Innovation Perception from a Customer Perspective

Transcrição

Innovation Perception from a Customer Perspective
Recognition, Assessment, and Comprehension of Innovations
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
Dennis Vogt
from
Germany
Approved on the application of
Prof. Dr. Torsten Tomczak
and
Prof. Dr. Sven Henkel
Dissertation no. 4189
Rosch-Buch, Schesslitz 2013
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, May 17, 2013
The President:
Prof. Dr. Thomas Bieger
Für Ines und Thomas
Vorwort
Die Promotion an der Universität St. Gallen stellt für mich eine ganz besondere
Erfahrung dar. Die vorliegende Dissertation ist das Ergebnis dieser unglaublich
spannenden und lehrreichen Zeit. Gerne möchte ich denjenigen Menschen danken, die
mich hierbei unterstützt haben.
In erster Linie möchte ich meinem Doktorvater, Prof. Dr. Torsten Tomczak danken.
Seine persönliche und fachliche Unterstützung haben das Gelingen dieser Dissertation
überhaupt erst möglich gemacht. Weiterhin möchte ich meinem Ko-Referenten Prof.
Dr. Sven Henkel danken, der mich als akademischer Mentor beim Entstehen dieser
Arbeit intensiv begleitet hat. Die konstruktiven Diskussionen mit ihm waren eine
grosse Bereicherung.
Dank möchte ich auch meinen Kollegen an der Forschungsstelle für Customer Insight
aussprechen: Simon Brösamle, Fabian Heuschele, Dr. Christian Hildebrand, Philipp
Scharfenberger und Miriam van Tilburg. Ganz besonderen Dank möchte ich in diesem
Zusammenhang Prof. Dr. Wibke Heidig aussprechen, die stets ein offenes Ohr für
mich hatte und mir jederzeit mit Rat und Tat zur Seite stand.
Spezieller Dank geht auch an meine Freundin, Paulina Jacob. Sie war in den
vergangenen Jahren immer für mich da und hat mir Rückhalt gegeben, wenn es
schwierig wurde. Ich bin sehr froh, einen so besonderen Menschen gefunden zu haben.
Ihre optimistische Lebenseinstellung hat mir in vielerlei Hinsicht die Augen geöffnet.
Der größte Dank gebührt meiner Mutter, Ines Vogt und meinem Vater, Dr. Thomas
Vogt. Ihr unerschütterlicher Glaube in meine Fähigkeiten, ihre grenzenlose
Unterstützung und ihre unermessliche Liebe haben mir immer wieder Kraft gegeben,
meine Ziele zu verfolgen und meine Träume zu verwirklichen. Ich bin sehr stolz, diese
beiden besonderen Menschen als Eltern zu haben. Ihnen widme ich diese Arbeit.
St. Gallen, im Mai 2013
Dennis Vogt
V
Abstract
Every year, firms invest billions of dollars in the creation of innovations with the aim
of conquering new markets and securing future revenue streams. Even though most
innovations are technically and functionally superior to existing practice, the majority
fails in the market. This is surprising, given that the world’s best minds stand behind
many of these failed innovations. The reason for this phenomenon stems from the fact
that, at the end of the day, customers decide whether an innovation succeeds or not.
However, experts and customers view innovations different: while experts are
primarily concerned with technical and functional aspects of innovations, customers
are driven by the superior experiences innovations provide. Thus, in order to turn a
greater number of innovations into a success, it is necessary to delve deep into the
minds of customers and develop a comprehensive understanding of how they form
perceptions of innovations, and how they decide whether to accept them or not.
This dissertation addresses this issue by providing a comprehensive investigation of
customer perception of innovations. First of all, the term innovation is defined in terms
of the customer’s perspective, and key dimensions of innovation perception are
derived. Thereby, it is also examined how these dimensions influence the market
success of innovations. Secondly, an extensive literature review is undertaken in which
core processes of innovation perception are identified and further analyzed. Two
processes of innovation perception are distinguished: innovation recognition and
innovation assessment. Innovation recognition refers to initial interactions with an
innovation, in which customers become aware of the innovation, and develop an initial
understanding of what the innovation is and does. The purpose of innovation
recognition is to encourage interest in the innovation, and motivate customers to
collect further information about it. This is where innovation assessment, the second
process of innovation perception, begins. During the innovation assessment process,
customers elaborate on the personal consequences an innovation has on their everyday
lives. The innovation assessment process culminates with customers’ accepting or
rejecting an innovation. Thirdly, this dissertation introduces innovation comprehension
as a previously neglected aspect of innovation perception. While innovation
recognition and innovation assessment are concerned with ‘what’ customers think
about an innovation, innovation comprehension details ‘how’ customers think about an
innovation. Generally, customers can think about an innovation in a creative, more
abstract manner, or in an analytic, more detail-oriented manner. These different ways
of thinking have the potential to profoundly change the perception of innovations.
VI
Zusammenfassung
Jedes Jahr investieren Unternehmen Milliarden von Dollar in die Entwicklung von
Innovationen, mit dem Ziel neue Märkte zu erschliessen und zukünftige Umsätze zu
sichern. Obwohl die meisten dieser Innovationen bestehenden Produkten technisch
und funktionsmässig deutlich überlegen sind, scheitert die Mehrheit von ihnen am
Markt. Dies ist überraschend, wurden sie doch von den klügsten Köpfen der Welt
entwickelt. Der Grund für das Scheitern der meisten Innovationen liegt darin
begründet, dass am Ende des Tages der Kunde über den Erfolg oder Misserfolg von
Innovationen entscheidet. Experten und Kunden haben jedoch eine sehr
unterschiedliche Sicht auf Innovationen. Während Experten ihr Augenmerk vor allem
auf technische und funktionale Aspekte legen, beschäftigen sich Kunden in erster
Linie mit den positiven oder negativen Erlebnissen, die Innovationen bieten. Um also
die Erfolgsaussichten von Innovationen zu steigern, ist es notwendig tief in die Psyche
der Kunden einzutauchen und ein fundiertes Verständnis dafür zu entwickeln, wie
genau Eindrücke von Innovationen entstehen und warum sich Kunden schliesslich für
oder gegen eine bestimmte Innovation entscheiden.
Hier setzt die vorliegende Dissertation an und führt eine umfassende Untersuchung der
Wahrnehmung von Innovationen durch. Erstens wird der Begriff Innovation aus
Kundensicht definiert und wesentliche Dimensionen der Wahrnehmung von
Innovationen abgeleitet. Dabei wird auch aufgezeigt, inwiefern diese Dimensionen auf
den Markterfolg von Innovationen einwirken. Zweitens werden zentrale Prozesse der
Innovationswahrnehmung identifiziert und analysiert. Zwei Prozesse werden
unterschieden: Innovation Recognition und Innovation Assessment. Bei Innovation
Recognition geht es darum, das Interesse für eine Innovation zu wecken. Kunden
sollten sich also einer Innovation bewusst werden und verstehen, worum es sich dabei
handelt. Darauf aufbauend setzt Innovation Assessment an. Im Rahmen von
Innovation Assessment setzen sich Kunden intensiv mit einer Innovation auseinander
und treffen eine Entscheidung für oder gegen die Innovation. Drittens wird im Rahmen
dieser Dissertation Innovation Comprehension, ein bislang vernachlässigter Aspekt
von Innovationswahrnehmung beleuchtet. Während sich Innovation Recognition und
Innovation Assessment darauf beziehen, was Kunden über Innovationen denken,
bezieht sich Innovation Comprehension auf die Art und Weise, mit der Kunden über
Innovationen nachdenken. Kunden können sich grundsätzlich auf kreative Weise oder
auf analytische Weise mit Dingen auseinandersetzen. Diese Denkweisen haben
massgeblichen Einfluss darauf, wie sie Innovationen wahrnehmen.
VII
Table of Contents
Abstract .......................................................................................................................... V
Zusammenfassung ........................................................................................................ VI
Table of Contents ........................................................................................................ VII
List of Tables ................................................................................................................ XI
List of Figures ............................................................................................................. XII
1 Introduction ............................................................................................................. 1
1.1 Problem Orientation ........................................................................................... 1
1.2 Research Questions and Structure of the Dissertation ....................................... 4
2 Conceptual Background of Innovation Perception ............................................. 7
2.1 Defining the Innovation Construct ..................................................................... 7
2.2 Determinants of Innovation Perception.............................................................. 8
2.2.1 Perception of Newness of Innovations......................................................... 9
2.2.1.1 Newness as an Experience ..................................................................... 9
2.2.1.2 Classification of Innovations based on Perceived Newness ................ 10
2.2.2 Perception of Meaningfulness of Innovations ........................................... 12
2.2.2.1 Perceived Desirability of Innovations .................................................. 13
2.2.2.2 Perceived Feasibility of Innovations .................................................... 15
2.2.3 Determinants of Innovation Perception and Market Success .................... 15
3 Processes of Innovation Perception ..................................................................... 17
3.1 Processes-Oriented View of Innovation Perception ......................................... 17
3.2 Innovation Recognition .................................................................................... 19
3.2.1 Identification of Innovations ...................................................................... 19
3.2.1.1 Formation of Awareness of Innovations .............................................. 19
3.2.1.2 Identification of Category Membership of Innovations ....................... 22
3.2.2 Learning of Innovations ............................................................................. 25
3.2.2.1 Customer Knowledge Transfer ............................................................ 25
3.2.2.2 Encouragement of Customer Knowledge Transfer .............................. 30
3.2.2.3 Single vs. Multiple Category Inferencing ............................................ 31
3.2.3 Perceived Differentiation of Innovations ................................................... 33
3.2.3.1 Feature Based Differentiation of Innovations ...................................... 33
VIII
3.2.3.2 Perception of Utilitarian versus Hedonic Differentiation..................... 36
3.2.3.3 Assimilation/Contrast Effects in Innovation Perception ...................... 38
3.2.4 Perceived Incongruity of Innovations ........................................................ 41
3.2.4.1 Perceived Incongruity and the Moderate Incongruity Effect ............... 41
3.2.4.2 Coping with Extreme Levels of Incongruity ........................................ 45
3.3 Innovation Assessment ................................................................................... 47
3.3.1 Determinants of Intention and Behavior .................................................... 47
3.3.1.1 Model of Reasoned Action ................................................................... 47
3.3.1.2 Formation of Intentions towards Behavior ........................................... 49
3.3.2 Determinants of Innovation Acceptance .................................................... 52
3.3.2.1 Performance Expectancy ...................................................................... 53
3.3.2.2
3.3.2.3
3.3.2.4
3.3.2.5
3.3.2.6
3.3.2.7
Effort expectancy ................................................................................. 55
Social Influence .................................................................................... 56
Facilitating Conditions ......................................................................... 58
Hedonic Motivation .............................................................................. 61
Price Value ........................................................................................... 64
Habit ..................................................................................................... 64
3.3.3 Risk Associated with Innovation Acceptance ............................................ 66
3.3.3.1 Types of Risks associated with Innovation Acceptance ...................... 66
3.3.3.2 Regulatory Focus and Risk Perception ................................................ 68
3.3.3.3 Information Acquisition and Risk Perception ...................................... 69
3.3.4 Formation of Mental Scenarios about Innovations .................................... 73
3.3.4.1 The Simulation Heuristic and Innovation Perception .......................... 73
3.3.4.2 Mental Simulation and Innovation Perception ..................................... 74
3.3.4.3 Difficulty of Imagination and Innovation Perception .......................... 77
3.3.5 Resolution of Trade-Offs in Evaluation of Innovations ............................. 78
3.3.5.1 Resolution of Trade-Offs between Capability and Usability ............... 78
3.3.5.2 Resolution of Trade-Offs between Functional and Hedonic Benefits . 79
3.3.6 Expectations about Usage of Innovations .................................................. 81
3.3.6.1 Formation of Expectations about Innovations...................................... 81
3.3.6.2 (Dis)Confirmation of Expectations about Innovations ........................ 81
3.3.6.3 Insight and Discontinuous Learning in Innovation Usage ................... 83
IX
4 Innovation Comprehension .................................................................................. 86
4.1 Introduction to Innovation Comprehension ..................................................... 86
4.2 Conceptual Development Innovation Comprehension .................................... 89
4.2.1 Specification of Global/Local Processing .................................................. 89
4.2.1.1 Distinguishing Global and Local Processing ....................................... 89
4.2.1.2 Inducing Global and Local Processing ................................................. 90
4.2.2 Implications of Global/Local Processing for Innovation Perception ......... 91
4.2.2.1 Assimilation/Contrast Effects............................................................... 92
4.2.2.2 Creative and Analytical Problem Solving Performance....................... 93
4.3 Experimental Analysis Innovation Comprehension ................................... 96
4.3.1 Overview over the Experimental Studies ................................................... 96
4.3.2 Experiment 1 .............................................................................................. 98
4.3.2.1
4.3.2.2
4.3.2.3
4.3.2.4
4.3.2.5
4.3.2.6
Hypothesis Development ..................................................................... 98
Design, Participants, and Procedure ..................................................... 99
Manipulation of Independent Variables ............................................... 99
Selection of Measures ........................................................................ 101
Results ................................................................................................ 102
Discussion .......................................................................................... 105
4.3.3 Experiment 2 ............................................................................................ 106
4.3.3.1 Hypothesis Development ................................................................... 106
4.3.3.2 Design, Participants, and Procedure ................................................... 107
4.3.3.3 Manipulation of Independent Variables ............................................. 107
4.3.3.4 Selection of Measures ........................................................................ 109
4.3.3.5 Results ................................................................................................ 110
4.3.3.6 Discussion .......................................................................................... 114
4.3.4 Experiment 3 ............................................................................................ 115
4.3.4.1 Hypothesis Development ................................................................... 115
4.3.4.2 Design, Participants, and Procedure ................................................... 116
4.3.4.3 Manipulation of Independent Variables ............................................. 117
4.3.4.4 Selection of Measures ........................................................................ 119
4.3.4.5 Results ................................................................................................ 120
4.3.4.6 Discussion .......................................................................................... 123
4.4 General Discussion ....................................................................................... 125
4.4.1 Summary of Results ................................................................................. 125
4.4.2 Theoretical Contributions ........................................................................ 126
X
4.4.3 Managerial Contributions ........................................................................ 126
4.4.4 Limitations ............................................................................................... 128
4.4.5 Future Research........................................................................................ 129
5 Conclusion ........................................................................................................... 131
6 References ............................................................................................................ 134
7 Appendices ........................................................................................................... 157
7.1 Appendix 1: Navon-Letters used in Experiment 1 ......................................... 157
XI
List of Tables
Table 2-1: Types of Customer Benefits ....................................................................... 14
Table 3-1: Congruent and Incongruent Additions of Functionalities (Gill, 2008) ...... 37
Table 3-2: Core Constructs UTAUT (Venkatesh et al., 2012) .................................... 53
Table 3-3: Performance Expectancy Scale (Venkatesh et al., 2012) ........................... 54
Table 3-4: Effort Expectancy Scale (Venkatesh et al., 2012) ...................................... 55
Table 3-5: Social Influence Scale (Venkatesh et al., 2012) ......................................... 57
Table 3-6: Facilitating Conditions Scale (Venkatesh et al., 2012) .............................. 59
Table 3-7: Hedonic Motivation (Venkatesh et al., 2012) ............................................ 62
Table 3-8: Price Value Scale (Venkatesh et al., 2012) ................................................ 64
Table 3-9: Habit Scale (Venkatesh et al., 2012) .......................................................... 65
Table 3-10: Extrabrand Attributes for Selected Innovations (Boyd & Mason, 1999) . 72
Table 3-11: Types of Process and Outcome Simulation (Zhao et al., 2011) ............... 75
Table 4-1: Measures Employed in Experiment 1 ...................................................... 102
Table 4-2: Results of the ANOVAs in Experiment 1 ................................................ 105
Table 4-3: Mean Values for the Dependent Variables in Experiment 1 .................... 105
Table 4-10: Primes of Global/Local Processing (Förster, 2012) ............................... 127
XII
List of Figures
Figure 1-1: Structure of the Dissertation ....................................................................... 6
Figure 2-1: Types of Innovations (Chandy & Tellis, 1998, 2000) .............................. 12
Figure 3-1: Car Segments and Associated Prototypes (Landwehr et al. 2011) ........... 24
Figure 3-2: The Knowledge Transfer Process (Gregan-Paxton et al., 2002) .............. 26
Figure 3-3: Model of Knowledge Transfer (Gregan-Paxton & Roedder John, 1997). 29
Figure 3-4: Congruent and Incongruent Functionality Additions (Gill, 2008) ........... 38
Figure 3-5: Comparisons and Innovation Evaluation (Ziamou & Ratneshwar, 2003) 40
Figure 3-6: The Moderate Incongruity Effect (Meyers-Levy & Tybout, 1989).......... 43
Figure 3-7: Perception of Product Incongruity (Jhang et al., 2012) ............................ 44
Figure 3-8: Model of Reasoned Action (Ajzen, 1991) ................................................ 49
Figure 3-9: Determinants of Behavior and Associated Beliefs ................................... 50
Figure 3-10: Basic Concept of Innovation Acceptance (Venkatesh et al., 2003)........ 52
Figure 3-11: The Affect Circumplex (Armstrong & Detweiler-Bedell, 2008) ........... 63
Figure 3-12: Expectations and Innovation Evaluation (Wood & Moreau, 2006) ....... 82
Figure 3-13: Learning to Use Innovations (Lakshmanan & Krishnan, 2011) ............. 84
Figure 4-1: Broad vs. Narrow Categorization Primes (Ulkumen et al., 2010) ............ 87
Figure 4-2: Example Item of the Kimchi-Figures task (Kimchi & Palmer, 1982) ...... 90
Figure 4-3: Sample-Item Navon-Letter task ................................................................ 91
Figure 4-4: Overview over the Experimental Studies ................................................. 97
Figure 4-5: Manipulation of Innovations in Experiment 1 ........................................ 100
Figure 4-6: Example of a Navon-Letter (Prime for Processing Style) ...................... 101
Figure 4-7: Product Evaluation Interaction (Experiment 1) ...................................... 103
Figure 4-8: Purchase Intention Interaction (Experiment 1) ....................................... 104
Figure 4-9: Manipulation of Innovations in Experiment 2 ........................................ 108
Figure 4-10: Product Evaluation Interaction (Experiment 2) .................................... 111
Figure 4-11: Purchase Intention Interaction (Experiment 2) ..................................... 112
Figure 4-12: Usefulness Interaction (Experiment 2) ................................................. 113
Figure 4-13: Manipulation of Innovations in Experiment 3 ...................................... 118
Figure 4-14: Product Evaluation Interaction (Experiment 3) .................................... 121
Figure 4-15: Purchase Intention Interaction (Experiment 3) ..................................... 122
1
1 Introduction
1.1 Problem Orientation
If a man can write a better book, preach a better
sermon, or make a better mousetrap than his
neighbor, though he builds his house in the woods,
the world will make a beaten path to his door.
Ralph Waldo Emerson (1803-1882)
This quotation from the iconic American essayist, lecturer, and poet, Ralph Waldo
Emerson, implies that an innovation which improves existing practice will inevitably
enjoy success. However, this happy picture does not seem to hold true in today’s
fiercely competitive marketplace (Chiesa & Frattini, 2011). Every year, firms right
across the spectrum of industry spend billions of dollars on their research and
development activities, with the purpose of creating successful innovations (Henard &
Dacin, 2010). However, despite being technically and functionally superior to existing
alternatives, a large number of these innovations fail (Chiesa & Frattini, 2011;
Cierpicki, Wright, & Sharp, 2000; Wilke & Sorvillo, 2005). In only a few cases do
firms successfully commercialize their innovations (Gourville, 2006; Hartley, 2005;
Schilling, 2005), thereby generating promising new revenue streams as a basis for
future growth and profitability (Danneels & Kleinschmidt, 2001).
First and foremost, it is the customer who decides whether an innovation ultimately
succeeds. Only if a customer becomes aware of an innovation and perceives it as
attractive will he or she respond favorably to it. However, a customer-centric
perspective on innovation represents a considerable challenge (Kunz, Schmitt, &
Meyer, 2011). Most firms have an expert-based view, which is diametrically opposed
to the way that customers perceive an innovation (Rogers & Shoemaker, 1971). While
experts take a technical or functional perspective, customers think about an innovation
primarily in terms of the experiences it provides (Danneels & Kleinschmidt, 2001).
Specifically, experiences such as ease, comfort, and safety represent important
determinants of the success of many innovations (Schmitt, 2003). Take the car
industry for example: innovations such as power steering, air-conditioning, and the
antilock-breaking system enjoyed success because they provided these positive
experiences to customers.
2
Despite repeated calls for the development of a broad-based customer-centric approach
to innovation perception (Danneels & Kleinschmidt, 2001; Rogers & Shoemaker,
1971), such an approach remains largely absent from the literature (Kunz et al., 2011).
During the last decades different research streams have emerged, with each of them
addressing a different aspect of innovation perception. Numerous lines of research
have investigated when customers take notice of an innovation and how they learn
what the innovation does. For example, various researchers have investigated the way
with which customers compare an innovation to familiar concepts stored in memory
(e.g., Jhang, Grant, & Campbell, 2012; Mandler, 1982; Meyers-Levy & Tybout, 1989;
Zheng Zhou & Nakamoto, 2007). Such categorization processes were found to play an
important role in determining the nature of an innovation and how it differs from
existing alternatives (Selinger, Dahl, & Moreau, 2006). Going a step further, different
researchers have also investigated how customers transfer prior knowledge that they
have gleaned in other domains in order to develop an initial understanding of an
innovation (e.g., El Houssi, Morel, & Hultink, 2005; Goode, Dahl, & Moreau, 2010;
Gregan-Paxton, Hibbard, Brunel, & Azar, 2002; Gregan-Paxton & Roedder John,
1997; Moreau, Markman, & Lehmann, 2001; Roehm & Sternthal, 2001). When
customers first encounter an innovation like a personal digital assistant, for example,
their understanding of the device can be significantly enriched by drawing on
completely different, yet comparable, concepts such as the office secretary. That is,
similar to a secretary, a personal digital assistant organizes one’s appointments,
informs one of when a friend or colleague celebrates his or her birthday, and provides
relevant contact details when one wishes to make a call (Gregan-Paxton et al., 2002).
Further, various research streams have addressed how customers cope with the
potential consequences of innovation adoption (e.g., Kulviwat, Bruner, Kumar, Nasco,
& Clark, 2007; Rogers, 2003; Venkatesh, Thong, & Xu, 2012). When customers
evaluate an innovation, they do not only evaluate the innovation itself, but also the
consequences associated with adoption. That is, even though an innovation might
appear attractive at the first glance, a more detailed analysis may reveal that it is
difficult to use or that its use presents considerable risk to health. A variety of different
models have been proposed which attempt to explain how these and other factors
influence innovation perception (Venkatesh, Morris, Davis, & Davis, 2003). Thereby,
it was also investigated how customers learn to make use of an innovation. Such
learning represents a considerable hurdle for innovation adoption. If this process is too
confusing, customers are likely to respond very unfavorably (e.g., Lakshmanan &
Krishnan, 2011; Wood & Moreau, 2006; Ziamou, Gould, & Venkatesh, 2012).
3
Finally, researchers have recently begun to investigate how innovation perception is
influenced by the way in which customers think about an innovation (e.g., Förster &
Dannenberg, 2010; Förster, Liberman, & Shapira, 2009; Förster, Marguc, &
Gillebaart, 2010). Literature refers to these ways of thinking as processing styles. Two
different processing styles are distinguished: global processing and local processing
(Förster, 2012). As an old proverb says, people can either attend to the forest or the
trees when looking at a stimulus. When they look at the forest, they are in global
processing, when they look at the trees, they are in local processing. Global and local
processing represent two different ways of thinking that profoundly change the way in
which one perceives the world (Förster & Dannenberg, 2010). Recent findings indicate
that processing styles may also significantly influence customer perception of
innovation. An individual is likely to perceive the same innovation completely
different depending on his or her current processing style (Förster, Liberman, et al.,
2009; Förster et al., 2010; Ulkumen, Chakravarti, & Morwitz, 2010).
This prior discussion underlines the importance of understanding of how customers
perceive innovations. To date, a wide range of literature streams have examined
individual aspects of innovation perception, with each of them taking a different
perspective. Thereby, the various research results have not yet been integrated into a
comprehensive view of innovation perception. Only if different aspects of innovation
perception are captured holistically is it possible to design and communicate
innovations so that they are broadly accepted in the marketplace.
4
1.2 Research Questions and Structure of the Dissertation
The main purpose of this dissertation is to provide a comprehensive analysis of
different aspects of innovation perception. Key customer perception processes are
examined, and it is investigated how these processes may be influenced to encourage
favorable responses to a given innovation. Based on an extensive literature review, two
core processes of innovation perception are identified and further specified. One of
these processes concerns innovation recognition. Prior to the evaluation of an
innovation, customers need to become aware of its existence. Consequently, a
customer’s first interaction with an innovation should provide an initial understanding
of what that innovation does. This first interaction should also motivate the customer
to further experiment with the innovation. That is, an innovation should pique a
customer’s interest rather than confusing him or her. The second innovation perception
process analyzed in the literature review concerns innovation assessment. This process
represents an intensive examination of an innovation in which customers make
inferences about its consequences. That is, customers ask themselves how well an
innovation satisfies specific needs, and whether the fulfillment of these needs is
associated with reasonable sacrifices. If these sacrifices are too high, customers are
likely to reject an innovation, even if it provides considerable benefits.
Building on the literature review, innovation comprehension is introduced as a new
aspect of innovation perception. Past research has tended to suggest that, for a specific
individual, innovation perception always follows the same pattern. However, recent
findings have cast doubt on this hypothesis: innovation perception may also be
determined by global and local processing, which represent different ways of thinking
about everyday things (i.e. ‘looking at the forest’ vs. ‘looking at the trees’). This
dissertation investigates the influence of global/local processing on innovation
perception. More formally, the following research questions are addressed:
Research Questions 1:
What determines an innovation from a customer perspective, and how do
different dimensions of innovation perception influence market success?
Research Question 2:
What kinds of innovation perception processes can be distinguished, and
how do these processes influence the perception of innovations?
5
Research Question 3:
How
does
global/local processing
of
innovations
(i.e. innovation
comprehension) influence the perception of innovations?
The dissertation is structured as follows. Chapter 1 provides an introduction to the
topic of the dissertation and describes the research questions. Chapter 2 develops a
theoretical conceptualization of innovation perception from a customer perspective.
Furthermore, this chapter provides an overview over key determinants of innovation
perception and reveals how these influence market success of an innovation. Chapter 3
examines innovation recognition and innovation assessment as core processes of
innovation perception. Innovation recognition is the process by which customers
become aware of an innovation and gain an initial understanding of what the
innovation does. Innovation assessment is the process by which customers actively
analyze an innovation and draw inferences about the consequences of that innovation.
Chapter 4 introduces innovation comprehension as a new aspect of innovation
perception. More precisely, this part contains an empirical analysis of the influence of
global/local processing on innovation perception. Chapter 5 concludes with a summary
of the dissertation’s findings. Figure 1-1 provides an overview over this structure.
6
Figure 1-1: Structure of the Dissertation
Chapter 1
Introduction
Problem orientation of the dissertation and formulation of
research questions
pp. 1-6
Conceptual Background Innovation Perception
Chapter 2
Definition of innovation from a customer perspective
pp. 7-16
Chapter 3
Literature Analysis Processes of Innovation Perception
Analysis of innovation recognition and innovation
assessment as core processes of innovation perception
pp. 17-85
Chapter 4
Empirical Investigation Innovation Comprehension
Empirical analysis of innovation comprehension as new
aspect of innovation perception
pp. 86-130
Chapter 5
Conclusion
Summary and concluding remarks on
innovation perception
pp. 131-133
7
2 Conceptual Background of Innovation Perception
2.1 Defining the Innovation Construct
Research provides numerous definitions for the term innovation, with each of them
viewing innovation from a different perspective. Firstly, from a strategic perspective,
innovation is defined as “the initial introduction of a new product or process whose
design departs radically from past practice. It is derived from advances in science, in
its introduction makes existing knowledge in that application obsolete. It creates new
markets, supports freshly articulated user needs in the new functions it offers, and in
practice demands new channels of distribution and aftermarket support. In its wake it
leaves obsolete firms, practices, and factors of production, while creating a new
industry (Abernathy & Clark, 1985, pp. 6-7).”
Secondly, from a process-oriented perspective, innovation is defined as “an iterative
process initiated by the perception of a new market and/or service opportunity for a
technology based invention which leads to development, production, and marketing
tasks striving for commercial success of the invention (Garcia & Calantone, 2002, p.
112).” This definition emphasizes that an invention does not constitute an innovation
until it has progressed through production and marketing, and is diffused into the
marketplace. In line with this, Smith & Barfield (1996) argue that “the solution to a
basic scientific puzzle or the invention of a new product only in a laboratory setting
makes no direct economic contribution. Innovation not only includes basic and applied
research but also product development, manufacturing, marketing, distribution,
servicing, and later product adaptation and upgrading (p. 1).”
