Virtual Collaboration and E-Democracy During the Pandemic Era: Insights on Digital Engagement, Infrastructure, and Social Dynamics

Abstract

The COVID-19 pandemic accelerated virtual collaboration, reshaping digital communication, remote work, education, and e-democracy. This study examines the impact of these tools on digital citizen participation through a quantitative cross-sectional survey of n = 1122 participants across diverse demographics. Using stratified purposive sampling, descriptive statistics, correlation analyses, and segmentation by demographic and psychological factors, we analyzed how infrastructure quality, personality traits, and social dynamics influenced virtual engagement. While digital platforms have improved accessibility, findings reveal that they often fail to foster interpersonal trust and democratic deliberation. Statistical analyses demonstrated significant correlations between communication effectiveness and relationship quality (ρ = 0.387, p < 0.001), with distinct patterns emerging across age groups, community sizes, and personality types. Infrastructure disparities significantly impacted participation, particularly in rural areas (χ² = 70.72, df = 12, p < 0.001, V = 0.145). Recommendations include enhancing digital infrastructure, developing adaptive e-governance platforms, and implementing trust-building mechanisms. Despite the limitations of self-reported data and the cross-sectional design, these insights contribute to building more inclusive digital governance frameworks. Future research should employ longitudinal approaches to explore evolving trends in e-democratic participation.

1. Introduction

The COVID-19 pandemic brought an overnight transformation in how people communicate, work, and learn. Organizations, governments, and institutions quickly transitioned to online platforms to adopt teleworking and e-learning, aiming to maintain societal continuity during lockdown and social distancing measures [1,2]. This revolutionized how individuals communicate, form relationships, and socialize [3,4]. While these technologies brought greater flexibility and accessibility, they also brought significant challenges, including increased stress, technical glitches, and inequities in infrastructure access [5,6]. Understanding how users manage this transition is crucial for improving the design and implementation of e-collaboration tools in a post-pandemic future [7,8,9].

The existing body of research emphasizes the significance of communication, interpersonal relationships, and socialization in enhancing human productivity and satisfaction in both physical and digital environments [10,11]. The critical role of interpersonal networks in influencing human behavior, particularly in communication and decision making, has been found [12,13]. Personal influence and relational ties shape collective action and participation. These foundational studies align with more recent work [14,15], which underscores the role of community and communication in fostering engagement and collaboration. However, little is known about how these factors were affected during the rapid and large-scale shift to virtual collaboration [16,17]. This study seeks to fill this gap by systematically analyzing the experiences of diverse user groups in teleworking and online learning contexts during the pandemic [18,19,20].

The existing literature emphasizes the importance of communication, interpersonal relations, and socialization to human productivity and satisfaction in both virtual and real-world contexts. Interpersonal networks constitute the cornerstone of human action in terms of communication and choice [21,22,23]. Relational connections and personal influence affect collective participation and action [24]. Supplementing these foundational studies of the past, more recent publications emphasize the importance of communication and community in fostering engagement and collaboration [25]. However, we hear little about how those aspects were impacted during the abrupt and large-scale shift to virtual cooperation [26,27,28].

Thus, this paper attempts to bridge this gap by examining, in a structured manner, the experiences of various user groups who engaged in teleworking and online learning amidst the pandemic. The present study examines the impact of infrastructure, personality, occupation, location, and demographics on user experiences related to teleworking and e-learning. It also attempts to capture how virtual environments influence socialization and communication patterns, the quality of relationships, and the contribution these bring to satisfaction and productivity in virtual environments. It also examines how e-collaboration impacts the affective states of stress, work–life balance, and well-being, identifying the benefits and challenges associated with virtual collaboration. The study also outlines the challenges to successful e-collaboration by determining disparities in access to physical infrastructure, enabling technologies, and support mechanisms, mainly based on community size and population characteristics. Ultimately, this study offers actionable insights for enhancing the design and implementation of e-collaboration tools, with a focus on usability, inclusivity, and adaptability. It distinguishes between the influence of intrinsic factors, such as personality traits and preferences, from external factors like digital infrastructure and organizational culture in shaping remote work and learning outcomes.

The novelty and significance of this research lie in its comprehensive multi-dimensional approach that uniquely integrates demographic, psychological, and infrastructural variables within the context of e-democracy and digital collaboration. Unlike previous studies that have examined isolated aspects of virtual engagement, our research employs a robust sample (n = 1122) across diverse population segments to develop a comprehensive understanding of the factors that enable or inhibit effective e-participation. This study offers unprecedented insights into the complex interplay of factors that shape digital citizen engagement by analyzing personality traits through the DISC model, community size influences, demographic variations, and the quality of technological infrastructure. Furthermore, our research contributes actionable recommendations for policy-makers, organizations, and technology developers to enhance inclusive digital participation in post-pandemic governance and educational settings, addressing a critical gap in the literature between theoretical frameworks of e-democracy and practical implementation strategies for diverse populations.

2. Theoretical Framework

The COVID-19 pandemic fundamentally transformed how democratic participation and collaborative work occur in digital spaces. Rather than examining these changes through isolated theoretical lenses, this framework integrates multiple perspectives to understand the interconnected nature of e-democracy and virtual collaboration during the pandemic era.

2.1. The Digital Transformation of Democratic Participation

The transition from physical to virtual democratic engagement during COVID-19 represents more than a simple technological shift—it fundamentally altered the nature of democratic participation itself. Building on Habermas’s foundational theory of the public sphere [29], we can understand pandemic-era digital spaces as hybrid democratic environments that simultaneously expand and constrain civic engagement. These spaces embody what Keane conceptualizes as “monitory democracy” [30], where constant digital scrutiny and engagement profoundly reshape traditional representative structures.

The pandemic accelerated this transformation with unprecedented speed and scope. When researchers documented how COVID-19 led to the rapid adoption of digital decision-making processes in parliaments worldwide [31], they highlighted the fragility of traditional democratic institutions and their remarkable adaptability under pressure. Simultaneously, legal scholars analyzed the ethical implications of emergency e-governance measures [32,33], raising critical questions about democratic legitimacy in crisis contexts. This forced digitization exposed what researchers identified as the fundamental paradox of e-democratic systems [34]: digital platforms simultaneously democratized information access while creating entirely new forms of inequality and exclusion.

The theoretical implications extend far beyond the adoption of simple technology. When we examine how researchers established distinctions between digital activism and institutionalized e-democracy practices [33], we see that pandemic-era changes blurred these boundaries in unprecedented ways. Studies documenting how non-state actors have increasingly influenced policy through digital channels [35,36] have revealed that participation remains stratified along socioeconomic lines, even as overall digital engagement expands. This paradox frames our understanding of how virtual collaboration technologies reshaped democratic engagement during the crisis.

Additionally, virtual collaboration technologies function not merely as communication tools but as a democratic infrastructure that fundamentally shapes who can participate and how they engage. The integration of established frameworks, such as Media Richness Theory [37] and Social Presence Theory [38], with pandemic-era empirical findings, reveals a more complex reality: effective democratic participation requires sophisticated sociotechnical systems that support meaningful deliberation and decision making, not just basic connectivity.

Research tracking the evolution of technology affordances for remote work throughout the pandemic [39] demonstrated that democratic capacity itself became technologically mediated. When scholars examined how communication and collaboration adapted in specialized professional contexts [40], they uncovered patterns that extend to civic engagement more broadly. These findings complement comprehensive analyses of engagement in online environments [41], suggesting that virtual collaboration technologies create new forms of democratic capacity that extend far beyond traditional notions of civic participation [42,43].

