Intellectual capital and performance in temporary teams
Bibliographic record
Abstract
Purpose The purpose of this paper is to deepen resource-based view theory by analyzing how intellectual capital (IC) affects performance in temporary teams and by showing the moderating role of integrative mechanisms. Design/methodology/approach The research context focuses on 153 national teams of football (NTF), also referred to as national soccer teams, as an example of temporary groups. A partial least squares (PLS) methodology was utilized on a data set built from transfermarkt.com and FIFA world rankings. Three main hypotheses were developed and tested using first a PLS and then an OLS approach. Findings The results show how IC contributes to performance, extending the findings of previous studies to the context of temporary teams. Additionally, the results show how some integrative mechanisms such as assembly decisions and team leader experience influence temporary team performance by creating an interaction effect with existing IC. Originality/value This study contributes to IC theories for three reasons. First, it applies IC research to a specific research context: temporary teams, where specific organizational capabilities are required to coordinate resources. Second, the study analyzes the role of integrative mechanisms as moderators of the relationship between IC and performance in temporary teams. Third, the study focuses on NTF as an example of temporary 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 | 0.002 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".