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Record W7099011632

Munich Personal RePEc Archive Job Satisfaction for Employees: Evidence from Karachi Electric Supply Corporation

2009· article· en· W7099011632 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsnot available
Fundersnot available
KeywordsJob satisfactionCorporationQuarter (Canadian coin)ObstacleSample (material)Order (exchange)Job analysisProductivityJob rotation
DOInot available

Abstract

fetched live from OpenAlex

Research has been conducted in order to critically evaluate and examine the level of employees ’ satisfaction as well as the factors of dissatisfaction among the employees of Karachi Electric supply Corporation (KESC). The purpose of this study is also to observe and analyze the factors which create job dissatisfaction especially among the hardworking managers, and to find out the reasons which make them realize that they don not have a clear career path along working with KESC. The primary data for this study was compiled through questionnaire filled in on a one-to-one basis by 60 respondents from a representative sample of employees of (KESC) in Karachi district in the last quarter of 2008. The results have shown that Working Environment, Total Compensation, Growth Opportunities and Training & Development are significant factor and these four are affecting Job Satisfaction and correlated with each others. The study was faced by certain limitations and those limitations included time constraints and resources constraints, which limited this research to only the Karachi Head office of the KESC organization. According to a number of literatures studied, lack of job satisfaction is a serious issue in various organizations and job dissatisfaction has become a major obstacle in employees ’ productivity and company’s growth. There are numbers of factors which can

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.337
Teacher spread0.297 · 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.

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
Published2009
Admission routes1
Has abstractyes

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