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This chapter analyzes the impact of terrorism in South Asian countries. The study is based on secondary data collected from South Asian Report, crime records, South Asia Terrorism Portal, and other reports. Descriptive statistics of South Asia shows that out of the total deaths due to terrorism, 52.63 percent of the deaths occurred among terrorists, 35.22 percent civilians, and only 12.15 percent among the security forces (SFs). Human Development Index (HDI) and total number of fatalities in the region are highly correlated with an expected negative sign. This means that terrorist activities have adversely affected human development in the South Asian region. Besides, human development of the SFs has been highly hampered by their fatalities, with that of terrorists being relatively low. Civilians are relatively less affected by the fatalities as the correlation results show a moderate (−0.543) value. Total number of deaths due to terrorism in India was 21,942 between 2005 and 2018 but was 57,840 in Pakistan, which is substantially higher compared to India. The number of deaths of civilians, SFs, and terrorists in Pakistan is almost double that of India during the same period. In India, civilian deaths due to terrorism have significantly reduced over time. In Pakistan, civilian deaths have increased from 2005 to 2013, thereafter reducing. Terrorist groups have been subjected to major loss due to more deaths among them. With regard to terrorism, Pakistan is the most critical country in the South Asian region. Regional cooperation in South Asia and multilateral discussions can reduce terrorism in this region.

This chapter covers the attributes of a well-designed questionnaire and on how to adopt a framework for developing questionnaires. Different types of questionnaires are discussed exhaustively, with tips on structure, procedures, and standard format examples. The author gives an elaborate example of a survey questionnaire, closely related to one of his major research project.

The introductory paper begins with the issue about the relevance of research in management. It emphasizes the need for scholars to adopt methodologies best suited to the research problem of their choice. This paper contains sections on the nature of management research, dominant research paradigms, the methodological domain, quantitative versus qualitative research, and triangulation in using multiple methodologies. The paper provides a background to the purpose of the book and summarizes in brief the purpose of each the subsequent papers.

In this research paper, the authors delve into the ethnography and socioeconomic status of the Chenchu tribal community that resides in Telangana's Nallamalla forest. The authors also examine the initiatives undertaken by the government to create fair entrepreneurial opportunities while preserving their unique culture. Additionally, the authors analyse potential entrepreneurial and business opportunities using the environmental, social and governance (ESG) framework.

To gain insight into the current socio-economic development and living standards of the Chenchu tribal people, the authors conducted interviews in nine different villages with 177 individual respondents. Statistical analysis was then used to determine the relationship between business opportunities and their impact on socio-economic development based on the data collected.

The Chenchu tribal community has faced significant challenges, including vulnerability, poverty and debt. To promote the development of the tribe, it is essential to provide equal opportunities in education, healthcare and means of subsistence. The government has implemented various social and developmental programs to support sustainable development and entrepreneurial opportunities while preserving the tribe's unique culture and improving their education and standard of living. These initiatives are aimed at benefiting the entire community.

The Chenchu tribal people are eager for progress but hesitant to leave their forest homes and integrate into modern society. This creates challenges for efficiently implementing welfare programs. It is crucial to strike a balance between forest regulations and the rights of indigenous communities to ensure their protection and prevent any hindrance to development efforts.

To promote sustainable development and preserve tribal cultures, the government should implement various social and developmental programs. These programs should improve living conditions and socioeconomic development while exploring business opportunities. Maintaining these communities' unique identities while striving for progress is essential.

Phishing is one of the major threats affecting businesses worldwide in current times. Organizations and customers face the hazards arising out of phishing attacks because of anonymous access to vulnerable details. Such attacks often result in substantial financial losses. Thus, there is a need for effective intrusion detection techniques to identify and possibly nullify the effects of phishing. Classifying phishing and non-phishing web content is a critical task in information security protocols, and full-proof mechanisms have yet to be implemented in practice. The purpose of the current study is to present an ensemble machine learning model for classifying phishing websites.

A publicly available data set comprising 10,068 instances of phishing and legitimate websites was used to build the classifier model. Feature extraction was performed by deploying a group of methods, and relevant features extracted were used for building the model. A twofold ensemble learner was developed by integrating results from random forest (RF) classifier, fed into a feedforward neural network (NN). Performance of the ensemble classifier was validated using k-fold cross-validation. The twofold ensemble learner was implemented as a user-friendly, interactive decision support system for classifying websites as phishing or legitimate ones.

Experimental simulations were performed to access and compare the performance of the ensemble classifiers. The statistical tests estimated that RF_NN model gave superior performance with an accuracy of 93.41 per cent and minimal mean squared error of 0.000026.

The research data set used in this study is publically available and easy to analyze. Comparative analysis with other real-time data sets of recent origin must be performed to ensure generalization of the model against various security breaches. Different variants of phishing threats must be detected rather than focusing particularly toward phishing website detection.

The twofold ensemble model is not applied for classification of phishing websites in any previous studies as per the knowledge of authors.