The forced transition to virtual work environments generated substantial evidence about collaborative dynamics with direct implications for democratic theory [44,45]. Studies documenting the impact of remote working and digital transformation on psychological drivers and economic outcomes across diverse organizational settings [42] have revealed that virtual engagement operates through complex social and psychological mechanisms [46,47]. Similarly, research exploring trade-offs between flexibility and structure in pandemic-era remote work arrangements [43] highlighted tensions that also characterize virtual democratic participation.

2.2. Platform-Mediated Democracy and Social Transformation

The migration to online platforms during the pandemic fundamentally reconfigured democratic participation through what we term “platform-mediated democracy” [48,49]. This concept acknowledges that digital platforms are not neutral spaces but rather active mediators that shape democratic discourse through algorithms, interface design, and governance structures [50,51]. When researchers documented how social and professional interactions were reconfigured in virtual environments [52,53], they identified patterns that extend to civic engagement, creating new forms of political participation that blend formal and informal democratic activities [54].

Educational research provides particularly revealing insights into these dynamics. Studies examining digital transformations in learning environments [44,45] and professional contexts [46,47] demonstrate that the pandemic accelerated pre-existing digital trends rather than creating entirely new phenomena. This acceleration necessitated more sophisticated theoretical frameworks capable of capturing the complex interplay of socioeconomic factors, technological access, and digital literacy that determined participation outcomes [55,56].

Social media platforms played a crucial role in this transformation, functioning as sites of democratic contestation where traditional boundaries dissolved [57,58]. Research examining how social media reconstructed urban identity during the pandemic [59,60] revealed new forms of civic engagement that challenge conventional theories of democratic participation. When scholars analyzed engagement with governmental versus non-governmental messages during crises, they documented how platforms became hybrid spaces where political and social engagement merged in unprecedented ways [60,61].

Furthermore, the pandemic highlighted how digital infrastructure serves as a critical determinant of democratic inclusion, functioning as both an enabler and gatekeeper to civic participation [62,63]. Research documenting digital inequality in communication during COVID-19 [64,65,66] revealed systematic patterns of exclusion that researchers exploring health inequalities during the pandemic [67] showed extended across multiple domains. These findings suggest that access to and quality of infrastructure create new forms of civic stratification based on technological capacity rather than traditional markers of political inclusion [68,69,70].

Connectivity challenges emerged as particularly significant barriers to meaningful participation. Studies examining digital inequality and faculty communication in higher education [71,72] demonstrated how connectivity problems operated at multiple levels, from basic infrastructure access to the quality and reliability of connections. When researchers analyzed how internet access influenced productivity, inequality, and resilience in remote work contexts [73], they uncovered patterns directly relevant to democratic participation, demonstrating how connectivity shapes not only access but also the quality of civic engagement itself.

The demographic dimensions of these challenges proved especially concerning. Research analyzing how being “disconnected in a pandemic” shaped COVID-19 outcomes across different populations [74] revealed that connectivity challenges disproportionately affected marginalized communities. Studies documenting inequalities in connectivity and their consequences [75] have demonstrated how digital exclusion exacerbates social inequalities, creating compounding disadvantages that extend from health outcomes to democratic participation.

2.3. Social Dynamics and Demographic Variations in Digital Democracy

The social dimensions of e-democracy received substantial attention during the pandemic as researchers moved beyond technological determinism to examine how social norms, trust relationships, and community dynamics shape digital political participation. Studies examining digital strategies to advance community stakeholder engagement during COVID-19 [76] revealed that e-democracy operates within existing social structures that both enable and constrain digital engagement in complex ways.

Demographic research proved particularly revealing about differential engagement patterns. When scholars compared generational differences between Gen Z and Gen X in resilience and adaptation during the pandemic [50], they uncovered systematic variations in how different age groups approached digital civic engagement. Research exploring how personality traits influenced technology adoption across generational cohorts [51] demonstrated that complex interactions mediate digital democratic participation between demographic characteristics, technological access, and individual psychological factors.

Social identity and community belonging emerged as crucial mediating factors. Studies analyzing the disproportionate impact of COVID-19 among Latinx patients and associated democratic responses [77] highlighted how marginalized communities developed distinct patterns of digital political participation during the crisis. This research demonstrates that pre-existing social inequalities were not simply reproduced but actively amplified in digital democratic spaces, creating new forms of civic exclusion that operate through technological mediation.

The pandemic highlighted the crucial importance of inclusive design in e-democratic systems, revealing gaps between technological capabilities and genuine civic engagement. Research documenting barriers for people with impairments in accessing digital solutions [78] demonstrated that technical accessibility requirements intersect with broader questions of democratic inclusion. When scholars systematically reviewed engagement interventions across diverse populations [79], they found that e-democratic design must account for the diverse needs and capabilities of users to achieve genuine inclusivity.

The relationship between participation quantity and quality emerged as a key theoretical concern with practical implications. Studies developing tools to measure the effectiveness of public participation in e-rulemaking [62] found that increased participation did not necessarily improve decision quality, challenging assumptions about digital democratization. Research examining senior citizen participation in European policy-making [63] documented both opportunities and barriers to digital engagement, revealing the need for more sophisticated models that distinguish between different forms and qualities of democratic participation [80,81].

The technological requirements proved more complex than anticipated, varying significantly across different collaborative contexts and user populations. When researchers examined how COVID-19 impacted tacit knowledge and social interaction in global development teams [68], they highlighted the inadequacy of standard videoconferencing tools for complex collaborative tasks. Studies analyzing digitization in the design and construction industries [69] identified domain-specific technological requirements that generic collaboration platforms failed to meet, suggesting similar challenges for civic engagement platforms [82,83].

3. Materials and Methods

3.1. Research Design and Rationale

This study employed a quantitative cross-sectional survey design to comprehensively assess user experiences with digital collaboration tools in teleworking and e-learning environments during the COVID-19 pandemic. A survey methodology was selected as the most appropriate approach to address our research objectives for several reasons. First, it allowed us to collect standardized data from a large, diverse sample across multiple geographic locations, occupations, and demographic categories, thereby enabling the broad generalizability of our findings. Second, the survey approach facilitated the examination of relationships between multiple variables simultaneously (infrastructure, personality traits, demographics, and experiential outcomes), which was essential for our multi-dimensional analysis. Third, this methodology enabled the statistical testing of associations between variables, providing quantifiable evidence of relationships between factors that affect virtual collaboration experiences.

We acknowledge the inherent limitations of self-reported data, including potential social desirability bias, recall inaccuracies, and subjective interpretation of questions. To mitigate these limitations, we implemented several methodological safeguards: (1) anonymous data collection to reduce social desirability concerns; (2) questions focused on current or recent experiences rather than distant recall; (3) pilot testing to identify and revise ambiguous questions; (4) inclusion of attention check items to identify careless responding; and (5) triangulation of related constructs through multiple question formats to enhance reliability.

3.2. Survey Design and Structure

3.2.1. Instrument Design Process

The survey instrument was developed through a systematic four-phase process:

• Initial Development Phase: Based on a comprehensive literature review, we identified key constructs relevant to virtual collaboration experiences. Initial item pools were created for each construct domain, drawing from validated measures where available and developing new items where necessary.
• Expert Review Phase: The preliminary instrument was evaluated by a panel of six experts in educational technology, organizational psychology, and survey methodology. Experts rated each item’s relevance, clarity, and appropriateness using a content validity index (CVI) approach. Items with CVI scores below 0.78 were either revised or eliminated.
• Cognitive Interview Phase: Ten participants representing diverse demographics engaged in cognitive interviewing while completing the draft survey, providing feedback on item interpretation, response processes, and survey structure. This process identified several problematic items that were subsequently modified or removed.
• Pilot Testing Phase: The revised instrument was pilot-tested with 48 participants from various occupational backgrounds. Statistical analyses, including item-total correlations, inter-item correlations, and preliminary factor analyses, were conducted to evaluate psychometric properties. Cronbach’s alpha coefficients for multi-item scales ranged from 0.72 to 0.89, indicating acceptable internal consistency. The pilot phase also established an estimated completion time of 10–12 min.

