The advent of the internet, digitization and e-commerce has changed the definition of business territory, re-invented direct selling, eradicated middle men and brought the customers and sellers closer. These changes in the business scenario must have had an impact on the intensity and nature of channel conflict which needs to be inspected to structure better channel relationship strategies in the changing context. This paper aims to attempt a systematic investigation into the determinants of channel conflict in today’s context and proposes a composite model by reconciling the research so far in the domain of channel relationships.
An exhaustive search was carried for extant research finding in the channels resulting in the identification of 284 research papers beyond the meta-analysis by Geyskens et al. (1999). The next step was to manually scan through each of these papers to identify the studies which involved quantitative analysis including measures of association such as correlations related to conflict and the determinants of conflict. This led to the finalization of 36 research papers for the meta-analysis.
This study proffers a model that illustrates ranking of major determinants of channel conflict. The results of the study suggest that determinants can be categorized into three major domains: organizational, interpersonal (communication, cooperation, relationship activities and opportunistic behaviour) and environmental factors (environmental volatility, competitive intensity and product or market volatility).
The analysis is based on static data in the sense that the correlations do not reflect supplier-channel member interactions in specific conflict situations. It may be argued that conflicts ultimately occur among firms/businesses run by individuals and individual traits may also impact the formation and resolution of conflict. Further, the quality of the measures capturing the constructs was not investigated in many studies. Final limitation pertains to the measurement of conflict. Conflict may not have been measured in a uniform manner in each of the studies analysed. As this study has evaluated extant research through a meta-analysis, it was not possible to identify the correlations between the determinant variables and the three factors (or constructs).
This study reconciles different research streams in this domain with the visualization of the composite model. It presents a quantitative analysis of the correlations of the determinants of conflict with channel conflict holistically. It creates a base through the composite model to carry forward the academic discussion in this domain holistically. It aims to be a ready reference for understanding the antecedents of conflict along with their significant correlations to enable prioritization of their channel strategies.
This meta-analysis and the suggested model that may be of use to practitioners in terms of prioritizing their activities to reduce channel conflicts through pre-emption. It is hoped that this study enhances the extant understanding of the determinants of channel conflict considerably based on the presented composite model. The results may assist to resolve channel conflicts, create channel synergies, identify optimal channel mix, reduce channel costs, increase channel efficiency and build partnerships in the changing business scenario.
A holistic view of the determinants of conflict would be of enormous use to practitioners and academics alike. Hence, a detailed study is required to enlist and categorize the determinants causing conflict in channels so that an attempt can be made to resolve channel conflict for better performance of the firms. This meta-analysis study is an attempt to fill this major gap in research in this domain to quantitatively analyse the major determinants of channel conflict on the basis of analysis of research work over the past 15 years.
Sharma, D. and Parida, B. (2018), "Determinants of conflict in channel relationships: a meta-analytic review", Journal of Business & Industrial Marketing, Vol. 33 No. 7, pp. 911-930. https://doi.org/10.1108/JBIM-08-2016-0195Download as .RIS
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Researchers contend that conflict is inevitable and endemic in nearly all channel relationships. As conflict is a deterrent for smooth functioning and performance of a channel, the importance of understanding the determinants of channel conflict gains significance. In response to the rising significance of understanding conflict, researchers have examined many possible determinants of conflict in distribution channel relationships over the past few decades (Gaski, 1984; Geyskens et al., 1999; Rutherford et al., 2012). Additionally, Geyskens et al. (1999) conducted a meta-analysis in the domain of satisfaction in marketing channel relationships. However, owing ubiquity of internet and internet channel in the past two decades, there might be several changes in the nature and scope of channel conflict and consequent conflict arising from it. In the past two decades, there are major changes in the relationship norms between channels and firms, overall financial performance of firms and relationship-specific activities undertaken by firms due to increasing ubiquity of internet channel (Harris and Cohen, 2003; Palmatier et al., 2016; Paul, 1996; Sharma and Gassenheimer, 2009; Sharma et al., 2010; Webb and Hogan, 2002).
More specifically, the emergence of electronic commerce has forced many manufacturers to adopt multiple channels to lower distribution cost and to reach unexplored markets (Chiang et al., 2003; Rangaswamy and Van Bruggen, 2005; Palmatier et al., 2016; Sharma and Gassenheimer, 2009; Sharma et al., 2010). Multi-channel approach resulted in re-demarcation of territories leading to more friction between channels (Lee et al., 2003; Sharma and Gassenheimer, 2009; Sharma et al., 2010). Additionally, intensified competition between internet channel, distributers and wholesalers has given rise to more conflict amongst them (Bannon, 2000; Cunningham, 2013; Sharma and Gassenheimer, 2009; Sharma et al., 2010; Gassenheimer et al., 1995).
Hence, in concurrence with past research, we contend that there are several challenges in managing multi-channel approach such as understanding priorities for channel members, resolving channel conflicts, creating channel synergies and identifying optimal channel mix (Achrol and Etzel, 2003; Rosenbloom, 1973). More recently, the complexity of channel conflict has increased owing to firm’s concomitant desire to reduce costs, improve efficiency and offer greater presence posing potential constraint in building channel firm relationship (Cunningham, 2013).
Finally, the micro and macro environment (Srivastava Dabas et al., 2012) in which the channel partners and the companies operate also seems to have evolved with the advent of digitization, social media and other management and monitoring tools. The ability to monitor every transaction has improved; the speed with which the products are delivered to customers has multiplied and the ability to monitor the transparency of processes and people involved is enhanced. The extent of manual intervention in processes undertaken by companies and channel partners is reducing with the help of advanced enterprise resource planning systems available and superior technology like RFID. At the same time, the competitive environment seems to have intensified because of diminishing information asymmetry because of access to internet, other transaction support systems and also company owned e-commerce channels along with other concurrent channel partners.
