This study investigates how meaningful work (MW) influences the three basic psychological needs, which, in turn, affect the four dimensions of hybrid working. It further explores how these outcomes contribute to work–family enrichment (WFE) among back-of-house employees in Hong Kong’s hospitality industry, a critical yet under-researched segment.
Data collected from 332 back-of-house employees in Hong Kong’s hospitality sector using a two-wave survey design were analyzed via the partial least squares structural equation modeling.
The results showed that MW positively influences the three basic psychological needs for autonomy, relatedness and competency. Only fulfilling the needs for autonomy positively influences the four dimensions of hybrid working (i.e. job effectiveness, well-being, relationship with organization and work–life balance). However, fulfilling other basic psychological needs showed differential results. Among the four dimensions of hybrid working, job effectiveness, well-being and relationship with the organization enrich the work–family domain.
This study improves our knowledge on hybrid workers’ work–family dynamics. Specifically, it highlights how MW and the fulfillment of basic psychological needs – particularly autonomy – enhance hybrid work outcomes such as job effectiveness, well-being and employee–organization relationships and in turn, positively influence WFE. This study also offers suggestions on how organizations can design a positive hybrid work experience that is especially valuable for hospitality employers seeking to retain and motivate back-of-house talent in a competitive labor market.
This study is among the first few studies that include hybrid working, basic psychological needs and WFE in one model. At the same time, this study extended literature by incorporating MW, an increasingly relevant constructs transforming workplaces.
Introduction
The recent pandemic has accelerated a new workplace arrangement – hybrid working. A recent South China Morning Post (SCMP) highlighted that 90% of the Chief Executive Officers in Hong Kong implemented a hybrid work model as employees view this arrangement as a necessity rather than a privilege (SCMP, 2024). PwC (2021) further spotlighted that hybrid working would be a permanent feature that organizations should provide to maintain their competitiveness in attracting and retaining talent. This arrangement is becoming so prevalent that the Labor Department of Hong Kong Special Administrative Region has issued a Flexible Work Arrangement guideline requesting employers to consider employees’ requests for hybrid work reasonably (Labour Department, 2025). Given the growing popularity and large body of research, such as the work of Grant et al. (2019), one would think there is sufficient evidence and knowledge to understand the psychological challenges of hybrid working. However, gaps remain.
First, hybrid working means changes to workplace design, engendering uncertainty due to reduced interpersonal cues that ensure smooth interactions (Gagne et al., 2022). In the process, hybrid working increases hybrid workers’ cognitive load and self-regulation and requires constant reminders of their work priorities and goals (Autin et al., 2022). Such changes affect how their psychological needs can be satisfied or frustrated, which has implications for both employees and employers. For some, these changes increase their sense of control and autonomy as they can decide how and when the work will be done. For others, it could mean frustrating their basic psychological need for relatedness due to the lack of support from their employers. This study addresses this gap by looking at how meeting these needs is essential in creating a positive hybrid work experience.
Second, most of the existing knowledge on hybrid working was developed when it was offered as a choice for employees with domestic commitments (Toniolo-Barrios and Pitt, 2021). Previously, this practice was used sporadically and applied to only selected groups of employees with unique circumstances. Compared to the current times, where hybrid working is an expectation, earlier results obtained by scholars could not represent the situation (Bailey and Kurland, 2002).
Third, literature examining the extent and influence of hybrid working on the work–family interface is relatively nascent, and it is even less developed for work–family enrichment (WFE) (Agarwal, 2021). Most existing works focus on conflicts between the two areas (Morilla-Luchena et al., 2021). Hence, this gap comes as a surprise, considering that the original intent of hybrid working is to support employees’ work by enriching their personal lives, not reducing it.
Another gap in the literature is examining meaningful work (MW) as an antecedent to the relationship between hybrid working effectiveness and WFE. MW can act as a catalyst in the relationship, resulting in effective hybrid working and enriching the work–family domain. When individuals find their work meaningful, they are more likely to proactively seek opportunities to integrate their work and family responsibilities to align with their values and goals (Laaser and Karlsson, 2021). By integrating the concept of MW into the analysis, we address the gap in how employees' perceptions of their work’s significance and purpose may impact their WFE experiences within a hybrid work arrangement.
This study further differentiates itself with its focus on predictive analysis, addressing a critical gap in the existing literature. Many earlier works, such as those by Kalliath et al. (2020) and Premchandran and Priyadarshi (2020), have emphasized explanatory modeling to determine whether hypotheses are significant and align with theoretical expectations. They often fall short in assessing the model’s ability to predict future similar scenarios.
Literature review
Research context
The research context of this study focuses on the back-of-house employees in the hospitality sector, a critical but often overlooked workforce segment responsible for operational efficiency and seamless service delivery behind the scenes. Traditionally, roles in hospitality, particularly in accommodation and food services, have centered on customer-facing (front-of-house) employees, with a wealth of research examining their experiences, challenges and work dynamics (Gursoy et al., 2023; Li et al., 2019). However, this focus has resulted in a significant research gap, as the experiences and challenges of back-of-house employees – working in areas such as finance, marketing, human resources and other administrative functions – remain underexplored.
As highlighted by Flanagan and Phi (2023), the growing competitiveness of the hospitality industry implies that all employees, regardless of areas of work, are key towards contributing to the differentiation and success of a hospitality organization in a crowded marketplace. Back-of-house employees play a pivotal role in supporting operational processes, ensuring strategic alignment and maintaining service quality, ultimately influencing customer satisfaction and organizational performance. With the rise of hybrid work models, these employees now face new opportunities and challenges balancing their professional and personal lives. As such, it is essential to understand how these arrangements impact the less visible yet indispensable back-of-house employees.
In the context of Hong Kong’s hospitality industry, this issue is particularly relevant. Hong Kong is a global hospitality hub with a high concentration of international hotel chains and a fast-paced, service-oriented economy (SCMP, 2024). While labor shortages and high employee turnover long challenged the industry, hybrid work is increasingly viewed as a strategic tool for workforce retention, particularly for roles that support operations behind the scenes (Ha, 2024). According to Tourism HR Canada (2022), hybrid work adoption is rising in the hospitality sector, especially in back-of-house functions. Similarly, Mckinsey (2024) highlighted the fact that workplace flexibility in the hospitality sector should be the norm to entice talent. Despite its growing relevance, little is known about how hybrid arrangements affect this unique employee group – hence the significance of this study.
