Multiple channel complexity: Conceptualization and measurement

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Multiple channel complexity: Conceptualization and measurement Nermin Eyuboglua, Sertan Kabadayib,⁎, Andreas Bujac,1 a b c

Department of Marketing and International Business, Zicklin School of Business, Baruch College, New York, NY 10010, United States Marketing Area, Gabelli School of Business, Fordham University, 140 West 62nd Street, New York, NY 10023, United States Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, United States

A R T I C L E I N F O

A B S T R A C T

Keywords: Multiple channel complexity Environment Performance Distance-based analysis

Nowadays, to better serve their customers, many companies are using multiple channels with different levels of complexity. Although the literature agrees that it is a challenge to design and manage multiple channels for improved performance in today's circumstances, there are no empirically determined guidelines offered to achieve that goal. One contributing factor to this is the lack of a clear conceptualization of multiple channel complexity in the literature. With no such construct and measure, researchers are unable to conduct studies to understand variability in the complexity of multiple channels in practice and hence to draw normative conclusions for managers. In this work, drawing upon the vast organizational complexity literature, we provide a conceptual definition and a measure of multiple channel complexity. Our construct describes the structure of a multiple channel system with respect to three complexity dimensions: channel number; channel levels, and channel member variety. The data from 305 sales/marketing managers in the electronics industry support the validity of the construct as we observe that in highly uncertain environments having highly complex multiple channels in place improves company performance.

1. Introduction In the past two decades, manufacturers have been increasingly using complex multiple channels to distribute their goods (Bairstow & Young, 2012; Vinhas & Anderson, 2005). Technological innovations, changing customer expectations and emergence of new forms of businesses have been contributing factors to this trend. The level of complexity of these multiple channels varies across industries and companies, showing various ownership structures (company owned versus independent) with different types and forms of individual channel members (e.g, distributors, sales agents, e-tailers) (Yan, 2011). For example, while a company in an industrial market may have a company sales force channel and a distributor channel, another one may have a mail order channel, an online channel, a call center channel, and a company sales force channel. Naturally, crafting and managing these multiple channels in a way to produce desirable performance are challenging for marketing managers (Chen & Chiang, 2011; Kabadayi, 2011). Complex multiple channels may be costly and risky if not designed and managed strategically, but serve customers better and improve performance if designed and managed strategically (Chung, Chatterjee, & Sengupta, 2012).

⁎

1

Although marketing channels literature has consistently acknowledged the importance of channel design for performance (Kabadayi, Eyuboglu, & Thomas, 2007; Sharma & Mehrotra, 2007), it offers limited insight and tools to understand and analyze today's complex multiple channels. Specifically, there is a lack of conceptualization of multiple channel complexity. Without having such a construct and its operational measure, it would not be possible to fully understand the variability in the complexity of multiple channels and its impact on channel performance. The importance of understanding organizational complexity is well documented in the management literature (Anderson, 1999; Damanpour, 1996). Analyzing the complexity of a system is a first step to understand the behavior and working of that system (Dooley, 2002). As complexity increases, so do the demands on management to ensure that all activities are working smoothly and together toward achieving the organization's goals (Anderson, 1999). Therefore to manage a system, the complexity of that system must be understood first; otherwise, interventions would lead to sub-optimization (Gottinger, 1983). By analogy then, it is very important to have the concepts and tools to understand and analyze a channel organization's complexity. Without such tools, one cannot theorize and test hypotheses linking multiple channel complexity to possible behaviors and sentiments of

Corresponding author. E-mail addresses: [email protected] (N. Eyuboglu), [email protected] (S. Kabadayi), [email protected] (A. Buja). The authors contributed equally to this article.

http://dx.doi.org/10.1016/j.indmarman.2017.03.010 Received 14 June 2016; Received in revised form 29 March 2017; Accepted 30 March 2017 0019-8501/ © 2017 Elsevier Inc. All rights reserved.

Please cite this article as: Eyuboglu, N., Industrial Marketing Management (2017), http://dx.doi.org/10.1016/j.indmarman.2017.03.010

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A highly complex organization, for example, is characterized by many levels of authority, a large number of occupational roles, and many and different subunits (departments and divisions) (Dooley, 2002). Since complexity is achieved through differentiation, two distinct types of complexity are possible: horizontal complexity is caused by a high level of horizontal differentiation, i.e. a large number of units/departments in an organization or a high level of functional differentiation, a large number of different types of units based on their tasks, characteristics and titles (Daft 1995; Hrebiniak, 1978). On the other hand, vertical complexity entails a high degree of vertical differentiation that involves a high number of hierarchical levels in an organization (Dooley, 2002; Price, 1972). Based on these similar and complementary definitions of complexity in the organization theory literature, we define multiple channel complexity as the degree of structural differentiation in the channel organization (Hall et al., 1967). Structural differentiation may come from horizontal differentiation, vertical differentiation, and variety of elements (ibid). A channel organization has a higher degree of horizontal complexity if it has a larger number of parallel channels operating concurrently. A channel organization has a higher degree of vertical complexity if it has a larger number of vertical levels between the manufacturer and the customer. Finally, a channel organization has higher complexity if it embodies a larger number of different types of independent channel members and in house departments which carry out the distribution tasks. In summary, multiple channel complexity is comprised of the following three dimensions:

channel members in the system and therefore to company performance. Given the importance of recognition and eventual management of multiple channel complexity for companies, such conceptualization could be critical for managers. To address this need of the literature, our potential contribution lies in developing a conceptual definition and a measure of multiple channel complexity. In this paper, multiple channel complexity is considered as a structural descriptor of a channel system. Following Hall, Haas, and Johnson's (1967) definition of organizational complexity, multiple channel complexity is defined as the degree of structural differentiation in the system. Structural differentiation may come from three sources: horizontal differentiation, vertical differentiation, and variety of elements (ibid). Horizontal differentiation corresponds to the number of concurrent channels in place; vertical differentiation is the number of channel levels in the system; and variety of elements is captured in the number of different channel member types in the system. Based on findings in the current channels literature, possible relationships among different dimensions of multiple channel complexity, environment, and performance are developed. Then, these relationships are used to empirically test the nomological validity of our multiple channel complexity measure. Specifically, it is suggested that company performance depends on the match between the multiple channel complexity and the environment: if complex (simple) multiple channels are matched with high (low) environmental uncertainty, company performance will be closer to the ideal. In terms of methodology, “distances from ideal” approach is used to generate normative insights from data (Doty, 1990; Doty, Glick, & Huber, 1993). This approach analyzes the association between distances of multiple channel complexity dimensions from their ideal types and distances of observed performance outcomes from ideal outcomes (Van de Ven & Drazin, 1985). In the next section first the conceptual background is discussed and multiple channel complexity is defined based on organization theory. Then, the details of empirical study are presented followed by modeling methodology and results. The final section discusses contributions and implications for future research and practice.

