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A Study on the Influencing Factors on Social Media Use Intensity and Fatigue, and the Moderating Effect of Process Incentive Expectations

소셜 미디어 사용 강도 및 피로감에 미치는 영향 요인과 성과기대의 조절 효과 연구

  • Park, Kiho (Dept of Management of Digital Technology, Hoseo University)
  • 박기호 (호서대학교 디지털기술경영학과)
  • Received : 2021.02.25
  • Accepted : 2021.05.20
  • Published : 2021.05.28

Abstract

This study empirically studied the factors affecting the intensity of use of mobile social media and fatigue. Theories for the research framework were based on the theory of planned behavior, the theory of private information protection, the theory of flow, and the theory of process incentives. As a result of data analysis, it was found that self-efficacy, user habits, and flow experience positively influence the intensity of mobile social media use. This study assumed that personal information protection issues negatively affect the intensity of mobile social media use, but have little influence on the use intensity. The intensity of media use had a positive effect on media fatigue. In other words, when the intensity of using mobile social media increased, the feeling of fatigue increased. The expected process incentives variable did not show a moderating effect between media use intensity and social media fatigue. The findings will have implications for social media-related companies and organizations that want to use social media tools for business and public services.

본 연구는 모바일 소셜 미디어 사용 강도와 사용 피로감에 미치는 영향 요인에 대하여 실증적으로 연구하였다. 연구의 프레임워크를 위한 이론으로는 계획된 행위 이론, 사적정보보호 이론, 몰입 이론, 절차적 성과 이론을 기반으로 하였다. 데이터 분석 결과 자기 효능감, 사용자 습관 및 몰입 경험이 모바일 소셜 미디어 사용 강도(intensity)에 긍정적 영향을 미치는 것으로 나타났다. 개인 정보 보호 문제는 모바일 소셜 미디어 사용 강도에 부정적인 영향을 미치기는 하나 사용행위에는 영향력이 미미하였다. 미디어 사용 강도는 미디어 피로감에 긍정적 영향을 미쳤다. 즉, 모바일 소셜 미디어 사용강도가 높아질 경우 피로감은 증가하였다. 절차적 성과 기대 변수는 미디어 사용 강도와 소셜 미디어 피로감 사이에 조절효과를 보이지 않았다. 연구 결과는 소셜 미디어 도구를 비즈니스 및 공공 서비스에 활용하고자 하는 소셜 미디어 관련 기업 및 조직에 시사점을 줄 것이다.

