Understanding and conceptualizing CEOs’ integration collaborative competencies (CEOs’ ICCs) for startups in Canada and Saudi Arabia
Bibliographic record
Abstract
CEOs play a major role in startups’ operations and development processes and are sometimes instrumental for startup survival. Accordingly, the strategic management literature proposes human capability as a source of competitive advantage. This dissertation explores the startup CEOs’ integration collaborative competencies (CEOs’ ICCs). Newly developed measures for the internal and external competencies were developed and resulted in findings of differential CEOs’ perceptions of firm performance and ability to survive. Based on the dynamic capabilities framework, the research link individual CEO social dynamic capability with the ICCs to explore startup-specific capabilities. Three modes of startup dynamic capabilities emerged from analyses of seven startup cases. Specifically, the traditional collaborative CEOs’ ICCs mode was shown to build business relations with external partners, reduce cost, and improve value. The competitive integration mode was associated with integrative processes that improved the effectiveness of internal capabilities. Finally, the ambidexterity mode was associated with the effectiveness of external collaboration and the integration of internal competencies. The findings indicate the three ICCs modes develop dynamic capabilities differently in startups based on the diversity of CEOs’ ICCs and organizational processes, while providing a greater balance among these CEOs’ ICCs both internally and externally. In other words, what CEOs do is just as important as who they are (personality traits and personal values) when it comes to performance and startup survival.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".