DOI QR코드

DOI QR Code

How IT Affordance Influences Engagement in Live Commerce: An Empirical Analysis Focusing on Social Cues as Moderating Effect

  • Received : 2021.11.11
  • Accepted : 2022.04.04
  • Published : 2022.06.30

Abstract

With the development of technology and media and the pursuit of non-face-to-face due to the corona pandemic, the influence of live commerce, a real-time streaming shopping channel, is growing. Starting from China, the popularity of live commerce is growing all over the world, and it has become an interesting topic among many practitioners and researchers. However, compared to its popularity, there are few studies on live commerce. Therefore, we build a theoretical model in terms of IT affordance such as visibility, guidance shopping, trading, and meta-voicing and investigate how live commerce affects engagement with customers. We empirically measure 428 individuals who have used live commerce using survey data. In addition, we conduct four types of scenario experiments on whether social cues on exposures of other consumers, influence customer engagement. Our results show that trading affordance has the most significant effect. This shows that the live commerce platform may want to devise a program that helps make payment easier for users who prefer a quick and simple process. Our study contributes to the literature by presenting the importance of IT affordance for live commerce.

Keywords

References

  1. Aladwani, A. M. (2017). Compatible quality of social media content: Conceptualization, measurement, and affordances. International Journal of Information Management, 37(6), 576-582. https://doi.org/10.1016/j.ijinfomgt.2017.05.014
  2. Bai, Y., Yao, Z., and Dou, Y. F. (2015). Effect of social commerce factors on user purchase behavior: An empirical investigation from renren. com. International Journal of Information Management, 35(5), 538-550. https://doi.org/10.1016/j.ijinfomgt.2015.04.011
  3. Bowden, J. L. H. (2009). The process of customer engagement: A conceptual framework. Journal of Marketing Theory and Practice, 17(1), 63-74. https://doi.org/10.2753/MTP1069-6679170105
  4. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238
  5. Brodie, R. J., Hollebeek, L. D., Juric, B., and Ilic, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of service research, 14(3), 252-271. https://doi.org/10.1177/1094670511411703
  6. Bygstad, B., Munkvold, B. E., and Volkoff, O. (2016). Identifying generative mechanisms through affordances: A framework for critical realist data analysis. Journal of Information Technology, 31(1), 83-96. https://doi.org/10.1057/jit.2015.13
  7. Cai, J., and Wohn, D. Y. (2019, January). Live streaming commerce: Uses and gratifications approach to understanding consumers' motivations. Proceedings of the 52nd Hawaii International Conference on System Sciences.
  8. Celine, D. (2017). Why is "live shopping" ah it in China? Innovation Is Everywhere, Retrieved from https://innovationiseverywhere.com/live-shopping-hit-china/.
  9. Chen, C. C., and Lin, Y. C. (2018). What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telematics and Informatics, 35(1), 293-303. https://doi.org/10.1016/j.tele.2017.12.003
  10. Chen, A., Lu, Y., and Wang, B. (2017). Customers' purchase decision-making process in social commerce: A social learning perspective. International Journal of Information Management, 37(6), 627-638. https://doi.org/10.1016/j.ijinfomgt.2017.05.001
  11. Chou, C. H., Wang, Y. S., and Tang, T. I. (2015). Exploring the determinants of knowledge adoption in virtual communities: A social influence perspective. International Journal of Information Management, 35(3), 364-376. https://doi.org/10.1016/j.ijinfomgt.2015.02.001
  12. Dahl, D. W., Argo, J. J., and Morales, A. C. (2012). Social information in the retail environment: The importance of consumption. alignment, referent identity, and self-esteem. Journal of Consumer Research, 38(5), 860-871. https://doi.org/10.1086/660918
  13. Dibble, J. L., Hartmann, T., and Rosaen, S. F. (2016). Parasocial interaction and parasocial relationship: Conceptual clarification and a critical assessment of measures. Human Communication Research, 42(1), 21-44. https://doi.org/10.1111/hcre.12063
  14. Dillon, W. R., and Goldstein, M. (1984). Multivariate analysis methods and applications (No. 519.535 D5).
  15. Doll, W. J., Xia, W., and Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly: Management Information Systems, 18(4), 453-460. https://doi.org/10.2307/249524
  16. Dong, X., Wang, T., Benbasat, I., (2016). IT affordances in online social commerce: Conceptualization validation and scale development. Twenty-Second Americas Conference on Information Systems, San Diego, American, 1-0.