Over the past 15 years, the proliferation of internet, changing relationships between agent and principle, changing nature of relationships from bounded contracts to more on pay as work systems and the changing nature of business relationship has changed the nature and intensity of channel conflict costing billions of dollars (Lamb et al., 2011, p. 340) to businesses. For example, Kodak has suffered around $500m a year in sales due to channel conflict with Walgreens (Bell et al., 2002). Similarly, there has been breaching of contracts between sellers and retailers as a result of channel conflict, for instance, Amazon.com and Toys “R” Us fought cases against each other in the court until 2006 on violation of agreements (Mangalingan, 2006). Also, the number of broken channel relationships is on the rise including breakdown of channel relationship even in large MNCs. For example, Tata Motors and Cargo motors relationship breakdown (Times of India, 2016), Tractors India and Caterpillar relationship issues were all triggered by possible channel conflict. Despite the new disruption in channel structures and rising magnitude of channel conflict, Krafft et al. (2015) contend that there is a declining in empirical research on channels conflict.
Plausibly, there is a need for a re-investigation of channels conflict with a holistic view of the determinants of conflict. More specifically, a detailed study is required to list and categorize the determinants of conflict into different buckets so that an attempt can be made to resolve them. The current study is a meta-analytic endeavour to quantitatively analyse the major determinants of channel conflict on the basis of published research over the past two decades.
This study attempts to integrate past research and proffer a holistic model with ranking of determinants in three categories of factors leading to channel conflict. Furthermore, this meta-analysis offers an orderly summarization of studies to extract knowledge from the myriad individual researches (Table I, Figure 2). The holistic model proffered in this study may be useful for managers as they are interested in business models which are ready to apply in current context (Stott et al., 2016; Tapio Salminen et al., 2014; Gustavsson and Åge, 2014; Åge, 2014; Anna Cederlund, 2014; Brennan et al., 2014).
The definition of conflict has been provided by Lusch (1976a) in a marketing channel setting. As per this definition, channel conflict refers to a situation in which one channel member perceives another channel member to be engaged in behaviour that prevents one from achieving the desired goals. Similarly, Stern and Gorman (1969) and Etgar (1979) state that channel conflict is present when a channel member perceives the behaviour of another channel member to be impeding for the effective performance of its instrumental behaviour patterns. Conflict has also been defined as the tension between two or more social entities (larger organizations, individuals or groups) which arise from incompatibility of desired or actual responses (Raven and Kruglanski, 1970). Thomas (1976) states conflict as “a frustration-conceptualization-behaviour-outcome sequence”, while Pondy (1967) classifies conflict into five stages:
latent conflict: underlying sources of conflict;
perceived conflict: perception only, when no conditions of latent conflict exist;
felt conflict: tension, anxiety, disaffection in addition to the perception;
manifest conflict: behaviour which blocks another’s goal achievement; and
conflict aftermath: post-conflict conduct, either resolution or suppression.
Manifest conflict is seen as overt actions and underlying conflict involves interpersonal attractions, interests and desires (Raven and Kruglanski, 1970). Overall, it has been suggested that conflict is virtually inevitable in marketing channels. Most agree that this condition is primarily due to the functional interdependence between channel members (Assael, 1968; Lusch, 1976a, 1976b; Mallen, 1963; Pondy, 1967; Reve and Stern, 1979; Stern and El-Ansary, 1977).
Power has been designated as both the independent and dependent variable in case of channel relationship. Bachrach and Baratz (1963) have suggested that power requires the pre-existence of conflict. There is fairly widespread acknowledgment that the causal sequence between power and conflict may proceed in either direction (Stern and Gorman, 1969). Empirical work in the area of marketing channels, generally of cross-sectional nature, has consistently presumed power to be the causative factor with respect to conflict (Shamdasani et al., 2001; Etgar, 1978; Lusch and Brown, 1982).
Among the most central relationships of channel power and conflict theory are the impact of sources of power on conflict and satisfaction developed by Hunt and Nevin (1974) and Lusch (1976a). Hunt and Nevin (1974) found that coercive sources of power reduce satisfaction, while non-coercive sources of power increase satisfaction within the marketing channel. Lusch (1976a) has reported that coercive sources increase conflict and non-coercive sources of power reduce intra-channel conflict.
Further, the absence of conflict in channel relationships is constantly pursued by managers as a mode of increasing loyalty, sales and commitment (Geyskens ET AL., 1999; Morgan and Piercy, 1998). Distribution channel managers have always tried to understand the determinants of conflict inter- and intra-channels primarily due to the adverse effects of channel conflict on channel relationships and ultimately channel performance. As both researchers and practitioners recognize the benefits of conflict avoidance, researchers must continue to focus on diminishing conflict (Reid et al., 2004; Geyskens et al., 1999; Anderson and Weitz, 1992; Anderson and Narus, 1990).
The primary goal in selecting the data sources for any meta-analysis is to secure a representative sample and avoid potential bias (Churchill et al., 1985). In this study, an exhaustive search was carried out on EBSCO and Google scholar for journals by using the following key words: “determinants”, “antecedents”, “conflict”, “distribution channels”, “channel relationships” and “determinants of conflict in channel relationships”. This led to the identification of 284 research papers beyond the meta-analysis by Geyskens et al. (1999).
The next step was to manually scan through each of these papers to identify the studies which involved quantitative analysis including measures of association such as correlations related to conflict and the determinants of conflict. This led to the finalization of 35 research papers for the meta-analysis. The meta-analysis presented in the following sections is based on these 35 research studies (Table AI in Appendix 2). Each of the studies reported measures of association between conflict and one or more predictor variable(s) in the form of a correlation coefficient (i.e. product-moment correlation coefficient).
