Digital transformation has intensified the use of digital technologies in the workplace (Stich et al., 2019). This transformation is reflected in the widespread adoption of digital-based work systems, including the use of digital devices, remote work arrangements, and real-time communication practices. While the integration of digital technologies enables greater work flexibility and efficiency, it also introduces new challenges that may adversely affect employees’ well-being (Marsh et al., 2024).
In increasingly digitalized work environments, employee well-being related to the use of digital technologies is conceptualized as digital well-being. Digital well-being is defined as a condition in which individuals can use digital technologies optimally while minimizing their potential negative impact on psychological and emotional functioning (Mayiwar et al., 2025). Previous studies have shown that digital well-being is associated with key organizational outcomes, including work engagement, job satisfaction, and performance. Employees with higher levels of digital well-being tend to manage technology-based job demands more effectively, experience greater work engagement, and report lower work-related stress (Qadaar et al., 2025).
To explain how the use of digital technology translates into employee well-being, this study draws on three complementary theoretical lenses: the Job Demands–Resources (JD-R) framework, technostress perspective, and digital self-regulation theory. Within the JD-R framework (Bakker & Demerouti, 2017), digital technologies function simultaneously as job resources—facilitating communication, flexibility, and efficiency—and as job demands that require cognitive, emotional, and temporal efforts to manage. When digital demands exceed the available resources, employees experience strain that erodes their well-being; when balanced, digital tools support engagement and performance. The technostress perspective (Ragu-Nathan et al., 2008; Tarafdar et al., 2019) further specifies the mechanisms through which digital tools become demanding: techno-overload (feeling compelled to work faster and longer because of technology), techno-invasion (constant connectivity blurring work–life boundaries), techno-complexity, techno-insecurity, and techno-uncertainty. These mechanisms clarify why prolonged or unregulated use of digital technologies is consistently associated with emotional exhaustion, sleep disturbance and reduced psychological recovery.
Critically, exposure to digital demand does not affect all employees equally. Digital self-regulation, the capacity to consciously monitor, evaluate, and control one's use of digital technologies in alignment with personal goals (Reinecke et al., 2022), plays a central role in determining whether digital engagement enhances or undermines well-being. Employees with stronger digital self-regulation are better able to set boundaries, manage notifications, allocate attention strategically, and disconnect when needed, thereby buffering the negative effects of this phenomenon. In contrast, employees with weaker self-regulatory capacities are more vulnerable to compulsive use, attentional fragmentation, and erosion of work–life boundaries. Together, the JD-R model, technostress, and digital self-regulation provide a coherent mechanistic account: digital technologies impose demands that, depending on individuals' regulatory resources and organizational support, are either adaptively integrated or experienced as chronic strain that undermines digital well-being.
Generation Z represents a cohort born and raised in an environment heavily shaped by digital technologies, leading them to be widely recognized as digital natives (Zoya & Chitrao, 2021). This extensive digital exposure strongly influences how Generation Z employees work, communication, and interaction of Generation Z employees in professional settings. Data from the Indonesian Internet Service Providers Association (Asosiasi Penyelenggara Jasa Internet Indonesia, 2025) indicate that Generation Z accounts for 23.49% of internet users in Indonesia, highlighting their substantial presence in digital spaces. Furthermore, the Work Trend Index survey conducted by Microsoft and LinkedIn (2024) reported that 68% of Generation Z employees experience emotional and mental difficulties in maintaining boundaries between work and personal life. Challenges in managing work–life boundaries may increase the risk of impaired digital well-being, particularly in the context of work–life integration, which has become a critical issue in research on digital well-being among young employees (Angela et al., 2025).
Despite the growing body of literature on employee well-being, several important gaps remain in the literature. First, most existing studies continue to focus on general psychological well-being without explicitly conceptualizing digital well-being as a distinct construct rooted in workplace technology use. Consequently, the specific mechanisms through which digital demands shape young employees' psychological functioning remain underexplored. Second, although Generation Z is widely characterized as digitally fluent, research has tended to assume that digital nativity equates to adaptive digital use—an assumption that overlooks the possibility that prolonged digital exposure may heighten vulnerability to technostress and weaken self-regulatory boundaries. Third, empirical evidence on digital well-being remains heavily concentrated in Western post-industrial work contexts, while research on Indonesian Generation Z employees is still limited. This is a critical omission given that Indonesia is one of the largest digital economies in Southeast Asia, where Gen Z constitutes a substantial share of internet users (Asosiasi Penyelenggara Jasa Internet Indonesia, 2025) and is increasingly populating the early career workforce. Cultural factors such as collectivist work norms, blurred role expectations, and the rapid digitalization of Indonesian organizations create a distinctive context in which digital well-being may unfold differently from that observed in Western settings.
Building on these gaps, the present study aims to provide a descriptive overview of digital well-being among Generation Z employees in Indonesia and identify variations in its dimensions across demographic and work-related characteristics. Theoretically, this study contributes to the literature by integrating the JD-R framework, technostress perspective, and digital self-regulation theory to clarify the mechanisms linking digital technology use to employee well-being, an integration that has rarely been applied to the Indonesian Gen Z workforce. Practically, the findings are expected to inform organizational strategies for managing digital demands, promoting healthy technology use, and supporting work–life integration among young employees in increasingly digitalized work environments in the future.

