The challenges facing corporate universities in dealing with open innovation
Bibliographic record
Abstract
Purpose – This paper aims to illustrate the quick rise in the popularity of corporate universities since the 1990s. Because knowledge management is becoming imperative to the survival and growth of firms in most industries, better management of corporate universities is becoming more and more critical. The purpose of this paper is to analyze three objectives: Why invest in corporate universities? Which model to adopt? and What are the key challenges facing corporate universities in dealing with the adoption of an open innovation approach? Design/methodology/approach – The article provides a general review of corporate universities dealing with open innovation by using a creative synthesis. Findings – This paper analyzes the challenges involved in the development of corporate universities and examines how they can deal with open innovation. While few corporate universities have a real strategic role, several initiatives have failed or have been seriously compromised. To create competitive advantages through a corporate university, upper management must dedicate significant resources and have a plan for building the corporate curriculum in order to deal with innovation management. Research limitations/implications – Due to the lack of scientific articles on the topic, most of the published articles made by practitioners was used. Further studies are needed to test the recommendations and models. Practical implications – This paper identifies some development models and growth avenues for corporate universities. It helps provide an understanding of the challenges associated with open innovation as well as their limits. Originality/value – It is among the first papers to link the development of corporate universities with the open innovation approach. It also provides practical advice for managers and academics.
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 | 0.040 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".