Advances in Social Science and Culture ISSN 2640-9682 (Print) ISSN 2640-9674 (Online) Vol. 6, No. 3, 2024 www.scholink.org/ojs/index.php/assc 92 Original Paper Juggling Work and Studies: A Focused Study on Work-Life Balance and Well-being of Part-Time Student Workers Raghid Ghassan El Ali1 & Khadija Kandil2 1 Raghid Ghassan El Ali, doctorate in business administration student at Beirut Arab University, The United Arab Emirates 2 Khadija Kandil, doctorate in business administration student at Beirut Arab University, The United Arab Emirates Received: April 27, 2024 Accepted: June 11, 2024 Online Published: June 19, 2024 doi:10.22158/assc.v6n3p92 URL: http://dx.doi.org/10.22158/assc.v6n3p92 Abstract Where social unrest, economic downturn, and political instability coexist, part-time workers and students have difficulties in balancing their personal, professional, and academic commitments. To understand how part-time students achieve work-life balance, this study examines work-related stress, flexible work schedules, productivity while working from home, supervisor and coworker support, and work-related characteristics among part-time employees who are also students in Lebanon. Data from a sample of part-time employees who are also students will be gathered using a quantitative survey approach in order to examine how these characteristics interact. In order to improve the academic performance and general well-being of part-time employees who are also students in Lebanon, the findings are intended to influence organizational practices, inform policy interventions, and contribute to the corpus of existing research on work-life balance. 1. Part 1 1.1 Introduction The juggling of work and studying has become increasingly common among university students, particularly those who are part-time workers. In Lebanon, where the cost of education is high and the job market is competitive, many students find themselves having to balance their academic responsibilities with part-time employment. This can take a toll on their well-being and work-life harmony, as they battle to juggle the demands of their jobs and their studies. In this study, we aim to examine the specific challenges faced by part-time student workers in Lebanon, with a focus on how this juggling act impacts their overall well-being. By conducting surveys with www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 93 students and employers, we hope to gain insights into the strategies employed by students to manage their time effectively and maintain a sense of equilibrium between their work and studies. Additionally, we will explore the support systems available to these students, both within their academic institutions and in the workplace. This study is important because it provides important information and understandings on the health and work-life balance of part-time student workers in Lebanon. The research findings hold potential implications for Higher Education Institutions (HEIs) in Lebanon, as they can inform the development of policies and programs aimed at supporting the health and work-life equilibrium of students who work part-time. In line with the expanding interest in this field worldwide, this study contributes to the body of information on work-life balance and its consequences on the wellbeing of part-time student workers. Understanding the specific challenges faced by part-time student workers in Lebanon contributes to the broader discourse on work-life balance, benefiting employees across different countries and industries. In summary, this research closes a significant gap in the research by shedding light on the experiences of part-time student workers in Lebanon with regard to work-life balance and by making insightful recommendations for improving their well-being and work-life balance, possibly both domestically and internationally. This study, titled "Juggling Work and Studies: A Lebanon-Focused Study on Work-Life Balance and Well-being of Part-Time Student Workers", seeks to investigate the difficulties encountered by part-time employee-students in managing their employment, academic, and personal responsibilities. 1.2 Research Question Limited research explores work-life balance for part-time student employees in Lebanon, despite its critical importance in a nation facing political and economic turmoil. Existing global studies offer a foundation, but Lebanon's unique context demands deeper understanding. Research Question How do work stress, flexible work arrangements, work-from-home productivity, supervisor support, and co-worker support relate to the work-life balance of a part time students? This study investigates this question through the lens of the following hypotheses: Main Hypothesis (H1):  Higher levels of Work Stress negatively impact Work-Life Balance among part-time student employees. Moderation Hypothesis 1 (H2):  Supervisor Support and Coworker Support moderates the relationship between Work Stress and Work-Life Balance, such that higher levels of Supervisor Support weaken the negative impact of Work Stress on Work-Life Balance. Mediating Hypothesis 1 (H3):  Work-from-Home Productivity mediates the relationship between Work Stress and Work-Life Balance, such that higher levels of Work-from-Home Productivity weaken the negative impact of Work www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 94 Stress on Work-Life Balance among part-time student employees. Mediating Hypothesis 2(H4):  Flexible Work Arrangements (FWAs) mediate the relationship between Work Stress and Work-Life Balance, such that the availability of FWAs weakens the negative impact of Work Stress on Work-Life Balance among part-time student employees 1.3 Justification of Research: Why This Topic Matters This topic is important and worth studying for several reasons: First, addressing a Critical Gap in Knowledge: Limited research exists on the experiences of part-time employee-students, emphasizing the importance of understanding their challenges, coping mechanisms, and the impact of their dual roles on various aspects of life. Existing literature predominantly focuses on full-time employees or traditional students, overlooking the unique dynamics of work-life balance for part-time employee-students. Despite extensive research on the quality of life, studies specific to this group are scarce. Consequently, there is a dearth of information in the literature about the experiences of part-time employees who are also students with regard to work-life balance, underscoring the need for more research on this understudied group. Furthermore, there is a lack of longitudinal studies tracking the progression of part-time employee-students over time, which could provide valuable insights into their evolving work-life balance and quality of life. This research gap is particularly notable in Lebanon. Second, Urgent Need for Solutions: Research has shown that a poor work-life balance can lead to negative outcomes like stress, burnout, and poor performance at work and in the classroom (Shockley, 2017) and (Amstad, 2011) This urgency underscores the necessity for tailored interventions and support mechanisms to address the specific needs and challenges faced by part-time employee-students, ultimately enhancing their overall quality of life and academic performance. Third, Impact on Future Workforce: Part-time employee-students, as emphasized by (Draugalis, 2009) have a significant impact on forming the future labor force and leaders of the nation's social and economic landscape. Finally, Policy and Practice Implications: Understanding the factors influencing their work-life balance is imperative for developing effective policies and practices to support their well-being and academic success, and the research will provide evidence-based recommendations for strengthening this understanding. 