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Perceived marketing–sales relationship effectiveness: a matter of justice

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Abstract

There is a compelling need to improve the relationship between managers in marketing and sales departments. This paper argues that one critical way of enhancing individual managers’ perceptions of relationship effectiveness between these departments is to view the issue as a matter of justice and suggests that perceived marketing–sales relationship effectiveness is positively influenced by managers’ perceptions of organizational justice. Furthermore, it proposes that interfunctional communication has the potential to enhance the proposed positive effects of justice and hence needs to be considered and effectively managed when looking at marketing–sales relationship effectiveness. Data drawn from a survey of 203 marketing and sales managers in 38 consumer packaged goods companies are used to empirically test these predictions. The authors find that perceived sales–marketing relationship effectiveness is influenced by perceptions of distributive, procedural and interactional justice. Greater interfunctional communication is found to further enhance the positive effects of distributive and procedural justice on perceived relationship effectiveness, but it does not contribute to the already strong positive effects of interactional justice. Furthermore, results reveal important differences in the effects of justice on perceived relationship effectiveness across the marketing and sales departments.

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Notes

  1. Following Cespedes (1995, pp. 31–44), we distinguish between “marketing” and “sales” managers in terms of their organizational responsibilities. Marketing managers assume broad strategic responsibilities for brand health, initiate and lead business development programs, execute and control the marketing plan, develop strong working relationships with ad agencies and train/develop the marketing group. Sales managers establish and maintain direct customer contact, work with orders, service products and/or accounts, work with resellers and solicit, interpret, and relay information from both customers and channel partners.

  2. In this paper, we view “fairness” and “justice” as essentially equivalent notions. We prefer to use the term justice for two primary reasons. First, in our empirical work we use the justice measures proposed by Tax et al. (1998). Second, empirical work in marketing often refers to this underlying phenomenon as “justice” (e.g., Maxham and Netemeyer 2003; Maxham et al. 2008; Smith et al. 1999; Tax et al. 1998).

  3. Note that while interdepartmental relationship effectiveness is an important and managerially relevant group-level outcome, our hypotheses and measures assess the perception of this outcome from the perspective of the individual sales and marketing managers working in the organization, in line with past literature discussed above, which has studied individual employees’ perceptions of interfunctional interactions (e.g., Dawes and Massey 2006; Ruekert and Walker 1987).

  4. The consumer packaged goods industry typically requires both sales and marketing to play important roles in achieving business success. Given the complexities of data collection (multiple responding managers from both sales and marketing departments, across multiple firms) we decided to focus here solely on the CPG industry.

  5. Separate questionnaires were created for the sales and marketing managers. The same measurement items were used in both versions, but appropriate wording changes were made.

  6. All measures employed here are based on 7-point Likert scales.

  7. In the interests of space, individual item loadings for these models are not reported here. However, details are available upon request from the first author.

  8. This means that the average PRE for all managers of a given firm is modeled as a random deviation from the grand mean PRE value (based on information from all 203 responding managers). This deviation reflects firm-specific effects not otherwise incorporated into the model. This approach is commonly employed in HLM models and uses one degree of freedom. An alternative approach would be to include firm-specific fixed effects variables in the model. This requires the use of k-1 dummies (and k-1° of freedom). Given our relatively small sample size, we prefer use of the former approach.

  9. In all models, the independent variables have been grand-mean centered (i.e., they represent deviations from the overall, cross-respondent means), except for MARKETING, which as a dichotomous variable is entered in its original form.

  10. We report these as β coefficients, although they are technically more correctly labeled as γ coefficients, as shown in equations 3b – 3 h. And for equation 3a, the expected value of β00k = γ000.

  11. Recall that the equations are estimated using grand-mean centered values for IJ, DJ and PJ. Thus, for an average firm, PRE is estimated by the constant term alone. The mean value of DJ is 4.40, and its standard deviation is 1.00.

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Acknowledgements

The first and second authors thank the Katz Graduate School of Management at the University of Pittsburgh for its ongoing financial support, and the first and third authors thank the Social Sciences and Humanities Council of Canada for its funding assistance. The first two authors are listed alphabetically and contributed equally. All three authors are also indebted to Chris Carr, Jeff Inman, Sophia Marinova, Joerg Dietz, Tomas Hult, and three anonymous reviewers for their comments on earlier versions of this paper.

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Authors and Affiliations

  1. Terry College of Business, University of Georgia, Athens, GA, 30602, USA

    John Hulland

  2. Carroll School of Management, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA, 02467, USA

    Gergana Y. Nenkov

  3. Ivey Business School, University of Western Ontario, 1151 Richmond Street, London, ON, N6A 3K7, Canada

    Donald W. Barclay

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  1. John Hulland
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  2. Gergana Y. Nenkov
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Correspondence to John Hulland.

