Selecting self-and-follower goal-aware leadership styles across sectors: a decision support approach

Current leadership research falls short of addressing multidimensional performance criteria such as employee engagement, team productivity, innovation, job security, and sustainability in a balanced manner. Instead, it largely focuses solely on follower-focused approaches and fails to systematically assess leaders' own goal awareness. This situation increases the need for holistic and data-driven decision-making models for leadership style selection across various sectors. Studies conducted on this objective are quite limited in the literature. The aim of this study is to determine the most appropriate self-and-follower goal-aware leadership model (SFGAL)-based leadership style for the energy, automotive, healthcare, and information and communication technologies sectors and to reveal differences across sectors. The study evaluates four leadership alternatives (win-win, self-oriented, self-neglecting/over-giving, and lose-lose). Expert weights are determined using a machine learning-based mechanism that considers the demographic and professional characteristics of the experts. Criteria weights are calculated using the criteria importance through intercriteria correlation (CIMAS) method, and the ranking of the alternatives is performed using the combined compromise solution (CoCoSo) technique. The innovative Koch Snowflake fuzzy set approach is used to model uncertainties and linguistic evaluations. The study's key contributions to the literature are as follows: (1) developing a sector-sensitive decision-making framework that enables the generation of unique leadership strategies for different sectors; (2) strengthening uncertainty modeling by applying the Koch Snowflake fuzzy set application within the context of MCDM in leadership assessment; and (3) increasing the reliability and validity of the decision process by employing a demographic and attribute-based approach to expert weighting. Key findings indicate that innovation is the most important criterion in the automotive and information technology sectors, while occupational safety is paramount in the energy sector and employee engagement in the healthcare sector. A win-win leadership style is identified as the most suitable option in all sectors, with self-oriented leadership being the second priority in the energy, healthcare, and information technology sectors.

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Publication Details

Journal
Frontiers in Psychology
Published
2026-04-01
DOI
https://doi.org/10.3389/fpsyg.2026.1685841
Primary Topic
Job Satisfaction and Organizational Behavior
Type
article
Field-Weighted Citation Impact
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article

Selecting self-and-follower goal-aware leadership styles across sectors: a decision support approach

Gergely Czukor, Serkan Eti, Hasan Dinçer, Serhat Yüksel
Frontiers in Psychology
Job Satisfaction and Organizational Behavior
article

Selecting self-and-follower goal-aware leadership styles across sectors: a decision support approach

Gergely Czukor, Serkan Eti, Hasan Dinçer, Serhat Yüksel
article en

Abstract

Current leadership research falls short of addressing multidimensional performance criteria such as employee engagement, team productivity, innovation, job security, and sustainability in a balanced manner. Instead, it largely focuses solely on follower-focused approaches and fails to systematically assess leaders' own goal awareness. This situation increases the need for holistic and data-driven decision-making models for leadership style selection across various sectors. Studies conducted on this objective are quite limited in the literature. The aim of this study is to determine the most appropriate self-and-follower goal-aware leadership model (SFGAL)-based leadership style for the energy, automotive, healthcare, and information and communication technologies sectors and to reveal differences across sectors. The study evaluates four leadership alternatives (win-win, self-oriented, self-neglecting/over-giving, and lose-lose). Expert weights are determined using a machine learning-based mechanism that considers the demographic and professional characteristics of the experts. Criteria weights are calculated using the criteria importance through intercriteria correlation (CIMAS) method, and the ranking of the alternatives is performed using the combined compromise solution (CoCoSo) technique. The innovative Koch Snowflake fuzzy set approach is used to model uncertainties and linguistic evaluations. The study's key contributions to the literature are as follows: (1) developing a sector-sensitive decision-making framework that enables the generation of unique leadership strategies for different sectors; (2) strengthening uncertainty modeling by applying the Koch Snowflake fuzzy set application within the context of MCDM in leadership assessment; and (3) increasing the reliability and validity of the decision process by employing a demographic and attribute-based approach to expert weighting. Key findings indicate that innovation is the most important criterion in the automotive and information technology sectors, while occupational safety is paramount in the energy sector and employee engagement in the healthcare sector. A win-win leadership style is identified as the most suitable option in all sectors, with self-oriented leadership being the second priority in the energy, healthcare, and information technology sectors.

Frontiers in PsychologyVol. 17
Khazar University (AZ), Istanbul Bilgi University (TR), Korea University (KR), Istanbul Medipol University (TR), The Ark (IE)
Industry, innovation and infrastructure
Openalex Percentile: Top 34%
Job Satisfaction and Organizational Behavior
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