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The implicit affiliation motive and self-regulation: exploring the differences with implicit achievement and power, and patterns of self-regulation.

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Title: The implicit affiliation motive and self-regulation: exploring the differences with implicit achievement and power, and patterns of self-regulation.
Authors: Marion-Jetten, Ariane S. (AUTHOR), Piesch, Ludwig (AUTHOR), Baumann, Nicola (AUTHOR), Wegner, Mirko (AUTHOR)
Source: Motivation & Emotion. Jun2025, Vol. 49 Issue 3, p210-225. 16p.
Subjects: Cognitive psychology, Achievement motivation, Path analysis (Statistics), Emotions, Motivation (Psychology)
Abstract: The implicit affiliation motive represents a concern with establishing and maintaining warm and friendly relationships. Unlike implicit achievement or power, it does not entail reaching an end goal such as mastering a skill or making a group of people laugh (Kuhl, 2018). Affiliation-motivated people should be more interested in maintaining affiliative motivational states (e.g., friendships) and get information about how they are doing through nonverbally expressed emotions (Sokolowski, 1994). We investigated whether the implicit affiliation motive is associated with different self-regulation components (in contrast to the implicit achievement and power motives) in a sample of 5940 people in search of employment, students, and leaders (Mage = 34.19, SD = 12.38, 48% identifying as women). Path analyses revealed similar, significant, patterns of associations for implicit achievement and power on the micro and macro subscales of the volitional components questionnaire, while implicit affiliation showed weaker associations for almost every scale and was often not a significant predictor of self-regulation. The theoretical and practical implications of, as well as suggestions for future research focusing on affiliative self-regulation are discussed. [ABSTRACT FROM AUTHOR]
Copyright of Motivation & Emotion 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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: The implicit affiliation motive represents a concern with establishing and maintaining warm and friendly relationships. Unlike implicit achievement or power, it does not entail reaching an end goal such as mastering a skill or making a group of people laugh (Kuhl, 2018). Affiliation-motivated people should be more interested in maintaining affiliative motivational states (e.g., friendships) and get information about how they are doing through nonverbally expressed emotions (Sokolowski, 1994). We investigated whether the implicit affiliation motive is associated with different self-regulation components (in contrast to the implicit achievement and power motives) in a sample of 5940 people in search of employment, students, and leaders (Mage = 34.19, SD = 12.38, 48% identifying as women). Path analyses revealed similar, significant, patterns of associations for implicit achievement and power on the micro and macro subscales of the volitional components questionnaire, while implicit affiliation showed weaker associations for almost every scale and was often not a significant predictor of self-regulation. The theoretical and practical implications of, as well as suggestions for future research focusing on affiliative self-regulation are discussed. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Motivation & Emotion 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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      – SubjectFull: Path analysis (Statistics)
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              Text: Jun2025
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