Individual and Group Level Antecedents of Team-Member Exchange (TMX) and its Associated Outcomes
Bibliographic record
Abstract
In recent years, teams have become a popular and efficient way of managing and performing work tasks. The idea behind teams is that if they are structured to maximize communication density, connectivity, and minimize hierarchy, there will be greater flexibility in communicating, cooperating, and collaborating on work-related tasks. Human resources are growing concern for today’s competitive organizations. Therefore it is very essential to focus on this issue seriously. In this review paper, we have integrated empirical research regarding the antecedents and consequences of Team-Member Exchange (TMX). An exchange relationship between team members is very critical but relatively unexplored phenomenon in the field of organizational behaviour. We have proposed a theoretical model to study certain selected antecedents (or predictor) and consequences of team-member exchange (TMX) process, both at the individual and group level. The individual level antecedents included in this paper are organizational justice, emotional intelligence, workplace friendship and group level antecedents are collectivistic orientation, team similarity, team identification, team-member affect, team reflexivity and group potency. Likewise, individual level outcomes associated with high quality team-member exchange are organizational citizenship behaviour, job performance, mental health and group level outcomes associated with high quality team-member exchange are team conflict, team climate, team commitment, team performance and team innovativeness. Further, several preliminary propositions have been offered to guide future research and the role of team-member exchange (TMX) within a broad theoretical and empirical context is discussed.Finally, we have discussed the gaps in the relevant literature, major issues for future research on team-member exchange (TMX) along with implications and interventions about how management can develop good interrelationships between co-workers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".