Keywords

References

  1. Gartner. (2017). Gartner survey highlights consumer fatigue with social media [EB/OL]. http://www.gartner.com/newsroom/id/1766814,2017.
  2. S. H., Kim & H. S. Park. (2015). Empirical Study on Antecedents and Consequences of Users' Fatigue on SNS and the Moderating Effect of Habit, Journal of Information Technology Service, 14(4), 137-157. https://doi.org/10.9716/KITS.2015.14.4.137
  3. K. H. Park. (2017). Exploring Differing Communication among Generation in a Social Network Age. Journal of Information Technology Applications & Management, 24(1), 11-24. UCI(KEPA) : I410-ECN-0101-2017-005-002410273 https://doi.org/10.21219/jitam.2017.24.1.011
  4. H. S. Park & S. H. Kim. (2014). A Study on the Effects of SNS Fatigue Factors on Intention to stop using SNS and The Moderating Effect of Service Flow, Kaemyung University, Business Management Review, 47(2), 1-24. UCI(KEPA) : I410-ECN-0101-2016-325-000940635
  5. M. S. Yoon & N. H. Kim. (2018). The Relationship between SNS addiction, SNS fatigue and Depression among Adult - The Moderated Mediating Effect of SNS Usage Intention, Mental Health & Social Work, 46(2), 120-149. DOI : 10.24301/MHSW.2018.06.46.2.120
  6. S. B. Lee & J. Y. Moon. (2017). The Impact of Technostress on Social Interaction Overload in Social Network Service, Journal of the Korea Contents Association, 17(12), 25-33, UCI(KEPA) : I410-ECN-0101-2018-310-001730930 https://doi.org/10.5392/JKCA.2017.17.12.025
  7. E. J. Lee. (2018). The Antecedents of SNS Fatigue : Influences on Intention to Continuous Usage and Discontinuing Intention, Journal of the HCI Society of Korea, 13(2), 21-29. DOI : 10.17210/jhsk.2018.05.13.2.21
  8. E. Y. Choi & N. M. Sin. (2020). The Relationship Between SNS Fatigue and University Students' Behaviors of Academic Procrastination and Learning Flow, Journal of Knowledge Information Technology, 15(3), 373-382.
  9. H. Aarts, & A. Dijksterhuis. (2000). Habits as knowledge structures: automaticity in goal directed behavior. Journal of Personality & Social Psychology, 78(1), 53-63. https://doi.org/10.1037/0022-3514.78.1.53
  10. I. Ajzen. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Process, 52(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  11. N. F. Awad & M. S. Krishnan. (2006). The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled on line for personalization. MIS Quarterly, 30(1), 13-28. https://doi.org/10.2307/25148715
  12. A. Bandura. (1982). Self-Efficacy mechanism in human agency. American Psychologist, 37(2), 122-147. https://doi.org/10.1037//0003-066X.37.2.122
  13. A. Bandura. (1986). Social foundations of thought and action: a social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
  14. K. Berchelmann. (2020). 6 steps to effective incentive compensation. https://www.iidmglobal.com/expert_talk/expert-talkcategories/managing-people/incentive_reward_bonus/id42560.html
  15. P. B. Brandtzaeg & J. Heim. (2009). Why people use social networking sites. 3rd International Conference on Online Communities and Social Computing: HeldAs. Springer-Verlag,143-152.
  16. L. F. Bright, S. B. Kleiser & S. L. Grau. (2015). Too much Facebook? An exploratory examination of social media fatigue. Computers in Human Behavior, 44(C): 148-155. https://doi.org/10.1016/j.chb.2014.11.048
  17. Y. Chang & D. Zhu. (2012). The role of perceived social capital and flow experience in building user's continuance intention to social networking sites in China. Computers in Human Behavior, 28, 995-1001. https://doi.org/10.1016/j.chb.2012.01.001
  18. C. M. K. Cheung & M. Limayem. (2005). The role of habit in information systems continuance: examining the evolving relationship between intention and usage. ICIS 2005 Proceedings. Caen, France, 39.
  19. China Internet Network Information Center. (2018). The 44th China Statistical Report on Internet Development.[EB/OL].
  20. M. Csikszentmihalyi (1990). Flow: The psychology of optimal experience: New York: Harper and Row.
  21. M. J. Culnan & P. K. Armstrong. (1999). Information privacy concerns, procedural, and impersonal trust: An empirical investigation[J]. Organization Science, 10(1). 104-115 https://doi.org/10.1287/orsc.10.1.104
  22. A. Dhir, Y. Yossatorn & P. Kaur. (2018). Online social media fatigue and psychological wellbeing-A study of compulsive use, fear of missing out, fatigue, anxiety and depression. International Journal of Information Management. 40, 141-152. https://doi.org/10.1016/j.ijinfomgt.2018.01.012
