Skip to main content
Springer Nature Link
Log in

M-learning adoption of management students’: A case of India

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

The present study aims to interpret management student’s motivation to adopt m-learning and assesses the determinates impacting the behavioral intent of m-learning adoption. A comprehensive research archetype is proposed by integrating two prominent theoretical models, namely UTAUT and UGT. The research model is tested using multi-analytic structural equation modeling (SEM) and advanced neural network (ANN) approach. The quantitative data was gathered and measured from 220 management students. The study outcomes reported that affective need, performance expectancy, effort expectancy, social influence and facilitating conditions positively impacted the student’s intent to use m-learning, whereas cognitive need was found to be insignificant in predicting and explicating the m-learning adoption. The results of sensitivity analysis revealed that effort expectancy showed the highest normalized importance (100%) followed by performance expectancy (97.2%) in explicating the m-learning adoption. The research archetype was able to elucidate 66% of variance in student’s intent towards m-learning adoption. In addition to that, Cohen’s f-square statistic resulted in effect size as 0.771 indicating that the study findings were relevant and substantial with the empirical data collected. Conclusively, the theoretic and managerial implications are described for the proposed model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abu-al-aish, A., & Love, S. (2013). Factors influencing students ’ acceptance of M-learning : An investigation in higher education. The International Review of Research in Open and Distance Learning, 14(5), 83–108.

    Google Scholar 

  • Acheampong, P., Zhiwen, L., Boateng, F., Boadu, A. B., & Acheampong, A. A. (2017). Determinants of behavioral intentions of generation Y adoption and use of computer-mediated communication tools in Ghana. British Journal of Interdisciplinary Research, 8(1), 34–47.

    Google Scholar 

  • Adel Ali, R., & Rafie Mohd Arshad, M. (2018). Empirical analysis on factors impacting on intention to use m-learning in basic education in Egypt. The International Review of Research in Open and Distance Learning, 19(2).

  • Adov, L., Must, O., & Pedaste, M. (2017). Attitudes towards Mobile devices in Estonian basic education: Using the framework of the UTAUT model. Lecture notes in computer science, 319–329. doi:https://doi.org/10.1007/978-3-319-58515-4_25.

  • Akgül, Y. (2018). A SEM-neural network approach for predicting antecedents of factors influencing Consumers' intent to install Mobile applications. In Mobile technologies and socio-economic development in emerging nations (pp. 262-308). IGI global.

  • Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020a). An empirical examination of continuous intention to use m-learning: An integrated model. Education and information technologies, 1-20.

  • Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2020b). Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance. Technology in Society, 101247.

  • Al-Emran, M., Mezhuyev, V., Kamaludin, A., & ALSinani, M. (2018, February). Development of M-learning application based on knowledge management processes. In proceedings of the 2018 7th international conference on software and computer applications (pp. 248-253).

  • Al-Shihi, H., Sharma, S. K., & Sarrab, M. (2018a). Neural network approach to predict mobile learning acceptance. Education and Information Technologies, 23(5), 1805–1824. https://doi.org/10.1007/s10639-018-9691-9.

    Article  Google Scholar 

  • Al-Shihi, H., Sharma, S. K., & Sarrab, M. (2018b). Neural network approach to predict mobile learning acceptance. Education and Information Technologies, 23(5), 1805–1824. https://doi.org/10.1007/s10639-018-9691-9.

    Article  Google Scholar 

  • Ali, R. A., & Arshad, M. R. M. (2016). Perspectives of students’ behavior towards mobile learning (M-learning) in Egypt: An extension of the UTAUT model. Engineering, Technology & Applied Science Research, 6(4), 1109–1114.

    Google Scholar 

  • Aliaño, Á. M., Hueros, A. D., Franco, M. G., & Aguaded, I. (2019). Mobile learning in university contexts based on the unified theory of acceptance and use of technology (UTAUT). Journal of New Approaches in Educational Research (NAER Journal), 8(1), 7–17.

