Social Network Analysis (SNA), a research method that focuses on the perspective of networks consisting of interconnected entities (e.g., individuals, organizations, countries, concepts), has been applied widely in multiple disciplines and is particularly suitable for interdisciplinary research, including fashion research, in this digital era. The purpose of this study is to examine the application of SNA in fashion studies published between 2000 and 2023, focusing on the topics, SNA purposes, and research designs. A systematic literature review on eligible articles was conducted using bibliometric analysis and content analysis. Findings indicated that the SNA application in fashion research is in its initial stage, primarily used as an inductive approach with limited network types and relationships. Suggestions for future research for deepening theoretical contribution and broadening topic areas were proposed along with a research process to help fashion scholars apply SNA to its fullest potential and expand the research scopes.
AriaM.CuccurulloC. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
2.
BertolottiF.TagliaventiM. R. (2007). Discovering complex interdependencies in organization settings: The role of social network analysis in qualitative research. Qualitative Research in Organizations and Management, 2(1), 43–61. https://doi.org/b348xd
3.
BorgattiS. P.EverettM. G.FreemanL. C. (2002). UCInet 6 for Windows: Software for social network analysis. Harvard Analytic Technologies.
4.
BorgattiS. P.EverettM. G.JohnsonJ. C. (2018). Analyzing social networks (2nd ed.). Sage Publications.
5.
BoydJ. P.FitzgeraldW. J.MahutgaM. C.SmithD. A. (2010). Computing continuous core/periphery structures for social networks. Social Networks, 32(2), 125–137. https://doi.org/10.1016/j.socnet.2009.09.003
6.
BrassD. J. (2022). New developments in social network analysis. Annual Review of Organizational Psychology and Organizational Behavior, 9, 225–246. https://doi.org/gnvn2j
7.
BrittR. K.HayesJ. L.BrittB. C.ParkH. (2020). Too big to sell? A computational analysis of narrative persuasion in health communication. Journal of Interactive Advertising, 20(2), 111–118. https://doi.org/10.1080/15252019.2020.1763873
8.
BurtR. S. (1992). Structural holes. Harvard University Press.
9.
CaglioA.DitilloA. (2012). Opening the black box of management accounting information exchanges in buyer-supplier relationships. Management Accounting Research, 23(2), 61–78. https://doi.org/10.1016/j.mar.2012.03.001
CattaniG.FerrianiS. (2008). A core/periphery perspective on individual creative performance: Social networks and cinematic achievements in the Hollywood film industry. Organization Science, 19(6), 824–844. https://doi.org/10.1287/orsc.1070.0350
12.
CavesR. E. (2000). Creative industries: Contracts between art and commerce. Harvard University Press.
13.
ChenH.MehraA.TasselliS.BorgattiS. P. (2022). Network dynamics and organizations: A review and research agenda. Journal of Management, 48(6), 1602–1660. https://doi.org/gpg
14.
ChoiY. H.KimS. E.LeeK. H. (2021a). Faux fur trade networks using macroscopic data: A social network approach. Sustainability, 13(3), 1427. https://doi.org/10.3390/su13031427
15.
ChoiY. H.YoonS.XuanB.LeeS. Y. T.LeeK. H. (2021b). Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis. Fashion and Textiles, 8(1), 1–27. https://doi.org/10.1186/s40691-021-00265-6
16.
CingolaniI.IapadreL.TajoliL. (2018). International production networks and the world economy: Understanding the dynamics. International Economics, 153, 11–33. https://doi.org/10.1016/j.inteco.2017.10.002
17.
CreswellJ. W. (2014). Research design: Qualitative, quantitative, and mixed-method approaches. Sage Publications.
18.
DonthuN.KumarS.MukherjeeD.PandeyN.LimW. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/J.JBUSRES.2021.04.070
FreemanL. C. (2004). The development of social network analysis: A study in the sociology of science. Empirical Press.
21.
