Exploration of Salespeople Activities and Behaviour in

Exploration of Salespeople Activities and Behaviour in Information Technology Selling

D I S S E R T A T I O N

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

IV

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.

V

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

VI

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 Natalia Bächli-Bolvako

TABLE OF CONTENTS

VII

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

TABLE OF CONTENTS

VIII

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

TABLE OF CONTENTS

IX

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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X

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

TABLE OF CONTENTS

XI

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

LIST OF ABBREVIATIONS

XII

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)

LIST OF FIGURES

XIII

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

LIST OF TABLES

XIV

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

LIST OF TABLES

XV


“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

LIST OF TABLES

XVI

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

ABSTRACT

XVII

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.

ZUSAMMENFASSUNG

XVIII

Zusammenfassung

Das Ziel dieser Studie war, ein umfangreiches "Makro"-Ebene-Bild der Verkäufer-

Aktivitäten 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 Informationstechnologie-

Marktes, 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.

INTRODUCTION

1

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

INTRODUCTION

2

(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).

INTRODUCTION

3

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

INTRODUCTION

4

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).

INTRODUCTION

5

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.

INTRODUCTION

6

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 non-

empirical. 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

INTRODUCTION

7

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.

INTRODUCTION

8

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.

INTRODUCTION

9

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.

INTRODUCTION

10

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.

2 THEORETICAL BACKGROUND AND LITERATURE REVIEW

11

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.


12

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;


13

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 Activity model components (aggregated) Lamont and Lundstrom (1974) (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

Moncrief (1986a, b) (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

Kerber and Campbell (1987) (1) order processing (2) customer contact (3) dealing with co-workers (4) miscellaneous activities

Darmon (1998) (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

Marshall, Moncrief and Lassk (1999) (1) communication2 (2) sales (3) relationships (4) team-building/team selling (5) database management

1 All theories in this overview table are listed in chronological order. 2 These activities aim to enlarge the model of Moncrief (1986a, b).


14

Author, year Activity model components (aggregated) Guenzi (2002) (1) selling (2) communication (3) value-adding relationship management

(4) analytical-strategic Moncrief, Marshall, and Lassk (2006) (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.


15

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 cross-

loadings and low factor loadings under 0.5. In this way, only 55 activities remained in the

final list.


16

Table 2-2: A summary of the activities in the study of Moncrief (1986a, b) Factor Name Activity Composition 1 Selling function

(α = 0.82) 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 (α = 0.80)

Correct orders (0.65) expedite orders (0.71) handle back orders (0.66) handle shipment problems (0.66)

3 Servicing the product (α = 0.81)

Test equipment (0.71) be present during repairs (0.70) supervise installations (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 (α = 0.62)

Provide feedback to supervisor (0.72) receive feedback from clients (0.61) check-in with superiors (0.59) read trade publications (0.54) provide technical information (0.52)

5 Servicing the account (α = 0.73)

Inventory (0.72) set-up point of purchase displays (0.65) handle local advertising (0.51) stock shelves (0.51)

6 Conferences/meetings (α = 0.75)

Attend sales conferences (0.66) attend regional sales meetings (0.64) work client conferences (059) set up exhibitions, trade shows (0.53) attend training sessions (0.63) fill out questionnaires (0.54)

7 Training/recruiting (α = 0.56)

Look for new sales representatives (0.61) train new representatives (0.55) travel with trainees (0.56) help company management plan selling activities (0.54)

8 Entertaining (α = 0.73)

Take clients to lunch (0.64) drink (0.68) evening meal (0.69) party (0.57) golf, fishing, tennis etc. (0.72)

9 Out of town travelling (α = 0.74)

Spend night on road (0.69) travel out of town (0.66)

10 Working with distributors (α = 0.59)

Sell to distributors (0.62) establish relations with distributors (0.53) extend credit (0.57) collect past due accounts (0.51)

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.


17

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


18

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.”


19

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).


20

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 2-

3 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 Activity Description 1 Size of the information load

required by the sales position 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


21

Item Characteristic 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

2 Extent and complexity of information processing required by the sales position

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

3 Importance of time management (relative to relationship management) implied by the sales position

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

Source: Darmon (1998: 39) 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


22

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.

Table 2-4: The list of new selling activities of Marshall et al. (1999) Communication Sales Relationship Team Database

Tec

hnol

ogy

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

Set up appointments, script sales pitch from database, use software for customer background, laptop for presentation, VCR for presentation, provide technology, ability to customer

Web page Conference calls Collect new information from database, enter information/data on laptop, update customer files

Non

-tec

hnol

ogy

Practise language skills

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

Bring in vendor/alliance, develop relationships, hand-hold 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

Source: Marshall et al. (1999: 89)


23

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


24

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 eleven-

activity 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


25

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

Factor 2 Communica-tion

Factor 3 Analytical-strategic

Factor 4 Value-adding relationship management

Selling 8.21 .899 Transmission of information and communication to the customer

7.01 .706

Acquisition and transfer to the company of written information about customers

4.58 .836

Acquisition and transfer to the company of written information about competitors

3.98 .854

Coordination of the sales team inside the selling company

5.67 .843

Market analysis and sales forecasts

5.95 .587

Customer profitability analysis

4.50 .883

Provision of pre-sales services

5.66 .813

Provision of after-sales services

5.86 .632

Customer relationship management and development

7.12 .793

Source: Guenzi (2002: 759)

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 two-

way 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


26

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


27

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 Description 1 Relationship selling 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 and sales service

Point of purchase (0.818) set up displays (0.780) handle advertising (0.779) 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)


28

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


29

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).


