  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. We suggest that artificially
intelligent technologies that can adapt information representations to overcome
cognitive limitations are necessary for these complex information environments.
To this end, we 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. We 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.
