Examines the gap between the cultural ideal of independent business ownership and the economic, institutional, and personal constraints that shape entrepreneurial autonomy, risk, and responsibility.
This page brings together my current research, working papers, applied analyses, and interactive projects across marketing strategy, customer experience, organizational behavior, innovation, and leadership.
My work often starts with a practical management question and then looks beneath the conventional answer. Why do certain metrics become dominant despite contested evidence? How do innovations diffuse through markets and organizations? What happens when formal ownership or managerial authority differs from the power people actually possess? And how can analytical tools—including artificial intelligence—improve decisions without obscuring the assumptions behind them?
Current projects include research on the diffusion of Net Promoter Score as a managerial innovation, customer segmentation and AI-enabled decision tools, the S-curve and diffusion of disruptive business models, and the organizational realities of entrepreneurship and small-business ownership. Where possible, I make working papers, tools, supporting analysis, and related writing publicly available.
Examines the gap between the cultural ideal of independent business ownership and the economic, institutional, and personal constraints that shape entrepreneurial autonomy, risk, and responsibility.
Uses Uber’s growth to examine how disruptive innovations move through adoption cycles, how markets respond as novelty becomes infrastructure, and what the S-curve reveals about competitive advantage.
Examines how Net Promoter Score became one of the world’s most widely adopted management metrics despite persistent disagreement over its empirical foundations and predictive value.
Examines how headline star ratings can obscure meaningful variation in underlying reviews, testing whether familiar summary scores provide as much insight as their apparent precision suggests.
Uses employee-review data to test how ratings, sentiment, and firm-level differences predict whether workers would recommend an employer, comparing traditional scores with text-based signals.
Tracks employment-arbitration filings over time to examine whether changes in case activity coincide with periods of financial strain, offering a longitudinal view of dispute patterns and organizational pressure.
I’ll email you periodically with developments.
