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Consensus and collaboration: norm-regulated behaviour in industrial marketing relationships.

Introduction

Because of their ability to co-ordinate and control resource flows between trading partners, relational norms have long been of interest to both marketing scholars and practitioners involved with business-to-business exchange relationships (e.g. Achrol, 1991; Bonoma, 1976; Dwyer et al., 1987; Ford, 1982). Relational norms are relationship-specific expectations about behaviour that are mutually held by the involved parties (Heide and John, 1992; MacNeil, 1980). As business-to-business exchange relationships across a number of industries are becoming closer, a particular type of relational norm is gaining in importance. Wilson (1995) argues that buyer and seller relationships have become an integral part of business-to-business operating strategies.

In a collaborative exchange relationship, the boundary between buyer and seller often becomes blurred so that it is hard to discern where one firm begins and the other ends. Firms are more frequently choosing to implement this type of exchange relationship because collaboration is increasingly viewed as necessary to gain competitive advantage. For example, both Xerox and Motorola attribute a substantial portion of their success to their close relationship with a limited number of committed suppliers who wilfully contribute technical support and innovative ideas to the new product development process. Turnbull and his colleagues have also documented an effort to reduce the supply base among UK automotive manufacturers (Turnbull et al., 1993). Owing to the inherent lack of hierarchical and/or contractual control in collaborative relationships, managers must often rely on shared collaborative norms to ensure desired outcomes. These norms evolve over the life of the trading relationship and become an integral part of the role behaviour (e.g. Heide and John, 1992). Collaborative norms direct parties to cooperate with each other in order to co-ordinate their activities for mutual benefit and forgo the opportunity to pursue their own interests at the expense of their partners (e.g. Dwyer et al., 1987; Frazier, 1983; Powell, 1988). With shared collaborative norms, partners are compelled to align their expectations and actions.

Given this, collaborative norms have become a focus of managerial interest and scholarly inquiry; however, gaps remain. Previous empirical research has, for the most part, examined the norm of collaboration from the perspective of one of the trading partners, and not from a dyadic perspective. Because effective collaboration requires appropriate perceptions by both parties, a research approach that incorporates perceptions from only one exchange partner is naturally incomplete. Our objective is to address this shortcoming by utilizing a dyadic empirical approach to study collaboration. To this end, we begin with the proposition that collaborative norms are indicated by a set of mutually held expectations: both sides expect to learn about each other's needs; share their knowledge and experience; work for mutually beneficial outcomes; and, jointly anticipate changes that could affect their future relationship. The emphasis on "both sides" should be underscored for there must be consensus across the dyad. That is, partners' subjective interpretations of their relationship must be congruent for collaborative norms to emerge.

Utilizing a dyadic conceptualization, we will attempt to answer empirically the following managerial and academic questions. How does the shared norm of collaboration emerge between trading partners? What distinguishes arms-length relationships that simply persist over time from enduring, collaborative relationships? And when are non-collaborative relationships preferable to collaborative relationships?

The paper begins with a brief discussion of the conceptual foundations on which the research propositions are built. After an explanation of our methodology and data analysis, we then combine findings from a discriminant analysis and a post hoc analysis of descriptive field interview data to explore factors that distinguish buyer-supplier relationships in which collaboration is the norm from those in which it is not.

Conceptual development

Interdependence and collaboration

Marketing literature supports the notion that the supplier and buyer must perceive similar levels of dependence for collaborative norms to emerge (e.g. Anderson and Narus, 1990). When interdependence is balanced, partners exhibit a working consensus to collaborate. Here, partners interpret and define their relationship as one in which the stakes are high for both parties. If one party acts in a manner that interferes with the goal attainment of the other, both the relationship and one's own self-interest are threatened. Thus, mutual recognition of interdependence tends to reduce the probability that one party will act in a manner that produces a suboptimal result for the partnership. With this shared understanding, collaboration is more likely to emerge. Wilson (1995) links value creation to interdependence and commitment. Through greater recognized interdependence both parties work to create value of mutual benefit. Where interdependence is asymmetrical one party might extract value creating concessions from the other.

Antecedents of interdependence: relationship specific investments and barriers to exit

Two elements that have been used extensively in the marketing literature to explain relationship interdependence are relationship specific investments and barriers to exit. As one party adapts processes to accommodate to the other, partners tend to strengthen the ties that bind them together (Hallen et al., 1991). Relationship specific investments, whose theoretic origins lie in transaction cost analysis (Williamson, 1975, 1979, 1983), are specialized investments that partners make that are of little value outside their relationship owing to the idiosyncratic nature of the investments. For example, a supplier who dedicates engineering expertise to solve a unique design problem for a manufacturer has made a relationship specific investment. At least in the short run, the supplier's investment is neither easily transferred nor recovered if the relationship terminates; thereby increasing the supplier's dependence on the relationship (e.g. Anderson and Weitz, 1991, Hallen et al., 1991). As the level of a partner's relationship specific investment increases, so does the partner's dependence on the relationship and willingness to collaborate. When both partners believe that they have similar levels of relationship specific investments, mutual recognition of interdependence exists, as should a mutual willingness to collaborate (Heide and John, 1990). Ouchi (1980) refers to this process as equity whereby each partner is satisfied with the effort and contribution of the other.

