Behavioral Appeals to Influence Product Return Behavior

Behavioral Appeals to Influence Product Return Behavior Theoretical Foundations and Experimental Applications
DISSERTATION
of the University of St.Gallen,
School of Management,
Economics, Law, Social Sciences
and International Affairs
to obtain the title of
Doctor of Philosophy in Management
submitted by
Thilo Pfrang
from
Germany
Approved on the application of
Prof. Dr. Thomas Rudolph
and
Prof. Dr. Heiner Evanschitzky
Dissertation no. 4484
Difo-Druck GmbH, Bamberg 2015
The University of St.Gallen , School of Management, Economics, Law, Social
Sciences and International Affairs hereby consents to the printing of the present
dissertation, without hereby expressing any opinion on the views herein expressed.
St.Gallen, November 2, 2015
The President:
Prof. Dr. Thomas Bieger
VORWORT
Wie lassen sich Retouren von online bestellten Waren reduzieren?
Die Beantwortung dieser Frage hat mich in den vergangenen drei Jahren fast täglich
beschäftigt und mir schlaflose Nächte, aber auch große Glücksgefühle bereitet. Es war
faszinierend, wissenschaftlich zu untersuchen und zu erfahren, wie sich psychologische
Mechanismen auf das Kauf- und Rücksendeverhalten von Online-Kunden auswirken
können. So entstand die vorliegende Arbeit, welche ohne die Unterstützung von
zahlreichen lieben Menschen in meinem Umfeld nicht möglich gewesen wäre. Ihnen
möchte ich an dieser Stelle herzlich danken:
Zuerst bedanke ich mich bei meinem Doktorvater, Prof. Dr. Thomas Rudolph, der mir
die Promotion am Forschungszentrum für Handelsmanagement der Universität
St.Gallen ermöglichte. Er hat mich auf die hohe Relevanz von Retouren im OnlineHandel hingewiesen und mir damit den initialen Anstoß für dieses faszinierende Projekt
gegeben. Mit seiner Begeisterung für praxisnahe Fragestellungen und seiner inhaltlichen
Akribie, aber auch mit seinen kreativen Ideen hat er mich stets herausgefordert und
inspiriert. Seine Betreuung trug maßgeblich zum Gelingen der vorliegenden Arbeit bei.
Ebenso möchte ich mich bei Prof. Dr. Heiner Evanschitzky für die Übernahme des
Korreferats, aber auch für die stets schnelle und unkomplizierte Unterstützung bei
konzeptionellen und methodischen Fragen bedanken.
Des Weiteren danke ich meinem „Coach“ und Co-Autor, Prof. Dr. Oliver Emrich für
seinen essentiellen Beitrag zu den Artikeln 3 und 4 dieser Arbeit, sein immer offenes
Ohr bei fachlichen Fragen, aber auch für seine ruhige und pragmatische
Herangehensweise, die ich bewundere und mich stets inspirierte.
In der finalen Phase der Dissertation hatte ich die Gelegenheit, drei Monate als Visiting
Scholar an der University of Michigan zu verbringen. Diese Zeit war nicht nur enorm
produktiv und hat jedem einzelnen Artikel dieser Dissertation entscheidende Impulse
gegeben, sondern hat auch zu meiner persönlichen Entwicklung beigetragen. Ich danke
meinem Freund, Prof. Dr. Philipp Rauschnabel, für diese großartige Zeit und freue mich
schon auf weitere gemeinsame Projekte und Aktivitäten in der Zukunft.
i
Mein großer Dank gilt außerdem den Geschäftsführern der an dieser Dissertation
beteiligten Versandhandelsunternehmen, Dr. Marcus Rodermann und Niels Degen. Sie
haben sehr viel Verständnis für die wissenschaftliche und praktische Relevanz meiner
Arbeit aufgebracht und mir großen Freiraum bei der Konzeption und Durchführung der
Feldexperimente unter realen Bedingungen gelassen. Für die technische Umsetzung und
Begleitung der Experimente in den jeweiligen Online-Shops danke ich Benjamin
Boltner, Franziska Spirig, Annette Dolpp, Rebekka Sauter und Stefan Wetzler. Ferner
danke ich Daniel Huber für das bereichernde Feedback aus Unternehmenssicht sowie
für die Herstellung zahlreicher Kontakte, ohne die eine Realisierung der Studien nicht
möglich gewesen wäre. Patrick Kessler danke ich für die Möglichkeit das damalige
Projektvorhaben zahlreichen Praxisvertretern zu präsentieren.
Dankbar bin ich auch meinen Kolleginnen und Kollegen am Forschungszentrum für
Handelsmanagement der Universität St.Gallen. Prof. Dr. Johannes Bauer danke ich für
sein konstruktives Feedback zu früheren Versionen einiger Manuskripte, sowie seine
methodischen Hilfestellungen bei der Konzeption der experimentellen Studien. Ich
danke ihm außerdem für zahlreiche gemeinsame Trainingseinheiten, die stets an die
Belastungsgrenze gingen und zumindest meinem Kopf immer die notwendige Frische
zurückbrachten. Dr. Tim Böttger danke ich für die fachliche Unterstützung und die tolle
Zusammenarbeit in gemeinsamen Beratungs- und Forschungsprojekten, aus denen ich
sehr viel für diese Arbeit lernen konnte. Ihm und David Biernath danke ich ausserdem
für gemeinsame Freizeitaktivitäten, die den Forschungsalltag aufgelockert haben und
die ich weiterhin nicht missen möchte. Ferner danke ich Prof. Dr. Liane Nagengast, Dr.
Maximilian Weber, Dr. Marc Linzmajer, Dr. Felix Brunner, Melanie Bassett, Elena
Essig, Dr. Jasmin Hödl, Thomas Metzler, Alexander Safaric, Kristina Kleinlercher,
Severin Bischof und Frauke Nitsch für ihre Unterstützung und die tolle Zeit am IRM.
Besonders danken möchte ich meiner lieben Freundin, Wiebke Lüders, für ihre
Unterstützung und ihr Verständnis vor allem während der Finalisierung dieser Arbeit,
sowie für die unvergesslichen Momente in den letzten zwei Jahren.
Mein größter Dank gilt meinen Eltern, die mir stets den nötigen Rückhalt für mein
Vorhaben gegeben haben und immer für mich da sind. Für ihre Liebe und
bedingungslose Unterstützung bin ich tief dankbar. Ihnen widme ich diese Arbeit.
St.Gallen im Dezember 2015
Thilo Pfrang
ii
ABSTRACT
The expanding role of the Internet as a distribution channel leads to increased product
returns that are associated with high handling costs. Accordingly, e-commerce
practitioners and scholars have become particularly interested in gaining knowledge
about how to reduce product returns without sacrificing sales. However, although high
return rates and opportunistic return behaviors of consumers require behavioral change,
research on ways to effectively change return behavior remains scarce. This cumulative
dissertation aims to narrow the research gap by investigating the foundations and the
experimental applications of behavioral appeals to change product return behavior. Four
individual papers contribute to this research purpose. The first paper summarizes and
structures research findings on social norms in order to provide a deeper understanding
of their potential to change human behavior, and the resulting use for marketing research
and practice. In the second paper, social norms are tested empirically together with a
self-benefit appeal as a standard approach to examine their impact on return intentions.
Two laboratory experiments reveal that social norms and self-benefits can reduce return
intentions without reducing purchase intention. Paper 3 complements and extends the
second paper by investigating the effects of social norms and self-benefits, depending
on consumers´ fraudulent return proclivity and situational opportunism. The results
suggest that, in general, self-benefits can reduce return intentions of customers after a
purchase. Social norms reduce return intentions of fraudulent customers, but increase
the return intentions of non-fraudulent customers. This detrimental effect is particularly
strong under situational opportunism. Paper 4 extends the contribution of the laboratory
experiments in Papers 2 and 3 by examining actual return and purchase behavior. Across
two large-scale field experiments, Paper 4 further extends knowledge about the
conditions in which social norms and self-benefits are more, or less, effective in
reducing product returns and increasing net sales. The first field study finds that social
norms reduce returns and increase sales of customers with a high return propensity when
presented in the purchase stage, but increase returns and decrease sales after purchase,
when self-benefits reduce returns and increase sales. The second field study shows that
social norms increase returns and decrease sales for customers with a low return
propensity, but reduce returns and increase sales for customers with a high return
propensity, particularly if the norm appeals refer to close others with whom customers
can identify. An umbrella article outlines the relevance of the research and concludes
the findings of this dissertation in a four-step management process for marketing
practitioners.
iii
ZUSAMMENFASSUNG
Die steigende Bedeutung des Internets als Distributionskanal führt zu mehr Retouren,
welche für Händler mit hohen Kosten verbunden sind. Dementsprechend sind im
Online-Handel sowohl Praktiker als auch Forscher zunehmend daran interessiert, wie
man Retouren reduzieren kann, ohne den Absatz zu beeinträchtigen. Doch obwohl ein
extremes Rücksendeverhalten seitens der Kunden eine Verhaltensänderung nahelegt,
existieren nur wenige Forschungserkenntnisse darüber. Um diese Forschungslücke zu
verringern, werden in dieser kumulativen Dissertation theoretische Fundierungen sowie
experimentelle Anwendungen von Verhaltensanreizen zur Änderung des
Rücksendeverhaltens in vier Beiträgen untersucht. Der erste Beitrag strukturiert
Forschungsergebnisse zu sozialen Normen, um ein tieferes Verständnis für deren
Verhaltensrelevanz und damit verbundenen Nutzen für die Marketingforschung und
-praxis zu generieren. Im zweiten Beitrag werden soziale Normen zusammen mit einem
Eigennutzenanreiz empirisch getestet, um deren Einfluss auf die Rücksendeabsicht zu
untersuchen. Zwei Laborexperimente zeigen, dass beide Anreizformen die Rücksende-,
aber nicht die Kaufabsicht reduzieren. Der dritte Beitrag ergänzt den Zweiten durch eine
Untersuchung der Norm- und Eigennutzeneffekte in Abhängigkeit von betrügerischer
Rücksendeneigung sowie situativem Opportunismus der Kunden. Die Ergebnisse
zeigen, dass Eigennutzen-Anreize die Rücksendeabsicht nach dem Kauf senken können.
Soziale Normen reduzieren die Retouren von Kunden mit betrügerischer
Rücksendeneigung, aber erhöhen Retouren von Kunden ohne betrügerische Neigung.
Dieser nachteilige Effekt verstärkt sich, wenn opportunistische Gedächtnisinhalte
aktiviert werden. Durch eine Untersuchung des Rücksende- und Kaufverhaltens
erweitert der vierte Beitrag die Erkenntnisse von Beitrag 2 und 3. Zwei Feldexperimente
liefern einen Erkenntnisfortschritt über die Bedingungen, unter denen soziale Normen
und Eigennutzen-Anreize mehr oder weniger effektiv Retouren senken und NettoUmsätze erhöhen. Die erste Studie zeigt, dass soziale Normen in der Kaufphase bei
Vielretournierern Retouren senken und Umsätze steigern. Sie erhöhen Retouren und
senken Umsätze jedoch, wenn sie in der Nachkaufphase eingesetzt werden, wo
Eigennutzen-Anreize Retouren senken und Umsätze steigern. Die zweite Studie zeigt,
dass soziale Normen bei Kunden mit einer geringen Rücksendeneigung Retouren
erhöhen und den Umsatz senken. Sie senken aber Retouren und steigern den Umsatz
von Vielretournierern, besonders dann, wenn sie sich auf Referenzgruppen beziehen,
mit denen sich Kunden identifizieren können. Ein Dachbeitrag stellt die Relevanz dieser
Forschung dar und fasst die Ergebnisse der Dissertation in einem vierstufigen
Managementprozess für Marketingpraktiker zusammen.
iv
TABLE OF CONTENTS
Vorwort .......................................................................................................................... i
Abstract ........................................................................................................................ iii
Zusammenfassung ....................................................................................................... iv
Table of Contents .......................................................................................................... v
List of Figures ............................................................................................................ viii
List of Tables ............................................................................................................... ix
A Umbrella Article: When and How Do Social Norm and Self-Benefit Appeals
Affect Product Return Behavior? ........................................................................... 1
1 Introduction ....................................................................................................... 3
1.1
1.2
Product Returns as a Challenge for Online Retailers ............................. 3
The Potential of Behavioral Appeals to Influence Return Behavior ...... 5
2 Research Gap ..................................................................................................... 7
3 Research Strategy .............................................................................................. 9
3.1
Summary of Paper 1: The Behavioral Impact of Social Norms and Their
Potential in Marketing: A Literature Review ....................................... 12
3.2
Summary of Paper 2: Retourensenkung im Online-Handel: Das Potenzial
von Eigennutzen und sozialen Normen ................................................ 13
3.3
Summary of Paper 3: The Influence of Social Norms and Self-Benefits
on Return Decisions of Fraudulent Returners ...................................... 14
3.4
Summary of Paper 4: The Desirable and Undesirable Influences of Social
Norms and Self-Benefits on Product Return Behavior: Evidence from
Two Field Experiments ......................................................................... 15
4 Synthesis .......................................................................................................... 16
4.1
Assess Current Return Behavior ........................................................... 17
4.2
Generate a Solution Space .................................................................... 18
4.3
Plan and Implement Appeals ................................................................ 24
4.4
Measure the Impact. .............................................................................. 27
5 Conclusion ....................................................................................................... 28
References................................................................................................................... 30
B Paper 1: The Behavioral Impact of Social Norms and Their Potential in Marketing:
A Literature Review ............................................................................................. 36
1 Introduction ..................................................................................................... 38
2 Research Gap ................................................................................................... 39
3 Theoretical Background .................................................................................. 41
v
3.1
An Evolutionary Approach ................................................................... 41
3.2
3.3
The Principle of Conformity ................................................................. 42
Focus Theory of Normative Conduct ................................................... 44
4 Methodology ................................................................................................... 46
5 Triggers of Social Norms ................................................................................ 49
5.1
The Manipulation of Social Norms ...................................................... 49
5.2
Normative Beliefs ................................................................................. 65
6 Salience of Social Norms ................................................................................ 67
6.1
Observing Others .................................................................................. 67
6.2
Environmental Cues .............................................................................. 68
7 Other Moderating Influences .......................................................................... 73
7.1
Contextual Moderators ......................................................................... 73
7.2
Characteristics ....................................................................................... 74
8 General Discussion .......................................................................................... 80
9 Implications for the Use of Social Norms in Marketing ................................. 81
10
Directions for Future Research in Marketing ....................................... 88
References................................................................................................................... 92
C Paper 2: Retourensenkung im Online-Handel: Das Potenzial von Eigennutzen und
sozialen Normen................................................................................................. 102
1 Einleitung ...................................................................................................... 104
2 Transformation der Kundenansprache .......................................................... 106
3 Verhaltensänderung durch soziale Normen und Eigennutzen ...................... 107
4 Studie 1 .......................................................................................................... 109
5 Studie 2 .......................................................................................................... 114
6 Implikationen für Wissenschaft und Praxis .................................................. 117
Literaturverzeichnis .................................................................................................. 120
D Paper 3: The Influence of Social Norms and Self-Benefits on Return Decisions of
Fraudulent Returners .......................................................................................... 124
1 Introduction ................................................................................................... 126
2 Theoretical Background and Hypotheses Development ............................... 128
3 Research Methodology .................................................................................. 131
4 Results ........................................................................................................... 132
5 Conclusions and Implications ....................................................................... 138
References................................................................................................................. 140
Appendix ................................................................................................................... 144
vi
E
Paper 4: The Desirable and Undesirable Influences of Social Norms and SelfBenefits on Product Return Behavior: Evidence from Two Field Experiments 146
1 Introduction ................................................................................................... 148
2 Conceptual Framework ................................................................................. 150
3 Hypothesis Development .............................................................................. 153
4 Study 1 ........................................................................................................... 157
4.1
Method ................................................................................................ 158
4.2
Results ................................................................................................. 160
4.3
Discussion ........................................................................................... 168
5 Study 2 ........................................................................................................... 169
5.1
Results ................................................................................................. 172
5.2
Discussion ........................................................................................... 187
6 General Discussion ........................................................................................ 188
7 Theoretical Implications ................................................................................ 190
8 Managerial Implications ................................................................................ 192
9 Limitations and Directions for Further Research .......................................... 195
References................................................................................................................. 197
Appendix A: Self-benefit appeal in Study 1 ............................................................. 205
Appendix B: Social norm appeal in Study 1 ............................................................ 206
Appendix C: Control appeal in Study 1.................................................................... 207
Appendix D: Social norm appeal in Study 2 ............................................................ 208
Appendix E: Social norm appeal in Study 2 when clicking “More” ........................ 209
Appendix F: Wording of “More” appeals in Study 2 ............................................... 210
Curriculum Vitae ...................................................................................................... 213
vii
LIST OF FIGURES
Figure 1 Reasons for Product Returns (following Pur et al., 2013) ............................... 4
Figure 2 Cyclical Process for Changing Product Return Behavior .............................. 17
Figure 3 Solution Space for Changing Return Behavior .............................................. 21
Figure 4 Solution Space for Influencing Post-Purchase Return Decisions .................. 23
Figure 5 Discussion Topology ...................................................................................... 41
Figure 6 Descriptive and Injunctive Norm Triggers ..................................................... 49
Abbildung 7 Beispiele für Verhaltensappell bei mirapodo ........................................ 105
Abbildung 8 Studienaufbau ........................................................................................ 109
Abbildung 9 Experimentalbedingungen von Studie 1 ................................................ 110
Abbildung 10 Wirkung der normativen Anreize ........................................................ 113
Abbildung 11 Experimentalbedingungen von Studie 2 .............................................. 115
Abbildung 12 Wirkung der Eigennutzen- und Norm-Anreize ................................... 117
Figure 13 Interaction of Fraudulent Return Proclivity and Appeal Type on Return
Probability ................................................................................................. 134
Figure 14 Interaction of Level of Self, Fraudulent Return Proclivity, and Appeal
Type on Return Probability ....................................................................... 137
Figure 15 Conceptual Framework .............................................................................. 153
Figure 16 Return Rate as a Function of Discretionary Room for Maneuver and
Appeal Type .............................................................................................. 163
Figure 17 Net Sales as a Function of Discretionary Room for Maneuver and
Appeal Type .............................................................................................. 165
Figure 18 Net Sales per Order as a Function of Discretionary Room for
Maneuver and Appeal Type ...................................................................... 167
Figure 19 Return Rate as a Function of Latent Return Propensity and Appeal Type 175
Figure 20 Return Rate as a Function of Latent Return Propensity and Appeal
Type (Detailed Appeals) ........................................................................... 179
Figure 21 Net Sales as a Function of Latent Return Propensity and Appeal Type
on Net Sales ............................................................................................... 182
Figure 22 Net Sales as a Function of Latent Return Propensity and Appeal Type
(Detailed Appeals) ..................................................................................... 185
viii
LIST OF TABLES
Table 1 Overview of Papers within the Cumulative Dissertation ................................ 10
Table 2 Descriptive and Injunctive Norms in Social Influence Research .................... 48
Table 3 Empirical Studies on Social Norms with Peer Information ............................ 54
Table 4 Empirical Studies on Social Norms and Social Comparison .......................... 59
Table 5 Empirical Studies on Social Norms with Specific Reference Groups ............ 64
Table 6 Empirical Studies on Normative Beliefs ......................................................... 66
Table 7 Empirical Studies on the Salience of Social Norms ........................................ 71
Table 8 Empirical Studies on Moderating Influences of Social Norms ....................... 77
Table 9 Examples of Appeal Types ............................................................................ 132
Table 10 Main Effects and One-Way Interactions between the Appeals and
Fraudulent Return Proclivity ....................................................................... 135
Table 11 Wordings of Appeals in Study 1 .................................................................. 159
Table 12 Main Effects of the Appeals on the Return Rate ......................................... 160
Table 13 Main Effects of the Appeals on Net Sales ................................................... 161
Table 14 Interaction Effects of the Appeals and Discretionary Room for
Maneuver on the Return Rate ...................................................................... 162
Table 15 Interaction of Appeals and Discretionary Room for Maneuver on
Net Sales ...................................................................................................... 164
Table 16 Wording of Appeals in Study 2 ................................................................... 171
Table 17 Interaction of Appeals and Latent Return Propensity on the
Return Rate .................................................................................................. 173
Table 18 Interaction of Appeals and Latent Return Propensity on the
Return Rate (Detailed Appeals) ................................................................... 177
Table 19 Interaction of Appeals and Latent Return Propensity on Net Sales ............ 180
Table 20 Interaction of Appeals and Latent Return Propensity on Net Sales (Detailed
Appeals) ....................................................................................................... 184
Table 21 Overview of Hypothesis Tests ..................................................................... 189
ix
A UMBRELLA ARTICLE: WHEN AND HOW DO
SOCIAL NORM AND SELF-BENEFIT APPEALS
AFFECT PRODUCT RETURN BEHAVIOR?
1
WHEN AND HOW DO SOCIAL NORM AND SELF-BENEFIT
APPEALS AFFECT PRODUCT RETURN BEHAVIOR?
Authors
Thilo Pfrang
Abstract
This umbrella article provides a general overview of the cumulative dissertation,
outlines the internal connections between the individual articles, and draws an overall
conclusion. First, it outlines the relevance of changing return behavior in light of
opportunistic consumer behavior that in an increasingly competitive environment incurs
high costs for online retailers. Second, it summarizes the overall research strategy of the
dissertation and explains how each of the four individual papers in this dissertation
contributes to marketing research. Finally, it provides a synthesis of the research
findings in the form of a four-step management process for practitioners in returns
management to implement behavioral appeals in order to influence return behavior
effectively.
2
1 Introduction
1.1 Product Returns as a Challenge for Online Retailers
Consumers’ increasing acceptance of the Internet as a distribution channel in recent
years has led to sharp increases in e-commerce. Between 2008 and 2014, e-commerce
sales have almost doubled in Germany (from 18.3 to 39 billion euros) and in Switzerland
(from 2.95 to 5.9 billion CHF), while forecasts suggest that strong growth will be
sustained (Lang 2015; HDE 2014). To further exhaust this growth potential, online
retailers must dismantle barriers that arise as a result of consumers’ physical remoteness
from products (Wood 2001). The spatial separation between supply and demand in
online shopping implies risks for consumers, as sensory examinations of products are
precluded. These risks are particularly acute in categories where customers need to touch
and feel the product to determine how well it fits their taste and needs (e.g., fashion
apparel, jewelry, sporting goods, furniture) (Ofek 2011). Online retailers try to
counteract consumer reservations that arise from this boundary by providing farreaching return options and lenient return policies intended to build trust. Moreover,
increasing competition forces online sellers to oblige customers in order to prevent
losing them to competitors. On the one hand, being obliging creates trust and reduces
risk for consumers, thereby increasing the probability of an order. However, although
lenient return policies and vigorous marketing campaigns have increased sales, the
better-informed educated customers have developed patterns of habitually returning
merchandise, thereby opportunistically exploiting retailer leniency (Petersen and Kumar
2009). Moreover, in the European Union consumers do not even need to depend on the
leniency of online retailers, because an extensive right of revocation allows consumers
to return products within 14 days for any reason (The Economist 2013).
As a result of the rise in online purchasing, product returns occur more frequently,
and result in high handling costs (Petersen and Kumar 2015; Walsh et al. 2015). In
European markets such as those of Germany and Switzerland, the textile sector reaches
an average return rate between 40% and 50% (Lang 2015). The big players in fashion
retailing, which account for a significant proportion of e-commerce and provide the most
popular shopping websites in the German-speaking market (Rudolph et al. 2015), have
a return rate as high as 60% (Reinhold 2014). Retailers and fulfilment service providers
3
have become accustomed to receiving returns of dirty sports clothes, suits with opera
tickets forgotten in the pockets, lederhosen after Oktoberfest, or televisions after big
soccer tournaments (Lütge 2014). Studies on the reasons for product returns confirm
that a significant proportion does not arise solely from necessary returns due to wrong
deliveries or damaged products, but also from avoidable returns where customers benefit
from or even exploit retailers’ lenient return policies (i.e., ordering with no real purchase
intention, or ordering several varieties or styles) (Pur et al. 2013, see Figure 1).
Figure 1
Reasons for Product Returns (following Pur et al. 2013)
Interview data from 357 online retailers on the question:
“Which are the three most frequent reasons for product returns from your point of view?”
Did not like
Did not fit
Order with several variants to choose
Product defect/damaged
Incorrect order
No real purchase intention/wardrobing
Did not conform to product description
Wrong delivery
Duplicated purchase
Delivery time too long
Delivery not complete
Duplicated delivery
Other reasons
59%
52%
38%
27%
26%
13%
12%
7%
5%
4%
2%
1%
6%
0%
10%
20%
30%
40%
50%
60%
70%
A cost range between four and twenty euros per returned item indicates the
relevance of this problem, in the form of significant losses in the already low margins
of online retailing (The Economist 2013; Foscht et al. 2013; Walsh et al. 2015).
Accordingly, retailers consulted in Germany suggest that a 10% decrease in the return
rate could increase profits by 5% (Pur et al. 2013).
The research is in agreement on the risks of product returns for maintaining
profitability. Studies confirm that product returns have potential benefits for purchase
behavior and profitability, but only up to a threshold limit (Petersen and Kumar 2009).
Increases in product returns beyond a certain point have been shown to significantly
decrease profits (Petersen and Kumar 2010). This is because customers with a
particularly high return rate disproportionally increase costs for returns (The Economist
2013; El Kihal, Schulze, and Skiera 2014). For instance, the optimal percentage of
4
product returns that maximize firm profits was 13%, according to Petersen and Kumar’s
2009 study. At 18% above this optimal return rate (31%), profits decrease by over 77%.
That the average return rates for many European online fashion retailers exceed 50% is
clear indication that current levels of product returns can lower profitability of online
retailers.
Accordingly, e-commerce practitioners and scholars have become particularly
interested in gaining knowledge on how to reduce product returns without sacrificing
sales (Walsh et al. 2015). On the one hand, retailers use preventive measures such as
optimizing product presentation, developing return forecasting algorithms, and
enforcing sanctions and exclusions of customers with excessive return behavior (Kontio,
Hortig, and Nagel 2013). However, sanctions can deteriorate customer relationships.
Preventive measures induce high costs, yet they affect return habits of consumers only
indirectly.
On the other hand, recent research examines motives of product returns (e.g.,
Powers and Jack 2013), the configuration of return policies (e.g., Bower and Maxham
2012), and the consideration of product returns in marketing resource allocation
(Petersen and Kumar 2015). Although consumers’ current return behaviors obviously
require measures that can induce behavioral change, behavioral approaches have been
widely ignored in research and practice. Hence, only limited knowledge is available
about how to effectively change return behavior.
1.2 The Potential of Behavioral Appeals to Influence Return Behavior
According to behavioristic approaches, one method would be to adjust the variables
that determine behavior so that a behavioral change will result from this adjustment.
However, consumers often tend to resist changing their behavior or engaging in
activities that involve cost to the individual-level self (e.g., additional time, increased
effort, the change itself), especially when they have become accustomed to a behavior
that generates benefits (White and Simpson 2013). Moreover, measures forcing a
behavioral change can have severe side effects, as has been shown in the sanctioning
efforts of online retailers that have resulted in boycotts and deterioration of customer
relationships (Kontio, Hortig, and Nagel 2013). In order to avoid ethical and practical
problems involved in attempting sanctions in marketing, behaviorists advocate
rewarding the customer, or having the customer observe the behavior of others in order
to inhibit undesirable behaviors (Nord and Peter 1980; Skinner 1953). Previous research
5
on behavioral changes has highlighted two theoretical concepts that are in line with
rewarding and observing others: self-benefits and social norms.
According to social exchange theory, human behavior is determined by the degree
to which a specific behavior benefits an individual. Self-benefits can be defined as a
personal advantage, a benefit for the self, that may arise from engaging in a specific
behavior (White and Peloza 2009). Alternatively, research has shown that human
behavior is influenced by social norms—referring to “rules and standards that are
understood by members of a group, and that guide and/or constrain social behavior
without the force of law” (Cialdini and Trost 1998). Recent findings referring to these
explanatory approaches of human behavior confirm that prevalence and adjustment of
both determinants, social norms and self-benefits, could lead to desirable behavioral
changes (e.g., White and Simpson 2013). Importantly, a closer look at the psychological
mechanisms of these concepts indicates that they could be especially applicable to
influencing return behavior.
The human tendency to conform to what others do has been proven in a variety of
behavioral contexts, where people face situations of uncertainty and orient themselves
toward the conduct of their peers (Cialdini, Reno, and Kallgren 1990; Martin 2012). In
addition, recent findings highlight the potential for social norms to provoke purchase
decisions (e.g., Noguti and Russell 2014). The variety of influenced behaviors and the
potential to influence purchase behavior could also be promising indicators for the
successful use of social norms in return decisions. Moreover, product return behavior is,
in itself, determined by social influence. Drawing on the theory of planned behavior,
research has shown that the beliefs of others about fraudulently merchandise (e.g.,
returning used or damaged products) determine their proclivity toward engaging in
fraudulent return behavior (Harris 2008; King, Dennis and Wright 2008). This indicates
that return behavior might represent human tendencies that can in turn be influenced by
adequate appeals of social influence corresponding to a desired behavior.
Furthermore, the underlying mechanisms determining the behavioral effectiveness
of social norms (see Paper 1) could be especially relevant for the research problem of
changing the return behavior of consumers who incur disproportionally high costs.
Social norms particularly affect the behavior of those persons who deviate from average
behavior (e.g., consumers with a particularly high return rate). This is because people
measure the appropriateness of their behavior by how far away they are from the norm
(Schultz et al. 2007). For instance, a student whose grade deviates significantly from the
class average might be more worried about his or her performance in the class than would
6
be the case if the grade was close to the class average. Theoretically, then, providing social
norm information could influence those customers with an above-average return level that
deviates from “normal” return behavior. In other words, the psychological mechanisms of
social norms could work for precisely those customer groups whose behavior retailers aim
to change.
Self-benefits have also been shown to influence a variety of behaviors, because
people are likely to engage in behaviors from which they can derive a personal benefit.
Numerous studies, by combining the request for the desired behavior with some form
of benefit to the self, have proven behavioral changes for many similar behaviors that
have been influenced by social norm appeals, such as engagement in unfamiliar
sustainable behaviors (White and Simpson 2013) or energy conservation (Nolan et al.
2008). The human tendency to weigh costs and benefits (Blau 1964) could also be
applicable to influencing return behavior. Online shoppers base return decisions on the
freedom to try out products and return those items that are not wanted. Retailers have a
special interest in providing customers with the most flexibility and autonomy possible
in order to decrease barriers arising through the spatial separation between supply and
demand (Ofek 2011). Providing customers with a self-benefit could be congruent with
online shoppers’ individual-level goals after purchase, and with their autonomy that they
use to their advantage (White and Simpson 2013). Thus, customers might be susceptible
to self-benefit appeals, especially when deciding whether or not to return an item after
purchase.
Finally, both social norms and self-benefits have proven their behavioral relevance
in the contexts of environmental protection and prosocial behaviors (Ayres, Raseman,
and Shih 2013; Nolan et al. 2008; Schultz et al. 2007; White and Simpson 2013). These
recent findings suggest that both appeals could also affect return behavior, as product
returns are associated with resource consumption and transport emissions (Srivastava
and Srivastava 2006).
2 Research Gap
Despite this practical relevance and potential, social norms and self-benefits have
received little attention in online retail practice. This may be a result of limited
understanding of their psychological mechanisms and behavioral effects (Griskevicius,
Cialdini, and Goldstein 2008). Instead, appeals currently being used by online retailers
7
are limited to intuitive measures such as monetary incentives offered to avoid returns
(e.g., bon prix) or normative instructions about high costs, resource consumption, and
environmental damages through product returns (e.g., mirapodo). However, the success
of such measures is yet to be seen, especially because initial research indicates that
detrimental reactance effects of currently used threat and normative appeals result in
increased return intentions and decreased loyalty (Garnefeld, Münkhof, and Raum
2013). Although these initial attempts indicate retailers’ aspirations for changing the
behavior of their customers, behavioral-scientific approaches have not yet been
examined in returns management.
In general, research on how to change consumers’ return behavior is scarce. Of the
existing material, one body of product returns research has focused on the exchange
relationships between manufacturers and retailers (e.g., Padmanabhan and Png 1997,
2004; Pasternack 2008; Wang 2004), and on optimizations of the returns management
process in the supply chain (Mollenkopf et al. 2007; Mollenkopf, Frankel, and Russo
2011; Mollenkopf, Russo, and Frankel 2007; O’Connell 2007; Rogers et al. 2002;
Savaskan, Bhattacharya, and Van Wassenhove 2004; Shulman, Coughlan, and Savaskan
2010). However, these studies do not consider consumers’ return behavior.
Another group of studies does focus on consumers’ return behavior, but has only
indirectly examined how to influence return propensities. Instead, the previous research
has focused on motives and antecedents of product returns (Harris 2008; Maity and
Arnold 2013; Powers and Jack 2013), the choice process that helps explain return
behavior (Bechwati and Siegal 2005; Petersen and Kumar 2009), and scrutinizing the
configuration of return policies as an optimization problem in order to increase sales and
profits (Anderson, Hansen, and Simester 2009; Bower and Maxham 2012; Wood 2001).
Because there is such a knowledge gap about how to directly influence return
behavior, it is also unclear how retailers can specifically address the critical behavior of
customers who have particularly high levels of product returns and opportunistic
propensities to return. The neglect of appeals to social norms and self-benefits in this
context is surprising, because their theoretical mechanisms, especially those of social
norms (see Paper 1), suggest the value of effectively influencing those consumers who
have a particularly high rate of returns.
Despite their theoretical potential and proven behavioral relevance in other contexts,
social norms and self-benefits have not yet been examined in product returns
management. Specifically, existing research does not (1) explicitly conceptualize why
8
social norms and self-benefits could exert an influence on product return behavior, (2)
examine how they affect return behavior and (3) consider the conditions in which social
norms and self-benefits are more, or less, effective in reducing product returns and
maintaining sales.
3 Research Strategy
The aim of this dissertation is to provide a deeper understanding of ways to change
the product return behavior of customers who are accustomed to returning, and who tend
to exploit lenient return policies to their own advantage. This objective leads to the
overall research question: How can behavioral appeals influence return behavior? This
multi-faceted question requires a differentiated examination in order to provide an
insightful answer. Therefore, a cumulative approach was chosen allowing to examine
the research question from different perspectives. Because social norms and selfbenefits have not yet been examined in the context of product returns, this cumulative
dissertation aims to shed light on complementary aspects of behavioral approaches in
the context of product returns—the theoretical foundations and experimental
applications regarding customer characteristics and stages of the purchase process.
Four papers make up the dissertation. Each paper has a unique focus and
contribution to the overall research problem. Moreover, the four papers address different
audiences. The first paper provides a general overview of the literature of social norms
and its theoretical foundations. The three subsequent papers apply social norms and selfbenefits in the context of product return behavior, which many retailers and online
sellers aim to change. Consequently, this cumulative dissertation aims to provide an arc
of tension from theoretical foundations to experimental applications and examinations
of proposed effects under different conditions. Table 1 provides an overview of the four
papers, including their titles, authors, and information regarding their publication status.
Paper 1 provides a literature-based overview of the potentials of social norms to
change human behavior. The research findings of different literature streams are
summarized and discussed to provide implications and directions for further research on
the use of social norms in marketing. Thus, Paper 1 aims to answer the research question:
Which mechanisms drive the behavioral influence of social norms, and how can this
behavioral relevance be used in marketing? The paper is based on theoretical concepts
of social influence such as the evolutionary development of norm adherence, the
9
principle of conformity, and the focus theory of normative conduct. Through
summarizing and structuring the findings of 49 studies, this paper develops a deeper
knowledge of the mechanisms of social norms in order to influence a variety of
behaviors and the use of these behavioral mechanisms for marketing research and
practice. Building on current research to provide knowledge about the mechanisms of
social norms for marketing, paper 1 primarily addresses marketing scholars. The derived
implications can also help marketing practitioners to optimize their customer approach.
Table 1
Overview of Papers within the Cumulative Dissertation
Paper 1
Paper 2
Title
The Behavioral Impact of Social Norms and Their
Potential in Marketing: A Literature Review
Retourensenkung im Online-Handel:
Das Potenzial von Eigennutzen und sozialen
Normen
Authors
Thilo Pfrang
Thilo Pfrang
Research
question
Which mechanisms drive the behavioral influence
of social norms and how can this behavioral
relevance be used in marketing?
How can social norms and self-benefit appeals
influence product return intentions?
Publication
status/
Conference
presentations
To be submitted (working paper)
Accepted for publication in Marketing Review
St.Gallen 05/2015
Paper 3
Paper 4
Title
The Influence of Social Norms and Self-Benefits
on Return Decisions of Fraudulent Returners
The Desirable and Undesirable Influences of Social
Norms and Self-Benefits on Product Return
Behavior:
Evidence from Two Field Experiments
Authors
Thilo Pfrang
Thomas Rudolph
Oliver Emrich
Thilo Pfrang
Thomas Rudolph
Oliver Emrich
Research
question
How can social norms and self-benefit appeals
influence product return intentions of fraudulent
returners?
How do behavioral appeals affect product return
and purchase behavior of customers with latent
return propensity compared to those of customers
without latent return proclivity?
At which point of the purchase process should
retailers induce behavioral appeals for customers
with latent return propensity?
Publication
status/
Conference
presentations
An earlier version was presented at the EMAC
2015 European Marketing Academy Conference in
Leuven, Belgium.
The paper was placed in the top twenty of
submitted papers and thus nominated for the
EMAC Best Paper Award.
To be submitted (working paper)
10
In Paper 2, social norms are tested empirically in a laboratory setting to examine
their main effects on return intentions. A second laboratory study integrates self-benefit
appeals as a standard behavioral approach for comparison. These two studies aim to
answer the research question: How can social norms and self-benefit appeals influence
product return intentions? Hence, the paper aims to create knowledge about the potential
for different behavioral appeals to reduce product returns. Focusing on main effects and
different stages of the online purchase process, the paper aims to provide initial,
practice-oriented implications on the use of behavioral appeals, for marketers and for
companies that are suffering from high return rates. Based on this content orientation,
the paper was submitted to the journal Marketing Review St. Gallen, which targets
marketing scholars and marketing practitioners. After a double-blind review process, it
was accepted for publication and was published in Issue 5, October 2015.
Paper 3 complements and extends the second paper by investigating the effects of
social norms and self-benefits in the after-purchase stage that depend on characteristic
and situational opportunism. Thus, the objective of the paper is to answer the question:
How do social norms and self-benefits influence return intentions after purchase under
different levels of characteristic and situational opportunism? Utilizing a laboratory
experiment, Paper 3 sheds light on how behavioral appeals can influence customers who
have a fraudulent return proclivity, representing a consumer segment that induces
particularly high costs for retailers. Moreover, the study provides insights on how the
activation of situational opportunism strengthens these influencing factors. An earlier
version of this paper was presented at the May 2015 European Marketing Academy
Conference (EMAC) in Leuven, Belgium. It placed in the top twenty of submitted
papers and was nominated for the “Best Paper Award.” By intent and practice, this paper
addresses the marketing research community.
Finally, Paper 4, which is the central contribution of this dissertation, extends the
contribution of the laboratory examinations of Papers 2 and 3 by examining the
influence of social norms and self-benefits on actual return and purchase behavior.
Across two large-scale field experiments in cooperation with a German and a Swiss
multi-channel fashion retailer, this paper further extends knowledge about the
conditions in which social norms and self-benefits are more, or less, effective in
reducing product returns and increasing net sales. This paper addresses the lack of
research examining how behavioral appeals can influence return behavior, and answers
the following research questions:
11
• How do behavioral appeals affect product return and purchase behavior of
customers with high return propensity, compared to customers with low return
propensity?
• At what point in the purchase process should retailers induce behavioral appeals
for customers with high return proclivity?
By examining the moderating influences that the effects of social norms and selfbenefits impose on return behavior, the paper primarily targets marketing scholars.
However, the practical relevance of the identified moderators (such as customers’ return
propensity and different stages of the purchase process) also make the subject interesting
for marketing practitioners. Moreover, the field experiment approach enables derivation
of valuable management implications and insights, particularly on potential cost
reductions and sales increases. The following sections provide short summaries of the
four papers of the cumulative dissertation, and elaborate on the purpose, research
methods, and results of each individual work.
