Research Article

The Role of Perceived Utilitarian and Hedonic Value in Predicting Use of Location-Based Anonymous Social Networking Sites

Priyanka Khandelwal 1 * , Melissa R. Gotlieb 2
More Detail
1 West Texas A&M University, USA2 Texas Tech University, USA* Corresponding Author
Online Journal of Communication and Media Technologies, 11(4), October 2021, e202118, https://doi.org/10.30935/ojcmt/11114
OPEN ACCESS   2845 Views   1495 Downloads
Download Full Text (PDF)

ABSTRACT

Recent years have seen a rapid rise in the popularity of location-based anonymous social networking sites (LB-ASNS). Although anonymity and site specificity have been reported to be the major attractions of these platforms, there exists a severe paucity of studies that have investigated the adoption behavior of LB-ASNS using the Technology Acceptance Model as the primary theoretical framework. The goal of this study is to utilize an extended version of TAM to explain the actual use of LB-ASNS. We demonstrate the applicability of TAM in the realm of LB-ASNS. Our results suggest that the quality and enjoyment afforded by these platforms are major predictors of actual use.
Keywords: location-based anonymous social networking sites, adoption behavior, TAM, college students, hedonic value

CITATION (APA)

Khandelwal, P., & Gotlieb, M. R. (2021). The Role of Perceived Utilitarian and Hedonic Value in Predicting Use of Location-Based Anonymous Social Networking Sites. Online Journal of Communication and Media Technologies, 11(4), e202118. https://doi.org/10.30935/ojcmt/11114

REFERENCES

  1. Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75-90. https://doi.org/10.1016/j.chb.2016.05.014
  2. Aboelmaged, M., & Gebba, T. R. (2013). Mobile banking adoption: an examination of technology acceptance model and theory of planned behavior. International Journal of Business Research and Development, 2(1), 35-50. https://doi.org/10.24102/ijbrd.v2i1.263
  3. Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 665-694. https://doi.org/10.2307/3250951
  4. Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314. https://doi.org/10.1016/j.chb.2013.10.035
  5. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  6. Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2020). Employing the technology acceptance model in social media: A systematic review. Education and Information Technologies, 25, 4961-5002. https://doi.org/10.1007/s10639-020-10197-1
  7. Ariff, M. S. M., Yeow, S. M., Zakuan, N., Jusoh, A., & Bahari, A. Z. (2012). The effects of computer self-efficacy and technology acceptance model on behavioral intention in internet banking systems. Procedia-Social and Behavioral Sciences, 57, 448-452. https://doi.org/10.1016/j.sbspro.2012.09.1210
  8. Arslan, A. (2008). The correlation between attitude and self-efficacy with regard to computer assisted education. Electronic Journal of Social Sciences, 7(24), 101-109. https://dergipark.org.tr/esosder/issue/6138/82343
  9. Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: technological antecedents and implications. MIS quarterly, 831-858. https://doi.org/10.2307/41409963
  10. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Inc.
  11. Bauer, R. A. (1960). Consumer behavior as risk taking. In R. S. Hancock (Ed.), Dynamic Marketing for a Changing World, Proceedings of the 43rd Conference of the American Marketing Association, pp. 389-98.
