Innovative work behavior and personality traits

五大性格特征 清晰 心理学 特质 人格 社会心理学 工作(物理) 特质理论 工作行为 机械工程 生物化学 化学 计算机科学 工程类 程序设计语言
作者
Stephen A. Woods,Michael Mustafa,Neil Anderson,Benjamin Sayer
出处
期刊:Journal of Managerial Psychology [Emerald Publishing Limited]
卷期号:33 (1): 29-42 被引量:177
标识
DOI:10.1108/jmp-01-2017-0016
摘要

Abstract Purpose The literature on individual differences in innovative work behavior (IWB) reveals inconsistencies in the relations of personality traits and tenure on innovation at work. To provide greater clarity about the effects of these antecedents, the purpose of this paper is to report a study of the moderating effects of tenure on the associations of traits and IWB, and apply a theoretical lens based on the trait-activation theory. Design/methodology/approach In all, 146 employees of a UK-based financial institution completed measures of conscientiousness and openness, and had three aspects of IWB (idea generation, promotion, and realization) rated by their line-supervisor. All participants were on graduate training programs. Hierarchical regression analyses were used to test the moderating effects of tenure on the associations of the self-reported traits with the supervisor-rated IWB outcomes. Findings Tenure moderated the effects of conscientiousness on IWB, with highly conscientious employees being less innovative with longer tenure. Tenure moderated the effect of openness with idea generation with highly open employees generating more ideas if they were longer tenured. Practical implications Management of innovation requires differentiated strategies based on the personality traits and tenure of individual employees. Implications for recruitment, socialization and development are discussed. Originality/value This is the first study to examine empirically the interactions of traits and contextual factors (i.e. organizational tenure) on IWB, framed around a strong theoretical foundation (i.e. trait activation theory). The study also makes notable contributions by measuring innovative behavior using a supervisor-rated and multidimensional approach.

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