Search results

While COVID-19 temporarily created worldwide immobility, the gradual opening up of borders spurred one of the largest return migration episodes ever, and it continues to this day. Disappearing jobs, decreasing wages, inadequate social protection systems and networks, xenophobia, wage theft and overall uncertainty are among the prominent factors that have influenced this movement. Emigrants from the Gulf-India Migration Corridor were particularly affected by these forces and returned en masse, uncertain of their future. When people come back to their home country after living abroad, particularly due to exogenous shocks, it raises concerns about whether their decision to return was truly voluntary, their ability to adjust to being back home and the long-term effects on their reintegration. Additionally, it is uncertain what kind of impact return migrants have on their home country’s development. In this chapter, the authors examine the recent trend of return migration since the outbreak of COVID-19 and how it affects the Gulf-India corridor. The authors also take a closer look at the state of Kerala through a unique survey conducted by the authors and provide possible future scenarios for emigration in this region, along with recommendations for policy.

The current research prioritizes the dimensions of a higher-order customer engagement construct in the tourism destination context in order of their relative importance as perceived by tourists to provide meaningful insights to the tourism industry player.

Data was collected from 429 tourists' from selected tourist destinations. Exploratory factor analysis (EFA) was used for data analysis using SPSS. The scale items were prioritized through hybrid evidence-based modeling using the RIDIT-GRA (Relative to an Identified Distribution and Grey Relational Analysis, respectively) approach.

The prioritization of customer engagement scale items is done through RIDIT analysis and is verified through GRA. The rankings of two independent methodologies show a 93% correlation, thereby ensuring the ranking's robustness.

Prioritizing customer engagement scale items may assist destination managers in strategy formulation to ensure tourists' high-level loyalty transcending purchase. The findings help develop key response areas (KRA's) for effective destination brand management.

Such precedence analysis of customer engagement scale items has not been done in the tourism studies yet. Also, the present study verified the results by incorporating two independent methodologies, thus providing more valid results.

Digital technology and SDGs have gained increasing interest from the research community. This chapter aims to explore the field through a holistic review of 188 publications from 2017 to 2022. For the systematic review of 188 articles, a three-step methodology comprising of PRISMA guidelines was performed, bibliometric analysis and text analysis using VOS-Viewer and Sentiment Analysis using RStudio had been undertaken. Bibliographic coupling revealed the following clusters Digital Space (Over all SDG), Localising SDGs, Financial Systems and Growth (SDG 8), Sustainable Supply Chain (SDG 9), Education (SDG 4), Energy Management (SDG 7), Smart Cities (SDG 11 and 13), Gender, Skills, and Responsibility (SDG 5 and 12), Food Management (SDG 1, 2 and 3), Business Innovation (SDG 8 and 9) and ICT (SDG 9). Next, co-occurrence analysis highlighted the following clusters Circular Economy (SDG 8), Higher Education System (SDG 4), Digital health (SDG 3), Industry 4.0 (SDG 9) and Supply Chain Management (SDG 9). Next, text analysis traced the most relevant areas of work within the theme. Finally, sentiment analysis revealed positive sentiments of the field. The research concluded that only a few SDGs had found major focus while the others don't have any solid ground in the literature. This chapter presents a knowledge structure by mapping the most relevant SDGs in the context of digital technology and sets directions for future research.

This paper aims to produce sustainable sport-hijab or veiling using cotton and bamboo as renewable and eco-material blending with polyester. Due to the unique characteristics of the knitting fabrics, the research focused on constructing the proposed samples using a circular knitting technique with a French terry structure, to achieve comfort, ease of care, good appearance and sustainability in different climatic conditions.

The researchers formed three different knitted samples using yarn count 30/1Ne for cotton and bamboo and 70 dens for polyester yarn, using the same blending ratio of 50:50% (cotton/polyester, bamboo/ polyester and cotton/ bamboo). They tested several mechanical and physical properties (weight, thickness, air permeability, water permeability, electrostatic charges, ultraviolet protection factor, stiffness, pilling resistance and bursting strength).

Using different tools, the researchers statistically analyzed the influence of variables on sample properties, including a Chart line, ANOVA test at p-value = 0.05 and the least significant differences values to identify the effect significantly as well as demonstrate the interaction among the samples at each tested property. Finally, radar chart areas to clarify the preferable sample performance.

The findings declared that blending materials used significantly affected most properties of the produced samples, except for the water permeability and an electrostatic charge. Furthermore, the findings pointed out that blending (cotton or bamboo/polyester) is more efficient and desirable than blending (cotton/ bamboo). Additionally, based on radar charts analysis, the cotton/polyester knitted outperforms other blended materials samples in producing sport-hijab or veiling fabric.

This paper aims to present comfort properties of bamboo-silk and cotton-silk Kota Doria fabrics.

Two types of Kota Doria fabrics were manufactured: one from the mixture of silk and bamboo yarns and the other from the mixture of cotton and silk yarns. Air permeability, thermal resistance and moisture management properties were determined.

Air permeability of bamboo-silk fabric was higher than that of cotton-silk fabric, whereas thermal resistance was less. Moisture management of both the fabrics was almost the same.

Halal integrity assurance is the primary objective of Halal supply chain management. Several halal-related risks are present that have the potential to breach halal integrity. Therefore, this study aims to develop the framework for the assessment of halal-related risk from a supply chain perspective.

