Work–life balance among higher-education professionals in Hong Kong and Thailand during the COVID-19 pandemic

Abstract

In the wake of the COVID-19 pandemic, the global workforce, including higher-education professionals, experienced substantial changes in their working conditions. Responding to the crisis, universities swiftly adapted their operational modes, thus inevitably impacting the work–life balance (WLB) of their staff. This study examines the WLB factors and their outcomes for higher-education staff, among both teaching and non-teaching staff, in Hong Kong and Thailand, during the initial stages of the pandemic through the lens of the job demands and resources model (JD-R model). The results of 1800 questionnaires completed by respondents from government-funded universities in both locations revealed a significant difference in the WLB between the two cases, with the Hong Kong respondents reporting lower levels of WLB compared with their Thai counterparts. The Hong Kong teaching staff exhibited a higher utilization of family-friendly policies (FFPs), whereas the non-teaching staff reported significantly lower levels of both policy utilization and overall WLB. The results also revealed significant differences in terms of how work, family, and personal-resource and demand factors were correlated to the WLB outcomes. This study advocates for tailored adjustments in university policies, basing the policies on the specific staff category and emphasizing the need for targeted support. In the Hong Kong context, attention should be directed toward enhancing WLB and FFP utilization among non-teaching staff. In contrast, in Thailand, a focus on refining the policies to better support the WLB of teaching staff is recommended.

Keywords:

• Work–life balance
• family-friendly policy
• higher education
• job demands and resources model
• Hong Kong
• Thailand

1. Introduction

Work–life balance (WLB) has become an important issue in the fields of organizational management, human resource management, and public policy. The concept has been broadly defined as the degree to which a person is equally engaged in and satisfied with both the role of work and the role of family in his or her life (Greenhaus, Collins, and Shaw 2003). Balancing one’s professional and personal life increases one’s life and work satisfaction while it decreases negative physiological conditions and increases one’s potential to achieve organizational goals (e.g. Johari, Yean Tan, and Tjik Zulkarnain 2018; Tamunomiebi and Oyibo 2020). However, research shows that work–life imbalance might lead to detrimental outcomes, including family dissatisfaction, poor work performance, and may have negative effects on the individual’s psychological and physical health (Chen et al. 2022; Padmanabhan and Srinivasan 2016). Thus, WLB can refer to the minimization of conflict or interference between one’s responsibilities at work and those in family/personal roles, and to the facilitation of a positive equilibrium between work life and family/personal life.

A university is a unique organizational setting in which employment status and career advancement are relatively competitive and depend largely on individual performance. In addition, the university world’s ranking system has forced universities around the world to compete (Kehm and Stensaker 2019; Li 2021). This system requires teaching staff to improve the quality of their teaching and publishing, and non-teaching staff are required to have good university management skills in order to compete. This situation, therefore, can affect the WLB of the entire university staff.

Several WLB studies were conducted in higher-education settings prior to the COVID-19 pandemic, particularly academic staff group. Thorough research had focused on universities in Europe (Krilić, Istenič, and Hočevar 2018), the United Kingdom (Fontinha, Van Laar, and Easton 2018), the United States (Azevedo et al. 2020; Denson, Szelényi, and Bresonis 2018), Africa (Jackson and Fransman 2018), and Asia (Abd Rahman et al. 2017; Badri and Panatik 2020). Whereas actual teaching is the primary role of teaching positions in higher education, non-teaching staff work in managerial and service-oriented areas of university administration (Avenali, Daraio, and Wolszczak-Derlacz 2023). However, there have been very few comparative studies on WLB that target both teaching and non-teaching staff. Those research works are, for example, the study in the United Kingdom (Johnson, Willis, and Evans 2019) and Australia (Winefield, Boyd, and Winefield 2014).

To the best of our knowledge, relatively few comparative studies on WLB had been conducted across multiple countries prior to the pandemic, and even fewer during the pandemic. Furthermore, only a few studies have examined WLB for both teaching and non-teaching staff in the higher-education sector, particularly in Asia. Even though both groups work in higher-education institutions, their roles and responsibilities are distinct, and thus they may have experienced different impacts from the pandemic.

This study, therefore, examines the WLB and factors that are associated with it, together with its outcomes among university staff in two cases in Asia: The Hong Kong Special Administrative Region (henceforth, Hong Kong), and Thailand. The performance of universities in Hong Kong and Thailand in the world university rankings is remarkably different, which may affect the WLB of their staff as they often reflect the resources and support systems available at these institutions. According to the Times Higher Education World University Rankings 2024 (Times Higher Education 2024), most public universities in Hong Kong were generally ranked within the top 100 universities worldwide, whereas the rankings of the Thai public universities ranged from 601 to 1500. Besides the differences in their performance, they differ markedly in their cultural orientations, societal norms, and economic structures, which are likely to influence how WLB is perceived and managed. Thailand, with its deep cultural emphasis on familial obligations, social harmony, and relaxed working norms, intersects significantly with cultural norms and expectations regarding WLB. On the other hand, Hong Kong is characterized by its high-pressure work environment, influenced by Chinese culture’s hard-working norms, which may lead to different stressors and coping mechanisms among higher education staff. Comparing these two distinct contexts can illustrate how different cultural and institutional factors influence the WLB of university staff. This comparison not only enriches our understanding of WLB as a multidimensional concept but also assists policymakers and educational leaders in both regions in designing more effective policies tailored to their unique socio-economic contexts.

2. Work–life balance policies in higher education in Hong Kong and Thailand

Work–life balance policies within higher education, often referred to as family-friendly policies (FFPs), encompass initiatives such as flexible work arrangements and family-friendly hours. These policies are strategically designed to enhance staff effectiveness and satisfaction while mitigating the risks inherent in academic work (Saltmarsh and Randell-Moon 2015). Their overarching goal is to establish a supportive and adaptable work environment that empowers employees to harmonize their professional obligations with family responsibilities

In Hong Kong, all employees, including university staff, are protected by the Employment Ordinance. The Ordinance sets out regulations regarding flexible work arrangements, living support, and leave entitlements to protect workers’ rights and ensure a family-friendly work environment (Labour Department 2024). The Labour Department has encouraged employers to adopt more FFPs to support the WLB of employees (Chou and Cheung 2013). Notably, each government-funded university in Hong Kong operates autonomously, governed by its own Ordinance and Governing Council. Consequently, universities have the discretion to offer FFPs tailored to their specific contexts (University Grant Committee 2024). While variations exist, common FFPs include flexible work arrangements – such as five-day work weeks, adaptable working hours, and telecommuting – alongside living support provisions encompassing medical coverage, child care services, and lactation facilities within the workplace. Additionally, leave entitlements, including marriage, parental, and filial leave, cater to employees’ diverse needs. Sabbatical and academic leaves are specific for academic staff to support scholarly pursuits.

