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Industry 4.0 has an inimitable potential to create competitive advantages for the apparel industry by enhancing productivity, production, profitability, efficiency and effectiveness. This study, thus, aims to assess the digitalisation level of the Tanzanian apparel industry through the Industry 4.0 perspectives.

A mixed-methods-based approach was deployed. This study deployed semi-structured interviews, document review and observation methods for the qualitative approach. For the quantitative approach, closed-ended questionnaires were used to ascertain the digitalisation levels and maturity level of the textiles and apparel (T&A) factories and small and medium-sized textile enterprises in Tanzania. The sample size was 110, with participants engaged through the purposive sampling technique.

Industry 4.0 frameworks evolved into practices mainly since 2011 in several service and manufacturing industries globally. For Tanzania, the findings indicate that the overall maturity level of the T&A industries is 2.5 out of 5.0, demonstrating a medium level of adoption. Thus, the apparel industries are not operating under the industry 4.0 framework; they are operating within the third industrial revolution – Industry 3.0 – framework. For such industries to operate within the fourth industrial revolution – Industry 4.0 – that is only possible if there is significantly well-developed industrial infrastructure, availability of engineering talent, stable commercial partnerships, demand from the marketplace and transactional relationship with customers.

This study’s limitations include: firstly, Industry 4.0 is an emerging area; this resulted in limited theoretical underpinnings in the Tanzanian perspectives. Secondly, the studied industries may not suffice the need to generalise the findings for the entire country, thus needing another study.

Although Industry 4.0 conceptual frameworks have been on trial in several industries since 2011, this is amongst the first empirical research on Industry 4.0 in the Tanzanian apparel industry that assesses the digitalisation levels.

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.

Driven by Davis’s (1989) technology acceptance model (TAM) and Westaby’s (2005) behavioral reasoning theory (BRT), the purpose of this study is to develop and test a conceptual model and examine consumers’ acceptance of artificial intelligence (AI) chatbots for apparel shopping.

Data from 353 eligible US respondents was collected through a self-administered questionnaire distributed on Amazon Mechanical Turk, an online panel. Confirmatory factor analysis and path analysis were used to test all hypothesized relationships using the structural equation model.

The results show that optimism and relative advantage of “reasons for” dimensions have a positive and significant influence on perceived ease of use (PEU), while innovativeness and relative advantage have a positive and significant influence on perceived usefulness (PUF). Discomfort and insecurity have no significant impact on PEU and PUF. However, complexity has a negative and significant impact on PEU but not on PUF. Additionally, PEU has a positive influence on PUF. Both PEU and PUF have a positive and significant influence on consumers’ attitudes toward using AI chatbots, which, in turn, affects the intention to use AI chatbots for apparel shopping. Overall, this study identifies that optimism, innovativeness and relative advantage are enablers and good reasons to adopt AI chatbots. Complexity is a prohibitor, making it the only reason against adopting AI chatbots for apparel shopping.

This study contributes to the literature by integrating TAM and BRT to develop a research model to understand what “reasons for” and “reasons against” factors are enablers or prohibitors that significantly impact consumers’ attitude and intention to use AI chatbots for apparel shopping through PEU and PUF.

The apparel industry of Bangladesh is rethinking lean manufacturing (LM) deployment because of the challenges imposed by the COVID-19 pandemic. Due to COVID-19, LM implementation in the apparel industry has become more difficult. Thus, the purpose of this study is to explore the barriers to implementing LM practices in the apparel industry of Bangladesh in the context of COVID-19 pandemic.

For evaluating the barriers, an integrated framework that combines the Delphi method and fuzzy total interpretive structural modeling (TISM) has been designed. The application of fuzzy TISM has resulted in a structured hierarchical relationship model of the barriers with driving and driven power.

The findings reveal that “lack of synchronization of lean planning with strategic planning”, “lack of proper understanding of lean concept” and “low priority from the top management” are the three top most important barriers of LM implementation in apparel industry.

These findings will help the apparel industry to formulate strategy for implementing the LM practices successfully. The proposed model is expected to contribute to the sustainable development goals (SDGs) such as Responsible Consumption and Production (SDG 12); Decent Work and Economic Growth (SDG 8); Industry, Innovation and Infrastructure (SDG 9) via resilient strategies.

This study is one of few initial efforts to investigate LM implementation barriers during the COVID-19 epidemic in a real-world setting.

The purpose of this study is to examine the impact of the shift towards a circular economy and marketing strategies on the collaborative purchasing behaviour of consumers in India. The study uses the theory of planned behaviour (TPB) and the marketing mix to understand the factors affecting a consumer’s intention to participate in collaborative consumption (CC).

A Web-based survey was conducted, and 349 valid responses were analysed using AMOS (Analysis of Moment Structures) structural equation modelling. The study emphasised the impact of price, promotion and perceived behaviour control on CC and provided direction and advice for companies that rent and swap apparel.

According to the study, promotion and perceived behaviour control are the two key characteristics that significantly impact a consumer’s willingness to participate in CC in India. The study also found that perceived behaviour control plays a significant direct role in behavioural usage. These findings emphasise the impact of price, promotion and perceived behaviour control on CC and offer direction and advice for companies that rent and swap apparel.

