Journal of Business Research 65 (2012) 896–906

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Journal of Business Research

Do suppliers' formal controls damage distributors' trust?

José M. Sánchez a,⁎, María L. Vélez b, María A. Ramón-Jerónimo c

a Universidad de Cádiz, Dep. Organización, Glorieta Carlos Cano s/n, 11002 Cádiz, Spainb Universidad de Cádiz, Dep. Economía de la Empresa, Glorieta Carlos Cano s/n, 11002 Cádiz, Spainc Universidad Pablo de Olavide, Dep. Dirección de Empresas, Ctra. Utrera Km. 1, 41013 Sevilla, Spain

⁎ Corresponding author.E-mail addresses: josem.sanchez@uca.es (J.M. Sánch

(M.L. Vélez), maramjer@upo.es (M.A. Ramón-Jerónimo)

0148-2963/$ – see front matter © 2011 Elsevier Inc. Aldoi:10.1016/j.jbusres.2011.06.002

a b s t r a c t

a r t i c l e i n f o

Article history:Received 1 February 2009Received in revised form 1 July 2010Accepted 1 June 2011Available online 30 June 2011

Keywords:Formal control systemTrustInter-organizational relationshipsVertical distribution channels

Do suppliers' formal controls damage distributors' trust? Extensive studies on this question show mixedresults. This article develops a theoretical model that (a) takes into account the use of formal controls for bothdecision control and decision management; (b) employs perceptual measures from the controlled party'spoint of view; (c) proposes that the control-trust association is contingent upon the character (coercive vs.enabling) of different control tools; and (d) focuses on a mature relationship. This model was empiricallytested with survey data from 107 distributors, trying to capture the moderator effect of two control tools onthe association between formal control uses and trust development. With this approach, the results seem toshow that decision-control and decision-management uses simultaneously substitute for and complementdistributors' trust, and that their effects are partly moderated by the coercive or enabling character of thespecific control tool used to manage the relationship.

ez), maria.velez@uca.es.

l rights reserved.

© 2011 Elsevier Inc. All rights reserved.

1. Introduction

Increasingly, independent suppliers and distributors are establish-ing close up- and downstream relations to increase strategicadvantages, reduce risks, or create customer value (Stern, El-Ansary,and Coughlan, 1996). These relationships come at the cost of higherdependence and coordination efforts (Gençtürk and Aulakh, 2007).Since risks and uncertainties always exist, both parties would benefitfrom a more enlightened approach to managing their relationships.While channel leadership may occur at any level within a givenchannel, the supply chain management literature shows the benefitsof one strong leader driving the relationship (Andraski, 1998;Majumder and Srinivasan, 2008), facilitating the integration andmanagement of processes across company boundaries (Lambert andCooper, 2000). The present study focuses on an asymmetrical market-oriented relationship where the supplier is the channel leader. In thisspecific case, the supplier, a manufacturing firm, has expert, referent,and legitimate power as a result of its larger size; its longevity in themarket, which provides it with more and better knowledge andinformation; its production and marketing expertise and responsibil-ity to the clients; and the contractual conditions to which thedistributors must agree (Jharkharia and Shankar, 2006).

Asymmetrical and market-oriented relationships entail risks andvulnerability for the supplier because distributors treat directly, andoften in the name of the supplier, with clients (Frazier, 1999). Toaccomplish strategic goals and create competitive advantages, thesupplier needs to systematically identify and capitalize on ways tocreate value with its distributors. In such settings, both formal controlsystems (CS) and trust serve simultaneously as key regulators of therelationship (Das and Teng, 1998).

Sometimes, the supplier relies on the unilateral development offormal CS (Frazier, 1999) in order to make better decisions, getorganizational control, achieve its goals efficiently and effectively, andprevent and resolve emergent disagreements (Dant and Schul, 1992). Anumber of different tools (Otley, 1999) shape formal CS. Arguments byAbernethy and Vagnoni (2004) imply that these control tools provideinformation for (1) decision-control use, that is, use by the leader firmto control distributors' behaviors; and (2) decision-management use,that is, use by distributors to manage their own day-to-day activities.

Suppliers can also pursue activities that maintain and promotedistributors' trust. Trust is a generalized expectancy held by one partythat the words and actions of the other party can be relied on (Morganand Hunt, 1994), and is necessary for a relationship both to be startedand to be completed successfully. Trust is the other key regulator ofthe relationship, facilitating communication, increasing relationshipcommitment, leading to the continuation of business (Anderson andWeitz, 1992), reducing the perception of vulnerability to the largersupplier's actions, and mitigating uncertainties (Sharif, Kalafatis, andSamouel, 2005).

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Because competition is increasingly between distribution channels(Stern et al., 1996), the literature exhorts suppliers to develop closerand collaborative relationships with their distributors, as a means ofachieving competitive advantage (Ganesan, 1994). In order to gatherthe benefits listed above, firms should design formal CS to foster anatmosphere in which trust grows (Van Der Meer-Kooistra andVosselman, 2000). However, this strategy is not trouble-free, sincethe association between control and trust is not straightforward and isstill open to debate (Tomkins, 2001; Velez, Sanchez, and Alvarez-Dardet, 2008; Weitz and Jap, 1995). Two main research streamsdescribe this association. Some authors (e.g., Andaleeb, 1995; Neu,1991) argue that formal CS damage trust among partners; others (e.g.,Coletti, Sedatole, and Towry, 2005; Poppo and Zenger, 2002), thatformal CS nurture trust. Despite extensive studies, the empiricalresearch has not provided compelling evidence for either association.

Focusing on the effect of the supplier's formal CS on distributors'trust in asymmetrical downstream relationships, this study contrib-utes to scholars' understanding of relationship management in fourways. All four together shape a new approach to try to shed empiricallight on the complex control-trust association. First, focusing on onepart of the relationship, the distributors, this article develops atheoretical model that considers their real use and the perception ofuse of the other part of formal CS. Departing from the model proposedby Abernethy and Vagnoni (2004), the present study allocates thedecision-control use to the supplier's (leader) side and the decision-management use to the distributors' (controlled) side. Most empiricalresearch on the topic views formal CS mainly in terms of theirdecision-control use for evaluating and rewarding performance(Abernethy and Vagnoni, 2004; Bijlsma-Frankema and Costa, 2005).However, formal CS are also used by distributors to improve theirchoices and actions, and to coordinate daily activities, mitigatinguncertainty and helping them to reduce divergences with thesupplier. This use by the controlled party, which is considered tohave a positive effect on trust (Woolthuis, Hillebrande, and Noote-boom, 2005), has received significantly less attention (exceptionsinclude Velez et al., 2008; Vlaar, Van den Bosch, and Volberda, 2007).

