Yıl: 2021 Cilt: 17 Sayı: 1 Sayfa Aralığı: 131 - 163 Metin Dili: İngilizce İndeks Tarihi: 29-07-2022

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

Öz:
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.
Anahtar Kelime: Technology Acceptance Model Mobile Anxiety Price Savings E-Conference Structural Equation Modeling

COVID-19 PANDEMİ SÜRESİNCE E-KONFERANS KABULÜNÜN BELİRLEYİCİLERİ

Öz:
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.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Albahri, O. S., Al-Obaidi, J. R., Zaidan, A. A., Albahri, A. S., Zaidan, B. B., Salih, M. M., ... & Zulkifli, C. Z. (2020). “Helping doctors hasten COVID19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods”. Computer Methods and Programs in Biomedicine, Vol. 196, 1-13.
  • Al-Adwan, A. S., Al-Madadha, A., & Zvirzdinaite, Z. (2018). “Modeling students’ readiness to adopt mobile learning in higher education: An empirical study”. International Review of Research in Open and Distributed Learning, Vol.19, No.1, 221-241. doi:10.19173/irrodl.v19i1.3256.
  • 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.
  • Amoroso, D. & Lim, R. (2017). “The mediating effects of habit on continuance intention”. International Journal of Information Management, Vol. 37, No. 6, 693-702.
  • 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.
  • 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.
  • Azjen, I. & Fisbein, M. (1980). Understanding Attitudes and Predicting Social Behaviour. Englewood Cliffs
  • Baker-Eveleth, L. & Stone, R.W. (2015). “Usability, expectation, confirmation, and continuance intentions to use electronic textbooks”. Behaviour & Information Technology, 34, 992-1004.
  • Basak, E., & Calisir, F. (2015). “An empirical study on factors affecting continuance intention of using Facebook”. Computers in Human Behaviour, 48, 181-189.
  • 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.
  • Becker, G. S. (1965). “A theory of the allocation of time”. The Economic Journal. Vol. 75, No. 299, 493-517.
  • Byrne, B. M. (2011). Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming (1st Edition): Routledge.
  • 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.
  • Bonifati, A., Guerrini, G., Lutz, C., Martens, W., Mazilu, L., Paton, N., ... & Zhou, Y. (2020). “Holding a Conference Online and Live due to COVID19”. arXiv preprint arXiv:2004.07668.
  • Chang, M.-Y., Pang, C., Tarn, J. M., Liu, T.-S., & Yen, D. C. (2015). “Exploring user acceptance of an e-hospital service: An empirical study in Taiwan”. Computer Standards & Interfaces, Vol. 38, 35-43. doi:10.1016/j.csi.2014.08.004.
  • Chang, C. T., Hajiyev, J., & Su, C. R. (2017). “Examining the students’ behavioural intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach”. Computers & Education, 111, 128-143.
  • 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).
  • Chin, W. W. (1998). “Commentary: Issues and Opinion on Structural Equation Modeling”. MIS Quarterly. Vol. 22, No. 1, pp. vii-xvi.
  • Davis, F. D. (1989). “Perceived usefulness, perceived ease of use, and user acceptance of information technology”. MIS Quarterly, 13, 319-340.
  • 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.
  • 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.
  • 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.
  • Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research. Addison-Wesley Publishing.
  • 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.
  • Fortes, N., & Rita, P. (2016). “Privacy concerns and online purchasing behaviour: Towards an integrated model”. European Research on Management and Business Economics, 22(3), 167–176. doi:10.1016/j.iedeen.2016.04.002.
  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). “Trust and TAM in online shopping: An integrated model”. MIS Quarterly, 27(1), 51-90.
  • 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.
  • Gómez-Ramirez, I., Valencia-Arias, A., & Duque, L. (2019). “Approach to m-learning acceptance among university students: an integrated model of TPB and TAM”. International Review of Research in Open and Distributed Learning, Vol. 20, No. 3, 141-164. doi:10.19173/irrodl.v20i4.4061.
  • 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.
  • Ha, S., & Stoel, L. (2009). “Consumer e-shopping acceptance: Antecedents in a technology acceptance model”. Journal of Business Research, Vol. 62, No.5, 565-571.
  • Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (1998). Multivariate data analysis. Prentice hall Upper Saddle River, NJ.
  • Hancerliogullari Koksalmis, G., & Pamuk, M. (2021). “Promoting an Energy Saving Technology in Turkey: The Case of Green Roof Systems”. Environmental Engineering and Management Journal (in press).