  17. Dong, X., and Wang, T. (2018). Social tie formation in Chinese online social commerce: The role of IT affordances. International Journal of Information Management, 42, 49-64. https://doi.org/10.1016/j.ijinfomgt.2018.06.002
  18. Faraj, S., and Azad, B. (2012). The materiality of technology: An affordance perspective. Materiality and organizing: Social interaction in a technological world, 237, 258.
  19. Fei, M., Tan, H., Peng, X., Wang, Q., and Wang, L. (2021). Promoting or attenuating? An eye-tracking study on the role of social cues in e-commerce livestreaming. Decision Support Systems, 142, 113466.
  20. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
  21. Fulk, J., and Yuan, Y. C. (2013). Location, motivation, and social capitalization via enterprise social networking. Journal of Computer-Mediated Communication, 19(1), 20-37. https://doi.org/10.1111/jcc4.12033
  22. Gibson, J. J. (1986). The Ecological Approach to Visual Perception Psychology Press. New Yorik NY USA.
  23. Grange, C., and Benbasat, I. (2013). The value of social shopping networks for product search and the moderating role of network scope. Proceedings of 34th International Conference on Information Systems (ICIS).
  24. Hamilton, W. A., Garretson, O., and Kerne, A. (2014, April). Streaming on twitch: Fostering participatory communities of play within live mixed media. Proceedings of the SIGCHI conference on human factors in computing systems, 1315-1324.
  25. Hansen, J. M., Saridakis, G., and Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers' use of social media for transactions. Computers in human behavior, 80, 197-206. https://doi.org/10.1016/j.chb.2017.11.010
  26. Hartmann, T., and Goldhoorn, C. (2011). Horton and Wohl revisited: Exploring viewers' experience of parasocial interaction. Journal of Communication, 61, 1104-1121. https://doi.org/10.1111/j.1460-2466.2011.01595.x
  27. Horton, D., and Wohl, R. (1956). Mass communication and parasocial interaction. Psychiatry, 19, 215-229. https://doi.org/10.1080/00332747.1956.11023049
  28. Iimedia Research, (2020, February). 艾媒報告|2019-2020年中國在線直播行業硏究報, Retrieved from https://www.iimedia.cn/c400/69017.html
  29. Hu, M., and Chaudhry, S. S. (2020). Enhancing consumer engagement in e-commerce live streaming via relational bonds. Internet Research, 30(3), 1019-1041. https://doi.org/10.1108/INTR-03-2019-0082
  30. Islam, J. U., and Rahman, Z. (2017). The impact of online brand community characteristics on customer engagement: An application of Stimulus-Organism-Response paradigm. Telematics and Informatics, 34(4), 96-109. https://doi.org/10.1016/j.tele.2017.01.004
  31. Jensen, B. B. (2006). Forbrugernes prisviden for, under og efter butiksbesoget: Hvilken prisviden er forbrugerne i besiddelse af for, under og efter kob af dagligvarer og hvad kan forklare forskelle i forbrugernes prisviden? Arhus: Aarhus School of Business, MAPP Centre. Ph.d.-afhandling, Nr. 2006:15.
  32. Kelman, H. (1958). Communing and relating. The American Journal of Psychoanalysis, 18(1), 77-98. https://doi.org/10.1007/BF01871880
  33. Kim, K. J., and Shin, D. H. (2015). An acceptance model for smart watches. Internet Research Electronic Networking Applications & Policy, 25(4), 527-541. https://doi.org/10.1108/IntR-05-2014-0126
  34. Ko, D. (2020, September25). 600 million in 1 minute on mobile, the power of 'live commerce'. Hankyung News, Retrieved March 24, from http://www.hankyung.com/economy/article.202009250365i
  35. Ko, H. C., and Chen, Z. Y. (2020, July). Exploring the factors driving live streaming shopping intention: A perspective of parasocial interaction. Proceedings of the 2020 International Conference on Management of e-Commerce and e-Government, 36-40.
  36. Kukar-Kinney, M., and Xia, L. (2017). The effectiveness of number of deals purchased in influencing consumers' response to daily deal promotions: A cue utilization approach. Journal of business research, 79, 189-197. https://doi.org/10.1016/j.jbusres.2017.06.012
  37. Labrecque, L. I. (2014). Fostering consumer-brand relationships in social media environments: The role of parasocial interaction. Journal of interactive marketing, 28(2), 134-148. https://doi.org/10.1016/j.intmar.2013.12.003
  38. La, K. W., and Oh, K. W. (2021). Effects of Wanghong marketing in live commerce on chinese consumers' purchase intention toward fashion products: Focusing on the mediating effect of Wanghong's characteristics and consumers' co-experience. Journal of the Korea Fashion and Costume Design Association, 23(1), 19-36. https://doi.org/10.30751/KFCDA.2021.23.1.19