3.2.2. Instrument Structure and Content

The final survey instrument comprised 62 items organized into five major sections:

• Demographics and Background (12 items): Collected information on age, gender, education level, community size, employment/educational role, family status, pre-pandemic experience with teleworking/e-learning, and geographic location. Age was measured in six brackets (18–24, 25–34, 35–44, 45–54, 55–64, and 65+), while community size was categorized as small (rural), medium, large, and metropolitan areas based on population thresholds.
• Digital Infrastructure (14 items): Assessed availability and usability of devices (5-point Likert scale from “completely inadequate” to “completely adequate”), type of internet connection (categorical: fiber optic, ADSL/VDSL, mobile data, other), frequency of connection problems (5-point scale from “never” to “very frequently”), workspace setup quality (5-point scale from “very poor” to “excellent”), and organizational support for remote work/learning (5-point scale from “none” to “comprehensive”).
• Personality Assessment (8 items): Incorporated a simplified version of the DISC personality assessment (Dominance, Influence, Steadiness, Conscientiousness), using paired forced-choice items validated in previous studies [84,85]. This abbreviated instrument has demonstrated adequate convergent validity (r = 0.72–0.81) in comparison to full-length DISC assessments in prior research while reducing respondent burden.
• Teleworking and E-Learning Experiences (20 items): Measured key outcome variables including stress levels (5-point scale from “significantly decreased” to “significantly increased”), productivity (5-point scale from “much less productive” to “much more productive”), communication quality (5-point scale from “much worse” to “much better”), educational/work engagement (5-point scale from “much less engaged” to “much more engaged”), and overall satisfaction (5-point scale from “very dissatisfied” to “very satisfied”). Specific subsections were tailored to respondents’ primary roles (student, educator, employee).
• Behavioral and Preference Assessment (8 items): Examined inclination toward hybrid or remote work models (5-point scale from “strong preference for traditional” to “strong preference for remote”), perceptions of fairness in virtual assessments (5-point scale from “much less fair” to “much more fair”), and goal attainment in virtual environments (5-point scale from “goals not achieved” to “goals exceeded”).

The complete survey instrument is provided in Supplementary Material (Table S1—Survey Instrument), including all questions, response options, and conditional logic pathways.

3.2.3. Reliability and Validity Assessment

Several approaches were employed to establish the reliability and validity of the survey instrument:

• Internal Consistency: Cronbach’s alpha coefficients were calculated for multi-item scales, with values ranging from 0.74 to 0.91, exceeding the conventional threshold of 0.70 for adequate reliability.
• Test–Retest Reliability: A subset of 42 participants completed the survey twice, two weeks apart, yielding intraclass correlation coefficients ranging from 0.68 to 0.85 across different sections, indicating satisfactory temporal stability.
• Construct Validity: Exploratory factor analysis, using principal components extraction with varimax rotation, confirmed the hypothesized factor structure for the multi-item scales, with factor loadings exceeding 0.50 and minimal cross-loadings.
• Convergent Validity: Where possible, we included established measures alongside our developed items. For example, our abbreviated DISC assessment demonstrated a strong correlation (r = 0.78) with standard DISC inventory results from participants who had previously completed formal assessments.

3.3. Sampling Strategy and Participant Recruitment

3.3.1. Sampling Framework

We employed a stratified purposive sampling approach to ensure representation across key demographic and occupational categories. While not entirely random sampling, this strategy allowed us to achieve sufficient diversity across critical dimensions while working within the practical constraints of pandemic-era data collection. The sampling framework was structured to ensure minimum representation thresholds across:

• Age groups: Minimum quotas established for each age bracket (18–24, 25–34, 35–44, 45–54, 55–64, 65+).
• Gender: Approximately balanced representation of male and female participants.
• Occupational/educational roles: Minimum quotas for university students, school students, educators, public sector employees, private sector employees, and self-employed/freelancers.
• Community sizes: Representation across rural, medium, large, and metropolitan communities.
• Geographic regions: Participants from northern, southern, eastern, western, and central areas of Greece.

Sampling quotas were monitored during data collection, with targeted recruitment efforts intensified for underrepresented categories to achieve balanced representation.

3.3.2. Recruitment Methods

Participants were recruited through multiple channels between January 2022 and March 2024:

• Academic networks: Invitations distributed through university mailing lists at eight institutions across Greece, with coordination through faculty contacts to reach student and staff populations.
• Educational authorities: Collaboration with secondary education administrators to distribute survey invitations to teachers and eligible students (ages 18+) across 24 schools from diverse geographic and socioeconomic contexts.
• Professional associations: Survey invitations were distributed through newsletters and member communications of twelve professional organizations representing diverse sectors (technology, healthcare, public administration, business, and education).
• Social media channels: Targeted advertisements on Facebook, Twitter, and Instagram using demographic filtering to reach underrepresented groups, with strategic boosting of posts to extend reach beyond primary networks.
• Community organizations: Partnerships with local community centers and municipalities to reach populations with potentially limited digital access, including the provision of paper surveys with later digital entry when necessary (accounting for 4.2% of total responses).

All recruitment materials included clear information about the study’s purpose, participation requirements, data handling procedures, and the voluntary nature of participation. No financial incentives were offered, though participants received access to anonymized summary findings if requested.

3.4. Data Collection Procedures

Data was collected primarily through a custom web application developed specifically for this study using PHP and MySQL. The application incorporated:

• Adaptive questioning: Conditional logic to present relevant questions based on participants’ roles and prior responses.
• Progress indicators: Visual feedback showing completion percentage to reduce abandonment.
• Save and resume functionality: Option to save partial responses and complete them later, reducing participant burden.
• Mobile optimization: Responsive design for completion on various devices.
• Accessibility features: Compliance with WCAG 2.1 guidelines for maximum accessibility.

Before accessing the survey, participants were presented with a detailed information sheet explaining the study objectives, confidentiality measures, data handling procedures, and their rights as participants. Electronic informed consent was obtained, and participants actively confirmed their voluntary participation and understanding of data usage before proceeding. The survey platform included the following built-in data validation features:

• Require responses to essential items (with “prefer not to answer” options where appropriate).
• Identify logically inconsistent responses.
• Flag unusually rapid completion times that might indicate inattentive responding.
• Prevent duplicate submissions through IP verification and browser cookies.

A minority of participants (4.2%) unable to access the online platform completed paper versions of the instrument, which were subsequently entered into the digital database by research staff using dual-entry verification to ensure accuracy.

3.5. Data Validation and Processing

3.5.1. Data Cleaning and Validation

Rigorous data cleaning procedures were implemented to ensure data quality:

• Completeness verification: Only responses with 100% completion of required items were included in the final dataset. Of 1384 initiated surveys, 1122 (81.1%) met this criterion and were retained for analysis.
• Response time analysis: Submissions completed in less than 5 min (calculated as two standard deviations below the mean completion time) were flagged for manual review, with 36 submissions excluded due to evidence of inattentive responding.
• Attention check analysis: Three embedded attention check items (e.g., “Please select ‘Somewhat agree’ for this question”) were used to identify careless responding, with 42 submissions failing multiple attention checks excluded from the analysis.
• Consistency verification: Logically inconsistent response patterns were identified through cross-validation of related items, with 23 submissions showing systematic inconsistencies, which were excluded from the dataset.
• Outlier detection: Univariate outliers were identified using z-scores (|z| > 3.29) for continuous response variables and examined individually. In most cases, outliers were retained as representing valid extreme experiences, but 14 cases with multiple extreme values inconsistent with overall response patterns were excluded.

