Bibliometric Analysis of International Academic Studies on Unconscious Bias

Abstract

Objective: This study aims to examine the international literature on “unconscious bias” using bibliometric methods. Method: The study seeks to uncover the main research topics, significant collaboration networks, and the most influential studies on unconscious bias. The bibliometric analysis was conducted on 474 articles published between 1996 and 2023, obtained from the Web of Science database. The data were subjected to performance analysis using the “bibliometrix” package in the R programming language, and scientific mapping techniques were applied. Performance analyses evaluated metrics such as the number of articles, the most productive journals, researchers, universities, and countries. Scientific mapping focused on the co-occurrence network of conceptual topics, the networks of co-cited articles and authors, and the collaboration networks among authors and countries producing articles. Results: The analysis results indicate a marked growth in the volume of articles on unconscious bias from 1996 to 2023, with a particularly sharp increase beginning in 2012. The “Journal of Experimental Social Psychology” emerged as the leading journal publishing these articles. The most productive researchers included John Francis Dovidio, Payne B. Keith, and Nilanjana Dasgupta. The United States was found to be the leading country with the most publications, having 334 articles on unconscious bias. The analyses also revealed the interdisciplinary nature of the collaboration networks and the cited studies in this research area. Conclusion: Research on unconscious bias has evidently attracted growing interest over the years, embracing an interdisciplinary approach. The studies on unconscious bias are increasingly recognized for their social and academic importance and are approached from a broad perspective.

Keywords

• Unconscious bias
• implicit bias
• bias
• discrimination
• bibliometric analysis

Bilinçsiz Önyargı Üzerine Yapılmış Uluslararası Akademik Çalışmaların Bibliyometrik Analizi

Öz

Amaç: Bu çalışma, “bilinçsiz önyargı” konusundaki uluslararası literatürü bibliyometrik yöntemlerle incelemeyi amaçlamaktadır. Yöntem: Bu bağlamda, bilinçsiz önyargıya dair temel araştırma konuları, önemli iş birliği ağları ve en etkili çalışmaları ortaya koymak hedeflenmiştir. Bibliyometrik analiz, Web of Science veri tabanından elde edilen ve 1996-2023 yılları arasında yayımlan 474 makale üzerinden gerçekleştirilmiştir. Veriler, R programlama dilinde “bibliometrix” paket programı kullanılarak performans analizine tabi tutulmuş ve bilimsel haritalama teknikleri uygulanmıştır. Performans analizlerinde, makale sayısı, en üretken dergiler, araştımacılar, üniversiteler ve ülkeler gibi metrikler değerlendirilmiştir. Bilimsel haritalamada ise kavramsal konuların birlikte oluşum ağı, ortak atıf yapılan makaleler ve yazarlar arasındaki ilişki ağları, makale üreten yazarlar ve ülkeler arasındaki iş birliği ağlarına odaklanılmıştır. Bulgular: Analiz sonuçları, 1996-2023 yılları arasında bilinçsiz önyargı konusundaki makale sayısında önemli bir artış olduğunu göstermektedir. Özellikle 2012 yılından itibaren makale sayısında belirgin bir artış gözlemlenmiştir. En fazla makale yayımlayan dergi “Journal of Experimental Social Psychology” olurken, en üretken araştırmacılar arasında John Francis Dovidio, Payne B. Keith ve Nilanjana Dasgupta’nın yer aldığı görülmüştür. Bilinçsiz önyargılar ile ilgili yayınlarda Amerika Birleşik Devletleri’nin, 334 makale ile en fazla yayına sahip ülke olduğu anlaşılmıştır. Analizler ayrıca, araştırma konusundaki iş birliği ağlarının ve atıf yapılan çalışmaların disiplinler arası doğasını da ortaya koymuştur. Sonuç: Bilinçsiz önyargı araştırmalarının, yıllar içinde giderek artan bir ilgi gördüğü ve bu konuyla ilgili disiplinler arası bir yaklaşımın benimsendiği anlaşılmıştır. Bilinçsiz önyargı üzerine yapılan çalışmaların toplumsal ve akademik öneminin giderek arttığı ve bu konunun geniş bir yelpazede ele alındığı görülmüştür.

Anahtar Kelimeler

• Bilinçsiz önyargı
• örtülü önyargı
• Önyargı
• ayrımcılık
• bibliyometrik analiz

Kaynakça

1. Aria M, Cuccurullo C (2017) Bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr, 11:959-975.
2. Aria M, Misuraca M, Spano M (2020) Mapping the evolution of social research and data science on 30 years of social indicators research. Soc Indic Res, 149:803-831.
3. Arif SA, Schlotfeldt J (2021) Gaps in Measuring and Mitigating Implicit Bias in Healthcare. Front Pharmacol, 12:633565
4. Banks R, Ford RT (2008) (How) does unconscious bias matter?: law, politics, and racial inequality. Emory L J, 58:1053-1122.
5. Bayer AE (1982) A bibliometric analysis of marriage and family literature, J Marriage Fam, 44:527-538.
6. Caballero AE, ElSayed NA, Golden SH, Bannuru RR, Gregg B (2023) Implicit or unconscious bias in diabetes care. Clin Diabetes, 42:308-313.
7. Castillo-Page L, Poll-Hunter N, Acosta DA, Fair M (2018) The Inconvenient truth about unconscious bias in the health professions. In Diversity and inclusion in quality patient care (Eds ML Martin, S Heron, L Moreno-Walton, M Strickland):5-13. Heidelberg, Springer.
8. Cobo MJ, López-Herrera AG, Herrera-Viedma E, Herrera F (2011) An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. J Informetr, 5:146-166.

