supplier evaluations: communication strategies to improve supplier performance

Journal of Operations Management 22 (2004) 39–62

Supplier evaluations: communication strategies toimprove supplier performance

Carol Prahinskia,∗, W.C. Bentonb,1

a Richard Ivey School of Business, Operations Management Area Group, University of Western Ontario,London, Ontario, N6A 3K7 Canada

b Fisher College of Business, Department of Management Sciences, The Ohio State University, Columbus, OH 43210, USA

Received 1 July 2002; received in revised form 7 October 2003; accepted 1 December 2003

Abstract

As firms increasingly emphasize cooperative relationships with critical suppliers, executives of buyer firms are using sup-plier evaluations to ensure that their performance objectives are met. Supplier evaluations, one type of supplier developmentprogram (SDP), are an attempt to meet current and future business needs by improving supplier performance and capabilities.The purpose of this study was to determine how suppliers perceive the buying firm’s supplier evaluation communicationprocess and its impact on suppliers’ performance. Three communication strategies (indirect influence strategy, formality andfeedback) were tested separately and one in unison (collaborative).

Using structural equation modeling (SEM) and data collected from 139 first-tier North American automotive suppliers,the results of this research have shown that, contrary to the SDP literature from the buying firm’s perspective, the supplier’sperceptions of the buying firm’s communication does not directly influence suppliers’ performance. Specifically, the supplierevaluation communication process does not ensure improved supplier performance unless the supplier is committed to thebuying firm. Buying firms can influence the supplier’s commitment through increased efforts of cooperation and commitment.The results also indicate that when a buying firm utilizes collaborative communication, the supplier perceives a positiveinfluence on the buyer–supplier relationship.© 2004 Elsevier B.V. All rights reserved.

Keywords:Supply chain management; Supplier evaluations; Supplier development; Supply chain communication strategies

1. Introduction

In today’s business environment, there is an em-phasis on developing long-term cooperative relation-ships with critical suppliers. Business managers arereducing their supply base and thereby increasingthe buying volume with the remaining suppliers.

∗ Corresponding author. Tel.:+1-519-661-3305.E-mail addresses:[email protected] (C. Prahinski),[email protected] (W.C. Benton).

1 Tel.: +1-614-292-8868.

Many executives are hesitant to rely on an untestedsupplier without first taking the time to build an ef-fective relationship to ensure specific performanceobjectives.

When a supplier is unable to conform to the buyingfirm’s expectations, the buying firm manager mustdetermine the most appropriate action to resolve theissue. To maintain the working relationship, the man-ager must find a way to communicate the problemand motivate the supplier to change its results. The re-search framework herein will focus on the suppliers’perceptions of a buying firm’s attempts to motivate

0272-6963/$ – see front matter © 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.jom.2003.12.005

40 C. Prahinski, W.C. Benton / Journal of Operations Management 22 (2004) 39–62

suppliers through supplier development programs, andin particular, supplier evaluations. The buying firmdevelops the supplier evaluation, or report card, andcommunicates the results to its suppliers with the hopeand expectation that the supplier will address notedshortcomings (Morgan, 2001; Purdy et al., 1994).

Many supplier development programs (SDPs),however, are not successful (Krause et al., 2000;Monczka et al., 1993; Porter, 1991; Purdy et al.,1994; Watts and Hahn, 1993). A number of studieshave emphasized the need to determine the con-tributing factors of SDP success or failure (Krauseet al., 2000; Purdy et al., 1994). To our knowledge, astudy byKrause et al. (2000)was the only study thatconsidered the importance of SDPs on supply chainperformance. Two studies have addressed the buyingfirm’s perspective of the impact of supplier evalu-ations on the buyer–supplier relationship (Krause,1999; Carr and Pearson, 1999). To date, there hasbeen little investigation of the suppliers’ reactions toSDPs and the impact of supplier evaluation communi-cation on the suppliers’ performance. It is not knownwhether SDPs are effective in improving the supplier’sperformance.

The purpose of this research is to assess thesupplier’s perceptions of four buying firm’s supplierevaluation communication strategies (indirect influ-ence strategy, formality, feedback and collaborativecommunication) and determine how specific commu-nication strategies influence suppliers’ performance.The supplier’s perceptions of the buyer–supplier rela-tionship and the supplier’s commitment to the buyingfirm are tested as possible mediators. This study isimportant because a buying firm’s performance in-creasingly hinges on the capabilities of its supplybase.

The following research questions are investigatedfrom the supplying firm perspective: (1) is the im-pact of the buying firm’s strategy for communicat-ing supplier performance evaluations mediated bythe buyer–supplier relationship and supplier’s com-mitment? (2) Do suppliers perceive that the buyingfirm’s communication of the evaluation affects theirperformance?

In the following section, the relevant literature is re-viewed. The conceptual model and research hypothe-ses are then developed. Subsequently, the researchmethodology is described. The analysis and results are

presented in section five. The paper concludes with acomprehensive discussion and conclusions.

2. Literature review

The literature review is organized into five sections:supplier development programs with an emphasis onsupplier evaluations, inter-organizational communica-tion strategies, buyer–supplier relationships, supplier’scommitment, and supplier’s performance.

2.1. Supplier development programs—supplierevaluations

SDPs are defined as activities undertaken by thebuying firms in their efforts to measure and improvethe products or services they receive from their sup-pliers. From the buyer’s perspective, SDPs are war-ranted when the buying firm perceives that the currentsupplier base is unable to meet short and long-termbusiness objectives (Handfield et al., 2000). The buy-ing firms’ typically selects a small number of criticalsuppliers to focus their improvement effort (Watts andHahn, 1993).

Although there are several different types of SDPs(Krause, 1997), the supplier evaluation process wasselected as the main focus of this research becausethe buying firm’s assessment of the supplier’s perfor-mance was considered a catalyst for all SDP’s. Basedon the evaluation process, the buying firm can deter-mine if the supply base is capable of meeting currentand future business needs. The buying firm needs toquantify and communicate the measurements and tar-gets to the supplier so that the supplier is made awareof the discrepancy between its current performanceand the buying firm’s expectations. Without an ef-fective measurement and communication system, theinter-organizational coordination and improvementinitiatives would be ineffective.

To our knowledge, no research has directly ad-dressed different supplier evaluation communicationstrategies. Supplier evaluations could include bothprocess and content (Hartley and Choi, 1996; Porter,1991; Purdy et al., 1994), however, many recentstudies emphasize only the quality performance as-pect (e.g.,Forker et al., 1999; Park et al., 2001).There has been no comprehensive survey research

C. Prahinski, W.C. Benton / Journal of Operations Management 22 (2004) 39–62 41

on supplier evaluation and SDP from the supplier’sperspective.

2.2. Inter-organizational communication

Communication has been described as “the gluethat holds together a channel of distribution” (Mohrand Nevin, 1990). According to Mohr and Nevin(1990), there are four categories of communication:content, medium, feedback and frequency. In orderto adequately assess communication, three of thefour components were included in this study and arefurther described below. Communicationfrequencywas excluded since it is often measured with othercommunication constructs, predominantly content ormedium (e.g.,Boyle and Alwitt, 1999; Hartley et al.,1997; Mohr and Sohi, 1995).

Contentrefers to the message that is transmitted.Two predominant subcategories are: the type of infor-mation exchanged and the type of influence strategyembedded in the exchange, such as direct or indirectinfluence. Indirect influence strategy is designed tochange the recipient’s beliefs and attitudes, such asthrough education and communication of the evalua-tion, so that the recipients have more complete knowl-edge for decision-making (Boyle and Dwyer, 1995;Frazier and Sheth, 1985). Since direct influence strat-egy, such as power influence, was studied from thesupplier’s perspective inMaloni and Benton (2000),this research will examine indirect influence strategywith supplier evaluations as the type of informationexchanged.2

Communicationmediumrefers to the method usedto transmit information. Two predominant classifica-tion schemes include: medium richness and formality.Medium richness is defined as the number of cues thatcan be used by the receiver to interpret the messageand ranges from face-to-face, which is considered therichest medium, to electronic data transfer which isconsidered the least rich medium (Daft and Lengel,1986). Formality assesses the structure and routine of

2 In Krause et al. (2000), the term for the buying firms’ per-ceptions of indirect influence strategy was “direct involvement,”where the buying firm directly involves itself in the supplier devel-opment effort. Since the focus of our research is on the suppliers’perceptions of the embedded communication strategy rather thanthe development effort expended by the buying firm, we will referto the construct as “indirect influence strategy.”

the communication (Carr and Pearson, 1999; Mohrand Sohi, 1995). Because of the categorical natureof medium richness, communication formality will bestudied in this research. Formality is defined as thedegree to which the inter-organizational communica-tion of the supplier evaluation is established throughstructured rules and fixed procedures.

