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Salesperson ambidexterity in customer engagement: do customer base characteristics matter?

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Abstract

Drawing from the interactional psychology of personality and multitasking paradigm, we examine the contingencies of salesperson orientation ambidexterity in the “exploration” of new customers (i.e., hunting) and the “exploitation” of existing customers (i.e., farming) to achieve sales growth and make time allocation decisions. The results from a field study and an experiment indicate that the impact of salesperson orientation ambidexterity is contingent on a salesperson’s customer base characteristics. First, a salesperson’s orientation ambidexterity in both hunting and farming leads to significantly higher (lower) sales growth when his or her existing customer base is large (small). Second, high levels of customer base newness in a salesperson’s customer portfolio weaken the relationship between hunting time allocation at time t – 1 and hunting time allocation at time t, suggesting that salespeople are not subject to a success trap in hunting. However, salespeople are subject to a success trap in farming. These findings shed new light on how a salesperson’s customer portfolio influences salesperson behaviors and performance, with implications for how to better manage ambidextrous behaviors in customer engagement.

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Notes

  1. We tested this assumption in a separate experiment and found that participants in the ambidextrous condition reported significantly higher levels of psychological tension than those in the farming condition but similar levels of tension to those in the hunting condition. Given the unique nature of hunting versus farming, this result corroborates the findings in these studies. Further details on this experiment are available from the authors on request.

  2. Visually, a crossover interaction effect is reflected by a positive (negative) relationship between hunting orientation and sales growth among salespeople who are low (high) on farming orientation (see Podsakoff et al. 1995).

  3. Other operationalizations of ambidexterity using difference scores, absolute difference scores, and ratios suffer from several limitations of the use of difference scores (Peter et al. 1993) and ratios (Certo et al. 2018).

  4. We did not use promotion focus as an instrumental variable for hunting orientation because it is directly related to sales performance (the dependent variable) and violates the exclusion restriction of the instrumental variable.

  5. We also refrained from using polynomial regression, an approach that allows for differentiating between the effect of being high on both orientations and that of being low on both orientations. Conceptually, both these combinations can be labeled as ambidexterity, though the former is more likely to create more psychological and resource tension. Empirically, however, such an approach is most appropriate and meaningful when the two variables are measured with the same set of items (Edwards and Parry 1993), which is not applicable in the majority of research on ambidexterity or in our data.

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Author information

Author notes

  1. Son K. Lam, Thomas E. DeCarlo and Ashish Sharma contributed equally to this work. Stefan Wuyts served as Area Editor for this article.

Authors and Affiliations

  1. Terry College of Business, University of Georgia, C328 Benson Hall, Athens, GA, 30602-6258, USA

    Son K. Lam

  2. Collat School of Business, University of Alabama – Birmingham, 250 CSB, 710 13th Street South, Birmingham, AL, 35294-4460, USA

    Thomas E. DeCarlo

  3. Belk College of Business, University of North Carolina – Charlotte, Friday 250B, 9201 University City Blvd., Charlotte, NC, 28223, USA

    Ashish Sharma

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Electronic supplementary material

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Appendices

Appendix 1: Key measurement scales

Constructs and measures

Standardized factor loadings

 

Study 1

Study 2

Hunting orientation (7-point, 1 = does not describe me at all, 7 = describes me very well, Study 1: AVE = .68, CR = .90; Study 2: AVE = .78, CR = .93)

  

[1] To “hunt” for a new sales opportunity is the most enjoyable part of the job.

.86

.85

[2] I am at my best when I engage a new prospect that I have never met before.

.87

.88

[3] I prefer to spend the majority of my day prospecting and closing new accounts.

.84

.91

[4] The most enjoyable part of the job is selling to new accounts.

.88

.89

Farming orientation (7-point, 1 = does not describe me at all, 7 = describes me very well, Study 1: AVE = .53, CR = .82; Study 2: AVE = .65, CR = .88)

  

[1] Spending time working with current customers is the most enjoyable part of the job.

.69

.75

[2] My best attributes are my customer relations skills where I work for the best interests of my current customers.

.66

.82

[3] The most gratifying is working with an established customer.

