PREDICTING AND EXPLAINING PRE-SERVICE TEACHERS’ SOCIAL NETWORKING TECHNOLOGY ADOPTION
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Published
Jan 26, 2022
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Abstract
This research study examined pre-service teachers’ (N=250) intentions to adopt Twitter for professional development. The study used the Technology Acceptance Model to test research hypotheses grounded in the literature. The data were collected with a survey questionnaire and analyzed with Structural Equation Modelling. Findings indicated that ease of use, subjective norms, and perceived connectedness explained the variability in intentions to use Twitter. Perceived mobility, mediated through perceived behavioral control, explained participant differences in the perceived ease of use. Implications for stakeholders include highlighting the role subjective norms and mobile applications play in facilitating the ease of use and connectedness because both variables appear to positively impact behavioral intentions to use Twitter for professional development. Supporting pre-service teachers with self-efficacy, resources, and positive social media subjective norms will positively influence Twitter adoption for cross-cultural collaboration and professional learning. Technology adoption mediates global collaboration among educators in heralding innovation and creativity.
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Articles - General topics
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References
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Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.). Action control: From cognition to behavior (pp. 11-39). New York, NY, US: Springer-Verlag.
Barnes, S., & Bohringer, M. (2011). Modeling use continuance behavior in microblogging services: The case of Twitter. Journal of Computer Information Systems, 51(4), 1-10.
Beach, L. R., & Mitchell, T. R. (1978). A contingency model for the selection of decision strategies, Academy of Management Review, 3(3), 439-449.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.
Bentler, P. M., & Bonett, D. G. (1989). Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606.
Birch, A., & Irvine, V. (2009). Preservice teachers' acceptance of ICT integration in the classroom: Applying the UTAUT model. Educational Media International, 46(4), 295-315.
Bollen, K., & Long, I. S. (1992). Tests for structural equation models. Sociological Methods and Research, 123–131.
Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230.
Brown, A. L., Ash, D., Rutherford, M., Nakagawa, K., Gordon, A., & Campione, J. C. (1993). Distributed Expertise in the Classroom. In G. Soloman (Ed.), Distributed Cognitions: Psychological and Educational Considerations (pp. 188-229). Cambridge, UK: Cambridge University Press.
Chen, B., Sivo, S., Seilhamer, R., Sugar, A., & Mao, J. (2013). User acceptance of mobile technology: A campus-wide implementation of Blackboard's Mobile™ learn application. Journal of educational computing research, 49(3), 327-343.
Choi, G., & Chung, H. (2013). Applying the technology acceptance model to social networking sites (SNS): Impact of subjective norm and social capital on the acceptance of SNS. International Journal of Human-Computer Interaction, 29(10), 619-628.
Cocosila, M., & Igonor, A. (2015). How important is the "social" in social networking? A perceived value empirical investigation. Information Technology & People, 28(2), 366-382.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
Compeau, D. R., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Quarterly, 23(2), 145-158.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35, 982-1003.
Fan, X., & Sivo, S. (2005). Evaluating the sensitivity and generalizability of SEM fit indices while controlling for severity of model misspecification. Structural Equation Modeling, 12(3), 343-367.
Fan, X., & Sivo, S. A. (2007). Sensitivity of fit indices to model misspecification and model types. Multivariate Behavioral Research, 42(3), 509–529. doi: 10.1080/00273170701382864
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA, US: Addison-Wesley.
Gefen, D., Straub, D. W., & Boudreau, M. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1-77.
Gollwitzer, P. M. (1993). Goal achievement: The role of intentions. In W. Stroebe & M. Hewstone (Eds.), European review of social psychology, 4, 141–185. Chichester, UK: Wiley.
Greenhow, C., & Askari, E. (2017). Learning and teaching with social network sites: A decade of research in K-12 related education. Education and Information Technologies, 22, 623–645.
Greenhalgh, S. P., Rosenberg, J. M., Willet, B. S., Koehler, M. J., & Akglau, M. (2020). Identifying multiple learning spaces within a single teacher-focused Twitter hashtag. Computers & Education, 148, 1–12.
Gurjar, N. (2020). Leveraging social networks for authentic learning in distance learning teacher education. TechTrends, 64(4), 666-677. doi: 10.1007/s11528-020-00510-7
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use, Management Science, 40(4), 400-465.
Hilgard, E. R. (1980). The trilogy of mind: Cognition, affection, and conation. Journal of the History of the Behavioral Sciences, 16(2), 107-117.
Hu, L. T, & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus. Structural Equation Modeling, 6(1), 1.
