A systematic procedure to evaluate an automobile manufacturer–distributor partnership

European Journal of Operational Research 205 (2010) 687–698

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European Journal of Operational Research journal homepage: www.elsevier.com/locate/ejor

Innovative Applications of O.R.

A systematic procedure to evaluate an automobile manufacturer–distributor partnership Shuo-Pei Chen a,*, Wann-Yih Wu b a b

Department of International Trade, Kun Shan University, Yung-Kang, Tainan 71003, Taiwan Department of Business Administration, National Cheng Kung University, Tainan 70101, Taiwan

a r t i c l e

i n f o

Article history: Received 27 October 2007 Accepted 21 January 2010 Available online 25 January 2010 Keywords: Marketing Partnership Analytic Hierarchy Process (AHP) Analytic Network Process (ANP) Interpretive Structure Modeling (ISM)

a b s t r a c t Automobile manufacturer–distributor partnerships are fundamental to the success of automobile companies. The complexity of the overall partnership model often causes difficulties in partnership study. This paper presents a systematic procedure to evaluate an automobile manufacturer–distributor partnership consisting of a large number of system variables. Firstly, Interpretive Structure Modeling (ISM) is used to sort system variables into groups of various characteristics. This sorting process provides an effective means to develop a three-stage hierarchic/network model of the partnership, including Stage I: partnership selection, Stage II: partnership establishment, and Stage III: partnership maintenance. Secondly, Analytic Hierarchy Process (AHP)/Analytic Network Process (ANP) are applied to partnership evaluation based on as many as 20 system variables. Relative importance weight of all variables is quantitatively determined. The most investment-worthy variables found are management strength and power. Finally, this paper makes a comparison between the optimum distributors identified by the present procedure and in practical cases. The usefulness and efficiency of the proposed procedure are ascertained with highly consistent results in the comparison. Ó 2010 Elsevier B.V. All rights reserved.

1. Introduction The automobile industry is a high capital, high technology, and high product-integrated industry. Competitive advantages of a single manufacturer rely not only on internal capability and resources, but also on close cooperation and solid relationships with external organizations (Fahy, 2002). Customers’ demands have become great diverse in the contemporary world. Without boundless power and resources, any individual company can hardly afford to satisfy all customer expectation. An automobile manufacturer has to manipulate an efficient manufacturer-distributor partnership deliberately and delicately. Taiwan automobile market resembles a miniature version of the world market to a certain degree, in which several global automobile manufacturers intensively compete through their surrogates. Most domestic automobile manufacturers produce their cars under technical support from specific global automobile manufacturers. For example, Hotai Corporation produces solely Toyota models and Ford Lio Ho Corporation produces solely Ford models. This paper focuses on investigating partnerships in Taiwan automobile industry and presents a systematic framework for evaluating

* Corresponding author. Tel.: +886 6 2050611; fax: +886 6 2757575. E-mail addresses: [email protected] (S.-P. Chen), [email protected] (W.-Y. Wu). 0377-2217/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.ejor.2010.01.036

automobile manufacturer-distributor partnerships. The rest of the paper is organized as follows: Section 2 defines the problem and explains the motivation. Literature about system variables and ISM/AHP/ANP is given in Section 3; Section 4 outlines the procedure of model development and importance weight determination; Section 5 presents the data and results; and Section 6 draws the conclusions from this research.

2. Motivation and problem description Partnerships in a variety of fields have been studied mostly from four main points of view: transaction cost theory, resources-based approaches, knowledge-based theory, and sociology approaches (Hoffmann and Schlosser, 2001). Different approaches focus on different cause–effect relationships in partnerships. Some studies emphasize the importance of managerial strength, marketing capability or potential benefits. Others focus on the interaction between commitment, trust and solidarity. An overall picture of a partnership is hard to achieve. Moreover, partnerships would form, grow, expand or even dissolve in their life cycles. Most studies do not consider the development processes of partnerships, and, therefore, limit their application to a specific stage of partnership development. Only few studies develop the overall model of partnerships with a broad view trying to cover the whole picture of partnerships

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(Pelton et al., 2002; Fang et al., 2002). They mostly explore the characteristics at different development stages of partnership, not the full hierarchical/network structure of partnerships. One of major difficulties of studying the full structure of partnerships is processing a large number of system variables. A conceptual model has to be derived before the multivariate method can be used to verify the interrelationship between variables. Increasing variable induces difficulties in model construction. Conventional multivariate analysis also demands enough survey data to verify each relationship between variables. The statistical limitation necessitates not only good characteristics of sample data but arduous work in partnership analysis. Above facts motivate the present study to investigate the overall partnership model. Since the automobile industry heavily relies on the manufacturer–distributor partnerships to sell their product, the automobile manufacturer–distributor partnership in Taiwan is used as the research object. The present study tries to solve the following problems. How to derive the overall model of a partnership with a large number of variables? How to construct the hierarchical/network structure of the overall partnership model efficiently? Once the hierarchical/network structure of the partnership model is set up, can the relative importance weight of system variables be determined when they are interrelated?

3. Literature background 3.1. Partnership model The transaction cost theory posits that partners pursue minimum transaction costs that benefit their partnerships. Resourcebased approaches consider partnerships as tools of partners to obtain irreplaceable or rare resources. Knowledge-based approaches emphasize on improving partners’ learning ability to survive from competition and creating value through knowledge integration. Sociological approaches explain partner collaboration through non-economic aspects such as trust or commit. Studies of partnership evaluation depend more or less on one of the previous approaches (Hoffmann and Schlosser, 2001). Rindfleisch and Heide (1997) discuss the development of the transaction cost analysis. Chen and Tseng (2005) propose that abundant resources for a mutually beneficial relationship are major criteria in selecting potential partners. Heide and John (1992) maintain that information exchange is a coordination mechanism to align common goals and coordinate activities. Anderson and Narus (1990) build a model of on-going, working partnerships in marketing channels from the sociological approach. Those studies do not give an overall picture of partnerships. There is also a wide diversity of criteria for evaluating partnerships. Criteria such as financial factors (Steuer and Na, 2003), organizational effectiveness, and efficiency have been used frequently. Stern and El-Ansary (1992) propose that partnership evaluation should be studied from either a societal perspective or a managerial perspective. Instead of objective measures, some researchers have emphasized perceptual measures of partner performance (Duarte and Davies, 2003). No consensus has been reached in partnership evaluation. Some researchers considered partnership performance as reflected in terms of either retailer or supplier performance (Frazier et al., 1989). Partners need to develop joint mechanisms for evaluating the partnership based on their common goal. Another topic of partnership studies is to investigate specific cause–effect relationships between system variables. For example, Geyskens et al. (1999) conduct a meta-analysis of the relationships of satisfaction and other variables such as trust, conflict in a marketing channel. Duarte and Davies (2003) test conflict-perfor-

