Employee Experience and Shareholder Value: A Human Capital Framework (International Journal of Environmental Sciences, 2023)
Human resource management and corporate finance have largely developed as separate literatures, yet a growing body of evidence suggests that how a firm treats its employees is materially connected to how that firm is valued and financed. This conceptual paper integrates human capital theory (Becker, 1964), the resource-based view (Barney, 1991), and research on high-performance work systems (Huselid, 1995) with asset-pricing evidence on the mispricing of employee satisfaction (Edmans, 2011; Edmans, Li, & Zhang, 2024) and on employee well-being as a determinant of corporate financial risk (Verwijmeren & Derwall, 2010) to develop a Human Capital–Firm Value Framework linking employee experience quality to both shareholder returns and downside financial risk. The framework specifies five constructs — employee experience quality, signal visibility, market attention constraints, the downside-risk channel, and labor-market context — and advances six propositions. To illustrate the framework's plausibility, the paper presents a small, transparent empirical analysis of the eight publicly traded companies ranked in the top ten of the 2026 Fortune 100 Best Companies to Work For list, comparing their 2023–2025 stock returns, individually and as an equal-weighted portfolio, against the S&P 500. The portfolio outperformed the index in all three years, though this result is shown to depend heavily on a single constituent (NVIDIA) whose returns reflect an AI-hardware cycle unrelated to workplace quality; excluding that firm, the portfolio still outperformed in two of three years but underperformed in one, illustrating both the plausibility and the fragility of small-sample tests of the employee-satisfaction-and-value relationship. The paper contributes an integrative, testable framework connecting human resource management and corporate finance and offers a template for combining conceptual theorizing with transparent, real-data illustration. Boundary conditions, limitations, and a future research agenda are discussed.