ReviewMethodsX2024
Hesitant and uncertain linguistics based executive decision making using risk and regret aversion: Methods, implementation and analysis.
Review in MethodsX, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed, 2 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Presence of globally-affecting issues, such as the recent COVID-19 pandemic is a major factor impacting the operation of services provided by high-stake companies. These factors create huge hindrances in the regular and proper operations of companies in staying relevant in market while catering to the services they provide. In such cases, in order to maintain and achieve their internal goals should any possible losses that the grave situation might incur, relevant experts within these firms must arrive at optimal decisions taking into account human cognition as well as all possibilities of risk and regrets. A suitable regret theory based linguistic decision-making model called THREAD which computes with inherent hesitancy using interval type-2 fuzzy sets (IT2 FS) and hesitant fuzzy linguistic term sets-based techniques is introduced in this paper.
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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.