Unpacking the effect of exploration during environmental uncertainty: Evidence from the information technology sector
Bibliographic record
Abstract
Abstract Environmental uncertainty can render managerial decision‐making about resource deployment particularly difficult. Integrating the knowledge‐based view of the firm and the organizational learning literature, we make a case for deploying specific knowledge‐based resources to cope with specific types of environmental uncertainty. We unbundle knowledge‐based resources into technology‐based and social‐network‐based resources and, using Milliken's (1987) typology of environmental uncertainty, we hypothesize that (a) technological exploration will be more effective during state uncertainty and (b) while being generally beneficial, social exploration will prove more effective during response uncertainty. An analysis of the financial performance of information technology (IT) firms in the United States over the period 1995–2004 generally supports our hypotheses. Copyright © 2015 ASAC. Published by John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".