mance in a business-to-business relationship. Gruen et al. (2000) investigate factors to enhance partners’ commitment. Morgan and Hunt (1994) study partnerships based on commitment-trust theory. Kumar et al. (1995) look into effects of perceived independent. Those studies often concentrate on specific variables, not on overall partnership models. They offer valuable information about the interaction within specific parts of partnerships. Though, there are still lack general views of partnerships. Despite partnerships have their own lifecycles, most research focuses on topics at a specific stage of partnership development, especially partner selection and partnership maintenance. For example, many researchers propose selection criteria (Medcof, 1997; Bierly and Gallagher, 2007) for partner selection or evaluate variable importance without considering interactions among system variables. Those who study partnership satisfaction or partnership performance are interested in partnership maintenance (Geyskens and Steenkamp, 2002; Bello et al., 2003). Most research interest lies in identifying system variables in partnership activities (Gruen et al., 2000), analyzing attribution of system variables (Fahy, 2002), describing partnership characteristics (Heide and John, 1992), or discussing relationships between system variables, such as conflict, satisfaction, trust and partnership performance (Duarte and Davies, 2003; Geyskens et al., 1999) for specific partnerships. There is limited work regarding overall conceptual partnership models that cover a complete partnership development process. Some researchers have proposed several stages for partnership development. The elementary stages in common are selection, establishment and security of a partnership (Ellram, 1990; Hoffmann and Schlosser, 2001). Pelton et al. (2002) propose that the five stages involving cultivating a positive functioning partnership are: recruiting, screening, selecting, motivating, and securing the recruit’s commitment. Fang et al. (2002) propose a four-stage model, including selection, establishment, maintenance, and decline, to study the partnership between small and middle firms. The present work defines an automobile manufacturer–distributor partnership as ‘‘an on-going relationship between automobile manufacturers and their distributors in which the parties collaborate on objectives, policies and procedures to create common profits by distributing the products of manufacturers”. Since awareness does not involve interaction between potential partners. Dissolution and decline of a partnership are caused by negative effects in maintaining a partnership. They somehow represent the reverse sides of variable attribution. The present study excludes these two stages in the research model. Modified from the aforementioned models, the present work proposes that the overall model of a partnership should include the following three stages: Stage 1: partnership selection, Stage 2: partnership establishment, and Stage 3: partnership maintenance. 3.2. System variables Partner selection is the process that manufacturers deliberate on which distributors are adequate partners before actual formation of a partnership. Partnership establishment is a stage of compromise, complement, and adaptation between manufacturers and distributors. Heavy knowledge flow and organizational change could be induced in this stage. Partnership maintenance is a ongoing task that manufacturers and distributors concentrate on activities that provide long-term effects and contributions to improve partnership quality. The present paper tries to establish the overall model of a partnership by selecting variables from literature to cover the partnership as large as possible. Variables with similar constructs are grouped into a single item. For example, non-economic satisfaction and economic satisfaction (Geyskens and Steenkamp, 2002) are treated as ‘‘satisfaction”, and normative

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commitment, continuance commitment and affective commitment as ‘‘commitment” in this study. Partner’s use of threats, promise and non-coercive influence strategies (Geyskens et al., 1999) are all categorized as power. In total, the present study categorized collected variables into 20 major variables. The variables are reviewed and defined in the following sections. For conveniences, variables are discussed into three groups corresponding to the three-stage model. The model is proposed in the previous section and completed after the ISM analysis. 3.2.1. Partner selection Culture (CU) is regarded as ‘‘cultural fit between a manufacturer and its associated distributor (Ellram, 1990)” (e.g., individualism among the employees of the distributors). Cultural diversity could cause practical barriers that hinder the smooth interaction between manufacturers and distributors. Companies with higher similarity are apt to cope with the adaptation involved in forming a partnership. Management strength (MS) refers to ‘‘the management team and managerial capabilities of distributors (Stern and El-Ansary, 1992; Chen and Tseng, 2005)” (e.g., the distributor’s corporation is wellorganized). Planning, staff relationships, strategy making and resource allocation as well as many other managerial capabilities of distributors could be useful to manufacturers who are considering developing a partnership. Marketing capability (MC) refers to ‘‘the advertising, promoting and selling capability of distributors, and/or distributors’ market coverage and market knowledge (Stern and El-Ansary, 1992)” (e.g., the distributor’s capability to understand what consumers need and to locate key consumer groups). A manufacturer relies on its associated distributors to contact customers, collect market information and extend market coverage. Potential benefit (PB) is ‘‘the expected reward of a manufacturer from a partnership (Stern and El-Ansary, 1992; Chen and Tseng, 2005)” (e.g., the possible sales growth after the formation of a partnership). The intent of forming a partnership is to lower total costs, increase channel value, and achieve mutual benefit. A manufacturer should also be aware of potential risks of losses resulting from the relationship it is to join. Power (PW) is conceived as ‘‘the ability of a manufacturer to control or influence an associated distributor over its beliefs, attitudes, behaviors, and related matters (Anderson and Narus, 1990)” (e.g., the ability of the manufacturer to determine profitsharing or promoting activities unilaterally). A manufacturer would assess its ability to use coercive or non-coercive power to force an associated distributor to carry out the strategies to attain its goals. Reputation (RP) reflects ‘‘a distributor’s characteristics in the areas of management, product quality, and financial position (Stern and El-Ansary, 1992)” (e.g., the distributor’s corporate ratings in professional or governmental surveys). Reputation is considered an important variable in partnership success. Manufacturers often form their first appraisal of distributors based on the general reputation established by their past efforts. Resource (RS) represents ‘‘a distributor’s resources that are unique or complementary to those of the manufacturers” (e.g., a distributor has branches in regions where manufacturers do not). A wide variety of resources should be invested, reallocated, shared or exchanged to properly develop a partnership (Hunt and Morgan, 1995). Manufacturers prefer to develop a partnership with distributors with unique or complementary resources to achieve its goals. Strategy fit (SF) indicates ‘‘the extent to which the strategy of distributors is compatible with that of manufacturers (Ellram, 1990)” (e.g., the distributor shares the same price-setting strategy with the manufacturer for their product). Medcof (1997) maintained that a partnership must have a good business strategy ratio-

