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Dependence Asymmetry and Relationship Outcome Evaluation in
Buyer-Supplier Relationships
An empirical investigation of the influence of relative dependence on conflict, satisfaction, commitment and trust.
Research Master Thesis of BSc Vivian A.M.E. Rutten s971271
June 30, 2010
Faculty of Economics and Business Administration, Department of Organization & Strategy and CentER Tilburg University
Warandelaan 2 P.O. Box 90153 5000 LE Tilburg
The Netherlands tel. +31-13-4662315
Supervisor: Prof. Bart Vos, Dep. of Organization & Strategy, Tilburg University
Acknowledgements: I would like to thank Dr. Mark vd Vijver, Prof. Bart Vos and Dr. Zi Lin He for their useful insights and comments provided during the writing process. I thank my parents Carla and Prof. Victor Rutten for five years of financial and mental support. I would like to thank my partner Anne Kees van Es for slowing me down when needed. Finally, I would like to thank Marcel Bon from NEVI for making available a list of respondents.
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Abstract: This study investigates the relationship between joint dependence and dependence asymmetries, and relationship outcome evaluation. The study builds on and contrasts the work of Kumar et al. (1995) and Gulati and Sytch (2007). We argue that it is necessary to split up dependence asymmetry into buyer dependence advantage and supplier dependence advantage. Findings indicate that joint dependence has a positive direct on commitment, trust, satisfaction and conflict. This last result provides an indication of a ‘dark side’ of close relationships. Moreover, buyer dependence advantage directly positively influences buyer evaluation of commitment only. Supplier dependence advantage negatively influenced trust, commitment and satisfaction, and positively influenced commitment.
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Contents
I. Introduction ........................................................................................................................ 4
II. Literature Review ............................................................................................................... 6
Buyer-supplier relationships .................................................................................................. 6
Relationship outcomes ........................................................................................................... 8
III. Hypotheses Development ................................................................................................ 16
Joint Dependence ................................................................................................................. 17
Buyer Dependence Advantage & Supplier Dependence Advantage ................................... 22
IV. Method ............................................................................................................................. 27
Data ...................................................................................................................................... 27
Survey Design ....................................................................................................................... 27
V. Results .............................................................................................................................. 32
VI. Discussion ......................................................................................................................... 38
VII. Implications, Limitations & Future Research ................................................................ 42
References ................................................................................................................................ 45
Appendix A – Descriptive Statistics .................................................................................. 49
Appendix B – Model Analyses Outcomes ........................................................................ 51
Appendix C - Survey.......................................................................................................... 58
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I. Introduction
The nature of buyer-supplier relationships has changed over time. It became more and more
common for relationships to be long-term oriented and of a strategic nature (Ganesan
1994). Firms are involved in fewer, but increasingly significant relationships (Anderson and
Narus 1990). This has triggered many researchers to investigate the quality of these
interorganizational relationships (Dorsch, Swanson and Kelley 1998; Naude and Buttle 2000;
Hennig-Thurau, Gwinner and Gremler 2002; Woo and Ennew 2004). The three most
commonly mentioned constructs of relationship quality in the literature are trust,
commitment and satisfaction (Walter, Müller, Helfert and Ritter 2003). These three
constructs together with conflict levels in a relationship are the most commonly linked
relationship outcomes (Geyskens, Steenkamp and Kumar 1999). The type and nature of the
relationship is often based on the degree of dependence of both partners on each other or
on the relationship (Dwyer, Schurr and Oh 1987; Heide and John 1988; Gundlach and
Cadotte 1994; Gulati and Sytch 2007; Ryu, Arslan and Aydin 2007). Some authors have
investigated effects of perceived interdependence on dealer attitudes (Kumar, Scheer and
Steenkamp 1995) and the influence of interdependency and dependence asymmetry on
performance (Gulati and Sytch 2007). Previous literature has often averaged or ignored
differences in perceptions between buyers and suppliers. It is of great importance for
managers to be aware of fluctuations in relationship outcome evaluation levels as well as
differences between partners in these levels in order to have correct knowledge on the state
of the relationship. In this study we explore a possible source of structural differences in
relationship outcome evaluation. We investigate the influence of joint dependence and
relative dependence on the perception of relationship outcomes. Relative dependence
corresponds to the level of dependence of one party on the relationship as compared to the
other party in the relationship. Relative dependence results in dependence asymmetry when
one of the parties is more dependent on the relationship than the other. Joint dependence is
the sum of dependence of the two exchange partners on the relationship. We look at
satisfaction, conflict, trust and commitment that are found to be relationship outcomes
(Geyskens, Steenkamp and Kumar 1999) specifically since performance at the relational level
is difficult to measure. In addition to that trust, satisfaction and commitment have shown to
be significant antecedents to relationship performance (Crosby, Evans and Cowles 1990;
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Mohr and Spekman 1994; Morgan and Hunt 1994; Zaheer, McEvily and Perrone 1998; Gulati
and Sytch 2007; Nyaga, Whipple and Lynch 2010). Moreover, by measuring several
relationship outcomes we are able to describe the causal mechanisms through which joint
and dependence asymmetries influence relationship outcome evaluation and possibly
indirectly exchange performance. Our main research question is: How do joint dependence
and dependence asymmetry influence relationship outcome evaluation? Moreover, previous
literature has found mixed evidence on whether dependence asymmetry is a construct that
is of negative influence to relationship quality in general (Kumar et al. 1995) or that it is of
importance to point out different influences on relationship quality when the buyer or
supplier has a dependence advantage over the other (Andaleeb 1996; Kumar, Scheer and
Steenkamp 1998; Gulati and Sytch 2007). This brings us to our second research question:
Does distinguishing between buyer and supplier dependence advantage provide a better
explanation for differences in relationship outcome evaluations than solely investigating the
influence of dependence asymmetry? In order to investigate this research question we test
two rival models. Our basic model investigates the two possibilities for relative dependence
and dependence asymmetry; buyer dependence advantage and supplier dependence
advantages (i.e. less relative dependence/more relative dependence for the buyer). The
second model tests the possibility that dependence asymmetry alone provides a sufficient
explanation for the relationship between relative dependence and relationship outcome
evaluation.
We contribute to academic literature in three ways. The first contribution comes from the
use of a thorough measure of dependency that was developed by Gulati and Sytch (2007)
recently. This measure contains several dimensions that are linked to dependency in the
literature. By using a more elaborate and recent measure we are able to test outcomes of
previous research such as Gundlach and Cadotte (1994), Kumar et al. (1995), Andaleeb
(1996). Our second contribution is related to the scope and generalizability of the outcomes
of this research. Influential papers on dependence and interorganizational relationships
measuring specific items of relationships quality (Kumar et al. 1995; Gulati and Sytch 2007)
have focused on single industry or firms. Our sample contains cross-industry data. The data
that we use also draws better inference on which items actually measure buyer and supplier
dependence in general. The third contribution of this paper is to deepen our knowledge on
the causal mechanisms between dependence asymmetries (buyer and supplier dependence
6
advantage) and relationship outcome evaluations. This last contribution also gives important
inference for managers with respect to improvements in relationship outcomes evaluations.
In the following section we briefly review the literature on buyer-supplier relationships and
thoroughly on relationship quality and interorganizational dependence literature. For the
latter two we provide both a conceptual overview as well as a summary of the most
important empirical findings. The third section provides a theoretical framework in which we
develop hypotheses on the relationship between the dependence asymmetry and
relationship quality constructs. A basic and a rival model are presented. In the fourth section
we describe our data collection as well as methods to test our hypotheses. Section five
describes the results from our analysis. In section six we discuss the results of our study. We
conclude with an integrated overview of implications, limitations and suggestions for future
research.
II. Literature Review
In this literature review we first discuss previous work on buyer-supplier relationship
typologies. Second, we discuss the conceptual literature and empirical findings with respect
to relationship quality and outcome evaluations and interorganizational dependence.
Buyer-supplier relationships
Buyer-supplier relationships have been widely investigated in the management, marketing,
supply chain management and strategy literature. The labels or specific names for these
exchange relationships differ somewhat from each other. For example channel relationships
(Geyskens et al. 1999), working partnerships (Anderson and Narus 1990), buyer-seller
relationships (Doney and Cannon 1997), and interorganizational relationships (Ring and Ven
1994). In this study we focus specifically on buyer-supplier relationships and the dynamics of
evaluation of these exchange relationships.
Although there is no single theory of interorganizational relationships, power dependency
theory and theories of uncertainty reduction seem most appropriate in explaining patterns
7
of interorganizational exchange relationships (Galaskiewicz 1985). Moreover, buyer-supplier
exchange relationships arise out of a basic need for resources in terms of capital, goods and
services by both parties (Galaskiewicz 1985). Kraljic (1983) identified strategic items,
leverage items, bottleneck items and non-critical items that are exchanged between
organizations.
In this study we are specifically interested in the relational level and dimension of the
exchange between two partners. We now highlight two studies from the management and
relationship marketing literature that both attempted to draw a typology or portfolio of
types of exchanges and relationships between buyers and suppliers. These typologies are
based on phenomena that indicate a source of dependence asymmetry. Bensaou (1999)
took an approach at the level of the relationship. He distinguished four types of exchange
profiles within buyer-supplier relationship portfolios: Market exchange, captive buyer,
captive supplier and strategic partnership. The types of relationships are determined and
presented in a matrix (figure 1). The two axes represent supplier’s specific investments
horizontally and buyers’ specific investments vertically. Bensaou (1999) argues that the type
of relationship is highly dependent upon the degree of relationship specific investment that
both parties make for this relationship. The types of relationships that arise can be
distinguished by product, market and supplier characteristics. We would like to highlight that
Bensaou (1999) describes that captive buyer relationships are characterized by a lack of
mutual trust, strategic partnership are characterized by high mutual trust and commitment
and captive supplier relationships will show high levels of mutual trust. Lindgreen and Pels
(2002) presented four types of exchange between buyers and suppliers. The matrix they
present is also shown in figure 1 and describes the following exchange situations from a
buyer point of view in general: transactional exchange, hostage exchange, free-rider
exchange and relational exchange. At which point along the dimensions the exchange can be
placed depends upon the offer proposition and need structure of both parties. The axes
represent by the buyer’s and supplier’s paradigms of their needs and offer propositions with
respect to the unit of exchange. A mismatch of these paradigms is said to be possibly caused
by the power of one of the actors. In hostage situations, buyers are offered only a
transaction exchange while they need a relational exchange. This causes the exchange
values offered and asked for to differ in value. Therefore, hostages might be generally
unsatisfied. The other mismatch includes a buyer offering a short transaction, while the
8
seller is in need of a committed relationship. This situation offers buyers the opportunity to
free-ride in their own interest and make calculations of expected returns compared to other
sellers. The description of characteristics of the relationship types provided by Lindgreen and
Pels (2002) shows resemblance with Bensaou (1999) with respect to lower satisfaction and
trust when interests or investments divert. Heide and John (1992) related investment in
transaction specific assets with a decrease in control over the other party, unless relational
norms are high. It is this type of source for dependence, amongst others, that we would like
to investigate in this research. Further elaboration will be done in the dependence section.
Relationship outcomes
It is important for managers and academia to gain and apply knowledge on what makes
business-to-business relationships a success. One of the issues addressed in the
determination of the success of interorganizational relationships the determination of the
quality of a relationship. Relationship quality is an abstract concept that describes the
general perceived atmosphere of the relationship (Ivens 2005; Ivens and Pardo 2007). A
considerate amount of research has been done on relationship outcomes, such as
satisfaction, conflict, trust and commitment (Geyskens et al. 1999). The concept of
relationship quality has been applied in slightly different ways in academic literature. No
clear-cut definition or application can be determined. What is clear however is that
relationship quality and outcome can be seen and should be measured as a higher order
construct of several variables. Many of the variables mentioned in literature, such as trust
Buyer Captive
Market Exchange
Supplier Captive
Strategic Partnership
Low Transactions
High
Relationships
Supplier’s Specific investments
Bu
yer’
s Sp
ecif
ic
Inve
stm
ents
High
Hostage
Transactional Exchange
Relational Exchange
Free-rider
Relationships
Supplier’s Paradigm
Bu
yer’
s P
arad
igm
Figure 1 – Bensaou (1999) matrix of Buyer-Supplier relationship and Lindgreen & Pels (2002) matrix of exchange situations.
Bensaou (1999) Lindgreen & Pels (2002)
9
and satisfaction, are important in developing long-term buyer-supplier relationships
(Ganesan 1994). The constructs mentioned vary from very broad models including
communication, co-operation, commitment, trust, adaptation and interdependence (Fynes,
Voss and de Búrca 2005; Cambra-Fierro and Polo-Redondo 2008) to more focused
investigations of commitment and trust (Anderson and Weitz 1992), to satisfaction, low
opportunism and trust (Dwyer et al. 1987) or to trust specifically (Ganesan 1994).
Dwyer et al. (1987) argued that satisfaction, trust and low opportunism are characteristics of
quality relationships. Anderson and Narus (1990) determined antecedents for relationships
to “work well”. They find that relative dependence has a positive, indirect effect on conflict
and a negative indirect effect on satisfaction through the influence over the partner firm.
Trust is also found to be an important factor. Satisfaction is here the main measure of
relationships that “work well”. Crosby, Evans and Cowles (1990) investigated relationship
quality in services selling. They used constructs of trust and satisfaction to measure
relationship quality and find a significant interpersonal influence on relationship quality in
their context. Mohr and Spekman (1994) described the characteristics of partnership success
as partnership attributes - amongst which commitment and trust, communication behavior
and conflict resolution techniques. Partnership success is here measured by satisfaction and
sales volume between the partners. Storbacka, Strandvik and Grönroos (1994) investigated
the dynamics of relationship quality. These authors point out that there has been too much
focus on perceptions of benefits of a relationship, and too little on the action outcomes.