Hu, T., Kettinger, W., & Poston, R. (2011). The effect of online social value on satisfaction and continued use of social media. European Journal Of Information Systems, 24(4), 391-410.
Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. Electronic Library, 25(5), 585–598.
Hung, H. T., & Yuen, S. C. Y. (2010). Educational use of social networking technology in higher education. Teaching in Higher Education, 15(6), 703–714.
Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587-605.
Kelman, H. C. (1958). Compliance, identification, and internalization: Three processes of attitude change. Journal of Conflict Resolution, 2, 51-60.
Kleijnen, M., Lievens, A., de Ruyter, K., & Wetzels, M. (2009). Knowledge creation through mobile social networks and its impact on intentions to use innovative mobile services. Journal of Service Research, 12(1), 15-35.
Kwon, S., Park, E., & Kim, K. (2014). What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter. Social Science Journal, 51(4), 534-544.
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The Technology Acceptance Model: Past, present, and future. Communications of AIS, 12, 752-780.
Lemon, N. (2014). Twitter and Teacher Education: Exploring teacher, social, and cognitive presence in the professional use of social media. Teacher Education and Practice, 27(4), 532-560.
Liang, T. P., Huang, C. W., Yeh, Y. H., & Lin, B. (2007). Adoption of mobile technology in business: A fit-viability model. Industrial Management and Data Systems, 107(8), 1154-1169.
Linck, K., Pousttchi, K., & Wiedemann, D. G. (2006). Security issues in mobile payment from the customer viewpoint. In Proceedings of the 14th European Conference on Information Systems (pp. 1-11).
López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45(6), 359-364.
Lowe, B., D'Alessandro, S., Winzar, H., Laffey, D., & Collier, W. (2013). The use of Web 2.0 technologies in marketing classes: Key drivers of student acceptance. Journal of Consumer Behaviour, 12(5), 412-422.
Madden, M., & Raine, L. (2015). Americans’ attitudes about privacy, security, and surveillance. Pew Research Center report.
Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: the influence of perceived user resources. ACM SIGMIS Database, 32(3), 86 - 112.
National Center for Education Statistics. (2021). Characteristics of Public School Teachers. Retrieved from https://nces.ed.gov/programs/coe/indicator/clr
Olmstead, K., Lampe, C., & Ellison, N.B. (2016). Social media and the workplace. Pew Internet Research. Retrieved from http://www.pewinternet.org/2016/06/22/social-media-and-the-workplace/
Pan, C., Gunter, G., Sivo, S., & Cornell, R. (2005). End-user acceptance of a learning management system in two-hybrid large-sized introductory undergraduate courses. Journal of Educational Technology Systems, 33(4), 355–365. doi: 10.2190/B7TV-X8RN-0L66-XTU8
Pan, C., Sivo, S., Gunter, G., & Cornell, R. (2005). Students’ perceived ease of use of an e-learning management system: An exogenous or endogenous variable? Journal of Educational Computing Research, 33(3), 285–307. Retrieved from doi: 10.2190/7M4G-R742-W9FT-JX1J
Pan, C. C., Sivo, S. A., & Brophy, J. (2003). Students’ attitude in a web-enhanced hybrid course: A structural equation modeling inquiry. Journal of Educational Media and Library Sciences, 41(2), 181–194. Retrieved from http://joemls.dils.tku.edu.tw/detail.php?articleId=41204&lang=en
Radner, R. & Rothschild, M. (1975). On the allocation of effort. Journal of Economic Theory, 10, 358-376.
Selwyn, N. (2010). Looking beyond learning: Notes towards the critical study of educational technology. Journal of Computer Assisted Learning, 26(1), 65–73.
Shin, D. H. (2010). Analysis of online social networks: A cross-national study. Online Information Review, 34(3), 473–495.
Shipps, B., & Phillips, B. (2013). Social networks, interactivity, and satisfaction: Assessing socio-technical behavioral factors as an extension to technology acceptance. Journal of Theoretical & Applied Electronic Commerce Research, 8(1), 35.
Siegel, D., Acharya, P. & Sivo, S. (2017). Extending the technology acceptance model to improve usage & decrease resistance toward a new technology by faculty in higher education. Journal of Technology Studies, 43(2), 58-69.
Sivo, S. A., Fan, X., Witta, E. L., & Willse, J. (2006). The search for “optimal” cutoff properties: Fit index criteria in structural equation modeling. Journal of Experimental Education, 74(3), 267–288. doi: 10.3200/JEXE.74.3.267-288
Sivo, S.A., Ku, C.H., & Acharya, P. (2018). Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australasian Journal of Technology, 34(4), 72-91.
