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http://dx.doi.org/10.15207/JKCS.2021.12.12.197

An empirical study on the influencing factors of learning through knowledge sharing live streaming - Based on live streaming platform in China  

Liu, Yi (Dept. of Experience Design, TED, Kookmin University)
Pan, Young-Hwan (Dept. of Experience Design, TED, Kookmin University)
Publication Information
Journal of the Korea Convergence Society / v.12, no.12, 2021 , pp. 197-211 More about this Journal
Abstract
The emergence of knowledge-sharing live streamers provides more diversified content to the live streaming platform. Analysis of the factors affecting the intention to use knowledge sharing live streaming users can allow the live streaming platform to understand better the adoption characteristics of users who follow this type of content. Help platform operators provide better services and help live streaming platforms innovate. Based on the TAM model, this research uses questionnaire surveys and structural equation models to construct a conceptual model of the influencing factors of users' intentions in the knowledge sharing live streaming and conduct an empirical analysis on the influencing factor models. The results of data analysis show that a significant influence of users' attitudes of knowledge sharing live streaming is perceived usefulness, followed by flow experience; perceived value has a positive impact on users' attitudes and intention to use, and the positive influence of users attitude significantly affect the user's intention.
Keywords
Knowledge sharing live streaming; TAM model; Perceived value; Flow experience; Attitude; Intention;
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1 F. H. Nah. B. Eschenbrenner & D. Dewester. (2011). Enhancing brand equity through flow and telepresence: a comparison of 2d and 3d virtual worlds. Mis Quarterly, 35(3), 731-747. DOI:10.2307/23042806   DOI
2 K. Payne, M. J. Keit, R. M. Schuetaler. (2017). Examining the learning effects of livestreaming video game instruction over Twitch. 2019. DOI: 110.1016/j.chb.08.029
3 W. Wang, R. Shi & X. Li. (2017). Research on the Influencing Factors of Online Learning Continuous Willingness Based on Flow Experience. China Distance Education, (05), 17-23. DOI:10.13541/j.cnki.chinade.20170517.004   DOI
4 Y. Li, Y. Peng. (2021) What drives gift-giving intention in live streaming? The perspectives of emotional attachment and flow experience. International Journal of Human-Computer Interaction. 1-13. DOI:10.1080/10447318.2021.1885224   DOI
5 F. D. Davis, R. P. BAGOZZI & P. R. WARSHAW. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, (8), 982-1003. DOI : 10.1287/mnsc.35.8.982   DOI
6 Z. Lu. (2021) Understanding and Supporting Live Streaming in Non-Gaming Contexts .Doctoral dissertation, University of Toronto.Canada.. DOI : 10.1145/3025453.3025642
7 China Internet Network Information Center.The 47th China Statistical Report on Internet Development. http://www.cnnic.cn/hlwfzyj/hlwxzbg/hlwtjbg/202102/P020210203334633480104.pdf
8 C. C. Chen & Y. C. Liu. (2018) What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telematics and Informatics, 293-303. DOI : 10.1016/j.tele.2017.12.003   DOI
9 Z. Lu, H. Xia, S. Heo & S. Wigdor. (2018). You Watch, You Give, and You Engage: A Study of Live Streaming Practices in China. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18), 2018, pages Paper, 466, 13. DOI : 10.1145/3173574.3174040   DOI
10 C. Chiu, E. T. G.Wang, F. Shih & Y. Fan. (2011). Understanding knowledge sharing in virtual communities. Online Information Review, 35(1), 134-153. DOI : 10.1016/j.dss.2006.04.001   DOI
11 J. W. Moom & Y. G. Kim. (2010) . Extending the TAM for a World-Wide-Web context. Information & management,38(4), 217-230. DOI : 10.1016/S0378-7206(00)00061-6   DOI
12 J. Ming, C. Guo & J. Zhang. (2017). A review of research on mobile library user perception behavior based on technology acceptance model in my country . Library Forum, 37(7), 125-131.