  39. Leonardi, P. M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35(1), 147-167. https://doi.org/10.2307/23043493
  40. Leonardi, P. M. (2013). When does technology use enable network change in organizations? A comparative study of feature use and shared affordances. MIS quarterly, 749-775.
  41. Li, C. Y. (2019). How social commerce constructs influence customers' social shopping intention? An empirical study of a social commerce website. Technological Forecasting and Social Change, 144, 282-294. https://doi.org/10.1016/j.techfore.2017.11.026
  42. Lichtenstein, D. R., Ridgway, N. M., and Netemeyer, R. G. (1993). Price perceptions and consumer shopping behavior: A field study. Journal of Marketing Research, 30(2) 234-245. https://doi.org/10.1177/002224379303000208
  43. Lin, J., Luo, Z., Cheng, X., and Li, L. (2019). Understanding the interplay of social commerce affordances and swift guanxi: An empirical study. Information & Management, 56(2), 213-224. https://doi.org/10.1016/j.im.2018.05.009
  44. Liu, M., Park, J. Y., & Lee, H. E. (2021). Technology acceptance model in live commerce context: The effect of para-social interactivity and source characteristics on consumers' shopping intention on live commerce platform. The Journal of the Korea Contents Association, 21(6), 138-154. https://doi.org/10.5392/JKCA.2021.21.06.138
  45. Lu, B., and Chen, Z. (2021). Live streaming commerce and consumers' purchase intention: An uncertainty reduction perspective. Information and Management, 103509.
  46. Lv, Z., Jin, Y., and Huang, J. (2018). How do sellers use live chat to influence consumer purchase decision in China? Electronic Commerce Research and Applications, 28, 102-113. https://doi.org/10.1016/j.elerap.2018.01.003
  47. MacCallum, R. C., and Hong, S. (1997). Power in covariance structure modeling using GFI and AGFI. Multivariate Behavioral Research, 32(2), 193-210. https://doi.org/10.1207/s15327906mbr3202_5
  48. Majchrzak, A., Faraj, S., Kane, G. C., & Azad, B. (2013). The contradictory influence of social media affordances on online communal knowledge sharing. Journal of Computer-Mediated Communication, 19(1), 38-55. https://doi.org/10.1111/jcc4.12030
  49. Marsh, H. W., Balla, J. R., and McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103(3), 391-410. https://doi.org/10.1037/0033-2909.103.3.391
  50. McQuail, D. (1972). The television audience: A revised perspective. Sociology of mass communications, 135-165.
  51. Nambisan, P., and Watt, J. H. (2011). Managing customer experiences in online product communities. Journal of Business Research, 64(8), 889-895. https://doi.org/10.1016/j.jbusres.2010.09.006
  52. Parchoma, G. (2014). The contested ontology of affordances: Implications for researching technological affordances for collaborative knowledge production. Computers in Human Behavior, 37, 360-368. https://doi.org/10.1016/j.chb.2012.05.028
  53. Park, S. C., and Ko, J. (2013). Determining factors for continued use intention of group purchase social commerce users: Focusing on the moderating effect of product involvement. Entrue Journal of Information Technology, 12(2), 139-154.
  54. Rubin, R. B., and McHugh, M. P. (1987). Development of para-social interaction relationships. Journal of Broadcasting and Electronic Media, 31(3), 279-292. https://doi.org/10.1080/08838158709386664
  55. Qi, J., Zhang, Z., Jeon, S., & Zhou, Y. (2016). Mining customer requirements from online reviews: A product improvement perspective. Information & Management, 53(8), 951-963. https://doi.org/10.1016/j.im.2016.06.002
  56. Sashi, C. M. (2012). Customer engagement, buyer seller relationships, and social media. Management Decision, 50(2), 253-272. https://doi.org/10.1108/00251741211203551
  57. Seo, H. S., and Jeong, H. Y. (2020). A study on the formation of behavior inducement (affordance) of mobile fashion O2O service using beacons. e-Business Research, 21(1), 113-130.
  58. Serences, J. T., and Yantis, S. (2007). Spatially selective representations of voluntary and stimulus-driven attentional priority in human occipital, parietal, and frontal cortex. Cerebral cortex, 17(2), 284-293. https://doi.org/10.1093/cercor/bhj146
  59. Shin, G. W., (2021). The era of 'live commerce' has accelerated due to COVID-19... Growing to 8 trillion in 23 years. News 1 News, Retrieved 2021, 08 18, from https://www.news1.kr/articles/?4074298
  60. Sokolova, K., and Kefi, H. (2020). Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. Journal of Retailing and Consumer Services, 53.
  61. Strong, D. M., Volkoff, O., Johnson, S. A., Pelletier, L. R., Tulu, B., Bar-On, I., ... and Garber, L. (2014). A theory of organization-EHR affordance actualization. Journal of the association for information systems, 15(2), 2.
