the impact of product, price, promotion and place logistics on customer satisfaction and share of...
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The Impact of Product, Price, Promotion and Place/Logistics on Customer Satisfaction and Share of Business
Dissertation
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Rudolf Leuschner, M.A.
Graduate Program in Business Administration
The Ohio State University
2010
Dissertation Committee:
Professor Douglas M. Lambert, Advisor
Professor Keely L. Croxton
Professor A. Michael Knemeyer
UMI Number: 3438241
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Copyright by
Rudolf Leuschner
2010
ii
Abstract
Customer service has been a topic in marketing and logistics research for many decades. Much
of the research was functionally focused and lacked the integration of logistics customer service
with the other components of the Marketing Mix (price, product and promotion). In addition
many prior studies focused only on a single industry and there is little replication and limited
possibilities for generalizability. This shortcoming is alleviated in this research by using a multi-
industry approach that allows for replication across the samples. The focus of this research was
on business-to business relationships in several industries, health care, electronics, plastics, and
sporting goods. The goals of the research were to test a general model that across multiple
samples and industries and to understand where differences occur. The outcome variables are
customer satisfaction and share of business. The results show that the impact of each
component of the Marketing Mix varies by sample. In no two samples do the same components
of the Marketing Mix show a significant impact on customer satisfaction. This does not diminish
the importance of the Marketing Mix, but it shows that a careful evaluation of individual
samples is necessary. The impact of customer satisfaction on share of business is significant in
most samples, but not all of them. As a result of this research future researchers should
investigate why differences occur between the samples. Managers should take away that they
must perform customer service studies in their own company and that the studies must be
repeated in regular intervals.
iii
Dedication
This dissertation is dedicated to my family who always supported me.
iv
Acknowledgments
My first thanks go to Dr. Douglas M. Lambert for all the advice, guidance, and support he
provided me with. His tireless pursuit of perfection is remarkable and hopefully my work is a
reflection of this spirit. He is the best editor I have had and he sets an example of
professionalism that we all should strive to achieve. In addition, I would like to thank him for
providing me with all the data for this dissertation.
I would like to also thank Dr. A. Michael Knemeyer for the insightful comments throughout the
writing of this dissertation. His great attitude and skill as a researcher have made positive
contributions to the quality of my work. Special thanks go to Dr. Keely Croxton for her counsel
regarding the dissertation. It is remarkable that she let me defend my dissertation within days
of her giving birth to her beautiful daughter. I would also like to thank Marley for not wanting to
come out during my defense, which would have caused an interesting situation.
Next, I would like to extend my gratitude to several special people in the Fisher College of
Business. I am grateful to Drs. Martha Cooper, Walter Zinn, John Saldanha, Rao Unnava,
Thomas Otter, Bob Leone, and Michael Browne for teaching the classes and seminars that
taught me so much. I am especially thankful to Shirley Gaddis who always took care of me. I
also appreciate the support from my friends Francois Charvet, Matias Enz, Steve Robeano,
Sebastián García-Dastugue, Chris Randall, Ned Sandlin, Tim Pettit, Ping Wang, Jason Miller, and
v
Matt Schwieterman. The members of the Global Supply Chain Forum have also provided me
with valuable feedback on various stages of the dissertation.
Last but not least, I would like to thank my family because without them none of this would
have been possible. First, I have to acknowledge my mother Maria Leuschner and my father
Wolfgang Leuschner. Only with their sacrifice, devotion and support was I able to do all the
things I did. Next, I have to thank my grandparents Alexandru and Stella Ioanitescu who were
the first ones to show me what hard work, dedication and perseverance really mean. Then, I
also would like to specifically mention my uncle Emil, my other uncle Lothar and my aunt Heidi.
Finally, I thank all the special people in my life who have cheered me on and kept me going.
vi
Vita
2004 ........................................................ B.S. Business Administration, University of Nevada
2006 ........................................................ M.B.A., University of Nevada
2009 ........................................................ M.A. Business, The Ohio State University
Publications
Lambert, Douglas M., Rudolf Leuschner, and Dale S. Rogers, “Implementing and Sustaining the
Supply Chain Management Processes,” in Douglas M. Lambert (editor), Supply Chain
Management: Processes, Partnerships, Performance, Third Edition, Sarasota, FL: Supply Chain
Management Institute, 2008, pp. 235-254.
Carter, Craig R., Rudolf Leuschner, and Dale S. Rogers (2007) “A Citation Analysis of the Journal
of Supply Chain Management: An Examination of Social Networks,” The Journal of Supply Chain
Management, Vol. 43, No. 2, pp. 15–28.
Rogers, Dale S. and Rudolf Leuschner (2004) “Supply Chain Management: Retrospective and
Prospective,” Journal of Marketing Theory and Practice, Vol. 12, No. 4, pp. 60-65.
Fields of Study
Major Field: Business Administration
Area of Specialization: Logistics
Minor Field: Marketing
vii
Table of Contents
Abstract ............................................................................................................................................ ii
Dedication ....................................................................................................................................... iii
Acknowledgments........................................................................................................................... iv
Vita .................................................................................................................................................. vi
Table of Contents ........................................................................................................................... vii
List of Tables ................................................................................................................................... xi
List of Figures ................................................................................................................................ xvii
CHAPTER 1. INTRODUCTION ........................................................................................................ 1
1.1. Background ...................................................................................................................... 2
1.2. Scope of the Research ..................................................................................................... 4
1.3. Objectives and Research Questions ................................................................................ 6
1.4. Research Hypotheses ...................................................................................................... 8
1.5. Methodology and Research Design ............................................................................... 10
1.6. Contributions and Future Research ............................................................................... 13
viii
1.7. Organization .................................................................................................................. 15
CHAPTER 2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT ....................................... 16
2.1. Introduction and Origins of Customer Service .............................................................. 17
2.2. Customer Service in Marketing and Logistics ................................................................ 25
2.3. Customer Satisfaction and Firm Performance .............................................................. 44
2.4. Hypothesis Development .............................................................................................. 52
2.5. Summary ........................................................................................................................ 58
CHAPTER 3. METHODOLOGY ..................................................................................................... 59
3.1. Data Collection .............................................................................................................. 60
3.2. Overview of the Samples ............................................................................................... 64
3.3. Data Analysis Preparation ............................................................................................. 69
3.4. Overview of Questions Used in the Samples ................................................................. 76
3.5. Structural Equation Modeling ....................................................................................... 83
3.6. Measurement Model Results for Sample A-1 ............................................................... 87
3.7. Measurement Model Results for Sample A-2 ............................................................... 92
3.8. Measurement Model Results for Sample A-3 ............................................................... 97
ix
3.9. Measurement Model Results for Sample B-1.............................................................. 101
3.10. Measurement Model Results for Sample B-2.............................................................. 106
3.11. Measurement Model Results for Sample C-1.............................................................. 110
3.12. Measurement Model Results for Sample D-1 ............................................................. 114
3.13. Measurement Model Results for Sample D-2 ............................................................. 119
3.14. Measurement Model Results for Sample D-3 ............................................................. 124
3.15. Summary ...................................................................................................................... 129
CHAPTER 4. RESULTS................................................................................................................ 130
4.1. Overview of the Results Evaluation ............................................................................. 131
4.2. A-1 Blood Banking Reagents Sample Results .............................................................. 132
4.3. A-2 Coagulation Reagents Sample Results .................................................................. 135
4.4. A-3 Coagulation Reagents Sample Results .................................................................. 137
4.5. B-1 Professional Video Tape Sample Results ............................................................... 139
4.6. B-2 Consumer Video Tape Sample Results .................................................................. 141
4.7. C-1 Plastics Resin Sample Results ................................................................................ 143
4.8. D-1 Golf Balls Sample Results ...................................................................................... 145
x
4.9. D-2 Golf Clubs Sample Results ..................................................................................... 147
4.10. D-3 Golf Shoes Sample Results .................................................................................... 149
4.11. Sample Comparison ..................................................................................................... 151
4.12. Summary ...................................................................................................................... 155
CHAPTER 5. CONCLUSIONS ...................................................................................................... 156
5.1. Summary of Research Purpose .................................................................................... 157
5.2. Review of Research Objectives and Hypotheses ......................................................... 158
5.3. Summary of Findings ................................................................................................... 161
5.4. Research Limitations ................................................................................................... 163
5.5. Opportunities for Future Research .............................................................................. 165
5.6. Implications for Theory ................................................................................................ 166
5.7. Implications for Practice .............................................................................................. 167
5.8. Overall Conclusions ..................................................................................................... 169
References ................................................................................................................................... 170
xi
List of Tables
Table 1: Sample Overview ............................................................................................................. 11
Table 2: Effective Sample Size ........................................................................................................ 12
Table 3: Summary of Results of Previous Customer Service Studies ............................................. 23
Table 4: Constructs of the Marketing Mix ..................................................................................... 43
Table 5: Outcome Variables in Customer Service Studies ............................................................. 51
Table 6: Overview of the Samples ................................................................................................. 64
Table 7: Number of Questions and Usable Cases .......................................................................... 71
Table 8: Summary of Composite Formation Methods................................................................... 74
Table 9: Product Attributes Across all Sample ............................................................................... 77
Table 10: Price Attributes Across all Samples ................................................................................ 80
Table 11: Promotion/Personal Selling Attributes Across all Samples ............................................ 80
Table 12: Place/Logistics Attributes Across all Samples ................................................................ 82
Table 13: A-1 Measurement Model Product Construct Loadings ................................................. 88
xii
Table 14: A-1 Measurement Model Price Construct Loadings ...................................................... 89
Table 15: A-1 Measurement Model Promotion Construct Loadings ............................................. 89
Table 16: A-1 Measurement Model Place Construct Loadings ...................................................... 90
Table 17: A-1 Discriminant Validity Test Results ........................................................................... 91
Table 18: A-2 Measurement Model Product Construct Loadings ................................................. 93
Table 19: A-2 Measurement Model Price Construct Loadings ...................................................... 93
Table 20: A-2 Measurement Model Promotion Construct Loadings ............................................. 94
Table 21: A-2 Measurement Model Place Construct Loadings ...................................................... 95
Table 22: A-2 Discriminant Validity Test Results ........................................................................... 96
Table 23: A-3 Measurement Model Product Construct Loadings ................................................. 97
Table 24: A-3 Measurement Model Price Construct Loadings ...................................................... 98
Table 25: A-3 Measurement Model Promotion Construct Loadings ............................................. 98
Table 26: A-3 Measurement Model Place Construct Loadings ...................................................... 99
Table 27: A-3 Discriminant Validity Test Results ......................................................................... 100
Table 28: B-1 Measurement Model Product Construct Loadings ................................................ 102
xiii
Table 29: B-1 Measurement Model Price Construct Loadings .................................................... 103
Table 30: B-1 Measurement Model Promotion Construct Loadings ........................................... 103
Table 31: B-1 Measurement Model Place Construct Loadings .................................................... 104
Table 32: B-1 Discriminant Validity Testing Results ..................................................................... 105
Table 33: B-2 Measurement Model Product Construct Loadings ................................................ 106
Table 34: B-2 Measurement Model Price Construct Loadings .................................................... 107
Table 35: B-2 Measurement Model Promotion Construct Loadings ........................................... 108
Table 36: B-2 Measurement Model Place Construct Loadings .................................................... 108
Table 37: B-2 Discriminant Validity Testing Results ..................................................................... 109
Table 38: C-1 Measurement Model Product Construct Loadings ................................................ 110
Table 39: C-1 Measurement Model Product Construct Loadings ................................................ 111
Table 40: C-1 Measurement Model Promotion Construct Loadings ........................................... 111
Table 41: C-1 Measurement Model Place Construct Loadings .................................................... 112
Table 42: C-1 Discriminant Validity Testing Results ..................................................................... 113
Table 43: D-1 Measurement Model Product Construct Loadings ............................................... 115
xiv
Table 44: D-1 Measurement Model Price Construct Loadings .................................................... 116
Table 45: D-1 Measurement Model Promotion Construct Loadings ........................................... 116
Table 46: D-1 Measurement Model Place Construct Loadings .................................................... 117
Table 47: D-1 Discriminant Validity Testing Results .................................................................... 118
Table 48: D-2 Measurement Model Product Construct Loadings ............................................... 119
Table 49: D-2 Measurement Model Price Construct Loadings .................................................... 120
Table 50: D-2 Measurement Model Promotion Construct Loadings ........................................... 121
Table 51: D-2 Measurement Model Place Construct Loadings .................................................... 122
Table 52: D-2 Discriminant Validity Testing Results .................................................................... 123
Table 53: D-3 Measurement Model Product Construct Loadings ............................................... 125
Table 54: D-3 Measurement Model Price Construct Loadings .................................................... 126
Table 55: D-3 Measurement Model Promotion Construct Loadings ........................................... 126
Table 56: D-3 Measurement Model Place Construct Loadings .................................................... 127
Table 57: D-3 Discriminant Validity Testing Results .................................................................... 128
Table 58: A-1 Blood Banking Results............................................................................................ 133
xv
Table 59: A-1 Blood Banking Results Alternative Model ............................................................. 134
Table 60: A-2 Coagulation Reagents Results ............................................................................... 135
Table 61: A-2 Coagulation Reagents Results Alternative Model ................................................. 136
Table 62: A-3 Coagulation Reagents Results ............................................................................... 137
Table 63: A-3 Coagulation Reagents Results Alternative Model ................................................. 138
Table 64: B-1 Professional Tape Results ...................................................................................... 140
Table 65: B-1 Professional Tape Results Alternative Model ........................................................ 140
Table 66: B-2 Consumer Tape Results ......................................................................................... 141
Table 67: B-2 Consumer Tape Results Alternative Model ........................................................... 142
Table 68: C-1 Plastic Resin Results ............................................................................................... 143
Table 69: C-1 Plastic Resin Results Alternative Model ................................................................. 144
Table 70: D-1 Golf Balls Results ................................................................................................... 145
Table 71: D-1 Golf Balls Results Alternative Model ..................................................................... 146
Table 72: D-2 Golf Clubs Results .................................................................................................. 147
Table 73: D-2 Golf Clubs Results Alternative Model .................................................................... 148
xvi
Table 74: D-3 Golf Shoes Results ................................................................................................. 149
Table 75: D-3 Golf Clubs Results Alternative Model .................................................................... 150
Table 76: Overall Impact of the Marketing Mix on Customer Satisfaction ................................. 152
Table 77: Overall Impact of Customer Satisfaction on Share of Business ................................... 153
Table 78: Overall Impact of Customer Satisfaction on Preferred Share of Business ................... 154
Table 79: Overall Impact of the Marketing Mix on Customer Satisfaction ................................. 161
Table 80: Overall Impact of Customer Satisfaction on Share of Business ................................... 162
xvii
List of Figures
Figure 1: Conceptual Model with Hypotheses ................................................................................. 9
Figure 2: A Model of Service Quality Improvement and Profitability ............................................ 48
Figure 3: Conceptual Model ........................................................................................................... 53
Figure 4: Conceptual Model with Hypotheses ............................................................................... 57
Figure 5: Structural Model and Hypotheses ................................................................................ 131
1
CHAPTER 1.
INTRODUCTION
Traditionally, logistics has been viewed as a cost center in companies and the function’s primary
contributions to the bottom line are cost and asset reductions. However, there is some
evidence that logistics attributes have a stronger and more consistent influence on customer
satisfaction and share of business than the factors that are commonly attributed to the
marketing function (Lambert and Harrington 1989; Sterling and Lambert 1988). By comparing
logistics attributes to other attributes, in multiple industries, it can be determined whether
logistics consistently has a stronger impact on customer satisfaction and share of business. In
this dissertation, data from several industries are used: health services, electronics, plastics, and
sporting goods. In order to gain a better understanding of the factors that contribute
consistently to business performance nine distinct samples are analyzed. The results suggest
that managers should look beyond the common belief that logistics can only contribute cost and
asset reductions.
2
1.1. Background
Customer service has been an important research stream in both the marketing and the logistics
areas. Early academic work in Marketing included a large number of areas that today would be
considered as logistics activities, like transportation and distribution (Shaw 1915). Over time
marketing and logistics became more specialized, just as businesses became more functionally
specialized. The problem with functional silos is that both Marketing and Logistics functions can
influence customer service but if their actions are not coordinated, it can lead to suboptimal
decisions. More general frameworks like the “Marketing Concept” and the “Marketing Mix”
incorporate elements from marketing and logistics.
The “Marketing Concept” is the philosophy that firms should analyze the needs of their
customers, then make decisions to satisfy those needs and do so better than the competition
(Kotler 1967). The “Marketing Mix” (Borden 1953) activities have often been conceptualized as
the “Four P’s” of marketing (McCarthy 1960), product, price, promotion, and place. Once the
channels of distribution have been selected, “place” generally occurs in the logistics function as
time and place utilities are created. For this reason, “place” is synonymous with logistics service
(Coyle, Bardi and Langley 1992, Stock and Lambert 2001). Marketing and logistics are involved
in the Marketing Mix, yet conflicting objectives can hinder effective integration of service
activities (Sterling and Lambert 1988; Sterling and Lambert 1989). Customer service is a
boundary-spanning activity that takes place within and beyond the firm (La Londe, Cooper and
Noordewier 1988; Rinehart, Cooper and Wagenheim 1989). Integration within the firm should
3
focus on marketing and logistics activities that interface with the customer (Rinehart, Cooper
and Wagenheim 1989).
A problem facing many manufacturing firms when marketing to downstream members of their
supply chain is the integration of logistics customer service with the other components of the
Marketing Mix: product, price, and promotion (Innis and La Londe 1994; Sterling and Lambert
1988; Sterling and Lambert 1989). How management allocates scarce resources to the
components of the Marketing Mix has a significant impact on the market share and profitability
of a company (Innis and La Londe 1994, Leuthesser and Kohli 1995; Sterling and Lambert 1988;
Sterling and Lambert 1989). Logistics scholars have attempted to understand how logistics
activities affect customer service, but often this happened without consideration of a broad set
of marketing activities (Innis and La Londe, 1994; Sterling and Lambert 1988; Sterling and
Lambert 1989). Although the link between marketing and logistics customer service has been
documented in several studies (Emerson and Grimm, 1996; Emerson and Grimm 1998; Innis and
La Londe 1994; Lambert and Harrington 1989; Sterling and Lambert 1988; Sterling and Lambert
1989) other studies focused exclusively on either logistics (Mentzer, Flint and Kent 1999;
Mentzer, Flint and Hult 2001; Stank, Goldsby and Vickery 1999; Stank, et al. 2003) or marketing
(Cronin and Taylor 1992; Parasurman, Zeithaml and Berry 1985; Parasurman, Zeithaml and Berry
1988; Parasurman, Zeithaml and Berry 1991).
4
1.2. Scope of the Research
The main goal for this research was to develop a model that shows the contribution of logistics
relative to the other components of the Marketing Mix across several industries. By comparing
the four components of the Marketing Mix in multiple industries, it can be determined whether
logistics consistently has a stronger impact on customer satisfaction and share of business. Such
a result could help change the common belief that logistics can only contribute cost and asset
reductions.
Many previous studies on customer service focused only on a single industry (Innis and La Londe
1994; Stank, Goldsby and Vickery, 1999; Sterling and Lambert 1988) and few were replication
studies (Lambert and Harrinton, 1990; Lambert, Lewis and Stock 1993; Stank, et al. 2003). As
such, no prior research can claim generalizability. Generalizability provides the confidence that
a theoretical model can be expanded beyond the situation in which it was developed. If a study
is conducted in one industry, then the results may be valid only for that industry. A customer
service model with a more universal application was called for in several previous studies (Davis-
Sramek, Mentzer and Stank 2008; Mentzer, Flint and Hult 2001; Stank, Goldsby and Vickery
1999; Stank, et al. 2003) and this research addresses that need.
By using a multi-industry approach with nine samples, a model can be developed on one sample
and then validated on the others (Hubbard and Armstrong 1994; Hubbard and Vetter 1996).
This approach yields stronger results because it minimizes the chance for misspecification of the
model (Ehrenberg 2004). The need for replication has been voiced several times in the past
(Furchtgott 1984, Lubin 1957, Sterling, Rosenbaum and Weinkam 1995). Often, researchers and
5
reviewers seem biased toward publishing research that reports significant results (Rosenthal
1979). This leads to a majority of published studies showing significant results, even if Type I
errors cause them. In addition, a large number of studies showing non-significant results are
not published.
Lindsay and Ehrenberg (1993) offer guidelines for designing replication studies. If a study is
performed for the first time, the result can be regarded as one-off. One can ask, under what
conditions, if any, will it hold again? Would the same result be obtained at a later point in time?
Would the result hold in a different situation? If the study is repeated, these questions can be
answered. It is important to note that replicated studies are not identical. First, identical
replication is virtually impossible and second pointless because that would mean the results
must be the same. If the same result is obtained even with varying conditions, researchers can
note these conditions and investigate why they did not change the result. For this research,
different products, industries and points in time are the major conditions that may influence the
outcomes. There are two types of replications, close and differentiated. Close replications
attempt to keep as many of the conditions constant as possible. An example would be the two
samples on coagulation reagents (see samples A-2 and A-3) used in this research. Differentiated
replications involve major differences, such as different industries, different products, and
different position in the supply chain. The other samples would fall into that category.
Generally with close replication one expects to see the same result, while under differentiated
replication, variations are more likely.
6
1.3. Objectives and Research Questions
One framework for determining and assessing all variables important for selecting and
evaluating suppliers is the framework first presented by Lambert and Zemke (1982). Since the
goal of this research is to develop a generalizable model, it is best to start with more variables
and reduce them as the need arises during scale purification. The variables that buyers in
companies use to select and evaluate suppliers can be summarized into one of the four
components of the Marketing Mix: product, price, promotion/personal selling and
place/logistics. The product construct was made up of attributes describing the performance of
the product. Price contained attributes regarding competitiveness of pricing and the
satisfaction with billing procedures. Promotion (including personal selling) had attributes
related to advertising efforts and salesperson performance. Place (logistics) evaluated different
aspects of logistics performance. The outcomes measured in this research are: customer
satisfaction and share of business. Customer satisfaction is the overall evaluation of satisfaction
with a supplier. Share of business denotes the percentage of business given to a supplier.
The suppliers with better products will usually be rewarded with higher customer satisfaction.
Lower prices also mean generally higher customer satisfaction. Satisfaction with the
salesperson also has an impact on overall satisfaction. Suppliers who can deliver their products
on time and without errors can create higher customer satisfaction as well. Each of these four
relationships was tested in this dissertation in addition to the impact that customer satisfaction
has on share of business. These relationships were tested on a range of samples from several
industries.
7
The specific research questions for this dissertation are:
1. What are multi-item scales to assess the performance of customer service elements in
business-to-business relationships across industries?
2. What is the relative importance of the components of the Marketing Mix on customer
satisfaction in business-to-business settings in several industries?
3. What is the influence of customer satisfaction on share of business?
8
1.4. Research Hypotheses
The four components of the marketing mix were all believed to influence the level of customer
satisfaction (Emerson and Grimm 1998; Innis and La Londe 1994; Lambert and Harrington 1989;
Sterling and Lambert 1988). Therefore, the first hypothesis was:
H1: All components of the Marketing Mix contribute to customer satisfaction
More specifically, the relationship between the individual components of the Marketing Mix
were analyzed. The nature of all the relationships is projected to be positive and significant.
H1a: Product has a significant impact on customer satisfaction.
H1b: Price has a significant impact on customer satisfaction.
H1c: Promotion/personal selling has a significant impact on customer satisfaction.
H1d: Place/logistics has a significant impact on customer satisfaction.
While customer satisfaction is an important construct in the literature, it does not directly
translate into profitability or market share. There is evidence that satisfaction can and should
be connected to “hard” financial measures (Rust, Zahorik and Keiningham 1996). Share of
business is a good indicator of the financial success of a business-to-business relationship.
Focusing on expanding business with current customers rather than attracting new customers
has been referred to as the “Leaky Bucket Theory” (Brown, et al. 2005; Dowling and Uncles
1997), which holds that over time, customers defect and business is lost just as water is lost
9
through the holes in a bucket. There are two ways to maintain the water level in the bucket: put
more water into the bucket or plug the holes. Defecting customers can either be replaced by
new customers or by increased volume from the remaining customers (Brown, et al. 2005;
Dowling and Uncles 1997). It is often suggested that it is a better strategy to increase the share
of business than to attract new customers (Fornell and Wernerfeld 1987; Rust, Zahorik and
Keiningham 1996). In order to understand the effect of customer satisfaction on share of
business, the direct link was tested. It is believed to be a generally positive relationship (Rust,
Zahorik and Keiningham 1996). In addition, indirect effects between the Marketing Mix and
share of business are assessed.
H2: Customer satisfaction has a significant impact on share of business.
All of these hypotheses were tested. The research model with the hypotheses is displayed in
Figure 1.
Figure 1: Research Model with Hypotheses
10
1.5. Methodology and Research Design
A database of nine customer satisfaction surveys was used for this dissertation. This section
begins with a description of the data collection methodology. Next, there is a brief overview of
the samples. Then, the hypothesis testing is described.
Questionnaire Design
The data were collected using mail questionnaires which are described in more detail in Chapter
3. The service attributes in each survey were identified during in-depth, personal interviews
with key decision-makers in the sponsoring organization and buyers in 20 to 32 of each
sponsor’s customer firms. Those who determined how much business was given to suppliers
were asked to review the attributes and (1) describe any that they used that were not on the list
and (2) evaluate the wording to determine if it was clear to them what each question meant.
Attributes were presented by product, price, promotion and place to make it easier for those
interviewed to identify attributes that they considered that were not included. The objective
was to compile a set of comprehensive and meaningful questions for the mail survey. The
attributes were randomized for the surveys.
Next, mail questionnaires were sent to representative decision-makers in firms served by major
suppliers in the respective industry. The sponsor of the research was not identified. The
questionnaires consisted of the following:
Part A: importance of attributes used to select and evaluate suppliers and the performance of the top three suppliers on those attributes.