FroehlichD. E.Van WaesS.SchäferH. (2020). Linking quantitative and qualitative network approaches: A review of mixed methods social network analysis in education research. Review of Research in Education, 44(1), 244–268. https://doi.org/gg6v3d
22.
GodartF. C. (2015). Trend networks: Multidimensional proximity and the formation of aesthetic choices in the creative economy. Regional Studies, 49(6), 973–984. https://doi.org/ggw2hw
GranovetterM. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. http://www.jstor.org/stable/2776392 https://doi.org/10.1086/225469
25.
HansenD. L.ShneidermanB.SmithM. A. (2011). Analyzing social media networks with NodeXL: Insights from a connected world. Morgan Kaufmann.
26.
HilemanJ.KallsteniusI.HäyhäT.PalmC.VeermanC. (2020). Keystone actors do not act alone: A business ecosystem approach to global sustainability. PLoS ONE, 15(11), e0241453. https://doi.org/gjdtqc
27.
HollanderA. (2016). Sex and suits: The evolution of modern dress. Bloomsbury Publishing.
28.
HollebeekL. D.SarstedtM.MenidjelC.SprottD. E.UrbonaviciusS. (2023). Hallmarks and potential pitfalls of customer- and consumer engagement scales: A systematic review. Psychology & Marketing, 40(6), 1074–1088. https://doi.org/10.1002/MAR.21797
29.
HongJ.ChangJ. S. (2016). The structure of supplier network and firm's performance in the fashion industry. Indian Journal of Science and Technology, 9(46), 1–8. https://doi.org/npvp
30.
JainA.ThukralS.PaulJ. (2024). Foreign market entry modes of family firms: A review and research agenda. Journal of Business Research, 172, https://doi.org/npvx
31.
JinB.KimG.MooreM.RothenbergL. (2021). Consumer store experience through virtual reality: Its effect on emotional states and perceived store attractiveness. Fashion and Textiles, 8(1), 1–21. https://doi.org/10.1186/s40691-021-00256-7
32.
KawamuraY. (2005). Fashion-ology: An introduction to fashion studies. Berg Publishers.
33.
KeatsD.SchmidtJ. P. (2007). The genesis and emergence of Education 3.0 in higher education and its potential for Africa. First Monday, 12(3), https://doi.org/ks45
34.
KimH. J.AhnS. K.ForneyJ. A. (2014). Shifting paradigms for fashion: From total to global to smart consumer experience. Fashion and Textiles, 1(1), 1–16. https://doi.org/ks46
35.
KimH.RheeE. Y.YeeJ. (2008). Comparing fashion process networks and friendship networks in small groups of adolescents. Journal of Fashion Marketing and Management: An International Journal, 12(4), 545–564. https://doi.org/10.1108/13612020810906182
36.
KimS.SkvoretzJ. (2013). Structural embeddedness, uncertainty, and international trade. International Journal of Comparative Sociology, 54(2), 124–143. https://doi.org/ks47
37.
KimS.WooH. (2022). Big data analysis of trends in the second-hand apparel market: A comparison between 2014 and 2019. Research Journal of Textile and Apparel, 26(2), 138–155. https://doi.org/10.1108/RJTA-12-2020-0139
38.
LaBatK. L.KnightD. (2015). Interdisciplinary research and education: Strengthening the textiles and apparel field. In Ha-BrookshireJ.LaBatD. (Eds.), Envisioning textile and apparel research and education for the 21st century (pp. 20–26). ITAA Monograph #11.
39.
LangC.XiaS.LiuC. (2021). Style and fit customization: A web content mining approach to evaluate online mass customization experiences. Journal of Fashion Marketing and Management: An International Journal, 25(2), 224–241. https://doi.org/10.1108/JFMM-12-2019-0288
40.
LazzerettiL.CaponeF. (2020). The role of education in the entrepreneurial ecosystem: The case of “made in Italy tuscany academy” in the florence fashion city. International Journal of Entrepreneurship and Small Business, 40(2), 270–290. https://doi.org/ks48
41.
