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The purpose of this paper is to analyze the twin transition of textile firms operating in an industrial district. The twin transition comprises two interconnected but distinct processes: the sustainable transition and the digital transition. The study specifically considers sustainability goals in terms of the triple bottom line and digitalization as the adoption of Industry 4.0 (I4.0) technologies. The study aims to understand how the characteristics of Italian districts influence the choices related to the twin transition and how it affects textile industrial firms.

The author conducts a multiple-case study involving five firms of industrial textile district of Prato.

The results show the relationship between the characteristics of the district, such as fragmentation of the supply chain, lean manufacturing, territorial proximity and attachment to origins and traditions and the sustainable goals and I4.0 technologies adoption. Moreover, the study proposes a framework for twin transition. Market and technology drive the process in which sustainability represents the aim, I4.0 serves as enablers and the relevant outcome is the implementation of the business model innovation.

These findings offer valuable insights for textile firms, policymakers and stakeholders seeking to navigate the complexities of twin transition.

The study contributes to the broader topic of twin transition. In particular, it links the particular context represented by the industrial district in which the textile firms operate to their conduct, and the two interconnected and distinct processes, sustainable transition and digital transition, with the business model innovation topic.

The widespread use of washing machines in textile dyeing and finishing boosts product quality while leading to significant wastewater production. This wastewater poses environmental risks due to the textile industry's high pollution levels and water consumption. Sustainability hinges on minimizing water usage and treating wastewater for reuse. This study employs Matlab R2020a and Python 2023 to model experimental designs for treating textile production wastewater using the Fenton oxidation method, aiming to address sustainability concerns in the industry.

The Fenton oxidation process's efficacy and optimal operating conditions were determined through experimental sets employing the Box–Behnken design. Assessing machine learning algorithms on the data, Matlab R2020a utilized an artificial neural network (ANN), while Python 2023 employed support vector regression (SVR), decision trees (DT), and random forest (RF) models. Evaluation of model performance relied on regression coefficient (R2) and mean square error (MSE) outcomes. This methodology aimed to refine the Fenton oxidation process and identify the most efficient parameters, leveraging a combination of experimental design and advanced computational techniques across different programming platforms.

The study identified optimal conditions: pH 3, Fe+2 concentration of 0.75 g/L, and H2O2 concentration of 5 mM, yielding 87% COD removal. The Box–Behnken design achieved a high R2 of 0.9372, indicating precise predictions. Artificial neural networks (ANN) and support vector regression (SVR) exhibited successful applications, notably achieving an R2 of 0.99936 and low MSE of 0.00416 in the ANN (LOGSIG) model. However, decision trees (DT) and random forests (RF) proved less effective with limited datasets. The findings underscore technology integration in treatment modeling and the environmental imperative of wastewater purification and reuse.

This study, in which water use and wastewater treatment are evaluated with technological integration such as machine learning and data management, reveals how to contribute to targets 6, 9, 12, and 14 within the scope of UNEP 2030 sustainable development goals.

The prospect of Sub-Saharan Africa (SSA) as an apparel-sourcing base for US fashion companies has been a growing heated debate among academia, industry practitioners and policymakers. This study aims to evaluate SSA countries’ readiness to serve as an alternative sourcing destination to Asia for US fashion companies, focusing on comparing the similarities and differences of US apparel imports from these two regions at the product level.

This study was based on a statistical analysis of detailed product features and assortment information of thousands of apparel items at the stock-keeping unit level sold by US retailers between January 2021 and December 2023.

US fashion companies seemed to leverage SSA countries as suppliers of “niche products,” such as those relatively simple and basic apparel categories containing African cultural elements and targeting the luxury and premium market segment. However, the range of apparel products available for US fashion companies to source from the SSA region remained significantly more limited than those from Asia. Also, US apparel imports from SSA countries were primarily made of cotton and polyester, with less use of other fiber types, including nylon, rayon, viscose, wool and those made from recycled textile materials.

The study’s findings provided fresh insights into why US fashion companies sourced from SSA countries and the specific types of products they were sourcing, going beyond existing studies based on macro trade statistics. The results also deepened the understanding of SSA countries’ competitiveness as an apparel-sourcing destination and their potential to serve as an alternative to sourcing from Asia, particularly from a unique product perspective.

The study aimed to investigate the impact of mechanically recycled cotton fibres from pre-consumer waste, blended with virgin cotton at varying ratios, on the mechanical and fastness properties of knitted fabrics.

Single jersey fabrics were produced using open-end rotor yarns with two different yarn counts, which were made from cotton blends obtained at three different mechanical recycled cotton blend ratios. The fabrics were then comparatively analysed for pilling resistance, bursting strength, dimensional stability, and fastness to perspiration, water, and rubbing. The investigations included fabrics made from 100% virgin cotton to determine the impact of mechanically recycled cotton fibres on the final fabric properties.

