Abstract
The international higher education system should be grounded in an educational approach in which teaching and learning methods aim to transform the student into an active agent in their learning process. The present study aims to learn how intention to use a personal learning environment based on Google applications for supporting collaborative learning is formed, in the context of university student learning. For this purpose, an expansion of the technology acceptance models was proposed including subjective norms and social image. The model was empirically evaluated using survey data collected from 267 students from a marketing management degree course, on which Google applications (apps) were used to design a learning environment to support project work and learning. The results show the suitability of the extended TAM to explain the intention to use Google apps as a personal learning environment in the university context. More specifically, subjective norms contributed to the indirect effect on the intention to use Google apps through social image and had a substantial positive influence on the social image. Meanwhile, social image had a significant positive direct effect on perceived usefulness. The results of the present study have a series of practical implications for the higher education sector.
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References
Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior,56, 238–256.
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.
Adell, J., & Castañeda, L. (2010). Los Entornos Personales de Aprendizaje (PLEs): Una nueva manera de entender el aprendizaje. In R. Roigvilla & M. Fiorucci (Eds.), Claves para la investigación e innovación y calidad educativas. La integración de las Tecnologías de la Información y la comunicación y la Interculturalidad en las aulas. Marfil—Roma TRE universita degli studi: Alcoy.
Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly,24(4), 665–694.
Agarwal, R., & Prasad, J. (1998). The antecedents and consequences of user perceptions in information technology adoption. Decision Support Systems,22(1), 15–29.
Ajjan, H., & Hartshorne, R. (2008). Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. The Internet and Higher Education,11(2), 71–80.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall.
Alier-Forment, M., Casany, M. J., Mayol, E., Piguillem, J., Galanis, N., García-Peñalvo, F. J., & Ángel, M. (2012). Docs4Learning: Getting Google docs to work within the LMS with IMS BLTI. Retrieved June 26, 2018 from https://gredos.usal.es/jspui/handle/10366/121865.
Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2013). IT infrastructure services as a requirement for e-learning system success. Computers and Education,69, 431–451.
Arenas-Gaitán, J., Ramírez-Correa, P. E., & Rondán-Cataluña, J. F. (2011). Cross cultural analysis of the use and perceptions of web based learning systems. Computers and Education,57(2), 1762–1774.
Arteaga Sánchez, R., Cortijo, V., & Javed, U. (2014). Students’ perceptions of Facebook for academic purposes. Computers and Education,70, 138–149.
Attwell, G. (2007). Personal learning environments—The future of eLearning? Elearning Papers,2(1), 1–8.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly,25(3), 351–370.
Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly,28(2), 229–254.
Caruso, J. B., & Salaway, G. (2008). The ECAR study of undergraduate students and information technology, 2008. ECAR Research Studies Colorado: EDUCAUSE Center for Applied Research,1, 1–4.
Castañeda, L., Dabbagh, N., & Torres-Kompen, R. (2017). Personal learning environments: Research-based practices, frameworks and challenges. Journal of New Approaches in Educational Research,6(1), 1–2.
Castañeda, J. A., Muñoz-Leiva, F., & Luque, T. (2007). Web acceptance model (WAM): Moderating effects of user experience. Information and Management,44(4), 384–396.
Chang, C.-T., Hajiyev, J., & Su, C.-R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers and Education,111, 128–143.
Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information and Management,39(8), 705–719.
Cheng, G. (2014). Exploring students’ learning styles in relation to their acceptance and attitudes towards using second life in education: A case study in Hong Kong. Computers and Education,70, 105–115.
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers and Education,63, 160–175.
Chien-Huang, L., Ya-Chung, S., Yueh-Chiang, L., & Shih-Chia, W. (2007). How instant messaging affects the satisfaction of virtual interpersonal behavior of Taiwan Junior high school students. Adolescence,42(166), 417–430.
Chow, M., Herold, D. K., Choo, T.-M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use second life for enhancing healthcare education. Computers and Education,59(4), 1136–1144.
Dabbagh, N., & Fake, H. (2017). College students’ perceptions of personal learning environments through the lens of digital tools, processes and spaces. Journal of New Approaches in Educational Research,6(1), 28–36.
Dabbagh, N., & Kitsantas, A. (2012). Personal learning environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education,15(1), 3–8.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly,13(3), 319–340.
