Evidence map›Paper›PMID 41365953›Full record

ArticleScientific reports2025

Evaluating corporate high performance work systems via an intelligent model using complex T spherical fuzzy CoCoSo method.

Chengxin Liu

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Chengxin LiuCollege of Smart Senior Care and Modern Home Economics, Guiyang Healthcare Vocational University, Guiyang, 550081, Guizhou, China. liuchengxin198207@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-performance work systems (HPWS) have become a key element for enhancing the performance and competitiveness of organizations in the corporate sector. Due to the complexity of HPWS, an effective and intelligent way of measuring its performance must be capable of handling uncertainty, interrelationships, and subjective expert judgments. This paper introduces a method that combines a high-quality multiple-criteria decision-making (MCDM) model operating under ambiguous and imprecise conditions, using the complex t-spherical fuzzy combined compromise solution (CTSF-CoCoSo) model to assess the features of HPWS. The seven criteria of evaluation in the current study are also determined by a thorough discussion of the HPWS literature and further adjusted by the experts to maintain construct validity and the applicability of the concept to corporate HR practices. An example involving three decision-makers, seven clearly defined evaluation criteria, and ten alternatives is presented to validate the model. In assessing alternatives, it was discovered that Alternative-2 is the best strategy to adopt in HPWS. The ranking stability and sensitivity of the proposed approach are greater than those of the current MCDM techniques. The implications of the findings are both theoretical and practical as they shed some light on the critical aspects of performance, as well as serving as a way of supporting strategy-based human resource decision-making. This model provides a dynamic and extensible model and is therefore not restricted to complex organizational evaluation situations.

Indexed as

CoCoSo approachComplex t-spherical fuzzy setsCorporate sectorHigh-performance work systemsMulti-criteria decision-making

Identifiers

PMID41365953
PMCPMC12770557

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.