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Record W2782147344 · doi:10.5430/ijba.v9n1p55

The Ripple Effects of Performance Management on Employees’Perceptions and Affective Commitment among Small and Medium Scale Enterprises (SMEs)

2017· article· en· W2782147344 on OpenAlexvenueno aff
Anita Asamany, Shaorong Sun

Bibliographic record

VenueInternational Journal of Business Administration · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessSmall and medium-sized enterprisesScale (ratio)Context (archaeology)Organizational commitmentKnowledge managementPerceptionMarketingOrganizational performancePerformance managementPsychologyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Management literature acknowledges the important role played by performance management systems (PMS) in business organization, however, a little empirical studies exist in the Ghanaian context.Using a sample data of 180 from managements and staffs of thirty-eight (38) Small and Medium Scale Enterprises (SMEs), the current study presents the effects of performance management systems on employees perception and organizational commitment (affective) among Small and Medium Scale Enterprises (SMEs) located in the Greater Accra region of Ghana.Statistical Package for Social Sciences 20.0 version (SPSS) and Microsoft Word 2010 were employed for the data analysis. From the result, performance management had a positive significant relationship with employees’ perceptions and affective organizational commitment.The study further examined the mediating role of both employees’ perceptions and affective commitment on performance management towards operational performance, it was revealed that both variables positively mediate the relationship between performance management and organizational performance of the SMEs in Ghana. This indicates that SMEs in Ghana have seen the need for implementing proper performance management systems based on their own capabilities to ensure effectiveness in meeting the organizational objectives.

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)0.000
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 score0.013
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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