Technology Adoption Propensity Among Hungarian Business Students

Berényi, László ORCID:, Deutsch, Nikolett ORCID:, Pintér, Éva, Bagó, Péter and Nagy-Borsy, Viktor (2021) Technology Adoption Propensity Among Hungarian Business Students. European Scientific Journal, 17 (32). pp. 1-21. DOI

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The emerging role of technology raises several management challenges. Beyond the ability to develop new tools and solutions, achieving the business goals on new technologies require capable users on the other side. Understanding the factors of technology acceptance has been appreciated in recent decades. The paper aims to explore the approach to technology by using the adoption propensity (TAP) index among Hungarian business students. Gender, study level, and work experience were applied as grouping factors. A voluntary online survey was used for data collection. Based on 345 responses, the results are engaging and progressive. Many of the students have an optimistic approach to new technologies, and a significant part of them shows higher than medium-level proficiency. Parallelly, fear from vulnerability is remarkable among the respondents, which suggests cautious behavior. Gender and study level show significant differences within the sample, but no difference is found based on work experience. The results can be used to evaluate technology adoption readiness or generally support action research in developing industrial technologies.

Item Type:Article
Uncontrolled Keywords:technology adoption, technology acceptance, business students, banking service

1. Abbad, M. M. M. (2021). Using the UTAUT model to understand
students’ usage of e-learning systems in developing countries.
Education and Information Technologies, Online, 1-20. DOI:
2. Agarwal, R., & Prasad, J. (1999). Are Individual Differences Germane
to the Acceptance of New Information Technologies? Decision
Sciences, 30(2), 361-391. DOI: 10.1111/j.1540-5915.1999.tb01614.x
3. Ajzen, I. (1991). The theory of planned behavior. Organizational
Behavior and Human Decision Processes, 50(2), 179-211. DOI:
4. Ajzen, I. (2012). Martin Fishbein’s legacy: The reasoned action
approach. The Annals of the American Academy of Political and Social
Science, 640(1), 11-27. DOI: 10.1177/0002716211423363
5. Arfi, W. B., Nasr, I. B., Khvatova, T., & Zaied, Y. B. (2021).
Understanding acceptance of eHealthcare by IoT natives and IoT
immigrants: An integrated model of UTAUT, perceived risk, and
financial cost. Technological Forecasting and Social Change, 163,
120437. DOI: 10.1016/j.techfore.2020.120437
6. Chan, A.H.S., & Chen, K. (2011). A review of technology acceptance
by older adults. Gerontechnology, 10(1), 1-12. DOI:
7. Chen, H.-H., & Chen, S.-C. (2009). The empirical study of automotive
telematics acceptance in Taiwan: Comparing three technology
acceptance models. International Journal of Mobile Communications,
7(1), 50-65. DOI: 10.1504/IJMC.2009.021672
8. Chen, J., Li, R., Gan, M., Fu, Z., & Yuan, F. (2020). Public acceptance
of driverless buses in China: an empirical analysis based on an
extended UTAUT model. Discrete Dynamics in Nature and Society,
Article ID: 4318182, p. 13, DOI: 10.1155/2020/4318182
9. Cheng, V. T. P., & Guo, R. (2021). The impact of consumers’ attitudes
towards technology on the acceptance of hotel technology-based
innovation. Journal of Hospitality and Tourism Technology. In Press,
DOI: 10.1108/JHTT-06-2020-0145
10. Chille, F. J., Shayo, F. A., & Kara, N. S. (2021). Adoption of Mobile
Marketing in the Telecommunication Industry of Tanzania: The
Effects of Perceived Usefulness, Ease of Use, and Customer’s
Knowledge. European Scientific Journal, 17(12), 160. DOI:
11. Collan, M., & Tétard, F., (2011). Lazy User Model: solution selection
and discussion about switching costs. In: Scandinavian conference on
information systems. Springer, Berlin, Heidelberg, 2011. p. 56-68.
12. Davis, F. D. (1986). A technology acceptance model for empirically
testing new end-user information systems: Theory and results.
Cambridge, US: Massachusetts Institute of Technology
13. Dulle, F. W., Minishi-Majanja, M. K., & Cloete, L. M. (2010). Factors
influencing the adoption of open access scholarly communication in Tanzanian public universities. World Library and Information
Congress: 76th IFLA General Conference and Assembly 10-15 August
2010, Gothenburg, Sweden.
14. Fishbein, M. (1967). Attitude and the prediction of behavior. In:
Fishbein, M. (ed.): Readings in attitude theory and measurement. New
York, US: Wiley. pp. 477-492.
15. Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology
acceptance model for web-based learning. Journal of Information
Systems Education, 15(4), 365-374.
16. Grewal, D., Gauri, D. K., Das, G., Agarwal, J., & Spence, M. T.
(2021). Retailing and emergent technologies, Journal of Business
Research, 134, 198-202. DOI: 10.1016/j.jbusres.2021.05.004.
17. Goswami, A., & Dutta, S. (2016). Gender Differences in Technology
Usage—A Literature Review. Open Journal of Business and
Management, 4, 51-59. DOI: 10.4236/ojbm.2016.41006
18. Hapuarachchi, C., & Samarakoon, A. (2020). Drivers Affecting Online
Banking Usage of Private Commercial Banks in Sri Lanka. Asian
Journal of Economics, Business and Accounting, 20(1), 1-10.
