A Resource Model of Team Resilience Capacity and Learning
Bibliographic record
Abstract
A team's capacity to bounce back from adversities or setbacks (i.e., team resilience capacity) is increasingly valuable in today's complex business environment. To enhance our understanding of the antecedents and consequences of team resilience capacity, we develop and empirically test a resource-based model that delineates critical team inputs and outputs of resilience capacity. Drawing from conservation of resources theory, we propose that voice climate is a critical resource that builds team resilience capacity by encouraging intrateam communication and that leader learning goal orientation (LGO) amplifies this relationship by orienting team discourse toward understanding and growing from challenges. In turn, we propose that team resilience capacity is positively related to team learning behaviors, as teams with a higher resilience capacity are well-positioned to invest their resources into learning activities, and that team information elaboration amplifies this relationship by facilitating resource exchange. Results of a time-lagged, multisource field study involving 48 teams from five Canadian technology start-ups supported this moderated-mediated model. Specifically, voice climate was positively related to team resilience capacity, with leader LGO amplifying this effect. Further, team resilience capacity was positively related to team learning behaviors, with information elaboration amplifying this effect. Altogether, we advance theory and practice on team resilience by offering empirical support on what builds team resilience capacity (voice climate) and what teams with a high resilience capacity do (learning), along with the conditions under which these relationships are enhanced (higher leader LGO and team information elaboration).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".