Theoretical framework
Self-determination theory (SDT) could provide a framework for understanding intrinsic motivation that contributes to creating optimal hybrid working environments for employees (Orsini and Rodrigues, 2020). The shift to hybrid work has naturally led workers to shed emotional barriers, offering a genuine glimpse into who they are outside the office – an aspect of their lives that many colleagues had never seen previously. Hence, motivating individuals means to afford an encouraging view on the level of organizational support that shapes their feeling of having work ownership, a sense of confidence to do the work well and a connection with the broader work environment (Gagne et al., 2022). Satisfying basic psychological needs at work can generate personal resources that spillover to the non-work space (Autin et al., 2022). Putting these together, Figure 1 argues that MW fulfils the needs of competence, autonomy and relatedness, which will positively influence the different outcomes of hybrid working, and, eventually, WFE.
The figure shows a small circle labeled “M W” in the left center. From “M W”, three arrows extend right and point to three small circles arranged vertically and labeled from top to bottom as follows: “Need for Autonomy”, “Need for Competence”, and “Need for Relatedness”. Along the horizontal axis at the bottom, two time phases are marked by double-headed arrows labeled “Time 1” and “Time 2”, with directional arrows indicating progression from left to right. From “Need for Autonomy”, “Need for Competence”, and “Need for Relatedness”, four arrows extend right from each and point to four circles labeled vertically from top to bottom as follows: “J E”, “R E L”, “W E L L”, and “W L B”. The flow from “M W” to these four arrows arising from “Need for Autonomy”, “Need for Competence”, and “Need for Relatedness” occurs under the time phase “Time 1”. From here, the time phase changes to “Time 2”, where from “J E”, “R E L”, “W E L L”, and “W L B”, an arrow arises from each and extends right, pointing to a circle labeled “W F E” on the right.Conceptual model. Notes: (1) WELL = Well-being; JE = job effectiveness; MW = meaningful work; REL = Relationship with organization; WFE = work–family enrichment; WLB = work–life balance. Source: Authors’ own work
The figure shows a small circle labeled “M W” in the left center. From “M W”, three arrows extend right and point to three small circles arranged vertically and labeled from top to bottom as follows: “Need for Autonomy”, “Need for Competence”, and “Need for Relatedness”. Along the horizontal axis at the bottom, two time phases are marked by double-headed arrows labeled “Time 1” and “Time 2”, with directional arrows indicating progression from left to right. From “Need for Autonomy”, “Need for Competence”, and “Need for Relatedness”, four arrows extend right from each and point to four circles labeled vertically from top to bottom as follows: “J E”, “R E L”, “W E L L”, and “W L B”. The flow from “M W” to these four arrows arising from “Need for Autonomy”, “Need for Competence”, and “Need for Relatedness” occurs under the time phase “Time 1”. From here, the time phase changes to “Time 2”, where from “J E”, “R E L”, “W E L L”, and “W L B”, an arrow arises from each and extends right, pointing to a circle labeled “W F E” on the right.Conceptual model. Notes: (1) WELL = Well-being; JE = job effectiveness; MW = meaningful work; REL = Relationship with organization; WFE = work–family enrichment; WLB = work–life balance. Source: Authors’ own work
Hybrid working
The concept of hybrid work encompasses a flexible working arrangement characterized by completing tasks and responsibilities from inside and outside office locations by using technology (Grant et al., 2013). A review of existing literature, such as works by Dale et al. (2024), Teng-Calleja et al. (2024), Benedic (2023), and Krajčík et al. (2023), on hybrid work reveals that much of the research focuses on key aspects such as employee performance, well-being, levels of engagement and role overload. These findings are broadly grouped into dual domains that align with Grant et al. (2019): job-related outcomes, including relationship with organization and job effectiveness, and individual-related outcomes, encompassing employee well-being and work–life balance.
Job effectiveness. Job effectiveness indicates how well employees can perform work responsibilities (Grant et al., 2019). In a hybrid working model, job effectiveness often relies on clear expectations, effective communication channels and the flexibility to adapt to different work environments (Forbes, 2021).
Relationship with the organization. This outcome refers to how hybrid workers view their interaction with their superiors and the degree of independence they believe they have (Grant et al., 2019). This gives workers the confidence to trust their employers and not be disadvantaged regarding career progressions and assessments (Collins et al., 2016).
Well-being. The third outcome of an effective hybrid working experience is well-being. As highlighted earlier, hybrid working necessitates a delicate balance between remote and in-person activities, which can impact employee well-being significantly. It is essential to support employees, let them have a sense of belonging and helping them to manage their workloads, no matter where they work (Mishra and Bharti, 2023).
Work–life balance. The final and fourth outcome is work–life balance. This outcome would rely upon hybrid workers’ ability to balance work and personal demands effectively, transition seamlessly between various roles and separate work and family in a way that does not conflict with each other (Grant et al., 2019).
Relationship between meaningful work and basic psychological needs
MW is an individual’s connection between their occupation and their larger self (Afota et al., 2024). It is about finding significance in what one does, whether contributing to society, helping others or pursuing personal growth (Mortimer, 2023). This connection to one’s work often becomes integral to one’s identity and leads to positive outcomes such as increased job satisfaction, improved well-being and enhanced productivity. As Tan et al. (2024) highlighted, individuals experiencing MW would have a stronger sense of ownership and responsibility. This phenomenon is expected as MW signifies an alignment with an individual’s core values and purpose, fostering intrinsic motivation and a sense of control over one’s tasks and environment. Based on this, our first hypothesis is.
Meaningful work positively influences autonomy.
MW also nurtures a sense of competence, where individuals feel capable and effective in their roles. When people experience MW, they will invest effort in mastering existing skills and develop new ones (Tan et al., 2024). It is further demonstrated that when individuals find work to be meaningful, they would be involved in job-crafting behavior that would increase their structural resources at work (Oprea et al., 2020; Sánchez-Cardona et al., 2019). We therefore hypothesize:
Meaningful work positively influences competence.
Similarly, we argue that MW will positively influence employees’ sense of relatedness in the workplace. When employees experience MW, they develop shared purposes and values, which can encourage collaboration, cultivate a supportive environment, promote open communication and create a culture of shared successes (Afota et al., 2024). Furthermore, when individuals find their work meaningful, they are more inclined to identify with the organization’s mission and values, contributing to a sense of connection among colleagues (Arora and Garg, 2024). With hybrid work reducing physical interaction, which may weaken opportunities for spontaneous social exchange and relational bonding, MW may act as a compensatory mechanism, helping maintain a sense of relatedness even without regular face-to-face interaction (Wulff and Finnestrand, 2023). We lay the following hypothesis:
Meaningful work positively influences relatedness.
