Trade liberalization, destination-specific export expansion and product quality upgrading: New evidence from export quota removal of textile and clothing industries in China

ABSTRACT The global clothing industry is one of the largest contributors to environmental and social challenges; however, firms face increasing demands to integrate environmental, social, and governance (ESG) practices into their core strategies. Prior studies have linked ESG to efficiency and competitiveness, but their findings remain inconsistent, partly because they overlook the role of competitive intensity (CI). This study examines how ESG influences eco‐efficiency and market efficiency under varying market conditions using a two‐stage network data envelopment analysis (DEA) and hierarchical regression on 56 publicly listed clothing firms from 2019 to 2023. We complemented the regression results with a strategic mapping framework that classifies firms by eco‐ and market efficiency and prescribes ESG strategies tailored to their competitive contexts. The DEA results show that firms perform better in terms of eco‐efficiency than market efficiency, with significant variations across regions and firm sizes. The regression results show that the impact of ESG is conditional: In highly competitive markets, environmental ESG improves market efficiency, whereas in concentrated markets, governance ESG enhances eco‐efficiency. Regional analysis confirms these dynamics: European firms in concentrated markets prioritize governance for legitimacy, whereas American and Asian/African firms in fragmented markets leverage environmental initiatives for differentiation. By positioning CI as a boundary condition, this study clarifies the conflicting evidence in the ESG‐performance literature and offers practical guidance for tailoring sustainability strategies to the market structure.

Indonesia, as a major global textile exporter, faces substantial sustainability challenges due to its linear production model, which generates massive volumes of post-industrial polyester waste (PIPW). However, reliable data and recycling pathways remain critically lacking. This study quantifies the volume, composition, and textile-to-textile (T2T) recyclability potential of PIPW across Indonesia’s national textile and clothing production chain, employing a mixed-methods approach that integrates material flow analysis (MFA), site visits, and stakeholder interviews. The results indicate that 572 kilotonnes of PIPW were generated in 2023, with garment manufacturing identified as the most waste-intensive. Nineteen waste types were identified; 61% comprise fibre blends, which significantly constrain closed-loop recycling. A novel five-tier waste typology was developed to classify waste streams based on material characteristics, technological availability, and economic feasibility. The circularity map reveals that Indonesia is trapped in pseudo-circularity. Scenario analysis suggests that up to 184 kilotonnes of PIPW could be feasibly redirected towards higher-value chemical recycling. The research recommends mandatory source segregation, fiscal incentives, investment in chemical recycling infrastructure, and the integration of circular design into national standards. The study provides the first national-level MFA of PIPW in Indonesia and establishes an empirical baseline to advance T2T recycling in emerging economies.

This research aims to analyze the marketing communication strategies implemented by MVP.CO in building its brand image as a clothing industry brand in Bengkulu City. The study focuses on the application of the 7P marketing mix, product, price, place, promotion, people, process, and physical evidence as well as the SWOT analysis used to identify internal strengths and weaknesses along with external opportunities and threats. Employing a qualitative descriptive approach, data were collected through in-depth interviews, observation, and documentation. The findings indicate that MVP.CO effectively applies the 7P marketing mix by offering premium-quality products, competitive pricing, strategic distribution channels, and dynamic promotional activities supported by competent human resources and structured operational processes. The SWOT analysis further shows that MVP.CO possesses significant internal strengths and opportunities to enhance its competitive position, although it must address several weaknesses, particularly in digital promotion, facility development, and production capacity. Overall, this research concludes that integrated marketing communication strategies play an essential role in strengthening MVP.CO’s brand image within the creative clothing industry in Bengkulu.

ABSTRACT South Africa’s textiles, clothing, leather, and footwear (TCLF) industry plays an important role as an employer and is hence supported significantly by the government. In this study, we focus on analyzing growth opportunities within its value chain by identifying which products appear to hold the most potential for supporting the country’s developmental aims. We first determine the optimal product categories by applying the input-output product space approach. Given the importance and potential of the bovine leather industry, we then use a Location Determinant Framework to dive into the prerequisites for a successful further development of this particular sub-sector by means of an in-depth survey targeting 97% of the manufacturers. The majority of the industry states they have benefited immensely from industrial policy interventions. The smaller bovine leather manufacturers believe that with such an incentive made available to them again, they will attain growth similar to what the larger companies experienced.

