THE CONUNDRUM OF INTERNET OF THINGS ADOPTION IN HIGHER EDUCATIONAL INSTITUTIONS
DOI:
https://doi.org/10.32770/rbaos.vol267-94Keywords:
Internet of things, digital mindset, trust, attitude, adoption behaviorAbstract
This study aims to extend the evaluation and understanding of an individuals’ adoption intention towards the Internet of Things (IoT) in a higher educational context and also to assess the relationship between Perceived Benefits, Digital Culture and Mindset, Technological Motivator, Technological Inhibitor and Attitude and how these factors relate to the adoption of Internet of Things (IoT) behavior. The research employed a quantitative and cross-sectional approach. A sample of 202 respondents from a Malaysian educational institution was collected through a self-designed questionnaire based on a snowball sampling technique. The data collected were analyzed using SmartPLS. The results indicate that attitude, technological motivator and digital mindset have a significant effect on the IoT adoption intention. Of these, attitude has the greatest influence with regard to the decision to adopt any IoT products or services. Digital mindset was a salient factor that explained user’s adoption intention behaviour on IoT technologies. Perceived benefits, however, showed insignificant direct effect whereas the technological inhibitor perspective affects the IoT adoption intention through attitude factor. The research provides further evidence that attitude and digital mindset built up within the individual are crucial elements to be considered in justifying the adoption behavior of IoT. The research findings show how the adoption of IoT could help academic staff and students leverage technologies' benefits to improve work and academic performance. It also highlights the importance of trust and builds the required attitude to support the technology to industry players. This study did not account for motivators such as incentives or influence from authority figures (leaders, top management, government and policy maker) as well as environmental conditions, namely the readiness of the infrastructure and the commonality of the usage in the social group.
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Aldowah, H., Rehman, S. U., Ghazal, S., & Umar, I. N. (2017, January). Internet of Things in higher education: a study on future learning. Journal of Physics: Conference Series, 892(1), 1-10.
AlHogail, A. (2018). Improving IoT technology adoption through improving consumer trust. Technologies, 6(3), 64-80.
Alsaadi, E., & Tubaishat, A. (2015). Internet of things: features, challenges, and vulnerabilities. International Journal of Advanced Computer Science and Information Technology, 4(1), 1-13.
Atif, A., Richards, D., Busch, P., & Bilgin, A. (2015). Assuring graduate competency: a technology acceptance model for course guide tools. Journal of Computing in Higher Education, 27(2), 94-113.
Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787-2805.
Bailey, J. S., & Burch, M.R. (2016). Ethics for Behavior Analysts, 3rd ed. United States: Routledge.
Bass, B. M., Avolio, B. J., Jung, D. I., & Berson, Y. (2003). Predicting unit performance by assessing transformational and transactional leadership. Journal of applied psychology, 88(2), 207-218.
Benke, V. (2013). The Digital Mindset: A theoretical discussion. (Master Thesis). Retrieved from http://projekter.aau.dk/projekter/files/77247472/Vivienne_Benke_Masters_thesis.pdf
Bienhaus, F., & Haddud, A. (2018). Procurement 4.0: factors influencing the digitisation of procurement and supply chains. Business Process Management Journal, 24(4), 965-984.
Boos, D., Guenter, H., Grote, G., & Kinder, K. (2012). Controllable accountabilities: The Internet of Things and its challenges for organisations. Behaviour & Information Technology, 32(5), 449-467.
Buchanan, E. A., & Hvizdak, E. E. (2009). Online survey tools: Ethical and methodological concerns of human research ethics committees. Journal of Empirical Research on Human Research Ethics, 4(2), 37-48.
Carter, S., & Yeo, A. C. M. (2016). Mobile apps usage by Malaysian business undergraduates and postgraduates. Internet Research., 26(3), 733-757.
Cegarra-Navarro, J. G., Garcia-Perez, A., & Moreno-Cegarra, J. L. (2014). Technology knowledge and governance: Empowering citizen engagement and participation. Government Information Quarterly, 31(4), 660-668.
Chen, L, Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & management, 39(8), 705-719.
Chin, W. W. (1998). The partial least squares approach for structural equation modeling, 1st ed., Hillsdale : Lawrence Erlbaum Associates Publishers.
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information systems research, 14(2), 189-217.
Choi, J., & Kim, S. (2016). Is the smartwatch an IT product or a fashion product? A study on factors affecting the intention to use smartwatches. Computers in Human Behavior, 63, 777-786.
Christensen, L. B., Johnson, B., Turner, L. A., & Christensen, L. B. (2015). Research Methods, Design, and Analysis, 12th ed. London: Pearson.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences, 2nd ed. Hillsdale: Lawrence Earlbaum Associates.
Cozby, P.C, & Bates, S.C. (2012). Methods in behavioral research, 11th ed. Boston: McGraw Hill Higher Education.
Creswell, J. W., & Creswell, J. D. (2003). Research design: Qualitative, quantitative, and mixed methods approaches, 2nd ed. Thousand Oaks: Sage publications.