  12. Benbasat, I., & Barki, H. (2007). Quo vadis TAM?. Journal of the Association for Information Systems, 8(4), 7. https://doi.org/10.17705/1jais.00126
  13. Biucky, S. T., & Harandi, S. R. (2017). The effects of perceived risk on social commerce adoption based on tam model. International Journal of Electronic Commerce Studies, 8(2), 173-196. https://doi.org/10.7903/ijecs.1538
  14. Black, E. W., Mezzina, K., & Thompson, L. A. (2016). Anonymous social media–Understanding the content and context of Yik Yak. Computers in Human Behavior, 57, 17-22. https://doi.org/10.1016/j.chb.2015.11.043
  15. Boyd, D., & Marwick, A. E. (2011, September). Social privacy in networked publics: Teens’ attitudes, practices, and strategies. In A decade in internet time: Symposium on the dynamics of the internet and society, pp. 1-29. https://papers.ssrn.com/sol3/papers.cfm?abstract_=1925128
  16. Bradner, E. (2001, March). Social affordances of computer-mediated communication technology: understanding adoption. In CHI'01 Extended abstracts on human factors in computing systems (pp. 67-68). https://doi.org/10.1145/634067.634111
  17. Burns, H. (2014). Yik Yak app stirring up chatter on college campuse. https://www.usatoday.com/story/tech/personal/2014/03/07/yik-yak-college-students/6124405/
  18. Caya. (2019) Company forensics| Yik Yak: A deadly combination. https://slidebean.com/blog/startups-startup-forensics-yik-yak
  19. Chen, C. C., & Tsai, J. L. (2019). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628-638. https://doi.org/10.1016/j.future.2017.02.028
  20. Chen, D. X. (2004). Students’ sense of campus community: What it means, and what to do about it. NASPA Journal, 41, 216-234. https://doi.org/10.2202/1949-6605.1331
  21. Choi, T. R., & Sung, Y. (2018). Instagram versus Snapchat: Self-expression and privacy concern on social media. Telematics and Informatics, 35(8), 2289-2298. https://doi.org/10.1016/j.tele.2018.09.009
  22. Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (2010). Age differences in perceptions of online community participation among non-users: An extension of the Technology Acceptance Model. Computers in Human Behavior, 26(6), 1674-1684. https://doi.org/10.1016/j.chb.2010.06.016
  23. Clark-Gordon, C. V., Bowman, N. D., Goodboy, A. K., & Wright, A. (2019). Anonymity and Online Self-Disclosure: A Meta-Analysis. Communication Reports, 32(2), 98-111. https://doi.org/10.1080/08934215.2019.1607516
  24. Clark-Gordon, C. V., Workman, K. E., & Linvill, D. L. (2017). College students and Yik Yak: An exploratory mixed-methods study. Social Media+ Society, 3(2), 2056305117715696. https://doi.org/10.1177/2056305117715696
  25. Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information systems research, 6(2), 118-143. https://doi.org/10.1287/isre.6.2.118
  26. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
  27. Currás‐Pérez, R., Ruiz‐Mafé, C., & Sanz‐Blas, S. (2013). Social network loyalty: evaluating the role of attitude, perceived risk and satisfaction. Online Information Review, 37(1), 61-82. https://doi.org/10.1108/14684521311311630
  28. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
  29. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
  30. de la Fuente, J., Gustafson, S., Twomey, C., & Bix, L. (2015). An affordance‐based methodology for package design. Packaging Technology and Science, 28(2), 157-171. https://doi.org/10.1002/pts.2087
  31. 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
  32. Eastin, M. S. (2002). Diffusion of e-commerce: an analysis of the adoption of four e-commerce activities. Telematics and informatics, 19(3), 251-267. https://doi.org/10.1016/S0736-5853(01)00005-3
  33. Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of computer-mediated communication, 6(1), JCMC611. https://doi.org/10.1111/j.1083-6101.2000.tb00110.x
  34. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison–Wesley.
  35. Fishbein, M., & Cappella, J. N. (2006). The role of theory in developing effective health communications. Journal of Communication, 56(s1), S1-S17. https://doi.org/10.1111/j.1460-2466.2006.00280.x
  36. Frith, J., & Saker, M. (2017). Understanding Yik Yak: Location-based sociability and the communication of place. First Monday, 22(10). https://doi.org/10.5210/fm.v22i10.7442
  37. Galib, M. H., Hammou, K. A., & Steiger, J. (2018). Predicting consumer behavior: an extension of technology acceptance model. International Journal of Marketing Studies, 10(3), 73-90. https://doi.org/10.5539/ijms.v10n3p73
  38. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
  39. Gerhart, N., & Koohikamali, M. (2019). Social network migration and anonymity expectations: What anonymous social network apps offer. Computers in Human Behavior, 95, 101-113. https://doi.org/10.1016/j.chb.2019.01.030