After applying these exclusion criteria, the final analytic sample consisted of n = 1122 participants, representing 81.1% of the initiated surveys.

3.5.2. Data Processing

Data processing procedures included

• Variable classification: Variables were classified as follows:

  ○

  Categorical (e.g., gender, profession, and internet connection type);

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  Ordinal (e.g., Likert-scale satisfaction scores and agreement levels);

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  Interval (e.g., age brackets and community size categories).

• Scale construction: For multi-item constructs, composite scores were created following psychometric analysis, which confirmed unidimensionality and internal consistency.
• Missing data handling: Although complete responses were required for submission, occasional missing values occurred for non-mandatory items. Given their limited extent, these represented less than 0.5% of all data points and were handled through pairwise deletion in specific analyses rather than imputation.
• Variable transformation: Several variables showed moderate skewness (|skewness| > 1.0) and were transformed to approximate normal distributions where appropriate for parametric analyses. Specifically, log transformations were applied to highly skewed frequency measures, while square root transformations were used for moderately skewed satisfaction scores.

Data processing was conducted using Microsoft Excel for initial organization and IBM SPSS Statistics (v28) for more complex procedures. To ensure transparency and reproducibility, all data transformations and processing steps were documented in a detailed analysis log.

3.6. Statistical Analysis

3.6.1. Descriptive Statistics

Descriptive statistical analyses were conducted to characterize the sample and summarize key variables:

• Frequency distributions and percentages for categorical variables.
• Measures of central tendency (mean, median) and dispersion (standard deviation and interquartile range) for ordinal and interval variables.
• Visual representations, including histograms, box plots, and bar charts, to illustrate distributions.

Descriptive statistics were generated for the overall sample and for specific subgroups defined by key demographic and occupational categories to identify potential patterns and differences.

3.6.2. Segmentation Analysis

Segmentation analysis was conducted to examine response patterns across key grouping variables:

• Age: Compared experiences across six age brackets using ANOVA with post hoc Tukey tests.
• Gender: Examined differences between male and female participants using t-tests and chi-square analyses.
• Personality traits: Analyzed variations across DISC personality profiles using ANOVA and post hoc comparisons.
• Profession/role: Compared experiences across occupational categories using ANOVA and chi-square tests.
• Community size: Examined differences across four community size categories using ANOVA with trend analysis.

For each segmentation analysis, effect sizes were calculated (Cohen’s d for t-tests, Eta-squared for ANOVA, Cramer’s V for chi-square) to assess practical significance beyond statistical significance.

3.6.3. Correlation Analysis

Correlation analyses were conducted to examine relationships between key variables:

• Spearman’s rho correlation: Used for ordinal and non-normally distributed variables to assess associations between infrastructure characteristics, experiential outcomes, and demographic factors.
• Point-biserial correlation: Applied to examine relationships between dichotomous and continuous variables.
• Partial correlation: Employed to control for potential confounding variables when examining specific relationships.

Correlation matrices were generated for key variable clusters, with significance levels adjusted using Bonferroni correction to control for multiple comparisons.

3.6.4. Advanced Statistical Techniques

Several advanced statistical techniques were employed to deepen the analysis:

• Chi-square tests of independence: Assessed associations between categorical variables (e.g., community size and device usability).
• One-way ANOVA: Compared mean differences across categorical groups with post hoc tests (Tukey HSD or Games–Howell, depending on variance homogeneity) to identify specific group differences.
• Hierarchical regression analysis: Examined predictors of key outcome variables (productivity, stress, satisfaction) while controlling for demographic characteristics.
• Mediation analysis: Explored potential mediating roles of infrastructure quality and personality traits in relationships between demographic factors and experiential outcomes.

All statistical tests used a significance threshold of α = 0.05, with specific p-values reported to allow readers to apply alternative thresholds if desired. Effect sizes were reported for all significant findings to facilitate the interpretation of practical significance.

3.7. Limitations and Methodological Considerations

Several methodological limitations should be acknowledged:

• Self-reported data: Despite mitigation strategies, self-reporting remains susceptible to various biases, including social desirability and recall inaccuracy.
• Cross-sectional design: The one-time measurement approach limits causal inference and cannot capture longitudinal changes in experiences throughout the pandemic.
• Sampling limitations: While stratified purposive sampling achieved diversity across key demographics, it does not guarantee full representativeness of the Greek population.
• Geographic concentration: Although participants were recruited from various regions, the sample shows some concentration in urban areas and western Greece, potentially limiting generalizability to other regions.
• Pandemic timing: Data collection occurred after the initial pandemic shock, potentially introducing retrospective biases in recall of early pandemic experiences.
• Language and cultural context: The instrument was developed and administered in Greek, with findings potentially specific to Greek cultural contexts and pandemic response policies.

These limitations are considered when interpreting the results and are discussed more comprehensively in the limitations section of the paper.

4. Results

This section presents key findings from our analysis of teleworking and e-learning experiences during the COVID-19 pandemic. Given the comprehensive nature of our study, we focused on the most significant patterns and relationships, organizing results into three main subsections: demographic characteristics, psychological aspects, and correlation analysis. Throughout this section, we provide concise interpretations supported by visualizations and statistical evidence.

4.1. Demographic Characteristics

Our analysis of n = 1122 respondents revealed distinct patterns in how different demographic groups experienced virtual collaboration environments. Figure 1 summarizes the demographic profile of our sample, which included balanced representation across age, gender, and community size. Working-age adults (25–44 years) comprised most respondents (59.5%), followed by smaller proportions of younger adults (18–24: 15.3%) and older adults (45+: 25.2%). Gender distribution was relatively balanced (50.7% male, 44.5% female, 4.8% other/preferred not to say). The sample represented diverse community sizes, with 66.5% from urban environments (large cities and metropolitan areas) and 33.5% from medium and small communities.

The occupational landscape included university students (26.9%), private-sector employees (16.0%), public-sector workers (15.0%), educators (10.3%), and self-employed professionals (10.1%). Most participants lived with a partner and children (35.7%) or with a partner without children (26.7%), while 22.4% were single without children. Figure 2 provides an overview of infrastructure availability and emotional impacts, revealing significant disparities in remote work conditions.

Most respondents (86.9%) had suitable devices for remote activities, though only 20.2% received institutional support for equipment needs. Infrastructure challenges were more pronounced among participants from smaller communities and those in primary and secondary education. Participants with inadequate technical infrastructure reported significantly higher stress levels (r = 0.31, p < 0.001) and lower productivity (r = −0.28, p < 0.001).

4.2. Psychological Aspects and Experiential Patterns

4.2.1. Age-Related Differences in Virtual Collaboration

Age significantly influenced adaptation to virtual environments, with distinct patterns emerging across generational groups (Figure 3). Mid-career participants (25–44) demonstrated the strongest adaptation to remote environments, reporting higher satisfaction with teleworking arrangements (F(4,369) = 8.36, p < 0.001, η² = 0.08) and better communication experiences (F(4,369) = 6.93, p < 0.001, η² = 0.07) compared to other age groups. These participants effectively balanced technological proficiency with established professional routines.

Younger adults (18–24) faced greater challenges with relationship quality and communication effectiveness, with significantly lower satisfaction scores (M = 2.87, SD = 0.89) compared to their mid-career counterparts (M = 3.64, SD = 0.92, p < 0.001). Older participants (55–64) reported mixed experiences, with comfort in routine tasks but concerns about e-learning effectiveness and compensation fairness. Their adaptation reflected established work habits and reliance on traditional interactions, resulting in more polarized responses. The financial impacts of teleworking were most pronounced among younger professionals (25–34), while time savings benefits were most significant for mid-career participants (35–54).

