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Acceptance of Information and Communication Technologies in Education: An Investigation Into University Students' Intentions to Use Mobile Educational Apps

Authors:

Victor ChangAuthors Info & Claims

International Journal of Enterprise Information Systems, Volume 15, Issue 1

Pages 24 - 44

https://doi.org/10.4018/IJEIS.2019010102

Published: 01 January 2019 Publication History

Abstract

This article explores the influential factors of acceptance of information communication technologies in high educational institutes. using intentions of the mobile educational information system. Based on available adoption models and theories, a research model was proposed and the data from the 250 questionnaires of Chinese students from Chinese and overseas colleges was analyzed by a quantitative method PLS-SEM method, indicating several factors influencing the use of mobile educational apps. This study was conducted to check the possible changes in these influential factors because some authors pointed out that there might be some possible differences in different countries, fields and types of IT. The results show that student status quo bias will reduce their motivation in using mobile educational apps; their perceived task-technology fit will positively influence their perceived usefulness and perceived ease of use; and students' perceived descriptive norms of using mobile apps will positively affect their adoption intentions. The study verifies the validity of technology acceptance model, the perceived task-technology fit in explaining technology using behavior. Additionally, the study examines the effect of status quo bias and the mechanism of how task-technology fitness, social norms and status quo bias influence adoption intentions. Finally, study inspires some new research points from the perspective of demographic variables. The study will also help educators and designers to understand the antecedents of acceptance of mobile educational system and promote the quality of education.

References

[1]

Abdullah, F., Ward, R., & Ahmed, E. 2016. Investigating the influence of the most commonly used external variables of TAM on students' Perceived Ease of Use PEOU and Perceived Usefulness PU of e-portfolios . Computers in Human Behavior, 63, 75-90.

Digital Library

[2]

Ajzen, I. 1985. From intentions to actions: A theory of planned behavior. In Action Control: From Cognition to Behavior pp. 11-39.

[3]

Ajzen, I. 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes, 502, 179-211. .

[4]

Ajzen, I., & Fishbein, M. 1973. Attitudinal and normative variables as predictors of specific behavior . Journal of Personality and Social Psychology, 271, 41-57.

[5]

Bagozzi, R., & Yi, Y. 1988. On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 161, 74-94.

[6]

Bass, F. M. 1969. A new product growth for model consumer durables . Management Science, 155, 215-227.

Digital Library

[7]

Bass, F. M., Krishnan, T. V., & Jain, D. C. 1994. Why the Bass model fits without decision variables . Marketing Science, 133, 203-223.

Digital Library

[8]

Benbasat, I., & Dexter, A. S. 1986. An investigation of the effectiveness of color and graphical information presentation under varying time constraints . Management Information Systems Quarterly, 101, 59-83.

Digital Library

[9]

Benbasat, I., Dexter, A. S., & Todd, P. 1986. An experimental program investigating color-enhanced and graphical information presentation: An integration of the findings . Communications of the ACM, 2911, 1094-1105.

Digital Library

[10]

Benford, T. L., & Hunton, J. E. 2000. Incorporating information technology considerations into an expanded model of judgment and decision making in accounting . International Journal of Accounting Information Systems, 11, 54-65.

[11]

Bhattacherjee, A. 2001. Understanding information systems continuance: An expectation-confirmation model . Management Information Systems Quarterly, 253, 351-370.

Digital Library

[12]

Blechar, J., Constantiou, I., & Damsgaard, J. 2006. Exploring the influence of reference situations and reference pricing on mobile service user behavior . European Journal of Information Systems, 153, 285-291.

[13]

Bogart, W., & Wichadee, S. 2015. Exploring students' intention to use LINE for academic purposes based on technology acceptance model . International Review of Research in Open and Distributed Learning, 163, 65-85.

[14]

Burmeister, K., & Schade, C. 2005. Status Quo Bias versus Variety Seeking . Marketing: Journal of Research and Management, 11, 14-25.

