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Owing to the significance of a healthy lifestyle, we investigate the antecedents of the healthy lifestyle of young consumers in Sri Lanka. 658 structured questionnaires were collected from young consumers in Sri Lanka as part of the survey procedure. The judgmental sampling method is used to choose the respondents. The analysis makes use of both descriptive and inferential statistics. The findings disclose a high degree of healthy lifestyle among young consumers in Sri Lanka. Further findings revealed that health consciousness, collective esteem, and neighborhood environment are the antecedents for a healthy lifestyle. As young consumers are more concerned about a healthy lifestyle, managers in certain industries such as food and beverages, hotels, and restaurants should adopt their products and services in line with a healthy lifestyle.

The main goal of this study is to employ unsupervised (lexicon-based) learning approaches to identify readers' emotional dimensions and thumbs-up empathy reactions to reviews of online travel agency apps based on appraisal and stimulus–organism–response (SOR) theories.

Using the Google Play Scraper, we gathered a total of 402,431 reviews from the Google Play Store for two travel agency apps, Tripadvisor and Booking.com. Following the filtering and cleaning of user reviews, we used lexicon-based unsupervised machine learning algorithms to investigate the associations between various emotional dimensions of reviews and review readers' thumbs-up reactions.

The study's findings reveal that the sentiment of different sorts of reviews has a substantial influence on review readers' emotional experiences, causing them to give the app a thumbs up review. Furthermore, readers' thumbs-up responses to the text reviews differed depending on the eight emotional aspects of the reviews.

The results of this research can be applied in the development of online travel agency apps. The findings suggest that app developers can enhance users' emotional experiences by considering the sentiment and emotional aspects of reviews in their design and implementation. Additionally, the results can be used by travel agencies to improve their online reputation and attract more customers by providing a positive user experience.

The findings of this research have the potential to have a significant impact on society by providing insights into the emotional experiences of users when they engage with online travel agency apps. The study highlights the importance of considering the emotional aspect of user reviews, which can help app developers to create more user-friendly and empathetic products.

The current study is the first to evaluate the impact of users' thumbs-up empathetic reactions on user evaluations of online travel agency applications using unsupervised (lexicon-based) learning methodologies.

This study aims to examine consumers' purchasing and consumption behavior regarding big data embedded in packaged food post-Covid-19. The second purpose is to determine whether consumer purchasing behavior varies depending on the variety and volume of big data on food packages.

Semi-structured interviews were conducted to investigate consumer sentiment regarding big embedded data in packaged foods during purchasing. Based on samples from packaged foods sold in international chain stores, interview data collected from 24 participants were subjected to systematic analytical procedures.

The results revealed that before Covid-19, consumers had positive thoughts about the expiration date, brand, and product contents but did not care much about other data. At the same time, post-Covid-19, there were changes in their attitudes and behaviors on this issue. Post-Covid-19, it has been observed that consumers have positive attitudes and behaviors toward human health and food safety issues regarding unprocessed big data in packaged foods.

This study provides a different perspective on consumer purchasing behavior through big data on packaged foods post-Covid-19. Embedded information in packaged foods provides important data regarding consumer purchasing behavior. As a powerful source of consumer sentiment, this data also provides a reference for consumer purchasing decisions.

An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

This study aims to investigate the effect of organizational ambidexterity (OA) and organizational green culture (OGC) on corporate sustainability (CS) while incorporating the mediating role of green innovation (GI) to provide a detailed insight into CS. The study also presents a research framework based on the Organizational Ambidexterity theory and Natural Resource-based view to explain the factors contributing to CS.

Using stratified sampling, the study collected data through survey-based empirical research from 307 textile companies registered with the Securities and Exchange Commission of Pakistan (SECP) or the All-Pakistan Textile Mills Association (APTMA). The collected data were analysed using path analysis, mediation analysis and moderation analysis through smart PLS-SEM version 4.0 to assess the composition and causal association of factors.

The study found a significant relationship between OA and OGC with CS. Furthermore, the study revealed that green innovation partially mediates the relationship between OGC and CS. The proposed research framework can be valuable for promoting and recommending actions to enhance CS.

The study on CS in the textile sector of Pakistan has limitations such as a narrow focus, cross-sectional design and reliance on self-reported data. Future research should explore additional factors, conduct longitudinal research, investigate contextual factors, scrutinize specific green innovation practices and broaden the scope of the study to include SMEs and other textile organizations.

The research framework can help senior executives to foster CS by promoting OGC, OA and GI. Practitioners and academicians can also utilize or further investigate the proposed framework for validation and to foster CS.

This study fills gaps in the existing literature by investigating the mediating effect of GI between OGC and CS. The proposed research framework provides a comprehensive understanding of the factors contributing to CS based on the Organizational Ambidexterity theory and Natural Resource-based view.

This study aims to identify and describe the relationships among sensory-driven pleasure, cognition-driven pleasure, symbolic-driven pleasure and organic food purchase behavior considering the moderating effects of functionality trust and authenticity trust from the viewpoint of the theory of planned behavior (TPB).

Data were collected using a structured questionnaire survey in first-tier cities in China. A total of 352 consumers of organic foods participated in the study. Structural equation modeling and hierarchical regression analysis were employed for data analysis.

The results indicated the significant association of perceived behavioral control, subjective norm, attitude, sensory-driven pleasure and cognition-driven pleasure with purchase intention. Perceived behavioral control and purchase intention had significantly positive effects on purchase behavior. Moreover, functionality trust had a positive moderating effect on purchase intention and purchase behavior.

