PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

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• Al-Madhrahi, Z., Singh, D., & Yadegaridehkordi, E. (2022). Integrating big data analytics into business process modelling: Possible contributions and challenges. International Journal of Advanced Computer Science and Applications, 13(6), 461–468. https://doi.org/10.14569/IJACSA.2022.0130657
  
  Barba-González, C., Caballero, I., Varela-Vaca, Á. J., Cruz-Lemus, J. A., Gómez-López, M. T., & Navas-Delgado, I. (2024). BIGOWL4DQ: Ontology-driven approach for big data quality meta-modelling, selection and reasoning. Information and Software Technology, 167, Article 107378. https://doi.org/10.1016/j.infsof.2023.107378
  
  Bui, K. Q., & Perera, L. P. (2021). Advanced data analytics for ship performance monitoring under localized operational conditions. Ocean Engineering, 235, Article 109392. https://doi.org/10.1016/j.oceaneng.2021.109392
  
  Chang, X., Huang, Y., Li, M., Bo, X., & Kumar, S. (2021). Efficient detection of environmental violators: A big data approach. Production and Operations Management, 30(5), 1246–1270. https://doi.org/10.1111/poms.13272
  
  Chen, C., Choi, H. S., & Ractham, P. (2022). Data, attitudinal and organizational determinants of big data analytics systems use. Cogent Business & Management, 9(1), Article 2043535. https://doi.org/10.1080/23311975.2022.2043535
  
  Chen, Y., Bai, R., Wu, Y., Li, T., & Zhou, H. (2023). A multidimensional data utility evaluation and pricing scheme in the big data market. Wireless Communications and Mobile Computing, 2023(1), Article 6217495. https://doi.org/10.1155/2023/6217495
  
  Clarke, V., & Braun, V. (2013). Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), 120–123.
  
  Côrte-Real, N., Ruivo, P., & Oliveira, T. (2020). Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value? Information & Management, 57, Article 103141. https://doi.org/10.1016/j.im.2019.01.003
  
  Hao, X., & Demir, E. (2024). Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol. Journal of Modelling in Management, 19(2), 605–629. https://doi.org/10.1108/JM2-01-2023-0009
  
  Hart, P., He, L., Wang, T., Kumar, V. S., Aggour, K., Subramanian, A., & Yan, W. (2022). Application of big data analytics and machine learning to large-scale synchrophasor datasets: Evaluation of dataset ‘Machine Learning-Readiness’. IEEE Open Access Journal of Power and Energy, 9, 386–397. https://doi.org/10.1109/OAJPE.2022.3197553
  
  Jha, A. K., Agi, M. A. N. N., & Ngai, E. W. T. T. (2020). A note on big data analytics capability development in supply chain. Decision Support Systems, 138(2020), Article 113382. https://doi.org/10.1016/j.dss.2020.113382
  
  Johnson, D. S., Sihi, D., & Muzellec, L. (2021). Implementing big data analytics in marketing departments: Mixing organic and administered approaches to increase data-driven decision making. Informatics, 8(4), Article 66. https://doi.org/10.3390/informatics8040066
  
  Konanahalli, A., Marinelli, M., & Oyedele, L. (2022). Drivers and challenges associated with the implementation of big data within U.K. facilities management sector: An exploratory factor analysis approach. IEEE Transactions on Engineering Management, 69(4), 916–929. https://doi.org/10.1109/TEM.2019.2959914
  
  Lavalle, A., Teruel, M. A., Maté, A., & Trujillo, J. (2020). Improving sustainability of smart cities through visualization techniques for big data from IoT devices. Sustainability, 12(14), Article 5595. https://doi.org/10.3390/su12145595
  
  Medeiros, M. M., MaçAda, A. C. G., & Hoppen, N. (2021). The role of big data stewardship and analytics as enablers of corporate performance management. Revista de Administracao Mackenzie, 22(6), Article eRAMD210063. https://doi.org/10.1590/1678-6971/eRAMD210063
  
  Patrucco, A. S., Marzi, G., & Trabucchi, D. (2023). The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions. Technovation, 126(2023), Article 102814. https://doi.org/10.1016/j.technovation.2023.102814
  
  Phan, D. T., & Tran, L. Q. T. (2022). Building a conceptual framework for using big data analytics in the banking sector. Intellectual Economics, 16(1), 5–23. https://doi.org/10.13165/IE-22-16-1-01
  
  Radhakrishnan, J., Gupta, S., & Prashar, S. (2022). Understanding organizations’ artificial intelligence journey: A qualitative approach. Pacific Asia Journal of the Association for Information Systems, 14(6), 43–77. https://doi.org/10.17705/1pais.14602
  
  Rana, N. P., Chatterjee, S., Dwivedi, Y. K., & Akter, S. (2022). Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm’s operational inefficiency and competitiveness. European Journal of Information Systems, 31(3), 364–387. https://doi.org/10.1080/0960085X.2021.1955628
  
  Savoska, S., & Ristevski, B. (2020). Towards implementation of big data concepts in a pharmaceutical company. Open Computer Science, 10(1), 343–356. https://doi.org/10.1515/comp-2020-0201
  
  Shahi, K. (2023). Volunteered Geographic Information (VGI) in Spatial Data Infrastructure (SDI) continuum. EAI Endorsed Transactions on Internet of Things, 9(1), Article e3. https://doi.org/10.4108/eetiot.v9i1.2979
  
