Advancing algorithmic bias management capabilities in AI-driven marketing analytics research

Algorithms in the age of artificial intelligence (AI) constantly transform customer behaviour, marketing programs, and strategies industrial markets. However, algorithms often fail to perform as expected due various data, model, market biases. Motivated by this challenge, study presents a framework algorithmic bias management capabilities for markets that contribute equity terms value, brand relationship equity. Drawing on dynamic capability theory, fills gap conducting literature review, thematic analysis, two rounds surveys (n=200 analytics professionals n=200 business customers) financial service industry Australia. The findings show consists three primary dimensions (data, deployment capabilities) nine subdimensions. These have important implications scholars managers interested developing influence