Teknoloji Kabul Modeli Bağlamında Öğrencilerin Davranışsal Niyetine Etki Eden Etmenlerin Belirlenmesi

Abstract

Bu çalışmanın amacı Teknoloji Kabul Modeli (TKM) bağlamında davranışsal niyete etki eden etmenlerin belirlenmesidir. Araştırmanın evrenini Afyon Kocatepe Üniversitesi Turizm Fakültesi öğrencileri oluşturmakta olup veriler kolayda örneklem metodu ile anket formu kullanarak 01-30 Mayıs 2022 tarihleri arasında yüz yüze ve çevrimiçi ortamlarda toplanmıştır. Veriler Warp PLS istatistik programında analiz edilmiş ve verilerin çözümlenmesinde betimsel istatistiklerin yanı sıra yapısal eşitlik modellemesinden faydalanılmıştır. Araştırma sonuçlarına göre kullanışlılık, kolaylık ve değerin tutum üzerinde pozitif yönlü anlamlı bir etkisi olduğu, hedonik motivasyonun ise tutum üzerinde anlamlı bir etkisinin olmadığı belirlenmiştir. Ayrıca tutumun davranışsal niyet üzerinde pozitif yönlü anlamlı bir etkisinin olduğu tespit edilmiştir. Araştırma sonuçları neticesinde öneriler geliştirilmiştir.

Keywords

• Teknoloji Kabul Modeli
• Davranışsal Niyet
• Turizm Eğitimi

Determining the Factors Affecting Students' Behavioral Intention in the Context of the Technology Acceptance Model

Abstract

The purpose of this study is to determine the factors that affect behavioral intention in the context of the Technology Acceptance Model (TAM). The study population consists of students at Afyon Kocatepe University Tourism Faculty, and data was collected through face-to-face and online surveys using the convenience sampling method between May 1 and 30, 2022. The data was analyzed using the Warp PLS statistical program, and both descriptive statistics and structural equation modeling were used to analyze the data. The results of the study showed that usability, ease of use, and value have a positive and significant effect on attitude, while hedonic motivation has no significant effect on attitude. In addition, it was found that attitude has a positive and significant effect on behavioral intention. Based on the results of the study, recommendations were developed.

Keywords

• Technology Acceptance Model
• Behavioral İntention
• Tourism Education

References

1. Adam, I. ve Amuquandoh, F. E. (2019). Ethnic-based motives and experiences at former slave sites. Journal of Travel & Tourism Marketing, 36(4), 497-510.
2. Agarwal, R. ve Prasad, J. (1998). A Conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204‐215.
3. Aggelidis, P. V. ve Chatzoglou, P. D. (2009). Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics, 78(2), 115-126.
4. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
5. Ali, F., Nair, P. K. ve Hussain, K. (2016). An assessment of students' acceptance and usage of computer supported collaborative classrooms in hospitality and tourism schools. Journal of Hospitality, Leisure, Sport & Tourism Education, 18, 51-60.
6. Al-Qaysi, N., Mohamad-Nordin, N. ve Al-Emran, M. (2020). Employing the technology acceptance model in social media: A systematic review. Education and Information Technologies, 25(1),
7. Avcı, İ. ve Yıldız, E. (2021). COVID-19 pandemi sürecinde uzaktan eğitimi kullanan öğrencilerin memnuniyet ve davranışlarının teknoloji kabul modeli çerçevesinde incelenmesi. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 12(3), 814-830.
8. Bagozzi, R. P. ve Burnkrant, R. E. (1979). Attitude organization and the attitude–behavior relationship. Journal of Personality and Social Psychology, 37(6), 913-929.