  • Hancerliogullari Koksalmis, G., & Gozudok, A. (2021). “What Impacts ECommerce Acceptance of Generation Z? A Modified Technology Acceptance Model”. In Al-Emran, M. & Shaalan, K. (Eds.), Recent Advances in Technology Acceptance Models and Theories, Springer, Cham., 335.
  • Hancerliogullari Koksalmis, G., & Damar, S. (2021). “An Empirical Evaluation of a Modified Technology Acceptance Model for SAP ERP System”. Engineering Management Journal, 1-16.
  • Hashemkhani Zolfani, S., Yazdani, M., Ebadi Torkayesh, A., & Derakhti, A. (2020). “Application of a gray-based decision support framework for location selection of a temporary hospital during COVID-19 pandemic”. Symmetry, Vol. 12, No. 6, 1-15.
  • Hazen, B. T., Overstreet, R. E., & Wang, Y. (2015). “Predicting public bicycle adoption using the technology acceptance model”. Sustainability, 7(11), 14558-14573.
  • 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.
  • 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.
  • Ho, C. H. (2010). “Continuance intention of e-learning platform: Toward an integrated model”. International Journal of Electronic Business Management, 8(3), 206.
  • 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.
  • Koksalmis, G. H. (2019). “Drivers to adopting B-flow ultrasonography: contextualizing the integrated technology acceptance model.” BMC Medical Imaging, 19(1), 56.
  • 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 (pp. 307-318). Springer, Cham.
  • Konana, P., Menon, N. & Balasubramanian, S. (2000). “Exploring the implications of online investing”. Comm. ACM, 43(1) 34-41.
  • Koufaris, M. (2002). “Applying the technology acceptance model and flow theory to online consumer behaviour”. Information Systems Research, 13(2), 205–223.
  • Kowitlawakul, Y. (2011). “The technology acceptance model: predicting nurses' intention to use telemedicine technology (eICU)”. CIN: Computers, Informatics, Nursing, Vol. 29, No. 7, 411-418. doi:10.1097/NCN.0b013e3181f9dd4a.
  • Lai, V. S., & Li, H. (2005). “Technology acceptance model for internet banking: an invariance analysis”. Information & Management, Vol. 42, No. 2, 373-386.
  • Lam, T., Cho, V., & Qu, H. (2007). “A Study of Hotel Employee Behavioural 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, Article 50.
  • 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.
  • Lee, Y.-H., Hsieh, Y.-C., & Hsu, C.-N. (2011). “Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems”. Journal of Educational Technology & Society, Vol. 14, No. 4, 124-137.
  • Liao, C., Palvia, P. & Chen, J.-L. (2009). “Information technology adoption behaviour life cycle: Toward a Technology Continuance Theory (TCT)”. International Journal of Information Management, 29, 309-320.
  • Lu, J., Yu, C.-S., Liu, C., & Yao, J. E. (2003). “Technology acceptance model for wireless Internet”. Internet research, Vol. 13, No. 3, 206-222. Marelli, S., Castelnuovo, A., Somma, A., Castronovo, V., Mombelli, S.,
  • Bottoni, D., ... & Ferini-Strambi, L. (2021). “Impact of COVID-19 lockdown on sleep quality in university students and administration staff”. Journal of Neurology, Vol. 268, No.1, 8-15.
  • Mbunge, E. (2020). “Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls”. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, Vol. 14, No. 6, 1631- 1636.
  • Mollenkopf, D. A., Ozanne, L. K., & Stolze, H. J. (2020). “A transformative supply chain response to COVID-19”. Journal of Service Management, Vol. 32, No. 2, 190-202.
  • 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.
  • Newsted, P. R., Huff, S. L., & Munro, M. C. (1998). “Survey instruments in information systems”. MIS Quarterly, 22(4), 553-554.
  • Özbek, V., Alnıaçık, Ü., Koc, F., Akkılıç, M. E., & Kaş, E. (2014). “The impact of personality on technology acceptance: A study on smart phone users”. Procedia-Social and Behavioural Sciences, Vol. 150, 541-551. doi:10.1016/j.sbspro.2014.09.073.
  • 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.
  • 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
  • Pedersen, P. E., & Nysveen, H. (2003). “Usefulness and selfexpressiveness: extending TAM to explain the adoption of a mobile parking service.” In Proceedings of the 16th Electronic Commerce Conference, Bled, Slovenia.
  • 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.
  • Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). “Technology acceptance model (TAM) and social media usage: an empirical study on Facebook”. Journal of Enterprise Information Management, Vol. 27, No. 1, 6-30. doi:10.1108/JEIM-04-2012-0011.
