Mobile Technology and Interactive Lectures: The Key Adoption Factors

Lecture classes are fundamental and essential for teaching and learning in higher education. The objective of this study is to investigate adoption factors for promoting interactive lectures in higher education from reviews of technology acceptance models, motivational factors, and cultural dimension theory. The study aims to elicit key factors influencing mobile technology adoption in the classrooms as an interaction tool, focusing on the notion of communication barriers caused by classes with large number of students. Survey involving higher education students enrolled in academic courses in Malaysia was conducted with a sample size of 396. Factor analysis produced three key factors: User system perception (USP), system and information quality (SIQ) and user uncertainty avoidance (UUA). Results of regression analysis revealed UUA as the strongest significant predictor of adoption (beta = −0.225, p < 0.001), and a high proportion of UUA was strongly explained by USP (r = −0.513) and SIQ (r = −0.537). This study underscores the need for researchers to further explore blended learning pedagogies using mobile technology.

[1]  Hissam Tawfik,et al.  MoHTAM: A Technology Acceptance Model for Mobile Health Applications , 2011, 2011 Developments in E-systems Engineering.

[2]  Chun-Chieh Wang,et al.  An empirical study of instructor adoption of web-based learning systems , 2009, Comput. Educ..

[3]  Li Zhao,et al.  Information quality and community municipal portal use , 2013, Gov. Inf. Q..

[4]  A. Chickering,et al.  Seven Principles for Good Practice in Undergraduate Education , 1987, CORE.

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

[6]  Wendy Beekes The ‘Millionaire’ method for encouraging participation , 2006 .

[7]  Vimala Balakrishnan,et al.  Mobile Wireless Technology and Its Use in Lecture Room Environment: An Observation in Malaysian Institutes of Higher Learning , 2013 .

[8]  Hsien-Cheng Lin,et al.  An investigation of the effects of cultural differences on physicians' perceptions of information technology acceptance as they relate to knowledge management systems , 2014, Comput. Hum. Behav..

[9]  Izak Benbasat,et al.  IT-Mediated Customer Service Content and Delivery in Electronic Governments: An Empirical Investigation of the Antecedents of Service Quality , 2013, MIS Q..

[10]  Ephraim R. McLean,et al.  The DeLone and McLean Model of Information Systems Success: A Ten-Year Update , 2003, J. Manag. Inf. Syst..

[11]  J. Lee,et al.  How cultural differences in uncertainty avoidance affect product perceptions , 2007 .

[12]  Peter A. Todd,et al.  Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication , 1992, MIS Q..

[13]  R. Shroff,et al.  Analysis of the technology acceptance model in examining students' behavioural intention to use an e-portfolio system , 2011 .

[14]  Philip M. Podsakoff,et al.  The effects of "intrinsic" and "extrinsic" reinforcement contingencies on task behavior. , 1988 .

[15]  Marjan Hericko,et al.  A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types , 2011, Comput. Hum. Behav..

[16]  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..

[17]  Wen-Hao David Huang,et al.  Comparison of Web 2.0 Technology Acceptance Level based on Cultural Differences , 2011, J. Educ. Technol. Soc..

[18]  Thomas C. Reeves,et al.  How do you know they are learning? The importance of alignment in higher education , 2006, Int. J. Learn. Technol..

[19]  Wen-Shan Lin,et al.  Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit , 2012, Comput. Educ..

[20]  Fan-Yun Pai,et al.  Applying the Technology Acceptance Model to the introduction of healthcare information systems , 2011 .

[21]  Timothy Paul Cronan,et al.  On the Test-Retest Reliability of Perceived Usefulness and Perceived Ease of Use Scales , 1993 .

[22]  A. F. M. Ayub,et al.  An Assessment of Students’ Mobile Self-Efficacy, Readiness and Personal Innovativeness towards Mobile Learning in Higher Education in Malaysia , 2012 .

[23]  Stephen Marshall,et al.  Mobile phones in the classroom: if you can't beat them, join them , 2009, CACM.

[24]  Girish H. Subramanian,et al.  A Replication of Perceived Usefulness and Perceived Ease of Use Measurement , 1994 .

