Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
권호 표지
DOI QR코드

DOI QR Code

A Multidimensional View of SNS Usage: Conceptualization and Validation

  • Received : 2022.01.28
  • Accepted : 2022.03.13
  • Published : 2022.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Social networking sites (SNSs) have become an essential part of people's lives. It is thus crucial to understand how individuals use these platforms. Previous literature has divided usage into numerous activities and then grouped them into dimensions to avoid excessive granularity. However, these categories have not been derived from a uniform theoretical background; consequently, these dimensions are dispersed, overlapping, and disconnected from each other. This study argues that "SNS usage" is a complex phenomenon consisting of multiple activities that can be grouped into dimensions under the umbrella of communication theories and these dimensions are related to each other in a particular multi-dimensional architecture. "SNS usage" is conceptualized as a third-order construct formed by "producing," "consuming," and "communicating." "Producing," in turn, is proposed as a second-order construct manifested by "commenting," "general information sharing," and "self-disclosure." The proposed model was assessed with data collected from 414 USA adult users and PLS-SEM technique. The results show empirical support for the theorized model. SNS providers now have this architecture that clarifies the role of each dimension of use, which will allow them to design effective strategies to encourage the use of these networks.

Keywords

References

  1. Aladwani, A. M. (2014). Gravitating towards Facebook (GoToFB): What it is? and how can it be measured? Computers in Human Behavior, 33, 270-278. https://doi.org/10.1016/j.chb. 2014.01.005
  2. Almakrami, H. A. A. (2015). Online Self-Disclosure Across Cultures: A Study of Facebook Use in Saudi Arabia and Australia. Queensland University of Technology.
  3. Barclay, D. W., Higgins, C. A., and Thompson, R. (1995). The partial least squares approach to causal modeling: Personal computer adoption and use as illustration. Technology Studies, 2, 285-309.
  4. Barki, H., Titah, R., and Boffo, C. (2007). Information system use-related activity: An expanded behavioral conceptualization of individual-level information system use. Information Systems Research, 18(2), 173-192. https://doi.org/10.1287/isre.1070. 0122
  5. Becker, J. M., Klein, K., and Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM: Guidelines for using reflective-formative type models. Long Range Plann, 45(5-6), 359-394. https://doi.org/10. 1016/j.lrp.2012.10.001 https://doi.org/10.1016/j.lrp.2012.10.001
  6. Bond, A. J., Widdop, P., Cockayne, D., and Parnell, D. (2021). Prosumption, networks and value during a global pandemic: Lockdown leisure and COVID-19. Leisure Sciences, 43(1-2), 70-77. https://doi.org/10.1080/01490400. 2020.1773985
  7. Bravo, E., and Bayona, H. (2020). Explaining multidimensional Facebook benefits: A task-technology fit approach. Proceedings of the 53rd Hawaii International Conference on System Sciences. Retrieved from http://hdl.handle.net/10125/ 64289
  8. Broughton, A., Nieuwoudt, M., Daly, M., Le Roux, D. B., Marx, N. J., and Parry, D. A. (2019). An exploratory investigation of online and offline social behaviour among digital natives. Proceedings of the South African Institute of Computer Scientists and Information Technologists. https://doi.org/10.1145/3351108.3351146
  9. Bruns, A. (2015). Making sense of society through social media. Social Media + Society, 1(1). https://doi.org/10.1177/2056305115578679
  10. Burke, M., and Kraut, R. E. (2016). The relationship between Facebook use and well-being depends on communication type and tie strength. Journal of Computer-Mediated Communication, 21(4), 265-281. https://doi.org/10.1111/ jcc4.12162
  11. Burke, M., Kraut, R., and Marlow, C. (2011). Social capital on Facebook: Differentiating uses and users. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 571-580. http://doi.org/:10.1145/1978942. 1979023
  12. Burke, M., Marlow, C., and Lento, T. (2010). Social network activity and social well-being. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. http://doi.org/10.1145/1753326.1753613
  13. Burton-Jones, A., and Straub Jr, D. W. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228-246. https://doi.org/10.1287/ isre.1060.0096
  14. Chang, C. (2015). Self-construal and Facebook activities: Exploring differences in social interaction orientation. Computers in Human Behavior, 53, 91-101. https://doi.org/10.1016/j.chb.2015. 06.049
  15. Chang, C. W., and Heo, J. (2014). Visiting theories that predict college students' self-disclosure on Facebook. Computers in Human Behavior, 30, 79-86. https://doi.org/ 10.1016/j.chb.2013.07.059
  16. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp. 295-358). Lawrence Erlbaum.