Communicationfeedback, also called bi-directio-nality, refers to two-way communication between twofirms (Mohr and Sohi, 1995; Purdy et al., 1994). Thisresearch will assess feedback as the supplier attemptsto discuss the buying firm’s evaluation of the supplier’sperformance. The focus is on clarifying the expecta-tions and the evaluation process.

Mohr and Nevin (1990)proposed that the dimen-sions of communication would function together ina specific combination based on channel conditions.They coined the phrase “collaborative communica-tion strategy,” which was more likely to occur inrelational structures, supportive climates and symmet-rical power. As inMohr et al. (1996), collaborativecommunication is defined in this research as a com-munication effort that emphasizes indirect influencestrategy, formality and feedback in unison.

There are several gaps in the communication lit-erature. First, the influence of the various types ofcommunication strategies on the supplier’s perfor-mance is unknown. Several studies from the buyingfirm’s perspective assessed the indirect influencestrategy or formality on the buying firm’s perfor-mance (D’Amours et al, 1999;Krause et al., 2000;Walton and Marucheck, 1997). From the supplier’sperspective, to our knowledge only one study has fo-cused on the impact of indirect influence strategy onone performance measure, JIT shipment performance(Srinivasan et al., 1994). Moreover, there are no stud-ies that have investigated the supplier’s perspectiveof the buying firm’s communication on supplier’sperformance.

To date, there have not been any studies to deter-mine whether the supplier evaluation communicationstrategy has an effect, either directly or indirectly, onthe supplier’s performance. Although several studieshave stated that they expect a direct effect (e.g.,Krauseet al., 2000), the relationship has not been empiricallytested. Third, if there is an indirect effect, the literaturesuggests that the buyer–supplier relationship (BSR)may be a mediator (Johnston and Lewin, 1996).


2.3. Buyer–supplier relationships

In the BSR literature, two predominant classifica-tions exist. The first is based on the relationship asa transformation process, e.g., from awareness, ex-ploration, expansion, and commitment to dissolution(Dwyer et al., 1987). The second classification isbased on the components of the BSR at one point intime, such as structural governance ranging from atransactional-based relationship to a strategic allianceor vertical integration (Cooper and Gardner, 1993;Webster, 1992), or the continuum between com-petitive and cooperative orientation (Ellram andHendrick, 1995). Researchers are increasingly assess-ing multiple dimensions of the characteristics thatsustain the relationship or partnership (Boyle et al.,1992; Cannon and Perreault, 1999; Heide and John,1990; Lambert et al., 1996; Maloni and Benton, 2000).

For this research, BSR is defined as the supplier’sperception of the buying firm’s behavioral and oper-ational relationship attributes: buying firm’s commit-ment, cooperation and operational linkages. Althoughmany possible dimensions could be included in thestudy (e.g.,Johnston and Lewin, 1996; Lambert et al.,1996; Wilson, 1995), these three dimensions provideda representative sample of several important relation-ship characteristics, both behavioral and operational.Two theories were relied upon in the development ofBSR: transactional cost analysis and social exchangetheory (e.g.,Blau, 1964; Ring and Van de Ven, 1994).

Based on prior empirical research,commitmentwasshown to contain three components: investment inthe trading partner, affective commitment and the ex-pectation of the relationship extending into the fu-ture (Kumar et al., 1995). For this research, the buy-ing firm’s commitment was defined as the suppliers’perception of the degree to which the buying firmfeels pledged or obligated to continue business witha specific supplier. This commitment can be reflectedby loyalty, willingness to make investments in thesupplier’s business, and confidence in the stability ofa long-term relationship (Anderson and Weitz, 1992).

Based on prior empirical research,cooperationwasshown to contain market flexibility (Boyle et al., 1992;Heide and Miner, 1992) and problem solving (Cannonand Perreault, 1999; Heide and Miner, 1992). For thecurrent study, cooperation is defined as the supplier’sperceptions of the degree to which the two trading part-

ners work together to solve problems, establish strate-gic directions and achieve their mutual goals (Cannonand Perreault, 1999; Maloni and Benton, 2000).

Operational linkageswere considered importantsince it would permit information exchange to bemeasured on a tactical level. The operational link-ages construct is defined as the supplier’s perceptionsof the degree to which the buying and selling firmscoordinate their systems, procedures and routines tofacilitate operations (Cannon and Perreault, 1999).

2.4. Supplier’s commitment

Anderson and Weitz (1992)found that each chan-nel member’s commitment to the relationship wasbased on its perceptions of the other party’s commit-ment. They found that the buying firm’s commitmentpositively influences the supplier’s commitment.Later, Krause (1999)found that the buying firm’sperceptions of the supplier’s commitment positivelyinfluenced the buying firm’s commitment to thesupplier.

In the current study, supplier’s commitment isdefined as the degree to which the supplier feels obli-gated to continue business with the particular buyingfirm. In several studies, commitment was based oninvestments, as developed in the transactional costanalysis literature (e.g.,Cannon and Perreault, 1999;Heide and John, 1990). For this study, we chose toexpand our definition to include loyalty and longevity,as developed in the social exchange theory (Blau,1964; Ring and Van de Ven, 1994).

2.5. Supplier’s performance

Business performance improvement is at the heartof supplier development programs.Mentzer andKonrad (1991)stated that performance measurementis the evaluation of effectiveness and efficiency ofcompleting a given task. Effectiveness is the extent towhich goals are accomplished. Efficiency is a mea-sure of how well resources are utilized.Venkatramanand Ramanujam (1986)focused on organizational ef-fectiveness, and classified business performance mea-sures as either financial or operational (non-financial).Operational measures of performance can be classi-fied in two streams: key competitive success factors(e.g., quality, delivery, price, service, and flexibility)


and internal indicators, such as defects, schedulerealization and cost.

In the current study, the supplier’s performance isan operational measure of key competitive successfactors, namely product quality, delivery performance,price, responsiveness to change requests, servicesupport, and overall performance. The supplier’s per-formance directly influences the buying firm and is,therefore, a critical criterion for the buying firm.

3. Conceptual model and hypotheses

The research herein will test the linkages amongseveral constructs: supplier evaluation communicationstrategy, buyer–supplier relationship, supplier com-mitment and supplier performance. The relationshipsamong several constructs are hypothesized and thegeneral hypothesized model is shown inFig. 1. Fourmodels are developed: three communication strategies(indirect influence strategy, formality and feedback)are each tested in separate models and in the fourthmodel, the three strategies are tested in unison (calledcollaborative communication).

3.1. Dimensions of communication and theirinfluence on the buyer–supplier relationship

3.1.1. Indirect influence strategyIndirect influence strategy, such as with education,

training and site visits between two trading part-

H 5

Supplier EvaluationCommunication

Strategy

Buyer-SupplierRelationship

OperationalLinkages

Buying Firm’s Commitment

H4

H 3

H2a, 2b, 2c, 2d

H1a, 1b, 1c, 1d

Supplier’s Performance

Supplier’s Commitment

Cooperation

Fig. 1. Hypothesized Model.

ners, would integrate the businesses together with acommon language and shared objectives.Boyle andAlwitt (1999) found that as information sharing andoverall frequency of contact increased, BSR was en-hanced.Sibley and Michie (1982)found that indirectinfluence strategy positively influenced the BSR co-operation dimension. To date, the impact of indirectinfluence strategy on BSR has not been investigated.The formal hypothesis is given below:

H1a. Indirect influence strategy expressed by thebuying firm to the supplier positively influences thebuyer–supplier relationship.

3.1.2. FormalityCarr and Pearson (1999)found that formal commu-

nication of supplier evaluations positively influencedBSR. Similarly, Vijayasarathy and Robey (1997)found that communication formality had a positiveinfluence on cooperation, one dimension of BSR.Mohr and Sohi (1995)found that formality negativelyinfluenced distortion and withholding of information.All three studies addressed formality and aspects ofthe relationship from the buying firm’s perspective. Itis anticipated that formality will positively influencethe buyer–supplier relationship from the supplier’sperspective. The formal hypothesis is given below:

H1b. Communication formality established betweenthe buying firm and the supplier positively influencesthe buyer–supplier relationship.