  40. Gibson, J. J. (1979). The ecological approach to visual perception. Houghton Mifflin.
  41. Golbeck, J., & Mauriello, M. L. (2016). User perception of Facebook app data access: A comparison of methods and privacy concerns. Future Internet, 8(2), 9. https://doi.org/10.3390/fi8020009
  42. Goodhue, D. L. (2007). Comment on Benbasat and Barki’s “Quo Vadis TAM” article. Journal of the Association for Information Systems, 8(4), 15. https://doi.org/10.17705/1jais.00125
  43. Govender, I., & Sihlali, W. (2014). A study of mobile banking adoption among university students using an extended TAM. Mediterranean Journal of Social Sciences, 5(7), 451. https://doi.org/10.5901/mjss.2014.v5n7p451
  44. Graham, J. (2015) Anonymity working for Whisper app. https://www.usatoday.com/story/tech/2015/08/12/anonymity-working-whisper-app-secret-snapchat/31514503/
  45. Graham, J. (2017) Yik Yak, the once popular and controversial college messaging app, shuts down. https://www.usatoday.com/story/tech/talkingtech/2017/04/28/yik-yak-shut-down/101045670/
  46. Gumussoy, C. A., Kaya, A., & Ozlu, E. (2018). Determinants of mobile banking use: An extended TAM with perceived risk, mobility access, compatibility, perceived self-efficacy and subjective norms. In Industrial Engineering in the Industry 4.0 Era (pp. 225-238). Springer, Cham. https://doi.org/10.1007/978-3-319-71225-3_20
  47. Hall, J. (2013) Hannah Smith suicide: Internet trolls target sister of tragic teenager who was driven to suicide by cyberbullies. https://www.independent.co.uk/news/uk/crime/hannah-smith-suicide-internet-trolls-target-sister-tragic-teenager-who-was-was-driven-suicide-cyberbullies-8749648.html
  48. Hsu, C. L. & Lu, H. P. (2007). Consumer behavior in online game communities: A motivational factor perspective. Computers in Human Behavior, 23(3), 1642-1659. https://doi.org/10.1016/j.chb.2005.09.001
  49. Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & management, 41(7), 853-868. https://doi.org/10.1016/j.im.2003.08.014
  50. Hsu, M. H., & Chiu, C. M. (2004). Internet self-efficacy and electronic service acceptance. Decision support systems, 38(3), 369-381. https://doi.org/10.1016/j.dss.2003.08.001
  51. Juetten, M. (Nov, 13, 2018). Failed Startups: YikYak. https://www.forbes.com/sites/maryjuetten/2018/11/13/failed-startups-yik-yak/
  52. Kamble, S., Gunasekaran, A., & Arha, H. (2019). Understanding the Blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7), 2009-2033. https://doi.org/10.1080/00207543.2018.1518610
  53. Kapoor, K., Dwivedi, Y., Piercy, N. C., Lal, B., & Weerakkody, V. (2014). RFID integrated systems in libraries: extending TAM model for empirically examining the use. Journal of Enterprise Information Management, 27(6), 731-758. https://doi.org/10.1108/JEIM-10-2013-0079
  54. Koohikamali, M., Gerhart, N., & Mousavizadeh, M. (2015). Location disclosure on LB-SNAs: The role of incentives on sharing behavior. Decision Support Systems, 71, 78-87. https://doi.org/10.1016/j.dss.2015.01.008
  55. Kutluca, T., & Ekici, G. (2010). Examining teacher candidates’ attitudes and self-effıcacy perceptions towards the computer assisted education. Hacettepe University Journal of Education, 38, 177-188. http://www.efdergi.hacettepe.edu.tr/shw_artcl-426.html
  56. LaRose, R., & Atkin, D. (1991). Attributes of movie distribution channels and consumer choice. Journal of Media Economics, 4(1), 3-17. https://doi.org/10.1080/08997769109358200
  57. Lauri, M. A., & Farrugia, L. (2020). Identity Exploration in Anonymous Online Spaces. The Routledge Companion to Digital Media and Children, 173-184. https://doi.org/10.4324/9781351004107-16
