Intention to use online meeting applications during Covid-19 pandemic: A Technology Acceptance Model perspective

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Gilang Puspita Rini
Imroatul Khasanah


This research aims to add to the repertoire on the knowledge of perceived risk effect on the intention to use online meeting applications during the COVID-19 pandemic. Data were obtained from 186 respondents using the questionnaire instrument, which had been tested, for validity and reliability. The obtained data were analyzed using the Structural Equation Model. Based on the data testing, it was found that all hypotheses in this research were accepted. Therefore, the intention to use online meeting applications was most affected by perceived ease of use comparing to perceived risk. The results showed that developingcompanies increased perceived ease of use, hence the technology is easily accepted by consumers. 



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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. doi:10.1287/isre.9.2.204

Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16-23. doi:10.1016/j.jretconser.2014.09.003

Ajitha, S., & Sivakumar, V. J. (2017). Understanding the effect of personal and social value on attitude and usage behavior of luxury cosmetic brands. Journal of Retailing and Consumer Services, 39, 103-113. doi:10.1016/j.jretconser.2017.07.009

Alcántara-Pilar, J. M. B.-E., Francisco Javier; Armenski, Tanja; Del Barrio-García, Salvador. (2018). The antecedent role of online satisfaction, perceived risk online, and perceived website usability on the affect towards travel destinations. Journal of Destination Marketing & Management, 9, 20-35. doi:10.1016/j.jdmm.2017.09.005

Aldás‐Manzano, J. L. N., Carlos; Ruiz‐Mafé, Carla; Sanz‐Blas, Silvia. (2009). The role of consumer innovativeness and perceived risk in online banking usage. International Journal of Bank Marketing, 27(1), 53-75. doi:10.1108/02652320910928245

Arbuckle, J. L. (2016). IBM® SPSS® Amos™ User’s Guide.

Balasubramanian, N. T., & Lingam, S. A. K. D. (2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37, 8-22. doi:10.1016/j.jretconser.2017.02.010

Basri, N. A. m. H. A., Roslina; Anuar, Faiz I.; Ismail, Khairul Azam. (2016). Effect of Word of Mouth Communication on Consumer Purchase Decision: Malay Upscale Restaurant. Procedia - Social and Behavioral Sciences, 222, 324-331. doi:10.1016/j.sbspro.2016.05.175

Bhatiasevi, V., & Yoopetch, C. (2015). The determinants of intention to use electronic booking among young users in Thailand. Journal of Hospitality and Tourism Management, 23, 1-11. doi:10.1016/j.jhtm.2014.12.004

Bhukya, R., & Singh, S. (2015). The effect of perceived risk dimensions on purchase intention. American Journal of Business, 30(4), 218-230. doi:10.1108/ajb-10-2014-0055

Chao, C. M. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front Psychol, 10, 1652. doi:10.3389/fpsyg.2019.01652

Chen, L. S.-L. (2010). The impact of perceived risk, intangibility and consumer characteristics on online game playing. Computers in Human Behavior, 26(6), 1607-1613. doi:10.1016/j.chb.2010.06.008

Chi, T. (2018). Understanding Chinese consumer adoption of apparel mobile commerce: An extended TAM approach. Journal of Retailing and Consumer Services, 44, 274-284. doi:10.1016/j.jretconser.2018.07.019

Dariyoush, J., & Nazimah, H. (2016). Forecasting patronage factors of Islamic credit card as a new e-commerce banking service: an integration of TAM with perceived religiosity and trust. Journal of Islamic Marketing. doi:10.1108/JIMA-07-2014-0050

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.

Dlodlo, N., & Dhurup, M. (2013). Selected Social Media Antecedents: Attitudes Towards And Behavioural Impacts On Its Usage Among Consumers In A Developing Country. Studia Universitatis Babes-Bolyai, 58(3), 90-109.

Evandio, A. (2020). Pengguna Aplikasi Video Conference di Indonesia, Zoom Pemenangnya. Retrieved from

Heikki, K. A., Töllinen, Janne, P., & Chanaka, J. (2014). Intention to use mobile customer relationship management systems. Industrial Management & Data Systems, 114(6), 966-978. doi:10.1108/IMDS-11-2013-0480

Hong, J.-C., Lin, P.-H., & Hsieh, P.-C. (2017). The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch. Computers in Human Behavior, 67, 264-272. doi:10.1016/j.chb.2016.11.001

Hotniar, M. S. S. S. (2013). How Word of Mouth Influence Brand Equity for Automotive Products in Indonesia. Procedia - Social and Behavioral Sciences, 81, 40-44. doi:10.1016/j.sbspro.2013.06.384

Iswara, G. T., Wialdy, K., & Sihombing, S. O. (2019). Predicting the Relationship of Antecedent Variables of Intention to Use: Empirical Analysis on E-Money Application. JDM (Jurnal Dinamika Manajemen), 10(2), 256-268.

Jackson, J. D., Yi, M. Y., & Park, J. S. (2013). An empirical test of three mediation models for the relationship between personal innovativeness and user acceptance of technology. Information & Management, 50(4), 154-161. doi:10.1016/

Juansyah, A. (2015). Pembangunan Aplikasi Child Tracker Berbasis Assisted – Global Positioning System (A-Gps) Dengan Platform Android. Jurnal Ilmiah Komputer dan Informatika (KOMPUTA), 1(1).

