A Bibliometric Research Trend Analysis on Emerging Technology in the Textile Industry

Abstract

Textile industry research and practices must comprehend the state-of-the-art digital transformation to effectively coordinate future research efforts in this rapidly growing field of study. Thus, the current study conducts a comprehensive bibliometric analysis of the literature on the textile industry’s digital transformation. A total of 324 publications were assessed using the bibliometric analysis tools VOSviewer and the R package Bibliometrix. Articles were analyzed for author contributions, citation counts, trending scientific journals, keywords, academic affiliations, and country affiliations, as well as to identify the overall research trend in these fields since 2001. The evidence demonstrates that the United Kingdom, the United States, and China are the top three major nations, as reflected by the annual number of publications. The thematic network analysis indicates that in Industry 4.0, innovations such as blockchain, artificial intelligence, COVID-19, and sustainability are playing the role of push factor toward the digital transformation of the textile industry. The article concludes with a plea for more research.

Keywords

Asia R bibliometrix Digitalization Textile Bibliometric analysis Co-occurrence Analysis

Introduction

Textiles are humans’ second basic need after food, making their research valuable because they are integral to human existence (Yan et al., 2022). The utilization of textiles dates back to the Stone Age; it has served as a means of self-image, revealing one’s social standing, gender, or customs (Halepoto et al., 2022a). The advent of new digital technologies and their potential strategic value is one of the businesses’ primary concerns (Korachi & Bounabat, 2019). As shown by the adoption of cyber-physical systems and new digital capabilities, the fourth industrial revolution is expected to enable fully autonomous and intelligent manufacturing systems, with enormous implications for the global economy (Piccarozzi et al., 2018).

Emerging technologies such as artificial intelligence (AI) have significantly advanced textiles’ production, testing, and analysis. Big data (Hack-Polay et al., 2020), blockchain technology (Yadlapalli & Rahman, 2022), and business intelligence (Ahmad et al., 2020) are transforming the business landscape of the textile industry. For example, AI is used in fibre development to assemble slivers, yarns, fabrics, and garments (Giri et al., 2019; Halepoto et al., 2022a). The utilization of blockchain technology is increasing due to its capacity to create a transparent textile manufacturing supply chain as part of sustainability reporting (Yadlapalli & Rahman, 2022). As emerging technologies are tapping into the textile industry to realize the development of the scientific body of knowledge, Halepoto et al. (2022a) suggested conducting bibliometric studies in textiles.

The existing bibliometric studies in the field of textiles covered diverse topics such as protective clothing, intelligent textile, and textile and clothing footprint (Kuilang & Qian, 2021; Tian & Li, 2019; Xiang et al., 2022) using CiteSpace. While Halepoto et al. (2022a) utilized VOSviewer in mapping research trends on AI in textiles, Halepoto et al. (2022b) researched antibacterial textiles’ using R bibliometric tool biblioshiney. However, there is a lack of bibliometric research, specifically on emerging technologies in the textile industry. To address this gap, this study aims to provide an overview of the research and citation trends, identify leading authors, institutions, journals, nations, and cited papers, and explore the thematic structure of the field.

Mapping the scientific knowledge of emerging technologies in the textile industry is crucial for suggesting future research directions. This study contributes to the knowledge by presenting the latest research trends based on metadata analysis regarding emerging technologies in the textile industry. It investigates the pattern of annual publications, collaboration, and co-citation trends, identifies major contributing authors and articles with the highest citation count, explores the most trending scientific journals and frequently used author keywords, and examines the most productive countries and universities.

In addition, there are several textile-related research, to the best knowledge of the authors, both VOSviewer and R package Bibliometrix have been used to examine research trends on emerging technologies’ influence on textiles. To fill those knowledge gaps, this study’s questions include: (a) what is the overall research and citation trend? (b) who is leading the field regarding authors, institutions, journals, nations, and cited papers? (c) what is the thematic structure of the domain?

Specifically, the study aims to investigate the pattern of the annual publication, collaboration, co-citation trends, major contributing authors, articles with the highest citation count, most trending scientific journals, most frequently used author keyword, most productive countries, and universities. This study significantly contributes to the knowledge by presenting the latest research trend based on metadata analysis regarding digital transformation in the textile industry.

The remainder of this study is as follows; the second section presents the method, followed by the third section, analysis and results. The fourth section, offers an additional discussion based on the findings. This section also discusses the influence of emerging technologies linked to sustainability and textile manufacturing in developing nations. The section also presents some suggestions on policy implications. In the fifth section, limitational and future research directions can be found. Finally, highlighting study findings and contributions, the sixth section concludes the study.

