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This paper aims to investigate the effect of economic policy uncertainty (EPU) shocks on Indian equity market sectors. The effect of domestic (Indian) and foreign (USA) EPU shocks is examined on ten major Bombay Stock Exchange sectors.

The study uses data covering the period from September 2005 to July 2023 and uses the methodology of quantile regression to investigate the heterogenous response of stock market sectors under diverse market conditions explained through the analysis of conditional quantiles distribution.

The results demonstrate that domestic and foreign EPU shocks negatively affect most of the sectors in bearish market conditions. Industrials, commodities, utilities, consumer discretionary and financial services are the most affected sectors by domestic EPU. However, the information technology sector is found to be immune to domestic EPU shocks but negatively affected by foreign EPU shocks. On the other hand, energy, financial services and fast-moving consumer goods sectors are found to be immune to foreign EPU shocks but are negatively affected by domestic EPU shocks.

Understanding the heterogeneous response of different sectors to EPU shocks could help investors and portfolio managers identify portfolio diversification opportunities.

This study makes an inaugural attempt to examine the responses of Indian stock market sectors to domestic and foreign EPU shocks using the approach of quantile regression and unveils the previously unexamined diverse reactions of Indian stock market sectors to EPU shocks originating from both India and USA.

Creditor rights reduce or increase agency problems in corporations, affecting financial decisions. This study examines the impact of India’s Insolvency and Bankruptcy Code of 2016 (hereinafter referred to as the Code) on firms’ investment and investment sensitivity based on their health, considering this Code as more creditor-friendly than pre-existing bankruptcy laws.

This study uses a quasi-natural experiment that employs the difference-in-differences (DID) and propensity score matching difference-in-differences (PSM DiD) approach by considering the Code as treatment and categorizing the firms into distressed (treated group) and healthy (control group) firms. For the purpose of analysis, a fixed-effect regression model is used.

This study finds that distressed firms reduce their investment after the Code, but healthy firms do not observe any change. It shows that the reduction in investment of the distressed group is significantly greater than that of the healthy group due to agency conflict and the liquidation bias hypothesis. However, the reduction in investment is not followed by the change in investment sensitivity.

This study adds to the existing studies on the impact of the Insolvency and Bankruptcy Code, 2016 on investment. No study explores the relationship between this Code and investment based on the financial health of the firms. Also, none of the studies explores the impact of the Code on investment sensitivity. The results show that this Code has provided stronger protection to the creditors, which hurts the internal stakeholders’ interests. The study has implications for policymakers and academicians.

The purpose of this paper is to investigate the impact of entrepreneurial orientation and total quality management on quality performance of pharmaceutical manufacturers in Pakistan. It also investigates the mediating role played by total quality management in the EO-QP relationship.

A structured questionnaire was used to collect data from 115 pharmaceutical manufacturers in Pakistan. Partial least squares structural equation modeling was used to analyze data.

The results show entrepreneurial orientation that and total quality management positively and significantly affect quality performance. Moreover, the effect of entrepreneurial orientation on quality performance is partially mediated by total quality management.

The study uses cross-sectional data. Therefore, the long-term effect of entrepreneurial orientation and total quality management on quality performance could not be determined. Moreover, quality performance has been measured based on perceptual data, which may be prone to biases, entrepreneurial orientation (EO), quality performance (QP), total quality management (TQM).

The study is unique in providing empirical evidence linking entrepreneurial orientation with quality performance through the mediating role of total quality management. Quality performance is vital in the pharmaceutical industry; thus, the study makes a valuable contribution by identifying a new mechanism to achieve it. Moreover, although both entrepreneurial orientation and total quality management are very important, only a few studies have so far investigated their interrelationship. By providing evidence from a new cultural and industrial context, the study makes a valuable contribution to an under-researched yet important area.

As the Metaverse emerges as a prominent paradigm, Artificial Intelligence (AI) plays a pivotal role in shaping its landscape. This chapter explores the convergence of AI and the Metaverse, exploring the symbiotic relationship between these two transformative technologies. From virtual assistants to immersive experiences, AI-driven innovations are redefining how individuals interact and navigate within the Metaverse. This chapter also assesses the challenges and prospects brought about by AI in the Metaverse which is impacting both businesses and society on a broader scale.

Artificial intelligence (AI) is increasingly applied by businesses to optimize their processes and decision-making, develop effective and efficient strategies, and positively influence customer behaviors. Businesses use AI to generate behaviors such as customer retention (CR). The existing literature on “AI and CR” is vastly scattered. The paper aims to review the present research on AI in CR systematically and suggest future research directions to further develop the field.

The Scopus database is used to collect the data for systematic review and bibliometric analysis using the VOSviewer tool. The paper performs the following analysis: (1) year-wise publications and citations, (2) co-authorship analysis of authors, countries, and affiliations, (3) citation analysis of articles and journals, (4) co-occurrence visualization of binding terms, and (5) bibliographic coupling of articles.

Five research themes are identified, namely, (1) AI and customer churn prediction in CR, (2) AI and customer service experience in CR, (3) AI and customer sentiment analysis in CR, (4) AI and customer (big data) analytics in CR, and (5) AI privacy and ethical concerns in CR. Based on the research themes, fifteen future research objectives and a future research framework are suggested.

