Exploring students' awareness and perceptions: Influencing factors and individual differences driving m-learning adoption

This study investigates students' awareness and perceptions of m-learning and examines the factors affecting students' behavioral intention to adopt m-learning, by using a modified research model that integrate technology acceptance model (perceived usefulness and perceived ease of use) and unified theory of acceptance and use of technology (social influence) along with other factors (m-learning services and mobile limitations). In addition, control (gender, field of study, study level) and moderator variables (mobile capabilities, level of mobile usage, and frequent use of m-services) were introduced to verify the individual differences between respondents on the key factors affecting the adoption and usage of m-learning. Structural equations modeling and path analysis were used to test the hypotheses and the proposed model. The results revealed that perceived usefulness and perceived ease of use were found to be the primary factors driving students' intentions to use m-learning. Both m-learning services and social influence have positive effects on the acceptance of m-learning, while mobile limitations were found to be the main obstacle restraining students' participation in a m-learning environment. Most of the control variables yield no significant differences between students, but all the moderator variables were found to be significant determinants that can influence students to adopt m-learning. Overall, students have great potential to engage and integrate mobile technology into their educational environment. An extended model is proposed based on TAM and UTAUT in the context of m-learning.The extended model reflects students' awareness and perceptions of m-learning.The most significant factors driving m-learning adoption were PEOU and PU.The main obstacles affecting students' use of m-learning were mobile limitations.The most vital determinants that affect m-learning usage were moderator variables.

[1]  Fred L. Kitchens,et al.  Web Services Architecture for M-Learning. , 2004 .

[2]  G. D. Chen,et al.  Ubiquitous learning website: Scaffold learners by mobile devices with information-aware techniques , 2008, Comput. Educ..

[3]  Felix B. Tan,et al.  Adult learners' intention to adopt mobile learning: A motivational perspective , 2015, Br. J. Educ. Technol..

[4]  M. Seliaman,et al.  Mobile Learning Adoption in Saudi Arabia , 2012 .

[5]  Tiong-Thye Goh,et al.  Exploring Gender Differences in SMS-Based Mobile Library Search System Adoption , 2011, J. Educ. Technol. Soc..

[6]  Chorng-Shyong Ong,et al.  Gender differences in perceptions and relationships among dominants of e-learning acceptance , 2006, Comput. Hum. Behav..

[7]  Tina Swee Kim Lim,et al.  Mobile Learning Initiative through SMS: A Formative Evaluation , 2009 .

[8]  Yu-Feng Lan,et al.  Using Mobile Learning to Improve the Reflection: A Case Study of Traffic Violation , 2012, J. Educ. Technol. Soc..

[9]  Marko Helenius,et al.  About malicious software in smartphones , 2006, Journal in Computer Virology.

[10]  Agnes Kukulska-Hulme,et al.  Will mobile learning change language learning? , 2009, ReCALL.

[11]  S. Howard,et al.  A Field Study of Perceptions and Use of Mobile Telephones by 16 to 22 Year Olds , 2002 .

[12]  Pavel Rosman,et al.  M-learning as a paradigm of a new forms in education , 2008 .

[13]  Enas Al-Lozi,et al.  Get Ready to Mobile Learning : Examining Factors Affecting College Students' Behavioral Intentions to Use M-Learning in Saudi Arabia = الاستعداد للتعلم الإلكتروني باستخدام الأجهزة النقالة : العوامل المؤثرة في نية طلاب الجامعات لاستخدام التعلم الإلكتروني في المملكة العربية السعودية , 2014 .

[14]  Viswanath Venkatesh,et al.  Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior , 2000, MIS Q..

[15]  Gary Garrison,et al.  Investigating mobile wireless technology adoption: An extension of the technology acceptance model , 2009, Inf. Syst. Frontiers.

[16]  Luvai Motiwalla,et al.  Mobile learning: A framework and evaluation , 2007, Comput. Educ..

[17]  Joseph Rene Corbeil,et al.  Are You Ready for Mobile Learning , 2007 .

[18]  M. Tagoe,et al.  Determining Distance Education Students' Readiness for Mobile Learning at University of Ghana Using the Theory of Planned Behavior. , 2014 .

[19]  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..