Davis, S., & Wiedenbeck, S. (2001). The mediating effects of intrinsic motivation, ease of use and usefulness perceptions on performance in first-time and subsequent computer users. Interacting with Computers,13(5), 549–580.
De Smet, C., Bourgonjon, J., De Wever, B., Schellens, T., & Valcke, M. (2012). Researching instructional use and the technology acceptation of learning management systems by secondary school teachers. Computers and Education,58(2), 688–696.
del Barrio-García, S., Arquero, J. L., & Romero-Frías, E. (2015). Personal learning environments acceptance model: The role of need for cognition, e-learning satisfaction and students’ perceptions. Journal of Educational Technology and Society,18(3), 129–141.
Drent, M., & Meelissen, M. (2008). Which factors obstruct or stimulate teacher educators to use ICT innovatively? Computers and Education,51(1), 187–199.
Dwivedi, Y. K., Mustafee, N., Carter, L. D., & Williams, M. D. (2010). A bibliometric comparison of the usage of two theories of IS/IT acceptance (TAM and UTAUT). In AMCIS 2010 Proceedings (Paper 183, p. 10).
Ebner, M., & Taraghi, B. (2010). Personal learning environment for higher education—A first prototype (pp. 1158–1166). Presented at the EdMedia: World conference on educational media and technology, association for the advancement of computing in education (AACE). Retrieved June 27, 2018 from https://www.learntechlib.org/primary/p/34779/.
Emmett, D. J. (2011). Student engagement with an ePortfolio: A case study of pre-service education students (professional_doctorate). Queensland University of Technology. Retrieved August 22, 2018 from https://eprints.qut.edu.au/40957/.
Escobar-Rodriguez, T., & Monge-Lozano, P. (2012). The acceptance of Moodle technology by business administration students. Computers and Education,58(4), 1085–1093.
Esteve, F. (2009). Bolonia y las TIC: De la docencia 1.0 al aprendizaje 2.0. La Cuestión Universitaria,5, 59–68.
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human–Computer Studies,59(4), 451–474.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Boston: Addison-Wesley.
Fisher, R. J., & Price, L. L. (1992). An investigation into the social context of early adoption behavior. Journal of Consumer Research,19(3), 477–486.
García-Peñalvo, F. J., Conde, M. Á., Alier, M., & Casany, M. J. (2011). Opening learning management systems to personal learning environments. Journal of Universal Computer Science,17(9), 1222–1240.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly,27(1), 51–90.
Gunawardena, C. G. (2014). Comparison of existing technology acceptance theories and models to suggest a well improved theory/model. International Technical Sciences Journal,1(1), 21–36.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1999). Análisis multivariante. Upper Saddle River: Prentice Hall Iberia.
Hajiyev, J. (2018). Assessing students’ attitude and intention to use M-learning in higher education. Journal of Contemporary Educational Research,2(2), 17–25.
Helsper, E. J., & Eynon, R. (2010). Digital natives: Where is the evidence? British Educational Research Journal,36(3), 503–520.
Heo, J., & Han, I. (2003). Performance measure of information systems (IS) in evolving computing environments: An empirical investigation. Information and Management,40(4), 243–256.
Ho, L.-H., Hung, C.-L., & Chen, H.-C. (2013). Using theoretical models to examine the acceptance behavior of mobile phone messaging to enhance parent–teacher interactions. Computers and Education,61, 105–114.
Hora, M. T., & Holden, J. (2013). Exploring the role of instructional technology in course planning and classroom teaching: Implications for pedagogical reform. Journal of Computing in Higher Education,25(2), 68–92.
Ifinedo, P. (2017). Examining students’ intention to continue using blogs for learning: Perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior,72, 189–199.
Ifinedo, P., Pyke, J., & Anwar, A. (2018). Business undergraduates’ perceived use outcomes of Moodle in a blended learning environment: The roles of usability factors and external support. Telematics and Informatics,35(1), 93–102.
Islam, A. K. M. N. (2013). Investigating e-learning system usage outcomes in the university context. Computers and Education,69, 387–399.
Jen, W., Lu, T., & Liu, P.-T. (2009). An integrated analysis of technology acceptance behaviour models: Comparison of three major models. MIS Review,15(1), 33.
Johnson, L., Adams, S., & Cummins, M. (2012). NMC Horizon Report > 2012K-12 Edition. Austin: New Media Consortium. Retrieved January 17, 2018 from https://www.nmc.org/publication/nmc-horizon-report-2012-k-12-edition/.