In line with global trends, Hong Kong’s University Grants Committee (UGC)-funded public universities exhibit proximity to neoliberalism – an ideology emphasizing socioeconomic gains through individual performance and productivity (Gill 2014). The UGC’s quality audits, driven by competitive criteria, assess university and staff performance (Singh 2020). This emphasis on output and accountability translates into specific requirements for contract renewal; teaching staff must meet performance-based thresholds. Consequently, the Hong Kong higher education sector is marked by extensive workloads, prolonged working hours, heightened performance expectations set by management, and contract instability. These challenges may hinder the effectiveness of WLB initiatives for university staff.

In Thailand, Rules of the Office of the Prime Minister on Leaves of Civil Servants, B.E.2555 outlines various types of leave entitlements for civil servants, including those working in public universities. These regulations ensure that employees have access to necessary leaves for both family and personal reasons. Overview of the types of leaves mentioned is: (1) family-related leaves: maternity leave; paternity leave; leave to follow spouse (which is granted to employees whose spouses are on governmental missions abroad). (2) Personal-related leaves: sick leave; personal leave; vacation leave; ordination or Hajj leave. Academic staff have some specific leaves such as sabbatical leave for academic research or study; short training leaves for professional development, subject to university policy. These leave policies reflect a commitment to supporting the WLB and personal development of civil servants, including those in the academic sector. As some government-funded universities in Thailand are autonomous organizations, each university may have additional policies that further define or extend these leave options for their staff. Besides, welfare policies can vary in respective public universities, but some common benefits include: social security schemes, health care insurance for both staff members and their families, provident funds, and, in some cases, tuition fee support for the children of university staff.

The spread of the COVID-19 pandemic prompted the introduction of additional policies and measures to mitigate its impact (Güner, Hasanoğlu, and Aktaş 2020). Among these measures, working from home (WFH) emerged as a crucial strategy. It allowed both teaching and non-teaching staff to fulfill their responsibilities remotely. The higher-education sector experienced significant impacts during the pandemic. Classes transitioned to an online mode, forcing teaching staff to adapt to the new pedagogy, which may have required preparing online teaching materials, online assessments, planning for interaction in online classes, and attending more online teaching-related training (Almazova et al. 2020; Leal Filho et al. 2024). Non-teaching staff participated in providing more support activities and university management while staying away from the office, such as administrative student and teaching staff support, emergency management, and planning for normality (Agasisti and Soncin 2021). Although WFH helps people to work anywhere at any time, it also yields negative impacts on the separation of work and life, and thus it can affect workers’ WLB.

3. Theoretical framework and hypotheses

To examine the WLB and its outcomes of the university staff in the two cases during the pandemic, this study employed the job demands and resources model (JD-R model) as a fundamental theoretical framework. The model was developed more than two decades ago, when the publication by Demerouti et al. (2001) based their original model on job demands and resources. That model was then expanded to make it more applicable to crisis situations, which included the demands and resources of both the individual and the family (Bakker, Demerouti, and Sanz-Vergel 2023; Demerouti and Bakker 2023). As evidenced by Bakker and Demerouti (2007) and Schaufeli (2017), the JD-R theory serves as a predictive framework for crucial workplace outcomes, encompassing employee engagement, organizational performance, and the risk of burnout.

Although the JD-R model offers an appropriate framework for studying WLB, some scholars have argued that the JD-R model is lacking in personal demands/resources (Schaufeli and Taris 2014; Xanthopoulou et al. 2007). This study, therefore, advanced the model by adding new elements – family demands, family resources, personal demands, and personal resources – in order to recognize the balance in work–life relations. Apart from advancing the JD-R model, this study also considered the effects of FFPs on WLB because FFPs can affect the WLB and the performance of university staff. As a result, this study applied the expanded model to explore a more extensive range of elements related to WLB. The proposed framework elaborates on the pillars of JD-R theory and includes the expanded components of work demands and work resources, family demands and family resources, personal demands and personal resources, and FFPs. The theoretical framework of this study is presented in Figure 1.

Figure 1. Theoretical framework based on the JD-R model.

Each key element of the theoretical framework is explained as follows which the definitions of each variable are used to construct the questionnaire for this study.

3.1. Work demands and work resources

Work demands are defined as “the efforts needed (physical and/or psychological) to perform the task given in paid employment excellently” (Jayasingam, Lee, and Mohd Zain 2023), whereas work resources include autonomy, decision control, and family-supportive supervisor behavior (Wayne et al. 2020).

3.2. Family demands and family resources

According to Boyar et al. (2008), the key variables used to measure family demands are hours spent caregiving, number of children, number of dependents, and being married. Wayne et al. (2020) defined family resources to include both support from family, and family responsibilities, and they can vary in autonomy, skill variety, and task significance.

3.3. Personal demands and personal resources

Personal demands, in this study, encompassed personal health concerns, socialization, personal development, and personal activities/hobbies (Damon 2020; Donati and Watts 2005). Xanthopoulou et al. (2007) proposed three types of personal resources: self-efficacy, organizational-based self-esteem, and optimism.

3.4. Family-friendly policies

According to Albrecht (2003) and Callan (2007), FFPs refer to organizational strategies that are intended to respond to the concerns of employees with family responsibilities, which include, but are not limited to, welfare that covers family members, leave arrangements, flexible working arrangements, and workplace facilities.

3.5. Work–life balance outcomes

The aforementioned factors are related to the outcomes of WLB. According to Sirgy and Lee (2018), the consequences of WLB or WLB outcomes involve work and nonwork life. Hence, in the present study, three categories represent WLB outcomes that an employee can perceive: work, family, and personal outcomes.