13 C. L. Hsu. K. C. Chang & M. C. Chen. (2012) Flow experience and internet shopping behavior: Investigating the moderating effect of consumer characteristics. Systems Research & Behavioral Science,29(3), 317-332. DOI:10.1002/sres.1101   DOI
14 J. A. Ghani & S. P. Deshpande. (1994) Task Characteristics and the Experience of Optimal Flow in Human-Computer Interaction. The Journal of Psychology: Interdisciplinary and Applied, 128(4), 381-394. DOI : 10.1080/00223980.1994.9712742   DOI
15 M. C. Lee. (2010). Explaining and predicting users'continuance intention toward e-learning: an extension of the expectation-confirmation model. Computers & Education, 54(2), 506-516. DOI:10.1016/j.compedu.2009.09.002   DOI
16 T. Kim & F. Biocca. (1997). Telepresence via television: two dimensions of telepresence may have different connections to memory and persuasion. Journal of Computer-Mediated Communication,3(2). DOI:10.1111/j.1083-6101.1997.tb00073.x   DOI
17 P. Sweetser & P. Wyteh. (2005). Game Flow: A Model for Evaluating Player Enjoyment in Games. Computer in Entertainment, 3(3), 3. DOI:10.1145/1077246.1077253   DOI
18 X. Su. (2019). An Empirical Study on the Influencing Factors of E-Commerce Live Streaming, In 2019 International Conference on Economic Management and Model Engineering (ICEMME). IEEE, 492-496. DOI : 10.1109/ICEMME49371.2019.00103   DOI
19 Z. Lu. (2019) Improving Viewer Engagement and Communication Efficiency within Non-Entertainment Live Streaming. In The Adjunct Publication of the 32nd Annual ACM Symposium on User Interface Software and Technology, 162-165. DOI : 10.1145/3332167.3356879   DOI
20 K. Scheibe, K. J. Fietkiewicz & W. G. Stock. (2016). Information behavior on social live streaming services. Journal of information science theory and practice 4(2), 6-20. DOI : 10.1633/JISTaP.2016.4.2.1   DOI
21 W. Song & X. Zhu. (2015). A study of mobile library users' behavioral intentions based on TAM model. Librarianship Research, (11), 71-77. DOI:10.15941/j.cnki.issn1001-0424.2015.11.014   DOI
22 M. L. Richins. (1997). Measuring Emotions in the Consumption Experience. Journal of Consumer Research 24(2), 127-146. DOI : 10.1086/209499   DOI
23 J. N. Sheth. B. I. Newmen & B. L. Gross. (1991). Why we buy what we buy: a theory of consumption values. Journal of Business Research,22(2), 159-170. DOI:10.1016/0148-2963(91)90050-8   DOI
24 M. Csikszentmihalyi. (1975). Beyond boredom and anxiety: Experiencing flow in work and play. Jossey-Bass Publishers.
25 W. Xiong, S. Wang & Q. Pan. (2015). Research on the influence of WeChat mobile social user's flow experience on user stickiness. News, (7), 13-18. DOI:10.15897/j.cnki.cn51-1046/g2.2015.07.003   DOI
26 C. C. L. Judy. (2007) Online stickiness: its antecedents and effect on purchasing intention. Behavior & Information Technology, 26(6), 507-516. DOI:10.1080/01449290600740843   DOI
27 Twitch Usage and Growth Statistics: How Many People Use Twitch in 2021. Available online:https://backlinko.com/twitch-users
28 R. L. Oliver & W. S. De Sarbo. (1988) Response determinants in satisfaction judgments. Journal of Consumer Research,14(4), 495-508. DOI:10.1086/209131   DOI
29 V. A. Zeithaml. (1988). Consumer perceptions of price,quality and value: a means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22. DOI:10.1177/002224298805200302   DOI
30 M. B. Holbrook & E. C. Hirschman. (1982) The experiential aspects of consumption: consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2), 132-140. DOI:10.1086/208906   DOI
31 R. L. Oliver. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research,17(4), 460-469. DOI:10.1177/002224378001700405   DOI
32 J. C. Sweeney & G. N. Soutar. (2001). Consumer perceived value: the development of a multiple item scale. Journal of Retailing, 77(2), 203-220. DOI:10.1016/S0022-4359(01)00041-0   DOI
33 J. C. Nunnally. (1978). Psychometric theory. McGraw Hill.
34 L. LaPointe & M. Reisetter. (2008). Belonging online: Students' perceptions of the value and efficacy of an online learning community. International Journal on E-learning, 7(4), 641-665.
35 D. L. Hoffman & T. P. Novak. (2009).Flow online: Lessons learned and future prospects. Journal of Interactive Marketing, 23(1), 23-34. DOI:10.1016/j.intmar.2008.10.003   DOI
36 M. M. Wong & M. Csikszentmihalyi. (2014). Affiliation motivation and daily experience: Some issues on gender differences. In Applications of Flow in Human Development and Education. Springer, Dordrecht, 305-326. DOI:10.1007/978-94-017-9094-9_16
37 Z. Guo, L. Xiao. C. V. Toorn. Y. Lai & C. Seo. (2015). Promoting online learners'continuance intention: an integrated flow framework. Information & Management, 53(2), 279-295. DOI:10.1016/j.im.2015.10.010   DOI
38 W. Yan. (2021). Whether "Ganhuo" are enough: feature extraction and problem mining of knowledge sharing live streaming. Knowledge of library information. 38(4), 4-14. DOI:10.13366/j.dik.2021.04.004   DOI