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Abstract # 008-0385 Delineating the “Ease of Doing Business”
Construct within the Supplier-Customer Interface: The Case of Thailand and South Korea
Mohammad Asif Salam
School of Management, Assumption University, 24 Ramkhamhaeng Road, Bangkok 10240, Thailand
E-mail: [email protected], Phone: ++66816392627
Abstract The purpose of current study is to examine an extension and modification of the ‘ease
of doing business’ construct as suggested by Stading and Altay (2007) (p.37) in an Asian
context. The current research provides an insight into the “ease of doing business” construct.
An analysis of survey responses from supply managers in the automobile industry was used to
test the proposed “ease of doing business” construct (EODB), which includes three
dimensions – Information and Material Services, Financial Contract Services and Personal
Relations Services. Personal interviews, and a review of the literature on relationship
marketing in business-to-business transactions was used to develop a conceptual model for
profiling relational interactions associated with a successful supplier-customer interface. A
survey was conducted with the supply management and purchasing executives from
automobile industry in Thailand and South Korea to explore their relationships with suppliers.
The findings suggest that the Information and Material Services and Personal Relations
Services relating to decision-making by the respondent’s firms are the significant predictors
of supplier-customer interface service performance (EODB). This study demonstrates an
application of the EODB model to explaining the supplier-customer interface performance in
a different cultural setting. The results support a link between a customer’s assessment of a
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supplier’s “ease of doing business” and the amount of business conducted with that supplier.
The attributes supported by this research provide the means for managers to improve and
grow business with customers in the Thai and South Korean automotive industries.
Keywords: Ease of Doing Business, Supplier-Customer Interface Performance, Customer
Satisfaction, Automotive Industry, Thailand, and South Korea.
Introduction Supply chain and logistics management has become an emergent strategy for a
developing country, like Thailand. Low labor cost – as a competitive factor – is no longer
sufficient for surviving in a highly volatile and competitive economy. However, at a time
when the concept plays a key role in the development of Thai industries, and a number of
research studies have been conducted, there is still no effective integration of the knowledge
in the field. The actual needs, trends and directions in supply chain management are still
unclear.
Supply chain management is generally regarded as the integration of the flows of
material, information and financing around three competitive priorities: price, delivery and
quality. However, sustained competitive advantage in a supply chain is not achieved through
these three dimensions alone. Supply chain partners generally acknowledge the pivotal
importance of relationship management, but often default to conventional measures that are
founded on easily quantifiable criteria such as price, delivery and quality. These measures,
however, do not necessarily reflect the complexities of the buyer-supplier interface.
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While suppliers constantly seek to improve their relationship with customers,
simultaneously customers attempt to objectively evaluate supplier performance. This duality
leads to a question frequently encountered in supplier evaluation assessments, and generally
referred to as ‘the ease of doing business’ (EODB). This refers to how easy it is to do business
with a supplier, and is a measure of the absence of barriers and hurdles in the buyer-supplier
interface (Vandenbosch and Dawar 2002; Goodman 2004). Thus, from the customer’s point
of view, EDOB is a customer service/satisfaction measure. From the supplier’s side, EODB is
often referred to as ‘customer relationship management’ (CRM). However, EODB is neither
clearly defined nor well understood by supply chain partners (i.e. the supplier is not able to
understand the reported measure, and still more seriously, customers often cannot explain
their own responses). The ease of doing business concept is infrequently discussed in the
academic literature and then often only in an informal way. In one of those rare accounts of
the concept, the difficulties are summarized by Bowersox and Closs:
Although most senior managers agree that customer service is important, they find it
difficult to explain exactly what it is and what it does. Two interpretations commonly
expressed are easy to do business with and sensitive to customer needs. While such
generalizations have appeal from qualitative perspective, it is difficult to interpret
what “easy to do business with” means for firms that deal with numerous customers
on a daily basis (Bowersox and Closs 1996, p. 66).
At first glance, an informal statement like EODB appears to minimize the complicated
process of assessing the depth and breadth of business relationships. In reality, an
appropriately defined EODB construct may actually enhance the assessment process. The
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distinct meaning of this concept, however, continues to elude practicing managers, creating
the need to postulate a precise construct and conduct exploratory research. The challenge for
supply managers comes from not knowing what to improve when customers indicate low
levels of EODB. Their frustration increases when, having received low scores from customers
on EODB and having taken corrective action on some perceived aspect of their business, they
continue to receive low scores on the next customer survey.
