Cravens, David W., Thomas N. Ingram, Raymond W. LaForge and Clifford E. Young (1993), Behavior-. Based and Outcome-Based Salesforce Control Systems,â ...
Outcome-based and Behavior-based Channel Coordination Efforts Kirti Sawhney University of Michigan Gary L. Frazier University of Southern California
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Outcome-based and Behavior-based Channel Coordination Efforts
BY Kirti Sawhney Celly and Gary L. Frazier*
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Kirti Sawhney Celly is Assistant Professor of Marketing and International Business, School of Business Administration, University of Michigan, Ann Arbor. Gary L. Frazier is Richard and Jar-da Hurd Professor of Distribution Management and Chairman of the Department of Marketing, School of Business Administration, University of Southern California. The authors thank the Institute for Study of Business Markets and the University of Southern California for financial assistance in conducting this research. They greatly appreciate the guidance offered by Bart Weitz and three anonymous JMR reviewers, and the assistance of Erin Anderson, INSEAD, Rick Bagozzi, University of Michigan, Bernie Jaworski, Debbie Machuris, and Dave Stewart, all of the University of Southern California, Everett Rogers, University of New Mexico, and Robert Spekman, University of Virginia, at earlier stages of this research. The contributions of numerous suppliers and industrial distributors to the empirical study are also appreciated.
Outcome-based and Behavior-based Channel Coordination Efforts ABSTRACT Distributor outcomes and behaviors are often discussed by supplier personnel in their interactions with distributors, providing an opportunity for supplier personnel to attempt to signal important product-market objectives, gain information about the distributors, and motivate distributors to better support their product lines. The purpose of this study is to improve our understanding of outcome-based and behavior-based coordination efforts of supplier personnel in their relationships with distributors. Primary data from a national survey of industrial distributors are used to test the conceptual framework. Empirical results suggest that a supplier’s use of outcome-based and behavior-based coordination efforts are affected by environmental uncertainty, the supplier’s farniliarity with distributor markets and supplier resource constraints, as well as by distributor experience and value-added.
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. INTRODUCTION The use of distributors is becoming a reality for a growing number of suppliers as they attempt to serve multiple, diverse vertical markets (Anderson and Weitz 1986), as markets mature, and as sales expense-to-revenue ratios increase. While the use of distributors offers advantages of lower capital investment, lower asset exposure, and greater flexibility compared to company-owned distribution (Corey, Cespedes and Rangan 1989; Day and Klein 1988), coordinating relationships with them is a challenge. Supplier personnel frequently attempt to motivate distributors to perform their responsibilities, follow channel policies, and adopt marketing programs in attempts to ensure that the supplier’s product-market objectives are achieved (cf. Stem and El-Ansary 1992). From the supplier’s point-of-view, the channel coordination problem stems, in part, from the fact that distributors are independent businesses with multiple product lines. Therefore, distributors likely have business objectives that differ from those of their suppliers (Eliashberg and Michie 1984). The problem is exacerbated since distributors take title to products (Bucklin 1973; Cespedes 1988) and often have more specialized market information than their suppliers, making it difficult in many cases for suppliers to specify desirable behaviors or evaluate distributor performance. Within the channels literature, a good deal of attention has been devoted to understanding how attempts at interfirm coordination are made, including the role of power and influence strategies (cf. El-Ansary and Stern 1972; Frazier and Summers 1984; Gaski and Nevin 1985), alternative governance structures (cf. Dwyer and Oh 1988; Heide and John 1988), and partnerships (cf. Anderson and Narus 1990; Anderson and Weitz 1992). Another approach for coordinating channel relationships is suggested by agency and organizational control research, though their focus on “control” is problematic in an interfirm context. Agency theory suggests that control problems in organizations are a consequence of differences in the objectives and risk preferences of the parties, as well as informational asymmetries. In this view, control may be achieved through an emphasis on outcomes or behaviors such that the interests of the parties are co-aligned (Bergen, Dutta and Walker 1992; Eisenhardt 1989; Levinthal 1988). In a similar vein, organizational control research suggests that either results or behaviors may be used as the basis for control within an organization, so as to ensure that its goals are met (Anderson 1990; Eisenhardt 1985; Govindarajan and Fisher 1990; Ouchi 1979). Within marketing, these principles have been applied in intraorganizational
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. 2 research on salesforce compensation (cf. John and Weitz 1989; La1 and Staelin 1986), and marketing and personnel controls (Anderson and Oliver 1987; Jaworski 1988; Jaworski and MacInnis 1991). Obvious limitations exist in applying research on intraorganizational control to interfirm channel relationships. “Control” implies actual impact on another’s decision making and behavior. Within an organization, formal authority relationships facilitate the monitoring and directing of behaviors, the use of performance evaluations, and the attainment of control (Ouchi 1979). In channel relationships, formal authority relationships are not nearly as strong. “Control” is much more difficult to achieve in an interfirm context. Therefore, the constructs of “outcome control” and “behavior control” in agency and organizational control research do not appear directly transferable to channels settings. However, a focus on outcomes and/or behaviors in channel relationships may be relevant if
coordination efforts are stressed rather than control. Outcome-based and behavior-based coordination efforts by supplier personnel within their interactions with distributors may be used to convey what is important to the supplier for achievement of its product-market objectives, glean information on distributor activities, capabilities and performance, and motivate distributors to better support the supplier’s product lines and marketing initiatives. Pre-study interviews with over twenty suppliers and distributors revealed that the lack of formal employer-employee authority does not prevent suppliers from attempting to coordinate their channel relationships (cf. Frazier 1983). The interviews further revealed that (1) supplier personnel often discuss distributor outcomes and behaviors with distributor personnel, and (2) such discussions are used in attempts to coordinate ongoing channel relationships. The purpose of this article is to improve our understanding of outcome-based and behavior-based coordination efforts of supplier personnel in their relationships with distributors. Outcome-based
coordination eforts are defined as a focus on bottom-line results (e.g., distributor sales, market share) within interactions of supplier and distributor personnel. Sole reliance on this approach might reflect an exclusive concern with the bottom-line, irrespective of the manner in which the distributor achieves such results. Behavior-based coordination eforts, on the other hand, are defined as a focus during interactions on tasks and activities of the distributor (e.g., selling techniques, customer education efforts) that are expected to lead to bottom-line results. A conceptual framework that attempts to identify the
conditions under which outcome-based and behavior-based coordination efforts are likely to be used by supplier personnel is developed and tested. This study intends to make two primary contributions to the channels literature. First, it underscores the importance of examining outcome-based and behavior-based coordination efforts in channel relationships. They appear to represent primary means of coordination in channel relationships that have not been studied to date. Second, our study provides some initial empirical evidence as to what factors are significantly related to the use of outcome-based and behavior-based coordination efforts by supplier personnel. Therefore, our study sheds new light on factors that may shape the nature of interactions among channel members. The empirical findings will, hopefully, lay the foundation for further theory building and empirical research on the constructs. In the next section, we develop the conceptual framework and hypotheses. A description of the research setting and method is then presented, followed by a discussion of the empirical results. We close with a discussion of limitations of our research, and opportunities for future research.