4.2.2. Community Size and Virtual Experience

Community size emerged as a significant predictor of teleworking satisfaction and infrastructure quality (Figure 4). Residents of smaller communities preferred remote work (χ²(6) = 19.71, p = 0.003, V = 0.09), likely due to reduced commuting needs and lifestyle compatibility. Conversely, those in metropolitan areas reported the most reliable infrastructure but moderate dissatisfaction with teleworking’s impact on socialization.

Connectivity problems varied significantly by community size (χ²(12) = 70.72, p < 0.001, V = 0.15), with rural participants experiencing more frequent disruptions. Small community residents reported the highest productivity gains from teleworking and the most significant financial strain due to increased household costs. Metropolitan participants benefited from reliable infrastructure and considerable time savings from eliminated commutes, but experienced more pronounced impacts on social relationships.

4.2.3. Personality Traits and Virtual Adaptation

Personality characteristics, measured using the DISC model, significantly influenced adaptation to remote environments (Figure 5).

Participants with high conscientiousness scores demonstrated the strongest adaptation to virtual environments, reporting higher productivity (F(3,370) = 12.34, p < 0.001, η² = 0.09) and more effective communication (F(3,370) = 15.60, p = 0.016, η² = 0.08). Their preference for structure and organization facilitated adjustment to remote work conditions. Conversely, those with dominant influence traits reported greater challenges with socialization aspects of virtual environments (F(3,370) = 62.68, p < 0.001, η² = 0.14), reflecting their preference for dynamic social interactions. Steady personality types showed remarkable adaptability, maintaining consistent productivity and relationship quality in virtual settings. Those with dominant personality traits experienced the most polarized outcomes, with some thriving in autonomous environments while others struggled with limited direct feedback channels.

4.2.4. Family Status and Virtual Experience

Family composition significantly influenced teleworking experiences, with single parents facing the greatest challenges. Single parents reported significantly higher stress levels (F(4,369) = 11.27, p < 0.001, η² = 0.11) and work–life balance difficulties (F(4,369) = 8.92, p < 0.001, η² = 0.09) compared to other groups. Their dual responsibilities as caregivers and professionals created substantial strain in remote environments. Childless couples reported the most positive teleworking experiences, with higher satisfaction (M = 3.81, SD = 0.78) and productivity (M = 3.72, SD = 0.79) compared to other family configurations. Shared living participants faced unique challenges with workspace availability and internet stability. At the same time, those living with partners and children reported moderate impacts but valued the flexibility of remote arrangements for managing family responsibilities.

4.2.5. Gender Differences in Virtual Experience

Gender differences in remote work experiences were modest but significant in specific domains. Female participants reported significantly higher stress levels in teleworking environments compared to males (t(372) = 3.47, p < 0.001, d = 0.36), potentially reflecting greater household responsibilities during the pandemic. Women also reported greater financial impacts from increased household costs (t(372) = 2.93, p = 0.004, d = 0.30).

Both genders showed similar productivity and educational goal achievement patterns, with no significant differences observed. While communication effectiveness and relationship quality showed no significant gender differences, women reported slightly better adaptation to virtual communication tools (M = 3.42, SD = 0.87) compared to men (M = 3.28, SD = 0.91). However, this difference did not reach statistical significance (p = 0.089).

4.3. Factors Influencing Remote Work and Learning Outcomes

Our correlation analysis revealed key relationships between infrastructure, personality, demographic factors, and experiences with teleworking and e-learning.

Table 1. Correlation of preference, productivity, and socialization measures.

Variable Pair	Correlation Coefficient (ρ)	p-Value	n
Preference for Teleworking/e-Learning ↔ Productivity	0.362	<0.001	374
Preference for Teleworking/e-Learning ↔ Educational Goal Achievement	0.378	<0.001	742
Impact on Socialization ↔ Teleworking/e-Learning Stress	0.229	<0.001	374
Impact on Socialization ↔ Productivity	0.173	<0.001	374
Impact on Socialization ↔ Educational Goal Achievement	0.167	<0.001	742

presents the correlations between preference for teleworking/e-learning, productivity, and socialization measures.

Those who preferred remote modalities reported significantly higher productivity and educational goal achievement, suggesting that personal alignment with virtual environments has a strong influence on outcomes. Reduced socialization was weakly but significantly associated with increased stress and lower productivity, implying that social connections remain essential even in virtual environments.

Table 2. Correlation of communication, relationships, and infrastructure measures.

Variable Pair	Correlation Coefficient (ρ)	p-Value	n
Communication Effectiveness ↔ Relationship Quality	0.387	<0.001	1116
Communication Effectiveness ↔ Teleworking/e-Learning Stress	0.256	<0.001	374
Preference for Teleworking/e-Learning ↔ Communication	0.324	<0.001	1116
Internet Quality ↔ Productivity	0.134	0.009	374
Internet Quality ↔ Educational Goal Achievement	0.117	0.001	742
Internet Quality ↔ Preference for Teleworking	0.100	<0.001	1116

presents correlations related to communication effectiveness, relationship quality, infrastructure quality, and stress.

Communication effectiveness was moderately correlated with relationship quality and preference for remote modalities, highlighting the central role of effective communication in virtual environments. While infrastructure quality showed significant associations with productivity and stress, the modest correlation coefficients suggest that other factors beyond infrastructure quality influence remote work and learning outcomes. The relationship between community size and infrastructure quality was more pronounced (χ²(12) = 70.72, p < 0.001, V = 0.15), with larger communities reporting better connectivity.

4.3.1. Gender, Age, and Work–Life Balance

Table 3. Correlations and associations of demographics with work–life balance and preferences.

Variable Pair	Statistical Method	Test Value	df	p-Value	Effect Size
Gender ↔ Preference for Teleworking/e-Learning	Pearson Chi-Square	4.201	2	0.122	V = 0.061
Age ↔ Preference for Teleworking/e-Learning	Pearson Chi-Square	90.364	12	<0.001	V = 0.201
Gender ↔ Work–Life Balance	Pearson Chi-Square	3.779	4	0.437	V = 0.071
Age ↔ Work–Life Balance	Pearson Chi-Square	11.695	8	0.165	V = 0.125
Work–Life Balance Changes ↔ Household Cost Impact	Spearman’s rho	0.024	-	0.640	-
Work–Life Balance Changes ↔ Time Savings	Spearman’s rho	0.224	-	<0.001	-
Fairness of Compensation ↔ Household Cost Impact	Spearman’s rho	0.094	-	0.123	-

presents the correlations and associations related to demographic factors, work–life balance, and teleworking preferences.

Gender did not significantly impact preference for teleworking or work–life balance, while age showed a moderate association with teleworking preferences but not with work–life balance. Time savings were weakly but significantly correlated with improved work–life balance, while household cost impacts and compensation fairness showed no significant relationship with changes in work–life balance.

4.3.2. Infrastructure and User Experience

Table 4. Infrastructure impact on user experience.

Variable Pair	Statistical Method	Test Value	df	p-Value	Effect Size
Connection Type ↔ Connectivity Issues	Pearson Chi-Square	83.195	12	<0.001	V = 0.157
Community Size ↔ Internet Connectivity Issues	Pearson Chi-Square	70.723	12	<0.001	V = 0.145
Community Size ↔ Productivity	Pearson Chi-Square	27.200	9	0.001	V = 0.156
Community Size ↔ Preference	Pearson Chi-Square	19.711	6	0.003	V = 0.094
Device Suitability ↔ Educational Goal Achievement	Spearman’s rho	0.091	-	0.013	-
Device Suitability ↔ Productivity	Spearman’s rho	−0.042	-	0.418	-
Connectivity Issues ↔ Productivity	Spearman’s rho	0.134	-	0.009	-
Connectivity Issues ↔ Educational Goal Achievement	Spearman’s rho	0.117	-	0.001	-

summarizes the impact of infrastructure elements on user experiences in virtual environments.