  23. T. Dienlin & S. Trepte. (2015). Is the privacy paradox a relic of the past? An in - depth analysis of privacy attitudes and privacy behaviors. European Journal of Social Psychology, 45(3), 285 - 297. https://doi.org/10.1002/ejsp.2049
  24. T. Dinev & P. Hart. (2006). An extended privacy calculus model for e-commerce transactions [J]. Information Systems Research, 17(1), 61-80. https://doi.org/10.1287/isre.1060.0080
  25. W. B. Dodds, K. B. Monroe & D. Grewal. (1991). Effects of price, brand, and store information on buyers' product evaluations. Journal of Marketing Research, 1991(28), 307-319.
  26. M. S. Eastin, & R. Larose. (2000). Internet self-efficacy and psychology of the digital divide [J]. Journal of Computer-Mediated Communication, 6(1):
  27. N. B. Ellison, C. Steinfield & C. Lampe. (2007). The benefits of Facebook "friends": exploring the relationship between college students[J]. Journal of Computer-Mediated Communication, 12, 1143-1168. https://doi.org/10.1111/j.1083-6101.2007.00367.x
  28. Li Fenghua, Li Hui, N. Ben & Chen Jinjun. (2019). Privacy Computing: Concept, Computing Framework, and Future Development Trends, Engineering 5(6), 1179-1192. https://doi.org/10.1016/j.eng.2019.09.002
  29. W. He, K. K. Wei. (2009). What drives continued knowledge sharing? an investigation of knowledge-contribution and - seeking beliefs [J]. Decision Support Systems, 46(4), 826-838. https://doi.org/10.1016/j.dss.2008.11.007
  30. M. B. Holbrool & E. C. Hirschman. (1982). The experiential aspects of consumption: Consumer fantasies, feelings and fun. Journal of Consumer Research, 9(2), 132-140 https://doi.org/10.1086/208906
  31. H. C. Huang, S. S. Chang & J. Lous. (2015). Preliminary investigation on recreation and leisure knowledge sharing by LINE [J]. Procedia-social and Behavioral Science, 174(12), 3072 - 3080. https://doi.org/10.1016/j.sbspro.2015.01.1100
  32. C. C. Judy. (2007). Online stickiness: its antecedents and effect on purchasing intention [J]. Behavior & Information Technology, 26(6), 507-516. https://doi.org/10.1080/01449290600740843
  33. M. Kasa & Z. Hassan. (2013). Antecedent and consequences of flow: lessons for developing human resources [J]. Procedia Social and Behavioral Sciences, 97(1), 209-213. https://doi.org/10.1016/j.sbspro.2013.10.224
  34. B. Kim. (2012). The diffusion of mobile data services and applications: exploring the role of habit and its antecedents[J]. Telecommunications Policy, 36(1), 69-81. https://doi.org/10.1016/j.telpol.2011.11.011
  35. B. Kim, & I. Han. (2009). The role of trust belief in community - driven knowledge and its antecedents [J]. Journal of the American Society for Information Science & Technology, 60(5), 1012-1026. https://doi.org/10.1002/asi.21041
  36. R. S. Laufer & M. Wolfe. (1977). Privacy as a concept and social issue: A multidimensional developmental the theory [J]. Journal of Social Issues, 3(3):22-41.
  37. A. R. Lee, S. M. Son & K. K. Kim. (2016). Information and communication technology overload and social networking service fatigue: a stress perspective [J]. Computers in Human Behavior, 55, 51-61. https://doi.org/10.1016/j.chb.2015.08.011
  38. M. Limayem & C. M. K. Cheung. (2008). Understanding information systems continuance: the case of internet - based learning technologies [J]. Information & Management, 45(4), 227 - 232 https://doi.org/10.1016/j.im.2008.02.005
  39. M. Limayem, S. G. Hirt & C. M. K. Cheung. (2007). How habit limits the predictive power of intentions: the case of IS continuance. MIS Quarterly, 31(4), 705-737. https://doi.org/10.2307/25148817
  40. J. Liu, Y. X. Zhao & Q. H. Zhu. (2012). A review of research on divers and their diving motives in the Internet environment [J]. Library and Information Service, 56(18), 65-72..
  41. L. C. Liu, X. Li & B. Q. Zhang. (2017). Research on social media user fatigue and negative use based on grounded theory. Information Theory and Practice, 40(12), 100-106.
  42. A. Luqman, X. Cao & Alia. (2017). Do you get exhausted from too much socializing? Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm. Computers in Human Behavior.
  43. A. Luqman, X. F. Cao, A. Ali, A. Masood & L. L. Yu. (2017). Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm [J]. Computers in Human Behavior, 70, 544-555. https://doi.org/10.1016/j.chb.2017.01.020