1.4 Literature Review Balancing work and studies are a significant challenge, especially for students who work more hours (Tuttle, 2005). This juggling act often leads to stress and exhaustion due to the mental and physical demands of both work and academic obligations. Financial constraints often compel students, particularly those from low or middle-income households, to work while studying (Devlin, 2008). The www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 95 cumulative burden placed on students can affect their confidence, time management abilities, and prospects for future employment in both positive and bad ways (Haqifa, 2013). While there are potential benefits, research suggests that working too much can lead to lower academic achievement and decreased social involvement (Tuttle, 2005). This is particularly true with rising tuition fees putting a financial strain on students (Devlin, 2008). Unfortunately, the pressure to balance work and study can also lead to unhealthy eating habits, with students opting for convenient but unhealthy choices (Dolan, 2015). These dietary patterns can increase the risk of obesity and chronic diseases over time (Bransford, 2000); (Busch, 2003). The impact of balancing work and study varies. On the positive side, working students can develop valuable skills like financial management, independence, time management, and problem-solving (Devlin, 2008). However, working more than 35 hours per week can negatively affect academic performance, potentially leading to failing classes or dropping out altogether (Devlin, 2008). With the increasing number of working students, understanding these effects is crucial (Alipio, 2020). Higher education, after all, aims to foster learning and personal development, empowering individuals to contribute meaningfully to the world. Work Life Balance The concept of a balanced work-life is widely accepted, offering a framework for individuals to manage work and personal commitments effectively (Akdere, 2006). Definitions vary, but the core idea is about juggling paid work with other meaningful aspects of life, leading to a fulfilling existence both inside and outside the workplace (New Zealand Department of Labour, 2008a; Pocock, 2005a; (Bruin, 2004). Research shows that work-life balance policies are effective in attracting and retaining talent in competitive markets, benefiting both individuals and organizations (De Cieri, 2005). These policies can lead to higher productivity, improved employee morale, and reduced absenteeism Defining work-life balance remains challenging due to its multifaceted nature. While some describe it as balancing time, emotional investment, and behavior (Hill, Hawkins, Ferris, & Weitzman, 2004), others emphasize achieving overall life harmony. Studies indicate a connection between enhanced well-being and a work-life balance and better quality of life (Greenhaus, 2011). However, in Lebanon, with its political and economic challenges, understanding the specific challenges faced by part-time student employees is crucial. Here, things like the amount of work they have to do in school, extracurricular activities, corporate culture and policies, social conventions, and cultural expectations all have a big impact on their work-life balance. Work Stress Stress as it is explained by Riggio (2018), encompasses both physiological and psychological responses to challenging or threatening circumstances, which are often triggered by environmental stressors. Robbins and Judge (2017) further elaborate that stress is an unpleasant psychological process resulting from external stressors, yet it can occasionally yield positive outcomes, such as enhancing work quality www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 96 and personal fulfillment. However, excessive stress may indicate mental health issues and can hinder work performance by increasing errors. Research conducted by (Allen, 2013) reveals a direct correlation between elevated work stress levels and reduced work-life balance. This result is in accordance with earlier research (Byron, 2005), which found that particular work stressors such high workloads, tense deadlines, and role ambiguity were significant contributors to work-life imbalance. Furthermore (Frone, 2003) highlighted the detrimental effects of work-related stress on general wellbeing, emphasizing the importance of managing these stressors at work. (Greenhaus, 1985) Discovered a negative relationship between workplace stress and work-life balance. suggesting that high stress levels make it difficult to manage both personal and professional obligations. (Grzywacz, 2000) Found that individuals facing high work stress perceive less control over their tasks and schedules, exacerbating challenges in achieving work-life balance. According to research, social support and coping mechanisms might lessen the negative impacts of job stress on work-life balance (Frone, 2003); (Ten, 2012)). (Kossek, 2011) affirmed this by highlighting the importance of supportive supervisors, colleagues, and flexible work arrangements in helping employees manage work stress and enhance work-life balance. (Thompson, 2005) Also emphasized that access to organizational resources for workload management contributes to improved work-life balance for employees. Flexible Work Arrangements: In recent years, striking a work-life balance has grown in significance, and flexible work arrangements, or FWAs, have become a practical means of granting employees more autonomy over their schedules. The effect of FWAs on the work-life balance of student employees is still largely unknown, though. FWAs include a range of strategies, including job sharing, telecommuting, flextime, and reduced workweeks (Golden, 2017). Research suggests that FWAs offer numerous benefits for students, including increased flexibility