689

nale. A manufacturer has to prudently examine if its potential distributor is capable of carrying out or developing appropriate strategies. Termination cost (TC) includes ‘‘the explicit costs that a manufacturer will incur should it prematurely end a partnership, and the expected loss from the perceived lack of a comparable potential alternative distributor (Morgan and Hunt, 1994)” (e.g., the manufacturer’s potential penalty or cost for replacing the distributor). Termination cost provides a manufacturer a very important threshold while considering participating in a partnership. 3.2.2. Partnership establishment Conflict (CF) refers to ‘‘the overall level of disagreement between manufacturers and distributors (Anderson and Narus, 1990; Duarte and Davies, 2003)” (e.g., the disagreement between partners on issues such as price setting, promote policy). Disagreements originate from goal divergences and differing perception of reality between partners. Conflict is inevitable, but it should be restricted to a level that will not affect the partnership function. Dependence (DP) is ‘‘the need or perceived need for the resources of both manufacturers and distributors to achieve common goals (Kumar et al., 1995)” (e.g., a manufacturer finds it very difficult to replace its distributor). Dependence is a major consideration while partners engage in the activities of partnership. Dependence can be considered as the degree to which manufacturers need the resources provided by their distributors to achieve their goals. Flexibility (FB) represents ‘‘a willingness of distributors to make adaptations to deal with different and changing environmental conditions (Bello et al., 2003; Heide and John, 1992)” (e.g., the distributor is open to the manufacturer’s request to modify a prior agreement). Distributors with high flexibility can adapt their operations, reacting to unforeseen changes in external conditions or partnership. Guanxi (GX) is a transliteration of the Chinese word representing ‘‘the personal social connections between manufacturers and distributors due to birth or blood, natures, or acquired relationships (Fan, 2002)” (e.g., the CEO of the distributors and the manufacturer are in-laws). It seems to be a special concern in the Chinese community. 3.2.3. Partnership maintenance Commitment(CM) is ‘‘the definite vows of channel members to keep the partnership eternal (Kim, 2001)” (e.g., the manufacturer will make its maximum efforts to maintain the relationship). It shows a partner’s resolve to perform an activity as promised and intention to continue in the relationship. Communication (CC) is considered as ‘‘a set of abilities and knowledge related to the willingness to conduct formal and informal sharing of meaningful and timely information between manufacturers and distributors (Anderson and Narus, 1990)” (e.g., the term of the partnershipd. The media of interaction between partners). Lack of effective communication could impede the learning capability and effectiveness of a partnership. Information exchange (IE) is defined as ‘‘the formal and informal sharing of meaningful and timely information between manufacturers and distributors (Anderson and Narus, 1990; Bello et al., 2003)” (e.g., the frequency of exchange of information between the manufacturer and the distributor). The exchange grants manufacturers and distributors the ability to coordinate and adjust accordingly. Performance (PF) refers to ‘‘the extent to which the manufacturer–distributor relationship is achieving the common goals of its members as a whole.” A wide variety of criteria could be used to assess the performance of a partnership (e.g., profits and growth generated by the partnership or efficiency of the partnership). All

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members should have a consensus on how to measure the performance. Satisfaction (SA) is regarded as ‘‘a manufacturer’s appraisal of overall results of its distributor (Geyskens and Steenkamp, 2002)” (e.g., the manufacturer is pleased with marketing/selling support of high quality by the distributor). It represents a manufacturer’s positive affection to the partnership from both an economic and a social viewpoint. Solidarity (SD) is defined as ‘‘a bilateral expectation that a high value is placed on the manufacturer–distributor relationship, and it prescribes behaviors directed specifically toward relationship maintenance (Heide and John, 1992)” (e.g., the manufacturer and the distributor treat problems as joint rather than individual responsibilities). It allows a manufacturer and his distributor to make certain moves in unison under no external constraints. Trust (TR) means ‘‘the state when a manufacturer has confidence in his distributor due to the ability of the distributor to provide expertise, dependability, and direction (Kumar et al., 1995)” (e.g., the manufacturer believes that the representative of the distributor has high integrity). It reflects the extent to which the manufacturer believes his distributor will not take advantage of him, nor sacrifice his benefits. 3.3. ISM, AHP and ANP The interrelationships between system variables in large-scale complex systems are often entangled, and thus it is hard to recognize their hierarchical features. Interpretative structural modeling (ISM), developed by Warfield (1973), renders a methodical approach to the analysis of such systems. Algebraic manipulation based upon Boolean operation helps to build an interaction map to identify the independent/dependent relationships between system variables. The capability of simplifying the lengthy process of obtaining an effective structure model with digital computers has extensively extended its applications to a variety of research fields. Hsiao and Liu (2005) introduced ISM in a design procedure, which decomposes a piece of apparatus into groups of components with different design characteristics. Wanga et al. (2008) used ISM to

