Bibliometric knowledge mapping of consumers’ inferring shopping experience in live E-commerce platform on data mining
Vol 8, Issue 3, 2023, Article identifier:
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Abstract
This study utilizes bibliometric knowledge mapping to guide the development of consumer behavior within a theoretical framework in live E-commerce environments. This analysis is based on data collected by existing customers on platforms such as Taobao, JD.com, Mogujie, Xiaohongshu, and Jumei Youpin. The literature survey chart is based on 2000 articles. The resulting theoretical concept shares the same structure as the stimulus-organism-response (S-O-R) model of consumer behaviors. The stimulus has social, technical, stream and viewer factors. The social stimulus includes social influence, and interaction among the live anchor and the consumers. The stream factor is represented by vicarious learning. The technical stimulus consists of performance and effort expectancies, the synchronicity of live session and system, and system and service quality. The viewer stimulus consists of impulsive buying tendency and innovativeness of consumers. Multilayer perceptron neural network (MLP-NN), which integrates many variables in bibliometrics, has become an effective means to guide Structural Equation Modeling (SEM) analysis and configuration, thus generating a knowledge base. The study offers many practical and theoretical implications, for instance, numerous theories are found fit to explain the roles of social stimuli such as cognitive development theory. Trust and enjoyment are found to significantly influence consumers’ flow state, which implies the working of cognitive appraisal theory, as an expanded insight into the flow theory of consumer behaviors. In addition, addiction to live is another factor that is significantly critical to influencing the impulsive buying of consumers.
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1. Ye M, Tan CC. Research and application of agricultural energy Internet intelligent system for live streaming E-commerce based on MATLAB analysis in China. Energy Reports 2022; 8: 227–239. doi: 10.1016/j.egyr.2022.10.308
2. Ye M, Tan CC. Research and application of compulsive buying behaviors of consumers in E-commerce live big data. Human-Centric Intelligent Systems 2022; 2(1). doi: 10.1007/s44230-22-00010-2
3. Xue J, Liang X, Xie T, Wang H. See now, act now: How to interact with customers to enhance social commerce engagement? Information & Management 2020; 57(6): 103324. doi: 10.1016/j.im.2020.103324
4. Yin S. A study on the influence of E-commerce live streaming on consumer’s purchase intentions in mobile internet. In: Stephanidis C, Salvendy G, Wei J, et al. (editors). HCI International 2020—Late Breaking Papers: Interaction, Knowledge and Social Media, Proceedings of the 22nd International Conference on Human-Computer Interaction; 19–24 July 2020; Copenhagen, Denmark. Springer, Cham; 2020. Volume 12427, pp. 720–732.
5. Lu Y, He Y, Ke Y. The influence of E-commerce of live streaming affordance on consumers’ gift-giving and purchase intention. Data Science and Management 2023; 6(1): 13–20. doi: 10.1016/j.dsm.2022.10.002
6. Omar S, Mohsen K, Tsimonis G, et al. M-commerce: The nexus between mobile shopping service quality and loyalty. Journal of Retailing and Consumer Services 2021; 60: 102468. doi: 10.1016/j.jretconser.2021.102468
7. Barta S, Flavián C, Gurrea R. Managing consumer experience and online flow: differences in handheld devices vs PCs. Technology in Society 2021; 64: 101525. doi: 10.1016/j.techsoc.2020.101525
8. Liu H, Chu H, Huang Q, Chen X. Enhancing the flow experience of consumers in China through interpersonal interaction in social commerce. Computers in Human Behavior 2016; 58: 306–314. doi: 10.1016/j.chb.2016.01.012
9. Ettis SA. Examining the relationships between online store atmospheric color, flow experience and consumer behavior. Journal of Retailing and Consumer Services 2017; 37: 43–55. doi: 10.1016/j.jretconser.2017.03.007
10. Serravalle F, Vanheems R, Viassone M. Does product involvement drive consumer flow state in the AR environment? A study on behavioral responses. Journal of Retailing and Consumer Services 2023; 72: 103279. doi: 10.1016/j.jretconser.2023.103279
