Journal of Information Science Theory and Practice

  • 제12권4호
  • /
  • Pages.1-15
  • /
  • 2024
  • /
  • 2287-9099(pISSN)
  • /
  • 2287-4577(eISSN)
한국과학기술정보연구원 과학기술정보센터 (Korea Institute of Science and Technology Information)
권호 표지
DOI QR코드

DOI QR Code

Examining Social Networking Sites Users' Benefits Using the DeLone and McLean Information System Success Model

  • Nusrat Jahan (Department of Management, Rabindra University Bangladesh) ;
  • Md. Shah Azam (Department of Marketing, University of Rajshahi) ;
  • Md. Alamgir Hossain (Department of Management, Hajee Mohammad Danesh Science and Technology University)
  • 투고 : 2023.11.20
  • 심사 : 2024.05.19
  • 발행 : 2024.12.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In the modern era of technological and communication amelioration, social networking sites (SNSs) open the door, not only in industrial sectors but also as a platform which mostly aims attention at easing the formation of social correspondence among people. On the basis of the DeLone and McLean model of information system success (2003), this study develops a modified IS model to examine the individual merits (i.e., enjoyment, expected relationships, and reputation) obtained from using SNSs. Structural equation modeling is used to investigate the above issues, and an online survey questionnaire technique is conducted with 421 respondents from the United States, and several interesting results are observed. System quality significantly affects both satisfaction and use intention, whereas information and service quality have a large impact on satisfaction but not on use intention. Individual benefits (such as enjoyment, expected relationships, reputation, and use intention) are all positively impacted by satisfaction. In addition, use intention acts as an intermediate construct and has positive effects on individual benefits. SNS practitioners should focus on awareness-raising initiatives regarding the personal benefits enabled by SNS use, as well as educational programs to encourage SNS usage at the user level, by identifying and addressing the specific individual benefits.

키워드

과제정보

This research is funded by the Rabindra University Bangladesh.