9. Bagozzi, R. P. ve Burnkrant, R. E. (1985). Attitude organization and the attitude-behavior relation: A reply to Dillon and Kumar. Journal of Personality and Social Psychology, 49(1), 47–57.
10. Bagozzi, R. P. ve Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
11. Becker, J. M., Klein, K. ve Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models. Long Range Planning, 45(5-6), 359-394.
12. Bruner, G. C. ve Kumar, A. (2005). Explaining consumer acceptance of handheld internet devices. Journal of Business Research, 58(5), 553-558.
13. Chen, S. Y. ve Lu, C. C. (2016). Exploring the relationships of green perceived value, the diffusion of innovations, and the technology acceptance model of green transportation. Transportation Journal, 55(1), 51-77.
14. Chien, Y. T., Chang, Y. H. ve Chang, C. Y. (2016). Do we click in the right way? A meta-analytic review of clicker-integrated instruction. Educational Research Review, 17, 1-18.
15. Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. Management Information Systems Quarterly, 22(1), 7-16.
16. Çılgınoğlu, H. ve Yazgan, I. (2022). Speleotherapy in the scope of health tourism: case of Çankırı salt cave in Turkey. International Journal of Tourism Policy, 12(3), 333-350.
17. Cooper, J. ve Croyle, R. T. (1984). Attitudes and attitude change. Annual Review of Psychology, 35, 395-426.
18. Davis, F. D. (1989). Perceived usefulness: Perceived ease of use and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 983-1003.
19. Delone, W. H. ve 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.
20. Dijkstra, T. K. ve Henseler, J. (2015). Consistent partial least squares path modeling. Management Information Systems Quarterly, 39(2), 297-316.
21. Dilek, Ö. ve Öztürk, A. (2021). COVID-19 sürecinde online yemek siparişlerinde teknolojinin kabulü. Üçüncü Sektör Sosyal Ekonomi Dergisi, 56(3), 1313-1332.
22. Dubey, R., Gunasekaran, A. ve Childe, S. J. (2018). Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility. Management Decision, 57(8), 2092-2112.
23. Fishbein, M. ve Ajzen, I. (1975). Belief, attitude, intention, and behavior: an introduction to theory and research. Addison-Wesley.
24. Fornell, C. ve Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
25. Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y. ve Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192-3198.
26. Gumpo, C. I. V., Madinga, N. W., Maziriri, E. T. ve Chuchu, T. (2020). Examining the usage of instagram as a source of information for young consumers when determining tourist destinations. South African Journal of Information Management, 22(1).
27. Hagger, M. S., Hamilton, K., Hardcastle, S. J., Hu, M., Lin, J., Nawawi, H. M. ve Watts, G. F. (2019). Predicting intention to participate in self-management behaviors in patients with familial hypercholesterolemia: A cross-national study. Social Science & Medicine, (242).
28. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P. ve Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A Workbook.
29. Hair, J. F., Hult, G. T. M., Ringle, C. M. ve Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Thousand Oaks: Sage.
30. Hajiha1, A., Shahriari, M. ve Vakilian, N. (2014). The role of perceived value on customer e-shopping intention using technology acceptance model, (TAM), IEEE International Conference on Industrial Engineering and Engineering Management, 9-12 December, Selangor, Malaysia.
31. Han, J. H. ve Sa, H. J. (2022). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): The COVID‑19 situation in Korea, Asia Pacific Education Review, 23, 403-415.
32. Henseler, J., Ringle, C. M. ve Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
33. Hu, L. T. ve Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological methods, 3(4), 424-453.
34. Jan, A. U. ve Contreras, V. (2011). Technology acceptance model for the use of information technology in universities. Computers in Human Behavior, 27(2), 845-851.
35. Julio, B. F., Emilio, J. M. ve Saenz-Diez, M. J. C. (2017). The impact of human resources on the agility, flexibility and performance of wine supply chains. Agricultural Economics, 63(4), 175-184.