This article can be used to evaluate the business in different countries and can be developed further. It does, however, have some restrictions. Because most respondents are from northern and central India, in addition, some respondents are from the southwestern and southern regions, especially in the Mumbai and Chennai locales. Hence, the geographical sample was not diverse in terms of demographics. Furthermore, the gender identity of the respondents might essentially affect how the authors interpret customer buying behaviour, but the study missed this. Researchers could enhance this by using various sampling techniques and ensuring that other demographic characteristics are considered in the future. Furthermore, the survey could not distinguish between online and in-person transactions.

The study provides practical advice for companies that rent and swap apparel, emphasising the impact of price, promotion and perceived behaviour control on consumer willingness to participate in CC. The findings suggest that companies can improve consumer participation by focusing on promotion and perceived behaviour control. In addition, the significance of perceived behaviour control on behavioural usage highlights the importance of empowering consumers to control their decisions to participate in CC.

To the best of the authors’ knowledge, this study is one of the first to examine the factors influencing consumer willingness to participate in CC in the context of the shift towards a circular economy in India. By examining the impact of the TPB and the marketing mix on consumer intention, the study provides valuable insights for companies that rent and swap apparel. The findings highlight the importance of promotion and perceived behaviour control in shaping consumer behaviour and provide practical direction for companies to promote and market their products effectively. The study adds to the existing knowledge on the circular economy and the role of CC in reducing waste and promoting sustainability.

The effect of dynamic capabilities on operational excellence and the moderating effect of environmental dynamism on the relationship between operational excellence and dynamic capabilities in the apparel industry in Sri Lanka were investigated while developing new psychometric scales to assess operational excellence and dynamic capacities constructs.

We followed the exploratory sequential research design with a mixed-method research approach, aligning with the pragmatic research philosophy. Thus, both qualitative and quantitative research methods were followed.

Dynamic capabilities positively affect operational excellence, and environmental dynamism moderates the relationship between operational excellence and dynamic capabilities in the apparel industry in Sri Lanka such that when a higher environmental dynamism exists, a weaker positive relationship exists between dynamic capabilities and operational excellence. The two main dimensions of the operational excellence construct are continuous improvement of sustainable operational performance and sustainable competitive advantages. It empirically confirmed that sensing, seizing and reconfiguring capabilities are the three main dimensions of the dynamic capabilities construct.

This study was limited to the apparel industry in Sri Lanka. This research phenomenon should be explored in other industrial sectors worldwide to generalize the findings. The practitioners in the apparel sector may improve the organizational dynamic capabilities to achieve operational excellence and keep a strong positive relationship between dynamic capabilities and operational excellence in a highly dynamic environment if they address out-of-family situations with out-of-the-box thinking.

We generated two new empirical findings: (1) dynamic capabilities positively affect operational excellence, and (2) environmental dynamism moderates the relationship between dynamic capabilities and operational excellence. Also, we introduced validated new scales for assessing operational excellence and dynamic capabilities.

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.

This research investigates apparel consumers' psychological and behavioral responses to omnichannel (OC) integration. Specifically, the study applies the cognitive–affective–conative (CAC) model to reveal consumers' decision-making process under the impact of channel integration quality (CIQ), perceived fluency (PF) and cognitive and affective trust (AT).

Primary data were collected through an online survey. In total, 657 eligible responses were received. This study applied partial least square structural equation modeling for data analysis.

The findings demonstrate that the extrinsic cognitive factor, CIQ, substantially affects consumers' intrinsic cognition (cognitive trust [CT] and PF), which consequently fosters consumers' AT and shopping intentions. Specifically, integrated promotion and transaction information positively affects CT, while integrated product and price and information access negatively impact CT. All the dimensions of CIQ, except integrated promotion (IP), significantly affect PF. CT and AT exhibit mediation effects in the CAC model.

Apparel brands and retailers may apply the findings to effectively design their retail channels and implement channel integration to boost consumers' shopping intentions and trust.

This study is one of the pioneering studies applying the CAC model to empirically examine OC consumers' decision-making process. It is also among the first to determine that cognitive and AT have theoretical distinctions in the OC retailing setting.

This study is to examine and develop sector skills strategies and action plans for the textile and apparel (T&A) sector.

The paper used a participatory action qualitative method anchored on the Skills for Trade and Economic Diversification (STED) framework, utilising the workshop-based approach with 24 key stakeholders of the sector. Content analysis was used with the help of Nvivo software.

The findings revealed that there are skills shortages, skills gaps, skills mismatches and skills diversification programmes available through higher education and work-based learning. Further, there are labour supply challenges such as national skills policy and strategy, government and stakeholder coordination, funding, relevance of curriculum and qualifications, access to practicals and the absence of a clear national vision for the sector.

This study possesses an inherent limitation in terms of generalising the findings derived from qualitative research.

This research is among the first of its kind to assess skills needs and gaps through the lens of STED framework, which has been overlooked in previous literature. Importantly, this study provides vocational insights into skill needs in the sector.

This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.

The study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.

The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.

This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.