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Group influences of selling teams on industrial salespeople’s cross-selling behavior

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Abstract

Cross-selling offers tremendous benefits for both vendors and customers. However, up to 75% of all cross-selling initiatives fail, usually for sales force–related reasons. Yet prior research has largely ignored the role of salespeople in the field of cross-selling. Using a motivation–opportunity–ability (MOA) framework, this research addresses factors that determine a salesperson’s cross-selling performance, including the predominant role of the selling team as a social environment in which individual behavior occurs. A dataset of 231 industrial salespeople working in 55 teams reveals that 37% of overall variation in behavior is caused by differences across teams. The team-specific hypotheses, based on social norms and reputation theory, are tested with a hierarchical linear modeling approach with matched data from three sources. Individual cross-selling motivation has a stronger effect when a selling team has strong cross-selling norms, and in the specific context of cross-selling, selling team reputation can constrain individual behavior that might damage that reputation. Salespeople also develop beliefs about the reasons for their team reputation, including its cross-selling ability, which can reduce an individual salesperson’s reputational concerns and hence reinforce individual cross-selling behavior. These results have significant theoretical and managerial implications.

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Notes

  1. We thank an anonymous reviewer for pointing out that research has distinguished groups and teams conceptually, such as according to the level of task interdependence (e.g., Chan 1998). From this perspective, members of groups are highly independent in their tasks, whereas members of teams are interdependent as they share collective effort and performance (Robbins et al. 2010). In other research, the term work group takes on a broader meaning (e.g., Hackman 1992; Kelly and Barsade 2001; Knippenberg et al. 2004), also embracing groups with high task interdependence among the members. The latter is consistent with a view broadly shared in team research, which defines a team as a task-specific or temporary group (e.g., Cohen and Bailey 1997; Moon and Armstrong 1994; Tyran and Gibson 2008). Following this view, we examine sales teams as a specific type of work group, in which there is at least moderate task interdependence among members. Thus, we use the terms work team and work group interchangeably when referring to sales teams, consistent with prior literature (e.g., Andersen and West 1998; Barrick et al. 1998; Chan 1998; Fiorelli 1988; Sundstrom et al. 1990).

  2. The Herfindahl index is a common measure of concentration in economic literature (Tirole 1989). It also has proved useful in marketing for measuring competitive intensity (e.g., Luo et al. 2010; McAlister et al. 2007; Putsis and Bayus 2001), the structure of retailers’ brand portfolios (e.g., Ailawadi et al. 2006), consumers’ spending across stores (e.g., Goldman 1978), consumers’ product alternative choices (e.g., Nowlis et al. 2010), and firm revenue as either concentrated or spread over the customer portfolio (e.g., Yli-Renko and Janakiraman 2008). To the best of our knowledge, this study is the first to use the Herfindahl index to measure sales concentration across products in the context of personal selling.

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Acknowledgement

I thank Christian Belz and Gary L. Lilien for insightful discussions, and Hans Baumgartner, Shankar Ganesan, Frank Germann, Simon de Jong, Jan Landwehr, and Daniel Wentzel for many helpful comments on previous drafts of this paper. Also, I gratefully acknowledge the constructive guidance of the four anonymous JAMS reviewers.

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  1. Institute of Marketing, University of St. Gallen, Dufourstr. 40a, 9000, St. Gallen, Switzerland

    Christian Schmitz

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Appendix

Appendix

Hierarchical models

Model 1

$$ \matrix{ {{\text{L1}}:{\text{ PP}}{{\text{A}}_{\text{ij}}} = {\beta_{0{\text{j}}}} + { }{{\text{r}}_{\text{ij}}}} \hfill \\ {{\text{L2}}:{\beta_{0{\text{j}}}} = {\gamma_{00}} + { }{{\text{u}}_{0{\text{j}}}}} \hfill \\ }<!end array> $$

Model 2

$$ \matrix{ {{\text{L1}}:{\text{PP}}{{\text{A}}_{\text{ij}}} = {\beta_{0{\text{j}}}} + {\beta_{\text{1j}}}\left( {{\text{MO}}{{\text{T}}_{\text{ij}}}} \right) + {\beta_{\text{2j}}}\left( {{\text{CD}}{{\text{O}}_{\text{ij}}}} \right) + {\beta_{\text{3j}}}\left( {{\text{RE}}{{\text{S}}_{\text{ij}}}} \right) + {{\text{r}}_{\text{ij}}}} \hfill \\ {{\text{L2}}:{\beta_{0{\text{j}}}} = {\gamma_{00}} + {{\text{u}}_{0{\text{j}}}}} \hfill \\ {{\text{L2}}:{\beta_{\text{1j}}} = {\gamma_{{1}0}}} \hfill \\ {{\text{L2}}:{\beta_{\text{2j}}} = {\gamma_{{2}0}}} \hfill \\ {{\text{L2}}:{\beta_{\text{3j}}} = {\gamma_{{3}0}}} \hfill \\ }<!end array> $$

