The International Arab Journal of Information Technology (IAJIT)

..............................
..............................
..............................


Unlocking Decision-Making Performance through Data Governance and Availability: Structural and Predictive Evidence from Jordan

In the context of increasingly data-driven organizational environments, effective Decision-Making (DM) relies substantially on sound data management practices. This study investigates the interconnections among Data Governance (DG), Data Availability (DA), and DM in Jordanian organizations. Utilizing Structural Equation Modeling (SEM) and regression analysis, both direct and mediating relationships were examined. A cross-sectional survey design was employed, and data were analyzed using Confirmatory Factor Analysis (CFA), SEM, and mediation testing, followed by regression analysis to assess additional determinants of decision Speed (SPD1) and accuracy. Findings indicate that DA significantly mediates the relationship between DG and DM, with robust model fit indices and notable explained variance (R²). Furthermore, data Accessibility (ACC1) emerged as a critical predictor of decision SPD1. These results contribute to the theoretical discourse by reinforcing Resource-Based Theory (RBT) perspectives on data as a strategic resource and provide empirical evidence within a developing-country context. Implications are offered for both policy development and managerial practices in Jordanian organizations.

 


[1] Adepoju A., Austin-Gabriel B., Eweje A., and Hamza O., “A Data Governance Framework for High-Impact Programs: Reducing Redundancy and Enhancing Data Quality at Scale,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 4, no. 6, pp. 1141-1154, 2023. https://www.allmultidisciplinaryjournal.com/uplo ads/archives/20250116173709_MGE-2025-1- 077.1.pdf

[2] Al-Hinawi A. and Alelaimat R., “Using Deep Learning for Profitable Concrete Forecasting Methods,” The International Arab Journal of Information Technology, vol. 21, no. 5, pp. 832- 843, 2024. https://doi.org/10.34028/iajit/21/5/5

[3] Almajali M., Nasrawin L., Alqudah F., Althunibat A., and Albalawee N., “Technical Service Error as a Pillar of Administrative Responsibility for Artificial Intelligence (AI) Operations,” International Journal of Advances in Soft Computing and its Applications, vol. 15, no. 3, pp. 274-287, 2023. DOI: 10.15849/IJASCA.231130.18

[4] Alzyadat W., Muhairat M., Alhroob A., and Rawashdeh T., “A Recruitment Big Data Approach to Interplay of the Target Drugs,” International Journal of Advances in Soft Computing and its Applications, vol. 14, no. 1, pp. 1-13, 2022. DOI: 10.15849/IJASCA.220328.01

[5] Asto Buditjahjanto I., Pratama A., and Samani M., “The Intelligent Decision System Based on Hybrid Decision Tree to Determine the Level of Lecturer Performance,” International Journal of Advances in Soft Computing and its Application, vol. 16, no. 3, pp. 219-232, 2024. DOI: 10.15849/IJASCA.240330.13

[6] Ates V. and Garip A., “Data Governance for Businesses: Challenges, Recommendations, and Critical Success Factors,” Acta Infologica, vol. 9, no. 1, pp. 339-358, 2025. file:///C:/Users/user/Downloads/ACTA_TamMeti nMakale%20(1).pdf

[7] Bena Y., Ibrahim R., Mahmood J., Al-Dhaqm A., and et al., “Big Data Governance Challenges Arising from Data Generated by Intelligent Systems Technologies: A Systematic Literature Review,” IEEE Access, vol. 13, pp. 12859-12888, 2025. https://ieeexplore.ieee.org/document/10839423

[8] Bliznak K., Munk M., and Pilkova A., “A Systematic Review of Recent Literature on Data Governance (2017-2023),” IEEE Access, vol. 12, pp. 149875-149888, 2024. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnu mber=10707270

[9] Bramantyo S. and Pujianto W., “Driving Structural Equation Modeling (SEM) Growth Innovation and Digital Capabilities,” Indonesian Journal of Innovation Studies, vol. 26, no. 3, pp. 1-18, 2025. https://doi.org/10.21070/ijins.v26i3.1376

[10] Brysbaert M., Questioning the Exclusive Focus on the Hu and Bentler Norms in Factor Analysis: Practice-Oriented Likert Scale Indicators Based on an Analysis of 161 Datasets, https://scispace.com/pdf/questioning-the- exclusive-focus-on-the-hu-and-bentler-norms- 4v79gvi9n4.pdf, Last Visited, 2025.