They use relationship quality as an overall description of the link between perception
measures (service quality, satisfaction, intentions) and action measures (loyalty, volume,
etc.). Dorsch et al. (1998) examined the extent to which businesses use relationship quality
perceptions to differentiate their qualified vendors. They characterize relationship quality by
high levels of trust, satisfaction, commitment, customer orientation, little opportunism and
strong ethical profile of the vendor, as perceived by the customer. Naude and Buttle (2000)
developed a study to assess relationship quality. They investigate what it is that determines
the quality of a relationship. Their assessment of this constitution is based on a survey
amongst a group of senior executives. Trust, needs, integration, power and profit are
identified as important for relationships. Results indicated that there were actually four
different types of relationships based on the different values of importance attached to the
dimensions.
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Several studies investigated relationship quality as compared to relationship value and
service quality. Hennig-Thurau et al. (2002) for example, linked relationship benefits with
relationship quality. It is found that several types of benefits that can arise from a
relationship will increase satisfaction and commitment. Roberts, Varki and Brodie (2003)
measured the quality of relationships in consumer services. They measure relationship
quality as a higer order construct of commitment, trust and satisfaction. They find that
relationship quality provides better explanation for behavioral intentions than service
quality. Lages, Lages and Lages (2005) determined a relationship quality scale for the
relationship between exporters and importers. Relationship quality here is a higher order
construct of amount of information sharing, communication quality, long-term orientation
and satisfaction with the relationship. Ulaga and Eggert (2005) distinguished between
relationship value and relationship quality. They find that part of relationship value is based
on perceptions of relationship quality as compared to other relationships. Relationship
quality is again a higher order construct of trust, commitment and satisfaction.
Conflict is a concept which is very tightly coupled to the above mentioned constructs of
relationship quality. Palmatier, Dant and Grewal (2007) identified that one of the
relationship outcomes measured when authors investigated dependence theory was
conflict. By adding the notion of conflict to the more general relationship outcomes of
relationship quality, we are able to combine the stream of ‘commitment-trust’ theories and
dependence theories described by Palmatier et al. (2007). When relationships are not
successful it is likely that conflict resolution techniques fail and perceived levels of conflict
are high (Mohr and Spekman 1994). Kumar et al. (1995) measure the influence of
interdependence and dependence asymmetry on commitment, trust and conflict find
opposite directions of the relationship for conflict. Several authors find strong negative
relationships between the constructs of relationship quality and conflict (Mohr and Spekman
1994; Kumar et al. 1995; Zaheer, McEvily and Perrone 1998; Lam and Chin 2005; Avivi,
Laurenceau and Carver 2009).
Concluding, we can say that the constructs of trust, commitment and satisfaction are very
commonly mentioned in literature. In our description we see a close link with conflict,
especially when the influence of dependence is investigated (Palmatier et al. 2007). We
focus our reasoning on the relationship outcomes mentioned by Geyskens et al. (1999):
11
trust, commitment, satisfaction and conflict. In the following paragraphs we elaborate on
the conceptual literature and most important findings on these relationship outcomes.
Trust
Anderson and Narus p.45 (1990) define trust as “the firm’s belief that another company will
perform action that will result in positive outcomes for the firm, as well as not take
unexpected action that would result in negative outcomes for the firm”. Trust is generally
accepted to be good for interorganizational relationships. However, researchers should be
well aware of a possible dark side to trust (Anderson and Jap 2005; Gargiulo and Ertug 2008).
Quite a few researchers have tried to identify, categorize and measure the different natures
of trust. The development of trust in a partnership relation depends on the formation of
expectations about the motives of the partner (Doney and Cannon 1997). Morgan and Hunt
(1994) conceptualize this trust is existing when one of the partners has confidence in an
exchange partner’s reliability and integrity. They find support for trust and commitment
acting as a key mediating variables for partnership success. Moorman, Zaltman and
Desphandé (1992) put stress on trust as being the willingness to rely on an exchange partner
in which one has confidence. Their results suggest that trust and relationship quality
significantly increases subsequent exchange. Zaheer, McEvily and Perrone (1998) find
evidence by empirical investigation that trust in interorganizational exchange relations
matters to performance. Geyskens et al. (1999) focus on trust as the belief that a partner is
honest in keeping its promises and acting in the interest of the welfare of the firm. They find
in their meta-analysis that trust is influenced directly via non-economic satisfaction and
indirectly trough conflict by economic satisfaction. Moreover, they find trust to be the most
important antecedent of commitment.
A number of different catagorizations of trust have been applied in academic literature. The
first is that of Mayer, Davis and Schoorman p.712 (1995) who define trust as “the willingness
of a party to be vulnerable to the actions of another party based on the expectation that the
other will perform a particular action important to the trustor, irrespective of the ability to
monitor or control the other party”. This willingness to trust is determined by the perceived
trustworthiness of the other party. Trustworthiness is build up out of ability, benevolence
and integrity. They build their framework around existing literature on antecedents of trust.
Ganesan (1994) used a similar definition of trust that could be splitted up into benevolence
12
and credibility. A second categorization is that of cognition. McEvily, Zaheer and Perrrone
(2003) related ability to calculative- or cognition based trust, and benevolence and integrity
to non-calculative or non-cognition based trust. Klein-Woolthuis, Hillebrand and Nooteboom
(2005) discussed a similar distinction and labeled competence trust and intentional trust.
They find that trust is an important determinant of the type of contract in a partnership.
Different types of relationships set up different contracts. This links the relationship
typologies (Bensaou 1999; Lindgreen and Pels 2002) with the level of trust in a relationship.
The third categorization can be found in the level of analysis. Many studies have
made a clear distinction between interorganizational and interpersonal trust (Doney and
Cannon 1997; Zaheer et al. 1998; Fang, Palmatier and Steenkamp 2008). Although attempts
have been made to separate the types of trust, it remains difficult to pinpoint the sources of
trust (Fang, Palmatier, Scheer and Li 2008). In this research we describe trust as an overall
perception of trust in the partner firm as described by Anderson and Narus (1990). This
definition provides a general description that leaves room for interpretation of both
interpersonal as well as interorganizational influences.
Commitment
Dwyer et al. (1987) indicate commitment as an implicit or explicit whish for continuation of
the relationship between exchange partners. They argue, like Jap and Anderson (2007) that
commitment is only built in the most advanced maturity phase of a buyer-supplier
relationship. In a buyer-supplier relationship commitment exists when a partner believes
that the exchange relationship with the partner is so important and worth to put in effort for
an indefinite period of time (Morgan and Hunt 1994). Some authors make a distinction
between affective and calculative commitment (Gounaris 2005). Calculative commitment is
more cognition based and affective commitment has a stronger emotional base. Geyskens et
al. (1999) use the definition of commitment which was given by Anderson and Weitz p.19
(1992): “Commitment is a desire to develop a stable relationship, a willingness to make
short-term sacrifices to maintain the relationship and a confidence of stability in the
relationship.” As suggested above, commitment was found to be preceded by trust.
Geyskens, Steenkamp, Scheer and Kumar (1996) find that interdependence influences the
development of commitment. In the following section we see that this actually one of the
13
sources for dependence in a relationship. In this research we measure overall commitment
or intention to continue the relationship, explicitly stated by the respondent.
Satisfaction
Relationship satisfaction has previously been defined as a positive affective state resulting
from the processes within a relationship (Anderson and Narus 1990; Ganesan 1994).
Satisfaction is often used to measure the performance or success of relationships (Mohr and
Spekman 1994; Andaleeb 1996; Hennig-Thurau et al. 2002; Nyaga et al. 2010) and
satisfaction is an antecedent of trust (Geyskens et al. 1999; Caceres and Paparoidamis 2007)
as well as a result of trust (Johnston, McCutcheon, Stuart and Kerwood 2004; Cambra-Fierro
and Polo-Redondo 2008). Moreover, several studies find that trust and satisfaction variables
are distinct, but very closely related (Smith 1998; Walter et al. 2003). It is possible to
distinguish between economic and noneconomic satisfaction (Geyskens et al. 1999;
Geyskens and Steenkamp 2000). Economic satisfaction is a positive affection of an individual
towards an economic reward gained from the relationship and non-economic satisfaction is
a positive affect towards non-economic outcomes of the relationship, such as gratification
and fulfillment (Geyskens and Steenkamp 2000). This means levels of satisfaction can be
influenced by interpersonal relations with the other organization and the overall positive
affect that one might get from an organization. Since we are interested in the quality of the
relationship in this research and the respondent might be affected by both types of
relationship, we investigate overall perceptions of satisfaction with the relationship.
Conflict
Conflict is often defined as the overall level of disagreement in buyer-supplier relationships
(Anderson and Narus 1990; Palmatier et al. 2007). Conflict represents the level of tension,
frustration and disagreement in the relationship (Gaski and Nevin 1985). Assael (1969)
already found that interorganizational conflict can be constructive, given certain
circumstance amongst which a balance of power between the two partners. The influence of
conflict on relationship performance or success has also been perceived to be either
negative or positive dependent upon the conflict resolution techniques (Mohr and Spekman
1994). It is important to investigate conflict in relationships because they are found to be
possibly disastrous for performance (Lam and Chin 2005; Peterson and Behfahr 2003).
Several categorizations can be made with respect to conflict. The first one is similar to that
14
of trust. Lam and Chin (2005) distinguish between cognitive and non-cognitive conflict. They
relate cognitive conflict to a functional conflict, which is task-oriented. A dysfunctional
conflict is related to non-cognitive conflicts and negatively influences relationship
performance.
Building on several studies (Gaski and Nevin 1985; Anderson and Narus 1990; Gundlach and
Cadotte 1994; Mohr and Spekman 1994; Kumar et al. 1995; Geyskens et al. 1999) we see
that conflict, trust, commitment and satisfaction are found to be closely related and to a
considerate degree determined together by dependence dynamics (Palmatier et al. 2007).
In this research we measure the overall level of conflict within a relationship. Since conflict
might differ for organizations, given their level of joint and relative dependence.
In sum, we can say that successful relationships are characterized by high evaluations of
trust, commitment and satisfaction and relatively low levels of conflict (Geyskens et al.
1999).
Interorganizational Dependence
There are many possible dimensions along which types of relationships can be identified. In
this research we highlight the relative dependence of partners in a particular relationship.
Some of the early studies of dependence used the concepts of power and relatively low
dependence interchangeably (Assael 1969; Salancik 1979; Gaski and Nevin 1985). These two
concepts are highly related. High relative dependence of say A, gives relative power to the B.
B therefore has a dependence advantage. A dependence disadvantage arises when B is
relatively more dependent on A, than A is on B (Emerson 1962). In the remainder of this
paper we use the terms; ‘buyer dependence advantage’ and ‘supplier dependence
advantage’ in this respect.
The basis of research on interorganizational dependence was developed in both sociology
and economics and lies in resource dependency. The most important factor identified by this
approach is how much an organization is dependent on other organizations that control
resources and markets that are needed for its survival (Mindlin and Aldrich 1975). Pfeffer
and Salancik (1978) draw the interdependence of social actors to an interorganizational
level. They pose that in social systems and social interactions, interdependence exists
15
whenever one actor does not entirely control all of the conditions necessary for achieving
the desired outcomes. Moreover, they recognize that interdependence does not necessarily
have to be balanced and that it is important to organizations because it impacts the ability of
organizations to achieve their goals. Lastly, they stress the importance of the object of
exchange for the organization is the main driver of dependence. Several other drivers of
dependence have been found in the literature; asset specific investment (Heide and John
1988), alternative buyers/suppliers, amount of capital involved in transaction, additional
relationships between partners (Gulati and Sytch 2007). Salancik (1979) found that
affirmative action towards demands of partners is more likely when the partner is
dependent upon the requestor. Heide and John (1988) pose a transaction cost analysis
approach on dependence balancing, but find that it is not sufficient to explain safeguarding
of transaction-specific assets. They find that firms who have made specific asset investments
try to reduce their dependence by bonding more in the specific transaction. This makes
them more knowledgeable and increases their chances when they have to replace the
supplier. So, dependence can be influenced by organizations. In this study we focus however
on given levels of dependence. Gundlach and Cadotte (1994) find that increasing magnitudes
of joint dependence are associated with more frequent use of noncoercive strategies, lower
residual conflict and more favorable evaluations of partner performance. Moreover, they
find that lower relative dependence is associated with less favorable performance
evaluations of exchange partners and less residual conflict. Kumar et al. (1995) investigate in
the automotive industry the effect of interdependence asymmetry and total
interdependence on interfirm conflict, trust and commitment. They find that increasing
interdependence asymmetry causes a dealer’s trust and commitment in the supplier to
decrease, whereas conflict increases. Greater total interdependence is associated with
higher trust and commitment and lower conflict. The authors test whether it matters if the
dependence advantage is on part of the buyer or the supplier, but find no significant results.
Our study contributes to this study by testing a cross-industry sample and providing a more
elaborate measure of dependency. Gulati and Sytch (2007) investigate the influence of
dependence asymmetry and joint dependence on performance in procurement
relationships. They especially investigate how the effect of joint dependence on
performance is mediated by joint action, trust and the quality and scope of information
exchange. Their findings show that joint dependence enhances performance for
16
manufacturers and that this effect is partially mediated by the level of joint action and
quality of information exchange. Moreover, they investigate the influence of manufacturer
dependence advantage and supplier dependence advantage on manufacturer performance.
They find that a manufacturer’s dependence advantage diminishes its performance and
supplier’s dependence advantage has a null effect. Like Kumar et al. (1995) this study also
has a sample in the automotive industry only. The latter two studies show some contrasting
results with respect to the influence of buyer and supplier dependence advantages versus
dependence asymmetry in general. We contribute to these studies by investigating whether
these differences can be attributed to the scope of the sample or the dependence measure.
In the present study we investigate the effect of dependence (i.e. joint dependence,
dependence advantage, and dependence disadvantage) on the perceived levels of trust,
commitment, satisfaction and conflict in a relationship outcome evaluation. Moreover, we
test two rival models on the influence of dependence asymmetry versus supplier and buyer
dependence advantages. The following section describes our conceptual framework and
poses hypotheses based on this framework.