(1) Channel number: the total number of parallel channels in a multiple channel system, (2) Channel level: the total number of vertical channel levels in a multiple channel system (3) Channel variety: the total number of different types of channel members in a multiple channel system. Therefore, a channel organization is complex if it has a high channel number (i.e. a large number of parallel channels), a high channel level (i.e. a large number of vertical channel levels), and a high channel variety (i.e. a large number of different types of both internal/company owned and external/independent channel members/departments). To give these measures full conceptual precision, we explain below how one could operationalize them: the first step of the operationalization is to produce a complete list of different channel configurations that are used in the industry of interest. For example, in our study, we developed this list on the basis of a review of the academic and trade literature coupled with pre-study interviews with industry executives. First, a total of 8 executives were briefly asked about the various channels that their companies were using to reach their final customers. Then, the list of channels created based on those interviews was shared with another group of 20 executives to make sure that their companies indeed employed those channels. This group of executives unanimously confirmed that the channels as presented below were commonly used in their industry. Therefore, we decided to include the seven channels as shown in Fig. 1 in our operationalization of multiple channel complexity.

2. Multiple channel complexity Organizational complexity has been discussed in a wide range of literatures. Several authors who studied organization design and social systems have offered various definitions of complexity (e.g. Simon, 1964). One common theme is that the degree of complexity is derived from the structural properties of the system as determined by the number and variety of elements and their interactions (Anderson, 1999; Daft, 1995). In a very general sense, a complex system can be defined as a system made up of a large number of parts that interact in a nonsimple way (Simon, 1964). An organization with lots of departments would necessarily be more complex than one with a few departments (Price, 1972). Complexity is also described as the degree of structural differentiation - the number of separate parts of the organization as reflected by the division of labor, number of hierarchical levels, and variety of elements (Hall et al., 1967). In other words, complexity is related to the numerousness and variety in the system (Scuricini, 1988).

(1) (2) (3) (4) (5) (6) [ (7)

Distributor Sales Agent/Broker Sales Agent/Broker Distributor Company Sales Branch/Office]

Fig. 1. Channels used in operationalization of multiple channel complexity.

2

Customer Customer Customer Customer Customer Customer Customer

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Fig. 2. Examples of two different multiple channel systems**In the diagrams, rectangular shape represents internal channels, while oval shape represents independent/external channels.

2.1. Multiple channel complexity and environmental uncertainty

The independent/external channel members are shown in italic font outside the square brackets; the company and the channel departments it owns (internal channel members) are shown in roman font inside square brackets. The second step of operationalization of channel complexity measures consists of counting operations that are best explained by way of example: assume a manufacturer MA has channels (1)–(5), and another manufacturer MB has channels (6)–(7) (See Fig. 2). According to our conceptualization, the number of parallel channels of MA is 5, and that of MB is 2. The number of vertical channel levels is obtained by counting the arrows, so that MA has 2 + 2 + 3 + 1 + 1 = 9 levels, and MB has 1 + 1 = 2 levels. In this case, ownership matters because we count only levels not owned by the company. Finally, the number of channel member types of MA is 4 (Distributor, Sales Agent/Broker, Company Sales Branch/Office, Company Sales Force), and that of MB is 2 (Company Catalog, Company Website). Organization theory implies that MA is exposed to greater management complexities than MB for several reasons: MA has to oversee the functioning of a greater number of parallel paths, each posing different operational challenges, yet requiring system-wide harmonization (Dalton, Todor, Spendolini, Fielding, & Porter, 1980; Simon, 1964). Also, MA has to contend with deeper channels than MB, in particular in channel (3) where the second level, the Distributor, is out of reach of direct dealings for MA, even though MA has an interest in the Distributor's workings (Kumar, Scheer, & Steenkamp, 1995). Finally, MA deals with a greater number of types (two external resellers and two internal sub-units) and hence with greater heterogeneity in operation and expertise and with the potential of goal incompatibilities among them (Coughlan, Anderson, Stern, & El-Ansary, 2006; Webb & Lambe, 2007). Our conceptualization of multiple channel complexity can apply in a retail context as well. For example, in financial services sector, there is now almost no company left that relies on one single channel to reach its customers (Chen & Chang, 2010). Many banks are adopting technology-based channels, such as internet, mobile, telephone and interactive TV applications along with traditional or physical channels such as branches and salespeople (e.g. Durkin, Jennings, Mulholland, & Worthington, 2008) and thus increasing the complexity in their channel systems.

According to contingency theory, organizations try to structure themselves such that their characteristics match with the demands of the environment, and the nature of that match determines their performance (e.g. Dooley, 2002). According to Galbraith (1982), matching specifically the complexity of an organization with the demands of its environment is important in improving the bottom-line outcomes. In the upcoming sections, we will develop arguments linking multiple channel complexity, environmental uncertainty, and performance. We chose environmental uncertainty as our environment variable since it has been a central construct of many studies that deal with organizations and marketing channels (e.g., Achrol & Stern, 1988; Krishnan, Geyskens, & Steenkamp, 2016; López-Gamero, MolinaAzorín, & Claver-Cortés, 2011; Paulraj & Chen, 2007). If the links that we propose hold in our empirical work, they will serve as support for the nomological validity for our multiple channel complexity construct. The extant literature (e.g. Dess & Beard, 1984; Luo & Yu, 2016; Sun & Price, 2016) suggests that environmental uncertainty has two distinct dimensions: environmental dynamism and environmental diversity. Environmental dynamism is about the rate and predictability of changes in the environmental factors. It refers to the frequency of market-related changes such as in the actions of customers and competitors and to the degree that those changes are unforeseeable (Homburg, Workman, & Krohmer, 1999; Krishnan et al., 2016). Rapid and unpredictable changes in the environment make it difficult for organizations to predict outcomes of their actions and thus create uncertainty about their performance (Dess & Beard, 1984). Environmental diversity, on the other hand, refers to the presence of a large variety of customers, competitors, suppliers, and products, and other environmental actors relevant for strategy (Bourgeois, 1980; Paulraj & Chen, 2007). Increasing level of diversity in terms of the external environmental factors creates high uncertainty that organizations need to contend with (Dess & Beard, 1984). Duncan (1972) found that environmental dynamism and diversity both contributed significantly to organizational decision makers' overall perceptions of environmental uncertainty, with high levels of dynamic and diverse environments inducing the highest amount of uncertainty. Similarly, it has been empirically shown that organizations facing highly dynamic and diverse environments experience the highest degree of environmental 3

