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Evaluating regression techniques for service advisor performance analysis in automotive dealerships

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  • Njoku, Judith Nkechinyere
  • Nwakanma, Cosmas Ifeanyi
  • Lee, Jae-Min
  • Kim, Dong-Seong

Abstract

Service advisors are a crucial contributor to the profit and loss of an automotive dealership. It is thus crucial that their performances are continuously evaluated to make decisions that ensure continued profitability. Artificial intelligence (AI) algorithms like the finite mixture of regression (FMR) have been previously explored as a tool for benchmarking the performance of dealerships. However, it is essential that the AI algorithm is trustworthy and that its predictions can be explained. This study aims to develop a performance analytics platform that is reliable and explainable. It develops and evaluates variants of FMR based on different regression techniques, focusing on both reliability and explainability. The results show that the implemented FMR variants not only provide accurate performance assessments but also offer transparent insights into the factors influencing dealership performance.

Suggested Citation

  • Njoku, Judith Nkechinyere & Nwakanma, Cosmas Ifeanyi & Lee, Jae-Min & Kim, Dong-Seong, 2024. "Evaluating regression techniques for service advisor performance analysis in automotive dealerships," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:joreco:v:80:y:2024:i:c:s0969698924002297
    DOI: 10.1016/j.jretconser.2024.103933
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    1. Shiyi Tang & Jiali Zheng, 2022. "Penalized estimation in finite mixture of ultra-high dimensional regression models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(17), pages 5971-5992, September.
    2. Kallweit, Katrin & Spreer, Philipp & Toporowski, Waldemar, 2014. "Why do customers use self-service information technologies in retail? The mediating effect of perceived service quality," Journal of Retailing and Consumer Services, Elsevier, vol. 21(3), pages 268-276.
    3. Junhui Yin & Liucang Wu & Lin Dai, 2020. "Variable selection in finite mixture of regression models using the skew-normal distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(16), pages 2941-2960, December.
    4. Omar Sabbagh & Mohd Nizam Ab Rahman & Wan Rosmanira Ismail & Wan Mohd Hirwani Wan Hussain, 2017. "The moderation influence of warranty on customer satisfaction’s antecedents: an empirical evidence from automotive dealerships," The Service Industries Journal, Taylor & Francis Journals, vol. 37(5-6), pages 381-407, April.
    5. Sara Dias & Valeska Andreozzi & Rosário Martins, 2013. "Analysis of HIV/AIDS DRG in Portugal: a hierarchical finite mixture model," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(5), pages 715-723, October.
    6. King, Robert P. & Park, Timothy A., 2004. "Modeling Productivity In Supermarket Operations: Incorporating The Impacts Of Store Characteristics And Information Technologies," Journal of Food Distribution Research, Food Distribution Research Society, vol. 35(2), pages 1-14, July.
    7. Darley, William K. & Luethge, Denise J. & Thatte, Ashish, 2008. "Exploring the relationship of perceived automotive salesperson attributes, customer satisfaction and intentions to automotive service department patronage: The moderating role of customer gender," Journal of Retailing and Consumer Services, Elsevier, vol. 15(6), pages 469-479.
    8. Yu, Wantao & Ramanathan, Ramakrishnan, 2009. "An assessment of operational efficiency of retail firms in China," Journal of Retailing and Consumer Services, Elsevier, vol. 16(2), pages 109-122.
    9. Braz, Renata Gomes Frutuoso & Scavarda, Luiz Felipe & Martins, Roberto Antonio, 2011. "Reviewing and improving performance measurement systems: An action research," International Journal of Production Economics, Elsevier, vol. 133(2), pages 751-760, October.
    10. Ahonen, Ilmari & Nevalainen, Jaakko & Larocque, Denis, 2019. "Prediction with a flexible finite mixture-of-regressions," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 212-224.
    11. Almohri, Haidar & Chinnam, Ratna Babu & Colosimo, Mark, 2019. "Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships," International Journal of Production Economics, Elsevier, vol. 213(C), pages 69-80.
    12. Arndt, Aaron D. & Karande, Kiran, 2012. "Is it better for salespeople to have the highest customer orientation or a strong fit with their group's customer orientation? Findings from automobile dealerships," Journal of Retailing and Consumer Services, Elsevier, vol. 19(3), pages 353-359.
    13. Elena Lascu & Irina Severin & Florina Daniela Lascu & Razvan Adrian Gudana & Gabriela Nalbitoru & Nicoleta Daniela Ignat, 2021. "Framework on Performance Management in Automotive Industry: A Case Study," JRFM, MDPI, vol. 14(10), pages 1-20, October.
    14. Qingguo Tang & R. J. Karunamuni, 2018. "Robust variable selection for finite mixture regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 489-521, June.
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