ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC Management 18 April 2023

E-commerce cart recommendation effects: A field experiment on entertainment products

Cite this:
https://doi.org/10.52396/JUSTC-2022-0130
More Information
  • Author Bio:

    Yongjun Li is an Associate Professor at School of Management, University of Science and Technology of China (USTC). He received his Ph.D. degree from USTC. His research mainly focuses on big data marketing, data envelopment analysis (DEA) methodology, and applications

    Hanbing Xue is a postdoctor of School at Management, University of Science and Technology of China (USTC). She received her Ph.D. degree from USTC. Her research mainly focuses on digital content marketing, entertainment marketing, and consumer behavior

  • Corresponding author: E-mail: xuehb@mail.ustc.edu.cn
  • Received Date: 09 September 2022
  • Accepted Date: 16 November 2022
  • Available Online: 18 April 2023
  • This study aims to compare the effects of e-cart recommendation and homepage recommendation in the field of entertainment products on the basis of a field experiment involving almost 13000 consumers supported by one of the leading digital reading platforms in China. The results indicate that e-cart recommendations have a significant positive impact on consumer downloads in comparison with homepage recommendations. Moreover, this positive effect decreases when the alternatives in the e-cart are of a larger quantity but increases when consumers are more active. Interestingly, this study also finds that e-cart recommendations can spill over to other products, leading to more downloads of non-recommended items. Our findings provide novel insights into consumer responses to e-cart recommendations of entertainment products for researchers and managers alike.
    The effects of e-cart recommendation of entertainment products on consumer responses. All hypotheses are supported.
    This study aims to compare the effects of e-cart recommendation and homepage recommendation in the field of entertainment products on the basis of a field experiment involving almost 13000 consumers supported by one of the leading digital reading platforms in China. The results indicate that e-cart recommendations have a significant positive impact on consumer downloads in comparison with homepage recommendations. Moreover, this positive effect decreases when the alternatives in the e-cart are of a larger quantity but increases when consumers are more active. Interestingly, this study also finds that e-cart recommendations can spill over to other products, leading to more downloads of non-recommended items. Our findings provide novel insights into consumer responses to e-cart recommendations of entertainment products for researchers and managers alike.
    • This study explores the effects of e-cart recommendation on consumer responses in the field of entertainment products.
    • For entertainment products, e-cart recommendation has a positive impact on consumer responses relative to homepage recommendation.
    • The positive effect of e-cart recommendation declines when the cart is of more products.
    • The positive effect of e-cart recommendation increases when the recommended products are offered to more active consumers.

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  • 加载中

Catalog

    Figure  1.  Proposed research model.

    Figure  2.  Example of recommendation in different groups.

    Figure  3.  Model-free evidence for the download behavior of treatment and control.