    Google Scholar 

  • Alioon, Y., & Delialioğlu, Ö. (2019). The effect of authentic m-learning activities on student engagement and motivation. British Journal of Educational Technology, 50(2), 655–668.

    Google Scholar 

  • Ally, M. (2009). Mobile learning: Transforming the delivery of education and training. Edmonton, Canada: Athabasca University Press.

  • Alvarez, C., Alarcon, R., & Nussbaum, M. (2011). Implementing collaborative learning activities in the classroom supported by one-to-one mobile computing: A design-based process. Journal of Systems and Software, 84(11), 1961–1976.

    Google Scholar 

  • Alsswey, A., & Al-Samarraie, H. (2019). M-learning adoption in the Arab gulf countries: A systematic review of factors and challenges. Education and Information Technologies, 24(5), 3163–3176.

    Google Scholar 

  • Asadi, S., Abdullah, R., Safaei, M., & Nazir, S. (2019). An integrated SEM-Neural Network approach for predicting determinants of adoption of wearable healthcare devices. Mobile Information Systems, 2019.

  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation model. Journal of academy of marketing science, 16(1), 74–94.

    Google Scholar 

  • Basak, S. K., Wotto, M., & Bélanger, P. (2018, November). University Students' M-leaming adaption behavioral factors: A pilot study. In 2018 IEEE 9th annual information technology, electronics and Mobile communication conference (IEMCON) (pp. 68-73). IEEE.

  • Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13(2), 139–161.

    Google Scholar 

  • Baydas, O., & Yilmaz, R. M. (2018). Pre-service teachers’ intention to adopt mobile learning: A motivational model. British Journal of Educational Technology, 49(1), 137–152.

    Google Scholar 

  • Bere, A. (2014). Exploring determinants for Mobile learning user acceptance and use: An application of UTAUT. 2014 11th international conference on information technology: New generations. https://doi.org/10.1109/itng.2014.114.

  • Bishop, C. M. (1996). Neural networks: A pattern recognition perspective.

  • Bolton, D., & Galloway, C. (2014). The holistic dilemma: Helping management students deal with risk. The International Journal of Management Education, 12(2), 55–67. https://doi.org/10.1016/j.ijme.2014.01.001.

    Article  Google Scholar 

  • Bornstein, M. H., Jager, J., & Putnick, D. L. (2013). Sampling in developmental science: Situations, shortcomings, solutions, and standards. Developmental Review, 33(4), 357–370.

    Google Scholar 

  • Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9–54.

    Google Scholar 

  • Browne, M.W. and Cudeck, R. (1993), Alternative ways of assessing model fit, sage publications, Newbury Park, CA. Harasim.

  • Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in need for cognition. Psychological Bulletin, 119, 197–253.

    Google Scholar 

  • Cazan, A. M., & Indreica, S. E. (2014). Need for cognition and approaches to learning among university students. Procedia-Social and Behavioral Sciences, 127, 134–138.

    Google Scholar 

  • Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652.

    Google Scholar 

  • Chatterjee, S., Majumdar, D., Misra, S., & Damaševičius, R. (2020). Adoption of mobile applications for teaching-learning process in rural girls’ schools in India: An empirical study. Education and Information Technologies. https://doi.org/10.1007/s10639-020-10168-6.

  • Cheng, Y., Liang, J., & Leung, L. (2015). Social network service use on mobile devices: An examination of gratifications, civic attitudes and civic engagement in China. New Media & Society, 17(7), 1096–1116.

    Google Scholar 

  • Chiu, C. M., & Wang, E. T. (2008). Understanding web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194–201.

    MathSciNet  Google Scholar 

  • Chong, A. Y. L. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240–1247.

    Google Scholar 

  • Chong, A. Y. L., Liu, M. J., Luo, J., & Keng-Boon, O. (2015). Predicting RFID adoption in healthcare supply chain from the perspectives of users. International Journal of Production Economics, 159, 66–75.