  44. C. Maier, S. Laumer & Eckhardta. (2013). When social networking turns to social overload: Explaining the stress, emotional exhaustion, and quitting behavior from social network sites'users [C]. ECIS 2012 Proceedings. AIS Electronic Library.
  45. C. Maier, S. Laumer & Eckhardta. (2015). Giving too much social support: social overload on social networking sites[J]. European Journal of Information Systems, 24(5), 447-464. https://doi.org/10.1057/ejis.2014.3
  46. G. Mark, D. Gudith & U. Klocke. (2008). The cost of interrupted work: more speed and stress [A]. Proceedings of the 2008 Conference on Human Factors in Computing Systems. Florence, Italy, 107-110.
  47. J. A. Oldmeadow, S. Quinn & R. Kowert. (2013). Attachment style, social skills, and Facebook use amongst adults. Computers in Human Behavior, 29(3), 1142-1149. https://doi.org/10.1016/j.chb.2012.10.006
  48. J. A. Ouellette & W. Wood. (1998). Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior [J]. Psychological Bulletin, 124(124), 54-74. https://doi.org/10.1037/0033-2909.124.1.54
  49. Kiho, Park & Ren, Gaufei. (2019). Does social media use increase or decrease learning performance? a meta-analysis based on international english journal studies, The Journal of Information Systems, 28(4), 293-311, https://doi.org/10.5859/KAIS.2019.28.4.293
  50. L. H. Peng, H. Li & Y. F. Zhang. (2018). Research on the factors influencing user privacy security on mobile social media fatigue behavior-the CAC research paradigm based on privacy computing theory. Information Science, 9, 96-102.
  51. M. G. Rodriguez, K. , Gummadi & B. Schoelkopf. (2014). Quantifying information overload in social media and its impact on social contagions. Physics.
  52. Rogers. (2002). Diffusion of innovation [M]. Trans. Xin xin. Beijing: Central Compilation and Translation Press.
  53. R. Ruth. (2001). An exploration of flow during internet use internet research [J]. Electronic Networking Applications and Policy, 11(2), 103-113. https://doi.org/10.1108/10662240110695070
  54. C. Q. I. Shun & X. U. Yunjie. (2011). Designing not just for pleasure: effects of Web Site esthetics on consumer shopping value [J]. International Journal of Electronic Commerce, 15(4), 159-188. https://doi.org/10.2753/JEC1086-4415150405
  55. D. J. Solove. (2006). A taxonomy of privacy [J]. University of Pennsylvania Law Review, 154(3), 477-564. https://doi.org/10.2307/40041279
  56. C. Speier, J. S. Valacich & I. Vessey. (2010). The influence of task interruption on individual decision making: An information overload perspective[J]. Decision Sciences, 30(2), 337-360. https://doi.org/10.1111/j.1540-5915.1999.tb01613.x
  57. C. S. Tang & Y. Y. Koh. (2017). Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective discorder. Asian Journal of Psychiatry, 25, 175-178. https://doi.org/10.1016/j.ajp.2016.10.027
  58. H. C. Triandis. (1971). Attitude and attitude change. New York: Wiley.
  59. B. Verplanken, H. Aarts & A. V. Knippenberg. (1997). Habit, information acquisition, and the process of making travel mode choices. European Journal of Social Psychology, 7(5), 539-560.
  60. H. Xu, S. Gupta & M. B. Rosson. (2012). Measuring mobile users' concerns for information privacy [C]. In proceeding of the 33rd International Conference on Information Systems, Orlando.
  61. H. Xu, H. H. Teo & B. Tan. (2009). The role of push- pull technology in privacy calculus: the case of location - based services. Journal of Management Information Systems, 26(3), 135-174. https://doi.org/10.2753/MIS0742-1222260305
  62. M. F. Xu & J. Y. Ye. (2011). A summary of research on knowledge sharing in academic virtual communities. Library and Information Work, 55(13), 67-71, 125.
  63. S. Zhang, L. Zhao & Y. Lu. (2016). Do you get tired of socializing? An empirical explanation of discontinuous usage behavior in social network services [J]. Information & management, 53(7), 904-914. https://doi.org/10.1016/j.im.2016.03.006
  64. W. Zhou & J. Q. Lu. (2011). Examining mobile instant messaging user loyalty from loyalty from the perspectives of network externalities and flow experience. Computers in Human Behavior, 27(2), 883-889. https://doi.org/10.1016/j.chb.2010.11.013
  65. R. K. Yin, Case Study Research:Design and Methods, second edition. SAGE Publications. 1994.