3.1 Summary of Paper 1: The Behavioral Impact of Social Norms and Their
Potential in Marketing: A Literature Review
Purpose. Although a large body of literature in various research areas has provided
insights into how the behavior of others shapes a person’s behavior, the different
domains and methodological differences between the studies have resulted in
inconsistencies and fragmentations. Research that structures this fragmented literature
on social influence in order to provide insights for the use of social norms in marketing
remains scarce. The first paper aims to structure the variety of research findings on social
norms as a way to provide a deeper understanding of its psychological mechanisms and
behavioral effects. Within the studies of this dissertation, social norms represent the
focal behavioral appeal that is tested empirically in several variants, and that is
compared with self-benefit and control appeals aiming to reflect standard approaches in
online retailing. Thus, by reviewing state-of-the-art research findings and summarizing
the underlying theoretical foundations, this article provides a theoretical basis for the
following articles examining the effects of social norms on return intentions and
behavior.
12
Method. The literature review summarizes and structures the methods and findings
of 49 research works based on qualitative content analysis. The findings are discussed
within and between derived categories, reflecting the mode of action of social norms
influence.
Results. The reviewed research works are organized into five conceptual categories,
providing insight into the ways in which social norms influence behavior: forms of
manipulating social norms, the salience of social norms, the influence of normative
beliefs, and the influence factors moderating the effects of social norms on human
behavior.
Based on the within- and between-discussion of these categories, the article
provides concrete guidelines on how to apply social norms in marketing. Moreover, the
structured composition of current research findings leads to directions for further
research on social norms in marketing.
3.2 Summary of Paper 2: Retourensenkung im Online-Handel: Das Potenzial
von Eigennutzen und sozialen Normen
Purpose. Returning products represents a behavior that retailers and online sellers
aim to change, especially for those customers who induce particularly high costs in
consequence of their extreme return behavior. Although research has examined motives
of product returns and the optimal configuration of return policies, there is a paucity of
work examining how product return behavior can be changed. Behavioral approaches
such as social norms and self-benefits have been shown to change human behavior in a
wide range of areas, but have not yet been applied as an appeal to online shoppers in
order to change their return behavior. This paper provides initial empirical examinations
focused on the potential of social norms and self-benefits in the context of product
returns.
Method. Two experimental studies represent initial empirical tests of behavioral
appeals in specific online shopping return scenarios. The first study tests the influence
of social norm appeals on return prevention intentions in an online-shopping scenario.
The second study tests how exposing participants to a social norm or self-benefit appeal
influences their return decision after the fictitious order of a garment.
Results. The first study found that social norms can increase consumers’ readiness
to prevent product returns. Social norm appeals referring to the behavior of people with
13
whom the targeted consumer can identify are especially effective in promoting return
prevention. The second study shows that social norms can also affect return intentions
after purchase if a product does not fully meet customers’ expectations. However, selfbenefits seem to influence return intentions more strongly in the post-purchase stage.
3.3 Summary of Paper 3: The Influence of Social Norms and Self-Benefits on
Return Decisions of Fraudulent Returners
Purpose. While little is generally known about the potential of behavioral appeals
to influence product return intentions, a next step requires an examination of conditions
in which social norm and self-benefit appeals are more effective, or or less effective, in
reducing return intentions. This leads to the question of whether social norms and selfbenefits can influence behaviors of those customers who test the limits of retailers’
leniency and returning fraudulently (e.g., returning used products). Moreover, besides
investigating the moderating influence of characteristic forms of opportunism, little
knowledge exists regarding situational incentives for opportunistic behavior and their
interrelation to behavioral appeals in the product returns context. This paper focuses on
an investigation of the ways that behavioral appeals affect return decisions, with regard
to consumers’ characteristic (i.e., fraudulent return proclivity) and situational incentives
for opportunism.
Method. A laboratory experiment tested the influence of behavioral appeals on
return intentions with regard to characteristic and situational opportunism. Specifically,
for participants who have either high or low fraudulent return proclivity, the study tests
how exposure to social norm and self-benefit appeals can reduce return intentions.
Moreover, by activating situational mindsets of opportunism through a self-accessibility
priming method, the experiment investigates how situational opportunism strengthens
the influence of social norms and self-benefits with regard to fraudulent return
proclivity. Moderated regression analyses were used to analyze main, two-way, and
three-way interaction effects.
Results. The results of the laboratory experiment suggest that self-benefits can
generally reduce return intentions of consumers after purchase, whereas social norms
reduce return intentions after purchase only for customers with a fraudulent return
proclivity. However, social norms increase return intentions of consumers without a
fraudulent return proclivity. This detrimental effect is strengthened under situational
opportunism through activation of the consumer’s individual self. These results indicate
14
that retailers could benefit from behavioral appeals in order to influence return behavior
of customers with a fraudulent proclivity after purchase. However, they have to assess
the desirable and undesirable effects of social norms with regard to the specific purchase
situation.
3.4 Summary of Paper 4: The Desirable and Undesirable Influences of Social
Norms and Self-Benefits on Product Return Behavior: Evidence from two
Field Experiments
Purpose. Customers with a high level of product returns disproportionally increase
costs for retailers, and can therefore have a negative impact on profitability. Despite the
value that a search for strategies to reduce product returns holds for retailers, research
on changing return behavior of customers who have a high return propensity is scarce.
Across two large-scale field experiments, this paper highlights the potential of social
norms and self-benefit appeals for influencing actual return behavior. Specifically, the
article examines the conditions in which social norms and self-benefits are more (or
less) effective in reducing product returns and increasing profitability. Therefore, two
moderators—the stage of the purchase process and the level of latent return
propensity—are considered as moderators influencing the effect of behavioral appeals
on product returns.
Method. Two large-scale field experiments, in cooperation with a Swiss and a
German multi-channel fashion retailer, test the influence of social norms and selfbenefits on product return behavior and purchase behavior with regard to the stage in
the purchase process, and to latent return propensity. With a sample of above-average
returners, the first experiment examines how social norms and self-benefits influence
the return rate and net sales in the purchase and the post-purchase stages. The second
field experiment tests the impact of a self-benefit appeal and three social norm appeal
variants on changing return behavior within the purchase process, with regard to the
level of customers’ latent return propensity.
Results. The first field study reveals that social norms presented in the purchase
stage reduce the return rate and increase net sales of the above-average returners.
However, norms increase the return rate and decrease net sales when presented after
purchase, where self-benefits reduce returns and increase sales. The results indicate that
social norms can reduce the return rate and thereby increase net sales when discretionary
room for maneuver is present, fueling a sense of deviating from the norm. In contrast,
15
self-benefits reduce the return rate and increase net sales more strongly than social
norms after purchase, indicating a stronger congruence with consumers’ individual
decisions after purchase than with social norms. The second field study replicates the
finding that social norms reduce the product returns of customers with a high return
propensity. In addition, it shows the detrimental effects of social norms for customers
with a low return propensity. The interaction effects are particularly strong if the social
norm appeal is enhanced by referring to a reference group with which the targeted
customers can identify (e.g., other customers with the same fashion taste).
4 Synthesis
The goal of this dissertation is to advance knowledge on the theoretical foundations
and experimental applications of behavioral appeals to change product return behavior.
Four individual papers contribute to the research gap individually, but from different
perspectives. Developing additional knowledge on changing behavior has a high
practical relevance because consumers’ high levels of product returns and opportunistic
return habits have a negative impact on retailers. Thus, the revealed insights have to be
translated into actionable guidelines for marketers and practitioners in returns
management. Each individual paper provides implications showing how the respective
insights can be used in marketing practice and returns management. The following
cyclical process of changing return behavior draws on these key findings of the
dissertation, and outlines their internal connection in the context of changing return
behavior (see Figure 2).
16
Figure 2
Cyclical Process for Changing Product Return Behavior
Assess current
return behavior
Measure the
impact
Change
return
behavior
Plan and
implement appeals
Generate a
solution space
- Target customer
- Touchpoint
- Return motives
- Strategy choice
- Desired behavior
- Norm type
- Reference group
- Benefit type
4.1 Assess Current Return Behavior
The implementation of behavioral appeals in returns management is illustrated in a
four-step management process (see Figure 2). The first step represents the assessment
of the current return behavior. The analysis of the status quo serves to identify the
behavioral return problem. As outlined in the introduction, product returns do not
damage profitability uniformly. A moderate level of returns can increase sales and
profitability up to a threshold (Petersen and Kumar 2009). Only return levels exceeding
this threshold significantly decrease profits as the costs outweigh the benefits (Petersen
and Kumar 2010). Notably, many retailers suffer under extreme and fraudulent levels of
product returns, which open up great potentials for cost reductions through minimizing
instances of product returns. However, recent studies show that retailers only
rudimentarily assess the return propensities of their customers (Rösch 2015), and fail to
include product returns as metrics in their customer selection and optimal resource
allocation algorithms when measuring and maximizing customer value (Petersen and
Kumar 2015).
17
In order to use the mechanisms of behavioral appeals effectively, it is recommended
that online sellers identify their need for behavioral change as a first step. In doing so,
they must assess their customers’ current return behavior to get an idea about what
segments require behavioral changes and those that do not. The analysis of this status
quo helps to describe measurable objectives (e.g., reductions of the return rate for a
specific customer segment). The following steps provide insights on how these
objectives can be achieved.
4.2 Generate a Solution Space
After assessing the status quo, retailers have to find ways to achieve the objective
established in step one. This requires assessing different alternatives in order to open a
solution space. Three empirical papers and one literature review in this dissertation
highlight several key conditions in which social norms and self-benefits are more or less
effective in reducing product returns and maintaining sales. The following sections
provide an overview of these conditions that retailers should consider in order to find
solutions on how to change return behavior effectively.
Select target customer (Who?)
A critical step for marketers and practitioners in returns management is the
identification of the target customer group. On the one hand, retailer must recognize
those customers whose behavior they aim to change. That is, the retailer must identify
customer groups for whom a behavioral change could be worthwhile. On the other hand,
online sellers must be aware of the varying effectiveness of behavioral appeals across
different customer segments. The analysis of the current research findings in Paper 1
and the results of the experiments in Papers 3 and 4 indicate that the behavioral
effectiveness of social norms, in particular, requires a specific assessment of customer
segments.
Paper 1 provides knowledge of moderating influences on social norms. The
systematic examination of the literature has revealed trait moderators that can limit but
also intensify the behavioral effectiveness of social norms. For instance, those
consumers who have high levels of trait reactance or attitude certainty might be
influenced by social norms to a lesser degree than consumers whose reactance traits are
less pronounced. On the other hand, consumers who trust normative information have
been shown to be more likely to conform to a proposed social norm. When applying
18
social norms to change return behavior, retailers must take these potential trait
influences into account, especially when selecting customer segments for implementing
behavioral appeals.
Importantly, the experimental results across the dissertation (Papers 3 and 4)
indicate that social norms and, to some extent self-benefits, are effective in reducing
product returns of those customers who induce particularly high costs due to their
opportunistic behavior and high level of past returns. That is, self-benefits and social
norms can help retailers deal with heavy returners. The results of Paper 3 show that selfbenefits and social norms can reduce return probability of customers who demonstrate
a proclivity for fraudulent returns. Analogously, Paper 4 shows that social norms can
reduce product returns and increase sales of customers with a high return propensity.
However, while behavioral changes of heavy returners might imply substantial cost
reductions, retailers should not lose sight of the effects of social norms on customers
with a low (fraudulent) return propensity. The experiment in Paper 3 shows that social
norms increase return probability of low-fraudulent customers. Paper 4 confirms
detrimental effects on returns and sales for customers with a low return propensity.
Accordingly, retailers could reduce product returns and increase net sales if they
selectively use social norms for fraudulent returners and for customers with high return
propensity. Thus, knowing the degree of their customers’ past return levels and
fraudulent return proclivity not only helps retailers to plan efforts to change return
behavior, but it is also necessary in order to prevent detrimental effects of appeal
interventions with social norms.
This segment-oriented approach of using behavioral appeals gains in practical
relevance because, currently, retailers insufficiently analyze the return behavior of their
customers. According to the results of this dissertation, retailers are advised to better
interpret the return propensities of their customers in order to influence cost-intensive
segments more effectively with promising appeals like social norms and self-benefits.
Importantly, the results of this dissertation indicate that using one appeal strategy for all
customers is not effective, and can even harm the company.
Select the touchpoint (When?)
When selecting adequate customer segments for the use of behavioral appeals,
retailers must also find an adequate touchpoint to appeal to their customers as a next
step. In addition to providing knowledge on the effects of social norms and self-benefits
across different levels of customers’ return propensity, Paper 4 sheds light on the various
19
effects of appeals across the stages of the purchase process. That is, social norms reduce
product returns and increase net sales for customers with a high return propensity in the
purchase phase (i.e., in the shopping cart view), but increase returns and decrease sales
after purchase. In contrast, self-benefits reduce returns and increase sales after purchase.
These results indicate that retailers have to carefully assess what appeals they use, and
at what touchpoints. Specifically, retailers could benefit when they implement social
norm appeals at touchpoints, wherein the customer can still make adaptations to his or
her order. In contrast, self-benefits might be more effective when implemented in the
post-purchase stage (see Papers 2, 3, and 4). This is because customers react more
desirably to an appeal that is congruent with their post-purchase return decisions, which
are based on the autonomy to act according to their own desires. Thus, the implication
of this research regarding use of social norms and self-benefits in a differentiated way
also applies to customer touchpoints. Retailers can use self-benefit appeals more
broadly. If they want to maximize the potential of the appeals to increase profitability,
however, they primarily need to focus on the adequate implementation of social norms
at touchpoints in the purchase phase (see also Figure 3).
20
Figure 3
Solution Space for Changing Return Behavior
Consider return motives (Why?)
After selecting the right customer and the right touchpoint, the customers’ reasons
for returns must be considered. The results of the studies in this dissertation show that it
is crucial to consider return motives in order to maximize the benefits of social norms
and self-benefits in changing return behavior. Papers 2, 3 and 4 indicate that the
effectiveness of social norms in reducing product returns also varies across different
return reasons.
Paper 2 shows that social norms can reduce return intentions in the pre- and the postpurchase stage. Paper 3 confirms directionally that for fraudulent returners, social norms
can reduce product returns in the post-purchase stage. However, Paper 4 shows
detrimental effects on product returns in the post-purchase stage for heavy returners.
21
Notably, the post-purchase scenarios of Papers 2 and 3 present situations in which the
customer must decide whether to keep or return a product that is acceptable but does not
fully meet his or her expectations. The first field experiment in Paper 4 naturally covers
all return motives that could happen in the experimental period. Thus, the post-purchase
condition of the field experiment probably contained a large proportion of choice orders
(i.e., ordering an item in several sizes or colors), which usually account for a large
percentage of product returns (Pur et al. 2013). In the situation of an acceptable product
that does not fulfill expectations, social norms might dissuade the customer from
returning, as is shown in Papers 2 and 3. However, if the customer has ordered several
items, intending to choose from among them, she or he will need to return a part of the
order (e.g., the items that do not fit). In this case, social norm information describing
others avoiding returns might cause feelings of reactance, as the targeted customer is
placed in a situation where the return is restricted. In contrast, a communication
promoting self-benefits is more congruent with post-purchase situations of autonomy to
decide what items to keep and what to return. This mechanism has been shown in the
first field study of Paper 4, and has been revealed in other contexts of previous studies
that analyze self-benefits and social norms (e.g., White and Simpson 2013; see also
Paper 1). That is, in post-purchase situations of unfulfilled expectations, social norms
and self-benefits might be effective in reducing product returns (as shown in Papers 2
and 3). In the case of choice orders that imply returns, however, social norms exert
detrimental effects when presented in the post-purchase stage, whereas self-benefits
could still prevent product returns and increase sales, as is shown in Paper 4.
Thus, when intending to implement social norm appeals, retailers must consider
their customers’ motives to return. If a major proportion of customers place choice
orders, which is often the case in fashion retailing, then social norms should be used
only across touchpoints in the purchase phase. Possible reactance effects of social norms
after purchase could then be avoided, whereas their potential could be used to avoid
choice orders in the purchase phase. Instead, for customers, who are often unsure about
whether to return or keep a product after purchase, social norms could be used, in
addition to self-benefits, as an effective post-purchase appeal (see Figure 4).
22
Figure 4
Solution Space for Influencing Post-Purchase Return Decisions
These results across the different studies of the dissertation indicate that retailers
must consider the return motives of their customers when intending to use the potentials
of social norms. Given that most retailers record the reasons behind every product return,
there is great potential for achieving changes in customers’ product return behavior by
adequately utilizing the self-benefit and social norm appeals. However, a recent practice
study of the Fraunhofer Institute shows that assessment of customer reasons for return
is not widely accomplished—that more than half of German mail order companies either
do not (21%) assess reasons for returns, or only partially do so (34%) (Rösch 2015).
According to the revealed potential for social norms and self-benefits to reduce product
returns and increase net sales, retailers might benefit in providing more resources to
analyze return reasons, in order to adequately use appeals to reduce product returns.
23
Choose an effective strategy
Assessment of the various circumstances influencing the effects of the appeals
opens a solution space that provides possible appeal strategies for affecting return
behavior (see Figures 2 and 3). The retailer can choose between possible appeal
strategies of implementing behavioral appeals. Based on the specified objective (e.g.,
reduce choice orders), the retailer can focus on a specific strategy (e.g., using social
norms in the purchase phase), and can also simultaneously choose several alternatives
(e.g., social norms in the pre-purchase stage and self-benefits in the post-purchase
stage).
4.3 Plan and Implement Appeals
The implementation phase puts the selected solution into practice. Based on the
appeal strategy that is chosen, marketers must craft adequate appeals to affect return
behavior of the right customer at the right touchpoint. Specifically, the results of this
dissertation suggest the formulation of behavioral appeals that create customers
awareness of a social norm or a self-benefit. Although the content of behavioral appeals
can be implemented textually at crucial customer touchpoints and with reasonable effort
and cost, the psychological mechanisms of social norms in particular (see Papers 1, 3,
and 4) require consideration of several factors in order to obtain desirable (but not
detrimental) effects on product returns and sales.
Proposing the desired behavior
Retailers who craft social norm appeals with the goal of creating behavioral change
must be aware of which specific behaviors they should highlight. Research findings
(reviewed in Paper 1) have shown that social norm appeals should always highlight the
desired behavior and not the undesired behavior. For instance, when a company aims to
prevent specific behaviors, it is tempting to point out mistakes and undesirable behaviors
of customers (e.g., reminding people that too many customers return products
fraudulently). However, according to empirical evidence in line with the focus theory of
normative conduct (see Paper 1), this could backfire, or even cause customers to engage
more in the undesirable behavior (Cialdini et al. 2006). This also applies to using low
prevalence rates to motivate people because, following the maxim “When so few people
engage in this behavior, it might not be a sensible thing to do,” this information might
result in a boomerang effect. Low prevalence increases undesired behavior because it
24
indicates that the undesired behavior is the normal behavior. Studies on the influence of
social norms show that the common tendency to postpone or refuse to engage in an
unpleasant behavior can easily be reinforced by conveying that it is “normal” not to
participate in the respective unpleasant behavior (see Paper 1; Sieverding, Decker, and
Zimmermann 2010).
Retailers must be aware of these effects, especially when they aim to prevent
specific behaviors or want customers to engage in behaviors that are generally seen as
rather unpleasant or unpopular, such as avoiding product returns. That is, retailers using
social norm appeals need to focus the target audience solely on the type of norm that is
consistent with the desired behavior (Cialdini et al. 2006; Cialdini, Reno, and Kallgren
1990), such as pointing out those customers who avoid product returns. Moreover,
retailers need to be certain that they present a high prevalence of desired behavior (i.e.,
that a majority of customers avoid returns). Therefore, in crafting the social norm
appeals of the field experiments (Paper 4), the context of choice orders is included and
the focus is on the socially accepted topic of environmental protection in order to
provide truthful and credible social norm information for the social norm appeals of the
field experiments (Paper 4),
The social norm appeals of both field experiments contain a high prevalence of, and
strong focus on, the desired behavior of product returns avoidance. The effects on the
return rate and net sales confirm the recommendations above.
Type of social norm
When planning social norm appeals, retailers must decide what type of social norm
they want to focus on. For instance, Paper 1 has summarized several forms of
communicating social norms, such as peer information or social comparison. The social
norm appeals in the experiments of this dissertation are based on providing peer
information—specifically that a majority of other customers avoid product returns.
Retailers could also benefit from alternative approaches such as comparing the return
rate of the individual customer with the average return rate of the other customers. This
would require ad hoc calculations to provide up-to-date return comparisons with every
order. The studies across the three empirical papers show that peer information can be
sufficient to influence return behavior. Moreover, in the studies for Papers 3 and 4, the
detrimental effects of peer information appeals for non-fraudulent and low-returning
customers are analogous to the boomerang effects of social comparison appeals shown
in other contexts (see Paper 1). Based on their own resource capacities and customer
25
knowledge, therefore, retailers must decide what methods would most effectively make
customers aware of social norms.
This also includes choosing between the injunctive and descriptive character of
social norms, which have been outlined in Papers 1 and 3. For example, the field
experiments focused on descriptive norms because the cooperating retailers preferred to
avoid appeals with a prescriptive character, as in the case of injunctive norms.
Accordingly, Paper 3 shows that injunctive norms can induce particularly strong
reactance effects for customers who feel restricted in their autonomy. Paper 2 shows a
stronger reduction in return intention achieved through descriptive rather than injunctive
norms. In contrast, Paper 1 demonstrates findings indicating that the behavioral effect
of injunctive norms is more robust than that of descriptive norms. This is especially the
case when the proposed norm is credibly socially important (e.g., avoiding littering).
Hence, retailers must decide for each individual case what framing method (descriptive
or injunctive) is better suited to ensure desirable effects and to avoid reactance.
According to the results of this dissertation, retailers can reduce product returns more
desirably when they communicate social norms in a descriptive rather than in an
injunctive way. Alternatively, it remains possible to test both variants, as is shown in
Papers 2 and 3.
The appropriate reference group
In addition to choosing the adequate frame for highlighting the desired behavior of
others, retailers should also consider to whom they refer the social norm appeal. The
first laboratory experiment in Paper 2, as well as the large-scale field of Study 2 in Paper
4, indicate that marketers could benefit from presenting the behavior of appropriate
reference groups in their social norm appeals. As both studies illustrate, this could be
done by conveying the behavior of persons with whom targeted customers identify. The
results of the experiments indicate that social norm appeals that refer to people with
similar characteristics (i.e., customers who bought or viewed the same products) have a
stronger influence on behavior than social norms that refer to unspecified customers.
Thus, retailers could increase the effectiveness of social norm appeals if they specify
and localize the norm message, or at least enable the target customers to identify with
those whom the company wants customers to mimic (see also in Paper 1 Gino, Ayal,
and Ariely 2009; Martin 2012). This could be done by referring to both sociodemographic and psychographic criteria, such as in the case of the experiments in Papers
2 and 4 (i.e., customers who bought the same product or have the same tastes).
26
Type of benefit
One aim of the studies in this dissertation was to compare social norms with standard
approaches, in order to create knowledge about social norms` behavioral effects in
relation to other behavioral appeals that are more common-sense (see also Nolan et al.
2008). The studies of this dissertation, therefore, use self-benefit appeals as well.
Notably, little knowledge on the effects of self-benefits on customers’ return behavior
is available. More importantly, self-benefits exerted reductions in product returns
(Papers 2, 3, and 4) without providing monetary benefits. The implication for retailers
is that it can be sufficient to make customers aware of benefits that are not associated
with costs for the company. The only costs lie in inventing benefits that can dissuade
customers from returning. The results of the studies have shown that making customers
aware of environmental benefits to their life quality through avoiding returns may be
one possibility for affecting return behavior, especially in the return decision after
purchase.
4.4 Measure the Impact
Finally, marketing practitioners should measure the impact of the implemented
behavioral appeals. As the studies of the different papers have shown, this can be done
by using experimental approaches. In practice, trial-and-error methods such as A/B
testing with test consumer samples enable tracking of whether or not customers accept
behavioral appeals or behave differently. For example, the experiments in Papers 2 and
3 have already achieved profound findings on the behavioral potential of social norms
and self-benefits in laboratory settings. Paper 2 found that social norms and self-benefits
have an impact on return intentions. Paper 3 sheds additional light on the impact of the
appeals in interactions with relevant customer segments (i.e., fraudulent returners).
However, to measure the impact of the appeals on actual behavior, retailers must
test the appeals in the field. The two large-scale field studies in Paper 4 provide insights
on the impact that social norms and self-benefits have on actual return and purchase
behavior. As outlined with the existing experimental results in the previous steps, an
experimental approach provides insights not only regarding the overall impact of
behavioral appeals. Importantly, the field and laboratory experiments of Papers 3 and 4
have also created knowledge on the conditions in which social norms and self-benefits
are more (or less) effective in reducing product returns. Therefore, in their efforts to
27
measure the impact of implemented appeals, retailers should not only focus on the
overall effects of behavioral interventions, but should also analyze data differentiatedly.
Similarly, retailers should not give up if there will be no overall reduction in product
returns through a social norm or a self-benefit across all customers. Importantly, they
must carefully analyze relevant conditions (i.e., touchpoints and customer segments) in
which the effects of social norms and self-benefits could vary significantly. For instance,
the first field experiment of Paper 4 shows that social norms and self-benefits do not
exert significant main effects on return behavior and net sales. However, the analysis
across different touchpoints (i.e., high and low levels of discretionary room for
maneuver) shows significant interaction effects, indicating that social norms reduce
product returns when presented in the purchase phase, but increase them when presented
after purchase. A differentiated analysis, including relevant additional influence factors
such as customer characteristics and the purchase process, is essential for gaining
relevant knowledge on the effects of behavioral appeals on return behavior. Based on
the results, the applier can optimize or adjust the appeals and the experimental design if
necessary, and then try again. Once marketers have measured the differentiated effects
of their appeal initiatives, they can assess the performance of each used appeal type
across various conditions, and may then implement them selectively.
Taken together, this cyclical process of implementing behavioral appeals provides
marketers and practitioners in returns management with an actionable framework for
systematically changing the return behavior of their customers. Based on the results of
the dissertation, the process considers relevant customer segments, touchpoints, and
return motives to enable retailers to selectively implement social norms and selfbenefits. Through such a method, retailers will be capable of effectively reducing
product returns and increasing net sales.
5 Conclusion
Current levels of product returns are threatening the profitability of retailers.
However, an intensifying competition in e-commerce, and the necessity to reduce
barriers arising through the spatial separation between supply and demand, make it
impossible for retailers to restrict return policies. Instead, their obliging nature fuels
product returns and provides a breeding ground for opportunistic return behavior.
Therefore, retailers use preventive measures to reduce the return rate, whereas research
28
examines return motives and optimal configurations of return policies. Although current
return behaviors make obvious the retailers’ need for a behavioral change of their
customers, behavioral approaches have been widely ignored in research and practice.
Four individual research papers examine the foundations and experimental
applications of behavioral appeals to influence product return behavior in online
retailing. The findings suggest that social norm and self-benefit appeals can be used
successfully to reduce product returns and maintaining sales. The experimental studies
in this dissertation show relevant boundary conditions that assist retailers in most
effectively implementing behavioral appeals. This umbrella article summarizes the key
findings of the four papers and derives an actionable guideline for marketing managers
and practitioners in product returns management. This cumulative dissertation is
intended to enhance knowledge as well as to provide valuable insights and actionable
implications for practitioners on the effectiveness of behavioral appeals to change
product return behavior.
29
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35
B PAPER 1: THE BEHAVIORAL IMPACT OF SOCIAL
NORMS AND THEIR POTENTIAL IN
MARKETING: A LITERATURE REVIEW
36
THE BEHAVIORAL IMPACT OF SOCIAL NORMS AND THEIR
POTENTIAL IN MARKETING: A LITERATURE REVIEW
Authors
Thilo Pfrang
Abstract
Social norms, the human tendency to orient conduct toward the behavior of others, have
been proven to influence various behaviors, arousing the interest of marketers who aim
to influence consumer behavior. However, examinations of the behavioral relevance of
social norms across various research areas with different methodological and conceptual
approaches have led to fragmentations and inconsistencies. Research that structures the
various social influences in order to provide insights into the use of social norms in
marketing is scarce. This article reviews empirical studies that focus on the influence of
social norms on behavior, then structures and summarizes them to provide an overview
of the behavioral mechanisms of social norms. The article discusses the ways in which
social norms are communicated, their salience, moderating influences, and effects of
normative beliefs. Between and within these conceptual categories of social influence
literature, the author explains how social norms influence human behavior and how the
identified categories are related. Finally, the author concludes with thoughts on how the
reviewed research results can help marketing practitioners in their own business
practices, and suggests directions for further research.
37
1 Introduction
“If a person jumped out of a window, would others follow?” “Do people follow one
another like lemmings?” In many situations, these figures of speech have a realistic
background. Scholars from various disciplines have shown that people’s behavior is
shaped, largely, by the behavior of those around them, especially those close to them or
with whom they identify (Griskevicius, Cialdini, and Goldstein 2008). This
phenomenon is what behavioral scientists call social norms (Martin 2012). People tend
to conform to what others do (Burchell, Rettie, and Patel 2013). By orienting toward the
behavior of others, a person can usually decide efficiently and well, especially in cases
of uncertainty or in situations where quick decisions are needed (Cialdini, Reno, and
Kallgren 1990). In addition to showing how an individual’s behavior is shaped by the
behavior of others, studies have also shown that social norms can be used to convert
undesirable behaviors into desirable ones, such as reducing energy consumption (e.g.,
Schultz et al. 2007), using hotel towels more than once (Goldstein, Cialdini, and
Griskevicius 2008), or paying taxes punctually (Martin 2012). These mechanisms of
behavioral change are naturally of interest to marketers who aim to create behavioral
change in their customers by increasing purchase behavior (e.g., selling more of a newly
launched product) and preventing behaviors that increase company costs (e.g., product
returns).
However, business executives and marketing practitioners have taken little notice
of the potency of social norms (Griskevicius, Cialdini, and Goldstein 2008). According
to Burchell, Rettie, and Patel (2013), only a small group of dedicated practitioners and
social psychology researchers utilize social norms to influence consumer behavior. This
may be a result of limited understanding of the psychological mechanisms of social
norms (Griskevicius, Cialdini, and Goldstein 2008). Many businesses are only now
beginning to experiment with social norms as a tool to drive profits, and they need
insights to guide them in implementing such appeals (Martin 2012).
Similarly, scientific examination of the effects of social norms on purchase behavior
has only recently been initiated, and includes only a small number of studies
(Griskevicius et al. 2009; Melnyk et al. 2011; Melnyk et al. 2013; Noguti and Russell
2014). Burchell, Rettie, and Patel (2013) note that the social norm approach represents
a lacuna in the academic marketing literature. Moreover, although a large body of
research in other fields examines how individual behavior is shaped by the behavior of
38
others, because of the domain and methodological differences between the studies,
results have been far from consistent (Melnyk, Van Herpen, and van Trijp 2010).
Examination of social norms across various fields has led to a fragmentation of its
effects and mechanisms. However, research that structures the fragmented social
influence literature in order to provide insights into using social norms in marketing is
scarce. This work aims to summarize and structure the recent knowledge of social norms
in order to provide insights for the use of social norms in marketing.
2 Research Gap
Previous literature reviews have addressed conformity and social norms in other
contexts (Claidière and Whiten 2012), whereas meta-analyses have examined the
overall strength of association between social norms and behavior on an aggregate level.
These studies fail to provide a deeper understanding of the mechanisms involved,
however, or the potential for marketing and business research as well as practice (Bond
and Smith 1996; Rivis and Sheeran 2003; Wood, Wong, and Chachere 1991).
The literature on social norms in the context of consumer research provides only
initial steps for a consideration of social norms in marketing. In their review on
consumer conformity, Lascu and Zinkhan (1999) focus on theoretical backgrounds and
basic conformity effects without taking into account the current studies and research
results on social norms. Burchell, Rettie, and Patel (2013) provide insights into the use
of social norms in social marketing. However, in addition to focusing on social
marketing, their conclusions are not based on current research of the academic literature.
The meta-analysis by Melnyk, Van Herpen, and van Trijp (2010) is the first overview
of social norms in a consumer context to have created initial knowledge on the variety
of effects of social norms, and their use in consumer and marketing research. However,
although individual meta-analyses and conceptual works have developed initial insights
into the mechanisms of social norms and their use in marketing research, these works
do not create a deeper understanding of the multitude of social norm mechanisms under
different conditions based on a systematic review of current state-of-the-art research.
Moreover, previous research does not focus on deriving avenues for further research on
social norms in marketing, nor does it provide concrete implications on how and in
which areas in marketing social norms could be used.
39
This research aims to extend knowledge of the mechanisms of social norms by
systematically reviewing current research in order to develop a deeper understanding
about social norms, and to provide implications for marketing practice and directions
for further research in marketing. For this purpose, the article takes up selected empirical
studies on the influence of social norms on behavior, then structures and summarizes
them to present a distinct overall picture (see Figure 5). Based on this review, the author
derives avenues for further research, and concludes with suggestions for ways that help
marketing practitioners to use the findings in their own business practices.
The literature review is structured as follows: First, the underlying theoretical
background is explained. Then the methodology of the literature search is presented,
followed by the literature review. The research works are organized according to
conceptual principles and influence factors that help to explain the psychological
mechanisms of social norms (see Figure 5). Within this structure, the findings of each
work are presented and discussed. Based on the findings, implications for the use of
social norms in marketing and directions for further research are presented.
40
Figure 5
Discussion Topology
3 Theoretical Background
3.1 An Evolutionary Approach
If humans want to join a group, or are facing an uncertain situation, they tend to
monitor and mimic the behavior of others. As social animals, humans tend to focus on
preserving group cohesion (Zeigler-Hill, Welling, and Shackelford 2015). The
orientation toward the conduct of others is a result of evolutionary success, and has been
shown to be an unconscious feature (Chartrand and Bargh 1999) programmed into our
genes (Chiao and Blizinsky 2010). Drawing on evolutionary theory to explain
behavioral orientation toward others was justified when nonhuman species such as
monkeys, as well as more distantly related animals such as rats and fish, showed similar
41
patterns of group cohesion and conformist behaviors (Claidière and Whiten 2012;
Zeigler-Hill, Welling, and Shackelford 2015).
Animals have highly evolved self-protection strategies that increase their
probability of survival. In perceived threat situations, these strategies are activated
automatically. For instance, many animal species mimic others to increase survival
benefits (Wickler 1968), whereas social animals adhere more closely to their group
when threatened by a predator. The decrease in individual salience to the predator and
the reciprocal protection of the group increase the probability of survival (Alcock 2005;
Hamilton 1971).
Consistent with the evolutionary survival strategies of animals, mimicry and
imitation serve a similar function in human self-protection (Dijksterhuis, Bargh, and
Miedema 2000; Griskevicius et al. 2006). Human ancestors secured the survival of
humankind by developing and adapting similar strategies of group cohesion to protect
themselves from physical threats and dangers. Making decisions that served their selfprotective interests and being successful at solving self-protection problems are two of
the reasons that the human species exists today (Daly and Wilson 1988; Griskevicius et
al. 2009).
The ancestral instinct for self-protection and for developing survival strategies
continues to appear in everyday situations. People tend to herd closer to their groups and
are more likely to conform to the opinions of others when they feel afraid (Griskevicius
et al. 2006; Griskevicius et al. 2009). We mimic others in situations of uncertainty and
thereby often adopt successful and efficient behaviors that provide informationprocessing advantages and decisional shortcuts (Cialdini 2008). This is especially true
when quick decisions have to be made and individual-acquired information is costly
(Boyd and Richerson 1988; Cialdini, Reno, and Kallgren 1990). Using these strategies,
human ancestors were able to survive, thereby establishing an infinitely remarkable
species.
3.2 The Principle of Conformity
The evolutionary phenomenon of normative behavior set the stage for a theoretical
approach that is compatible with behavioral and evolutionary sciences, and that helps to
explain why people tend to conform to what other people do—following the principle
of conformity. Conformity can be defined as a condition in which individuals engage in
42
a particular behavior because it is frequently observed in others (Claidière and Whiten
2012). Cialdini and Trost (1998) define conformity as behavioral change designed to
match or imitate the beliefs, expectations, or behaviors of real or imagined others, and
the authors focus on the potential for conformity to change behaviors. That is,
conformity refers to changing attitudes and behaviors so as to fall in line with the social
norms accepted by other people (Cialdini and Goldstein 2004; Kim and Hommel 2015).
Social psychology distinguishes between two types of motivations for conformity,
both introduced by Deutsch and Gerard (1955): informational and normative conformity
motivations. Informational conformity refers to situations of uncertainty where
individuals are trying to behave accurately and therefore need evidence about reality.
People conform to the behavior and information of others because they believe that these
others may be correct (Campbell and Fairey 1989). Hence, informational conformity
refers to procuring nonsocial information and orienting toward the behavior of others in
a nonsocial environment. Other persons’ awareness of the individual’s behavior does
not affect this type of conformity, which exists in the absence of social feedback on
behavior (Claidière and Whiten 2012). Thus, this belief in the superior knowledge of the
group can occur even when others are absent—for example, if an individual is merely
informed about the opinion or behavior of a group that he or she does not personally
know (Kim and Hommel 2015). For instance, you are staying in a foreign city and want
to have something to eat. You might assume that it is best go to a restaurant where a
good number of other people are eating, as the number of individuals eating at a specific
restaurant provides information regarding the quality of the food and of the facility. At
the same time, the other peoples’ awareness of your behavior (deciding whether or not
to go to the restaurant) does not affect your decision to eat at this restaurant. Anyone
who is uncertain of what to do looks around at what others are doing. However, although
informational orientation toward others in situations of uncertainty can be helpful in
making good, efficient decisions (Cialdini, Reno, and Kallgren 1990), it can also have
dramatic consequences. Such an outcome may develop if the other people do not know
what to do either, so that no one does anything. This pluralistic ignorance, a form of
informational conformity, is a frequent reason for people’s failure to render assistance,
for example helping in a scene of an accident (Latane and Darley 1968).
In contrast, normative conformity occurs in situations where people need to manage
social interactions with the goal of obtaining social approval and meeting the
expectations of others (Bardsley and Sausgruber 2005; Cialdini and Goldstein 2004).
That is, individuals conform because they believe that similarity promotes favor, and
43
that agreeing with others will help them to fit in with the group and avoid rejection
(Papyrina 2012). Based on the desire to maximize social outcomes, this type of
conformity is influenced by the social consequences of one’s behavior (Campbell and
Fairey 1989). For instance, even if a person knows that drinking alcohol influences how
he or she will feel the next day, and thus does not feel comfortable drinking alcohol in
specific situations, that person might still be motivated to drink if others do so (e.g., on
a company outing). In that case, the social consequences of not joining in with a group
of people drinking alcohol can outweigh personal preference, knowledge, and
experience (Claidière and Whiten 2012). A classic experiment showing the mechanism
of normative conformity was carried out by Asch (1951), and is often cited to explain
the general concept of conformity. Asch asked participants to judge the lengths of
different lines. The results showed that pressure to conform made the participants pass
clearly incorrect judgments. Specifically, a considerable proportion of participants
matched their responses with the estimates of a group of research confederates, despite
the fact that the opinions expressed by the confederates were blatantly incorrect.
Although the literature emphasizes that normative and informative conformity are
conceptually independent (Campbell and Fairey 1989; Cialdini, Reno, and Kallgren
1990; Deutsch and Gerard 1955), both functions of conformity are interrelated and may
vary between different individuals and situations when conformity becomes salient
(Cialdini and Goldstein 2004). For instance, there can be situations in which an
individual conforms for both normative and informational reasons simultaneously—for
formal occasions, as an example (Reno, Cialdini, and Kallgren 1993). Moreover, an
individual might also first conform for normative reasons, only to find out that this
brings important information about reality. Conversely, informational conformity can
also lead to normative conformity when a conformity action for informational reasons
later becomes socially approved (Claidière and Whiten 2012). Given that people
encounter situations of conformity in all areas of daily life, research has highlighted
various influences that conformity imposes on human behavior, with overwhelming
support for the classical finding that people tend to conform to what others do (Kim and
Hommel 2015).
3.3 Focus Theory of Normative Conduct
Based on the principle of conformity and the distinction between informational and
normative social motivations, researchers (e.g., Cialdini, Reno, and Kallgren 1990;
44
Kallgren, Reno, and Cialdini 2000; Reno, Cialdini, and Kallgren 1993) have developed
a theory in which norms effectively influence behavior only when an individual’s
attention is focused on the norm (Jonas et al. 2008; Krupka and Weber 2007). This
theory projects informational and normative conformity on two distinct types of norms.
According to Cialdini, Reno, and Kallgren (1990), it is crucial to discriminate between
descriptive norms and injunctive norms.
Descriptive norms include what is commonly done and what motivates people, and
in this way they provide evidence of what is likely to be an effective and adaptive action
(Cialdini et al. 2006). As the term itself suggests, descriptive norms describe what others
are doing (Goldstein, Cialdini, and Griskevicius 2008); for example, they might provide
information on the average percentage of people who save energy (Nolan et al. 2008).