This study aims to explore the underlying assumptions behind the expression of
EODB. The objectives of this study are twofold. The first objective is to understand whether
EODB plays a role in predicting satisfaction within the supplier-customer interface. The
second is to suggest a framework for the measurement of EODB and to provide some context
for the EODB construct. What factors make up EODB? What should business managers try to
change if they want to want to improve their EODB rating from a specific customer? The
current research proposes a construct for EODB based on survey results from the automotive
industry of two major Asian economies, Thailand and South Korea. A general background on
the use of EODB and how it is linked to managing customer-supplier relationships is initially
presented. This is followed by a discussion of the relationships conceptualized and the
methodology used. The results of the analysis are discussed, and the paper culminates in a
working definition for EODB and its managerial implications.
Why Apply EODB in the Asian Context? Researchers can only have strong confidence in a theory when multiple attempts are
made to falsify that theory (Popper, 1962). In addition, as noted by Carter (2004), replication
research is needed in order to advance the state of knowledge in the discipline of supply
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management. More research is needed to understand the performance outcomes of relational
exchanges. This is particularly true in the case of the relationships between manufacturing
firms and their suppliers, as manufacturing firms spend more than half their income on
materials (Burt, et al., 2002; Leenders, et al., 2002). As noted by Anderson (1995) although
“the essential purpose for a customer firm and supplier firm engaging in a collaborative
relationship is to work together in ways that add value or reduce cost in the exchange
relationship between the firms…[h]ow well do practitioners or academics understand this
event, or the mechanisms through which it occurs?” Stading and Altay (2007) pointed out
“what should be central to understanding supplier-customer interface performance (EODB)”.
However, they left such an examination of the supplier-customer interface performance
(EODB) for future research.
An Overview of Thai and South Korean Economy Today’s high competitive global market climate drives Thai industrial demand for
supply chain and logistics management. Running the old style management and competing
with competitors only on the sales and marketing side will not work for Thailand. The
advantages of lower labor costs and cheaper raw materials than other exporting countries are
disappearing. Markets for Thai products in most sectors are gradually being lost to other
countries. To stay competitive, companies in Thailand have started cultivating supply chain
and logistics management. The corporate crusade to gain competitive advantage using new
supply chain and logistics management methods is extending to all Thai industry. (Research
direction and knowledge management of supply chain and logistics in Thailand,
www.kmutt.ac.th/gmi/2005/mambo/images/stories/LRN2003.pdf)
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Thailand is one of Asia’s manufacturing powerhouses, and a periodical entitled
Logistics Manager provides a vital source of local, regional and international information for
shippers, carriers, and logistics providers in the country’s working language. Published every
two weeks, Logistics Manager has been an integral part of the country’s shipping and
logistics industry since 1997.
(http://vector.menloworldwide.com/vectorscm/en/pressroom/prarchives/10_13_2004_1.shtml)
EODB is important to the Thai and South Korean automobile industry because, as a
result of research and experience, supply chain and logistics management is now recognized
as a key competitive area for these two Asian economies. The presence of manufacturing
intensive end-user sectors such as automotive manufacture has fueled the growth of these
countries, according to a report by Frost & Sullivan.
Demand for welding equipment will continue to grow as strong global demand drives
South Korea’s export-focused industrial sectors with the principal demand originating from
the shipbuilding and automotive sectors. However, the loss of international competitiveness in
relation to China threatens to have a detrimental effect on export demand, on which South
Korea is heavily reliant. The impact on some industries could be particularly severe and could
dampen demand for several capital equipment inputs, including welding equipment.
Frost & Sullivan Research Analyst Titus Hocevar said that, “China was attracting
most international investments in the Far East region at present, and at the same time, South
Korea’s automotive, shipbuilding and other industrial sectors were under constant competitive
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pressure from other South-east Asian economies, meaning that they had to continually invest
in new capital equipment to remain competitive”.
(http://www.manufacturing.net/article.aspx?id=9460&terms).
