What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors

Abstract This study investigated how network externalities affect users' persistence in completing massive online open courses (MOOCs) through the mediation of human factors. 346 students from a public university were recruited into the study. The data were collected using a survey and analyzed by partial least square structural equation modeling (PLS-SEM). The findings indicate that users' persistence in completing MOOCs was a function of network benefit, user preference, and motivation to achieve. Network benefit, which was strongly predicted by network size (direct network externalities) and perceived complementarity (indirectly network externalities), also indirectly influenced users' persistence in completing MOOCs through user preference and motivation to achieve. Furthermore, this study found that the duration of MOOC usage made a significant difference in the effect of network externalities on users' persistence in completing MOOCs. For instance, user preference had a stronger influence on users' persistence in completing MOOCs for one-year users than above-one-year users, while motivation to achieve in MOOCs had a stronger effect on users’ persistence in completing MOOCs for above-one-year users than one-year users. This study could benefit MOOC providers and researchers seeking to improve the retention and completion rates of MOOCs.

[1]  Xiaohui Chen,et al.  Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model , 2017, Comput. Hum. Behav..

[2]  Taek-Soo Kim,et al.  A formal model for user preference , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[3]  Carl Gutwin,et al.  The effects of interaction sequencing on user experience and preference , 2017, Int. J. Hum. Comput. Stud..

[4]  Aytaç Göğüş,et al.  Educational technology acceptance across national and professional cultures: a European study , 2013 .

[5]  Cathy Sandeen.  Integrating MOOCS into Traditional Higher Education: The Emerging “MOOC 3.0” Era , 2013 .

[6]  Som Naidu,et al.  MOOCs: emerging research , 2014 .

[7]  D. Schunk.  Learning Theories: An Educational Perspective , 1991 .

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

[9]  Ray-I Chang,et al.  Survey of learning experiences and influence of learning style preferences on user intentions regarding MOOCs , 2015, Br. J. Educ. Technol..

[10]  Rachel B. Baker,et al.  Persistence Patterns in Massive Open Online Courses (MOOCs) , 2015 .

[11]  Richard P. Bagozzi,et al.  The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift , 2007, J. Assoc. Inf. Syst..

[12]  John Hulland,et al.  Use of partial least squares (PLS) in strategic management research: a review of four recent studies , 1999 .

[13]  Mingming Zhou,et al.  Chinese university students' acceptance of MOOCs: A self-determination perspective , 2016, Comput. Educ..

[14]  Kevin Zhu,et al.  Migrating to internet-based e-commerce: Factors affecting e-commerce adoption and migration at the firm level , 2006, Inf. Manag..

[15]  Philip A. Houle,et al.  Perceived network externalities and communication technology acceptance , 2007, Eur. J. Inf. Syst..

[16]  Daniel S. J. Costa,et al.  Testing complex models with small sample sizes: A historical overview and empirical demonstration of what Partial Least Squares (PLS) can offer differential psychology , 2015 .

[17]  Judy Kay,et al.  MOOCs: So Many Learners, So Much Potential ... , 2013, IEEE Intelligent Systems.

[18]  David F. Larcker,et al.  Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics: , 1981 .

[19]  Karen Janman,et al.  Achievement motivation theory and occupational choice , 1987 .

[20]  Stephen Kinsella,et al.  Peer Effects in the Diffusion of Innovations: Theory and Simulation , 2015 .

[21]  V. E. Vinzi,et al.  A global Goodness – of – Fit index for PLS structural equation modelling 1 , 2004 .

[22]  George Veletsianos.  Toward a generalizable understanding of Twitter and social media use across MOOCs: who participates on MOOC hashtags and in what ways? , 2017, J. Comput. High. Educ..

[23]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

[24]  Antoine Doucet,et al.  Building engagement for MOOC students: introducing support for time management on online learning platforms , 2014, WWW.

[25]  Scott B. MacKenzie,et al.  Working memory: theories, models, and controversies. , 2012, Annual review of psychology.

[26]  John Gallaugher,et al.  Network Effects and the Impact of Free Goods: An Analysis of the Web Server Market , 1999, Int. J. Electron. Commer..

[27]  Geoff Sharrock,et al.  Making sense of the MOOCs debate , 2015 .

[28]  Wynne W. Chin.  How to Write Up and Report PLS Analyses , 2010 .

[29]  Yaobin Lu,et al.  Enhancing perceived interactivity through network externalities: An empirical study on micro-blogging service satisfaction and continuance intention , 2012, Decis. Support Syst..

[30]  Mukun Cao,et al.  The effects of network externalities and herding on user satisfaction with mobile social apps , 2017 .