Work outcomes refer to job satisfaction derived from motivation and work performance. Job satisfaction is an emotional response of the employees to their job. This response is the work outcomes led by the WLB. A high WLB employee could be highly satisfied and an excellent performer (French et al. 2020). Likewise, a study by Jung, Hwang, and Yoon (2023) found that a good WLB of employees can enhance their job satisfaction and performance, similar to the findings of Aruldoss et al. (2022).

Family outcomes encompass one’s family support system, which increases one’s quality of life, and one’s healthy family relationships. WLB is conducive to marital and family stability, family cohesion, and family happiness (Rao and Indla 2010). In addition, Chan et al. (2016) found that WLB has a positive influence on family outcomes. Also, family outcomes, such as family satisfaction can be predicted by WLB (Joseph and Sebastian 2019).

Personal outcomes are personal goals in terms of achieving career advancement (Denson, Szelényi, and Bresonis 2018). The literature review revealed that most previous studies mainly focused on work outcomes but were very limited in their focus on personal outcomes. Only a few studies have been conducted on WLB outcomes that focus on personal outcomes. For example, Haar et al. (2014) found that balancing employees’ work and life roles could lead to good physical and mental health. Recent study conducted by Leal Filho et al. (2024) shows that faculty members in European universities experienced significant disruptions in their personal domains during the pandemic, marked by increased stress and complexities related to reconciling professional responsibilities with personal obligations. The struggle to reconcile professional and private life has been cited as one of the most harmful consequences of lockdown measures.

This study tested the following hypotheses.

Hypothesis 1: There are significant differences between all staff, teaching staff, and non-teaching staff in the higher-education sector in Hong Kong and their counterparts in Thailand, in terms of resource and demand factors (work resources and demands, family resources and demands, personal resources and demands), family-friendly policies, and work–life balance, and WLB outcomes.

Hypothesis 2: There are significant differences between Hong Kong’s and Thailand’s all staff, teaching staff, and non-teaching staff in the higher-education sector, in terms of the correlations between their resource and demand factors, family-friendly policies, and work–life balance, when comparing WLB outcomes.

4. Methodology

4.1. Case selection

This comparative study employed a quantitative approach by examining the WLB and its outcomes of staff in the higher-education sectors in Hong Kong and in Thailand during the pandemic. Despite Hong Kong and Thailand both having collectivist cultures, the working culture in Thailand is more flexible than the Chinese work-driven lifestyle (Chan and Sheridan 2020; Rojanaporn, Niyomsin, and Bangpan 2022). University staff in Hong Kong are pushed to work hard due to the influence of neoliberal ideology. As a result, they tend to perform better in World University Rankings compared to their Thai counterparts. However, this intense work culture may impact the staff’s WLB. It should be noted that the researchers have affiliations with institutions in Hong Kong and in Thailand, which provided good access to information. This study is one of the few comparative studies that have compared WLB across higher-education institutions in Asia.

4.2. Sample

Samples of the study were obtained from eight government-funded universities in both cases, resulting in a total of 16 universities. In Thailand, eight universities were selected, with two chosen from each of the four regions. Data were collected from both teaching and non-teaching staff. In this study, teaching staff included professors, lecturers, and other teaching staff, and by non-teaching staff referred to administrative and research staff. Before collecting the data, we obtained ethical approval from the researchers’ institutions, both in Hong Kong and in Thailand. The contact information of the research team and the aims and objectives of this study were provided to the participants, to ensure their consent to participate. All personal information has been kept confidential, privacy has been maintained, and there was no potential risk involved. The respondents were free to withdraw from participation in this study at any time if they felt uncomfortable responding to the questionnaire. All respondents’ personal data were kept confidential.

4.3. Data collection

The data were collected using a questionnaire adapted from Cheung, Wu, and Wong (2013), Hill et al. (2001), Mukhtar (2012), Toderi and Balducci (2015), and Xanthopoulou et al. (2007), and developed based on the theoretical framework. The study administered a questionnaire that comprised 69 questions which covered the following elements: personal information, work resources and work demands, family resources and family demands, personal resources and personal demands, FFPs, WLB, work outcomes, family outcomes, and personal outcomes. A sample of the questionnaire statements is given in Appendix.

The majority of the questions were answered using Likert-scale items, with answers ranging from “1 = strongly disagree” to “5 = strongly agree”. The remaining items were questions asking for personal information. The same set of questionnaires was administered in both territories; English supplemented with Chinese was used in the Hong Kong questionnaire, and English supplemented with Thai was used in the Thai questionnaire.

Because the study was conducted during the pandemic and most of the university staff were WFH, the data collection in Hong Kong was carried out by a telephone survey between July and November 2020. The population included all of the staff working in eight UGC-funded universities and three vocational colleges in Hong Kong, and individuals were selected by simple random sampling. Their phone numbers were published by the universities’ official websites. In Thailand, an online questionnaire was distributed to university staff via email and social media (LINE and Facebook), as there were no telephone number data available for Thailand universities. The survey in Thailand was conducted between August and December 2020. The total number of respondents in this study was 1800 and comprised 1400 university staff in Hong Kong and 400 university staff in Thailand. The difference in sample size between the two locations was a result of the different survey methods used – the telephone survey was more proactive and therefore received far more responses than the online survey distributed via email and social media did.

4.4. Data analysis

Wilcoxon’s rank-sum test was employed to test Hypothesis 1: whether there were significant differences between university staff in Hong Kong and those in Thailand in terms of resources and demands, FFPs, WLB, and WLB outcome factors. Wilcoxon’s rank-sum test is a classic nonparametric alternative to the two-sample t-test for comparing two independent samples (Westfall and Henning 2013). To test Hypothesis 2, Fisher’s z-transformation was utilized to test the significance of the differences between Spearman’s correlation coefficients for the Hong Kong university staff and those for the staff in Thailand, in regard to resource and demand factors, FFPs, WLB, and WLB outcomes. Fisher’s z-transformation aims to stabilize the variance of the sampling distribution of correlation coefficients (Zimmerman, Zumbo, and Williams 2003). Spearman’s correlation coefficient measures the strength and direction of association between two ranked variables (Schober, Boer, and Schwarte 2018).