The year 2005 marks the 50th year since Korea first began manufacturing
automobiles. From the time when Korea started with a simple KD assembly process in the
1960s it has grown to be the only nation, among those that started developing an auto industry
after World War II, which is capable of manufacturing cars with its independently-developed
technologies, thereby becoming the world’s sixth-largest producer today. As of the end of
2004, the number of cars manufactured in Korea reached 3.47 million, of which exports
amounted to 2.38 million units, making Korea the world’s 6th-largest manufacturer (5.4%),
after the United States, Japan, Germany, China and France. In terms of domestic consumption
it ranked 10th in the world by consuming 1.1 million units in 2004 When it comes to
manufacturing scale, Korea – situated in the hub of Northeast Asia between China and Japan
– has been playing a leading role, together with emerging China, in the region. The combined
manufacturing capacity of these three countries puts them in first place in terms of
manufacturing output and third in terms of the scale of domestic consumption in the world,
when compared by regional unit.
(http://www.investkorea.org/InvestKoreaWar/work/ik/eng/bo/bo_01.jsp?code=1020202)
Literature Review Ease of Doing Business (EODB) has been a bit like the weather – everybody talks
about it, but doing something about it is another matter. While you cannot change the
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weather, you can equip yourself and your organization not only to cope with it, but to thrive
within it.
Practical Considerations of EODB
While academic studies involving EODB have been limited, publications targeting
practitioners have recognized the growing significance of the concept in customer
satisfaction. Hammer (2001) defines EODB by arguing that it has nothing to do with
products, features, quality or price. Rather, it is a measure of how “complex, problematic and
fatiguing” it is for a customer to conduct business with the supplier. Similarly, focusing on
customer retention, Coyles and Gokey (2002) imply that EODB is separate from price and
quality as purchase criteria. They describe customers reassessing their purchases based on
price, performance and the ease of doing business with a company. Likewise, Robinson and
Kalakota (2004) suggest that having a perspective on customers’ interaction with internal
services and aligning these service capabilities to that interaction allows a company to be
perceived as one with which it is easy to do business.
Hammer supports the case of EODB further with the argument that it enhances
customer relationships by making customer interfaces more efficient and effective. In one
recent example of enhancing customer relationships with EODB (similar to other high-profile
cases from companies like Staples and Geico that actually utilized EODB approaches in their
advertising campaigns), National Grange Mutual received the highest rating for its EODB
among property and casualty insurance providers. This rating originated from a nationwide
survey of property and casualty insurance carriers (Insurance Journal 2003). The factors being
considered by respondents included responsiveness, flexibility, timeliness, technical support
and effective use of technology.
Evolution of an EODB Construct
The proposed construct uses the predictions of disconfirmation theory to examine the
role that EODB could eventually play in the customer-supplier interface. In disconfirmation
theory, customers compare actual performance levels of a product or service to expected
performance levels (Oliver 1980). Customers then make varying degrees of behavioural
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decisions. The decisions range from approach behaviours (verbal positive reinforcement) and
confirmation judgments (performance as expected) to avoidance behaviours, which include
behaviour that leads to the selection of alternative suppliers (Dadzie, Chelariu and Winston
2005).
This study proposes that the EODB construct contributes to measuring customer
satisfaction and is a direct result of the services provided from a supplying organization.
Lloyd (2003) argues that EODB could be a mainstay of an organization’s customer
relationship strategy. Independently, Lacobucci, Grisaffe, Duhachek and Marcati (2003)
proposed that the extent to which a customer perceives a company as one that is easy to do
business with should be a function of high quality and enhanced customer service. Response
levels are affected by the strategic attributes of three relational determinants: Information and
Material Services, Financial Contract Services and Personal Relations Services. The existence
of these three attributes could be interpreted as service excellence (Johnston 2001). According
to Johnston (2004), service excellence includes EODB. All three proposed determinants have
an effect on EODB, and they subsequently converge towards a common definition when
considered by customers (Figure 1).
Swaddling and Miller (2002) warn that, despite many attempts to correlate customer
satisfaction and customer-repurchase decisions, this correlation is complex and not yet fully
understood. They argue that linkages are not yet in place to warrant a role in strategy
formulation. They suggest that EODB may play a contributory role in establishing that link
and that an EODB construct with theoretical underpinnings is necessary.
