Exploring values affecting e-Learning adoption from the user-generated-content: A consumption-value-theory perspective

ABSTRACT The aim of this study is to utilise the user-generated content from social media platforms and merchandise websites to explore various values affecting behavioural intention in context of e-Learning services from the consumption-value-theory perspective. This study has utilised a novel mixed-method approach based on natural language processing (NLP) techniques for the both the qualitative and quantitative analysis. This study has used user-generated content of Coursera (an e-Learning service) consisting of online reviews from Coursera-100 k-dataset and tweets about Coursera. Some of the important themes generated from the thematic-based analysis of tweets are ‘value addition’, ‘course content’, ‘topic cover’, ‘reliability of course’, ‘course quality’, ‘enjoyed course’, ‘recommend the course’, ‘value for money’, ‘facilitator skills’, etc. Results of the empirical study reveal that offers and deals, emotional connect, facilitator quality, course reliability, platform innovativeness, and compatibility are important predictors of behavioural intention. This study concludes with the various limitations and future directions.

[1]  Ryosuke Saga,et al.  LDA-based path model construction process for structure equation modeling , 2016, Artificial Life and Robotics.

[2]  Amita Goyal Chin,et al.  A bidirectional perspective of trust and risk in determining factors that influence mobile app installation , 2018, Int. J. Inf. Manag..

[3]  S. Naidu,et al.  Political Language in Economics , 2015 .

[4]  Rdouan Faizi,et al.  Cloud computing and sentiment analysis in E-learning systems , 2016, 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech).

[5]  Pradip Kumar Bala,et al.  Role of authenticity and perceived benefits of online courses on technology based career choice in India: A modified technology adoption model based on career theory , 2019, Int. J. Inf. Manag..

[6]  Liang-Yi Li,et al.  Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance , 2017, Comput. Educ..

[7]  Kai Wang,et al.  How flow experience affects intention to use music streaming service: Model development , 2014, MoMM.

[8]  Lei-da Chen,et al.  Enticing online consumers: an extended technology acceptance perspective , 2002, Inf. Manag..

[9]  Edwin R. Griff,et al.  Evaluation of an adaptive online learning system , 2013, Br. J. Educ. Technol..

[10]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[11]  Differences in Characteristics of Online versus Traditional Students: Implications for Target Marketing , 2007 .

[12]  France Bélanger,et al.  Trust and Risk in eGovernment Adoption , 2008, AMCIS.

[13]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[14]  I. Wong,et al.  Exploring the effect of geographic convenience on repeat visitation and tourist spending: the moderating role of novelty seeking , 2016 .

[15]  Andrea Back,et al.  Evaluating the Role of Trust in Adoption: A Conceptual Replication in the Context of Open Source Systems , 2018, AIS Trans. Replication Res..

[16]  Fang-Ming Hsu,et al.  The role of customer values in accepting information technologies in the public information service sector , 2010 .

[17]  Atika Qazi,et al.  Students' perspective on knowledge quality in eLearning context: a qualitative assessment , 2016, Internet Res..

[18]  Rolph E. Anderson,et al.  Multivariate Data Analysis (7th ed. , 2009 .

[19]  Lingling Gao,et al.  Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation , 2015, Information Systems Frontiers.

[20]  Sabine Seufert.  Trust and Reputation in eLearning at the Workplace: The Role of Social Media , 2012, 2012 IEEE 12th International Conference on Advanced Learning Technologies.

[21]  Anthony Stefanidis,et al.  #Earthquake: Twitter as a Distributed Sensor System , 2013, Trans. GIS.

[22]  Anand Shanker Tewari,et al.  e-Learning Recommender System for Teachers using Opinion Mining , 2015 .

[23]  Ali Delice,et al.  The Sampling Issues in Quantitative Research. , 2010 .

[24]  I JordanMichael,et al.  The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies , 2010 .

[25]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[26]  Swagato Chatterjee,et al.  Explaining customer ratings and recommendations by combining qualitative and quantitative user generated contents , 2019, Decis. Support Syst..

[27]  J. Choe,et al.  Effects of tourists’ local food consumption value on attitude, food destination image, and behavioral intention , 2018 .

[28]  Parsa Ghaffari,et al.  Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment , 2016, 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService).

[29]  W. Bruce Croft,et al.  LDA-based document models for ad-hoc retrieval , 2006, SIGIR.

[30]  Andrew J. Rohm,et al.  Time for a Marketing Curriculum Overhaul: Developing a Digital-First Approach , 2018, Journal of Marketing Education.

[31]  Dheeraj Sharma,et al.  Online learning: Adoption, continuance, and learning outcome - A review of literature , 2018, Int. J. Inf. Manag..

[32]  Hsiu-Fen Lin,et al.  An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust , 2011, Int. J. Inf. Manag..

[33]  Jie Zhang,et al.  Antecedents of student MOOC revisit intention: Moderation effect of course difficulty , 2017, Int. J. Inf. Manag..

[34]  Zhen Shao,et al.  Examining the impact mechanism of social psychological motivations on individuals' continuance intention of MOOCs: The moderating effect of gender , 2017, Internet Res..

[35]  M. Santally,et al.  Designing a predictive model of student satisfaction in online learning , 2016 .

[36]  Pradip Kumar Bala,et al.  Use of NLP and SEM in Determining Factors for E-Service Adoption , 2019 .

[37]  Nor Hazlin Nor Asshidin,et al.  Perceived Quality and Emotional Value that Influence Consumer's Purchase Intention towards American and Local Products , 2016 .

[38]  Thomas L. Griffiths,et al.  The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies , 2007, JACM.