III. Hypotheses Development
Table 1 summarizes the investigated empirical findings of relationships between the
variables of interest to this study (i.e. dependence, trust, commitment, satisfaction and
conflict). These findings are used to hypothesize how dependence may affect the outcomes
of a relationship outcome evaluation. In figure 2 we present a matrix of dependence, which
is based on a similar typology as Bensaou’s (1999) portfolio of relationships. The horizontal
axis represents the dimension of supplier dependence from low to high. The vertical axis
represents the buyer dependence. The boxes supplier dependence advantage and buyer
dependence advantage correspond respectively to a buyer captive and a supplier captive
situation. Next, we discuss these three dependence states separately.
17
Joint Dependence
Joint dependence is this study is measured as the total level of dependence of the two
parties in the relationship. In figure 2 we see that although dependence can be symmetrical,
level of joint dependence differs from low to high. This implies that in the different
relationship types described by Bensaou (1999) the degree of joint dependence plays an
important role as well. We discuss empirical findings on effects of joint dependence on trust,
commitment, satisfaction and conflict and build our hypothesis on these findings.
Trust Joint dependence increases the embeddedness of firms in the relationship (Gulati and
Sytch 2007). When embeddedness is higher, in general relationships are closer. Close
relationships are characterized by high levels of trust (Anderson and Jap 2005), since parties
are more able to either base trust on ability to monitor or specific knowledge (Ryu et
al.2007). Joint dependence gives parties a basis to trust the other party, since defecting from
the relationship will harm them.
Commitment Anderson and Weitz (1992) find that idiosyncratic investments, which we
found are a base of dependence, lead to more commitment in relationships. Geyskens et al.
(1996) find that total interdependence increases both levels of affective and calculative
commitment. When firms are more dependent upon a relationship we expect that their
need to maintain this relationship is high, since they might not be able to continue their
business without this exchange relationship.
Satisfaction Andaleeb (1996) finds that dependence will lead to commitment and
Supplier Advantage
(med joint dependence)
Dependence Symmetry
(low joint dependence)
Buyer Advantage
(med joint dependence)
Dependence Symmetry
(high joint dependence)
Low
High
Supplier Dependence
Bu
yer
Dep
end
ence
High
Dependence Matrix
Figure 2 – Dependence Matrix of conceptual framework based on Bensaou (1999).
18
satisfaction. Jap and Ganesan (2000) find that interdependence magnitude increases both
levels of satisfaction and conflict as well as commitment of the buyer as perceived by the
supplier. Moreover, Gundlach and Cadotte (1994) find that higher joint dependence makes
partners use less coercive strategies and more non-coercive strategies, which leads to a
decrease in conflict and an increase in satisfaction. If joint dependence is high, both parties
have the incentive to put effort into the relationship. This effort is likely to result in
satisfaction.
Conflict Conflict is closely and negatively related to relationship quality. Kumar et al. (1995)
find that total interdependence between dealers and suppliers will lead to increased
commitment, trust and a decreased level of conflict on the dealer’s side. Put differently, a
successful relationship faces lower perceived conflict. There is however always the
possibility that a dark side of trust and close relationships arise (Anderson and Jap 2005). If
this is the case, the extreme closeness of partners might result in increases in conflict.
In general both Smith (1998) and Nyaga et al. (2010) find support for the view that dedicated
relationship investments lead to higher commitment, satisfaction and trust. The measures
for dependence in previous studies have differed from investment, switching cost, number
of alternative supplier and perceived dependence. In this study we use a measure for
dependence with a wider scope (Gulati and Sytch 2007) which takes into account several
sources of dependence that we have seen from existing literature. Concluding we expect
that joint dependence is associated with a positive evaluation of trust, commitment and
satisfaction and is negatively related to the level of conflict in relationships. Our first set of
hypotheses states the following:
H1: Joint dependence is positively related to buyer evaluation of trust. H2: Joint dependence is positively related to buyer evaluation of commitment. H3: Joint dependence is positively related to buyer evaluation of relationship satisfaction. H4: Joint dependence is negatively related to buyer evaluation of conflict.
19
Table 1 - Empirical Findings on Relationship Outcome constructs and dependence in IOR's (+ positively influences, - negatively influences, 0 null-effect)
Author(s) Relevant Contribution Research Context
Assael (1969) Balance Power (+) → Constructive Conflict Business-to-business, Automobile
Salancik (1979) Power (-) → Affirmative Action Business-to-government, Defense contractors
Gaski & Nevin (1985) Coercive Power → Conflict (+), Satisfaction (-) Business-to-Business, Manufacturer/Distributor
Crosby & Stevens (1987) Rational Evalution > Relationship Generalization Business-to-consumer, Life Insurance
Heide & John (1988) Asset specific investments → Bonding (+) → Dependence (-) Business-to-business, Manufacturing
Anderson & Narus (1990) Influence over/by partner → Conflict (-/+), Satisfaction (+/-) Business-to-business, Wholesale/Distributor
Crosby et al. (1990) Relationship quality(trust, satisfaction) → Future intention to buy (+) Salesperson-to-consumer, Life Insurance Policy
Anderson & Weitz (1992) Idiosyncratic investments (→),Perceived partner commitment (+) → Commitment (+)
Business-to-Business, Manufacturer/Distributor
Buchanan (1992) Dependence → Achieve stated objectives (+), given value of trade partners' resources and willingness to work.
Business-to-Business, Field research, Department stores
Heide & John (1992) Relational norms → Loss of control due to dependence (-) Business-to-Business, Purchasing agents/directors
Moorman, Zaltman & Desphande (1992) Perceived interaction quality (+), Trust (+) indirectly, Commitment & Involvement 0) → Research use
Business-to-Market, Research providers
Ganesan (1994) Trust and mutual dependence (+) → Long-term orientation, similarities and differences exist between retailers and vendors.
Business-to-Business, Retail
Gundlach & Cadotte (1994) Joint dependence → noncoercive strategies (+), coercive strategies (-), conflict (-), satisfaction (+). Power advantage → satisfaction (-), conflict (-), use of rewards (-), other partner: Use of rewards, promises and persuasion (+).
Business-to-Business, Simulation
Mohr & Spekman (1994) Commitment, coordination, trust; communication quality and participation; and conflict resolution technique (+) → Partnership success (satisfaction and sales volume).
Business-to-Business, Personal Computer Industry
Morgan & Hunt (1994) Commitment, trust (+) mediating → Successful relationship marketing. Business-to-Business, Tire dealers
Provan & Gassenheimer (1994) Dealer dependence → Exercised power (+): Strong in short-term relationships, weaker in long-term relationships.
Business-to-Business, Office Systems/Furniture dealers
Andaleeb (1996) Dependence → Satisfaction (+), Commitment (+). Trust (+) → Commitment (+) - regardless of dependence -, Satisfaction (+)
Experimental design, Business executives
Kumar, Scheer & Steenkamp (1995) Total Interdependence → Trust (+), Commitment (+), Conflict (-). Interdependence Asymmetry → Trust (-), Commitment (-), Conflict (+).
Business-to-Business, Automobile dealers
20
Table 1 Ctd. - Author(s)
Relevant Contribution
Research Context
Doney & Cannon (1997) Supplier firm size, Willingness to Customize, Trust of sales person → Trust of supplier firm. Sales person expertise, likability, similarity, contact frequency and trust of supplier firm → Trust of sales person.
Business-to-Business, Salesperson
Dorsch et al. (1998) Vendor status → Trust (+), Satisfaction (+), Commitment (+). Business-to-Business, Purchasing Executives
Kumar et al. (1998) Interdependence Asymmetry (0), Punitive Capability Asymmetry (+) → Punitive actions.
Business-to-Business, Automobile
Smith (1998) Relationship investment → Commitment (+), Satisfaction (+), Trust (+) Business-to-Business, Purchasing Association
Zaheer et al. (1998) Interorganizational trust → Exchange performance (+) Business-to-Business, Electronical
Blankenburg et al. (1999) Mutual commitment (measured by investment) → Mutual dependence (+) → Value creation (+)
Business-to-Business Network, European Suppliers
Brennan & Turnbull (1999) Commitment (+), Trust (+) → Adaptation (+) and vice versa. Customer power (+) → Supplier Adaptation.
Business-to-Business, Field Research Telecommunication
Geyskens et al. (1999) Conflict (-), Satisfaction (+) → Trust (+) → Commitment (+) (all relationship outcomes indirectly influenced by own dependence)
Business-to-Business, Meta-analysis
Jap & Ganesan (2000) Retailer transaction specific investment→ perception of supplier commitment (-). Suppliers’ transaction specific investment → retailer perception of supplier commitment. Retailer Perception of Supplier Commitment → Conflict (-), Satisfaction (-). Interdependence Asymmetry → Conflict (-), Satisfaction (+). Interdependence Magnitude→ Conflict (+), Satisfaction (+). Alternative suppliers → Satisfaction (-).
Business-to-Business
Jap (2001) Specialized investments both → Joint competitive advantage (+) → Behavioral continuance outcomes (+)
Business-to-Business, Longitudinal, Industrial
Hennig-Thurau et al. (2002) Relational benefits → Commitment (+), Satisfaction (+) → Relationship marketing outcomes (performance) (+)
Business-to-Consumer, Services
Roberts et al. (2003) Relationship quality > Service quality in predicting behavioral outcomes. Business-to-Business, Services
Walter et al. (2003)
Jap & Anderson (2003)
Direct supplier functions > Indirect supplier functions → Relationship quality (+) (commitment, trust, satisfaction)
Business-to-Business, German purchasing professionals
High opportunism: Goal congruence (+), Bilateral Idiosyncratic investments (+), Interpersonal Trust (-) → Performance (+) and Future Expectation (+). Low opportunism: Goal congruence (0), Bilateral idiosyncratic investment (+), Interpersonal Trust (+) → Performance (+), Future Expectation (+).
Business-to-Business, Four Fortune 50 companies
Johnston et al. (2004) Supplier Trust → Cooperative relationship behavior (+) → Buyer Satisfaction (+). Business-to-Business, Purchasing Association, Partnerships
21
Table 1 Ctd. - Author(s)
Relevant Contribution
Research Context
Woo & Ennew (2004) Relationship quality (cooperation, adaptation, atmosphere) → Service quality (+), Customer satisfaction (+), Behavioral intentions (+).
Business-to-Governement, Government Engineers
Barnes et al. (2005) Soft Intangibles (trust, reliability, reputation, affection etc.) highly correlate with Supplier Commitment and Dependency from a buyer perspective. Soft Intangibles highly correlate with Supplier commitment and Hard Intangibles highly correlate with Hard Tangbles (switching cost, investment stakes etc.) from a supplier perspective.
Business-to-Business, SME supplier and MNE buyers
Fynes et al. (2005) Supply chain RQ is a higher order construct of Communication, Trust, Adaptation, Commitment and Cooperation. Interdependence (0) for RQ.
Business-to-Business, Electonical, Ireland
Gounaris (2005) Trust → Calculative commitment (-), Affective Commitment (+) → Invest in and Maintain relation (-/+)
Business-to-Business, Consulting Greece
Lages et al. (2005) Relationship quality is a higher order construct of Information Quality, Communication Quality, Long-term Orientation and Satifsaction → Financial and Strategic Exchange Performance (+).
Business-to-Business, Export/Import Markets
Caceres & Paparoidamis (2007) Relationship Satisfaction → Commitment (+), Trust (+) Business-to-Business, Advertising Agencies Clients
Gulati & Sytch (2007) Supplier dependence advantage (0), Manufacturer dependence advantage (-) → Manufacturer Performance. Joint Dependence → Manufacturer Performance (+). Trust not found to mediate between Joint Dependence and Manufacturer Performance.
Business-to-Business, Automobile, Two firms' Procurement
Ryu et al. (2007) High interdependence → monitoring and norm of information sharing. Buyer dependence low → Buyer does not rely on information sharing. Supplier dependence low → Buyer relies on monitoring, regardless own dependence level.
Business-to-Business, Manufacturing
Cambra-Fierro & Pollo-Redondo (2007) Communication (+), Cooperation (+), Adaptation to expectation of buyer (+), Trust (+) → Buyer satisfaction.
Business-to-Business, Manufacturing
Nyaga et al. (2010) Information sharing, Joint relationship effort, dedicated investments → Trust (+), Commitment (+). Trust, Commitment → Satisfaction (+), Performance (+). Buyers focus somewhat more on relationship outcomes, suppliers more on collaborative activities.
Business-to-Business, Cross industry, respondents not always purchase functions.
22
Buyer Dependence Advantage & Supplier Dependence Advantage
Trust Kumar et al. (1995) find that dependence advantage and dependence disadvantage are
both related to lower levels of trust and commitment and higher levels of conflict. It is found
that if dependence is low the party does not rely on information sharing (Ryu et al. 2007).
The lack of information leads to a decrease in trust for the other party. Salancik (1979) finds
that lower power in a relationship leads to more affirmative action and vice versa. The less
dependent party knows in this case that the other party will confirm to his whishes and is
more able to trust the other.
Commitment Geyskens et al. (1996) find that a dependence advantage has a negative
influence on calculative commitment and that a dependence disadvantage might lead to an
increase in affective commitment. Salancik (1979) finds that lower power in a relationship
leads to more affirmative action and vice versa. This affirmative action reflects commitment
to the relationship. Kumar et al. (1995) find opposing to this a lower level of commitment,
whenever there is a dependence advantage or disadvantage.
Satisfaction & Conflict Anderson and Narus (1990) find that having influence over a partner
is related to higher levels of satisfaction and lower levels of conflict. Influence by the partner
on the buyer is associated with lower satisfaction and an increase in conflict. Anderson and
Narus (1990) thus find a mirroring relationship between the degree of influence by and on
the partner. No direct effect from relative dependence on conflict and satisfaction is found.