This study not only provides novel and original insights for understanding organic consumption but also provides a reference for organic producers, sellers and policymakers to develop effective strategies to guide organic consumption that are conducive to promoting sustainable consumption.

For the first time, this research introduces the construct of food pleasure into the TPB to explore the relationships between food pleasure and purchase behavior based on the TPB. It may expand the scope of the TPB and provide valuable insights regarding how to improve the existing intention–behavior gap in organic consumption.

Electric cars have very little market share in developing countries despite their environmental benefits. Thus, governments have started promoting electric cars by providing financial incentives to consumers. The current article aims to examine the direct and indirect effects of government financial incentives on consumer electric car adoption in India.

The study followed a quantitative research method that employed a self-administered survey questionnaire. Structural Equation Modelling and Multi-Group Analysis were followed for data analysis.

The study revealed that financial incentives have an indirect effect on electric car adoption intention rather than a direct effect. In addition, financial incentives were found to have a direct effect on attitude and Perceived Behavioural Control (PBC). Attitude and PBC positively influenced consumer adoption intention.

The insights and implications from the present study would help policymakers and marketers to formulate better incentive policies and market strategies to increase consumer acceptance of electric cars in developing countries.

The study contributes to the literature by analysing the underlying mechanism that links government financial incentives to electric car adoption intention. This study also explored the direct effect of financial incentives on attitude and PBC, which are less investigated in electric vehicle literature. In addition, the present article also assessed the moderating role of age in electric car adoption, which has mixed evidence in the literature, and such studies are scarce in the Indian context.

Purpose: Human capital development strategies and components in hospitality are covered in this chapter. This study examines hospitality human capital development, including training, leadership, technology integration, staff well-being, diversity, equality, and inclusion (DEI).

Design/methodology/approach: This chapter uses secondary data from the relevant research article literature. The design shows how human capital development techniques affect hospitality. This study addresses human capital development needs in the hospitality business as technology becomes increasingly essential.

Findings: Hospitality worker adaptation and flexibility for human capital development, including wellness efforts for work–life balance, are discussed in the chapter. The results also address recognition and incentive systems to foster a healthy hospitality workplace culture that supports employee human capital development.

Research limitations: Human capital research in hospitality is minimal. Future research should examine additional human capital determinants affecting hospitality sector success and their relevance in varied settings for global hospitality and tourism growth.

Practical implications: This study can help hospitality and tourism stakeholders, policymakers, and local communities improve the symbiotic relationship for human capital development. Practical implications emphasise the necessity of inclusive development initiatives that empower hospitality communities and maintain global hospitality and tourism.

Originality/value: This study examines how human capital development affects hospitality, particularly tourism. The novelty is studying development strategies as drivers for good change, teaching sustainable global human capital development in international tourism.

The present study apotheosises on the relationship between blockchain and fintech and its impact on the financial services sector. It then gauges into the various aspects that have been included in the published literature by the authors all across the world.

Post-pandemic, technology has led to tremendous opportunities in the financial sector, and the customers have started assessing financial services in online mode, thus enabling companies to innovate their business strategies. The present chapter aims to explore the areas where blockchain is benefitting the financial sector.

The objectives of this study were achieved through systematic review of literature performed with the help of bibliographic analysis. Further, a PRISMA model was developed, and the networks were derived with the help of Scopus analyser software and VOSviewer Version 1.6.15.

It is observed that since the introduction of the term and development of the first blockchain, a lot of work has been performed in the said domain, but it is still at a nascent stage. It is thus discovered that the string specific research documents got published in Scopus database from 2016 onwards, and a considerable amount is being performed by authors from China, the United States and India.

The present chapter exhibits the various dimensions in which a lot has to be yet explored and needs to devise innovative and full proof strategies which will enable to overcome the challenges, thus strengthening the position and working of the companies in Industry 5.0.

Natural extracts produced with Annona squamosa and Moringa oleifera leaves through the methanol-based solvent were coated on 100% cotton and 80%:20% polyester/cotton blends to improve the functional properties such as antimicrobial activity, wicking, stiffness and crease recovery of the fabric using an eco-friendly 1,2,3,4-butane tetracarboxylic acid (BTCA) crosslinking agent.

In this study, 100% cotton and 80:20% Polyester/Cotton fabrics with surface densities of 113.5 g/m2 and 101 g/m2 were treated BTCA. Eight different samples were produced by padding through the natural extracts. The FTIR investigation was performed on all the fabric samples. These coated fabrics were studied for their antimicrobial activity, wicking, stiffness and crease recovery properties.

It was found that the BTCA cross-linked fabrics showed higher antimicrobial activity against gram-positive and gram-negative bacteria. Similarly, the percentage crease recovery angle was higher for the Annona squamosa coated sample than for Moringa Oleifera leaf extract in both cotton and polyester cotton blend samples. Furthermore, no significant variation in stiffness values was discovered between the control samples of cotton and polyester cotton blend and its treatment one. It was interesting to note that treating the fabrics with cross-linker showed improved vertical wicking properties, which were closer to control fabric values. The study confirms that crosslinking the fabrics with BTCA has improved the functional properties of the fabrics. The zone of inhibition values of BTCA cross-linked moringa methanolic leaves extract coated cotton and polyester cotton blend were 6 to 6.5 cm, which was more than 50% higher than non-BTCA cross-linked fabric, and BTCA cross-linker has improved the vertical wicking properties.

The outcome of this study will help to gain a better understanding of BTCA cross-linkers for improving the functional coating on textile substrates.

This study was conducted to improve the natural extract coating on textile material with eco-friendly aspects, enhancing the commercial utility of these finished fabrics