Conceptual Model
Theoretical Connection of EODB with the Customer interface
The relationship between customer service and customer satisfaction is rich and deep.
The introduction of the SERVQUAL measure stimulated a stream of continuing research
measuring consumer perceptions and satisfaction with service quality (Parasuraman, Zeithaml
and Berry 1988; Parasuraman, Berry and Zeithaml 1991). This literature stream emphasized
the role of customer service psychometric properties, including such factors as reliability,
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responsiveness, assurance and empathy. The customer service literature specific to logistics
includes traditional measures of success which centre on delivery, including availability,
timeliness and delivery quality (e.g. Mentzer, Gomes and Krapfel 1989; Dahlstrom, McNeilly
and Speh 1996; Emerson and Grimm 1996; Morris and Carter 2005).
Figure 1: Model Linking Relations Services with Supplier’s Ease of Doing Business
These dimensions can be expanded to include a comprehensive list of supplier
evaluation criteria in the areas of customer relationship and communication factors (Simpson,
Siguaw and White 2002). Frazier (1983) argued that the value of these relationships is
demonstrated when suppliers use support services. Supporting this view, Hunt and Jones
(1998) suggested that subsidiary factors, such as after sales support and total service
capability, play an important part in selecting suppliers. They added that these factors
highlight criteria which may affect ease of doing business between customer and supplier.
Managing the supplier-customer interface has theoretical linkages to sustainable
competitive advantage (Tscng and Huang 2007). Supply chain revenues are not optimized in
the absence of loyalty, satisfaction and anticipation of customer needs (Verhoef 2002).
Information and Material Services
Financial Contract Services
Personal Relations Services
Supplier-Customer Interface Service
Performance (EODB)
H1
H3
H2
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Elements of these determinants include long-term commitment of a customer to a supplier and
favourable attitudes towards that supplier on the part of the customer (Cronin and Morris
1992; Dick and Basu 1994; Morgan and Hunt 1994). It is through this commitment that the
repurchase intention and the customer’s willingness for relationship renewal is reinforced
(Kumar, Scheer and Steenkamp 1995). As a stop-gap, managers already accept the application
of a poorly defined concept of EODB as a minimum predictive measure for those more
difficult to quantify dimensions of longitudinal commitment and repurchase intention
(Stading 2000).
Information and Material Services
Customer satisfaction is a complex construct and has been defined in various ways
(Besterfield, 1994; Barsky, 1995; Kanji and Moura, 2002; Fecikova, 2004). Recently,
researchers have argued that there is a distinction between customer satisfaction as related to
tangible products and as related to service experiences. This distinction is due to the inherent
intangibility and perishability of services, as well as the inability to separate production and
consumption. Hence, customer satisfaction with services and with goods may derive from,
and may be influenced by, different factors and therefore should be treated as separate and
distinct (Veloutsou et al., 2005).
Managing information in a supply chain has a number of benefits and is important in
building supply chain relationships (Manoochehri 1984; Russell and Krajewski 1992). The
first hypothesis in the present study considers the individual association between EODB and
the management of information needed to help materials flow more efficiently. In a supply
chain, contact points between the customer and the supplier are critical in building a solid
relationship. These contact points and supplier functions are associated with daily or routine
order processing, supporting the availability of inventories and the making on-time deliveries.
Managing these contact points falls inside the sales function of a supplier with responsibilities
for such elements as pricing information and direct quality quotes to the customer. Daily
contact points influence the customer’s perception of how easy it is to do business with a
supplier. These are important attributes for suppliers in maintaining relationships with
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customers and should be managed carefully (Verwijmeren, van der Vlist and van Donsellar
1996; Shin, Collier and Wilson 2000; Goodman 2004).
The attributes of this determinant in the EODB framework are structured around
recurring arguments which support availability and responsiveness in the customer service
and customer satisfaction literature (e.g., Bitner 1992; Dadzie et al. 2005). Supplier
availability and responsiveness can make an impression on a customer in various ways.
