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Sales Force Behavior, Pricing Information, and Pricing Decisions

Author

Listed:
  • Wedad Elmaghraby

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • Wolfgang Jank

    (College of Business, University of South Florida, Tampa, Florida 33620)

  • Shu Zhang

    (Alipay.com, Hangzhou, Zhejiang Province 310000, China)

  • Itir Z. Karaesmen

    (Kogod School of Business, American University, Washington, DC 20016)

Abstract

This paper focuses on salespeople behavior in business-to-business transactions. The paper investigates how salespeople use the information provided to them to set prices; of particular interest is how salespeople use price recommendations from a decision support tool. The investigation builds reduced-form models and tests them on a data set obtained by a grocery products distributor. The analysis shows that salespeople’s decisions are explained well by a two-stage decision model whereby salespeople make an initial decision on whether or not to change the price (a binary decision) and then decide on the magnitude of change (a continuous response). We find that salespeople in our data set do not blindly adopt the recommended price change generated by the pricing tool. Rather, our two-stage model allows for us to uncover a nuanced association between the recommended price and the actual price change by identifying customer-specific and salesperson-specific market factors that moderate the influence of price recommendations.

Suggested Citation

  • Wedad Elmaghraby & Wolfgang Jank & Shu Zhang & Itir Z. Karaesmen, 2015. "Sales Force Behavior, Pricing Information, and Pricing Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 495-510, October.
    • Handle: RePEc:inm:ormsom:v:17:y:2015:i:4:p:495-510
    DOI: 10.1287/msom.2015.0537
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    References listed on IDEAS

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    2. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Eric Bergerson & Megan Kurka & Ludek Kopacek, 2020. "Learning Demand Curves in B2B Pricing: A New Framework and Case Study," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1287-1306, May.
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    5. 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.
    6. Sun, Libo & Jiao, Xiaoting & Guo, Xiaolong & Yu, Yugang, 2022. "Pricing policies in dual distribution channels: The reference effect of official prices," European Journal of Operational Research, Elsevier, vol. 296(1), pages 146-157.
    7. Huina Gao & Michael O. Ball & Itir Z. Karaesmen, 2016. "Distribution-free methods for multi-period, single-leg booking control," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(6), pages 425-453, December.
    8. Khosrowabadi, Naghmeh & Hoberg, Kai & Imdahl, Christina, 2022. "Evaluating human behaviour in response to AI recommendations for judgemental forecasting," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1151-1167.
    9. Bhavani Shanker Uppari & Sameer Hasija, 2019. "Modeling Newsvendor Behavior: A Prospect Theory Approach," Manufacturing & Service Operations Management, INFORMS, vol. 21(3), pages 481-500, July.
    10. Saravanan Kesavan & Tarun Kushwaha, 2020. "Field Experiment on the Profit Implications of Merchants’ Discretionary Power to Override Data-Driven Decision-Making Tools," Management Science, INFORMS, vol. 66(11), pages 5182-5190, November.
    11. Käki, Anssi & Kemppainen, Katariina & Liesiö, Juuso, 2019. "What to do when decision-makers deviate from model recommendations? Empirical evidence from hydropower industry," European Journal of Operational Research, Elsevier, vol. 278(3), pages 869-882.

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