30

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 1. Locating and prospecting

Step 2. Pre-approach

Step 3. Approach

Step 4. Sales presentation

Step 6. Close

Step 5. Handling objections

Step 7. Post-sales follow-up

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


31

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).


32

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


33

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


34

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 pre-

approach: “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.


35

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).


36

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


37

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. face-

to-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).


38

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).


39

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


40

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.


41

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.


42

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).


43

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 non-

customer interaction behaviour impact on sales performance.


44

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.


45

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


46

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


47

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.


48

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


49

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 self-

ratings. 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.


50

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 close7.” 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).


51

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,


52

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 selling8” 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.

3 METHODOLOGY OF ACTIVITY DOMAINS DEFINITION AND VALIDATION

53

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 under-

representation 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.


54

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.


55

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


56

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).


57

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 seller-

buyer 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).


58

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).


59

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


60

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


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(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, 1980-

1981).

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 “product-

benefit 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 “win-

win” 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


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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;


73

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 time-

consuming, 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 self-


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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 non-


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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).


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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 non-

verbal. 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 value-

added 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.

4 EMPIRICAL STUDY RESULTS

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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


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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).


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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 non-

respondents (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 F Sig. Total selling experience .041 .839 Company selling experience 2.146 .148 Age .105 .747 Identify new leads through customer organisation .013 .909 Identify new contacts in social network .870 .355 Confirm agreement .271 .605 Importance: sales presentation .924 .340 Customer day: sales presentation .360 .551 Office day: personal development .007 .926 Average sales performance (multi-item) 3.367 .072


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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

Little’s MCAR test Variables

Question block(s)

Average missing data χ2 df Sig.

Socio-demographic variables 2 -10 .2% 18 2 .000 Frequency variables 11 - 21 1.5% 2042 1894 .009 Importance variables 22 1% 49 39 .127 Time evaluation variables 23 - 25 8% 33 34 .487 Sales performance variables 26 4% 17 28 .983

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).


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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)


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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

0.5

1.4

2.1

1.4

0.8

1.1

0.8

0.2

1.5

0.7

1.4

0.9

1.2 1.2

1.81.7

0.6

0.2

Customer day/ hrs Office day/ hrs

Customer days= 44% per month office days = 66% per month

Average customer day: 9.7 hrsAverage office day: 9.6 hrs

Locating buyersPre-sales preparationSales presentationFollow-upPlanningInternal activitiesAdministrationPersonal developmentTravel

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


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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

6.3 5.9

14.2

6.0

14.9

6.0

13.9

6.4

10.5

5.9

15.0

4.5

12.8

3.74.5

5.7

7.8

4.7

Locatingbuyers

Pre-salespreparation

Salespresentation

Follow-up Planning Internalactivities

Administration Personaldevelopment

Travel

Time per month spent, %Importance of the activity

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.


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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 4 5 6 1. Importance: Sales presentation 1 2. Customer day: sales presentation, hrs .088 1 3. Office day: sales presentation, hrs .150 .046 1 4. Customer-oriented presentation, frequency, multi-scale .281** .034 -.115 1 5. Product-oriented presentation, frequency, multi-scale .275** -.056 .236* .436** 1 6. Sales presentation, hrs per month .160 .631** .676** -.011 .054 1 *. All coefficients (Pearson) significant at p < 0.05 (two-sided). **. All coefficients (Pearson) significant at p < 0.01 (two-sided).

Table 4-4 indicates some correlations between the importance measurement with all other

measurement types: time allocation and frequency multi-scale. However, the correlations


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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 3 4 5 1. Importance: Personal development 1 2. Customer day: personal development, hrs .227* 1 3. Office day: personal development, hrs .219* .257** 1 4. Average of personal development, frequency .352** 0.053 0.168 1 5. Personal development, hrs per month .249** .510** .922** 0.125 1 *. All coefficients (Pearson) significant at p < 0.05 (two-sided). **. All coefficients (Pearson) significant at p < 0.01 (two-sided).

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


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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


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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.


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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.


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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 cross-

loadings 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.


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Table 4-5: Factor analysis results 1 Component

1Identify contacts and new leads from internet

.782

Identify contacts and new leads customer organisation

.752

Identify new contacts in social network

.710

Qualify, select and deactivate contacts

.701

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.


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Table 4-6: Descriptive statistics, inter-item and item-total correlations for the factor “Contact and lead management”

Variable Mean SD 1 2 3 4

Corrected item - total correlation

1. Identify new leads and decision-makers from internet 3.90 1.870 1 .5592. Identify new leads and decision-makers through customer organisation 5.65 1.471 .418** 1 .5223. Identify new contacts/leads in social network 5.03 1.836 .401** .350** 1 .4784. Qualify, select and deactivate contacts 4.74 1.712 .389** .395** .321** 1 .464**. All coefficients (Pearson) significant at p < 0.01 (two-sided); N = 116.

4.2.2.2 Factor analysis 2 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 cross-

loadings 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.


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Table 4-7: Factor analysis results 2

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.

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 information

.048 .836 .192

Work with DBs to input customer information

.101 .821 .181

Search company tools for new contacts

.025 .818 -.031

Collect more specific info in own records

.199 .062 .807

Collect more specific info in internet

.022 .200 .802

Eigenvalue 3.392 2.121 1.099

Variance explained, % 34 21 11

Cronbach's alpha .813 .773 .530


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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.