참고문헌

  1. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411
  2. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage.
  3. Chang, Y. P., & Zhu, D. H. (2012). The role of perceived social capital and flow experience in building users' continuance intention to social networking sites in China. Computers in Human Behavior, 28(3), 995-1001. https://doi.org/10.1016/j.chb.2012.01.001
  4. Chen, K. H., Shen, K. S., & Ma, M. Y. (2012). The functional and usable appeal of Facebook SNS games. Internet Research, 22(4), 467-481. https://doi.org/10.1108/10662241211250999
  5. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95. https://doi.org/10.1287/isre.3.1.60
  6. DeLone, W. H., & McLean, E. R. (2002). Information systems success revisited. Paper presented at Proceedings of the 35th Hawaii International Conference on System Sciences, Hawaii, USA.
  7. Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A tenyear update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748
  8. DeLone, W. H., & McLean, E. R. (2004). Measuring e-commerce success: Applying the DeLone & McLean information systems success model. International Journal of Electronic Commerce, 9(1), 31-47. https://doi.org/10.1080/10864415.2004.11044317
  9. Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 453-461. https://doi.org/10.2307/249524
  10. Dong, T. P., Cheng, N. C., & Wu, Y. C. J. (2014). A study of the social networking website service in digital content industries: The Facebook case in Taiwan. Computers in Human Behavior, 30, 708-714. https://doi.org/10.1016/j.chb.2013.07.037
  11. Dunne, A., Lawlor, M. A., & Rowley, J. (2010). Young people's use of online social networking sites - a uses and gratifications perspective. Journal of Research in Interactive Marketing, 4(1), 46-58. https://doi.org/10.1108/17505931011033551
  12. 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/10.2307/3151312
  13. Gronroos, C. (1984). A service quality model and its marketing implications. European Journal of Marketing, 18(4), 36-44. https://doi.org/10.1108/EUM0000000004784
  14. Hair, J. F., Black, W. C., Babin, B. J., & Anderson R. E. (2010). Multivariate data analysis: A global perspective. Pearson Education.
  15. Hennig-Thurau, T. (2000). Relationship quality and customer retention through strategic communication of customer skills. Journal of Marketing Management, 16(1-3), 55-79. https://doi.org/10.1362/026725700785100497
  16. Hennig-Thurau, T., Malthouse, E. C., Friege, C., Gensler, S., Lobschat, L., Rangaswamy, A., & Skiera, B. (2010). The impact of new media on customer relationships. Journal of Service Research, 13(3), 311-330. https://doi.org/10.1177/1094670510375460
  17. Hew, K. F. (2011). Students' and teachers' use of Facebook. Computers in Human Behavior, 27(2), 662-676. https://doi.org/10.1016/j.chb.2010.11.020
  18. Hoadley, C. M., Xu, H., Lee, J. J., & Rosson, M. B. (2010). Privacy as information access and illusory control: The case of the Facebook News Feed privacy outcry. Electronic Commerce Research and Applications, 9(1), 50-60. https://doi.org/10.1016/j.elerap.2009.05.001
  19. Hoque, S., & Hossain, M. A. (2023). Social media stickiness in the Z generation: A study based on the uses and gratifications theory. Journal of Information Science Theory and Practice, 11(4), 92-108. https://doi.org/10.1633/JISTaP.2023.11.4.6
  20. Ibanez-Sanchez, S., Orus, C., & Flavian, C. (2022). Augmented reality filters on social media. Analyzing the drivers of playability based on uses and gratifications theory. Psychology & Marketing, 39(3), 559-578. https://doi.org/10.1002/mar.21639
  21. Jahan, N., & Kim, S. W. (2021). Understanding online community participation behavior and perceived benefits: A social exchange theory perspective. PSU Research Review, 5(2), 85-100. https://doi.org/10.1108/PRR-12-2019-0036
  22. Kulkarni, U. R., Ravindran, S., & Freeze, R. (2006). A knowledge management success model: Theoretical development and empirical validation. Journal of Management Information Systems, 23(3), 309-347. https://doi.org/10.2753/MIS0742-1222230311
  23. Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean's model perspective. Interacting with Computers, 21(5), 385-392. https://doi.org/10.1016/j.intcom.2009.06.004
  24. Lee, S. L. (2024). Positive orientation and social capital: The insignificance of the mediating effects of social network sites usages. Psychological Studies, 69(1), 92-104. https://doi.org/10.1007/s12646-023-00767-x
  25. Li, D. C. (2011). Online social network acceptance: A social perspective. Internet Research, 21(5), 562-580. https://doi.org/10.1108/10662241111176371
  26. Lin, H. F. (2007). Measuring online learning systems success: Applying the updated DeLone and McLean model. CyberPsychology & Behavior, 10(6), 817-820. https://doi.org/10.1089/cpb.2007.9948
  27. Lin, H., Fan, W., & Chau, P. Y. K. (2014). Determinants of users' continuance of social networking sites: A self-regulation perspective. Information & Management, 51(5), 595-603. https://doi.org/10.1016/j.im.2014.03.010
  28. Lin, K. Y., & Lu, H. P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152-1161. https://doi.org/10.1016/j.chb.2010.12.009
  29. Lin, X., Featherman, M., & Sarker, S. (2017). Understanding factors affecting users' social networking site continuance: A gender difference perspective. Information & Management, 54(3), 383-395. https://doi.org/10.1016/j.im.2016.09.004
  30. Madden, M., & Smith, A. (2010). Reputation management and social media. https://www.pewresearch.org/internet/2010/05/26/reputation-management-and-social-media/
  31. Mouakket, S. (2015). Factors influencing continuance intention to use social network sites: The Facebook case. Computers in Human Behavior, 53, 102-110. https://doi.org/10.1016/j.chb.2015.06.045
  32. Ortiz, J. A. F., De Los M. Santos Corrada, M., Lopez, E., Dones, V., & Lugo, V. F. (2023). Don't make ads, make TikTok's: Media and brand engagement through Gen Z's use of TikTok and its significance in purchase intent. Journal of Brand Management, 30(6), 535-549. https://doi.org/10.1057/s41262-023-00330-z
  33. Pavlou, P. A., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31(1), 105-136. https://doi.org/10.2307/25148783
  34. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
  35. Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean Model of IS success. Information Systems Research, 8(3), 240-253. https://doi.org/10.1287/isre.8.3.240
  36. Silveira, P., Morais, R., & Petrella, S. (2022). A communication study of young adults and online dependency during the COVID-19 pandemic. Societies, 12(4), 109. https://www.mdpi.com/2075-4698/12/4/109 109
  37. Singh, S. (2024). How many people use social media (2024 Statistics). https://www.demandsage.com/social-ediausers/#:~:text=USA%2DSpecific%20Social%20Media%20 Statistics&text=The%20USA%20has%20302.35%20million,74.2%25%20of%20adults%20using%20it
  38. Statista (2024). Social media & user-generated content. https://www.statista.com/markets/424/topic/540/social-media-user-generated-content/#overview
  39. van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704. https://doi.org/10.2307/25148660
  40. Wang, Y. S., & Liao, Y. W. (2007). The conceptualization and measurement of m-commerce user satisfaction. Computers in Human Behavior, 23(1), 381-398. https://doi.org/10.1016/j.chb.2004.10.017
  41. Wang, Y. S., Li, H. T., Li, C. R., & Wang, C. (2014). A model for assessing blog-based learning systems success. Online Information Review, 38(7), 969-990. https://doi.org/10.1108/OIR-04-2014-0097
  42. Wilson, R. E., Gosling, S. D., & Graham, L. T. (2012). A review of Facebook research in the social sciences. Perspectives on Psychological Science, 7(3), 203-220. https://doi.org/10.1177/1745691612442904
  43. Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25(1), 17-41. https://doi.org/10.2307/3250957
  44. Yuthas, K., & Young, S. T. (1998). Material matters: Assessing the effectiveness of materials management IS. Information & Management, 33(3), 115-124. https://doi.org/10.1016/S0378-7206(97)00028-1
  45. Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56, 513-524. https://doi.org/10.1016/j.dss.2012.11.008