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Furthermore, when uncertainty is low, decisions and procedures tend to be more routine in nature, and the need for specialization is reduced to a minimum (Tung, 1979). In summary, the undesirability of differentiation in simple environments implies that the multiple channels should have simple structure, meaning fewer parallel channels, fewer levels and fewer channel member types. Again, it is apparent that these requirements — routines, generalization rather than specialization, and a united front against competition — are best met by the capabilities of simple multiple channel structures. Low uncertainty environments pose a deterrence problem for manufacturers where the primary goal is to deter competition and defend existing market share with a defensive posture that requires market muscle. It is this deterrence problem that simple multiple channels are designed to address. Based on the discussions above and organizational literature that supports that firms should design their multiple channel structures to match their environments to maximize their performance, we offer the following hypothesis: Hypothesis: The closer the match between the level of multiple channel complexity and the level of uncertainty in its environment, the higher the outcomes created in that multiple channel system.

uncertainty (Krishnan et al., 2016; Tung, 1979). Below we explain why complex multiple channels would generate better outcomes in highly uncertain environments, while simple multiple channels result in higher performance in less uncertain environments. 2.2. Complex multiple channels in high uncertainty environments The literature suggests that the primary concern of firms facing highly uncertain, i.e. dynamic and diverse environment, is adaptation to their environments (Jia, Cai, & Xu, 2014). Then an important question to address is: what kinds of attributes of a multiple channel system would enable a firm to adapt or to respond quickly to the challenges of an uncertain environment? First, rapid changes in the markets must be recognized by equally rapid intelligence and countered by rapid deployment of marketing initiatives, all of which require a multiple channel structure with agile units that operate under minimal constraints. Second, unpredictability, unforeseeable changes, and surprise developments call for flexibility in the multiple channel structure. Third, the diversity component of an uncertain environment with many types of actors (customers, suppliers, competitors) and products necessitates highly differentiated multiple channels with expertise that is as diverse and localized as required by the actors. Furthermore, a high level of diversity implies more concretely that manufacturers face many customer segments with potentially different channel preferences or multi-channel habits (Balasubramanian, Raghunathan, & Mahajan, 2005). If all types of customers are offered their desired types of channels, manufacturers are likely to extend market coverage and reach (Dholakia, Zhao, & Dholakia, 2005; Vinhas & Anderson, 2005). Greater exposure from the greater coverage and reach also increases awareness and trial of the product, which in the long run may enhance the company bottom-line in terms of sales, growth, and profits (Sharma & Mehrotra, 2007). In summary, the demands of a high uncertainty environment for agility, flexibility, expertise, and creativity are more likely to be met by highly complex multiple channel structures that are subdivided into small, differentiated units operating under few constraints but empowered by specialized expertise. Also, the resulting high information demands in high uncertainty environments can be provided by flexible and differentiated structure of a multiple channel systems (Tung, 1979). All of these attributes together could help firms solve the adaptation problems posed by uncertain environments. Fundamentally, adaptation ensures outcomes necessary for survival (Eyuboglu & Buja, 2007).

3. Empirical study and methodology 3.1. Research context, sample and respondents The present empirical study was conducted in a single industry consisting of manufacturers of electronic components such as receiving antennas, switches, and waveguides (SIC Group 3679). While using a single industry allowed us to isolate the relationships of interest and to control for potentially confounding industry-specific factors, the subindustries under the same SIC code provided enough variance in the data (Kabadayi et al., 2007). In fact, a closer examination of the SIC 3679 showed that it contains a range of sub-industries, from commoditized components for consumer products to innovative high-tech products for hospitals. In addition, a look at trade publications and interviews with industry executives showed that firms in subcategories of this SIC did not only rely on multiple channel systems but also featured a variety of channel anatomies. These findings seemed sufficiently promising that desirable variation would emerge, which was indeed the case. The sampling frame was a national list of manufacturing firms in this SIC code from Dun and Bradstreet's online directory. Initially, we randomly selected 925 manufacturers from that list. We chose sales/marketing managers as our key informants since they were determined in preliminary interviews to be the qualified informants in charge of channel design strategies. From our first phone contact with the sales/marketing managers, we identified 913 firms as using multiple-channel organizations; we excluded the 12 single-channel firms as atypical for modern channel organizations (given their small number they would be unlikely to have made much of a difference). We contacted the respondents three times after this initial screening contact. First, we mailed them the study packages including a cover letter with instructions, the questionnaire, and a return envelope. Two weeks later, we mailed them reminder post cards, and, after another two weeks, we had follow-up phone calls to 300 randomly chosen firms and asked informants who had not yet responded to do so. This procedure yielded a total of 305 completed questionnaires, for a 33.4% overall response rate. We used a promised incentive—a donation of $2 to a charity of the respondent's choice—along with multiple contacts to achieve this high response rate. We used two different methods to control for non-response bias and the results confirmed that no such bias existed in our study. The final data set consisted of firms that employed on average 65 employees, which matched the industry average of 77 (1997 Economic Census) quite well. The maximum number of employees was 130. The informants were asked to respond

2.3. Simple multiple channels in low uncertainty environments When the environment is less uncertain, i.e. stable, predictable and homogenous, multiple channels are confronted with relative certainty and a fixed business base. Adaptation is a non-issue because changes are generally slow and predictable and products and actors do not proliferate. When there is no need for adaptation, differentiation is not only unnecessary, but it can even be detrimental. First, in the presence of a stable business base a proliferation of channel members would diminish the returns to scale, resulting in lower sales and profits for each member (Sharma & Mehrotra, 2007; Vinhas & Anderson, 2005). Second, differentiation can be detrimental because homogeneous markets do not call for specialized expertise; rather, all channels serve the same market, fighting for the same customers, leading to intra-brand competition. This sets in motion a chain of destructive developments, starting with price competition, leading to reduction of services, ensuing free-ride problems, and deteriorating profits. The multiple channels become conflict-ridden and the channel members lose interest in the manufacturer's product (Coughlan et al., 2006, p. 116). Thus, for reasons of diminished returns to scale and intra-brand competition, channel members' loyalty to the manufacturer's products vanishes and with it the manufacturer's satisfaction with the channel. 4

