Exploration of Salespeople Activities and Behaviour in Information

Transcrição

Exploration of Salespeople Activities and Behaviour
in Information Technology Selling
DISSERTATION
of the University of St. Gallen,
Graduate School of Business Administration,
Economics, Law and Social Sciences (HSG)
to obtain the title of
Doctor Oeconomiae
submitted by
Natalia Bächli-Bolvako
from
Belarus
Approved on the application of
Prof. Dr. Christian Belz
and
Prof. Dr. Marcus Schögel
Dissertation no. 3808
ZSUZ-Druckerei Irchel, Zurich 2011
The University of St. Gallen, Graduate School of Business Administration, Economics,
Law and Social Sciences (HSG) hereby consents to the printing of the present dissertation,
without hereby expressing any opinion on the views herein expressed.
St. Gallen, October 26, 2010
The President:
Prof. Ernst Mohr, PhD
for all salespeople who want to be more,
and for my parents
Acknowledgements
“For a man – even one very well versed in learning – will
attain unto nothing more perfect than to be found to be
most learned in the ignorance which is distinctively his.”
- Nicholas of Cusa, 1440, De Docta Ignorantia I, 1
The idea to write this dissertation was born, on the one hand, as a personal challenge to
myself, and on the other hand, as a wish to improve something important.
My genuine interest in the personality, success factors, and behaviour of salespeople
determined my research direction. Being fascinated by “real-life” salespeople, their
personalities and success stories, I asked myself whether the notion of a successful sales
job can be best explained and predicted by science and mathematics. My personal interest
and intellectual curiosity took me through a number of barriers to the accomplishment of
this work. But of course, this work could not have been done alone without professional
guidance, collegial support, and my own deep belief that I could accomplish it.
First of all, I would like to thank my supervisor, Professor Dr. Christian Belz, who
professionally guided my research process and positively supported me with constructive
criticism. I would like to extend my thanks to my second supervisor, Professor Dr.
Marcus Schögel, for taking up the co-supervision role and for his valuable suggestions,
advice and remarks.
I am very grateful to Hans-Jürg Roth for showing genuine interest in my dissertation and
providing executive support for the study data collection.
IV
My special thanks go to Peter Frei and Philippe Roux, who supported my start as a
doctoral student at the HSG with a part-time position within their team and motivated me
through my studies. To my former and present managers, Cynthia Kümin, Edzard
Paulussen, Matthias Sailer, Marc Scriba and Roger Altorfer I am thankful for their
ongoing understanding and support in all respects during the writing and finishing process
of this dissertation. My heartfelt thanks go to my dear mentor, Marcel Luginbühl, for his
advice, mental encouragement and friendship throughout this period.
I would also like to express my gratitude to Professor Dr. Olga V. Tereshchenko and Dr.
Jürg Schwarz. I benefited greatly from your critical comments and recommendations on
the empirical part of my study. I would like to thank Joanna Niederer for proofreading
this dissertation.
Although the writing process was challenging and exhausting at times, I was inspired by
my peer PhD colleagues, who became my great friends – Olena Kos-Kalia, Dmitri
Kharitonenko, Agung Wicaksono, Dmitri Dailido, Rustem Bektenov, Natalia Outecheva,
Anna Wozniak, Rashmi Rai, Pierre Huang, Marina Rogacheva, and many others not
mentioned here. The time spent together reflecting on our research, sharing knowledge
and ideas, identifying ways to improve, as well as time spent outside of our studies, will
always remain with me.
I was very fortunate to get to know some “real-life” top sellers and great people: André
Bigler, Dominik Meli, Josef Meyer, Michel Hübscher, Dr. Guy Anastaze, Tina Zybur,
Esther Weiss, Manuel Seybold, and many others not mentioned here, who gave up their
valuable time to meet me and discuss my research. I thank them for the numerous
practical selling examples that contributed greatly to the quality of this study.
I thank my husband, Markus Bächli, for motivating and supporting me, for accepting my
busy lifestyle, and making all high barriers smaller. In addition, I especially thank my
V
father-in-law, Dr. Gerhard Bächli, for his valuable advice, and for the emotional support
of my mother- and sister-in-law: Helena and Astrid Bächli.
Finally, I owe much of my success in all my beginnings to my dear parents – Elena Sugak
and Ivan Bolvako – who motivated and encouraged me at a distance during all these years,
and rushed everywhere if help was needed. All this time I felt your love and support
although I was far away from you. This dissertation is also yours. (И наконец, своим
успехом во всех начинаниях я обязана своим дорогим родителям – Елене Сугак и
Ивану Болвако, которые мотивировали, воодушевляли меня на расстоянии все эти
годы, и поддерживали в любых ситуациях. Все это время я чувствовала вашу
любовь и поддержку будучи далеко от вас. Эта диссертация – тоже ваша).
Zurich, 29th of November, 2010
VI
TABLE OF CONTENTS
Table of Contents
ACKNOWLEDGEMENTS ............................................................................................ IV
TABLE OF CONTENTS .............................................................................................. VII
LIST OF ABBREVIATIONS ....................................................................................... XII
LIST OF FIGURES ......................................................................................................XIII
LIST OF TABLES ........................................................................................................ XIV
ABSTRACT................................................................................................................. XVII
ZUSAMMENFASSUNG ........................................................................................... XVIII
1 INTRODUCTION.......................................................................................................... 1
1.1 RELEVANCE OF THE CURRENT RESEARCH AND THE RESEARCH BACKGROUND .......... 1
1.2 RESEARCH QUESTIONS AND RESEARCH AIMS OF THE STUDY ...................................... 5
1.3 CHOOSING RESEARCH METHODOLOGY ....................................................................... 6
1.4 OUTLINE OF THE DISSERTATION ................................................................................. 9
2 THEORETICAL BACKGROUND AND LITERATURE REVIEW .................... 11
2.1 SALESPERSON ACTIVITIES AND BEHAVIOUR: DEFINITION AND MAIN
CHARACTERISTICS .......................................................................................................... 11
2.2 A DISCUSSION OF SALESPERSON ACTIVITY MODELS ................................................. 13
2.2.1 The model of Lamont and Lundstrom (1974).................................................... 14
2.2.2 The model of Moncrief (1986a, b)..................................................................... 15
2.2.3 The model of Kerber and Campbell (1987)....................................................... 18
2.2.4 The model of Darmon (1998) ............................................................................ 20
2.2.5 The model of Marshall, Moncrief, and Lassk (1999) ........................................ 21
2.2.6 The model of Guenzi (2002) .............................................................................. 24
2.2.7 The model of Moncrief, Marshall, and Lassk (2006) ........................................ 26
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TABLE OF CONTENTS
2.3 A CRITICAL REVIEW OF THE CLASSICAL PERSONAL SELLING PROCESS MODEL ........ 29
2.3.1 Review of Step 1: Prospecting for customers.................................................... 31
2.3.2 Review of Step 2: Pre-approach........................................................................ 33
2.3.3 Review of Step 3: Approach .............................................................................. 35
2.3.4 Review of Step 4: Sales presentation................................................................. 36
2.3.5 Review of Step 5: Handling objections.............................................................. 38
2.3.6 Review of Step 6: Closing the sale .................................................................... 39
2.3.7 Review of Step 7: Post-sale follow-up............................................................... 41
2.4 THEORIES ON THE LINK BETWEEN SALES ACTIVITIES AND SALES PERFORMANCE .... 43
2.4.1 The model of Kerber and Campbell (1987)....................................................... 45
2.4.2 The model of Brashear, Bellenger, Ingram, and Barksdale (1997).................. 47
2.4.3 The models of Dwyer et al. (2000) and Jaramillo and Marshall (2004) .......... 48
2.4.4 Study hypotheses................................................................................................ 52
3 METHODOLOGY OF ACTIVITY DOMAINS DEFINITION AND
VALIDATION.................................................................................................................. 53
3.1 THE DEVELOPMENT OF A SALESPERSON ACTIVITY/BEHAVIOUR LIST: THE
METHODOLOGICAL FRAMEWORK .................................................................................... 53
3.2 DESCRIPTION OF THE STUDY RESEARCH CONTEXT ................................................... 56
3.2.1 An overview of the information technology market and the implications for
personal selling .......................................................................................................... 57
3.2.2 The study research site ...................................................................................... 61
3.3 DEFINITION OF THE DOMAINS OF SALESPERSON ACTIVITY AND BEHAVIOUR ........... 62
3.3.1 Definition of the “customer prospecting and qualification” domain ............... 62
3.3.2 Definition of the “pre-approach and sales call preparation” domain ............. 64
3.3.3 Definition of the “approach and opening dialogue” domain ........................... 65
3.3.4 Definition of the “sales presentation, demonstration and product marketing”
domain ........................................................................................................................ 65
3.3.5 Definition of the “overcoming objections, problem-solving” domain.............. 66
3.3.6 Definition of the “closing and satisfying needs” domain ................................. 67
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TABLE OF CONTENTS
3.3.7 Definition of the “follow-up and relationship maintenance” domain .............. 67
3.3.8 Definition of the “inter-company coordination activities and administration”
domain ........................................................................................................................ 69
3.3.9 Definition of the “planning” domain ................................................................ 70
3.3.10 Definition of the “personal development” domain ......................................... 71
3.3.11 Definition of the “travel” domain ................................................................... 72
3.3.12 Operationalisation of the “sales performance” variable ............................... 72
3.4 THE METHODOLOGY OF ACTIVITY MEASUREMENTS ................................................. 76
3.4.1 The time use methodology ................................................................................. 77
3.4.2 The measurements of activity importance ......................................................... 82
3.4.3 The implementation of measurements in the current study............................... 83
3.5 SURVEY STUDY DESIGN ............................................................................................ 86
3.5.1 The questionnaire outline .................................................................................. 86
3.5.2 Survey design considerations to maximise the response rates.......................... 88
3.5.3 Survey implementation ...................................................................................... 90
3.5.4 Addressing possible survey errors .................................................................... 91
3.6 THE QUALITATIVE INTERVIEW AS A METHOD OF VALIDATING A QUESTIONNAIRE ... 93
3.6.1 Cognitive interviewing as a qualitative questionnaire pre-test method............ 94
3.6.2 Introduction to the cognitive processes underlying question answering .......... 95
3.6.3 Using the “think aloud” technique and probing questions during cognitive
interviews.................................................................................................................... 97
3.6.4 Measures of cognitive processing ..................................................................... 98
3.6.5 Implementation of the cognitive interviews..................................................... 100
3.6.6 Cognitive interview results .............................................................................. 101
4 EMPIRICAL STUDY RESULTS ............................................................................ 108
4.1 INITIAL DATA ANALYSIS ......................................................................................... 108
4.1.1 General sample information and socio-demographic data............................. 108
4.1.2 Nonresponse bias examination........................................................................ 110
4.1.3 Data examination ............................................................................................ 111
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TABLE OF CONTENTS
4.1.4 Describing the time allocation of the salespeople........................................... 114
4.1.5 The evaluation of different measurement approaches of sales activities........ 116
4.2 EMPIRICAL ANALYSIS OF THE UNDERLYING STRUCTURE OF THE SALESPEOPLE’S
ACTIVITIES .................................................................................................................... 118
4.2.1 Factor analysis methodology .......................................................................... 118
4.2.2 Factor analyses results.................................................................................... 121
4.2.2.1 Factor analysis 1 .......................................................................................... 121
4.2.2.2 Factor analysis 2 .......................................................................................... 123
4.2.2.3 Factor analysis 3 .......................................................................................... 127
4.2.2.4 Factor analysis 4 .......................................................................................... 131
4.2.2.5 Factor analysis 5 .......................................................................................... 132
4.2.2.6 Factor analysis 6 .......................................................................................... 134
4.2.2.7 Factor analysis 7 .......................................................................................... 135
4.2.2.8 Factor analysis 8 .......................................................................................... 137
4.2.2.9 Factor analysis 9 .......................................................................................... 139
4.3 ANALYSIS OF THE DIFFERENCES IN SALES ACTIVITIES OF TOP AND BOTTOM
PERFORMERS ................................................................................................................. 142
4.3.1 Methodology of one-way analysis of variance ANOVA .................................. 142
4.3.2 Results of the statistical analysis using the one-way ANOVA......................... 145
4.4 AN ANALYSIS OF SALES PERFORMANCE PREDICTION FROM THE SALESPERSON
ACTIVITIES BY MEANS OF MULTIPLE REGRESSION ........................................................ 160
4.4.1 Testing the assumptions of multiple regression .............................................. 161
4.4.2 Selection of the regression estimation technique ............................................ 162
4.4.3 The regression model summary....................................................................... 162
5 DISCUSSION AND CONCLUSIONS ..................................................................... 165
5.1 DISCUSSION OF THE RESULTS AND FINDINGS OF THIS STUDY ................................. 165
5.2 THE CONTRIBUTION OF THIS STUDY TO THEORY..................................................... 170
5.3 PRACTICAL IMPLICATIONS OF THE DISSERTATION .................................................. 173
5.4 LIMITATIONS AND SUGGESTIONS FOR FURTHER RESEARCH.................................... 179
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TABLE OF CONTENTS
6 BIBLIOGRAPHY ...................................................................................................... 182
7 APPENDICES ............................................................................................................ 199
APPENDIX 1. MAJOR JOB ROLE SUMMARIES IN THE SAMPLE COMPANY ....................... 199
APPENDIX 2. LIST OF THE PERSONAL PRE-TEST INTERVIEWS BASED ON COGNITIVE
TECHNIQUES.................................................................................................................. 201
APPENDIX 3. INITIAL PRE-TEST VERSION OF A QUESTIONNAIRE ................................... 202
APPENDIX 4. THE FINAL VERSION OF THE QUESTIONNAIRE .......................................... 209
APPENDIX 5. DESCRIPTIVE STATISTICS FOR THE VARIABLES IN THE STUDY ................ 217
CURRICULUM VITAE................................................................................................ 221
XI
LIST OF ABBREVIATIONS
List of abbreviations
ANOVA
Analysis of Variance
CD ROM
Compact Disk Read-Only Memory
CEO
Chief Executive Officer
CFO
Chief Financial Officer
CRM
Customer Relationship Management
CSF
Critical Success Factors
DB
Database
df
Degrees of Freedom
e.g.
exempli gratia (for example)
et al.
et allii (and others)
etc.
et cetera (and so on)
hrs.
Hours
i.e.
id est (that is to say)
IT
Information Technology
KMO
Kaiser-Meyer-Olkin measure of sampling adequacy
MRI
Magnetic Resonance Imaging
N
Number
R2
Coefficient of Determination
SD
Standard Deviation
Sig.
Significance
Std.
Standard
TD
Tailored Design
TDM
Tailored Design Method
VCR
Video Cassette Recorder
VIF
Variance Inflation Factor
vs.
versus (against)
XII
LIST OF FIGURES
List of figures
Figure 2-1: The classical selling process steps ................................................................. 30
Figure 3-1: Methodology of the development of dimensions of salesperson activities..... 55
Figure 4-1: Sample size characteristics .......................................................................... 109
Figure 4-2: Missing data pattern..................................................................................... 111
Figure 4-3: Distribution of the time estimation variable (example of the “office day
planning” variable) ......................................................................................................... 113
Figure 4-4: Salesperson estimated time allocation during customer and office days .... 114
Figure 4-5: Estimated average monthly activity time allocation versus activity importance
ratings .............................................................................................................................. 115
Figure 4-6: Regression equation of the model on sales activities and performance ...... 163
Figure 5-1: The model of empirically confirmed dimensions of salesperson activities.. 166
XIII
LIST OF TABLES
List of tables
Table 2-1: An overview of the existing salesperson activity models ................................. 13
Table 2-2: A summary of the activities in the study of Moncrief (1986a, b) ..................... 16
Table 2-3: An overview of the activities that underlie a sales position – Darmon (1998) 20
Table 2-4: The list of new selling activities of Marshall et al. (1999)............................... 22
Table 2-5: Factor solution of the activity model of Guenzi (2002) ................................... 25
Table 2-6: The inventory of salesperson activities of Moncrief et al. (2006).................... 27
Table 4-1: ANOVA test of differences between the “early” and “late” repliers (selected
variables) ......................................................................................................................... 110
Table 4-2: Missing data characteristics .......................................................................... 112
Table 4-3: Correlations of different measurement variations for the variable block “sales
presentation” ................................................................................................................... 116
Table 4-4: Correlation between different measurement variations for the variable
“personal development”.................................................................................................. 117
Table 4-5: Factor analysis results 1 ................................................................................ 122
Table 4-6: Descriptive statistics, inter-item and item-total correlations for the factor
“Contact and lead management” .................................................................................... 123
Table 4-7: Factor analysis results 2 ................................................................................ 124
“Sales call preparation”.................................................................................................. 125
“Database management” ................................................................................................ 126
“Other information gathering” ....................................................................................... 127
Table 4-11: Factor analysis results 3 .............................................................................. 128
“Customer-oriented sales presentation”......................................................................... 129
XIV
LIST OF TABLES
“Product-oriented sales presentation” ........................................................................... 130
“Sales presentation close” .............................................................................................. 132
“Customer relationship maintenance”............................................................................ 133
“Technical support and maintenance”............................................................................ 134
“Sales planning” ............................................................................................................. 135
“Internal coordination and administration”................................................................... 137
“Internal partnerships and delivery” .............................................................................. 137
“Personal development” ................................................................................................. 139
“Travel”........................................................................................................................... 140
Table 4-27: Summary of factor solutions ........................................................................ 140
Table 4-28: One-way ANOVA results for the socio-demographic variables .................. 145
Table 4-29: One-way ANOVA results for the items of construct “Contact and lead
management”................................................................................................................... 146
Table 4-30: One-way ANOVA results for the items of the construct “Database
management”................................................................................................................... 147
XV
LIST OF TABLES
Table 4-31: One-way ANOVA results for the items of the construct “Other information
gathering”........................................................................................................................ 147
Table 4-32: One-way ANOVA results for the items of the construct “Sales
call/presentation preparation” ........................................................................................ 148
Table 4-33: One-way ANOVA results for the items of the construct “Customer-oriented
sales presentation” .......................................................................................................... 149
Table 4-34: One-way ANOVA results for the items of the construct “Product-oriented
sales presentation” .......................................................................................................... 150
Table 4-35: One-way ANOVA results for the items of the construct “Sales presentation
close” ............................................................................................................................... 150
Table 4-36: One-way ANOVA results for the items of the construct “Customer
relationship maintenance”............................................................................................... 151
Table 4-37: One-way ANOVA results for the items of the construct “Internal partnership
management and delivery” .............................................................................................. 152
Table 4-38: One-way ANOVA results for the items of the construct “Personal
development” ................................................................................................................... 153
Table 4-39: One-way ANOVA results for the items of the construct “Internal coordination
and administration”......................................................................................................... 154
Table 4-40: One-way ANOVA results for the items of the construct “Planning“ .......... 154
Table 4-41: One-way ANOVA results for the activity variables that did not appear in the
factor constructs............................................................................................................... 155
Table 4-42: One-way ANOVA results for the variables of activity importance evaluation
.......................................................................................................................................... 156
Table 4-43: One-way ANOVA results for the variables of activity time evaluation ....... 157
Table 4-44: Summary of the ANOVA results indicating the variables with identified
differences between top and bottom sellers at different p levels (a top-performer profile)
.......................................................................................................................................... 159
Table 4-45: Multiple regression results summary........................................................... 163
XVI
ABSTRACT
Abstract
This study aimed to investigate an extensive “macro” level picture of salesperson
activities/behaviour, and to identify which elements of these activities/this behaviour are
associated with sales job performance. We examined the context of information
technology selling, which is characterised by a high level of product complexity, market
uncertainty, pressure to find a solution, and the intensiveness of the business relationship.
Based on the empirical study results, we built a model of salesperson behaviour that
indicates eleven domains, such as prospecting contact management, database contact
management, detailed information gathering, call preparation/gathering, customer- and
product-oriented presentation, sales close, customer relationship management, and
customer technical support. By means of ANOVA, we found the elements of behaviour
that differentiate top and bottom performers, and by means of regression analysis – the
activities which can be the strongest predictors of salesperson job performance. Our study
reinforces the importance of both customer-interaction activities and non-customer
interaction activities in achieving sales success, and re-emphasizes the roles of long-term
planning, social networking, a “win-win agreement on relationship” close, and the
importance of personal development as the strongest factors of sales job performance.
XVII
ZUSAMMENFASSUNG
Zusammenfassung
Das Ziel dieser Studie war, ein umfangreiches "Makro"-Ebene-Bild der VerkäuferAktivitäten bzw. des Verkäufer-Verhaltens zu untersuchen und die Elemente dieser
Aktivitäten bzw. dieses Verhaltens, welche die Verkaufsleistung beeinflussen, zu
identifizieren. Wir untersuchten den Verkaufskontext des InformationstechnologieMarktes, der durch einen hohen Grad an Produktkomplexität, an Marktunsicherheit, am
Zwang, eine effiziente Kundenlösung zu finden, sowie durch die Intensität der
Geschäftsbeziehung geprägt ist.
Der empirische Beitrag unserer Studie lag in der Entwicklung eines Modells der
Verkäuferaktivitäten, das elf Dimensionen aufweist. Diese Dimensionen sind Lead- und
Kontakt-Management, Kundendatenbank-Management, Kunden-Informationserhebung,
Verkaufsgesprächs-Vorbereitung, kunden- und produktorientiertes Präsentationsgespräch,
Verkaufsgesprächs-Abschluss,
Kundenbeziehungs-Management
sowie
technische
Unterstützung des Kunden. Mittels einer univariaten Varianzanalyse (ANOVA) wurden
einerseits die Verhaltenselemente identifiziert, welche die erfolgreichsten von den
schwächsten Verkäufern unterscheiden, und andererseits – mittels einer multiplen
Regressionsanalyse – die Aktivitäten ermittelt, die am stärksten die Verkaufsleistung
beeinflussen. Unsere Studie betont die Bedeutung der Aktivitäten der Verkäufer für deren
Verkaufsleistung, sowohl innerhalb als auch ausserhalb der Kundeninteraktion. Ebenfalls
bestärkt unsere Analyse die Wichtigkeit der langfristigen Verkaufsplanung, des sozialen
Netzwerks, des strategischen, beziehungsorientierten Verkaufsabschlusses sowie die
Bedeutung der persönlichen Entwicklung des Verkäufers für dessen Verkaufserfolg.
XVIII
INTRODUCTION
1 Introduction
1.1 Relevance of the current research and the research background
Personal selling is a crucial marketing tool for different types of products (Parasuraman
and Day, 1977: 22). It plays at least three main roles: (1) disseminating factual
information, (2) presenting persuasive information, and (3) rendering a service
(Montgomery and Urban, 1969: 245). The main role of the salesperson is to inform the
market about all the product’s characteristics, technical details, prices, and conditions of
contracts. And in its turn, the market informs the company through the salespeople about
its recent changes. “In his persuasive role, he (the sales person, – Author) marshals
evidence in the social-psychological environment of the salesman-customer interaction in
an effort to influence the customer to purchase the product and service offering of his
firm” (Montgomery and Urban, 1969: 245).
Personal selling not only performs the important demand creation function, but also
creates a great promotional effect for other marketing tools, multiplying it in several times
(Montgomery and Urban, 1969: 246-247). In most companies, it is the single most
important link to the customer, and at the same time, for the customers, the salesperson
represents “the company” (Jobber and Lancaster, 2009: 4).
The personal selling function is a dynamic one, and it has been affected by a number of
environmental and social factors over the last few decades. Behavioural, technological,
and managerial forces such as globalisation, customer avoidance of buyer-seller
negotiations, rising customer expectations, sales force automation, electronic channels,
and teleselling are dramatically altering the way salespeople perform their job.
Salespeople are becoming more independent of the company management; they serve as
customer consultants, and focus on developing ongoing relationships with customers
1
INTRODUCTION
(Anderson, 1996: 17).
The focus of today’s selling and its survival in the “new
marketing” age in highly competitive and rapidly changing markets is dependent on
moving away from managing transactions and instead – focusing on gaining a very
detailed understanding of customer needs (Chaston, Megicks, and Williams, 2005: 847).
The changing sales environment and the increasing customer demands are putting a lot of
pressure on salespeople. The current sales jobs “encompass a wide range of duties,
behaviours, and challenges” (Wotruba, 1991: 1). Salespeople need to struggle with market
specific demands such as intense competition and market fluctuations, internationality of
sales, and sales complexity (Belz and Schmitz, 2007: 10). Sales complexity is a recently
defined phenomenon which is characterised by a high level of customer, information,
organisation, and production complexities. The sales complexity drivers include IT,
priority problems, ambiguous performance requirements, information overload, and
internal logistics (Rader, 2008).
As a consequence of this complexity and role stress, salespeople experience high levels of
stress, role ambiguity, and feel that they need to waste their time on non-customer facing
tasks that do not bring value (e.g. administration, planning, or internal work). These
negative perceptions are closely linked to emotional exhaustion and intentions to quit
(Jaramillo, Mulki, and Locander, 2006).
The salesperson’s role is developing and expanding at the same time, and more emphasis
is being placed on sales performance (Wilson, 1993: 127). Selling has become
increasingly more difficult: prospects have less time, while the decision makers are
receiving more sales calls than ever before. “No” has become the standard response and
the vast majority of calls end in rejection. As a result, sales productivity decreases (Freese,
2003: 3).
2
INTRODUCTION
Being the main focus of this study, the information technology market assumes a central
role in becoming a driving force of the economy. It stands at the core of different
industries and markets, from small business and consumer products to the public and
financial sectors (John, Weiss, and Dutta, 1999: 78).
No field of selling is more complex than IT technology selling, where personal contact is
as important as the product itself (Dunn, Frian, and Thomas, 1991: 149). The selling task
in the IT technology market is characterised by solution complexity, time pressure, the
information load, the length of the selling cycle, and high customer demands (Newell and
Simon, 1972 cited in Avila and Fern 1986: 55). Complex technical products require
extensive effort and time to contact the decision makers and influencers across different
customer departments and management levels of the customer’s organisation (Marshall
and Johnston, 2009: 510).
Since selling occurs in a continually developing organisational and economic
environment, the sales jobs and roles are rarely standardised and up-to-date (Lamont and
Lundstrom, 1974: 102). Theoretical progress in the field of personal selling will depend
greatly on the availability of empirically based classifications of sales activities (Kerber
and Campbell, 1987: 48). Therefore, the strategic development of the personal selling
function has to be the focus of companies (Marshall et al., 1999: 87) and academics. In
this way, a precise definition and an extended integrated knowledge of the salespeople
activity dimension on a “macro” level is crucial for the definition of the sales job roles,
and for further sales management topics, such as sales development, training, selection,
hiring and evaluation.
The empirical exploration of salesperson activities is a narrow research stream within
personal selling research. Since many conception theories on the identification and update
of the basic dimensions of salesperson behaviour did not end in empirical research (e.g.
Darmon, 1998; Marshall et al., 1999) or a number of them overlooked such strategic
3
INTRODUCTION
functions as planning, prospecting, or sales close (e.g. Darmon, 1998; Guenzi, 2002;
Moncrief et al., 2006), the number of empirical theoretical models on the definition of
sales behaviour from a “macro” approach remains limited.
In order to address the large number of complexity factors and in the face of sales
productivity concerns, it is crucial to manage sales organisation in the direction of task
prioritisation, while considering the company’s objectives (Belz and Schmitz, 2007: 10).
The overall sales and marketing strategy of an organisation must be developed “within a
context of accurate knowledge of what is truly important to the success of an individual
salesperson” (Moncrief et al., 1999: 97). Kerber and Campbell (1987) outlined that “if
specific behaviours or activities are associated with higher performance, this information
could improve our understanding of effective sales techniques, provide models for
training sales personnel, and facilitate the efficient use of sales resources” (1987: 41).
A number of notions from previous research highlight the importance of the empirical
models on the link between sales behaviour and sales performance. Walker, Churchill,
and Ford (1979) outlined that “sales performance is the result of carrying out a number of
discreet and specific activities which may vary greatly across different types of selling
jobs and situations” (1979: 22). Weitz (1981) mentioned that behaviour impacts on the
effectiveness of sales interactions in the microenvironment and, in this way, influences
the performance of salespeople. Avila, Fern, Mann (1988) suggested that sales behaviour
is related to both overall performance and goal achievement. Plank and Reid (1994)
assumed that customer interaction behaviour as well as noncustomer interaction
behaviour impact on sales performance and stressed the strong necessity to propose a
“taxonomy consisting of customer interaction behaviours and noncustomer interaction
behaviours (…) as a logical means of differentiating between the broad types of sales
behaviours and facilitating our understanding of the wide array of behaviours involved in
selling” (1994: 54).
4
INTRODUCTION
Although a lot has been written in well-known books about the success factors in sales,
contradictory and limited theoretical research exists on the factors of behaviour that
influence sales success from a “macro” approach on an empirical level, and no research
studies exist which contain facts and theories from the complex context of IT selling.
The current study aims to provide strong empirical evidence in order to address these
research gaps.
1.2 Research questions and research aims of the study
In view of the above-mentioned academic directions and research gaps and given
practical relevance of the research problem, the following research questions are
formulated for the current study.
Research question 1: What are the activities and activity domains of a salesperson in the
information technology selling context?
Research question 2: Do top and bottom performers differ in their execution of certain
activities/behaviours?
Research question 3: Which activities can be the best predictors of sales job
performance?
The aims of the current research are as follows: (a) to obtain an extensive current “macro”
level picture of salesperson behaviour based on emerging trends in the context of
information technology selling, and (b) to identify which elements of these activities and
this behaviour are associated with sales job performance.
5
INTRODUCTION
In view of these research aims, our research objectives are: (1) systematisation of the
previous theoretical background; (2) the proposal and development of a comprehensive
model of salesperson activities and behaviour related to the sales process and non-selling
activities in the IT industry; (3) identification of the activity elements of successful
performance in the context of comparing top and bottom salespeople, and identification of
activities that can be the best predictors of sales job performance.
Since a number of studies in sales activity research delivered inconsistent results, which
was thought to be highly dependent on the selling context environment, our research will
take the exploratory perspective and exploratory hypotheses will be developed in the
following chapters.
1.3 Choosing research methodology
Literature on research methods proposes a large number of options. Different researchers
suggest different approaches to methodologies. Based on these sources of information,
Black (1999) defines the main conceptual scientific approaches – empirical and nonempirical. The non-empirical approach implies a large amount of “authoritarian”
knowledge, while the empirical approach “indicates that the information, knowledge and
understanding are gathered through experience and direct data observation” (Black,
1999: 4).
Most of the researchers distinguish between two methodology approaches: qualitative and
quantitative. The qualitative approach in management is a research direction which
“provides a greater understanding of a concept and crystallises a problem rather than
providing precise measurement” (Zikmund, 1994: 88). It purposively abandons the
quantity aspect in order to provide an in-depth analysis of the object studied. Qualitative
research aims to provide a holistic overview of the context and perceptions around it. It is
6
INTRODUCTION
conducted through intense contact with the “field” or life situation which reflects the daily
life of individuals (Miles and Huberman, 1994: 6).
Unlike qualitative research, the purpose of quantitative research is “to determine the
quantity or extent of some phenomenon” (Zikmund, 1994: 88). This type of research is
concerned with numbers, mathematic or statistical modelling and it aims to classify
features, count them, and construct statistical models in an attempt to explain what is
observed (Miles and Huberman, 1994: 40).
Although quantitative data is more efficient because it is able to test hypotheses, it may
miss relevant contextual detail (Miles and Huberman, 1994: 40) as well as bring a number
of technical and concept limitations. Since qualitative studies take place in a real social
world, and can have real consequences in people’s lives, there is a reasonable view of
“what happened” in any particular situation for a closer correspondence to reality (Miles
and Huberman, 1994: 227). These constraints can be easily eliminated by adding the
qualitative methodology for the purpose of theory validation or deeper examination of
these constraints. The combination of both the quantitative and qualitative methodologies
is often called the eclectic approach. In this case, quantitative methods are used in
conjunction with qualitative methods to minimise the existing limitations of both of them
(Black, 1999: 46).
There is a wide choice of methods which researchers can use to develop their concepts
and theories. For every type of research question, there is a corresponding method or a
combination of them (Black, 1999).
Building knowledge and analytical structuring of salesperson behaviour and its
relationship to sales job performance would support the primary use of the quantitative
methods in our research, such as descriptive statistics, ANOVA, factor analysis,
regression and correlation analysis.
7
INTRODUCTION
Descriptive statistics will help to analyse the data on the first steps of this research study
as well as the consolidated average data from the activity lists. We will use graphical
techniques, such as histograms or scatter diagrams, to check the frequency distributions,
bar charts to analyse the sample characteristics, as well as time allocation and importance
evaluations of different activity sets.
Another level of quantitative data analysis will be the use of complex statistical
procedures, such as regression and correlation analysis, ANOVA and factor analysis. The
assumptions for all of the methods will be checked and reported before utilising a relevant
technique in the appropriate chapter.
Factor analysis is a statistical technique that is used to identify a small number of factors
which explain observed correlations among variables (Norušis, 2003: 395). Factor
analysis will help us to identify the dimensions of sales behaviours in our case company.
The one-way analysis of difference (ANOVA) will enable us to provide a comparative
analysis of the use of different selling behaviours among top and bottom performers.
Correlation analysis is used when we want to determine if there is a relationship between
the variables (Keller, 1997: 23). Correlation coefficients will show whether the
relationship is observed or not observed between the variables of activities, performance
and time allocation. By means of correlation analysis, we will check whether there are
relationships between different measurements of selling activities (time, importance and
frequency).
And finally, regression analysis, which is used to predict the value of one variable on the
basis of other variables (Keller, 1997: 725), will propose a mathematical equation that
describes the relationship between the dependent variable of sales job performance and
the independent variables of selling behaviours.
8
INTRODUCTION
Since quantitative methods are often used to predict results and build quantitative models,
and provide support for theories, research can turn to evaluation and validation of
practices in the real world of an organisation. In this way, the study results may have
immediate practical implications. Generalisability may be limited, but the value to the
organisation is huge (Black, 1999: 11).
For this reason, we chose the single company study approach, which stands at the centre
of our research and defines its sampling strategy. One-company research is not
uncommon in sales management and has already been used in a number of studies, mostly
of an exploratory nature and to find evidence for theories in different selling contexts (e.g.
Dwyer, Hill, and Martin, 2000; Kerber and Campbell, 1987). The case company where
the research was conducted wished to remain anonymous, but it allowed itself to be
described as a major information technology firm.
A single case was used for time, cost and cooperation reasons, and the possibility to build
our theory around a real life context. This is where we applied qualitative methodology
represented primarily by qualitative pre-test interviews that were conducted to validate
the content of the pre-test questionnaire and preliminary measures of the sellers’
behaviour. The techniques of cognitive evaluation were the major focus of these
interviews.
1.4 Outline of the dissertation
The research problem, objective and approach outlined provided the following
dissertation structure.
Chapter 1 (“Introduction”) outlines the practical and academic relevance of this
dissertation project. In this chapter, the research aims are defined, the research questions
are given, and the corresponding methodology choice is discussed.
9
INTRODUCTION
Chapter 2 (“Theoretical background”) presents a comprehensive review of the
theoretical background and relevant literature to uncover the research problem. The
existing theoretical models of salesperson activities and the classical paradigm of the
selling process activities are critically reviewed within this section. Hereafter follows a
discussion of the models that investigated the link of selling activities with sales
performance. Subsequently, the exploratory hypotheses are derived for empirical testing.
The main purpose of Chapter 3 (“Methodologies of activity domain definition and
validation”) is the definition of the activity domains and their validations. In this chapter,
different activity measurement approaches are discussed, the study research context is
described, and the survey design principles are outlined. The proposed definitions of the
activity domains are described in this part of the dissertation.
In Chapter 4 (“Empirical study results”) we analyse and characterise the data received
from the survey in our study, we describe the statistical techniques in use and check their
assumptions, and we present the findings of the empirical test on analysing the structure
of the seller’s activities by means of factor analysis which compares top and bottom
performers with ANOVA. Then, we investigate the prediction activity variables of sales
performance.
Finally, Chapter 5 (“Discussion and conclusions”) concludes the study. The study results
and finding are discussed, the limitations are outlined, and suggestions for further
research are made. The study’s implications and contributions to theory and practice are
also presented in this chapter.
10
2 THEORETICAL BACKGROUND AND LITERATURE REVIEW
2 Theoretical background and literature review
2.1 Salesperson activities and behaviour: definition and main
characteristics
In his study in 1986, Moncrief defines activities as “the overt actions that may include
complex mental task elements” that are the “fundamental elements” in a one person’s job
(1986: 309). Depending on the level of detail, the activity list may even consist of
thousands of records (Moncrief, 1986: 309).
In this meaning, activities are associated with actions that underlie a sales job.
In a number of research works, sales activities are frequently used as synonyms for sales
behaviour (e.g. Kerber and Campbell, 1987; Walker, Churchill, and Ford, 1977).
Walker, Churchill, and Ford (1979) conceptualise the behaviour of salespeople as “what
people do (the tasks they expend effort on) in the course of working” (1979: 33). This
definition is conceptually close to the activities definition of Moncrief (1986a, b). Walker,
Churchill and Ford (1977) also suggest that the set of activities or behaviours to be
performed by a person is connected to a certain role and position defined at the company.
Avila, Fern, and Mann (1988) propose that “sales behaviour refers to specific job skills
and/or attributes, i.e. the work that a salesperson is expected to do (…) and presumably
lead to profitably closing the sale” (1988: 46). As mentioned above, this definition
emphasises that sales behaviour may involve an application of certain skills with regard
to one’s own sales role and with the purpose of achieving a successful sales outcome.
