Skip to main content
SpringerLink
Log in

Utilitarian and experiential aspects in acceptance models for learning technology

Educational Technology Research and Development volume 69, pages 627–654 (2021)Cite this article

Abstract

Identifying and understanding factors influencing the adoption of a specific technology in various educational settings is critical for maximizing the effectiveness of using the technology. Research based on Technology Acceptance Model (TAM) in education provides an extensive insight into constructs that influence the adoption of learning technology. Most of these constructs represent either the utilitarian or the experiential aspect, e.g., self-efficacy and system quality (utilitarian) or satisfaction and perceived enjoyment (experiential). However, no prior review tried to systematize how these aspects have been addressed in different learning contexts. This review investigates to what extent and how these aspects have been addressed in TAM-based studies in general and relative to the contextual factors: types of participants, types of technology, and learning environment factors. Therefore, 112 good-quality articles have been reviewed. 132 constructs that addressed the utilitarian aspect have been classified into categories such as user characteristics, technology characteristics, learning/teaching process characteristics, etc. 64 constructs from the pre-coded categories of ‘social influence’ and ‘experience of use’ addressed the experiential aspect. The utilitarian aspect has been largely studied in some learning contexts (e.g., adult learners and educators/teachers as participants), whereas the experiential aspect is more prominent in some other learning contexts (e.g., students in primary and secondary education as participants, hedonic technology). The review discusses and summarizes the identified research gaps, as well as some implications for future research.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Notes

  1. A period of time might be short (e.g., several days of intensive use or of multiple uses of technology) or long (e.g., use of technology over a semester, a school year or even several years).

References

  • Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256.

    Article  Google Scholar 

  • Abyaa, A., Idrissi, M. K., & Bennani, S. (2019). Learner modelling: systematic review of the literature from the last 5 years. Educational Technology Research and Development, 1-39.

  • Adukaite, A., van Zyl, I., Er, Ş, & Cantoni, L. (2017). Teacher perceptions on the use of digital gamified learning in tourism education: The case of South African secondary schools. Computers & Education, 111, 172–190.

    Article  Google Scholar 

  • Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301–314.

    Article  Google Scholar 

  • Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). The effect of universal design for learning (UDL) application on e-learning acceptance: A structural equation model. The International Review of Research in Open and Distributed Learning, 18(6), 54–87.

    Article  Google Scholar 

  • Beaudry, A., & Pinsonneault, A. (2010). The other side of acceptance: studying the direct and indirect effects of emotions on information technology use. MIS Quarterly, 34(4), 689–710.

    Article  Google Scholar 

  • Bourgonjon, J., De Grove, F., De Smet, C., Van Looy, J., Soetaert, R., & Valcke, M. (2013). Acceptance of game-based learning by secondary school teachers. Computers & Education, 67, 21–35.

    Article  Google Scholar 

  • Cabada, R. Z., Estrada, M. L. B., Hernández, F. G., Bustillos, R. O., & Reyes-García, C. A. (2018). An affective and Web 3.0-based learning environment for a programming language. Telematics and Informatics, 35(3), 611–628.

    Article  Google Scholar 

  • Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.

    Article  Google Scholar 

  • Chow, M. (2016). Determinants of presence in 3D virtual worlds: A structural equation modelling analysis. Australasian Journal of Educational Technology, 32(1).

  • Codish, D., & Ravid, G. (2015). Detecting playfulness in educational gamification through behavior patterns. IBM Journal of Research and Development, 59(6), 6–1.

    Article  Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.

    Article  Google Scholar 

  • Dæhlen, M., & Ure, O. B. (2009). Low-skilled adults in formal continuing education: does their motivation differ from other learners? International Journal of Lifelong Education, 28(5), 661–674.

    Article  Google Scholar 

  • Deci, E., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York, USA: Springer Science & Business Media.

    Book  Google Scholar 

  • Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30.

