Integrated TTF and self-determination theories in higher education: The role of actual use of the massive open online courses

The purpose of this study was to examine the relationships between users’ satisfaction with actual use of massive open online courses (MOOCs) and intrinsically motivated, task-technology fit, attitudes toward using MOOCs, and behavioral intention to use MOOCs. As the primary technique for data collection, a survey questionnaire on self-determination theory (SDT) as well as task-technology fit (TTF) was distributed to a total of 228 students. The results of the users’ (TTF) to attitude toward using MOOCs and their behavioral intention to use MOOCs had a positive impact on their satisfaction and actual use of MOOCs in higher education institutes. However, the users’ perceived autonomy was not entirely satisfied, based on the results of their intrinsic motivation for the actual use of learning courses. Similarly, technology characteristics were insignificant with TTF for the actual use of MOOCs in academic institutions. Additionally, mediation studies showed that the correlations between independent factors on the one hand and users’ satisfaction with their actual use of MOOCs on the other were significantly mediated by intrinsic motivation, TTF attitude, and behavioral intention to use. Finally, practical ramifications were examined, and recommendations were made with regards to the direction of future studies.

[1]  Nadira Saab,et al.  Do learners share the same perceived learning outcomes in MOOCs? Identifying the role of motivation, perceived learning support, learning engagement, and self-regulated learning strategies , 2022, Internet High. Educ..

[2]  E. Wahab,et al.  Social media use in higher education: Building a structural equation model for student satisfaction and performance , 2022, Frontiers in Public Health.

[3]  A. Hershkovitz,et al.  Interest in Educational Data and Barriers to Data Use Among Massive Open Online Course Instructors , 2022, Journal of Science Education and Technology.

[4]  Urvashi Tandon,et al.  Analyzing Impact of Aesthetic Visual Design on Usability of E-Learning: An Emerging Economy Perspective , 2022, Higher Learning Research Communications.

[5]  Nabil Hasan Al-Kumaim,et al.  A Model of Motivational and Technological Factors Influencing Massive Open Online Courses’ Continuous Intention to Use , 2022, Sustainability.

[6]  A. Kafy,et al.  Mapping emerging massive open online course (MOOC) markets before and after COVID 19: A comparative perspective from Bangladesh and India , 2022, Spatial Information Research.

[7]  Uthman T. Alturki,et al.  Integrating the Role of UTAUT and TTF Model to Evaluate Social Media Use for Teaching and Learning in Higher Education , 2022, Frontiers in Public Health.

[8]  I. A. Ismail,et al.  Influencing Factors in MOOCs Adoption in Higher Education: A Meta-Analytic Path Analysis , 2022, Sustainability.

[9]  Ahmad A. Mousa,et al.  Modeling the Impact of Massive Open Online Courses (MOOC) Implementation Factors on Continuance Intention of Students: PLS-SEM Approach , 2022, Sustainability.

[10]  Lyv Wenfei,et al.  Understanding the impact of quality elements on MOOCs continuance intention , 2022, Education and Information Technologies.

[11]  Renée S. Jansen,et al.  Exploring the link between self-regulated learning and learner behaviour in a massive open online course , 2022, J. Comput. Assist. Learn..

[12]  Singha Chaveesuk,et al.  Comparative analysis of variables that influence behavioral intention to use MOOCs , 2022, PloS one.

[13]  M. Bauters,et al.  Indicators for enhancing learners’ engagement in massive open online courses: A systematic review , 2022, Computers and Education Open.

[14]  A. Kaklauskas,et al.  A common framework for MOOC curricular development in climate change education - Findings and adaptations under the BECK project for higher education institutions in Europe and Asia , 2022, Progress in Disaster Science.

[15]  O. Noroozi,et al.  The role of motivation in MOOCs’ retention rates: a systematic literature review , 2022, Res. Pract. Technol. Enhanc. Learn..

[16]  Mohammed Alqahtani,et al.  Factors Influencing University Students’ Adoption of Digital Learning Technology in Teaching and Learning , 2022, Sustainability.

[17]  Sogol Rabiee Savoji,et al.  Exploring factors influencing students’ continuance intention to use the learning management system (LMS): a multi-perspective framework , 2020, Interact. Learn. Environ..

