Abstract
Orientation: Remote work has become a prevalent feature in today’s workplace and significantly transforming how organisations operate. This shift created both opportunities and challenges for sustaining employee motivation.
Research purpose: This study examines how communication, technology and work–life balance collectively impact employee motivation among remote workers within a Namibian organisation, thereby identifying which of these factors is mostly associated with motivational outcomes in remote work environment.
Motivation for the study: This study is motivated by the need to explore the triple impact of communication, technology and work life balance interactions on employee motivation in remote work settings.
Research approach/design and method: A quantitative study was conducted using a causal-comparative design. Data were collected from employees in a Namibian service industry company using an online structured questionnaire. By using a census sampling technique, the survey was self-administered and anonymous. We utilised multiple linear regression analysis in SPSS version 28 software to analyse the data and test our hypotheses.
Main findings: The findings reveal that technological support during remote work has the strongest positive impact, work–life balance has a strong positive influence, and effective communication has a moderate effect on employee motivation. This proves all the relationships to be statistically significant, implying that they are worth managerial considerations.
Practical/managerial implications: To ensure employee motivation during remote work, organisations could continuously offer technological and communication support and implement work–life balance strategies.
Contribution/value-add: Following the job demand-resource theory (JD-R), this study enriches the existing literature on remote work by examining the combined impact of communication, technology and work–life balance on employee motivation within remote work environments in Namibia.
Keywords: communication; employee motivation; job-demand resources; remote work; technology; work–life balance.
Introduction
In today’s business environment, a rapid shift to remote work, particularly among information workers has emerged. Following the coronavirus disease 2019 (COVID-19) pandemic, many employees perform their duties outside the traditional office setting (Atiku et al., 2020), using information and communication technology. Working remotely has not only transformed workplace dynamics but also challenged the existing structures of technology usage (Raisien et al., 2020; Urbaniec et al., 2022), communication (Olawale et al., 2024; Raisien et al., 2020) and work–life balance (Olawale et al., 2024; Shaanika & Bhebhe, 2023). Despite offering employees with job autonomy and flexible scheduling, remote work has brought forward new challenges for both organisations and employees. Employees often experience digital fatigue and social isolation because of the absence of in-person interaction (Wang et al., 2021), while the blurring of work-home boundaries creates challenges of maintaining a healthy work–life balance (Oakman et al., 2020). Organisations, on the other hand, often report communication gaps, inappropriate digital monitoring practices and trust issues in virtual teams, undermining employee autonomy (Contreras et al., 2020). In addition, remote work has exacerbated cybersecurity risks and difficulties in monitoring collaboration across dispersed teams. These emerging challenges require new forms of human resource management (HRM) practices to support motivation and performance (Shimaneni & Kiley, 2025).
As these challenges emerge, advancement in digital infrastructures has simultaneously made it possible for non-traditional sectors to implement remote work. Historically, remote work was more common in sectors such as tech and freelancing, but now many sectors are implementing it. As a result, various scholars have explored the influences of remote work on organisational structures (Olawale et al., 2024), employee engagement (Nwoko & Yazdani, 2022) and employee productivity (Drisu et al., 2023). However, remote work also presents new challenges, particularly in communication, technology support and managing work–life balance, which are key elements of focus for this study. Empirical research on these factors abounds in other jurisdictions, whereas it is limited in Namibia.
Research purpose and objectives
This study primarily aims to examine the combined impact of communication, technological support and work–life balance on employee motivation among remote workers in a Namibian service-based organisation and to further determine which of these factors has the strongest impact on motivational outcomes.
The specific objectives are to:
- assess the impact of effective communication on employee motivation in remote work environment
- analyse the effect of technological support on employee motivation in remote work environment
- evaluate the influence of work–life balance on employee motivation in remote work environment.
Literature review
Remote communication
To maintain productivity and collaboration, effective communication is crucial in remote work environments (Sulaiman et al., 2023). Both synchronous and asynchronous interactions are made possible by the rise of digital communication tools, such as Microsoft Teams and Zoom, allowing geographically dispersed teams to stay connected (Das et al., 2021; Nwoko & Yasdan, 2022). Recently, research has shown that instant messaging and real-time video conferencing not only enhance collaboration but also lead to cognitive load and time-zone misalignment (Wang et al., 2021). Consequently, employees experience communication fatigue, particularly if their jobs required them to maintain constant connectivity (Galanti et al., 2021). On the one hand, Namibia has limited infrastructure in some areas, and this disparity can limit seamless communication.
On the other hand, asynchronous communication offers flexibility and reduces the pressure for immediate response, although it slows down decision-making processes, making remote workers feeling isolated. However, both synchronous and asynchronous communications are regarded essential for maintaining team cohesion and effective collaboration among remote work environments (Das et al., 2021; Olawale et al., 2024). Therefore, Begumisa et al. (2023) suggest that organisations need to utilise both methods to optimise remote work outcomes.
