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This paper aims to examine and compare the export performance and competitiveness of Indian and Chinese textile and clothing industry in post-multifibre arrangement (MFA) era.

Balassa’s revealed comparative advantage Index is used to assess the competitiveness of Indian and Chinese textile and clothing exports.

The results indicate that China’s textiles and garments sector holds a greater proportion of the global market compared with India. India has a robust comparative advantage in silk, carpets and cotton post-MFA. Vegetable textile fibers, paper yarn and woven fabrics of paper yarn are also competitive. China had a strong comparative advantage in silk and fabrics; special woven fabrics, tafted textile fabrics, lace, tapestries, trimmings and embroidery in 2005. China also recorded comparative advantage in silk, man-made filaments: strip and the like of man-made textile materials, fabrics; special woven fabrics, tafted textile fabrics, lace, tapestries, trimmings and embroidery and fabrics; knitted or crocheted in 2021.

This study’s results and recommendations could assist the Indian and Chinese Governments develop policies to upgrade their garment industries.

Though vast literature reviews are available for textile and apparel export performance in India and China separately, there are few studies on comparisons. This study is a significant attempt to evaluate India and China’s competitiveness in the global market.

The apparel and textile sector poses a significant environmental challenge due to its substantial contribution to pollution in the form of air, water and soil pollution. To combat these issues, the adoption of sustainable practices is essential. This study aims to identify and analyse the barriers that hinder the progress of sustainability in the apparel and textile industry. By consulting experts in the field, critical barriers were identified and given special attention.

To achieve the research objective, an integrated approach involving Interpretive Structural Modelling (ISM) and fuzzy MICMAC decision-making techniques was employed. The results were further validated through the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method.

The findings highlight that barrier related to clothing disposal, inadequate adaptation to modern technology, challenges affecting sector efficiency and issues related to fashion design are crucial in influencing the remaining six barriers. Based on the outcomes of the DEMATEL method, a comprehensive cause-and-effect diagram was constructed to gain a deeper understanding of these challenges.

This research provides valuable insights for policymakers and stakeholders in the apparel and textile industry. It offers a strategic framework to address and overcome sustainability barriers, promoting the development of a more environmentally responsible and resilient sector.

The purpose of this research is to conduct an in-depth investigation of the barriers apparel and textile sectors. It is feasible that both the management team and the medical experts who provide direct patient care could benefit from this research.

The purpose of the study is to examine the obstacle in doing business and determinants of credit adoption by the textile enterprises in India.

The study is based on World Bank’s Enterprises Survey, there are 571 enterprises involved in textile business. The enterprises survey has response on wide range of business obstacles which are categorized under three broad categories, namely, access to resource, business regulations and market externalities. Chi-square test and analysis of variance (ANOVA) have been used to examine the significant difference among firm’s profile and perceived business obstacles across the firm size. Furthermore, binary logistic regression model has been applied to explore the determinants of credit adoption by textile enterprises.

A statistically significant difference has been found in size of firms and legal status nature of establishment, gender of top manager, main product market and credit adoption from financial institutions. Majority of small- and medium-sized enterprises (SMEs) are sole proprietorship firm while large enterprises are limited partnership firms. Similarly, large enterprises have relatively more female as a top manager and international market for their product. ANOVA reveals equal degree of obstacles in doing textile business across the firm size. The logistic regression coefficient and marginal effects reveal that firm size, main market,gender of owner, number of establishment in the firms positive and significantly affects the credit adoption by 3 textile enterprises.

The study has some policy implications for various stakeholders such as textile business managers and promoters, government, investors and bankers for entrepreneurship development in textile sector. The study suggests that the government should incentivize small- and medium-sized businesses to increase their exports. The results show that despite government efforts to finance SMEs, fewer SMEs are receiving both short- and long-term credit. To help SMEs in the textile industry overcome financial difficulties and expand their main product market to both domestic and international levels, a soft loan should be provided based on the characteristics of textile enterprises.