Joo, Y. J., Lee, H. W., & Ham, Y. (2014). Integrating user interface and personal innovativeness into the TAM for mobile learning in Cyber University. Journal of Computing in Higher Education,26(2), 143–158.
Karahanna, E., & Limayem, M. (2000). E-mail and V-mail usage: Generalizing across technologies. Journal of Organizational Computing and Electronic Commerce,10(1), 49–66.
Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information and Management,35(4), 237–250.
Karahanna, E., Straub, D., & Chervany, N. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. Management Information Systems Quarterly,23(2), 183–213.
Kelman, H. C. (1958). Compliance, identification, and internalization three processes of attitude change. Journal of Conflict Resolution,2(1), 51–60.
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management,43(6), 740–755.
Knezek, G., & Christensen, R. (2016). Extending the will, skill, tool model of technology integration: Adding pedagogy as a new model construct. Journal of Computing in Higher Education,28(3), 307–325.
Kripanont, N. (2007). Examining a technology acceptance model of internet usage by academics within Thai business schools (Ph.D.). Melbourne: Victoria University. Retrieved August 22, 2018 from http://www.vu.edu.au/research.
Kuskaya-Mumcu, F., & Kocak-Usluel, Y. (2010). ICT in vocational and technical schools: Teachers’ instructional, managerial and personal use matters. Turkish Online Journal of Educational Technology,9(1), 98–106.
Lay, J.-G., Chi, Y.-L., Hsieh, Y.-S., & Chen, Y.-W. (2013). What influences geography teachers’ usage of geographic information systems? A structural equation analysis. Computers and Education,62, 191–195.
Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers and Education,61, 193–208.
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems,50(12), 752–780.
Lin, C.-P., & Bhattacherjee, A. (2010). Extending technology usage models to interactive hedonic technologies: A theoretical model and empirical test. Information Systems Journal,20(2), 163–181.
Lin, S. C., Persada, S. F., & Nadlifatin, R. (2014). A study of student behavior in accepting the blackboard learning system: A technology acceptance model (TAM) approach. In Proceedings of the 2014 IEEE 18th international conference on computer supported cooperative work in design (CSCWD) (pp. 457–462). IEEE.
Lin, S., Zimmer, J. C., & Lee, V. (2013). Podcasting acceptance on campus: The differing perspectives of teachers and students. Computers and Education,68, 416–428.
Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C.-H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers and Education,54(2), 600–610.
Marín, V., & de Benito, B. (2011). A design of a postgraduate course on Google Apps based on an Institutional Personal Learning Environment (iPLE). Paper presented at the PLE conference 2011. Southampton: University of Southampton.
Mohammadi, H. (2014). The moderating role of individual and social factors in Internet banking loyalty: An exploratory study. Transforming Government: People, Process and Policy,8(3), 420–446.
Mohammadi, H. (2015). Factors affecting the e-learning outcomes: An integration of TAM and IS success model. Telematics and Informatics,32(4), 701–719.
Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a world-wide-web context. Information and Management,38(4), 217–230.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research,2(3), 192–222.
Morris, M. G., & Dillon, A. (1997). How user perceptions influence software use. IEEE Software,14(4), 58–65.
Muñoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing—ESIC,21(1), 25–38.
Nami, F., & Vaezi, S. (2018). How ready are our students for technology-enhanced learning? Students at a university of technology respond. Journal of Computing in Higher Education (in press).
Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers and Education,48(2), 250–267.
Nunnally, J. C. (1978). Psychometric. New York: McGraw-Hill.
O’Cass, A., & Fenech, T. (2003). Web retailing adoption: Exploring the nature of internet users Web retailing behaviour. Journal of Retailing and Consumer Services,2(10), 81–94.
Patterson, C., Stephens, M., Chiang, V., Price, A. M., Work, F., & Snelgrove-Clarke, E. (2017). The significance of personal learning environments (PLEs) in nursing education: Extending current conceptualizations. Nurse Education Today,48, 99–105.
Pynoo, B., Tondeur, J., van Braak, J., Duyck, W., Sijnave, B., & Duyck, P. (2012). Teachers’ acceptance and use of an educational portal. Computers and Education,58(4), 1308–1317.
Rahimi, E., van den Berg, J., & Veen, W. (2015). Facilitating student-driven constructing of learning environments using Web 2.0 personal learning environments. Computers and Education,81, 235–246.