[18]  Tingru Zhang,et al.  Key characteristics in designing massive open online courses (MOOCs) for user acceptance: an application of the extended technology acceptance model , 2019, Interact. Learn. Environ..

[19]  A. Mittal,et al.  E-learning adoption by undergraduate architecture students: facilitators and inhibitors , 2021, Engineering, Construction and Architectural Management.

[20]  Lang Ma,et al.  Online Learning Satisfaction During COVID-19 Pandemic Among Chinese University Students: The Serial Mediation Model , 2021, Frontiers in Psychology.

[21]  Yumei Luo,et al.  Students’ motivation and continued intention with online self-regulated learning: A self-determination theory perspective , 2021, Zeitschrift für Erziehungswissenschaft.

[22]  Nour Awni Albelbisi,et al.  Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach , 2021, Sustainability.

[23]  Uthman T. Alturki,et al.  Exploring the Factors Affecting Mobile Learning for Sustainability in Higher Education , 2021, Sustainability.

[24]  Abdulmajeed A. Aljeraiwi,et al.  The Influence of Information System Success and Technology Acceptance Model on Social Media Factors in Education , 2021, Sustainability.

[25]  W. Al-rahmi,et al.  Information and Communications Technology Used in Higher Education: An Empirical Study on Digital Learning as Sustainability , 2021, Sustainability.

[26]  Singha Chaveesuk,et al.  Factors influencing behavioural intention to use MOOCs , 2021, Engineering Management in Production and Services.

[27]  Cesar Alexandre de Souza,et al.  The use of Massive Open Online Courses (MOOCs) in blended learning courses and the functional value perceived by students , 2021, Comput. Educ..

[28]  Debajyoti Pal,et al.  University Students’ Perception of Video-Based Learning in Times of COVID-19: A TAM/TTF Perspective , 2020, Int. J. Hum. Comput. Interact..

[29]  Yusri Kamin,et al.  Integrating innovation diffusion theory with technology acceptance model: supporting students’ attitude towards using a massive open online courses (MOOCs) systems , 2019, Interact. Learn. Environ..

[30]  I. Alyoussef.  Massive Open Online Course (MOOCs) Acceptance: The Role of Task-Technology Fit (TTF) for Higher Education Sustainability , 2021, Sustainability.

[31]  Abeer Qashou,et al.  Influencing factors in M-learning adoption in higher education , 2020, Education and Information Technologies.

[32]  C. Cheung,et al.  The MOOC dropout phenomenon and retention strategies , 2020 .

[33]  Maryam Altalhi,et al.  Toward a model for acceptance of MOOCs in higher education: the modified UTAUT model for Saudi Arabia , 2020, Education and Information Technologies.

[34]  Ahmed Ibrahim Alzahrani,et al.  Factors affecting student dropout in MOOCs: a cause and effect decision‐making model , 2019, Journal of Computing in Higher Education.

[35]  Mostafa Al-Emran,et al.  The impact of knowledge management practices on the acceptance of Massive Open Online Courses (MOOCs) by engineering students: A cross-cultural comparison , 2020, Telematics Informatics.

[36]  Liyong Wan,et al.  Toward an Understanding of University Students’ Continued Intention to Use MOOCs: When UTAUT Model Meets TTF Model , 2020, SAGE Open.

[37]  Annisa R. Sari,et al.  A comprehensive systematic review of MOOC research: Research techniques, topics, and trends from 2009 to 2019 , 2020 .

[38]  K. R. Pillai,et al.  Intention and barriers to use MOOCs: An investigation among the post graduate students in India , 2020, Education and Information Technologies.

[39]  J. Ángel Velázquez-Iturbide,et al.  Relationship between motivations, personality traits and intention to continue using MOOCs , 2020, Education and Information Technologies.

[40]  Shadi Aljawarneh,et al.  Blockchain-based approach to create a model of trust in open and ubiquitous higher education , 2020, J. Comput. High. Educ..

[41]  Muesser Nat,et al.  Continuance Intentions to Use Gamification for Training in Higher Education: Integrating the Technology Acceptance Model (TAM), Social Motivation, and Task Technology Fit (TTF) , 2020, IEEE Access.