CONCEPTUAL FRAMEWORK
As indicated above, the concepts of “outcome control” and “behavior control” do not appear to be directly applicable to interorganizational contexts. However, many of the underlying conceptual arguments of agency and organizational control research (e.g., informativeness of results about behaviors, behavior programmability) appear useful for examining outcome-based and behavior-based interorganizational coordination efforts. Anderson and Oliver (1987), Eisenhardt (1989), and Bergen, Dutta and Walker (1992) all indicate that the underlying conceptual arguments in these streams of research appear appropriate for interorganizational settings. Based on these conceptual arguments, prior channels research on coordination, and our pre-study interviews, we developed the conceptual framework--exhibited in Figure l--for this study. Exogenous variables were selected as they were expected to impact the supplier’s outcome-based and behavior-based channel coordination efforts based on one or more of the following: (1) the information that distributor outcomes convey about the extent of distributor support for supplier product lines; (2) the extent to which suppliers are able to specify appropriate distributor activities; (3) suppliers’ ability to monitor distributor activities, and the costs
4 associated with such monitoring; (4) distributors’ confidence about the consequences of their sales and support efforts and (5) distributor risk aversion (Eisenhardt 1985, 1988; Jaworski and MacInnis 1989; John and Weitz 1989; Oliver and Weitz 199 1; Ouchi and Maguire 1975). [Place Figure 1 About Here]
Environmental uncertainty refers to the difficulty of making accurate predictions about the future (Achrol and Stern 1988; Aldrich 1979; Pfeffer and Salancik 1978). When uncertainty is high--due to volatility in demand, buyer preferences, and competition--achieved sales results may not provide much meaningful information to the supplier on the extent of distributor support for its lines. In addition, outcome-based efforts are unlikely to serve a motivational role under high uncertainty because cause-effect ambiguity for the distributor may be high. That is, the distributor may be unsure of the relationship between its efforts and sales performance (Merchant 1985). As arguments in Jaworski (1988) and Jaworski and MacInnis (1989) suggest, under such conditions, an emphasis on outcomes is equivalent to holding the distributor responsible for uncontrollable factors, potentially leading to dissatisfaction and dysfunctional behavior. Finally, a focus on outcomes under high uncertainty might transfer excessive risk to the distributor, especially if the distributor perceives that its rewards from the supplier are likely to be tied to outcome performance (Eisenhardt 1989; Oliver and Weitz 1991). As a consequence, the distributor may become more risk averse, unwilling to make any additional investments in sales or support for the supplier’s line since the associated returns are uncertain (March and Shapira 1987). Therefore, outcome-based coordination efforts are expected to be lower when environmental uncertainty is high. On the other hand, the supplier, based on general knowledge of its products and crucial functions in its channel system, may be in a position to provide inputs to the distributor on what needs to be done in the face of high uncertainty to improve performance (Stern and El-Ansary 1992). Focusing on distributor activities may reduce cause-effect ambiguity and the perceived riskiness of its efforts. For example, in the absence of behavior-based coordination efforts, the distributor may be unwilling to increase sales effort or advertise since it does not know whether adding a salesperson or local advertising will result in improved sales of the line. By holding the distributor responsible for its efforts--“action accountability” in Merchant’s (1985) words--the supplier serves two additional purposes. One, the
5 supplier may be able to more readily guard against the potential opportunistic behavior that is likely when uncertainty is high (John 1984; Williamson 1985). In addition, it provides the supplier with information on at least some of the reasons behind the distributor’s performance. Hl:
Environmental uncertainty will be inversely related to outcome-based coordination efforts.
H2:
Environmental uncertainty will be positively related to behavior-based coordination efforts.
Supplier product-market familiarity reflects the supplier’s knowledge of the idiosyncrasies of distributor markets where its products are sold. Suppliers that are familiar with customer requirements in a trade area are more likely to be able to set precise outcome standards, which can then be used in evaluating distributor performance, thereby enhancing outcome usefulness (Ouchi 1977). This ability to set performance benchmarks may enhance the ability of suppliers to meaningfully assess the extent of distributor support by comparing achieved results with such standards. Therefore, familiarity is expected to be positively related with outcome-based coordination efforts. In terms of behavior-based coordination efforts, an answer to the question ‘I-- do you know what people should do?” (Anderson 1990, p. 24)-- requires that the supplier understand the relationship between distributor efforts and outcomes. Such understanding may stem, in large part, from knowledge of local business conditions and customer requirements. A supplier that is highly familiar with the distributor’s trade area and customers will be in a better position to specify desirable behaviors (i.e., behavior programmability is high), enhancing behavior-based coordination efforts (Eisenhardt 1985; Ouchi and Maguire 1975). H3:
Supplier product-market familiarity will be positively related to outcome-based coordination efforts.
H4:
Supplier product-market familiarity will be positively related to behavior-based coordination efforts.
Supplier replaceability reflects the difficulty the distributor would face in finding alternative suppliers to replace the focal supplier (Heide and John 1988). Distributors will be more dependent on suppliers that are difficult to replace (Emerson 1962; Heide and John 1988). When distributor dependence on a supplier is high, it is more likely that the distributor will make serious efforts to sell and support the supplier’s product line (cf. Frazier and Rody 1991; Gaski and Nevin 1985). This should
I 6 result in a better association between distributor efforts and outcomes than in relationships where dependence levels are low. Therefore, under conditions of high dependence, distributor outcomes are more likely to be reasonable indicators of their support for suppliers’ product lines. Consequently, a focus on distributor outcomes during interactions makes more sense. Since the distributor is an independent business, attempts by the supplier to monitor activities are at the discretion of the distributor. Given time constraints and the need to pay attention to several aspects of their business, distributors may not give suppliers that are easily replaced significant access to their facilities to monitor their activities, nor enough time to discuss appropriate behaviors with their personnel (Frazier and Rody 1991; Pfeffer and Salancik 1978). Thus, for suppliers that are easily replaced, measuring distributor behaviors is likely to be very difficult and the costs of distributor behavior measurement are likely to be correspondingly high. H5:
Supplier replaceability will be inversely related to outcome-based coordination efforts.
H6:
Supplier replaceability will be inversely related to behavior-based coordination efforts.