Relationship between autonomy and outcomes of hybrid working
Fulfilling the basic psychological need for autonomy is foundational in understanding how it contributes directly to the outcomes of hybrid working: job effectiveness, relationship with the organization, work–life balance and well-being (Van den Broeck et al., 2016). By not being physically co-located in the office, hybrid workers are expected to have more freedom to decide how and when work is to be done (Charalampous et al., 2018). By the nature of these empowerments, they will become more deeply invested in their work, leading to better performance (Vassiley et al., 2025). Hybrid workers with autonomy would feel more valued and empowered, which can lead to higher levels of engagement, strengthening the overall relationship between employees and their organization. At the same time, studies like that of Wang et al. (2021) demonstrated that flexibility associated with autonomy helps employees find an equilibrium between work and non-work domain, reducing stress and burnout. A recent systematic review by Slemp et al. (2024) further confirmed our earlier arguments that autonomy supports positive associations with different outcomes, including personal and work-related. As such, we postulate:
Autonomy positively influences job effectiveness.
Autonomy positively influences relationship with organization.
Autonomy positively influences well-being.
Autonomy positively influences work–life balance.
Relationship between competency and outcomes of hybrid working
An individual’s need for competence is met when they can successfully adapt to a complex and changing environment (Krause et al., 2019). For instance, hybrid workers need to have excellent communication skills to communicate their thoughts and ideas effortlessly, especially when hybrid workers will do most of their communication through email and communication applications (Gagne et al., 2022). Therefore, when hybrid workers feel competent in their skills and knowledge, they are more likely to approach challenges with confidence and resilience (Gagne et al., 2022). Naturally, this improves performance as employees adapt to the changing environment (Heyns et al., 2021). Fulfilling the need for competence goes beyond just doing their job well. Studies like that of Martela and Riekki (2018) found that competent employees better manage work-related stress. Their proficiency in their roles allows them to work more independently, contributing to a better work experience and reduced burnout. Similarly, competent employees can complete their tasks more efficiently, which frees up time for personal life and allows them to take full advantage of the flexibility offered by hybrid work models. They also understand the importance of setting boundaries between work and personal life and maintaining a healthy balance (McIntyre et al., 2023). Our next set of hypotheses is:
Competence positively influences job effectiveness.
Competence positively influences the relationship with the organization.
Competence positively influences well-being.
Competence positively influences work–life balance.
Relationship between relatedness and outcomes of hybrid working
The need for relatedness can be described as feeling connected to people, being part of a group and giving and receiving love (Gagne et al., 2022). When employees feel connected to their colleagues, teams and the broader organizational community, they are more likely to communicate openly, share ideas and support one another, increasing productivity and innovation (Kluwer et al., 2020). Furthermore, relatedness supports work–life balance by encouraging a positive integration of work and personal life (Martela and Riekki, 2018). Once they discover a sense of belongingness, they will be more forthcoming to voice boundaries that help them balance their responsibilities effectively.
Relatedness positively influences job effectiveness.
Relatedness positively influences the relationship with the organization.
Relatedness positively influences well-being.
Relatedness positively influences work–life balance.
Outcomes of hybrid working to work–family enrichment (WFE)
WFE describes that experiences in one domain enhance the quality of life in the other (Hassan et al., 2021). According to Greenhaus and Powell’s (2003) enrichment theory, this occurs through instrumental and affective pathways. The former involves the transfer of resources from one role to the other, while the latter focuses on the spillover of positive emotions and moods between roles (Kacmar et al., 2014). These works highlight the importance of creating an environment that supports both work and family domains to maximize individuals' positive outcomes.
In this regard, the experience of job effectiveness is an aspect of enriching work to family domain. According to Kramer and Kramer (2020), the increased effectiveness leads to the acquisition of valuable skills such as time management, problem-solving and communication, which can be applied to family roles, thereby enriching family life. At the same time, effective work performance evokes positive emotions such as a sense of accomplishment, which can spill over into the family domain, fostering positive interactions and relationships (Ab Wahab and Tatoglu, 2020). Our hypothesis is:
Job effectiveness positively influences WFE.
Similarly, positive relationships with the organization in a hybrid working model foster a sense of trust and support. When hybrid workers feel supported and valued by their organization, they are less likely to experience work-related stress, which can otherwise negatively affect family life (De Klerk et al., 2014). This proposition is further supported by Bowen (2024), where empirical research suggests that organizational support, including flexible working arrangements and family-friendly policies, is positively influenced by WFE (Wayne et al., 2006).
Relationship with organization positively influences WFE.
We further argue that experiencing well-being from hybrid work is another antecedent leading to WFE. According to Hobfoll (1989), well-being is a critical resource that employees can bring into their family lives, enhancing hybrid workers’ ability to engage positively with family members and participate in family activities. This resource gain cycle, where well-being in the work domain enhances family life, further supports the concept of WFE (Chen et al., 2015).
Well-being positively influences WFE.
Finally, hybrid working models promote work–life balance by enabling employees to better allocate time and resources between work and family responsibilities. Literature has demonstrated that work–life balance is essential for preventing role conflict and ensuring that both domains receive adequate attention (French et al., 2018). Shirmohammadi et al. (2022) found that work–life balance facilitates the transfer of positive experiences and skills between work and family domains, leading to mutual enrichment. By setting clear boundaries and ensuring that work demands do not encroach on family time, hybrid working arrangements support a balanced and enriched family life.
Work–life balance positively influences WFE.
Method
Ethics procedure
With ethics obtained from the university’s Human Research Ethics Office, the team commenced with survey data collection. As part of the ethics procedure, we confirmed that all participants were fully informed about the purpose of the study, their rights as participants to refuse participation or withdraw from the study at any time and measures were taken to ensure anonymity, protect their confidentiality and privacy. Respondents’ permission were given before commencing the survey.
Sample size and data-collection procedure
Online surveys via Qualtrics were distributed to the respondents. Qualtrics allowed a structured questionnaire flow using branching logic to ensure that only eligible respondents – specifically, back-of-house employees who had engaged in hybrid work within the past year – could proceed. Before beginning the survey, participants were presented with information about the study’s purpose and rights and were required to provide explicit consent. Each survey wave was distributed via Qualtrics using a snowball sampling strategy, leveraging professional networks within the Hong Kong hospitality sector.
A two-wave time-lagged approach reduces bias. Similarly, studies such as those by Tan et al. (2024) adopted the same methodology. The first wave obtained constructs relating to MW and the three basic psychological needs of autonomy, competence and relatedness. The next collection time point focuses on the outcomes of hybrid work (i.e. job effectiveness, relationship with organization, well-being and work–life balance) and WFE.
