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Usage Experience with Decision Aids and Evolution of Online Purchase Behavior

Author

Listed:
  • Savannah Wei Shi

    (Leavey School of Business, Santa Clara University, Santa Clara, California 95053)

  • Jie Zhang

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

Abstract

This study investigates how prior usage experience with various decision aids available in an Internet shopping environment contributes to online purchase behavior evolution. Four types of decision aids are examined: those for (1) nutritional needs, (2) brand preference, (3) economic needs, and (4) personalized shopping lists. We construct and estimate nonhomogeneous hidden Markov models of store- and category-level purchase decisions, in which parameters vary over time across hidden states as driven by usage experience with different decision aids. We find that consumers evolve through distinct behavioral states over time, and the evolution is attributable to their prior usage experience with various decision aids. Moreover, the impact varies by the specific decision aid, behavioral state, and category characteristics. In addition, consumers gravitate toward habitual decision processes in online grocery stores, and their average price and promotion sensitivities increase first and then decrease but the level of heterogeneity rises continuously. We identify beneficial versus potentially undesirable decision aids and demonstrate how the proposed research method can help online retailers improve their store environments, design customized promotions, and quantify the payoffs of these strategies.

Suggested Citation

  • Savannah Wei Shi & Jie Zhang, 2014. "Usage Experience with Decision Aids and Evolution of Online Purchase Behavior," Marketing Science, INFORMS, vol. 33(6), pages 871-882, November.
    • Handle: RePEc:inm:ormksc:v:33:y:2014:i:6:p:871-882
    DOI: 10.1287/mksc.2014.0872
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    References listed on IDEAS

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    Cited by:

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    2. Kannan, P.K. & Li, Hongshuang “Alice”, 2017. "Digital marketing: A framework, review and research agenda," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 22-45.
    3. Benjamin Avanzi & Greg Taylor & Bernard Wong & Alan Xian, 2020. "Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework," Papers 2003.13888, arXiv.org, revised May 2020.
    4. Gielens, Katrijn & Steenkamp, Jan-Benedict E.M., 2019. "Branding in the era of digital (dis)intermediation," International Journal of Research in Marketing, Elsevier, vol. 36(3), pages 367-384.
    5. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    6. Melis, Kristina & Campo, Katia & Lamey, Lien & Breugelmans, Els, 2016. "A Bigger Slice of the Multichannel Grocery Pie: When Does Consumers’ Online Channel Use Expand Retailers’ Share of Wallet?," Journal of Retailing, Elsevier, vol. 92(3), pages 268-286.
    7. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    8. Sigitas Urbonavicius & Karina Adomaviciute – Sakalauske, 2023. "Learning from Pandemic Periods: Elements of the Theory of Behavioral Transformation," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(2), pages 251-266.
    9. Park, Joonyong & Kim, Renee B., 2018. "A new approach to segmenting multichannel shoppers in Korea and the U.S," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 163-178.
    10. Guo, Wenhao & Tian, Jin & Li, Minqiang, 2023. "Price-aware enhanced dynamic recommendation based on deep learning," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    11. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    12. Singh, Reema & Rosengren, Sara, 2020. "Why do online grocery shoppers switch? An empirical investigation of drivers of switching in online grocery," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    13. Gui Liberali & Alina Ferecatu, 2022. "Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models," Marketing Science, INFORMS, vol. 41(4), pages 769-794, July.
    14. Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Xian, Alan, 2021. "Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework," European Journal of Operational Research, Elsevier, vol. 290(1), pages 177-195.
    15. Choudhary, Vidyanand & Currim, Imran & Dewan, Sanjeev & Jeliazkov, Ivan & Mintz, Ofer & Turner, John, 2017. "Evaluation Set Size and Purchase: Evidence from a Product Search Engine," Journal of Interactive Marketing, Elsevier, vol. 37(C), pages 16-31.
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