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dc.contributor.authorDust, Scott
dc.date.accessioned2020-03-05T02:30:50Z
dc.date.available2020-03-05T02:30:50Z
dc.identifier.urihttp://hdl.handle.net/2374.MIA/6593
dc.description.abstractOrganizational research commonly proposes that leader and/or follower extraversion will be positively related to leader-member exchange (LMX). The assumption is that being social translates into high-quality relationships. We move beyond this intrapersonal hypothesis and offer an interpersonal hypothesis. Specifically, we draw from person-supervisor (PS) fit literature and apply similarity-attraction theory to suggest that leader-follower extraversion similarity positively relates to LMX. Importantly, we also suggest that it is necessary to incorporate followers’ perspectives regarding appropriate power dynamics to determine the degree to which followers are reliant on interpersonal similarity. We therefore hypothesize that the relationship between leader-follower extraversion similarity and LMX will hold for followers high in power distance orientation (PDO), but will be neutralized for followers low in PDO. Our findings illustrate support for our moderation hypothesis. Supplemental analyses also reveal that low PDO enhances the effect of follower extraversion on LMX. Additionally, for high PDO followers, LMX was highest when leader-follower similarity occurred at extreme levels along the extraversion-introversion continuum compared to similarity at moderate levels (i.e., ambiverts). To test our hypotheses, we apply polynomial regression and moderated polynomial regression analysis using 374 leader-follower dyads.en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.titleFinal data and measures for JSP 2020 - extroversion congruenceen_US
dc.typeJournal Articleen_US
dc.date.published2020


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  • Dust, Scott
    Dr. Scott Dust - Assistant Professor, Management

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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States