Technology usage in remote work environments
The proliferation of digital tools has made it possible for the adoption of remote work widely. These tools facilitate communication, collaboration and productivity. Cloud-based platforms and project management software enable real-time collaboration, seamless document sharing and task tracking, ensuring work productivity beyond office physical space (Urbaniec et al., 2022).
On the contrary, Begumisa et al. (2023) warned about significant challenges associated with heavy reliance on technology. Constant use of technology leads to technostress, a leading concern in remote work literature (Das et al., 2021; Urbaniec et al., 2022). The pressure to be continuously available, a lack of downtime, and constant connectivity are reported as key contributors to digital burnout (Drisu et al., 2023; Nwoko & Yazdani, 2022). Moreover, digital literacy gaps further exacerbate technostress, as less tech-savvy employees struggle to use the required technologies (Urbaniec et al., 2022).
Further studies have raised cybersecurity concerns as a critical issue in the remote work settings. Borkovich and Skovira (2020) emphasised the need for robust security measures to protect organisations’ information as well as employee training. While technology enables remote work, relying heavily on it poses a range of operational risks (Borkovich et al., 2020) that organisations must proactively address through policy development, training and support.
Work–life balance in remote work
The impact of remote work on work–life balance is one of the aspects discussed extensively in literature. Alongside its benefits of flexibility, remote work blurs the boundaries between personal and work life, making it difficult for employees to switch off from their work responsibilities (Raisien et al., 2020). This often leads to role conflict, with remote workers grappling to balance their professional demands with family responsibilities, a phenomenon referred to as work–family conflict (Dockery & Bawa, 2020).
Recent studies have also examined the gender dimension in work from home settings. According to Marcén and Morales (2024) women are more likely to experience intensified work–family conflict, particularly those who bear a disproportionate share of household responsibilities. In a recent Namibian empirical study, it was found that working mothers experience family-to-work conflict, which significantly reduces both vigour and absorption, key dimensions of employee engagement (Murangi & Groenewald, 2024). Consequently, women report greater work–family conflict compared to men, raising gender equity concerns in remote work environments (Marcén & Morales, 2024).
Dockery and Bawa (2020) noted that the blurring boundaries between work and home life can result in increased loneliness, stress and procrastination, negatively affecting motivation. As a result, research consistently finds work–life balance as a central pathway linking remote work to motivation and job satisfaction (Dockery & Bawa, 2020; Galanti et al., 2021). Consequently, scholars have proposed various strategies including, clear boundary setting between work and home, flexible work schedules and organisational support structures to address challenges associated with work–family conflict (Galanti et al., 2021; Urbaniec et al., 2022). For instance, Das et al. (2021) emphasised adopting a work culture that respects employee boundaries, encouraging regular breaks and limiting after-hours communications. These strategies are necessary to enhance employee well-being in remote work environments.
These insights highlight the motivational implications of communication, technology and work–life balance. However, most studies examine these factors in isolation and largely focus on well-being rather than motivation. Limited evidence exists on how the combined effects of communication, technological support and work–life balance interact to influence employee motivation in Namibian remote work environments. This study addresses that gap by adopting a synergistical approach and comparative effects.
Theoretical framework and hypotheses
The job demand-resource (JD-R) model was initially formulated by Bakker and Demerouti, who laid its foundational groundwork in 2006. According to the JD-R theory, employee motivation and well-being are influenced by a balance between job demands and job resources. Job demands refer to aspects of the job that require sustained efforts and can lead to stress or burnout, whereas job resources are aspects of the job that help achieve work goals and reduce job demands (Bakker & Demerouti, 2006).
The JD-R theory provides a compelling framework for understanding the interplay of communication, technology and work–life balance on employee motivation in remote work environments. Because it analyses the correlation between job demands and job resources in the workplace (Drisu et al., 2023), this study proposes that employee motivation may be affected by job demands, such as workload and time pressure, as well as job resources, such as effective communication and technological support. Within the scope of this study, the JD-R theory allows for an examination of how the demands and resources associated with remote work affect employee motivation.
There can be various and challenging job demands in remote work environments. The prominent one is the workload (Das et al., 2021), which often increases because of blurred boundaries between personal life and work. In these settings, employees may find themselves working long and odd hours without clear start and end times. In addition, pressure to use and adapt to new technologies (technostress) can be overwhelming, and employees may struggle with constant technical issues, connectivity and the need to quickly learn and master digital tools (Urbaniec et al., 2022). Another job demand in these setting is social isolation and disconnection from colleagues and the organisation, which may lead to poor mental health and demotivation (Galanti et al., 2021). Furthermore, employees struggle to juggle professional responsibilities with family obligations, often in the same physical space.
