Morande, Swapnil (2022) Augmenting Psychological Well-being using Artificial Intelligence: Reflections on the Workplace Productivity. [Tesi di dottorato]

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The presented doctoral thesis is titled “Augmenting Psychological Well-being using Artificial Intelligence: Reflections on Workplace Productivity”. It offers insight into how technology can be used to support psychological health. This study makes use of Healthcare IoT (IoMT) and Artificial Intelligence (A.I.) to fulfill the same. The study, with its interdisciplinary approach, focuses on the augmentation of psychosomatic health using A.I. and considers its impact on an individual to extend reflections on organizational performance. Health is an essential component of life. We take care of physical health, but mental health is usually taken for granted where it must be given the same care and importance. Psychosomatic health is nothing but a holistic reflection of both the physical and mental health of an individual. As per the pilot study, the root causes of the same are related to events relating to workplaces, finances, and relationships. As studies indicate, stress, anxiety, and depression are the signs of degrading mental health; considering service research priorities, the presented research empirically explores the impact of positive emotions on psychological well-being. Observing the complexity of neural constructs, Artificial Intelligence is deployed to be able to gain valuable insights. At the same time, keeping a managerial point of view in research reflects on co-created value in an organization achieved through a person’s well-being. Literature suggests that seeking therapy may be the only option while dealing with psychological issues, but it could be a time-consuming, expensive process with limited access to society. We do have technologies that have advanced over the last few decades but are mainly focused on supporting physical health. The presented study offers insight into how it can be used to support psychological health in the form of Machine Learning. The study reflects on EEG retrieved in the form of brain signals. Based on the adaption of research design termed as ‘Sequential Mixed Method,’ the study extends its application from the personal to a professional arena for enhancing workplace productivity. Research design includes experiments with predictive analytics and drives discussions using Qualitative and Quantitative data. Based on the information retrieved from the subjects - captured through a BCI and a survey questionnaire, a Machine Learning (ML) model was developed. In this study, we hypothesize that such treatment protocol can accelerate treatments by therapists for the betterment of Psychosomatic health. Not only that, but the use of the ML model can also offer greater scalability in reaching out to the masses for greater access. The well-being achieved can positively reflect on the individual. Through a comprehensive view, it would support a person in improving their personal and professional life. Ultimately given study suggests that the well-being achieved could further impact organizations, enhancing their overall performance as validated in the presented thesis. The contribution of this study was the interlinking of interdisciplinary domains such as Management - Technology and Healthcare. Also, this study uniquely utilized Data science due to the large size of the dataset that is collected in this study.

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