summarize the critical barriers hindering the project of energy saving in China and to explain the interrelationships among them. Vivek et al. (2008) resorted to ISM to establish changing emphases of the specific elements in offshore alliances. The ISM approach gives a better understanding of a system structure and draws up a useful guideline in generating a graphical representation of the structure. Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP), proposed by Saaty (1980, 1996), have been widely recognized as a distinguished decision-making model for quantifying preference in a multi-criterion environment. The evaluation process consists of a series of pairwise comparisons, which generate a positive reciprocal matrix. Importance weight of system variables are calculated through the eigenvector method and the consistency ratio provides an index to ensure that judgments are neither random nor illogical. AHP has wide applications in almost all research fields regarding multiple-criteria decision-making problems (Steuer and Na, 2003; Vaidya and Kumar, 2006; Crary et al., 2002). Recently, AHP is frequently combined with other tools in many applications. A detailed literature review paper is given in Ho (2008). The AHP can not handle network structures with interdependent variables. The analysis of such systems necessitates the application of ANP, also known as AHP with feedback or supermatrix approach. ANP is a general form of AHP that starts the determination of primitive importance weight of system variables using AHP. Then, the final importance weight of system variables is given by a limiting supermatrix that is formed by the aggregation of primitive importance weight and raised the powers until it converges. ANP can be found in many multi-criterion decisionmaking problems (Ivanov et al., 2010; Partovi and Corredoira, 2002; Aragonés-Beltrán et al., 2008; Agarwal et al., 2006). Some integrated ANP are published recently (Yu and Cheng, 2007; Kahraman et al., 2006; Feng et al., 2010). 4. Research design and methodology This study will integrate ISM, AHP/ANP to analyze an automobile partnership between manufacturers and distributors. The

Fig. 1. Research flowchart.

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Fig. 2. Algorithm of the ISM procedure to rearrange a reachability matrix.

research flowchart is shown in Fig. 1. The procedure is divided into three steps. Step I: Structure determination: The overall model of the partnership is constructed through the ISM procedure. Step II: Importance weight calculation: The importance weight of system variables is determined through the AHP/ANP approach. Step III: Comparison: The comparison between the present results and practical cases are presented. The brief description of ISM, AHP/ ANP and research procedure is given in the following sections. 4.1. Interpretive Structure Modeling (ISM) ISM reduces complex system interactions to a logically oriented matrix which is useful for structure determination. The procedures of ISM are illustrated as follows: (1) Formation of incidence matrix: For a system with n variables, denoted as m1 ; m2 ; . . . ; mn , the incidence matrix is an n  n matrix E ¼ ½eij , in which the entry eij is assigned as follows  1 if variable mj is under the direct influence of variable mi eij ¼ 0 otherwise

ð1Þ (2) Deduction of reachability matrix: The reachability matrix R ¼ ½rij  is the limit of the Boolean n-multiple product of E + I, where I is a Boolean unity matrix, and ‘‘+” is addition in Boolean sense. The reachability matrix R represents all linkages between system variables. The entry rij ¼ 1 indicates that variable mj is under the direct or indirect influence of variable mi , or it is said that variable mj is reachable by variable mi if rij ¼ 1. (3) Construction of system levels: The ‘entry level’ of a system is a set of variables that cannot be reached by any variables in other sets. All system levels could be identified one by one using a recursive algorithm that exclude the current ‘‘entry level” from consideration in the next round after it is identified. The algorithm listed in Fig. 2 is used to rearrange variables in the reachability matrix R in order of the level number. The rearranged reachability matrix R provides a basic guideline when establishing the system structure.

4.2. Analytic Hierarchy Process (AHP) AHP takes a series of pairwise comparisons of variables within a single cluster to determine their relative importance weight judged on a specific criterion in a higher level of the system. Suppose a

cluster of m variables includes, denoted as m1 ; m2 ; . . . mm ; when judged on a certain criterion ck , the pairwise comparison matrix A of the cluster takes the form of

A ¼ ½aij mm ;

ð2Þ

where aij represents the relative importance of mi over mj judged on the criterion ck . Specifically, aij ranges from 1, 2,. . . to 9 as the relative importance of mi increases. On the contrary, if mi is less important than mj ; aij ranges from 1, 1/2,. . . to 1/9 as the importance of mi declines. The relative importance weight of mvariables is arranged in a column vector w ¼ ½w1 ; w2 ; . . . ; wm T , which is the corresponding eigenvector of the largest eigenvalue kmax in the following eigenvalue problem Aw ¼ kw. In practice, a pairwise comparison matrix A is rarely perfectly consistent. Consistency has to be checked by examining the consistent ratio CR, which is given by CR = CI/RI, where consistent index CI ¼ ðkmax  mÞ=ðm  1Þ is the deviation of eigenvalue and random index RI is the average CI over a large number of reciprocal matrices with random entries. If CR = 0 then the pairwise comparison matrix is perfectly consistent. An importance weight is acceptable if CR < 0:1; otherwise, it should be rejected. 4.3. Analytic Network Process (ANP) The interaction among variables requires use of ANP in determining the relative importance of variables. Preliminary relative importance weight of variables is calculated in the same way as in AHP, and then the supermatrix S based on the preliminary importance weight is raised to a limiting power to find the final stable importance weight of variables. Major procedures are outlined as follows. If a network system comprises N clusters, C 1 ; C 2 ; . . . C N , and the kth cluster C k consists of nk variables, mk;1 ; mk;2 ; . . . ; mk;nk , then the interaction between clusters may be given by the supermatrix S that takes the form of

S ¼ ½W ij NN ;

where W ij ¼ ½wjlik ni ni ;

ð3Þ

where W ij is a ni  nj sub-matrix representing the relative importance of the variables in C i on the criteria C j . Therefore, wjlik represents the relative importance weight of mi;k in C i on the criterion mj;l in C j . Each column vector in W ij refers to the eigenvectors of the pairwise comparison matrix through the AHP procedure. If there is no direct interaction between two clusters, the corresponding sub-matrix is zero. After the normalization of every column vector, the supermatrix S reflects the preliminary importance weight of all variables. The ultimate importance weight of all variables is given by its limit S L ¼ lim S n . n!1

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4.4. Survey sampling, data collection and analysis

Meanwhile, they were also asked to indicate which distributor had developed the optimal partnership with their companies. The consistency between the choices made by the managers and those identified through the proposed procedure were examined.