Barriers to exit are present when partners believe that terminating the established relationship would be costly. A long standing proposition in the marketing literature is that the extent to which one party is dependent on the other is directly proportional to the level of difficulty faced in gaining access to alternative sources of valued outcomes (e.g. Cadotte and Stern, 1979). To the extent that valued resources and outcomes are available outside of the established relationship, dependency is minimized and partners are more easily pulled away from the relationship. Thus, if partners believe that both face high barriers to exit, they are likely to reach a working consensus to collaborate as a means of managing resource flows between their firms.

Facilitation of collaboration: working consensus

Central to this research on collaborative norms is the concept of working consensus because, although dependence may motivate collaboration, working consensus is necessary to facilitate collaboration. In other words, unless the trading parties have congruent perceptions of their interdependence, interdependence cannot motivate collaboration. From a symbolic interaction perspective, parties exhibit a working consensus when they mutually recognize and agree on their definition of their exchange relationship (Blummer, 1969; Goffman, 1959; Scheff, 1967). Utilizing symbolic interaction theory, we adopt the premiss that both buyers' and sellers' interpretations of events, and not simply the events themselves, become the bases for understanding their relationship. The phenomena of interest become the subjective interpretations and meanings that people give to events, behaviours, memories, and intentions as they construct mental models that define the situations in which they find themselves (e.g. Waller, 1970).

For example, when a supplier makes a relationship specific investment it is an event. The supplier may see the investment as substantial and highly valued by the buyer. Wilson (1995) speaks about this investment as a structural bond whereby the relationship becomes difficult to end due to the complexity of the investment. These bonds grow over time and partners' willingness to continue the relationship grows. The buyer, however, may expect the supplier to make such an investment as a routine part of doing business. Each party's interpretation of this investment will influence their interpretation of other aspects of their relationship. Problems arise if one party holds an uncorroborated belief that both sides are in agreement about the state of their relationship. If there is no working consensus and the ignorant party acts based on an uncorroborated perception of simple agreement, the unknowing party has trouble realizing its desired outcomes. Working consensus resolves problems by requiring exchange parties to have a common understanding from which to base their judgements, decisions, and actions.

Thus, consistent with the symbolic interaction paradigm, we examine each party's subjective interpretations of the other and the extent to which these interpretations are congruent across the dyad. This approach addresses issues that are important in marketing research and practice. Marketers have described viewpoint differences between partners' perceptions of their working relationship (e.g. John and Reve, 1982). For instance, Heide and John (1990) discuss how industrial buyers and suppliers screen partners and assess each other's probable behaviour. In another example, Bleeke and Ernst (1993) argue for the need to understand the goals of one's alliance partner. The implication for relationship managers is that it is not possible for partners to change their behaviour or align their actions without first understanding subjective definitions of their situation and their normative expectations. These norms are the rules by which values are operationalized and are designed to enhance the wellbeing of the relationship as a whole.

Conceptual model and hypotheses

Figure 1 summarizes the conceptual model guiding our investigation. This study tests the hypothesis that partners for whom collaboration is the norm will be distinguished from other relationships by their degree of mutually recognized interdependence. We expect that partnerships exhibiting a working consensus to collaborate will be those in which:

(1) both sides believe that their own investments are substantial;

(2) each recognizes the substantial investments of the other;

(3) both sides believe that they themselves would face difficulties accessing alternative partners; and

(4) each believes that the other would face costly consequences if the established relationship were terminated.

Method

Sampling and data collection procedure

We employed an in-depth field study of established and ongoing relationships between one major manufacturing firm and 46 of its major suppliers. The buying firm participating in the study was in the early stages of implementing a vendor management programme. The unit of analysis is the individual trading relationship. In this fashion, the study takes a relational perspective in both data collection and analysis. The sampling procedure began with the construction of a sampling frame of buyer-supplier relationships. Five of the buying firm's purchasing experts selected a set of suppliers from the firm's master list of major vendors and then classified them into one of three categories: "collaborative", "potentially collaborative", or "unlikely to be collaborative". The experts interpreted the categories for themselves. This list helped primarily to achieve variance in the final sample; it was not used as a means by which relationships were classified for data analysis. It is important to note that this procedure ensured only that a range of buyer-seller relationships entered the final sample. It was important that the potential sample should contain both collaborative and uncollaborative dyads. If the experts could not agree on the classification of the relationship, it was not included in the sampling frame. In this manner, 64 relationships entered the sampling frame.