In line with informational conformity, descriptive norms provide information about how
to behave accurately in a given situation of uncertainty. On the other hand, injunctive
norms cover what is commonly approved/disapproved (what “ought” to be done). This
type of norm informs people about what others think should be done (Cialdini, Reno,
and Kallgren 1990; Schultz et al. 2007); injunctive norms motivate behavior through
promises of social rewards and punishments (Cialdini et al. 2006). By providing
information about what receives approval from others, injunctive norms conceptually
rest upon normative conformity (Cialdini and Goldstein 2004).
Cialdini et al. (2006) tested this theory in several field experiments, providing
support for the theory´s central postulates about norm focus and the different effects of
descriptive and injunctive norms. That is, social norms directly influence behaviors
(e.g., littering) only when they are focal (Kallgren, Reno, and Cialdini 2000), and
activating descriptive or injunctive norms can lead to significantly different behavioral
responses (Reno, Cialdini, and Kallgren 1993). For instance, a research confederate
picking up litter (injunctive norm) could decrease littering behavior in both clean and
littered environments, whereas a confederate littering (descriptive norm) only increased
littering in a littered environment. Another experiment showed that the frequency of
litter was higher when there was no litter in the environment than when there was one
piece of litter. Cialdini et al. (1990) attribute this result to a “focus of attention,” in which
the one piece of litter makes the antilittering norm salient, whereas the absence of litter
results in a lack of focus on the antilittering norm. Accordingly, the meaning of this
theory becomes especially apparent in situations where people are intentionally
persuaded to conform to existing norms.
45
Research highlighting the mechanisms of this theory has shown that many attempts
arising from a positive intent to change disapproved conduct often achieve just the
opposite. This is because of a tendency to highlight a regrettable frequency of the
undesirable behavior (e.g., by appealing to visitors not to remove wood from a park, and
emphasizing this appeal by informing them about how many past visitors have removed
wood). Cialdini et al. (2006) shows that such normative communication inadvertently
instills a counterproductive descriptive norm in the minds of the audience, increasing
undesirable behaviors (i.e., removing wood). The various empirical evidences indicate
that a successful behavioral change depends on the users´ awareness of the different
effects of descriptive and injunctive norms, and on his or her ability to focus the target
audience only on the type of norm that is consistent with the desired behavior (Cialdini
et al. 2006; Cialdini, Reno, and Kallgren 1990). Scholars have utilized these individual
mechanisms—which manifest themselves in the focus theory of normative conduct—in
several different areas in order to provoke behavioral changes, predominantly to induce
pro-environment and prosocial behaviors.
4 Methodology
The focus theory of normative conduct, as a further development of the principle of
conformity, provides the theoretical basis for empirical examinations of the influence of
social norms on behavior. Accordingly, a large number of articles focusing on the
influence of social norms on behavior refer to the focus theory of normative conduct by
citing Cialdini, Reno, and Kallgren (1990), whose theory-building work achieved 2062
citations in Google Scholar (as of July 2015). Thus, the present review focuses on works
that cite Cialdini, Reno, and Kallgren (1990) and apply social norms in various forms
as an independent variable, in order to examine their influence on behavior or behavioral
intentions. After a search of scientific databases such as EBSCO, Elsevier, and Google
Scholar, the final sample consists of 49 articles, and include works that were published
in top-tier academic journals (rating A+, A, or B according to VHB-JOURQUAL3 and
SJR SCImago Journal & Country Rank) and were cited five times or more. Notably, we
made exceptions for related papers that had been recently published in top-tier journals
and had, therefore, not yet received five citations (e.g., Iyengar, Van den Bulte, and Lee
2015 in Marketing Science).
In contrast to the prior literature and conceptual overviews on social norms (e.g.,
Burchell, Rettie, and Patel (2013), solely academic studies in leading scientific journals
46
from different research areas such as psychology (e.g., Psychological Science,
Psychological Bulletin, and the Journal of Personality and Social Psychology),
economics (e.g., the Journal of Public Economics and the Journal of Economic Behavior
and Organization), consumer research (e.g., the Journal of Consumer Research),
marketing (e.g., the Journal of Marketing Research, Marketing Science, and the Journal
of Marketing), finance (e.g., the Journal of Finance), and politics (e.g., the Journal of
Politics) form the basis of the present literature review. One exception is the work of
Martin (2012) in the Harvard Business Review, which is also based on empirical studies
providing crucial knowledge on the transfer of the social norms approach to marketing
and business practice.
The total of 49 articles (108 empirical studies) were selected for a qualitative content
analysis. Due to the wide variety of examined behaviors, the articles were categorized
based on the conceptual components and influence factors of social norms, and then
discussed within and between these categories (see Figure 5). The categorization helped
to provide a deeper understanding of the mechanisms determining the behavioral effects
of social norms, which will be useful for deriving implications for marketing and
directions for further research. For an overview of the reviewed studies, see Table 2.
47
Table 2
Descriptive and Injunctive Norms in Social Influence Research
Authors
Descriptive
norms
Injunctive
norms
Examined behavior
Osterhus 1997
Schultz et al. 2007
Nolan et al. 2008
Göckeritz et al. 2010
Allcot 2011
Ayres, Raseman, and Shih 2013
Loock, Staake, and Thiesse 2013
Bardsley and Sausgruber 2005
Goldstein and Cialdini 2007
Jonas et al. 2008
Krupka and Weber 2009
Anik, Norton, and Ariely 2014
Griskevicius et al. 2009
Melnyk et al. 2011
Melnyk et al. 2013
Mourali and Young 2013
Warren and Campbell 2014
Noguti and Russell 2014
Goldstein, Cialdini, and Griskevicius 2008
Thøgersen 2008
White and Simpson 2013
McDonald, Fielding, and Louis 2013
Cialdini, Reno, and Kallgren 1990
Reno; Cialdini, and Kallgren 1993
Kallgren, Reno, and Cialdini 2000
Keizer, Lindenberg, and Steg 2008
Sanderson, Darley, and Messinger 2002
Grier et al. 2007
McFerran, Dahl, and Fitzsimons 2009
Aldrovandi, Brown, and Wood 2015
Wenzel 2005
Martin 2012
Bobek, Hageman, and Kelliher 2013
Zaki, Schirmer, and Mitchell 2011
Kim and Hommel 2015
Vries et al. 1995
Buunk, Van Den Eijnden, and Siero 2002
Conlin, Lynn, and O’Donoghue 2003
Gerber and Rogers 2009
Sieverding, Decker, and Zimmermann, 2010
Jacobson, Mortensen, and Cialdini 2011
Ashraf, Bandiera, and Lee 2014
Wood, Wong, and Cachere 1991
Paluck and Shepherd 2012
Iyengar, Van den Bulte, and Lee 2015
Beshears et al. 2015
Aarts and Dijksterhuis 2003
Gino, Ayal, and Ariely 2009
Galinsky et al. 2008
X
X
X
X
X
X
X
X
X
X
X
Energy consumption
Energy consumption
Energy consumption
Energy consumption
Energy consumption
Energy consumption
Energy consumption
Prosocial behavior
Prosocial behavior
Prosocial behavior
Prosocial behavior
Prosocial behavior
Attitudes toward product
Attitudes toward product
Attitudes toward product
Attitudes toward product
Attitudes toward product
Purchase intention
Pro-environmental behavior
Pro-environmental behavior
Pro-environmental behavior
Pro-environmental intentions
Littering behavior
Littering behavior
Littering behavior
Littering behavior, stealing
Eating disorder
Fast food consumption
Quantity of food
Willingness to pay for healthy food
Tax compliance
Tax compliance
Tax compliance
Attractiveness rating
Attractiveness rating
Smoking behavior
Condom use intention
Tipping behavior
Vote intention
Cancer screening
Conflict/Conformity decisions
Exam performance
Aggression behavior
Harassment behavior
New product adoption and repeat
Savings behavior
Normative behavior
Cheating behavior
Task evaluation
Total: 49
44 (90%)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
25 (51%)
48
5 Triggers of Social Norms
Researchers have applied various methods and techniques to investigate behavioral
influences of social norms. The main body of research covers the experimental
manipulation of social norms. However, individual studies examine social influence by
measuring social norms with surveys. Figure 6 provides an overview of the different
possibilities for triggering social norms in the minds of the recipients. The following
sections discuss and summarize the associated research findings.
Figure 6
Descriptive and Injunctive Norm Triggers
5.1 The Manipulation of Social Norms
The principle of conformity and the focus theory of normative conduct suggest that
people orient their behavior toward their fellow human beings and tend to conform to
49
what others do. However, if people are asked whether they always conform to the
behavior of others, they usually deny it. Research has shown that people are not likely
to believe that the behavior of others influences their own behavior (Nolan et al. 2008).
Instead, the mechanism of conformity and norm adherence is often subtle, unconscious,
and heuristic-based (Cialdini and Goldstein 2004). Nevertheless, provoking this
mechanism requires making people aware of the behavior of others. Research has
examined several ways to “frame” a social norm and communicate it to recipients. The
following sections discuss the different methods of providing information about what
others are doing or think should be done and their impact on the behavior of individuals.
The relevant research providing knowledge on the manipulation of social norms consists
nearly exclusively of experimental studies in various behavioral contexts.
Peer information
Peer information interventions involve disseminating information regarding what a
target population’s peers are typically doing or think should be done (Cialdini, Reno,
and Kallgren 1990). Sharing this social norm information aims to teach people that a
certain behavior (i.e., the desired behavior) is more common than they had previously
believed, and to thereby motivate them to engage in the disseminated behavior (Beshears
et al. 2015). Being exposed to peer information can provoke conformity with the
presented social norm for two reasons: First, an individual may mimic fellow human
beings because their behavior provides evidence as to what will likely be an effective
and adaptive action in times of uncertainty (Cialdini, Reno, and Kallgren 1990). Second,
peer information highlights social norms from which people do not want to deviate, due
to the risk of social sanctions, identity considerations, strategic complementarities, or
proclivity for conformity (Asch 1951; Beshears et al. 2015; Festinger 1954; Schultz et
al. 2007).
The most popular way to communicate descriptive and injunctive norms with peer
information is to expose subjects to appeals in which behavioral descriptions of a
specific reference group are provided (Jacobson, Mortensen, and Cialdini 2011; Melnyk
et al. 2013; White and Simpson 2013). Most studies use appeals that employ facts and
statistics to highlight the information that a majority of a specific reference group
engages in the desired behavior (e.g., Goldstein, Cialdini, and Griskevicius 2008; Nolan
et al. 2008) or has the opinion that everybody should (Melnyk et al. 2011). Numerous
studies have been able to demonstrate the effects on various behaviors by applying this
type of social norm framing (see Table 3).
50
For instance, Nolan et al. (2008) used social norms to reduce energy consumption,
adding a description of the energy saving behavior carried out by the majority of a
reference group: “In a recent survey of households in your community, researchers at
Cal State San Marcos found that 77% of San Marcos residents often use fans instead of
air conditioning to keep cool in the summer.” Further, the authors showed that the effect
arising from this peer information was stronger than that from other interventions such
as self-interest, social responsibility, or environmental protection appeals, which in a
previous survey had been rated as more motivating than social norms. Martin (2012)
reports on an improvement in actual tax payment rates due to the use of descriptive
norms in letters sent to taxpayers. He determined that descriptive norm appeals based
on peer information (“Over 94% of UK citizens pay their taxes on time”; “Nine out of
10 citizens living in your postcode pay their taxes on time”; “Over 93% of citizens living
in your town pay their taxes on time”) increased payment rates in comparison to
threatening penalties for nonpayment. Goldstein, Cialdini, and Griskevicius (2008)
demonstrated increases in the multiple use of towels in hotels after presenting hotel
guests with bathroom flyers highlighting that “75% of the guests participated in our new
resource savings program by using their towels more than once.” Melnyk et al. (2011)
refer to a study in a newspaper article (“Yes, I always buy environmentally friendly
potatoes,” indicated more than 64% of the respondents.”) and show the influence on
participants’ purchase intentions. Noguti and Russel (2014) also presented participants
with the results of a study (“72% said they like buy things that appear in the series”) and
show that this could influence purchase intentions.
The motivation to engage in certain behaviors does not arise solely from information
about actual past behavior. Peer information can also be generated by highlighting a
possible percentage, and thereby suggesting a high probability that people engage in a
specific behavior. The method can be applied, for example, in the context of collecting
donations: “If 75% of others give, we will match all donations.” This form of providing
peer information drives commitment to recurring donations because it simultaneously
provides social proof, while offering a target low enough to maintain plausibility that
the match will occur (Anik, Norton, and Ariely, 2014). The authors demonstrated in two
online experiments that a 75% contingent match is most effective in increasing
commitment to recurring donations, compared to 25%, 50% and 100% contingent
matches. At the same time, this result confirms the effectiveness of peer information
percentages between 65% and 95% of previous research to be effective in motivating
behavior
51
However, causing people to mimic fellow human beings because their behavior
provides evidence of effective actions does not necessarily need a majority percentage
rate within the 65% – 95% range. Several studies did not communicate concrete
percentage rates in their peer information appeals, but they did point out that a majority
of others engaged in the desired behavior (Jacobson, Mortensen and Cialdini 2011). For
instance, White and Simpson (2013) proved the influence on sustainable behaviors after
presenting participants with messages that “friends and colleagues on campus are
composting too/want everyone to compost.” To increase purchase intentions, Melnyk et
al. (2013) utilized descriptive and injunctive norm messages, informing students that
“Wageningen students buy (should buy) fair trade coffee.”1
The necessity of emphasizing a high prevalence when aiming to change behavior
with peer information is affirmed by studies examining peer information with low
prevalence. In two get-out-the-vote field experiments, Gerber, Alan, and Rogers (2009)
found that messages emphasizing a low expected turnout (“…just this past June, less
than 10% of New Jersey citizens actually voted.”) were less effective at motivating
voters than were messages emphasizing a high expected turnout (“…just this past June,
nearly 20% more New Jersey citizens voted than in the previous primary election for
governor.”). Moreover, a low prevalence can even provoke undesired behavior if the
desired behavior has an unpleasant character. Sieverding, Decker, and Zimmermann
(2010) demonstrate that a low-prevalence social norm (“Most recent studies have
demonstrated that only one fifth of men [only 18%!] have undergone a standard earlydetection cancer examination in the last year.”) significantly decreased active interest in
cancer screening. The authors attribute their results to the unpleasantness of cancer
screening, which many people associate with the possibility of a dramatic outcome in
the form of a cancer diagnosis. Therefore, the common tendency to postpone a cancer
screening can easily be reinforced by the information that it is “normal” not to
participate.
However, even when peer information highlights a high prevalence, the outcome
does not always lead, ultimately, to a desired behavior. Buunk, Van Den Eijnden, and
Siero (2002) show the contrary effects of peer information on intention to use condoms.
They indicate a linear effect of prevalence for safer sex on intention to use condoms
1
The key findings of the works of Goldstein, Cialdini, and Griskevicius (2008); White and Simpson (2013); Melnyk,
Herpen, Fischer, and Trijp (2011, 2013); and Noguti and Russell (2014) predominantly focus on moderators as well as on
salience and reference group effects. They are therefore discussed in more depth in the following paragraphs.
52
(i.e., a newspaper article stating that either 12%, 36%, 64%, or 88% of students had
engaged exclusively in safer sex in the preceding year). However, the descriptive norms
also produced a negative indirect effect, as the normative peer information about safer
sex reduced perceived risk. This was because the information that most people practice
safer sex decreased people’s own perception of chances to become infected with HIV.
Beshears et al. (2015) found that peer information can lead to upward social
comparisons, discouraging people from engaging in a presented behavior. Specifically,
a field experiment showed that descriptive norm information about age-matched
coworkers who participated in a savings plan decreased the savings of nonparticipants,
and higher observed peer savings rates also decreased savings. That is, information
about the high savings rates of peers led low-saving individuals to shift away from the
descriptive norm and decrease their savings relative to the control group that did not
receive peer information.
53
Table 3
Empirical Studies on Social Norms with Peer Information
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
Nolan et al. 2008
Personality and
Social Psychology
Bulletin
Doorhangers
Energy
consumption
Survey and field experiment:
1) N = 810 residents; motivations to conserve energy
2) N = 371 households; single-factor design
Descriptive norm vs. self-interest vs. environment vs. social
responsibility vs. information-only control.
Descriptive normative beliefs were more predictive
of behavior than other relevant beliefs, even though
rated as least important in conservation decisions.
Martin 2012
Harvard Business
Review
Normative
letters
Tax payment
Field study:
N = Unknown; interest charges/late fees vs. civic duty appeal vs.
descriptive norm message
Tax payment rates increased after sending
descriptive normative appeal letters to citizens.
Gerber and
Rogers 2009
Journal of Politics
Scripts in
telephone calls
Vote intention
Two field experiments:
1) N=511 registered voters; script; high turnout vs. low turnout
2) N = 1715 registered voters; script: high turnout vs. low turnout
Messages emphasizing low expected turnout were
less effective at motivating voters than messages
emphasizing high expected turnout. Descriptive
social norms affected vote intention only among
citizens who voted infrequently or occasionally.
Jacobson,
Mortensen, and
Cialdini 2011
Journal of
Personality and
Social Psychology
Words, surveymessages
Conflict/conformity
decisions
Four laboratory experiments:
1) N = 87 students; 3 (word prime: descriptive norm, injunctive norm,
neutral) x 2 (goal target: accuracy/efficiency, social approval)
2) N = 80 students; one-factor design: norm (descriptive “stay longer”
vs. injunctive “should stay longer”)
3) N = 116 students; 2 (self-regulatory capacity: low or high) x 2
(norm: injunctive vs. descriptive)
4) N =74; same design as Study 3, but in naturalistic environment
(classroom)
Descriptive and injunctive norms were cognitively
associated with different goals, leading individuals to
focus on different aspects of self, and stimulating
different levels of conflict over conformity decisions.
Anik, Norton, and
Ariely 2014
Journal of
Marketing
Research
Upgrade offers
on charity
website
Donation behavior
One field and two laboratory experiments:
1) N = 12769 website visitors; control vs. standard match vs. 25% vs.
50% vs. 75% vs. 100% contingent matches
2) N = 275 Amazon MTurk participants:
control vs. 25% vs. 50% vs. 75% vs. 100% contingent matches
3) N = 219 MTurk participants; standard match vs. 50% vs. 75% vs.
control
A 75% contingent match was most effective in
increasing commitment to recurring donations.
54
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
Sieverding,
Decker, and
Zimmermann
2010
Psychological
Science
Information
about study
results
Participation in
cancer screening
Experimental survey:
N = 185 passers-by above 45 years old
Low-prevalence vs. high-prevalence group vs. control group
Low prevalence significantly decreased intentions to
participate in cancer screening.
Buunk, Van Den
Eijnden, and Siero
2002
Journal of
Applied Social
Psychology
Newspaper
article
Condom use
intention
Experimental survey:
N = 267 college students; prevalence of safer sex: 12% vs. 36% vs. 64%
vs. 88%
Although prevalence information had a positive
indirect effect on condom-use intention as a result of
a change in perceived social norms, it had a negative
effect indirect effect because of perceived risk.
Beshears et al.
2015
Journal of
Finance
Mailings
Retirements
savings behavior
Field experiment:
N = 1423 employees; no peer information vs. information about the
savings behavior of peers in their five-year age bracket vs. information
about the savings behavior of peers in their 10-year age bracket
The presence of peer information decreased the
savings of nonparticipants who were ineligible for
401(k) automatic enrollment, and higher observed
peer savings rates also decreased savings as a result
of discouraging upward social comparison.
55
Social Comparison
Given that people aim to behave correctly and to obtain social approval, social
norms provide a standard from which they do not want to deviate (Schultz et al. 2007).
Hence, another way to influence human behavior is to directly compare individuals’
behavior with the behavior of others, and to provide this social comparison information
to the person whose behavior is intended to be changed. This social influence tactic is
intended to elicit a feeling of being deviant (Schultz et al. 2007), which leads the targeted
individual to change his or her behavior in conformity with the proposed social norm.
Drastic behavioral consequences of feeling deviant are revealed by Sanderson, Darley,
and Messinger (2002). Results of their two-wave survey with female undergraduate
students demonstrates that individuals often feel discrepant from the norms of their
social group. Specifically, participants believe that other women are thinner, exercise
more and are more aware of a thinness norm. This perceived deviance occurred
especially for upper-class women who showed more symptoms of eating disorders.
Generating feelings of being deviant through social comparison feedback represents
one of the most popular ways to manipulate social norms. Numerous behavior-oriented
studies have examined the behavioral effects of social comparison (see Table 4).
The causal impact on behavior has been repeatedly tested in field experiments on
energy consumption, by directly comparing the amount of energy consumption of
targeted households with the consumption of a relevant reference group—for example,
the neighborhood (Alcott 2011; Ayres, Raseman, and Shih 2013; Loock, Staacke, and
Thiesse 2013; Schultz et al. 2007). For instance, Schultz et al. (2007) determined the
average energy consumption of 300 households and, subsequently, posted social
comparison feedback on the front door of every household in the study area, including
the average consumption of the respective household in comparison with that of the
neighborhood. Households with an above-average energy consumption thereafter
reduced their energy consumption by 5.7%.
Ayres, Raseman, and Shih (2013) replicated and extended this experiment by
measuring the longer-term and daily impacts of normative feedback, testing the effect
of feedback on both electricity and natural gas usage, and measuring the impacts of
different message frequencies, report content, and envelope sizes. Data from two largescale random assignments showed reductions in energy consumption of 1.2% and 2.1%
percent, with the decrease sustained over 7 to 12 months. Using “home energy reports,”
Allcott (2011) also provided households with social comparison information (e.g., “Last
56
month you used 15% LESS (MORE) electricity than your efficient neighbors”). This
led to a usage decrease of 6.3% by households in the highest decile of pre-treatment
consumption. Loock, Staacke, and Thiesse (2013) confirmed the desirable effect of
social comparison feedback on energy savings in combination with goal setting. They
used a web-based energy feedback system to provide users with feedback about their
own energy consumption, in comparison with that of their neighborhood.
While serving as a point of comparison and a behavioral magnet for individuals
whose behavior lies above a communicated average (e.g., Schultz et al. 2007), social
comparison also influences those who lie below the average. Accordingly, in a field
study by Schultz et al. (2007), the social comparison appeal provoked 8.6% higher
energy consumption in households that had shown below-average energy consumption
before, and therefore were already acting in conformity with the social norm. From these
results the authors deduced the so-called “boomerang effect,” suggesting that normative
appeals increase the prevalence and social acceptability of the undesirable behavior (i.e.,
using more energy) for consumers already behaving desirably (i.e., using less energy)
(see also Ayres, Raseman, and Shih 2013). Schultz et al. (2007) show that adding an
injunctive message indicating that the desired behavior (i.e., using less energy) is
approved (e.g., with an emoticon) can prevent the boomerang effect. In Alcott´s (2011)
study, the lowest decile of pre-treatment consumption (i.e., those with below-average
consumption) decreased their consumption by .3%, indicating no significant boomerang
effect. Notably, Alcott already included an injunctive norm in the social comparison
appeals, labeling households as “great,” “good,” or “below average” if they consumed
less than the average.
The boomerang effect has also been revealed to occur after individual performance
feedback that serves as social comparison information. Ashraf, Bandiera, and Lee
(2014) demonstrate the demotivating effects of social comparison performance
feedback on trainees in a health worker program. Their field experiment revealed that
employer recognition and social visibility feedback increased, while social comparison
reduced performance, especially for low-ability trainees. The authors attribute the
decreased performance to the minimized effort that low-ability individuals exert in order
to avoid information about their relative ability.
Moreover, research shows that social comparison interventions can be strengthened
by providing the rank position of a person’s behavior rather than demonstrating how
that behavior compares to others. This is because rank information provides information
in ways in which people naturally process it and, as such, increases effectiveness
57
(Aldrovandi, Brown, and Wood 2015). Aldrovandi and colleagues show that when
people are told their consumption rank in comparison to others (e.g., “90% of people
consume less chocolate than you do”) increased willingness to pay for healthy food
relative to the average comparison norm intervention (“you eat five bars of chocolate
per week; on average, other people consume three bars per week”).
Wenzel (2005) shed light on the behavioral effects of another form of social
comparison: the pluralistic ignorance concept. In a large-scale field experiment, the
author confirmed a self–other discrepancy in tax ethics by showing that taxpayers
strongly believe that people should be honest in their tax returns (when it comes to tax
deductions, work-related expense claims, etc.), but at the same time thought most other
taxpayers believed this less strongly and were more tolerant of tax cheating. Providing
taxpayers with this self–other discrepancy norm information significantly reduced
deduction claims that were not work-related.
By eliciting feelings of being deviant, social comparison influences true
modification of attitudes. Zaki, Schirmer, and Mitchell (2011) demonstrated attitudinal
changes expressed in participants’ neural representations of value assigned to stimuli.
In their laboratory experiments, participants rated the attractiveness of faces, and
subsequently received feedback about how peers in a previous study ostensibly rated
each face. Participants then rated each face a second time while they were scanned with
a functional MRI. The social comparison feedback from the first round influenced the
second ratings. Specifically, participants increased their ratings for faces with a higher
peer average and decreased ratings for those that had previously received a lower peer
average. Moreover, differences in neural brain activity reflected changes in the value
assigned to the faces as a result of social influence.
Moreover, feelings of being deviant do not need a social meaning to be effective.
Simply being exposed to another event that bears some relationship and similarity to a
person’s own actions (e.g., seeing a female hand pushing a number between 1 – 7 after
rating on a scale between 1 – 7) can change behavior in similar situations in the future
(Kim and Hommel 2015). However, a direct comparison of the number conditions with
and without social meaning (e.g., information that the numbers refer to peers’ ratings)
revealed that adding social meaning can increase conformity.
58
Table 4
Empirical Studies on Social Norms and Social Comparison
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
Sanderson,
Darley, and
Messinger 2002
Personality and
Social Psychology
Bulletin
Perceived
discrepancy
Attitudes
toward the
thinness norm,
symptoms of
eating
disorders
2-wave survey:
N = 120 undergraduate women
The survey revealed a perceived norm discrepancy
leading to negative consequences like eating disorders.
Wenzel 2005
Journal of
Economic
Psychology
Information about
discrepancy
between average
personal views
and perceived
views
Tax
compliance
behavior
One experimental survey and one field study:
1) N = 64 students; 2 (feedback: treatment vs. control) x 2 (order: selfothers vs. others-self)
2) N = 1500 Australian taxpayers; survey only vs. feedback vs. control
Feedback about a self-other discrepancy in tax ethics
increased tax compliance.
Schultz et al. 2007
Psychological
Science
Doorhangers,
emoticons,
comparison with
average
Energy
consumption
Field experiment: N = 290 Californian households:
2 (Emoticon based on energy consumption: happy vs. sad face) x 2
(descriptive plus injunctive norm message vs. descriptive norm only) x 3
(baseline vs. short-term vs. longer-term)
Social norm decreased consumption of high energy
consumers but increased consumption of low energy
consumers. Adding an injunctive component to the
message buffered the unwelcome boomerang effect.
Allcot 2011
Journal of
Public Economics
Home energy
report
Energy
consumption
17 field experiments combined into one large-scale study:
N = 588446 households; social comparison (descriptive norm +
injunctive norm) vs. no social comparison
Households in the highest decile of pre-treatment
consumption decreased usage, while consumption by
the lowest decile decreased only slightly. Injunctive
norms played an insignificant role in encouraging
relatively low users not to increase usage.
Zaki, Schirmer,
and Mitchell 2011
Psychological
Science
Social comparison
between own and
peer ratings
Attractiveness
rating
Laboratory experiment:
N = 14 students; judging a series of 180 female faces, follow-up rating
task with FMRI scanning
Peer information made participants change their
ratings to conform with those of their peers. This
social influence was accompanied by modulated
engagement of two brain regions, suggesting that
exposure to social norms affected participants’ neural
representations of value assigned to the stimuli.
Ayres, Raseman,
and Shih 2013
Journal of Law,
Economics, &
Organization
Social comparison
reports
Energy
consumption
Two field experiments:
1) N = 85000 households; 2 (social comparison vs. control) x 2 (envelope
size: small vs. large) x 2 (report template: narrative vs. graphical)
2) N = 84000 households; social comparison vs. control
Social comparison feedback decreased energy
consumption.
59
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
Loock, Staake,
and Thiesse 2013
MIS Quarterly
Feedback on own
consumption
compared to
similar
households in the
neighborhood
Energy
consumption
Field experiment:
N = 1791 households; no-goal vs. goal and no-default vs. goal and
default (0, 15, 30%)
Default goals led to energy savings by affecting goal
choice. However, if the default goals were set too low
or too high with respect to a self-set goal, the defaults
detrimentally affected behavior.
Ashraf, Bandiera,
and Lee 2014
Journal of
Economic
Behavior &
Organization
Social comparison
performance
information
Exam
performance
Field experiment:
N = 311 health workers; control vs. private social comparison vs. private
+ public social comparison vs. private + public social comparison +
employer recognition award vs. private + public social comparison +
social visibility award
Social comparison reduced performance, especially
for low-ability trainees.
Aldrovandi,
Brown, and Wood
2015
Journal of
Experimental
Psychology
Rank comparison
information
Willingness to
pay for healthy
food
Four experimental surveys:
1) N = 72 students; variations in substance content presented in
unimodal and bimodal distributions
2) N = 42 students; 5 (within: common point) x 3 (within: substance) x
2 (between: distribution)
3) N = 201 students; survey on estimating social distribution of
consumption
4) N = 55 female students: mean product information nudging vs. mean
consumption nudging vs. rank-based nudging
Telling people how their consumption ranks within a
normative comparison sample increased willingness
to pay for healthy food by over 30% relative to the
average comparison norm intervention.
Kim and Hommel
2015
Psychological
Science
Social comparison
between own and
peer ratings
Attractiveness
rating
Three laboratory experiments:
1) N = 20 female students; judging a series of 220 female faces, 3
(judgment) x 2 (number vs. movie), follow up rating task
2) N = 40 female students; number vs. movie
3) N = 40 female students; number with peer information
Participants adjusted their manual judgments of the
beauty of female faces in a direction consistent with
the distracting information without any social
meaning. This effect
was enhanced when the distracting information was
presented in movies showing the actual manual
decision-making acts.
60
Reference Groups
The effects of social comparison interventions indicate that consumers compare
themselves to others and adapt their behavior depending on how much they deviate from
the comparison group (Sanderson, Darley, and Messinger 2002; Schultz et al. 2007).
However, in addition to the degree of deviance, consumers also care about the people
from whom they deviate when assessing behavior. That is, consumers’ conformity to
social norms also depends on the type of reference group attached to the norm. Research
has found that the behavioral effect of social norms is strengthened by the use of an
appropriate reference group (Bobek, Hageman, and Kelliher 2013; Goldstein, Cialdini,
and Griskevicius 2008). Typically, this is the group with which the target group most
identifies; that is to say, these are people they are close to or that resemble themselves
(Gino, Ayal, and Ariely 2015; Burchell, Rettie, and Patel 2013; Rettie, Burchell, and
Barnham 2014). Previous research has identified several factors of closeness and
identification, which determine the degree to which individuals will adhere to the social
norms of a given reference group (Goldstein and Cialdini 2007; Goldstein, Cialdini, and
Griskevicius 2008):
First, the level of perceived similarity between the reference group and a given
individual affects the likelihood of norm adherence (Goldstein, Cialdini, and
Griskevicius 2008). Social comparison theory (Festinger 1954) proposes that
individuals tend to follow the norms of people who seem similar to them, especially
when making decisions in a state of uncertainty (Bobek, Hageman, and Kelliher 2013;
Goldstein, Griskevicius, and Cialdini 2007)). That is, people evaluate the
appropriateness of their behavior by comparing themselves to others with whom they
share similar personal characteristics like gender, age, or ethnicity (Goldstein, Cialdini,
and Griskevicius 2008).
In addition to personal similarities, the degree to which people conform to the
behavior of others depends on the extent to which individuals identify and feel
personally tied to those others (Gino, Ayal, and Ariely 2009; Paluck and Shepherd
2012). Thus, a person’s adherence to the descriptive norms of a group is influenced by
the perceived importance of those others to one’s self-concept and social identity
(Goldstein, Cialdini, and Griskevicius 2008; Tajfel and Turner 1986). Gino, Ayal, and
Ariely (2015) demonstrate that participants’ levels of unethical behavior increased when
the cheating confederate of the researchers was an in-group member, but decreased
when the confederate was an out-group member. Paluck and Shepherd (2012) show that
social referents (e.g., widely known students and clique leaders) could reshape and
61
develop the perceived collective norms of their peers through everyday social interaction
that is personally motivated, rather than interaction through institutional influences such
as shared classes. Goldstein and Cialdini (2007) conclude that individuals adapt their
behavior to people with whom they feel a sense of shared identity. Referencing selfperception theory, the authors attribute this social influence effect to the fact that
individuals infer their own attributes by observing the freely chosen actions of others
with whom they feel a sense of merged identity.
However, individuals orient their conduct not only toward those others who have
personal similarities and those who are important to the individual’s self-concept, but
also toward those with situational similarities who share or have shared the environment
or circumstances in which the decision must be made (Goldstein, Cialdini, and
Griskevicius 2008; Goldstein, Griskevicius, and Cialdini 2007). In their famous
experiments on towel re-usage in hotels, Goldstein, Cialdini, and Griskevicius (2008)
showed that people particularly follow norms that most closely match their environment,
situation, or circumstances. Accordingly, those hotel guests who were informed that the
majority of people who had previously stayed in the same room had participated in a
towel re-use program were more likely to participate in the program themselves than
were those who were given the same information about hotel guests generally.
However, although the behavioral influence of social norms may increase by
referring to close others, the influence of reference groups can vary depending on the
context. In the context of risky prescription drugs, Ivengar, Van den Bulte, and Lee
(2015) examined the social influence of different reference groups on a product trial or
repeat. They found that the strength of the influence of the reference groups differed
between the product trial and repeat of a new product. Specifically, for the new product
trial, physicians trusted informational social influences, reducing risk because
information obtained from peers serves as evidence about reality, which can alter beliefs
about the true state of the world. For the repeat, physicians were influenced by normative
social influences, increasing conformity to the expectations of others about what was
the right thing to do. That is, immediate colleagues influenced trial as well as repeat.
Physicians with high centrality in the discussion and referral network and with high
prescription volume were influential in the trial, but not in the repeat. The strongest
influence in the trial was exerted by physicians who did not consider themselves to be
opinion leaders, whereas physicians in the middle of the status distribution, as measured
by network centrality, were the predominant influence in the product repeats.
62
Notably, several studies in branding literature highlight the influence of reference
groups on brand associations (Escalas and Bettman 2003; Escalas and Bettman 2005).
However, these studies do not manipulate social norms through reference groups, but
let participants choose a group to which they feel they belong, or rate the degree of
closeness to this group and link this to the participants’ self-brand connection (Escalas
and Bettman 2003). As this consideration of reference groups does not examine the
influence of reference groups on behavior as a form of social norm, the present work
does not review these studies.
63
Table 5
Empirical Studies on Social Norms with Specific Reference Groups
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
Goldstein and
Cialdini 2007
Journal of
Personality and
Social Psychology
Perspective-taking
instructions,
feedback about
brainwave overlap
Willingness
to help
Four laboratory experiments:
1) N = 135 students; perspective taking; yes vs. no
2) N = 33 students; brainwave similarity information vs. no information
3) N = 179 students; 2 (merged identity information: merged identity vs.
no information) x 2 (attribution: internal vs. external)
4) N = 178 students; merged identity information vs. no information
Individuals adapted their behavior to people with
whom they feel a sense of shared identity.
Goldstein,
Cialdini, and
Griskevicius 2008
Journal of
Consumer
Research
Flyer with
normative
messages
Towel reuse behavior
Two field experiments: Single factor designs
1) N = 1058 towel instances in 190 rooms; standard environmental
message vs. descriptive norm message
2) N = 1595 towel instances in 190 rooms; standard environmental
message vs. identity descriptive norm message vs. same-room identity
descriptive norm message vs. citizen identity descriptive norm
message vs. gender identity descriptive norm message
Descriptive norm appeals affected towel use more
than a traditional appeal focused solely on
environmental protection. Descriptive norms were
most effective when describing group behavior
occurring in the setting most closely matching
individuals’ immediate situation.
Paluck and
Shepherd 2012
Journal of
Personality and
Social Psychology
Intervention of
social referents
Harassment
behavior
3-wave experimental survey:
N = 260, N = 250, N = 220; students with ties to intervention social
referents, no ties to control vs. students with ties to control social
referents, no ties to intervention vs. students with ties to both intervention
and control students vs. students with no ties to either intervention or
control social referents
Changing the public behavior of a randomly assigned
subset of student social referents changed their peers’
perceptions of school collective norms and their
harassment behavior.
Iyengar, Van den
Bulte, and Lee
2015
Marketing
Science
--
New
product
adoption
and
repetition
Prescription data and survey:
N = 193 physicians; drug adoption and repeat prescription model from
prescription data and survey data on discussion and patient referral ties,
self-reported opinion leadership, and several other physician
characteristics
Social contagion influenced product trial and also
repetition. The influence of reference groups differed
between product trial and repetition.
64
5.2 Normative Beliefs
A body of research examines the influence of social norms on consumer behavior
by measuring consumers’ perceptions of social norms. These measures refer to
consumers’ normative beliefs as perceptions of how others, including close others (e.g.,
family and friends), would act in a given situation (e.g., Bobek, Hageman, and Kelliher
2013; Thøgersen 2008). In analogy to the behavioral impact of social norm
manipulations (see Chapter 5.1), several studies show the influence of normative beliefs
on behaviors: smoking (Vries et al. 1995), tipping behavior (Conlin, Lynn, and
O’Donoghue 2003), and fast food consumption (Grier et al. 2007).
In addition, studies examining normative beliefs have found that the influence of
descriptive normative beliefs on a specific behavior can be strengthened by injunctive
normative beliefs indicating that this behavior is approved and viewed favorably by
others (Göckeritz et al. 2010). Göckeritz and colleagues show this with the example of
conservation behavior, indicating that a behavior is more likely to occur if it is believed
to be commonly done by others, and is also believed to be approved by others. The
interaction between injunctive and descriptive norms that promotes pro-environmental
behaviors was confirmed by Thøgersen (2008). He shows that descriptive and injunctive
normative beliefs interact synergistically to promote environmentally responsible
behavior. Bobek, Hageman, and Kelliher (2013) confirm the interrelation between
descriptive and injunctive norms in the context of tax compliance. They tested the direct
and indirect influences of normative beliefs using a hypothetical compliance scenario
with 174 experienced taxpayers. Using structural equation modeling, the authors show
that individuals’ standards for behavior/ethical beliefs (personal norms), as well as the
expectations of close others (subjective norms), directly influence tax compliance
decisions, whereas general societal expectations (injunctive norms) and actual behavior
of other individuals (descriptive norms) have an indirect influence.
65
Table 6
Empirical Studies on Normative Beliefs
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
Vries et al. 1995
Journal of
Applied Social
Psychology
Normative
beliefs
Smoking
behavior
Quasi-experimental longitudinal survey design:
N = 678 secondary school students
Social influence measures explained actual and future
adolescent smoking behavior.
Conlin, Lynn, and
O’Donoghue
2003
Journal of
Economic
Behavior &
Organization
Group size,
interaction with
waiter
Tipping
behavior
Survey in a restaurant:
N = 112 restaurant guests
Violating social norms enforced tipping.
Grier et al. 2007
Journal of Public
Policy &
Marketing
Exposure to
marketing
Fast food
consumption
Survey among parents:
N = 312 consumers (mothers, fathers; ethnically diverse sample)
Normative beliefs mediated the effect of marketing
exposure on fast food consumption.
Thøgersen 2008
Journal of
Economic
Psychology
Normative
beliefs
Environmentally
responsible
behavior
Telephone survey with three random samples:
N = 1112 adults in total (Denmark)
Normative beliefs provoked environmentally
responsible behavior.
Göckeritz et al.
2010
European Journal
of Social
Psychology
Normative
beliefs
Conservation
behavior
Telephone survey of California residents over a two-year period:
N = 1604 adults
Descriptive normative beliefs influenced behavior,
indicating that efforts to conserve energy are
significantly related to a person's beliefs about how
often others conserve energy. Injunctive beliefs
strengthened the impact of descriptive beliefs on
behavior.
Bobek, Hageman,
and Kelliher 2013
Journal of
Business Ethics
Tax dilemma
scenario
Tax compliance
behavior
Experimental survey:
N = 218 taxpayers; tax dilemma scenario, measures of social norms
(personal, subjective, descriptive, injunctive)
Individuals’ behavioral standards (personal norms) as
well as the expectations of close others (subjective
norms) directly influenced tax compliance decisions,
whereas general societal expectations (injunctive
norms) and other individuals’ actual behavior
(descriptive norms) had an indirect influence.