11. Wang IK, Seidle R. Ambition in innovation: Vicarious learning in the nascent electric scooter market in Taiwan. Technological Forecasting & Social Change 2020; 152: 119886. doi: 10.1016/j.techfore.2019.119886
12. Catani L, Grassi E, di Montanara AC, et al. Essential oils and their applications in agriculture and agricultural products: A literature analysis through VOSviewer. Biocatalysis and Agricultural Biotechnology 2022; 45: 102502. doi: 10.1016/j.bcab.2022.102502
13. Gavrilla Gavrilla S, de Lucas Ancillo A. COVID-19 as an entrepreneurship, innovation, digitization and digitalization accelerator: Spanish internet domains registration analysis. British Food Journal 2021; 123(10): 3358–3390. doi: 10.1108/BFJ-11-2020-1037
14. Zhu L, Li H, Wang FK, et al. How online reviews affect purchase intention: a new model based on the stimulus-organism-response (S-O-R) framework. Aslib Journal of Information Management 2020; 72(4): 463–488. doi: 10.1108/AJIM-11-2019-0308
15. Alanadoly A, Salem S. Fashion involvement, opinion-seeking and product variety as stimulators for fashion E-commerce: An investigated model based on S-O-R model. Asia Pacific Journal of Marketing and Logistics 2022; 34(10): 2410–2434. doi: 10.1108/APJML-06-2021-0447
16. Zhang JX, Ip RKF. E-commerce advertising in social networking sites and implications for social commerce. In: Proceedings of the 19th Pacific Asia Conference on Information Systems; 5–9 July 2015; Singapore.
17. Baboli A, Okamoto J, Tsuzuki MSG, et al. Intelligent manufacturing system configuration and optimization considering mobile robots, multi-functional machines and human operators: New facilities and challenge for industrial engineering. IFAC-PapersOnLine 2015; 48(3): 1912–1917. doi: 10.1016/j.ifacol.2015.06.366
18. Han F, Li B. Exploring the effect of an enhanced E-commerce institutional mechanism on online shopping intention in the context of E-commerce poverty alleviation. Information Technology & People 2021; 34(1): 93–122. doi: 10.1108/ITP-12-2018-0568
19. Kim J, Lennon SJ. Effects of reputation and website quality on online consumers’ emotion, perceived risk and purchase intention: Based on the stimulus‐organism‐response model. Journal of Research in Interactive Marketing 2013; 7(1): 33–56. doi: 10.1108/17505931311316734
20. Wu YL, Li EY. Marketing mix, customer value, and customer loyalty in social commerce: A stimulus-organism-response perspective. Internet Research 2018; 28(1): 74–104. doi: 10.1108/IntR-08-2016-0250
21. Hewei T, Youngsook L. Factors affecting continuous purchase intention of fashion products on social E-commerce: SOR model and the mediating effect. Entertainment Computing 2022; 41: 100474. doi: 10.1016/j.entcom.2021.100474
22. Tarka P, Kukar-Kinney M, Harnish RJ. Consumers’ personality and impulsive buying behavior: The role of hedonistic shopping experiences and gender in mediating-moderating relationships. Journal of Retailing and Consumer Services 2022; 64: 102802. doi: 10.1016/j.jretconser.2021.102802
23. Zheng Y, Yang X, Liu Q, et al. Perceived stress and online impulsive buying among women: A moderated mediation model. Computers in Human Behavior 2020; 103: 13–20. doi: 10.1016/j.chb.2019.09.012
24. Mrad M, Cui CC. Comorbidity of impulsive buying and brand addiction: An examination of two types of addictive consumption. Journal of Business Research 2020; 113: 399–408. doi: 10.1016/j.jbusres.2019.09.023
25. Han MS, Hampson DP, Wang Y, Wang H. Consumer confidence and green purchase intention: An application of the stimulus-organism-response model. Journal of Retailing and Consumer Services 2022; 68: 103061. doi: 10.1016/j.jretconser.2022.103061
26. Tan CC, Patthracholakorn AI. Towards a community-based theory of brand community engagement. Advanced Science Letters 2018; 24(7): 5167–5170. doi: 10.1166/asl.2018.11296
27. Yang SU, Grunig JE. Decomposing organizational reputation: The effects of organization-public relationship outcomes on cognitive representations of organizations and evaluations of organizational performance. Journal of Communication Management 2005; 9(4): 305–325. doi: 10.1108/13632540510621623
28. Vojvodic K, Matic M. Challenges of e-tailing: Impulsive buying behavior. International Business & Management 2013; 29: 155–171. doi: 10.1108/S1876-066X(2013)0000029013
