COVID-19 PANDEMİ SÜRESİNCE E-KONFERANS KABULÜNÜN BELİRLEYİCİLERİ

Abstract

Küresel olarak hissedilmeye devam eden COVID-19 salgınının etkisiyle, insanların yeni bir sanal iş ortamına, yani yüz yüze görüşmeden sanal toplantı formatına, geçişe hızla adapte olması oldukça önemli. E-konferans, diğer bir deyişle web konferansı veya sanal konferans, fiziksel bir yerde toplantı yapmak yerine web üzerindeki sanal bir ortam aracılığıyla bir konferansa katılan kişileri içeren çevrimiçi bir konferanstır. Bu çalışmanın amacı, değiştirilmiş teknoloji kabul modelini (TKM) kullanarak e-konferansı kullanmak için çeşitli davranışsal niyet faktörlerini incelemektir. TKM’nin temel unsurları ile birlikte, bu makalede memnuniyet, zaman, fiyat tasarrufu, teknik destek, mobil kaygı, sosyal etki ve kolaylık gibi ek yapılar dikkate alınmıştır. Türkiye'deki akademisyenler aracılığıyla toplam 203 anket toplanmıştır. Verileri değerlendirmek ve önerilen hipotezleri test etmek için SmartPLS 3.2.7 yazılımı kullanılarak Yapısal Eşitlik Modelleme (YEM) metodolojisi uygulanmıştır. Sonuçlar, kolaylık, mobil kaygı, memnuniyet, algılanan yararlılık ve sosyal etkinin davranışsal niyeti anlamlı şekilde etkilediğini göstermiştir. Bu makale, ekonferansı uygulamak isteyen yetkililer için teorik ve pratik birtakım çıkarımlar sağlamaktadır.

Keywords

• E-konferans
• Yapısal Eşitlik Modellemesi
• Teknoloji Kabul Modeli
• Fiyat Tasarrufu
• Mobil Kaygı

THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC

Abstract

With the impact of the COVID-19 pandemic continuing to be felt globally, it is essential that people quickly adapt to a new virtual business landscape, in order to continue to provide a valuable conference experience. E-conference, in other words web conference or virtual conference, is an online conference that involves people participating in a conference through a virtual environment on the web, rather than meeting in a physical location. The objective of this paper is to inspect several reasons of behavioural intention to use an e-conference system by utilizing the modified technology acceptance model (TAM). Together with primary elements of TAM, in this particular paper, additional constructs such as satisfaction, time, price savings, technical support, mobile anxiety, social influence and convenience are taken into account. Total of 203 questionnaires is gathered through academicians in Turkey. To evaluate the data and examine the proposed hypotheses, the Structural Equation Modeling (SEM) methodology is implemented by utilizing SmartPLS 3.2.7. The results indicate that convenience, mobile anxiety, satisfaction, perceived usefulness and social influence are significantly predicting the behavioural intention. This paper enables theoretical and practical implications for authorities seeking to implement an e-conference.

Keywords

• E-Conference
• Structural Equation Modeling
• Technology Acceptance Model
• Price Savings
• Mobile Anxiety

References

1. Al-Emran, M., Mezhuyev, V. & Kamaludin, A. (2020). “Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance”. Technology in Society, Vol. 61, 101247.
2. Amoroso, D. & Lim, R. (2017). “The mediating effects of habit on continuance intention”. International Journal of Information Management, 37 (6) (2017), pp. 693-702.
3. Anderson, J.C. & Gerbing, D.W. (1992). “Assumptions and comparative strengths of the two-step approach: Comment on Fornell and Yi”. Sociological Methods & Research, 20, 321-333.
4. Arasanmi, C. N., Wang, W.Y.C. & Singh, H. (2017). “Examining the motivators of training transfer in an enterprise systems context”. Enterprise Information Systems, 11(8), 1154-1172.
5. Azjen, I. & Fisbein, M. (1980). “Understanding attitudes and predicting social behavior”. Englewood Cliffs.
6. Baker-Eveleth, L. & Stone, R.W. (2015). “Usability, expectation, confirmation, and continuance intentions to use electronic textbooks”. Behaviour & Information Technology, 34, 992-1004.
7. Basak, E., & Calisir, F. (2015). “An empirical study on factors affecting continuance intention of using Facebook”. Computers in Human Behavior, 48, 181-189.
8. Bayraktar, C.A., Hancerliogullari, G., Cetinguc, B. & Calisir, F. (2017). “Competitive strategies, innovation, and firm performance: an empirical study in a developing economy environment”. Technology Analysis & Strategic Management, 29, 38-52.