Creswell, J.W. (2014). Research Design: Qualitative, quantitative and Mixed Methods Approaches, 4th ed. Thousand Oaks: SAGE Publications.
Daniel, W. W. (1975). Nonresponse in sociological surveys: A review of some methods for handling the problem. Sociological Methods & Research, 3(3), 291-307.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
Diekmann, A. (2007). Not the first digit! using benford’s law to detect fraudulent scientif ic data. Journal of Applied Statistics, 34(3), 321-329.
Field, A.P. (2013). Discovering statistics using IBM SPSS Statistics: and sex and drugs and rock ‘n’ roll, 4th ed. London : SAGE Publications,.
Fisher, M. J., & Marshall, A. P. (2009). Understanding descriptive statistics. Australian Critical Care, 22(2), 93-97.
Fusilier, M., & Durlabhji, S. (2005). An exploration of student internet use in India. Campus-Wide Information Systems, 22(4), 233-246.
Gao, L., & Bai, X. (2014). An empirical study on continuance intention of mobile social networking services. Asia Pacific Journal of Marketing and Logistics, 26(2), 168-189.
Geissbauer, R., Vedso, J., & Schrauf, S. (2016). Industry 4.0: Building the digital enterprise. Retrieved from PwC Website: https://www. pwc. com/gx/en/industries/industries-4.0/landing-page/industry-4.0-building-your-digital-enterprise-april-2016. pdf.
Goethals, G. R., Sorenson, G. J., & Burns, J. M. (Eds.). (2004). Encyclopedia of leadership. California: Sage Publications.
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7), 1645-1660.
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 1st ed. Thousand Oaks: SAGE Publications.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range planning, 46(1-2), 1-12.
Hair, J.F., Sarstedt, M. & Ringle, C.M. (2017). Partial Least Squares Structural Equation Modeling, Chapter 15: Handbook of Market Research. New York: Springer Publishing.
Hawk, E.T., Matrisian, L.M., Nelson, W.G., Dorfman, G.S., Stevens, L., Kwok, J., Viner, J., Hautala, J., & Grad, O. (2008). The Translational Research Working Group Developmental Pathways: Introduction and Overview. Clinical Cancer Research, 14(18), 5664–5671.
Heneman, G.H. & Patterson, D.G. (1949). Refusal rates and interviewer quality. International Journal of Opinion and Attitude Research, 3(3), 392-398.
Henseler, J., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17(1), 82-109.
Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565-580.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20(1), 277–319.
Howard, P.N. (2015). Pax Technica: How the Internet of Things may set us free or lock us up, 1st ed. New Haven: Yale University Press.
Hoy, WK and Curt, A 2016, Quantitative research in education: A primer, 2nd ed. Los Angeles: SAGE Publications.
Hsu, C. L., & Lin, J. C. C. (2016). An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior, 62, 516-527.
Hsu, H. Y., & Tsou, H. T. (2011). Understanding customer experiences in online blog environments. International Journal of Information Management, 31(6), 510-523.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic management journal, 20(2), 195-204.
Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence that structural equations models perform better than regressions. Journal of consumer psychology, 17(2), 139-153.
InvestKL 2019, Malaysia gets serious on developing IoT ecosystem, http://www.investkl.gov.my/Relevant_News-@-Malaysia_Gets_Serious_on_Developing_ IoT_ Ecosystem.aspx
Jahanmir, S. F., & Cavadas, J. (2018). Factors affecting late adoption of digital innovations. Journal of business research, 88(1), 337-343.
Kaiser, H. F., & Rice, J. (1974). Little jiffy, mark IV. Educational and psychological measurement, 34(1), 111-117.
Karahoca, A., Karahoca, D., & Aksöz, M. (2018). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47(4), 742-770.
Khan, R., Khan, S. U., Zaheer, R., & Khan, S. (2012, December). Future internet: the internet of things architecture, possible applications and key challenges. In 2012 10th international conference on frontiers of information technology (pp. 257-260). IEEE.
Kim, J. B. (2012). An empirical study on consumer first purchase intention in online shopping: integrating initial trust and TAM. Electronic Commerce Research, 12(2), 125-150.
Kline, R. B. (2016). Principles and practice of Structural Equation Modeling, 4th ed. New York: The Guilford Press.
Lee, W. I., Chiu, Y. T., Liu, C. C., & Chen, C. Y. (2011). Assessing the effects of consumer involvement and service quality in a self‐service setting. Human Factors and Ergonomics in Manufacturing & Service Industries, 21(5), 504-515.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology, 22(140), 5-55.
Mahbub, M. (2020). A smart farming concept based on smart embedded electronics, internet of things and wireless sensor network. Internet of Things, 9(1), 1-30.
Makkonen, P. (1997, June). Does collaborative hypertext support better engagement in learning of the basics in informatics?. In Proceedings of the 2nd conference on Integrating technology into computer science education (pp. 130-132).