For wave 1, respondents are requested to provide their email addresses for the survey. A total of 423 responses were received. For Time 2, 423 were subsequently requested to participate in wave 2 where they will provide similar identifying information. Responses from both periods were matched. The final sample is 322 participants, representing 76.1% of participation. This sample size exceeds the suggested number of 160 (Kock and Hadaya, 2018). Table 1 shows the breakdown of the respondents.
Respondents’ profile
| Demographic variable | Category | Frequency (n = 332) | Percentage |
|---|---|---|---|
| Gender | Male | 147 | 44.3% |
| Female | 185 | 55.7% | |
| 0.0% | |||
| Marital status | Married | 213 | 64.2% |
| Single | 119 | 35.8% | |
| Age group | 17 and below | 0 | 0.0% |
| 18–24 | 6 | 1.8% | |
| 24–34 | 93 | 28.0% | |
| 35–44 | 109 | 32.8% | |
| 45–54 | 93 | 28.0% | |
| 55 and above | 31 | 9.3% | |
| Education level | Doctorate | 25 | 7.5% |
| Master | 48 | 14.5% | |
| Bachelor | 166 | 50.0% | |
| Diploma | 64 | 19.3% | |
| Certificate | 19 | 5.7% | |
| Other | 10 | 3.0% |
| Demographic variable | Category | Frequency (n = 332) | Percentage |
|---|---|---|---|
| Gender | Male | 147 | 44.3% |
| Female | 185 | 55.7% | |
| 0.0% | |||
| Marital status | Married | 213 | 64.2% |
| Single | 119 | 35.8% | |
| Age group | 17 and below | 0 | 0.0% |
| 18–24 | 6 | 1.8% | |
| 24–34 | 93 | 28.0% | |
| 35–44 | 109 | 32.8% | |
| 45–54 | 93 | 28.0% | |
| 55 and above | 31 | 9.3% | |
| Education level | Doctorate | 25 | 7.5% |
| Master | 48 | 14.5% | |
| Bachelor | 166 | 50.0% | |
| Diploma | 64 | 19.3% | |
| Certificate | 19 | 5.7% | |
| Other | 10 | 3.0% |
Source(s): Authors’ own work
Data analysis
The data collected were analyzed using PLS-SEM. PLS-SEM is appropriate for this study as it is more inclined to be used for social sciences (Sarstedt et al., 2014). Additionally, PLS-SEM has been leveraged in studies such as leadership (Ali et al., 2021), purchasing psychology (Ting et al., 2015), human resources (Ringle et al., 2020), education (Sim et al., 2020), adopting new technologies (Tan et al., 2025) as well as hospitality (Fam et al., 2020).
Measures
The ten items measuring MW were adapted from Steger et al. (2012) and are measured on a 5-point Likert scale. Measured on a 7-point Likert scale, the 21 items on basic psychological needs for autonomy, relatedness and competence are adapted from Deci and Ryan (2000). Items for job effectiveness, relationship with organizations, work–life balance and well-being were adapted using the 18-item instrument from Grant et al. (2019). Finally, WFE was adapted from Carlson et al. (2006). The nine-item instrument is measured on a 5-point Likert scale.
Common method bias
Besides collecting data at two time intervals, we pretested survey questions to remove ambiguities in the instructions and the items. At the same time, we remove any apprehension by emphasizing that the data collected is confidential and all participation is voluntary. Statistically, the variance inflation factor (VIF) values were between 1.276 and 1.496 (see Table 4), which are all lower than 3.3, indicating that common method bias is not a major concern in this study (Kock, 2015)
Results
Measurement model
The measurement model was assessed by evaluating the constructs' reliability, convergent validity and discriminant validity. First, the composite reliability for all constructs, as shown in Table 2, was above 0.70 (Hair et al., 2017), establishing the constructs' reliability. Secondly, convergent validity is evaluated by inspecting the outer loadings and the average variance extracted (AVEs). As shown in Table 2, most outer loadings were above 0.707. Some items between 0.40 and 0.70 were retained because the AVEs for all constructs were already above 0.50 (Hair et al., 2017). Thirdly, discriminant validity is established as ratios (see Table 3) between the constructs were below 0.85 (Hair et al., 2017).
Measurement model assessment
| Construct | Item | Loading | CA | AVE | CR |
|---|---|---|---|---|---|
| Meaningful work | MW1 | 0.807 | 0.929 | 0.935 | 0.615 |
| MW2 | 0.783 | ||||
| MW3 | 0.622 | ||||
| MW4 | 0.811 | ||||
| MW5 | 0.721 | ||||
| MW6 | 0.823 | ||||
| MW7 | 0.751 | ||||
| MW8 | 0.867 | ||||
| MW9 | 0.770 | ||||
| MW10 | 0.858 | ||||
| Need for autonomy | NFA1 | 0.815 | 0.836 | 0.872 | 0.509 |
| NFA2 | 0.840 | ||||
| NFA3 | 0.618 | ||||
| NFA4 | 0.439 | ||||
| NFA5 | 0.773 | ||||
| NFA6 | 0.784 | ||||
| NFA7 | 0.635 | ||||
| Need for competence | NFC1 | 0.691 | 0.852 | 0.866 | 0.576 |
| NFC2 | 0.840 | ||||