Second, this research uses the perspective of the controlled party.The distributors' view is key, yet perceptual measures of their use offormal CS are rare or nonexistent in the literature. Such measuresprovide further insights for two reasons: (a) distributors' own reportsare likely to provide a fair representation of their understanding of thesupplier's formal CS; and (b) distributors' trust is a complex forward-looking psychological construct that is based on subjective interpre-tations of past actions taken by the supplier. Control is relevant ininterfirm relationships to the extent that it is exerted by the leaderfirm, is noticed by the controlled party, and provides some utility tothe parties involved, aspects of it that are rarely taken into account inempirical studies on the association between formal CS and trust.

Third, Adler and Borys (1996) have shown that certain character-istics of a specific control tool affect the controlled party's perceptionsof it as enabling or coercive. Because formal CS are shaped by differentcontrol tools that are used to manage the relationship, this studyexamines whether the enabling or coercive nature of each tool usedinfluences the intensity of the effect that formal CS uses have on trust.

Fourth, some researchers propose that whether formal CS andtrust are complements or alternatives might depend partly on thestage of the relationship (Inkpen and Curral, 2004; Tomkins, 2001).For example, Tomkins (2001) suggests that in early stages the CS-trustassociation will be positive, but in mature stages it will be negative. Inorder to avoid confounding effects, this study focuses on a well-established relationship.

Finally, the model proposed was empirically tested with surveydata obtained from 107 distributors, members of a chemicaldistribution channel, in which the supplier had developed a newformal CS to manage the channel. This natural setting furnishes theopportunity to study what happens to distributors' trust after formal

CS implementation by the supplier. This model highlights theconsideration of some methodological aspects derived from thedifficulty to capture the real effect of both (1) the CS uses, and (2)the coercive or enabling character of the tool used. We introduce adiscussion about conventional methodological issues related tomeasures of CS uses when different tools and common variancederived from a single informant are considered in distributionchannels. Under these considerations, the results seem to show thatdecision-control and decision-management uses simultaneouslysubstitute and complement distributors' trust, and that their effectsare partly moderated by the coercive or enabling character of eachspecific control tool used to manage the relationship.

The rest of the paper is structured as follows. First we develop atheoretical model and hypotheses that include (a) the associationsbetween the twofold use of formal CS and trust, and (b) themoderatoreffect of the character of different control tools on these associations.Then it explains the methods used to conduct the empirical study andcarry out the analysis, and discusses the results. The final sectionspresent conclusions and managerial implications, note the mainlimitations of the study, and suggest new lines of further research.

2. Theoretical model and hypotheses

Fig. 1 summarizes the theoretical model and hypotheses.

2.1. Perceived supplier's use of formal control systems for decisioncontrol and its effect on distributor trust

Abernethy and Vagnoni (2004) examine how the leader side usesformal CS to monitor the controlled side's behaviors within a singleorganization. Looking instead at an interorganizational relationship,the present study defines decision-control use as the extent to whichthe distributor perceives that the supplier uses CS to appraise distributors'actions and performance. This use is critical for developing relation-ships, producing greater benefits (Lambert and Cooper, 2000) andreducing opportunism and goal divergence. Formal CS are used tomeasure and reward performance (Noordewier, John, and Nevin,1990) by supervising the accomplishment of objectives, providingwarning signals, aligning incentives, and constraining distributors'autonomy (Ittner and Larcker, 1997).

According to Neu (1991), trust is self-enhancing because it reducesthe expectation of opportunism and thus relaxes the necessity forcontrols. Consequently, as Perrow (1986) and Stump and Heide(1996) note, decision-control use can be seen by distributors asobtrusive and may undermine the relationship. Trust is alsodependent on the perceived reciprocity of trust in the relationship,and changes with experience (Mayer, Davis, and Schoorman, 1995).When the leader firm uses CS to transfer risk to partners (Donada andNogatchewsky, 2006), establishing specific performance targets forthem (Bergen, Dutta, and Walker, 1992), the distributors mayperceive that the supplier lacks belief in their honesty, competence,and intentions (Ghoshal and Moran, 1996; Inkpen and Curral, 2004).Because the decision-control use assumes opportunistic behavior(Abernethy and Vagnoni, 2004), it could make suspicion, rather thantrust, dominate the relationship (Das and Teng, 1998), particularlywhen trust has already been established. H1: Distributors' perceptionof the supplier's use of formal control systems for decision controldecreases distributors' trust in the supplier.

2.2. Distributors' use of formal control systems for decision managementand its effect on distributor trust

Within an organization, when leaders implement formal CS,controlled subordinates with delegated decision-making authorityalso have access to the information and use it in decision making(Baiman and Demski, 1980). This access is particularly significant for

Supplier’s use of formalCS for decision control

(as perceived bydistributors)

Distributors’ use offormal CS for decision

management (self-reported)

Character of control tools(enabling vs. coercive)

H3a (+)

H3b (-)

H2 (+)

H1 (-)

Δ Distributors’ trust insupplier

Fig. 1. Theoretical model and hypotheses.

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interorganizational relationships in which tasks have to be continu-ously geared to each other and in which partners' decision making isrequired (Gulati and Singh, 1998). This study defines decisionmanagement as the distributors' use of formal CS to manage theirown day-to-day activities.

Formal CS generate information that can be shared (on informa-tion sharing, see Mohr and Spekman, 1994). Some authors accept thatcommunication is an important component of trust (Morgan andHunt, 1994), claiming that giving useful information creates trust.When the supplier shares information through formal CS, as part ofthe information flow facility structure (Lambert and Cooper, 2000), itprovides distributors with critical information needed for effectivemanagement. And when distributors use it, this information allowsthem to make decisions by themselves, updating knowledge, focusingattention, resolving their cognitive limitations, coordinating activities,and facilitating learning (Abernethy and Vagnoni, 2004), and in turn,as this paper argues, creating trust in the supplier.

