Do Business Schools Walk the Talk? A Critical Examination of Espoused Values and Reputational Façades
Bibliographic record
Abstract
Amid increasing pressures from external stakeholders, business schools are adopting characteristics similar to corporations to enhance their ability to address these demands and enhance their competitiveness. One such feature that has gained popularity is the explicit articulation of a school’s espoused values. Drawing upon previous work on organizational façades and authenticity, we argue that external pressures often lead business schools to superficially adopt espoused values without genuine integration. Consequently, this leads to the construction and manipulation of reputational façades aimed at influencing reputational performance. Our study examines 841 accredited post-secondary business schools from 62 countries across six continents. Using established frameworks by Bourne, Jenkins, and Parry (2019) and Cardona and Rey (2008), we analyze and map the espoused values of each institution, probing the relationship between these values and reputational performance. We find evidence that business schools espouse values that are disconnected from academic principles. Furthermore, we observe a disconnect between the articulation of more espoused values and reputational performance, as evidenced by poorer ranking outcomes. Overall, our research demonstrates that schools utilize espoused values as a low-cost signal to construct a reputational façade, which may ultimately harm perceptions of organizational authenticity and undermine reputational performance.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".