| NFC3 | 0.845 | ||||
| NFC4 | 0.646 | ||||
| NFC5 | 0.796 | ||||
| NFC6 | 0.712 | ||||
| Need for relatedness | NFR1 | 0.637 | 0.893 | 0.901 | 0.511 |
| NFR2 | 0.747 | ||||
| NFR3 | 0.687 | ||||
| NFR4 | 0.713 | ||||
| NFR5 | 0.796 | ||||
| NFR6 | 0.713 | ||||
| NFR7 | 0.777 | ||||
| NFR8 | 0.573 | ||||
| NFR9 | 0.764 | ||||
| NFR10 | 0.714 | ||||
| Job effectiveness | JE1 | 0.887 | 0.851 | 0.884 | 0.699 |
| JE2 | 0.913 | ||||
| JE3 | 0.637 | ||||
| JE4 | 0.879 | ||||
| Relationship with organization | REL1 | 0.773 | 0.708 | 0.708 | 0.631 |
| REL2 | 0.793 | ||||
| REL3 | 0.817 | ||||
| Well-being | WELL1 | 0.730 | 0.739 | 0.759 | 0.660 |
| WELL2 | 0.809 | ||||
| WELL3 | 0.890 | ||||
| Work–life balance | WLB1 | 0.784 | 0.852 | 0.866 | 0.533 |
| WLB2 | 0.702 | ||||
| WLB3 | 0.785 | ||||
| WLB4 | 0.765 | ||||
| WLB5 | 0.511 | ||||
| WLB6 | 0.765 | ||||
| WLB7 | 0.758 | ||||
| Work–family enrichment | WFE1 | 0.832 | 0.959 | 0.961 | 0.755 |
| WFE2 | 0.842 | ||||
| WFE3 | 0.832 | ||||
| WFE4 | 0.872 | ||||
| WFE5 | 0.901 | ||||
| WFE6 | 0.903 | ||||
| WFE7 | 0.902 | ||||
| WFE8 | 0.863 | ||||
| WFE9 | 0.869 |
| Construct | Item | Loading | CA | AVE | CR |
|---|---|---|---|---|---|
| Meaningful work | MW1 | 0.807 | 0.929 | 0.935 | 0.615 |
| MW2 | 0.783 | ||||
| MW3 | 0.622 | ||||
| MW4 | 0.811 | ||||
| MW5 | 0.721 | ||||
| MW6 | 0.823 | ||||
| MW7 | 0.751 | ||||
| MW8 | 0.867 | ||||
| MW9 | 0.770 | ||||
| MW10 | 0.858 | ||||
| Need for autonomy | NFA1 | 0.815 | 0.836 | 0.872 | 0.509 |
| NFA2 | 0.840 | ||||
| NFA3 | 0.618 | ||||
| NFA4 | 0.439 | ||||
| NFA5 | 0.773 | ||||
| NFA6 | 0.784 | ||||
| NFA7 | 0.635 | ||||
| Need for competence | NFC1 | 0.691 | 0.852 | 0.866 | 0.576 |
| NFC2 | 0.840 | ||||
| NFC3 | 0.845 | ||||
| NFC4 | 0.646 | ||||
| NFC5 | 0.796 | ||||
| NFC6 | 0.712 | ||||
| Need for relatedness | NFR1 | 0.637 | 0.893 | 0.901 | 0.511 |
| NFR2 | 0.747 | ||||
| NFR3 | 0.687 | ||||
| NFR4 | 0.713 | ||||
| NFR5 | 0.796 | ||||
| NFR6 | 0.713 | ||||
| NFR7 | 0.777 | ||||
| NFR8 | 0.573 | ||||
| NFR9 | 0.764 | ||||
| NFR10 | 0.714 | ||||
| Job effectiveness | JE1 | 0.887 | 0.851 | 0.884 | 0.699 |
| JE2 | 0.913 | ||||
| JE3 | 0.637 | ||||
| JE4 | 0.879 | ||||
| Relationship with organization | REL1 | 0.773 | 0.708 | 0.708 | 0.631 |
| REL2 | 0.793 | ||||
| REL3 | 0.817 | ||||
| Well-being | WELL1 | 0.730 | 0.739 | 0.759 | 0.660 |
| WELL2 | 0.809 | ||||
| WELL3 | 0.890 | ||||
| Work–life balance | WLB1 | 0.784 | 0.852 | 0.866 | 0.533 |
| WLB2 | 0.702 | ||||
| WLB3 | 0.785 | ||||
| WLB4 | 0.765 | ||||
| WLB5 | 0.511 | ||||
| WLB6 | 0.765 | ||||
| WLB7 | 0.758 | ||||
| Work–family enrichment | WFE1 | 0.832 | 0.959 | 0.961 | 0.755 |
| WFE2 | 0.842 | ||||
| WFE3 | 0.832 | ||||
| WFE4 | 0.872 | ||||
| WFE5 | 0.901 | ||||
| WFE6 | 0.903 | ||||
| WFE7 | 0.902 | ||||
| WFE8 | 0.863 | ||||
| WFE9 | 0.869 |
Note(s): CA: Cronbach’s Alpha; CR = composite reliability; AVE = average variance extracted
Source(s): Authors’ own work
Discriminant validity
| MW | NFA | NFC | NFR | WELL | JE | REL | WLB | WFE | |
|---|---|---|---|---|---|---|---|---|---|
| MW | |||||||||
| NFA | 0.587 | ||||||||
| NFC | 0.489 | 0.443 | |||||||
| NFR | 0.537 | 0.600 | 0.455 | ||||||
| WELL | 0.342 | 0.581 | 0.241 | 0.330 | |||||
| JE | 0.071 | 0.191 | 0.286 | 0.084 | 0.155 | ||||
| REL | 0.379 | 0.667 | 0.345 | 0.392 | 0.747 | 0.279 | |||
| WLB | 0.092 | 0.335 | 0.226 | 0.163 | 0.337 | 0.518 | 0.410 | ||
| WFE | 0.769 | 0.648 | 0.376 | 0.514 | 0.400 | 0.185 | 0.464 | 0.168 |
| MW | NFA | NFC | NFR | WELL | JE | REL | WLB | WFE | |
|---|---|---|---|---|---|---|---|---|---|
| MW | |||||||||
| NFA | 0.587 | ||||||||
| NFC | 0.489 | 0.443 | |||||||
| NFR | 0.537 | 0.600 | 0.455 | ||||||
| WELL | 0.342 | 0.581 | 0.241 | 0.330 | |||||
| JE | 0.071 | 0.191 | 0.286 | 0.084 | 0.155 | ||||
| REL | 0.379 | 0.667 | 0.345 | 0.392 | 0.747 | 0.279 | |||
| WLB | 0.092 | 0.335 | 0.226 | 0.163 | 0.337 | 0.518 | 0.410 | ||
| WFE | 0.769 | 0.648 | 0.376 | 0.514 | 0.400 | 0.185 | 0.464 | 0.168 |
Note(s): (1) WELL = Well-being; JE = job effectiveness; MW = meaningful work; NFA = need for autonomy; NFC = need for competence; NFR = need for relatedness; REL = relationship with the organization; WFE = work–family enrichment; WLB = work–life balance, (2) Discriminant validity achieved at HTMT0.85
Source(s): Authors’ own work
Hypotheses testing
First, VIF was inspected to determine whether collinearity exists between the constructs. The VIF for all exogenous constructs was below 3.3 (see Table 4), indicating no collinearity issue in the structural model (Hair et al., 2017).