[42]  Uthman Alturki,et al.  Task-Technology Fit and Technology Acceptance Model Application to Structure and Evaluate the Adoption of Social Media in Academia , 2020, IEEE Access.

[43]  Carlos Alario-Hoyos,et al.  Self-regulated learning in MOOCs: lessons learned from a literature review , 2019, Educational Review.

[44]  Muhammad Usman Riaz,et al.  The moderating role of intrinsic motivation in cloud computing adoption in online education in a developing country: a structural equation model , 2019, Asia Pacific Education Review.

[45]  Y. Punie,et al.  Typology of motivation and learning intentions of users in MOOCs: the MOOCKNOWLEDGE study , 2019, Educational Technology Research and Development.

[46]  Habibah Ab. Jalil,et al.  Attitude and Behavioral Intention to Develop and Use MOOCs among Academics , 2019, Int. J. Emerg. Technol. Learn..

[47]  Baldreck Chipangura,et al.  Conceptualizing Factors that Influence South African Students' Intention to Choose Mobile Devices as Tools for Learning , 2019, ICITL.

[48]  Niu Lung-Guang,et al.  Decision-making determinants of students participating in MOOCs: Merging the theory of planned behavior and self-regulated learning model , 2019, Comput. Educ..

[49]  Wanli Xing,et al.  Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention , 2019 .

[50]  Yusri Bin Kamin,et al.  How Cyber Stalking and Cyber Bullying Affect Students’ Open Learning , 2019, IEEE Access.

[51]  Yongqiang Sun,et al.  Understanding students' engagement in MOOCs: An integration of self-determination theory and theory of relationship quality , 2018, Br. J. Educ. Technol..

[52]  Chei Sian Lee,et al.  Investigating the adoption of MOOCs: A technology-user-environment perspective , 2018, J. Comput. Assist. Learn..

[53]  Chun Meng Tang,et al.  Driving High Inclination to Complete Massive Open Online Courses (MOOCs): Motivation and Engagement Factors for Learners , 2019, Electronic Journal of e-Learning.

[54]  Yusri Bin Kamin,et al.  A Model of Factors Affecting Cyber Bullying Behaviors Among University Students , 2019, IEEE Access.

[55]  Ahmed Aldraiweesh,et al.  Massive open online courses (MOOCS): systematic literature review in Malaysian higher education , 2018, International Journal of Engineering & Technology.

[56]  Kamsuriah Ahmad,et al.  Develop a Model to Measure the Ethical Effects of Students Through Social Media Use , 2018, IEEE Access.

[57]  Rafael Molina-Carmona,et al.  Study on the Attitude of Young People Towards Technology , 2018, HCI.

[58]  C. Blewett,et al.  Factors Affecting MOOC Usage by Students in Selected Ghanaian Universities , 2018 .

[59]  Zenun Kastrati,et al.  MOOC dropout prediction using machine learning techniques: Review and research challenges , 2018, 2018 IEEE Global Engineering Education Conference (EDUCON).

[60]  Esra Harmandaoglu Baz,et al.  Introducing an innovative technology integration model: Echoes from EFL pre-service teachers , 2018, Education and Information Technologies.

[61]  Miri Barak,et al.  Motivating factors of MOOC completers: Comparing between university-affiliated students and general participants , 2018, Internet High. Educ..

[62]  Tahir Islam,et al.  Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory , 2017, Telematics Informatics.

[63]  Teck-Soon Hew,et al.  Applying Channel Expansion and Self-Determination Theory in predicting use behaviour of cloud-based VLE , 2017, Behav. Inf. Technol..

[64]  Jon-Chao Hong,et al.  Internet cognitive failure relevant to users' satisfaction with content and interface design to reflect continuance intention to use a government e-learning system , 2017, Comput. Hum. Behav..

[65]  Boonlert Watjatrakul,et al.  Interactive Technology and Smart Education Online Learning Adoption : Effects of Neuroticism , Openness to Experience , and Perceived Values , 2016 .

[66]  Viswanath Venkatesh,et al.  Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead , 2016, J. Assoc. Inf. Syst..

[67]  Rupert Ward,et al.  Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors , 2016, Comput. Hum. Behav..

