The Informed Insider: A Leading Measure of Quality -- by Wei Cai, Dennis Campbell, Yaxuan Chen, Yufei Chen, Andrea Prat

Product and service quality is fundamental to firm value creation, yet it is well recognized as difficult to observe ex ante. Existing quality proxies are limited in coverage, lack cross-firm comparability, and primarily rely on lagging indicators that capture product or service failures only after they materialize. Exploiting employees’ informational advantage as informed insiders and firsthand observers of firms’ internal operations, we develop and validate a novel, forward-looking measure of firm-year-level product and service quality using over 4.3 million employee reviews on Glassdoor. Leveraging machine learning models trained on a subset of firms with third-party customer satisfaction data, we construct quality indices for S&P 1500 firms spanning 2008 to 2023. The resulting quality measures exhibit meaningful variation across firms and within firms over time. In out-of-sample tests, our quality indices demonstrate strong predictive power for future quality provision, emerging as the single most important predictor relative to firm fundamentals and Glassdoor ratings. We validate our measure by examining its association with alternative quality metrics. We show that our quality measures are useful in predicting important quality-related firm outcomes such as product recalls, brand value, and profitability. We also construct an alternative set of quality measures using a zero-shot prompt-based approach and a supervised fine-tuning approach with GPT models to assess the potential of LLMs and generative AI in capturing firm-level quality provision. Our paper shows the value of employee voices as a powerful, forward-looking, and scalable signal of firm quality provision. The paper offers implications for stakeholders seeking to identify quality-related risks and opportunities before they become externally visible.