On the other hand, remote work presents various demands. On the other hand, mitigating these demands and fostering motivation require job resources. Effective communication is a crucial resource in ensuring that employees collaborate and feel connected to their teams and the organisation (Shaanika & Bhebhe, 2023). To reduce uncertainties and the feeling of social isolation, organisations must maintain clear and consistent communication. Technology, while potentially can be a source of stress (job demand), it can also be an essential resource when managed properly. Access to reliable digital platforms, adequate technical support and training on how to use digital tools (Urbaniec et al., 2022) can facilitate employee motivation, as they feel control over their work. Work–life balance strategies, such as flexible working hours and clear work expectations are also essential job resources that can help employees manage their personal and professional responsibilities effectively (Galanti et al., 2021).
Based on these discussions, a balance between job demands and job resources is essential for maintaining high motivation among remote employees. High job demands can lead to stress and burnout (Schaufeli & Bakker, 2004), thereby reducing employee motivation. Conversely, adequate job resources can mitigate the impact of job demands (Schaufeli & Bakker, 2004) and enhance employee motivation by providing employees with the necessary tools and support. Providing technological support to remote employees equally boosts motivation by improving flexibility and efficiency (Drisu et al., 2023). A healthy work–life balance prevents burnout (Das et al., 2021) and enhances employee motivation by assisting employees in managing their work and personal responsibilities effectively.
In conclusion, the JD-R model illustrates that maintaining motivation for remote employees depends on balancing job demands and job resources. The combined effects of communication, technology and work–life balance are crucial in this regard, as they interact to influence employee motivation in remote work environments. After analysing the given theoretical derivation, we put forward hypotheses that suggest correlations between communication, technology and work–life balance and their impact on employee motivation:
H1: Effective communication during remote work has no positive impact on employee motivation of a Namibian service industry.
H2: Technology support during remote work has no positive effect on employee motivation of a Namibian service industry.
H3: Work–life balance during remote work has no positive impact on employee motivation of a Namibian service industry.
These hypotheses lay the foundation for our conceptual framework, which illustrates the relationship between these variables and their combined effect on employee motivation. The conceptual framework delineates the pathways through which communication, technological support and work–life balance in the context of remote work influence employee motivation. As informed by the literature (Jamaludin & Kamal, 2023; Nwoko & Yazdani, 2022; Sulaiman et al., 2023), the model assumes a direct impact of effective communication in the context of remote work on employee motivation, which the study validated by testing hypothesis one (H1). Similarly, the framework also suggests a direct effect of technological support in the context of remote work on employee motivation, as informed by the literature (Jamaludin & Kamal, 2023). The study confirmed this relationship by testing hypothesis two (H2). Finally, based on the evidence in the literature (Faulds & Raju, 2021; Gorlick, 2020; Wilson, 2021), the model hypothesises a direct influence of work–life balance in the context of remote work on employee motivation, which the study validated by measuring hypothesis three (H3). The conceptual model is shown in Figure 1.
Job demands such as workload, isolation, work–life conflict and technostress require sustained effort and can lead to burnout if not managed effectively. On the other hand, job resources that are crucial in reducing job demands include effective communication, technological support and a balanced work–life interface. At the centre of this interaction is employee motivation, which is influenced by the equilibrium between job demands and job resources. Transformation of job demands into valuable job resources is achieved by effective communication, well-managed technology and a balanced work–life interface.
Research design
Research approach
This study adopted a quantitative research approach to examine the combined effects of communication, technology, and work–life balance on employee motivation in a remote work environment.
Research method
A casual comparative design was used to determine whether differences in the predictor variables (communication, technology support and work–life balance) were associated with difference in levels of employee motivation in remote work environments.
Research participants
The research was conducted within a single Namibian service industry organisation using a census sampling technique, where all remote-working employees were invited to participate. This method treats the entire population of interest as the sample (Kothari, 2004), thus reducing sampling bias.
Measuring instruments
The survey instrument comprised five sections. Section A gathered demographic data (e.g. gender, tenure, department, current role). Section B focused on communication (clarity, effectiveness, frequency). Section C assessed technology access, availability, and usability. Section D addressed perceptions of work–life balance support. Section E measured overall employee motivation. Apart from the demographic variables, all items in the questionnaire were evaluated using a 5-point Likert scale. They were rated on a scale ranging from ‘1 to 5’, with 1 indicating strong disagreement and 5 indicating strong agreement.
Effective communication
A 6-item scale adapted from Hartner-Tiefenthaler et al. (2022) was used to measure effective communication. This scale captured focused communication, knowledge-sharing and spontaneous communication among remote teams where Cronbach’s alpha values above 0.7 for all dimensions were reported. For this study, items include statements such as, ‘The management promotes effective communication through online collaborations with colleagues during remote work’, ‘The management converts in-person meetings to video meetings during remote work’, ‘Employees are always provided with feedback on time’, ‘Clarity of instructions is received through different communication channels during remote work’, ‘I am happy with the regularity with which the leadership communicates about updates and modifications to remote work procedures during remote work’ and ‘I am happy with the overall effectiveness of communication during remote work’. The scale has Cronbach’s alpha coefficient of 0.857 in this study as shown in Table 1.