[15]

Burt, J. 2010. Global IT spending to grow 5.3 percent in 2010: Gartner. Eweek. Retrieved from eWeek.com. http://www.eweek.com/c/a/IT-Infrastructure/Global-IT-Spending-to-Grow-53-Percent-in-2010-Gartner-839373/

[16]

Chandra, S., Srivastava, S., & Theng, Y. 2010. Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis . Communications of the Association for Information Systems, 27, 561-588.

[17]

Chau, P. 1996. An empirical investigation on factors affecting the acceptance of CASE by systems developers . Information & Management, 306, 269-280.

Digital Library

[18]

Chau, P. Y. K., & Lai, V. S. K. 2003. An empirical investigation of the determinants of user acceptance of internet banking . Journal of Organizational Computing and Electronic Commerce, 132, 123-146.

[19]

Chen, L.-D., Gillenson, D. L., & Sherrell, M. 2002. Enticing on-line consumers: An extended technology acceptance perspective . Information & Management, 398, 705-719.

Digital Library

[20]

Cialdini, R. B., Kallgren, C. A., & Reno, R. R. 1991. A focus theory of normative conduct: A theoretical refinement and reevaluation of the role of norms in human behavior . Advances in Experimental Social Psychology, 24, 201-234.

[21]

Cialdini, R. B., Reno, R. R., & Kallgren, C. A. 1990. A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public place . Journal of Personality and Social Psychology, 586, 1015-1026.

[22]

Conti, V., Militello, C., Sorbello, F., & Vitabile, S. 2009. A multimodal technique for an embedded fingerprint recognizer in mobile payment systems . Mobile Information Systems, 52, 105-124.

Digital Library

[23]

Cronin, J. J.Jr, Brady, M. K., & Hult, G. T. M. 2000. Assessing the Effects of Quality, Value, and Customer Satisfaction on Customer Behavioral Intentions in Service Environments . Journal of Retailing, 762, 193-218.

[24]

Davis, F. 1993. User acceptance of information technology system characteristics, user perceptions and behavioral impacts . International Journal of Man-Machine Studies, 383, 475-487.

Digital Library

[25]

Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology . Management Information Systems Quarterly, 133, 319-339.

Digital Library

[26]

Dishaw, M. T., & Strong, D. M. 1999. Extending the technology acceptance model with task-technology fit constructs . Information & Management, 361, 9-21.

[27]

Dlodlo, N. 2009. Access to ICT education for girls and women in rural South Africa: A case study . Technology in Society, 312, 168-175.

[28]

Durresi, A., & Denko, M. 2009. Advances in wireless networks . Mobile Information Systems, 51, 1-3.

Digital Library

[29]

Falk, T., Schepers, J., Hammerschmidt, M., & Bauer, H. 2007. Identifying Cross-Channel Dissynergies for Multichannel Service Providers . Journal of Service Research, 102, 143-160.

[30]

Fishbein, M., & Ajzen, I. 1975. Belief, attitude, intention and Behavior: An introduction to theory and research. Addison-Wesley.

[31]

Fornell, C., & Larcker, D. F. 1981. Evaluating structural equation models with unobservable variables and measurements error . JMR, Journal of Marketing Research, 184, 39-50.

[32]

GaoS.KrogstieJ.GransætherP. 2008. Mobile services acceptance model. In International conference on convergence and hybrid information technology pp. 446-453.

Digital Library

[33]

Gao, S., Krogstie, J., & Siau, K. 2011. Developing an instrument to measure the adoption of mobile services . Mobile Information Systems, 71, 45-67.

Digital Library

[34]

Gefen, D., Karahanna, E., & Straub, D. 2003. Trust and TAM in online shopping: An integrated model . Management Information Systems Quarterly, 271, 51-90.

Digital Library

[35]

Goodhue, D. L. 1995. Understanding user evaluations of information systems . Management Science, 4112, 1827-1844.

[36]

Goodhue, D. L., & Thompson, R. L. 1995. Task-technology fit and individual performance . Management Information Systems Quarterly, 192, 213-236.