11
Plank and Reid (1994) suggest that sales behaviour should be looked at from a “macro”
perspective and behaviour should be defined as “what salespeople do”, which “involves
the execution of selling-related activities by salespeople in the performance of their jobs”
(1994: 45). They suggest dividing salesperson behaviours into customer interaction
behaviours and noncustomer interaction behaviours, and investigating the “wide array of
behaviours involved in selling” (1994: 54).
Brashear, Bellenger, Ingram, and Barksdale (1997) introduce the following definition of
behaviours: “salesperson behaviours include sales force activities required in the sales
process and activities related to the development of ongoing relationships” (1997: 178). In
this definition, they generally follow Plank and Reid (1994) and list the sales behaviour
process activities of prospecting and fact finding, as well as supporting activities such as
planning and development.
With the aim of following the previous definitions, and for the purpose of this study,
salesperson activities or behaviours are defined as “the wide array of actions performed
by salespeople within and out of customer interactions with regard to their sales job
roles.”
In view of the above-mentioned definition, it is worth emphasising the following:
a) activities and behaviours go beyond the job and task descriptions of salespeople in a
certain job position, but they are the freely chosen patterns of actions performed by
salespeople in real life;
b) activities and behaviours in this study are studied at the “macro” level proposed by
Plank and Reid (1994), and both terms will be used as synonyms in the current study;
12
c) the term behaviour in this study will not be viewed from a psychological or a
behavioural economic perspective, but will be analysed from the angle of sales
management (the marketing discipline);
d) interconnections between various behaviour or activity elements will not be studied in
this dissertation;
e) the study does not a priori hypothesise that the skills associated with a certain activity/
behaviour underlie its actual performance. Finding such interconnections is not part of the
current research.
2.2 A discussion of salesperson activity models
The empirical exploration of salesperson activities represents a narrow research stream
within personal selling research. Since 1974, when the first model appeared, only 6
further models or model propositions have been built.
Table 2-1 summarises the existing empirical studies and the original models of
salesperson activities.
Table 2-1: An overview of the existing salesperson activity models1
Author, year
Lamont and Lundstrom (1974)
Moncrief (1986a, b)
Kerber and Campbell (1987)
Darmon (1998)
Marshall, Moncrief and Lassk (1999)
1
2
Activity model components (aggregated)
(1) working with management (2) customer service (3) ethics (4) direct
selling (5) customer relations (6) keeping abreast of market conditions (7)
meeting sales objectives (8) keeping customer records
(1) selling function (2) working with orders (3) servicing product (4)
information management (5) servicing the account (6) conferences/
meetings (7) training/ recruiting (8) entertaining (9) out of town travel
(10) working with distributors
(1) order processing (2) customer contact (3) dealing with co-workers
(4) miscellaneous activities
(1) information activities: information acquisition from own firm,
information acquisition from field, information provision to
customers/prospects and to the firm (2) time management activities
(1) communication2 (2) sales (3) relationships (4) team-building/team
selling (5) database management
All theories in this overview table are listed in chronological order.
These activities aim to enlarge the model of Moncrief (1986a, b).
13
Author, year
Guenzi (2002)
Moncrief, Marshall, and Lassk (2006)
Activity model components (aggregated)
(1) selling (2) communication (3) value-adding relationship management
(4) analytical-strategic
(1) relationship selling (2) promotional activities and sales service (3)
entertaining (4) prospecting (5) computer (6) travel (7) training/recruiting
(8) delivery (9) product support (10) educational activities (11) office (12)
channel support
Source: Author, 2010
As seen from table 2-1, all of the authors tried to represent their own view of the
salesperson activities based on different classification criteria. Most of the models were
tested empirically; however, several models were only proposed conceptually (Darmon,
1998, Marshall et al., 1999).
2.2.1 The model of Lamont and Lundstrom (1974)
The study of Lamont and Lundstrom (1974) cited in Moncrief (1986a) and (1986b) was
the first attempt to identify the main dimensions of a salesperson’s behaviour. The
researchers conducted face-to-face interviews and identified 60 salesperson activities.
Following personal interviews, factor analysis was used to find underlying dimensions of
sales behaviours. Its results indicated the existence of the following eight constructs of
salesperson activities: working with management, customer service, ethics, direct selling,
customer relations, keeping abreast of market conditions, meeting sales objectives, and
keeping customer records (Moncrief, 1986b: 309).
Interestingly, by the middle of the 1970s, the activity of working with management and
the activity of ethics, as well as keeping customer records were already listed along with
the functions of direct selling and customer relations. No further information was
available on the factors’ details and the context of the study.
14
Although this first attempt was a remarkable one among the theories on salespeople
activities, its main limitation was its restrained generalisability of the results because the
study was only conducted in one company (Moncrief, 1986a: 262).
2.2.2 The model of Moncrief (1986a, b)
Moncrief (1986a, b) was the first to identify a comprehensive list of 121 sales activities
based on evidence from 51 industries – a total of 1291 observations.
Moncrief (1986a) pointed out that one fundamental characteristic of selling is that
“regardless of the job and industry, all salespeople perform sales activities, and these
activities can be identified readily” (1986a: 262).
In his study, Moncrief (1986a, b) first created an inventory of sales activities, and then
built an empirical taxonomy of sales positions.
The preliminary list of 121 activities was created based on a very limited literature
review, but mostly it was derived during personal interviews and within focus groups.
The measures of frequencies of activity execution as well as the time spent on an activity
per day were used to create the activity model. The time measure failed to produce
consistent results, therefore the data from frequency measurements was only taken for
further statistical analysis (Moncrief, 1986b: 313).
Through factor analysis, ten activity dimensions were identified (see the table 2-2). Many
of the 121 originally identified activities were eliminated from the list due to crossloadings and low factor loadings under 0.5. In this way, only 55 activities remained in the
final list.
15
Table 2-2: A summary of the activities in the study of Moncrief (1986a, b)
Factor Name
1
Selling function
(α = 0.82)
Activity Composition
Select products for calls (0.55)3 make sales pres. (0.70) call on potential accts.
(0.53) overcome objections (0.64) plan selling activities (0.59) prepared sales
presentations (0.69) introduce new products (0.62) identify person in authority
(0.55) search out leads (0.55) design visuals (0.54) call on new accounts (0.55)
help clients plan (0.52)
2
Working with orders
Correct orders (0.65) expedite orders (0.71) handle back orders (0.66) handle
(α = 0.80)
shipment problems (0.66)
3
Servicing the product
Test equipment (0.71) be present during repairs (0.70) supervise installations
(α = 0.81)
(0.69) learn about product by watching technician (0.52) perform maintenance
(0.68) make delivery using product (0.54) order accessories (0.53) teach safety
instructions (0.59) train customers to use products (0.61)
4
Information management Provide feedback to supervisor (0.72) receive feedback from clients (0.61)
(α = 0.62)
check-in with superiors (0.59) read trade publications (0.54) provide technical
information (0.52)
5
Servicing the account
Inventory (0.72) set-up point of purchase displays (0.65) handle local
(α = 0.73)
advertising (0.51) stock shelves (0.51)
6
Conferences/meetings
Attend sales conferences (0.66) attend regional sales meetings (0.64) work
(α = 0.75)
client conferences (059) set up exhibitions, trade shows (0.53) attend training
sessions (0.63) fill out questionnaires (0.54)
7
Training/recruiting
Look for new sales representatives (0.61) train new representatives (0.55)
(α = 0.56)
travel with trainees (0.56) help company management plan selling activities
(0.54)
8
Entertaining
Take clients to lunch (0.64) drink (0.68) evening meal (0.69) party (0.57) golf,
(α = 0.73)
fishing, tennis etc. (0.72)
9
Out of town travelling
Spend night on road (0.69) travel out of town (0.66)
(α = 0.74)
10
Working with
Sell to distributors (0.62) establish relations with distributors (0.53) extend
distributors
credit (0.57) collect past due accounts (0.51)
(α = 0.59)
Sources: Moncrief (1986a: 264-266); Moncrief (1986b: 314)
As seen from table 2-2, the activity 1 “selling function” encompasses the activities similar
to the direct selling process items of Dubinsky (1980-81). Interestingly, this factor
includes planning and preparation activities as well. The existence of the order function
and technical servicing of the product in the list leads us to the conclusion that these
activities were performed intensively by salespeople in the middle of the 1980s.
“Customer entertainment” and “working with distributors” are two important factors in
Moncrief’s list (1986a, b). Most probably these factors are strongly associated with
manufacturing industry.
In his analysis, Moncrief also showed that sales jobs include different frequencies of sales
activities, and that the activities vary greatly depending on the industry. He proposed that
3
The number in the parenthesis indicates the factor loading of each activity.
16
this very diversity might lead to conflicting research results on sales person-related issues
(Moncrief, 1986b: 314).
One of the main conclusions of this study is that the job of a salesperson consists not only
of customer contact activities, such as making presentations and product demonstrations
in front of the customer, but also contains such functions as information management,
servicing the account and the product, and many other related activities (Johnston and
Marshall, 2006: 49).
Moncrief’s study (1986a, b) is considered to be the earliest, most comprehensive and the
most cited empirical study on sales activities. However, as any study, this one has its own
limitations.
The first limitation of the study is that the industries investigated in his study were mainly
industrial and manufacturing companies (in total 51), such as machinery production,
chemicals, metal and electrical industries, and industries which trade with tangible goods.
This makes the generalisations of this study to other industries, such as services,
problematic.
Another limitation is that Moncrief’s activity list (1986a, b), as well as the study of
Lamont and Lundstrom (1974), paid little attention to important activities such as sales
planning, and did not really consider the internal company work of a salesperson or
his/her administrative activities. Moreover, the factors “working with distributors” and
“training and recruiting” failed to approach the widely acceptable level of the Cronbach’s
alpha of 0.6, which evidences the low reliability of these items.
And finally, Moncrief’s study (1986a, b) focused primarily on the elaboration of the
activity model, and on its basis – the development of the taxonomy of sales positions.
Therefore it does not focus on the relationship between the activities and sales
17
performance. For this reason, Moncrief (1986b) proposed that firms need to “examine the
activities of their most successful salespeople to determine what activities to emphasise.”
This suggested the need for further research (1986b: 316).
2.2.3 The model of Kerber and Campbell (1987)
Kerber and Campbell (1987) investigated the link between the time spent on 23 activities,
sales performance, and turnover on a sample of salespeople working for a large computer
company. They identified 23 activities of salespeople by holding 45 qualitative interviews
with its sales force. A two-week time diary instrument was developed to measure the time
allocations of the company salespeople. After the time use reporting on the activity list,
the activities were aggregated into 4 major groups by means of principal components
analysis (factor analysis).
The component I “order processing” (13% of variance) included such activities as
“presentation preparation”, “sales administration tasks”, “information and reference
searches”, “credit related tasks”, “customer service”, “field engineering upgrades”, and
“legal/policy requests.”
Component II was “customer contact” (10% of variance) and contained such activities as
“customer contact in person”, “travel”, “configuration checks”, “clerical tasks” (filing),
“quotes” and “proposals.“ The activity “customer contact in person” was the most time
consuming of all the activities in the list, accounting for 22.6% of time.
Component III was “dealing with co-workers” as opposed to “customers” (8.5% of
variance) and consisted of “field engineering service”, “credit tasks”, and “commission
matters.”
18
The last component was “miscellaneous activities”, which did load on any of the three
components above and included activities such as “vacation”, “paperwork”, “product
marketing tasks”, “mail campaigns”, “systems engineering tasks”, and “telephone
customer contact” (Kerber and Campbell, 1987: 43-44). The activity “telephone customer
contact” was the second most time consuming activity of the whole list, accounting for
11.9 % of the salespeople’s time.
When compared to the list of activities developed by Moncrief (1986a, b), the activity
items of Kerber and Campbell (1987) turned out to have very few common items, with
the most similarity for the “working with orders” activities. However, Kerber and
Campbell (1987) proposed the activities of “dealing with co-workers”, “administration”,
“product marketing” and “credit-related activities”, which were not present in the list of
Moncrief (1986a, b). Many of these missing activities were also relevant for the
manufacturing industry, but were not considered by Moncrief (1986a, b).
An initial glance at the factor structure of the generated list reveals several logical
inconsistencies in the factor solutions. The component “order processing”, for example,
included items logically far away from each other, such as “preparation of presentations”,
“field engineering upgrades” and “customer service.”. The model also showed some
statistical problems, such as cross-loadings and lower factor loadings, which greatly
depleted its discriminant and construct validity. Furthermore, the reliability measures for
these factor constructs are unknown.
The problematic output of the factor model can probably be explained by two things: the
reliance on statistical analysis rather than on theory; and the usage of time diary reporting
as the main data gathering technique. The latter method failed to provide consistent input
data to principal component analysis. The low fit of time use measurements for the
principal component analysis had already been reported in Moncrief’s study (1986b: 313).
19
2.2.4 The model of Darmon (1998)
In his model, René Darmon (1998) proposed an original approach to activity and sales
positions systematisation – based on the size of information load to be managed by the
salesperson, the extent and complexity of the information processing required; and on
importance of time management implied by the sales position. Darmon (1998) followed
the vision of the cognitive selling paradigm and built his theory on the information
processing characteristics, or the cognitive mental processes (1998: 35). Using,
processing, and getting information are regarded by Darmon (1998) as the main
characteristics of activity classification. Darmon (1998) postulated that efficient
management of information and time underlies the success factors of salespeople.
The list of activities “that are indicants of the relative importance of the three selected
dimensions characterising a sales position” (Darmon, 1998: 39) are represented in table 23 below.
Table 2-3 indicates three main dimensions of activities which are characterised by the size
of the information load, information complexity load, and importance of time
management. It can be seen that many of the activities from this list are taken from the
activity list of Moncrief (1986a, b).
Table 2-3: An overview of the activities that underlie a sales position – Darmon (1998)
Item Characteristic
1
Size of the information load
required by the sales position
Activity Description
A. Information acquisition from own firm (obtain leads from the
company, learn about new products, practise using products, read
company literature, meet with supervisors, find about competitors’
products, attend sales conferences/meetings/training sessions,
handle back orders/shipment problems/lost orders/delayed
deliveries, follow-up clients’ orders
B. Information acquisition from the field (find leads, find out client
needs, listen to objections and questions, research client’s
background/potential, learn about competitive offers, sales trends,
read trade publications, take inventory for clients
C. Information provision to customers/prospects (make sales
presentation, introduce new products, demonstrate the products,
Overcome objections, answer questions, make closure and obtain
orders, teach safety instructions, train customers, provide technical
20
Item Characteristic
2
Extent and complexity of
information processing
required by the sales position
3
Importance of time
management (relative to
relationship management)
implied by the sales position
Source: Darmon (1998: 39)
Activity Description
information, help client plan, help client display products,
Report on order status, back orders, shipment problems, follow-up
orders, entertain clients, enhance goodwill
D. Information provision to the firm (write up and expedite
orders/order accessories/repair parts, fill out credit forms,
Fill out sales activity reports/expense accounts, transmit market
intelligence, assist management surveys, train new salespeople,
report to supervisors
Interpret and analyse information received (1.A and 1.B), study
client’s/prospects’ needs, prepare sales presentation/proposals to clients,
Co-ordination with existing lines, determine own price/legalities, forecast
demand, study market trends, design sales plan, select selling strategies,
Process information for time management/planning
Plan selling activities and time allocations (prospects/clients, various
accounts…), plan daily routine, prepare visual displays, make deliveries,
travel (alone/with supervisors/with trainees), travel in town/office, of
town, work after hours, from home, plan selling tours/routes/schedules
The framework of Darmon (1998) is an original representation of the selling dimensions
framework; however it is limited to only two mental criteria. Some other criteria are
neglected and could be added to explain the fuller range of dimensions. However, the
main limitation of the study is the lack of empirical work validating the proposed
classification.
2.2.5 The model of Marshall, Moncrief, and Lassk (1999)
The model of Marshall et al. (1999) proposed a systematic update of the original model of
Moncrief (1986a, b), which had been created roughly 15 years earlier. Although no
quantitative empirical study was conducted within their research, they aimed to expand on
Moncrief’s study by using the qualitative technique of focus groups. The wider industry
assortment with a mix of high and low technical products in their sample was used in this
study.
Marshall et al. (1999) questioned whether the activities of a salesperson, indicated by
Moncrief (1986a, b), had evolved over time. Their results showed that the majority of
21
1981 activities were still being performed, and the sales job had experienced significant
job enlargement with an increase of sales activities required for successful job
performance. In course of their research, they found 49 new activities, many of which
appeared around new philosophies of selling, such as consultative and adaptive selling,
value-added selling, team selling, and relationship selling. The greatest of all changes
Marshall et al. (1999), however, named the increase in availability and the use of
advanced technology that gave a salesperson instant access to the company information,
and provided a new opportunity for efficient communication.
Table 2-4 indicates the list of new selling activities which appeared in the late 1990s.
Technology
Table 2-4: The list of new selling activities of Marshall et al. (1999)
Communication
Email, dictaphone,
internet, laptops,
CD ROM, voice
mail, fax, cellular
phone, pager, web
page, newsletter,
audio and video
conference, provide
technical
information,
overnight services,
maintain virtual
office
Practise
language skills
Sales
Set up appointments,
script sales pitch
from database, use
software for
customer
background, laptop
for presentation,
VCR for
presentation, provide
technology, ability to
customer
Non-technology
Adaptive selling,
conduct research at
customer’s business,
avoid potential
litigation, plan for
multiple calls to
close the deal, sell
value-added
services, respond to
referrals, write
thank-you letters,
target key accounts,
pick up sales
supplies,
consultative selling,
listen, ask questions,
read body language,
sell unique
competencies
Source: Marshall et al. (1999: 89)
Relationship
Web page
Team
Conference calls
Bring in
vendor/alliance,
develop
relationships, handhold customer,
write thank-you
letters, purchase
dealers, call on
CEOs, build rapport
with buying centre,
network build trust,
train brokers
Mentor, make sales
and turn them over
to someone else,
coordinate with
sales support
22
Database
Collect new
information from
database, enter
information/data on
laptop, update
customer files
As shown in table 2-4, the 49 new activities fell into the following five categories:
“communication”, “sales”, “relationship”, “team-building”, and “database management.
“These categories were divided into two major types: “technology” and “non-technology”
(Marshall et al., 1999: 92).
Activities linked with communication, such as “using a laptop”, “email”, “Internet”, and
“managing virtual office”, indicated that the salesperson needed to spend much of his/her
time on these activities (Marshall et al., 1999: 93). Although the communication level
improved the salesperson’s effectiveness, appropriate skills were necessary to properly
manage the overwhelming number of technological advances. In the non-technological
activities, Marshall et al. (1999) named the activity of practising foreign languages in
view of the increasing diversity in buying and selling, and high level of
internationalisation of sales flows.
The next new activities concern the dimension of selling. Here the communication
technology brought new communication devices into the selling process, the use of
databases to set appointments or search customer backgrounds, and the use of the laptop
for customer presentations (Marshall et al., 1999: 95). In addition, the non-technological
terrain of selling was reinforced by “new” sales jargon words, such as “adaptive”,
“consultative”, and “value-added selling.” The activities of “listening”, “asking
questions”, and “reading body language” were not new, but they were not on the list of
Moncrief (1986a, b) (Marshall et al., 1999: 95). The relatively “new” activities on list of
Marshall et al. (1999) were “planning of multiple calls”, and “spending time at the
customer’s locations to gather necessary information.”
The relationship dimension of “new” activities reflects the general business topic of the
1990s – “relationship selling.” In this respect, the relationship-building approach is
represented by such activities as “calling on the CEO”, “writing “thank you” notes”, and
“networking.” The webpages of a sales company built the basis for the technological
support of relationship maintenance that kept customers conveniently informed on the
23
range of issues about the product or the organisation of the salesperson (Marshall et al.,
1999: 96).
The next activity category pointed out by Marshall et al. (1999) was “teaming”, including
such activities as “participation in conference calls”, as well as “working within selling
teams” and “using the services of sales support personnel.”
The last category described in this study was “database”, which reflected the beginning of
wide use of this technology for reporting, updating customer files, and drawing
information about potential customers (Marshall et al., 1999: 97).
The study of Marshall et al. (1999) contributed greatly to the model update of Moncrief
(1986a, b); however, its main limitation was that its conceptual proposition was not
supported by any empirical analysis or any empirical model.
2.2.6 The model of Guenzi (2002)
Paolo Guenzi (2002) was the first to empirically examine the current state of activities in
the European sales research. The sample for his study consisted of 113 Italian sales
managers from different industries (consumer, durable, industrial goods, services, and
pharmaceuticals) with different company sizes. In his study, Guenzi (2002) aimed to
describe the current status of selling activities performed by salespeople, and postulated
the “new role” of a salesperson in the “relationship era” (2002: 757). Based on a literature
review (including e.g. Darmon, 1997, Marshall et al., 1999, Wilson, 1993) the elevenactivity list was compiled (see table 2-5). The average frequency measures shown in table
2-5 indicate that “selling” was the core activity with the highest frequency score of 8.21
out of 9, followed by “customer relationship management and development” (7.12 out of
9). The activities of “acquisition and transfer of market information about customers and
competitors” (4.58 and 3.98), as well as “customer profitability analysis” (4.5) showed
24
low frequency ratings. Guenzi (2002) explained this low frequency as follows: the “sales
force formally acquires information and transmits reports”, but there might be problems
with their accuracy or promptness (2002: 758). This fact is not surprising due to the lack
of incentives and control mechanisms on the execution of these activities (Guenzi, 2002:
758).
Table 2-5: Factor solution of the activity model of Guenzi (2002)
Activities
Frequency,
average
(from 1 to 9)
Factor 1
Selling
Selling
Transmission of information
and communication to the
customer
Acquisition and transfer to
the company of written
information about customers
Acquisition and transfer to
the company of written
information about
competitors
Coordination of the sales
team inside the selling
company
Market analysis and sales
forecasts
Customer profitability
analysis
Provision of pre-sales
services
Provision of after-sales
services
Customer relationship
management and
development
Source: Guenzi (2002: 759)
8.21
7.01
.899
Factor 2
Communication
Factor 3
Analyticalstrategic
Factor 4
Valueadding
relationship
management
.706
4.58
.836
3.98
.854
5.67
.843
5.95
.587
4.50
.883
5.66
.813
5.86
.632
7.12
.793
Table 2-5 also shows the four factors which were obtained using principal component
analysis with the purpose of grouping the activity items and identifying major activity
roles. The four factors were: a) the selling role; b) communication role, which is the twoway information exchange between the salespeople and company and customers; c) the
analytical-strategic role, which included market analysis and forecasts, customer
profitability analysis and team coordination; and d) the value-adding relationship
25
management role, which included the provision of pre- and after-sales service, and
customer relationship management (Guenzi, 2002: 762).
The study of Guenzi (2002) proposed a new original approach to a sales force activity
structure, identifying four major activity roles played by the salesperson from an
information-communication role perspective. His activity lists were tested empirically,
and included several propositions of Marshall et al. (1999) and other authors. The
activities of “relationship”, “information transmission”, and “customer profitability
analysis” were not present in the list of Marshall et al. (1999). However, as mentioned by
Guenzi (2002) himself, his list included only the most important activities (mainly from
the relationship and information perspective), and therefore, could not be viewed as being
comprehensive enough. The list omits the activities of planning and delivery. The activity
of selling was also not detailed enough; the activities were not defined precisely and, from
our perspective, needed some clarification or definition. It was not clear which activities
come under the construct “customer relationship management”, and how this activity
differed from “selling.” This may have resulted from an insufficient pre-test of the initial
questionnaire, which was judged by only 4 academic experts, and was not discussed by
any sales field specialist. Additionally, the sample of his study consisted of sales
managers only, and in this way, the view of sales specialists was omitted.
2.2.7 The model of Moncrief, Marshall, and Lassk (2006)
The most current list of the salespeople activities and sales position taxonomies was
developed by Moncrief et al. (2006). They postulated that a sales job was totally different
than 10 or 15 years ago. Rising customer expectations, the existence of technological
changes, globalisation, sales automation, outsourcing of components of the sales and
marketing function, and many other factors had contributed to the nature and scope of
salespeople’s tasks, responsibilities and work assignments (Moncrief et al., 2006: 55).
Taking into account these factors, Moncrief et al. (2006) aimed to develop a
26
contemporary classification of sales activities and on its basis – the taxonomy of job
positions. The data for their study was collected from 1042 respondents from 15 industrial
manufacturing companies from different industries, such as chemical, food,
manufacturing, machinery, metals. The 105 activities were analysed by means of
principal component analysis, and, based on this analysis, 12 factors were obtained. The
factor structure indicated clean and interpretable results and most factor loadings
exceeded 0.4, whereas the cross-loadings were retained due to the theory reasons.
Table 2-6 shows the results of the factor analysis representing the new inventory of the
salesperson’s activities.
Table 2-6: The inventory of salesperson activities of Moncrief et al. (2006)
Factors Name
1
Relationship selling
Description
Build trust (0.675) ask questions (0.661) build rapport (0.650) listen (0.559)
consult with customers (0.567) adapt presentations (0.565) sell value added
(0.558) overcome objections (0.555) sell unique competencies (0.518) close the
sale (0.517) work with key accounts (0.495) ID person in authority (0.488) read
body language (0.483) plan selling activities (0.477) call on multiple individuals
(0.445) correspond with customers (0.427) help clients plan (0.408)
2
Promotional activities Point of purchase (0.818) set up displays (0.780) handle advertising (0.779)
and sales service
demonstrate the product (0.612) train customers with product (0.594) use VCR to
sell (0.514) sell product accessories (0.492) create newsletters (0.474) check
customer inventory (0.455) work trade shows (0.445) write up orders (0.443)
handle back orders (0.437) introduce new products (0.417)
3
Entertaining
Entertain with leisure (0.851) take clients to dinner (0.737) take clients for drinks
(0.687) play golf (0.680) take clients to lunch (0.610) throw parties (0.522)
4
Prospecting
Call on potential accounts (0.729) search out new leads (0.723) respond to
referrals (0.679) submit bids (0.532) make multiple calls (0.429)
5
Computer
Use the internet (0.757) work on Web (0.706) check e-mail (0.618) learn software
(0.588) enter data on laptop (0.527) collect database information (0.476)
presentation with laptop (0.422)
6
Travel
Spend the night on the road (0.697) travel out of town (0.659)
7
Training / recruiting
Train new sales reps (0.810) mentor junior sales reps (0.792) recruit sales reps
(0.632) ride with reps (0.475)
8
Delivery
Deliver product samples (0.696) deliver product (0.693) stock shelves (0.581)
9
Product support
Supervise installation (0.518) modify the product (0.505) perform maintenance
(0.448) take clients on site (0.400) expedite orders (0.400)
10
Educational activities Attend sales meeting (0.583) attend training sessions (0.447) learn about products
(0.410)
11
Office
Fill out expense reports (0.603) check voice mail (0.400)
12
Channel support
Establish relationships with distributors (0.586) train brokers / middlemen (0.455)
Source: Moncrief et al. (2006: 59)
27
As can be seen from table 2-6, a number of new items which were not on the list of
Moncrief (1986a, b) had appeared: “computer”, “delivery”, “office”, and “education.”
The factors “entertaining”, “training/recruiting” remained almost the same, with some
extension with a few more items.
The factor “relationship” selling included the items from both the factor “selling” of
Moncrief (1986a, b) and the “sales” activities of Marshall et al. (1999), thus, reflecting the
new paradigms of selling, such as consultative selling, adaptive selling, or value-added
selling. The new factor of “prospecting” appeared as well, and included several items,
such as “search out leads”, or “call on potential accounts” from the “selling” factor of
Moncrief (1999). The factor “product support” turned out to have similarities with
Moncrief (1986a, b)’s “servicing the product.” The “order management” construct from
Moncrief (1986a, b) disappeared as a factor, but several items moved to the “promotional
activities and sales service” factor. This could probably be explained by diminishing
function of the order management in the salesperson’s role. On contrary, the new factor
“promotional activities and sales service” replaced the old “servicing the account”
enlarging it with such new activities as “demonstrate product”, “sell accessories”, “use
VCR to sell”, and others. The large list of the items under this construct might propose the
enlargement of the sales service function and its increasing importance in the range of
salesperson activities.
With its thoroughly empirically elaborated structure of activities, the study of Moncrief et
al. (2006) made a solid contribution to the models of sales activities by including most of
the recent updates of the personal selling function. However, as with any other study, this
one has its own limitations as well.
Several terms, such as “make multiple calls” or “point of purchase” are not immediately
understandable. Therefore, they need some clarification. Planning is also represented only
with one single item within the dimension of “relationship selling.” The team-selling
28
activities did not appear in the list as well (although they were proposed by Marshall et al.,
1999). Although the activity “computer” demonstrates just how important technology is
in the work of the salesperson, it is referred to in other activities (“presentation with
laptop” can be part of “presentation”; “using the Internet” may be part of the “prospecting
process”).
The reliability measures are not indicated in their publication study, which makes it
impossible to judge unidimensionality on any scale. The linkage to sales performance is
also not regarded as a research purpose of the study.
Another limitation of the study, as mentioned by Moncrief (2006), is the investigation of
the activities within only one industry – manufacturing. Therefore, they suggested that
their classification procedure be applied to other industries, such as services, to compare
the obtained results (2006: 64).
2.3 A critical review of the classical personal selling process model
In order to focus on one theory, this study aims to build the largest part of its sales activity
model based on approach of classical sales process paradigm of “seven steps.” The “seven
steps” of the selling process are an old classical paradigm widely used in sales training
and sales text books since the emergence of the professional selling discipline (Moncrief
and Marshall, 2005: 13).
The “seven steps” had already been discussed in American sales literature and personal
selling handbooks during the 1970s (Crissy, Cunningham, and Cunningsham, 1977;
Jolson, 1977; Peterson, 1978; Reid, 1970; Russell, Beach, and Buskirk, 1978 cited in
Dubinsky, 1980-81).
29
In the context of this paradigm, the personal selling process is defined as a set of steps
that a salesperson passes through when making a sale (Dubinsky, 1980-81: 26; Dwyer,
Hill, and Martin, 2000: 151; Hite and Bellizzi, 1985: 19). These steps are locating and
prospecting for customers, pre-approach, approach, sales presentation, handling
objections, close and post-sales follow-up (see figure 2-1).
Figure 2-1: The classical selling process steps
Step 7. Post-sales follow-up
Step 6. Close
Step 5. Handling objections
Step 4. Sales presentation
Step 3. Approach
Step 2. Pre-approach
Step 1. Locating and prospecting
Source: Author, 2010 based on Dubinsky (1980-81: 26-27).
Dubinsky (1980-81) was the first to conduct an empirical study on a sample from
manufacturing and service companies. He identified the underlying behaviour, technique
and method elements of these sales process steps.
Due to recent changes in the selling environment, such as the appearance of relationship
marketing and the development of technology, 20 years later, Moncrief and Marshall
(2005) proposed changes to the classical paradigm. Their propositions as well as the
30
classical paradigm description, the Dubinsky (1980-81) model, and the insights from
several related works are discussed in the sections below.
2.3.1 Review of Step 1: Prospecting for customers
The prospecting step in its classic meaning (the “seven steps” of the sales process)
involved searching and identifying leads or potential buyers who have the “need,
willingness, ability and authority to buy.” This step included the activities of gathering
information about the prospect’s name and contact information, and receiving potential
contact names from other sources (Dubinsky, 1980-81: 26). In a number of companies
and situations, this happened by directly “knocking on the door” of the customer
(Anderson and Dubinsky, 2004: 70).
Prospecting is believed to be a future-oriented task which leads to the generation of a new
customer base and a flow of sales (Anderson and Dubinsky, 2004: 70).
Dubinsky (1980-81) identified 4 main ways of identifying buyers: from external and
internal sources, through personal contact and others, and 13 techniques that underlie
these factors (such as identification of prospects by using a variety of information
sources: company directories, references, social, or professional contacts).
In today’s selling, it is very common that the amount of time invested in the development
of the new customer base versus the old customer base differs for different products and
company strategies, and the company, depending on the importance of the customer and
the product sold, may assign salespeople to work either with new or with established
customers (Johnston and Marshall, 2006: 54).
31
Moncrief and Marshall (2003) propose that the prospecting step is not longer needed in
organisations, and that the sales representative should be released from this function,
which could be taken over by other departments.
Since experienced sales representatives would cost the company more if they looked for
prospects themselves, today’s prospecting function is very often taken over by the
“outbound” telemarketing when the lead is generated by calling on potential customers. It
is then forwarded to the sales representative with a client facing role for further
development or the “inbound” marketing when the lead is generated outside of the direct
customer contact, or when the customer comes back with questions for the sales
representative about the specific products found on the Internet website (Marshall and
Johnston, 2009: 251).
In today’s business, the focus should be mainly put on existing customers or customer
validations and, if necessary, on customer deletions. In this respect, the database
marketing and the customer management tools, like CRM databases, should help in
performing this step. The qualification of a prospect is made based on the prospect’s need
for company products, financial ability and customer profitability (Marshall and Johnston,
2009: 510). The new qualification criteria are also long-term profitability and the
individual’s accessibility (Anderson and Dubinsky, 2004: 73).
In their empirical study, Moncrief et al. (2006) found that the following activities
characterise prospecting in manufacturing companies: call on potential accounts, search
out new leads, respond to referrals, submit bids, and make multiple calls. This list lacks
consistency because the calls are rarely made at this stage, and bids are not submitted.
Instead, they are done at the latter stages of the process.
The studies on the relationship of prospecting activities and skills to sales performance
delivered contradictory results. In the study of Marshall et al. (2003), prospecting skills
32
were put on the 2nd level of importance in hiring decisions by the sales managers (5.673
points out of 7).
In the study of Brashear et al. (1997), time spent for prospecting had a significantly
negative impact on the performance of insurance salespeople. They explained this
outcome with the seller’s avoidance behaviour and call reluctance, and recommended not
to invest too much time in this activity.
The use of “cold call” techniques, centres of influence, participation in public exhibitions,
and pre-notification of potential clients (“warm call”) proved to have a positive impact on
sales performance in the study of Dwyer et al. (2000) on insurance sales.
Jaramillo and Marshall (2004) found that the technique of organising and participating in
public seminars and company trade shows as a prospecting method yielded positive sales
performance results in banking sales.
2.3.2 Review of Step 2: Pre-approach
The classical sales process view of the pre-approach step describes such activities as
collecting more specific information about the prospect’s needs and interests, further
qualification of the prospect, developing an effective way to approach, tailoring the sales
presentation, and arranging an interview (Dubinsky, 1980-1981: 26).
Dubinsky (1980-81) found two main factors combining ten different techniques used in
performing the pre-approach: “interview pre-approaches” which include such activities as
using mutual friends to arrange an interview, setting customer appointments per telephone,
and “information sources” with activities of reading newspapers to obtain necessary
33
customer information, and asking salespeople working at the same company about
potential customers.
In their study on banking sales, Jaramillo and Marshall (2004) combined several original
techniques of Dubinsky (1980-81) and proposed the use of three activities for the preapproach: “gaining prospect information from current customers, newspapers, and
prospects themselves”, the “intermediary approach: have mutual friends arrange a sales
interview”, and the “direct contact approach: contact prospects directly to arrange an
interview. “
Moncrief and Marshall (2005) propose that, in view of the technological developments in
the sales field, the pre-approach step has changed its role, and now includes the functions
of contributing customer knowledge and gaining customer knowledge from the existing
customer databases. The sellers not only review their own notes on the customer, but
work more consistently with the existing company knowledge, gaining information and
analysing records in customer-related company tools. The internal support representatives
are very often involved to support the implementation of this step.
As some organisations are extremely hierarchical, multinational or have multiple levels of
buying or/and user organisation, this step may involve working in a sales/project team in
order to identify and plan the appropriate approach for the different levels of this
buying/user organisation (Marshall and Johnston, 2009).
The step of pre-approach and call preparation is defined as important for sales success in
a number of different literature sources. For example, Darmon (1998) pointed out that the
effective management of relationships with prospects and customers needs the deep
awareness of “customers’ and prospects’ needs, problems, buying processes, objectives,
and constraints” (1998: 38). Sujan, Weitz, and Sujan (1988) suggested that successful
selling requires detailed knowledge of different types of customers and sales situations.
34
Jaramillo and Marshall (2004) found a significant positive relationship between sales
performance and the pre-approach activity of gaining prospect information from different
sources. However, neither Dwyer et al. (2000) nor Brashear et al. (1997) found any
evidence of a significant link between any pre-approach activities and sales performance
in their studies.
2.3.3 Review of Step 3: Approach
This step is omitted in several literature sources (Anderson and Dubinsky, 2004; Johnston
and Marshall, 2006) or is seen as a part of the next step – sales presentation.
The approach step of the selling process in its traditional sense involves the initial few
minutes of a sale when the prospect or customer is being contacted for the first time, as
well as a number of approaches for gaining and holding a customer’s attention and
interest (Dubinsky, 1980-1981: 26). The main purpose of this step is to build a good first
impression and “open the prospect’s door” to the main presentation.
The approach step can be done for existing and new customers (contacts for separate sales
opportunities). Dubinsky (1980-81) classified approaches into “non-product related”,
“peaking interest”, “consumer- and product-oriented” (Dubinsky, 1980-1981: 26).
Jaramillo and Marshall (2004) proposed the “prospect-focused”, “product-benefit”,
“statement” and “peak interest” approaches.
Moncrief and Marshall (2005) proposed that the approach step is no longer the approach
in its traditional understanding but rather a sequence of actions across all other steps
which are designed build a relationship basis for future sales transactions. Since
relationship building is a constant process, this step seems inconsequential. “The
approach is easier when the relationship exists” (Moncrief and Marshall, 2005: 20).
35
Jaramillo and Marshall (2004) found that statement approach – “opening the dialogue
with a statement about yourself, the name of your company, or the name of the person
who referred you” was linked to sales performance in their study on banking sales. Dwyer
et al. (2000) did not find any significant association between any of the approach
activities and sales performance.