It was observed that using MR-CO at different ratios generally produced results similar to the usage properties obtained when using virgin cotton.

The study looked in detail at the effect of using mechanically recycled cotton (MR-CO) on the yarn properties and the mechanical and colour fastness properties of the fabrics produced using them. It was found that MR-CO has the potential to be an alternative fibre source to virgin cotton, not only mechanically but also in terms of colour fastness. Previous studies have commonly used MR-CO in fixed ratios or by incorporating various fibres into the blend. However, in this study, we determined the suitability of fabrics for their intended use by gradually increasing the MR-CO blend ratios and more clearly assessing the impact of MR-CO on fabric properties.

To comprehend the influence of green HR practices (GHRM) on employee outcomes, strategic HRM researchers are gradually adopting an employee-centric approach, a subject that has sparked recent discussions among scholars in the field of green HR. These scholars have emphasized the need for studies that shed light on the reasons behind the differences in employees' perceptions of GHRM. To address this concern, we investigated (1) supervisors perceived GHRM (SUP-GHRM) and subordinates perceived GHRM (SUB-GHRM) as the fundamental source of variation in employee eco-friendly behavior and green performance, (2) the association between SUP-GHRM and SUB-GHRM, (3) the mediation role of SUB-GHRM toward green performance and eco-friendly behavior, and (4) the moderation of perceived HRM system strength (HRMSS) on supervisor-subordinate perceived GHRM.

Applying a survey approach, we collected data from 217 supervisors and 624 subordinates from Large-Scale Manufacturing Organizations in the Textile sector of Pakistan. Since the data is hierarchical, we applied the Hierarchical Linear Model (HLM) and bootstrapping techniques to examine the hypothesized relationship.

The results of HLM revealed that (1) the SUP-GHRM and SUB-GHRM were key in determining green performance and eco-friendly behavior, (2) the SUP-GHRM significantly influenced SUB-GHRM, (3) the SUP-GHRM indirectly affected the eco-friendly behavior and green performance through SUB-GHRM, (4) the HRM system’s strength positively moderated the association between the SUP-GHRM and SUB-GHRM.

The corporations need to ensure that both supervisors and subordinates have a consistent understanding of GHRM practices and foster positive relationships between them. It is also important for companies to actively enhance supervisors' knowledge of GHRM and encourage them to effectively communicate the company’s GHRM practices to their subordinates. This is vital for improving employee job-related outcomes. Furthermore, corporations should emphasize developing a strong HRM system designed to create a climate where employees understand the behaviors and responses that are valued and recognized, leading them to perceive situations in line with their managers.

This study suggests SUP-GHRM and SUB-GHRM as critical factors that influence eco-friendly behavior and green performance, and HRMSS is key to aligning the perception gaps between subordinates and supervisors about what GHRM is in place in their organization, which is empirically analyzed in a developing country context.

This study investigates and explores sustainable fashion based on social attitudes toward culture and sustainable fashion effects in sub-Saharan Africa (SSA), based on environmental knowledge and consumer satisfaction initiatives. It explicates sustainable fashion on the sustainable development agenda in addressing the gap of cultural value, environmental knowledge and sustainable fashion in SSA.

Using a quantitative approach, the study employed a web-based online cross-sectional survey to extract tangible information from 620 participants from SSA. The study integrated theory of planned behaviors (TPB) model and hypotheses. A structural equation model (SEM) was used to test all proposed hypotheses.

The results show that low environmental knowledge, influenced by geographical and cultural differences, affected fashion value, as which is predictively significant for sustainable fashion. However, attitude and cultural value results found statistical significance for consumer satisfaction in sustainable fashion. Furthermore, mediation was attained between consumer behavioral and environmental knowledge of sustainable fashion. The study recommends government policies on educational awareness and textile regulations for environmental garbage disposal possible harmful effects of climate change and finally, designing innovative initiatives for environmentally friendly fashion.

This study examines the environmental and social attitudes as well as behavioral effects, of an ecosystem that would most likely have a short life period, eliminate disposal dumps and foster an environmental control policy. Consequently, the study’s conceptual model and extended TPB contribute to how sustainable fashion supports environmental knowledge, consumer attitudes and cultural behaviors in fashion among Sub-Saharan Africans.

Through the tracer fiber method, we strive to more accurately obtain the hook degree, straightening degree, percentage and other characteristic indexes. In order to intuitively represent the hook state from sliver to yarn, and feed back production information in combination with quality test.

Taking the cotton fiber as an example, the hooked fibers were studied by using the tracer fiber method. Tracer fibers were made from cotton-type viscose fibers. Tracer fibers and combed cotton fibers were uniformly mixed for many times and used to produce the card sliver, semi-drawn sliver, drawn sliver, roving and yarn. With the help of ZF-20D ultraviolet analyzer, geometric parameters of hooked fibers were measured, and characterization indexes were calculated. And hook indexes and quality indexes were compared.