to manage academic schedules, leading to reduced conflicts between work and study commitments (Allen, 2013). Moreover, FWAs contribute to improved job satisfaction, reduced stress levels, and enhanced productivity among student employees (Shockley, 2017). Notwithstanding these benefits, FWAs may cause problems for students, such as making it difficult to draw boundaries between work and personal life, which could lead to burnout and poor academic achievement (Berg, 2018). Additionally, students in demanding academic programs may face challenges in negotiating suitable FWAs with employers, limiting their access to flexible work options (Golden, 2017). Organizational support and culture play a crucial role in the effectiveness of FWAs for students. Supportive managerial practices and clear communication channels facilitate successful implementation of FWAs (Hill, 2019). Furthermore, organizations prioritizing work-life balance initiatives are more likely to attract and retain student employees, thereby enhancing their overall www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 97 well-being and academic success (Kossek, 2014). Work From Home Productivity: The rise of remote work (WFH) presents a complex picture for student work-life balance. While WFH offers potential benefits like flexibility and reduced commutes (Allen, 2015); (Gajendran, 2007), it can also create challenges. On the positive side, WFH allows students to tailor their work schedules around academic commitments, minimizing conflicts and freeing up time for studies and personal pursuits (Allen, 2015); (Gajendran, 2007) However, WFH can also blur the lines between work and personal life, making it more difficult to separate and raising the possibility of burnout (Golden & Geisler, 2017). Social isolation, technological issues, and increased home responsibilities can further complicate work-life balance for remote students (Gajendran, 2007); (Golden, 2017); (Mesmer-Magnus, 2005). Supervisor and co-worker support Supervisor and co-worker support significantly impact student WLB (Kossek et al., 2014). WLB can be fostered at work by understanding the needs of their students and providing flexibility in scheduling, workload, and communication (Kossek, 2014); (Golden & Geisler,2014). Clear expectations and regular feedback further empower students to manage time and reduce stress. Positive relationships with co-workers are equally important. Supportive colleagues who offer assistance, collaborate effectively, and provide social support can alleviate isolation and contribute to overall well-being (Shockley, 2017); (Golden, 2017). Conversely, a lack of support from supervisors and co-workers can be detrimental. It can lead to isolation, stress, and difficulty managing work responsibilities, ultimately compromising work-life balance (Kossek, 2014). 1.5 Methodology 1.5.1 Research Design For this research, a quantitative survey design is the most appropriate methodology. This study will investigate the factors influencing work-life balance among part-time student workers. A cross-sectional survey approach will be utilized to collect data. 1.5.2 Data Collection To assess the factors influencing work-life balance among student employees, this study will employ a two-pronged approach to data collection. First, a structured questionnaire was distributed to 500 student employees. This questionnaire, built on validated scales and existing research, it evaluates work stress, flexible work options, work-from-home productivity, Considering the degree of assistance obtained from supervisors and co-workers. A pilot test at a modest gathering was conducted beforehand to ensure clarity, effectiveness, and minimize potential biases in the questionnaire. Ethical considerations will be paramount. Participants will be provided informed consent, fully briefed on the study's goals, and informed of their right to withdraw at any point. Confidentiality of all responses will be strictly maintained. www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 98 Second, to enrich the analysis, secondary data was collected from various credible sources, including websites, journals, research reports, books, magazines, and newspapers relevant to the topic. Variables:  Independent Variables: Work stress  Moderating Variables: supervisor support, co-worker support.  Mediating Variable: flexible work arrangements, WFHP.  Dependent Variable: WLB. Theoretical framework Greenhaus and Arthur model (1986) proposes that work-life balance hinges on managing three key factors: work demands, family demands, and individual characteristics. These characteristics encompass personality traits, values, and coping mechanisms that influence how individuals perceive work and family pressures. The model suggests that striving for balance between these components is crucial. When work and family demands become overwhelming, work-life conflict arises. Conversely, individuals achieve better balance when demands are lower or they possess strong coping skills. Work-Life Balance Model for Part-Time Student Employees. Adapted from (Demerouti, 2012); (Allen, 2015). Work Stress Supervisor and coworker support Work Life Balance Flexibility Work Arrangements Work from Home Productivity Figure 1. Research Conceptual Framework 1.6 Data Analysis Plan Variable’s Definitions and Measurements: The conceptualization of the variables, as well as the operating definitions of the variables, are presented in the Table below: www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 99 Table 1. Variable’s Definitions andMeasurements Variables Conceptual definition Operational definition Work Stress 1. I am discouraged about my work. 2. I feel many things are beyond my control and ability while working from home. 3. I feel unable to get out from my work during working from home. 4. I feel like giving up on work during working from home. 4 items on 5-point Likert scale were adopted from (Lait and Wallace, 2002). Flexible Work Arrangements 1. Flexible work arrangements are an important benefit that accounting majors use to select the firm they plan to work for. 2. Flexible work arrangements generally enhance morale and improve the quality of work life for those involved. 3. Flexible work arrangements are a women’s issue; most men don’t want or need these options. 3 items on 5-point Likert scale were adopted from (Anderson, J. C. ,2020). Work From Home Productivity 1. I am very productive while working from home. 2. I can concentrate on getting work done even when there are distractions from family members during working from home. 3. Working from home motivates me to work better. 4. I receive technical assistance from my workplace in completing work during working from home 4 items on 5-point Likert scale were adopted from (Neufeld and Fang, 2005). Supervisor & Co-worker support 1. My supervisor is understanding when I have personal or family problems which interfere with my work. 2. My supervisor is helpful when I have a personal emergency. 