User Selections on Social Network Applications
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33087
User Selections on Social Network Applications

Authors: C. C. Liang

Abstract:

MSN used to be the most popular application for communicating among social networks, but Facebook chat is now the most popular. Facebook and MSN have similar characteristics, including usefulness, ease-of-use, and a similar function, which is the exchanging of information with friends. Facebook outperforms MSN in both of these areas. However, the adoption of Facebook and abandonment of MSN have occurred for other reasons. Functions can be improved, but users’ willingness to use does not just depend on functionality. Flow status has been established to be crucial to users’ adoption of cyber applications and to affects users’ adoption of software applications. If users experience flow in using software application, they will enjoy using it frequently, and even change their preferred application from an old to this new one. However, no investigation has examined choice behavior related to switching from Facebook to MSN based on a consideration of flow experiences and functions. This investigation discusses the flow experiences and functions of social-networking applications. Flow experience is found to affect perceived ease of use and perceived usefulness; perceived ease of use influences information ex-change with friends, and perceived usefulness; information exchange influences perceived usefulness, but information exchange has no effect on flow experience.

Keywords: Consumer behavior, social media, technology acceptance model.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339127

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1405

References:


[1] Aulakh, P.S., and Gencturk, E.F. (2000), “International Principal – agent relationships –control, governance and performance.” Industrial Marketing Management, 29, 521-538.
[2] Brunner, P., Bianchi, L., Guger, C., Cincotti, F., and Schalk, G. (2011). Current trends in hardware and software for brain–computer interfaces (BCIs). Journal of Neural Engineering, 8(2), DOI:10.1088/1741-2560/8/2/025001.
[3] Byrne, B. M. (2013). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Psychology Press.
[4] Castells, M. (2011). The rise of the network society: The information age: Economy, society, and culture. The 2nd Edition, Wiley-Blackwell.
[5] Centola, D. (2010). The Spread of Behavior in an Online Social Network Experiment. Science 3, 329(5996), 1194-1197.
[6] Chan, K.W., and Li, S.Y. Understanding consumer-to-consumer interactions in virtual communities: The salience of reciprocity. Journal of Business Research, 63(9), 1033-1040.
[7] Chen, J.S., Yen, H.J.R., Li, E.Y., Ching, R.K.H. (2009), “Measuring CRM effectiveness: Construct development, validation and application of a process-oriented model,” Total Quality Management & Business Excellence, 20(3), 283-299.
[8] Chiu, H.C. (2003), Quantitative Research and Statistical Analysis in Social &Behavioural Sciences, Wu-Nan Book Inc, Taipei.
[9] Chow, K.P., and Shenoi, S. (2010). Advances in Digital Forensics, 16(8), 582-587.
[10] Csikszentmihalyi, M. (1990). Flow: The psychology of optimal performance. New York: Cambridge University Press.
[11] Ellison, N.B., Steinfield, C., and Lampe, C. (2011). Connection strategies: Social capital implications of Facebook-enabled communication practices. New Media & Society, 13(6), 873-892.
[12] Evans, B.M., Kairam, S., and Pirolli, P. (2010). Do your friends make you smarter?: An analysis of social strategies in online information seeking. Information Processing & Management, 46(6), 679-692.
[13] Fornell, C., and Larcker, D.F. (1981), “Evaluating structure equation models with unobservable variables and measurement error.” Journal of Marketing Research, 18, 39-50.
[14] Frangos, C.C., Frangos, C.C., Sotiropoulos, I. (2011). Problematic Internet Use Among Greek University Students: An Ordinal Logistic Regression with Risk Factors of Negative Psychological Beliefs, Pornographic Sites, and Online Games. Cyberpsychology, Behaviour, and Social Networking, 14(1-2), 51-58.
