RMSc Thesis Final VivianRuttens971271

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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.

Transcript of RMSc Thesis Final VivianRuttens971271

Page 1: RMSc Thesis Final VivianRuttens971271

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

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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

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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

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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)

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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):

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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

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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

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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

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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

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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

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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.

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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).

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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.

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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

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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

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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.

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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

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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.

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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.

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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

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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

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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.

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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

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(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

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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

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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

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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.

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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.

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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.

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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

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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

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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

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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).

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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

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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

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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

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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

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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

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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.

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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

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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

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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

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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)

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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)

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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

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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

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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

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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

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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?