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This study explores the relevance of film texts in Indian social studies classrooms for students at the upper primary level. It examines how different types of visual texts can facilitate awareness, critical thinking, discussion and action.

Using multimodal discourse analysis (MDA), this paper critically evaluates five selective films from regional cinemas in India and explores their teaching implications for social studies. The study argues that films are vital multimodal resources that can challenge the prevailing narratives in social studies pedagogy. It conveys how the suggestive revised taxonomy could benefit the students. It proposes a classification system for film analysis with discussion and states how films can bring out interrelated themes and encourage deeper, critical inquiry within the curriculum. The study finds discourse around the films and qualitatively stresses dialogue exchange with sentiment analysis using MAXQDA software. Developed by VERBI software, MAXQDA is a product whose name is inspired by the German Sociologist Max Weber, while the ‘QDA' stands for Qualitative Data Analysis.

The films act as multimodal texts, navigators, metaphors, communicative circuits and catalysts. The paper concludes that films can improve and expand multimodal learning of social studies in three ways: films help learners connect emotionally with the concepts, films make the learning process more appealing and extend it beyond classroom boundaries and films offer a unique insight into the socio-cultural subtleties that are often limited in textbooks.

This research pioneers an intersectionality-driven framework for film analysis in the curriculum for Indian upper primary social studies, offering innovative pedagogical tools to enrich Indian curriculum insights and bridge existing knowledge gaps.

Research in the domain of behavioral finance has proven that investors demonstrate irrational behavior while making investment decisions. In a similar domain, the primary objective of this research is to prioritize the behavioral biases that influence cryptocurrency investors' investment decisions in the Indian context.

A fuzzy analytic hierarchy process (F-AHP) was used to prioritize the behavioral factors impacting cryptocurrency investors' investment decisions. Overconfidence and optimism, anchoring, representativeness, information availability, herding, regret aversion, and loss aversion are among the primary biases evaluated in the present study.

The findings suggested that the two most important influential criteria were herding and regret aversion, with loss aversion and information availability being the least influential criteria. Opinions of family, friends, and colleagues about investment in cryptocurrency, the sale of cryptocurrencies that have increased in value, the avoidance of selling currencies that have decreased in value, the agony of holding losing cryptocurrencies for too long rather than selling winning cryptocurrencies too soon, and the purchase of cryptocurrencies that have fallen significantly from their all-time high are the most important sub-criteria.

This survey only covered active cryptocurrency participants. Additionally, the study was limited to individual crypto investors in one country, India, with a sample size of 467 participants. Although the sample size is appropriate, a larger sample size might reflect the more realistic scenario of the Indian crypto market.

The study is relevant to individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, market regulators, and society at large.

To the best of the authors' knowledge, no prior research has attempted to explain how the overall importance of various criteria and sub-criteria related to behavioral factors that influence the decision-making process of crypto retail investors can be assessed and how the priority of focus can be established, particularly in the Indian context.