Method

The methodological approaches and software tools used in this investigation are explained in this section. A set of articles linked to digital transformation in the textile industry was collected, scanned, processed, and evaluated using advanced software to yield various bibliometric data and technology trends.

Bibliometric Analysis

Bibliometric research is a quantitative method that employs statistics to quantify text information and examine published documents (Ahmi & Mohd Nasir, 2019). Bibliometric analysis is research in library and information studies that uses quantitative approaches to evaluate bibliographic material. Alan Pritchard pioneered the notion of bibliometric analysis in 1969. This analytic approach in a certain field of study has existed since the nineteenth century. Researchers used to collect data for bibliometric analysis by hand, which took a long time. The expansion of bibliometric analytic studies has been expedited and aided by advances in information and communication technology, which have increased scholars’ access to scholarly publications in their areas of specialization (Hira et al., 2020). It can also assess the quantity and quality of published materials to track trends or patterns in a particular study field. The bibliometric study could reveal descriptive patterns of finished publications depending on a domain, field, country, years, or a combination of these variables. Furthermore, a rigorous approach to bibliometric analysis could uncover more precise information about the articles, such as authors, keyword frequency, and citations (Ahmi & Mohd Nasir, 2019).

This bibliometric analysis extracts quantitative information on publication metrics, geographical characteristics, author collaboration via co-authorship, and the relevance of research institutions, among other things, by analyzing data from a corpus of scientific publications on digital transformation in the textile industry. The results of this analysis are used to summarize the state of digital transformation in textile industry research at the time of writing. Specialized software, Bibliometrix and VOSviewer (version 1.6.14), were used to conduct the analyses in this study. Bibliometrix is a new open-source software tool for systematically mapping scientific literature written in the R environment (Aria & Cuccurullo, 2017). Van Eck and Waltman also produced VOSviewer, a widely used and freely available software package. It can create and display bibliometric maps of scientific publications, authors, journals, countries, organizations, and keywords (van Eck & Waltman, 2010).

The term ‘items’ in VOSviewer refers to the object of interest. Any two elements (i.e., author, country, and keyword) can be linked together to provide a level of strength (curved line represents interlink). The link’s ‘link strength’, which measures its positive arithmetic value, is displayed. The stronger the link, the higher the value. The number of journals in which two keywords appear together is referred to as link strength in co-occurrence analysis. The word ‘co-authorship’ refers to the number of journals published by two countries/authors associated with each other.

Data Search

Elsevier has named Scopus the primary scientific database for transdisciplinary research literature and computational methods. On October 12, 2022, data mining occurred. The researchers were particularly interested in the digital transformation of the textile industry. After examining the abstract, keywords and full-text irrelevant papers were excluded during the scanning process. This search yielded 324 documents for further analysis. The data extraction flow diagram is illustrated in Figure 1.

Figure 1.

Flow Diagram of the Search Strategy.

Analysis and Results

Descriptive Analysis

The basic information on the selected 324 articles is presented in Table 1. The time span of the articles yielded is between 2001 and 2022. The authors’ keywords associated with these articles are 1271. A total of 942 authors have contributed to this phenomenon of interest. Other than 50 articles (single-authored), all the papers are prepared with at least two authors.

Table 1.

Main Information Regarding Selected Articles.

Description	Results
Time span	2001:2022
Sources (Journals, Books, etc.)	220
Documents	324
Average years from publication	3.54
Average citations per document	11.4
Average citations per year per doc	2.352
References	22718
article	324
Keywords plus (ID)	1888
Author’s keywords (DE)	1271
Authors	942
Author appearances	1023
Authors of single-authored documents	48
Authors of multi-authored documents	894
Single-authored documents	50
Documents per author	0.344
Authors per document	2.91
Co-authors per documents	3.16
Collaboration index	3.26

Publication Trends

The research on textile or apparel industry digitalization has grown in line with the development of information and communication technology, and scholarly articles have been available since 2001. However, the publications and citations (see Figures 2 and 3) have noticeably increased since 2014, while Industry 4.0 began changing conventional business models with an annual growth rate of 13.39%. The upward research trend demonstrates that the COVID-19 pandemic and revolutionary innovations (i.e., the Internet of Things [IoTs], blockchain technology, AI) have a major influence in grabbing researchers’ attention to the digitalization of the textile industry.

Figure 2.

Annual Publication Trend.

Figure 3.

Average Article Citation Per Year.

