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The tourism has become more responsible in terms of tourists choosing environmentally friendly alternatives and resorting to green options in food, travel, and purchase. In view of this, “ecotourism” appears as a sustainable means of promoting tourism in the less explored areas which are endowed with esthetic beauty of nature. Ecotourism is based on the pillars of biodiversity, wildlife, natural wealth, rivers, and authentic cuisine. The proposed chapter is an attempt to explore the ecotourism potential of the Southern Rajasthan in India which has a rich heritage of natural wealth and cultural diversity.

In recent years, due to fast growth of Kabul city in Afghanistan, newest construction has been taking place in unplanned ways and entailed illegal squatting on land and mountains. Such unplanned urban expansion, without any type of urban infrastructure and insufficient drainage systems, has created many problems including an increasing number of urban floods. Recent reports show that the number of female victims is higher than that of males affected by floods. First, this chapter highlights the country overview of the disaster and vulnerability, and then particularly focus on the vulnerability factors of urban women who are affected by floods and living in Kabul city. Data for this study were obtained through focus group discussions with urban women, and men and face-to-face interviews with governmental and non-governmental organizations (GOs-NGOs) at the local as well as national levels in Afghanistan. Interpretive Structural Modelling (ISM) was used to map the relationship among factors, and to stratify factors according to their importance. Findings show that the lack of education on disaster management and the lack of protective measures play an important role in increasing women’s vulnerability during disasters. Furthermore, the study finds that cultural issues represent a dominant vulnerability factor which affect women. The comparative results show that the women affected by the cultural norms during the flood are less important than the post-disaster. The study also found that the perception regarding these cultural norms and how they affect women’s behaviour during disasters differs between men and women. This chapter concluded some policy recommendations towards resilient livelihoods.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

The objective of this chapter is to study the symmetric and asymmetric impact of macroeconomic variables on the Indian stock prices (SPs) of the Bombay Stock Exchange index. This chapter further investigates whether the asymmetric impact of macroeconomic variables on SP is due to the impact of any tail events like the global financial recession. An autoregressive distribution lag and non-autoregressive distribution lag approach is used for the full sample covering the period from January 2000 to June 2019 and later this sample is further subdivided into before and after the crisis period to study the variations in result. The findings show that macroeconomic variables and SP have a symmetric relation in the long run whereas an asymmetric relationship in the short run when the whole sample is analyzed. However when data are segregated into “before and after” crisis period this relationship turns to be asymmetric in long run too, meaning that in the long run, the negative and positive changes in a macroeconomic variable do not affect SPs similarly.