Shailendra Kumar
Shailendra Kumar is Tech. philosophers and AI researcher. When it comes to AI, he has been an early trailblazer in terms of thought. In AI coding and programming, he has been first in advocating drawing on the normative notions of classical philosophers. His innovative arguments on the identity of AI humanoids as artificial persons and legal entities are a significant addition to the literature on AI ethics and philosophy. He sheds new light on the rapidly developing subject of artificial intelligence and robotics through his ground-breaking work on defining the identity of an AI humanoid and considering it as an artificial person. His writings appeal to a wide audience curious about the effects of artificial intelligence on human-robot interaction, including both experts and laypeople on the subject.
Shailendra Kumar Singh is an Assistant Professor at Sikkim's Central University in Gangtok, India. Artificial intelligence and ethics, business ethics, corporate social responsibility, business and religion, business and gender, business and human rights, and marketing management are among his research and specialisation areas. He has a rich teaching experience of more than 15 years and is widely published. His books are used in courses at well-known business schools in India and abroad, and his research papers are published by all the top publishers around the world, such as Elsevier, Taylor & Francis, Springer, Nature, MDPI, Cengage, Marszalek, McMillan, etc.
Shailendra Kumar Singh is an Assistant Professor at Sikkim's Central University in Gangtok, India. Artificial intelligence and ethics, business ethics, corporate social responsibility, business and religion, business and gender, business and human rights, and marketing management are among his research and specialisation areas. He has a rich teaching experience of more than 15 years and is widely published. His books are used in courses at well-known business schools in India and abroad, and his research papers are published by all the top publishers around the world, such as Elsevier, Taylor & Francis, Springer, Nature, MDPI, Cengage, Marszalek, McMillan, etc.
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Papers by Shailendra Kumar
Design/methodology/approach-A meta-correlation analysis was done using sample size and correlation (r) data from several relevant studies that look at how emotional biases (loss aversion bias, regret aversion bias, and overconfidence bias) affect investment decisions. Additionally, beta coefficients (ß) were also converted to correlation coefficients (r) from six studies.
Findings-This study analysed 31 empirical studies and found a significant positive correlation between emotional biases and investment decisions [loss aversion bias (r 5 0.492), regret aversion bias (r 5 0.401), and overconfidence bias (r 5 0.346)]. We set the statistical significance threshold at 0.05.
Research limitations/implications-The review covered 31 online research publications that showed significant heterogeneity, possibly influenced by various methodological, population, or other factors. Furthermore, the use of correlational data restricts the ability to establish causation.
Originality/value-This is a novel attempt to integrate the results of various studies through meta-analysis on the relation between these emotional biases (loss aversion, overconfidence, and regret aversion) and investment decisions.
Design/methodology/approach-A meta-correlation analysis was done using sample size and correlation (r) data from several relevant studies that look at how emotional biases (loss aversion bias, regret aversion bias, and overconfidence bias) affect investment decisions. Additionally, beta coefficients (ß) were also converted to correlation coefficients (r) from six studies.
Findings-This study analysed 31 empirical studies and found a significant positive correlation between emotional biases and investment decisions [loss aversion bias (r 5 0.492), regret aversion bias (r 5 0.401), and overconfidence bias (r 5 0.346)]. We set the statistical significance threshold at 0.05.
Research limitations/implications-The review covered 31 online research publications that showed significant heterogeneity, possibly influenced by various methodological, population, or other factors. Furthermore, the use of correlational data restricts the ability to establish causation.
Originality/value-This is a novel attempt to integrate the results of various studies through meta-analysis on the relation between these emotional biases (loss aversion, overconfidence, and regret aversion) and investment decisions.