4.4.1. Step I: Structure determination This study starts with the collection of potential system variables through literature review to cover the problem domain as widely as possible. After extensive survey, discussions and interviews with three senior managers from Ford Lio Ho Corp. (Ford) and China Motor Corp. (Mitsubishi), 20 most-relevant variables were identified and used in this work. An ISM questionnaire was designed and mailed to 12 senior managers from five major auto manufacturers, including Hotai Corp. (Toyota), China Motor Corp. (Mitsubishi), Yulong Corp. (Nissan), Ford Lio Ho Corp. (Ford) and Sanyang Industry Corp. (Hyundai), to determine the entries in the incidence matrix E. Those managers were carefully selected in that every single one has had considerable practical experience in superintending his/her companies’ relationships with distributors. Twelve responses were received; one was discarded because it filled in identical answers to all questions. The entry eij of the incidence matrix was set to one if at least 10 managers confirmed the direct influence from variable mi onto variable mj . The structure of the system was developed with the help of the rearranged reachability matrix obtained through the ISM procedure.

5. Results and discussions 5.1. Results of Step I: structure determination Twenty most-relevant system variables are: 1-Conflict (CF), 2Commitment (CM), 3-Communication (CA), 4-Culture (CU), 5Dependence (DP), 6-Flexibility (FB), 7-Guanxi (GX), 8-Information exchange (IE), 9-Marketing capability (MC), 10-Management strength (MS), 11-Performance (PR), 12-Potential benefit (PB), 13-Power (PW), 14-Reputation (RP), 15-Resource (RS), 16-Satisfaction (SA), 17-Solidarity (SD), 18-Strategy fit (SF), 19-Termination cost (TC), 20-Trust (TR). Each system variable can also be referred to by the number in front of its name or by its abbreviation in parentheses. The incidence matrix E is listed in Table 1. The entry eij is set to one if the ith variable has a direct influence on the jth variable. For example, e52 ¼ 1 indicates that dependence has a direct influence on commitment. Because discrepancies between experts’ opinions unavoidably exist, a entry is set to 1 if at lease 10 managers agree that the corresponding influence exists. In total, 21 direct relationships were confirmed in the survey. Indirect relationships between variables are not included in the matrix E. Firstly, it is impractical to include questions regarding indirect relationships in one single questionnaire because too many questions would be needed. Secondly, it would be more difficult to reach a consistent conclusion since indirect relationships are less tangible to managers. Through the Broolean operation, the indirect relationships among system variables are represented by the reachability matrix R based on relation transitivity. The stable reachability matrix R, listed in Table 2, is obtained after 5-multiple product of (E+I), i.e., ðE þ IÞn ¼ ðE þ IÞnþ1 for all n > 5. It represents all direct and indirect relationships between system variables. To establish a manufacturer–distributor relationship structure, the reachability matrix R is rearranged through the ISM procedure. A five-layer relationship structure was identified and listed in Table 3. The number in the bottom line indicates the layer number of the corresponding variable. The layer structure is based on the

4.4.2. Step II: Importance weight calculation After the establishment of system structure, a pairwise-comparison questionnaire was mailed to the same senior managers to determine the importance weight of each individual variable in the AHP/ANP analysis. Eleven responses were received. Answers to each question were geometrically averaged before calculating the importance weight. Consistency ratios (CR) were checked for each pairwise comparison. If CR > 0:1, the comparison was repeated again. AHP analysis was used to determine the preliminary importance weight. The ultimate importance weight was determined by raising the power of the supermatrix S. 4.4.3. Step III: Comparison For the purpose of comparison, each manager was asked to evaluate four of his/her current distributors. To avoid unnecessary complications, the managers did not reveal the true name of their distributors. Without prior knowledge of importance weight of all variables, the managers were asked to evaluate four of their distributors, using the Likert’s 5-score scale, on each of the 20 criteria.

Table 1 The incidence matrix E of a manufacturer–distributor relationship. No.-Variable name 1-Conflict 2-Commitment 3-Communication 4-Culture 5-Dependence 6-Flexibility 7-Guanxi 8-Information exchange 9-Marketing capability 10-Management strength 11-Performance 12-Potential benefit 13-Power 14-Reputation 15-Resource 16-Satisfaction 17-Solidarity 18-Strategy fit 19-Termination cost 20-Trust All zero entries are left blank.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1 1

1 1

1 1

1

1 1 1 1 1

1

1 1

1

1 1

1

1

1

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S.-P. Chen, W.-Y. Wu / European Journal of Operational Research 205 (2010) 687–698 Table 2 The converged reachability matrix R of a manufacturer–distributor relationship. No.-Variable name

1

2

3

1-Conflict 2-Commitment 3-Communication 4-Culture 5-Dependence 6-Flexibility 7-Guanxi 8-Information exchange 9-Marketing capability 10-Management strength 11-Performance 12-Potential benefit 13-Power 14-Reputation 15-Resource 16-Satisfaction 17-Solidarity 18-Strategy fit 19-Termination cost 20-Trust

1

1 1 1

1 1 1

1

1

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

1

1 1 1

1 1 1

1 1 1

1 1 1

1

1

1

1

1 1

1 1 1 1 1

1 1

1 1

1

1

1 1 1 1 1

1 1

1 1 1

1 1

1 1

1

1

1

1

1

1 1 1

1

1

1

1 1

1 1

1 1 1

1

1

1

1

1

1

All zero entries are left blank.

Table 3 The converged reachability matrix R after rearrangement.