5. Findings

5.1. Characteristics of the respondents

Table 1 presents the characteristics of the respondents in the two groups: teaching staff and non-teaching staff. The respondents in both groups had similar characteristics in terms of gender, age, level of education, mode of employment, and position. Whereas most of the Hong Kong participants were married, the majority of the Thai participants were not. Employment status represented another primary difference between the two groups: the Hong Kong staff worked mostly on a contract basis, whereas the Thai university respondents were in tenured/permanent positions. Employment status clearly affected career longevity in both cases – whereas the majority of respondents in both groups exhibited an average work duration of one to five years, nearly half of the Thai respondents had worked for their university for more than 10 years, compared with a mere 25% of the Hong Kong respondents having worked for the same duration. The average numbers of working hours between the two cases were similar, at 40–50 hours per week. Although the maximum number of working hours in both groups was 48, more than 20% of both groups worked more than 50 hours per week. In terms of family variables, the two groups shared some similarities in terms of family structure, often with two household members earning income. In both cases, more than 70% of the respondents had no children under the age of 12 living in the family, nor did they have domestic helpers. Although the majority of families in both groups had no elderly members, 30% of the Thai respondents had two or more elderly people in the family.

Table 1. Descriptive statistics of respondents.

	Hong Kong		Thailand
	Obs.	Percent	Obs.	Percent
Gender
Male	525	37.5	156	39.0
Female	875	62.5	244	61.0
Age
20–29	203	14.5	39	9.8
30–39	624	44.6	143	35.8
40–49	318	22.7	140	35.0
50–59	205	14.6	65	16.3
60 and above	50	3.6	13	3.3
Education
Associate degree	41	2.9	2	0.5
Bachelor’s degree	338	24.1	98	24.5
Master’s degree	467	33.4	136	34.0
Doctoral degree	520	37.1	164	41.0
Others	34	2.4
Marital status
Never married	574	41.0	202	50.5
Married	781	55.8	185	46.3
Widowed	11	0.8	4	1.0
Divorced	30	2.1	8	2.0
Separated	4	0.3	1	0.3
Mode of employment
Full time	1365	97.5	398	99.5
Part time	35	2.5	2	0.5
Position
Teaching staff	581	41.5	180	45
Non-teaching staff	819	58.5	220	55
Employment status
Permanent/tenured	287	20.5	315	78.8
Contract	1113	79.5	85	21.3
Length of work at the university
Less than 1 year	95	6.8	26	6.5
1 year to less than 5 years	624	44.6	101	25.3
5 years to less than 10 years	323	23.1	76	19.0
10 years to less than 15 years	170	12.1	59	14.8
15 years to less than 20 years	55	3.9	55	13.8
20 years and above	133	9.5	83	20.8
Average number of working hours per week at this university
Less than 30 hours	48	3.4	12	3.0
30 hours to less than 40 hours	239	17.1	128	32.0
40 hours to less than 50 hours	744	53.1	179	44.8
50 hours and above	369	26.4	81	20.3
Number of immediate family members living together in the household (including yourself)
Alone	202	14.4	52	13.0
2	396	28.3	71	17.8
3	387	27.6	85	21.3
4	316	22.6	112	28.0
5 and above	99	7.1	80	20.0
Family characteristics
Alone	202	14.4	80	20.0
Nuclear family	1021	72.9	236	59.0
Extended family	177	12.6	84	21.0
Number of children (aged 0–12) living together in your nuclear family
None	983	70.2	289	72.3
1	231	16.5	75	18.8
2 or more	186	13.3	36	9.0
Number of elderly people (aged 60 and above) living together in your nuclear family (including yourself)
None	911	65.1	162	40.5
1	244	17.4	116	29.0
2 or more	245	17.5	122	30.5
Number of household members who earn income in the nuclear family (including yourself)
1	472	33.7	81	20.3
2	707	50.5	177	44.3
3	157	11.2	70	17.5
4 or more	64	4.6	72	18.0
Number of domestic helpers
None	1065	76.1	305	76.3
1 or more	335	23.9	95	23.8

5.2. Resource and demand factors and WLB outcome factors

To test Hypothesis 1, Wilcoxon’s rank-sum test was employed. As is shown in Table 2, the scales for the two groups were internally consistent. Respondents among the university staff – both teaching and non-teaching – in Hong Kong had significantly lower levels of work resources and work demands, family resources and family demands, WLB, work outcomes, family outcomes, and personal outcomes than those in Thailand did. However, there were no statistical differences in their personal resources, personal demands, and FFP variables.

Considering the university teaching staff groups, the Hong Kong respondents had significantly lower levels of family resources, family demands, work outcomes, and family outcomes, but higher levels of FFPs, compared with their Thai counterparts.

Table 2. Statistics for resource and demand factors and WLB outcome factors.

	All staff						Teaching staff						Non-teaching staff
	Hong Kong		Thailand		Wilcoxon’s rank sum test		Hong Kong		Thailand		Wilcoxon’s rank sum test		Hong Kong		Thailand		Wilcoxon’s rank sum test
	Mean	SD	Mean	SD	z	p Value	Mean	SD	Mean	SD	z	p Value	Mean	SD	Mean	SD	z	p Value
Work resources and work demands	3.206	0.588	3.321	0.675	−3.570	.000	3.087	0.635	3.140	0.718	−0.739	.460	3.289	0.525	3.502	0.577	−5.097	.000
Family resources and family demands	3.344	0.636	3.604	0.637	−6.969	.000	3.285	0.681	3.430	0.634	−2.732	.006	3.368	0.580	3.778	0.592	−7.915	.000
Personal resources and personal demands	3.143	0.729	3.179	0.802	−0.961	.337	2.911	0.740	2.951	0.818	−0.483	.629	3.326	0.657	3.408	0.717	−1.379	.168
Family-friendly policies	2.761	0.767	2.730	0.959	0.606	.544	2.801	0.805	2.515	0.924	4.258	.000	2.770	0.721	2.945	0.946	−2.942	.003
Work–life balance	3.045	0.881	3.166	0.960	−2.621	.009	2.859	0.982	2.915	1.009	−0.510	.610	3.185	0.756	3.418	0.838	−4.089	.000
Work outcomes	3.329	0.607	3.611	0.718	−7.566	.000	3.370	0.656	3.531	0.737	−2.995	.003	3.299	0.562	3.691	0.691	−7.301	.000
Family outcomes	3.776	0.662	4.080	0.684	−8.579	.000	3.803	0.670	3.971	0.790	−3.659	.000	3.765	0.628	4.188	0.539	−8.340	.000
Personal outcomes	3.290	0.744	3.383	0.847	−2.197	.028	3.449	0.739	3.377	0.922	1.081	.280	3.176	0.700	3.389	0.767	−3.749	.000

Significant level at p < .05.