3.1.3. FeedbackAnderson et al. (1987)found that channel mem-

bers allocated more time to suppliers with whomthey have good communication, participation andfeedback. Later,Anderson and Weitz (1989)foundmixed results regarding the impact of communica-tion feedback on BSR. As such, these studies didnot clearly establish the influence of feedback onBSR. It is anticipated that supplier evaluation feed-back opportunities will eliminate ambiguity, lead-ing to a closer perceived relationship, enhancedcommitment and cooperation between the supplierand buying firm. The specific hypothesis is givenbelow:

H1c. Communication feedback between the buy-ing firm and the supplier positively influences thebuyer–supplier relationship.

3.1.4. Collaborative communicationCollaborative communication coupled with the

supplier development program would more fully in-tegrate the buying firm and supplier. For the buyingfirm, the focus of the SDP is on meeting its cur-rent and future needs through a reduced number ofsuppliers. The development of BSR is integral tothe SDP communication effort. As a recipient of theSDP communication effort, suppliers can achievetheir business objectives of cost minimization, mar-ket growth and future sales with an improvement ofBSR.

From the buying firm’s perspective,Mohr et al.(1996) found that collaborative communication wassignificantly related to commitment, coordination andsatisfaction. They measured collaborative communi-cation as more frequent medium richness, feedback,formality and indirect influence.

By using collaborative communication, it is hypoth-esized that the supplier’s perceptions of BSR will bepositively influenced. The SDP communication effortcould be interpreted as an example of the buying firm’scommitment and cooperation attempts. The specifichypothesis is given below:

H1d. Collaborative communication between the buy-ing firm and the supplier positively influences thebuyer–supplier relationship.

3.2. The influence of communication strategy onperformance

3.2.1. Indirect influence strategyIn previous studies, researchers found that an in-

crease in the indirect influence strategy, such as witheducation programs, EDI communication and infor-mation sharing, improves the buying firm’s perfor-mance as measured by cost (D’Amours et al., 1999),delivery performance (Walton and Marucheck, 1997),sales, time, product design, and quality (Krauseet al., 2000). Each of these studies addressed theindirect influence strategy from the buying firm’sperspective.

Srinivasan et al. (1994)considered the impact ofone specific type of indirect influence strategy, EDItechnology, on JIT shipment performance from the au-tomotive supplier’s perspective. They found that whenthe buying firm shared their JIT schedule, deliveryperformance improved and shipment discrepancies de-creased. The hypothesis is given as:

H2a. Indirect influence strategy expressed by the buy-ing firm (source) to the supplier (target) positively in-fluences the supplier’s performance.

3.2.2. FormalityFormality of the evaluation process in relation to

supplier’s performance has been assessed in the SDPliterature.Krause et al. (2000)found that formality ofsupplier evaluations did not have a direct impact on thebuying firm’s performance, but rather, it was mediatedby the buying firm’s direct involvement through sitevisits, training and education programs. [As noted ear-lier, we use the term indirect influence strategy ratherthan direct involvement to represent the suppliers’perspective.]

It is hypothesized in the current study that withroutine and formal communication, the supplierevaluation process will create a more effective con-duit in communicating the buying firm’s operationaltargets and expectations. When the supplier under-stands the buying firm’s expectations, they can ef-fectively manage their business operations to meetthe needs and specifications of the buying firm. Itis hypothesized that the formality of the supplierevaluation process positively influences supplier’sperformance.


H2b. Communication formality established betweenthe buying firm and the supplier positively influencesthe supplier’s performance.

3.2.3. FeedbackWhen managers in firms conduct dialogue, feed-

back and clarification, they attempt to reduce theequivocality and uncertainty in the relationship (Daftand Lengel, 1986). In turn, organizational perfor-mance would be improved since the participants willhave a clearer understanding of the requirements.To our knowledge, the influence of communicationfeedback between two firms on organizational perfor-mance has not been empirically tested.

In the job characteristics model in the organizationalbehavior literature, job performance feedback impactspersonal and work-related outcomes, such as workperformance quality, work satisfaction, absenteeismand turnover (Robbins, 2001). Several studies foundsupport that high performance work practices, ofwhich feedback is a component, impact organizationalfinancial performance (Huselid, 1995; Huselid et al.,1997). The organizational behavior literature providessome support for the impact of individual communi-cation feedback on organizational performance; how-ever, inter-organizational feedback between buyingand selling firms has not been tested. The followinghypothesis is:

H2c. Communication feedback between the buy-ing firm and the supplier positively influences thesupplier’s performance.

3.2.4. Collaborative communicationCollaborative communication measures three

communication strategies in unison, indirect influ-ence strategy, formality and feedback. From ourreview of the literature,Mohr et al. (1996)foundthat collaborative communication was significantlyrelated to all three of their outcome measures:commitment, satisfaction and coordination. Wewere unable to find prior empirical research test-ing the relationship between collaborative commu-nication and business performance. This researchwill test if collaborative communication influencessupplier’s performance. The hypothesis is givenbelow.

H2d. Collaborative communication between the buy-ing firm and the supplier positively influences thesupplier’s performance.

3.3. Buyer–supplier relationship and supplier’scommitment

The marketing literature contains some evidencethat the buyer–supplier relationship has some influ-ence on the supplier’s commitment to the buyingfirm. From the dyadic perspective,Anderson andWeitz (1992)found that supplier’s commitment wasa function of three components of the buyer–supplierrelationship: the buying firm’s commitment as per-ceived by the supplier, the supplier’s investment in therelationship, and communication feedback. It is an-ticipated that the supplier’s commitment is a functionof BSR. Therefore, the following hypothesis is:

H3. The buyer–supplier relationship positively influ-ences the supplier’s commitment to the buying firm.

3.4. Buyer–supplier relationship and supplier’sperformance

One key to improving suppliers’ short-term pro-ductivity benefits and long-term strategic advantagesis to effectively manage the partnering relationship(Stuart, 1993). Many researchers have found thatcloser unidimensional buyer–supplier relationshipspositively influence performance measures, such asROI (Carr and Pearson, 1999); order lead-time, ser-vice levels, out-of-stock situations, inventory levels(Vijayasarathy and Robey, 1997); quality, deliveryreliability, lead-time and on-time delivery (Shin et al.,2000). Maloni and Benton (2000)found that a strongbuyer–supplier relationship was perceived to have abeneficial influence on supply chain performance.

However, several researchers concluded that aclose relationship is not universally desirable (e.g.,Cannon and Perreault, 1999; Heide and John, 1990;Noordewier et al., 1990). These researchers foundthat certain dimensions of the relationship constructcan influence either positively or negatively the per-formance outcome.Heide and John (1990)foundthat close relationships are only useful when spe-cific assets and uncertainty evoke a need to protectand to adapt.Cannon and Perreault (1999)found


that procurement obstacles and importance of supplydiscriminate among the different types of relation-ships. Each of these studies relied on transaction–costtheory.

As described inSection 2.3, for this research, BSRis based on both transaction–cost theory and socialexchange theory. From the supplier’s perspective,it is anticipated that BSR will have a positive im-pact on performance, as delineated in the followinghypothesis:

H4. The buyer–supplier relationship positively influ-ences the supplier’s performance.

3.5. Supplier’s commitment and supplier’sperformance

When the supplier is committed to a buying firm,the supplier will want to ensure the continued suc-cess of the business relationship and therefore, meetand/or exceed the needs of the buying firm. Conse-quently, the supplier’s commitment should influencethe supplier’s performance. Empirical research that di-rectly measures the impact of supplier’s commitmenton performance was not found.

In the organizational behavior literature, employeecommitment to their firm was found to influence jobperformance (e.g.,Riketta, 2002). Arthur (1994)foundthat organizations that use systems to develop higheremployee commitment had higher productivity, lowerscrap rates and lower employee turnover. However,individual-based and intra-organizational measuresare significantly different from organization-basedand inter-organizational measures. Therefore, thehypothesis is given below:

H5. The supplier’s commitment to the buying firmpositively influences the supplier’s performance.

4. Methodology

Four models were developed to assess the influenceof each supplier evaluation communication strategy(indirect influence, formality, feedback and collabo-rative communication) on BSR and supplier’s perfor-mance. The hypothesized model, which depicts the

implied causal relationships among the research vari-ables, is shown inFig. 1. Given the many linked,causal relationships in the model, structural equationmodeling (SEM) was selected as the most appropri-ate research methodology. SEM enables us to con-currently test the hypothesized relationships for eachmodel.