[15] Franko, O.I., Tirrell, T.F. (2011). Smartphone app use among medical providers in ACGME training programs. Journal of medical systems, 36(5), 3135-3139.
[16] Götz, O., Liehr-Gobbers, K., Krafft, M. Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach, Springer Handbooks of Computational Statistics 2010, pp 691-711.
[17] Heckathorn, D.D. (2011). COMMENT: SNOWBALL VERSUS RESPONDENT-DRIVEN SAMPLING. Socilogical Methodology, 41(1), 355-366.
[18] Hrastinski, S., &Aghaee, N. M. (2012). How are campus students using social media to support their studies? An explorative interview study. Education and Information Technologies, 17(4), 451-464
[19] Jackson, D.L. (2003). Revisiting sample size and number of parameter estimates: Some support for the N:q hypothesis. Structure Equation Modeling, 10, 128-141.
[20] Jung, Y., Perez-Mira, B., & Wiley-Patton, S. (2009). Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human Behavior, 25(1), 123-129.
[21] Kaplan, A.M., and Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68.
[22] Kim, W., Jeong, O.R., Lee, S.W. (2010). On social Web sites. Information Systems, 35(2), 215-236.
[23] Kwon, O., and Wen, Y. (2010). An empirical study of the factors affecting social network service use. Computers in Human Behavior, 26(2), 254-263.
[24] Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabassi, A.L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., and Alstyne, V. (2009). Life in the network: the coming age of computational social science. Science, 323(5915), 721-723.
[25] Lee, K., Yan, A., and Joshi, K. (2011). Understanding the dynamics of users' belief in software application adoption. International Journal of Information Management, 31(2), 160-170.
[26] Liang, C.C., and Huang, Y.W. (2013). Employee’s Intention on Social Media Evolvement – A Case Study of MSN Messenger and Facebook. Business Review, 18(2), 85-114.
[27] 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.
[28] Louridas, P. (2010). Up in the air: Moving your applications to the cloud. Software, IEEE, 27(4), 6-11.
[29] Lu, Y., Zhou, T., and Wang, B. (2009). Exploring Chinese users' acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29-39.
[30] MacCallum, R.C., Browne, M.W., and Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-149.
[31] Maguire, M. (2013). Using Human Factors Standards to Support User Experience and Agile Design. Lecture Notes in Computer Science, 8009, 185-194.
[32] Mauri, M., Cipresso, P., Balgera, A., Villamira, M., and Riva, G. (2011). Why Is Facebook So Successful? Psychophysiological Measures Describe a Core Flow State While Using Facebook. Cyberpsychology, Behaviour, and Social Networking, 14(12), 723-731.
[33] Mazman, S.G., and Usluel, Y.K. (2010), Modeling educational usage of Facebook. Computers & Education, 55(2), 444-453.
[34] Maksl, A., and Young, R. (2013). Affording to Exchange: Social Capital and Online Information Sharing. Cyberpsychology, Behaviour, and Social Networking, 16(8), 588-592.
[35] Marchewka, J.T., Liu, C., and Kostiwa, K. (2007). An Application of the UTAUT Model for Understanding Student Perceptions Using Course Management Software. Communication of IIMA, 7(2), 93-104.
[36] Micorsoft, Inc. (2013), “Windows Live Messenger”
[37] http://en.wikipedia.org/wiki/Windows_Live_Messenger.
[38] Moreno, M.A., Kota, R., Schoohs, S., and Whitehill, J.M. (2013). The Facebook Influence Model: A Concept Mapping Approach. Cyberpsychology, Behaviour, and Social Networking, 16(7), 504-511.
[39] Nilsson, E.G. (2009). Design patterns for user interface for mobile applications. Advances in Engineering Software, 40(12), 1318-1328.
[40] Nunnally, J. C. (1978), Psychometric Theory, McGraw-Hill.
[41] Riva, G., Banos, R.M., Botella, C., Wiederhod, B.K., and Gaggioli, A. (2012). Positive Technology: Using Interactive Technologies to Promote Positive Functioning. Behaviour, and Social Networking, 15(2), 69-77.
[42] Saadé, R., and Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information &Managerment, 42(2), 317-327.
[43] Shin, D.H. (2010). The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption. Interacting with Computers, 22(5), 428-438.