Financial Contract Services
Part of a supplier’s responsibility in the supply chain includes customer awareness and
the sharing of programmes which result in mutual cost savings and financial efficiencies
gained through supply chain improvement projects. This is important for maintaining
customer relationships (Milgrom and Roberts 1988; Newnan 1991). Savings, however, are not
necessarily realized, shared or recognized without contracts. Contracts of various kinds
(formal, informal, policies, etc.) are instrumental in realizing shared benefits between
customers and suppliers. Negotiation and implementation of financial arrangements between
supply chain partners easily affect customer-supplier relationships. Contract negotiations and
those supplier functions that are associated with various points of shared benefits affect a
customer’s satisfaction level with a supplier and can affect a customer’s perception of the ease
of doing business.
The first hypothesis in the present study addresses the management of contracts.
Financial contracts focus on areas of potential contention, such as credit terms, product
quality non-conformance issues and the subsequent handling of potentially returned material.
The speed and ease of producing those contracts is addressed within the contract turnaround
and negotiation process. These logistical issues are important for suppliers in realizing savings
and maintaining relationships with customers (Berry 1980; Collier 1987; Bowen, Siehl and
Schneider 1989; Goodman 2004).
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Hypothesis 1: The information and material (IM) services determinant is positively related to
the EODB, such that the daily service contact points strengthen the EODB
rating.
Personal Relations Services
The importance of personal interaction in creating satisfied customers has been
recognized in the customer service literature (Crosby and Stephens 1987; Dwyer, Schurr and
Oh 1987). It has been shown that future sales opportunities depend mostly on the quality of
the relationship (Crosby, Evans and Cowles 1990). It is at the individual level that the quality
of the relationship between supply managers and suppliers is affected (Brennan and Turnbull
1999). Components of relationship commitment include extent to which partners are willing
to share confidential information and level of investment in the relationship, including both
current and future investment (Gundlach and Achrol 1995). The components of the
commitment dimension stem from the importance of a relationship as measured by how hard
a partner is willing to work at preserving the relationship (Morgan and Hunt 1994). Ability to
answer customer questions from a technical perspective should influence a customer’s
perception of that supplier (Hartley, Zirger and Kamath 1997).
The third determinant of the EODB framework proposed in this study is the
effectiveness of the Personal Relations Services. Services of individual attention with
attributes like on-location (outside) sales support or technical support can foster important
relations with suppliers. These types of services are a potential source of sustained
competitive advantage (Sheth and Parvatiyar 1995; Hartley et al. 1997; Shin et al. 2000). In
addition, supplier functions that impact on personalized services, such as order follow-up,
customization or Web based e-services, can influence a customer’s perception of a supplier’s
EODB (Collier 1987; Goodman 2004; Zahay and Griffin 2004; Dadzie et al. 2005). The
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following hypotheses relate to the individual association between EODB and those services
which cater to the customer at a personal level.
Hypothesis 2: The financial contract (FC) services determinant is positively related to the
EODB, such that those services strengthen the EODB rating.
Hypothesis 3: The personal relations (PR) services determinant is positively related to the
EODB, such that those services strengthen the EODB rating.
The determinants affecting the perceived measures of EODB begin with the
transference of necessary information between customers and suppliers; they include the
financial alignment of these provisions and the technical support and follow-up for the
product or service. These are considerations in building a framework for EODB. The
hypotheses presented in this research are tested to identify the significant contributions of
each attribute to the EODB (Dess and Davis 1984; Johnson and Fornell 1991).
Methodology This study seeks to isolate those attributes of EODB which supply managers use to
evaluate suppliers. The identifiable benefits of this research include understanding customer
behaviour patterns predicted by the EODB customer response. Given that EODB predicts
behaviour patterns, identifying which of those attributes influences the EODB response
subsequently affects how managers can utilize this measure to grow their business with a
given customer.
Process Measuring the proposed construct establishes the foundation for theory development
around that concept. A construct is needed for theory construction when a concept moves
from case studies and anecdotes to testable models (Bagozzi and Fornell 1982; Sheth and
King 1994). Practical case studies of EODB, as well as anecdotes and construct measures
already exist. Therefore, constructs developed in an earlier study by Stading and Altay (2007)
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should be extended and tested. In the present studies this is done, applying those constructs in
an Asian context to contribute towards the development of grounded theory on EODB.