4.1. Second-order factors

In the four structural equation models, there aretwo second-order factors, collaborative communica-tion and BSR, each of which is modeled to influencethree first-order factors. Second-order factors arecompletely latent and unobservable since the covari-ation among the first-order factors is explained bythe second-order factor (Byrne, 1995). The first-orderfactors are considered consequences (endogenousvariables) of the second order factor. (SeeByrne,1995; Gorsuch, 1983; Hair et al., 1998for moreinformation).

There are several benefits of using a second-orderfactor. First, each of the first-order factors is signifi-cantly correlated and thereby, the second-order factorincreases the breadth of generalizability (Gorsuch,1983). Second, when compared to aggregation,second-order factors retain the number of parametersin the model and thereby maximize the degrees offreedom for estimating the path coefficients. Third,due to the higher level of degrees of freedom, statisti-cal power is higher. Fourth, measurement error is cap-tured within the model. Finally, the outside influenceson the first-order factors are captured in the model(Bollen, 1989). One concern of the second-order fac-tor analysis is that when there are a high number ofmanifest variables per factor, the model has a higherlevel of measurement error, which negatively influ-ences model fit (Bagozzi and Heatherton, 1994). Instudies that utilize a high number of manifest vari-ables per factor, the cutoff criteria for fit indices insecond-order factor models may need to be relaxed.

For this study, our objective was to enhance thegeneralizability of collaborative communication andbuyer–supplier relationship based on empirical meth-ods that supported a high-powered model. The resultsof a second-order factor model, such as the measure-ment error and outside influences, are richer as com-pared to the aggregated model.


4.2. The survey

In the development of the survey instrument, pre-viously tested and validated instruments were reliedon wherever possible (such asAnderson and Weitz,1992; Cannon and Perreault, 1999; Carr and Pearson,1999; Krause, 1995; Maloni and Benton, 2000; Mohrand Sohi, 1995; Mohr and Spekman, 1994). Whereapplicable, minor wording changes were made to re-flect the supplier’s perspective rather than the buyingfirm’s perspective. New items for feedback, formal-ity, cooperation, commitment and operational linkagesconstructs were developed. Extensive validation stepswere implemented for the new constructs.

Content validity was supported by (1) an extensiveliterature review, (2) in-depth interviews with two au-tomotive manufacturing executives, which provided anunderstanding of BSR, the supplier evaluation com-munication process and business performance, and (3)a pre-test of the survey by three first-tier automotivesupplier executives and five experienced researchers,providing suggestions on wording and format modifi-cations. There were four versions of the survey instru-ment, each of which asked the supplier to characterizea specific automobile manufacturer: Daimler-Chrysler,Ford, General Motors or Honda.

4.3. The sample

The decision to study the communication be-tween first-tier automotive suppliers and automobilemanufacturer was based on the following: (1) the rela-tionships between suppliers and the automobile man-ufacturers are well developed and fairly stable; manyof the buyer–seller relationships have developed overdecades. (2) Suppliers within the automobile industryrepresent a wide diversity of industries. Thus, conclu-sions drawn from the study may be generalized acrossa variety of businesses. (3) The automobile industryhas been well studied, so results drawn from this studywould fit within our knowledge base of the industry.

Only critical suppliers, as defined by the buyingfirms, were selected for this study. The four largestNorth American automotive manufacturers were se-lected. Honda and DaimlerChrysler provided contactinformation for their most critical suppliers. Suppliersfor Ford and General Motors were selected based onrecent publicized awards recognition. If a supplier was

Table 1Summary of responses

Daimler-Chrysler

Ford GeneralMotors

Honda Total

Number Sent 171 67 91 331 660Non-deliverable 25 6 6 47 84Ineligible 1 1 1 2 5

Sample Size 145 60 84 282 571Respondents 32 22 32 53 139

Table 2Average of the reported years of business with the buying firm

Daimler–Chrysler

Ford GeneralMotors

Honda Average

Means (years) 30 37 31 14a 25

a Bonferroni difference test showed statistically significant dif-ferences between the mean for Honda and the three other means(P < 0.001).

listed for multiple manufacturers, the supplier was ran-domly retained for either Ford or General Motors be-cause there were fewer potential respondents for thosetwo firms. The unit of analysis was the business unitand the targeted respondents were the chief executiveofficers. Thus, of the targeted 660 automobile suppli-ers, 139 usable surveys were received for a responserate of 24.3% (139/571).Table 1provides a summaryof the respondents.

Due to the differences in sampling frames, therewas a potential selection bias. We attempted to con-trol for selection bias by measuring for differencesbetween the four supplier groups. Only one statisti-cally significant difference was found; since Honda isa relatively recent entrant into the US market, the av-erage lengths of Honda suppliers’ relationships withtheir manufacturer were statistically less than averagefor the other manufacturers (P < 0.001), as reflectedin Table 2.3 Statistically significant differences werenot found between the supplier groups on the follow-ing: (1) the percentage of the supplier’s business to

3 Due to the sample size of Honda suppliers (n = 53), amulti-group SEM comparison was not a viable option. Althoughit was not hypothesized, we inserted a dummy variable represent-ing Honda suppliers as an influencing variable of buying firm’scommitment or BSR. The model results did not change, indicatingthat the model is robust. Thet-statistics were significant, whichindicates that the age of the relationship should be considered infuture models as a possible influencing variable.


all automotive manufacturers, (2) annual gross salesdollars, (3) supplier’s main business for the buyingfirm and (4) respondents’ position titles.

Attempts were made to minimize non-responsethrough frequent and easy-to-understand correspon-dence (Dillman, 2000). Evidence of non-responsebias was assessed by comparing answers betweenquestionnaires that were returned early and thosereturned late to determine if there were statisticaldifferences (Lambert and Harrington, 1990; Lesslerand Kalsbeek, 1992). Specifically, the sample wassplit into two groups based on if the surveys werereturned before or after the mailing of the second sur-vey package. Twelve of the sixty survey items (20%)were randomly selected and t-tests were performedon each item (n1 = 72, n2 = 68). Thet-tests yieldedno statistically significant differences.

Prior to the analysis, the data characteristics wereexamined. The variables, scales, associated factors,and descriptive statistics are shown inTable 3. Elevensurveys were missing data for the independent vari-ables in the initial measurement model. Multiplelinear regression was used to impute 15 missingcompletely-at-random items (0.24%) associated withthe independent variables.4 One returned survey wasexcluded due to a lack of dependent variable data (asrecommended byHair et al., 1998).

4.4. The measurement model

Using the two-step approach proposed byAndersonand Gerbing (1988), the first step was to purify andtest the measurement model. A systematic, iterativeprocess was used to determine which items should beeliminated from the measurement model. Please referto Table 4 for the final factor loadings. Item elimi-nation was based on weak loadings, cross loadings,multiple loadings, communalities, error residuals andtheoretical determination.5 Since the suggested elim-

4 The dependent variable was calculated by linearly regressingall of the items that were hypothesized within the same construct(Lessler and Kalsbeek, 1992). We also tested the final structuralmodel using the most conservative method, listwise deletion (n =135). We found no statistically significant differences between theresults of the two sets of data although test statistics were slightlyimproved with listwise deletion.

5 Items S2, B2, I1, I2 and I3 had low loadings on all constructs.Items B6 and L1 loaded onindirect influence strategy. Item L2

ination of L1 and L2 would leave only two items torepresent the operational linkages construct, the con-struct was eliminated (Ding et al., 1995). Of the 44initial scale items, 28 items were retained during themeasurement purification process. Each retained itemwas a statistically significant indicator of its respec-tive construct.6 The fit indices inTable 5 indicatedacceptable fit for the purified measurement model.

4.5. Validity of measurement model

The validation process for the survey instrumenthad three steps: content validity, which we cov-ered in designing the survey instrument; constructvalidity, which includes reliability; and nomolog-ical validity (O’Leary-Kelly and Vokurka, 1998).The literature review and in-depth interviews con-ducted with business executives and researchers es-tablished the basis of content validity of the surveyinstrument.

The purpose of construct validity is to show that theitems measure and are correlated with what they pur-port to measure, and that the items do not correlatewith other constructs. Unidimensionality was estab-lished with target rotation factor analysis, where 0.30was considered to be the lowest significant factor load-ing to define the construct (Guadagnoli and Velicer,1988; Hair et al., 1998).

