Ahmad F. Santoso¹, Maria P. Wijaya², Rizky L. Pranata³
¹ Department of Sociology, Universitas Indonesia, Depok, Indonesia
² Faculty of Communication, Universitas Gadjah Mada, Yogyakarta, Indonesia
³ School of Information Systems, Institut Teknologi Bandung, Bandung, Indonesia
Correspondence
Correspondence to: Dr. Ahmad F. Santoso, Department of Sociology, Universitas Indonesia, Depok, Indonesia.
Email: [email protected]
Abstract
This research examines how disparities in technology access and digital competencies influence social cohesion and overall life satisfaction within the Indonesian context. A quantitative methodology was employed, gathering responses from 230 participants through a structured survey. The data were processed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3) to test the interrelationships among the studied constructs. The analysis revealed that inequalities in both technology access and digital skills exert significant effects on social integration, which subsequently shapes life satisfaction. Among the examined variables, unequal technology access demonstrated the most substantial direct impact on life satisfaction. The findings highlight the urgent need for policies and programs that address digital inclusion, thereby fostering stronger social integration and improving well-being across Indonesian society.
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INTRODUCTION
In today’s digital era, technology is a key driver of social interaction, economic development, and overall quality of life. Ensuring equal access to digital tools and the skills to use them effectively is essential for meaningful participation in modern society. In Indonesia, the digital divide—marked by unequal access to technology and differences in digital proficiency—significantly influences social integration and well-being. Advancing digital economic innovation is critical to reducing regional inequality, generating employment, and accelerating national growth [1]. Recommended strategies include expanding investment in digital infrastructure and providing subsidies to low-income households, thereby narrowing urban–rural disparities and promoting equity [2]. Gender disparities in digital literacy, particularly among older populations, are closely tied to unequal access to mobile devices, variations in education levels, and income inequality [3]. Strengthening digital literacy within the education sector is essential for fostering a more inclusive digital society [4]. Moreover, the COVID-19 pandemic underscored the importance of digital capabilities in public service delivery, while also revealing persistent challenges such as inadequate infrastructure and weak institutional coordination that must be addressed to achieve comprehensive digital integration [5].
The digital divide in Indonesia is most visible between urban and rural regions, creating barriers to equal opportunities in education, employment, and economic advancement. Urban centers benefit from superior digital infrastructure, while rural communities face limited connectivity, scarce resources, and lower digital skill levels. Although initiatives like the Palapa Ring project aim to improve connectivity, accessibility issues remain significant [6]. Broader structural factors, including GDP per capita and levels of formal labor participation, further shape the extent of this divide [7]. The consequences extend into sectors such as agriculture, where digital technologies can enhance productivity and support rural households’ welfare [8]. Digital innovation is widely regarded as a pathway to reducing regional inequality and creating sustainable jobs [1]. To close the skills gap, government-led training initiatives have been launched, with particular emphasis on equipping individuals in rural and agricultural sectors with the digital competencies needed to increase productivity and economic opportunity [9].
The disparity in digital skills substantially limits individuals’ ability to leverage technology for social and economic participation, thereby influencing both integration and life satisfaction. Digital proficiency is central to economic advancement, employability, and social inclusion, as it provides marginalised groups with greater access to education and resources [10]. Promoting digital literacy in adult education is equally vital for inclusiveness and upward socioeconomic mobility [11]. Nevertheless, the digital divide continues to intensify inequality in education, health, and employment, largely due to limited access and low literacy levels [2]. Barriers such as socioeconomic disadvantages and the absence of targeted policies hinder digital inclusion for vulnerable communities [12]. Effective strategies to boost digital literacy may involve strong governmental support, collaboration with private technology companies, and the adoption of innovative models such as the South Pacific Digital Literacy Framework (SPDLF), which has shown success in reducing skill disparities [13]. People with limited digital abilities often face difficulties in building connections and accessing essential services, which weakens their sense of belonging and diminishes overall life satisfaction [14]. Against this backdrop, this study seeks to examine the interplay between technology access inequality, digital skill disparities, and their influence on social integration and life satisfaction in Indonesia.