The construct of supplier resource constraints refers to the insufficiency of supplier personnel available to coordinate channel relationships (Rosenbloom 1992; Stern and El-Ansary 1992). Currently, many suppliers are making reductions in field personnel in an attempt to lower selling costs. Since behavior-based coordination is more resource and labor intensive than outcome-based coordination (Anderson and Oliver 1987), its use requires that supplier personnel get involved to some degree in the activities of the distributor. As such, the supplier must have sufficient personnel available to spend time with distributors in its channel system. Insufficient resources may reduce the supplier’s ability to adequately monitor, evaluate, and discuss a particular distributor’s activities (Eisenhardt 1985; John and Weitz 1989). That is, both the difficulty of measuring distributor behavior and the costs of doing so are expected to be high, reducing the emphasis on behaviors. H7:
Supplier resource constraints will be inversely related to behavior-based coordination efforts.
Supplier resource constraints are expected to have no direct effect on outcome-based coordination efforts. As previous research has suggested, any effect of resource constraints on outcome-based mechanisms is likely indirect, through behavior-based mechanisms. This is evidenced in Eisenhardt
7 (1985, 1988) and John and Weitz’s (1989) argument that as the span of control increases, the costs of monitoring becomes prohibitive. Behavior-based coordination efforts are, therefore, less feasible, which in turn may have an effect on outcome-based coordination efforts.
Distributor experience reflects the distributor’s exposure to the supplier’s product lines and target markets. Inexperienced distributors are unlikely to grasp the connections between their efforts and the sales performance of the supplier’s lines and, therefore, are likely to be more risk-adverse than their experienced counterparts (Merchant 1985). Consequently, focusing on distributor outcomes leads to excessive transfer of risk to the distributor. Oliver and Weitz (1991) find support for the notion that cause-effect ambiguity is negatively associated with preference for outcome-based compensation. For inexperienced distributors, therefore, a heavy emphasis on outcomes may dampen motivation (Merchant 1985). H8:
Distributor experience will be positively related to outcome-based coordination efforts.
Distributor experience is not expected to have an effect on the extent of behavior-based coordination efforts. Whatever the distributor’s experience, suppliers that focus more on behaviors are likely to do so because of the need to regularly update their distributors on product information, demand and competitive conditions, and their marketing programs.
Distributor value-added reflects the degree to which a distributor contributes to the value of the supplier’s product line with end customers (e.g., customer education applications assistance) (Corey, Cespedes and Rangan 1989). When value-added in the channel is relatively low, the most important performance criterion for the supplier may be the sales volume achieved by the distributor. In such cases, a focus on outcomes seems appropriate, since sales performance may be a reasonable indicator of the extent to which the distributor is performing basic channel functions (e.g., selling, product availability). In contrast, when distributor value-added is high, short-term sales performance may contain less information on the extent to which the distributor is performing the full-range of value-added services. In addition outcome-based coordination efforts may lead to a myopic preoccupation with sales performance (Churchill, Ford and Walker 1990; Merchant 1989), possibly resulting in misplaced selling
8 efforts and price-cutting, with insufficient effort put into pre-sales and post-sale activities to promote user value and satisfaction. Heavy reliance on behavior-based efforts is likely unnecessary when distributor value-added is low. The complexity of the tasks to be performed by the distributor is simply not high enough to warrant such an effort. However, when value-added in the channel is relatively high, a focus on distributor behaviors is likely needed, since the tasks the distributor needs to perform are relatively complex and the returns associated with a particular task or behavior may not be evident. Since the relationship between distributor value-added activities and sales may not be immediate or direct (i.e., cause-effect ambiguity is high), performing value-added activities to support a particular supplier’s lines is risky. Further, high value-added may make it more difficult to replace the distributor, prompting the supplier to invest in behavior-based coordination in an attempt to directly assure distributor support, guard against opportunism, and develop a long term relationship (Heide 1994). Behavior-based coordination efforts are likely to signal the importance of the value-added activities and the willingness of the supplier to share in the risks. Further, they may help ensure that the distributor’s efforts are focused in the right direction (John and Weitz 1989). H9:
Distributor value-added will be inversely related to outcome-based coordination efforts.
H 10:
Distributor value-added will be positively related to behavior-based coordination efforts.
We expect a positive, bi-directional relationship between the two types of coordination efforts for the following reasons. First, the two types of coordination efforts are likely to serve complementary purposes. A focus on outcomes conveys to the distributor an interest in ends--product-market performance. On the other hand, a focus on behaviors conveys an interest in the means by which the distributor achieves these ends. Thus, the former facilitates the maintenance of short-term performance pressure on the distributor, while the latter indicates that the capabilities and activities of the distributor are themselves of importance. Second, suppliers with a proclivity for coordinating their channel relationships, or those that want greater participation in the downstream marketing of their products, are likely to focus on both outcomes and behaviors Hl 1:
Outcome-based and behavior-based coordination efforts will be positively related to each other.
9 RESEARCH METHOD
Setting and Sample Industrial product channels were selected as the setting for our empirical study since coordination attempts by supplier personnel are more clear-cut than in many consumer product channels where interfirm power has shifted downstream to a degree. Specifically, data were collected via a mailed, national survey of distributors in four product categories--machine tools and metalworking machinery, air compressors and packaging equipment, industrial tools, and abrasives-adhesives. Our interviews confirmed that there are varying levels of coordination efforts, some amount of goal incongruence, and relatively stable channel structures, suggesting that these channels are suitable for testing our hypotheses. It became clear early in the research process that in order to get the necessary information on coordination attempts from the suppliers’ perspective, we would need supplier cooperation in identifying the boundary person(s) most responsible for dealing with each distributor. Additionally, there was a question of whether we would find sufficient variation in the use of outcome-based and behavior-based coordination efforts within a single manufacturing organization. This pointed to the need for a large number of suppliers, compounding the problem associated with identifying respondents. Given the financial and time constraints faced, we felt that gathering data from industrial product suppliers would be infeasible. On the other hand, the distributors in our sample frame were relatively small, owner-managed firms, which facilitated the identification of the primary person interacting with supplier personnel in each firm (cf. Lusch and Brown 1982). Further, when informed about a new distributor-supported distribution management program at our university, distributors reacted with enthusiasm. Assuming a conservative response rate of 20% and allowing for some screening related attrition, an initial random sample of 1245 distributors was drawn from Dun’s Marketing Services’ Direct Access service. The screening criteria were: primary line of business in one of the four industries referred to above; sales over $3 million; personal contact, address and phone number were available. Pre-study phone calls were used to screen distributors, to identify and speak with the person in the distributorship with primary responsibility for dealing with key suppliers, and to verify the accuracy of the mailing
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information. After excluding persons that could not be reached and those that did not qualify (e.g., suppliers or reps), our final sample consisted of 1031 distributors.