Table 4-8: Descriptive statistics, inter-item and item-total correlations for the factor “Sales call preparation”

Variable Mean SD 1 2 3 4 5


1. Prepare meeting guidelines 6.40 .941 1 .7722. Tailor sales presentation 6.28 1.060 .713** 1 .7083. Prepare for potential objections 6.03 1.054 .654** .536** 1 .6034. Respond to customer requests 6.19 1.141 .490** .474** .420** 1 .5665. Arrange call/interview 6.28 1.125 .488** .459** .330** .394** 1 .529**. All coefficients (Pearson) significant at p < 0.01 (two-sided); N = 116

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).


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Table 4-9: Descriptive statistics, inter-item and item-total correlations for the factor “Database management”

Variable Mean SD 1 2 3


1. Work with DBs to gain customer information 4.02 1.734 1 .661

2. Work with DBs to input customer information 3.76 1.635 .639** 1 .635

3. Search company tools for new contacts 4.00 1.810 .501** .467** 1 .534**. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 120.

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.


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Table 4-10: Descriptive statistics, inter-item and item-total correlations for the factor “Other information gathering”

Variable Mean SD 1 2

Corrected item - total

correlation 1. Collect more specific information in internet 5.45 1.560 1 .3742. Collect more specific information in own records 5.47 1.198 .374** 1 .374**. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 121.

4.2.2.3 Factor analysis 3 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 cross-

loadings 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.


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Table 4-11: Factor analysis results 3 Components

1 2Listen 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 opening interview

.616 -.014

Statement approach of opening interview

.520 .123

Repeat the benefits of the product .139 .867Product-benefit approach of opening interview

.080 .826

Product benefit approach presentation

.305 .768

Eigenvalue 5.668 1.641

Variance explained, % 47 14

Cronbach's alpha .885 .783

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


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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.

Table 4-12: Descriptive statistics, inter-item and item-total correlations for the factor “Customer-oriented sales presentation”

Variable Mean SD 1 2 3 4 5 6 7 8 9

Corrected

item - total

correlation

1. Listen actively 6.60 .811 1 .8352. Build trust 6.60 .899 .754** 1 .7773. Ask questions 6.60 .911 .870** .693** 1 .7984. Use consultative selling 6.21 1.097 .640** .569** .713** 1 .6835. Use adaptive selling 5.80 1.241 .594** .651** .568** .518** 1 .6916. Clarify objections 6.04 1.028 .533** .562** .566** .473** .431** 1 .6217. Help clients plan 5.65 1.416 .521** .397** .541** .410** .474** .513** 1 .5848. Statement approach of opening dialogue 5.59 1.231 .424** .443** .363** .223* .362** .258** .272** 1 .4569. Customer-focused approach of opening interview 5.94 1.330 .460** .429** .385** .370** .412** .261** .333** .403** 1 .519**. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 114.

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


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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).

Table 4-13: Descriptive statistics, inter-item and item-total correlations for the factor “Product-oriented sales presentation”



correlation 1. Repeat the benefits of the product while handling objections 5.21 1.513 1 .6062. Product benefit focused presentation 5.14 1.414 .602** 1 .6173. Product benefit approach (opening) 4.90 1.727 .573** .488** 1 .594**. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 118.

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.


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4.2.2.4 Factor analysis 4 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 .788Confirm agreement on mutual goals .749Provide solutions for customer problems

.712

Ask directly for the order .638Eigenvalue 2.096


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.


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Table 4-15: Descriptive statistics, inter-item and item-total correlations for the factor “Sales presentation close”

Variable Mean SD 1 2 3 4 Corrected item - total correlation

1. Negotiate prices, terms, conditions 5.50 1.614 1 .5672. Confirm agreement on mutual goals 5.93 1.305 .450** 1 .4673. Provide solutions for customer problems 6.33 .936 .331** .445** 1 .450

4. Ask directly for the order 4.59 1.858 .478** .237* .241** 1 .407**. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 118.

4.2.2.5 Factor analysis 5 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.

Table 4-16: Factor analysis results 5 Components

1 2Check customer satisfaction .820 .167

Thank customer .767 -.046

Arrange financing .665 .321

Socialise with customer .648 .275

Perform maintenance -.055 .860Organise customer workshops .279 .652Supervise installations .250 .633



Cronbach’s alpha .733 .603


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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.

Table 4-17: Descriptive statistics, inter-item and item-total correlations for the factor “Customer relationship maintenance”



correlation 1. Check customer satisfaction 5.64 1.264 1 .633 2. Arrange financing for customer 4.53 1.996 .495** 1 .552 3. Socialise with customer 4.76 1.572 .501** .412** 1 .527 4. Thank customer 6.41 .989 .532** .411** .338** 1 .509 **. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 120.

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).


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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.

Table 4-18: Descriptive statistics, inter-item and item-total correlations for the factor “Technical support and maintenance”



1. Perform maintenance 3.01 1.765 1 .447 2. Organise customer workshops 5.00 1.722 .379** 1 .400 3. Supervise installations 4.77 1.547 .365** .287** 1 .391 **. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 115.

4.2.2.6 Factor analysis 6 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


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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.

Table 4-20: Descriptive statistics, inter-item and item-total correlations for the factor “Sales planning”



1. Plan internal meetings 5.87 1.147 1 .6852. Plan customer calls and visits 6.04 1.130 .625** 1 .6733. Develop long-term plans 5.66 1.318 .577** .562** 1 .638**. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 120.

