Understanding how values shape human behavior is a central question in psychology and organizational research. Yet, traditional value measures often show weak or inconsistent links to actual behavior. According to Maio's value framework, people are more likely to act in accordance with an abstract value when a behavior is perceived as a direct expression, i.e., instantiation, of that value. To advance this line of research beyond labor-intensive manual coding, we applied AI-based language models to automatically identify value instantiations in employee work goals.
Our computational approach leverages the structure of Schwartz's value system to derive behavioral patterns from textual instantiations. Our findings offer empirical validation for Maio's framework by showing that instantiation-based measures outperformed abstract value scores in predicting workplace behavior and outcomes, including status, tenure, income and employment in nonprofit sector. Cross-cultural analyses further revealed that Asian-American and Indian employees showed stronger alignment at the instantiation level than at the abstract value level, suggesting that globalization may promote the emergence of shared work-related values while attenuating historically entrenched cultural distinctions.
We also validated the robustness of our computational approach. When trained on Asian-American work-goal texts, the model achieved an F1-score of 0.82 against expert annotations. Applied to Indian data without further fine-tuning, performance remained high (F1 = 0.67). These findings demonstrate the portability and efficiency of our AI-based method for uncovering value instantiations.