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Although significantly more organizations have recently invested in Data Analytics (DA), most business users do not execute DA recommendations. Conceptualizing the novel concept of DA recommendation quality, shaped by tool, data and analyst quality, this study draws on the Stimulus-Organism-Response framework to investigate its effect on shaping users’ perceptions of concordance, actionability, and risk, ultimately influencing their DA recommendation execution. The theoretical model is empirically validated using a sample of senior managers across North America. Enriching DA literature, this study shows that DA recommendation quality is positively associated with recommendation execution, while actionability is the dominant factor in increasing it.

Understanding Data Analytics Recommendation Execution: The Role of Recommendation Quality