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Recommendation and repurchase intention thresholds: A joint heterogeneity response estimation

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  • Jin, Ying
  • Su, Meng

Abstract

Recommendation and repurchase intentions are the two most important dimensions of customer loyalty. Latent satisfaction thresholds at the individual level, if obtained, can function as an additional valid and effective criterion for satisfaction ratings in determining customer loyalty. To improve the existing estimation methodology on satisfaction thresholds, this paper proposes a joint heterogeneity response model to simultaneously calibrate the individual-level recommendation and repurchase thresholds through a joint specification over the heterogeneity. It conducts a simulation study to examine the parameter recovery and model validation. This paper further applies the method to two different datasets of satisfaction surveys in the automotive industry, with the first involving owners of vehicles of a single brand, and the second involving owners of vehicles of multiple brands. The results from the two empirical studies show that the proposed model consistently outperforms the two competing models and the extant approach. In addition, the results provide different insights into enhancing customer loyalty by using a new segmentation scheme based on estimated satisfaction thresholds.

Suggested Citation

  • Jin, Ying & Su, Meng, 2009. "Recommendation and repurchase intention thresholds: A joint heterogeneity response estimation," International Journal of Research in Marketing, Elsevier, vol. 26(3), pages 245-255.
    • Handle: RePEc:eee:ijrema:v:26:y:2009:i:3:p:245-255
    DOI: 10.1016/j.ijresmar.2009.06.004
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    2. Pomirleanu, Nadia & Chennamaneni, Pavan Rao & Krishen, Anjala S., 2016. "Easy to please or hard to impress: Elucidating consumers' innate satisfaction," Journal of Business Research, Elsevier, vol. 69(5), pages 1914-1918.
    3. Ravula, Prashanth & Jha, Subhash & Biswas, Abhijit, 2022. "Relative persuasiveness of repurchase intentions versus recommendations in online reviews," Journal of Retailing, Elsevier, vol. 98(4), pages 724-740.
    4. Jiang, Yuanchun & Shang, Jennifer & Liu, Yezheng & May, Jerrold, 2015. "Redesigning promotion strategy for e-commerce competitiveness through pricing and recommendation," International Journal of Production Economics, Elsevier, vol. 167(C), pages 257-270.
    5. Choi, Il Young & Oh, Myung Geun & Kim, Jae Kyeong & Ryu, Young U., 2016. "Collaborative filtering with facial expressions for online video recommendation," International Journal of Information Management, Elsevier, vol. 36(3), pages 397-402.

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