Digital Library

[37]

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. 2017. A primer on partial least squares structural equation modeling PLS-SEM. Sage Publications, Inc. Retrieved from: https://liverpool.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat00003a&AN=lvp.b5097371&site=eds-live&scope=site

Digital Library

[38]

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. 2012. An assessment of the use of partial least squares structural equation modeling in marketing research . Journal of the Academy of Marketing Science, 403, 414-433.

[39]

Harland, P., Staats, H., & Wilke, H. A. 1999. Explaining pro-environmental intention and behavior by personal norms and the theory of planned behavior . Journal of Applied Social Psychology, 2912, 2505-2528.

[40]

Henseler, J., Ringle, C. M., & Sarstedt, M. 2014. A new criterion for assessing discriminant validity in variance-based structural equation modeling . Journal of the Academy of Marketing Science, 431, 115-135.

[41]

Hew, J.-J., Lee, V.-H., Ooi, K.-B., & Wei, J. 2015. What catalyses mobile apps usage intention: An empirical analysis . Industrial Management & Data Systems, 1157, 1269-1291.

[42]

Hopper, J. R., & Nielsen, J. M. 1991. Recycling as altruistic behavior normative and behavioral strategies to expand participation in a community recycling program . Environment and Behavior, 232, 195-220.

[43]

Horowitz, J. K., & McConnell, K. E. 2002. A Review of WTA/WTP Studies . Journal of Environmental Economics and Management, 443, 426-447.

[44]

Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. 1997. Personal computing acceptance factors: A structural equation model . Management Information Systems Quarterly, 213, 279-302.

Digital Library

[45]

Jones, C., Connolly, M., Gear, A., & Read, M. 2002. Group interactive learning with group process support technology . British Journal of Educational Technology, 325, 571-586.

[46]

Julien, C., & Roman, G. 2006. Ego spaces: Facilitating rapid development of context-aware mobile applications . IEEE Transactions on Software Engineering, 325, 281-298.

Digital Library

[47]

Kahneman, D., Knetsch, J. L., & Thaler, R. H. 1991. Anomalies: The endowment effect, loss aversion, and status quo bias . The Journal of Economic Perspectives, 51, 193-206.

[48]

Kauffman, R. J., & Techatassanasoontorn, A. A. 2009. Understanding early diffusion of digital wireless phones . Telecommunications Policy, 338, 432-450.

Digital Library

[49]

Kim, H. J., & Rha, J. Y. 2018. Predicting the Drivers of the Intention to Use Mobile Learning in South Korea . International Journal of Interactive Mobile Technologies, 121, 116-132.

[50]

Kim, S. H., Mims, C., & Holmes, K. 2006. An Introduction to Current Trends and Benefits of Mobile Wireless Technology Use in Higher Education . AACE Journal, 141, 77-100. Retrieved from https://www.learntechlib.org/primary/p/6158/.

[51]

Koç, T., Turan, A. H., & Okursoy, A. 2016. Acceptance and usage of a mobile information system in higher education: An empirical study with structural equation modeling . International Journal of Management Education, 143, 286-300.

[52]

Kumar, R. 2008. Convergence of ICT and Education . World Academy of Science, Engineering and Technology, 40, 556-559.

[53]

Legris, P., Ingham, J., & Collerette, P. 2003. Why do people use information technology? A critical review of the technology acceptance model . Information & Management, 403, 191-204.

Digital Library

[54]

Lin, T., & Huang, C. 2008. Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit . Information & Management, 456, 410-417.

Digital Library

[55]

Liu, Y., Li, H., & Carlsson, C. 2010. Factors driving the adoption of m-learning: An empirical study . Computers & Education, 553, 1211-1219.

Digital Library

[56]

Luarn, P., & Lin, H.-H. 2005. Toward an understanding of the behavioral intention to use mobile banking . Computers in Human Behavior, 216, 873-891.

[57]

March, J. G., & Herbert, A.S. 1993. Organizations. Cambridge, MA: Blackwell.

[58]

Marchewka, J., & Liu, C. 2007. An application of the UTAUT model for understanding student perceptions using course management software. Communications of the IIMA, 72, 93-104. Retrieved from: http://scholarworks.lib.csusb.edu/ciima/vol7/iss2/10

[59]

Mathieson, K. 1991. Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior . Information Systems Research, 841, 123-136.

