Business Intelligence and Decision Support to Enhance Decision-Making Quality in Higher Education
DOI:
https://doi.org/10.35870/ijsecs.v5i2.4273Keywords:
Business Intelligence, Decision Support System, Decision MakingAbstract
The availability of accurate and reliable data is essential for organizational sustainability. Business intelligence (BI) enhances an organization's ability to analyze challenges, support decision-making, and improve performance. The term “Business Intelligence System” refers to applications and technologies that facilitate BI activities, including data collection, storage, access, and analysis—thus providing insights into performance and aiding informed decisions. These activities include decision support systems, querying, reporting, OLAP, statistical analysis, forecasting, and data mining. BI applications encompass reporting tools, analytics platforms, dashboards, alerts, and portals, and involve technologies such as data integration, quality management, warehousing, and content analysis. Accordingly, a Business Intelligence System can function as a Decision Support System. This study uses SPSS version 17 for data analysis to evaluate the impact of BI and decision support on decision-making quality in colleges in Jakarta and Bekasi. ANOVA (F-test) results show an F-value of 117.041, exceeding the F-table value of 3.29, with a significance of 0.000 < α = 0.05. Since the calculated F-value surpasses the critical value and the significance level is below 0.05, the null hypothesis is rejected. Thus, BI and decision support significantly and simultaneously influence decision-making quality (Y). These findings highlight the essential role of BI and decision support in improving decision-making within higher education institutions.
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Zarour, K., & Benmerzoug, D. (2019). A decision-making support for business process outsourcing to a multi-cloud environment. International Journal of Decision Support System Technology, 11(1), 66–92. https://doi.org/10.4018/IJDSST.2019010104
Ali, M. S., & Khan, S. (2019). Organizational capability readiness towards business intelligence implementation. International Journal of Business Intelligence Research, 10(1), 42–58. https://doi.org/10.4018/IJBIR.2019010103
Güngör-Demirci, G., Lee, J., Keck, J., Harrison, S. J., & Bates, G. (2019). Development of a risk-based tool for groundwater well rehabilitation and replacement decisions. Journal of Water Supply: Research and Technology-Aqua, 68(6), 411–419. https://doi.org/10.2166/aqua.2019.021
Hayajneh, S., & Harb, Y. (2023). Understanding the continuous use of business intelligence: The case of Jordan. Journal of Decision Systems, 1–32. https://doi.org/10.1080/12460125.2023.2253587
Huang, J.-C., Huang, H.-C., & Chu, S.-H. (2019). Research on image quality in decision management system and information system framework. Journal of Visual Communication and Image Representation, 63, 102588. https://doi.org/10.1016/j.jvcir.2019.102588
Sousa, M. J., Pesqueira, A. M., Lemos, C., Sousa, M., & Rocha, Á. (2019). Decision-making based on big data analytics for people management in healthcare organizations. Journal of Medical Systems, 43(9), 290. https://doi.org/10.1007/s10916-019-1419-x
Miah, S. J. (2021). Tailorable technologies for improving business intelligence systems. In Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering (pp. 814–829). IGI Global. https://doi.org/10.4018/978-1-7998-9023-2.ch039
Kirilov, L., Guliashki, V., & Staykov, B. (2021). Web-based decision support system for solving multiple-objective decision-making problems. In Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering (pp. 594–620). IGI Global. https://doi.org/10.4018/978-1-7998-9023-2.ch029
Esteves, M., Miranda, F., & Abelha, A. (2021). Pervasive business intelligence platform to support the decision-making process in waiting lists. In Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering (pp. 848–863). IGI Global. https://doi.org/10.4018/978-1-7998-9023-2.ch041
Wiwik, L., Dwiningrum, S. I. A., & Sujarwo, S. (2023). Decision support systems: A game changer in the field of education. AL-ISHLAH: Jurnal Pendidikan, 15(4). https://doi.org/10.35445/alishlah.v15i4.4245
