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Artificial Intelligence (AI) is a branch of computer science, and Chatbot relies on the technology of AI. Chatbot has an AI feature which can be used for many messaging applications. A chatbot has a very advanced and promising interaction between humans and machines. They are a representative of systematic evolution of a question-answering natural language processing (NLP). Today, India has over 4.6 crores investor accounts with Central Depository Services (India) Limited (CDSL) who are actively investing in stocks, mutual funds, insurance and banking schemes. Chatbot plays an important role today in investor awareness and grievance resolution. Chatbots offer several advantages like standardised operations, higher efficiency and time saving but it has several disadvantages such as lack of human touch and inability to understand fragmented or grammatically incorrect sentences. There are several objections as it may contribute to further unemployment in many sectors. Overall analysis indicates that Chatbot application has a lot of potential in the investment sector in country like India.

This paper aims to explore factors impacting citizen intention toward artificial intelligence (AI) adoption, considering government regulation as a moderating variable. It focuses on the Palestinian Cellular Communications Sector in Gaza Strip, providing insights into the citizen-AI relationship dynamics. The research contributes to enhancing comprehension of AI technology from clients’ perspective.

To test the hypotheses, a questionnaire was used in an empirical study to collect primary data. In total, 347 Palestinian citizens responded to the survey.

The findings of this paper reveal that perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns significantly influence citizen intention toward AI adoption. Furthermore, government regulations as a moderating variable strengthen the impact of perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns on citizen intention toward AI acceptance and adoption. Thus, further research should explore specific domains and cultural contexts to gain a more comprehensive understanding of the factors shaping acceptance and adoption.

The findings of the study should be understood in the context of their limitations. First, the study ignored cultural or domain-specific subtleties in favor of generic characteristics, which calls for more research in these particular circumstances. Second, relying on self-reported data might result in biases and limitations due to subjectivity in reporting, indicating the necessity for alternate data gathering methods and approaches in future research.

Policymakers, developers and organizations working to promote the acceptability and implementation of AI applications should consider the practical implications of this study’s results. To secure the long-term use of AI technologies in a responsible and user-centric way, policymakers should give priority to public education and awareness, user-centered design and ethical AI development techniques. They should also stimulate partnerships and create monitoring systems.

This paper investigates the originality of factors that influence citizen intention toward AI acceptance and adoption. It uniquely examines the moderating role of government regulations in shaping this intention. By addressing this novel aspect, the paper contributes to advancing our understanding of the complex dynamics surrounding citizen intentions toward AI applications.

This paper aims to explore the mechanisms by which employees’ happiness at work (HAW) can be promoted. Drawing on the social exchange theory (SET), this study examined the relationships among discretionary human resource (HR) practices, perceived organizational support (POS), meaning of work (MOW) and HAW.

A three-path mediation model was developed to test the proposed relationships. The data were collected from Pakistani business professionals (n = 361), and hypotheses were tested using the PROCESS macro for SPSS .

The results suggest that POS mediates the relationship between discretionary HR practices and HAW. Also, MOW mediated the relationship between discretionary HR practices and HAW. Hence, both POS and MOW were found to be independent mediators. Further, the data provided support for the serial mediation of POS and MOW in the relationship between discretionary HR practices and HAW.

This research provides insights to organizations and their management on how discretionary HR practices can enhance employees’ POS, MOW and HAW.

The findings show that discretionary HR practices are associated with employees’ HAW. In addition, two mediators (POS and MOW) were found to serially mediate the aforesaid relationships. These findings are novel, as no prior research has used this nascent methodological approach to deepen our understanding by examining the associations between discretionary HR practices, POS, MOW and employees’ HAW.

The present research examines an underlying mechanism outlining how features of the influencers’ content influence the consumers’ urge to buy impulsively through the mediation of trust. Moreover, the moderating role of persuasion knowledge is also investigated in this study.

By employing persuasion theory and social capital theory, this study develops a framework that is tested on 251 social media users. The PLS-SEM modeling technique is employed for data analysis.

