Exploring the Strategic Interdependence of Innovation Capabilities: A perspective of the Canadian Manufacturing Sector
Bibliographic record
Abstract
Innovation and strategy are central issues for businesses operating in an ever increasingly competitive market place. For many firms the pursuit of a sustained competitive advantage through strategy is the focus of senior leadership. A key driver of this focus is the firms’ capability to innovate. This research demonstrates that the pursuit of a sustained competitive advantage, firm performance and long-term growth can be achieved through strategies focused on innovation capabilities (IC) development. The author cites literature which argues there are two, three or four innovation capabilities strategies based on Schumpeter (1934). However, the author argues, that there are five distinct innovation capability strategies that are interdependent, based on resource interdependencies and are essential to an overall firm strategy.The research employs an extensive literature review, leader interviews and quantitative analysis of data collected from an industry survey to highlight the significant factors influencing a firms IC. The findings of this research provide empirical support for five distinct Innovation Capabilities strategies, as well as supporting interdependence between the five strategies. Additionally, support was found for three outcomes tested. For Business the implication is that interdependencies between distinct innovation capabilities resources and strategies can either support or adversely affect outcome performance where interdependencies between resources and strategies is not fully understood. From a theoretical perspective, this study tests the combination and application of Resource-based View and Social Interdependence theories and proposes a less classical perspective of RBV as well as a combination of RBV with SI theory in extending IC research of the firm.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".