.67

.83

[4] Of all my responsibilities, I most enjoy using my skills to maintain and grow existing accounts.

.76

.82

Sales growth (company record)a: average sales growth in three consecutive months after survey (Study 1)

  

Selling time allocation (excluding administrative tasks, Study 2)

• Hunting time allocation: % of time spent on hunting for new customers (including prospecting new customers, generating sales proposals for new customers, closing new customers)

• Farming time allocation: % of time spent on farming existing customers (including generating proposals for selling to existing customers, cross-selling, up-selling to existing customers)

  

Customer base size (company record) a: average number of active customers in the previous quarter (Study 1)

  

Customer base newness a

Percentage of first-time customers accounting for salesperson sales revenues in the previous quarter (manipulated, Study 2)

  

Job satisfaction (7-point Likert, 1 = strongly disagree, 7 = strongly agree. AVE = .71; CR = .88; adapted from Hackman and Oldham 1975)

  

[1] Generally speaking, I am very satisfied with this job.

[2] I am generally satisfied with the work I do in this job.

[3] I frequently thinking about quitting this job. (reverse coded)

.91

.80

.82

 
  1. All factor loadings are significant at p < .01. aSingle item

Appendix 2: Study 1: Additional analyses and robustness check

Selection model, first-stage results

Variables

DV = Inclusion

Intercept

.54*** (.06)

Salesperson tenure with firm

.26** (.07)

Salesperson job satisfaction

.08 (.06)

Business unit size

.02 (.06)

Business unit growth at time (t – 1)

.05 (.06)

Salesperson performance

−.13** (.06)

Pseudo-R2

.032

χ2(5)

19.99***

N

504

  1. *p < .10, **p < .05, ***p < .01. Note: Standard errors are in parentheses

Quadratic effects

We added the quadratic effects of salesperson hunting orientation and farming orientation to control for the impacts of these higher-order terms on sales growth. Neither term was significant (hunting orientation squared: β = .055, robust SE = 2.89, ns; farming orientation squared: β = 2.98, robust SE = 3.67, ns). This result is consistent with the specification error test (RESET test; Ramsey 1969). Finally, one salesperson appeared to be an outlier. When we removed this potential outlier and reestimated the model, we found that the results remained supportive of our focal hypothesis.

Additional robustness checks

We examined robustness of our results by using other control variables. We controlled for respondents’ gender but do not find any significant effect (β = −0.56, SE = 16.02); thus, the hypothesis about the three-way interaction is still confirmed. We also included control variables for the U.S. states in our model. We included all the states that are significant at 10% level. However, the three-way interaction is robust and significant (p < .05). In addition, we examined whether the result can be explained by salesperson self-efficacy in sales. We measured salesperson self-efficacy with three items, adapted from Sujan et al. (1994). We found that salesperson self-efficacy is not predictive of sales growth (β = 1.49, SE = 5.26, ns). That is, while self-efficacy can drive sales, salespeople rely on strategic allocation of resources over time to achieve sales growth. Finally, salesperson customer orientation was not significantly related to sales growth (β = .05, p > .31) and was not included as a control variable in the final model estimation.

Accounting for measurement errors using Mplus

As a robustness test, we also estimated the full model with interactions using Mplus 8 (Muthén and Muthén 2017). In the model, hunting orientation and farming orientation are specified as latent constructs. The latent interaction is estimated using numerical integration, and is robust against moderate violation of distribution assumptions (Klein and Moosbrugger 2000). The results from this analysis also confirm the hypothesis about the three-way interaction (H2a). The results are available in Web Appendix B.

Social desirability

We followed Baumgartner and Steenkamp’s (2001) approach to create response-style indexes to check whether social desirability influences the two focal constructs in the same way. We found no evidence of this bias. The results are available on request.

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Lam, S.K., DeCarlo, T.E. & Sharma, A. Salesperson ambidexterity in customer engagement: do customer base characteristics matter?. J. of the Acad. Mark. Sci. 47, 659–680 (2019). https://doi.org/10.1007/s11747-019-00650-0

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  • DOI: https://doi.org/10.1007/s11747-019-00650-0

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