3. My supervisor is concerned about me as a person. My co-workers encourage each other when someone feels down. 4. My co-workers try to act like peacemakers when there are disagreements. 5. My co-workers willingly share their expertise with 5 items on 5-point Likert scale were adopted from (Stankevičiūtė & Kunskaja, 2022). www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 100 each other. Work-Life Balance 1. Time for hobbies is difficult for me 2. Do you ever miss out any quality time with your family or your friends because of pressure of work? 3. Forgetting work issues is hard to do 4. I have to take work home most evening 4 items on 5-point Likert scale were adopted from (Shange et al., 2022). Source: Developed by the researcher. 1.6.1 Quantitative Data Collection: Surveys The target population for this study comprises university students enrolled in various Lebanese universities. Sampling Strategy: A multi-pronged approach is employed to reach a diverse sample of students: Randomized WhatsApp Group Distribution: A survey link is disseminated through WhatsApp groups targeting 200 students across several Lebanese universities (AUB, LAU, USJ, UOB, USEK, AUST). Targeted Distribution: 200 surveys are directly distributed at the Modern University for Business and Science (MUBS). Additionally, 300 surveys are distributed at the Lebanese International University (LIU). Convenience Sampling: Hard copies of the survey are distributed at Lebanese University's Beqaa campus to target students there. Sample Size: The target sample size is 211 completed surveys. This allows for sufficient data for statistical analysis while acknowledging potential limitations due to the non-randomized sampling methods employed through WhatsApp groups and convenience sampling. Survey Instrument: A self-administered, online survey instrument is developed using Google Forms. The survey is designed to be user-friendly and takes approximately 3-4 minutes to complete. Data Analysis: SPSS is described as a tool for data management and statistical analysis, with a focus on descriptive analysis and regression analysis. Descriptive analysis involves summarizing dataset features using statistics like frequencies, means, and standard deviations, aiding in understanding variables' characteristics. Regression analysis examines relationships between dependent and independent variables, helping identify factors predicting part-time student work-life balance. SPSS steps for both analyses are detailed, highlighting the importance of interpreting results to inform interventions and support mechanisms. Overall, SPSS facilitates thorough analysis of convenience-sampled data, supporting the study's exploration of work-life balance among part-time student workers. 1.7 Timeline and Budget 1.7.1 Timeline As for the timeline, to ensure a well-structured and efficient research process, a detailed timeline was www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 101 established. Table 1. Outlines the Key Milestones and Deadlines for Completing This Research Project Week 1 Week 2 Day 1-2: Select research topic and refine research questions. Day 8-9: Begin drafting the literature review section. Day 3-4: Conduct initial literature review and gather relevant sources. Day 10-11: Continue drafting the literature review section and distribute the survey/questionnaire. Day 5: Formulate research methodology and data collection plan. Day 12: Write the methodology section. Enter SPSS and analyze the data. Day 6: Develop survey/questionnaire (if applicable) and finalize data collection tools. Day 13: Analyze collected data and begin drafting the results section. Day 7: Obtain approvals required in order to collect data. Day 14: Finalize the results section and begin drafting discussion/conclusion. Submit the research paper. Source: Developed by the researcher. 1.7.2 Budget This research project prioritized cost-effectiveness throughout the data collection process. Data collection expenses were minimal, totaling approximately $150. This included fuel for stakeholder interviews across Lebanon and the printing of 300 hard copies of the survey distributed. The survey instrument itself was built and distributed for free using Google Forms. Using free student versions of specialized data analysis tools, such SPSS, for statistical analysis and qualitative data coding helped to minimize software costs. It's crucial to remember that the budget will change based on a number of variables, including the size of the research team, the difficulty of data gathering techniques, and any unique project requirements. 1.8 Ethical Considerations Even though we didn't initially get informed consent using a separate form, our survey explained everything about the study: what we wanted to find out, how we were collecting data, keeping it safe, and what rights participants had. This helped them understand the research and decide if they wanted to take part, including the option to stop at any time. We made sure the survey didn't ask for personal details like names or addresses, so everyone stayed anonymous and their information confidential. We also used strong security measures like passwords and encryption to protect the data we collected. While we see the importance of getting consent in a separate way in the future, our survey didn't cause any harm because we were clear about what we were doing and didn't collect personal details. This shows we're serious about ethical standards. Moving forward, we'll make sure to explain things clearly and follow stricter consent rules in our future research. www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 102 2. Part 2 2.1 Data Collection and Analysis 2.1.1. Descriptive Analysis 2.1.2 Demographic Statistics Table 2. Gender Frequency Percent Valid Percent Cumulative Percent Valid Female 119 56.4 56.4 56.4 Male 89 42.2 42.2 98.6 Prefer not to say 3 1.4 1.4 100.0 Total 211 100.0 100.0 Source: SPSS. The study provides data that demonstrates the distribution of respondents by gender. With a frequency of 119, 56.4% of the 211 people surveyed identified as females. In contrast, 89 people (or 42.2% of the total) identified as male. Three people, or 1.4% of the total, chose not to reveal their gender. Looking at the total proportion, we can see that 98.6% of those who took the survey identified as male or female; 1.4% chose not to say. Table 3. Education Frequency Percent Valid Percent Cumulative Percent Valid Graduate 61 28.9 28.9 28.9 Graduate, Post-graduate 1 .5 .5 29.4 Post-graduate 43 20.4 20.4 49.8 Undergraduate 104 49.3 49.3 99.1 Undergraduate, Graduate 1 .5 .5 99.5 Undergraduate, Post-graduate 1 .5 .5 100.0 Total 211 