Size of correlations
The average correlation across the years is 0.3325 (standard deviation = 0.0976). These inter-correlations were not analysed because, by and large, they were not specifically reported in the papers. Further, the correlations support the notion that models of the determinants of conflict in channel relationships must incorporate multiple causes.
Given the large number of variables that have been investigated as possible determinants of conflict in channel relationships, we were required to categorize the reported correlations to make sense out of them. To do this, we initially listed out the determinant variables as they were reported in the papers reviewed. This came to a total of 33 determinant variables. These determinant variables were then grouped together based on their qualitative relationships to arrive at an intermediate set of 13 determinant factors. These 13 determinant factors were then categorized into three factor categories (i.e. organizational, interpersonal and external factors) as visualized in the composite model (Table AII in Appendix 2).
To evaluate the strength of the relationship between conflict and each of the each of the proffered major determinants, we followed the analytic procedures suggested by Hunter et al. (1982). They have suggested that:
The best estimate of the size of the correlation between criterion and predictor is not the simple mean r (i.e. mean correlation) across studies, but a weighted average in which each ftablelation is weighted by the number of persons in that study. Their rationale is that a correlation based on 500 persons contains more information than one based on 50 persons because the estimate based on the larger number of observations has smaller sampling error.
There appears to be some risk, though, in relying exclusively on the weighted average correlation in that a few large-sample studies could dominate the analysis. We therefore also report the un-weighted or simple average correlation in the analysis that follows (Table AIII in Appendix 2).
In this section, the composite model is discussed in terms of the determinants of conflict (Figure A2 in Appendix 1) along with a summary of the absolute values of the correlations of each of the determinant variables with conflict in channel relationships (Table AIV in Appendix 2). Based on the number of determinant variables and the size of the correlations, it appears that of the three factors outlined in the composite model, organizational factors play the most significant role followed by interpersonal factors. Environmental factors play a secondary role in channel conflict. Within organizational factors, the determinants which have higher correlations with conflict and have been studied across multiple relationships are trust followed by power, transactional support and relationship investments respectively in that order. While use of concurrent channels is a major cause for conflict the data is based on six relationships with conflict and hence the robustness of the correlation size (0.34) may be questionable. Hence, we suggest that concurrent channels may be considered as a secondary organizational determinant of conflict at this stage along with overall financial performance of the channel, interdependence and organizational commitment. Among the interpersonal determinants, opportunistic behaviour emerged as the determinant with the highest correlation size followed by information asymmetry, cooperation and relationship-specific activities in that order. External factors were found to have a smaller correlation with conflict than as compared to the other factors. The studies exploring the impact of environmental factors such as environmental volatility, product or market volatility and competitive intensity were also limited during the review period.
The determinants of channel conflict
Trust has been defined as the confidence a supplier has in an exchange or channel partner’s reliability and integrity, it has emerged as the single largest determinant of conflict with an average weighted mean of −0.46 and simple mean of −0.43 (negative sign indicates that greater the trust, lower is the possibility of conflict in a channel relationship). The correlations reported by these studies vary from 0.16 to 0.78 with a range of 0.62.
It would not be incorrect to conclude that a manufacturer desirous of keeping conflict in channel relationships down to manageable levels needs to focus on building mutual trust (i.e. from the channel members towards the supplier and vice-versa) with a long-term perspective (Paswan and Young, 1999).
Power has been concisely defined as “the ability to cause someone to do something he/she would not have done otherwise” (Gaski, 1984). Further:
The power of a channel member is his ability to control the decision variables in the marketing strategy of another member in a given channel at a different level of distribution. For this control to qualify as power, it should be different from the influenced member’s original level of control over his own marketing strategy (El-Ansary and Stern, 1972).
Conflict in a channel has been observed to occur due to power asymmetry between suppliers and channel members as well as among channel members themselves. The dimensions linked to conflict in these studies include power asymmetry, the impact of coercive power and the impact of non-coercive power bases. Together, power asymmetry constitutes the second largest determinant of channel conflict with a weighted mean correlation with conflict of 0.41 from 4,649 samples totally in the 18 studies. The correlations range from 0.04 to 0.93.
This determinant of conflict encapsulates the organizational aspects of cooperative work or joint working including order handling, fairness, financial support, cultural sensitivity and centralization. These have been studied across 4,982 samples in various studies during the review period. The weighted mean correlation of transactional support with conflict is 0.31, ranging from 0.04 to 0.64.
Relationship investments relate to the seller’s investment of effort, time, spending and resources focussed on constructing a stronger relationship. These may be manifested through support, prizes/gifts, resources, investments and loyalty programmes. They have a beneficial influence on channel relationships and tend to reduce the incidence and/or magnitude of conflict. The weighted mean correlation coefficient is 0.30 ranging from 0.09 to 0.61.
Overall financial performance
The influence of the overall financial performance of the channel on conflict was investigated by 11 studies. The correlation between financial performance and conflict was found to be 0.24 (weighted mean) ranging from a very low 0.03 to a high 0.61.
Interdependence or dependence asymmetry
Interdependence in a channel has been defined as a firm’s need to uphold an exchange relationship to achieve the desired goals which is reciprocated by the other firm’s need (Frazier, 1983; Gundlach and Cadotte, 1994). As interdependence is manifested through reciprocal dependencies, high interdependence or an asymmetry in dependence is likely to give rise to conflict. There were 14 studies which examined the role of interdependence in conflict with a weighted mean correlation of 0.22 ranging from a low 0.02 to a high 0.43.
