Prioritizing Competitive Intelligence Needs Using AHP: A Contingency-Based Decision Framework

Abstract

Abstract

This paper addresses the problem of misalignment between Competitive Intelligence (CI) systems and the actual intelligence needs of organizations. Many firms implement CI tools without first identifying what specific intelligence is required to support strategic decision-making. To overcome this gap, the paper introduces a decision-oriented framework for prioritizing Competitive Intelligence Needs (CIN). Unlike well-established (CI) practices, which have received greater attention in both research and application, CIN represents an upstream construct that guides the design, implementation, and strategic alignment of CI practices with actual organizational requirements. This orientation is particularly relevant in resource-constrained environments, such as emerging economies, where formal intelligence infrastructures are often lacking.Grounded in contingency theory and operationalized through the Analytic Hierarchy Process (AHP), the CIN framework is structured around four core intelligence sub-needs: data collection, information analysis, dissemination, and protection, each linked to internal and external contingency factors.Empirical results, based on expert evaluations, show that data collection and analysis are top priorities, primarily driven by environmental uncertainty. In contrast, dissemination and protection are more strongly influenced by organizational size and task interdependence. Internal contingencies emerge as equally important as external ones, challenging traditional models focused mainly on external threats, a legacy of CI’s military origins.Although the framework was developed using input from Moroccan experts, it is designed as a transferable diagnostic model for aligning intelligence priorities with organizational context across diverse sectors and geographical settings.