36. Kalayou, M. H., Endehabtu, B. F. ve Tilahun, B. (2020). The applicability of the modified technology acceptance model (TAM) on the sustainable adoption of ehealth systems in resource-limited settings. Journal of Multidisciplinary Healthcare, 13, 1827-1837.
37. Kalıpçı, M. B. (2021). Konaklama İşletmeleri Satın Alma Müdürlerinin Teknoloji Kabul ve Hizmet İnovasyonu Algılarının Değerlendirilmesinde Öğrenen Örgütün Aracılık Rolü: Antalya Örneği. (Yayınlanmamış doktora tezi). Alanya Alaaddin Keykubat Üniversitesi Sosyal Bilimler Enstitüsü, Antalya.
38. Kılıç, A. ve Yılmaz, R. (2021). Youtube’un eğitsel amaçlı kabul durumunun incelenmesi. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi, 3(1), 69-89.
39. Kim, T. G., Lee, J. H. ve Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29(3), 500-513.
40. King, W. R. ve He, J. (2006). A meta-analysis of the technology acceptance model, Information & Management, 43(6), 740-755.
41. Kock, N. (2021). Moderated mediation and j-curve emergence in path models: an information systems research perspective. Journal of Systems and Information Technology.
42. Kock, N. (2022). Contributing to the success of PLS in SEM: An action research perspective. Communications of the Association for Information Systems.
43. Koehler, M. J. ve Mishra, P. (2008). What is technological pedagogical content knowledge (TPACK)? In AACTE Committee on Innovation and Technology (Ed.), Handbook of technological pedagogical content knowledge (TPACK) for educators (3–29). Routledge.
44. Kwun, D. J. W. (2011). Effects of campus foodservice attributes on perceived value, satisfaction, and consumer attitude: A gender-difference approach. International Journal of Hospitality Management, 30(2), 252-261.
45. Legris, P., Inghamb, J. ve Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.
46. Lew, S., Tan, G. W. H., Loh, X. M., Hew, J. J. ve Ooi, K. B. (2020). The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model. Technology in Society, 63.
47. McGuire, W. J. (1985). Attitudes and attitude change, (Eds: G. Lindzey, E. Aronson (Eds.), Handbook of Social Psychology, Random House, New York, 233-346.
48. Mishra, P. ve Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054.
49. Moutinho, L. (1987). Consumer behaviour in tourism. European Journal of Marketing, 21(10), 3-44.
50. Nadim, J. ve Noorjahan, B. (2008). The role of perceived usefulness, perceived ease of use, security and privacy, and customer attitude to engender customer adaptation in the context of electronic banking. African Journal of Business Management, 2(2), 032-040.
51. Newman, T., Beetham, H. ve Knight, S. (2018). Digital experience insights survey, Findings from students in UK further and higher education. Jisc
52. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150-162.
53. 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.
54. Park, S. Y., Nam, M. W. ve Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592-605.
55. Pavlou P. A. (2003). Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 69‐103.
56. Porter, C. E. ve Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999-1007.
57. Prasetyo, Y. T., Ong, A. K. S., Concepcion, G. K. F., Navata, F. M. B., Robles, R. A. V., Tomagos, I. J. T., Young, M. N., Diaz, J. F. T., Nadlifatin, R. ve Redi, A. A. N. P. (2021). Determining factors affecting acceptance of e-learning platforms during the COVID-19 pandemic: integrating extended technology acceptance model and DeLone & McLean IS success model. Sustainability, 13(15).
58. Purnawirawan, N., De Pelsmacker, P. ve Dens, N. (2012). Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions. Journal of Interactive Marketing, 26(4), 244-255.
59. Ringle, C., Da Silva, D. ve Bido, D. (2015). Structural equation modeling with the SmartPLS. Bido, D., da Silva, D. ve Ringle, C. (2014). Structural equation modeling with the SmartPLS. Brazilian Journal of Marketing, 13(2).
60. Rivera, M., Gregory, A. ve Cobos, L. (2015). Mobile application for the timeshare industry: The influence of technology experience, usefulness, and attitude on behavioral intentions. Journal of Hospitality and Tourism Technology, 6(3), 242-257.
61. Salehzadeh, R. ve Pool, J. K. (2017). Brand attitude and perceived value and purchase intention toward global luxury brands. Journal of International Consumer Marketing, 29(2), 74-82.