Cronbach’s alpha and alpha-if-item-deleted werecalculated to determine construct reliability. Asshown inTable 4, all Cronbach’s alphas were above0.70, where 0.70 is the suggested cutoff for estab-lished scales (Carmines and Zeller, 1979). Althoughthe alpha-if-item-deleted indicated that three items(C5, FM4 and P3) had limited contribution to theCronbach’s alpha, each of the items was retaineddue to their significance in defining the theoreticalconstruct.

loaded oncooperation. Item C1 loaded onbuying firm’s commit-ment. Item C3 loaded onsupplier’s commitment. Items C2 andC4 loaded on bothbuying firm’s commitmentand cooperation.Item S6 loaded on bothoperational linkagesand indirect influ-ence strategy. Item FB3 loaded onoperational linkages, indirectinfluence strategyand feedback.

6 FM1 and FM4 were retained for theoretical reasons. FM1represents this study’s definition of formality and FM4, areverse-coded item, represents one end of the formality continuum,word-of-mouth.

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Table 3Factors, variables, scales and descriptive statistics

Factor Variable and survey questiona Scale Mean S.D.

Indicate the extent to which�Mfg� has engaged in each of the following activities within the past year to increase theperformance or capabilities of your business:

Indirect influence strategy I1*: Assessment of your firm’s performance through formal evaluation,using guidelines and procedures

1 = always to 7= never 2.70 1.56

I2*: Use of a supplier certification program to certify your firm’sprocess control


I3*: Public recognition of your firm’s achievements/performance 1= always to 7= never 3.32 1.84I4: Site visits by�Mfg� to your premises to help your firm improveits performance


I5: Inviting your personnel to�Mfg�’s site to increase yourawareness of how the product is used


I6: Training and education of your personnel 1= always to 7= never 3.88 1.74

Formality FM1: In coordinating our activities with�Mfg�, formalcommunication channels are followed (i.e., channels that are regularized,structured modes versus casual, informal, word-of-mouth modes)

1 = strongly agree to 7= strongly disagree 2.77 1.31

FM2: �Mfg� has a formal system to track the performance of theirsuppliers


FM3: �Mfg� has a formal program for evaluating and recognizingsuppliers


FM4: The source of our information about�Mfg�’s evaluationprogram is predominantly word-of-mouth. (R)


FM5: �Mfg�’s evaluation process is conducted through standardprocedures

1 = strongly Agree to 7= strongly disagree 2.63 1.38

Our firm can easily approach�Mfg� for discussion:Feedback FB1: To clarify their expectations of our firm’s performance 1= strongly agree to 7= strongly disagree 2.35 1.30

FB2: Regarding their evaluation of our firm’s performance 1= strongly agree to 7= strongly disagree 2.47 1.36FB3*: Regarding ideas for performance improvement. 1= strongly agree to 7= strongly disagree 2.61 1.43FB4: To establish goal activities for performance improvement 1= strongly agree to 7= strongly disagree 2.57 1.42

Buying firm’s commitment B1:�Mfg� is loyal to our firm 1= strongly agree to 7= strongly disagree 3.20 1.70B2*: �Mfg� is continually on the lookout to reduce dependence onour firm. (R)


B3: �Mfg� expects to buy our products for a long time 1= strongly agree to 7= strongly disagree 2.66 1.40B4: �Mfg� see this relationship as a long-term partnership 1= strongly agree to 7= strongly disagree 2.79 1.63B5: How strong is�Mfg�’s commitment with your firm? 1= very strong to 7= very weak 3.01 1.59B6*: How significant are�Mfg�’s investments in your firm ascompared to investments provided by other customers?

1 = significantly higher to7 = significantly lower

3.60 1.38

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Table 3 (Continued)

Factor Variable and survey questiona Scale Mean S.D.

Cooperation C1*:�Mfg� is concerned about our firm’s success 1= strongly agree to 7= strongly disagree 3.55 1.89C2*: �Mfg� will not take advantage of a strong bargaining position 1= strongly agree to 7= strongly disagree 4.91 1.71C3*: �Mfg� and our firm must work together to achieve our mutualgoals


C4*: Our relationship with�Mfg� is better described as acooperative effort rather than an adversarial effort


C5: When our firm has a problem,�Mfg� helps us solve it 1= always to 7= never 3.19 1.48C6: How flexible is�Mfg� in response to requests your firm makes? 1= very flexible to 7= very inflexible 3.57 1.38C7: When we are solving problems jointly, how flexible is�Mfg� inresolving them?

1 = very flexible to 7= very inflexible 3.26 1.32

Operational linkages L1*: Our business activities are closely linked with�Mfg� 1 = strongly agree to 7= strongly disagree 2.47 1.29L2*: We feel like we never know what we are supposed to be doingfor �Mfg�. (R)


L3*: Our activities with�Mfg� are well coordinated 1= strongly agree to 7= strongly disagree 2.86 1.40L4*: We have a routine and well-established system that facilitates thelinks of our operations with�Mfg�’s operations


Supplier’s commitment S1: Our firm is loyal to�Mfg� 1 = strongly agree to 7= strongly disagree 1.66 1.00S2*: Our firm is continually on the lookout to reduce dependence on�Mfg� as a customer. (R)


S3: Our firm expects�Mfg� to buy our products for a long time 1= strongly agree to 7= strongly disagree 2.03 1.40S4: Our firm sees this relationship as a long-term partnership 1= strongly agree to 7= strongly disagree 1.94 1.38S5: How strong is your firm’s commitment with�Mfg�? 1 = very strong to 7= very weak 1.60 0.96S6*: How significant are your firm’s investments in equipment dedicatedto �Mfg� as compared to investments dedicated to other customers?

1 = significantly higher to 7= significantlylower

2.61 1.39

Compared to your competitors, how well does your firm perform on the following aspects?Supplier’s performance P1: Product quality (R) 1 = strongly agree to 7= strongly disagree 2.32 1.25

P2: Delivery performance (R) 1 = strongly agree to 7= strongly disagree 2.37 1.31P3: Price (R) 1 = strongly agree to 7= strongly disagree 3.36 1.32P4: Responsiveness to requests for changes (R) 1 = strongly agree to 7= strongly disagree 2.40 1.31P5: Service support (R) 1 = strongly agree to 7= strongly disagree 2.18 1.17P6: Overall performance (R) 1 = strongly agree to 7= strongly disagree 2.28 1.08

a Variables denoted with an asterisk (∗) were subsequently dropped from the study. Variables denoted with an (R) were reverse coded for all analyses.


Table 4Final factor loadings

Note: Questions FM4 and P(1–6) were reverse coded for all analyses.

The third step of construct validity is to establishconvergent and discriminant validity. Convergent va-lidity was supported with allt-values greater than 2.0(Pedhazur and Schmelkin, 1991). Discriminant valid-ity was assessed by calculating the 95% confidence in-terval from the data inTable 6by adding and subtract-ing twice the standard error of a correlation betweentwo latent variables (Anderson and Gerbing, 1988).

4.6. Validity of second-order factors

Higher-order factors are determined by factoringthe correlations among the first-order factors. The ma-

trices can be determined in the same manner as thefirst-order factors, with two main differences. First, itshould be noted that higher order factors reduce ac-curacy as a tradeoff for an increase in the breadth ofgeneralization. Second, significance tests are not ap-plicable because the distribution characteristics of thecorrelation coefficients are a function of the rotationprocedure of the first-order factors and sample size(Gorsuch, 1983).

Based on theoretical justification described in theliterature review section, it was anticipated that up totwo second-order factors exist within the four mod-els. Using the correlations among the seven first-order

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Table 5Measures of model fit and statistical power

Desirable range Measurementmodel

Indirect influencestrategy model

Formalitymodel

Feedbackmodel

Collaborativecommunication model

χ2 test statistic 606 384 438 379 630χ2/d.f. ≤3.0 1.84 2.34 2.16 2.31 1.85Non-normed fit index ≥0.90 0.89 0.87 0.88 0.89 0.89Bollen’s 1989 fit index ≥0.90 0.91 0.89 0.90 0.91 0.90Relative non-centrality index ≥0.90 0.91 0.89 0.90 0.91 0.90Comparative fit index ≥0.90 0.91 0.89 0.90 0.91 0.90RMSEA ≤0.08 for

reasonable fit0.078 0.099 0.092 0.097 0.078

RMSEA confidence interval 0.068, 0.088 0.086, 0.111 0.080, 0.103 0.085, 0.110 0.069, 0.088Degrees of freedom 329 164 203 164 341Effective number of parameters 77 46 50 46 65Power atα = 0.05 and alternate

RMSEA = 0.08>0.99 >0.99 >0.99 >0.99 >0.99


Table 6Correlations among latent variables (lower triangle) and standard errors after rotation (upper triangle)

factors, shown in the lower triangle ofTable 6, therotated matrix in Table 7 supports three factors:buyer–supplier relationship, collaborative communi-cation, and performance. In general, the loadings sup-port the theoretical second-order factor relationships.

