Strategic Entrepreneurship’s Impact on Product Innovation Performance: An Analysis of ICT Firms
Bibliographic record
Abstract
The purpose of this study is to examine the driving effects of information technology (IT) capabilities on product innovation performance (PIP) by exploring the mediating role of strategic entrepreneurship (SE) in a firm level context. The new and exciting field of SE or the interface of entrepreneurship and strategic management research which aims to answer the question of how firms create value or wealth and sustain success in increasingly competitive and dynamic environments is the appropriate catalyst to explore this link. Data were collected from small to medium sized information communication technology (ICT) firms in Canada. Partial Least Squares (PLS) regression tested the hypotheses derived from the research model on 112 firms surveyed. The results of this study show that IT capabilities drive product innovation performance and thereby create value. Secondly, SE had a direct impact on PIP and SE partially mediates IT capabilities effect on product innovation performance in this research context. This study contributes to the IT business management and the strategic entrepreneurship literature by confirming the importance of IT capabilities on product innovation performance as well as confirming SE's impact on PIP and its role in mediating the relationship between IT capabilities and product innovation performance. This research informs ICT managers of the importance of balancing both strategic (advantage seeking) and entrepreneurial (opportunity seeking) activities. Keywords: IT capabilities; product innovation performance; strategic entrepreneurship; Canadian ICT firms
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".