Consequently, whatever the level of trust in the relationship,insofar as formal CS are used by distributors to mitigate theiruncertainty and to reduce divergences with the supplier, they canbe seen as needed to align activities and to manage relationships(Woolthuis et al., 2005) and foster trust (Velez et al., 2008). Thedecision management lacking the negative characteristics of decisioncontrol (Abernethy and Vagnoni, 2004) informs the second hypoth-esis. H2: Distributors' use of formal control systems for decisionmanagement increases distributors' trust in the supplier.

2.3. The moderating effect of control tools

Formal CS comprise multiple control tools that work interdepen-dently to achieve goals (Otley, 1999). Adler and Borys (1996)distinguish two types of formalization process—enabling vs. coercive—by analyzing their characteristics (internal transparency, globaltransparency, flexibility, and repair), the design process, and imple-mentation differences. They argue that the positive impact offormalization on employees' attitudes is a function of the extent towhich this formalization is enabling rather than coercive. Any controltool can be categorized on the enabling vs. coercive continuum.Enabling tools provide detailed, objective, accurate information; buildconnections among people and business units, giving people a sense ofthe whole; and facilitate analysis and what-if thinking. Recently,Wouters and Wilderom (2008), building on Adler and Borys'sframework, have found that a process that is experience based (usingexisting skills, local practice, and local know-how) and allowsexperimentation enhances the enabling function of a formal controltool.

As Adler and Borys (1996) propose, enabling formalizationprovides needed guidance and clarifies responsibilities, thereby

easing role stress and helping individuals be and feel more effective.Thus, an enabling tool is experienced as a cooperative endeavor ratherthan as an abrogation of autonomy. Enabling tools should, first,reinforce the positive effect on trust of formal CS used for decisionmanagement, and second, mitigate the negative effect of formal CSused for decision control. H3a: Enabling control tools positivelymoderate the effect of both distributors' decision-management useand distributors' decision-control perceptions on distributors' trust.

In contrast, when a coercive tool is used to manage therelationship, controlled firms will consider formal CS in a morenegative way, and the coercive tool may lessen the positive effect ofdecision-management use on trust, and strengthen the negative effectof decision-control use. H3b: Coercive control tools negativelymoderate the effect of both distributors' decision-management useand distributors' decision-control perceptions on distributors' trust.

3. Methods

3.1. Research site and data collection

The research site is a unique chemical distribution channel in Spaincharacterized by a relatively stable market, in which five largecompanies share most of the world market and large entry barriersdiscourage other potential manufacturers. The supplier is a bigmanufacturer founded in the late nineteenth century, having morethan 1000 employees and 30 production factories. This supplier iscurrently the leader in its branch of the Spanish chemical industry.Although the supplier bills all clients directly, it sells its products (a)directly to major clients and (b) to a large number of small andmedium clients via its distribution channel, which currently generatesmore than 75% of sales and caters to 90% of the supplier's clients, andgenerates a supplier's ROI 3.4 times higher than do direct sales.

Collectively, the distribution channel is formed by 178 indepen-dent nano-firms (physical people or firms with 1–4 workers) thathave had a long and successful vertical and asymmetrical relationshipwith the supplier. They receive the chemical products on consignmentfor distribution and sale. By contract, no internal competition isallowed; each distributor acts exclusively in a given geographical areaand receives commissions on the supplier's invoicing. This commis-sion system and themarket stability enable distributors to obtain highand stable profits. In addition, most of these distributors also carryother (noncompeting) products from other manufacturers, andbecause of the synergy between businesses, large-scale investmentsare not necessary to join the channel.

This channel was selected as the research site for the empiricalstudy for several reasons: (1) The supplier has been acting as leader ofthe channel for more than 19 years. (2) The channel is in a maturestage (according to Dwyer et al., 1987), as 97% of its members have

Table 1Main features associating with control tools A and B.

Enabling tool A Coercive tool B

Description In 1999, the supplier began to issue reports of commercial management information. “Theintention was that the information would allow the distributors to

In 2001, the supplier developed a Distributors Evaluation System (called “Distributor IncentivesSystem” when it was first presented, to obtain greater acceptance from distributors). “This toolhas two objectives:

(i) obtain better knowledge of the evolution of their clients;

(ii) plan and solve problems more quickly;

(iii) coordinate their work with the sales network.” (Project Memory).

(i) to reward those who obtain the best results; and

(ii) to pinpoint those who need more assistance or more support in order to pre-emptpossible conflicts.” (Project Memory).

Framework characteristics Enabling tool A evidences Coercive tool B evidences

Features of structure(from Adler andBorys, 1996)

Repair Information can be reconfigured by both boundary employees and distributors. One year afterimplementation, the tool was changed to include information suggested by users. It providesdetailed information in order to investigate causes of deviations.

It facilitates routine reports. The supplier's top managers decide the list of performance measures.It is not open to suggestions by distributors.

"It allows an ongoing dialogue, helping in the analysis of contingencies" (Distributors 1, 6). It flatly asserts duties. It provides aggregated information on the execution of objectives. Ithighlights weak points to improve, but it does not show how to improve them.“The supplier gives us a list of targets and objectives to reach” (Distributors 3, 4).

Internaltransparency

It provides objective, accurate, monthly feedback on how each distributor works, showing theirinternal process as a whole. It provides information on the drivers of sales, distributors' source ofrevenues.

It provides yearly feedback on 37 performance indicators. Some measures are subjective andinaccurate. It assesses all activities but does not increase distributors' knowledge of theiroperations. It does not provide distributors with the rationale for their work process. Informationis presented in language familiar to the supplier, but not to distributors.

“It is a working tool” (Supplier managers 1, 10, 12). “It helps in our management, allowing us totake quicker decisions and to establish plans” (Distributors 4, 8). “It allows us to have ourindependence and not to depend on the supplier” (Distributors 2, 7).

“It is an evaluating device” (Distributors 1, 6). “We can see our defects and try to correct them"(Distributors 5, 6). “The supplier controls us” (Distributors 3, 4, 7).