The actual selection of relationships for investigation occurred in the field. Purchasing managers in five of the firm's plants identified suppliers in the sampling frame with whom their plant/division did a substantial volume of business. Purchasing managers also identified the one buyer[1] at the plant which had major responsibility for overseeing the relationship with a given supplier. Twenty-two key informants were then matched to those relationships. Similar criteria for key informant selection were used by Noordewier et al. (1990). Except in the infrequent case when a plant purchasing manager had responsibility for a given relationship, informants did not self-select the relationships on which they reported. Thus, a possible source of bias was diminished.

Data collection began with plant visits and personal interviews with each of the 22 buy-side informants. During the interview, informants were asked to identify their corresponding supply-side informant. This person was then asked to participate in a telephone interview. Screening interviews established that all informants interacted with each other and that both sides had intimate knowledge of the trading relationship between their two companies (e.g. Phillips, 1981).

As with many companies and purchasing departments, buyers typically had responsibility for more than one product class and, therefore, managed multiple trading relationships. On average, buy-side informants reported on two different trading relationships. These 22 buyers were paired with 46 supplier representatives. When a buy-side informant reported on more than one relationship, data collection procedures focused the buyer on one relationship at a time.

Complete sets of buyer-supplier data (i.e. a dyad) were obtained for 46 of 64 potential relationships for a response rate of 72 per cent. Each dyad reflected a particular trading relationship in which a specific product was purchased. Purchased products included such commodities as raw and moulded plastics, castings and stampings, fasteners, machine tooled accessories, motors, batteries, switches, electronic components, and packaging. Of the 46 supply-side informants, 30 held sales or sales management positions and 16 were general managers or production managers.

Measurement

In a manner similar to the dyadic scales developed by John and Reve (1982), buyer and supplier versions of each measure contained mirrored sets of items in order to accommodate the perspectives of the partners. Senior executives in the buying firm reviewed all items for face validity. The collaboration scale was also pre-tested in a pilot survey of 120 purchasing managers from a cross-section of industries. Two sets of measures were analysed in this study. One set, used to develop scales of specialized investments and exit barriers used in the discriminant analysis, was administered through the structured mail questionnaires (see Appendix 1). A second set of descriptive items was administered through personal interviews or through the mail questionnaires (see Appendix 2). These items include characteristics of the purchased product, type of contract and work arrangements, and strategic considerations such as goal compatibility and complementarity.

The collaboration scale contains four items measuring the extent to which both sides engaged in collaborative behaviours. Scale items focused on the mutual benefits derived by both parties; information sharing; and, the willingness of one partner to learn the needs of the other. Four scales were developed to measure barriers to exit. Each informant assessed the degree of difficulty his or her own firm would face in accessing alternative partners if the established relationship were terminated. Because the practical steps in identifying and qualifying alternative partners are different for buyers and suppliers, items in the buy-side scale and the supply-side scale are different. Each informant made an assessment of the other's exit cost in terms of the negative consequences they would face if they switched partners. The items in these scales are also somewhat different for each side of the dyad.

Indices, not scales, were developed to measure levels of relationship specific investment[2] since the individual items are indicators of different dimensions of investment. For example, there is no reason to expect that an investment in warehousing is necessarily associated with an investment in product development, but higher levels of each connote greater levels of relationship specific investments. Four measures of investment were taken: the buyer's viewpoint of both the buying firm's investment and the supply firm's investment; and, the supplier's viewpoint of both the supply firm's investment and the buying firm's investment. The Cronbach's alpha computed for the collaboration and barriers to exit scales were satisfactory (see Appendix 1). Inter-item correlations are not appropriate for the indices and, therefore, Cronbach's alpha is not reported for measures of investment (i.e. Howell, 1987). Given the size of the sample and the number of items and scales, evidence of discriminant validity was found by performing a series of factor analyses. Using data from all 46 dyads, items for all possible pairs of the eight independent measures were factor analysed. In all but one case, items from different measures loaded highly on separate factors[3]. Eigenvalues were greater than or equal to one and factor loadings were typically 0.70 or higher. Low and/or insignificant correlations among measures provide further evidence of discriminant validity (e.g. Dwyer and Oh, 1988).

From Table I, it can be seen that significant correlations (r [less than] 0.50, p [less than or equal to] 0.05) were found among three independent variables. On the supply-side, as informants perceived increased investments by the manufacturer, they also reported that their own firm would have greater difficulty accessing alternative customers[4]. On the buy-side, as informants perceived increased investment by their supply partner, they also expected their supply partners to face higher switching costs. As the supplier on one side of a dyad reported more substantial supply firm investment, the buy-side partner also perceived greater supply-firm investment.

