The Impact of Relational Variables on Value Creation in Buyer-Seller Business Relationships_Sachez

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Journal of Business-to-Business Marketing, 17:62–94, 2010Copyright © Taylor & Francis Group, LLCISSN: 1051-712X print/1547-0628 onlineDOI: 10.1080/10517120903000389

WBBM1051-712X 1547-0628 Journal of Business-to-Business Marketing, Vol. 17, No. 1, Jan 2010: pp. 0–0 Journal of Business-to-Business Marketing

The Impact of Relational Variables on ValueCreation in Buyer–Seller Business Relationships

Relational Variables and Value Creation J. Á. L. Sánchez et al.

JOSÉ ÁNGEL LÓPEZ SÁNCHEZUniversity of Extremadura, Badajoz, Spain

MARÍA LETICIA SANTOS VIJANDE and JUAN ANTONIOTRESPALACIOS GUTIÉRREZ

University of Oviedo, Oviedo, Spain

Purpose: This research was aimed at attaining a deeper knowledge of how customer value creation can be improved in business mar-kets. Although trust and commitment (as relational governance mechanisms) appear to have a positive effect on customer value creation, limited empirical evidence exists about the combined effect of the aforementioned variables on improving customer value creation. This article studies why trust and commitment are key precursors to improving customer value creation in commercial relationships among companies.

Methodology: Following a review of the literature, we introduce and contrast a conceptual model on a sample of 181 manufactur-ing companies located in Spain by means of a structural equation

system.Originality: The study of these causal relationships is relevant

because it provides greater knowledge of the role played by the key relational variables of trust and commitment on improvingcustomer value creation in business markets. These variables also

have an important influence on the development and mainte-nance of a relationship in the long term and have been the focus of recent marketing research.

The authors thank the anonymous Journal of Business-to-Business Marketing reviewersfor their constructive comments on earlier versions of this article.

Address correspondence to José Ángel López Sánchez, PhD, assistant professor of marketing, University of Extremadura, Facultad de Ciencias Económicas y Empresariales,Departamento de Dirección de Empresas y Sociología, Avda. de Elvas s/n, 06071 Badajoz,

Spain. E-mail: [email protected]



Relational Variables and Value Creation 63

Findings: The empirical results reveal that: (1) distributor commitment is a direct and positive antecedent of value creationin a relationship, understood from a functionalist perspective; (2)distributor trust, the other relational variable, has an indirect

effect on value creation through the distributor’s commitment; and (3) this research does not tie in with previous studies that found that direct value-creating functions have a multiple-component nature representing a second-order factor.

KEYWORDS trust, commitment, value creation, buyer–seller relationships, Spain

The creation of customer value has been widely acknowledged as being

one of the main objectives in buyer–seller relationships (Anderson, Jain, andChintagunta 1993; Holbrook 1994; Menon, Homburg, and Beutin 2005;Ulaga and Eggert 2006). Adding value to the basic market offer may improve customer satisfaction while strengthening attachment between theparties, thus promoting customer retention (Walter et al. 2003). Understandinghow value can be identified, created, and delivered to the customer cantherefore be considered a key source of competitive advantage in businessmarkets (Haksever, Chaganti, and Cook 2004). Accordingly, the MarketingScience Institute has included this concept among its research priorities inrecent years. The specialist journals have followed suit, devoting special

issues to recent developments in this field of knowledge. They include the Journal of the Academy of Marketing Science in 1997 and Industrial Market-ing Management in 2001 (Lapierre 2000; Ulaga and Eggert 2005). However,despite these efforts, the complexity of the concept of customer value hasresulted in this term remaining ambiguously defined in the literature, whichprovides many different approaches to its critical components (Ulaga andEggert 2005).

In addition, academic research has identified that value creation can beimproved in a relationship through: (1) product innovations resulting fromresearch and development (Mizik and Jacobson 2003; Swaminathan, Feisal,and Hulland 2008); (2) technology improvements in the production-distribution processes (Nambisan and Nambisan 2008); (3) market-basedassets (relational and intellectual) (Srivastava, Shervani, and Fahey 1998);(4) business processes (product development, supply chain, and customerrelationship management) (Srivastava, Shervani, and Fahey 1999); (5) col-laborative communication between exchange partners (Mohr, Fisher, andNevin 1996); (6) marketing, research and development, and operationscapability (Krashnikov and Jayachandran 2008); and (7) a high level of cooperation (Fang et al. 2008). The present work was aimed at attaining a

deeper knowledge of how customer value creation can be improved in



64 J. Á. L. Sánchez et al.

business markets. In particular, despite the fact that the improvement of customer value creation is very likely to be based on the trust and commit-ment of the commercial partners, limited empirical evidence exists aboutthe combined effect of the aforementioned variables on improving customer

value creation. In assessing why companies promote or inhibit the develop-ment of customer value creation we consider trust and commitment asrelational governance mechanisms that can act as antecedents to customer

value creation. Understanding the effect of these factors can help compa-nies manage them efficiently in order to improve customer value creation.The compelling nature of the case has attracted the attention of scholarssuch as Moller (2006) and Holcomb and Hitt (2007), who have insisted onthe need for empirical studies in this domain.

The present study is an inquiry into the role of trust and commitmentas antecedents of customer value creation in business markets in Spain. The

study of these causal relationships is relevant because it provides greaterknowledge of the role played by the key relational variables of trust andcommitment in improving customer value creation in business markets, andbecause these variables also have an important influence on the develop-ment and maintenance of a relationship in the long term. The article isstructured as follows. First, the various value-creating functions in a relation-ship are studied. A theoretical model linking trust and commitment with

value-creating functions is then proposed. The empirical section presentsthe results of research based on a sample of 181 manufacturer–distributorrelationships. Finally, the most significant conclusions are presented, andbusiness implications as well as the limitations of the study are highlighted.

VALUE-CREATING FUNCTIONS

The functionalist approach is a line of research that focuses on analysis of the creation of value for the customer (Anderson, Hakansson, and Johanson1994; Walter, Ritter, and Gemunden 2001; Lindgreen and Wynstra 2005). Itis called the functionalist approach because it is based on the premise that

buyer–seller relations may take the form of a series of tasks or functions thatcreate value for the agents involved (Anderson, Hakansson, and Johanson1994). More specifically, they allow the parties to “link their activities, tietogether their resources, and to develop bonds between people . . .enabling the accumulation of knowledge, the creation of new resources,and the development of new activities” (Harris and Wheeler 2005: 188).Functions in relationships can be classified as direct or indirect (Vazquez,Santos, and Sanzo 1998; Ryssel, Ritter, and Gemunden 2004).

Direct or primary functions represent, in general, the basic benefitsderived from the main dyadic relationship. These are performed within the

relationship as its actors look for efficiency by means of the interconnection




of activities, creativity from the heterogeneity of resources, and mutuality based on the interest of the actors themselves.

Indirect or secondary functions , also known as network functions,involve the effects of a relationship deriving from its interconnection with

other relationships. This type of function includes the chains of activitiesthat involve several organizations, the resource portfolio controlled by morethan one company, and the perceptions shared on the network by morethan two companies.

Among the most significant recent contributions to this analyticalapproach is that of Walter, Ritter, and Gemunden (2001) who take the func-tions that create value for the customer to be the activities carried out andthe resources used by the supplier company, that is, whatever the suppliercontributes to both the main dyadic relationship and to the network of relationships connected to it. The following is a description of each of the

value-creating functions that the supplier carries out for the customer.