Micro, Small, and Medium Enterprises (MSMEs) in the clothing industry are one of the key pillars of the economy, contributing significantly to Gross Domestic Product (GDP) and employment. However, MSMEs face considerable challenges related to market competition, shifting consumer trends, and fluctuating demand. Advances in data analytics and machine learning offer solutions to improve sales forecasting accuracy, thereby supporting more effective business strategies. This study aims to develop a sales forecasting model based on Long Short-Term Memory (LSTM) tailored to the characteristics of clothing MSMEs in Indonesia. The research was conducted at Ananda Kids MSME in Purbalingga, using 30,885 daily transaction records collected over 23 months. The dataset included product categories, sales volume, and revenue, which were further processed through normalization, handling of missing values, and the addition of seasonal features. The LSTM model was designed with 128 neurons and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the LSTM model achieved high accuracy for certain product categories. The “Set” and “Children’s Fashion” categories recorded MAPE values below 10%, demonstrating the model’s effectiveness in forecasting stable sales patterns. In contrast, categories with high volatility, such as accessories, produced larger prediction errors. These results highlight that data quality and sales pattern stability are crucial factors in enhancing model performance. Overall, the study demonstrates that the application of LSTM holds significant potential in supporting strategic decision-making for MSMEs through more accurate sales forecasting. Beyond its practical contributions for business actors, the study also provides a basis for the development of digitalization policies for the MSME sector in Indonesia.

Abstract India's textile and clothing industry plays a significant role in the nation's social, cultural, and economic structure. The textile industry is in a unique position to serve as both a growth engine and a tool of inclusive development as India moves closer to its national goal of becoming a Viksit Bharat (Developed India) by 2047. With a workforce of more than 45 million people and a whole value chain that extends from fibre production to finished clothing, the industry has enormous potential to boost employment, exports, sustainability, and regional development. In line with sustainability and innovation imperatives, this paper offers a strategic roadmap to reach USD 100 billion in textile and apparel exports, analyses global competitiveness, assesses policy interventions, and provides a thorough analysis of India's textile ecosystem. In India's economic, social, and cultural framework, the textile and apparel industry plays a crucial role. The industry is anticipated to make a substantial contribution through job creation, export growth, sustainability leadership, and technology-driven transformation as India moves closer to its national goal of becoming a Viksit Bharat (Developed India) by 2047. This study examines how India's textile industry is changing in response to recent shifts in international trade, particularly the US lowering its tariffs on Indian clothing and textiles from previously high levels to about 18%. India's export competitiveness in the US market has significantly increased as a result of this change. Newly signed Free Trade Agreements (FTAs) with the UK and the EU offer diversified and privileged market access. The study makes the case, using secondary data and policy analysis, that India can increase its textile and apparel exports from the current USD 37–38 billion to USD 100 billion by 2030–2035 through a balanced strategy that makes use of improved US market conditions, increases exports to the EU and the UK, and boosts domestic consumption. According to the paper, India's 2047 development vision places the textile industry at the forefront of inclusive, sustainable, and design-led growth. Keywords: Textile and Apparel Industry; Viksit Bharat 2047; US Tariffs; Free Trade Agreements; Export Growth; Sustainability; Design Education, Viksit Bharat, PM MITRA, PLI Scheme

The clothing and textile industry is under increasing pressure to comply with European sustainability directives, including the European Strategy for Sustainable and Circular Textiles, the Circular Economy Action Plan, and the revised Waste Framework Directive, effective October 2025. While global interest in sustainable textile practices grows, limited research has examined clothing consumption and disposal behaviors in Greece, particularly through the lens of practice theory. This study addresses that gap by exploring the dynamics of these practices using a structured questionnaire distributed online via Google Forms in 2024 with 250 valid responses. Chi-square (χ2) tests and regressions analyses were used to assess associations among certain categorical variables. Our findings reveal that older consumers tend to spend more on clothing and show a preference for fast fashion. Frequent shoppers also lean toward fast fashion, yet they demonstrate greater concern for material composition. Higher sustainability awareness is associated with a preference for purchasing fewer garments or opting for higher-quality items. Notably, discomfort with recycled materials predicts reluctance toward industrial recycling and reinforces the tendency to choose durable clothing that lasts longer.

The rapid integration of freemium AI tools is shaping how clothing industries streamline their operations and engage with customers online. In emerging markets like the Philippines, SMEs face several challenges due to limited resources, consumer demands, and the fast pace of digitalization. This study investigates the effects of freemium AI on organizational efficiency and customer engagement strategies among all clothing e-commerce industries within Bulacan, with the use of Dynamic Capabilities Theory, it examines how enterprises sense opportunities, utilize digital resources, and transform processes. A qualitative research design was employed, utilizing in-depth interviews with 15 registered clothing business owners with at least three years of operational history. Findings reveal that free AI tools improve centralized management, targeted advertising, content creation, and communication, which significantly enhance both operational efficiency and customer satisfaction. Despite challenges, the study highlights that free AI tools serve as scalable, accessible, and democratizing resources for digital transformation. The study concludes that strategic adoption of free AI tools not only strengthens business performance but also promotes inclusive and sustainable growth in the global clothing e-commerce.