11
Part B: measurement of overall performance.
Part C: expected performance levels.
Part D: meaningful demographic data.
Overview of the Samples
For this study, nine samples were used for which the data were collected using the methodology
that was described previously. An overview of the samples is presented in Table 1.
Industry Sample Name Sample Size Responses Response Rate
Health services A-1 (Blood Banking Reagents) 2,015 754 37.42%
Health services A-2 (Coagulation Reagents) 1,005 299 29.75%
Health services A-3 (Coagulation Reagents) 667 212 31.78%
Electronics B-1 (Professional Tape) 1,369 342 24.98%
Electronics B-2 (Consumer Blank Tape) 434 77 17.74%
Plastics C-1 (Commodity Resin) 1,854 540 29.13%
Sporting goods D-1 (Golf Balls) 1,012 134 13.24%
Sporting goods D-2 (Golf Clubs) 2,240 172 7.68%
Sporting goods D-3 (Golf Shoes) 1,001 95 9.49%
Table 1: Sample Overview
The nine samples involve five distinct industries. The overall response rates vary from 7.68 to
37.42 percent. While the response rates in the Sporting goods industry are fairly low, this is not
unusual due to the fact that surveys of retailers generally have lower response rates (Ellram, La
Londe and Weber 1999). The sample sizes are sufficiently large and non-response bias is
assessed in two ways (Armstrong and Overton 1979; Lambert and Harrington). Therefore,
reliable conclusions can be drawn (Boyer and Swink 2008). The number of responses in each
sample is adequate for the type of analysis that is performed because each respondent was
12
asked to evaluate up to three suppliers, thus effectively increasing the number of cases for the
analysis. In Table 2, the effective sample size and the number of attributes is shown.
Industry Sample Name Responses Usable Cases Attributes
Health services A-1 (Blood Banking Reagents) 753 1,400 88
Health services A-2 (Coagulation Reagents) 299 435 78
Health services A-3 (Coagulation Reagents) 205 279 71
Electronics B-1 (Professional Tape) 347 508 83
Electronics B-2 (Consumer Blank Tape) 113 229 69
Plastics C-1 (Commodity Resin) 534 759 91
Sporting goods D-1 (Golf Balls) 141 288 130
Sporting goods D-2 (Golf Clubs) 120 265 149
Sporting goods D-3 (Golf Shoes) 89 205 135
Table 2: Effective Sample Size
Hypothesis Testing
All of the main hypotheses were tested using structural equation modeling (Bagozzi and Yi 1988;
Bollen 1989). Each of the four components of the Marketing Mix was modeled as a latent
variable with multiple indicators. The structural regression equations were used to test the
hypotheses. The advantage of this approach is the ability to use multi-item constructs and
jointly estimate structural parameters that correspond to the research hypotheses. Each sample
was analyzed individually. Then, values of the latent variables from the individual samples were
compared. In the industries where multiple samples exist, exploratory model development and
confirmatory analysis were performed.
13
1.6. Contributions and Future Research
The customer satisfaction and revenue implications of superior logistics service should not be
ignored. But such an argument can only be made with solid evidence. If the place/logistics
construct has a consistently higher influence on customer satisfaction and share of business,
then that could provide the necessary evidence. Determining if this is indeed the case is the
main motivation for this dissertation.
The most important theoretical contribution is the extension of the frameworks integrating the
Marketing Mix variables into a customer service context (Innis and La Londe, 1994; Lambert and
Harrington 1989; Lambert, Lewis and Stock 1993; Sterling and Lambert 1988; Sterling and
Lambert 1989). This extends previous theory by including the outcome variables customer
satisfaction and share of business. By using multiple samples from multiple industries, a stable
model can be developed (Rentz 1987). The power of replication should not be underestimated
because theoretical models should be tested extensively before theory can be accepted as valid
(Ehrenberg 2004).
The strength of replication lies not only in attempting to obtain the same result in multiple
samples, but also to test the impact of the Marketing Mix on customer satisfaction and the
impact of customer satisfaction on share of business. Therefore it is not only important to
determine if any of the 4P’s is significant more often than the others, but also to determine if all
of them have an impact at all. As most of the replication takes the form of differential
replication, larger deviations in the results are expected (Lindsay and Ehrenberg 1993).
14
By providing a number of validated scales, the research also has the potential to provide
direction for those interested in building questionnaires for measuring customer service
attributes. Scale purification will provide guidance on which questions are most applicable in
each industry. The research evaluates the data collection approach recommended by Lambert
and Zemke (1982) for identifying service attributes that are important for customers. This will
help managers who want to identify a useful set of questions for customer service and customer
satisfaction attributes. The methodology to identify attributes is general enough that it can be
used in any industry and it provides an accurate representation of all important attributes.
Managers can use the results of this study as a starting point to determine which attributes
increase customer satisfaction and share of business in their firms.
15
1.7. Organization
In Chapter 2, the relevant literature is reviewed. It includes sections on the origins of customer
service, customer service in marketing and logistics, and customer satisfaction. The literature is
then used to build the research hypotheses. Chapter 3 contains a description of the
methodology and contains sections on data collection, questionnaire development, sample
description and measurement model development. The results are presented in Chapter 4.
Chapter 5 includes a summary of the research purpose, a review of research objectives and
hypotheses, a summary of findings, research limitations, opportunities for future research, and
implications for theory and practice.
16
CHAPTER 2.
LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
In this chapter, a review of the relevant literature in the customer service domain is provided.
The review is divided in the following subsections: introduction and origins of customer service,
customer service research in the fields of marketing and logistics, and customer satisfaction and
firm performance. Following the literature review, the conceptual model is presented and the
research hypotheses are developed. The chapter concludes with a summary.
17
2.1. Introduction and Origins of Customer Service
Customer service research has a long tradition in business and understanding how to better
serve customers has always been part of the Marketing domain. This section will include the
development of customer service as a field of study. All research in this section is based on data
from surveys or experiments. The main analysis technique is rank-ordered lists, although some
studies used analysis of variance and this is a major difference from later work in this area. Early
in the twentieth century, business activities were classified into “three great divisions” (Shaw
1915):
the activities of production, which change the form of materials the activities of distribution, which change the place and ownership of the commodities
thus produced the facilitating activities which aid and supplement the operations of production and
distribution
“The accepted system of distribution had been built up on the satisfying of staple needs” (Shaw
1915). The ability of manufacturers to produce to the demand of the market was reached
quickly and then emphasis shifted to aspects like distribution performance in order to reach an
expanded market base (Shaw 1915).
Later, distribution was separated into its transportation and storage functions and the
importance of service was recognized (Clark 1922). Service is, “as far as the purchaser is
concerned, a part of the product, a part of the thing which he is purchasing” (Clark 1922). Later,
the importance of physical distribution was once again highlighted by including a chapter on it in
a Marketing textbook (Clark 1924). This was the first use of the term physical distribution
(Bowersox, Smykay and LaLonde 1968) which would be replaced by the term logistics in 1985
18
when the National Council of Physical Distribution Management changed its name to the
Council of Logistics Management (Bowersox 2007).
Until the early 1950’s, commercial and academic interest in distribution was “traditionally
fragmentary and most often a secondary consideration” (Bowersox 1969). “Manufacturers all
too often fail to realize the marketing penalty they pay when even a small proportion of the
outlets normally handling their type of product does not have their brand in stock. Generally
speaking, all marketing, selling, and advertising effort which has been put behind the product
fails to the extent that potential buyers do not find it on hand when they are buying” (Brown
1955). In a survey of factors that affect industrial buying decisions, the important areas were
identified as product quality, delivery performance, quality of salesperson, price, and effective
communication (Klass 1969).
It was suggested that stockouts, excess delivery time, or excess variability of delivery time all can
result in lost sales (LeKashman and Stolle 1965). This concept was later expanded by specifying
six steps to help companies achieve cost reductions through improved customer service
(Hutchinson and Stolle 1968):
Define the elements of service Determine the customer’s viewpoint Design a competitive service package Develop a program to sell service Market-test the program Establish performance controls
In an experimental approach, factors that are considered when selecting a supplier were
identified (Dickson 1966). The subject was assigned to read one of four hypothetical situations,
19
put himself in the position of the purchasing manager, and rate the importance of 23 purchasing
factors. The ranking of the factors did vary by individual case situation but quality, delivery, and
past performance were always in the top five and the cumulative ranking of the top five factors
were quality, delivery, performance, warranties, and facilities. Analysis of variance on the factor
rating showed that there was general agreement on factors with high and low importance, but
not for factors between the extremes (Dickson 1966). This was one of the first studies that
highlighted the importance of good delivery systems in purchasing decisions.
Researchers in another study attempted to determine the relative importance of determinants
of industrial buyers’ vendor selection (Wind, Green and Robinson 1968). Subjects were asked to
consider a list of 10 vendor characteristics and assign 100 to the most important, zero to the
least important and proportional values to the remainder (Wind, Green and Robinson 1968).
These results indicated agreement among the buyers as to the ranking of characteristics and
quality/price ratio and delivery reliability were indicated as the two most important
characteristics. In contrast, reciprocity and personal benefits to the buyer were grouped at the
bottom. This study confirmed the importance of logistics aspects in customer service, which in
this case was conceptualized as delivery reliability.
A similar approach was used in a later study by examining the role of customer service in
business-to-business purchasing situations (Cunningham and Roberts 1974). Buyers were asked
to name the five most important service factors and to rank them in order. Service factors were
then compared by three criteria: times mentioned, times ranked in top 5, times ranked first.
The rankings from combined results were (1) delivery reliability, (2) technical advice, (3) test
20
facilities, and (4) replacement guarantee. It was also found that 80% of the buyers formed a
favorable impression of suppliers if they would meet the buyers’ need for quality, service, and
price (Cunningham and Roberts 1974).
The first research to examine the role of physical distribution in industrial purchasing decisions
was by Perreault and Russ (1976c) who studied the importance of physical distribution, the
determinants of its importance, and the determinants of buyer satisfaction regarding physical
distribution. Aggregate results indicated the five most important supplier characteristics were
quality, distribution service, price, supplier management, and distance. The results showed the
relative importance of supplier characteristics varied widely across the six product categories
(semiconductors, bearings, acid, sheet plastic, fasteners, and lubricants), but only quality and
distribution service were consistently ranked as first and second most important. The highest
satisfaction was with billing procedures, order methods, and accuracy in filling orders, while the
lowest satisfaction was with delivery time and delivery time variation (Perreault and Russ 1976c).
The major contribution of this study is that customer service was assessed across different
products and industries.
Gilmour et al. (1977) examined the service provided by major suppliers in the scientific
instrument and supplies industry in Australia. Each respondent was shown a list of 17 customer
service elements and asked to rank order the five most important. The average importance of
each of the nine most mentioned elements was noted for all customers, for all suppliers and for
five types of customer organizations. The five most important purchasing elements for all
customers were availability, after-sales service, delivery reliability, delivery time, and technical
21
competence of the representatives. There were some differences in the rankings depending on
the segment which indicates a possible benefit for applying different customer service policies in
different segments (Gilmour, et al. 1977). However, the importance of the elements across the
five customer groups was quite similar, which supports the conclusion that customer service is
perceived uniformly.
The relative importance of physical distribution aspects continued to be an area of interest.
Anderson, Jerman and Constantin (1978) used a mail survey to ask respondents to make 20
paired comparisons of goals for physical distribution. The comparisons then were converted to
an interval scale. The results of the rankings were (1) order cycle time reliability, (2) percent
orders filled, (3) minimum physical distribution cost, (4) minimum order cycle time, and (5)
minimum damage in transit. The importance of elements is the same whether the respondent
was from top or middle management (Anderson, Jerman and Constantin 1978).
A mail survey of manufacturers and wholesalers in the over-the-counter pharmaceutical
products industry compared manufacturer and wholesaler views of customer service (Levy
1978). The wholesaler questionnaire requested information on the wholesalers’ perceptions of
suppliers’ service performance. The manufacturers’ questionnaire requested information on
their perceptions of the importance of each service to wholesale customers. The results of the
rank ordering of the customer service elements in terms of perceived dollar value were (1) fill
rate, (2) terms of sale, (3) lead time, (4) order placement policy, and (5) consistent delivery (Levy
1978).
22
A survey of purchasing managers (located in two industrial areas in Brazil) on physical
distribution service added more findings to the body of knowledge about customer service (Luce
1982). Respondents were asked to rank order the five overall purchasing factors and the five
specific physical distribution elements which they perceived as most important. The overall
purchasing factors mentioned most often were: quality, price, physical distribution, location,
and minimum order size. The five specific physical distribution elements were: accuracy in filling
orders, average delivery time, rush services and billing, action on complaints, and order status
information (Luce 1982).
Jackson, Keith and Burdick (1986) studied the perceived relative importance of six physical
distribution service components. Purchasing agents from 25 large industrial manufacturing
firms were randomly assigned to one product type and one buy class condition. The elements
ranked in the following order: (1) consistent delivery, (2) in-stock, (3) lead time, (4) cooperation,
and (5) order processing information. The results supported earlier research which found order
cycle time and in-stock performance to be important physical distribution service elements. No
differences were found based on size of firm or industry type (Jackson, Keith and Burdick 1986).
The relationship between service level, the resulting customer satisfaction level, and the
customer’s purchase decision has implications for the entire firm (Mentzer, Gomes and Krapfel
1989). The previously cited research revealed that elements of logistics were among the most
important sub-factors of customer service. It is also apparent that purchasing managers ranked
elements of logistics customer service high. A summary of the results of these studies is shown
in Table 3.
23
Study Research Method Main Findings
(Dickson 1966)
Experiment: read one of four scenarios and rate the importance of 23 purchasing factors
The ranking of the factors did vary by individual case situation but quality, delivery, and past performance were always in the top five and the cumulative ranking of the top five factors were quality, delivery, performance, warranties, and facilities. Analysis of variance on the factor rating showed that there was general agreement on factors with high and low importance, but not for factors between the extremes.
(Wind, Green and Robinson 1968)
Experiment: assign between zero and 100 based on importance to a list of 10 vendor characteristics.
The results indicated agreement among the buyers as to the ranking of characteristics and quality/price ratio and delivery reliability were indicated as the two most important characteristics. In contrast, reciprocity and personal benefits to the buyer were grouped at the bottom.
(Cunningham and Roberts 1974)
Experiment: name the five most important service factors and to rank them in order.
The rankings were (1) delivery reliability, (2) technical advice, (3) test facilities, and (4) replacement guarantee. It was also found that 80% of the buyers formed a favorable impression of suppliers if they would meet the buyers’ need for quality, service, and price.
(Perreault and Russ 1976c)
Survey: Importance and satisfaction of supplier characteristics in six product categories (semiconductors, bearings, acid, sheet plastic, fasteners, and lubricants)
The top five important supplier characteristics were quality, distribution service, price, supplier management, and distance. The relative importance of supplier characteristics varied widely across product categories, but only quality and distribution service were consistently ranked as first and second. The highest satisfaction was with billing procedures, order methods, and accuracy in filling orders, while the lowest satisfaction was with delivery time and delivery time variation.
Continued Table 3: Summary of Results of Previous Customer Service Studies
24
Table 3 continued
Study Research Method Main Findings
(Gilmour, et al. 1977)
Interviews: rank order the five most important of 17 customer service elements.
The five most important purchasing elements for all customers were availability, after-sales service, delivery reliability, delivery time, and technical competence of the representatives. There was some difference of ranking by segment which indicates a possible benefit for applying different customer service policies in different segments.
(Anderson, Jerman and Constantin 1978)
Survey: sales representatives for motor and rail transportation; each respondent completed 20 paired comparisons of goals that were then ranked.
The top five were (1) order cycle time reliability, (2) percent orders filled, (3) minimum physical distribution cost, (4) minimum order cycle time, and (5) minimum damage in transit.
(Levy 1978) Survey: wholesalers’ perceptions of their suppliers’ service performance, and the manufacturers’ perception of the importance of each service to their wholesalers.
The results of the rank ordering of the customer service elements in terms of perceived dollar value were (1) fill rate, (2) terms of sale, (3) lead time, (4) order placement policy, and (5) consistent delivery.
(Jackson, Keith and Burdick 1986)
Experiment: purchasing agents from 25 large industrial manufacturing firms were randomly assigned to one product type and one buy class condition.
The perceived relative importance of six physical distribution service components was assessed. The importance varied across five product types and three buy classes. Overall, the elements ranked in the following order: (1) consistent delivery, (2) in-stock, (3) lead time, (4) cooperation, and (5) order processing information.
25
2.2. Customer Service in Marketing and Logistics
In this section, more recent customer service research is described. The following research has
been published from the mid 1980s onwards and there are differences from the previous
research. Previous research mostly used rank-ordered lists or analysis of variance compared to
regression and structural equation modeling. The second difference is the connection of
customer service elements to outcome variables like customer satisfaction. Customer service
research in the Marketing domain is related to the SERVQUAL framework. In the Logistics
domain research can be divided into studies that solely focus on logistics elements, the logistics
service quality scale, and studies comparing elements related to Marketing and Logistics.
SERVQUAL
For more than two decades, the definition and measurement of service quality has occupied a
prominent position in the Marketing literature. Unlike a physical product where one can often
quantify quality, measuring service quality is different because there are no objective metrics
(Parasurman, Zeithaml and Berry 1985). Exploratory research (Parasurman, Zeithaml and Berry
1985) initially offered support for the idea that service quality is an overall evaluation similar to
an attitude. The difficulty of measuring service quality as a psychometric concept was overcome
by measuring the customer’s expectations on service quality in a general service category and
concurrently the perceptions about the particular firm whose service quality was being assessed.
The difference between expectation scores and perception scores was conceptualized as
perceived service quality. The total SERVQUAL score for service quality was calculated by
26
averaging the difference scores. The findings show that regardless of the type of service,
customers used the same general criteria for making an evaluative judgment about service
quality. Based on those results, it was concluded that it was possible to construct one multi-
item scale that could be used to evaluate universal service quality (Parasurman, Zeithaml and
Berry 1985).
Following the exploratory research, a multi-item scale for surveys was developed (Parasurman,
Zeithaml and Berry 1988). Ten aspects of service quality and 97 individual items were tested.
Each item was converted into two statements, one to measure service expectations about firms
in general and another to measure perceptions about the service performance of a particular
firm. After scale purification, the refined scale had 22 items spread among five dimensions
(Parasurman, Zeithaml and Berry 1988):
Tangibles: Physical facilities, equipment, and appearance of personnel Reliability: Ability to perform the promised service dependably and accurately Responsiveness: Willingness to help customers and provide prompt service Assurance: Knowledge and courtesy of employees and their ability to inspire trust and
confidence Empathy: Caring, individualized attention the firm provides its customers
Despite the obvious popularity of SERVQUAL in literature (Carman 1990; Johnson, Dotson and
Dunlop 1988), several researchers questioned its usefulness in measuring service quality and
proposed alternative approaches (Babakus and Boller 1992; Brown, Churchill and Peter 1993;
Cronin and Taylor 1992; Teas 1993). There is little, if any, theoretical or empirical evidence to
support the relevance of the expected service-perceived service gap as a basis for measuring
service quality (Brown, Churchill and Peter 1993; Cronin and Taylor 1992; Teas 1993). It was
shown that this operationalization of service quality confounds satisfaction and attitude (Cronin
27
and Taylor 1992). In addition to theoretical issues, the usefulness of SERVQUAL for making
managerial decisions is doubtful because numerous aspects of service are not covered and some
of the SERVQUAL questions are vague in some contexts.
Although SERVQUAL was originally intended as a generic measure of service performance
(Parasurman, Zeithaml and Berry 1985), subsequent research has shown that the SERVQUAL
items must be customized to the specific situation to which it is applied (Donnelly, et al. 1995;
Finn and Lamb 1991; Reidenbach and Sandifer-Smallwood 1990). Other researchers argue that
it takes more than a simple adaptation of the SERVQUAL items to effectively address service
quality in some environments (Brown, Churchill and Peter 1993; Carman 1990; Finn and Lamb
1991). It is also important to note that managers and researchers are advised to carefully assess
which issues are important to service quality in a particular situation and to modify the
SERVQUAL scale accordingly or develop a proprietary scale (Parasurman, Zeithaml and Berry
1991):
By design, the iterative procedure retained only those items that are common and relevant to all service firms included in the study. However, by the same token, this procedure may have deleted certain "good" items relevant to some but not all firms. Therefore, while SERVQUAL can be used in its present form to assess and compare service quality across a wide variety of firms or units within a firm, appropriate adaptation of the instrument may be desirable when only a single service is investigated.
While many researchers acknowledge the theoretical validity of the individual items comprising
the SERVQUAL scale, the usability of the conceptualization has been challenged several times
(Babakus and Boller 1992; Carman 1990; Cronin and Taylor 1992; Finn and Lamb 1991). Some
empirical evidence suggests that the proposed delineation of the five components is not
28
consistent when used on different industries (Babakus and Boller 1992; Carman 1990; Cronin
and Taylor 1992; Finn and Lamb 1991). Another unclear issue is the application of SERVQUAL to
business-to-business relationships versus the business-to-consumer context in which it was
developed. Some of the SERVQUAL items did not load on the same constructs when compared
across different types of industries and different situations in subsequent research (Babakus and
Boller 1992; Carman 1990; Cronin and Taylor 1992; Finn and Lamb 1991). This suggests that the
dimensions of service quality may vary between different industries. An additional area of
concern is whether a generic conceptual scheme like that has merit at all. Using the same
questions to evaluate a situation-specific concept like service has not been a successful strategy
in previous research. It would have merit to use the same constructs because it enables
comparing results from several samples.
Simply measuring service quality alone is of limited interest and therefore service quality was
linked to outcome variables in the service marketing literature. While studies in Marketing
identified significant relationships among service quality, marketing variables and profitability
and market share (Buzzell and Gale 1987; Gale 1992; Phillips, Chang and Buzzell 1983), other
researchers have shown that the link between service quality and business performance is
neither straightforward nor simple (Greisig 1994; Rust and Zahorik 1993). Some researchers
have focused on intermediate links between service quality and profitability (Zeithaml, Berry
and Parasurman 1996). The findings offered empirical support for the notion that improving
service quality can increase favorable behavioral intentions on the part of a customer.
29
Cronin and Taylor (1992) developed a competing scale to SERVQUAL called SERVPERF. The main
difference between the two scales is that SERVQUAL is made up of the difference between
actual performance and expectations of performance and SERVPERF only contains actual
performance. It was found that the SERVPERF was an antecedent of customer satisfaction. In
addition, customer satisfaction exerted a stronger influence on purchase intentions than did
service quality (Cronin and Taylor 1992).
The SERVQUAL scale also had a direct influence on logistics research. Stank, Goldsby and
Vickery (1999) used the five dimensions of SERVQUAL to build two scales: operational and
relational service quality. Both dependability and accuracy relate to the consistent quality or
conformance quality aspect of operational performance. The other dimensions, responsiveness,
assurance, and empathy are all aspects of relational performance. Tangibles might also be
viewed as being related to relational performance, at least to some degree, as they encompass
the physical appearance of employees (Stank, Goldsby and Vickery 1999). The logistics
customer service research is reviewed in the next section.
Logistics Customer Service
In both marketing and logistics, the nature of interactions between buyers and service suppliers
has been identified as an important influence on buyer satisfaction and is a significant predictor
of the continuation of a successful business relationship (Daugherty, Stank and Ellinger 1998;
Innis and La Londe 1994; Leuthesser and Kohli 1995). Empirical research revealed that relational
behavior is an important complement to offering quality in determining customer satisfaction
(Leuthesser and Kohli 1995). It was found that service employees that engaged in deliberate
30
efforts to understand their customers’ unique business conditions cause higher levels of buyer
satisfaction (Leuthesser and Kohli 1995). Logistics studies have revealed that both operational
and relational performance relative to the logistics aspect of service quality had significant
positive links to customer satisfaction and repurchase intentions (Daugherty, Stank and Ellinger
1998; Innis and La Londe 1994; Stank, Goldsby and Vickery 1999). Operational elements are
aspects related to product availability, product condition, delivery reliability, and delivery speed.
Relational elements are aspects related to communications and responsiveness.
There are many definitions and descriptions in the literature of how logistics activities create
value for the customer. The most traditional are based on the creation of time and place
utilities (Perreault and Russ 1976). Another approach are the “Seven Rs” that describe the
attributes of the company’s product/service offering that lead to utility creation through
logistics value: the company’s ability to deliver the right product in the right amount at the right
place at the right time for the right customer in the right condition at the right price (Coyle,
Bardi and Langley 1992; Stock and Lambert 2001). This definition implies that a significant part
of the value of a product is created by logistics service. The service-dominant logic of Marketing
also provides evidence for that argument (Vargo and Lusch 2004). Logistics customer service is
often defined as a component of, or used as a substitute for, logistics value (Langley and
Holcomb, 1992). Customer service adds value through three components (La Londe and Zinszer
1976):
31
An activity to satisfy customers’ needs Performance measures to ensure customer satisfaction A philosophy of firm-wide commitment
In a subsequent book customer service was defined as “a process for providing significant value-
added benefits to the supply chain in a cost effective way” (La Londe, Cooper and Noordewier
1988). The supply chain view of this definition points to the notion that the benefits of good
service go beyond the four walls of a company.
Using a sample from the personal products industry, the link between logistics capabilities,
customer satisfaction, customer loyalty, and market share was investigated (Daugherty, Stank
and Ellinger 1998). The path from logistics service through satisfaction to loyalty to market
share is not linear as previously believed. The results of that study indicate that both satisfaction
and loyalty are required to influence market share positively, however this is not a
straightforward process. Positive market share benefits accrue only when firms create
customers that are not only satisfied but also committed to repurchasing from a vendor over
time (Daugherty, Stank and Ellinger 1998). This highlights the importance of measuring the
financial benefits of customer service.
In addition, research has revealed that the relationship between service quality and outcome
measures is complex. For example, Stank, Goldsby and Vickery (1999) found that:
The covariance between operational and relational performance, is supported by a significant positive value.