Globaltransparency

It provides information designed to help distributors interact creatively with clients, otherdistributors, the supplier, and the environment. It shares information that was previouslyconsidered private. Currently, it is used by both sides. It allows distributors to interact withpreviously distant supplier employees.

It shows each distributor's absolute results, but not ranking. It informs about strategic aspects ofthe channel, allowing a global vision of the contribution from each distributor to the globalstrategy. It signals areas in which distributors need to change. The information is available todistributors on a restrictive need-to-know basis.

''It is very useful, to analyze, to compare, to negotiate" (Supplier managers 3, 8). "It allows us tohave a more direct relationship with the supplier. This tool allows us to be more integrated"(Distributor 2). “This tool allows us to control our market” (Distributors 1, 3, 8).

“We do not want distributors to know the results of others” (Supplier managers 5, 14, 16). “Theytell us only how an ideal distributor is" (Distributors 5, 7).

Flexibility Use is voluntary. It can be customized for different purposes by both sides. It is a compulsory tool. All distributors are measured with the same parameters.Design process and development(from Wouters and Wilderom,2008)

It was derived from the distributors' daily work. It was developed by a team that includeddistributors. A pilot experience that also involved some distributors allowed correcting the toolbefore its definitive development.

It was developed by supplier demand, to satisfy the need for control. It was designed by topmanagers, with the help of an external consultant firm; distributors did not participate in anyphase of its development and did not even know that the supplier was working on it. It wascompletely new for distributors. Its items and measures had never been used until then.

It standardizes and formalizes information from boundary personnel and distributors. “Wemiss our participation to create a mixed system that was good for both parties” (Distributors4, 5, 8). “We only agree with them, each year, what each one is willing to give” (all suppliermanagers).

“It picks up information that some distributors were already demanding” (Supplier managers 3,12, 19)

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Table 2Study items and confirmatory multitrait multimethod analysis−standardized solution with EQS.

DM λ (t-value) DC λ (t-value) T λ (t-value) Tool A λ (t-value) Tool B λ (t-value) CMF λ (t-value) Errora R2 αb AVE

Distributors' use of control system for decision management Since the introduction of the A/B tool, you use the information provided to… .81 .371 Dg1 Identify flaws to improve your management .82(8.40) −.30(−2.18) −.19(−1.24) .46 .792 Dg2 Make decisions to improve your relationship with clients .64(6.56) −.30(−2.35) .02(.128) .71 .503 Dg3 Improve in your administrative, warehouse, and distribution management .70(7.47) .13(1.03) .18(1.23) .68 .544 Dg4 Improve your coordination with the supplier .72(7.13) .37(2.86) .12(.80) .58 .675 Ds1 Identify flaws to improve your management .40(4.06) .68(7.74) −.21(−1.61) .58 .676 Ds2 Make decisions to improve your relationship with clients .44(4.50) .71(8.32) .08(.55) .54 .717 Ds3 Improve in your administrative, warehouse, and distribution management .61(6.42) .40(4.73) −.23(−1.71) .64 .598 Ds4 Improve your coordination with the supplier .33(3.20) .52(5.57) −.33(−2.6) .72 .49

Perceived supplier's use of control system for decision control In your opinion, after the introduction of the A/B tool, you think the supplier uses it to…. .81 .341 Cg1 Influence you through this information .77(8.40) .06(.525) .25(1.70) .58 .662 Cg2 Make you personally accountable for variances occurring in your geographical area .57(5.65) .23(2.20) −.33(−2.43) .71 .493 Cg3 Develop incentives and rewards for reaching targets .47(4.38) .48(4.88) −.34(−2.52) .66 .574 Cg4 Specify and clarify what the supplier wants from you .68(7.34) .27(2.56) −.11(−.77) .67 .555 Cs1 Influence you through this information .73(8.28) .18(2.20) .01 (.07) .66 .576 Cs2 Make you personally accountable for variances occurring in your geographical area .63(6.69) .15(1.66) −.17(−1.21) .75 .447 Cs3 Develop incentives and rewards for reaching targets .34(3.48) .35(4.05) −.08(−.58) .87 .248 Cs4 Specify and clarify what the supplier wants from you .43(4.20) .39(4.00) .27(2.06) .77 .40

Changes in distributors' trust in supplier Since the introduction of these tools, you perceive that… .91 .681 Trust1 The supplier has been frank in dealing with us .82(9.73) .32(2.36) .47 .782 Trust2 Promises made by the supplier are reliable .86(10.97) .03(.18) .50 .753 Trust3 The supplier does not make false claims .87(10.94) −.15(−1.04) .47 .774 Trust4 The supplier is open in dealing with us .80(9.65) −.02(−.11) .61 .635 Trust5 If problems arise, the supplier is honest about the problems .77(9.13) −.11(−.77) .63 .60

Note: Standardized solution and t-value, in brackets, are shown.DM = Decision management; DC = Decision control; T = Trust; CMF = Common method factor;

Factor correlation Decision management Decision control Enabling tool A Coercive tool B Trust

Decision management 1.00Decision control −.54 1.00Enabling tool A −.05 .26 1.00Coercive tool B −.11 .04 .21 1.00Trust .36 −.40 .07 .17 1.00

Overall fit indexes χ2=222,93(145); pb .01; NFI=.83; NNFI=.90; CFI=.93; RSMR=.06; RMSEA=.07 (.052–.089)

a Standardized coefficient associated with the residual variable.b α = reliability.

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DG1

DG2

DG3

DG4

DS1

DS2

DS3

DS4

DM

CG1

CG2

CG3

CG4

CS1

CS2

CS3

CS4

DC

Enablingtool A

Coercivetool B

DM: Distributors’ use for decision managementDC: Perceived supplier use for decision controlEnabling tool A: Monthly distributors score cardCoercive tool B: Yearly distributors evaluation system

Fig. 2. CFA approach for testing measurement properties.

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been distributors for more than 5 years. (3) The supplier has recentlydeveloped a new formal CS to manage the channel. This naturalsetting furnishes the opportunity to study, in a real situation, whathappens to distributors' trust after formal CS implementation by thesupplier.

