References: | 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:
10.1007/s10639-021-10573-5
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:
10.1016/0749-5978(91)90020-T
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:
10.4017/gt.2011.10.01.006.00
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:
10.19044/esj.2021.v17n12p160
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:
10.14267/VEZTUD.2017.10.05
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:
10.1016/j.im.2006.05.003
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:
10.1016/j.ijhm.2020.102633.
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:
10.1287/mnsc.46.2.186.11926
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:
10.3390/jtaer16040037
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:
10.1201/9781420074086-b2
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
|
---|