Regarding the non-teaching staff, the results show that, compared with respondents in Thailand, respondents in Hong Kong had significantly lower levels in almost all variables, except for the personal resources and personal demands variables, which were not statistically different.

Hypothesis 2 was tested using Spearman’s rank correlation coefficient analysis. The correlations between resource and demand factors and WLB outcome factors are presented in Table 3. In both of the locations, the work resources and work demands variable had relatively high positive correlations with the work outcomes (Hong Kong 0.632, Thailand 0.716). Likewise, and in both locations, the family resources and family demands variable had moderate positive correlations with the family outcomes (Hong Kong 0.475, Thailand 0.579). The personal resources and personal demands variable had a moderate positive correlation with the personal outcomes in the Hong Kong case (0.455), whereas in the Thailand case, we found a high positive correlation (0.645). The FFPs aspect was found to have moderate positive correlations with the work outcomes and the personal outcomes in Hong Kong (0.441 and 0.408, respectively), whereas FFPs had a relatively high positive correlation with the personal outcomes in Thailand (0.603). The WLB variable was found to have a moderate positive correlation with the work outcomes and the personal outcomes in Hong Kong (0.531 and 0.439, respectively), whereas it was found to have a relatively high positive correlation with these two outcomes in Thailand (0.675 and 0.670, respectively).

Table 3. Spearman’s correlation coefficients regarding resource and demand factors and WLB outcome factors.

	All staff						Teaching staff						Non-teaching staff
	Work outcomes		Family outcomes		Personal outcomes		Work outcomes		Family outcomes		Personal outcomes		Work outcomes		Family outcomes		Personal outcomes
	HK	TH	HK	TH	HK	TH	HK	TH	HK	TH	HK	TH	HK	TH	HK	TH	HK	TH
Work resources and work demands	0.632	0.716	0.246	0.368	0.507	0.667	0.695	0.746	0.277	0.352	0.542	0.568	0.624	0.673	0.221	0.359	0.542	0.568
Family resources and family demands	0.284	0.389	0.475	0.579	0.355	0.384	0.358	0.417	0.503	0.544	0.367	0.246	0.243	0.323	0.451	0.616	0.367	0.246
Personal resources and personal demands	0.482	0.581	0.279	0.416	0.455	0.645	0.575	0.585	0.306	0.393	0.523	0.615	0.469	0.555	0.247	0.417	0.523	0.615
Family-friendly policies	0.441	0.552	0.233	0.229	0.408	0.603	0.458	0.536	0.263	0.281	0.393	0.659	0.417	0.554	0.199	0.150	0.393	0.659
Work–life balance	0.531	0.675	0.305	0.408	0.439	0.670	0.600	0.695	0.296	0.410	0.506	0.569	0.508	0.643	0.287	0.346	0.506	0.569

HK: Hong Kong; TH: Thailand.

All correlation coefficients are significantly different from zero at the .05 significance level.

Relying on Fisher’s z-transformation, as is shown in Table 4, we tested the significance of the differences between Spearman’s correlation coefficients for the two samples. In most cases, the correlations for the Hong Kong sample were significantly lower than those for the Thailand sample, with the exceptions of the correlation between FFPs and family outcomes and the correlation between family resources and demands and personal outcomes.

Table 4. Comparison of the correlations between resource and demand factors and WLB outcome factors for Hong Kong and Thailand university staff (fisher z-transformation).

	All staff						Teaching staff						Non-teaching staff
	Work outcomes		Family outcomes		Personal outcomes		Work outcomes		Family outcomes		Personal outcomes		Work outcomes		Family outcomes		Personal outcomes
	z	p	z	p	z	p	z	p	z	p	z	p	z	p	z	p	z	p
Work resources and work demands	−2.719	.003	−2.386	.009	−4.331	.000	−1.289	.099	−1.010	.156	−4.151	.000	−1.026	.152	−1.831	.034	−0.456	.324
Family resources and family demands	−2.073	.019	−2.530	.006	−0.586	.279	−0.842	.200	−0.685	.247	−1.821	.034	−1.055	.146	−2.819	.002	1.622	.052
Personal resources and personal demands	−2.422	.008	−2.745	.003	−4.871	.000	−0.183	.428	−1.203	.115	−3.151	.001	−1.416	.078	−2.325	.010	−1.654	.049
Family-friendly policies	−2.589	.005	0.082	.467	−4.648	.000	−1.258	.104	−0.236	.407	−2.861	.002	−2.183	.015	0.613	.270	−4.554	.000
Work–life balance	−2.166	.015	−2.084	.019	−5.969	.000	−1.993	.023	−1.582	.057	−5.455	.000	−2.464	.007	−0.795	.213	−1.075	.141

Significant level at p < .05.

Of the correlations found for the teaching staff group, the correlations between the resources and demands factors and personal outcomes for the Hong Kong sample were significantly lower than those for the Thailand sample in almost all factors, with the exceptions of the family resources and family demands, which were higher, as shown in Tables 3 and 4. However, most of the correlations between work outcomes and family outcomes were not significantly different between the two groups, with the exception that the Hong Kong sample’s correlation between WLB and work outcomes was lower than that found in the Thailand sample.

For the non-teaching staff, the Hong Kong sample had lower correlations between various factors than the Thailand sample did: FFPs, WLB, and work outcomes; work resources and demands, family resources and family demands, personal resources and demands, and family outcomes; personal resources and demands; and personal resources and demands, FFPs, and personal outcomes.

Overall, then, regardless of whether the respondents were university teaching staff or non-teaching staff, the associations between resource and demand factors and WLB outcome factors were weaker in the Hong Kong group than in the Thailand group, which indicates that resources and demands may have a larger effect on corresponding outcomes in Thai universities.