2.3.4 Review of Step 4: Sales presentation
The presentation to the customer is “the core of the selling process.” This is when the
salesperson transmits information about the offering and tries to persuade the customer to
buy. At this stage of the traditional process, the client representative presents the offering,
demonstrating its strength and advantages with the purpose of arousing the customer’s
interest and trying to convince the customer to buy (Dubinsky, 1980-1981: 26).
Compared to the traditional style of presenting, the new selling includes the following
selling types and techniques used in sales presentations: adaptive selling, consultative
selling, and value-added selling (Moncrief and Marshall, 2005: 20). Active listening is
now even more valued than talking. The sales presentation now tends to be conducted
through a number of meetings and usually – by different sales team members rather than
only by a single salesperson. According to Moncrief and Marshall (2005), in today’s
contemporary sales setting, the salesperson performs more of a marketing function on the
customer site, doing customer research and market segmentation than simply delivering
the sales presentation (Moncrief and Marshall, 2005: 20). Since some of the presentations
can be even emailed or sent by post to inform the client of the offering, at times it is not
necessary to visit the customer personally (Moncrief and Marshall, 2005: 21).
Face-to-face contact demands high costs (Cannon and Homburg, 2001: 22). It can be
justified in the transactions which involve consultative and relationship selling.
Consultative selling is “best suited for a relationship, rather than a transactional
36
orientation“ (Hawes, Strong, and Winick, 1996: 350). The disadvantage of the
consultative approach is its great time expenditure, the need for a broader product line,
and the inability to use standardised presentations if one has a large number of prospects
(Hawes et al., 1996: 350).
The value of the sales presentation, as pointed out by Daft and Lengel (1984) cited in
Cannon and Homburg (2001), is based on the richer modes of communication, e.g. faceto-face, which allows for not only customer-orientated communication and feedback, but
also makes it possible to gather non-verbal information through observation. In their way,
electronic and written communication media are suitable for large volumes of
standardised and coded information (Cannon and Homburg, 2001: 22).
Along with consultative selling, IT product demonstrations, simulation and trial-size
demos are an important part of the sales process in the IT environment. “Good demos
move quickly to hold the customer’s interest” and make the customer “believe the
software works” and the product meets their expectations (Kiamy, 1993: 219). Depending
on the product sold, the presentations are too small or overly product-oriented, i.e. the
“low-key” selling is taking place.
A number of research studies proved the existence of a positive association between
behaviour characterised by adaptive selling (e.g. Giacobbe et al., 2006; Marks, Vorhies,
and Badovick, 1996; Predmore and Bonnice, 1994; Spiro and Weitz, 1990), consultative
or solution selling (e.g. Eades, 2003) and the sales performance. The usage of customer
language during the presentation, which is consistent with the adaptive selling approach,
as well as the “need identification and satisfaction approach” consistent with consultative
selling were the techniques that influenced the sales performance the most in the study of
Jaramillo and Marshall (2004) on banking selling. Offering visualisations as well as the
use of a product-benefit approach in selling standardised products – insurance policies –
were shown to directly influence the sales job performance (Dwyer et al., 2000).
37
2.3.5 Review of Step 5: Handling objections
Objections are “statements, questions, or actions by the prospect that indicate resistance
or an unwillingness to buy” (Anderson and Dubinsky, 2004: 172). Objections can concern
the product itself, product need, the company trust, pricing, etc. (Marshall and Johnston,
2009).
At this stage of the traditional process, the salesperson tries to overcome the prospect’s
unwillingness to buy by reiterating the benefits of the product, reassuring the customer of
his/her potential decision, and by helping the client to decide (Dubinsky, 1980-1981: 26).
Dubinsky (1980-81) defined the four dimensions of activities underlying the sales
objections step: “create strife techniques” (include techniques such as accept objections,
tell a good story, smile and pass off objections), “offset objection” (includes techniques
such as admit objection, minimize objection by comparing, describe how another
customer purchased the product standardised presentation), the “clarify objections
technique“ and “other techniques” (include techniques such as asking questions about the
objection and making comparisons with rival products).
According to Moncrief and Marshall (2005), the function of handling objections in the
solution selling has outgrown its boundaries. It is not enough to just assure the customer
of the benefits of the product in question or assuage their concerns, the objections are to
be solved, and the techniques of solution selling need to be applied. Each of the
customer’s problems needs to have a solution (2005: 20).
Darmon (1998) also pointed out that “the activity of answering customer objections
involves much more sophisticated and complex information processing for a high-tech
product salesperson than for a consumer product” (1998: 35).
38
In view of the complexity of IT products, we would propose that the step of handling
objections still exists as a consequential part of the presentation process, although it has
taken on a consultative form (i.e. it is a part of consultative selling approach to the
customer presentation).
2.3.6 Review of Step 6: Closing the sale
During the sales close, the seller negotiates, finalises the details of the transaction, and
asks directly for the order (Dubinsky, 1980-81: 27). Dubinsky (1980-1981) found four
main types of close: clarification, psychologically-oriented, straightforward, and
concession.
During the close, the buyers often tend to delay the purchase decisions and “the time it
takes the salesperson to close the sale increases, the profit to be made from the sales may
go down, and the risk of losing the sale increases.” In this respect, the task of a
salesperson is “to facilitate the client in making a timely final decision” (Johnston and
Marshall, 2006: 56).
Closing is one of the most contradictory and complex steps in the IT sales process in
terms of its role in the successful sales completion and sales performance. This is the step
where the buyer and the seller complete and commit to the deal.
The avoidance of close happens frequently with both experienced and inexperienced
sellers. Salespeople find it difficult to close for three main reasons: a lack of confidence in
themselves or in a product, feeling guilty to ask for customer’s money, or the general fear
of failure (Anderson and Dubinsky, 2004: 208-209).
Moncrief and Marshall (2005) questioned the actuality of an “order-oriented close.” They
proposed that the key purpose of this process step has moved from the short-term goal of
39
getting an order to the mutual strive towards the long-term realisation of joint relationship
goals.
Di Modica (2006) suggested that spending a disproportional amount of the available sales
time with a decision influencer will not necessarily increase the success of the salesperson.
He said that it should be the salesperson’s goal to move the leads through the sales cycle
as quickly as possible, i.e. precisely utilise the possibilities of the close techniques. In this
way, decreasing the time needed for a sales cycle and at the same time increasing the
available selling time could bring the most value in terms of sales success.
Too much time closing has proven to have a negative influence on performance in the
insurance industry (Brashear et al., 1997). Extending the time spent for a close, as well as
repeated closing attempts often “indicate premature closing attempts and a lack of
understanding about how business customers like to buy” (Brashear et al., 1997: 183).
Hawes et al. (1996) found that the use of classical closing techniques may diminish the
customer’s trust, which is a prerequisite of long-term relationships with the customer (this
effect must be considered).
Despite this finding, closing skills – the ability to properly and persuasively ask for the
sales – were found to be positively related to the sales performance in the study of Johlke
(2006), and were ranked among the most valued by managers in the study of Marshall et
al. (2003).
The technique of clarification close was found to be linked to sales performance in the
study of Jaramillo and Marshall (2004). The closing should be proper and highly skilled.
Most probably, the properly managed closing situation rather than the straight close is a
potential success factor.
40
2.3.7 Review of Step 7: Post-sale follow-up
In the classical theory, follow-up included activities were undertaken to assuage the
customer’s negative post-purchase concerns, to reassure the customer of his/her purchase
decision, to increase the possibility of future deals, to teach customer how to use the
product, and to organise credit if necessary (Dubinsky, 1980-1981: 26).
Experienced salespeople realise that the customer not only buys a “great product” but also
the “great service” that persuades them to be loyal customers. In this way, post-sale
service is another pillar of successful customer relationships (Anderson and Dubinsky,
2004: 238).
Successful customer support is an attitude and it “can also sell products.” The primary
goals of the product support are to satisfy the customer and to follow through with
customers by keeping commitments. These commitments are the basis for an ongoing
successful relationship (Kiamy, 1993: 219).
Post-sale service may lead to the purchase of replacement goods and to the appearance of
service contracts. The assurance of the delivery schedule and quality of goods,
appropriate financial billing, and supervision of installation are the main activities of this
step (Johnston and Marshall, 2006: 57).
Depending on the characteristics of the product sold and on the company structure, the
post-sales activities are usually performed by other departments, technicians or call
centres, and the main activities include delivery, installations, training, effective
resolution of customer problems, and customer complaints handling (Marshall and
Johnston, 2009). Although they are not usually part of the direct role of the salesperson,
these activities are often coordinated by him/her to ensure their effective completion, and
the most difficult issues are solved face-to-face directly with the client.
41
Relationship maintenance activities are the crucial element of the follow-up. Meeting the
customer socially to eat and drink together, organising events and training seminars or
charity events build an excellent basis for a relationship (Piercy, 1995: 7). If customer
complaints are responded to positively, this will provide a way to ensure customer loyalty
(Piercy, 1995: 8). There are a number of ways to measure customer satisfaction, such as
face-to-face meetings, or formal reactive systems like surveys and feedback cards (Piercy,
1995: 7).
Moncrief and Marshall (2005) noted that post-sale problem solving and the follow-up step
are becoming more important.
Time invested in servicing clients or following-up after the sale additional to the time
invested in the direct selling activities proved to have a significantly positive influence on
sales performance in the study of Brashear et al. (1997).
Jaramillo and Marshall (2004) found that two activities of follow-up were linked to sales
performance: “follow-up questions, complaints and adjustments within the product
delivery” and a “periodic check of customer satisfaction.” Dwyer et al. (2000) did not find
any significant link between any of these activities and sales performance.
Follow-up skills were rated as the most important (first level of importance) compared to
all other sales process skills in the study of Marshall et al. (2003). This confirms the
statement that managers are interested in prolonging their relationship with the new and
established customers.
It should be also mentioned that “follow-up” activities are ongoing. The border between
servicing the account and prospecting for new contacts disappears. New contacts appear
from the old ones and from past cooperation during past sales transactions (Moncrief and
Marshall, 2005).
42
Within the frames of this study, the primary purpose is to utilise the concept of Dubinsky
(1980-81) with the suggestions of Moncrief and Marshall (2005) as a conceptual input to
the seller behaviour model developed in this study. Empirical validation and a suggestion
for amendments to the model of Dubinsky (1980-81) are regarded as a secondary aim.
Several dimensions of the proposed models will be aggregated to the level of detailed
analysis and the burden connected with answering long questionnaires.
2.4 Theories on the link between sales activities and sales
performance
In their meta-analysis of the sales performance studies, Churchill, Ford, Harvey, and
Walker (1985) found that the determinants of salesperson performance are based on six
major factors: skills, motivation, role perceptions, and personal characteristics,
organisational or environmental variables. The notions of selling activities or behaviours
did not exist in empirical literature at that time.
Nevertheless, Walker, Churchill, and Ford (1979) proposed that “sales performance is the
result of carrying out a number of discreet and specific activities which may vary greatly
across different types of selling jobs and situations” (1979: 22). Weitz (1981) mentioned
that behaviour impacts on effectiveness in sales interactions in the microenvironment and,
in this way, influence the performance of salespeople.
Avila, Fern, Mann (1988) hypothesised that sales behaviour is related to both overall
performance and goal achievement.
Plank and Reid (1994) assumed that customer interaction behaviour as well as noncustomer interaction behaviour impact on sales performance.
43
In 1975, Henry came up with a conceptual scheme to view sales management as a system
with output and input variables. As input variables he named the salesperson activities
which he believed the salesperson can change in order to alter the output variables – the
sales volume, the product mix and the sales costs. The concept of Henry (1975) suggested
that the number of sales calls and the quality of the sales call impacts on sales
performance. Henry’s approach was the first attempt to propose a conceptual model, but
this needed to be developed and measured further.
Sujan (1986) created his theory of effort allocation, which is linked with the terms of
“working hard” and “working smart.” “Working hard” explained that salespeople work
more hours (persistence), and “working smart” – how well they performed during those
hours, and the way they allocate this time. Both “working smart” and “working hard”
proved to have a positive significant relationship with sales performance (Sujan, Weitz,
and Kumar, 1994).
The quantity and quality of the selling effort was believed to impact on the market
response (Cravens, Moncrief, Lamb, and Dielman, 1990: 218). The discretionary effort as
a part of salesperson’s behaviour was an indication of extra-role performance (Dubinsky
and Skinner, 2002: 590).
A number of effort allocation theories concentrated on finding hypothetical or
mathematical models of time and effort allocation. Several effort allocation theories
concentrated on finding mathematical models of efficient time and effort allocation. The
efficient effort deployment has also been widely discussed in sales literature, such as
allocation of effort among customers, prospects, territories in the study of Montgomery
and Urban (1969), across products and over time in the study of Montgomery et al. (1971),
across different accounts with different sales potential in the study of Bommer, O’Neil,
and Sethna (1994). The CALLPLAN of Lodish (1971) was a well-known computer
system algorithm that aimed to develop call frequency norms for each client and prospect.
44
The SCHEDULE programme of Armstrong (1976) was an algorithm to determine an
optimal solution for configuration of call frequencies that maximise the probability of a
desired level of return. The travelling salesman problem (e.g. studies of Van Dal et al.,
1993, Malandraki et al., 1996, Papadimitriou et al., 1993) was also discussed in
mathematical disciplines. The travelling salesman problem represents the mathematical
problem of salesman route optimisation.
The conception propositions theories did not end in empirical research, whereas the
mathematical models on effort allocation were extremely complex and had a number of
methodological limitations. Empirical research on the elements of successful selling
behaviour, in particular from a “macro” approach, remains scarce. The following four
studies aim to explore the association of different activity elements with sales
performance.
2.4.1 The model of Kerber and Campbell (1987)
The first published research with empirical evidence on the relationship of work activities
and performance belongs to Kerber and Campbell (1987). The researchers investigated
the link between the objective performance, tenure, activities and turnover of salespeople
of a computer company. They pointed out the necessity of investigating the link between
the main work activities and objective sales performance, and that this link was complex.
Kerber and Campbell (1987) proposed that “if specific behaviours or activities are
associated with higher performance, this information could improve our understanding of
effective sales techniques, provide models for training sales personnel, and facilitate the
efficient use of sales resources” (1987: 41).
Kerber and Campbell (1987) hypothesised that the time spent with the customer may
affect the dollar amount of new sales, and time spent processing orders may be related to
45
the dollar amount of unshipped orders because the time spent in problem solving may
delay shipments.
In the course of their research, Kerber and Campbell (1987) found that sellers spend
almost one third (34.5%) of their total working time on customer contact in person and on
the phone (1987: 42). The next most time consuming was travel to customer. They found
that the time activities varied strongly depending on a salesperson’s job function.
The only significant correlation found in this study was order processing activities (which
were preparation for presentations, sales administration, software and reference searches,
credit related tasks, customer service, legal requests, and field engineering upgrades) and
aggregate sales performance consisting of the average indices of the dollar amount of
orders not shipped yet during the month, the dollar amount of orders shipped to
customers during the month, and the dollar amount of new orders during the month.
By the means of stepwise multiple regression (with performance as the criterion), Kerber
and Campbell (1987) found that additional time spent processing orders together with
longer tenure at the company was associated with high aggregate performance (R2 =
0.38); a higher unshipped orders (R2 = 0.28) and higher new orders (R2 = 0.31). The
customer contact and dealing with co-workers activities were not part of this regression
due to the lack of correlation relationships, which means that these activities were found
to be unrelated to the performance indicators. This lack of a link between the customer
contact is explained by the lag between the actual contact and the actual results of this
contact (dollar volumes of goods received). For this reason, we would propose that the
measurement of performance (though being one of the earliest in the sales research),
needs to take into account other factors than the amount of shipped orders, and consider a
period longer than one month. These factors may be the quality of the customer
relationship or the percent of quota achieved. Due to this limitation, we would propose the
46
use of a more multidimensional measurement of sales performance than we will aim to do
in the empirical part of the current study.
Also, the factor analysis of Kerber and Campbell (1987) produced some inconsistent
results. Most probably, the methodological issue of the time allocation measurement may
be the reason, which means that the measurement has to be validated to check its
appropriateness for such complex studies on sales performance relationships. This check
is also one of the aims of our research.
2.4.2 The model of Brashear, Bellenger, Ingram, and Barksdale (1997)
Brashear et al. (1997) studied the activity level differences between the high and low
performers in the insurance industry. The sample selected by the researcher consisted of
497 Canadian and US insurance agents.
Brashear et al. (1997) used the classification of the sales process steps behaviour as the
basis of their model development. The sales process stets were: prospecting, fact finding,
selling, closing and servicing the clients, as well as planning and development of
professionals. They proposed that salesperson behaviours in the sales process will have
different effects on sales performance, whereby the selling and servicing clients will have
greater effect on performance (Brashear et al., 1997: 178).
The results of the model of Brashear et al. (1997) showed the positive and significant
relationship of “selling” (β = 0.161; p < 0.005) and “servicing customers” (β = 0.152; p <
0.005). Negative signs were found for “prospecting” (β = – 0.148; p < 0.05), “closing” (β
= – 0.119; p < 0.05), and “planning” (β = – 0.112; p < 0.005) – which were claimed to be
the characteristics of low performers.
47
Brashear et al. (1997) explained this result as follows: investing too much time in
activities other than selling may be an indicator of avoidance behaviour, fear of failure,
call reluctance, i.e., investing a lot of time in “over-analysing” and “under-acting.”
Furthermore, the negative sign for the sales close may mean that it does not to be long.
This may indicate the pre-maturity of closing attempts and a lack of customer
understanding.
Development of professionals and fact finding did not show significant relationships with
sales performance. The relationship between the overall total hours worked per week and
performance had not been hypothesised first, but was found in the statistical analysis (β =
-0.211; p < 0.05) (Brashear et al., 1997: 180). This finding is consistent with the models
of “working hard” that found this type of association with high performance.
Interestingly, the model of Brashear et al. (1997) produced consistent results, also with
the implied measurement of the estimated hours per week worked on a certain activity.
However, the study utilised “aggregated” measures, and did not deliver any descriptions
or details of behaviours on these measures. For example, it is not clear what is meant
under “close” or “selling” in this research paper. In addition, the model only considered
seven variables, and did not capture the whole range of salesperson behaviours, such as
working with internal specialists and travelling.
In view of this limitation, goal of this dissertation is to enlarge the activity list with more
precise measures, and to recheck the character time estimation measurement to get an
understanding of why this measurement worked in this study and did not work in others.
2.4.3 The models of Dwyer et al. (2000) and Jaramillo and Marshall (2004)
The models of Dwyer et al. (2000) and Jaramillo and Marshall (2004) aimed to
investigate the critical success factors in the personal selling process. Both of the models
48
utilised the factor model of Dubinsky (1980-81) and aimed to identify whether there are
relationships between each of the selling techniques with the measures of sales
performance. Dwyer et al. (2000) investigated life insurance sales, and Jaramillo and
Marshall (2004) the banking sales. Both of the models used ANOVA to compare the
sample that was divided into top and bottom deciles based on the sales performance
measurement, i.e. to compare top and bottom performers. Afterwards, multiple
regressions were run, and indicated the variables and their coefficients of association with
sales performance.
Both Dwyer et al. (2000) and Jaramillo and Marshall (2004) used the same sales
performance measurements with very slight adjustments: sales commissions earned,
exceeding sales objectives, generating current customer sales, and overall sales
performance. The measurement of Jaramillo and Marshall (2004) was based on the
evaluations of sellers and their managers, whereas Dwyer et al. (2000) used only selfratings. The measurement was adopted from Behrman and Perreault (1982).
Interestingly, the results of both models turned out to be different. Dwyer et al. (2000)
found that 12 techniques were linked to sales performance. There were six techniques
from the prospecting dimension which were characteristic of top performers: the usage of
centres of influence, examination of records, and visiting public exhibitions. Low
performers were more prone on using “cold call” specialists, non-competing salespeople,
and pre-notification (“warm call”) techniques (2000: 156). In contrast, Jaramillo and
Marshall (2004) found the following techniques of prospecting characteristic of top
salespeople:
“personal
observation”,
“public
exhibitions”,
“referral
approach”,
“introduction approach”, and “seek contacts through community groups.” Only one
technique – “organise and participate in public exhibitions” proved to have a positive
relationship with performance in both studies.
49
Dwyer et al. (2000) did not find any link between the pre-approach activities and sales
performance. On contrary, Jaramillo and Marshall (2004) found positive associations
between the techniques of “obtaining prospect information from customer, newspapers,
etc.” and “direct contact approach: contact prospect directly to arrange interview” with
performance. The same discrepancy was observed for the step of approach. Here, Dwyer
et al. (2000) did not discover any link. Jaramillo and Marshall (2004) detected that the
technique of “statement approach of opening interview4” characterised top performers.
In the dimension of presentation, Dwyer et al. (2000) found that greater use of “helping
prospects visualise offering” distinguished top performers, and the use of “product-benefit
approach”, as well as “partially standardised presentation” – the bottom performers.
Jaramillo and Marshall (2004) identified two other techniques different from those of
Dwyer et al. (2000) that differentiated top and bottom performers – “talk prospect’s
language during presentation”, and the “need-satisfaction approach5.” The latter focused
on identifying a prospect’s needs and is similar to the consultative selling method of the
sales interview. Likewise, in the “overcoming objections” step, Dwyer et al. (2000) found
the technique of “comparative item method6” more characteristic of low performers and
in the closing step – “single obstacle close 7 .” Jaramillo and Marshall (2004) did not
identify significant differences in the “overcoming objections” or in the “close” step. In
the same way, Dwyer et al. (2000) found no significant differences for the last step of
“follow-up” as opposed to Jaramillo and Marshall (2004), who identified four out of five
techniques which characterise top performers: “follow-up questions, complaints and
adjustments”, “periodic follow-up of customer satisfaction”, and “explain your firm’s
proper billing procedures.”
4
The statement approach is opening an interview with a statement about yourself, the name of your company, or the
name of the person who referred you (Dwyer et al., 2000: 159).
5
The “need satisfaction” approach is the focus on identifying the prospects needs during the sales interview by using
probing questions (Dwyer et al., 2000: 159).
6
The “comparative item method” means showing the prospect two or more products and when the prospect objects
to a feature in one product, reject it and substitute with other (Dwyer et al., 2000: 159).
7
The “single obstacle close” is the close when the prospect is almost ready to buy the product except for one reason,
so the salesperson attempts to eliminate that obstacle (Dwyer et al., 2000: 159).
50
The studies of Dwyer et al. (2000) and Jaramillo and Marshall (2004) offered an original
way of studying success factors within the sales process steps based on the analysis of
techniques proposed by Dubinsky (1980-81). Certain elements of their methodological
approach, such as the comparison of top and low performers by means of ANOVA and
using the consequent regression analysis will be applied in the methodology of the current
study.
Interestingly, their findings were very different. This meant that it was difficult to judge
with a high degree of certainty which of the factors played the major role in creating such
differences. Both studies had a different cultural setting (Latin American in Jaramillo and
Marshal, 2004 vs. US American in Dwyer, 2000), a different sample industry (banking in
Jaramillo and Marshall, 2004 vs. life insurance in Dwyer, 2000), and different
measurement bases of sales performance (self-reported evaluations in Dwyer, 2000 vs.
self-reported and manager evaluations in Jaramillo and Marshall, 2004).
There were some “hints” from other research which suggested the answer. Kerber and
Campbell (1987) hypothesised that because work activities cover a wide range of
functions, and they frequently change over time, the salespeople might vary in their
activities (1987: 47). Plank and Reid (1997) noted that the customer type and sales
situation could have an impact “on whether the salesperson performs certain behaviour
and whether or not that behaviour is effective” (1997: 51). These hypotheses suggest that
in our current research in the setting of the IT industry, the salesperson’s behaviour may
not necessarily be explained by the findings from the models described above.
Another two limitations of the studies of Dwyer et al. (2000) and Jaramillo and Marshall
(2004) are as follows. First, both studies relied on the Dubinsky model (1980-81).
According to Moncrief and Marshall (2005), the Dubinsky model (1980-81) has
experienced a number of changes because of the changing selling environment. In this
way, these changes need to be revised and the model needs to be amended. Secondly,
51
both of the studies analysed only techniques and activities within the sale process. In this
way, the activities outside of the process remain under-researched. This was therefore the
exploratory purpose of this dissertation.
2.4.4 Study hypotheses
Since this dissertation is exploratory in nature and aims to fill the above-mentioned
research gaps, the study hypotheses are formulated in an exploratory manner.
H1: Top and bottom performers differ in the level of their selling activities.
H10: There is no difference in the activity levels of top and bottom sales performers.
H2: The amount of effort spent on “direct selling 8 ” or “sales process” activities is
positively associated with sales performance in the information technology selling context.
H20: The amount of effort spent on “direct selling” or “sales process” activities is not
positively associated with sales performance in the information technology selling context.
H3: The amount of effort spent on “non-direct selling” or “non-sales process” activities
is positively associated with sales performance in the information technology selling
context.
H30: The amount of effort spent on “non-direct selling” or “non-sales process” activities
is not associated with sales performance in the information technology selling context.
The results of the empirical test of these hypotheses are discussed in details in Chapter 5.
8
The “direct selling” or “sales process” or “customer interaction activities” are the activities associated with the sales
process and involve direct selling effort. The “non-direct selling” or “non-sales process” or “non-customer
interaction activities” are supportive and are not usually client facing.
52
3 METHODOLOGY OF ACTIVITY DOMAINS DEFINITION AND VALIDATION
3 Methodology of activity domains definition and validation
3.1 The development of a salesperson activity/behaviour list: the
methodological framework
The aim of operationalisation in theory development or testing is to “provide an empirical
estimate of each theoretical construct of interest” (Gerbing and Anderson, 1988: 186).
The measures used in literature sources may be directly used or adapted from previous
studies.
Measure and item adaptations are generally done for the following reasons: (a) the need to
shorten the original questionnaire, (b) a different population being studied, (c) translations
to another language need to be done, or (d) the need to expand, reorder the questions due
to the change of the data collection method (Bourque, 2003: 37). If modifications are
made to the original measurement instrument, it needs to be retested, along with the
assessments of reliability and validity measures of the “new” constructs (Bourque, 2003:
37).
Our main reasons for measurements adaptation and reconstruction include: (a) the underrepresentation and the need for expansion of several important salesperson activities, such
as the selling customer-function and planning into the lists available in literature (e.g.
Marshall et al., 2006); (b) the adaptation of the existing measurements of salesperson
behaviours from the US-American to the European context; (c) different populations
being studied and the adaptation of the existing measurements to the specificity of selling
in the information technology industry.
53
The models of selling activities from earlier studies were developed by using a
combination of different methodologies.
Guenzi (2002) compiled the initial questionnaire based on the review of previous
theoretical and empirical works, refined the list during interviews with four academic
experts, modified the questionnaire accordingly, and mailed the final version of
questionnaire to the target group of respondents.
As a first step in their time diary creation, Kerber and Campbell (1987) conducted initial
interviews. Their preliminary instrument was then criticised by another set of field
salespeople during face-to-face interviews. Afterwards, the diary was pretested, and only
then was the final version delivered to the study participants.
Moncrief (1986) based his activity classification on a series of personal interviews and
focus group discussions. The respondents were asked to describe their “average” day and
if they performed each of the activities found during the literature review. Focus groups
were used to identify additional activities not identified during interviews. Afterwards, the
final version of the questionnaire was created and sent out to the study participants for
completion.
Based on this previous report of academic methods, as well as based on recommendations
derived from the study of Churchill (1979) on the development of constructs, we designed
the following methodology steps for our study.
Figure 3-1 shows the flow of actions involved in the development of the salesperson
activity list to ensure its validity.
54
Figure 3-1: Methodology of the development of dimensions of salesperson activities
As the first step in developing a list of salesperson behaviours, we consulted literature for
various available activity model compositions and analysed the structures of these
existing models. Churchill (1979) noted that the use of literature sources and measure
compositions can at times make it difficult to “compare and assimilate findings and
develop syntheses” (1979: 67). We observed this situation in our research. The activity
models and their developments from different angles, combined with evidence from
different industries and years, meant it was not an easy task to generate our preliminary
activity list. This is why the deep analysis of the context information was extremely
necessary in this case. The analysis of company work activity reports, observations, and
55
analysis of different salesperson job profiles helped to refine the design of the preliminary
activity list. Based on these analyses, the preliminary questionnaire was developed (step 2
of the framework).
The third step of the framework was the pre-test of the questionnaire. The purpose of this
step was to increase the content and face validity of the instrument. The technique of
cognitive interviewing was chosen due to its high face validity and the immediateness of
results. Several elements of this technique (e.g. probing questions) were already used in
the study of Moncrief (1986a, b) and proved its methodological suitability.
The fourth step is the analysis of the pre-test results, the purification of the activity list
and posting the final questionnaire online. We gathered data by means of an online survey
(step 5).
The last step of the methodological framework was the actual generation of the activity
dimension list. Exploratory factor analysis is widely used for this purpose – suggesting
dimensions (Churchill, 1979: 69). If factor analysis forms dimensions in a different way
as expected, the factor analysis needs to be restructured, and the Cronbach’s alpha needs
to be recalculated each time (Churchill, 1979: 69). The issues of construct validity and
reliability are assessed in the Chapter 4.
The steps described in the model below are discussed in detail further in text of this
chapter.
3.2 Description of the study research context
The behaviour setting concept of Roger Barker (1968) cited in Harvey (1999) proposes
that “behaviour settings are units of the environment that have relevance for behaviour.
They coerce people and things to conform to their temporal spatial pattern” (1999: 38).
56
These settings are the set of rules, symbols, or participants. Understanding how patterns
of behaviour can be facilitated by the context and the contextual basis itself could offer
many answers to the questions of “how” and “why” (Harvey, 1999).
The following subchapter will examine the activity settings of this study: the underlying
factors of the IT market and IT selling. Furthermore, the sample company setting will be
described.
3.2.1 An overview of the information technology market and the implications
for personal selling
The information technology market assumes a central role in becoming a driving force of
the economy. It stands at the core of different industries and markets from small business
and consumer products to the public and financial sector (John, Weiss, and Dutta, 1999:
78).
Technology is “the stock of relevant knowledge that allows new techniques to be derived
and includes both product and process know-how” (Mohr, Sengupta and Slater, 2005: 3).
It is is closely related to “know-how.” Therefore, when know-how is traded, the sellerbuyer exchanges are characterised as intellectual property transactions (John et al., 1999:
78).
The information technology market is a market with high uncertainty, competitive
volatility, network externalities, and an important role in industry standards (Mohr et al.,
2005). Its other characteristics are competitive volatility (permanent change of market
structures), the leading role of distribution channels, and the importance of relationship
management (Mohr et al., 2005: 29).
57
Market uncertainty is defined as the degree of ambiguity about the precise knowledge of
changing customer needs. It arises from technological uncertainty – the customer’s fear
and doubt about the usability of a new technology (Mohr et al., 2005: 9). This is the
reason for the long decision-making processes in the customer organisation when new IT
products are purchased.
From the beginning of the 1990s, the technology market witnessed a shift from hardware
selling to customised software and services sales. With this trend, both the sales
orientation and sales behavioural repertoire changed dramatically (Blustain, 1992:67).
Software, services and systems integration became the current sources of value and
margins creation. “Solutions” and “systems integration 9 ” and “establishing business
relationships” are the key worlds in today’s IT business (Blustain, 1992: 68). The IT
selling strategy is now not centred on the product any more, but is focused on value
creation, solution finding and not the product, partnership and customer needs (Blustain,
1992: 68).
During the 1960s and 1970s, the successful sellers of the technology were those who
could offer the best hardware platform (Blustain, 1992: 68). Nowadays, those people who
decide to purchase technological products are not necessarily technicians, but are the
users. Therefore, they are more interested in “what the computer can do, not in how it
works” (Blustain, 1999: 68).
In the era of “systems integration” salespeople need to have an in-depth knowledge of the
whole range of information technologies: from local area networks to telecommunications
and beyond” (Blustain, 1999: 69).
9
Systems integration is a combination of different elements of the IT palette (Blustain, 1992: 69).
58
Dunn, Frian, and Thomas (1991) mentioned that no field of selling is more complex than
high technology selling, where the personal selling is as important as the product itself
(1991: 149).
Personal selling is the critical asset for the success of the new technological products and
their consistent market success (Sharma et al., 2008: 289). “The critical role of personal
selling in high-technology markets is evident from the high levels of product complexity
that require more in-depth communications” (Sharma et al., 2008: 289).
The selling task in IT selling is characterised by complexity, time pressure, information
format, information load, the length of the selling cycle and high customer demands
(Newell and Simon, 1972 cited in Avila and Fern 1986: 55).
The complexity of IT offerings impacts on sellers in different ways. When selling
complex technical products, they must make more effort and take more time to call on the
decision makers and influencers across different departments and management levels of
the customer organisation (Marshall and Johnston, 2009: 510). Because of the complexity
of technological offerings, customers in the computer industry “are increasingly looking
to the salespeople to tell them which products to purchase. They need the guidance and
validation and the product literature to ensure them that they are getting the product with
the best price, quality and performance” (Alonzo, 1997: 22).
Another peculiarity of IT selling is the selling of complete package solutions rather than
“stand-alone” products. This requires a solution selling approach, which is conceptually
more “demand driven” than “supply driven.” An important role of the salesperson is to
deliver the information on the customer reaction to the product supply and suggestions for
product/service redesign according to the customer’s needs. The salespeople play an
additional role here as “conduits of information of future R&D investments and new
products” rather than being purely sales oriented and achieve short-term revenue (Sharma
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et al., 2008: 289). Emergence of relationship marketing has impacted on IT selling as well.
Since system suppliers and system integrators install large-scale complex systems, they
search and value personal relationships with the purpose of supply facilitation (Sharma et
al., 2008: 299). The relationship in its turn fosters customer loyalty and customer trust,
and satisfaction (Crosby, Evans, and Cowles, 1990; Donney and Cannon, 1997).
In IT selling, a sales representative who used to perform fulfilment functions besides
actual selling 10 years ago is now supposed to be released from them and concentrate
solely on selling-related activities (Sharma et al, 2008: 302).
Sharma et al. (2008) points out that the consultative selling era brings the classic sales
process of “seven steps” to an end, and instead, will concentrate on problem identification,
presentation of solutions, and continued customer support (2008: 303).
Ligos (2004) suggested that IT salespeople talk with customers’ CEOs about the view of
their own business goals and with CFOs about the financial ramifications. The sellers in
IT do not rely on standard presentations, they “pepper each presentation with carefully
crafted questions designed to uncover a client’s changing needs.” The client
representatives also spend a great amount of time with clients generally, “they attend
clients’ staff meetings, go on plant tours, and spend time getting to know customers,
rather than just selling to them” (2004: 4). Identification and connection to the key
individuals in the buyer organisations responsible for purchase decisions and developing
other influential supporters in customer organisations is crucial in the IT sales roles (Due
to the complexity of the deals, the identification of internal supporters “may not be as
tough as in other industries” (Heiman, 2008: 2).
Since salespeople have a complex job, Kerber and Campbell (1987) suggested that they
should be familiar with all technical aspects of the product line and all possible technical
combinations; therefore, intensive training for salespeople is strongly recommended
60
(1987: 40). Sharma et al. (2008) emphasised that rapid technological change leads to
shorter product cycles, and as a consequence – requires ongoing product and technology
training (i.e., product knowledge which enables a salesperson to anticipate the demand for
new technology and suggest changes to existing products).
Having outlined the peculiarities of the IT selling context, the following conclusion can
be reached: the complex world of IT selling has a direct impact on the structure and
intensity of the salesperson’s activities. Taking all of this into account, the activities of
consultative and value-added selling, adaptive selling, knowledge management, training
as well as relationship maintenance and channel work will be strongly emphasised in our
model.
3.2.2 The study research site
In our research we investigate the activities of salespeople based on the data gained from
a single company research site. The choice of this research site defines the sampling
policy of the current study.
The one-company research is not uncommon in similar sales activity studies (e.g. Dwyer,
Hill, and Martin, 2000; Kerber and Campbell, 1987). In many cases, it is preferred due to
the considerations of cost, time, seller and executive cooperation. This kind of research
facilitates the reduction of sampling errors coming from difficulties in identifying and
reaching the proper sample of the sellers mostly hidden in organisations.
The case company, where the research was conducted, wished to remain anonymous. All
documents related to selling activities, models, processes, and performance data had to be
handled with the utmost confidentiality.
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In our study we deal with a major international information technology firm with a
number of branches in Switzerland and all over the world. The sellers perceive the
company as “complex” due to the existence of multiple levels of management control on
national and international levels.
Appendix 1 contains several examples of sales job position descriptions that exist at our
research company.
The work activity report was the second important instrument used in the validation of the
sales activity list. The work activity report consists of 27 items related to the seller’s job
in all of the company’s branches. This activity report is to be completed regularly by all
of the sellers during the request phases. This report contains a list of activities conducted
by salespeople in this particular company. It contains specific information in the
company’s language, and it took a considerable amount of time to compare it with the
theoretical lists. To confidentiality reasons the content of the work report can not
disclosed.
Apart from this company information, we researched newspaper articles, job company
case studies, sales models, job profiles, and sales job descriptions found in the intranet to
obtain a fuller picture of the behaviour elements crucial for the sellers in the research site
company.
3.3 Definition of the domains of salesperson activity and behaviour
3.3.1 Definition of the “customer prospecting and qualification” domain
The construction of the activity battery of “customer prospecting and qualification” (name
adapted from Anderson and Dubinsky, 2004) is based on the classical description of the
prospecting step of the “seven stage process” with recommended activities from the
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studies of Marshall and Johnston (2009), Moncrief and Marshall (2005), Moncrief et al.