While technology innovation and learning research draw considerable interest, most studies neglect low- and medium-tech (LMT) industries, which comprise over 70% of the GDP in emerging economies like Brazil. This study bridges that gap by analyzing technological capability accumulation and learning modes within the Brazilian clothing industry. Based on qualitative, longitudinal research with two Brazilian clothing firms, the findings reveal similar trajectories. Both firms advanced from production technological capability (Level 1) to intermediate innovation (Level 3) in management and product. In production organization and equipment, they reached basic innovation (Level 2), with varied speeds of accumulation. Firms primarily relied on the DUI (Doing, Using, and Interacting) learning mode, largely underutilizing STI (Science, Technology, and Innovation) mechanisms. The study underscores the significance of effectively managing learning modes by employing both STI and DUI learning in tandem to facilitate the development of innovation activities and, consequently, technological capabilities.

This research aims to analyze the impact of product quality on increasing market share in the clothing manufacturing sector in Baghdad, Iraq. The study explores the factors influencing consumers' evaluation of clothing quality and how this evaluation affects their purchasing decisions, thereby influencing the market share of local companies. The importance of this study arises in light of increasing competition from imported products, which often offer higher quality or lower prices, as well as the economic challenges facing the local market. This makes it critically important to examine the relationship between quality and market share. The research problem highlights that local companies face significant difficulties maintaining their competitive advantage due to the superiority of some imported products in terms of quality or price. To achieve the research objectives, a sample of 384 consumers was selected, and a questionnaire was used as the primary data collection tool. This questionnaire was developed based on a set of validated scales, with its reliability and validity confirmed through established statistical tests. Data were analyzed using SPSS, which facilitated systematic data entry and processing. The study yielded several significant findings, most notably that understanding customer needs and meeting their expectations are critical to enhancing market share. The results also showed that product quality is a fundamental factor contributing to increased sales volume in the local clothing industry in Baghdad.

The problem of textile industry waste has become increasingly relevant. Recycling clothing industry waste to build acoustic panels is one of the most popular and relatively inexpensive ways to use clothing industry waste. We see a lack of information on the acoustic properties of panels made from waste from the clothing industry. The aim of this research is to determine the acoustic properties of a wide range of clothing industry waste recycled into acoustic panels. The acoustic panels were made from clothing industry waste, a different composition of textile and paper residues generated during digital printing processes. We see that panels made from square-cut scraps knitted and woven fabrics, and from yarns and fibers have relatively good acoustic properties. The panel made only of paper had good acoustic properties, the production of panels from paper and textile resulted in similar acoustic properties. Analyzing the acoustic properties of the double specimen, it was found that testing the double-layered panels, the insertion loss is better; by tripling the samples, it was found that although the acoustic properties improved, they were only marginal. Cellulose fiber boards were characterized by significantly higher air resistance. The air resistance of the boards made from fabric scraps was lower.

Cross-media dynamics and prioritized risks of PFAS in textile-impacted environments: using geospatial machine learning.

Customers' purchasing habits have significantly improved in Odisha due to the establishment of organized retail in malls and brand outlets. This trend has gained popularity primarily due to consumers' increased purchasing power and interest in fashion. The impulsive purchasing habits of female consumers in the clothing industry are highlighted in this article. The present study mainly clarifies the different elements that affect a woman's decision-making process, particularly in the clothing sector. Data is gathered using quantitative methods and pre-established scales. Regression analysis was used to determine the impact of salesperson influence, income level, credit card, store environment, and promotional strategy on a customer's impulsive purchase decision in the clothing industry.

Exposed to unprecedented internal as well as external levels of uncertainties, supply chains have become much more vulnerable than ever before. Although artificial intelligence holds transformative potential to create resilient supply chains, its adoption remains intricate particularly within the industrial zones of developing economies. Previous studies have indicated that there is paucity of research in connection with artificial intelligence and supply chain risk management. Collecting data from twenty-six participants, this qualitative study revolves seeks to explore the impediments which hinder Pakistani textile and clothing firms from adopting artificial intelligence to control supply chain disruptions. Both thematic and content summative analysis techniques were used to attain interpretative depth as well as empirical substantiation. Lack of general understanding and trust in artificial intelligence, digital infrastructure deficits, high cost of investment, scarcity or substandard quality of operational and supply chain data and confidentiality and security issues are the core constraints The outcomes of the investigation are immensely valuable in developing pragmatic strategies to leverage artificial intelligence effectively in order in addressing supply chain risks.