29. Csikszentmihalyi M. Good Business: Leadership, Flow, and the Making of Meaning. Viking Adult; 2003.
30. Csikszentmihalyi M. Flow: The Psychology of Happiness. Rider; 2022.
31. Rathunde K, Csikszentmihalyi M. Middle school students’ motivation and quality of experience: A comparison of montessori and traditional school environments. American Journal of Education 2005; 111(3): 341–371. doi: 10.1086/428885
32. Faqih KMS. Internet shopping in the COVID-19 era: Investigating the role of perceived risk, anxiety, gender, culture, and trust in the consumers’ purchasing behavior from a developing country context. Technology in Society 2022; 70: 101992. doi: 10.1016/j.techsoc.2022.101992
33. Bolton ML, Siminiceanu RI, Bass EJ. A systematic approach to model checking human-automation interaction using task analytic models. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 2011; 41(5): 961–976. doi: 10.1109/TSMCA.2011.2109709
34. Roethke K, Kumpe J, Adam M, Benlian A. Social influence tactics in E-commerce onboarding: The role of social proof and reciprocity in affecting user registrations. Decision Support Systems 2020; 131: 113268. doi: 10.1016/j.dss.2020.113268
35. Borisova EA. Development of acceptable risk skills among students of technical higher educational institutions based on interactive case technologies. In: Proceedings of the 2022 International Conference on Information Science and Communications Technologies (ICISCT); 28–30 September 2022; Tashkent, Uzbekistan. pp. 1–5.
36. Crespo ÁH, del Bosque RI. The effect of innovativeness on the adoption of B2C E-commerce: A model based on the theory of planned behaviour. Computers in Human Behavior 2008; 24(6): 2830–2847. doi: 10.1016/j.chb.2008.04.008
37. Cao CW, Reid M, Hung YC. Vicarious innovativeness or vicarious learning: The role of existing vicarious innovativeness in new product purchase intentions. Australasian Marketing Journal 2016; 24(1): 87–92. doi: 10.1016/j.ausmj.2016.01.006
38. Lissitsa S, Kushnirovich N, Aharoni M. Domestication of remote threats: From vicarious learning of foreign events to local intergroup relations. International Journal of Intercultural Relations 2022; 87: 157–168. doi: 10.1016/j.ijintrel.2022.02.004
39. Donmez-Turan A. Does the unified theory of acceptance and use of technology (UTAUT) reduce resistance and anxiety of individuals towards a new system? Kybernetes 49(5): 1381–1405. doi: 10.1108/k-08-2018-0450
40. Ang T, Wei S, Anaza N. live vs pre-recorded: How social viewing strategies impact consumers’ viewing experiences and behavioral intentions. European Journal of Marketing 2018; 52(9/10): 2075–2104. doi: 10.1108/ejm-09=2017-0576
41. Picot-Coupey K, Krey N, Huré E, Ackermann CL. Still work and/or fun? Corroboration of the hedonic and utilitarian shopping value scale. Journal of Business Research 2021; 126: 578–590. doi: 10.1016/j.jbusres.2019.12.018
42. Akdim K, Casalo LV, Flavian C. The role of utilitarian and hedonic aspects in the continuance intention to use social mobile apps. Journal of Retailing and Consumer Services 2022; 66: 102888. doi: 10.1016/j.jretconser.2021.102888
43. Liu Y. Developing a scale to measure the interactivity of websites. Journal of Advertising Research 2003; 43(2): 207–216. doi: 10.1017/S0021849903030204
44. Lubis M, Handayani DOD. The relationship of personal data protection towards internet addiction: Cyber crimes, pornography, and reduced physical activity. Procedia Computer Sciences 2022; 197: 151–161. doi: 10.1016/j.procs.2021.12.129
45. Forza C. Survey research in operations management: A process-based perspective. International Journal of Operations & Production Management 2002; 22(2): 152–194.