Direct Functions

Benefit function. The fact that the supplier, in its relationship with a givencustomer, is able to offer products or services at the best prices, or at leastat competitive prices, without the customer having to renounce certainminimum technical quality standards, is a significant factor in customer prof-itability (Walter, Ritter, and Gemunden 2001). This function reflects theimportance of the supplier in the generation of financial benefits for thecustomer. It also helps customers—because of the cash generated by thepurchase of products from which they gain a high profit margin—to stimu-late relational exchanges with different suppliers who can carry out comple-mentary functions in their business strategy (Kalwani and Narayandas 1995).

Volume function. The supplier carries out this function, creating valuefor the customer when it can meet the high demand of its counterpart(Walter, Ritter, and Gemunden 2001). It may happen that, for strategic rea-sons, stable relations with suppliers are more important than obtaining highprofits in subsequent transactions. The fact that a supplier can carry out the

volume function means that the customer can be sure it will be suppliedproperly and on time with a large amount of product when necessary (Turnbull 1982).

Safeguard function. The supplier carries out this function in a rela-tional exchange once it can guarantee that the client will receive both acertain level of supply, even if this is not so favorable from an economicpoint of view, and a reduction in its dependence on other suppliers(Hakansson 1982). The seller party therefore acts as an “emergency sup-plier” for the trading partner. The argument is that market uncertainty makes it necessary for customers to maintain relations with additional

suppliers because, even though these may not always have a benefit




function and/or a volume function, they provide a sort of insurance againstcrises or difficulties in other exchange relationships (Walter et al. 2003).

Indirect Functions

Innovation function. In the development of new products and pro-cesses, customers tend to establish cooperation relations with suppliers whoare technological leaders and who have extensive product experience(Walter, Ritter, and Gemunden 2001). In fact, both product and processinnovation are often the result of interactions between two or more agents(Hakansson 1987) so that, under some circumstances, the customer may even sacrifice financial and/or volume benefits because of the long-termusefulness of cooperation of this type (Walter et al. 2003).

Market function. Customers can take advantage of the prestige or

good commercial reputation of some of their suppliers and use it as a refer-ence point to help gain access to new markets and make new trading links(Walter, Ritter, and Gemunden 2001). The market function can be found

when working with suppliers who adopt strict criteria and conditions forselecting their commercial partners (Corstjens and Merrihue 2003). Working

with this type of supplier can have a knock-on effect on other trading rela-tionships, even if the suppliers are not the most important in the market ineconomic terms (e.g., in terms of sales volume).

Scout function. It can be argued that a company’s success may belargely the result of its being able to obtain significant information fromother agents located outside itself (Walter, Ritter, and Gemunden 2001).

A possible criterion to be used when selecting new suppliers, therefore, istheir capacity to provide relevant commercial information to the customer.Such suppliers can become useful channels of information because of theirextensive knowledge about their own sector and about other related mar-kets (Walter et al. 2003). Such useful commercial information may includedetails of competitors’ product lines and matters relating to market trends.

THE EFFECT OF TRUST AND COMMITMENTON VALUE-CREATING FUNCTIONS

In this research we adopt the commitment-trust perspective of interorganiza-tional relationship performance to develop a conceptual model with which

we try to attain a deeper knowledge of how customer value creation can beimproved in business markets (Blau 1964; Cook and Emerson 1978; Morganand Hunt 1994; Palmatier, Dant, and Grewal 2007). Consistent with this per-spective, trust and commitment are regarded as key factors for the successof a relationship in the long term (Dwyer, Schurr, and Oh 1987; Morgan and

Hunt 1994; Wilson 1995). More specifically, trust and commitment (as




relational governance mechanisms) are considered to have a positiveimpact on customer value creation (Dyer 1996; Dyer and Singh 1998). Acommercial partner is, therefore, expected to behave positively toward andin the best interest of a committed and trusted counterpart in the dyad

(Hakansson 1982; Jap 1999; Palmatier, Dant, and Grewal 2007). Analysis of this perspective shows its usefulness for explaining trustand commitment as key precursors of customer value creation. However,despite the importance of this topic for gaining a greater understandingof how buyer–seller relationships work (Dwyer, Schurr, and Oh 1987;

Anderson and Narus 1990; Mohr and Spekman 1994; Morgan and Hunt1994; Fontenot and Wilson 1997; Palmatier, Dant, and Grewal 2007), thereare few works providing empirical evidence about the combined effect of these two factors on improving the creation of value for the customer(Gustafsson, Johnson, and Roos 2005; Lindgreen and Wynstra 2005; Moller

2006). Figure 1 shows the theoretical model proposed. The factors aremeasured on the basis of the evaluation made by one of the members of the main dyadic relationship: the manufacturer who, on the basis of thethorough knowledge it can be expected to have on the relationship, evaluatesthe level of trust and commitment of its main distributor. The manufactureralso indicates its own degree of fulfillment of a series of direct and indirectfunctions that provide value to the main distributor. Accordingly, the modelevaluates whether the manufacturer’s value creation activities in the maindyadic relationship depend on its perceptions of its counterpart’s level of trust and commitment.

This approach has been used to determine how organizationalresources are related to customers’ marketing measures or performanceindicators (Roth and Jackson 1995; Soteriou and Zenios 1999; Theoharakisand Hooley 2003). We also verified that the respondents’ companies wereISO 9001:2000 certified. It can then be guaranteed that the companies understudy carry out reiterative procedures to evaluate their customers as is

FIGURE 1 Trust, commitment, and value-creating functions.

Distributor Commitment

Distributor Trust

Direct FunctionsCreating Valuefor the Distributor

Indirect FunctionsCreating Value

for the Distributor

H5

H3

H4

H1

H2

Value-Creating Functions




required by this certification. Thus, manufacturer’s perceptions about theirdistributors in the main dyadic relationship are well known (as a conse-quence of the nature of the relationship) as well as being supported moreobjectively. In the literature, one can find diverse examples in which this

methodological approach is employed (Matear, Gray, and Garrett 2004; Agarwal, Erramilli, and Dev 2003; Baker, Simpson, and Siguaw 1999;Gilliland and Bello 2002; Ryu, Min, and Zushi 2008). The following explainseach of the causal links identified.

Trust and Value-Creating Functions

Trust is a key element for creating and developing buyer–seller relationshipsover the long term (Lusch, O’Brien, and Sindhav 2003; Bart et al. 2005). Thetangible benefits of a relationship in which there is trust are greater than

those generated when this is not the case, because trust makes a greaterlevel of commitment and sales attainable. There is also a greater probability that the relationship will survive difficulties, that confidential information

will be shared, that investments will be made in shared resources, and thatthe costs of monitoring the trading partner will be reduced (Morgan andHunt 1994; Kumar 1996; Nielson 1998; Williams 2001). In this study, trust isdefined as “a willingness to rely on an exchange partner in whom one hasconfidence” (Moorman, Zaltman, and Deshpande 1992: 315). This impliesadoption of the two criteria on which this concept is currently based:honesty or credibility and benevolence (Sirdeshmukh, Singh, and Sabol2002; Sanzo et al. 2003).

