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Assessing Impacts of Store and Salesperson Dimensions of Retail Service Quality on Consumer Returns

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  • Necati Ertekin
  • Michael E. Ketzenberg
  • Gregory R. Heim

Abstract

This study contributes to the understanding of consumer return behavior by examining associations between in‐store customer shopping experiences and subsequent customer returns. Return rates can vary a great deal across stores within a company and across salespersons within a store. We empirically examine returns across these two levels with respect to three retail service quality dimensions: salesperson friendliness, salesperson competence, and store environment. We conduct a detailed analysis using transaction data and customer survey responses from 25,131 customers at a national jewelry retailer. We find salesperson friendliness, salesperson competence, and store environment are significantly associated with subsequent return events, since, theoretically, customers use the three service quality dimensions as information cues to form their product quality perceptions. Our analysis reveals managerially relevant insights for retailers. The empirical associations suggest retailer management might obtain the most benefit in reducing returns from improving salesperson competence, which is followed by improving store environment and improving salesperson friendliness. We also conduct analyses using customer shopping attributes to identify how retailers might modify service for different customer segments to increase the efficacy of return prevention. Lastly, our counterfactual analysis predicts substantial improvements in return rates and net sales due to potential store execution efforts targeted at improving salesperson friendliness, salesperson competence, or store environment. The predictions support the idea that return prevention should start at the point of sale.

