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The present research study aims to explore the impact of the most recent Israeli–Palestinian conflict, which unfolded in October 2023, on global equity markets, including a wide range of both emerging and developed markets (as per the Morgan Stanley Capital Investment country classification).

The market model of event study methodology, with an estimation window of 200 days and 28-day event window (including event day, i.e. October 7, 2023), has been employed to investigate the event’s impact on the stock markets of different countries, with 24 emerging countries and 23 developed countries. The daily closing prices of the prominent indices of all 47 countries have been analyzed to examine the impact of the conflict on emerging markets, developed markets and overall global equity markets. Additionally, cross-sectional regression analysis has been performed to investigate the possible explanations for abnormal returns.

The findings of the study suggest the heterogeneous impact of the selected event on different markets. Notably, emerging markets and the overall global equity landscape exhibited substantial negative responses on the event day, as reflected in average abnormal returns of −0.47% and −0.397%, respectively. In contrast, developed markets displayed resilience, with no significant negative impact observed on the day of the event. A closer examination of individual countries revealed diverse reactions, with Poland, Egypt, Greece, Denmark and Portugal standing out for their positive or resilient market responses. Poland, in particular, demonstrated significantly positive cumulative abnormal returns (CARs) of 7.16% in the short-term and 8.59% in the long-term event windows (−7, +7 and −7, +20, respectively), emphasizing its robust performance amid the geopolitical turmoil. The study also found that, during various event windows, specific variables had a significant impact on the CARs.

The study suggests diversification and monitoring of geopolitical risks are key strategies for investors to enhance portfolio resilience during the Israeli–Palestinian conflict. This study identifies countries such as Poland, Egypt, Greece, Denmark and Portugal with positive or resilient market reactions, providing practical insights for strategic investment decisions. Key takeaways include identifying resilient markets, leveraging opportunistic strategies and navigating market dynamics during geopolitical uncertainties.

As per the authors’ thorough investigation and review of the literature, the present study is the earliest attempt to explore the short-term and long-term impact of the 2023 Israeli–Palestinian conflict on equity markets worldwide using the event study approach and cross-sectional regression analysis.

The present study aims to comprehensively examine the impact of the Union Bank of Switzerland (UBS) takeover of Credit Suisse on the banking and financial services sector in the Indian stock market. To fully comprehend the impact of the event, the study separately investigates the response of private sector banks, public sector banks, overall banking companies and financial services companies to the takeover of the second-largest financial institution in Switzerland.

The study employs event study methodology, using the market model, to analyze the event's impact on Indian banking and financial services sector stocks. The data consists of daily closing prices of companies included in the Nifty Private Bank Index, Nifty PSU Bank Index, Nifty Bank Index and Nifty Financial Services Index from the National Stock Exchange (NSE). Furthermore, cross-sectional regression analysis has been conducted to explore the factors that drive abnormal returns.

The empirical findings of the study suggest the event had a heterogeneous impact on the stock prices of Indian banks and financial services companies. While public sector banks experienced a significant negative impact on select days within the event window, the overall Indian banking sector and financial services companies also witnessed notable declines. In contrast, Indian private sector banks were relatively resilient, exhibiting minimal effects. However, the cumulative effect is found to be insignificant for all four categories across different event windows. The study also observed that the cumulative abnormal returns (CARs) were significantly influenced by certain variables during different event windows.

To the best of the authors' knowledge, the present study is the earliest attempt that investigates the impact of the UBS takeover of Credit Suisse on the Indian banking and financial services sector using event study methodology and cross-sectional regression model.

Given the dearth of thorough summaries in the literature, this systematic review and bibliometric analysis attempt to take a meticulous approach meant to present knowledge on the constantly developing subject of stock market volatility during crises. In outline, this study aims to map the extant literature available on stock market volatility during crisis periods.

The present study reviews 1,283 journal articles from the Scopus database published between 1994 and 2022, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow diagram. Bibliometric analysis through software like R studio and VOSviewer has been performed, that is, annual publication trend analysis, journal analysis, citation analysis, author influence analysis, analysis of affiliations, analysis of countries and regions, keyword analysis, thematic mapping, co-occurrence analysis, bibliographic coupling, co-citation analysis, Bradford’s law and Lotka’s law, to map the existing literature and identify the gaps.