    Article  Google Scholar 

  • Dicheva, D., Irwin, K., & Dichev, C. (2018). Gamifying with OneUp: For Learning, Grades or Fun?. In International Conference on Games and Learning Alliance (pp. 343-353).

  • Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7), 895–910.

    Article  Google Scholar 

  • Fiore, A. M., & Kim, J. (2007). An integrative framework capturing experiential and utilitarian shopping experience. International Journal of Retail & Distribution Management, 35(6), 421–442.

    Article  Google Scholar 

  • Gan, C. L., & Balakrishnan, V. (2017). Predicting acceptance of mobile technology for aiding student-lecturer interactions: An empirical study. Australasian Journal of Educational Technology, 33(2).

  • Harter, S. (1981). A new self-report scale of intrinsic versus extrinsic orientation in the classroom: Motivational and informational components. Developmental Psychology, 17, 300–312.

    Article  Google Scholar 

  • Harter, S., & Jackson, B. K. (1992). Trait vs. nontrait conceptualizations of intrinsic/extrinsic motivational orientation. Motivation and Emotion, 16, 209–230.

    Article  Google Scholar 

  • Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), 101–123.

    Article  Google Scholar 

  • Hassenzahl, M. (2003). The thing and I: understanding the relationship between user and product. In M. Blythe, & A. Monk (Eds). Funology 2. Human–Computer Interaction Series (pp. 31-42). Cham, Switzerland: Springer.

  • Hassenzahl, M., Schöbel, M., & Trautmann, T. (2008). How motivational orientation influences the evaluation and choice of hedonic and pragmatic interactive products: The role of regulatory focus. Interacting with Computers, 20(4–5), 473–479.

    Article  Google Scholar 

  • Helmefalk, M., & Eklund, A. A. (2018). Fun and Function?-The impact of experiential learning styles on hedonic and utilitarian values in classrooms. Journal of Interdisciplinary Studies in Education, 7(1), 1–18.

    Article  Google Scholar 

  • Hertzum, M. (2010). Images of usability. International Journal of Human-Computer Interaction, 26(6), 567–600.

    Article  Google Scholar 

  • Hornbæk, K., & Hertzum, M. (2017). Technology acceptance and user experience: a review of the experiential component in HCI. ACM Transactions on Computer-Human Interaction (TOCHI), 24(5), 33.

    Article  Google Scholar 

  • Hong, J. C., Hwang, M. Y., Liu, M. C., Tsai, C. R., & Tai, K. H. (2020). Metacognition in covariation reasoning relevant to performance achievement mediated by experiential values in a simulation game. Educational Technology Research and Development, 68, 929–948.

    Article  Google Scholar 

  • Huang, T. K. (2018). How to lessen the effects of user resistance on the adoption of an e-learning environment: screenshot annotation on Flickr. Interactive Learning Environments, 26(4), 506–524.

    Article  Google Scholar 

  • Huang, H. M., & Liaw, S. S. (2018). An analysis of learners’ intentions toward virtual reality learning based on constructivist and technology acceptance approaches. The International Review of Research in Open and Distributed Learning, 19(1), 91–115.

    Article  Google Scholar 

  • Huang, H. M., Liaw, S. S., & Lai, C. M. (2016). Exploring learner acceptance of the use of virtual reality in medical education: A case study of desktop and projection-based display systems. Interactive Learning Environments, 24(1), 3–19.

    Article  Google Scholar 

  • Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13(1), 127–143.

    Article  Google Scholar 

  • Ifinedo, P. (2017). Examining students’ intention to continue using blogs for learning: Perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior, 72, 189–199.

    Article  Google Scholar 

  • ISO. (2010). ISO 9241: Ergonomics of Human-System Interaction - Part 210: Human-Centred Design for Interactive Systems. Geneva, CH: International Standard Organization.

    Google Scholar 

  • Islam, A. N. (2015). The moderation effect of user-type (educators vs students) in learning management system continuance. Behaviour & Information Technology, 34(12), 1160–1170.

