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Presenting a Model of Self-sacrificial behaviors of Leaders in Organizations; Interpretive Structural Modeling (ISM) Method

2024· article· en· W6888739491 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsConceptual modelLeadership styleData collectionStructural equation modelingOrientation (vector space)Component (thermodynamics)

Abstract

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Objective One of the most significant topics discussed in management and leadership literature is the concept of self-sacrifice. Due to its numerous positive implications, organizations need to develop leadership styles based on self-sacrificial behaviors. Despite various examples and instances of self-sacrifice exhibited by managers and leaders, research on self-sacrifice and its leadership implications has been neglected. Therefore, further research in this area can illuminate the dimensions and aspects of self-sacrificial behaviors in organizations. Methods This study employs the Interpretive Structural Modeling (ISM) method. It is an applied research project that utilizes interviews as the primary data collection method. Results The findings of the research indicate that the conceptual model of self-sacrificial behaviors in organizations consists of eleven components: "positive self-concept," "resilience," "social representation," "motivation to serve," "empathy and compassion," "awareness and knowledge," "goal orientation and idealism," "collective identity," "social learning and social contagion," "core values," and "crisis." According to the findings, the two dimensions of "awareness and knowledge" and "collective identity" are the foundational components of the model, as they influence all other components and have a two-way relationship with each other. This means that, in addition to being influencing factors for other components, they also impact each other. Additionally, based on the model, the components of "empathy and compassion" and "social learning and social contagion" are ranked next. These two components also have a mutual relationship with each other and are further influenced by the "crisis" component, which ranks below them. In fact, the critical condition, in addition to affecting the higher-level components, also impacts the lower-level components, indicating the significant influence of this variable in the model. The "motivation to serve" component is placed at the fifth level of the model. As shown by the direction of the arrows, this component is dependent on the three components of critical conditions, core values, and goal orientation and idealism. This means that the motivation to serve, as one of the antecedents of altruistic behavior, is influenced by the occurrence of critical conditions and the presence of core values, goals, and ideals of the individual. The remaining three components in the model—positive self-concept, resilience, and social representation—have the least influence and the most dependence on other components, indicating that they are more influenced by other components in the model. The "social representation" component is the most dependent in the model, meaning that a person's desire for social expressiveness is reliant on all other components except resilience, as resilience does not affect social expressiveness. Conclusion Based on the results of the study using ISM, two components are identified in the linkage region: "empathy and compassion" and "awareness and knowledge." These components are considered dynamic, meaning that any change in them can impact the entire system. The independent region includes five components: "goal orientation and idealism," "collective identity," "social learning and social contagion," "core values," and "crisis," indicating their strong influence and guiding role in the model. Additionally, the "social representation" component is placed in the dependence region, signifying its high reliance on other components.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.348
GPT teacher head0.601
Teacher spread0.253 · 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.

Study designSimulation or modeling
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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