By redefining and reclassifying hooked fibers, the change of hooked fibers in the process was tracked and characterized carefully. Some hooks in card sliver are straightened but not eliminated, and will form longer zero-angled hooks in the subsequent process. The straightening degree and number of zero-angled hooks affect the evenness CV mainly.

The characterization of hooked fibers is important for reducing hooked fibers and spinning high quality yarns. There is no uniform standard for the characterization of hooked fibers at present. Most studies are about relationship between process and hook in carding and drawing. There is no research on hooked fibers in the whole spinning process. In the paper, hooked fibers were redefined and reclassified, the change of hooked fibers in the process was tracked and characterized carefully.

The COVID-19 outbreak has been accompanied by a massive “infodemic”, characterized by an overabundance of information, both accurate and inaccurate, making it hard for people to find trustworthy sources and reliable guidance. This study aims to investigate how the COVID-19 infodemic (i.e. information overload and untrustworthiness) influences consumers’ emotions (i.e. fear, anxiety and hope) by shaping their cognitive appraisals of the pandemic (i.e. perceived risk and uncertainty). Additionally, this study also investigates how individual differences (i.e. COVID-19 involvement and infection experience) impact their emotion formation process.

Data were collected from 815 US consumers aged between 18 and 65 in November 2021 via an online survey. Structural equation modeling and multi-group comparison from AMOS 23 were used to test the proposed relationships.

Information overload increased one’s perceived risk and perceived uncertainty of COVID-19, which, in turn, structured the emotional states of fear, anxiety and hope. Information untrustworthiness had a significant impact on risk perception, which led to an increased feeling of fear. Additionally, individuals’ COVID-19 involvement and their infection experience with the coronavirus were found to moderate the cognitive appraisal process in developing emotions.

This study offers insights into the relationships between the information landscape and cognitive appraisals regarding health crises, specifically the COVID-19 pandemic. Not only enriching emotional well-being literature, it also lends managerial implications for effective communication strategies in global health emergencies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2023-0616

This study aims to develop a robust predictive model for anticipating financial distress within Pakistani companies, providing a crucial tool for proactive economic turbulence management.

To achieve this objective, the study examines a comprehensive data set comprising nonfinancial firms listed on the Pakistan Stock Exchange from 2005 to 2022. It investigates 23 financial ratios categorized under profitability, liquidity, leverage, asset efficiency, size and growth.

The study reveals that financial ratio indices are more effective in forecasting financial distress compared to individual ratios. These indices achieve impressive accuracy rates, ranging from a robust 93.90% in the first year leading up to bankruptcy to a commendable 73.71% in the fifth year. Furthermore, the research identifies profitability, liquidity, leverage, asset efficiency, size and growth as pivotal indicators for financial distress prediction.

This research underscores the utility and practicality of financial ratio indices, offering a comprehensive perspective on risk assessment and management. In conclusion, this pioneering study provides valuable insights into financial distress prediction, highlighting the enhanced information capture made possible by financial ratio indices. It equips stakeholders in the Pakistan Stock Exchange with an effective means to proactively address financial risks.

Capital markets are the backbone of an economy. COVID-19 has created an unacceptable and unpredictable economic environment worldwide, resulting in a significant setback for securities exchanges. India also experienced two waves of this pandemic, which led to a significant downturn in the capital market.

Researchers have endeavored to study the impact of the first and second waves of COVID-19 on the performance of various sectoral stocks in India. The performance of selected sectoral indices of the Bombay Stock Exchange was compared with the market performance of the S&P BSE 100. An event study was conducted to analyze the normal return, abnormal return (A.R.), and t-statistics of A.R. for various sectoral stocks. In addition, the abnormal returns of sectoral stocks between the first and second waves of COVID-19 in India were compared

The results of the tests showed heterogeneous A.R. between different sectors in both the first and second waves of COVID-19 in India. Positive investor outlook and government financial support programs for various sectors helped them recover from the second wave of COVID-19.

The study analyzed the impact of the peak of the first and second waves of COVID-19 on selected sectoral indices. There may be several reasons for the performance of this particular stock index. However, we have tried to analyze the best possible reasons for this turbulence in the performance of stocks of selected sectoral indices. The study can be further analyzed to examine the long-term impact of such a pandemic on other sectors.

The study is based on the panic behavior of investors during such a pandemic. No one was prepared for this and expected this pandemic to last this long. This pandemic has taught so many lessons to everyone involved. Investors need to be prepared and cautious for such unforeseen disasters before making any investment decision. They need to analyze which industry can survive under such circumstances, and then they should invest there. Industries and enterprises must adapt and improve by honestly looking at their weaknesses and trying to meet investors' expectations.