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Would I Really Make a Difference? Moral Typecasting Theory and its Implications for Helping Ethical Leaders.

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Title: Would I Really Make a Difference? Moral Typecasting Theory and its Implications for Helping Ethical Leaders.
Authors: Yam, Kai Chi1 (AUTHOR), Fehr, Ryan2 (AUTHOR), Burch, Tyler C.3 (AUTHOR), Zhang, Yajun4 (AUTHOR) zhangyajun@mail.gufe.edu.cn, Gray, Kurt5 (AUTHOR)
Source: Journal of Business Ethics. Dec2019, Vol. 160 Issue 3, p675-692. 18p. 3 Charts, 3 Graphs.
Subject Terms: Business ethics, Professional ethics, Leadership, Judgment (Psychology)
Abstract: Ethical leadership research has primarily relied on social learning and social exchange theories. Although these theories have been generative, additional theoretical perspectives hold the potential to broaden scholars' understanding of ethical leadership's effects. In this paper, we examine moral typecasting theory and its unique implications for followers' leader-directed citizenship behavior. Across two studies employing both survey-based and experimental methods, we offer support for three key predictions consistent with this theory. First, the effect of ethical leadership on leader-directed citizenship behavior is curvilinear, with followers helping highly ethical and highly unethical leaders the least. Second, this effect only emerges in morally intense contexts. Third, this effect is mediated by the follower's belief in the potential for prosocial impact. Our findings suggest that a follower's belief that his or her leader is ethical has meaningful, often counterintuitive effects that are not predicted by dominant theories of ethical leadership. These results highlight the potential importance of moral typecasting theory to better understand the dynamics of ethical leadership. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Business Ethics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Business+Ethics%22">Journal of Business Ethics</searchLink>. Dec2019, Vol. 160 Issue 3, p675-692. 18p. 3 Charts, 3 Graphs.
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  Data: Ethical leadership research has primarily relied on social learning and social exchange theories. Although these theories have been generative, additional theoretical perspectives hold the potential to broaden scholars' understanding of ethical leadership's effects. In this paper, we examine moral typecasting theory and its unique implications for followers' leader-directed citizenship behavior. Across two studies employing both survey-based and experimental methods, we offer support for three key predictions consistent with this theory. First, the effect of ethical leadership on leader-directed citizenship behavior is curvilinear, with followers helping highly ethical and highly unethical leaders the least. Second, this effect only emerges in morally intense contexts. Third, this effect is mediated by the follower's belief in the potential for prosocial impact. Our findings suggest that a follower's belief that his or her leader is ethical has meaningful, often counterintuitive effects that are not predicted by dominant theories of ethical leadership. These results highlight the potential importance of moral typecasting theory to better understand the dynamics of ethical leadership. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Business Ethics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10551-018-3940-0
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      – SubjectFull: Leadership
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              Text: Dec2019
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