[25]  E. Diener,et al.  Further validation of the Satisfaction with Life Scale: evidence for the cross-method convergence of well-being measures. , 1991, Journal of personality assessment.

[26]  Deborah Compeau,et al.  Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study , 1999, MIS Q..

[27]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[28]  Joel Geske,et al.  Overcoming the Drawbacks of the Large Lecture Class , 1992 .

[29]  Deborah Allen,et al.  Infusing active learning into the large-enrollment biology class: seven strategies, from the simple to complex. , 2005, Cell biology education.

[30]  Julie Ratcliffe,et al.  Measuring technology self efficacy: reliability and construct validity of a modified computer self efficacy scale in a clinical rehabilitation setting , 2012, Disability and rehabilitation.

[31]  Jonathan Matusitz,et al.  Power Distance, Uncertainty Avoidance, and Technology: Analyzing Hofstede's Dimensions and Human Development Indicators , 2013 .

[32]  Jared Keengwe,et al.  Students’ perceptions of clickers as an instructional tool to promote active learning , 2011, Education and Information Technologies.

[33]  L. V. Dijk,et al.  Interactive lectures in engineering education , 2001 .

[34]  Jason M. Lodge,et al.  Capturing dynamic presentation: Using technology to enhance the chalk and the talk , 2013 .

[35]  Patrick J. Moreo,et al.  The influence of the cultural dimension of uncertainty avoidance on business strategy development: A cross-national study of hotel managers , 2008 .

[36]  Mi-Jin Noh,et al.  Customer acceptance of IPTV service quality , 2011, Int. J. Inf. Manag..

[37]  Jeffrey R. Stowell,et al.  Using Student Response Systems (“Clickers”) to Combat Conformity and Shyness , 2010 .

[38]  Abdul Razak Yaakub,et al.  Students' Awareness and Requirements of Mobile Learning Services in the Higher Education Environment , 2011 .

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

[40]  Abdul R. Ashraf,et al.  The Application of the Technology Acceptance Model under Different Cultural Contexts: The Case of Online Shopping Adoption , 2014 .

[41]  Vimala Balakrishnan,et al.  Determinants of mobile wireless technology for promoting interactivity in lecture sessions: an empirical analysis , 2014, J. Comput. High. Educ..

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

[43]  Mark R. Lehto,et al.  User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model , 2013, Comput. Educ..

[44]  Tzy-Ling Chen,et al.  Using a personal response system as an in-class assessment tool in the teaching of basic college chemistry , 2013 .

[45]  Chien-Hung Liu,et al.  Adopt Technology Acceptance Model to Analyze Factors Influencing Students' Intention on Using a Disaster Prevention Education System , 2013, EMC/HumanCom.

[46]  Sung Youl Park,et al.  University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model , 2012, Br. J. Educ. Technol..

[47]  Antonio Padilla-Meléndez,et al.  Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario , 2013, Comput. Educ..

[48]  Eunil Park,et al.  Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model , 2014, Telematics Informatics.

[49]  Alain Yee-Loong Chong,et al.  An empirical analysis of the adoption of m-learning in Malaysia , 2011, Int. J. Mob. Commun..

[50]  Birgit Loch,et al.  Closing the feedback loop: engaging students in large first-year mathematics test revision sessions using pen-enabled screens , 2013 .

[51]  Kexin Zhao,et al.  The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation , 2013, Decis. Support Syst..

[52]  Deborah Compeau,et al.  Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..

[53]  A. E. Bayraktaroglu,et al.  Predicting the Intention to Use a Web‐Based Learning System: Perceived Content Quality, Anxiety, Perceived System Quality, Image, and the Technology Acceptance Model , 2014 .

[54]  Barbara H Wixom,et al.  A Theoretical Integration of User Satisfaction and Technology Acceptance , 2005, Inf. Syst. Res..

[55]  Xiaohui Liu,et al.  Measuring the Moderating Effect of Gender and Age on E-Learning Acceptance in England: A Structural Equation Modeling Approach for An Extended Technology Acceptance Model , 2014 .

[56]  A. Bandura Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychological review.

[57]  Yi-Shun Wang,et al.  What drives purchase intention in the context of online content services? The moderating role of ethical self-efficacy for online piracy , 2013, Int. J. Inf. Manag..