[20]  Viswanath Venkatesh,et al.  Gender and age differences in employee decisions about new technology: an extension to the theory of planned behavior , 2005, IEEE Transactions on Engineering Management.

[21]  H. Kaiser.  A second generation little jiffy , 1970 .

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

[23]  V. Venkatesh,et al.  AGE DIFFERENCES IN TECHNOLOGY ADOPTION DECISIONS: IMPLICATIONS FOR A CHANGING WORK FORCE , 2000 .

[24]  Magdalena Mateescu,et al.  Smartphones as Multimodal Communication Devices to Facilitate Clinical Knowledge Processes: Randomized Controlled Trial , 2013, Journal of medical Internet research.

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

[26]  Charles R. Graham,et al.  A framework for institutional adoption and implementation of blended learning in higher education , 2013, Internet High. Educ..

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

[28]  M. Prensky.  Do They Really Think Differently , 2001 .

[29]  Douglas McConatha,et al.  MOBILE LEARNING IN HIGHER EDUCATION: AN EMPIRICAL ASSESSMENT OF A NEW EDUCATIONAL TOOL , 2008 .

[30]  M. Prensky.  Digital Natives, Digital Immigrants Part 1 , 2001 .

[31]  France Bélanger,et al.  Gender differences in perceptions of web-based shopping , 2002, CACM.

[32]  Zhongyi Hu,et al.  Exploring Gender Differences on General and Specific Computer Self-Efficacy in Mobile Learning Adoption , 2013, ArXiv.

[33]  Matteo Gaeta,et al.  A system for adaptive platform-independent mobile learning , 2004 .

[34]  M. Browne,et al.  Alternative Ways of Assessing Model Fit , 1992 .

[35]  Fred D. Davis,et al.  Development and Test of a Theory of Technological Learning and Usage , 1992 .

[36]  Yen-Ting Lin,et al.  Toward interactive mobile synchronous learning environment with context-awareness service , 2008, Comput. Educ..

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

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

[39]  Birgit Bomsdorf,et al.  Adaptation of Learning Spaces: Supporting Ubiquitous Learning in Higher Distance Education , 2005, Mobile Computing and Ambient Intelligence.

[40]  Mabel K. Kiplang'at Joseph Minishi-Majanja.  The diffusion of innovations theory as a theoretical framework in Library and Information Science research : research article , 2005 .

[41]  Geoffrey S. Hubona,et al.  The mediation of external variables in the technology acceptance model , 2006, Inf. Manag..

[42]  Fred D. Davis.  A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .

[43]  Y. Cheng.  Globalisation and Education Reforms in Hong Kong: Paradigm Shifts , 2005 .

[44]  Yeonjeong Park,et al.  A Pedagogical Framework for Mobile Learning: Categorizing Educational Applications of Mobile Technologies into Four Types. , 2011 .

[45]  Lori Baker-Eveleth,et al.  Students' expectation, confirmation, and continuance intention to use electronic textbooks , 2013, Comput. Hum. Behav..

[46]  Gwo-Jen Hwang,et al.  Criteria, Strategies and Research Issues of Context-Aware Ubiquitous Learning , 2008, J. Educ. Technol. Soc..

[47]  L. Currie,et al.  Integrating mobile devices into nursing curricula: opportunities for implementation using Rogers' Diffusion of Innovation model. , 2014, Nurse education today.

[48]  S. Barnes,et al.  Driving consumer acceptance of mobile marketing: a theoretical framework and empirical study , 2005 .

[49]  Chechen Liao,et al.  Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT) , 2009, Int. J. Inf. Manag..

[50]  Jeffrey N. Lowenthal.  Using Mobile Learning: Determinates Impacting Behavioral Intention , 2010 .

[51]  Supyan Hussin,et al.  Mobile Learning Readiness among Malaysian Students at Higher Learning Institutes , 2012 .

[52]  Valerie M. Crawford,et al.  Creating a Powerful Learning Environment with Networked Mobile Learning Devices. , 2007 .

[53]  L. Cronbach,et al.  Construct validity in psychological tests. , 1955, Psychological bulletin.

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

[55]  Gwo-Jen Hwang,et al.  An adaptive navigation support system for conducting context-aware ubiquitous learning in museums , 2010, Comput. Educ..