While multiple formal control tools are present in the CSimplemented by the supplier (more than 7 control tools), this articlefocuses on the two most discriminant mechanisms. In order to selectthem, we developed a preliminary analysis using archival data and in-depth interviews with 20 supplier firm managers and 8 distributors.Semi-structured interviews were focused on the intrinsic andperceived control tools characteristics (structure and design processand development features), and analyzed using the Adler and Borys'(1996) framework. Then, all control tools were classified as enablingor coercive. Because in reality formalization is a continuous variablewith a zone of indifference (Adler and Borys, 1996), the two mostdifferent, enabling and coercive tools were selected. Table 1 summa-rizes Enabling tool A and Coercive tool B, justifying this distinction.

Enabling tool A, a monthly distributors' scorecard, was developedin 2000 by a team formed mainly of the supplier's commercialemployees, that is, boundary employees. Through this scorecard, thesupplier shared objective commercial management information,extracted from its sales databases, that covered falls in demand,new clients, analysis of sales, itemized sales breakdowns, and theevolution of the client portfolio. Each supplier sales employee helpedhis/her assigned distributors to understand this information, moti-vating them to use it. These boundary employees also analyzed thisinformationmonthly, providing an extra management tool to monitordistributors and enabling both parties to establish and manage salesobjectives. Although distributors already had access to this informa-

tion (through client invoice copies), they perceived this moreorganized and detailed format as more helpful.

Coercive tool B is a yearly distributors evaluation system,established in 2001 by another team, this one composed of topmanagers and experts from a consultant firm. This tool comprises 37indicators that evaluate all the channel activities (warehousing,delivery, administration, and commercialization), using objectiveand subjective, and financial and nonfinancial information sources.It allows the supplier to identify distributors who require moreassistance, enhancing strong points and eliminating weak ones.Weighting these indicators, and establishing the desired levels ofdevelopment, the supplier also ranks its distributors every year inorder to reward best performances. In this way, the supplier can nowdetect shortfalls item by item, distributor by distributor.

3.2. Measures

The questionnaire items, shown in Table 2, were adapted fromexisting scales (Abernethy and Vagnoni, 2004; Bisbe, 2002) usingLikert scales. Ganesan (1994) developed the five items regardingtrust. After assessment by several academic researchers in the fields ofmanagement and control, the questionnaire was submitted toextensive pretesting and refinement through personal interviewswith supplier managers and distributors in order to ensure contentvalidity.

The questionnaires were mailed in 2004 to the whole populationof distributors, addressed to owners or general managers. Respon-dents were asked to report changes in their trust towards the supplierafter the introduction of both control tools, alongwith howmuch theythought each tool was used, either by them for decision managementor by the supplier for decision control. When the questionnaires wereadministered, the distributors had been using the tools for over twoyears, long enough for distributors to get used to it and thus to formrealistic conclusions about how it was working, while permittingthem to retain a perspective of the relationship prior to theirdevelopment and so enable the assessment of their effects on trust.The response ratio was 61.23%, with 109 questionnaires received (107useful). Along the lines suggested by Armstrong and Overton (1977),nonresponse bias was ascertained by determining whether early andlate respondents differed (late respondents are traditionally used as aproxy for nonrespondents (Diamantopoulos and Souchon, 1999). Theresults were nonsignificant, suggesting no differences in the responsepatterns of early and late respondents.

3.3. Data analysis

In a real research site, to analyze how the twofold CS uses couldsimultaneously substitute and complement distributors' trust, andhow these effects could be moderated by the coercive or enablingcharacter of the specific control tool used to manage the relationship,appears to be methodologically difficult. We try to capture themoderator effect of two tools in a sample where all units (distribu-tors) are using both of them. In this case, a multi-group analysis has tobe discarded; because we cannot split the population into those thatapply Enabling tool A and those applying Coercive tool B, as naturalgroups. This fact introduces a serious problem in this study, i.e., howto capture the moderator effect of tools which cannot be directlyobserved. We are also measuring the distributors' use for decisionmanagement and their perception about the supplier's use fordecision control from one single informant, the distributor, with theensuing need to control the common variance associated with onerespondent.

In order to solve these problems we follow different stepsusing the Structural Equations Modeling software EQS 6.1. First, wepropose a general factor analysis (CFA) approach to the multitrait-multimethod technique (MTMM)(Campbell and Fiake, 1959;Widaman,

Table 3Path analysis coefficients using factor scores.

Dependent variable: changes in a distribution channel's trust in the supplier after the implementation of formal CS

Independent variables β(ε) T-value β0 (ε) T0-value

Perception of supplier's use of CS for decision control −.24 (.09)a −2.53 –.22(.10)a −2.25Distributors' use of CS for decision management .21 (.10)a 2.18 .21(.10)a 2.12Interorganizational use of Enabling tool A .13 (.07)b 1.86 .01(.07) .145Interorganizational use of Coercive tool B .17 (.08)a 2.19 .22(.08)c 2.74Decision control * Enabling tool A .15 (.07)a 2.15 .15(.07)a 2.15Decision control * Coercive tool B −.17 (.09)b 1.92 −.16(.08)c 2.74Decision management * Enabling tool A .09 (.09) 0.97 .05(.09) .53Decision management * Coercive tool B .05 (.08) 0.57 .07(.09) .83

R-squared .34 .31Overall fit indexes χ2=20.19(12); pN .05; NFI=.94; CFI=.95; IFI=.96; MFI=.97; SRMR=.05;Fit indexes not including common method factor χ2=17.75(12); pN .05; NFI=.96; CFI=.97; IFI=.98; MFI=.98; SRMR=.04;

Note: β0 and T0-value are parameters without considering common method factor in the measurement model.a Significant at 5%.b Significant at 10%c Significant at 1%.