Thirdly, from a product-oriented perspective, innovation refers to discontinuities in
product benefits, technological capability, and/or consumption patterns (Danneels &
Kleinschmidt, 2001; McNally, Cavusgil, & Calantone, 2010; Veryzer, 1998a).
Changes in product benefits are based on the new capabilities an innovation provides
in terms of customer need fulfillment (Veryzer, 1998a). In other words, innovations
offer greater functionality relative to existing offerings (Ali, Krapfel, & LaBahn,
1995). Changes in technological capability are based on the degree to which an
innovation expands technological capabilities beyond existing boundaries (Veryzer,
1998a). That is, innovations improve technical performance compared to other
products (Colarelli O’Connor, 1998). Changes in consumption patterns denote the
degree to which customers need to adapt their thinking and behavior to utilise an
8
innovation (Veryzer, 1998a). Research suggests that innovations frequently involve
significant learning, as well as considerable behavioral change (McNally et al., 2010).
Finally, in a sociological context, innovation may be defined as an idea, practice, or
object that the members of a social system perceive as new. Thereby, an innovation
needs to initiate a communication process in which the members of the social system
exchange information about the innovation and develop a mutual understanding of it.
As a consequence, the innovation results in some kind of social change. That is, the
innovation alters the structure or function of a social system (Rogers, 2003).
From these different definitions it follows that innovation is not restricted only to new
products and services, but also includes any other kind of innovative output. More
precisely, innovations may manifest themselves in product and service innovations,
“design innovations (e.g., the styling of a car, the look and feel of a MP3 player, the
lifestyle services of a mobile phone service), process innovations (e.g., a new delivery
process), marketing innovations (e.g., new communication campaigns or a new web
site), and in broad based business innovations (e.g., selling directly to consumers)
(Kunz et al., 2011, p. 817).” Hence, from a customer perspective, innovation refers to
any kind of a firm’s innovative output. However, to be perceived as an innovation, this
output needs to have market impact (Henard & Dacin, 2010; Kunz et al., 2011).
Even though innovation perception may refer to all kinds of innovative outputs, this
dissertation solely examines perception of product innovations. The restriction to
product innovations is primarily done for comprehensibility reasons. However, many
of the findings may also be generalized to other kinds of a firm’s innovative outputs.
2.2 Determinants of Innovation Perception
In line with the previous discussion, research suggests that innovation perception is
determined by the extent to which an innovation is perceived as being different from
existing alternatives in a way that is meaningful to customers (Sethi, Smith, & Park,
2001). Two determinants of innovation perception are distinguished: perceived
newness and perceived meaningfulness. Newness denotes the degree to which an
innovation differs from established practice (Amabile, 1983; Andrews & Smith, 1996;
Jackson & Messick, 1965; Szymanski, Kroff, & Troy, 2007). Meaningfulness refers to
the degree to which customers perceive an innovation as desirable and feasible
(Andrews & Smith, 1996; Im & Workman, 2004; Jackson & Messick, 1965).
9
2.2.1 Perception of Newness of Innovations
2.2.1.1 Newness as an Experience
In general, research refers to newness as ‘not previously experienced’ or ‘lack of
familiarity’. Newness is typically related to different characteristics such as
unexpectedness, atypicality, obscurity, ambiguity, complexity, and uncertainty (Förster
et al., 2010). It seems that perceived newness is independent of valence. That is,
something new may be perceived as an opportunity, and evoke interest and curiosity,
or it may be perceived as a potential threat, and evoke safety concerns (Förster et al.,
2010). Loewenstein (1994), for example, argues that new things may trigger positive
and negative affect at once. On the one hand, new things represent knowledge gaps
that may be perceived as unpleasant in the first instance. However, by exploring new
things, based on a desire to know, people may experience positive affect from
developing new knowledge. Similarly, Scherer (2001) argues that the experience of
something new involves both a novelty check and a pleasantness check. A novelty
check determines whether someone further attends to a stimulus. At the same time, the
pleasantness check determines whether to avoid or approach the stimulus.
Furthermore, research suggests that perceived newness represents a subjective
experience (Radford & Bloch, 2011; Rogers, 2003) which is highly dependent on
decision context (Förster et al., 2010). That is, perceived newness is not only
determined by the objective features of a stimulus itself, but also by a number of other
variables (Förster et al., 2010). These include contextual factors such as framing,
priming, categorization, and motivational orientation (Förster, Liberman, et al., 2009).
Building on Heraclit’s well known aphorism, ‘you could never step twice in the same
river; for other waters are ever flowing onto you’, Förster et al. (2010) argue that
almost any event can be experienced as new. More precisely, one may perceive a
familiar event as new simply by adopting a different perspective on that event (Gati &
Ben-Shakhar, 1990). At the same time, it is suggested that almost anything can be
perceived as familiar, just by adopting a ‘been there, done that’ attitude. That is, when
taking a trip to a foreign country, one can refer to this experience as just another
adventure (Förster et al., 2010). This implies that perceived newness may not only
vary from individual to individual, but also from situation to situation: the same
individual may judge newness of the same stimulus in one situation as very low and in
another situation as very high.
10
2.2.1.2 Classification of Innovations based on Perceived Newness
From a customer perspective, research distinguishes two broad types of innovations:
incrementally new products and really new products (e.g., Dahl & Hoeffler, 2004;
Zhao, Hoeffler, & Dahl, 2009, 2012). Incrementally new products represent minor
changes to established ideas or concepts (Dahl & Hoeffler, 2004). Research also refers
to these innovations as evolutionary or continuous innovations, which do not seem
very new to customers (Veryzer, 1998b). Incrementally new products neither involve
dramatically new science nor provide very new benefits. They represent
improvements, upgrades, or line-extensions rather than groundbreaking departures
from existing practice (Zhao et al., 2009). Put differently, incrementally new products
are based on mere adaptations, refinements, or enhancements of established products
and/or associated systems of production and distribution (Song & Montoya-Weiss,
1998). When trying to make sense of incrementally new products, customers can draw
on prior experiences (Zhao et al., 2012). Thus, incrementally new products are easy to
understand and involve only small learning costs (Veryzer, 1998a).
In comparison, really new products are perceived as very new from a customer
perspective (Veryzer, 1998a). This perceived newness may be based on technological
newness and/or newness of customer benefits (Chandy & Tellis, 1998, 2000).
Technological newness is the degree to which an innovation involves technologies that
depart from prior technologies (Chandy & Tellis, 1998). In other words, technological
newness is based on the extent to which an innovation is based on advanced
technological capabilities (Veryzer, 1998a). Generally, a technology is defined as “a
design for instrumental action that reduces the uncertainty in the cause effect
relationship involved in achieving a desired outcome (Rogers, 2003, p. 13).” A
technology involves both a hardware aspect which refers to the tool that embodies the
technology as a material or physical object, and a software aspect which refers to the
information base for the tool (Rogers, 2003). Technological newness may be based on
changes to the core design concepts of a technological system. In such case, research
speaks of modular innovations (Henderson & Clark, 1990). An example of a modular
innovation is runflat tires that provide emergency running properties for cars. In
contrast to established tires, runflat tires either involve reinforced sidewalls or a
support ring in the rim. In addition to modular innovation, technological newness may
be based on architectural innovation. This type of innovation is the configuration of
new and/or established design concepts in a new architecture (Henderson & Clark,
1990). The engine of an electric car is an instance of architectural innovation. The
architecture of electric engines differs markedly from traditional combustion engines.
11
Newness of customer benefits denotes the degree to which an innovation fulfills
customer needs substantially better than existing alternatives (Chandy & Tellis, 1998).
A high level of newness of benefits occurs if an innovation has the “potential to
produce one or more of the following: an entirely new set of performance features,
improvements in known performance features of five times or greater, a significant
(30% or greater) reduction in cost (Leifer, McDermott, Colarelli O’Connor, G., Peters,
& Veryzer, 2000, p. 5).” More generally, innovations characterized by a high level of
newness of benefits go beyond previously recognized demand (Garcia & Calantone,
2002) and provide customers with totally different ways of doing things, sometimes
even allowing them to do things they have not been able to do before (Lehmann,
1997). Consequently, these innovations often involve a steep learning curve for
customers. That is, customers have to alter their thinking and behavior to understand
and make use of these innovations (Veryzer, 1998a). This is because customers lack
prior experience with completely new benefits and are likely to feel uncertain about
associated consumption utilities (Hoeffler, 2003).
Arising from this discussion, three types of really new products can be distinguished:
technological breakthroughs, market breakthroughs, and radically new products.
Technological breakthroughs are innovations that “adopt a substantially different
technology than existing alternatives but do not provide new customer benefits
(Chandy & Tellis, 1998, p. 476).” Research also refers to technological breakthroughs
as technologically discontinuous innovations (Veryzer, 1998b). Market breakthroughs
denote innovations that “are based on core technology that is similar to existing
alternatives but provides substantially higher customer benefits (Chandy & Tellis,
1998, p. 476).” Research also refers to market breakthroughs as commercially
discontinuous innovations (Veryzer, 1998b). Radically new products refer to
innovations “that involve substantially new technology and provide completely new
customer benefits (Chandy & Tellis, 1998, p. 476).” Research also refers to these
innovations as revolutionary innovations (Veryzer, 1998b).
Figure 2-1 summarizes the different types of innovations. These include:
incrementally new products characterized by low levels of technological newness and
newness of customer benefits. Conversely, really new products refer to innovations
with a high level of technological newness and/or a high level of newness of customer
benefits. Three sub-types of really new products are distinguished: (1) technological
breakthroughs characterized by a high level of technological newness and a low level
of newness of customer benefits; (2) market breakthroughs characterized by a low
level of technological newness and a high level of newness of customer benefits; and
12
(3) radically new products characterized by a high level of technological newness and
a high level of newness of customer benefits.
Figure 2-1: Types of Innovations (Chandy & Tellis, 1998, 2000)
Technological Newness
Newness of Benefits
Low
High
Low
Incrementally New
Products
Technological
Breakthroughs
High
Market
Breakthroughs
Radically New
Products
2.2.2 Perception of Meaningfulness of Innovations
According to its formal definition, perceived meaningfulness comprises the
desirability of an innovation as well as its feasibility (Arts, Frambach, & Bijmolt,
2011; Trope & Liberman, 2003). The higher an innovation scores in these dimensions,
the higher is its perceived meaningfulness and the more favorable is customers’
response. However, if customers perceive a lack of desirability and/or feasibility, they
won’t regard an innovation as meaningful and reject it (Rogers, 2003). An example for
such a case represents the Dvorak keyboard which was introduced in the 1930s as an
alternative to the conventional QWERTY keyboard. Even though the Dvorak
keyboard increased efficiency of typewriting, typists rejected this innovation. More
precisely, the Dvorak keyboard required typists to learn a new way of typing. From the
perspective of typists, the effort associated with this learning overweighed the
perceived benefits of the Dvorak keyboard. As a consequence, typists did not perceive
it as meaningful and responded unfavorably to this innovation (Rogers, 2003).
13
2.2.2.1 Perceived Desirability of Innovations
The desirability of an innovation refers to the benefits that arise when putting an
innovation into use. It is based on the specific advantages an innovation provides over
existing alternatives (Sethi et al., 2001). Research distinguishes three types of benefits:
functional benefits, hedonic benefits, and symbolic benefits (Smith & Colgate, 2007).
Functional benefits refer to the degree “to which a product (good or service) has
desired characteristics, is useful, or performs a desired function (Smith & Colgate,
2007, p. 10).” Firstly, functional benefits arise from superior features, functions,
attributes, and/or characteristics of an innovation (Woodruff, 1997). In other words,
functional benefits may be based on superior quality (Sethi & Sethi, 2009). Secondly,
an innovation provides functional benefits if it enhances performance compared to
existing practice (Dahl & Hoeffler, 2004; Norman, 2004; Zhao et al., 2012; Zhao,
Hoeffler, & Zauberman, 2007). Thus, the better an innovation fulfills its instrumental
or physical purpose, the higher its functional benefits (Sheth, Newman, & Gross,
1991). In extreme cases, an innovation may even offer “a completely new way of
doing something, or a completely new thing to do, something that was not possible
before (Norman, 2004, p. 77).” Thirdly, functional benefits relate to favorable
outcomes and consequences. That is, an innovation has high functional benefits if it
solves current problems or helps to prevent future problems (Smith & Colgate, 2007).
Hedonic benefits refer to the degree to which a product “creates appropriate
experiences, feelings, and emotions (Smith & Colgate, 2007, p. 10).” Firstly, hedonic
benefits may be based on the special sensory experiences an innovation provides
(Rafaeli & Vilnai-Yavetz, 2004). Typically, these derive from an innovation’s formal
attributes such as color, shape, proportions, materials, or craftsmanship (Rindova &
Petkova, 2007). Secondly, hedonic benefits may arise from the emotional experiences
an innovation provides. Examples of such emotional experiences include pleasure, fun,
enjoyment and excitement (Smith & Colgate, 2007). Thirdly, hedonic benefits relate to
the social-relational experiences an innovation provides (Ulaga & Eggert, 2005).
Facebook is a prominent example of an innovative firm that primarily provides socialrelational benefits. Through its social platform, Facebook allows customers to interact
with friends and family in completely new ways. Finally, hedonic benefits may be
based on epistemic experiences (Sheth et al., 1991) which involve a “sudden
expansion, recombination, or ordering of previously adopted information
(Csikszentmihalyi & Robinson, 1990, p. 18).” Examples of epistemic experiences
include knowledge emotions, such as interest, curiosity, or fantasy (Silvia, 2005).
14
Symbolic benefits refer to the degree to which “customers attach or associate
psychological meaning to a product (Smith & Colgate, 2007, p. 10).” Firstly, symbolic
benefits may address customers’ self-identity and self worth. That is, an innovation
provides symbolic benefits if it contributes to the way that customers would like to see
themselves. Secondly, symbolic benefits relate to social acceptance. Put differently,
symbolic benefits may be based on the messages customers can convey to others by
their use of an innovation (Norman, 2004). Frequently, customers adopt innovations so
that they will be associated or disassociated with specific socio-cultural groups (Sheth
et al., 1991). Thirdly, symbolic benefits involve personal and conditional associations.
Personal associations are events that are relevant for a particular customer only.
Conditional associations refer to sociocultural-ethnic events and traditions (Smith &
Colgate, 2007). Generally, symbolic benefits arise from the deeper meaning of an
innovation (Verganti, 2008). Such meaning includes the signs that distinguishes an
innovation from existing practice, the general significance of an innovation, as well as
an innovation’s relations to other objects, concepts, or ideas (Krippendorff, 1989).
Verganti (2008) argues that such meaning arises particularly from specific design
languages, consisting of signs, symbols, and icons incorporated into an innovation.
The Bang & Olufsen Beosound 4000 stereo, for example, represented an innovation
that markedly changed the meaning of music players by transforming them from
electronic devices to items of furniture.
Table 2-1 provides a summarizing overview of the different types of customer benefits
as a basis for the perception of meaningfulness of innovations.
Table 2-1: Types of Customer Benefits
Functional
Hedonic
Symbolic
Correct/accurate
Sensory
Self-identity/
attributes
experience
self-worth
Appropriate
Emotional
Social
performances
experience
Acceptance
Appropriate
Social/relational
Personal
outcomes
experience
Associations
Appropriate
Epistemic
Conditional
consequences
experience
Associations
15
2.2.2.2 Perceived Feasibility of Innovations
Feasibility refers to the cost and other sacrifices that may be involved in the purchase,
ownership, and use of an innovation (Arts et al., 2011; Smith & Colgate, 2007). It
includes economic costs, psychological costs, and risk (Smith & Colgate, 2007).
Firstly, an innovation reduces economic costs if it provides an existing functionality at
a lower price. An example of this would be Netbooks which provide similar
functionalities as Notebooks, only at a much lower price (Rogers, 2003). Secondly, an
innovation may reduce psychological costs by increasing the usability of existing
alternatives, that is, by making usage easier and more intuitive, thereby decreasing
learning costs (Norman, 2004). Specifically, innovations by the Apple brand provide a
high level of usability. Finally, many innovations reduce the risks associated with
existing practice (Rogers, 2003). An example of this is the air bag in cars, which
significantly reduced the risk of severe injuries in car accidents.
2.2.3 Determinants of Innovation Perception and Market Success
As the previous discussion suggests, newness and meaningfulness represent key
determinants of innovation perception. This invites the question whether each of these
dimensions influences innovation perception in the same way. Important implications
of the influence of determinants of innovation perception may be derived from studies
which examined the impact of newness and meaningfulness on market success.
Research indicates that market success/failure of innovations represents a key indicator
of favorable/unfavorable innovation perception (Kunz et al., 2011).
In a recent study, Im & Workman (2004) examined the impact of the newness and
meaningfulness of new products and associated marketing programs on new product
performance. 312 new product project managers were surveyed in the study.
Respondents indicated the newness and meaningfulness of new products, and
associated marketing programs that were introduced into the market within 6 months
prior to the study. New product success was measured on the basis of relative sales,
relative market share, relative return on investment, and relative profits of the new
products. The study revealed that new product performance was driven more by
meaningfulness than by newness of new products and associated marketing programs.
Similar results are provided by Szymanski, Kroff, & Troy (2007) who conducted a
meta-analysis in which they examined the impact of newness and meaningfulness of
new products on new product success. The authors distinguished between studies that
solely used newness as predictor of new product success, and studies which used
16
newness and meaningfulness as predictors of new product success. The analysis
revealed that newness only does not represent an appropriate predictor of new product
success. For some studies, the meta-analysis even showed a negative relationship
between newness and new product success. Hence, Szymanski et al. (2007) suggest
that new products will be successful only if they are characterized by a sufficient
degree of meaningfulness in addition to newness.
These findings also appear to confirm the findings of a study by Moldovan,
Goldenberg, & Chattopadhyay (2011) which examined the impact of the newness and
meaningfulness of new products on word of mouth. The authors distinguished between
valence of word of mouth and amount of word of mouth. Valence refers to whether
word of mouth is positive or negative. Positive word of mouth creates favorable
attitudes towards products, while negative word of mouth creates negative attitudes.
Amount of word of mouth depends on how many people talk about a product. It refers
to the total buzz generated in a given market. The study demonstrated that newness
significantly increased the amount of word of mouth, but had no impact on the valence
of word of mouth. The valence of word of mouth was primarily determined by
meaningfulness. At a high level of meaningfulness, word of mouth was positive,
whereas, at low levels of meaningfulness, word of mouth even became negative.
Together, these findings imply that perceived newness represents a necessary, yet not
sufficient driver of innovation perception (Henard & Szymanski, 2001). As a matter of
fact, newness itself is unlikely to evoke positive reactions to an innovation. Only if
customers perceive an innovation as considerably meaningful they will respond
favorably to it (Im & Workman, 2004). It could be concluded that newness solely
draws attention to an innovation. However, whether customers perceive an innovation
as attractive or not primarily depends on meaningfulness (Moldovan et al., 2011).
Now that the term innovation has been specified from a customer perspective, key
processes of innovation perception are discussed in the next chapter (chapter 3). The
main emphasis of this chapter will be to reveal how processes of innovation perception
can be influenced so that innovations are perceived as favorable.
17
3 Processes of Innovation Perception
3.1 Processes-Oriented View of Innovation Perception
Literature indicates that innovation perception does not represent an instantaneous act,
but a process that involves a variety of different interactions with an innovation.
Research distinguishes five stages through which individuals pass when adopting an
innovation. These stages include a knowledge stage, a persuasion stage, a decision
stage, an implementation stage, and a confirmation stage (Rogers, 2003). The
knowledge stage occurs, when individuals are exposed to an innovation and develop
an understanding of what the innovation does. In the persuasion stage, individuals deal
with potential consequences of innovation adoption and form a favorable or
unfavorable attitude towards the innovation. The decision stage takes place when
individuals engage in activities that lead to the adoption or rejection of an innovation.
During the implementation stage, individuals attempt to put an innovation to use. In
the confirmation stage, individuals seek reinforcement of a previous adoption decision.
However, they may also reverse their decision in this last stage (Rogers, 2003).
Following the five stages of adoption, two primary processes of innovation perception
may be distinguished: innovation recognition and innovation assessment. Innovation
recognition is a person’s taking note of an innovation and becoming interested in it
(Ziamou & Gregan-Paxton, 1999). It relates to the knowledge stage of the adoption
process and involves both exposure to as well as learning of what an innovation does
(Rogers, 2003). Innovation recognition may arise from an internal or external change
of one’s situation. Perception of external change is typically based on the
communication of innovations via advertising, personal selling, publicity, or word of
mouth. Furthermore, perception of external change may occur if individuals come
across an innovation by engaging in unplanned activities such as exploratory shopping.
Conversely, perception of internal change is the internal recognition of a specific
problem or need (Bagozzi & Lee, 1999). In the case of internal change, an individual
actively searches for an innovation that suits his or her problem or need (von Hippel,
1988). Innovation recognition may result either in initial resistance or openness to an
innovation (Bagozzi & Lee, 1999).
In comparison, innovation assessment is the persuasion, decision, implementation, and
confirmation stages of the adoption process. In case of innovation assessment,
individuals become intensively involved with an innovation (Rogers, 2003). During
this process, individuals repeatedly interact with an innovation and actively seek
18
information about it. They analyze key attributes and characteristics of the innovation
and make inferences about potential favorable and unfavorable consequences that may
result from adoption (Bagozzi & Lee, 1999). A key aspect of innovation assessment
refers to the effort required for effective use of an innovation. Individuals will only
form positive attitudes toward an innovation if adoption is associated with reasonable
effort (Venkatesh et al., 2003, 2012). Furthermore, individuals will only react
favorably to an innovation as long as adoption is not perceived as too risky (Castaño,
Sujan, Kacker, & Sujan, 2008; Ostlund, 1974).
As Innovation Recognition and Innovation Assessment have now been specified,
chapters 3.2 and 3.3 will provide a more detailed discussion of these processes.
19
3.2 Innovation Recognition
3.2.1 Identification of Innovations
3.2.1.1 Formation of Awareness of Innovations
Before individuals can develop a favorable attitude towards an innovation in the
recognition phase, they must become aware of its existence. Research indicates that
individuals form a high awareness of an innovation if they are repeatedly exposed to
that innovation, and if they develop strong associations with relevant purchase or
consumption cues (Keller & Lehmann, 2006; Keller, 1993). This relationship follows
from the way in which information is structured in human memory. Specifically,
human memory consists of a network of interlinked nodes in which information is
stored (Anderson, 1983; Srull & Wyer, 1989). Thereby, the probability of information
retrieval from a specific node is a function of how often that node is activated. Such
activation can either occur directly, or indirectly through activation of other nodes to
which a link was previously established (Ratcliff & McKoon, 1988). Thus, in order to
build awareness of an innovation - especially a highly novel innovation - a new node
needs to be created and interlinked with other nodes. This particularly occurs if
individuals come across an innovation repeatedly and are thereby able to relate the
innovation to important aspects of their lives. In other words, the more often
individuals experience an innovation by seeing it, hearing about it, or thinking about it
in a way that is personally relevant to them, the more likely these individuals are to
strongly register that innovation in memory (Keller, 2007).
Repeated exposure is not only likely to increase awareness of an innovation, but also
to evoke a favorable attitude towards it. Research found that repeated presentation of a
stimulus generally enhances its liking because of an experience of perceptual fluency
(Reber, Winkielman, & Schwarz, 1998). Perceptual fluency may be defined as the ease
of identifying the physical identity of a stimulus (Reber, Schwarz, & Winkielman,
2004). If an individual encounters a specific stimulus, he/she will form a mental image
of that stimulus. Through repeated exposure, the vividness of this mental image is
enhanced. The more vivid the mental image, the easier it is to identify the physical
identity of the stimulus. This fluency experience evokes a positive feeling, which is
attributed to the stimulus and, as a consequence, leads to a favorable attitude
(Bornstein, 1989). Thus, if an individual encounters an innovation repeatedly,
20
perceptual fluency of this innovation is likely to increase. This in turn results in a more
favorable attitude towards that innovation.
Perceptual fluency is not only a function of quantity of exposure, but also of quality of
exposure (Reber et al., 2004). More precisely, perceptual fluency of an innovation is
likely to increase as people become more intensively involved with that innovation
(Gordon & Holyoak, 1983). Typically, this involvement depends on an individual’s
motivation, ability, and opportunity to process information about the innovation
(Maclnnis & Jaworski, 1989; Petty & Cacioppo, 1986). Motivation may be defined as
the degree to which an individual is interested and willing to put effort in processing
information about an innovation. Ability denotes the degree to which an individual
possesses the necessary knowledge to interpret that information appropriately.
Opportunity may be defined as the degree to which situational factors facilitate
processing that information (Chandy, Tellis, MacInnis, & Thaivanich, 2001).
Following this, research identified different types of message cues that promote or
prevent involvement with an innovation. These include appeal mode, appeal
prominence, and appeal frame (Chandy et al., 2001). Appeal mode refers to the degree
to which a message is argument-based or emotional (Chandy et al., 2001). Argumentbased messages emphasize the technological superiority of an innovation over its
competitors. That is, they clearly emphasize what an innovation does for the individual
(Golden & Johnson, 1983). Argument-based messages convince customers by means
of functional appeals such as quality, economy, or value (Lee & Colarelli O’Connor,
2003). Conversely, emotional messages evoke positive feelings associated with the
product and its use (Friestad & Thorson, 1986). In other words, the purpose of
emotional messages is to create some kind of emotional experience that people
associate with an innovation (Ray & Batra, 1983). With emotional messages,
customers are convinced by appeal to such feelings as joy, humor, love, or pride (Lee,
Lin, Wong, & Calantone, 2011).
For really new products, argument-based messages are likely to be more effective than
emotional messages. Because of their inherent newness, really new products create an
unwanted information gap (Loewenstein, 1994) which motivates customers to collect
further information of what the product does (Schumann, Petty, & Clemons, 1990).
Accordingly, in such a situation, customers are more willing to consider factual
information. Factual information reduces purchase risks and differentiates the product
more effectively from existing alternatives (Talke & Colarelli O’Connor, 2011).
Conversely, emotional messages are counterproductive for really new products.
Emotional messages neither provide a compelling reason for buying a product nor
21
change fundamental beliefs about it (Chandy et al., 2001). Sometimes, emotional
messages may even distract customers from critical information (Moore &
Hutchinson, 1983).
In contrast, with incrementally new products, emotional messages are more effective
than argument-based ones (Chandy et al., 2001). Incrementally new products do not
create information gaps. Individuals can easily make sense out of these products by
drawing on prior experiences (Zhao et al., 2009). Thus, individuals are less responsive
to argument-based messages when encountering incrementally new products. In
extreme cases, they may even respond negatively to these kinds of messages because
of satiation, boredom, or irritation (Batra & Ray, 1986). Things are different for
emotional messages. They draw attention to incrementally new products by
stimulating the visualization of concrete self-related consumption experiences (Adaval
& Wyer, 1998; Shiv & Huber, 2000).
The second message cue, appeal prominence, refers to the prominence of key
attributes of an innovation. Messages may be prominent by virtue of their size (e.g. a
large font as opposed to a small font), duration on screen, or the number of times they
are shown (Stewart & Furse, 1986). As individuals are less familiar with really new
products, they experience difficulties in assimilating key message information into
memory. Relative to incrementally new products, customers will require more time to
assimilate key message information for really new products. Thus, anything that
increases attention to and assimilation of key message information should increase
effectiveness of messages about really new products (Chandy et al., 2001).
The third message cue, appeal frame, may be defined in terms of the positive and
negative goal framing of a message. Generally, research refers to positive goal frames
as messages that are concerned with obtaining favorable consequences. In contrast,
negative goal frames refer to messages that are concerned with avoiding unfavorable
consequences (Levin, Schneider, & Gaeth, 1998). In the marketing context, a positive
goal frame highlights a product’s ability to provide gains or obtain benefits, whereas a
negative goal frame highlights a product’s ability to avoid losses or solve problems
(Chandy et al., 2001). Negative goal frames are more effective for really new products.
These products allow individuals to do things they have not been able to do before and
provide solutions to previously unsolved problems (Dahl & Hoeffler, 2004). Messages
that contain information for avoiding or eliminating unsolved problems are likely to
motivate individuals to process these messages (Maheswaran & Meyers-Levy, 1990).
Conversely, positive goal frames are more effective for incrementally new products.
For these products, individuals are already aware of the problems they solve. To
22
motivate product use, individuals should be provided with information about how the
product fulfills appetitive and positive states (Chandy et al., 2001).
