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Record W4411067796 · doi:10.59141/jrssem.v4i10.820

Relational and Embedded Leadership for Navigating Structural Conflict: A Strategic Model for Adaptive Organizations

2025· article· en· W4411067796 on OpenAlexaboutno aff
Rendy Ardiansyah, Arenal Arenal, Budi Priyono, Indra Pahala, Wahyu Handaru

Bibliographic record

VenueJournal Research of Social Science Economics and Management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge managementStrategic leadershipBusinessProcess managementShared leadershipComputer scienceLeadership stylePsychologyPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

This article develops the Relational and Embedded Strategic Leadership model in response to the limitations of conventional strategic leadership in navigating structural conflicts and adaptive challenges in modern organizations. Through theoretical synthesis and case analysis across sectors including Nokia, Microsoft, Telkom Indonesia, the UK’s NHS, and Finnish & Canada universities, GE, this paper reveals that successful transformation depends more on social relations, distributed leadership roles, and collective sensemaking than on structural control or top-down vision. The proposed model emphasizes dialogic, context-sensitive, and operationally embedded leadership practices. It contributes conceptually to leadership theory by integrating relational and institutional perspectives, while offering practical direction for organizations seeking sustainable change through cross-functional and cross-level participation. Key implications include the need for organizational design that enables distributed leadership, leadership development programs rooted in reflection and empathy, and institutional reforms based on collaboration—particularly in public and higher education sectors.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.238
GPT teacher head0.373
Teacher spread0.135 · 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 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
Published2025
Admission routes1
Has abstractyes

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