Rahman, M. M., Lesch, M. F., Horrey, W. J., & Strawderman, L. (2017). Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accident; Analysis and Prevention,108, 361–373.
Rejón-Guardia, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2013). The acceptance of microblogging in the learning process: The µBAM model. Journal of Technology and Science Education,3(1), 31–48.
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.
Rogers, E. M. (2010). Diffusion of innovations. New York: Simon and Schuster.
Saadé, R., & 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 and Management,42(2), 317–327.
Sanchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior,26(6), 1632–1640.
Sanchez-Franco, M. J. (2010). WebCT—The quasimoderating effect of perceived affective quality on an extending Technology Acceptance Model. Computers and Education,54(1), 37–46.
Sánchez-Franco, M. J., & Roldán, J. L. (2005). Web acceptance and usage model: A comparison between goal-directed and experiential web users. Internet Research,15(1), 21–48.
Sangrà, A., Vlachopoulos, D., & Cabrera, N. (2012). Building an inclusive definition of e-learning: An approach to the conceptual framework. The International Review of Research in Open and Distance Learning,13(2), 145–159.
Schoonenboom, J. (2012). Teachers’ acceptance and use of an educational portal. Computers and Education,59(4), 1309–1316.
Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computers and Education,71, 247–256.
Selim, H. M. (2003). An empirical investigation of student acceptance of course websites. Computers and Education,40(4), 343–360.
Siemens, G. (2004). Conectivismo: Una teoría de aprendizaje para la era digital. CC. Retrieved August 22, 2018 from http://www.diegoleal.org/docs/2007/Siemens(2004)-Conectivismo.doc.
Smith, J. Z. S., & Western, M. (2012). Beneath the ‘Digital Native’ myth: Understanding young Australians’ online time use. Journal of Sociology,49(1), 97–118.
Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior,27(6), 2067–2077.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research,6(2), 144–176.
Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the technology acceptance model (TAM). Computers and Education,53(3), 1000–1009.
Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers and Education,56(4), 1032–1044.
Tian, K. T., Bearden, W. O., & Hunter, G. L. (2001). Consumers’ need for uniqueness: Scale development and validation. Journal of Consumer Research,28(1), 50–66.
Toots, A., & Idnurm, T. (2001). Tiger under magnifying glass: Study on ICT in Estonian Schools in 2000. Retrieved October 10, 2017 from http://www.tiigrihype.ee/eng/publikatsioonid/tiigerluup_eng/tiigerluup_eng.html.
Townsend, T. (2017). Here’s how Google’s rival to Microsoft Office, G Suite, came together. Retrieved June 10, 2017 from https://www.recode.net/2017/3/18/14955654/short-history-g-suite.
University Coordination Council. (2005). Spanish Ministry of Education. Retrieved February 14, 2016 from http://www.boe.es/boe/dias/2005/03/15/pdfs/A09068-09069.pdf.
van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: Contributions from technology and trust perspectives. European Journal of Information Systems,12(1), 41–48.
van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers and Education,50(3), 838–852.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences,39(2), 273–315.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science,46(2), 186–204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly,27(3), 425–478.
Wiggers, K. (2017). Google’s G Suite for Education app platform now has over 70 million users. Retrieved June 10, 2017 from https://www.digitaltrends.com/web/google-g-suite-70-million/.
Wojciechowski, R., & Cellary, W. (2013). Evaluation of learners’ attitude toward learning in ARIES augmented reality environments. Computers and Education,68, 570–585. https://doi.org/10.1016/j.compedu.2013.02.014.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior,67, 221–232.
Yen, C.-J., Tu, C.-H., Sujo-Montes, L., & Sealander, K. (2016). A predictor for PLE management: Impacts of self-regulated online learning on student’s learning skills. Journal of Educational Technology Development and Exchange,9(1), 29–48.
Yu, J., Lee, H., Ha, I., & Zo, H. (2017). User acceptance of media tablets: An empirical examination of perceived value. Telematics and Informatics,34(4), 206–223.
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This study was carried out thanks to financing received from the Teaching innovation project 12-64 by the University of Granada (Spain).
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Rejón-Guardia, F., Polo-Peña, A.I. & Maraver-Tarifa, G. The acceptance of a personal learning environment based on Google apps: the role of subjective norms and social image. J Comput High Educ 32, 203–233 (2020). https://doi.org/10.1007/s12528-019-09206-1
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DOI: https://doi.org/10.1007/s12528-019-09206-1