2.1. Research Design
This study employed a quantitative non-experimental research design with a cross-sectional survey approach to measure the level of digital well-being among Generation Z employees. A descriptive quantitative approach was selected because the primary objective was to obtain a systematic and measurable depiction of digital well-being within this population, as well as to identify variations across demographic and work-related characteristics, rather than to establish causal relationships.

2.2. Participants and Sampling
The participants in this study were Generation Z employees aged 18–27. The inclusion criteria required participants to (1) belong to the Generation Z age cohort, (2) be actively employed at the time of data collection, (3) have a minimum of one year of work experience, and (4) use digital technology as part of their daily work activities. The study did not restrict participants based on gender or educational backgrounds.
Sampling was conducted using a non-probability sampling method with a convenience sampling technique, in which participants were selected based on their accessibility and willingness to participate in the study (Etikan et al., 2016). This technique was chosen for several practical and methodological reasons. First, no comprehensive sampling frame of Generation Z employees in Indonesia was available, making probability sampling unfeasible in this study. Second, convenience sampling enabled efficient data collection through digital platforms, which is particularly appropriate for reaching Gen Z respondents, given their high level of digital engagement and active use of social media. Third, this technique is widely accepted in organizational and psychological research conducted in online settings, especially when targeting working populations with limited availability to participate in face-to-face surveys.

2.3. Data Collection Procedure
Data were collected online between September and October 2025 using a self-administered questionnaire. The link to the questionnaire was disseminated through multiple social media platforms, including LINE, WhatsApp, Instagram, and LinkedIn, to maximize the reach across diverse organizational backgrounds and industry sectors. Participants accessed and completed the questionnaire using personal electronic devices, such as computers or smartphones, connected to the Internet.
The questionnaire consisted of four main sections presented sequentially. The first section contained an informed consent form, in which the participants were informed about the purpose of the study, the voluntary nature of participation, the confidentiality of their responses, and their right to withdraw at any time. Only respondents who provided consent proceeded to the subsequent sections. The second section collected information regarding the participants' characteristics in using digital technology, including the duration and frequency of digital technology use in the workplace. The third section comprised the Digital Well-Being Scale items, while the final section gathered demographic information such as gender, age, educational level, and length of work experience.

2.4. Measurement Instrument
Digital well-being was measured using the Digital Well-being Scale developed by Mathew et al. (2023), which was adapted into Indonesian by Masruro (2024). The original scale conceptualizes digital well-being across four dimensions: mental, physical, social, and emotional. However, during the adaptation process, the social health dimension demonstrated high correlations and conceptual overlap with the mental and emotional health dimensions and was, therefore, excluded. The final adapted scale used in this study consisted of 20 items distributed across three dimensions: mental health (12 items), emotional health (4 items), and physical health (4 items).
The adaptation process followed the standard cross-cultural adaptation procedures. The original English version of the scale was translated into Indonesian and subsequently back-translated into English to verify semantic equivalence and conceptual fidelity of the translation. Linguistic and cultural adjustments were made to ensure that the item wording was appropriate for the Indonesian work context while preserving the original meaning. The adapted instrument was pilot-tested to assess clarity, readability, and reliability before being used in the main study. Items are rated on a six-point Likert-type scale ranging from 1 (never) to 6 (always), with higher scores indicating higher digital well-being.
Sample items included "I feel productive when I am online" (mental health dimension), "Information available from online sources helps me think clearly" (emotional health dimension), and "Spending more time online affects my sleep patterns" (physical health dimension).
Reliability testing using Cronbach's alpha indicated that the initial scale yielded a coefficient of 0.791. However, item 12 within the mental health dimension showed a corrected item-total correlation below 0.30 and was, therefore, removed. Following this adjustment, the overall Cronbach's alpha increased to 0.835, with dimension-level reliabilities of 0.910, 0.848, and 0.696 for mental, emotional, and physical health, respectively. These values indicate that the Digital Well-being Scale used in this study demonstrated satisfactory internal consistency, exceeding the commonly accepted threshold of 0.60.