The literature on the effects of crises on volatility in financial markets has grown in recent years. It was discovered that volatility intensified during crises. This increased volatility can be linked to COVID-19 and the global financial crisis of 2008, as both had massive effects on the world economy. Moreover, we identify specific patterns and factors contributing to increased volatility, providing valuable insights for further research and decision-making.

The present study is confined to the areas of economics, econometrics and finance, business, management and accounting and social sciences. Future studies could be conducted considering a broader perspective.

Most of the available literature has focused on the impact of some particular crises on the volatility of financial markets. The present study is not limited to some specific crises, and the suggested research directions will serve as a guide for future research.

This study aims to investigate the impact of FTX bankruptcy on the global stock markets, including both the developed and emerging markets, as per the Morgan Stanley Capital Investment (MSCI) country classification.

Using the daily closing prices for leading stock market indices of all 47 countries in the MSCI market classification, comprising 23 developed markets and 24 emerging markets, the event study methodology is used to examine the impact of the event on developed markets, emerging markets and overall global equity markets.

The study finds heterogeneous effects of the event on different countries. Results indicate that overall global equity markets experienced a statistically significant positive cumulative average abnormal returns of 15.8533% in the complete event window of 28 days from t − 7 to t + 20. The authors conclude that traditional global equity markets can be used as a hedge against potential financial risk posed by unfavorable events in the cryptocurrency markets and have safe haven properties.

The study emphasizes the global financial system’s interconnectedness and the potential of traditional equity markets to hedge risks in the cryptocurrency market. The findings are relevant for investors seeking portfolio diversification and mitigating their exposure to potential risks in the cryptocurrency market.

To the best of the authors’ knowledge, the present study is the earliest attempt to comprehensively examine the impact of the bankruptcy of the world’s fourth largest cryptocurrency exchange, FTX, on the global equity markets.

This study evaluated the bundled impact of high performance work practices (HPWPs) on Generation Y (Gen Y) employee engagement (EE) while considering empowering leadership (EL) as a mediator.

The data for the study are received from 404 Gen Y frontline service employees from three to five star Indian hotels and examined using structural equation modeling.

Gen Y employees' perception of HPWPs directly explains their engagement, and EL partially mediates the link between HPWPs and Gen Y EE.

This study suggests managers to gain from implementing HPWPs and their impact on Gen Y engagement to boost their organizational performance.

This study suggests managers to gain from implementing HPWPs and their impact on Gen Y EE to boost their employee and hotel's performance.

The present research is one of the few attempts to study how HPWPs can engage the Gen Y cohort in the workplace, especially in developing countries (i.e. India).

The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.

This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.

For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.

The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to store, maintain, and analyze large sets of data provided by different systems of health. It is essential to manage and analyze these data to get meaningful information. Pharmaceutical companies are accumulating their data in the medical databases, whereas the payers are digitalizing the records of patients. Biomedical research generates a significant amount of data. There has been a continuous improvement in the health sector for past decades. They have become more advanced by recording the patient’s data on the Internet of Things devices, Electronic Health Records efficiently. BD is undoubtedly going to enhance the productivity and performance of organizations in various fields. Still, there are several challenges associated with BD, such as storing, capturing, and analyzing data, and their subsequent application to a practical health sector.

This study aims to quantify and prioritize the financial performance (FP) determinants in Indian small and medium-sized enterprises (SMEs).

Analytic hierarchy process, a multi-criteria decision-making tool, was used. Experts were allowed to express the opinion regarding the relative importance of each factor and sub-factors by making pairwise comparisons through a structured questionnaire based on a nine-point scale.

Market orientation (0.4529) was perceived as the most important FP determinant followed by the entrepreneurial orientation (0.3382) and corporate social responsibility (0.2089) in SMEs.

This study can be considered as a pilot study because it is confined to Indian SMEs. Future research studies can incorporate the opinion or insights of other stakeholders and may target the SMEs situated in different geographical areas.

The inferences drawn in this study would clarify the conceptual and contextual applicability of competitive strategies in SMEs. Indeed, proposed hierarchy and developed framework would guide the SMEs in strategic planning. Moreover, it would help in repositioning and alignment of core strategies duly with business objectives.

The study represents the foremost step and a unique effort in the area of development of hypothetical model (a hierarchal model) with the framework considered to prioritize the FP determinants in SMEs.

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.