    Google Scholar 

  • Chuang, Y. T. (2015). SSCLS: A smartphone-supported collaborative learning system. Telematics and Informatics, 32(3), 463–474.

    Google Scholar 

  • Cochrane, T. (2013). M-learning as a catalyst for pedagogical change. In Z. L. Berge & L. Y. Muilenburg (Eds.), Handbook of mobile learning. New York, NY: Routledge.

    Google Scholar 

  • Cohen, J. E. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.

    MATH  Google Scholar 

  • Coutinho, S. A. (2006). The relationship between the need for cognition, metacognition, and intellectual task performance. Educational Research Review, 1, 162–164.

    Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

    Google Scholar 

  • DeLuca, J. R., & Braunstein-Minkove, J. (2016). An evaluation of sport management student preparedness: Recommendations for adapting curriculum to meet industry needs. Sport Management Education Journal, 10(1), 1–12.

    Google Scholar 

  • Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18, 453–461.

    Google Scholar 

  • Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information systems Frontiers, 1-16.

  • Eighmey, J. (1997). Profiling user responses to commercial web sites. Journal of Advertising Research, 37(3), 59–66.

    Google Scholar 

  • Elliott, P. (1974). Uses and gratifications research: A critique and a sociological alternative. In J. G. Blumler & E. Katz (Eds.), The uses of mass communications: Current perspectives on gratifications research (pp. 249–268). Beverly Hills, CA: Sage.

    Google Scholar 

  • Fadol, Y., Aldamen, H., & Saadullah, S. (2018). A comparative analysis of flipped, online and traditional teaching: A case of female middle eastern management students. The International Journal of Management Education, 16(2), 266–280. https://doi.org/10.1016/j.ijme.2018.04.003.

    Article  Google Scholar 

  • Fathi, E., & Shoja, B. M. (2018). Deep neural networks for natural language processing. In handbook of statistics (Vol. 38, pp. 229-316). Elsevier.

  • FICCI & EY (2017). Leapfrogging to Education 4.0: Student at the core. Retrieved from https://www.ey.com/Publication/vwLUAssets/ey-leap-forgging/$File/ey-leap-forgging.pdf

  • Fidler, F., Cumming, G., Thomason, N., Pannuzzo, D., Smith, J., Fyffe, P., Edmonds, H., Harrington, C., & Schmitt, R. (2005). Toward improved statistical reporting in the journal of consulting and clinical psychology. Journal of Consulting and Clinical Psychology, 73(1), 136–143.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Google Scholar 

  • Gamlem, S. M., Kvinge, L. M., Smith, K., Engelsen, K. S., & Hansen, V. L. (2019). Developing teachers’ responsive pedagogy in mathematics, does it lead to short-term effects on student learning? Cogent Education, 6(1), 1676568.

    Google Scholar 

  • García Botero, G., Questier, F., Cincinnato, S., He, T., & Zhu, C. (2018). Acceptance and usage of mobile assisted language learning by higher education students. Journal of Computing in Higher Education, 30(3), 426–451. https://doi.org/10.1007/s12528-018-9177-1.

    Article  Google Scholar 

  • Garrison, D. R., & Anderson, T. (2003). E-learning in the 21st century: A framework for research and practice. London: Routledgefalmer.

    Google Scholar 

  • Gibson, M. (2019). Crafting communities of practice: The relationship between making and learning. International Journal of Technology and Design Education, 29(1), 25–35.

    Google Scholar 

  • Givens, S. M. (2010). Using affective assessment to understand our students’ identities as readers (and non-readers). Inquiry: The Journal of the Virginia Community Colleges, 15(1), 2.

    Google Scholar 

  • Gómez-Ramirez, I., Valencia-Arias, A., & Duque, L. (2019). Approach to M-learning acceptance Among University students: An integrated model of TPB and TAM. The International Review of Research in Open and Distance Learning, 20(3).