Organizational commitment in the context of channel relationships may be seen as a persistent desire to maintain a prized relationship. This may take the form of affective, behavioural, obligatory or normative commitment. One can easily see that higher organizational commitment would lead to lower conflict and vice versa. Out of the 35 papers reviewed, 12 of them focussed on the positive and negative impact of commitment. The weighted mean correlation was 0.21. The correlations ranged from 0.10 to 0.47.
Concurrent channels imply distribution through multiple channels to the same geographic territory or to the same customer group. They may take the form of vertically integrated (direct) channels or distribution through a third party (indirect channel). Concurrent channels have been studied across their degree of concurrency, extent of differentiation, ownership of orders, compensation duality, direct service and integrated service contracts. While the mean correlation is 0.34, they have been evaluated across 108 firms which appear to be on the lower side. The correlations with conflict range from 0.12 to 0.57.
At the interpersonal level in the context of distribution channels, lack of commitment may be manifest through opportunistic behaviour. Opportunistic behaviour is the tendency of individuals in a relationship to opt for personal short term gains at the cost of a more stable long-term relationship. Such behaviour is likely to cause uncertainty in a relationship and conflict is a natural outcome. The weighted mean correlation was 0.45, and the correlations ranged from 0.19 to 0.64. This is one of the major determinants of interpersonal factors that influence channel conflict.
Information or communication asymmetry
This is one of the largest interpersonal determinants of conflict with a weighted mean correlation of 0.34 and simple correlation mean of 0.36. The correlations range from a low of 0.05 to a high of 0.79.Communication has been measured across the quality, frequency and amount of information shared between exchange partners. It is also manifested through collaborative or bilateral communication, sharing and information exchange. Gaps in each of these granular components of communication result in asymmetries which lead to conflict.
Cooperation has been defined as “coordinated and complementary actions between exchange partners to achieve mutual goals” (Morgan and Hunt, 1994; Anderson and Narus, 1990) which is manifested through coordination and joint actions. In this analysis, cooperation has been used to include the interpersonal aspects of coordination and joint actions. The organizational aspect has been encapsulated by the variable transactional support. A range of transactional variables include order handling, fairness, financial support, cultural sensitivity and centralization. The weighted mean correlation of interpersonal cooperation with conflict is 0.34 ranging from 0.01 to 0.82.
While relationship investments occur at the firm level, relationship-specific activities are carried out at the interpersonal level by individuals to maintain and grow the channel relationship. This is a major interpersonal determinant of channel conflict leading to a mean correlation with conflict of 0.33 and a range from 0.08 to 0.84.
In addition to factors within the distribution channel, some of the studies have also evaluated the impact of external factors including environmental volatility, product/market volatility and competitive intensity on conflict in channel relationships. It was felt that external factors constitute an additional dimension to the determinants of channel conflict and hence are shown as the third factor in the composite model. The weighted mean of the correlations for the external factors was 0.18 ranging from 0.03 to 0.29.
Based on the meta-analysis, 13 major determinants of channel conflict were identified (Tables AII and AV in Appendix 2) by analysing their reported correlations with channel conflict (Tables AIII and AIV in Appendix 2). The major organizational factors identified include trust, power asymmetries and transactional support and relationship investments. Power demonstrates maximum range in terms of correlation indicating clearly the importance of power in channel conflict. The major interpersonal factors identified include information asymmetries, opportunistic behaviour and cooperation. External factors impacting conflict include environmental volatility and competitive intensity. However, external factors have been found not to have been studied adequately by researchers so far.
Extant research has been inadequate in two major areas. First, given that each extant study has focussed on one (and rarely two) determinants, a reader (practitioner or researcher) may feel that conflict occurs due to a limited number of variables. This would be fallacious. Second, each research tries to highlight the importance of the variables considered in their respective studies, which may be at the cost of other significant variables impacting conflict which have not been considered. It is precisely this contradiction emerging from multiple streams of research which is attempted to be reconciled through the meta-analysis and composite model presented in this study (Figure A2 in Appendix 1). The composite model incorporates different research streams to provide readers (practitioners and researchers) a more complete picture of the determinants of channel conflict. The meta-analysis has enabled us to first group the determinants into three factors and then order the determinants on the basis of their correlation sizes with channel conflict.
The composite model presented in this study (Figure A2 in Appendix 1) provides a foundation for exciting opportunities for future research. As this study has evaluated extant research through a meta-analysis, it was not possible to identify the correlations between the determinant variables and the three factors (or constructs). The composite model can be tested out empirically using structural equation modelling or partial least squares-based techniques. The reflective nature of the model would enable such an evaluation. Given the above discussion, the key implications and contributions of the study are as follows:
reconciliation different research streams in this domain with the visualization of the composite model;
a quantitative analysis of the correlations of the determinants of conflict with channel conflict holistically;
creation of a base through the composite model to carry forward the academic discussion in this domain holistically; and
a ready reference for understanding the antecedents of conflict along with their significant correlations to enable prioritization of their channel strategies.
However, only a few studies have examined the comparison between challenges related to physical vs virtual channels (Blut et al., 2015). This paper will help researchers compare the determinants, consequences of channel conflict both in case of physical, and virtual channels based on the holistic model given with ranking of the determinants. It is hoped that this study enhances the extant understanding of the determinants of channel conflict considerably especially from a holistic perspective based on the composite model and the meta-analysis.
The key challenges before the businesses are to develop strategies to incentivise all channel members to achieve the same goal by catering to the same customer base. On the other hand, the reason for conflict between different channel members is to figure out the actual value proposition for the business and the consumers which was hardly a worry prior to e-commerce boom.
