A Look Back and a Leap Forward: A Review and Synthesis of the Individual Work Performance Literature

规则网络 格里芬 主动性 领域(数学) 集合(抽象数据类型) 多样性(控制论) 清晰 具身认知 计算机科学 心理学 认知心理学 知识管理 数据科学 社会心理学 人工智能 结构方程建模 生物化学 化学 数学 考古 机器学习 纯数学 历史 程序设计语言
作者
Joseph A. Carpini,Sharon K. Parker,Mark Griffin
出处
期刊:The Academy of Management Annals [Academy of Management]
卷期号:11 (2): 825-885 被引量:118
标识
DOI:10.5465/annals.2015.0151
摘要

Individual work performance has been a central topic for scholars over the past century. There is a mass of research on performance but it is embodied in a variety of disconnected literatures each using their own set of constructs and theoretical lenses. In this paper, we synthesize this disparate literature to better understand individual work performance and pave the way for future research. First, using a bibliometric technique to analyze 9,299 articles, we identify the cumulative intellectual structure of the field and show how the field has evolved over the past 40 years. Second, drawing on the Griffin, Neal, and Parker (2007) model, we classify 97 performance constructs according to their form (proficiency, adaptivity, proactivity) and level of contribution (individual, team, organization). We conclude this model is useful for understanding the similarities and differences among many distinct performance constructs. Third, using the Griffin et al. (2007) model, we illuminate the nomological network by mapping the antecedents and outcomes of each form and level of contribution. Our synthesis identified theoretically relevant and differentiating antecedents of form; whereas the nomological network is underdeveloped in relation to the level of contribution. Finally, we propose 18 recommendations which include ensuring conceptual clarity for performance constructs, expanding theoretical models to account for more performance dimensions, greater attention to the underlying mechanisms through which individual performance contributes to higher-level outcomes, increased consideration of how performance changes over time and across contexts, and more investigations into how multiple performance constructs interact with each other to shape effectiveness.
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