902 J.M. Sánchez et al. / Journal of Business Research 65 (2012) 896–906

1985) to capture the overall interorganizational use of each control toolas the concepts: Enabling tool A and Coercive tool B (see Fig. 2); and testmeasurementproperties. As shown in Fig. 2, exogenous constructs in ourmodel are sharing items, that is, the factor labeled by “DM” is measuredby the items referred to the distributors' use for decision-management,however, these items are simultaneously reflecting part of the tools Aand B. The same problem arises from the “DC” items. This fact makes itdifficult to apply traditional assumptions aboutmeasurement validation.That is, our model violates the first assumption of the classical measurevalidation for theoretical concepts: “a unidimensional item or indicatorhas only one underlying construct” (see Ping, 2004 for a review).Anyway, correlations and extracted variances resulting for this MTMMmodel have been compared in order to test discriminant and convergentvalidity considering a traditional approach (Ping, 2004) added to thediscriminant and convergent validity tested in the MTMM approach(Widaman, 1985). Second, due to having one single source using thesame questionnaire, common variance was controlled using Harman'ssingle factor test (Podsakoff, MacKenzie, Lee, and Podsakoff, 2003).

Third, hypotheses were tested introducing the Bentler–Yuan optimalGLS factor scores in a path analysis. Usually, composite factor scorescontrolling errors of the interactions terms are used to test non-linearrelationships in Structural Equations Models. However, the firstassumption for applying this method is that unidimensionality must beproven (Ping, 1998). Here, by construction, each itemdid not correspondwith one single factor; consequently we opt for using the Bentler–Yuan

-1,5

-1

-0,5

0

0,5

1

-2 -1,7 -1,4 -1,1 -0,8 -0,5 -0,2 0,1 0,4 0,7

Distributors' perception of supplier's u(A) for Decision Contr

Trust

Fig. 3. Interaction effects of decisio

optimal GLS factor scores implemented in the EQS 6.1 software (BentlerandYuan, 1997). The interaction variables in this case are latent, then thefactor scores approach is probably the most reasonable to use (Jöreskog,1998). This section continues developing measurement model andhypothesis testing in accordance with previous assumptions.

CFA (see Fig. 2) was performed in combination with MTMM andCVM as follows:

Item = λTrait + λMethod + λCMV + ε: ð1Þ

MTMM was employed with two purposes: first, to test forevidence that the decision-control and decision-management con-structs (traits) were valid regardless of the method employed tomeasure them (tools A and B); and second, to extract two latentmeasures of the interorganizational use of the two control tools andtest for interaction effects (Baltes, Bauer, Bajdo, and Parket, 2002).

In the CFA approach to MTMM (see Table 2), each item presentssignificant factor loadings onto a designated trait. In the case ofmethod measures, two items presented nonsignificant factor load-ings, Dg3 and Cg1 for tool A, and the loading factor of Cs2 for Tool B issignificant at 10%. Since each factor loading is specific to the particulartrait-method combination (Bagozzi, 1994) and most of the traitloading factors were significant, a more exhaustive analysis of the CFAapproach to MTMM was performed in order to guarantee convergentand discriminant validity. In accordance with Widaman's (1985)

High use of Enabling tool Aβ= 0.03(0.11)

Average use of Enabling tool Aβ=-0.25 (0.09)

Low use of Enabling tool Aβ=-0.17 (0.12)

1 1,3 1,6 1,9

se of Enabling tool ol

n control and Enabling tool A.

High use of Coercive tool Bβ=-0.67 (0.15)

Average use of Coercive tool Bβ=-0.25(0.09)

Low use of Coercive tool Bβ=-0.17 (0.12)

-1

-0,5

0

0,5

1

1,5

2

-2 -1,7 -1,4 -1,1 -0,8 -0,5 -0,2 0,1 0,4 0,7 1 1,3 1,6 1,9

Distributors' perception of the supplier's use of a Coercive tool (B) for Decision control

Trust

Fig. 4. Interaction effects of decision control and Coercive tool B.

903J.M. Sánchez et al. / Journal of Business Research 65 (2012) 896–906

approach, four nested MTMM models were compared: (1) a modelwith freely correlated traits and methods; (2) a model with no traitsand freely correlated methods; (3) a model with perfectly correlatedtraits and freely correlated methods; and (4) a model with freelycorrelated traits and perfectly correlated methods. Convergentvalidity was tested by comparing the chi-squared value of a modelin which traits are specified (model 1) with one in which they are not(model 2) (Widaman, 1985). Discriminant validity was tested bycomparing model 1 with model 3 for traits, and model 1 with model 4for methods. The difference in chi-square between the modelsprovides the basis for judgment. In addition, decision control, decisionmanagement, and distributors' trust in the supplier were tested fordiscriminant validity using Fornell and Larcker (1981).

The test for bias from Common Method Variance (CMV, variancethat is attributable to the measurement method) used Harman'ssingle-factor test in CFA (see Podsakoff et al., 2003, for a review).

As mentioned above, although our model violates classicalassumptions about the unidimensionality needed to validate latentconcepts, we show traditional values of reliability and validity of themeasures proposed. All trait loadings met the minimum criteria forfactor loadings (higher than 0.3), reliability (higher than 0.7). Sincereliability and AVE are related, it is not surprising that our resultsshow acceptable reliability and unacceptable AVE using existingcutoffs for the acceptability of these statistics (Ping, 2004) showingdoubts about convergent validity (the minimum Variance Extractedvalue was .34). Discriminant validity was supported using Fornelland Larcker (1981) after accounting for control tool (A and B) andcommon method variance. We want to point out that the traitDecision Control (DC) has an AVE below 0.5, this is due to the Cs3item which presents an R-squared below 0.5. Although it would beeasy to increase this AVE dropping thementioned itemwe decide notto eliminate it in order to maintain the content validity of theproposed measures across tools (this item is measured for tool A andB using Cg3 and Cs3 respectively). Omitting items to attainconsistency could affect content validity (Ping, 2004) not capturingconcepts defined at the proposed theory. Zero-order correlationsamong decision-control, decision-management, and trust constructsranged from −.57 to .36.

4. Results

The hypotheses were tested using path analysis introducing theBentler–Yuan optimal GLS factor scores implemented in the EQS 6.1

software (Bentler and Yuan, 1997). The equation tested was

Trust = α + β1DM–β2DC + β3Tool A + β4Tool B + β5�DM�Tool A

+ β5�DM�Tool B + β5

�DC�Tool A + β5�DC�Tool B + ε; ð2Þ

where DM refers to decision management and DC refers to decisioncontrol.

The procedure used for testing moderation effects follows Baronand Kenny (1986), and variables have been centered before beingincluded into the regression equation containing interactions asCohen, Cohen, West, and Aiken (1983) suggested. Table 3 illustratesthe results, showing acceptable fit indexes.

