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.
[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.
[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.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:72132", author = "C. C. Liang", title = "User Selections on Social Network Applications", 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.", volume = "8", number = "8", pages = "1173-4", }