  17. Chin, W. W. (2010). How to write up and report PLS analyses. In I. V. V. Esposito, W. W. Chin, J. Henseler, and H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing and Related Fields. Springer.
  18. Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159. http://doi.org/10.1037//0033-2909.112.1.155
  19. Datareportal. (2022a). Digital 2022: Global overview report, Retrieved from https://datareportal.com/reports/digital-2022-global-overview-report
  20. Datareportal. (2022b). Digital 2022: Instagram's surge continues, Retrieved from https://datareportal.com/reports/digital-2022-instagram-headlines
  21. Datareportal. (2022c). Digital 2022: The potential future of Facebook, Retrieved from https://data reportal.com/reports/digital-2022-future-of-facebook
  22. Datareportal. (2022d). Digital 2022: Tiktok's rapid rise continues, Retrieved from https://datareportal.com/reports/digital-2022-tiktok-headlines
  23. Datareportal. (2022e). Digital 2022: YouTube's ad reach passes 2.5 billion, Retrieved from https://datareportal.com/reports/digital-2022-youtube-headlines
  24. Diamantopoulos, A., and Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269-277. https://doi.org/10.1509/jmkr.38.2.269.18845
  25. Edwards, J. R. (2001). Multidimensional constructs in organizational behavior research: An integrative analytical framework. Organizational Research Methods, 4(2), 144-192. https://doi.org/10.1177/109442810142004
  26. Ellison, N. B., Steinfield, C., and Lampe, C. (2007). The benefits of Facebook "friends:" Social capital and college students' use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143-1168. https://doi.org/10.1111/j.1083-6101.2007.00367.x
  27. Ghose, A., and Han, S. P. (2011). An empirical analysis of user content generation and usage behavior on the mobile Internet. Management Science, 57(9), 1671-1691. https://doi.org/ 10.1287/mnsc.1110.1350
  28. Goodhue, D., Thompson, R. L., and US. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236. https://doi.org/10.2307/249689
  29. Goo, J., Huang, C. D., and Koo, C. (2015). Learning for healthy outcomes: Exploration and exploitation with electronic medical records. Information & Management, 52(5), 550-562. https://doi.org/10.1016/j.im.2015.04.002
  30. Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2010). Multivariate Data Analysis. Englewood Cliffs. Prentice Hall.
  31. Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage.
  32. Hair, J. F., Ringle, C. M., and Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069 -6679190202
  33. Hsu, M. H., and Lin, H. C. (2017). An investigation of the impact of cognitive absorption on continued usage of social media in Taiwan: The perspectives of fit. Behaviour & Information Technology, 36(8), 768-791. https://doi.org/10.1080/0144929X.2017.1288267
  34. Hulten, B. (2007). Customer segmentation: The concepts of trust, commitment and relationships. Journal of Targeting, 15, 256-269. https://doi.org/10.1057/palgrave. jt.5750051
  35. Hu, T., Kettinger, W. J., and Poston, R. S. (2015). The effect of online social value on satisfaction and continued use of social media. European Journal of Information Systems, 24(4), 391-410. http://doi.org/10.1057/ejis.2014. 22
  36. Hu, X., Lin, Z., Whinston, A. B., and Zhang, H. (2004). Hope or hype: On the viability of escrow services as trusted third parties in online auction environments. Information Systems Research, 15(3), 236-249. http://doi.org/10.1287/isre. 1040.0027
  37. Johnson, R. E., Rosen, C. C., Djurdjevic, E., and Taing, M. U. (2012). Recommendations for improving the construct clarity of higher-order multidimensional constructs. Human Resource Management Review, 22(2), 62-72. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S1053482211000532 1053482211000532
  38. Junco, R. (2012). Too much face and not enough books: The relationship between multiple indices of Facebook use and academic performance. Computers in Human Behavior, 28(1), 187-198. https://doi.org/10.1016/j.chb. 2011.08.026
  39. Kim, C., and Shen, C. (2020). Connecting activities on Social Network Sites and life satisfaction: A comparison of older and younger users. Computers in Human Behavior, 105, 106222. https://doi.org/10.1016/j.chb.2019.106222
  40. Kim, C., and Yang, S. U. (2017). Like, comment, and share on Facebook: How each behavior differs from the other. Public Relations Review, 43(2), 441-449. https://doi.org/10.1016/j.pubrev.2017.02.006