Pilot Study A pilot study was used to collect data through initial questionnaires with subsequent
follow-up discussions. Discussions were recorded to identify a filtered list of potential
attributes of EODB. This information was then used to build the survey instrument used in
this study. To measure the instrument for content validity 12 industry personnel formed three
groups during the pilot study. Supplier personnel and customers from the automobile
components industry were represented in the focus groups. Supplier representatives included
sales professionals, operational personnel, and management executives. Participants from the
customer side were typically professionals with supply management responsibilities.
Participant responses were analyzed using multi-attribute utility theory. This approach
allows the choices and alternatives to expand beyond those initially considered (Keeney and
Raiffa 1976). Participants were asked to respond to a scenario presented to them by selecting
their preferences. These preferences were used to clarify questions on the survey instrument.
Survey A customer survey was designed based on the findings of the pilot study and sent to
procurement professionals of 60 selected companies that purchase components in the
automotive industry. The supply managers were asked to evaluate the importance of different
aspects of EODB using a 6-point rating scale. These EODB survey questions were aligned to
the determinants and attributes specified in the hypotheses. In addition, when considering a
supplier, respondents were asked to indicate the nature of their relationship and the level of
trust involved. These responses were used to measure commitment to their relationships.
After an initial contact by telephone a total of 60 respondents out of 200 (100 each in
Thailand and South Korea) accepted our invitation to participate in the survey. Accordingly,
survey instruments were sent out to all these respondents. 52 were surveys returned, but only
48 of them had usable responses. Out of the total usable samples 30 (62.5%) were from
Thailand and 18 (37.5%) from South Korea. Over 60% of the respondents had more than 10
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years of experience in their professional procurement positions. Professional supply managers
typically purchased through a variety of mechanisms, including annual contracts, competitive
bid processes or simply re-ordering as needed. All respondents indicated that they use
multiple suppliers when purchasing automotive components. Usable responses represented
80% of the companies that had the surveys mailed to them. The following industry segments
were represented by the responses: 35% body parts, 24% contract manufacturers, 16% audio
components and 15% from industrial and manufacturing control industries. The remaining
10% of respondents were from a variety of other industries.
Non-response bias Non-response bias can exist with survey research, even with relatively high response rates
(Lohr, 1999). One commonly employed means of assessing non-response bias is to compare
the answers of early survey respondents to those of late respondents (Lambert and Harrington,
1990). The assumption here is that late respondents are more characteristic of non-
respondents than are early respondents (Armstrong and Overton, 1977). The study used a
naturally occurring breakpoint between the two response waves of the survey to represent
early versus late respondents, and computed a multivariate t-test along the key study variables
to assess whether significant differences existed between the two groups. The results suggest
that early respondents did not demonstrate statistically significant differences from late
respondents.
As an additional test for non-response bias, 20 non-respondents were randomly selected and
sent an abbreviated form of the questionnaire via overnight mail. Follow-up phone calls were
also made to these non-respondents, to ensure that all 20 of the selected non-respondents
completed and returned the abbreviated survey (Lohr, 1999). A second multivariate t-test was
then computed, comparing the responses to the full-length questionnaire with those of the
abbreviated questionnaire. There were no significant differences between respondents and
non-respondents.
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Key informant issue The study took two measures to ensure that survey respondents were knowledgeable
(Campbell, 1955) and appropriate, or key, informants. First, it addressed the survey to
purchasing managers and executives, as the results of the pre-test and pilot test indicated that
personnel at the manager level or higher were capable of answering the study’s questions
(John and Reve, 1982). In addition, the survey included questions that addressed the
respondents’ knowledge and capacity to answer the scale items that measured the study’s
constructs (Kumar et al., 1993). These questions included the number of years that the
respondents had been involved with the purchasing function, questions about their
involvement in socially responsible initiatives and their degree of involvement in purchasing.
Two informants rated themselves as only ‘somewhat involved’ in PSR initiatives, and had
been involved for one year or less. These two were eliminated from further analyses.
Analysis and findings The findings of the study are presented on the basis of two types of analysis: (1) Descriptive
Analysis, and (2) Inferential Analysis. The descriptive part of the analysis provides an
overview of the data. Table 1 depicts the mean, standard deviation and reliability indices of all
the constructs in this study. The average response to the scales varied from a low of 1.71 to a
high of 3.06, which means that the average responses show the relative importance of each of
the dimensions of EODB this study measured. The study also found the measurement scales
to be reliable.
