Feedback environment: A meta‐analysis

verifiedmedium profile · International Journal of Selection and Assessment · 2021

Katz, Ian M.; Rauvola, Rachel S.; Rudolph, Cort W.

10.1111/ijsa.12350 · CrossRef: verified

How this was studied

paradigm: positivistpurpose: explanatory causaldesign: meta analytic synthesisidentification: nonetime: cross sectionaldata: quantitative survey

A meta-analytic synthesis — it pools many primary studies rather than running a single design, so there is no single-study diagram.

analysis: meta analysis, glm t anova regression · validity (validity): internal addressed · external addressed · construct addressed · statistical addressed · classifier confidence 0.9

What this article reports

The findings we extracted from this work, normalized into Principia’s relationship form (X → Y). Each feeds the synthesized models below.

performance feedbackorganizational commitment

r = 0.46 · k=112 · grade B · unverified

performance feedbackjob satisfaction

r = 0.51 · k=112 · grade B · unverified

Models this article informs

The meta-analytic priors this article contributes evidence to — its place in the broader synthesis.

In the authors’ words

The article’s own abstract.

Abstract Feedback environment reflects the perceptions of the contextual, day‐to‐day feedback process within supervisor–subordinate relationships. Here, we present a comprehensive meta‐analysis of the feedback environment literature. On the basis of K = 112 independent samples, representing N = 31,089 workers, results suggest that feedback environment is positively related to feedback orientation ( r c = .42), leader–member exchange ( r c = .81), supervisor‐rated performance ( r c = .29), and negatively related to burnout ( r c = −.51). Moreover, we present multiple regression and relative weights analyses to consider the unique and incremental predictive power of feedback environment above and beyond two related constructs: leader–member exchange and feedback orientation. The results suggest that the feedback environment explains unique variance in several correlates and is a particularly important predictor of (lower) burnout.

What the literature says about it

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