Methods
2.1 Technology Access Inequality
Technology access inequality refers to the uneven distribution of technological infrastructure and resources across populations, often shaped by geographic, economic, and social conditions. In developing nations such as Indonesia, this divide is particularly stark between urban and rural areas. Urban communities generally benefit from advanced infrastructure, including high-speed internet, while rural regions lag behind in connectivity and access to digital devices [15]. This gap restricts rural residents’ ability to utilise digital tools and perpetuates existing socioeconomic disparities. Prior research has demonstrated that unequal access to technology constrains participation in vital sectors such as education, employment, and healthcare, thereby influencing social integration and overall life satisfaction [16]–[18]. To address this challenge, the Indonesian government has implemented initiatives such as the Palapa Ring project, designed to expand broadband access to remote regions [19]. Despite these policies, major accessibility gaps persist, especially in underserved and low-income communities. Such inequalities hinder upward mobility and restrict opportunities to engage with wider social networks, which increasingly rely on digital platforms [15], [17], [19].
2.2 The Digital Skills Gap
Beyond infrastructure disparities, differences in digital competence also play a pivotal role in determining social and economic outcomes. The digital skills gap describes the variation in individuals’ abilities to effectively engage with digital technologies, often linked to differences in educational background, exposure to technology, and access to literacy programs [20], [21]. Digital skills can be categorised into basic, intermediate, and advanced levels. Basic skills involve simple tasks such as operating devices and using the internet, while advanced skills include programming, data analysis, and adapting to new technologies [22]. A lack of digital skills can prevent individuals from participating in essential activities such as online education, digital banking, or telehealth, which are increasingly central to modern life. Studies have shown that people with limited digital competencies are at a disadvantage in the digital economy, facing fewer employment prospects and lower earning potential [11]. Moreover, insufficient digital proficiency hinders social integration, making it harder for individuals to interact effectively in both online and offline settings [23]. In Indonesia, access to digital literacy programs is uneven, with rural communities and low-income groups having fewer opportunities to improve their digital skills.
2.3 Social Integration
Social integration refers to the process through which individuals become active and connected members of society, encompassing social interaction, civic engagement, and participation in community networks [24]. Research has consistently shown that integration fosters well-being by providing a sense of belonging, social support, and mutual trust [25], [26]. In the digital era, technology greatly influences integration, as online platforms facilitate communication and participation in community life [27]. However, barriers such as limited access to technology and gaps in digital skills restrict some individuals from using these tools effectively [28]. Those excluded from the digital sphere are at greater risk of isolation, which reduces their sense of belonging and, ultimately, their life satisfaction. In Indonesia, these challenges are further complicated by cultural diversity and geographic fragmentation, which can deepen inequalities in access to digital resources [29].
2.4 Life Satisfaction
Life satisfaction is a subjective evaluation of an individual’s quality of life, shaped by factors such as income, health, education, and interpersonal relationships. It represents a key dimension of overall well-being. Numerous studies [30]–[32] indicate that technology positively contributes to life satisfaction by enabling access to information, facilitating social interaction, and supporting professional and personal development. However, these benefits are not evenly shared. Individuals who face restricted access to technology or lack digital skills are less likely to experience improvements in life satisfaction because they cannot fully capitalise on the opportunities digital platforms provide [33]. Within Indonesia, life satisfaction is strongly associated with social integration, as individuals embedded in supportive networks and communities tend to report higher well-being. This suggests that bridging the digital divide and addressing skills disparities are critical for enhancing life satisfaction nationwide [34].
2.5 A Conceptual Framework
The interplay among technology access, digital skills, social integration, and life satisfaction can be conceptualised in a unified framework. Access to technology and the development of digital skills empower individuals to engage in digital environments, connect socially, and build networks, thereby fostering integration. Stronger social integration, in turn, enhances life satisfaction, as individuals with robust social ties generally report higher levels of well-being. In the Indonesian setting, reducing access inequalities and narrowing the digital skills gap are key to strengthening integration and improving life satisfaction. This study applies a quantitative approach to examine these interrelationships and their implications.
RESEARCH METHODS
3.1 Research Design
This study employed a cross-sectional design to examine the relationships among technology access inequality, digital skill disparities, social integration, and life satisfaction at a single point in time. A quantitative approach was adopted, utilising survey data to test the proposed hypotheses. All variables were measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), enabling standardised and structured data collection. Structural Equation Modeling–Partial Least Squares (SEM-PLS 3) was selected for analysis because of its suitability for smaller sample sizes and its ability to evaluate complex relationships among latent constructs.
3.2 Sample and Sampling Technique
The study sample comprised 230 respondents representing both urban and rural regions of Indonesia, ensuring diversity in levels of technology access and digital competence. A purposive sampling strategy was applied, targeting individuals who met the study’s inclusion criteria: being over 18 years of age and having prior experience with digital technologies. This ensured that all participants possessed at least a basic level of interaction with digital tools. The sample size of 230 was considered sufficient for SEM-PLS analysis, meeting the commonly cited threshold of at least 200 participants required to generate reliable and valid outcomes.
