Essays on innovation and relational capital
Bibliographic record
Abstract
This dissertation is composed of three essays. Its central theme is a study of the antecedents to technological innovation. Essay One examines an important relationship that has been overlooked in the literature, i.e., the impact of prior alliance relationship between firms on their current innovation performance when they become competitors. I used a comprehensive longitudinal dataset that includes information on historical alliance activities and current innovation races between firms in the U.S. pharmaceutical industry, over two decades (1985-2004). I found that the impact of prior collaborations on current competitions is a function of both the type of prior alliance relationships between firms, and the number of prior allies of different types in the current competition. Essay Two helps reconcile an ongoing debate in the literature regarding whether competition positively or negatively influences innovation. I used panel data containing innovation races from 1991 to 2004 in the U.S. pharmaceutical industry. I found that the degree of knowledge resource similarity (in both structure and amount) between the focal firm and its rivals is an important determinant of the balance between the positive and negative externalities of competition. The focal firm’s innovation was likely to suffer from competition where rivals had relatively larger amounts of knowledge resources. Such negative effect, however, can be attenuated and the net effect may turn positive, as the knowledge structure similarity between the rivals and the firm increases. While Essay One focuses on inter-firm relational capital, i.e., alliances, Essay Three focuses on the development of relational capital (i.e., trust) in the workplace, touching upon some of the fundamental conditions of innovation. I studied the antecedents to social trust in the workplace, a unique form of relational capital that draws an increasing research interest. Using two field studies conducted in Canada and China representing distinct cultures, I found that the diversity of one’s social network in the community was positively associated with one’s social trust in the workplace, in both societies, while the diversity of social network in the workplace was only positively associated with social trust in the workplace in China, and not in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".