Investigating the Role of Tenure Diversity in Information System Project Teams: A Multilevel Analysis
Bibliographic record
Abstract
Diversity in information system project (ISP) teams can be a double-edged sword. Since many ISP teams bring together different specialists who have different backgrounds, knowledge, and skills, managing their diverse nature represents an important concern in the IS field. However, so far, few studies have examined the influence that project teams’ diversity has on IS project outcomes. To better understand this influence, we developed a multilevel research model that examined job tenure and organizational tenure diversity in ISP teams and their influence on team members’ satisfaction. We tested our hypotheses via hierarchical linear modeling (HLM) with data that we collected from 200 participants in 41 ISP teams. Our results indicate that job tenure influences the effect that job tenure diversity has on a team member’s satisfaction: while team “rookies” were more satisfied in teams that had greater job tenure diversity, team “veterans” were more satisfied when their teams had lower job tenure diversity. Via systematically applying both conceptual and methodological recommendations in combination, we address several limitations in past research and underscore the need to adopt a more nuanced and rigorous approach to examine diversity in project teams.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".