Research articles (Quantitative, Qualitative, case study) on the Scopus database specific to digital transformation in the textile industry can be found from 2001. The number of publications before 2014 is negligible. It is noteworthy that industrial digitalization swiftly increased during the COVID-19 pandemic. A paradigm shift in the textile industry due to industry 4.0-driven technologies was already a reality. The pandemic pushed businesses to switch towards innovation immediately. The undeniable fact that the growing digitalization trend in the textile industry increases in BcT research indicates an increasing study area.

The topic trends based on keywords plus highlight that industry 4.0-driven innovation is being heavily discussed in the scholarly articles. Particularly, the traceability features of blockchain technology across garment supply-chain and the implication of AI in manufacturing and automation. Besides, image processing, fibre, design and weaving have been spotlighted.

Most Productive Authors

In terms of article output, Table 2 summarizes the classification of the twenty most influential authors on digital transformation in the textile sector who have been published in the Scopus database. It is critical to highlight that the authors have only a few published papers between them, as the topic has only lately garnered prominence among scientists. The author in the first position has four articles, the second and third-ranked authors each have three articles, and the fourth to twentieth-ranked authors each have two articles.

Table 2.

Twenty Most Productive Authors.

Element	h_index	g_index	m_index	TC	NP	PY_start
Kim H	3	4	0.2	40	4	2008
Koehl L	3	3	0.429	30	3	2016
Ozuem W	3	3	0.6	47	3	2018
Agrawal Tk	2	2	0.4	17	2	2018
Campagne C	2	2	0.4	17	2	2018
Howell K	2	2	0.667	19	2	2020
Hracs Bj	2	2	0.4	21	2	2018
Huh J-H	2	2	0.4	15	2	2018
Jain G	2	2	0.4	13	2	2018
Kumar V	2	2	0.286	43	2	2016
Lancaster G	2	2	0.4	41	2	2018
Lee S	2	2	1	12	2	2021
Li D	2	2	0.143	68	2	2009
Li X	2	2	0.222	28	2	2014
Liu R	2	2	0.667	67	2	2020
Madsen Dø	2	2	0.5	27	2	2019
Matthyssens P	2	2	1	20	2	2021
Mishra S	2	2	0.667	33	2	2020
Moodley S	2	2	0.095	50	2	2002
Özdemir H	2	2	0.143	40	2	2009

Note: NP = Number of publications, TC = Total citations, PY_start = Publication year starting.

Leading Intuitions

Seoul National University leads the list of most productive institutions with 12 published articles. The second leading university is Donghua University, having seven publications. Third to sixth positions are held by Hanyang University, Macquarie University, Soochow University, and the University of Southampton, with five publications each. Amsterdam University of Applied Sciences (7th), Manchester Metropolitan University (8th), Queen Mary University of London (9th), Taipei Medical University (10th), and University of Florence (11th), each having four articles. Universities from position 12th to 15th, as mentioned in Table 3, have three published articles. Notably, South Korea, China, and England each have the three most productive institutions.

Table 3.

Two Most Productive Institutions.

Ranking	Affiliations	Articles	Country
1.	Seoul National University	12	South Korea
2.	Donghua University	7	China
3.	Hanyang University	5	South Korea
4.	Macquarie University	5	Australia
5.	Soochow University	5	China
6.	University of Southampton	5	England
7.	Amsterdam University of Applied Sciences	4	Netherlands
8.	Manchester Metropolitan University	4	England
9.	Queen Mary University of London	4	England
10.	Taipei Medical University	4	Taiwan
11.	University of Florence	4	Italy
12.	Aalto University	3	Finland
13.	Carnegie Mellon University	3	United States
14.	Dokuz Eyll University	3	Turkey
15.	Jiangnan University	3	China

Leading Journals

Industrial digital transformation and sustainable development goals are top priorities in academia. The leading high-ranked journals publish articles on the textile industry’s digital transformation. The list shows the ranking of journals based on the total number of published articles. As mentioned in Table 4, Sustainability (1st position, 12 papers), International Journal of Fashion Design Technology and Education (2nd position, eight papers), Journal of Fashion Marketing and Management (3rd position, seven articles), Research Journal of Textile And Apparel (4th, seven documents), and International Journal of Retail and Distribution Management (5th posting, six papers). Journal ranked 6th to 8th has five articles each, whereas each journal ranked 9th to 12th published four articles on this POI. From the 13th to the 20th, journals have three papers.

Table 4.

Twenty Leading Journals.