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3.2.2. Outcome measures The first set of outcome variables was about channel organizations' contribution to manufacturers' outcomes: contribution to (1) overall sales, (2) business profit, and to (3) growth. The second set was about the channel organization's behavioral response, operationalized in terms of (1) channel members' loyalty to the manufacturer, (2) the latter's perception of conflict in the channel organization, and (3) satisfaction with the channel organization. To measure these outcome variables we adopted and modified existing scales from the channels literature (see Appendix A).

to the questionnaire based on the entire channel organization used in their business units. When a business unit had different subunits for different markets or products, we asked them to refer only to the unit responsible for the most important market or product. Our key informant bias checks assured us that our key informants were knowledgeable about their companies to respond to our questionnaire. In order to test for common method bias, we performed the Harman single-factor test (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003), which easily rejected the one-factor model as an egregious indicator of common method variance. More importantly, the rotated factor loading matrix demonstrated that the items for different constructs loaded on different factors, which alleviated our concerns over common method bias.

3.3. Psychometric analyses We first examined item-to-total correlations for each construct to see if the respective items belonged to its specific domain. Next, we conducted a confirmatory factor using LISREL 8.71 (Joreskog, Du Toit, & Du Toit, 2000). We assessed the fit of the measurement model with a series of indexes, all of which met or exceeded the critical values for acceptable fit: χ2 = 507.84 df = 261, p < 0.01, CFI = 0.95, GFI = 0.94, RMSEA = 0.05. To assess convergent validity, we examined the path coefficients (loadings) for all latent factors to their manifest indicators. The analysis indicated that all items loaded significantly on their corresponding latent factors (see the Appendix A for item loadings). To assess discriminant validity, we calculated shared variance between all possible pairs of constructs and verified that they were lower than the average variance extracted for each individual construct (Anderson & Gerbing, 1982; Bagozzi & Yi, 1988) (see Table 1). In addition, all constructs had a Cronbach alpha greater than the preferred level of 0.70 and relatively high composite reliability scores (Churchill, 1979). These results collectively demonstrated that our measures had adequate reliability, convergent and discriminant validity. For further evidence of measure quality, we checked the correlations of our outcome variables with a measure of global channel performance. The latter consisted of a 4-item scale developed by Kumar, Stern, and Achrol (1992) and reflected respondents' overall impressions and summary evaluations about their channel organizations. The global channel performance measure correlated highly and positively with the contribution to manufacturers' sales, profits, and growth, as well as channel loyalty and manufacturer satisfaction (r = 0.71, 0.70, 0.68, 0.56, and 0.70, respectively), and negatively with channel conflict (r = − 0.67). These results are evidence for the nomological validity of our outcome variables. Finally, for predictive validity purposes, we included the following item in the questionnaire: “How likely is your company to reorganize/rearrange your current channel system in the near future?” (1 = very unlikely, 7 = very likely). As expected, all our outcome measures, except for conflict, correlated negatively with responses to this item (r = −0.52, −0.49, − 0.41, − 0.38, −0.57). The same item correlated positively with conflict (r = 0.34), further supporting the predictive validity of our outcome variable measures.

3.2. Measures With the exception of multiple channel complexity, we measured the constructs/dimensions with multi-item scales. The scale items are listed in the Appendix A along with literature sources, reliabilities, and item loadings. Our discussion here is limited to the complexity measures that are new to the channels literature, and to the channel outcome measures.

3.2.1. Measures of multiple channel complexity The starting point is the list of channel diagrams (shown in the earlier subsection “Multiple channel complexity”) which we developed from pre-study interviews and academic and trade literatures. This list was intended to be comprehensive for the industry at hand, and indeed respondents did not make use of the opportunity to write in other channel diagrams into our questionnaire. As the first step, we reviewed the trade publications and company profiles to generate an initial list of the available channels used in this industry. Meanwhile, brief interviews were conducted with the executives who were either in charge of or knowledgeable about their companies' channel structures to get insights about the specific channels used by those companies. Finally, the lists generated from trade publications and industry interviews were discussed with a separate group of business professionals to confirm those channel systems. Their feedback provided the final set of channels that included in our questionnaire (please see Appendix B). Furthermore, their feedback also provided validity to our conceptualization as industry executives unanimously agreed that high number of channels with many levels and different types presents more complexity for their decision making and management purposes. In the questionnaire we asked the respondents in our sample to check all those diagrams describing channels used by their business units. While this way of asking respondents to describe the channel systems used by their companies is not very common in the channels literature, given the recent options available to managers, we believe that it is an effective way to capture the channel system structure. The complexity measures were then derived as outlined in the subsection “Multiple channel complexity”: (1) the number of parallel channels was obtained as the number of channel diagrams checked by the respondent. (2) The number of levels was calculated as the total number of levels in the checked diagrams. (3) The number of channel member types was calculated as the number of different types of channel members in the checked diagrams. The use of counting measures is unusual in this line of research, but it is appropriate in this context because greater numbers of organizational strata — horizontal, vertical and functional — imply a greater degree of differentiation and hence greater complexity. From a measurement point of view, the counting process proposed here provides considerable objectivity as it reduces the task of the respondents to selection from a manageable number of choices, while deferring the actual counting processes to the data analysis stage.

4. Analysis and results In testing our hypothesis, we draw on the distance-based analysis of Doty et al. (1993), a version of which was also used by Kabadayi et al. (2007). The approach is based on forming “distances from ideals” in the base dimensions, and on analyzing the association between two kinds of distances: 1) distances of multiple channel complexity dimensions from their ideal types, and 2) distances of observed channel outcomes from ideal outcomes. The hypothesis is that these distances are positively associated: a multiple channel that is closer to its ideal type has channel outcomes that are closer to ideal as well. Thus we first measured the distance of a multiple channel from its ideal channel type and then subjected to potential refutation the normative prediction based on our hypothesis. To this end we 5

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Table 1 Correlation table and descriptive statistics.