Technological support
A 4-item scale (including tangible tools, provisions of software, technical support and training) was used to measure employees’ perceived technological support while executing their work, and Cronbach’s alpha is 0.834 as illustrated in Table 1. This was adapted from the technology-organisation-environment framework that captures key dimensions such as technological competence, information technology infrastructure and the use of sharing tools to facilitate remote collaborations. This scale demonstrated good reliability with Cronbach’s alpha coefficients typically ranging between 0.78 and 0.85 (Ng et al., 2022).
Work–life balance
A 4-item scale adapted from Raisien et al. (2020) was used to measure employees’ work–life balance. The original instrument evaluated various aspects of remote working during COVID-19 quarantine period. For this study, four items that specifically captured employees’ balance between work and personal life were selected and slightly rephrased to fit the organisational context (e.g. replacing ‘quarantine period’ with ‘remote working arrangement’). This study’s items included ‘Working remotely does not affect my ability to perform my organisational roles’, ‘Remote work allows me to balance work and social life’, ‘Working remotely enables me to have control over my work schedule’, and ‘The institution provides support and resources to help me maintain a healthy work–life balance while working remotely’. The Cronbach’s alpha is 0.705.
Employee motivation
Respondents completed a 4-item motivation scale adapted from Nwoko and Yazdani (2022). The survey items solicited respondents to quantify the degree to which they feel motivated during remote work: ‘The institution recognises my achievements during remote work’, ‘The management ensures that employees are engaged during remote work’, ‘The institution helps employees set up a dedicated workspace during remote work’ and ‘Management rewards me for exceptional performance during remote work’. The Cronbach’s alpha is 0.81.
Research procedure
All responses data were stored in a secure password-protected file. A total of 105 valid responses were obtained from a target population of 180 employees, resulting in a response rate of 58.3%. The study met the minimum statistical guidelines of 15–20 respondents per predictor variable needed for adequate power in multiple regression analysis with three predictors (Tabachnick & Fidell, 2019). This implies a sample size of 45–60 respondents, which the current study meets.
Statistical analysis
We utilised SPSS version 28 software to analyse the data and test our hypotheses. To summarise the characteristics of the sample and the main study variables, descriptive statistics were utilised. Prior to performing Pearson correlation and multiple regression analysis, we examined key statistical assumptions. Variables normality was assessed through the inspection of skewness and kurtosis values, all of which fell within the acceptable ranges. Linearity was confirmed through scatter plots, homoscedasticity was assessed through inspection of residuals, while multicollinearity was assessed using variance inflation factors (VIF), with all values below the recommended threshold of 5.
To ensure construct validity, Cronbach’s alpha coefficients were calculated for each scale. All constructs exceeded the acceptable threshold of 0.7, confirming good internal consistency. Pearson correlation analysis was conducted to assess the relationship between the variables. Thereafter, multiple regression analysis was performed to determine the extent to which communication, technological support and work–life balance were associated with employee motivation. Statistical significance was assessed at the p < 0.05 level. Thus, Equation 1 (the regression equation) was derived as:
Y = B0 + BnXn + e, where Y is the dependent variable (employee motivation), B0 is the constant, Bn is the coefficient of the independent variables, Xn is the independent variables (effective communication, technological support, and work–life balance during remote work), while e is the error term. Hence, the study specified the regression model as:
Ethical considerations
Ethical clearance to conduct this study was obtained from the Namibia University of Science and Technology, Faculty of Management Sciences High Degree Committee (HSS/1242/025F). Participation was voluntary and anonymous, and informed consent was obtained prior to data collection. Data were collected through a structured, self-administered online questionnaire via Google Forms where an information sheet embedded on the first page of the google form explained the purpose of the study and assured respondents of strict confidentiality. The form did not collect any personal identifiers such as names or organisational emails. Participants could not proceed if they did not tick the consent checkbox. The organisation’s Human Resource (HR) department acted as a distribution channel of the online survey link to eligible participants. To ensure confidentiality and anonymity, participants’ responses were submitted directly to the researcher without being accessible to HR personnel. In addition, the opening page of the questionnaire included a clear consent statement, explicitly emphasising that individual responses would remain anonymous and no personal identifiers would be collected.
Results
Descriptive analysis
This study analysed four latent constructs: Effective Communication, Technological Support, Work–Life Balance, and Employee Motivation. Each construct was measured using multiple items: Effective Communication (6 items), Technological Support (4 items), Work–Life Balance (4 items), and Employee Motivation (4 items).
Table 2 presents the descriptive statistics for each construct. Technological Support recorded the highest mean (M = 4.05, standard deviation [SD] = 0.67), indicating general agreement among participants regarding the availability and adequacy of technological resources. The remaining constructs – Work–Life Balance (M = 3.89, SD = 0.72), Employee Motivation (M = 3.44, SD = 0.70), and Effective Communication (M = 3.31, SD = 0.81) – had means close to the midpoint of the scale, reflecting more neutral to moderately positive perceptions.