The e-commerce players offer platforms for transactions among three different groups of buyers and sellers: consumer to consumer (C2C), business to consumer (B2C) and business to business (B2B) (Lazarus, 2016). These platforms also enable four different flows: product, cash, information and reverse product logistics (Figure A1 in Appendix 1). The customer determined flexibility of time, location and speed puts additional pressure on the channel members and logistics partners. The evolution from traditional retail models to modern trade and now to e-tail models call for greater importance for channel members and logistics partners.
As more firms are contemplating integration of online channels with pre-existing offline channels to achieve efficiency (Steinfield, 2004), this might pose a challenge in terms of embracing all channel members together and have them working for the same goal. Blending the online and offline channels together would require managers to prepare a guideline to avoid any potential conflict, going forward and to make these channels work in the same direction.
The offered visualization (Figure 2) will help managers to execute the decisions to drive channel efficiency by optimizing the inputs in carrying out particular marketing channel decisions. The decision makers in firms have to decide on special effort or inducements that are necessary to achieve channel goals on emergence of e-platforms and Web technologies as channel efficiency is of importance for any manager making decisions for any particular unit of channel.
The control over customers and power to influence customer choice is also distributed across channels and third parties involved as the customer touch points increase. The seamless or harrowing experience at any touch point such as the user interface, payment, customer care service, delivery and reverse pick up decides the traffic to the website and impact customers’ opinion. The instant customer feedback, reviews and word-of-mouth in social media exerts pressure on various stake holders from manufacturing to delivery. The question of ownership over the customers is another rising issue with e-commerce boom which further complicates the channel member relationship. In this scenario, relationship investments, transactional support, cooperation and relationship-specific activities can play a central role to encourage prompt actions by the channel members across channels.
In addition to the complex nature of channel conflict and channel member relationship, the external environment has become extremely volatile. Surge in product innovation, duplicate brands, private level brands and the highest level of customizations offered by different e-commerce platforms add to the product/market and environmental volatility. The e-commerce platforms exercising both marketplace and inventory model, though entice customers with a great deal of options, face setbacks in terms of real-time flow of data and information between vendor and market place systems and high holding cost. So trust on the vendors is very critical to survive the information asymmetry and environmental volatility in the market place. Trust can be established with communication and cooperation between the channels and the manufacturer.
The firms engaging in multiple channels to serve the same customer base have to revise their policies to curb opportunistic behaviour of nay channel member. These firms have to frame strategies to have standard pricing and exclusive product and promotion rites across all dedicated channels on rotational basis to avoid undercutting, overlapping and manage margins. Companies may also consider a cart transfer policy across different channels to fulfil orders and manage revenues. Similarly, linking customers to a channel partners’ site can act as a gesture to encourage trust and cooperation.
Moreover, firms must adopt to explicate well-defined objectives and roles for each channel member and logistic partner. All pervasive firms must chart out the strategic relevance of existing channels in short term and long term to manage both in-house and company external conflict. The company external conflict such as conflict with sales and distribution partners, suppliers and customers can be reduced by clarifying ownership, setting branding and pricing standards, imparting customer education and building technical expertise to manage product, information, cash and reverse logistics flows.
The presented meta-analysis has a few limitations. First, the analysis is centred on static data which means that the correlations do not actually reflect supplier–channel member interactions in specific conflict situations. It may be argued that conflicts ultimately occur among firms/businesses run by individuals and individual traits may also impact the formation and resolution of conflict.
The second limitation is related to the measurement and conceptualization of the variables. Under each major grouping, there is an assortment of variables. The variable called co-operation for example may contain diverse determinants which have been clubbed together. Further, the quality of the measures capturing the constructs was not investigated in many studies.
The third limitation pertains to the measurement of conflict itself. Conflict may not have been measured in a uniform manner in each of the studies analysed. Hence, collating correlations with conflict across studies may not lead to very reliable estimates. This aspect should be borne in mind while using the values of the collated correlations. One way to overcome this problem could be by exploring bootstrapping procedures which however would require sufficiently large number of relationships between any determinant variable and conflict to be evaluated.
Finally, this paper does not examine any moderating variable. The future studies may consider the possibility of including moderating variables in the presented composite model. For example, future research direction could be multi-channel vs single-channel contexts as a moderator variable that could account for heterogeneity in the effect sizes of the other conflict determinants considered in this study.