Instead, this relationship is mediated by ability to influence the partner. The exercise of
power leads to higher perception of conflict and a decrease in relationship satisfaction
(Gaski and Nevin 1985). Gundlach and Cadotte (1994) find in a simulation study that a power
advantage is related to less satisfaction on performance and less conflict. Gundlach and
Cadotte (1994) also find that a dependence disadvantage is related to the use of rewards
promises and persuasion, which makes the other party more satisfied. Provan and
Gassenheimer (1994) also find that dependence of one partner might lead to more exercise
of power towards the dependent partner. The need to exercise power might however be
due to an existing level of dissatisfaction and not the other way around. Andaleeb (1996)
finds main effects of trust and dependence on satisfaction. Jap and Ganesan (2000) find that
the greater the relative power (positive interdependence asymmetry) for the retailer, the
higher the level of satisfaction and the lower the level of conflict perceived in the
23
relationship.
In general, there is no particular consensus on the influence of having a dependence
advantage or disadvantage with respect to the other party on evaluations of relationship
outcomes. Measures used have differed from study to study. What is specifically striking is
the differences in the independent variable used in these studies (i.e. dependence
asymmetry vs. buyer advantage & supplier advantage). Dependence asymmetry is related
mostly to lower evaluations of relationship quality and higher levels of conflict (Salancik
1979; Kumar et al. 1995). In the meanwhile, when authors discuss dependence advantage
(which resembles only one side of dependence asymmetry) evaluations of relationship
quality seem to show more positive results (Jap and Ganesan 2000). When a partner has a
dependence disadvantage (supplier advantage in this study) the results seem to highlight
lower relationship quality evaluations due to an overall dissatisfied feeling (Gaski and Nevin
1985; Anderson and Narus 1990). Therefore, we argue that it indeed makes a difference and
that it is important to distinguish between buyer and supplier dependence advantages. In
line with the findings above we argue that when a buyer has dependence advantage the
buyer is generally more likely to be able to select suppliers that would satisfy his needs in
the best manner.
Given the fact that the supplier is more dependent upon the relationship than vice versa, the
buyer is more able to trust the supplier. The advantageous position of the buyer leads him to
pick the most advantageous supplier and the levels of conflict and satisfaction are likely to
be higher and the buyer will be committed to maintain this advantageous relationship
(Anderson and Narus 1990; Geyskens et al. 1999). This leads to the formation of the
following hypotheses:
H5: Buyer dependence advantage is positively related to buyer evaluation of trust. H6: Buyer dependence advantage is positively related to buyer evaluation of commitment. H7: Buyer dependence advantage is positively related to buyer evaluation of relationship satisfaction. H8: Buyer dependence advantage is negatively related to buyer evaluation of conflict.
24
In the situation that the buyer is more dependent upon the supplier than vice versa we have
a supplier dependence advantage. Buyers are likely to feel uncomfortable in this
relationship, since the supplier has power to exercise influence and determine what happens
in the relationship (Salancik 1979; Gaski and Nevin 1985; Provan and Gassenheimer 1994). It
is likely that interests clash more often in this buyer-supplier relationship, which leads to
higher perceptions of conflict by the buyer (Anderson and Narus 1990). Satisfaction is lower,
resulting in lower trust, since the less dependent party can defect from the relationship
more easily and therefore commitment of the buyer is expected to be lower due to an
overall feeling of dissatisfaction (Geyskens et al. 1999). This leads to our next set of
hypotheses:
H9: Supplier dependence advantage is negatively related to buyer evaluation of trust. H10: Supplier dependence advantage is negatively related to buyer evaluation of commitment. H11: Supplier dependence advantage is negatively related to buyer evaluation of relationship satisfaction. H12: Supplier dependence advantage is positively related to buyer evaluation of conflict. Our basic model can be found in Figure 3.
25
Rival Model
The currently proposed basic model focuses on buyer and supplier dependence advantages
rather than dependence asymmetry. However, we have also seen that there is no clear
consensus on the significance of buyer and supplier dependence advantages instead of
dependence asymmetry. Therefore we also test a rival model that is presented in figure 4
and based on the findings by Kumar et al. (1995) that dependence asymmetry is the most
important explanatory factor and not buyer or supplier dependence advantage. The main
hypothesis for this rival model is:
HR: Dependence asymmetry provides a better explanation for buyer relationship quality
evaluation differences.
Joint Dependence
Buyer dependence advantage
Supplier dependence advantage
Conflict
Satisfaction
Commitment
Trust
+++-
+++-
---+
Figure 3 – Basic model of hypotheses
H1-4
H5-8
H9-12
26
Concluding we would like to state again why we test these specific hypotheses. Previous
research has investigated several of the relationships described above. Still, some of them
were narrow due to single industry sample (Kumar et al. 1995; Geyskens et al. 1996; Kumar
et al. 1998; Gulati and Sytch 2007). Our first contribution is to provide a sample test that
raises external validity by broadening the scope of these studies.
Others used single-item measures for the variables we discussed (Anderson and Narus 1990;
Andaleeb 1996). Moreover, measures for dependence often only covered few of the sources
for dependence. Especially relationship specific investments were often not considered
(Anderson and Narus 1990; Provan and Gassenheimer 1994; Kumar et al. 1995; Geyskens et
al. 1996; Kumar et al. 1998). Gulati & Sytch (2007) provide a measure for dependence with a
broader scope covering more sources for dependence. We divert from Gulati and Sytch
(2007) by focusing on relationship evaluation outcomes rather than performance. Moreover,
by adding conflict to our analysis we are able to contribute to theory on buyer supplier
relationships. In the next section we describe our sample, data collection and method to test
the hypotheses stated above.
Joint Dependence
Dependence Asymmetry
Conflict
Satisfaction
Commitment
Trust
+++-
---+
Figure 4 – Rival Model of Dependence Asymmetry
H1-4
HR’s
27
IV. Method
Data
Our sample consists out 830 buyers which are all a member of NEVI (Dutch Association of
Purchasing Management). The sample was constructed by selecting all companies that were
active the Dutch industrial sector. The respondents are all buyers and knowledgeable about
several relationships with suppliers. Two versions (see survey design) of our survey were
sent out by personal mailing to two separate, but random groups of 415 buyers. A unique
link to an online survey was sent to each person. Surveys could be exited and finished later
at any time up to the closing date. After this first mailing it turned out that 27 of our e-mail
addresses were not valid. Therefore our corrected sample size is 803. Three weeks after our
initial mailing we send out a reminder to potential respondents who had not (or not fully)
answered the online questionnaire. Two weeks after this reminder we closed the
questionnaire and ended up with a total of 147 completed surveys, and a response rate of
18,3%. This response rate is above the average of web-based or internet surveys of 13,5%
(Cook, Heath and Thompson 2000).
Survey Design
We are aware of the fact that when respondents are asked to randomly take in mind a
relationship, the likelihood that a respondent refers to a more significant or strategic
relationship increases. This bias is avoided as follows: A random half of the respondents
were asked to take in mind a relationship of little to moderate importance. The other
random half was asked to take in mind a relationship of considerable importance. Apart
from this instruction the surveys were identical. With this type of pre-stratification approach
we aimed to ensure an adequate spread in the type of relationship dependencies. Our
survey was set out for a pretest amongst 10 purchasing professionals of a Dutch chemical
company. Moreover, several academic experts have judged the viability of our survey.
Suggestions and corrections were used to modify the survey. We avoided using the word
dependence in both this stratification of importance as well as the actual measure for
dependence to limit the chances of biasing respondents. In our data-analysis no use of the
‘importance’ stratification is made and results of the two survey version were put together.
28
We now discuss the separate variables used in this research. A full version of the ´high
importance’ survey is presented in the Appendix C.
Construct measurement
Dependent variables
Our proposed framework identifies four dependent variables in our basic model: trust,
commitment, satisfaction and conflict. All these relationship outcomes are measured by the
perception of the buyer for a single and specific relationship with a supplier. We performed
a factor analysis in AMOS to determine discriminant and convergent validity of the
relationship outcomes. Although trust, commitment, satisfaction and conflict are highly
related, they are distinct constructs. Table 2 represents the standardized regression
estimates of item loadings on the four relationship outcome constructs, taking into account
covariance between the constructs.
Trust is measured by 8 items on a 7-point likert scale that ranges from strongly disagree to
strongly agree. The measure we use was adopted from Doney and Cannon (1997) and is
found particularly useful in this research context since these researchers investigated the
nature of trust in buyer-supplier relationships. Moreover, this measure is widely used in both
operations management as well as (relationship) marketing and strategic literature
(Johnston et al. 2004; Caceres and Paparoidamis 2007; Nyaga et al. 2010). All items, except
for the item on the necessity to be cautious with this supplier, were found to be significantly
loading on trust. This last item was dropped in further analysis.
Commitment is measured again with a 7-point likert scale for three items and is adapted
from Morgan and Hunt (1994). The scale has been widely used in previous research on
commitment (e.g. Andaleeb 1996; Gounaris 2005; Nyaga et al. 2010). Moreover, this
measurement was used in vertical business-to-business relationship. All items loaded
significantly on commitment.
Satisfaction of the relationship is measured by three items adapted from the research of Jap
(2001). These measures represent an overall satisfaction with the relationship, in which we
are interested in this study. All three items loaded significantly on satisfaction.
Conflict is measured by the level tension, frustration and disagreement felt in the
relationship (Anderson and Narus 1990; Geyskens et al. 1999). Both Kumar, Stern and Achrol
29
(1992) and Jap and Ganesan (2000) have used this measure. All items loaded significantly on
conflict.
Table 2 – Standardized regression weights of FA relationship outcomes and loadings dependence FA.
Independent variables
The independent variables in our basic model are measured by the same scale as used by
Gulati and Sytch (2007) since this scale incorporates multiple sources of dependence in a
buyer-supplier relationship. The items include volumes, capital, switching cost an
possibilities as well as relationship specific investments. In order to calculate buyer and
supplier dependence advantage we first construct measures for the absolute buyer and
supplier dependence. The results of the common factor analysis on these items are
presented Table 2. After common factor analysis with orthogonal varimax rotation of the
standardized values of the dependence items we labeled the first factor Buyer dependence
(Db) and the second factor Supplier dependence (Ds). We use a cutoff point of 0.3 to
Relationship Quality/OutcomesScale: 'Strongly disagree ---- Strongly agree' (7-point likert).
Trust (Doney and Cannon 1997) Standardized regression weights α=0,84
T1 This supplier keeps promises it makes to our firm. 0,78
T2R This supplier is not always honest with us. ( R ) 0,65
T3 We believe the information that this vendor provides us. 0,65
T4 This supplier is genuinely concerned that our business succeeds. 0,47
T5 When making important decisions, this supplier considers our welfare as well as its own. 0,63
T6 We trust this vendor keeps our best interest in mind. 0,68
T7 This supplier is trustworthy. 0,83
T8R We find it necessary to be cautious with this supplier. ( R ) Insignificant, dropped from analysis
Conflict (Kumar, Stern and Achrol 1992; Jap and Ganesan 2000) Standardized regression weights α=0,68
CF1 The level of conflict in the relation with this supplier can be best described as tense. 0,56
CF2 We have significant disagreements in our working relationship with this supplier. 0,77
CF3 We frequently clash with this supplier on issues relating to how we should conduct our business. 0,60
Commitment (Morgan and Hunt 1999) Standardized regression weights α=0,66
CM1 The relationship that our organization has with this buyer/supplier is something my organization intends to maintain indefinitely. 0,67
CM2 My organization plans to have a close relationship with this buyer/supplier in the near future. 0,73
CM3 Enhancing our relationship with this buyer/supplier is an important objective for our organization. 0,47
Satisfaction (Jap 2000) Standardized regression weights α=0,81
S1 Our relationship with this supplier has been a successful one. 0,74
S2 Our relationship with this supplier has more than fulfilled our expectations. 0,72
S3 We are satisfied with the outcomes of the relationship with this supplier. 0,86
Dependency (Gulati and Sytch 2007) Common Factor Analysis of Standardized values w ith Orthogonal Varimax Rotation Factor1 Factor2
DEP1 It would require much trouble for our firm to switch supplier for this product/service. 0.4276 0.2670
DEP2R There are enough potential supplier to ensure adequate competition among the current suppliers. 0.6889 0.0300
DEP3R There are satisfactory alternate sources of short-term supply for this product/service. 0.6988 0.1362
DEP4 This supplier has an advantage over other competitors in their field. -0.0060 0.3076
DEP5 This supplier has adapted its management methods to work effectively with our firm. -0.1668 0.4959
DEP6 Our firm has made significant investments for this specific relationship. 0.2991 0.5545
DEP7R There are enough potential alternative buyers for this supplier to ensure adequate competition among current buyers. 0.5953 0.0766
DEP8 This supplier would face a serious financial crisis if our firms withdrew business from them. 0.1858 0.3692
DEP9 This supplier has made significant investments for this specific relationship. 0.1184 0.7279
DEP10 What is the approximate total euro volume purchased from this supplier for this specific product/service?* 0.1537 0.3614
DEP11 What percentage of the total volume needed of this product/service is purchased from this particular supplier? 0.1621 0.1191
DEP12 What percentage of total sales of the supplier sold to your firm, approximately? 0.1468 0.1603
R Scales with R are inverted scales.