Suggested Citation

  • Necati Ertekin & Michael E. Ketzenberg & Gregory R. Heim, 2020. "Assessing Impacts of Store and Salesperson Dimensions of Retail Service Quality on Consumer Returns," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1232-1255, May.
  • Handle: RePEc:bla:popmgt:v:29:y:2020:i:5:p:1232-1255
    DOI: 10.1111/poms.13077
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    1. Bonifield, Carolyn & Cole, Catherine & Schultz, Randall L., 2010. "Product returns on the Internet: A case of mixed signals?," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1058-1065, September.
    2. Andrea Ichino & Fabrizia Mealli & Tommaso Nannicini, 2008. "From temporary help jobs to permanent employment: what can we learn from matching estimators and their sensitivity?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 305-327.
    3. Mehmet Sekip Altug & Tolga Aydinliyim, 2016. "Counteracting Strategic Purchase Deferrals: The Impact of Online Retailers’ Return Policy Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 376-392, July.
    4. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, Decembrie.
    5. Jeffrey D. Shulman & Marcus Cunha & Julian K. Saint Clair, 2015. "Consumer Uncertainty and Purchase Decision Reversals: Theory and Evidence," Marketing Science, INFORMS, vol. 34(4), pages 590-605, July.
    6. Debanjan Mitra & Peter N. Golder, 2006. "How Does Objective Quality Affect Perceived Quality? Short-Term Effects, Long-Term Effects, and Asymmetries," Marketing Science, INFORMS, vol. 25(3), pages 230-247, 05-06.
    7. Yili (Kevin) Hong & Paul A. Pavlou, 2014. "Product Fit Uncertainty in Online Markets: Nature, Effects, and Antecedents," Information Systems Research, INFORMS, vol. 25(2), pages 328-344, June.
    8. Nachiketa Sahoo & Chrysanthos Dellarocas & Shuba Srinivasan, 2018. "The Impact of Online Product Reviews on Product Returns," Information Systems Research, INFORMS, vol. 29(3), pages 723-738, September.
    9. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    10. Guangzhi Shang & Bikram P. Ghosh & Michael R. Galbreth, 2017. "Optimal Retail Return Policies with Wardrobing," Production and Operations Management, Production and Operations Management Society, vol. 26(7), pages 1315-1332, July.
    11. Xuanming Su, 2009. "Consumer Returns Policies and Supply Chain Performance," Manufacturing & Service Operations Management, INFORMS, vol. 11(4), pages 595-612, March.
    12. James G. Maxham, III & Richard G. Netemeyer & Donald R. Lichtenstein, 2008. "The Retail Value Chain: Linking Employee Perceptions to Employee Performance, Customer Evaluations, and Store Performance," Marketing Science, INFORMS, vol. 27(2), pages 147-167, 03-04.
    13. Puccinelli, Nancy M. & Goodstein, Ronald C. & Grewal, Dhruv & Price, Robert & Raghubir, Priya & Stewart, David, 2009. "Customer Experience Management in Retailing: Understanding the Buying Process," Journal of Retailing, Elsevier, vol. 85(1), pages 15-30.
    14. Jeffrey D. Shulman & Anne T. Coughlan & R. Canan Savaskan, 2009. "Optimal Restocking Fees and Information Provision in an Integrated Demand-Supply Model of Product Returns," Manufacturing & Service Operations Management, INFORMS, vol. 11(4), pages 577-594, December.
    15. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    16. Sophia Rabe-Hesketh & Anders Skrondal, 2012. "Multilevel and Longitudinal Modeling Using Stata, 3rd Edition," Stata Press books, StataCorp LP, edition 3, number mimus2, March.
    17. Ajay Kalra & Mengze Shi & Kannan Srinivasan, 2003. "Salesforce Compensation Scheme and Consumer Inferences," Management Science, INFORMS, vol. 49(5), pages 655-672, May.
    18. Grewal, Dhruv & Levy, Michael & Kumar, V., 2009. "Customer Experience Management in Retailing: An Organizing Framework," Journal of Retailing, Elsevier, vol. 85(1), pages 1-14.
    19. Jeffrey D. Shulman & Anne T. Coughlan & R. Canan Savaskan, 2011. "Managing Consumer Returns in a Competitive Environment," Management Science, INFORMS, vol. 57(2), pages 347-362, February.
    20. Prabuddha De & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2013. "Product-Oriented Web Technologies and Product Returns: An Exploratory Study," Information Systems Research, INFORMS, vol. 24(4), pages 998-1010, December.
    21. Enno Siemsen & Aleda V. Roth & Sridhar Balasubramanian & Gopesh Anand, 2009. "The Influence of Psychological Safety and Confidence in Knowledge on Employee Knowledge Sharing," Manufacturing & Service Operations Management, INFORMS, vol. 11(3), pages 429-447, April.
    22. Shannon W. Anderson & L. Scott Baggett & Sally K. Widener, 2009. "The Impact of Service Operations Failures on Customer Satisfaction: Evidence on How Failures and Their Source Affect What Matters to Customers," Manufacturing & Service Operations Management, INFORMS, vol. 11(1), pages 52-69, November.
    23. Eric T. Anderson & Karsten Hansen & Duncan Simester, 2009. "The Option Value of Returns: Theory and Empirical Evidence," Marketing Science, INFORMS, vol. 28(3), pages 405-423, 05-06.
    24. Hildebrandt, Lutz, 1988. "Store image and the prediction of performance in retailing," Journal of Business Research, Elsevier, vol. 17(1), pages 91-100, August.
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    3. Ruan, Yanya & Mezei, József, 2022. "When do AI chatbots lead to higher customer satisfaction than human frontline employees in online shopping assistance? Considering product attribute type," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    4. Guo, Xiongfei & Chen, Jing, 2023. "Manufacturer’s quality improvement and Retailer’s In-store service in the presence of customer returns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    5. Arzum Akkaş & Nachiketa Sahoo, 2020. "Reducing Product Expiration by Aligning Salesforce Incentives: A Data‐driven Approach," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1992-2009, August.
    6. Tien-Hsiang Chang & Kuei-Ying Hsu & Hsin-Pin Fu & Ying-Hua Teng & Yi-Jhen Li, 2022. "Integrating FSE and AHP to Identify Valuable Customer Needs by Service Quality Analysis," Sustainability, MDPI, vol. 14(3), pages 1-15, February.
    7. Yang, Xi & Zhao, Quanwu & Sun, Heshan, 2022. "Seekers’ complaint behavior in crowdsourcing: An uncertainty perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    8. Robert P. Rooderkerk & Nicole DeHoratius & Andrés Musalem, 2022. "The past, present, and future of retail analytics: Insights from a survey of academic research and interviews with practitioners," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3727-3748, October.

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