6. Discussion

This study compared the WLB and factors relating to it, together with analyzing the correlations with the WLB outcomes among teaching and non-teaching staff in the higher-education sector in Hong Kong and Thailand during the COVID-19 pandemic. This comparative study distinguished between three groups: the university staff as a whole in the higher-education sector, the teaching staff, and the non-teaching staff, in Hong Kong and in Thailand. The objective was to gain insights into the factors relating to the WLB of each respective group in each location, and thus to inform the customization of policies intended to enhance the WLB for each specific category.

6.1. Staff in the higher-education sector

Overall, the results show that the Hong Kong respondents had significantly lower work and family resources and demands and a lower level of WLB than did those in Thailand. Regarding work resources and demands, employment status played an important role in distinguishing between the two territories; the respondents from Hong Kong were mainly employed on a contract basis, whereas those in Thailand were mainly in tenured/permanent positions. Therefore, employment status was found to be related to work resources and demands and WLB. These findings are consistent with those of previous studies, which confirmed that low work resources and demands contribute to a low quality of work and life (Mudrak et al. 2018). Due to their contracts, staff in the higher-education sector in Hong Kong have irregular working hours, high expectations and strong competition, and a trend of internationalization; thus, contract workers have to labor actively to accomplish their goals and secure their jobs at the same time (Annink, den Dulk, and Steijn 2016). However, when a permanent contract is guaranteed, workers are able to experience income security and greatly benefit from work resources (Krumbiegel, Maertens, and Wollni 2018).

Moreover, this study’s results revealed that the university staff in Hong Kong worked longer hours than those in Thailand did – nearly 80% of the respondents in Hong Kong spent at least 40 hours or more per week working, whereas approximately 77% of the respondents in Thailand spent between 30 and 50 hours working per week. On this subject, our study was also consistent with research by the Federation of Hong Kong and Kowloon Labour Unions, which had found that Hong Kong employees worked an average of 44 hours per week (The Standard 2022), and with research by the Swiss Bank UBS, which indicated that Hong Kong workers had the most prolonged working hours in the world (April International 2023). Working from home may encourage employees in Hong Kong to work longer hours compared to being in the office due to the culture of hard work and the lack of clear separation between work and personal life. Many studies (e.g. Chen et al. 2022; Fontinha, Easton, and Van Laar 2019) have revealed that overtime work hours are associated with an increase in work–family imbalance because time spent on work interferes with family life. Indeed, those findings also align with our result concerning the longer working hours affecting the lower level of WLB in Hong Kong. Overall, the staff in the higher-education sector in Hong Kong experienced lower levels in all of the WLB outcomes – the work, family, and personal aspects. Our results regarding Hong Kong staff in the higher-education sector indicated that they had a lower level of demands and resources and a low level of WLB; thus, the WLB outcomes in Hong Kong were lower than those in Thailand.

In terms of family resources and demands, the family’s characteristics can have an impact. Particularly during the pandemic, the staff in the higher-education sectors in both Hong Kong and Thailand were forced to embrace the work-from-home arrangements, along with other sectors. This study found that most of the respondents in both cases did not have domestic helpers; however, respondents in Thailand had relatively more immediate family members living with them, resulting in more family resources, than was the case with the respondents in Hong Kong. Significantly, while WFH during the pandemic, people in Thailand tended to have more immediate family members available who could help with caregiving for children or older people, and with other household responsibilities. In contrast, those in Hong Kong most often lived in nuclear families, in which the parents may have needed to take care of their children, their household responsibilities, their chores, and their work duties by themselves, while also WFH during the pandemic. This would undoubtedly have made it challenging for the Hong Kong workforce to attain a high level of WLB (Vyas and Butakhieo 2020). Therefore, this study’s findings illustrate that the extended family system in Thailand enriches family resources and enables university staff to experience better WLB. In this regard, too, this study is congruent with previous research, which found that support from family can generate positive effects and enhance workers’ contentment with their WLB (Landolfi et al. 2021; Wayne et al. 2020).

Regarding FFPs, this study found that FFPs had only moderate positive correlations with family outcomes in both locations, which may imply that the universities’ FFPs, in both cases, largely do not yet help the staff achieve the desired family outcomes. One reason that the utilization of FFPs is low is the staffs’ “fear of being labeled” if they utilize any available FFPs, and the influence of organizational culture and supervisor and peer support (Vyas, Cheung, and Chou 2024; Vyas, Lee, and Chou 2017).

As more than half of the study’s respondents were women, which is a limitation of the study, the result tended to reflect the needs of female staff. In Asian contexts such as those of Hong Kong and Thailand, families, societies, and governments expect women, more than men, to perform nurturing duties or engage in unpaid care work for their family members. According to Chandra (2012), in Asian countries, WLB is seen as an issue for women more than for men, while in Western countries it is often seen as an issue common to both men and women. Owing to these cultural and gender factors, using FFPs to improve WLB would need to be specific for female staff.

In the case of Thailand, the majority of the Thai respondents have never married; thus, their family and personal resources and demands are different from those who are married and those who have children. Consequently, universities in Thailand should focus more on providing policies that support the needs of unmarried staff. Moreover, more than 70% of the respondents had no children under the age of 12 living in the family, and 30% of the respondents had two or more elderly people in the family. The data reflect the characteristics of an aging society in Thailand, wherein the elderly are the dependents in the family, not young children. Therefore, FFPs that support an aging society should be encouraged in Thai universities.

Theoretically, FFPs are addressed in labor laws to support employees in achieving WLB. However, in practice, there is a wide range of limitations – for instance, more support from employers and supervisors, more governmental legislative intervention, and more leave policies in organizations are needed (Kaewthaworn 2019; Vyas, Lee, and Chou 2017). Therefore, the current situation makes it challenging to practice FFPs, and that was especially the situation during the early stages of the pandemic when employees were forced to work from home and their work interfered with their work–life boundaries (Iwu et al. 2022).

6.2. Teaching staff

In terms of the teaching staff in particular, the study’s results revealed that the Hong Kong respondents had significantly less secured employment status than the Thai counterparts owing to the tenure track system. Together with lower family resources and demands, the Hong Kong teaching staff experienced challenging WLB. Regardless of whether they were employed on a tenure or non-tenure basis, the staff members in both cases similarly worked within the competitive university ranking system. Their promotion and contract renewal indicators were based primarily on their research output and publications to meet the requirements for international competitiveness in global university rankings (Chou 2021; Li 2021). This turned the academic staff into paper producers (Chou 2021) and ultimately affected the WLB of them. It was evident in a study by Fontinha, Van Laar, and Easton (2018) that academics who work in lower-ranked universities have less stress at work and less pressure from the need for publications, compared with their counterparts working in higher-ranked universities. Because most universities in Hong Kong rank higher than those in Thailand (e.g. Times Higher Education and QS World University Rankings), results from this study show that the Hong Kong respondents had a lower WLB than those in Thailand. This could have resulted from greater pressure to publish in the Hong Kong universities, which in turn led to long working hours, lower levels of WLB, and poorer family and personal outcomes.