The relationship between operational performance and customer satisfaction is statistically significant.
Improvements in operational performance yield higher levels of customer satisfaction.
Improvements in relational performance only marginally affect customer satisfaction as evidenced by the weak statistical significance.
32
Customer satisfaction has a highly significant positive effect on customer loyalty.
One issue that was raised was the fact that there could be other constructs that affect customer
satisfaction and loyalty (Stank, Goldsby and Vickery 1999). Operational service performance and
relational service performance are not the only variables affecting customer satisfaction. If
service quality is operationalized just as operational and relational service performance (Stank,
Goldsby and Vickery 1999), then product, pricing and service expectations that are set by the
sales person are neglected. It is very likely, however that such attributes influence the outcome
variables like satisfaction and loyalty. Another limitation is the fact that financial implications
are omitted.
A subsequent study alleviated some of the issues by adding two more constructs: cost
performance and market share (Stank, et al. 2003). Unlike previous studies, the findings show
relational performance had a significant relationship with customer satisfaction, while the
operational performance-satisfaction and cost performance-satisfaction relationships were not
significant. It can be interpreted that operational performance and cost performance are order
qualifiers and relational performance elements are the main drivers of determining which
suppliers are excellent. The link between customer loyalty and market share was significant but
at a lower level than the satisfaction-loyalty link (Stank, et al. 2003).
Because logistics service can be used by managers as a differentiating competitive tool, it is
important to discern whether suppliers and customers have a similar understanding about
logistics service expectations. Data from qualitative interviews showed a close match between
the supplier's perception of what the customer expects and actual customer expectations (Davis
33
and Mentzer 2006). A gap in perceptions about what loyalty means to customers and suppliers
was revealed as well. The more powerful customers had a very behavioral view of loyalty, while
suppliers took a more affective perspective. This is likely due to asymmetry in the relationship
(large customers and small suppliers). The zone of tolerance concept seems to show its effect in
this circumstance as well. The concept portrays service performance as a range rather than a
distinct point. Service levels can vary within the zone without changes in customers’ satisfaction.
Larger customers have a narrower zone, and suppliers differentiate service offerings in order to
cater to the demands of more powerful customers (Davis and Mentzer 2006).
In a later article, the results of the previously described exploratory research were tested with a
survey (Davis-Sramek, Mentzer and Stank 2008). Relational- and operational order fulfillment
are based on the conceptualization of Stank, Goldsby and Vickery (1999). Relational order
fulfillment service was modeled as an antecedent to operational order fulfillment service. The
relational component of order fulfillment was conceptualized like the personnel contact quality
construct in Mentzer, Flint and Hult (2001), which referred to the customer orientation of the
supplier’s customer service contact people. Operational order fulfillment was conceptualized as
driving customer satisfaction. Satisfaction is the result of a cognitive evaluation based on total
purchase experience over time and more specifically it is affected by (1) general satisfaction, (2)
confirmation of expectations, and (3) the distance from the customer’s hypothetical ideal
product. The final outcome is measured as loyalty, which is conceptualized as the causal
relationship between two variables: affective commitment and purchase behavior (Davis-
Sramek, Mentzer and Stank 2008).
34
To examine the model, data were collected from retailers of consumer durable manufacturing
goods (Davis-Sramek, Mentzer and Stank 2008). The results show the existence of a complex,
mediating relationship between satisfaction, affective commitment, and purchase behavior.
Just satisfying customers may not be enough to influence future behavior; forging emotional
bonds and trust in the relationship stems from satisfying customers and consequently influences
purchase behavior. The results justify the importance of looking at the emotional and
behavioral components of loyalty not only as distinctly different constructs, but as a causal
relationship between affective commitment and purchase behavior (Davis-Sramek, Mentzer and
Stank 2008). However, affective commitment and purchase behavior were measured as
perceptions. It would be revealing to see if measuring actual purchase behavior would change
that result.
Logistics Service Quality
In this section the stream of research centered on the logistics service quality scale (LSQ) is
reviewed. The LSQ conceptualization refers to a distinct operationalization of logistics customer
service. The quality of all aspects of logistics service several articles were published. Two
articles involved literature reviews (Bienstock, Mentzer and Bird 1997, Mentzer, Gomes and
Krapfel 1989) and three are based on empirical research (Mentzer, Flint and Hult 2001, Mentzer,
Flint and Kent 1999, Rafiq and Jafaar, 2007).
Twenty-six elements of logistics and customer service reported in the literature were
synthesized in order to obtain a three-dimensional construct composed of availability,
35
timeliness, and quality (Mentzer, Gomes and Krapfel 1989). This structure was generally
supported by later empirical evidence, with adaptations based on qualitative research
(Bienstock, Mentzer and Bird 1997). A multi-item scale of service quality in the logistics context
was developed as an extension of physical distribution service (Mentzer, Flint and Kent 1999).
As a result, service is believed to have considerable value as a competitive advantage input to
strategic planning.
The research project was made up of a qualitative stage and a quantitative stage. In the
qualitative phase, 13 focus group interviews were conducted with customers. The general
topics covered four basic areas (Mentzer, Flint and Kent 1999):
The nature of the participants' work in relation to the sponsoring organization Evaluation of the working relationship with the sponsoring organization Assessment of sponsoring organization’s performance Perceptions of what the sponsoring organization does well or poorly
In the quantitative phase, surveys were mailed and the results were analyzed. Respondents
were divided into 10 separate data sets based on industry, one for scale purification, eight for
scale validation. Respondents who failed to indicate the segment to which they belonged were
excluded from the data analysis. Following the analysis, nine constructs were identified that
make up LSQ (Mentzer, Flint and Kent 1999):
Information Quality: Value of information provided by the supplier. Ordering Procedures: Efficiency and effectiveness of the order process of the supplier. Ordering Release Quantities: Product availability (customers should be the most
satisfied when they are able to obtain the quantities they desire). Timeliness: Whether orders arrive when promised, but more broadly, timeliness is
dependent on the length of time between order placement and receipt.
36
Order Accuracy: How closely actual shipments match orders upon arrival (having the right items, the correct number of items, and no substitutions).
Order Quality: How well products work in two ways: how well they conform to product specifications and how well they meet customers' needs.
Order Condition: Ensuring undamaged products. Order Discrepancy Handling: How well the suppliers deal with issues that the customer
experiences due to the supplier’s fault. Personnel Contact Quality: Whether the customer service employees are
knowledgeable, empathize with the customer’s situation, and help them resolve their problems.
In an attempt to connect customer service with customer satisfaction, the LSQ constructs were
split into two categories, order placement and order receipt (Mentzer, Flint and Hult 2001).
Personnel contact quality, order release quantities, information quality, and ordering
procedures fall into the order placement category. The others, order accuracy, order condition,
order quality, timeliness, and order discrepancy handling fall into the order receipt category.
The two categories then drive customer satisfaction as an outcome construct (Mentzer, Flint
and Hult 2001). The single outcome variable is one of the limitations of this study and “LSQ
must be linked to other customer outcome measures, such as loyalty, word of mouth, and price
sensitivity, as well as supplier outcome measures, such as revenues, market share, and
profitability” (Mentzer, Flint and Hult 2001).
An extension of this scale was used in a survey of logistics managers in the United Kingdom
about their perceptions of third-party logistics (3PL) providers (Rafiq and Jafaar 2007). This was
an independent validation of the LSQ scale. The researchers conceptualized the constructs for
information quality and ordering procedures differently and achieve improved reliability of both
constructs. In contrast to previous research (Mentzer, Flint and Hult 2001), it was found that
the components of LSQ do not contribute equally to customer satisfaction. The functional LSQ
37
elements (personnel contact quality, ordering procedures, order discrepancy handling, and
information quality) are perceived as more important than the technical ones (Rafiq and Jafaar
2007).
Integration of Marketing and Logistics Elements of Customer Service
Organizationally, customer service involves a variety of people at different levels within the
organization and from different functions. A survey from ICSA showed that 51.0% of customer
service respondents report to sales/marketing, 14% to administration, 13% to logistics and the
remaining 22% report to other functions (ICSA 1988). A logistics organizational study shows that
customer service reports to logistics in 56.1% of companies (Bowersox 1987). Customer service
is a pervasive, boundary-spanning activity that takes place from within and beyond the firm and
integration within the firm should focus on marketing and logistics activities as the primary
functions which interface with the customer (Rinehart, Cooper and Wagenheim 1989). However,
traditionally marketing and logistics have evolved separately within many corporations and
therefore this can pose some serious challenges because both functions influence the service
experience of the customer. If the functions do not coordinated their effort, it can deteriorate
the customer satisfaction.
One of the earliest comprehensive frameworks for assessing the importance of customer service
was the work of Sterling and Lambert (1988). They focused on the office systems and furniture
industry. Before this study, customer service research had several shortcomings (Sterling and
Lambert 1987):
38
Either multiple industries were examined as a homogeneous group or if a single industry was assessed, the findings were not generalizable beyond a specific firm.
A majority of the studies examined customer service in isolation from the rest of the marketing mix.
Others perceived no functional boundaries to customer service, and consequently included in the "customer service" component variables which in fact were related specifically to product, price or promotion. In such instances "customer satisfaction" has, in effect, been mistakenly replaced by "customer service" as the output of all marketing effort.
Several studies used a cross-sectional approach to assess customer service within an industry
(Daugherty, Stank and Ellinger 1998; Davis-Sramek, Mentzer and Stank 2008; Stank, Goldsby and
Vickery 1999; Stank, et al. 2003). LSQ could be considered a generalizable model, but it lacks a
holistic perspective because it excludes Marketing aspects and it does not use actionable
outcome variables like financial outcomes (Mentzer, Flint and Kent 1999; Mentzer, Flint and
Hult 2001). The second shortcoming is a more serious issue because often outcome variables
such as customer satisfaction are used. If only a subset of attributes affecting the outcome
variable is analyzed, the findings of the entire study are in jeopardy. Namely the effect of the
factors that are included in the study is grossly overestimated. The third shortcoming illustrates
another problem that can arise by not including the other parts of the marketing mix. Thus,
several research gaps existed 20 years ago (Sterling and Lambert 1987):
It is uncertain which of the marketing mix variables are more important, and how these variables interact in affecting sales.
Most empirically based studies were conducted using restricted data, such as advertising or price data exclusively, and were aimed at testing alternative model formulations and therefore, the generalizability of their findings is questionable.
Most users of share measures have failed to: Carefully explore the role of share in their marketing and corporate strategic models Assess its relative importance under different environmental scenarios Establish empirically for their own brands, the historical and projected relationship
between share and the effectiveness of their marketing strategy variables
39
With few exceptions, researchers have failed to obtain direct empirical evidence concerning the cause-effect relationship between share or other objectiveness, such as profits—and the effectiveness of marketing strategy variables.
The first research gap is still valid because no study is known to have systematically included the
marketing mix components and connected it to share of business or other measures in a single
structural equation model. The second research gap is not so much of an issue any more
because there were several surveys that focused at least on an industry level so that cross-
sectional data of several companies could be analyzed (Daugherty, Stank and Ellinger 1998;
Davis-Sramek, Mentzer and Stank 2008; Stank, Goldsby and Vickery 1999; Stank, et al. 2003).
The third and fourth research gaps are difficult to assess because causal relationships are
inherently difficult to prove with statistical methods.
A factor analysis was performed on the office systems and furniture industry data and eleven
functionally oriented factors were obtained (Sterling and Lambert 1987). Each of the four
marketing mix components was represented by two or three factors:
Product
Product Flexibility
Product Development
Product Breadth/Quality Price
Discount Structure
All Inclusive Bids Promotion
Personal Selling
Sales Assistance/Training Place/Logistics
Physical Distribution Information System Capability
Transportation Services
Order Completeness
40
Scales developed from these factors were then used for hypothesis testing. The results of the
stepwise regression show that the four components of the marketing mix did not contribute
equally to the share of business allocated to vendors by end users. Specifically place/logistics
factors, which were referred to as physical distribution/customer service, consistently
contributed more to the share of business. Price and promotion factors were inconsistent in
their ability to predict share of business. Therefore, concentrating on only one or a few
elements of the marketing mix would be dangerous, if a firm's objective was to gain market
share (Sterling and Lambert 1987). Improved performance on the components of the Marketing
Mix may lead to increased market penetration.
Customer service, one of the key elements provided by logistics, was seen to have a significant
and positive impact on customer satisfaction, cognitive attitudes, and repurchase intentions
(Innis and La Londe 1994). Customer satisfaction is one of the key objectives of the marketing
function in most firms and cross-functional coordination should be encouraged to allow
marketing and logistics to work together to provide the optimal marketing mix output to the
customer (Innis and La Londe 1994). It can be concluded that only with efficient cross-functional
integration can an optimal level of customer satisfaction be achieved. Innis and La Londe (1994)
proposed but never tested a model where sales were driven by product, price, and promotion as
well as physical distribution and customer service (logistics activities).
The marketing and logistics dimensions of customer service proposed by Mentzer, Gomes and
Krapfel (1989) were tested with a survey by Emerson and Grimm (1996). A factor analysis was
performed and several constructs were revealed (Emerson and Grimm 1996):
41
Quality Product support – customer service Availability Product support – sales Pricing policy Communication Delivery quality
Three logistics factors and four marketing factors were obtained (Emerson and Grimm 1996).
The availability construct (proposed by Mentzer, Gomes and Krapfel 1989) was confirmed
without any adaptation. While quality of physical distribution service was suggested as a
construct by Mentzer, Gomes and Krapfel (1989), one modification was the renaming of this
construct as delivery quality, to better represent the two indicators (Emerson and Grimm 1996).
Communication formed the third logistics construct.
Of the four marketing constructs, pricing policy loaded on one factor as expected. However, the
product support items for sales representatives and product support items for customer service
representatives loaded on two separate factors. Customer service representatives are viewed
differently from sales representatives by customers, because a customer service representative
might communicate with only a small subset of accounts and there were many customers in the
sample who have never had any interaction with a customer service representative and would
certainly view a customer service representative differently from their sales representative
(Emerson and Grimm 1996). The last marketing construct was named quality, but the measures
contained questions on warranty, which was interpreted as a result of quality products.
In a later study, the difference in importance between marketing and logistics elements of
customer service was assessed under different environmental conditions (Emerson and Grimm
42
1998). The informants were asked to distribute 100 points across eight attributes, with
assignment of more points indicating greater importance (Emerson and Grimm 1998). The eight
attributes included four from logistics (percentage of order filled, order cycle-time consistency,
accuracy of orders shipped, and order status information) and four from marketing (terms of
sale, competence of customer service representatives, overall product quality, and action on
complaints) (Emerson and Grimm 1998). In order to calculate a score, the total assigned to the
four items representing marketing customer service were subtracted from the sum of the four
items representing logistics customer service (Emerson and Grimm 1998). Several findings were
reported (Emerson and Grimm 1998):
The more indirect a channel (the higher the number of intermediaries), the more important logistics customer service becomes.
Larger customers will place more importance on marketing customer service. Smaller customers often perceive the level of logistics service to be lower than the level
of marketing service they receive. Firms experiencing high levels of supplier flexibility are obtaining high levels of logistics
service.
A summary of constructs of the Marketing Mix variables obtained in three previous research
studies is shown in Table 4. The selected studies used customer service elements that could be
classified into the Marketing Mix components. Only studies where constructs could be assigned
the categories were used. It is apparent that the constructs are different across multiple
industries (office systems and furniture, plastics, and large power tools). But it is seems that
each component of the Marketing Mix is important and at least one factor was obtained for
each component.
43
Marketing Mix Component
Study/ Industry
Product Price Promotion Place/Logistics
(Sterling and Lambert 1987) / Office Systems and Furniture
Product Flexibility Product Development Product Breadth/
Quality
Discount Structure All Inclusive Bids
Personal Selling Sales Assistance/
Training
Information System Capability
Order Completeness Transportation
Services
(Lambert and Harrington 1989) / Plastics
Product Quality Credit Discount Structure
Direct Mail Gifts, Entertainment,
and Trade Shows Sales Support Quality of Sales Force
Information System Capability
Lead Time Order Servicing
Product Availability
(Emerson and Grimm 1996) / Large Power Tools
Quality Product support –
customer service
Pricing policy Communication Product support –
sales
Availability Delivery quality
Table 4: Constructs of the Marketing Mix
44
2.3. Customer Satisfaction and Firm Performance
Customer Satisfaction has long been a central topic in Marketing research and practice. High
customer satisfaction ratings are widely believed to be the best indicator of a company's future
profits (Kotler 1991). Managers often use customer satisfaction as a criterion for diagnosing
product or service performance and often tie customer satisfaction ratings to employee
compensation. To encourage actions that will lead to an optimal level of satisfaction, it is
necessary to understand the link between the antecedents of satisfaction and satisfaction's
behavioral and economic consequences (Anderson and Sullivan 1993). Satisfaction was
conceptualized as a post-purchase evaluation of product quality given pre-purchase
expectations (Kotler 1991).
Using data from Sweden, several experimental findings of satisfaction research were tested
(Anderson and Sullivan 1993). Satisfaction was found to increase with both perceived quality
and disconfirmation. The term disconfirmation refers to the concept that a customer perceives
a change in satisfaction only when their expectations are not confirmed. Both positive and
negative disconfirmation increased with the ease of evaluating quality. When quality is
ambiguous or difficult to evaluate, then expectations will play a greater role in determining
satisfaction, and more importantly, quality which falls short of expectations was found to have a
greater impact on satisfaction and retention than quality which exceeds expectations.
Satisfaction was found to have a positive impact on repurchase intentions, and the elasticity of
45
repurchase intentions with respect to satisfaction is found to be lower for firms that provide
high satisfaction (Anderson and Sullivan 1993).
Using the same dataset as above, the link between customer satisfaction, market share, and
profitability was investigated (Anderson, Fornell and Lehmann 1994). Firms that achieve high
customer satisfaction enjoy superior economic returns, but the findings also indicate that
economic returns from improving customer satisfaction are not immediately realized. Because
efforts to increase current customers' satisfaction primarily affect future purchasing behavior,
the greater portion of any economic returns from improving customer satisfaction will be
realized in subsequent periods. Overall, customer satisfaction actually may fall as market share
increases. This may be because gains in market share may come from attracting customers with
preferences more distant from the target market. The firm may overextend its capabilities as
the number of customers and/or segments grows. In such a situation, even though the overall
level of customer satisfaction is falling, a firm's sales and profits may be increasing. It is
important to note that this may be a short-run versus long-run phenomenon. In the long run, it
is possible that customer satisfaction and market share go together, but there is evidence that
this is not always the case in the short run (Anderson, Fornell and Lehmann 1994). This effect
may also not be recognized in a cross-sectional research design, because changes cannot be
measured.
The American Customer Satisfaction Index (ACSI) is a customer-based measurement system for
evaluating and enhancing the performance of firms, industries, economic sectors, and national
46
economies (Fornell, et al. 1996). It was designed to be representative of the economy as a
whole and covers more than 200 firms, with 1994 sales in excess of $2.7 trillion competing in
over 40 industries in the seven major consumer sectors of the economy. On an annual basis,
the ACSI system estimates a firm-level customer satisfaction index for each company in the
sample and weights these firm-level indices to calculate industry, sector, and national indices.
Overall customer satisfaction (ACSI) has three antecedents: perceived quality, perceived value,
and customer expectations. The immediate consequences of increased customer satisfaction
are decreased customer complaints and increased customer loyalty (Fornell, et al. 1996).
Many studies up to that point used the standard customer service paradigm (Oliver 1980).
Those models reveal anomalies and omissions of previous approaches and to propose
extensions and new discoveries that address the limitations and exclusions in existing theory.
Fournier and Mick (1999) chose a different approach to explore and describe satisfaction by
using firsthand accounts of subjects. This approach enabled the development of a more realistic
perspective of satisfaction as it unfolds in the course of daily life. Satisfaction was investigated
through extensive and repeated in-home interviews that focused on consumers' purchase and
usage experiences with technological products. The study resulted in five main conclusions
(Fournier and Mick 1999):
Consumer product satisfaction is an active, dynamic process. The satisfaction process often has a strong social dimension. Meaning and emotion are integral components of satisfaction. The satisfaction process is context-dependent and contingent, encompassing multiple
paradigms, models, and modes.
47
Product satisfaction is invariably intertwined with life satisfaction and the quality of life itself.
In order to assess the value of customer satisfaction in financial terms, a mathematical model
approach was used (Rust and Zahorik 1993). The research is built on the premise of defensive
marketing (Fornell and Wernerfeld 1987), which in contrast to offensive marketing focuses more
on retaining customer rather than acquiring new ones. It is generally believed that it is more
costly to add a new customer than to keep an existing one (Brown et. al 2005). The authors
show how customer satisfaction can be linked to individual loyalty, aggregate customer
retention rate, market share, and profitability. However, this is not a straightforward process
(Rust and Zahorik 1993).
Quantifying the results of quality expenditures by linking service quality and customer
satisfaction to financial outcomes can show the effect service improvements have on the
bottom line. The return on quality (ROQ) approach is characterized by the following
assumptions (Rust, Zahorik and Keiningham 1996):
Quality is an investment. Quality efforts must be financially accountable. It is possible to spend too much on quality. Not all quality expenditures are equally valid.
The relationship between service quality improvement efforts and profitability is modeled as a
chain of effects that can be seen in Figure 2. The improvement effort, if successful, leads to an
improvement in service quality. Improved service quality typically results in increased customer
satisfaction. Increased customer satisfaction in turn leads to higher levels of customer retention,
48
and also positive word-of-mouth. Revenue and market share increases are driven by losing
fewer existing customers and gaining more new customers. The increased revenues lead to
greater profitability. The effect of word-of-mouth is very difficult to measure in a practical
business situation, however it is important and it has a real effect (Kumar, Petersen and Leone
2007).
Source: (Kumar, Petersen and Leone 2007)
Figure 2: A Model of Service Quality Improvement and Profitability
Analysis of repurchase intention as a function of overall satisfaction showed that disappointed
customers had only a 45% probability of returning, whereas the satisfied and very satisfied
customers had a probability of over 90% (Rust, Zahorik and Keiningham 1996). The ROQ
approach also considers a shift in market share as a result of the shift in satisfaction and given
49
the right data specific ROQ figures can be calculated. Clearly, these numbers have little meaning
without knowing the details of the particular situation, but it is important to note that there are
approaches to quantify the results of increased customer satisfaction.
A review of outcome measures for customer service in prior research revealed that several
outcome variables have been used. They can be classified into perceptual and financial
measures. In general perceptual measures are about the subject’s opinion, while financial
measures are about quantifiable data. It is also apparent that most studies either choose to use
one type or the other and only few combine both approaches.
The most popular perceptual outcome variable is customer satisfaction (see Table 5). Other
related variables are loyalty (Daugherty, Stank and Ellinger 1998; Davis-Sramek, Mentzer and
Stank 2008; Stank, Goldsby and Vickery 1999; Stank, et al. 2003; Zeithaml, Berry and
Parasurman 1996) and future purchase intentions/behavior (Cronin and Taylor 1992; Davis-
Sramek, Mentzer and Stank 2008). Often several outcome variables are used in order to provide
a more accurate picture of the results. It is noteworthy, especially with exclusively perceptual
outcome measures, that there might be some issues with common method variance. The
problem may occur because strictly perceptual measures could mask actual differences. Data
collected with the same method may limit variance, which makes it more difficult to recognize
differences between the subjects.
In the other category, quantifiable measures of financial performance were used as outcome
variables. Market share was the most popular measure used (see Table 5). Others used
50
measures of profitability to determine success (Anderson, Fornell and Lehmann 1994; Phillips,
Chang and Buzzell 1983; Rust, Zahorik and Keiningham 1996). Financial outcome measures,
such as profitability and market share are often measured as perceptions. Often, not even the
managers themselves have accurate profitability data on individual customers and as such
obtaining accurate data is difficult, if not impossible (Lambert and Pohlen 2001; Lambert and
Sterling 1987). Market share data can be obtained from databases covering an industry (Stank,
et al. 2003).
51
Study Perceptual Outcomes Financial Outcomes
(Phillips, Chang and Buzzell 1983)
Relative Market Position Relative Direct Costs Relative Prices Return on Investment
(Sterling and Lambert 1987)
Share of business
(Cronin and Taylor 1992)
Customer Satisfaction Purchase Intentions
(Anderson, Fornell and Lehmann 1994)
EXPECTt = f1(EXPECTt-1, QUAL t-1, E1t) SATt = f2(QUALt-1, PRICEt-1, EXPECTt-2, E2t)
PROFITt = f3(SATt-1, , E3t) where Eit = vector of other factors (e.g., environmental trends, firm-specific factors, error)
(Zeithaml, Berry and Parasurman 1996)
Behavioral Intentions: Loyalty Switch Pay More External Response Internal Response
(Rust, Zahorik and Keiningham 1996)
Customer Attitudes, Emotions and Perceptions
Customer Satisfaction
Profitability Market Share Return on Quality
(Daugherty, Stank and Ellinger 1998)
Customer Satisfaction Loyalty
Market Share
(Stank, Goldsby and Vickery 1999)
Customer Satisfaction Loyalty
(Mentzer, Flint and Hult 2001)
Customer Satisfaction
(Stank, et al. 2003) Customer Satisfaction Loyalty
Market Share
(Rafiq and Jafaar 2007)
Customer Satisfaction
(Davis-Sramek, Mentzer and Stank 2008)
Customer Satisfaction Loyalty:
Affective Commitment Purchase Behavior
Table 5: Outcome Variables in Customer Service Studies
52
2.4. Hypothesis Development
The literature presented in this chapter revealed that there was an opportunity to add new
insights to the customer service domain. Although this area has a long stream of excellent
research, there are still gaps that should be addressed. While there were several studies that
presented a framework for assessing customer service, many of those were focused exclusively
on logistics (Stank, Goldsby and Vickery 1999) or marketing (Parasurman, Zeithaml and Berry
1985). The research revealed that narrow approaches to customer service needed to be
extended to include additional variables (Mentzer, Flint and Kent 1999) or customized to
individual situations (Parasurman, Zeithaml and Berry 1991). The only framework for
determining and assessing all variables important for selecting and evaluating suppliers is the
framework first presented by Lambert and Zemke (1982). This approach has been refined over
time and repeated in various industries and provides the direction for future research (Sterling
and Lambert 1987; Lambert and Harrington 1989).