[11] Caligiuri P., Collings D., De Cieri H., and Lazarova M., “Global Talent Management: A Critical Review and Research Agenda for the New Organizational Reality,” Annual Review of Organizational Psychology and Organizational Behavior, vol. 11, no. 1, pp. 393-421, 2024. https://doi.org/10.1146/annurev-orgpsych- 111821-033121

[12] Chukwurah N., Ige A., Idemudia C., and Eyieyien O., “Integrating Agile Methodologies into Data Governance: Achieving Flexibility and Control Simultaneously,” Open Access Research Journal of Multidisciplinary Studies, vol. 8, no. 1, pp. 045- 056, 2024. https://doi.org/10.1146/annurev- orgpsych-111821-033121

[13] Eryurek E., Gilad U., Lakshmanan V., Kibunguchy-Grant A., and Ashdown J., Data Governance: The Definitive Guide, O’Reilly Media, Inc., 2021. https://www.oreilly.com/library/view/data- governance-the/9781492063483/

[14] Grebovic M., Filipovic L., Katnic I., Vukotic M., and Popovic T., “Machine Learning Models for Statistical Analysis,” The International Arab Journal of Information Technology, vol. 20, no. 3A, pp. 505-514, 2023. https://www.iajit.org/upload/files/doi- 61684824431-38.pdf

[15] Hannila H., Silvola R., Harkonen J., and Haapasalo H., “Data-Driven Begins with DATA; Potential of Data Assets,” Journal of Computer Information Systems, vol. 62, no. 1, pp. 29-38, 2022. https://www.tandfonline.com/doi/full/10.1080/08 874417.2019.1683782

[16] Hunziker S. and Blankenagel M., Research Design in Business and Management: A Practical Guide for Students and Researchers, Springer Gabler, 2024. https://link.springer.com/chapter/10.1007/978-3- 658-42739-9_10

[17] Ibrahim A., Mohamed I., and Satar N., “Factors Influencing Master Data Quality: A Systematic Review,” International Journal of Advanced 414 The International Arab Journal of Information Technology, Vol. 23, No. 3, May 2026 Computer Science and Applications, vol. 12, no. 2, pp. 181-192, 2021. file:///C:/Users/user/Downloads/Factors_Influenc ing_Master_Data_Quality_A_Systemat.pdf

[18] Jaradat Y., Masoud M., Manasrah A., Alia M., et al., “Exploring the Intersection of Information Theory and Machine Learning,” The International Arab Journal of Information Technology, vol. 22, no. 5, pp. 845-858, 2025. https://doi.org/10.34028/iajit/22/5/1

[19] Koziol-Nadolna K. and Beyer K., “Determinants of the Decision-Making Process in Organizations,” Procedia Computer Science, vol. 192, pp. 2375-2384, 2021. https://www.sciencedirect.com/science/article/pii /S1877050921017439

[20] Li J. and Wang R., “An Anomaly Detection Method for Weighted Data Based on Feature Association Analysis,” The International Arab Journal of Information Technology, vol. 21, no. 1, pp. 117-127, 2024. https://doi.org/10.34028/iajit/21/1/11

[21] Mohamed A. and Jebapillai C., “A Machine Learning Attempt for Anatomizing Software Risks in Small and Medium Agile Enterprises,” The International Arab Journal of Information Technology, vol. 22, no. 3, pp. 547-559, 2025. https://doi.org/10.34028/iajit/22/3/10