The type of internet connection significantly influenced connectivity issues, with fiber optic connections reporting fewer problems than ADSL/VDSL and mobile data. Community size is significantly associated with internet reliability, productivity, and preferences for teleworking. Device suitability was weakly related to educational goal achievement but showed no significant relationship with productivity in teleworking contexts.

4.3.3. Personality and Experience Factors

Table 5. Personality and Experience Factors.

Variable Pair	Statistical Method	Test Value	df	p-Value	Effect Size
DISC Test ↔ Educational Goal Achievement	Pearson Chi-Square	24.667	12	0.016	V = 0.105
DISC Test ↔ Productivity	Pearson Chi-Square	13.244	9	0.152	V = 0.109
DISC Test ↔ Stress Levels	Pearson Chi-Square	7.290	6	0.295	V = 0.099
DISC Test ↔ Work–Life Balance	Pearson Chi-Square	4.141	6	0.658	-
DISC Test ↔ Communication Effectiveness	Pearson Chi-Square	15.595	6	0.016	V = 0.084
DISC Test ↔ Relationship Quality	Pearson Chi-Square	4.783	6	0.572	V = 0.046
DISC Test ↔ Socialization Shifts	Pearson Chi-Square	62.682	12	<0.001	V = 0.137
DISC Test ↔ Preference for Teleworking/e-Learning	Pearson Chi-Square	14.930	6	0.021	V = 0.082

presents the associations between personality traits and various experience factors in virtual environments.

Personality traits showed significant but weak associations with educational goal achievement, communication effectiveness, socialization shifts, and teleworking preferences. The strongest relationship was between personality and socialization shifts (V = 0.137), suggesting that different personality types adapted their social behaviors differently in virtual environments. Personality did not significantly influence stress levels, work–life balance, or relationship quality, suggesting that other factors may have a substantial impact on these outcomes.

Figure 6 presents a comprehensive correlation heatmap of key variables, visualizing the interconnections between different aspects of remote work and learning experiences.

The strongest correlations appeared between preference for remote modalities and productivity/goal achievement (ρ = 0.36–0.38), and between communication effectiveness and relationship quality (ρ = 0.39). Moderate correlations emerged between socialization and stress (ρ = 0.23), while infrastructure factors showed weaker but significant associations with outcome variables (ρ = 0.10–0.13).

Figure 7 illustrates how personality traits influenced adaptation across key remote work and learning dimensions.

Conscientiousness and steadiness traits were associated with stronger adaptability and goal orientation in virtual environments, while dominance and influence traits showed more variable outcomes, particularly in collaboration and stress dimensions.

These findings collectively demonstrate that remote work and learning experiences were shaped by a complex interplay of demographic, psychological, and infrastructural factors, with individual preferences and communication effectiveness emerging as particularly influential determinants of success in virtual environments.

4.4. Key Findings Summary for Policy-Makers

To assist policy-makers and practitioners in interpreting our findings, we provide this comprehensive summary that translates statistical results into actionable policy guidance. The following analysis bridges the gap between statistical evidence and practical decision making by evaluating each finding for both statistical strength (the reliability of the evidence) and practical importance (the magnitude of its real-world impact).

Table 6. Key findings summary for policy decision making.

Research Finding	Statistical Evidence	Effect Size and Practical Importance	Real-World Meaning	Policy Priority Level	Recommended Action
Rural-Urban Digital Divide	χ2(12) = 70.72, p < 0.001	V = 0.145—Medium-High (14% of connectivity variation explained)	Rural residents experience significantly more connectivity problems, directly limiting e-democracy participation	HIGH PRIORITY	Immediate rural broadband expansion and infrastructure investment
Communication Quality Impact	ρ = 0.387, p < 0.001	Medium-Large effect (~15% of relationship success explained)	Better virtual communication tools substantially improve democratic engagement and satisfaction	HIGH PRIORITY	Invest in advanced communication platforms and user training programs
Age-Related Digital Adaptation	F(4,369) = 8.36, p < 0.001	η2 = 0.08—Medium (8% of satisfaction variance explained)	Mid-career adults (25–44) adopt best; younger and older adults face distinct challenges	MEDIUM PRIORITY	Develop an age-responsive interface design and targeted support
Personality and Virtual Socialization	χ2(12) = 62.68, p < 0.001	V = 0.137—Medium (~14% of socialization patterns explained)	Different personality types require different virtual interaction approaches	MEDIUM PRIORITY	Include diverse communication styles and interaction options in platform design
Family Status and Stress	F(4,369) = 11.27, p < 0.001	η2 = 0.11—Medium (11% of stress variance explained)	Single parents experience significantly higher stress in virtual environments	MEDIUM PRIORITY	Develop family-friendly virtual participation options and childcare support
Gender and Virtual Stress	t(372) = 3.47, p < 0.001	d = 0.36—Small-Medium effect	Women report higher stress levels in teleworking environments	MEDIUM PRIORITY	Address work–life balance and household responsibility inequities
Device Adequacy Impact	ρ = 0.091, p = 0.013	Small effect (<1% of achievement explained)	Device quality has minimal impact compared to infrastructure and communication factors	LOW PRIORITY	Monitor but focus resources on higher-impact interventions
Organizational Support	No significant correlation with major outcomes	Minimal practical impact	Generic organizational support shows limited effectiveness	LOW PRIORITY	Replace generic support with targeted interventions based on the above findings

below presents our key research findings, organized by their potential to inform evidence-based policy decisions. Each row represents a significant finding from our analysis of 1122 participants’ experiences with virtual collaboration and e-democracy tools during the pandemic era.

The statistical evidence column reports the specific test statistics and p-values, indicating the reliability of each finding. The effect size and practical importance column translates these statistics into measures of real-world impact, helping readers understand not just whether a relationship exists but how meaningful it is for policy purposes. The Real-World Meaning column explains the practical implications of each finding for the implementation of e-democracy. At the same time, the policy priority level provides an evidence-based ranking to inform resource allocation decisions. Most importantly, the recommended action column translates our empirical findings into specific, actionable steps that policy-makers can implement. These recommendations are directly proportional to both the statistical strength of our evidence and the practical magnitude of expected improvements, ensuring that the most robust findings receive the highest priority for implementation.

5. Discussion

This section synthesizes observed trends, segmentation insights, and statistical relationships to provide an understanding of how virtual environments influence user experiences in remote work and learning contexts. Here, we synthesize observed trends, segmentation findings, and statistical correlations to show how virtual environments shape user experiences in remote work and learning environments. Descriptive analysis revealed data trends that guided our research. Infrastructure is a significant aspect: participants’ experiences differed based on internet quality and device sufficiency. Fiber optic connections were consistently associated with fewer connectivity problems. Stress was reported overall. Having a proper and dedicated space at home was found to act as a de-stressor. Teleworking/e-learning attitude has been mixed. They were driven by age and sex demographic variables, with younger respondents appearing more adaptable. Socialization patterns have shifted significantly, and maintaining relationships in a virtual setting is a challenging goal. Communication effectiveness and relationship quality were the critical factors in workplace and classroom environments, indicating that interpersonal communication in computer-mediated settings was necessary [85].

Segmentation analysis added more detail. Factors such as preference for teleworking, for example, or partial factors like stress or productivity, were influenced by age and gender in some ways. Relationship quality improved to a certain extent with larger communities, probably due to more significant opportunities for contact. Poor internet connection quality or inadequate equipment can increase stress and decrease productivity, highlighting infrastructural inequalities. The DISC personality traits affected socialization and preferences for virtual settings but had fewer effects on productivity and stress [86].