Ranking	Sources	Articles
1.	Sustainability	13
2.	International Journal of Fashion Design Technology and Education	8
3.	Journal of Fashion Marketing and Management	7
4.	Research Journal of Textile and Apparel	7
5.	International Journal of Retail and Distribution Management	6
6.	Journal of Retailing and Consumer Services	5
7.	Journal of The Textile Institute	5
8.	Technological Forecasting and Social Change	5
9.	Ieee Access	4
10.	Industria Textile	4
11.	International Journal of Advanced Manufacturing Technology	4
12.	Journal of Business Research	4
13.	Computers and Industrial Engineering	3
14.	Fashion Theory – Journal of Dress Body and Culture	3
15.	Fibers and Textiles in Eastern Europe	3
16.	Industrial Marketing Management	3
17.	International Journal of Clothing Science and Technology	3
18.	Journal of Cleaner Production	3
19.	Production Planning and Control	3
20.	Textile Progress	3

Most Productive Countries

Figure 4 shows the most productive 20-country list (based on the document number). China is leading the list with 86 articles. The UK is ranked 2 and has 80 articles. The USA and Italy are holding 3rd and 4th position with 63 and 45 articles, respectively. Other countries on the list are South Korea (5th, 34 documents), India (6th, 33 papers), Australia (7th, 26 documents), and Spain (8th, 25 papers). The remaining listed countries (see Figure 4) have at least nine published articles.

Figure 4.

Twenty Most Productive Countries.

Most Cited Papers

The distribution of the most frequently cited documents on a global scale is depicted in Table 5. According to the Scopus database, the work by Acharya (2018) has total citations (86), followed by Ito (2016), with 79 citations. Leist (2017) is in third place, with 65 total citations in the Scopus database. Yang (2009) is rated fourth in terms of total citations (59), whereas Bertola (2018) is placed fifth with 49 total citations.

Table 5.

Ten Most Cited Papers.

Title	Paper	DOI	Total Citations	TC per Year	Normalized TC
Big data, knowledge co-creation, and decision-making in fashion industry	Acharya A, 2018, Int J Inf Manage	10.1016/j.ijinfomgt.2018.06.008	86	17.2	3.0714
Long-term natural remediation process in textile dye-polluted river sediment driven by bacterial community changes	Ito T, 2016, Water Res	10.1016/j.watres.2016.05.050	79	11.2857	2.7526
Investigating the shape memory properties of 4D printed polylactic acid (PLA) and the concept of 4D printing onto nylon fabrics for the creation of smart textiles	Leist Sk, 2017, Virtual Phys Prototyping	10.1080/17452759.2017.1341815	65	10.8333	2.472
A new approach for image processing in foreign fibre detection	Yang W, 2009, Comput Electron Agric	10.1016/j.compag.2009.04.005	59	4.2143	3.0412
Fashion 4.0. Innovating fashion industry through digital transformation	Bertola P, 2018, Res J Text Appar	10.1108/RJTA-03-2018-0023	49	9.8	1.75
A functional-based segmentation of human body scans in arbitrary postures	Werghi N, 2006, Ieee Trans Syst Man Cybern Part B Cybern	10.1109/TSMCB.2005.854503	45	2.6471	1.0227
Prototype garment pattern flattening based on individual 3D virtual dummy	Yunchu Y, 2007,Int J Cloth Sci Technol	10.1108/09556220710819528	44	2.75	1
Fast fashion: a financial snapshot	Hayes Sg, 2006,J Fash Mark Manage	10.1108/13612020610679277	43	2.5294	0.9773
Fashion retailing—past, present, and future	Mccormick H, 2014, Text Prog	10.1080/00405167.2014.973247	41	4.5556	3.0496
Acceptance of mass customization of apparel: merchandising issues associated with preference for product, process, and place	Lee S-E, 2002, Cloth Text Res J	10.1177/0887302X0202000302	39	1.8571	1.5

Collaboration Analysis and Co-occurrence Network

Co-occurrence of Author Keywords

Figure 5 presents the visual illustration of author keywords. The author’s keywords were assessed for their frequency of occurrence and relevancy. Other than search keywords, Digital Transformation (18), Textile Industry (68), another 30 frequently used author keywords are listed in Table 6. It is vital to mention that, in this study context, keywords, the fashion industry (26), clothing industry (23), apparel industry (10), and garment industry (16) are of the same meaning to the textile industry.

Figure 5.

Co-occurrence of Author Keywords.

Table 6.

Most Frequently Used 20-author Keywords.