Data analysis plan

We first computed mean scores on the collaboration scale for buyers and suppliers. Dyads were assigned to groups based on partners' above and/or below average scores on the collaboration scale. A working consensus to collaboration was indicated when both dyad members scored above average on their respective collaboration measures; these dyads were assigned to Group I. [TABULAR DATA FOR TABLE I OMITTED] A working consensus not to collaborate existed when both dyad members had below average scores; these dyads were assigned to Group III. Group II, the non-consensus group, had average scores that were different between buyer and seller (i.e. no working consensus).

Thus, consensus is indicated when both sides hold similar interpretations of their interaction patterns; this is captured in the "above-above" and "below-below" average cells of the assignment matrix. Non-consensus is commonplace and is captured in off-diagonal cells. Blalock (1965) states that cross-tabulating high and low scores to create groups is appropriate for capturing a phenomenon when interaction is expected; working consensus assumes interaction. In addition, there was a significant association between the a priori classification of relationships by purchasing experts and the group assignments based on buy-side informant collaboration scores (c2 is significant at 0.05 level).

We then employed discriminant analysis both to test differences and to build profiles of the three groups. After the completion of this procedure, descriptive and anecdotal information was incorporated to deepen the interpretation of these empirical profiles and to enrich our understanding of when and why partners consent to collaborate. Each of these stages of data analysis adds to our interpretation of the findings and explores in greater detail those factors that distinguish enduring collaborative relationships from other ongoing, non-collaborative and non-consensus relationships.

Analysis and results

Discriminant analysis

Discriminant analysis requires that groups have equal covariance matrices on the independent variables. The data satisfy this assumption. In addition, we verified that each independent variable was distributed normally. Table II presents univariate statistics for each of the independent variables.

Using discriminant analysis, we tested for differences among the groups on all eight independent variables simultaneously. The test of the null hypothesis is based on Wilk's Lambda with functions 1 and 2 considered simultaneously. The test is significant (p [less than] 0.01), indicating that the average score profiles of the three a priori groups are different.

Prediction of group membership

Although prediction was not our primary research objective, assessing the rate of correct classification is one indication of the effectiveness of discriminant functions. The classification matrix based on a "jackknife" method is presented in Figure 2.

The hit-rate is 58.7 per cent; 27 dyads were correctly classified by their scores on the discriminant functions. These results indicate an improvement in classification accuracy of 73 per cent relative to chance when compared to the fairness criterion, [C.sub.FAIR] (.33.9%) for three groups of unequal sizes (Lehmann, 1990). No dyads exhibiting a working consensus to collaborate were [TABULAR DATA FOR TABLE II OMITTED] misclassified as non-collaborators and only one non-collaborative dyad was misclassified as collaborating.

Interpretation of the group profiles

Interpretation begins by mapping group means on the discriminant functions [ILLUSTRATION FOR FIGURE 3 OMITTED] and then inspecting the correlations between each independent variable and the discriminant functions (see Table III). From the plot of group means it is clear that function 1 differentiates collaborators from non-collaborators and function 2 differentiated consensus from non-consensus relationships. We labelled function 1 "collaboration factors". Our interpretation of function 1 is that collaborators are distinguished from non-collaborators by three variables. First, supply-side investment was more substantial in collaborative relationships compared to non-collaborative relationships. Second, buyers in collaborative relationships recognized these substantial supply-side investments. Third, these buyers believed that their supply partners faced significant costs of exit. [TABULAR DATA FOR FIGURE 2 OMITTED] [TABULAR DATA FOR TABLE III OMITTED] We labelled function 2 "consensus factors". Our interpretation of function 2 is that three variables distinguish consensus relationships. First, suppliers correctly detected that the buyer had not made substantial investments. Second, suppliers believed that the buyer faces high exit costs. And finally, suppliers believed that their own firm faced little difficulty gaining access to alternative customers.

The data do not lend support to the proposition that both sides make substantial investments. Rather, investment appears to be asymmetrical with suppliers committing more resources to the relationship than buyers. Buy-side self-reports of their own investment did not contribute significantly to either discriminant function. Further, the data do not lend support to the proposition that a working consensus to collaborate is motivated by difficulties accessing alternative partners. In all of its relationships this buying firm appears to maintain access to alternative suppliers, regardless of its level of collaboration. One wonders if the manufacturer maintains a posture of not wanting to place "all its eggs in one basket". In addition, it appears that collaborating suppliers are not unilaterally dependent on this manufacturer as they perceive they could replace this buyer's business. This result empirically supports the proposition advanced by Dwyer and Oh (1988) that collaborative processes do not circumvent market tests. Dwyer et al. (1987, p. 19) suggest that in advanced interdependence, participants do not cease attending to alternatives; they maintain awareness of alternatives but without "constant and frenetic testing" (see also Scanzoni, 1979). This finding is somewhat contrary to results hypothesized by Anderson et al. (1994) who expect that commitment fosters co-operation. Our findings suggest a more guarded approach.