Risk related to halal is identified through the combined approach of the systematic literature review and experts’ input. Further, these risks are assessed using the integrated approach of intuitionistic fuzzy number (IFN) and D-number based on their severity score. This integrated approach can handle fuzziness, inconsistency and incomplete information that are present in the expert’s input.

Eighteen significant risks related to halal are identified and grouped into four categories. These risks are further prioritised based on their severity score and classified as “high priority risk” or “low priority risks”. The findings of the study suggests that raw material status, processing methods, the wholesomeness of raw materials and common facilities for halal and non-halal products are more severe risks.

This study only focusses on halal-related risks and does not capture the other types of risks occurring in the supply chain. Risks related to halal supply chain management are not considered in this study. Prioritisation of the risks is based on the expert’s input which can be biased to the experts' background.

The proposed risk assessment framework is beneficial for risk managers to assess the halal related risks and develop their mitigation strategies accordingly. Furthermore, the prioritisation of the risks also assists managers in the optimal utilisation of resources to mitigate high-priority risks.

This study provides significant risks related to halal integrity, therefore helping in a better understanding of the halal supply chain. To the best of the authors' knowledge, this is the first comprehensive study for developing a risk assessment model for the halal supply chain.

By relying on a sustainable career perspective and recent studies on senior employees’ late career phase, this study aims to examine senior (50+) nurses’ late career narratives in the context of extending retirement age. Given the current global nursing shortage, there is a pressing need to find ways on how to promote longer and sustainable careers in the health-care field. Yet, there is limited knowledge about the extended late career phase of senior nurses.

Empirical data were derived from 22 interviews collected among senior (50+) nursing professionals working in a Finnish university hospital. The qualitative interview data were analysed using a narrative analysis method. As a result of the narrative analysis, four career narratives were constructed.

The findings demonstrated that senior nurses’ late career narratives differed in terms of late career aspirations, constraints, mobility and active agency of one’s own career. The identified career narratives indicate that the building blocks of sustainable late careers in the context of extending retirement age are diverse.

The qualitative interview data were restricted to senior nurses working in one university hospital. Interviews were conducted on site and some nurses were called away leaving some of the interviews shorter than expected.

To support sustainable late careers requires that attention be based on the whole career ecosystem covering individual, organizational and societal aspects and how they are intertwined together.

So far, few studies have investigated the extended late career phase of senior employees in the context of a changing career landscape.

Globalization of markets and pace of technological change have caused the growing importance of paying attention to supplier selection problem. Therefore, this study aims to choose the best suppliers by providing a mathematical model for the supplier selection problem considering the green factors and stochastic parameters. This paper aims to propose a multi-objective model to identify optimal suppliers for a green supply chain network under uncertainty.

The objective of this model is to select suppliers considering total cost, total quality parts and total greenhouse gas emissions. Also, uncertainty is tackled by stochastic programming, and the multi-objective model is solved as a single-objective model by the LP-metric method.

Twelve numerical examples are provided, and a sensitivity analysis is conducted to demonstrate the effectiveness of the developed mathematical model. Results indicate that with increasing market numbers and final product numbers, the total objective function value and run time increase. In case that decision-makers are willing to deal with uncertainty with higher reliability, they should consider whole environmental conditions as input parameters. Therefore, when the number of scenarios increases, the total objective function value increases. Besides, the trade-off between cost function and other objective functions is studied. Also, the benefit of the stochastic programming approach is proved. To show the applicability of the proposed model, different modes are defined and compared with the proposed model, and the results demonstrate that the increasing use of recyclable parts and application of the recycling strategy yield more economic savings and less costs.

This paper aims to present a more comprehensive model based on real-world conditions for the supplier selection problem in green supply chain under uncertainty. In addition to economic issue, environmental issue is considered from different aspects such as selecting the environment-friendly suppliers, purchasing from them and taking the probability of defective finished products and goods from suppliers into account.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).

The purpose of this paper is to investigate effect of material properties in 3D knitted fabrics on thermo-physiological comfort.

In the present study six different spacer fabrics were developed. Among these six fabrics, it was classified into two groups for convenient analysis of results, the first group has been developed using polyester/polypropylene blend with three different proportion and second group with polyester/polypropylene/lycra blend having another three different composition. As a spacer yarn, three different types of 88 dtex polyester monofilament yarn and polyester multifilament yarns (167 dtex and 14.5 tex) were used and 14.5 tex polypropylene and 44 dtex lycra multifilament yarns were also used for the face and back side of the spacer fabrics (Table I). These fabrics were developed in Syntax Pvt Ltd Czech Republic.

The main influence on the water vapour permeability of warp knitted spacer fabrics is the kind of raw material, i.e. fibre wetting and wicking. Also there is no correlation between air permeability and water vapour permeability. It is found that both air permeability and thermal conductivity are closely related to the fabric density. It is also found that the fabric characteristics of spacer fabric show a very significant effect on the air permeability, thermal conductivity and mechanical properties of spacer fabric. Therefore, selection of spacer fabric for winter clothing according to its fabric characteristics.

The main objective of the present study is to produce spacer knitted 3D fabrics suitable for defined climatic conditions to be used as clothing or in sports goods.

New 3D knitted spacer fabrics can be produced with improved comfort properties.