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Record W4386899490 · doi:10.26562/ijiris.2023.v0904.02

Fringe Benefits Effects on Employee Productivity in the Public Sector Tamilnadu Water Supply and Drainage Board Namakkal

2023· article· en· W4386899490 on OpenAlexaboutno aff
K. Jayapriya, A Akilan

Bibliographic record

VenueInternational Journal of Innovative Research in Information Security · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicLeadership, Behavior, and Decision-Making Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmployee benefitsProductivityBusinessPensionRelocationWork (physics)Compensation of employeesEmployee moraleTurnoverMarketingPublic sectorHealth careJob securityCompensation (psychology)EconomicsFinanceEconomic growthManagement

Abstract

fetched live from OpenAlex

The research purpose is to determine the study of the fringe benefits important of employees. Fringe benefits are additions to compensation that companies give their employees. This research project is on Fringe Benefits and Employees productivity in public sector. This research work is generally about the Benefits and Employees productivity Public Sector. The project has undertook the general introduction into the research work led to the review of various literature that relates to the major variables involved in the research work especially employees productivity. The purpose of employee benefits is to increase the economic security of staff members, and in doing so, improve worker retention across the organization. As such, it is one component of reward management. In any case, employers use fringe benefits to help them recruit, motivate, and keep high-quality people. According to Mathis and John (2003), productivity is a measure of the quantity and quality of work done, considering the cost of the resources used. The more productive an organization, the better its competitive advantage, because the costs to produce its goods and services are lower. Employee benefits in Canada usually refer to employer sponsored life, disability, health, and dental plans. Employee benefits in the United States include relocation assistance; medical, prescription, vision and dental plans; health and dependent care flexible spending accounts; retirement benefit plans (pension, 401(k), 403(b). fringe benefits refers to the regular review of an employee’s job performance and overall contribution to a company. The objective is to know the effect of fringe benefits on employee motivation. The reveals that fringe benefits lead to improved employees’ performance. This results from increased productivity in the organization. The majorities of the employees are motivated of the organization through feedback and increased productivity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.221
GPT teacher head0.463
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 source (direct Gemma or distilled Codex), 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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