9. Dasgupta N (2004) Implicit ingroup favoritism, outgroup favoritism, and their behavioral manifestations. Soc Justice Res, 17:143-169.
10. Dasgupta N, Greenwald AG (2001) On the malleability of automatic attitudes: combating automatic prejudice with images of admired and disliked individuals. J Pers Soc Psychol, 81:800-814.
11. Dehon E, Weiss N, Jones J, Faulconer W, Hinton E, Sterling S (2017) A systematic review of the ımpact of physician implicit racial bias on clinical decision making. Acad Emerg Med, 24:895-904.
12. Demirgil H (2018) Süleyman Demirel Üniversitesi Yayınlarında Bilimsel Yoğunlaşma Alanları ve Bibliyometrik Ağ Analizi, Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi, 13:36-53.
13. Devine PG (1989) Stereotypes and prejudice: their automatic and controlled components. J Pers Soc Psychol, 56:5-18.
14. Devine PG, Forscher PS, Austin AJ, Cox WT (2012) Long-term reduction in implicit race bias: A prejudice habit-breaking intervention. J Exp Soc Psychol, 48:1267-1278.
15. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res, 133:285-296.
16. Dovidio JF, Kawakami K, Gaertner SL (2002) Implicit and explicit prejudice and interracial interaction. J Pers Soc Psychol, 82:62-68.
17. Dovidio JF, Gaertner SL, Esses VM, Brewer MB (2003) Social conflict, harmony, and integration. In Handbook of psychology: Personality and social psychology (Eds T Millon, MJ Lerner):Vol. 5, 485-506. Hoboken, NJ, Wiley.
18. Dovidio JF, Fiske ST (2012) Under the radar: how unexamined biases in decision-making processes in clinical interactions can contribute to health care disparities. Am J Public Health, 102:945-52.
19. Egghe L (2006) Theory and practise of the g-index. Scientometrics, 69:131-152.
20. Elsbach KD, Stigliani I (2019) New information technology and implicit bias. Acad Manag Perspect, 33:185-206.
21. Emberton M (2021) Unconscious bias is a human condition. Perm J, 1-4.
22. Fiske ST (2002) What we know now about bias and intergroup conflict, the problem of the century. Curr Dir Psychol Sci, 11:123-128.
23. FitzGerald C, Hurst S (2017) Implicit bias in healthcare professionals: a systematic review. BMC Med Ethics, 18:19.
24. Frisby CL (2021) Science versus the unconscious bias paradigm in education: a review essay. J Sch Choice, 15:139-156.
25. Glas KE, Faloye A (2021) Unconscious (implicit) bias. J Cardiothorac Vasc Anesth, 35:991-992.
26. Gopal D, Chetty U, O’Donnell P, Gajria C, Blackadder-Weinstein J (2021) Implicit bias in healthcare: clinical practice, research and decision making. Future Healthc J, 8:40-48.
27. Greenwald AG, Banaji MR (1995) Implicit social cognition: attitudes, selfesteem, and stereotypes. Psychol Rev, 102:4-27.
28. Greenwald AG, McGhee DE, Schwartz, JLK (1998) Measuring individual differences in implicit cognition: the implicit association test. J Pers Soc Psychol, 74:1464-1480.
29. Greenwald AG, Nosek BA, Banaji MR (2003) Understanding and using the implicit association test: I. An improved scoring algorithm. J Pers Soc Psychol, 85:197-216.
30. Greenwald AG, Krieger LH (2006) Implicit bias: scientific foundations. Calif L Rev, 94:945-967.
31. Greenwald AG, Poehlman TA, Uhlmann EL, Banaji MR (2009) Understanding and using the implicit association test: III. meta-analysis of predictive validity. J Pers Soc Psychol, 97:17-41.
32. Gursoy M (2022) A bibliometric analysis of covid-19 research in sociology. Jass Studies-The Journal of Academic Social Science Studies, 15:503-528.
33. Heffernan T (2021) Sexism, racism, prejudice, and bias: a literature review and synthesis of research surrounding student evaluations of courses and teaching. Assess Eval High Educ, 47:144-154.
34. Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Natl Acad Sci U S A, 102:16569-16572.
35. Hsueh M, Yogeeswaran K, Malinen SK (2015) “Leave your comment below”: can biased online comments influence our own prejudicial attitudes and behaviors? Hum Commun Res, 41:557-576.
36. Jost JT, Stern C, Kalkstein DA (2012) What’s so insidious about “peace, love, and understanding”? a system justification perspective. Behav Brain Sci, 35:438-9.
37. Kagetsu N, Gunderman R (2018) Unconscious bias. J Am Coll Radiol, 14:1253-1255.