Results indicate that vicarious expressions and informational value are the two main characteristics of the influencer’s content, which develop trust in influencers’ posts and instigate an UBI. In addition, trust in influencer posts serves as a mediator between content characteristics and UBI. However, no moderating role of persuasion knowledge was found.

The present study offers an inclusive understanding of how marketers can strategically use influencers by leveraging the influential power of their content.

The purpose of the present study is to investigate the role of responsible leadership in impacting organizational sustainability performance grounded in the stakeholder theory. Further, it delves into the underlying mechanism that accentuates the link between responsible leadership and organizational sustainability performance via mediating role of sustainable HRM practices within the organization.

The study follows a cross-sectional research design using data collected via questionnaire-based survey from employees working in top 100 NSE listed organizations in India. The proposed hypotheses of the research were tested using SPSS Process Macro.

Results depicted that there is a direct relationship between responsible leadership and organizational sustainability performance. However, the relationship is better explained with a mediating role of sustainable HRM practices within the organization.

The study is helpful for the practitioners to understand the instrumentality of responsible leadership and sustainable HRM practices within Indian organizations. Moreover, the study promotes the relevance of achieving sustainability-oriented goals of the organizations.

The study fosters the limited research on the integration of leadership style and HRM function. The results further add to literature on sustainable HRM by studying its antecedents and consequences.

This chapter examines the relative influence of parents (vertical channel) and friends (horizontal channel) when deciding between employment in the public or private sector. Using a novel database and applying network analysis, the influence of peers is first measured. Next, the peer effect is compared with the impact of parental background on individual preferences. For the private sector, findings indicate that the influence (marginal effect) of friends is greater than that of parents. The opposite is observed for the public sector. However, in the case of public sector employment, the overall effect of the horizontal channel may surpass the vertical channel, as individuals typically have two parents but may have many friends. Additionally, it is found that both parents and friends exert a greater influence on women than on men.

Purpose: In this chapter of this book, the role and contributions of blockchain in Industry 5.0 are examined. Especially, the advantages offered by blockchain in mass customization, hyper-personalization, human–robot collaboration and cognitive systems, which constitute the main theme of Industry 5.0, are mentioned.

Need for the study: With developing technology, revolutions are taking place in the industry. While these revolutions are occurring, various technologies serve as enablers. In this chapter, one of these technologies, blockchain, is examined.

Methodology: Industrial revolutions represent important technological developments for societies. In this chapter, Industry 5.0, one of these revolutions, is discussed. In the first section, the main themes of Industry 5.0 were examined. Afterward, the transition from Industry 4.0 to Industry 5.0 was analyzed. Then, the opportunities offered by Industry 5.0 were reviewed. Subsequently, the contributions of blockchain to Industry 5.0 were examined. Finally, the role of blockchain in Industry 5.0 is summarized.

Findings: The main themes of Industry 5.0 enable customized processes and smart production approaches. Blockchain makes significant contributions to these processes with its security and traceability features. In addition, smart contracts can increase transparency, traceability and security among stakeholders in the production process with their distributed ledger structure and immutability features. In blockchain networks, each transaction is carried out and approved by consensus. This consensus, provided by smart contracts, also secures transactions by reducing administrative costs. With these contributions, blockchain meets the security and smart management requirements of Industry 5.0.

Purpose: In this study, monolith analysis methods, microservice identification, and decomposition methods proposed for the transition to microservice architectures that enable the development of appropriate solutions by adapting to the complex demands that will shape the technological infrastructure of the future are evaluated.

Need for the study: Decomposition from monolithic architectures to microservices has become a popular approach in organizations and companies with Industry 5.0. This transformation of Industry 5.0 enables businesses to gain a competitive advantage and can provide a quick solution to personalized problems such as personal service systems.

Methodology: The study, decomposition from monolith to microservice, initially includes monolith analysis, followed by microservice decomposition review. Various classification methods have been proposed for microservice identification and decomposition and are aligned with Industry 5.0 principles, focusing on artificial intelligence (AI)-based approaches, especially human-centered AI.

