Sustainable Competitive Intelligence for Academic Capability Development: A Validation-Ready Governance, Sensing and Foresight Framework

Abstract

Purpose: The purpose of this study is to create a Sustainable Competitive Intelligence (SCI) framework for academic capability development, which explains how the faculty competence evidence can be translated into the input of the intelligence cycle, namely sensing, analysis, dissemination, governance and use in decision-making. Methodology/approach: The study is based on a secondary study design that is validation-ready. Rather than serving as a direct measure of CI adoption, a public Figshare faculty competence dataset is used as evidence input. The analysis establishes the competence domains of the intelligence cycle, formulates a clear prioritisation logic and describes specific organisational validation measures those future institutions have to implement via expert review, Delphi/AHP weighting and KPI tracking. Originality/Relevance: The article looks at educational analytics with a new perspective, as CI is introduced as an organizational ability instead of a dashboard or a descriptive data exercise. Key findings: The framework demonstrates that competence evidence can be used to contribute to intelligence products like capability-gap briefs, segmentation notes, risk signals, development-priority maps and governance dashboards. The proposed indicators of digital competence are not considered as an empirical measure of CI, but rather as one source of intelligence. Theoretical/methodological contributions: Provides a CI-SHRM-KM-TR framework and a logic that can be validated (CPI) without empirical data from organizations in the future, and the boundaries of claims are just explicit.