Finally, the findings indicate support for the proposition that collaborating partners assess each other's exit costs and modify their behaviour accordingly. It may be that each party assumes that since the other faces high exit costs, coercive and competitive behaviour is less feasible and is really counterproductive. Therefore, collaborative behaviour becomes a less risky option and is more likely to result in reciprocity.

Combining findings from functions 1 and 2 provides a profile of the factors that distinguish relationships that exhibit consensus and collaboration. When a norm of collaboration is operating, the supplier has alternative courses of action and is not solely dependent on the buyer. Nonetheless, the supplier selects to make substantial relationship specific investments. Believing that these additional investments are valued by the buyer, the supplier conjectures that the buyer faces higher exit costs. In fact, the data suggest that the buyer does recognize the supplier's investments as important and believes that the supplier will forgo opportunities to earn a return on its investments if the relationship were terminated.

Thus, the data imply that when there is consensus to collaborate suppliers wilfully choose to make investments. But, why invest in a relationship when alternative customers are accessible? If the manufacturer does have alternative suppliers, why collaborate with these particular suppliers? To advance tentative answers to these questions the next section presents findings based on consistent and recurring patterns in descriptive data for the three groups of correctly classified dyads.

Descriptive profiles

Questionnaire and in-depth interviews provided an abundant source of information about each of the buyer-supplier dyads. This additional information helps us gain a better understanding of how dyad partners define their collaborative, non-collaborative, and non-consensus relationships. Our first step was to rule out competing explanations for intergroup differences in consensus to collaborate. We were concerned that since norms emerge over time, differences in consensus to collaborate may be accounted for by the length of time the partners had been doing business. Non-consensus, for example, might be associated with the early stages of relationship development and consensus to collaborate might emerge later. The data show that length of relationship (personal and corporate) was not significantly different among the groups. On average, individual dyad members knew each other for a period of 5.3 years; and firms had been doing business together for an average of 11.6 years.

The quality of the interpersonal relationship between informants could also have biased the partner's perceptions of the interfirm relationship. However, there appeared to be no evidence to this potential bias. Measures of rapport and friendliness did not vary significantly across the three groups. We also ruled out the possibility that single source relationships were more prevalent in one group than in another. The mean percentage of the plant's total purchases from a single supplier was 68.6 per cent (sd = 29.5). There was not a significant difference in the mean percentage across the groups. Given the lack of support for these competing explanations of differences, we then constructed post hoc profiles of the groups in terms of three aspects of industrial marketing relationships: characteristics of the purchased product, strategic complementarity of the buying and selling firms, and the work arrangements between them. Each of these characteristics has been shown to affect the nature and form of buyer-seller relationships. These findings are summarized in Tables IV to VI.

Factors used to construct profiles

Taking the buyer's point of view, we defined the purchased products in terms of whether the purchased product was viewed as a standard off-the-shelf item, a part designed by the buyer, or a jointly designed product. In addition, buyers made an assessment of their firm's capacity to make the purchased product. Perceived strategic complementarity of the buying and selling firm was [TABULAR DATA FOR TABLE IV OMITTED] [TABULAR DATA FOR TABLE V OMITTED] suggested by partners' perceptions of goal compatibility between their firms and their willingness to discuss openly the state of their firms' businesses. Strategic complementarity exists when buyers believed that the supply firm was willing to apply their engineering expertise to the buyer's problems; and, in response, suppliers were more likely to believe that buyers would put to use the supply firm's expertise. Diffenbach and Higgins (1987) believe that these factors build perceived "strategic credibility" which influences interactions between companies.

The term "work arrangements" refers to actual tasks and work flows that link the buying and selling firms. We noted whether on-time delivery programmes were in place; whether there was a computerized inventory management system that provided suppliers with long-term forecasts of the buyer's product demand; and whether buyers included suppliers in pre-design phases of product development. Similar considerations were examined by Turnbull et al. (1992) in their study of the adoption of the Japanese model of manufacturing in the UK auto industry. Using this descriptive data, we constructed group profiles of these collaborative, non-collaborative, and non-consensus dyads that emerged from the discriminant analysis.

[TABULAR DATA FOR TABLE VI OMITTED]

Descriptive profile of collaborative dyads

Buyers in collaborative relationships frequently reported that the purchased product was designed jointly by the partners and that the product tended to represent a substantial proportion of their firm's purchasing budgets. For example, findings presented in Tables IV to VI suggest that buyers were likely to report that their firm did not have the engineering expertise to make the purchased product. Thus, the buying firm encouraged joint design efforts, thereby leveraging its need for engineering capabilities by selecting a capable supplier.