The conceptual model of this research is depicted in Figure 3. The variables that buyers in
companies use to select and evaluate suppliers can be summarized into one of the four
categories of the Marketing Mix: product, price, promotion and place. While there were several
studies that included fewer variables responsible for driving customer satisfaction (Mentzer,
Flint and Kent 1999; Stank, Goldsby and Vickery 1999), they later would be extended to include
additional variables (Stank, et al. 2003). Since the goal of this research was to develop a
53
generalizable model, it was determined that the best strategy was to start with more variables
and reduce them as the need arises during scale development.
Figure 3: Conceptual Model
The four components of the marketing mix are all believed to influence the level of customer
satisfaction. The product construct generally covers attributes like performance, reliability,
quality, and product development. The price attributes address billing, discounts and
competitiveness. The promotion/personal selling attributes deal with characteristics of the
sales representative. While advertising would certainly be part of the promotion construct in
business-to-consumer relationships, business-to-business relationships are different (Mudambi
2002). There may be some advertisements in trade magazines, but during data analysis it was
evident that they only played a minor role compared to characteristics of the sales
54
representative. In addition, training the sales force to deliver the branding message is of critical
importance (Lynch and de Chernatony 2004). Attributes in the place/logistics category are
focused on delivery issues like reliability, availability, accuracy, lead time and logistics service.
Several studies focused on marketing and logistics components of customer service (Emerson
and Grimm 1998; Innis and La Londe 1994; Lambert and Harrington 1989). The Marketing Mix
variables are believed to drive satisfaction (Innis and La Londe 1994), however empirical testing
of these relationships is still lacking. Therefore, the first hypothesis is:
H1: The Marketing Mix has a positive significant impact on customer
satisfaction
Beyond the general relationship between the overall Marketing Mix, the individual components
must be assessed as well. Because customers first perceive the benefits of the product they are
buying it is likely the primary source of satisfaction they derive from the purchase. There is
evidence that better products increase customer satisfaction, however this is not a linear
relationship (Maddox 1981; Swan and Combs 1976). In the samples that are analyzed in this
research, a basic level of performance is present because only existing suppliers are evaluated.
H1a: Product has a positive significant impact on customer satisfaction.
Pricing is an important factor in determining which products to procure, and it is not necessarily
the absolute level of pricing but price level in relation to other factors (Herrmann, et al. 2004;
Hoyer, Herrmann and Huber 2002). This notion of price fairness is captured in the way the
55
construct is measured in this study. Because respondents are asked to indicate their
perceptions on a seven-point scale relative pricing is measured. Pricing has shown a significant
impact on satisfaction in several previous studies (Herrmann, et al. 2004; Hoyer, Herrmann and
Huber 2002; Morganoski 1988; Voss, Parasuraman and Grewal 1998). However, most of the
studies were experiments and this study would strengthen the understanding of the hypothesis.
H1b: Price has a positive significant impact on customer satisfaction.
Previous research has shown that good salespeople can help increase satisfaction (Goof, et al.
1997; Grewal and Sharma 1991; Johnson, Barksdale and Boles 2001; Liu and Leach 2001). This
effect has been shown to take place with manufacturers and retailers, which is important for
this research because in some samples of this research manufacturers are surveyed, while in
others retailers are surveyed (Goof, et al. 1997). Salespeople also set the expectations regarding
the other three components of the Marketing Mix, and if that is done well, it should result in
higher satisfaction.
H1c: Promotion/personal selling has a positive significant impact on customer
satisfaction.
The three constructs that belong to the Marketing function have all received attention in the
Marketing literature. The contrast between Marketing and Logistics attributes has not been
thoroughly explored and only a few studies have evaluated constructs from both areas together
(see Table 5). Logistics attributes have been shown to have an impact on customer satisfaction
56
in some studies (Davis-Sramek, Mentzer and Stank 2008; Mentzer, Flint and Hult 2001; Stank,
Goldsby and Vickery 1999). However, in the presence of other constructs like pricing attributes,
this effect is not consistent (Stank, et al. 2003).
H1d: Place/logistics has a positive significant impact on customer satisfaction.
While customer satisfaction is an important construct in the literature, the downside is that it
does not directly translate into financial success of a company. There is some evidence in the
literature that satisfaction can and should be connected to hard financial measures (Heskett, et
al. 1994; Heskett, Sasser and Schlesinger 2003; Rust, Zahorik and Keiningham 1996). The link
between satisfaction and business outcomes is well-documented in the Service-Profit Chain
framework (Heskett, et al. 1994; Heskett, Sasser and Schlesinger 2003; Homburg, Wieseke and
Hoyer 2009). Share of business is a good indicator for determining financial success within a
business-to-business relationship and therefore a useful outcome variable for this research. If
the goal of management is to increase sales from existing customers then share of business
must be expanded. In addition, understanding how the direct effect of the Marketing Mix on
share of business differs from the effect on customer satisfaction provides additional insight.
The relationship between customer satisfaction and share of business is also useful in
determining the accuracy of satisfaction as a predictor of financial success. If satisfied
customers are contributing to larger share of business, then managers can focus their attention
on activities that have a significant impact on customer satisfaction that are identified in the first
hypothesis. It would seem that high customer satisfaction should result in a large share of
57
business, but there have been examples where this was not clear (Homburg, Wieseke and Hoyer
2009; Kamakura, et al. 2002). The last hypothesis deals with the relationship between these
variables directly:
H2: Customer satisfaction has a positive significant impact on share of
business.
These hypotheses are tested in the course of this research. Figure 4 shows the conceptual
model with the hypotheses. While some of the individual hypotheses have been addressed in
previous research, connecting these links in one model adds a more holistic perspective. This
perspective is useful when evaluating the model in several industries because it is able to show
differences between the samples. If consistent patterns emerge across the different samples
they could be regarded as generalizable. The main contribution of this research is the
replication of the model over the nine samples.
Figure 4: Conceptual Model with Hypotheses
58
2.5. Summary
In this chapter, the relevant literature that influenced this research was presented. Although an
extensive literature base already exists, there are gaps that this research addresses. The main
gaps that are being addressed are:
The effect of logistics attributes relative to the other components of the Marketing Mix is examined.
Both customer satisfaction and share of business are used as outcome variables. Differences between industries are examined.
The described hypotheses are believed to advance knowledge in the field and hopefully will spur
new research in this area. The next chapter contains a description of the methodology. In
Chapter 4, the results are presented and analyzed. Chapter 5 contains the conclusions.
59
CHAPTER 3.
METHODOLOGY
In this chapter the research methodology is described. First, the data collection and research
instrument are reviewed. Next, the data analysis preparation including non-response bias
testing, missing data mitigation and parceling technique are described. Then, scale
development is reviewed for each sample:
Health Services (A-1): blood banking reagents
Health Services (A-2): coagulation reagents
Health Services (A-3): coagulation reagents
Electronics (B-1): professional video tape
Electronics (B-2): consumer video tape
Plastics (C-1): commodity resin
Sporting Goods (D-1): golf balls
Sporting Goods (D-2): golf clubs
Sporting Goods (D-3): golf shoes
For each of these samples the measurement model part of the structural equation modeling is
described. Last, an overview of the questions used for each sample and a summary are
provided.
60
3.1. Data Collection
A dataset of nine customer satisfaction studies used to conduct this research. For the data
collection, the methodology proposed by Lambert and Zemke (1982) was followed on all
samples. This methodology has been validated in several articles (Sterling and Lambert 1988,
Lambert and Harrington 1989, Lambert, Lewis and Stock, 1993). The studies consisted of the
following steps: external audit, internal audit, evaluation of customer perceptions and
identification of opportunities. Because this research requires customer provided data, only the
external audit part of each dataset was used. In the next section, the steps that were used for
obtaining the data are described in detail.
Data Collection Methodology
The external audit part in the Lambert and Zemke (1982) methodology was used to identify
attributes for evaluating suppliers. There are two parts in the data collection procedure: in-
depth, personal interviews and a mail survey. For each of the nine samples used for this
research, interviews were conducted with key decision-makers in each of the sponsoring
manufacturer organizations and then in 20-32 customer firms for each product category.
Customer firms were selected on the basis of their geographic location, size, marketing needs
and the major suppliers they used. The objective was to obtain a perspective that was as broad
as possible in order to compile a comprehensive and meaningful set of questions for the mail
survey. Any attribute that was mentioned during an interview was used on the questionnaire
because the goal was to provide the sponsoring organization with a comprehensive
61
understanding of factors that customers use to select and retain suppliers. The interviews were
continued until a saturation point was reached and no new attributes could be identified. The
number of interviews conducted is consistent with previous research (Blair and Presser 1993).
In the second part, the mail survey, all potential respondents from industry databases were
contacted by telephone and asked to participate in the survey before the mail questionnaire
was sent to them. Pre-qualifying the respondents improved the overall response rate (Allen,
Schewe and Wijk 1980, Hornik 1982). It also ensured that the informant was responsible for
choosing supplier, was knowledgeable about the suppliers and could provide the information to
complete the questionnaire. During the telephone conversations, the name and position of the
decision-maker was determined and the mailing address was verified. Respondents were
offered a summary of the research findings in return for their participation in the survey.
Then, questionnaires were sent to the representatives identified during the telephone
interviews. The questionnaires were sent in three waves. In addition to the three waves of
regular questionnaires, a short version of the main questionnaire was sent to non-respondents
in order to assess non-response bias (Lambert and Harrington 1990).
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Research Instrument
The questionnaires consisted of four parts:
Part A:
Importance of attributes used to select and evaluate suppliers.
Performance of the top three suppliers on those attributes. Part B: measurement of overall performance. Part C: expected performance levels. Part D: meaningful demographic data.
The main data sources for the analysis in this research are the first two parts of the
questionnaires. The questions in Part A fell into one of the following four categories: product,
price, promotion/personal selling, and place/logistics. The product questions generally covered
attributes like performance, reliability, quality and product development. The price questions
addressed billing, discounts and competitiveness. The promotion/personal selling questions
dealt with characteristics of the sales representative. Advertising in trade magazines was not an
important factor for purchasing managers based on the low importance scores in the
questionnaires. Questions in the place/logistics category focused on delivery issues like
reliability, availability, accuracy, and lead time.
In Part A of the questionnaire, there were two tasks that must be completed. The first task was
an evaluation of the importance of the attributes. The respondent was asked to circle on a scale
from one to seven the number which best expressed the importance of each attribute when
deciding how much business to give to each supplier. If an attribute was not used or possessed
very little weight in the evaluation of suppliers, the respondent was asked to circle the number
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one (not important). A rating of seven (very important) should be reserved for those factors
that would cause the respondent to reevaluate the amount of business done with a supplier, or
cause the manager to drop the supplier in the event of inadequate performance.
The second task in Part A was to evaluate the current performance of three major suppliers.
Using the scale labeled “perceived performance”, the respondent was asked to insert a number
between one and seven which best expresses the perception of the supplier's current
performance under the appropriate supplier heading. A rating of one indicates poor
performance and seven should be used for excellent performance. If a service was not available
from a supplier, then the respondent should use “NA” (not available).
In Part B, the current performance of all three major suppliers was evaluated. The two most
important questions were about overall customer satisfaction and current and ideal share of
business for each of the three major suppliers. These measures are used directly in the
structural equation models. Another question in this section was whether the customer would
recommend each of the suppliers they evaluated. The next two questions are about problems,
whether any were reported and if they were solved satisfactory. The question after that
assessed the percentage of on-time shipments during a typical month. The last question in that
section referred to price differences. The lowest price supplier receives a score of 0 percent and
for the other two the price premium as a percentage is indicated.
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3.2. Overview of the Samples
For this study, nine independent samples were collected using the methodology described
above. An overview of the samples is presented in Table 6.
Industry Sample Name Sample Size Responses Response Rate
Health Services A-1 (Blood Banking Reagents) 2,015 754 37.42%
Health Services A-2 (Coagulation Reagents) 1,005 299 29.75%
Health Services A-3 (Coagulation Reagents) 667 212 31.78%
Electronics B-1 (Professional Tape) 1,369 342 24.98%
Electronics B-2 (Consumer Blank Tape) 434 77 17.74%
Plastics C-1 (Commodity Resin) 1,854 540 29.13%
Sporting Goods D-1 (Golf Balls) 1,012 134 13.24%
Sporting Goods D-2 (Golf Clubs) 2,240 172 7.68%
Sporting Goods D-3 (Golf Shoes) 1,001 95 9.49%
Table 6: Overview of the Samples
The nine samples represented four distinct industries. The overall response rates varied from
7.68 to 37.42 percent. While the response rates in the Golf industry are fairly low, this is not
unusual due to the fact that surveys of retailers generally have lower response rates (Ellram, La
Londe and Weber 1999). The sample sizes are sufficiently large. Non-response bias was
assessed in two ways (Armstrong and Overton 1979; Lambert and Harrington) and no significant
differences were identified indicating that non-response bias was not a problem. Therefore,
reliable conclusions can be drawn (Boyer and Swink 2008). The number of responses in each
sample is adequate for the type of analysis that was performed because each respondent was
asked to evaluate up to three suppliers, thus bringing the sample size into an acceptable level
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for each sample. It is often suggested that at least 200 observations are necessary to run a
structural equation model (Shah and Meyer Goldstein 2006). This is also dependent on the
specifics of the model in particular the number of latent variables. In this model, there are six
latent variables (LV) so in the smallest samples an adequate ratio of observations to LVs was
achieved. In the next sections, each of the samples is described.
Health Services Industry
The health services industry yielded three separate samples. For sample A-1, a total of 2,015
hospitals with on-site blood banks were surveyed. Each hospital had 100 or more beds, which
was believed to be the smallest sized hospital to have an on-site blood bank. Three separate
mailings, spaced at three to four week intervals, were conducted. A total of 753 usable surveys
were obtained, representing an overall response rate of 37.42 percent. In addition, 55
responses of the short version were obtained.
For sample A-2, a total of 1,005 of the largest hospitals in the continental United States having
on-site coagulation/hematology laboratories were surveyed. Also included in the survey were a
limited number of commercial laboratories (approximately 40) and other hospitals. Three
separate mailings, spaced at three to four week intervals, were conducted. The second mailing
replicated the initial mailing list; all respondents contacted in the first survey were polled a
second time. The final, third mailing was restricted to those institutions not responding to the
first two mailings. A total of 299 usable surveys were obtained, representing an overall
response rate of 29.75 percent. An additional 147 short surveys were obtained.
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For sample A-3, a total of 667 hospitals in the continental United States having on-site
coagulation/hematology laboratories were surveyed. Three separate mailings, spaced at three
to four week intervals, were conducted. The second mailing replicated the initial mailing list; all
respondents contacted in the first survey were polled a second time. The third mailing was
restricted to those institutions not responding to the first two mailings. A total of 212 usable
surveys were obtained representing an overall response rate of 31.78 percent. In addition to
the regular surveys, 100 short surveys were received.
Electronics Industry
The customers in the electronics industry sample B-1 were mainly TV Broadcasters, TV
Producers/Post Production and various distributors. Personal in-depth interviews were
conducted at 32 companies in order to develop and pre-test the survey instrument. The
questionnaire was sent to representatives from 1,369 firms. There were 342 completed
questionnaires that were returned after three mailings. The response rate was 24.98 percent.
In addition, a two-page short version of the questionnaire was sent to the remaining non-
respondents and 70 completed questionnaires were returned.
In sample B-2, vendors of blank video cassettes were surveyed. In order to develop and pre-test
the questionnaire in-depth personal interviews at 23 firms were conducted. The complete
questionnaire was sent to 434 recipients in three mailings. At various times during the mailing
of surveys, the recipients were contacted by phone in order to encourage participation. There
were 77 completed questionnaires which represents a response rate of 17.74 percent. In
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addition, a four-page version of the survey was sent to non-respondents and 36 additional
surveys were received.
Plastics Resin Industry
The customers in the plastics resin industry were molders and extruders that purchased plastic
resins from approximately 24 manufacturers and more than a dozen distributors. Most of the
manufacturers sold directly to large original equipment manufacturers and used distributors to
reach smaller volume molders and extruders as well as to provide fast turnaround on small
volume orders. The market was very competitive with many of the manufacturers being well
known Fortune 1000 companies. There were two large national distributors and many regional
and local distributors that also served the market.
For sample C-1 a random sample of 1,920 purchasers was selected from a mailing list of
approximately 8,000 firms. Phone calls to each of the 1,920 buyers confirmed that 1,858 of
them had responsibility for purchasing plastic resins. Next, the questionnaires were mailed to
each of the 1,858 buyers. There were 260 completed questionnaires returned from the first
mailing. Another round of phone calls was followed by a second mailing. As a result of both
mailings, the number of usable surveys was 540 which represented a 29.13 percent response
rate. In addition, 161 responses from the mailing of the short version of the questionnaire were
obtained.
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Sporting Goods Industry
The sporting goods industry yielded three samples. For sample D-1, 1012 sellers of golf balls
were surveyed. Of these sellers, 762 were “Green Grass” pro shops and 250 were retailers.
Overall, 134 responses for the complete survey and seven for the reduced version were received,
which represents a 13.93 percent response rate.
For sample D-2, 2240 sellers of golf clubs were surveyed. Of these, 1737 were “Green Grass”
pro shops and 504 were retailers. Overall, 172 responses for the complete survey and 50 for the
reduced version were received, which represents a 7.68 percent response rate.
For sample D-3, 1001 sellers of golf shoes were surveyed. Of these 751 were “Green Grass” pro
shops and 250 were retailers. Overall, 95 responses for the complete survey and eight for the
reduced version were received, which represents a 9.49 percent response rate.
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3.3. Data Analysis Preparation
Before the data analysis can begin, several issues must be resolved: non-response bias, missing
data, and parceling. Non-response bias is a concern in surveys because it can lead to an
incorrect sampling frame. If the sample that is taken from a population has different
characteristics, then the results of the survey cannot be generalized to the population. Some
respondents did not answer every question, so there were some missing data with which to deal.
Testing for non-response bias is described next, then missing data issues and parceling is
covered.
Non-Response Bias Testing
There are two popular methods of determining non-response bias in the literature. In one, early
and late respondents are compared (Armstrong and Overton 1977) and in the other a subset of
questions is sent to non-respondents and the results are compared to the full version of the
questionnaire (Lambert and Harrington 1990). In all of the surveys used for this research, a
short version of the survey was sent to non-respondents. In order to test for non-response bias,
an analysis of variance (ANOVA) was performed on each sample to test for differences between
the full version of the survey and the reduced version.
Questions from Part A for each of the three suppliers were used to determine the difference
between the three waves of the regular survey and the short version (Lambert and Harrington
1990). Based on the results of the ANOVA, most questions that were analyzed showed no
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difference in means between the four different mailings (three mailings of the regular
questionnaire and one mailing of the short questionnaire). On the few questions where
differences between the groups were detected, further analysis was performed with multiple
comparisons using Tukey’s method (Lambert and Harrington 1990). The results of that analysis
showed that differences did not consistently occur in the short version of the survey. Overall,
there was no evidence to indicate that non-response bias is a concern in any of the samples.
Missing Data Mitigation
There are several procedures that are commonly used to deal with missing data: pairwise
deletion (or available-case analysis), listwise deletion (or complete-case analysis), mean
substitution, and imputation methods like maximum likelihood and Bayesian (Kamakura and
Wedel 2000, Schafer and Graham 2002). When listwise deletion is used, only cases with
complete data are used. Pairwise deletion is a procedure where incomplete cases are only
removed if they are used in one calculation. Both methods have disadvantages: listwise
deletion severely reduces the sample size and pairwise deletion may yield a covariance matrix
that is not positive-definite (Little and Rubin 1987). Mean substitution is another simple
procedure which may be used to deal with missing data, where missing data is replaced by the
average score. An issue with mean substitution is that covariances are systematically
underestimated, but simulations have shown that for low levels of missing data (less than 10
percent) this procedure can yield equally good results compared to more sophisticated methods,
like maximum likelihood imputation (Kamakura and Wedel 2000).
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When the amount of missing data was assessed, it was evident that there were some
respondents who only answered a few questions. Those responses with less than 75 percent of
the questions answered were deemed unreliable and removed. In addition, there were a few
questions that had much fewer responses than other questions. Therefore, all questions where
at least 80 percent of data were present were deleted. The remaining amount of missing data
were less than 10 percent, and as such mean substitution would be adequate (Kamakura and
Wedel 2000). In order to minimize undervalued correlations, mean replacement was conducted
for suppliers A, B, and C separately. So, missing data for supplier A was replaced by the mean
score for all responses for supplier A. The same procedure was used for data on suppliers B and
C. Table 7 shows the number of attributes used and the number of usable cases for data
analysis.
Industry Sample Name Responses Usable Cases
Attributes
Health Services A-1 (Blood Banking Reagents) 753 1,400 88
Health Services A-2 (Coagulation Reagents) 299 435 78
Health Services A-3 (Coagulation Reagents) 205 279 71
Electronics B-1 (Professional Tape) 347 508 83
Electronics B-2 (Consumer Blank Tape) 113 229 69
Plastics C-1 (Commodity Resin) 534 759 91
Sporting Goods D-1 (Golf Balls) 141 288 130
Sporting Goods D-2 (Golf Clubs) 120 265 149
Sporting Goods D-3 (Golf Shoes) 89 205 135
Table 7: Number of Questions and Usable Cases
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Parceling of Items to Build Composite Scores
Parceling is a technique in which factors are estimated using composites of items instead of
individual items (Garver and Mentzer 1999, Little, et al. 2004). So, instead of estimating the
constructs with the ratings of the questions, the scores of several questions are combined into a
composite that is then used to estimate the construct. The first reference for the parceling
technique was in the Psychology literature (Cattell 1956) and this technique has since been used
in other areas such as education, psychology, and marketing (Bandalos and Finney 2001).
While there is debate about the use of parcels (Cattell and Burdsal Jr. 1975, Little, et al. 2004),
parcels built on items that have a unidimensional structure are shown to accurately reflect the
scale (Bandalos 2002). In this study, before the parcels were constructed, the unidimensionality
of the construct was tested with a confirmatory factor analysis and only items that had
sufficiently high loadings were used for the parcels. The concerns that are voiced about this
technique generally relate to the fact that modeled data should be as close to the response of
the individual as possible in order to avoid the potential imposition or arbitrary manufacturing
of a false structure (Little, et al. 2004). It is questionable that Likert-type scales by definition do
not impose an arbitrary structure on the data. However, there are several advantages as well.
From a psychometric perspective the advantages are: higher reliability, higher communality, a
larger ratio of common-to-unique factor variance, and a smaller likelihood of distributional
violations (Little, et al. 2004). Advantages from an estimation perspective are that models based
on parceled data are more parsimonious, have fewer chances for residuals to be correlated or
dual loadings to emerge, and lead to reductions in various sources of sampling error (Little, et al.
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2004). In this dataset multivariate normality is potentially enhanced because the data are
transformed from categorical to continuous, which is another reason why some researchers
choose to use parcels (Landis, Beal, and Tesluk 2000).
The main reason parcels are used in this research is because too many items load on each
construct. Ideally three to five manifest variables should be used to estimate a latent variable
(Bollen 1989). There are two options to estimate a construct with more variables. The focal
construct can be estimated as a second-order latent variable or several variables can be
combined into a composite. Second-order models use several first-order constructs that make
up the second-order construct (Bollen 1989). When parcels are used to combine several items
the parcels are then used to estimate the construct (Bandalos 2002, Garver and Mentzer 1999).
The parcels in this research are built as importance-weighted averages of the performance
scores. When the respondents answer the question about an attribute in the questionnaire,
they are required to perform two tasks, indicate the importance of that attribute in selecting
and evaluating suppliers and the performance of each major supplier. Since the parcel score
takes into account importance and performance ratings, a more precise understanding of how
the respondent rates a particular attribute is obtained. When respondents answer a question,
they are required to consider the importance and performance of the attribute, and therefore
just using one or the other may cause bias in the data. Another reason for importance-weighted
parcels is that second-order models would not take importance scores into account, and the
estimation procedure would assign weights to each manifest variable in a manner that would
not reflect how respondents would assign relative importance to them. It was shown that
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weighted composites are preferred to un-weighted or equal-weighted composites (Rozeboom
1979). Generally, parcels where individual items have different weights are closer to the true
structure of the latent variable they represent (Bollen 1991). In this case, weighing the items by
relative importance should reasonably lead to a closer assessment of the latent variables that
are used in this research.
There are two issues regarding how parcels are built, how the items are combined and which
items are combined (see Table 8). Regarding the calculation of composites, the simplest
method is to add all variables. This procedure requires that each parcel has the same number of
variables, otherwise parcels with more variables are systematically overemphasized. Another
method is to average the items. The downside is that all attributes are assumed to be equally
important, which is not always accurate. Therefore, the procedure used in this study is a
weighted average in which weights are based on relative importance.
Method Description
Single factor Pair off items with highest and lowest loadings as first composite based on a single-factor solution; continue pairing until items are exhausted
Correlational Pair off items with highest interrorrelation as first composite; continue pairing until items are exhausted
Random Randomly assign items to composites Content Create composites based on rational grouping (s) of items Exploratory factor analysis Create composites based on results from exploratory factor
analysis Empirically equivalent Create composites with equal means, variances, and reliabilities
Adapted from: Landis, Beal, and Tesluk 2000 Table 8: Summary of Composite Formation Methods
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For this study, questions on similar issues are combined to build the parcels (see content
method in Table 8) because it allows better conceptualization of the construct (Bagozzi and
Edwards 1998). More on the results of the parceling is provided in the next section of this
chapter. The parcel scores were calculated using Equation 1, which follows the standard
formula for weighted averages.