  • Grant, M. M., & Barbour, M. K. (2013). Mobile teaching and learning in the classroom and online: Case studies in K-12. In Z. Berge & L. Muilenburg (Eds.), handbook of mobile learning (pp.285-292). New York, NY:Routledge.

  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis prentice Hall. Upper Saddle River, NJ, 730.

  • Hair, J. F., Celsi, M., Money, A., Samouel, P., & Page, M. (2015). The essentials of business research methods. New York: Routledge.

    Google Scholar 

  • Hara, N., & Kling, R. (2001). Student distress in web-based distance education, Educause. Retrieved from http://www.educause.edu/ir/library/pdf/EQM01312.pdf.

  • Hashim, K. F., Tan, F. B., & Rashid, A. (2015). Adult learners' intention to adopt mobile learning: A motivational perspective. British Journal of Educational Technology, 46(2), 381–390.

    Google Scholar 

  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.

  • Hew, T. S., Leong, L. Y., Ooi, K. B., & Chong, A. Y. L. (2016). Predicting drivers of mobile entertainment adoption: A two-stage SEM-artificial-neural-network analysis. Journal of Computer Information Systems, 56(4), 352–370.

    Google Scholar 

  • Hossain, M., Kim, M., & Jahan, N. (2019). Can “liking” behavior Lead to usage intention on Facebook? Uses and gratification theory perspective. Sustainability, 11(4), 1166.

    Google Scholar 

  • Hox, J. J., & Bechger, T. M. (2009). Introduction to structural equation modeling. Family science review, 11, 354–373. https://doi.org/10.1080/10705510903008345.

    Article  Google Scholar 

  • Hsia, J. W. (2016). The effects of locus of control on university students’ mobile learning adoption. Journal of Computing in Higher Education, 28(1), 1–17.

    Google Scholar 

  • Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91–112.

    Google Scholar 

  • Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning. The electronic library.

  • IBM SPSS Statistics, 2019. Output (Multilayer Perceptron). Retrieved from https://www.ibm.com/support/knowledgecenter/SSLVMB_sub/statistics_mainhelp_ddita/spss/neural_network/idh_idd_mlp_output.html

  • Iqbal, S., & Qureshi, I. A. (2012). M-learning adoption: A perspective from a developing country. The International Review of Research in Open and Distributed Learning, 13(3), 147–164.

    Google Scholar 

  • Isaac, O., Abdullah, Z., Aldholay, A. H., & Ameen, A. A. (2019). Antecedents and outcomes of internet usage within organisations in Yemen: An extension of the unified theory of acceptance and use of technology (UTAUT) model. Asia Pacific Management Review, 24(4), 335–354.

    Google Scholar 

  • Jager, J., Putnick, D. L., & Bornstein, M. H. (2017). II. More than just convenient: The scientific merits of homogeneous convenience samples. Monographs of the Society for Research in Child Development, 82(2), 13–30.

    Google Scholar 

  • Jambulingam, M. (2013). Behavioural intention to adopt mobile technology among tertiary students. World Applied Sciences Journal, 22(9), 1262–1271.

    Google Scholar 

  • Jena, R. K. (2020). Measuring the Impact of Business Management Student’s Attitude towards Entrepreneurship Education on Entrepreneurial Intention: A Case Study. Computers in Human Behavior, 106275. doi:https://doi.org/10.1016/j.chb.2020.106275

  • Jennings, M. (2002). What do good designers know that we don’t. In M. Khosrow-Pour (Ed.), Web-based instructional learning (pp. 235–241). London: IRM Press.

    Google Scholar 

  • Jeno, L. M., Adachi, P. J., Grytnes, J. A., Vandvik, V., & Deci, E. L. (2019). The effects of m-learning on motivation, achievement and well-being: A self-determination theory approach. British Journal of Educational Technology, 50(2), 669–683.