In this context, we can assume that when there is sufficiently well-developed trust, this being a basic norm of conduct, it will facilitate theexchange and sharing of resources, new ways of coordinating activities, anda warmer relationship between the parties (Walter and Ritter 2003). To bemore specific, the link trust → value creation can be explained on the basisof the following arguments.

Direct value-creating functions. If the manufacturer perceives that itsmain distributor complies with the terms of the agreement and is interested

in a mutually beneficial relationship, the immediate consequence will bethat the manufacturer will be prepared to do whatever it can to ensure thatthe relationship lasts (Williams 2001; Lines et al. 2005). This can lead to themanufacturer ensuring that the distributor receives quality products at com-petitive prices, that the required product volume is supplied, and that thesupply continues even in changing demand conditions (Brashear et al.2003; Mezgar 2003). All this means that

H1: The direct value-creating functions carried out by the manufacturerfor its main distributor are positively influenced by the distributor’s

trust in the manufacturer.




Indirect value-creating functions. Whenever trust exists in a relation-ship, the situation will be appropriate for innovative activities. Without thecustomer’s trust, the manufacturer would not be prepared to cooperate inactions of this type, because there would be a high risk of the distributor

acting in an opportunistic way and using previously shared ideas and/orexperiences against it (McAllister 1995; Ruppel and Harrington 2000). More-over, the manufacturer would never agree in principle to work with anunreliable distributor because the latter might make use of the former’sgood reputation (without its consent and/or knowledge) to gain access toother markets in which the manufacturer has no control over its image. It

would also be critical for the manufacturer to reveal information about newbusiness possibilities in other markets to the distributor because in thefuture the latter might use it to compete against the former (Homburg,Koschate, and Hoyer 2005; Singh et al. 2005). These arguments suggest that

H2: The indirect value-creating functions carried out by the manufac-turer for its main distributor are positively influenced by the distrib-utor’s trust in the manufacturer.

Commitment and Value-Creating Functions

In the same way as trust, commitment in relational exchanges has becomean essential aspect in marketing research on commercial distribution(Morgan and Hunt 1994; Brown, Lusch, and Nicholson 1995). The fact that

the members of the channel act in a committed way may improve their pos-sibilities both of survival and of profitability in the long term (Geyskenset al. 1996). In particular, working in this way makes it possible to generateand offer higher value to the parties involved. In the case of a manufacturer,it provides “easier access to market information and stronger support for itsproducts” (Kim and Frazier 1997: 848), whereas for distributors it facilitates“access to products desired by their customers and more opportunity todifferentiate themselves from other distributors” (Anderson and Weitz 1992:18). Commitment is defined as “an enduring desire to maintain a valued

relationship” (Moorman, Zaltman, and Deshpande 1992: 316). That is, com-mitment involves an emotional component because it is based on a state of positive feelings toward the trading partner expressed through the existenceof an emotional link and a spirit of cooperation with the opposite party.It implies the identification of one of the partners with the objectives and

values of the other, as well as a certain feeling of belonging and “loyalty.” Just as with trust, commitment can be associated with one of the main

causes of value creation in a relationship. Once the parties to a deal havetaken the decision to commit in one way or another, they are recognizingthe importance of the relationship for achieving individual and shared

goals. It therefore seems reasonable to think that if there is commitment,




greater value can be created than when this is not so (Ryssel, Ritter, andGemunden 2004). The link commitment → value creation is based on thefollowing arguments.

Direct value-creating functions. When commitment is present in a

relationship, it is very likely that the relationship will take on a long-termorientation. Organizations assume that long-term cooperation favors greaterbenefits than discrete transactions with changing partners (Friman et al.2002; Lambert and Knemeyer 2004). The way in which the manufacturerrelates to the distributor will vary greatly depending on the degree to whichthe distributor is committed. If there is a lasting wish on the part of the dis-tributor to maintain a relationship, the manufacturer will be more likely tooffer it products or services under beneficial conditions, allowing the dis-tributor to improve its profitability (McQuiston 2001; Ghosh et al. 2004). Themanufacturer, in turn, will be more prepared to provide the volume of

products requested by the distributor and, when necessary, to meet any supply needs resulting from changes in demand specifications caused by market uncertainty (Walter and Ritter 2003). This allows us to draft thefollowing research hypothesis.

H3: The direct value-creating functions carried out by the manufacturerfor its main distributor are positively influenced by the distributor’scommitment toward the manufacturer.

Indirect value-creating functions. When commitment exists, the parties

in the exchange recognize that their objectives depend on each other, andthis means that if the distributor is successful in innovation activities thenthe manufacturer will also benefit (Simonin 1997; Wong, Tjosvold, andZhang 2005). In those contexts the manufacturer may act as a sort of cata-lyst, using its prestige if required so that a distributor considered strategiccan set up new contacts and thus expand its own commercial activities. Thiseffort may even include the manufacturer providing the distributor with

valuable information on other organizations that could help its insertion intonew markets, and business strategies adopted by rivals in end-usersegments that were not initially served (Walter and Ritter 2003; Boddy,MacBeth, and Wagner 2000). This reasoning allows us to conclude that

H4: Indirect value-creating functions carried out by the manufacturer forits main distributor are positively influenced by the distributor’scommitment toward the manufacturer.

Trust and Commitment

Trust can be expected to have a direct, positive impact on commitment

because trading relationships based on trust are usually highly appreciated by




those involved in them. The fact that the parties act sincerely or credibly— and that they aim to protect, whenever possible, the interests and well-being of the counterpart—generally results, as shown empirically, in thegeneration of commitment (Morgan and Hunt 1994; Hadjikhani and Thilenius

2005). This supports the argument that distributors prefer to work in the longterm with manufacturers they can trust who will act sincerely and benevo-lently toward the relationship, thus covering themselves against the risk of damage caused by the manufacturer in any actions taken (Ruyter, Moorman,and Lemmink 2001; Friman et al. 2002). This leads us to suggest that

H5: The trust of the main distributor in the manufacturer has a positiveeffect on the distributor’s commitment toward the manufacturer.

EMPIRICAL STUDY Sample and Information Collection

The SABI1 database was used to establish a population of 1,820 companies with the following characteristics: (1) manufacturing companies located inSpain; (2) of medium size, according to the European Union (EU) criterionof 2003; and (3) belonging to more than one economic sector (Table 1).

TABLE 1 Description of the Sample

POPULATION SAMPLENo. of firms % No. of firms %

MAIN ACTIVITY OF FIRMSFood 406 22.3 48 26.5Chemicals and plastics 397 21.8 45 24.9Iron and steel, other metals 590 32.4 50 27.6Other machinery 172 9.5 13 7.2Machinery and electric, electronic,

and optical equipment149 8.2 20 11.0

Transport equipment 106 5.8 5 2.8

Total 1,820 100.0 181 100.0

NUMBER OF EMPLOYEESBetween 50 and 100 943 51.8 80 44.2Between 101 and 200 737 40.5 71 39.2Between 201 and 249 140 7.7 30 16.6

Total 1,820 100.0 181 100.0

AGE OF THE FIRMBefore 1970 428 23.5 45 24.9Between 1970 and 1979 361 19.8 31 17.1Between 1980 and 1989 553 30.4 67 37Between 1990 and 1999 407 22.4 34 18.8Between 2000 and 2005 71 3.9 4 2.2

Total 1,820 100.0 181 100.0




Medium-sized companies were chosen for two reasons (Roth 1992; Zahra et al.2000; Dawson and Larke 2005). First, these companies have a significantrole in the development of the socioeconomic circumstances of a country and, consequently, they can be considered a relevant unit of analysis (Roth