Table 3 also presents an additional test for common methodvariance. Because predictor and criterion variables could not beobtained from different sources measured in different contexts, norcould the source(s) of the method bias be identified (Podsakoff et al.,2003), items were allowed to load on their theoretical constructs aswell as on a latent common methods variance factor, with theaddition of a first-order factor. The significance of the structuralparameters, shown in Table 3, is examined both with and without thelatent common method variance factor in the measurement model.The parameters and significance for both models suggest thatcommon method variance does not affect the significance of theresulting parameters (Podsakoff et al., 2003).

The main effect on trust of the distributors' perception of thesupplier's use of CS for decision control (H1) was negative andsignificant (β=−.24; pb0.01), supporting hypothesis 1. Results alsosupport hypothesis 2 (β=+.21; pb0.05): the use of formal CS fordecision management is positively associated with distributors' trustin the supplier.

Results supported a moderating effect of both enabling andcoercive tools on the relationship between decision control andtrust, but not on the relationship between decision management andtrust. That is, the positive (complementary) relationship betweendecision management and trust (β=+.21; pb0.05) did not vary withthe type of control tool used.

In contrast and as predicted, Tool A (enabling) had a positive andsignificant interaction effect on the relationship between decisioncontrol and trust (β=+.15; pb0.05), and Tool B (coercive) had asignificant and negative effect (β=−.17; p=0.05). Therefore,hypotheses 3a and 3b were partially supported. Figs. 3 and 4represent these interaction effects and a post hoc probing test todetermine the statistical significance of the coefficients.

904 J.M. Sánchez et al. / Journal of Business Research 65 (2012) 896–906

The negative effect of decision control on trust (β=−.24; pb0.01)is offset when the enabling Tool A is used a great deal (β=.17;pN0.05), but is significantly stronger when this tool is used little (β=−.67; pb0.01). In contrast, higher use of the coercive Tool B increasesthe magnitude of the negative effect of decision control on trust (β=−.53; pb0.01), while lower use counterbalances this effect (β=.03;pN0.05). That is, the negative (alternative) relationship betweendecision control and trust was not significant under two conditions:(a) heavy use of the enabling tool, and (b) low use of the coercive tool.

5. Discussion and conclusions

This research suggests that, in order to advance in the under-standing of the complex control-trust association, studying both (a)the uses of these CS and (b) the coercive or enabling character of eachspecific control tool used is necessary. However, in practice, capturingthe effect of CS uses using real data seems to be difficult. In adistribution channel, in which uniform CS are used by all thedistributors, natural groups cannot be made up because all companiesenrolled in the real research site are using the CS under analysis. Inthese cases, control tools become latent concepts sharing items acrosstraits or theoretical concepts; they are latent factors that additionallyinteract with theoretical concepts in the model. In this way, wehighlight an important issue, how to empirically evaluate themoderator effect of different control tools on the development oftrust.

An appropriate approach seems to be the use of MTMM as asolution to make up the measurement model. However, although inthe MTMM approach convergent and discriminant validity are tested,these conventional tests are traditionally used to control the methodeffect as a nuisance not as a usable concept. In consequence, themeasurement model, where different concepts are sharing items,violates the principle of unidimensionality needed to guaranteevalidity and usual cutoffs for convergent validity and reliabilityseem not to be directly applicable. These facts limit the considerationof any proposed measures for the CS uses in a distribution channelwhere the control tools want to be taken into account properly.Additionally, when one single respondent is considered commonvariance must be controlled. Consequently, measures developed toassess concepts (uses in this case) present some validation problemsthat must be taken into account in order to consider hypothesissupport. Accordingly, our analysis highlights that the measuresdeveloped in prior studies could fail to display adequate validitywhen you control for the tools and common methods bias.

Because formal CS are shaped by different control tools that areused to manage the relationship, in this paper we propose a solutionto the problem shown. Combining the MTMM with Harman's test forcommon variance, and introducing factor scores in the path analysisto capture the moderator effect of control tools in the model understudy. This solutionmust be considered as a first step in the analysis ofthemoderator effect of different tools in the success of relationships inthe supply chain, pointing out that common variance derived fromone single respondent or method has to be controlled and differentcontrol tools interact with the concepts under study.

According to this approach, although the results must be evaluatedwith caution, this research offers interesting empirical evidence as afirst step in this analysis. The association between the supplier'sformal CS and distributors' trust seems to be simultaneouslycomplementary and alternative depending on the uses of formal CSfor decision management and decision control, respectively. Thealternative association appears to depend on the use of the set ofcontrol tools that shape the CS, with a coercive tool strengthening thealternative association and an enabling tool weakening it. That is,control and trust might not function as alternatives under twoconditions: when distributors perceived that an enabling tool was

used more, and when they perceived that a coercive tool was usedless.

5.1. Decision-management use and trust

The finding that decision management use of CS increases trustseems to support Nooteboom, Berger, and Noorderhaven's (1997)argument. When CS developed and shared by the supplier are used bydistributors in their daily management, these systems will beassociated with a greater interest on the part of the supplier inimproving communication and mutual knowledge by providingrelevant information (Morgan and Hunt, 1994).

The finding that distributors' perception of their own use of formalCS is not affected by control tool characteristics provides evidence forBrashear, Manolis, and Brooks's (2005) proposal that favorableemotional responses ensue when a distributor feels that the suppliershares control information and empowers the distributor to makedecisions. No matter whether distributors rate a particular tool asmore or less enabling, they associate its use for decision managementwith a higher frankness, reliability, openness, and honesty on the partof the supplier.

5.2. Decision-control use and trust

In accordance with the view that trust and formal CS arealternatives (Ghoshal and Moran, 1996; Stump and Heide, 1996),the results in the first step above show that distributors trust thesupplier less when they think the supplier is using these systems fordecision control. As Inkpen and Curral (2004) argue, when distribu-tors perceive that the supplier uses formal CS for decision control,they find their autonomy constrained, so their own trust decreasesreciprocally.