4.2.2.7 Factor analysis 7 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.

Component 1

Plan internal meetings .867 Plan customer calls and visits .861Develop long-term plans .836

Eigenvalue 2.192


Cronbach's alpha .812


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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 .848Socialise with internal specialists .156 .815Coordinate orders, delivery .291 .643



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.


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Table 4-22: Descriptive statistics, inter-item and item-total correlations for the factor “Internal coordination and administration”



1. Coordinate aligned team 5.45 1.522 1 .3462. Participate in internal meetings 5.91 1.017 .441** 1 .4883. Document, report market information 5.25 1.537 .291** .301** 1 .505

4. Do administration 5.80 1.291 .121 .338** .483** 1 .402**. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 118.

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.

Table 4-23: Descriptive statistics, inter-item and item-total correlations for the factor “Internal partnerships and delivery”

Variable Mean SD 1 2 3 Corrected item - total correlation

1. Work with distributors 4.68 1.987 1 .5752. Coordinate orders, delivery 4.68 1.806 .458** 1 .4973. Socialise with internal specialists 5.51 1.335 .510** .403** 1 .546**. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 118.

4.2.2.8 Factor analysis 8

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


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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. Table 4-24: Factor analysis results 8

Component 1

Participate in sales trainings .806Set up and visit conferences .791Train tool skills .772Train product skills .766Study market trends .608Train new hires .599

Eigenvalue 3.185


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.


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Table 4-25: Descriptive statistics, inter-item and item-total correlations for the factor “Personal development”

Variable Mean SD 1 2 3 4 5 6

Corrected

item - total

correlation

1. Participate in sales trainings 4.72 1.317 1 .6622. Set up and visit conferences 4.46 1.460 .575** 1 .6603. Train tool skills

3.74 1.487 .658** .454** 1 .6264. Train product skills 5.08 1.446 .606** .518** .533** 1 .6165. Study market trends 5.03 1.387 .321** .509** .309** .304** 1 .4756. Train new hires 4.58 1.513 .298** .387** .355** .358** .413** 1 .466**. All coefficients (Pearson) significant at p < 0.01 (two-sided), N = 117.

4.2.2.9 Factor analysis 9 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.


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Table 4-26: Descriptive statistics, inter-item and item-total correlations for the factor “Travel”

Variable Mean SD 1 2 Corrected item - total correlation

1. Travel 5.73 1.328 1 .1922. Overnight stays 3.15 1.556 .200* 1 .192*. All coefficients (Pearson) significant at p < 0.05 (two-sided), N = 120.

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)

Factor Database management

(α = .773) 1. Identify new contacts in social network .782 1. Work with DBs to gain customer

information .836

2. Identify new leads through customer organisation

.752 2. Work with DBs to input customer information

.836

3. Qualify, select and deactivate contacts .710 3. Search company tools for new contacts .821 4. Identify new leads from internet .701

Factor Other information gathering

(α = .530)

Factor Sales call preparation

(α = .813)

1. Collect more specific information in internet

.807 1. Prepare meeting guidelines .865

2. Collect more specific information in own records

.802 2. Tailor sales presentation .841

3. Prepare for potential objections .750 4. Respond to customer requests .739 5. Arrange call/interview .664

Factor Customer-oriented sales presentation (α

= .885)

Factor Product-oriented sales presentation

(α = .783) 1. Listen actively .877 1. Repeat the benefits of the product while

handling objections .867

2. Ask questions .873 2. Product-benefit approach (opening) .826 3. Build trust .822 3. Product-benefit focused presentation .768 4. Use consultative selling .784 5. Use adaptive selling .721 6. Clarify objections .693 7. Help clients plan .641 8. Customer-focused approach of opening sales interview

.616

9. Statement approach of opening dialogue .520


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Table 4-27: Summary of factor solutions (continued)

Factor Sales presentation close

(α = .670)

Factor Customer relationship maintenance

(α = .733) 1. Negotiate prices, terms, conditions .788 1. Check customer satisfaction .820 2. Confirm agreement on mutual goals .749 2. Thank customer .767 3. Provide solutions for customer problems

.712 3. Arrange financing for customer .665

4. Ask directly for the order .638 4. Socialise with customer .648

Factor Technical support and maintenance

(α = .603)

Factor Internal partnership

management and delivery (α = .706) 1. Perform maintenance .860 1. Work with distributors .848 2. Organise customer workshops .652 2. Socialise with internal specialists .815 3. Supervise installations .633 3. Coordinate orders, delivery .643

Factor Internal coordination and administration (α = .694)

Factor Personal development

(α = .821) 1. Document, report market information .751 1. Participate in sales trainings .806 2. Participate in internal meetings .747 2. Set up and visit conferences .791 3. Do administration .670 3. Train tool skills .772 4. Coordinate aligned team .527 4. Train product skills .766 5. Study market trends .608 6. Train new hires .599

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”.


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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).


143

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 quantile-

quantile (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


144

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.


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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

ANOVA Low performers

Top performers

Variable and Source SS df MS F Mean SD Mean SD

Between groups 221.28 1 221.28 3.47* 12.13 7.68 15.93 8.34 Within groups 3827.51 60 63.79

Total Selling Experience

Total 4048.79 61 Between groups 223.99 1 223.99 6.61* 8.07 4.92 11.89 6.76 Within groups 2032.74 60 33.88

Company Selling Experience Total 2256.73 61

Between groups 130.94 1 130.94 1.92 42.8 7.75 45.71 8.84 Within groups 4091.27 60 68.19

Age

Total 4222.21 61 * 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”


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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”


Top performers


Between groups 22.25 1 22.25 7.19*** 3.12 1.61 4.32 1.93 Within groups 185.64 60 3.09

Identify new leads from internet Total 207.89 61


Identify new leads through customer organisation

Total 145.48 61

Between groups 45.12 1 45.12 15.77*** 4.00 1.79 5.71 1.56 Within groups 171.71 60 2.86

Identify new contacts in social network Total 216.84 61

Between groups 19.19 1 19.19 6.54** 4.21 1.82 5.36 1.55 Within groups 167.32 57 2.94

Qualify, select and deactivate contacts Total 186.51 58 * 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.”