1992; Storey 2002). Second, targeting a specific population (e.g., themedium-sized company domain) can further guarantee that the findings aremeaningful with reference to that specific domain (McKelvey 1978; Bresnenand Fowler 1996). It was also important to consider more than one eco-nomic sector for two reasons. First, empirical results will not be affected somuch by the uncontrollable, idiosyncratic effects of any particular sector,thus giving the study a higher degree of external validity (Tippins and Sohi2003; Gonzalez, Iglesias, and Trespalacios 2005). Second, key relational

variables such as commitment and trust are considered to contribute tocustomer value creation in a wide range of economic sectors (Dyer 1996;

Dyer and Singh 1998).The research questionnaire was prepared on the basis of a significant

literature review. We carried out a pretest by means of in-depth interviews with five outstanding academic scholars and three senior managers who were aware of the subject under study and were knowledgeable about theSpanish market. The information obtained was used to introduce modifica-tions into the wording of some items and to determine satisfactorily thatdistributor trust, distributor commitment, and the value-creating functionmeasurement scales covered the domain of each of the corresponding latent

variables under study.Once the final questionnaire had been established, a four-stage field

study was carried out. First, we contacted by telephone the entire targetpopulation of 1,820 manufacturing companies. Despite the laboriousness of this procedure, we considered it essential to work this way in order to becertain that the questionnaire would reach key informants (Dillman 2000;Podsakoff et al. 2003). General managers2 and sales managers were a priori identified as potential key informants because they can be expected to haveboth information on a wide variety of departments or areas within the com-pany (Santos et al. 2005; Thorpe and Morgan 2007) and detailed information

on the main dyadic relationship (Walter, Ritter, and Gemunden 2001; Walteret al. 2003). Telephone conversations allowed the most suitable key infor-mant in each firm (either the general manager or the sales manager) to beidentified as well as to personalize the survey (we were always provided ane-mail address or a fax number to which the questionnaire could be sent).

Second, whenever possible, questionnaires were sent out on the sameday that the contact had been made. Our understanding was that the imme-diacy with which we sent out the questionnaires would lead to greater pos-sibilities of the key informants identifying our study as a priority andsending back their responses as soon as possible. Questionnaires were

accompanied by a cover letter that mentioned the general purpose of our




study (respondents were purposely not informed of the causal connectionsunder study), the absolute and rigorous confidentiality of the informationcollected, the commitment to provide them with an executive summary, andthe different options for contacting us if necessary while completing the

questionnaire.Third, we contacted again those manufacturers that had not yetresponded to the questionnaire. In particular, an e-mail or fax reminder(depending on the procedure selected initially) was sent to them at two-

week intervals, reminding them of the urgency and necessity of their contri-bution for the correct development of the fieldwork. This process wasextended, if needed, for up to four waves of reminders.

Finally, we concluded the fieldwork with the analysis of the responsesreceived to the questionnaires. There were a total of 181 valid responses,the equivalent of a response rate of 9.95 percent. This rate is comparable

with other survey-based studies carried out in Spain (Ordoñez 2002; Santoset al. 2005; Perez, Montes, and Vazquez 2006; Prieto and Revilla 2006).

A key informant competence test was carried out (Campbell 1955).The parameters considered were the number of years of existence of thebusiness relationship between the manufacturing company and its maindistributor, the number of years that the key informant had been workingin the company, and his or her years of experience in their current posi-tion. The results showed that the respondents’ companies, on average, had8.2 years of relationship with their main distributor (which reduces thepotential bias associated with relationship newness) (Zahra et al. 2000).The respondents, on average, have been an employee of the company for10.3 years, and also have been working in their present position for anaverage of 6 years. We tried to guarantee that the respondent had therequired knowledge of the issues under study and was capable of commu-nicating this knowledge properly (Kumar, Stern, and Anderson 1993;Thorpe and Morgan 2007).

To deal with the potential no-response bias, we followed the proce-dure suggested by Armstrong and Overton (1977). The results suggestedthat there were no statistically significant differences between early and late

respondents. We also compared the sample and the population accordingto the criteria used in Table 1: the main activity of firms, the number of employees, and the age of the firm. The chi-squared tests showed that there

were no significant differences between the sample and the population interms of the main activity of the firms ( X 2 = 5.008, df = 5, p = 0.414) and of the age of the firm ( X 2 = 3.204, df = 4, p = 0.524). However, there weresignificant differences in terms of the number of employees ( X 2 = 11.444,df = 2, p = 0.004). A firm’s size (measured in terms of the number of employees) predisposed them to participation in our survey. Firms of between 201 and 249 employees exceeded by almost 9 percentage points

the corresponding group in the population, whereas firms of between




50 and 100 employees were more than 7 percentage points less than thecorresponding group in the population. The logic behind this could wellbe that the larger organizations in our sample (firms of between 201 and249 employees) have resources specifically dedicated to participating in

survey studies (Almeida et al. 2003).

Measurement Scales

With respect to the measurement of the model’s variables, we used multi-item scales as suggested by consideration of the related literature. The

Appendix gives these scales in complete detail. All the variables weremeasured on a seven-point Likert scale, with 1 meaning “completely disagree” and 7 meaning “completely agree.” We used reflective indicatorsas measures of all the model’s variables. To measure the value-creating

functions , we used the scale proposed by Walter, Ritter, and Gemunden(2001) as referent. In particular, in our study these functions were adaptedto express the manufacturer’s perception of how it contributes to creating

value for its most important distributor by performing a series of tasks orfunctions (both direct and indirect). We assessed value-creating functionscales during the pretest by means of in-depth interviews with five out-standing academic scholars and three senior managers (general managers orsales managers) who were aware of the subject under study and who wereknowledgeable about the Spanish market (Hoskisson et al. 1993; Boyd andReuning-Elliott 1998).

The rest of the measurement scales were also assessed during the pre-test. This allowed us to confirm that the items were valid in the context of our research setting. With regard to the key relational variables of trust andcommitment, preexisting measurement scales were selected and adapted tothe idiosyncrasies of our study. In particular, the trust of the most importantdistributor as perceived by the manufacturer (CO) was evaluated from anoverall point of view in terms of the elements on which this construct isbased at present: honesty or credibility and benevolence (Ganesan 1994;Nielson 1998; Kumar, Scheer, and Steenkamp 1995). The commitment of the

most important distributor as perceived by the manufacturer (CM) involvesan emotional aspect. It expresses a positive emotional link felt by thedistributor toward the manufacturer, which can also be interpreted as a feel-ing of unity, cohesion, or belonging, or essentially as the wish to continue

with the relationship (Moorman, Zaltman, and Deshpande 1992; Morganand Hunt 1994).

Procedures and Statistical Techniques

A set of procedures and statistical techniques were used to test the hypoth-

eses formulated. Two different issues were considered in this process.3




1. Evaluating the measurement model.

To examine the reliability and the validity of the measurement scales, aseries of recommendations were followed that can be grouped into two

phases (Churchill 1979; Anderson and Gerbing 1988; Bagozzi and Yi 1988;Gerbing and Anderson 1988; Steenkamp and Van Trijp 1991). First phase. The process of purifying the measurement scales was

initiated with the calculation of Cronbach’s a , the minimum acceptable valuebeing 0.7 (Nunally 1978). We continued with an exploratory factor analysis(EFA) using the method of principal components analysis with varimax rota-tion to evaluate the unidimensionality of the latent variables. In this evalua-tion, two types of tests were used: Bartlett’s sphericity test with which, if thelevel of significance is less than 0.05, the underlying factorial structure of thedata is acceptable, and the Kaiser-Meyer-Olkin (KMO) sample adequacy test

for which it is recommended that the statistic should exceed 0.7 (Trespalacios, Vazquez, and Bello 2005; Uriel and Aldas 2005).