1. Env. dynamism 2. Env. diversity 3. #Parallel ch. 4. #Ch. levels 5.#Ch. types 6. Contrib. sales 7. Contrib. profit 8. Contrib. growth 9. Loyalty 10. Conflict 11. Satisfaction Mean Std. deviation Composite reliability Ave. var. extracted Highest. shared var. a

1

2

3

4

5

6

7

8

9

10

11

1.00 0.43a 0.37a 0.40a 0.38a 0.10 0.08 0.07 0.09 −0.11 0.10 3.92 1.50 0.90 0.67 0.10

1.00 0.43a 0.41a 0.37a 0.10 0.16a 0.14a 0.09 − 0.09 0.10 4.22 1.49 0.90 0.68 0.12

1.00 0.83a 0.85a 0.08 0.07 0.05 0.09 0.11 0.08 4.09 1.11 n/a n/a n/a

1.00 0.71a 0.09 0.10 0.07 0.08 0.08 0.07 5.83 1.87 n/a n/a n/a

1.00 0.08 0.06 0.02 0.10 0.10 0.05 3.82 1.01 n/a n/a n/a

1.00 0.51a 0.44a 0.37a −0.43a 0.41a 4.38 1.29 0.87 0.72 0.12

1.00 0.52a 0.48a − 0.56a 0.53a 4.40 1.26 0.82 0.66 0.11

1.00 0.46a − 0.53a 0.59a 3.96 1.35 0.78 0.64 0.07

1.00 − 0.50a 0.55a 4.28 1.09 0.82 0.64 0.08

1.00 − 0.41a 3.92 1.26 0.83 0.66 0.06

1.00 4.17 1.36 0.76 0.67 0.10

Correlations are significant at < 0.05 significance.

Table 2 Determination of a firm's environment. Vector symbol

Equation Equation Equation Equation

#1 #2 #3 #4

Extreme of high uncertainty environment Extreme of less uncertainty environment Firm j environment Distance to extreme of high uncertainty environment Distance to extreme of low uncertainty environment Firm j is in high uncertainty environment Firm j is in low uncertainty environment

Dynamism

Diversity

7 7 ZHighUncEnv ZLessUncEnv 1 1 ZEnv(j) ZDyn(j) ZDiv(j) 2 2 1/2 || ZEnv(j) − ZHighUncEnv || = [(ZDyn(j) − 7) + (ZDiv(j) − 7) ] || ZEnv(j) − ZLowUncEnv || = [(ZDyn(j) − 1)2 + (ZDiv(j) − 1)2]1/2 || ZEnv(j) − ZHighUncEnv || < || ZEnv(j) − ZLowUncEnv || ⟺ 1HighUncEnv(j) = 1 || ZEnv(j) − ZHighUncEnv || > || ZEnv(j) − ZLowUncEnv || ⟺ 1LowUncEnv(j) = 1

(Note: The case of equality in the last two rows did not occur in the sample, hence 1HighUncEnv (j) = 1 − 1LowUncEnv (j).)

proceeded in four steps: 1) we determined each firm's environmental type with a distance-based method, 2) we measured distances of the firm's multiple channel from ideal patterns for its environmental type, 3) we quantified the distance of a firm's channel outcomes from ideal, and 4) we predicted the firm's closeness to ideal channel outcomes from its closeness to its ideal channel type. In the following sections, we describe the four steps.

Table 3 Mean comparison of the variables for environment and multiple channel complexity in the two types of environment.a

Environmental uncertainty Multiple channel complexity

4.1. Step 1: determining a firm's environment type To decide a firm's environmental type, we first determined the environmental patterns (consisting of measures of dynamism and diversity) that characterize the extremes of high and low uncertainty environments, as shown in the first two rows of Table 2. Then, for each firm, we used the observed environmental pattern (Table 2, row 3) to calculate the Euclidean distances to the two extreme patterns (Table 2, Equations 1 and 2) (Doty, 1990; Van de Ven & Drazin, 1985). Whichever was the nearer extreme pattern determined the environment type of the firm. This procedure divided the sample into two nearly balanced groups of 161 and 144 firms corresponding to the two environment types. Table 3 shows the separation of the two groups in terms of mean patterns. We defined dummy variables for the two environment types: 1HighUncEnv(j) = 1 or 0 if firm j is in high uncertainty environment or not, and conversely 1LowUncEnv(j) = 1–1HighUncEnv(j) (Table 2, Equations 3 and 4). These dummy variables are the environmental surrogates in our analysis.

a

Variables

High uncertainty environment n = 161

Low uncertainty environment n = 144

Dynamism Diversity # Parallel channels # Levels # Types

5.01 4.97 4.91

2.95 3.04 3.49

7.72 4.27

5.04 3.07

All means are significantly different at the 5% level between the two environments.

that provide the translation between the verbal descriptions of the ideal types in the theory and the operational measures used to assess real organizations.” These authors propose that, when two ideal types define endpoints of a continuum as is the case here, one ideal type can be scored as the maximum and the other as the minimum on each measurement scale. Thus we identified ideal patterns for each environment type in terms of multiple channel complexity measures. The multiple channel complexity measures are count variables for which there exist semi-theoretical extremes due to the finiteness of the list of channels shown in the subsection “Multiple channel complexity”. This list was established in pre-study research and thus is empirical, yet not derived from the sample. The derivation thereafter of the possible extreme values for the three complexity variables is theoretical: (1) by considering a maximally complex multiple channel structure that has all seven channels, we find that the maximal possible values are, respectively, 7 for the number of parallel channels, 11 for the number of channel levels, and 6 for the number of channel member types; (2) by considering a minimally complex (maximally simple) channel structure

4.2. Step 2: quantifying a firm's distance from its ideal channel organization We next needed to quantify the patterns of ideal multiple channel structures for each type of environment. Ideal patterns are according to Doty and Glick (1994, p.237) “multivariate models of the ideal types 6

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Table 4 Distance of a firm from its ideal multiple channel complexity level. Vector symbol

Equation #1 Equation #2 Equation #3

Extreme of complex channel Extreme of simple channel Firm j ch. complexity Distance to extreme of complex channel Distance to extreme of simple channel Distance of firm j to ideal channel complexity

# Parallel ch.

# Ch. levels

# Ch. mem. types

7 11 6 ZcomplCh ZsimplCh 2 2 2 ZCC(j) ZPar(j) ZLev(j) ZTypes(j) XcomplCh(j) = || ZCC(j) − ZcomplCh || = [(ZPar(j) − 7)2 + (ZLev(j) − 11)2 + (ZTypes(j) − 6)2]1/2 2 2 2 1/2 XsimplCh(j) = || ZCC(j) − ZsimplCh || = [(ZPar(j) − 2) + (ZLev(j) − 2) + (ZTypes(j) − 2) ] XcomplCh(j) · 1HighUncEnv(j) + XsimplCC(j) · 1LowUncEnv(j)