[56]  Hao Chen,et al.  Exploiting MMS Vulnerabilities to Stealthily Exhaust Mobile Phone's Battery , 2006, 2006 Securecomm and Workshops.

[57]  Fahad N. Al-FAHAD,et al.  STUDENTS' ATTITUDES AND PERCEPTIONS TOWARDS THE EFFECTIVENESS OF MOBILE LEARNING IN KING SAUD UNIVERSITY, SAUDI ARABIA , 2009 .

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

[59]  Ting-Ting Lee,et al.  Nurses' adoption of technology: application of Rogers' innovation-diffusion model. , 2004, Applied nursing research : ANR.

[60]  Chien Chou,et al.  Ubiquitous knowledge construction: mobile learning re‐defined and a conceptual framework , 2009 .

[61]  I. Ajzen,et al.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[62]  Gwo-Jen Hwang,et al.  A Context-Aware Mobile Learning System for Supporting Cognitive Apprenticeships in Nursing Skills Training , 2012, J. Educ. Technol. Soc..

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

[64]  Wynne W. Chin,et al.  On the use, usefulness, and ease of use of structural equation modeling in MIS research: a note of caution , 1995 .

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

[66]  S. Phillipson.  Learning diversity in the Chinese classroom: Contexts and practice for students with special needs , 2007 .

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

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

[69]  Elliot Soloway,et al.  Anatomy of a mobilized lesson: Learning my way , 2009, Comput. Educ..

[70]  Garry Wei-Han Tan,et al.  Determinants of Mobile Learning Adoption: An Empirical Analysis , 2012, J. Comput. Inf. Syst..

[71]  Albert Bandura,et al.  Organisational Applications of Social Cognitive Theory , 1988 .

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

[73]  E. Rogers.  Diffusion of Innovations , 1962 .

[74]  Shengnan Han,et al.  Understanding the factors driving m‐learning adoption: a literature review , 2010 .

[75]  Anastasios A. Economides,et al.  Computer based assessment: Gender differences in perceptions and acceptance , 2011, Comput. Hum. Behav..

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

[77]  Yi-Shun Wang,et al.  Investigating the determinants and age and gender differences in the acceptance of mobile learning , 2009, Br. J. Educ. Technol..

[78]  I. Qureshi,et al.  M-learning adoption: A perspective from a developing country , 2012 .

[79]  Gregg Orr.  A Review of Literature in Mobile Learning: Affordances and Constraints , 2010, 2010 6th IEEE International Conference on Wireless, Mobile, and Ubiquitous Technologies in Education.

[80]  Yu-Ru Lin,et al.  Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model , 2007, Electron. Libr..

[81]  Xiaohui Liu,et al.  The effects of individual differences on e-learning users' behaviour in developing countries: A structural equation model , 2014, Comput. Hum. Behav..

[82]  Younghwa Lee,et al.  The Technology Acceptance Model: Past, Present, and Future , 2003, Commun. Assoc. Inf. Syst..

[83]  K. Ciampa,et al.  Learning in a mobile age: an investigation of student motivation , 2014, J. Comput. Assist. Learn..

[84]  Chih-Kai Chang,et al.  A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students , 2013, Comput. Educ..

[85]  M. Prensky.  H. Sapiens Digital: From Digital Immigrants and Digital Natives to Digital Wisdom , 2009 .

[86]  Ismail Sahin,et al.  Using Rogers’ Theory to Interpret Instructional Computer Use by COE Faculty , 2006 .

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

[88]  Su-Chao Chang,et al.  An empirical investigation of students' behavioural intentions to use the online learning course websites , 2007, Br. J. Educ. Technol..

[89]  Mike Sharples,et al.  The Design and Implementation of a Mobile Learning Resource , 2002, Personal and Ubiquitous Computing.

[90]  Biju Issac,et al.  The mobile devices and its mobile learning usage analysis , 2008, IMECS 2008.

[91]  Ahmad Abu-Al-Aish,et al.  International of Research in Open and Distributed Learning Factors Influencing Students’ Acceptance of M-Learning: An Investigation in Higher Education , 2022 .

[92]  Georgina Parsons.  Information provision for HE distance learners using mobile devices , 2010, Electron. Libr..

[93]  Lung-Hsiang Wong,et al.  What seams do we remove in mobile-assisted seamless learning? A critical review of the literature , 2011, Comput. Educ..