Digital Library

[60]

McKenzie, J. 2001. The unwired classroom: Wireless computers come of age . Educational Technology, 104. Retrieved from http://www.fno.org/jan01/wireless.html.

[61]

Mital, M., Chang, V., Choudhary, P., Papa, A., & Pani, A. K. 2017. Adoption of Internet of Things in India: A test of competing models using a structured equation modeling approach . Technological Forecasting and Social Change, 136, 339-346.

[62]

Montoya-Weiss, M. M., Voss, G. B., & Grewal, D. 2003. Determinants of online channel use and overall satisfaction with a relational multichannel service provider . Journal of the Academy of Marketing Science, 314, 448-458.

[63]

Motaghian, H., Hassanzadeh, A., & Karimzadgan, D. 2013. Factors affecting university instructors' adoption of web-based learning systems: Case study of Iran . Computers & Education, 61, 158-167.

Digital Library

[64]

Neighbors, C., Lee, C. M., Lewis, M. A., Fossos, N., & Larimer, M. E. 2007. Are social norms the best predictor of outcomes among heavy-drinking college students? Journal of Studies on Alcohol and Drugs, 684, 556-565. 17568961.

[65]

Nguyen, T. N., Cao, T. K., Dang, P. L., & Nguyen, H. A. 2016. Predicting consumer intention to use mobile payment services: Empirical evidence from Vietnam . International Journal of Marketing Studies, 81, 117-124.

[66]

O'Cass, A., & Fenech, T. 2003. Web retailing adoption: Exploring the nature of internet users' web retailing behavior . Journal of Retailing and Consumer Services, 102, 81-94.

[67]

Olsen, L. L., & Johnson, M. D. 2003. Service Equity, Satisfaction, and Loyalty: From Transaction-Specific to Cumulative Evaluations . Journal of Service Research, 53, 184-195.

[68]

Oye, N., Iahad, N., & Ab. Rahim, N. 2014. The history of UTAUT model and its impact on ICTs acceptance and usage by academicians . Education and Information Technologies, 191, 251-270.

Digital Library

[69]

Paluck, E. L., Ball, L., Poynton, C., & Sieloff, S. 2010. Social norms marketing aimed at gender based violence: A literature review and critical assessment. New York: International Rescue Committee.

[70]

Park, H. S., Klein, K. A., Smith, S., & Martell, D. 2009. Separating subjective norms, university descriptive and injunctive norms, and US descriptive and injunctive norms for drinking behavior intentions . Health Communication, 248, 746-751. 20183383.

[71]

Pavlou, P. A. 2003. Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model . International Journal of Electronic Commerce, 73, 69-103.

Digital Library

[72]

Rai, A., Ravichandran, T., & Samaddar, S. 1998. How to anticipate Internet's global diffusion . Communications of the ACM, 4110, 97-106.

Digital Library

[73]

Ritov, I., & Baron, J. 1992. Status-Quo and Omission Bias . Journal of Risk and Uncertainty, 51, 49-61.

[74]

Rogers, E. M. 1995. Diffusion of Innovations: modifications of a model for telecommunications. In Die Diffusion von Innovationen in der Telekommunikation pp. 25-38. Springer.

[75]

Sachs, J. D. 2008. The End of Poverty: Economic Possibilities for Our Time. New York, NY: Penguin Press.

[76]

Safar, M., Sawwan, H., Taha, M., & Al-Fadhli, T. 2009. Virtual social networks online and mobile systems . Mobile Information Systems, 53, 233-253.

Digital Library

[77]

Samuelson, W., & Zeckhauser, R. 1988. Status Quo Bias in Decision Making . Journal of Risk and Uncertainty, 11, 7-59.

[78]

Saxenian, A. L. 1994. Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Boston, MA: Harvard University Press.

[79]

Saxenian, A. L. 2007. The New Argonauts: Regional Advantage in a Global Economy. Boston, MA: Harvard University Press.

