One Step Forward, Two Steps Back: How Negative External Evaluations Can Shorten Organizational Time Horizons
Bibliographic record
Abstract
Researchers have endeavored to explain the causes of short organizational time horizons because of the organizational and societal costs of corporate short-termism. These explanations, however, tend to confound cognitive with behavioral explanations, which masks the importance of cognitive biases. We address this oversight by situating our work in prospect theory and organizational search, which underscores the importance of external evaluations on organizational time horizons and the asymmetry of positive and negative evaluations. Specifically, we argue that negative evaluations will shorten organizational time horizons more than positive evaluations will lengthen them. In our research context of financial analysts, this means that “sell” recommendations will shorten time horizons more than “buy” recommendations will lengthen them. Our main thesis can help to explain rising short-termism among some publicly traded companies. We operationalize organizational time horizons by the language managers use during 3,136 quarterly earnings conference calls. We test our main hypothesis and other timing-related moderating effects on 98 extractives firms from 2006 to 2013.
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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 | 0.005 | 0.029 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".