Kai, L., Xiaowen, W., Kunrong, L., & Jianguo, C. (2016). Information privacy disclosure on social network sites: An empirical investigation from social exchange perspective. Nankai Business Review International, 7(3), 282-300. doi:10.1108/NBRI-02-2015-0005

Kang, H., Lee, M. J., & Lee, J. K. (2012). Are You Still with Us? A Study of the Post-Adoption Determinants of Sustained Use of Mobile-Banking Services. Journal of Organizational Computing and Electronic Commerce, 22(2), 132-159. doi:10.1080/10919392.2012.667710

Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322. doi:10.1016/j.chb.2009.10.013

Lee, M.-c. (2009). Factors Influencing the Adoption of Internet Banking: An Integration of TAM and TPB with Perceived Risk and Perceived Benefit. Electronic Commerce Research and Applications, 8(3), 130-141. doi:10.1016/j.elerap.2008.11.006

Lee, S. M., & Trimi, S. (2021). Convergence innovation in the digital age and in the COVID-19 pandemic crisis. J Bus Res, 123, 14-22. doi:10.1016/j.jbusres.2020.09.041

Li, R., Chung, T.-L., & Fiore, A. M. (2017). Factors affecting current users’ attitude towards e-auctions in China: An extended TAM study. Journal of Retailing and Consumer Services, 34, 19-29. doi:10.1016/j.jretconser.2016.09.003

Lingam, N. T. B. S. A. K. D. (2018). The moderating role of device type and age of users on the intention to use mobile shopping applications. Technology in Society, 53, 79-90. doi:10.1016/j.techsoc.2018.01.003

Marriott, H. R., & Williams, M. D. (2018). Exploring consumers perceived risk and trust for mobile shopping: A theoretical framework and empirical study. Journal of Retailing and Consumer Services, 42, 133-146. doi:10.1016/j.jretconser.2018.01.017

Nunnaly, J. C., & Bernstein, I. H. (1994). Psychometric Theory.

Nzaramyimana, L., & Susanto, T. D. (2019). Analysis of Factors Affecting Behavioural Intention to Use EGovernment Services in Rwanda. The Fifth Information Systems International Conference 2019.

Pavlou, P. A. (2014). 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

Prendergast, G., Ko, D., & Siu Yin, V. Y. (2015). Online word of mouth and consumer purchase intentions. International Journal of Advertising, 29(5), 687-708. doi:10.2501/s0265048710201427

Rahman, F., & Soesilo, P. K. M. (2018). The effect of information exposure of contract manufacturing practice on consumers' perceived risk, perceived quality, and intention to purchase private label brand. Journal of Retailing and Consumer Services, 42, 37-46. doi:10.1016/j.jretconser.2018.01.010

Ratriani, V. R. (2020). Jokowi Instruksikan Bekerja dari Rumah, Ini Arti Work From Home. Retrieved from

Raymond, R. K. (2015). When Word-Of-Mouth Goes Online: Evaluating The Characteristics And Effects Of Ewom Communication. International Journal of Arts & Sciences,, 8(5), 499-508.

Rini, G. P. (2011). Studi Mengenai Pengaruh Persepsi Nilai, Persepsi Risiko Dan Kesadaran Merek Terhadap Minat Mengambil Kredit Pt Bank Tabungan Pensiunan Nasional, Tbk Kcp Pecangaan. Universitas Diponegoro, Semarang.

Rogers, E. M. (1983). Diffusion of Innovations: The Free Press.

Rouibah, K., Lowry, P. B., & Hwang, Y. (2016). The effects of perceived enjoyment and perceived risks on trust formation and intentions to use online payment systems: New perspectives from an Arab country. Electronic Commerce Research and Applications, 19, 33-43. doi:10.1016/j.elerap.2016.07.001

Sandhu, R. A. L. H. K. (2017). Factors Influencing Users‟ Intentions to Use Mobile Government Applications in Saudi Arabia: TAM Applicability. International Journal of Advanced Computer Science and Applications, 8(7), 200-211.

Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. doi:10.1016/j.compedu.2018.09.009

Singh, N., & Sinha, N. (2020). How perceived trust mediates merchant's intention to use a mobile wallet technology. Journal of Retailing and Consumer Services, 52. doi:10.1016/j.jretconser.2019.101894

Stephanie, C. (2020). Layanan Google Melonjak Selama Wabah Covid-19. Retrieved from

Szlezak, M. R. N. L. P. C. (2020). CRISIS MANAGEMENT: Lead Your Business Through the Coronavirus Crisis. In (pp. 7).

Turja, T. A., Iina; Taipale, Sakari; Oksanen, Atte. (2019). Robot acceptance model for care (RAM-care): A principled approach to the intention to use care robots. Information & Management. doi:10.1016/

Verma, S., Bhattacharyya, S. S., & Kumar, S. (2018). An extension of the technology acceptance model in the big data analytics system implementation environment. Information Processing & Management, 54(5), 791-806. doi:10.1016/j.ipm.2018.01.004

Wang, L. Y., Lew, S. L., Lau, S. H., & Leow, M. C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6), e01788. doi:10.1016/j.heliyon.2019.e01788

Wang, Y., & Yu, C. (2015). Social interaction-based consumer decision-making model in social commerce: The role of word of mouth and observational learning. International Journal of Information Management. doi:10.1016/j.ijinfomgt.2015.11.005

Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232. doi:10.1016/j.chb.2016.10.028

Xia, M., Zhang, Y., & Zhang, C. (2017). A TAM-based approach to explore the effect of online experience on destination image: A smartphone user's perspective. Journal of Destination Marketing & Management. doi:10.1016/j.jdmm.2017.05.002

Yang, K. C. C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and Informatics, 22. doi:10.1016/j.tele.2004.11.003

Yang, S., Chen, Y., & Wei, J. (2015). Understanding Consumers' Web-Mobile Shopping Extension Behavior: A Trust Transfer Perspective. Journal of Computer Information Systems, 55(2), 78-87. doi:10.1080/08874417.2015.11645759