Keyword	Frequency	Keyword	Frequency
Industry 4.0	22	Consumption behaviour	8
Sustainability	19	E-commerce	8
Blockchain	17	Manufacturing	8
Fashion	17	Product design	8
Image processing	14	Automation	7
Innovation	13	Internet of things	7
Social media	13	SMEs	7
COVID-19	12	Textile fibres	7
Supply chain management	11	Artificial intelligence	6
Weaving	11	Bangladesh	6
Retailing	10	Big data	6
China	9	Smart factory	6
Sales	9	Case study	6
Clothes	8	Interoperability	6
Traceability	8	Smart contract	5

Industry 4.0-driven vertical innovation and technologies such as blockchain, AI, big data, IoTs have been utilized repeatedly. Research on conventional supply-chain management issues regarding interoperability and traceability has been studied, as blockchain’s smart contract can solve those issues. Furthermore, the smart factory concept is shifting the manufacturing industry toward production automation. The influence of horizontal innovations in product design (i.e., fabric, cloths, and textile fibre) and manufacturing (i.e., weaving and image processing) has been important in this study domain. Most of the studies were qualitative, case studies by nature, as reflected in the keyword. China and Bangladesh, the world’s leading garments manufacturing countries, are also on this list (Anner, 2020). The COVID-19 pandemic is one of the factors contributing to the rapid digitization of the textile sector. The pandemic abruptly pushed the business models to shift toward digitalization.

Country Collaboration Analysis

Figure 6 illustrates that 30 of 52 countries have 62 links, with 73 total link strengths. Therefore, this study included 30 affiliated countries in terms of co-authorship analysis. The link strength between the USA and the United Kingdom is 3. In the case of Malaysia and Japan, the link strength is 1. The higher the link value, the higher the link strength. China, the United Kingdom, Italy, and the USA have the most collaboration. The finding also aligns with the result of the most productive county presented in Figures 4 and 7.

Figure 6.

Co-occurrence Analysis of Countries.

Figure 7.

Corresponding Author’s Country.

Author Collaboration Analysis

The bibliometric network displays the relationships between scholars, research organizations, and countries based on the number of publications co-authored in co-authorship analysis. As illustrated in Figure 8, the bibliometric map of co-authorship created by VOSviewer employing author names reveals seven clusters of 42 items connected by 107 linkages with a total link strength of 110. A cluster is a collection of nodes that are closely linked. Each node in a network is uniquely assigned to a cluster. A resolution parameter determines the number of clusters. Author Zhang X. belongs to cluster six with 12 links, 12 total link strengths, and three documents with an average year of publication of 2018.33. Similarly, author Shen l., from cluster 1, has eight links, 10 total link strengths, and four documents, with an average publication year 2021.0. The strength of the connection between the authors is 1.

Figure 8.

Author Collaboration Analysis.

Co-occurrence Network

Multiple correspondence analysis (MCA) is a frequently used method in sociology. It compresses big data sets with several variables into a low-dimensional space to create an intelligible two-dimensional (or three-dimensional) graph that employs plane distance to indicate similarity between the keywords. The plane distance between the keywords is used to signify their similarity. Keywords that approach the centre point have attracted much attention in recent years. The closer it gets to the edge, the more focused the study theme or transition to other themes becomes (Xie et al., 2020). As demonstrated in Figure 9, industry 4.0, traceability, blockchain, case study, firm performance, and supply-chain management is the most researched topics.

Figure 9.

Multiple Correspondence Analysis (MCA) of High-frequency Keywords.

Thematic Structure of the Field

Thematic Map

Keywords plus for analysis accurately depict the study’s knowledge structure and aid in identifying and connecting disparate research fields. The author’s keywords highlight the study’s primary concerns. The database provides additional keywords briefly explaining the article’s contents, called Keywords Plus. It offers additional descriptive trends in addition to the author’s selected keywords. The use of ‘biblioshiny’, the R-bibliometric program’s analysis tool, helps discover research streams and themes based on keywords from the literature (Nasir et al., 2020). We have identified several study themes that will allow a more accurate assessment of the results. We can organize the detected themes into a strategic diagram to examine the research theme’s significance and evolution.

The thematic map’s density (y-axis) and centrality (x-axis) are depicted in Figure 10 (x-axis). The centrality metric indicates the significance of the selected subject, while the density metric indicates the extent to which the chosen theme has developed. The graph is separated into four distinct sections. The themes that are shown in the lower-left section are emerging or waning. These fresh themes may emerge to improve the research area or disappear entirely. The basic or transversal themes fall within the lower right corner of the thematic map. These are low-density motifs with a high degree of centrality. Numerous studies have been conducted on these subjects. The upper left section shows a dense population with a low centrality; these themes are strongly developed yet isolated. The upper right portion shows a dense population and a high centrality. The themes in this section are a developed and vital motor topic. The scale of the thematic map corresponds to the number of components included in the theme. The thematic map in Figure 10 is generated using a full-time span and the top 250 keywords. Still, the items displayed in the clusters have a minimum frequency of 5 in the ‘biblioshiny’ online program. Each topic has a maximum of three representative labels. This has no bearing on the literature but reflects the author’s subjective assessment of the dynamics and best depiction of relevant literature.