  58. Lee, Y. H., Hsieh, Y. C., & Chen, Y. H. (2013). An investigation of employees” use of e-learning systems: applying the technology acceptance model. Behaviour & Information Technology, 32(2), 173-189. https://doi.org/10.1080/0144929X.2011.577190
  59. Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50. https://doi.org/10.17705/1CAIS.01250
  60. Li, Q., & Literat, I. (2017). Misuse or misdesign? Yik Yak on college campuses and the moral dimensions of technology design. First Monday, 22(7). https://doi.org/10.5210/fm.v22i7.6947
  61. Lin, H.F. (2008). Antecedents of virtual community satisfaction and loyalty: An empirical test of competing theories. CyberPsychology & Behavior, 11(2),138-144. https://doi.org/10.1089/cpb.2007.0003
  62. Liu, X. (2010). Empirical testing of a theoretical extension of the technology acceptance model: An exploratory study of educational wikis. Communication Education, 59(1), 52-69. https://doi.org/10.1080/03634520903431745
  63. Lorenzo-Romero, C., & Constantinides, E. (2011). Consumer adoption of social networking sites: implications for theory and practice. Journal of research in Interactive Marketing, 5(2/3), 170-188. https://doi.org/10.1108/17505931111187794
  64. Lowry, P. B., Gaskin, J., Twyman, N., Hammer, B., & Roberts, T. (2012). Taking “fun and games” seriously: Proposing the hedonic-motivation system adoption model (HMSAM). Journal of the Association for Information Systems, 14(11), 617-671. https://doi.org/10.17705/1jais.00347
  65. Machkovech, S. (2016). Yik Yak’s “handles” are just lipstick on an ugly, anonymous yak. https://arstechnica.com/information-technology/2016/03/yik-yaks-handles-are-just-lipstick-on-an-ugly-anonymous-yak/
  66. Madden, M., & Rainie, L. (2015). Americans” attitudes about privacy, security and surveillance. Pew Research Center. http://www.pewinternet.org/2015/05/20/americans-attitudes-about-privacy-security-and-surveillance/
  67. Madden, M., Lenhart, A., Cortesi, S., Gasser, U., Duggan, M., Smith, A., & Beaton, M. (2013). Teens, social media, and privacy. Pew Research Center, 21(1055), 2-86. https://assets.pewresearch.org/wp-content/uploads/sites/14/2013/05/PIP_TeensSocialMediaandPrivacy_PDF.pdf
  68. Mahler, J. (2015). Who spewed that abuse? Anonymous Yik Yak app isn”t telling. https://www.nytimes.com/2015/03/09/technology/popular-yik-yak-app-confers-anonymity-and-delivers-abuse.html
  69. Majchrzak, A., Faraj, S., Kane, G. C., & Azad, B. (2013). The contradictory influence of social media affordances on online communal knowledge sharing. Journal of Computer-Mediated Communication, 19(1), 38-55. https://doi.org/10.1111/jcc4.12030
  70. Makki, T. W., DeCook, J. R., Kadylak, T., & Lee, O. J. Y. (2018). The social value of snapchat: An exploration of affiliation motivation, the technology acceptance model, and relational maintenance in Snapchat use. International Journal of Human-Computer Interaction, 34(5), 410-420. https://doi.org/10.1080/10447318.2017.1357903
  71. Mao, C. M., & Hovick, S. R. (2020). Adding affordances and communication efficacy to the technology acceptance model to study the messaging features of online patient portals among young adults. Health Communication, 1-9. https://doi.org/10.1080/10410236.2020.1838106
  72. Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access Information Society, 14, 81-95. https://doi.org/10.1007/s10209-014-0348-1
  73. Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173
  74. McGuire, W. J. (1969). The nature of attitudes and attitude change, In G. Lintzey & E. Aronson (Eds.), Handbook of Social Psychology. Addison-Wesley.