    Google Scholar 

  • Johnson, J. E., Giannoulakis, C., Felver, N., Judge, L. W., David, P. A., & Scott, B. F. (2017). Motivation, satisfaction, and retention of sport management student volunteers.

  • Katz, E., Blumler, J. G., & Gurevitch, M. (1974). Utilization of mass communication by the individual. In J. G. Blumler & E. Katz (Eds.), The uses and gratifications of mass communications: Current perspectives on gratifications research (pp. 19–34). Beverly Hills, CA: Sage Publications.

    Google Scholar 

  • Katz, E., Haas, H., & Gurevitch, M. (1973). On the use of the mass media for important things. American Sociological Review, 38, 164–181.

    Google Scholar 

  • Kearney, P. (1994). Affective learning. In R. B. Rubin, P. Palmgreen, & H. E. Sypher (Eds.), Communication research measures: A sourcebook (pp. 81–85). New York: Guilford.

    Google Scholar 

  • Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404–416.

    Google Scholar 

  • Kirsh, D. (2000). A Few Thoughts on Cognitive Overload. Retrieved from http://interactivity.ucsd.edu/articles/Overload/Cognitive_Overload.pdf

  • Kline, R. B. (1998). Software review: Software programs for structural equation modeling: Amos, EQS, and LISREL. Journal of Psychoeducational Assessment, 16(4), 343–364.

    Google Scholar 

  • Kline, R. B. (2004). Beyond significance testing: Reforming data analysis methods in behavioral research. American Psychological Association.

  • Korac, D., Damjanovic, B., & Simic, D. (2020). Information security in M-learning systems: Challenges and threats of using cookies. 2020 19th international symposium INFOTEH-JAHORINA (INFOTEH). doi:https://doi.org/10.1109/infoteh48170.2020.9066344.

  • Kruk, B., & Zhuravleva, O. (2013). Modern approaches to engineering education. International Journal of Experimental Education, 6, 10–13.

    Google Scholar 

  • Kuehn, S. A. (1994). Computer-mediated communication in instructional settings: A research agenda. Communication Education, 43, 171–182.

    Google Scholar 

  • Lee, M. J. (2005). Expanding hypertext: Does it address disorientation? Depends on individuals' adventurousness. Journal of Computer-Mediated Communication, 10 (3), Retrieved from http://jcmc.indiana.edu/vol10/issue3/lee.html.

  • Lee, S. (2006). Propensity score adjustment as a weighting scheme for volunteer panel web surveys. Journal of Official Statistics, 22(2), 329.

    Google Scholar 

  • Littlejohn, S. W. (1996). Theories of human communication. New York: Wadsworth.

    Google Scholar 

  • Liu, T. Z., Huang, T. Y., & Hsu, C. S. (2015). Investigating E-learning effects on continuance intentions of hospitality management students. In information and communication Technologies in Tourism 2015 (pp. 873–883). Springer, Cham.

  • Liu, Y., Li, H., & Carlsson, C. (2010b). Factors driving the adoption of mLearning: An empirical study. Computers & Education, 55(3), 1211–1219.

    Google Scholar 

  • Lometti, G. E., Reeves, B., & Bybee, C. R. (1977). Investigating the assumptions of uses and gratifications research. Communication Research, 4, 321–328.

    Google Scholar 

  • Lowenthal, J. N. (2010). Using mobile learning: Determinates impacting behavioral intention. The American Journal of Distance Education, 24(4), 195–206.

    Google Scholar 

  • Luo, X. (2002). Uses and gratifications theory and E-consumer behaviors. Journal of Interactive Advertising, 2(2), 34–41.

    Google Scholar 

  • Lynn, T. G., & Alexopoulos, A. (2010). Technology in the Daily Lives and Educational Experiences of Business Students. Available at SSRN 1973030.

  • Madigan, R., Louw, T., Wilbrink, M., Schieben, A., & Merat, N. (2017). What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transportation Research Part F: Traffic Psychology and Behaviour, 50, 55–64.