The present study suggests the evidence-based understanding of textile business environment. The value and uniqueness of this study is to explore an ease of business textile sector using comprehensive enterprises survey data of World Bank.

This study aims to explore the mediating role of self-efficacy and the relationship between job satisfaction and employee commitment.

The study is designed based on social cognitive theory. To collect the data, survey method is used in the present study. Questionnaires were distributed to the 75 randomly selected textile industry registered and located in Bengaluru city, India. Employees of 71 organizations participated in the survey. Out of 700 employees who received the email survey, 452 valid responses were considered for the data analysis. To examine the defined research hypothesis, a structural equation model is used.

The mediating analysis explored that the direct effect is 0.700, the indirect effect is 0.1730 and the total effect is 0.8731; it indicated that self-efficacy mediates the relationship between job satisfaction and employee commitment. Research also reveals that there is a positive relationship between self-efficacy, employee commitment and job satisfaction of employees working in the textile industry. The proposed measurement model statistics are as follows: CMIN = 2.322; df = 49; GFI = 0.958; AGFI = 0.934; NFI = 0.943; RFI = 0.923; IFI = 0.967; TLI = 0.955; CFI = 0.966. All these indices were nearer to unity.

The research findings provide insights to the management, practitioners and employers about the status of job satisfaction, self-efficacy and commitment of employees in textile organizations which will help make the strategies to increase the overall performance of the organization by enhancing the levels of job satisfaction, self-efficacy and commitment of textile industry employees.

To the best of the authors’ knowledge, this is the first study that tests the relationship among self-efficacy, job satisfaction and the mediating effect of self-efficacy of employees in Indian textile industries.

The purpose of this paper is to present the perception of the textile industry stakeholders (manufacturers, wholesalers, retailers, consumers and tax professionals) on India’s new goods and services tax (GST) system and find whether the introduction of GST has made doing business easier or not.

The researchers used interviews and surveys to capture the perceptions of the textile industry stakeholders at Surat, a major textile hub in India. To econometrically verify the perceptions, the researchers used a logit regression model.

The researchers found that the provision of monthly tax filing has increased textile businesses’ dependency on tax professionals, which increased business costs. Also, the GST system has made tax compliance easier and is user-friendly. However, tax refund-related issues are a significant factor that negatively impacts the ease of doing business post-GST.

The findings of the research shall be helpful for the GST Council of India and policymakers to understand the problems faced by the textile businesses and cater to their problems.

To the best of the authors’ knowledge, this study is original as none of the available studies captures the perception of all the textile industry stakeholders, namely, manufacturers, wholesalers, retailers, consumers and tax professionals, on the GST system applying econometric techniques to validate the perceptions.

The COVID-19 pandemic has caused widespread disruptions to global industries, with the textile sector in South Asia being particularly hard hit. While previous studies have focused on the performance of textile sectors in individual countries, there is a gap in the literature on the comparative impact of the pandemic on the textile industry in South Asian nations. This study aims to fill this gap by investigating the performance of the textile sector in South Asian countries and identifying best practices for overcoming the pandemic’s adverse effects.

Using a comparative approach, this study analyzes the impact of COVID-19 on the performance of the textile sector in Pakistan, India and Bangladesh.

Our findings reveal that COVID-19 significantly negatively impacts the textile industry in Pakistan and India. However, Bangladesh has shown effective practices to support the textile industry and mitigate the pandemic’s adverse effects.

The findings of this study hold considerable implications for legislators, leaders, investors and supply chain management professionals operating within the South Asian textile sector. This research has the potential to inform policymakers in formulating strategies to facilitate the textile sector’s resilience during emergencies like the COVID-19 pandemic.

This paper provides significant theoretical additions to the current body of literature regarding the impact of COVID-19 on the textile sector in South Asia. The research uses the global value chain (GVC) theory as a theoretical framework to enhance understanding of the impact of global supply chains and interdependencies on the textile sector in the region.

