Individual investor’s behavior is extensively influenced by various biases that highlighted in the growing discipline of behavior finance. Therefore, this study is also one of another effort to assess the impact of behavioral biases in investment decision-making in National Stock Exchange. A questionnaire is designed and through survey responses collected from 243 investors. The present research has applied inferential statistics and descriptive statistics. In the existing study, four behavioral biases have been reviewed namely, overconfidence, anchoring, disposition effect and herding behavior. The results show that overconfidence and herding bias have significant positive impact on investment decision. Overall results conclude that individual investors have limited knowledge and more prone towards making psychological errors. The findings of the study also indicate the existence of these four behavioral biases on individual investment decisions. This study will be helpful to financial intermediaries to advice their clients. Further, study can be elaborated to study other behavioral biases on investment decisions.

The Internet of Things (IoT) has emerged as a transformative technology paradigm, interconnecting a vast array of devices and enabling unprecedented levels of data collection, analysis, and automation. This study explores the foundational concepts, applications, and implications of IoT across various domains. IoT facilitates real-time monitoring and control, enhancing efficiency and productivity in sectors such as healthcare, agriculture, manufacturing, and smart cities. By integrating sensors, actuators, and connectivity technologies, IoT enables seamless communication between devices and systems, facilitating intelligent decision-making and predictive analytics.However, the rapid proliferation of IoT devices also raises significant challenges, including privacy concerns, data security risks, and interoperability issues. Addressing these challenges requires robust protocols, standards, and regulatory frameworks to ensure the reliable and secure operation of IoT ecosystems.Looking ahead, IoT continues to evolve with advancements in edge computing, artificial intelligence, and 5G .

Revolutionizing industries and civilizations alike, data analysis and artificial intelligence (AI) have emerged as transformative forces in modern technology. Machine learning and other forms of artificial intelligence allow systems to gain knowledge from data, see trends, and make judgments with more independence and precision. To enhance AI's decision-making and forecasting capacities across a wide range of disciplines, data analysis is essential for mining massive datasets for actionable insights. Companies can improve their operations, provide better customer experiences, and come up with new goods and services with the help of AI and data analysis. The significance of ethically deploying AI is highlighted by factors like data privacy and bias prevention. Future technologies, such as healthcare diagnostics, autonomous systems, and smart cities, are expected to be transformed by the combination of AI and data analysis. Efforts in research, development, and governance must be coordinated if these areas are to realize their full potential, overcome obstacles.

United Nations SDGs (sustainable development goals) have prompted health-care organisations in and around this world to implement health care initiatives that aim to improve their long-term sustainability. Analysis of 637 documents from the Scopus database was retrieved using bibliometric methods for the existing study. Research on health care sustainability has been documented over almost the past 12 years (2010-November, 2022) in this review. Results have indicated that the intellectual structure of this knowledge body comprises of five clusters. First, the authors saw that this review database had 22 writers listed as authors. This indicates that a high number of academicians are interested in this topic. But at the same time, it is also evident that majority of contributors produce only few publications on health care sustainability.

This study aims to explore and explain the history of neuromarketing, techniques, examples, pros and cons of neuromarketing, major limitations, as well as its major findings and draw conclusions for its future development. This is a theoretical chapter. An extensive literature research was done for its goals. Additionally, to provide more clarity on the influence of neuromarketing, researchers employing neuromarketing technologies combined existing information from multiple literatures. In this study, the authors realized that the consumer's psychology has been and is still being affected by the digital world where neuromarketers manipulate consumer's choosing capacities towards a product through digital network advertising. However, it has been discovered that researchers are still working on it, to figure out a significant scientific solution to the existing problems, such as privacy and price of equipment(tools) to be used. This chapter might be useful for neuroeconomics theorists and practitioners who wish to learn more about neuromarketing technology.

Objective: In India, incorporating structural transformation in corporate social responsibility for achieving sustainable development goals in the Covid-19 Pandemic has become a priority. Therefore, the present article aims to review the corporate social responsibility activities conducted by Nine Indian companies (two public sector organizations and seven private sector organizations) towards attaining Sustainable Development Goals in times of global pandemic.   Method: For our research, we chose India since it was one of the earliest and worst-hit countries during the recent pandemic. Even though huge Asian corporations are a minority, It is a good fit for our study goal for various reasons. Less and medium-sized businesses, on the other hand, have a far smaller influence on society, Due to their prominence in the press, clients or consumers are more likely to provide feedback if they are proactive. As a second point, major companies have greater resources (e.g., human and financial) to spend on reporting and distributing social and environmental information. The existing study applied bibliometric analysis in the exploratory research. The data collection done through secondary sources in which articles extracted with the use of PRISMA flowchart.   Result: The study's findings state that most selected Indian Companies spend Rupees 50-1000 crores as corporate social responsibility activities and contribute to the Pradhan Mantri Relief Fund as a business response to face challenges in times of pandemic. Moreover, private companies spend more on CSR activities than public companies in India. The study provides suggestions to the government to make the corporate social responsibility activities compulsory for all the profitable companies so that country can maintain a corporate pool of contingent funds that can utilize to meet such times of critical circumstances.   Conclusion: Companies are taking steps to ensure the safety and protection of their personnel. This has been reflected in corporate social responsibility initiatives as well. Corporate social responsibility efforts must include pushing for and facilitating access to health insurance programmes and other equity indicators as a result.