wow-inequalities/02-data/intermediate/wos_sample/3c7ab11d531e7adfcefd1bdbf8d9b3cb-jafari-amirhosein-a/info.yaml

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abstract: 'Wage inequality is a source of many social and economic problems, and is
the target of mitigating programs both nationally and internationally.
The primary step toward developing effective programs to reduce or
eliminate wage inequality is identifying employees at risk of such
inequalities. This study used 17,889 data points from USDOT workforce
demographic information and salary data to analyze wage inequality and
develop a novel framework to identify employees at risk of wage
inequality. The evaluation framework includes (1) a salary prediction
model, developed using artificial neural networks (ANNs), to estimate
employees'' salaries based on demographic information and identify
underpaid employees; (2) a minority index, which is defined to score the
underrepresentation of each employee regarding gender, ethnicity, and
disability, based on the current status of employee diversity in the
organization; and (3) a decision model, which uses the salary prediction
model and minority index based on historical data to determine if new
employees are at risk of wage inequality. The analysis showed that
although women are underrepresented among USDOT employees, there was no
significant wage inequality between men and women. Furthermore, the
lowest minority index was for White men without disability, and the
highest for American Indian/Alaska Native women with disability. In
addition, the results of evaluating the proposed framework had an
accuracy of 98\%, with a harmonic mean (F1) score of 81.8\%. The
framework developed in this study can enable any engineering
organization to establish an unbiased wage rate for its employees,
resulting in reduction or elimination of wage inequality and its
consequent challenges among its employees. (C) 2020 American Society of
Civil Engineers.'
affiliation: 'Jafari, A (Corresponding Author), Louisiana State Univ, Bert S Turner
Dept Construct Management, Baton Rouge, LA 70803 USA.
Jafari, Amirhosein, Louisiana State Univ, Bert S Turner Dept Construct Management,
Baton Rouge, LA 70803 USA.
Rouhanizadeh, Behzad; Kermanshachi, Sharareh, Univ Texas Arlington, Dept Civil Engn,
Arlington, TX 76019 USA.
Murrieum, Munahil, Calif State Univ East Bay, Coll Business \& Econ, Hayward, CA
94542 USA.'
article-number: '04020072'
author: Jafari, Amirhosein and Rouhanizadeh, Behzad and Kermanshachi, Sharareh and
Murrieum, Munahil
author-email: 'ajafari1@lsu.edu
behzad.rouhanizadeh@mavs.uta.edu
sharareh.kermanshachi@uta.edu
mmurrieum@horizon.csueastbay.edu'
author_list:
- family: Jafari
given: Amirhosein
- family: Rouhanizadeh
given: Behzad
- family: Kermanshachi
given: Sharareh
- family: Murrieum
given: Munahil
da: '2023-09-28'
doi: 10.1061/(ASCE)ME.1943-5479.0000841
eissn: 1943-5479
files: []
issn: 0742-597X
journal: JOURNAL OF MANAGEMENT IN ENGINEERING
keywords-plus: 'JOB QUALITY; GENDER INEQUALITY; UNITED-STATES; RACE; GAP; IMPACT;
WOMEN;
LABOR; DISABILITY; EMPLOYMENT'
language: English
month: NOV 1
number: '6'
number-of-cited-references: '77'
orcid-numbers: 'Jafari, Amirhosein/0000-0002-0356-2282
Kermanshachi, Ph.D., F.ASCE, F.ICE, P.E., PMP, LEED AP, DBIA, ENV SP, CMIT, Sharareh
(Sherri)/0000-0003-1952-2557'
papis_id: ce56fe89b41b5e757e9b8e47fb6d0296
ref: Jafari2020predictiveanalytics
researcherid-numbers: 'Jafari, Amirhosein/B-7375-2016
'
times-cited: '9'
title: Predictive Analytics Approach to Evaluate Wage Inequality in Engineering Organizations
type: article
unique-id: WOS:000609482800020
usage-count-last-180-days: '0'
usage-count-since-2013: '14'
volume: '36'
web-of-science-categories: Engineering, Industrial; Engineering, Civil
year: '2020'