Outperforming managers in setting strategic targets by using a novel Computer-Aided Management (CAM) approach

Abstract

The minimal availability of scientific literature suggests that managers hardly consider internal organizational consequences as organizational Alignment, implementation effort, and Capacity to change when setting strategic targets. This study bridges this gap in the literature by employing a self-developed algorithm that assists managers by focusing on consequences that would make the target’s implementation nearly impossible. In our study: too little organizational alignment, setting too ambitious targets, and insufficient capacity to change. We first quantified how 3,300 managers in 500+ organizations set targets by themselves in terms of these three consequences. We defined this group as Classical Management (CM). Then, in the second batch of 1,000 managers in 90 organizations, we provided our algorithm that quantified their targets' internal consequences. We defined this group as Computer-Aided Management (CAM). Our finding is that comparing two target-setting approaches (CM versus CAM) indicated that the latter chose targets with a “consequence score” six times better than the former. Our recommendation: in an organizational transformation, ask as many employees and managers as possible and let an algorithm upgrade their input to refine the decision-making process.