100.0 100.0 Source: SPSS. Table 2 displays the respondents' educational backgrounds based on the data supplied. With 104 out of 211 respondents possessing an undergraduate degree, 49.3% of the total were in the education bracket. Be that as it may, 61 people (or 28.9% of the total) reported having a graduate degree. Forty-three www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 103 people, or 20.4% of the total, reported having post-graduate degrees. Furthermore, a tiny percentage of respondents (.5%) claimed to have a mix of educational degrees, such as a graduate with post-graduate or an undergraduate with graduate credentials. Table 4. Age Frequency Percent Valid Percent Cumulative Percent Valid 18 – 25 years old 123 58.3 58.3 58.3 26-30 years old 19 9.0 9.0 67.3 29-39 years old 50 23.7 23.7 91.0 40 years old and above 19 9.0 9.0 100.0 Total 211 100.0 100.0 Source: SPSS. Based on the responses, we can see that respondents were mostly between the ages of 18 and 25, 26–30, 31–39, and 40 and higher (Table 3). According to the results, 123 people (or 58.3% of the entire sample) are between the ages of 18 and 25, making up the biggest demographic of responders. Afterward, 9.0% of the total, or 19 people, are in the 26–30 age bracket, and almost a quarter, or 23.7%, are in the 29–39 age bracket, with 50 people making up this group. Similarly, there are nineteen people (or 9.0% of the total) who are forty years old or older. More over two-thirds of the respondents (67.3%) are under the age of 30, and almost all (91.0%) are under the age of 39. Table 5. Working Hours Frequency Percent Valid Percent Cumulative Percent Valid 10-12 Hours 15 7.1 7.1 7.1 7-8 Hours 107 50.7 50.7 57.8 8-9 Hours 51 24.2 24.2 82.0 9-10 Hours 29 13.7 13.7 95.7 More than 12 hours 9 4.3 4.3 100.0 Total 211 100.0 100.0 Source: SPSS. The five categories of respondents' usual workweeks are shown in Table 4: 10-12 hours, 7-8 hours, 9-10 hours, and more than 12 hours. Statistical analysis revealed that 107 respondents, or 50.7% of the total, reported working for 7-8 hours. Then, 51 individuals, or 24.2% of the total, reported working 8 to www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 104 9 hours daily. Thirteen persons, or 7.1% of the total, reported working for 10-12 hours, while thirty-nine, or 13.7%, reported working for 9-10 hours. On top of that, nine individuals (4.3% of the total) reported working above 12 hours daily. Many individuals work 8 hours or less (57.8%), 82.0 percent work 9 hours or fewer, and 95.7 percent work 10 hours or fewer, as seen in the total percentage column. Conversely, only 4.3% of workers put in shifts over 12 hours. Table 6. Job Nature Frequency Percent Valid Percent Cumulative Percent Valid Field Work 45 21.3 21.3 21.3 Managerial 32 15.2 15.2 36.5 Office Work 82 38.9 38.9 75.4 Staff 31 14.7 14.7 90.0 Technical 21 10.0 10.0 100.0 Total 211 100.0 100.0 Source: SPSS. Table 5 provides insights into the distribution of job natures among respondents, categorized into five groups: Field Work, Managerial, Office Work, Staff, and Technical. The data reveals that the largest proportion of respondents, constituting 38.9%, are engaged in office work, with 82 individuals in this category. Following this, 21.3% of respondents reported being involved in field work, with 45 individuals engaged in such roles. Of the responders, 15.2% (or 32 people) hold managerial roles, and 14.7% (or 31 people) work as staff members. Additionally, a smaller proportion of respondents, 10.0%, reported having technical roles, with 21 individuals in this category. The cumulative percentage column indicates that 75.4% of respondents are involved in either office work, staff, or technical roles, while 90.0% are engaged in managerial roles or below. 2.2.1 Workplace Stress Table 7.Workplace Stress N Minimum Maximum Mean Std. Deviation I am discouraged about my work. 211 1 5 3.18 1.088 I feel many things are beyond my control and ability while working from home. 211 1 5 2.77 1.058 I feel unable to get out from my work during working from home. 211 1 5 2.75 .955 www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 105 I feel like giving up on work during working from home. 211 1 5 2.86 .988 Valid N (listwise) 211 Source: SPSS. Using a scale from 1 (strongly agree) to 5 (strongly disagree), the supplied data analyzes workplace stress among respondents for four statements pertaining to their experiences working remotely. The average score was 3.18, indicating that respondents were somewhat discouraged about their employment. It seems that they were on the fence about the idea of being discouraged. Also, while working remotely, the average score of 2.77 suggests that people prefer to disagree with the idea that there are a lot of things that are beyond their control and abilities. Respondents gave their ability to step back from work when working from home a mean score of 2.75, indicating a significant disagreement. In addition, those who work remotely are somewhat more likely to disagree with the statement "I feel like giving up on work" (mean score of 2.86). 2.2.2 Flexible Working Arrangements Table 8. Flexible Working Arrangements N Minimum Maximum Mean Std. Deviation Flexible work arrangements are an important benefit that accounting majors use to select the firm they plan to work for. 211 1 5 2.19 .751 Flexible work arrangements generally enhance morale and improve the quality of work life for those involved. 211 1 5 2.13 .603 Flexible work arrangements are a women’s issue; most men don’t want or need these options. 211 1 5 2.97 1.066 Valid N (listwise) 211 Source: SPSS. The average score given by respondents regarding the significance of flexible work arrangements was 2.19, indicating a tendency towards agreement but not strong agreement. Extremely consistent responses from the participants are demonstrated by the low standard deviation of 0.751. Respondents gave flexible work arrangements an average score of 2.13 out of 5 for raising morale and bettering work-life balance. With a standard deviation of just 0.603, it seems that most people have the same opinion. With a mean score of 2.97, respondents generally agreed that flexible work arrangements are www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 106 more of a problem for women than males. This statement seems to have a larger level of response variability than the others, as shown by the higher standard deviation of 1.066. 2.2.3 Work from Home Productivity Table 9.Working from Home Productivity N Minimum Maximum Mean Std. Deviation I am very productive while working from home. 211 1 5 2.55 1.015 I can concentrate on getting work done even when there are distractions from family members during working from home. 211 1 5 2.69 1.072 Working from home motivates me to work better. 