Summary of major conflict research from 1999 to 2016
|Study||Context||Conceptualization of conflict||Operationalization of conflict||Antecedents/ linkages|
|Krafft et al. (2015)||177 channel research articles with empirical data||Felt conflictPerceived conflict||The degree to which power is distributed to control decision variables||Power dependence in relationships, Mutually incompatible goals, Relational outcomes, Negotiation outcomes|
|Scheer et al. (2015)||211 empirical studies||Manifest conflict||Relation-specific investments||Interdependence|
|del Mar Benavides-Espinosa and Ribeiro-Soriano (2014)||74 international jointventures (IJVs)||A multi-dimensional construct, consisting of at least two dimensions: a) work-related conflicts, and b) relationship conflicts||Scale developed||Control|
|Grewal et al. (2013)||Field datafrom German and Japanese MNCs in the USA||Disharmony||Scale||Relational disharmony, Goal clarity, Conflicting perspectives, Output standards, Process control, Efficacy ofcontrol mechanisms, Goals lacking legitimacy, MNC country of origin, HQ–subsidiary relationships, Task coordination, Subsidiary decision involvement, Output control, Firm dependence, Environmental munificence, Environmental dynamism, Strategic performance, Sales performance, Economic performance|
|Grace et al. (2013)||339 Australianfranchisees||The amount, frequency and intensity ofconflict perceived by the franchisee to exist in their relationshipwith their franchisor||Brown and Day (1981), and King and Grace (2010)||Perceived support and communication openness|
|Kang et al. (2013)||Survey data from 300 franchisees of a leading Korean fast foodfranchise organization||The degree of perceived tension in the relationship||Five-point Likert-type scale||Communication, Exchanges, Investments, GoalIncongruence, Changing policies, Changing environments, or Violation of obligations|
|Antia et al. (2013)||FDDs filed during 1995-2003 by a randomlyselected sample of 75 franchisors offering business formatfranchise, 411 conflicts reported by 61 of the 75 franchisorsin our sample||Ifone partner perceives the other as indulging in acts thatimpede the achievement of its own goals||FDDs filed during 1995-2003||Regulatory environment, The ownership structureof the channel system, Registrationvs relationship law|
|Rutherford et al. (2012)||229 respondents working in consultancy and new business||Overall level ofdisagreement between exchange partners that arises whenone channel member engages in actions designated toharm, ruin, or advance him- or herself at the cost of othermembers||Seven-point Likert-type scale||Economic satisfaction(−), Relational duration(−), Communication frequency alignment (−)|
|Blut et al. (2011)||Survey on 2, 668 franchisees which found that Franchisors should strive for “stability on high levels” before operationalrealities influence the franchisees||Level of conflict perceived by franchisees||Level of conflict between the franchisor andthe franchisee -(Kumar et al., 1995)||Level of coordination between franchisors and Franchisees, Cooperation, DependenceLevels of experience, Distribution conflicts (e.g., distribution of wealth, encroachment)|
|Arndt et al. (2011)||Surveys were administered at new car dealerships in a Mid-Atlantic state, 112 salespeople participated||Opposite of integration||Seven-point Likert scale||Cross-functional integration, Communication quality, Relationship effectiveness, Cross-functional training, Joint reward valenceCohesion|
|Runyan et al. (2010)||467 buying managers in Japan of which 121 were usable response||One channel member perceives another channelmember to be engaged in behaviour that is preventing or impeding goalachievement, State of frustration brought about by arestriction of role performance, An overall level of disagreement between members||Scales adapted from Frazier et al. (1989)||Cultures (Individualistic vs collectivistic)Incompatible goals, values and interests, Frequent use of coercive strategies|
|Gilliland et al. (2010)||Sixteen in-depth field interviews were conducted in a majorUS business centre with marketing managers of firms thatproduce industrially distributed products. Finally 1, 800after accounting for duplications in company listings, firmsthat had recently gone out of business, and managerialtransfers||Attempt to direct reseller behaviours by providing extrafinancial incentives and enforcing agreements, reflectingthe way relative dependence erodes their basis of authority||Scale||Relative dependence, Exchange, GovernanceProcesses, Incentive, Monitoring, Enforcement|
|De Clercq et al. (2009)||Analyses of a sample of 232 Canadian-based firms todemonstrate that at higher levels of social interaction, thepositive relationship between task conflict and innovationis stronger, and so is the negative relationship betweenrelationship conflict and innovation. Furthermore, athigher levels of trust, the positive relationship betweentask conflict and innovation weakens||Conflict – the perceived incompatibilitiesor disagreements among exchange partnersTask conflict pertains to disagreements between functional departmentsabout ideas and opinions pertaining to a particular taskRelationship conflict pertains to personality clashes between people indifferent departments and is characterized by negativefeelings such as tension, annoyance, frustration, and irritationInnovation – the extent to whicha firm’s strategic posture is directed toward the developmentof new products and services or entry in new markets||Task conflict - Dyer and Song (1998), Jehn and Mannix (2001) RelationshipConflict – Dyer and Song (1998), and Jehn and Mannix (2001) Trust – Rempel et al. (1985), and Morgan and Hunt (1994)||Task conflict(+), Relationship conflict(+), Trust(−)|
|McFarland et al. (2008)||23 in-depth field interviews were conducted with dealers and product based wholesalers, service providers from 14 industries in different regions of the USA which found that intermediaries frequently imitate the downstream behaviours of manufacturers/suppliers in their interactions with end customers . Data was collected for 400 triads (1 dealer per triad and approximately 3 customers per triad||Downstream influence strategy as similarity between manufacturer and dealer and between dealer and customer||Compliant, Reflexive and Normative imitation of the downstream in terms of “Information exchange” “Recommendation” “Promises” “Threats” “Ingratiation” “Inspirational appeals”||Environmental uncertainty(+), Similarity(+), Interdependence(+), Dependence Asymmetry(−), Frequency of contact(+), Industry tenure(+)|
|Palmatier et al. (2007)||Four years of longitudinal data (N = 396)||Overall level of disagreement and ill will between exchange partner||Confirmatory measurement models||Commitment, Trust, Relationship-specific investment, Dependency, Transaction cost economics, Relational norms, Power structure among exchange partners, Seller’s opportunistic behaviour, Interdependence, Dependence asymmetry, Communication, Environmental dynamism, Market diversity, Need for negotiated solution|