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Table 4-30: One-way ANOVA results for the items of the construct “Database management”


Top performers


Between groups .11 1 .11 .036 3.94 1.77 3.86 1.67 Within groups 179.31 60 2.99

Work with DBs to gain customer information

Total 179.42 61

Between groups 1.36 1 1.36 .547 3.56 1.48 3.86 1.69 Within groups 149.81 60 2.49

Work with DBs to input customer information

Total 151.17 61


Search company tools for new contacts

Total 197.74 61

* p < 0.1. ** p < 0.05. *** p < 0.01 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”


Top performers


Between groups 1.68 1 1.68 .72 5.15 1.54 5.48 1.50Within groups 137.01 59 2.32

Collect more specific info in internet Total 138.69 60

Between groups 5.24 1 5.24 3.40* 5.06 1.28 5.64 1.19Within groups 92.31 60 1.54

Collect more specific info in own records Total 97.55 61 * p < 0.1. ** p < 0.05. *** p < 0.01


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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”


Top performers


Between groups 2.08 1 2.08 2.99* 6.27 .94 6.64 .68 Within groups 40.97 59 .69

Prepare meeting guidelines Total 43.05 60

Between groups .00 1 .00 .01 6.41 .86 6.43 .88 Within groups 45.09 60 .75

Tailor sales presentation

Total 45.10 61 Between groups 1.14 1 1.14 1.26 6.03 .87 6.31 1.05 Within groups 52.51 58 .91

Prepare for objections

Total 53.65 59 Between groups .08 1 .08 .07 6.21 1.17 6.29 .98 Within groups 69.23 59 1.17

Respond to customer requests Total 69.31 60

Between groups 1.38 1 1.38 1.53 6.09 1.01 6.39 .88 Within groups 53.41 59 .91

Arrange call/interview

Total 54.79 60 * 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.”


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Table 4-33: One-way ANOVA results for the items of the construct “Customer-oriented sales presentation”


Top performers


Between groups .29 1 .29 .44 6.47 .71 6.61 .92 Within groups 39.15 60 .65

Listen actively

Total 39.44 61 Between groups 1.57 1 1.57 1.88 6.32 0.81 6.64 1.03 Within groups 49.87 60 .83

Build trust

Total 51.44 61 Between groups .40 1 .40 .54 6.52 .71 6.68 1.02 Within groups 44.35 59 .75

Ask questions


Use consultative selling Total 63.87 61


Use adaptive selling

Total 96.23 60 Between groups 8.44 1 8.44 8.10*** 5.79 .98 6.54 1.07 Within groups 62.52 60 1.04

Sell value of company solutions12 Total 70.97 61

Between groups .43 1 .43 .40 5.94 .85 6.11 1.25 Within groups 64.55 59 1.09

Clarify objections

Total 64.98 60 Between groups 3.02 1 3.02 1.66 5.44 1.26 5.89 1.45 Within groups 107.05 59 1.81

Help clients plan


Statement approach of opening interview


Customer focused approach of opening interview

Total 118.27 61

* p < 0.1. ** p < 0.05. *** p < 0.01

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.


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(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”


Top performers



Repeat the benefits of the product Total 148.84 61


Product benefit approach presentation Total 137.93 60


Product-benefit approach

Total 191.05 61 * 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”


Top performers



Negotiate prices, terms, conditions Total 141.05 60

Between groups 18.90 1 18.90 13.29*** 5.38 1.45 6.50 .79 Confirm agreement on Within groups 82.50 58 1.42


151


Top performers

mutual goals Total 101.40 59 Between groups 3.74 1 3.74 5.13** 6.18 .73 6.68 .98 Within groups 43.02 59 .73

Provide solution for customer problems

Total 46.75 60


Ask directly for the order

Total 194.07 60 * 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”


Top performers


Between groups 4.00 1 4.00 3.49* 5.38 .85 5.89 1.29 Within groups 68.71 60 1.15

Check customer satisfaction

Total 72.71 61 Between groups 44.90 1 44.90 14.02*** 3.65 1.82 5.36 1.75 Within groups 192.19 60 3.20

Arrange financing


Socialise with customer

Total 145.18 61 Between groups 4.24 1 4.24 3.97** 6.12 .91 6.64 1.16 Within groups 63.96 60 1.07

Thank customer

Total 68.19 61 * p < 0.1. ** p < 0.05. *** p < 0.01


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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”


Top performers



Work with distributors


Coordinate orders, delivery


Socialise with internal specialists Total 137.24 61 * 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:


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Table 4-38: One-way ANOVA results for the items of the construct “Personal development”


Top performers



Participate in sales trainings

Total 123.69 61 Between groups 14.28 1 14.28 5.46** 4.00 1.74 4.96 1.45 Within groups 156.96 60 2.62

Set up and visit conferences Total 171.24 61


Train your tools skills


Train product skills

Total 171.41 60 Between groups 8.40 1 8.40 5.02** 4.62 1.33 5.36 1.25 Within groups 100.46 60 1.67

Study market trends


Train new hires

Total 154.19 61 * p < 0.1. ** p < 0.05. *** p < 0.01.

