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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.

Drawing on the social presence theory, this study aims to explore how supplier–customer social media interactions influence supplier observers’ trust in the customers and what mechanisms contribute to variation in trust experience.

A total of 36 semi-structured interviews were conducted with Chinese suppliers using WeChat for business-to-business interactions. Data were analyzed in three steps: open coding, axial coding and selective coding.

Findings reveal that varied trust is based not only on the categories of social presence of interaction – whether social presence is embedded in informative interactions – but also on the perceived selectivity in social presence. Observer suppliers who experience selectivity during social and affective interactions create a perception of hidden information and an unhealthy relationship atmosphere, and report a sense of emotional vulnerability, thus eroding cognitive and affective trust.

The findings contribute new understandings to social presence theory by exploring the social presence of interactions in a supplier–supplier–customer triad and offer valuable insights into business-to-business social media literature by adopting a suppliers’ viewpoint to unpack the mechanisms of how social presence of interaction positively and negatively influences suppliers’ trust and behavioral responses.

This collaborative paper presents three case studies on four scholars' experiences with remote data collection. The authors highlight the challenges and strengths of online qualitative research across three disparate projects: an interdisciplinary exploration of matrilineal heritage, an examination of Irish women's sexual identity and an investigation of dress practices among Tz'utujil-Maya.

Qualitative researchers traditionally go into the field to explore and understand social phenomena. At the onset of the COVID-19 pandemic, while people faced the daily realities of a worldwide crisis from within their homes, remote data collection became a necessary strategy to pursue knowledge. As a result, researchers adapted to unknowns regarding recruiting, scheduling, technology, interviewing and analysis.

Participant and researcher experiences during the adaptation to remote interviewing yielded important lessons on research strategies.

Outcomes from these studies highlight the potential value of online data collection alongside the necessity for flexibility in designing and conducting qualitative research.

This study is the maiden attempt to understand the effect of specific human resource practices (HRPs) on employee retention (ER) with the mediation of job satisfaction (JS) and moderation of work experience (WE) and job hopping (JH) in the context of the textile industry.

This study adopted a quantitative methodology and applied quota sampling to gather data from employees (n = 365) of leading textile companies in India. The conceptual model and hypotheses were tested with the help of Partial Least Squares-Structural Equation Modelling (PLS-SEM).

The findings of a path analysis revealed that compensation and performance appraisal (CPA) have the highest impact on JS followed by employee work participation (EWP). On the other hand, EWP had the highest impact on ER followed by grievance handling (GRH). The study revealed that JS significantly mediates between HRPs like CPA and ER. During Multi-group analysis (MGA) it was found that the importance of EWP and health and safety (HAS) was more in employee groups with higher WE, but it was the opposite in the case of CPA. In the case of JH behavior, the study observed that EWP leads to JS in loyal employees. Similarly, JS led to ER, and the effect was more pronounced for loyal employees.

In the context of the Indian textile industry, this work is the first attempt to comprehend how HRPs affect ER. Secondly, it confirmed that JS is not a guaranteed mediator between HRPs and ER, it could act as an insignificant, partial or full mediator. Additionally, this study establishes the moderating effects of WE and JH in the model through multigroup analysis.

Carpal tunnel syndrome (CTS) is a hand disease caused by the pressing of the median nerve present in the palmar side of the wrist. It causes severe pain in the wrist, triggering disturbance during sleep. Different products like splints, braces and gloves are available in the market to alleviate this disease but there was still a need to improve the wearability, comfort and cost of the product. This study was about designing a comfortable and cost-effective wearable system for mild-to-moderate CTS. Transcutaneous electrical nerve stimulation (TENS) therapy has been used to reduce the pain in the wrist.

After simulation by using Proteus software (which allowed the researchers to draw and simulate electrical circuits using ISIS, ARES and PCB design tools virtually), the circuit with optimum frequency, i.e. 33 Hz was selected, and the circuit was developed on a printed circuit board (PCB). The developed circuit was integrated successfully into the half glove structure.

The developed product had good thermophysiological comfort and hand properties as compared to the commercially available product of the same kind. In vivo testing (It involves the testing with living subjects like animals, plants or human beings) was performed which resulted in 85% confirmed viability of the product against CTS. A glove with an integrated circuit was developed successfully to accommodate various sizes without any sex specifications in a cost-effective way to mitigate the issue of CTS.

Industrial workers, individuals frequently using their hands or those diagnosed with CTS may wish to use this product as therapy. The attention could not be paid to the aesthetic or visual appeal of the developed product.

A very comfortable glove with integrated TENS electrodes was developed successfully to accommodate various sizes without any sex specifications in a cost-effective way to mitigate the issues of CTS.

Accurate and automatic clothing pattern making is very important in personalized clothing customization and virtual fitting room applications. Clothing pattern generating as well as virtual clothing simulation is an attractive research issue both in clothing industry and computer graphics.

