Explosive growth in big data technologies and artificial intelligence (AI) applications have led to increasing pervasiveness of information facets and a rapidly growing array of information representations. Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information and consequently affect human performance. Extant research in cognitive fit, which preceded the big data and AI era, focused on the effects of aligning information representation and task on performance, without sufficient consideration to information facets and attendant cognitive challenges. Therefore, there is a compelling need to understand the interplay of these dominant information facets with information representations and tasks, and their influence on human performance.
In a new article, “Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations,” (International Journal of Information Management, August 2022), Bloustein School Associate Professor of Practice Jim Samuel and co-authors Rajiv Kashyap (William Paterson University), Yana Samuel (Northeastern University), and Alexander Pelaez (Hofstra University) suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary for these complex information environments.
To this end, the authors propose and test a novel “Adaptive Cognitive Fit” (ACF) framework that explains the influence of information facets and AI-augmented information representations on human performance. They draw on information processing theory and cognitive dissonance theory to advance the ACF framework and a set of propositions. We empirically validate the ACF propositions with an economic experiment that demonstrates the influence of information facets and a machine learning simulation that establishes the viability of using AI to improve human performance.