본문 바로가기
KERD
논문KCI 등재

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

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류학제간연구

초록

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.

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.

참고문헌 (38)

  1. Chia-Chen Chen, "What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement", Telematics and Informatics, 35(1), pp.293~303 (2018), Elsevier BV[DOI]
  2. "Twitch Usage and Growth Statistics: How Many People Use Twitch in 2021"[URL]
  3. Z. Lu., "Understanding and Supporting Live Streaming in Non-Gaming Contexts", Doctoral, pp.2021
  4. "The 47th China Statistical Report on Internet Development"[URL]
  5. Z. Lu, "You Watch, You Give, and You Engage: A Study of Live Streaming Practices in China", Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18), pp.466~ (2018)
  6. X. Su, "An Empirical Study on the Influencing Factors of E-Commerce Live Streaming", 2019 International Conference on Economic Management and Model Engineering (ICEMME)., pp.492~496 (2019)
  7. Z. Lu., "Improving Viewer Engagement and Communication Efficiency within Non-Entertainment Live Streaming", The Adjunct Publication of the 32nd Annual ACM Symposium on User Interface Software and Technology, pp.162~165 (2019)
  8. Chao-Min Chiu, "Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories", Decision Support Systems, 42(3), pp.1872~1888 (2006), Elsevier BV[DOI]
  9. Katrin Scheibe, "Information Behavior on Social Live Streaming Services", Journal of Information Science Theory and Practice, 4(2), pp.6~20 (2016), 한국과학기술정보연구원[DOI]
  10. Examining the learning effects of livestreaming video game instruction over Twitch, "K. Payne", ~ (2017)
  11. Fred D. Davis, "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models", Management Science, 35(8), pp.982~1003 (1989), Institute for Operations Research and the Management Sciences (INFORMS)[DOI]
  12. Ji-Won Moon, "Extending the TAM for a World-Wide-Web context", Information & Management, 38(4), pp.217~230 (2001), Elsevier BV[DOI]
  13. W. Song, "A study of mobile library users' behavioral intentions based on TAM model", Librarianship Research, 11, pp.71~77 (2015)[DOI]
  14. J. Ming, "A review of research on mobile library user perception behavior based on technology acceptance model in my country", Library Forum, 37(7), pp.125~131 (2017)
  15. Marsha L. Richins, "Measuring Emotions in the Consumption Experience", Journal of Consumer Research, 24(2), pp.127~146 (1997), Oxford University Press (OUP)[DOI]
  16. M. Csikszentmihalyi., "Beyond boredom and anxiety: Experiencing flow in work and play" (1975), Jossey-Bass Publishers
  17. Jawaid A. Ghani, "Task Characteristics and the Experience of Optimal Flow in Human—Computer Interaction", The Journal of Psychology, 128(4), pp.381~391 (1994), Informa UK Limited[DOI]
  18. W. Xiong, "Research on the influence of WeChat mobile social user's flow experience on user stickiness", News, 7, pp.13~18 (2015)[DOI]
  19. Judy Chuan-Chuan Lin, "Online stickiness: its antecedents and effect on purchasing intention", Behaviour & Information Technology, 26(6), pp.507~516 (2007), Informa UK Limited[DOI]
  20. Ming-Chi Lee, "Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model", Computers & Education, 54(2), pp.506~516 (2010), Elsevier BV[DOI]
  21. W. Wang, "Research on the Influencing Factors of Online Learning Continuous Willingness Based on Flow Experience", China Distance Education, 05, pp.17~23 (2017)[DOI]
  22. Yi Li, "What Drives Gift-giving Intention in Live Streaming? The Perspectives of Emotional Attachment and Flow Experience", International Journal of Human–Computer Interaction, 37(14), pp.1317~1329 (2021), Informa UK Limited[DOI]
  23. "Journal of Computer-Mediated Communication", 1997, 0~0, pp.Oxford University Press (OUP), 10.1111[DOI]
  24. Nah, "Enhancing Brand Equity Through Flow and Telepresence: A Comparison of 2D and 3D Virtual Worlds", MIS Quarterly, 35(3), pp.731~ (2011), JSTOR[DOI]
  25. Donna L. Hoffman, "Flow Online: Lessons Learned and Future Prospects", Journal of Interactive Marketing, 23(1), pp.23~34 (2009), Elsevier BV[DOI]
  26. M. M. Wong, "Applications of Flow in Human Development and Education", pp.305~326 (2014), Springer
  27. Zixiu Guo, "Promoting online learners’ continuance intention: An integrated flow framework", Information & Management, 53(2), pp.279~295 (2016), Elsevier BV[DOI]
  28. L. LaPointe, "Belonging online : Students' perceptions of the value and efficacy of an online learning community", International Journal on E-learning, 7(4), pp.641~665 (2008)
  29. Richard L. Oliver, "Response Determinants in Satisfaction Judgments", Journal of Consumer Research, 14(4), pp.495~ (1988), Oxford University Press (OUP)[DOI]
  30. Valarie A. Zeithaml, "Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence", Journal of Marketing, 52(3), pp.2~22 (1988), SAGE Publications[DOI]
  31. Morris B. Holbrook, "The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun", Journal of Consumer Research, 9(2), pp.132~ (1982), Oxford University Press (OUP)[DOI]
  32. Jagdish N. Sheth, "Why we buy what we buy: A theory of consumption values", Journal of Business Research, 22(2), pp.159~170 (1991), Elsevier BV[DOI]
  33. Richard L. Oliver, "A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions", Journal of Marketing Research, 17(4), pp.460~469 (1980), SAGE Publications[DOI]
  34. Jillian C Sweeney, "Consumer perceived value: The development of a multiple item scale", Journal of Retailing, 77(2), pp.203~220 (2001), Elsevier BV[DOI]
  35. Penelope Sweetser, "Game Flow: A Model for Evaluating Player Enjoyment in Games", Computers in Entertainment, 3(3), pp.3~3 (2005), Association for Computing Machinery (ACM)[DOI]
  36. J. C. Nunnally., "Psychometric theory" (1978), McGraw Hill
  37. Chia-Lin Hsu, "Flow Experience and Internet Shopping Behavior: Investigating the Moderating Effect of Consumer Characteristics", Systems Research and Behavioral Science, 29(3), pp.317~332 (2012), Wiley[DOI]
  38. W. Yan, "Whether"Ganhuo"are enough : feature extraction and problem mining of knowledge sharing live streaming", Knowledge of library information, 38(4), pp.4~14 (2021)[DOI]

관련 논문AI추천

이 간행물의 다른 논문 (59건)