9. Becker, G. S. (1965). “A theory of the allocation of time”. The Economic Journal. Vol. 75, No. 299, 493-517.
10. Bhattacherjee, A. (2001). “Understanding information systems continuance: An expectation-confirmation model”. Management Information Systems Quarterly, Vol. 25, No. 3, 351–370. doi:10.2307/3250921.
11. Chang, Y.P. & Zhu, D.H., (2012). “The role of perceived social capital and flow experience in building users’ continuance intention to social networking sites in China”. Computers in Human Behavior, 28, 995-1001.
12. 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.
13. Chau, K. Y., Lam, M. H. S., Cheung, M. L., Tso, E. K. H., Flint, S. W., Broom, D. R., ... & Lee, K. Y. (2019). “Smart technology for healthcare: Exploring the antecedents of adoption intention of healthcare wearable technology”. Health psychology research, 7(1).
14. Chin, W. W. (1998). “Commentary: Issues and Opinion on Structural Equation Modeling”. MIS Quarterly. Vol. 22, No. 1, pp. vii-xvi.
15. Davis, F. D. (1989). “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, 13, 319-340.
16. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). “User acceptance of computer technology: a comparison of two theoretical models”. Management Science, 35(8), 982-1003. doi:/10.1287/mnsc.35.8.982.
17. Dellaert, B. G., Arentze, T. A., Bierlaire, M., Borgers, A. W., & Timmermans, H. J. (1998). “Investigating consumers’ tendency to combine multiple shopping purposes and destinations”. Journal of Marketing Research, 35(2), 177-188.
18. Devaraj, S., Fan, M., & Kohli, R. (2002). “Antecedents of B2C channel satisfaction and preference : Validating e-conference metrics”, Information Systems Research. 13(3), 316–333. doi:10.1287/isre.13.3.316.77.
19. Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley Publishing.
20. Fornell, C., & Larcker, D. F. (1981). “Evaluating structural equation models with unobservable variables and measurement error”. Journal of Marketing Research. Vol. 18, No. 1, 39–50. doi:10.2307/3151312.
21. Fortes, N., & Rita, P. (2016). “Privacy concerns and online purchasing behavior: Towards an integrated model”. European Research on Management and Business Economics, 22(3), 167–176. doi:10.1016/j.iedeen.2016.04.002.
22. Gefen, D., Karahanna, E., & Straub, D. W. (2003). “Trust and TAM in online shopping: An integrated model”. MIS Quarterly, 27(1), 51-90.
23. Gefen, D., Straub, D. (2000). “The relative importance of perceived ease of use in IS adoption: A study of e-conference adoption”. J. Assoc. Inform. Systems, 1(8) 1-28.
24. Götz, O., Liehr-Gobbers, K. & Krafft M. (2010). “Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach”. In V. E. Vinzi, W. W. Chin, J. Henseler, H. Wang (Eds.), Handbook of Partial Least Squares (pp. 691-711). Springer Handbooks of Computational Statistics. Springer.
25. Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R.E. (1998). “Multivariate data analysis”. Prentice hall Upper Saddle River, NJ.
26. Hazen, B. T., Overstreet, R. E., & Wang, Y. (2015). “Predicting public bicycle adoption using the technology acceptance model”. Sustainability, 7(11), 14558-14573.
27. Heinssen Jr, R. K., Glass, C. R., & Knight, L. A. (1987). “Assessing computer anxiety: Development and validation of the computer anxiety rating scale”. Computers in human behavior, 3(1), 49-59.
28. Henseler, J., Ringle, C.M. & Sinkovics, R.R., (2009). “The use of partial least squares path modeling in international marketing, New challenges to international marketing”. Emerald Group Publishing Limited, 277-319.
29. Ho, C. H. (2010). “Continuance intention of e-learning platform: Toward an integrated model”. International Journal of Electronic Business Management, 8(3), 206.
30. Hsu, H. H., & Chang, Y. Y. (2013). “Extended TAM model: Impacts of convenience on acceptance and use of Moodle”. US-China Education Review A, 3(4), 211-218.
31. Koksalmis, G. H. (2019). “Drivers to adopting B-flow ultrasonography: contextualizing the integrated technology acceptance model.” BMC Medical Imaging, 19(1), 56.