According to Nunnally (1978), the cut-off criterion for Cronbach’s Alpha should be 0.70. All
of the constructs in this study met the minimum reliability requirements, as indicated in Table
1, alpha ranging from a low of 0.725 to high of 0.826. A comparison of the Cronbach value
and the correlations among the variables revealed that the Cronbach Alpha values are bigger
than correlations the among the variables. Hence, it can be said that there is decomposition
validity of the scales (Gaski, 1984). When the results of correlation between the variables are
taken into account, both between the variables and with PSR, there is a positive value at p <
0.01 level. These results may therefore be considered significant in statistical terms.
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Table 1: Summary of means and standard deviations of the scale items
Information and Material Servicesb (.826)a Mean S.D. • On-time deliveries 1.83 1.10 • Inside sales responsiveness 2.42 1.23 • Pricing and negotiation 2.23 1.12 • Quote quality and turnaround 1.71 0.99 • Inventory Availability 2.04 1.17
Financial Contract Servicesb (.725) a Mean S.D. • Contract turnaround time 2.06 0.78 • Contract negotiations 2.15 1.11 • Nonconforming material handling 2.52 0.92 • Return material authorizations 2.50 1.05 • Web-enabled e-business servicesc 3.06 1.26 • Credit terms and credit limits 2.13 1.48
Personal Relations Servicesb (.738) a Mean S.D. • Customized operations 2.19 0.89 • Technical sales availability 2.10 0.90 • Outside sales available 2.10 1.12 • Order follow-up 1.92 1.07
Supplier-Customer Performance (EODB)b (.761) a Mean S.D. • Relationship has personal meaning (Customers and suppliers) 2.92 1.20 • Having a trusted relationship with your supplier 2.06 1.12 • Your supplier's technical product quality 2.15 0.90 • Your supplier's functional product quality 1.94 1.02 • Your supplier has excellent experience 2.31 1.09
aConstruct reliability (Cronbach's Alpha) shown in parentheses (Fornell and Larcker 1981) b(1=very important and 6=very unimportant) cThis item was proposed to be a part of the personal relations construct, but it loaded on the financial contract construct.
Inferential analysis provides findings and discussion based on the results of the test of the
hypotheses. The study tested the hypothesized model in Figure 1 using multiple regression
analysis. The main objective of this test was to examine the predictive power of the
independent variables in explaining the dependent variable.
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ANOVAb
13.210 3 4.403 21.266 .000a
9.110 44 .20722.320 47
RegressionResidualTotal
Model1
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), PRS, FCS, IMSa.
Dependent Variable: EODBb.
Coefficientsa
.516 .263 1.958 .057
.263 .109 .330 2.426 .019 .502 1.992
.208 .121 .221 1.728 .091 .568 1.760
.382 .117 .372 3.251 .002 .707 1.415
(Constant)IMSFCSPRS
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig. Tolerance VIFCollinearity Statistics
Dependent Variable: EODBa.
Model Summaryb
.769a .592 .564 .45503 .592 21.266 3 44 .000 1.715Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change StatisticsDurbin-Watson
Predictors: (Constant), PRS, FCS, IMSa.
Dependent Variable: EODBb.
Table 2: Regression Model Summary
Table 3: ANOVA Results
Table 4: Regression Coefficients
Discussion Jain (1994, p. 173) suggested, the goodness of fit of the multiple regression model is
measured by R2. R-square commonly referred to as the coefficient of determination, which
tells us how well the regression equation fits the observed data (Jain 1994, p. 168). The
variance explained (R2) by the model is a good indicator of the fit of the data to the
hypothesised model. In this study, R2 is 0.564, and this can be interpreted as stating that the
amount of variance explained by the regression model is 56.40%, which is acceptable in
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behavioural research. The result from the regression analysis in the table 2 indicates that the
regression equation fits the observed data well.
Jain (1994, p. 173) suggested that, “the closer R-square (coefficient of determination)
is to 1 the better is the fit of the model to the observed data”. Also, the regression model (table
4) suggests that two out of the three predictor variables (Information and Material Services
and Personal Relations Services) are positively and significantly associated with the criterion
variable (EODB). That means it found two of the three hypothesized paths to be substantiated.