* Categories: (1) up to 1 milion, (2) 1-5 milion, (3) 5-20 milion, (4) 20-50 milion. Variance explained: 0.4345 0.3955
30
determine significant factor loadings, similar to Gulati and Sytch (2007). It provides us with
sufficient items (at least three) loading on each factor. Our results for this factor analysis
differ somewhat from those of Gulati and Sytch (2007). We have labeled Factor 1 with
‘buyer dependence’ and Factor 2 with ‘supplier dependence’. Dep1, Dep2 and Dep3 load on
buyer dependence and were found by Gulati and Sytch as well. Although Dep 7 was an
inverted scale to measure supplier dependence, it loads significantly on our buyer
dependence factor. The availability of alternative buyers for the supplier could signal the
relative vulnerability of a buyer and therefore can be associated with buyer dependence. Six
items loaded significantly on supplier dependence. Significant loadings of Dep 5, Dep8, Dep9
and Dep10 are in line with the findings of Gulati and Sytch (2007). Dep5 did not load
significantly in Gulati and Sytch (2007). The positive loading of the advantage the supplier
has over others in the field (Dep4) is surprising, but might be attributed to the fact that our
respondents are buyers. If a supplier has an advantage over other suppliers for this specific
buyer this might be due to an adaptation of its product specific to this buyer. The supplier
has difficulties to sell its products elsewhere. This specificity might lead to dependence for
both the buyer and the supplier. The second surprising loading is that of the investment of
the buyer leading to supplier dependence. It is possible that this item resembles some
loyalty aspect. When the buyer has invested, the supplier owes services to the buyer, which
makes them dependent. It however also loads close to .3 on buyer dependence. Therefore,
our model will provide room for some covariance between buyer and supplier dependence
advantage. The differences with Gulati and Sytch (2007) may be attributed to the single
(automotive) industry focus of that study. In this industry it is generally the manufacturer
that has most of the power and is able to let the suppliers bid up against each other. It is
likely that the base for dependency measured by Gulati and Sytch (2007) is very specific to
this industry. Overall the constructs of buyer and supplier dependence show a correlation of
.42. This indicates that it is possible that the bases for dependence are likely to be
interrelated and different from industry to industry.
We calculated buyer and supplier dependence advantage by averaging the standardized
values for the items that loaded significantly on the factors. We also calculated the values by
multiplying the factor score weights with the standardized values for the items. The
correlation between using the average and factor score weights was above .9. This allows us
31
to use the average values and make interpretation more straight forward. After calculating
buyer dependence and supplier dependence we are interested in buyer dependence
advantage or supplier dependence advantage. We do this by using split estimation. This
estimation was previously used in similar research by Emerson (1962). We construct two
dummy variables. Buyer advantage = Ds-Db if positive and otherwise zero. Supplier
advantage = Db-Ds if positive and otherwise zero. Joint dependence is constructed by
aggregating buyer and supplier dependence. Dependence asymmetry is an absolute
measure of Db-Ds and thus combines buyer and supplier dependence advantages in one
variable.
Controls
We control for several characteristics of the respondent, the firm and the relationship. We
take into account the respondents’ age, duration of employment with the current firm and
duration of contact between respondent and supplier. Age and tenure take into account the
experience the respondent has in several fields. To control for the influence of the firm´s size
on dependency we measure the number of employees and the annual turnover of the
respondent´s firm. Moreover, since relationships tend to evolve over time (Ring and Ven
1994) we identify the duration of the relationship between the respondent’s firm and the
supplier taken in mind since perceptions might also differ in different durations of the
relationship (Barnes, Naudé and Michell 2007). In essence, over time organizations and
individuals become more attached to specific relationships. The table of correlations
between all the variables and items used can is presented in Appendix A.
Moreover, we are aware of the high likelihood of relationship between trust, commitment,
satisfaction and conflict themselves. In our measurement model we control for the existence
of these relationships by incorporating the causal path that was found by Geyskens et al.
(1999) in their meta-analysis on satisfaction. This path leads from satisfaction to conflict, and
from conflict and satisfaction to trust, and from trust to commitment.
In the next section we test our hypotheses posed in section III with the measures described
in this section. First we test our basic model and variations of this model including the
controls mentioned above. After that we also test the rival model that was described in the
32
theoretical framework to determine which model provides a better explanation for the
relationship between dependence asymmetries and relationship outcome evaluation.
V. Results
In this section we present the results of several analyses we have performed to make
inferences about our hypotheses. In order to test our models we perform Structural
Equation Modeling (SEM) in AMOS, which is an extended version of SPSS developed
especially for models with latent variables. All full summaries of the analyses described in
this section can be found in the appendix. We perform a regression that is based on
Maximum Likelihood (ML) estimation, a procedure that allows for correlation between our
items. Descriptive statistics on the variables mentioned in section IV can also be found in the
Appendix.
Basic Model
To test H1-H12 we modeled our basic model, excluding the control variables mentioned
above. Table 3 presents the ML estimation results of this model. We have already included a
covariance link between Supplier and Buyer dependence advantage since their correlation
shows to be moderately high and significant. On the basis of this model we are not able to
reject H1, H2, H3, H6, H9 and H10, H11 and H12. Both joint dependence and buyer
dependence advantage (H4 and H8) have an effect which is not significantly different from
zero on conflict. Moreover, results of buyer advantage relations with trust and satisfaction
are positive, but not significant either. With respect to the overall fit of this model we cannot
be particularly satisfied since the Goodness Fit Index (GFI) is below and the Root Mean
Square Error of Approximation (RMSEA) is above satisfactory levels of .9 and .08.
33
Maximum Likelihood Estimates GFI = .7494, RMSEA = .12, Chisquare = 454,62 p<.001
Regression Weights: (Group number 1 - Default model)
*** = <0.001 Estimate S.E. C.R. P
H1: Trust <--- JD_AVG 0,1432 0,0668 2,1442 0,032
H2: Commit <--- JD_AVG 0,417 0,0814 5,1218 ***
H3: Satisf <--- JD_AVG 0,0961 0,0568 1,6902 0,091
H4 Conflict <--- JD_AVG 0,0881 0,0597 1,477 0,1397
H9: Trust <--- SAD_AVG -1,0591 0,184 -5,7548 *** H10: Com-mit <--- SAD_AVG -0,6718 0,1832 -3,6677 ***
H11: Satisf <--- SAD_AVG -0,6902 0,1459 -4,7323 *** H12: Con-flict <--- SAD_AVG 0,6472 0,1891 3,4234 ***
H5: Trust <--- BAD_AVG 0,0466 0,1964 0,2373 0,8124
H6: Comm <--- BAD_AVG 0,635 0,2055 3,0906 0,002
H7: Satisf <--- BAD_AVG 0,104 0,1656 0,6278 0,5301
H8:Conflict <--- BAD_AVG 0,1121 0,1744 0,6429 0,5203
Table 3 – Maximum Likelihood Estimates Basic model
At this point we want to determine if adding the relationships between trust, commitment,
satisfaction and conflict as described by Geyskens et al. (1999) increases the fit of our model.
Table 4 presents the results of adding these relationships. The GFI of this model has
significantly increased and the RMSEA is significantly lower. The drop in Chisquare can be
attributed to an increase in the number of parameters to be estimated and can thus be
ignored. In this model we cannot reject our hypothesized relationships of joint dependence
and commitment (H2) and satisfaction (H3). The relationship between joint dependence and
trust (H1) shows an insignificant result. The relationship between joint dependence and
conflict (H4) is significant, but positive, opposite to our expectations. Three of the
hypotheses concerning buyer dependence advantage are insignificant (H5, H6, and H8). A
significant result is found for the positive relationship between buyer dependence advantage
and commitment (H7). Supplier dependence advantage negative relationship with
satisfaction (H11) and trust (H9) cannot be rejected. We find non-significant results for the
relationships between supplier dependence advantage and conflict (H12) and commitment
(H10). Based on the significant relationship between our dependent variables, the pathways
as identified by Geyskens et al. (1999) are supported by this model.
34
Maximum Likelihood Estimates GFI = .8376 , RSMEA =.076, Chisquare = 259,38 p<.001
Regression Weights: (Group number 1 - Default model) *** = <0.001
Estimate S.E. C.R. P
H3: Satisf <--- JD_AVG 0,0945 0,0557 1,6979 0,0895
H11:Satisf <--- SAD_AVG -0,686 0,1439 -4,7665 ***
H6: Satisf <--- BAD_AVG 0,1009 0,1635 0,6172 0,5371
H4: Conflict <--- JD_AVG 0,1576 0,0628 2,511 0,012
H12: Conflict <--- SAD_AVG 0,2351 0,1681 1,3982 0,1621
H8:Conflict <--- BAD_AVG 0,21 0,1764 1,1903 0,2339
Conflict <--- Satisf -0,6689 0,1604 -4,169 ***
H1:Trust <--- JD_AVG 0,0651 0,0537 1,2128 0,2252
H9:Trust <--- SAD_AVG -0,2776 0,137 -2,0264 0,0427
H5: Trust <--- BAD_AVG -0,0238 0,1453 -0,1641 0,8697
Trust <--- Conflict -0,1528 0,1283 -1,1907 0,2338
Trust <--- Satisf 1,0947 0,1794 6,1028 ***
H2: Commit <--- JD_AVG 0,3368 0,0685 4,9156 ***
H10: Commit <--- SAD_AVG -0,0591 0,1747 -0,3381 0,7353
H7: Commit <--- BAD_AVG 0,6069 0,1828 3,3193 ***
Commit <--- Trust 0,5565 0,1079 5,1574 ***
Table 4 – Maximum Likelihood Estimates Basic model with Geyskens (1999).
The next step is to check whether incorporating our control variables will significantly
increase our model fit. We performed the same analysis as in table 4, but this time added all
control variables mentioned in the method section. A full summary of this estimation can be
found in Appendix B. The Generalized Fit Index (GFI) of this model is .7749 which is below
the normal threshold of .9 and below our basic model including Geyskens et al. (1999). The
RMSEA is .0872 which is higher than the generally used cutoff point of .08. Only few of the
control variables -age, multiplexity, tenure and turnover- show slightly significant results.
Incorporating the control variables does not change the main effects we are investigating.
Therefore, in the following analysis we discuss our basic model only including the dynamics
of Geyskens et al. (1999). For our rival model test, we apply the same reasoning.
35
We continue our analysis by estimating the standardized regression weights in our basic
model (Figure 5). Thick arrows and values represent significant relationships. The advantage
of using standardized regression weights is that the relationship magnitudes can be directly
interpreted and compared. In this figure we can see that for example for supplier
dependence advantage there is no significant direct effect on conflict, but there is an
indirect effect of -.64*-.46 = .29. These indirect effects are important to determine the total
effect of our independent variables on our dependent variables. Standardized direct and
indirect effects for this model are provided in Table 5. Moreover, in our discussion we are
able to figure out what exact pathways are important in these relationships. We performed
a robustness check with a Bollen-Stine bootstrap of 200 samples. This specific bootstrap
method was chosen, since not all our variables showed complete normality. The p-value of
this test was .005, therefore we can say that our model is acceptable and the non-normality
has no significant influence on the usefulness of our model.
Joint Dependence
Buyer dependence advantage
Supplier dependence advantage
Conflict
Satisfaction
Commitment
Trust
.07
.39
.14
.22
-.01.26.06.11
-.14-.03-.46.15
Figure 5 – Basic model standardized regression weights
H1-4
H5-8
H9-12
.60
-.64
.79
-.12
36
Rival Model
In order for us to find out whether the distinction between buyer and supplier dependence
advantages does a better job in explaining the relationships in H1-H12 we test our rival
model. In Table 5 we present the Maximum Likelihood estimates. With these values we can
determine which relationships are significant and can be interpreted by the standardized
regression weights provided in figure 6. From the regression estimates we see that
dependence asymmetry is significantly and negatively related to satisfaction. For the
relationship with conflict and trust we do not find significant results. The relationship with
commitment is found to be positive and significant, which is in contrast with previous
literature on dependence asymmetry in general (Kumar et al. 1995). However, if we take a
look at the direct and indirect effects we see that the indirect effect of dependence
asymmetry on commitment is negative. Appendix B provides an overview of direct and
indirect effects in this rival model. Joint dependence again has a significant positive
relationship with conflict (H4 not supported) and a positive significant relationship with
commitment (H2). Joint dependence and satisfaction (H3) no longer show a significant
relationship. The relationship of trust and joint dependence (H1) is again not significant.
Maximum Likelihood Estimates
GFI = .8452, RMSEA = .0736, Chisquare = 224,00 p<.001
Regression Weights: (Group number 1 - Default model)
*** = <0.001
Estima-
te S.E. C.R. P
Satisf <--- JD_AVG 0,0547 0,0594 0,9212 0,357
Satisf <--- DASS_AVG -0,418 0,1393 -2,9995 0,0027
Conflict <--- JD_AVG 0,159 0,0612 2,5966 0,0094
Conflict <--- DASS_AVG 0,2242 0,1423 1,575 0,1153
Conflict <--- Satisf -0,6714 0,1489 -4,5085 ***
Trust <--- JD_AVG 0,0476 0,0536 0,8868 0,3752
Trust <--- DASS_AVG -0,1686 0,1206 -1,3977 0,1622
Trust <--- Conflict -0,1501 0,1314 -1,1425 0,2532
Trust <--- Satisf 1,1561 0,1776 6,5114 ***
Commit <--- JD_AVG 0,2961 0,0695 4,2618 ***
Commit <--- DASS_AVG 0,2631 0,1573 1,6724 0,0944
Commit <--- Trust 0,6849 0,1139 6,0123 ***
Table 5 – Maximum Likelihood Estimates Rival Model
37
Concluding we can say that our results partly support the hypotheses stated. An overview of
hypotheses supported directly or indirectly per model can be found in Table 6. The lower
right corner of this table describes the found effects of dependence asymmetry on trust,
commitment, satisfaction and conflict.
Support? Dir/Ind/Total
Basic Model Rival Model
H1 No/Yes/Yes No/Yes/Yes H2 Yes/Yes/Yes Yes/Yes/Yes H3 Yes/No/Yes
No/No/No H4 No/Yes/No positive! No/No/No positive! H5 No/No/No Dependence Asymmetry Findings:
Trust: Indirectly Negative H6 Yes/No/Yes Commit: Directly Positive, Indirectly H7 No/No/No Negative and Total Positive H8 No/No/No Satis: Directly Negative H9 Yes/Yes/Yes Confl: Indirectly Positive H10 No/Yes/Yes By Dependence Asymmetry! H11 Yes/No/Yes H12 No/Yes/Yes
Table 6 – Summary of findings on Hypotheses
Joint Dependence
Dependence Asymmetry
Conflict
Satisfaction
Commitment
Trust
.05
.36
.08
.23
-.08.14-.27.14
Figure 6 – Rival Model standardized regression weights
H1-4
HR’s
.76
-.63
.84
-.12
38
VI. Discussion
In this section we discuss the results we found and provide answers to our research
questions. We discuss our findings and possible reasons for these findings separately for
each hypothesis. This will answer our first research question on the influence of dependence
states on relationship outcome evaluations. After that we answer our second research
question by discussing and comparing the results of our basic and rival model. We determine
which model performs better in explaining the relationships between dependence and
relationship outcome evaluation.