Model 3

$$ \matrix{ {{\text{L1}}:{\text{PP}}{{\text{A}}_{\text{ij}}} = {\beta_{0{\text{j}}}} + {\beta_{\text{1j}}}\left( {{\text{MO}}{{\text{T}}_{\text{ij}}}} \right) + {\beta_{\text{2j}}}\left( {{\text{CD}}{{\text{O}}_{\text{ij}}}} \right) + {\beta_{\text{3j}}}\left( {{\text{RE}}{{\text{S}}_{\text{ij}}}} \right) + {{\text{r}}_{\text{ij}}}} \hfill \\ {{\text{L2}}:{\beta_{0{\text{j}}}} = {\gamma_{00}} + {\gamma_{0{1}}}\left( {{\text{TNOR}}{{\text{M}}_{\text{j}}}} \right) + {\gamma_{0{2}}}\left( {{\text{TABI}}{{\text{L}}_{\text{j}}}} \right) + {\gamma_{0{3}}}\left( {{\text{TRE}}{{\text{P}}_{\text{j}}}} \right) + {{\text{u}}_{0{\text{j}}}}} \hfill \\ {{\text{L2}}:{\beta_{\text{1j}}} = {\gamma_{10}}} \hfill \\ {{\text{L2}}:{\beta_{\text{2j}}} = {\gamma_{{2}0}}} \hfill \\ {{\text{L2}}:{\beta_{\text{3j}}} = {\gamma_{{3}0}}} \hfill \\ }<!end array> $$

Model 4

$$ \matrix{ {{\text{L1}}:{\text{PP}}{{\text{A}}_{\text{ij}}} = {\beta_{0{\text{j}}}} + {\beta_{\text{1j}}}\left( {{\text{MO}}{{\text{T}}_{\text{ij}}}} \right) + {\beta_{\text{2j}}}\left( {{\text{CD}}{{\text{O}}_{\text{ij}}}} \right) + {\beta_{\text{3j}}}\left( {{\text{RE}}{{\text{S}}_{\text{ij}}}} \right) + {{\text{r}}_{\text{ij}}}} \hfill \\ {{\text{L2}}:{\beta_{0{\text{j}}}} = {\gamma_{00}} + {\gamma_{0{1}}}\left( {{\text{TNOR}}{{\text{M}}_{\text{j}}}} \right) + {\gamma_{0{2}}}\left( {{\text{TABI}}{{\text{L}}_{\text{j}}}} \right) + {\gamma_{0{3}}}\left( {{\text{TRE}}{{\text{P}}_{\text{j}}}} \right) + {\gamma_{0{4}}}\left( {{\text{TABI}}{{\text{L}}_{\text{j}}} \times {\text{ TRE}}{{\text{P}}_{\text{j}}}} \right) + {{\text{u}}_{0{\text{j}}}}} \hfill \\ {{\text{L2}}:{\beta_{\text{1j}}} = {\gamma_{{1}0}} + {\gamma_{{11}}}\left( {{\text{TNOR}}{{\text{M}}_{\text{j}}}} \right) + {\gamma_{{12}}}\left( {{\text{TABI}}{{\text{L}}_{\text{j}}}} \right) + {\gamma_{{13}}}\left( {{\text{TRE}}{{\text{P}}_{\text{j}}}} \right) + {\gamma_{{14}}}\left( {{\text{TABI}}{{\text{L}}_{\text{j}}} \times {\text{ TRE}}{{\text{P}}_{\text{j}}}} \right)} \hfill \\ {{\text{L2}}:{\beta_{\text{2j}}} = {\gamma_{{2}0}}} \hfill \\ {{\text{L2}}:{\beta_{\text{3j}}} = {\gamma_{{3}0}}} \hfill \\ }<!end array> $$

Regression model

$$ {\text{PE}}{{\text{R}}_{\text{i}}} = {\beta_0} + {\beta_1}\left( {{\text{PP}}{{\text{A}}_{\text{i}}}} \right) + {\beta_2}\left( {{\text{CD}}{{\text{O}}_{\text{i}}}} \right) + {\beta_3}\left( {{\text{RE}}{{\text{S}}_{\text{i}}}} \right) + {{\text{r}}_{\text{i}}}, $$

where salespeople i = 1−n, PER = cross-selling performance, PPA = product portfolio adoption, CDO = cross-divisional orientation, and RES = resource availability.

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Schmitz, C. Group influences of selling teams on industrial salespeople’s cross-selling behavior. J. of the Acad. Mark. Sci. 41, 55–72 (2013). https://doi.org/10.1007/s11747-012-0304-7

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