Appendices

Appendix A

Table 4 Measurement items by construct
Full size table

Appendix B

Reduced form equation

Equations 1, 2a through 2d, and 3a through 3h can be combined to create the following reduced form equation:

$$ \begin{array}{*{20}{c}} {{\text{PR}}{{\text{E}}_{\text{ijk}}} = {\gamma_{{000}}} + {\gamma_{{010}}}{\text{MARKETING}} + \left( {{\gamma_{{100}}} + {\gamma_{{110}}}{\text{MARKETING}}} \right){\text{D}}{{\text{J}}_{\text{ijk}}} + \left( {{\gamma_{{200}}} + {\gamma_{{210}}}{\text{MARKETING}}} \right){\text{P}}{{\text{J}}_{\text{ijk}}} + \left( {{\gamma_{{300{\text{k}}}}} + {\gamma_{{310}}}*{\text{MARKETING}}} \right){\text{I}}{{\text{J}}_{\text{ijk}}} + {{\text{e}}_{\text{ijk}}} + {{\text{r}}_{{0{\text{jk}}}}} + {{\text{r}}_{\text{1jk}}} + {{\text{r}}_{{2{\text{jk}}}}} + {{\text{r}}_{{3{\text{jk}}}}} + {{\text{u}}_{{00{\text{k}}}}}} \\ { = {\gamma_{{000}}} + {\gamma_{{010}}}*{\text{MARKETING}} + {\gamma_{{100}}}{\text{D}}{{\text{J}}_{\text{ijk}}} + {\gamma_{{110}}}{\text{MARKETING}}*{\text{D}}{{\text{J}}_{\text{ijk}}} + {\gamma_{{200}}}{\text{P}}{{\text{J}}_{\text{ijk}}} + {\gamma_{{210}}}{\text{MARKETING}}*{\text{P}}{{\text{J}}_{\text{ijk}}} + {\gamma_{{300{\text{k}}}}}{\text{I}}{{\text{J}}_{\text{ijk}}} + {\gamma_{{310}}}{\text{MARKETING}}*{\text{I}}{{\text{J}}_{\text{ijk}}} + {{\text{e}}_{\text{ijk}}} + {{\text{r}}_{{0{\text{jk}}}}} + {{\text{r}}_{{1{\text{jk}}}}} + {{\text{r}}_{{2{\text{jk}}}}} + {{\text{r}}_{{3{\text{jk}}}}} + {{\text{u}}_{{00{\text{k}}}}}} \\ \end{array} $$
(B1)

Careful inspection of Eq. (B1) shows that an individual manager’s perception of relationship effectiveness is a function of a constant (i.e., constant to all respondents), the main effects of MARKETING, DJ, PJ, and IJ, three interaction terms (between MARKETING and each justice dimension perception) and a set of error components. This equation varies in form from a traditional OLS regression equation only in that it contains extra variance components (the r and u terms). This means that an individual’s perception (PRE) is influenced by (1) an idiosyncratic personal error component (eijk), (2) potential departmental level error components (r0jk, r1jk, r2jk, r3jk), and (3) a firm-specific error component (u00k). Thus, the effects of DJ, PJ, and IJ on PRE are estimated while taking into account individual differences, departmental differences, and firm-specific differences.

Appendix C

Results from organizational outcomes survey of senior managers

A total of 28 senior managers completed a second survey, one informant per firm. None of these individuals was part of the original survey. This follow-up survey was administered about three months after the main survey. (These data could not be combined with the earlier observations for two main reasons. First, we were able to obtain responses for only 28 of the 38 firms originally surveyed. Second, these measures represent aggregate, firm-level outcomes, and cannot therefore be combined with the individual perceptual measures we use in our HLM models.)

We compared the organizational outcome measures obtained from these senior managers to an aggregated measure of perceived relationship effectiveness (computed as the average across all informants from each company) using bivariate correlations. Despite the small sample size, a number of significant relationships were found. Over a six month period, superior relationship effectiveness was significantly correlated with greater new product success (r = .46, p < .05), market share growth (r = .42, p < .05), sales growth (r = .47, p < .05), increased profits (r = .42, p < .05), greater customer focus (r = .44, p < .05), a reduction in the number of major accounts lost (r = −.33, p < .1) and decreased staff turnover (r = −.51, p < .01). These results provide solid support for our claim that informants’ perceptions of relationship effectiveness are related to meaningful organizational performance outcomes.

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Hulland, J., Nenkov, G.Y. & Barclay, D.W. Perceived marketing–sales relationship effectiveness: a matter of justice. J. of the Acad. Mark. Sci. 40, 450–467 (2012). https://doi.org/10.1007/s11747-011-0257-2

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