(Note: Even though the three count variables differ in range, we considered the differences as insufficient to warrant differential weighting in the Euclidean distances.)

such as manufacturer MB of page 9, we find that the minimal possible value is 2 for all three variables. These semi-theoretical extremes are taken on in the data, hence they are also empirical extremes. We defined an ideal multiple channel structure in high uncertainty environments as consisting of the maxima 7, 11 and 6 for number of parallel channels, number of channel levels, and number of channel member types. Similarly, the ideal multiple channel structure in low uncertainty environments consisted of the minima 2, 2 and 2 for the same variables. (See Table 4, rows 1 and 2). We defined the distance of a firm from its ideal multiple channel structure patterns as follows: we first determined a firm's environment type according to Step 1; we then picked for this environment type the ideal multiple channel complexity patterns and calculated the Euclidean distances to the observed patterns (for mathematical details see Table 4). If the distances of firm j to the two ideal complexity patterns are denoted by XcomplCh(j), XsimplCh(j), then XcomplCh(j) will be predictors of outcomes if firm j is in a high uncertainty environment, whereas XsimplCh(j) will be predictors if firm j is in a low uncertainty environment.

estimated for the two environments, which is desirable because differences in size effects between the environments are not of interest to us. In detail, the model works out as follows:

4.3. Step 3: quantifying a firm's distances from its ideal outcomes

The slopes and their statistical indicators for each of the six regressions are shown in Table 5. The regressions produced twelve

Y (j ) = (βHighUncEnv + βcomplCh ·XcomplCh (j ) ) ·1HighUncEnv (j ) + (βLowUncEnv + βsimplCh ·XsimplCh (j ) ) ·1LowUncEnv (j ) + βS ·log (Size (j )) + Error (j ) where Y(j) is generic for anyone of the six component distances of Firm j from ideal outcomes (Step 3), and where the β's are the regression slopes and intercepts for the respective predictors in the respective environments. Because the response variables are distances from ideal outcomes, we expect the relevant slopes to be positive: small distance from the ideal outcomes should be positively associated with small distance from the ideal multiple channel complexity levels. It is therefore appropriate to use one-sided significances when testing these slopes. 5. Results

Ideal patterns in channel outcome variables can again be specified using extremes of the 7-point Likert scales. The highest score of 7 reflected the ideal point in five of the six outcome variables: channel organization's contribution 1) to sales, 2) to profits, and 3) to growth, 4) channel loyalty, and 5) manufacturer's satisfaction with the channel organization. The lowest score of 1 reflected the ideal point of the sixth outcome variable, conflict. It would be natural to again measure the closeness of firm j to the ideal outcome pattern by the Euclidean distance. We decided, however, against a compound measure of distance and used instead the individual six distances, such as 7 – contrib. sales(j) or conflict(j) – 1, as six separate response variables. The reason is that putting six component responses to the test in six regressions is a more difficult but more convincing exercise than one regression of a single compound response.

Table 5 Multiple regression results where the independent variables are the distances from matching ideals. Dependent variables

Total deviation in terms of… Contribution to sales

4.4. Step 4: modeling outcome distances based on ideal channel distances Finally, we modeled the six observed distances from best outcomes (Step 3) using essentially the distance from ideal multiple channel complexity (Step 2) as the independent variables. In order to observe different effects in the two environments, one would have to run each regression twice, one per environment. A more powerful approach, however, is to combine the pairs of regressions in a single regression using the dummy variables for environments that interact with the linear effects from the independent variables. This amounts to performing two separate regressions in the two environments but with combined statistical inference that pools the residuals across the two environments. We also added a control variable to the model: firm size, measured as the logarithm of the number of employees. This was meant to adjust for potential differences due to sheer size of the firms. An additional advantage of the combined model is that only one size effect is

Contribution to profit Contribution to growth Loyalty

Satisfaction

Conflict (Absence)

⁎

7

p < 0.01.

?A3B2 tbcolw 7pc?>Distance from congruent ideals for high uncertainty environments β (t-stat) (p-value)

Distance from congruent ideals for low uncertainty environments β (t-stat) (p-value)

R2

Adj. R2

F-value

0.70 (5.412) (0.000) 0.60 (4.708) (0.000) 0.57 (3.975) (0.000) 0.54 (4.045) (0.000) 0.54 (3.891) (0.000) 0.45 (2.568) (0.001)

0.57 (3.415) (0.001) 0.41 (2.812) (0.001) 0.38 (2.456) (0.000) 0.51 (3.542) (0.000) 0.53 (3.912) (0.000) 0.41 (2.542) (0.001)

0.339

0.334

24.27⁎

0.334

0.326

24.23⁎

0.303

0.294

21.87⁎

0.318

0.311

22.98⁎

0.298

0.292

22.73⁎

0.254

0.249

20.71⁎

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Table 6 Multiple regression results where the independent variables are the distances from nonmatching ideals. Dependent variables

Total deviation in terms of… Contribution to sales Contribution to profit Contribution to growth Loyalty

Satisfaction

Conflict (Absence)

⁎

Distance from in-congruent ideals for high uncertainty environments β (t-stat) (p-value)

Distance from in-congruent ideals for low uncertainty environments β (t-stat) (p-value)

R2

Adj. R2

−0.27 (− 2.378) (0.007) −0.19 (− 2.005) (0.001) −0.28 (− 2.932) (0.000) −0.19 (− 1.904) (0.003) −0.19 (− 1.815) (0.005) −0.20 (− 2.072) (0.000)

−0.22 (− 2.187) (0.009) −0.22 (− 2.216) (0.000) −0.25 (− 2.343) (0.000) −0.17 (− 1.701) (0.005) −0.21 (− 2.182) (0.004) −0.21 (− 2.119) (0.002)

0.131

0.128

12.37⁎

0.145

0.139

14.02⁎

0.189

0.183

19.24⁎

0.120

0.117

12.03⁎

Table 7 Correlations between outcome measures and multiple channel complexity measures by environment type. High uncertainty environment

F-value

#Par. channels Contrib. sales Contrib. profit Contrib. growth Loyalty Conflict Satisfaction

0.104

9.91

0.123

0.119

11.02*

#Ch. levels

#Ch. types

0.22 0.25a 0.17b 0.14b 0.12 0.12

0.28a 0.21a 0.19b 0.16b − 0.18b 0.10

#Par. channels

#Ch. levels

#Ch. types

− 0.22a − 0.25a − 0.30a − 0.12 0.14 − 0.18

− 0.19b − 0.24a − 0.21a − 0.11 0.15 − 0.24a

− 0.17b − 0.18b − 0.23a − 0.10 0.15 − 0.19b

0.17 0.26a 0.20a 0.18b 0.12 0.21b

a

Low uncertainty environment

Contrib. sales Contrib. profit Contrib. growth Loyalty Conflict Satisfaction

Of the 36 correlations, only two correlations violate the expected pattern (shown in italics). a Significant at the 0.01 level (2-sided). b Significant at the 0.05 level (2-sided).