Equation 1: Calculation of Parcel Scores
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3.4. Overview of Questions Used in the Samples
In order to get a better understanding of which questions are used in each sample an overview
of the questions that are used across the nine samples is provided next. There are 56 questions
that are used for the product construct. The main reason why there are that many questions is
because most of them address very specific issues that are used to evaluate the product
construct. The questions and in which samples they are used is displayed in Table 9. For the
price construct, fewer questions are used (25). Overall, the constructs are much more
consistent across the nine samples. The main differences occur because questions in some
sample are more general while others use more specific questions. There are a few questions
that are used in only one sample. Table 10 provides an overview of the questions and in which
samples they are used. There are 18 questions that are used for the promotion/personal selling
construct across the nine samples. One difference is that some questions ask the respondent to
evaluate “quality of sales force” and others use “sales representative characteristics”. Both
terms have been used interchangeably. The questions and which ones are used in each sample
are shown in Table 11. There are 25 different questions that are used for the place/logistics
construct. Some constructs are adapted to reflect specific industry conditions. The questions
and in which samples they are used is displayed in Table 12.
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Questions A-1 A-2 A-3 B-1 B-2 C-1 D-1 D-2 D-3
Adequate availability of newly introduced products X Advance information (literature, specs, prices, etc.) on new product introductions X X X X Consistent development of new golf balls by supplier X Development of new products by supplier X X X X Frequency of new product introduction X Speed at which vendor responds to industry technical improvements X Supplier adequately tests new products before delivering to market X X X X
Appropriate range of sizes X Availability of men's and women's products X Availability of different widths X Supplier has complete assortment of footwear items X
Consistency of supplier's delivered product after initial evaluation of samples by my facility
X X X X
Durability of product X Overall quality of resin relative to price X Past experience with supplier's product X Product durability (tape continues play back without loss of video and audio quality): after multiple passes
X
Product durability (tape continues play back without loss of video and audio quality): after extensive shuttling
X
Product quality relative to price X Product reliability (consistent performance from shipment to shipment) X X X X Quality of product X Quality of product line above minimum standards X Supplier replaces entire allotment when there is evidence of defective product X X X X Supplier's resins are of consistent color X Supplier's resins are of consistent quality X Warranty program for footwear X
Continued Table 9: Product Attributes Across all Sample
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Continued
Table 9 continued
Questions A-1 A-2 A-3 B-1 B-2 C-1 D-1 D-2 D-3
Prompt notification of technical analysis results X X Service support if salesperson is not available X X
Consistency of product performance X Consistent shaft quality & performance X Consistent sizing X Fit and comfort X Footwear consistent with most preferred styles and trends X Functionality X Level of product performance X Overall appearance of shoe X Performance of premium balls X Processability of resin X Product features (distance, spin rate) X Product stability (Shelf life): Antiserum X X X Product stability (Shelf life): Red cells X X X Sensitivity (specificity) of reagent X X X Supplier at leading edge of technology X Supplier's products are at the leading edge of technology X X Supplier's resins are of consistent melt flow X Waterproofing X
Adequate identification/labeling of package contents X X Availability of the following package: type-bag X Durability of packaging X Packaging: aids in consumer purchasing decision X Packaging: product/technical info on package X Packaging: visual appeal X Quality/durability of packaging X X Vendor's willingness to work with your firm to develop custom packaging configurations X
Ability of vendor to handle: consumer complaints X
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Ability of vendor to handle: defective product returns X Past experience with vendor's product X
Questions A-1 A-2 A-3 B-1 B-2 C-1 D-1 D-2 D-3
Adequate advance notice of price changes provided X X X X X X X
Responsiveness of vendor to competitor's price reductions X
Supplier does not raise prices more than once per year X X X X
Supplier gives you an adequate period of price protection after a price increase is announced X X X X
Supplier gives you an adequate period of price protection after a price decrease is announced X X
Vendor gives you adequate price protection and/or markdown funds X
Integrity of suggested retail price X
Margin reflects selling effort X
Profit margin X
Simplicity of pricing program X
Prompt and comprehensive response to competitive bid quotations X X X X X X X
Supplier reacts quickly to competitive price reductions X X X X X X
Pre-book discount program X X
Quantity discount structure X
Quantity discount structure based on size of individual order X X X X X X
Quantity discount structure based on total annual purchases X X X X X X
Sales rep will give you volume price even if you are buying less X
Supplier combines purchases of different products in order to compute volume discount X X X X
Assurance that my target price at retail will equal that of my competitors X
Competitiveness of price X X X X X X X X
Low price X
Lowest price X X X
Realistic, consistent pricing policy by supplier over time X
Sales rep has authority to negotiate special prices X X X X X
Willingness of sales rep to be flexible in offering special/volume discounts, pricing, etc. X
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Table 10: Price Attributes Across all Samples
Questions/Parcels A-1 A-2 A-3 B-1 B-2 C-1 D-1 D-2 D-3
Ability of vendor to: provide unique promotions to your firm X
Customer service backup if salesperson is not available X
Number of sales calls you personally receive per year from: vendor's sales representatives
X
Sales force characteristics: follows up promptly X
Sales representative characteristics: accessibility X X X X X X X
Timely response to requests for assistance from supplier's sales representative X X X X X X X X X
Quality of sales force: knowledge of industry trends X
Quality of sales force: knowledge of merchandising techniques X X X X
Quality of sales force: knowledge of my business X
Quality of sales force: knowledge of my competitor's business X
Sales representative characteristics: industry knowledge X X X X X X X
Sales representative characteristics: product knowledge X X X X X X X X
Sales representative characteristics: technical knowledge X X X X X
Quality of sales force: adequate preparation for sales calls X X X X
Quality of sales force: prompt follow-up X X X X
Quality of sales force: understands logistics issues X
Sales representative characteristics: concern/empathy X X X X
Sales representative characteristics: honesty X X X X X X X X X Table 11: Promotion/Personal Selling Attributes Across all Samples
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Questions/Parcels A-1 A-2 A-3 B-1 B-2 C-1 D-1 D-2 D-3
Action on complaints related to order servicing and shipping X X X X X X
Advance notice of shipping delays X X
Assistance from supplier in handling carrier loss and damage claims X X X X X X
Prompt handling of claims due to overages, shortages or shipping errors X X X X X X X X
Supplier absorbs cost of freight and handling on returns due to damage or product shipped in error
X X X
Ability of supplier to meet specific service and delivery needs X X X X X
Ability of supplier to respond to changes in requested delivery dates X X X X
Ability to expedite emergency orders X
Adequate availability (Supplier' ability to deliver) of new products at time of introduction X X X X X X
Availability of reorder product X X X
Availability of supplier to meet specific and/or unique customer service/delivery needs of individual customers
X
Supplier expedites emergency orders in a fast, responsive manner X X X X X
Supplier's adherence to special shipping instructions X X X X X X X X X
Consistent lead times (supplier consistently meets promised delivery date) X X X X X X X
Length of promised lead times (from order submission to delivery): pre-booked order/initial stocking
X X X
Length of promised lead times (from order submission to delivery): Normal orders X X X X X X X X
Length of promised lead times (from order submission to delivery): Emergency orders X X X X
Length of promised lead times: ad or promotional orders X
Length of promised lead times: ASAP or emergency orders X
Ability of supplier to automatically backorder out-of-stock items X
Ability to meet/keep dates for pre-booked shipments X X X
Accuracy in filling orders (correct product is shipped) X X X X X X X X
Availability of inventory status information X
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Availability of status information on orders X X X X X X X X
Supplier ships complete orders and within specified windows (no incomplete or split shipments)
X X X
Table 12: Place/Logistics Attributes Across all Samples
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3.5. Structural Equation Modeling
The statistical analysis was performed using the two-step approach for structural equation
modeling (SEM), which combines the features of a factor analysis with a path analysis (Anderson
and Gerbing 1988, Bollen 1989). In the first step, the measurement model is established with a
confirmatory factor analysis. Then, hypotheses are tested with the structural model in the
second step. This analysis technique allows the researcher to examine the validity of the
measures and the relationship of the constructs at the same time.
Scale Development and Measurement Model
In SEM, before starting the hypothesis testing, the scales used to measure the constructs must
be developed and validated. For this dissertation, the recommendations for building scales by
Gerbing and Anderson (1988) are followed. A necessary condition for building a scale
estimating the constructs is that the measures must be acceptably unidimensional (Gerbing and
Anderson 1988). That is, each set of indicators has only one underlying trait or construct in
common. A confirmatory factor analysis (CFA) measurement model is used to establish
unidimensionality (Anderson and Gerbing 1988). The criteria for assessing the CFA are the
overall model fit and the validity of the components (Garver and Mentzer 1999). The criteria are
described next and the results from the samples are provided in the later sections.
The fit indices that are used to assess model fit are chi-square, Tucker-Lewis index (TLI),
comparative fit index (CFI), and root mean squared error (RMSEA), which have been shown to
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be reliable (Hu and Bentler 1999). The chi-square goodness-of-fit statistic is the probably most
commonly used measure of fit and a starting point for other fit indices; however one drawback
is the increased sensitivity to large sample sizes. Additionally, chi-square divided by degrees of
freedom is another revealing fit statistic. The TLI compares a proposed model's fit to a nested
null model and it measures parsimony by assessing the degrees of freedom from the proposed
model to the degrees of freedom of the null model (Tucker and Lewis 1973). A well-fitting
model should have a TLI of 0.95 or higher (Hu and Bentler 1999), but values above 0.90 are
acceptable. The CFI is a noncentrality parameter-based index to overcome the limitation of
sample size effects (Bentler 1990). A well-fitting model should have a CFI of 0.96 or higher (Hu
and Bentler 1999) , but values above 0.90 are acceptable. The RMSEA index measures the
discrepancy between the observed and estimated covariance matrices per degree of freedom
(Steiger and Lind 1980). Ideally RMSEA values are less than 0.60 (Hu and Bentler 1999), but
values between 0.60 and 0.80 are still acceptable (Browne and Cudek 1993). In addition to
good overall fit indices, an acceptable measurement of unidimensional constructs should reveal
relatively small standardized residuals of and modification indices (Garver and Mentzer 1999). A
large residual is larger than 2.58 (Medsker, Williams and Holahan 1994). A significant
modification index is larger than 7.88 (Jöreskog and Sorborn 1993).
In order to assess construct validity, several components of the model are evaluated: reliability,
convergent validity, and discriminant validity (Garver and Mentzer 1999). Reliability was
assessed using internal consistency method via Cronbach’s alpha (Cronbach 1951, Nunnally
1978). Typically, reliability coefficients of 0.70 or higher are considered adequate (Cronbach
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1951, Nunnally 1978). Additionally, composite reliability (CR) scores were calculated to assess
construct reliability (Bagozzi and Yi 1988). A CR greater than 0.70 would imply that the variance
captured by the factor is significantly more than the variance indicated by the error components
(Bagozzi and Yi 1988). In addition to CR, average variance extracted (AVE) was calculated
(Bagozzi and Yi 1988). An AVE of more than 0.50 implies that more variance is captured than
error (Bagozzi and Yi 1988). Convergent validity is the extent to which a latent variable
correlates to the items used to measure it. This is achieved by using manifest variables that load
highly on the latent variables (Dunn, Seaker and Waller 1994). Most factor loadings should be
above 0.60 and ideally above 0.70 (Chin 1998). Discriminant validity is established using CFA.
Measurement models are constructed for all possible pairs of the theoretical constructs. These
models were tested on each selected pair by fixing the correlation between the constructs at
1.00. A significant difference in chi-square values for the fixed and free solutions indicates the
distinctiveness of the two constructs (Bagozzi, Yi and Phillips 1991). In addition, the confidence
interval for each pair of constructs was set to be equal to plus or minus two standard errors of
the respective correlation coefficient.
The development of the scales for each of the constructs representing the Marketing Mix
variables was performed on sample A-1 (blood banking reagents). Next, validation of the scales
was performed on sample A-2 (coagulation reagents). For the remaining samples, slight
adaptations were necessary because of industry and product differences. The attributes that
are used to evaluate blood banking reagents, video tapes, plastic resin, golf balls, golf clubs, and
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golf shoes have to be different because the products are very different. Not all questions for
one survey were available in the others.
The outcome variables, customer satisfaction and share of business, are measured as single
indicators. This has the consequence that there is no specific error associated with those
variables. However, the informant was prequalified and it was ensured that the key informant
was answering the questionnaire (Phillips and Bagozzi 1986). Based on that, the measurement
of the outcome variables should be as precise as possible and measurement error is not likely to
improve the overall model. Specifically for share of business it is not possible build multi-item
scales, and as such, it is not possible but to use the single item scale.
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3.6. Measurement Model Results for Sample A-1
A CFA was run on sample A-1 (blood banking reagents) to develop the scales for the constructs
and to establish the measurement model. The overall fit of the measurement model was
adequate (chi-square = 632.172 /98 d.f.; TLI = 0.955; CFI = 0.968; RMSEA = 0.062) and with the
exception of the chi-square, the fit indexes were above the recommended thresholds. The chi-
square has the property that it increases with larger sample sizes and as such the high value
should not cause dismissal of the model (Hu and Bentler 1999). The model did not show any
high modification indexes or residuals. All of this evidence points to a well-fitting measurement
model. Next, the constructs are assessed.
The product construct has four parcels representing 11 questions. The questions and parcels
are shown in Table 13. The first parcel is made up of questions on new product development.
The second parcel has questions on reagent performance. The third parcel is based on
questions regarding reagent quality. The fourth parcel is about service regarding the product.
Overall, the construct exhibits good reliability with a Cronbach’s alpha of 0.866, a CR of 0.874,
and an AVE of 0.634. All the parcels have significant loadings and the standardized loadings are
well above the minimum recommended values.
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Questions/Parcels Standard Loadings
Supplier adequately tests new products before delivering to market
0.821 Development of new products by supplier
Advance information (literature, specs, prices, etc.) on new product introductions
Product stability (Shelf life): Red cells
0.765 Product stability (Shelf life): Antiserum
Sensitivity (specificity) of reagent
Supplier replaces entire allotment when there is evidence of defective product
0.797 Consistency of supplier's delivered product after initial evaluation of samples by my facility
Product reliability (consistent performance from shipment to shipment)
Service support if salesperson is not available 0.800
Prompt notification of technical analysis results
Table 13: A-1 Measurement Model Product Construct Loadings
The price construct has four parcels representing 11 questions. The questions and parcels are
shown in Table 14. The first parcel has several questions on price changes. The second parcel
uses questions regarding price competition. The third parcel has questions regarding discounts.
The fourth parcel uses questions on price level. Overall, the construct exhibits good reliability
with a Cronbach’s alpha of 0.847, a CR of 0.840, and an AVE of 0.569. All the parcels have
significant loadings and the standardized loadings are well above the minimum recommended
values.
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Questions/Parcels Standard Loadings
Adequate advance notice of price changes provided
0.770 Supplier gives you an adequate period of price protection after a price increase is announced
Supplier does not raise prices more than once per year
Supplier reacts quickly to competitive price reductions 0.796
Prompt and comprehensive response to competitive bid quotations
Supplier combines purchases of different products in order to compute volume discount
0.703 Quantity discount structure based on total annual purchases
Quantity discount structure based on size of individual order
Sales rep has authority to negotiate special prices 0.745
Competitiveness of price
Table 14: A-1 Measurement Model Price Construct Loadings
The promotion construct has four parcels representing seven questions. The questions and
parcels are shown in Table 15. The first parcel has questions regarding sales representative
accessibility. The second parcel uses questions regarding knowledge of the sales representative.
The third parcel is on questions regarding personal characteristics of the sales representative.
Overall, the construct exhibits excellent reliability with a Cronbach’s alpha of 0.909, a CR of
0.912, and an AVE of 0.776. All the parcels have significant loadings and the standardized
loadings are well above the minimum recommended values.
Questions/Parcels Standard Loadings
Sales representative characteristics: accessibility 0.942
Timely response to requests for assistance from supplier's sales representative
Sales representative characteristics: product knowledge
0.868 Sales representative characteristics: industry knowledge
Sales representative characteristics: technical knowledge
Sales representative characteristics: honesty 0.829
Sales representative characteristics: concern/empathy
Table 15: A-1 Measurement Model Promotion Construct Loadings
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The place construct has four parcels representing 14 questions. The questions and parcels are
shown in Table 16. The first parcel summarizes various aspects of problem solving on shipping.
The second parcel has questions on delivery flexibility. The third parcel is about lead times. The
fourth parcel summarizes delivery quality. Overall, the construct exhibits excellent reliability
with a Cronbach’s alpha of 0.888, a CR of 0.890, and an AVE of 0.670. All the parcels have
significant loadings and the standardized loadings are well above the minimum recommended
values as shown in Table 16.
Questions/Parcels Standard Loadings
Prompt handling of claims due to overages, shortages or shipping errors
0.861
Supplier absorbs cost of freight and handling on returns due to damage or product shipped in error
Action on complaints related to order servicing and shipping
Assistance from supplier in handling carrier loss and damage claims
Ability of supplier to meet specific service and delivery needs
0.885
Adequate availability (Supplier' ability to deliver) of new products at time of introduction
Supplier's adherence to special shipping instructions
Ability of supplier to respond to changes in requested delivery dates
Supplier expedites emergency orders in a fast, responsive manner
Consistent lead times (supplier consistently meets promised delivery date)
0.796 Length of promised lead times (from order submission to delivery): Normal orders
Length of promised lead times (from order submission to delivery): Emergency orders
Availability of status information on orders 0.721
Accuracy in filling orders (correct reagent is shipped)
Table 16: A-1 Measurement Model Place Construct Loadings
All constructs in the measurement model for sample A-1 exhibit high reliability and seem to fit
the data well. Next, discriminant validity is tested. The results of the discriminant validity testing
are shown in Table 17. The first row of each cell contains the chi square value after fixing the
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correlation between two constructs to 1.00. The second row shows the difference to the chi
square value of the original model of 632.2 and the difference in degrees of freedom from 98 in
parentheses. The last row displays the p-value of the chi square difference test. All of the fixed
correlations cause the fit of the model to increase significantly and none of the correlation
confidence intervals include 1.00. This evidence supports the conclusion that the constructs are
distinct from each other and thus discriminant validity is achieved.
Product Price Promotion
Price New chi square: 835.6 Difference: 203.4 (1) p < 0.01
Promotion New chi square: 805.6 Difference: 173.4 (1) p < 0.01
New chi square: 641.3 Difference: 9.1 (1) p < 0.01
Place New chi square: 984.9 Difference: 352.7 (1) p < 0.01
New chi square: 710.2 Difference: 78 (1) p < 0.01
New chi square: 689.8 Difference: 57.6 (1) p < 0.01
Table 17: A-1 Discriminant Validity Test Results
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3.7. Measurement Model Results for Sample A-2
As the second step in validating the constructs, the measurement model is tested on an
independent sample, which in this case is sample A-2 (coagulation reagents). The overall fit of
the measurement model was adequate (chi-square = 270.454 /98 d.f.; TLI = 0.950; CFI = 0.964;
RMSEA = 0.064) and the fit indices are above the recommended thresholds. The model did not
show any high modification indexes or residuals. All of this evidence points to a well-fitting
measurement model. Next, the constructs are assessed.
The product construct has the same four parcels as sample A-1. The questions and parcels are
shown in Table 18. Overall, the construct exhibits good reliability with a Cronbach’s alpha of
0.822, a CR of 0.813, and an AVE of 0.533. All the parcels have significant loadings and all but
one have standardized loadings that are well above the minimum recommended values as
shown in Table 18. The third parcel has a lower standardized loading but since it is only one
variable the overall construct exhibits acceptable reliability.
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Questions/Parcels Standard Loadings
Supplier adequately tests new products before delivering to market
0.866 Development of new products by supplier
Advance information (literature, specs, prices, etc.) on new product introductions
Product stability (Shelf life): Red cells
0.670 Product stability (Shelf life): Antiserum
Sensitivity (specificity) of reagent
Supplier replaces entire allotment when there is evidence of defective product
0.556 Consistency of supplier's delivered product after initial evaluation of samples by my facility
Product reliability (consistent performance from shipment to shipment)
Service support if salesperson is not available 0.789
Prompt notification of technical analysis results
Table 18: A-2 Measurement Model Product Construct Loadings
The price construct has the same four parcels as sample A-1. Overall, the construct exhibits
good reliability with a Cronbach’s alpha of 0.842, a CR of 0.854, and an AVE of 0.595. All the
parcels have significant loadings and the standardized loadings are well above the minimum
recommended values.
Questions/Parcels Standard Loadings
Adequate advance notice of price changes provided
0.692 Supplier gives you an adequate period of price protection after a price increase is announced
Supplier does not raise prices more than once per year
Supplier reacts quickly to competitive price reductions 0.773
Prompt and comprehensive response to competitive bid quotations
Supplier combines purchases of different products in order to compute volume discount
0.779 Quantity discount structure based on total annual purchases
Quantity discount structure based on size of individual order
Sales rep has authority to negotiate special prices 0.835
Competitiveness of price
Table 19: A-2 Measurement Model Price Construct Loadings
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The promotion construct has the same three parcels that are used for sample A-1. Overall, the
construct exhibits very good reliability with a Cronbach’s alpha of 0.874, a CR of 0.845, and an
AVE of 0.645. All the parcels have significant loadings and the standardized loadings are well
above the minimum recommended values as shown in Table 20.
Questions/Parcels Standard Loadings
Sales representative characteristics: accessibility 0.846
Timely response to requests for assistance from supplier's sales representative
Sales representative characteristics: product knowledge
0.744 Sales representative characteristics: industry knowledge
Sales representative characteristics: technical knowledge
Sales representative characteristics: honesty 0.816
Sales representative characteristics: concern/empathy
Table 20: A-2 Measurement Model Promotion Construct Loadings
The place construct has the same four parcels as in sample A-1. Overall, the construct exhibits
very good reliability with a Cronbach’s alpha of 0.882, a CR of 0.880, and an AVE of 0.648. All
the parcels have significant loadings and the standardized loadings are well above the minimum
recommended values.
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Questions/Parcels Standard Loadings
Prompt handling of claims due to overages, shortages or shipping errors
0.686
Supplier absorbs cost of freight and handling on returns due to damage or product shipped in error
Action on complaints related to order servicing and shipping
Assistance from supplier in handling carrier loss and damage claims
Ability of supplier to meet specific service and delivery needs
0.896
Adequate availability (Supplier' ability to deliver) of new products at time of introduction
Supplier's adherence to special shipping instructions
Ability of supplier to respond to changes in requested delivery dates
Supplier expedites emergency orders in a fast, responsive manner
Consistent lead times (supplier consistently meets promised delivery date)
0.800 Length of promised lead times (from order submission to delivery): Normal orders
Length of promised lead times (from order submission to delivery): Emergency orders
Availability of status information on orders 0.824
Accuracy in filling orders (correct reagent is shipped)
Table 21: A-2 Measurement Model Place Construct Loadings
All constructs in the measurement model for sample A-2 exhibit high reliability and seem to fit
the data well. Next, discriminant validity is tested. The results of the discriminant validity testing
are shown in Table 22. The first row of each cell contains the chi square value after fixing the
correlation between two constructs to 1.00. The second row shows the difference to the chi
square value of the original model of 270.5 and the difference in degrees of freedom from 98 in
parentheses. The last row displays the p-value of the chi square difference test. All of the fixed
correlations cause the fit of the model to increase significantly and none of the correlation
confidence intervals include 1.00. This evidence supports the conclusion that the constructs are
distinct from each other and thus discriminant validity is achieved.
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Product Price Promotion
Price New chi square: 337.4 Difference: 66.9 (1) p < 0.01
Promotion New chi square: 319.8 Difference: 49.3 (1) p < 0.01
New chi square: 283.6 Difference: 13.1 (1) p < 0.01
Place New chi square: 383.3 Difference: 112.8 (1) p < 0.01
New chi square: 326.8 Difference: 56.3 (1) p < 0.01
New chi square: 311.3 Difference: 40.8 (1) p < 0.01
Table 22: A-2 Discriminant Validity Test Results
Following the validation of the measurement model in two separate samples, it can be
concluded that the measures for the Product, Price, Promotion, and Place constructs are
validated and reliable. Next, the measurement models in the remaining samples are assessed as
adaptations to the validated scales.
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3.8. Measurement Model Results for Sample A-3
For sample A-3 slight adaptations had to be made because not all questions from the previous
surveys were available. The overall fit of the measurement model was adequate (chi-square =
198.088 /74 d.f.; TLI = 0.918; CFI = 0.942; RMSEA = 0.078) and the fit indexes are close to the
recommended thresholds. The model did not show any high modification indexes or residuals.
All of this evidence points to a well-fitting measurement model. Next, the constructs are
assessed.
The product construct has three of the four parcels from the previous two samples. The
questions and parcels are shown in Table 23. The construct has a Cronbach’s alpha of 0.774, a
CR of 0.785, and an AVE of 0.550. All the parcels have significant loadings and all have
standardized loadings that are above the minimum recommended values.
Questions/Parcels Standard Loadings
Supplier adequately tests new products before delivering to market
0. 703 Development of new products by supplier
Advance information (literature, specs, prices, etc.) on new product introductions
Product stability (Shelf life): Red cells
0.780 Product stability (Shelf life): Antiserum
Sensitivity (specificity) of reagent
Supplier replaces entire allotment when there is evidence of defective product
0.740 Consistency of supplier's delivered product after initial evaluation of samples by my facility
Product reliability (consistent performance from shipment to shipment)
Table 23: A-3 Measurement Model Product Construct Loadings
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The price construct has three of the four parcels that appeared in the previous two samples.
The questions and parcels are shown in Table 24. Overall, the construct exhibits good reliability
with a Cronbach’s alpha of 0.829, a CR of 0.798, and an AVE of 0.569. All the parcels have
significant loadings and standardized loadings that are above the recommended values.
