Keywords

E-participation, Social Capital, Social Interaction, Digital Governance, Trust in Government, Civic Engagement, Public Policy, Online Communities, Social Media.

Introduction

Citizen engagement in the process of decision-making with the local governments is a fundamental pillar of modern democracy. In recent years, Sæbø et al., (2008) e-participation has emerged as a tool that allows citizens to involve in the consultation, deliberation, & decision-making processes by digital platforms. According to Kipenis & Askounis, (2016), this shift towards digital engagement is seen as a means to strengthen democratic practices and improve government transparency. Research indicates that e-participation can yield favorable results for communities, particularly when citizens actively engage with the system on a regular basis (Naranjo-Zolotov et al., 2019). However, since participation is entirely voluntary, citizens must be motivated to use e-participation platforms.E-participation programs have adopted a range of ICT tools, such as email communication, online surveys, virtual discussion forums, group support systems, and online chat platforms which reflect significant progress in implementing e-participation tools, local governments still face challenges in fully engaging citizens at the decision-making level because, the adoption and success of e-participation, despite technological advancements and user-friendly interfaces, do not solely depend on information technology factors. Rather, they are also influenced by various socio-cultural factors, including the social capital of the community.

Social capital, which refers to trust, social networks, and civic engagement within a community that encourages collaboration and group efforts, plays a significant role in shaping citizen participation in both offline and online environments. Social capital exists in the social realm, referring to the “resources embedded in networks between individuals and their communities” (Putnam, 1994; Wang et al., 2018). In cities, where communities form the social structure, social capital is essential for engaging individuals in e-participation (Acedo et al., 2017). Local governments can enhance citizens' trust by fostering social capital through online civic engagement (Warren et al., 2014). E-participation provides a platform for such engagement.

Thus, even if the value of social capital in promoting civic involvement is becoming more widely acknowledged, limited research has focused on its impact on the usage of e-participation tools (Gil de Zúñiga, 2012). The aim of this research study is to address this gap by examining how social capital influences the intention to use e-participation platforms. By integrating social capital theory, we aim to explore the role of community trust, social networks, & civic norms in promoting e-participation usage.

With this study, we seek to advance the understanding of the variables that influence citizens’ engagement with e-participation tools and the implications for enhancing democratic processes at the local government level.

The present study contributes to the theoretical comprehension of e-participation. First, it addresses the lack of research on the role of citizens' social capital in e-participation (Gil de Zúñiga, 2012). Second, the study offers valuable insights for local governments implementing e-participation platforms, based on the evaluation of the proposed research model.

Review of Literature

E-Participation

The definition of "e-participation" is technological usage to enable interactions between official politics or administration and civil society so as to promote citizen participation in decision-making processes (Sæbø et al., 2008). As per United Nations, (2014) three levels make up the concept e-participation: “e-information”, “e-consultation” & “e-decision-making”. Active engagement as well as involvement of citizens in the long-term scenario are necessary for the success of the highest level of the e-participation model, decision-making. E-participation relies heavily on the adoption of technology by communities. (Gil de Zúñiga, 2012; Warren et al., 2014) found that the extent to which citizens engage with e-participation platforms is influenced by the social capital within that community. Citizens' decisions to participate in online platforms are often a result of the trust and relationships they have within their social networks.

Social Capital

According to Oxford Dictionary on Lexico.com, social capital includes interpersonal connections, mutual understanding, trust, collaboration, reciprocity, common norms, shared values, and a sense of identity. Researchers have discussed social capital from various perspectives,(Siisiäinen, 2000; N. Lin et al., 2001) defining it as diverse resources that can help others or as embedded resources within a society that members can share. Putnam's theory (1993), social capital is made up of things like networks, norms, and social trust that make it easier for people to work together and coordinate their efforts for everyone's gain. According to Machalek & Martin, (2015), social relationships may lead to the development and accumulation of human capital.

E-participation is a tool for creating online communities, and social capital research sheds light on the variables affecting citizens' usage of these sites. Social capital theory suggests that the level of social capital across a community significantly influences community formation effectiveness (Sæbø et al., 2008). Further Putnam et al., (1993) highlights social capital as a pivotal asset for ministries to implement policies, deliver services, and increase innovation legitimacy. Social networks, civic standards and trust are linked to citizens' usage of e-participation platforms. Social capital is discussed in literature as a set of dimensions.