Osuszek, L., & Ledzianowski, J. (2020). Decision support and risk management in a business context. Journal of Decision Systems, 29(sup1), 413–424. https://doi.org/10.1080/12460125.2020.1780781
Pereira, A. M., et al. (2022). Customer models for artificial intelligence-based decision support in fashion online retail supply chains. Decision Support Systems, 158, 113795. https://doi.org/10.1016/j.dss.2022.113795
Zarghami, S. A., & Zwikael, O. (2022). Measuring project resilience – Learning from the past to enhance decision making in the face of disruption. Decision Support Systems, 160, 113831. https://doi.org/10.1016/j.dss.2022.113831
Zhdanov, D., Bhattacharjee, S., & Bragin, M. A. (2022). Incorporating FAT and privacy aware AI modeling approaches into business decision-making frameworks. Decision Support Systems, 155, 113715. https://doi.org/10.1016/j.dss.2021.113715
Hsu, M.-F., & Lin, S.-J. (2021). A BSC-based network DEA model equipped with computational linguistics for performance assessment and improvement. International Journal of Machine Learning and Cybernetics, 12(9), 2479–2497. https://doi.org/10.1007/s13042-021-01331-7
Sujith, A. V. L. N., Qureshi, N. I., Dornadula, V. H. R., Rath, A., Prakash, K. B., & Singh, S. K. (2022). A comparative analysis of business machine learning in making effective financial decisions using structural equation model (SEM). Journal of Food Quality, 2022, 1–7. https://doi.org/10.1155/2022/6382839
Iftekhar, M. S., & Pannell, D. J. (2022). Developing an integrated investment decision-support framework for water-sensitive urban design projects. Journal of Hydrology, 607, 127532. https://doi.org/10.1016/j.jhydrol.2022.127532
Lennerholt, C., van Laere, J., & Söderström, E. (2023). Success factors for managing the SSBI challenges of the AQUIRE framework. Journal of Decision Systems, 32(2), 491–512. https://doi.org/10.1080/12460125.2022.2057006
Suboyin, A., Eldred, M., Thatcher, J., Rehman, A., Gee, I., & Anjum, H. (2023, January). Environomics framework for sustainable business practices: Industrial case studies on true impact reduction and process optimization through AI. In Day 1 Tue, 17 January 2023. SPE. https://doi.org/10.2118/214459-MS
Hmoud, H., Al-Adwan, A. S., Horani, O., Yaseen, H., & Al Zoubi, J. Z. (2023). Factors influencing business intelligence adoption by higher education institutions. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100111. https://doi.org/10.1016/j.joitmc.2023.100111
Al-Surmi, A., Bashiri, M., & Koliousis, I. (2022). AI-based decision making: Combining strategies to improve operational performance. International Journal of Production Research, 60(14), 4464–4486. https://doi.org/10.1080/00207543.2021.1966540
Parsamehr, M., Perera, U. S., Dodanwala, T. C., Perera, P., & Ruparathna, R. (2023). A review of construction management challenges and BIM-based solutions: Perspectives from the schedule, cost, quality, and safety management. Asian Journal of Civil Engineering, 24(1), 353–389. https://doi.org/10.1007/s42107-022-00501-4
Maluleka, M. L., & Chummun, B. Z. (2023). Competitive intelligence and strategy implementation: Critical examination of present literature review. SA Journal of Information Management, 25(1). https://doi.org/10.4102/sajim.v25i1.1610
Tewari, A., Gabarro, J., Sole, J., Lapouble, B., & Montull, L. (2020). Artificial intelligence based decision making for venture capital platform. In Proceedings (pp. 136–149). https://doi.org/10.1007/978-3-030-46224-6_11
Yie, L. F., Susanto, H., & Setiana, D. (2021). Collaborating decision support and business intelligence to enable government digital connectivity. In Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering (pp. 830–847). IGI Global. https://doi.org/10.4018/978-1-7998-9023-2.ch040
Samihardjo, R., & Nugraha, U. (2020). Design of the Business Intelligence Dashboard for Sales Decision Making. International Journal of Psychosocial Rehabilitation, 24(2), 3498–3513. https://doi.org/10.37200/IJPR/V24I2/PR200670
Indriantoro, N., & Supomo, B. (2002). Metode penelitian bisnis. Yogyakarta: BPFE.
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