The (Bounded) Role of Stated-Lived Value Congruence and Authenticity in Employee Evaluations of Organizations
Bibliographic record
Abstract
A growing body of research documents that audiences reward organizations perceived to be authentic with positive evaluations. In the current work, we adopt a mixed-methods approach—using data collected from Glassdoor.com and two experiments—to establish that perceptions of authenticity are elicited by perceived congruence between an organization’s stated values (i.e., the values it claims to hold) and its lived values (i.e., values members perceive as embodied by the organization), which in turn lead to more positive organizational evaluations. We then explore the conditions under which audiences are less likely to respond favorably to organizational authenticity, finding that the positive effects of stated-lived value congruence on evaluations are attenuated when audiences have a lower preference for stated values. Although scholars have often explored whether and how organizations can successfully make themselves appear authentic to reap rewards, our findings suggest that the perceived authenticity that results from stated-lived value congruence may not prove fruitful unless the audience holds a higher preference for an organization’s stated values. History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory. Funding: This research was supported by the Interdisciplinary Research Award from the Management and Organizations Department at the Kellogg School of Management and by the Michael-Lee Chin Institute for Corporate Citizenship Research Grant from the Rotman School of Management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1578 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".