Planning and performance in teams: A Bayesian meta-analytic structural equation modeling approach

Udo Konradt, Alexander Nath and Martina Oldeweme

PLOS ONE, 2023, vol. 18, issue 1, 1-24

Abstract: We meta-analyzed the relationship between team planning and performance moderated by task, team, context, and methodological factors. For testing our hypothesized model, we used a meta-analytic structural equation modeling approach. Based on K = 33 independent samples (N = 1,885 teams), a mixed-effects model indicated a non‐zero moderate positive effect size (ρ = .31, 95% CI [.20, .42]). Methodological quality, generally rated as adequate, was unrelated to effect size. Sensitivity analyses suggest that effect sizes were robust to exclusion of any individual study and publication bias. The statistical power of the studies was generally low and significantly moderated the relationship, with a large positive relationship for studies with high-powered (k = 42, ρ = .40, 95% CI [.27, .54]) and a smaller, significant relationship for low-powered studies (k = 54, ρ = .16, 95% CI [.01, .30]). The effect size was robust and generally not qualified by a large number of moderators, but was more pronounced for less interdependent tasks, less specialized team members, and assessment of quality rather than quantity of planning. Latent class analysis revealed no qualitatively different subgroups within populations. We recommend large‐scale collaboration to overcome several methodological weaknesses of the current literature, which is severely underpowered, potentially biased by self-reporting data, and lacks long-term follow-ups.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0279933

DOI: 10.1371/journal.pone.0279933

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