2.5. Ethical Considerations
This study was conducted in accordance with the established ethical standards for psychological research involving human participants. Prior to data collection, all participants were provided written information regarding the purpose of the study, the voluntary nature of their participation, and the confidentiality of their responses. Informed consent was obtained electronically before the participants could access the questionnaire. No personally identifiable information was collected, and all responses were stored securely and used only for research purposes.

2.6. Data Analysis
Data were analyzed using IBM SPSS Statistics. Prior to inferential analysis, raw data were transferred from the questionnaire into Microsoft Excel for cleaning and coding, and then imported into SPSS for statistical processing. The analysis began with descriptive statistics to provide an overview of the participants' characteristics and the distribution of digital well-being scores, including the means and standard deviations across the mental, emotional, and physical health dimensions.
A normality test was conducted using the Kolmogorov–Smirnov method, with data considered normally distributed when the significance value (p) was ≤ 0.05. Internal consistency was examined using Cronbach's alpha, with a threshold of 0.60 to determine adequate reliability. Subsequently, difference tests were performed to examine variations in digital well-being levels based on participants' characteristics, including gender, educational level, duration of digital technology use, and length of work experience. The selection of parametric or non-parametric difference tests was based on the results of the normality test.

3.1. Results

3.1.1. Participant Characteristics
For more details, see Table 1.

Table 1. Participant Characteristics

Based on the demographic data, this study involved 237 participants, the majority of whom were women (74.3%). Most participants were aged between 19 and 28 years, with the largest proportion being 24 years old (21.1%). The majority held a bachelor’s degree (D4/S1; 89.0%) and had 1-2 years of work experience (56.1%). In terms of work location, the participants were predominantly employed in the Greater Jakarta Area (Jabodetabek; 92.4%) and mostly held staff-level positions (82.3%). The largest proportion of participants worked in the Marketing department (16.2%), followed by the Human Resources (14.4 %) and Accounting (12.6 %) departments. Regarding their work activities, participants reported using digital technologies for an average of 4-6 hours per day (35.9%).

3.1.2. Reliability Analysis
For more details, see Table 2.

The Digital Well-Being Scale demonstrated good internal consistency, with a Cronbach’s alpha coefficient of 0.841. At the dimensional level, the mental health dimension showed excellent reliability (α = 0.905), followed by the emotional health (α = 0.855) and physical health dimensions (α = 0.722). Overall, these findings indicate that the Digital Well-Being Scale exhibits acceptable to excellent reliability and is suitable for use in the current study.

3.1.3. Normality Test
For more details, see Table 3.

Table 3. Normality Test

The normality assumption was examined using the one-sample Kolmogorov–Smirnov test. As presented in Table 3, the asymptotic Sig. (2-tailed) value was 0,084 (p > 0,05), indicating that the data were normally distributed

3.1.4. Inferential Statistical Analysis
For more details, see Table 4.

Table 4. Differences in Digital Well-Being Based on Digital Technology Use Duration

Differences in digital well-being were examined based on the duration of digital technology use. The homogeneity test yielded a significance value of 0.473 (p > 0.05), indicating that the variances were homogeneous. The one-way ANOVA results showed a significance value of < 0.001 (p < 0.05), indicating statistically significant differences in digital well-being across groups with different durations of digital technology use.
Notably, mean digital well-being scores increased progressively as the duration of digital technology use increased, ranging from 3.13 in the 1–2 h group to 4.18 in the more than 8 h group, with the lowest score observed in the shortest duration group. This pattern is theoretically meaningful and warrants careful interpretation, particularly because it appears to contradict the conventional assumption that prolonged screen exposure is uniformly detrimental to one’s well-being.