⁎

0.108

b

p < 0.01.

5.1. Post-hoc analysis involving adaptability and compliance The strongest associations in Table 7 are in the dependencies of contributions to sales, profit and growth on multiple channel complexity in high uncertainty environments. These associations can be put in the context of our earlier theorizing as follows: The purpose of a complex multiple channel organization in a high uncertainty environment is to adapt to the environment so the firm can take advantage of opportunities. For a low uncertainty environment we theorized that the purpose of simple channels is to control the channel and to reach compliance from members to fairly partition limited resources and coordinate in the face of persistent competition. To examine these explanations we performed a post hoc analysis by correlating adaptability and compliance with distances from ideal multiple channel structure. We had measured adaptability with a three-item scale asking manufacturers to rate their multiple channels' efforts in adjusting their selling practices, being innovative, and meeting changes in their areas (Kumar et al., 1992). We had also measured compliance with a three-item scale asking respondents to rate their channels' efforts to conform to the company's procedures, terms, and conditions (Kumar et al., 1992). Because this analysis is post hoc, we calculated correlations for the two environments separately. For each we correlated distance from the extremes of adaptability and compliance with overall distances from the two ideal multiple channel patterns, both matching and mismatching. The results, shown in Table 8, are striking: only adaptability in high uncertainty environments and compliance in low uncertainty environments are significantly associated with the distances from their concordant ideal multiple channel patterns. These findings support the argument that the salient mechanisms in the two environments differ as follows: in high uncertainty environments, there is greater adaptability as the multiple channels resemble more the complex type; and in low uncertainty environments, there is greater channel compliance as the multiple channels resemble more the simple type.

slopes in all: for each of the six responses there is one slope for each of the two environments. The slopes are consistently positive and statistically significant, while the adjusted R Square values range between 0.249 and 0.334 and are highly statistically significant. In particular the three response variables corresponding to contribution to sales, profit and growth reveal very strong associations with distance from ideal channel complexity level in both environments. Thus the data and model lend sound support to our hypothesis: opposite types of environments in terms of level of uncertainty have opposite types of ideal channel complexity levels, and closeness to these ideals is associated with improved outcomes. We performed a second series of regressions using ideal patterns that were incongruent with the firm's environmental conditions. For example, if a firm operated in a high uncertainty environment, we used distances from the ideal complexity patterns for a low uncertainty environment as independent variables. All associations between outcomes and multiple channel complexity measures changed signs and weakened in terms of R Square, now ranging between 0.104 and 0.183, adding to the evidence for the results (see Table 6). As final evidence for the reality of inverse associations in the two environments, we obtained plain correlations between the outcome measures and multiple channel complexity measures separately for the two environments. The results are shown in Table 7: the inverse associations in the two environments are overwhelmingly confirmed even at this level of disaggregation. Among 36 correlations a vast majority of 34 have the expected signs: in high uncertainty environments multiple channel complexity measures are largely positively associated with outcomes (after inversion of “conflict”); in low uncertainty environments these associations are largely in the inverse direction. The two exceptions turn to be statistically insignificant. Thus the findings of inverse association between environments are robust and invariant to analysis approaches.

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6.2. Managerial implications

Table 8 Post hoc analysis — correlations of adaptability and compliance deviations with distances to ideal multiple channel patterns. Environment

Overall distance to

Adaptability deviation

Compliance deviation

High uncertainty

Congruent ideal

Low uncertainty

In-congruent ideal Congruent ideal

0.369** (0.000) − 0.111 (0.209) − 0.118 (0.191) 0.127 (0.102)

0.109 (0.148) −0.123 (0.140) 0.389** (0.000) 0.089 (0.214)

In-congruent ideal

One important implication of our conceptualization and proposed measurement is that multiple channel complexity has different dimensions and any decision that involves the complexity should take those dimensions into consideration. Many managers base their multiple channel design decisions solely on the number of parallel channels (channel number) that their companies deploy to reach their final customers (Rosenbloom, 2007). However, our conceptualization suggests they also need to include channel level and variety dimensions as well as they also affect the level of complexity in their channel system. Furthermore, our conceptualization and measurement of multiple channel complexity as described in this paper give the managers the opportunity to identify the level of complexity in their channel system and to make adjustments to that complexity level if needed. For example, when faced with a dynamic and diverse environment, they may consider increasing the channel variety by adding new types of channels members to their channel portfolio in addition to only adding new channels. Alternatively, in a more stable and homogeneous environment, they may decide not only decreasing the number of channels that they have but also reducing the number of levels that those channels. Finally, our conceptualization of multiple channel complexity also offers managers an opportunity for a cost-benefit analysis when it comes to managing multiple channel complexity and maximizing performance. As the results indicate, the closer they get to the ideal points in terms of multiple channel complexity for a given environment, the better the overall performance of their channel system. Therefore, making changes to the level of their multiple channel complexity, by increasing and decreasing it as required by their environment, can enable them to improve their performance. However, they may also consider the cost of making such changes and compare that cost to the performance gain and benefits that such changes bring.

