International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023
p-ISSN: 2395-0072
www.irjet.net
Understanding Work-Life Balance: An Analysis of Quiet Quitting and Age Dynamics using Deep Learning Devang Shah1, Masumee Parekh2 1Student, Department of Computer Engineering, NMIMS University, Maharashtra, India
2Student, Department of Computer Engineering, NMIMS University, Maharashtra, India
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Abstract - In today's fast-paced society, achieving work-life
One notable phenomenon that has emerged in recent years is Quiet Quitting. This term refers to employees who, do not openly resign but disengage from their work and become less committed to their organisations. Quiet Quitting can have detrimental effects on both the individual and the organisation, resulting in reduced job satisfaction, decreased productivity, and increased turnover rates [8]. Exploring the factors contributing to quiet quitting and understanding its implications is crucial for organisations to create an environment that promotes engagement and job satisfaction.
balance is increasingly becoming a challenge for individuals across various age groups. To gain a comprehensive understanding of this phenomenon, we leverage the power of Deep Learning (DL) and Machine Learning (ML) techniques in this research study. We explore how ML can be instrumental in understanding the determinants of work-life balance, with a particular focus on the concept of "quiet quitting" and how different attributes possess varying degrees of importance in establishing work-life balance across different age groups. Our study is based on a comprehensive dataset of 15,977 survey responses encompassing 25 attributes related to work and personal life. To extract meaningful insights from the dataset, we have implemented Artificial Neural Network (ANN) algorithm. Our model's performance showcases promising results, with a test accuracy of 73.91%. This underscores the efficacy of the ANN implementation in capturing the complexity of work-life balance dynamics. Additionally, we discuss various potential solutions and recommendations derived from our findings, enabling organizations and individuals to proactively address work-life balance challenges and foster healthier, more sustainable work environments.
As individuals progress in age, their priorities, responsibilities, and overall life circumstances change. The factors influencing work-life balance can vary significantly based on age, as different life stages bring forth distinct priorities and responsibilities [12]. Understanding how various attributes hold different weightage in determining work-life balance for different age groups is crucial for organisations to tailor their strategies and support systems effectively. For instance, younger employees may place greater importance on career advancement, achievement, and social networking. These individuals may strive for recognition and engage extensively in professional networks to expand their opportunities. Consequently, work-life balance for this age group may be influenced by the interplay between their ambition, social connections, and the time dedicated to personal pursuits.
Key Words: Deep Learning, Quiet Quitting, Work Life Balance, Artificial Neural Networks, Data Analysis.
1.INTRODUCTION
On the other hand, as individuals enter mid-career stages, their work-life balance considerations may shift. The ability to maintain financial stability, allocate time for personal interests and hobbies, and strike a healthy equilibrium between work and personal life becomes increasingly significant. Mid-career professionals often seek fulfilment beyond material success and strive for a more holistic approach to their well-being.
In today's fast-paced and ever-evolving world, the influence of machine learning (ML) and deep learning (DL) algorithms is ubiquitous. These advanced computational techniques have significantly impacted various aspects of our lives, including the workplace. As industries undergo rapid transformations and individuals face increasing personal and work-related challenges, the concept of work-life balance has become crucial for maintaining well-being and productivity.
The Lifestyle and Wellbeing dataset from Kaggle consisting of survey responses from authentic-happiness website, includes factors such as fruits and vegetables consumption, stress levels, social network size, achievement orientation, physical health indicators like BMI range and daily steps, demographic variables like age and gender and various other parameters.
The changing landscape of industries has introduced novel demands and complexities, often leading to personal and work-related problems for individuals. The relentless pursuit of professional success and the pressures of modernday work environments can take a toll on employees, affecting their physical and mental well-being [1]. Consequently, organisations have started recognizing the importance of work-life balance as a means to foster a healthier and more productive workforce.
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In this paper, we have used the Artificial Neural Network (ANN) algorithm which captures complex patterns and
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