211 1 5 2.73 1.067 I receive technical assistance from my workplace in completing work during working from home 211 1 5 2.65 .893 Valid N (listwise) 211 Source: SPSS. Using a strong disagreement rating of 1 to a strong agreement scale of 5, respondents' views of productivity and assistance when working from home are shown in the table. The average response from those who have worked from home on their own productivity was 2.55, suggesting some disagreement but not outright severe disagreement. Along similar lines, they gave themselves a 2.69 (skewed toward disagreement) for their ability to focus on work despite interruptions from loved ones. With an average score of 2.73 on the desire to perform better when working from home survey, we can see a tendency of disagreement, but not a significant one. Also, respondents gave their workplace's technical support for remote work a mean score of 2.65 out of 5, suggesting some dissatisfaction but not outright significant disagreement. 2.2.4 Supervisor & Coworker Support Table 10. Supervisor & Coworker Support N Minimum Maximum Mean Std. Deviation www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 107 My supervisor is understanding when I have personal or family problems which interfere with my work. 211 1 5 2.41 .913 My supervisor is helpful when I have a personal emergency. 211 2 5 2.46 .715 My supervisor is concerned about me as a person. 211 1 5 2.63 .954 My co-workers encourage each other when someone feels down. 211 2 5 2.50 .662 My co-workers try to act like peacemakers when there are disagreements. 211 2 5 2.64 .669 My co-workers willingly share their expertise with each other. 211 1 5 2.43 .827 Valid N (listwise) 211 Source: SPSS. From 1 (strongly agree) to 5 (strongly disagree), across six categories, the table sheds light on how respondents perceived the level of support from their supervisors and coworkers. When asked how well their supervisor understood when their personal or family issues got in the way of work, most respondents gave it a moderate rating of 2.41. They also indicated a comparable amount of perceived assistance when they assessed their supervisor's helpfulness during personal situations as 2.46. On top of that, with an average score of 2.63, respondents indicated a modest degree of perceived worry about their supervisor's care for them as an individual. On average, respondents gave their colleagues a 2.50 for encouraging them when circumstances became difficult and a 2.64 for being ready to mediate conflicts, indicating modest levels of support from coworkers in these areas. On top of that, they gave a 2.43 for their coworkers' readiness to share information, which suggests a reasonable amount of support for sharing expertise between colleagues. 2.2.5 Work-Life Balance Table 11. Work-Life Balance N Minimum Maximum Mean Std. Deviation Time for hobbies is difficult for me 211 1 5 2.56 .984 Do you ever miss out any quality time with your family or your friends because of pressure of work? 211 1 5 2.86 1.014 Forgetting work issues is hard to do 211 1 5 2.59 .887 I have to take work home most evening 211 1 5 2.92 1.013 www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 108 N Minimum Maximum Mean Std. Deviation Time for hobbies is difficult for me 211 1 5 2.56 .984 Do you ever miss out any quality time with your family or your friends because of pressure of work? 211 1 5 2.86 1.014 Forgetting work issues is hard to do 211 1 5 2.59 .887 I have to take work home most evening 211 1 5 2.92 1.013 Valid N (listwise) 211 Source: SPSS. Respondents, on average, rated the difficulty of allocating time for hobbies as 2.56, suggesting moderate challenges in finding time for personal interests. Additionally, they rated the extent to which they miss quality time with family or friends due to work pressure as 2.86, indicating a tendency towards agreement with this statement. Moreover, respondents rated the difficulty of forgetting work issues as 2.59, suggesting some challenges in mentally disengaging from work. Lastly, they rated the frequency of needing to take work home in the evenings as 2.92, indicating a moderate level of agreement with this statement. 2.3 Reliability Analysis Table 12. Reliability Analysis Cronbach Alpha Work Life Balance .708 Workplace Stress .539 Flexible Working Arrangements .771 Work From Home Productivity .642 Supervisor Coworker Support .746 Source: SPSS. The table presents Cronbach's alpha coefficients for various constructs, which assess the internal consistency or reliability of scales used to measure each construct. WLB: The Cronbach's alpha coefficient for the WLB scale is .708, indicating moderate internal consistency among the items assessing this construct. Workplace Stress: The workplace stress scale displays an alpha of Cronbach's coefficient of .539, www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 109 suggesting relatively lower internal consistency among its items. Flexible Working Arrangements: The flexible working arrangements scale shows a Cronbach's alpha coefficient of .771, suggesting good internal consistency among its items. Work From Home Productivity: For the work from home productivity scale, the Cronbach's alpha coefficient is .642, indicating moderate internal consistency among its items. Supervisor Coworker Support: The supervisor coworker support scale demonstrates a Cronbach's alpha coefficient of .746, suggesting good internal consistency among its items. Overall, constructs such as work-life balance, flexible working arrangements, and supervisor coworker support exhibit relatively higher internal consistency, while the workplace stress scale shows lower consistency. Improvements in the measurement items for workplace stress may enhance its internal reliability. 2.4 Pearson Correlations Table 13. Pearson Correlations Workplace Stress Flexible Working Arrangements Work From Home Productivity Supervisor Coworker Support Work Life Balance Workplace Stress Pearson Correlation 1 -.601** -.439** -.516** -.525** Sig. (2-tailed) .000 .000 .000 .000 N 211 211 211 211 211 Flexible Working Arrangements Pearson Correlation .601** 1 .618** .736** -.642** Sig. (2-tailed) .000 .000 .000 .000 N 211 211 211 211 211 Work From Home Productivity Pearson Correlation .439** .618** 1 .618** -.629** Sig. (2-tailed) .000 .000 .000 .000 N 211 211 211 211 211 Supervisor Coworker Support Pearson Correlation .516** .736** .618** 1 -.663** Sig. (2-tailed) .000 .000 .000 .000 N 211 211 211 211 211 Work Life Balance Pearson Correlation -.525** .642** .629** .663** 1 Sig. (2-tailed) .000 .000 .000 .000 www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 110 N 211 211 211 211 211 **. Correlation is significant at the 0.01 level Source: SPSS. The Table displays the Pearson correlation coefficients between several dimensions, such as work-life balance, supervisor-coworker support, work-from-home