1) Adaptability is the driving issue for complex multiple channels in high uncertainty environments. 2) Compliance is the driving issue for simple multiple channels in low uncertainty environments. (p-Values are two-sided.) ** means those numbers are significant at 0.01 level

6. Discussion 6.1. Theoretical contributions In recent decades multiple channels have grown in complexity due to technological innovations, changing customer expectations and the emergence of novel forms of businesses. These complex multiple channels impose burdens on manufacturers in several ways: deploying a complex multiple channel is risky, running it is costly as well as cognitively demanding on managers, and control by manufacturers over the marketing of their products becomes challenging. Despite these burdens complex multiple channels often seem to arise almost by necessity and to prove beneficial in terms of company bottom lines. This fact raises questions as to the conditions under which complexity is justifiable in terms of overall performance. While this is a managerially relevant and important question, there have been limited insights in the literature with regards to the different implications of using complex multiple channel systems. This has been mostly due to the fact that there was no real attempt to conceptualize and measure multiple channel complexity. The overall contribution of this study is to offer a novel conceptualization and measurement of multiple channel complexity that both academics and managers can use in their efforts to better understand this important construct and its effects on other variables. Our conceptualization is partly a synthesis of strategic marketing, and organization science, partly an autonomous theory specific to channel organizations — with the potential to affect how channels research is framed, how managers design their multiple channels based on an environment. Furthermore, the interviews that we conducted with industry experts and company executives reinforced our conceptualization and measurement, assuring us the validity of our efforts. Also, the empirical study that we performed to test the nomological validity of our conceptualization provided further support for the contingency theory's claim that an organization's structure should match its environment to maximize its performance. We believe that this new conceptualization of multiple channel complexity richly characterizes contemporary channel organizations, and it focuses on the multiple channels as a whole as opposed to dyadic channel relationships that have been the focus of past literature (Antia & Frazier, 2001, p.67).

6.3. Limitations and future research directions While this paper offers a novel conceptualization and measurement of multiple channel complexity, it unavoidably has some limitations. For the empirical study, only one industry was included as the study context. While this choice was made to ensure that potentially confounding industry-specific factors were controlled for, similar studies in the future may consider including other industries to test the generalizability of the findings. Also, this study focused on the multiple channel complexity and the environmental dimensions; governance and coordination dimensions were missing. Future studies may want to include such dimensions as formalization and centralization in their analyses and see how they would align with different levels of multiple channel complexity. Channel intensity was explicitly excluded from this study as one of the potential sub-dimensions of channel complexity. This can be remedied by incorporating the concept in the conceptualization and measurement of channel complexity with a counting measure or measurement scale. Finally, while some of the conclusions we draw from this study may sound somewhat “obvious and simple” we have to remind that this is the first conceptual and empirical work that measures multiple channel complexity in this novel way and links it to environment. Future studies should build upon our conceptualization and measures as described in this paper and further investigate this important and relevant concept in different contexts.

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Appendix A. Scale items, reliabilities and item loadings Environmental dynamism (Seven point scale: “very few”–“very frequent”) (α = 0.85) Source: Achrol and Stern (1988) and Homburg et al. (1999). 1. 2. 3. 4. 5. 6.

Changes Changes Changes Changes Changes Changes

in in in in in in

products offered by your business unit and your competitors. (0.82) sales strategies by your business unit and your competitors. (0.83) customer preferences and expectations about product features. (0.77) distribution arrangements and strategies. (0.83) competitive strategies and competitive intensity. (0.80) your company's sales volume. (0.83)

(Seven point scale: “highly predictable”–“highly unpredictable”) 1. 2. 3. 4. 5. 6.

Changes Changes Changes Changes Changes Changes

in in in in in in

products offered by your business unit and your competitors. (0.84) sales strategies by your business unit and your competitors. (0.82) customer preferences and expectations about product features. (0.74) distribution arrangements and strategies. (0.82) competitive strategies and competitive intensity. (0.81) your company's sales volume. (0.85)

Environmental diversity (Seven point scale: “strongly disagree”–“strongly agree”) Source: Achrol and Stern (1988) and Homburg et al. (1999). 1. 2. 3. 4. 5. 6.

(α = 0.86)

The number of products/brands sold in our market is very high. (0.84) The number of different customer segments in our market is very high. (0.81) The number of companies competing in our market is very high. (0.82) Customer requirements vary very much across different customer segments. (0.84) There is a lot of variety in products for sale. (0.84) There is a lot of variety in terms of customers involved in our market. (0.85) Multiple channels' contribution to manufacturer's performance (Seven point scale: “strongly disagree”–“strongly agree”) Source: Kumar et al. (1992) Contribution to sales (α = 0.90)

1. 2. 3. 4.

Over Over Over Over

the the the the

past past past past

three three three three

years, years, years, years,

Contribution to profit

your your your your

channel channel channel channel

has been successful in generating high sales for your company. (0.90) system has generated high sales revenues. (0.86) system has enabled your company to achieve high level of market penetration. (0.83) system has met the sales target you had set for it. (0.89)

(α = 0.82)

1. Your company's cost of servicing your channel system is unreasonable. (R) (0.81) 2. The channel system's demands for support have resulted in inadequate profits for your company. (R) (0.80) 3. Your company has made inadequate profits from your channel system. (R) (0.83) Contribution to growth

(Corr = 0.78)

1. In the past three years, your current channel system has contributed enormously to your company's revenue growth (0.91). 2. In the past three years, your current channel system has been very successful in expanding your business (0. 83). Multiple channels' response (Seven point scale: “strongly disagree”–“strongly agree”) Source: Kumar et al. (1992). Channel loyalty (α = 0.80) 1. Your channels want to sell your products and show their desire to do so in a number of positive ways (0.82). 2. Your channels show motivation to further your company's business (0.72). 3. Your channels place higher amount of time and effort behind your products relative to other businesses that they engage in (0.85). Manufacturer's perception of conflict with channels Source: Anderson and Narus (1990), Frazier (1983)

(α = 0.82)

1. The relationship between your company and your channels has been tense (0.81). 2. Your company and your channels have significant disagreements in your relationship (0.87). 10

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3. Your company and your channels frequently dispute over issues to business (0.80). Manufacturer's satisfaction with channels Source: Anderson and Narus (1990)

(Corr = 0.81)

1. Generally, your company is very satisfied with its overall relationship with the channel system (0.82). 2. Your company is very pleased with its working with the channel system (0.81).

Appendix B. Channel systems This section is about the different types of channel systems your company uses for its most important product or market. Please look at the charts below describing different distribution channel system alternatives and put X next to all alternatives that your company uses for its most important product or market. If you think that some of your distribution channels are not represented below, please feel free to describe them using the empty chart.

____

Your Company

Distributor

Customer

____

Your Company

Sales Agent/ Broker

Customer

____

Your Company

Sales Agent/ Broker

Distributor

Customer

____

Your Company

Company sales branch/office

Distributor

Customer

____

Your Company

Company Sales Force

Customer

____

Your Company

Company Catalog

Customer

____

Your Company

Company Website

Customer

Other (please specify) you may add boxes if you need necessary or use the space in the margins to describe different channel alternatives your company uses for its most important product or market.

____

Your Company

Customer

____

Your Company

Customer

____

Your Company

Customer

____

Your Company

Customer

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