3.2.1.2 Identification of Category Membership of Innovations
An important aspect during identification of innovations involves categorization of an
innovation. Categorization research is concerned with how people organize knowledge
in memory, and how this knowledge influences perception of novel objects such as
innovations (e.g., Goode, Dahl, & Moreau, 2012; Ozanne, Brucks, & Grewal, 1992;
Ratneshwar & Shocker, 1988). This line of research argues that knowledge is
organized around partially integrated knowledge structures in memory. Each of these
structures contains information about a group of objects that are perceived as alike in
important respects. Research refers to these entities as categories (J. Cohen & Basu,
1987). Every time an individual encounters a novel object, a categorization process is
initiated. During this process, the object is compared to categories already stored in
memory (Piaget, 1969; Rosch, 1978). The purpose of categorization is to make
information processing more efficient, thereby decreasing cognitive load (Bruner,
Goodnow, & Austin, 1956; Lingle, Altom, & Medin, 1984).
Identification of category membership is determined by how similar an object is
perceived to other objects within a given category (Creusen & Schoormans, 2005;
Loken & Ward, 1990). Research identified analytical and non-analytical categorization
processes as a basis for the identification of an object’s category membership (J.
Cohen & Basu, 1987). In case of an analytical categorization process, perceived
similarity is based on a set of core attributes that jointly determine the membership of
a category. The more of these critical category defining features a novel object
possesses, the more similar it will be perceived to the associated category. However, if
a novel object does not possess any of the critical features that define a category, it
will be perceived as highly dissimilar to that category (Dominowski, 1974).
In comparison, non-analytical categorization processes are not concerned with a set of
category defining attributes, but with an object’s overall representativeness of specific
category exemplars or its general prototypicality (Rosch & Mervis, 1975). Nonanalytical categorization is based on knowledge abstracted from prior experience. The
overall representativeness of specific category exemplars is determined by the degree
to which an object is representative of integral representations of available exemplars
of a specific category (Cantor & Mischel, 1979). Similarly, prototypicality is defined
in terms of a set of features commonly associated with members of a category. It is
23
determined by an object’s departure from the average values of these features (J.
Cohen & Basu, 1987; Langlois & Roggman, 1990; Veryzer & Hutchinson, 1998).
Accordingly, the prototypicality of an innovation’s appearance depends on how far its
design deviates from the means of characteristic feature points in a given product
category. Drawing on this insight, Landwehr, Labroo, & Herrmann (2011) developed a
methodology for determining the prototypicality of car designs. Specifically, they
defined 50 characteristic features of car designs, including the vertex of headlights,
grill, and windshield. For each of the features the mean position was calculated across
different models within a given segment (see exemplars of different car segments in
Figure 3-1). This procedure yielded a segment-specific prototype for each segment
(see prototypes of different car segments in Figure 3-1). This prototype was then used
to determine the prototypicality of the designs in the segments on the basis of the
Euclidian distances of each of the 50 feature points from the corresponding feature
points in the respective prototypes.
24
Figure 3-1: Car Segments and Associated Prototypes (Landwehr et al. 2011)
Exemplars Compact Segment
Exemplars Premium Segment
Prototype Compact Cars
Prototype Compact Cars
Research demonstrates that the identification of category membership plays an
important role in innovation perception. Goode, Dahl, & Moreau (2012) provide
empirical evidence that the certainty of identifying an innovation’s superordinate
product category leads to more favorable response to that innovation. Specifically,
they find that certainty of identification results in more positive evaluation of an
innovation. This is because the successful identification of an appropriate category
provides individuals with a clear point of reference from which to compare and
evaluate an innovation with confidence. If such a point of reference is missing due to
uncertainty of an innovation’s category membership, people are likely to conduct more
conservative evaluations.
25
3.2.2 Learning of Innovations
3.2.2.1 Customer Knowledge Transfer
Besides identification of an innovation, innovation recognition is also concerned with
the development of an initial understanding of what an innovation does. In the course
of such a learning process, people often appeal to prior knowledge. The literature
refers to this process as knowledge transfer. It occurs when people make something
unfamiliar intelligible by relating it to something familiar. More precisely, knowledge
transfer „is concerned with the use of a familiar domain (the base) to understand a
novel domain (the target) (Gregan-Paxton & Roedder John, 1997, p. 267).“ For
example, when cars were first introduced, many people made sense out of this new
technology on the basis of their prior knowledge of horse carriages.
In the process of knowledge transfer, representations of the target domain and the base
domain consist of systems of objects, attributes, and relations. Objects may refer to
clear entities such as a car as a whole, individual parts of a larger object such as a door
of a car, or even combinations of entities such as a complete model range of cars
(Gentner, 1983). Attributes are independent properties or components of objects. They
can be concrete or abstract. An example of a concrete attribute is the steering wheel of
a car. Reliability provides an example of an abstract attribute of a car. In comparisons,
relations refer to the links between the attributes of a domain. For a car, such a relation
exists between the gas pedal and the car’s velocity. Specifically, by pushing the gas
pedal, the velocity increases (Gregan-Paxton & Roedder John, 1997).
Gregan-Paxton et al. (2002) suggest that knowledge transfer occurs in a three-stage
process. The stages of this process comprise: (1) accessing the base domain, (2)
mapping the elements from the target onto the base, and (3) transfer of knowledge
from the base to the target (see Figure 3-2). When people first encounter an
innovation, they typically follow this process in order to learn what the innovation
does. In each stage of the process a variety of factors need to be considered so that
customers can develop an adequate understanding of an innovation.
26
Figure 3-2: The Knowledge Transfer Process (Gregan-Paxton et al., 2002)
Access
Mapping
Transfer
Accessing the Base Domain
In the access stage, the target should activate an appropriate base domain in the mind
of an individual. This base domain should represent a potential source of information
that is suitable for making sense out of the target domain. Thereby, access may either
occur spontaneously or on the basis of a prompt provided by an external source
(Gregan-Paxton et al., 2002). If the access stage is successful, a mental representation
of the base domain is evoked. In doing so, knowledge potentially relevant for
understanding the target domain becomes active in memory (Gregan-Paxton &
Roedder John, 1997). In case of the car example, the access stage is completed if a
mental representation of a horse carriage is evoked.
The success of the access stage is strongly determined by the degree to which the
target domain and the base domain share common attributes (Nisbett & Ross, 1980).
Put differently, a base domain is more likely to be activated if it is characterized by a
high degree of overlap to the target domain (Gentner, 1983). Research provides
considerable empirical evidence for this relationship. For example, Gentner et al.
(1993) conducted a study in which they presented participants with sets of stories that
varied in the degree to which they contained common attributes such as characters.
They found that an increase in attribute overlap of the stories resulted in a higher
number of participants who would retrieve a base story.
Mapping from the Base to the Target
The purpose of the mapping stage is to relate the base domain to the target domain so
that knowledge about the base domain can be transferred. Therefore, individuals need
to construct one-to-one correspondences between the mental representations of the
base domain and the target domain (Falkenhainer, Forbus, & Gentner, 1989). Put
differently, in the mapping stage, individuals create paths between the base and the
target. Across these paths, they can transport relevant knowledge (Gregan-Paxton &
Roedder John, 1997). Mapping occurs in the car example by identifying
27
commonalities between cars and horse carriages. Such commonalities might include
the transportation of heavy objects or comfortable traveling of large distance.
In the mapping stage, individuals prefer relation-based matches over attribute-based
matches. Research provides considerable empirical evidence to support this claim
(Gregan-Paxton & Roedder John, 1997). For example, Gentner et al. (1993) conducted
a study in which they presented participants with different stories of varying degrees
of matching attributes and relations. They found that the addition of matching relations
increased the perceived soundness of a match between stories, whereas the addition of
common attributes had no effect on the perceived soundness of a match between
stories. Furthermore, in a study by Spellman & Holoyak (1992), participants were
asked to compare the Persian Gulf War and World War II. The study showed that
participants tended to generate mappings that preserved the relation between a country
and its leader, in spite of mappings that separated the two.
Research suggests that individuals prefer relation-based matches in the mapping stage,
because relation-based matches allow them to derive causal principles (Gentner,
1983). In line with this, Gregan-Paxton & Roedder John (1997) argue that
comparisons building on relation-based matches are more informative than
comparisons building on attribute-based matches. They use the example of laundry
detergents to illustrate this point. Specifically, they argue that, when customers
compare a store-brand laundry detergent (target) with a laundry-detergent of an
established brand like Ariel (base), customers are likely to be more concerned about
common relations than about common attributes. This is because the store-brand is
more likely to clean properly if it has an identical chemical configuration (i.e. common
relation) as the established brand, Ariel. Whether or not the store-brand is packaged in
white and green boxes (common attribute) is irrelevant.
Transferring Knowledge from the Base to the Target
In the transfer phase, the actual transfer of knowledge takes place. By moving
knowledge from the base to the target, people make sense of the target. This stage is
driven by the belief that domains which seem similar in certain respects may also be
similar in other respects. With the car example, the fact that cars and horse carriages
allow the transportation of heavy objects may encourage individuals to assume that
both devices share more characteristics. Relying on their experience of horse carriages,
they may conclude, for instance, that cars require horses as drive (Gregan-Paxton &
Roedder John, 1997).
28
Research distinguishes different kinds of knowledge transfer. Knowledge transfer can
occur in a schema-based process, which builds on more abstract knowledge structures
(Gick & Holyoak, 1983; Spencer & Weisberg, 1986). Additionally, knowledge
transfer can occur in a similarity-to-exemplar process, which is entirely based on the
similarity between the target domain and a specific exemplar of the base domain (Alba
& Hutchinson, 1987; Beattie, 1982; Carey, 1985; Klein, Loftus, Trafton, & Fuhrman,
1992; Rumelhart, 1989). In general, schema-based knowledge transfer leads to better
results than similarity-to-exemplar transfer. This is because schema-based knowledge
transfer concentrates on the essential matters and prevents the transfer of inappropriate
information. In contrast, similarity-to-exemplar transfer represents more of a backupstrategy. It is used primarily when an appropriate schema is absent or the schema is
insufficient to guide knowledge transfer (Gregan-Paxton & Roedder John, 1997).
It must be stressed that similarities between different domains are limited (GreganPaxton & Roedder John, 1997). In general, only a subset of information relating to a
base is appropriate for the transfer of knowledge to a specific target (Ortony, 1975).
This implies that errors may occur in the knowledge transfer process (Novick, 1988).
Figure 3-3 provides a summarizing overview over the knowledge transfer model. As
this model indicates, the knowledge transfer process begins with the access stage in
which individuals search for common attributes between a target domain and
appropriate base domain. If a base domain is activated, the individual proceeds to the
mapping stage. In this stage, individuals create one-to-one correspondences between
the target and the base. Thereby, they prefer relation-based mappings over attributebased mappings. However, relation-based mappings will occur only if individuals
possess sufficient knowledge about the target and the base domain. If this is not the
case, attribute-based mapping will occur. After the mapping stage, actual knowledge
transfer occurs in the transfer phase. In this phase, an individual transfers knowledge
from the base to the target, thereby developing a deeper understanding of the target
domain (Gregan-Paxton & Roedder John, 1997).
29
Figure 3-3: Model of Knowledge Transfer (Gregan-Paxton & Roedder John, 1997)
Identification of
Commmon Attributes
Accessing the
Base Domain
Perceive
Common Relations
Don’t Perceive
Common Relations
Map Relations
Map Attributes
Schema-Based
Transfer
Similarity-to-Exemplar
Transfer
30
Research identified knowledge transfer as a key aspect of innovation recognition. It
may help people to develop a much better understanding of what an innovation does
(e.g., El Houssi et al., 2005; Hoeffler, 2003). Thereby, knowledge transfer may be
based on closely related knowledge structures as well as more disparate knowledge
structures. Take a personal digital assistant (PDA), for example. People could make
sense out of such a device by transferring prior knowledge they have developed about
organizers. In this case, people would draw on a closely related knowledge structure.
In comparison, people could also draw on a more disparate knowledge structure when
learning about a PDA. Specifically, they could make sense out of a PDA by building
on prior knowledge they have gained with secretaries. That is, people can conclude
that a PDA may be similar to a secretary in that it performs many routine tasks such
taking a dictation or setting up appointments (Gregan-Paxton et al., 2002).
It is suggested that extensive knowledge transfer is likely to provide a comprehensive
understanding of an innovation and, as a consequence, lead to more favorable response
to an innovation (Hoeffler, 2003). Given the high relevance for innovation recognition,
it is next discussed how knowledge transfer can be encouraged.
3.2.2.2 Encouragement of Customer Knowledge Transfer
Research demonstrates that knowledge transfer is effective only in certain situations.
Specifically, individuals need the ability to map attribute relations from a base domain
to understand the benefits of a target domain, and they need to allocate substantial
resources to complete this mapping. If one of these conditions is absent, sufficient
knowledge transfer is unlikely to occur. However, research has identified a variety of
devices that help to overcome the challenge of mapping relations, thereby improving
the understanding of a target domain (Roehm & Sternthal, 2001).
One of these devices represents expertise in the base domain. This is likely to promote
extensive relation-based mappings. The advantage associated with such expertise is
the difference in base knowledge structures between experts and novices (Roehm &
Sternthal, 2001). Specifically, the knowledge structures of novices contain some
attribute information, but little information about structural relations. In contrast, the
knowledge structures of experts contain extensive attribute information as well as
considerable abstract information about structural relations (Alba & Hutchinson,
1987). In line with this, Reidenbach & Grimes (1984) showed that individuals with
prior knowledge in the portable phone category were able to develop a much more
comprehensive understanding of an innovative portable phone system. Similarly,
31
Dickerson & Gentry (1983) demonstrated that prior experience with computer-related
products significantly improved appreciation of home computers.
If expertise in the base domain is lacking, relation-based mapping may be alternatively
promoted through training individuals how to process base information (Roehm &
Sternthal, 2001). A positive relationship between training and relation-based mappings
was identified by studies examining the impact of knowledge transfer on negotiation
performance of individuals (Loewenstein, Thompson, & Gentner, 1999; Thompson,
Gentner, & Loewenstein, 2000). In one of these studies, management students had to
read two case studies on the topic of negotiation and were subsequently asked to
perform a face-to-face negotiation task. While one group of students had to study the
case studies separately (control condition), another group of students had to compare
the two cases and derive an overall negotiation principle (training condition). It was
found that students in the training condition were almost three times more likely to
make relation-based mappings when transferring knowledge from the cases to the
subsequent negotiation task (Loewenstein et al., 1999). Following this, Roehm &
Sternthal (2001) demonstrated that instructing individuals to focus their attention on
structural relations among attributes of a base domain is likely to prompt relationbased mappings, thereby significantly improving knowledge transfer.
3.2.2.3 Single vs. Multiple Category Inferencing
To develop an adequate understanding of a target domain, it is often necessary to
transfer knowledge from more than one base domain. Specifically, when learning
about innovations, people frequently need to make inferences from multiple product
categories. Understanding the benefits of a camera phone, for example, requires
inferences from both the mobile phone category as well as the digital camera category.
The literature distinguishes multiple category inferencing (transferring knowledge
from two or more categories) and single category inferencing (transferring knowledge
from only one category) (e.g., Gregan-Paxton et al., 2002; Moreau, Markman, et al.,
2001).
Research indicates that learning about innovations is characterized by a disposition to
make single category inferencing. Specifically, Moreau, Markman, et al. (2001)
showed that people tend to draw inferences about an innovation on the basis of the
first category cue that becomes available. A subsequent second category cue was
found to have hardly any effect. The tendency for single category inferencing may be
overcome, however, by pointing out specific correspondences between different
32
category cues and a target domain. Take digital cameras for example: to learn about
such an innovation, it might be helpful to draw inferences from both film based
cameras as well as computer scanners. Individuals can be focused on specific
correspondences to these categories in the following way: First of all, a digital camera
manufacturer could point out that a digital camera resembles a film based camera in
that one can take pictures with it. The manufacturer can also point out that the digital
camera is like a computer scanner in that one can process pictures with it (Moreau,
Markman, et al., 2001). This approach was applied when Steve Jobs presented the first
Apple iPhone. He started his presentation by informing the audience that he was about
to present three separate devices, including a media player, a mobile phone, and an
internet communications device. Only later in his presentation did he reveal that these
three devices were part of a single product, namely, the iPhone.
Research suggests that multiple category inferencing may be evoked though a specific
priming approach derived from literature on conceptual combinations in
psycholinguistics. This research examines primarily how people interpret novel nounnoun combinations such as a book-magazine. People predominantly adopt one of two
different strategies when interpreting such novel combinations: relational
interpretations and property interpretations (Wisniewski & Love, 1998). In case of a
relational interpretation, a relation is formed between the categories such that a bookmagazine is interpreted as a magazine about books. In comparison, property
interpretation occurs if a particular property or attribute of one category maps to the
second category such that a book-magazine is interpreted as a magazine that is as thick
as a book. Following these considerations, Rajagopal & Burnkrant (2009) conducted a
study in which they primed participants with the aim of evoking relational and
property interpretations. Specifically, examples of relational priming in the study
included ‘An ink pen is a pen that writes with ink,’ while examples for property
priming in the study included ‘A pencil pen is a pen and a pencil together in a single
product.’ The study revealed that multiple category inferencing was more likely to
occur under property priming relative to relational priming.
Furthermore, multiple category inferencing depends on the propensity to adapt prior
category knowledge. When individuals apply multiple category inferencing, they
frequently need to change or update at least one of the categories involved in that
process. An example of multiple category inferencing might be a product that
combines a personal digital assistant (PDA) and a cell phone. If individuals make
predictions about the performance of this ambiguous device, they are likely to make
inferences that involve discrepant attribute values. Specifically, if they make
33
inferences about the scheduling capability of a PDA/cell phone, the PDA category is
likely to prompt a favorable response, whereas the cell phone category is likely to
prompt an unfavorable response. In contrast, if individuals make inferences about the
device’s capability to make phone calls, the PDA category is likely to prompt an
unfavorable response, whereas the cell phone category is likely to prompt a favorable
response. Following this, Gregan-Paxton, Hoeffler, & Zhao (2005) argue that in such a
situation individuals either need to update the PDA category to accommodate the new
value for making phone calls, or they need to update the cell phone category to
accommodate the new value for scheduling.
Gregan-Paxton, Hoeffler, & Zhao (2005) show that category updating is in part
determined by the familiarity of category cues. Individuals are more likely to update
low-familiarity categories than high-familiarity categories (Elliott & Anderson, 1995).
The authors further argue that category updating is determined by the nature of
category cues. Research distinguishes between perceptual and conceptual category
cues. Perceptual category cues arise from visual depiction of categories, whereas
conceptual category cues arise from the provision of category labels. Generally,
individuals perceive perceptual category cues as more diagnostic of a specific
functionality than conceptual category cues (Matan & Carey, 2001). Gregan-Paxton,
Hoeffler, & Zhao (2005) demonstrate that, when an ambiguous product is described in
terms of conflicting perceptual and conceptual category cues, multiple category
inferencing is applied when the perceptually cued category is as familiar as or less
familiar as the conceptual cued category. Where these conditions do not obtain,
individuals are more likely to apply single category inferencing.
3.2.3 Perceived Differentiation of Innovations
3.2.3.1 Feature Based Differentiation of Innovations
Furthermore, innovation recognition is determined by how different an innovation is
perceived from existing alternatives. Differentiation of an innovation occurs where
individuals are able to identify one or more features that distinguish the innovation
from existing alternatives (Carpenter, Glazer, & Nakamoto, 1994). Specifically,
features on which an innovation is perceived as being about equal with existing
alternatives will result in unsuccessful differentiation. Features on which an innovation
is perceived to be different will result in successful differentiation (Fuchs &
Diamantopoulos, 2012).
34
Research distinguishes between unique and enhanced features as a basis of perceived
differentiation. Unique features are defined in terms of the extent to which an
innovation is differentiated vis-ä-vis existing alternatives by virtue of perceptions
unique to that innovation (Chaturvedi & Carroll, 1998). Unique features are typically
new and unexpected, often causing considerable levels of attention (Kardes &
Kalyanaram, 1992). Owing to their high level of newness, unique features are typically
perceived as highly differentiating (Ravi Dhar & Sherman, 1996). At the same time,
however, unique features are likely to raise performance concerns, because individuals
lack prior experience with them (Hsee, 1996; Nowlis & Simonson, 1996). For the
same reason, unique features may also evoke negative learning-cost inferences. This is
particularly the case, if unique features are added to high-complexity products such as
personal computers or programmable cameras as opposed to low-complexity products
such as refrigerators or washing machines (Mukherjee & Hoyer, 2001).
Enhanced features, on the other hand, are based on performance improvements along
existing product characteristics (Zheng Zhou & Nakamoto, 2007). They refer to the
degree to which an innovation outperforms competing offerings along familiar
attributes and functionalities (Rijsdijk, Langerak, & Hultink, 2011). On account of
their lack of newness, enhanced features are likely to be perceived as less
differentiating (Carpenter et al., 1994). Nevertheless, enhanced features have a crucial
advantage: enhanced features allow individuals to compare an innovation to
established products on the basis of a common dimension (Hsee, 1996). In doing so,
they do not cause the same level of performance uncertainty as unique features (Zhang
& Markman, 1998; Zheng Zhou & Nakamoto, 2007). Furthermore, enhanced features
are unlikely to evoke negative learning-cost inferences. When encountering an
enhanced feature, customers can easily make sense of it by drawing on prior
experiences (Mukherjee & Hoyer, 2001).
Research suggests that already the mere existence of a distinguishing feature may lead
to successful differentiation, even if the feature fails to provide actual value. Literature
refers to this phenomenon as meaningless differentiation. More formally, it may be
defined as the successful differentiation of a product on the basis of a feature that does
not provide any benefit. A series of studies have revealed that people are likely to
respond favorably towards products to which a distinguishing but irrelevant feature
was added. This effect even persists when individuals are informed that the
distinguishing feature has no value (Carpenter et al., 1994).
In line with this, it has also been shown that specifications may change product
preferences even if experiences are available, and specifications provide little or no
35
consumption relevant information (Hsee, Yang, Gu, & Chen, 2009). This occurs
because, similar to meaningless differentiation, specifications represent an illusion of
advantage over an otherwise not advantageous option (Hsee, Yu, Zang, & Zhang,
2003). This implies that providing appropriate specifications can significantly increase
the success of new products. Where a quantitative specification does not exist, a new
one should be invented. If a well-known quantitative specification is already
established, it can be converted to accentuate the advantage of a product. By way of
example, the size of a television could be stated in square inches rather than by
conventional diagonal length (Hsee et al., 2009).
However, it needs to be accentuated that meaningless differentiation is only effective
under certain circumstances. If one of these circumstances is not apparent, the addition
of a distinguishing but irrelevant feature is likely to result in very negative reactions.
Firstly, meaningless differentiation is only effective if people have difficulty in
evaluating the differentiating feature. This is especially the case where people do not
care about, or are not able to judge the true value of a feature. An example of such a
case is shampoo that contains silk. Such a shampoo suggests that users’ hair will be
silky. The truth is, however, that silk has no benefits for hair (Carpenter et al., 1994).
A second factor is price. Specifically, meaningless differentiation will have an effect
only if the underlying product is priced appropriately. Specifically, individuals will
evaluate an irrelevant feature favorably only as long as the price of the associated
product corresponds to higher priced market offerings. If this is not the case, either
because the price corresponds to low-priced competitive offerings or is much higher
than existing alternatives, individuals are likely to respond unfavorably (Carpenter et
al., 1994).
A third factor is the attractiveness of the irrelevant feature’s label. An attractive label
will increase evaluation whereas an unattractive label will decrease evaluation
(Carpenter et al., 1994). Broniarczyk & Gershoff (1997) provide empirical evidence
for this relationship. They examined how individuals evaluate jackets that were
differentiated by their down fill. Specifically, participants were asked to evaluate
jackets that had down fills from different kinds of birds. Prior to the study, they were
informed, however, that for down fill the age of the bird matters but not the type of
bird. Results showed that jackets with an attractive down fill label of ‘goose’ received
positive evaluations. Even more positive evaluations were garnered by replacing
‘goose’ with the even more attractive label ‘alpine’. By contrast, jackets with the
unattractive down fill label ‘duck’ received negative evaluations. These evaluations
36
became even more negative when ‘duck’ was replaced with the even less attractive
label ‘regular’.
Hence, even though meaningless differentiation needs to be handled carefully, this
research indicates that the addition of distinguishing but irrelevant features may be
applied strategically in certain cases, specifically, to evoke a more positive response to
an innovation which hardly differs from existing alternatives. Firstly, awareness and
familiarity may be increased by distinctiveness. Secondly, perceived quality may be
enhanced by setting premium prices especially. Thirdly, positive associations may be
evoked if the irrelevant feature is labeled attractively. However, to build a long-term
competitive advantage through meaningless differentiation, it is necessary to make
sure that a distinguishing feature cannot be duplicated (Carpenter et al., 1994).
3.2.3.2 Perception of Utilitarian versus Hedonic Differentiation
When differentiating innovations from existing market offerings, it is necessary to
distinguish whether the differentiation is based on utilitarian or hedonic functionalities.
Generally, the addition of a new functionality may be congruent or incongruent with
the base (see Table 3-1). Where a utilitarian (hedonic) functionality is added to a
utilitarian (hedonic) base product, one speaks of an added functionality that is
congruent to the base. However, if a hedonic (utilitarian) functionality is added to a
utilitarian (hedonic) base product, one speaks of an added functionality that is
incongruent to the base. Consequently, the addition of congruent functionalities is
likely to lead to diminishing marginal utility (Gill, 2008). Generally, the addition of
congruent functionalities is believed to decrease with the overall value of a product
(Nowlis & Simonson, 1996).
37
Table 3-1: Congruent and Incongruent Additions of Functionalities (Gill, 2008)
Base Product
Added Functionality
Nature of Addition
Personal Digital Assistant
Global Positioning System
CONGRUENT
(utilitarian)
(utilitarian)
Handheld Email Device
MP3 Music Player
(utilitarian)
(hedonic)
MP3 Music Player
Video Capability
(hedonic)
(hedonic)
Television
Internet Access
(hedonic)
(utilitarian)
INCONGRUENT
CONGRUENT
INCONGRUENT
However, adding incongruent functionalities does not necessarily lead to favorable
evaluation of innovations. The addition of utilitarian and hedonic functionalities
fulfills different consumption goals. Hedonic functionalities serve goals associated
with pleasure and excitement, whereas utilitarian functionalities serve practical and
instrumental goals (Hirschman & Holbrook, 1982). Adding a hedonic functionality to
a utilitarian base product normally provides more pleasure in using this product. Such
pleasure is likely to have a stronger effect on overall evaluation than established
utilitarian functionalities of the base (Keller & McGill, 1994). By contrast, adding a
utilitarian functionality to a hedonic base product is often perceived as a loss in
hedonic value. Such a loss is likely to be weighted more heavily than the gain in
utilitarian value (Dhar & Wertenbroch, 2000; Tversky & Kahneman, 1991).
Gill (2008) conducted a study in which he examined how participants evaluated
products with added functionalities. He distinguished between utilitarian and hedonic
base products to which either a utilitarian or hedonic functionality was added. The
study demonstrated that the addition of congruent functionality led principally to
diminishing marginal utility. Furthermore, the study showed that the addition of an
incongruent hedonic functionality to a utilitarian base significantly increased product
evaluation compared to addition of a congruent utilitarian functionality. This was
because participants thought that a hedonic functionality provided more
pleasure/excitement to the utilitarian base product, resulting in a gain of hedonic value.
Conversely, the addition of an incongruent utilitarian functionality to a hedonic base
38
significantly decreased evaluation compared to the addition of a congruent hedonic
functionality. Participants regarded the addition of such functionality as less
pleasurable/exciting, and also weighted this loss in hedonic value more heavily than
the gain in utilitarian value. Figure 3-4 provides an overview over this relationship.
Figure 3-4: Congruent and Incongruent Functionality Additions (Gill, 2008)
Utilitarian
Product
New Functionality from
Another Category
Hedonic
Product
Converged Product
with a Utilitarian Base
Converged Product
with a Hedonic Base
Goal
Congruence
Goal
Congruence
yes
Diminishing
Marginal Utility
no
Gain in Hedonic
Value
yes
Diminishing
Marginal Utility
no
Loss in
Hedonic Value
3.2.3.3 Assimilation/Contrast Effects in Innovation Perception
Moreover, perceived differentiation of innovations strongly depends on whether an
innovation is contrasted away from or assimilated to existing alternatives. Research
identified these so called assimilation/contrast effects as a robust psychological
phenomenon, which occurs every time a novel object is encountered. While
39
contrasting promotes perceived differentiation, assimilation decreases perceived
differentiation (Gill, 2008). It was found that pioneering products are contrasted away
from existing products, and are evaluated more positively than follower products
which are assimilated to the pioneer, and therefore do not provide sufficient
differentiation (Carpenter & Nakamoto, 1989). The application of assimilation versus
contrast depends on the degree to which a novel object is perceived as similar to a
specific category. High levels of similarity are likely to lead to assimilation, whereas
low levels of similarity are likely to lead to contrast (Herr, Sherman, & Fazio, 1983).