As mentioned earlier, the characteristics of the family and the long work hours could have had an impact on WLB outcomes. Thus, the Hong Kong respondents may have perceived less support from their families, since most of the respondents lived alone or in a nuclear family – situations that tend to cause lower work and family outcomes. However, the teaching staff in Hong Kong in this study had a significantly higher level of FFPs than those in Thailand, which implies that the Hong Kong universities have provided FFPs that match the teaching staff members’ needs, and that the utilization of FFPs is the norm in Hong Kong.

Although universities in Thailand have similar FFPs, such as parental leave and flexible work arrangements, the utilization of FFPs are lower there. One of the reasons is that Thai teaching staff have a larger teaching load: the minimum number of working hours is 35 hours per week (on a five-day basis), and some universities require teaching staff to teach at least 25 hours per week. In addition to this, some universities have extra courses which do not count toward the regular teaching workload, and which are held in the evenings and weekends, and thus require teaching staff to work extra hours. Teaching responsibilities are difficult to assign to others and thus prevent teaching staff from utilizing FFPs, such as taking leave. A sabbatical leave policy has been introduced in most universities in Thailand to support the teaching staff in enhancing their research knowledge and skills; however, teaching responsibilities might not encourage workers to apply for such leave as their teaching load will be assigned to other staff. As was discussed above, the main reason for the low uptake of such a policy could be the fear of being labeled self-centered. Online teaching during the pandemic enhanced flexibility of work for teaching staff and can help encouraging the use of FFPs as teaching can be done online anywhere at any time. Thus, the Thai universities should consider reducing teaching load for teaching staff, allow online teaching in certain circumstances, and promote the use of FFPs to become the norm in organizations.

6.3. Non-teaching staff

In the context of non-teaching staff, the Hong Kong respondents had significantly lower levels of work and family resources and demands, FFPs, WLB, and WLB than their counterparts in Thailand did. As was mentioned previously, the FFPs in Hong Kong could be a privilege afforded to professions with higher educational levels (Vyas, Lee, and Chou 2017), but leaving non-teaching staff excluded from some policies because of their educational level or work position. In line with the Indeed Editorial Team’s study (Indeed Editorial Team 2023), individuals in a nonprofessional career with little training or education are likely to be concerned about job security, job conditions, and work relationships, and such concerns in turn create stressors and a work–life imbalance (Johnson, Willis, and Evans 2019). Lack of job security can put more stress on the Hong Kong non-teaching respondents than the Thais as the majority of respondents in Hong Kong are employed on a contract basis, whereas the majority of respondents from Thailand have permanent employment status. These concerns might have been accelerated, especially during the pandemic (Rajabimajd, Alimoradi, and Griffiths 2021).

The nature of the administrative work of non-teaching staff requires fixed-working hours, meaning their jobs have less flexibility than the teaching staff’s. Moreover, given that the Hong Kong respondents have fewer family resources than those in Thailand, more pressure is put on the Hong Kong non-teaching staff to manage family and personal demands. Besides this, non-teaching staff in Hong Kong experienced lower benefits from the FFPs than did the Thai non-teaching staff. In terms of the Thai non-teaching staff, higher levels of family resources and FFPs may help them to achieve better WLB. A study by Phetpankan and Thabhiranrak (2018) found that non-teaching staff at one university in Thailand had a good quality of working life, and had acquired appropriate compensation, and accordingly had a good workplace environment and a favorable WLB. Therefore, the Hong Kong universities should be focused on providing job security, facilitating flexible working hours, encouraging the WFH practice, and providing a wider range of FFPs for non-teaching administrative staff such as medical assistance for family members of staff, longer periods of leave for family and personal reasons.

7. Policy recommendations

Based on the findings discussed above, some policies should be designed specifically to enhance the WLB among university staff in both cases, not only in time of the pandemic but also in non-pandemic situation. In Hong Kong, the results show that all respondents had low work and family resources and demands and perceived their WLB to be low across all aspects of their work, family, and personal outcomes. The Hong Kong teaching staff also perceived themselves as having less support from their families than those in Thailand did, while the non-teaching staff in Hong Kong experienced lower benefits from the FFPs than did the Thai non-teaching staff. Thus, two policy recommendations are proposed for Hong Kong.

First, to address the issue of low work resources and demands, Hong Kong universities should support additional tenured positions, with the intention of increasing job security, so that the staff can feel a sense of belonging and be more engaged with their work (Krumbiegel, Maertens, and Wollni 2018). Supporting systems such as mentoring or supervision for career advancement should be emphasized for teaching staff. Regarding non-teaching staff, long term contacts and flexible working hours can enhance the work resource and therefore enhance WLB and WLB outcomes. Long work hours in most Hong Kong workplaces, including higher-education institutions, do not necessarily equate to productive hours and should not be encouraged. In particular, both contract and temporary staff should have a maximum number of work hours stipulated. Furthermore, because their jobs are not permanent, they are likely to choose to work excessive hours or are made to do so to secure their position. Flexible work arrangements for all, including teaching, administrative, and research staff, should be encouraged, in an effort to allow the entire staff more autonomy and encourage job commitment (Aik 2022). Flexible job arrangements will allow the workers to spend more time with their families (Peek, Im-em, and Tangthanaseth 2016) and can thus result in better family outcomes.

Second, to increase family resources, because the majority of Hong Kong’s families are characterized as nuclear, family support policies should be addressed, particularly for the non-teaching group. For example, increasing the number and flexibility of family leave policies are essential steps. It is also vital that university staff perceive the effectiveness of FFPs and that universities encourage them to enjoy the policies’ benefits (Vyas, Lee, and Chou 2017). Even though the teaching staff in Hong Kong seemed to utilize the FFPs, the FFPs they took advantage of were usually those mandated by the government, such as the five-day work week, the teaching staff seldom utilized other policies because they feared being stigmatized. In that light, the university administration should proactively encourage and train supervisors to support their supervisees in freely making use of the available policies.

