Building a Strong Organizational Culture: Key Drivers and Best Practices
Bibliographic record
Abstract
This qualitative study explores the key drivers and best practices for building a strong organizational culture within private sector organizations in Ottawa, Ontario, Canada. Recognizing that organizational culture significantly influences employee engagement, performance, and retention, the research seeks to understand how culture is formed, sustained, and adapted in contemporary work environments. Data was collected through semi-structured interviews with 18 professionals occupying leadership, HR, and managerial roles across six private organizations. Using thematic analysis, six core themes emerged: Leadership Influence, Recruitment and Cultural Fit, Recognition and Motivation, Culture in Hybrid Work Models, Technology and Culture Transmission, and Feedback Loops and Culture Assessment. The findings highlight the central role of leadership in modeling and reinforcing cultural values, the importance of value-based recruitment in maintaining cultural coherence, and the critical need for recognition systems that reflect organizational priorities. Additionally, the study reveals how hybrid work and digital platforms have redefined cultural expression and interaction. Regular feedback mechanisms emerged as vital tools for assessing and refining culture in dynamic business contexts. This study contributes to the growing body of knowledge on organizational behavior by offering practical recommendations for cultivating resilient and high-performing workplace cultures. It also identifies areas for future research, including sector-specific analysis, longitudinal studies, and quantitative validation of cultural indicators.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".