Nahapiet & Goshal, (1998) described social capital is “the sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit”. Social ties, shared objectives, and trust are indicators of a citizen's social capital (Sauk et al., 2013). Online communities are a crucial element of citizen-initiated e-participation, as noted by (Lee & Kim, 2014), who also argued that the literature on social capital can provide a useful theoretical framework for comprehending the behaviour of e-participants. Additionally, Warren et al., (2014) noted that citizens' perceptions of high-quality responsiveness during the e-participation process, their lack of offline social ties, their volunteer experiences, and their trust in government all positively influence their active online engagement. The characteristics of social capital (bonding, bridging, and linking) and psychological empowerment were examined by Babaei et al., (2012). They discovered a positive correlation between social capital and psychological empowerment. Poor people can engage in local governance, get basic services, and have greater access to resources and economic opportunities through social capital. Choi & Song, (2020) elaborated that E-participation is more frequently used by individuals if they have higher levels of ownership, commitment, and trust in their community. But lack of trust in the government may contribute to low levels of online involvement. Research has assessed social capital as a second-order construct (Sauk et al., 2013), which may limit the analysis in depth of the effects of each social capital dimension on e-participation usage behavior. Thus, the current study focuses on social capital at the individual level because e-participation is a behaviour that belongs to individuals as well as it is an antecedent to behaviours like citizen involvement (Gil de Zúñiga, 2012). In the current study, the four dimensions of social capital, namely trust in the government, identification, interaction, and reciprocity is taken to study the citizens' intentions to use e-participation. The dimensions of social capital are discussed below:

The Dimensions of Social Capital are Discussed Below

1)Trust in Local Government: In the framework of E-participation, citizens communicate directly or indirectly with other community members and the government. With e-participation, the government is ultimately responsible for putting the results of citizen-involved consultation or decision-oriented procedures into action. For this reason, trust in the government is essential to increasing the desire to employ/use e-participation (Teo et al., 2008).

H₁: “Trust in local government will have a positive effect on the intention to use e-participation”.

2)Identification:  It can be characterised as a citizen's sense of community belonging (Zhao et al., 2012) and is falls under the relational dimension of social capital, which states that citizens are more likely to utilise e-participation or to use it more often to give back to the community when they have a sense of identification with it. According to Chang & Chuang, (2011), the individual`s feeling of identification has a favorable impact on knowledge sharing behaviour in terms of quality and quantity.

H₂: “Identification will have a positive effect on the intention to use e-participation”.

3)Interaction: In the words of e-participation, citizens can interact with each other and with the government. As per (Chang & Chuang, 2011; Lin & Lu, 2011b), interaction has a favorable impact on both the continuous intention to use online social networks and on the quality of knowledge sharing behavior respectively. Despite the possibility that thousands of citizens`s may participate in e-participation even though they are strangers to each other, citizen`s can use a variety of e-participation platforms to promote their interests in using e-participation tools that are approved by local governments for consultation and decision-making sessions.

H₃: “Interaction will have a positive effect on the intention to use e-participation”.

4)Reciprocity: In terms of e-participation usage, it refers as “sense of mutual support among participatory community members”(Wellman, B., & Gulia, 1999), members may contribute to e-participation for a common objective despite not knowing one another, which could foster a sense of reciprocity among the community as a whole.

H4: “Reciprocity will have a positive effect on the intention to use e-participation”.

The study explores the impact of social capital theory on e-participation, highlighting the importance of understanding the variables influencing citizens' online participation. It aims to develop better E-participation by examining how social capital elements affect individual behaviors and the influence of social capital on citizen intention to use E-participation Figure 1.

Research Methodology

Measurement

For this present study, a cross-sectional study was considered most suitable as the researcher attempted to access the influence of Social Capital on citizens’ “intention to use” e-participation

In the present study, researcher designed structured questionnaire to record the opinion of experts. The experts included students, self-employed, government employees, private employees, retired and unemployed from the chosen five districts of Punjab (i.e Ludhiana, Patiala, Bathinda, Amritsar & Jalandhar). The sampling unit will consist of any individual aged 18 years or older residing in the districts being studied in Punjab. The reason for selecting this age range as the sampling unit is that individuals in this age bracket are believed to be mature and knowledgeable.

A sample size of 450 citizens is determined based on the total number of voters, a 95% - “confidence level” & a 5% - “margin of error”. As of the Punjab Legislative Assembly election in February 2022, a total of 21499804 voters were included in the sample calculation. A sample of 90 respondents was selected from each district to make a total sample of 450 from Punjab state. To remove the bias in choosing a sample, multistage quota sampling was used. Partial least squares structural equation modelling method (PLS-SEM)(Hair et al., 2019) was used to evaluate the research model.

Data Analysis and Results

To assess the research model, Hair et al., (2019) PLS-SEM utilizing Smart PLS was used. Fornell & Bookstein, (1982) PLS, a SEM technique based on an iterative strategy that maximizes the explained variance of endogenous components, was first developed by (Wold, 1974, 1985). PLS-SEM operates much like a multiple regression analysis (Hair et al., 2011).