The relatively low standard deviations suggest that responses were clustered closely around the means, implying data reliability and a low risk of multicollinearity (Field, 2018; Kline, 2018). These results support the use of inferential analysis in subsequent sections.
Correlation analysis
To examine the relationships between the variables, a Pearson correlation analysis was conducted. Table 3 presents the correlation coefficients among the four constructs: Effective Communication, Technological Support, Work–Life Balance, and Employee Motivation.
| TABLE 3: Pearson correlation matrix (N = 105). |
The analysis reveals that Technological Support has the strongest positive correlation with Employee Motivation (r = 0.773, p < 0.01), suggesting that employees who perceived higher technological support also reported higher motivation levels during remote work. A moderate positive correlation was found between Work–Life Balance and Employee Motivation (r = 0.621, p < 0.01), indicating that better balance between personal and work life is associated with increased motivation. Similarly, Effective Communication was moderately correlated with Employee Motivation (r = 0.442, p < 0.05), showing that communication contributes meaningfully, albeit to a lesser degree.
Weaker, non-significant correlations were observed between Work–Life Balance and Technological Support (r = 0.251, p > 0.05), and between Work–Life Balance and Effective Communication (r = 0.336, p > 0.05). In addition, the correlation between Technological Support and Effective Communication was modest (r = 0.350, p > 0.05).
These findings suggest that, although the variables are positively related, none of the correlation coefficients exceed 0.80, indicating that multicollinearity is not a concern (Field, 2018). As such, the variables are suitable for inclusion in subsequent regression analyses to assess causal effects.
Regression analysis
To assess the effects of effective communication, technological support, and work–life balance on employee motivation during remote work, a multiple linear regression analysis was conducted. Prior correlation results indicated no multicollinearity concerns, allowing for all three independent variables to be included in the regression model.
Model fit
The regression model was statistically significant and demonstrated a strong fit with the data. As shown in Table 4, the model explains the adjusted R2 value of 0.772 indicating that approximately 72.2% of the variance in employee motivation is explained by the combined effect of communication, technological support and work–life balance.
Furthermore, Table 5 shows that the F-statistics is adequately yielding a value of 33.743. A significant F-statistic demonstrates that the regression model provides a better fit to the data than a model with no predictors (Field, 2018). The results also provide evidence that the model is significant at a 5% level of significance, given a p-value < 0.001. These results validate the model’s goodness of fit to the data, hence the accuracy of the results. After confirming the model fit, the next section presents the individual regression coefficients for communication, technological support and work–life balance.
| TABLE 5: ANOVA for regression analysis of employee motivation. |
Regression coefficient
To determine the causal impacts between the variables of the research, the study treated employee motivation as the dependent variable and effective communication, technological support, and work–life balance as the independent variables. Consequently, the study simplified the regression model (Equation 2) as follows, based on the regression coefficient results:
The results show that a constant is negative (−0.102), which demonstrates that when there is no effective communication, technological support, and work–life balance during remote work, employee motivation decreases by 10.2%. The study proves this relationship statistically significant, given a t-value of −2.131 and a p-value of 0.023. In a nutshell, this implies the importance of maintaining effective communication, technological support, and work–life balance during remote work to help employees stay motivated. However, when one of the dimensions (effective communication, technological support, and work–life balance) improves, employee motivation is enhanced too, as detailed in this section based on each objective.
Discussion
The study examines the combined impact of communication, technological support and work–life balance on employee motivation in a remote setting to further determine, which of these factors has the strongest impact on motivational outcomes.
Communication as a job resource
The study’s findings demonstrate that effective communication has a small yet modest positive association with employee motivation. This is consistent with previous studies that emphasise the role of clear, transparent and frequent feedback in sustaining motivation during remote work (Jamaludin & Kamal, 2023; Nwoko & Yazdani, 2022; Sulaiman et al., 2023). In line with the JD-R model, communication is an essential job resource that clarifies expectations and provides employees with feedback needed to manage remote work demands (Bakker & Demerouti, 2006; Das et al., 2021; Van Zoonen & Sivunen, 2021). To mitigate the social isolation commonly reported in remote work environments, communication practices such as clear instructions and regular updates are often implemented (Van Zoonen & Sivunen, 2021). However, the relatively weaker impact of communication compared to technological support observed in this study, suggests that communication alone may not be sufficient to sustain high motivation. In highly technology-dependent remote work environments, communication practices must be complemented by technological resources to meaningfully impact motivation. This contrasts with prior research findings where effective communication often emerges as a dominant motivator in knowledge-intensive sectors (Van Zoonen & Sivunen, 2021).