Hypotheses
Joint Dependence
H1: In both models we have not been able to identify a significant relationship between joint
dependence and trust. This finding is in line with that of Gulati & Sytch (2007). Joint
dependence does not seem a sufficient condition for trust. However, both models are able
to find a positive and significant indirect effect, following a path through satisfaction. In line
with Geyskens et al. (1999) a smaller indirect negative effect is found via conflict. This non-
direct finding is This negative finding might point out the dark side of close relationships as
mentioned by Anderson and Jap (2005). Having a close relationship might lead to more
friction, exactly because partners are so close. This conflict might in turn lead to reduced
trust.
H2: Our results clearly indicate that joint dependence is significantly and positively related to
commitment, both directly and indirectly. The standardized regression estimate indicates a
fairly strong relationship. We indicate a relationship here, not necessarily causality. It is of
course also possible that the investments done are a signal of commitment. Both models
support this hypothesis, in line with previous literature (Kumar et al. 1995).
H3: Our basic model finds a significant and positive relationship between joint dependence
and satisfaction. In the rival model it is no longer significant. It is possible that due to the
higher correlation between dependence asymmetry and joint dependence compared to the
separate dependence dimensions, the effect is picked up by dependence asymmetry in this
model. Although the significant finding is not very large, it provides a better explanation if
we look at existing literature (Gundlach and Cadotte 1994, Andaleeb 1996).
39
H4: We have not found support for a negative relationship between joint dependence and
conflict. On the contrary, both models show a positive relation. This finding provides an
indication of the existence of a dark side of close relationship as described by Anderson and
Jap (2005). High joint dependence is inherent to being close to each other, which might
more often lead to direct confrontation of problems, resulting in conflict. The negative
indirect effects found for the hypotheses above underline this idea. However, our literature
review on conflict indicates that when the quality of a relationship is good, conflicts might
functional. These functional or constructive conflicts actually add value to the relationship
(Assael 1969) when the right conflict resolution techniques are used (Mohr and Spekman
1994).
Concluding, we find support for the direct positive relationship between joint dependence
and buyer’s evaluations of the relationship outcome constructs commitment, satisfaction
and conflict. All indirect effects on trust are positive. If the levels of relationship quality are
high, the significant relations between the relationship outcome variables indicate that there
is a negative relationship with conflict. However, we also find a direct positive relationship
between joint dependence and conflict, which might hamper the interorganizational
relationship to develop. Moreover, our model finds support for the model of Geyskens et al.
(1995).
Buyer dependence advantage
H5: For buyer dependence advantage we find no direct, nor indirect relationship with trust.
It seems to be the case that buyer dependence advantage is no sufficient base for trust to
develop. A supplier captive situation does not seem to imply high levels of mutual trust as
Bensaou (1999) suggested.
H6: The only significant relationship between buyer dependence advantage and relationship
outcomes is that with commitment. We find a positive relationship in this case, supporting
H6. Thus, the argument that buyers are more able to reap relational benefits (Hennig-Thurau
et al. 2002) of or to free-ride (Lindgreen and Pels 2002), due to their advantageous position,
increases their willingness to maintain the relationship.
H7 & H8: For both conflict and satisfaction we find a positive, but insignificant relationship
with buyer dependence advantage. Buyers that have a dependence advantage in the
40
relationship do not seem to be influenced by this advantage when evaluating their trust,
conflict and relationship satisfaction.
Concluding, we only found a significant direct positive relationship between buyer
dependence advantage and commitment. The other relational outcomes are likely to be
driven by factors outside our model. From our control variables we cannot determine a
clear-cut strong influence in this. Age, turnover and others do not significantly influence all
relationship outcomes (Appendix B).
Supplier dependence advantage
H9: We find a negative direct and indirect significant relationship between supplier
dependence advantage and a buyer’s evaluations of trust. The buyer captive situation
implies that the level of trust is lower for the buyer, a finding that is supported by Bensaou
(1999). The likelihood of opportunistic behavior by the less dependent increases levels of
suspicion for the buyer and negatively influences his perceived levels of trust (Jap and
Anderson 2003).
H10: With respect to buyer’s commitment we do not find a direct relationship with supplier
dependence advantage. The indirect significant negative relationships that we find are via
trust, satisfaction, and satisfaction and conflict. This result implies that having a
disadvantageous position with respect to dependence in the relationship, does not directly
negatively influences the willingness to maintain the relationship as suggested by Kumar et
al. (1995). The idea of Andaleeb (1996) that firms that are highly dependent are more
committed to these relationships, because they have few other options might offset the
direct negative relationship. It is clear though that through the other indirect mechanisms of
relationship outcomes the relative disadvantage does lead to reduced commitment.
H11: We find a direct negative and significant relationship between supplier dependence
advantage and buyers perception of relationship satisfaction. This is in line with the findings
by Anderson and Narus (1990). Being in a weaker hostage or captive (Bensaou 1999;
Lindgreen and Pels 2002) position negatively influences the buyer’s perception of
satisfaction with the relationship.
H12: We do not find a significant direct relationship of supplier dependence advantage and
the buyer’s perceived level of conflict in the relationship. This mechanism fully operates via
41
the perceived level of satisfaction in the relationship. So, supplier dependence advantage
does lead to higher perceived conflict, but indirectly.
Concluding, we find a strong negative and significant relationships between supplier
dependence advantage and trust and satisfaction. The indirect but significant positive
relationship between supplier dependence advantage and negative relationship between
supplier dependence advantage and commitment does provide some support for at least
the negative nature of evaluation in these relationships.
Basic vs. Rival model
The question that remains after the thorough analysis of the two models is which explains
the variation in and the mechanism that lead to our dependent variables better. We can look
at several model fit statistics (Appendix B) to answer the first question. Moreover, we can
make inference on the second question more intuitively.
The values for GFI and RSMEA as well as the Akaikes Information Criterion (AIC) seem to
imply that the rival model provides a better overall fit to the data. However, the increase in
values is fairly small. Moreover, these methods put a high penalty on adding extra
parameters and there is no specific criterion for which one of the models can be ‘rejected’
(Burnham and Anderson 2002). So, according to these fit-indexes we should have a
preference for the rival model. However, we are interested specifically in whether splitting
up dependence asymmetry into buyer and supplier dependence advantage will tell us more
about relationship outcomes. After investigation of the squared multiple regression
coefficients of our dependent variables of interest we identify a higher degree of explanation
specifically with respect to commitment and satisfaction.
If we compare the direct effects find that dependence asymmetry explains two direct
relationships and supplier and buyer dependence advantages explain three. If managers
know which dependence states influence certain relationship outcome evaluations, they
immediately know that for example low evaluations of trust might be directly related to a
supplier dependence advantage.
Moreover, dependence asymmetry was found to be directly positively related to
commitment and indirectly negative to commitment. When we split up dependence
asymmetry in buyer and supplier dependence advantage, we see that the positive relation
42
with commitment is solely found for buyer dependence advantage. Supplier dependence
advantage only shows a negative indirect relationship. In this case buyer dependence
advantage and supplier dependence advantage show at least opposing results with respect
to perceived levels of commitment. Therefore, it is less recommended to make a single
measure out of these two.
Last but not least it is found that buyer dependence advantage has no significant
relationship with trust, satisfaction and conflict. Supplier dependence advantage does have a
significant direct relationship with trust and satisfaction and an indirect relationship with
commitment and conflict. Therefore, to explain specifically how trust, commitment,
satisfaction and conflict are influenced by relative dependence, we prefer to make a
distinction between buyer and supplier dependence advantage. Managers will be more able
to disentangle possible dependence dynamic influences on relationship evaluations when
this distinction is made.
In sum, we have found some interesting dynamics with respect to the influence of relative
dependence on relationship outcome evaluation. We can conclude that joint dependence
does not seem to be ‘just’ good for the relationship. Moreover, we find that it is important
to distinguish between buyer and supplier dependence advantages. Buyer dependence
advantage in the relationship does not influence most of the relationship outcome
evaluations by the buyer, except for commitment. Supplier dependence advantage does
influence relationship outcome evaluation. All relationship outcomes are influenced, either
directly or indirectly, as hypothesized. Supplier dependence advantage seems to have a
negative effect on the evaluations of relationship success by the buyer. In the last section of
this paper, we shortly discuss the implications of these findings for academic research, the
limitations and open pathways for future research and managerial implications.
VII. Implications, Limitations & Future Research
Academic Implications
The first contribution of this paper is that it is important to distinguish between different
types of dependence asymmetry when investigating its influence on relationship outcome
43
evaluations. Our findings are not in line with the findings of Kumar et al. (1995). This could
be explained by the single industry that Kumar et al. (1995) investigate. Future research
could point out what the exact dynamics in this respect are by investigating the differences
between industries. This cross-industry comparison could also be applied to which items
determine buyer and supplier dependence.
With respect to joint dependence and relationship outcome evaluations we found a
direct positive effect between joint dependence and the level of conflict. This finding is an
indication of support for the dark side of close relationships (Anderson and Jap 2005) and
should be further investigated. It seems to be the case that joint dependence does not have
a direct effect on trust. This is in contrast with the findings of Kumar et al. (1995) and the
portfolio of Bensaou (1999). Similar to Gulati and Sytch (2007) we find an insignificant
relationship between joint dependence and trust, indicating the current measure of
dependence provides results with higher generalizability than those of Kumar et al. (1995).
Moreover, the differing results took into account meta-analysis based relations between the
relationship outcomes.
Our single significant finding between buyer dependence advantage and commitment
indicates that this variable does not directly drive the other relationship outcomes. The
controls that we investigated could not provide a clear cut explanation of other factors
influencing the other relationship outcomes. Moreover, our research gathered information
from a single respondent. This could cause single respondent bias. We were able to some of
the objective company measures, such as number of employees and turnover, which were in
line with the actual numbers. Future research should investigate drivers for the other
relationship outcomes in this case. Interesting research lies ahead in investigating what
being less dependent upon the other party than vice versa does with the overall attitude in
buyer-supplier relationships.
Our findings with respect to supplier dependence advantage and buyer evaluations of
relationship outcome show an overall negative trend for the state of the relationship. These
findings in combination with the findings on buyer dependence advantage are interesting.
They support the very basic marketing rule: A negative experience has greater effect on
evaluation than a positive experience (Taylor 1991). Future research should find out whether
drawing this parallel is indeed valid.
The use of the Geyskens et al. (1999) pathway has provided us with better inference on the
44
actual mechanisms through which relative dependence influences relationship outcomes.
However, the use of SEM in AMOS does not allow us to make definite inference on causal
relationship. To make this inference on the causality, longitudinal research should be
performed. In order to conduct this research, in-depth investigation must be done on how
long time gaps between the different constructs should be. This last step is not yet taken by
research.
Finally, there are some other limitations of this research.. First of all, the research is conduct
with responses from buyers only. It is possible that buyers and supplier might react
differently to their relative dependence (Nyaga et al. 2010). Second, we are aware of the
possibility of common method bias in the items of our constructs and a social desirability
scale. We have tried to minimize these effects by randomizing the order of questions that
asked for likert-scale responses. Moreover, we have not labeled the items in the
questionnaire with construct names. Lastly, future researchers may test for interaction
effects in our model between joint dependence and buyer and supplier dependence
advantages.
Managerial Implications
In practice, our results and conclusions can be of use as well. When managers evaluate the
relationships that they have with their buyers or suppliers, it is important that they take
account the relative dependencies of both parties. Specifically when partners have a
dependence disadvantage, it is very likely that their evaluations are negatively influenced by
this. For example: In a situation where there are many possible buyers and the supplier all of
a sudden decides to sell its products via auctioning, products might be sold at a prices that is
much than the production cost, plus a reasonable margin. This procedure of auctioning
possibly gives the buyer a negative feeling, possibly a feeling of betrayal when the buyer and
supplier have done business before via other negotiation processes. This mechanism is likely
to negatively influence the buyer evaluation of the relationship. Moreover, it is important
that joint dependence might be a source of interfirm conflict. In sum, our framework and
findings provide a guideline for managers on which handles can be switched, and which are
difficult to change in certain dependence situations.