Second phase. Each of the measurement scales analyzed in the firstphase were then subjected to confirmatory factor analysis (CFA) by meansof structural equation modeling using the statistical package EQS for

Windows 6.1. The estimation procedure followed was robust maximumlikelihood (ML) estimation to avoid problems of non-normality with the data(Dunn, Everitt, and Pickles 1994; Byrne 1994; Bentler 1995). The followingfit statistics were used (Bentler 1990; Hair et al. 1999; Uriel and Aldas 2005):S-Bc

2 (Satorra-Bentler’s chi-squared), Bentler Bonnett Non-normed Fit Index(BBNNFI), Comparative Fit Index (CFI), Incremental Fit Index (IFI), Good-ness of Fit Index (GFI), and Standardized Root Mean-Squared Residual(SRMR). Once it had been verified that the measurement model had accept-able fit statistics, the convergent validity was examined. Standardizedparameter estimates (l ) between the latent variable and its indicator wererequired to be greater than 0.6, and an associated robust t value greater than1.96 for a p value of 0.05. Two complementary reliability indices were alsocalculated: average variance extracted (AVE) and composite reliability (CR).In the former, a value that surpasses 0.5 is tolerable, and in the latter it is

recommendable that the value is at least 0.7 (Bagozzi and Yi 1988; Hairet al. 1999). Finally, we checked that the latent variables of the measure-ment model were discriminative. This circumstance is satisfied wheneverthe square root measure of the AVE exceeds correlations between the latent

variables (Fornell and Larcker 1981; Johnson, Herrmann, and Huber 2006;Ulaga and Eggert 2006).

2. Evaluating the structural model and research hypotheses.

The above fit statistics were also used to evaluate the fit of the structural

model, in this case examining the structural parameters (or model paths).




Again, they were considered to be significant when the robust t value wasgreater than 1.96 for a p value of 0.05. Finally, Akaike’s Information Criterion(AIC) and the Consistent AIC (CAIC) were used to compare the proposedstructural model with the competing model (Bozdogan 1987; Steenkamp and

Baumgartner 1998; Aragon, Garcia, and Cordon 2007; Rudd et al. 2008).

MEASUREMENT MODEL OF VALUE-CREATING FUNCTIONS

As explained above, value-creating functions can be classified a priori according to two different second-order factors: direct value-creatingfunctions (the benefit, volume, and safeguard functions) and indirect value-creating functions (the innovation, market, and scout functions).

Direct functions. Before proceeding with the study of the reliability and validity of each of the components of the direct value-creating functions

(DF), we must point out that the benefit, volume, and safeguard functionshad to satisfactorily pass a study of their internal consistency (Cronbach’s a )and an exploratory factor analysis separately. At this phase of the process of purifying the measurement scales we had to drop the DF4 indicator(Nunally 1978). We then proceeded to carry out a confirmatory factor anal-

ysis of the direct value-creating function measurement model in order toconfirm the degree of reliability—composite reliability (CR) and AVE—anddetermine the degree of convergent validity. The results pointed to theexistence of an improper estimate in this measurement model, that is, there

was a negative error variance associated with the DF8 indicator. We there-fore respecified the measurement model (Byrne 1994; Ganesan, Malter, andRindfleisch 2005). The aim was to test the hypothesis that there is no overallmeasure for the direct value-creating functions, but rather three alternativetypes that correspond to the aforementioned value-creating functions (Table 2).The results corroborated this hypothesis for the following reasons: no

TABLE 2 Direct Value-Creating Functions Measure: Validity and Reliability

MeasuresStandardized

lambdaRobustt value Scale CR Scale AVE

First-order measurement DF1←BENEFIT FUNCTION 0.892 15.594 0.886 0.795DF2←BENEFIT FUNCTION 0.891 15.779DF3← VOLUME FUNCTION 0.854 11.633DF5← VOLUME FUNCTION 0.841 12.676 0.836 0.718DF7←SAFEGUARD FUNCTION 0.683 9.025DF8←SAFEGUARD FUNCTION 0.700 10.295 0.647 0.478

Summary statistics

First-order model: S-B c 2 (9) = 17.960 p = 0.035 BBNNFI = 0.944 CFI = 0.972 IFI = 0.973GFI = 0.966 SRMR = 0.040

Note: DF = Direct value-creating function, CR = Composite reliability, AVE = Average variance extracted.




improper estimates were made and the quality of fit was greater than withany of the previous measurement models (Bentler 1995). In sum, thisresearch does not tie in with previous studies that found that direct value-cre-ating functions have a multiple-component nature representing a second-

order factor (Walter et al. 2003). Indirect functions. With regard to the analysis of the reliability and validity of each of the components comprising the indirect value-creatingfunctions (INDF), that is, the innovation function, the market function, andthe scout function, we began by studying the internal consistency andcarrying out an exploratory factor analysis of each of the measurementscales separately. After satisfactorily evaluating this circumstance for theinnovation, market, and scout functions, we continued with a confirmatory factor analysis of the indirect value-creating function measurement model.The results were not acceptable because the INDF7 indicator had a stan-

dardized lambda less than 0.6 and the INDF8 indicator had a negative error variance. We therefore had to respecify the measurement model (Byrne1994; Ganesan, Malter, and Rindfleisch 2005). In particular, we consideredthe procedure suggested by Hayduck (1987) and Bollen (1989) for working

with an alternative factorial structure (where the scout function has only one indicator), obtaining results (see Table 3) that were better than those

TABLE 3 Indirect Value-Creating Functions Measure: Validity and Reliability

Measures

Standardized

lambda

Robust

t value

Scale

CR

Scale

AVE

First-order measurement

INDF1←INNOVATION FUNCTION 0.780 12.634 0.878 0.642INDF2←INNOVATION FUNCTION 0.833 13.571INDF3←INNOVATION FUNCTION 0.772 12.349INDF4←INNOVATION FUNCTION 0.819 17.409

INDF5←MARKET FUNCTION 0.913 13.662 0.802 0.672INDF6←MARKET FUNCTION 0.715 11.124

INDF9←SCOUT FUNCTION 0.901 14.539 0.812 0.812

Second-order factor model

INNOVATION FUNCTION←INDIRECT VALUE-CREATING FUNCTIONS 0.836 9.805 0.848 0.737

MARKET FUNCTION←INDIRECT VALUE-CREATING FUNCTIONS

0.880 12.361

SCOUT FUNCTION←INDIRECT VALUE-CREATING FUNCTIONS

0.665 7.513

Summary statistics

First-order model: S-B c 2 (12) = 32.681 p = 0.001 BBNNFI = 0.0.857 CFI = 0.922 IFI = 0.925GFI = 0.942 SRMR = 0.044 AIC = 8.680 CAIC = −41.701

Second-order model: S-B c 2 (13) = 32.408 p = 0.002 BBNNFI = 0.876 CFI = 0.927 IFI = 0.929GFI = 0.942 SRMR = 0.044 AIC = 6.407 CAIC = −48.172

Note: INDF = Indirect value-creating functions, CR = Composite reliability, AVE = Average variance extracted.




we obtained with any preceding measurement model. In consequence, theindirect value-creating function measurement model tests of reliability,

validity, and fit were within the acceptable limits for theory developmentpurposes.

