Roles of Perceived Knowledge, Risk, and Trust in Cybersecurity Solution Implementation A Study in Bangkok, Thailand
Article Sidebar
Main Article Content
Abstract
Thailand has adopted an economic model (Industrial 4.0) that merges physical manufacturing processes and services with digital connectiveness. Hence, cybersecurity cannot be ignored. The aim of this research was to promote cybersecurity awareness among technology and information executives, along with top-level managers, develop a more efficient security management system, and implement an effective cybersecurity framework for the private and public sectors in Thailand. An extension of the Technology Acceptance Model was developed that used three variables, namely, perceived knowledge of cybersecurity, perceived risk of cyberattacks, and perceived trust in cybersecurity solutions. A quantitative research approach was used to collect data from both online and offline survey forms (N = 394). Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling were used to analyze this data. The findings permitted the Technology Acceptance Model to be extended. Positive relationships were found among perceived knowledge of cybersecurity, perceived risk of cyberattacks, and perceived trust in cybersecurity solutions. These variables, together with perceived ease of use, usefulness, and attitude towards using cybersecurity solutions, all played a pivotal role in organizations/businesses in Thailand and their intention to implement cybersecurity solutions.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright: Asia-Pacific International University reserve exclusive rights to publish, reproduce and distribute the manuscript and all contents therein.
References
Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82, 261–277. https://doi.org/10.1037/h0076477
Alassafi, M. O. (2022). E-learning intention material using TAM: A case study. Materialstoday: Proceedings, 61, 873–877. https://doi: 10.1016/j.matpr.2021.09.457
Al-Zahrani, M. S. (2020). Integrating IS success model with cybersecurity factors for e-government implementation in the Kingdom of Saudi Arabia. International Journal of Electrical and Computer Engineering (IJECE), 10(5), 4937–4955. https://doi:10.11591/ijece.v10i5.pp4937-4955
Bada, M., & Nurse, J. R. (2019). Developing cybersecurity education and awareness programmes for small and medium-sized enterprises (SMEs). Information and Computer Security, 27(3), 393–410. https://doi: 10.1108/ICS-07-2018-0080
Basuki, R., Tarigan, Z. J., Siagian, H., Limanta, L. S., Setiawan, D., & Mochtar, J. (2021). The effects of perceived ease of use, usefulness, enjoyment and intention to use online platforms on behavioral intention in online movie watching during the pandemic era. International Journal of Data and Network Science, 6(1), 253–262. https://doi: 10.5267/j.ijdns.2021.9.003
Bhuyan, S. S., Kabir, U. Y., Escareno, J. M., Ector, K., Palakodeti, S., Wyant, D., Kumar, S., Levy, M., Kedia, S., Dasgupta, D., & Dobalian, A. (2020). Transforming healthcare cybersecurity from reactive to proactive: Current status and future recommendations. Journal of Medical Systems, 44(5), 98. https://doi.org/10.1007/s10916-019-1507-y
Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. https://doi:10.1016/j.technovation.2021.102312
Catal, C., Ozcan, A., Donmez, E., & Kasif, A. (2022). Analysis of cyber security knowledge gaps based on cyber security body of knowledge. Education and Information Technologies, 28, 1809–1831. https://doi:10.1007/s10639-022-11261-8
Chen, L., & Aklikokou, A. K. (2019). Determinants of E-government adoption: Testing the mediating effects of perceived usefulness and perceived ease of use. International Journal of Public Administration, 43(1), 850–865. https://doi.org/10.1080/01900692.2019.1660989
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
Coulter, K. S., & Coulter, R. A. (2003). The effects of industry knowledge on the development of trust in service relationships. International Journal of Research in Marketing, 20(1), 31–43. https://doi:10.1016/S0167-8116(02)00120-9
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. https://www.jstor.org/stable/2632151
Faulkner, P. (2011). The epistemology of testimony: Knowledge on trust. Oxford University Press. https://doi:10.1093/acprof:oso/9780199589784.001.0001
Fung, S. (2023, July 7). Cybercrime in Thailand: Current trends and solutions. Pacific Prime Thailand. https://www.pacificprime.co.th/blog/cybercrime-thailand-trends/
Hair J. F., Jr., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hansen, J. M., Saridakis, G., & Benson, V. (2017). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in Human Behavior, 81, 197–206. https//doi:10.1016/j.chb.2017.11.010
Humida, T., Mamun, M. B., & Keikhoscrokiani, P. (2021). Predicting behavioral intention to use e learning system: A case study in Begum Rokeya University, Rangpur, Bangladesh. Education and Information Technologies, 27(2), 2241–2265. https//doi:10.1007/s10639-021-10707-9
