Exploring gender stereotypes in financial reporting: An aspect-level sentiment analysis using big data and deep learning
This study delves into the intricate interplay between gender stereotypes and financial reporting through an aspect-level sentiment analysis approach. Leveraging Big Data comprising 129,251 human face images extracted from 2085 financial reports in Chile, and employing Deep Learning techniques, we u...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-10-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024149468 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850194194088329216 |
|---|---|
| author | Fabiola Jeldes-Delgado Tiago Alves Ferreira David Diaz Rodrigo Ortiz |
| author_facet | Fabiola Jeldes-Delgado Tiago Alves Ferreira David Diaz Rodrigo Ortiz |
| author_sort | Fabiola Jeldes-Delgado |
| collection | DOAJ |
| description | This study delves into the intricate interplay between gender stereotypes and financial reporting through an aspect-level sentiment analysis approach. Leveraging Big Data comprising 129,251 human face images extracted from 2085 financial reports in Chile, and employing Deep Learning techniques, we uncover the underlying factors influencing the representation of women in financial reports. Our findings reveal that gender stereotypes, combined with external economic factors, significantly shape the portrayal of women in financial reports, overshadowing intentional efforts by companies to influence stakeholder perceptions of financial performance. Notably, economic expansion periods correlate with a decline in women's representation, while economic instability amplifies their portrayal. Furthermore, the financial inclusion of women positively correlates with their presence in financial report images. Our results underscore a bias in image selection within financial reports, diverging from the neutrality principles advocated by the International Accounting Standards Board (IASB). This pioneering study, combining Big Data and Deep Learning, contributes to gender stereotype literature, financial report soft information research, and business impression management research. |
| format | Article |
| id | doaj-art-7e0c3b83b4f14a929f68d6f343bfa237 |
| institution | OA Journals |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-7e0c3b83b4f14a929f68d6f343bfa2372025-08-20T02:14:03ZengElsevierHeliyon2405-84402024-10-011020e3891510.1016/j.heliyon.2024.e38915Exploring gender stereotypes in financial reporting: An aspect-level sentiment analysis using big data and deep learningFabiola Jeldes-Delgado0Tiago Alves Ferreira1David Diaz2Rodrigo Ortiz3Escuela de Negocios Internacionales, Universidad de Valparaíso, Valparaíso, Chile; Centro de Análisis de la Incorporación Social, Valparaíso, ChilePontificia Universidad Católica de Valparaíso–Escuela de Comercio, Chile; Corresponding author.Facultad de Economía y Negocios, Departamento de Administración, Universidad de Chile, Santiago, ChileFacultad de Economía y Negocios, Universidad Alberto Hurtado, Santiago, ChileThis study delves into the intricate interplay between gender stereotypes and financial reporting through an aspect-level sentiment analysis approach. Leveraging Big Data comprising 129,251 human face images extracted from 2085 financial reports in Chile, and employing Deep Learning techniques, we uncover the underlying factors influencing the representation of women in financial reports. Our findings reveal that gender stereotypes, combined with external economic factors, significantly shape the portrayal of women in financial reports, overshadowing intentional efforts by companies to influence stakeholder perceptions of financial performance. Notably, economic expansion periods correlate with a decline in women's representation, while economic instability amplifies their portrayal. Furthermore, the financial inclusion of women positively correlates with their presence in financial report images. Our results underscore a bias in image selection within financial reports, diverging from the neutrality principles advocated by the International Accounting Standards Board (IASB). This pioneering study, combining Big Data and Deep Learning, contributes to gender stereotype literature, financial report soft information research, and business impression management research.http://www.sciencedirect.com/science/article/pii/S2405844024149468Financial reportsGender stereotypesAspect-level sentiment analysisBig dataDeep learningGender inclusion |
| spellingShingle | Fabiola Jeldes-Delgado Tiago Alves Ferreira David Diaz Rodrigo Ortiz Exploring gender stereotypes in financial reporting: An aspect-level sentiment analysis using big data and deep learning Heliyon Financial reports Gender stereotypes Aspect-level sentiment analysis Big data Deep learning Gender inclusion |
| title | Exploring gender stereotypes in financial reporting: An aspect-level sentiment analysis using big data and deep learning |
| title_full | Exploring gender stereotypes in financial reporting: An aspect-level sentiment analysis using big data and deep learning |
| title_fullStr | Exploring gender stereotypes in financial reporting: An aspect-level sentiment analysis using big data and deep learning |
| title_full_unstemmed | Exploring gender stereotypes in financial reporting: An aspect-level sentiment analysis using big data and deep learning |
| title_short | Exploring gender stereotypes in financial reporting: An aspect-level sentiment analysis using big data and deep learning |
| title_sort | exploring gender stereotypes in financial reporting an aspect level sentiment analysis using big data and deep learning |
| topic | Financial reports Gender stereotypes Aspect-level sentiment analysis Big data Deep learning Gender inclusion |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024149468 |
| work_keys_str_mv | AT fabiolajeldesdelgado exploringgenderstereotypesinfinancialreportinganaspectlevelsentimentanalysisusingbigdataanddeeplearning AT tiagoalvesferreira exploringgenderstereotypesinfinancialreportinganaspectlevelsentimentanalysisusingbigdataanddeeplearning AT daviddiaz exploringgenderstereotypesinfinancialreportinganaspectlevelsentimentanalysisusingbigdataanddeeplearning AT rodrigoortiz exploringgenderstereotypesinfinancialreportinganaspectlevelsentimentanalysisusingbigdataanddeeplearning |