productivity, flexible working arrangements, and workplace stress. The analysis that focuses on work-life balance is as follows: A strong negative relationship (r = -0.525, p < 0.01) exists between workplace stress and work-life balance. This suggests that a decline in work-life balance is associated with elevated levels of workplace stress. Higher levels of occupational stress are correlated with a weaker work-life balance. There is a strong positive correlation (r = 0.642, p < 0.01) between flexible work arrangements and work-life balance. It appears that when flexible work arrangements proliferate, work-life balance tends to get better. Increased job flexibility is associated with a better work-life balance. When working from home, there is a positive association between productivity and work-life balance. (r = 0.629, p < 0.01). This suggests that a better work-life balance is connected with increased productivity while working from home. Positive correlations have been found between work-life balance and supervisor colleague support. (r = 0.663, p < 0.01). This provides further evidence that a healthy work-life balance is linked to encouragement from superiors and peers. 2.5 Regression Analysis 2.5.1 Regression One Table 14. Workplace Stress andWork-Life Balance Regression Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .525a .275 .272 .63583 a. Predictors: (Constant), Workplace Stress ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 32.086 1 32.086 79.367 .000a Residual 84.494 209 .404 Total 116.581 210 a. Predictors: (Constant), Workplace Stress b. Dependent Variable: Work Life Balance www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 111 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients T Sig.B Std. Error Beta 1 (Constant) 1.398 .189 7.395 .000 Workplace Stress -.506 .057 -.525 -8.909 .000 a. Dependent Variable: “Work Life Balance” Source: SPSS. Table 13 displays the results of a regression study that looks at how stress in the workplace relates to the concept of WLB. Based on the model summary and the R-squared value of.275, workplace stress is the predictor variable that explains 27.5% of the variation in WLB. Accounting for the number of factors in the model, workplace stress explains roughly 27.2% of the variation in work-life balance, according to the modified R-squared value of.272. The standard error of the estimate, 0.63583, indicates the average deviation of the observed values from the regression line. Workplace stress is a major predictor of work-life balance, as shown by the statistically significant regression model (F = 79.367, p <.001) in the ANOVATable. According to the coefficients table, there is a considerable negative correlation between workplace stress and WLB (Beta = -.525, p <.001). Workplace stress is inversely connected with work-life balance, according to this evidence. Beta, the standardized coefficient, is -.525, which shows how strong and in what direction this association is. Moreover, the constant term in the model (B = 1.398, p <.001) represents the projected mean score for WLB in the absence of workplace stress. 2.5.2 Regression Two Table 15. Supervisor Support Moderates the Relationship between Work Stress and Work-Life Balance Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .711a .506 .501 .52631 a. Predictors: (Constant), WS SCP, Workplace Stress ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 58.964 2 29.482 106.431 .000a Residual 57.617 208 .277 Total 116.581 210 www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 112 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .525a .275 .272 .63583 a. Predictors: (Constant), WS SCP, Workplace Stress b. Dependent Variable: Work Life Balance Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig.B Std. Error Beta 1 (Constant) 2.162 .175 12.379 .000 Workplace Stress -.126 .080 -.131 -1.589 .114 WS x SCP .135 .014 .813 9.850 .000 a. Dependent Variable: Work Life Balance Source: SPSS. With an R-squared value of.506, the model summary shows that the predictors, workplace stress and supervisor support (WS SCP), explain 50.6% of the variation in work-life balance. With a modified R-squared value of.501, we may deduce that the number of predictors accounts for about 50.1% of the variation in WLB. With a value of 0.52631, The amount that the observed data normally depart from the regression line is displayed by the standard error of the estimate. The ANOVA table reveals a statistically significant regression model (F = 106.431, p <.001), indicating that workplace stress and supervisor support together have a considerable predictive power for work-life balance. Workplace stress is not a significant predictor of WLB (Beta = -.131, p =.114), according to the coefficients table. However, there is a substantial positive correlation between WLB and the interaction term "WS x SCP" (supervisor support multiplied by workplace stress; beta =.813, p <.001). This suggests that having a supportive supervisor mediates the connection between stress at work and maintaining a healthy work-life balance. Moreover, the constant term in the model (B = 2.162, p <.001) represents the projected mean work-life balance score when both workplace stress and supervisor support are set to zero. All things considered; the results show that having a supportive supervisor lessens the impact of stress on WLB. When employees feel more support from their supervisors, it helps to mitigate the negative effects of stress at work on their work-life balance. This emphasizes how important it is to have supportive leadership in order to lessen the detrimental effects of workplace stress on employees' ability to maintain a good WLB. www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 113 2.5.3 Regression Three Table 16. Work from Home Mediates the Relationship between Workplace Stress and Work Life Balance Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .687a .472 .467 .54411 a. Predictors: (Constant), Work from Home Productivity, Workplace Stress ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 55.001 2 27.500 92.888 .000a Residual 61.580 208 .296 Total 116.581 210 a. Predictors: (Constant), Work from Home Productivity, Workplace Stress b. Dependent Variable: Work Life Balance Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig.B Std. Error Beta 1 (Constant) .863 .173 4.995 .000 Workplace Stress -.297 .054 -.308 -5.494 .000 Work From Home Productivity .406 .046 .493 8.798 .000 a. Dependent Variable: Work Life Balance Source: SPSS. The Model Summary Table's regression analysis looks into the connection between office stress, work-from-home productivity, and how these factors affect work-life balance. Together, the predictors of work-from-home productivity and office stress explain for 47.2% of the variance in work-life balance, as indicated by the R-squared score