Sivo, S., & Pan, C. (2005). Undergraduate engineering and psychology students’ use of a course management system: A factorial invariance study of user characteristics and attitudes. The Journal of Technology Studies, 31(2), 94–103. Retrieved from http://www.jstor.org/stable/43604057
Sivo, S. A., Pan, C. C., & Brophy, J. (2004). Temporal cross-lagged effects between subjective norms and students’ attitudes regarding the use of technology. Journal of Educational Media and Library Sciences, 42(1), 63–74. Retrieved from http://joemls.dils.tku.edu.tw/detail.php?articleId=42106&lang=en
Sivo, S. A., Pan, C. C., & Hahs-Vaughn, D. (2007). Combined longitudinal effects of attitude and subjective norms on student outcomes in a web-enhanced hybrid course: A structural equation modeling approach. British Journal Educational Technology,38(5), 861–875. doi:10.1111/j.1467-8535.2006.00672.x
Smith, J., & Sivo, S. A. (2012). Predicting continued use of online teacher professional development and the influence of social presence and sociability. British Journal of Educational Technology, 43(6), 871–882. doi: 10.1111/j.1467-8535.2011.01223.x
Tao, H., Poston, R. S., & Kettinger, W. J. (2011). Non-adopters of online social network services: is it easy to have fun yet? Communications of the Association for Information Systems, 29, 441-458.
Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 119-186.
Venkatesh, V., Morris, M., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3),425-478.
Vie, S. (2015). What’s going on? Challenges and opportunities for social media use in the writing classroom. Journal of Faculty Development, 29(2), 33-44.
Yan, T., Chu, D., Ganesan, D., Kansal, A., & Liu, J. (2012). Fast app launching for mobile devices using predictive user context. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (pp. 113-126). New York, NY, US: ACM Press.
Yang, H. C., & Zhou, L. (2011). Extending TPB and TAM to mobile viral marketing: An exploratory study on American young consumers’ mobile viral marketing attitude, intent and behavior. Journal of Targeting, Measurement and Analysis for Marketing, 19(2), 85-98.
Yenisey, M. M., Ozok, A. A., & Salvendy, G. (2005). Perceived security determinants in e-commerce among Turkish university students. Behavior and Information Technology, 24(4), 259–274.
Yuen, A. H. K., & Ma, W. W. K. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36, 229–243.
Yoo, J., Choi, S., Choi, M., & Rho, J. (2014). Why people use Twitter: social conformity and social value perspectives. Online Information Review, 38(2), 265-283.
Zhao, L., & Lu, Y. (2012). Enhancing perceived interactivity through network externalities: An empirical study on microblogging service satisfaction and continuance intention. Decision Support Systems, 53(4), 825-834. doi: 10.1016/j.dss.2012.05.019
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.). Action control: From cognition to behavior (pp. 11-39). New York, NY, US: Springer-Verlag.
Barnes, S., & Bohringer, M. (2011). Modeling use continuance behavior in microblogging services: The case of Twitter. Journal of Computer Information Systems, 51(4), 1-10.
Beach, L. R., & Mitchell, T. R. (1978). A contingency model for the selection of decision strategies, Academy of Management Review, 3(3), 439-449.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.
Bentler, P. M., & Bonett, D. G. (1989). Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606.
Birch, A., & Irvine, V. (2009). Preservice teachers' acceptance of ICT integration in the classroom: Applying the UTAUT model. Educational Media International, 46(4), 295-315.
Bollen, K., & Long, I. S. (1992). Tests for structural equation models. Sociological Methods and Research, 123–131.
Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230.
Brown, A. L., Ash, D., Rutherford, M., Nakagawa, K., Gordon, A., & Campione, J. C. (1993). Distributed Expertise in the Classroom. In G. Soloman (Ed.), Distributed Cognitions: Psychological and Educational Considerations (pp. 188-229). Cambridge, UK: Cambridge University Press.
Chen, B., Sivo, S., Seilhamer, R., Sugar, A., & Mao, J. (2013). User acceptance of mobile technology: A campus-wide implementation of Blackboard's Mobile™ learn application. Journal of educational computing research, 49(3), 327-343.
Choi, G., & Chung, H. (2013). Applying the technology acceptance model to social networking sites (SNS): Impact of subjective norm and social capital on the acceptance of SNS. International Journal of Human-Computer Interaction, 29(10), 619-628.
