Social Sciences & Humanities Open· 2026Q1
Integrated governance intelligence for higher education in resource-constrained contexts: A design science study of BI-SI integration and decision support
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- 2026year
Short summary
A new Integrated Business Intelligence and Strategic Intelligence Framework (IBISIF) prototype improved data standardization for higher education institutions in resource-constrained settings, increasing rule-conformance exact-match rates from 62.1% to 96.7% across 5000 administrative rows.
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Key points
- Developed the Integrated Business Intelligence and Strategic Intelligence Framework (IBISIF) prototype for higher education in resource-constrained environments.
- Improved data standardization, achieving a descriptive increase in rule-conformance exact-match rate from 62.1% to 96.7% across 5000 administrative rows.
- Prototype demonstrated feasible scheduled refresh and dashboard response times in controlled runs.
- Institutional staff reported a mean System Usability Scale score of 75.5, indicating good usability in a formative study.
AI-generated from the title and abstract; the full text is not read.
Abstract
Higher education institutions increasingly depend on digital systems that generate data on enrolment, finance and academic processes. In resource-constrained settings, however, internal records are often fragmented while external signals—such as policy reforms, funding calls and scholarship opportunities—are monitored informally. This study examines Business Intelligence–Strategic Intelligence (BI–SI) integration as a socio-technical governance and decision-support problem. Using Design Science Research (DSR), it develops the Integrated Business Intelligence and Strategic Intelligence Framework (IBISIF), a single-site prototype combining context-aware Extract, Transform and Load (ETL) procedures, a dimensional data warehouse, a strategic-intelligence feed and governance dashboards. Four coordinated evaluation components were used: a rule-conformance data-standardization assessment, a functional strategic-intelligence illustration, controlled prototype benchmarking and a formative usability study. Across 5000 matched administrative rows, the unweighted mean rule-conformance exact-match rate increased descriptively from 62.1% to 96.7%. Because the deterministic reference applies the same documented dictionaries and conventions used to define normalized categories, this result measures conformance to predefined normalization rules rather than independent external validation of semantic correctness. Category-specific changes remained statistically detectable under every paired-transition allocation compatible with the retained margins, although the original row-level transition file was unavailable for exact McNemar reconstruction. Ten controlled runs indicated feasible scheduled refresh and dashboard response times, but the measurements are not production service-level or scalability evidence. Thirty institutional staff members produced a mean System Usability Scale score of 75.5 (95% confidence interval 70.9–80.1) in a purposive formative sample. The single-source, one-day strategic-intelligence log is treated only as a functional illustration, not as an information-retrieval performance test. The study contributes case-based design considerations for linking data quality, external scanning and institutional sensemaking, while making no claim that the prototype improved governance outcomes, predicted financial distress or changed decision behaviour.
The authors' abstract, as published at the source. Social Sciences & Humanities Open, 2026 · DOI ↗
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Field: Strategy and Management
Strategy and ManagementBusiness, Management and Accounting