(2006). When producing this activity list, only the major prospecting activities were
considered. A lot of details and specifics on its techniques were not included.
We found that within our company setting, the prospecting step not only means gaining
new customers in the sense of “accounts” but also identifying new contacts and new leads
or opportunities through these contacts, which is regarded as crucial for sales success.
Because account management is more a management task in the researched context, the
salespeople are focused on locating and identifying potential decision-makers, or contacts
with a high propensity to buy, and leads generated out of this contacts.
The dimension of “customer prospecting and qualification” is constructed as follows.
The first activity “identify new leads and potential buyers who have the need, ability and
authority to buy” and the second activity “gather information about the prospect’s name,
his contact information” represent the classical meaning of this step (Dubinsky, 19801981).
The activity “search CRM, customer databases or other written documentation for new
contacts” is based on suggestions of Moncrief and Marshall (2005) of the wider use of
CRM and marketing tools in prospecting step.
The inclusion of these activities – “identify customers to retain or delete from your
records” and “qualify and select customers to call on” – is based on the suggestion of
Moncrief and Marshall (2005). They proposed careful customer and contact selection, and
concentrating on customers who generate higher margins.
The next item, “respond to referrals”, is adopted word-for-word from the current activity
list of Moncrief et al. (2006) and represents a passive way of obtaining new contacts.
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The item “involve telemarketers” is based on the concept of the usage of the
telemarketing channel for lead generation and passing it to the core sales team (adapted
from and Moncrief and Marshall (2005), repeated in Marshall and Johnston (2009).
3.3.2 Definition of the “pre-approach and sales call preparation” domain
We develop the dimension of “pre-approach and sales call preparation“ using the leads of
Dubinsky (1980-1981), Moncrief et al. (2006), and Moncrief and Marshall (2005). The
items “collect more specific information on customer needs and interests”, “prepare
call/meeting guidelines, questions, objectives”, “tailor the sales presentation/proposal”,
and “arrange call/interview with customer” represent the classical meaning of this step,
which is still being performed by most of the sellers in their roles (adapted from Dubinsky,
1980-1981, repeated in Anderson and Dubinsky, 2004). These items were found to be
consistent with the observations of salespeople in the sample company.
Since the wide spread of sales technology and CRM databases in the last decades used for
sales calls and customer visit preparations, these important elements are not presented in
the model of Dubinsky (1980-1981).
Based on the conceptual propositions of Moncrief and Marshall (2005), we incorporate
the following activities into the pre-approach step: “working with customer databases and
tools to gain customer information” and “working with customer databases and tools to
input customer information.”
Moncrief et al. (2006) included the activity “collect database information”, which is
similar to our proposed items. The activity “engage presales support to analyse customer
information” is included in the list on the lead of Moncrief and Marshall (2005).
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3.3.3 Definition of the “approach and opening dialogue” domain
Moncrief and Marshall (2005) stated that the approach is no longer used in its traditional
meaning, but is now seen as the sequence of relationship building actions across other
steps. Nevertheless, we tend to regard this step as still existing and important. Regardless
of whether the customer is current or new, the way a seller approaches him/her in each
sales presentation defines the impression of the whole customer visit. For this reason, this
dissertation will follow the domain definition from Jaramillo and Marshall (2004), who
adapted and shortened the items from the original list of Dubinsky (1980-81).
The activities in this item battery include: a “prospect-focused approach: an open sales
interview with a compliment or a question to gain attention and interest”, a “productbenefit approach: state the potential benefits of using your product”, and a “statement
approach: an open dialogue with a statement about yourself, company, your intent, or the
name of the person who referred you.”
3.3.4 Definition of the “sales presentation, demonstration and product
marketing” domain
The definition of this construct is based on the lead of Moncrief and Marshall (2005), who
suggested naming the previous step of sales presentation “marketing the product.” When
constructing this item battery, we followed the following authors: Dubinsky (1980-1981);
Jaramillo and Marshall (2004); Moncrief and Marshall (2005). We also confirmed
through the IT sales literature that the activities mentioned herewith are crucial in the IT
salesperson.
The item “adaptive selling, consultative selling and value-added selling” is included in
our battery on the lead of Moncrief and Marshall (2005: 20). “The need satisfaction
approach: focus on the sales talk around identifying the prospect’s needs, using probing
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questions when necessary, refine understanding” is adapted from Jaramillo and Marshall
(2004) because it best describes the character of IT selling. “The product benefit
approach: focus the sales talk on the offering, visual demonstration of a product/proposal”
is adapted from Dubinsky (1980-1981), repeated in Jaramillo and Marshall (2004). As
mentioned by Kiamy (1993), the demonstration of an IT solution plays a central role in
the IT selling process.
The following two items were introduced to this domain on the conceptual proposal of
Moncrief and Marshall (2005): “assessing and collecting market information on the
customer site for product, market development”, and “email product information, updates
and presentations”, as described in subchapter 2.3.
We adopted the following items from Moncrief et al. (2006) as they fully represent the IT
sales presentation activity domain: “adapt presentations”, “build trust”, “ask questions”,
and “consult with customers.” The item “sell value added of your company solutions”
was borrowed from Moncrief et al. (2006) as well, and was even found in an IT Seller
Services position description.
3.3.5 Definition of the “overcoming objections, problem-solving” domain
The construction of this domain is based on the principle of combining both the
consultative selling approach proposed by Moncrief and Marshall (2005) and the classical
model actions and activity components of Dubinsky (1980-81), such as “clarify objections,
release customer concerns”, and “reiterate the benefits of the product.”
The activity “plan and prepare for objections” is added to the domain and is based on
Anderson and Dubinsky (2004).
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3.3.6 Definition of the “closing and satisfying needs” domain
In this dimension construction, we rely on Dubinsky (1980-81) but expand it with two
recent activities from Anderson and Dubinsky (2004) and Moncrief and Marshall (2005).
When constructing this measurement, we did not go into the same level of detail as
Dubinsky (1980-81), who uncovered 13 close techniques, or Jaramillo and Marshall
(2004) using 7 techniques, but we use the aggregated scale with the most relevant items.
The dimension of “sales close” is constructed as follows. The first item is “confirm “winwin” agreement on relationship and close” follows Moncrief and Marshall (2005) and
Anderson and Dubinsky (2004). This item reflects the relationship selling aspect of the
new selling paradigm. If the sale did not take place, further customer relationship is going
to be maintained in view of possible future deals. “The goals to be achieved between
seller and buyer must be mutually beneficial” (Moncrief and Marshall, 2005: 17).
The next item is “finalization of the details of transaction” represents a clarification close
of Dubinsky (1980-1981). “Ask for the order” of Dubinsky (1980-81) is the
representation of a straightforward close when the customer is asked directly for the order.
And finally, the last item “provide solution for customer problems” is included here on
the recommendation of Moncrief and Marshall (2005), who said that the salesperson
needs to concentrate on solving the business problem of the customer during the closing
situation.
3.3.7 Definition of the “follow-up and relationship maintenance” domain
The activities in the domain “follow-up and relationship maintenance” rely on the main
two sources: Dubinsky (1980-81) and Moncrief et al. (2006). Many of the items from two
factors, “promotional activities and sales service” and “product support” in the study of
Moncrief et al. (2006), correspond to the techniques of the dimension “customer service
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activities” of the list of Dubinsky (1980-81). Interestingly, several important activities
analogical to the “customer-satisfaction oriented activities“ of Dubinsky (1980-81) (such
as “sending thank you notes” or “follow-up of customer satisfaction”) are not present in
the list of Moncrief et al. (2006).
The first three items of the “follow-up” domain, “writing “thank you” and appreciation
note”, “periodic check of customer satisfaction”, and “handling customer complaints”,
were adapted from Dubinsky (1980-81). These items also appear in Jaramillo and
Marshall (2004), and in the sales handbook of Anderson and Dubinsky (2004).
The activity “perform maintenance, test equipment with customer” is adapted by using
the activity “installation/maintenance” Dubinsky (1980-81) and “perform maintenance”
of Moncrief et al. (2006). This activity was also allocated in company job descriptions.
The item “supervise installations” was adapted from Moncrief et al. (2006); this activity is
similar to the “serve as consultants and give technical advice” of Dubinsky (1980-81).
The activity “deliver technical workshops for customers” was adapted from the technical
sales engineer position description and is conceptually close to “serve as consultants and
give technical advice” of Dubinsky (1980-81).
“Train customer on product” was borrowed from Dubinsky (1980-81) and Moncrief et al.
(2006).
The activity “write up, handle, expedite orders” was obtained by combining two activities
of Moncrief et al. (2006) – “write up orders”, “expedite orders” and “handle orders.” This
activity corresponds to the “follow-up activity when the order is signed” and “ensure
proper billing procedures” of Dubinsky (1980-81). We decided to combine the activities
proposed in literature due to the fact that they all represent the same dimension – order
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management, and it is not directly part of a seller’s responsibilities at the researched
company, and therefore is seldom performed.
The activity of “socialising with customer: drink together, organising events, training
seminars, playing golf” was included in the activity domain following the lead of Piercy
(1995), Moncrief (1986a, b) and Moncrief et al. (2006). The latter discovered the activity
domain “entertaining.” This activity represents the post-sale relationship maintenance
aspect.
The activities “take clients on site” and “arranging customer credit” were adapted from
Moncrief et al. (2006) and Moncrief 1986 (a, b) respectively.
3.3.8
Definition
of
the
“inter-company
coordination
activities
and
administration” domain
High inter-company specialisation leads to the high level of interdependence between
different units, where salespeople tend to play a “boundary-spanning role” between their
customers and their own sales and support organisation (Marshal et al., 1999: 97).
Because of the high use of selling teams, the team coordination plays a crucial role.
Interdepartmental conflict often appears. The interdepartmental conflict is “working at
cross-purposes, having incompatible goals, being obstructive and not appreciating each
other’s roles” (Le Meunier-FitzHugh and Piercy, 2007: 209). There is pressure to retain
the originality of their tasks and, at the same moment, facilitate the collaboration and
coordination of efforts around organisational aims (Le Meunier-FitzHugh and Piercy,
2007: 209).
Darmon (1998) pointed out that salespeople need to keep, improve relationships with
their supervisors and sales support, technical, production and other staff because they use
69
their services to accomplish their tasks (Darmon, 1998: 37). This is also a relationship
aspect of a sales job, and plays an extremely high role in internal cooperation and teaming
(Darmon, 1998: 37).
To describe the aspect of team selling in the activity domain of a salesperson, we included
the item “coordinating the aligned sales and marketing team.” This item comes from the
study of Guenzi (2002), and was mentioned in several sales job descriptions. The item
“planning, working with distributors” was taken from a Channel Sales Manager position
description. “Doing territory management”, “doing personnel tasks”, “participation in
internal meetings” was applied in our domain from the company seller work activity
report. “Coordinating orders and delivery” is adapted from Moncrief and Marshall (2006).
3.3.9 Definition of the “planning” domain
Call planning is supposed to be a crucial element in the sales activities mix (e.g. Gwin and
Perreault, 1981; Henry, 1975; Lodish, 1971).
In order to accomplish their tasks, salespeople need to plan and manage their time
correctly (Darmon, 1998: 36). Time management orientated behaviour results in
salespeople performing their jobs more efficiently (Nonis and Sager, 2003). This refers
both to short-term and long-term planning.
Because the activity of planning was underrepresented in all existing empirical models on
salesperson activities, we aim to fill this research gap with the following proposed domain
construction.
The short-term planning is represented by the activities of “planning internal meetings”
and “planning customer calls and customer visits”, both of which follow the lead of
Darmon (1998). The existence of these activities was proven through the analysis of the
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sample company’s records, such as activity reports, and by observing the behaviour of
their salespeople.
The activity “planning your relationship, customer analysis, initiatives, decision process”
represents the long-term strategic planning perspective. The two activities: “planning on
quarter, year, and monthly basis” and “planning your relationship, customer analysis,
initiatives, and decision process” are based on the analysis of the company seller activity
report and sellers’ observations.
3.3.10 Definition of the “personal development” domain
The value of training, development of skills and knowledge of the IT salesperson was
emphasized by Kerber and Campbell (1987), who explained the crucial necessity of
intensive training for the sellers of IT because they should be familiar with all the
technical aspects of the product line and with all possible technical combinations.
Sharma et al. (2008) outlined that rapid technological change leads to shorter product
cycles, and, as a consequence, requires permanent product and technology training (the
product knowledge which enables a salesperson to anticipate the demand for new
technology and suggest changes to existing products).
In view of this fact, we included the dimension “personal development” into our activity
model list. We adopted the activity of “study market trends, read trade publications” from
Darmon (1998). Trade and consumer magazines are the most obvious source of
competitive information (Kiamy, 1993: 66).
Because of the increasing sales force automation and the growth in the number of sales
tools, salespeople are under a lot of added pressure to use the latter. The complexity of
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these tools is an additional role stress factor for salespeople (Rangarajan, Jones, and Chin,
2005: 345).
For this reason, learning technology would best relieve this perceived role stress.
Therefore, we included the item “learn software”, which was found in the list of Moncrief
et al. (2006).
Other activities in the list are “participate in sales trainings” and “train new hires” adapted
from Moncrief et al. (2006), also found in Moncrief (1986a, b); “setup and visit
conferences” – adapted from Moncrief (1986a,b) by combining “attend conferences” and
“set up conferences.”
3.3.11 Definition of the “travel” domain
The activities “travelling out of town” and “spend night on road” were adapted from
Moncrief et al. (2006) and Moncrief 1986 (a, b) respectively. The activity reports and the
company records included these activities as well.
3.3.12 Operationalisation of the “sales performance” variable
There is a huge body of research existing on measurement of sales performance. There is
an approach of objective measurement, which uses the available company information,
such as the total volume of sales or orders, sales commission, and percent of the quota
achieved. This type of measurement was widely used in the early studies of sales research
(e.g. Futrell and Parasuraman, 1984; Kerber and Campbell, 1987). The subjective
evaluations of performance have gained in importance in recent years. The subjective
measurements vary from manager evaluations (e.g. Behrman, Bigones and Perreault,
1981), self-report evaluations (e.g. Behrman and Perreault, 1972; Dwyer et al., 2000;
72
Johlke, 2006; Nonis and Sager, 2003), combined self-performance and manager
evaluation (e.g. Jaramillo and Marshall, 2004; Weeks and Kahle, 1990) to combined
manager evaluation and objective numbers (e.g. Lamont and Lundstrom, 1977).
Rich et al. (1999) found a correlation of 0.447 between self-evaluations and manager
evaluations.
Chonko et al. (1986) defended the approach of self-reported measures because these
measurements are easier to collect and they showed high correlations with objective
measurements. Considering these arguments, the self-evaluations were used in our study
as well.
Avila et al. (1988) postulated that there is no universal “best” measurement of sales
performance. Moreover, no single measurement of performance can be perfect. The
overall sales performance is a multidimensional concept (1988: 32). Most measurements
are more or less appropriate depending on the sample of studied companies, job role, and
job situations.
Nevertheless, one measurement is cited the most: the measurement methodology of
Behrman and Perreault (1982). Its basis was used in a large number of studies, such as
Kidwell et al., 2007; Miao and Evans, 2007; Pettijohn et al., 2007; Piercy, Piercy,
Cravens, and Lane, 2001; Sujan, Weitz, and Kumar, 1994, and many others.
Reid et al. (1997) pointed out that Behrman and Perreault (1982) developed a measure of
sales performance that “in essence incorporates the notion of behaviours and the
performance of those behaviours.”
In the studies on the association of activities and sales performance, the following
measurements of sales performance were utilised.
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Kerber and Campbell (1987) used only the objective measurements of performance
obtained from company internal documentation: the dollar amount of orders not shipped
yet during the month, the dollar amount of orders shipped to customers during the month,
and the dollar amount of new orders during the month. The three indices were
intercorrelated (p < 0.1) with a correlation of 0.52. An aggregate measure was calculated
by averaging the three indices of performance which had not been standardised. The
measured period of one month is considered to be too short for valid evaluations.
While investigating the relationship between the time calling on potential and established
accounts and sales performance, Weeks and Kahle (1990) gathered information from nine
companies from different industries across the USA. Weeks and Kahle (1990) included
both annual sales performance measures (objective measures of performance standardised
for each salesperson as a deviation from his or her company’s mean sales) as well as
subjective measures received by sales managers. The latter were based on the eight
dimensions of sales performance from Futrell and Parasuramann (1984). The reliability
index for the scale was 0.90. As an outcome of their research, they proved the thesis of
Kerber and Campbell (1987) that different measures of performance can yield different
results. The subjective evaluation (the manager rating) was more successful than the
objective performance, because this measurement provided a deeper insight into all
aspects of the salesperson’s activities and performance.
In the study of Brashear et al. (1997), performance was measured using the self-reported
measure of the number of policies sold in the past 12 months, which was the standard
measure of salesperson performance in the insurance industry. The measurement showed
good model results.
We analysed these and other available measurement approaches on the appropriateness of
them to our research. Owing to the outdated character of the above mentioned studies, and
for the reason of the specificity of the sample studied, we chose one of the recent scales
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used in the sales research: the sales job performance scale from the paper of Jaramillo and
Mulki (2008), who studied the relationship between effort, job performance, supportive
leadership, self-efficacy and motivation.
This scale was adopted from the study of Piercy, Cravens, and Lane (2001), which is an
adaptation of the scale of Behrman and Perreault (1982). The Cronbach’s alpha was 0.76
in the study of Jaramillo and Mulki (2008) and 0.79 in the study of Piercy, Cravens, and
Lane (2001).
The elements of the scale are as follows:
• Building effective relationships with customers;
• Making effective presentations to customers;
• Keeping expenses at acceptable levels;
• Achieving sales targets and other business objectives;
• Understanding our products and services;
• Providing feedback to management;
• Understanding customer needs and work processes; and
• Contributing to the salesperson’s own sales unit’s revenue.
This scale was chosen for our study for the following reasons: on the other hand, it is
specific enough to consider both outcome and behavioural performance elements, and, on
the other hand, it is common to all sales positions in the case company.
The inclusion of the elements of sales targets achievement (e.g. quota) is a strong measure
of performance because its controls for many other performance measures (Churchill,
Ford, Hartley, and Walker, 1985: 106).
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3.4 The methodology of activity measurements
Researchers in sales management have developed three main measurement approaches to
assess the execution of activity/ behaviour: time use measurements with a) actual time
spent on an activity or b) frequency of the activity performance, and c) the measurement
of the importance of the activity for achieving success.
Kerber and Campbell (1987) used a time diary in their study on the link of activities,
performance, and turnover. Each participant was asked to record the number of minutes
they spent each day on each of the 23 activities for a period of two weeks. A daily log
sheet was provided to the participants to record their activities. Unfortunately, the model
of Kerber and Campbell (1987) produced inconsistent results.
Recall measures proved to have a good reliability in the model of Weeks and Kahle
(1990). The researchers implied the recall values for the time-budget use (measured with
the question “how many hours in a typical week do you spend doing the following…?”)
that were implied for the two variables: time spent on potential and time spent on
established accounts.
As a basis for their research, Brashear et al. (1997) used the recall measurement, where
the participants were asked to report the number of hours per week they spent on seven
activities. The activity data was analysed by means of regression analysis with the
dependent variable of sales performance. The model of Brashear et al. (1997) yielded
significant results.
Guenzi (2002) utilised frequency and intensity of the activities measurements to find the
interrelationships of 11 activities.
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Moncrief (1986) used the two measures of activities: how long the respondents spent
performing the activity when they performed it (here the time diary measurement did not
produce consistent results) and how frequently the respondents performed each of the
activities (this measurement proved its validity).
About 20 years later, Moncrief et al. (2006) only used the frequency of activity
performance measured over a month; Jaramillo and Marshall (2004) adapted the
measurement of "importance" that was being placed by the sellers on a number of selling
techniques adopted from the list of Dubinsky (1980-81).
As can be seen, different studies utilised different measures of activities to achieve
different research aims. We can suppose that the time diary results with longer activity
lists provide less consistent and valid results (e.g. Moncrief, 1986) than the time reporting
for only several activities (Brashear et al., 1997). Studies with longer lists of activities
preferred more estimative “frequency” or “importance” measurement approaches.
The research objective of this chapter is to describe and compare the use of different
methodological measurements of behaviour and activities, and to propose the approach
most suitable for the current study.
3.4.1 The time use methodology
Time use data are powerful illustrators of role behaviour and its variations (Pentland and
McColl, 1999: 169). Time use methodology provides an insight into actual lifestyles,
supplies information about what people do, the tasks they perform, their social
environmental context, level of stress, etc. This method is valuable because it provides an
understanding of human behavioural problems, and is used to guide the improvement and
development of areas where an activity is limited or constrained (Harvey, 1999: 3).
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The practical applicability of this method as a research instrument is that it uncovers both
qualitative as well as quantitative information about certain behaviour elements (Vermaas
and Van De Wijngaert, 2005: 121).
Time is a valuable resource and “we speak of time as a commodity, a resource, an ally, an
enemy, and a gift (...) that may be on our side, on our hands, with us, or against us”
(Pentland and McColl, 1999: 170). Many of the highly characteristic features of social life
are connected with timing: office hours, rush hours, shifts, or timetables (Szalai, 1972: 3).
The time use method has been popularly used in the social and related sciences. A large
number of social aspects were investigated with this method. For example, the time use
measurements were applied for investigations of internet usage (Ishii, 2004; Vermaas et
al., 2005), for time spent travelling, housework, and educational activities (Robinson,
Andreyenkov, and Patrushev, 1989), in examination of family and community activities
of rural families (Bollman, Moxley, and Elliot, 1975), in cross-cultural studies (Robinson
et al., 1989; Szalai, 1972), in clinical psychology and studies on disabilities (Pentland and
McColl, 1999), and in national surveys, such as the Information Behaviour Census
Survey in Japan (1995-2004) (Ishii, 2004).
Apart from the studies mentioned above (e.g. of Brashear et al., 1997; Kerber and
Campbell, 1987), the time measurement in sales management research is also widely used
to measure the total picture of time allocation of salespeople within and across companies.
For management purposes, the work activity reports of the sales force are usually part of
monthly routine and employee surveys.
The time measurement studies generally use a time diary or a recall questionnaire.
The time diary is “a log of the sequence and duration of activities engaged by a test
person over a certain period of time” (Bishop, Jeanrenaud, and Lawson, 1975: 74).
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The time diary asks respondents to record the duration of each occurrence and measures
the exact timing of activities with frequencies, patterns of sequence of activities, the
issues, which are difficult to measure with interviews or recall questionnaires (Bishop et
al., 1975: 74). The possibility to provide unique and detailed time information is the
obvious strength of the time-diary approach (Harvey, 1999: 27).
The time diary design may be with open or coded categories (where most researchers
prefer the coded format); closed (e.g. a diary with fixed-intervals of 5-20 minutes) or open
time interval, yesterday (diaries about a day before) or tomorrow diaries (Harvey, 1999:
22).
Although the collection of data about activities by the use of time diaries is an old
research technique, it was used infrequently in contrast to such methods as interviews or
recall questionnaires (Bishop et al., 1975: 74).
Compared to the time diary where a respondent indicates the exact time, a recall
questionnaire commonly asks participants to estimate by recall how often he/she
participated in each of the activities during a certain period of time, or to indicate the total
time devoted to this activity (Bishop et al., 1975: 74).
Time diaries are assumed to provide more reliable and valid information than recall
questionnaires, although they require much more time and investment from both the
researcher and the respondent (Bishop et al., 1975: 75).
Bishop et al. (1975) compared the responses to recall questionnaires and time diaries in
the investigation of leisure activities of the university students, and they found a high
correlation between both methods (0.88; p < 0.05).
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For the studies where the summary and average information are sufficient for answering
the research questions, and the exact day/week time consequences are not important,
Bishop et al. (1975) recommended a recall questionnaire because it is simpler, less timeconsuming, and an economical alternative to the time diary.
The measures of time frequency are often used instead of actual time measurements in the
recall questionnaires. Frequency is the surrogate measure of time allocation and it is “the
number of episodes of a given activity occurring during a specified period of time”
(Harvey, 1999: 36). Bishop et al. (1975) found evidence of good convergent validity and
high significant correlation (0.78, p < 0.005) between the frequencies of the recall
questionnaire and the amount of time reported in diaries (1975: 78).
The recall period length has a significant influence on a number of activities reported
(Fisher, Egerton, Gershuny, and Robinson, 2007: 6). The general rule for the diary
research is that the recall should not be made for more than 2 days (Harvey, 1999).
The usual period of reporting can be one or two days, or one week, and the period should
be “long enough to capture the behaviour patterns of interest without putting at risk the
completion by making it too much of a burden for the respondents” (Vermaas and Van De
Wijngaert, 2005: 124).
Many diary studies capture only 1-2 days per respondent. The 2-day approach is believed
to provide better reliability. Cost and administration consideration favour the random day
approach (Harvey, 1999: 20).
One-day recall is significantly better than more extended recall, where people tend to
recall the primary activity and not to recall episodes that take place in the middle of an
activity, which might reduce the number of activities reported (Juster, 1986: 392).
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The one-day diary approach overcomes the limitations of recall difficulties, ambiguous
borders of activities, social desirability, existence of atypical days or weeks by focusing
on a clearly defined time period that involves minimal memory loss (Robinson et al.,
1989: 7).
When cumulated across fairly large representative samples, single-day diaries indicate
validity and reliability, and thus provide a basis for long-term assessments. Statistically,
in large samples those respondents who spend more time in some activities rather than in
others are balanced by those who do the opposite (Robinson, 1999: 54).
The easiest questions for the respondents to answer are those which contain an indication
of a central tendency or a normal value (Tourangeau et al., 2000: 108).
Fischer et al. (2007) favour the quickness of a “usual” day or week approach, indicating
that it has considerably lower time consummation (about 10 seconds) in contrast to the
full time diary, which can “take up to 40 minutes depending on the degree of details
needed” (p. 3). There are certain type of questions where reporting past behaviour by
asking “how much time did you watch TV last Sunday” may make it difficult for
respondents to answer accurately (Dillman, 2000: 37). To solve this problem, it is
recommended that the respondent be asked what he/she usually does (Dillman, 2000: 37).
Although it seems to be a simple task, a recall questionnaire involves calculations in mind
producing a consequence of overestimation that was proved by different studies (Fischer
et al., 2007: 5). Moreover, the Vierordt’s law postulates that the participants overestimate
the short intervals and underestimate the long ones (Tourangeau et al., 2000: 118). In this
way, all under- and overreportings are all sources of measurement error (Tourangeau et
al., 2000: 122).
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The frequency estimations arise complex interexchange between memory retrievals and
judgment in responding (Tourangeau et al., 2000: 137). The frequency is not stored in our
memory per se, but it is computed from other facts that are remembered (Tourangeau et
al., 2000: 140). Because the frequency is based on the recall memory, it can be reported
only if the items connected with the frequency estimates are recalled. The item or event
familiarity or strength increases the possibility that the event is remembered (Tourangeau
et al., 2000: 141). Tversky and Kahneman (1973) opened the effect of availability
heuristic that is the effect when the retrieval of information fails and the people conclude
that the event happens rare (Tourangeau et al., 2000: 137). It is not only the recall
function which contributes to the frequency judgements, but also the familiarity with the
information in the question. For the purpose of the above mentioned estimation effects
clarification, the cognitive interview pre-test is conducted in our study.
3.4.2 The measurements of activity importance
The measurement of “importance” is widely used in management research, and is based
on the concept of critical success factors.
Critical success factors (CSFs) are the “critical areas of the business that management
must constantly monitor to ensure that the business flourishes” (Magal, Carr, and Watson,
1988: 414). Critical success factors are the conditions and characteristics that, when
properly managed, have a significant impact on the success of a firm (Leidecker and
Bruno, 1984: 24).
The first discussions of critical success factors appeared in early 1960s, and already a
decade later this concept had become widely utilised. The critical success factors are the
key result areas, strategic factors, and pulse points that significantly impact on
profitability (Leidecker and Bruno, 1984: 23). The CSFs focus on areas where “things
must go right” (Magal et al., 1988: 413). The CSFs provide standards for performance
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measurements as well as a set of criteria for strengths, weaknesses, threats and
opportunity assessments (Leidecker and Bruno, 1984: 23).
CSFs can be implemented on different levels and units of analysis, e.g. individual
managers, functional units, or firms (Williams and Ramaprasad, 1996: 251). Based on
their type of criticality, the CSFs can be causal, necessary and sufficient, and necessary
and associated (Williams and Ramaprasad, 1996: 256).
There are differences in the meanings of two particular measurements: “importance” and
“frequency.” The “importance” measurement is more subjective and evaluative than
“frequency”, whereas “frequency” is more objective in that it measures what people
perform in reality (Moncrief, 1986b: 312).
3.4.3 The implementation of measurements in the current study
Three factors are crucial for the diary content design: the need for the information which
is in line with the research questions, the need for validity and reliability, and the concern
for “the burden for the respondent” that directly affects the response rate (Harvey, 1999).
Considering the highly occupancy of the respondents, and the evidenced low validity and
data usability for the time duration reporting of the long activity lists (e.g. as in the studies
of Kerber and Campbell, 1987, or Moncrief, 1986a,b) we decided not to use the time
duration approach in our study. As a rule of thumb and following the successful
implementation of Moncrief et al. (2006), we employed the “frequency” measurement
instead of “importance” on the whole list of activity variables because it depicts the actual
behaviour of salespeople.
Because the time-diary method is mainly a self-reported instrument, the verification of the
results by an independent method may produce 20-30% higher results than the self83
reported measures alone, and increase the validity of these results (Robinson, 1999: 82).
The activity lists, logs, continuous or random observation, activity and duration surveys,
and beeper studies may provide more detailed information (Harvey, 1999: 20).
The multimethod of “triangulation” is widely used and recommended when searching for
the generalisable laws across subjects by the combination of a time-diary or a recall
questionnaire with other methods, e.g. in an in-depth interview or “field” observations
(Wilcock, 1999).
Webb, Campbell, Schwartz and Sechrest (1965) cited in Wilcock (1999) propose that
“when a hypothesis can survive the confrontation of a series of complementary methods
of testing, it contains a degree of validity unattainable by one tested within the more
constricted framework of a single method” (1999: 20). Bailey, McDermott, and Wilson
(1976) suggest as well that due to the weakness of the reliability of the seller’s call report,
other measurements needs to be taken into account (1976: 344).
The recommended triangulation approaches that are used in the study are field
observations and in-depth interviews provide rich descriptive information gathered in a
natural context (Wilcock, 1999: 202). Especially useful for triangulation within the time
use study is a “story-telling” approach that enables people to “reflect upon their own lived
experience and to describe in depth their own subjective, multiple realities.” It is
recommended that this be done in the same period of time when the respondents recorded
their time use to provide self-reflection (Wilcock, 1999: 202).
The triangulation in our study will be implemented by the use of qualitative cognitive
interviews to validate the proposed activity structure lists, field observations and literature
review which happen at the stage of the initial questionnaire development (for details see
Subchapter 3.6). Moreover, we implemented one more technique of triangulation – the
use of different measurement approaches to compare the results which came out of
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different techniques. Questions 22, 23, 24, 25 in the final version of the questionnaire
were designed to generate richer and more comprehensive information on the behaviour
patterns not only from the frequency perspective, but also from a time allocation and
importance evaluation perspective. The simultaneous use of these different measurement
types aims to provide a unique opportunity to compare them by means of empirical
analysis and give recommendations for their further usage. In order to reduce the burden
on the respondent, the time allocation and importance measurements were provided for a
shortened (aggregated) variable list.
After the time allocation data has been gathered, it is analysed by aggregating the
summarised amount of time and the duration (Harvey, 1999: 28). In most studies, the
focus is laid on hours and days, weekly or monthly time estimates can be calculated easily
from daily estimates (Harvey, 1999: 28). When activities are aggregated, they provide the
“totality of the observable human behaviour” (Harvey, 1999: 38).
The period of measurement for our variables is implemented based on the following
considerations. For the frequency estimates: because the year period may be too long to
be reliable, and one week or one month may be too short to cover all of the activities due
to the differences in the sales cycles of different technology products, we decided to
formulate the question about how the salesperson perceives the “average” or “usual”
frequency of a particular activity in his/her specific job situation. In this way, the general
estimation or average strategy builds the basis for this measurement (Dillman, 2000: 69).
The period of measurement for the time allocation variables uses a one-day approach.
However, the observations of the company salespeople showed that there are certain days
in a week when the sellers make presentations to a customer, and there are days when
they work in the office or at home and mainly do not make sales presentations (sales
phone calls usually very seldom). Taking into account this fact, we suggested that the
respondents evaluate one day when they visit a customer – a so-called “customer visit
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day” and an “office” day when they work mainly from the office or home – without
travelling to customers. Using this “two-day” approach increases the validity of the
method of the “one-day” approach (Harvey, 1999: 20).
The measurement scale used in our study is the 7-item Likert scale, which is in line with
Moncrief et al. (2006); Moncrief (1986); Marshall, Moncrief, and Lassk (1999). They
used the 7-point Likert scale with 1 being “never performed in a month’s time” and 7
being “very frequently performed in a month’s time.”
3.5. Survey study design
The choice of the survey method as the main data collecting technique in our study is
justified by its wide use in quantitative sales and other research. The survey is an effective
tool of capturing the data of a large population through a relatively small representative
sample (Ghauri, 2005: 124).
In this subchapter, we will describe the outline of the questionnaire, the utilised survey
design theories to increase response rates, as well as the ways of addressing possible
survey errors.
3.5.1 The questionnaire outline
The online Web survey approach was chosen for our study. A Web survey involves “a
computerised, self-administered questionnaire in which the researcher announces the
survey on a World Wide website where individuals access and complete the questionnaire
by using compatible Web browsers” (Simsek and Veiga, 2001: 219).
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The invitation to the survey is usually sent by e-mail with an embedded URL message – a
hyperlink to the survey, “which evokes his or her Web browser, presenting the recipient
with a Web-based survey” (Simsek and Veiga, 2001: 219).
The main advantages of the Web or online surveys are the lower costs compared to
traditional surveys, ease of navigation, the ecological consideration (no paper needed), the
possibility of a rapid send-out and data collection, download and analysis. Moreover, all
the respondents of the survey receive the questionnaire regardless of their location
(Simsek and Veiga, 2001: 219). For these reasons, we chose this method of data
collection.
For the purpose of convenience, we chose the approach of self-administered
questionnaires. The main advantage of self-administration in the survey process is the
possibility to think, reflect, and give more “thoughtful answers” (Dillman, 2000: 67).
Another advantage is that the respondents have an overview of the questionnaire as a
whole, and can come back to some questions later if necessary. The self-administered
questionnaires cost less, allow for more geographic coverage, and are suitable for larger
samples compared to in-person or telephone interviews (Bourque, 2003: 9). The
likelihood of receiving more truthful information on a sensitive topic where the
anonymity and confidentiality for participants may be important is much higher for the
self-administered questionnaires (Bourque, 2003: 9).
The online questionnaire outline can be seen in the Appendix 4 to this study. The
questions were ordered in several sections segmented by topic. The first block of
questions (1 to 10) was devoted to the socio-demographic questions that were considered
to be relatively “easy” to fill in. Activity frequency blocks of questions (11 to 21)
followed. The activity importance questions (block 22) and the time allocation questions
(blocks 24 and 25) were the next sections of the questionnaire. The sales performance
self-evaluation questions were the last ones. Because the participants are generally
reluctant to complete open-ended questions, the answer categories were created.
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The questionnaire was one page long, and it was designed to be easy and convenient to
fill in. We checked the potential problems connected with unfamiliarity and questionnaire
administration during the pre-test phase, and came to the conclusion that the respondents
from the sample company were familiar with online questionnaires, and felt comfortable
filling them in.
3.5.2 Survey design considerations to maximise the response rates
Different survey participation theories, such as the tailored design of Dillman (2000), as
well
as
the
social
exchange
theory
with
its
reference
to
self-perception,
commitment/involvement and cognitive dissonance theories, were applied as a basis for
the survey design of our study.
The Tailored Design (TD) describes the survey development procedures which aim to
obtain high response rates by considering direct survey outcomes and effects, such as the
perceived rewards, associated survey costs, and trust creation in the benefits of the survey
results, as well as considering the survey errors (Dillman, 2000: 5, 27).
Dillman (2000) builds his Tailored Design (TD) as an adaptation of the classic Tailored
Design Method (TDM), including the developments from the social exchange theory of
Homan (1958). He postulates that the TD “uses the knowledge about the causes of human
behaviour to identify and understand why people do or do not respond to surveys”
(Dillman, 2000: 13). The social exchange theory utilisation includes the corresponding
choice of different survey stimuli to increase respondents’ motivation (Dillman, 2000: 13).
Because the answering behaviour is a complex perception of the perceived gains and
losses from participation in a survey, as well as a matter of trust, our study based its
survey design on the propositions of Dillman (2000).
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As its basis, the social exchange theory analyses survey response behaviour and utilises
some of the features of the self-perception and cognitive dissonance theories.
The theory of cognitive dissonance postulates that the incentives would increase the
number of responses because the respondents would feel discomfort “if they were to
accept the incentive without returning the survey” (Festinger, 1957 sited in Carter et al.,
2008: 404). The small gift included with the mailing to each respondent, such as one to
five dollar bills, proved to be more powerful in generating high response rates than
promises of rewards or payments afterwards (Dillman, 2000: 15).
The implication of the self-perception theory and social desirability effect in the
questionnaire design will use the appeal to the “individual’s internally motivated desire to
help others”, as well as motivate their participation by invoking other social exchange
possibilities, like communication with new people (Carter et al., 2008: 404).