  41. Kim, J., Lee, C., and Elias, T. (2015). Factors affecting information sharing in social networking sites amongst university students. Online Information Review, 39(3), 290-309. https://doi.org/10.1108/OIR-01-2015-0022
  42. Kourouthanassis, P., Lekakos, G., and Gerakis, V. (2015). Should I stay or should I go? The moderating effect of self-image congruity and trust on social networking continued use. Behaviour & Information Technology, 34(2), 190-203. http://doi.org/10.1080/0144929X.2014.948489
  43. Lallmahomed, M. Z. I., Rahim, N. Z. A., Ibrahim, R., and Rahman, A. A. (2013). Predicting different conceptualizations of system use: Acceptance in hedonic volitional context (Facebook). Computers in Human Behavior, 29(6), 2776-2787. https://doi.org/10.1016/j.chb. 2013.07.018
  44. Lankton, N. K., and McKnight, D. H. (2011). What does it mean to trust Facebook?: Examining technology and interpersonal trust beliefs. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 42(2), 32-54. https://doi.org/10. 1145/1989098.1989101 https://doi.org/10.1145/1989098.1989101
  45. Law, K. S., Wong, C. S., and Mobley, W. M. (1998). Toward a taxonomy of multidimensional constructs. Academy of Management Review, 23(4), 741-755. https://doi.org/10.2307/259060
  46. Lee, J. Y., Park, S., Na, E. Y., and Kim, E. (2016). A comparative study on the relationship between social networking site use and social capital among Australian and Korean youth. Journal of Youth Studies, 19(9), 1164-1183. https://doi.org/10.1080/1367 6261.2016.1145637
  47. Lee, S. Y., and Lee, S. W. (2020). Social media use and job performance in the workplace: The effects of facebook and kakaotalk use on job performance in South Korea. Sustainability, 12(10), 4052. https://doi.org/10.3390/su12104052
  48. Limayem, M., and Cheung, C. M. K. (2011). Predicting the continued use of Internet-based learning technologies: The role of habit. Behaviour & Information Technology, 30(1), 91-99. https://doi.org/ 10.1080/0144929X.2010.490956
  49. Lin, K. Y., and 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
  50. Lowry, P. B., and Gaskin, J. (2014). Partial Least Squares (PLS) Structural Equation Modeling(SEM) for Building and Testing Behavioral Causal Theory:When to Choose It and How to Use It. IEEE Transactions on Professional Communication, 57(2), 123-146. http://doi. org/10.1109/TPC.2014.2312452
  51. Lu, H. P., and Yang, Y. W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323-332. https://doi.org/10.1016/j.chb.2013.10.020
  52. MacKenzie, S. B. (2003). The dangers of poor construct conceptualization. Journal of the Academy of Marketing Science, 31(3), 323-326. https://doi.org/10.1177/00920703 03031003011
  53. MacKenzie, S. B., Podsakoff, P. M., and Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293-334. https://doi.org/10.2307/23044045
  54. Malhotra, N. K., Kim, S. S., and Agarwal, J. (2004). Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model. Information Systems Research, 15(4), 336-355. https://doi.org/10.1287/isre.1040. 0032
  55. Ma, L., Sian Lee, C., and Hoe-Lian Goh, D. (2014). Understanding news sharing in social media: An explanation from the diffusion of innovations theory. Online Information Review, 38(5), 598-615. https://doi.org/10.1108/OIR-10- 2013-0239
  56. Mantymaki, M., and Islam, A. K. M. N. (2016). The Janus face of Facebook: Positive and negative sides of social networking site use. Computers in Human Behavior, 61, 14-26. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0747563216301418 https://doi.org/10.1016/j.chb.2016.02.078
  57. Mingers, J., and Willcocks, L. (2017). An integrative semiotic methodology for IS research. Information and Organization, 27(1), 17-36. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S1471772716300306 https://doi.org/10.1016/j.infoandorg.2016.12.001
  58. Nahapiet, J., and Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 23(2), 242-266. https://doi.org/10.5465/ amr.1998.533225
  59. Numally, J. C. (1978). Psychometric Theory. McGraw-Hill.
  60. O'Leary, D., and Volkmer, S. A. (2021). Are the negative effects of social networking a privilege of the rich? Social network usage and life satisfaction across European countries. Computers in Human Behavior Reports, 3, 100078. https://doi.org/10.1016/j.chbr.2021.100078
  61. Park, J. H. (2014). The effects of personalization on user continuance in social networking sites. Information Processing & Management, 50(3), 462-475. https://doi. org/10.1016/j.ipm.2014.02.002
  62. Partridge, K., and Begole, B. (2011). Activity-based advertising. In Pervasive Advertising: Human-Computer Interaction Series (J. Muller,). Springer.
