Latest Research
The Diffusion of a Contested Metric: Net Promoter Score as a Managerial Innovation ▸
This working paper examines an enduring management puzzle: why did the Net Promoter Score (NPS) diffuse so widely despite persistent disagreement about its validity, reliability, scoring methodology, and relationship to business performance? The project approaches NPS not only as a measurement instrument, but also as a managerial and organizational phenomenon. It traces how a simple, visible, portable, and benchmarkable metric spread through consulting practices, software platforms, executive reporting, training, and professional networks, eventually moving far beyond its original commercial setting. The paper considers how NPS can function simultaneously as a customer-feedback measure, a management-control device, and a source of organizational legitimacy. It also examines the extension of the underlying logic into employee measurement through eNPS, where adoption has similarly moved faster than the development of a substantial psychometric evidence base. The broader question is how management practices become institutionally durable even when their scientific foundations remain contested.
Strength Overshoot ▸
Strength Overshoot is a developing conceptual framework for understanding how a genuine leadership strength can become counterproductive when it is applied beyond the point at which a situation benefits from it. Rather than treating strengths and weaknesses as fixed opposites, the framework focuses on the interaction among the leader, the strength, its intensity, and situational demands. A behavior such as decisiveness, empathy, confidence, persistence, or inclusiveness may contribute to effectiveness in one context but impede it in another—or when taken too far. The project builds on established scholarship concerning leadership effectiveness, contextual fit, and the overuse of strengths while proposing a more explicit way to think about the transition from productive strength to maladaptive behavior. Current work includes refinement of the conceptual model and development of the Strength Overshoot Scale (SOS) for future empirical study.
AI Customer Segmentation: Models and Best Practices ▸
This project examines the expanding use of artificial intelligence and machine learning for customer segmentation and challenges the assumption that organizations should search for a single “best” segmentation technique. Its central proposition is that there is no universally best segmentation model; there is a better-fit model for the decision an organization needs to make. The guide reframes model selection around managerial purpose. Different approaches may be better suited to discovering latent structure, identifying meaningful customer groups, explaining behavioral differences, predicting future outcomes, or supporting individualized action. Accordingly, technical measures of model performance are only part of the evaluation. Interpretability, stability, data requirements, organizational usability, actionability, and the ability to deploy the resulting segments also matter. The project is intended to bridge the gap between increasingly sophisticated analytical methods and the practical decisions marketers must make with them. Its broader argument is that greater algorithmic precision does not necessarily produce greater business value when the resulting segmentation cannot be understood, operationalized, or connected to a consequential decision.
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Biography ▸
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Personal Website
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