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Customer query handling in sales interactions

Author

Listed:
  • Sunil Singh

    (University of Nebraska-Lincoln)

  • Detelina Marinova

    (University of Missouri-Columbia)

  • Jagdip Singh

    (Case Western Reserve University)

  • Kenneth R. Evans

    (Lamar University)

Abstract

Using a novel approach with video-recordings of sales interactions, this study focuses on a dynamic analysis of salesperson effectiveness in handling customer queries. We conceptualize salesperson behaviors, namely, resolving, relating, and emoting, as separate elements of customer query handling and empirically identify the distinct verbal and nonverbal cues that salespeople use to display these behaviors during sales interactions. We draw from compensation effects in social cognition theory to propose that customers’ perceptions of a salesperson’s effectiveness are prone to trade-offs between competence (resolving behaviors) and warmth (relating and emoting behaviors). Results, robust to endogeneity corrections, support the proposed tradeoffs such that the effectiveness of salesperson’s resolving behavior is significantly curtailed, even neutralized, by the salesperson’s relating and emoting behaviors. We situate these counterintuitive results within the extant theory and research on sales interactions, and outline implications for practice.

Suggested Citation

  • Sunil Singh & Detelina Marinova & Jagdip Singh & Kenneth R. Evans, 2018. "Customer query handling in sales interactions," Journal of the Academy of Marketing Science, Springer, vol. 46(5), pages 837-856, September.
    • Handle: RePEc:spr:joamsc:v:46:y:2018:i:5:d:10.1007_s11747-017-0569-y
    DOI: 10.1007/s11747-017-0569-y
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    References listed on IDEAS

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    1. Alavi, Sascha & Wieseke, Jan & Guba, Jan H., 2016. "Saving on Discounts through Accurate Sensing – Salespeople's Estimations of Customer Price Importance and Their Effects on Negotiation Success," Journal of Retailing, Elsevier, vol. 92(1), pages 40-55.
    2. Maryse Koehl & Juliet F. Poujol & John F. Tanner Jr, 2016. "The impact of sales contests on customer listening: an empirical study in a telesales context," Post-Print hal-02013750, HAL.
    3. Campbell, Margaret C & Kirmani, Amna, 2000. "Consumers' Use of Persuasion Knowledge: The Effects of Accessibility and Cognitive Capacity on Perceptions of an Influence Agent," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 27(1), pages 69-83, June.
    4. Zaichkowsky, Judith Lynne, 1985. "Measuring the Involvement Construct," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(3), pages 341-352, December.
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    Cited by:

    1. H. R., Ganesha & Aithal, Sreeramana, 2020. "Sales Personnel Attrition Control and Retention – An Integrated Framework for Lifestyle Retailers in India (RSPR-LS)," MPRA Paper 102867, University Library of Munich, Germany.
    2. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    3. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    4. Tobias Scholz & Jörn Redler & Sven Pagel, 2021. "Re-designing adaptive selling strategies: the role of different types of shopping companions," Review of Managerial Science, Springer, vol. 15(5), pages 1243-1280, July.
    5. Michael Ahearne & Yashar Atefi & Son K. Lam & Mohsen Pourmasoudi, 2022. "The future of buyer–seller interactions: a conceptual framework and research agenda," Journal of the Academy of Marketing Science, Springer, vol. 50(1), pages 22-45, January.

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