International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 12, Issue 1 (January 2025), Pages: 69-77

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 Original Research Paper

Understanding data privacy: How Bangkok workers perceive Thai personal data protection act regulations

 Author(s): 

 Kattakamon Pislae-Ngam *, Sureerut Inmor

 Affiliation(s):

 Department of Information Systems, Faculty of Business Administration, Rajamangala University of Technology Thanyaburi, Pathum Thani, Thailand

 Full text

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-5641-3926

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2025.01.006

 Abstract

This study examined how demographic factors influence awareness of the Personal Data Protection Act (PDPA) among workers in Bangkok. Data were collected from 389 individuals using a structured questionnaire and analyzed using descriptive statistics (percentage, mean, standard deviation) and inferential statistics (one-way ANOVA and post-hoc analysis with the Least Significant Difference method). The findings show that overall awareness of the PDPA among Bangkok's working population is high. However, demographic factors such as gender, age, education, occupation, and organizational type do not significantly affect awareness. In contrast, work experience has a statistically significant influence on PDPA awareness at the 0.05 level, supporting the study's hypothesis. These results suggest that while traditional demographic factors are not key predictors of PDPA awareness, individuals with greater work experience tend to have a better understanding of the law. This study provides empirical evidence highlighting the limited role of demographic characteristics in shaping PDPA awareness and identifies work experience as a crucial factor. The findings can help organizations design targeted strategies, such as training programs, to improve PDPA awareness and compliance across different experience levels in the workforce.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 PDPA awareness, Demographic factors, Work experience, Compliance strategies, Bangkok workforce

 Article history

 Received 29 August 2024, Received in revised form 12 December 2024, Accepted 21 December 2024

 Funding

This research was funded by the Human Resource Development Fund. Personnel Development Project with Professional Experience Training in Workplaces 2023, Faculty of Business Administration, Rajamangala University of Technology, Thanyaburi.

 Acknowledgment

No Acknowledgment.

 Compliance with ethical standards

 Ethical considerations

Participants provided informed consent, and their data were handled with confidentiality. The study adhered to ethical principles for research involving human subjects.

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Pislae-Ngam K and Inmor S (2025). Understanding data privacy: How Bangkok workers perceive Thai personal data protection act regulations. International Journal of Advanced and Applied Sciences, 12(1): 69-77

 Permanent Link to this page

 Figures

 Fig. 1 

 Tables

 Table 1 Table 2 Table 3 

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 References (32)