Technological support: A critical enabler
The strongest association was found between technological support and employee motivation in remote work environments, highlighting the essential role of digital resources in enhancing motivation (Nwoko & Yazdani, 2022; Olawale et al., 2024; Wang et al., 2021). By providing reliable devices, high-speed internet connectivity and technical support, organisations reduce technology-related frustrations and cognitive overload (Olawale et al., 2024; Tarafdar et al., 2010). This aligns with the JD-R model’s emphasis on technology as a powerful job resource that mitigates job demands while also promoting motivational outcomes (Bakker & Demerouti, 2006). This result highlights the significance of infrastructural readiness as remote work increasingly becomes entrenched globally. Therefore, organisations cannot expect remote work to succeed without investing strategically in digital infrastructures. In this way, they can reduce stress and technology-related disruptions, enhancing employee motivation. The strong association found here reinforces the basic provision of reliable connectivity and devices as a fundamental motivator in Namibia, unlike studies conducted in more digitally advanced context. This suggests that contextual differences shape the relative weight of job resources.
Work–life balance as a supportive resource
The study’s findings demonstrate a moderate positive relationship between work–life balance and motivation, confirming prior studies, which emphasise that balancing personal and professional responsibilities during remote work fosters higher motivation and greater job satisfaction (Dockery & Bawa, 2020; Faulds & Raju, 2021; Galanti et al., 2021; Wilson, 2021). Where there are wellness programmes and supportive organisational policies, employees can manage their schedule flexibly and maintain well-being (Das et al., 2021; Galanti et al., 2021). In terms of the JD-R model, work–life balance acts as an essential psychosocial resource to mitigate burnout and facilitate well-being (Bakker & Demerouti, 2006). While the study’s findings indicate that remote work enables employees greater control over their schedule, challenges of remote work such as blurred boundaries between work and home life increase stress. This finding extends prior research by demonstrating that work–life balance outcomes depend on supportive cultures and resources such as flexible scheduling options and mental health programmes (Kossek & Lautsch, 2018; Susanto et al., 2022).
Practical implications
The study’s findings have practical implications for organisations aiming to enhance employee motivation in remote settings. Although the study found that effective communication slightly influences employee motivation during remote work, the importance of maintaining clear and regular communication channels between line supervisors and employees, as well as among team members cannot be ignored. Organisations should, therefore, invest in communication tools that facilitate seamless collaborations such as video conferencing and instant messaging apps. Team meetings, regular check-ins and virtual team building activities can also foster a sense of connectedness and motivation among remote employees (Jamaludin & Kamal, 2023; Nwoko & Yazdani, 2022).
The study further demonstrated a strong positive relationship between technological support and employee motivation during remote work settings. By ensuring that employees have access to necessary technology and resources when performing their remote work tasks, organisations can create a supportive environment that promotes employee motivation. This does not only include providing employees with laptops, high-speed internet and other relevant tools and software but also providing training and technical support to assist employees navigate any technological challenges they may encounter (Atiku et al., 2020; Nwoko & Yazdani, 2022).
Lastly, it is crucial for organisations to promote work–life balance and pay attention to remote employees by encouraging them to establish clear boundaries between personal and work life. Employees who set realistic expectations regarding working hours and take regular breaks are inclined to promote self care and well-being, resulting in high levels of motivation. As a result, organisations must allow flexible schedules or compressed workweeks to contribute to a better work–life balance among remote work employees (Dockery & Bawa, 2020; Faulds & Raju, 2021; Wilson, 2021).
Limitations and recommendations for future research
To give potential directions for future research, the study relied on the inherent limitations and delimitations within its framework. Firstly, this study was limited to a quantitative methodology, resulting in a methodological gap in the existing literature, according to the notion of Miles (2017) on research gaps. Hence, the study suggests that future research should investigate the phenomenon by using a qualitative approach in addressing this research gap and gather diverse reality about the phenomenon. Secondly, the investigation of this study was limited to one organisation and to the use of a sensus sampling method, which may hinder the generalisation of the findings outside the specified geographical settings of the study. This resulted in a population gap according to Miles (2017). Thirdly, a sample of N = 105 may limit the statistical power of the results; therefore, the study proposes that future research should cover a wide range of entities and employ a larger sample to enhance representativeness of the current findings. Lastly, the use of HR as a distribution channel may have reduced confidentiality despite assurances of anonymity.
Conclusion
The combined effect of communication, technological support and work–life balance on employee motivation in remote work environments is highlighted by this study. The study’s findings demonstrate the importance of managing these job resources effectively to mitigate job demands and enhance employee motivation. In a chronological order of effect size, ranked from descending order, technological support, work–life balance, and effective communication during remote work all have positive impact on employee motivation. On that basis, the study enriches the existing literature and offers valuable insights to both scholars and organisations that are eager to implement policies that enhance employee motivation in remote work settings.
