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Record W4235942613 · doi:10.1108/ict-07-2013-0045

Organizational outcomes of leadership style and resistance to change (Part Two)

2015· article· en· W4235942613 on OpenAlexaff
Steven H. Appelbaum, Medea Cesar Degbe, Owen MacDonald, Thai-Son NGUYEN-QUANG

Bibliographic record

VenueIndustrial and Commercial Training · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsUniversité du QuébecConcordia University
Fundersnot available
KeywordsResistance (ecology)Context (archaeology)Organizational commitmentOrganizational studiesLeadership stylePsychologyOrganizational changeChange management (ITSM)Organizational learningKnowledge managementPlanned changePerceptionOrganizational performanceOrganizational cultureOrganization developmentOrder (exchange)Management stylesSocial psychologyPublic relationsBusinessPolitical scienceMarketingComputer science

Abstract

fetched live from OpenAlex

Purpose – Organizations must react rapidly to evolving environments by engaging in change, ranging from minor adjustments to radical transformation. Many obstacles are encountered on the path towards achieving positive organizational outcomes, among which resistance to change prevents the level of mobilization critical to achieve a successful transformation. The purpose of this two-part paper is to offer a review of the body of knowledge explaining how leadership styles may address resistance to change in order to achieve desired organizational outcomes. For this purpose, multiple organizational concepts are visited and linked through a synthesized model proposing causality relationships between the various elements. Design/methodology/approach – A range of recently published empirical and practitioner research papers were reviewed to analyse the relationships in search of the variables that affect resistance during a major organizational change. In order to synthesize and bridge many concepts that are often studied separately, an overall model is proposed to help establish causal relationships between the elements of interest influencing organizational outcomes, in the context of a change. Findings – Leadership acts as an input at multiple levels, influencing organizational outcomes both directly – by continuously shaping employee attitude throughout change – and indirectly – by regulating the antecedents and moderators of their predisposition to change. These subsequently shape the extent of resistance to change, which translates from the perception of, commitment to and involvement in the change process. The interaction of the organizational environment with these factors ultimately determines the organizational outcome resulting from the change initiatives. Research limitations/implications – The model must be tested in another empirical article to measure its effectiveness. The complexity of the model may, however, hinder the ability to successfully correlate all the concepts. Practical implications – The paper suggests an overall framework that may help leaders and organizational development practitioners identify the major factors which may be considered during a change initiative or a transformation. Social implications – This paper highlights the multi-dimensional role of leadership style in successfully achieving organizational changes. Leaders’ emotional aptitude to influence their followers and employees’ natural and contextual predisposition to change transact to shape organizational outcomes. These social elements must be carefully assessed not only prior to embarking on a change implementation, but also to proactively invest in psychologically directed organizational training and development, at all hierarchical levels. Originality/value – The synthesis model is the novel contribution of the paper. It proposes an organized approach to relate multiple close yet distinct concepts that have so far been predominantly discussed separately.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.408
GPT teacher head0.292
Teacher spread0.116 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations40
Published2015
Admission routes1
Has abstractyes

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