J
John D. Kammeyer-Mueller
Researcher at University of Minnesota
Publications - 60
Citations - 5531
John D. Kammeyer-Mueller is an academic researcher from University of Minnesota. The author has contributed to research in topics: Context (language use) & Core self-evaluations. The author has an hindex of 32, co-authored 58 publications receiving 4753 citations. Previous affiliations of John D. Kammeyer-Mueller include University of Florida & College of Business Administration.
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Unwrapping the organizational entry process: disentangling multiple antecedents and their pathways to adjustment.
TL;DR: Results suggest that preentry knowledge, proactive personality, and socialization influences from the organization, supervisors, and coworkers are independently related to proximal adjustment outcomes, consistent with a theoretical framework highlighting distinct dimensions of organizational and work task adjustment.
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Predictors and outcomes of proactivity in the socialization process.
TL;DR: Two personality variables, extraversion and openness to experience, were associated with higher levels of proactive socialization behavior and highlighted the importance of 2 control variables in the experience of socialization into a new job.
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The role of core self-evaluations in the coping process.
TL;DR: In 2 studies, the authors investigated whether core self-evaluations (CSE) serve as an integrative framework for understanding individual differences in coping processes and demonstrated that emotional stability was uniquely related to the stress and coping process and thatotional stability moderated the relationship between stressors and strain.
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Job attitudes, job satisfaction, and job affect: A century of continuity and of change.
TL;DR: This review of research on job attitudes addresses a rich panoply of topics related to the daily flow of affect, the complexity of personal motives and dispositions, and the complex interplay of attitude objects and motivation in shaping behavior.
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Comparing meta-analytic moderator estimation techniques under realistic conditions.
TL;DR: Monte Carlo analyses are developed in this article that compare bivariate correlations, ordinary least squares and weighted least squares (WLS) multiple regression, and hierarchical subgroup (HS) analysis for assessing the influence of continuous moderators under conditions of multicollinearity and skewed distribution of study sample sizes (heteroscedasticity).