The Guide for Information Technology Management Regarding Personal Data Protection of the Industrial Business Sector in Thailand
Faculty of Business Administration, King Mongkut’s University of Technology North Bangkok, Thailand.
Faculty of Business Administration, King Mongkut’s University of Technology North Bangkok, Thailand.
Faculty of Business Administration, King Mongkut’s University of Technology North Bangkok, Thailand.
Abstract
The Personal Data Protection Act 2019 (PDPA) is a statute that governs the protection of personal data, making it crucial for the industrial business sector to adhere to the law and establish effective guidelines. This research aims to investigate the approaches for managing personal data protection in the industrial business sector through qualitative and quantitative research methods. The qualitative research included In-Depth Interviews with nine experts, as well as Focus Group Discussions with 11 qualified specialists. The quantitative study involved a survey of 500 executives within the industrial business sector, with the data analysed using descriptive, inferential, and multivariate statistics. The findings revealed that the Guidelines for Information Technology Management concerning Personal Data Protection in the Industrial Business Sector in Thailand comprise four key aspects, ranked according to their mean levels of importance as follows: 1) Laws and Governance (X̄ = 4.20), 2) Audit and Evaluation (X̄ = 4.19), 3) Internal Control (X̄ = 4.16), and 4) Organisational Support (X̄ = 4.15). The most significant aspects identified within each category were: understanding the legal standards associated with the Personal Data Protection Act, establishing regular audits and monitoring of personal data storage in accordance with legal standards, developing guidelines for managing personal data security systems, and collaborating with leading external technology organisations such as Microsoft to assess the security of information systems. The hypothesis test results indicated that executives from different industrial business sectors, including Manufacturing and Services, consistently recognised a statistically significant level at 0.05. The analysis of the Structural Equation Model (SEM) demonstrated that the model met the assessment criteria and aligned with the empirical data. The assessed values for the chi-square probability, relative chi-square, goodness-of-fit index, and root mean square error of approximation were 0.188, 1.096, 0.965, and 0.014, respectively.