All zero entries are left blank.

converged reachability matrix R. Therefore, the entry r ij ¼ 1 indicates that the ith variable has a direct or indirect influence on the jth variable. The variable at the ith row is called an ‘‘influencing variable” and the variable at the jth column is called an ‘‘influenced variable”. A variable is said to be located in the rth layer if the highest layer of its ‘‘influencing variable” is not influenced by the variable in the r  1 layer. Variables are independent if they neither influence other variables nor are influenced by others. Some variables in the first layer are independent. The entries in the corresponding position of independent variables are all zero except those on the diagonal. Independent variables include culture, resource, strategy fit, potential benefit, termination cost, reputation, and marketing capability. The development of a manufacturer–distributor partnership is divided into three stages: Stage I: partner selection, Stage II: partnership establishment, and Stage III: partnership maintenance. We propose that all six independent variables should be considered

at Stage I: partner selection. Several variables in the first layer are also considered at the first stage, including marketing capability, management strength, and power. In order to reduce the number of variables in a single stage, we grouped six independent variables into a single cluster named ‘‘threshold.” ‘‘Threshold” represents the overall effects of all six independent variables on a manufacturer–distributor relationship. It is noted that not all variables in the first layer have to belong to the first stage. When a new stage begins, the characteristics of relationships may induce new requirements of variables. A ‘‘starting point” of a channel relationship can enter the relationship schematic in any stage. Nevertheless, no dependent variables can locate on a layer higher than their influencing variables. Dependence is considered as a new ‘‘starting point” in Stage II: partnership establishment. Satisfaction is considered as a new ‘‘starting point” at Stage III: partnership maintenance. All variables in layer 2 and layer 3 are the variables included in Stage II: partnership

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Fig. 3. A schematics of a manufacturer–distributor partnership.

establishment. Therefore, there are four variables at this stage, including conflict, flexibility, guanxi, and dependence. In this stage, conflict is directly influenced by power from the first stage. Flexibility is also directly influenced by marketing capability and management strength from the first stage. Guanxi is directly influenced by conflict in the same stage. Power is located at the second stage but extends its influence over commitment in the next stage. Stage III: partnership maintenance covers variables in the fourth and fifth layers as well as satisfaction in layer 1. Many variables in this stage are highly interactive, including trust, information exchange, commitment, communication, and solidarity. The interactive group, shown in Fig. 3, is bounded by five pairs of variables that have direct influences on each other, including (information exchange/communication), (communication/trust), (trust/ commitment) and (trust/solidarity). Performance is a subordinate variable in this cluster because it is directly influenced by many variables from other stages and within the same stage, including marketing capability and management strength in the first stage, and communication, commitment and flexibility in the third stage. Satisfaction is also another variable worthy of note. It is a starting

Table 4 The original incidence matrix E after rearrangement.

All zero entries are left blank.

point of a relationship in Stage III, and it is reasonable that satisfaction should only have meaning for an existing relationship. There are several connections between Stage II and Stage III, including information exchange, which is directly influenced by guanxi and dependence which has a direct influence on commitment and trust. The converged reachability matrix R shows the direct relationships as well as indirect relationships between row variables and column variables. On the other hand, the rearranged incidence matrix, Table 4, shows the direct relationships between variables only. Fig. 3 is the schematic of a manufacturer–distributor partnership. The schematic is very helpful to construct the network graph of our research model, shown in Fig. 4. The development of a manufacturer–distributor partnership is divided into three stages: Stage I: partner selection, Stage II: partnership establishment, and Stage III: partnership maintenance. There are nine criteria in Stage I, four criteria in Stage II and seven criteria in Stage III. Some direct interactions are allowed between criteria over different stages. As shown in Fig. 4, Stage I does not involve any closed-loop relationship. Though three variables extend their influence over Stage II, no

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Fig. 4. A model of manufacturer–distributor partnership.

reverse influence on any variables in this stage is found. At Stage II, the variables either receive influence from Stage I or exert their influence over the follow-up stage. Highly interactive variables all locate in Stage III. 5.2. Results of Step II: importance weight calculation The pairwise comparison results of the three developing stages are listed in Table 5 with very low consistent ratio CR ¼ 0:00754 < 0:1. Stage I: partner selection has the largest importance weight of 0.6385, followed by Stage III: partnership maintenance (0.2754) and Stage II: partnership establishment (0.0861). It implies that a deliberate selection process is essential for the success of a partnership. A proper partner can save considerable of effort in the follow-up stages. Stage I consists of nine variables. In order to reduce the possibilities of inconsistency in the process of pairwise comparison, six Table 5 Relative importance weight of the three developing stages. Goal

Stage I Stage II Stage III Eigenvector Importance weight

Stage I: Selection 1 8.1393 Stage II: Establishment 0.1229 1 Stage III: Maintenance 0.4735 2.9113

2.1118 0.3435 1

0.91128 0.12294 0.39299

0.6385 0.0861 0.2754

kmax ¼ 3:00875. CR ¼ 0:00754 < 0:1.

independent variables are grouped into one single cluster denoted as ‘‘threshold”, which functions as certain entrance standards that distributors have to meet to be accepted as a partner. Then, instead of nine, pairwise comparisons are made among four variables: threshold, power, marketing capability, and management strength. Table 6 shows their relative importance weight, among which threshold has the largest value of 0.3861. The value accounts for the total contribution of six variables under the cluster ‘‘Threshold”. One single comparison could determine the relative importance weight of these six variables based on ‘‘Threshold”, Table 6. Except termination cost, the others are approximately equal in importance weight, ranging from 0.13 to 0.21. At Stage II, one pairwise comparison is enough to determine all relative importance weight. The results listed in Table 6 show that the most important variable is dependence (0.3890) at this stage. Stage III: Partnership maintenance is a highly interactive stage. One pairwise comparison among its seven variables only generates temporary relative importance weight because of its network structure, Table 7. This is an on-going stage whose long-term effects should be considered. ANP is necessary to deal with network relationships. Six additional sets of pairwise comparisons were conducted to form a supermatrix. Six interdependent variables, including information exchange, communication, commitment, trust, solidarity and performance, were assigned successively a local goal with all its precedent variables as criteria in each comparison. Some variables in the other two stages are included as criteria. For example, guanxi was included as one cri-

Table 6 Relative importance weight of the variables in partner selection (threshold, partnership establishment). Goal

Partner selection IW

Threshold (TH) Management strength (MS) Power (PW) Marketing capability (MC) CR ¼ 0:00252 < 0:1

0.3861 0.2554 0.2131 0.1454

IW: importance weight.