Data Collection Procedure and Response Rate We used a four-stage procedure to enhance respondent involvement and response rate. In addition to priming the respondents with the preliminary phone contact, a personalized letter was sent to each distributor indicating the purpose of our study and informing them of the forthcoming survey. A week later, the first wave of questionnaires was sent with a cover letter assuring confidentiality and a pre-paid reply envelope. A brochure of our new distribution management program and a complimentary pen were included. A total of 78 questionnaires were returned either as undeliverable, or by companies that did not qualify as distributors. After ten days, we made call-backs to non-respondents, a number of whom indicated that they would respond. We mailed a second wave of questionnaires with a cover letter to such distributors. This procedure yielded 254 responses for an overall response rate of about 27%, which compares favorably with rates reported in previous channels research. Of the 254 responses, 207 responses were received in the first wave, and 4 were incomplete and unusable. We examined the likelihood of non-response bias in two ways. First, we examined the differences between first and second wave respondents (Armstrong and Overton 1977). The two groups were essentially similar descriptively; about 8 1% of the respondents in the first wave and 79% in the second wave are owners, chairpersons or presidents; the average experience of the distributorship in the general market served for both groups is about 27 years; the approximate time spent with major supplier personnel in a typical month is about 15 hours for the first wave and 14 hours for the second. A further examination of the first and second wave respondents along study variables revealed no significant differences (in both univariate and a MANOVA analyses). Second, we had information on overall sales volume and number of employees for every distributor in our initial sample. Such data for non-respondents were compared with data from the respondents and no significant differences were found. Based on these analyses, it appears that non-response bias is not a serious problem in our study. Responses from both waves were pooled for further analysis.
Finally, while we felt that gathering data from the primary, qualified respondent in each of these firms would be sufficient, we performed a check for possible informant bias. Following Frazier and Rody (199 l), a question was included in the survey that asked whether, for each of three major decisions related to the supplier, (1) the primary respondent had decision making authority and (2) other personnel also had significant input. In 91% of the cases, the respondent was the primary decision maker in at least two of the three decisions. In 26% of the cases, however, another individual, identified by position, had significant input on at least two of the decisions. A shortened version of the survey was mailed to the individuals identified by the primary respondent in 63 distributorships, of which 43 were returned and usable. We compared responses for the primary and secondary respondents for 14 different items, including nine study variables and five descriptive items. Inter-informant correlations, presented in Table 1, are all reasonably high and significant, providing us with greater confidence in using the primary responses. [Place Table 1 About Here] Construct Measures Measure development was guided by conceptual definitions and prior research, coupled with feedback received during pre-study interviews with over twenty suppliers and distributors. The pre-study interviews served to confirm that the questionnaire format and language was understandable and interpreted consistently by distributors. In addition to identifying that much of the coordination took place during interactions with supplier personnel, we refined items that were confusing or lacked consistency in interpretation. The procedure recommended by Churchill (1991) was used in developing and refining the measures. An examination of the item intercorrelations, means and standard deviations and exploratory principal components factor analysis were used to develop scales. Final measures were all unidimensional with factor loadings being at least 0.6 in all cases (See Appendix for measures). The correlation matrix--with alpha coefficients, means and standard deviations--is shown in Table 2. All of the alpha coefficients are greater than the 0.70 level recommended for early stages of basic research (Nunnally 1978). Finally, discriminant validity was assessed as satisfactory by examining the results of a
I 12 principal components factor analysis of all items together with an orthogonal rotation; results for outcome-based and behavior-based coordination efforts are exhibited in Table 3. [Place Table 2 and Table 3 About Here] The fourteen items included to measure outcome-based coordination and behavior-based coordination were examined together. Three items--inventory levels, coverage of accounts, and extent of distributor selling efforts--had mixed loadings, and were dropped from further analysis. As evident, factor analysis results for the outcome-based and behavior-based measures revealed two distinct factors. In addition, the measures are reliable with alpha for the six-item behavior-based coordination efforts scale being 0.88 and alpha for the five-item outcome-based coordination effort scale being 0.87. Environmental uncertainty is operationalized following John and Weitz (1988) by asking distributors to describe the predictability of the market for the supplier’s product line. Of a total of eight semantic differential items (see Appendix), three correlated poorly with the rest, and were dropped. The remaining five items loaded on one factor, revealing a uni-dimensional measure of uncertainty. The final uncertainty scale has a coefficient alpha of 0.85. Supplier product-market familiarity reflects the supplier’s knowledge of distributor markets where its products are sold. It was measured using a five item semantic differential scale of which all items proved satisfactory with coefficient alpha of 0.85. Supplier resource constraints refers to the insufficiency of supplier personnel available to coordinate channel relationships. Five items were included in the questionnaire addressing this construct. All five Likert items, two of which were reverse coded, proved satisfactory, with an alpha coefficient of 0.79. Supplier replaceability, the difficulty of replacing the focal supplier (cf. Heide and John 1988), was measured using four items, three of which were Likert items assessing the difficulty of (1) replacing the supplier, (2) switching to another supplier, and (3) compensating for lost income should a switch be made. The fourth item was a semantic differential. Each of the items was standardized before summing to arrive at the scale. It has an alpha of 0.91; a high score indicates it is relatively easy to replace the focal supplier. In terms of distributor experience, pre-test interviews indicated a possible response bias with distributors tending to rate themselves as highly experienced with the supplier’s product lines and target markets. Based on pre-study interviews, an attempt was made to overcome this potential problem by
measuring the distributor’s experience with this supplier relative to its experience with its other suppliers. The final measure was unidimensional with an alpha of 0.86, though it has a relatively high mean and low variance. Distributor value-added reflects the degree to which a distributor contributes to the value of the supplier’s product line with end customers. The list of value-added functions included in the survey was carefully constructed based on a perusal of trade publications and talks with distributors. In an attempt to avoid response bias, we included a number of items related to selling and logistics functions to anchor the responses of the distributor. In addition, we provided a response category for them to indicate that particular activities were just not performed in their channel. Each distributor indicated which of the fourteen functions they performed in the context of the focal supplier. We excluded six basic channel functions from the final measure because our pre-study interviews suggested that these functions would be performed in all channels. Our responses confirm that personal selling and order processing were performed by 100% of the distributors, inventory holding by 98%, customer financing by 95%, and lead generation and evaluation by 94% of the respondents. The final value-added measure is a count of how many of the eight remaining functions are performed in the channel.