    [1]
    Häubl G, Trifts V. Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 2000, 19 (1): 4–21. doi: 10.1287/mksc.19.1.4.15178
    [2]
    Wang R, Sahin O. The impact of consumer search cost on assortment planning and pricing. Management Science, 2018, 64 (8): 3649–3666. doi: 10.1287/mnsc.2017.2790
    [3]
    Virdi P, Kalro A D, Sharma D. Online decision aids: The role of decision-making styles and decision-making stages. International Journal of Retail & Distribution Management, 2020, 48 (6): 555–574. doi: https://doi.org/10.1108/IJRDM-02-2019-0068
    [4]
    Lo L Y S, Lin S W, Hsu L Y. Motivation for online impulse buying: A two-factor theory perspective. International Journal of Information Management, 2016, 36 (5): 759–772. doi: 10.1016/j.ijinfomgt.2016.04.012
    [5]
    Close A G, Kukar-Kinney M. Beyond buying: Motivations behind consumers’ online shopping cart use. Journal of Business Research, 2010, 63 (9-10): 986–992. doi: 10.1016/j.jbusres.2009.01.022
    [6]
    Kapoor A P, Vij M. Following you wherever you go: Mobile shopping “cart-checkout” abandonment. Journal of Retailing and Consumer Services, 2021, 61: 102553. doi: 10.1016/j.jretconser.2021.102553
    [7]
    Senecal S, Nantel J. The influence of online product recommendations on consumers’ online choices. Journal of Retailing, 2004, 80 (2): 159–169. doi: 10.1016/j.jretai.2004.04.001
    [8]
    Xiao B, Benbasat I. An empirical examination of the influence of biased personalized product recommendations on consumers’ decision making outcomes. Decision Support Systems, 2018, 110: 46–57. doi: 10.1016/j.dss.2018.03.005
    [9]
    Lee D, Hosanagar K. How do recommender systems affect sales diversity? A cross-category investigation via randomized field experiment. Information Systems Research, 2019, 30 (1): 239–259. doi: 10.1287/isre.2018.0800
    [10]
    Lee D, Gopal A, Park S H. Different but equal? A field experiment on the impact of recommendation systems on mobile and personal computer channels in retail. Information Systems Research, 2020, 31 (3): 892–912. doi: 10.1287/isre.2020.0922
    [11]
    Chinchanachokchai S, Thontirawong P, Chinchanachokchai P. A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations. Journal of Retailing and Consumer Services, 2021, 61: 102528. doi: 10.1016/j.jretconser.2021.102528
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    iResearch. China Internet Entertainment Market Data Release Report 2020Q1&2020Q2e (2020). [2022-08-09]. https://report.iresearch.cn/report_pdf.aspx? id=3603.
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    iResearch. Overseas Development of Chinese Network Literature in 2021. 2021. https://report.iresearch.cn/report_pdf.aspx?id=3840
    [14]
    Shi A, Tan C H, Sia C L. Timing and basis of online product recommendation: The preference inconsistency paradox. In: International Conference on Human Interface and the Management of Information. Berlin, Heidelberg: Springer, 2013: 531–539.
    [15]
    Yan Q, Zhang L, Li Y, et al. Effects of product portfolios and recommendation timing in the efficiency of personalized recommendation. Journal of Consumer Behavior, 2016, 15 (6): 516–526. doi: 10.1002/cb.1588
    [16]
    Hennig-Thurau T, Houston M B. Entertainment Science. Cham, Switzerland: Springer, 2019.
    [17]
    Foutz N Z. Entertainment Marketing (Foundations and Trends® in Marketing). Boston: Now Publishers Inc, 2017.
    [18]
    Dhar R, Wertenbroch K. Consumer choice between hedonic and utilitarian goods. Journal of Marketing Research, 2000, 37 (1): 60–71. doi: 10.1509/jmkr.37.1.60.18718
    [19]
    Lee D, Hosanagar K. How do product attributes and reviews moderate the impact of recommender systems through purchase stages? Management Science, 2020, 67 (1): 524–546. doi: 10.1287/mnsc.2019.3546
    [20]
    Okada E M. Justification effects on consumer choice of hedonic and utilitarian goods. Journal of Marketing Research, 2005, 42 (1): 43–53. doi: 10.1509/jmkr.42.1.43.56889
    [21]
    Clement M, Fabel S, Schmidt-Stolting C. Diffusion of hedonic goods: A literature review. The International Journal on Media Management, 2006, 8 (4): 155–163. doi: 10.1207/s14241250ijmm0804_1
    [22]
    Aggarwal P, Vaidyanathan R. Perceived effectiveness of recommendation agent routines: Search vs. experience goods. International Journal of Internet Marketing and Advertising, 2005, 2 (1): 38–55. doi: 10.1504/IJIMA.2005.007503
    [23]
    Fitzsimons G J, Lehmann D R. Reactance to recommendations: When unsolicited advice yields contrary responses. Marketing Science, 2004, 23 (1): 82–94. doi: 10.1287/mksc.1030.0033
    [24]
    Wang J, Zhang Y. Opportunity model for e-commerce recommendation: Right product; right time. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 303–312.
    [25]