45
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49
Appendix A – Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max
DUROR 147 14,034010 9,262840 2 50
DURPR 147 6,278912 8,818592 0 95
EXCH 147 1,7142860 0,9065968 1 3
T1 147 5,2108840 1,3253650 1 7
T2 147 5,1156460 1,4406860 1 7
T3 147 5,4013610 1,0832460 2 7
T4 147 5,0204080 1,3970090 1 7
T5 147 4,6326530 1,4481050 1 7
T6 147 5,1020410 1,1688580 1 7
T7 147 5,4829930 1,0939040 1 7
T8 147 3,7959180 1,5302380 1 7
CF1 147 3,1768710 1,4929090 1 7
CF2 147 2,7619050 1,4727350 1 6
CF3 147 3,2517010 1,6168780 1 7
CM1 147 4,4285710 1,4524420 1 7
CM2 147 4,8571430 1,5702720 1 7
CM3 147 4,5102040 1,3465700 1 7
S1 147 5,5374150 1,0018620 3 7
S2 147 4,5646260 1,3802320 1 7
S3 147 5,2040820 1,2710620 2 7
DEP1 147 4,2517010 1,8088200 1 7
DEP2 147 3,00000000 1,57897100 1 7
DEP3 147 3,68707500 1,81244800 1 7
DEP4 147 5,16326500 1,16478500 2 7
DEP5 147 4,29932000 1,56334300 1 7
DEP6 147 3,82993200 1,60211400 1 7
DEP7 147 3,39455800 1,55077500 1 6
DEP8 147 3,48299300 1,79555700 1 7
DEP9 147 4,34013600 1,51475200 1 7
MP 147 2,29932000 1,45439800 1 7
DEP10 147 1,69387800 0,91121060 1 4
DEP11 147 53,18367000 33,27203000 0 100
DEP12 147 14,31293000 18,24971000 0 85
AGE 147 43,16327000 7,98889700 22 61
TEN 147 9,17006800 8,07340000 .5 35
EMP 147 2,59183700 1,41302700 1 5
TURN 147 2,29932000 1,11290700 1 4
50
Sample Correlations (Group number 1)
MP
EXCH
TEN
EMP
DURPR
AGE
DUROR
TUR
N
BAD_AVG
SAD_A
VG
JD_AV
G T7
CF3
CF2
CF1
S3
S2
S1
CM3
CM2
CM1
T6
T5
T4
T3
T2R
MP 1 EXC
H 0,
1328 1
TEN
-0,11
55 0,
0385 1
EMP
-0,00
01 0,
1436
0,0118 1
DUR
PR
-0,01
78 0,
0289
0,2748
-0,0
724 1
AGE
-0,07
08 0,
2108
0,43
5
-0,0
402
0,26
4 1
DUROR
0,0242
0,1349
0,0547
0,1214
0,0771
-0,0
119 1
TURN
-0,08
96 0,
0989
0,0137
0,6967
0,0284
0,0314
0,0515 1
BAD
_AVG
-0,01
4 0,
1088
0,02
2
0,1561
-0,0
842
-0,112
0,1188
0,1118 1
SAD
_AVG 0,
0566
-0,00
6
-0,0
774
-0,0
157
-0,0
452
0,1216
0,0303
-0,0
389
-0,47
13 1
JD_AVG
0,2812
0,2841
-0,1
223
0,1809
-0,0
369
0,0262
0,0633
0,1242
-0,05
95 0,
2346 1
T7
-0,02
69 0,
0296
0,0891
-0,009
0,1279
-0,0
976
0,0687
0,1392
0,1713
-0,40
75
0,0266 1
CF3 0,
2357 0,
1148
-0,0
172
0,0303
-0,0
674
-0,0
525
0,0799
-0,0
764 0,
0272 0,
1498
0,1205
-0,2
822 1
CF2 0,
1102 0,
1693
-0,047
0,1077
-0,0
526
0,0941
0,0076
0,0103
-0,15
3 0,
4113
0,2142
-0,4
553
0,45
1 1
CF1 0,
3067 0,
0224
-0,0
059
0,1903
-0,0
522
0,0716
0,05
8
0,0957
-0,07
97 0,
1769
0,0574
-0,254
0,3645
0,4367 1
S3
-0,10
37 0,
0034
0,1084
-0,0
181
0,1174
-0,1
294
-0,025
-0,0
096 0,
2291
-0,42
54
-0,0
182
0,6429
-0,3
851
-0,4
678
-0,3
368 1
S2
-0,02
34
-0,00
16
0,0156
-0,0
285
0,03
4
-0,1
196
-0,0
588
0,1033
0,1613
-0,33
95
0,0779
0,5213
-0,2
299
-0,3
782
-0,1
585
0,6015 1
S1
-0,00
3 0,
0269
0,06
4
-0,0
472
0,1713
-0,1
069
0,0083
0,03
9 0,
2382
-0,28
84
0,0606
0,5365
-0,2
743
-0,3
583
-0,2
243
0,6448
0,5419 1
CM
3 0,
1803 0,
0866
0,0433
0,0202
-0,0
432
-0,053
-0,0
036
-0,0
706 0,
1887
-0,05
14
0,3172
0,1989
0,1954
-0,035
0,0706
0,1989
0,2198
0,2218 1
CM
2 0,
1808 0,
154
-0,1
553
-0,0
542
0,0687
-0,1
778
0,0837
-0,0
028 0,
3563
-0,33
73
0,29
4
0,50
7
-0,153
-0,3
139
-0,2
258
0,3785
0,4199
0,4714
0,3716 1
CM
1 0,
1723 0,
0208
-0,0
431
-0,0
577
0,1066
0,0258
-0,0
922
-0,0
545 0,
2664
-0,28
31
0,2272
0,34
3
-0,0
871
-0,2
113
-0,1
458
0,4123
0,4935
0,4102
0,3322
0,4685 1
T6 0,
0141 0,
0471
0,1818
0,0752
0,1328
-0,0
854
0,0313
0,0553
0,3023
-0,50
68
0,0015
0,5558
-0,2
456
-0,4
195
-0,2
655
0,5483
0,4947
0,3798
0,2539
0,3849
0,3896 1
T5 0,
0688 0,
0917
0,04
9
-0,0
169
-0,019
-0,1
546
0,0336
0,0049
0,2603
-0,29
91
0,19
8
0,5322
-0,0
977
-0,3
079
-0,2
992
0,5434
0,4814
0,4675
0,3181
0,4617
0,4466
0,4634 1
T4 0,
0745 0,
0046
0,0635
0,1014
0,1082
-0,1
218
0,0672
0,13
7 0,
336
-0,40
28
0,1052
0,4058
-0,0
751
-0,3
438
-0,1
659
0,2715
0,2746
0,2906
0,3148
0,4322
0,2522
0,4601
0,4066 1
T3
-0,08
11
-0,04
98
0,0442
-0,0
086
0,0387
-0,082
-0,0
669
-0,0
094 0,
2237
-0,32
73
0,0324
0,5983
-0,2
614
-0,2
488
-0,1
797
0,4923
0,3696
0,4626
0,2296
0,2957
0,2643
0,4489
0,3348
0,2525 1
T2R
-0,19
97
-0,04
79
0,1788
-0,0
372
0,06
4
-0,0
534
-0,1
307
0,1021
-0,01
13
-0,32
41
-0,0
831
0,5902
-0,3
742
-0,3
969
-0,3
726
0,5369
0,4044
0,3742
0,0329
0,2617
0,23
8
0,4729
0,3061
0,1928
0,4923 1
T1
-0,06
49 0,
0505
0,0376
0,02
8
0,14
5
-0,055
0,0022
0,1519
0,2033
-0,37
1
-0,0
258
0,6379
-0,4
341
-0,4
268
-0,2
821
0,7102
0,5523
0,6104
0,0736
0,4029
0,3157
0,4635
0,4082
0,3269
0,5227
0,4857
51
Appendix B – Model Analyses Outcomes
Basic Model, without Geyskens, no controls
Standardized Regression Weights: (Group number 1 - Default model)
Estimate Trust <--- JD_AVG 0,1649 Commit <--- JD_AVG 0,4954 Satisf <--- JD_AVG 0,1422 Conflict <--- JD_AVG 0,1336 Trust <--- SAD_AVG -0,5534 Commit <--- SAD_AVG -0,3622 Satisf <--- SAD_AVG -0,4636 Conflict <--- SAD_AVG 0,4452 Trust <--- BAD_AVG 0,02 Commit <--- BAD_AVG 0,2807 Satisf <--- BAD_AVG 0,0572 Conflict <--- BAD_AVG 0,0632 T1 <--- Trust 0,7291 T2R <--- Trust 0,659 T3 <--- Trust 0,683 T4 <--- Trust 0,5109 T5 <--- Trust 0,6086 T6 <--- Trust 0,7105 CM1 <--- Commit 0,6387 CM2 <--- Commit 0,7816 CM3 <--- Commit 0,5148 S1 <--- Satisf 0,7523 S2 <--- Satisf 0,7174 S3 <--- Satisf 0,8621 CF1 <--- Conflict 0,4991 CF2 <--- Conflict 0,8821 CF3 <--- Conflict 0,5142 T7 <--- Trust 0,8583
Covariances: (Group number 1 - Default model)
Estimate S.E. C.R. P Label
SAD_AVG <--> BAD_AVG -0,1001 0,0194 -5,1516 ***
Correlations: (Group number 1 - Default model)
Estimate SAD_AVG <--> BAD_AVG -0,4713 Squared Multiple Correlations: (Group number 1 - Default model)
Estimate Conflict
0,1935
Satisf
0,2634 Commit
0,5512
Trust
0,3443 T7
0,7366
CF3
0,2644 CF2
0,7781
CF1
0,2491 S3
0,7432
S2
0,5146 S1
0,5659
CM3
0,265 CM2
0,6108
CM1
0,408 T6
0,5048
T5
0,3704 T4
0,261
T3
0,4664 T2R
0,4343
T1 0,5315 Model Fit
Summary
CMIN
52
Model NPAR CMIN DF P CMIN/DF
Default model 48 454,622 142 0 3,2016
Saturated model 190 0 0
Independence model 19 1312,929 171 0 7,6779
RMR, GFI
Model RMR GFI AGFI PGFI
Default model 0,2987 0,7494 0,6648 0,5601 Saturated
model 0 1
Independence
model 0,5502 0,3056 0,2285 0,2751 RMSEA
Model RMSEA LO 90 HI 90 PCLOSE
Default model 0,1228 0,1102 0,1355 0
Independence model 0,2139 0,2032 0,2247 0
AIC
Model AIC BCC BIC CAIC
Default model 550,622 565,8601 694,1627 742,1627
Saturated model 380 440,3175 948,1822 1138,1822
Independence model 1350,929 1356,9607 1407,7472 1426,7472
Basic Model, with Geyskens, no controls
Standardized Regression Weights: (Group number 1 - Default model)
Estimate Satisf <--- JD_AVG 0,141 Satisf <--- SAD_AVG -0,4646 Satisf <--- BAD_AVG 0,056 Conflict <--- JD_AVG 0,2243 Conflict <--- SAD_AVG 0,1518 Conflict <--- BAD_AVG 0,1112 Conflict <--- Satisf -0,6379 Trust <--- JD_AVG 0,0701 Trust <--- SAD_AVG -0,1357 Trust <--- BAD_AVG -0,0096 Trust <--- Conflict -0,1157 Trust <--- Satisf 0,7903 Commit <--- JD_AVG 0,3925 Commit <--- SAD_AVG -0,0312 Commit <--- BAD_AVG 0,2632 Commit <--- Trust 0,6021 T1 <--- Trust 0,7778 T2R <--- Trust 0,6487 T3 <--- Trust 0,6459 T4 <--- Trust 0,4904 T5 <--- Trust 0,6421 T6 <--- Trust 0,7038 CM1 <--- Commit 0,6506 CM2 <--- Commit 0,7841 CM3 <--- Commit 0,4933 S1 <--- Satisf 0,7462 S2 <--- Satisf 0,718 S3 <--- Satisf 0,8663 CF1 <--- Conflict 0,5317 CF2 <--- Conflict 0,8107 CF3 <--- Conflict 0,5696 T7 <--- Trust 0,8286
Covariances: (Group number 1 - Default model)
53
Estimate S.E. C.R. P Label
SAD_AVG <--> BAD_AVG -0,1001 0,0194 -5,1516 ***
Correlations: (Group number 1 - Default model)
Estimate SAD_AVG <--> BAD_AVG -0,4713 Squared Multiple Correlations: (Group number 1 - Default model)
Estimate Satisf
0,2634
Conflict
0,4925 Trust
0,9063
Commit
0,7855 T7
0,6866
CF3
0,3245 CF2
0,6573
CF1
0,2828 S3
0,7505
S2
0,5155 S1
0,5568
CM3
0,2433 CM2
0,6149
CM1
0,4232 T6
0,4953
T5
0,4122 T4
0,2405
T3
0,4172 T2R
0,4209
T1 0,605 Model Fit
Summary
CMIN
Model NPAR CMIN DF P CMIN/DF
Default model 52 259,3808 138 0 1,8796
Saturated model 190 0 0
Independence model 19 1312,929 171 0 7,6779
RMR, GFI
Model RMR GFI AGFI PGFI
Default model 0,1353 0,8376 0,7763 0,6083 Saturated
model 0 1
Independence
model 0,5502 0,3056 0,2285 0,2751
RMSEA
Model RMSEA LO 90 HI 90 PCLOSE
Default model 0,0776 0,063 0,0921 0,0015 Independence
model 0,2139 0,2032 0,2247 0
AIC
Model AIC BCC BIC CAIC
Default model 363,3808 379,8887 518,8833 570,8833 Saturated
model 380 440,3175 948,1822 1138,1822 Independence
model 1350,929 1356,9607 1407,7472 1426,7472
Basic Model with Geyskens and controls
Maximum Likelihood Estimates
Regression Weights: (Group number 1 - Default model)
54
Estimate S.E. C.R. P Label
Satisf <--- JD_AVG 0,1335 0,0593 2,2509 0,0244
Satisf <--- SAD_AVG -0,622 0,1395 -4,4581 ***
Satisf <--- BAD_AVG 0,1407 0,1622 0,8678 0,3855
Satisf <--- TURN 0,0479 0,0749 0,6391 0,5227
Satisf <--- AGE -0,0189 0,0087 -2,1712 0,0299
Satisf <--- DURPR 0,0119 0,0071 1,6678 0,0954
Satisf <--- EMP -0,0735 0,06 -1,2258 0,2203
Satisf <--- TEN 0,0115 0,0083 1,3838 0,1664
Satisf <--- EXCH 0,0013 0,0696 0,0191 0,9847
Satisf <--- MP -0,0506 0,0423 -1,1964 0,2316
Conflict <--- JD_AVG 0,0997 0,07 1,4243 0,1544
Conflict <--- SAD_AVG 0,2616 0,1776 1,4729 0,1408
Conflict <--- BAD_AVG 0,1818 0,1902 0,9554 0,3394
Conflict <--- Satisf -0,7188 0,1636 -4,3933 ***
Conflict <--- TURN -0,0623 0,0863 -0,7223 0,4701
Conflict <--- DUROR -0,0021 0,0074 -0,2851 0,7756
Conflict <--- AGE -0,009 0,0103 -0,8696 0,3845
Conflict <--- DURPR 0,0037 0,0083 0,4417 0,6587
Conflict <--- EMP 0,0849 0,0713 1,1914 0,2335
Conflict <--- TEN 0,0089 0,0098 0,9014 0,3674
Conflict <--- EXCH 0,114 0,0815 1,3995 0,1617
Conflict <--- MP 0,0962 0,0581 1,6553 0,0979
Trust <--- JD_AVG 0,0469 0,0556 0,8424 0,3996
Trust <--- SAD_AVG -0,2689 0,1354 -1,9866 0,047
Trust <--- BAD_AVG -0,0582 0,1473 -0,3951 0,6928
Trust <--- Conflict -0,1842 0,1356 -1,3582 0,1744
Trust <--- Satisf 1,0589 0,1863 5,6835 ***
Trust <--- TURN 0,1168 0,0675 1,7284 0,0839
Trust <--- DUROR 0,0036 0,0055 0,6482 0,5168
Trust <--- AGE -0,0013 0,0079 -0,1624 0,871
Trust <--- DURPR -0,0011 0,0063 -0,1804 0,8568
Trust <--- EMP -0,0259 0,0543 -0,4768 0,6335
Trust <--- TEN 0,0068 0,0075 0,9023 0,3669
Trust <--- EXCH 0,0243 0,063 0,3867 0,699
Trust <--- MP 0,0383 0,0397 0,9648 0,3347
Commit <--- JD_AVG 0,3111 0,0698 4,459 ***
