Multisource leadership ratings rely on the assumption that-in addition to the leader's self-evaluation-different rater groups (i.e., subordinates, peers, and superiors) bring in unique perspectives and thus provide a more well-rounded analysis of the leader's behavior. However, the way in which multisource data are typically treated in research offers little information about the precise levels of overlap and uniqueness that are encapsulated in these different perspectives. Drawing on the Trait-Reputation-Identity (TRI) model, we propose a model that conceptualizes these shared and unique perspectives in terms of latent factors reflecting, respectively, (a) the consensus about the leader (i.e., the leadership Arena), (b) the impressions conveyed to others that are distinct from self-perceptions (i.e., the leader's Reputation), and (c) the unique self-perceptions of the leader (i.e., the leader's Identity). This Leadership Arena-Reputation-Identity (LARI) model is formalized by means of bifactor modeling, which allows to statistically decompose the variance captured by multisource ratings. The LARI model was tested against five alternative models in two large multisource samples (N1 leaders = 537, N1 observers = 7,337; N2 leaders = 1,255, N2 observers = 15,777), each using different leadership instruments. In both samples, the LARI bifactor model outperformed the alternative models. A subsequent variance decomposition showed that each rater source indeed provides unique information about the target's behavior, although in varying degree. Across all leadership dimensions in both samples, superiors consistently provided the largest share of unique information among the three observer groups. Implications and future directions are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Bibliografische notaFunding Information:
We would like to thank Robert B. Kaiser (Kaiser Leadership Solutions) and Patrick Vermeren (PerCo) for enabling the use of their multisource data in this article (i.e., Samples 1 and 2 respectively). Further, we thank Louis Tay for his valuable and constructive feedback on an earlier version of the manuscript. This work was supported by the Research Foundation— Flanders (FWO), Grant number 1200919N. The authors have no conflict of interest to disclose.
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