Bridging Technology and Well-being - Applying Machine Learning to Employee Mental Health - An Investigation into the Integration, Impact, and Adoption of Intelligent Solutions

Aug 4, 2026

Upload by Noah

This master’s thesis explores the use of machine learning techniques to tackle business-oriented challenges, centering on strengthening support for employee mental health. The research targets gaps in understanding how machine learning can improve employee well-being and workplace performance. Advanced statistical and probabilistic methods evaluate multiple data sources, such as surveys and IT company records, to inform improvement opportunities. It also examines integration strategies and adoption barriers to guide effective rollout within workplace wellness programs, supporting organizational performance and well-being.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages111

Reading Time4 h 40 min

AccessFree to download

1. Introduction

1.1. Scientific & Research Question

2. Literature review

2.1. Mental Health

2.2. IT Companies

2.3. Predictive Analytics and Machine Learning

MGI Master Degree Program in Information Management Bridging Technology and Well-being: Applying Machine Learning to Employee Mental Health An Investigation into the Integration, Impact, and Adoption of Intelligent Solutions Guilherme Dinis Landeiro Vaz Castel-Branco Master Thesis presented as a partial requirement for obtaining the Master Degree in Information Management NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Bridging Technology and Well-being: Applying Machine Learning to Employee Mental Health An Investigation into the Integration, Impact, and Adoption of Intelligent Solutions by Guilherme Dinis Landeiro Vaz Castel-Branco Master Thesis presented as a partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Information Systems and Technologies Management. Supervised by Prof. Mauro Castelli, PhD, NOVA Information Management School February, 2024 STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School. Guilherme Castel-Branco Lisbon, 2023 DEDICATION My dear Family, I want to express my heartfelt gratitude for the unwavering support and boundless love you've provided throughout my journey. Your presence has been a constant anchor, guiding me through challenges and celebrating my triumphs. Grandparents, you hold a special place in my heart as a wellspring of wisdom, strength, and inspiration without limits. The stories you've shared, your enduring values, and the legacy of resilience and kindness you've imparted continue to illuminate my path. This achievement underscores the strength of our family bond and the limitless love that courses through it. Each one of you has played a pivotal role in shaping the person I am today, and for that, I am endlessly thankful. ABSTRACT This master's thesis explores the application of machine learning techniques to address business-related challenges, with a specific focus on enhancing support for employee mental health. The research aims to fill existing gaps in our understanding of how machine learning can positively impact employee well-being and workplace performance. Utilizing advanced statistical and probabilistic methods, the study will analyze a diverse range of data sources, including surveys and IT company records, to gain insights into various aspects of employee mental health and identify opportunities for improvement. The study will critically evaluate the influence of machine learning tools on employee well-being and performance while also investigating effective strategies for seamlessly integrating these tools into existing workplace wellness programs. Additionally, the thesis will address potential barriers to the adoption and utilization of machine learning tools in supporting employee mental health, offering practical solutions to overcome these challenges. This paper aims to contribute to the development of innovative tools and methodologies that foster a supportive work environment conducive to the improvement of employee mental health, thereby enhancing overall organizational performance and well-being. KEYWORDS Machine Learning; Employee Mental Health; Data Analysis; Workplace Wellness; BusinessOriented Issues JEL Codes – C38; C55; I12; I18; J24; J28; M12; M54 TABLE OF CONTENTS Statement of Integrity ....................................................................................................... ii Dedication .................

FAQs

What is the core objective of the thesis?

To apply machine learning to enhance support for employee mental health by clarifying how such methods can improve well-being and workplace performance.

Which data sources does the study plan to analyze?

The thesis analyzes diverse data sources, including surveys and IT company records, to examine aspects of employee mental health and identify improvement opportunities.

How does the thesis address practical adoption of intelligent solutions?

It evaluates integration strategies for embedding machine learning tools into existing workplace wellness programs and investigates barriers to adoption, proposing practical solutions to overcome them.

Aug 4, 2026

Upload by Noah