In addition, research suggests that assimilation/contrast effects depend on the context
in which a novel object is presented. In a series of studies of electronic agent
recommendations, the evaluation of unfamiliar recommendations was examined. It
was found that individuals tended to react negatively to unfamiliar recommendations.
However, these negative reactions were overcome by embedding the unfamiliar
recommendation among a set of familiar recommendations that individuals were
known to like. Although it was unlikely that they would purchase the familiar
recommendations (because they might have already owned these products), the
presence of the familiar recommendations increased the attractiveness of the
unfamiliar recommendations (Cooke, Sujan, Sujan, & Barton, 2002). By embedding
unfamiliar recommendations within a set of familiar recommendations, individuals
were provided with a set of appropriate category exemplars with which the unfamiliar
recommendation could be contrasted. This resulted in more favorable evaluations.
Similarly, it is possible to influence assimilation/contrast effects by making explicit
comparisons between an innovation and an existing product category. In marketing
practice, innovations are often explicitly compared to existing alternatives. By making
such comparisons, typical products on which innovation evaluation is based are
evoked in memory. Thereby, the perception of the innovation may either shift towards
existing alternatives or away from them (Buchanan, Simmons, & Bickart, 1999;
Herbert, Schwarz, & Bless, 1998; Schwarz & Bless, 1992). Whether assimilation or
contrast occurs in such circumstances depends on whether the new functionality is
offered in a product design that is typical of an existing functionality, or a design that
is atypical of an existing functionality (Ziamou & Ratneshwar, 2003).
Empirical evidence demonstrates that explicit comparisons are only effective when a
new functionality is offered in a product with an atypical product design. In this case,
the new product is contrasted with established products because individuals perceive a
mismatch between the salient features of the new product and the typical products
evoked in working memory. However, if a new functionality is offered in a product
40
with a typical product design, explicit comparisons are counter-productive because
they lead to assimilation. Customers will perceive a high overlap between the salient
features of the new product and existing products evoked in working memory (Ziamou
& Ratneshwar, 2003). Figure 3-5 summarizes the effects of explicit comparisons on
product evaluation depending on typicality of design.
Figure 3-5: Comparisons and Innovation Evaluation (Ziamou & Ratneshwar, 2003)
Explicit comparisons of a
new functionality with an
existing functionality
Customers mentally access the
goal-derived category that corresponds
to the existing functionality
New functionality is
offered in a product
that is typical of the
Customers evoke in working
memory products that are
typical of the goal-derived
category that corresponds to the
New functionality is
offered in a product
that is atypical of the
High feature overlap between
stimulus and the product(s)
evoked in working memory
Low feature overlap between
stimulus and the product(s)
evoked in working memory
Assimilation of new
functionality to existing
functionality
Contrast of new functionality
to existing functionality
41
3.2.4 Perceived Incongruity of Innovations
3.2.4.1 Perceived Incongruity and the Moderate Incongruity Effect
As the previous discussion implies, an innovation needs to be clearly differentiated
from existing alternatives so that customers respond favorably. From this arises the
question, whether an innovation is also evaluated positively if it is perceived as very
different from existing alternatives. The so-called moderate incongruity effect sheds
light on this.
In psychology and consumer behavior literature, incongruity refers to „the extent that
structural correspondence is achieved between the entire configuration of attribute
relations associated with an object, such as a product, and the configuration specified
by an associated schema (Meyers-Levy & Tybout, 1989, p. 40).“ Two extreme cases
of incongruity are distinguished. A complete match between an object and an activated
category schema refers to congruity. In contrast, a complete mismatch between
multiple features of an object and an activated category schema refers to incongruity.
Between these two extreme cases, different levels of incongruity may occur involving
both congruent and incongruent attributes (Mandler, 1982).
An innovation that is perceived as highly congruent with existing alternatives is
unlikely to result in a particularly positive response. Even though people appreciate
things that are predictable and correspond to their expectations, they evaluate high
congruity unfavorably, because they do not perceive it as exciting (Meyers-Levy &
Tybout, 1989). This is supported by research on curiosity which argues that events
characterized by a lack of unknown information are likely to evoke feelings of
boredom and disinterestedness (Min Jeong et al., 2009).
However, as an innovation becomes more incongruent with existing alternatives,
people begin to respond more favorably (Meyers-Levy & Tybout, 1989). This is
because increasing levels of incongruity represent knowledge gaps that are perceived
as interesting (Loewenstein, 1994) and motivate learning and exploration. Typically,
people engage in such processes in order to develop new knowledge, skills, and
experiences. If they are successful, they will experience positive affect (Silvia, 2008).
Thereby, incongruity will only yield positive affect as long as people expect they can
handle it. In contrast, if people think that they lack the ability to resolve incongruity,
they will be confused and respond unfavorably (Silvia, 2010).
Generally, confusion arises when the “environment is giving insufficient or
contradictory information (Keltner & Shiota, 2003, p. 82).” Research suggests that
42
such a situation is likely to occur at extreme levels of incongruity. Specifically, if
people are confronted with extreme levels of incongruity, they can only resolve it by
making fundamental changes to established knowledge structures (Jhang et al., 2012).
Such a process typically results in mental overload and unfavorable evaluation of an
innovation (Mandler, 1982). Support for a general aversion against extreme levels of
incongruity is provided by brand extension research. Specifically, this line of research
demonstrated that customers will only evaluate brand extensions as favorable as a long
as they perceive a certain fit between the extending product and the existing product
portfolio of a brand. If this is not the case, customers are likely to react unfavorably
towards the extending product (Aaker & Keller, 1990; Maoz & Tybout, 2002; Park,
Milberg, & Lawson, 1991).
Meyers-Levy & Tybout (1989) examined how individuals evaluate innovations with
different levels of incongruity. In line with the previous discussion, they identified an
inverted U-shaped relationship between perceived incongruity and innovation
evaluation (see Figure 3-6). Specifically, at high levels of perceived congruity, people
get bored and respond unfavorably to an innovation. However, as perceived
incongruity increases, curiosity is evoked, and people begin to find innovations
interesting and respond favorably. Increasing levels of incongruity will further
enhance innovation evaluation until moderate incongruity is reached. Beyond that
point, innovation evaluation will decrease again, because resolution of incongruity
becomes more and more difficult. At extreme levels of perceived incongruity, people
will become confused and respond unfavorably (Kashdan & Silvia, 2009).
43
Figure 3-6: The Moderate Incongruity Effect (Meyers-Levy & Tybout, 1989)
While research consistently defines incongruity as the function of a mismatch between
an innovation and an associated category schema, it less clear what exactly
distinguishes moderate incongruity from extreme incongruity. Jhang et al. (2012) have
proposed that different levels of incongruity may be distinguished on the basis of
associations individuals need to make in order to resolve incongruity. According to
this view, congruent innovations provide attributes that are directly related to preexisting category knowledge. An example of this is vitamin-fortified orange juice that
is congruent with the orange juice schema, because vitamins represent an existing
association of the orange juice category (see A in Figure 3-7). In comparison,
moderately incongruent innovations provide attributes that are not part of prior
category knowledge, but can be resolved by activating preexisting shared associations
between a category and the attribute. An example of this is vitamin-fortified coffee
(see B in Figure 3-7). Vitamins do not represent a common association for coffee.
However, vitamin and coffee are both associated with a “good start to the day”. By
activating this shared association, incongruity can be resolved. By contrast, extremely
incongruous innovations neither include attributes that are part of established category
associations nor hold any associations that they share with a category. An example is
vitamin-fortified vodka (see C in Figure 3-7). Vitamins are neither associated with
vodka, nor do they share a common association with it.
44
FigureNew
3-7:
Perception
of Product
Enhancing
Product
Acceptance
Incongruity (Jhang et al., 2012)
249
Figure 1
DEFINITION OF PRODUCT INCONGRUITY
Enhancing New Product Acceptance
A: Congruent:
249
Vitamin-Fortified Orange Juice
A: Congruent: Vitamin-Fortified Orange Juice
Figure 1
Enhancing New Product Acceptance DEFINITION OF PRODUCT INCONGRUITY
249
Figure 1
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45
It needs to be accentuated that the moderate incongruity effect does not apply, if
incongruity-based affect is overwhelmed by other sources of affect. Specifically, an
innovation may relate to a category schema, which evokes strong affective reactions.
These reactions are typically based on associations to specific attributes of other
products in that category (Peracchio & Tybout, 1996). That is, previous experiences
with products of a category are likely to influence affective reactions to new products
in that category (Boush et al., 1987). This relationship is supported by research on
brand extensions which found that extending products evoke affective impressions
previously developed with other products of a brand (Aaker & Keller, 1990).
3.2.4.2 Coping with Extreme Levels of Incongruity
Research suggests that the perception of extreme incongruity does not always lead to
negative evaluation of an innovation. In certain situations, people may be able to
resolve extreme incongruity. If this is the case, even extremely incongruent new
products are likely to be evaluated favorably. Literature provides different possibilities
of extreme incongruity resolution. Firstly, extreme incongruity can be resolved, if
individuals are provided with comprehensive knowledge about an innovation and
associated ideas. More developed knowledge structures generally enable people to
cope with both schema-consistent and schema-inconsistent information (Fiske &
Taylor, 1991). Accordingly, research found that building extensive and wellinterconnected knowledge structures, allows people to resolve extreme incongruity
with reasonable cognitive effort (Peracchio & Tybout, 1996).
Secondly, resolution of extreme incongruity may be enabled by prompting people to
consider multiple perspectives and alternatives when learning about an innovation. In
other words, people are likely to resolve extreme incongruity if they think flexibly
about an innovation (Murray, Sujan, Hirt, & Sujan, 1990). This was shown in a study
by Jhang et al. (2012) in which participants were asked to evaluate an extremely
incongruent new product. Prior to evaluation, however, participants were either primed
with flexible or with non-flexible thinking. Therefore, they received a short story in
which a can of cola explodes inside a car on a hot day. After they had read the story,
participants in the flexible thinking condition were asked to think of as many
explanations for the explosion as they could. In contrast, participants in the nonflexible thinking condition were asked to think of only one explanation for the
explosion of the can. The results of the study showed that participants in the flexible
thinking condition evaluated extremely incongruent new products significantly more
favorably than participants in the non-flexible thinking condition.
46
Thirdly, extreme incongruity may be resolved, if a strong benefit rationale is apparent.
Empirical evidence for this relationship is provided by a recent experimental study, in
which participants were asked to evaluate an extremely incongruent new product,
vitamin-fortified vodka. In this study, each participant was assigned to one of two
conditions: a benefit rationale-absent condition and a benefit rationale-present
condition. Prior to evaluation, participants in the benefit rational-absent condition only
received a short description of the vitamin-fortified vodka. Participants in the benefit
rational-present condition were additionally informed that vodka is dehydrating, and
that replacing lost vitamins can help people feel better. The study showed that the
presence of a strong benefit rationale significantly improved evaluation of the
extremely incongruent new product. This implies that a strong benefit rationale
generally helps people to make sense out of extremely incongruent new products and,
as a consequence, is likely to result in positive responses (Jhang et al., 2012).
Now that all important aspects of innovation recognition have been discussed, the
second process of innovation perception, innovation assessment, is examined next.
47
3.3 Innovation Assessment
3.3.1 Determinants of Intention and Behavior
3.3.1.1 Model of Reasoned Action
In case of innovation assessment, people get intensively involved with an innovation
and decide whether to accept it or not. Accordingly, innovation acceptance represents
the ultimate measure for favorable perception of an innovation in the innovation
assessment process. Thereby, innovation acceptance is strongly determined by the
formation of mental scenarios about an innovation’s impact on everyday life (Ziamou,
2002). In other words, innovation acceptance represents an informed decision about
the behavioral consequences arising from the usage of an innovation (Bagozzi, Davis,
& Warshaw, 1992). A model that provides valuable insights about innovation
acceptance represents the Model of Reasoned Action (MRA). MRA is a general model
which predicts human behavior across different domains. It represents an important
starting point for the examination of innovation acceptance.
MRA assumes that most behavior is strongly determined by people’s intention to
perform that behavior. Specifically, people are more likely to perform a specific action
if they have formed strong intentions towards it (Fishbein & Ajzen, 2010). Intentions
capture the motivational factors which determine whether to engage or not engage in a
behavior. In other words, intentions indicate how hard people are willing to try and
how much effort they plan to exert (Ajzen, 1991). Even though there will not always
be perfect congruence between intention and behavior, people will normally act
according to their intentions, as long as no unforeseen events occur (Ajzen & Fishbein,
1980). This is also the case for innovation acceptance.
Davis, Bagozzi, & Warshaw (1989) provide empirical evidence for the positive
relationship between intention and innovation acceptance. In their study, they collected
data from 107 full-time MBA students on their acceptance of the word-processing
program WriteOne. At the beginning of their first semester, students were introduced
to the program and asked to answer a questionnaire containing measures of intention
towards its usage. After 14 weeks, at the end of the first semester, the students were
asked to answer a second questionnaire with intention measures and a 2-item selfreported usage measure. It was shown that intentions measured directly after
introduction correlated at 0.35 with behavior 14 weeks later. Intentions and usage
measured contemporaneously at the end of the semester correlated at 0.63.
48
The notion that intentions predict behavior is intuitively understandable and does not
yet provide valuable insight. To actually explain behavior, the motivational factors of
intention must be specified. MRA distinguishes attitude toward behavior and
subjective norm as key motivational factors towards behavior. Attitude toward
behavior refers to “the individual’s positive or negative feelings (evaluative affect)
about performing the target behavior’ (Fishbein & Ajzen, 1975, p. 216).” It refers to
the evaluation of an action as good or bad. Subjective norm concerns “the person’s
perception that most people who are important to him think he should or should not
perform the behavior in question (Fishbein & Ajzen, 1975, p. 302).” It refers to the
social pressures a person associates with the action.
However, motivational factors will only predict behavior sufficiently if a people can
decide at will whether or not to engage in the behavior in question. This is often not
the case. As a matter of fact, numerous actions depend on the availability of requisite
opportunities and resources. These aspects refer to people’s behavioral control over
actions. Research suggests that behavior is normally jointly determined by
motivational factors (attitude toward behavior and subjective norm) and ability
(behavioral control). Motivation influences performance where a person has
behavioral control, and behavioral control increases performance if the person is
motivated to try. Accordingly, MRA identified behavioral control as another predictor
of intention and behavior. Perceived behavioral control refers to the perceived ease or
difficulty associated with an action. It reflects anticipated impediments and obstacles
as well as past experiences (Ajzen, 1991).
MRA points out that attitude toward behavior and subjective norm have a different
impact on behavior than perceived behavioral control (see overview of MRA Figure
3-8). Specifically, attitude toward behavior and subjective norm only influence
behavior indirectly via intention. In contrast, perceived behavioral control additionally
influences behavior directly. Research assumes a direct influence of perceived
behavioral control on behavior, because perceived behavioral control increases
people’s confidence towards performing a behavior effectively. Specifically, it is
argued that if two persons have strong intentions towards engaging in some type of
behavior, the person with higher confidence in his ability to master this challenge is
more likely to succeed than the person who has doubts in his ability (Ajzen, 1991).
49
Figure 3-8: Model of Reasoned Action (Ajzen, 1991)
Attitude toward
the behavior
Subjective norm
Intention
Behavior
Perceived behavioral
control
The question which arises, then, concerns the relative weight of attitude toward
behavior, subjective norm, and perceived behavioral control in influencing intention
and behavior. MRA argues that the relative weights of these factors are situation
specific. That is, relative weights must be determined with regard to the respective
behavior in question. In some situations, one or two of the factors may have priority.
In many other situations, all three may be equally important (Fishbein & Ajzen, 1975).
3.3.1.2 Formation of Intentions towards Behavior
MRA suggests that people form intentions towards a behavior on the basis of beliefs
about that behavior. Three kinds of beliefs are distinguished: behavioral beliefs,
normative beliefs, and control beliefs (Fishbein & Ajzen, 2010). As Figure 3-9
indicates, behavioral beliefs determine attitudes towards behavior, normative beliefs
determine subjective norm, and control beliefs determine perceived behavioral control.
50
Figure 3-9: Determinants of Behavior and Associated Beliefs
Beliefs about Behavior
Determinants of Behavior
Behavioral beliefs
Attitude toward behavior
Normative beliefs
Subjective norms
Control beliefs
Perceived behavioral control
Behavioral Beliefs
Behavioral beliefs are concerned with the positive or negative consequence that people
might experience when they perform the behavior. People will form more positive
attitudes towards the behavior if they expect more positive and less negative outcomes
to follow from that behavior (Fishbein & Ajzen, 2010). Thus, people are more likely to
accept an innovation if they believe that its use will provide many favorable
consequences and only a few unfavorably consequences (Davis et al., 1989).
The relationship between behavioral beliefs and attitude towards behavior has been
expressed using a mathematical equation. Attitude towards behavior results from the
sum of the product of behavioral beliefs (bi) about outcomes and the evaluation (ei) of
these outcomes (Davis et al., 1989).
Attitude towards behavior =
∑b e
i i
51
Normative Beliefs
Normative beliefs refer to perceived social pressure to engage or not engage in the
behavior. Normative beliefs are based on expectations that important others would
approve or disapprove of the behavior. If many others think that the behavior should
be carried out, and if the majority of them actually perform the behavior, people are
likely to feel high social pressure to engage in that behavior (Fishbein & Ajzen, 2010).
Accordingly, people are more likely to accept an innovation, if they think that
important others will appreciate acceptance (Davis et al., 1989).
Research formulated a mathematical equation of the relationship between normative
beliefs and subjective norm. According to this equation, subjective norm associated
with the behavior results from the sum of the product of normative beliefs (nbi) and the
motivation to comply (mci) with these beliefs (Davis et al., 1989):
Subjective norm =
∑ nb mc
i
i
It must be emphasized that a belief about performing a behavior that involves a
referent does not automatically represent a normative belief. In many cases, one may
have a belief that engaging in some type of behavior is likely to please another person.
Such a belief represents more of a behavioral belief, and therefore influences attitude
towards behavior rather than subjective norm. According to MRA, normative beliefs
refer only to situations in which another person thinks one should or should not engage
in the behavior in question (Ajzen & Fishbein, 1980).
Control Beliefs
Control beliefs refer to expectations of personal and environmental factors that
promote or impede people’s attempts to engage in that behavior (Fishbein & Ajzen,
2010). They deal with the presence or absence of resources and opportunities required
for performing an action effectively. The more resources and opportunities people
believe they possess, and the fewer obstacles or impediments they anticipate, the
greater should be their perceived behavioral control (Ajzen, 1991). Hence, people are
more likely to accept an innovation, if they think they are capable of using the
innovation effectively (Davis et al., 1989).
52
A mathematical equation of the relationship between control beliefs and perceived
behavioral control has been formulated. According to this equation, perceived
behavioral control results from the sum of products of control beliefs (ci) and the
perceived power (pi) of the control beliefs (Ajzen, 1991):
Perceived behavioral control =
∑c p
i
i
3.3.2 Determinants of Innovation Acceptance
Even though MRA provides a good starting point for explaining innovation
acceptance, it only represents a general model to predict behavior. In this way, it treats
important aspects of innovation acceptance rather superficially. Following this,
research derived a series of models which predict innovation acceptance more
specifically (Venkatesh et al., 2012). These models include the Technology
Acceptance Model (TAM), the Motivational Model (MM), the Theory of Planned
Behavior (TPB), the Model of PC Utilization (MPCU), Innovation Diffusion Theory
(IDT), and the Social Cognitive Theory (SCT). All of these models are based on the
same basic concept of innovation acceptance depicted in Figure 3-10. In accordance
with MRA, these models assume that innovation acceptance is strongly determined by
the behavioral consequences arising from the use of an innovation. Each of the models
suggests a different set of distinct factors as determinants of innovation acceptance.
These were intensively examined during the last decades (Venkatesh et al., 2003).
Figure 3-10: Basic Concept of Innovation Acceptance (Venkatesh et al., 2003)
From the different models of innovation acceptance, Venkatesh et al. (2003) derived
the Unified Theory of Acceptance and Use of Technology (UTAUT). It represents a
comprehensive synthesis of existing models. Initially, UTAUT identified four key
factors that influence innovation acceptance. These factors include: performance
expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh
et al., 2003). To fit the consumer context, research identified three additional factors
53
that complement the initial factors of UTAUT. These include: hedonic motivation,
price value, and habit (Venkatesh et al., 2012).
Table 3-2 provides an overview over key factors of UTAUT. Each of these factors
will now be discussed in more detail.
Table 3-2: Core Constructs UTAUT (Venkatesh et al., 2012)
Construct
Definition
Performance Expectancy
Degree to which one perceives that using an innovation provides
benefits in performing certain activities
Effort Expectancy
Degree to which one perceives the use of a particular innovation as
easy or difficult
Social Influence
Degree to which one perceives that important others (e.g. friends and
family) believe one should use an innovation
Facilitating Conditions
Degree to which one perceives that resources and support is available
to facilitate the use of an Innovation
Hedonic Motivation
Degree to which one perceives the use of an innovation to be
associated with fun or pleasure
Price Value
Cognitive tradeoff between perceived benefits of using an innovation
and monetary costs associated with its use
Habit
Degree to which individuals tend to perform automatically during
innovation usage because of learning
3.3.2.1 Performance Expectancy
Performance expectancy is defined as the degree to which using a technology will
provide benefits to potential customers in performing certain activities (Venkatesh et
al., 2012). Generally, people are more likely to perform behaviors they believe will
result in valued outcomes than those which they do not expect to provide favorable
consequences (Compeau & Higgins, 1995). Accordingly, performance expectancy is
related to task accomplishment and productivity (Venkatesh et al., 2003). This implies
that it is primarily based on functional or instrumental considerations (Smith &
Colgate, 2007). Performance expectancy has consistently been demonstrated to be the
strongest predictor of innovation acceptance (Venkatesh et al., 2003). Following these
54
considerations, Venkatesh et al. (2012) developed a measurement scale for
performance expectancy which is depicted in Table 3-3.
Table 3-3: Performance Expectancy Scale (Venkatesh et al., 2012)
1.
I find […] useful in my daily life.
2.
Using […] helps me accomplish things more quickly.
3.
Using […] increases my productivity.
Performance expectancy is closely related to relative advantage, a construct initially
introduced by IDT (Tornatzky & Klein, 1982). Relative advantage denotes the degree
to which an innovation is perceived as being better than the idea it supersedes.
Subdimensions of relative advantage include economic profitability, low initial cost,
decrease in discomfort, savings in time and effort, and immediacy of reward (Rogers,
2003). To be effective, an innovation must add significant value in at least one of these
subdimensions. However, if an innovation adds little or no improvements to existing
alternatives, people will react unfavorably (Simonson, Carmon, & O’Curry, 1994).
Performance expectancy and relative advantage particularly arise from an innovation’s
perceived usefulness. Perceived usefulness is defined as the perception that an
innovation provides benefits in the performance of some task (Davis, 1989; Karahanna
& Straub, 1999; Kulviwat et al., 2007). People form usefulness judgments by
cognitively comparing what an innovation is capable of doing with what they need to
get done. This process is based on so-called cognitive instrumental determinants of
behavior. These comprise goal relevance, output quality, result demonstrability, and
perceived ease of use. Goal relevance refers to the degree to which an innovation is
perceived as applicable to the achievement of one’s goals. The more goal relevant
tasks an innovation is capable of supporting, the more useful it is. Over and above the
considerations of what tasks an innovation is capable of, and the degree to which these
tasks match one’s goals, people will take into account how well the innovation
performs those tasks, which is referred to as perceptions of output quality.
Furthermore, result demonstrability refers to the tangibility of the results of using an
innovation. If an innovation produces goal relevant results desired by customers, but
does so in an obscure manner, customers are unlikely to understand how useful the
innovation really is. Finally, perceived ease of use has a positive impact on usefulness
55
such as the less effortful an innovation is to use, the more it can increase task
performance (Venkatesh & Davis, 2000).
3.3.2.2 Effort expectancy
Effort expectancy is defined as the degree of ease associated with the use of a
technology (Venkatesh et al., 2012). It derives from the difficulty of learning how to
make use of an innovation effectively, the propensity to make errors when using it, and
inefficiencies in using it (McLaughin & Skinner, 2000). The time required to perform
a task, the ratio of favorable to unfavorable interactions, and the number of errors are
typical operationalizations of effort expectancy (Nielsen, 1993). Effort expectancy is
constructed rather than retrieved. It may be based on prior knowledge, the observable
experiences of others, and marketing messages (Wood & Moreau, 2006). Following
these considerations, Venkatesh et al. (2012) developed a measurement scale for effort
expectancy which is depicted in Table 3-4.
Table 3-4: Effort Expectancy Scale (Venkatesh et al., 2012)
1.
Learning how to use […] is easy for me.
2.
My interaction with […] is clear and understandable.
3.
I find […] easy to use
4.
It is easy for me to become skillful at using […].
Effort expectancy strongly relates to the easy-of-use construct which was originally
introduced by TAM (Davis, 1989; Venkatesh & Davis, 1996). Ease-of-use is formally
defined as “the degree to which a person believes that using a particular system would
be free of effort (Davis, 1989, p. 320).” This definition was derived from the word
‘ease’, which means ‘freedom from difficulty or great effort’ (Davis, 1989). The
construct is based on the consideration that effort is a limited resource, and therefore
one needs to deploy it economically (Radner & Rothschild, 1975). Perceived ease-ofuse is associated with people’s self-efficacy beliefs and procedural knowledge about
how to effectively make use of an innovation (Venkatesh & Bala, 2008). Thereby,
ease-of-use involves both mental and physical effort. Mental effort is determined by
the degree to which the operation and benefits of an innovation are difficult to learn
(Veryzer, 1998a). By comparison, physical effort is determined by the degree to which
56
using an innovation feels uncomfortable or requires high bodily strain (Mugge &
Schoormans, 2012).
Effort expectancy and perceived ease-of-use particularly arise from an innovation’s
usability. Usability refers to the convenience associated with the use of an innovation
(Mugge & Schoormans, 2012). Thereby, usability is viewed as a multidimensional
construct which consists of the following attributes: Learnability, efficiency,
memorability, errors, and satisfaction. Learnability is the ease with which people can
learn to use an innovation. The more rapidly people can get some work done with an
innovation, the higher is its learnability. Efficiency is the level of productivity people
can achieve with an innovation. Memorability is the ease with which people can
remember how to effectively make use of an innovation. Memorability is high when
people can return to an innovation after some period of time and use it effectively
without having to learn everything all over again. Errors refer to negative
consequences resulting from faulty operation or misuse of an innovation. Thus, an
innovation should be designed in such a way that errors are unlikely to occur. If,
nonetheless, an error does occur, an innovation should rapidly recover from the error.
Satisfaction is the degree to which an innovation is pleasant to use. Put differently,
people should be satisfied when using an innovation (Nielsen, 1993).
3.3.2.3 Social Influence
Social influence is the extent to which potential customers perceive that important
others (e.g. friends, family, or colleagues) will evaluate their usage of an innovation
favorably (Venkatesh et al., 2012). It represents one of the most important motivations
to adopt an innovation (Rogers, 2003). The construct follows from the assumption that
the behavior of potential customers is influenced by the way that others will view them
as a result of innovation usage (Venkatesh et al., 2003). Following these
considerations, Venkatesh et al. (2012) developed a measurement scale for social
influence which is depicted in Table 3-5.
57
Table 3-5: Social Influence Scale (Venkatesh et al., 2012)
1.
People who are important to me think that I should use […].
2.
People who influence my behavior think that I should use […].
3.
People whose opinions I value prefer that I use […].
Social influence arises on the one hand from subjective norm. It is defined as the
degree to which one believes that most people who are important to oneself believe
one should or should not use an innovation (Venkatesh & Bala, 2008; Venkatesh &
Davis, 2000). People may choose to adopt an innovation, even if they themselves do
not have a favorable attitude towards that innovation (Venkatesh & Davis, 2000). A
social influence mechanism that is very similar to subjective norm is compliance. This
refers to a situation in which one performs a certain behavior with the purpose of
attaining reward or avoiding punishment (Miniard & Cohen, 1979; Venkatesh &
Davis, 2000). Compliance occurs if one believes that a referent wants him or her to
perform a specific behavior, and the referent has the ability to reward behavior or
punish non-behavior (French & Raven, 1959; Kelman, 1961; Venkatesh & Davis,
2000; Warshaw, 1980).
Subjective norm may influence innovation acceptance directly based on compliance
(Venkatesh & Bala, 2008). Additionally, subjective norm may influence innovation
acceptance indirectly via usefulness. This is because subjective norm may stimulate
internalization. The concept of internalization is the process by which an individual
perceives that important others belief that he or she should use an innovation and begin
to integrate that belief into his or her own belief structure. Over time, the individual
actually begins to belief that the innovation is useful. Following this, if important
others suggest that a specific innovation might be useful, one may come to the
conclusion that it is actually useful, and one may develop an intention to use that
innovation (Venkatesh & Bala, 2008).