LeeS. E.JuN.LeeK. H. (2021). Visioning the future of smart fashion factories based on media big data analysis. Applied Sciences, 11(16), 7549. https://doi.org/10.3390/app111
42.
LeonelliS. (2020). Scientific research and big data. In ZaltaE. N. (Ed.), The Stanford Encyclopedia of Philosophy (Summer 2). Metaphysics Research Lab, Stanford University.
43.
MarckettiS.PaulinsV. A.BanningJ. (2023). SoTL special issue editor's note. Clothing and Textiles Research Journal, 41(1), 3–5. https://doi.org/10.1177/0887302X221138215
44.
ModiD.ZhaoL. (2021). Social media analysis of consumer opinion on apparel supply chain transparency. Journal of Fashion Marketing and Management: An International Journal, 25(3), 465–481. https://doi.org/10.1108/JFMM-09-2019-0220
45.
MoherD.LiberatiA.TetzlaffJ.AltmanD. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151(4), 264–269. https://doi.org/10.7326/0003-4819-151-4-200908180-00135
46.
Muñoz-LeivaF.Rodríguez LópezM. E.Liebana-CabanillasF.GuillénP. (2021). Past, present, and future research on self-service technologies: A literature review. European Journal of Marketing, 55(8), 2269–2307. https://doi.org/10.1108/EJM-02-2019-0179
47.
NaY. K.KangS. (2018). Sustainable diffusion of fashion information on mobile friends-based social network service. Sustainability, 10(5), 1474–1497. https://doi.org/gdtd9h
48.
PattonM. Q. (2015). Qualitative evaluation and research methods (4th ed). Sage Publications.
49.
PodolnyJ. M. (2005). Status signals: A sociological study of market competition. Princeton University Press.
50.
PranckutėR. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 12. https://doi.org/10.3390/publications9010012
51.
PrellC.FengK. (2016). Unequal carbon exchanges: The environmental and economic impacts of iconic U.S. Consumption items. Journal of Industrial Ecology, 20(3), 537–546. https://doi.org/10.1111/jiec.12377
52.
SimoesI.RibeiroM. M. (2019). Collaborative learning in fashion education. In CarlottoF.McCreeshN. (Eds.), Engaging with fashion (pp. 149–162). Brill Rodopi.
53.
SongK.HwangS.KimY.KwakY. (2013). The effects of social network properties on the acceleration of fashion information on the web. Multimedia Tools and Applications, 64(2), 455–474. https://doi.org/10.1007/s11042-012-1068-2
54.
SongS. Y.KimY. K. (2022). Factors influencing consumers’ intention to adopt fashion robot advisors: Psychological network analysis. Clothing and Textiles Research Journal, 40(1), 3–18. https://doi.org/10.1177/0887302X20941261
55.
TabassumS.PereiraF. S. F.FernandesS.GamaJ. (2018). Social network analysis: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(5), e1256. https://doi.org/10.1002/WIDM.1256
56.
WassermanS.FaustK. (1994). Social network analysis: Methods and applications. The Press Syndicate of the University of Cambridge.
57.
WilliamsT. A.ShepherdD. A. (2017). Mixed method social network analysis: Combining inductive concept development, content analysis, and secondary data for quantitative analysis. Organizational Research Methods, 20(2), 268–298. https://doi.org/gc5tcb
58.
WilsonS. M.PetersonP. L. (2006). Theories of learning and teaching: What do they mean for educators?National Education Association.
59.
YuY.MooreM.ChapmanL. P. (2021). Social network analysis of an emerging innovation: Direct-to-garment printing technology. Journal of Fashion Marketing and Management, 25(2), 247–289. https://doi.org/10.1108/JFMM-03-2020-0053
60.
ZhaoL.MinC. (2019). The rise of fashion informatics: A case of data-mining-based social network analysis in fashion. Clothing and Textiles Research Journal, 37(2), 87–102. https://doi.org/10.1177/0887302X18821187
