Does innovativeness matter in technology adoption? Addressing pre-service teachers' intention to use ITs

Abstract

This study aims to identify the factors influencing pre-service teachers' use of information technologies in educational settings and to validate a technology acceptance model that is extended by employing variables related to innovativeness. The data were collected from 1819 pre-service teachers studying in 12 different teacher education programs of a state university. For the analysis, PLS-SEM technique was employed. The results showed that the most influential construct on intention was the perceived usefulness, and the strongest relationship was found between social influence and perceived usefulness. In addition, the relationships between openness and the core technology acceptance constructs were found to be significant. These findings suggest that the openness to change trait is crucial for instructors and the opinions of people who are important to pre-service teachers and social pressure are the primary factors influencing their views in performance increase they can achieve by using technology. Accordingly, social norms, motivational-emotional factors and personality traits regarding innovativeness may have a vital role in technology adoption both theoretically and practically.

Keywords

• Technology acceptance
• Innovativeness
• Information technologies
• Personality traits
• Pre-service Teachers

References

1. Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information systems research, 9(2), 204-215.
2. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Cliffs, NJ: Prentice-Hall.
3. Ali, I. (2019). Personality traits, individual innovativeness and satisfaction with life. Journal of Innovation & Knowledge, 4(1), 38-46.
4. Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the association for information systems, 8(4), 3.
5. Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191. https://doi.org/10.1037/0033-295X.84.2.191
6. Bauer, R. A. (1960). Consumer behavior as risk taking. In Proceedings of the 43rd National Conference of the American Marketing Assocation, June 15, 16, 17, Chicago, Illinois, 1960. American Marketing Association.
7. Baydaş, Ö. (2015). Öğretmen Adaylarının Gelecekteki Derslerinde Bilişim Teknolojilerini Kullanma Niyetlerini Belirlemeye Yönelik Bir Model Önerisi (Unpublished doctoral dissertation). Atatürk Üniversitesi, Erzurum.
8. Baydas, O., & Goktas, Y. (2016). Influential factors on preservice teachers' intentions to use ICT in future lessons. Computers in Human Behavior, 56, 170-178. https://doi.org/10.1016/j.chb.2015.11.030

