Predicting the acceptance of cloud-based virtual learning environment: The roles of Self Determination and Channel Expansion Theory

Intrinsic motivations of teachers in cloud-based VLE were examined.Effects of relatedness, competence, autonomy and media richness were examined.Content design, interactivity, trust, KS attitude & school support were examined.1064 teachers were randomly selected nation wide in two waves of survey.The integrated SDT-CET model provides 65.96% variance explained. The emergence of the cloud computing technology has further enhanced the capabilities of the cloud-based virtual learning environment (VLE) compared to the grid computing based VLE as teaching resources can be accessed, saved, retrieved and shared on the cloud any time any where without any limitation. Unlike existing VLE literature that examines extrinsic motivation (e.g. TAM; UTAUT) from the perspective of the learners in the context of the conventional grid-computing VLE; this study examine the intrinsic motivation from the teachers' perspective in the context of the cloud-based VLE. So far, the influences of Self Determination Theory (i.e. relatedness, competence, autonomy) and Channel Expansion Theory (i.e. media richness) have been over-looked. In this study, the roles of SDT, CET, VLE content design and interactivity together with the trust-in-website, attitude toward knowledge sharing and school support are being examined. A sample of 1064 respondents was gathered using simple random sampling across the country and analyzed with PLS-SEM. The research model is able to predict intention to use with 65.96% variance explained. SDT, CET, VLE content design, Attitude toward knowledge sharing, trust-in-website, school support and education significantly effects intention to use VLE. This study provides theoretical and practical implications while contributing to the VLE literature.

[1]  Chun-Ming Chang,et al.  Determinants of repurchase intention in online group-buying: The perspectives of DeLone & McLean IS success model and trust , 2014, Comput. Hum. Behav..

[2]  Tom Page,et al.  Using Virtual Reality for Developing Design Communication , 2010 .

[3]  Andrew Dillon,et al.  User analysis in HCI - the historical lessons from individual differences research , 1996, Int. J. Hum. Comput. Stud..

[4]  In Lee,et al.  Learners' acceptance of e-learning in South Korea: Theories and results , 2009, Comput. Educ..

[5]  Marko Sarstedt,et al.  The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications , 2012 .

[6]  Rick Kazman,et al.  Investigating antecedents of technology acceptance of initial eCRM users beyond generation X and the role of self-construal , 2007, Electron. Commer. Res..

[7]  Viswanath Venkatesh,et al.  Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..

[8]  R. Kelly Rainer,et al.  The Influence of Individual Differences on Skill in End-User Computing , 1992, J. Manag. Inf. Syst..

[9]  R. Daft,et al.  Information Richness. A New Approach to Managerial Behavior and Organization Design , 1983 .

[10]  R. Arteaga Sánchez,et al.  Motivational factors that influence the acceptance of Moodle using TAM , 2010, Comput. Hum. Behav..

[11]  Nauman Saeed,et al.  Media richness and user acceptance of Second Life , 2008 .

[12]  Alain Yee-Loong Chong,et al.  Influence of individual characteristics, perceived usefulness and ease of use on mobile entertainment adoption , 2011, Int. J. Mob. Commun..

[13]  Tuncay Ercan,et al.  Effective use of cloud computing in educational institutions , 2010 .

[14]  Edward L. Deci,et al.  Intrinsic Motivation and Self-Determination in Human Behavior , 1975, Perspectives in Social Psychology.

[15]  Øystein Sørebø,et al.  The role of self-determination theory in explaining teachers' motivation to continue to use e-learning technology , 2009, Comput. Educ..

[16]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[17]  Chee-Sing Yap,et al.  Top Management Support, External Expertise and Information Systems Implementation in Small Businesses , 1996, Inf. Syst. Res..

[18]  Alireza Hassanzadeh,et al.  Factors affecting university instructors' adoption of web-based learning systems: Case study of Iran , 2013, Comput. Educ..

[19]  Hui-Min Lai,et al.  Factors influencing secondary school teachers' adoption of teaching blogs , 2011, Comput. Educ..

[20]  Marjan Hericko,et al.  An Empirical Study of Virtual Learning Environment Adoption Using UTAUT , 2010, 2010 Second International Conference on Mobile, Hybrid, and On-Line Learning.

[21]  W. Poon,et al.  A study of Web‐based learning (WBL) environment in Malaysia , 2004 .

[22]  Pep Simo,et al.  Evolution of online discussion forum richness according to channel expansion theory: A longitudinal panel data analysis , 2013, Comput. Educ..

[23]  Garry Wei-Han Tan,et al.  NFC mobile credit card: The next frontier of mobile payment? , 2014, Telematics Informatics.