Threshold IW Potential benefit (PB) Reputation (RP) Strategy fit (SF) Resource (RS) Culture (CU) Termination cost (TC) CR ¼ 0:00168 < 0:1

0.2131 0.2097 0.1896 0.1874 0.1269 0.0733

Partnership establishment IW Dependence (DP) Flexibility (FB) Guanxi (GX) Conflict (CF) CR ¼ 0:00453 < 0:1

0.3890 0.2444 0.2418 0.1248

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Table 7 Relative importance weighs w.r.t. global goal and local goals within Stage III. Global goal

Local goal

Partnership maintenance

Commitment

Goal

IW

Performance Trust Communication Solidarity Commitment Satisfaction Information exchange

0.3091 0.1557 0.1365 0.1147 0.1125 0.0798 0.0917

Information exchange

IW

Communication

IW

PW 0.3062 DP 0.2873 CM 0.2154 TR 0.1911 CR = 0.00839

Solidarity

IW

GX 0.4478 CC 0.2926 IE 0.2596 CR = 0.00264

Trust

IW

TR 0.3943 CC 0.3318 IE 0.2739 CR = 0.00426

Performance IW

SD 0.5163 TR 0.4837 C R = 0.00000

IW

DP 0.2981 SD 0.2275 CC 0.1664 CM 0.1551 TR 0.1529 CR = 0.00389

MC 0.2730 PF 0.2216 MS 0.2008 CC 0.1258 SF 0.1078 CM 0.0795 CR = 0.00889

CR = 0.00773. IW: importance weight.

Table 8 Initial supermatrix S and convergent S L after it is raised to the power of 68. Initial supermatrix S

MC MS SA GX PW DP IE CC CM TR SD PF

Convergent supermatrix SL

MC

MS

SA

GX

PW

DP

IE

CC

CM

TR

SD

PF

MC

MS

SA

GX

PW

DP

IE

CC

CM

TR

SD

PF

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0.448 0 0 0.260 0.293 0 0 0 0

0 0 0 0 0 0 0.274 0.332 0 0.394 0 0

0 0 0 0 0.306 0.287 0 0 0.215 0.191 0 0

0 0 0 0 0 0.298 0 0.166 0.155 0.153 0.228 0

0 0 0 0 0 0 0 0 0 0.484 0.516 0

0.273 0.201 0.108 0 0 0 0 0.126 0.071 0 0 0.222

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0.046 0.047 0.142 0.080 0.150 0.084 0.263 0.188 0

0 0 0 0.046 0.047 0.142 0.080 0.150 0.084 0.263 0.188 0

0 0 0 0.046 0.047 0.142 0.080 0.150 0.084 0.263 0.188 0

0 0 0 0.046 0.047 0.142 0.080 0.150 0.084 0.263 0.188 0

0 0 0 0.046 0.047 0.142 0.080 0.150 0.084 0.263 0.188 0

0 0 0 0.046 0.047 0.142 0.080 0.150 0.084 0.263 0.188 0

terion when information exchange is considered as a local goal because it is a precedent variable of information exchange. Table 7 lists the relative importance weight in these six pairwise comparisons. The column vectors of relative importance weight in Table 7 are plugged into the corresponding entries of the initial supermatrix S, Table 8. Since the first six variables in Table 7 have unidirectional influences on the other variables, the corresponding column vectors are set to zero such that they only have the initial effects on the results, and the rest of column vectors are normalized. The converged supermatrix S L is obtained after S is raised to the power of 68, where the maximum deviation of each entry between two consecutive steps is less than 1010 . The converged supermatrix S L is listed in Table 8. Table 8 shows that guanxi, dependence, and power contribute some weight at Stage III, though they are not considered variables of this stage. On the other hand, zero weight of performance and satisfaction in the converged supermatrix only implies that their importance is not included in the interactive process. The relative importance weight of these two variables within the stage should be given directly from the first column of Table 7 in which performance has the largest relative importance weight of 0.3091 and satisfaction has a rather small value of 0.0798. The sum of the importance weight of interrelated variables amounts for the rest, 0.6111, which is distributed over eight other variables according to their weight listed in Table 8. The most important one is trust (0.263). The results show that manufacturers and distributors have to trust one another and express strong solidarity to overcome obstacles and establish a competitive value chain. Table 9 shows the breakdown of the importance weight of all stages and variables. For example, from Tables 5 and 6, the individual relative importance weight of threshold is 0.6835  0.3861 = 0.2465. The process successfully determines the breakdown of the relative importance weight among three stages and 20 variables.

According to Table 9, the foundation of a successful partnership may be mostly determined in partner selection (0.6385). Manufacturers should place emphasis on potential distributors’ marketing capability (0.0928), management strength (0.1630), and power (0.1360). Marketing capability and management strength are the core skills of a company in fulfilling it promises. An appropriate management team could help distributors successfully merchandise the product and bridge the marketing gap between manufacturers and customers. Before a partnership is established, manufacturers should seriously consider their power to dominate over their distributors. However, once having set up the partnership, manufacturers should resort to other characteristics, such as mutual trust, to maintain the partnership. In the third stage, when long-term relationships are the major concerns, manufacturers rely more on the effects of communication, trust, solidarity and dependence than on the effects of power. Six independent variables account for the importance weight of 0.3861 in total, and each has approximately the same weight (0.031–0.051) except termination cost (0.0181). Partnership establishment only has a moderate importance weight of 0.0867. This is a short stage, in which most of its relationships are cross-stage relationships, except conflict. Dependence (0.03349) is the most substantial variable when a manufacturer is establishing a partnership. Complementary characteristics would help members to achieve the mutual goal of a partnership. Interdependence can lay a solid foundation to build trust and commitment between partners in the long run. Investment in each other could be a good way to increase interdependence between partners. Partnership maintenance (0.2754) has an importance weight second only to partner selection. This stage is an on-going process. Information exchanges, communication, trust, commitment and solidarity form a highly interrelated network. These variables all depend on a mutual attitude and take time to develop. Though,

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S.-P. Chen, W.-Y. Wu / European Journal of Operational Research 205 (2010) 687–698 Table 9 Overall relative importance weight of stages and variables.