Acknowledgements
This article is based on the research originally conducted as part of Erika Shikusinde’s Honours thesis titled: ‘Assessing factors that impact employee motivation in remote work’, submitted to the Faculty of Commerce, Human Sciences and Education, Department of Governance and Management Sciences, Namibia University of Science and Technology in 2024. The thesis was supervised by Fiina Shimaneni. The manuscript has since been revised and adapted journal publication. The authors would like to thank Dingi Consultancy for the editorial work before submission to the journal.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
Authors’ contributions
E.S. conducted the research, while F.S. provided guidance and oversight throughout the research project. F.S. also wrote the article for publication.
Funding information
The publication of this manuscript was made possible by the funding received from Namibia University of Science and Technology.
Data availability
The data that support the findings of this study are available from the corresponding author, F.S. upon reasonable request.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency, or that of the publisher. The authors are responsible for this article’s results, findings, and content.
References
Atiku, S.O., Jeremiah, A., & Boateng, F. (2020). Perceptions of flexible work arrangements in selected African countries during the coronavirus pandemic. South African Journal of Business Management, 51(1), a2285. https://doi.org/10.4102/sajbm.v51i1.2285
Bakker, A.B., & Demerouti, E. (2006). The Job Demands-Resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115
Begumisa, I., Kamaduuka, R.Z., Pontious, M., & Kizza, J. (2023). Remote working adoption strategies and employee performance during COVID-19 pandemic: A case study of MTN Uganda limited. East African Journal of Business and Economics, 6(2), 125–137. https://doi.org/10.37284/eajbe.6.2.1578
Borkovich, D.J., & Skovira, R.M. (2020). Working from home: Cybersecurity in the age of COVID-19. Issues in Information Systems, 21(4), 234–246.
Contreras, F., Baykal, E., & Abid, G. (2020). E-leadership and teleworking in times of COVID-19 and beyond: What we know and where do we go. Frontiers in Psychology, 11, a590271. https://doi.org/10.3389/fpsyg.2020.590271
Das, M., Tang, J., Ringland, K.E., & Piper, A.M. (2021). Towards accessible remote work: Understanding Work-from-Home practices of neurodivergent professionals. Proceedings of the ACM on Human Computer Interaction, 5(CSCW1), 1–30. https://doi.org/10.1145/3449282
Dockery, M., & Bawa, S. (2020). Working from home in the COVID-19 lockdown (vol. 19, pp. 1–5). Bankwest Curtin Economics Centre.
Drisu, I.M., Peter, O.J., & Fumba, J. (2023). Hybrid work models and employee productivity in Hewlett-Packard (hp) regional office Abuja, Nigeria. Nigerian Journal of Management Sciences, 24(2b), 222–230. Retrieved from https://nigerianjournalofmanagementsciences.com/wp-content/uploads/2023/09/28.-HYBRID-WORK-MODELS-AND-EMPLOYEE-PRODUCTIVITY-IN-HEWLETT.pdf
Faulds, D.J., & Raju, P.S. (2021). The work-from-home trend: An interview with Brian Kropp. Business Horizons, 64(1), 29–35. https://doi.org/10.1016/j.bushor.2020.10.005
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications Ltd. Retrieved from http://journal.umsurabaya.ac.id/index.php/JKM/article/view/2203
Galanti, T., Guidetti, G., Mazzei, E., Zappala, S., & Toscano, F. (2021). Work from home during the COVID-19 outbreak the impact on employees’ remote work productivity, engagement, and stress. Journal of Occupational and Environmental Medicines, 63(7), 426–432. https://doi.org/10.1097/JOM.0000000000002236
Gorlick, A. (2020). The productivity pitfalls of working from home in the age of COVID-19. Stanford News. Retrieved from https://news.stanford.edu/stories/2020/03/productivity-pitfalls-working-home-age-covid-19
Hartner-Tiefenthaler, M., Loerinc, I., Hodzic, S., & Kubicek, B. (2022). Development and validation of a scale to measure team communication behaviors. Frontiers in Psychology, 13, 961732. https://doi.org/10.3389/fpsyg.2022.961732
Jamaludin, N.L., & Kamal, S.A. (2023). The relationship between remote work and job satisfaction: The mediating role of perceived autonomy. Information Management and Business Review, 15(3), 10–22. https://doi.org/10.22610/imbr.v15i3(SI).3453
Kline, R.B. (2018). Response to Leslie Hayduk’s review of principles and practice of structural equation modeling, 4th edition. Canadian Studies in Population, 45(3–4), 188–195. https://doi.org/10.25336/csp29418
Kossek, E.E., & Lautsch, B.A. (2018). Work-life flexibility for whom? Occupational status and work-life inequality in upper, middle, and lower-level jobs. Academy of Management Annals, 12(1), 5–36. https://doi.org/10.5465/annals.2016.0059
Kothari, C.R. (2004). Research methodology: Methods and techniques (2nd ed.). New Age International Publishers.