The use of tangible and intangible rewards is usual in survey study design. The tangible
rewards can be a necessary and meaningful thing for the respondents that they can use
immediately after the survey. The intangible rewards include the promise of the study
results, making the questionnaire interesting to fill in, the social meaningfulness of the
scientific results, and recognition for each of the responses (Dillman, 2000). As a tangible
award, we selected the coffee coupons of the company due to their relatively low cost and
high immediate usability. Two coupons were sent to every participant in the study.
The “perceived costs” of the survey should be carefully studied and minimised to achieve
better response rates. The salesperson’s time was the highest cost identified in this study.
It was estimated that it would take 20 minutes of the respondents’ personal time to answer
the questions. This was communicated to the respondents in the email communication and
in the header of the questionnaire. Other “social costs” included: survey inconvenience,
embarrassment, and long and boring questions (Dillman, 2000: 27). To avoid these social
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costs, especially in the section on the self-evaluation of the sales performance, we made
the questionnaire anonymous and promised full confidentiality. The anonymity also
aimed to reduce the social desirability effects associated with the responses.
Building trust is also an important element of the survey implementation (Dillman, 2000:
29). We selected executive sponsorship as a way of building trust, which is one of the
common and well working methods of organisational survey research (e.g. Kerber and
Campbell, 1987; Weeks and Kahle, 1990). The use of executive sponsorship was also
based on the perception that some people “are more likely to comply with a request if it
comes from and an authoritative source” (Dillman, 2000: 30). Our study had executive
sponsorship from this organisation, and the original email invitation was sent from the
executive sponsor with the link to the study.
Carter et al. (2008) suggested employing more intensive facilitators for sales research
than for non-sales marketing research in view of the former’s lower response rates (2008:
413). They found that the existing efficient response facilitators used for the sales
research were pre-announcement, follow-ups, executive cooperation, and incentive
(Carter et al., 2008: 407). For the survey design in a single company, Carter et al. (2008)
strongly suggested gaining the cooperation of management and providing incentives. We
followed this recommendation in our research.
3.5.3 Survey implementation
Dillman (2000) proposes the “four communications plus one” method of survey
implementation to maximise its effectiveness. The first one is the brief pre-notification
letter sent out a few days prior to the actual send out of the questionnaire. The second
mailing includes the questionnaire with a detailed cover letter explaining the importance
of the study. A “thank you” postcard follows some days later, which aims to thank the
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participants for taking part in the survey, or remind them to do so. A replacement
questionnaire is sent to non-respondents 2-4 weeks later. The final contact is
recommended by phone a week later (Dillman, 2000: 151).
The commitment/involvement theory of the studies participation also postulates that the
preannouncement and the follow-ups might increase the response rates (Carter et al.,
2008: 404)
In our study, the pre-notification email was sent out on the 8th of March, 2010; the actual
invitation to the survey with the embedded online link was sent by the company executive
sponsor encouraging study participation on the 11th of March, 2010; the first reminder was
sent by post mail for each of the target respondents with a personalised heading and
attached incentive from the 19th to 21st of March; the last reminder was sent out on the
13th of April, 2010. In our study, we abandoned the fifth element of communication
suggested by Dillman (2000) due to the sufficient number of questionnaires received back
and in order to reduce non-response bias. Because it was not possible to attach a tangible
incentive to the Internet survey, we attached it to the first reminder letter sent out by
traditional mail. Also, the reminders were personalised letters to show the respondent that
he/she was important (Schaefer and Dillman, 1998: 380).
3.5.4 Addressing possible survey errors
There are four main sources of survey errors which exist in most of surveys: sampling,
coverage, measurement and non-response errors (Dillman, 2000: 11).
The sampling error is related to the sample size, and therefore could be reduced by
getting an acceptable response rate, covering most of the survey population, and drawing
reliable conclusions (Dillman, 2000: 12). In our study, we used all existing survey
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participation incentives to achieve the highest possible rate, and in this way encourage the
survey population to participate in the survey.
The coverage error is the “result of not allowing all members of the survey population to
have an equal or known nonzero chance of being sampled” (Dillman, 2000: 11). The
coverage error in this study was reduced by working with company databases and the
consequent multiple respondent lists validations with the sales unit managers, controlling
units, and human resources departments. The aim of this action was to include every
single respondent in the sample population. Coverage error may occur in the survey over
the Internet due to the lack of Internet access, and therefore, does not provide equal
opportunity for all respondents to answer the questions (Simsek and Veiga, 2001: 226).
This concern was reduced in our study: all the respondents had Internet access and most
of them even had wireless Internet access, because of their mobile sales role.
The measurement error comes from incorrect or unclear wording (Dillman, 2000: 11). In
our study, this was addressed by the detailed design of the questions and their thorough
pre-testing (the pre-test steps are described in Subchapter 3.6). The measurement errors as
well as the overall response quality in the organisational studies over the Internet are
greatly impacted by confidentiality (Veiga and Simsek, 2001: 226). Moreover, the
anonymous studies proved to have better response rates than non-anonymous ones
(Linsky, 1975: 91). In view of this fact, and as already mentioned above, our study was
conducted under conditions of strict confidentiality and anonymity.
The non-response error may occur when not all members of the sample respond to the
survey (Simsek and Veiga, 2001: 223). Because the non-response occurs due to multiple
factors, such as the attitude to the survey topic or any social norms, it has been proven
that the non-respondents tend to have different characteristics than the respondents, e.g.
they tend to show less organisational commitment, less job satisfaction, and have a
generally negative attitude (Simsek and Veiga, 2001: 224). In this respect, the non92
response bias was examined by using the extrapolation method of Armstrong (1976) – see
sub-chapter 4.1.2 for more details.
3.6 The qualitative interview as a method of validating a
questionnaire
While developing or adapting a questionnaire, it is highly recommended to pre-test it in
order to finalise its content and design (Dillman, 2000: 141). The purpose of the pre-test
is to identify if the questions are understood, the instructions are clear, and the order of
the questions is appropriate (Bourque, 2003: 90). During the pre-test, the response
categories, motivation, inconvenience, embarrassment, and scale difficulty are checked
(Dillman, 2000: 142). The preliminary questions are compared for mutual exclusiveness,
items are checked for possible eliminations or additions, and the language is adjusted to
the company terminology (Dillman, 2000: 67).
Although a number of different methods for the survey pre-test exist, the author chose the
qualitative face-to-face interview technique. This technique has a convincing advantage –
i.e. the personal interview is the richest of all the media – because it provides prompt
feedback and is very personalised, which facilitates understanding on the side of the
respondents (Simsek and Veiga, 2001: 221).
The strongest advantage of the qualitative pre-test is its setting – it does not require a
large number of subjects. The qualitative pre-test is also perfectly suitable for exploratory
research where new theory is created.
In this study, the primary focus of the qualitative interviews was on validation of the
activity list from both the content and outline angles. The main purpose was to validate
the list of activities that were worked out in subchapter 3.3, and to see whether they fully
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described the context of IT selling. With the qualitative interviews, the author aimed to
increase the face validity of the whole instrument and the research results as a whole.
The questionnaire pre-test originally comes from sociological studies which have
introduced elements of psychology over the last few years (Beatty, 2003: 3). Cognitive
interviewing was the technique used in the pre-test.
3.6.1 Cognitive interviewing as a qualitative questionnaire pre-test method
Cognitive survey research is an interdisciplinary technique that has existed since the
1980s and bridges two disciplines: survey methodology and cognitive psychology
(Holleman and Murre, 2008: 709).
The application of cognitive and communication theory to survey research aims to
understand and reduce survey error, and to facilitate the valid interpretation of responses
Cognitive interviewing is defined by Beatty and Willis (2007) as “the administration of
draft survey questions while collecting additional verbal information about the survey
responses, which is used to evaluate the quality of the response or to help determine
whether the question is generating the information that its author intends” (2007: 287).
The main advantage of the technique of cognitive interviewing is that it is a powerful
technique, and is high in face validity (Tourangeau, Rips, and Rasinski, 2000: 333).
However, cognitive interviewing, as any other methodological technique, has several
limitations. The first limitation is that the laboratory and field settings during face-to-face
interviews are different from those when the participants answer the final questions,
which may influence the results. The second limitation is the limitation of working
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memory, i.e. if an event left working memory, people cannot report it any more during the
interview (Beatty and Willis, 2007: 303). If an event is not remembered, it can also be
reported as seldom. This is named the effect of availability heuristic, and it was
introduced by Tversky and Kahneman in 1973 (Tourangeau et al., 2000: 137). The third
limitation is that it is impossible to ever state that the questionnaire is absolutely free of
“problems.” Every additional interview adds to the general understanding of the
questionnaire structure and provides new insights, and at the end it is assumed that, as the
result of cognitive interviewing, the questionnaire is freed from the “most egregious
problems evident in most participant cases” (Beatty and Willis, 2007: 303).
3.6.2 Introduction to the cognitive processes underlying question answering
Answering survey questions is a complex act. When a survey question is asked, it not
only awakens a number of associated stored memories of events about the question
subject, but also the related general knowledge, memory traces of related events, and
others, all of which contribute to the final result – the answer (Holleman and Murre, 2008:
717).
Tourangeau, Rips and Rasinski (2000) proposed the following four-stage model of a
survey response process:
1. Comprehension of a question;
2. Retrieval of memories or attitudinal information;
3. Derivation of a judgement; and
4. Mapping the judgement into a response option.
The first stage, comprehension “encompasses such processes as attending to the question
and accompanying instructions, assigning a meaning to the surface form of the question,
and inferring the question’s point – that is, identifying the information thought”
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(Tourangeau et al., 2000: 9). Comprehension is influenced by attentive listening or
attentive reading questions, unfamiliarity with the terms, complicated sentences, detailed
qualifications that are difficult to understand. In this way certain categories may be
misclassified (Tourangeau et al., 2000: 9).
Retrieval is the second step in the response model. It involves recalling relevant
information from one’s long-term memory and covers the processes of adopting a
retrieval strategy, generating retrieval cues, recollecting memories, and filling in partial
memories through inference (Tourangeau et al., 2000: 9). The quality and the
characteristics of the retrieval memory define the accuracy of the materials and
completeness of the response (Tourangeau et al., 2000: 9).
The third step of the model, judgement, “comprises the process that respondents use to
combine or supplement what they have retrieved” (Tourangeau et al., 2000: 10). For the
factual questions, such as on the total time spent during the reference period or the
number of hours worked, the respondent must sum up the individual events for the
retrieval period to find the total number (2000: 10). The retrieval of the events may be
generally difficult, and the respondent may tend to conclude that the events did not
happen at all (2000: 10).
During the judgement phase, one of the trade-offs the respondents may face is whether to
responding truthfully or not to the sensitive questions.
Over- or underreporting of socially desirable or undesirable behaviours is common.
Sensitive questions may evoke feelings of social (un)desirability, worries invasion of
privacy, and risk of disclosure to third parties (Tourangeau et al., 2000: 257). If a
participant is engaged in socially undesirable behaviour, he/she will have a choice to tell
the truth or not to tell it, but to appear in a more socially desirable category (Tourangeau
et al., 2000: 258). The existence of sensitive questions leads to a very high risk of
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producing missing data. Though, the existence of sensitive questions and the bias of their
non-response can be best cured by self-administration and promising survey respondents
of confidentiality (Tourangeau et al., 2000: 260).
Mapping the judgement into a response option is the last step of the model. In this step,
the respondents map the answer to the scale and edit the response for acceptability
(Tourangeau et al., 2000: 265). The boundaries between different options of the
questionnaire and meanings they underlie should be clearly described.
The techniques of “thinking aloud” as well as using probing questions may diagnose and
solve many of the potential problems stemming from question answering.
3.6.3 Using the “think aloud” technique and probing questions during
cognitive interviews
The “think aloud” paradigm describes the situation when the interviewer asks the
respondents to formulate all their thoughts that lead to the answer during the
questionnaire completion (Mittag et al., 2003: 57). To motivate the participants to “think
aloud”, the interviewer may ask the respondents to reflect about the situations and decides
which answer to choose (asking respondent to comment by “thinking aloud” and giving
the details). The “think aloud” transcripts or verbal reports are produced as a result of this
technique to get an insight into the thinking process of the participants while they are
completing the questionnaire (Beatty and Willis, 2007: 290).
The “think aloud” technique may be complemented by the use of probing questions to
find out how participants interpreted terms and question meanings (Beatty and Willis,
2007: 292).
97
Because using probing questions can be a outcome of a unique interaction between
interview partners, it is recommended they be carefully selected and only a few of them
be utilised per interview (Beatty and Willis, 2007: 293). Under no circumstances should
the probing questions suggest the “answer” (Beatty and Willis, 2007: 299). The probing is
to be implemented where some of the previous participants had difficulties interpreting
certain terms, and the probing aims to double check their understanding. These questions
are called “anticipated” probes. “Conditional” probes are the questions that are asked if
the participant hesitates about the answer. And the “spontaneous” probes are applied
without any pre-planning for spontaneous checks of the respondents’ understanding
(Beatty and Willis, 2007: 300).
Conrad and Blair (2009) recommended the following situation for the use of the
conditional probes. When a respondent cannot answer a question because the task is
difficult, or he/she does not know the answer, or the answers are accompanied with
uncertainty or pauses, then the respondent frequently uses “um”, “ah” or frequently
changes the answer (2009: 38). If the respondent indicates misconception or gives an
inappropriate response during the probing, the question should be a potential for
paraphrasing (Conrad and Blair, 2009: 38).
In some cases, reactive thinking may occur as a consequence of the use of probing
questions. The interviewer should manage the situation and reduce the concerns of his/her
counterpart (Conrad and Blair, 2009: 35).
3.6.4 Measures of cognitive processing
The common measures of cognitive processing in survey research can be verbal and nonverbal. The verbal feedback is recorded in the verbal report, the transcripts of the
interviews, reports on respondents’ thoughts, and the notes on negotiations about the
question meaning (Holleman and Murre, 2008: 720). However, not all aspects can be
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verbalised by the respondents. Therefore, observation of the respondents’ nonverbal
behaviour may be considered applicable (2008: 720). Reaction times serve as an
indication of the cognitive processes and are thought to reflect the cognitive difficulty of
the task because it takes more mental steps to retrieve the necessary information
The nonverbal methods also include eye-tracking, which is the observation of a
respondent’s fixation on a certain point; if it is long, it means more intensive cognitive
processing. For example, respondents may have longer fixation duration for difficult
words respondents have longer fixation durations (Holleman and Murre, 2008: 721).
Some other nonverbal sophisticated measurements are the use of physiological
measurements, such as brain imaging tools, functional Magnetic Resonance Imaging, and
heart rate measurements. These methods were not part of the research.
The verbal report as the main measurement instrument of the cognitive survey consists of
a) comments from the respondents on how they constructed their answers; b) explanations
of what they interpreted the question to mean; c) elaborations on any difficulties they had
had when answering questions; and d) other opinions on why they had answered this way
and not the other (Beatty and Willis, 2007: 288).
The verbal report of the participants certainly helps to decrease the measurement error
associated with surveys in general (Conrad and Blair, 2009: 33).
Eriksson and Simon (1993)’s theory of verbal report postulated on the possible sources of
error of verbal reports, i.e. that people can only report those events which they store in
their active working memory (Conrad and Blair, 2009: 34). The information is only
retrieved when it is accessible, i.e. if the respondent knows the immediate answer.
Pressing respondents to retrieve the information if “they do not have access to their
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thinking” may produce vague statements, and therefore allow for multiple interpretations
(Conrad and Blair, 2009: 35).
In order to avoid results like these, the interviewer should behave neutrally, not intervene,
and not indicate the presence of problems (Conrad and Blair, 2009: 35).
This study followed the described recommendations. All reactions, including memory
problems, were recorded.
3.6.5 Implementation of the cognitive interviews
The above-described methodology of cognitive interviewing was implemented during
face-to-face interviews in February 2010. The pre-test questionnaire was designed online,
and the participants were sent the link to the questionnaire by email before the interview.
Cognitive interviews are conducted usually with a small number of respondents (Conrad
and Blair, 2009: 33). Following the recommended number of pre-test interviews of 5 to
20, the author conducted 19 pre-test interviews with participants from the sample
company. The list of interviews can be found in Appendix 2.
Beatty and Willis (2007) recommended that the sample for the cognitive interview should
not be chosen out of convenience, but be randomly selected to ensure the
representativeness of the results. It is also strongly suggested that the participants of the
interviews cover most of the questionnaires’ conceptual domain, and come from diverse
fields of expertise (Beatty and Willis, 2007: 296; Dillman, 2000: 141).
As recommended, the interviewees came from different sales departments, performed
various sales roles, and sold different products to different customer sets. The author also
included several specialists from the sales support and sales controlling departments to
evaluate the non-selling terrain of the salespeople’s activities.
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Out of 19 interview participants, 15 were in a sales role (2 in a managerial position), 2 –
in a sales operation role, 1 – in a sales support role, and 1 was from the human resources
department. The participants were chosen randomly to ensure the representativeness of
the results.
During the interviews, the participants were asked to fill in the questionnaire online and to
“think aloud”: to verbalise their thoughts during its completion. During the pre-test, based
on the situation, the author asked several probing questions to ensure the proper
understanding of the questionnaire content.
The participants were asked if the activities on the list were applicable for their position,
company, and industry. During an interview, the author selected several activities where
the participants had to give bright examples of how they precisely do the task, or give an
example of any successful accomplishment of an activity. Several participants were
requested to describe their typical working day or/and week. The respondents had also to
indicate which of the activities were not present in the list, and what would they change in
the order of the questions.
3.6.6 Cognitive interview results
As a result of the cognitive interviews and analysis of verbal reports, the updated
questionnaire version was created and posted online to be used in the main survey (see
Appendix 4).
The following observations were made during the interviews:
a) the respondents were mostly interested in the topic and motivated to answer the
questions, many of them enjoyed talking about their experiences;
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b) the “social desirability” effect was clearly observed for many sellers, and as a
result, certain variables were overestimated (in a round 50% of cases);
c) several respondents had obvious difficulty with the terms and concepts of the study
as well as understanding several nontrivial terms in English; others (mostly with
higher educational levels) handled questions without any difficulties;
d) several respondents felt that the use of incentives would increase the response rate
greatly;
e) many respondents reacted emotionally to the questions about internal activities and
administration, which indicates that that most of the sellers were not comfortable
with these activities; another concern was associated with the questions on
Customer Relationship Management (CRM);
f) no difficulties were reported during the completion of the time allocation questions,
although some calculations in mind were still made (was this again an indication of
a social desirability effect?). The calculation looked as follows: generally,
salespeople started with the hourly evaluation of separate activities, then calculated
the total hours worked, corrected the total number of hours worked, and
accordingly corrected the hours reported for each of the activities;
g) some of the sellers said that time estimation answers were very; and
h) generally, all respondents tended to invest more effort in answering the
“frequency” questions about sales process terms (like “approach”, “pre-approach”)
than the other non-selling process questions, like planning or personal
development. Therefore, several anticipated probes were applied for the questions
of sales process to control the respondents’ understanding.
The following changes were made to the initial version of the questionnaire based on the
results of the pre-test (all question numbers refer to the initial questionnaire if otherwise
not indicated):
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a) the questionnaire header was amended with the content about the target audience
of the research, the message that the study was confidential and fully anonymous,
as well as with the reminder to click on the button “Finished” to save the answered
questions;
b) the company position examples were refined and amended for question 1;
c) the question on “manager/non-manager” was added to the questionnaire;
d) the question on seller education was added to the questionnaire;
e) questions 2, 3 about selling experience were paraphrased;
f) question 5 about the products that the specialists are selling was refined with other
products not mentioned in the previous questionnaire version;
g) for all of the frequency measurement variables, the explanation of the question
blocks and terms were introduced (questions 9 to 19) for example: “value-added
sales presentation – represent the activities of an actual sales call/presentation with
the customer”;
h) Web sales representatives were excluded from the sample size due to their very
special role;
i) no questions were marked as obligatory so as to make the respondents feel more
comfortable with responses;
j) the general question “please rate the frequency of use of the following sales
behaviours for your job” was reported by many sellers as being difficult to
understand quickly. Therefore, this question was changed to “how frequently do
are you do the following in your sales job?”
k) question block 9 “customer prospecting and qualification” caused the most
confusion. Therefore, it was almost totally reworked. The question block name was
changed to “locating potential buyers”, which was much clearer for most of the
sellers than the word “prospecting” (also for the reason that we talked mostly about
leads identification through effective contact management in this block). The
question “respond to referrals” was the stumbling block for many salespeople
because they did not understand the word “referrals.” For this reason, we changed
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this question to “respond to customer or prospect requests”; two questions “qualify
and select customers to call on” and “identify customers to retain or deleted from
your records” were put together under one question “qualify and select
contacts/customers in your territory to call on or deactivate from your records”
because both of the questions logically refer to one activity (the word “customers”
was also changed to “customers/contacts” as some of the sellers are responsible for
only one customer or for multiple small ones, and generally we are talking about
people – i.e. contacts); the question “identify new leads and potential buyers…”
was divided into three questions indicating different channels of buyer
identification (suggested by several respondents): “in social network”, ”in the
Internet”, and “through customer organisation”; the question “gather information
about the prospect’s name” was deleted because it was repeated in the other
questions; “involve telemarketers” was deleted as well because the sellers felt this
was not their task, but rather a managerial decision to involve the telemarketing
channel in selling; the question order was changed to appear more logical;
l) in question block 10 “pre-approach and sales call preparation” caused some
confusion. The word “pre-approach” was unfamiliar for many respondents because
it seemed “too theoretical” for them. We changed this name to “pre-sales call
preparation”; “collect more specific information on customer interests” was
perceived as being too general, and therefore was divided into two sections
indicating two sources of information identification: “in the Internet” and “in own
records” (based on the four salespeople input); “working with company databases”
was rephrased to “working with (company name) databases like CRM” to make it
clear that the internal customer information databases were meant in this context;
m) “approach and opening dialogue” was reworded to “opening dialogue with
customer approach” because it seemed to be more understandable for the
salespeople; the first question “customer-focused approach…” was amended to
“small talk” based on the input of three sellers describing their common way of
opening sales presentation; the question “peak interest approach: open with
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dramatic efforts such as shock, showmanship, or a gift” was not understood well
and was found to reflect the American culture and to be very uncommon in the
Swiss market. Therefore, it was deleted from the list;
n) the name of the question block 12 “sales presentation, demonstration and product
marketing” was misunderstood by the sellers who could not imagine the
combination of marketing with selling presentation in one question. As a
consequence, the name was reworded to “value-added sales presentation.” The
question “need satisfaction approach” was deleted from its construct because it
came up in “consultative selling”; “adaptive selling, consultative selling and valueadded selling” were all separated and put into the three different variables with a
detailed explanation of what they all mean; “preparing sales forecasts” was put to
the “planning” section of questions; “assessing and collecting market information
on customer site … ” was reworded to “document and report market and customer
information to company management and tools” and was moved to the logically
close construct of “internal activities”;
o) question block 13 “overcoming objections, problem solving” was paraphrased to
“handling customer objections” as the latter was better understood by the sellers;
“plan and prepare for objections” was moved to the “pre-sales call preparation”
block due to its logical closeness with this block; “reiterate the benefits of the
product” was adapted to “repeat the benefits of the product” as the English word
“reiterate” seemed to be too complex for the salespeople; “use consultative selling”
was deleted because it was mentioned in previous sections and was logically more
appropriate there;
p) in the “closing presentation” block, the author changed the question “finalisation of
the details of transaction” to “negotiate prices, terms, conditions, sign the contract”
because it provides a more detailed description of the deal close and increases
respondents’ understanding of what is meant by this expression; “confirm win-win
agreement on relationship and close” was reworded to “confirm agreement on
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achieving mutual goals in terms of long-term relationship” (“win-win” was
incomprehensible for the respondents);
q) in the 15th block, the author changed “writing thank-you and appreciation notes” to
“thank customer on the phone/by email/face-to-face” because it describes not only
the written way of communication, but includes also phone and face-to-face
interaction that is even more frequent and more personal than the written way;
“supervise installations” was amended to “supervise installation project” to cover
those of the salespeople selling technology services; “write up, handle and expedite
orders” was deleted because all of the sellers were not doing this in their job any
more; “arranging customer credit” was reworded to “arrange financing of customer
solution/product/service”; “train customer in product” and “deliver technical
workshops” was consolidated into one question “organise customer workshops and
training on product” because the sellers do not usually do the training themselves.
They only organise these events and act as an intermediary to other technical
departments of the company;
r) in construct block 16, “planning” the wording of “planning your relationship, sales
initiatives, select strategies” was modified to a more coherent one “develop your
long-term relationship, territory, sales initiatives plans and forecasts”, as this
represents the long-term dimension of a seller’s planning; “help clients plan” was
moved to the question block “sales presentation” because of the logical
constellations (as proposed by the interview respondents); “planning on quarter,
year, monthly basis” was deleted because was similar to other questions in the
block;
s) in block 17, the item “doing territory management” was deleted because it was not
applicable for the sellers responsible for one single customer and also had a similar
meaning to “planning territory”; “doing personnel tasks” was removed due to its
vague meaning; “socialise with (company name) employees” was added to the list;
“planning, working with distributors and channels: recruitment, development,
management and enablement of business partners” was perceived as too complex.
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Therefore, it was renamed to “work with distributors, channels and business
partners”;
t) in block 18, “personal development”, the item “learn software” was renamed to
“train your sales tools skills, like CRM” due to the close association of software as
the selling object; the new item “train product skills” was added as proposed by the
interview partners; “train new hires” was enlarged with “colleagues, doing
mentoring”;
u) in block 19, “travel”, the phrase “spend night on road” was perceived as culturally
inappropriate, and therefore was formulated to “overnight stay”;
v) in block 20, in the questions describing the importance of the activities we refined
the question instruction from “rate the importance of the following activities” to
“how important are the following activities to your sales success?”, as proposed by
several respondents;
w) an introduction for the time estimation questions was inserted; “customer day” was
changed to “customer visit day” to point out the meaning of a “customer” day as a
day when the customer presentation takes place;
x) question 24 on sales performance was edited in the following way: “contributing to
my sales unit targets revenue” was changed to “contributing to my sales unit
targets revenue/signings/gross profit” in order to make it more precise, which
targets are meant for the sellers depending on their sales role; “understanding
customer needs and work processes” was paraphrased to “understanding customer
business and requirements”; in “understanding company products and services”,
the test company name was given for the purpose of precision; “efficiently
providing feedback to management” was amended to “about customer and market”
to make it more understandable to make it clear what feedback is meant; and
y) one text-free field was added at the end of the questionnaire for potential
comments.
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4 EMPIRICAL STUDY RESULTS
4 Empirical study results
In this chapter, the results of the empirical study are outlined. In the first section, we
conduct initial data analysis describing the main characteristics of the data and the sample,
and analyse the descriptive statistics for the time allocation variables. The next section is
concerned with identifying and empirical validation of the salesperson activities model by
means of a factor analysis. Afterwards, a comparison is made on the activity domains
between top and bottom employees by means of an ANOVA analysis. And finally,
multiple regression is run to predict the value of sales performance from a set of
salesperson behaviour elements.
4.1 Initial data analysis
Initial data analysis aims to provide the first view of the character of data, to check the
existence of data bias, which underlie the further choice of research methods and validity
of the empirical results.
4.1.1 General sample information and socio-demographic data
A total of 285 salespeople were identified (initial sample size) as a target group for the
study – professionals with a primary sales role, including a client relationship and product
sales role. A total number of 122 completed questionnaires came back, which
corresponded to a response rate of 43%. The response rate of our study lied within the
average values of the similar studies in sales management according to the review of
Carter, Dixon, and Moncrief (2008), who analysed the studies conducted between the
years 1990 and 2005 – 46.1% (mean value) and 41.7 % (median). Considering the general
trend towards lower response rates in sales marketing research, and their tendency to be
108
lower than for nonsales marketing studies, the current study response rate seemed to be a
relatively good one (Carter, Dixon, and Moncrief, 2008: 412).
Figure 4-1 shows the main socio-demographic characteristics of the sample. As can be
seen, our sample consisted of 36% salespeople with a client relationship role, and 64%
with a product sales role selling different types of products. We had a proportionally high
amount of men (91%) compared to women (9%), and most of the sellers were not in a
managerial role (85%) compared to a relatively small proportion of sales managers (15%).
The educational level of our sample sales group was diverse with more than a half of
salespeople (58%) possessing a university degree.
Figure 4-1: Sample size characteristics
In our sample, the average seller was 42 years old (SD = 8.2 ranging from 27 to 58), had a
total sales experience of 12.8 years (SD = 7.7; ranging from 1 to 35 years), and
experience with the company of 8.9 years (SD = 6.3; ranging from 1 to 35 years).
109
4.1.2 Nonresponse bias examination
The examination of the nonresponse bias was conducted using the extrapolation method
of Armstrong and Overton (1977). This method is based on the assumption that there
might be differences in the personalities of the respondents and non-respondents, and
proposes that people who respond in later “waves“ are believed to have responded
because of the additional stimulus, and therefore, are expected to be similar to nonrespondents (Armstrong and Overton, 1977: 397).
In their study on key account managers’ behaviours, Guenzi, Pardo and George (2007)
analysed the differences between the first and last quartiles labelled as “early“ and the
“late” respondents by the means of ANOVA test run on the main variables of the study.
Following this approach, we conducted an ANOVA analysis of the “early repliers”
quartile and the “late repliers” quartile, and found no significant differences for the
majority of the variables (table 4-1). Only the variables “listen actively” and “ask
questions” were found to be significantly different at p < 0.05. Because these two
variables represented only 1.7 % of the whole group of variables, this finding did not raise
any concern for the further statistical analysis and did not threaten the external validity.
Therefore, we can conclude that our analysis was free of the nonresponse bias.
Table 4-1: ANOVA test of differences between the “early” and “late” repliers (selected
variables)
Variable
Total selling experience
Company selling experience
Age
Identify new leads through customer organisation
Identify new contacts in social network
Confirm agreement
Importance: sales presentation
Customer day: sales presentation
Office day: personal development
Average sales performance (multi-item)
F
.041
2.146
.105
.013
.870
.271
.924
.360
.007
3.367
110
Sig.
.839
.148
.747
.909
.355
.605
.340
.551
.926
.072
4.1.3 Data examination
As the first step in data analysis, the data obtained from the questionnaires were proofread
to avoid out-of-range entries or data errors. The non-population members were not
identified in the sample. The data were screened for outliers. Two outliers were found
(with z-scores greater than 3), but they were kept in the data set for two reasons: the
sensitivity of the multivariate methods to the sample reduction and the representativeness
of the outlier cases of the studied population.
The next step in the data analysis was the identification of missing values. As the missing
data represent a huge problem for most research statistics, it needs to be analysed in terms
of its types, extent, and a decision had to be made whether to ignore or impute these data
(Hair et al., 2006: 98).
Figure 4-2 shows the pattern of the existing missing data and the high concentration of
missing data in specific questions, i.e. – time estimation questions – mainly in question
blocks 24 and 25.
Figure 4-2: Missing data pattern
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Table 4-2 indicates the highest average amount of missing data (about 8%) in the time
estimation variables. This high proportion of missing data in the “time estimation”
questions may have come from the reluctance of the salespeople to make calculations in
mind or the inability to answer these questions.
Table 4-2: Missing data characteristics
Variables
Socio-demographic variables
Frequency variables
Importance variables
Time evaluation variables
Sales performance variables
Question
block(s)
2 -10
11 - 21
22
23 - 25
26
Average missing
data
.2%
1.5%
1%
8%
4%
Little’s MCAR test
df
Sig.
18
2
.000
2042
1894
.009
49
39
.127
33
34
.487
17
28
.983
χ2
As seen in table 4-2, the Little’s MCAR test results indicate the random character of the
missing data only in the socio-demographic and frequency estimation variables (MCAR –
Missing Completely At Random). The rest of the variables were shown to be MAR
(Missing At Random), which means that the missing data imputation for these variables
may result in bias (Hair et al., 2006: 98).
Based on the relatively low amount of missing data (under 10%), and its MAR character
for several independent variables, we did not impute them to avoid unexpected changes in
relationships. After the missing data were analysed, we investigated the descriptive
statistics of the variables in the study. The descriptive statistical summary for the
variables used in the study can be found in Appendix 5.
A graphical examination of the distributions, normal probability plots as well as the
skewness and kurtosis data showed that some of the several variables are negatively
skewed. However, as measures of normality, skewness and kurtosis “seem to be overly
sensitive” (Garson, 2010a).
112
The negatively skewed distribution for some variables, such as “build trust”, “ask
questions” is due to the nature of the variable itself, and is even a desirable outcome of
such a survey. It indicates the tendency of salespeople to evaluate the activities which
they find important as frequently done. This type of distribution may also be an indication
of a social desirability effect that is obviously unavoidable in such type of studies, in spite
of the fact that the study was conducted anonymously.
An unusual type of distribution was observed for the time allocation variables. Figure 4-3
shows the distribution of the variable “office day planning” as an example. As can be seen,
the values in the chart are mainly 0, 0.5, 1, 2, and 3 with 1 as a median. This type of
distribution indicates that the salespeople’s answers were only estimates, which means
that they were not precise enough. The issue of the validity of such a measurement, i.e.,
the measurement does not measure very precisely what it is intended to measure, may
endanger the statistical validity of the results.
Figure 4-3: Distribution of the time estimation variable (the example of the “office day
planning” variable)
113
The examination of the variable “sales job performance” used in our study, adopted from
the measurement of Jaramillo and Mulki (2008), and based on Piercy, Cravens, and Lane
(2001), showed the reliability of its composite scale of 0.76. The same Cronbach’s alpha
of 0.76 was reported by Jaramillo and Mulki (2008) for this scale.
4.1.4 Describing the time allocation of the salespeople
Figure 4-4 shows the graphical presentation of the descriptive statistics for the time
estimation variables. It can be seen that the average customer and office day last about 9.6
– 9.7 hours, and customer days constitute only 44% against 66% of office days.
Figure 4-4: Salesperson estimated time allocation during customer and office days
Customer days= 44% per month
office days = 66% per month
Average customer day: 9.7 hrs
Average office day: 9.6 hrs
2.1
1.8
1.4
1.5
1.4
Locating buyers
Pre-sales preparation
1.4
1.2 1.2
1.1
0.8
1.7
0.8
0.7
Sales presentation
Follow-up
0.9
Planning
0.6
0.5
Internal activities
Administration
0.2
0.2
Personal development
Travel
Customer day/ hrs
Office day/ hrs
It is obvious that salespeople spend their time during customer and office days differently.
A customer day is devoted to customer presentation (2.1 hours), travel (1.5 hours),
preparation (1.4 hours) and follow-up (1.4 hours). It is also a daily routine to spend some
amount of time on internal activities (1.1 hours), planning (0.8 hours), administration (0.8
hours), and prospecting (0.5 hours). An office day, on the contrary, is spent for internal
activities (1.8 hours) and administration (1.7 hours), pre-sales preparation (1.4 hours),
follow-up (1.2 hours) and planning (1.2 hours). Some time is still spent for sales
114
presentation (0.9 hours), either on the phone or on-site, and for travel. Personal
development activities usually take place during an office day.
Figure 4-5 depicts the calculated average of the time spent per month as a percentage of
all the activities against the importance estimations of these activities.10
Figure 4-5: Estimated average monthly activity time allocation versus activity
importance ratings
14.2
Time per month spent, %
Importance of the activity
15.0
14.9
13.9
12.8
10.5
7.8
6.3
5.9
6.0
6.0
6.4
5.9
5.7
4.5
Locating
buyers
Pre-sales
Sales
preparation presentation
Follow-up
Planning
4.5
4.7
3.7
Internal Administration Personal
activities
development
Travel
It is really striking to see that internal activities are the most time consuming occupation
of salespeople (15% of their monthly time). Almost the same amount of time is spent on
sales presentation (14.9%) and pre-sales preparation (14.2%). Follow-up (13.9%) and
administration (12.8%) conclude the first top five activities with the largest time
investment. Furthermore, the difference in time spent and the importance is the most
visible for internal activities and administration, which are considered to be less important
but most time consuming. This finding, together with some comments from the personal
interviews, indicates exactly how much time these activities add to the workload of the
salespeople. On the contrary, some activities were measured as very important, but the
10
The monthly time allocation was calculated based on the statistics of the customer/office day data, and the number
of customer/office days per month. Please note: due to the high estimation character of these data, they are only for
general evaluation purposes.
115
time invested in them was relatively small (locating customers – importance 5.9 and 6.3%
of time spent a month), planning (5.9 – 10.5%) and personal development (5.7 – 4.5%).
4.1.5 The evaluation of different measurement approaches of sales activities
The three main measurements of the selling activities, behaviours and selling techniques
used in the sales management research are: time allocation (e.g. Brashear et al., 1997),
activity importance (e.g. Jaramillo and Marshall, 2005), and activity frequency (e.g.
Moncrief and Marshall, 2006). Tables 4-3 and 4-4 contain examples of correlation
analysis of the main measurement methods: the importance evaluation, time evaluation
and frequency evaluation. Table 4-3 shows no correlation between the importance
measurement and time measurement, although there is a correlation of 0.281 and 0.275
between the importance and frequency measurements of sales presentation which is
significant at p < 0.01. No significant correlation was uncovered for the frequency – time
allocation measurements.
Table 4-3: Correlations of different measurement variations for the variable block
“sales presentation”
1
2
3
1. Importance:
1
Sales presentation
2. Customer day: sales
.088
1
presentation, hrs
3. Office day: sales presentation,
.150
.046
1
hrs
4. Customer-oriented
presentation, frequency, multi.281**
.034
-.115
scale
5. Product-oriented presentation,
.275**
-.056
.236*
frequency, multi-scale
6. Sales presentation, hrs per
.160
.631**
.676**
month
*. All coefficients (Pearson) significant at p < 0.05 (two-sided).
