New Frontiers in Innovation Research
Bibliographic record
Abstract
Innovation has been a significant and widely studied topic in strategy and management research for decades, yet certain crucial phenomena and related theoretical questions remain unexamined, creating opportunities to explore new frontiers in innovation studies. This symposium brings together a unique set of papers that explore new perspectives on understanding various aspects of innovation, providing implications for both future research and practice. Specifically, the first paper differentiates between research and development to discern the distinct impacts of organizational structure on R&D: centralization of development is related to reduced duplication of development effort; however, the likelihood that a given invention being commercialized is lower in centralized development compared to decentralized development. The second paper proposes managerial prosocial preferences, specifically the inclination to avoid harming employees, as a negative antecedent to firm investment in automation and AI innovation. The third paper studies the fast-growing call for firms’ environmental innovations and delves into a paradox concerning the effect of institutional pressures—normative institutional pressures incentivize firms to innovate, but they also motivate them to shift focus toward short-term “brown” innovations, rather than long-term “green” innovations. The last study moves beyond the conventional binary choice between trade secret and patent protection, investigating vagueness as a novel patenting strategy in response to the desire to withhold technological information. As a set, these papers offer fresh insights into understanding the creation, adoption, commercialization, and protection of innovation, shedding new light on expanding the frontier of innovation research. Innovation and Commercialization as a Function of the Organization Structure of Development Author: Jiho Yang; Imperial College Business School Author: Paola Criscuolo; Imperial College London Author: Brian Silverman; U. of Toronto Managerial Prosocial Preferences and Automation Innovation Author: Dongil Daniel Keum; Columbia Business School Institutional Pressures Promote Short-Termism in Environmental Innovation Author: Raffaele Conti; ESSEC Business School Author: Marcin Kacperczyk; Imperial College London Trade Secrets and Vagueness in Patent Applications Author: Yang Liu; Fordham U.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".