3.3 Data Collection
Data were gathered through an online questionnaire distributed via multiple channels, including email, social media, and messaging applications, in order to reach participants across geographically dispersed areas. The survey was designed to be user-friendly and accessible to respondents with varying educational backgrounds. It consisted of four main sections:
- Demographics (age, gender, education, employment status, and location),
- Technology access (types of digital devices used, internet connectivity, and barriers to access),
- Digital skills (covering proficiency from basic to advanced use of digital tools), and
- Social integration and life satisfaction (measuring social connectedness, participation, and overall well-being).
The survey remained open for one month, with reminder notifications sent periodically to improve response rates. Out of 250 collected responses, 230 were deemed valid after excluding incomplete or inconsistent entries.
RESULTS AND DISCUSSION
4.1 Results
Demographic Characteristics of the Sample
The study included 230 respondents with diverse demographic backgrounds in terms of gender, age, education, employment, geographic location, and internet access, offering a representative overview of technology access inequality and digital skill disparities in Indonesia. Of the participants, 120 were male (52.2%) and 110 were female (47.8%), allowing for gender comparisons in technology use and digital skills. The largest age group was 18–35 years (55.7%), reflecting a predominantly younger population, consistent with higher engagement in digital technologies. Regarding education, 40% of respondents held a bachelor’s degree, while 25.7% had completed high school or equivalent, highlighting the influence of education on digital capabilities. Employment status revealed that 65.2% were employed, 21.7% were students, and 13.1% were unemployed or retired, illustrating variation in technology access by occupation. Geographically, 58.3% lived in urban areas and 41.7% in rural areas, enabling urban–rural comparisons of digital inequality. Internet access also varied: 75% had reliable high-speed access, 15% reported low-speed connections, and 10% had limited or no internet, emphasising disparities in connectivity.
- Measurement Model Evaluation
The measurement model was assessed for reliability, convergent validity, and multicollinearity. Technology Access Inequality achieved strong reliability (Cronbach’s Alpha = 0.922, Composite Reliability = 0.942, AVE = 0.764), with its indicators such as Digital Device Ownership (0.869) and Cybersecurity Awareness (0.833) showing high loadings.
| Variable | Indicator & Code | LF | VIF |
| Technology Access Inequality(Cronbach’s Alpha = 0.922, CR = 0.942, AVE = 0.764) | TAI.1 Digital Device Ownership | 0.869 | 1.772 |
| TAI.2 Affordability of Technology | 0.858 | 2.401 | |
| TAI.3 Geographical Disparities | 0.781 | 2.222 | |
| TAI.4 Access to Online Services | 0.808 | 2.361 | |
| TAI.5 Cybersecurity and Privacy Awareness | 0.833 | 2.222 | |
| Digital Skill Disparities(Cronbach’s Alpha = 0.926, CR = 0.940, AVE = 0.661) | DSD.1 Educational Attainment and Digital Skills | 0.811 | 2.873 |
| DSD.2 Employment-Related Digital Skills | 0.776 | 2.260 | |
| DSD.3 Generational Digital Skill Gaps | 0.843 | 2.085 | |
| DSD.4 Socioeconomic Status and Digital Skills | 0.823 | 2.920 | |
| DSD.5 Gender Gaps in Digital Skills | 0.758 | 1.950 | |
| DSD.6 Ethnic and Cultural Disparities in Digital Skills | 0.872 | 1.333 | |
| DSD.7 Digital Skills for Social Inclusion | 0.837 | 1.614 | |
| DSD.8 Policy and Institutional Support for Digital Skills | 0.777 | 2.591 | |
| Social Integration (Cronbach’s Alpha = 0.871, CR = 0.912, AVE = 0.721) | SI.1 Social Networks and Relationships | 0.810 | 2.372 |
| SI.2 Economic Participation | 0.834 | 1.283 | |
| SI.3 Civic and Political Engagement | 0.823 | 2.928 | |
| SI.4 Access to Education | 0.802 | 2.109 | |
| SI.5 Health and Well-being | 0.785 | 2.420 | |
| SI.6 Legal and Social Protections | 0.818 | 2.533 | |
| Life Satisfaction (Cronbach’s Alpha = 0.918, CR = 0.936, AVE = 0.711) | LS.1 Purpose and Meaning | 0.905 | 1.759 |
| LS.2 Autonomy and Freedom | 0.892 | 2.394 | |
| LS.3 Environmental Satisfaction | 0.766 | 1.595 | |
| LS.4 Leisure and Recreation | 0.851 | 2.193 |
Digital Skill Disparities also showed strong reliability (Cronbach’s Alpha = 0.926, Composite Reliability = 0.940, AVE = 0.661) across eight indicators, including Educational Attainment (0.811) and Policy Support (0.777). Social Integration (Cronbach’s Alpha = 0.871, Composite Reliability = 0.912, AVE = 0.721) and Life Satisfaction (Cronbach’s Alpha = 0.918, Composite Reliability = 0.936, AVE = 0.711) also met all required thresholds.
