The factors that predispose students to continuously use cloud services: Social and technological perspectives

Cloud services have been widely used in education in recent years. However, the factors that determine students' continuance intention to use such services have received surprisingly little scholarly attention. Previous studies have shown that students may discontinue using a specific technology even if they have initially accepted it. Therefore, this research seeks to identify what factors may influence students' continuance intention to use cloud services. This research not only developed a research model by incorporating social and technological factors but also tested a series of hypotheses derived from the model. Our research findings suggest that (1) attitude toward using is the most important factor behind students' continuance intention to use cloud services; (2) social presence is the most significant factor that directly influences students' attitude toward using cloud services; (3) perceived ease of use plays a more important role than perceived usefulness in influencing students' attitude toward using cloud services. We explore factors that predispose students to continuously use cloud services.Attitude toward using is the most important determinant.Social presence is the most significant factor that influences attitude.Perceived ease of use is more than perceived usefulness in influencing attitude.

[1]  Ryan T. Wright,et al.  Deconstructing and Operationalizing Interactivity: An Online Advertising Perspective , 2011 .

[2]  Namkee Park,et al.  Factors influencing intention to upload content on Wikipedia in South Korea: The effects of social norms and individual differences , 2012, Comput. Hum. Behav..

[3]  Yong-Ming Huang,et al.  Exploring the Factors That Affect the Intention to Use Collaborative Technologies: The Differing Perspectives of Sequential/Global Learners. , 2015 .

[4]  C. Gunawardena,et al.  Social presence as a predictor of satisfaction within a computer‐mediated conferencing environment , 1997 .

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

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

[7]  Juho Hamari,et al.  Social Motivations To Use Gamification: An Empirical Study Of Gamifying Exercise , 2013, ECIS.

[8]  I. Ajzen.  The theory of planned behavior , 1991 .

[9]  Ming-Chi Lee,et al.  Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model , 2010, Comput. Educ..

[10]  C. Stevens,et al.  ANTECEDENTS OF INTERVIEW INTERACTIONS, INTERVIEWERS' RATINGS, AND APPLICANTS' REACTIONS , 1998 .

[11]  R. Oliver.  A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .

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

[13]  R. Abelson.  Script processing in attitude formation and decision making. , 1976 .

[14]  Hyunseung Choo,et al.  Smartphones as smart pedagogical tools: Implications for smartphones as u-learning devices , 2011, Comput. Hum. Behav..

[15]  Ibrahim Arpaci,et al.  Effects of security and privacy concerns on educational use of cloud services , 2015, Comput. Hum. Behav..

[16]  Yung-Ming） 程永明（Cheng,et al.  What Drives Nurses' Blended e-Learning Continuance Intention?. , 2014 .

[17]  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..

[18]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

[19]  Wynne W. Chin,et al.  Structural equation modeling analysis with small samples using partial least squares , 1999 .

[20]  CHRISTOPHER W. TAYLOR,et al.  A Publication of the Association of Management A STUDY OF STUDENT USE OF CLOUD COMPUTING APPLICATIONS , 2011 .

[21]  Francisco Muñoz-Leiva,et al.  Web Acceptance Model (WAM): Moderating effects of user experience , 2007, Inf. Manag..

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

[23]  Ling Liu,et al.  Understanding Chinese users’ continuance intention toward online social networks: an integrative theoretical model , 2013, Electronic Markets.

[24]  Icek Ajzen,et al.  From Intentions to Actions: A Theory of Planned Behavior , 1985 .

[25]  Mohammed Al-Zoube,et al.  E-Learning on the Cloud , 2009, Int. Arab. J. e Technol..

[26]  David C. Yen,et al.  Factors influencing the continuance intention to the usage of Web 2.0: An empirical study , 2012, Comput. Hum. Behav..

[27]  Yongbeom Kim,et al.  Cloud Computing for Education: A Case of Using Google Docs in MBA Group Projects , 2011, 2011 International Conference on Business Computing and Global Informatization.

[28]  Kun Chang Lee,et al.  Effects of Knowledge Sharing and Social Presence on the Intention to Continuously Use Social Networking Sites: The Case of Twitter in Korea , 2010, FGIT-UNESST.

[29]  Hong Xie,et al.  Supporting ease-of-use and user control: desired features and structure of Web-based online IR systems , 2003, Inf. Process. Manag..

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

[31]  Matthew K. O. Lee,et al.  Online social networks: Why do students use facebook? , 2011, Comput. Hum. Behav..

[32]  Szu-Yuan Sun,et al.  What influences college students to continue using business simulation games? The Taiwan experience , 2009, Comput. Educ..

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

[34]  Antoni Meseguer-Artola,et al.  Interactive Learning Environments , 2022 .

[35]  Hsiao-Hwa Chen,et al.  Trust, Security, and Privacy in Next-Generation Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[36]  Stephen A. Sivo,et al.  Predicting continued use of online teacher professional development and the influence of social presence and sociability , 2012, Br. J. Educ. Technol..

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

[38]  F. Fischer,et al.  Toward a Script Theory of Guidance in Computer-Supported Collaborative Learning , 2013, Educational psychologist.

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

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

[41]  Ian Douglas Sanders,et al.  An online collaborative document creation exercise in an ODL research project module , 2014, Comput. Educ..

[42]  Jung-Min Lee,et al.  Cloud-Based RF-Inspection for Ship Maintenance , 2013, Int. J. Distributed Sens. Networks.

[43]  Yong-Sheng Chen,et al.  Advancing Collaborative Learning with Cloud Service , 2013, ITCS.

[44]  Sanjay Misra,et al.  Learning management systems and cloud file hosting services: A study on students' acceptance , 2014, Comput. Hum. Behav..

[45]  Anis Allagui,et al.  Web Interface and Consumers Buying Intention in E-Tailing: Results From an Online Experiment , 2007 .

[46]  Bing Wu,et al.  Empirical study on continuance intentions towards E-Learning 2.0 systems , 2014, Behav. Inf. Technol..

[47]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[48]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[49]  Chyuan Perng,et al.  E-Learning Usability Measurement-Using Technology Acceptance Model and Usability Test , 2009 .

[50]  Hans van Buuren,et al.  Determining Sociability, Social Space, and Social Presence in (A)synchronous Collaborative Groups , 2004, Cyberpsychology Behav. Soc. Netw..

[51]  Yung-Ming Cheng,et al.  What Drives Nurses' Blended e-Learning Continuance Intention? , 2014, J. Educ. Technol. Soc..

[52]  Eduardo Gómez-Sánchez,et al.  Cloud computing and education: A state-of-the-art survey , 2015, Comput. Educ..

[53]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[54]  Nikiforos M. Papachristos,et al.  The role of environment design in an educational Multi-User Virtual Environment , 2013, Br. J. Educ. Technol..

[55]  Tzung-Ru Tsai,et al.  What Drives People to Continue to Play Online Games? An Extension of Technology Model and Theory of Planned Behavior , 2010, Int. J. Hum. Comput. Interact..