Understanding Data Analytics Recommendation Execution: The Role of Recommendation Quality

ABSTRACT

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.

KEYWORDS:

• DA recommendation execution
• DA recommendation quality
• concordance
• DA recommendation actionability
• perceived execution risk

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ 1 A 12 months time frame is sufficiently recent for accurate recall.⁸⁸ Collecting participants’ perceptions of Information Technology-related events that have occurred in the past has been utilized in the past by IS researchers (e.g.,⁸⁸)

² To confirm the discriminant validity the internal consistency should be higher with the construct than any other construct in the model.⁸¹

³ Gefen and Straub⁸² state that indicators’ cross-loading on any latent construct should be at least 0.1 higher than any other constructs in the research model.

⁴ According to Straub et al.,⁸⁹ for constructs appearing successive in the research model, comparing to any other constructs, the items should have higher loadings with their own construct.