32. Koksalmis, G. H., & Damar, S. (2019). “Exploring the adoption of ERP systems: An empirical investigation of end-users in an emerging country”. In H. Camgoz Akdag, F. Çalışır & E. Cevikcan (Eds.), Industrial Engineering in the Big Data Era, Springer, Cham., 307-318.
33. Konana, P., Menon, N. & Balasubramanian, S. (2000). “Exploring the implications of online investing”. Comm. ACM, 43(1) 34-41.
34. Koufaris, M. (2002). “Applying the technology acceptance model and flow theory to online consumer behavior”. Information Systems Research, 13(2), 205–223.
35. Lam, T., Cho, V., & Qu, H. (2007). “A Study of Hotel Employee Behavioral Intentions towards Adoption of Information Technology”. International Journal of Hospitality Management, Vol. 26, Issue 1, 49–65. doi:10.1016/j.ijhm.2005.09.002. Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). “The Technology Acceptance Model: Past, Present, and Future”. Communications of the Association for Information Systems (CAIS). Vol. 12. doi:10.17705/1CAIS.01250.
36. Lee, M.-C. (2010). “Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model”. Computers & Education, 54, 506-516.
37. Liao, C., Palvia, P. & Chen, J.-L. (2009). “Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT)”. International Journal of Information Management, 29, 309-320.
38. Moore, G. C., & Benbasat, I. (1991). “Development of an instrument to measure the perceptions of adopting an information technology innovation”. Information systems research, 2(3), 192-222.
39. Newsted, P. R., Huff, S. L., & Munro, M. C. (1998). “Survey instruments in information systems”. MIS quarterly, 22(4), 553.
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.
41. Park, J., Lee, D., & Ahn, J. (2004). “Risk-focused e-commerce adoption model: A cross-country study”. Journal of Global Information Technology Management, Vol. 7, No. 2, 6–30. doi:10.1080/1097198X.2004.10856370.
42. 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), 101–134. doi:10.1080/10864415.2003.11044275
43. Pedersen, P. E., & Nysveen, H. (2003). “Usefulness and self-expressiveness: extending TAM to explain the adoption of a mobile parking service.” In Proceedings of the 16th Electronic Commerce Conference, Bled, Slovenia.
44. Pijpers, G. G., Bemelmans, T. M., Heemstra, F. J., & van Montfort, K. A. (2001). “Senior executives' use of information technology”. Information and Software Technology, 43(15), 959-971.
45. Roca, J.C., Chiu, C.-M. & Martínez, F.J., (2006). “Understanding e-learning continuance intention: An extension of the Technology Acceptance Model”. International Journal of human-computer studies, 64, 683-696.
46. Sheikh, Z., Islam, T., Rana, S., Hameed, Z. & Saeed, U., (2017). “Acceptance of social commerce framework in Saudi Arabia”. Telematics and Informatics, 34, 1693-1708.
47. Son, H., Park, Y., Kim, C., & Chou, J. S. (2012). “Toward an understanding of construction professionals' acceptance of mobile computing devices in South Korea: An extension of the technology acceptance model”. Automation in Construction, 28, 82-90.
48. Stone, R.W., Baker-Eveleth, L., (2013). “Students’ expectation, confirmation, and continuance intention to use electronic textbooks”. Computers in Human Behavior, 29, 984-990.
49. Vasić, N., Kilibarda, M., & Kaurin, T. (2018). “The influence of online shopping determinants on customer satisfaction in the Serbian market”. Journal of Theoretical and Applied Electronic Commerce Research. Vol. 14, Issue 2. doi:10.4067/S0718-18762019000200107.
50. Venkatesh, V., & Davis, F. D. (1996). “A model of the antecedents of perceived ease of use: Development and test.” Decision Sciences. 27(3), 451-481. doi:10.1111/j.1540-5915.1996.tb00860.x.
51. Venkatesh, V. (2000). “Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model”. Information Systems Research. Vol. 11, No. 4, 342-365.doi:10.1287/isre.11.4.342.11872.
52. Venkatesh, V., & Morris, M. G. (2000). “Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior.” MIS quarterly, Vol. 24, No. 1, 115-139.
53. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). “User acceptance of information technology: Toward a unified view”. MIS quarterly, Vol. 27, No. 3, 425-478.