Hypotheses testing
| Path coefficient | SE | t_value | 5.00% | 95.00% | VIF | f2 | R2 | ||
|---|---|---|---|---|---|---|---|---|---|
| H1a | MW → NFA | 0.562 | 0.045 | 12.553*** | 0.489 | 0.635 | 1.000 | 0.463 | 0.316 |
| H1b | MW → NFC | 0.447 | 0.053 | 8.485*** | 0.362 | 0.536 | 1.000 | 0.250 | 0.200 |
| H1c | MW → NFR | 0.504 | 0.043 | 11.634*** | 0.437 | 0.58 | 1.000 | 0.341 | 0.254 |
| H2a | NFA → JE | 0.158 | 0.082 | 1.934* | 0.019 | 0.287 | 1.482 | 0.018 | 0.073 |
| H2b | NFA → REL | 0.473 | 0.054 | 8.719*** | 0.382 | 0.561 | 1.482 | 0.211 | 0.283 |
| H2c | NFA → WELL | 0.466 | 0.058 | 7.967*** | 0.369 | 0.561 | 1.482 | 0.190 | 0.231 |
| H2d | NFA → WLB | 0.280 | 0.073 | 3.816*** | 0.164 | 0.404 | 1.482 | 0.058 | 0.093 |
| H3a | NFC → JE | 0.222 | 0.066 | 3.378*** | 0.119 | 0.334 | 1.247 | 0.043 | 0.073 |
| H3b | NFC → REL | 0.080 | 0.050 | 1.595(NS) | −0.001 | 0.166 | 1.247 | 0.007 | |
| H3c | NFC → WELL | 0.007 | 0.058 | 0.119(NS) | −0.087 | 0.102 | 1.247 | 0.000 | |
| H3d | NFC → WLB | 0.117 | 0.067 | 1.748* | 0.01 | 0.229 | 1.247 | 0.012 | |
| H4a | NFR → JE | −0.129 | 0.073 | 1.783* | −0.251 | −0.009 | 1.491 | 0.012 | |
| H4b | NFR → REL | 0.038 | 0.061 | 0.622(NS) | −0.059 | 0.14 | 1.491 | 0.001 | |
| H4c | NFR → WELL | 0.022 | 0.061 | 0.362(NS) | −0.077 | 0.127 | 1.491 | 0.000 | |
| H4d | NFR → WLB | −0.080 | 0.077 | 1.030(NS) | −0.209 | 0.043 | 1.491 | 0.005 | |
| H5a | JE → WFE | 0.113 | 0.068 | 1.661* | 0.002 | 0.225 | 1.275 | 0.012 | 0.182 |
| H5b | REL → WFE | 0.267 | 0.076 | 3.516*** | 0.136 | 0.387 | 1.496 | 0.058 | |
| H5c | WELL → WFE | 0.193 | 0.064 | 3.021** | 0.09 | 0.301 | 1.436 | 0.032 | |
| H5d | WLB → WFE | −0.037 | 0.066 | 0.559(NS) | −0.143 | 0.073 | 1.389 | 0.001 |
| Path coefficient | SE | t_value | 5.00% | 95.00% | VIF | f2 | R2 | ||
|---|---|---|---|---|---|---|---|---|---|
| MW → NFA | 0.562 | 0.045 | 12.553*** | 0.489 | 0.635 | 1.000 | 0.463 | 0.316 | |
| MW → NFC | 0.447 | 0.053 | 8.485*** | 0.362 | 0.536 | 1.000 | 0.250 | 0.200 | |
| MW → NFR | 0.504 | 0.043 | 11.634*** | 0.437 | 0.58 | 1.000 | 0.341 | 0.254 | |
| NFA → JE | 0.158 | 0.082 | 1.934* | 0.019 | 0.287 | 1.482 | 0.018 | 0.073 | |
| NFA → REL | 0.473 | 0.054 | 8.719*** | 0.382 | 0.561 | 1.482 | 0.211 | 0.283 | |
| NFA → WELL | 0.466 | 0.058 | 7.967*** | 0.369 | 0.561 | 1.482 | 0.190 | 0.231 | |
| NFA → WLB | 0.280 | 0.073 | 3.816*** | 0.164 | 0.404 | 1.482 | 0.058 | 0.093 | |
| NFC → JE | 0.222 | 0.066 | 3.378*** | 0.119 | 0.334 | 1.247 | 0.043 | 0.073 | |
| NFC → REL | 0.080 | 0.050 | 1.595(NS) | −0.001 | 0.166 | 1.247 | 0.007 | ||
| NFC → WELL | 0.007 | 0.058 | 0.119(NS) | −0.087 | 0.102 | 1.247 | 0.000 | ||
| NFC → WLB | 0.117 | 0.067 | 1.748* | 0.01 | 0.229 | 1.247 | 0.012 | ||
| NFR → JE | −0.129 | 0.073 | 1.783* | −0.251 | −0.009 | 1.491 | 0.012 | ||
| NFR → REL | 0.038 | 0.061 | 0.622(NS) | −0.059 | 0.14 | 1.491 | 0.001 | ||
| NFR → WELL | 0.022 | 0.061 | 0.362(NS) | −0.077 | 0.127 | 1.491 | 0.000 | ||
| NFR → WLB | −0.080 | 0.077 | 1.030(NS) | −0.209 | 0.043 | 1.491 | 0.005 | ||
| JE → WFE | 0.113 | 0.068 | 1.661* | 0.002 | 0.225 | 1.275 | 0.012 | 0.182 | |
| REL → WFE | 0.267 | 0.076 | 3.516*** | 0.136 | 0.387 | 1.496 | 0.058 | ||
| WELL → WFE | 0.193 | 0.064 | 3.021** | 0.09 | 0.301 | 1.436 | 0.032 | ||
| WLB → WFE | −0.037 | 0.066 | 0.559(NS) | −0.143 | 0.073 | 1.389 | 0.001 |
Note(s): (1) *p < 0.05; **p < 0.01; ***p < 0.001 (one-tailed) (2) WELL = well-being; JE = job effectiveness; MW = meaningful work; NFA = need for autonomy; NFC = need for competence; NFR = need for relatedness; REL = relationship with the organization ; WFE = work–family enrichment; WLB = work–life balance (3) SE = Standard error
Source(s): Authors’ own work
Second, the hypotheses were tested by running bootstrapping, and results, as shown in Table 4, revealed that MW positively influenced the three basic psychological needs of autonomy (H1a. β = 0.562, p < 0.001), competence (H1b. β = 0.447, p < 0.001) and relatedness (H1c. β = 0.504, p < 0.001). Therefore, H1a to H1c are supported.
Similarly, fulfilling the need for autonomy positively influenced all outcomes of hybrid work, which are job effectiveness (H2a. β = 0.158, p < 0.05), relationship with the organization (H2b. β = 0.473, p < 0.001), well-being (H2c. β = 0.466, p < 0.001) and work–life balance (H2d. β = 0.280, p < 0.001). H2a, H2b, H2c and H2d are supported.