  75. Milosevic, T. (2016). Social media companies’ cyberbullying policies. International Journal of Communication, 10, 5164-5185. https://ijoc.org/index.php/ijoc/article/view/5320
  76. Moon, J.-W. & Kim, Y.-G. (2001) Extending the TAM for a World-Wide-Web context. Information and Management, 38, 217-230. https://doi.org/10.1016/S0378-7206(00)00061-6
  77. Moore, M. J., Nakano, T., Enomoto, A., & Suda, T. (2012). Anonymity and roles associated with aggressive posts in an online forum. Computers in Human Behavior, 28(3), 861-867. https://doi.org/10.1016/j.chb.2011.12.005
  78. Murimi, R. (2018, November). Online Social Networks for Meaningful Social Reform. In 2018 World Engineering Education Forum-Global Engineering Deans Council (WEEF-GEDC) (pp. 1-6). IEEE. https://doi.org/10.1109/WEEF-GEDC.2018.8629713
  79. Mynatt, E. D., O’Day, V. L., Adler, A., & Ito, M. (1998). Network communities: Something old, something new, something borrowed... Computer Supported Cooperative Work (CSCW), 7(1-2), 123-156. https://doi.org/10.1023/A:1008688205872
  80. Ngai, E. W., Poon, J. K. L., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250-267. https://doi.org/10.1016/j.compedu.2004.11.007
  81. Norman, D. A. (1988). The design of everyday things. Doubleday.
  82. O’Brien, S. A. (2015). 20 million people are sharing secrets on this app. https://money.cnn.com/2015/12/11/technology/whisper-20-million-users-privacy
  83. Ortutay, B. (2014). Anonymous apps like Secret and Whisper find a niche in Silicon Valley. https://www.mercurynews.com/2014/03/24/anonymous-apps-like-secret-and-whisper-find-a-niche-in-silicon-valley/
  84. Park, E., Baek, S., Ohm, J., & Chang, H. J. (2014). Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model. Telematics and Informatics, 31(1), 3-15. https://doi.org/10.1016/j.tele.2013.07.001
  85. Park, N. (2010). Adoption and use of computer-based voice over Internet protocol phone service: Toward an integrated model. Journal of Communication, 60(1), 40-72. https://doi.org/10.1111/j.1460-2466.2009.01440.x
  86. Pedersen, D. M. (1997). Psychological functions of privacy. Journal of environmental psychology, 17(2), 147-156. https://doi.org/10.1006/jevp.1997.0049
  87. Peter, J., & Valkenburg, P. M. (2011). Adolescents” online privacy: Toward a developmental perspective. In Privacy online (pp. 221-234). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21521-6_16
  88. Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: models, dimensions, measures, and interrelationships. European journal of information systems, 17(3), 236-263. https://doi.org/10.1057/ejis.2008.15
  89. Pinsonneault, A., & Heppel, N. (1997). Anonymity in group support systems research: A new conceptualization, measure, and contingency framework. Journal of Management Information Systems, 14(3), 89-108. https://doi.org/10.1080/07421222.1997.11518176
  90. Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of business research, 59(9), 999-1007. https://doi.org/10.1016/j.jbusres.2006.06.003
  91. Posteuca-Esi, N. L. (2019). Social and psychological benefits of self-disclosure. International Journal of Social and Educational Innovation (IJSEIro), 6(12), 25-29. https://www.journals.aseiacademic.org/index.php/ijsei/article/view/136
  92. Putzke, J., Fischbach, K., Schoder, D., & Gloor, P. (2010). The evolution of interaction networks in massively multiplayer online games. Journal of the Association for Information Systems, 11(2), 69-94. https://doi.org/10.17705/1jais.00221
  93. Ramírez-Correa, P., Grandón, E. E., Ramírez-Santana, M., & Belmar Órdenes, L. (2019). Explaining the use of social network sites as seen by older adults: The enjoyment component of a hedonic information system. International journal of environmental research and public health, 16(10), 1673. https://doi.org/10.3390/ijerph16101673
  94. Razmak, J., & Bélanger, C. (2018). Using the technology acceptance model to predict patient attitude toward personal health records in regional communities. Information Technology & People. https://doi.org/10.1108/ITP-07-2016-0160
  95. Ritz, W., Wolf, M., & McQuitty, S. (2019). Digital marketing adoption and success for small businesses. Journal of Research in Interactive Marketing. https://doi.org/10.1108/JRIM-04-2018-0062
  96. Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585-1604. https://doi.org/10.1016/j.chb.2007.06.001
  97. Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human–Computer Studies, 64(8), 683-696. https://doi.org/10.1016/j.ijhcs.2006.01.003