**. All coefficients (Pearson) significant at p < 0.01 (two-sided).
4
5
6
1
.436**
1
-.011
.054
1
Table 4-4 indicates some correlations between the importance measurement with all other
measurement types: time allocation and frequency multi-scale. However, the correlations
116
are not so high – only 0.227 and 0.219 for time measurement variables at p < 0.05, and
0.352 for the average frequency measurement scale. Again, no significant correlation was
found for the frequency – time allocation measurements.
Table 4-4: Correlation between different measurement variations for the variable
“personal development”
1
2
1. Importance: Personal
1
development
2. Customer day: personal
.227*
1
development, hrs
3. Office day: personal
.219*
.257**
development, hrs
4. Average of personal
.352**
0.053
development, frequency
5. Personal development,
.249**
.510**
hrs per month
*. All coefficients (Pearson) significant at p < 0.05 (two-sided).
**. All coefficients (Pearson) significant at p < 0.01 (two-sided).
3
4
5
1
0.168
1
.922**
0.125
1
Based on the above-mentioned analysis, we conclude that different measurements of sales
activities and sales behaviour are not very closely related. This might be the result of the
slightly different meanings that each measurement relies on. This is probably the reason
why different studies on selling behaviour and activities produced different results.
Nevertheless, the best possible correlations were received for the measurements of
importance and frequency. The importance measurement is a subjective evaluation of
what needs to be done to be successful. Frequency is more an objective measurement of
what event happened with which regularity in real life. Frequency has a less evaluative
character than importance (Moncrief, 1986: 263).
In many cases, we observed that the activity frequency was reported as high because of its
high importance. The meanings of these two dimensions are very close, but they are not
fully identical.
Taking into account the evaluative character of the time measurement method based on its
special art of distribution, the highest amount of missing data for the time estimation
117
variables, and its low to not existing correlations with other measures, we would propose
that this measurement be excluded in further studies on selling behaviours because of its
lower validity. Furthermore, time measurement studies may be conducted only for very
rough evaluation purposes, and their feed into the multivariate models is not
recommended.
4.2 Empirical analysis of the underlying structure of the
salespeople’s activities
4.2.1 Factor analysis methodology
The research questions on the proposed updated structure of the salesperson’s activities
and the sales process were analysed by means of statistical methodology and factor
analysis.
Factor analysis is an interdependence technique which is used to examine the underlying
structure, patterns or relationships for a large number of variables, and to determine a
smaller number of factors or components (Hair et al, 2006: 101). Factor analysis is a
commonly used technique for finding and determining not directly observable constructs
in complex social phenomenon (Norušis, 2003: 396).
Like the majority of statistical procedures, factor analysis has several assumptions. The
critical assumptions of are of a conceptual rather than a statistical nature – the existence
of a logical structure between the variables (Hair et al., 2006: 113). The interpretation of
factor analysis results needs to “have face validity and be rooted in theory” (Garson,
2010a). Other assumptions include correlations between variables, and the sample size
(Garson, 2010a), which has more cases than factors (Garson, 2010a). Principle
118
component analysis has no distributional assumptions and homoscedasticity is not
regarded as critical (Garson, 2010a).
In our study, we fulfilled all the necessary assumptions of the factor analysis: face validity
rooted in theory, a large number of significant correlations over 0.3 (Hair et al., 2006:
114), and a satisfactory sample size.
The following measures of the appropriateness of the factor analysis were estimated for a
good factor model fit: the KMO index and the results of the Bartlett's test of sphericity.
The KMO stands for the Kaiser-Meyer-Olkin measure of sampling adequacy. If the KMO
index is high – close to 1, all partial correlations are small, compared to the ordinary
coefficients, and the variables are linearly related (Norušis, 2006: 390). Small values of
KMO (those below 0.5 are generally unacceptable) indicate a lower appropriateness of
factor analysis, as the correlations between pairs of variables cannot be explained by other
variables (Norušis, 2006: 390).
The Bartlett's test of sphericity was conducted to test the null hypothesis that “the
observed data are a sample from a multivariate normal population in which all correlation
coefficients are 0” (Norušis, 2006: 390). In other words, this test shows how significant
the correlations in the factor matrix are. The test’s cut-off is p < 0.05 and was used for
testing the appropriateness of the analysis.
We utilised the principal component analysis with varimax rotation due to its wide
popularity and credit in social sciences. The principle component analysis forms linear
combinations of variables where the first principal component is the combination that
accounts for the largest amount of variance (Norušis, 2006: 394). A principal component
analysis with oblimin rotation was conducted as well and it showed similar results to
those received with the varimax rotation method.
119
The criteria used for determining the number of extracted factors were the visual
examination of the Cattell scree plot, the Kaiser criterion of an Eigenvalue of 1, and the
existence of meaning dimensions with a theoretical background (Garson, 2010a). The
purpose was to extract the meaningful factors, and in this case the variance explained was
taken into account, but was not the premium decision basis for the number of factors.
We analysed the variables in the preliminary proposed constructs (item batteries or
variable questionnaire blocks) and estimated the rotated matrix. If the solutions were not
satisfactory, several blocks were combined and the factor model was respecified. After
the satisfactory solutions had been received, the factors were labelled so that the labels
represented the common component solution, and were meaningful for all components.
This approach was chosen for two main reasons: theoretical and sample size
considerations.
The evaluation of the factor analysis results was primarily based on theoretical
considerations and finding meaningful factor solutions. As a rule of thumb, the factor
loadings should be a minimum of 0.5 to be practically significant; factor loadings in the
range between 0.3 and 0.4 are minimally acceptable to provide convergent validity (Hair
et al, 2006: 128). The loadings and communalities have to be interpreted in the light of
theory, and not rely only on cut-off measures (Garson, 2010b).
The evaluation of construct reliability was made by computing Cronbach’s alpha, where
0.6 may be satisfactory for exploratory research (Hair et al., 2006: 139). In the study of
Moncrief (1986) on sales activities, several alpha measurements were even at 0.5 level.
The unidimensionality of the dimensions was assessed by the widely used measure of
item-total correlations (Gerbing and Anderson, 1988: 186). Item-total correlations over
0.3 are regarded as acceptable, and over 0.4 as very good.
120
The discriminant validity may be important in factor solutions. It is concerned with the
“construct’s uniqueness when it captures some phenomena other measures do not” (Hair
et al., 2006: 778). This validity is checked from a logical perspective as well as
statistically – by the absence of cross-loadings over 0.4.
4.2.2 Factor analyses results
The factor analyses results are reported for each of the analyses of the proposed constructs,
and follow the variable structure of the survey questionnaire.
4.2.2.1 Factor analysis 1
In this factor analysis, we aimed to identify the factor structure for the “locate potential
buyers” item battery (questionnaire block 11). The factor analysis was initially run on all
of the items in the variable block. The factorability of the items was examined, and
proved to have a good appropriateness for the factor analysis: the KMO measure was
0.730, and the Bartlett’s test of sphericity was significant (χ2 (15) = 112.602), p < 0.001).
We decided to remove two items – “search company tools” and “respond to customer
requests” – because they did not contribute well to this factor solution and had crossloadings on two factors. Therefore, the model was respecified and analysed again.
The respecified factor model again evidenced a good factorability: the KMO measure of
0.750 and the significant result of the Bartlett's test of sphericity (χ2 (15) = 82.161), p <
0.001).
The factor analysis (table 4-5) showed one factor with 54% of variance extracted the
factor loadings over 0.70, reliability of 0.715 and item–total correlations, which indicated
a good construct fit.
121
Table 4-5: Factor analysis results 1
Component
1
Identify contacts and new leads
.782
from internet
Identify contacts and new leads
.752
customer organisation
Identify new contacts in social
.710
network
Qualify, select and deactivate
.701
contacts
Eigenvalue
2.172
Variance explained, %
54
Cronbach's alpha
.715
Four items in the factor analysis formed a unique factor solution. The factor content
includes the variables, which clearly represent the activities of contact and leads
identification from different sources, such as the Internet, customer organisation and
social network, as well as contact and lead qualification. Based on this description, we
labelled this factor as “Contact and lead management.”
The descriptive statistics for this factor (table 4-6) show that the identification of contacts
from the Internet is quite seldom (mean of 3.9) compared to the contact and lead
identification through the customer organisation (5.65) and social network (5.03).
The correlation matrix indicates significant correlations between all of the factor items,
from 0.321 to 0.418 at the level of p < 0.01. The item-total correlations exceed 0.4, which
suggests good unidimensionality.
122
“Contact and lead management”
Variable
Mean
SD
1
2
3
1. Identify new
leads and decisionmakers from
internet
3.90
1.870
1
2. Identify new
leads and decisionmakers through
customer
organisation
5.65
1.471
.418**
1
3. Identify new
contacts/leads in
social network
5.03
1.836
.401**
.350**
1
4. Qualify, select
and deactivate
contacts
4.74
1.712
.389**
.395**
.321**
**. All coefficients (Pearson) significant at p < 0.01 (two-sided); N = 116.
Corrected
item - total
correlation
4
.559
.522
.478
1
.464
In the second factor analysis, we examined the factor structure for the “pre-sales
preparation” item battery (questionnaire block 12). The factor analysis was conducted on
all of the items in this variable block, including the two items “search company tools” and
“respond to customer requests.” They were removed in the factor analysis due to their
logical and theoretical closeness with the construct.
The factorability of the factor solution showed the good appropriateness of the factor
analysis: the KMO measure was 0.762, and the Bartlett’s test of sphericity was significant
(χ2 (45) = 376.982) at p < 0.001. The analysis identified three factors. Due to crossloadings of the variable “engage pre-sales support”, it was removed from the model, and
the model was respecified without this item.
Table 4-7 represents the results of the factor analysis for this construct.
123
Components
1
2
3
Prepare meeting guidelines
.865
.064
.145
Tailor sales presentation
.841
-.073
.102
Respond to customer requests
.739
.018
-.063
Prepare for objections
.750
.138
.062
Arrange call/interview
.664
.091
.159
Work with DBs to gain customer
.048
.836
.192
.101
.821
.181
.025
.818
-.031
.199
.062
.807
.022
.200
.802
Eigenvalue
3.392
2.121
1.099
34
21
11
Cronbach's alpha
.813
.773
.530
information
Work with DBs to input customer
information
Search company tools for new
contacts
Collect more specific info in own
records
Collect more specific info in
internet
The initial Eigenvalues show that the first factor explained most of the variance – 34%,
the second – 21%, and the third – 11%. Internal consistency was examined for each of the
constructs; the Cronbach’s alpha was good for the first and second factor, and quite low
for the third factor. Probably, the number of items was too small for this factor.
Nevertheless, some of the reliability measures in the study of Moncrief (1988) indicated
values of about 0.5, and apparently, can appear in exploratory studies.
Five components in the first component formed a unique solution. The factor content
included the variables which describe activities of sales call preparation: “preparing
meeting guidelines”, “tailoring presentation”, “preparing for objections”, “arranging
interview.” This description clearly proposes the factor label “Sales call preparation” for
this new construct.
124
The descriptive statistics for this factor (table 4-8) show that all activities in this factor are
performed very frequently. The most frequent is “preparing meeting guidelines” (mean of
6.4). The correlation matrix indicates mutual correlations from 0.33 to 0.71 at the level of
p < 0.01, and item-total correlations exceeding 0.5.
“Sales call preparation”
Variable
Mean
SD
1
2
3
4
1. Prepare
meeting
guidelines
6.40
.941
1
2. Tailor sales
presentation
6.28
1.060
.713**
1
3. Prepare for
potential
objections
6.03
1.054
.654**
.536**
1
4. Respond to
customer
requests
6.19
1.141
.490**
.474**
.420**
1
5. Arrange
call/interview
6.28
1.125
.488**
.459**
.330**
.394**
**. All coefficients (Pearson) significant at p < 0.01 (two-sided); N = 116
Corrected
item - total
correlation
5
.772
.708
.603
.566
1
.529
The second component in the factor solution clearly describes the activities of working
with company databases and tools in searching for contact and lead information. In this
way, we labelled this factor “Database management.”
The descriptive statistics for this factor (table 4-9) indicate that the activities in this factor
are performed less frequently than expected – the frequency means are around 4.
Obviously, the salespeople do not often work with databases to input customer
information (the lowest mean of 3.76). This is not a surprising finding. In the personal
interviews, salespeople said they thought database work was an unimportant
administrative task. This may indicate that sales and marketing technology is still not
widely accepted in the sample company.
The correlation matrix indicates a solid mutual correlation from 0.467 to 0.639 at the level
of p < 0.01, and large inter-item correlations (over 0.5).
125
“Database management”
Variable
Mean
SD
1
2
1. Work with DBs
to gain customer
4.02
1.734
1
information
2. Work with DBs
to input customer
3.76
1.635
.639**
1
information
3. Search company
tools for new contacts
4.00
1.810
.501**
.467**
**. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 120.
Corrected
item - total
correlation
3
.661
.635
1
.534
The third component in the factor solution includes the variables which clearly represent
the activities of collecting more specific information about customers who cannot be
found in company databases or who could be identified by means of personal contacts or
through customer organisation. The label for this factor could be “Other information
gathering.”
The descriptive statistics (table 4-10) show that the activities of this factor were
performed at the approximately equal “middle” frequency of about 5.4. Surprisingly,
these activities are more frequently performed as database information gathering. This
again may indicate the low acceptability of sales automation trend and reliance on
privately kept information.
The correlation matrix indicates significant correlation from 0.374 between these
variables at the level of p < 0.01. The item-scale correlations exceed the level of 0.3, and
indicate the lower border of acceptance for unidimensionality.
126
“Other information gathering”
Variable
Mean
SD
1
1. Collect more
specific
information in
internet
5.45
1.560
1
2. Collect more
specific
information in
own records
5.47
1.198
.374**
Corrected item
- total
correlation
2
.374
1
.374
In the third factor analysis, we searched for the factor structure for the “sales
presentation” construct (questionnaire blocks 13, 14, 15). The factor analysis conducted
separately on variable blocks 13 “opening dialogue”, 14 “value-added presentation” and
“handling customer objections” did not show consistent results. Therefore, the variable
blocks were put together. In addition, Moncrief and Marshall (2005) suggested that the
boundaries between “opening dialogue”, “main presentation” and “overcoming
objections” are disappearing since the focus of presentations content is problem-solving
and the sales presentation is no longer just a single act, but a series of events (2005: 17).
A factor analysis was run on all of the items in these three variable blocks. The variables
“submit bids” and “sell value of company solutions” were removed due to the crossloadings in order to ensure factor discriminant validity.
The factor solution demonstrated the “marvellous” appropriateness of the factor analysis:
the KMO measure was 0.867, and the Bartlett’s test of sphericity was significant (χ2 (66)
= 748.640) at p < 0.001.
The analysis identified two factors. Table 4-11 represents the results of the factor analysis
for this construct.
127
Components
1
2
Listen actively
.877
.203
Ask questions
.873
.133
Build trust
.822
.219
Use consultative selling
.784
.139
Use adaptive selling
.721
.279
Clarify objections
.693
.198
Help clients plan
.641
.158
Customer focused approach of
.616
-.014
.520
.123
Repeat the benefits of the product
.139
.867
Product-benefit approach of opening
.080
.826
.305
.768
Eigenvalue
5.668
1.641
47
14
Cronbach's alpha
.885
.783
opening interview
Statement approach of opening
interview
interview
Product benefit approach
presentation
As can be seen from table (4-11), the initial Eigenvalues show that the first factor explains
47% of the variance, and the second – 14%. The reliability was estimated as good for
each of the factors: 0.885 and 0.783 correspondingly.
The first component in the factor solution includes those factors which clearly represent
the activities of sales call/presentation focused on the customer, i.e. active listening,
consultative selling, customer-focused sales interview opening. We labelled this factor
“Customer-oriented sales presentation.”
The descriptive statistics (table 4-12) for this factor (table 4-13) show that the activities in
this factor are performed very frequently, in particular listening (6.6), building trust (6.6),
asking questions (6.6), and consultative selling (6.21). These activities are the most
frequently performed of all those listed in the study. The correlation matrix indicates
128
mutual correlations mostly over 0.3, and all of them are significant at the level of p < 0.01,
as well as item-total correlations over 0.45.
“Customer-oriented sales presentation”
Variable
Mean
SD
1
2
3
4
5
6
7
8
1. Listen
actively
6.60
.811
1
2. Build
trust
6.60
.899 .754**
1
3. Ask
questions
6.60
.911 .870** .693**
1
4. Use
consultativ
e selling
6.21 1.097 .640** .569** .713**
1
5. Use
adaptive
selling
5.80 1.241 .594** .651** .568** .518**
1
6. Clarify
objections
6.04 1.028 .533** .562** .566** .473** .431**
1
7. Help
clients plan
5.65 1.416 .521** .397** .541** .410** .474** .513**
1
8.
Statement
approach
of opening
dialogue
5.59 1.231 .424** .443** .363** .223* .362** .258** .272**
1
9.
Customerfocused
approach
of opening
interview
5.94 1.330 .460** .429** .385** .370** .412** .261** .333** .403**
Corre
cted
item total
correl
ation
9
.835
.777
.798
.683
.691
.621
.584
.456
1
The second component in the factor solution includes the variables that obviously
describe the activities of sales call/presentation that focus on the product and offering
instead of the customer. We labelled this factor “Product-oriented sales presentation.”
The descriptive statistics show that the activities in this factor are performed less
frequently than the customer-oriented selling activities. The product-oriented opening of
129
.519
the sales interview (4.9) is the least common among the salespeople, especially compared
to the customer-focused opening (5.9).
The product focus of presentation has changed its primary role over the last few decades.
Customer-orientated selling is now more common. Nevertheless, this does not mean that
product-orientation is totally out-of-date. Especially in the technology market, the high
degree of technological uncertainty of complex technological products and solutions
should be reduced by clear communication of the product or solution benefits (Mohr et al.,
2005: 27).
“Product-oriented sales presentation”
Variable
Mean
SD
1
2
1. Repeat the benefits of
the product while
5.21
1.513
1
handling objections
2. Product benefit
focused presentation
5.14
1.414
.602**
1
3. Product benefit
approach (opening)
4.90
1.727
.573**
.488**
Corrected item
- total
correlation
3
.606
.617
1
.594
The correlation matrix (table 4-13) indicates mutual correlations equal to and over 0.488.
All of them are significant at the level of p < 0.01. The item-total correlations exceed 0.5,
which suggests good unidimensionality.
130
In this factor analysis, we aimed to identify the factor structure for the “sales presentation
close” item battery (questionnaire block 16). The factor analysis was run on all of the
items in this variable block.
The factorability of the items was examined, and it proved to have a good appropriateness
for the factor analysis: the KMO measure was 0.691, and the Bartlett’s test of sphericity
was significant (χ2 (6) = 80.833), p < 0.001). The factor analysis results (see table 4-14)
showed one factor with 52% of variance extracted and reliability of 0.670, which
evidence of a satisfactory construct fit.
Component
1
Negotiate prices, terms, conditions
.788
Confirm agreement on mutual goals
.749
Provide solutions for customer
.712
problems
Ask directly for the order
.638
Eigenvalue
2.096
52
Cronbach's alpha
.670
The descriptive statistics (table 4-15) show that the salespeople tend to provide solutions
to customer problems as a part of closing actions (6.33), and rarely ask directly for the
order (4.59). This is not surprising in view of the high popularity of the “solution-selling”
paradigm. The correlation matrix indicates that the majority of mutual correlations are
over 0.3, and all of them are significant at the level of p < 0.01. The item-total
correlations exceed 0.4, which suggests good unidimensionality.
131
“Sales presentation close”
Variable
1. Negotiate prices, terms, conditions
2. Confirm agreement on mutual goals
3. Provide solutions for customer
problems
Mean
5.50
5.93
SD
1.614
1.305
6.33
.936
1
1
.450**
2
3
Corrected item total correlation
.567
.467
4
1
.331** .445**
1
4. Ask directly for the order
4.59 1.858
.478** .237* .241**
.450
1
.407
In this factor analysis, we identified the factor structure for the “follow-up” block of
variables (questionnaire block 17). The factor analysis was run on all of the items in this
variable block. The variables “invite clients on site” and “handle customer complaints”
were removed due to the existing cross-loadings. The factorability of the items was
examined, and it proved to have good appropriateness for the factor analysis: the KMO
measure was .738, and the Bartlett’s test of sphericity was significant (χ2 (21) = 175.245),
p < 0.001). The factor analysis (table 4-16) showed two-factor solution with total of 58%
of variance extracted, 41% for the first factor and 17% for the second one.
Components
1
2
Check customer satisfaction
.820
.167
Thank customer
.767
-.046
Arrange financing
.665
.321
Socialise with customer
.648
.275
-.055
.860
Organise customer workshops
.279
.652
Supervise installations
.250
.633
Eigenvalue
2.844
1.197
41
17
Cronbach’s alpha
.733
.603
Perform maintenance
132
The first component in the solution combines the variables that obviously describe the
activities of sales follow-up and relationship maintenance activities, such as thanking and
socialising with the customer, checking customer satisfaction, etc. We labelled this factor
as “Customer relationship maintenance.” The reliability analysis shows that the scale is
consistent enough (.733) to satisfy the conditions for a good construct validity.
As seen from the table (4-17), the salespeople frequently thank customers (mean of 6.41),
and rarely socialise with customers (4.76) and arrange customer financing (mean of 4.53).
The correlation matrix (table 4-17) indicates that the majority of mutual correlations are
over 0.3, and all of them are significant at the level of p < 0.01. The item-total
correlations exceed 0.5, which suggests good unidimensionality.
“Customer relationship maintenance”
Variable
Mean
SD
1
2
3
1. Check customer
satisfaction
5.64
1.264
1
2. Arrange financing
for customer
4.53
1.996 .495**
1
3. Socialise with
customer
4.76
1.572 .501**
.412**
1
4. Thank customer
6.41
.989 .532**
.411**
.338**
Corrected item
- total
correlation
4
.633
.552
1
.527
.509
The second component of the factor solution combines the variables which clearly feature
the activities of the technical part of the sales follow-up, i.e. technical maintenance
activities, organising workshops with customers and supervise installations. Therefore,
the factor name could be set as “Technical support and maintenance.” This factor is very
similar to the factor “product support” in the study of Moncrief (2006).
133
The reliability analysis shows that the scale is satisfactory enough for exploratory analysis
(0.603), although it might be enhanced with more items in further research to strengthen
its reliability.
The descriptive statistics show that the salespeople in our study are not often involved in
technical support activities, because of the specialised nature of these functions. However,
face-to-face contact with the customer at this stage may actually be needed to increase the
effectiveness of the follow-up activities.
The correlation matrix (table 4-18) indicates that the majority of mutual correlations are
around 0.3, and all of them are significant at the level of p < 0.01. The item-total
correlations are close to 0.4, which suggests sufficient unidimensionality.
“Technical support and maintenance”
Variable
1. Perform maintenance
Mean
SD
1
2
3.01
1.765
1
5.00
1.722
.379**
Corrected
item - total
correlation
3
.447
2. Organise customer
workshops
3. Supervise installations
4.77
1.547
.365**
1
.287**
.400
1
.391
This factor analysis was conducted for the variables of the questionnaire construct “sales
planning.” The KMO measure of 0.812 and the significant result of the Bartlett's test of
sphericity (χ2 (3) = 121.521), p < 0.001) demonstrated the “marvellous” appropriateness
of factor analysis for this construct.
The analysis (table 4-19) identified one single factor with an Eigenvalue of 2.192. This
factor is truly unique, describing the job aspects of customer and internal planning
procedures of the salespeople surveyed in this dissertation
134
Component
1
Plan internal meetings
.867
Plan customer calls and visits
.861
Develop long-term plans
.836
Eigenvalue
2.192
73
Cronbach's alpha
.812
The descriptive statistics (table 4-20) show that the salespeople plan very frequently, with
the highest frequency estimate of 6.04 for planning customer visits and calls. The
correlation matrix indicates that the majority of mutual correlations are over 0.56, and all
of them are significant at the level of p < 0.01. The item-total correlations exceed 0.6,
which suggests very good unidimensionality.
“Sales planning”
Variable
1. Plan internal meetings
2. Plan customer calls and
visits
Mean
SD
1
2
Corrected
item - total
correlation
3
5.87
1.147
1
.685
6.04
1.130
.625**
1
3. Develop long-term plans
5.66
1.318
.577**
.562**
.673
1
.638
In the seventh factor analysis, we searched for the factor structure for the “internal
activities and administration” construct (questionnaire variable block 19). The factor
analysis was run on all of the items in this variable block. The factorability of the factor
solution showed the “good” appropriateness of the factor analysis: the KMO measure was
0.723, and the Bartlett’s test of sphericity was significant (χ2 (21) = 177.018 at p < 0.001.
135
The analysis identified two factors. The table (4-21) represents the results of the factor
analysis.
Components
Variable
1
2
Document, report market information
.751
.104
Participate in internal meetings
.747
.100
Do administration
.670
.175
Coordinate aligned team
.527
.294
Work with distributors
.080
.848
Socialise with internal specialists
.156
.815
Coordinate orders, delivery
.291
.643
Eigenvalue
2.857
1.112
41
16
Cronbach's alpha
.694
.706
As can be seen from table (4-21), the initial Eigenvalues show that the first factor explains
the 41% of the variance, and the second – 16%. Internal consistency is evaluated as good
for each of the factors, with alpha values of 0.694 and 0.706 correspondingly.
The first component of the factor solution combines the variables which clearly describe
the activities of the administration and internal team coordination. Therefore, the factor
name could be set as “Internal coordination and administration.”
The descriptive statistics for the items of this factor (table 4-22) show that the salespeople
frequently do all the internal activities, especially participate in internal meetings (5.91).
This conclusion confirms the earlier finding on the salespeople’s time allocation, where
the internal activities are ranked in first place in the time budget of the salespeople. The
correlation matrix indicates the majority of mutual correlations are round 0.3, and all of
them are significant at the level of p < 0.01 and the item-total correlations over 0.3. This
also proves to be a good factor model fit for this factor solution.
136
“Internal coordination and administration”
Variable
1. Coordinate aligned team
2. Participate in internal
meetings
3. Document, report market
information
Mean SD
5.45 1.522
1
2
3
Corrected
item - total
correlation
.346
4
1
5.91 1.017
.441**
1
.488
5.25 1.537
.291**
.301**
1
4. Do administration
5.80 1.291
.121
.338**
.483**
.505
1
.402
The second component of the factor solution unites the variables that describe the
activities of building partnerships, i.e. with distributors and internal specialists, as well as
the activities of the delivery coordination. Therefore, the factor name could be set as
“Internal partnership management and delivery.”
The descriptive statistics for this factor (table 4-23) show that salespeople perform these
activities with average frequency, mostly “socialisation with internal specialists” (5.51).
The correlation matrix indicates correlations over 0.403 at p < 0.01, and the item-total
correlations over 0.4, which proposes a good fit.
“Internal partnerships and delivery”
Variable
Mean
SD
1
2
1. Work with
distributors
4.68
1.987
1
2. Coordinate orders,
delivery
4.68
1.806
.458**
1
3. Socialise with
internal specialists
5.51
1.335
.510**
.403**
3
.575
.497
1
.546
This factor analysis was conducted for the variables of the questionnaire construct
“personal development.” The KMO measure of 0.821 and the significant result of the
137
Bartlett's test of sphericity (χ2 (15) = 253.303), p < 0.001) demonstrated the “marvellous”
appropriateness of factor analysis for this construct.
The analysis (table 4-24) identified one single factor with an Eigenvalue of 3.185. This
factor describes the activities of personal development, such as trainings, visiting
conferences, and studies of market trends. The explained variances of 53%, as well as the
high value of the Cronbach’s alpha indicate good construct convergent fit.
Component
1
Participate in sales trainings
.806
Set up and visit conferences
.791
Train tool skills
.772
Train product skills
.766
Study market trends
.608
Train new hires
.599
Eigenvalue
3.185
53
Cronbach's alpha
.821
Table (4-25) shows descriptive statistics and correlation matrix for the items of the factor
construct. As can be seen, the majority of the correlations exceed 0.3. The frequencies of
all the item activities are estimated as average – neither frequent nor seldom. The least
frequently performed activity is “training tool skills” (3.74), which again indicates the low
acceptance of sales technology among sellers. The most frequently performed is “training
of product skills” (5.08). The item-total correlations for this block exceed 0.4, which
suggests good unidimensionality.
138
“Personal development”
Variable
1. Participate in
sales trainings
2. Set up and visit
conferences
3. Train tool skills
Mean
SD
1
2
4.72
1.317
1
4.46
1.460
.575**
3
4
5
Correc
ted
item total
correla
tion
6
.662
1
3.74
1.487
.658** .454**
1
4. Train product
skills
5.08
1.446
.606** .518** .533**
1
5. Study market
trends
5.03
1.387
.321** .509** .309** .304**
6. Train new hires
4.58
1.513 .298** .387** .355** .358**
.660
.626
.616
1
.413**
1
.475
.466
In factor analysis 9, we analysed the variables “travelling out of town” and “overnight
stays” for a factor structure. The factorability of the factor solution showed a very low
appropriateness of the factor analysis for these variables: the KMO measure was 0.500,
though the Bartlett’s test of sphericity was still significant (χ2 (1) = 4.63 at p < 0.05). The
correlation matrix shows a very small correlation between the variables (0.2) and at p <
0.05. Taking this finding into account, we could not form any factor with these variables.
The low correlations between variables can be explained in the following way. We see in
table 4-26 that although the sellers travel quite often (5.73), they stay overnight very
seldom (3.15). The item battery was adopted from the study of Moncrief and Marshall
(2006), where the sample consisted of US salespeople, where travelling by plane and
stays in the hotels are common. On the contrary, due to the small size of the country, the
Swiss salespeople work in a very limited geographic area. Therefore, they do not usually
travel long distances (although they travel frequently, but shorter distances), and in this
way, stay in hotels very seldom.
139
“Travel”
Variable
Mean
SD
1
1. Travel
5.73
1.328
1
2. Overnight stays
3.15
1.556
.200*
*. All coefficients (Pearson) significant at p < 0.05 (two-sided), N = 120.
2
1
.192
.192
The factor analyses results described above show the total existence of 13 factors
underlying the salesperson activities in the sample company. Table (4-27) summarises all
the factor solutions indicating their factor loadings and Cronbach’s alpha values.
Table 4-27: Summary of factor solutions
Factor
Contact and lead management
(α = .715)
1. Identify new contacts in social network
2. Identify new leads through customer
organisation
3. Qualify, select and deactivate contacts
4. Identify new leads from internet
Factor
Other information gathering
(α = .530)
1. Collect more specific information in
internet
2. Collect more specific information in
own records
.782
.752
.710
.701
Factor
Database management
(α = .773)
1. Work with DBs to gain customer
information
2. Work with DBs to input customer
information
3. Search company tools for new contacts
.836
.836
.821
.807
Factor
Sales call preparation
(α = .813)
1. Prepare meeting guidelines
.865
.802
2. Tailor sales presentation
.841
3. Prepare for potential objections
4. Respond to customer requests
5. Arrange call/interview
Factor
Product-oriented sales presentation
(α = .783)
1. Repeat the benefits of the product while
handling objections
2. Product-benefit approach (opening)
3. Product-benefit focused presentation
.750
.739
.664
Factor
Customer-oriented sales presentation (α
= .885)
1. Listen actively
.877
2. Ask questions
3. Build trust
4. Use consultative selling
5. Use adaptive selling
.873
.822
.784
.721
6. Clarify objections
.693
7. Help clients plan
.641
8. Customer-focused approach of opening
sales interview
9. Statement approach of opening dialogue
.616
.520
140
.867
.826
.768
Table 4-27: Summary of factor solutions (continued)
Factor
Sales presentation close
(α = .670)
1. Negotiate prices, terms, conditions
2. Confirm agreement on mutual goals
3. Provide solutions for customer
problems
4. Ask directly for the order
Factor
Technical support and maintenance
(α = .603)
1. Perform maintenance
2. Organise customer workshops
3. Supervise installations
.788
.749
.712
Factor
Customer relationship maintenance
(α = .733)
1. Check customer satisfaction
2. Thank customer
3. Arrange financing for customer
.820
.767
.665
.638
4. Socialise with customer
.648
Factor
Internal partnership
management and delivery (α = .706)
1. Work with distributors
2. Socialise with internal specialists
3. Coordinate orders, delivery
.848
.815
.643
Factor
Personal development
(α = .821)
1. Participate in sales trainings
2. Set up and visit conferences
3. Train tool skills
4. Train product skills
5. Study market trends
6. Train new hires
.806
.791
.772
.766
.608
.599
.860
.652
.633
Factor
Internal coordination and
administration (α = .694)
1. Document, report market information
2. Participate in internal meetings
3. Do administration
4. Coordinate aligned team
.751
.747
.670
.527
Factor
Sales planning
(α = .812)
1. Plan internal meetings
.867
2. Plan customer calls and visits
.861
3. Develop long-term plans
.836
Note. The following items were removed from the factor structures for different statistical
reasons: “engage pre-sales support”, “submit bids”, “sell value of company solutions”,
“invite clients on site”, “handle customer complaints”, “travel”, and “overnight stays”.
141
4.3 Analysis of the differences in sales activities of top and bottom
performers
4.3.1 Methodology of one-way analysis of variance ANOVA
One-way analysis of variance (ANOVA) was used in this study to test the null hypothesis
that the group means of top and bottom performers are equal. In case of two groups, the
one-way ANOVA test results correspond to those of the two-independent-samples t test.
Either of the tests can be generally chosen for this purpose, but we chose the ANOVA.
The approach of comparing the bottom and top performers in sales research is not totally
new. The studies of Jaramillo and Marshall (2005), as well as Dwyer (2000) used this
technique in identifying the preferences of top vs. low performers for different selling
techniques. The two group comparison allowed us to work out what activities can be
associated with high sales performance, and where the top and bottom performers do not
differ.
We divided all the subjects in this study into two observational categories: bottom and top
performers based on their average performance. We defined the bottom performers as
those subjects who fall into the first quartile of population (n = 28), and correspondingly,
the top performers as those who fall into the fourth quartile (n = 34). A comparison was
made between the socio-demographic variables, variables of factor items, variables of
importance evaluation, and the variables of time use evaluation.
Before conducting the ANOVA, the assumptions of this statistical procedure were tested.
The main assumptions of ANOVA are the independence and randomness of samples,
equal variances of populations, and multivariate normality (Norušis, 2006: 143).
142
The first assumption, the independence of observations, is generally fulfilled by a
properly conducted research design, when the groups of subjects are not treated
differently, or the observations are made at the same time (Norušis, 2006: 143). This
assumption was fulfilled for this study.
The assumption of residual normality is widely discussed in statistical literature. The
ANOVA and two-sample t test are supposed to be robust against moderate violations of
normality (Garson, 2010). Ramsey and Schafer (2002) also consider estimates of the
coefficients and their standard errors to be robust against violations of normal distribution
(2002: 211). The normality violations are viewed to be robust even for fairly sample sizes
(Online Discussion about checking data for compliance with a normality assumption of
East Carolina University).
In our study, this assumption was checked by the graphical inspection of the quantilequantile (Q-Q) plot of residuals. The graphics showed the absence of strong violations of
normality.
The assumption of equal variances in all groups depends on the equality of the sample
sizes (Norušis, 2006: 144). The groups should be approximately the same size. In our
study of top and bottom performers, the sample of top performers consisted of 34 cases,
whereas the sample of bottom performers contained 28 cases. The values varied slightly
depending on the existence of missing data in certain variables.
The equality of variances assumption was checked by conducting the Levene test. The
Levene test showed that a small number of variables violated this assumption, such as
“prepare meeting guidelines“, and “train product skills.”
In case of violation of the equality of variances assumption, it is recommended to evaluate
the original p value, and, if it is too large or too small, it would probably not impact on the
143
conclusions (Norušis, 2006: 144). One could conduct the Welch and Brown-Forsythe
tests, which do not require the equality of population variances (and uses median instead
of mean), or compute the Kruskal-Wallis test, and compare the results with violations
with the results of a test with less stringent assumptions (Norušis, 2006: 144, 148).
Kruskal-Wallis is the nonparametric one-way analysis of variance by ranks, and it
compares the medians of two or more groups (Norušis, 2006: 458). Although the
nonparametric tests are less sensitive on data quality, and do not impose on the normality
or homogeneity assumption, they are less powerful, and the p value tends to be higher
than in its parametric equivalent (Vaughan, 2001: 153).
We conducted the Kruskal-Wallis test for all the variables in the study. As expected, the
test delivered more or less the same results as the ANOVA test.11
Generally, in case of assumption violations, the nonparametric test may be appropriate,
However, most of the assumptions of ANOVA are robust against distributional and
equality of variances violations (Zar, 1996: 128).
Taking these considerations into account, we suggest the results of ANOVA can be fully
trusted.
11
For example, the Levene test indicated the violation of the equality of variances assumption for the variable “train
product skills” (p < 0.05). ANOVA result indicated F = 8.68 significant at p < 0.01. The Kruskal-Wallis test also
showed the significance of the variable „train product skills” (χ2 = 7.600, p < 0.01), as well as the robust tests for the
equally of means Welch/Brown-Forsyth show the value of 9.11 significant at p < 0.001. Based on the results of these
tests, we conclude that there is a strong association between this variable and performance at the level of p < 0.01,
and the ANOVA result can be fully trusted, even with the existing assumption violations.
144
4.3.2 Results of the statistical analysis using the one-way ANOVA
We conducted the ANOVA test on all the “frequency” variable constructs, “importance”
variables and time estimation variables. The results of the one-way ANOVA are
presented in 16 tables.
Table 4-28 shows the results of the ANOVA test for the socio-demographic variables.
