Technology Adoption in Education-Based Business Services

The objectives of this study are two folds. Firstly is to identify the advantages and disadvantages factors of electronic learning’s adoption. Secondly is to measure the influence of innovation adoption components toward users’ attitude in using electronic learning. A mixed method of study was carried out in response to the research’s objectives. The qualitative approach was conducted by means of interviewing 25 participants of users to identify e-learning advantages and disadvantages. The quantitative approach was used to test the hypotheses. A questionnaire was distributed to 313 e-learning system users. The results show that the three advantages and disadvantages of e-learning adoption factors were formed. SEM-Smart PLS was used to test the hypothetical relationships. The results indicate that three dimensions of innovation diffusion significantly influenced the attitude toward e-learning, while two dimensions were not significant. The findings suggest that education-based business services should use the advantages factors and influential dimensions to promote their teaching-learning services delivery and eliminate weaknesses and insignificant dimensions.

[1]  Sevil Orhan,et al.  The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the Unified Theory of acceptance and use of technology , 2015, Comput. Educ..

[2]  Joseph F. Hair,et al.  Partial Least Squares Structural Equation Modeling , 2021, Handbook of Market Research.

[3]  Hossein Mohammadi,et al.  Investigating users' perspectives on e-learning: An integration of TAM and IS success model , 2015, Comput. Hum. Behav..

[4]  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..

[5]  Zetian Fu,et al.  A study on e-learning take-up intention from an innovation adoption perspective: A case in China , 2010, Comput. Educ..

[6]  Huong May Truong Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities , 2016, Comput. Hum. Behav..

[7]  Douglas R. Vogel,et al.  Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning , 2013, Comput. Educ..

[8]  Geoffrey S. Hubona,et al.  Using PLS path modeling in new technology research: updated guidelines , 2016, Ind. Manag. Data Syst..

[9]  Sung Youl Park,et al.  An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning , 2009, J. Educ. Technol. Soc..

[10]  Victor Chang,et al.  Review and discussion: E-learning for academia and industry , 2016, Int. J. Inf. Manag..

[11]  Faizan Ali,et al.  An Assessment of the Use of Partial Least Squares Structural Equation Modeling (PLS-SEM) in Hospitality Research , 2017 .

[12]  R. Masa’deh,et al.  Factors influencing students’ adoption of e-learning: a structural equation modeling approach , 2017 .

[13]  Hsi-Peng Lu,et al.  The role of experience and innovation characteristics in the adoption and continued use of e-learning websites , 2008, Comput. Educ..

[14]  Valentina Arkorful,et al.  The role of e-learning, the advantages and disadvantages of its adoption in Higher Education. , 2014 .

[15]  E. Rogers A Prospective and Retrospective Look at the Diffusion Model , 2004, Journal of health communication.

[16]  Chun-Chieh Wang,et al.  An empirical study of instructor adoption of web-based learning systems , 2009, Comput. Educ..

[17]  LuHsi-Peng,et al.  The role of experience and innovation characteristics in the adoption and continued use of e-learning websites , 2008 .

[18]  Martin Wetzels,et al.  Getting a Discount or Sharing the Cost: The Influence of Regulatory Fit on Consumer Response to Service Pricing Schemes , 2010 .

[19]  Yi-Chuan Hsieh,et al.  Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to use E-Learning Systems , 2011, J. Educ. Technol. Soc..

[20]  Dan Bouhnik,et al.  Interaction in distance-learning courses , 2006, J. Assoc. Inf. Sci. Technol..

[21]  Zuhal Husseina,et al.  Leading to Intention : The Role of Attitude in Relation to Technology Acceptance Model in E-Learning , 2018 .

[22]  Zetian Fu,et al.  E-learning adoption intention and its key influence factors based on innovation adoption theory , 2010, Math. Comput. Model..

[23]  Richard Boateng,et al.  Determinants of e-learning adoption among students of developing countries , 2016 .

[24]  Tsai-Hsin Chu,et al.  With Good We Become Good: Understanding e-learning adoption by theory of planned behavior and group influences , 2016, Comput. Educ..

[25]  Shu-Sheng Liaw,et al.  Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments , 2013, Comput. Educ..

[26]  Yi-Shun Wang,et al.  Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications , 2008, Comput. Educ..

[27]  Corinne Mantle-Bromley Positive Attitudes and Realistic Beliefs: Links to Proficiency , 1995 .

[28]  Man-Shin Cheng,et al.  Adding Innovation Diffusion Theory to Technology Acceptance Model: Understanding Consumers' Intention to Use Biofuels in Viet Nam , 2017 .

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

[30]  W. Johnston,et al.  Innovation adoption and diffusion in business-to-business marketing , 2014 .

[31]  LiDaoliang,et al.  A study on e-learning take-up intention from an innovation adoption perspective , 2010 .

[32]  Francisco Herrera,et al.  E-learning and educational data mining in cloud computing: an overview , 2014, Int. J. Learn. Technol..

[33]  Shintaro Okazaki,et al.  Understanding e-learning adoption in Brazil: Major determinants and gender effects , 2012 .