Integration of Artificial Intelligence in Management Information Systems to Improve the Effectiveness of Strategic Decision-Making in the Digital Era

Istikha Ruchitra Hayudirga Wasesa, Dhyta Permatasari, Fhatiya Alzahra Angkat, Al-Khowarizmi Al-Khowarizmi

Abstract


The integration of Artificial Intelligence (AI) into Management Information Systems (MIS) has emerged as a strategic imperative for enhancing the effectiveness of organizational decision-making in the digital era. This study aims to analyze the factors influencing successful AI adoption in MIS, evaluate its impact on strategic decision-making effectiveness, and explore the mediating role of dynamic capabilities. Grounded in the Technology Acceptance Model (TAM) and Dynamic Capabilities Theory, a conceptual framework was developed and tested using a mixed-methods approach. Quantitative data were collected from 715 respondents across six industry sectors in Indonesia, while qualitative insights were derived from case studies in 25 organizations with varying levels of AI implementation maturity. Results from Structural Equation Modeling revealed that perceived usefulness, ease of use, organizational readiness, and management support significantly influence AI adoption in MIS. The integration of AI was found to improve decision quality (34.7%), speed (42.3%), predictive accuracy (28.6%), strategic alignment (31.2%), and risk assessment capabilities (36.8%). Qualitative findings highlighted key implementation challenges, including data quality, skills gaps, employee resistance, and integration complexity. This study contributes theoretically by enriching TAM with organizational and strategic dimensions, and practically by offering a comprehensive framework to guide AI integration in MIS for sustained competitive advantage.

Keywords


Artificial Intelligence; Management Information Systems; Strategic Decision-Making

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References


Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052

Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.

Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities.

Borges, A. F., Laurindo, F. J. B., Spínola, M. de M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225

Brynjolfsson, E., & McAfee, A. (2019). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.

Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., & Trench, M. (2020). The age of AI: Artificial intelligence and the future of humanity. McKinsey Global Institute.

Chandra, S., Verma, S., Lim, W. M., Kumar, S., & Donthu, N. (2022). Personalization in personalized marketing: Trends and ways forward. Psychology & Marketing, 39(8), 1529–1562. https://doi.org/10.1002/mar.21670

Chen, H., Chiang, R. H. L., & Storey, V. C. (2021). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 45(4), 1836–1882. https://doi.org/10.25300/MISQ/2021/16474

Company, M. &. (2024). The state of AI in Indonesia: Digital transformation accelerating. McKinsey Global Institute.

Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). Sage Publications.

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data--evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021

Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harvard Business Review, 97(4), 62–73.

Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. In European Business Review (Vol. 31, Issue 2). https://doi.org/10.1108/EBR-11-2018-0203

Institute, M. G. (2021). The state of AI in 2021. McKinsey & Company.

Laudon, K. C., & Laudon, J. P. (2020). Management information systems: Managing the digital firm (16th ed.).

Liu, Y., & Chen, X. (2021). Exploring the relationship between customer experience and satisfaction in e-commerce platforms. Journal of Retailing and Consumer Services, 60, 102510.

Marr, B. (2020). Artificial intelligence in practice: How 50 successful companies used AI and machine learning to solve problems. John Wiley & Sons.

Myers, M. D., & Newman, M. (2021). The qualitative interview in IS research: Examining the craft. Information and Organization, 17(1), 2–26. https://doi.org/10.1016/j.infoandorg.2006.11.001

Provost, F., & Fawcett, T. (2023). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media, Inc.

Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2020). The future of work in technology. MIT Sloan Management Review, 61(4), 1–4.

Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Global Edition, 4ed. Pearson Education.

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H.,

Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2016). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, August. https://doi.org/10.1016/j.jbusres.2016.08.001

Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2020). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 21(2), 478–509. https://doi.org/10.17705/1jais.00611

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003

Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001

Winfield, A. F. T., Jirotka, M., & Winfield, A. F. T. (2018). Ethical governance is essential to building trust in robotics and artificial intelligence systems Subject Areas : Author for correspondence : Philosophical Transactions A, 376.




DOI: https://doi.org/10.30596/jcositte.v6i2.26051

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