Marcén, M., & Morales, M. (2024). The effect of COVID-19 on the gender gap in remote work. GLO Discussion Paper, 1379. Global Labor Organization (GLO). Retrieved from https://hdl.handle.net/10419/281174
Miles, D.A. (2017). A taxonomy of research gaps: Identifying and defining the seven research gaps methodological gap. Journal of Research Methods and Strategies. Retrieved from https://www.researchgate.net/profile/Hamidul-Shohel/publication/354691106_Types_of_Research_Gaps/links/61476d66a3df59440b9b9855/Types-of-Research-Gaps.pdf
Murangi, A., & Groenewald, S. (2024). Does work-life balance matter for working mothers’ engagement at work? A case of working mothers in Windhoek, Namibia. Undergraduate Research in Health Journal, 2(1), 19–21.
Ng, P.M.L., Lit, K.K., & Cheung, C.T.Y. (2022). Remote work as a new normal? The technology-organization-environment (TOE) context. Frontiers in Psychology, 13, a9187876. https://doi.org/10.1016/j.techsoc.2022.102022
Nwoko, C., & Yazdani, K. (2020). Remote working during the COVID-19 global pandemic and its implications for employee motivation: Some evidence from Nigeria through the lens of self-determination theory. Journal of Business and Management Studies, 4(2), 423–447. https://doi.org/10.32996/jbms.2022.4.2.31
Oakman, J., Kinsman, N., Stuckey, R., Graham, M., & Weale, V. (2020). A rapid review of mental and physical health effects of working at home: How do we optimise health?. BMC Public Health, 20, a1825. https://doi.org/10.1186/s12889-020-09875-z
Olawale, O., Ajayi, F.A., Udeh, C.A., & Odejide, O.A. (2024). Remote work policies for it professionals: Review of current practices and future trends. International Journal of Management & Entrepreneurship Research, 6(4), 1236–1258. https://doi.org/10.51594/ijmer.v6i4.1056
Raisien, A.G., Rapuano, V., Varkulevi, K., & Stachova, K. (2020). Working from Home – Who is happy? A survey of Lithuania’s employees during the COVID-19 quarantine period. Sustainability, 12(13), 2–21. https://doi.org/10.3390/su12135332
Schaufeli, W., & Bakker, A.B. (2004). Job demands, job resources, and their relationship with burnout and engagement. Journal of Organizational Behavior, 25(3), 293–315. https://doi.org/10.1002/job.248
Shaanika, H.H., & Bhebhe, T. (2023). Effects of telecommuting on employee motivation and productivity during the COVID-19 pandemic: A case of new era publication corporation, Namibia. Journal of Economics and Finance, 14(2), 1–12.
Shimaneni, F., & Kiley, J. (2025). Challenges of managing academic staff performance in a Namibian private higher education. Transformation in Higher Education, 10, 2–10. https://doi.org/10.4102/the.v10i0.442
Sulaiman, N.S., Abdullah, Z., & Man, N.I. (2023). The effects of employee communication and engagement on organisational performance: A conceptual study. International Journal of Accounting, Finance and Business (IJAFB), 8(46), 233–251.
Susanto, P., Hoque, M.E., Jannat, T., Emely, B., Zona, M.A., & Islam, M.A. (2022). Work-life balance, job satisfaction, and job performance of SMEs employees: The moderating role of family-supportive supervisor behaviors. Frontiers in Psychology, 13, 906876. https://doi.org/10.3389/fpsyg.2022.906876
Tabachnick, B.G., & Fidell, L.S. (2019). Using multivariate statistics (7th ed.). Pearson.
Tarafdar, M., Tu, Q., & Ragu-Nathan, T.S. (2010). Impact of technostress on end-user satisfaction and performance. Journal of Management Information Systems, 27(3), 303–334. https://doi.org/10.2753/MIS0742-1222270311
Urbaniec, M., Małkowska, A., & Włodarkiewicz-Klimek, H. (2022). The impact of technological developments on remote working: Insights from the Polish managers’ perspective. Sustainability, 14(1), 552. https://doi.org/10.3390/su14010552
Van Zoonen, W., & Suvinen, A.E. (2021). The impact of remote work and mediated communication frequency on isolation and psychological distress. European Journal of Work and Organisational Psychology, 31(4), 610–621. https://doi.org/10.1080/1359432X.2021.2002299
Wang, B., Liu, Y., Qian, J., & Parker, S.K. (2021). Achieving effective remote working during the COVID-19 pandemic: A work design perspective. Applied Psychology, 70(1), 16–59. https://doi.org/10.1111/apps.12290
Wilson, J. (2021). Impact of work from home policies on workplace productivity and employee sentiments during the COVID-19 pandemic. Journal of Occupational and Environmental Medicine, 63(3), 181–190. Retrieved from https://scholarworks.uark.edu/cgi/viewcontent.cgi?article=1042&context=acctuht
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