Questions/Parcels Standard Loadings
Supplier reacts quickly to competitive price reductions 0.738
Prompt and comprehensive response to competitive bid quotations
Supplier combines purchases of different products in order to compute volume discount
0.817 Quantity discount structure based on total annual purchases
Quantity discount structure based on size of individual order
Sales rep has authority to negotiate special prices 0.704
Competitiveness of price
Table 24: A-3 Measurement Model Price Construct Loadings
The promotion construct has the same three parcels that are used for sample A-1 and A-2.
Overall, the construct exhibits very good reliability with a Cronbach’s alpha of 0.863, a CR of
0.865, and an AVE of 0.682. All the parcels have significant loadings and the standardized
loadings are well above the minimum recommended values.
Questions/Parcels Standard Loadings
Sales representative characteristics: accessibility 0.827
Timely response to requests for assistance from supplier's sales representative
Sales representative characteristics: product knowledge
0.866 Sales representative characteristics: industry knowledge
Sales representative characteristics: technical knowledge
Sales representative characteristics: honesty 0.783
Sales representative characteristics: concern/empathy
Table 25: A-3 Measurement Model Promotion Construct Loadings
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The place construct has the same four parcels as in sample A-1 and A-2. The only adaptation is
the removal of one question due to missing data. Overall, the construct exhibits very good
reliability with a Cronbach’s alpha of 0.846, a CR of 0.854, and an AVE of 0.595. All the parcels
have significant loadings and the standardized loadings are well above the minimum
recommended values.
Questions/Parcels Standard Loadings
Prompt handling of claims due to overages, shortages or shipping errors
0.722 Supplier absorbs cost of freight and handling on returns due to damage or product shipped in error
Action on complaints related to order servicing and shipping
Ability of supplier to meet specific service and delivery needs
0.891
Adequate availability (Supplier' ability to deliver) of new products at time of introduction
Supplier's adherence to special shipping instructions
Ability of supplier to respond to changes in requested delivery dates
Supplier expedites emergency orders in a fast, responsive manner
Consistent lead times (supplier consistently meets promised delivery date)
0.737 Length of promised lead times (from order submission to delivery): Normal orders
Length of promised lead times (from order submission to delivery): Emergency orders
Availability of status information on orders 0.723
Accuracy in filling orders (correct reagent is shipped)
Table 26: A-3 Measurement Model Place Construct Loadings
All constructs in the measurement model for sample A-3 exhibit adequate reliability and seem
to fit the data well. Next, discriminant validity is tested. The results of the discriminant validity
testing are shown in Table 27. The first row of each cell contains the chi square value after fixing
the correlation between two constructs to 1.00. The second row shows the difference to the chi
square value of the original model of 291.5 and the difference in degrees of freedom from 74 in
parentheses. The last row displays the p-value of the chi square difference test. All of the fixed
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correlations cause the fit of the model to increase significantly and none of the correlation
confidence intervals include 1.00. This evidence supports the conclusion that the constructs are
distinct from each other and thus discriminant validity is achieved.
Product Price Promotion
Price New chi square: 325.5 Difference: 34.0 (1) p < 0.01
Promotion New chi square: 319.3 Difference: 27.5 (1) p < 0.01
New chi square: 292.8 Difference: 22.3 (1) p < 0.01
Place New chi square: 339.7 Difference: 46.7 (1) p < 0.01
New chi square: 291.8 Difference: 38.6 (1) p < 0.01
New chi square: 292.2 Difference: 14.7 (1) p < 0.01
Table 27: A-3 Discriminant Validity Test Results
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3.9. Measurement Model Results for Sample B-1
For sample B-1 adaptations had to be made because not all questions from the previous surveys
were available and other more specific questions were used. The largest differences were on
the product construct, which is not surprising since the video tape industry is very different and
the surveys were designed to be industry- specific. The overall fit of the measurement model
was good (chi-square = 236.847/ 86 d.f.; TLI = 0.964; CFI = 0.974; RMSEA = 0.059) and the fit
indexes are meet the recommended thresholds. The model did not show any high modification
indexes or residuals. All of this evidence points to a well-fitting measurement model. Next, the
constructs are assessed.
The product construct has three parcels which are mostly specific to the video tape industry.
The first parcel summarizes consistency of performance in different aspects. The second parcel
summarizes the level of performance on the same aspects as the first parcel. The third parcel
summarizes various aspects of product quality. The questions, parcels and standardized
loadings are shown in Table 28. Overall, the construct exhibits very good reliability with a
Cronbach’s alpha of 0.883, a CR of 0.829, and an AVE of 0.622. All the parcels have significant
loadings and all have large enough standardized loadings that are above the minimum
recommended values.
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Questions/Parcels Standard Loadings
Consistency of product performance: electromagnetics
Consistency of product performance: audio playback levels
Consistency of product performance: bit error rate
Consistency of product performance: dropouts
Consistency of product performance: scratches
Consistency of product performance: edge damage
Consistency of product performance: slitting errors
Consistency of product performance: creases/cinches
Consistency of product performance: wind quality
0.685
Level of product performance: electromagnetics
0.718
Level of product performance: audio playback levels
Level of product performance: bit error rate
Level of product performance: dropouts
Level of product performance: scratches
Level of product performance: edge damage
Level of product performance: slitting errors
Level of product performance: creases/cinches
Level of product performance: wind quality
Vendor replaces entire allotment when there is evidence of defective product
0.939
Consistency of vendor's delivered product after initial evaluation of samples by my facility
Product durability (tape continues play back without loss of video and audio quality): after multiple passes
Product durability (tape continues play back without loss of video and audio quality): after extensive shuttling
Quality of product line above minimum standards Table 28: B-1 Measurement Model Product Construct Loadings
The price construct has the same four parcels as the previous samples, however there are slight
adaptations in the questions that make up the parcels, but overall they can be considered
equivalent. The questions and parcels are shown in Table 29. Overall, the construct exhibits
good reliability with a Cronbach’s alpha of 0.838, a CR of 0.824, and an AVE of 0.540. All the
parcels have significant loadings and all have large enough standardized loadings that are above
the minimum recommended values.
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Questions/Parcels Standard Loadings
Adequate advance notice of price changes provided 0.718
Vendor gives you an adequate period of price protection after a price increase is announced
Vendor reacts quickly to competitive price reductions 0.750
Prompt and comprehensive response to competitive bid quotations
Supplier combines purchases of different products in order to compute volume discount
0.744 Quantity discount structure based on total annual purchases
Quantity discount structure based on size of individual order
Sales rep will give you volume price even if you are buying less
Competitiveness of price 0.819
Low price
Table 29: B-1 Measurement Model Price Construct Loadings
The promotion construct has the same three parcels that are used in the previous samples. The
questions, parcels, and standardized factor loadings are shown in Table 30. One question was
added to the first parcel. Overall, the construct exhibits very good reliability with a Cronbach’s
alpha of 0.870, a CR of 0.886, and an AVE of 0.722. All the parcels have significant loadings and
the standardized loadings are well above the minimum recommended values.
Questions/Parcels Standard Loadings
Sales force characteristics: accessibility
0.788 Sales force characteristics: follows up promptly
Timely response to requests for assistance from supplier's sales representative
Sales force characteristics: product knowledge
0.861 Sales force characteristics: industry knowledge
Sales force characteristics: technical knowledge
Sales force characteristics: honesty 0.897
Sales force characteristics: concern/empathy
Table 30: B-1 Measurement Model Promotion Construct Loadings
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The place construct has the same four parcels as in previous samples with some slight
adaptations. Overall, the construct exhibits very good reliability with a Cronbach’s alpha of
0.904, a CR of 0.912, and an AVE of 0.723. All the parcels have significant loadings and the
standardized loadings are well above the minimum recommended values.
Questions/Parcels Standard Loadings
Action on complaints related to order servicing and shipping 0.830
Prompt handling of claims due to overages, shortages or shipping errors
Vendor meets specific customer service and delivery needs of individual customers
0.899 Vendor’s adherence to special shipping instructions
Vendor’s ability to respond to changes in requested delivery dates
Vendor expedites emergency orders in a fast, responsive manner
Consistent lead times (supplier consistently meets promised delivery date)
0.876 Length of promised lead times (from order submission to delivery): Normal orders
Length of promised lead times (from order submission to delivery): Emergency orders
Availability of status information on orders 0.791
Accuracy in filling orders
Table 31: B-1 Measurement Model Place Construct Loadings
All constructs in the measurement model for sample B-1 exhibit adequate reliability and fit the
data well. Next, discriminant validity is tested. The results of the discriminant validity testing are
shown in Table 32. The first row of each cell contains the chi square value after fixing the
correlation between two constructs to 1.00. The second row shows the difference to the chi
square value of the original model of 236.8 and the difference in degrees of freedom from 86 in
parentheses. The last row displays the p-value of the chi square difference test. All of the fixed
correlations cause the fit of the model to increase significantly and none of the correlation
confidence intervals include 1.00. This evidence supports the conclusion that the constructs are
distinct from each other and thus discriminant validity is achieved.
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Product Price Promotion
Price New chi square: 362.2 Difference: 125.4 (1) p < 0.01
Promotion New chi square: 416.4 Difference: 179.6 (1) p < 0.01
New chi square: 390.3 Difference: 153.5 (1) p < 0.01
Place New chi square: 376.8 Difference: 140 (1) p < 0.01
New chi square: 334.8 Difference: 98 (1) p < 0.01
New chi square: 434.3 Difference: 197.5 (1) p < 0.01
Table 32: B-1 Discriminant Validity Testing Results
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3.10. Measurement Model Results for Sample B-2
For sample B-2 adaptations had to be made because not all questions from sample B-1 were
available. The overall fit of the measurement model was good (chi-square = 163.050/ 61 d.f.; TLI
= 0.933; CFI = 0.955; RMSEA = 0.086) and the fit indexes are close to the recommended
thresholds. The model did not show any high modification indexes or residuals. All of this
evidence points to a well-fitting measurement model. Next, the constructs are assessed.
The product construct has three parcels which are mostly specific to the video tape industry.
The first parcel summarizes aspects of new product development. The second parcel
summarizes several aspects of packaging. The third parcel summarizes various aspects of
service related to the product. The questions, parcels and standardized loadings are shown in
Table 33. Overall, the construct exhibits very good reliability with a Cronbach’s alpha of 0.883, a
CR of 0.829, and an AVE of 0.622. All the parcels have significant loadings and all have large
enough standardized loadings that are above the minimum recommended values.
Questions/Parcels Standard Loadings
Speed at which vendor responds to industry technical improvements 0.879
Adequate availability of newly introduced products
Quality/durability of packaging
0.652 Adequate identification/labeling of package contents
Vendor's willingness to work with your firm to develop custom packaging configurations
Ability of vendor to handle: defective product returns
0.933 Ability of vendor to handle: consumer complaints
Past experience with vendor's product
Table 33: B-2 Measurement Model Product Construct Loadings
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The price construct has the three of the four parcels from previous samples. The questions and
parcels are shown in Table 34. Overall, the construct exhibits good reliability with a Cronbach’s
alpha of 0.905, a CR of 0.908, and an AVE of 0.767. All the parcels have significant loadings and
all have large enough standardized loadings that are above the minimum recommended values.
Questions/Parcels Standard Loadings
Prompt and comprehensive response to competitive bid quotations 0.866
Competitiveness of price
Lowest price
0.931 Willingness of sales rep to be flexible in offering special/volume discounts, pricing, incentives and other offers
Assurance that my target price at retail will equal that of my competitors
Responsiveness of vendor to competitor's price reductions
0.828 Adequate advance notice of price changes
Vendor gives you adequate price protection and/or markdown funds
Table 34: B-2 Measurement Model Price Construct Loadings
The promotion construct has the same three parcels that are used in the previous samples, but
some questions were added. The questions, parcels, and standardized factor loadings are
shown in Table 35. Overall, the construct exhibits very good reliability with a Cronbach’s alpha
of 0.901, a CR of 0.858, and an AVE of 0.670. All the parcels have significant loadings and the
standardized loadings are well above the minimum recommended values.
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Questions/Parcels Standard Loadings
Customer service backup if salesperson is not available
0.907 Ability of vendor to: provide unique promotions to your firm
Number of sales calls you personally receive per year from: vendor's sales representatives
Timely response to requests for assistance from vendor's sales rep
Quality of sales force: knowledge of merchandising techniques
0.795
Quality of sales force: product knowledge
Quality of sales force: knowledge of industry trends
Quality of sales force: knowledge of my business
Quality of sales force: knowledge of my competitor's business
Quality of sales force: adequate preparation for sales calls
0.746 Quality of sales force: honesty
Quality of sales force: understands logistics issues
Table 35: B-2 Measurement Model Promotion Construct Loadings
The place construct has the three of the four parcels from previous samples. The questions,
parcels and standard loadings are shown in Table 36 Overall, the construct exhibits very good
reliability with a Cronbach’s alpha of 0.902, a CR of 0.887, and an AVE of 0.725. All the parcels
have significant loadings and the standardized loadings are well above the minimum
recommended values.
Questions/Parcels Standard Loadings
Ability to expedite emergency orders
0.873 Ability of vendor to meet specific and/or unique customer service and delivery needs
Vendor's adherence to your specific shipping instructions
Length of promised lead times: normal reorders
0.809 Length of promised lead times: ad or promotional orders
Length of promised lead times: ASAP or emergency orders
Action on complaints related to order servicing and shipping
0.870 Prompt handling of claims due to: overages, shortages/pricing errors
Advance notice of shipping delays
Table 36: B-2 Measurement Model Place Construct Loadings
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All constructs in the measurement model for sample B-2 exhibit adequate reliability and fit the
data well. Next, discriminant validity is tested. The results of the discriminant validity testing are
shown in Table 37. The first row of each cell contains the chi square value after fixing the
correlation between two constructs to 1.00. The second row shows the difference to the chi
square value of the original model of 163.1 and the difference in degrees of freedom from 61 in
parentheses. The last row displays the p-value of the chi square difference test. All of the fixed
correlations cause the fit of the model to increase significantly and none of the correlation
confidence intervals include 1.00. This evidence supports the conclusion that the constructs are
distinct from each other and thus discriminant validity is achieved.
Product Price Promotion
Price New chi square: 219.2 Difference: 56.1 (1) p < 0.01
Promotion New chi square: 197.5 Difference: 34.4 (1) p < 0.01
New chi square: 304.2 Difference: 141.1 (1) p < 0.01
Place New chi square: 216 Difference: 52.9 (1) p < 0.01
New chi square: 212.4 Difference: 49.3 (1) p < 0.01
New chi square: 213.8 Difference: 50.7 (1) p < 0.01
Table 37: B-2 Discriminant Validity Testing Results
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3.11. Measurement Model Results for Sample C-1
For sample C-1, adaptations had to be made because not all questions from previous samples
were available. The overall fit of the measurement model was good (chi-square = 330.718 / 74
d.f.; TLI = 0.922; CFI = 0.945; RMSEA = 0.075) and the fit indexes are close to the recommended
thresholds. The model did not show any high modification indexes or residuals. All of this
evidence points to a well-fitting measurement model. Next, the constructs are assessed.
The product construct has three parcels which are mostly specific to the plastics resin industry.
The first parcel summarizes different aspects of packaging. The second parcel summarizes the
level of performance of the supplier’s product. The third parcel summarizes various aspects of
product quality. The questions, parcels and standardized loadings are shown in Table 38.
Overall, the construct exhibits very good reliability with a Cronbach’s alpha of 0.823, a CR of
0.821, and an AVE of 0.606. All the parcels have significant loadings and all have large enough
standardized loadings that are above the minimum recommended values.
Questions/Parcels Standard Loadings
Adequate identification/labeling of package contents
0.690 Quality/durability of packaging materials (bag, box, drum)
Availability of the following package: type-bag
Processability of resin 0.781
Supplier's resins are of consistent melt flow
Supplier's resins are of consistent quality
0.856 Overall quality of resin relative to price
Supplier's resins are of consistent color
Table 38: C-1 Measurement Model Product Construct Loadings
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The price construct has three parcels and there are slight adaptations in the questions from
previous samples. The questions and parcels are shown in Table 39. Overall, the construct
exhibits good reliability with a Cronbach’s alpha of 0.742, a CR of 0.760, and an AVE of 0.513. All
the parcels have significant loadings and all have large enough standardized loadings that are
above the minimum recommended values.
Questions/Parcels Standard Loadings
Realistic, consistent pricing policy by supplier over time 0.719
Competitiveness of price
Adequate advance notice of price changes 0.688
Supplier gives you an adequate period of price protection after a price increase is announced
Prompt and comprehensive response to competitive bid quotations 0.741
Quantity discount structure
Table 39: C-1 Measurement Model Product Construct Loadings
For the promotion construct the parceling technique that is used in the previous samples, is not
employed because fewer questions are available. Thus, with only four questions not enough
parcels could be built and therefore individual items are used. The questions and standardized
factor loadings are shown in Table 40. Overall, the construct exhibits good reliability with a
Cronbach’s alpha of 0.835, a CR of 0.831, and an AVE of 0.622. All the items have significant
loadings and the standardized loadings are well above the minimum recommended values.
Questions/Parcels Standard Loadings
Timely response to requests for assistance from supplier's sales representative 0.660
Quality of sales force-technical knowledge 0.732
Quality of sales force-prompt follow-up 0.847
Quality of sales force-honesty 0.782 Table 40: C-1 Measurement Model Promotion Construct Loadings
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The place construct has the three of the four parcels from previous samples. Overall, the
construct exhibits good reliability with a Cronbach’s alpha of 0.844, a CR of 0.846, and an AVE of
0.647. All the parcels have significant loadings and the standardized loadings are well above the
minimum recommended values as shown in Table 41.
Questions/Parcels Standard Loadings
Accuracy in filling orders (correct product is shipped)
0.793 Availability of status information on orders
Ability of supplier to automatically backorder out-of-stock items
Availability of inventory status information
Ability to expedite emergency orders in a fact responsive manner
0.789 Supplier's adherence to special shipping instructions
Availability of supplier to meet specific and/or unique customer service/delivery needs of individual customers
Action on complaints (e.g. order servicing, shipping, product, etc.)
0.831 Advance notice of shipping delays
Assistance from supplier in handling carrier loss and damage claims
Table 41: C-1 Measurement Model Place Construct Loadings
All constructs in the measurement model for sample C-1 exhibit acceptable reliability and fit the
data well. Next, discriminant validity is tested. The results of the discriminant validity testing are
shown in Table 42 . The first row of each cell contains the chi square value after fixing the
correlation between two constructs to 1.00. The second row shows the difference to the chi
square value of the original model of 330.2 and the difference in degrees of freedom from 74 in
parentheses. The last row displays the p-value of the chi square difference test. All of the fixed
correlations cause the fit of the model to increase significantly and none of the correlation
confidence intervals include 1.00. This evidence supports the conclusion that the constructs are
distinct from each other and thus discriminant validity is achieved.
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Product Price Promotion
Price New chi square: 518.2 Difference: 188 (1) p < 0.01
Promotion New chi square: 534.7 Difference: 204.5 (1) p < 0.01
New chi square: 420.7 Difference: 100.5 (1) p < 0.01
Place New chi square: 417.8 Difference: 87.6 (1) p < 0.01
New chi square: 363.5 Difference: 33.3 (1) p < 0.01
New chi square: 457.5 Difference: 127.3 (1) p < 0.01
Table 42: C-1 Discriminant Validity Testing Results
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3.12. Measurement Model Results for Sample D-1
For sample D-1, adaptations had to be made because of industry differences for the golf ball
industry and the fact that retailers were surveyed. The overall fit of the measurement model
was good (chi-square = 204.435 / 103 d.f.; TLI = 0.962; CFI = 0.971; RMSEA = 0.059) and the fit
indexes are well beyond the recommended thresholds. The model did not show any high
modification indexes or residuals. All of this evidence points to a well-fitting measurement
model. Next, the constructs are assessed.
The product construct has five parcels which are mostly specific to the golf ball industry. The
first parcel summarizes development of new products. The second parcel summarizes various
aspects of product packaging. The third and fourth parcels are about performance and quality
of the golf balls. The questions, parcels and standardized loadings are shown in Table 43.
Overall, the construct exhibits excellent reliability with a Cronbach’s alpha of 0.902, a CR of
0.910, and an AVE of 0.718. All the parcels have significant loadings and they have large enough
standardized loadings that are above the minimum recommended values.
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Questions/Parcels Standard Loadings
Consistent development of new golf balls by supplier
0.849 Frequency of new product introduction
Advance information (literature, specs, prices, etc.) on new product intros
Packaging: visual appeal
0.826 Packaging: aids in consumer purchasing decision
Packaging: product/technical info on package
Durability of packaging
Performance of premium balls
0.786 Supplier's products are at the leading edge of technology
Product features (distance, spin rate)
Past experience with supplier's product
0.919 Quality of product
Durability of product
Table 43: D-1 Measurement Model Product Construct Loadings
The price construct has the same four parcels as the previous samples, however there are slight
adaptations in the questions that make up the parcels, but overall they can be considered
equivalent. The questions and parcels are shown in Table 44. The first parcel summarizes the
effect of pricing on the retailer. The second parcel is about price increases. The third parcel is
made up of questions on discounts. In the fourth parcel there are questions concerning the
price level. Overall, the construct exhibits good reliability with a Cronbach’s alpha of 0.859, a CR
of 0.850, and an AVE of 0.587. All the parcels have significant loadings and all have large enough
standardized loadings that are above the minimum recommended values.
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Questions/Parcels Standard Loadings
Profit margin 0.776
Simplicity of pricing program
Supplier does not raise prices more than once per year
0.844 Supplier gives you an adequate period of price protection after a price decrease is announced
Quantity discount structure based on total annual purchases
0.714 Quantity discount structure based on size of individual order
Pre-book discount program
Lowest price
0.725 Competitiveness of price
Sales rep has authority to negotiate special prices
Table 44: D-1 Measurement Model Price Construct Loadings
The promotion construct has the same three parcels that are used in the previous samples. The
questions, parcels, and standardized factor loadings are shown in Table 45. Overall, the
construct exhibits excellent reliability with a Cronbach’s alpha of 0.908, a CR of 0.916, and an
AVE of 0.784. All the parcels have significant loadings and the standardized loadings are well
above the minimum recommended values.
Questions/Parcels Standard Loadings
Sales force characteristics: accessibility 0.818
Timely response to requests for assistance from supplier's sales rep
Sales force characteristics: product knowledge
0.921 Sales force characteristics: industry knowledge
Sales force characteristics: knowledge of merchandising techniques
Sales force characteristics: honesty
0.914 Sales force characteristics: concern/empathy
Sales force characteristics: adequate preparation for sales calls
Table 45: D-1 Measurement Model Promotion Construct Loadings
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The place construct uses the same four parcels as previous samples. Although some questions
are different it should not change the overall validity of the construct. Overall, the construct
exhibits excellent reliability with a Cronbach’s alpha of 0.900, a CR of 0.910, and an AVE of 0.705.
All the parcels have significant loadings and the standardized loadings are well above the
minimum recommended values as shown in Table 46.
Questions/Parcels Standard Loadings
Supplier ships complete orders and within specified windows (no incomplete or split shipments)
0.891 Availability of status information on orders
Ability to meet/keep dates for pre-booked shipments
Accuracy in filling orders (correct product is shipped)
Adequate availability (supplier's ability to deliver) of new products at time of introduction
0.848 Availability of reorder product
Supplier's adherence to special shipping instructions
Consistent lead times (supplier consistently meets promised delivery date)
0.838 Length of promised lead times (from order submission to delivery): pre-booked order/initial stocking
Length of promised lead times (from order submission to delivery): reorders
Prompt handling of claims due to overages, shortages or shipping errors 0.777
Assistance from supplier in handling carrier loss and damage claims
Table 46: D-1 Measurement Model Place Construct Loadings
All constructs in the measurement model for sample D-1 exhibit very good reliability and fit the
data well. Next, discriminant validity is tested. The results of the discriminant validity testing are
shown in Table 47. The first row of each cell contains the chi square value after fixing the
correlation between two constructs to 1.00. The second row shows the difference to the chi
square value of the original model of 206.4 and the difference in degrees of freedom from 103
in parentheses. The last row displays the p-value of the chi square difference test. All of the
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fixed correlations cause the fit of the model to increase significantly and none of the correlation
confidence intervals include 1.00. This evidence supports the conclusion that the constructs are
distinct from each other and thus discriminant validity is achieved.
Product Price Promotion
Price New chi square: 315.3 Difference: 108.9 (1) p < 0.01
Promotion New chi square: 416.8 Difference: 210.4 (1) p < 0.01
New chi square: 319.4 Difference: 113.0 (1) p < 0.01
Place New chi square: 284.1 Difference: 77.7 (1) p < 0.01
New chi square: 296.7 Difference: 90.3 (1) p < 0.01
New chi square: 402.4 Difference: 196.0 (1) p < 0.01
Table 47: D-1 Discriminant Validity Testing Results
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3.13. Measurement Model Results for Sample D-2
For sample D-2, only slight adaptations had to be made from sample D-1. The overall fit of the
measurement model was excellent (chi-square = 144.763 / 74 d.f.; TLI = 0.949; CFI = 0.964;
RMSEA = 0.060) and the fit indexes are at the recommended thresholds. The model did not
show any high modification indexes or residuals. All of this evidence points to a well-fitting
measurement model. Next, the constructs are assessed.
The product construct has four parcels which are mostly specific to golf clubs. The first parcel
summarizes development of new products. The second parcel is about golf club performance.
The third parcel covers golf club quality. The questions, parcels and standardized loadings are
shown in Table 48. Overall, the construct exhibits good reliability with a Cronbach’s alpha of
0.819, a CR of 0.815, and an AVE of 0.595. All the parcels have significant loadings and all have
large enough standardized loadings.