    Todri V, Ghose A, Singh P V. Trade-offs in online advertising: Advertising effectiveness and annoyance dynamics across the purchase funnel. Information Systems Research, 2019, 31 (1): 102–125. doi: 10.1287/isre.2019.0877
    [26]
    Campbell M C, Keller K L. Brand familiarity and advertising repetition effects. Journal of Consumer Research, 2003, 30 (2): 292–304. doi: 10.1086/376800
    [27]
    Toubia O, Iyengar G, Bunnell R, et al. Extracting features of entertainment products: A guided latent dirichlet allocation approach informed by the psychology of media consumption. Journal of Marketing Research, 2019, 56 (1): 18–36. doi: 10.1177/0022243718820559
    [28]
    Platania M, Platania S, Santisi G. Entertainment marketing, experiential consumption and consumer behavior: The determinant of choice of wine in the store. Wine Economics and Policy, 2016, 5 (2): 87–95. doi: 10.1016/j.wep.2016.10.001
    [29]
    Setyani V, Zhu Y Q, Hidayanto A N, et al. Exploring the psychological mechanisms from personalized advertisements to urge to buy impulsively on social media. International Journal of Information Management, 2019, 48: 96–107. doi: 10.1016/j.ijinfomgt.2019.01.007
    [30]
    Longoni C, Cian L. Artificial intelligence in utilitarian vs. hedonic contexts: The “word-of-machine” effect. Journal of Marketing, 2022, 86 (1): 91–108. doi: 10.1177/0022242920957347
    [31]
    Botti S, McGill A L. The locus of choice: Personal causality and satisfaction with hedonic and utilitarian decisions. Journal of Consumer Research, 2011, 37 (6): 1065–1078. doi: 10.1086/656570
    [32]
    Sinha S K, Verma P. Impact of sales promotion’s benefits on perceived value: Does product category moderate the results? Journal of Retailing and Consumer Services, 2020, 52: 101887. doi: 10.1016/j.jretconser.2019.101887
    [33]
    Parra J F, Ruiz S. Consideration sets in online shopping environments: The effects of search tool and information load. Electronic Commerce Research and Applications, 2009, 8 (5): 252–262. doi: 10.1016/j.elerap.2009.04.005
    [34]
    Ghiassaleh A, Kocher B, Czellar S. Best seller!? Unintended negative consequences of popularity signs on consumer choice behavior. International Journal of Research in Marketing, 2020, 37 (4): 805–820. doi: 10.1016/j.ijresmar.2020.04.003
    [35]
    Wang J, Sarwar B, Sundaresan N. Utilizing related products for postpurchase recommendation in e-commerce. In: Proceedings of the Fifth ACM Conference on Recommender Systems. New York: ACM, 2011: 329–332.
    [36]
    Lee L, Ariely D. Shopping goals, goal concreteness, and conditional promotions. Journal of Consumer Research, 2006, 33 (1): 60–70. doi: 10.1086/504136
    [37]
    Kwon K, Cho J, Park Y. Influences of customer preference development on the effectiveness of recommendation strategies. Electronic Commerce Research and Applications, 2009, 8 (5): 263–275. doi: 10.1016/j.elerap.2009.04.004
    [38]
    Song T, Yi C, Huang J. Whose recommendations do you follow? An investigation of tie strength, shopping stage, and deal scarcity. Information & Management, 2017, 54 (8): 1072–1083. doi: 10.1016/j.im.2017.03.003
    [39]
    Schreiner T, Rese A, Baier D. Multichannel personalization: Identifying consumer preferences for product recommendations in advertisements across different media channels. Journal of Retailing and Consumer Services, 2019, 48: 87–99. doi: 10.1016/j.jretconser.2019.02.010
    [40]
    Luo X, Lu X, Li J. When and how to leverage e-commerce cart targeting: The relative and moderated effects of scarcity and price incentives with a two-stage field experiment and causal forest optimization. Information Systems Research, 2019, 30 (4): 1203–1227. doi: 10.1287/isre.2019.0859
    [41]
    Tsao W Y. The fitness of product information: Evidence from online recommendations. International Journal of Information Management, 2013, 33 (1): 1–9. doi: 10.1016/j.ijinfomgt.2012.04.003
    [42]
    Dai Q, Cui X L. The influence and moderating effect of trust in streamers in a live streaming shopping environment. JUSTC, 2022, 52 (2): 6. doi: 10.52396/JUSTC-2021-0219
    [43]
    Hauser J R, Wernerfelt B. An evaluation cost model of consideration sets. Journal of consumer research, 1990, 16 (4): 393–408. doi: 10.1086/209225
    [44]
    Iyengar S S, Lepper M R. When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 2000, 79 (6): 995–1006. doi: 10.1037/0022-3514.79.6.995
    [45]
    Kuksov D, Villas-Boas J M. When more alternatives lead to less choice. Marketing Science, 2010, 29 (3): 507–524. doi: 10.1287/mksc.1090.0535
    [46]
    Mittal B. The maximizing consumer wants even more choices: How consumers cope with the marketplace of overchoice. Journal of Retailing and Consumer Services, 2016, 100 (31): 361–370. doi: 10.1016/j.jretconser.2016.05.003
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