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Economies and societies are not digitally isolated. Digital technologies are widely recognised as key drivers of information dissemination, knowledge sharing, income and employment. Digital technologies also influence the interlinkages of digitalisation, gender and labour market outcomes. Digital technologies impact every sphere of day-to-day life. It impacts ways of communication, trade and business; influences networking abilities; shapes societal norms, attitudes and behaviours. It is hence argued that digital technologies may have crucial implications for women's participation in the workforce.

Gender equality and increasing women's workforce participation is an important goal under the United Nations Sustainable Development Goals (SDGs). Research indicates that women are mainly involved in agricultural work, blue-collar formal work, while collar formal work, and entrepreneurship. Digital technologies significantly impact the ways of working in all these sectors. Consequently, it is argued that digital technologies influence women's participation across all such types of work.

This chapter aims at unravelling the linkages between digital technologies and women's workforce participation. To this end, the influences of digital technologies on women's participation in agricultural work, blue-collar formal work, white-collar formal work and entrepreneurship are discussed. The implications and impacts of the use of broadband, internet and mobile technologies are also discussed. This chapter also includes important theories of women's workforce participation and discusses them in light of digitalisation.

COVID-19 placed millions of employees under work-from-home/telework. Employers intend extending telework for the long-term, anticipating business benefits. But the benefits are impacted by employees' well-being/ill-being, which is affected by the satisfaction of psychological needs. In turn, need satisfaction is influenced by employees' personal/job attributes. As work-from-home's blended environment disrupts routines, the satisfaction of the psychological need for structure or routines was examined in this study, along with the effect of personal/job attributes.

Cross-section primary data were collected from 500 teleworking information technology employees from India and analysed using partial least squares structural equation modelling. Vigour and exhaustion represented well-being and ill-being. Telework self-efficacy, standardised job, technology assistance and supervisor social support were the determinants or personal/job attributes. Need for structure satisfaction was the mediator.

Telework self-efficacy, technology assistance and supervisor social support were positively associated with structure satisfaction. In turn, structure satisfaction was related positively with vigour and negatively with exhaustion, and thus mediated between personal/job attributes and vigour/exhaustion. Standardised job did not affect vigour, exhaustion or structure satisfaction.

Need for structure is mostly studied as a trait, with implications of greater/lesser preference for structure examined. However, this work acknowledges structure as a basic ubiquitous need. Everyone needs some structure. Hence, need for structure is researched from the novel perspective of its satisfaction. This paper also uniquely combines job demands–resources model which identifies personal/job attributes, with concepts of epistemic which posit the need for structure.

The United Nations (UN) adopted Sustainable Development Goals (SDGs); agenda 2030 focuses on Climate Action (goal 13), targeting climate adaptability, as well as resilience, awareness and improving policy mechanisms on climate change. In order to enhance climate adaptability, climate-smart agricultural practices (CSAP) is a necessary step. CSAP is a sustainable agriculture approach with a strong focus on climate dimensions. The three pillars of climate-smart agriculture (CSA) are ‘Adaptation’: adapting to climate change; ‘Resilience’: building resilience against it and ‘Remove’: reducing carbon emissions. The new world economy uses Industry 4.0 technologies for sustainable advancement, including blockchain technology, big data analytics, artificial intelligence (AI), augmented and virtual reality, industrial Internet of Things (IoT) and services. Hence, technology plays a significant role in climate sustainable agriculture practices. This chapter shall consider three technologies consisting of IoT, AI and blockchain technology which contribute to CSAP in pre-harvesting (monitoring climate as well as fertility status, soil testing, etc.), harvesting (tilling, fertilisation, seed operations, etc.) and post-harvesting (predicting weather factors, seed varieties, etc.) periods of agriculture. All these three technologies work like the human nervous system; IoT helps in converting various information regarding demography, climate change, local agricultural needs, etc. into world data; AI works like a brain in combination with IoT, helps predict the use of climate-smart technology and blockchain, the memory part of the nervous system which deals with supply-side and ensures traceability as well as transparency for consumers as well as farmers. Hence, this chapter shall contribute to the importance of these three technologies in adopting CSAP in three stages of agriculture.

This study aims to identify demographic characteristics, personal attributes and attitudes and social support factors that adversely or favourably affect the likelihood of career persistence amongst women workers of the Indian IT sector.

The research, grounded in the social cognitive career theory, analyses primary data collected from 850 women working in IT via a survey. Based on an original definition of career persistence, the sample was segregated into 427 persistent and 423 non-persistent women. Logistic regression was performed to test for the effect of various determinants on the likelihood of women being career persistent versus non-persistent.

Being married, having children, as well as high levels of belief in gender disadvantage and work–family conflict lowered the likelihood of career persistence amongst women. While being a manager, possessing high career identity, high occupational culture fit, positive psychological capital and family support boost the likelihood.

The study examines women's actual continuance in an IT career vis-à-vis exit from the workforce/IT field, rather than women's stated intent to persist/quit as previously investigated. It uses logistic regression to identify both hurdles and aids on the path of women's career persistence. The findings can help recognize women more likely to struggle, thus be a first step in targeted organizational interventions to plug a leaky talent pipeline.