[39]  Nour Awni Albelbisi,et al.  Factors Influencing Learners’ Self –Regulated Learning Skills in a Massive Open Online Course (MOOC) Environment , 2019, Turkish Online Journal of Distance Education.

[40]  Chen Li,et al.  Students’ Expectations and Perceptions on Service Quality of E-Learning in a Selected Faculty of a Public University in Malaysia , 2018 .

[41]  Charu C. Aggarwal,et al.  A Survey of Text Clustering Algorithms , 2012, Mining Text Data.

[42]  Tomer Simon,et al.  Socializing in emergencies - A review of the use of social media in emergency situations , 2015, Int. J. Inf. Manag..

[43]  Barbara L. Gross,et al.  Why we buy what we buy: A theory of consumption values , 1991 .

[44]  Jenny Headlam-Wells,et al.  Beyond the organisation: The design and management of E-mentoring systems , 2006, Int. J. Inf. Manag..

[45]  D. DavisFred,et al.  User Acceptance of Computer Technology , 1989 .

[46]  Jen-Her Wu,et al.  What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model , 2005, Inf. Manag..

[47]  Yogesh Kumar Dwivedi,et al.  Using Clickers in a Large Business Class , 2016 .

[48]  Hong Gao,et al.  Chances and Challenges: What E-Learning Brings to Traditional Teaching , 2018, 2018 9th International Conference on Information Technology in Medicine and Education (ITME).

[49]  Amit V. Deokar,et al.  Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews , 2018, Decis. Support Syst..

[50]  Rikuto Kunimoto,et al.  Purchase Factor Expression for Game Software Using Structural Equation Modeling with Topic Model in User's Review Texts , 2014 .

[51]  Min Zhang,et al.  Learner control, user characteristics, platform difference, and their role in adoption intention for MOOC learning in China , 2016 .

[52]  Yu-Wei Chang,et al.  Cooperation and competition between online travel agencies and hotels , 2019, Tourism Management.

[53]  Brian J. Higginbotham,et al.  The Influence of Facilitator and Facilitation Characteristics on Participants' Ratings of Stepfamily Education , 2010 .

[54]  Weng Marc Lim,et al.  It is not about what you read, but how you read it: the effects of sequencing rational and emotional messages on corporate and product brand attitudes , 2016 .

[55]  L. Robinson.  Embracing online education: exploring options for success , 2017 .

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

[57]  Chu-Chi Liu,et al.  Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs , 2017 .

[58]  Zetian Fu,et al.  An empirical study on behavioural intention to reuse e-learning systems in rural China , 2012, Br. J. Educ. Technol..

[59]  Yogesh Kumar Dwivedi,et al.  An empirical validation of a unified model of electronic government adoption (UMEGA) , 2017, Gov. Inf. Q..

[60]  Yogesh Kumar Dwivedi,et al.  Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model , 2017, Information Systems Frontiers.

[61]  D. Turley,et al.  Fatal errors: unbridling emotions in service failure experiences , 2007 .

[62]  Chris Hackley,et al.  The integration of video games in family-life dynamics , 2019, Inf. Technol. People.

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

[64]  C. Boddy.  Sample size for qualitative research , 2016 .

[65]  Felix B. Tan,et al.  International Journal of Information Management the Mediating Role of Trust and Commitment on Members' Continuous Knowledge Sharing Intention: a Commitment-trust Theory Perspective , 2022 .

[66]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[67]  Eue-Hun Lee,et al.  Consumer choice of on-demand mHealth app services: Context and contents values using structural equation modeling , 2017, Int. J. Medical Informatics.

[68]  Kit Hong Wong,et al.  The effects of consumption values and relational benefits on smartphone brand switching behavior , 2019, Inf. Technol. People.

[69]  Marko Sarstedt,et al.  Editorial - Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance , 2013 .

[70]  P. Paulus,et al.  Performance and Perceptions of Brainstormers in an Organizational Setting , 1995 .

[71]  Lisa Emerson,et al.  A comparison between paper-based and online learning in higher education , 2011, Br. J. Educ. Technol..

[72]  Carlos G Gallo,et al.  The Cascading Effects of Multiple Dimensions of Implementation on Program Outcomes: a Test of a Theoretical Model , 2018, Prevention Science.

[73]  Rui Guo,et al.  The Defining Role of Environmental Self-Identity among Consumption Values and Behavioral Intention to Consume Organic Food , 2019, International journal of environmental research and public health.

[74]  Shih-Ming Pi,et al.  Factors Affecting Lifelong Learners' Intention to Continue Using E-Learning Website: An Empirical Study , 2010, ICWL Workshops.

[75]  Nils Urbach,et al.  Structural Equation Modeling in Information Systems Research Using Partial Least Squares , 2010 .

[76]  C. Fornell,et al.  Evaluating Structural Equation Models with Unobservable Variables and Measurement Error , 1981 .

[77]  Chia-Lin Hsu,et al.  Understanding Users' Continuance of Facebook: An Integrated Model with the Unified Theory of Acceptance and Use of Technology, Expectation Disconfirmation Model, and Flow Theory , 2011, Int. J. Virtual Communities Soc. Netw..

[78]  Gökhan Daghan,et al.  Modeling the continuance usage intention of online learning environments , 2016, Comput. Hum. Behav..

[79]  Guicheng Shi,et al.  Asymmetric Effects of Regulatory Focus on Expected Desirability and Feasibility of Embracing Self-Service Technologies , 2012 .

[80]  Ibrahim M. Al-Jabri,et al.  Mobile Banking Adoption: Application of Diffusion of Innovation Theory , 2012 .

[81]  Nripendra P. Rana,et al.  Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model , 2019, Int. J. Inf. Manag..