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Clinching the deal: An empirical study of the drivers of diffusion of daily deals

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  • Xia, Feihong
  • Chatterjee, Rabikar
  • Venkatesh, R.

Abstract

Daily deals, exemplified by proponents such as Groupon, Deals-for-Deeds and Pinduoduo, have been very popular across the globe among both online shoppers and businesses seeking to attract customers. A daily deal typically consists of a deep discount for a targeted group of shoppers valid for a single day. In this paper, we rely on the Rogers-Bass new product diffusion framework to delve into the adoption process of daily deals and untangle the drivers of adoption by analyzing a unique dataset we have scraped from Woot, an Amazon-owned daily deal website in the United States. Our results show that factors such as consumers’ prior experience with daily deals, time-of-day, and the price discount frame have a substantial quantifiable impact. Providing price discount information has a strong positive effect, in addition to just the deal price. We discuss managerial implications of the study and propose directions for future research.

Suggested Citation

  • Xia, Feihong & Chatterjee, Rabikar & Venkatesh, R., 2022. "Clinching the deal: An empirical study of the drivers of diffusion of daily deals," Journal of Business Research, Elsevier, vol. 149(C), pages 824-832.
    • Handle: RePEc:eee:jbrese:v:149:y:2022:i:c:p:824-832
    DOI: 10.1016/j.jbusres.2022.05.054
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    1. Mantian (Mandy) Hu & Chu (Ivy) Dang & Pradeep K. Chintagunta, 2019. "Search and Learning at a Daily Deals Website," Marketing Science, INFORMS, vol. 38(4), pages 609-642, July.
    2. Kukar-Kinney, Monika & Xia, Lan, 2017. "The effectiveness of number of deals purchased in influencing consumers' response to daily deal promotions: A cue utilization approach," Journal of Business Research, Elsevier, vol. 79(C), pages 189-197.
    3. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    4. Kim, Sang-Hoon & Park, Hyun Jung, 2011. "Effects of social influence on consumers' voluntary adoption of innovations prompted by others," Journal of Business Research, Elsevier, vol. 64(11), pages 1190-1194.
    5. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    6. Collinson, Jeremy & Mathmann, Frank & Chylinski, Mathew, 2020. "Time is money: Field evidence for the effect of time of day and product name on product purchase," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    7. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    8. Wu, Feng-Shang & Chu, Wen-Lin, 2010. "Diffusion models of mobile telephony," Journal of Business Research, Elsevier, vol. 63(5), pages 497-501, May.
    9. Bone, Paula Fitzgerald, 1995. "Word-of-mouth effects on short-term and long-term product judgments," Journal of Business Research, Elsevier, vol. 32(3), pages 213-223, March.
    10. Harper Roehm & Michelle Roehm, 2004. "Variety-Seeking and Time of Day: Why Leader Brands Hope Young Adults Shop in the Afternoon, but Follower Brands Hope for Morning," Marketing Letters, Springer, vol. 15(4), pages 213-221, December.
    11. Jiahua Wu & Mengze Shi & Ming Hu, 2015. "Threshold Effects in Online Group Buying," Management Science, INFORMS, vol. 61(9), pages 2025-2040, September.
    12. Ieva, M. & De Canio, F. & Ziliani, C., 2018. "Daily deal shoppers: What drives social couponing?," Journal of Retailing and Consumer Services, Elsevier, vol. 40(C), pages 299-303.
    13. Parsons, Andrew G. & Ballantine, Paul W. & Ali, Ashleigh & Grey, Hannah, 2014. "Deal is on! Why people buy from daily deal websites," Journal of Retailing and Consumer Services, Elsevier, vol. 21(1), pages 37-42.
    14. Zheng, Dan & Chen, Yuxin & Zhang, Zhe & Che, Hai, 2022. "Retail price discount depth and perceived quality uncertainty," Journal of Retailing, Elsevier, vol. 98(3), pages 542-557.
    15. Marshall, Pablo & Dockendorff, Monika & Ibáñez, Soledad, 2013. "A forecasting system for movie attendance," Journal of Business Research, Elsevier, vol. 66(10), pages 1800-1806.
    16. Puller, Steven L. & Taylor, Lisa M., 2012. "Price discrimination by day-of-week of purchase: Evidence from the U.S. airline industry," Journal of Economic Behavior & Organization, Elsevier, vol. 84(3), pages 801-812.
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    1. Park, Yookyung & Yi, Youjae, 2023. "Morning deals make me feel smart: Consumer evaluations of online sales promotions differ by time of day," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).

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