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Record W4372348943 · doi:10.18280/ijsdp.180402

The Impact of the Enterprise Reorganization Process on the Efficiency of Human Resources- Case Study

2023· article· en· W4372348943 on OpenAlexvenueno aff
Bedri Statovci, Gentiana Gega, Gani Asllani, Simon Grima

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanking, Crisis Management, COVID-19 Impact
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Process managementBusinessHuman resourcesComputer scienceEconomicsManagement

Abstract

fetched live from OpenAlex

The research aims to analyse the importance of the reorganisation process on the efficiency of human resources. The company cannot develop a successful business if it does not manage the reorganisation process effectively. Also, the importance of this topic consists of the role of the human resources department as the main carrier and developer of the efficiency of organisations and institutions. In addition to the theoretical side, the paper also has a practical side realised through research in the banking sector in Kosovo. The research was carried out using the questionnaire to collect primary data through which the raised hypotheses were tested. The collected data were coded in the SPSS program through which the necessary results were reached. Through the analysis of this research, we expect to get some positive results which can tell us that the reorganisation process positively impacts the efficiency of human resources. The findings and recommendations will firstly help the organisations participating in the research in improving the reorganisation process as well as in increasing the efficiency of human resources even wider. This scientific paper presents actual and consistent results about the relevant conclusions. It contributes to the knowledge of reorganisation processes and his importance at the company.

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 score0.152
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.310
Teacher spread0.286 · 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
Published2023
Admission routes1
Has abstractyes

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