But this alternative association does not always have the sameintensity and can be neutralized; it depends on the enabling orcoercive character of the control tool used to manage the relationship.Our analysis indicates that, on one hand, a higher use of the enablingtool counteracts the negative effect of perceived decision-control useon trust. This fact seems to indicate that enabling control tools areperceived as technical assistance, so that even if distributors knowthat they are used tomeasure and reward performance or to supervisethe accomplishment of objectives, the damage to trust is low ornonexistent. In this sense, the results of this study are consistent withprevious qualitative research (e.g., Langfield-Smith and Smith, 2003)arguing that formal CS do not damage trust when they are developedin a supportive and collaborative way.

When use of the enabling control tool is high, the tool's owncharacteristics, shown by Table 1, give distributors a clear under-standing of why formal CS are in place, and where their own tasks fitinto the whole. Accordingly, distributors develop a strong under-standing of the supplier, seeing the supplier's decision control use asneeded (Velez et al., 2008), so that control and trust go hand in handwithout tension (Bijlsma-Frankema and van de Bunt, 2003). Further-more, the distributors' involvement in the development processmitigates suspicions and allows them to see the supplier's decision-control use as effort invested into achieving the relationship's jointobjectives.

On the other hand, the negative effect of decision-control use ontrust could increase when distributors perceive higher use of thesomewhat coercive tool and is neutralized when they perceive lowuse. Evidently, the coercive tool makes formal CS look like devices toforce distributor compliance. As Table 1 shows, the lack of transpar-ency of coercive tools that contain new and aggregated informationreinforces the distributors' perception that their performance is nottruthfully measured, and impairs their perception of being treatedwith fairness, reciprocity, and equity. An external development

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processmakes the supplier's decision-control use seem to signal a lackof confidence.

The supply chain management literature highlights the impor-tance of an appropriate relationship between members to achievingan effective competitive advantage, noting that trust is the mostcritical predictor of successfully cooperative and collaborative re-lationships (Trent, 2005). In an asymmetrical relationship, wherecontrolled firms' behaviors are monitored, influenced, or directed(either explicitly or implicitly) by the leader firm, that firm must beproactive in its pursuit of better relationships, but controlled firms'trust is hard to build because they tend to see the leader's acts withsuspicion (Sitkin, 1995; Fawcett et al. 2004). Taking our methodo-logical approach into account, our results indicate that formal CS canbe developed without damaging the established trust: (1) becauseperceived decision-control use by the supplier and the distributor'sown decision-management use of formal CS have different andsimultaneous effects on distributors' trust and (2) because thenegative effect of decision-control use on distributor trust iscontingent upon the coercive vs. enabling character of differentcontrol tools.

5.4. Managerial implications

The following guidelines can help proactive managers design andimplement formal CS while improving or maintaining trust-basedrelationships. First, leader firmmanagers need to motivate partners touse the CS information to manage their daily activities. Trust increaseswith the use of formal CS for decision-management purposes.

Second, Adler and Borys' (1996) framework, setting out the set ofcharacteristics that enabling tools should have, appears as a clear anduseful guide to designing and implementing formal CS so as toincrease trust. Our results support Jap's (1999) argument that, ratherthan playing a zero-sum game by developing coercive tools thatweaken its partners' trust, the leader firm can develop enabling toolsin order to increase their capacities, counteracting the potentialnegative effect of decision control on trust.

A better understanding of the association between formal CS andtrust can be highly relevant to the current business environment.Although this study is focused on downstream relationships, theresults could also be translated to other situations (e.g., supplierrelationship management) where a strong leader seeks to developformal CS to proactively manage relationships, but is also concernednot to damage trust. The secret of collaborative relationships is notonly what the parties should do together but also what they believeabout each other and how they interact (Hughes, 2008).

5.3. Limitations

The results and the constraints of this study suggest avenues forfurther research and research challenges. First, control tools areincluded in the model as latent factors making necessary specialconsiderations to guarantee content, as well as convergent anddiscriminant validity. The solution proposed here needs furtherresearch to achieve a robust model to measure simultaneously toolsand concepts as latent factors. This analysis needs more applicationsconsidering that different control tools shape formal CS (Otley, 1999).Consequently in real research sites, where you cannot manipulateconditions and tools as in experimental settings, when you areanalyzing complex constructs as CS it is necessary to take into accountthe relative weight of each tool used.

New measures and methods need to be developed to displayadequate validity when you capture the control tools and commonmethods bias as we have done here. Second, the study does notinclude the leader firm's perspective; a deeper understanding of therelationship between formal CS and trust would require understand-ing both parties simultaneously collecting data from the dyad

(supplier and distributor). We want to remark that this propositiondoes not simplify the fact that different control tools are analyzed andintroduce the multi-rate difficulty (Coovert, Craiger, and Teachout,1997) to a per se complicated model wheremethods and concepts areindispensable for testing hypotheses.

Third, its focus on well-established interorganizational relations invertical channels limits the generalizability of the results, whichmightnot be applicable to nonvertical channels and/or relationships in earlystages. Fourth, the data come from a single industry and a singledistribution channel; input from other industries and other distribu-tion channels would be needed to further validate the resultspresented here. Fifth, the study uses surveys, and the data obtainedwere self-reported and cross-sectional. Since a summary or databaseincluding demographic characteristics of the population is unavail-able, analyzing differences in demographics between respondents andnon-respondents is impossible (Blair and Zinkhan, 2006).

Although the early and late respondents test results wereacceptable, an uncontrolled non-response bias might exist thatwould need to be taken into account in order to generalize results.Likewise, the study includes several steps to limit any survey bias;nevertheless, it would be of great interest to study the evolution oftrust with longitudinal self-reported data. Sixth, the present studyexamines only two control tools. A fruitful avenue for future researchlies in providing more insight, through experimental design, into howtool characteristics reinforce or attenuate the negative effect of thedecision-control use on trust and how a broader array of control toolsinfluences the controlled party's trust in the leader party. Anotherextension of this work might consider how each different use offormal CS affects relationship performance, directly and indirectly.

Acknowledgments

The authors thank Clara Agustin and Concha Álvarez-Dardet forcomments on earlier drafts. The authors thank the survey respondentsfor their time generously provided. This research was supported bythe Ministry of Education of Spain (ECO2008-0550 and ECO2009-13378).

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