Perspectives on Firm Strategies in the Non-Market Environment
Bibliographic record
Abstract
The non-market environment, which includes “the rules of the game” that govern economic interactions in the market, plays a central role in the ability of firms to create economic value and to sustain competitive advantage. This dissertation focuses on firms’ interactions with different elements of the non-market environment and is comprised of three essays.\nEssay 1 examines the conditions under which firms reach agreements with environmental activists in regulatory agency proceedings to mutually settle disputes. The chapter argues that actions of firms will be dictated by the features of the regulatory decision-making process and the heterogeneity in the attributes of firms, activists, and the regulators. Statistical analysis in the context of regulatory proceedings in the U.S. electric utilities sector demonstrates that alongside firms and activist attributes, external pressures emanating from multiple stakeholder interests and the political ideology of regulators influence the likelihood of agreements.\nEssay 2 examines how differences in the political resources and capabilities of incumbent firms and sharing economy market entrants manifest in their lobbying strategies when they compete in the non-market environment to shape regulatory entry standards. Results from a statistical analysis in the ridesharing industry in Toronto, Canada, demonstrate that the incumbent (taxicab firms) and the market entrant (Uber), targeted different types of legislators for lobbying as they seek the legislators’ support for their respective regulatory positions.\nEssay 3 examines how government intervention in the management of private firms affects the performance of firms. Undertaken in the context of Chrysler’s bailout by the U.S. Federal Government in 2008, the chapter quantifies the effect of government’s intervention, which extended beyond capital injection and affected the day-to-day operations of the firm, on Chrysler and its constituent brands. Results demonstrate that all four brands of Chrysler experienced a decrease in their sales during the period of government intervention. However, this decrease was felt differentially across the brands and ranged between a 51 percent and 19 percent of average monthly sales.\nOverall, this dissertation aims to expand and enrich the knowledge on strategies of firms as they navigate the uncertainties associated with the non-market environment.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".