Figure 10.

Thematic Map.

The first cluster comprises articles on emerging technologies, particularly blockchain in supply-chain management, the IoTs, industry digitalization, and human factors. According to the thematic map, these subjects are classified as fundamental or transversal, with a high degree of centrality and low density. There is much effort in these areas, but it is difficult to discern future directions because most topics have been addressed. However, since blockchain is in the embryonic stage of development and the use of a case in the textile industry is a low, further study on blockchain adoption readiness, the case study is needed. The issues covered in these clusters are related to the second research theme, which significantly contributes to emerging and transversal themes. Textiles, textile production, image processing, colour, and weaving all contribute to smart textiles and garment manufacturing. This section discusses many aspects of the virus-host relationship. A sizable study has been conducted, and additional work is necessary, which is why this theme is transitioning from emergent to transversal. Under this theme, research will be conducted on the effect of descriptive innovation in the garment sector, marketing, and sales. The following clusters are represented: digital transformation, product design, and apparel manufacturing. It is a developing theme in the literature devoted to the textile industry. This theme denotes the emergence of industrial management, manufacturing, industry 4.0, big data, decision support studies for technological implementation, and adoption through various approaches.

The thematic network is presented in Figure 11. demonstrates four interrelated clusters yet distinguishing clusters: (a) Industry 4.0 and digital transformation, (b) Supply–chain and innovations such as blockchain, (c) Sustainability in the fashion industry, (d) COVID-19 and fashion marketing. The COVID-19 pandemic pushed technology in industrial settings even further. Besides, business activity alignment with sustainable development goals calls for assurance of transparency and traceability in the manufacturing industry. The potential of blockchain technology is therefore being considered to support the textile supply chain. Furthermore, the Firm’s cost-profit structure, operating process’s sensitivity, reliability, and transparency will likely undergo major transformation due to desalinization. The pandemic and Industry 4.0 influence shaping consumer behaviour (Chang et al., 2021).

Figure 11.

Thematic Network.

Figure 12.

Thematic Evolution.

Thematic Evolution

In addition to the thematic map, there is thematic evolution (Figure 13), showing the historical development of coronavirus literature. Using the keywords plus the thematic evolution depicts the history of themes and how these themes evolved. The thematic evolution is made using ‘biblioshiny’ and three-time segments. This time segmentation is based on the authors’ subjective judgment, keeping the better representation of thematic evolution. The first segment is from 2001 to 2015, the second is from 2016 to 2020, and the last segment represents 2021 to the current year, 2022. Themes have evolved with time. As analyzed from 2001 to 2015, the clothing and textile industry regarding image processing and chemistry focused. From 2016 to 2020, the literature shifted toward innovations like blockchain technology’s influence on supply-chain, product design, and farm decision-making to adopt the technology. In 2021, and 2022, the influence of COVID-19 on IR 4.0-driven technologies, the IoTs, big data, and blockchain held the lion’s share of industrial research. Blockchain technology grabbed researchers’ attention due to the sustainability assurance possibility in the circular economy.

Figure 13.

The Three-field Plot of Keywords Plus–Country–Institute.

Three-fields Plot

Along with annual production and article citations, it is critical to understand publications’ primary topics, locations, and affiliations. Figure 12 illustrates a three-tiered study of publications on digital transformation, beginning with a keyword plus on the left, affiliations on the right, and countries of interest in the centre. China collaborates with the majority of the top affiliations on POI-related issues. Additionally, Malaysia, the United Kingdom, Italy, South Korea, India, Pakistan, and Brazil contribute significantly to this field of research. A significant discussion has occurred on issues relating to the sector and emerging technologies such as blockchain and AI. Besides, numerous scientific studies have been conducted on consumer behaviour and sustainability.

Additional discussion

Harnessing Emerging Technologies for Sustainable Textile Manufacturing in Developing Nations

Developing countries such as Bengalese, India, Pakistan, and Vietnam serve as the textile manufacturing hub (Gambhir, 2023; Nayak & Padhye, 2018). Based on this finding, it is essential to illuminate the relationship between emerging technologies, sustainability, and their impact on textile manufacturing in developing countries. Historically, the manufacturing industry has been associated with significant environmental impacts, including depletion of natural resources, emissions, and wage slavery. However, emerging technologies have the potential to address these concerns and promote the sustainability of garment manufacturing. The introduction of Industry 4.0, characterized by the incorporation of digital technologies and automation, presents opportunities for enhancing sustainability practices in the industry.

