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Record W2083647673 · doi:10.17722/ijrbt.v6i2.393

Organizational Restructuring Influences Employee Quit Decision Through Employee Dissatisfaction In Commercial Banks in Kenya

2015· article· en· W2083647673 on OpenAlexvenueno aff
Wilson O Odadi, Medina Halako T., Peter K’Obonyo

Bibliographic record

VenueInternational Journal of Research in Business and Technology · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsEmployee researchRestructuringBusinessEmployee engagementBusiness administrationFinanceManagementEconomics

Abstract

fetched live from OpenAlex

This study was aimed at investigating the antecedents and predictors of employee quit decision process during organizational restructuring. This was done by examining the nature of relationships between Organizational restructuring, employee dissatisfaction and quit decisions. The literature review revealed that a number of studies have been conducted on the predictors and antecedents of employee quit decisions. However, these studies did not examine any integration between them. The objective of this study was to explore the integrated relationship amongst organizational restructuring, employee quit decisions and dissatisfaction. A sample size of 375 was selected from a total population of 15,017 employees from commercial banks in Kenya. A structured questionnaire with Likert-type statements anchored on a five-point scale ranging from “Not at all (1)” to “To a great extent (5)” was used to collect data.  The study employed Pearson's Product Moment Correlation and hierarchical Regression analysis to test the hypotheses. The findings showed a significance relationship between organizational restructuring and employee quit decisions. Further, the findings revealed that the effect of organizational restructuring on employee quit decisions is mediated by employee dissatisfaction.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.055
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.365
Teacher spread0.306 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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