[68]  Mohd Shahizan Othman,et al.  Using Social Media for Research: The Role of Interactivity, Collaborative Learning, and Engagement on the Performance of Students in Malaysian Post-Secondary Institutes , 2015 .

[69]  Mohd Shahizan Othman,et al.  The effect of social media on researchers’ academic performance through collaborative learning in Malaysian higher education , 2015 .

[70]  Mohd Shahizan Othman,et al.  Social Media for Collaborative Learning and Engagement: Adoption Framework in Higher Education Institutions in Malaysia , 2015 .

[71]  Mohd Shahizan Othman,et al.  Effect of engagement and collaborative learning on satisfaction through the use of social media on Malaysian higher education , 2015 .

[72]  Neda Abasi,et al.  Information systems success: the quest for the dependent variable , 2015 .

[73]  Hangjung Zo,et al.  Understanding the MOOCs continuance: The role of openness and reputation , 2015, Comput. Educ..

[74]  David C. Yen,et al.  Online shopping drivers and barriers for older adults: Age and gender differences , 2014, Comput. Hum. Behav..

[75]  Birgitta König-Ries,et al.  Progressor: social navigation support through open social student modeling , 2013, New Rev. Hypermedia Multim..

[76]  Sangwon Lee,et al.  Understanding User Experience with Computer-Based Applications with Different Use Purposes , 2013, Int. J. Hum. Comput. Interact..

[77]  Kai H. Lim,et al.  Contributing high quantity and quality knowledge to online Q&A communities , 2013, J. Assoc. Inf. Sci. Technol..

[78]  Marko Sarstedt,et al.  An assessment of the use of partial least squares structural equation modeling in marketing research , 2012 .

[79]  E. Deci,et al.  Motivation, personality, and development within embedded social contexts: An overview of self-determination theory. , 2012 .

[80]  Ángel L. Meroño-Cerdán,et al.  International Journal of Information Management Strategic Knowledge Management, Innovation and Performance , 2022 .

[81]  Blanca Hernández Ortega,et al.  The role of social motivations in e-learning: How do they affect usage and success of ICT interactive tools? , 2011, Comput. Hum. Behav..

[82]  I. Ajzen.  The theory of planned behaviour: Reactions and reflections , 2011, Psychology & health.

[83]  Judy Chuan-Chuan Lin,et al.  The Effect of Social Influence on The Bloggers' Usage Intention , 2011, Online Inf. Rev..

[84]  Tai-Kuei Yu,et al.  Modelling the factors that affect individuals' utilisation of online learning systems: An empirical study combining the task technology fit model with the theory of planned behaviour , 2010, Br. J. Educ. Technol..

[85]  Chao-Min Chiu,et al.  Exploring and mitigating social loafing in online communities , 2010, Comput. Hum. Behav..

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

[87]  Tung-Ching Lin,et al.  Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit , 2008, Inf. Manag..

[88]  K. Schulz.  Learning in complex organizations as practicing and reflecting , 2005 .

[89]  Kishan Dholakia,et al.  Imaging in optical micromanipulation using two-photon excitation , 2004 .

[90]  Kennon M. Sheldon,et al.  PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Motivating Learning, Performance, and Persistence: The Synergistic Effects of Intrinsic Goal Contents and Autonomy-Supportive Contexts , 2004 .

[91]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[92]  B. Byrne.  Structural equation modeling with EQS : basic concepts, applications, and programming , 2000 .

[93]  E. Deci,et al.  Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. , 2000, The American psychologist.

[94]  Diane M. Strong,et al.  Extending the technology acceptance model with task-technology fit constructs , 1999, Inf. Manag..

[95]  Dale Goodhue,et al.  Task-Technology Fit and Individual Performance , 1995, MIS Q..

[96]  Randy D. Jacobs,et al.  Exploring the determinants of cable television subscriber satisfaction , 1995 .

[97]  R. Bagozzi,et al.  On the evaluation of structural equation models , 1988 .

[98]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[99]  H. Teicher,et al.  Probability theory: Independence, interchangeability, martingales , 1978 .

[100]  Mary Henle,et al.  The Influence of Attitudes on Syllogistic Reasoning , 1956 .