The textile industry contributes 2 and 3% to the global and Indian Gross Domestic Product (GDP), respectively. India supplies a quarter of global cotton yarn. Yet, most yarn manufacturing companies use outdated methods and lack organisational skills and strategies. Improvement in processes in India could significantly help the industry worldwide.

The variables that influence the performance of the system were identified. Their interrelationships and impact were identified from the employees in the chosen case study, a yarn manufacturing industry. A System Dynamics (SD) approach was employed to study the benefits of implementing 5S lean strategies. The impact of each variable on various performance measures such as throughput, Work In Progress, processing time, waiting time, idle time, over-processing and scraps was analysed.

Improvement in outcomes reflected an enhanced adoption of leanness in the industry. The decision-makers can utilise this study to optimise the necessary parameters in the system and attain the desired productivity levels. Better resource management and reduced processing time helped increase the despatch rate by 9.735% and decrease the WIP by 23.01%. Time management helped to reduce the inventory, idle time and waiting time. Over-processing, defects and scraps were minimised, indicating a shift towards lean.

This study pioneers the use of SD simulation models for optimising yarn manufacturing using lean strategies. Improvement in performance measures by integrating these strategies opens avenues for future research using multiple approaches to address a problem.

Implementing 5S lean principles and simulations enhances productivity, reduces waste and optimises resource management for the yarn manufacturing industry. Decision-makers can employ simulation to witness the outcomes of their changes without investing cost and time and without associated implementation risks.

The use of a simulation model to witness the benefits of incorporating lean strategies in yarn production has not been explored. This approach could help the managers and policymakers understand their existing system's shortcomings and critical areas that require improvement.

The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

This study aims to assess the efficiency of managing working capital in 1,388 Indian manufacturing firms from 2008 to 2019 and investigate the effects of firm-specific and macro-level determinants on working capital management (WCM) efficiency.

The current study accommodates a slack-based measure (SBM) in data envelopment analysis (DEA) for computing WCM efficiency. Further, we implement a panel data fixed-effects model that controls for heterogeneity across firms in determining the relationships of selected variables with WCM efficiency.

The results highlight that manufacturing firms operate at around 50 percent efficiency, which is constant throughout the study period. Furthermore, among the selected variables, yield, earnings, age, size, ability to create internal resources, interest rate and gross domestic product (GDP) significantly affect WCM efficiency.

Instead of the traditional models used for assessing efficiency, the SBM-DEA model is unit-invariant and monotone for slacks, implying that it can handle zero and negative data, which overcomes the incapability of prior DEA models. Hence, this provides accurate efficiency scores for robust analysis. Additionally, this paper provides a holistic working capital model recognizing firm-specific and macro-level determinants for a more explicit estimation of the relationship between WCM efficiency and the selected determinants.

The primary objective of this research is to determine critical success factors (CSFs) that enable textile enterprises to effectively implement Kaizen, a Japanese concept of continuous development, particularly during disruptive situations. The study aims to provide insights into how Kaizen is specifically employed within the textile sector and to offer guidance for addressing future crises.

This study employs a structured approach to determine CSFs for successful Kaizen implementation in the textile industry. The Triple Helix Actors structure, comprising business, academia and government representatives, is utilized to uncover essential insights. Additionally, the Matriced Impacts Croises-Multiplication Applique and Classement (MICMAC) analysis and interpretative structural modeling (ISM) techniques are applied to evaluate the influence of CSFs.

The research identifies 17 CSFs for successful Kaizen implementation in the textile industry through a comprehensive literature review and expert input. These factors are organized into a hierarchical structure with 5 distinct levels. Additionally, the application of the MICMAC analysis reveals three clusters of CSFs: linkage, dependent and independent, highlighting their interdependencies and impact.

Major contribution of this study is understanding how Kaizen can be effectively utilized in the textile industry, especially during disruptive events. The combination of the Triple Helix Actors structure, MICMAC analysis and ISM provides a unique perspective on the essential factors driving successful Kaizen implementation. The identification of CSFs and their categorization into clusters offer valuable insights for practitioners, policymakers and academia seeking to enhance the resilience and sustainability of the textile industry.