Unveiling scholarly insights: A bibliometric analysis of literature on gender bias at the workplace
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.3.45Keywords:
Gender bias, Gender discrimination, Bibliometric analysis, Systematic literature review, Data visualization.Dimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Gender bias and discrimination in the workplace remain significant global challenges, impacting individuals and organizations. Despite heightened awareness and scholarly focus, a comprehensive, up-to-date evaluation of the literature’s scientific impact and citation trends is missing. This research article addresses this gap through a bibliometric analysis from 2000 to 2023, assessing gender bias’s scientific significance, citations, and pre-publication information. Utilizing tools like RStudio, VOS viewer, Dimensions analytics, and MS Excel, the study analyzes manuscripts from the Dimensions database. The analysis reveals notable trends, showing a steady rise in publications from 2003, with fluctuations in 2002 and 2008-2011, stability from 2012-2015, and a significant surge from 2016-2023, peaking in 2019-2022. The United States leads in publication quantity and collaboration. Key topics such as "Economics and Identity," the "glass cliff phenomenon," and the "climate for women in academic science" dominate citations. Prominent journals like "Building A New Leadership Ladder" and "Plos One" highlight the interdisciplinary nature of gender bias research. Influential contributors like Geffner CJ, Kim S, and Ryan MK are acknowledged for their dedication. This study underscores the interdisciplinary reach of gender bias research across Human Society, Commerce, Law, Biomedical Sciences, and Psychology, offering valuable insights into publication trends, collaborative networks, and thematic developments. The findings emphasize the need for continued exploration and collaboration to address gender-related challenges in professional settings.Abstract
How to Cite
Downloads
Similar Articles
- Chandrasekaran M, Rajesh P K, Optimization of cost to customer of power train in commercial vehicle using knapsack dynamic programming influenced by vehicle IoT data , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Roop Kanwal, Children’s literature as a tool for social change: Teaching values and social awareness , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Sangeeta Modi, P Usha, Fault analysis in hybrid microgrid for developing a suitable protection scheme , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- J. Pavithra, Status of investment in startup in India – An analysis , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
- Bhavikgiri Vishnugiri Goswami, Vaseemahmed G. Qureshi, Reclaiming identity: transgender perspectives on inclusion in contemporary India , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Rasheedha A, Santhosh B, Archana N, Sandhiya A, Foot sens - foot pressure monitoring systems , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Pratik Ghosh, Sriram M, A systematic review of social media communication with respect to fashion brands , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Sachin V. Chaudhari, Jayamangala Sristi, R. Gopal, M. Amutha, V. Akshaya, Vijayalakshmi P, Optimizing biocompatible materials for personalized medical implants using reinforcement learning and Bayesian strategies , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Farheen Najma B, Faseeha Begum, Resistance to digital banking by senior citizens in India - A review , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Milindkumar N. Dandale, Amar P. Yadav, P. S. K. Reddy, Seema G. Kadu, Madhusudana T, Manthan S. Manavadaria, Deep learning enhanced drug discovery for novel biomaterials in regenerative medicine utilizing graph neural network approach for predicting cellular responses , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
<< < 4 5 6 7 8 9 10 11 12 13 > >>
You may also start an advanced similarity search for this article.