After selecting this last measurement model, the next step was to analyzethe existence of discriminant validity. The latent variables were found to dis-criminate between each other, because for every pair of latent variables thesquare root of AVE exceeded correlations between the latent variables. Wetherefore proposed a second-order confirmatory factor analysis for theabove first-order factors. The second-order measurement model presentedlower comparative criteria (AIC and CAIC) and higher fit indices than thefirst-order measurement model. In particular, the results can be consideredacceptable as a whole: (S-B c 2 (12) = 32.408, p = 0.002, BBNNFI = 0.876,CFI = 0.927, IFI = 0.929, GFI = 0.942, SRMR = 0.044). The fit indices, except for

BBNNFI, lie within reasonable ranges of values bearing in mind the complexity of the factorial structure of the second-order measurement model. The individ-ual reliability (R 2) of the first-order factors varied between a minimum of 0.429(scout function) and a maximum of 0.774 (market function). The standardizedgamma coefficients linking the innovation, market, and scout functions with thesecond-order factor, indirect value-creating functions, exceeded 0.6 and were allsignificant at a p value of 0.05, thus showing the presence of convergent validity in the measurement model. In short, we can assert that indirect value-creatingfunctions represent a second-order factor (Walter, Ritter, and Gemunden 2001).

MEASUREMENT MODEL OF TRUST AND COMMITMENT

In measuring trust and commitment, we treated them as first-order factors.Based on this premise, we initially carried out a study of their internal con-sistency (Cronbach’s a ) and an exploratory factor analysis of each of theaforementioned measurement scales separately. We continued with a con-firmatory factor analysis of the measurement model of the key relational

variables (trust and commitment) in order to confirm their degree of reliabil-ity, that is, composite reliability (CR) and AVE, and determine the degree of

convergent validity. The results gave fit indices that fell within the accept-able range of values. The lambda parameter estimates indicated a highdegree of convergent validity since they exceeded 0.6, with robust t valuesexceeding the critical value of 1.96 for a significance level of 0.05 (Table 4).

STRUCTURAL MODEL AND RESEARCH HYPOTHESES

After evaluating satisfactorily the measurement models of the value-creatingfunctions (both direct and indirect), trust, and commitment, we proceeded

to analyze the paths of the structural model: H1 –H5, given in Table 5. It was




TABLE 4 Key Relational Variables Measure: Validity and Reliability

MeasuresStandardized

lambdaRobustt value

ScaleCR

Scale AVE

First-order measurement

CO1←DISTRIBUTOR TRUST 0.729 8.987 0.893 0.582CO2←DISTRIBUTOR TRUST 0.814 10.421CO3←DISTRIBUTOR TRUST 0.758 12.448CO4←DISTRIBUTOR TRUST 0.770 12.351CO5←DISTRIBUTOR TRUST 0.788 9.716CO6←DISTRIBUTOR TRUST 0.713 10.227

CM1←DISTRIBUTOR COMMITMENT 0.899 13.089 0.899 0.748CM2←DISTRIBUTOR COMMITMENT 0.830 15.419CM3←DISTRIBUTOR COMMITMENT 0.864 15.215

Summary statistics

First-order model: S-B c 2 (26) = 51.006 p = 0.002 BBNNFI = 0.944 CFI = 0.959 IFI = 0.960

GFI = 0.914 SRMR = 0.040 Note: CR = Composite reliability, AVE = Average variance extracted.

TABLE 5 Structural Model

Paths specifiedExpected

signStandardizedcoefficient

Robustt value

H1: DISTRIBUTOR TRUST→DIRECT VALUE-CREATINGFUNCTIONS

H1.1: DISTRIBUTOR TRUST→BENEFIT FUNCTION + 0.011 ns

H1.2: DISTRIBUTOR TRUST→ VOLUME FUNCTION + 0.106 ns

H1.3: DISTRIBUTOR →SAFEGUARD FUNCTION + 0.012 ns

H2: DISTRIBUTOR TRUST→INDIRECT VALUE-CREATING FUNCTIONS

+ 0.076 ns

H3: DISTRIBUTOR COMMITMENT→DIRECT VALUE-CREATING FUNCTIONS

H3.1: DISTRIBUTOR COMMITMENT→BENEFITFUNCTION

+ 0.506 2.811

H3.2: DISTRIBUTOR COMMITMENT→ VOLUME

FUNCTION

+ 0.338 2.053

H3.3: DISTRIBUTOR COMMITMENT→SAFEGUARDFUNCTION

+ 0.495 2.183

H4: DISTRIBUTOR COMMITMENT→INDIRECT VALUE-CREATING FUNCTIONS

+ 0.508 3.000

H5: DISTRIBUTOR TRUST→DISTRIBUTOR COMMITMENT

+ 0.819 10.342

Summary statistics:

S-B c 2 (126) = 179.791 p = 0. -001 BBNNFI = 0.946 CFI = 0.955 IFI = 0.956 GFI = 0.874SRMR = 0.058

Note: ns = not significant.




first necessary to verify the existence of discriminant validity. We thereforesimplified the higher-order factor (indirect value-creating functions) by taking the mean of the scores of the indicators of each of the lower-orderfactors (innovation, market, and scout functions). The results of the first-

order confirmatory factor analysis based on the aforementioned factorsshowed a good quality of fit. We thus corroborated the reliability and convergent validity of these factors, as well as their discriminant validity.

In light of these results, we continued by evaluating under ideal condi-tions the structural model specified in this study. The results showed thatthe fit indices of the model as a whole were acceptable (Table 5). In analyzingthe paths of the structural model, it was noted that the influence of distribu-tor commitment was significant in: (1) the generation of profit—the benefitfunction; (2) the circumstance of being able to guarantee a large volume of supply—the volume function; (3) the ability to create commercial links

guaranteeing a certain level of supply in times of crisis or difficulty—thesafeguard function; and (4) the development of indirect value-creating func-tions for the distributor. In the case of the other relational variable, distribu-tor trust, it was noted that this does not have a significant direct effect on

value-creating functions (direct and indirect functions). A possible explana-tion for this could be the mediatory effect of distributor commitment. Fromthis point of view, we noted the following indirect effects.