    Google Scholar 

  • Majumdar, D. (2018). In a market of 7.5 M-plus mobile self-learners, how many are actually learning?. Retrieved from https://yourstory.com/2018/04/mobile-self-learners?utm_pageloadtype=scroll

  • Malhotra, Y., & Galletta, D. F. (1999). Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. Proceedings of the 32nd annual Hawaii international conference on systems sciences. HICSS-32. Abstracts and CD-ROM of full papers. doi:https://doi.org/10.1109/hicss.1999.772658.

  • Martin, M. M., Mottet, T. P., & Myers, S. A. (2000). Students' motives for communicating with their instructors and affective and cognitive learning. Psychological Reports, 87(3), 830–834.

    Google Scholar 

  • Mckinsey Global Institute (2019). Digital India - Technology to transform connected nation. Retrieved from https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Digital%20India%20Technology%20to%20transform%20a%20connected%20nation/MGI-Digital-India-Report-April-2019.ashx

  • McLeod, J. M., & Becker, L. B. (1981). The uses and gratifications approach. Handbook of political communication, 67–99.

  • McQuiggan, S., McQuiggan, J., Sabourin, J., & Kosturko, L. (2015). Mobile learning: A handbook for developers, educators, and learners. John Wiley & Sons.

  • MHRD (2019). Press information bureau, Government of India, Ministry of Human Resource Department. Retrieved from https://pib.gov.in/Pressreleaseshare.aspx?PRID=1577240

  • Mondi, M., Woods, P., & Rafi, A. (2008). A’ uses and gratification expectancy model’ to predict students’ perceived e-learning experience. Educational Technology and Society, 11(2), 241–261.

    Google Scholar 

  • Mondi, M., Woods, P., & Rafi, A. (2007). Students uses and gratification expectancy conceptual framework in relation to E-learning resources. Asia Pacific Education Review, 8(3), 435–449.

    Google Scholar 

  • Morris, M., & Ogan, C. (1996). The internet as mass medium. Journal of Communications, 46(1), 39–50.

    Google Scholar 

  • Morris, S. A., Greer, T. H., Hughes, C., & Clark, W. J. (2004). Prediction of CASE adoption: A neural network approach. Industrial Management & Data Systems.

  • Mtebe, J., & Raisamo, R. (2014). Investigating students’ behavioural intention to adopt and use mobile learning in higher education in East Africa. International Journal of Education and Development using ICT, 10(3).

  • Mubuke, F., Kituyi, G. M., Masaba, K. A., Ogenmungu, C., & Nagujja, S. (2018). Social influence: Testing the predictive power of its dimensions in explaining the intention to use Mobile learning Systems in Universities-Empirical Evidence from Ugandan universities. I-Manager's journal of educational technology, 15(3), 52.

  • Naismith, L., & Corlett, D. (2006). Reflections on success: A retrospective of the mLearn conference series 2002–2005.

  • Nakagawa, S., & Cuthill, I. C. (2007). Effect size, confidence interval and statistical significance: A practical guide for biologists. Biological Reviews, 82(4), 591–605.

    Google Scholar 

  • Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56–73.

    Google Scholar 

  • Okai-Ugbaje, S., Ardzejewska, K., & Imran, A. (2020). Readiness, roles, and responsibilities of stakeholders for sustainable Mobile learning adoption in higher education. Education Sciences, 10(3), 49.

    Google Scholar 

  • Palmgreen, P. (1984). Uses and gratifications: A theoretical perspective. Communication Yearbook, 8, 20–55.

    Google Scholar 

  • Priyadarshinee, P., Raut, R. D., Jha, M. K., & Gardas, B. B. (2017). Understanding and predicting the determinants of cloud computing adoption: A two staged hybrid SEM-neural networks approach. Computers in Human Behavior, 76, 341–362.