of.472. The adjusted R-squared value of.467 indicates that roughly 46.7% of the variance in work-life balance is explained after adjusting for the number of factors. The average deviation of the observed values from the regression line is indicated by the standard error of the estimate, which is 0.54411. The findings of the ANOVA demonstrate that the regression model is statistically significant (F = 27.500, p <.001), suggesting that work-life balance is significantly predicted by the combination of www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 114 workplace stress and productivity from home. The regression's sum of squares is 55.001, indicating that the independent variables—work-from-home productivity and office stress—explain the variability of the dependent variable, work-life balance. Regarding coefficients, the constant term in the model (B = 0.863) represents the estimated mean work-life balance score when work-from-home productivity and workplace stress are both zero. Workplace stress has a negative coefficient (B = -0.297), implying that a poorer work-life balance is linked to higher levels of workplace stress. Conversely, work-from-home productivity has a positive coefficient (B = 0.406), indicating that higher levels of work-from-home productivity are associated with better work-life balance. 2.5.4 Regression Four Table 17. Flexible Working Arrangements Mediates the Relationship between Work Stress and Work Life Balance Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .665a .442 .437 .55912 a. Predictors: (Constant), Flexible Working Arrangements, Workplace Stress ANOVAb Model Sum of Squares Df Mean Square F Sig. 1 Regression 51.557 2 25.778 82.460 .000a Residual 65.024 208 .313 Total 116.581 210 a. Predictors: (Constant), Flexible Working Arrangements, Workplace Stress b. Dependent Variable: Work Life Balance Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig.B Std. Error Beta 1 (Constant) .965 .175 5.516 .000 Workplace Stress .209 .062 .217 3.353 .001 Flexible Working Arrangements .476 .060 .511 7.892 .000 a. Dependent Variable: Work Life Balance Source: SPSS. www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 115 Table 16's regression analysis looks into the role that flexible work schedules play as a mediator in the relationship between work-life balance and workplace stress. The.442 R-squared value shows that the predictors—flexible work schedules and workplace stress—account for 44.2% of the variation in work-life balance. After adjusting for the number of predictors, the adjusted R-squared value of.437 indicates that 43.7% of the variation in work-life balance can be explained. The average departure of the observed values from the regression line is represented by the standard error of the estimate, which is 0.55912. Work-life balance is substantially predicted by the combination of workplace stress and flexible working options, according to the statistically significant regression model (F = 82.460, p <.001). Work-life balance is the dependent variable, and the regression's sum of squares, or 51.557, shows how the independent variables explain variation in this measure (Workplace stress and flexible employment arrangements). In terms of coefficients, the constant term in the model (B = 0.965) represents the estimated mean work-life balance score when flexible working arrangements and workplace stress are both zero. Workplace stress has a positive coefficient (B = 0.209, p = .001), suggesting that a better work-life balance is linked to higher levels of workplace stress. Additionally, flexible work schedules have a positive coefficient (B = 0.476, p < .001), suggesting that higher levels of flexible working arrangements are associated with higher work-life balance. The standardized coefficient (Beta) for flexible working arrangements is 0.511, indicating its stronger influence on work-life balance compared to workplace stress (Beta = 0.217). Comparing these results with the previous regression analysis, the mediation effect of flexible working arrangements is evident in the change in beta coefficients. Workplace stress had a higher direct impact on work-life balance in the prior regression analysis (Beta = -0.506), but this mediation model shows that the effect of workplace stress on work-life balance is less pronounced (Beta = 0.209). This attenuation suggests that flexible work schedules are more beneficial to work-life balance and that the relationship between workplace stress and work-life balance is somewhat mitigated. Thus, although work-life balance is still impacted by workplace stress, this effect is lessened when flexible working arrangements are available, suggesting that flexible working arrangements have a mediating function in this interaction. 2.6 Conclusion and Recommendation The analysis of the data collected on workplace stress, work-from-home productivity, flexible working arrangements, and WLB among employees provides valuable insights into the dynamics of modern work environments. Key findings from the study reveal significant relationships between these variables, illuminating the elements that affect work-life balance and employee well-being. Recommendations stemming from these insights include prioritizing supportive leadership practices to mitigate the negative impact of workplace stress, promoting flexible working arrangements to www.scholink.org/ojs/index.php/assc Advances in Social Science and Culture Vol. 6 No. 3, 2024 Published by SCHOLINK INC. 116 accommodate diverse employee needs, enhancing work-from-home productivity through support and resources, fostering peer and supervisor support to create a supportive work environment, and continuing to measure and improve these factors through regular assessments and adjustments to policies and practices. 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Journal of Occupational Health Psychology, 10(4), 100-118. https://doi.org/10.1037/1076-8998.10.4.100 Abstract 1.Part 1 1.1 Introduction 1.2 Research Question 1.3 Justification of Research: Why This Topic Matt 1.4 Literature Review Work Life Balance Flexible Work Arrangements: Work From Home Productivity: Supervisor and co-worker support 1.5 Methodology 1.5.1 Research Design 1.5.2 Data Collection Theoretical framework 1.6 Data Analysis Plan Variable’s Definitions and Measurements: 1.6.1 Quantitative Data Collection: Surveys 1.7 Timeline and Budget 1.7.1 Timeline 1.7.2 Budget 1.8 Ethical Considerations 2.Part 2 2.1.2 Demographic Statistics 2.2.1 Workplace Stress 2.2.2 Flexible Working Arrangements 2.2.3 Work from Home Productivity 2.2.4 Supervisor & Coworker Support 2.2.5 Work-Life Balance 2.3 Reliability Analysis 2.4 Pearson Correlations 2.5 Regression Analysis 2.5.1 Regression One 2.5.2 Regression Two 2.5.3 Regression Three 2.5.4 Regression Four 2.6 Conclusion and Recommendation References