Transformation of Management Information Systems in the Digital Era: Integration of Big Data, Artificial Intelligence, and Strategic Decision-Making
DOI:
https://doi.org/10.55583/ijisim.v3i1.2055Keywords:
Management Information Systems, Digital Transformation, Big Data, Artificial Intelligence, Decision-MakingAbstract
The rapid development of digital technologies has driven organizations to adopt Management Information Systems (MIS) that are more adaptive, intelligent, and integrated. MIS no longer functions merely as an administrative record-keeping tool but has evolved into a strategic system that supports real-time, data-driven decision-making. This article aims to examine recent developments in Management Information Systems within the context of digital transformation, with a particular focus on the integration of big data, artificial intelligence (AI), and predictive analytics. The method employed is a literature review of international scholarly publications and recent industry reports. The findings indicate that modern MIS significantly enhances managerial decision effectiveness, operational efficiency, and organizational competitiveness. These results underscore that strengthening MIS through intelligent technologies has become a strategic necessity for organizations in the digital economy era.
References
Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). Digital transformation challenges and opportunities: A systematic literature review. Journal of Enterprise Information Management, 34(1), 1–22. https://doi.org/10.1108/JEIM-01-2020-0007
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. ACM Transactions on Human-Computer Interaction, 28(2), 1–44. https://doi.org/10.1145/3449285
Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383
Côrte-Real, N., Oliveira, T., & Ruivo, P. (2022). Assessing business value of big data analytics in European firms. Journal of Business Research, 142, 111–124. https://doi.org/10.1016/j.jbusres.2021.12.043
Davison, R. M., Wong, L. H. M., & Peng, J. (2022). The art of digital transformation as crafted by a chief digital officer. International Journal of Information Management, 69, 102617. https://doi.org/10.1016/j.ijinfomgt.2022.102617
Dennehy, D., Griva, A., Pouloudi, N., Mäntymäki, M., & Pappas, I. (2022). Artificial intelligence for decision-making and the future of work. International Journal of Information Management, 69, 102574. https://doi.org/10.1016/j.ijinfomgt.2022.102574
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Galanti, R., de Leoni, M., Monaro, M., Navarin, N., Marazzi, A., Di Stasi, B., & Maldera, S. (2023). An explainable decision support system for predictive process analytics. Engineering Applications of Artificial Intelligence, 120, 105904. https://doi.org/10.1016/j.engappai.2023.105904
Gkinko, L., & Elbanna, A. (2022). The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users. International Journal of Information Management, 69, 102568. https://doi.org/10.1016/j.ijinfomgt.2022.102568
Haenlein, M., & Kaplan, A. (2022). Artificial intelligence and robotics: Shaking up the business world and society at large. Journal of Business Research, 141, 1–15. https://doi.org/10.1016/j.jbusres.2021.11.036
Herm, L.-V., Heinrich, K., Wanner, J., & Janiesch, C. (2022). Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability. International Journal of Information Management, 69, 102538. https://doi.org/10.1016/j.ijinfomgt.2022.102538
Jackson, S., & Panteli, N. (2022). Trust or mistrust in algorithmic grading? An embedded agency perspective. International Journal of Information Management, 69, 102555. https://doi.org/10.1016/j.ijinfomgt.2022.102555
Kostopoulos, G., Davrazos, G., & Kotsiantis, S. (2024). Explainable artificial intelligence-based decision support systems: A recent review. Electronics, 13(14), 2842. https://doi.org/10.3390/electronics13142842
Kumar, P., Sharma, S. K., & Dutot, V. (2022). Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation. International Journal of Information Management, 69, 102598. https://doi.org/10.1016/j.ijinfomgt.2022.102598
Meske, C., & Bunde, E. (2023). Design principles for user interfaces in AI-based decision support systems: The case of explainable hate speech detection. Information Systems Frontiers, 25, 743–773. https://doi.org/10.1007/s10796-021-10234-5
Muchenje, G., & Seppänen, M. (2022). Unpacking task–technology fit to explore the business value of big data analytics. International Journal of Information Management, 69, 102619. https://doi.org/10.1016/j.ijinfomgt.2022.102619
Namvar, M., Intezari, A., Akhlaghpour, S., & Brienza, J. P. (2022). Beyond effective use: Integrating wise reasoning in machine learning development. International Journal of Information Management, 69, 102566. https://doi.org/10.1016/j.ijinfomgt.2022.102566
Oesterreich, T. D., Anton, E., Teuteberg, F., & Dwivedi, Y. K. (2022). The role of social and technical factors in creating business value from big data analytics: A meta-analysis. Journal of Business Research, 153, 128–149. https://doi.org/10.1016/j.jbusres.2022.08.028
Onari, M. A., Rezaee, M. J., Saberi, M., & Nobile, M. S. (2024). An explainable data-driven decision support framework for strategic customer development. Knowledge-Based Systems, 295, 111761. https://doi.org/10.1016/j.knosys.2024.111761
Plekhanov, D., Franke, H., & Netland, T. H. (2022). Digital transformation: A review and research agenda. European Management Journal, 41(6), 821–844. https://doi.org/10.1016/j.emj.2022.09.007
Schoonderwoerd, T. A. J., Jorritsma, W., Neerincx, M. A., & Van Den Bosch, K. (2021). Human-centered XAI: Developing design patterns for explanations of clinical decision support systems. International Journal of Human–Computer Studies, 154, 102684. https://doi.org/10.1016/j.ijhcs.2021.102684
Senoner, J., Netland, T., & Feuerriegel, S. (2022). Using explainable artificial intelligence to improve process quality: Evidence from semiconductor manufacturing. Management Science, 68(8), 5704–5723. https://doi.org/10.1287/mnsc.2021.4190
Sun, W., Zhang, X., Li, M., & Wang, Y. (2023). Interpretable high-stakes decision support system for credit default forecasting. Technological Forecasting and Social Change, 196, 122825. https://doi.org/10.1016/j.techfore.2023.122825
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
