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The Dual Effects of Leader Self-Complexity on Leadership Adaptability: The Underlying Mechanisms

2024· article· en· W4400442821 on OpenAlexaff
Lanyue Fan, Yanjun Guan, Zhuang Liu

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsWestern University
Fundersnot available
KeywordsAdaptabilityDual (grammatical number)PsychologyMechanism (biology)BiologyEcologyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

In a complex and dynamic business world, effective leadership requires adaptability. Researchers have long recognized the importance of leader self-complexity in enhancing this adaptability. However, the underlying mechanisms by which leader self-complexity affects adaptability remain unclear. Furthermore, current theories and empirical studies on leader self-complexity mainly emphasize its advantages in facilitating leadership adaptability, while often overlooking potential costs. This research employs conservation of resources theory to uncover two distinct processes linking leader self-complexity to adaptability: a cognitive pathway (i.e., cognitive flexibility) and an affective pathway (i.e., role conflict and well-being), thereby exploring its dual effects. Data were collected from 330 US leaders at three time points. The results demonstrated that different components of leader self-complexity influenced adaptability via cognitive and affective pathways differently. The number of leadership roles did not show any significant positive or negative effect on adaptability through either pathway. Differentiation among roles increased role conflict and decreased well-being, which in turn negatively influenced cognitive flexibility and adaptability. Conversely, integration among roles showed positive effects on adaptability through both pathways.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.087
GPT teacher head0.270
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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