Resumen
El objetivo del presente trabajo es contrastar un modelo de los determinantes perceptuales de la intención de uso de Internet. Se llevó a cabo un estudio no experimental con una selección no probabilística de 188 usuarios de una biblioteca pública de la Ciudad de México. Se encontró que la utilidad es determinante de la intención de uso de Internet. Se advierten líneas futuras de investigación en torno a la gobernanza de la sociedad informacional.Citas
Amoako, K. & Salam, A. (2004). An extension of the Technology Acceptance Model in an ERP implementation environment. Information & Management. 41, 731-745.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review. 84, 191-215.
Bandura, A. (1982). Self-efficacy. Mechanism in human agency. American Psychologist. 37, 122-147.
Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist. 28, 117-148.
Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (ed.). Encyclopedia and Human Behavior. (pp. 71-88). New York: Academic Press.
Bandura, A. (1995). Exercise of personal and collective efficacy in changing societies. In A. Bandura (ed.). Self-efficacy in changing societies. (pp. 1-45). New York: Cambridge University Press.
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology. 52, 1-26.
Barbeite, F. & Weiss, E. (2004). Computer self-efficacy and anxiety scales for a internet sample: Testing measurement equivalence of existing measures and development of new scales. Computers in Human Behaviors. 20, 1-15.
Bigne, E., Ruíz, C. & Sanz, S. (2007). Key drivers of mobile commerce adoption. An exploratory study of Spanish mobile use. Journal of Theoretical and Applied Electronic Commerce Research. 2, 48-60.
Briceño, M. y Godoy, E. (2012). El talento humano: Un capital intangible que otorga valor en las organizaciones. International Journal of Good Conscience. 7, 57-67.
Busch, T. (1995). Gender differences in self-efficacy and attitudes towards computers. Journal of Educational Computers Research. 12, 147-158.
Chang, H. (2009). Application of the extended the Technology Acceptance Model to picture archiving and communication systems in dental hospital. Journal of Korean Informatics. 15, 265-272.
Chu, Y. (2006). Levels of computer self efficacy, computer use and earnings in China. Economics Letters. 90, 427-432.
Chuo, Y-H., Tsai, C-H., Lan, Y-L. & Tsai, C-S. (2011). The effect of organizational support, self efficacy and computer anxiety on the usage intention of e–learning system in hospital. African Journal of Business Management. 5, 5518-5523.
D’ambra, J. & Wilson, C. (2004). Explaining perceived performance of the world wide web; Uncertainly and the Task Technology Fit Model. Internet Research. 14, 294-310.
Davis, F. (1993). User acceptance of information technology: Systems, characteristics, user perception and behavioral impacts. International Journal of Man Machine Studies. 8, 475-487.
Dishaw, M. & Strong, D. (1999). Extending the Technology Acceptance Model with Task Technology Fit construct. Information & Management. 36, 9-21.
Gong, M., Xu, Y. & Yu, Y. (2004). An enhanced Technology Acceptance Model for web-based learning. Journal of Information Systems Education. 1, 365-375.
Ha, S. & Stoel, L. (2009). Consumer e-shopping acceptance; Antecedents in a Technology Acceptance Model. Journal of Business Research. 62, 565-571.
Hsu, M-H. & Chiu, C-M. (2004). Internet self efficacy and electronic service acceptance. Decision Support Systems. 38, 369-381.
Jonhson, D. & Warldlow, J. (2004). Computers experiences, self efficacy and knowledge of undergraduate student entering a land grant college of agriculture by year and gender. Journal of Agricultural Education. 45, 53-64.
Klopping, I. & McKinney, E. (2004). Extending the Technology Acceptance Model and the Task Technology Fit Model to consumer e-commerce. Information Technology, Learning and Performance Journal. 22, 35-49.
Liu, S., Liao, H. & Peng, C. (2005). Applying the Technology Acceptance Model and flow theory to online e-learning user’s acceptance behavior. Issues in Information Systems. 2, 175-182.
Obisi, C. & Anyim, F. (2012). Developing the human capital for entrepeneurship challenges and successes. International Journal of Academic Research in Business and Social Science. 2, 128-134.
Paraskeva, F., Bouta, H. & Papagianni, A. (2008). Individual characteristics and computer self–efficacy in secondary education teachers to integrated technology in education practice. Computers & Education. 50, 1084-1091.
Porter, S. (2006). Using the technology acceptance model to explain how attitudes determine internet usage: The role perceived access barriers and demographic. Journal of Business Research. 59, 999-1007.
Reed, E. & Wolniak, G. (2005). Diagnosis or determination: Assessment explained through Human Capital Theory and the concept of aptitudes. Journal of Sociology. 1, 1-5.
Reid, M. & Levy, Y. (2008). Integrating trust and computer self-efficacy with TAM: An empirical assessment of customers’ acceptance of banking information system. Journal of Internet Banking and Commerce. 12, 1-18.
Roca, J., Chiu, C. & Martínez, F. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human Computer Studies. 64, 683-696.
Saadé, R., Nebebe, F. & Tan, W. (2007). Viability on the Technology Acceptance Model in multimedia learning environments: A comparative study. Interdisciplinary Journal of Knowledge and Learning Objects. 3, 1-10.
Sadeé, R. & Kira, D. (2009). Computer anxiety in e-learning: the effect of computer self efficacy. Journal of Information Technology Education. 8, 177-192.
Shepers, J. & Wetzels, M. (2007). A meta-analysis of the Technology Acceptance Model: Investigating subjective norm and moderation effects. Information & Management. 47, 90-110.
Shroff, R., Deneen, C. & Ng, E. (2011). Analysis of the Technology Acceptance Model in examining students’ behavior intention to use an e-portfolio system. Australasian Journal Education Technology. 27, 600-618.
Su, L., Hsiu, L. & Cheng, P. (2005). Applying the Technology Acceptance Model and flow theory to online e-learning user’s acceptance behavior. Issues in Information System. 6, 175-182.
Teh, P-L., Chong, C-W., Yong, C-C. & Yew, S-Y. (2010). Internet self–efficacy, computer self–efficacy, and cultural factor on knowledge sharing behavior. African Journal of Business Management. 4, 4086-4095.
Torkzadeh, G., Cha, J. & Demirhan, D. (2006). A contingency model of computer and internet self efficacy. Information & Management. 43, 541-550.
Wai, W., Andersson, R. & Oslear, K. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted Learning. 21, 387-395.
Zhangxi, L., Binjia, S. & Linhua, Y. (2007). Understanding Internet banking: An empirical investigation of potential customers’ acceptance in Mailand, China. American Conference of Information System. 485, 1-14.