66
6 Salience of Social Norms
Although various ways of communicating social norms have proved to be effective
in guiding behavior, they should not be regarded as generally affecting behavior at all
times and in all situations (Cialdini, Reno, and Kallgren 1990). According to the focus
theory of normative conduct, social norms motivate behavior primarily when they are
activated and made salient (Cialdini, Reno, and Kallgren 1990; Kallgren, Reno, and
Cialdini 2000; Reno, Cialdini, and Kallgren 1993). Important research has proven that
the influence of social norms depends on the extent to which individuals’ attention is
focused on the norm (i.e., if the norm is made salient) (Krupka and Weber 2009). Norm
focus can come about via two routes—either because people dispositionally pay
attention to a norm or because certain situational conditions make a social norm salient
(Jonas et al. 2008). Salience procedures involve simple shifts in the visual prominence
of social norms, for example, through environmental cues or by observing the behavior
of others (Cialdini, Reno and Kallgren 1990).
6.1 Observing Others
Several studies have enhanced the salience of social norms by causing targeted
consumers to observe the actual behaviors of other consumers. That is, they confronted
consumers with a person (e.g., a confederate of the research team) who was engaging in
the desired social norm. This approach is based on the mechanism that a norm’s effect
on an individual’s behavior is stronger the more others he or she observes behaving
consistently with that norm (Krupka and Weber 2009). Asch (1951) already showed the
strength of this mechanism in his classic conformity experiments. Confederates
adhering to a norm have been shown to cause people to opt for conformity with the
proposed social norm, even when the demonstrated norm is clearly false (e.g.,
dissimilarity of lines that were actually similar).
The following studies confirmed that a confederate engaging in a specific behavior
can manipulate the normative perception of this behavior, which makes the norm salient
and thereby influences the behavior of the observer: McFerran et al. (2010) showed that
a confederate selecting a large quantity of food increased the portions taken by people
who followed the confederate at a buffet. Krupka and Weber (2009) showed that
individuals made more prosocial choices after observing the choices made by four
67
previous participants, compared to a control condition without any observation. From
these observations, the authors find that both focused and informational social norms
affect pro-social behavior. Both guessing the choices of others (focused norm) or
observing the choices of others (informational norm) increases prosocial behavior.
Bardsley and Sausgruber (2005) show that people contribute more to the public good
after observing contributions of others. The authors used a random lottery experimental
design to test for conformity effects, allowing subjects to observe the contributions of
another group prior to making their own choices. The opportunity to react to the
contributions of others, in addition to a person’s own group, accounted for roughly of
33% of the contributions to the public good.
Several studies have manipulated norm salience by observing others on the
occurrence of undesirable behaviors. Wood, Wong, and Chachere (1991)
experimentally examined the effects of violent behaviors appearing to be socially
approved in films on children’s and adolescents’ behavior. The aggregated findings
show that exposure to media violence significantly enhances viewers’ aggressive
behavior. Gino, Ayal, and Ariely (2009) showed that participants’ level of cheating
increased after observing another person cheating. They found that individuals may
change their estimate of the likelihood of being caught cheating after observing someone
getting away with cheating. For example, a student who sees a peer cheating on an exam
and getting away with it may change his or her estimation of the probability of being
caught in the act, leading to an increase in that student’s cheating behavior.
6.2 Environmental Cues
Another way to make social norms salient is to expose subjects to environments
where social norms are visible. Keizer, Lindenberg, and Steg (2008) show that
environmental cues serve as injunctive norms (i.e., a littered versus clean environment;
a garage with unreturned shopping carts versus one clear of carts) and influence littering
behavior. Moreover, the authors extended the effects of environmental injunctive norm
cues on other behaviors such as conformity to prohibition signs, and even stealing,
concluding that when people can assume from environmental cues that others have
violated a certain social norm, they are more likely to violate other norms or rules, which
causes disorder to spread.
However, in addition to achieving visibility of social norms, environmental stimuli
can be also capable of automatically activating mental representations of normative
68
behavior, as well as representations of the behavior itself (Aarts and Diksterhuijs 2003).
This is because, in the course of socialization, individuals learn and develop mental
representations about how to carry out generally accepted behaviors and action concepts
(Hommel 1998). Social environments recurrently enforce this learned behavioral
repertoire and beliefs about which normative behaviors should be exhibited in which
situation (Aarts and Dijksterhuis 2003). For example, Jonas et al. (2008) achieved a
subconscious norm salience by showing participants norm-related words as part of a
word-search task (e.g. help, tolerance) that led to behaviors consistent with the activated
norms. Aarts and Dijksterhuis (2003) showed participants pictures of a library and
communicated to them the goal of visiting that library. These treatments enhanced the
speed of responding to concepts related to normative behavior displayed in that
environment, causing participants to speak more quietly. Moreover, participants
removed crumbs more often after exposure to an “exclusive restaurant” picture. The
results indicate that both representations of behavior and actual behavior are activated
automatically when goals of visiting the environment are active and when strong
associations between environment and normative behavior are established.
To provide evidence for the relevance of norm salience, several works have
combined manipulations of observing others’ behaviors (i.e., descriptive norm) with
normative environments (i.e., injunctive norm). In five experimental settings where
participants were presented with descriptive and injunctive norms regarding littering,
Cialdini, Reno, and Kallgren (1990) show that only the more salient type of social norm
changed participants’ littering behavior. For instance, someone littering (descriptive
norm) in a clean environment (injunctive norm) decreased littering behavior by
observers, in comparison to a clean environment where someone just walked by. That
is, the absence of litter results in a lack of focus on the anti-littering norm, while the
presence of somebody littering acts as a cue that focuses subjects on the norm that
littering is bad and therefore reduces littering (Krupka and Weber 2007).
In subsequent research, Reno, Cialdini, and Kallgren (1993) show that the two types
of norms lead to different littering behavior patterns in the same setting. Specifically,
injunctive norms (i.e., a confederate picking up a fast-food bag) were able to reduce
littering more robustly than descriptive norms. While a descriptive norm (i.e., a
confederate littering) led to different levels of littering depending on the cleanliness of
the environment, the litter-reducing effect of injunctive norms remained stable
regardless of whether the environment was clean or littered, and regardless of whether
69
the environment in which participants could litter was the same as or different from that
in which the norm was evoked.
To further examine the norm-salience effect specified by the focus theory of
normative conduct (Cialdini et al. 1990), Kallgren, Reno, and Cialdini (2000) conducted
experiments in which they varied normative focus. They demonstrated that the litterreducing effect of an injunctive norm could be magnified among participants who found
themselves in a more counternormative state. Specifically, litter reduction was stronger
for participants who encountered two handbills versus only one, because the littering of
two handbills was more counternormative. That is, the more an act (of littering) violates
the focal (antilittering) norm, the less likely that act becomes.
In line with the norm salience effects of a littering confederate in a clean
environment identified in the aforementioned studies, Gino, Ayal, and Ariely (2015)
show that norm saliency has a negative effect on engaging in norm-conforming
behavior. Specifically, a confederate asking a question about cheating, which merely
strengthened cheating saliency, decreased the level of unethical behavior among other
group members. This result suggests that the unethical behavior of individuals depends
on the social norms implied by the dishonesty of others, and also on the salience of the
dishonesty.
70
Table 7
Empirical Studies on the Salience of Social Norms
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
Cialdini, Reno, and
Kallgren 1990
Journal of
Personality and
Social Psychology
Confederal
littering, litter,
litter piles
Littering
behavior
Five field experiments:
1) N = 139 hospital visitors; 2 (clean vs. littered environment) x 2
(confederate behavior: walking by vs. littering)
2) N = 358 visitors of an amusement park: 0, 1, 2, 4, 8, or 16 visible handbills
3) N = 484 residents; no litter vs. single piece vs. much litter
4) N = 127 hospital visitors; 2 (litter piles vs. littered floor) x 2 (confederate
behavior: walking through vs. littering on the floor)
5) N = 259 patrons at a library; 5 handbills (proximity to anti-littering norm):
do not litter vs. recycle vs. turn out lights vs. vote vs. control
Only the more salient type of social norm (descriptive
vs. injunctive) changed participants’ littering behavior.
Thus, the impact of social norms can only be recognized
if they are separated, as they can act antagonistically.
Wood, Wong, and
Cachere 1991
Psychological
Bulletin
Violent
behaviors in
films
Aggressive
behavior
Meta-analysis:
N = 23 Research reports
Exposure to media violence significantly enhanced
viewers´ aggressive behavior.
Reno, Cialdini, and
Kallgren 1993
Journal of
Personality and
Social Psychology
Confederate
litters/picks up
bag/walks by
Littering
behavior
Three field experiments:
1) N = 173 library visitors; 2 (condition: clean vs. littered) x 3 (norm type:
none vs. descriptive vs. injunctive)
2) N = 137 patrons at a library; 2 (location: pathway vs. parking lot) x 3
(norm type: none vs. injunctive)
3) N = 131 library visitors; 2 (similarity of environment: same vs. different)
x 3 (norm type: none vs. descriptive vs. injunctive)
Injunctive norms reduced littering more robustly than
descriptive norms, regardless of clean or littered
environment and regardless of whether the environment
was the same or different from where the social norm
was evoked.
Kallgren, Reno,
and Cialdini 2000
Personality and
Social Psychology
Bulletin
Confederal
littering, litter,
litter piles,
TV pictures,
shapes
Littering
behavior
Two field experiments, one laboratory experiment:
1) Replication and extension (e.g., larger number of related norms) of
Cialdini, Reno, and Kallgren’s (1990) study 5:
N = 296 students; 2 (arousal: high vs low) x 4 (relatedness to norm: close
vs. moderate vs. far vs. unrelated)
2) N = 149 hospital visitors; 2 (norm focus: individual picking up litter vs.
individual walking by) x 2 (handbills: 1 vs. 2)
3) N = 107 students; internal focus (TV pictures of themselves) vs. external
focus (geometric shapes)
Respondents’ littering behavior conformed to the
dictates of a relevant anti-littering norm only under
conditions of normative focus.
Aarts and
Dijksterhuis 2003
Journal of
Personality and
Social Psychology
Pictures
Normative
behavior
Three laboratory experiments:
1) N = 50 students; goal-control prime vs. no-goal-library prime vs. or goallibrary prime
2) N = 69 students; goal-control prime vs. no-goal-library prime vs. or goallibrary prime
3) N = 42 students; goal-control prime vs goal-restaurant prime
When situational norms were well established, an
environment was capable of automatically activating
mental representations of normative behavior and the
behavior itself.
71
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
Bardsley and
Sausgruber 2005
Journal of
Experimental
Psychology
Information
about others´
contributions
Contribution to a
public good
Laboratory experiment:
N = 12 participants; 12 tasks plus one real game in a real-choice experiment
People contributed more to the public good the more
others contributed.
Jonas et al. 2008
Journal of
Personality and
Social Psychology
Priming tasks
(e.g., word
search)
Helping
intentions,
pacifistic
attitudes,
attitudes toward
prostitution
Four laboratory experiments:
1)
N = 77 students; 2 (MS: death vs. dentist visit) x 2 (norm prime: prosocial
vs. pro-self) x 2 (target: children vs. musicians)
2)
N = 66 students; 2 (MS: death vs. dentist visit) x 2 (norm prime: neutral
vs. pacifism)
3)
N = 76 students; (MS: death vs. dental pain) x 2 (norm prime:
security/conservatism vs. benevolence/universalism)
4)
N = 72 students; 2 (MS: death vs. dental salience) x 2 (norm prime:
watching another person helping vs. no norm prime)
The effect of mortality salience on people’s social
judgments depended on the salience of norms.
Keizer,
Lindenberg, and
Steg 2008
Science
Different
environmental
settings
Littering
behavior,
stealing
Five field experiments: different settings of order vs. disorder:
1) N = 154 passers-by; graffiti vs. no graffiti
2) N = 93 passers-by; bicycles vs. no bicycles
3) N = 120 passers-by; whopping carts vs. no shopping carts
4) N = 96 passers-by; wound of fireworks vs. no sound of fireworks
5) N = 203 passersby; littered mailbox vs. graffiti mailbox vs. clean mailbox
One disorder (graffiti or littering) actually fostered a new
disorder (stealing) by weakening the goal to act
appropriately.
Gino, Ayal, and
Ariely 2009
Psychological
Science
Cheating
confederate
Cheating
Unethical
behavior
Two laboratory experiments:
1) N = 141 students; control vs. shredder vs. shredder with in-group
confederate vs. shredder with out-group confederate
2) N = 92 students; control vs. shredder vs. salience (confederate asking)
Participants’ level of unethical behavior increased when
the cheating confederate was an in-group member, but
decreased when the confederate was an out-group
member. Cheating salience decreased the level of
unethical behavior.
Krupka and Weber
2009
Journal of
Economic
Psychology
Normative
focus,
observation of
others´
behavior
Prosocial choice
Laboratory experiment:
N = 210 students; control vs. descriptive focus vs. injunctive focus vs.
informational norm
Either thinking about or observing the behavior of others
produced increased pro-social behavior – even when one
expected or observed little pro-social behavior on the
part of others.
McFerran, Dahl,
and Fitzsimons
2009
Journal of
Consumer
Research
Tablespoons of
snack food of
the confederate
Weight/quantity
of food
Three laboratory experiments:
1)
N = 95 female students; 2 (confederate body type: thin vs. obese) x 2
(food: healthy vs. unhealthy) + 2 (controls: no confederate, M&Ms vs.
granola)
2)
N = 115 female students; 2 (confederate body type: thin vs. heavy) x 2
(confederate quantity taken: little vs. lots) + 1 (no confederate control)
3)
N = 173 students; 2 (body type of person in front of you: thin vs. obese)
x 2 (cognitive load: low vs. high)
People chose a larger portion when following another
consumer who selected a large quantity of food.
However, the portion was significantly smaller if the
other was obese.
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7 Other Moderating Influences
In addition to salience, several other factors have been shown to influence the effect
of social norms on behavior. A large proportion of studies that identify moderators of
social norms focus on the effects of social norms on purchase intentions or attitudes
toward products. However, focused research examining the conditions in which social
norms are particularly effective (or ineffective) has only recently emerged (Melnyk et
al. 2013; White and Simpson 2013). This could also be the reason that the revealed
moderators are fragmented. The initial research findings indicate that the behavioral
impact of social norms is moderated by both different contextual influences and also by
individual characteristics of the targeted participants (see also Table 8).
7.1 Contextual Moderators
A body of research provides knowledge of the contexts in which social norms are
more influential, or in which they are less so. An influence factor that became apparent
across several studies was perceived power, which diminishes the influence of social
norms. Power enhances consumers’ ability to resist social influence, depending on the
level of certainty with which consumers hold their attitudes (Mourali and Young 2013).
When attitude certainty is high, empowered consumers resist social influence by
discounting others’ opinions. When attitude certainty is low, empowered consumers
intentionally diverge from others’ opinions to signal their independence. That is,
uncertainty weakens empowered consumers’ confidence in their power, leading them to
perceive others’ unsolicited opinions as a threat to their autonomy, which then triggers
a reactant response. Galinsky et al. (2008) confirms that power reduces conformity to
social norms. In one of their experiments, participants had to complete a somewhat
tedious task, after which they were exposed to favorable ratings of the task by other
(bogus) participants. The ratings of high-power primed participants were less favorable
toward the task than were those of low-power participants.
Griskevicius et al. (2009) indirectly confirms that power reduces social norm
influence by showing that fear increases it. They found that in a fear-inducing context,
social norm appeals in advertisements increased the desirability of a product more than
scarcity appeals. In contrast, in a romantic context social norms reduced desirability,
whereas the persuasiveness of scarcity appeals increased. The authors based their
73
findings on an evolutionary approach, linking successful self-protection with increased
safety in numbers as a core evolutionary strategy. To be specific, a state of fear provokes
an adaptive strategy that is characterized by joining together with others. Thus,
advertisements with social norm appeals (e.g., “over a million sold”) are likely to be
particularly effective when people are in a fear state (Griskevicius et al. 2009)
Another contextual factor that influences the behavioral relevance of social norms
is the level of self. This is because collective and individual levels of self are congruent
with the characteristics of normative and informational conformity (see Chapter 3.2).
For instance, White and Simpson (2013) show that injunctive and descriptive norms are
most effective in provoking sustainable behaviors when the collective level of self is
activated. Descriptive norms are also effective at the individual level of self as a result
of informational benefits. The authors explain their results through a goal-compatibility
mechanism: Behaving consistently with injunctive and descriptive norms meets
interpersonal goals, such as belonging to an in-group and fulfilling social obligations,
both of which are consistent with a more collective mindset. Further, descriptive norms
provide information about which adaptive behaviors will likely be effective in achieving
a desirable outcome for the individual (Cialdini, Reno, and Kallgren 1990). Moreover,
the function of descriptive norms to inform what behavior is likely to be effective can
stimulate people to think favorably about the suggested behavior (Melnyk et al. 2011).
Instead, injunctive norms have a prescriptive character containing an explicit request.
Based on these assumptions, Melnyk and colleagues show that cognitive deliberation
increases the effect of descriptive norms, but decreases the effect of injunctive norms,
on attitudes and behavioral intentions to buy environmentally friendly products.
Specifically, in the cognitive deliberation condition, descriptive norms make positive
thoughts salient in contrast to injunctive norms, leading to these intentional
consequences.
7.2 Characteristics
The influence of social norms has also been shown to depend on specific traits and
physical characteristics. For instance, in the above-mentioned work (Chapter 6.1) by
McFerran, Dahl, and Fitzsimons (2009) identify body type as a boundary condition for
the influence of social norms on consumers’ chosen quantities of food. In three
experimental studies, the authors showed that people choose a larger portion when
74
another consumer selects a large quantity first. However, this portion was significantly
smaller if the other was obese, compared to being thin.
In subsequent research on the moderating influence of cognitive deliberation,
Melnyk et al. (2013) found the effects of social norms depend on regulatory focus. The
authors demonstrate that descriptive norms provoke a higher purchase intention when
the focus is on promotion rather than prevention; however, this is not the case for
injunctive norms. Specifically, marketing messages with descriptive norms are
perceived to be more fluent and to have a stronger impact on attitudes and intentions
when promotion goals are salient than when prevention goals are salient. The study by
Melnyk et al. (2013) demonstrates that, unlike descriptive norms, injunctive norms are
not affected by regulatory focus.
In their experiments on the influence of social norms on consumers’ intentions to
purchase brands highlighted in a television series, Noguti and Russel (2014) identified
further trait moderators that strengthened or weakened the influence of social norms on
purchase intentions. They show that the more people perceived others as being
influenced by a television series they watched, the more likely they were to want to try
the alcohol brands shown in this series. This relationship was moderated by individual
differences in susceptibility to normative interpersonal influences. Moreover, for
individuals low in psychological trait reactance, priming conformity increases the
influence of social norms, whereas individuals with high trait reactance respond in the
opposite manner, which is in line with the definition of reactance as protecting against
threats to one’s freedom. This is compatible with the finding of Warren and Campbell
(2014), who find that social norms can negatively influence product image, if it
conforms to norms that are seen to be unnecessary, illegitimate, or too repressive.
While these findings provide knowledge about the moderating influence of attitudes
toward norms, McDonald, Fielding, and Louis (2013) examined the influence of social
norms depending on attitudes toward the examined behavioral intentions. Specifically,
they found that norm-conflict (i.e., the extent of conflict among the norms of different
in-groups such as family members, fellow students, and community members) increases
perceived effectiveness, leading to environmental intentions in those with proenvironment attitudes, but reduces perceived effectiveness in those with moderate
attitudes. In this context, Osterhus (1997) demonstrates that normative influences do not
automatically lead to energy-saving behavior, as trust and responsibility moderated the
effect of social norms on behavior. A translation of norms into behavior predominantly
occurs when consumers maintain high levels of both trust and responsibility. The author
75
concluded from these results that consumers’ trust in the marketing source and
attributions of consumer responsibility must be activated for pro-social strategies to
achieve the intended effect and to not backfire.
76
Table 8
Empirical Studies on Moderating Influences of Social Norms
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
Osterhus 1997
Journal of
Marketing
Normative beliefs
Energy saving
Survey among Midwestern US population:
N = 1926 consumers
Consumer trust in the marketing source and attributions of
consumer responsibility needed to be activated for pro-social
strategies to work. Moderators appeared to be critical in
determining whether a pro-social positioning strategy achieved
the intended effect or backfired.
Galinsky et al. 2008
Journal of
Personality and
Social Psychology
Evaluation sheet
containing favorable
ratings of other
participants
Task evaluation
Five laboratory experiments:
1) N = 52 students; 2 (condition: high power vs. baseline) x 3 (product
category: pasta vs. nuclear element vs. pain reliever)
2) N = 75 students; 2 (power: high vs. low) x 2 (example provided: none vs.
winged alien)
3) N = 45 students; high-power prime/ conformity pressure vs.
low-power prime/conformity pressure vs.
no-power prime/conformity pressure (conformity baseline) vs.
no-power prime/no conformity pressure (attitude baseline)
4) N = 72 students; 3 (power: baseline vs. high-power prime vs. high-power
role) x 2 (opponent reputation: competitive vs. cooperative)
5) N = 49 students; 2 (power: high vs. low) x 2 (attitude topic: lake fill vs.
reading week)
Powerful participants generate creative ideas that were less
influenced by salient examples and expressed attitudes that
conformed less to the expressed opinions of others.
Griskevicius et al.
2009
Journal of
Marketing Research
Product review,
Advertisements
Product desirability
Three laboratory experiments:
1) N = 154 students; 2 (emotion: fear vs. romantic desire) × 3 (persuasion
heuristic: social proof vs. scarcity vs. control)
2) N = 157 students; 2 (emotion: fear vs. romantic desire) × 3 (persuasion
heuristic: social proof vs. scarcity vs. control)
3) N = 454; 3 (emotion: fear vs. romantic desire, neutral) × 4 (persuasion
heuristic: behavioral social proof vs. attitudinal social proof vs.
distinctiveness scarcity vs. limited-opportunity scarcity) × 2 (product:
museum vs. Las Vegas)
Fear increased persuasiveness of social proof appeals but led
scarcity appeals to be counterpersuasive. Romantic desire
could lead social proof appeals to be counterpersuasive.
Melnyk et al. 2011
Psychology &
Marketing
Newspaper article
Attitudes toward
buying
environmentally
friendly products,
intentions to follow
the advocated
behavior,
positive/negative
thoughts
Laboratory experiment:
N = 1010 participants of a Dutch panel; 2 (norm formulation: injunctive vs.
descriptive) x 3 (cognitive deliberation level: cognitive load vs. control vs.
cognitive deliberation) experiment and a two-level measured factor (belief in the
content of the message: nonbelievers vs. believers)
Cognitive load limited the influence of both norm
formulations. Cognitive deliberation increased the effect of
descriptive norms and decreased the effect of
injunctive norms.
77
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
McDonald, Fielding,
and Louis 2013
Personality and
Social Psychology
Bulletin
Writing task
Pro-environmental
intentions
One survey, two laboratory experiments:
1) N = 157 students; survey
2) N = 113 students; norm conflict vs. no conflict vs. control
3) N = 138 Indian adults; 2 (norm-conflict vs. no conflict) × 2 (effective vs.
ineffective)
Norm-conflict was associated with increased perceived
effectiveness for those with positive attitudes to the issue and
reduced perceived effectiveness for those with moderate
attitudes. Norm-conflict only influenced intentions when
perceived effectiveness was high.
Melnyk et al. 2013
Marketing Letters
Fictitious webpages
with normative
messages
Attitudes toward the
product,
buying intentions
Three laboratory experiments:
1) N = 110 students; 2 (social norm: descriptive “students buy fair trade coffee”
vs. injunctive “should buy fair trade coffee”) × 2 (regulatory focus: situations
to avoid vs. situations to achieve)
2) N = 60 students; comparing two messages (descriptive vs. injunctive)
regarding fluency
3) N = 120 students; 2 (social norm: descriptive vs. injunctive) × 2 (ingrained
regulatory focus: prevention vs. promotion)
Descriptive norms influenced attitudes and
intentions more strongly when promotion goals were salient
than when prevention goals were salient.
Regulatory focus exerted no effect on the influence of
injunctive norms.
Mourali and Young
2013
Journal of
Consumer Research
Peer feedback
Attitude toward the
product
Four laboratory experiments:
1) N = 216 students; 2 (power: low vs. high) x 2 (product type: low vs. high
attitude certainty) x 3 (peer feedback: control vs. positive vs. negative)
2) N = 202 students; 2 (power: high vs. low) x 2 (attitude certainty: high vs.
low) x 3 (feedback: positive vs. negative vs. none)
3) N = 209 students; 2 (power: high vs. low) x 2 (attitude certainty: high vs.
low) x 2 (feedback: negative vs. none)
4) N = 216 students; 2 (high power vs. control) x 2 (private vs. public
evaluation) x 3 (negative feedback vs. positive feedback vs. no feedback)
Power differently enhanced consumers’ ability to resist social
influence.
White and Simpson
2013
Journal of
Marketing
Flyer with
normative messages
Sustainable
behaviors
Two laboratory, one field experiment:
1) N = 119 employees; 2 (level of self: individual vs. collective) x 3 (appeal:
descriptive vs. injunctive vs. self benefit) + control group
2) N = 676 households; 2 (level of self: individual vs. collective) x 3 (appeal:
descriptive vs. injunctive vs. self benefit)
3) N = 358 students; 2 (autonomy: neutral vs. autonomy affirmation) x 2 (level
of self: individual vs. collective) x 3 (appeal: descriptive vs. injunctive vs.
self benefit)
Injunctive and descriptive normative appeals were most
effective in collective levels of self. Benefit appeals were less
effective in encouraging sustainable behaviors. Self-benefit
and descriptive appeals were particularly effective in
individual levels of self.
Noguti and Russell
2014
Journal of
Advertising
Cover stories
describing the
findings of a study
conducted at another
university
Purchase intention,
brand attitude
Three laboratory experiments:
1) N =150 students; majority vs. minority norm
2) N = 135 students; neutral vs. positive norm information
3) N = 116 students; 2 (conformity vs. non-conformity) x 2 (positive vs.
negative presumed influence)
Social norms could affect consumers’ intentions to purchase
brands placed in a TV series. The normative effect depended
on individual differences in susceptibility to normative
interpersonal influence and, for individuals low in
psychological trait reactance, by contextual effects that primed
conformity.
78
Reference
Treatment
Dependent
Variable
Experimental Design
Key Findings
Warren and
Campbell 2014
Journal of
Consumer Research
Brand
advertisements
Perceived coolness
Five laboratory experiments:
1)
N = 190 Amazon MTurk participants; 2 (divergence: low vs. high) x 2
(brand replicate: familiar vs. unfamiliar)
2)
N = 196 Amazon MTurk participants; 2 (autonomy: high vs. low) x 2 (norm:
legitimate vs. illegitimate)
3)
N = 133 Amazon MTurk participants; low autonomy vs. bounded autonomy
vs. extreme autonomy
4)
N = 58, N = 132 students; 2 (low vs. high counterculturalism) x 4 (low vs.
moderate vs. high vs. extreme autonomy)
5) N = 74 students; 2 (trait valence: positive, negative) x 2 (trait type: cool vs.
classy) x 2 (context autonomy: desired more vs. desired less) x 2 (context
replicate: job vs. dinner)
Behaviors expressing autonomy increased perceived coolness,
but only when the autonomy seemed appropriate. Autonomy
seemed appropriate, and hence increased perceptions of
coolness, when a behavior diverged from a norm considered
unnecessary or illegitimate, when the autonomy was bounded,
and when the consumer viewed social norms as being overly
repressive.
79
8 General Discussion
This literature review illustrates the current state of research on social norms and
their influence on behavior. The reviewed studies provide evidence that social norms
can influence a great variety of human behaviors (see Table 2). This paper discusses the
findings and the different mechanisms that determine behavioral influence (see also
Figure 5). First, it provides an overview of the triggers of social norms, describing
different social norm manipulations and findings on normative beliefs. The highlighted
findings show that presenting target consumers with what the majority of their peers do
or think should be done can influence behavior (e.g., Nolan et al. 2008). Another
effective way to manipulate social norms that was revealed was the comparison of target
consumers’ own behavior with the behavior of a reference group (e.g., Ayres, Raseman,
and Shih 2013). In this context, the author sheds also light on the undesirable boomerang
effect of this procedure, caused when consumers already behave more desirably than the
compared peer average (e.g., Schultz et al. 2007).
Second, it was shown that the behavioral effects can be strengthened by varying the
reference group and referring to peers with whom the targeted consumer shares
similarities or can particularly identify with (e.g., Goldstein, Griskevicius, and Cialdini
2007). Third, by highlighting studies measuring social norms as trait variables, the
literature review demonstrates the behavioral potential of normative beliefs (e.g., Bobek,
Hageman, and Kelliher 2013) as an alternative trigger of social norms.
Fourth, the author demonstrates through various findings that social norms do not
uniformly affect behavior in all situations. In line with the focus theory of normative
conduct, the review of the relevant literature reveals that social norms have to be salient
in order to be behaviorally effective, e.g., through adequate environments (e.g., Keizer,
Lindenberg, and Steg 2008), the possibility to observe others’ actual behavior (e.g.,
Gino, Ayal, and Ariely 2009), or a combination of both (e.g., Cialdini, Reno, and
Kallgren 1990). Finally, the literature review highlights further moderating conditions.
By discussing contextual and trait moderators, it provides structured knowledge on the
conditions in which social norms are more or less effective in influencing human
behavior. Among the fragmented moderating influences, the reviewed studies indicate
that perceived power and certainty limit social norm influence, whereas fear-contexts
can strengthen it (e.g., Mourali and Young 2013). Moreover, traits like susceptibility to
normative influence and reactance (e.g., Noguti and Russell 2014) decrease the
80
behavioral relevance of social norms, while trust and positive attitudes toward the
proposed behavior can increase the effectiveness of social norms (e.g., Osterhus 1997).
Not least, several studies provide evidence that moderating influences can vary between
descriptive and injunctive norms (e.g., Melnyk et al. 2013).
Taken together, the literature review structures current knowledge on social norms
by showing how social norms can be triggered and which factors influence their
behavioral effects.
9 Implications for the Use of Social Norms in Marketing
The studies in this review indicate that social norms in several variants significantly
drive behavioral changes. Even though most effects were shown outside of the
marketing context, they provide valuable insights for applying social norms in
marketing to influence consumer behavior. Companies should consider the presented
results as a motivation for the use of social norms in their customer contact. In the
following section, several key findings from the highlighted studies that might benefit
marketers are presented.
In line with the focus theory of normative conduct, several of the presented works
show that social norms drive behavior particularly in situations of uncertainty (Cialdini,
Reno, and Kallgren 1990; Gino, Ayal, and Ariely 2009). These effects could have
implications for marketers. When consumers have little or no experience with a brand
or general type of product or service, the resultant uncertainty can make them especially
receptive to peer influence, and therefore to social norm feedback (Griskevicius,
Cialdini, and Goldstein 2008). Innovations or new brands that customers need to get to
know, especially, could gain acceptance if potential buyers become aware of the new
product´s benefits. For instance, this could be achieved by creating a social norm where
consumers can observe how (close) others are using and enjoying the new product. This
basic principle, shown in the highlighted studies where confederates manipulated social
norms (e.g., Kallgren, Reno, and Cialdini 2000; McFerran et al. 2010) is already used
in word of mouth or buzz marketing campaigns employing highly visible consumers to
use companies’ new products, and to create “buzz” around them (Griskevicius, Cialdini,
and Goldstein 2008).
For most of the studies evaluated in this article, a major concern was how to convert
undesirable behaviors into desirable ones. Thereby, a large proportion of the studies
81
show that social norms are particularly effective in promoting sustainable or prosocial
behaviors such as energy saving. Melnyk et al. (2010) state that social norms have a
relatively large effect on socially responsible behaviors, and could therefore be
particularly influential on consumer behavior in this domain. Companies and brands
already using topics like sustainability and corporate social responsibility as sales
arguments could draw on this potential of social norms to encourage sustainable
behaviors. Environmental protection is recognized as a socially important issue, and an
increasing number of consumers reward businesses that address environmental concerns
through their business practices (Goldstein, Cialdini, and Griskevicius 2008; Meise et
al. 2014; Menon and Menon 1997). Contributing to the environment represents
something that ought to be done. The research results have shown that consumers are
susceptible to this topic, which could be combined with social norm feedback.
Companies could use the proven susceptibility that consumers show toward
environmental topics and the effectiveness of social norms in this area as a marketing
tool, with regard to all areas where behavioral changes could contribute to
environmental protection. In addition to the inherent benefits to the environment and to
society, normative appeals that induce behavioral changes could also benefit the
company in cost savings in areas such as labor, energy, materials, or transportation
(Goldstein, Cialdini, and Griskevicius 2008). For instance, a company could
normatively appeal to the sense of environmental awareness in the context that product
returns create transportation emissions and resource consumption. Behavioral appeals
providing social norm feedback could be presented at various customer touchpoints
(e.g., online shopping carts), and thus provoke a change in return behavior that also
reduces costs associated with product returns, such as transportation, diagnosing, and
repacking.
Importantly, in addition to the numerous studies that used social norms to induce
socially desirable behaviors, a small number of studies have recently emerged that
indicate the role of social norms in increasing product desirability (Griskevicius et al.
2009) and promoting purchase decisions (e.g., to purchase fair trade coffee) (Melnyk et
al. 2013), environmentally friendly products (Melnyk et al. 2011), or certain alcohol
brands (Noguti and Russel 2014). Although these studies provide initial knowledge
about the influence of social norms on purchase behavior, its potential employment in
marketing practice is limited. While companies already use social norm appeals in
advertising (e.g., “best-selling brand”), considerable potential remains to develop a more
systematic use of social norms in the purchase process, which has not yet been utilized.
82
The presented effects are encouraging with regard to integrating social norms at
customer touchpoints. Digital technologies, big data management systems, and
communication technologies have yielded innovative possibilities for providing
customers with personalized social norm feedback (Burchell, Rettie, and Patel 2013).
Different examples show how companies initially use the behavioral relevance of social
norms, not only in advertising but also to influence purchase decisions at the point of
sale. By providing information on what other “Customers Who Bought This Item Also
Bought,” Amazon set an example of using light normative feedback. In its newsletter,
Ebay highlights the most popular items by means of customer visits on the website. The
Brazilian subsidiary of the German fashion retailer C&A displayed Facebook “likes” of
items on its Facebook page and on the clothes hooks of in-store items, and in this way
dovetailed normative influences from both online and offline channels (Robles 2012).
These examples show first steps toward applying descriptive norm feedback at different
stages of the purchase process. According to the presented research results, opportunities
for marketers lie in exploiting the potential of the presented insights more effectively
with the help of existing technical capacities. For instance, a next step in using the
behavioral relevance of social norms could be a simple enhancement of the Amazon
example—that is, not only showing what other “customers who bought this item also
bought,” but also informing target customers how many other customers bought a given
additional product. This approach is a way to generate a higher level of cross-selling:
“Did you forget cereals? 93% of our customers who buy the same milk as you buy it
together with cereals.”
Moreover, the efficient use of social norms in marketing requires an adequate
framing of social norm information. In addition to providing basic peer information
(“93% of our customers…”), marketers could benefit from various possibilities for
framing social norm information. The analyzed studies showed several variants of
conveying normative information, for example, social comparison (Ayres, Raseman,
and Shih 2013), contingent matches (Anik, Norton, and Ariely 2014), or rank
information (Aldrovandi, Brown, and Wood 2015), which have been shown to
strengthen the normative influence. With this knowledge, marketers could optimize the
persuasiveness of their communication measures for consumers. Some of the presented
studies indicated that in specific contexts it might be worthwhile to deviate from basic
peer information and present variants that could be even more persuasive. For instance,
marketers could enhance the persuasiveness of basic peer information (“90% of our
customers order [amount] of [product].”) by providing individual social comparison
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feedback to increase revenue from low-volume customers: “Are you unsatisfied with
us? Your dropout rate is 90%. On average, our customers abandon the transaction in
50% of cases. Please tell us if something was out of order or if we could do more for
you.”
A further rise in the persuasiveness of the social norm feedback demonstrated in the
extant research can be achieved through the presentation of rank-based social norms.
Like Aldrovandi, Brown, and Wood (2015) revealed, the information that “90% of
people consume less chocolate than you do” could be even more persuasive in increasing
sales of healthy food than a social comparison norm intervention might be (for example,
“you eat five bars of chocolate per week; on average, other people consume three bars
per week”). Although companies would be ill-advised to give their customers advice
about what and how much to eat, the psychological mechanism behind rank information
could increase persuasiveness in interactions with customers. This is because rank
information provides norm feedback in ways in which people naturally process
information, and in this way substantially increases effectiveness (Aldrovandi, Brown,
and Wood 2015). For instance, a retailer or service provider could inform the customer
about their rank: “Are you unsatisfied with us, or did you find everything? 90% of our
customers have a lower dropout rate than you/order more of/buy more of, etc. Please tell
us if something was out of order or if we could do more for you.” Another application
for rank-based social norms in marketing arises in contexts where companies want to
prevent undesirable behavior by their customers—that is, returning products. The
normative statement “your return rate lies in the highest 10% of all customers” might
make heavy returners more aware of their opportunistic behavior than a basic norm
statement about the average return rate.
However, while different framings of social norms have been shown to influence
human behavior in a number of scientific studies, they can also backfire when appealing
to customers if they are implemented at the wrong touchpoint, or in a context where
such feedback is not appropriate for persuading customers. Messages such as “You
purchase less than do 90% of our customers” could also provoke feelings of reactance.
Therefore, companies must carefully assess and test to determine which framing will be
more or less effective in a given situation, and for a specific product or customer group.
Moreover, even if several possibilities exist for framing a social norm in a
persuasive way, companies also must appreciate that sometimes no norm exists whose
communication could benefit the applying company. The reviewed research provides
insights into how marketers can bypass this issue. To invent statistics that support a
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desired behavior is unethical, and can backfire if customers expose them. Companies
could instead communicate facts that are close to social approval and therefore result in
increased demonstrations of the desired behavior (e.g., opinions on environmental
protection, consumption of healthy products). Thus, it can be helpful to cite popular
opinion on what people think should be done (injunctive norm), even if they do not
engage in this behavior (descriptive norm) (Martin 2012). One of the reviewed studies
provides a further possibility for facing the challenge of providing correct norm
information about a behavior that is desired by the marketer, but that may not exist.
Specifically, companies do not need to provide customers with information about the
actual past behavior of other customers, but rather they should consider a possible percentage of people who may perform a behavior in the future: “If 75% of our customers
buy this product, we will donate 10% of the revenues to charity.” With this normative
framing, marketers could simultaneously provide social proof while offering a target
low enough to remain plausible.
When considering an adequate framing, companies could also benefit from
considering to whom the social norm information refers. The highlighted studies
indicated that marketers should focus on presenting the behavior of adequate reference
groups by conveying the behaviors of people with which the targeted customer can
especially identify. The results showed that social norms referring to people with similar
characteristics and with a personally relevant social identity have a stronger influence
on behavior than do social norms referring to distant or unspecified others (Melnyk, Van
Herpen, and van Trijp 2010). When appealing to customers, companies can make use
of this principle by highlighting behavior of similar customers based on
sociodemographic and psychographic criteria (e.g., customers who bought the same
product, or have the same tastes). For instance, the music-streaming service Spotify
identifies the music that people listen to in specific neighborhoods. On the so-called
“musical map” of 1000 cities, Spotify displays the songs that are listened to by other
users in the respective cities (Van Buskirk 2015). The well-proven principle of
highlighting peer information of those with whom the target can identify also has
potential for personalized product recommendations. Using the stronger behavioral
influence of close others, marketers can build a bridge between the collective character
of social norms and individualized customer appeal.
Consumers often take the expectations and behaviors of close others into account
when deciding what behavior is appropriate (Cialdini, Reno, and Kallgren 1990), and
this mechanism also has implications for branding. The strength of brands is based on
85
consumer identification—consumers purchase brands that they know and identify with
(Kuenzel and Halliday 2008). If customers can identify with a brand, they might also
identify with other customers who like the brand. This is in line with related research
examining consumer behavior in brand communities (Schouten, McAlexander, and
Koenig 2007) and research showing the influence of reference groups on self-brand
associations (e.g., Escalas and Bettman 2005). Thus, a customer who buys a specific
brand might be particularly susceptible to social norm information about how other
customers or fans of this brand behave. Brands with which customers have a strong
emotional relationship could benefit from these emotional connections between their
customers and fans. For instance, if an exclusive car brand wants to increase sales of a
specific equipment package, highlighting in the car-configurator how many other
drivers of the specific car model also ordered this package might be helpful.
