The Relationship among Self-Efficacy, Social Influence, Performance Expectancy, Effort Expectancy, and Behavioral Intention in Mobile Learning Service

The purpose of this study is to examine the structural relationship among self-efficacy, social influence, effort expectancy, performance expectancy, and behavioral intention of mobile learning, which is based on the extended technology acceptance model. We performed a study to determine the impacts that social influence, performance expectancy, and effort expectancy have on behavioral intention of mobile learning through selfefficacy. Appropriate measures were developed and tested on 226 university students of Gyeongnam province in South Korea with a cross-sectional questionnaire survey. The path relationship of the research model was analyzed by structural equation modeling (SEM) using AMOS 18.0. The results revealed that firstly, self-efficacy has positive effects on performance expectancy, social influence, and effort expectancy. Second, social influence has positive effects on performance expectancy, behavioral intention, and effort expectancy. Third, effort expectancy has positive effects on performance expectancy and behavioral intention. Fourth, performance expectancy has a positive effect on behavioral intention. Managers of mobile learning should focus on self-efficacy to enhance behavioral intention.

[1]  Nee Nee Chan,et al.  An exploration of students' lived experiences of using smartphones in diverse learning contexts using a hermeneutic phenomenological approach , 2015, Comput. Educ..

[2]  S. Ball Motivation in Education , 1977 .

[3]  Peter A. Todd,et al.  Understanding Information Technology Usage: A Test of Competing Models , 1995, Inf. Syst. Res..

[4]  Chin-Lung Hsu,et al.  Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation , 2008, Inf. Manag..

[5]  Thomas R. Guskey,et al.  Teacher efficacy, self-concept, and attitudes toward the implementation of instructional innovation☆ , 1988 .

[6]  J. Bruce Overmier,et al.  Choice behavior under differential outcomes: Sample stimulus control versus expectancy control , 2015 .

[7]  Norbert Pachler,et al.  Mobile Learning | Some Considerations › , .

[8]  Jaeik Shin,et al.  The Effects of Self-Efficacy and Social Influence on Behavioral Intention in Mobile Learning Service , 2015 .

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

[10]  Yongqiang Sun,et al.  Understanding the relationships between motivators and effort in crowdsourcing marketplaces: A nonlinear analysis , 2015, Int. J. Inf. Manag..

[11]  Kristine Peters,et al.  m-Learning: Positioning educators for a mobile, connected future , 2007 .

[12]  Robert W. Zmud,et al.  Behavioral Intention Formation in Knowledge Sharing: Examining the Roles of Extrinsic Motivators, Social-Psychological Factors, and Organizational Climate , 2005, MIS Q..

[13]  Juho Hamari,et al.  Transforming homo economicus into homo ludens: A field experiment on gamification in a utilitarian peer-to-peer trading service , 2013, Electron. Commer. Res. Appl..

[14]  Jane M. Howell,et al.  Personal Computing: Toward a Conceptual Model of Utilization , 1991, MIS Q..

[15]  Juho Hamari,et al.  "Working out for likes": An empirical study on social influence in exercise gamification , 2015, Comput. Hum. Behav..

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

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

[18]  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..

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

[20]  Megan Tschannen-Moran,et al.  Teacher efficacy: capturing an elusive construct , 2001 .

[21]  E. Giammarco,et al.  Relationships between general self-efficacy, planning for the future, and life satisfaction , 2015 .

[22]  Viswanath Venkatesh,et al.  Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model , 2000, Inf. Syst. Res..

[23]  Fred D. Davis User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts , 1993, Int. J. Man Mach. Stud..

[24]  Magid Igbaria,et al.  User acceptance of microcomputer technology: An empirical test , 1993 .

[25]  Bernardo de la Trinidad [Motivation in education. I]. , 1966, ANEC.

[26]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[27]  Viswanath Venkatesh,et al.  Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..