    Article  Google Scholar 

  • Karapanos, E., Zimmerman, J., Forlizzi, J., & Martens, J. B. (2009). User experience over time: an initial framework. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 729-738), New York, US: ACM.

  • Kim, B. W., Lee, W. G., Lee, B. R., & Shon, J. G. (2015). Influencing factors in OER usage of adult learners in Korea. The International Review of Research in Open and Distributed Learning, 16(2).

  • Kitchenham B. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering. Version 2.3, EBSE Technical Report, EBSE-2007-01. Keele, UK: Keele University. Retrieved from https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf.

  • Lepper, M. R., Corpus, J. H., & Iyengar, S. S. (2005). Intrinsic and extrinsic motivational orientations in the classroom: Age differences and academic correlates. Journal of educational psychology, 97(2), 184.

    Article  Google Scholar 

  • Mac Callum, K., & Jeffrey, L. (2014). Comparing the role of ICT literacy and anxiety in the adoption of mobile learning. Computers in Human Behavior, 39, 8–19.

    Article  Google Scholar 

  • Malaquias, R. F., Malaquias, F. F., & Hwang, Y. (2018). Understanding technology acceptance features in learning through a serious game. Computers in Human Behavior, 87, 395–402.

    Article  Google Scholar 

  • Mei, B., Brown, G. T., & Teo, T. (2018). Toward an understanding of preservice English as a Foreign Language teachers’ acceptance of computer-assisted language learning 20 in the People’s Republic of China. Journal of Educational Computing Research, 56(1), 74–104.

    Article  Google Scholar 

  • Merhi, M. I. (2015). Factors influencing higher education students to adopt podcast: An empirical study. Computers & Education, 83, 32–43.

    Article  Google Scholar 

  • Moghavvemi, S., Paramanathan, T., Rahin, N. M., & Sharabati, M. (2017). Student’s perceptions towards using e-learning via Facebook. Behaviour & Information Technology, 36(10), 1081–1100.

    Article  Google Scholar 

  • Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374.

    Article  Google Scholar 

  • Moreno, V., Cavazotte, F., & Alves, I. (2017). Explaining university students’ effective use of e-learning platforms. British Journal of Educational Technology, 48(4), 995–1009.

    Article  Google Scholar 

  • O’Brien, H. L. (2010). The influence of hedonic and utilitarian motivations on user engagement: The case of online shopping experiences. Interacting with Computers, 22(5), 344–352.

    Article  Google Scholar 

  • Oluwajana, D., Idowu, A., Nat, M., Vanduhe, V., & Fadiya, S. (2019). The adoption of students’ Hedonic motivation system model to gamified learning environment. Journal of theoretical and applied electronic commerce research, 14(3), 156–167.

    Article  Google Scholar 

  • Online supplementary document. (2019). Data Extraction. https://docs.google.com/spreadsheets/d/1-XBdHWmkIxNa7QAXcj4Cm_7vTdAEEdNINoNsaacDn-M/edit?usp=sharing

  • Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use E-learning. Educational Technology & Society, 12(3), 150–162.

    Google Scholar 

  • Philipsen, B., Tondeur, J., Roblin, N. P., Vanslambrouck, S., & Zhu, C. (2019). Improving teacher professional development for online and blended learning: A systematic meta-aggregative review. Educational Technology Research and Development, 1-30.

  • Pribeanu, C., Balog, A., & Iordache, D. D. (2017). Measuring the perceived quality of an AR-based learning application: a multidimensional model. Interactive Learning Environments, 25(4), 482–495.

    Article  Google Scholar 

  • Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604.

    Article  Google Scholar 

  • Rodríguez-Ardura, I., & Meseguer-Artola, A. (2016). What leads people to keep on e-learning? An empirical analysis of users’ experiences and their effects on continuance intention. Interactive Learning Environments, 24(6), 1030–1053.

    Article  Google Scholar 

  • Rothes, A., Lemos, M. S., & Gonçalves, T. (2017). Motivational profiles of adult learners. Adult Education Quarterly, 67(1), 3–29.