  Shidaganti, G., & Prakash, S. (2021). A comprehensive framework for big data analytics in education. International Journal of Advanced Computer Science and Applications, 12(9), 218–227. https://doi.org/10.14569/IJACSA.2021.0120926
  
  Song, F. (2024). Incorporating Morris’ design thoughts for AI and big data-enabled coverage optimization in China’s wireless communication network. Journal of Information Systems Engineering and Management, 9(1), Article 23622. https://doi.org/10.55267/iadt.07.14076
  
  Song, J., Xia, S., Vrontis, D., Sukumar, A., Liao, B., Li, Q., Tian, K., & Yao, N. (2022). The source of SMEs’ competitive performance in COVID-19: Matching big data analytics capability to business models. Information Systems Frontiers, 24, 1167–1187. https://doi.org/10.1007/s10796-022-10287-0
  
  Spanaki, K., Karafili, E., & Despoudi, S. (2021). AI applications of data sharing in agriculture 4.0: A framework for role-based data access control. International Journal of Information Management, 59, Article 102350. https://doi.org/10.1016/j.ijinfomgt.2021.102350
  
  Šprem, Š., Tomažin, N., Matečić, J., & Horvat, M. (2024). Building advanced web applications using data ingestion and data processing tools. Electronics, 13(4), Article 0709. https://doi.org/10.3390/electronics13040709
  
  Stach, C., Behringer, M., Bräcker, J., Gritti, C., & Mitschang, B. (2022). SMARTEN - A sample-based approach towards privacy-friendly data refinement. Journal of Cybersecurity and Privacy, 2(3), 606–628. https://doi.org/10.3390/jcp2030031
  
  Szukits, Á., & Móricz, P. (2023). Towards data-driven decision making: The role of analytical culture and centralization efforts. Review of Managerial Science, 18(10), 2849-2887. https://doi.org/10.1007/s11846-023-00694-1
  
  Teh, H. Y., Kempa-Liehr, A. W., & Wang, K. I. K. (2020). Sensor data quality: A systematic review. Journal of Big Data, 7(1), 1–49. https://doi.org/10.1186/s40537-020-0285-1
  
  Timotijevic, L., Carr, I., De La Cueva, J., Eftimov, T., Hodgkins, C. E., Seljak, B. K., Mikkelsen, B. E., Selnes, T., Van’t Veer, P., & Zimmermann, K. (2022). Responsible governance for a food and nutrition e-infrastructure: Case study of the determinants and intake data platform. Frontiers in Nutrition, 8, Article 795802. https://doi.org/10.3389/fnut.2021.795802
  
  Widad, E., Saida, E., & Gahi, Y. (2023). Quality anomaly detection using predictive techniques: An extensive big data quality framework for reliable data analysis. IEEE Access, 11, 103306–103318. https://doi.org/10.1109/ACCESS.2023.3317354
  
  Wook, M., Hasbullah, N. A., Zainudin, N. M., Jabar, Z. Z. A., Ramli, S., Razali, N. A. M., & Yusop, N. M. M. (2021). Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling. Journal of Big Data, 8(1), 1-15. https://doi.org/10.1186/s40537-021-00439-5
  
  Wurster, F., Beckmann, M., Cecon-Stabel, N., Dittmer, K., Jes Hansen, T., Jaschke, J., Köberlein-Neu, J., Okumu, M. R., Rusniok, C., Pfaff, H., & Karbach, U. (2024). The implementation of an electronic medical record in a German Hospital and the change in completeness of documentation: Longitudinal document analysis. JMIR Medical Informatics, 12(1), Article e47761. https://doi.org/10.2196/47761
  
  Yahia, N. B., Hlel, J., & Colomo-Palacios, R. (2021). From big data to deep data to support people analytics for employee attrition prediction. IEEE Access, 9, 60447–60458. https://doi.org/10.1109/ACCESS.2021.3074559
  
  Yu, J., Taskin, N., Nguyen, C. P., Li, J., & Pauleen, D. J. (2022). Investigating the determinants of big data analytics adoption in decision making: An empirical study in New Zealand, China, and Vietnam. Pacific Asia Journal of the Association for Information Systems, 14(4), 62–99. https://doi.org/10.17705/1pais.14403
  
  Zairul, M. (2020). A thematic review on student-centered learning in the studio education. Journal of Critical Reviews, 7(2), 504–511. https://doi.org/10.31838/jcr.07.02.95
  
  Zairul, M. (2021). A thematic review on Industrialised Building System (IBS) publications from 2015-2019: Analysis of patterns and trends for future studies of IBS in Malaysia. Pertanika Journal of Social Sciences and Humanities, 29(1), 635–652. https://doi.org/10.47836/PJSSH.29.1.35
  
  Zairul, M. (2023). Thematic Review template (Patent No. CRLY2023W02032). Controller of Copyright.
  
  Zairul, M., Azli, M., & Azlan, A. (2023). Defying tradition or maintaining the status quo? Moving towards a new hybrid architecture studio education to support blended learning post-COVID-19. Archnet-IJAR: International Journal of Architectural Research, 17(3), 554–573. https://doi.org/10.1108/ARCH-11-2022-0251
  
  Zairul, M., & Zaremohzzabieh, Z. (2023). Thematic trends in Industry 4.0 Revolution potential towards sustainability in the construction industry. Sustainability, 15, Article 7720. https://doi.org/10.3390/su15097720
  
  Zhang, G. (2022). Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation. ISPRS International Journal of Geo-Information, 11(1), Article 55. https://doi.org/10.3390/ijgi11010055