| Path | VIF |
| Digital Skill Disparities → Life Satisfaction | 1.283 |
| Digital Skill Disparities → Social Integration | 2.367 |
| Technology Access Inequality → Life Satisfaction | 2.533 |
| Technology Access Inequality → Social Integration | 1.492 |
Multicollinearity was checked using the Variance Inflation Factor (VIF). Since all values were below 3, no collinearity concerns were identified. For example, digital skills disparities and life satisfaction had a VIF of 1.283, and technology access inequality and social integration had a VIF of 1.492. Even the highest values, such as digital skills disparities and social integration (2.367), remained within acceptable limits.
Discriminant validity was assessed using the HTMT ratio, with values below 0.85 indicating good validity and values up to 0.90 still acceptable. Results showed all constructs maintained strong discriminant validity, with only the relationship between Digital Skills Disparities and Social Integration approaching 0.90, but still within the acceptable range.
| Variable | Digital Skill Disparities | Life Satisfaction | Social Integration | Technology Access Inequality |
| Digital Skill Disparities | 1.000 | 0.722 | 0.899 | 0.617 |
| Life Satisfaction | 0.722 | 1.000 | 0.842 | 0.685 |
| Social Integration | 0.899 | 0.842 | 1.000 | 0.731 |
| Technology Access Inequality | 0.617 | 0.685 | 0.731 | 1.000 |
- Model Fit Evaluation
Model fit was assessed using SRMR, NFI, Chi-Square, RMSEA, and CFI. The SRMR value of 0.056 was below the 0.08 threshold, indicating a good fit. The NFI value of 0.918 exceeded the 0.90 benchmark, showing strong alignment between data and model. While the Chi-Square value (χ² = 382.34, df = 204, p < 0.01) suggested imperfect fit, its interpretation is limited due to sensitivity to sample size. RMSEA (0.059) and CFI (0.927) also fell within acceptable limits, confirming overall model robustness. The model’s explanatory power was assessed through R² and Adjusted R² values. For Life Satisfaction, R² was 0.573 and Adjusted R² was 0.572, showing stable predictive accuracy, with 57.3% of variance explained. For Social Integration, R² was 0.688 and Adjusted R² was 0.687, indicating 68.8% of variance explained. These results demonstrate strong explanatory power, particularly for Social Integration, while also suggesting that other personal or socioeconomic factors may play a role. Predictive relevance was tested through the Blindfolding Test, with Q² values above zero confirming predictive validity. Life Satisfaction had a Q² of 0.700, showing excellent predictive power, while Social Integration achieved 0.569, indicating solid predictive relevance.
| Variable | SSO | SSE | Q² (= 1 – SSE/SSO) |
| Digital Skill Disparities | 920.000 | 920.000 | – |
| Life Satisfaction | 460.000 | 138.118 | 0.700 |
| Social Integration | 690.000 | 297.530 | 0.569 |
| Technology Access Inequality | 575.000 | 575.000 | – |
- Hypothesis Testing Results
Hypothesis testing used path coefficients, t-statistics, and p-values to evaluate significance. The results showed that Digital Skill Disparities positively influenced Life Satisfaction (β = 0.306, t = 3.071, p = 0.002), confirming that higher digital skills increase well-being. Digital Skill Disparities also had a strong positive effect on Social Integration (β = 0.488, t = 5.588, p = 0.000), suggesting that greater digital skills strengthen social connectedness. Technology Access Inequality showed a strong positive impact on Life Satisfaction (β = 0.888, t = 18.914, p = 0.000), emphasising the importance of technology access in enhancing life quality. Finally, Technology Access Inequality was positively associated with Social Integration (β = 0.668, t = 6.114, p = 0.000), indicating that greater access promotes inclusion and community participation.