Furthermore, if a salesman provides such peer information personally to the customer,
the normative effect might be even stronger.
Importantly, when using social norms for brands, marketers must balance between
highlighting what most other customers of the brand are doing and maintaining
exclusivity. Marketers could relate a social norm appeal to an exclusive reference group
(i.e., members of the “BMW M community”) or propose behaviors that do not damage
the exclusivity of the product/brand but provide other benefits for the company (e.g.,
cost reduction) or sharpen the profile of the brand. For instance, for a brand with a
sustainable profile, a normative appeal could highlight that “93% of our customers
participate in our resource saving program.”
The highlighted research provides several insights relevant to the application of
social norms in marketing. However, in addition to assessing the various fields of
application, marketers must be aware of boundary conditions that have been shown to
influence the effects of social norms on behavior. The highlighted examples indicate
that social norms do not uniformly influence the behavior of targeted customers in favor
of the company, but may have detrimental effects. The following section summarizes
the boundary conditions revealed in the reviewed studies, and those that may influence
the practical use of social norms in marketing contexts.
First, studies referring to the focus theory of normative conduct emphasize the
importance of making a norm salient (Kallgren, Reno, and Cialdini 2000). Hence, in
addition to providing an appropriate framing for a normative appeal, companies must
ensure that a social norm becomes salient, and is activated in customers’ minds.
Normative appeals that are prominent and visible without being obtrusive or interfering
86
with the purchase process can achieve an adequate level of salience. However, this can
backfire if a highlighted social norm is accompanied by an environment that makes an
opposite norm salient; such a condition will discourage conformity with the desired
norm—for example, when a retailer suggests that 99% of customers like a product that,
upon examination, is revealed to be an obvious shelf warmer.
Second, several studies show that the effect of social norms can vary across different
contexts and customer segments. This results in moderators that can diminish the
influence of social norms (e.g., trait reactance, power, and cognitive load) or that can
strengthen the influence (e.g., trust, fear, and susceptibility to normative influences).
These insights can be helpful when using social norms appeals that are selectively based
on lifestyle segmentations. Although the findings show that appealing to susceptible
customers can result in substantial behavioral changes, studies also indicate that not all
customers are susceptible to peer information and may, instead, feel deprived of their
freedom, leading to reactance (White and Simpson 2013). Moreover, studies also show
that social norms can elicit undesirable behaviors in customers who previously
displayed more desirable actions than the proposed average; this occurs when
undesirable norms are made salient (the boomerang effect) (Schultz et al. 2007). Thus,
the company or retailer must carefully assess whether social norms could be helpful in
a specific situation for a specific group of customers.
Third, in addition to implementing social norm appeals to susceptible customer
segments, marketers must carefully examine and test to determine which products and
purchase stages would be suitable for the application of social norms. For instance, a
normative appeal might not exert the same effects across all purchase stages. If a
customer has ordered a garment in three different sizes, it is difficult to conform to a
social norm that addresses avoiding returns after purchase. This customer may be
susceptible to the social norm appeal, but, having purchased three items with the
intention of keeping the one that fit best, may still want to return the items that did not
fit. The same appeal presented in the purchase stage might have prevented the customer
from ordering three different versions and instead made him or her have a look at the
retailer’s size chart.
Finally, the moderators reveal ethical principles about the implementation of social
norms in marketing. Studies suggest that a translation of norms into behavior occurs
only when consumers have trust in the proposed social norm, and perceive it to be
realistic and credible (Anik, Norton, and Ariely 2014; Osterhus 1997). These findings
indicate that if people do not believe in the message, it will not exert influence on their
87
behavior. Therefore, marketers need to ensure that they present social norm information
that is credible and that provides a comprehensive attribution of the facts presented
(Burchell, Rettie, and Patel 2013). For instance, marketers can increase the credibility
of appeals by citing or indicating their data source. Importantly, the revealed trust
moderator (Osterhus 1997) also points to the ethical use of social norms. If targeted
customers find out that they were manipulated on the basis of false information, the
attempt can backfire, resulting in a damaged image and in lost patronage, while future
appeal attempts will be regarded with suspicion (Martin 2012).
10 Directions for Future Research in Marketing
As well as describing valuable implications of using the principle of social influence
in marketing practice, the reviewed studies suggest several avenues for further research
on social norms in marketing. The presented studies use various framings for providing
social norm feedback, such as peer information (e.g., Goldstein, Cialdini, and
Griskevicius 2008), social comparison (e.g., Ayres, Raseman, and Shih 2013; Schultz
et al. 2007), rank information (Aldrovandi, Brown, and Wood 2015), and contingent
match incentives (Anik, Norton, and Ariely 2014). However, to the best of the author’s
knowledge, no study exists that compares different norm framings in order to analyze
which norms are more effective or less effective in specific conditions. A comparison
of different framings of social norm appeal could contribute to the knowledge on social
influence. A comparison of different norm framings with regard to their effectiveness in
provoking purchase behaviors could provide additional knowledge about how to use
social norms efficiently to influence consumer behavior.
Many of the reviewed studies indicate that peer information is particularly effective
if it highlights that a majority of a relevant reference group engages in the desired
behavior (e.g., Goldstein, Cialdini, and Griskevicius 2008; Nolan et al. 2008). One study
even shows, in contrast, that a low prevalence increases the undesired behavior,
following the motto “When so few people engage in this behavior, it might not be a
sensible thing to do!” (Sieverding, Decker, and Zimmermann 2010). However, a social
norm statement highlighting that a very high majority engages in a specific behavior
could also be perceived as being unlikely, and thus would make behavioral change less
probable (Anik, Norton, and Ariely 2014). Accordingly, several of the reviewed studies
in which participants received social norm appeals about the previous behavior of others
in order to influence their own behavior, provide evidence that norms highlighting
88
majorities in the 70%-80% range are effective at motivating behavior (e.g., Anik,
Norton, and Ariely 2014; Gerber and Rogers 2009; Goldstein, Cialdini, and Griskevicius
2008; Nolan et al. 2008). However, except for Anik, Norton, and Ariely (2014), who
focus on contingent match incentives and not on basic norm information, research
comparing the effectiveness of different percentages in social norm appeals is scarce.
Future research could shed more light on the ideal value required to make a social norm
appeal most persuasive. Similarly, marketing research could examine this question to
create knowledge on the ideal percentage level for normative appeals to influence or
change purchase decisions.
In addition to different framings of social norms, the nature of the social norm itself
may also exert varied influences on purchase behavior. For instance, the goal of
obtaining social approval as a characteristic of normative conformity might affect
purchase behavior, especially for branded products (i.e., buying a specific brand for
social approval). Future research could examine the conditions in which informational
conformity (i.e., descriptive norms) and normative conformity (i.e., injunctive norms)
have an effect on purchase behavior, in order to provide knowledge about which type of
social norm works best for specific products and customer segments.
Several of the reviewed studies show that an appropriate relevance group could
strengthen the behavioral effect of social norms. Specifically, varying the reference
group in social norm appeals revealed that a stronger influence of social norms can be
exerted from reference groups that share similar characteristics with the targeted
individual and/or from those with whom the individual can identify (Gino, Ayal, and
Ariely 2009; Goldstein, Cialdini, and Griskevicius 2008). In a marketing context, this
leads to the question: What would be appropriate reference groups to provoke purchase
decisions and affect consumer behavior in purchase situations? Analogous to the
analyzed studies, adequate reference groups could refer to other customers who bought
the same product or who share the same tastes. However, little knowledge exists about
which principle of norm adherence, similarity, or social identity is most effective in
strengthening the influence of a social norm appeal in a purchase situation, and under
which circumstances. Future research could examine how variations in customer
reference groups influence the effect of social norms on purchase behavior. Moreover,
in shopping decisions, reference groups could be influential in ways other than similarity
and social identity—for example, a person may not want to buy products that all others
in his or her social environment buy. Moreover, situational similarity, such as being in
the same choice decision, could have an influence on purchase behavior. The specific
89
conditions of shopping situations are an unknown field with regard to social norms with
varying reference groups; therefore, their examination has the potential to provide
theoretical knowledge about which types of reference groups could strengthen the
influence of social norms in purchase situations.
Table 7 indicates that the effect of social norms on consumer behavior also depends
on environmental cues and on the salience of the norm. For instance, salience might
vary across various stages of the purchase process (e.g., between the pre-purchase and
post-purchase stages). Therefore, future research examining social norms in a marketing
context could investigate how social norms influence purchase behavior across various
stages of the purchase process.
Further, Table 8 illustrates several psychological moderators that influence the
effect of social norms on purchase behavior, including regulatory focus and intuitive
influence factors such as trait reactance, susceptibility, and cognitive deliberation.
Future research examining the effects of social norms in marketing contexts could focus
on moderators that are strongly related to shopping motivations and purchase behavior.
For instance, testing the influence of social norms in the context of different shopping
motivations (e.g., Arnold and Reynolds 2003; Evanschitzky et al. 2015) could create
additional knowledge about the effect of social norms across different customer types.
Moreover, as alluded to in the implications, the underlying psychological
mechanisms of social norms may be helpful for strengthening emotional connections to
brands. Related research has already shown that reference groups influence self-brand
associations (e.g., Escalas and Bettman 2005), whereas the reviewed studies on social
norms provided initial evidence that social norm feedback can increase purchase
intentions of brands (Noguti and Russell 2014). Future research could create a deeper
understanding of the influence of social norms on brand outcomes, by examining
experimentally how social norm interventions (e.g., peer information or social
comparison feedback) drive emotional connections with brands and the resulting
willingness to purchase.
Most of these suggested research directions that developed from the highlighted
findings place a central focus on the potential for social norms to convert undesirable
behaviors into desirable ones. However, as was shown by those studies revealing
boomerang effects (Schultz et al. 2007; Sieverding, Decker, and Zimmermann 2010),
salience of opposite norms (e.g., Kallgren, Reno, and Cialdini 2000), or the effects of
norms being seen as illegitimate and repressive (Warren and Campbell 2014), the
90
mechanism of norm adherence can also include behavioral effects that will not be in
favor of the applying party. Therefore, in addition to examining the potential for social
norms to influence behaviors that are desired by companies (e.g., to increase purchase
behavior), future research in marketing could also examine the dark side of social norms
in marketing. For instance, social norms might be an explanatory factor for damages to
a brand’s reputation (e.g., Abercrombie & Fitch). Initial research has already provided
an overview of case studies demonstrating that social influence can damage brands, e.g.,
via social media (Rauschnabel, Kammerlander, and Ivens 2015). Future research could
examine the underlying mechanisms behind such phenomena, reflecting the dark side
of social norms in marketing.
Finally, the variety of behaviors investigated in the outlined research, as well as the
derived examples of application, indicate that further research on social norms in
marketing could look beyond influencing purchase decisions. Future research in this
field could exploit the potential of social norms in a wider marketing context, including
purchase behavior but also focusing on related behaviors that are relevant in marketing.
For instance, further research could provide insight into the ways that social norms can
influence the adoption of new products and innovations (e.g., e-mobility). Also,
investigating consumer behaviors that companies desire to prevent rather than
encourage (e.g., return behavior, complaints) may reveal useful insights. Moreover, in
addition to examining the potential of social norms on further dependent variables of
consumer behavior, there is also great potential for research on crafting the focal
independent variable. New technologies within the “Internet of things”, big data
analytics, and innovative forms of online communication provide various sources for
normative information that consumers might use to orient themselves (e.g., customer
comments or reviews, social media). These sources provide new possibilities for
examining social influence and for crafting social norm appeals beyond traditional peer
information treatments. Future research could examine the influence of these forms of
social norms in order to create additional knowledge on social influence in marketing.
91
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101
C PAPER 2: RETOURENSENKUNG IM ONLINEHANDEL: DAS POTENZIAL VON EIGENNUTZEN
UND SOZIALEN NORMEN
102
RETOURENSENKUNG IM ONLINE-HANDEL:
DAS POTENZIAL VON EIGENNUTZEN UND
SOZIALEN NORMEN
Author
Thilo Pfrang
Zusammenfassung
Online-Händlern fällt es schwer, das Rücksendeverhalten ihrer Kunden zu ändern.
Soziale Normen und Eigennutzen-Anreize sind als Ansätze zur Verhaltensänderung
etabliert, wurden zur Ansprache von Online-Kunden bisher jedoch nicht untersucht.
Zwei Experimente zeigen, dass beide Anreize die Rücksendeabsicht senken können,
ohne die Kaufabsicht zu beeinträchtigen.
103
1 Einleitung
Mit dem zunehmenden Wachstum des Onlinehandels steigt auch die Zahl der
Retouren. Viele Online-Händler zeigen sich diesbezüglich sehr kulant gegenüber ihren
Kunden. Großzügige Rücksendebedingungen können die Zufriedenheit erhöhen, indem
sie Vertrauen aufbauen und ein Qualitätssignal darstellen (Bonifield, Cole und Schultz
2010). Große Online-Händler wie Otto oder Zalando erachten Retouren als Bestandteil
ihres Geschäftsmodells, während sich Konsumenten an die Kulanz der Händler gewöhnt
haben und großzügige Rücksendebedingungen erwarten (Lütge 2014; e-velopment und
bevh 2014). Die Kulanz hat sich jedoch auch als Auslöser für hohe Retourenquoten
erwiesen und bereitet Nährboden für Missbrauch (Walsh und Möhring 2015; Petersen
und Kumar 2009). Die Schwelle, an welcher die Kosten für Retouren deren Nutzen
übersteigen, ist bei vielen Online-Händlern längst erreicht, wenn nicht gar überschritten.
Jüngste Studien zeigen, dass in Branchen wie Bekleidung, Schuhe, Sport und Freizeit
die Retourenquote häufig über 50% liegt (Birger 2014; EHI, ECC und Capgemini 2013),
während aktuellen Schätzungen zufolge pro zurückgesendetem Artikel bis zu 15 Euro
an Kosten entstehen (Rösch 2013). Aus diesem Grund streben Online-Händler vermehrt
danach, Strategien zu entwickeln, die die Anzahl an Retouren minimieren können. Eine
Befragung von Pur et al. (2013) unter 357 Online-Händlern deutet darauf hin, dass ein
zehnprozentiger Rückgang der Retourenquote die Profitabilität um 5% erhöhen könnte.
In der Praxis zeigen sich zahlreiche Beispiele, wie Unternehmen versuchen, dies zu
schaffen:
Um das Rücksendeverhalten direkt zu beeinflussen, integrieren Online-Händler
beispielsweise Verhaltensappelle in ihre Kundenansprache. Aufgrund der
Kundensensibilität gegenüber strengeren Rücksendebedingungen setzen sie diese
jedoch –wenn überhaupt– nur sehr dosiert ein, um den Kunden während des
Kaufprozesses nicht zu behindern und dadurch Umsätze einzubüßen. Verhaltensappelle
beschränken sich bisher auf monetäre Anreize bei Unterlassen einer Rücksendung (z.B.
bei bon prix) oder Belehrungen über hohe Kosten und Umweltschäden durch Retouren
(z.B. bei mirapodo, siehe Abbildung 7). Sie erfordern damit entweder indirekte
Preisreduktionen oder bergen die Gefahr beim Kunden Reaktanz auszulösen (Garnefeld,
Münkhoff und Raum 2013).
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Abbildung 7
Beispiele für Verhaltensappell bei mirapodo
Händler wie BestBuy, Zalando oder Amazon greifen im Extremfall auch auf
Sanktionen in Form von Vorkasse-Zahlung oder Kontensperrungen gegenüber Kunden
zurück (Rösch 2013; Janakiraman und Ordóñez 2012; Boyle 2006). Sanktionen senken
zwar die Zahl der Retouren und können die Profitabilität kurzfristig erhöhen (El Kihal,
Schulze und Skiera 2014). Sie haben jedoch negative Wirkungen auf den Umsatz,
schmälern zukünftige Ertragspotenziale und können die Marke durch Imageverluste
empfindlich beschädigen (Kontio 2012, Rösch 2013).
Letztendlich gilt es für den Online-Handel, eine Balance zwischen Befriedigung der
Kulanzansprüche der Kunden und den eigenen Profitabilitätszielen zu finden. Viele
Online-Händler rücken daher auch nach Inkrafttreten der neuen EU-Richtlinie nicht von
kulanten Rücksendebedingungen ab und nutzen stattdessen indirekte Maßnahmen der
Retourenvermeidung.
Dazu
zählen
beispielsweise
Optimierungen
der
Produktdarstellung (z.B. 360 Grad-/Zoom-Funktionen, virtuelle Anprobe, Apps zur
Größenbestimmung) oder Prognosetools, die aus den zur Verfügung stehenden
Retourendaten Muster erkennen und daraus Erkenntnisse für geeignete
Gegenmaßnahmen wie z.B. Sortimentsanpassungen liefern (Rösch 2013). Solche
105
Optimierungen sind jedoch mit hohen Kosten verbunden. Zudem beeinflussen sie das
Rücksendeverhalten nur indirekt. Die detaillierteste Produktdarstellung oder der beste
Retourenprognose-Algorithmus ändern nur wenig an den Rücksendegewohnheiten der
Kunden sowie der Tatsache, dass für den Großteil der Kunden geringe Barrieren für eine
Rücksendung, geschweige denn für Missbrauch der Händlerkulanz existieren (Lütge
2014; King, Dennis und Wright 2008).
Aus Händlersicht stellt sich somit die Frage, wie man das Rücksendeverhalten
direkt beeinflussen kann, ohne Kunden zu verprellen, Rücksendebedingungen zu
verschärfen oder monetäre Anreize bieten zu müssen. Erfordert dies ein Umdenken in
der Kundenansprache? Wie gelingt eine solche Marketing-Transformation?
2 Transformation der Kundenansprache
In der Konsumentenforschung haben sich vor allem zwei Ansätze in verschiedenen
Bereichen als wirksam erwiesen, unerwünschtes Verhalten in wünschenswertes
Verhalten zu ändern: Soziale Normen und Eigennutzen. Die Wirksamkeit dieser
verhaltenstheoretischen Ansätze basiert darauf, dass Menschen sich in ihrem Verhalten
an ihren Mitmenschen orientieren (soziale Norm) und ein Verhalten vor allem dann
ändern, wenn sie dadurch einen eigenen Vorteil (Eigennutzen) genießen.
Die Beispiele mirapodo und bon prix zeigen, dass eine normative Kundenansprache
sowie monetäre Eigennutzenanreize bereits ansatzweise genutzt werden. Unternehmen
schöpfen bisher jedoch nicht das volle Potenzial aus diesen Anreizformen und setzen
diese sogar unvorteilhaft ein. So hat sich die normative Kundenansprache (siehe
Abbildung 7) nicht als Verhinderungsinstrument, sondern auch schon als Treiber von
Retouren herausgestellt (Garnefeld, Münkhoff und Raum 2013). Psychologische
Untersuchungen zeigen hingegen, dass die adäquate Vermittlung einer sozialen Norm
das Verhalten in eine gewünschte Richtung lenken kann (Schultz et al. 2007; Chialdini,
Reno und Kallgren 1990), während das Bewusstmachen eines Eigennutzens auch ohne
monetären Anreiz gewünschte Verhaltensänderungen induzieren kann (White und
Peloza 2009). Dies lässt vermuten, dass eine Transformation der bisherigen
Kundenansprache den Kunden auch in puncto Retouren umkonditionieren könnte.
Doch neben der Praxis hat auch die Retourenforschung dieses Potenzial für eine aus
Händlersicht wünschenswerte Änderung im Rücksendeverhalten bisher nicht
untersucht. Der Fokus bisheriger Forschung liegt zum einen in der Analyse von
106
Konsequenzen, Treibern (Petersen und Kumar 2009) und Motiven von Retouren wie
z.B. der betrügerischen Neigung (Harris 2008; Piron und Young 2000), Opportunismus
(Powers und Jack 2013) oder kognitiven Dissonanzen (Maity 2012). Andererseits
untersucht der Großteil bisheriger Studien die Ausgestaltung und die Auswirkungen von
kulanten (gegenüber strengen) Rücksendebedingungen auf die Profitabilität (Bower und
Maxham III 2012; Kim und Wansink 2012; Janakiraman und Ordóñez 2012; Anderson,
Hansen und Simester 2009) und bezieht dabei auch Größen wie Risikoreduktion
(Petersen und Kumar 2015) oder Qualitätssignalisierung (Bonifield, Cole und Schultz
2010; Wood 2001) mit ein. Diese Studien untersuchen jedoch nicht, wie das
Rücksendeverhalten direkt beeinflusst und gesenkt werden kann. Experimentelle
Untersuchungen zur Wirkung von Anreizen gegen Retouren sind kaum Bestandteil
bisheriger Forschung zum Rücksendeverhalten.
Der vorliegende Beitrag versucht, diese Forschungslücke mit zwei experimentellen
Studien zum Einfluss von sozialen Normen und Eigennutzen auf die Rücksendeabsicht
zu adressieren. Daraus entstehen erste Erkenntnisse über die Wirkung einer
transformierten Kundenansprache mit Eigennutzen- und Norm-Anreizen auf die
Rücksendeabsicht. Diese Erkenntnisse können Händlern dabei helfen, die
Rücksendeabsichten ihrer Kunden zu beeinflussen, ohne die Kaufabsicht zu
beeinträchtigen.
3 Verhaltensänderung durch soziale Normen und Eigennutzen
Nach der Austauschtheorie investiert der Mensch in Austauschbeziehungen auf
Basis einer subjektiven Kosten-Nutzenanalyse (Blau 1964). In diesem Zusammenhang
impliziert das Konzept des Eigennutzens, dass Konsumenten sich vor allem dann so
verhalten, wie es ein Austauschpartner (z.B. ein Händler) wünscht, wenn sie aus diesem
Verhalten einen eigenen Vorteil generieren können (Holmes, Miller und Lerner 2002).
Ein Eigennutzen kann materieller bzw. monetärer (z.B. Geld sparen) oder immaterieller
Natur sein (z.B. Erhöhung der Lebensqualität).
Soziale Normen implizieren, dass das Verhalten der Menschen hauptsächlich von
ihren Mitmenschen geprägt ist. Nach der Fokustheorie des normativen Verhaltens
(Cialdini, Reno und Kallgren 1990) ziehen Menschen Erwartungen und
Verhaltensweisen in ihrem sozialen Umfeld in Betracht, um zu entscheiden, was
angemessen ist. Der Mensch fühlt sich wohl, wenn er gruppenkonform handelt. Nach
107
Cialdini, Reno und Kallgren (1990) gilt es, zwischen deskriptiven (was andere tun) und
injunktiven Normen (was andere denken, was getan werden sollte) zu unterscheiden. Die
Wirkung von sozialen Normen variiert außerdem in Abhängigkeit der Referenzgruppe.
Mitmenschen, mit denen man sich gut identifizieren kann, haben einen besonders
starken Einfluss auf das eigene Verhalten (Goldstein, Cialdini und Griskevicius 2008).
Eigennutzen- und Norm-Anreize haben sich in verschiedenen Bereichen als
verhaltensrelevant erwiesen. Eigennutzen kann beispielsweise nachhaltiges (White und
Simpson 2013) oder prosoziales Verhalten (Holmes, Miller und Lerner 2001)
hervorrufen. Studien zu sozialen Normen belegen Verhaltensänderungen durch
normative Anreize z.B. die Senkung des Energieverbrauchs (Schultz et al. 2007),
Mehrfachbenutzung von Handtüchern in Hotels (Goldstein, Cialdini und Griskevicius
2008), pünktliche Bezahlung von Steuern (Martin 2012) oder Müllvermeidung
(Kallgren, Reno und Cialdini 2000). Melnyk und Kollegen (2013) haben gezeigt, dass
normative Anreize auch die Kaufabsicht beeinflussen können.
Die Wirksamkeit in verschiedenen Bereichen sowie die Fähigkeit unerwünschte in
erwünschte Verhaltensweisen umzukehren lassen erahnen, dass eine adäquate
Vermittlung dieser Anreize auch das Rücksendeverhalten beeinflussen könnte, ohne
damit negative Wirkungen auf das Kaufverhalten zu entfachen. Vor allem die
bewiesenen Effekte für nachhaltige und prosoziale Verhaltensänderungen könnten für
eine Beeinflussung des Rücksendeverhaltens von unmittelbarer Relevanz sein (Möhring
et al. 2015). Obwohl die Retourenforschung dies bisher nicht untersucht hat, liegt es
nahe, die psychologischen Mechanismen von sozialen Normen und EigennutzenAnreizen für eine Änderung des Rücksendeverhaltens einzusetzen.
Die nachfolgenden Studien überprüfen den Einfluss von Eigennutzen- und NormAnreizen auf die Rücksende- sowie die Wiederkaufsabsicht anhand von zwei
Laborexperimenten. In beiden Experimenten wurden Probanden unterschiedliche
Online-Einkaufsszenarien und Anreiztexte präsentiert und anschließend dazu befragt
(siehe Abbildung 8).
108
Abbildung 8
Studienaufbau
Da der Modehandel eine besonders hohe Retourenquote aufweist und die
Modebranche einen großen Marktanteil am Online-Handel insgesamt hat (Pur et al.
2013), beziehen sich beide Experimente auf den Online-Einkauf von Bekleidung.
4 Studie 1
Teilnehmer und Design. Die erste Studie testet den Einfluss von sozialen Normen
auf die Verhaltensabsicht, Retouren zu vermeiden. Zur Wirkungsuntersuchung von
sozialen Normen wurden mittels eines einfaktoriellen Experimentaldesigns 118
Probanden (MAlter= 28.5 Jahre, SD=8.6, 58% weiblich) 2 im Rahmen einer Online-Studie
mit Szenario-Technik und anschließendem Fragebogen zufällig einer von drei
Experimentalbedingungen (soziale Norm, verstärkte soziale Norm und Kontrollanreiz,
siehe Abbildung 9) zugewiesen.
Vorgehen. Im ersten Schritt des Szenarios wurden die Teilnehmer über die
Merkmale eines fiktiven Fashion-Online-Shops "FashionCo" informiert. Anschließend
wurden sie in die Situation versetzt, bei „FashionCo“ ein Paar Converse-Schuhe zu
kaufen und dieses sicherheitshalber in mehreren Größen in den Warenkorb zu legen.
Die Wahl des Produktes fiel auf das Modell „Converse All-Stars“, da es sich um ein
Unisex Produkt mit großem Bekanntheitsgrad handelt. Die Teilnehmer wurden mit einer
visuellen Darstellung ihres Warenkorbes und der jeweiligen Manipulation in Form eines
2
M=Mittelwert, SD=Standardabweichung (standard deviation)
109
Pop-Up-Fensters konfrontiert. Durch eine Variation des Textes im Pop-Up-Fenster
wurden die Probanden zufällig einem der drei Anreiztypen zugeteilt (siehe Abbildung
9).
Abbildung 9
Experimentalbedingungen von Studie 1
110
111
Anschließend beantworteten alle Probanden einen kurzen strukturierten Fragebogen
mit wissenschaftlichen Skalen. Dieser enthielt zunächst die Abfrage der abhängigen
Variable Rücksendeabsicht in Form von verschiedenen Single-Items wie z.B. „Nach
dem Lesen dieser Nachricht würde ich versuchen meine Auswahl auf eine Größe zu
reduzieren.“. Zudem erfolgte die Abfrage der Wiederkaufsabsicht mit einem SingleItem in Anlehnung an Finn (2012). Danach folgten Skalen zu Manipulationschecks und
Realitätsnähe (Darley und Lim 1993) sowie die Abfrage soziodemographischer Daten.
Die Abfrage sämtlicher Skalen erfolgte jeweils auf einer Likert-Skala von 1 (stimme gar
nicht zu) bis 7 (stimme voll zu).
Manipulationschecks. Die Effektivität der sozialen Norm- sowie der verstärkten
sozialen Norm-Manipulation wurde mit je einem item („Ich wurde durch einen Text
informiert, dass ein Großteil der anderen Kunden sich für eine Größe entscheidet.“, „Ich
wurde durch einen Text informiert, dass ein Großteil der anderen Converse-Käufer sich
für eine Größe entscheidet.“) gemessen. Eine Varianzanalyse (ANOVA) zeigt, dass
sowohl die Manipulation der sozialen Norm (MSoziale Norm=4.7, SD=1.9; MSoziale Norm
verstärkt=4.3,
SD=2.3; MKontrollanreiz=1.5, SD=.9; F(2,115)=41.83, p<0.001) 3 als auch die
Manipulation der verstärken sozialen Norm (MSoziale Norm=2.7, SD=1.9; MSoziale Norm
verstärkt=4.0, SD=2.1; MKontrollanreiz=1.7, SD=1.1; F(2,115)=19.4, p<0.001) erfolgreich
war.
Um die Realitätsnähe des Experimentaldesigns zu messen, wurden außerdem die
Items („Die dargestellte Situation war realistisch.“; „Die dargestellte Situation war
glaubwürdig.“; „Ich konnte mich insgesamt gut in die dargestellte Situation
hineinversetzen.“) abgefragt. Die aggregierten Mittelwerte der Antworten auf diese
Items (α= .83) deuten in sämtlichen Experimentalgruppen auf ein homogenes Level an
Realitätsnähe hin (MSoziale Norm=5.4, SD=1.0 MSoziale Norm verstärkt=5.4, SD=1.3
MKontrollanreiz=5.4, SD=1.6).
Wirkung der sozialen Normen. Die Wirkung der sozialen Norm auf die
Rücksendeverhaltensabsicht wurde ebenfalls mit Varianzanalysen getestet und zeigt
signifikante Haupteffekte der sozialen Norm auf die Verhaltensabsicht „meine Auswahl
nochmals zu überdenken“ (MSoziale Norm=4.9, SD=1.9; MSoziale Norm verstärkt=5.5, SD=1.6;
MKontrollanreiz=4.4, SD=2.1; F(2, 111)=3.6, p=0.03) und „versuchen meine Auswahl auf
eine Größe zu reduzieren“ (MSoziale Norm=4.6, SD=1.8; MSoziale Norm verstärkt=5.1, SD=1.8;
MKontrollanreiz=4.0, SD=1.9; F(2, 111)=3.6, p=0.03). Die Tendenz zum Überdenken bzw.
3
M= Mittelwert; SD= Standardabweichung; F= F-Wert; p= p-Wert (Irrtumswahrscheinlichkeit)
112
Reduzieren der Produktauswahl ist in der Gruppe mit der verstärkten sozialen Norm
(„Converse Käufer“) signifikant höher als in der Kontrollgruppe (p=0.03).
Für die Verhaltensabsichten „direkt zur Kasse fortzufahren“ (F(2, 110)=.3, p=0.7)
bzw. „meine Bestellung ganz abbrechen“ (F(2, 110)=1.0, p=0.4) ergeben sich keine
signifikanten
Mittelwertunterschiede.
Ebenfalls
kein
signifikanter
Mittelwertunterschied zwischen den Experimentalgruppen und der Kontrollgruppe
ergibt sich für die Wiederkaufswahrscheinlichkeit (F(2, 111)=.03, p=0.98). Es zeigt sich
damit, dass (verstärkte) soziale Normen die Bereitschaft zur Retourenvermeidung
erhöhen konnten, ohne damit die (Wieder-)Kaufabsicht zu beeinträchtigen (siehe
Abbildung 10).
Abbildung 10
Wirkung der normativen Anreize
113
Studie 1 beleuchtet Verhaltensabsichten in der Kaufphase. Die Ergebnisse zeigen,
dass Kunden infolge von normativen Anreizen während des Kaufs Anpassungen an
ihrer Produktauswahl vornehmen würden, um Retouren zu vermeiden. Das gilt
insbesondere dann, wenn ihnen das Verhalten einer relevanten Referenzgruppe
(„Converse Käufer“) bewusst gemacht wird. Studie 2 untersucht die Wirkungen von
sozialen Normen in der Nachkaufphase. Das Untersuchungsdesign bezieht sich nun auf
eine Situation, in der bereits eine Bestellung getätigt wurde und folglich keine
Anpassungen an der Produktauswahl mehr möglich sind. Darüber hinaus wird in Studie
2 neben der deskriptiven sozialen Norm eine injunktive soziale Norm sowie ein
Eigennutzenanreiz ergänzt.
5 Studie 2
Teilnehmer und Design. Die Wirkungsuntersuchung von Eigennutzen sowie
deskriptiven und injunktiven sozialen Normen erfolgte ebenfalls anhand eines
einfaktoriellen Experimentaldesigns im Rahmen einer Online-Studie mit SzenarioTechnik und anschließendem Fragebogen. 162 Probanden (MAlter= 22.7 Jahre; SD= 5.3;
50.7% weiblich) wurden zufällig einer von vier Experimentalbedingungen
(Eigennutzen, deskriptive Norm, injunktive Norm oder Kontrollanreiz) zugewiesen.
Vorgehen. Im ersten Schritt des Szenarios wurden die Teilnehmer über die
Merkmale eines fiktiven Fashion-Online-Shops „FashionAndMore“ informiert.
Anschließend wurden sie in die Situation versetzt, bei „FashionAndMore“ ein
Kleidungsstück gekauft zu haben: „Stellen Sie sich vor, Sie haben ein beliebiges
Kleidungsstück im Online-Shop unter www.FashionAndMore.de bestellt. (…) Sie
probieren es an und es passt gut. Nur die Farbe ist nicht ganz so, wie Sie es sich
vorgestellt haben. FashionAndMore bietet einen gratis Rückversand (30 Tage). Daher
überlegen Sie, das Kleidungsstück zurückzuschicken, obwohl es Ihnen ansonsten gut
gefällt. Während Sie überlegen, fällt Ihnen der Flyer auf, der noch im Paket liegt.
Darauf steht Folgendes:“
Die Teilnehmer wurden anschließend mit dem Text des fiktiven Flyers konfrontiert.
Der Text variierte zufällig je nach Anreiztyp (siehe Abbildung 11):
114
Abbildung 11
Experimentalbedingungen von Studie 2
Analog zu Studie 1 beantworteten alle Probanden anschließend einen kurzen
strukturierten Fragebogen. Da sich die Szenarien nun auf die Nachkaufphase beziehen,
erfolgte die Erfassung der Rücksendeabsicht anhand der Abfrage der
Rücksendewahrscheinlichkeit in % („Mit welcher Wahrscheinlichkeit (in %) werden
Sie das Kleidungsstück zurückschicken?“) (Garnefeld, Münkhoff und Raum 2013).
Manipulationschecks. Analog zu Studie 1 wurde die Effektivität der Eigennutzensowie der beiden Norm-Manipulationen mit drei Items („Ich wurde durch einen Text
informiert, dass die Vermeidung von Rücksendungen einen Beitrag für eine intakte
Umwelt und meine eigene Lebensqualität leistet.“; „Ich wurde durch einen Text
informiert, dass andere Kunden selbst auf unnötige Rücksendungen verzichten, um
etwas für die Umwelt zu tun.“; „Ich wurde durch einen Text informiert, dass andere
Kunden der Meinung sind, jeder sollte auf unnötige Rücksendungen verzichten.“) auf
einer Likert-Skala von 1 (stimme gar nicht zu) bis 7 (stimme voll zu) gemessen. Die
ANOVA zeigt, dass die Manipulation des Eigennutzens (MEigennutzen=5.8, SD=1.6;
MDeskriptive Norm=5.1, SD=2.0; MInjunktive Norm=4.9, SD=2.0; MKontrollanreiz=5.3, SD=1.8; F(3,
158)=2.3, p=0.07), der deskriptiven sozialen Norm (MEigennutzen=2.4, SD=1.6; MDeskriptive
Norm=5.8, SD=1.8; MInjunktive Norm=4.5, SD=2.1; MKontrollanreiz=2.1, SD=1.3; F(3,
115
158)=50.3, p<0.001) und der injunktiven sozialen Norm (MEigennutzen=2.0, SD=1.5;
MDeskriptive Norm=2.8, SD=1.9; MInjunktive Norm=4.6, SD=2.2; MKontrollanreiz=2.0, SD=1.1; F(3,
158)=2.7, p<0.01) erfolgreich war.
Um die Realitätsnähe des Experimentaldesigns zu messen, wurden dieselben Items
wie in Studie 1 abgefragt. Die aggregierten Mittelwerte der Antworten auf diese Items
(α= .81) deuten in sämtlichen Experimentalgruppen auf ein homogenes Level an
Realitätsnähe hin (MEigennutzen=5.7, SD=1.1; MDeskriptive Norm=5.6, SD=1.1; MInjunktive
Norm=5.4,
SD=1.5 ; MKontrollanreiz=5.8, SD=1.2).
Wirkung der sozialen Normen und des Eigennutzens. Die Wirkung der Anreize auf
die
Rücksendewahrscheinlichkeit
in
%
zeigt
sich
in
signifikanten
Mittelwertunterschieden zwischen den Experimentalgruppen und der Kontrollgruppe
(F(3, 158)=3.7, p=0.01). Probanden mit Eigennutzenanreiz, sowie Probanden mit
deskriptiv-normativem Anreiz sind signifikant eher dazu bereit, das Kleidungsstück zu
behalten und nicht zurückzuschicken als die Kontrollgruppe (MEigennutzen=40.7,
SD=33.9; MDeskriptive Norm=43.3, SD=31.8; MKontrollanreiz=65.9, SD=29.1). Die
Rücksendewahrscheinlichkeit der Gruppe mit der injunktiven Norm liegt zwar mit
durchschnittlich 53.2% (SD=32.9) ebenfalls deutlich niedriger, unterscheidet sich
jedoch nicht signifikant von der Kontrollgruppe (siehe Abbildung 12).
Für die Wiederkaufswahrscheinlichkeit ergeben
Experimentalgruppen
und
der
Kontrollgruppe
Mittelwertunterschiede (F(3, 158)=.5, p=0.7).
sich zwischen den
keine
signifikanten
Studie 2 bestätigt damit die Befunde aus Studie 1, dass soziale Normen die
Bereitschaft zur Retourenvermeidung erhöhen können. Ferner zeigt Studie 2, dass auch
Eigennutzenanreize die Rücksendewahrscheinlichkeit signifikant beeinflussen. Sowohl
die normativen Anreize als auch der Eigennutzenanreiz beeinträchtigen dabei die
Wiederkaufswahrscheinlichkeit nicht. Die Wiederkaufswahrscheinlichkeit der Gruppe
mit Eigennutzenanreiz liegt mit 68.2% sogar leicht über der Kontrollgruppe. Die
Rücksendewahrscheinlichkeit konnte mit Eigennutzen-Anreiz und deskriptiver Norm
um über 20% gesenkt werden. Dass laut befragten Online-Händlern bereits ein
zehnprozentiger Rückgang bei der Retourenquote die Profitabilität um 5% erhöhen
könnte (Pur et al. 2013), unterstreicht die Bedeutung dieser Ergebnisse. Unterstellt man
durchschnittliche Rücksendekosten von 15€ pro Artikel (Rösch 2013; Pur et al. 2013)
zeigt sich, dass das Rücksenderisiko als Produkt aus Rücksendekosten und ermittelter
Rücksendewahrscheinlichkeit durch deskriptive Normen (15€ x 43.3%= 6.50€) um
116
34.3% und durch Eigennutzen-Anreize (15€ x 40.7%= 6.11€) um 38.2% im Vergleich
zur Kontrollgruppe (15€ x 65.9%= 9.88€) abnimmt.
Abbildung 12
Wirkung der Eigennutzen- und Norm-Anreize
6 Implikationen für Wissenschaft und Praxis
Die vorliegende Untersuchung liefert einen Erkenntnisgewinn in Bezug auf die
Wirksamkeit sozialer Normen und Eigennutzen zur Verhaltensänderung im RetourenKontext. Die Ergebnisse können Online-Shops helfen, aus Verhaltensappellen an
Kunden mehr Potenzial in Bezug auf die Retourensenkung zu schöpfen. Der adäquate
Einsatz von sozialen Normen und Eigennutzenanreizen stellt eine Transformation in der
Kundenkommunikation dar, welche in dieser Form bisher weder im Online-Handel
noch in der Forschung zum Rücksendeverhalten verwendet wird. Bisherige Studien
untersuchen Motive und Treiber von Retouren oder widmen sich dem
117
Optimierungsproblem der Ausgestaltung von Rücksendebedingungen zur Erhöhung der
Profitabilität. Letztere Forschungsarbeiten akzeptieren dabei eine hohe Retourenquote
und fokussieren sich stattdessen auf ein Absatzwachstum durch kulante
Rücksendebedingungen. Die vorliegende Wirkungsanalyse von Norm und
Eigennutzen-Anreizen erreicht dadurch einen Erkenntnisfortschritt zur bisherigen
Forschung, welche eine direkte Beeinflussung des Rücksendeverhaltens nur
unzureichend untersucht hat.