Questions/Parcels Standard Loadings
Supplier adequately tests new products before delivering to market 0.731
Consistent development of new products by supplier
Consistent shaft quality & performance
0.775 Supplier at leading edge of technology
Supplier's products are at the leading edge of technology
Past experience with supplier's product
0.806 Quality of product
Product reliability (consistent performance from shipment to shipment) Table 48: D-2 Measurement Model Product Construct Loadings
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The price construct has three of the four parcels from the previous samples. The only parcel
that was removed was the one about discounts. The questions and parcels are shown in Table
49. The first parcel summarizes the effect of pricing on the retailer. The second parcel is about
price changes. The third parcel is made up of questions concerning the price level. Overall, the
construct exhibits good reliability with a Cronbach’s alpha of 0.798, a CR of 0.804, and an AVE of
0.578. All the parcels have significant loadings and all have large enough standardized loadings
that are above the minimum recommended values.
Questions/Parcels Standard Loadings
Supplier reacts quickly to competitive price reductions 0.680
Profit margin
Adequate advance notice of price changes provided 0.811
Supplier does not raise prices more than once per year
Lowest price
0.784 Competitiveness of price
Sales rep has authority to negotiate special prices
Table 49: D-2 Measurement Model Price Construct Loadings
The promotion construct has the same three parcels that are used in the previous samples. The
questions, parcels, and standardized factor loadings are shown in Table 50. Overall, the
construct exhibits excellent reliability with a Cronbach’s alpha of 0.878, a CR of 0.907, and an
AVE of 0.765. All the parcels have significant loadings and the standardized loadings are well
above the minimum recommended values.
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Questions/Parcels Standard Loadings
Sales force characteristics: accessibility 0.836
Timely response to requests for assistance from supplier's sales rep
Sales force characteristics: product knowledge
0.971 Sales force characteristics: industry knowledge
Sales force characteristics: knowledge of merchandising techniques
Sales force characteristics: honesty
0.809 Sales force characteristics: concern/empathy
Sales force characteristics: adequate preparation for sales calls
Table 50: D-2 Measurement Model Promotion Construct Loadings
The place construct uses the four parcels from previous samples. The first parcel covers the
efficiency and effectiveness of delivery. The second parcel summarizes the flexibility of the
logistics system. The third parcel is about lead time. The last parcel covers problem solving. The
construct exhibits excellent reliability with a Cronbach’s alpha of 0.839, a CR of 0.856, and an
AVE of 0.600. All the parcels have significant loadings and the standardized loadings are well
above the minimum recommended values as shown in Table 51.
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Questions/Parcels Standard Loadings
Supplier ships complete orders and within specified windows (no incomplete or split shipments)
0.890 Availability of status information on orders
Ability to meet/keep dates for pre-booked shipments
Accuracy in filling orders (correct product is shipped)
Adequate availability (supplier's ability to deliver) of new products at time of introduction
0.749 Availability of reorder product
Supplier's adherence to special shipping instructions
Consistent lead times (supplier consistently meets promised delivery date)
0.787 Length of promised lead times (from order submission to delivery): pre-booked order/initial stocking
Length of promised lead times (from order submission to delivery): reorders
Prompt handling of claims due to overages, shortages or shipping errors 0.654
Assistance from supplier in handling carrier loss and damage claims
Table 51: D-2 Measurement Model Place Construct Loadings
All constructs in the measurement model for sample D-2 exhibit very good reliability and fit the
data well. Next, discriminant validity is tested. The results of the discriminant validity testing are
shown in Table 52. The first row of each cell contains the chi square value after fixing the
correlation between two constructs to 1.00. The second row shows the difference to the chi
square value of the original model of 144.8 and the difference in degrees of freedom from 74 in
parentheses. The last row displays the p-value of the chi square difference test. All of the fixed
correlations cause the fit of the model to increase significantly and none of the correlation
confidence intervals include 1.00. This evidence supports the conclusion that the constructs are
distinct from each other and thus discriminant validity is achieved.
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Product Price Promotion
Price New chi square: 309.7 Difference: 164.9 (1) p < 0.01
Promotion New chi square: 279.3 Difference: 134.5 (1) p < 0.01
New chi square: 344.1 Difference: 199.3 (1) p < 0.01
Place New chi square: 210.9 Difference: 66.1 (1) p < 0.01
New chi square: 253.5 Difference: 108.7 (1) p < 0.01
New chi square: 338.6 Difference: 193.8 (1) p < 0.01
Table 52: D-2 Discriminant Validity Testing Results
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3.14. Measurement Model Results for Sample D-3
For sample D-3, adaptations had to be made because of industry differences for the golf shoe
industry and the fact that retailers were surveyed. The overall fit of the measurement model
was adequate (chi-square = 186.527/ 74 d.f.; TLI = 0.935; CFI = 0.955; RMSEA = 0.086) and the fit
indexes are close to the recommended thresholds. The model did not show any high
modification indexes or residuals. All of this evidence points to a well-fitting measurement
model. Next, the constructs are assessed.
The product construct has three parcels which are mostly specific to the golf shoe industry. The
first parcel summarizes the assortment of products offered by the manufacturer. The second
parcel and third parcels are about golf shoe performance and quality. The questions, parcels
and standardized loadings are shown in Table 53. Overall, the construct exhibits very good
reliability with a Cronbach’s alpha of 0.883, a CR of 0.899, and an AVE of 0.749. All the parcels
have significant loadings and they have large enough standardized loadings that are above the
minimum recommended values.
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Questions/Parcels Standard Loadings
Appropriate range of sizes
0.800 Availability of men's and women's products
Availability of different widths
Supplier has complete assortment of footwear items
Waterproofing
0.888
Functionality
Consistent sizing
Fit and comfort
Overall appearance of shoe
Footwear consistent with most preferred styles and trends
Product quality relative to price
0.905 Warranty program for footwear
Product reliability (consistent product performance from shipment to shipment
Table 53: D-3 Measurement Model Product Construct Loadings
The price construct has three of the four parcels from the previous samples, and there are slight
adaptations in the questions that make up the parcels, but overall they can be considered
equivalent. The questions and parcels are shown in Table 54. The first parcel summarizes the
effect of price changes. The second parcel is made up of questions on discounts. In the third
parcel there are questions concerning the margin. Overall, the construct exhibits good reliability
with a Cronbach’s alpha of 0.772, a CR of 0.785, and an AVE of 0.549. All the parcels have
significant loadings and all have large enough standardized loadings that are above the
minimum recommended values.
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Questions/Parcels Standard Loadings
Adequate advance notice of price changes provided
0.758 Supplier gives you an adequate period of price protection after a price decrease is announced
Quantity discount structure based on total annual purchases
0.699 Quantity discount structure based on size of individual order
Pre-book discount program
Integrity of suggested retail price
0.765 Margin reflects selling effort
Profit margin
Table 54: D-3 Measurement Model Price Construct Loadings
The promotion construct has the same three parcels that are used in the previous samples. The
questions, parcels, and standardized factor loadings are shown in Table 45. Overall, the
construct exhibits excellent reliability with a Cronbach’s alpha of 0.892, a CR of 0.919, and an
AVE of 0.791. All the parcels have significant loadings and the standardized loadings are well
above the minimum recommended values.
Questions/Parcels Standard Loadings
Sales force characteristics: accessibility 0.800
Timely response to requests for assistance from supplier's sales rep
Sales force characteristics: product knowledge
0.971 Sales force characteristics: industry knowledge
Sales force characteristics: knowledge of merchandising techniques
Sales force characteristics: honesty
0.889 Sales force characteristics: concern/empathy
Sales force characteristics: adequate preparation for sales calls
Table 55: D-3 Measurement Model Promotion Construct Loadings
The place construct uses the same four parcels as previous samples. Overall, the construct
exhibits excellent reliability with a Cronbach’s alpha of 0.918, a CR of 0.914, and an AVE of 0.780.
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All the parcels have significant loadings and the standardized loadings are well above the
minimum recommended values as shown in Table 56.
Questions/Parcels Standard Loadings
Supplier ships complete orders and within specified windows (no incomplete or split shipments)
0.883 Availability of status information on orders
Ability to meet/keep dates for pre-booked shipments
Accuracy in filling orders (correct product is shipped)
Adequate availability (supplier's ability to deliver) of new products at time of introduction
0.894 Availability of reorder product
Supplier's adherence to special shipping instructions
Consistent lead times (supplier consistently meets promised delivery date)
0.873 Length of promised lead times (from order submission to delivery): pre-booked order/initial stocking
Length of promised lead times (from order submission to delivery): reorders
Prompt handling of claims due to overages, shortages or shipping errors 0.800
Assistance from supplier in handling carrier loss and damage claims
Table 56: D-3 Measurement Model Place Construct Loadings
All constructs in the measurement model for sample D-3 exhibit very good reliability and fit the
data well. Next, discriminant validity is tested. The results of the discriminant validity testing are
shown in Table 57. The first row of each cell contains the chi square value after fixing the
correlation between two constructs to 1.00. The second row shows the difference to the chi
square value of the original model of 185.6 and the difference in degrees of freedom from 74 in
parentheses. The last row displays the p-value of the chi square difference test. All of the fixed
correlations cause the fit of the model to increase significantly and none of the correlation
confidence intervals include 1.00. This evidence supports the conclusion that the constructs are
distinct from each other and thus discriminant validity is achieved.
128
Product Price Promotion
Price New chi square: 232.1 Difference: 46.5 (1) p < 0.01
Promotion New chi square: 336.5 Difference: 148.9 (1) p < 0.01
New chi square: 255.0 Difference: 69.4 (1) p < 0.01
Place New chi square: 265.6 Difference: 80.0 (1) p < 0.01
New chi square: 206.4 Difference: 20.8 (1) p < 0.01
New chi square: 382.2 Difference: 196.6 (1) p < 0.01
Table 57: D-3 Discriminant Validity Testing Results
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3.15. Summary
This chapter contains a description of the research methodology. There are nine samples that
are used for this research and each one is analyzed separately. There is no evidence that non-
response bias is a problem in any of the samples. Because too many items loaded on each
latent variable representing the Marketing Mix variables, parcels were used to summarize
several items into one importance-weighted composite score. The composite scores were then
used to estimate the constructs. The same constructs were used in all samples, but the
questions that were used to estimate each construct were not the same across all samples. All
measurement models exhibit adequate fit indices and there is enough evidence that the
variables represent the constructs well. The results of the structural part of the model are
presented in the next chapter.
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CHAPTER 4.
RESULTS
In this chapter, the results of the research are described. The measurement model part was
described in Chapter 3. In this chapter, the structural relationships between the constructs are
evaluated. In order to test the hypotheses, the structural relationships between the latent
variables of the structural equation modeling (SEM) are evaluated. The remainder of the
chapter is comprised of the structural model results from the nine samples. In order to gain a
more holistic understanding of the differences between the samples, a comparison of the
results is provided. The chapter ends with a summary.
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4.1. Overview of the Results Evaluation
In order to evaluate the strength of a relationship between two constructs, the estimate,
standard error, critical ratio, and p-value are assessed. The estimate represents a regression
coefficient that describes the magnitude of the relationship between the two constructs
(Arbukle 2008). The standard error (S.E.) is the amount of variability that is associated with the
estimate. The critical ratio (C.R.) is related to the significance of the regression weight. A C.R.,
larger than 1.96, indicates that a path that is significant at the 0.05 level (Arbukle 2008). The p-
value (P) denotes the absolute level of significance and refers to the probability of obtaining an
estimate that is zero. For this research, p-values of less than 0.01 are considered highly
significant, those between 0.01 and 0.05 are significant, and those between 0.05 and 0.10 are
marginally significant. In order to compare the relative strength of all paths in the model,
standardized estimates are presented. All relationships shown in Figure 5 as arrows are
estimated simultaneously in the SEM model.
Figure 5: Structural Model and Hypotheses
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4.2. A-1 Blood Banking Reagents Sample Results
The results for the blood banking reagents sample are displayed in Table 58. The overall fit of
the structural model was good (TLI = 0.950; CFI = 0.962; RMSEA = 0.065) and the fit indexes
were above the recommended thresholds. The difference between the measurement model
and the structural model is not large enough to be significant and that points to a well-fitting
structural model.
The first hypothesis shows the impact of the Marketing Mix on customer satisfaction. Three out
of four paths are significant. Promotion/personal selling has the strongest impact on
satisfaction (H1c), followed by place/logistics (H1d) and then by price (H1b). Only the product
construct does not have significant impact on satisfaction (H1a). With three out of four
constructs having a significant impact on customer satisfaction, it can be concluded that
Hypothesis one is supported.
One explanation for why product does not have a significant impact on customer satisfaction is
that blood banking reagents are regulated commodities that do not vary significantly in
performance and quality among the different manufacturers. New blood banking reagents must
be approved by the food and drug administration (FDA). As such, reagents must at least fulfill a
minimum level of performance and quality. New products would have to go through the same
approval process for any supplier. This explanation seems to be supported by the fact that the
parcel describing product performance has the highest average rating and the lowest standard
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deviation of all parcels in the model, which indicates that the different blood banking reagents
perform similarly well at a high level.
The second hypothesis describes the impact of customer satisfaction on share of business and
the analysis shows a significant and positive effect. In addition, indirect effects of the price,
promotion/personal selling and place/logistics constructs can be observed as well. This means
that those constructs have a significant impact on satisfaction, which in turn has a significant
impact on share of business. So, going back to the premise of the research, logistics attributes
are important part of the Marketing Mix and their impact on business generation must not be
ignored. Satisfaction explains 38.1 percent of the variance in the model and share of business
explains 6.5 percent of the variance.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction -3.240 2.717 -1.192 0.233 -0.091
H1b: Price Satisfaction 2.743 1.232 2.227 0.026 0.140
H1c: Promotion Satisfaction 6.257 0.498 12.554 <0.001 0.415
H1d: Place Satisfaction 5.421 2.063 2.628 0.009 0.209
H2: Satisfaction Share 0.481 0.049 9.810 <0.001 0.254
Table 58: A-1 Blood Banking Results
In addition to the impact of satisfaction on current share of business, a variation of the model
was evaluated with preferred share of business as an alternative outcome variable. Preferred
share of business is the percentage of business that a respondent would ideally want to award
to a supplier. Introducing preferred share as an alternative outcome variable enables the
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assessment whether ideal and preferred share show the same level of significance and explain
similar amounts of variance.
The significance and the magnitude of the variables generally remains the same, but the link
between satisfaction and preferred share of business is stronger in magnitude. Overall, the
model fit was comparable (TLI = 0.951; CFI = 0.963; RMSEA = 0.065). Preferred share of business
explains a larger percentage of the variance (10.7 percent) in the model than current share of
business (6.5 percent). While the estimates are not significantly different there is still an
indication that customer satisfaction is a better predictor of preferred share of business.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction -3.281 2.718 -1.207 0.227 -0.092
H1b: Price Satisfaction 2.786 1.229 2.267 0.023 0.142
H1c: Promotion Satisfaction 6.258 0.498 12.574 <0.001 0.415
H1d: Place Satisfaction 5.402 2.062 2.619 0.009 0.208
H2: Satisfaction Preferred Share 0.585 0.045 12.878 <0.001 0.326
Table 59: A-1 Blood Banking Results Alternative Model
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4.3. A-2 Coagulation Reagents Sample Results
The next sample that is assessed is the coagulation reagents sample. The overall fit of the
structural model was good (TLI = 0.941; CFI = 0.955; RMSEA = 0.066). The difference between
blood banking and coagulation reagents largely refers to the type of test that is performed in
the laboratory. Blood banking reagents are necessary for every surgery, but coagulation tests
are not and they may be used less frequently. The pattern of results for the coagulation
reagents sample is different than in the blood banking sample and they are shown in Table 60.
The relationship between the promotion/personal selling construct and customer satisfaction is
significant (H1c), as it was in the blood banking sample. The effect of product attributes on
customer satisfaction is significant (H1a), and it was not significant in the blood banking sample.
There is a significant impact of the product construct on customer satisfaction. Overall, two of
the four components of the Marketing Mix have a significant relationship with customer
satisfaction. The impact of customer satisfaction on share of business is marginally significant.
Therefore, hypothesis two is not supported and no indirect effects are supported either.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 20.304 7.116 2.853 0.004 0.558
H1b: Price Satisfaction 3.986 2.541 1.568 0.117 0.175
H1c: Promotion Satisfaction 9.146 2.014 4.542 <0.001 0.459
H1d: Place Satisfaction 6.780 7.381 0.919 0.358 0.205
H2: Satisfaction Share 0.163 0.094 1.733 0.083 0.083
Table 60: A-2 Coagulation Reagents Results
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In addition to the impact of satisfaction on current share of business, a variation of the model
was evaluated with preferred share of business as an alternative outcome variable. The
significance and the magnitude of the variables generally remains the same, but the link
between satisfaction and preferred share of business is now significant. Overall, the model fit
was comparable (TLI = 0.944; CFI = 0.957; RMSEA = 0.065). Preferred share of business explains
a larger percentage of the variance (3.6 percent) in the model than current share of business
(0.7 percent). The differences are statistically significant and there is an indication that
customer satisfaction is a better predictor of preferred share of business. Medical laboratories
must go through a difficult procedure in order to switch suppliers. They must run parallel tests
with both the new and the old reagent for a week and compare results to ensure the tests are
interpreted correctly (Ezzelle, et al. 2008). This creates significant extra work for the staff and
may cause customers to keep a current supplier even if the performance is subpar.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 20.304 7.116 2.853 0.004 0.558
H1b: Price Satisfaction 3.986 2.541 1.568 0.117 0.175
H1c: Promotion Satisfaction 9.146 2.014 4.542 <0.001 0.459
H1d: Place Satisfaction 6.780 7.381 0.919 0.358 0.205
H2: Satisfaction Preferred Share 0.368 0.091 4.049 *** 0.191
Table 61: A-2 Coagulation Reagents Results Alternative Model
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4.4. A-3 Coagulation Reagents Sample Results
The second survey on the coagulation sample was performed three years after A-2 and used the
same sampling frame. The overall fit of the structural model was good (TLI = 0.914; CFI = 0.935;
RMSEA = 0.080). The results are different than in the first survey on coagulation reagents. The
results are shown in Table 62. One component of the Marketing Mix, the price construct,
showed a significant impact on customer satisfaction (H1b). The product construct, which was
highly significant in the earlier sample, is not significant (H1a). This could be because some
suppliers who previously had worse performance improved and large differences exist no longer.
The opposite also could be possible, where some high-performing suppliers became worse.
Overall, hypothesis one has minimal support. The impact of customer satisfaction on share of
business is not significant and hypothesis two is not supported.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction -0.251 9.855 -0.025 0.980 -0.004
H1b: Price Satisfaction 21.862 4.353 5.023 *** 0.489
H1c: Promotion Satisfaction -1.590 5.120 -0.311 0.756 -0.041
H1d: Place Satisfaction 10.762 7.778 1.384 0.166 0.215
H2: Satisfaction Share 0.084 0.085 0.994 0.320 0.059
Table 62: A-3 Coagulation Reagents Results
In addition to the impact of satisfaction on current share of business, a variation of the model
was evaluated with preferred share of business as an alternative outcome variable. The
significance and the magnitude of the variables generally remains the same, but the magnitude
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of the relationship between customer satisfaction and share of business increased and moved
closer to significance. However, the impact of customer satisfaction on preferred share of
business is still not significant. Overall, the fit of the alternative model shown in Table 63 was
slightly better (TLI = 0.917; CFI = 0.938; RMSEA = 0.078). Preferred share of business explains a
larger percentage of the variance (0.9 percent) in the model than current share of business (0.4
percent), but it is at a very low level in both models. Although the explanatory power of
customer satisfaction decreases from 15.3 percent with current share of business to 12.8
percent with preferred share of business.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction -2.837 9.571 -0.296 0.767 -0.048
H1b: Price Satisfaction 19.295 4.121 4.682 <0.001 0.447
H1c: Promotion Satisfaction 0.251 4.971 0.05 0.96 0.007
H1d: Place Satisfaction 6.969 7.517 0.927 0.354 0.143
H2: Satisfaction Preferred Share 0.135 0.087 1.547 0.122 0.092
Table 63: A-3 Coagulation Reagents Results Alternative Model
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4.5. B-1 Professional Video Tape Sample Results
In the professional tape sample, the results show a different impact of the Marketing Mix on
firm performance. The results are shown in Table 64. The overall fit of the structural model was
very good (TLI = 0.963; CFI = 0.972; RMSEA = 0.059). The product in this sample was video tapes
sold to professional recording studios. Two of the four components of the Marketing Mix have a
significant impact on customer satisfaction, product (H1a) and place/logistics (H1d). The
place/logistics construct has the strongest impact on customer satisfaction. Customer
satisfaction does have a significant effect on share of business and as such hypothesis two is
supported. In addition, the product and place/logistics constructs have an indirect effect on
share of business through customer satisfaction. This result highlights the importance of
superior logistics performance as it directly affects customer satisfaction and indirectly affects
share of business. This means that suppliers who provide superior products and logistics
services to their customers are the most successful.
Availability of the desired tape is critical because of tight schedules, which makes logistics
performance very important. The best way for a supplier to succeed is to provide a high-quality
video tape with characteristics that professional users desire and make them easily available
when customers need them. Users of professional video tape placed a higher importance on
logistics attributes than product attributes as evidenced by the larger estimate. It may be
concluded that the video tape that is available is more appreciated than the one that has the
highest technical specifications.
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Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 7.369 2.201 3.348 <0.001 0.222
H1b: Price Satisfaction 1.005 1.920 0.523 0.601 0.049
H1c: Promotion Satisfaction 0.003 1.354 0.002 0.998 0.000
H1d: Place Satisfaction 9.297 2.166 4.292 <0.001 0.434
H2: Satisfaction Share 0.643 0.059 10.953 <0.001 0.437
Table 64: B-1 Professional Tape Results
In addition to the impact of satisfaction on current share of business, a variation of the model
was evaluated with preferred share of business as an alternative outcome variable. The
significance and the magnitude of the variables generally remains the same, but the magnitude
of the relationship between customer satisfaction and share of business increased in magnitude.
The difference is not statistically significant. Overall, the fit of the alternative model shown in
Table 65 was equal (TLI = 0.962; CFI = 0.972; RMSEA = 0.060). Preferred share of business
explains a slightly larger percentage of the variance (20.8 percent) in the model than current
share of business (19.1 percent). The explanatory power of customer satisfaction stays the
same at 43.7 percent.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 7.369 2.201 3.348 <0.001 0.222
H1b: Price Satisfaction 1.005 1.920 0.523 0.601 0.049
H1c: Promotion Satisfaction 0.003 1.354 0.002 0.998 0.000
H1d: Place Satisfaction 9.297 2.166 4.292 <0.001 0.434
H2: Satisfaction Preferred Share 0.660 0.057 11.535 <0.001 0.456
Table 65: B-1 Professional Tape Results Alternative Model
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4.6. B-2 Consumer Video Tape Sample Results
In the second electronics sample, video tapes for consumers sold to retailers, slightly different
results were obtained. The results are shown in Table 66. Overall, the fit of the model was good
(TLI = 0.935; CFI = 0.954; RMSEA = 0.084). Two of the four components of the Marketing Mix
have a significant impact on customer satisfaction, promotion/personal selling (H1c) and
place/logistics (H1d). In contrast to the professional users, retailers seem to value good
salespeople. In this sample, the product performance did not have a significant impact.
Customer Satisfaction has a significant effect on share of business and hypothesis two is
supported. In addition indirect effects are observed as promotion/personal selling and
place/logistics impact share of business through customer satisfaction. This adds additional
evidence to the importance of logistics attributes regarding business performance.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction -2.417 2.984 -0.810 0.418 -0.103
H1b: Price Satisfaction -0.159 2.297 -0.069 0.945 -0.009
H1c: Promotion Satisfaction 10.309 3.613 2.853 0.004 0.544
H1d: Place Satisfaction 5.073 2.444 2.076 0.038 0.237
H2: Satisfaction Share 0.222 0.055 4.074 <0.001 0.260
Table 66: B-2 Consumer Tape Results
In addition to the impact of satisfaction on current share of business, a variation of the model
was evaluated with preferred share of business as an alternative outcome variable. The
significance and the magnitude of the variables generally remains the same, but the magnitude
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of the relationship between customer satisfaction and share of business increased in magnitude.
The difference is not statistically significant. Overall, the fit of the alternative model shown in
Table 67 was similar (TLI = 0.932; CFI = 0.951; RMSEA = 0.086). Preferred share of business
explains a slightly larger percentage of the variance (9 percent) in the model than current share
of business (6.8 percent). The explanatory power of customer satisfaction stays the same at
43.1 percent.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction -2.417 2.984 -0.810 0.418 -0.103
H1b: Price Satisfaction -0.159 2.297 -0.069 0.945 -0.009
H1c: Promotion Satisfaction 10.309 3.613 2.853 0.004 0.544
H1d: Place Satisfaction 5.073 2.444 2.076 0.038 0.237
H2: Satisfaction Preferred Share 0.233 0.049 4.740 <0.001 0.299
Table 67: B-2 Consumer Tape Results Alternative Model
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4.7. C-1 Plastics Resin Sample Results
In the plastic resin sample, the respondents’ were buyers in a manufacturing environment, as
compared to the hospitals, movie studios, and retailers from previous samples. The results are
shown in Table 68. The overall model fit was good (TLI = 0.926; CFI = 0.943; RMSEA = 0.073).
One component of the Marketing Mix, price, has a significant impact on customer satisfaction
(H1b). The direct effects of product and promotion/personal selling factors are not significant.
Customer satisfaction has a significant effect on share of business, which supports hypothesis
two. In addition, the results provide evidence for a significant indirect effect of the price
construct through customer satisfaction on share of business.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 0.124 0.112 1.112 0.266 0.086
H1b: Price Satisfaction 0.480 0.230 2.083 0.037 0.324
H1c: Promotion Satisfaction 0.082 0.212 0.390 0.697 0.061
H1d: Place Satisfaction 0.087 0.118 0.739 0.460 0.064
H2: Satisfaction Share 1.796 0.666 2.699 0.007 0.108
Table 68: C-1 Plastic Resin Results
In addition to the impact of satisfaction on current share of business, a variation of the model
was evaluated with preferred share of business as an alternative outcome variable. The
significance and the magnitude of the variables generally remains the same, but the magnitude
of the relationship between customer satisfaction and share of business increased in magnitude.