  • Rizou, M., Galanakis, I. M., Aldawoud, T. M., & Galanakis, C. M. (2020). “Safety of foods, food supply chain and environment within the COVID-19 pandemic”. Trends in Food Science & Technology, Vol.102, 293-299.
  • Roca, J. C., Chiu, C.-M. & Martínez, F. J. (2006). “Understanding elearning continuance intention: An extension of the Technology Acceptance Model”. International Journal of Human-Computer Studies, Vol. 64, No. 8, 683-696.
  • Schumacker, R. E., & Lomax, R. G. (2012). A Beginner's Guide to Structural Equation Modeling (3rd Edition). Routledge.
  • 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.
  • 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.
  • Sternad, S., Gradisar, M., & Bobek, S. (2011). “The influence of external factors on routine ERP usage”. Industrial Management & Data Systems, Vol.111, No.9, 1511–1530. doi:10.1108/02635571111182818.
  • Sternad, S., & Bobek, S. (2013). “Impacts of TAM-based external factors on ERP acceptance”. Procedia Technology, Vol. 9, 33-42.
  • Tirkolaee, E. B., Abbasian, P., & Weber, G. W. (2021). “Sustainable fuzzy multi-trip location-routing problem for medical waste management during the COVID-19 outbreak”. Science of the Total Environment, Vol. 756, 1-10.
  • Ulucan, O. (2018). Yapısal Eşitlik Modellemesi ile Radyasyon Farkındalığının Radyasyondan Korunma Üzerindeki Etkisinin İncelenmesi: Afyon Kocatepe Üniversitesi Sağlık Bilimleri Öğrencileri Üzerinde Bir Uygulama (Yüksek Lisans Tezi), Afyon Kocatepe Üniversitesi.
  • 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.
  • 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.
  • 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.
  • 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 behaviour”. MIS Quarterly, Vol. 24, No. 1, 115-139.
  • 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.
  • Whitelaw, S., Mamas, M. A., Topol, E., & Van Spall, H. G. (2020). “Applications of digital technology in COVID-19 pandemic planning and response”. The Lancet Digital Health, Vol. 2, No. 8, 435-440.
  • World Health Organization (WHO) (2019). Middle East respiratory syndrome coronavirus (MERS-CoV).Retrieved from https://www.who.int/news-room/fact-sheets/detail/middle-east-respiratorysyndrome-coronavirus-(mers-cov)
  • World Health Organization (WHO) (2020a, January). Coronavirus disease (COVID-19) outbreak. Retrieved from https:// www.who.int/westernpacific/emergencies/covid-19
  • World Health Organization (WHO) (2020b, January). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Retrieved from https://www.who.int/docs/default-source/ coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf
APA Köksalmış E, AYDIN S (2021). THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC. , 131 - 163.
Chicago Köksalmış Emrah,AYDIN SERHAT THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC. (2021): 131 - 163.
MLA Köksalmış Emrah,AYDIN SERHAT THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC. , 2021, ss.131 - 163.
AMA Köksalmış E,AYDIN S THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC. . 2021; 131 - 163.
Vancouver Köksalmış E,AYDIN S THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC. . 2021; 131 - 163.
IEEE Köksalmış E,AYDIN S "THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC." , ss.131 - 163, 2021.
ISNAD Köksalmış, Emrah - AYDIN, SERHAT. "THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC". (2021), 131-163.
APA Köksalmış E, AYDIN S (2021). THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC. Journal of Naval Sciences and Engineering, 17(1), 131 - 163.
Chicago Köksalmış Emrah,AYDIN SERHAT THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC. Journal of Naval Sciences and Engineering 17, no.1 (2021): 131 - 163.
MLA Köksalmış Emrah,AYDIN SERHAT THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC. Journal of Naval Sciences and Engineering, vol.17, no.1, 2021, ss.131 - 163.
AMA Köksalmış E,AYDIN S THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC. Journal of Naval Sciences and Engineering. 2021; 17(1): 131 - 163.
Vancouver Köksalmış E,AYDIN S THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC. Journal of Naval Sciences and Engineering. 2021; 17(1): 131 - 163.
IEEE Köksalmış E,AYDIN S "THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC." Journal of Naval Sciences and Engineering, 17, ss.131 - 163, 2021.
ISNAD Köksalmış, Emrah - AYDIN, SERHAT. "THE DETERMINANTS OF E-CONFERENCE ACCEPTANCE DURING COVID-19 PANDEMIC". Journal of Naval Sciences and Engineering 17/1 (2021), 131-163.