Research suggests that the effects of subjective norm decline over time. It was found
that the opinions of others primarily play a role when direct experience with an
innovation is lacking (Hartwick & Barki, 1994). However, after implementation, when
an individual has experienced the strengths and weaknesses of an innovation first
hand, influence of subjective norm begins to diminish (Venkatesh & Davis, 2000).
58
Similarly, Agarwal & Prasad (1997) demonstrated that subjective norm first and
foremost motivates people to overcome the hurdle of initial use.
Besides subjective norm, social influence may also arise from image considerations.
Image is understood as the degree to which the adoption of an innovation is perceived
to improve one’s image or social status (Moore & Benbasat, 1991). For some
innovations, image enhancement is believed to be almost the sole benefit provided.
This is particularly the case for innovations in fashion. Trends in fashion typically gain
momentum within a very short period of time. As soon as a fashion trend is broadly
established, however, it does not improve image anymore and vanishes. Arising from
these considerations, it is argued that status-conferral plays an important role in any
innovation that is highly visible, such as new cars or hairstyles (Rogers, 2003).
Research shows that image considerations may even result in an overadoption of
innovations. In case of overadoption, an individual adopts an innovation despite the
fact that it does not make sense from a rational point of view. Mobile devices and
laptops represent two product classes that are often overadopted. Many people buy
high-speed computers and solely use these for word processing or other tasks for
which a much less powerful computer would be sufficient. Similarly, many people buy
multifunctional smart phones and then use them only for making phone calls or for
text messaging (Rogers, 2003).
Similar to subjective norm, image has a direct effect on innovation acceptance. In
addition, image influences innovation acceptance indirectly via usefulness (Venkatesh
& Bala, 2008). The influence of image on perceived usefulness is based on the fact
that modern society is characterized by a high degree of interdependence between the
individual and other social actors in carrying out one’s duties. Social status is a source
of power and influences processes such as social exchange, coalition formation, and
resource allocation. Power and influence resulting from status gains often provide an
important basis for greater productivity in a given task (Venkatesh & Davis, 2000).
3.3.2.4 Facilitating Conditions
Facilitating conditions are potential customers’ perceptions of the resources and
support available to perform a behavior (Venkatesh et al., 2012). All of these concepts
reflect aspects of the technological and/or organizational environment with the purpose
of removing barriers to adoption (Venkatesh et al., 2003). Following these
considerations, Venkatesh et al. (2012) developed a measurement scale for facilitating
conditions which is depicted in Table 3-6.
59
Table 3-6: Facilitating Conditions Scale (Venkatesh et al., 2012)
1.
I have the resources necessary to use […].
2.
I have the knowledge necessary to use […].
3.
[…] is compatible with other technologies I use.
4.
I can get help from others when I have difficulties using […].
Facilitating conditions arise from an innovation’s compatibility with potential
customers. Compatibility is the degree to which an innovation is perceived as
consistent with existing values, past experiences, and the needs of customers. It allows
customers to give meaning to an innovation, so that it is regarded as more familiar.
Naming an innovation and positioning it relative to previous ideas are important means
of making innovations more compatible (Rogers, 2003).
Research distinguishes between two interpretations of the compatibility construct. The
first interpretation refers to normative compatibility. This depends on an innovation’s
correspondence to the values and norms of customers. Normative compatibility is an
innovation’s compatibility with what people feel or think (Tornatzky & Klein, 1982).
It is concerned with the compatibility of an innovation with previously adopted ideas.
Old ideas represent an important means of assessing innovations and give these
innovations meaning. Customers cannot make sense of a new idea other than by
reference to something familiar. Previous ideas provide a standard that makes it easier
to draw inferences about an innovation (Rogers, 2003).
A study by Hawley (1946) demonstrates the important role of normative compatibility.
The study examines the adoption of Roman Catholicism by Native Americans. It finds
that Eastern Pueblo Indians in Arizona and New Mexico readily accepted the religion.
By contrast, Western Pueblo Indians rejected the religion, and even killed the Spanish
priests that proselytized it. Hawley concludes that different reactions to Roman
Catholicism may be best explained by normative compatibility. Specifically, the
family structure of Eastern Pueblo Indians was heavily patrilineal and father-oriented,
and therefore corresponded to a religion in which god is described as a male figure.
For Western Pueblo Indians, however, this compatibility was not apparent because
their family structure was mother-centered.
60
Normative compatibility also concerns whether an innovation addresses current, latent
or future needs. Current needs are customers’ expressed needs. Latent needs are those
that customers cannot articulate. Future needs are those of which customers are not yet
aware of (Narver, Slater, & MacLachlan, 2004). If an innovation appeals to current
needs, normative compatibility is high. However, if an innovation addresses latent and
future needs, normative compatibility is low. In such a situation, normative
compatibility needs to be increased first. This may be done by revealing the specific
needs that are addressed by the innovation.
The second interpretation of compatibility refers to operational compatibility. This is
defined as the extent to which an innovation is consistent with existing practices.
Operational compatibility is determined by how smoothly customers can integrate an
innovation into their daily routines (Tornatzky & Klein, 1982). For adoption of electric
cars, for example, operational compatibility is very important. Electric cars have a
significantly lower range than cars with petrol engine. Furthermore, charging times of
electric cars are relatively long, and a widespread network of charging stations does
not exist yet. Thus, for many potential customers, particularly those who have to travel
long distances on a daily basis, the adoption of electric cars does not make sense.
Operational compatibility depends on whether an innovation disrupts existing
complementarities between a product category and the context in which it is used. Two
types of complementarities are distinguished: product-user complementarities and
product-product complementarities. Product-user complementarities refer to
interrelationships between a product category and the user, whereas product-product
complementarities refer to interrelationships between a product category and other
products with which it is used. If an innovation disrupts one or more of these
established complementarities, the rate of adoption is likely to decrease significantly
(Dhebar, 1995).
Disruption of product-customer complementarities may occur in three ways: firstly, an
innovation may be a disruption in terms of its touch and feel. Touch and feel are
highly important: they often represent the defining traits of established product
categories. Secondly, an innovation may disrupt the way in which customers are
informed about the state and performance of a given product category. Many products
contain devices such as gauges that keep the customer informed about the current state
of the product. Typically, customers become familiar with the way they are informed
about the state and performance of a specific product category. Often, this familiarity
is neither easy to attain nor easily forgotten. An example is the speedometer in cars,
which is typically very similar across manufacturers. Thirdly, an innovation may
61
disrupt the way in which the customer interacts with the underlying product category.
In many cases, customers have to make substantial investments in learning how to
operate products. Once these skills are developed, it is difficult to unlearn them. An
example is the placement of keys on a computer keyboard (Dhebar, 1995).
Furthermore, disruption of product-product complementarities may occur in different
ways. A large number of products are used along with other products as part of a
multicomponent system. To function effectively, the different system components
must be compatible with each other. Industrial-organization economics literature has
invested considerable effort in the examination of intercomponent complementarity,
that is, the standardization of the method by which different components interface with
each other. Consequently, a distinction should be drawn between technical and
physical complementarity. Disruption of technical complementarity occurs if a product
lacks a standardized interface that is necessary for the product to work effectively with
other products. In contrast, disruption of physical complementarity occurs if a product
is designed to be physically connected with other products but the new product does
not support these connections (Dhebar, 1995).
3.3.2.5 Hedonic Motivation
Hedonic motivation’ is the positive feelings and emotions derived from using a
technology (Agarwal & Karahanna, 2000; Heijden, 2004; Venkatesh et al., 2012). So
far, only a few studies have addressed the influence of hedonic motivation on
innovation acceptance. This is surprising, given the fact that it was repeatedly found to
play an important role in acceptance and use of technology (Brown & Venkatesh,
2005; Childers, Carr, Peck, & Carson, 2001). Childers et al. (2001), for example,
conducted a study in which they identified enjoyment as a core predictor of attitude
towards interactive shopping. Similarly, Dabholkar & Bagozzi (2002) found that fun
significantly affects technology-based self-service acceptance. Finally, Bruner &
Kumar (2005) showed that fun has a direct effect on actual usage of a new product.
Following these considerations, Venkatesh et al. (2012) developed a measurement
scale for performance expectancy which is depicted in Table 3-7.
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Table 3-7: Hedonic Motivation (Venkatesh et al., 2012)
1.
Using […] is fun.
2.
Using […] is enjoyable.
3.
Using […] is very entertaining.
Besides fun and enjoyment, many other emotions may influence hedonic motivation.
On the positive side, people may be pleasantly surprised, excited, or confident.
Conversely, they may be annoyed, worried, or scared (Kulviwat et al., 2007). In order
to capture the full range of emotions that people may experience when interacting with
an innovation, Kulviwat et al. (2007) applied emotion theory to explain innovation
adoption more effectively. Emotion theory suggests that any kind of emotion can be
described in terms of two core dimensions: pleasure and arousal (Armstrong &
Detweiler-Bedell, 2008). Pleasure is the degree to which one experiences an enjoyable
reaction. Arousal refers to a combination of mental alertness and physical activity
(Kulviwat et al., 2007; Nasco, Kulviwat, Kumar, & Bruner, 2008).
Thereby, pleasure ranges from unpleasant to pleasant emotional states and arousal
ranges from deactivating to activating emotional states. Together, pleasure and arousal
open a two-dimensional space. Within this space, different kinds of emotional states
arrange themselves along a circle around the intersection of the pleasure and arousal
dimensions of emotion (see Figure 3-11). Research refers to this circle as the affectcircumplex (Russell & Barrett, 1999). An example for a pleasant deactivating
emotional state is tranquility, whereas an example for a pleasant activating emotional
state is elation. In comparison, an example for an unpleasant activating emotional state
is anger, while an example for an unpleasant deactivating emotional state is sadness
(Armstrong & Detweiler-Bedell, 2008).
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Figure 3-11: The Affect Circumplex (Armstrong & Detweiler-Bedell, 2008)
High Arousal
e.g. elation
Pleasant
Unpleasant
e.g. anger
e.g. sadness
e.g. tranquility
Low Arousal
It was found that pleasure and arousal represent important predictors of innovation
acceptance. More precisely, the more pleased or excited and the less frustrated or
confused people are about an innovation, the more likely it is that they will form a
favorable attitude toward the innovation and adopt it. Thus, when designing and
communicating an innovation, marketers should not only focus on its functionality, but
also on the full range of emotional reactions that it may evoke. That is, strong positive
emotional reactions should be created, while negative emotional reactions should be
avoided (Kulviwat et al., 2007; Nasco et al., 2008). Positive emotions can be
particularly evoked by designing an innovation in such a way that it is aesthetically
appealing (Creusen & Schoormans, 2005; Goode et al., 2012; Radford & Bloch,
64
2011). At the same time, negative reactions can be prevented by making innovations
highly comfortable to use (Billeter, Kalra, & Loewenstein, 2011; Lakshmanan &
Krishnan, 2011; Mugge & Schoormans, 2012; Wood & Moreau, 2006).
3.3.2.6 Price Value
Price value is defined as potential customers’ cognitive tradeoff between the perceived
benefits of the applications and the monetary costs of using them. Potential customers
perceive price value as positive as long as the perceived benefits associated with
acceptance and use of technology are perceived as greater than the monetary costs
(Venkatesh et al., 2012). Following these considerations, Venkatesh et al. (2012)
developed a measurement scale for performance expectancy which is depicted in
Table 3-8.
Table 3-8: Price Value Scale (Venkatesh et al., 2012)
1.
[…] is reasonably priced.
2.
[…] is good value for money.
3.
At the current price, […] provides good value.
Specifically, it is argued that many promising innovations are rejected because they
are too expensive for potential customers. To succeed, an innovation must be offered
at a reasonable price. This was the case with the personal digital assistant, PalmPilot.
At a selling price of only a few hundred dollars, it provided a real bargain to customers
by combining a digital address book, a digital date book, and a digital notebook in a
small portable handheld device. In fact, 65% of PalmPilot customers purchased the
device because they heard of it from other people. In many cases, these people
mentioned the attractive price of the device as a factor in their decision to purchase the
device (Rogers, 2003).
3.3.2.7 Habit
Habit is the extent to which potential customers tend to perform automatically because
of learning. This implies that habit is determined by the degree of interaction and
familiarity with a certain technology. Consequently, prior use represents a strong
predictor of habit. However, research suggests that habit extends beyond mere
65
experience, which solely reflects the passage of time from the initial use of the
technology by potential customers (Venkatesh et al., 2012). Following these
considerations, Venkatesh et al. (2012) developed a measurement scale for habit which
is depicted in Table 3-9.
Table 3-9: Habit Scale (Venkatesh et al., 2012)
1.
The use of […] has become a habit for me.
2.
I am addicted to using […].
3.
I must use […].
Research demonstrates that acceptance and use of innovation is likely to increase
considerably if their use corresponds to the existing habits of customers. The Nintendo
Wii game console represents an innovation which is strongly based on existing habits.
When playing with the Wii, people can rely on body movements that feel natural to
them, such as swinging a tennis racket or rolling a bowling ball. This represents a key
success factor of this innovative game console (Billeter et al., 2011). Another
successful innovation that is strongly based on existing customer habits represents the
Apple iPad. On the iPad, all eBooks are visualized on a wooden bookshelf. This
corresponds to the way with which customers have stored their books in the past.
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3.3.3 Risk Associated with Innovation Acceptance
3.3.3.1 Types of Risks associated with Innovation Acceptance
Innovations generally represent a risk to customers, and pose potential unfavorable
side effects that customers cannot anticipate (Ram & Sheth, 1989). Risks associated
with innovations may be defined as the degree to which risks are perceived as
associated with an innovation (Ostlund, 1974). Research identified a constellation of
such risks (Rijsdijk & Hultink, 2003), including:
− performance risk
− financial risk
− social risk
− psychological risk
− physical risk
− risk of time loss.
Performance risk is the risk that there is something faulty with an innovation, or that
the innovation will not work correctly (Jacoby & Kaplan, 1972). It also includes
concerns about the expected utility of an innovation (Castaño et al., 2008). Many
customers develop performance concerns for an innovation because they lack prior
experience. They may be uncertain about the need for or benefit of an innovation.
Furthermore, they may have difficulty in assessing the technological advancement of
an innovation (Veryzer, 1998a). Performance risk is often based on the perception that
an innovation has not been fully tested prior to market introduction (Ram & Sheth,
1989).
Financial risk is the chance that the adoption of an innovation will have unfavorable
financial consequences for potential customers (Jacoby & Kaplan, 1972). The more
expensive an innovation is, the higher customers’ perceived financial risk. If an
innovation is very expensive, many customers may even postpone its purchase and
wait until a more advanced generation of the product is offered at a lower price (Ram
& Sheth, 1989). Besides the purchase price, financial risk may also arise from
implementation costs. Take electric cars, for example. For fast charging, customers
need to buy a so-called wallbox which shortens charging times significantly. In such a
case, financial risk involves both the purchase price of the car as well as the price of
the wallbox.
Social risk refers to the chance that others will judge the adoption of an innovation
unfavorably (Jacoby & Kaplan, 1972). Owing to a lack of prior experience, customers
67
may encounter difficulty in assessing whether an innovation is socially undesirable or
inappropriate (Castaño et al., 2008). If this is actually the case, adoption of the
innovation may have unfavorable social consequences, such as ostracism or peer
ridicule (Ram & Sheth, 1989). Social risk is based on concerns regarding the social
values of self-esteem and status. If potential customers feel uncertain towards these
values, they are likely to respond unfavorably to an innovation (Mick & Fournier,
1998). Social risk plays an important role if usage of an innovation is visible to others
(Thompson & Norton, 2011).
Psychological risk denotes the possibility that the adoption of an innovation will not
cohere with one’s self-image or self-concept (Jacoby & Kaplan, 1972). In many cases,
innovations are rejected because of emotional attachment to established technology or
traditional products that have become a central part of everyday life. Potential
customers may experience difficulty in letting go of these devices (Castaño et al.,
2008). An example represents automatic shifting in cars. Even though automatic
shifting is much more convenient than manual shifting, many customers reject this
technology. For them, manual shifting represents an important aspect of driving which
they do not want to give up.
Physical risk is the chance that the adoption of an innovation may be harmful or
injurious to the adopter’s health (Jacoby & Kaplan, 1972). Physical risk represents a
major issue for adoption of many innovations. Innovations based on highly advanced
technologies often evoke safety concerns (Veryzer, 1998a). In line with this, research
showed that physical risk is particularly evoked if an innovation is characterized by a
high level of autonomy. Autonomy refers in this context to the degree to which an
innovation is able to operate in an independent and goal-directed way without
interference of the user (Rijsdijk et al., 2011).
Risk of time loss refers to the chance that the adoption of an innovation is associated
with a waste of time, convenience, or effort (Roselius, 1971). Lack of experience using
the product necessitates that customers make significant investments of time and effort
before they can make use of an innovation effectively. Accordingly, many customers
are concerned with the risk of time loss and the associated difficulty to switch from an
old to a new technology (Castaño et al., 2008).
Research commends a variety of different strategies for reducing perceived risk. On
the one hand, perceived risk may be reduced by making the results of an innovation as
visible as possible (Rogers, 2003). The easier it is for customers to observe the results
of an innovation, the more likely they are to form a favorable opinion of that
68
innovation (Tornatzky & Klein, 1982). Personal channels, such as trade shows,
customer shows and personal selling demonstrations represent important means of
increasing the visibility of the results of an innovation. Personal channels are an
effective means of revealing benefits and relating them to specific usage situations.
Furthermore, personal channels show how to effectively make use of the benefits of an
innovation, and overcome obstacles related to changes in consumption patterns
(Guiltinan, 1999).
Another way to reduce perceived risk is to provide potential customers with an
opportunity to experiment with an innovation. Research demonstrates that a new idea
that can be tried on an installment plan will be adopted more quickly than an
innovation that is not divisible. By trying out an innovation, people can give meaning
to it, and find out how the innovation works under their own conditions. Thus, an
innovation should be designed in a way that allows potential customers to easily
experiment with it. Trying an innovation may also involve its re-invention; customers
can customize it more closely to their individual preferences (Rogers, 2003).
3.3.3.2 Regulatory Focus and Risk Perception
It is emphasized that perception of risk represents a subjective experience, which is
determined by people’s self-regulatory system. The self-regulatory system is guided
by the fundamental instinct to seek pleasure and avoid pain (i.e. the hedonic principle).
Research across different domains provides considerable evidence for this proposition.
Biological models, for example, have identified an appetitive system associated with
approach and an aversive system associated with avoidance (Lang, 1995). Similarly,
personality and social psychology literature has identified a motivational system that
prompts people to move toward desirable end-states, and a motivational system that
prompts people to move away from undesirable end-states (Carver & Scheier, 1981).
Building on the hedonic principle, research suggests that the self-regulatory system
may be based either on a desirable or an undesirable end-state as a reference point
guiding behavior. Depending on decision context, the system motivates people to
move as close as possible to desirable end-states and as far away as possible from
undesirable end-states. Research refers to the motivation towards desirable end-states
as the approach system and the motivation away from undesirable end-states as the
avoidance system (Carver & Scheier, 1981, 1990). The motivation to achieve the same
goal may be guided by the approach system in some cases and the avoidance system in
other cases (Higgins, Roney, Crowe, & Hymes, 1994). Take the decision to adopt an
69
electric car, for example. Some people may adopt this innovation to achieve low fuel
consumption (approach motivation), whereas others adopt this innovation to avoid
high fuel consumption (avoidance motivation).
Furthermore, research suggests that the self-regulatory system not only distinguishes
approach and avoidance motivation, but also ideal self-regulation and ought selfregulation. Ideal self-regulation concerns people’s hopes, whishes, and aspirations.
Ought self-regulation refers to people’s duties, obligations, and responsibilities
(Higgins, 1987). It was found that ideal self-regulation is concerned with the presence
or absence of positive outcomes (Higgins, 1989). In contrast, ought-self-regulation
was found to be concerned with the presence or absence of negative outcomes (Rotter
1982). Furthermore, it was found that ideal-self regulation relates to a predilection for
approach means to achieve desirable end-states associated with positive outcomes,
whereas ought self-regulation relates to a predilection for avoidance means of
obtaining desired end-states associated with negative outcomes (Higgins et al., 1994).
Following these considerations, research identified two types of regulatory focus:
promotion focus and prevention focus. Promotion focus concerns advancement,
growth, and accomplishment. In contrast, prevention focus concerns security, safety,
and responsibility (Crowe & Higgins, 1997). These definitions imply that a prevention
focus is likely to increase risk perception in innovation adoption, while a promotion
focus is likely to decrease risk perception. In line with this, it was found that people in
a prevention focus associate more risks with innovations because they are more
concerned with the avoidance of unfavorable consequences that may result from
adoption. In contrast, people in a promotion focus associate less risk with innovations,
because they are more concerned with favorable consequences or opportunities
associated with adoption Thus, potential customers of an innovation are likely to
perceive less risk in situations in which innovation communication addresses
advancement and achievement goals as opposed to security and responsibility goals
(Herzenstein, Posavac, & Brakus, 2007).
3.3.3.3 Information Acquisition and Risk Perception
Typically, customers reduce perceived risk of an innovation by collecting extensive
information about the innovation (Rogers, 2003). This is supported by (Jacoby et al.,
1994) who examined how information acquisition reduces risk perception. They found
an accelerating power function for the relationship between information acquisition
and risk reduction. According to this relationship, risk remains relatively high until a
70
considerable amount of information has been collected. This occurs, because people
want to reduce risk to a minimum and, thus try to avoid closure (Kruglanski, 1989).
Accordingly, when collecting information about an innovation, customers look for
information that specifically addresses their risk-related concerns. If these risk-related
concerns are not addressed sufficiently, customers are likely to respond unfavorably
towards an innovation (Castaño et al., 2008; Herzenstein et al., 2007).
Generally, the information acquisition process is determined by so-called contextual
cues that function as risk reduction mechanisms. Contextual cues are risk relevant
pieces of information that are salient in the decision context (Zhu, Billeter, & Inman,
2012). Research distinguishes two specific types of contextual cues: product cues
(Bearden & Shimp, 1982) and extrabrand attributes (Boyd & Mason, 1999).
Product Cues
Product cues refer to information about an innovation that indicates its quality and
performance. Research distinguishes between intrinsic and extrinsic product cues
(Bearden & Shimp, 1982). Intrinsic cues represent cues which, if changed, would
result in a change in physical product characteristics (Kaplan, Szybillo, & Jacoby,
1974). They refer to product composition characteristics such as taste, aroma, color,
style, size, or touch and feel (Jacoby, Olson, & Haddock, 1971). Intrinsic cues result
from different interactions with a product. These so called product experiences
(Brakus, Schmitt, & Zarantonello, 2009) can be direct when an individual has physical
contact with a product (Hoch & Ha, 1986), or indirect when an individual interacts
with a product virtually or experiences it in an advertisement (Hoch & Ha, 1986;
Kempf & Smith, 1998).
Extrinsic cues go beyond physical product characteristics. They are particularly
important if intrinsic cues have low confidence and predictive value. In other words,
people turn to extrinsic cues when they have problems in determining how well a
product will perform, how safe it is, or how socially acceptable it might be (Bearden &
Shimp, 1982). Price, for example, represents one such extrinsic cue. People view price
information with much confidence, because it is concrete and measurable (Jacoby et
al., 1971). Furthermore, warranties represent an effective extrinsic cue. Warranties
assure customers that redress is possible if a product does not perform satisfactorily.
Reputation or credibility of the manufacturer represents another extrinsic cue of high
importance. Research provides empirical evidence for the relationship between these
extrinsic cues and perceived risk of innovations. Specifically, a series of studies
demonstrates that high prices increase perceived risks associated with innovations,
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while warranties and a favorable manufacturer reputation decrease perceived risks
associated with innovations (Bearden & Shimp, 1982).
Extrabrand Attributes
Extrabrand attributes (EBAs) go beyond a specific product and reflect considerations
at the product category level. The key difference between product cues and EBAs is
that product cues refer to a specific model, whereas EBAs reflect all models available
in a given product category. Research shows that customers use EBAs to reduce the
risks associated with adoption (Boyd & Mason, 1999). Table 3-10 provides an
overview over different EBAs, and their importance ratings for selected product
categories.
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Table 3-10: Extrabrand Attributes for Selected Innovations (Boyd & Mason, 1999)
Overall
Digital
Camera
High-Def
Television
Photo
CD
Videophone
Cost
6.28
6.28
6.44
6.38
6.03
Key benefits
6.21
6.21
6.36
6.46
5.85
Manufacturers’ reputations
5.94
5.92
5.84
5.84
6.14
Accessories and service
5.77
5.96
6.24
5.76
5.17
Poor reviews
5.72
5.82
5.44
6.11
5.53
Product complexity
5.64
5.96
5.28
5.61
5.67
Relative advantage
5.64
5.82
5.72
5.92
5.14
Favorable reviews
5.42
5.75
5.04
5.69
5.21
Level of standardization
5.29
4.96
5.28
5.23
5.71
Alternatives
5.15
5.14
5.48
5.15
4.89
Variety of features/models
4.91
4.85
4.92
4.96
4.92
Time firms in business
4.64
4.21
5.08
4.34
4.96
Future enhancements
4.37
4.28
4.80
4.57
3.89
Future price trends
4.08
3.85
3.88
4.11
4.46
Number of model choices
3.95
3.75
4.12
4.19
3.78
How many others own it
3.88
3.85
3.48
3.69
4.46
How many friends own it
3.66
3.96
2.52
3.19
4.82
Future sales expectations
3.64
3.89
3.48
3.46
3.71
Number of stores selling
3.57
3.32
3.16
3.57
4.21
Advertising expenditures
2.57
2.53
2.20
2.53
2.96
Size of competing firms
2.49
2.07
2.20
2.80
2.89
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3.3.4 Formation of Mental Scenarios about Innovations
3.3.4.1 The Simulation Heuristic and Innovation Perception
The evaluation of innovations is strongly determined by the simulation heuristic, a
well-established phenomenon from psychological research (Tversky & Kahneman,
1974). It builds on the notion that the probability of an outcome depends on mental
scenarios associated with that outcome. The more readily these scenarios are apparent,
the more likely it seems that the outcome will occur (Kahneman & Miller, 1986;
Kahneman, 1997; Tversky & Kahneman, 1974). Hence, people will place a high
likelihood that an innovation performs unsatisfactorily if different scenarios are
evoked in which the innovation malfunctions (Ziamou & Ratneshwar, 2002).
Unfavorable mental scenarios associated with new product use are often evoked by
innovations that incorporate a new interface and a preexisting functionality. An
example of such an innovation is a cell phone with speech recognition as a new
interface that provides familiar functionalities such as dialing a telephone number.
Research shows that customers evaluate these kinds of innovations negatively:
innovations with a new interface and a preexisting functionality focus customers’
attention on the interface, which is the sole differentiator relative to existing product
categories (Hastie, 1981; Lynch & Srull, 1982). As a consequence, customers are more
likely to generate failure scenarios about the performance of the interface which they
associate with considerable learning costs (Ziamou & Ratneshwar, 2002).
Potential customers generate more favorable scenarios if a new product incorporates a
new interface and a new functionality. An example of such an innovation is a cell
phone with speech recognition as a new interface that provides new functionalities
such as local news on demand and directions to the nearest parking lot. In this case, the
new functionality offers a considerable advantage over existing products. Competitive
offerings are unlikely to provide similar benefits (Gregan-Paxton & Roedder John,
1997). Accordingly, customers will focus their attention on the functionality and put
less emphasis on the learning costs associated with the new interface (Shneiderman,
1998). In line with this, research shows that customers evaluate positively innovations
with a new interface and a new functionality (Ziamou, 2002).
The evaluation of an innovation that combines a new interface with either a
preexisting or new functionality may be influenced by providing customers with
information about the interface. For an innovation that incorporates a new interface
with a preexisting functionality, customers automatically focus on the interface and
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generate unfavorable scenarios. Several of these possible failure scenarios can be
rejected by providing specific information about the interface which allows customers
to assess their plausibility (Folkes, 1988; Kahneman & Miller, 1986; Kahneman,
1997). Conversely, information about the interface is counter-productive for
innovations that incorporate a new interface and new functionality. When customers
are confronted with these innovations, they automatically focus on the new
functionality and generate favorable usage scenarios. If they receive information on
the new interface, however, they are distracted from the new functionality, and their
focus shifts on the interface itself. As a consequence, unfavorable usage scenarios are
generated (Ziamou & Ratneshwar, 2002).
