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International Journal of Academic Research in Business and Social Sciences

Open Access Journal

ISSN: 2222-6990

From Data Chaos to Decision Clarity: How Information Governance and Integration Shape Information Use in SME Inventory Decisions

Ahmad Amru Mohamad Zaid, Nur Athirah Abd Rahim

http://dx.doi.org/10.6007/IJARBSS/v16-i10/29195

Open access

This study examines how information governance (IG) and data integration capability (DIC) relate to decision information use (DIU) through information quality (IQ) among small and medium-sized enterprises (SMEs) in Selangor, Malaysia. Drawing on Organisational Information Processing Theory, the research distinguishes arrangements for managing and reconciling inventory records from their perceived quality and subsequent use in replenishment decisions. Survey responses from 360 SME participants, recruited through purposive sampling in October 2026, were analysed using four-item construct composites, measurement diagnostics and standardised ordinary least squares regressions with 2,000-resample percentile bootstrap intervals. IG (? = 0.344) and DIC (? = 0.335) were positively associated with IQ, explaining 29.6% of its variation. IQ was associated with DIU (? = 0.451; R² = 0.204). The respective product-of-coefficients indirect associations were 0.155 and 0.151, with confidence intervals excluding zero. The findings support a decision-specific statistical linkage among information arrangements, perceived quality and reported use, without establishing causal mediation or objective improvements in inventory performance.

DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36. https://doi.org/10.1287/inte.4.3.28
Goel, K., Martin, N., & ter Hofstede, A. H. M. (2024). Demystifying data governance for process mining: Insights from a Delphi study. Information & Management, 61(5), 103973. https://doi.org/10.1016/j.im.2024.103973
Hautala-Kankaanpää, T. (2023). Complementary and contingent value of SMEs’ data capability and supply chain capability in the competitive environment. Industrial Management & Data Systems, 123(8), 2128–2149. https://doi.org/10.1108/IMDS-01-2023-0013
Hayes, A. F., & Scharkow, M. (2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: Does method really matter? Psychological Science, 24(10), 1918–1927. https://doi.org/10.1177/0956797613480187
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
Jonsson, P., & Myrelid, P. (2016). Supply chain information utilisation: Conceptualisation and antecedents. International Journal of Operations & Production Management, 36(12), 1769–1799. https://doi.org/10.1108/IJOPM-11-2014-0554
Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148–152. https://doi.org/10.1145/1629175.1629210
Lem, K. W. (2024). Data analytics strategy and internal information quality. Contemporary Accounting Research, 41(2), 1376–1410. https://doi.org/10.1111/1911-3846.12942
Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44. https://doi.org/10.1037/1082-989X.12.1.23
Nordin, M. S., Che Hussin, A. R., & Mohamad Zaid, A. A. (2025). Key components for evaluating paid digital advertising (PDA) performance for SMEs in Malaysia. International Journal of Academic Research in Business and Social Sciences, 15(12), 1806–1813. https://doi.org/10.6007/IJARBSS/v15-i12/27302
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Raj, A., Kumar, R. R., Narayanan, S., Kirca, A. H., & Jeyaraj, A. (2025). Analytics capability, supply chain capabilities, and operational performance: A meta-analytic investigation. Journal of Operations Management, 71(6), 786–805. https://doi.org/10.1002/joom.1376
Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107–120. https://doi.org/10.1007/s11336-008-9101-0
Teerasoponpong, S., & Sopadang, A. (2022). Decision support system for adaptive sourcing and inventory management in small- and medium-sized enterprises. Robotics and Computer-Integrated Manufacturing, 73, 102226. https://doi.org/10.1016/j.rcim.2021.102226
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33. https://doi.org/10.1080/07421222.1996.11518099

Zaid, A. A. M., & Rahim, N. A. A. (2026). From Data Chaos to Decision Clarity: How Information Governance and Integration Shape Information Use in SME Inventory Decisions. International Journal of Academic Research in Business and Social Sciences, 16(10), 906-917.