ARTÍCULO
TITULO

Enhancing competitive response to market challenges with a strategic intelligence maturity model

Gianita Bleoju    
Alexandru Capatina    

Resumen

Tracking meaningful insights about companies? exposures to high risk of failure in competitive markets, intelligence studies in business should listen to practitioners? signals and act in providing decision making support to systematic scanning for valuable information. In order to gain robustness in confronting unexpected events in real markets, companies should adopt an unstructured learning perspective with maturity assessment tools, while purposely pooling strategic intelligence (SI) skills. By bridging organizational maturity modeling with a future orientation stream of literature and intelligence studies in business, this conceptual research aims to highlight a genuine Strategic Intelligence Capability Maturity Model (SI CMM), capable of purposely addressing the challenge of aligning detective and anticipatory organizational capabilities. The conceptual model highlights the degree of preparedness of four SI profiles behaviors (intelligence provider, vigilant learner, opportunity captor and opportunity defender ? previously developed by the authors) against seven levels of maturity. The SI CMM framework outlines both conditioned scanning capabilities (the first five SI readiness levels) and enablers to anticipate future market trends (the last two SI readiness levels). The novel approach of the strategic intelligence readiness framework supplies companies with a valuable organizational learning tool to close the skills gap through an opportunity provider profile. The main features lie in coordination and sharing SI common knowledge to enhance preparedness in forward-looking competitive pressures. The conceptual framework invites academia and the community of intelligence experts in business to evaluate the relevance of the new conceptualization, clarity of constructs and complementary nature of correlation and causation with the proposed SI CMM model

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Revista: Applied Sciences

 
Gianita Bleoju,Alexandru Capatina