[80]

Schierz, P., Schilke, O., & Wirtz, B. 2010. Understanding consumer acceptance of mobile payment services: An empirical analysis . Electronic Commerce Research and Applications, 93, 209-216.

Digital Library

[81]

Schwartz, S. H. 1977. Normative influences on altruism . Advances in Experimental Social Psychology, 10, 221-279.

[82]

Shim, M. K., & Shim, S. J. 2001. Mobile computing in higher education: Faculty perceptions of benefits and barriers . Journal of Educational Technology Systems, 294, 345-354.

[83]

Steg, L., & Vlek, C. 2009. Encouraging pro-environmental behavior: An integrative review and research agenda . Journal of Environmental Psychology, 293, 309-317.

[84]

Swett, C. 2002. College students' use of mobile wireless-internet connections becomes more common. Washington, DC: Knight Ridder Tribune Business News.

[85]

ThorntonP.HouserC. 2001. Learning on the move: Vocabulary study via email and mobile phone SMS. In Proceedings of ED-MEDIA, Tampere, Finland pp. 1846-1847.

[86]

Vega-Hernández, M., Patino-Alonso, M., & Galindo-Villardón, M. 2018. Multivariate characterization of university students using the ICT for learning . Computers & Education, 121, 124-130.

[87]

Venkatesh, V. 2000. Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model . Information Systems Research, 114, 342-365.

Digital Library

[88]

Venkatesh, V., & Davis, F. D. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies . Management Science, 462, 186-204.

Digital Library

[89]

Venkatesh, V., Morris, M., Davis, G., & Davis, F. 2003. User acceptance of information technology: Toward a unified view . Management Information Systems Quarterly, 273, 425-478.

Digital Library

[90]

Venkatesh, V., Thong, J., & Xu, X. 2012. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology . Management Information Systems Quarterly, 361, 157-178.

Digital Library

[91]

Wu, B., & Chen, X. 2017. Continuance intention to use MOOCs: Integrating the technology acceptance model TAM and task technology fit TTF model . Computers in Human Behavior, 67, 221-232.

Digital Library

[92]

Wu, J. H., & Wang, S. C. 2005. What drives mobile commerce? An empirical evaluation of the revised technology acceptance model . Information & Management, 425, 719-729.

Digital Library

[93]

Yen, D. C., Wu, C. S., Cheng, F. F., & Huang, Y. W. 2010. Determinants of users' intention to adopt wireless technology: An empirical study by integrating TTF with TAM . Computers in Human Behavior, 265, 906-915.

Digital Library

[94]

Yoon, C. 2011. Theory of planned behavior and ethics theory in digital piracy: An integrated model . Journal of Business Ethics, 1003, 405-417.

[95]

Yoon, C. 2018. Extending the TAM for Green IT: A normative perspective . Computers in Human Behavior, 83, 129-139.

Digital Library

[96]

You, I., & Hara, T. 2010. Mobile and wireless networks . Mobile Information Systems, 61, 1-3.

Digital Library

[97]

Zigurs, I., Buckland, B. K., Connolly, J. R., & Wilson, E. V. 1999. A test of task-technology fit theory for group support systems . ACM SIGMIS Database, 303-4, 34-50.

Digital Library

Cited By

Waheed AWang DSarwar-A Alam M(2020)Impact of Digital Marketing on Consumers' Impulsive Online Buying Tendencies With Intervening Effect of Gender and EducationInternational Journal of Enterprise Information Systems10.4018/IJEIS.201907010315:3(44-59)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.4018/IJEIS.2019070103

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cover image International Journal of Enterprise Information Systems

International Journal of Enterprise Information Systems Volume 15, Issue 1

January 2019

134 pages

ISSN:1548-1115

EISSN:1548-1123

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IGI Global

United States

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Published: 01 January 2019

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Waheed AWang DSarwar-A Alam M(2020)Impact of Digital Marketing on Consumers' Impulsive Online Buying Tendencies With Intervening Effect of Gender and EducationInternational Journal of Enterprise Information Systems10.4018/IJEIS.201907010315:3(44-59)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.4018/IJEIS.2019070103

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