19. Hornbaek, K., & Hertzum, M. (2017). Technology Acceptance and
User Experience: A Review of the Experiential Component in HCI.
ACM Transactions on Computer-Human Interaction, 24(5), 1–30,
DOI: 10.1145/3127358
20. Isaias, P. & Issa, T. (2015). High Level Models and Methodologies for
Information Systems. New York¸ NY: Springer
21. Jeon, H. M., Sung, H. J., & Kim, H. Y. (2020). Customers’ acceptance
intention of self-service technology of restaurant industry: expanding
UTAUT with perceived risk and innovativeness. Service Business,
14(4), 533-551. DOI: 10.1007/s11628-020-00425-6
22. Keszey, T., & Zsukk, J. (2017). Az új technológiák fogyasztói
elfogadása. A magyar és nemzetközi szakirodalom áttekintése és
kritikai értékelése. Vezetéstudomány, 48 (10), 38-47. DOI:
23. King, B. M. (2010) Analysis of Variance. In: Peterson, P. L., Baker,
E., & McGaw, B. (2010). International encyclopedia of education.
London: Elsevier, 32-36.
24. King, W., R., & He, J. (2006). A meta-analysis of the technology
acceptance model. Information & Management, 43(6), 740-755, DOI:
25. Koul, S., & Eydgahi, A. (2017). A systematic review of technology
adoption frameworks and their applications. Journal of technology
management & innovation, 12(4), 106-113.
26. Li, S., Glass, R., & Records, H. (2008). The Influence of Gender on
New Technology Adoption and Use–Mobile Commerce. Journal of
Internet Commerce, 7(2), DOI: 10.1080/15332860802067748
27. Mlekus, L., Bentler, D., Paruzel, A., Kato-Beiderwieden, A.-L., &
Maier, G. W. (2020). How to raise technology acceptance: User
experience characteristics as technology-inherent determinants.
Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte
Organisationspsychologie, 51(3), 273–283.
28. Murugan, A., Magid, I., & Uzoamaka, P. A. (2000). Technology
acceptance in the banking industry. A perspective from a less
developed country. Information Technology & People, 13(4), 298-312.
29. Pallant, J. (2020). SPSS Survival Manual: A Step by Step Guide to Data
Analysis Using IBM SPSS, 7th ed. London, UK: Open University Press
30. Parasuraman, A., & Colby, C. L. (2014). An Updated and Streamlined
Technology Readiness Index: TRI 2.0. Journal of Service Research,
18(1), 1-16. DOI: 10.1177/1094670514539730
31. Pikkarainen, T., Pikkarainen, K., Karjaluoto, H. & Pahnila, S. (2004).
Consumer acceptance of online banking: An extension of the
technology acceptance model. Internet Research, 14(3), 224-235.
DOI: 10.1108/10662240410542652
32. Ratchford, M., & Barnhart, M. (2012). Development and validation of
the technology adoption propensity (TAP) index. Journal of Business
Research, 65, 1209-1215. DOI: 10.1016/j.jbusres.2011.07.001
33. Radeskog, J., Strömstedt, P., & Söderström, O. (2009). UserTechnology-Acceptance among doctors: a case study examining the
effects of pre-implementation efforts made during a systemimplementation. Jönköping. Country Council, 2-30.
34. Rogers, E. M. (1995). Diffusion of innovation. New York: Free Press
35. Sajtos, L., & Mitev, A. (2007). SPSS kutatási és adatelemzési
kézikönyv. Budapest: Alinea Kiadó
36. Samaradiwakara, G.D.M.N., & Gunawardena, C.G. (2014).
Comparison of existing technology acceptance theories and models to
suggest a well improved theory/model. International Technical
Sciences Journal, 1(1), 21-36.
37. Sun, S., Lee, P.C., Law, R., & Hyun, S.S. (2020). An investigation of
the moderating effects of current job position level and hotel work
experience between technology readiness and technology acceptance.
International Journal of Hospitality Management, 90, 102633, DOI:
38. Székely, M., & Barna, I. (2013). Túlélőkészlet az SPSS-hez. Budapest: Typotex Kiadó
39. Taherdoost, H. (2018). A review of technology acceptance and
adoption models and theories. Procedia manufacturing, 22, 960-967.
40. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and
a research agenda on interventions, Decision Sciences, 39(2), 273-315.
DOI: 10.1111/j.1540-5915.2008.00192.x
41. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the
technology acceptance model: four longitudinal field studies,
Management Science, 46(2), 186-204. DOI:
42. 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, DOI: 10.2307/30036540
43. Wei, M. F., Luh, Y. H., Huang, Y. H., & Chang, Y. C. (2021). Young
generation’s mobile payment adoption behavior: Analysis based on an
extended UTAUT model. Journal of Theoretical and Applied
Electronic Commerce Research, 16(4), 618-637. DOI:
44. Williams, E., Slade, E., Hodges, D., & Morgan, P. (2020). Individual
Differences in the Adoption and Secure Use of Smart Home
Technology. British Academy of Management Conference: BAM2020
Conference in the Cloud, Online, 2-4 September 2020. p. 8.
45. Wu, J., & Lederer (2009). A Meta-Analysis of the Role of
Environment-Based Voluntariness in Information Technology
Acceptance. MIS Quarterly, 33(2), 419-432, DOI:
46. Žvanut, B., Pucer, P., Ličen, S., Trobec, I., Plazar, N., & Vavpotič, D.
(2011). The effect of voluntariness on the acceptance of e-learning by
nursing students. Nurse Education Today, 31(4), 350-355. DOI:10.1016/j.nedt.2010.07.004

ID Code:7014
Deposited By: MTMT SWORD
Deposited On:11 Nov 2021 09:47
Last Modified:11 Nov 2021 09:47

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