    Google Scholar 

  • Quinn, C. (2013). A future for M-learning. In Z. L. Berge & L. Y. Muilenburg (Eds.), Handbook of mobile learning. New York, NY: Routledge.

    Google Scholar 

  • Rosenstein, A. W., & Grant, A. E. (1997). Reconceptualizing the role of habit: A new model of television audience. Journal of Broadcasting & Electronic Media, 41, 324–344.

    Google Scholar 

  • Rubin, A. M. (1983). Television uses and gratifications: The interactions of viewing patterns and motivations. Journal of Broadcasting, 27(1), 37–51.

    Google Scholar 

  • Rubin, A. M. (1984). Ritualized and instrumental television viewing. Journal of Communication, 34(3), 67–77.

    Google Scholar 

  • Ruggiero, T. E. (2000). Uses and gratifications theory in the 21st century. Mass communication & society, 3(1), 3–37.

    MathSciNet  Google Scholar 

  • Sabah, N. M. (2016). Exploring students' awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior, 65, 522–533.

    Google Scholar 

  • Salloum, S. A., & Shaalan, K. (2018, September). Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. In international conference on advanced intelligent systems and informatics (pp. 469-480). Springer, Cham.

  • Scott, J. E., & Walczak, S. (2009). Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance. Information & Management, 46(4), 221–232.

    Google Scholar 

  • Shachak, A., Kuziemsky, C., & Petersen, C. (2019). Beyond TAM and UTAUT: Future directions for HIT implementation research. Journal of Biomedical Informatics, 103315, 103315. https://doi.org/10.1016/j.jbi.2019.103315.

    Article  Google Scholar 

  • Sharma, S. K., Gaur, A., Saddikuti, V., & Rastogi, A. (2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behaviour & Information Technology, 36(10), 1053–1066.

    Google Scholar 

  • Sharma, S. K., Sharma, H., & Dwivedi, Y. K. (2019). A hybrid SEM-neural network model for predicting determinants of mobile payment services. Information Systems Management, 36(3), 243–261.

    Google Scholar 

  • Sharples, M., 2005. Learning as conversation: Transforming education in the Mobileage. Educase Online.

  • Srivastava, M. (2015). 80% Indian employees find mobile learning useful, says study. Retrieved from https://www.livemint.com/Consumer/Jx5FFXAsYA6nB4hob1f0gJ/80-Indian-employees-find-mobile-learning-useful-says-study.html

  • Stanford, S. W. (1983). Comments on Palmgreen and Rayburn: Gratifications sought and media exposure. Communication Research, 10, 247–258.

    Google Scholar 

  • Stoerger, S. (2013). Becoming a digital nomad: Transforming learning through mobile devices. In Z. L. Berge & L. Y. Muilenburg (Eds.), Handbook of mobile learning. New York, NY: Routledge.

    Google Scholar 

  • Sun, J. C.-Y., & Rueda, R. (2011). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204. https://doi.org/10.1111/j.1467-8535.2010.01157.x.

    Article  Google Scholar 

  • Swanson, D. L. (1977). The uses and misuses of uses and gratifications. Human Communication Research, 3, 214–221.

    Google Scholar 

  • Tan, G. W. H., Ooi, K. B., Leong, L. Y., & Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-neural networks approach. Computers in Human Behavior, 36, 198–213.

    Google Scholar 

  • Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review, 11(2), 253–262.

    Google Scholar 

  • Thao, T. T. P., Thai, L. D., Thanh, H. T., Tran, T., & Vuong, Q. H. (2019). Mobile learning for high-school mathematics as a path to better sustainability in a fast-changing society: An exploratory study from Vietnam. Problems and perspectives in management, 17(2), 392–403. https://doi.org/10.21511/ppm.17(2).2019.30.

    Article  Google Scholar 

  • Thomas, T., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development using ICT, 9(3).