46. Navani N, Brown JM, Nankivell M, et al. Suitability of endobronchial ultrasound-guided transbronchial needle aspiration specimens for subtyping and genotyping of non-small cell lung cancer: a multicenter study of 774 patients. American Journal of Respiratory & Critical Care Medicine 2012; 185(12): 1316–1322. doi: 10.1164/rccm.201202-0294OC
47. Al Nageim H, Nagar R, Lisboa PJG. Comparison of neural network and binary logistic regression methods in conceptual design of tall steel buildings. Construction Innovation 2007; 7(3): 240–253. doi: 10.1108/14714170710754731
48. Bakti IGMY, Sumaedi S. P-TRANQUIL: A service quality model of public land transport services. International Journal of Quality & Reliability Management 2015; 32(6): 534–558. doi: 10.1108/IJQRM-06-2013-0094
49. Wolf EJ, Harrington KM, Clark SL, Miller MW. Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Education Psychology Measurement 2013; 76(6): 913–934. doi: 10.1177/0013164413495237
50. Agarwal V. Investigating the convergent validity of organizational trust. Journal of Communication Management 2013; 17(1): 24–39. doi: 10.1108/13632541311300133
51. Lima-Junior FR, Carpinetti LCR. Predicting supply chain performance based on SCOR metrics and multilayer perceptron neural networks. International Journal of Production Economics 2019; 212(C): 19–38. doi: 10.1016/j.ijpe.2019.02.001
52. Ayodeji Y, Rjoub H, Özgit H. Achieving sustainable customer loyalty in airports: The role of waiting for time satisfaction and self-service technologies. Technology in Society 2023; 72: 102106. doi: 10.1016/j.techsoc.2022.102106
53. Herbelin B, Benzaki P, Françoise R, et al. Using physiological measures for emotional assessment: A computer-aided tool for cognitive and behavioural therapy. International Journal on Disability and Human Development 2005; 4(4): 276–284. doi: 10.1515/IJDHD.2005.4.4.269
54. Kim H, Huh C, Song C, Lee MJ. How can hotel smartphone apps enhance hotel guest experiences? An integrated model of experiential value. Journal of Hospitality and Tourism Technology 2021; 12(4): 791–815. doi: 10.1108/JHTT-07-2020-0176
55. Kranjčev M, Hlupić TV. Personality, anxiety, and cognitive failures as predictors of flow proneness. Personality and Individual Differences 2021; 179: 110888. doi: 10.1016/j.paid.2021.110888
56. Lu H, Wang S. The role of Internet addiction in online game loyalty: An exploratory study. Internet Research 2008; 18(5): 499–519. doi: 10.1108/10662240810912756
57. Grew B, Collins JC, Schneider CR, Carter SR. How does perceived cost and value influence pharmacy patronage? A scoping review. International Journal of Pharmaceutical Healthcare Marketing 2020; 14(4): 641–663. doi: 10.1108/IJPHM-12-2019-0077
58. Schilke O, Cook KS. Sources of alliance partner trustworthiness: Integrating calculative and relational perspectives. Strategic Management Journal 2015; 36(2): 276–297. doi: 10.1002/smj.2208
59. Wu CG, Ho JC. The influences of technological characteristics and user beliefs on customers’ perceptions of live chat usage in mobile banking. International Journal of Bank Marketing 2022; 40(1): 68–86. doi: 10.1108/IJBM-09-2020-0465
60. Shen H, Zhao C, Fan DXF, Buhalis D. The effect of hotel live on viewers’ purchase intention: Exploring the role of parasocial interaction and emotional engagement. International Journal of Hospitality Management 2022; 107: 103348. doi: 10.1016/j.ijhm.2022.103348
61. Qian TY, Matz R, Luo L, Xu C. Gamification for value creation and viewer engagement in gamified live services: The moderating role of gender in esports. Journal of Business Research 2022; 145: 482–494. doi: 10.1016/j.jbusres.2022.02.082
DOI: https://doi.org/10.54517/esp.v8i3.1858
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