Die Ergebnisse zeigen, dass Eigennutzen und soziale Normen die Rücksendeabsicht
signifikant senken können. Gleichzeitig beeinflussen beide Anreize die
Wiederkaufsabsicht nicht signifikant. Händler können sich damit Verhaltensweisen, die
in der Natur des Menschen liegen, zunutze machen und dadurch Kunden vom
Zurücksenden abhalten, ohne den Absatz zu beeinträchtigen. Sinkende Retouren bei
stabilem Absatz erhöhen den Deckungsbeitrag pro Kunde und damit den Kundenwert
(Wagner, Hennig-Thurau und Rudolph 2009).
Der entscheidende Vorteil der eingesetzten Anreizformen liegt darin, dass sie im
Vergleich zu anderen wirksamen Retourensenkungsmaßnahmen kostengünstig zu
implementieren sind. Beide Studien deuten an, dass bereits anhand von geschickt
formulierten Kurztexten Wirkungen erzielt werden können. Die Anreiztexte könnten in
einem Online-Shop mit geringem Aufwand an relevanten Kundenkontaktpunkten
flexibel und vergleichsweise kostengünstig platziert werden.
Zweitens könnte eine Kundenansprache mit sozialen Normen und Eigennutzen es
Händlern sogar ermöglichen, Viel-Retournierer nicht zwangsläufig sanktionieren zu
müssen. Mit dem Potenzial zur Verhaltensänderung könnten die entsprechenden
Anreize betrügerisches Verhalten regulieren (Pfrang, Rudolph und Emrich 2015).
Bisher durch Sanktionen verschenkte Ertragspotenziale blieben dadurch bestehen.
Die gewünschte Verhaltenswirkung steht und fällt jedoch mit der Formulierung der
Anreize. Studien haben bereits gezeigt, dass bisher eingesetzte Verhaltensappelle (siehe
Abbildung 7) die Retourenabsicht sogar steigern können (Garnefeld, Münkhoff und
Raum 2013). Zudem kann eine soziale Norm bei Kunden, die sich bereits der Norm
entsprechend verhalten, eine gegenteilige Wirkung erzielen, den sogenannten
boomerang effect (Schultz et al. 2007).
Trotz der signifikanten Effekte muss an dieser Stelle daher auf den spezifischen
Charakter der Ergebnisse hingewiesen werden. Die vorliegenden Studiendesigns
unterstellen zwei verschiedene Retourengründe, in Studie 1 eine potenzielle
118
Auswahlbestellung und in Studie 2 eine Nichterfüllung von Erwartungen. In der Realität
beeinflussen noch weitaus mehr Motive das Rücksendeverhalten. Obwohl die
Wirksamkeit von beiden Anreizformen für zwei der häufigsten Retourengründe (Pur et
al. 2013) bestätigt werden kann, könnten in der Realität andere Rücksendemotive die
Anreizwirkung verändern. Ein Beispiel hierfür wäre, wenn ein Käufer eine
Auswahlbestellung getätigt hat und für die Ware, die nicht passt, keine
Verwendungsmöglichkeit hat. Eine Rücksendung wäre dann nahezu unvermeidbar,
selbst wenn der Käufer einen entsprechenden Verhaltensappell nach dem Kauf gerne
berücksichtigen würde.
Die Anreizwirkung kann daher mit der Platzierung im Rahmen des Kaufprozesses
variieren, obwohl die vorliegenden Studien eine Retourensenkung in der Kauf- und
Nachkaufphase andeuten. Aus Sicht des Händlers bedarf es folglich eines besonderen
Feingefühls für Präferenzen und Gewohnheiten der eigenen Kundschaft, um Norm- und
Eigennutzen-Anreize so auszugestalten und zu formulieren, dass daraus nicht Reaktanz,
sondern eine vorteilhafte Verhaltensänderung resultiert. Studie 1 hat gezeigt, dass es
darüber hinaus darauf ankommt, relevante Referenzgruppen (z.B. Kunden mit ähnlichen
Produktpräferenzen) für die normative Kundenansprache zu nutzen.
Weitere Limitationen der Untersuchung bestehen vor allem darin, dass es sich um
Online-Experimente handelt, in welchen die Wirkung fiktiver Szenarien überprüft
wurde. Somit weisen sie eine sehr hohe interne Validität auf. Die feldexperimentelle
Überprüfung der externen Validität sollte in Folgestudien aufgegriffen werden. Weiteres
Erkenntnispotenzial verspricht die Untersuchung von Interaktionen mit produkt-,
kontext- und konsumentenbezogenen Größen wie Rücksendemotiven oder
opportunistischen Neigungen (Pfrang, Rudolph und Emrich 2015; Harris 2008).
Erkenntnisse bezüglich einer differenzierten Anreizwirkung würden weitere
Aufschlüsse darüber geben, unter welchen Bedingungen und für welche
Kundensegmente Norm- oder Eigennutzenanreize das Rücksendeverhalten besonders
stark beeinflussen.
Der Autor bedankt sich bei Larissa Zengerling und Maximilian Baumann für die
Unterstützung bei der Datenerhebung der Studien.
119
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123
D PAPER 3: THE INFLUENCE OF SOCIAL NORMS
AND SELF-BENEFITS ON RETURN DECISIONS OF
FRAUDULENT RETURNERS
124
THE INFLUENCE OF SOCIAL NORMS AND SELF-BENEFITS ON
RETURN DECISIONS OF FRAUDULENT RETURNERS
Authors
Thilo Pfrang, Thomas Rudolph, Oliver Emrich
Abstract
Fraudulent product returns induce high costs for online retailers. As a consequence,
online sellers seek strategies for changing customer return behavior. Though the success
of preventive and sanctioning measures is yet to be seen, behavioral modification
approaches such as social norms and self-benefits have neither been considered by
retailers nor researched in relation to product returns. This research explores the effects
of social norms and self-benefit appeals on product return intentions. An online
experiment shows that self-benefits are able to reduce the return probability of
consumers after purchase, while social norms only reduce returns by customers with a
proclivity for fraudulent returns. In contrast, social norms increase return intentions by
non-fraudulent consumers, as a consequence of the boomerang effect and reactance.
This detrimental effect is particularly strong if the fraudulent proclivity becomes
amplified by the activation of situational opportunism. Implications for retailers are
discussed and directions for future research are identified.
125
1 Introduction
Product returns represent a significant part of the online retail business model.
Although product returns incur high costs through reverse logistics, many online
retailers appear obliging and provide lenient return policies to their customers. These
forgiving policies can increase customer satisfaction by building trust and providing a
quality signal that can increase sales (Bonifield, Cole, and Schultz 2010). However,
retailers’ leniency has also proven fertile ground for abuse, which can cost sellers more
than it benefits them (Petersen and Kumar 2009). Scientific studies and analyses of
product returns to mass retailers suggest that 50% of apparel returns are fraudulent, for
example premeditating returns and using/wearing/damaging ordered products before
returning them (Harris 2010). In the German-speaking market, 10% of online fashion
retailers classified returns of used goods as a serious problem (Reinhold 2014). In the
US, fraudulent returns cost retailers an estimated $9.1 billion dollars in 2013. Of these
returns, 62 % arise from the so-called “wardrobing”—when customers typically
purchase a dress for a special occasion or a big-screen television for the Super Bowl and
return the item after it has been used (Shearman 2014). Similarly, European online
retailers report returns of dirty sports clothes, suits with forgotten opera tickets in the
pockets, or the German “Zalando parties,” where teenage girls order crateloads of goods
for a party weekend, only to send it all back (The Economist 2013). One of the reasons
for such behavior is the impersonal nature of online shopping, which offers discretionary
room for maneuvering in opportunistic or even fraudulent ways. Fraudulent return
proclivity (Harris 2008) can turn into actual return behavior if customer opportunism
becomes situationally activated. Thereby, fraudulent return behaviors consume retailers’
resources. In addition to the cost of reshipping, used or even damaged products incur
costs for diagnosing, cleaning, and conditioning. In the worst cases, items cannot be
resold. King (2004) states that fraudulent returns reduce retail profitability by 10% –
20%. Conversely, other studies indicate that a reduction in opportunistic and fraudulent
returns represents a strong lever for reducing costs and increasing profitability (El Kihal,
Schulze, and Skiera 2014).
Consequently, retailers are seeking strategies to change return behavior and enact
preventive measures (e.g., enhanced product presentation and information, return
forecast algorithms) or even sanctions (e.g., prepay requirement or account freezing).
Some online fashion shops are integrating into the shopping cart view appeals regarding
126
high costs and environmental damage resulting from returns; some are even providing
monetary bonuses in cases of an order without return (e.g., Otto Group). However, the
success of such appeals is yet to be seen, especially because they have also proved to
evoke reactance and increases in return intention (Garnefeld, Münkhof, and Raum
2013).
Although the issue of handling fraudulent product returns constitutes a challenge for
retailers, marketing research has mainly focused on the configuration of (lenient) return
policies (e.g., Bonifield, Cole, and Schultz 2010) and the choice process that helps to
explain return behavior (Bechwati and Siegal 2005; Petersen and Kumar 2009).
Moreover, research has scrutinized the drivers and motives of undesirable return
behaviors such as “retail borrowing” (Piron and Young 2000), “fraudulent returning”
(Harris 2008) or “deshopping” (King, Dennis and Wright, 2008). Piron and Young
(2000) highlight social motives (e.g., to look good for a party), economic motives (e.g.,
low income, risk reduction) and personal motives (e.g., to feel better) as reasons for
returning opportunistically. Drawing on the theory of planned behavior, King, Dennis
and Wright (2008) show that the attitude toward opportunistic returning, the behavior
from other opportunistic returning related persons, and the perceived ease of returning
due to lenient return policies predict the extent of “deshopping” behavior. Harris (2008)
found that the so-called “fraudulent return proclivity” is based on demographic and
psychographic antecedents such as self-consciousness, knowledge of return policies,
past experiences, attitude toward complaints, perceived impact of fraudulent returning,
and social norms. Having examined the motives and drivers of fraudulent return
behaviors, however, this body of research did not investigate how these behaviors can
be influenced and changed.
As a result, little knowledge exists about how to change opportunistic return
intentions and behaviors. Behavioral research does show that behavioral modification
approaches such as social norms and self-benefits can provoke desirable behaviors (e.g.,
White and Simpson 2013). What remains unclear is whether these approaches also have
an effect on product returns in an environment characterized by sensitive and
opportunistic customers (Garnefeld, Münkhoff, and Raum 2013; Harris 2008). This
research contributes to the literature by examining the effect of self-benefit and social
norm appeals on product return intentions by considering the moderating role of
characteristic (i.e., fraudulent return proclivity) and situationally activated opportunism.
The findings lead to some initial insights and implications about how to reduce
fraudulent return behavior.
127
2 Theoretical Background and Hypotheses Development
Encouraging customers to avoid product returns requires a change in their
opportunistic habits and behavior. Online shoppers have grown accustomed to the
possibility of returning merchandise for almost any reason, and expect lenient return
policies (Petersen and Kumar 2009). Behavioral research has highlighted two
predominant appeal types that are able to change undesirable behaviors into desirable
ones: self-benefits and social norms.
The concept of self-benefits relates to social exchange theory, implying that
consumers are more likely to engage in a behavior that is desired by an exchange partner
(e.g., the retailer) if they can derive a self-benefit from that behavior (Blau 1964). Selfbenefit appeals have proved to promote a variety of prosocial behaviors, such as
donating (White and Peloza 2009) and grasscycling and composting (White and
Simpson 2013). Thus, we assume that self-benefits could also affect return behavior.
Moreover, the influence of self-benefits is assumed to be particularly effective in postpurchase return decisions because referring to advantages to the individual (White and
Peloza 2009) could be congruent with autonomy-based decisions about keeping or
returning a product after purchase. Thus:
H1a: Self-benefit appeals will generally decrease return probability.
Moreover, self-benefits should particularly affect those customers who actively seek
benefits for themselves and have already acted in their own interests anyway (Holmes,
Miller, and Lerner 2002). Opportunistic customers could be especially receptive to
benefits that the retailer confers on them:
H1b: The decreasing return effect of self-benefits is further strengthened for fraudulent
consumers.
Social norms have also been shown to change various behaviors, as people tend to
conform to what others do (descriptive norm) or think should be done (injunctive norm)
(Cialdini, Reno, and Kallgren 1990)—for example, saving energy (Schultz et al. 2007),
paying taxes (Martin 2012), or being environmentally conscious with towel use in hotels
(Goldstein, Cialdini, and Griskevicius 2008). The behavioral relevance of social norms
is particularly strong for those individuals who deviate from a proposed norm. This
could be being above or below the norm (Schultz et al. 2007). For instance, learning that
peers consume less energy could increase feelings of guilt about damaging the
environment and thereby motivate a behavioral change. Moreover, learning about the
128
behavior of others can provide information about how to behave appropriately, and
about the relative benefits of that behavior (Ayres, Raseman, and Shih 2013). On the
other hand, social norm information can provoke undesirable behaviors by consumers
who already act in conformity with the social norm (Schultz et al. 2007). Within this socalled “boomerang effect,” social norms increase the prevalence and social acceptability
of the undesirable behavior (e.g., using more energy than average) for consumers
already behaving desirably (e.g., using less energy than average) (Burchell, Rettie, and
Patel 2013). This suggests that social norm appeals would increase return intentions for
customers returning products for legitimate reasons, but decrease returns of those who
return fraudulently.
However, the behavioral effect of a social norm also depends on the specific
circumstances making the norm more or less salient (Cialdini, Reno, and Kallgren 1990)
or even leading to feelings of reactance (White and Simpson 2013). In the purchase
stage, a customer can still make adjustments to his or her order, whereas after purchase
the customer is limited in his or her decisions. For instance, if the order was a choice
order or a premeditated return, or if the product does not meet expectations, the customer
has limited opportunity to avoid returning the product. Research has shown that
perceiving restrictions on autonomy can elicit feelings of reactance (Brehm 1966). As
well, research indicates that for the consumer, normative information highlighting
others’ avoidance of returns while being restricted in changing return behavior, might
make feelings of reactance salient (Garnefeld, Münkhof, and Raum 2013).
Hence, we assume that if a customer is limited after purchase in his or her return
decision, social norm information highlighting the behavior of other customers in which
the targeted customer can only partially engage can create feelings of reactance. That is,
non-fraudulent consumers, who are assumed to increase their product returns due to the
boomerang effect, might additionally develop feelings of reactance that reinforce the
return-increasing potential of social norms. For fraudulent consumers, the feelings of
reactance are suggested to neutralize the feeling of being deviant which isolatedly are
assumed to reduce product returns. Thus, social norms are assumed to increase return
intentions for non-fraudulent consumers, but to exert no impact on the return intentions
of consumers with a fraudulent return proclivity. The reactance effect leading to higher
return intentions should be particularly strong for injunctive norms, which have a
prescriptive character that threatens a person’s ability to choose freely (White and
Simpson 2013).
129
H2: Social norms will increase return probability for non-fraudulent consumers, but
exert no impact on the return intentions of fraudulent consumers. The increasing effect
for non-fraudulent consumers should be stronger than the decreasing effect for
fraudulent consumers.
Whereas fraudulent return proclivity describes a characteristic and continuous
attitude of consumers (Harris 2008), opportunism can also occur situationally (Aquino
et al. 2009). Manipulations that enhance consumers’ accessibility of the individual self
have been shown to situationally activate opportunistic mindsets (Brebels, De Cremer,
and Sedikides 2008). Apart from this opportunism-inducing effect, there is evidence that
an enhanced self-focus amplifies internalized attitudes and values (Utz 2004;
Verplanken and Holland 2002). That is, fraudulent consumers will behave even more
opportunistically when their self-focus is activated. Their behavior would become more
consistent with their internal values (i.e., fraudulent return proclivity). As customers
with a fraudulent return proclivity generally have a higher tendency to return products,
their return probability is expected to increase if this internal attitude becomes amplified.
Following Brebels, De Cremer, and Sedikides (2008), we call this the return
amplification hypothesis:
H3: For fraudulent consumers, activation of opportunism increases return probability.
In contrast, the return-reducing effect of social norms for consumers who
undesirably deviate from the proposed norm should be increased by analogy with H2, if
fraudulent proclivity becomes amplified by enhanced accessibility of the self. Whereas
a feeling of being deviant in fraudulent consumers and the simultaneous feeling of
reactance should neutralize each other (H2), an amplified behavioral relevance of social
norms through the activation of the individual self, and therefore a stronger feeling of
being deviant (Schultz et al. 2007), is assumed to exceed the detrimental influence of
reactance. In sum, this should result in a lower return probability.
H4: For fraudulent consumers, enhanced self-accessibility leads to a decreasing impact
of social norms on return probability.
However, enhanced self-focus also activates the rule-consistent and dutiful values
that are internalized in non-fraudulent consumers (Utz 2004). For consumers who
already abstain from fraudulent product returns, normative appeals raise awareness of
the undesirable behavior (i.e., returning more) in accordance with the boomerang effect
(Schultz et al. 2007). In combination with feelings of reactance that might emerge after
purchase as a result of social norm feedback (White and Simpson 2013), the
130
strengthened activation of non-fraudulent internal values fueling the boomerang effect
of social norms is expected to increase return intentions for non-fraudulent consumers.
H5: For non-fraudulent consumers, enhanced self-accessibility strengthens the
increasing impact of social norms on return probability.
3 Research Methodology
For our study, we used a 2 (self vs. neutral) x 4 (self-benefit vs. descriptive norm
vs. injunctive norm vs. control) between-subjects design. A total of 1,245 respondents
from a European online consumer panel (64.3% female; Mage = 35.17, SD = 13.33)
participated in an online experiment that applied a scenario technique and a subsequent
survey. First, we asked participants to engage in a pronoun-circling task that included
the manipulation of self-accessibility (Brewer and Gardner 1996). Participants read a
paragraph describing a trip to a city, with instructions to circle all pronouns in the text.
In the self (situational opportunism) condition, the text contained pronouns such as I,
me, and mine. In the neutral condition, the story contained pronouns such as he (she),
him (her), and his (her), depending on the gender of the participant queried before.
Subsequently, participants were presented with a scenario about an online shopping
situation. They were asked to imagine that they had ordered a garment at a fictitious
online retailer named “FastFashion” that offered free returns. The garment fitted well
and had the right quality, but some details were slightly different from what they had
expected. Participants were then told that while considering returning the garment, they
would discover a flyer in the parcel. The text on the flyer varied in each experimental
appeal condition (see Table 9).
131
Table 9
Examples of Appeal Types
Control appeal
Self-benefit appeal
Environment protection instead of product returns!
You have the right to return products, but please keep in
mind that each return pollutes the environment through
additional transport routes and material expenses.
You are welcome to return products, but please support
the environment!
Your benefit: Environment protection instead of
product returns!
You have the right to return products, but please keep in
mind that each return pollutes the environment through
additional transport routes and material expenses.
According to a study in May 2014, you can help save the
environment by preventing unnecessary returns, which—
above all—benefits your own life quality.
You are welcome to return products, but please support the
environment for your own life quality!
Descriptive norm appeal
Injunctive norm appeal
8 out of 10 FastFashion customers opt for
environment protection instead of product returns!
You have the right to return products, but please keep in
mind that each return pollutes the environment through
additional transport routes and material expenses.
According to a study in May 2014, 83% of our customers
prevent unnecessary returns to help save the
environment.
You are welcome to return products, but please join your
fellow customers in supporting the environment!
8 out of 10 FastFashion customers believe that
everybody should opt for environment protection
instead of product returns!
You have the right to return products, but please keep in
mind that each return pollutes the environment through
additional transport routes and material expenses.
According to a study in May 2014, 83% of our customers
are of the opinion that everyone should prevent unnecessary
returns to help save the environment.
You are welcome to return products, but we should also
support the environment!
Subsequently, all participants accessed a survey including questions about their
probability of returning the garment (Garnefeld, Münkhoff, and Raum 2013),
repurchase intentions (Finn 2012), and manipulation checks. Further, we measured
participants’ degree of fraudulent return proclivity using an adapted version of the scale
(cronbach’s α= .700) by Harris (2008). In addition, we measured covariates such as
online purchase frequency (Schlosser, White, and Lloyd 2006), involvement (Mittal
1995; Laurent and Kapferer 1985; cronbach’s α= .929), and consumers’ past experience
with returning (Harris 2008; cronbach’s α= .814) on seven-point Likert scales (see
Appendix). Finally, participants completed demographic items and were debriefed.
4 Results
The manipulation checks confirmed the effectiveness of our manipulations: Those
in the self (situational opportunism) condition were more likely to think about
themselves than their neutral (third-person) counterparts (t(1241) = 8.657, p = .010).
ANOVAs supported the effectiveness of the self-benefit appeal (F(3, 1241) = 15.015,
132
p < .001), as well as the descriptive (F(3, 1241) = 227.803, p < .001) and injunctive
norm appeals (F(4, 1241) = 122.833, p < .001).
We tested our hypotheses with hierarchical moderated regression analyses (Dawson
and Richter 2006) to detect the effect of self-benefit and social norm appeals on return
probability, depending on fraudulent return proclivity and self-accessibility (i.e.,
activation of opportunism) (see Table 10). The main effect model (Model 1) showed
that self-benefit reduced return probability significantly, in support of H1 (B = -4.766;
p = .055). The social norm appeals tended to increase return probability, which indicates
the presence of detrimental effects of social norms in the post-purchase situation.
However, these effects were not significant. Model 2 revealed a significant two-way
interaction effect between self-benefit appeal and fraudulent return proclivity (B =
-9.083; p = .056). The conditional effect (see Figure 13) shows that the self-benefit
appeal significantly reduced the return probability of consumers with a high fraudulent
return proclivity (60.33% vs. 68.09%; t(1217) = -2.714, p = .007) but only directionally
reduced the return probability of consumers with a low fraudulent return proclivity
(60.91% vs. 61.75%; t(1217) = -.983, p = .326), supporting H1b. The interaction
between the descriptive norm and fraudulent return proclivity also reached significance
(B = -12.595; p = .009). As H2 predicted, the descriptive norm marginally decreased
return probability of customers with a high fraudulent return proclivity, compared with
the control group’s returns (63.52% vs. 68.09%; t(1217) = -1.370, p = .171), but
significantly increased return probability of customers with a low latent return
probability (66.78% vs. 61.75%; t(1217) = 1.725, p = .085). This indicates that
descriptive norms increased return probability to a stronger degree for customers with
low fraudulent proclivity than they reduced returns for customers with high fraudulent
return proclivity, in support of H2. The injunctive norm x fraudulent return proclivity
interaction was not significant (B = -2.860; p = .576). The conditional effects (Figure
13) indicate that the injunctive norm directionally increased return probability regardless
of consumers’ level of fraudulent proclivity when presented in a post-purchase situation.
133
Figure 13
Interaction of Fraudulent Return Proclivity and Appeal Type on Return
Probability
75%
73%
71%
69%
67%
68.09%
61%
69.33%
65.17%
65%
63%
66.78%
61.75%
60.91%
63.52%
60.33%
59%
57%
55%
Control
Self-benefit
Low Fraudulent
Descriptive norm
Injunctive norm
High Fraudulent
Model 3 shows a significant interaction between fraudulent return proclivity and
self-accessibility, supporting H3 (B = 12.840; p = .072). The three-way interactions
among both norms, fraudulent return proclivity, and self-accessibility were significant
(B = -24.068, p = .019; B = -17.255, p = .096). In the condition of enhanced selfaccessibility, the descriptive norm tended to decrease return probability of customers
with a high fraudulent return proclivity (64.70% vs. 70.07%; t(1210) = -1.185, p = .236),
partially supporting H4. However, the descriptive norm significantly increased return
probability of customers with a low fraudulent return proclivity when the individual self
was activated (70.39% vs. 58.99%; t(1210) = 2.842, p = .005), which supports H5 (see
Figure 14). Similarly, the conditional effects for the injunctive norm confirmed
increases in return probability as a result of social norm interventions in the postpurchase stage. That is, the injunctive norm did not decrease the return probability of
fraudulent returners with enhanced self-accessibility (69.39% vs. 70.07%; t(1210) = .135, p = .893). However, the injunctive norm significantly increased the return
probability of customers with a low fraudulent return proclivity when the individual self
was activated (66.59% vs. 58.99%; t(1210) = 1.910, p = .056). For fraudulent returners,
the directional but not significant reductions in return probability through descriptive
norms when the individual self was activated indicate partial support for H4, whereas
the significant detrimental effects for customers with a low fraudulent proclivity support
H5.
134
Table 10
Main Effects and One-way Interactions between the Appeals and Fraudulent
Return Proclivity
Model 1 (R2=.096; F= 16.278; d.f.1/d.f.2= 8/1220; p=.000)
B
SE
t-Value
p-Value
Constant
63.556
1.869
34.010
.000
Self-benefit
-4.766
2.486
-1.917
.055
Descriptive Norm
1.746
2.501
.698
.485
Injunctive Norm
2.767
2.464
1.123
.262
Prime
1.762
1.789
.985
.325
Fraudulent return proclivity
1.645
1.717
.958
.338
Involvement
2.110
.620
3.401
.001
Online purchase frequency
-2.029
.992
-2.046
.041
Experience with returning
6.942
.808
8.588
.000
B
SE
t-Value
p-Value
Constant
63.568
1.865
34.091
.000
Self-benefit
-4.840
2.481
-1.951
.051
Descriptive norm
1.719
2.496
.689
.491
Injunctive norm
2.734
2.459
1.112
.266
Covariates
Prime
1.783
1.97
.998
.318
8.325
3.572
2.331
.020
Self-benefit x Fraudulent return proclivity
-9.083
4.748
-1.913
.056
Descriptive x Fraudulent return proclivity
-12.595
4.804
-2.622
.009
Injunctive x Fraudulent return proclivity
-2.860
5.117
-.559
.576
Model 3 (R2=.109; F= 8.233; d.f.1/d.f.2= 18/1210; p=.000)
B
SE
t-Value
p-Value
Constant
65.383
2.268
28.833
.000
Self-benefit
-7.493
3.531
-2.122
.034
Descriptive norm
-1.640
3.500
-.469
.639
Injunctive norm
1.115
3.375
.330
.741
Covariates
Prime
-2.313
3.384
-.684
.494
1.711
5.160
.332
.740
Interactions
Self-benefit x Self accessibility
5.685
4.982
1.141
.254
Descriptive norm x Self accessibility
7.504
5.011
1.497
.135
Injunctive norm x Self-accessibility
3.885
4.930
.788
.431
Fraudulent return proclivity x Self-accessibility
12.840
7.132
1.800
.072
Self-benefit x Fraudulent return proclivity
-3.034
6.577
-.461
.645
Descriptive norm x Fraudulent return proclivity
2.045
8.110
.252
.801
Injunctive norm x Fraudulent return proclivity
6.389
7.740
.825
.409
Self-benefit x Fraudulent return proclivity x Self-accessibility
-11.464
9.575
-1.197
.231
Descriptive norm x Fraudulent return proclivity x Selfaccessibility
-24.068
10.211
-2.357
.019
Injunctive norm x Fraudulent return proclivity x Self-accessibility
-17.255
10.343
-1.668
.096
Covariates
Model 2
(R2=.103;
F= 12.670; d.f.1/d.f.2= 11/1217; p=.000)
Fraudulent return proclivity (further covariates omitted for
brevity)
Interactions
Fraudulent return proclivity (further covariates omitted for
brevity)
135
The three-way interaction among self-benefit, fraudulent return proclivity, and selfaccessibility was not significant (B = -11.464; p = .231). Instead, the conditional effects
confirmed the significant main effect of self-benefits in the given post-purchase
situation. That is, when the individual self was activated, self-benefits significantly
reduced the return probability of consumers with a high fraudulent return proclivity
(62.41% vs. 70.07%; t(1210) = -1.852, p = .064). Moreover, the self-benefit appeal
reduced the return probability of consumers with both low (59.32% vs. 64.37%; t(1210)
= -1.765, p = .078) and high fraudulent proclivity (58.32% vs. 65.67%; t(1210) = -1.823,
p = .069) at low values of self-accessibility.
In addition, further analyses showed no significant effects of the appeals on
repurchase intentions under different values of self and fraudulent return proclivity.
136
Figure 14
Interaction of Level of Self, Fraudulent Return Proclivity, and Appeal Type on
Return Probability
Individual self
73%
71%
69%
70.39%
70.07%
69.39%
67%
66.59%
65%
64.70%
63%
62.41%
62.37%
61%
59%
57%
55%
58.99%
Control
Self-benefit
Descriptive norm
Low Fraudulent + Ego
Injunctive norm
High Fraudulent + Ego
Neutral self
71%
70.13%
69%
67%
65%
65.67%
65.38%
64.37%
63%
63.96%
62.52%
61%
59%
59.32%
57%
55%
58.32%
Control
Self-benefit
Low Fraudulent + Neutral
Descriptive norm
Injunctive norm
High Fraudulent + Neutral
137
5 Conclusions and Implications
Our study shows that behavioral appeals can affect return probability of fraudulent
returners without reducing repurchase intentions. Self-benefits can reduce return
probability in the post-purchase stage in general, whereas social norms tend to reduce
return probability after purchase for opportunistic customers, but exert detrimental
effects for non-opportunistic customers as a result of reactance and the boomerang
effect. These detrimental effects increase under enhanced self-accessibility,
strengthening the prevalence of undesirable return behavior for customers with low
fraudulent return proclivity. That is, social norm appeals in combination with a postpurchase return decision scenario can elicit reactance, thus increasing return intentions,
especially for non-fraudulent consumers. However, social norms tend to decrease return
intentions for fraudulent consumers and when this fraudulent return proclivity becomes
amplified by enhanced self-accessibility. For fraudulent consumers, feelings of being
deviant elicited through social norms tend to outperform reactance effects in the given
post-purchase situation, leading to directionally lower return intentions. These findings
extend previous research that has examined fraudulent return proclivity merely as a
driver of product returns (Harris 2008, King, Dennis and Wright 2008). Furthermore,
our results extend previous research on social influence (Cialdini, Reno, and Kallgren
1990; Schultz et al. 2007) and self-benefits (White and Peloza 2009) that has not
examined the effectiveness of social norms and self-benefit appeals in the context of
fraudulent product returns.
Moreover, the findings have several implications for retailers. First, it has been
shown that retailers could benefit from approaches that address human nature, but must
carefully assess their desirable and undesirable effects, depending on the specific
purchase situation. Notably, self-benefits could help retailers persuade customers to
avoid a return after purchase. Importantly, this effect was especially strong for
customers with a high fraudulent return proclivity; these customers cause particularly
high costs. However, the implementation of social norm appeals at post-purchase
touchpoints can backfire, unless they are selectively presented to customers with a high
proclivity for fraudulent returns.
These insights could prevent retailers from sanctioning or suspending fraudulent
customers, and thus the retailers can maintain earnings potential. A further advantage
for retailers is that such textual appeals can be implemented at crucial customer
touchpoints at negligible effort and cost. However, a desirable behavioral change
138
depends on the implementation of the specific appeal at an adequate touchpoint within
the purchase process. Whereas self-benefits have been shown to be congruent with
customers’ freedom of choice in the post-purchase stage, normative appeals only
slightly reduce return intentions for fraudulent customers, and exert detrimental effects
on non-fraudulent customers. A desirable behavioral change benefitting retailers
requires a special instinct for customer habits, which will ensure that the right selfbenefit and social norm appeals are used for the right customer segments, and thus will
prevent the boomerang effect. Moreover, retailers need to ensure that they implement
social norm appeals at those stages in the purchase process where they will not provoke
reactance. Future research should explore the effects of social norms and self-benefits
on return behavior in field experiments. Additional studies also need to explore wider
contexts, including various products and various stages of the purchase process. In
particular, the detrimental effects of social norms could be reversed in the pre-purchase
stage, when customers can still alter their order, and when information about the
behavior of other customers would be more helpful than in a more restricted postpurchase situation.
139
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Appendix
Fraudulent return proclivity (Harris 2008)
Respondents indicated their responses to the items on a seven-point Likert scale:
I often return goods, even when I know it is against the rules.
I often return goods, knowing that I’ll have to lie to get my money back.
I often return goods that I know I have broken.
I often return goods after using them.
Product Involvement (Mittal 1995; Laurent and Kapferer 1985)
Respondents indicated their response to the items on a seven-point Likert scale:
Clothes and fashion are very important to me.
For me, clothes and fashion do matter.
Clothes and fashion are an important part of my life.
Online purchase frequency (adapted from Schlosser, White, and Lloyd 2006)
Respondents indicated their response to the items on a seven-point Likert scale:
How often do you purchase online (e.g., on Amazon, Zalando, etc.)?
1 = “Never,” 2 = “Once a year,” 3 = “Two to three times a year,” 4 = “Every month,”
5 = “Several times per month,” 6 = “Several times per week,” and 7 = “Daily.”
Experience with returning (adapted from Harris 2008; Schlosser, White, and Lloyd
2006)
Respondents indicated their response to the items on a seven-point Likert scale:
I have a lot of experience with returning products back to online retailers or mail-order
companies. (1 = “Strongly disagree” and 7 = “Strongly agree”)
144
I often order products that I know in advance I will return. (1 = “Strongly disagree” and
7 = “Strongly agree”)
How often do you return products back to online retailers or mail-order companies in
general? (1 = “very rarely” and 7 = “very often”)
How often do you place choice orders, like ordering products in several sizes or
variants? (1 = “Very rarely” and 7 = “Very often”)
145
E PAPER 4: THE DESIRABLE AND UNDESIRABLE
INFLUENCES OF SOCIAL NORMS AND SELFBENEFITS ON PRODUCT RETURN BEHAVIOR:
EVIDENCE FROM TWO FIELD EXPERIMENTS
146
THE DESIRABLE AND UNDESIRABLE INFLUENCES OF SOCIAL
NORMS AND SELF-BENEFITS ON PRODUCT RETURN
BEHAVIOR:
EVIDENCE FROM TWO FIELD EXPERIMENTS
Authors
Thilo Pfrang, Oliver Emrich, Thomas Rudolph
Abstract
Although a higher number of product returns is associated with a higher number of sales,
customers with a high level of product returns disproportionally inflate costs, which
reduces retailer profitability. Despite its relevance, research on changing return behavior
of customers with a high return propensity is scarce. This research examines how social
norm (i.e., highlighting what others are doing) and self-benefit appeals (i.e., highlighting
the benefits of the action) can change the return behavior of customers with a high latent
return propensity across various stages of the purchase process. The authors demonstrate
across two large-scale field experiments that the effectiveness of social norms and selfbenefits in reducing the return rate and increasing net sales depends on the level of
customers’ latent return propensity and the degree of the discretionary room for
maneuver across the purchase process. Study 1 shows that under discretionary room for
maneuver (i.e., the purchase stage), social norm appeals reduce the return rate and
increase net sales for customers who have a high latent return propensity. However,
social norms increase returns and decrease net sales when presented at the post-purchase
stage (low discretionary room), when self-benefits reduce returns and increase sales.
Study 2 shows that the combination of a low level of latent return propensity with the
communication of social norms can also lead to more product returns and lower net
sales. However, the study replicates the effect that social norms reduce returns and
increase net sales of customers with a high return propensity, particularly if the norm
appeals refer to close others, with whom customers can identify. We discuss the
implications for online retailers and provide directions for further research.
147
1 Introduction
The Internet’s increasing role as a distribution channel is associated with increasing
product returns (Bonifield, Cole, and Schultz 2010). In order not to lose customers to
competitors in a highly competitive market environment, retailers have little choice but
to be generous with their return policies (The Economist 2013). However, lenient return
policies and offensive marketing campaigns such as Zalando’s “Scream for Joy or Send
It Back!” have taught customers to not only return habitually, but also to test the limits
of retailers’ goodwill (Lawton 2008; Powers and Jack 2013). Recent studies indicate
high levels of product returns in textile sectors—for example, 30% for online fashion
retailers in the US and up to 60% for big pure players in the German-speaking market
(Walsh et al. 2015; Reinhold 2014).
Retailers’ product return costs range between 4 and 20 euros per returned item,
indicating losses in margins for diagnosing, conditioning, and repacking (The Economist
2013; Rösch 2015). Research confirms that product returns can cost retailers more than
they benefit from them (Petersen and Kumar 2009). Although product returns can
increase future purchase behavior (Griffis, Rao, Goldsby, and Niranjan 2012), they also
lead to declining profitability because customer segments with disproportionally high
product returns inflate costs (El Kihal, Schulze, and Skiera 2014; Foscht et al. 2013;
Petersen and Kumar 2010; Venkatesan and Kumar 2004). In other words, if the rate of
returns is too high, the costs to the company outweigh the benefit of the increased sales
(Petersen and Kumar 2009a). For example, Petersen and Kumar (2009) found an optimal
return rate of 13% with an exemplary profit of 58.4 million $. At 18% above this optimal
percentage that maximized firm profits (return rate = 31%), the profit was only 13.1
million $. Given that average return rates of the most fashion online retailers exceed
31%, these research results indicate the cost problem of current levels of product returns.
Some retailers have tried to exclude customers with extreme return behavior, but
these attempts have led to consumer protests and have damaged customer relationships
(The Economist 2013; Kontio, Hortig, and Nagel 2013). Therefore, how retailers can
reduce return behavior, particularly for customer segments with a high return propensity,
becomes an important research question.
Despite its relevance, empirical studies on product return behavior are scarce.
Research has focused on the motives of product returns (Harris 2008; Powers and Jack
148
2013; Maity and Arnold 2013) and the role of returns in the exchange processes between
the customer and the company (Petersen and Kumar 2009; Nasr-Bechwati and Siegal
2005), and has scrutinized the configuration of return policies as an optimization
problem to increase profits. Most of the latter studies have investigated purchase rates
(Anderson, Hansen, and Simester 2009), post-return spending (Bower and Maxham
2012; Petersen and Kumar 2009; Wood 2001), and overall profits (Petersen and Kumar
2015) as dependent variables. These studies analyze the antecedents and outcomes of
return behavior but do not investigate how retailers can induce customers to reduce their
return behavior. Initial evidence suggests that product returns can be influenced by
communicating deadlines (Janakiraman and Ordóñez 2012), by product-oriented Web
technologies (De, Hu, and Rahman 2013), by sequential product presentation when
customers receive disconfirming information (Nasr-Bechwati and Siegal 2005), or by
the product search as an experience (Maity and Arnold 2013). However, there is a lack
of research that investigates how retailers can specifically address the critical segments
of customers who have disproportionally high product returns.
In this article, we will use behavioral appeals in the context of product returns. Selfbenefit and social norm appeals were shown to change consumer behavior in the context
of pro-environmental (e.g., White and Simpson 2013) and prosocial behaviors (e.g.,
Holmes, Miller, and Lerner 2002). In contrast to these contexts, the retail context of
product returns has two specific challenges: First, behavioral appeals should address
specific customer segments because product returns are detrimental only for customers
with a latent return proclivity, whereas product returns can increase profits for other
customer segments (Petersen and Kumar 2009). Second, retailers have to decide at
which stage of the purchase process they should induce the behavioral appeals.
Behavioral appeals may be induced at the point of the purchase decision or after the
decision—after receipt of the products. At the purchase decision, customers have more
leeway to avoid product returns because they still have control over the decision about
which products to order. However, behavioral appeals at the purchase decision may have
a negative impact on the customer value of the order compared with behavioral appeals
after the purchase decision. Therefore, our article investigates two central questions for
retailers:
1) How do behavioral appeals affect the product returns and purchase behavior of
customers with high latent return propensity, compared to that of customers with
low latent return propensity?
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2) At which point in the purchase process should retailers induce behavioral appeals
for customers with high latent return propensity?
Below, we present the study’s conceptual framework, propose hypotheses, and then
test them in two field studies. We then discuss the results and their implications, offering
guidance to retailers on how to use behavioral appeals to reduce return behavior.
2 Conceptual Framework
Encouraging customers to avoid returning requires a behavioral change because
customers have grown accustomed to making returns and expect lenient return policies
(Petersen and Kumar, 2009). However, ethical and practical problems arise from
retailers’ attempts to punish customers who display excessive return behaviors (Kontio,
Hortig, and Nagel 2013). According to behavioristic approaches, incentives and appeals
can be more effective in changing customers’ behavior (Nord and Peter 1980;
Rothschild and Gaidis 1981). Research has stressed the use of two appeal types to
encourage consumers to engage in certain behaviors: self-benefits and social norms.
Social exchange theory proposes that consumers are more likely to engage in
behavior desired by an exchange partner (e.g., the retailer) if they can derive a selfbenefit from that behavior, because individuals invest in relationships on the basis of a
subjective analysis of their costs and benefits (Bendapudi, Singh, and Bendapudi, 1996;
Blau 1964; Bagozzi 1975). Self-benefits are “own” advantages, benefits for the self, that
can arise from engaging in a specific behavior (White and Peloza 2009). This egoistic
attitude is confirmed by numerous studies showing behavioral changes in various areas
by combining the request for the desired behavior with some form of benefit to the self
(White and Simpson 2013; Brunel and Nelson 2000). For instance, self-benefit appeals
have been shown to provoke engagement in unfamiliar sustainable behaviors (White
and Simpson, 2013), short-term reductions in energy consumption (Nolan et al. 2008),
or an increase in charitable support by offering a product in exchange for a donation
(Holmes, Miller, and Lerner, 2002).