Table 4-28: One-way ANOVA results for the socio-demographic variables
Low
performers
ANOVA
Variable and Source
Total Selling
Experience
Between groups
Within groups
Total
Company
Selling
Experience
Between groups
Within groups
Total
Age
Between groups
Within groups
Total
Top
performers
SS
df
MS
F
Mean
SD
Mean
SD
221.28
3827.51
4048.79
223.99
2032.74
2256.73
130.94
4091.27
4222.21
1
60
61
1
60
61
1
60
61
221.28
63.79
3.47*
12.13
7.68 15.93
8.34
223.99
33.88
6.61*
8.07
4.92 11.89
6.76
130.94
68.19
1.92
42.8
7.75 45.71
8.84
* p < 0.1. ** p < 0.05. *** p < 0.01
It can be seen that the null hypothesis cannot be rejected for the variables “total selling
experience” and “company selling experience” at p < 0.05, although some weak
association was found for the variables “total selling experience” and “company selling
experience” at p < 0.1.
The association of the categorical variables of “sex”, “education”, and “people manager”
with sales job performance was checked by means of contingency analysis. The Fischer’s
exact test showed the values of p = 0.058 (two-sided) for “sex”, p = 0.203 for “education”,
and p = 0.063 for “manager.” Based on these values, we cannot reject the null hypothesis
on p < 0.05 level that the top and bottom performers differ from each other on these
demographic criteria, even though the p values of the “sex” and “manager position”
145
variables were very close to 0.05. This can be thought as having a very weak association
with the dependent variable.
Table 4-29 shows the ANOVA results for the construct “Contact and lead management.”
Table 4-29: One-way ANOVA results for the items of construct “Contact and lead
management”
Low
performers
ANOVA
Variable and Source
Identify new
leads from
internet
Between groups
Within groups
Total
Identify new
leads through
customer
organisation
Identify new
contacts in
social network
Between groups
Within groups
Total
Qualify, select
and deactivate
contacts
Between groups
Within groups
Total
Between groups
Within groups
Total
Top
performers
SS
df
MS
F
Mean
SD
Mean
SD
22.25
185.64
207.89
8.69
136.80
145.48
1
60
61
1
60
61
22.25
3.09
7.19***
3.12
1.61 4.32
1.93
8.69
2.28
3.81*
5.18
1.64 5.93
1.33
45.12
171.71
216.84
19.19
167.32
186.51
1
60
61
1
57
58
45.12
2.86
15.77***
4.00
1.79 5.71
1.56
19.19
2.94
6.54**
4.21
1.82 5.36
1.55
* p < 0.1. ** p < 0.05. *** p < 0.01
It can be seen that the null hypothesis on the differences between the means of the top and
bottom performers can be rejected. All items of the factor “Contact and lead
management” are significantly different for the two groups of performers at various p
levels. The top performers do more leads identification from the Internet (4.32 vs. 3.12 of
bottom performers) and identify leads and contacts in their social networks (5.71 vs. 4.00),
as the variables are significant at p < 0.01. They also qualify and deactivate contacts more
often (5.36 vs. 4.21).
Table 4-30 shows the ANOVA results for the construct “Database management.”
146
Table 4-30: One-way ANOVA results for the items of the construct “Database
management”
Low
performers
ANOVA
Variable and Source
SS
Work with DBs Between groups .11
to gain
Within groups
179.31
customer
Total
179.42
information
Work with DBs Between groups 1.36
to input
Within groups
149.81
customer
Total
151.17
information
Between groups
Search
5.05
company tools Within groups
192.69
for new
Total
197.74
contacts
* p < 0.1. ** p < 0.05. *** p < 0.01
Top
performers
df
MS
F
Mean
SD
Mean
SD
1
60
61
.11
2.99
.036
3.94
1.77 3.86
1.67
1
60
61
1.36
2.49
.547
3.56
1.48 3.86
1.69
1
60
61
5.05
3.21
1.573
3.68
1.79 4.25
1.80
We do not reject the null hypothesis that the population means for items of the construct
database management are equal based on the results of the one-way ANOVA. An
interesting observation is that the lower performers work with databases to gain customer
information (mean of 3.94) more frequently than the top performers (mean of 3.86) do,
although this finding is not supported by a significant p value.
Table 4-31 shows the results of the one-way analysis of variance for the construct “Other
information gathering.”
Table 4-31: One-way ANOVA results for the items of the construct “Other information
gathering”
Low
performers
ANOVA
Variable and Source
Collect more
specific info in
internet
Between groups
Within groups
Total
Collect more
specific info in
own records
Between groups
Within groups
Total
SS
df
MS
F
1.68
137.01
138.69
5.24
92.31
97.55
1
59
60
1
60
61
1.68
2.32
.72
5.15 1.54
5.48 1.50
5.24
1.54
3.40*
5.06 1.28
5.64 1.19
* p < 0.1. ** p < 0.05. *** p < 0.01
147
Mean
SD
Top
performers
Mean
SD
The null hypothesis of the population differences for these two items cannot be rejected at
p < 0.05, though there might be a weak association at the level of p < 0.1 for the variable
“collect specific information in own records.”
Table 4-32 shows the results of the one-way analysis of variance for the construct “Sales
call/presentation preparation.”
Table 4-32: One-way ANOVA results for the items of the construct “Sales
call/presentation preparation”
Low
performers
ANOVA
Variable and Source
Prepare
meeting
guidelines
Between groups
Within groups
Total
Tailor sales
presentation
Between groups
Within groups
Total
Prepare for
objections
Between groups
Within groups
Total
Respond to
customer
requests
Between groups
Within groups
Total
Arrange
call/interview
Between groups
Within groups
Total
Top
performers
SS
df
MS
F
Mean
SD
Mean
SD
2.08
40.97
43.05
.00
45.09
45.10
1.14
52.51
53.65
.08
69.23
69.31
1.38
53.41
54.79
1
59
60
1
60
61
1
58
59
1
59
60
1
59
60
2.08
.69
2.99*
6.27
.94
6.64
.68
.00
.75
.01
6.41
.86
6.43
.88
1.14
.91
1.26
6.03
.87
6.31
1.05
.08
1.17
.07
6.21
1.17 6.29
.98
1.38
.91
1.53
6.09
1.01 6.39
.88
* p < 0.1. ** p < 0.05. *** p < 0.01
As seen from the results, we cannot reject the hull hypothesis on the difference of means
at p < 0.05 for all of the variables in this construct. Very weak evidence was found only
for the variable “prepare meeting guidelines” at p < 0.1.
Table 4-33 shows the results of the one-way analysis of variance for the construct
“customer-oriented sales presentation.”
148
Table 4-33: One-way ANOVA results for the items of the construct “Customer-oriented
sales presentation”
Low
performers
ANOVA
Variable and Source
Listen actively
Between groups
Within groups
Total
Build trust
Between groups
Within groups
Total
Ask questions
Between groups
Within groups
Total
Use
consultative
selling
Between groups
Within groups
Total
Use adaptive
selling
Between groups
Sell value of
company
solutions12
Between groups
Clarify
objections
Between groups
Within groups
Total
Within groups
Total
Within groups
Total
Help clients
plan
Between groups
Within groups
Total
Top
performers
SS
df
MS
F
Mean
SD
Mean
SD
.29
39.15
39.44
1.57
49.87
51.44
.40
44.35
44.75
2.99
60.88
63.87
9.45
86.77
96.23
8.44
62.52
70.97
.43
64.55
64.98
3.02
107.05
110.07
1.85
83.39
85.25
1
60
61
1
60
61
1
59
60
1
60
61
1
59
60
1
60
61
1
59
60
1
59
60
1
59
60
.29
.65
.44
6.47
.71
6.61
.92
1.57
.83
1.88
6.32
0.81 6.64
1.03
.40
.75
.54
6.52
.71
6.68
1.02
2.99
1.01
2.95*
6.06
1.01 6.50
1.00
9.45
1.47
6.43**
5.42
1.20 6.21
1.23
8.44
1.04
8.10*** 5.79
.98
6.54
1.07
.43
1.09
.40
5.94
.85
6.11
1.25
3.02
1.81
1.66
5.44
1.26 5.89
1.45
1.85
1.41
1.31
5.35
1.20 5.70
1.17
4.03
1.90
2.12
5.56
1.28 6.07
1.49
Between groups
Statement
approach of
Within groups
opening
Total
interview
Between groups
Customer
4.03
focused
Within groups
114.24
approach of
Total
118.27
opening
interview
* p < 0.1. ** p < 0.05. *** p < 0.01
1
60
61
As seen from the table above, there is a convincing evidence (p < 0.01) of differences
between top and bottom performers for the variable “selling value of company solutions”
(means 6.54 vs. 5.79). Moderate evidence was found for the “using adaptive selling” item
12
This item was deleted from the factor construct due to its high cross-loading.
149
(means 6.21 vs. 5.42; p < 0.05) and weak evidence for “use consultative selling” (variable
means 6.50 vs. 6.05; p < 0.1).
The results of the one-way analysis of variance for the construct “Product-oriented sales
presentation” are presented in the following table 4-34:
Table 4-34: One-way ANOVA results for the items of the construct “Product-oriented
sales presentation”
Low
performers
ANOVA
Variable and Source
Repeat the
benefits of the
product
Between groups
Within groups
Total
Product benefit
approach
presentation
Between groups
Within groups
Total
Product-benefit Between groups
approach
Within groups
Total
Top
performers
SS
df
MS
F
Mean
SD
Mean
SD
2.90
145.93
148.84
4.38
133.56
137.93
.00
191.05
191.05
1
60
61
1
59
60
1
60
61
2.90
2.43
1.19
5.03
1.58 5.46
1.52
4.38
2.26
1.93
4.79
1.53 5.33
1.47
.00
3.18
.00
4.82
1.60 4.82
1.98
* p < 0.1. ** p < 0.05. *** p < 0.01
Table (4-34) indicates that the null hypothesis on the difference in the activities of top and
bottom performers are supported by a lack of significant differences between all of the
items of the “product-oriented sales presentation” construct.
The following table, 4-35, shows the results of the ANOVA for the construct “Sales
presentation close.”
Table 4-35: One-way ANOVA results for the items of the construct “Sales presentation
close”
Low
performers
ANOVA
Variable and Source
Negotiate
prices, terms,
conditions
Between groups
Within groups
Total
Confirm
agreement on
Between groups
Within groups
Top
performers
SS
df
MS
F
Mean
SD
7.43
133.62
141.05
18.90
82.50
1
59
60
1
58
7.43
2.26
3.28*
5.12
1.60 5.82
1.39
18.90
1.42
13.29***
5.38
1.45 6.50
.79
150
Mean
SD
Low
performers
ANOVA
mutual goals
Provide
solution for
customer
problems
Ask directly
for the order
Total
Between groups
Within groups
Total
101.40
3.74
43.02
46.75
59
1
59
60
Between groups
Within groups
Total
24.08
169.99
194.07
1
59
60
Top
performers
3.74
.73
5.13**
6.18
.73
24.08
2.88
8.36*
4.06
1.62 5.32
6.68
.98
1.79
* p < 0.1. ** p < 0.05. *** p < 0.01
bottom performers can be rejected. All items of this construct are significantly different
for the two groups of performers at various p levels. The strongest evidence was found for
the variable “confirm agreement on mutual goals” (means of 6.50 vs. 5.38, p < 0.01). The
top performers strive to provide solutions for customer problems as a part of the
presentation close (means of 6.68 vs. 6.18, p < 0.05). A weak association was also found
for the variables “negotiate prices”, and “ask directly for the order” at p < 0.1.
The results of the one-way analysis of variance for the construct “Customer relationship
maintenance” are presented in the following table – 4-36.
Table 4-36: One-way ANOVA results for the items of the construct “Customer
relationship maintenance”
Low
performers
ANOVA
Top
performers
Variable and Source
SS
df
MS
F
Mean
SD
Mean
SD
Check customer Between groups
satisfaction
Within groups
4.00
68.71
72.71
44.90
192.19
237.10
4.80
140.38
145.18
4.24
63.96
68.19
1
60
61
1
60
61
1
60
61
1
60
61
4.00
1.15
3.49*
5.38
.85
5.89
1.29
44.90
3.20
14.02***
3.65
1.82 5.36
1.75
4.80
2.34
2.05
4.44
1.52 5.00
1.54
4.24
1.07
3.97**
6.12
.91
1.16
Total
Arrange
financing
Between groups
Within groups
Total
Socialise with
customer
Between groups
Within groups
Total
Thank
customer
Between groups
Within groups
Total
* p < 0.1. ** p < 0.05. *** p < 0.01
151
6.64
It can be seen that the null hypothesis about the differences between the means of the top
and bottom performers can be rejected for the “arrange customer financing” item (5.36 vs.
3.65), which showed the strongest evidence of association (p < 0.01). The top and bottom
performers also differed significantly on “thanking the customer after sales” (6.64 vs.
6.12, p < 0.05) and “checking customer satisfaction” (5.89 vs. 5.38, p < 0.1).
The results of the ANOVA for the construct “Internal partnership management and
delivery” are presented in the following table – 4-37:
Table 4-37: One-way ANOVA results for the items of the construct “Internal
partnership management and delivery”
Low
performers
ANOVA
Variable and Source
Work with
distributors
Between groups
Within groups
Total
Between groups
Coordinate
orders, delivery Within groups
Total
Socialise with
internal
specialists
Between groups
Within groups
Total
Top
performers
SS
df
MS
F
Mean
SD
Mean
SD
32.82
193.77
226.59
6.85
183.74
190.60
21.56
115.68
137.24
1
59
60
1
60
61
1
60
61
32.82
3.28
9.99**
4.24
1.84 5.71
1.78
6.85
3.06
2.24
4.62
1.61 5.29
1.90
21.56
1.93
11.18***
5.03
1.57 6.21
1.13
* p < 0.1. ** p < 0.05. *** p < 0.01
As seen from the table, the null hypothesis on the differences between the means of the
top and bottom performers can be rejected for the “socialise with internal specialists”
variable (6.21 vs. 5.03), which showed the strongest evidence of association (p < 0.01).
The top and bottom performers significantly differed also in the frequency that they
worked with distributors (5.71 vs. 4.24, p < 0.05).
The results of the one-way analysis of variance for the construct “Personal development”
are presented in table 4-38:
152
Table 4-38: One-way ANOVA results for the items of the construct “Personal
development”
Low
performers
ANOVA
Variable and Source
Between groups
Within groups
Total
SS
11.12
112.58
123.69
Between groups
14.28
Set up and
visit
Within groups
156.96
conferences
Total
171.24
Between
groups
7.70
Train your
tools skills
Within groups
144.58
Total
152.27
Train product Between groups 21.98
skills
Within groups
149.43
Total
171.41
Study market Between groups 8.40
trends
Within groups
100.46
Total
108.85
Between groups
9.28
Train new
hires
Within groups
144.92
Total
154.19
* p < 0.1. ** p < 0.05. *** p < 0.01.
Participate in
sales trainings
Top
performers
df
MS
F
Mean
SD
Mean
SD
1
60
61
1
60
61
1
60
61
1
59
60
1
60
61
1
60
61
11.12
1.88
5.92**
4.47
1.31 5.32
1.44
14.28
2.62
5.46**
4.00
1.74 4.96
1.45
7.70
2.41
3.19*
3.47
1.54 4.18
1.56
21.98
2.53
8.68***
4.55
1.79 5.75
1.32
8.40
1.67
5.02**
4.62
1.33 5.36
1.25
9.28
2.42
3.84*
4.29
1.64 5.07
1.44
The results from the table above indicate there is an association between all the variables
and job sales performance at different p levels.
bottom performers can be rejected. All items of this construct are significantly different
for the two groups of performers at various p levels. The strongest evidence was found for
the variable “train product skills” (means of 5.75 vs. 4.55, p < 0.01). The top performers
also participated more frequently in sales trainings (means of 5.32 vs. 4.47, p < 0.05), set
up and visited conferences (4.96 vs. 4.00, p < 0.05), and studied market trends (5.36 vs.
4.62, p < 0.05). Weak evidence was found also for the variables “train tool skills”, and
“train new hires” at p < 0.1.
The results of the one-way analysis of variance for the construct “Internal coordination
and administration” are presented in table 4-39.
153
Table 4-39: One-way ANOVA results for the items of the construct “Internal
coordination and administration”
Low
performers
ANOVA
Variable and Source
Coordinate
aligned team
Between groups
Within groups
Total
Participate in
internal
meetings
Between groups
Within groups
Total
Document,
report market
information
Between groups
Within groups
Total
Do
administration
Between groups
Within groups
Total
Top
performers
SS
df
MS
F
Mean
SD
Mean
SD
6.55
114.99
121.55
4.31
51.62
55.93
10.91
140.24
151.15
.45
80.40
80.85
1
60
61
1
59
60
1
59
60
1
59
60
6.55
1.92
3.42*
5.38
1.44 6.04
1.32
4.31
.87
4.93**
5.79
1.05 6.32
.77
10.91
2.38
4.59**
5.15
1.64 6.00
1.41
.45
1.36
.33
5.97
1.16 6.14
1.18
* p < 0.1. ** p < 0.05. *** p < 0.01
bottom performers can be rejected for the three variables of the construct: “coordinate
aligned team”, “participate in internal meetings”, and “document market information.”
The strongest evidence was found for the items “document market information” (6.00 vs.
5.15) and “participate in internal meetings” (6.32 vs. 5.79) at p < 0.05. The item
“coordinate aligned team” (6.04 vs. 5.38) showed weak evidence at level p < 0.1.
Table 4-40: One-way ANOVA results for the items of the construct “Planning“
Low
performers
ANOVA
Variable and Source
Plan customer
calls and visits
Between groups
Within groups
Total
Plan internal
meetings
Between groups
Within groups
Total
Develop longterm plans
Between groups
Within groups
Total
Top
performers
SS
df
MS
F
Mean
SD
9.57
56.43
66.00
7.09
55.76
62.85
16.42
84.82
101.25
1
59
60
1
59
60
1
59
60
9.57
.96
10.00***
5.65
1.04 6.44
.89
7.09
.95
7.50***
5.65
1.07 6.33
.83
16.42
1.44
11.42***
5.03
1.17 6.07
1.24
* p < 0.1. ** p < 0.05. *** p < 0.01
154
Mean
SD
The results shown in table 4-41 show that there is an association between all the variables
of “planning” with sales job performance at p < 0.01. The top sellers tend to develop
long-term plans (6.07 vs. 5.03), plan customer visits (6.44 vs. 5.65), and internal meetings
(6.33 vs. 5.65).
The following table, 4-41, represents the results of the ANOVA for the construct
variables which were not included in the factor model but could still provide valuable
information about differences between top and bottom employees.
Table 4-41: One-way ANOVA results for the activity variables that did not appear in the
factor constructs
Low
performers
ANOVA
Variable and Source
Travel out of
town
Between groups
Within groups
Total
Overnight stays Between groups
Within groups
Total
Handle
complaints
Between groups
Within groups
Total
Invite clients
on site
Between groups
Within groups
Total
Submit bids
Between groups
Within groups
Total
Engage presales support
Between groups
Within groups
Total
SS
df
5.74
102.73
108.47
2.00
106.98
108.98
5.74
102.73
108.47
16.62
137.05
153.67
4.80
232.88
237.68
6.77
136.47
143.24
1
60
61
1
60
61
1
60
61
1
59
60
1
60
61
1
60
61
MS
Top
performers
F
Mean
SD
Mean
SD
5.74
1.71
3.35*
5.35
1.32 5.96
1.29
2.00
1.78
1.12
2.85
1.21 3.21
1.47
5.74
1.71
3.35
5.38
1.10 5.89
1.45
16.62
2.32
7.16***
4.67
1.57 5.71
1.46
4.80
3.88
1.24
3.94
1.86 4.50
2.10
6.77
2.27
2.98*
5.26
1.58 5.93
1.41
* p < 0.1. ** p < 0.05. *** p < 0.01
The table above shows significant differences between top and bottom performers for the
variable “invite clients on site” at p < 0.01. A weak association also exists for the
variables “travel” and “engage pre-sales support” at p < 0.1.
155
The results of the ANOVA for the variables of activity importance evaluation are shown
in the table below – 4-42.
Table 4-42: One-way ANOVA results for the variables of activity importance evaluation
Low
performers
ANOVA
Variable and Source
Importance:
locating
potential buyers
Between groups
Within groups
Total
Importance: PreSales preparation
Between groups
Within groups
Total
Importance:
Sales
presentation
Between groups
Within groups
Total
Importance:
Handling
objections
Between groups
Within groups
Total
Importance:
Close
Between groups
Importance:
Follow-up
Within groups
Total
Between groups
Within groups
Total
Importance:
Planning
Between groups
Within groups
Total
Importance:
Internal activities
Between groups
Within groups
Total
Importance:
Administration
Between groups
Within groups
Total
Importance:
Personal
development
Importance:
Travel
Between groups
Within groups
Total
Between groups
Within groups
Total
Top
performers
SS
df
MS
F
Mean
SD
11.20
158.98
170.18
1.46
79.13
80.60
6.94
73.40
80.34
4.51
51.83
56.34
5.43
90.85
96.27
5.78
63.40
69.18
13.42
67.43
80.85
2.42
189.01
191.44
.14
187.74
187.89
14.89
89.80
104.69
7.63
160.77
168.40
1
58
59
1
60
61
1
60
61
1
60
61
1
60
61
1
60
61
1
60
61
1
60
61
1
60
61
1
59
60
1
58
59
11.20
2.74
4.09**
5.31
1.77 6.18
1.52
1.46
1.32
1.11
5.94
1.13 6.25
1.17
6.94
1.22
5.67**
5.97
1.27 6.64
.87
4.51
.86
5.22**
6.03
.97
.88
5.43
1.51
3.58*
5.94
1.25 6.54
1.20
5.78
1.06
5.47**
6.03
1.14 6.64
.87
13.42
1.12
11.94***
5.53
1.26 6.46
.74
2.42
3.15
.77
4.35
1.79 4.75
1.76
.14
3.13
.05
3.62
1.86 3.71
1.65
14.89
1.52
9.78***
5.26
1.40 6.26
.98
7.63
2.77
2.75
4.09
1.56 4.81
1.79
* p < 0.1. ** p < 0.05. *** p < 0.01
156
Mean
6.57
SD
As seen from table 4-42, we found strong evidence that the evaluation of the importance
of planning and personal development is associated with sales job performance (p < 0.01).
Moderate evidence was observed for the importance perception of follow-up, locating
buyers, sales presentation, and handling objections at p < 0.05, and for the importance of
close at p < 0.1.
The ANOVA results for the variables of activity time evaluation are presented in the
following table 4-43.
Table 4-43: One-way ANOVA results for the variables of activity time evaluation
Low
performers
ANOVA
Top
performers
Variable and Source
SS
df
MS
F
Mean
SD
Customer days: Between groups
number
Within groups
Total
Office days:
Between groups
number
Within groups
Total
Customer day: Between groups
locating buyers Within groups
Total
pre-sales
Within groups
preparation
Total
sales
Within groups
presentation
Total
follow-up
Within groups
Total
planning
Within groups
Total
internal
Within groups
activities
Total
administration Within groups
Total
personal
Within groups
2.52
814.85
817.37
5.74
809.23
814.97
.01
11.02
11.03
.07
33.87
33.94
.12
55.23
55.36
.08
58.48
58.55
.10
16.14
16.25
.00
65.20
65.20
.69
35.48
36.17
.41
6.68
1
60
61
1
60
61
1
56
57
1
56
57
1
56
57
1
56
57
1
56
57
1
56
57
1
56
57
1
56
2.52
13.58
.19
8.94
4.05 8.54
3.18
5.74
13.49
.43
10.85
4.03 11.46
3.18
.01
.20
.05
0.40
0.48 0.43
0.39
.07
.60
.11
1.39
0.81 1.46
0.73
.12
.99
.13
2.23
1.03 2.14
0.94
.08
1.04
.07
1.36
1.20 1.29
0.72
.10
.29
.36
0.85
0.53 0.76
0.54
.00
1.16
.00
1.11
1.16 1.09
0.95
.69
.63
1.09
0.84
0.76 1.06
0.85
.41
.12
3.46*
0.16
0.30 0.33
0.40
157
Mean
SD
Low
performers
ANOVA
Variable and Source
SS
Total
7.09
Between groups 1.03
Within groups 42.57
Total
43.60
Customer day: Between groups 4.60
total hours
Within groups 154.98
worked
Total
159.58
Office day:
Between groups .85
locating buyers Within groups 38.58
Total
39.43
Office day: pre- Between groups .21
sales
Within groups 69.15
preparation
Total
69.36
Office day:
Between groups .01
sales
Within groups 43.88
presentation
Total
43.89
Office day:
Between groups .22
follow-up
Within groups 31.42
Total
31.64
Office day:
Between groups .07
planning
Within groups 26.16
Total
26.23
Office day:
Between groups .13
internal
Within groups 62.40
activities
Total
62.53
Office day:
Between groups 1.76
administration Within groups 87.20
Total
88.96
Between groups .77
Office day:
personal
Within groups 10.10
development
Total
10.87
Office day:
Between groups .01
travel
Within groups 23.80
Total
23.82
Office day:
Between groups 2.87
total hours
Within groups 153.27
worked
Total
156.14
* p < 0.1. ** p < 0.05. *** p < 0.01
development
Customer day:
travel
df
57
1
56
57
1
56
57
1
55
56
1
55
56
1
55
56
1
55
56
1
55
56
1
55
56
1
55
56
1
55
56
1
55
56
1
55
560
Top
performers
MS
F
Mean
SD
Mean
SD
1.03
.76
1.36
1.25
0.81 1.52
0.95
4.60
2.77
1.66
9.54
1.42 10.11
1.94
.85
.70
1.21
0.59
0.95 0.84
0.67
.21
1.26
.17
1.50
1.14 1.38
1.09
.01
.80
.01
0.82
0.84 0.84
0.95
.22
.57
.38
1.12
0.72 1.24
0.81
.07
.48
.16
1.09
0.73 1.16
0.64
.13
1.13
.11
1.92
0.90 1.82
1.25
1.76
1.59
1.11
1.99
1.41 1.64
1.03
.77
.18
4.19**
0.49
0.46 0.72
0.38
.01
.43
.03
0.33
0.69 0.30
0.61
2.87
2.79
1.03
9.51
1.62 9.96
1.73
The ANOVA test results shown in the table above indicate the existence of significant
differences between only two variables. Interestingly, the two variables with a significant
p value represent the same activity – personal development that is done during both days:
158
customer visit and office day. The time spent during the office day for personal
development showed the existence of significant differences between top and bottom
performers at p < 0.05 (72 minutes for top performers vs. 49 minutes for low performers).
The top performers also invested more time in personal development during the customer
visit day (33 minutes vs. 16 minutes, significant at p < 0.1).
To summarise, the results of the ANOVA tests of means differences, table 4-44 depicts
the list of variables where differences were found on various p levels. Variables outside of
the table summary indicated no evidence related to performance.
As can be seen from table 4-44, convincing evidence at p < 0.01 was found for 13
variables, which originate from the constructs of “contact and lead management”,
“presentation close”, “planning”, “personal development”, “internal activities”, “followup”, and “sales presentation.” These variables build the top-performer profile in IT selling.
Table 4-44: Summary of the ANOVA results indicating the variables with identified
differences between top and bottom sellers at different p levels (a top-performer profile)
Variables that demonstrated
significant differences at p < 0.01
level (strong convincing
evidence)
Identify leads in Internet
Identify new leads and contacts in
social network
Sell value of company solutions
Confirm agreement on mutual
goals
Invite clients on site
Arrange customer financing
Socialize with internal specialists
Plan customer calls and visits
Importance of planning
Importance of personal
development
level (strong to moderate
evidence)
Qualify, select contacts
significant differences at p <
0.1 level (very weak evidence)
Provide solutions to customer
problems in presentation close
Thank customer
Identify leads in customer
organisation
Collect more specific information
in own records
Prepare meeting guidelines
Negotiate prices, terms, and
conditions
Engage pre-sales support
Participate in sales trainings
Set up and visit conferences
Study market trends
Participate in internal meetings
Document, report market
information
Importance of locating potential
buyers
Importance of sales presentation
Importance of handling objections
Importance of follow-up
159
Total selling experience
Company selling experience
Train tool skills
Train new hires
Travel
level (strong convincing
evidence)
level (strong to moderate
evidence)
Time for personal development
during office day
significant differences at p <
0.1 level (very weak evidence)
Importance of presentation close
Time for personal development
during customer visit day
The importance of these activities was also found to be significant at different p levels,
with the lowest p values for importance of planning and personal development. Moreover,
the time spent on personal development during both office and customer days was
substantially lower for bottom sellers compared to top sellers, which clearly postulates the
importance of this activity in performance prediction.
Another interesting finding from the analysis above is that the top performers generally
reported a more intensive frequency of behaviour than the lower performers. This finding
fully supports the proposition of Sujan (1986) that not only “working smart”, but
“working hard” directly influences sales performance.
4.4 An analysis of sales performance prediction from the salesperson
activities by means of multiple regression
Regression analysis is used to predict the value of one variable on the basis of other
variables. This method involves the creation of a mathematical equation which describes
the relationship between the dependent and independent variables (Keller, 1997: 725).
With this statistical technique, we aimed to check the hypothesis about the association of
the sales process and non-sales process related activities, and to determine their relative
importance in the prediction of the dependent variable – the job performance of the
salespeople. The variables of time estimation were not included in the regression analysis
160
due to their low validity and extremely low (if any) correlations with the dependent
variable.
4.4.1 Testing the assumptions of multiple regression
The assumptions of the multiple regression analysis are the linearity, homoscedasticity,
independence, and normality of the error term (Hair et al., 2006: 204).
Assumptions of a multiple regression are important for the representativeness of the
results of the sample (Hair et al., 2006: 236). It has been widely discussed that “regression
analysis has been shown to be robust even when the normality assumption is violated”
(Hair et al., 2006: 236) because the statistical tests of the regression are not affected by
moderate violations from normality (Norušis, 2006: 258). The violation of the
assumptions of linearity and homoscedasticity does not invalidate the regression analysis
so much as weaken it (Abrams, 2007).
Linearity is assessed through the residuals analysis (standardised predicted value vs.
studentised residual plot) and partial regression plots for each of the independent variables
in the model (Hair et al., 2006: 251). The plots of residuals showed no nonlinear pattern
to the residuals, and in this way, we assume that the linearity assumption is met.
Homoscedasticity is examined by analysing the same standardised predicted value vs.
studentised residual plot as for linearity (Hair et al., 2006: 251). The plot showed the
natural “cloud“, where no pattern of decreasing or increasing residuals was identified.
Independence of observations is usually fulfilled in the proper design of a study (Osborn
and Waters, 2002). In our study, the subjects were not treated differently during the whole
period of field research. Therefore, this assumption was met by our analysis.
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The normality of the error term is checked by the analysis of the normal probability plots
of the residuals where the standardised residuals – observed vs. expected – need to follow
the normal line to be considered normally distributed. The visual inspection of the P-P
plot and the non-significant Kolmogorov-Smirnov test (p > 0.4) proved that the regression
variate wass normally distributed in our data.
4.4.2 Selection of the regression estimation technique
The methods of sequential search and the approaches of stepwise regression compared to
the confirmatory specification – entry method, estimates the regression equations by
selectively adding or deleting the variables based on their contribution to the prediction of
the dependent variable (Hair et al., 2006: 209).
The method of forward stepwise regression was chosen in our study. This is the method
where the independent variables which best correlate with the independent variable are
added to the regression (Hinton, 2004: 331). Thus, we can observe the “strongest”
predictors of the dependent variable. The choice of this stepwise method is justified when
building regression models with a large number of predictor variables, as well as in
building the models for exploratory purposes (Jaramillo and Marshall, 2005: 20).
4.4.3 The regression model summary
The final regression model received in the course of this statistical analysis included five
independent variables and a constant (table 4-45 and figure 4-6).
As seen from table 4-45, the overall model fit explains 40% of the variance in the
salespeople’s job performance, which confirms a very good fit. In social sciences, where a
single variable rarely explains a large variation, the R2 values of at least 0.5 can be
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considered “remarkably good” (Ramsey and Schafer, 2002: 221). The overall F-test for
the model indicates that the multiple regression is statistically significant at p < 0.001.
Multicollinearity did not affect the outcome of the model. As can be seen from table 4-45,
all tolerance values are high, and far over 0.1, the common cut-off threshold, which
means that the variables are not highly correlated, and therefore, did not pose a problem
for our analysis (Hair et al., 2005: 230).
Table 4-45: Multiple regression results summary
Variable
Unstandardised
Standardised
coefficients
coefficients
B
Constant
Std. error
Collinearity
T
Beta
Sig.
Tolerance
VIF
3.858
.417
9.261 .000
.191
.061
.373
3.158 .002
.640
1.562
Identify new contacts and
.069
.033
.219
2.085 .041
.810
1.234
.144
.048
.308
3.016 .004
.854
1.172
-.180
.058
-.349
-3.088 .003
.697
1.435
.109
.050
.212
2.165 .034
.929
1.076
leads in social network
Confirm agreement on
mutual goals (sales
presentation close)
Importance: Personal
development
N = 107; R2 = .403; adjusted R2 = .358; F = 9.03 (p < 0.001); dependent variable: “salesperson job performance“
Figure 4-6: Regression equation of the model on sales activities and performance
y = 3.858 + 0 .191 x1 + 0.069 x2 + 0 .144 x3 – 0.180 x4 + 0.109 x5 + ε
3.858 – constant
y – “sales job performance“ variable
x1 – “develop long-term plans” variable
x2 – “identify new contacts in social network” variable
x3 – “confirm agreement on mutual goals” variable
x4 – “check customer satisfaction” variable
x5 – “importance of personal development” variable
ε – prediction error (residual)
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where
As shown above, all the coefficients of the model are significant at the 5% level. The
coefficients of the regression for all variables except for “check customer satisfaction” are
positive, which means that there is a positive relationship between these variables and
sales job performance.
The “check customer satisfaction” came out with the negative sign. In several situations,
the coefficients may be reversed if the multicollinearity is violated (Hair, 2005: 229). In
our model, this is not the case. Therefore, it can be concluded that the negative sign
appeared due to the model itself. The beta coefficients eliminate the problem with
different measurements and show the relative impact of each independent variable on the
dependent variables (Hair, 2005: 225). The beta coefficients tell us that the variable
“develop long-term plans” (0.373) has the strongest impact on sales job performance,
followed by “check customer satisfaction” (-0.349) and “confirm agreement on mutual
goals” (0.308). The other two variables are roughly comparable in their influence
“identify new contacts in social network” (0.219) and “importance of personal
development” (0.212).
Using regression analysis, we found that the highly frequent activities of developing longterm plans, identifying new contacts and leads in the salesperson’s social network,
confirming agreement on mutual goals in the sales presentation close, focusing on
personal development, as well as infrequent checks of customer satisfaction proved to be
good predictors of the salesperson’s job performance.
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5 DISCUSSION AND CONCLUSIONS
5 Discussion and conclusions
5.1 Discussion of the results and findings of this study
This study aimed to define an extensive “macro” level picture of salesperson behaviour in
the context of information technology selling and identify which elements of
behaviour/activities are associated with sales job performance. The obtained study results
show that the research objectives defined for this dissertation were fully accomplished.
This study entailed extensive exploratory research, which examined the systematisation of
the previous theoretical background, suggested a comprehensive model of salesperson
activities and behaviours with regard to the sales process and non-selling activities, pretested, and checked this model by means of principal component analysis. In addition, the
activity elements of successful behaviour were identified by comparing top and bottom
salespeople and the activities that showed the best prediction power of sales job
performance were highlighted.
The results of the principal component analysis indicated the existence of eleven domains
of salesperson activities: “prospecting contact management”, “database contact
management”, “detailed information gathering”, “call preparation/gathering”, “customerand product-oriented presentation”, “sales close”, “customer relationship management”,
and “customer technical support” (figure 5-1 based on table 4-27). The activity
dimensions of this model can be divided into direct selling/customer interaction activities
– activities involved in the direct selling process (such as customer-oriented presentation,
contact and lead management) and non direct selling/customer interaction activities (such
as planning, or personal development). The proposed dimensions built a model which
supports the recent picture of a “macro” level of salesperson behaviour adjusted to the
information technology selling context.
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Figure 5-1: The model of empirically confirmed dimensions of salesperson activities
Classical “7 step“ Sales Process Model (1970s)
Direct selling /customer interaction activities
1 Locating and
prospecting
for customers
Contact and
lead
management
2 Preapproach
DB
management
4 Sales
presentation
3 Approach
Other
information
gathering
Sales call
preparation
Customeroriented
presentation
Internal
coordination and
administration
Internal
Partnership
and delivery
6 Close
Sales
presentation
Close
Productoriented
presentation
Empirically derived model of the current study
(2010)
Sales
planning
5 Handling
objections
Customer
relationship
maintenance
7 Post sales
follow-up
Technical
Support
and
maintenance
Customer interaction activities involved
in the sales process
Personal
development
Non-customer interaction activities
Figure 5-1 indicates the interconnection of the classical “seven-step” selling process
model from the 1970s with the empirically derived model of the current study. In this way,
our model suggests new conceptual updates to the classical “seven step” model of selling.
Step 1 of the classical paradigm “prospecting” in the model was reconceptualised to
“contact and lead management.” This new step incorporates the relationship-building
approach, where the contemporary seller does not focus on the “customer search” but
instead identifies relationship contacts in different contemporary environments (the new
ones are the Internet, e-social networks, and the salesperson’s own records). An important
part of this step is precise prequalification of these contacts and focusing only on those
contacts with a high propensity to buy. Step 2 of the classical model, the “pre-approach”,
turned out to have three analogue “contemporary” dimensions in our model, namely “DB
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management”, “sales call preparation”, and “other information gathering.” Since the sales
force automation is widely deployed in the field of professional selling, the search for
relevant contact information belongs to the usual daily life of the salesperson of today. Of
course, this is not the only source of relevant information. Other sources, such as the
Internet and the salesperson’s own records, are also consulted where possible. The sales
call preparation with the design of an appealing presentation, the meeting agenda, and
gearing oneself up for potential objections belongs to this step of our model.