Despite efforts to reduce environmental pollution and wasteful fossil fuel use, electric vehicles (EVs) are still rare on the road. Why is it so challenging to get widespread EV adoption? One significant factor on which it heavily depends is one's awareness and understanding of EVs. However, due to an absolute lack of knowledge on the part of the populace, this factor becomes a huge impediment to the uptake of EVs. A systematic review of the electronic database Scopus for the years 2003–2022 was carried out on ‘EV awareness and adoption of EV’ while considering the ‘Preferred Reporting Items for Systematic Reviews and Meta-analysis’ (PRISMA) standards. A three-step identification process resulted in the ultimate detection of 41 papers, which were then thoroughly examined. A conceptual framework that encompasses the three key awareness aspects that influence EV adoption is developed. To encourage greater uniformity among EV researchers, this study's conclusions serve as a foundation for operationalising upcoming research efforts within a predetermined framework. The authors must therefore be optimistic that lingering technological, legislative, cultural, behavioural and business-model barriers may be overcome over time through widespread dissemination of knowledge and awareness related to EVs, making it possible for everyone to switch to greener, more economical and more efficient transportation solutions.

The current study investigates the role of consumer ethnocentrism (CE) in the context of locally produced organic food. This research work further extends the extended theory of planned behavior (TPB) model by examining the mediating effect of extended TPB variables (ATT, SN, PBC, PV) between CE and PI for locally produced organic food.

Data were obtained from the visitors of two shopping malls situated in a large, heavily populated city of India using survey method. Further, two-step approach was applied to analyze the hypothesized model.

Findings indicate that CE is a substantial determinant of PI for locally produced organic food. Moreover, extended TPB mediates the relation between CE and PI for locally produced organic food.

Post Covid-19, market size of organic food is rapidly growing in India. In this regard, this study presents useful implications for the marketers of organic food for gaining better consumer insights to further develop appropriate marketing strategies.

CE has been found to be a useful predictor of their food attitudes. However, studies, investigating the role of CE in the context of sustainable food consumption, are scant. Moreover, studies exploring the mediating effect of extended TPB variables are also very limited.

The purpose of this paper is to narrow the research gap by examining the relationship between new ecological paradigm (NEP), consideration of future consequences (CFC), the intention to buy and the intention to pay a premium in the context of electric car (EC) purchase in India.

This study used a structured questionnaire to measure the variables of the research. The study successfully obtained useable data from a sample of 491 consumers residing in India. The analysis of the variables and their relationships was done using structural equation modelling using SMARTPLS4 software.

The relationship between the values of NEP and CFC was observed in the context of electric cars that has a significant impact on the intention to buy and pay a premium. It also highlights the role of CFC future and CFC immediate on the intention to buy and between NEP and the intention to pay a premium.

The study only covers electric cars, and therefore further testing of these relationships is required in the context of other forms of environmentally friendly transportation. The results are generalizable across the potential consumers of EC but are even more pertinent to higher-income millennial consumers.

Potential buyers of electric cars, having a positive orientation towards the environment and also consideration for future consequence, were observed to have a stronger intention to buy EC. The study finds a way in increasing the intention to buy an EC by catalyzing environmental concern of consumers through CFC future.

This is the first study that has examined the NEP-CFC relationship, and provides evidence that the intention to buy an electric car is not only NEP (environmental concern)-dependent but also considers CFC's future orientation. This study adds the CFC aspect as another important variable regarding the purchase of EC, and proves that environmental concern is not the only moderating factor to buy an EC.

In the realm of tourism and sustainability, transportation and mobility hold a crucial position. Among the green product categories, three-wheeled electric vehicles have gained significant attention due to their environmental benefits. However, research on consumers’ intentions to purchase these vehicles is limited, particularly in Asian destinations like Sri Lanka. This study aims to bridge this gap by examining the direct and indirect impacts of green brand awareness, green brand associations and green perceived quality of local Sri Lankans’ intention to choose branded electric three-wheelers in future purchase decisions.

A questionnaire survey garnered 400 usable responses, and structural equation modeling was used to test hypotheses.

The results indicate that green brand awareness, green brand associations and green perceived quality have a direct positive and significant effect on consumers’ intention to choose branded electric three-wheelers in future purchase decisions and green trust. Also, green trust has a positive and significant direct effect on consumers’ intention to choose branded electric three-wheelers in future purchase decisions. Furthermore, the results indicate that only green brand associations and green perceived quality have an indirect positive and significant effect on consumers’ intention to choose branded electric three-wheelers in future purchase decisions through green trust.

This research contributes to the tourism industry and other sectors involved in sustainability efforts in several ways. First, it emphasizes the importance of fostering positive associations with eco-friendly attributes and perceived product quality to build consumer trust and influence their purchase intentions for green products. Second, the study underscores the relevance of highlighting eco-friendly product attributes to stimulate consumer interest and adoption of green products. Finally, it theoretically underscores the significance of building trust through transparent and credible sustainability initiatives.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.