ANALYSES RESULTS Model Specification The conceptual model was specified as follows: OCE= a, + a,, UNC + a,, FAM + aI2 SREP + a,, EXP + aI4 VADD + a,, BCE + e, BCE= a, + a20 UNC +
a21
FAM + q, SREP + %3 RCON + a, VADD + %, OCE + e,
where: OCE BCE UNC FAM SREP RCON EXP VADD
= = = = = = = =
Outcome-based Coordination Efforts Behavior-based Coordination Efforts Environmental Uncertainty Supplier Product-Market Familiarity Difficulty of Replacing Focal Supplier Supplier Resource Constraints Distributor Experience Distributor Value-Added
14 The model was estimated using a two stage least-squares procedure, appropriate for non-recursive, simultaneous equation systems, which allows us to test whether a reciprocal relationship exists between OCE and BCE. The standardized regression results are reported in Table 4. The outcome-based coordination efforts model is significant (F = 7.46, p < 0.0001) with 15.77% of the variance explained. The behavior-based coordination efforts model has 35.55% of the variance explained and is also significant (F = 21.97, p < 0.0001). [Place Table 4 About Here] Hypotheses Tests Hl and H2, dealing with the effects of uncertainty, are both supported, suggesting that uncertainty has a negative effect on outcome-based coordination efforts (a,, = - 0.30, p < 0.05) and a positive effect on behavior-based coordination efforts &, = 0.41, p < 0.01). The lower emphasis on outcomes is likely driven by the fact that distributor outcomes contain less information on its support for the supplier’s lines when uncertainty is high. In addition, when uncertainty is high, outcome-based efforts are not expected to motivate the distributor to support the supplier’s lines because of the effects of uncontrollable external factors on performance. The effect of uncertainty on behavior-based coordination efforts is particularly strong. When uncertainty is high, suppliers may tend to focus on their distributors’ efforts, thereby reducing the perceived riskiness to the distributor of unpredictable demand. In addition, by focusing on distributor selling and promotional support when sales are uncertain, suppliers obtain direct information on distributor efforts and possibly guard against the potential opportunistic behavior of their distributors (Williamson 1985). The large size of this effect supports the argument that the choice of coordination mechanism should be guided by a consideration of the effects of uncontrollable external factors on distributor perceptions of the effort-market response relationship (Oliver and Weitz 1991). Contrary to H3, the results suggest that supplier product-market familiarity has a negative effect on outcome-based efforts (a,, = -0.22, p < 0.05). A possible explanation for our finding is that when market familiarity is low, a focus on results provides some objective basis for distributor performance evaluation, even if there are no standards to evaluate them against or if sales targets are imprecise. This relationship is consistent with Ouchi and Maguire’s finding (1975) that a manager’s use of outcome
15 control is inversely related to their familiarity with the subordinate’s task. As hypothesized in H4, supplier familiarity with the local market appears to have a positive effect on the use of behavior-based coordination (q, = 0.14, p < 0.05). When familiarity is high, the supplier appears more able to specify desirable behaviors and provide the distributor with direction on what needs to be done to effectively represent it and improve market performance. The hypotheses on the proposed negative effects of supplier replaceability on outcome-based and behavior-based coordination efforts are rejected (aI2 = -0.07, p = 0.38; a,,, = -0.08, p = 0.50). These results are surprising, as the theoretical basis for relationships between these constructs appears reasonably strong. Perhaps distributor efforts and outcomes for the supplier are better connected when the supplier is difficult to replace, but this in itself may not compel supplier personnel to place greater emphasis on outcome-based coordination efforts. Moreover, greater access to the distributor’s business may result when the supplier is difficult to replace. However, if other conditions are not conducive to an emphasis on behavior-based coordination efforts, such access may not be used to any extent (cf. Frazier and Rody 1991). The supplier resource constraints construct appears to negatively affect behavior-based coordination efforts (q3 = -0.19, p < 0. lo), providing support for H7. When a supplier has insufficient personnel allocated to managing a channel relationship, behavior measurability is likely difficult, making behavior-based efforts less feasible. H8, dealing with the effects of distributor experience on outcome-based coordination is supported (aI3 = 0.09, p < 0. lo), consistent with our reasoning that an outcome focus, while perhaps not motivational for inexperienced distributors because of their likely risk aversion, is more appropriate for experienced distributors. H9, which proposed a negative effect of value-added on outcome-based coordination efforts is supported (aI4 = -0.11, p < 0.10). This result supports our contention that when the distributor’s task includes a number of non-selling functions (cf. John and Weitz 1989), suppliers outcome-based coordination efforts are lower. Achieved outcomes are possibly less reflective of distributor efforts when value-added is high. Further, the results suggest that value-added has a positive effect on behavior-based efforts (h = 0.09, p < 0.05), providing support for HlO and our reasoning that as task complexity and cause-effect ambiguity increase, the pay-off from any investment in the supplier’s lines by way of
16 increased promotional activity, customer support, or training of their own employees becomes unclear. Behavior-based coordination efforts, by signaling the importance of such value-added activities and reducing the riskiness and ambiguity associated with performing them, is, therefore, likely to be motivational. A bi-directional relationship between behavior-based and outcome-based coordination efforts was expected. Hl 1 is only partially supported, as the results in Table 4 indicate. While the parameter estimate for the effect of behavior-based efforts on outcome-based efforts is positive and significant (aI5 = 1.20, p < O.Ol), the estimate for the effect of outcome-based efforts on behavior-based efforts is not significant (%5 = 0.06, p = 0.91). We expected that suppliers with a greater interest in participating in the downstream marketing of their products would focus on both outcomes and behaviors. Our finding of a uni-directional relationship suggests, however, that when supplier personnel emphasize behaviors, they will also tend to emphasize outcomes in an attempt to maintain short term performance pressure. The reverse does not appear to hold based on study results.