Goal

Stage

Stage importance

Stage I: Partner selection

0.6385

Individual importance

Variables

Individual importance

Threshold

0.24652

Strategy fit (SF) Termination cost (TC) Reputation (RP14) Potential benefit (PB) Culture (CU) Resource (RS)

0.046741 0.018070 0.051696 0.052534 0.031284 0.046199

Marketing capability (MC) Management strength (MS) Power (PW)

0.092838 0.163073 0.136064

State II: Partnership establishment

0.0861

Dependence (DP) Conflict (CF) Flexibility (FB) Guanxi (GX)

0.033493 0.010745 0.021043 0.020819

Stage III: Partnership maintenance *

0.2754

Satisfaction (SA) Performance (PR) Information exchange (IE) Communication (CC) Solidarity (SD) Trust (TR) Commitment (CM) Guanxi (GX) Power (PW) Dependence (DP)

0.021977 0.085126 0.013464 0.025245 0.031640 0.044262 0.014137 0.007742 0.007910 0.023898

*

Total weight of the last eight variables (IE, CC, SD, TR, CM, GX, PW, DP) accounts for 0.1682, distributed according to relative importance weight in Table 6.

Table 10 Consistency check of the optimal distributors from two different approaches.

Manager #9 takes charge of three major distributors and manager 3 takes charge of two distributors only.

performance weight most (0.0851) at his stage, it is still lower than the total weight (0.1682) of highly interrelated variables. Manufacturers should pay attention to long-term effects. The stability and reliability of the partnership seem more valuable than its performance alone. A highly interrelated group provides a strong mechanism to support the development of a partnership. Communication and information exchange remove barriers that impede mutual understanding. Trust, commitment and solidarity encourage members to invest the necessary resources to maintain the interrelationship, and not to take high-risk action. The development of a long-term partnership necessitates relying on solidarity and trust between partners. The level of trust and solidarity will substantially affects the long-term relationship between channel members.

their companies. The final scores of distributors were determined by the product sum of scores and importance weight over all variables. The results are listed in Table 10. The proposed method identified the same optimal distributors as the managers did in seven out of 10 practical cases. In two other cases, managers #2 and #10 considered the distributors with the second highest scores as the optimal partners. Only manager #1 considered the distributor with the lowest score as the optimal partner. However, in this case, the scores of the four distributors are very close to one other; the differences are minimal. Generally speaking, the proposed procedure provides a reasonable tool to help managers make appropriate decisions in the management of manufacturer–distributor relationships.

5.3. Results of Step III: comparison

6. Conclusions

For the purpose of comparison between the present procedure and conventional procedures in practical applications, a survey was carried out to check if the managers’ evaluations of their distributors through the proposed model were consistent with those they made in practical situations. Without the knowledge of Table 9, the managers were asked to evaluate four of their own distributors, using the Likert’s 5-score scale, on each of 20 criteria. The distributors had to be those with whom they were familiar or had evaluated in the past. Meanwhile, they were also asked to indicate the distributor that had developed the optimal partnership with

This paper presents an application of the integrated ISM/AHP/ ANP method to the evaluation of a manufacturer–distributor partnership with a large number of system variables in the automobile industry. The ISM is able to identify a sophisticated system hierarchy through a series of matrix manipulation, which otherwise, would be rather difficult in a system with a wide variety of system variables. The AHP/ANP enables the determination of relative priorities of independent/interdependent system variables in the conceptual model through a series of pairwise comparisons.

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In this research, the partnership model with a large number of variables (20) was constructed in a hierarchical form. Relative priorities of all variables were quantitatively determined. The valuable information obtained in this study can help managers to understand the nature of a partnership better and to plan appropriate strategies accordingly. These results demonstrate convincingly that the proposed procedure has sufficient capability of analyzing a complicated problem systematically. Although the proposed method was specifically applied to evaluate the automobile manufacturer–distributor partnership, a similar procedure can be easily applied to a partnership in a supply chain of any kind. The method is beneficial to both academics and professionals in this or a related field. References Agarwal, A., Shankar, R., Tiwari, M.K., 2006. Modeling the metrics of lean, agile and leagile supply chain: an ANP-based approach. European Journal of Operational Research 173 (1), 211–225. Anderson, J.C., Narus, J.A., 1990. A model of distributor firm and manufacturer firm working partnerships. Journal of Marketing 54, 42–58. Aragonés-Beltrán, P., Aznar, J., Ferrís-Oñate, J., García-Melón, M., 2008. Valuation of urban industrial land: an Analytic Network Process approach. European Journal of Operational Research 185 (1), 322–339. Bello, D.C., Chelariu, C., Zhang, L., 2003. The antecedents and performance consequences of relationalism in export distribution channels. Journal of Business Research 56, 1–16. Bierly, P.E., Gallagher, S., 2007. Explaining alliance partner selection: fit, trust and strategic expediency. Long Range Planning 40, 134–153. Chen, H., Tseng, C., 2005. The performance of marketing alliances between the tourism industry and credit card issuing banks in Taiwan. Tourism Management 26, 15–24. Crary, M., Nozick, L.K., Whitaker, L.R., 2002. Sizing the US destroyer fleet. European Journal of Operational Research 136 (3), 680–695. Duarte, M., Davies, G., 2003. Testing the conflict-performance assumption in business-to-business relationships. Industrial Marketing Management 32, 91– 99. Ellram, L.M., 1990. The supplier selection decision in strategic partnerships. Journal of Purchasing and Materials Management 26 (4), 8–14. Fahy, J., 2002. A resource-based analysis of sustainable competitive advantage in a global environment. International Business Advantage 7 (1), 38–39. Fan, Y., 2002. Questioning Guanxi: definition, classification and implications. International Business Review 11, 543–561. Fang, S.R., Chiang, S.C., Fang, S.C., 2002. An integrative model for partner relationship – an empirical research of small and middle firms. Journal of Management 19 (4), 615–645 (in Chinese). Feng, C.M., Wu, P.J., Chia, K.C., 2010. A hybrid fuzzy integral decision-making model for locating manufacturing centers in China: a case study. European Journal of Operational Research 200 (1), 63–73. Frazier, G.L., Gill, J.D., Kale, S.H., 1989. Dealer dependence levels and reciprocal actions in a channel of distribution in a developing country. Journal of Marketing 53, 50–69. Geyskens, I., Steenkamp, J.E.M., 2002. Economic and social satisfaction: measurement and relevance to marketing channel relationships. Journal of Retailing 76 (1), 11–32.

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