Cocosila, M., & Igonor, A. (2015). How important is the "social" in social networking? A perceived value empirical investigation. Information Technology & People, 28(2), 366-382.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
Compeau, D. R., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Quarterly, 23(2), 145-158.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35, 982-1003.
Fan, X., & Sivo, S. (2005). Evaluating the sensitivity and generalizability of SEM fit indices while controlling for severity of model misspecification. Structural Equation Modeling, 12(3), 343-367.
Fan, X., & Sivo, S. A. (2007). Sensitivity of fit indices to model misspecification and model types. Multivariate Behavioral Research, 42(3), 509–529. doi: 10.1080/00273170701382864
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA, US: Addison-Wesley.
Gefen, D., Straub, D. W., & Boudreau, M. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1-77.
Gollwitzer, P. M. (1993). Goal achievement: The role of intentions. In W. Stroebe & M. Hewstone (Eds.), European review of social psychology, 4, 141–185. Chichester, UK: Wiley.
Greenhow, C., & Askari, E. (2017). Learning and teaching with social network sites: A decade of research in K-12 related education. Education and Information Technologies, 22, 623–645.
Greenhalgh, S. P., Rosenberg, J. M., Willet, B. S., Koehler, M. J., & Akglau, M. (2020). Identifying multiple learning spaces within a single teacher-focused Twitter hashtag. Computers & Education, 148, 1–12.
Gurjar, N. (2020). Leveraging social networks for authentic learning in distance learning teacher education. TechTrends, 64(4), 666-677. doi: 10.1007/s11528-020-00510-7
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use, Management Science, 40(4), 400-465.
Hilgard, E. R. (1980). The trilogy of mind: Cognition, affection, and conation. Journal of the History of the Behavioral Sciences, 16(2), 107-117.
Hu, L. T, & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus. Structural Equation Modeling, 6(1), 1.
Hu, T., Kettinger, W., & Poston, R. (2011). The effect of online social value on satisfaction and continued use of social media. European Journal Of Information Systems, 24(4), 391-410.
Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. Electronic Library, 25(5), 585–598.
Hung, H. T., & Yuen, S. C. Y. (2010). Educational use of social networking technology in higher education. Teaching in Higher Education, 15(6), 703–714.
Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587-605.
Kelman, H. C. (1958). Compliance, identification, and internalization: Three processes of attitude change. Journal of Conflict Resolution, 2, 51-60.
Kleijnen, M., Lievens, A., de Ruyter, K., & Wetzels, M. (2009). Knowledge creation through mobile social networks and its impact on intentions to use innovative mobile services. Journal of Service Research, 12(1), 15-35.
Kwon, S., Park, E., & Kim, K. (2014). What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter. Social Science Journal, 51(4), 534-544.
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The Technology Acceptance Model: Past, present, and future. Communications of AIS, 12, 752-780.
Lemon, N. (2014). Twitter and Teacher Education: Exploring teacher, social, and cognitive presence in the professional use of social media. Teacher Education and Practice, 27(4), 532-560.
Liang, T. P., Huang, C. W., Yeh, Y. H., & Lin, B. (2007). Adoption of mobile technology in business: A fit-viability model. Industrial Management and Data Systems, 107(8), 1154-1169.
Linck, K., Pousttchi, K., & Wiedemann, D. G. (2006). Security issues in mobile payment from the customer viewpoint. In Proceedings of the 14th European Conference on Information Systems (pp. 1-11).
López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45(6), 359-364.
Lowe, B., D'Alessandro, S., Winzar, H., Laffey, D., & Collier, W. (2013). The use of Web 2.0 technologies in marketing classes: Key drivers of student acceptance. Journal of Consumer Behaviour, 12(5), 412-422.
Madden, M., & Raine, L. (2015). Americans’ attitudes about privacy, security, and surveillance. Pew Research Center report.
Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: the influence of perceived user resources. ACM SIGMIS Database, 32(3), 86 - 112.
National Center for Education Statistics. (2021). Characteristics of Public School Teachers. Retrieved from https://nces.ed.gov/programs/coe/indicator/clr
Olmstead, K., Lampe, C., & Ellison, N.B. (2016). Social media and the workplace. Pew Internet Research. Retrieved from http://www.pewinternet.org/2016/06/22/social-media-and-the-workplace/
Pan, C., Gunter, G., Sivo, S., & Cornell, R. (2005). End-user acceptance of a learning management system in two-hybrid large-sized introductory undergraduate courses. Journal of Educational Technology Systems, 33(4), 355–365. doi: 10.2190/B7TV-X8RN-0L66-XTU8
Pan, C., Sivo, S., Gunter, G., & Cornell, R. (2005). Students’ perceived ease of use of an e-learning management system: An exogenous or endogenous variable? Journal of Educational Computing Research, 33(3), 285–307. Retrieved from doi: 10.2190/7M4G-R742-W9FT-JX1J
Pan, C. C., Sivo, S. A., & Brophy, J. (2003). Students’ attitude in a web-enhanced hybrid course: A structural equation modeling inquiry. Journal of Educational Media and Library Sciences, 41(2), 181–194. Retrieved from http://joemls.dils.tku.edu.tw/detail.php?articleId=41204&lang=en
Radner, R. & Rothschild, M. (1975). On the allocation of effort. Journal of Economic Theory, 10, 358-376.