The difference is not statistically significant. Overall, the fit of the alternative model shown in
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Table 69 was similar (TLI = 0.927; CFI = 0.944; RMSEA = 0.073). Preferred share of business
explains a slightly larger percentage of the variance (2.1 percent) in the model than current
share of business (1.2 percent). The explanatory power of customer satisfaction stays the same
at 25.4 percent.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 0.124 0.112 1.112 0.266 0.086
H1b: Price Satisfaction 0.480 0.230 2.083 0.037 0.324
H1c: Promotion Satisfaction 0.082 0.212 0.390 0.697 0.061
H1d: Place Satisfaction 0.087 0.118 0.739 0.460 0.064
H2: Satisfaction Preferred Share 2.302 0.625 3.682 <0.001 0.146
Table 69: C-1 Plastic Resin Results Alternative Model
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4.8. D-1 Golf Balls Sample Results
The last three samples are from the golf industry and in each of them volume retailers and pro
shops were surveyed. The overall model fit was good (TLI = 0.950; CFI = 0.961; RMSEA = 0.069).
The results of the golf balls sample are shown in Table 70. None of the components of the
Marketing Mix have a significant impact on customer satisfaction and as such there is no
support for the hypothesis that the Marketing Mix has a significant impact on customer
satisfaction. An analysis of the performance scores shows that variability of performance scores
is lower for this sample than the other two sporting goods samples. Customer satisfaction has a
significant impact on share of business, so there is support for hypothesis two.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 1.261 3.649 0.345 0.730 0.055
H1b: Price Satisfaction 3.127 3.176 0.985 0.325 0.144
H1c: Promotion Satisfaction -0.666 2.040 -0.326 0.744 -0.040
H1d: Place Satisfaction -1.763 4.143 -0.426 0.670 -0.078
H2: Satisfaction Share 0.310 0.055 5.586 <0.001 0.313
Table 70: D-1 Golf Balls Results
In addition to the impact of satisfaction on current share of business, a variation of the model
was evaluated with preferred share of business as an alternative outcome variable. The
significance and the magnitude of the variables generally remains the same, but the magnitude
of the relationship between customer satisfaction and share of business increased in magnitude.
The difference is not statistically significant. Overall, the fit of the alternative model shown in
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Table 71 was similar (TLI = 0.953; CFI = 0.963; RMSEA = 0.067). Preferred share of business
explains a slightly larger percentage of the variance (13.6 percent) in the model than current
share of business (9.8 percent). The explanatory power of customer satisfaction stays the same
at one percent.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 1.261 3.649 0.345 0.730 0.055
H1b: Price Satisfaction 3.127 3.176 0.985 0.325 0.144
H1c: Promotion Satisfaction -0.666 2.040 -0.326 0.744 -0.040
H1d: Place Satisfaction -1.763 4.143 -0.426 0.670 -0.078
H2: Satisfaction Preferred Share 0.346 0.052 6.713 <0.001 0.368
Table 71: D-1 Golf Balls Results Alternative Model
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4.9. D-2 Golf Clubs Sample Results
The data in the second sample in the golf industry is a survey of volume retailers and pro shops
regarding golf clubs. The overall model fit was good (TLI = 0.939; CFI = 0.955; RMSEA = 0.066).
The results are shown in Table 72. Two constructs have a significant impact on customer
satisfaction, product (H1a) and promotion/personal selling (H1c). These are the only significant
relationships in this sample. This is a disconnect because the relationship between customer
satisfaction and share of business is not significant. In other words, there is no statistical
relationship between customer satisfaction and share of business. In this sample no indirect
effects from the Marketing Mix, through customer satisfaction, on share of business can be
reported.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 12.883 6.746 1.910 0.056 0.260
H1b: Price Satisfaction 0.710 3.788 0.188 0.851 0.018
H1c: Promotion Satisfaction 7.991 2.533 3.155 0.002 0.263
H1d: Place Satisfaction -2.668 6.703 -0.398 0.691 -0.062
H2: Satisfaction Share 0.056 0.031 1.824 0.068 0.112
Table 72: D-2 Golf Clubs Results
In addition to the impact of satisfaction on current share of business, a variation of the model
was evaluated with preferred share of business as an alternative outcome variable. The
significance and the magnitude of the variables generally remains the same, but the strength of
the relationship between customer satisfaction and share of business is higher. The difference
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is statistically significant. Overall, the fit of the alternative model shown in Table 73 was similar
(TLI = 0.942; CFI = 0.957; RMSEA = 0.068). Preferred share of business explains a much larger
percentage of the variance (62.9 percent) in the model than current share of business (1.2
percent). The explanatory power of customer satisfaction stays the same at 18.9 percent. The
indirect effects of product and promotion/personal selling, through customer satisfaction, on
preferred share of business are significant.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 12.883 6.746 1.910 0.056 0.260
H1b: Price Satisfaction 0.710 3.788 0.188 0.851 0.018
H1c: Promotion Satisfaction 7.991 2.533 3.155 0.002 0.263
H1d: Place Satisfaction -2.668 6.703 -0.398 0.691 -0.062
H2: Satisfaction Preferred Share 0.766 0.036 21.157 <0.001 0.793
Table 73: D-2 Golf Clubs Results Alternative Model
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4.10. D-3 Golf Shoes Sample Results
The results of the golf shoes sample are shown in Table 74. The overall fit of them model is
good (TLI = 0.958; CFI = 0.971; RMSEA = 0.071). One component of the Marketing Mix,
promotion/personal selling had a significant impact on customer satisfaction (H1c). The
relationship between customer satisfaction and share of business is significant which offers
support for hypothesis two. Therefore, the indirect effect between promotion/personal selling
and share of business is significant.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 5.021 3.432 1.463 0.143 0.186
H1b: Price Satisfaction 3.879 3.624 1.070 0.284 0.201
H1c: Promotion Satisfaction 8.379 1.644 5.097 <0.001 0.502
H1d: Place Satisfaction 4.267 4.178 1.021 0.307 0.193
H2: Satisfaction Share 0.594 0.086 6.878 <0.001 0.434
Table 74: D-3 Golf Shoes Results
In addition to the impact of satisfaction on current share of business, a variation of the model
was evaluated with preferred share of business as an alternative outcome variable. The
significance and the magnitude of the variables generally remains the same, but the magnitude
of the relationship between customer satisfaction and share of business increased in magnitude.
The difference is not statistically significant. Overall, the fit of the alternative model shown in
Table 75 was similar (TLI = 0.961; CFI = 0.972; RMSEA = 0.069). Preferred share of business
explains a much larger percentage of the variance (24.7 percent) in the model than current
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share of business (18.8 percent). The explanatory power of customer satisfaction stays the
same at 46.9 percent.
Structural Path Estimate S.E. C.R. P Standard Estimate
H1a: Product Satisfaction 5.021 3.432 1.463 0.143 0.186
H1b: Price Satisfaction 3.879 3.624 1.070 0.284 0.201
H1c: Promotion Satisfaction 8.379 1.644 5.097 <0.001 0.502
H1d: Place Satisfaction 4.267 4.178 1.021 0.307 0.193
H2: Satisfaction Preferred Share 0.649 0.079 8.176 <0.001 0.497
Table 75: D-3 Golf Clubs Results Alternative Model
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4.11. Sample Comparison
After analyzing the results of each sample individually, an overall assessment of the results from
all samples is provided. The impact of the Marketing Mix components on customer satisfaction
is shown in Table 76. The table shows the sign and the significance of the coefficient
representing each hypothesis. A marginal significance represented by a p-value between 0.05
and 0.10 is denoted by “†”. One star (*) denotes a p-value between 0.01 and 0.05. Two stars
(**) denote a p-value between 0.001 and 0.01. Three stars (***) denote a p-value of less than
0.001. If a coefficient is not significant it is denoted with “n.s.”. In the last row of the table the
number of significant relationships is shown.
The same the Marketing Mix components are not significant for more than one sample. Even in
samples A-2 and A-3, which are based on the same product, the results are different. It seems
that promotion/personal selling has a more consistent impact on customer satisfaction than the
other components of the Marketing Mix. The other constructs are significant three out of nine
times. Based on the fact that differences between samples appear in the results, it must be
made clear that different types of industries, products and organizations were surveyed. Based
on the results in this study, customer service attributes are not equally important nor do they
have the same influence on customer satisfaction across all samples. It is not possible to use
previous data to generalize what components of the Marketing Mix have a significant impact on
customer satisfaction. If managers want to know what areas are key to the success of their
company, they must collect data and analyze it.
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Industry Sample Name Product Price Promotion/ Pers. Selling
Place/ Logistics
Health Services
A-1 (Blood Banking)
n.s. 0.14 * 0.42 *** 0.21 **
Health Services
A-2 (Coagulation)
0.56 ** n.s. 0.46 *** n.s.
Health Services
A-3 (Coagulation)
n.s. 0.49 *** n.s. n.s.
Electronics B-1
(Professional Tape) 0.22 *** n.s. n.s. 0.43 ***
Electronics B-2
(Consumer Tape) n.s. n.s. 0.54 ** 0.24 *
Plastics C-1
(Commodity Resin) n.s. 0.32 * n.s. n.s.
Sporting Goods
E-1 (Golf Balls)
n.s. n.s. n.s. n.s.
Sporting Goods
E-2 (Golf Clubs)
0.26 † n.s. 0.26 ** n.s.
Sporting Goods
E-3 (Golf Shoes)
n.s. n.s. 0.50 *** n.s.
Statistically Significant Relationships: 2/9 3/9 5/9 3/9
Table 76: Overall Impact of the Marketing Mix on Customer Satisfaction
The impact of the customer satisfaction on share of business is shown in Table 77. Overall, the
hypothesis was supported five out of nine times. In addition there were several indirect effects,
where there were significant relationships between a construct and customer satisfaction and
between customer satisfaction and share of business.
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Industry Sample Name Satisfaction Indirect Effects
Health Services A-1
(Blood Banking) 0.25 ***
Price, Promotion, and Place
Health Services A-2
(Coagulation) 0.08 † n/a
Health Services A-3
(Coagulation) n.s. n/a
Electronics B-1
(Professional Tape) 0.44 *** Product and Place
Electronics B-2
(Consumer Tape) 0.26 ** Promotion and Place
Plastics C-1
(Commodity Resin) 0.11 ** Price
Sporting Goods E-1
(Golf Balls) 0.31 *** Price
Sporting Goods E-2
(Golf Clubs) 0.11 † n/a
Sporting Goods E-3
(Golf Shoes) 0.43 *** Product
Statistically Significant Relationships: 6/9
Table 77: Overall Impact of Customer Satisfaction on Share of Business
The impact of customer satisfaction on preferred share of business was assessed as well and the
results are shown in Table 78. In sample A-2 current share of business is marginally significant,
but preferred share of business is highly significant. Another difference is in sample E-2, where
current share of business has a marginally significant impact on customer satisfaction, but
preferred share of business is highly significant.
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Industry Sample Name Satisfaction Indirect Effects
Health Services A-1
(Blood Banking) 0.33 ***
Price, Promotion, and Place
Health Services A-2
(Coagulation) 0.19 *** n/a
Health Services A-3
(Coagulation) n.s. n/a
Electronics B-1
(Professional Tape) 0.46 *** Product and Place
Electronics B-2
(Consumer Tape) 0.30 ** Promotion and Place
Plastics C-1
(Commodity Resin) 0.15 *** Price
Sporting Goods E-1
(Golf Balls) 0.37 *** Price
Sporting Goods E-2
(Golf Clubs) 0.79 *** Promotion
Sporting Goods E-3
(Golf Shoes) 0.50 *** Product
Statistically Significant Relationships: 8/9
Table 78: Overall Impact of Customer Satisfaction on Preferred Share of Business
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4.12. Summary
In this chapter the research results were described. The nine samples were analyzed and the
results were compared. The promotion/personal selling construct had the most consistent
impact on customer satisfaction. The other constructs were significant in three samples each.
Customer satisfaction had a significant impact on share of business in five samples. In the next
chapter, the implications of the results for theory and practice are described. In addition, the
limitations of the study are described and extensions for future research are proposed.
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CHAPTER 5.
CONCLUSIONS
In this chapter, a summary and the conclusions of the research are presented. The results were
provided in Chapter 5, and this chapter contains an evaluation and interpretation of the results.
As specific hypotheses were tested and evaluated in the previous chapter, the overall impact of
the Marketing Mix on both outcome variables is described in this chapter. Based on the results,
it is not possible to argue that one component of the Marketing Mix has a stronger impact on
firm performance than the others. The primary research objective was to investigate the
relative effect of logistics attributes versus the other components of the Marketing Mix
customer satisfaction and share of business and replicate the model under different conditions.
First, a summary of the research purpose is provided. Second, the research objectives and
hypotheses are reviewed. Third, the findings are summarized. Fourth, the research limitations
are described. Fifth, opportunities for future research are illustrated. Sixth, implications for
theory are described. Seventh, implications for practice are described. The chapter ends with
overall conclusions.
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5.1. Summary of Research Purpose
Many of the previous studies on customer service focused only on a single industry and few
were replication studies. If a study is conducted in a particular industry, then the results may be
valid only for that industry. The goal for this dissertation was to address this shortcoming by
using a multi-industry approach with nine samples enabling replication of the research model
(Hubbard and Armstrong 1994, Hubbard and Vetter 1996). By using multiple samples, a model
can be developed on one sample and then validated with the others. This approach yields
stronger results because it minimizes the chance for misspecification of the model (Ehrenberg
2004).
The need for replication has been voiced several times in the past (Furchtgott 1984, Lubin 1957,
Sterling, Rosenbaum and Weinkam 1995). The need for replication is adressed with this
dissertation. The results of this research show that the Marketing Mix components that have a
significant impact in one industry or even in one sample may not have a significant impact in
others. Consequently, the findings of past research studies that were based on a single sample
may be questioned. This research reinforces the need for replication.
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5.2. Review of Research Objectives and Hypotheses
The variables that managers use to select and evaluate suppliers were summarized into one of
the four components of the Marketing Mix: product, price, promotion/personal selling and
place/logistics. The main gap that has been addressed is the differential effect of logistics
attributes versus the other components of the Marketing Mix. The use of both customer
satisfaction and share of business as outcome variables enabled a better understanding of how
the Marketing Mix may affect supplier financial performance. As established in Chapter 1, the
specific research questions of the dissertation were:
1. What are multi-item scales to assess the performance of customer service elements in
business-to-business relationships across multiple industries?
2. What is the relative importance of the components of the Marketing Mix in business-to-
business settings in several industries?
3. What is the influence of the components of the Marketing Mix on customer satisfaction
and share of business?
Each component of the Marketing Mix is measured as a latent variable with several attributes,
making up the variable. The attributes are summarized as importance-weighted averages of
different aspects of the construct. In order to clarify the research model, a short review of the
constructs is provided:
1. The product construct is made up of attributes describing the performance, quality, new
product development and support provided for the product.
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2. Price contains attributes related to level and competitiveness of pricing and the
satisfaction with billing procedures.
3. Promotion/personal selling attributes are related to the efforts of the salesperson.
4. Place/logistics attributes evaluated different aspects of logistics performance like
delivery reliability, delivery flexibility, lead time, and problem solving of delivery issues.
5. Customer satisfaction is the overall evaluation of satisfaction with a supplier.
6. Share of business denotes the percentage of business given to a supplier.
Hypotheses
The following formal hypotheses were tested in using structural equation modeling (SEM).
Each hypothesis relates to a relationship in the structural model. The components of the
Marketing Mix are believed to influence customer satisfaction. More specifically, the
relationships between the individual components of the Marketing Mix were analyzed.
H1a: Product has a significant impact on customer satisfaction.
H1b: Price has a significant impact on customer satisfaction.
H1c: Promotion/personal selling has a significant impact on customer satisfaction.
H1d: Place/logistics has a significant impact on customer satisfaction.
While customer satisfaction is an important construct in the literature, it does not directly
translate into profitability or market share. In order to understand the effect of customer
160
satisfaction on share of business, the direct link was tested. It is believed to be a generally
positive relationship (Rust, Zahorik and Keiningham 1996). In addition, indirect effects between
the Marketing Mix and share of business through customer satisfaction are assessed.
H2: Customer satisfaction has a significant impact on share of business.
The research model, shown in Figure 6, displays the tested hypotheses in a path diagram.
Figure 6: Research Model with Hypotheses
161
5.3. Summary of Findings
The overall impact of the Marketing Mix on customer satisfaction is shown in Table 79. One star
(*) denotes a p-value between 0.01 and 0.05. Two stars (**) denote a p-value between 0.001
and 0.01. Three stars (***) denote a p-value of less than 0.001. A marginal significance is
denoted by “†”. If a coefficient is not significant it is denoted with “n.s.”.
Industry Sample Name Product Price Promotion/ Pers. Selling
Place/ Logistics
Health Services
A-1 (Blood Banking)
n.s. 0.14 * 0.42 *** 0.21 **
Health Services
A-2 (Coagulation)
0.56 ** n.s. 0.46 *** n.s.
Health Services
A-3 (Coagulation)
n.s. 0.49 *** n.s. n.s.
Electronics B-1
(Professional Tape) 0.22 *** n.s. n.s. 0.43 ***
Electronics B-2
(Consumer Tape) n.s. n.s. 0.54 ** 0.24 *
Plastics C-1
(Commodity Resin) n.s. 0.32 * n.s. n.s.
Sporting Goods
E-1 (Golf Balls)
n.s. n.s. n.s. n.s.
Sporting Goods
E-2 (Golf Clubs)
0.26 † n.s. 0.26 ** n.s.
Sporting Goods
E-3 (Golf Shoes)
n.s. n.s. 0.50 *** n.s.
Statistically Significant Relationships: 2/9 3/9 5/9 3/9
Table 79: Overall Impact of the Marketing Mix on Customer Satisfaction
The first result is that no consistent pattern emerged as to which components of the Marketing
Mix affect customer satisfaction. This shows that the customer service attributes that have the
162
most impact on customers can vary by circumstances in the market. The impact of customer
satisfaction on share of business is shown in Table 80.
Industry Sample Name Satisfaction Indirect Effects
Health Services A-1
(Blood Banking) 0.25 ***
Price, Promotion, and Place
Health Services A-2
(Coagulation) 0.08 † n/a
Health Services A-3
(Coagulation) n.s. n/a
Electronics B-1
(Professional Tape) 0.44 *** Product and Place
Electronics B-2
(Consumer Tape) 0.26 ** Promotion and Place
Plastics C-1
(Commodity Resin) 0.11 ** Price
Sporting Goods E-1
(Golf Balls) 0.31 *** Price
Sporting Goods E-2
(Golf Clubs) 0.11 † n/a
Sporting Goods E-3
(Golf Shoes) 0.43 *** Product
Significant Relationships: 6/9
Table 80: Overall Impact of Customer Satisfaction on Share of Business
The impact of customer satisfaction on share of business is fairly consistent, with six significant
relationships, two that are marginally significant and one non significant relationship. This also
enabled several indirect relationships of the Marketing Mix on share of business, when the link
between a component of the Marketing Mix and customer satisfaction was significant and the
link between customer satisfaction and share of business.
163
5.4. Research Limitations
As with any research, there are limitations that must be considered. The first limitation is the
possible impact for multicollinearity on the results. The second limitation is the use of context-
specific attributes to estimate the components of the Marketing Mix. Each of these limitations
will be described briefly.
Multicollinearity
The latent variables are left to correlate freely in the structural model. The correlations are
always significant and removing those paths would have resulted in poor fit of the model. One
possible problem is that multicollinearity may persist in the SEM models (Grewal, Cote and
Baumgartner 2004). The most common problem with multicollinearity is that type II errors may
occur, meaning that a relationship, which in reality is significant, is shown as non-significant
(Jagpal 1982). Restricting the latent variables to be uncorrelated would not be an option,
because conceptually it would be unreasonable to expect Product, Price, Promotion, and Place
to not be correlated. This modeling strategy was used previously (Stank, Goldsby and Vickery
1999), and is a compromise that seems prudent in this research. The best safeguards against
multicollinearity issues are large sample size, discriminant validity, and high measure reliability
(Grewal, Cote and Baumgartner 2004). For all samples, adequate sample size, discriminant
validity, and sufficiently high AVE and CR exist, but the threat of multicollinearity cannot be
eliminated completely.
164
Context-Specific Attributes
It was not possible to use exactly the same questions across all the samples, because each had
to give adequate customization to specific industry nuances. Across the product construct there
are 56 different questions. For the price construct, there are 25 different questions. There are
16 questions used for the promotion/personal selling construct. And 25 different questions are
used for the place/logistics construct. As a result, the questions are not entirely consistent
across the samples. Completely standardized surveys like SERVQUAL (Parasurman, Zeithaml and
Berry 1988) have suffered because of their inability to be specific enough and take into account
heterogeneity of different industries (Parasurman, Zeithaml and Berry 1991). The questions for
the surveys were developed during interviews with customers of the sponsoring firms. The
surveys were developed to provide an accurate account of the attributes used to select and
evaluate suppliers. If the surveys were developed with the premise that differences between
industries would be examined, then some common and some industry-specific questions may
be used (Baumgartner and Steenkamp 1998).
165
5.5. Opportunities for Future Research
The premise of this research was to investigate the impact of place/logistics versus the other
components of the Marketing Mix on customer satisfaction and indirectly share of business.
While this research question was investigated, several other issues emerged that warrant
further investigation. Each sample represents the situation at the point in time when the data
were collected. While A-2 and A-3 are two surveys that dealt with the same product, three
years apart, they are analyzed as two samples. An opportunity for future research is to use
longitudinal data to investigate how the impact of the Marketing Mix changes over time for a
particular industry.
The current model evaluates primary, secondary and tertiary suppliers in an equal manner.
However, it could be determined how the effect of these attributes varies between primary
suppliers and other suppliers. Establishing empirical evidence of logistics management’s impact
on business outcomes improves logistics managers’ understanding of their contribution to
financial performance. Another source of variance that could be investigated is the difference
between small specialized stores and larger mass retailers in the sporting goods samples.
In this research structural modeling was used to determine the current impact of the
components of the Marketing Mix on customer satisfaction and indirectly on share of business.
It does not consider areas where all suppliers might be underperforming, where an
improvement in the performance of one supplier might lead to increases in customer
satisfaction and share of business.
166
5.6. Implications for Theory
Across the nine samples, it was determined which of the components of the Marketing Mix had
a significant impact on customer satisfaction and indirectly on share of business. One piece of
prior evidence that points to logistics having a stronger impact on share of business is the work
by Sterling and Lambert (1987). The place/logistics construct has a significant impact on
customer satisfaction in three samples (A-1, B-1, and B-2). Similar to previous results
(Daugherty, Stank and Ellinger 1998, Stank, et al. 2003), the current study does point to a
complex relationship between the place/logistics construct and customer satisfaction and firm
performance. Logistics attributes, together with the other components of the Marketing Mix
are an important driver of customer satisfaction and share of business and the impact varies by
industry.
The impact of promotion/personal selling on customer satisfaction is evident in five samples,
which is the most consistent relationship of any component of the Marketing Mix. This is not a
surprising result because the salesperson can influence the expectations of the customer. A
salesperson that can set realistic expectations has a stronger effect on customer satisfaction
than one who overpromises. The salesperson can be regarded as a promise-maker and product
and place/logistics are fulfilling that promise. Price has a different relationship because it is
subject to promise-making during the negotiation and serves as a background to evaluate
product and place/logistics.
167
5.7. Implications for Practice
Due to the general nature of this research, no specific recommendations to managers are made,
but general suggestions can be asserted. If specific recommendations for a company are
required, then an analysis that charts importance and performance of several attributes versus
competitors is preferable (Stock and Lambert 2001). This research is focused on a high-level
holistic perspective in each cross-sectional sample. This research was focused on what was
leading to customer satisfaction and share of business. Business people are more interested in
what could impact customer satisfaction and share of business if performance was improved.
The result that promotion/personal selling has a strong impact on customer satisfaction and
only a minimal direct impact on share of business has also implications for managers. It is
possible that salespeople, who customers regard as good, do not drive higher share of business
directly. It is important at this point to remember that each sample only provides a snapshot in
time. No longitudinal analysis is performed. Samples A-2 and A-3 are on the same product
three years apart, and it seems that in the later sample the effect of promotion/personal selling
is not significant as it was in the previous sample.
The construct that is controlled by the logistics function – place - has a strong impact on firm
performance in some samples. Managers must be aware that in those samples better logistics
performance can lead to better outcomes for the firm. This is especially important in those
samples where the place/logistics construct is significant. In those samples one or more of the
other components of the Marketing Mix are significant and it is important that the marketing
168
and logistics functions coordinate their efforts. Firm performance is influenced by activities that
take place in these two functions.
In four of the nine samples retailers are surveyed (B-2, D-1, D-2, and D-3). There is one
distinction between the retailers in the electronics industry and the sporting goods industry.
Video tapes are sold in larger electronics or general retailers, and some of the golf equipment is
often sold in smaller more specialized stores. It must not be assumed that differences in results
are only explained by differences in industries because the type of retailer may also be a reason
why different components of the Marketing Mix are significant.
For managers interested in understanding which factors drive customer satisfaction and share of
business in their companies, this research shows the importance of collecting data on their
actual customers and analyzing it. Relying on data collected in other industries or on other
products cannot provide management with the data necessary to make informed decisions that
are relevant in their specific situations. Even looking at data that is several years old, may prove
troublesome if the differences between A-2 and A-3 are considered.
169
5.8. Overall Conclusions
The analysis of the nine samples revealed the importance of individual components of the
Marketing Mix varied by situation. It was not possible to determine a generalizable pattern in
the components of the Marketing Mix that has a significant impact on customer satisfaction and
indirectly on share of business. Other efforts to create a general framework for customer
service did not succeed (Parasurman, Zeithaml and Berry 1991). This research provides further
evidence that caution should be used in generalizing findings based on one study. The results
that were significant in one situation may not be significant again, but without replicating the
research it would not be possible to know.
170
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