3.2. Discussion
This study aimed to examine and analyze the level of digital well-being among Generation Z employees in a digitalized work environment. The findings revealed significant differences in digital well-being based on the duration of digital technology use. These results indicate that the amount of time spent using digital technologies plays a crucial role in shaping employees’ digital well-being in digitalized workplaces.
The results showed that employees who used digital technology for a longer duration tended to report higher levels of digital well-being. This finding is consistent with the concept of digital well-being, which emphasizes that technology use does not inherently lead to negative outcomes, provided that individuals can manage the intensity and purpose of their technology use in an adaptive manner (Abeele, 2020; Mayiwar et al., 2025). In the work context, digital technologies can function as enablers of job performance, communication, and work flexibility, which, in turn, contribute to employees’ psychological and emotional well-being.
From a theoretical perspective, these findings can be explained by the Job Demands–Resources (JD–R) model, which posits that digital work demands may have positive effects on well-being when accompanied by adequate resources, such as job control, autonomy, and digital competence (Bakker & Demerouti, 2017). For Generation Z employees, who have been exposed to digital technologies from an early age, relatively high levels of technology use may be perceived as a job resource rather than a burden, thereby fostering work engagement and digital wellbeing. Moreover, previous studies suggest that individuals with strong digital self-regulation can utilize digital technologies effectively without experiencing excessive psychological strain (Li et al., 2025).
Mayiwar et al. (2025) further emphasized that digital self-control plays a critical role in maintaining a balance between digital demands and individuals’ psychological states. Accordingly, high levels of digital technology use do not necessarily reflect a risk of diminished well-being but may instead indicate individuals’ adaptive capacity to manage technology use in a healthy manner. Nevertheless, several studies have highlighted that excessive and poorly regulated digital technology use may lead to adverse outcomes, including digital fatigue, mental exhaustion, and physical health complaints (Al-Mansoori et al., 2023; Marsh et al., 2024). Therefore, the present findings should be interpreted contextually, suggesting that the positive association between a longer technology use duration and digital well-being is likely to occur when technology use is functional, work-oriented, and supported by effective self-regulation.
Additional analyses revealed that demographic characteristics such as gender, educational level, work experience, and job position did not show significant differences in digital-wellbeing levels. This finding indicates that digital well-being is more strongly influenced by patterns and quality of technology use than by demographic factors alone. This result aligns with prior research suggesting that digital well-being is subjective and largely shaped by how individuals interact with digital technologies in their daily activities (Qadaar et al., 2025).
This study provides evidence that the duration of digital technology use is a key factor in shaping the digital well-being of Generation Z employees. Intensive technology use, when managed in an adaptive and balanced manner, can support employees’ psychological and emotional well-being. Therefore, organizations are encouraged to develop policies and workplace cultures that promote healthy digital practices, such as managing digital workloads, enhancing digital literacy, and strengthening employees’ self-regulation skills, to sustain digital well-being in the digital era.

The finding that Generation Z employees with longer durations of digital technology use reported higher levels of digital well-being challenges the popular discourse that frames extensive technology exposure as being inherently harmful. This counterintuitive pattern can be explained by several interrelated theoretical perspectives.
From the perspective of digital competence and self-efficacy, Gen Z employees who use technology more intensively are likely to have developed greater technical fluency and confidence in managing digital tools (Schroth, 2019; Zoya & Chitrao, 2021). This accumulated competence allows them to perform digital tasks efficiently and integrate technology into their workflow with minimal friction; thus, prolonged use reflects mastery and control rather than strain. Consistent with the Job Demands–resources framework (Bakker & Demerouti, 2017), digital technologies function more as job resources that facilitate communication, flexibility, and access to information than as demands that deplete psychological resources. In contrast, employees with shorter usage durations may experience more fragmented or under-integrated digital engagement, limiting the development of functional fluency.
This pattern is also consistent with the technostress perspective (Ragu-Nathan et al., 2008; Tarafdar et al., 2019) and the concept of digital self-regulation (Reinecke et al., 2022). Longer technology use may indicate the development of adaptive coping repertoires, such as managing notifications, batching digital tasks, and setting boundaries using digital tools, which buffer the strain typically associated with heavy use. In digitally intensive roles such as marketing, information technology, and human resources, which dominate the present sample, intensive digital engagement is often purposeful and goal-directed, aligning with the notion of techno-eustress, in which meaningful technology use enhances rather than diminishes well-being. Taken together, these findings suggest that the relationship between digital technology use and well-being is not linear but is shaped by the interplay of digital competence, self-regulatory capacity, and role characteristics.
These findings offer several practical implications for organizations seeking to support digital well-being among Generation Z employees. First, organizations should focus on managing the quality of digital engagement rather than reducing screen time. Concrete measures include establishing protected focus periods, defining clear after-hours communication norms, and setting realistic expectations for response times in digital channels to address the techno-invasion dimension of stress.
Second, organizations should invest in continuous digital literacy and competency training. Although Gen Z employees are often assumed to be digitally competent, digital nativity does not automatically translate into a professional digital fluency. Training programs should include advanced topics such as efficient workflow design, cybersecurity awareness, integration of artificial intelligence tools, and ergonomic digital practices to strengthen the perceived control over technology.
Third, organizations should support the development of digital self-regulation skills through workshops on attention management, mindful technology use, and digital detoxification practices. Managers can reinforce a culture of self-regulation by modeling healthy digital behavior, such as respecting offline boundaries and avoiding late-night messages. Particular attention should also be given to the physical health dimension of digital well-being through ergonomic workstations, movement breaks, and wellness reminders, especially for hybrid and remote workers.
Finally, digital well-being should be integrated into broader employee well-being and human resource programs, including regular well-being assessments, accessible mental health resources, and peer support networks. Collectively, these recommendations encourage organizations to move beyond a deficit-based view of digital technology use and cultivate the conditions of competence, regulation, and role design under which digital engagement enhances rather than undermines well-being.