The Ambiguity Tolerance Interface: A Modified Social Cognitive Model For Leading Under Uncertainty

This paper proposes a modification of McCormick's (2001) self-regulatory leadership confidence model by including an intervening variable referred to as the Ambiguity Tolerance Interface (ATI). After a review of theoretical approaches relevant to developing a framework of leadership at the inte...

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Veröffentlicht in:Journal of leadership & organizational studies 2004-12, Vol.10 (3), p.69-81
Hauptverfasser: Lane, Michael S., Klenke, Karin
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creator Lane, Michael S.
Klenke, Karin
description This paper proposes a modification of McCormick's (2001) self-regulatory leadership confidence model by including an intervening variable referred to as the Ambiguity Tolerance Interface (ATI). After a review of theoretical approaches relevant to developing a framework of leadership at the interface of tolerance of ambiguity and uncertainty, we introduce the ambiguity tolerance interface (ATI) and discuss a number of variables we identified as focal constructs that comprise the proposed ATI cluster. These variables include ambiguity tolerance, spirituality, creativity, aesthetic judgment and mindfulness. The paper concludes with a discussion of instruments that can be used to operationalize the variables of interest and the implications of ATI for leadership theory and praxis. Finally, we refocused existing research to address the influence of uncertainty on leaders in the dynamic global environment.
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subjects Ambiguity
Ambiguity (Context)
Beliefs
Cognition & reasoning
Cognitive models
Company business management
Creativity
Entrepreneurs
Epistemology
Goal Orientation
Information overload
Information processing
Intervening variables
Knowledge management
Leaders
Leadership
Leadership Effectiveness
Management
Managerial skills
Methods
Praxis
Predictor Variables
Resistance (Psychology)
Self Efficacy
Social Psychology
Social Theories
Uncertainty
Variables
title The Ambiguity Tolerance Interface: A Modified Social Cognitive Model For Leading Under Uncertainty
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