  1. An Q, Hong WCH, Xu X, Zhang Y, and Kolletar-Zhu K (2023). How education level influences internet security knowledge, behaviour, and attitude: A comparison among undergraduates, postgraduates and working graduates. International Journal of Information Security, 22(2): 305-317. https://doi.org/10.1007/s10207-022-00637-z   [Google Scholar] PMid:36466362 PMCid:PMC9702960
  2. Baharum Z, Ahmad F, Qureshi MI, Nasien D, and Adiya MH (2023). Mobile-based applications: The legal challenges on data privacy. International Journal of Online and Biomedical Engineering, 19(9): 4-14. https://doi.org/10.3991/ijoe.v19i09.40915   [Google Scholar]
  3. Bertolaccini L, Falcoz PE, Brunelli A, Batirel H, Furak J, Passani S, and Szanto Z (2023). The significance of general data protection regulation in the compliant data contribution to the European Society of Thoracic Surgeons database. European Journal of Cardio-Thoracic Surgery, 64(3): ezad289. https://doi.org/10.1093/ejcts/ezad289   [Google Scholar] PMid:37589648
  4. Bumpenboon T (2020). Thailand's personal data protection act: An understanding from the perspectives of the European privacy law. Thammasat Review of Economic and Social Policy, 6(1): 50-82.   [Google Scholar]
  5. Chatsuwan P, Phromma T, Surasvadi N, and Thajchayapong S (2023). Personal data protection compliance assessment: A privacy policy scoring approach and empirical evidence from Thailand's SMEs. Heliyon, 9(10): e20648. https://doi.org/10.1016/j.heliyon.2023.e20648   [Google Scholar] PMid:37886776 PMCid:PMC10597812
  6. Chokesuwattanaskul P (2021). The nexus between competition and personal data protection laws: Thailand's perspective. In: Van Uytsel S (Ed.), The digital economy and competition law in Asia, Springer, Cham, Switzerland. https://doi.org/10.1007/978-981-16-0324-2_4   [Google Scholar]
  7. Corning GP (2024). The diffusion of data privacy laws in Southeast Asia: learning and the extraterritorial reach of the EU's GDPR. Contemporary Politics, 30(5): 565-677. https://doi.org/10.1080/13569775.2024.2310220   [Google Scholar]
  8. Deepa S, Umamageswari A, Neelakandan S, Bhukya H, Sai Lakshmi Haritha IV, and Shanbhog M (2024). Deep belief network-based user and entity behavior analytics (UEBA) for web applications. International Journal of Cooperative Information Systems, 33(2): 2350016. https://doi.org/10.1142/S0218843023500168   [Google Scholar]
  9. Dowpiset K and Nuangjamnong C (2021). An investigation of factors affecting intention to comply Thailand PDPA with e-services in private university towards social media. International Journal of Economics and Business Administration, 9(2): 374-393. https://doi.org/10.35808/ijeba/709   [Google Scholar]
  10. Gao M (2021). Mitigating selective exposure in social media forums. Ph.D. Dissertation, University of Illinois at Urbana-Champaign, Champaign, USA.   [Google Scholar]
  11. Ha H and Chuah CP (2023). Digital economy in Southeast Asia: Challenges, opportunities and future development. Southeast Asia: A Multidisciplinary Journal, 23(1): 19-35. https://doi.org/10.1108/SEAMJ-02-2023-0023   [Google Scholar]
  12. Hamidon H, Radzi SM, Alias NR, Arifin N, and Zukarnain ZA (2022). Personal data abuse: Preliminary survey among Malaysian youth netizens. Journal of Information and Knowledge Management, 1: 192-210.   [Google Scholar]
  13. Hariansah S, Suganda A, Sinaulan RL, and Angelina M (2023). Personal data protection in ASEAN: Indonesia's role in developing ASEAN's personal data protection legal framework. Novateur Publications, 1: 453-465.   [Google Scholar]
  14. Kanungo S (2024). Consumer protection in cross-border FinTech transactions. International Journal of Multidisciplinary Innovation and Research Methodology, 3(1): 48-51.   [Google Scholar]
  15. Kennedy H, Oman S, Taylor M, Bates J, and Steedman R (2021). Public understanding and perceptions of data practices: A review of existing research. University of Sheffield, Sheffield, UK.   [Google Scholar]
  16. Khan NF, Ikram N, and Saleem S (2023). Digital divide and socio-economic differences in smartphone information security behaviour among university students: Empirical evidence from Pakistan. International Journal of Mobile Communications, 22(1): 1-24. https://doi.org/10.1504/IJMC.2023.131802   [Google Scholar]
  17. Klymenko O, Kosenkov O, Meisenbacher S, Elahidoost P, Mendez D, and Matthes F (2022). Understanding the implementation of technical measures in the process of data privacy compliance: A qualitative study. In Proceedings of the 16th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ACM, Helsinki, Finland: 261-271. https://doi.org/10.1145/3544902.3546234   [Google Scholar]
  18. Nanjundeswaraswamy TS and Divakar S (2021). Determination of sample size and sampling methods in applied research. Proceedings on Engineering Sciences, 3(1): 25-32. https://doi.org/10.24874/PES03.01.003   [Google Scholar]
  19. Niosi A (2021). Introduction to consumer behaviour. BCcampus Open Education, Victoria, Canada.   [Google Scholar]
  20. Payton T and Claypoole T (2023). Privacy in the age of big data: Recognizing threats, defending your rights, and protecting your family. Rowman and Littlefield, Lanham, USA. https://doi.org/10.5771/9781538167830   [Google Scholar]
  21. Penn JM, Petrolia DR, and Fannin JM (2023). Hypothetical bias mitigation in representative and convenience samples. Applied Economic Perspectives and Policy, 45(2): 721-743. https://doi.org/10.1002/aepp.13374   [Google Scholar]
  22. Pimdee P and Leekitchwatana P (2022). Appropriate Internet use behavior (AIUB) of Thai preservice teachers: A hierarchical linear model (HLM) analysis. International Journal of Instruction, 15(1): 489-508. https://doi.org/10.29333/iji.2022.15128a   [Google Scholar]
  23. Prawiyogi AG (2023). Southeast Asia's cyber security strategy: Multilateralism or self-help. IAIC Transactions on Sustainable Digital Innovation, 4(2): 119-127. https://doi.org/10.34306/itsdi.v4i2.581   [Google Scholar]
  24. Preeputtarat W, Sutthisai W, Kietjareon S, Saiyaros R, and Boonruan S (2024). Assessing the quality of life in a rural Thai province: A case study. Edelweiss Applied Science and Technology, 8(4): 1106-1118. https://doi.org/10.55214/25768484.v8i4.1485   [Google Scholar]
  25. Rangaraju S (2023). Secure by intelligence: Enhancing products with AI-driven security measures. EPH-International Journal of Science and Engineering, 9(3): 36-41. https://doi.org/10.53555/ephijse.v9i3.212   [Google Scholar]
  26. Rehan H (2024). AI-driven cloud security: The future of safeguarding sensitive data in the digital age. Journal of Artificial Intelligence General Science, 1(1): 132-151. https://doi.org/10.60087/jaigs.v1i1.89   [Google Scholar]
  27. Richmond G, Cho C, Gallagher HA, He Y, and Bartell T (2021). Fast and slow thinking to address persistent and complex problems in teaching and learning. Journal of Teacher Education, 72(4): 401-404. https://doi.org/10.1177/00224871211030367   [Google Scholar]
  28. Schillaci MA and Schillaci ME (2022). Estimating the population variance, standard deviation, and coefficient of variation: Sample size and accuracy. Journal of Human Evolution, 171: 103230. https://doi.org/10.1016/j.jhevol.2022.103230   [Google Scholar] PMid:36115144
  29. Sudarwanto AS and Kharisma DBB (2022). Comparative study of personal data protection regulations in Indonesia, Hong Kong, and Malaysia. Journal of Financial Crime, 29(4): 1443-1457. https://doi.org/10.1108/JFC-09-2021-0193   [Google Scholar]
  30. Sudirman L, Disemadi HS, and Aninda AM (2023). Comparative analysis of personal data protection laws in Indonesia and Thailand: A legal framework perspective. Jurnal Etika Demokrasi, 8(4): 497-510.   [Google Scholar]
  31. Wong B (2021). Data protection implications of modern employee monitoring software. Singapore Academy of Law Journal, 33: 101-126.   [Google Scholar]
  32. Yuspin W, Wardiono K, Nurrahman A, and Budiono A (2023). Personal data protection law in digital banking governance in Indonesia. Studia Iuridica Lublinensia, 32(1): 99-130. https://doi.org/10.17951/sil.2023.32.1.99-130   [Google Scholar]