However, fulfilling the need for competency would only positively influence job effectiveness (H3a. β = 0.222, p < 0.001) and work–life balance (H3d. β = 0.117, p < 0.05), but not relationship with an organization (H3b. β = 0.080, p = 0.055) and well-being (H3c. β = 0.007, p = 0.453). Hence, H3a and H3d are accepted, and not H3b and H3c.
At the same time, our results show that fulfilling the need for relatedness would reduce job effectiveness (H4a. β = −0.129, p < 0.05), but not with relationship to organization (H4b. β = 0.038, p = 0.267), well-being (H4c. β = 0.022, p = 0.359) and work–life balance (H4d. β = −0.080, p = 0.152). Given that H4a runs contrary to the hypothesized direction, all H4a, H4b, H4c and H4d are rejected.
Finally, among the four outcomes of hybrid working, only job effectiveness (H5a. β = 0.113, p < 0.05), relationship with organization (H5b. β = 0.267, p < 0.001) and well-being (H5c. β = 0.193, p < 0.01) have a positive influence on WFE. Only work–life balance (H5d. β = −0.037, p = 0.288) has a non-significant relationship. As such, only H5a, H5b and H5c are accepted.
The coefficient of determination (R2) and the effect sizes (f2) were computed to determine how well the exogenous variables explain the endogenous variables. Based on Cohen’s (1988) threshold, most of them are considered as medium and large. Some effects are smaller than 0.02 due to their non-significant relationships.
We generated predictions of the model following the work of Shmueli et al. (2016). As shown in Table 5, the linear model (LM) of the root mean squared error (RMSE) is greater than the PLS-SEM for all of the endogenous indicators, thus indicating that the model has high predictive power (Shmueli et al., 2019)
PLSpredict
| Item | PLS-SEM | LM RMSE | PLS-SEM – LM RMSE | |
|---|---|---|---|---|
| RMSE | Q2 predict | |||
| NFA1 | 1.223 | 0.213 | 1.283 | −0.060 |
| NFA2 | 1.275 | 0.231 | 1.321 | −0.046 |
| NFA3 | 1.313 | 0.106 | 1.337 | −0.025 |
| NFA4 | 1.158 | 0.057 | 1.178 | −0.020 |
| NFA5 | 1.313 | 0.166 | 1.316 | −0.003 |
| NFA6 | 1.160 | 0.245 | 1.168 | −0.008 |
| NFA7 | 1.484 | 0.061 | 1.499 | −0.015 |
| NFC1 | 1.526 | 0.024 | 1.557 | −0.031 |
| NFC2 | 1.172 | 0.065 | 1.183 | −0.010 |
| NFC3 | 1.072 | 0.071 | 1.100 | −0.028 |
| NFC4 | 1.450 | 0.024 | 1.492 | −0.042 |
| NFC5 | 0.991 | 0.046 | 1.017 | −0.026 |
| NFC6 | 1.127 | 0.052 | 1.162 | −0.035 |
| NFR1 | 1.608 | 0.047 | 1.658 | −0.050 |
| NFR2 | 1.223 | 0.103 | 1.247 | −0.024 |
| NFR3 | 1.515 | 0.012 | 1.573 | −0.059 |
| NFR4 | 1.444 | 0.067 | 1.457 | −0.014 |
| NFR5 | 1.433 | 0.019 | 1.440 | −0.007 |
| NFR6 | 1.321 | 0.025 | 1.370 | −0.049 |
| NFR7 | 1.402 | 0.053 | 1.440 | −0.038 |
| NFR8 | 1.583 | 0.034 | 1.623 | −0.040 |
| NFR9 | 1.435 | 0.029 | 1.464 | −0.030 |
| NFR10 | 1.560 | 0.005 | 1.586 | −0.026 |
| WFE1 | 0.838 | 0.113 | 0.863 | −0.025 |
| WFE2 | 0.851 | 0.110 | 0.857 | −0.006 |
| WFE3 | 0.847 | 0.099 | 0.877 | −0.030 |
| WFE4 | 0.935 | 0.125 | 0.975 | −0.040 |
| WFE5 | 0.921 | 0.116 | 0.951 | −0.030 |
| WFE6 | 0.911 | 0.145 | 0.930 | −0.020 |
| WFE7 | 0.873 | 0.140 | 0.899 | −0.025 |
| WFE8 | 0.852 | 0.109 | 0.878 | −0.026 |
| WFE9 | 0.831 | 0.099 | 0.844 | −0.014 |
| Item | PLS-SEM | LM RMSE | PLS-SEM – LM RMSE | |
|---|---|---|---|---|
| RMSE | Q2 predict | |||
| NFA1 | 1.223 | 0.213 | 1.283 | −0.060 |
| NFA2 | 1.275 | 0.231 | 1.321 | −0.046 |
| NFA3 | 1.313 | 0.106 | 1.337 | −0.025 |
| NFA4 | 1.158 | 0.057 | 1.178 | −0.020 |
| NFA5 | 1.313 | 0.166 | 1.316 | −0.003 |
| NFA6 | 1.160 | 0.245 | 1.168 | −0.008 |
| NFA7 | 1.484 | 0.061 | 1.499 | −0.015 |
| NFC1 | 1.526 | 0.024 | 1.557 | −0.031 |
| NFC2 | 1.172 | 0.065 | 1.183 | −0.010 |
| NFC3 | 1.072 | 0.071 | 1.100 | −0.028 |
| NFC4 | 1.450 | 0.024 | 1.492 | −0.042 |
| NFC5 | 0.991 | 0.046 | 1.017 | −0.026 |
| NFC6 | 1.127 | 0.052 | 1.162 | −0.035 |
| NFR1 | 1.608 | 0.047 | 1.658 | −0.050 |
| NFR2 | 1.223 | 0.103 | 1.247 | −0.024 |
| NFR3 | 1.515 | 0.012 | 1.573 | −0.059 |
| NFR4 | 1.444 | 0.067 | 1.457 | −0.014 |
| NFR5 | 1.433 | 0.019 | 1.440 | −0.007 |
| NFR6 | 1.321 | 0.025 | 1.370 | −0.049 |
| NFR7 | 1.402 | 0.053 | 1.440 | −0.038 |
| NFR8 | 1.583 | 0.034 | 1.623 | −0.040 |
| NFR9 | 1.435 | 0.029 | 1.464 | −0.030 |
| NFR10 | 1.560 | 0.005 | 1.586 | −0.026 |
| WFE1 | 0.838 | 0.113 | 0.863 | −0.025 |
| WFE2 | 0.851 | 0.110 | 0.857 | −0.006 |
| WFE3 | 0.847 | 0.099 | 0.877 | −0.030 |
| WFE4 | 0.935 | 0.125 | 0.975 | −0.040 |
| WFE5 | 0.921 | 0.116 | 0.951 | −0.030 |
| WFE6 | 0.911 | 0.145 | 0.930 | −0.020 |
| WFE7 | 0.873 | 0.140 | 0.899 | −0.025 |
| WFE8 | 0.852 | 0.109 | 0.878 | −0.026 |
| WFE9 | 0.831 | 0.099 | 0.844 | −0.014 |
Note(s): (1) NFA = need for autonomy; NFC = need for competence; NFR = need for relatedness; WFE = work–family enrichment
Source(s): Authors’ own work
Discussions and conclusions
Conclusions
From the results, we see that MW positively influences the three psychological needs of autonomy, competence and relatedness. There could be several reasons. Firstly MW is about doing work that aligns with individuals’ values and sense of purpose (Wulff and Finnestrand, 2023). This alignment will foster self-direction and autonomy as respondents perceive themselves to have valence in tasks and making decisions. At the same time, MW supports connecting to a larger purpose outside of just discharging tasks. Aligning with existing literature, such as Yeoman (2014), our results support the explanation that in MW settings, hybrid workers rely on collaboration and teamwork, leading them to enhance interpersonal relationships and the feeling of being part of a community.