|Terawatanavong et al. (2007)||Mail survey from a sample of 162Australian buyers||Level of disagreement in the working relationship||The degree to whicha high level of conflict characterizes the relationship, three-item scale by Kumar et al. (1992)||Interdependency among channel members, Interaction|
|Schmitz and Wagner (2007)||236 wholesalers involved in the international distributionof industrial goods||Tension, frustration, or disagreement in the relationship||Mohr et al. (1996)||Product(−), Marketing(+), Order Handling(−), Fairness(−), Financial support(−), Communication(−), Cultural sensitivity(−), Satisfaction(−), Competitive intensity(+), Output control(−)|
|Palmatier et al. (2006)||94 empirical studies||Manifest and perceived conflict||Overall disagreement between exchange partners||Dependency, Frequency of communication|
|Leonidou et al. (2006)||122 producers of industrial goods of average age of 24.3 years and the majority were concentratedin the Greater Athens and Salonica areas||Disagreements, frustration, and tension between the parties of a working relationshipwhich arise from the incompatibility of goals, aims, ideas, and values, and aiming at oneparty deterring the other from gaining the resources or conducting an activity necessaryfor its own advancement||Seven-pointLikert scale||Trust, Understanding, Dependence, Commitment, Communication, Adaptation, Cooperation|
|Chung et al. (2006)||National survey of Japanese 250 retail buyers||Disagreements||In-depth interview and scale||Retailer long-term orientation with a supplier(−), Trust(−), Economic dependence(−), Satisfaction(−)|
|Brown et al. (2006)||Conflict within 433 wholesaler–supplier relationships was studied.These wholesalers primarilyhandled either durable goods (e.g. office equipment, hardware, jewellery) or non-durable goods (e.g. officesupplies, groceries, books and newspapers, tobacco products)||Manifest conflict within the marketingchannel as the level of disagreement between thewholesaler and its supplier||10 salient businessissues (Brown et al., 1991)||Normative contracting(−), Explicit contracting(+), Distributiveand procedural justice (+), Wholesaler satisfaction(−)|
|Jaramillo et al. (2006)||Responses from 138 salespeople who work for a large retailer selling high-end consumer durables at 68 stores in 16 states were used to examine the process through which ethical climate affects organizational variables which discovered that ethical climate results in lower role conflict and role ambiguity and higher satisfaction, which, in turn, leads to lower turnover intention and organizational commitment and organizational commitment is a significant predictor of job performance||(1) the existence of a written code of ethics, (2) the communication of ethical expectations to employees, (3) a commitment from management to ethical values, and (4) perceptions about the enforcement of ethical codesAre employees aware of what is expected of themDo employees get incompatible requests||Ethical climate - Schwepker (2001) seven-item scale Role conflict and role ambiguity-Singh, Verbeke, and Rhoads (1996) three-item scale||Ethical climate(+), Role ambiguity(+), Role conflict(+)|
|Avlonitis and Panagopoulos (2006)||134 managers, 46 managers working for pharmaceutical, 37 for food companies, and 5 1 for companies selling machinery||No clarity of roles||Existing measurement scale||Role ambiguity, Type of selling situation|
|Román and Luis Munuera (2005)||280 financial services salespeople mainlyspecializing in selling high-involvement financial products (e.g. mortgages, life insurance) to final consumers||Dimensions of congruency-in-congruency or compatibility-incompatibility||Multiple-item Likert scale||Role conflict(+), Pay systems|
|Vinhas and Anderson (2005)||29 prominent manufacturers in diverse industries||Not observable||Survey||Conflict of interest, Concurrent channels, Customer hetero/homogeneity, Competitive market, Customers’ behaviour, Purchase in group, Standardised brand offering, Differentiated offers, Ownership clarification, Compensation, Market growth|
|Sahadev and Jayachandran (2004)||217distributors of branded computer hardware products located in the twosouthern states (namely, Kerala and Tamil Nadu) of India. Distributorsbelonging to six different suppliers participated in the survey||Disagreement||Scale by Ganesan (1993)||Supplier expertise(−), Problem solving strategy(−), Behavioural-based coordination strategy(−), Collaborative communication(−), Cooperation(−), Trust(−)|
|Peters and Fletcher (2004)||42 organizational teams (237 respondents)||No consensus and misunderstanding||Scale||(1) the degree of self-disclosure, (2) thedegree and richness of knowledge each partner has of another and, (3) theability to predict and anticipate each other’s reactions and responses Direct communication strategies, Cohesion, Coordination, Openness|
|Bradford et al. (2004)||81 four-person networks.The networks, composed of MBA students, engagedin a mixed-motive exercise developed by Beggs et al. (2000)||Disagreements within networksbased on task and non-task related incompatibilities||Scale developed||Accommodation(−), Confrontation(−), Collaboration(−)|
|Rose and Shoham (2004)||Data for this study were collected from Israeli manufacturersin three industries, chosen for diversity of products andmarkets (food, plastics and high tech). To be included, firmshad to be manufacturers, exporters and use independentchannels of distribution||A situation in which one channel member perceivesanother channel member(s) to be engaged in behaviour that ispreventing or impeding it from achieving its goals||Scale by Jehn (1994)||Organizational context variables (team spirit and inter-organizational connectedness)|
|Duarte and Davies (2004)||Sample is a large marketing channel distributing financial services tothe public in the UK (887 response). Trust was found to mediate the impact of the waypower is used on the agent’s perception of cooperation, satisfaction, andconflict, emphasizing the pivotal role of trust in understanding the behavioural aspects of channel behaviour||Conflict - feeling of hostility, frustration and anger Power - the difference between thefirm’s dependence on its partner and the partner’s dependence on the firm coercive power - principal’s use of threats, legalistic pleas andnegative activities/impositionsNon-coercive power - the agent’s perception of the value of the assistance provided by the principal||Likert scale measurement||Power Asymmetry infavour of Partner(+), Partner’s use of non-coercive power(−), Partner’s use of coercive power(+), Trust(−)|
|Hwan Choi et al. (2004)||210 salespeople||Dysfunctional behaviour||Existing scale||Supervisory trust, Participation, Information controls|
