ArticleScientific reports2025
Employee loyalty evaluation using machine learning in technology-based small and medium-sized enterprises.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Explainable attrition risk scoring for managerial retention decisions in human resource analytics.Frontiers in big data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Employee loyalty is a major issue of sustainable human resource management. Small and medium-sized enterprises with high technology content and strong innovation ability are the main body of innovation with great vitality and potential. Employee loyalty is an important factor for the success and development of Technology-based Small and Medium-sized Enterprises (TSMEs). This research starts with the historical evaluation data of employees in Chinese TSMEs to analyze the relevant factors of employee loyalty and the relationship between these factors and employee loyalty. Through several machine learning models and algorithms to predict employee loyalty, the feasibility of machine learning to predict employee loyalty is proved, and the evaluation of talent in TSMEs is supported by decision analysis. This research aims to build an objective and applicable intelligent evaluation model of employee loyalty to support TSMEs to accurately identify, motivate, attract, cultivate and retain outstanding scientific and technological talents. This study contributes to the promotion of sustainable and high-quality management of TSMEs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.