The construction industries have been severely affected by the global pandemic Covid 19. The purpose of this paper is to identify and categorize the challenging factors faced by construction organizations during Covid 19 era and to explore the interrelationship among the factors using Total Interpretive Structural Modelling (TISM) approach. The results of the study depict shortage of labour, cautious buyers, material delay, uncertainty of demands and underutilization of renewable resources are independent factors that that are a major challenge for construction organizations. Furthermore, this is the first attempt to understand the challenging factors in construction organizations using the theory building approach like TISM.

The current study develops a second order measurement instrument for sustainable tourist behaviour through confirmatory factor analysis from tourists' responses based on three prominent destinations in India namely, Goa, Munnar and Manali. Factorial validity, invariance analysis and nomological validity were also assessed. Resultsshowed that STB is a second order reflective factor consisting of destination specific pro-environmental, socially responsible, environmental learning, cultural and economically favourable behaviours. Furthermore, while environmental engagement is a significant enabler for STB, the latter contributes significantly in the development of memorable experiences and tourist satisfaction.

Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process and analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. AI has become increasingly pervasive across a wide range of industries and sectors, with healthcare, finance, transportation, manufacturing, retail, education, and agriculture are a few examples to mention. As AI technology continues to advance, it is expected to have an even greater impact on industries in the future. For instance, AI is being increasingly used in the agri-food sector to improve productivity, efficiency, and sustainability. It has the potential to revolutionize the agri-food sector in several ways, including but not limited to precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, and food safety. This review emphasizes how recent developments in AI technology have transformed the agri-food sector by improving efficiency, reducing waste, and enhancing food safety and quality, providing particular examples. Furthermore, the challenges, limitations, and future prospects of AI in the field of food and agriculture are summarized.

No abstract

No abstract