3.3.4.2 Mental Simulation and Innovation Perception
The formation of mental scenarios about an innovation may be influenced by
encouraging potential customers to imagine specific scenarios involving the usage of
that innovation. In such a case, research speaks of mental simulation, which occurs
when people are prompted to cognitively construct real or hypothetical events in
memory. Mental simulation enables people to re-experience past events and to
generate different versions of future events (Escalas, 2004). Research distinguishes
between different types of mental simulation. Depending on decision context, these
may lead to the generation of favorable or unfavorable mental scenarios of a given
event. For innovation adoption, the following types of mental simulation are of
particular importance: process versus outcome simulation, memory-focused versus
imagination-focused simulation, and self-related versus other-related simulation. Each
of these different types of mental simulation is discussed next.
Process versus Outcome Focused Simulation
Process simulation focuses people on the step-by-step process of reaching a goal. In
case of process simulation, people set a goal and they then actively mentally rehearse
the steps needed to reach that goal (Taylor, Pham, Rivkin, & Armor, 1998). Process
simulation may be evoked through advertisements that encourage people to think of
the process of using a product, and of how they would incorporate the product into
their daily routine (Escalas & Luce, 2003). By contrast, outcome simulation focuses
people on the desirable outcome of fulfilling a goal. It helps people to envision the
outcome associated with goal attainment, thereby facilitating efforts to achieve that
goal and improve perceptions of self-efficacy (Pham & Taylor, 1999). Outcome
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simulation may be evoked through advertisements that encourage people to imagine
the benefits they would receive by using a product (Escalas & Luce, 2003).
Process and outcome simulation may be based either on a cognitive processing mode
or on an affective processing mode. Generally, cognitive processing refers to cold,
deliberate, and analytic thinking, whereas affective processing refers to hot, rapid, and
emotional feelings. Following these considerations, research distinguishes two types of
process simulation - cognitive process simulation and affective process simulation and two types of outcome simulation - cognitive outcome simulation and affective
outcome simulation. Table 3-11 summarizes the different types of process and
outcome simulation of product use (Zhao, Hoeffler, & Zauberman, 2011).
Table 3-11: Types of Process and Outcome Simulation (Zhao et al., 2011)
Cognitive process simulation
Focus is on the specific features of a product, the
process of how to use the product, and how to
incorporate it into one’s daily routine.
Affective process simulation
Focus is on the emotions one may feel during the
process of using a product, and how one may feel
when incorporating it into one’s daily routine.
Cognitive outcome simulation Focus is on the specific benefits of using a product,
the outcome of using the product, and the benefits
that one would receive after using it.
Affective outcome simulation
Focus is on the emotions one may feel after
receiving the benefits of using a product, and how
one may feel about the outcome of product use.
Research demonstrates that different types of process and outcome simulations play an
important role in the evaluation of innovations. Under a cognitive mode, outcome
simulation leads to more favorable evaluation of utilitarian innovations, whereas
process simulation leads to more favorable evaluation of hedonic innovations.
Conversely, under an affective mode, process simulation leads to a more favorable
evaluation of utilitarian innovations, whereas outcome simulation leads to a more
favorable evaluation of hedonic innovations (Zhao et al., 2011).
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Memory versus Imagination Focused Simulation
Memory-focused simulation is thinking about an innovation in terms of consumption
situations and experiences that are most readily apparent. In other words, memoryfocused simulation limits mental images on consumption scenarios of established
products. In contrast, imagination-focused simulation is thinking about an innovation
in an imaginative focus (Zhao et al., 2009). In cases of imagination-focused
simulation, people create new, never-before-experienced consumption scenarios (Dahl,
Chattopadhyay, & Gorn, 1999).
A series of studies demonstrates that for really new products, imagination-focused
simulation leads to more favorable evaluation than memory-focused simulation.
Memory-focused simulation of a really new product is likely to focus people on the
behavioral change required to integrate it into existing consumption patterns. As a
consequence, people are more concerned about associated learning costs. In contrast,
imagination-focused simulation is more likely to focus people on the new benefits of a
really new product that may otherwise not be fully appreciated (Zhao et al., 2009).
Self versus Other Related Simulation
Self-related simulation contains images of the self when evaluating an innovation
(Dahl & Hoeffler, 2004). The self-referencing literature indicates that people who are
able to relate an advertisement to themselves remember the ad better, and develop
more positive attitudes toward the ad and the advertised product (Bone & Pam
Scholder, 1992; Burnkrant & Unnava, 1995; Debevec & Romeo, 1992). Selfreferencing encourages relevance and persuasion by enabling people to link ad or
product characteristics to a cognitive network of established associations (Debevec &
Romeo, 1992). Following these considerations, it was found that self-related
simulation leads to more favorable evaluation of incrementally new products. These
innovations build on established products, and therefore provide a context in which
people can draw on self-related experiences with similar products and/or consumption
scenarios (Dahl & Hoeffler, 2004).
However, in case of really new products, prior experience is lacking, and self-related
simulation leads to unfavorable evaluation. Specifically, self-related simulation is
likely to raise concerns about how such an innovation fits into existing personal
consumption patterns. This may create feelings of uncertainty. Accordingly, research
shows that, for really new products, other-related simulation should be preferred which
contains images of others when evaluating an innovation. Other-related simulation is
less likely to evoke thoughts about changes to existing personal consumption patterns.
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Instead, it focuses people on reasons why others may value specific attributes or
benefits of a really new product (Dahl & Hoeffler, 2004).
3.3.4.3 Difficulty of Imagination and Innovation Perception
Research suggests that, not only does the content of imagination influence innovation
evaluation, but also the difficulty of imagination. Specifically, if the benefits of an
innovation are difficult to imagine, customers are likely to respond negatively towards
it. This consideration follows from a study by Wänke, Bohner, & Jurkowitsch (1997)
which demonstrated that, when customers named 10 reasons for buying a BMW, the
difficulty of imagination was perceived to be more diagnostic than the content of
imagination, and evaluations were lower compared with when only 1 reason was
named.
Ziamou (2002) provides direct evidence of the negative influence of imagination
difficulty on innovation evaluation. In her study, participants were asked to imagine
the use of a new product that either incorporated a new interface with a preexisting
functionality or a new functionality. They subsequently had to evaluate the product.
The study revealed that participants who were asked to imagine the use of the new
product with a new interface and a preexisting functionality evaluated the new product
favorably. This was because imagining the use of a preexisting functionality was easy
for customers. Imagining product use increased the perceived likelihood that the new
interface would perform as promised, and provide the particular functionality.
Conversely, participants who were asked to imagine the use of a new product with a
new interface and a new functionality evaluated the product negatively. In this case,
imagination of product use was rather difficult, because participants lacked prior
experience with the new functionality. From the difficulty of imagination, participants
inferred that the new interface would not perform as expected, and its functionality
would be unlikely to yield favorable results.
Following these findings, it was shown that the evaluation of innovations can be
significantly improved by facilitating the imagination of new benefits. This may be
achieved by focusing customers’ attention on only a limited number of benefits (Zhao
et al., 2012). Another possibility is to provide customers with specific, new usage
scenarios (Feiereisen, Wong, & Broderick, 2008). This may be done through
interactions with sales people, TV commercials and print advertisements, and even
through descriptions on the product (Zhao et al., 2012).
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3.3.5 Resolution of Trade-Offs in Evaluation of Innovations
3.3.5.1 Resolution of Trade-Offs between Capability and Usability
Adding features to products may encourage both favorable and unfavorable
evaluation. People may respond favorably to added product features, because they
associate more capability with these features. Perceived capability refers to beliefs
about a product’s ability to perform desired functions (Thompson, Hamilton, & Rust,
2005). Research provides considerable evidence for a positive relationship between
added features and perceived capability. Carpenter, Glazer, & Nakamoto (1994) have
shown, for example, that added features may provide perceived advantages of a
product over existing alternatives. It has also been found that people perceive a
product with a greater number of features as superior in a given choice set (Brown &
Carpenter, 2000).
In contrast, people may respond unfavorably to added product features, because they
associate them with less usability (Thompson et al., 2005). Literature on usability and
user-centered design suggests that added features reduce people’s confidence in their
ability to effectively make use of a product. This relationship is believed to occur
across a variety of product categories (Wiklund, 1994). It is argued that people may
think of additional features as more things to learn, more things to possibly
misunderstand, and more things to search through when looking for what they want
(Nielsen, 1993).
Research suggests that the relative weights of capability and usability in overall
product evaluation are determined by temporal distance. Specifically, capability plays
a more important role in distant future considerations, whereas usability plays a more
important role in near future considerations (Thompson et al., 2005; Ziamou &
Veryzer, 2005). When people evaluate options for the distant future, they prefer high
capability options over high usability options. When people evaluate options for the
near future, they prefer high usability options over high capability options (Liberman
& Trope, 1998). Similarly, it has been shown that people attribute more weight to
capability and less weight to usability after product use (Thompson et al., 2005).
Furthermore, social visibility of product choice influences relative weights of
capability and usability in overall innovation evaluation. People prefer high capability
options over high usability options when product choice is public, but not when
product use is public. Specifically, people tend to choose feature rich products (high
capability and low usability products) in public choices in which they have the
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possibility to display their preferences to others. The public choice of feature rich
products enables people to signal their technological skills as well as their openness to
new experiences. However, if people anticipate that they have to use a product in front
of others, they tend to prefer feature poor products (high usability and low capability
products). In such a circumstance, people place higher weights on usability to avoid
the appearance of incompetence (Thompson & Norton, 2011).
3.3.5.2 Resolution of Trade-Offs between Functional and Hedonic Benefits
When evaluating an innovation, potential customers are frequently confronted with
trade-offs between functional and hedonic benefits. A considerable body of research
examined how potential customers resolve such trade-offs. Chitturi, Raghunathan, &
Mahajan (2007), for example, conducted a series of studies, in which they offered
participants different options of a product that varied in functional and hedonic
desirability. The study demonstrates that customers prefer the hedonically superior
option in gain-gain situations, where all options meet or exceed desired cutoffs on both
functional and experiential dimensions. However, in loss-loss situations, where all
options either meet the functional cutoffs or the experiential cutoffs but not both,
customers will choose the functional one.
Similar results were found in a subsequent study by Chitturi, Raghunathan, & Mahajan
(2008) which examined the impact of functional versus hedonic benefits on
postconsumption feelings of customer delight and satisfaction. The authors argue that
functional benefits address prevention goals associated with confidence and security,
whereas hedonic benefits address promotion goals associated with cheerfulness and
excitement. This implies that customers regard the fulfillment of prevention goals as a
necessity. A product that fails to meet prevention goals is likely to lead to highly
arousing negative reactions such as anger. Conversely, a product that meets prevention
goals will lead to satisfaction, which is only a low arousing positive emotion. In cases
where prevention goals are already met, further functional improvements are unlikely
to have an additional influence. In cases of hedonic expectations, things are different;
by meeting or exceeding high arousing promotion goals, customer delight is reached.
At the same time, the failure to meet promotion goals will only result in low arousing
negative emotions such as sadness or disappointment. This is because promotion goals
are regarded as luxury as opposed to necessity.
In another line of research, Noseworthy & Trudel (2011) examine how customers
evaluate incongruent product form. Incongruent product form is frequently used to
80
increase hedonic appeal of new products. The question which arises then relates to the
circumstances under which customers evaluate an increase in hedonic appeal
favorably. Among others, these circumstances depend on whether a product with
incongruent form is functionally or hedonically positioned. An example is toning
shoes which were introduced by the shoemakers Skechers and Reebok. While
Skechers chose a functional product positioning with a focus on benefits for
customers’ backs, cores, and muscles, Reebok chose a hedonic positioning, claiming
that their shoes help customers to get better legs and glutes. It was found that
functional product positioning favors the evaluation of incongruent product form. This
is because incongruent form leads to functional uncertainty if functional attributes are
not communicated explicitly. Thus, with hedonic positioning, customers will question
functional benefits of a product with incongruent form, and evaluate it more negatively
than congruent form. Conversely, if incongruent product form is functionally
positioned, customers are aware of functional benefits and evaluate it more favorably
than congruent form. Thus, customers must first understand a product’s functionality
before they can engage in hedonic consumption.
Moreover, technological trajectory influences relative weights of capability and
usability in product evaluation. Buying decisions concerning high-tech products
typically follow a buying hierarchy, with performance considerations at the top,
followed by convenience considerations. It was found that performance considerations
will dominate choices, as long as performance plays a discriminating role. If it fails to
do so, however, convenience becomes the primary choice criterion. In line with this, it
was found that, at the high end of technological trajectory, people prefer dedicated
products, while, at the lower end of technological trajectory, they prefer converged
products (Han, Chung, & Sohn, 2009).
Together, these findings imply that an inflection point exists, up to which functional
aspects play a more important role than hedonic ones, and beyond which the opposite
is true. Thus, it follows that innovations generally need to meet or exceed some basic
functional goals. If they fail to do so, customers are unlikely to consider them in their
choice sets. However, the dominance of functional considerations only persists up to a
certain functional level. If this level is met, further functional improvements are
unlikely to convince customers. In such a situation, resources should be devoted to
hedonic dimensions (Chitturi et al., 2007, 2008).
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3.3.6 Expectations about Usage of Innovations
3.3.6.1 Formation of Expectations about Innovations
Potential customers form expectations about innovations based on mass media
information and interpersonal information. Mass media information comprises
messages that involve a mass medium, such as radio, television, or newspapers
(Rogers, 2003). Two types of mass media information are distinguished: customer
education and anticipation creation. Customer education refers to messages describing
the functionality of an innovation, thereby creating a sense of familiarity with it. The
purpose of these messages is to educate potential customers about the innovation and
how to use it. Anticipation creation refers to messages building momentum and
demand for an innovation by heightening expectations of it (Lee & Colarelli
O’Connor, 2003).
Interpersonal channels refer to messages that involve a face-to-face exchange between
two or more individuals. This includes events such as trade and customer shows, as
well as personal selling demonstrations. These events may either be benefit-oriented or
process-oriented (Wood & Moreau, 2006). The purpose of benefit-oriented events is to
reveal an innovation’s benefits and relate them to specific usage situations of potential
customers. Process-oriented events are targeted towards showing potential customers
how to effectively make use of an innovation, and overcome obstacles associated with
changes in consumption patterns (Guiltinan, 1999).
Irrespective of whether expectations about an innovation are formed on the basis of
mass media or interpersonal channels, innovation acceptance is strongly determined by
whether subsequent interactions with an innovation comply with previously developed
expectations. If expectations about an innovation are disconfirmed, prior attitudes
toward an innovation may change completely.
3.3.6.2 (Dis)Confirmation of Expectations about Innovations
Research suggests that the creation of unrealistic expectations about innovations may
have a detrimental effect on innovation evaluation. When customers have the
possibility of experiencing an innovation first hand, their expectations are either
confirmed or disconfirmed (Heiman & Muller, 1996). While confirmation of
expectations does not have an impact on subsequent innovation evaluation,
disconfirmation may either have a positive or negative impact. If an innovation
performs better than expected, potential customers may be positively surprised and
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experience positive emotions. However, if an innovation performs worse than
expected, potential customers are likely to be negatively surprised and experience
negative emotions. Following this, the model of influence of expectations on
innovation evaluation (see Figure 3-12) was developed (Wood & Moreau, 2006).
Figure 3-12: Expectations and Innovation Evaluation (Wood & Moreau, 2006)
A study by Wood & Moreau (2006) provides empirical evidence for this model. They
asked participants to perform a series of tasks on a Personal Digital Assistant (PDA).
Prior to the performance of these tasks, participants were asked to indicate their
expectations concerning the usage complexity of the PDA. It was found that, if actual
usage was easier than expected, participants experienced positive emotions and
evaluated the PDA favorably. However, if actual usage was more difficult than
expected, participants experienced negative emotions and evaluated the PDA
unfavorably. These findings imply that promises of low complexity that create
overoptimistic complexity expectations will have an unfavorable effect in the long run.
Ziamou, Gould, & Venkatesh (2012) identified three basic factors as potential causes
of ineffective usage of innovations. One of these factors is prior knowledge regarding
interface and functionality practices. When learning about a new product, people are
often confronted with interface or functional elements that are missing or that work
differently from what they are used to. Such inappropriate construals are likely to lead
people down the wrong path, and consume substantial time and effort. A second factor
is social influence in new product learning. In many cases, people learn how to use a
new product by casually observing others using it. However, such observations are
likely to provide insufficient insight into the actual details of use. A third factor that
may cause ineffective usage of innovations is causal attributions generated from
failure to learn satisfactorily. Sometimes people blame themselves for not being able
to effectively making use of a new product. Arising from such negative selfattributions, people do not only feel less competent but also act less competent. To
decrease the negative impact of these factors, innovations should incorporate some
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degree of flexibility that allow individuals to modify usage to fit their own preferences
(Norman, 1998), or indicators that provide feedback about whether usage is effective
(Rijsdijk & Hultink, 2003).
3.3.6.3 Insight and Discontinuous Learning in Innovation Usage
When learning how to make use of an innovation, individuals typically follow one of
two different learning paths. In the first path, an individual uses a product repeatedly
and gradually improves the efficiency with which the usage steps are performed. In the
second path, the individual plays around with a product and explores its usage space
(Lakshmanan & Krishnan, 2011). This process is likely to lead to greater conceptual
learning and, as a consequence, to the discovery of optimal ways of using of the
product. As a result, the individual is likely to develop a more comprehensive mental
model of the product (Norman, 2002), which often results in a positive affective
reaction, frequently referred to as ‘insight’ (Lakshmanan & Krishnan, 2011).
The first path refers to power law learning which improves an individual’s procedural
knowledge (Cohen & Squire, 1980). Here, procedural knowledge involves the
development of sensorimotor and cognitive skills associated with product use (Squire,
1986). Procedural knowledge is often associated with the development of behavioral
routines such as habits. Conversely, the second path refers to discontinuous learning
which improves an individual’s conceptual or declarative knowledge. This type of
knowledge involves learning, representation, and use of knowledge pertaining to facts
and events. Consequently, power law learning typically follows a gradual learning
curve, whereas discontinuous learning typically follows a learning curve with abrupt
improvements (see Figure 3-13) (Lakshmanan & Krishnan, 2011).
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Figure 3-13: Learning to Use Innovations (Lakshmanan & Krishnan, 2011)
Lakshmanan & Krishnan (2011) examined the consequences of power law learning
versus discontinuous learning of innovations. They demonstrated that discontinuous
learning is more likely to create a feeling of insight. This insight, in turn, was found to
evoke positive affect and enhance usage intentions of innovations. Thereby,
Lakshmanan & Krishnan identified exploration as key antecedent of discontinuous
learning. Specifically, they found that encouraging exploration leads to the
development of broader, more flexible mental models, and enhances individuals’
ability to use an innovation in different contexts. Furthermore, they found that the
propensity to conduct discontinuous learning is significantly reduced if individuals are
constrained by detailed usage instructions. Detailed usage instructions usually prevent
the propensity for extensive exploration.
Empirical evidence by Dahl & Moreau (2007) indicates that innovations provide the
most favorable usage experiences if their use is associated with a sense of autonomy.
In this context, autonomy denotes the enjoyment derived from the freedom to choose
the process of using an innovation. However, such autonomy has a positive effect only
if individuals feel a sense of competence when using an innovation. Competence may
be defined as anticipated satisfaction derived from making use of an innovation
successfully. A feeling of competence in innovation usage particularly arises if
85
customers are provided with clear feedback about the appropriateness of their
interaction with the innovation.
Together, these findings break with the predominant perspective that initial trials of
innovations always represent a hurdle which individuals must surmount to become
proficient users. In addition to this learning-cost view, Lakshmanan & Krishnan
(2011) propose a benefit-based view. They suggest that initial trials may actually
provide value to the overall product experience of an innovation. Such value manifests
itself if an innovation encourages active exploration of its usage space, thereby
resulting in an experience of insight. The authors argue that the Nintendo Wii and the
Apple iPhone represent two successful examples of where active discovery plays an
essential role in the product experience. Specifically, the motion sensitive remote of
the Wii and the touch-screen-enabled interaction of the iPhone encouraged extensive
exploration, triggering an almost cultlike attachment to these products.
Chapter 3 provided a detailed overview over innovation recognition and innovation
assessment as key processes of innovation perception. In the next chapter (chapter 4),
innovation comprehension is addressed. Innovation comprehension represents a
previously neglected, yet highly relevant aspect of innovation perception.
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4 Innovation Comprehension
4.1 Introduction to Innovation Comprehension
So far, research on innovation perception has been primarily concerned with the
‘what’ of perception. Specifically, this research has tried to discover, what makes an
innovation attractive and sets it apart from existing alternatives in a way that is
desirable (Venkatesh et al., 2012). Recent findings indicate, however, that, not only the
‘what’ plays a role, but also the ‘how’. This ‘how’ refers to innovation comprehension
which is the way in which people process information about an innovation (Förster et
al., 2010; Marguc, Förster, & Van Kleef, 2011; Marguc, Van Kleef, & Förster, 2012).
Ulkumen, Chakravarti, & Morwitz (2010) provide initial empirical evidence for the
important role of different ways of processing on innovation perception. In a series of
studies, these authors manipulated processing on the basis of a categorization task, and
subsequently asked participants to evaluate an unrelated innovation (wireless
speakers). In the categorization task, participants were told that they were shopping for
a friend’s party and needed to make choices in eight different categories such as wine,
cheese, beer, and music. One group of participants had to choose products from a few
broad categories. The other group of participants had to choose the identical products
from many narrow categories (see Figure 4-1). Even though the shopping task had
nothing to do with the innovation, the study showed that participants who performed
the broad categorization task evaluated the innovation significantly better than
participants who performed the narrow categorization task.
Figure 4-1: Broad vs. Narrow Categorization Primes (Ulkumen et al., 2010)
Figure 1
MANIPULATIONS, STIMULI, AND SUMMARY OF RESULTS FROM STUDY 2A
Broad Categorization Prime
A: Decision Context 1: Shopping Study
JOURNAL OF MARKETING RESEARCH, AUGUST 2010
esponded to the dependent measure by
l attitude toward the product on a 100disliked it very much/liked it very
sponded to several process measures.
ponses, they first briefly listed the key
sidered in their evaluation, and then
ber of factors they considered in their
rally. We also measured participants’
od (across the entire experiment sesamiliarity with, knowledge of, awarep of the product; and recorded study
Results
Dependent measure. In support of H2, compared with
participants in the narrow condition, those in the broad condition had more favorable attitudes toward the new product
(MBROAD = 71.40, MNARROW = 62.83; F(1, 94) = 6.41, p <
.02) (for stimuli and results, see Figure 1).
Process measures. Participants previously exposed to
narrow categories reported considering a greater number of
factors when evaluating the speakers (MBROAD = 2.96,
Site Wireless speakMNARROW = 3.57;“Broad”
F(1, 93)Shopping
= 4.43, pWeb
< .04).
of Screen Shot from Cheese Category
ers have Example
several
attributes,
such
as
no-clutter
technology,
•24 cheese alternatives
MANIPULATIONS, STIMULI, AND SUMMARY OF RESULTS FROM STUDY 2A
less Speakers
“Narrow” Shopping Web
Example of Screen Shot from Che
•24 cheese alternatives
•8 categories (firm–cow, firm–g
•2 categories (firm and soft)
Figure 1
B: Decision Context 2: New Product Evaluation
Narrow Categorization Prime
Stimuli: Study
A: Decision Context 1: Shopping
Sony SRS-RF90RK Wireless Speakers
Results:
.92
3.57
71.40
2.96
62.83
d” Shopping Web Site
een Shot from Cheese Category
alternatives
s (firm and soft)
2.83
dition had more favorable attitudes tow
(MBROAD = 71.40, MNARROW = 62.83;
.02) (for stimuli and results, see Figure
Process measures.
Participants pr
87
narrow categories reported considering
factors when evaluating the speaker
MNARROW = 3.57; F(1, 93) = 4.43, p <
ers have several attributes, such as no
In two open-ended responses, they first briefly listed the key
factors they had considered in their evaluation, and then
they reported the number of factors they considered in their
evaluation more generally. We also measured participants’
involvement and mood (across the entire experiment session); assessed their familiarity with, knowledge of, awareness of, and ownership of the product; and recorded study
completion times.
.29
“Narrow” Shopping Web Site
Broad
Narrow
Broad
NarrowShot from Cheese Category
Example of Screen
Attitude
Toward
Number of Factors Considered
•24
cheeseProduct
alternatives
•8 categories (firm–cow, firm–goat, …, soft–sheep)
Broad
Number of Innovatio
B: Decision Context 2: New Product Evaluation
Ulkumen et al. (2010) argued that this relationship occurred because the exposure to
broad categories evokes processing in which individuals base their subsequent
decisions on easily available information, whereas the exposure to narrow categories
evokes processing in which individuals go beyond available information and base their
.92
3.57
subsequent decisions
also on non-salient information. Put differently, narrow (versus
.77
2.96
.73
broad) categories evoke more (versus less) multidimensional information processing
.29
Innovation thoughts
88
(Ulkumen et al., 2010). This is supported by research on individual-level categorywidth which demonstrates that individuals may associate narrow equivalence ranges
with a preference for greater dimensionality (Jackson & Messick, 1965; Sloane,
Gorlow, & Jackson, 1963). Accordingly, Ulkumen et al. (2010) found in their study
that participants exposed to broad categories considered fewer factors when evaluating
the innovation. These participants focused particularly on the salient benefit-related
aspects of the innovation, which led to more favorable responses. In contrast,
participants exposed to narrow categories also considered the less salient risk-related
aspects of the innovation, which led to less favorable responses.
Based on the findings of Ulkumen et al. (2010), the present research further examines
how different ways of processing influence innovation perception. Thereby, this
research draws on Novelty Categorization Theory (NCT), a recent theory from social
psychology, which “attempts to predict when people perceive events as novel and how
they process novel events across different domains (Förster et al., 2010, p. 736).” NCT
distinguishes two distinct processing styles: global processing that is focused on the
overall Gestalt of a stimulus and local processing that is focused on the constituting
details of a stimulus. Thereby, NCT argues that global/local processing considerably
influences the way with which people perceive novel versus familiar events.
Specifically, NCT proposes that global processing improves evaluation of novel
events, whereas local processing improves evaluation of familiar events (Förster et al.,
2010). Following this, it could be concluded that global/local processing may also
influence the perception of innovations with varying degrees of newness. More
precisely, as global processing favors response to novel events, it may improve
evaluation of really new products, which represent groundbreaking departures from
established product categories (i.e. high level of perceived newness). Conversely, local
processing may improve evaluation of incrementally new products, which solely
represent improvements of established products (i.e. low level of perceived newness).
Accordingly, building on NCT, the present research examines how global/local
processing influences perception of really new products as opposed to incrementally
new products. For this purpose, global and local processing is specified in chapter 4.2
and implications of NCT for innovation perception are derived. Based on this
theoretical foundation, the influence of processing styles on perception of
incrementally new vs. really new products is empirically investigated in chapter 4.3.
Further, chapter 4.4 contains a summary of the results of the empirical investigation of
the influence of processing styles on innovation perception. This chapter also includes
theoretical and managerial implications as well as propositions for future research.
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4.2 Conceptual Development Innovation Comprehension
4.2.1 Specification of Global/Local Processing
4.2.1.1 Distinguishing Global and Local Processing
As previously discussed, NCT distinguishes between global and local processing.
Specifically, NCT suggests that people may process the same object in two different
ways: they can either zoom out and focus on the overall impression of the object, or
they can zoom in and focus on the object’s constituting elements. An old proverb says
that people either see the forest or the trees when attending to a stimulus set. When
people see the forest rather than the trees, they are in global processing (Förster &
Dannenberg, 2010). In such a situation, they attend to the overall Gestalt of a stimulus
set and broaden perceptual scope. However, when people see the trees rather than the
forest, they are in local processing. In this situation, they attend to the details of a
stimulus set and narrow perceptual scope (Navon, 1977; Schooler, 2002).
Processing styles may be determined by the so-called Kimchi-Palmer-Figures task. In
this task, people are presented with different sets of geometrical figures, such as
squares or triangles, that are made up of smaller squares or triangles (Kimchi &
Palmer, 1982). Each of these sets consists of a target figure and two comparison
figures (see example in Figure 4-2). For each set of figures, people have to decide
which of the comparison figures is more similar to the target figure. One of the
comparisons figures is similar to the target figure from a global perspective
(comparison figure A in Figure 4-2), whereas the other comparison figure is similar to
the target figure from a local perspective (comparison figure B in Figure 4-2). The
selection of a larger number of global figures in the Kimchi-Palmer-Figures task
indicates a global processing style, and the selection of a larger number of local figures
indicates a local processing style (Förster & Dannenberg, 2010).