[22] Naqvi R., Soomro T., Alzoubi H., Ghazal T., and Alshurideh M., Advances in Intelligent Systems and Computing, Springer, 2021. https://link.springer.com/chapter/10.1007/978-3- 030-76346-6_73

[23] Petersen A., Ekstrom C., Spirtes P., and Osler M., “Constructing Causal Life-Course Models: Comparative Study of Data-Driven and Theory- Driven Approaches,” American Journal of Epidemiology, vol. 192, no. 11, pp. 1917-1927, 2023. https://pubmed.ncbi.nlm.nih.gov/37344193/

[24] Ristyawan M., Putro U., and Siallagan M., “Decision Making Mechanism in Resource Based Theory: A Literature Review, Synthesis, and Future Research,” Cogent Business and Management, vol. 10, no. 2, pp. 1-28, 2023. https://www.tandfonline.com/doi/full/10.1080/23 311975.2023.2247217

[25] Roehl U. and Hansen M., “Automated, Administrative Decision‐Making and Good Governance: Synergies, Trade‐Offs, and Limits,” Public Administration Review, vol. 84, no. 6, pp. 1184-1199, 2024. https://onlinelibrary.wiley.com/doi/10.1111/puar. 13799

[26] Sahoo S. and Goswami S., “A Comprehensive Review of Multiple Criteria Decision-Making (MCDM) Methods: Advancements, Applications, and Future Directions,” Decision Making Advances, vol. 1, no. 1, pp. 25-48, 2023. https://www.dma- journal.org/index.php/dema/article/view/7/5

[27] Salerno F. and Macada A., “Data-Driven Culture and Orchestrated Data Ecosystems: A Conceptual Model Based on the Resource-Based View,” Revista de Gestao, vol. 32, no. 2, pp. 123-135, 2025. https://doi.org/10.1108/REGE-12-2024- 0184

[28] Salerno F. and Macada A., “The Effect of Data Governance on Data-Driven Culture: The Mediating Effect of Data Quality,” The TQM Journal, 2025. https://doi.org/10.1108/TQM-08- 2024-0304

[29] Sargiotis D., Data Governance: A Guide, Springer, 2024. https://link.springer.com/chapter/10.1007/978-3- 031-67268-2_1

[30] Sathyanarayana S. and Mohanasundaram T., “Fit Indices in Structural Equation Modeling and Confirmatory Factor Analysis: Reporting Guidelines,” Asian Journal of Economics, Business and Accounting, vol. 24, no. 7, pp. 561- 577, 2024. https://scispace.com/papers/fit- indices-in-structural-equation-modeling-and- confirmatory-3mdlif5qmo

[31] Sharairi J., Saatchi S., Rahahle M., Maabreh H., and et al., Artificial Intelligence and Economic Sustainability in the Era of Industrial Revolution 5.0, Springer, 2024.

[32] Shukla S., Bisht K., Tiwari K., and Bashir S., Data Economy in the Digital Age, Springer, 2023. https://link.springer.com/chapter/10.1007/978- 981-99-7677-5_4

[33] Tatipamula S., “Real-Time vs. Batch Data Processing: When Speed Matters,” World Journal of Advanced Research and Reviews, vol. 26, no. 1, pp. 1612-1631, 2025. https://doi.org/10.30574/wjarr.2025.26.1.1213

[34] Walsh M., McAvoy J., and Sammon D., “Grounding Data Governance Motivations: A Review of the Literature,” Journal of Decision Systems, vol. 31, no. sup1, pp. 282-298, 2022. https://doi.org/10.1080/12460125.2022.2073637

[35] Wibisono A., Sammon D., and Heavin C., “Data Availability Issues: Decisions as Patterns of Action,” Journal of Decision Systems, vol. 31, no. sup1, pp. 241-254, 2022. https://doi.org/10.1080/12460125.2022.2070945