Selwyn, N. (2010). Looking beyond learning: Notes towards the critical study of educational technology. Journal of Computer Assisted Learning, 26(1), 65–73.
Shin, D. H. (2010). Analysis of online social networks: A cross-national study. Online Information Review, 34(3), 473–495.
Shipps, B., & Phillips, B. (2013). Social networks, interactivity, and satisfaction: Assessing socio-technical behavioral factors as an extension to technology acceptance. Journal of Theoretical & Applied Electronic Commerce Research, 8(1), 35.
Siegel, D., Acharya, P. & Sivo, S. (2017). Extending the technology acceptance model to improve usage & decrease resistance toward a new technology by faculty in higher education. Journal of Technology Studies, 43(2), 58-69.
Sivo, S. A., Fan, X., Witta, E. L., & Willse, J. (2006). The search for “optimal” cutoff properties: Fit index criteria in structural equation modeling. Journal of Experimental Education, 74(3), 267–288. doi: 10.3200/JEXE.74.3.267-288
Sivo, S.A., Ku, C.H., & Acharya, P. (2018). Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australasian Journal of Technology, 34(4), 72-91.
Sivo, S., & Pan, C. (2005). Undergraduate engineering and psychology students’ use of a course management system: A factorial invariance study of user characteristics and attitudes. The Journal of Technology Studies, 31(2), 94–103. Retrieved from http://www.jstor.org/stable/43604057
Sivo, S. A., Pan, C. C., & Brophy, J. (2004). Temporal cross-lagged effects between subjective norms and students’ attitudes regarding the use of technology. Journal of Educational Media and Library Sciences, 42(1), 63–74. Retrieved from http://joemls.dils.tku.edu.tw/detail.php?articleId=42106&lang=en
Sivo, S. A., Pan, C. C., & Hahs-Vaughn, D. (2007). Combined longitudinal effects of attitude and subjective norms on student outcomes in a web-enhanced hybrid course: A structural equation modeling approach. British Journal Educational Technology,38(5), 861–875. doi:10.1111/j.1467-8535.2006.00672.x
Smith, J., & Sivo, S. A. (2012). Predicting continued use of online teacher professional development and the influence of social presence and sociability. British Journal of Educational Technology, 43(6), 871–882. doi: 10.1111/j.1467-8535.2011.01223.x
Tao, H., Poston, R. S., & Kettinger, W. J. (2011). Non-adopters of online social network services: is it easy to have fun yet? Communications of the Association for Information Systems, 29, 441-458.
Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 119-186.
Venkatesh, V., Morris, M., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3),425-478.
Vie, S. (2015). What’s going on? Challenges and opportunities for social media use in the writing classroom. Journal of Faculty Development, 29(2), 33-44.
Yan, T., Chu, D., Ganesan, D., Kansal, A., & Liu, J. (2012). Fast app launching for mobile devices using predictive user context. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (pp. 113-126). New York, NY, US: ACM Press.
Yang, H. C., & Zhou, L. (2011). Extending TPB and TAM to mobile viral marketing: An exploratory study on American young consumers’ mobile viral marketing attitude, intent and behavior. Journal of Targeting, Measurement and Analysis for Marketing, 19(2), 85-98.
Yenisey, M. M., Ozok, A. A., & Salvendy, G. (2005). Perceived security determinants in e-commerce among Turkish university students. Behavior and Information Technology, 24(4), 259–274.
Yuen, A. H. K., & Ma, W. W. K. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36, 229–243.
Yoo, J., Choi, S., Choi, M., & Rho, J. (2014). Why people use Twitter: social conformity and social value perspectives. Online Information Review, 38(2), 265-283.
Zhao, L., & Lu, Y. (2012). Enhancing perceived interactivity through network externalities: An empirical study on microblogging service satisfaction and continuance intention. Decision Support Systems, 53(4), 825-834. doi: 10.1016/j.dss.2012.05.019