9. Baydas, O., & Goktas, Y. (2017). A model for preservice teachers’ intentions to use ICT in future lessons. Interactive Learning Environments, 25(7), 930-945. https://doi.org/10.1080/10494820.2016.1232277
10. Baydaş, O., & Yilmaz, R. M. (2018). Pre‐service teachers’ intention to adopt mobile learning: A motivational model. British Journal of Educational Technology, 49(1), 137-152. https://doi.org/10.1111/bjet.12521
11. Baylor, A. L., & Ritchie, D. (2002). What factors facilitate teacher skill, teacher morale, and perceived student learning in technology-using classrooms?. Computers & education, 39(4), 395-414.
12. Bervell, B., Umar, I. N., & Kamilin, M. H. (2020). Towards a model for online learning satisfaction (MOLS): Re-considering non-linear relationships among personal innovativeness and modes of online interaction. Open Learning: The Journal of Open, Distance and e-Learning, 35(3), 236-259. https://doi.org/10.1080/02680513.2019.1662776
13. Bhattacherjee, A., & Hikmet, N. (2007). Physicians' resistance toward healthcare information technology: a theoretical model and empirical test. European Journal of Information Systems, 16(6), 725-737.
14. Bingimlas, K. A. (2009). Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics, science and technology education, 5(3), 235-245.
15. 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. https://doi.org/10.1016/j.compedu.2013.02.010
16. Bozionelos, N., Bozionelos, G., Polychroniou, P., & Kostopoulos, K. (2014). Mentoring receipt and personality: Evidence for non-linear relationships. Journal of Business Research, 67(2), 171-181.
17. Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143. https://doi.org/10.1016/j.compedu.2017.04.010
18. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
19. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–47. https://doi.org/10.1177/002224378101800104
20. Garone, A., Pynoo, B., Tondeur, J., Cocquyt, C., Vanslambrouck, S., Bruggeman, B., & Struyven, K. (2019). Clustering university teaching staff through UTAUT: Implications for the acceptance of a new learning management system. British Journal of Educational Technology, 50(5), 2466-2483.
21. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal
22. of Marketing Theory and Practice, 19(2), 139–151. https://doi.org/10.2753/MTP1069-6679190202
23. Hair, J. J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). London: SAGE Publications.
24. Huang, F., & Teo, T. (2019). Influence of teacher-perceived organisational culture and school policy on Chinese teachers’ intention to use technology: An extension of technology acceptance model. Educational Technology Research and Development, 1-21.
25. Hurt, H. T., Joseph, K., & Cook, C. D. (1977). Scales for the measurement of innovativeness. Human Communication Research, 4(1), 58-65.
26. Im, I., Kim, Y., & Han, H. J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information & Management, 45(1), 1-9.
27. Jeong, H. I., & Kim, Y. (2017). The acceptance of computer technology by teachers in early childhood education. Interactive Learning Environments, 25(4), 496-512. https://doi.org/10.1080/10494820.2016.1143376
28. Kılıçer, K., & Odabaşı, H. F. (2010). Individual innovativeness scale (IS): The study of adaptation to Turkish, validity and reliability. Hacettepe University Journal of Education, 38 (38), 150-164. Retrieved from https://dergipark.org.tr/en/pub/hunefd/issue/7798/102155
29. Kılıçer, K. (2011). Bilgisayar ve öğretim teknolojileri eğitimi öğretmen adaylarının bireysel yenilikçilik profilleri (Unpublished doctoral dissertation). Anadolu University, Eskişehir.
30. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755. https://doi.org/10.1016/j.im.2006.05.003
31. Kirton, M. J., & De Ciantis, S. M. (1986). Cognitive style and personality: The Kirton adaption-innovation and Cattell's sixteen personality factor inventories. Personality and Individual Differences, 7(2), 141-146.
32. Korukonda, A. R. (2007). Differences that do matter: A dialectic analysis of individual characteristics and personality dimensions contributing to computer anxiety. Computers in human behavior, 23(4), 1921-1942.
33. Lai, C., Wang, Q., & Lei, J. (2012). What factors predict undergraduate students' use of technology for learning? A case from Hong Kong. Computers & Education, 59(2), 569-579. https://doi.org/10.1016/j.compedu.2012.03.006
34. Lai, H. M., & Chen, C. P. (2011). Factors influencing secondary school teachers’ adoption of teaching blogs. Computers & Education, 56(4), 948-960.
35. Lu, Y., Papagiannidis, S., & Alamanos, E. (2019). Exploring the emotional antecedents and outcomes of technology acceptance. Computers in Human Behavior, 90, 153-169. https://doi.org/10.1016/j.chb.2018.08.056
36. Luan, W. S., & Teo, T. (2009). Investigating the technology acceptance among student teachers in Malaysia: An application of the technology acceptance model (TAM). Asia-Pacific Education Researcher, 18(2), 261-272.
37. Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010). Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision support systems, 49(2), 222-234.
38. Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International journal of information management, 34(1), 1-13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
39. Mayya, S. (2007). Integrating New Technology to Commerce Curriculum: How to Overcome Teachers' Resistance?. Online Submission, 6(1).
40. Ngai, E. W., Poon, J. K. L., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250-267. https://doi.org/10.1016/j.compedu.2004.11.007
41. Nistor, N., Baltes, B., & Schustek, M. (2012). Knowledge sharing and educational technology acceptance in online academic communities of practice. Campus-Wide Information Systems. https://doi.org/10.1108/10650741211212377
42. Nov, O., & Ye, C. (2008, January). Personality and technology acceptance: Personal innovativeness in IT, openness and resistance to change. In Proceedings of the 41st annual Hawaii international conference on system sciences (HICSS 2008) (pp. 448-448). IEEE.
43. Oreg, S. (2003). Resistance to change: Developing an individual differences measure. Journal of applied psychology, 88(4), 680.
44. Rogers, E. M., & Shoemaker, F. F. (1971). Communication of Innovations; A Cross-Cultural Approach.
45. Saadé, R. G., & Kira, D. (2007). Mediating the impact of technology usage on perceived ease of use by anxiety. Computers & education, 49(4), 1189-1204.