Hwang, J., Lee, J.-S., & Kim, H. (2019). Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age. International Journal of Hospitality Management, 81, 94–103. https//doi:10.1016/j.ijhm.2019.03.002
Karim, M.W., Haque, A., Ulfy, M. A., Hossain, M. A., & Anis, M. Z. (2020). Factors influencing the use of E-wallet as a payment method among Malaysian young adults. Journal of International Business and Management, 3(2), 1–12. https//doi:10.37227/jibm-2020-2-21
Kate, P. T. (2021, August 18). Cyber attacks more than double since COVID-19, PwC Thailand says. PwC Thailand. https://www.pwc.com/th/en/press-room/press-release/2021/press-release-18-08-21-en.html
Khoa, B. T. (2020). The impact of the personal data disclosure's tradeoff on the trust and attitude loyalty in mobile banking services. Journal of Promotion Management, 27(4), 585–608. https//doi:10.1080/10496491.2020.1838028
Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404–16. https//doi:10.1016/j.ijmedinf.2008.12.005
Kim, R., & Song, H.D. (2022). Examining the influence of teaching presence and task-technology fit on continuance intention to use MOOCs. The Asia-Pacific Education Researcher, 31, 395–408. https://doi.org/10.1007/s40299-021-00581-x
Kini, A., & Choobineh, J. (1998). Trust in electronic commerce: Definition and theoretical considerations. Proceedings of the Thirty-First Hawaii International Conference on System Sciences, 4, 51–61. https//doi.org/10.1109/HICSS.1998.655251
Klerck, D., & Sweeney, J. C. (2007). Types on consumer-perceived risk and adoption of genetically modified foods. Psychology and Marketing, 24(2), 171–193. https//doi:10.1002/mar.20157
Kuo, YF, & Yen, SN (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103–110. https://doi.org/10.1016/j.chb.2008.07.007
Liao, CH, Tsou, CW, & Huang, MF. (2007). Factors influencing the usage of 3G mobile services in Taiwan. Online Information Review, 31(6), 759–774. https://doi:10.1108/14684520710841757
Limbu, Y. B., Wolf, M., & Lusford, D. (2012). Perceived ethics of online retailers and consumer behavioral intentions: The mediating roles of trust and attitude. Journal of Research in Interactive Marketing, 6(2), 133–154. https://doi.org/10.1108/17505931211265435
Liu, P., Teng, M., & Han, C. (2020). How does environmental knowledge translate into pro-environmental behaviors?: The mediating role of environmental attitudes and behavioral intentions. Science of the Total Environment, 728. 138126. https://doi:10.1016/j.scitotenv.2020.138126
Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e learning during the COVID 19 pandemic: An extended TAM model. Education and Information Technologies, 26, 7057–7077. https://doi:10.1007/s10639-021-10557-5
Minghao, P., & Wei, G. (2021). Determinants of the behavioral intention to use a mobile nursing application by nurses in China. BMC Health Services Research, 21, 228. https://doi:10.1186/s12913-021-06244-3
Morgan, S. (2020, November 13). Cybercrime to cost the world $10.5 trillion annually by 2025. Cybercrime Magazine. https://cybersecurityventures.com/cybercrime-damage-costs-10-trillion-by-2025/
Mortensen, M., & Neeley, T. B. (2012). Reflected knowledge and trust in global collaboration. Management Science, 58, 2207–2224. https://:10.2307/23359587
National Cyber Security Agency (NCSA). (2023). Annual Report 2022. https://www.dlt.go.th/web-upload/ 1xff0d34e409a13ef56eea54c52a291126/filecenter/admin_admin.k/cybersec/Annual%20Report%20NCSA%202022.pdf
National Institute of Standards and Technology (NIST). (2018). Framework for improving critical infrastructure cybersecurity. https://doi.org/10.6028/NIST.CSWP.04162018
Park, C. W., Gardner, M. P., & Thukral, V. K. (1987). Self-perceived knowledge: Some effects on information processing for a choice task. The American Journal of Psychology 101(3), 401–424. https://doi:10.2307/1423087
Sinclair, J., & Irani, T. (2013). Advocacy advertising for biotechnology: The effect of public accountability on corporate trust and attitude toward the ad. Journal of Advertising, 34(3), 59–73. https://doi:10.1080/00913367.2005.10639203
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & 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), e05410. https://doi: 10.1016/j.heliyon. 2020.e05410
The Economist. (2017, May 6). The world’s most valuable resource is no longer oil, but data. https://www. economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data
Trumbo, C. W. (1999). Heuristic‐systematic information processing and risk judgment. Risk Analysis, 19(3), 391–400. https://doi: 10.1023/a:1007092410720
Trumbo, C. W., & McComas, K. A. (2003). The function of credibility in information processing for risk perception. Risk Analysis, 23(2), 343–53. https://doi: 10.1111/1539-6924.00313
Zhu, W., Wei, J., & Zhao, D. (2016). Anti-nuclear behavioral intentions: The role of perceived knowledge, information processing, and risk perception. Energy Policy, 88, 168–177. https://doi.org/10.1016/j.enpol.2015.10.009
Zywiolek, J., Rosak-Szyrocka, J., Khan, M. A., & Sharif, A. (2022). Trust in renewable energy as part of energy-saving knowledge. Energies, 15(4), 1566. https://doi:10.3390/en15041566