[24]  John R. Carlson,et al.  Channel Expansion Theory and the Experiential Nature of Media Richness Perceptions , 1999 .

[25]  W. S. Chow,et al.  Social network, social trust and shared goals in organizational knowledge sharing , 2008, Inf. Manag..

[26]  Nauman Saeed,et al.  Effects of Media Richness on User Acceptance of Web 2.0 Technologies in Higher Education , 2009 .

[27]  Chien-Hung Liu,et al.  Learning effectiveness in a Web-based virtual learning environment: a learner control perspective , 2005, J. Comput. Assist. Learn..

[28]  Erik M. van Raaij,et al.  The acceptance and use of a virtual learning environment in China , 2008, Comput. Educ..

[29]  Yu-chen Hsu,et al.  Effect of interactivity on learner perceptions in Web-based instruction , 2013, Comput. Hum. Behav..

[30]  Sean B. Eom,et al.  Effects of LMS, self‐efficacy, and self‐regulated learning on LMS effectiveness in business education , 2012 .

[31]  Garry Wei-Han Tan,et al.  Understanding and predicting the motivators of mobile music acceptance - A multi-stage MRA-artificial neural network approach , 2014, Telematics Informatics.

[32]  M. Sarstedt,et al.  A new criterion for assessing discriminant validity in variance-based structural equation modeling , 2015 .

[33]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[34]  Victor R. Prybutok,et al.  Investigating factors affecting social presence and user satisfaction with Mobile Instant Messaging , 2014, Comput. Hum. Behav..

[35]  Chee Sing Yap,et al.  Issues in Managing Information Technology , 1989 .

[36]  P. Bentler,et al.  Fit indices in covariance structure modeling : Sensitivity to underparameterized model misspecification , 1998 .

[37]  E. Deci,et al.  The "What" and "Why" of Goal Pursuits: Human Needs and the Self-Determination of Behavior , 2000 .

[38]  Joseph Lee,et al.  An analysis of students' preparation for the virtual learning environment , 2001, Internet High. Educ..

[39]  Yun Yang,et al.  Effect of media richness on user acceptance of blogs and podcasts , 2010, ITiCSE '10.

[40]  D. A. Kenny,et al.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.

[41]  Kar Yan Tam,et al.  Determinants of User Acceptance of Digital Libraries: An Empirical Examination of Individual Differences and System Characteristics , 2002, J. Manag. Inf. Syst..

[42]  William H. DeLone Determinants of Success for Computer Usage in Small Business , 1988, MIS Q..

[43]  Mohamed Khalifa,et al.  Knowledge contribution in virtual communities: accounting for multiple dimensions of social presence through social identity , 2010, PACIS.

[44]  M. S. Balaji,et al.  Student Interactions in Online Discussion Forum: Empirical Research from "Media Richness Theory" Perspective. , 2010 .

[45]  Shu-Sheng Liaw,et al.  Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system , 2008, Comput. Educ..

[46]  Erik Rolland,et al.  Knowledge-sharing in virtual communities: familiarity, anonymity and self-determination theory , 2012, Behav. Inf. Technol..

[47]  Alan R. Dennis,et al.  Testing Media Richness Theory in the New Media: The Effects of Cues, Feedback, and Task Equivocality , 1998, Inf. Syst. Res..

[48]  Marylène Gagné,et al.  Understanding e-learning continuance intention in the workplace: A self-determination theory perspective , 2008, Comput. Hum. Behav..

[49]  Yueh-Min Huang,et al.  A blog article recommendation generating mechanism using an SBACPSO algorithm , 2009, Expert Syst. Appl..

[50]  A. Bandura Self-efficacy mechanism in human agency , 2024, Psihologìâ ì suspìlʹstvo.

[51]  Andrea Kübler,et al.  Empathy, motivation, and P300 BCI performance , 2013, Front. Hum. Neurosci..

[52]  Raduan Che Rose,et al.  Teachers' readiness to use technology in the classroom: an empirical study , 2008 .

[53]  Detmar W. Straub,et al.  Common Beliefs and Reality About PLS , 2014 .

[54]  Choy-Har Wong,et al.  Mobile advertising: The changing landscape of the advertising industry , 2015, Telematics Informatics.

[55]  Chun-Ming Chang,et al.  Exploring the antecedents of trust in virtual communities , 2011, Behav. Inf. Technol..

[56]  E. Deci,et al.  Self‐determination theory and work motivation , 2005 .

[57]  Marylène Gagné,et al.  Facilitating Acceptance of Organizational Change: The Importance of Self-Determination' , 2000 .

[58]  Kuan-Chung Chen,et al.  Motivation in online learning: Testing a model of self-determination theory , 2010, Comput. Hum. Behav..