Informants on both sides held positive feelings about the strategic complementarity of their firms. Findings reported in Table IV show that buyers believed that the suppliers were willing to apply their engineering expertise to the buyer's problems; and, in response, the suppliers were willing to apply their engineering expertise to the buyer's problems; and in response, the supplier believed that the buyer would use this expertise. Note that these collaboration buyers were more likely to report that their suppliers were more active in pre-design work. These results lend support to the notion that collaborative norms develop when both partners recognize a resource leveraging opportunity. This opportunity exists when a purchased product is important, the buyer lacks certain product specific expertise, and each partner views the other as credible. This finding supports work by Hakansson and Laage-Hellman (1984) who develop a framework for developing a network R&D strategy. Having confidence in each other, these partners structured elaborate work arrangements. On-time delivery programmes were in place in seven of these nine relationships and were often supported by a computerized inventory management system. These elaborate and integrative work arrangements are consistent with work by Frazier et al. (1988), who predict that partners engaged in collaborative exchange tend to continually enlarge the kinds of rewards they supply to each other.

Such was not the case with non-collaborative dyads which, notably, exhibited a lack of strategic complementarity and, anecdotally, good faith dealing. For example, several suppliers complained that they were kept in the dark and did not know whether their assistance was utilized. In one case, the buyer had asked for compliance with quality control procedures and the supplier had flatly refused. In another instance, a supplier admitted sending "junk" products to the buyer. Given the lack of perceived strategic complementarity, trading parties viewed work arrangements as truncated rather than integrated. Without shared goals, the exchange was viewed as transactional in nature. They described exchanges that were typically limited in scope and bound by traditional purchase orders. On-time delivery systems were in place in only two of seven relationships, thereby differentiating these exchanges from the other two groups.

Proposal of an archetypical collaborative relationship

Abstracting from both the discriminant analysis results and the post hoc descriptive profiles, we propose an archetypical industrial marketing relationship characterized by consensus and collaboration (see Figure 4). We represent this relationship as a system in order to capture a pattern of interrelated elements that are part of a process that repeats over time. The elements are described as events and behaviours (i.e. resource leveraging opportunities, strategic complementarity, exit costs, work arrangements, collaborative interaction norms) that are perceived and interpreted by the interacting buyer and supplier. Senge (1994) suggests that the systemic diagram portrayed in Figure 4 views each element as part of a process that does not have a beginning, a middle, and an end. Starting at any point and seen from the vantage point of either the buyer or the seller, one can see the recurring pattern in the system.

We believe that collaborators come to a common understanding of how they might leverage each other's resources and how they strategically complement each other. Their high level of mutual credibility facilitates their ability to structure elaborate work arrangements. The risks associated with these co-operative strategies are diminished because both parties appear committed to the partnership. The supplier makes a substantial investment in the relationship, partly as a result of the partner's elaborate work arrangements. The supplier also understands that these investments are both recognized and valued by the buyer. The buyer's acknowledgement of the importance of these investments further commits the buyer to the relationship and increases the buyer's exit costs. Similarly, the buyer perceives that the supplier also faces high exit costs due to the supplier's investments. Given this mutual commitment and perceived interdependence, a working consensus to collaborate emerges. Over time, mutual adaptations deepen the level of mutual interdependence and the process repeats itself.

Implications and limitations

Perhaps the most significant managerial and academic contribution of this paper is its empirical support of the notion that collaboration requires appropriate perceptions of both parties, not just one. This importance of shared perceptions among collaborative trading partners has long been supported by the IMP research programme (Hakansson, 1982). Specifically, we show the importance of mutual alignment on strategic goals. This position is supported also by recent research on alliances formation and management. Lei and Slocum (1991) show, for example, that the alignment of expectations and goals is an essential ingredient of alliance success.

From a managerial perspective, an important implication derived from these empirical and anecdotal findings is that non-consensual relationships may be risky and ultimately very costly to maintain. Non-consensus relationships are dysfunctional to the extent that information shared, tasks performed, and resources invested by one party are not recognized or valued by the other. In such a situation, efforts should be made to increase the level of communication between parties so that both partners might more accurately assess the scope and nature of their relationship. Such a strategy would permit parties to recalibrate their relationship and make more informed decisions as to which factors add value to the relationship and whether close interaction processes are appropriate and appreciated. For a marketer to squander scarce resources on a relationship that is viewed by the buyer as nothing more than a transaction is wasteful. Indeed, the proper management of buyer-seller relationships is an integral part of the development of strategic advantage in the marketplace (Turnbull and Wilson, 1989).

Additionally, managers should recognize that norms, such as the norm of non-collaboration, can also become standards for behaviour. Anecdotal evidence suggests that consensus towards non-collaboration emerges as the standard for behaviour in situations where opportunistic behaviour is likely and/or when products are easily standardized. Developing a norm of non-collaboration appears to create a more equitable bargaining position for both buyer and supplier. That is, both parties have more realistic expectations of what the trading relationship holds. Both sides will avoid making relationship specific investments; will claim to have access to alternative partners; and will believe the other could fairly easily exit the relationship. These relationships may persist because the nature of the transaction does not warrant closer, more collaborative ties[5].

