A closer look at determinants of organizational capability to innovate (OCI)
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a portrait of the main managerial and organizational determinants of organizational capability to innovate (OCI). Despite its importance, research on the subject seems limited, and little attempt has been made, over the years, to offer an in-depth and simultaneous analysis of these particular determinants, as well as an exploration of the underlying and complex mechanisms explaining their relationships to OCI. Design/methodology/approach A systematic review of articles published between 1991 and 2018 was conducted in ProQuest (ABI/INFORM Collection) and Scopus databases. A total of 64 articles were selected and analysed through the use of a coding grid. Findings Results highlight five key OCI determinants, namely: leadership, support, communication, culture, and learning. By using the dynamic capabilities theory (DCT) as a framework, this research suggests ways to better understand the dynamic action of these determinants as well as their contributions to OCI. Findings also suggest that OCI should be defined at the confluence of three perspectives (human, procedural and environmental aspects) to embrace the multiple facets of this complex construct. Proposals for future research are provided on how OCI can be better examined. Originality/value This research helps to understand the five core determinants through an integrated and holistic view and represents the first attempt to systematically analyse the scientific literature on OCI through the DCT lens.
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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".