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Figure 4-2: Example Item of the Kimchi-Figures task (Kimchi & Palmer, 1982)
4.2.1.2 Inducing Global and Local Processing
Global/local processing may be induced actively by evoking a carry-over of a
processing style from one task to other, unrelated tasks. Research refers to this process
as procedural priming (Förster, Liberman, & Friedman, 2007; Förster & Liberman,
2007). It differs from semantic priming in that the ‘how’ rather than the ‘what’ is
primed. Semantic priming refers to the activation of semantic concepts in memory,
which influence subsequent access to specific information (Förster & Dannenberg,
2010). For example, people become faster at identifying the word ‘car’ when they are
semantically primed with the word ‘street’. A study by Smith & Branscombe (1987)
illustrates the difference between procedural and semantic priming. Participants in this
study were asked to rate the aggressiveness of an ambiguously aggressive person.
Before this judgment, they either received a semantic prime or a procedural prime.
Specifically, participants in the semantic priming condition were asked to unscramble
sentences describing hostile behaviors such as ‘leg her break he’. In the procedural
priming condition, participants were presented with the same sentences in an
unscrambled form and had to choose matching traits for these sentences (e.g. hostile).
This matching task represents a procedural prime, because participants had to follow
the same procedure when they judged aggressiveness of the ambiguously aggressive
person in the second phase. The study showed that procedural priming increased
aggressiveness ratings for a considerably longer delay (15 min) than semantic priming
(15 s).
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According to NCT, global versus local processing may be procedurally primed in
various ways. One of these ways represents the so-called Navon-Letter task (Gasper &
Clore, 2002). In the Navon-Letter task, people are presented with a set of global letters
that consist of local letters (e.g. an H made of Ls, see Figure 4-3). These letters appear
in a random order on a computer screen. In the global processing condition, people are
asked to identify the global letters. The identification of global letters evokes global
processing by broadening perceptual scope. Conversely, in the local processing
condition people are asked to identify the local letters. The identification of local
letters evokes local processing by narrowing perceptual scope (Förster & Dannenberg,
2010).
Figure 4-3: Sample-Item Navon-Letter task
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
4.2.2 Implications of Global/Local Processing for Innovation Perception
NCT argues that global/local processing does not only influence perceptual scope, but
also conceptual scope (Friedman & Förster, 2008). More precisely, research indicates
that mechanisms, which influence perceptual scope and let people focus visually on
one percept while excluding others might be related to mechanisms that influence
conceptual scope, such as the selective activation of semantic networks. Conceptual
scope is determined by whether a concept such as ‘car’ spreads narrowly to concrete
associates such as ‘steering-wheel’, or broadly to more abstract and remote associates
such as ‘mobility’ (Förster & Dannenberg, 2010). Research identified a variety of
variables that are influenced by narrowing or broadening conceptual scope through
priming of global versus local processing. These include assimilation/contrast effects
(e.g., Förster, Liberman, & Kuschel, 2008) as well as performance in creative and
analytical problem solving tasks (e.g., Ayelet, Fishbach, Förster, & Werth, 2003).
Empirical evidence on the influence of global/local processing on these variables
provides important implications for innovation perception.
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4.2.2.1 Assimilation/Contrast Effects
NCT argues that global processing leads to inclusion and assimilation, thereby
rendering similarities between different stimuli more accessible. In contrast, local
processing leads to exclusion and contrast, thereby rendering dissimilarities between
different stimuli more accessible (Förster & Dannenberg, 2010).
In a series of experiments, Förster (2009) provides empirical evidence for the
relationship between processing styles and assimilation/contrast effects. In one of
these experiments, participants were primed with global/local processing by means of
the Navon-Letter task. Subsequently, they were asked to identify similarities and
dissimilarities between two comparable TV shows. It was found that global priming
promoted the identification of similarities, whereas local priming promoted the
identification of dissimilarities. In the remaining experiments, Förster conceptually
replicated these findings by carefully varying both independent and dependent
variables. In one of these experiments, the results were replicated by manipulating
global/local processing with a map task that was similar to the Navon-Letter task. In
another experiment, global processing was found to cause spontaneous similarity
generation, and local processing was found to cause spontaneous dissimilarity
generation even without instructions to generate similarities or dissimilarities. Further,
a bi-directional link was identified between global/local processing and dis/similarity
generation in another experiment. In this experiment, participants were asked to focus
on similarities versus dissimilarities between different stimuli. Subsequently, they
were asked to perform a task similar to the Kimchi-Figures task as a measure for
global/local processing. The experiment revealed that the focus on similarities led to
global processing, and the focus on dissimilarities led to local processing.
The relationship between global/local processing and assimilation/contrast effects was
also demonstrated for social judgment. In a series of experiments by Förster,
Liberman, & Kuschel (2008), participants were primed with global and local
processing styles. Subsequently, they had to compare themselves to high and low
standards, and estimate how they would score on this dimension. Specifically, in one
of these studies, participants had to estimate their athletic skills compared to the
Formula 1 racer Michael Schumacher (i.e. high standard) or the former American
president Bill Clinton (low standard i.e.). It was found that, in global processing,
participants thought that they could do more push-ups when they compared themselves
to Michael Schumacher and fewer push-ups when they compared themselves to Bill
Clinton. Conversely, in local processing, participants thought they could do fewer
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push-ups when they compared themselves to Michael Schumacher and more push-ups
when they compared themselves to Bill Clinton.
From these findings, important implications for perception of really new versus
incrementally new products can be derived. As the moderate incongruity effect
suggests, really new products are very dissimilar to existing alternatives and are likely
to be perceived as extremely incongruent. Because of an extreme level of incongruity,
customers typically respond unfavorably to really new products. In contrast,
incrementally new products are very similar to existing alternatives and are likely to be
perceived as highly congruent. Such congruity is also likely to result in unfavorable
response (Chandy et al., 2001; Feiereisen et al., 2008; Moreau, Markman, et al., 2001).
Because global processing triggers assimilation effects and local processing triggers
contrast effects, it can be concluded that global processing improves evaluation of
really new products and local processing improves evaluation of incrementally new
products. Specifically, global processing broadens conceptual scope and prompts
individuals to generate similarities between an innovation and existing alternatives.
Such a process is likely to decrease perceived incongruity and, as a consequence,
improve evaluation of really new products. At the same time, local processing narrows
conceptual scope and prompts individuals to create dissimilarities between an
innovation and existing alternatives. Such a process is likely to increase perceived
incongruity and, as a consequence, improve evaluation of incrementally new products
(Förster et al., 2010; Jhang et al., 2012; Meyers-Levy & Tybout, 1989).
4.2.2.2 Creative and Analytical Problem Solving Performance
NCT argues that global and local processing also has a differential effect on creative
and analytical problem solving performance. Research distinguishes creative insight
tasks as a measure of creative performance, and analytical reasoning tasks as a
measure of analytical performance. Creative generation tasks that require people to
generate unusual uses for a brick represent an example for a creative insight task. Ideas
such as “grind it up and use it as make-up” indicate a high level of creative
performance, whereas ideas such as “build a wall” indicate low levels of creative
performance (Förster & Dannenberg, 2010). Furthermore, the following classical
insight problem represents another creative insight task (Förster, Friedman, &
Liberman, 2004):
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A prisoner wanted to escape from a tower. He found a rope in his cell that was
half as long enough to permit him to escape safely. He divided the rope in half,
tied the two parts together, and escaped. How did he do this? [Solution: He
unraveled the rope lengthwise and tied the remaining strands together.]
Conversely, the Graduate Record Examination (GRE) Analytical test represents an
analytical reasoning task. This test involves the evaluation of the true value of a
number of propositions given an initial set of basic facts. Solving such problems
requires systematic organization of the given information, analyzing it carefully to
reach a conclusion about the verity of a series of logical conclusions (Friedman &
Förster, 2000). The following question represents an example problem from the GRE:
Evan has four times as many books as David and five times as many as Jason. If
Jason has more than 40 books, what is the least number of books that Evan could
have? (A) 200 (B) 205 (C) 210 (D) 220 (E) 240 [Solution: D]
A series of studies by Förster, Epstude, & Özelsel (2009) suggests that global
processing promotes creative performance, whereas local processing promotes
analytical performance. In one of the studies, participants were either primed with the
concepts of love or lust. Subsequently, they had to perform the Kimchi-Palmer-Figures
task in order to measure global/local processing. The results showed that love led to
global processing, whereas lust led to local processing. Thereby, the study also showed
that love priming improved performance on creative insight problems. At the same
time, lust priming improved performance in the GRE. These results indicate that
global processing (love priming) relates to creative performance, while local
processing (lust priming) relates to analytical performance.
In accordance with the discussion in chapter 4.2.2.1, the findings concerning the
influence of processing styles on creative and analytical problem solving imply that
global processing improves evaluation of really new products, while local processing
improves evaluation of incrementally new products. Specifically, really new products
allow customers to do things they have not been able to do before (Lehmann, 1997).
Such completely new functionalities are likely to be evaluated more favorably under
global processing, because it prompts creative thinking (Förster et al., 2010). That is,
in a creative mind-set, individuals typically generate atypical usage scenarios which
are more likely to reveal the new benefits provided by really new products (Zhao et al.,
2009). In contrast, incrementally new products solely provide improvements to
existing functionalities (Chandy & Tellis, 1998, 2000). Individuals are more likely to
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identify such improvements under local processing which evokes an analytical, detailoriented mind-set (Förster & Dannenberg, 2010; Förster et al., 2010).
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4.3 Experimental Analysis Innovation Comprehension
4.3.1 Overview over the Experimental Studies
Following the previous discussion, the purpose of the empirical investigation of the
present research was to examine the influence of global/local processing on the
evaluation of different innovations. More specifically, three experiments were
conducted in which participants were primed with global/local processing and asked to
evaluate really new products (RNPs) and incrementally new products (INPs). In
experiment 1, participants were primed with global/local processing and it was
investigated how different processing styles influence evaluation, purchase intention,
and perceived meaningfulness of RNPs and INPs. In experiment 2, the results of
experiment 1 were replicated with a different sample. In addition, experiment 2
examined the influence of global/local processing on perceived usefulness of RNPs
and INPs. Finally, in experiment 3, it was analyzed how global/local processing
influences perception of very easy-to-use versus very difficult-to-use RNPs. Figure
4-4 provides an overview over the experimental studies.
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Figure 4-4: Overview over the Experimental Studies
Experiment 1:
Processing Styles and Perception of INPs vs. RNPs
− Design: 2 (innovation: RNP vs. INP) x (processing: global vs. local)
− Scenario: Evaluation of a digital camera depicted in a mock advertisement
− Participants: 243 adults from a representative German online panel
− Pages: 98-106
Experiment 2:
Processing Styles and Perception of Usefulness of INPs vs. RNPs
− Design: 2 (innovation: RNP vs. INP) x (processing: global vs. local)
− Participants: 149 students from the University of St. Gallen
− Pages: 106-115
Experiment 3:
Processing Styles and Perception of Easy-to-Use (EtU) vs. Difficult-to-Use (DtU) RNPs
− Design: 2 (innovation: EtU RNP vs. DtU RNP) x (processing: global vs. local)
− Participants: 161 adults from a representative German online panel
− Pages: 115-124
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4.3.2 Experiment 1
4.3.2.1 Hypothesis Development
According to NCT, global processing helps to make sense out of new things by
activating superordinate or abstract representations in memory, and broadening
conceptual scope. In doing so, global processing evokes assimilation effects, thereby,
helping individuals to integrate novel events more effectively into existing knowledge
structures. At the same time, global processing also enhances creativity, which
promotes the development of new solutions (Förster & Dannenberg, 2010). Following
this, global processing should favor perception of RNPs for two reasons. Firstly,
because of their inherent newness, RNPs are likely to be perceived as extremely
incongruent to previous experiences (Veryzer, 1998a). By evoking assimilation
effects, global processing is likely to reduce extreme incongruity and, as a
consequence, lead to more favorable perception. Secondly, RNPs provide completely
new functionalities (Dahl & Hoeffler, 2004). By evoking creative thinking, global
processing is more likely to help individuals to identify new benefits associated with
these functionalities.
In contrast, local processing activates subordinate or concrete representations in
memory and narrows conceptual scope. Such thinking is likely to lead to contrast
effects. Further, local processing is likely to evoke more detail-oriented, analytical
thinking. As a consequence, local processing should promote perceived differentiation
(Förster & Dannenberg, 2010). Accordingly, local processing is likely to favor
perception of INPs for two reasons. Firstly, because of their similarity to existing
alternatives, INPs are likely to be perceived as highly congruent to prior experiences
(Veryzer, 1998a). By evoking contrast effects, local processing is likely to increase
perceived incongruity and, as a consequence, lead to more favorable perception.
Secondly, INPs solely provide improvements to existing functionalities (Dahl &
Hoeffler, 2004). By evoking detail-oriented, analytical thinking, local processing is
more likely to reveal differences between INPs and existing alternatives.
A favorable perception of an innovation may best be captured by overall evaluation of
the innovation (Zhao et al., 2012), intentions to purchase the innovation (Zhao et al.,
2011), and perceived meaningfulness of the innovation (Rubera, Ordanini, & Griffith,
2011). Thus, these variables were used in the present study as key measures for
favorable perception of an innovation.
99
Arising from this discussion, the following formal hypotheses are proposed:
H1: (a) Global processing (relative to local processing) improves the evaluation
of RNPs, whereas (b) local processing (relative to global processing)
improves the evaluation of INPs.
H2: (a) Global processing (relative to local processing) increases the purchase
intentions for RNPs, whereas (b) local processing (relative to global
processing) increases the purchase intentions for INPs.
H3: (a) Global processing (relative to local processing) increases the perceived
meaningfulness of RNPs, whereas (b) local processing (relative to global
processing) increases the perceived meaningfulness of INPs.
4.3.2.2 Design, Participants, and Procedure
The experiment was a 2 (innovation: INP vs. RNP) x 2 (processing: global vs. local)
between-subjects design. A total of 243 participants of a representative German online
panel (aged 18 to 40 years) completed the study (54.7% female, 45.3% male, average
age of 29.5 years). Participants were randomly assigned to either an INP condition or a
RNP condition. In the INP condition, a mock advertisement of an INP was provided,
and participants were asked to evaluate the INP. By contrast, in the RNP condition, a
mock advertisement of a RNP was provided and participants were asked to evaluate
the RNP. Prior to the presentation of the mock advertisement, participants were
randomly assigned either to a global processing condition or a local processing
condition. In both conditions, processing styles were primed by a version of the
Navon-Letter task.
4.3.2.3 Manipulation of Independent Variables
The manipulation of innovation was derived from a recent study by Zhao, Hoeffler, &
Dahl (2009) which examined the impact of visualization on evaluation of RNPs and
INPs. Specifically, participants received a mock advertisement of a digital compact
camera (see Figure 4-5). In the INP condition, participants received a mock
advertisement of a conventional digital compact camera. In the RNP condition,
participants received a mock advertisement of a new light field camera, which was not
available in the German market at the time the research was conducted. The company
logo, as well as all other brand identification information, was removed prior to the
experiment. The product was called XR-500 in all conditions. The mock
advertisements consisted of four components: a headline, a picture, a short description,
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and a set of product features (3 distinctive features and two common features). All
product features were taken from consumer reports and represented real features.
Figure 4-5: Manipulation of Innovations in Experiment 1
Incrementally New Product
Really New Innovation
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Furthermore, global and local processing was manipulated by a version of the NavonLetter task (Macrae & Lewis, 2002). Therefore, all participants were presented with a
series of 12 global letters that consisted of local letters (see Appendix). Participants in
the global processing condition were asked to identify the global letters and type these
in a text field below. By contrast, participants in the local processing condition were
asked to identify the local letters and type these in the text field below. Figure 4-6
contains an example Navon-Letter that was used in the experiment. In this example,
participants in the global condition had to identify the ‘A’, while participants in the
local condition had to identify the ‘C’.
Figure 4-6: Example of a Navon-Letter (Prime for Processing Style)
4.3.2.4 Selection of Measures
Participants were asked to rate product newness on a four-item scale ranging from 1 =
totally disagree to 7 = totally agree (i.e. This product is out of the ordinary, …can be
considered as revolutionary, …provides radical differences to industry norms,
…shows an unconventional way of solving problems). Furthermore, product
meaningfulness was measured on a four-item scale ranging from 1 = totally disagree to
7 = totally agree (This product is relevant to my needs and expectations, …is suitable
for my desires, …is appropriate for my needs and expectations, …is useful to me).
Both of these scales were derived from a study by Im & Workman (2004) which
examined the influence of newness and meaningfulness on new product success.
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Product evaluation was measured on a three-item scale ranging from 1 (bad, dislike,
poor) to 7 (good, like, excellent). This scale was derived from a study by Dahl &
Hoeffler (2004) which initially examined the impact of self-related vs. other-related
mental simulation on perception of innovations. This scale was also used in a variety
of other studies on innovation perception (e.g., Zhao et al., 2012, 2011).
Purchase intention was measured by asking participants to indicate how interested they
would be in purchasing the product on a scale ranging from 1 (not interested at all) to
7 (very interested). This scale was derived from a study by Zhao, Hoeffler, &
Zauberman (2011) which originally investigated the impact of process and outcome
simulation on perception of innovations.
Table 4-1 provides a summary of all measures and their corresponding levels of
reliability.
Table 4-1: Measures Employed in Experiment 1
Measures
Items
Reliability
Source
Product Evaluation
3
α = .91
Dahl & Hoeffler (2004)
Purchase Intention
1
-
Zhao, Hoeffler, & Zauberman (2011)
Meaningfulness
4
α = .96
Im & Workman (2004)
Newness
4
α = .92
Im & Workman (2004)
Processing Style
12
-
Macrae & Lewis (2002)
Dependent Variables
Independent Variables
4.3.2.5 Results
Manipulation checks. The four product newness-related items were aggregated into a
newness index (α= .92). As anticipated, participants rated the RNP as significantly
newer than the INP (Ms = 5.32 vs, 3.67; t(241) = 10.46, p < 0.001). Regarding
processing style, all participants in the global condition correctly identified the global
letters, while all participants in the local condition correctly identified the local letters.
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Product evaluation. For product evaluation, a two-way analysis of variance (ANOVA)
showed no main effect of innovation (F(1, 239) = .46, p = .50) or processing (F(1, 239)
= .05, p = .83). However, it did show the expected significant interaction between the
two factors (F(1, 239) = 7.11, p < .01). Specifically, for the INP, local processing led
to significantly better evaluation than global processing (Ms = 5.06 vs. 4.61; t(121) =
2.12, p < 0.05). For the RNP, global processing led to marginally better evaluation
than local processing (Ms = 5.13 vs. 4.75; t(118) = 1.67, p < .10). The results for
product evaluation are depicted in Figure 4-7.
Figure 4-7: Product Evaluation Interaction (Experiment 1)
Product Evaluation
5.50
5.25
5.13
5.06
5.00
4.75
4.75
4.61
Local processing
Global processing
4.50
4.25
4.00
INP
RNP
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Purchase intention. For purchase intention, a 2x2 ANOVA showed no main effect of
innovation (F(1, 239) = .61, p = .44) or processing (F(1, 239) = .47, p = .50), but did
show a marginally significant interaction between the two factors (F(1, 239) = 2.66, p
= .10). For the INP, local processing led to marginally higher purchase intention than
global processing (Ms = 4.17 vs. 3.66; t(121) = 1.69, p < 0.1). For the RNP, global
processing did not lead to higher purchase intention than local processing (t(121) =
.65, p = .52). The results for purchase intention are depicted in Figure 4-8.
Figure 4-8: Purchase Intention Interaction (Experiment 1)
Purchase Intention
4.50
4.25
4.17
4.00
3.75
3.85
3.66
3.64
Local processing
Global processing
3.50
3.25
3.00
INP
RNP
Meaningfulness. For meaningfulness, a 2x2 ANOVA showed a main effect of
innovation (F(1,239) = 12.31, p < 0.01), but no main effect of processing (F(1,239) =
.11, p = .74). Furthermore, this analysis did not show a significant interaction between
the two factors (F(1,239) = .26, p = .61).
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Table 4-2 and Table 4-3 provide a summarizing overview over the results of
experiment 1.
Table 4-2: Results of the ANOVAs in Experiment 1
Measures
Dependent Variable
F(1,239)
p
Innovation
Product Evaluation
.46
p = .50
Purchase Intention
.61
p = .44
Meaningfulness
12.31
p < .01
Product Evaluation
.05
p = .83
Purchase Intention
.47
p = .50
Meaningfulness
.11
p = .74
Product Evaluation
7.11
p < .01
Purchase Intention
2.66
p = .10
Meaningfulness
.26
p = .61
Processing
Innovation x Processing
Table 4-3: Mean Values for the Dependent Variables in Experiment 1
INP
RNP
Local
processing
Global
processing
Local
processing
Global
processing
Product Evaluation
5.06
4.61
4.75
5.13
Purchase Intention
4.17
3.66
3.64
3.85
Meaningfulness
4.13
3.97
3.39
3.42
4.3.2.6 Discussion
The purpose of this first experiment was to test the basic assumption that different
processing styles influence the perception of RNPs and INPs. Specifically, the
experiment provides initial evidence that global processing leads to more favorable
evaluation of RNPs than global processing. Thus, hypothesis 1a is confirmed. At the
same time, this experiment indicates that local processing leads to more favorable
evaluation of INPs than local processing. This is in support of hypothesis 1b.
106
Although the results of the first experiment are promising, the expected effects of
processing styles on innovation perception could not be shown for all dependent
variables. More precisely, global processing did not lead to higher purchase intention
of the RNP; only local processing increased purchase intention of the INP. Thus,
hypothesis 2a is not supported, whereas hypothesis is 2b supported. Furthermore, the
results of the experiment did not demonstrate any influence of processing styles on
perceived meaningfulness of RNPs and INPs. Accordingly, hypotheses 3a and 3b were
not confirmed by the experimental results.
4.3.3 Experiment 2
The purpose of experiment 2 was to replicate the findings of experiment 1, and
examine why experiment 1 showed only the effect of processing styles on evaluation
and purchase intentions, but not on perceived meaningfulness of different innovations.
It could be argued that processing styles did not have an impact on perceived
meaningfulness, because the meaningfulness construct involves both usefulness and
ease-of-use considerations (Im & Workman, 2004; Sethi et al., 2001). More precisely,
research suggests that RNPs are typically characterized by low levels of perceived
ease-of-use, whereas INPs are characterized by high levels of perceived ease-of-use
(Dahl & Hoeffler, 2004; Veryzer, 1998a; Zhao et al., 2009). Following this, it could be
the case that effects of processing styles on perceived meaningfulness of RNPs and
INPs are diminished by the inherent difficulty-of-use of RNPs and the inherent easeof-use of INPs. In line with this, it could be concluded that different processing styles
affect the perceived usefulness of RNPs and INPs instead of their perceived
meaningfulness.
Arising from this, the following additional formal hypothesis is proposed.
H4: (a) Global processing (relative to local processing) increases usefulness of
RNPs, whereas (b) local processing (relative to global processing) increases
usefulness of INPs.
107
The experiment was a 2 (innovation: INP vs. RNP) x 2 (processing mode: global vs.
local) between-subjects design. A total of 149 students of the University of St. Gallen
completed the study (26.2% female, 73.8% male, average age of 21.1 years).
The procedure of experiment 2 was the same as the procedure in experiment 1.
Participants were randomly assigned to either an INP condition or a RNP condition. In
the INP condition, a mock advertisement of an INP was provided, and participants
were asked to evaluate the INP. By contrast, in the RNP condition, a mock
advertisement of a RNP was provided, and participants were asked to evaluate the
RNP. Prior to presentation of the mock advertisement, participants were randomly
assigned either to a global processing condition or a local processing condition. In both
conditions, processing styles were primed by a version of the Navon-Letter task. This
procedure was the same as in experiment 1.
Similar to experiment 1, participants in the INP condition received a mock
advertisement of a conventional digital compact camera, whereas in the RNP
condition, participants received a mock advertisement of the new light field camera
used in experiment 1 (see Figure 4-9). The company logo and all other brand
identification information were removed prior to the experiment. The product was
called XR-500 in all conditions. The mock advertisements consisted of four
components: a headline, a picture, a short description, and a set of product features (4
distinctive features and two common features). All product features were taken from
consumer reports and represented real features.
108
Incrementally New Product
Really New Innovation
109
Global and local processing styles were manipulated with the same version of the
Navon-Letter task used in experiment 1 (Macrae & Lewis, 2002). That is, all
participants were presented with a series of 12 global letters that consisted of local
letters (see Appendix). Participants in the global processing condition were asked to
identify the global letters and write these in a text field below. By contrast, participants
in the local processing condition were asked to identify the local letters and write these
in the text field below.
Participants were asked to rate product newness on a three-point scale ranging from 1
(not very innovative, not very novel, not very original) to 7 (very innovative, very
novel, very original). The scale was derived from a study by Zhao, Hoeffler, Dahl
(2009) which initially examined the influence of imagination-focused visualization on
innovation perception.
Usefulness was measured on a three-point scale ranging from 1 = totally disagree to 7
= totally agree (This product is advantageous, …is useful, …is an improvement).
Ease-of-use was measured on a three-point scale ranging from 1 = strongly disagree to
7 = strongly agree (Usage of this product may be learned with little effort, …is clear
and understandable, …is easy). Both of these scales were derived from Kulviwat et al.
(2007) who examined the impact of perceived usefulness and ease-of-use on
innovation acceptance in the consumer context.
Product evaluation was measured by a three-point scale ranging from 1 (bad, dislike,
poor) to 7 (good, like, excellent). Additionally, purchase intention was measured by
asking participants to indicate how interested they would be in purchasing the product
on a scale ranging from 1 (not interested at all) to 7 (very interested). Both of these
scales were the same as in experiment 1. Table 4-4 provides a summary of all
measures and their corresponding levels of reliability.
110
Measures
Items
Reliability
Source
Product Evaluation
3
α = .86
Purchase Intention
1
-
Zhao et al. (2011)
Usefulness
3
α = .79
Kulviwat et al. (2007)
Newness
3
α = .93
Zhao et al. (2009)
Ease-of-Use
3
α = .90
Processing Style
12
-
Dependent Variables
4.3.3.5 Results
Manipulation checks. The three product newness-related items were aggregated into a
newness index (α= .93). As anticipated, participants rated the RNP as significantly
newer than the INP (Ms = 4.95 vs, 2.48; t(147) = 14.99, p < .001). Further, participants
rated the RNP as significantly less easy to use than the INP (Ms = 4.57 vs. 5.62; t(147)
= 5.64, p < .001). Regarding processing style, all participants in the global condition
correctly identified the global letters, while all participants in the local condition
correctly identified the local letters.
111
showed a main effect of innovation (F(1, 145) = 5.99, p < .05), but no main effect of
processing (F(1, 145) = .01, p = .94). Furthermore, the analysis showed the expected
significant interaction between the two factors (F(1, 145) = 11.75, p < .01).
Specifically, for the INP local processing led to significantly better evaluation than
global processing (Ms = 4.67 vs. 4.07; t(77) = 2.52, p < 0.05). For the RNP, global
processing led to significantly better evaluation than local processing (Ms = 5.07 vs.
4.50; t(68) = 2.34, p < .05). The results for product evaluation are depicted in Figure
4-10.
Product Evaluation
5.50
5.25
5.07
5.00
4.75
4.67
4.50
4.50
4.25
Global processing
4.07
4.00
3.75
3.50
INP
Local processing
RNP
112
Purchase intention. For purchase intention, a 2x2 ANOVA showed no main effect of
innovation (F(1, 145) = 2.42, p = .12) or processing (F(1, 145) = .10, p = .75), but
showed a significant interaction between the two factors (F(1, 145) = 6.86, p < .05).
For the INP, local processing led to significantly higher purchase intention than global
processing (Ms = 3.00 vs. 2.31; t(77) = 2.15, p < 0.05; one-tailed). For the RNP, global
processing led to a marginally higher purchase intention than local processing (Ms =
3.29 vs. 2.75; t(68) = 1.59, p < 0.10; one-tailed). The results for purchase intention are
depicted in Figure 4-11.
Purchase Intention
3.50
3.25
3.00
3.29
3.00
2.75
2.75
2.50
Local processing
2.31
Global processing
2.25
2.00
1.75
1.50
INP
RNP
113
Usefulness. For usefulness, a 2x2 ANOVA showed a marginally significant main
effect of innovation (F(1, 145) = 3.11, p < .10), but no main effect of processing (F(1,
145) = .74, p = .39). Further, this analysis showed a significant interaction between the
two factors (F(1, 145) = 9.14, p < .01). For the INP, local processing led to marginally
higher usefulness than global processing (Ms = 4.32 vs. 3.88; t(77) = 1.51, p < 0.10;
one-tailed). For the RNP, global processing led to a significantly higher usefulness
than local processing (Ms = 4.84 vs. 4.06; t(68) = 2.82, p < 0.01; one-tailed). The
results for usefulness are depicted in Figure 4-12.
Figure 4-12: Usefulness Interaction (Experiment 2)
Usefulness
5.00
4.84
4.75
4.50
4.32
4.25
4.00
4.06
3.88
3.75
3.50
INP
RNP
Local processing
Global processing
114
experiment 2.