1. Distributor trust → Distributor commitment → Development of thebenefit function for the distributor (0.819 × 0.506 = 0.414)

2. Distributor trust → Distributor commitment → Development of the volume function for the distributor (0.819 × 0.338 = 0.277)

3. Distributor trust → Distributor commitment → Development of thesafeguard function for the distributor (0.819 × 0.495 = 0.405)

4. Distributor trust → Distributor commitment → Development of theindirect value-creating functions for the distributor (0.819 × 0.508 = 0.416)

To further examine the validity of our model, we compared it with acompeting model (Table 6). The competing model stated that direct and indi-

rect value-creating functions drive trust and commitment (without eliminatingthe mediating link, H5) in business markets. In this regard, previous research

TABLE 6 Comparison of Theoretical Model and Competing Model

Fit statisticsComparative

information criteria

BBNNFI CFI IFI GFI SRMR AIC CAIC

Theoretical model 0.946 0.955 0.956 0.874 0.058 −72.209 −601.219

Competing model 0.893 0.912 0.914 0.844 0.151−

19.982−

548.993




has also argued that improving customer value creation may enhance thetrust and commitment of the commercial partners (Palmatier et al. 2006). Theresults indicated that the theoretical model (Figure 1) is preferable to thecompeting model. Akaike’s Information Criterion (AIC) and the Consistent

AIC (CAIC) in particular were lower in our theoretical model (AIC = −72.209and CAIC = −601.219). That the theoretical model gives lower comparativecriteria and better fit indices than the competing model is an aspect that isadvisable when selecting a model (Bozdogan 1987; Steenkamp and Baum-gartner 1998; Aragon, Garcia, and Cordon 2007; Rudd et al. 2008).

CONCLUSIONS AND BUSINESS IMPLICATIONS

This research has aimed at attaining a deeper knowledge of “how value can

be created through relationships with customers” (Lindgreen and Wynstra2005: 739). The basic underlying premise was that buyer–seller relationshipsentail a series of functions, both direct and indirect, that can improve valuecreation for those involved. The analysis of the data showed that direct

value-creating functions (benefit, volume, and safeguard functions) do notrepresent a second-order factor, whereas indirect value-creating functions(INDF) do.

The results of this study lend partial support to the commitment–trust perspective to understand the improvement of customer value creation inbusiness markets, which is particularly interesting considering the limitedempirical evidence available to shed light on this question. The importanceof trust and commitment as precursors of value creation in a relationship

was confirmed from a functionalist point of view. However, the type of effect of such relational variables on the proposed model varied. In the caseof distributor commitment, it had a significant impact on the benefit, vol-ume, and safeguard functions as well as on the INDFs performed by themanufacturer. In the case of distributor trust, this was seen to have no directinfluence on value-creating functions. This is attributable to the mediatory effect of distributor commitment in a relationship.

In the light of this, and given the quantitative confirmation of this cir-cumstance—that there are indirect effects involving distributor commitmentas a leading variable—it is argued that all this may be caused by the exist-ence of a significant risk of opportunistic behavior by the trading partner(i.e., an exchange vulnerability). Conceptually, this is what is known as asemistrong form of trust in a relationship, or, in other words, a scenario in

which such a concept may grow and develop but only if the other agentsinvolved in the relationship have some type of protection against the typeof risk in question (Barney and Hansen 1994; Lopez 1999).

In this debate, it is assumed that there is commitment in a relationship

acting as a socially oriented form of governance. Such a commitment, which




is characterized by being long term, adopts this protective role in the manu-facturer–distributor relationship to the extent that any benefits, whetherfinancial or nonfinancial, resulting from opportunistic behavior wouldalways be less than the cost involved, that of putting an end to a much-

appreciated dyadic relationship. According to this view, the existence of distributor commitment in a relationship is identified as an essential media-tory variable in the link established between distributor trust and the value-creating functions performed by the manufacturer (i.e., there is trustthrough a social governance mechanism: commitment).

With regard to the business implications derived from this research, themain aspect is that a manufacturer should promote commitment with itsmost important distributor (Palmatier, Dant, and Grewal 2007). If this isachieved, it can provide important support and be the key to financial sur-

vival in difficult commercial situations. Above all, when commitment does

exist, because of the long-term view adopted throughout a series of interac-tions, it will necessarily facilitate the exchange and/or sharing of resources,help coordinate activities in new ways, and even lead to emotional linksbetween the parties. In other words, from the instant in which the decisionis taken to commit to a relationship, in one way or another, there would berecognition of the relevance this action has for the attainment of certainshared and individual goals pursued by the exchange partners. In short, thiscould be regarded as the first step toward improving value creation on bothsides of the dyad.

As a result, trust could be considered a powerful tool to impel andmaintain commitment in a commercial relationship. In particular, trust couldbe treated as a variable that in some way helps discriminate betweenpartners with whom it might or might not truly be of help to maintain abusiness relationship with a long-term orientation. It also enables transac-tion costs and the risk of opportunistic behavior to be reduced, andimproves the existing level of cooperation, an aspect that could be material-ized in a more efficient use of the resources available to the relationshipthan when trust is not present. It could be argued, therefore, that opting to

work with trading partners who can be trusted is an indispensable step

toward commitment, that is, with partners who act sincerely and display benevolent behavior toward the relationship. In this way, the members of the exchange will feel less vulnerable to the risk of being adversely affected

when carrying out their actions.

LIMITATIONS AND FUTURE RESEARCH DIRECTIONS

The results of this research are, similar to many empirical studies, subject tolimitations. First, we used a single key informant in a dyadic relationship,

the manufacturer, to evaluate the constructs in question. We recognize that




measuring the same latent concepts on both sides of the dyad would allowone to work at a higher level of precision. Nevertheless, we considered thatthe difficulties in reaching an adequate response rate using paired data inSpanish industrial markets makes this information collection procedure not

truly viable today. Indeed, the present approach has been used elsewhereto determine how organizational resources are related to customers’ market-ing measures or performance indicators (Roth and Jackson 1995; Soteriouand Zenios 1999; Theoharakis and Hooley 2003). In addition, we verifiedthat the respondents’ companies were ISO 9001:2000 certified. This certifica-tion emphasizes that companies should know or estimate their maincustomers’ satisfaction. In support of our analytical perspective, there havebeen studies noting the existence of a significant correlation between themeasurements of the same construct that are made simultaneously on bothsides of the dyad (Schneider and Bowen 1985; Heide 1994; Soteriou and

Zenios 1999; Cullen, Johnson, and Sakano 2000; Homburgh et al. 2002).Moreover, the respondents with whom we communicated in the fieldworkshowed themselves to have a profound knowledge of their main distributor.This was a natural reflection of the high level of interaction between thetwo partners in daily operations that was seen as a guarantee for the rela-tionship to be efficient and enduring. Second, we employed cross-sectionaldata. This makes it difficult for us to know for certain if there is a possibility that the causal relationships identified may vary or even lose their meaningin the long run. A longitudinal study would overcome this limitation andstrengthen the results. Finally, although there were no significant differencesbetween the sample and the population in terms of the main activity of thefirm and the age of the firm, this was not the case for the number of employees criterion, which prevents us from extrapolating the presentresults to other populations.

Possibilities for future research are other antecedents for the value-creatingfunctions not considered in this study, such as: the influence of the learningprocess in an organization, the effect of power relations within a company, andthe impact of the type of organizational design used. Also, further effort shouldbe devoted to comparing and synthesizing other theoretical perspectives that

can help explain the creation of customer value in business markets.

NOTES

1. The Sistema de Análisis de Balances Ibéricos (SABI) is a database of Spanish and Portuguesecompanies. It is the result of the collaboration among Informa (responsible for the database of Spanishcompanies), Coface Mope (responsible for the database of Portuguese companies), and Bureau van Dijk(responsible for the data search, processing, and analysis software). SABI allows access to general infor-mation on more than 480,000 Spanish and 40,000 Portuguese companies.

2. In this study general manager is the manager that oversees all of the firm’s functions as well as

the day-to-day operations of the business.