[31]  Anol Bhattacherjee,et al.  Elucidating Individual Intention to Use Interactive Information Technologies: The Role of Network Externalities , 2008, Int. J. Electron. Commer..

[32]  Steven J. Landry,et al.  Introduction to Human Factors and Ergonomics for Engineers , 2007 .

[33]  Jeff Haywood,et al.  Emerging patterns in MOOCs: Learners, course designs and directions , 2015 .

[34]  C. Shapiro,et al.  Network Externalities, Competition, and Compatibility , 1985 .

[35]  K. E. Barron,et al.  Predictors and Consequences of Achievement Goals in the College Classroom: Maintaining Interest and Making the Grade , 1997 .

[36]  Vinish Kathuria,et al.  Role of Externalities in Inducing Technical Change , 1999 .

[37]  Hsi-Peng Lu,et al.  Why people use social networking sites: An empirical study integrating network externalities and motivation theory , 2011, Comput. Hum. Behav..

[38]  Chieh-Peng Lin,et al.  Modeling IT relationship quality and its determinants: A potential perspective of network externalities in e-service , 2011 .

[39]  Gilly Salmon,et al.  Designing Massive Open Online Courses to take account of participant motivations and expectations , 2017, Br. J. Educ. Technol..

[40]  C W Clegg,et al.  Sociotechnical principles for system design. , 2000, Applied ergonomics.

[41]  António Teixeira,et al.  Opportunities and Threats of the MOOC Movement for Higher Education: the European Perspective , 2015 .

[42]  William Jobe,et al.  No university credit, no problem? Exploring recognition of non-formal learning , 2014, 2014 IEEE Frontiers in Education Conference (FIE) Proceedings.

[43]  A. Bandura.  Social cognitive theory: an agentic perspective. , 1999, Annual review of psychology.

[44]  James A. Middleton,et al.  Motivation for Achievement in Mathematics: Findings, Generalizations, and Criticisms of the Research , 1999 .

[45]  Tayeb Brahimi,et al.  Learning outside the classroom through MOOCs , 2015, Comput. Hum. Behav..

[46]  Wynne W. Chin,et al.  A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic - Mail Emotion/Adoption Study , 2003, Inf. Syst. Res..

[47]  David E. Pritchard,et al.  Studying Learning in the Worldwide Classroom Research into edX's First MOOC. , 2013 .

[48]  Bo Wu,et al.  How WeChat can retain users: Roles of network externalities, social interaction ties, and perceived values in building continuance intention , 2017, Comput. Hum. Behav..

[49]  Kan-Min Lin,et al.  e-Learning continuance intention: Moderating effects of user e-learning experience , 2011, Comput. Educ..

[50]  Jaeki Song,et al.  An investigation of mobile learning readiness in higher education based on the theory of planned behavior , 2012, Comput. Educ..

[51]  Marek Rejman-Greene.  User Acceptance , 2015, Encyclopedia of Biometrics.

[52]  Kevin Oliver,et al.  A Social Network Perspective on Peer Supported Learning in MOOCs for Educators , 2014 .

[53]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[54]  Hangjung Zo,et al.  Continuance usage of corporate SNS pages: A communicative ecology perspective , 2016, Inf. Manag..

[55]  Mike Kuniavsky,et al.  Observing the User Experience: A Practitioner's Guide to User Research (Morgan Kaufmann Series in Interactive Technologies) (The Morgan Kaufmann Series in Interactive Technologies) , 2003 .

[56]  J. Hair.  Multivariate data analysis , 1972 .

[57]  Nathan W. Twyman,et al.  Taking "Fun and Games" Seriously: Proposing the Hedonic-Motivation System Adoption Model (HMSAM) , 2012, J. Assoc. Inf. Syst..

[58]  Wynne W. Chin.  The partial least squares approach for structural equation modeling. , 1998 .

[59]  N. Economides.  The Economics of Networks , 1995 .

[60]  Khe Foon Hew,et al.  Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS , 2016, Br. J. Educ. Technol..

[61]  Maha Bali,et al.  MOOC Pedagogy: Gleaning Good Practice from Existing MOOCs , 2014 .

[62]  Albert Bandura,et al.  Social Cognitive Theory and Clinical Psychology , 2001 .

[63]  Rudolf R. Sinkovics,et al.  The Use of Partial Least Squares Path Modeling in International Marketing , 2009 .

[64]  Francisco José García-Peñalvo,et al.  From massive access to cooperation: lessons learned and proven results of a hybrid xMOOC/cMOOC pedagogical approach to MOOCs , 2016, International Journal of Educational Technology in Higher Education.