62. Sameer, S. K. ve Priyadarshi, P. (2020). Role of Big Five personality traits in regulatory-focused job crafting. South Asian Journal of Business Studies, 10(3), 377-395.
63. Schmid, R. F., Bernard, R. M., Borokhovski, E., Tamim, R. M., Abrami, P. C., Surkes, M. A., Wade, C. A. ve Woods, J. (2014). The effects of technology use in postsecondary education: A meta-analysis of classroom applications. Computers & Education, 72, 271-291.
64. Shen, S., Xu, K., Sotiriadis, M., & Wang, Y. (2022). Exploring the factors influencing the adoption and usage of augmented reality and virtual reality applications in tourism education within the context of COVID-19 pandemic. Journal of Hospitality, Leisure, Sport & Tourism Education, 30, 100373.
65. Shih, H. P. (2003). Extended technology acceptance model of internet utilization behavior. Information & Management, 41(6), 719-729.
66. Shroff, R. H., Deneen C. C. ve Ng, E. M. W. (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an eportfolio system. Australasian Journal of Educational Technology, 27(4), 600-618.
67. Sukendro, S., Habibi, A. Khaeruddin, K., Indrayana B., Syahruddin, S., Makadada, F. A. ve Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during COVID-19: Indonesian sport science education context. Heliyon, 6(11).
68. Suki, N. M. ve Suki, N. M. (2011). Exploring the relationship between perceived usefulness, perceived ease of use, perceived enjoyment, attitude and subscribers’ intention towards using 3G mobile services. Journal of Information Technology Management, 22(1), 1-7.
69. Surendran, P. (2012). Technology Acceptance Model: A Survey of Literature. International Journal of Business and Social Research, 2(4), 175-178.
70. Tavitiyaman, P., Qu, H., Tsang, W. L. ve Lam, C. H. (2021). The influence of smart tourism applications on perceived destination image and behavioral intention: The moderating role of information search behavior. Journal of Hospitality and Tourism Management, (46), 476-487.
71. Taylor, S. ve Todd, P. A (1995a). Understanding information technology usage: A test of competing models. Institute for Operations Research and the Management Sciences. Information Systems Research, 6(2), 144–176.
72. Taylor, S. ve Todd, P. A. (1995b) Assessing IT usage: The role of prior experience. MIS Quarterly, 19, 561-570.
73. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y. M. ve Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205
74. 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.
75. Teo, T., Lee, C. B. ve 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.
76. Tornatzky, L. ve Klein, K. (1982). Innovation characteristics and innovation adoption–implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29, 28–45.
77. Untaru, E. N., Ispas, A., Candrea, A. N., Luca, M. ve Epuran, G. (2016). Predictors of individuals’ intention to conserve water in a lodging context: The application of an extended theory of reasoned action. International Journal of Hospitality Management, (59), 50-59.
78. Vahdat, A., Alizadeh, A., Quach, S. ve Hamelin, N. (2020). Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Marketing Journal, 29(2), 187-197.
79. Venkatesh V. ve Speier C. (1999), Computer technology training in the workplace: A longitudinal investigation of the effect of the mood. Organizational Behavior and Human Decision Processes, 79(1), 1-28.
80. Venkatesh, V., Thong, J. Y. L. ve Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Forthcoming in MIS Quarterly, 36(1), 157-178.
81. Yang, H. D. ve Yoo, Y. (2003). It’s all about attitude: Revisiting the technology acceptance model. Decision Support Systems, 38(1), 19-31.
82. Yıldırır, S. C. ve Kaplan, B. (2019). Mobil uygulama kullanımının benimsenmesi: Teknoloji kabul modeli ile bir çalışma. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(19), 22-51.
83. Zeithaml, V. A., Berry, L. L. ve Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.
84. Zhang, B., Li, Z. ve Jiang, L. (2021). The intentions to wear face masks and the differences in preventive behaviors between urban and rural areas during COVID-19: An analysis based on the technology acceptance model. International Journal of Environmental Research and Public Health, 18(19), 1-15.