DISCUSSION We contribute to the channels literature by underscoring the value of examining outcome-based and behavior-based coordination efforts in channel relationships. The focus of supplier personnel on outcomes and behaviors in ongoing interactions provides informational signals to the distributor on what is important in the channel relationship. Effective use of these coordination efforts is likely to have an impact on distributor effort allocation and motivation, thereby affecting overall support for the supplier’s product lines (Cespedes 1990; Jaworski and Kohli 1991; Lawler and Rhode 1976). However, simply pointing out that such coordination efforts are important is not enough, as there are costs and risks associated with each. While there may be a tendency to frequently rely on outcome-based efforts because of the routine availability of sales performance data, its use may be completely inappropriate given the situation. Similarly, behavior-based efforts, when unnecessary, will be very costly to the firm, as they are relatively expensive to implement because of the time and effort involved. An exclusive focus on outcomes may convey that the supplier is primarily concerned with the bottom-line and is likely to result in distributor emphasis on selling activities at the expense of other
I . 17 functions such as customer education and after-sales service. An exclusive focus on behaviors, while reflecting concern for service quality and distributor activities, may result in sub-optimal sales performance. A further contribution of this study lies in the empirical test of its conceptual framework, which identifies factors posited to influence the outcome-based and behavior-based coordination efforts of supplier personnel. Environmental uncertainty appears to exhibit a negative effect on outcome-based coordination efforts and a positive effect on behavior-based coordination efforts. In the face of high external uncertainty, distributors are likely to have less control over their achieved results. Also, they are likely to be much less confident about the pay-offs from their efforts. Therefore, an outcome focus may hamper distributor motivation, while a behavior focus is likely motivational. Supplier familiarity with the distributor’s local markets for its product line appears to have a positive effect on behavior-based coordination efforts. Behavior programmability should be facilitated bY the supplier’s understanding of the distributor’s markets, thereby promoting a focus on distributor behaviors. Contrary to expectations, supplier familiarity is negatively related to outcome-based coordination efforts. Originally, we felt that familiarity with a distributor’s market would aid the supplier in setting meaningful performance standards and, therefore, facilitate an outcome orientation. However, suppliers that are unfamiliar with their distributors’ local markets still need some basis on which to coordinate these relationships, for which outcome-based coordination efforts represent a relatively low-cost approach. The absence of significant results for the relationships between supplier replaceability and both types of coordination efforts is surprising. We expected inverse relationships based on the arguments that when distributors are more dependent on their suppliers, distributor efforts are strongly related to outcomes and greater access to the distributor’s business is afforded. While we speculate on why these relationships did not turn out earlier in the paper, no doubt, further research on the connections between supplier replaceability and outcome- and behavior-based coordination efforts is needed. As anticipated, supplier resource-constraints appear to negatively affect behavior-based coordination efforts. If the supplier has insufficient personnel available in the channel to work with distributors, the measurement of distributor behaviors will be problematic, inhibiting behavior-based
18 coordination efforts. Further, as expected, our results suggest that distributor experience has a positive effect on outcome-based coordination efforts” Inexperienced distributors are likely to be risk-averse, inhibiting reliance on an outcome-based approach. Distributor value-added appears to have a negative effect on outcome-based coordination efforts and a positive effect on behavior-based coordination efforts. As the distributor’s task complexity increases, in this case by a greater number of functions to be performed, the risks and ambiguity associated with performing its task are likely to be high. Under such conditions, an emphasis on outcomes would likely be frustrating to the distributor, possibly resulting in misplaced efforts and lowered motivation. Moreover, where distributor value-added is high, a focus on short-term financial results is unlikely to provide the supplier with much information on full-range of services and support being offered by the distributor. On the other hand, behavior-based coordination efforts in such circumstances will likely serve to reinforce the importance of the value-added activities and provide guidance to the distributor as to what efforts are worthwhile. We failed to find a bi-directional relationship between the two types of coordination efforts. Instead, the relationship appears to be uni-directional, with behavior-based coordination efforts having an apparent positive effect on outcome-based coordination efforts. When behaviors are focused upon, an added focus on bottom-line results may help to ensure that the supplier simultaneously maintains performance pressure on the distributor. On the other hand, suppliers emphasizing outcomes may not be that concerned with the means that distributors use to achieve those outcomes, perhaps because they prefer taking a “hands-off” approach to their distributors, or gain sufficient information on distributor support through an outcome-focus. No doubt, additional research is needed on this relationship. Overall, the empirical results of our study suggest that outcome-based and behavior-based coordination efforts across a company’s channels are influenced by supplier, distributor, and market characteristics. Using a coordination approach not suited to the situation may provide suppliers with poor information on the distributor’s extent of support. Even worse, such an approach could result in dysfunctional behaviors on the part of distributors. An appropriate focus on outcomes and behaviors, based on a consideration of the uncertainty the distributor faces, the distributor’s experience and task complexity, and the supplier’s knowledge about the markets and resource constraints could be beneficial.
19 Limitations Ours is the first study to empirically examine the antecedents of outcome-based and behavior-based coordination efforts in a channels context. While our results are suggestive of the usefulness of our approach, the study has several limitations that must be taken into account. First, using distributor perceptions to measure all the constructs of our study is weakness. The supplier familiarity and resource constraints constructs would have been better measured, in all likelihood, from the supplier’s perspective. Dyadic data would have made the study much stronger. Second, there is a potential for inflated construct relationships as a result of common method variance. This threat to the validity of our findings is particularly likely if respondents provided us with responses on what they thought was the optimal or desirable extent of supplier emphasis on outcomes and behaviors. However, the fact that distributors lacked information on the purpose of the study and were not aware of our interest in outcome-based and behavior-based coordination efforts argues against such a response pattern. We did not use the terms outcome-based and behavior-based coordination efforts in any of our correspondence with the distributors, nor in the questionnaire. Finally, there is a likelihood of some specification error in our study. While we were careful to base our conceptual arguments in economic and organizational theory, model specification is at an early stage. An area of particular concern are the interrelationships that the coordination efforts we examine possess with other aspects of an interorganizational coordination system, which can be referred to as the set of activities and processes one channel member uses in attempts to enhance the likelihood that associated channel members’ actions are consistent with its requirements (cf. Anderson and Oliver 1987; Frazier 1983; Jaworski and MacInnis 1989). Such a system includes the nature of the contract between the firms, governance structure, goal-setting, coordination attempts of various kinds, control or achieved influence, performance evaluation and feedback, and distributor compensation and rewards.
Future Research As suggested above, a critical need in future research is to examine the interrelationships that exist among the various components of an interorganizational coordination system. Specifically, research examining how outcome- and behavior-based coordination efforts are related to this system of constructs
20 would make a major contribution to knowledge development on how different channel relationships operate. Whether outcome-based or behavior-based coordination efforts are used may be related to the nature of the explicit or implicit contract and governance structure that exists in the channel relationship. What impact does a simple contract that requires only 30 days notice of termination by either channel member versus a franchise contract have on these coordination efforts? Do outcome-based and behavior-based coordination efforts of suppliers in contractual channel systems differ from those in administered and conventional channel systems (Stern and El-Ansary 1992)‘? Suppliers often meet with distributors at the beginning of the fiscal year to develop a marketing plan. Distributor goals are usually set at this time (e.g., supplier sales broken down by product line). How does the process of goal-setting and the actual goals set impact the use of outcome-based and behavior-based coordination efforts? Further, how are other influence approaches (e.g., use of coercion) related to these coordination efforts (cf. Frazier and Summers 1984)? How outcome-based and behavior-based coordination efforts are related to the supplier’s control in the channel relationship (i.e., achieved influence on the distributor’s behavior) represents another important research issue. When such coordination efforts are emphasized a good deal, is supplier control straightforwardly heightened? Or is it more complex than this? One might conjecture that if outcome-based and behavior-based coordination efforts are each used under inappropriate conditions, that control would not be forthcoming. For example, if outcomes are emphasized when uncertainty is high, the distributor might become frustrated, leading to less supplier control as a result. Suppliers with little knowledge of the distributor’s markets may attempt to utilize behavior-based coordination efforts, but actually lose ground on control because of poor and self-centered guidance. A key question becomes how does channel member commitment (Anderson and Weitz 1992) and performance (Kumar, Achrol, and Stern 1992) impact these interrelationships? Further, whether external uncertainty primarily impacts control through coordination efforts or has direct effects on control as well must be confronted. Distributor compensation could be outcome-based (e.g., gross margin, quantity discounts) or behavior-based (e.g., functional discounts) or a combination of the two. A key question becomes whether the coordination efforts we focus upon are complements or substitutes to these compensation approaches.