38. Korkmaz L (2017) Tutumlarımızın ne kadar farkındayız? örtük tutumlar ve örtük ölçüm yöntemleri. Türk Psikoloji Yazıları, 20:109-127.
39. Lai C (2020) Confronting bias. Science, 368:1435-1435.
40. Lopez B (2018) Unconscious bias in action. In Diversity and Inclusion in Quality Patient Care (Eds ML Martin, S Heron, L Moreno-Walton, M Strickland):69-74. Heidelberg, Springer.
41. Lopez P (2021) Bias does not equal bias: a socio-technical typology of bias in data-based algorithmic systems. Internet Policy Rev, 10:1-29.
42. Malgorzata K, Noa M (2024) The more, the better? An active learning tool on unconscious bias in everyday decision-making. In Promoting Inclusion and Justice in University Teaching A Transformative-Emancipatory Toolkit for Educators (Eds TM Cappiali and JP Johanne):76-89. Massachusetts, Edward Elgar Publishing.
43. Marcelin JR, Siraj DS, Victor R, Kotadia S, Maldonado YA (2019) The impact of unconscious bias in healthcare: how to recognize and mitigate it. J Infect Dis, 62-73.
44. Maretti M, Tontodimamma A, Biermann P (2019) Environmental and climate migrations: an overview of scientific literature using a bibliometric analysis, Int Rev Sociol, 29:142-158.
45. Marino KR, Vishnubala D, Ahmed OH, Zondi PC, Whittaker JL, Shafik A et al (2021) Embrace your discomfort: leadership and unconscious bias in sport and exercise medicine. Br J Sports Med, 55:303-304.
46. Moral-Munoz JA, Herrera-Viedma E, Santisteban-Espejo A, Cobo M J (2020) Software tools for conducting bibliometric analysis in science: an up-to-date review, Profesional De La informacion, 29:1-20.
47. Oberai H, Anand IM (2018) Unconscious bias: thinking without thinking. Hum Resour Manag Int Dig, 26:14-17.
48. Popadic M, Milohnic I (2016) Structure of the organizational ambidexterity field: qualitative literature review, article co-citation analysis, and science mapping. Informatologia, 49: 47-60.
49. Rejeb A, Rejeb K, Abdollahi A, Treiblmaier H (2022) The big picture on Instagram research: insights from a bibliometric analysis. Telemat Inform, 73:101876.
50. Rodríguez-Soler R, Uribe-Toril J, Valenciano JDP (2020) Worldwide trends in the scientific production on rural depopulation, a bibliometric analysis using bibliometrix R-tool. Land Use Policy, 97:104787.
51. Sampson RJ, Raudenbush SW (2004) Seeing disorder: neighborhood stigma and the social construction of “broken Windows”. Soc Psychol Q, 67:319-342.
52. Schoenberg E, Wang JV, Duffy R, Keller M (2019) Uncovering unconscious bias and health disparities in the practice of dermatology. Skinmed, 17:367-368.
53. Skinner‐Dorkenoo AL, George M, Wages JE, Sánchez S, Perry, SP (2023) A systemic approach to the psychology of racial bias within individuals and society. Nat Rev Psychol, 2:392-406.
54. Sun Y, Xu L, Luo X, Ren Y, Ding X (2022) Unconscious social relation threats: invisible boss face biases attention. Atten Percept Psychophys, 84:76-88.
55. Suveren Y (2022) Bilinçsiz önyargı: tanımı ve önemi. Psikiyatride Güncel Yaklaşımlar, 14:414-426.
56. Şenyurt AY (2018) Tehdit manipülasyonu ve olumlu bilgilendirmenin örtük ve açık tutumlar üzerindeki etkisi (Doktora tezi). Bolu, Abant İzzet Baysal Üniversitesi.
57. Şenyurt AY, Coşkun H, Ünlü ES (2020) Örtük çağrışım testi’ni Türkçe’ye uyarlama çalışması. OPUS-Uluslararası Toplum Araştırmaları Dergisi, 15:4437-4453.
58. Ukşul E (2016) Türkiye’de eğitimde ölçme ve değerlendirme alanında yapılmış bilimsel yayınların sosyal ağ analizi ile değerlendirilmesi: Bir bibliyometrik çalışma, (Yüksek lisans tezi). Antalya, Akdeniz Üniversitesi.
59. Walker I, Wang S (2018) Implicit Bias. In The Routledge International Handbook of Discrimination, Prejudice and Stereotyping (Eds C Tileagă, M Augoustinos, K Durrheim):197-210. London, Routledge.
60. Webster CS, Taylor S, Thomas C, Weller JM (2022) Social bias, discrimination and inequity in healthcare: mechanisms, implications and recommendations. BJA Educ, 22:131-137.
61. Woolway DG (2021) Unconscious bias in schools: A developmental approach to exploring race and racism. J Educ Res, 114:306-306.
62. Zupic I, Cater T (2015) Bibliometric methods in management and organization. Organ Res Methods, 18:429-472.