3. Two statistics programs were used for the data analysis. To evaluate the reliability and validity of the scales used, we first used the SPSS 14.0 for Windows statistics package, and subsequently the EQS6.1 for Windows program. We also used EQS 6.1 for Windows to evaluate the proposed structuralmodel.

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APPENDIX Value-Creating Functions

Direct value-creating functions (DF)

Our most important distributor has achieved…

Benefit function (Scale Cronbach’s a = 0.886)DF1 A high profit margin per product as a result of the nature of our product supply.DF2 High profit.

Volume function (Scale Cronbach’s a = 0.836)DF3 Is able to meet high demand volumes because we want and can meet the supply

of large-volume orders.DF4 Important long-term supply agreements with our firm.DF5 Satisfaction of global demand in the product category or categories bought from us.

Safeguard function (Scale Cronbach’s a = 0.647)DF6 Knowing it can receive orders at short notice.DF7 Knowing it can place unexpected orders.DF8 Reduced dependence on other manufacturers.

Indirect value-creating functions (INDF)

Our most important distributor has achieved…

Innovation function (Scale Cronbach’s a = 0.876)INDF1 Suggestions about how to improve or develop its production or management

processes.INDF2 Ideas about new products or services that it can sell to develop its business.INDF3 Participation in joint projects thus increasing its technical knowledge about our

product services and applications.INDF4 Testing of prototypes so is able to prepare sales strategy in advance.

Market function (Scale Cronbach’s a = 0.790)INDF5 Knowledge of new potential segments or user groups previously not considered.

INDF6 Knowledge of possible customers in other markets.Scout function (Scale Cronbach’s a = 0.810 ♠)INDF7 Information on outside organizations (such as research centers, business

associations, etc.).INDF8 Information on market trends.INDF9 Information on possible competitors.

Key Relational Variables

Distributor trust (CO) (Scale Cronbach’s a = 0.888)How would you describe your relationship with your main distributor?CO1 We can be sure this distributor will meet its commitments.CO2 This distributor is trustworthy.CO3 Takes our interests into account in its work.CO4 Is honest with us if problems arise.CO5 The distributor is well prepared for meeting our demands efficiently.CO6 The distributor has plenty of experience and can advise us, when needed, as to

what is best for the company as regards distribution.Distributor commitment (CM) (Scale Cronbach’s a = 0.895)How would you describe your relationship with your main distributor?CM1 This distributor is very committed to this relationship.CM2 This distributor plans to keep this relationship going indefinitely.CM3 This distributor is keen to make every effort to sustain the relationship.CM4 The distributor is understanding toward us if we make any sort of mistake.

Note: Italics indicate items that were eventually removed. Cronbach’s a is as calculated by Walter, Ritter,

and Gemunden (2001).




IMPLICATIONS FOR BUSINESS MARKETING PRACTICE

Trust and commitment are focal constructs in business relationships. When there is commitment in a relational exchange it is possible for there

to be improvement of value creation for the parties involved. In this con-text, credibility and mutuality need to be considered. Credibility emerges when exchange partners perform actions that make both the promisedconduct and the long-term orientation of the relationship likely. Mutuality exists when exchange partners collaborate as a means to obtaining greaterbenefits than they would from discretionary transactions with changingexchange partners. Trust is also a key factor for the improvement of valuecreation in business relationships that have a long-term orientation. Thebenefits when there is trust in a relationship are greater that when this isnot the case. For example, it is possible to attain a higher level of sales, to

share confidential information, and to reduce the cost of monitoring theexchange partner. In consequence, commitment and trust as relationalgovernance mechanisms breed an emotional link and a spirit of coopera-tion in the exchange partners that favor positive behavior in the bestinterests of the relationship.

Thus, as a manufacturer works with its most important distributor, overtime it will have to promote commitment in the relationship. If this isachieved, it can provide important support and be the key to financialsurvival in difficult commercial situations. The presence of commitment in arelationship as a result of the long-term view adopted throughout a series of interactions will necessarily facilitate new ways of sharing resources andcoordinating activities. From the moment exchange partners decide to com-mit to a relationship, there is recognition of the importance that thisrelationship has in attaining individual and shared goals. In consequence,this circumstance can be identified as the first step toward improving valuecreation in a relationship.

At the same time, trust can be regarded as a powerful mechanism toimpel commitment in a relationship. In particular, trust can be treated as a

variable that, in some way, helps distinguish the partners whom one might

not truly want to maintain in a relational exchange. It also enables bothtransaction costs and the risk of opportunistic behavior to be reduced.Therefore, more efficient use can be made of the resources available to therelationship than when trust is not present. Opting to work with exchangepartners who can be trusted is an indispensable step toward commitment.In this regard, exchange partners will feel less vulnerable to the risk of being adversely affected when performing their actions. Finally, determiningprecisely whether opportunistic behavior will arise is a very complex task asthere is bounded rationality about the real intentions of the parties involved.In some cases, in order to gain some intuition about these intentions it will

be necessary to examine the level of perceived risk, the power balance




between the parties, and the available options in the case of deciding toleave the relationship.

Another business implication is that the value-creating function framework may help the manufacturer to comprehend how value can be created

for distributors. This could be done by the direct functions , which areexpected to look for efficiency by means of the interconnection of activities,creativity from the heterogeneity of resources, and mutuality based on theinterest of the actors themselves. With these functions the manufacturer cansupport the distributor in the generation of economic benefits (benefit func-tion), guarantee a large volume of supply (volume function), and help it tofoster commercial connections that guarantee a certain level of supply incase of possible crises or difficulties (safeguard function). Another way of comprehending how value can be created for distributors is through theindirect functions, which include the chains of activities that involve several

organizations, the resource portfolio controlled by more than one company,and the perceptions shared by more than two companies. In this case themanufacturer would benefit its counterpart in activities related to innovation(innovation function), access to other markets (market function), and moreefficient commercial decision making (scout function).

This analysis can be applied even to comparing the different distribu-tors that conform the manufacturer’s distributor portfolio. It can help inthe control and follow-up of how and why it is possible to create more

value for one distributor than for another. This framework can be used as well as a mechanism or “alert” device that warns of those inadequate orundesired customers for companies’ long-term survival. It may also helpthe manufacturer to face changing situations that may arise while working

with the distributor. However, this is not the same as thinking that the value-creating function framework is a panacea for the problems that arisein dealing with distributors. This would be a seriously mistaken appraisal.One can only say that it is a framework that may assist the way a manufac-turer interacts with its distributors, but does not fully explain the reason

why, with the passage of time, some distributors continue doing businessdeals and others do not.

Finally, it seems reasonable for a manufacturer to invest or allocateresources for the improvement and development of the trust and commit-ment of the commercial partners, as this action influences the value-creatingfunctions that it performs for the distributor. The manufacturer is expected,therefore, to behave positively toward and in the best interest of a commit-ted and trusted counterpart in the dyad. This allows it to gain an importantadvantage over its most direct competitors, who in addition may not sharethis work philosophy. It is also more likely that the manufacturer will admitits own mistakes if they occur when creating value for its commercialpartner, and will be able to change a given course of action when

circumstances permit. This allows the manufacturer to present itself to the




distributor as a reliable partner who can carry out commercial operationssatisfactorily, a fundamental aspect in working with a long-term orientation.Similarly, this orientation makes it possible for the parties to reducetransaction costs and the appearance of opportunistic patterns of behavior,

and above all to improve the existing level of cooperation, which can bematerialized in a more useful employment of resources.



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