The correlation analysis validated and refined these trends. Internet quality, device sufficiency, and workspace availability were strongly correlated with productivity, stress, and the attainment of educational goals. Fiber optic internet outperformed other internet types by a significant margin. Socialization exhibited a moderate relationship with stress. DISC traits moderately influenced individuals’ shifts in socialization. Communication effectiveness was strongly related to relationship quality. Connectivity issues are a significant obstacle to smooth communication. DISC traits correlate moderately but significantly with teleworking preferences and socialization. Still, their practical impacts proved to be constrained compared to infrastructure and team dynamics [87,88].

Infrastructure appears as a sheer enabler. A reliable internet connection, proper equipment, and a dedicated work area are essential in reducing stress and enhancing productivity in remote work arrangements. Socialization and communication were powerful drivers: overall, socialization affects stress, while work-specific communication has a substantial impact on the quality of relationships and collaboration [89,90]. Personality plays a peripheral role: the traits of DISC provide some insight into adaptability and preference, but are secondary to exogenous circumstances. There were demographic diversions, with gender and age exhibiting minimal but detectable trends [91,92]. For instance, older respondents enjoyed teleworking, and younger ones found it more stressful to balance work and life [93,94].

The findings reflect the complicated nature of virtual workplaces. Infrastructure, social processes, and individual characteristics ultimately shape the experience. Practical recommendations include addressing the requirements of infrastructural transformation to remove the gaps in connectivity and access to suitable devices and home workplaces. Enabling general socialization is crucial, especially for individuals who struggle to adapt to virtual work. Refining communication tools and encouraging team-building exercises will solidify the relationships at the workplace [95,96]. Developing remote work and learning systems that consider demographic and personality variations and prioritizing the function of external enablers as a key success driver is recommended.

5.1. Key Insights—Interpretation of Results

The research revealed how infrastructure, personality, occupation, location, and demographics influence teleworking and e-learning experience. The results emphasize that infrastructure is the most influential factor. Internet connectivity and the availability of a home workspace have a significant impact on productivity, stress, and work–life balance. Reliable internet connections through fiber optic connections provided fewer issues with connectivity and more positive results, reflecting a continuing digital divide between urban and rural locations.

Personality traits measured by the DISC model had small but statistically significant influences. Specifically, Influence and Conscientiousness traits were mildly related to communication effectiveness, as well as a preference for teleworking and e-learning. The effects of personality traits on the magnitude of stress, work–life balance, and relationship quality were minor. These results suggest that although personality can influence certain aspects of remote work, it is more often supplanted by structural and organizational influences [97]. The other two dominant dimensions were Communication and Socialization. Good communication and workplace relationships were moderately correlated with higher satisfaction and improved productivity. Lower socialization was associated with weaker socialization but was significantly related to higher stress and lower goal accomplishment. These results confirm that interpersonal relationships do need to be developed in the virtual space.

The demographic factors of gender and age did not significantly impact the teleworking experiences, except for age-related differences in preference for remote settings. This thus demonstrates that technological nimbleness and individual circumstances can overcome broad demographic characteristics.

5.2. Implications for E-Democracy and Digital Governance

While our findings initially focused on virtual collaboration in work and educational settings, they have profound implications for e-democracy and digital governance in the post-pandemic era. The pandemic-forced transition to virtual environments offers valuable insights into the requirements for effective digital citizen participation.

5.2.1. Infrastructure as the Foundation of Digital Citizen Participation

Our research demonstrated that infrastructure quality is the primary determinant of effective digital engagement. The significant correlation between internet quality and productivity (ρ = 0.134, p = 0.009), along with the strong association between community size and connectivity issues (χ²(12) = 70.72, p < 0.001, V = 0.145), underscores a fundamental challenge to inclusive e-democracy—when physical barriers to participation are removed through digitalization, they are often replaced by technological barriers that follow similar socioeconomic and geographic patterns.

This finding has profound implications for e-governance, suggesting that genuine democratic participation requires addressing fundamental infrastructure inequalities before implementing sophisticated e-democracy platforms. The digital divide observed in our study mirrors what researchers have termed the “digital inverse care law” [67], where those most in need of digital services are often least able to access them. Our data confirms that these disparities tend to disadvantage already marginalized populations, particularly those in rural communities and lower socioeconomic brackets.

5.2.2. Communication Quality as the Mediator of Democratic Engagement

Our analysis revealed a strong correlation between communication effectiveness and relationship quality (ρ = 0.387, p < 0.001), indicating that technical communication capacity does not automatically translate into substantive engagement. This finding has critical implications for e-democratic processes, suggesting that providing digital platforms without attention to communication quality may lead to superficial and unengaging participation. Participants with high conscientiousness personality traits demonstrated significantly better communication outcomes (F(3,370) = 15.60, p = 0.016, η² = 0.084), suggesting that e-democratic systems may inadvertently favor certain cognitive and behavioral styles. This highlights the need for e-democracy platforms to accommodate diverse communication preferences and behaviors, ensuring inclusive participation.

These findings aligned with previous research on deliberative e-rulemaking [62], which found that increased digital participation did not necessarily improve decision quality. Our study provides empirical evidence for why this disconnect might occur—when digital platforms fail to support effective communication and relationship building, increased participation may be nominal rather than substantive.

5.2.3. Demographic and Psychological Factors in E-Democratic Inclusion

Our segmentation analysis revealed significant variations in how different demographic and personality groups experienced virtual environments. Mid-career professionals (25–44) demonstrated the strongest adaptation to virtual environments, while both younger adults (18–24) and older participants (55+) reported greater challenges with engagement. These findings are concerning from an e-democracy perspective, as they suggest digital governance systems may systematically favor certain age groups, potentially excluding younger and older citizens from meaningful participation.

Similarly, our finding, where personality traits significantly influenced adaptation to virtual environments (with dominant and influence types facing greater challenges), suggests that e-democratic systems designed with a “one-size-fits-all” approach risk reproducing or amplifying existing participation inequalities. This aligns with research on senior citizen participation in European policy-making [63], which documents opportunities and barriers to digital engagement for specific demographic groups.

5.3. Limitations of the Study and Methodological Considerations

Several methodological limitations must be acknowledged when interpreting our findings:

5.3.1. Threats to Internal Validity

• Self-reported data limitations: Despite implementing multiple validation strategies (anonymous collection, attention checks, consistency verification), our reliance on self-reported measures introduces potential social desirability and recall biases. This may have particularly affected stress and productivity measures, which could not be objectively verified.
• Cross-sectional design constraints: The one-time measurement approach limits causal inference regarding the relationships between infrastructure, personality traits, and e-democracy outcomes. The identified associations may reflect bidirectional or complex relationships that are not fully captured in our analysis.
• Measurement validity: While we utilized established scales where possible, some constructions (particularly those related to e-democratic participation) required newly developed measures with limited prior validation. The abbreviated DISC assessment, while showing adequate convergent validity with full-length assessments (r = 0.72–0.81), may not capture the full complexity of personality dimensions.
• Potential confounding variables: Despite our efforts to control for key demographic and contextual factors, unmeasured variables (such as pre-pandemic attitudes toward technology or political engagement) may have influenced the relationships observed.