Additionally, autonomy is key to fulfilling all needs. Aligned with Autin et al. (2022), it shows that focusing on the provision of autonomy would enhance empowerment in hybrid workers, leading them to work-process optimization and hence improving job effectiveness. At the same time, providing autonomy is a signal of trust and respect from the organization. As Heyns et al. (2021) highlight, when employees feel trusted, their loyalty and commitment to the organization increase, strengthening the employer–employee relationship.
We further demonstrated that fulfilling the need for competency has a positive relationship with job effectiveness and work–life balance. There are several reasons for this result. First, competence enhances job effectiveness by enabling employees to utilize their skills and expertise effectively, leading to higher-quality work and more efficient task completion (Slemp et al., 2024). Employees who feel confident about their abilities demonstrate superior problem-solving and decision-making skills, contributing to improved performance (Heyns et al., 2021).
While fulfilling the need for competence significantly enhances job effectiveness and work–life balance, our results demonstrated that it did not have a similarly pronounced impact on employees' relationship with the organization or their overall well-being. This result is somewhat of a departure from research such as that of Autin et al. (2022). The relationship with the organization often hinges more on organizational culture, leadership quality and perceived organizational support rather than individual competence. This argument aligns with several studies suggesting that trust in leadership, fairness and recognition play a more substantial role in fostering strong organizational relationships (Hassan et al., 2021; Shockley et al., 2021).
Unlike conclusions drawn from studies such as Martela and Riekki (2018), our results demonstrated that fulfilling the needs for relatedness did not support any hybrid working outcomes. This finding can be understood from a closer examination of the constructs of each of them. As highlighted earlier, well-being is a multifaceted construct, encompassing physical health, emotional stability and overall life satisfaction, which may not be fully addressed by fulfilling the need for relatedness alone (Van Dierendonck, 2004). Moreover, relatedness can lead to increased social obligations and interactions that make individuals to work harder, encroaching into their non-work hours (Smite et al., 2023). Building strong relationships within an organization also involves elements such as trust in leadership, perceived fairness, recognition and alignment with organizational values (Figueroa-Armijos et al., 2022). Therefore, the impact of relatedness on well-being, work–life balance and organizational relationships is limited due to these outcomes' complex and multifaceted nature.
Among the outcomes of hybrid working, work–life balance is the only dimension that does not contribute to WFE which contradicts existing studies like Smite et al. (2023). This divergence is explainable considering the conceptualization of work–life balance. In other words, while work–life balance is an effort to design an equal allocation of time and energy across the two domains, its construct does not inherently promise positive spillover.
Theoretical implications
First, we re-examine the boundaries between MW and SDT from a new perspective of hybrid workers. While previous studies, such as those of Gerdenitsch (2017), have sought to identify this, it should be noted that these studies are conducted in a different societal setting where hybrid working was not seen as an entitlement to all employees but a privilege to the selected few. Besides, the psychological challenges of hybrid working during a crisis still lack a theoretical response. On this note, this study provided an answer by measuring how basic psychological needs influence the dimensions of hybrid working and, thereafter, WFE.
Unlike earlier studies examining hybrid working as a unidimensional construct, we investigated the interplay of the different dimensions of hybrid working. In the process, we respond to Grant et al.’s (2019) call to further expand the understanding of hybrid working. From this perspective, we provide new insights that further clarify the theoretical boundaries of the SDT and hybrid working literature during a global crisis.
Lastly, our study confirms the robustness of the research model, showcasing strong predictive power. Following Sharma et al. (2018), the ability to support explanatory insights with a predictive perspective ensures research efficiency that provides actionable insights to practitioners.
Practical implications
First, organizations should nurture a positive and supportive relationship with hybrid workers. In turn, it enriches their work–family interfaces, which is becoming increasingly important to today’s employees (Braun et al., 2019). Further, organizations should foster WFE by incentivizing programs and policies that help employees manage their work issues.
Managers would need to be trained in formal and informal communications to provide employees new to hybrid working with the necessary work support. Managers should provide hybrid workers the autonomy to decline official meetings after office hours or unreasonable work requests, such as overworking, that could further frustrate their needs, leading to work–family conflict.
To strengthen their sense of autonomy, hybrid workers should actively cultivate skills tailored to the hybrid work model, including effective self-management, goal-setting, task prioritization and maintaining high -performance standards. Equally important is recognizing when to set boundaries and decline tasks outside of work hours to prevent conflicts between professional and personal responsibilities.
Limitations and future research directions
First, the context of each country differs, especially in how hybrid working is being managed. The cultural, economic and regulatory environments can significantly influence the implementation and perception of hybrid working models. Besides, cultural values regarding work–life balance, organizational commitment and familial responsibilities vary widely, impacting how employees perceive WFE. Therefore, future researchers can conduct similar studies in other contexts to examine if and how there are any differences in this study’s results. Second, we encourage future researchers to consider adopting a mixed methodology to have a qualitative study to detail the challenges hybrid workers face while working.
Additionally, future researchers can replicate the model on specific occupational groups. Different occupations have unique characteristics and demands that influence how hybrid working affects WFE. Examining the curvilinear effects would add depth to the research as highly competent employees might overwork, worsening work–life balance and depleting the meaning of work. Furthermore, understanding the intricacies involved in work performance, such as feeling confident to execute tasks, being self-directed at work and face-to-face interaction, can shed light on how hybrid working influences WFE in various contexts.
The authors thank Dr Nguyen Dang Hat for his support in this project.