The clothing industry is rapidly transitioning from traditional mass production to multi-product, small-batch manufacturing systems, addressing increasingly diverse consumer needs. This study proposes an integrated scheduling optimization model that spans the entire apparel production process, from material inbound to packing, reflecting the complexity of modern ODM (Original Design Manufacturing) and OBM (Own Brand Manufacturing) environments. Leveraging real production data and FlexSim simulation, the model incorporates productivity dynamics through learning curves and applies Mixed-Integer Linear Programming (MILP) and Greedy algorithms for scheduling optimization. Simulation and real-case analyses demonstrate that dynamic allocation and process balancing enhance line utilization, decrease makespan, and increase production efficiency. These results suggest the proposed model offers practical and effective strategies for scheduling optimization and digital transformation in high-mix, low-volume apparel manufacturing settings.

Unisa emphasises responsive student-centredness, recognising the role of an engaged student for future career success and lifelong learning. This research adopted a learner-centred approach, integrating point cloud scans from a 3D full-body scanner into a module for third-year fashion and textile undergraduates. Traditionally, the fashion sector designs apparel for an hourglass woman’s body type. However, South African researchers report that this shape does not represent the majority of women in the country. This research aimed to assess the competence of undergraduate fashion students in visually assessing women’s body shapes to develop cognitive skills for the workplace. Twenty-nine students visually assessed 31 3D scans provided as 2D black and white images. The experts and students identified the dominant shapes of spoon, hourglass and rectangle. In addition, the students reported the triangle, inverted triangle and diamond. Of the 31-point cloud images, 52% of the student’s classifications correlated with those of the clothing industry experts; 48% of the students experienced challenges in accurately assessing asymmetrical body shapes with varying frame sizes across different classifications. The findings suggest that fashion students demonstrate a degree of skill in visually assessing women’s body shapes. Recommendations include using 3D printed models and interactive 360° videos to enhance cognitive abilities. Integrating 3D scanning technology into the curriculum offers sustainable benefits by streamlining processes, improving digital competence and standardising body measurements to SANS 8559-1. This integration not only develops essential workplace skills but also educates students on minimising waste throughout the design-to-garment construction process, thereby aligning academic instruction with industry norms.

Background of the study: The rise of collaborative consumption in the clothing industry and technology developments have made online fashion rentals both acceptable and accessible, increasing consumer desire for more affordable and eco-friendly fashion products. According to the current study, expectancy-value is crucial in the formation of behavioral beliefs that could lead to positive attitudes as well as intentions for collaborative fashion consumption. Purpose of the Study: The central objective of the study is to find the mediating role of CFC between different factors and economic, social and environmental wellbeing. Theoretical Framework & Methodology: The study uses a quantitative methodology, and a conceptual framework based on Capability approach theory. Through a survey, information was obtained as of 337 fashion consumers who rent articles from rental stores and social media. SEM is used to know the relation among the factor influencing CFC and wellbeing. Key Findings: According to the findings, CFC mediates between face, embarrassment, pride, social rejection and economic, environmental and subjective wellbeing. Practical Implications: Since consumer awareness is the key to success of fashion renting as well as its approval in the Pakistan’s market for the customer’s communication might be crucial in building confidence about online fashion leasing, this study offers recommendations for both renters and rentals.

Given the importance and necessity of branding in the clothing industry, this research was conducted with the aim of developing clothing brands in online businesses. This research is applied in terms of purpose and descriptive-survey in terms of method, and it has adopted a mixed (qualitative-quantitative) approach. In the first step of qualitative research, the method used was meta-analysis, and the statistical population included studies, books, and articles from 2014 to 2024 in the field of clothing brand development in online businesses, including works published in reputable scientific and promotional journals in this period. In the second step, the quantitative research method was used using confirmatory factor analysis and the statistical population included 238 senior and middle managers of clothing manufacturing companies in Mazandaran, of which a sample of 147 was determined using the Cochran formula. In the qualitative stage, meta-analysis was used, and in the quantitative stage, PLS structural equation modeling was used. The findings identify five main dimensions: brand strategy, design and visual variables, competitive advantage, content production and feedback, and communication, each of which includes specific components. The relationships between dimensions and components were verified through path analysis, validating the proposed model. The overall fit of the model, calculated using the GOF formula, indicates a strong fit. This study provides practical insights into the strategic development of apparel brands in online markets, enhancing brand competitiveness, and customer engagement.

Managing Quality Improvement in the Soviet Clothing Industry in Perm City in the 1960s and early 1970s