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:

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.

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.

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.

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:

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.

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.

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.

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.

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.

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.

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:

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.

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.

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.

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.

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.

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.

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.

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).

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.

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).

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).

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.