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Statistical Hypothesization and Predictive Modeling of Reactions to COVID-19-Induced Remote Work: Study to Understand the General Trends of Response to Pursuing Academic and Professional Commitments
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Author(s): Arjun Sharma (Vellore Institute of Technology, Chennai, India), Hemanth Harikrishnan (Vellore Institute of Technology, Chennai, India), Sathiya Narayanan Sekar (Vellore Institute of Technology, Chennai, India), Om Prakash Swain (Vellore Institute of Technology, Chennai, India), Utkarsh Utkarsh (Vellore Institute of Technology, Chennai, India)and Akshay Giridhar (Vellore Institute of Technology, Chennai, India)
Copyright: 2023
Pages: 23
Source title:
Principles and Applications of Socio-Cognitive and Affective Computing
Source Author(s)/Editor(s): S. Geetha (Vellore Institute of Technology, Chennai, India), Karthika Renuka (PSG College of Technology, India), Asnath Victy Phamila (Vellore Institute of Technology, Chennai, India)and Karthikeyan N. (Syed Ammal Engineering College, India)
DOI: 10.4018/978-1-6684-3843-5.ch012
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Abstract
The initial outbreak of the coronavirus was met with lockdowns being enforced all over the world in March 2020. A prominent change in human lifestyle is the shift of professional and academic work to online platforms, as opposed to previously attending to them in person. As with any major change, the implementation of complete remote work and study is expected to affect different people differently. Through the results of a questionnaire designed as per the implications of the self-efficacy theory shared with people who were either students, working professionals, entrepreneurs, or homemakers aged between 12 and 60 years, the authors perform statistical analysis and subsequently hypothesize how different aspects of remote work affect the population from a mental standpoint using t-test, with respect to their professional or academic work. This is followed by predictive modelling through machine learning algorithms to classify working preference as ‘remote' or ‘in-person'.
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