21 Do suppliers that primarily use outcome-based compensation also emphasize outcome-based coordination efforts? Or are they more prone to rely on behavior-based coordination efforts? Quantity discounts would provide incentives for financial performance, while a focus on behavior-based coordination efforts may help to ensure that distributors engage in appropriate behaviors. Do suppliers that use functional discounts focus primarily on behaviors in their interactions with distributor personnel? Or do such suppliers use functional discounts instead of intensive behavior-based coordination efforts in order to reduce selling costs? The above discussion is suggestive of just a few of many the research issues that must be examined in the context of an interorganizational coordination system. Future research should also more thoroughly examine the value-added construct, including assessing the relative importance of various functions to channel customers. One must explicitly recognize whose perspective is being taken; that is, functions that provide value to the supplier may not be what customers value. This issue flows logically from the current dominant concern with firms’ market orientations (cf. Jaworski and Kohli 1993) and is, therefore, critical for effective channel management. Finally, two broad questions related to distributor experience merit further investigation. One possible explanation for the relatively high mean and low variance of our measure is that, despite the care we took in developing this measure, a response bias exists. A viable alternate explanation would be that, given the multi-line nature of distributors, one could expect them to be relatively experienced. This leads us to question what length of time it takes distributors to become experienced with a supplier and its lines. Is it one or two years; several years? Does the answer vary based on the nature of the product. Second, is it experience or expertise that is the most theoretically relevant variable influencing coordination efforts?
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24 Jaworski, Bernard J. (1988), “Toward a Theory of Marketing Control: Environmental Context, Control Types, and Consequences,” Journal of Marketing, 52 (July), 23-39. Jaworski, Bernard J. and Ajay K. Kohli (1991), “Supervisory Feedback: Alternative Types and Their Impact on Salespeople’s Performance and Satisfaction, Journal of Marketing Research, 28 (May), 190201. Jaworski, Bernard J. and Ajay K. Kohli (1993), “Market Orientation: Antecedents and Consequences,” Journal of Marketing, 57, (July), 53-70. and Deborah J. MacInnis (1989), “Marketing Jobs and Management Controls: Toward a Framework,” Journal of Marketing Research, 26 (November), 406-419. John, George (1984), “An Empirical Investigation of Some Antecedents of Opportunism in a Marketing Channel,” Journal of Marketing Research, 21 (August), 278-289. and Barton A. Weitz (1988), “Forward Integration into Distribution: Empirical Test of Transaction Cost Analysis,” Journal of Law, Economics, and Organization, 4 (Fall), 2, 121- 139. and Barton A. Weitz (1989), Salesforce Compensation: An Empirical Investigation of Factors Related to Use of Salary Versus Incentive Compensation, Journal of Marketing Research, 26 (February), 1-14. Johnston, J. (1984), Econometric Methods, Singapore, McGraw-Hill. Kennedy, P. (1985), A Guide to Econometrics, Cambridge, MA: The MIT Press. Kumar, Nirmalya, Louis W. Stern and Ravi S. Achrol (1992), “Assessing Reseller Performance from the Perspective of the Supplier,” Journal of Marketing Research, 29 (May), 238-253. Lal, Rajiv and Richard Staelin (1986), “Salesforce Compensation Plans in Environments with Asymmetric Information,” Marketing Science, 5 (Summer), 3, 179-198. Lawler III, Edward E., and J. G. Rhodes (1976), Information and Control in Organizations, Pacific Palisades, CA: Goodyear Publishing Co. Levinthal, Daniel (1988), “A Survey of Agency Models of Organizations,” Journal of Economic Behavior and Organization, vol. 9, 153-85. Lusch, Robert F. and James R. Brown (1982), “A Modified Model of Power in the Marketing Channel,” Journal of Marketing Research, 19 (August), 3 12-23. March, James. G. and 2. Shapira (1987), “Managerial Perspectives on Risk and Risk-Taking,” Management Science, 33 (November), 1404- 18, Merchant, Kenneth A. (1985), Control in Business Organizations, Boston, MA: Pitman Publishing.
25 (1986), “Research and Control in Complex Organizations: An Overview,” Journal of Accounting Literature, vol. 5, 183-203. (1988), “Progressing Toward a Theory of Marketing Control: A Comment,” Journal of Marketing, 52 (July), 40-44. (1989), Rewarding Results: Motivating Profit Center Managers, Boston, MA: Harvard Business School Press. Nunnally, Jum C. (1978), Psychometric Theon/, New York,NY: McGraw Hill. Oliver, Richard L. and Barton A. Weitz (1991), “The Effects of Risk Preference, Uncertainty, and Incentive Compensation on Salesperson Motivation,” MS1 Report No. 91-104, (February). Ouchi William G. (1977), “The Relationship between Organizational Structure and Organizational Control,” Administrative Science Quarterly, 22 (March), 1, 95-l 13. (1979), “A Conceptual Framework for the Design of Organizational Control Mechanisms,” Management Science, 25 (September), 833-48. and Mary Ann Maguire (1975), “Organizational Control: Two Functions,” Administrative Science Quarterly, 20 (December), 4, 559-69. Perrow, Charles (1986), Complex Organizations: A Critical Essay, Third edition, New York: Random House. Pfeffer, Jeffrey and Gerald R. Salancik (1978), The External Control of Organizations: A Resource Dependence Perspective, New York: Harper. Rosenbloom, Burt (1978), “Motivating Independent Distribution Channel Members,” Industrial Marketing Management, vol. 7, 275-28 1. (1991), Marketing Channels: A Management View, Fourth Edition, Chicago, IL: Dryden. Stern, Louis W. and Adel I. El-Ansary (1992), Marketing Channels, Third edition, Englewood Cliffs, NJ: Prentice-Hall. Williamson, Oliver E. (1985), The Economic Institutions of Capitalism, New York: Free Press.
26
APPENDIX CONSTRUCT MEASURES Outcome-based and Behavior-based Coordination Efforts” Please consider all your personal interactions, both formal and informal, with this supplier’s sales and marketing personnel (e.g., sales representatives, sales managers, marketing managers) during the last year. Include both phone and face-to-face contacts regarding business issues. During such interactions in the past year, indicate below the extent to which the supplier’s personnel focused on or emphasized each of the following areas of your business. individual product line sales total sales volume market share performance sales relative to targets distributor product & applications knowledge inventory levels* extent of distributor selling efforts* coverage of accounts* sales growth selling techniques used by distributor sales reps distributor participation in promotional programs extent of distributor promotional efforts distributor customer education & support activities distributor service capabilities (Seven point scale anchored with Very Little Emphasis (1) and A Great Deal of Emphasis (7) a
In the questionnaire, the areas of the distributor’s business were presented as a single column with the seven point response scale to the right of each area.