Measures
Dependent Variable
F(1,145)
p
Innovation
Product Evaluation
5.99
p < .05
Purchase Intention
2.42
p = .12
Usefulness
3.11
p < .10
Product Evaluation
.01
p = .94
Purchase Intention
.10
p = .75
Usefulness
.74
p = .39
Product Evaluation
11.75
p < .01
Purchase Intention
6.86
p < .05
Usefulness
9.14
p < .01
Processing
Innovation x Processing
INP
RNP
Local
processing
Global
processing
Local
processing
Global
processing
Product Evaluation
4.67
4.07
4.50
5.07
Purchase Intention
3.00
2.31
2.75
3.29
Usefulness
4.32
3.88
4.06
4.84
4.3.3.6 Discussion
The aim of experiment 2 was to replicate the results of experiment 1, and examine why
experiment 1 did not show the effect of processing styles on the perceived
meaningfulness of different innovations. The results concerning hypotheses 1a and 1b
could be replicated. More precisely, global processing improved evaluation of the
RNP relative to local processing. At the same time, local processing improved
evaluation of the INP relative to global processing. Furthermore, the results of
experiment 2 also confirmed hypotheses 2a and 2b. That is, global processing led to
higher purchase intentions for the RNP, whereas local processing led to a higher
115
purchase intention for the INP. Finally, experiment 2 supports hypotheses 4a and 4b
that global/local processing influences the perceived usefulness of different
innovations. Specifically, global processing increased the perceived usefulness of the
RNP, while local processing increased the perceived usefulness of the INP.
Two questions arise from the results of experiment 2. First of all, one could question
whether global processing also increases evaluation of a very easy-to-use RNP. It may
be the case that local processing leads only to less favorable evaluations of RNPs,
because of a lower level of perceived ease-of-use. Secondly, one could question
whether global processing even increases evaluation of very difficult-to-use RNPs.
From the findings of experiment 2, it could be concluded that people in a global
processing style generally do not mind low levels of ease-of-use.
4.3.4 Experiment 3
The purpose of experiment 3 was to examine whether global processing (relative to
local processing) even increases the evaluation of RNPs that are characterized by
extreme levels of ease-of-use. Specifically, experiment 3 examined the impact of
processing styles on the evaluation of very easy-to-use RNPs and very difficult-to-use
RNPs.
Research indicates that global processing is focused on the desirability of behavior,
whereas local processing is focused on the feasibility of behavior (Liberman & Trope,
1998). In the context of innovation adoption, desirability refers to the benefits of an
innovation. Feasibility denotes the obstacles associated with adoption. These obstacles
particularly include learning costs (Arts et al., 2011). Following this, it may be the case
that people in global processing concentrate on the benefits of an innovation, and place
less emphasis on learning costs. In contrast, people in a local processing style may put
more emphasis on learning costs and place less emphasis on the benefits.
From this, the following two conclusions could be drawn for the influence of
global/local processing on the perception of RNPs: firstly, one may conclude that
global/local processing has no influence on the perception of very easy-to-use RNPs.
Specifically, as very easy-to-use RNPs are hardly associated with any learning costs,
people in local processing can shift their attention from feasibility to desirability, and
focus on the benefits of that innovation. In such a situation, global processing would
not provide an advantage over local processing. Secondly, one may conclude that
116
global processing (relative to local processing) leads to more favorable perception of
RNPs, even if these are characterized by an extremely low level of ease-of-use. As
people in global processing generally concentrate on the benefits of an innovation and
neglect the costs associated with adoption, they may also ignore exceptionally high
learning costs and, as a consequence, even perceive very difficult-to-use RNPs
favorably.
However, contrary to these arguments, research indicates that global processing leads
to a more favorable perception of very easy-to-use RNPs, but not of very difficult-touse RNPs. According to NCT, local processing is associated with a general aversion to
newness. This implies that people under local processing evaluate RNPs unfavorably
irrespective of how easy or difficult their usage can be learned (Förster et al., 2010).
Furthermore, the tendency of global processing to focus attention on the benefits of an
innovation and neglect the learning costs associated with adoption does not mean that
people in global processing even evaluate RNPs more favorably, if these are
characterized by very high learning costs. That is, in case of very difficult-to-use
RNPs, global processing is unlikely to provide an advantage (Herzenstein et al., 2007).
Arising this discussion, the following formal hypotheses are proposed:
H5: (a) Global processing (relative to local processing) increases the evaluation
of easy-to-use RNPs, (b) but not of difficult-to-use RNPs.
H6: (a) Global processing (relative to local processing) increases the purchase
intentions of easy-to-use RNPs, (b) but not of difficult-to-use RNPs.
H7: (a) Global processing (relative to local processing) increases the perceived
usefulness of easy-to-use RNPs, but not of difficult-to-use RNPs.
The experiment was a 2 (innovation: easy-to-use RNP vs. difficult-to-use RNP) x 2
(processing: global vs. local) between-subjects design. A total of 161 participants of a
representative German online panel (aged 18 to 40 years) completed the study (50.3%
females, 49.7% males, average age of 29.4 years).
Participants were randomly assigned to either a easy-to-use condition or a difficult-touse condition. In the easy-to-use condition, participants received a mock advertisement
of a RNP whose usage was very easy to learn. In the difficult-to-use condition,
participants received a mock advertisement of a RNP whose usage was very difficult
to learn. In both conditions, participants were asked to evaluate the RNP. However,
117
before participants received the mock advertisements of the RNP, they were randomly
assigned to a local processing condition or a global processing condition. Similar to
experiments 1 and 2, processing was primed by a version of the Navon-Letter task.
Participants received two mock advertisements of a RNP (see Figure 4-13). Similar to
experiments 1 and 2, a light field camera was used as RNP. The company logo as well
as all other brand identification information were removed prior to the experiment.
The product was called XR-500 in all conditions. The mock advertisements included a
headline, a picture, a short description, and a set of four product features. These
elements were the same across conditions. The learning cost factor was manipulated
by providing two types of usage information about the RNP. In the easy-to-use
condition, participants were informed that all camera settings were made
automatically. In contrast, in the difficult-to-use condition, participants were informed
that all camera settings needed to be made manually.
118
Easy-to-Use RNP Manipulation
Difficult-to-Use RNP Manipulation
119
Furthermore, global and local processing was manipulated by a version of the NavonLetter task similar to experiments 1 and 2. Therefore, all participants were presented
with a series of 14 global letters that consisted of local letters (see Appendix).
Participants in the global processing condition were asked to identify the global letters
and type these in a text field below. By contrast, participants in the local processing
condition were asked to identify the local letters and type these in the text field below.
Participants were asked to rate product newness on a three-point scale ranging from 1
(not very innovative, not very novel, not very original) to 7 (very innovative, very
novel, very original). Usefulness was measured on a three-point scale ranging from 1
(totally disagree) to 7 (totally agree) (This product is advantageous, …is useful, …is
an improvement). Ease-of-use was also measured on a three-point scale ranging from
1 (strongly disagree) to 7 (strongly agree) (Usage of this product may be learned with
little effort, …is clear and understandable, …is easy). Product evaluation was
measured by a three-point scale ranging from 1 (bad, dislike, poor) to 7 (good, like,
excellent). Finally, purchase intention was measured by asking participants to indicate
how interested they would be in purchasing the product on a scale ranging from 1 (not
interested at all) to 7 (very interested). All scales were the same as the scales used in
experiment 2. Table 4-7 provides a summary of all measures and their corresponding
levels of reliability.
120
Measures
Items
Reliability
Source
Product Evaluation
3
α = .93
Purchase Intention
1
-
Zhao et al. (2011)
Usefulness
3
α = .93
Newness
3
α = .95
Zhao et al. (2009)
Ease-of-Use
3
α = .96
Processing Style
12
-
Dependent Variables
4.3.4.5 Results
Manipulation checks. The three product newness-related items were aggregated into a
newness index (α= .95). As anticipated, participants rated the RNP as very new (M =
5.12; t(160) = 10.34, p < .001, compared with the scale midpoint). Further, participants
rated the difficult-to-use RNP as significantly less easy to use than the easy-to-use
RNP (Ms = 3.83 vs. 4.97; t(159) = 5.06, p < .001). Regarding global/local processing,
all participants in the global condition correctly identified the global letters, while
those in the local condition correctly identified the local letters.
121
showed no main effect of innovation (F(1, 157) = 1.08, p = .30) or processing (F(1,
157) = .06, p = .81). Furthermore, the analysis showed the expected significant
interaction between the two factors (F(1, 157) = 5.34, p < .05). Specifically, the easyto-use RNP was evaluated marginally better under global processing than under the
local processing (Ms = 5.14 vs. 4.60; t(76) = 1.94, p < 0.10). In contrast, for the
difficult-to-use RNP, global/local processing did not influence product evaluation
(t(81) = .1.39, p = .17). The results for product evaluation are depicted in Figure 4-14.
Product Evaluation
5.50
5.14
5.25
5.00
4.75
4.50
4.87
Local processing
4.60
4.43
4.25
4.00
Easy-to-Use RNP Difficult-to-Use RNP
Global processing
122
Purchase Intention. For purchase intention, a 2x2 ANOVA showed no main effect of
innovation (F(1,157) = 1.56, p = .21) but a marginally significant main effect of
processing (F(1,157) = 3.56, p < .10). Furthermore, this analysis showed a marginally
significant interaction between the two factors (F(1,157) = 2.82, p < .10). Specifically,
for the easy-to-use RNP global processing led to higher purchase intentions than local
processing (Ms = 4.43 vs. 3.44; t(76) = 2.62, p < 0.05). In contrast, for the difficult-touse RNP, processing styles did not influence purchase intention (t(81) = .14, p = .89).
The results for purchase intention are depicted in Figure 4-15.
Purchase Intention
5.00
4.75
4.43
4.50
4.25
Local processing
4.00
3.75
3.50
3.44
3.56 3.62
Global processing
3.25
3.00
Easy-to-Use RNP Difficult-to-Use RNP
Usefulness. For usefulness, a 2x2 ANOVA showed a marginally significant main
effect of innovation (F(1,157) = 3.83, p < 0.10) but no main effect of processing
(F(1,157) = .00, p = .99). Furthermore, this analysis did not show a significant
interaction between the two factors (F(1,157) = 2.54, p = .11).
123
experiment 3.
Measures
Dependent Variable
F(1,157)
p
Innovation
Product Evaluation
1.08
p = .30
Purchase Intention
1.56
p = .21
Usefulness
3.83
p < .10
Product Evaluation
.06
p = .81
Purchase Intention
3.56
p < .10
Usefulness
.00
p = .99
Product Evaluation
5.34
p < .05
Purchase Intention
2.82
p < .10
Usefulness
2.54
p = .11
Processing
Ease-of-Use x Processing
Easy-to-Use RNP
Difficult-to-Use RNP
Local
processing
Global
processing
Local
processing
Global
processing
Product Evaluation
4.60
5.14
4.87
4.43
Purchase Intention
3.44
4.43
3.56
3.62
Usefulness
4.55
4.89
4.47
4.13
4.3.4.6 Discussion
The third and final experiment of the empirical investigation examined hypotheses 5 to
7. The purpose of the experiment was to show how processing styles influence
evaluation of RNPs that are either very easy to use or very difficult to use. The results
of the experiment showed that global processing increases evaluation of very easy-touse RNPs, but not of very difficult-to-use RNPs. Furthermore, the results also showed
that global processing increases purchase intentions for very easy-to-use RNPs, but not
of very difficult-to-use RNPs. These results support hypotheses 5 and 6. Contrary to
124
expectations, the experiment did not show the expected interaction effect of processing
styles on perceived usefulness. Accordingly, hypothesis 7 is not supported.
Together, the results of experiment 3 indicate that global processing even leads to a
more favorable evaluation than local processing when a RNP is very easy to use. This
implies that local processing relates to a general aversion to newness. Furthermore,
experiment 3 demonstrates that global processing tolerates difficulty-of-use of RNPs
only to a certain extent. At very high levels of difficulty-of-use, global processing does
not lead to more favorable evaluation of RNPs.
125
4.4 General Discussion
The following section provides a summarizing discussion of the empirical
investigation of the influence of processing styles on perception of RNPs and INPs.
The remainder of this chapter is structured as follows: the first section contains a brief
review of the three experimental studies. The theoretical and managerial implications
are drawn out in sections 2 and 3. Section 4 then discusses the limitations of the
conducted studies. The purpose of the last section is to reveal potential avenues for
future research.
4.4.1 Summary of Results
Research distinguishes between global and local processing as fundamental ways of
perceiving the world. Global processing broadens perceptual and conceptual scope
with the result that people focus on the overall impression of an object and think of it
in more abstract terms. In contrast, local processing narrows perceptual and conceptual
scope with the result that people focus on an object’s constitutive elements and
conceive it in more concrete terms (Förster, 2012). Thereby, global processing
promotes assimilation, whereas local processing promotes contrast. Further,
global/local processing improves performance in different kinds of problem solving
tasks. More precisely, global processing enhances creative thinking, while local
processing enhances analytical, detailoriented thinking. Finally, global processing is
more suitable for dealing with novel events, whereas local processing is more suitable
for dealing with familiar events (Förster & Dannenberg, 2010).
Following these considerations, the present study examined the impact of processing
styles on perception of different kinds of innovations. Processing styles were
manipulated on the basis of simple visual primes. Specifically, global processing was
evoked by focusing individuals on the overall Gestalt of a visual stimulus, whereas
local processing was evoked by focusing individuals on the details of that same
stimulus. The empirical results showed that these subtle manipulations are already
sufficient to fundamentally change the perception of RNPs and INPs.
Specifically, global processing was found to lead to higher perceived usefulness, better
evaluation, and stronger purchase intentions of RNPs. Thereby, the results indicate that
global processing even tolerates a certain degree of difficulty-of-use, a typical
characteristic of RNPs. In contrast, local processing was found to lead to higher
perceived usefulness, better evaluation, and stronger purchase intentions of INPs. The
findings further suggest that local processing is characterized by a general aversion to
126
newness. More precisely, local processing will even lead to less favorable evaluation
of RNPs if these are very easy to use.
4.4.2 Theoretical Contributions
The present study provides important theoretical contributions. First of all, it
contributes to innovation perception theory. Previous research focused primarily on
the ‘what’ of innovation perception (see chapter 3). However, recent findings suggest
that innovation perception is also determined by the ‘how’. That is, customers may
perceive the same innovation completely different depending on the way with which
they think about that innovation (Ulkumen, Chakravarti, & Morwitz, 2010). Research
refers to these ways of thinking as processing styles (Förster & Dannenberg, 2010).
The present study represents the first study that examined the influence of global and
local processing styles on innovation perception. The results suggest that even subtle
visual primes such as Navon-Letters can influence these processing styles and
fundamentally change the way people perceive these RNPs and INPs.
Secondly, the present study contributes to NCT, which evolved from psychological
research on novelty perception. Research has only recently started to investigate the
implications of this theory for consumers (Förster et al., 2009). Thus, this study is
among the first to apply NCT to the marketing context. Thereby, the empirical
findings extend NCT in an important way. So far, NCT suggests that local processing
favors response to familiar stimuli, whereas global processing favors response to novel
stimuli. However, the empirical results of this study show that global processing does
not favor response to novel stimuli under any circumstances. A global processing style
will improve evaluation of RNPs only as long as individuals do not perceive them as
too difficult to use. That is, at extreme levels of perceived difficulty-of-use, global
processing does not lead to more favorable evaluation than local processing.
4.4.3 Managerial Contributions
Processing styles may not only be evoked by Navon-Letters, but also by various reallife variables (see Table 4-10). With this in mind, the present study has important
implications for managing innovation perception.
127
Table 4-10: Primes of Global/Local Processing (Förster, 2012)
Global Processing
Local Processing
Good Mood
Bad Mood
Interoceptive Approach
Interoceptive Avoidance
(e.g., Arm Flexion)
(e.g. Arm Extension)
Conceptual Approach
Conceptual Avoidance
Promotion Focus
Prevention Focus
Blue Color
Red Color
Distal Events
Proximal Events
High Power
Low Power
Love
Lust
Novelty
Familiarity
Obstacles
No Obstacles
Interdependent Selves
Independent Selves
Firstly, implications for innovation communication can be derived. Research suggests
that global/local processing may be evoked by colors. For instance, the color blue
evokes global processing, and the color red evokes local processing (Förster &
Dannenberg, 2010). Hence, color schemes in innovation communication can be
adjusted so that global processing is evoked for RNPs and local processing is evoked
for INPs. More precisely, perception of RNPs can be improved by using blue color
schemes to evoke global processing, whereas perception of INPs can be improved by
using red color schemes to evoke local processing.
Secondly, implications for product positioning of innovations can be derived. Research
suggests that a promotion focus evokes global processing, whereas a prevention focus
evokes local processing (Förster & Dannenberg, 2010). A promotion focus is
associated with advancement, growth, and accomplishment. In contrast, a prevention
focus is concerned with security, safety, and responsibility (Crowe & Higgins, 1997).
128
Following this, RNPs should be positioned so that people perceive them as a means for
achieving their dreams and aspirations, whereas INPs should be positioned in such a
way that people perceive them as means for better fulfilling their duties and
obligations. Similarly, implications for the positioning of innovations may be derived
from self-concepts. Research distinguishes between interdependent self-concepts
which evoke global processing and independent self-concepts which evoke local
processing. Interdependent self-concepts refer to groups of people. When speaking of
‘we’ and ‘us’, interdependent self-concepts are addressed. In contrast, independent
self-concepts refer to the individual. When speaking of ‘I’ and ‘me’, independent selfconcepts are addressed (Förster & Dannenberg, 2010). Thus, when introducing RNPs
to the market, product positioning should address interdependent self-concepts. It
should be revealed how RNPs consider the needs of the collective, such as lower CO2
emissions of a car. When bringing INPs into the market, product positioning should
address independent self-concepts. In other words, it should be revealed how INPs
consider individual needs, such as lower fuel costs of a car.
4.4.4 Limitations
As with any experimental investigation, the present study involves some limitations
with regard to external validity. In academic research, external validity depends in part
on whether experimental results may be replicated in other settings (Winer, 1999). One
might worry that this study used digital cameras only to examine the effect of
processing styles on innovation perception. Whether the use of a single product
category is adequate depends on the underlying research goals (Calder, Phillips, &
Tybout, 1981). That is, if the purpose of this research is to apply the findings to
specific real-world situations, then the use of a single product category is not
appropriate. However, if the purpose of the research is to develop scientific theories
that allow a more general understanding of real-world phenomena, then the use of a
single category is acceptable.
For the purpose of the present study, the sole use of innovations from a single product
category seems appropriate. The primary research goal was to extend theoretical
understanding of processing styles and innovation perception. Digital cameras seemed
to be very well suited for such an investigation. That is, digital cameras represent
familiar products that can potentially be perceived as rather new (Herzenstein et al.,
2007). By using digital cameras, it was possible to use RNPs and INPs that were
highly comparable and, therefore, differed primarily on the newness and ease-of-use
dimensions.
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The external validity of the present study might be criticized for another reason.
Research suggests that external validity depends also on whether the research context
is realistic and, therefore, whether the results are also likely to hold in a more natural
environment (Winer, 1999). In all three of the experiments, Navon-Letters were used
as a prime for processing styles. That is, participants were required to follow detailed
instructions to evoke global/local processing. Such a prime is difficult to integrate into
a ‘real-live’ setting. In other words, the application of Navon-Letters in the actual
communication of innovations is very unlikely to occur.
However, for the purpose of this study, the use of Navon-Letters seemed appropriate.
Navon-Letters represent a prime of global/local processing that is broadly established
in academic research (Förster & Dannenberg, 2010). If applied adequately, NavonLetters solely evoke global/local processing, excluding other potential influences.
Hence, this procedure was very well suited for revealing the actual effect of different
processing styles on the perception of RNPs and INPs.
4.4.5 Future Research
One potential avenue for further research arises from the fact that, in addition to
newness and ease-of-use, innovations may be characterized by a multitude of other
kinds of variables. For example, in future research, a distinction could be made
between RNPs and INPs that provide different kinds of customer benefits. Zhao,
Hoeffler, & Dahl (2011) found, for example, that different kinds of mental simulation
have a different impact on the evaluation of functional versus hedonic innovations.
Thus, in future research on the influence of processing styles on innovation perception,
a distinction could be drawn between innovations that primarily provide functional
benefits and innovations that primarily provide hedonic benefits. Alternatively to
customer benefits, innovations could also be distinguished on the basis of the specific
customer goals these innovations address. Research distinguishes between promotion
goals associated with achievement, and prevention goals associated with safety
(Higgins, 1997). RNPs and INPs may address either of these goals. It could be the
case, for example, that global processing particularly improves evaluation of RNPs
that address promotion goals, whereas local processing particularly improves
evaluation of INPs that address prevention goals.
Another potential route for further research is the use processing primes other than
Navon-Letters. Processing styles are evoked by a variety of real world variables such
as colors, power, or self-concepts (Förster, 2012). These variables seem to be more
130
suitable for application in innovation communication rather than Navon-Letters. It
could be investigated whether these variables have a similar effect on innovation
perception. Thereby, it would also be interesting to investigate, whether different
primes of processing styles work only prior to the presentation of an innovation, or if
they also work directly in innovation presentation. More precisely, in the present
study, participants were first primed with global/local processing styles via the NavonLetter task, and then received the mock-advertisements of the innovations. In contrast,
other primes of global/local processing may be integrated directly into the mockadvertisement. For example, using red color schemes in mock-advertisements may
directly evoke local processing, whereas using blue color schemes in mockadvertisements may directly evoke global processing (Förster & Dannenberg, 2010).
131
5 Conclusion
The fundamental aim of this dissertation was to provide a comprehensive analysis of
innovation perception from a customer perspective. The dissertation began with a
conceptualization of customer perception of innovations. It was found that customers
perceive innovations in terms of two dimensions: perceived newness and perceived
meaningfulness (Sethi et al., 2001). Perceived newness is the degree to which an
innovation is understood as deviating from existing practice (Amabile, 1983; Andrews
& Smith, 1996; Jackson & Messick, 1965; Szymanski et al., 2007). It may arise from
the technological newness of an innovation and/or the newness of the benefits an
innovation provides (Chandy & Tellis, 1998, 2000). Newness is not perceived as good
or bad per se. People may regard newness either as an opportunity and respond
favorably to it, or they may perceive it as a potential threat and respond unfavorably to
it. The question of which of these two outcomes will occur depends on people’s
situation-specific motivational state. If they are driven by a need for accomplishment,
they are attracted to novel events. In contrast, if people are driven by a need for safety
and predictability, they avoid novel events. Research suggests that the perception of
newness is also determined by people’s situation specific attitude towards newness.
This attitude can, on the one hand, relate to Heraclit’s well-known aphorism, ‘you
could never step twice in the same river; for other waters are ever flowing onto you’.
On this view, almost everything may be perceived as new simply by adopting a
slightly different perspective. In contrast, people may adopt a ‘been there, done that’
attitude in which they perceive almost anything as familiar and boring (Förster et al.,
2010). Which of these attitudes predominates in a given situation depends on a variety
of context factors that can be influenced through appropriate design and
communication of innovations (Förster, 2012).
The second dimension of innovation perception, perceived meaningfulness, denotes
the degree to which an innovation is perceived as desirable and feasible (Arts et al.,
2011; Trope & Liberman, 2003). Perceived desirability arises from the benefits an
innovation provides over and above those provided by existing alternatives (Rogers,
2003). An innovation can provide benefits in terms of superior functional or
instrumental value, hedonic or experiential value, and/or symbolic or expressive value
(Smith & Colgate, 2007). The perceived feasibility of an innovation is based on the
cost and other sacrifices that may occur during its purchase, ownership, and use (Smith
& Colgate, 2007). This implies that a high level of desirability is not enough for an
innovation to be perceived as meaningful. Perceived meaningfulness can only be
achieved if an innovation is highly desirable, and at the same time, sufficiently
132
feasible. That is, people should be able to obtain the benefits of an innovation with
reasonable financial and non-financial resources.
Following the conceptualization of innovation perception, this dissertation then moved
to an extensive literature review of the key processes of innovation perception. These
included innovation recognition and innovation assessment. Innovation recognition
refers to the initial interactions with an innovation. During the innovation recognition
phase, people should form a positive awareness of an innovation that motivates them
to gather further information about it. The innovation recognition phase will only lead
to a favorable response if an innovation is clearly differentiated from existing practice
(Ziamou & Ratneshwar, 2003). For incrementally new products, this represents a
considerable hurdle. Because incrementally new products provide only minor
improvements, people frequently perceive them as literally the same as existing
alternatives (Chandy & Tellis, 1998, 2000). Besides differentiation, innovation
recognition is also determined by whether people understand what an innovation is and
does. Such understandability represents a considerable hurdle for really new products,
which represent groundbreaking departures from existing practice (Veryzer, 1998a).
As people lack prior knowledge of really new products, they frequently overlook
essential aspects of these innovations and fail to grasp them (Moreau, Lehmann, &
Markman, 2001; Moreau, Markman, & Lehmann, 2001).
In comparison, in case of innovation assessment, people get actively involved with an
innovation, and gather information about the personal consequences associated with
adoption. During this process, people ask themselves whether an innovation is actually
capable of providing desirable outcomes in their everyday lives (Venkatesh et al.,
2012). More precisely, in the innovation assessment process, people try to ascertain
how productive an innovation will be in their own context; how easy it will be for
them to make use of the innovation; what kinds of investments they need to make
before they can operate the innovation; or whether important others appreciate or
depreciate their adoption of the innovation. All of these aspects represent potential
risks that people need to address before they can make an informed decision about an
innovation (Castaño et al., 2008). Consequently, innovation assessment is strongly
determined by the degree to which people develop favorable or unfavorable mental
scenarios associated with adoption of an innovation. Typically, such scenarios arise
from the way in which an innovation is communicated (Ziamou & Ratneshwar, 2002).
Innovation assessment is also determined by whether previously considered
hypothetical scenarios about an innovation are confirmed or disconfirmed.
Specifically, the disconfirmation of favorable scenarios is critical. Frequently,
133
innovations are presented in a way that suggests they are easily operated. However,
oftentimes this is not the case. If they encounter difficulties in operating the device,
people are likely to respond very unfavorably to the innovation (Wood & Moreau,
2006). The damage arising from this situation is typically irreparable. In most of these
cases, such negative experiences generate a large amount of negative word of mouth
from which an innovation is unlikely to recover (Moldovan et al., 2011).
Besides innovation recognition and innovation assessment, this dissertation identified
innovation comprehension as a new, highly relevant, aspect of innovation perception.
While innovation recognition and innovation assessment are primarily concerned with
the ‘what’ of innovation perception, innovation comprehension is concerned with the
‘how’ of innovation perception. More precisely, innovation comprehension refers to
the way in which people think about an innovation. Building on social psychology
literature, this dissertation identified global processing and local processing as two
distinct ways of thinking that have considerable influence on innovation perception
(Förster et al., 2010). Specifically, it was found that already subtle visual primes (i.e.
Navon-Letters) are sufficient to evoke global and local processing, thereby,
fundamentally changing the way in which people perceive really new and
incrementally new products. Specifically, global processing was demonstrated to
significantly improve responses to really new products, whereas local processing was
found to significantly improve responses to incrementally new products.
Because social psychology literature provides a variety of variables that may evoke
global and local processing, such as colors, self-concepts, and regulatory focus
(Förster, 2012), innovation comprehension has extensive managerial implications for
communicating and positioning innovations. Additionally, innovation comprehension
is of high theoretical relevance. This dissertation represents the first empirical
investigation of the influence of global/local processing on innovation perception. The
promising empirical results provide numerous avenues for future research.
Accordingly, this dissertation represents an important basis for further inquiry into the
influence of processing styles on customer perception of innovations.
134
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7 Appendices
7.1 Appendix 1: Navon-Letters used in Experiment 1
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Curriculum Vitae
Name
Dennis Vogt
Date of Birth
4th of October 1981 in Munich, Germany
Education
2010-2013
University of St. Gallen, Switzerland
Doctoral Candidate in Business Administration
2004-2008
Technical University Munich, Germany
Diploma Studies in Business Administration and Engineering
2002-2004
University of Bayreuth, Germany
Diploma Studies in Business Administration
1992-2001
Michaeli-Gymnasium Munich, Germany
Abitur
Work Experience
2010-2013
Center for Customer Insight, University of St. Gallen, Switzerland
Research Associate
2009-2010
Raffel GmbH Corporate Development, Munich, Germany
Junior Consultant
2009
BMW AG, Munich, Germany
Internship
2008
goetzpartners Management Consultants, Munich, Germany
Working Student
2007
goetzpartners Corporate Finance, London, United Kingdom
Internship
2004
Roland Berger Strategy Consultants, Munich, Germany
Working Student
2002
Audi AG, Ingolstadt, Germany
Internship

Innovation Perception from a Customer Perspective

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