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Record W2062178588 · doi:10.5539/ibr.v5n4p124

Effects of Strategic Performance Appraisal, Career Planning and Employee Participation on Organizational Commitment: An Empirical Study

2012· article· en· W2062178588 on OpenAlexvenueno aff
Danlami Sani Abdulkadir, Sulu Babaita Isiaka, Salami Isaac Adedoyin

Bibliographic record

VenueInternational Business Research · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsPerformance appraisalOrganizational commitmentBusinessStrategic human resource planningEmployee engagementHuman resource managementEmployee researchHuman resourcesEmpirical researchTest (biology)Organizational performanceWork (physics)Public relationsMarketingEmployee motivationStrategic planningManagementEconomicsPolitical science

Abstract

fetched live from OpenAlex

Despite the recent research efforts into the antecedents of organizational commitment most especially in the developed economies, little empirical work has been conducted examining the effect of some human resource management practices such as performance appraisal system, career planning system and employee participation on organizational commitment. In this article, we examined the effect of these human resource management practices in explaining employee job commitment in the Nigerian banking sector. Based on a survey of 14 banks in Nigeria, the study applies regression analysis, correlation analysis and G-test in testing the hypotheses. Results indicate that performance appraisal system, career planning system and employee participation significantly influence employee job commitment and that the level of organizational commitment of employees in the Nigerian banking sector is low. The study therefore, recommends that for employees to be genuinely committed to their jobs, organizations should make conscious efforts at strategically managing performance appraisal, career planning and employee participation with a view to ensuring effective implementation and achieving desired results.

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)0.000
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 score0.002
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.147
GPT teacher head0.419
Teacher spread0.272 · 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

Citations53
Published2012
Admission routes1
Has abstractyes

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