Environmental Uncertainty For each pair of items below, please circle a number to indicate which term better describes the market
for this product line. Predictable Stable market shares Easy to monitor trends Stable industry volume Certain that selling efforts will pay off Sales forecasts are likely to be accurate Sufficient information marketing decisions Confident of results of marketing actions
Unpredictable Volatile market shares* Difficult to monitor trends Unstable industry volume Uncertain whether selling efforts will pay off” Sales forecasts are likely to be inaccurate Insufficient information for marketing decisions Unsure of the results of marketing actions*
(Five point semantic differential scale)
Supplier Product-Market Familiarity Indicate the extent to which the following terms which you sell its lines: Very familiar In-depth user information Well developed customer contacts Excellent knowledge of buying practices Good understanding of customer requirements (Five point semantic differential scale)
describe this supplier’s familiarity with the markets to Not at all familiar (R)’ Limited user information (R) Poor customer contacts (R) Little knowledge of buying practices (R) Poor understanding of customer requirements (R)
27
Supplier Resource Constraint 1. This supplier pays enough attention to managing its distribution in this trade area. (R)’ 2. The number of supplier salespeople assigned to this channel is insufficient. 3. This supplier has assigned enough personnel to coordinate its relationship with its distributors in this trade area. (R) 4. Supplier sales representatives are thinly stretched over several territories of accounts. 5. Supplier sales personnel are unable to devote the time necessary to manage this channel. (Seven point scale, anchored with Strongly Disagree (1) and Strongly Agree (7))
Supplier Replaceability 1. If we no longer represented this supplier, we could easily compensate for the loss of income by switching to other suppliers. 2. It would be quite easy for my distributorship to find an adequate replacement for this supplier. 3. If we wanted to, we could switch to another supplier’s line quite easily. (Seven point scale, anchored with Strongly Disagree (1) and Strongly Agree (7)) Indicate how difficult it would be for your distributorship to find an adequate replacement for this supplier. (Seven point response scale anchored with Very Difficult (1) and Very Easy (7))
Distributor Experience Relative to our other suppliers, our familiarity with customers for this supplier’s lines is... knowledge of customer requirements for this supplier’s lines is... overall experience with the markets for this supplier’s lines is... (Five point response scale anchored with Limited (1) and Substantial (5))
Distributor Value-Addedb For each of the following activities, indicate (circle N.A. if not performed). Lead generation* Lead evaluation* Personal selling (phone or face-to-face)* Order processing* Inventory holding* Customer financing and credit
whether that activity is performed by your distributorship Customization Customer education and product training Delivery Installation Applications assistance Technical support and information Customer maintenance and repair services Customer inventory management
b
In the questionnaire, the activities were presented as a single column.
1
(R) Indicates item is reverse-coded Items not included in final measure
*
I
,
Table 1 INTER-INFORMANT CORRELATIONS Outcome-based Coordination Efforts Total Sales Volume
0.50 (p=O.O006) .I
Market Share Performance
0.58 (p=O.OOO 1)
Sales Growth
0.42 (p=O.O053)
Behavior-based Coordination Efforts Selling Techniques Used By Distributor Sales Reps.
0.49 (p=O.OO 10)
Extent of Distributor Promotional Efforts
0.42 (p=O.O058)
Distributor’s Customer Education and Support Activities
0.52 (p=O.O004)
Distributor’s Service Capabilities
0.57 (p=O.OOO 1)
Supplier Replaceability If we no longer represented this supplier, we could easily compensate for the loss of income by switching to other suppliers.
0.63 (p=O.OOO 1)
Supplier Resource Constraint Supplier sales representatives are thinly stretched over several territories or accounts.
0.42 (p=O.O06 1)
Descriptive Variables Year Distributorship Established
0.82 (p=O.OOOl)
Family Owned Distributorship?
0.76 (p=O.O076)
Number of Employees
0.72 (p=O.OOO 1)
Distributor Sales
0.66 (p=O.OOO 1)
Type of Distributor
0.78 (p=O.OOO 1)
Table 2 Construct Inter-Correlations, Means and Variation OCE
BCE
UNC FAM SREP RCON EXP
VADD
OCE
o.s7*
BCE
0.55
0.88
UNC
0.24
0.47
0.85
FAM
0.11
0.28
0.10
0.85
SREP -0.26
-0.21
-0.12
-0.15
0.91
RCON -0.28
-0.3 1
-0.07
-0.45
0.21
0.79
EXP
0.16
0.07
0.02
0.07
-0.23
-0.11 0.86
VADD 0.07
0.18
0.23
-0.05
-0.08
0.03 0.13
N.A.**
MEAN 4.14
3.75
2.65
3.28
3.49
3.93 4.44
7.21
STDV 1.41
1.45
0.81
0.93
1.83
1.28
1.10
Legend: OCE
Outcome-based Coordination Efforts
BCE
Behavior-based Coordination Efforts
UNC
Environmental Uncertainty
FAM
Supplier Product-Market Familiarity
SREP
Difficulty of Replacing Focal Supplier
RCON Supplier Resource Constraint EXP
Distributor Experience
VADD Distributor Value-Added * **
Construct reliabilities in diagonal Measure is an index
0.66
Table 3 Discriminant Validity of the Outcome-based and Behavior-based Coordination Efforts Measures
ITEM’
Coordination Efforts Behaviorbased2
Outcomebased
distributor’s customer education and support activities
0.83
0.13
distributor’s service capabilities
0.78
0.11
extent of distributor promotional efforts
0.76
0.30
distributor’s product and applications knowledge
0.74
0.28
selling techniaues used bv distributor sales reps.
0.71
0.33
distributor participation in promotional programs
0.71
0.26
total sales volume
0.07
0.83
sales growth
0.29
0.81
sales relative to targets
0.25
0.78
market share performance
0.29
0.75
individual product line sales
0.27
0.68
Variance explained (%)
49.87
14.59
CRONBACH’S ALPHA
0.88
0.87
1
3 items--inventory levels, coverage of accounts and selling efforts were dropped.
2
The determinant factor for each item is indicated in bold typeface.
I
n
Table 4 Estimation Results
Predictor Variable
timated Coeffkients for Coordination Efforts’ Outcome-based Behavior-based
Environmental Uncertainty Supplier Familiarity Supplier Replaceability Distributor Experience Distributor Value-added Supplier Resource Constraints Behavior-based Coordination Efforts Outcome-based Coordination Efforts R2 = 15.77 %
R2 = 35.55 %
F 6,239 = 7-46 p < .OOOl
F6,z9 = 21.97 p < .OOOl
Overall model
1 2
*
** ***
Directional hypotheses; p-values based on one-tailed tests Based on two-tailed test p c .Ol p c .05 p c .lO
