Bibliographic record
Abstract
Purpose Work in distributed project teams is always a challenge for organizations. Many researchers have studies different aspects of distributed project teams, as witnessed by the impressive number of papers published in the last decade. However, it appears that the dimensions related to organizational support have still not received much attention in empirical studies. This study investigates the dimensions of organizational support in distributed project teams that contribute most to the quality of the decision‐making process and teamwork effectiveness in distributed project teams. Design/methodology/approach The initial intent of this research was to test a theoretical model on the basis of data from the field, namely real‐life situations. A two‐step approach (qualitative and quantitative method) was used. The research model was tested in a sample of experienced project managers on distributed project teams. Findings The results suggest that strategic staffing and training and tools provided to team members have a positive impact on the quality of decision making and teamwork effectiveness. Team autonomy is more salient and influential in fostering decision quality in a highly culturally diverse context. Our findings also re‐confirm the link between the quality of decision making and team effectiveness. Thus, teams are perceived as vehicles for identifying and integrating various individual viewpoints and combining knowledge. Practical implications This study underscores the importance of selecting practices that enhance the recognition of team members’ contributions in the context of distributed project teams. It is now clear that managers cannot treat these distributed project teams in the same way as conventional teams. Several intervention and support methods are possible. This research contributes to identifying which of them are the most appropriate in this context. Originality/value This study contributes to research on distributed project teams and on organizational support theory. It highlights the importance of understanding the processes or dimensions underlying the consequences of perceived organizational support. It bolsters the need to select practices that enhance the recognition of team members’ contributions and treat them favourably in the context of distributed project teams.
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 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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".