Academically, symbolic interactionism offers a promising paradigm for investigating relational exchange. Based on our examination of a small set of relationships, there appears to be merit in conceptualizing and measuring norm-regulated behaviour in terms of each side's subjective interpretations of their own and their partner's behaviour. There is also value in gaining a better appreciation for the degree to which partners are in agreement as to interaction patterns. Relying on one-sided reports can lead to a misinterpretation of the actual state of the relationship.

That is not to say that there are not limitations with our paper. For our proposed archetypical collaborative relationship to be validated, additional field research is needed. Ideally, dyad partners should be studied over time. We offer a snapshot at one point in the relationship. In addition, the generalizability of this field study is limited somewhat because we relied on a non-probability sample of relationships within one buying firm. Nonetheless, by using experts within the buying firm to construct a heterogeneous sampling frame we did increase external validity (Cook and Campbell, 1979, p. 75). An advantage of sampling within one buying firm is that intra-firm norms that characterize the corporate culture of the buying firm are constant and cannot account for differences in relational norms observed in different dyads. Further, differences in collaboration cannot be attributed solely to industry norms that might influence partnership norms.

In addition, our focus on dyadic relationships might not capture the complexities of firms' relationships in business markets. At its extreme, Granovetter (1992) cautions against abstracting pairs of firms from their embedded network. Moore (1996) and others have begun to articulate the importance of constellations of firms which co-operate with other constellations of firms in sets of complex alliances. To be sure, such a focus echoes words spoken by our European colleagues (e.g. the IMP Group). Nonetheless, attention to the relationship between dyadic firms is still of paramount importance (Anderson et al., 1994) and serves to highlight fundamental properties and relationships that exist within business networks.

Finally, this research emphasizes the importance of examining the relationship between norm-regulated behaviour and performance outcomes in business-to-business relationships. And in doing so, we hope we have shed light on the complex issues that underpin a discussion of norm regulated behaviour and have provided guidance for future researchers. The importance of such concerns for the successful management of inter-firm relationships cannot be underscored enough.

Notes

1. Buy-side informants included plant purchasing managers, commodity specialists, senior buyers and purchasing engineers.

2. Following Howell (1987, p. 121) indices are described as "multidimensional composites or 'checklists' ... wherein each item represents a single dimension and 'more' of the construct is defined as higher frequency or intensity across its dimensions ... The construct is not defined by the joint intercorrelations among the indicants, but rather by the total potential influence across the ... separate aspects or dimensions...".

3. The one exception was an item on the supply-side index of buy-side investment (supply-side assessment that the buy-side bought obsolete inventory which the supplier held for the manufacturer) that loaded with the items measuring the supplier's own difficulty in exiting the relationship.

4. This correlation may be a measurement artefact since in the factor analysis of items on these two measures, one item on this investment index loaded with items on this exit barrier scale.

5. While not a direct thrust of this research, those results also address topics that are emerging in the area of relationship marketing (see Morgan and Hunt, 1994). We support the notion that not all exchanges should result in close relationships. In fact, we shed light on characteristics that distinguish relationships from market transactions.

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Appendix 1. Measures used in the discriminant analysis

Collaboration scale (four item scales)

Buy-side view of collaborative interaction

On a scale of 1 to 7, to what extent does each of the following describe this relationship?

(1) Engineers and manufacturing people on both sides spend time getting to know each other's needs.

(2) Parties on both sides benefit from the knowledge and experience possessed by the other.

(3) Both sides discuss with each other changes in technology, substitutes, and other factors that could have a long run affect on our trading relationship.

(4) Both sides work to achieve true productivity improvements from which both sides benefit.

Mean = 19.86, SD = 4.26, Cronbach Alpha = 0.81.

Supply-side view of collaborative interaction

On a 7 point scale, to what extent does each of the following phrases describe this relationship ... (items parallel buy-side measure). Mean = 20.70, SD = 4.15, Cronbach Alpha = 0.77.

Exit barrier scales (three items per scale)

Buy-side view of manufacturer's difficulty accessing alternative suppliers (BMALTER)

On a 1 to 7 scale, how difficult would it be;

(1) for your other established suppliers to meet your needs for the purchased component;

(2) for you to identify a new supplier to take the place of this supplier; and

(3) for you to qualify a new supplier to take the place of this supplier?

Mean = 11.85, SD = 5.25, Cronbach Alpha = 0.95.

Buy-side view of supply firm's exit costs (BSCOST)

On a 1 to 7 scale, how significant to the supplier would be the following consequences, if this relationship terminated;

(1) supplier's forgone return on their investment of time and effort to work with you;

(2) supplier's forgone return on their investment of technical/engineering support for you; and

(3) supplier's forgone return on design and engineering modifications they made to accommodate you?

