Add wos sample results library

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
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+  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'