Engaging with Uncertainty: Three Empirical Studies
Bibliographic record
Abstract
Engaging with uncertainty is vital in business because it can either generate or destroy wealth. My dissertation, comprising three empirical studies, investigates management decision-making and firm performance under uncertainty. The first study examines how organizational stress represented by resource constraints impacts firm performance. Drawing from the psychology-based Yerkes-Dodson (1908) Law, we propose that, while some amount of stress activated by constraints enhances performance, too much stress hampers performance. Using textual measures to gauge constraints that activate stress at the organizational level, we find an inverted-U relationship between constraints and return on assets. This relationship is more aligned with creativity, reflected by profit margin and innovation activities, than with efficiency in resource usage captured by asset turnover. My second study analyzes the compensation structure of the top leadership team (TLT), a group of executives responsible for navigating the organization through uncertain times. This study recognizes the importance of both the CEO's unique role and the dynamics among team members through: (1) CEO pay slice, reflecting payment for the CEO’s team leadership and management skill, and (2) pay dispersion among the CEO’s top team, capturing the weights on team versus individual based payments. We find that TLTs characterized by a large CEO pay slice and low degree of pay dispersion among the CEO’s top team outperform others in terms of return on assets. These results highlight complementary relations between CEO team leadership and team-based compensation in compensating TLTs. My third study analyzes how a strategic focus on balance sheet strength influences investment decisions and performance among Canadian oil and gas firms that navigate through uncertainties. Based on discussions with industry experts, we identify two groups of firms: those aggressively investing during favorable conditions – “making hay while the sun shines”, and those investing more prudently – “saving for a rainy day”. While investment in downturns declined generally for both types of firms, the decline in investment was significantly less for rainy day companies. These rainy day firms make shrewder acquisitions and achieve greater operational efficiency over time. However, rainy day firms have lower market valuations during upturns compared to making hay firms.
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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 | 0.018 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".