  63. Pavlou, P. A., and Fygenson, M. (2006). Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior. Management Information Systems Research Center, 30(1), 115-143. http://doi.org/10.2307/25148720
  64. Pempek, T. A., Yermolayeva, Y. A., and Calvert, S. L. (2009). College students' social networking experiences on Facebook. Journal of Applied Developmental Psychology, 30(3), 227-238. https://doi.org/10.1016/j.appdev.2008.12.010
  65. Podsakoff, P. M., and Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531-544. https://doi.org/10.1177/0149206 38601200408
  66. Polites, G. L., Roberts, N., and Thatcher, J. (2012). Conceptualizing models using multidimensional constructs: a review and guidelines for their use. European Journal of Information Systems, 21(1), 22-48. https://doi.org/10.1057/ejis. 2011.10
  67. Ranzini, G., Newlands, G., and Lutz, C. (2020). Sharenting, peer influence, and privacy concerns: A study on the Instagram-sharing behaviors of parents in the United Kingdom. Social Media + Society, 6(4), 1-13. https://doi.org/10.1177/2056305120978376
  68. Rauniar, R., Rawski, G., Yang, J., and Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6-30. https://doi.org/10.1108/JEIM-04-2012-0011
  69. Ringle, C. M., Wende, S., and Becker, J. M. (2015). SmartPLS 3. Bonningstedt: SmartPLS, Retrieved from http://www. smartpls.com
  70. Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M., and Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197-211. https://doi.org/10.1016/j.ausmj.2019.05.003
  71. Statista. (2019). Main activities carried out by YouTube users in Mexico as of June 2019, Retrieved from https://www.statista.com/statistics/1071648/main-activities-carried-out-by-youtube-users/
  72. Statista. (2020a). Most popular Facebook activities in the United States as of March 2020, Retrieved from https://www.statista.com/statistics/275788/shareof-facebook-user-activities/
  73. Statista. (2020b). Number of social network users worldwide from 2017 to 2025 (in billions), Retrieved from https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/
  74. Statista. (2022). Most popular social networks worldwide as of October 2020, ranked by number of active users (in millions), Retrieved from https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/
  75. Stone, C. B., and Wang, Q. (2019). From conversations to digital communication: The mnemonic consequences of consuming and producing information via social media. Topics in Cognitive Science, 11(4), 774-793. https://doi.org/10.1111/tops.12369
  76. Straub, D. W., Boudreau, M. C., and Gefen, D. (2004). Validation Guidelines for IS Positivist Research. Communications of the AIS, 13, 380-427. https://doi.org/10.17705/ 1CAIS.01324
  77. Sun, J., and Teng, J. T. C. (2012). Information Systems Use: Construct conceptualization and scale development. Computers in Human Behavior, 28(5), 1564-1574. https://doi.org/10.1016/j.chb.2012.03.016
  78. Vlachos, P. A., and Theotokis, A. (2009). Formative versus reflective measurement for multidimensional constructs. http://dx.doi. org/10.2139/ssrn.1521095
  79. Wenninger, H., Krasnova, H., and Buxmann, P. (2019). Understanding the role of social networking sites in the subjective well-being of users: A diary study. European Journal of Information Systems, 28(2), 126-148. https://doi.org/10.1080/0960085X.2018.1496883
  80. Wong, C. S., Law, K. S., and Huang, G. h. (2008). On the importance of conducting construct-level analysis for multidimensional constructs in theory development and testing. Journal of Management, 34(4), 744-764. https://doi.org/10.1177/0149206307312506
  81. Yamamoto, M., Nah, S., and Bae, S. Y. (2020). Social media prosumption and online political participation: An examination of online communication processes. New Media & Society, 22(10), 1885-1902. https://doi.org/10.1177/146144 4819886295
  82. Yang, C., and Brown, B. B. (2013). Motives for using Facebook, patterns of Facebook activities, and late adolescents' social adjustment to college. Journal of Youth and Adolescence, 42(3), 403-416. http://doi.org/10.1007/s10964- 012-9836-x
  83. Yu, J., Hu, P. J. H., and Cheng, T. H. (2015). Role of affect in self-disclosure on social network websites: A test of two competing models. Journal of Management Information Systems, 32(2), 239-277. https://doi.org/10. 1080/07421222.2015.1063305 https://doi.org/10.1080/07421222.2015.1063305
  84. Zhao, X., Lynch Jr, J. G., and Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206. http://doi.org/10.1086/651257