Findings: Four analysis methods (domain, static, dynamic, and version) are identified for monolith analysis, with static and dynamic being the most common. Version analysis is not typically used alone. In the decomposition phase, clustering-based methods are prevalent due to the uncertain dimensions of microservices. Rule-based and unsupervised methods are identified for decomposition, with AI algorithms like affinity propagation, Kmeans clustering, hierarchical clustering, Hungarian algorithm, genetic algorithm, latent Dirichlet allocation (LDA), and minimum spanning tree (MST) being employed.

Practical implications: Microservice architecture enables flexibility, scalability, and resilience compared to monolithic structures. Decomposing large-scale monolith projects into microservices is challenging, requiring selection of appropriate monolith analysis methods based on project details (e.g., domain analysis for detailed Unified Modelling Language (UML) diagrams) before proceeding with decomposition. This transformation improves deployment, maintenance, fault isolation, and scalability, while allowing for diverse service-specific databases and programming languages.

Exact solution of time–cost trade-off problem (TCTP) by the state-of-the-art meta-heuristic algorithms can be obtained for small- and medium-scale problems, while satisfactory results cannot be obtained for large construction projects. In this study, a hybrid heuristic meta-heuristic algorithm that adapts the search domain is developed to solve the large-scale discrete TCTP more efficiently.

Minimum cost slope–based heuristic network analysis algorithm (NAA), which eliminates the unfeasible search domain, is embedded into differential evolution meta-heuristic algorithm. Heuristic NAA narrows the search domain at the initial phase of the optimization. Moreover, activities with float durations higher than the predetermined threshold value are eliminated and then the meta-heuristic algorithm starts and searches the global optimum through the narrowed search space. However, narrowing the search space may increase the probability of obtaining a local optimum. Therefore, adaptive search domain approach is employed to make reintroduction of the eliminated activities to the design variable set possible, which reduces the possibility of converging into local minima.

The developed algorithm is compared with plain meta-heuristic algorithm with two separate analyses. In the first analysis, both algorithms have the same computational demand, and in the latter analysis, the meta-heuristic algorithm has fivefold computational demand. The tests on case study problems reveal that the developed algorithm presents lower total project costs according to the dependent t-test for paired samples with α = 0.0005.

In this study, TCTP is solved without considering quality or restrictions on the resources.

The proposed method enables to adapt the number of parameters, that is, the search domain and provides the opportunity of obtaining significant improvements on the meta-heuristic algorithms for other engineering optimization problems, which is the theoretical contribution of this study. The proposed approach reduces the total construction cost of the large-scale projects, which can be the practical benefit of this study.

Depression and anxiety are the most common comorbidities in TB patients, adversely impacting TB treatment outcomes. The purpose of this study is to assess the effect of lay counselling in reducing symptoms of depression and anxiety and TB treatment completion.

The study used a pre-post interventional research design. Patients were screened for depression and anxiety. All symptomatic TB patients were followed up and offered four to six or more lay counselling sessions whenever necessary. The authors assessed changes in depression and anxiety symptoms before lay counselling intervention and after fourth lay counselling session or the completion of TB treatment, whichever occurred the last.

Approximately 6,974 TB patients were screened for symptoms of depression and anxiety. The mean age was 36.7 ± 14.7 years. Total 25.9% patients were symptomatic. About 99.8% were provided lay counselling and received at least one to two follow-up sessions, while two patients who screened with severe depression were referred to a mental health specialist. Nearly 96.9% TB patients did not report symptoms of depression or anxiety after four lay counselling sessions, and TB treatment completion rate was higher among symptomatic TB patients who completed at least four counselling sessions (92.5%).

Lay counselling services delivered by field coordinators offer a promising approach to address mental health comorbidities among TB patients in resource-limited settings.

It explores a novel approach – lay counselling delivered by field coordinators – in tackling depression and anxiety among TB patients, which is a potentially scalable solution in resource-limited settings.