  • Thongsri, N., Shen, L., Bao, Y., & Alharbi, I. M. (2018). Integrating UTAUT and UGT to explain behavioural intention to use M-learning: A developing country’s perspective. Journal of Systems and Information Technology, 20(3), 278–297.

    Google Scholar 

  • Traxler, J. (2009). Learning in a Mobile age. International journal of Mobile and blended learning, 1(1), 1–12. https://doi.org/10.4018/jmbl.2009010101.

    Article  MathSciNet  Google Scholar 

  • Uğur, N. G., Koç, T., & Koç, M. (2016). An analysis of mobile learning acceptance by college students. Journal of Educational & Instructional Studies in the World, 6(2).

  • Uzunboylu, H., Cavus, N., & Ercag, E. (2009). Using mobile learning to increase environmental awareness. Computers & Education, 52(2), 381–389.

    Google Scholar 

  • Vázquez-Cano, E. (2014). Mobile distance learning with smartphones and apps in higher education. Educational Sciences: Theory and Practice, 14(4), 1505–1520.

    Google Scholar 

  • Venkatesh, V. (2006). Where to go from here? Thoughts on future directions for research on individual-level technology adoption with a focus on decision making. Decision Sciences, 37(4), 497–518.

    Google Scholar 

  • Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27, 451–481.

    Google Scholar 

  • Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: U.S. Vs. China. Journal of Global Information Technology Management, 13(1), 5–27. https://doi.org/10.1080/1097198x.2010.10856507.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.

    Google Scholar 

  • Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118.

    Google Scholar 

  • Williams, M. D., Dwivedi, Y. K., Lal, B., & Schwarz, A. (2009). Contemporary trends and issues in IT adoption and diffusion research. Journal of Information Technology, 24(1), 1–10.

    Google Scholar 

  • Williams, P. W. (2009). Assessing mobile learning effectiveness and acceptance (Doctoral dissertation, The George Washington University).

  • Windahl, S. (1981). Uses and gratifications at the crossroads. Mass communication review yearbook, 2(2), 174–185.

    Google Scholar 

  • Wong, W. T., & Huang, N. T. N. (2011). The effects of e-learning system service quality and users’ acceptance on organizational learning. International Journal of Business and Information, 6(2).

  • Xu, H., & Gupta, S. (2009). The effects of privacy concerns and personal innovativeness on potential and experienced customers’ adoption of location-based services. Electronic Markets, 19(2–3), 137–149.

    Google Scholar 

  • Xu, Y., Zhang, W., Bao, H., Zhang, S., & Xiang, Y. (2019). A SEM–neural network approach to predict customers’ intention to purchase battery electric vehicles in China’s Zhejiang Province. Sustainability, 11(11), 3164.

    Google Scholar 

  • Yang, J. (2005). Knowledge integration and innovation: Securing new product advantage in high technology industry. The Journal of High Technology Management Research, 16(1), 121–135.

    Google Scholar 

  • Yang, S. (2013). Understanding undergraduate students' adoption of mobile learning model: A perspective of the extended UTAUT2. Journal of Convergence Information Technology, 8(10), 969.

    Google Scholar 

  • Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129–142.

    Google Scholar 

  • Yeap, J. A., Ramayah, T., & Soto-Acosta, P. (2016). Factors propelling the adoption of m-learning among students in higher education. Electronic Markets, 26(4), 323–338.

  • Zhong, X., & Tan, F. B. (2017). Employees' intention to continue using organizational microblogs: A Uses & Gratification Perspective. In CONF-IRM (p. 10).

Download references

Author information

Authors and Affiliations

  1. Gurugram, India

    Sadhna Shukla

Authors
  1. Sadhna Shukla
    View author publications

    You can also search for this author inPubMed Google Scholar

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 9 Survey Items
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shukla, S. M-learning adoption of management students’: A case of India. Educ Inf Technol 26, 279–310 (2021). https://doi.org/10.1007/s10639-020-10271-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-020-10271-8

Keywords