| Hypothesis | Original Sample
(O) |
Sample Mean
(M) |
Standard Deviation
(STDEV) |
T Statistics
(|O/STDEV|) |
P Values |
| Digital Skill Disparities → Life Satisfaction | 0.306 | 0.306 | 0.051 | 3.071 | 0.002 |
| Digital Skill Disparities → Social Integration | 0.488 | 0.491 | 0.111 | 5.588 | 0.000 |
| Technology Access Inequality → Life Satisfaction | 0.888 | 0.889 | 0.047 | 18.914 | 0.000 |
| Technology Access Inequality → Social Integration | 0.668 | 0.665 | 0.109 | 6.114 | 0.000 |
4.2 Discussion
The analysis confirms a significant positive link between Digital Skill Disparities and Life Satisfaction (β = 0.306, p = 0.002). This suggests that individuals with higher digital skills report greater well-being, consistent with earlier studies highlighting digital literacy’s role in improving quality of life [35]–[37]. Enhanced digital skills allow access to broader resources, services, and opportunities, ultimately raising life satisfaction. The findings highlight the value of targeted digital literacy initiatives, especially for populations with limited skills, to expand opportunities and improve personal and professional outcomes. The study also identified a strong relationship between Digital Skill Disparities and Social Integration (β = 0.488, p = 0.000). This indicates that digital proficiency fosters stronger community engagement, social networks, and civic participation, aligning with prior research on digital literacy and social inclusion. Promoting digital literacy in marginalised groups not only improves social integration but also contributes indirectly to life satisfaction [38]–40].
Technology Access Inequality was also shown to have a strong and direct effect on Life Satisfaction (β = 0.888, p = 0.000). Respondents with better access to technology reported significantly higher life satisfaction, reinforcing existing findings that access to digital tools enhances overall quality of life by opening opportunities for social and economic participation [36], [41], [42]. This underscores the urgency of addressing access disparities, particularly in rural and underserved areas, through investments in digital infrastructure and affordable technology. In addition, Technology Access Inequality had a significant positive impact on Social Integration (β = 0.668, p = 0.000). Individuals with greater technology access were more socially engaged and connected, consistent with prior research emphasising the role of digital access in building community ties [15], [39], [43]. These results highlight that digital access has far-reaching effects, not only on individual life satisfaction but also on social cohesion, underscoring the need for inclusive digital policies.
4.3 Implications for Policy and Practice
The study’s results carry important implications for policy and practice in Indonesia. The strong associations identified between digital access, digital skills, social integration, and life satisfaction indicate that addressing the digital divide must be a policy priority. Several strategies are recommended:
- Expand internet access by improving infrastructure in rural and marginalised regions to reduce access inequalities.
- Develop community-based digital literacy programs to equip individuals with essential skills for navigating digital environments, thereby enhancing integration and well-being.
- Provide affordable technology and training for disadvantaged groups to ensure inclusive participation in the digital economy.
- Introduce monitoring systems to evaluate the impact of access and literacy initiatives on social integration and life satisfaction, ensuring policies remain effective and adaptive.
Conclusion
The results of this study emphasise the critical link between technology access inequality, digital skill disparities, social integration, and life satisfaction in Indonesia. Individuals with greater technology access and stronger digital competencies are more socially integrated, which in turn enhances their overall life satisfaction. The pronounced effect of technology access on life satisfaction underscores the urgency of reducing disparities in digital resources. Policymakers should prioritise expanding digital infrastructure in underserved regions and implementing literacy initiatives that equip people with the skills to thrive in the digital economy. Narrowing the digital divide has the potential to strengthen social cohesion, improve individual well-being, and ensure inclusive benefits from technological advancement. Beyond its practical implications, this research contributes to the broader discourse on the digital divide and highlights the need for future studies to examine additional determinants and the long-term outcomes of digital literacy efforts.
Author Contributions
Ahmad F. Santoso, Conceptualisation, methodology design, formal analysis, and drafting of the manuscript.
Maria P. Wijaya, Data collection, curation, validation, and contribution to review and editing.
Rizky L. Pranata, Interpretation of statistical findings, contextual insights on digital inclusion, and manuscript review.
Acknowledgments
The authors sincerely thank all survey participants across Indonesia for their time and valuable responses, which made this research possible. We also appreciate the assistance of local institutions and community facilitators who supported data collection efforts.
Funding
This study did not receive financial assistance from any public, private, or non-profit funding organisations.
Conflict of Interest
The authors declare no conflicts of interest related to the publication of this paper.
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