A Brave New World: The Impact of Technology on Innovation Management
Bibliographic record
Abstract
ABSTRACT This special issue—focused on the transformative impact of emerging technologies (e.g., artificial intelligence, Internet of Things, service robots, blockchain) on innovation management—identifies the ways that firms are rethinking how they generate ideas, develop products, and engage with stakeholders across organizational and societal levels. The articles in this special issue span multiple domains, including ride‐sharing, smart homes, health care, and digital communication. In addition, they demonstrate how new methods are reshaping new product development (NPD) processes. These contributions highlight how emerging technologies enable novel ideation through tools like large language models and topic modeling, as well as how they support agile, data‐informed NPD and dynamic commercialization strategies. Leveraging these insights, this editorial proposes a conceptual framework that encompasses a simplified, three‐step NPD process (ideation, development, and commercialization), along with key drivers of innovation and their implications for individuals, firms, society, and academia.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".