Trust and Knowledge Sharing in Hybrid Project Teams
Bibliographic record
Abstract
The COVID-19 pandemic resulted in a rapid and significant change in the work settings of many organizations. Today, many businesses have adopted a hybrid way of working as opposed to returning to the traditional way. Due to this topic being relatively new, there is little research done within the area. Furthermore, knowledge and, by extension, knowledge sharing are two of the most important resources or procedures for an organization to remain long-term competitive. One major factor influencing knowledge sharing is trust, therefore, this study aims to investigate how knowledge sharing and trust within project teams have been impacted by the hybrid context. Further, four aspects of trust, benevolence, competence, openness, and integrity, will also be studied to see how the hybrid context has influenced them. This master’s thesis is based on a case study with an exploratory approach. Qualitative interviews and literature research were conducted, which together form the basis for the analysis and conclusions. It has become evident that the hybrid context has had an impact on all factors considered, in other words, knowledge sharing, trust, and the aspects of trust. One significant finding was that it appears to be difficult and more time-consuming to establish social relationships in the hybrid context, and this has had a negative impact on all variables examined. Despite the negative consequences, the research findings also show that the hybrid context has some positive impacts in regard to benevolence, openness, and integrity. The findings in this master’s thesis contribute to the research by providing further understanding of how the hybrid context has affected knowledge sharing and trust, including its aspects. Practitioners will get valuable insights into trust and knowledge sharing, both of which are critical to the success of teams and organizations. Further, a set of managerial implications are given.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".