Mean = 11.74, SD = 3.84, Cronbach Alpha = 0.91.

Supply-side view of difficulty accessing alternative customers (SSALTER)

On a 1 to 7 scale, how difficult would it be:

(1) to increase sales of this component to your firm's current customers;

(2) for you to identify a new customer for this component; and

(3) for your firm to sell this component to a new customer?

Mean = 12.33 SD = 5.15, Cronbach Alpha = 0.81.

Supply-side view of manufacturer's exit costs (SMCOST)

On a 1 to 7 scale, how significant to the buyer would be the following consequences, if this relationship terminated:

(1) customer's time and effort spent finding a supplier to replace your firm;

(2) customer's engineering changes needed to accommodate a new supplier; and

(3) customer's forgone efficiencies gained through experience working with your firm? Mean = 12.28, SD = 3.60, Cronbach Alpha = 0.71.

Investment indices

Buy-side view of manufacturer's relationship specific investment (BMINV)

On a 1 to 7 scale, how substantial have been your firm's investments in this relationship in terms of:

(1) deviating from engineering specs to accommodate this supplier's product;

(2) buying obsolete inventory which this supplier produced to meet your old specifications; and

(3) guaranteeing the placement of orders in the short term so that this supplier could break even on new products developed specifically for your firm?

Mean = 7.80, SD = 5.39.

Supply-side view of manufacturer's relationship specific investment (SMINV)

On a 7 point scale, how substantial an investment has the buyer made ... (items parallel the supply-side measure BMINV). Mean = 7.80, SD = 5.39.

Supply-side view of supply firm's relationship specific investment (SSINV)

On a 7 point scale, how substantial have been your firm's investments in this relationship in terms of:

(1) investments in warehousing/silos to meet this customer's needs;

(2) gearing your firm's production equipment and schedules to accommodate this customer; and

(3) dedicating engineering resources to this customer's new product development?

Mean = 13.15, SD = 4.55.

Buy-side view of supplier's relationship specific investment (BSINV)

On a 7 point scale, how substantial an investment has the supplier made ... (items parallel the buyside measure SSINV).

Mean = 12.65, SD = 4.31.

Appendix 2. Single item measures used in descriptive profile

Length of inter-personal relationship

To both sides, how many years have you known this person (the key informant on the other side of the relationship)?

Quality of inter-personal relationship

To both sides, on a 7 point scale, to what extent do you agree or disagree that this vendor representative (purchasing agent) "has good rapport with me?" And, to what extent do you agree or disagree that this vendor representative (purchasing agent) "is friendly"?

Length o f inter-firm relationships

To both sides, how many years has this plant/division purchased this component (product) from this vendor?

Supplier's share of buyer's purchases

To the buyer, what percentage of this plant's total purchases of the component/product come from this vendor?

Purchased product type

To the buyer, is the purchased component (product):

(1) a shelf item;

(2) a product designed by the buyer; or

(3) a product designed jointly by the buyer and supplier?

Buyer's engineering expertise

To the buyer, how strongly do you agree or disagree that your firm "has the engineering expertise to make this component".

Purchased product's share of purchasing budget

To the buyer, how strongly do you agree or disagree that compared to other products purchased by this plant/division, "the total annual expenditure for this component (purchased product) is high".

Contractual arrangement

To both, describe the type of arrangement under which you purchase this component (product) from this supplier:

(1) traditional purchase orders;

(2) formal contract with this plant/division; or

(3) formal corporate contract.

Just-in-time delivery

To the buyer, do you have a just-in-time delivery arrangement with this vendor? Briefly describe your arrangement.

Early involvement in product design

To the buyer, on a 7 point scale, to what extent have "this supplier's engineers/R&D people been involved in your product design before engineering release?"

Goal compatibility

To both sides, on a 7 point scale, to what extent is this relationship described by the phrase "the goals of our firm and this supplier's (buyer's) goals are compatible"?

Strategic communications

To both sides, on a 7 point scale, to what extent does this supplier (buyer) "communicate the state of their business"?

Strategic complementarity

To the buyer, on a 7 point scale, to what extent is this supplier "willing to apply their engineering expertise to your plant/division's problems"? To the supplier, on a 7 point scale, to what extent does this buyer "apply your firm's engineering expertise"?

This research was funded in part by the Marketing Science Institute and by the Black & Decker Corporation. The authors are grateful for this support; and would particularly like to thank Mr George Huper, Vice President of Commodity Purchases at Black & Decker, for his efforts on their behalf.
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Author:Spekman, Robert E.; Salmond, Deborah J.; Lambe, C. Jay
Publication:European Journal of Marketing
Date:Nov 1, 1997
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