46. Sánchez-Prieto, J. C., Hernández-García, Á., García-Peñalvo, F. J., Chaparro-Peláez, J., & Olmos-Migueláñez, S. (2019). Break the walls! Second-Order barriers and the acceptance of mLearning by first-year pre-service teachers. Computers in Human Behavior, 95, 158-167. https://doi.org/10.1016/j.chb.2019.01.019
47. Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644-654. https://doi.org/10.1016/j.chb.2016.09.061
48. Šumak, B., Pušnik, M., Heričko, M., & Šorgo, A. (2017). Differences between prospective, existing, and former users of interactive whiteboards on external factors affecting their adoption, usage and abandonment. Computers in Human Behavior, 72, 733-756. https://doi.org/10.1016/j.chb.2016.09.006
49. Svendsen, G. B., Johnsen, J. A. K., Almås-Sørensen, L., & Vittersø, J. (2013). Personality and technology acceptance: the influence of personality factors on the core constructs of the Technology Acceptance Model. Behaviour & Information Technology, 32(4), 323-334. https://doi.org/10.1080/0144929X.2011.553740
50. Şahin, F. (2016). Öğretmen adaylarının bilişim teknolojileri kabul düzeyleri ile bireysel yenilikçilik düzeyleri arasındaki ilişkinin incelenmesi (Unpublished master’s thesis). Anadolu University, Eskişehir.
51. Şahin, F. (2021). Öğretmen adaylarının bilişim teknolojileri kullanım niyetlerinde duyguların ve temel psikolojik ihtiyaçların rolü: Teknolojinin kabulüne motivasyonel bir yaklaşım (Unpublished doctoral dissertation). Anadolu University, Eskişehir.
52. Şahin, F., & Şahin, Y. L. (2021). Examining the acceptance of e-learning systems during the pandemic: The role of compatibility, enjoyment and anxiety. International Technology and Education Journal, 5(1), 01-10.
53. Şahin, F., Doğan, E., İlic, U., & Şahin, Y. L. (2021). Factors influencing instructors’ intentions to use information technologies in higher education amid the pandemic. Education and Information Technologies, 4795–4820. https://doi.org/10.1007/s10639-021-10497-0
54. Şahin, F., Doğan, E., Okur, M. R., & Şahin, Y. L. (2022). Emotional outcomes of e-learning adoption during compulsory online education. Education and Information Technologies, 1-23.
55. Şahin, F., & Şahin, Y. L. (2022). Drivers of technology adoption during the COVID-19 pandemic: The motivational role of psychological needs and emotions for pre-service teachers. Social Psychology of Education, 1-26. https://doi.org/10.1007/s11218-022-09702-w
56. Şahin, F., Doğan, E., Yıldız, G., & Okur, M. R. (2022). University students with special
57. needs: Investigating factors influencing e-learning adoption. Australasian Journal of Educational
58. Technology, 38(5), 146-162. https://doi.org/10.14742/ajet.7454
59. Teo, T. (2009). The impact of subjective norm and facilitating conditions on pre-service teachers' attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Journal of Educational Computing Research, 40(1), 89-109. https://doi.org/10.2190/EC.40.1.d
60. Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: a structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review, 11(2), 253-262. https://doi.org/10.1007/s12564-009-9066-4
61. Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. https://doi.org/10.1016/j.compedu.2011.06.008
62. Teo, T., & Van Schaik, P. (2012). Understanding the intention to use technology by preservice teachers: An empirical test of competing theoretical models. International Journal of Human-Computer Interaction, 28(3), 178-188.
63. Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre‐service teachers' computer attitudes: Applying and extending the technology acceptance model. Journal of Computer Assisted Learning, 24(2), 128-143. https://doi.org/10.1111/j.1365-2729.2007.00247.x
64. Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: a Chinese perspective. Interactive Learning Environments, 27(4), 530-546. https://doi.org/10.1080/10494820.2018.1489290
65. Thakur, R., Angriawan, A., & Summey, J. H. (2016). Technological opinion leadership: The role of personal innovativeness, gadget love, and technological innovativeness. Journal of Business Research, 69(8), 2764-2773. https://doi.org/10.1016/j.jbusres.2015.11.012
66. Thatcher, J. B., & Perrewe, P. L. (2002). An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS quarterly, 381-396.
67. Toquero, C. M. (2020). Challenges and opportunities for higher education amid the COVID-19 Pandemic: The Philippine context. Pedagogical Research, 5(4).
68. Ursavaş, Ö. F. (2014). Öğretmenlerin Bilişim Teknolojilerini Kullanmaya Yönelik Davranışlarının Modellenmesi (Unpublished doctoral dissertation). Gazi Üniversitesi, Ankara.
69. Ursavaş, Ö., Şahin, S., & McIlroy, D. (2014). Technology acceptance measure for teachers: T-TAM/Öğretmenler için teknoloji kabul ölçeği: Ö-TKÖ. Eğitimde Kuram ve Uygulama, 10(4), 885-917. Retrieved from https://dergipark.org.tr/en/pub/eku/issue/5462/74152
70. Valtonen, T., Kukkonen, J., Kontkanen, S., Sormunen, K., Dillon, P., & Sointu, E. (2015). The impact of authentic learning experiences with ICT on pre-service teachers' intentions to use ICT for teaching and learning. Computers & Education, 81, 49-58. https://doi.org/10.1016/j.compedu.2014.09.008
71. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
72. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
73. Weele, I. (2013). The effects of CEO’s personality traits (Big 5) and a CEO’s external network on innovation performance in SMEs (Bachelor's thesis, University of Twente).
74. Wong, G. K. (2015). Understanding technology acceptance in pre-service teachers of primary mathematics in Hong Kong. Australasian Journal of Educational Technology, 31(6). https://doi.org/10.14742/ajet.1890
75. Wong, K. T., Teo, T., & Russo, S. (2012). Influence of gender and computer teaching efficacy on computer acceptance among Malaysian student teachers: An extended technology acceptance model. Australasian Journal of Educational Technology, 28(7). https://doi.org/10.14742/ajet.796
76. Yi, M. Y., Fiedler, K. D., & Park, J. S. (2006). Understanding the role of individual innovativeness in the acceptance of IT‐based innovations: Comparative analyses of models and measures. Decision Sciences, 37(3), 393-426. https://doi.org/10.1111/j.1540-5414.2006.00132.x
77. Yuen, A. H., & Ma, W. W. (2008). Exploring teacher acceptance of e‐learning technology. Asia‐Pacific Journal of Teacher Education, 36(3), 229-243. https://doi.org/10.1080/13598660802232779