  98. Rogers E., M. (1995). The diffusion of innovation (4th ed). Free Press.
  99. Saker, M., & Evans, L. (2016). Everyday life and locative play: An exploration of Foursquare and playful engagements with space and place. Media, Culture & Society, 38(8), 1169-1183. https://doi.org/10.1177/0163443716643149
  100. Tao, D., Wang, T., Wang, T., Zhang, T., Zhang, X., & Qu, X. (2020). A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies. Computers in Human Behavior, 104, 106147. https://doi.org/10.1016/j.chb.2019.09.023
  101. Tate, M., & Evermann, J. (2009). Perceptive users with attitudes-some heuristics on theorizing. ICIS 2009 Proceedings, 139.
  102. Taylor, S., & Todd, P. A. (1995a). Understanding information technology usage: A test of competing models. Information Systems Research, 6, 144-176. https://doi.org/10.1287/isre.6.2.144
  103. Taylor, S., & Todd, P. A. (1995b). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561-570. https://doi.org/10.2307/249633
  104. Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers’ computer attitudes: applying and extending the technology acceptance model. Journal of Computer Assisted Learning, 24(2), 128-143. https://doi.org/10.1111/j.1365-2729.2007.00247.x
  105. Tsai, T. H., Chang, H. T., & Ho, Y. L. (2016). Perceptions of a specific family communication application among grandparents and grandchildren: An extension of the technology acceptance model. PloS one, 11(6), e0156680. https://doi.org/10.1371/journal.pone.0156680
  106. Tsay-Vogel, M., Shanahan, J., & Signorielli, N. (2018). Social media cultivating perceptions of privacy: A 5-year analysis of privacy attitudes and self-disclosure behaviors among Facebook users. New Media & Society, 20(1), 141-161. https://doi.org/10.1177/1461444816660731
  107. Valkenburg, P. M., & Peter, J. (2011). Online communication among adolescents: An integrated model of its attraction, opportunities, and risks. Journal of Adolescent Health, 48(2), 121-127. https://doi.org/10.1016/j.jadohealth.2010.08.020
  108. van der Heijden, H. (2003). Factors influencing the usage of websites: The case of a generic portal in The Netherlands. Information & Management, 40(6), 541-549. https://doi.org/10.1016/S0378-7206(02)00079-4
  109. Vaterlaus, J. M. (2017). Yik Yak: An exploratory study of college student uses and gratifications. Bulletin of Science, Technology & Society, 37(1), 23-33. https://doi.org/10.1177/0270467617738902
  110. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  111. Vossen, H. G., & Valkenburg, P. M. (2016). Do social media foster or curtail adolescents” empathy? A longitudinal study. Computers in Human Behavior, 63, 118-124. https://doi.org/10.1016/j.chb.2016.05.040
  112. Wakefield, R. L., & Whitten, D. (2006). Mobile computing: A user study on hedonic/utilitarian mobile device usage. European Journal of Information Systems, 15(3), 292-300. https://doi.org/10.1057/palgrave.ejis.3000619
  113. Wang, G., Wang, B., Wang, T., Nika, A., Zheng, H., & Zhao, B. Y. (2014, November). Whispers in the dark: analysis of an anonymous social network. In Proceedings of the 2014 Conference on Internet Measurement Conference (pp. 137-150). https://doi.org/10.1145/2663716.2663728
  114. Wirtz, B. W., & Göttel, V. (2016). Technology acceptance in social media: review, synthesis and directions for future empirical research. Journal of Electronic Commerce Research, 17(2), 97. http://www.jecr.org/sites/default/files/17_2Paper1.pdf