Physics-based method is an effective way to model dynamic process and generate realistic clothing animation. Due to conceptual simplicity and computational speed, mass-spring model is frequently used to simulate deformable and soft objects follow the natural physical rules. We present a physics-based clothing pattern generating framework by using scanned human body model. After giving a scanned human body model, first, we extract feature points, planes and curves on the 3D model by geometric analysis, and then, we construct a remeshed surface which has been formatted to connected quad meshes. Second, for each clothing piece in 3D, we construct a mass-spring model with same topological structures, and conduct a typical time integration algorithm to the mass-spring model. Finally, we get the convergent clothing pieces in 2D of all clothing parts, and we reconnected parts which are adjacent on 3D model to generate the basic clothing pattern.

The results show that the presented method is a feasible way for clothing pattern generating by use of scanned human body model.

The main contribution of this work is twofold: one is the geometric algorithm to scanned human body model, which is specially conducted for clothing pattern design to extract feature points, planes and curves. This is the crucial base for suit clothing pattern generating. Another is the physics-based pattern generating algorithm which flattens the 3D shape to 2D shape of cloth pattern pieces.

This public-sourced case describes the latest restructuring efforts by Deutsche Bank (DB) and gives a short history of prior restructuring efforts from the decade before. In July 2019, Christian Sewing, the new CEO of DB, announced a series of measures that included, among others, the elimination of global equity trading, the layoff of 18,000 employees, the creation of a “bad bank” to transfer noncore assets, and the suspension of dividends until 2022. The case describes key decisions a bank CEO makes when a bank needs to change course to return to profitability and growth. The case offers an opportunity to debate these key decisions, as well as discuss some of the prior ones during earlier restructuring efforts, and put the students in the CEO's shoes: What would you do and why? The case also describes key banking performance metrics (e.g., ROE, ROA) and other critical variables such as those reflecting capital health (Tier 1 ratio), as well as gives an overview of the bank business model and factors impacting bank profitability and value.

This research aims to investigate how manufacturing flexibility can address the challenges of an ever-changing and unpredictable business environment in Bangladesh’s labor-intensive ready-made garment (RMG) industry, which is underserved and situated in a developing country.

Using Partial Least Square Structural Equation Modeling, this study empirically evaluated the relationships between manufacturing flexibility, environmental uncertainty and firm performance. The analysis utilized 320 survey responses from potential RMG experts, representing 95 organizations.

The study achieved a decision-making model for implementing manufacturing flexibility in the RMG industry of Bangladesh with acceptable model fit criterion. The research pinpointed that workforce flexibility plays the maximum mediating among different types of manufacturing in coping with demand and supply uncertainty in the RMG sector.

The study made valuable contributions to theoretical and practical knowledge in the context of manufacturing flexibility in Bangladesh’s RMG and other underserved labor-intensive sectors in developing economies. It suggests that managers should shift from defensive and risky business strategies to more aggressive and proactive approaches by utilizing workforce flexibility resources adaptively to enhance manufacturing capabilities and align with dynamic market demand. Additionally, the study offers recommendations for future research to build upon its findings.

This study is unique in its approach because it presents a decision model for implementing manufacturing flexibility in a labor-intensive industry in a developing economy, specifically the RMG industry in Bangladesh, whereas previous research has primarily focused on high-tech industries in developed economies.

Devices for step estimation are body-worn devices used to compute steps taken and/or distance covered by the user. Even though textiles or clothing are foremost to come to mind in terms of articles meant to be worn, their prominence among devices and systems meant for cadence is overshadowed by electronic products such as accelerometers, wristbands and smart phones. Athletes and sports enthusiasts using knee sleeves should be able to track their performances and monitor workout progress without the need to carry other devices with no direct sport utility, such as wristbands and wearable accelerometers. The purpose of this study thus is to contribute to the broad area of wearable devices for cadence application by developing a cheap but effective and efficient stride measurement system based on a knee sleeve.

A textile strain sensor is designed by weft knitting silver-plated nylon yarn together with nylon DTY and covered elastic yarn using a 1 × 1 rib structure. The area occupied by the silver-plated yarn within the structure served as the strain sensor. It worked such that, upon being subjected to stress, the electrical resistance of the sensor increases and in turn, is restored when the stress is removed. The strip with the sensor is knitted separately and subsequently sewn to the knee sleeve. The knee sleeve is then connected to a custom-made signal acquisition and processing system. A volunteer was employed for a wearer trial.

Experimental results establish that the number of strides taken by the wearer can easily be correlated to the knee flexion and extension cycles of the wearer. The number of peaks computed by the signal acquisition and processing system is therefore counted to represent stride per minute. Therefore, the sensor is able to effectively count the number of strides taken by the user per minute. The coefficient of variation of over-ground test results yielded 0.03%, and stair climbing also obtained 0.14%, an indication of very high sensor repeatability.

The study was conducted using limited number of volunteers for the wearer trials.

By embedding textile piezoresistive sensors in some specific garments and or accessories, physical activity such as gait and its related data can be effectively measured.

To the best of our knowledge, this is the first application of piezoresistive sensing in the knee sleeve for stride estimation. Also, this study establishes that it is possible to attach (sew) already-knit textile strain sensors to apparel to effectuate smart functionality.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.