  62. Sun, Y., Shao, X., Li, X., Guo, Y., and Nie, K. (2019). How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 37, 100886.
  63. Thorson, K. S., and Rodgers, S. (2006). Relationships between blogs as eWOM and interactivity, perceived interactivity, and parasocial interaction. Journal of Interactive Advertising, 6(2), 5-44. https://doi.org/10.1080/15252019.2006.10722117
  64. Tian, Y., and Yoo, J. H. (2015). Connecting with the biggest loser: An extended model of para-social interaction and identification in health-related reality TV shows. Health communication, 30(1), 1-7. https://doi.org/10.1080/10410236.2013.836733
  65. Tsalis, G. (2020). What's the deal? Consumer price involvement and the intention to purchase suboptimal foods. A cross-national study. Food Quality and Preference, 79, 103747.
  66. Tucker, C., and Zhang, J. (2011). How does popularity information affect choices? A field experiment. Management Science, 57(5), 828-842. https://doi.org/10.1287/mnsc.1110.1312
  67. Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., and Verhoef, P. C. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of service research, 13(3), 253-266. https://doi.org/10.1177/1094670510375599
  68. Vivek, S. D., Beatty, S. E., and Morgan, R. M. (2012). Customer engagement: Exploring customer relationships beyond purchase. Journal of marketing theory and practice, 20(2), 122-146. https://doi.org/10.2753/MTP1069-6679200201
  69. Volckner, F. (2008). The dual role of price: Decomposing consumers' reactions to price. Journal of the academy of marketing science, 36(3), 359-377. https://doi.org/10.1007/s11747-007-0076-7
  70. Wang, H. M., Lee, K. T., and Kim, N. Y. (2020). The effect of technological incentives on purchase intention of live streaming commerce. Proceedings of the integrated academic presentation of the Korean Management Association, 304-305.
  71. Wang, Z., Lee, S. J., and Lee, K. R. (2018). Factors influencing product purchase intention in Taobao live streaming shopping. Journal of Digital Contents Society, 19(4), 649-659. https://doi.org/10.9728/DCS.2018.19.4.649
  72. Williamson, O. E. (2000). The new institutional economics: Taking stock, looking ahead. Journal of economic literature, 38(3), 595-613. https://doi.org/10.1257/jel.38.3.595
  73. Windels, K., Heo, J., Jeong, Y., Porter, L., Jung, A. R., and Wang, R. (2018). My friend likes this brand: Do ads with social context attract more attention on social networking sites? Computers in Human Behavior, 84, 420-429. https://doi.org/10.1016/j.chb.2018.02.036
  74. Wongkitrungrueng, A., and Assarut, N. (2020). The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117, 543-556 https://doi.org/10.1016/j.jbusres.2018.08.032
  75. Xiang, L., Zheng, X., Lee, M. K., and Zhao, D. (2016a). Exploring consumers'impulse buying behavior on social commerce platform: The role of parasocial interaction. International Journal of Information Management, 36(3), 333-347. https://doi.org/10.1016/j.ijinfomgt.2015.11.002
  76. Xiao, B., and Benbasat, I. (2011). Product-related deception in e-commerce: A theoretical perspective. MIS Quarterly, 35(1), 169-195. https://doi.org/10.2307/23043494
  77. Xu, X., Wu, J. H., and Li, Q. (2020). What drives consumer shopping behavior in live streaming commerce? Journal of Electronic Commerce Research, 21(3), 144-167.
  78. Yim, M. Y. C., Chu, S. C., and Sauer, P. L. (2017). Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective. Journal of Interactive Marketing, 39, 89-103. https://doi.org/10.1016/j.intmar.2017.04.001
  79. Yoo, B., Jeon, S., & Han, T. (2016). An analysis of popularity information effects: Field experiments in an online marketplace. Electronic Commerce Research and Applications, 17, 87-98. https://doi.org/10.1016/j.elerap.2016.03.003
  80. Yoo, J. (2016). Individual broadcasting: The source of benefit. Journal of the Korean Institute of Communication Sciences, 33(4), 71-78
  81. Yu, E., Jung, C., Kim, H., and Jung, J. (2018). Impact of viewer engagement on gift-giving in live video streaming. Telematics and Informatics, 35(5), 1450-1460 https://doi.org/10.1016/j.tele.2018.03.014
  82. Zammuto, R. F., Griffith, T. L., Majchrzak, A., Dougherty, D. J., and Faraj, S. (2007). Information technology and the changing fabric of organization. Organization science, 18(5), 749-762. https://doi.org/10.1287/orsc.1070.0307
  83. Zhao, K., Stylianou, A. C., and Zheng, Y. (2018). Sources and impacts of social influence from online anonymous user reviews. Information & Management, 55(1), 16-30.  https://doi.org/10.1016/j.im.2017.03.006