The hypothesized structural model required minormodification since the operational linkages constructwas dropped from the measurement model. With onlytwo first-order factors, BSR would be under-identified(Gorsuch, 1983; Byrne, 1994) unless the path coeffi-cient for buying firm’s commitment,β1, and cooper-ation,β2, were set equal to each other.

Content and construct validities of the scales weresupported. The next step to validate the scales is toassess nomological validity, which was supported withresults of the hypothesis testing described in the nextsection.

5. Results

Structural equation modeling (SEM) was utilizedto simultaneously measure the hypothesized multiplelinear relationships. As discussed inSection 4.4, the fit

Table 7Second-order factor loadings

Factor loadings greater than 1.0 (#) are possible due to the nature of the oblique rotation method.

for the revised model was deemed acceptable. Sincesecond-order factors were incorporated into the struc-tural model, the validity of the second-order factorswas established and discussed inSection 4.6. UsingAnderson and Gerbing’s two-step approach (1988),the second step is to simultaneously test the hypoth-esized relationships among the factors using SEM.Figs. 2–5represent the four models, each shown withtheir associated path coefficients and specific mathe-matical equations.

As shown inTable 5, the fit indices of the struc-tural model were similar for the first three models:indirect influence, formality and feedback. Whencompared to the cut-off criteria, most of the fit in-dices (e.g.,χ2/d.f., NNFI, Bollen’s 89, RNI, CFIand RMSEA) were consistent with the rule-of-thumbmeasures (Hair et al., 1998). The RMSEA fit statis-tic for the collaborative communication model hadsignificantly improved fit as compared to the othermodels. Based on the fit and theoretical support, thestructural models were deemed acceptable and revi-sions were not made. The results for the hypothesestesting for each of the four models are summarized inTable 8.


Indirect InfluenceStrategy

ξ1

Buyer-Supplier Relationship

η4


η1

β4 = 0.836***

γ2 = -0.040

γ1 = 0.560***


η6


η5

Cooperationη2

ζ1 = 0.278

β5 = -0.059

β2 = 0.850***

β6 = 0.386**

β1 = 0.850***

ζ4 = 0.687

ζ2 = 0.278

ζ5 = 0.302

ζ6 = 0.896

Fig. 2. Indirect Influence Strategy Structural Equation Model and Equations. Solids paths indicate significant results, where: (∗) indicatessignificance atP < 0.10, (∗∗) indicates significance atP < 0.05; (∗∗∗) indicates significance atP < 0.01; Dashed paths indicatesnon-significant results.β1 andβ2 were set equal to each other so that the second-order factor was fully identified. Operational Linkages,η3, was dropped since the construct could not be validated.η1 = 0.850η4 + 0.278, η2 = 0.850η4 + 0.278, η4 = 0.560ξ1 + 0.687,η5 = 0.836η4 +0.302,η6 = −0.040ξ1 + (−0.059η4)+0.386η5 +0.896 where:ηi, is the endogenous variable, i;ξ1, the exogenous variable,indirect influence strategy.

Formalityξ2


η4


η1

β4 = 0.868***

γ2 = -0.004

γ1 = 0.603***


η6


η5

Cooperationη2

ζ1 = 0.305

β5 = -0.122

β2 = 0.834***

β6 = 0.432*

β1 = 0.834***

ζ4 = 0.636

ζ2 = 0.305

ζ5 = 0.247

ζ6 = 0.891

Fig. 3. Formality Structural Equation Model and Equations. Solids paths indicate significant results, where (∗) indicates significance atP < 0.10; (∗∗) indicates significance atP < 0.05; (∗∗∗) indicates significance atP < 0.01; Dashed paths indicates non-significant results.β1 and β2 were set equal to each other so that the second-order factor was fully identified. Operational linkages,η3, was droppedsince the construct could not be validated.η1 = 0.834η4 + 0.305, η2 = 0.834η4 + 0.305, η4 = 0.603ξ2 + 0.636, η5 = 0.868η4 + 0.247,η6 = −0.004ξ2 + (−0.122η4) + 0.432η5 + 0.891 where:ηi, is the endogenous variable, i;ξ2, the exogenous variable, formality.


Feedbackξ3


η4


η1

β4 = 0.841***

γ2 = 0.068

γ1 = 0.700***


η6


η5

Cooperationη2

ζ1 = 0.285

β5 = -0.158

β2 = 0.845***

β6 = 0.420**

β1 = 0.845***

ζ4 = 0.510

ζ2 = 0.285

ζ5 = 0.293

ζ6 = 0.887

Fig. 4. Feedback Structural Equation Model and Equations. Solids paths indicate significant results, where (∗) indicates significance atP < 0.10; (∗∗) indicates significance atP < 0.05; (∗∗∗) indicates significance atP < 0.01; Dashed paths indicates non-significant results.β1 and β2 were set equal to each other so that the second-order factor was fully identified. Operational Linkages,η3, was droppedsince the construct could not be validated.η1 = 0.845η4 + 0.285, η2 = 0.845η4 + 0.285, η4 = 0.700ξ3 + 0.510, η5 = 0.841η4 + 0.293,η6 = 0.068ξ3 + (−0.158η4) + 0.420η5 + 0.887 where:ηi, is the endogenous variable,i; ξ3, the exogenous variable, feedback.

CollaborativeCommunication

ξ4


η4


η1

β4 = 0.846***

γ2 = 0.067

γ1 = 0.866***


η6


η5

Cooperationη2

ζ1 = 0.286

β5 = -0.179

β2 = 0.845***

β6 = 0.430**

β1 = 0.845***

ζ4 = 0.251

ζ2 = 0.286

ζ5 = 0.284

ζ6 = 0.887

Indirect Influence Strategy

η7

Formalityη8

Feedbackη9

β8 = 0.708***

β7 = 0.666***

β9 = 0.801***ζ9 = 0.358

ζ8 = 0.499

ζ7 = 0.556

Fig. 5. Collaborative Communication Structural Equation Model and Equations. Solids paths indicate significant results, where (∗) indicatessignificance atP < 0.10; (∗∗) indicates significance atP < 0.05; (∗∗∗) indicates significance atP < 0.01; Dashed paths indicatesnon-significant results.β1 andβ2 were set equal to each other so that the second-order factor was fully identified. Operational Linkages,η3, was dropped since the construct could not be validated.η1 = 0.845η4 + 0.286, η2 = 0.845η4 + 0.286, η4 = 0.866ξ4 + 0.251,η5 = 0.846η4 + 0.284,η6 = 0.067ξ4 + (−0.179η4)+ 0.430η5 + 0.887,η7 = 0.666ξ4 + 0.556,η8 = 0.708ξ4 + 0.499,η9 = 0.801ξ4 + 0.358,whereηi, is the endogenous variable,i; ξ4, the exogenous variable, collaborative communication.


Table 8Summary of test results for structural model

Model Hypothesis Path Pathcoefficient

R2 t-value Hypothesissupported?