Marshall, G., & Jonker, L. (2010). An introduction to descriptive statistics: A review and practical guide. Radiography, 16(4), 1-7.
Miltgen, C. L., Popovič, A., & Oliveira, T. (2013). Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context. Decision Support Systems, 56(1), 103-114.
Newman, D. (2019). Breaking down the six pillars of digital transformation from the CEO’s perspective. Retrieved from: https://www.forbes.com/sites/danielnewman/2019/01/02/ breaking-down-the-6-pillars-of-digital-transformation-from-the-ceos-perspective/ #4998898b6fdc.
O’Brien, H. L., & Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American society for Information Science and Technology, 59(6), 938-955.
Ozkan, M., & Solmaz, B. (2015). The changing face of the employees–generation Z and their perceptions of work (a study applied to university students). Procedia Economics and Finance, 26(1), 476-483.
Park, E., Cho, Y., Han, J., & Kwon, S. J. (2017). Comprehensive approaches to user acceptance of Internet of Things in a smart home environment. IEEE Internet of Things Journal, 4(6), 2342-2350.
Peppet, S. R. (2014). Regulating the internet of things: first steps toward managing discrimination, privacy, security and consent. Tex. L. Rev., 93(1), 85-178.
Porter, M. E., & Heppelmann, J. E. (2015). How Smart, Connected Products Are Transforming Companies. Harvard Business Review, 93(10), 96–114.
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior research methods, instruments, & computers, 36(4), 717-731.
Proctor, R.W. & Capaldi, E.J. (2008). Why science matters: Understanding the methods of psychological research, 2nd ed. Malden: Blackwell Publishing.
Ramayah, T., Hwa, C.J., Chuah, F., Ting, H. & Memon, M.A. (2016). Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0: An updated and practical
Ridley, M. (2015). The evolution of everything – How new ideas emerge, 1st ed. New York: Harper Perennial.
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), 3-14.
Rogers, E.M. (1995). Diffusion of innovations, 4th ed. New York: The Free Press.
Rogers, E.M. (2003). Diffusion of innovations, 5th ed., New York: The Free Press.
Rogers, E. M. (2004). A prospective and retrospective look at the diffusion model. Journal of health communication, 9(S1), 13-19.
Salvo, M. J. (2002). Critical engagement with technology in the computer classroom. Technical Communication Quarterly, 11(3), 317-337.
Schmidt, E. & Rosenberg, J. (2014). How Google works, 1st ed. New York: Grand Central Publishing.
Schwab, K. (2016). The Fourth Industrial Revolution. Quality Management Journal, 25(2), 108–109.
Skelly, T., Fries, K., Linnett, B., Nass, C., & Reeves, B. (1994, April). Seductive interfaces: Satisfying a mass audience. In Proceedings of the ACM CHI 94 Human Factors in Computing Systems Conference, 24-28 April 1994, Boston (pp. 359-360).
Sosik, J. J., Kahai, S. S., & Piovoso, M. J. (2009). Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group & Organization Management, 34(1), 5-36.
Stamm, B.V. (2008). Managing innovation, design and creativity, 2nd ed. New Jersey: Wiley.
Sundmaeker, H., Guillemin, P., Friess, P., & Woelfflé, S. (2010). Vision and challenges for realising the Internet of Things. Cluster of European Research Projects on the Internet of Things, European Commision, 3(3), 34-36.
Tianbo, Z. (2012, November). The internet of things promoting higher education revolution. In Proceedings of the 2012 Fourth International Conference on Multimedia Information Networking and Security (pp. 790-793). IEEE.
Heinis, T. B., Hilario, J., & Meboldt, M. (2018). Empirical study on innovation motivators and inhibitors of Internet of Things applications for industrial manufacturing enterprises. Journal of Innovation and Entrepreneurship, 7(1), 10.
Trott, P. (2017). Innovation management and new product development, 6th ed. London: Pearson.
Uckelmann, D., Harrison, M., & Michahelles, F. (2011). An architectural approach towards the future Internet of Thing, 1st ed. New York: Springer.
Uzelac, A. (2010). Digital culture as a converging paradigm for technology and culture: Challenges for the culture sector. Digithum, 12, 28-35.
Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0–a glimpse. Procedia Manufacturing, 20, 233-238.
Veryzer, R. W. (2003). Marketing and the development of innovative new products. In The International Handbook on Innovation (pp. 845-855). Amsterdam: Elsevier Science Ltd.
Wang, X., Li, J., Yang, M., Chen, Y., & Xu, X. (2018). An empirical study on the factors influencing mobile library usage in IoT era. Library Hi Tech, 36(4), 605-621.
Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & management, 42(5), 719-729.
Mun, Y. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350-363.
Yoon, J. S. (2002). Teacher characteristics as predictors of teacher-student relationships: Stress, negative affect, and self-efficacy. Social Behavior and Personality: an international journal, 30(5), 485-493.
Zikmund, W.G., Babin, B.J., Carr, J.C. & Griffin, M. (2010). Business Research Methods, 8th ed., Boston: Cengage Learning.
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