|McFarland (2003)||290 sales people, each working at a different dealership from across the United States for a Fortune 500 company that manufactures and sells heavy farm equipment through an extensive dealer network||Role conflict occurs when there is perceived incompatibility between role expectations and demands; role ambiguity occurs when information is lacking about role expectation||Venkatesh et al. (1995)||Coercive sales tactics, Physical and mental stress|
|Johnson et al. (2003)||Business-to-business setting - 406 decision makers||Arises due tothe different goals of the two parties||Existing scale||Perceived tension, Incompatible goals, Bad negotiation, Trust, Perceived relationship continuity, Relationship, Appraisal of salesperson, Willingness to refer|
|Webb and Hogan Z (2002)||65 channel managers from four organizations||Arises when one party seeks to gain scarce resources at the expense of another||Conflict intensity and frequency scales (Eliashberg and Michie, 1984)||Domain similarity, Overlap of resource requirements, Scarce resources, Misuse of channels, Goal incompatibility|
|Nygaard and Dahlstrom (2002)||Norwegian distribution system of two oil refiners – 218 managers of dual-branded retail oil outlets||Degree of incongruity orincompatibility of expectations associated||Existing scales||Role stress, Role ambiguity, Transaction specific assets, Bargaining efforts, Stress in mergers and acquisitions|
Determinant variables, determinant factors, factor categories
|Determinant variable||Determinant factor||Factor category|
|Power asymmetry||Power asymmetry||Organizational|
|Coercive power||Power asymmetry||Organizational|
|Non-coercive power||Power asymmetry||Organizational|
|Systematic bargaining efforts||Power asymmetry||Organizational|
|Firm’s commitment||Organizational commitment||Organizational|
|Order hANDLING||Transactional support||Organizational|
|Fin support||Transactional support||Organizational|
|Cultural sensitivity||Transactional support||Organizational|
|Current-specific assets||Relationship investments||Organizational|
|Relational norms||Relationship investments||Organizational|
|Concurrent channels (Degree)||Concurrent channels||Organizational|
|Concurrent channels (differentiated offers)||Concurrent channels||Organizational|
|Concurrent channels (order ownership)||Concurrent channels||Organizational|
|Concurrent channels (double compensation)||Concurrent channels||Organizational|
|Concurrent channels (direct service)||Concurrent channels||Organizational|
|Concurrent channels (integrated supply contracts)||Concurrent channels||Organizational|
|Overall financial performance||Financial performance||Organizational|
|Personal communication||Information asymmetry||Interpersonal|
|Information assymetry||Information asymmetry||Interpersonal|
|Communication frequency||Information asymmetry||Interpersonal|
|Perceived role ambiguity||Relation-specific activities||Interpersonal|
|Relation-specific activities||Relation-specific activities||Interpersonal|
|Opportunistic behaviour||Personal commitment||Interpersonal|
|Environmental volatility||External factors||External|
|Product/market volatility||External factors||External|
|Competitive intensity||External factors||External|
Values of r’s (correlations) for determinant variables
|Independent variables||No of r’s||Simple mean||Weighted mean||Variance (T)||Sampling error (SE)||Corrected variance||SE/T||95% confidence interval for r|
Summary of correlations of determinant variables with channel conflict
|Independent variables||Minimum correlation||Maximum correlation||Range||Mean|
Definitions of all determinants of channel conflict
|Trust||The confidence a supplier has in an exchange or channel partner’s reliability and integrity (Morgan and Hunt, 1994, p. 23)|
|Power||The ability to cause someone to do something he/she would not have done otherwise (Gaski, 1984)|
|Transactional support||The organizational aspects of cooperative work or joint working including order handling, fairness, financial support, cultural sensitivity and centralization|
|Relationship investments||The seller’s investment of time, effort, spending and resources focussed on building a stronger relationship with the channel members|
|Overall financial performance||How the company is performing financially|
|Interdependence or dependence asymmetry||A firm’s need to maintain an exchange relationship to achieve desired goals which is reciprocated by the other firm’s need (Frazier, 1983; Gundlach and Cadotte, 1994)|
|Organizational commitment||An enduring desire to maintain a valued relationship, may take the form of affective, behavioural, obligatory or normative commitment|
|The tendency of individuals in a relationship to opt for personal short term gains at the cost of a more stable long term relationship. Usually occurs when distribution happens through multiple channels to the same geographic territory or to the same customer group|
|Information or communication asymmetry||The amount, frequency, and quality of information shared between exchange partners, and are manifested through bilateral or collaborative communication, information exchange and sharing|
|Cooperation||Coordinated and complementary actions between exchange partners to achieve mutual goals (Anderson and Narus, 1990; Morgan and Hunt, 1994) which is manifested through coordination and joint actions|
|Relationship-specific activities||Activities that are carried out at the interpersonal level by individuals to maintain and grow a channel relationship|
|External factors||External factors include environmental volatility, product/market volatility and competitive intensity|
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About the authors
Dr Dheeraj Sharma is the Director of Indian Institute of Management Rohtak, and a Professor at Indian Institute of Management Ahmedabad (on lien). Dr Sharma earned his doctoral degree from Louisiana Tech University, USA. His primary research interests are strategic marketing management, behavioural channel theory, corporate social responsibility, ethics and international business. Dr Sharma’s publications have appeared/accepted for publication in Advances in Marketing. Dr Sharma’s publications have appeared and/or are accepted for publication in Advances in Marketing, Developments in Marketing, Business and Society Review, European Journal of Marketing, International Journal of Emerging Markets, Journal of Consumer Marketing, Journal of Marketing Channels, Journal of Marketing Education, Journal of Personal Selling and Sales Management, Journal of Business Ethics, Journal of Business Research, Journal of Product and Brand Management, Journal of International Business Strategy, Marketing Management Journal among other prominent publications.
Biswajita Parida is an Assistant Professor in the marketing area at School of Business Management, Mumbai, NMIMS University, India.