Indirect influencestrategy

1a γ1 Influence→ BSR 0.560 0.313 6.65∗∗∗ Yes

2a γ2 Influence→ performance −0.040 0.025 −0.30 No3 β4 BSR → supplier’s commitment 0.836 0.698 18.25∗∗∗ Yes4 β5 BSR → performance −0.059 0.076 −0.24 No5 β6 Supplier’s commitment→ performance 0.386 0.098 1.77∗∗ Yes

Formality 1b γ1 Formality → BSR 0.603 0.364 9.18∗∗∗ Yes2b γ2 Formality → performance −0.004 0.028 −0.04 No3 β4 BSR → supplier’s commitment 0.868 0.753 20.53∗∗∗ Yes4 β5 BSR → performance −0.122 0.084 −0.41 No5 β6 Supplier’s commitment→ performance 0.432 0.100 1.63∗ Yes

Feedback 1c γ1 Feedback→ BSR 0.700 0.490 12.96∗∗∗ Yes2c γ2 Feedback→ performance 0.068 0.043 0.50 No3 β4 BSR → supplier’s commitment 0.841 0.707 19.40∗∗∗ Yes4 β5 BSR → performance −0.158 0.082 −0.57 No5 β6 Supplier’s commitment→ performance 0.420 0.103 1.86∗∗ Yes

Collaborative 1d γ1 Collaborative→ BSR 0.866 0.749 16.94∗∗∗ Yes2d γ2 Collaborative→ performance 0.067 0.071 0.23 No3 β4 BSR → supplier’s commitment 0.846 0.716 20.31∗∗∗ Yes4 β5 BSR → performance −0.179 0.080 −0.44 No5 β6 Supplier’s commitment→ performance 0.430 0.103 1.86∗∗ Yes

Operations linkages was dropped since the construct could not be validated. BSR represents the construct “buyer–supplier relationship”.Influence represents the construct “indirect influence strategy”. Performance represents the construct “supplier’s performance”.

∗ Path significant atP < 0.10.∗∗ Path significant atP < 0.05.∗∗∗ Path significant atP < 0.01.

5.1. The effect of indirect influence strategy

We hypothesized that indirect influence strategyexpressed by the buying firm to the supplier pos-itively influences the buyer–supplier relationship(H1a). The results inTable 8 and Fig. 2 show thatindirect influence strategy significantly influences thebuyer–supplier relationship (P < 0.01), as indicatedby theγ1 path coefficient of 0.56. Buying firms thatemphasize education, training and site visits enhancethe supplier’s perceptions of the buying firm’s com-mitment and cooperation.

It was hypothesized that indirect influence strategyexpressed by the buying firm (source) to the supplier(target) positively influences the supplier’s perfor-mance (H2a). This hypothesis was not supported. Theγ2 path coefficient of−0.04 in Table 8 and Fig. 2was not significantly different from zero (P > 0.10).Although the buying firm is attempting to educate and

train the supplier, suppliers do not perceive that thebuying firm’s attempts directly enhance the supplier’sperformance.

5.2. The effect of formality

In the formality model shown inFig. 3, the hy-pothesis H1b is supported; communication formalityestablished between the buying firm and the supplierpositively influences the buyer–supplier relation-ship. When the buying firm utilizes a formal systemfor evaluations, the supplier’s perceptions of thebuyer–supplier relationship are enhanced. InTable 8,γ1 path coefficient of 0.60 is statistically significant(P < 0.01).

We hypothesized that communication formalitywould directly influence the supplier’s performance(H2b). This hypothesis was not supported. The�2 pathcoefficient of−0.004 shown inTable 8andFig. 3was


not significantly different from zero (P > 0.10). Al-though the buying firm is attempting to use formal sys-tems of communicating the supplier evaluation, thereis not a direct influence on the supplier’s performance.

5.3. The effect of feedback

In the feedback model, we hypothesized that com-munication feedback between the buying firm andthe supplier positively influences the buyer–supplierrelationship (H1c). With the statistically significantγ1path coefficient of 0.70, the results shown inTable 8andFig. 4 indicate that the hypothesisis strongly sup-ported (P < 0.01). When the buying firm establishesan environment conducive to open dialogue regardingthe supplier evaluation, the supplier perceives that thebuying firm is committed to the relationship and thatthe buying firm will cooperate in solving problems.

The hypothesis H2c that communication feedbackbetween the buying firm and the supplier positivelyinfluences the supplier’s performance was not sup-ported. Theγ2 path coefficient of 0.068 was not sig-nificantly different from zero (P > 0.10). The resultsof the study show that supplier’s perceptions of im-provements in understanding gained through feedbackof the supplier’s performance evaluation do not di-rectly impact the supplier’s performance.

5.4. The effect of collaborative communication

We hypothesized that collaborative communicationbetween the buying firm and the supplier positivelyinfluences the buyer–supplier relationship. With thestatistically significantγ1 path coefficient of 0.87,the results shown inTable 8andFig. 5 indicate thatrespondents perceive collaborative communicationto positively influence supplier’s perceptions of thebuyer–supplier relationship; H1d is strongly supported(P < 0.01). When the buying firm uses indirect influ-ence strategy, formality and feedback, in unison, fortheir supplier development program, the suppliers’perceive an improvement in the buyer–supplier rela-tionship.

The disturbance variable,ζ4, includes the variablesthat influence buyer–supplier relationship but are ex-cluded from theη4 equation (Bollen, 1989). In thecollaborative communication model shown inFig. 5,ζ4 of 0.25 is significantly less than theζ4 of the other

models (0.69, 0.64, and 0.51 in the indirect influencestrategy, formality and feedback model, respectively.)As shown inTable 8, the pathγ1 in the collaborativecommunication model accounts for approximately75 per cent of the variance in the BSR construct, asindicated by theR2. Respondents indicate that thebuying firm’s collaborative communication explainsa significant amount of the suppliers’ perceptions ofthe buyer–supplier relationship.

Hypothesis 2d, that collaborative communicationbetween the buying firm and the supplier positivelyinfluences the supplier’s performance, was not sup-ported. The�2 path coefficient of 0.067 was not signif-icantly different from zero (P > 0.10). Respondentsindicated that the buying firm’s collaborative commu-nication does not directly influence the supplier’s per-formance.

5.5. The influence of buyer–supplier relationship onsupplier’s commitment

It was hypothesized that the buyer–supplier relation-ship positively influences the supplier’s commitmentto the buying firm (H3). The statistically significant�4 path coefficients (P < 0.01) in Table 8of 0.84,0.87, 0.84 and 0.85 for the indirect influence strategy,formality, feedback and collaborative communicationmodels, respectively, indicate support for the hypoth-esis in all four models. Suppliers are committed tothe buying firm when suppliers perceive that the buy-ing firm is cooperative and committed to the supplier.Two measures of commitment were validated from thesupplier’s perspective: buying firm’s commitment andsupplier’s commitment.

5.6. The influence of buyer–supplier relationship onsupplier’s performance

It was hypothesized that the buyer–supplier rela-tionship positively influences the supplier’s perfor-mance (H4). For each model inTable 8, the �5 pathcoefficients of−0.06,−0.12,−0.16 and−0.18 in theindirect influence strategy, formality, feedback andcollaborative communication models, respectively, arenot significantly different from zero. Therefore, H4was not supported for any of the four models. The re-spondents do not perceive that the buyer–supplier re-lationship directly influences supplier’s performance.


5.7. The influence of supplier’s commitment onsupplier’s performance

We hypothesized that the supplier’s commitmentto the buying firm positively influences the supplier’sperformance (H5). The β6 paths of 0.39, 0.43, 0.42,and 0.43 for the indirect influence strategy, formal-ity, feedback and collaborative communication model,were statistically significant (P < 0.05) as shown inTable 8. Therefore, the data indicates that there is sup-port for H5. When the supplier has a higher level ofcommitment to a buying firm, the supplier perceivesthat they have a higher level of performance associ-ated with the buying firm.

5.8. Power analysis

High statistical power in research improves theprobability of repeatability of the study results. Poweris the probability of correctly rejecting the null hy-pothesis when it should be rejected. A post hoc poweranalysis was conducted for each of the four commu-nication models using STATISTICA software. Thepower of each of the models was very near 1.00, asreflected inTable 5, and therefore, the decisions thatindicated ‘support’ for the hypotheses would appearto have a high probability of being correct.

6. Discussion and managerial implications

The purpose of this research was to determine thesupplier’s perceptions of the buying firm’s supplierevaluation communication strategies and the influenceof the communication on supplier’s performance.Based on the results, several key insights emerge.

6.1. The effect of indirect influence communicationstrategy

Executives at buying firms who want to cultivateimproved relationships with their suppliers shouldconsider site visits, education and training programstargeted to the supplier’s personnel. The focus ofthese programs should be on production and pro-cess techniques that impact the supplier’s output andfuture capabilities.