The original steps 3 “approach”, 4 “sales presentation”, and 5 “handling objections” from
the classical “seven steps” model correspond to the two dimensions – “customer-oriented
presentation” and “product-oriented presentation” in our model. The existence of these
two factors may indicate the diminishing border between the classical steps of “approach”,
“presentation”, and “handling objections” performed during customer presentations. This
finding may also support the inclusion of the consultative and relationship approaches
into contemporary selling. The salesperson uses these approaches to focus his/her
presentation on identifying customer needs and establishing a positive relationship with
the client. The appearance of the factor “product-oriented presentation“ is not a
coincidence in our case. Since the marketing of complex offerings is as important as
building a good customer relationship in the IT sales field, the focus on the product
remains part of the convincing presentation policy of the salespeople (Alonzo, 1997).
The traditional sales step “close” remained, but new elements such as “the relationship
agreement close” (coming from the relationship management perspective), “negotiation of
prices and conditions of a sale” (coming from the IT market perspective) and “providing
solutions to customer problems” (as the basis of consultative selling) were incorporated.
The “post-sales follow up” step in the classical model was divided into two parts in our
model. The first new factor was “customer relationship management” – representing the
relationship aspect of the successful post-sales stage, and the second was “customer
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technical support”, characterising the technical part of the follow-up, which is a crucial
element of the IT marketing coming from the complexity of the IT offerings. Several of
the dimensions of this model (e.g. “customer technical support” and “customer
relationship management”) are consistent with the dimension models of Moncrief (1986a,
b), Moncrief (2006), and Dubinsky (1981-80).
Apart from the selling activities, the model in this dissertation incorporates the noncustomer interaction activities: “sales planning”, “internal coordination”, “internal
partnerships”, and “personal development”, showing that one of the main conclusions of
our study is that the job of a salesperson does not just consist of customer contact
activities, such as making presentations and product demonstrations in front of the
customer, but also contains other crucial functions and related activities.
The results of the ANOVA and comparison of top and bottom performers confirmed the
rejection of the null hypothesis on the levels of activity performance, and in this way a
high performer profile was suggested. A high performer identifies new leads and contacts
in his/her social network as a part of prospecting and qualifies them. He/she is able to
identify leads in the Internet as well as sell value of company solutions, and adapts his/her
selling style during the presentation. He/she realises the relationship-oriented close of
confirming agreement on mutual goals. He/she frequently invites clients on site, thanks
customers and arranges customer financing. He/she socialises with internal specialists and
frequently trains his/her product skills. He/she emphasises the importance of planning and
personal development to his/her own success and does it frequently. He/she regularly
revises market trends, visits conferences, and works with distributors.
An interesting finding was also that the top performer works more hours a day and
performs all of the activities with higher frequency than the lower performer. This finding
is in line with the studies on “working smart” and “working hard” (e.g. Sujan, 1986;
Sujan, Weitz, and Sujan, 1988) and their positive relationship to sales performance.
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Interestingly, the results of this comparative study of top and bottom performers were
different from the outcomes of the studies of Dwyer et al. (2000) and Jaramillo and
Marshall (2005). For example, Jaramillo and Marshall (2004) found positive associations
between techniques of “obtaining prospect information from customer, newspapers, etc.”
and the “direct contact approach: contact prospect directly to arrange interview.” Dwyer
et al. (2000) and our research did not find evidence of any associations between these
activities. The link of our item “identify new contact from social network” was consistent
with the result of ANOVA on the item “seek contact through community groups” of
Jaramillo and Marshall (2005). At the same time, Dwyer et al. (2000) did not find any
association for the variables, such as “personal networking”, “referral approach”, or
“community contact.” This finding may be explained in two ways: on the other hand, the
implied different measurement approaches could cause these result differences – selfrated performance in Dwyer et al. (2000) and in our research vs. combined self- and
manager-rated performance in Jaramillo and Marshall (2005). On the other hand, and this
is the convincing argument confirmed by our study results, the different types of selling
and different situations often apply different and contradictory CSFs (DeVicentis and
Rackham, 1999 cited in Jaramillo and Marshall, 2005).
Using regression analysis, we found that the highly frequent activities of developing longterm plans, identifying new contacts and leads in the salesperson’s social network,
confirming agreement on mutual goals in the sales presentation close, considering
personal development important for one’s own success, as well as less frequent checks of
customer satisfaction proved to be good predictors of a salesperson’s job performance. In
this way, we rejected the null hypothesis on the prediction of direct selling and non-direct
selling activities. Since it has been widely discussed that only the customer-facing
activities of the salesperson contribute to his/her success, our study proposes that the noncustomer facing ones must not be overlooked since they influence the success of a
salesperson to the same extent.
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When we analysed the time allocation of the salespeople who took part in our research,
we found that internal activities were the most time consuming (i.e., took up 15% of their
week). Almost the same amount of time is spent on sales presentations and pre-sales
preparation. Follow-up (13.9%) and administration (12.8%) concluded the first top five
activities with the largest time investment. We observed that the difference in time spent
and the importance given to the activity is most obvious for internal activities and
administration, which are considered to be less important but most time consuming. This
finding, together with some comments from the personal interviews, indicates exactly
how much time these activities add to the workload of salespeople. On the contrary, the
activities of “locating customers”, “planning”, and “personal development” were
measured as being very important, but the time invested in them was relatively small.
This finding may indicate the extent of the time constraints on salespeople.
5.2 The contribution of this study to theory
The results of the current study have several implications for the theory of personal
selling.
First, our research documented the current state of the salespeople activities and
behaviour, and delivered an original view of the latest customer interaction and noncustomer interaction activities of IT salespeople. Based on the newly created activity and
behaviour dimensions, an empirical model of prediction of salesperson performance was
proposed from a set of behavioural elements. We created a measurement instrument of
salesperson behaviour and contributed to the current theory with new facts and evidence
from the information technology industry, which is itself a very complex, interesting and
under-researched area in the sales field.
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Second, to our knowledge, no research has been done recently on the empirical
revalidation of the classical sales process model, and in particular, on the model of
Dubinsky (1980-81) by partly incorporating the conceptual suggestion or model elements
of Marshall et al. (1999), Moncrief and Marshall (2005), Darmon (1998), and several
other authors. None of the aforementioned authors could provide a statistical
measurement approach and an empirically tested model of the sales process with
incorporated changes in the personal selling function.
Third, we expanded on the activity models of Moncrief (1986a, b) and Moncrief et al.
(2006) with evidence from the information technology industry, and provided a more
detailed classification of the selling function by analysing it along the “seven step”
classical selling process and its critiques. We enlarged on their models as well as the
models of Dwyer et al. (2000) and Jaramillo and Marshall (2004) with important
constructs, such as “internal activities” and “planning”, which were not present there.
Our study made several methodological contributions to the theoretical field of personal
selling.
First, we utilised the measurement of “frequency” (from Moncrief, 1986 a, b and
Moncrief et al., 2006) instead of “importance” in the examination of top and bottom
employees, and received consistent results (as Dwyer et al., 2000 and Jaramillo and
Marshall, 2004 did).
Second, this research demonstrated the effective use of cognitive interviewing as a pretest method for sales research. No known research in this field has documented the use of
the “think-aloud” technique as a part of a qualitative pre-test. The cognitive interviewing
techniques proved to be a highly suitable and effective qualitative method to validate a
new instrument. Since the field of personal selling is closely related to psychology, the
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use of further complimentary methods from this field of knowledge in the personal selling
discipline may yield interesting outcomes.
Third, we analysed different approaches for the measurement of selling activities/
behaviours and found that these measurements are not closely related. This might be the
result of the slightly different meanings that each measurement relies on. This is probably
the reason why different studies on selling behaviour and activities produced different
results. Nevertheless, the best possible correlations were received for the measurements of
“importance” and “frequency.”
This can be explained in the following way. The “importance” measurement is a
subjective evaluation of what is considered necessary to be successful. “Frequency” is
more an objective measurement of what event happened with which regularity in a reallife situation. “Frequency” has a less evaluative meaning than “importance” (Moncrief,
1986: 263). In many cases, we observed that the activity “frequency” was reported as high
because of its high importance (e.g. activities of planning and personal development). The
meanings of these two dimensions are very close, but they are not fully identical. This
must be taken into account in future studies utilising these types of measurements.
And finally, we discovered that the time measurement method has an evaluative character.
The special art of distribution, the highest amount of missing data, and its low to nonexistent correlations with other measures indicated the low validity of this measurement
method. Nevertheless, we proposed that evaluative time measurement studies should only
be conducted in the first steps of research or for evaluation purposes, or the precise time
measurement should be applied in more rigorous statistical models.
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5.3 Practical implications of the dissertation
It is possible to derive a number of implications for sales managers, human resources
specialists and sales specialists themselves based on the results of this dissertation. The
proposed implications for managerial and sales practice are related to the major fields of
sales job role definition and improvement of sales performance.
Management implications for the sales job definition and improvement of job perception
Sound management of a sales force involves understanding of the complexity of selling
activities by sales managers on all levels, as well as making the appropriate decisions
connected to managing these activities (Johnston and Marshall, 2006: 10).
The managers should understand that the set of activities and behaviours defines the role
of the salesperson, i.e. those that he/she associates with the job and that he/she is expected
to perform. The expectations and boundaries on this role performance should be set and
communicated to role partners: top management, supervisors, and private life members. A
salesperson has an accurate perception of his/her job role when he/she understands
correctly what his/her role partners expect when he/she performs the job (Johnston and
Marshall, 2006: 217).
The model of salesperson activities developed in this study should enable managers and
sales practitioners to perform a structured analysis of every aspect of the sales role
function, taking into consideration both selling and non-selling activity dimensions, and
thus minimise the risks of overlooking important factors associated with the sales job role
and its performance. The developed framework of the dimensions of the seller’s
behaviour and the reviewed sales process will guide both salespeople and their managers
to ensure that their efforts are consistent with the company’s way of selling.
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The instrument developed by the author in this study (subchapters 3.3, table 4-27) may be
directly used in sales job analysis to re-determine appropriate tasks and activities which
need to be performed by specific groups of sellers, to review and set up detailed job
descriptions, and write job and hiring qualifications. Since the instrument was developed
for a specific company in a specific industry setting, it needs to be adapted for each
product, geography, and customer set.
Sales managers should not forget that the salesperson’s role is constantly developing at a
high rate, reflecting the changes in the sales and economic environments. Therefore, it is
highly recommended that the sellers, sales managers, and human resources managers
revise the sales role descriptions, sales position descriptions, and evaluation criteria for
each sales job on a regular basis based on discussions with their salespeople, their own
observations, analysis reports of practice, and assessments of the economic forces and
sales labour market.
By profoundly restructuring sales behaviour and the sales job role, sales managers
reinforce a positive role perception in their sales force, thus releasing them from
perceived role ambiguity.
Management implications for sales performance improvement
This dissertation demonstrates that salesperson behaviour is multidimensional, and it
includes a number of links. The most important of these is the link to sales performance.
The results of the statistical comparison of top and bottom performers in this study
suggested a high performer profile (subchapter 4.3, table 4-44). This knowledge of the
elements of behaviour mostly associated with sales success should enable companies to
reformulate sales programmes, organise and plan selling efforts, as well as use the
identified factors for structured monitoring and controlling of their sales force along the
proposed activity dimensions.
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Knowledge of the success factors in personal selling enables managers to direct the
selling effort and behaviour of their employees towards activities mostly associated with
sales success. Salesperson behaviour may be either formally influenced through
organisation policies, operating procedures, and training programmes, or informally
through social pressure, rewards, and sanctions (Johnston and Marshall, 2006: 207). On
the formal side, companies could also include the components of success into the
formulations of their sales force’s mission and sales organisation philosophy around these
success milestones.
This study identified that the development of long-terms plans, identifying new contacts
and leads in the salesperson’s social network, confirming agreement on mutual goals in
the sales presentation close, considering personal development important for own success
might be the best predictors of a salesperson’s job performance (details in subchapter 4.4,
table 4-45).
These activities emphasise the strategic, future-oriented function of the salesperson’s
behaviour. This strategic view of the customer and sales person development activities
should be more strongly emphasised in the company’s sales culture, sales model, in
existing sales tools, the CRM system, and be incorporated into company excellence and
sales development programmes. For example, companies need to re-emphasise their longterm plans as part of their quarterly and half-yearly account management review process
or encourage their salespeople to locate new contacts in their social network by
organising networking events, conferences, and trade shows, and holding events where
clients are invited to the company site and accompanied by the sales representatives.
Companies need to develop solid and diversified IT financing offerings so that their
salespeople introduce these as part of their sales strategy. This will not only expand the
offering base for the company solutions, but also strengthen customer relationship, trust,
and foster loyalty.
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This study identified that customer satisfaction is an important element of sales success.
However, it also discovered that customer satisfaction checks should be done less
frequently than they are being done at the moment, especially regarding the whole mix of
activities. Satisfied customers are important for a sustainable customer relationship
management, but the customer satisfaction must not be viewed as a bureaucratic “musthave” component by both the seller and the customer. It is recommended that companies
revise the customer satisfaction measurement approach: simplify it, reduce its frequency,
and do it less officially, i.e. make it more personal. In any case, neither the sales
representative nor the customer should be frequently overburdened with a long list of
questions that are difficult to answer. More informal satisfaction checks as part of the
consultative selling approach might be preferred by both the sales representative and the
customer.
When they know which factors differentiate top performers, companies must not only
motivate the average employees, but also to create conditions which generate top
performance (Belz and Belz, 2010: 37). In this respect, training and mentoring plays a
crucial role. The training effort should be directed in the first place at lower performers
and new hires. Training needs to focus on product knowledge development, skills of
developing plans, choosing an appropriate sales close, prospecting for new customers
while socialising and navigating in the Web, selling value of company solutions, using
adaptive selling, and doing other activities associated with sales job performance.
The results of the analysis of time allocation and time utilisation profiles (subchapter
4.1.4) contain interesting implications for managers as well. The results indicated that
internal activities were the most time consuming occupation of salespeople, whereas the
salespeople in question felt that they did not invest enough time in crucial elements of
their job, i.e. locating customers, planning and personal development. In this respect, we
recommend that sales management reduce the role stress resulting from the increasing
amount of non-customer related and non-development related activities, such as
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administration and internal tasks of their salespeople as much as possible by relocating
these tasks to other departments and functions. By taking appropriate measures, sales
managers will allow their salespeople to spend more time on locating new leads and
customers, planning and working on their own personal development. These are activities
that the salespeople felt they did not have enough time to do properly.
Since a time utilisation profile is a widely used instrument in a number of companies to
assess effort invested in different types of activities, our study demonstrated that this
measurement instrument is not only expensive, but should also be used with caution as it
has proven its low validity. Although the output of such time utilisation studies may
provide a general picture of where the time is generally spent by the sales force, it is not
recommended that this data be used as an input in rigorous models on association of time
utilisation with other measurements.
Implications for sales practitioners
An understanding of the factors and patterns of behaviour and their success elements are
of high practical value to salespeople since they allow the evaluation of risks and
opportunities connected with the sales opportunity identification and progression. The
developed instrument of behaviour and its success elements may guide this evaluation in
a structured way. The concrete implications are can be as follows.
Prospecting is the most important activity salespeople do to add new customers or replace
lost ones (Weitz, Castleberry, and Tanner, 2007: 170). Our study results showed that
selling in IT requires great emphasis on locating new contacts and efficient contact and
lead management. The social network helps salespeople to identify potential new sales
opportunities. The most unsophisticated and the best hint here could be: “Do not go far
away for your leads; they may be closer than you think.” The easiest sources of leads
generation, even for a new salesperson, may probably be the people he/she already knows
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(Anderson and Dubinsky, 2004: 77). Neighbours, friends, or relatives are, amongst others,
helpful in assisting to find connections, especially if a seller is a beginner (Anderson and
Dubinsky, 2004: 77). The newly popular Internet platforms, such as XING or LinkedIn,
offer a great many new contact possibilities. At the present time, the Internet is a huge
source of hidden leads: websites, boards, feedback templates, and organisational charts
offer potential contact possibilities (Weitz, Castleberry, and Tanner, 2007: 178).
Conferences and trade shows should not be underestimated as well. Most attendees at
such events are mainly potential or existing customers (Weitz, Castleberry, and Tanner,
2007: 181).
It is also suggested that salespeople carefully select and qualify their identified contacts,
and focus on those who will bring the most potential business to the sales company.
This study found that planning had a significant impact on sales performance.
Salespeople should permanently focus on developing long-term plans for their
relationships with clients, territory plans, and sales initiative plans. The analysis of market
trends and the evaluation of market and customer potential may provide valuable input for
the formulation of this strategic sales vision. The sharing of this and other market
information with the team or company management would also facilitate the
implementation of sales initiatives, thus improving opportunity identification and
progression. Short-term planning of customer calls and visits, as well as internal meeting
planning should not be underestimated as well since they are connected with the time
management skills.
Every sales presentation of an IT salesperson needs to include contemporary techniques,
such as value-added selling, consultative selling and adaptive selling. During sales
presentations and their preparation, sellers of IT products need to focus on both the
customer and the product so that the delivered message increases the customer’s
understanding of the IT offering.
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It is crucial that the salespeople use the proper sales close techniques during closing
situations. A successful close should confirm a win-to-win agreement reached by both
seller and the buyer and focus on continuation of the existing relationship (Anderson and
Dubinsky, 2004: 207). Since a properly managed close is mainly a matter of experience, it
is important for sellers to improve their closing skills by visiting appropriate training
sessions or requesting mentoring from more experienced salespeople.
The after-sale customer relationship maintenance may be a way of developing further
opportunities. It is recommended that the sellers thank the customers for the successful
sales transaction on the phone/by email/ or face-to-face; regularly invite clients on site,
and arrange customer financing.
As a part of their non-customer related activities, the salespeople should invest a
considerable amount of time in personal development: visiting training session on sales
topics, developing product skills crucial in the complex world of IT offerings, and, as
already mentioned above, study market trends important for the strategic vision creation.
The establishment and maintenance of a good relationship base not only with direct
customers, but with internal specialists, as well as the development of relationships with
distributors and business partners would help salespeople to do their jobs more efficiently,
and thus, more successfully.
5.4 Limitations and suggestions for further research
This study had several limitations. First, it only examined one industry – information
technology, and one company, which means that it is only possible to generalise the
results within the frame of the research company and a similar company in this industry.
In this way, the results may not be generalisable to other industries. This generalisability
issue is characteristic of many research studies in sales management. These studies (e.g.
179
Dwyer et al., 2000; Kerber and Campbell, 1987) were also consistent with the
contingency theory of Weitz (1979) and achieved high internal but low external validity.
On the other hand, as we mentioned from the results of previous studies, the context plays
a considerable role in the study results, and “what has been found to hold for a specific
selling situation may not be true for others” (Darmon, 1998: 31).
The second limitation was of a statistical nature – the sample size. Although our sample
of about 122 people is considered to be average in sales research, a larger number of
participants would have offered more room for statistical analysis. For this reason, we
would suggest that future studies increase the sample size and cover other companies or
industries to address these statistical and external validity issues.
Our study offers several areas for further research. Because of the exploratory nature of
this study, we aimed to identify the main behavioural and activity elements that underlie
the job of a seller. With the results of this research, further studies may concentrate on
improving several domain constructs (e.g. increase the number of correlated variables in
the factor constructs with lower reliability measures) by conducting more pre-testing. The
same recommendation is valid for the theory of Moncrief and Marshall (2005) which did
not end with empirical validations. Our model is the first exploratory proposal for
contemporary dimensions of salespeople behaviour, and other studies may focus on the
further development of the sales process model based on the findings from our study. The
next step could be also the use of the dimensions received in our research in multivariate
analysis.
Another idea for further research could be to reconceptualise the list of selling techniques
elaborated by Dubinsky (1980-81), and developed further in different studies of Dwyer et
al. (2000) and Jaramillo and Marshall (2004), taken into account recent developments in
the selling function. Of course, analysis of one particular activity domain, such as
prospecting, i.e. from a “micro” perspective, could be also done in more detail.
180
As already mentioned in Kerber and Campbell (1987), Moncrief (1986a, b), or Moncrief
(2006) the activity lists are also supposed to be dependent on a salesperson’s role, the
product sold or the customer type. In this respect, the investigation of the activity
differences and similarities across different customer types and products could be studied
further.
Another interesting approach could be to investigate different cluster types of behaviour
patterns based on the set of activity dimensions received in this study. A similar approach
has already been implemented on a set of American manufacturing companies (e.g.
Moncrief, 1986; Moncrief et al., 2006), but other industries and countries would be
interesting to investigate and compare.
One more possible direction for further research could also be the situational
investigations of conditions and consequences of behaviours in different selling contexts
(e.g. in the form of cases studies) – i.e., in which situations which behaviours deliver
results in terms of trust, commitment, relationship, and finally contribute to sales
performance.
In addition, it would be interesting to know whether the “out-of-work” activities and not
only the “duty activities” of a salesperson influence his/her sales success. For example,
spending time socialising out of work, exercising, spending time for hobbies and with
families.
Although the current study included the service component in the investigation of
activities, the activity and behaviour research in the service company context remains
scarce (Moncrief et al., 2006), which suggests possibilities for further detailed analysis.
181
6 BIBLIOGRAPHY
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198
7 APPENDICES
7 APPENDICES
Appendix 1. Major job role summaries in the sample company
Technical sales engineer works directly with business partners and sales representatives
to identify technical fit between the implemented and sold technology and customer needs.
Demonstrates and explains benefits of technology, establishes value proposition. Provides
technical presentations and product demonstrations, develops prototypes, assists
feasibility studies, follows-up technically with the customers, supports trade shows, and
delivers workshops through territory for existing customers and prospects.
Software Sales Representative promotes customer awareness of software solutions,
drives opportunity identification and progression, coordinates multiple brand offerings.
Territory Sales Representative develops through direct customer contact an effective
business relationship with clients. Identifies opportunities, provides solutions to the
business needs, uses available marketing resources. He selects and prioritises high-growth
opportunities, participates in cross functional marketing teams, develops customer
propositions, selects and works with channels, identifies, and utilises marketing resources.
Sales Specialist Software Brand works closely with sector client teams to move
opportunities forward, holds customer presentations, delivers proposals, and closes
business.
Sales Representative Small and Medium Business develops through customer contact
an effective relationship with major clients in a specific customer segment. Identifies
opportunities, provides solutions to customer’s business needs to improve their
performance. He makes use of available marketing resources. Selects high value
199
7 APPENDICES
opportunities, develops customer propositions, selects offerings to meet client’s needs,
selects channels.
Systems Sales Representative is a cross-brand sales specialist responsible for
opportunity ownership for his brand segment. He is responsible for closing deals, working
with prospects for products and for opportunity identification.
Integrated Technology Services Manager is responsible for identifying opportunities
for infrastructure solutions, establishing new and maintaining existing relationships with
customers from the client’s business units and specially on executive level. Collaborates
with client team and other brands during opportunity identification and validation.
Engages technical professionals for transition of opportunity ownership.
Services Sales Specialist specialises in selling of one specific line of business (services
type). He is deployed to specific opportunities to apply his sales and solution knowledge.
He is selling offerings directly and through business partners. He presents the value of
solutions to all levels of customer management, teaming with other sales and technical
sales.
Channel Sales Manager is responsible for sales of products to business partners:
recruitments, development, management and enablement of business partners. He works
closely in territory teams to track leads.
IT Services Sales Representative develops and maintains executive business
relationships and is responsible for identifying opportunities. He collaborates with client
teams and brands. Receives opportunity ownership. Is responsible for relationship support
and business management in new and existing relationships.
200
7 APPENDICES
Appendix 2. List of the personal pre-test interviews based on
cognitive techniques
1.
Interview counterpart, position
Date of interview; time; place
Client relationship executive of a large scale
02.02.2010; 9.00-10.00; Zurich
account
2.
Senior sales specialist technology services
03.02.2010; 13.00-14.00; Zurich
3.
Marketing manager, sales representative for
03.02.2010; 15.00-16.00; Zurich
financial services institutions
4.
Sales representative small and medium
04.02.2010; 9.30-13.00; Zurich
business; senior bid manager
5.
Sales leader top accounts and complex deals
04.02.2010; 9.00-10.30; Zurich
6.
Sales manager hardware
05.02.2010; 10.30-11.30; Zurich
7.
Sales leader of consulting services
05.02.2010; 17.00-19.00; Zurich
8.
Web sales representative
05.02.2010; 14.00-15.00; Zurich
9.
Sales manager small accounts
09.02.2010; 19.00-20.00; Zurich
10. Senior sales operation manager hardware group
10.02.2010; 19.00-20.00; Zurich
11. Senior sales operation manager software group
29.01.2010; 15.00-16.00; Zurich
07.02.2010; 21.00-22.00; Zurich
12. Senior sales support professional of technology
08.02.2010; 17.00-18.00, Zurich
services sales unit
13. Senior engagement manager for consulting
11.02.2010; 9.00-10.00, Zurich
services
14. Software services sales representative
15.02.2010; 15.00-16.00; Zurich
15. Account manager small and medium business
17.02.2010; 11.00-12.00; Zurich
16. Sales representative for technology services
16.02.2010; 10.00-11.00; Zurich
17. Client relationship executive for the customers
22.02.2010; 9.45-10.45, Zurich
of public sector
18. Human resources manager
16.02.2010; 14.00-15.00; Zurich
19. Sales manager hardware
09.03.2010; 12.00-13.00; Zurich
201
7 APPENDICES
Appendix 3. Initial pre-test version of a questionnaire
202
7 APPENDICES
203
7 APPENDICES
204
7 APPENDICES
205
7 APPENDICES
206
7 APPENDICES
207
7 APPENDICES
208
7 APPENDICES
Appendix 4. The final version of the questionnaire
209
7 APPENDICES
210
7 APPENDICES
211
7 APPENDICES
212
7 APPENDICES
213
7 APPENDICES
214
7 APPENDICES
215
7 APPENDICES
216
7 APPENDICES
Mean
Median
Mode
Standard
deviation
Skewness
Kurtosis
Minimum
Maximum
People manager
120
2
Age
121
1
42.6
43
40
8.3
-0.1
-0.8
27
58
Total Selling Experience
Company Selling
Experience
122
0
12.9
13
15
7.7
0.7
0.4
0.1
35
122
0
8.9
8
10
6.3
1.2
2.3
0.1
35
Sex
122
0
Search company tools
Identify new leads in
customer organisation
Identify new leads from
internet
Identify new contacts in
social network
Qualify, select and
deactivate contacts
Respond to customer
requests
Collect more specific info
in internet
Collect more specific info
in own records
Work with DBs to gain
customer information
Work with DBs to input
customer information
120
2
4.0
4
4
1.8
0.2
-1.0
1
7
120
2
5.6
6
6
1.5
-1.3
1.0
1
7
120
2
3.9
4
4
1.9
0.1
-1.1
1
7
120
2
5.0
6
6
1.8
-0.7
-0.6
1
7
116
6
4.7
5
6
1.7
-0.5
-0.7
1
7
119
3
6.2
7
7
1.1
-2.0
5.4
1
7
121
1
5.4
6
6
1.6
-1.2
0.8
1
7
122
0
5.5
6
6
1.2
-0.9
1.0
1
7
122
0
4.0
4
2
1.7
0.0
-1.1
1
7
121
1
3.8
4
2
1.6
0.2
-1.0
1
7
Arrange call/interview
121
1
6.3
7
7
1.1
-2.0
5.2
1
7
Tailor sales presentation
Prepare meeting
guidelines
122
0
6.3
7
7
1.1
-2.3
6.7
1
7
121
1
6.4
7
7
0.9
-2.0
4.9
2
7
Prepare for objections
120
2
6.1
6
6
1.0
-1.5
2.9
2
7
Engage pre-sales support
Customer focused
approach: open interview
122
0
5.7
6
7
1.5
-1.1
0.5
1
7
121
1
5.9
6
7
1.3
-1.5
2.5
1
7
Product-benefit approach
121
1
4.9
5
6
1.7
-0.7
-0.5
1
7
Statement approach
Product benefit approach
presentation
120
2
5.6
6
6
1.2
-1.0
1.3
1
7
121
1
5.1
5
5
1.4
-0.7
0.3
1
7
122
0
6.2
7
7
1.1
-2.0
4.9
1
7
121
1
6.1
6
7
1.1
-2.0
5.1
1
7
Sell value of company
solutions
Missing
Variable
N Valid
Appendix 5. Descriptive statistics for the variables in the study
217
Variable
N Valid
Missing
Mean
Median
Mode
Standard
deviation
Skewness
Kurtosis
Minimum
Maximum
7 APPENDICES
121
1
5.8
6
6
1.2
-1.4
2.1
1
7
Build trust
122
0
6.6
7
7
0.9
-3.7
17.5
1
7
Listen actively
122
0
6.6
7
7
0.8
-3.0
11.4
2
7
Ask questions
121
1
6.6
7
7
0.9
-3.2
11.8
2
7
Help clients plan
119
3
5.6
6
7
1.4
-1.2
1.3
1
7
Submit bids
122
0
4.1
4
2
1.8
0.1
-1.1
1
7
Clarify objections
120
2
6.0
6
6
1.0
-1.5
2.9
2
7
Repeat the benefits
Provide solution for
customer problems
119
3
5.2
6
6
1.5
-0.9
0.3
1
7
121
1
6.3
7
7
0.9
-2.3
7.5
2
7
Negotiate prices, terms,
conditions
121
1
4.6
5
7
1.9
-0.3
-1.1
1
7
121
1
5.5
6
7
1.6
-1.1
0.5
1
7
Confirm agreement
118
4
5.9
6
7
1.3
-1.6
2.8
1
7
Thank customer
Check customer
satisfaction
122
0
6.4
7
7
1.0
-2.6
9.7
1
7
122
0
5.6
6
6
1.3
-1.4
2.7
1
7
Handle complaints
122
0
5.6
6
6
1.3
-1.0
0.4
2
7
Supervise installations
120
2
4.8
5
5
1.6
-0.7
0.3
1
7
Perform maintenance
118
4
3.0
3
1
1.8
0.5
-1.0
1
7
Invite clients on site
Organize customer
workshops
121
1
5.2
6
6
1.6
-0.9
0.2
1
7
121
1
5.0
5
6
1.7
-0.9
-0.1
1
7
Arrange financing
121
1
4.5
5
5
2.0
-0.5
-1.0
1
7
Socialize with customer
Plan customer calls and
visits
121
1
4.7
5
5
1.6
-0.6
-0.3
1
7
120
2
6.0
6
7
1.1
-1.4
1.7
2
7
120
2
5.9
6
7
1.2
-1.1
1.1
2
7
120
2
5.6
6
7
1.3
-0.8
0.0
2
7
Participate in internal
meetings
Coordinate orders,
delivery
121
1
5.5
6
6
1.5
-1.0
0.3
1
7
120
2
5.9
6
6
1.0
-1.0
1.1
2
7
119
3
4.7
5
6
1.8
-0.4
-0.8
1
7
Socialize with internal
specialists
Document, report market
information
120
2
4.7
5
7
2.0
-0.5
-1.1
1
7
121
1
5.5
6
6
1.3
-1.3
1.7
1
7
119
3
5.2
6
6
1.5
-0.8
0.1
1
7
Do administration
118
4
5.8
6
7
1.3
-1.0
0.5
2
7
218
Variable
N Valid
Missing
Mean
Median
Mode
Standard
deviation
Skewness
Kurtosis
Minimum
Maximum
7 APPENDICES
Participate in sales
trainings
121
1
4.7
5
4
1.3
-0.2
-0.4
2
7
Train your tools skills
121
1
3.8
4
4
1.5
0.0
-0.5
1
7
117
5
5.1
5
6
1.5
-0.8
0.1
1
7
Train new hires
Set up and visit
conferences
121
1
4.6
5
6
1.5
-0.5
-0.6
1
7
121
1
4.5
5
5
1.5
-0.6
-0.3
1
7
Study market trends
121
1
5.0
5
5
1.4
-0.6
0.2
1
7
Travel out of town
118
4
5.7
6
7
1.3
-1.0
0.5
2
7
Overnight stays
Importance: locating
potential buyers
Importance: Pre-Sales
preparation
Importance: Sales
presentation
Importance: Handling
objections
121
1
3.2
3
2
1.6
0.9
-0.1
1
7
120
2
5.9
6
7
1.4
-1.7
2.8
1
7
122
0
6.0
6
7
1.2
-1.4
1.7
2
7
122
0
6.0
6
7
1.2
-1.1
0.5
2
7
122
0
6.3
7
7
0.9
-1.0
0.1
4
7
Importance: Close
122
0
6.0
6
7
1.2
-1.4
1.6
2
7
Importance: Follow-up
122
0
6.4
7
7
1.0
-2.2
5.5
2
7
Importance: Planning
Importance: Internal
activities
Importance:
Administration
Importance: Personal
development
121
1
5.9
6
7
1.1
-1.1
1.4
2
7
122
0
4.5
5
5
1.6
-0.1
-1.0
1
7
122
0
3.6
3
2
1.7
0.5
-0.8
1
7
121
1
5.7
6
6
1.3
-1.0
0.9
1
7
Importance: Travel
119
3
4.7
5
6
1.7
-0.6
-0.5
1
7
Customer days: number
119
3
8.8
8
10
3.5
0.4
-0.4
2
19
Office days: number
Customer day: locating
buyers
Customer day: pre-sales
preparation
Customer day: sales
presentation
119
3
11.1
12
12
3.6
-0.4
-0.5
1
18
110
12
0.5
1
0
0.5
0.7
0.3
0
2
110
12
1.4
1
1
1.0
1.5
4.4
0
6
110
12
2.1
2
2
0.9
1.0
1.0
0.5
5
Customer day: follow-up
110
12
1.4
1
1
0.9
1.7
5.9
0
6
Customer day: planning
Customer day: internal
activities
Customer day:
administration
Customer day: personal
development
110
12
0.8
1
1
0.5
0.4
0.3
0
2
110
12
1.1
1
1
1.1
2.1
6.9
0
7
110
12
0.8
1
1
0.8
1.4
3.0
0
4
110
12
0.2
0
0
0.4
1.2
0.1
0
1
219
Missing
Mean
Median
Mode
Standard
deviation
Skewness
Kurtosis
Minimum
Maximum
N Valid
7 APPENDICES
Customer day: travel
Customer day: total hours
worked
Office day: locating
buyers
Office day: pre-sales
preparation
Office day: sales
presentation
110
12
1.5
1
1
0.9
0.9
1.6
0
5
110
12
9.7
10
10
1.7
1.1
3.5
4
16
109
13
0.7
1
1
0.7
2.1
10.3
0
5
109
13
1.4
1
2
1.0
0.8
0.9
0
5
109
13
0.9
1
0
0.9
0.9
0.5
0
4
Office day: follow-up
109
13
1.2
1
1
0.8
0.8
1.1
0
4
Office day: planning
Office day: internal
activities
Office day:
administration
Office day: personal
development
109
13
1.2
1
1
0.7
0.6
0.3
0
3
109
13
1.8
2
2
0.9
0.7
0.0
0
4
109
13
1.7
2
2
1.1
2.2
9.6
0
8
109
13
0.6
1
1
0.6
2.1
9.3
0
4
109
13
0.2
0
0
0.6
2.4
4.5
0
2
109
13
9.6
9
9
1.6
1.6
3.3
7
16
114
8
5.7
6
6
1.1
-0.5
-0.3
3
7
114
8
5.8
6
6
0.9
-0.6
0.0
3
7
118
4
6.0
6
6
0.9
-0.7
0.3
3
7
118
4
5.8
6
6
0.7
-0.4
0.0
4
7
119
3
6.1
6
6
0.8
-0.3
-0.6
4
7
118
4
5.8
6
6
0.8
-0.4
-0.2
4
7
118
4
5.4
6
6
1.2
-1.0
0.8
2
7
117
5
5.5
6
6
1.1
-0.6
-0.3
2
7
112
10
5.8
6
6
0.6
-0.5
0.2
4
7
Variable
Office day: travel
Office day: total hours
worked
Achieve sales targets
Contribute to sales unit
targets
Build effective
relationships
Make effective
presentations
Understand Customer
Understand own
company
Provide feedback to
management
Keep expenses
Average sales
performance measure
220
Curriculum vitae
Born: February 15th, 1979
in Minsk, Belarus
Email: [email protected]
Professional Experience:
since 2009
International IT Company, Zurich: Consultant
2004 – 2008
International IT Company, Zurich: Client Process Transformation
Team Central and Eastern Europe, CRM Deployment within Sales
Operations
2004
International IT Company, Zurich: Trainee in Sales Operations, Sector
Sales
2001 – 2003
Minsk Wheel Tractor Plant, Belarus: International Purchasing Manager
2001
Ministry of Foreign Affaires, Minsk: Trainee in Investment Office
1999 – 2006
Part-time jobs as freelancer: journalist, interpreter, teaching assistant,
study trip programme writer and organiser, court secretary, editor
Education:
2004 – 2010
University of St. Gallen, Switzerland: Doctoral Student
2003 – 2004
University of St. Gallen, Switzerland: MAPOW-Scholarship Holder
1996 – 2001
Belarus State Economic University, Minsk: Diplom (Honours) in
International Business and Economics; Award of the Special Fund of
President of Belarus for talented students
1986 – 1996
Elementary School, Music School and Francisk Skaryna Gymnasium,
Minsk
1995
Lewiston Porter High School, USA: Exchange Student
221

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