Various behavioral changes have also been produced through conformity to social
norms, as people orient their conduct and actions toward their fellow human beings
(Cialdini, Reno, and Kallgren 1990). Social norms can be defined as “rules and
standards that are understood by members of a group, and that guide and/or constrain
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social behavior without the force of law” (Cialdini and Trost 1998). According to the
focus theory of normative conduct, people tend to believe that others’ behavior is based
on more thorough knowledge and that others behave in the most effective way in a given
situation (Melnyk et al. 2011; Reno, Cialdini, and Kallgren 1993), as reflected in the
maxim “If everybody is doing it, it must be a sensible thing to do” (Cialdini, Reno, and
Kallgren 1990). By providing examples of preferred and appropriate behaviors, and in
this way providing information on which behavior is likely to be effective (Melnyk et
al. 2013), social norms establish a behavioral standard from which consumers do not
want to deviate (Schultz et al. 2007). Numerous studies have shown the capacity of
social norm appeals to convert undesirable behaviors into desirable ones, such as saving
energy (Ayres, Raseman, and Shih 2013; Schultz et al. 2007), paying taxes (Martin
2012), using towels in hotels in an environmentally conscious way (Goldstein, Cialdini,
and Griskevicius 2008), being silent in libraries (Aarts and Dijksterhuis 2003), and
increasing motivation to vote (Gerber and Rogers 2009). Studies have also shown that
social norm appeals increase purchase intentions (Melnyk et al. 2011, 2013; Noguti and
Russell 2014; Griskevicius et al. 2009).
Both the opportunistic nature and context of product returns suggest that self-benefit
and social norm appeals can influence return behavior. First, self-interest and normative
influence have been shown to affect opportunistic behavior (King, Dennis and Wright
2008; Piron and Young 2000). Second, product returns are associated with resource
consumption and transport emissions (Srivastava and Srivastava 2006). A wide range
of the behavioral influence of self-benefits and social norms has been achieved in
contexts of environmental protection and prosocial behaviors. Thus, consumers’
susceptibility to these influences could also be used in behavioral appeals to convert
undesirable return behavior into desirable return behavior.
Consumers’ return behavior results from their actual product returns. Studies on
product returns have used different variables to measure product return intentions, such
as the “likelihood” (Bechwati and Siegal 2005), “probability” (Garnefeld, Münkhoff,
and Raum 2013) “frequency” (Powers and Jack 2013), and “propensity” (De, Hu, and
Rahman 2013) of product returns, serving as proxy variables to predict actual returns
(Maity and Arnold 2013). In this field research, we measure the actual return rates based
on actual customer behaviors.
Net sales is the difference between sales and the value of product returns. In
measuring the profitability of behavioral appeals, this dependent variable is important
for determining how behavioral appeals affect future sales, in addition to reducing the
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costs associated with product returns. We assume that the effects of behavioral appeals
on customers’ return behavior and net sales depend on customers’ latent return
propensity and situational decision-making leeway (i.e., discretionary room for
maneuver).
Latent return propensity refers to customers’ frequency and extent of past product
return behavior (Powers and Jack 2013; De, Hu, and Rahman 2013), implying a certain
degree of opportunistic behavior (e.g., ordering products in numerous colors and sizes,
returning used products, premeditating returns when ordering; King, Dennis and Wright
2008; Harris 2008). Thus, following Petersen and Kumar (2009), past return behavior is
reflected by past purchases and returns, as well as by customer characteristics and
attitudes (Piron and Young 2000; King, Dennis, and Wright 2008; Harris 2010).
Accordingly, a body of research conceptualizes the propensity to return products as
consisting of habits and attitudes (Harris 2008) that can easily be converted to
opportunistic behaviors (Ülkü, Dailey, and Yayla-Küllü 2013; King, Dennis, and Wright
2008).
During their purchase process stages, customers have various degrees of leeway to
influence the probability of product returns, which we refer to as discretionary room for
maneuver (Heuvelhof and Heuvelhof 2009). Principal agent theory posits that
discretionary room for maneuver (i.e., situational leeway to act opportunistically) arises
from information asymmetries between transaction partners (Roth and O’Donnell 1996;
Williamson 1989). Constituting a classical principal–agent problem, retailers provide
the opportunity for product returns without knowing why a customer orders a specific
product in a specific amount (Harris, 2010; Cooper and Ross 1985). This information
asymmetry is particularly pronounced in online retailing because it is difficult for a
retailer to determine whether product returns result from imperfect information on the
Internet (e.g., insufficient product images, lack of touch) or opportunistic customer
behavior (Chu, Gerstner, and Hess 1998). Customer opportunism can arise situationally
during the purchase process; for instance, the multitude of order options can entice
customers to add items they do not intend to buy (Powers and Jack 2013; Heiman,
McWilliams, and Zilberman 2001). To avoid jeopardizing net sales, retailers often try
to address product return behavior in the post-purchase stage, when the customer
receives the products (Garnefeld, Münkhoff, and Raum 2013), at which point
customers’ discretionary room has been reduced because they have already made a
decision concerning their opportunistic behavior.
152
Therefore, apart from the question of whom to address with behavioral appeals, we
also assume that the effects of social norms and self-benefits depend on when customers
are exposed to them. Addressing a highly relevant problem for retailers, this study
investigates how self-benefits and social norms influence return behavior and net sales,
depending on customers’ latent return propensity and situational incentive for
opportunistic behavior (i.e., discretionary room for maneuver) as determined by the
stage of the purchase process (see Figure 15).
Figure 15
Conceptual Framework
3 Hypothesis Development
We suggest that the effectiveness of social norm appeals will be moderated by a
customer’s high or low level of latent return propensity. Specifically, we assume that
for customers with a high latent return propensity, social norm appeals will reduce
returns more strongly than will self-benefit appeals (see Figure 15 for the conceptual
framework). Studies show that social norms exert a significantly stronger influence on
energy-saving behavior than do self-interested motives such as saving money, even
though people rate normative information as the least motivating reason to change
behavior (Nolan et al. 2008). Thus, social norms are more influential in provoking pro153
environmental behaviors than the factors people intuitively believe will affect their
behavior, such as the provision of benefits to the self (White and Simpson 2013). We
assume that this effect is particularly strong for customers with a latent return
propensity. In line with the focus theory of normative conduct (Cialdini, Reno, and
Kallgren 1990), studies suggest that the behavioral effects of social norms are driven by
feelings of having deviated from a proposed social norm (e.g., Ayres, Raseman, and
Shih 2013). As people measure the appropriateness of their behavior by their distance
from a norm (Sanderson, Darley, and Messinger 2002; Schultz et al. 2007), we assume
that customers with a high return propensity have a stronger feeling of deviating from a
social norm than do customers with a low return propensity who are behaving desirably.
Thus, if a social norm conveys the message that most customers try to avoid product
returns to protect the environment, deviating applies especially strongly to customers
with a high latent return propensity. Given that a social norm appeal serves as a point of
comparison for an individual’s own behavior (Schultz et al. 2007), the social norm
appeal’s power as a magnet for behavior should be especially strong for customers with
a high return propensity. Therefore, we hypothesize the following:
H1: For customers with a latent return propensity, social norm appeals will reduce
return behavior more strongly than will self-benefit appeals.
We also suggest that the effects of social norm and self-benefit appeals not only
vary across different levels of customers’ latent return propensity but also depend on
situational opportunism diverging between the pre- and post-purchase stages. Social
cognitive theory indicates that, if a situational factor decreases the current accessibility
of moral identity, it weakens the motivation to act morally (Aquino et al. 2009). Thus,
if a customer with a latent return propensity also receives discretionary room for
maneuver during the purchase process, the incentive for opportunistic behavior should
be particularly strong. Discretionary room for maneuver could therefore situationally
strengthen the opportunistic intentions of those customers with a latent return
propensity. Accordingly, if latent return propensity is amplified by the situational
accessibility of discretionary room for maneuver, the perception of deviance should also
increase when a customer receives social norm feedback. Analogous to H1, as
consumers compare themselves with others and adapt their behavior depending on their
distance from the comparison group (Schultz et al. 2007), the desire for behavioral
change (i.e., to return less often) should also increase. Thus, social norm appeals should
reduce the return rate of customers who have a latent return propensity and, particularly,
should do so markedly if discretionary room for maneuver appears (see also Figure 15).
154
Moreover, the theory of planned behavior posits that the perceived ease of
performing a specific behavior (i.e., perceived behavioral control) influences the
probability of a behavioral change (Fishbein and Ajzen 1975; King, Dennis, and Wright,
2008). The point of purchase (i.e., pre-choice stage) offers more possibilities for
behavioral change (e.g., taking size variants out of the shopping cart) than the postpurchase stage, where the customer has already made a decision and has received items
that he or she may not want to keep. Thus, the return-reducing effect of social norms on
customers with a latent return propensity should be especially strong if a situational
accessibility of discretionary room for maneuver (i.e., the pre-choice stage) appears.
In summary, the feeling of deviance and thus the incentive to act in conformity with
the norm is strongest if discretionary room for maneuver is present. Moreover, the
perceived ease of performing a desirable behavior is higher in the purchase-stage, where
the customer has discretionary room for maneuver. Thus:
H2: The reduction effect of social norm appeals on return behavior will be strengthened
if customers’ discretionary room for maneuver is high.
By contrast, if no discretionary room for maneuver appears (i.e., after purchase), the
perceived relevance of social norm information should be smaller, as the customer will
have few appropriate options other than a product return. For instance, customers who
receive a parcel that includes a flyer with a behavioral appeal will have already made a
purchase decision before receiving the appeal. If this purchase decision is opportunistic
(e.g., ordering products with the intention to return), it is difficult to reverse the decision
without returning the product. For instance, a customer who has ordered a product for a
special occasion or in several sizes (Heiman, McWilliams, and Zilberman 2001) can
hardly avoid a return, as the only alternative is paying for the whole order with additional
items he or she does not need. Such a situation, where the customer’s reaction to
information on what other customers are doing is limited, might even cause reactance
due to the customer’s restricted post-purchase behavioral freedom. Reactance theory
posits that feelings of reactance can occur, particularly, when people feel that their
freedom is threatened (Brehm 1966). A social norm appeal highlighting others´ return
avoidance behavior in which a targeted customer can engage in only a limited way after
purchase could make this restricted autonomy salient. As a result, people seek to restore
their freedom by behaving in oppositional ways (Chartrand, Dalton, and Fitzsimons
2007), which could provoke acts of defiance and therefore higher product returns.
155
By contrast, self-benefit appeals refer to customers’ autonomy and individual-level
goals by focusing on advantages for the self (White and Peloza 2009). The autonomy to
try products after purchase and to decide to keep or return them is one of the fundamental
motivations for online shopping (Foscht et al. 2013). Appeals promoting self-benefits
would be more congruent with such autonomy-based decisions in the post-purchase
stage than social norms, making post-purchase restrictions more salient (White and
Simpson 2013). Consequently, when presented in the post-purchase stage, social norms
should increase the return rate, whereas self-benefit appeals should reduce it.
Moreover, research has shown that social norms can provoke undesirable behaviors
in consumers who already act in conformity with the social norm (Schultz et al. 2007).
This so-called “boomerang effect” suggests that normative appeals increase the
prevalence and social acceptability of the undesirable behavior (e.g., using more energy
than average) for consumers already behaving desirably (e.g., using less energy than
average; Burchell, Rettie, and Patel 2013). Thus, social norm appeals should increase
the return behavior of customers who return products for legitimate reasons and who
return less than the average. As self-benefits do not convey a social comparison by
which customers may perceive a deviation, but only an individual benefit that consumers
generally consider behaviorally relevant (Nolan et al. 2008), we expect boomerang
effects solely for customers with a low return propensity who receive social norm
appeals.
In summary, we assume that normative appeals can raise awareness of undesirable
behavior in consumers who already abstain from opportunistic product returns or
provoke reactance in consumers who have low discretionary room for maneuver,
leading to a higher product return rate. We further propose that this is not so for selfbenefit appeals, which communicate only the individual benefits to which customers
could be especially susceptible after purchase. We thus hypothesize:
H3: If latent return propensity or discretionary room for maneuver is low, social norm
appeals will increase return behavior more strongly than will self-benefit appeals.
Reference groups. In addition to their degree of deviating from the norm, when
assessing their behavior people also show concern about the persons from whom they
deviate when assessing their behavior. Consumers’ conformity to social norms thus
depends on the type of reference group attached to the norm (Goldstein, Cialdini, and
Griskevicius 2008).
156
Drawing on social comparison theory (Festinger 1954) and social identity theory
(Tajfel and Turner 2004), research on social influence shows that individuals conform
to fellow human beings in a particularly strong way when they share similar
characteristics or represent personally relevant social identities with whom they can
identify (Martin 2012; Goldstein, Cialdini, and Griskevicius 2008). For instance, Bobek,
Hageman, and Kelliher (2013) found that the expectations of close others directly
influenced tax compliance decisions. Gino, Ayal, and Ariely (2015) show that
participants’ levels of unethical behavior increase when the cheating confederate of the
researchers was an in-group member, but decreased when the confederate was an outgroup member. Goldstein, Cialdini, and Griskevicius (2008) showed that hotel guests
who were informed that most of the previous occupants of their rooms had participated
in towel reuse programs were more likely to participate in the program than were those
given the same information about all the hotel guests.
Hence, we assume that social norm appeals are most effective in changing
customers’ return behavior when the social norm refers to a reference group that shares
similarities with the targeted customers (e.g., who bought the same product, have similar
fashion tastes). We thus hypothesize:
H4: The reduction effect of social norm appeals on return behavior will be strengthened
if the social norm refers to a relevant reference group.
4 Study 1
We tested our assumptions via field experiments to examine the effects of social
norms and self-benefits on actual return and purchase behavior. We focused our
experimental approach on online fashion retailing, as the apparel product category
implies particularly high risks due to the preclusion of sensory product examinations
(Ofek, Katona, and Sarvary 2011). Therefore, online fashion retailers are especially
interested in examining return propensities and reducing return behavior (Walsh et al.
2015). Moreover, in the German-speaking market major players in fashion retailing
account for a significant proportion of e-commerce, and provide the most popular
shopping websites (Reinhold 2014; Rudolph et al. 2015).
157
4.1 Method
Participants and design. Between October 2014 and March 2015, we collected
purchase data from online customers of a European multi-channel fashion retailer of
women’s clothing, shoes, and accessories in the mid- to high-priced product segments.
A total of 1,821 participants, who were logged into their Webshop account (100%
female; Mage = 58.36 (SD = 9.85)) participated in the field experiment. Customers were
not aware that they were participants in an experiment. They were randomly assigned
to one of six conditions in a 2 (touchpoint: shopping cart vs. flyer in the parcel) x 3
(appeal type: self-benefit vs. social norm vs. control) between-subjects design.
In cooperation with the retailer, we randomly selected Swiss and Austrian
customers 4 with above-average return rates (> 45%) based on observations of the 24
months preceding the start of the experiment, to obtain an exclusive sample of customers
with a latent return propensity. The selection of the customers ensured that, for every
single order during the experiment, each customer received the same appeal at the same
touchpoint. The retailer’s return policy is relatively lenient, in that customers purchase
on trial and can return products for any reason within 14 days.
Procedure. We tested our hypotheses on return and purchase behavior using a
between-subjects field experiment. We investigated online shoppers’ behavioral
responses to both self-benefit appeals and social norm appeals, at two different
touchpoints across the purchase process—in the shopping cart view and via a flyer in
the parcel 5. In conjunction with the retailer, we drafted three different appeal messages
soliciting customers to help avoid product returns by deciding on a product in only one
size and color. One message, designed to reflect a standard retailing approach, focused
on the importance of environmental protection but provided no explicit self-benefit or
social norm. A second message conveyed a self-benefit by informing customers that,
besides protecting the environment, avoiding product returns by deciding on one size
and color benefited their own life quality. The third message highlighted a social norm
by informing customers that most of the retailer’s customers avoid product returns, for
Robustness checks showed that the location of participants (i.e., Austria or Switzerland) had no influence on the
results.
5
The full field study contains additional experimental groups (i.e., one group received appeals via order
confirmation and one group received the appeals at all three touchpoints shopping cart view, order confirmation
and flyer). The full sample consists of 7,285 female customers (Mage = 58.11; SD = 10.13). To clearly test the
outlined hypotheses between the purchase and post-purchase stages, we included only the two distinct touchpoints
“shopping cart view” (purchase phase) and “flyer” (post-purchase phase) in this experiment, resulting in the
announced sample of 1,821 female customers.
4
158
instance by ordering products in only one size and color (see Table 11 and Appendix A
- Appendix C).
Table 11
Wordings of Appeals in Study 1
Appeal type
Manipulation
Control
For the good of the environment
With the avoidance of product returns, you actively reduce transport emissions and material
consumption!
You can support the environment by ordering a product only in one size/color and trying your best to
avoid returns.
Self-benefit
For the good of the environment
With the avoidance of product returns, you actively reduce transport emissions and material consumption
for your own benefit!
You can support the environment and your own life quality by ordering a product only in one size/color
and trying your best to avoid returns.
Social Norm
For the good of the environment
83% of the [company name] customers avoid product returns and thereby actively reduce transport
emissions and material consumption.
You can join your fellow customers in supporting the environment by ordering a product in only one
size/color and by trying your best to avoid returns.
Customers received one of these appeals either at the point of purchase (i.e., the
shopping cart view) or as a flyer in the parcel received. Except for the difference between
the physical flyer and the digital text message in the Webshop, the appeals were identical
in appearance across both touchpoints (see also Appendix A - Appendix C). Each appeal
had the same text framework. Only the text modules of the key manipulation varied
across the three appeal types.
Measures. We analyzed the participants’ purchase and return data during the
experimental period, measuring the return rate as the share of product returns out of the
total order delivery quantity. We calculated the difference between sales and returns as
the measure of net sales to consider both return and purchase behavior. For instance, if
a customer ordered three size variants of an item and sent back two, net sales amounted
to one item. If the same customer reduced the order volume in reaction to an appeal from
three to two size variants and thus sent back only one item, net sales still amounted to
one item; however, the return rate dropped from two-thirds (66.7%) to one-half (50%).
Thus, the retailer achieved the same net sales but had lower costs for product returns. If
the reduction in product returns exceeded the reduction in the order amount, net sales
increased.
159
As all participants were logged in, we could record the age and the order frequency
within the experiment of each one. Moreover, implementing the appeals in the purchase
(i.e., shopping cart view) and the post-purchase phase (i.e., flyer in the parcel) allowed
us to analyze the effects of the appeals at high (i.e., purchase stage) and low (i.e., postpurchase stage) values of discretionary room for maneuver.
4.2 Results
Regression models revealed no significant main effects of the behavioral appeals on
the return rate or net sales (see Table 12 and Table 13). Both the social norm appeal and
the self-benefit appeal tend to reduce the return rate and exert no influence on net sales.
A comparison between self-benefit and social norms showed no significant differences.
Thus, at this point, the results did not support our assumption that social norms have a
stronger return-reducing effect than self-benefit appeals for customers with a high latent
return propensity.
Table 12
Main Effects of the Appeals on the Return Rate
Dependent variable: Return rate
Intercept
Baseline: Control
Baseline: Self-benefit
R2=.006;
F= 2.803;
d.f.1/d.f.2= 4/1816; p=.025
R2=.006;
F= 2.803;
d.f.1/d.f.2= 4/1816; p=.025
Estimate
t-Value
Estimate
t-Value
.531
31.742***
.509
30.337***
.022
1.071
Control
Self-benefit
-.022
-1.071
Social norm
-.013
-.651
.009
.423
Discretionary room for maneuver
.014
.828
.014
.828
Age
-.003
-3.079***
-.003
-3.079***
Covariates
*p < .10
**p < .05
***p < .01
160
Table 13
Main Effects of the Appeals on Net Sales
Dependent variable: Net sales
Intercept
Baseline: Control
Baseline: Self-benefit
R2=.001;
F= .497;
d.f.1/d.f.2= 4/1816; p=.738
R2=.001;
F= .497;
d.f.1/d.f.2= 4/1816; p=.738
Estimate
t-Value
Estimate
t-Value
982.031
13.005***
1040.394
13.733***
-58.263
-.631
Control
Self-benefit
58.263
.631
Social norm
-1.787
-.019
-60.050
-.649
Discretionary room for maneuver
-85.505
-1.136
-85.505
-1.136
Age
-1.406
-.368
-1.406
-.368
Covariates
*p < .10
**p < .05
***p < .01
We tested H2 and H3 by analyzing the effects of the appeals depending on the
touchpoint. Specifically, we analyzed the effect of the appeals on return rate and net
sales at high (i.e., shopping cart view) and low (i.e., flyer in the parcel) values of
discretionary room for maneuver (see Table 14).
A moderated multiple regression model revealed the anticipated interaction effect
between the social norm and discretionary room for maneuver (B= -.070; ΔR2= .002;
p= .084) on the return rate. The interaction between the self-benefit appeal x
discretionary room for maneuver does not reach significance (B= .023; ΔR2= .000; p=
.582).
Specifically, the conditional effect of the social norm on return rate confirmed that,
as H2 predicted, when discretionary room for maneuver is high (i.e., in the shopping
cart view), the social norm appeal significantly reduced the return rate of customers with
a latent return propensity (49.6% vs. 54.49%; t(1813)= -1.676, p= .094). The conditional
effect of the social norm at low values of discretionary room for maneuver increased the
return rate, as assumed, but did not reach significance (53.63% vs 51.49%; t(1813)=
.767, p= .443). The self-benefit appeal led to non-significant return reductions under
high and low discretionary room for maneuver (see Figure 16).
161
Moreover, a comparison between the social norm and the self-benefit appeal
revealed that, if discretionary room for maneuver appears, the return rate was
directionally lower in the social norm than in the self-benefit condition (B= -.093, ΔR2=
.003; p= .024; 49.6% vs. 53.76%; t(1813)= -1.322, p= .186). However, if low
discretionary room for maneuver appeared, the return rate was significantly higher in
the social norm condition than in the self-benefit condition (53.63% vs 48.48%,
t(1813)= 1.88, p=.060), supporting H3.
Table 14
Interaction Effects of the Appeals and Discretionary Room for Maneuver on the
Return Rate
Dependent variable: Return rate
Baseline: Control
Baseline: Self-benefit
R2=.009;
F= 2.813;
d.f.1/d.f.2= 6/1814; p<.01
Intercept
Estimate
t-Value
Estimate
t-Value
.523
25.354***
.490
23.838***
.033
1.129
Control
Self-benefit
-.033
-1.129
Social norm
.021
.745
.054
1.891*
Discretionary room for maneuver
.030
1.047
.053
1.810*
Age
-.003
-3.134***
-.003
-3.143***
-.023
-.559
-.093
-2.273**
Covariates
Interactions
Control x Discretionary room for maneuver
Self-benefit x Discretionary room for maneuver
.023
.558
Social norm x Discretionary room for maneuver
-.070
-1.732*
*p < .10
**p < .05
***p < .01
162
Figure 16
Return Rate as a Function of Discretionary Room for Maneuver and Appeal
Type
55%
54.49%
54%
53.63%
53.76%
53%
52%
51.49%
51%
50%
49.60%
49%
48.48%
48%
Control
Flyer
Social norm
Self-benefit
Shopping cart
The interactions on net sales were analogous to the effects on the return rate, but
were not significant (see Table 15): at high values of discretionary room for maneuver
(i.e., in the shopping cart view), net sales were directionally higher in the social norm
condition than in the control condition (999.96 vs. 893.46, t(1814)= .672, p=.502), in
line with H2 (see Figure 17). At low values of discretionary room for maneuver (i.e.,
the flyer), however, social norms led to directionally lower net sales than in the control
appeal (917.19 vs. 983.45, t(1814)= -.661, p=.509). By contrast, the self-benefit appeal
led to directionally higher net sales at low values of discretionary room for maneuver
(1123.06 vs. 983.45, t(1814)= 1.062, p=.288), but did not influence net sales at high
values of discretionary room for maneuver (868.82 vs. 893.46, t(1814)= -.190, p=.849).
A comparison between the social norm and the self-benefit appeal revealed a
significant interaction between social norms and discretionary room for maneuver (B=
337.008, ΔR2= .002; p= .069): if discretionary room for maneuver was present, net sales
163
were directionally higher in the social norm than in the self-benefit condition (999.96
vs. 868.82, t(1814)= .845, p= .398). However, under low discretionary room for
maneuver, net sales were significantly lower in the social norm condition than in the
self-benefit condition (917.19 vs 1123.06, t(1814)= -1.738, p=.082), in support of H3.
Table 15
Interaction of Appeals and Discretionary Room for Maneuver on Net Sales
Dependent variable: Net sales
Baseline: Control
Baseline: Self-benefit
R2=.003;
F= .885;
d.f.1/d.f.2= 6/1814; p=.505
Intercept
Estimate
t-Value
Estimate
t-Value
984.392
10.572***
1124.003
12.110***
-139.612
-1.062
Control
Self-benefit
139.611
1.062
Social norm
-85.816
-.661
-225.428
-1.738*
Discretionary room for maneuver
-89.981
-.695
-254.236
-.1.929*
Age
-1.307
-.342
-1.307
-.342
164.254
.889
337.008
1.821*
Covariates
Interactions
Control x Discretionary room for maneuver
Self-benefit x Discretionary room for maneuver
-164.254
-.889
Social norm x Discretionary room for maneuver
172.754
.942
*p < .10
**p < .05
***p < .01
164
Figure 17
Net Sales as a Function of Discretionary Room for maneuver and Appeal Type
1150
1123.06
1100
1050
1000
999.96
983.45
950
900
917.19
893.46
868.82
850
800
Control
Flyer
Social norm
Self-benefit
Shopping cart
As our sample consisted of Austrian and Swiss customers, paying in two different
currencies (euro vs. Swiss franc), we standardized the dependent variable in an
additional analysis, resulting in equivalent effects. Given a standard deviation for net
sales of 1467.85, the social norm appeal should increase net sales by an estimated 86.90
currency units (B= .059; t(1814)= .672; p= .502) in the presence of discretionary room
for maneuver, whereas the social norm appeal in the post-purchase stage with low
discretionary room for maneuver should reduce net sales by an estimated 85.87 currency
units (B= -.059; t(1814)= -.661; p= .509). 6 By contrast, the self-benefit appeal led to a
directional increase in net sales of an estimated 139.45 currency units (B= .095; t(1814)=
1.062; p= .288) at low values of discretionary room for maneuver, but did not influence
net sales at high values. Compared with the self-benefit, social norm appeals increased
net sales directionally by an estimated 111.56 currency units under discretionary room
The estimated increases/decreases in net sales can slightly deviate from the plots seen in Figure 17 due to
deviations in the data for plotting for each individual appeal in the PROCESS tool (Hayes 2013).
6
165
for maneuver (B= .076; t(1814)= .845; p= .398), but reduced net sales significantly by
an estimated 226.05 currency units (B= -.154; t(1814)= -1.738; p= .082) when no
discretionary room for maneuver existed.
Robustness Checks of the Effects on Net Sales. We assume that net sales (excluding
the return rate) are also influenced by the order frequency of customers, as more orders
naturally result in higher net sales. Thus, we checked for robustness of the effects,
depending on different considerations of the order frequency. Neither including the
order frequency as a covariate nor using “net sales per order” as a dependent variable
created any meaningful change in the results.
Importantly, when analyzing the effects with net sales per order as a dependent
variable (see Figure 18), the social norm even interacted significantly with discretionary
room for maneuver (B= 92.117; ΔR2=002; p= .093), whereas the self-benefit appeal
exerted no significant impact on net sales in interaction with discretionary room for
maneuver (B= -23.967; ΔR2=000; p= .664), as with the effects on the return rate.
Specifically, at high values of discretionary room for maneuver (i.e., in the shopping
cart view), net sales were directionally higher in the social norm condition than in the
control condition (413.88 vs. 360.35, t(1813)= 1.384, p=.166), supporting H2. By
contrast, at low values of discretionary room for maneuver (i.e., the flyer), social norms
led to directionally lower net sales than the control appeal (334.71 vs. 373.29, t(1813)=
-.994, p=.320).
Moreover, a comparison between the social norm and the self-benefit appeal reveals
a significant interaction effect (B= 116.083, ΔR2=002; p= .036): if discretionary room
for maneuver was present, net sales per order were directionally higher in the social
norm than in the self-benefit condition (413.88 vs. 372.41, t(1813)= .833, p= .405).
However, if there was low discretionary room for maneuver, net sales per order were
significantly lower in the social norm condition than in the self-benefit condition (334.7
vs 409.3, t(1813)= -2.147, p=.031), in support of H3.
166
Figure 18
Net Sales per Order as a Function of Discretionary Room for Maneuver and
Appeal Type
420
413.88
409.33
400
380
360
373.29
372.41
360.35
340
334.71
320
300
Control
Flyer
Social norm
Self-benefit
Shopping cart
Thus, given a standard deviation of net sales per order of 459.479, the social norm
appeal in the presence of discretionary room for maneuver should increase net sales per
order by an estimated 53.53 7 currency units (B= .117; t(1813)= 1.384; p= .166), whereas
the social norm appeal in the after-purchase stage with low discretionary room for
maneuver reduced net sales by an estimated 38.59 currency units (B= -.084; t(1813)=
-.994; p= .320). Compared with the self-benefit, social norm appeals increased net sales
per order directionally by an estimated 33.08 currency units (B= .072; t(1813)= .833;
p= .405) but reduced net sales significantly by an estimated 83.17 currency units (B=
-.181; t(1813)= -2.147; p= .031) when discretionary room for maneuver was absent.
Contributions to Profitability. We assume that positive influences on profitability
result only from those conditions in which the strongest return reductions occur. Thus,
The estimated increases/decreases in net sales can slightly deviate from the plots seen in Figure 18 due to
deviations in the data for plotting for each individual appeal in the PROCESS tool (Hayes 2013).
7
167
we illustrate possible cost savings with an exemplary cost calculation of those
experimental conditions where significant reductions in the return rate occurred. This
applies to the social norm appeal in the purchase stage (i.e., discretionary room for
maneuver) compared with the control appeal. The calculation is approximate, being
based on the estimated values of the return rates in the plots of the regression models
(see Figure 16 and Figure 17) and on the average costs per return of the retailer. We
computed return cost savings per item as follows:
= (Costs per return 8 x Return rateControl) – (Costs per return x Return rateSocial norm)
= (4.87€ x 0.545) – (4.87€ x 0.496)
= 2.65 € - 2.42 €
= 0.23 € per item
This represents an estimated return cost reduction through social norms of 8.8% per
item (0.23 € / 2.65€) when presenting the social norm in the purchase stage compared
to the control group.
In addition to these return cost reductions, social norms exert an estimated increase
in net sales of 12% in the purchase stage. Thus, the application of social norms in the
purchase stage for customers with a high return propensity should contribute
significantly to an increase in profitability.
4.3 Discussion
The results of the field experiment differed between the purchase and post-purchase
stages concerning the effects of social norms and self-benefits for customers with a
latent return propensity depending on the presence of discretionary room for maneuver.
Importantly, the social norm appeal resulted in a lower return rate and higher net sales
if it was presented in the purchase stage, where discretionary room for maneuver is high.
However, it increased the return rate and reduced net sales when presented after
purchase, where discretionary room for maneuver is low. This detrimental effect
contrasts with the self-benefit appeal, which reduced the return rate and increases net
sales more strongly than social norms in the post-purchase stage. These findings support
our assumption that social norms reduce product returns only if discretionary room for
8
According to the retailer, average costs per return amount to 4.87€
168
maneuver is present, whereas the absence of discretionary room for maneuver after
purchase can lead to the detrimental effect whereby social norms increase product
returns and decrease net sales more strongly than self-benefits.
These results reflect a difference in the perceived relevance of the appeals across
the various stages of the purchase process. Whereas social norms influence customers
while compiling an order in presence of discretionary room for maneuver, self-benefits
only lead customers to return less often after purchase. As H2 predicted, the social norm
appeal seems to strengthen feelings of deviance (Schultz et al. 2007) when the
opportunity for opportunism is present, leading to a strong urge to change behavior (i.e.,
adjust the order in a way that will result in fewer returns). By contrast, social norm
appeals seem to be less effective after customers have received an order, for which they
may have already paid, including for items they intend to return for a refund. In this
case, descriptions about what other customers are doing might not count anymore and
can even make a restricted autonomy more salient for customers who ordered items they
do not want to keep. This might induce feelings of reactance, leading to reactions
opposite to normatively approved behavior. Instead, self-benefits might be more
congruent with customers’ autonomy to decide whether to keep or return a product after
purchase. Thus, in the post-purchase stage, the customer’s own advantage seems to be
more relevant, resulting in a stronger behavioral effect through the self-benefit appeal.
Whereas this field experiment revealed the different effects of social norms and selfbenefits on return behavior at high and low levels of discretionary room for maneuver,
the conclusions are limited to customers with a high latent return propensity. The
experimental examination of customers with above-average return rates cannot address
how social norms and self-benefit appeals affect the return intentions of customers with
a low return propensity.
5 Study 2
In Experiment 2, we focused on the purchase stage, which has confirmed our
assumption that this stage is the moment when the appeals (i.e., social norms) reduce
the return rate most strongly. In planning to replicate the results of our first study and
address its limitations, we extended the design of our second field experiment in two
ways. First, we focused not only on above-average returners but on all customers in the
process of placing a choice order, including below-average returners, enabling an
169
examination of the effects of social norms and self-benefits for customers with both a
low and high latent return propensity (H3). Second, we extended the appeal design of
the first field study by adding two social norm variants. This allows for covering a
broader spectrum of the mechanisms of social norms by additionally examining whether
highlighting a return norm of a personally important or similar reference group
motivates customers to avoid product returns to a greater extent than the norm of a usual
reference group (Goldstein, Cialdini, and Griskevicius, 2008).
Participants and design. Over a period of four weeks between February and March
2015, we collected purchase data on the online customers of a German multi-channel
fashion retailer of apparel, shoes, accessories, furniture, and living accessories in midto high-priced product segments 9. A total of 21,405 female shoppers, logged into their
Webshop accounts (Mage = 50.6 (SD = 9.8), participated in the field experiment.
Customers were not aware that they were participants in an experiment. They were
randomly assigned to one of five experimental shopping cart messages within a onefactorial design. We also included a default group that received no experimental
message while placing an order.
Procedure. Online customers were exposed to one of five appeals, presented as short
text messages in the shopping cart view, only if customers were placing a choice order
(i.e., placing products of several sizes and/or colors in the shopping cart). We drafted
five different appeal messages that solicited customers placing a choice order to help
avoid product returns by deciding on a product in only one size and color (see Table 16
and Appendix D for a visual example).
9
In consistency with Study 1, we only recorded data for fashion orders excluding furniture and living accessoires.
170
Table 16
Wording of Appeals in Study 2
Appeal type
Manipulation
Control
For the good of the environment
Avoid returns and thus decide on one size/color. More
Self-benefit
For the good of the environment
Avoid returns to your advantage and thus decide on one size/color. Thereby, you will be doing something
for your own life quality! More
Social norm
For the good of the environment
Help to avoid returns and thus decide on one size/color. 83% of our customers have already succeeded
in avoiding returns! More
Self norm
“product”
For the good of the environment
Help to avoid returns and thus decide on one size/color. 83% of our customers with the same product
choice have already succeeded in avoiding returns! More
Social norm
“taste”
For the good of the environment
Help to avoid returns and thus decide on one size/color. 83% of our customers with similar taste have
already succeeded in avoiding returns! More
One message, designed to reflect a standard retailing approach, focused on the
importance of environmental protection but provided no explicit self-benefit or social
norm. A second message conveyed a self-benefit by informing customers that, besides
protecting the environment, avoiding product returns by deciding on one size and color
benefited their own life quality. The third message highlighted a social norm by
informing participants that most of the retailers’ customers avoided product returns by
ordering products in only one size and color (see also Appendix D). Based on our
assumption that consumers follow the norms of those with similar characteristics and
with whom they can identify, we altered the reference group identity in the remaining
two social norm messages by varying the groups to which the norms referred. These two
messages conveyed the norms of reference groups that were considered important and
personally meaningful to people’s social identities (Goldstein, Cialdini, and
Griskevicius 2008). Specifically, one of these two messages highlighted that most of the
customers who bought the same product had avoided product returns by ordering
products in only one size and color (social norm “product”), whereas the other message
paired the social norm with customers with similar tastes (social norm “taste”).
In addition, all of the messages included a “more”-link. By clicking on “more”, a
pop-up window opened that described the appeal in more detail (see Appendix E and
171
Appendix F). Customers who ordered more than once within the experimental period
received the same appeal for every purchase when placing a choice order.
Measures. We measured the return and purchase behavior of participating
customers in the experimental period. Again, we measured the return rate as the share
of product returns out of total order delivery quantity and calculated the difference
between sales and returns as the measure of net sales.
We assessed latent return propensity by recording the participants’ return rates over
the last 24 months before the start of the experiment. As all participants were logged in,
we could record the age and order frequency within the experiment of each one.
5.1 Results
Return rate. A moderated multiple regression model revealed a significant
interaction effect between the social norm “taste” and latent return propensity (B= -.056;
ΔR2= .001; p= .028) on the return rate (see Table 17). Compared with the control appeal,
the average return rate was significantly higher for customers with a low latent return
propensity (32.53 vs. 30.46; t(18592)=1.691, p= .091) but significantly lower for
customers with a high latent return propensity (65.19 vs. 67.08; t(18592)=-1.846, p=
.065). As the self-benefit appeal directionally increased the return rate (B= .008; p=
.211), the significant interaction between social norm “taste” and latent return propensity
was in line with H1 H2, and H3 (see also Figure 19). However, this support is based
primarily on the effect of the social norm “taste” appeal, while the other social norm
appeals had no significant influence on the return rate. This result indicated that a social
norm referring to a similar reference group (i.e., customers with similar tastes) exerted
a stronger influence than the basic social norm appeal, in support of H4. Notably, the
finding that a social norm variant reduced returns of customer with a high return
propensity more strongly than self-benefits when presented in the purchase stage is
consistent with the results of Study 1.
172
Table 17
Interactions of the Appeals and Latent Return Propensity on the Return Rate
Dependent variable:
Return rate
Intercept
Baseline: Control
Baseline: Default
Baseline: Self-benefit
R2=.221
F= 440.288;
d.f.1/d.f.2= 12/18592; p<.01
R2=.221
F= 440.288;
d.f.1/d.f.2= 12/18592; p<.01
R2=.221
F= 440.288;
d.f.1/d.f.2= 12/18592; p<.01
Estimate
t-Value
Estimate
t-Value
Estimate
t-Value
0.516
109.345***
.525
113.531***
.524
110.899***
-.010
-1.485
-.008
-1.250
Control
Self-benefit
.008
1.250
-.001
-0.11
Social norm
.005
.722
-.005
-0.638
-.004
-.532
Social norm “product”
.001
.090
-.009
-1.403
-.008
-1.167
Social norm “taste”
-.002
-.264
-.012
-1.759*
-.010
-1.519
Default
.010
1.485
.001
.223
Latent return propensity
.518
28.401***
.508
28.793***
.515
28.340***
Age
-.003
-14.049***
-.003
-14.049***
-.003
-14.049***
.010
.387
.003
.126
Covariates
Interactions
Control x
Latent return propensity
Self-benefit x
Latent return propensity
-.003
-.126
.007
.260
Social norm x
Latent return propensity
.021
0.838
.031
1.242
.025
.967
Social norm “product” x
Latent return propensity
-.008
-0.294
.002
.088
-.004
-.169
Social norm “taste” x
Latent return propensity
-.056
-2.191**
-.046
-1.837**
-.053
-2.069**
Default x
Latent return propensity
-0.010
-.387
-.007
-.260
* p < .10
**p < .05
***p < .01
173
Similar effects occur in comparison with the default group consisting of participants
who did not receive any appeal. Similarly, the regression model reveals a significant
interaction effect between the social norm “taste” and latent return propensity (B= -.046;
ΔR2= .001; p= .066) on the return rate. Although the social norm “taste” barely increased
the average return rate for customers with a low latent return proclivity (32.53 vs. 31.75;
t(18592)=.564, p= .573), it reduced returns significantly for customers with a high latent
return propensity (65.19 vs. 67.78; t(18592)=-2.533, p= .011), compared with the default
group. The social norm appeal “taste” also had a significant simple effect on the return
rate (B= -.012; p= .079). The other appeals did not influence the return rate significantly.