The buying firm’s underlying objective of the SDPis to enhance the supplier’s performance (Handfieldet al., 2000; Krause, 1997; Watts and Hahn, 1993).Krause et al. (2000)found that buying firm’s percep-tions of direct involvement with SDP influenced thefirm’s performance. The results of the current studysuggest that the supplier may perceive the effect ofindirect influence strategy differently than the buyingfirm. The supplier does not perceive that the buyingfirm’s indirect influence strategy directly affects thesupplier’s performance. In general, when the buyingfirm establishes education and training programs, theyshould not expect that the supplier’s performance andcapabilities would improve, except when the inter-vening BSR and supplier’s commitment are positivelyaffected.

6.2. The effect of formality

This research is the first to illustrate the impor-tance of the formal communication structure from thesupplier’s perspective. The results are consistent withthe existing literature from the buying firm’s perspec-tive (Carr and Pearson, 1999). Executives at supplyingfirms positively perceive the standardized proceduresand formal channels of communicating the supplierevaluations.

However, the buying firm cannot simply establishSDPs, such as a formal evaluation program, and ex-pect that the supplier’s performance and capabilitieswill improve. An improved supplier’s performancerequires the coordination of factors that are outsideof the buying firm’s realm of control. The buyingfirm needs to establish an environment that is con-ducive to improving the controllable factors, such asthe buyer–supplier relationship.

6.3. The effect of feedback

The impact of feedback on the buyer–supplier re-lationship is addressed for the first time in this study.For an enhanced buyer–supplier relationship, buyingfirm executives need to listen to their suppliers’ sug-gestions for performance improvement and to clar-ify the buying firm’s objectives, evaluation proceduresand evaluation results. This feedback opportunity en-hances the supplier’s perceptions of the buying firm’scooperation and commitment to the supplier.


Although the supplier may be more receptive to sug-gestions and may become more committed to the buy-ing firm, the bottom line is not directly impacted byfeedback opportunities. Since effective communica-tion of the buying firm’s objectives aids the supplier’sunderstanding, the feedback mechanisms should beutilized in a similar manner as the indirect influencestrategy, i.e., a mechanism to increase the educationand training of the supplier’s personnel.

6.4. The effect of collaborative communication

Collaborative communication positively influencedbuyer–supplier relationship, which influenced buyingfirm’s commitment and cooperation. When imple-menting SDPs, executives at buying firms shoulduse indirect influence strategy, formal program struc-ture and feedback with their most critical suppliers.Each of these three communication strategies usedtogether are more powerful in their influence ofbuyer–supplier relationship than any one strategy usedin isolation. Successfully implemented collaborativecommunication of the SDP effort significantly andpositively influences the supplier’s perceptions of thebuyer–supplier relationship. This finding is consistentwith theMohr et al. study (1996).

This study also assessed the supplier’s perceptionsof the buying firm’s collaborative communicationinfluence on suppliers’ performance. However, aswith the other communication strategy efforts, datado not support the hypothesis that collaborative com-munication is positively associated with suppliers’performance. The buying firm manager should notanticipate improved supplier performance as a resultof the supplier development communication effort.

6.5. The influence of buyer–supplier relationshipon supplier’s commitment

When suppliers perceive that executives at buy-ing firms emphasize improved buyer–supplier rela-tionships, the suppliers’ commitment to the buyingfirm will increase. Since businesses are increasinglyreducing their supplier base and becoming more de-pendent on their suppliers, buying firm managers areincreasingly vulnerable to the supplier’s whims. Tocounter this risk, buying firm managers need their sup-pliers to also become more committed to the relation-

ship. Without the supplier’s commitment, the buyingfirm may be unable to meet their business objectives.This commitment could easily become a competitiveadvantage for successful buying firms in an oligopolis-tic environment.

Both the buying firm’s commitment and thesupplier’s commitment were composed of items thatmeasured these three dimensions: loyalty, willingnessto make investments and expectations of relationshipcontinuity. The final measurement model, however,excluded the willingness to make investment in thetrading partner’s business, a measure thatWilliamson(1985) supported with his transactional cost eco-nomics.

6.6. The influence of buyer–supplier relationship onperformance

Prior studies that utilized a one-dimensionalbuyer–supplier relationship construct and/or consid-ered the buying firm’s perspective (Carr and Pearson,1999; Shin et al., 2000; Vijayasarathy and Robey,1997) found that buyer–supplier relationship influ-enced performance. The results of this study appearto contradict findings from the buying firm’s vantagepoint. Two possible implications of this result arethat the complexity of the buyer–supplier relationshipconstruct should not be ignored, and that the buyingfirm’s perspective could be distinctly different fromthe supplier’s perspective.

The managerial implications of this result are that‘good relations’ with suppliers do not directly influ-ence the suppliers’ performance. The buying firm’scooperative efforts and expression of commitment donot directly translate into better product quality, deliv-ery performance, price, responsiveness, service, andoverall performance from the supplier. However, aswill be further discussed inSection 6.7, the resultsdo not indicate that managers should be unconcernedwith the development of good business relationships.

6.7. The influence of supplier’s commitment onperformance

This research provides empirical support for theproposition presented inLambert et al. (1996)thatwhen a business considers a firm to be critical forits success, managers should focus on improving the


relationship with that firm.Lambert et al. (1996)postulated that an improved relationship enhancesbusiness performance for both firms. The currentstudy found that when managers focus on improvingthe relationship, such as with increased cooperationand commitment, the supplier’s commitment is en-hanced (H3), which, in turn, improves the supplier’sperformance (H5).

The managerial significance of this result is thatSDPs are effective when the supplier is committedto the buying firm. If the supplier is not committed,the buying firm cannot influence the supplier’s per-formance through the supplier evaluation communica-tion process. The buying firm needs to influence thesupplier’s commitment in order to positively influencethe supplier’s performance.

7. Conclusions

When the buying firm uses collaborative commu-nication for the supplier development programs, it isperceived by the supplier as an effective mechanismto improve the buyer–supplier relationship. Col-laborative communication includes indirect influ-ence strategy, formality and feedback. However,this study shows that the implementation of sev-eral supplier evaluation communication strategiesby itself is not enough to influence the supplier’sperformance.

Although the buying firm currently considers theircommitment and the importance of the supplier’sproduct and/or service to the supplier when initiatingtheir targeted supplier development programs (Krauseet al., 1998), the buying firm may want to also con-sider the supplier’s perspective of the relationshipprior to initiating supplier development programs.Supplier development programs will be successfulin terms of operational performance measures if thesupplier is committed to the buying firm. As noted bya buying firm manager inPorter (1991), “Are we thecustomer of choice with our suppliers?” The supplierevaluation communication process could be the cata-lyst that strengthens the buyer–supplier relationshipand supplier’s commitment.

There appears to be an explanation for why somesuppliers have not adequately improved their per-formance to meet the buying organization’s SDP

initiatives. The supplier must feel a strong sense ofcommitment, loyalty, and longevity in the relationshipwith the buying firm. The buying firm can influencethe supplier’s commitment through enhanced com-munication and relationship development. Relation-ship development includes enhancing cooperation,problem solving, and expressing their commitment,loyalty and desire to continue the relationship formany years into the future.

Several implications for business managers can bedrawn from this research. For the buying firm manager,specific communication strategies should be designedinto their SDP efforts. The program should be formal-ized with routine communication; incorporate suppliertraining, education and site visits to aid in the learn-ing process; and provide opportunities for feedback toclarify program objectives and improvement sugges-tions. The result of the SDP collaborative communi-cation effort should enhance supplier’s perceptions ofthe business relationship and their commitment to thebuying firm.

Buying firm managers should focus their SDPimplementation efforts on suppliers that exhibit com-mitment to the buying firm. Although the buyingfirm’s perceptions of the supplier’s commitment areinherently biased, it represents the best proxy for thesupplier’s commitment.

As the recipient of their customer’s SDP efforts, thesupply firm manager has the opportunity to improvethe relationship with the customer. Improved relation-ships can result in increased market share, growth op-portunities and other benefits. In addition, when SDPsare implemented, the supply firm can take advantageof the learning opportunities and improve its overallperformance with the buying firm and with their othercustomers.

This research has explored a relatively new areaof supply chain communication and supplier de-velopment programs. Insights from this study haveimplications in the marketing area for specific chan-nel conditions, such as structure, climate and power.Further theoretical work could expand the modelby including other dimensions of communicationstrategy. This research incorporated indirect influ-ence strategy, formality and feedback, as well asan encompassing construct, collaborative communi-cation. Other communication dimension strategiesinclude: direct influence strategy, informality (such


as word-of-mouth communication) and measures offrequency and media richness.

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