The role of business intelligence systems (BIS) in promoting sustainability in the textile and clothing industry was spotlighted by Ahmad et al. (2020). BIS uses data analytics, machine learning, and AI to collect, analyze, and comprehend vast volumes of data to make informed decisions. Textile producers can supervise and optimize the consumption of resources, waste management, and energy efficiency by implementing BIS. This reduces adverse ecological consequences and costs, enhancing sustainability performance.

In addition, emerging technologies such as the IoTs and blockchain can improve garment manufacturing’s environmental performance. IoT devices could be implemented to track and control systems, recognize waste and inefficiency, and facilitate real-time recourses, inventory control and tracking. This enables manufacturing companies to identify areas for improvement and implement sustainable supply chain practices. In contrast, blockchain technology enables brands and consumers to verify sustainable practices, ethical sourcing, and fair labour conditions.

Furthermore, Islam et al. (2021) identify numerous sustainable strategies in the textile, apparel, and fashion industries. These include the use of organic and recycled materials, the implementation of cleaner production methods, the adoption of environmentally friendly dyeing and finishing techniques, and the promotion of circular economy principles. By providing improved monitoring, control, and optimization of manufacturing processes, emerging technologies could support and enhance the execution of these sustainable practices.

In the context of developing nations, the implementation of emerging textile manufacturing technologies can yield substantial benefits. It can contribute to enhanced environmental performance, resource efficiency, and cost savings, thereby boosting the competitiveness of regional industries. In addition, implementing sustainable practices can result in improved working conditions, decreased water and air pollution, and a positive social impact in these regions.

These same articles conclude by noting that emerging technologies possess the prospects to revolutionize the apparel manufacturing sector, thereby promoting sustainability and addressing environmental challenges. Utilizing BIS, IoT, blockchain, and other technological innovations can optimize the consumption of resources, increase traceability, and facilitate the adoption of sustainable textile manufacturing practices. These developments are especially significant for developing nations, where sustainable growth and responsible production are essential for environmental and social progress.

Policy Implication

Governments must develop policies and plans to protect and enhance the textile industry’s competitiveness. They should not only increase the number of start-ups and adhere to the mandatory guidelines imposed by buyers and international regulatory bodies but also make efforts to influence the quality characteristics of these enterprises, comprehend the characteristics and prerequisites of enterprises, support high-quality early-stage technology-related initiatives, and recognize the importance of textile industries to gradual economic development, particularly in developing countries. The study’s yearly publication trend and keyword analysis reflect the need for digital transformation in the textile industry.

With the COVID-19 public health emergency outbreak, businesses worldwide witnessed a forced paradigm shift from traditional offline business modes into online modes for business continuity. With regard to the COVID-19 situation, many governments have pursued divergent solutions. Certain structural reforms should be made following a country’s current status and progress rather than ignoring the unique circumstances. For such a crisis, governments should implement new policies to promote digitalization in the textile sector to mitigate the crisis’s detrimental impact on people’s lives. Institutional adjustments constrain corporate activity in a crisis-stricken economy (Xu et al., 2021). It is high time for governments, textile firms, and garment associations to realize the need for digitalization and focus on improving institutional environments so that digital technologies can be in place. Digitalization can help recover from an economic crisis or public health emergencies (Guo et al., 2020). Promoting digitization as a key policy is mandatory for enterprises to remain competitive in the digital global economy (Sepashvili, 2020). To bolster firms’ capacity to react to long-term crises, governments must address structural weaknesses, introduce new commercial policies, and increase resilience (Xu et al., 2021).

Limitation and Future Research Direction

Given the substantial evaluation, it should be recognized that the present study has some limitations. This research, for example, is based on empirical evidence supplied by Scopus, which is certainly, among the most reliable and accurate sources of scientific knowledge; even so, the pattern may change if documents from other scholarly databases are added, and journals from core collections are included. This researcher did not include data about the reviewers, including which organizations or critics evaluated the articles in this field. Consequently, additional research must be conducted in this area. In addition, the between-linkage selection of the journal, editor, and chosen reviewer may be suggested for further investigation.

Additionally, we chose a typical bibliometric technique as the primary way of analyzing the literature. Still, future research can use various alternative types of literature reviews in addition to bibliometrics. We considered only articles published in English. Publications in other languages have been eliminated from the search.