All of the variables also show acceptable amount of variance being explained by them.
The study checked all of the assumptions of regression analysis prior to conducting the
test and found the data to be normally distributed. The model has no problem of
multicollinearity. That is, the VIF (collinearity index) suggests that there is no
multicollinearity (association between the explanatory variables) between the predictor
variables. As Jain (1994) suggested, a maximum VIF greater than ten is thought to signal
harmful collinearity. This is not the case in the present study. The results from testing the
hypotheses are also displayed in Table 4, where p < 0.05 is taken to represent significant
relationships. Interestingly, two out of the three hypothesized relationships were found to be
significant at the p < 0.01 level. The exception was Financial Contract Services, for which the
relationship has not been confirmed. Among all the hypothesized paths, Personal Relations
Services were found to be the strongest predictor of EODB, followed by Information and
Material Services.
The EODB construct will continue to evolve as future research examines various
aspects of this construct. This may produce different results in different settings such as
different types of businesses, different countries or different cultures.
This study presents findings which disagree with those found in an earlier study
conducted by Stading and Altay (2007) based on US samples. In their study, all three of the
determinants of EODB were supported. These were Information and Material Flows,
Financial Contract Services and Personal Relations Services. This implies that the US
21
respondents differ from the Asian respondents on how they perceived Financial Contract
Services and how this construct was associated with the ease of doing business construct.
The results of the present study support linking two of the three proposed determinants
of EODB. Attributes showing positive association with the EODB include relational aspects
of doing business, such as negotiating contracts, providing technical support and providing
customization for the customer. As a note of caution, the predictors that were not supported
should not necessarily be interpreted as being of less importance to customers. Interestingly,
the Web e-services attribute loaded on the Financial Contract Service dimension. This was
unanticipated, but it should probably have been expected. The Web appears to be playing a
growing role not only for communicating financial terms, but also for providing the means for
business to business transaction. A Web-based e-service is certainly a dynamic and evolving
field and will undoubtedly continue to grow in significance in future studies.
Implications
Several implications may be drawn from the present study. First, this study presents an
attempt to conceptualize and test a construct based on the input of various authors who have
discussed contributing factors of an enigmatic customer-supplier measure. This work has been
conducted from an Asian perspective. Second, the results support a link between a customer’s
assessment of a supplier’s “ease of doing business” and the amount of business conducted
with that supplier. Third, the attributes supported by this research provide the means for
managers to improve and grow business with customers.
Future Research It would be interesting to see how the non-personal (e.g. credit terms, system
capabilities) and personal (e.g. negotiation) components of the EODB construct interact with
each other and contribute to the satisfaction of customers. In addition, customer relational
attributes should be examined for influence on EODB. The initial indications from this study
show that customer relationship management (CRM) attributes, like negotiating contracts and
providing technical services, influence this rating. These two findings, EODB being tied to
the percent of business conducted with a supplier and consisting of CRM-like attributes,
22
together, strongly indicate that EODB may be tied to customer satisfaction. This is a
suggestion that should be further investigated in the future.
Completing a study in one industry, while providing convenient foundation for the
research, also represents an inherent limitation for generalizing the findings. EODB appears to
be a complicated measure that requires additional clarification, but because of this
prominence in practice, both executives and academicians should work towards the further
elaboration of this measure. A plethora of research in marketing has been devoted linking
customer satisfaction to customer loyalty by comparing satisfaction to expected performance
levels. This specific link remains an enigma. The present paper demonstrates a positive
relationship between EODB and personal relationship services.
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Appendix: Measurement Scales
Information and Material Servicesa
• On-time deliveries • Inside sales responsiveness • Pricing and negotiation • Quote quality and turnaround • Inventory Availability
Financial Contract Servicesa
• Contract turnaround time • Contract negotiations • Nonconforming material handling • Return material authorizations • Web-enabled e-business services • Credit terms and credit limits
Personal Relations Servicesa
• Customized operations • Technical sales availability • Outside sales available • Order follow-up
Supplier-Customer Interface Service Performance (EODB)a
• Relationship has personal meaning (Customers and suppliers) • Having a trusted relationship with your supplier • Your supplier's technical product quality • Your supplier's functional product quality • Your supplier has excellent experience
a(1=very important and 6=very unimportant)
