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'.
TopBackground
Self-efficacy theory (Bandura, 1986) claims that the behavior, environment, and cognitive factors of an individual share high levels of interrelation. Self-efficacy is defined as “a judgement of one’s ability to execute a particular behavior pattern.” Further assessment shows that self-efficacy is crucial in levels of motivation and performance exhibited by an individual (Wood & Bandura, 1989), implying that self-efficacy levels also influence the amount of effort and/or time invested on a particular task. Those with higher beliefs of self-efficacy take more effort to complete their tasks, conversely, those with lower self-efficacy beliefs tend to undertake relatively lesser effort, spend less time and sometimes even abandoning it.
According to self-efficacy theory, 4 major sources are considered by an individual through the formation of their self-efficacy judgements, they are: Performance Accomplishment, Vicarious Experience, Social Persuasion and, Physiological and Emotional State (Wrycza & Maslankowski, 2020).
Self-efficacy theory excels in behavior and performance prediction (Bandura, 1978). The theory and evidence supporting its empirical implications are robust and its implications strongly suit the study of virtual organizations (Staples et al., 1999). Employees working remote have little support or guidance, strongly fitting the current situation across the world due to COVID-19.
Key Terms in this Chapter
H0: Null Hypothesis, the true difference between the group means is zero.
Recall: Number of true positives divided by the sum of the number of true positives and number of false negatives.
CI: Confidence Interval, it measures the degree of certainty in a sampling method. The most common probability limit is 95%, which is also the limit specified in this work.
F1 Score: Harmonic mean of the recall and precision.
Accuracy: The number of correct prediction divided by the total number of predictions.
p: Probability that the sample data results occurred by chance.
DF: The largest number of logically independent values, that can vary within the dataset.
Precision: Number of true positives divided by sum of the number of true positives and the number of false positives.
T: Calculated difference in units of standard error.