Commit <--- SAD_AVG -0,0286 0,1662 -0,1722 0,8633
Commit <--- BAD_AVG 0,7245 0,1843 3,9319 ***
Commit <--- Trust 0,6042 0,111 5,4444 ***
Commit <--- TURN -0,1209 0,0816 -1,4822 0,1383
Commit <--- DUROR -0,0028 0,0069 -0,4042 0,686
Commit <--- AGE 0,0059 0,0096 0,6125 0,5402
Commit <--- DURPR 0,0092 0,0075 1,2231 0,2213
Commit <--- EMP -0,0521 0,0634 -0,8214 0,4114
Commit <--- TEN -0,0232 0,0092 -2,5126 0,012
Commit <--- EXCH -0,0126 0,0748 -0,1683 0,8664
Commit <--- MP 0,1075 0,046 2,3376 0,0194
T1 <--- Trust 1
T2R <--- Trust 0,9131 0,1153 7,9181 ***
T3 <--- Trust 0,6603 0,0867 7,6192 ***
T4 <--- Trust 0,665 0,1159 5,7358 ***
T5 <--- Trust 0,8925 0,1172 7,6158 ***
T6 <--- Trust 0,797 0,0936 8,5115 ***
CM1 <--- Commit 1
CM2 <--- Commit 1,2976 0,179 7,2483 ***
CM3 <--- Commit 0,6473 0,1363 4,7509 ***
S1 <--- Satisf 1
S2 <--- Satisf 1,3134 0,1576 8,3359 ***
S3 <--- Satisf 1,4866 0,1459 10,1903 ***
CF1 <--- Conflict 1
CF2 <--- Conflict 1,3323 0,2403 5,5448 ***
CF3 <--- Conflict 1,1359 0,2184 5,2008 ***
T7 <--- Trust 0,8847 0,0841 10,5189 ***
Standardized Regression Weights: (Group number 1 - Default model)
Estimate Satisf <--- JD_AVG 0,1957 Satisf <--- SAD_AVG -0,4139 Satisf <--- BAD_AVG 0,0768 Satisf <--- TURN 0,0694 Satisf <--- AGE -0,1964 Satisf <--- DURPR 0,1363 Satisf <--- EMP -0,1353 Satisf <--- TEN 0,121 Satisf <--- EXCH 0,0016 Satisf <--- MP -0,096 Conflict <--- JD_AVG 0,1316 Conflict <--- SAD_AVG 0,1568 Conflict <--- BAD_AVG 0,0893 Conflict <--- Satisf -0,6472 Conflict <--- TURN -0,0813 Conflict <--- DUROR -0,0231
55
Conflict <--- AGE -0,0841 Conflict <--- DURPR 0,038 Conflict <--- EMP 0,1408 Conflict <--- TEN 0,0839 Conflict <--- EXCH 0,1213 Conflict <--- MP 0,1641 Trust <--- JD_AVG 0,0492 Trust <--- SAD_AVG -0,1283 Trust <--- BAD_AVG -0,0228 Trust <--- Conflict -0,1466 Trust <--- Satisf 0,7591 Trust <--- TURN 0,1213 Trust <--- DUROR 0,0307 Trust <--- AGE -0,0096 Trust <--- DURPR -0,0094 Trust <--- EMP -0,0342 Trust <--- TEN 0,0511 Trust <--- EXCH 0,0206 Trust <--- MP 0,052 Commit <--- JD_AVG 0,352 Commit <--- SAD_AVG -0,0147 Commit <--- BAD_AVG 0,305 Commit <--- Trust 0,6505 Commit <--- TURN -0,1353 Commit <--- DUROR -0,0262 Commit <--- AGE 0,0472 Commit <--- DURPR 0,0818 Commit <--- EMP -0,074 Commit <--- TEN -0,1881 Commit <--- EXCH -0,0115 Commit <--- MP 0,1572 T1 <--- Trust 0,7857 T2R <--- Trust 0,6653 T3 <--- Trust 0,6408 T4 <--- Trust 0,504 T5 <--- Trust 0,6476 T6 <--- Trust 0,7136 CM1 <--- Commit 0,6662 CM2 <--- Commit 0,7903 CM3 <--- Commit 0,4717 S1 <--- Satisf 0,7518 S2 <--- Satisf 0,718 S3 <--- Satisf 0,8749 CF1 <--- Conflict 0,5707 CF2 <--- Conflict 0,7703 CF3 <--- Conflict 0,5985 T7 <--- Trust 0,8388
Covariances: (Group number 1 - Default model)
Estimate S.E. C.R. P Label
SAD_AVG <--> BAD_AVG -0,1001 0,0194 -5,1516 ***
Correlations: (Group number 1 - Default model)
Estimate SAD_AVG <--> BAD_AVG -0,4713 Squared Multiple Correlations: (Group number 1 - Default model)
Estimate Satisf
0,3496
Conflict
0,5772 Trust
0,92
Commit
0,8806 T7
0,7035
CF3
0,3582 CF2
0,5933
CF1
0,3257 S3
0,7655
S2
0,5155 S1
0,5653
CM3
0,2225 CM2
0,6246
CM1
0,4438 T6
0,5093
T5
0,4194 T4
0,254
T3
0,4106 T2R
0,4427
T1 0,6173 Model Fit
Summary
CMIN
56
Model NPAR CMIN DF P CMIN/DF
Default model 91 605,5585 287 0 2,11
Saturated model 378 0 0
Independence model 27 1709,5563 351 0 4,8705
RMR, GFI
Model RMR GFI AGFI PGFI
Default model 2,0602 0,7749 0,7035 0,5883 Saturated
model 0 1
Independence
model 2,1139 0,3597 0,3105 0,334 RMSEA
Model RMSEA LO 90 HI 90 PCLOSE
Default model 0,0872 0,0775 0,0969 0
Independence model 0,1628 0,1551 0,1706 0
AIC
Model AIC BCC BIC CAIC
Default model 787,5585 830,7449 1059,6878 1150,6878
Saturated model 756 935,3898 1886,3835 2264,3835
Independence model 1763,5563 1776,3698 1844,2979 1871,2979
Rival Model
Standardized Regression Weights: (Group number 1 - Default model)
Estimate Satisf <--- JD_AVG 0,0825 Satisf <--- DASS_AVG -0,2714 Conflict <--- JD_AVG 0,2261 Conflict <--- DASS_AVG 0,1372 Conflict <--- Satisf -0,6328 Trust <--- JD_AVG 0,0519 Trust <--- DASS_AVG -0,0792 Trust <--- Conflict -0,1153 Trust <--- Satisf 0,8367 Commit <--- JD_AVG 0,3582 Commit <--- DASS_AVG 0,137 Commit <--- Trust 0,7587 T1 <--- Trust 0,7745 T2R <--- Trust 0,6502 T3 <--- Trust 0,6405 T4 <--- Trust 0,4746 T5 <--- Trust 0,6336 T6 <--- Trust 0,6909 CM1 <--- Commit 0,6378 CM2 <--- Commit 0,7599 CM3 <--- Commit 0,4899 S1 <--- Satisf 0,7428 S2 <--- Satisf 0,7144 S3 <--- Satisf 0,863 CF1 <--- Conflict 0,5317 CF2 <--- Conflict 0,8147 CF3 <--- Conflict 0,5681 T7 <--- Trust 0,8298 Squared Multiple Correlations: (Group number 1 - Default model)
Estimate Satisf
0,0805
Conflict
0,4939 Trust
0,8948
Commit
0,7065
57
T7
0,6885 CF3
0,3227
CF2
0,6637 CF1
0,2827
S3
0,7448 S2
0,5104
S1
0,5517 CM3
0,24
CM2
0,5775 CM1
0,4068
T6
0,4774 T5
0,4014
T4
0,2252 T3
0,4102
T2R
0,4227 T1 0,5998 Model Fit
Summary
CMIN
Model NPAR CMIN DF P CMIN/DF
Default model 46 223,9897 125 0 1,7919
Saturated model 171 0 0
Independence model 18 1191,5943 153 0 7,7882
RMR, GFI
Model RMR GFI AGFI PGFI
Default model 0,1345 0,8452 0,7883 0,6179 Saturated
model 0 1
Independence
model 0,5759 0,3172 0,2369 0,2838 RMSEA
Model RMSEA LO 90 HI 90 PCLOSE
Default model 0,0736 0,0578 0,0891 0,0085
Independence model 0,2156 0,2043 0,2271 0
AIC
Model AIC BCC BIC CAIC
Default model 315,9897 329,7535 453,5496 499,5496
Saturated model 342 393,1654 853,364 1024,364
Independence model 1227,5943 1232,9801 1281,4221 1299,4221
58
Appendix C - Survey
INSTRUCTIONS:
Please take in mind the relationship with a supplier that is of considerable importance to your firm. It is
important that answers to all questions consider the relationship with this specific supplier. After closing
you can return to this questionnaire at any time to complete or change your entries.
The information that you provide here will be processed anonymously.
Thank you for taking the time to fill out this questionnaire.
BSc. Vivian Rutten, RMsc student Organisation & Strategy, Tilburg University, CentER institute for
applied research
Prof. Bart Vos, Department of Organisation & Strategy, Tilburg University
General
DUROR For how many years has your firm had an exchange relationship with this supplier?
DURPR For how many years have you yourself been in contact with this specific supplier?
EXCH What is the nature of the unit of exchange between your firm and this supplier? (Product, Service, Both)
Relationship Quality/OutcomesScale: 'Strongly disagree ---- Strongly agree' (7-point likert).
Trust (Doney and Cannon 1997) Standardized regression weights α=0,84
T1 This supplier keeps promises it makes to our firm. 0,78
T2R This supplier is not always honest with us. ( R ) 0,65
T3 We believe the information that this vendor provides us. 0,65
T4 This supplier is genuinely concerned that our business succeeds. 0,47
T5 When making important decisions, this supplier considers our welfare as well as its own. 0,63
T6 We trust this vendor keeps our best interest in mind. 0,68
T7 This supplier is trustworthy. 0,83
T8R We find it necessary to be cautious with this supplier. ( R ) Insignificant, dropped from analysis
Conflict (Kumar, Stern and Achrol 1992; Jap and Ganesan 2000) Standardized regression weights α=0,68
CF1 The level of conflict in the relation with this supplier can be best described as tense. 0,56
CF2 We have significant disagreements in our working relationship with this supplier. 0,77
CF3 We frequently clash with this supplier on issues relating to how we should conduct our business. 0,60
Commitment (Morgan and Hunt 1999) Standardized regression weights α=0,66
CM1 The relationship that our organization has with this buyer/supplier is something my organization intends to maintain indefinitely. 0,67
CM2 My organization plans to have a close relationship with this buyer/supplier in the near future. 0,73
CM3 Enhancing our relationship with this buyer/supplier is an important objective for our organization. 0,47
Satisfaction (Jap 2000) Standardized regression weights α=0,81
S1 Our relationship with this supplier has been a successful one. 0,74
S2 Our relationship with this supplier has more than fulfilled our expectations. 0,72
S3 We are satisfied with the outcomes of the relationship with this supplier. 0,86
Dependency (Gulati and Sytch 2007) Common Factor Analysis of Standardized values w ith Orthogonal Varimax Rotation Factor1 Factor2
DEP1 It would require much trouble for our firm to switch supplier for this product/service. 0.4276 0.2670
DEP2R There are enough potential supplier to ensure adequate competition among the current suppliers. 0.6889 0.0300
DEP3R There are satisfactory alternate sources of short-term supply for this product/service. 0.6988 0.1362
DEP4 This supplier has an advantage over other competitors in their field. -0.0060 0.3076
DEP5 This supplier has adapted its management methods to work effectively with our firm. -0.1668 0.4959
DEP6 Our firm has made significant investments for this specific relationship. 0.2991 0.5545
DEP7R There are enough potential alternative buyers for this supplier to ensure adequate competition among current buyers. 0.5953 0.0766
DEP8 This supplier would face a serious financial crisis if our firms withdrew business from them. 0.1858 0.3692
DEP9 This supplier has made significant investments for this specific relationship. 0.1184 0.7279
DEP10 What is the approximate total euro volume purchased from this supplier for this specific product/service?* 0.1537 0.3614
DEP11 What percentage of the total volume needed of this product/service is purchased from this particular supplier? 0.1621 0.1191
DEP12 What percentage of total sales of the supplier sold to your firm, approximately? 0.1468 0.1603
R Scales with R are inverted scales.
* Categories: (1) up to 1 milion, (2) 1-5 milion, (3) 5-20 milion, (4) 20-50 milion. Variance explained: 0.4345 0.3955
MP We are extensively tied to this supplier trough additional business ties (e.g. shared board member, charity boards etc.).
AGE What is your age?
TEN For how many years have you been working for your firm?
EMP How many employees does your firm have?
TURN What is the approximate annual turnover of your firm in millions of euro's?