The paper has important implications for researchers and managers as it reveals vital insights into the latest trends and paths in AI-CR research and practices. It focuses on privacy and ethical issues of AI; hence, it will help the government develop policies for sustainable AI adoption for CR.

To the author's best knowledge, this paper is the first attempt to comprehensively review the existing research on “AI and CR” using bibliometric analysis.

In the framework of Industry 4.0, the study seeks to clarify the intricate connection between modern digitalization and sustainability and the ways in which these developing technologies support sustainable practices in the societal, environmental and economic spheres. Utilizing a conceptual framework, this study integrates insights from existing literature and provides a structured investigation to explore the impact of Industry 4.0 technologies, including smart manufacturing, artificial intelligence, the internet of things (IoT) and blockchain, on sustainability. The research reveals that the integration of environmental, social, economic and human aspects of sustainability. The study emphasizes how these elements support Industry 4.0 by encouraging moral and sustainable business practices. The insights derived from this research offer practical implications for policymakers, industry leaders and researchers. By understanding the symbiotic relationship between digitalization and sustainability, stakeholders can formulate strategies that leverage new-age technologies to achieve optimal efficiency while ensuring ethical and sustainable business practices. The originality lies in the holistic approach to understanding the environmental, social and economic dimensions impacted by new-age digitalization offering a comprehensive examination of the interplay between Industry 4.0 and sustainability.

Land value in urban areas in India is influenced by regulations, bylaws and the amenities associated with them. Planning interventions play a significant role in enhancing the quality of the neighbourhood, thereby resulting in a change in its value. Land is a distinct commodity due to its fixed location, and planning interventions are also specific to certain locations. Consequently, the factors influencing land value will vary across different areas. While recent literature has explored some determinants of land value individually, conducting a comprehensive study specific to each location would be more beneficial for making informed policy decisions. Therefore, this article aims to examine and identify the critical factors that impact the value of residential land in the National Capital Territory of Delhi, India.

The study employed a combination of semi-structured and structured interview methods to construct a Relative Importance Index (RII) and ascertain the critical determinants affecting residential land value. A sample of 36 experts, comprising property valuers, urban planners and real estate professionals operating within the National Capital Territory of Delhi, India, were selected using snowball sampling techniques. Subsequently, rank correlation and ANOVA methods were employed to evaluate the obtained results.

Location and stage of urban development are the most critical determinants in determining residential land values in the National Capital Territory of Delhi, India. The study identifies a total of 13 critical determinants.

A scenario planning approach can be developed to achieve an equitable distribution of values and land use entropy. A land value assessment model can also be developed to assist professional valuers.

There has been a lack of emphasis on assessing the impact of planning interventions and territorial regulation on land values in the context of Delhi. This study will contribute to policy decision-making by developing a rank list of planning-based determinants of land value.

This study illustrates the conceptual framework that expands the knowledge of the fundamental components that describe how AI-driven servant leadership (SEL) influences the job resources (JR), work engagement (WE) and job performance (JP) of tourism and hospitality employees.

The empirical study was conducted on a sample of 953 international tourism and hospitality employees who were selected via a purposive and snowball sampling approach in a cross-sectional survey. The analysis was performed using a partial least square-structural equation modeling.

The results of this study confirmed the positive impact of AI-driven SEL on employee JR with the boundary conditions of AI-driven SEL.

This study finding assists tourism and hospitality practitioners in understanding that in the near future, AI will have a major effect on the nature of work, including the impact on leadership styles. Hence, AI-driven SEL holds both positive (through direct impact on JR) and negative (via boundary conditions) impacts on employees’ JP and ultimately organizational success. Accordingly, managers should employ AI-driven SEL to increase employees’ JR, and once employees achieve high WE, they should constrict AI-driven SEL boundary conditions and their influence between JR and WE and WE and JP.

This study offers a novel and original conceptual model that advances AI-driven social theory, SEL theory and job demands-resources (JD-R) theory by synthesizing, applying and generalizing gained knowledge in a methodical way.

The purpose of this research was to conduct a comprehensive bibliometric analysis to explore supply chain resilience and operations management practices in the construction industry, with a particular focus on the transition from Industry 4.0 to Industry 5.0. The study addressed a significant gap in the literature regarding the impact of these advanced technologies on the construction sector’s ability to anticipate, respond to and recover from disruptions.

The methodology employed a bibliometric analysis using the Scopus database to identify key trends, influential publications and emerging research areas using keywords such as “supply chain”, “operations management”, “Industry 4.0”, “Industry 5.0” and “construction”. This approach allowed for a quantitative evaluation of existing literature, offering insights into the intellectual structure of the field.

The findings revealed that while Industry 4.0 technologies, such as IoT and AI, have enhanced the construction industry’s supply chain visibility and efficiency, the shift towards the Industry 5.0 paradigm introduces a human-centric approach that further strengthens resilience through collaboration and sustainability.

The study’s practical implications suggest to both industry and academia that embracing Industry 5.0 principles could significantly enhance the construction industry’s resilience, enabling it to withstand disruptions better and maintain project quality, timelines, and budgets in an increasingly complex global environment.

This research examines the shift from Industry 4.0 to Industry 5.0 within construction supply chains, offering a novel perspective on integrating these technologies.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.