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.


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Table 4-39: One-way ANOVA results for the items of the construct “Internal coordination and administration”


Top performers



Coordinate aligned team

Total 121.55 61 Between groups 4.31 1 4.31 4.93** 5.79 1.05 6.32 .77 Within groups 51.62 59 .87

Participate in internal meetings Total 55.93 60


Document, report market information Total 151.15 60


Do administration

Total 80.85 60 * 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 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“


Top performers


Between groups 9.57 1 9.57 10.00*** 5.65 1.04 6.44 .89 Within groups 56.43 59 .96

Plan customer calls and visits

Total 66.00 60 Between groups 7.09 1 7.09 7.50*** 5.65 1.07 6.33 .83 Within groups 55.76 59 .95

Plan internal meetings


Develop long-term plans

Total 101.25 60 * p < 0.1. ** p < 0.05. *** p < 0.01


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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


Top performers



Travel out of town


Overnight stays


Handle complaints


Invite clients on site


Submit bids


Engage pre-sales support

Total 143.24 61 * 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.


156

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


Top performers



Importance: locating potential buyers Total 170.18 59


Importance: Pre-Sales preparation

Total 80.60 61 Between groups 6.94 1 6.94 5.67** 5.97 1.27 6.64 .87 Within groups 73.40 60 1.22

Importance: Sales presentation Total 80.34 61

Between groups 4.51 1 4.51 5.22** 6.03 .97 6.57 .88 Within groups 51.83 60 .86

Importance: Handling objections Total 56.34 61


Importance: Close

Total 96.27 61 Between groups 5.78 1 5.78 5.47** 6.03 1.14 6.64 .87 Within groups 63.40 60 1.06

Importance: Follow-up

Total 69.18 61 Between groups 13.42 1 13.42 11.94*** 5.53 1.26 6.46 .74 Within groups 67.43 60 1.12

Importance: Planning

Total 80.85 61 Between groups 2.42 1 2.42 .77 4.35 1.79 4.75 1.76 Within groups 189.01 60 3.15

Importance: Internal activities

Total 191.44 61 Between groups .14 1 .14 .05 3.62 1.86 3.71 1.65 Within groups 187.74 60 3.13

Importance: Administration

Total 187.89 61 Between groups 14.89 1 14.89 9.78*** 5.26 1.40 6.26 .98 Within groups 89.80 59 1.52

Importance: Personal development


Importance: Travel

Total 168.40 59 * p < 0.1. ** p < 0.05. *** p < 0.01


157

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


Top performers


Between groups 2.52 1 2.52 .19 8.94 4.05 8.54 3.18 Within groups 814.85 60 13.58

Customer days: number

Total 817.37 61 Between groups 5.74 1 5.74 .43 10.85 4.03 11.46 3.18 Within groups 809.23 60 13.49

Office days: number

Total 814.97 61 Between groups .01 1 .01 .05 0.40 0.48 0.43 0.39 Within groups 11.02 56 .20

Customer day: locating buyers


Customer day: pre-sales preparation Total 33.94 57

Between groups .12 1 .12 .13 2.23 1.03 2.14 0.94 Within groups 55.23 56 .99

Customer day: sales presentation Total 55.36 57


Customer day: follow-up


Customer day: planning


Customer day: internal activities Total 65.20 57

Between groups .69 1 .69 1.09 0.84 0.76 1.06 0.85 Within groups 35.48 56 .63

Customer day: administration

Total 36.17 57 Between groups .41 1 .41 3.46* 0.16 0.30 0.33 0.40 Customer day:

personal Within groups 6.68 56 .12


158


Top performers


development Total 7.09 57 Between groups 1.03 1 1.03 1.36 1.25 0.81 1.52 0.95 Within groups 42.57 56 .76

Customer day: travel


Customer day: total hours worked Total 159.58 57

Between groups .85 1 .85 1.21 0.59 0.95 0.84 0.67 Within groups 38.58 55 .70

Office day: locating buyers


Office day: pre-sales preparation Total 69.36 56


Office day: sales presentation Total 43.89 56


Office day: follow-up


Office day: planning


Office day: internal activities Total 62.53 56


Office day: administration

Total 88.96 56 Between groups .77 1 .77 4.19** 0.49 0.46 0.72 0.38 Within groups 10.10 55 .18

Office day: personal development Total 10.87 56


Office day: travel


Office day: total hours worked Total 156.14 560 * p < 0.1. ** p < 0.05. *** p < 0.01

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:


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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”, “follow-

up”, 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)

Variables that demonstrated significant differences at p < 0.05 level (strong to moderate evidence)

Variables that demonstrated significant differences at p < 0.1 level (very weak evidence)

Identify leads in Internet Qualify, select contacts Total selling experience Identify new leads and contacts in social network

Use adaptive selling Company selling experience

Sell value of company solutions Provide solutions to customer problems in presentation close

Identify leads in customer organisation

Confirm agreement on mutual goals

Thank customer Collect more specific information in own records

Invite clients on site Work with distributors Prepare meeting guidelines Arrange customer financing Participate in sales trainings Use consultative selling Socialize with internal specialists Set up and visit conferences Negotiate prices, terms, and

conditions Train product skills Study market trends Ask directly for the order Plan customer calls and visits Participate in internal meetings Check customer satisfaction Plan internal meetings Document, report market

information Engage pre-sales support

Develop long-term plans Importance of locating potential buyers

Train tool skills

Importance of planning Importance of sales presentation Train new hires Importance of personal development