In the context of Thailand, two key policy recommendations are suggested. First, considering Thailand has an aging society wherein elderly people are more likely to be the dependents in the family rather than young children, Thai universities should adjust their FFPs to be more focused on supporting the elderly in the family, such as providing medical care that covers family members, and providing leave when family members become sick or die. The utilization of FFPs should also be made easier by simplifying the bureaucratic process, as the current system requires staff to navigate many levels of authorization. Reducing teaching workload for teaching staff and introducing flexible work arrangements, such as hybrid working, online classrooms, online meetings, or online consultations, will allow staff to work virtually outside the university and increase their personal time (Eaton and Heckscher 2021), and permit them to spend time with their families (Peek, Im-em, and Tangthanaseth 2016).

Second, because the results suggest that the Thailand respondents acquired sufficient family support from their extended families, the FFPs in Thailand should focus on enriching family aspects. This could include engaging family members in organization activities, and extending welfare to family members to demonstrate the organization’s support of the family. Considering the results showed that the majority of the Thai respondents are single, personal preoccupations of single people should be considered, such as personal health concerns, financial security, socialization, personal development, and personal activities/hobbies. According to Jain (2022), personal demand factors influence one’s perception of WLB. Thus, Thai universities can improve the WLB of their staff by focusing on personal resources and demands.

8. Conclusions

This study compared data from university staff in higher-education institutions in Hong Kong and in Thailand, investigating WLB factors and outcomes among teaching and non-teaching staff during the COVID-19 pandemic. The study’s primary focus was on exploring the relationships between work and life spheres among higher-education staff in both regions, and that was guided by two primary hypotheses.

In general, the study’s respondents from Hong Kong exhibited significantly lower levels of work and family resources and demands their counterparts in Thailand had, and that resulted in them having an overall lower level of WLB. This discrepancy may be attributed to variations in employment statuses, family characteristics, and prolonged working hours. However, when specifically examined the teaching staff, the Hong Kong respondents demonstrated lower work and family outcomes, but notably higher utilization of FFPs than their Thai counterparts did. In contrast, the comparative analysis of non-teaching staff indicated that Hong Kong respondents exhibited considerably lower levels of WLB and FFP utilization, and of overall WLB outcomes, than their counterparts in Thailand did – implying that the current FFPs in Hong Kong may not effectively support higher education staff in balancing their work and life domains.

In addition, when comparing correlations among the variables, the study revealed that the correlations between the work resources and work demands variable and work outcomes, and correlations between the family resources and family demands variable and family outcomes, were similar in both the Hong Kong and Thailand cases. Hong Kong was found to have positive moderate correlations between the personal resources and demands variable and personal outcomes, as well as between FFPs and work and personal outcomes, while relatively high positive correlations were found in the Thailand case. However, FFPs had moderate positive correlations with family outcomes in both cases.

The findings of this study contribute to informing both policy design and practices to improve the WLB of staff in the higher-education sector in Asia. The key recommendation is to tailor policies according to the diverse needs of the various types of staff. Despite the unconventional circumstances surrounding the COVID-19 pandemic during the study, the insights garnered remain valuable for policymakers in higher education. The findings demonstrate the importance of prioritizing WLB initiatives, not only to enhance the overall quality of life for staff, but also to improve work performance within the sector.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was funded by the Faculty of Political Science and Public Administration, Chiang Mai University (Project number: R000019983) and the Department of Asian and Policy Studies, The Education University of Hong Kong.

Appendix

The questionnaire statements

Please rate your agreement with the following statements on a scale of 1–5, where: 1 = “strongly disagree”, 2 = “disagree”, 3 = “neutral”, 4 = “agree”, and 5 = “strongly agree”.

Table

Variables	Statements
Work resources and work demands	The salary and welfare I received from the university are fair and competitive. My university offers a good career opportunity to me. I can decide the priority of my work order. My supervisor uses his or her influence to help me to solve my work-related problems. My supervisor encourages and supports me to attend training and capacity-building programs that help me to do my job better. My supervisor welcomes ideas and thoughts from me. I have access to facilities (e.g. lab, classroom, meeting room, library, etc.) in the university when need arises. My colleague can substitute for me when I am on leave. I rarely have to work overtime. I am able to do the assigned work without worries and stress. After work, I never feel drained because of work stress. The promotion (or tenure-application) process has NOT brought extra pressure on my job. The emphasis of the university on improving its global university rankings has NOT brought extra pressure on my job.
Family resources and family demands	My family can help handle family matters. My family show their support when I have work and personal problems. My family is NOT entirely dependent on my income. I do NOT have to look after my family after work. I have enough quality time with my family. I can handle my family responsibilities.
Personal resources and personal demands	I have time and resources to look after my physical and mental health. I have strong faith/belief to hold on to. I can reach out for help from others when I have problems. I do NOT (or rarely) spend time on work on weekends or holidays. I have time for my hobbies. I am satisfied with my current personal development. When I am on vacation, I am able to detach myself from work and enjoy myself.
Family-friendly policies	My university provides welfare that covers my family members. My university has flexible policies that allow me to balance my work–family life. My university arranges social functions at times suitable for families.
Work outcomes	I am satisfied with my work. I have never thought of changing my job. I am driven to improve my work performance. I never feel burnout with my current work. My work performance is above average. I find my professional expertise and job performance to be duly recognized and appreciated by the career advancement and reward system. I have a good relationship with my colleagues.
Family outcomes	I can count on my family when I have problems. My family and I can look after each other. My work and personal matters could never distract me from my family. I am happy to be part of this family. I have a good relationship with my family.
Personal outcomes	I have good physical and mental health. My work compensation is sufficient to fulfill my personal needs. I have enough savings to live without future worries. I am proud to tell others about my profession. I have a good career path in my profession. My work helps me to realize my ultimate capability.
Work–life balance	It is easy for me to balance the demands of my work and my personal and family life. My job at the university allows adequate work and personal/family life balance. Flexible working arrangements and other family-friendly policies enable me to focus more on my work. All in all, I feel successful in balancing work and personal/family life.