5.3.2. Threats to External Validity

• Sampling limitations: While our stratified purposive sampling achieved diversity across key demographics, it cannot guarantee full representativeness of the broader population. The slight overrepresentation of urban participants (66.5% vs. approximately 60% in the general Greek population) may have affected our assessment of infrastructure challenges.
• Pandemic context specificity: Data collection occurred after the initial pandemic shock, potentially capturing experiences specific to later adaptation stages rather than the whole pandemic experience. This timing may limit the generalizability of the findings to future non-emergency contexts of e-democracy implementation.
• Geographic and cultural context: Our findings are situated within the specific Greek context, including its digital infrastructure landscape, pandemic response policies, and cultural attitudes toward technology and governance. Caution should be exercised when generalizing to other national contexts with different infrastructure conditions or governance traditions.
• Selection bias: Despite efforts to include digitally marginalized populations through paper surveys (4.2% of responses), our methodology inherently favored participants with some level of digital access and comfort, potentially underrepresenting those most severely affected by digital divides.

5.3.3. Mitigation Strategies

We implemented several strategies to address these validity threats:

• Triangulation of measures: Key constructs were assessed through multiple question formats to enhance reliability and validity.
• Mixed-mode data collection: Including paper surveys for participants with limited digital access.
• Statistical controls: Demographic variables were systematically controlled in analyses to minimize confounding effects.
• Effect size reporting: All findings were assessed not only for statistical significance but for practical significance through appropriate effect size metrics.
• Transparency in limitations: Explicitly acknowledging these constraints to prevent overinterpretation of findings.

Despite these acknowledged limitations, the research provides valuable insights into the factors influencing virtual collaboration experiences, forming a valid foundation for future research and practical applications in e-democracy.

5.4. Implications for Future Use of E-Collaboration Tools and Future Work

These results have practical implications for the development and implementation of e-collaboration technology. A strong infrastructure remains key: investments in high-speed internet and digital equity must be pursued to help the heterogeneous users. Supplementing virtual communication platforms with utilities that allow frictionless interaction will attenuate socialization issues and enhance team dynamics. Personalization based on personality traits can also improve the remote experience. Customizable features that accommodate different communication styles or focus on structured environments can help users translate their preferences into digital processes more efficiently. Adding stress-mitigating features, such as workload tracking and mental wellness support modules, can also address concerns about well-being. Organizations should adopt an integrated strategy that balances infrastructure development with interpersonal and technological measures. Practical virtual communication training, team building, and support for work–life balance could be vital parts of a thriving e-collaboration environment.

Future studies should prioritize longitudinal designs that capture the emerging trends in remote work and learning. Diversifying the dataset by including different cohorts and underrepresented populations can enable greater generalization and deeper insight into the complexity of e-collaboration experiences. Examining how external variables, such as organizational culture and digital tool affordances, influence the outcomes could enrich the understanding of virtual environments.

Building on our findings and acknowledging the limitations above, we propose several high-priority directions for future research:

• Longitudinal studies of e-democratic engagement: Track how citizen participation in digital governance evolves beyond the pandemic crisis context into normalized practice.
• Comparative, cross-cultural analysis: Examine how different cultural contexts, governance traditions, and infrastructure landscapes shape e-democratic participation.
• Platform-specific evaluations: Assess how specific technological design choices within e-governance platforms influence participation patterns across demographic groups.
• Mixed-method approaches: Complement quantitative assessments with in-depth qualitative analysis of participation quality and democratic deliberation in virtual environments.
• Intervention studies: Test the effectiveness of the policy recommendations outlined above through controlled implementation and evaluation.

By addressing these research priorities, scholars and policy-makers can develop more sophisticated frameworks for implementing inclusive e-democratic systems that overcome the barriers identified in our study.

5.5. Privacy and Data Security

When the COVID-19 outbreak led to a general lockdown in the Western world last spring, governments and businesses did not promptly grasp the magnitude of the shift from physical to digital environments, both in practical and strategic aspects. Before the 2020 COVID-19 crisis, labor markets and urban locales were changing due to new technologies and innovative practices, which altered the need for space and equipment while shifting many office tasks to homes or third spaces. The pandemic significantly accelerated these processes, leading to radical reorganizations of professional activities and physical settings, with broader implications for city life, social relations, and the potential emergence of new forms of socio-spatial disparity [98]. However, when the concept of projection towards the digital was first and foremost embodied in teleworking, it commanded an immediate acknowledgment of the peculiar privacy concerns that might accompany it. Indeed, the sudden, large, and unexpected transfer of bureaucratic personnel to domestic environments has provoked awareness of the issue due to the potential exposure of sensitive information or trade secrets to unfamiliar hazards, alongside a reevaluation of the emotional and psychological benefits of privacy in traditional office settings [99,100]. Privacy concerns were also fueled by fears of surveillance, as well as worries about data protection [101,102]. While a consensus matured within the expert community on the experience of agreeing to counter-act the digital divide, the related discussions crucially excluded the fundamental dimensions of privacy and data security [103,104,105,106].

Education and work environments in the pandemic era heavily shifted to online formats. However, the digital divide concerning the education and social mobility of students, as well as the economic and emotional well-being of workers, remained on the discussion table with the new course of the pandemic norms [107,108]. The highlights of this collection read about shattered fears, secret puzzles, unjust inequalities, manipulative invitation approaches that developed and prospered more than ever in the face of the crisis, insecure learning environments, entrepreneurial excitement in difficult times, boom economy in specific sectors, and existing practices that have been in the margins until today [109,110]. Digital inequality during physical distancing is likely to shift as people rely on online alternatives to in-person activities, such as work, school, and communication. Vulnerable groups who are least prepared to engage in these online activities are likely the least prepared to manage these shifts [111,112]. This may prevent them from forming daily habits that make these activities more likely to occur. The COVID-19 pandemic’s digital transformation exposed critical vulnerabilities in privacy and data security infrastructure while simultaneously amplifying existing social and economic inequalities. The rapid shift to online platforms created a two-tiered system where those with adequate digital resources and literacy could maintain productivity and privacy. At the same time, vulnerable populations faced increased exposure to both digital exclusion and privacy risks. Moving forward, addressing these disparities requires comprehensive policy interventions that ensure equitable access to secure digital technologies while establishing robust privacy protections that safeguard all users, particularly those most vulnerable to exploitation. The pandemic’s legacy in digital transformation must include lessons about the fundamental importance of building inclusive, secure, and privacy-respecting digital infrastructure that serves all members of society equally [113,114,115,116,117].

6. Conclusions

In conclusion, this study provides a comprehensive analysis of how virtual collaboration tools influence digital citizen engagement, communication, and e-democratic participation in the post-pandemic era. The findings reveal that while digital platforms increased accessibility, flexibility, and participation in remote work, education, and governance, they also introduced critical challenges related to socialization, trust-building, and digital infrastructure disparities. The effectiveness of these tools was significantly shaped by demographic factors (age, gender, family status), personality traits, community size, and access to technology, resulting in differentiated experiences within virtual environments.

The analysis highlights key patterns: mid-career professionals and urban residents adapted more effectively to remote collaboration, while younger, older, and rural populations faced greater difficulties due to infrastructure gaps and socialization barriers. Personality traits also played a defining role, with conscientious and steady types benefiting the most from structured remote environments. In contrast, dominant and influential types struggled with the lack of dynamic social interactions. Work–life balance, stress levels, and digital fluency further influenced engagement levels, with single parents and individuals in shared accommodations facing higher stress and financial burdens.

While virtual platforms facilitated democratic engagement by enabling participation beyond physical constraints, they lacked the depth necessary for meaningful deliberation, thereby reducing the quality of engagement and exacerbating digital inequalities. The research underscores the need for policy-driven improvements, including infrastructure expansion, adaptive digital governance models, and enhanced virtual communication strategies. Future studies should investigate long-term behavioral shifts, platform-specific innovations, and the role of AI-driven tools in fostering inclusive and participatory e-democracy. Addressing these structural and behavioral challenges is crucial to ensuring that digital transformation leads to a more equitable and engaged digital society in the post-pandemic world.