Importance of handling objections Coordinate aligned team

Importance of follow-up Travel


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Variables that demonstrated significant differences at p < 0.01 level (strong convincing evidence)

Variables that demonstrated significant differences at p < 0.05 level (strong to moderate evidence)

Variables that demonstrated significant differences at p < 0.1 level (very weak evidence)

Time for personal development during office day

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


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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 Unstandardised

coefficients Standardised coefficients Collinearity

Variable

B Std. error Beta T Sig. Tolerance VIF Constant 3.858 .417 9.261 .000

Develop long-term plans .191 .061 .373 3.158 .002 .640 1.562

Identify new contacts and leads in social network

.069 .033 .219 2.085 .041 .810 1.234

Confirm agreement on mutual goals (sales presentation close)

.144 .048 .308 3.016 .004 .854 1.172

Check customer satisfaction -.180 .058 -.349 -3.088 .003 .697 1.435

Importance: Personal development

.109 .050 .212 2.165 .034 .929 1.076

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 where

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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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 long-

term 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.

5 DISCUSSION AND CONCLUSIONS

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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, pre-

tested, 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”, “customer-

and 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

1 Locating and prospecting

for customers2 Preapproach 3 Approach 4 Sales

presentation5 Handlingobjections 6 Close 7 Post sales

follow-up

Contact andlead

management

DBmanagement

Otherinformationgathering

Sales call preparation

Customer-oriented

presentation

Sales presentation

Close

Customerrelationship

maintenance

TechnicalSupport

and maintenance

Salesplanning

Internalcoordination and

administration

Internal Partnershipand delivery

Personal development

Product-oriented

presentation

Classical “7 step“ Sales Process Model (1970s)

Empirically derived model of the current study (2010)

Non-customer interaction activities

Customer interaction activities involved in the sales process

Direct selling /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 non-

customer 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 – self-

rated 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 long-

term 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 non-

customer 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 non-

customer 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 pre-

test 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 real-

life 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 non-

existent 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 long-

term 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 “must-

have” 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


178

(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.


179

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.


180

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.


181

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.

6 BIBLIOGRAPHY

182

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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

7 APPENDICES

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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.

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Appendix 2. List of the personal pre-test interviews based on

cognitive techniques

Interview counterpart, position Date of interview; time; place

1. Client relationship executive of a large scale

account

02.02.2010; 9.00-10.00; Zurich

2. Senior sales specialist technology services 03.02.2010; 13.00-14.00; Zurich

3. Marketing manager, sales representative for

financial services institutions

03.02.2010; 15.00-16.00; Zurich

4. Sales representative small and medium

business; senior bid manager

04.02.2010; 9.30-13.00; Zurich

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

services sales unit

08.02.2010; 17.00-18.00, Zurich

13. Senior engagement manager for consulting

services

11.02.2010; 9.00-10.00, Zurich

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

of public sector

22.02.2010; 9.45-10.45, Zurich

18. Human resources manager 16.02.2010; 14.00-15.00; Zurich

19. Sales manager hardware 09.03.2010; 12.00-13.00; Zurich

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Appendix 3. Initial pre-test version of a questionnaire

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Appendix 4. The final version of the questionnaire

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Appendix 5. Descriptive statistics for the variables in the study

Variable N

Val

id

Mis

sing

Mea

n

Med

ian

Mod

e

Stan

dard

de

viat

ion

Skew

ness

Kur

tosi

s

Min

imum

Max

imum

People manager 120 2

Age 121 1 42.6 43 40 8.3 -0.1 -0.8 27 58

Total Selling Experience 122 0 12.9 13 15 7.7 0.7 0.4 0.1 35 Company Selling Experience 122 0 8.9 8 10 6.3 1.2 2.3 0.1 35

Sex 122 0

Search company tools 120 2 4.0 4 4 1.8 0.2 -1.0 1 7 Identify new leads in customer organisation 120 2 5.6 6 6 1.5 -1.3 1.0 1 7 Identify new leads from internet 120 2 3.9 4 4 1.9 0.1 -1.1 1 7 Identify new contacts in social network 120 2 5.0 6 6 1.8 -0.7 -0.6 1 7 Qualify, select and deactivate contacts 116 6 4.7 5 6 1.7 -0.5 -0.7 1 7 Respond to customer requests 119 3 6.2 7 7 1.1 -2.0 5.4 1 7 Collect more specific info in internet 121 1 5.4 6 6 1.6 -1.2 0.8 1 7 Collect more specific info in own records 122 0 5.5 6 6 1.2 -0.9 1.0 1 7 Work with DBs to gain customer information 122 0 4.0 4 2 1.7 0.0 -1.1 1 7 Work with DBs to input customer information 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 122 0 6.3 7 7 1.1 -2.3 6.7 1 7 Prepare meeting guidelines 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 122 0 5.7 6 7 1.5 -1.1 0.5 1 7 Customer focused approach: open interview 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 120 2 5.6 6 6 1.2 -1.0 1.3 1 7 Product benefit approach presentation 121 1 5.1 5 5 1.4 -0.7 0.3 1 7

Use consultative selling 122 0 6.2 7 7 1.1 -2.0 4.9 1 7 Sell value of company solutions 121 1 6.1 6 7 1.1 -2.0 5.1 1 7

