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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 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.

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.

This study aims to emphasize green energy-driven solutions to address environmental sustainability issues, particularly to promote the uptake of electric vehicles (EVs). This study intends to investigate user adoption of EVs as the existing predicament of converting car owners to EV buyers, demanding a push to create a facilitating environment for EV uptake.

A survey-based quantitative study involving 330 car owners and potential buyers was conducted involving four predictors, i.e. financial benefits, social influence, charging infrastructure and range consciousness. Environmental concerns and socio-demographic factors such as age, family income and gender were considered as moderators between these predictors and EV adoption intention. Partial least square structural equation modelling was used to analyse the proposed relationships.

The findings indicated that financial benefits (ß = 0.169, t = 3.930), social influence (ß = 0.099, t = 2.605), range consciousness (ß = 0.239, t = 3.983) and charging infrastructure (ß = 0.142, t = 4.8) significantly impact EV adoption. Family income was the most significant moderator with a large effect size (F square = 0.224), followed by environmental concern (F square = 0.182) and age (F square = 0.042) having a medium moderation effect and, subsequently, gender (F square = 0.010) as a mild moderator.

By analysing environmental concerns as a moderator, this study fosters a novel understanding of how environmental concerns impact EV adoption, which has not been explored. Additionally, the empirical assessment of the socio-economic and socio-demographic factors of EV adoption helps to offer a consumer perspective to the government and policymakers in undertaking initiatives to promote EV adoption.

The world is increasingly threatened by climate change. As the dimensions of this danger grow, it becomes essential to develop the most effective policies to mitigate its impacts and adapt to these new conditions. Technology is one of the most crucial components of this process, and this study focuses on examining climate change adaptation technologies. The aim of the study is to investigate the entire spectrum of technology actors and to concentrate on the technology citation network established from the past to the present, aiming to identify the core actors within this structure and provide a more comprehensive outlook.

The study explores patent citation relationships using social network analysis. It utilizes patent data published between 2000 and 2023 and registered by the US Patent and Trademark Office.

Study findings reveal that technologies related to greenhouse technologies in agriculture, technologies for combatting vector-borne diseases in the health sector, rainwater harvesting technologies for water management, and urban green infrastructure technologies for infrastructure systems emerge as the most suitable technologies for adaptation. For instance, greenhouse technologies hold significant potential for sustainable agricultural production and coping with the adverse effects of climate change. Additionally, ICTs establish intensive connections with nearly all other technologies, thus supporting our efforts in climate change adaptation. These technologies facilitate data collection, analysis, and management, contributing to a better understanding of the impacts of climate change.

Existing patent analysis methods often fall short in detailing the unique contributions of each technology within a technological network. This study addresses this deficiency by comprehensively examining and evaluating each technology within the network, thereby enabling us to better understand how these technologies interact with each other and contribute to the overall technological landscape.

This paper aims to analyse how financial literacy (FL) is conceptualised and operationalised in the Indian context.

A systematic literature review (SLR) was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol. Thirty-six articles published between 2010 and 2020 were considered for analysis. The FL conceptualisation was examined based on knowledge, ability, skill, attitude and confidence elements. The FL operationalisation was analysed using the modified version of the Organisation for Economic Co-operation and Development’s (OECD) Programme for International Student Assessment (PISA) 2012 model for organising the domain for an assessment framework.

The findings indicate that, despite offering operationalisation details of the FL, 13 out of 36 studies did not include a conceptual definition of FL. Of the 23 studies that mentioned a conceptual definition, 87% are primarily focused on the “knowledge” element and only 39% have combined knowledge, ability/skill and attitude elements in defining FL. As in the developed countries, the Indian studies also preferred investment/saving-related contents in their FL measures. The volume of content focusing on the financial landscape is meagre amongst the FL measures used in India and developed countries. The survey instruments of most studies have been designed in the individuals’ context but have failed to measure the extent to which individuals apply the knowledge in performing their day-to-day financial transactions. Further, it was found that 20 out of 36 studies did not convert the FL level of their target groups into a single indicator or operational value.

To the best of our knowledge, this is the first study that explores the FL’s assessment practices in India. Further, this study offers new insights by comparing the contents of FL measures used in Indian studies with those used in developed countries.

This paper conceptually aims to discuss the dual role of knowledge management (KM) and technological innovation, which brings about innovations, although it can be limited by psychological and emotional ownership.

This study examines the real impact of the paper on KM and technological innovation in family small to medium enterprises (FSMEs). This is a unique context affected by psychological and emotional ownership. However, COVID-19 has forced FSMEs to consider new strategies and practices to preserve their competitive advantage.

In this scenario, knowledge exchange, knowledge absorption and technology adoption appear relevant to the innovation process. This study offers a framework for how the duality of KM and technological innovation affects innovation.

Although extant research has explored technological innovation outcomes, a literature review reveals that accumulated studies on the drivers of technological innovation and KM in the context of FSMEs require further inquiry. Family members’ emotional ownership may foster KM because identification with organizational goals enhances individuals’ willingness to access and share information and stimulates new products and technological development.