The Industrial Revolution 4.0, in conjunction with the COVID-19 issues, heightened the urgency of digitalization. Governments, business management, and groups should take proactive measures toward digitization. According to available research, the textile sector has numerous obstacles. Scholars should devote further attention to the impediments to digitalization in the future. Developing countries from Asia are manufacturers of textile goods (Gambhir, 2023). Measures are needed to overcome issues like labour-intensive production processes, weak infrastructure, and lack of sufficient funding and expertise for digitalization. How future innovation opportunities may be seized? Firms must prioritize game-changing breakthroughs and the establishment of a new technological paradigm. Existing research examines this topic via the lens of information system theory and case studies. The textile industry’s environment is extremely complex due to its reliance on worldwide supply chains.

Additionally, the industry must adhere to various local and global rules and principles, such as the Sustainable Development Goals. As a result, the external and internal environments complicate decision-making for policymakers, regulators, and industry participants. It is also meaningful to study internal and personal crises to address the sudden resignation of decision-makers in enterprises. To support decision-making, multicriteria decision analysis is required for a holistic investigation of the issue.

It is vital to highlight that while the bibliometric criteria analyzed in this study are quantitative, they should not be construed too broadly when evaluating a university’s research contributions, particularly in the very interdisciplinary topic of textiles. Each of these measures has distinct advantages and disadvantages. The quantity of publications is a proxy for production, not for quality. The citation count and h-index are calculated assuming that the frequency with which a work is cited in other papers reflects its excellence, which is not necessarily accurate. Suppose these bibliometric indicators are interpreted as absolute measurements of scientific outputs. In that case, a skewed picture will emerge as the quality of research contributions in terms of novelty, scientific rigour, and societal effect may not be reflected in their publication and citation counts.

Furthermore, study fields have significant output, citation patterns, and citation dynamics disparities. For instance, a researcher or textile school specializing in textile materials or chemistry will typically have more citations than those specializing in apparel product development. Furthermore, it cannot be stressed enough that while research papers are an essential indicator of scholarship, they are only one component of textile schools’ contributions. A full ranking of textile schools should consider additional factors such as education, economic and social influence, etc. Recognizing the limits of this study, it is advised that the textile educational and professional communities pursue a complete benchmarking process to promote the textile discipline’s professionalism (Li et al., 2021). As the primary restriction of this study, because we searched using keywords, thus cannot guarantee that all relevant articles have been included.

Additionally, the finding identifies research gaps that require further investigation. Most of the studies are case studies textile industry has a complex ecosystem that runs businesses in a competitive global supply chain environment. Also, the industry players are bound to follow sustainable development measures, several local and international regulations, guidelines, and policies. These external environment factors and the Firm’s internal issues associated with human resources, budget, innovativeness, culture, etc., make decision-making complex. Multicriteria decision analysis is recommended for policymaker’s and managers’ decision support towards digital organizational transformation, which is lacking in current literature. Moreover, this study serves as a foundation for future research by offering an overview of the digital transformation in the textile industry’s research trends.

Conclusion

The study between 2001 and 2022 examined the bibliometric evaluations of specialized research institutions in the textile, garments, apparel, fashion, and clothing industries. The study identified 20 leading journals in this research field by analyzing 458 Scopus-identified research publications, providing authors with valuable options for publishing their articles.

The study revealed that major research institutions in the United States and the United Kingdom played a substantial role in supporting textile research; notwithstanding being one of the chief garment-manufacturing nations, Bangladesh and Vietnam have fallen short behind in terms of research output, demonstrating significant challenges such as poor governance, insufficient training facilities, fiscal constraints, and insufficient infrastructure. This finding highlights the significance of strong programmes and government policies to impact the digitalization of the textile industry on a global scale.

In addition, the findings of this study provide crucial insights for various stakeholders, including managers, professionals, academics, decision-makers, and policymakers concerned with advanced manufacturing digitalization. Through a comprehensive literature review, the study revealed the dynamics of digitalization through emerging technologies over time. In addition, the report spotlighted the most influential institutions influencing the technological change in the textile industry.

The academic contributions discussed in the study highlight the need for additional research in the exciting field of the digital revolution of the textile industry. In addition, the study could be useful for notable authors, journals, research organizations, nations, trending keywords, and subject areas associated with the digital transformation of the textile industry. This information can aid researchers in identifying potential collaborators and relevant journals in which to publish their work.

Overall, this research contributes to the growing body of knowledge on emerging technologies and state-of-the-art research in the context of the textile industry. By shedding light on the existing research landscape and identifying areas for improvement and collaboration, the study provides a foundation for future research endeavours aimed at sustainable development and innovation in this sector.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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