    Article  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.

    Article  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67.

    Article  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.

    Article  Google Scholar 

  • Sadaf, A., Newby, T. J., & Ertmer, P. A. (2016). An investigation of the factors that influence preservice teachers’ intentions and integration of Web 2.0 tools. Educational Technology Research and Development, 64(1), 37–64.

    Article  Google Scholar 

  • Sánchez-Franco, M. J., & Roldán, J. L. (2005). Web acceptance and usage model: A comparison between goal-directed and experiential web users. Internet Research, 15(1), 21–48.

    Article  Google Scholar 

  • Song, Y., & Kong, S. C. (2017). Investigating students’ acceptance of a statistics learning platform using technology acceptance model. Journal of Educational Computing Research, 55(6), 865–897.

    Article  Google Scholar 

  • Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077.

    Article  Google Scholar 

  • Teo T. (2011) Technology Acceptance Research in Education. In Teo T. (ed.) Technology Acceptance in Education. Leiden: SensePublishers.

  • Teo, T., & Wong, S. L. (2013). Modeling key drivers of e-learning satisfaction among student teachers. Journal of Educational Computing Research, 48(1), 71–95.

    Article  Google Scholar 

  • Teo, T., Doleck, T., Bazelais, P., & Lemay, D. J. (2019). Exploring the drivers of technology acceptance: a study of Nepali school students. Educational Technology Research and Development, 1-23.

  • Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use Technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475.

    Article  Google Scholar 

  • Teo, T., Zhou, M., & Noyes, J. (2016). Teachers and technology: development of an extended theory of planned behavior. Educational Technology Research and Development, 64(6), 1033–1052.

    Article  Google Scholar 

  • Traxler, J. (2005). Defining mobile learning. In IADIS International Conference Mobile Learning (pp. 261-266).

  • Van Schaik, P., & Ling, J. (2011). An integrated model of interaction experience for information retrieval in a Web-based encyclopedia. Interacting with Computers, 23(1), 18–32.

    Article  Google Scholar 

  • Voss, K. E., Spangenberg, E. R., & Grohmann, B. (2003). Measuring the hedonic and utilitarian dimensions of consumer attitude. Journal of Marketing Research, 40(3), 310–320.

    Article  Google Scholar 

  • Wan, Z. H. (2019). Exploring the effects of intrinsic motive, utilitarian motive, and self-efficacy on students’ science learning in the classroom using the expectancy-value theory. Research in Science Education. https://doi.org/10.1007/s11165-019-09891-z.

    Article  Google Scholar 

  • Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761–774.

    Article  Google Scholar 

  • Weng, C., Tsai, C. C., & Weng, A. (2015). Social support as a neglected e-learning motivator affecting trainee’s decisions of continuous intentions of usage. Australasian Journal of Educational Technology, 31(2).

  • Yang, M., Shao, Z., Liu, Q., & Liu, C. (2017). Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. Educational Technology Research and Development, 65(5), 1195–1214.

    Article  Google Scholar 

  • Yoo, S. J., & Huang, W. D. (2013). Engaging online adult learners in higher education: Motivational factors impacted by gender, age, and prior experiences. The Journal of Continuing Higher Education, 61(3), 151–164.

    Article  Google Scholar 

Download references

Acknowledgements

The research described in this paper was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia.

Author information

Authors and Affiliations

  1. Mihailo Pupin Institute, Volgina 15, 11060, Belgrade, Serbia

    Sonja Dimitrijević

  2. Faculty of Organizational Sciences, FON, University of Belgrade, Jove Ilića 154, Belgrade, Serbia

    Vladan Devedžić

Authors
  1. Sonja Dimitrijević
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vladan Devedžić
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sonja Dimitrijević.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dimitrijević, S., Devedžić, V. Utilitarian and experiential aspects in acceptance models for learning technology. Education Tech Research Dev 69, 627–654 (2021). https://doi.org/10.1007/s11423-021-09970-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11423-021-09970-x

Keywords

search

Navigation