Compared with the self-benefit, there was also a significant interaction effect
between the social norm “taste” and latent return propensity (B= -.053; ΔR2=.001; p=
.039) on the return rate. Specifically, the social norm “taste” led to a directionally higher
return rate for customers with a low latent return propensity (32.53 vs. 31.29; t(18592)=
.894, p= .372), partially supporting H3. However, for customers with a high latent return
propensity, the social norm “taste” appeal led to a significantly lower return rate than
the self-benefit appeal (65.19 vs. 67.67; t(18592)= -2.561, p= .011), supporting H1 and
H2.
174
Figure 19
Return Rate as a Function of Latent Return Propensity and Appeal Type
Low return propensity
34%
32.53%
32%
31.75%
31.29%
30.46%
30.81%
30%
28%
30.20%
Default
Social Norm Social norm Social norm Self-benefit
"product"
"taste"
Control
High return propensity
69%
68.15%
67%
65%
67.68%
67.67%
66.90%
67.08%
65.19%
Default
Social Norm Social norm Social norm Self-benefit
"product"
"taste"
Control
Return rate for detailed appeals. When selecting for those customers who clicked
on the “More” link (i.e., detailed appeal), stronger effects occured, confirming the
effectiveness of the social norms (see Table 18 and Figure 20). The regression model
revealed a marginal interaction between the social norm appeal and latent return
proclivity (B= -.131; ΔR2=.002; p= .207). Compared with the control appeal, customers
with a low return proclivity who received a social norm appeal tended to return more
(38.84 vs. 32.17; t(830)=1.424, p= .155), whereas customers with a high return
proclivity returned directionally less (68.91 vs. 71.71; t(830)= -.569, p= .569).
Moreover, the social norm “product” had a significant impact on the return rate in
175
interaction with latent return proclivity (B= -.213; ΔR2=.004; p= .046), indicating that
the appeal provokes a higher return rate for customers with a low return propensity
(42.58 vs. 32.17; t(830)= 1.989, p= .047) but a directionally lower return rate for
customers with a high return propensity (66.70 vs. 71.71; t(830)=-1.094, p= .274).
The interaction between the social norm “taste” x latent return propensity did not
reach significance (B= -.018; ΔR2=.000; p= .861). Moreover, the self-benefit appeal
exerts a significantly positive simple effect on the return rate (B= .053; p= .051).
These effects show that if customers see the detailed treatment, social norms (except
for the variant “taste”) reduce the return rate for customers with a high latent return
propensity, but have a detrimental effect for customers with a low return propensity. The
social norm variant “product” strengthens this effect, whereas the self-benefit actually
increases the return rate compared to the control group. These results support H1, H2,
H3, and H4.
176
Table 18
Interaction of the Appeals and Latent Return Propensity on the Return Rate
(Detailed Appeals)
Dependent variable:
Return rate
Intercept
Baseline: Control
Baseline: Default
Baseline: Self-benefit
R2=.197
F= 20.373;
d.f.1/d.f.2= 10/830; p<.01
R2=.193
F= 127.426;
d.f.1/d.f.2= 12/6393; p<.01
R2=.197
F= 20.373;
d.f.1/d.f.2= 10/830; p<.01
Estimate
t-Value
Estimate
t-Value
Estimate
t-Value
.531
33.006***
.596
144.499***
.583
26.997***
-.066
-3.265***
-.053
-1.957*
Control
Self-benefit
.053
1.957*
-.013
-.490
Social norm
.019
.752
-.047
-1.900*
-.034
-1.147
Social norm “product”
.026
.955
-.040
-1.495
-.027
-.885
Social norm “taste”
.020
.759
-.046
-1.857*
-.033
-1.125
Latent return propensity
.547
8.428***
.550
36.095***
.485
5.798***
Age
-.002
-1.937*
-.002
-4.197***
-.002
-1.937*
-.003
-.033
.059
.563
Default
Covariates
Interactions
Control x
Latent return propensity
Self-benefit x
Latent return propensity
-.062
-.584
-.065
-.624
Social norm x
Latent return propensity
-.131
-1.264
-.134
-1.332
-.069
-.595
Social norm “product” x
Latent return propensity
-.213
-2.000**
-.216
-2.071**
-.151
-1.271
Social norm “taste” x
Latent return propensity
-.018
-.175
-.021
-.207
.043
.371
Default x
Latent return propensity
*p < .10
**p < .05
***p < .01
177
In comparison with the default group, the social norm exerted a negative simple
effect on the return rate (B= .-047; p= .058) and interacted directionally with the latent
return proclivity (B= -.134; ΔR2=.001; p= .183). Thus, social norms barely increased
the return rate for customers with a low return propensity but significantly reduced the
return rate of customers with a high latent return propensity (68.91 vs. 78.24; t(6393)=
-2.197, p= .028). The assumed interaction between social norms and latent return
proclivity became clearer for the social norm “product” variant (B= -.216; ΔR2=.001;
p= .038), indicating a stronger but not significant increase in the return rate for
customers with a low return propensity and a stronger significant decrease in the return
rate for customers with a high return propensity (66.70 vs. 78.24; t(6393)=-2.869; p=
.004), in support of H4 (see Figure 20). Furthermore, the social norm variant “taste”
significantly reduced the return rate in a simple effect (B= -.046; p= .063), mostly for
customers with a high return propensity (72.88 vs. 78.24; t(6393)= -1.628; p= .103),
confirming the support for H4. Finally, the control appeal had a significant simple effect
(B= -.066; p= .001), resulting in plausible return reductions for both customers with a
low (32.17 vs. 38.51; t(6393)= -1.704; p= .088) and high (71.71 vs. 78.24; t(6393)=
-2.027; p= .043) return propensity, in comparison with the participants who did not
receive any appeal. The self-benefit appeal had no impact on the return rate and did not
interact with latent return propensity, compared with the default group.
Compared with the self-benefit, the social norm “product” appeal interacted
marginally with latent return propensity (B= -.151; ΔR2= .002; p= 204). The conditional
effects showed that, for customers with a high latent return propensity, the “product”
variant significantly reduced product returns compared to the self-benefit appeal (66.70
vs. 74.58; t(830)= -1.729, p= .084), in support of H1 and H2. For customers with a low
return propensity, the variant “product” (42.58 vs. 39.52; t(830)= .393, p= .694)
increased the return rate only directionally, partially supporting H3.
178
Figure 20
Return Rate as a Function of Latent Return Propensity and Appeal Type
(Detailed Appeals)
Low return propensity
45%
42.58%
40%
38.51%
39.52%
38.84%
35%
34.66%
32.17%
30%
Default
Social Norm Social norm Social norm Self-benefit
"product"
"taste"
Control
High return propensity
80%
78.24%
75%
72.88%
70%
71.71%
68.91%
66.70%
65%
60%
74.58%
Default
Social Norm Social norm Social norm Self-benefit
"product"
"taste"
Control
Net sales. While significantly influencing the return rate at high and low values of
latent return propensity, the appeals exert less influence on net sales, except in
comparison with the default group and for those customers who read the detailed appeal
(i.e., clicked on “More”). The results indicate that, when the appeals could significantly
reduce product returns (e.g., the social norm “taste” for customers with a high return
propensity in the whole sample and social norm “product” for those customers who read
the detailed appeal), no negative, and even directionally positive, effects on net sales
179
occurred (see also Figure 21 and Figure 22). Reduced returns, while stabilizing or even
increasing net sales, indicate a positive contribution to profitability.
Table 19
Interactions of the Appeals and Latent Return Propensity on Net Sales
Dependent variable:
Net sales
Baseline: Control
Baseline: Default
Baseline: Self-benefit
R2=.019
F= 29.425
d.f.1/d.f.2= 12/18592; p<.01
Intercept
Estimate
t-Value
Estimate
t-Value
Estimate
t-Value
129.549
53.549***
132.171
55.621***
129.062
53.245***
-2.623
-.774
.487
.142
Control
Self-benefit
-.487
-.142
-3.109
-.916
Social norm
-2.819
-.826
-5.441
-1.610*
-2.332
-.683
Social norm “product”
-2.069
-.609
-4.692
-1.392
-1.582
-.465
Social norm “taste”
-2..128
-.163
-.150
-.044
2.959
.869
Default
2.623
.774
3.109
.916
Latent return propensity
-59.727
-6.385***
-90.201
-9.964***
-54.014
-5.798***
Age
-.944
-9.103***
-.944
-9.102***
-.944
-9.102***
30.474
2.345**
-5.713
-.433
Covariates
Interactions
Control x
Latent return propensity
Self-benefit x
Latent return propensity
5.713
.433
36.187
2.790***
Social norm x
Latent return propensity
-7.272
-.555
23.202
1.800*
-12.984
-.993
Social norm “product” x
Latent return propensity
5.075
.385
35.549
2.737***
-.638
-.048
Social norm “taste” x
Latent return propensity
-2.128
-.162
28.346
2.200**
-7.840
-.600
Default x
Latent return propensity
-30.474
-2.345**
-36.187
-2.790***
*p < .10
**p < .05
***p < .01
180
Compared with the control group, the appeals did not significantly influence net
sales. The regression model revealed no significant simple or interaction effects between
the appeals and latent return proclivity on net sales (see Table 19). Only customers with
a low return propensity in the default group have higher net sales than customers with
the control appeal (165.74 vs. 150.55; t(18592)= 2.375; p= .018), whereas default
customers with a high return propensity have directionally lower net sales than high
return customers receiving a control appeal (101.96 vs. 108.32; t(18592)= -1.304; p=
.192; B= -30.474; ΔR2=.001; p= .019).
Compared with the default group, the self-benefit (B= 36.187; ΔR2=.001; p= .005),
the social norm appeal (B= 23.202; ΔR2=.001; p= .072), the social norm “product”
appeal (B= 35.549; ΔR2=.001; p= .006), the social norm “taste” appeal (B= 28.346;
ΔR2=.001; p= .028), and the control appeal (B= 30.474; ΔR2=.001; p= .019) influenced
net sales in interaction with latent return propensity. Thus, for customers with a low
latent return propensity, the self-benefit appeal significantly decreased net sales (147.86
vs. 165.74; t(18592)= -2.817; p= .005), whereas, for customers with a high latent return
propensity, self-benefits tended to increase net sales compared with the default group
(109.67 vs. 101.96; t(18592)= 1.554; p= .120). The social norm appeal reduced net sales
significantly for customers with a low return propensity (151.11 vs. 165.74; t(18592)=
-2.390; p= .017) but tended to increase net sales for customers with a high latent return
propensity (103.73 vs. 101.96; t(18592)= .338; p= .735).
The social norm “product” appeal decreased net sales significantly for customers
with a low return propensity (146.85 vs. 165.74; t(18592)= -3.051; p= .002) but tended
to increase net sales for customers with a high latent return propensity (108.20 vs.
101.96; t(18592)= 1.210; p= .226), analogously to the aforementioned effects of the
social norm variants on the return rate.
The social norm “taste” appeal decreased net sales significantly for customers with
a low return propensity (153.49 vs. 165.74; t(18592)= -1.864; p= .062) but increased net
sales significantly for customers with a high latent return propensity (109.75 vs. 101.96;
t(18592)= 1.651; p= .099), analogous to the aforementioned effects of the social norm
variants on the return rate.
Finally, the control appeal decreased net sales significantly for customers with a low
return propensity (150.55 vs. 165.74; t(18592)= -2.375; p= .018) but increased net sales
significantly for customers with a high latent return propensity (108.32 vs. 101.96;
181
t(18592)= 1.304; p= .192). Compared with self-benefit, no significant differences in net
sales occurred, except for the default group, as mentioned.
As with the check for net sales robustness in the first study, we checked for
robustness of the effects when we include order frequency as a covariate or used net
sales per order as dependent variable. In both cases, the effects did not change
meaningfully, confirming that in those cases where the appeals reduce returns, they do
not negatively influence, but even tend to increase, net sales.
Figure 21
Net Sales as a Function of Latent Return Propensity and Appeal Type on Net
Sales
Low return propensity
170
165
165.74
160
155
153.49
151.11
150
146.85
145
140
150.55
Default
Social norm
Social norm
"product"
147.86
Social norm
"taste"
Self-benefit
Control
High return propensity
115
110
108.2
105
109.75
109.67
108.32
103.73
101.96
100
Default
Social norm
Social norm
"product"
Social norm
"taste"
Self-benefit
Control
182
Net sales for detailed appeals. When selecting for those customers who clicked on
the “More” link, the appeals did not significantly influence net sales in comparison with
the control appeal (see Table 20). Figure 22 illustrates that social norms (except for the
variant “taste”) even tended to increase net sales of customers with a high latent return
propensity compared to the control appeal.
Compared to the default group, all appeals significantly increased net sales without
interacting with latent return propensity: all appeals increase net sales for both customers
with a low and high latent return propensity who read the detailed appeals compared
with the default group, which received no appeal. The social norm appeal particularly
increased net sales for customers with a high return propensity (133.61 vs. 66.87;
t(6433)= 2.979; p= .003). Finally, the appeals do not significantly influence net sales in
comparison with the self-benefit appeal.
As with the check for net sales robustness for the whole sample, we also tested the
effects including order frequency as a covariate or net sales per order as dependent
variable for customers who read the detailed appeals. Again, the results did not change
meaningfully, confirming that in those cases where the appeals reduce returns, they do
not negatively influence, but even increase, net sales compared with the default group.
183
Table 20
Interactions of the Appeals and Latent Return Propensity on Net Sales (Detailed
Appeals)
Net sales
Control
Default
Self-benefit
R2=.028
R2=.033
F= 18.293;
d.f.1/d.f.2= 12/6433; p<.01
R2=.028
F= 2.395;
d.f.1/d.f.2= 10/830; p<.01
F= 2.395;
d.f.1/d.f.2= 10/830; p<.01
Intercept
Estimate
t-Value
Estimate
t-Value
Estimate
t-Value
141.484
17.444***
99.392
45.788***
131.467
12.062***
41.977
3.953***
10.017
.738
Control
Self-benefit
-10.017
-.738
31.887
2.257**
Social norm
-3.261
-.254
38.795
3.003***
6.756
.459
Social norm “product”
-2.709
-.200
39.565
2.820***
7.308
.475
Social norm “taste”
-12.796
-.988
29.087
2.212**
-2.779
-.187
Latent return propensity
-104.395
-3.189***
-103.004
-12.863***
-78.111
-1.851*
Age
-.758
-1.636*
-.629
-3.022***
-.758
-1.636*
-.711
-.017
-26.283
-.493
Default
Covariates
Interactions
Control x
Latent return propensity
Self-benefit x
Latent return propensity
26.283
.493
25.241
.461
Social norm x
Latent return propensity
80.788
1.545
79.916
1.508
54.504
.928
Social norm “product” x
Latent return propensity
29.820
.556
28.240
.513
3.537
.059
Social norm “taste” x
Latent return propensity
24.986
.474
24.187
.451
-1.297
-.022
Default x
Latent return propensity
*p < .10
**p < .05
***p < .01
184
Figure 22
Net Sales as a Function of Latent Return Propensity and Appeal Type (Detailed
Appeals)
Low return propensity
190
185
180
175
170
165
160
155
150
145
140
135
130
182.89
170.37
164.01
161.57
150.29
142
Default
Social norm
Social norm
"product"
Social norm
"taste"
Self-benefit
Control
High return propensity
140
135
130
125
120
115
110
105
100
95
90
85
80
75
70
65
60
55
50
133.61
116.37
104.65
107.84
107.98
67
Default
Social norm
Social norm
"product"
Social norm
"taste"
Self-benefit
Control
185
Contributions to Profitability. As in the first field experiment, we illustrate possible
cost reductions with exemplary cost calculations of those experimental conditions in
which significant reductions in the return rate occurred. This applied to customers with
a high return propensity receiving the social norm “taste” appeal and those with a high
return propensity receiving the social norm “product” appeal when selecting for those
who read the detailed appeals (by clicking “More”). These calculations are approximate,
as they are based on the estimates of the return rate in the plots of the regression models
(see Figure 19 and 20) and on the retailer’s average costs per return. We computed return
cost savings per item as follows:
= (Costs per return 10 x Return rateControl) – (Costs per return x Return rateSocial norm taste)
= (4.42€ x 0.671) – (4.42€ x 0.652)
= 2.97€ - 2.88€
= 0.09 € per item
This represents a return cost reduction through the social norm “taste” appeal of
3.0% per item (0.09 €/2.97€) compared to the control group when presenting the appeal
to customers with a high latent return propensity. Compared with the default group cost
reduction amounts
= (4.42€ x 0.677) – (4.42€ x 0.652)
= 2.99 € - 2.88 €
= 0.11€ per item
This represents a return cost reduction through social norms of 3.7% per item
(0.11€/2.99€) compared to the default group. Notably, net sales remain stable and even
tend to increase in these conditions.
When selecting for customers who clicked on the “More” link and read the detailed
treatments, cost reductions would increase. Compared with the control group, the social
norm variant “product” would reduce return costs by (4.42€ x 0.717) – (4.42€ x 0.667)
= 0.22€ per item, and, compared with the default group, by (4.42€ x 0.782) – (4.42€ x
0.667) = 0.51€ per item, which would reduce return costs through the social norm
“product” appeal by 6.99% and 14.82% respectively. In addition, whereas the net sales
of customers who read the detailed social norm “product” appeal directionally increased
10
According to the retailer, average costs per return amount to 4.42€
186
compared with the control appeal, all appeals considerably increase net sales compared
with the default. Compared with the default, the social norm “product” increased net
sales by 73.69% for customers who read the detailed appeal, in addition to the reductions
in return costs.
5.2 Discussion
The social norm variant “taste” exerted by far the strongest influence on the return
rate, whereas the basic social norm appeal and the variant “product” affect return
behavior when selecting for participants who read the detailed appeals by clicking on
the “More” link. Importantly, the social norm “taste” increased the return rate for
customers with a low latent return propensity but reduced it for customers with a high
latent return propensity. In addition, the “taste” variant reduced the return rate
significantly, compared to the self-benefit appeal. For customers who also clicked on
“More,” all three norm appeals significantly reduced product returns for customers with
a high latent return propensity (compared with the default group). Furthermore, the
variant “product” reduced product returns for customers with a high latent return
propensity significantly more than the self-benefit appeal, but significantly increased the
return rate for customers with a low return propensity (compared with the control
group). The overall stronger effects of the two norm variants “taste” and “product”
support our assumption that normative appeals addressing similar others strengthen the
reducing effect of social norms on return behavior (H4). Moreover, the return reductions
for customers with a high return propensity through the social norm variants are
consistent with Study 1.
In contrast to the effects on the return rate, net sales remained stable, with some
insignificant minor deviations, compared to the control group. However, compared to
the default group, all appeals increased net sales of customers with a high latent return
propensity. Customers who read the detailed appeals increased net sales regardless of
their return propensity. Notably, the treatments of this experiment appealed to customers
who were placing a choice order to adapt the order regarding size or color variants.
These interventions necessarily resulted in reductions of the order amount. Thus, it is all
the more valuable that net sales remained stable or even increased as a result of the
reduced product returns, particularly for customers with a high latent return propensity.
Increased net sales due to significant return reductions indicate that the reductions in
product returns exceed reductions in the order amount, which would have a positive
187
effect on profitability. Despite these positive effects on net sales through social norms
for customers with a high return propensity, it must be mentioned that all appeals
decreased net sales for customers with a low return propensity, compared with those
customers who received no appeal. This indicates that behavioral appeals, in general,
may cause reactance for customers who are already behaving desirably (i.e., returning
less than average), leading to detrimental effects on purchase behavior.
In summary, the results of this study confirm that the combination of a high latent
return propensity and social norm appeals is particularly effective in preventing product
returns, and is more effective than using self-benefit appeals. The strongest effects
occured through the social norm variants referring to similar others, thereby
strengthening the behavioral relevance of social norms. For customers who clicked on
the “More” link, the reduction effects of the social norm appeals increased. However,
the combination of low latent return propensity and social norms can lead to detrimental
increases in the return rate. Importantly, net sales of customers with a high latent return
propensity remained stable or even increased through social norm appeals. In
combination with significant reductions in product returns, this could create positive
effects for retailers’ profitability. However, for customers with a low return propensity,
all appeals seem to exert reactance effects, which can decrease the order amount.
6 General Discussion
The results across two large-scale field experiments highlight the potential of social
norms and self-benefits to induce a behavioral change concerning product returns.
Importantly, this research identifies the mechanisms by which social norms and selfbenefits are more or less effective in reducing product returns and increasing net sales.
The two studies confirm the influence of two important moderators of social norms and
self-benefits: whether the customer has a low or high latent return propensity, and
whether discretionary room for maneuver exists (i.e., in the purchase stage) or not (i.e.,
after purchase).
The findings produce counterintuitive, but all the more encouraging, results for
retailers and online sellers (see also Table 21). Social norms reduce the product returns
of customers who have a latent return propensity at high values of discretionary room
for maneuver (i.e., when customers complete their order) but increase returns when
discretionary room for maneuver is low (i.e., in the post-purchase stage). Thereby, social
188
norms tend to reduce returns more strongly than self-benefits in the purchase stage, but
they increase returns after purchase, where self-benefits reduce returns (in Study 1). The
reduction in product returns through social norms for customers with a high latent return
propensity under discretionary room for maneuver is particularly strong if the social
norm refers to other customers who share similar characteristics (e.g., have similar
tastes, or have bought the same product; see Study 2). However, the combination of a
low level of latent return propensity and the communication of social norms can lead to
increased product returns.
Table 21
Overview of Hypothesis Tests
Hypothesis
Study 1
Study 2
H1: For customers with a latent return propensity, social norm appeals will
reduce return behavior more strongly than self-benefit appeals.
Partial support
Partial support
H2: The reduction effect of social norm appeals on return behavior
will be strengthened if customers’ discretionary room for maneuver is high.
Support
Support
H3: If latent return propensity or discretionary decision room for maneuver
is low, social norm appeals will increase return behavior more strongly than
self-benefit appeals.
Support
Support
H4: The reduction effect of social norm appeals on return behavior
will be strengthened if the social norm refers to a relevant reference group.
N/A
Support
Importantly, the two field studies indicate not only the potential of social norms and
self-benefits to reduce product returns, but also the positive effects on retailers’
profitability, depending upon the degree of discretionary room for maneuver and latent
return propensity. Study 1 shows that social norms tend to increase net sales more
strongly than do self-benefits when discretionary room for maneuver is present. The
social norm interventions in Study 2 that focused on an adaptation of the order amount
tended to increase net sales, while significantly reducing the return rate of customers
who have a high latent return propensity. Thus, while significantly reducing returns,
social norms do not exert detrimental effects—and even tend to increase net sales for
customers with a high latent return propensity at the purchase stage. However, social
norms reduce net sales in the post-purchase stage, whereas self-benefits increase net
sales when presented after purchase (Study 1). While both appeals can increase sales of
customers with a high latent return propensity, depending on the level of discretionary
189
room for maneuver (Study 1), behavioral appeals seem to exert detrimental reactance
effects, which reduce sales for customers with a low return propensity (Study 2).
Consequently, by examining the pre- and post-purchase stages and the various customer
segments according to their return propensity, both field experiments identify the
conditions in which behavioral appeals can decrease costs for product returns while
stabilizing and even increasing sales. The findings also shed light on the circumstances
in which social norms and self-benefits exert detrimental effects on return and purchase
behavior.
7 Theoretical Implications
By empirically demonstrating the effects of social norms and self-benefits on
product return behavior, this research makes several theoretical contributions to the
existing literature.
It builds on work that posits the behavioral relevance of social norms (Cialdini etc.)
and self-benefits (White and Peloza 2009) by examining how social norms and selfbenefits work in the sensitive context of online shopping and product return behavior.
This extends the research that has examined the characteristics of return behavior (e.g.,
Petersen and Kumar 2009; Powers and Jack 2013; Harris 2008) and the effects of
differently designed return policies (Bonifield, Cole, and Schultz 2010; Anderson,
Hansen, and Simester 2009) without using behavioral appeals (e.g., White and Simpson
2013) to influence customer return behavior.
Importantly, we found that both self-benefit and social norm appeals can induce
desirable behavior in a challenging context, where customers are used to returning
products and where returning thus represents a social norm (King, Dennis, and Wright
2008; The Economist 2013), whereas retailers’ leniency already provides a self-benefit
(Wood 2001). Using self-benefits and social norms, instead, to change return behavior
extends the studies that examined normative influence in contexts where the social norm
is uncontested, such as preventing littering (Cialdini, Reno, and Kallgren 1990), paying
taxes on time (Martin 2012), using towels more than once in a hotel (Goldstein, Cialdini,
and Griskevicius 2008), or being silent in libraries (Aarts and Dijksterhuis 2003).
Moreover, although research has stressed self-benefit appeals and social norms to
encourage consumers to engage in certain behaviors, very few studies have compared
both within the same study (exceptions include White and Simpson [2013] and Nolan
190
et al. [2008]). We do so by using social norm and self-benefit appeals in two field
experiments, and comparing their behavioral effects on return and purchase behavior.
Importantly, we examine at which stages of the purchase process and for
whichcustomer segments self-benefits and social norms will be more or less effective in
reducing product returns. We propose and demonstrate that, for customers with a high
latent return propensity, social norms reduce product returns and increase net sales more
strongly than self-benefits (in Studies 1 and 2). In contrast, Study 2 indicates that social
norms can be detrimental to reducing product returns for customers with a low latent
return propensity. The reduction in product returns of customers with a latent return
propensity through social norms appears to be a counterintuitive finding that extends the
extant literature on return behavior assessing latent return propensity as a driver of
product returns (Harris 2008; King, Dennis, and Wright 2008). Moreover, the
detrimental effect of social norms on customers with a low latent return propensity adds
to the scarce literature that shows the boomerang effects of social norms for consumers
solely in the domain of energy consumption (Schultz et al. 2007).
Furthermore, besides examining a moderator that represents the attitudes and
characteristics of returning consumers, we highlight a situational moderator that
influences the effects of the appeals: discretionary room for maneuver. Across the two
field studies, we demonstrate that social norms reduce product returns for customers
who have a latent return propensity only if discretionary room for maneuver is present
(i.e., at the purchase stage); when discretionary room for maneuver is absent, social
norms increase product returns, in contrast to self-benefits (in Study 1). This result
extends previous research suggesting that discretionary room for maneuver provides an
incentive to premeditate product returns (Heiman, McWilliams, and Zilberman 2001;
Powers and Jack 2013) by showing that discretionary room for maneuver can also
represent a condition where social norms are especially effective in preventing product
returns.
The examination of characteristic (i.e., latent return propensity) and situational (i.e.,
discretionary room for maneuver) moderators identifies important boundary conditions
for the behavioral effectiveness of social norms and self-benefits, providing valuable
insights into consumers’ acceptance of behavioral appeals in the context of product
returns. We thus add to the scarce and recent line of research on the moderators of
normative influence (Melnyk et al. 2013; White and Simpson 2013; Jacobson et al.
2011). Furthermore, the investigation of the moderating roles of latent return propensity
and discretionary room for maneuver extends the research on product returns examining
191
how opportunistic return behavior is characterized (Harris 2008), and which factors
drive opportunistic returns (King, Dennis, Wright 2008) by showing how opportunistic
return behavior can be influenced and changed.
Moreover, besides examining the effectiveness of social norms in reducing product
returns and identifying its boundary conditions, this research provides knowledge about
the type of social norm that is particularly effective in inducing a behavioral change in
return behavior. Study 2 suggests the effectiveness of social norms that refer to other
customers who share similar characteristics, adding to the literature on the enhanced
social influence of close others (Gino, Ayal, and Ariely 2009; Goldstein, Cialdini, and
Griskevicius 2008).
Finally, this work extends the research on product returns, most of which has
measured return intentions merely in the form of the “likelihood,” “probability,” or
“propensity” of product returns in order to estimate actual returns (Maity and Arnold
2013). Importantly, this research presents two large-scale field experiments that
investigate actual return behavior. This research also considers not only product return
behavior in isolation, but observes purchase behavior as well, which provides substantial
insights into the tradeoff between reducing product returns and maintaining sales.
8 Managerial Implications
This research addresses an important issue faced by online retailers, mail-order
companies, and all firms selling products online that will increase in significance as ecommerce expands: retail firms are continually looking for ways to reduce product
returns, especially those of opportunistic customers, who particularly jeopardize
profitability. Retailers suffering from this cost problem have adopted stricter return
policies to curb inappropriate returns, by increasing the cost (in money and effort) of
returning an item and/or reducing the time available to customers for product return
(Janakiraman and Ordóñez 2012).
However, such restrictions can also impose barriers to purchases, in that they
increase customers’ perceived risk (Wood 2001) and thereby reduce satisfaction, trust,
and purchase intentions (Anderson, Hansen, and Simester 2009). Thus, online retailers
face a dilemma between cost-efficiency and maintaining customer satisfaction and sales.
192
This research provides retailers with a new approach to dealing with cost-intensive
product returns. By experimentally examining behavioral appeals, we provide insights
into how to draft effective appeals that change the return behavior of critical customer
groups at relevant customer touchpoints. Two large-scale field experiments, conducted
in cooperation with multi-channel fashion retailers, show that these manipulations can
influence actual return behavior and thereby increase profitability (in the form of stable,
and even increased, net sales) while reducing costs for returns.
Specifically, this research highlights the conditions in which the use of behavioral
appeals such as social norms and self-benefits might be more (or less) effective in
reducing product returns and increasing the value of those customers who induce
particularly high costs for retailers.
Importantly, we found that social norms reduce the return rates of customers with a
latent return propensity more strongly than do self-benefit appeals (in Studies 1 and 2)
but increase product returns for customers who do not demonstrate a latent return
propensity (in Study 2). These results suggest that social norms could provoke a
reduction in the product returns of cost-intensive customer segments (i.e., customers
with a latent return propensity). Moreover, the significant reductions in product returns
have also led to increased net sales (Study 1), indicating that the product-return
reductions exceed the losses in sales that might occur as a result of customer order
adaptations. Hence, retailers could benefit from implementing social norm appeals in
their marketing communications directed toward customer segments that have an aboveaverage return rate. Social norms could thus prevent retailers from sanctioning or
suspending opportunistic customers, and in this way maintain earnings potential.
However, appealing normatively to customers who already behave desirably can lead to
higher product returns and lower sales (Study 2). Therefore, marketers need to ensure a
differentiated implementation of behavioral appeals, in which they present social norms
only to those segments that can be expected to engage in desirable behavioral responses.
This particularly applies to customers with a high return propensity.
In addition, the findings regarding the moderating role of discretionary room for
maneuver specify these implications for marketers. The results of Study 1 show that a
combination of discretionary room for maneuver and a social norm appeal can be
significantly effective in reducing the product returns and increasing the net sales from
customers with a latent return propensity. In an absence of discretionary room for
maneuver (i.e., after purchase), however, social norms can increase product returns.
Thus, if a retailer needs to limit choice orders or premeditated returns, an effective
193
strategy could be to present social norm appeals to return-critical customers at
touchpoints that provide discretionary room for maneuver. Specifically, online-retailers
may profit most from normative appeals if they implement the appeals at purchase
process stages when customers can still alter their choices. This could be done by
presenting social norm appeals to customers at the shopping cart view or (considering
probable reactance effects) via pop-up windows during the choice process. Moreover,
to avoid the detrimental effects of post-purchase social norm appeals, retailers could
benefit from the effectiveness of the self-benefit appeal in reducing returns and
increasing net sales when presented in the post-purchase stage (found in Study 1).
Though normative appeals can be effectively applied at touchpoints where customers
can still change their minds, self-benefits could be implemented after purchase, when
customers have received their order (e.g., via a flyer in the parcel) in order that retailers
achieve a return reduction and net sales increase.
Furthermore, the findings provide insights that can help retailers draft behavioral
appeals to influence return behavior while maintaining purchase behavior. First, the
results show that self-benefit appeals can make customers aware of advantages to
themselves, preventing them from returning after purchase without providing monetary
benefits. Although these effects were not as strong as those from social norms at the
purchase stage, they could elicit reductions in product returns as well as increases in net
sales without providing monetary incentives—which would save the company money.
Second, the studies provide concrete insights into how retailers could present social
norm information affecting return behavior. The findings indicate that peer information
highlighting the desired behavior of the majority of other customers can significantly
affect return behavior without reducing net sales, and potentially increasing them. In
addition, Study 2 provides insights into how variations in the reference group can
reinforce the behavioral effect. The findings suggest that retailers can strengthen the
effects of social norms if they refer the social norm appeals to the appropriate reference
groups—those groups with whom the targeted customer can identify. As the second
field experiment indicates, marketers could optimize the effectiveness of behavioral
appeals by referring to customers with similar taste in fashion, or who have purchased
the same product.
However, while implementing behavioral appeals adequately within the purchase
process, retailers and online sellers should not lose sight of their sales. Though social
norm interventions in the purchase stage reduce product returns, they can also reduce
sales when customers reduce their choice orders (in Study 2). Although the significant
194
reductions in product returns have exceeded the lost gross sales, resulting in higher net
sales (in Studies 1 and 2), retailers should carefully choose the categories in which they
use social norm appeals. Retailers could achieve the greatest potential from norm
appeals if they carefully assess the categories in which preventing choice orders makes
sense and produces a profit. For some high-margin products, conversely, choice orders
may even create additional sales and should not, therefore, be prevented.
Moreover, all appeals reduced the net sales from customers with a low latent return
propensity, compared with customers who received no appeals (in Study 2). As
mentioned, when intending to implement behavioral appeals, retailers must be aware of
possible boomerang effects for customers who already behave in conformity with the
behavioral appeal messages, and should therefore focus more strongly on appealing to
those customers with a high return propensity.
The design of the experimental studies and their results highlight actionable
opportunities for marketers to increase customer value by reducing product returns.
They can implement the examined appeals at crucial customer touchpoints with
reasonable effort and cost. Overall, this examination of characteristic moderators (i.e.,
latent return propensity) and situational moderators (i.e., discretionary room for
maneuver) helps marketers to identify the conditions in which the appeals will be most
successful.
9 Limitations and Directions for Further Research
The retailers that were used in both field studies describe their customers as selfconfident, middle-aged to elderly women in middle to high income brackets who do not
allow themselves to be told what to do. Thus, it is a tribute to the effectiveness of the
behavioral appeals (particularly the social norms) to change product return behavior that
they are effective in this sample. At the same time, the experimental studies have several
limitations that suggest avenues for further research.
First, the female sample in both field experiments indicates the value of running
additional field experiments with female and male online shoppers in order to replicate
the effects for both sexes.
Second, this research focuses on product returns in the fashion sector, which
constitutes a relevant part of overall e-commerce, where product returns represent a
195
particularly important cost driver (Foscht et al. 2013). Future research should consider
experimental data across several different retailers with varying customers and products.
Doing so will provide valuable insights into the optimal use of social norm and selfbenefit appeals for different types of e-commerce sectors (e.g., electronics vs. apparel
vs. sports equipment), different stores (e.g., catalog vs. online store), and different
customer characteristics (Petersen and Kumar 2009; Anderson, Hansen, and Simester
2009).
Third, we manipulated social norm and self-benefit appeals in an environmental
context. Future research could extend the context and/or the appeals. For instance, in
addition to an environmental context, the manipulation of the appeals could involve a
competition or cost information (e.g., high product returns will force us to raise prices).
Future research could also test related appeal types such as other-benefits (White and
Peloza 2009) or vary the existing appeals. For instance, while we focus on a nonmonetary self-benefit (increasing life quality), future research could compare the effects
of a non-monetary self-benefit appeal to those of a monetary one.
Fourth, researchers could examine additional touchpoints at which the appeal type
is communicated. Beside the shopping cart view and the flyer in the parcel, studies could
consider and contrast further means and touchpoints in the purchase and post-purchase
stages, such as pop-up windows, or order confirmation emails. Moreover, future
research could examine the effects of the appeals when customers receive them at all
touchpoints.
Finally, we collected data in Austria, Switzerland, and Germany. Previous research
has shown that social norms are particularly effective in affecting behavior in
collectivistic cultures that give greater priority to the maintenance of group harmony
(Cialdini and Wosinska 1999). Thus, social norm appeals likely influence return
behavior even more strongly in cultures that are more collectivistic, as in East Asia.
Experimental studies in cooperation with online retailers operating in East Asian
markets could shed more light on how social norms affect product return behavior
differently across cultures.
196
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Appendix A: Self-benefit appeal in Study 1
For the good of the environment
With the avoidance of product returns, you actively reduce transport emissions and
material consumption for you own benefit! You can support the environment and your
own life quality by ordering a product only in one size/color and trying your best to
avoid returns.
205
Appendix B: Social norm appeal in Study 1
For the good of the environment
83% of the [company name] customers avoid product returns and thereby actively
reduce transport emissions and material consumption. You can join your fellow
customers in supporting the environment by ordering a product only in one size/color
and trying your best to avoid returns.
206
Appendix C: Control appeal in Study 1
For the good of the environment
With the avoidance of product returns, you actively reduce transport emissions and
material consumption!
You can support the environment by ordering a product only in one size/color and trying
your best to avoid returns.
207
Appendix D: Social norm appeal in Study 2
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Appendix E: Social norm appeal in Study 2 when clicking “More”
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Appendix F: Wording of “More” appeals in Study 2
Appeal type
Manipulation
Social norm
Avoiding returns for the good of the environment – Join your fellow customers!
Please keep in mind: Each return pollutes the environment through additional transport routes and
material expenses.
83% of our customers decide on one size/color and thereby avoid returns.
You can join your fellow customers to help reduce transport emissions by ordering items in only one
size/color and trying your best to avoid returns.
You are welcome to return products any time. But please join the other [company name] customers in
supporting the environment!
Thank you very much
Your [company name] team
Self norm
“product”
Avoiding returns for the good of the environment – Join your fellow customers with the same
product choice!
Please keep in mind: Each return pollutes the environment through additional transport routes and
material expenses.
83% of our customers with the same product choice have decided on one size/color and thereby avoid
returns.
You can join your fellow customers with similar taste to help reduce transport emissions by ordering
items in only one size/color and trying your best to avoid returns.
You are welcome to return products any time. But please join the other [company name] customers in
supporting the environment!
Thank you very much
Your [company name] team
Social norm
“taste”
Avoiding returns for the good of the environment – Join your fellow customers with a similar
fashion taste!
Please keep in mind: Each return pollutes the environment through additional transport routes and
material expenses.
83% of our customers who viewed the same products have decided on one size/color and thereby avoid
returns.
You can join your fellow customers with similar taste to help reduce transport emissions by ordering
items in only one size/color and trying your best to avoid returns.
You are welcome to return products any time. But please join the other [company name] customers in
supporting the environment!
Thank you very much
Your [company name] team
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Self benefit
Avoiding returns for the good of the environment and to your own advantage!
Please keep in mind: Each return pollutes the environment through additional transport routes and
material expenses.
With the decision on one size/color, you avoid returns, to your own benefit.
You can reduce transport emissions and thereby do something for your own life quality by ordering
items in only one size/color and trying your best to avoid returns.
You are welcome to return products any time. But please support the environment – to your own benefit!
Thank you very much
Your [company name] team
Control
Avoiding returns for the good of the environment!
Please keep in mind: Each return pollutes the environment through additional transport routes and
material expenses.
With the decision on one size/color, you avoid returns.
You can reduce transport emissions by ordering items in only one size/color and trying your best to
avoid returns.
You are welcome to return products any time. But please support the environment!
Thank you very much
Your [company name] team
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CURRICULUM VITAE
Name
Thilo Pfrang
Date of Birth
November 6th, 1984
Place of Birth
Karlsruhe
Education
since 04/2011
University of St.Gallen, St.Gallen, Switzerland
Ph.D. Student in Marketing
06/2015 – 08/2015
University of Michigan, Dearborn, USA
Visiting Scholar (faculty sponsor: Prof. Dr. Philipp
Rauschnabel)
10/2008 - 03/2011
Georg-August-University Göttingen, Göttingen, Germany
Master of Science in Marketing and Distribution
Management
09/2010 - 03/2011
Universidad Autónoma de Madrid, Madrid, Spain
Exchange Semester
10/2005 - 09/2008
Cooperative State University, Karlsruhe, Germany
Diplom-Betriebswirt: Business Administration
09/1995- 07/2004
Goethe Gymnasium Gaggenau, Gaggenau, Germany
Diploma: German Abitur, University Entrance Degree
Professional Experience
03/2010 - 04/2010
Daimler AG, Wörth, Germany
Internship
10/2009 - 04/2010
YouGov Germany AG, Cologne, Germany
Applied research project
08/2009 –09/2009
Glatfelter GmbH & Co KG, Gernsbach, Germany
Internship
10/2005 – 09/2008
PROTEKTOR GmbH & Co. KG, Gaggenau, Germany
Trainee and degree candidate
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