7 APPENDICES

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Variable

N V

alid

Mis

sing

Mea

n

Med

ian

Mod

e

Stan

dard

de

viat

ion

Skew

ness

Kur

tosi

s

Min

imum

Max

imum

Use adaptive selling 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 119 3 5.2 6 6 1.5 -0.9 0.3 1 7 Provide solution for customer problems 121 1 6.3 7 7 0.9 -2.3 7.5 2 7

Ask directly for the order 121 1 4.6 5 7 1.9 -0.3 -1.1 1 7 Negotiate prices, terms, conditions 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 122 0 6.4 7 7 1.0 -2.6 9.7 1 7 Check customer satisfaction 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 121 1 5.2 6 6 1.6 -0.9 0.2 1 7 Organize customer workshops 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 121 1 4.7 5 5 1.6 -0.6 -0.3 1 7 Plan customer calls and visits 120 2 6.0 6 7 1.1 -1.4 1.7 2 7

Plan internal meetings 120 2 5.9 6 7 1.2 -1.1 1.1 2 7

Develop long-term plans 120 2 5.6 6 7 1.3 -0.8 0.0 2 7

Coordinate aligned team 121 1 5.5 6 6 1.5 -1.0 0.3 1 7 Participate in internal meetings 120 2 5.9 6 6 1.0 -1.0 1.1 2 7 Coordinate orders, delivery 119 3 4.7 5 6 1.8 -0.4 -0.8 1 7

Work with distributors 120 2 4.7 5 7 2.0 -0.5 -1.1 1 7 Socialize with internal specialists 121 1 5.5 6 6 1.3 -1.3 1.7 1 7 Document, report market information 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

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Variable

N V

alid

Mis

sing

Mea

n

Med

ian

Mod

e

Stan

dard

de

viat

ion

Skew

ness

Kur

tosi

s

Min

imum

Max

imum

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

Train product skills 117 5 5.1 5 6 1.5 -0.8 0.1 1 7

Train new hires 121 1 4.6 5 6 1.5 -0.5 -0.6 1 7 Set up and visit conferences 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 121 1 3.2 3 2 1.6 0.9 -0.1 1 7 Importance: locating potential buyers 120 2 5.9 6 7 1.4 -1.7 2.8 1 7 Importance: Pre-Sales preparation 122 0 6.0 6 7 1.2 -1.4 1.7 2 7 Importance: Sales presentation 122 0 6.0 6 7 1.2 -1.1 0.5 2 7 Importance: Handling objections 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 121 1 5.9 6 7 1.1 -1.1 1.4 2 7 Importance: Internal activities 122 0 4.5 5 5 1.6 -0.1 -1.0 1 7 Importance: Administration 122 0 3.6 3 2 1.7 0.5 -0.8 1 7 Importance: Personal development 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 119 3 11.1 12 12 3.6 -0.4 -0.5 1 18 Customer day: locating buyers 110 12 0.5 1 0 0.5 0.7 0.3 0 2 Customer day: pre-sales preparation 110 12 1.4 1 1 1.0 1.5 4.4 0 6 Customer day: sales presentation 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 110 12 0.8 1 1 0.5 0.4 0.3 0 2 Customer day: internal activities 110 12 1.1 1 1 1.1 2.1 6.9 0 7 Customer day: administration 110 12 0.8 1 1 0.8 1.4 3.0 0 4 Customer day: personal development 110 12 0.2 0 0 0.4 1.2 0.1 0 1

7 APPENDICES

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Variable

N V

alid

Mis

sing

Mea

n

Med

ian

Mod

e

Stan

dard

de

viat

ion

Skew

ness

Kur

tosi

s

Min

imum

Max

imum

Customer day: travel 110 12 1.5 1 1 0.9 0.9 1.6 0 5 Customer day: total hours worked 110 12 9.7 10 10 1.7 1.1 3.5 4 16 Office day: locating buyers 109 13 0.7 1 1 0.7 2.1 10.3 0 5 Office day: pre-sales preparation 109 13 1.4 1 2 1.0 0.8 0.9 0 5 Office day: sales presentation 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 109 13 1.2 1 1 0.7 0.6 0.3 0 3 Office day: internal activities 109 13 1.8 2 2 0.9 0.7 0.0 0 4 Office day: administration 109 13 1.7 2 2 1.1 2.2 9.6 0 8 Office day: personal development 109 13 0.6 1 1 0.6 2.1 9.3 0 4

Office day: travel 109 13 0.2 0 0 0.6 2.4 4.5 0 2 Office day: total hours worked 109 13 9.6 9 9 1.6 1.6 3.3 7 16

Achieve sales targets 114 8 5.7 6 6 1.1 -0.5 -0.3 3 7 Contribute to sales unit targets 114 8 5.8 6 6 0.9 -0.6 0.0 3 7 Build effective relationships 118 4 6.0 6 6 0.9 -0.7 0.3 3 7 Make effective presentations 118 4 5.8 6 6 0.7 -0.4 0.0 4 7

Understand Customer 119 3 6.1 6 6 0.8 -0.3 -0.6 4 7 Understand own company 118 4 5.8 6 6 0.8 -0.4 -0.2 4 7 Provide feedback to management 118 4 5.4 6 6 1.2 -1.0 0.8 2 7

Keep expenses 117 5 5.5 6 6 1.1 -0.6 -0.3 2 7 Average sales performance measure 112 10 5.8 6 6 0.6 -0.5 0.2 4 7

221

Curriculum vitae

Natalia Bächli-Bolvako 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

Documents

Exploration of Salespeople Activities and Behaviour in