In this study, a dependent variable is intention to use, whereas an independent variable is social capital, which further incorporates four variables i.e. Identification, Reciprocity, Social Interaction & Trust in Government. The analysis involved in this study is testing of adjusted R2 & structural model path coefficients. Hence, a structural model was evaluated to examine the hypothesized relationships from H1 to H4 within the model.

Measurement Model

The study focuses on assessing the reflective outer model of a theoretical model, which includes reflective constructs. Reflective indicators are representative items in a construct's conceptual domain, interchangable and strongly correlated. Two stages of testing are the validity test and the reliability test. The validity test ensures that the instruments accurately represent latent variables and two kinds of validity tests that must be performed:  convergent and discriminant validity tests. 

“Convergent validity is the extent to which the construct converges to explain its items' variance”(Hair Jr. et al., 2014). If the outer loading of a variable is more than 0.7, it is considered to reflect its latent variable, and if the AVE threshold value is 0.5, it indicates strong convergent validity (Hair Jr. et al., 2014). As per Henseler et al., (2015), the determination of discriminant validity is assessed by examining the Heterotrait-Monotrait Ratio (HTMT). In  case of HTMT, Heterotrait-Monotrait Ratio needing to be less than the 0.9 threshold (Henseler et al., 2015).

In the present study, Hair et al., (2019), the AVE for all latent constructs exceeds the value 0.6, which is well above the required minimum level of 0.50. Therefore, the measures of all the constructs can be said to have high level of convergent validity. Furthermore, it is regarded as highly acceptable for observed variables to have an outer load of 0.7 or higher. As per Table 1, outer loads varied from 0.72 to 0.91. This indicates that the structure represents more than half of the indicator's variance, resulting in acceptable item validity. Table 2, highlights the HTML values which is well above the required minimum level i.e. 0.9. As a result, the model has strong discriminant validity.

Table1 Htmt Ratio (Discriminant Validity)
Constructs	R	SIN	TIG	ID	IU
R-Reciprocity
SIN-Social Interaction	0.539
TIG- trust in Government	0.613	0.688
ID-Identification	0.556	0.410	0.441
IU-Intention to Use	0.748	0.758	0.804	0.554

Table 2 Results of Reflective Construct Assessment
Latent	Indicators	Outer Loading Indicators	Composite Reliability (CR)	Cronbach`s Alpha	AVE	Decision
Identification	ID1	0.91	*0.864	*0.862	*0.783	Retain
	ID2	0.87
	ID3	0.875
Intention to Use	IU1	0.765	*0.952	*0.952	*0.633	Retain
	IU10	0.812
	IU11	0.793
	IU12	0.812
	IU13	0.823
	IU2	0.772
	IU3	0.802
	IU4	0.808
	IU5	0.787
	IU6	0.802
	IU7	0.804
	IU8	0.775
	IU9	0.783
Reciprocity	R1	0.812	*0.915	*0.914	*0.700	Retain
	R2	0.828
	R3	0.846
	R4	0.853
	R5	0.845
	R6	0.835
Social Interaction	SIN1	0.857	*0.915	*0.896	*0.763	Retain
	SIN2	0.891
	SIN3	0.892
	SIN4	0.853
trust in Government	TG1	0.773	*0.939	*0.937	*0.613	Retain
	TG10	0.81
	TG11	0.799
	TG2	0.73
	TG3	0.787
	TG4	0.787
	TG5	0.763
	TG6	0.728
	TG7	0.781
	TG8	0.797
	TG9	0.85

Reliability Test

The reliability test is the second stage of measurement model testing, assessing whether a questionnaire accurately measures the variable being measured (Hair et al., 2011). It ensures consistency in the research study by assessing internal consistency reliability (IC) of constructs and considering Cronbach's α and composite reliability. Cronbach's α measures “the extent to which items intended to measure a construct are interrelated and whose variance is derived from a common source”(Netemeyer et al., 2003).

Composite reliability measures consistency between multiple measurements of a variable (Bacon et al., 1995). According to Hair Jr. et al., (2014) both have the cut-off values for Cronbach's α and composite reliability usually is 0.7.

In the present study, Table 2, indicates that the CR & alpha for each variable are more than 0.7 which shows that the values of both Cronbach's alpha and composite reliability are greater than the minimum required level i.e. greater than 0.7. It signifies a good level of internal consistency reliability for the scale. Thus, the scales are measuring the same construct and very reliable. Table 2 represents the reliability statistics. Thus, the scales are measuring the same construct and very reliable. Table 2 represents the reliability statistics.

Structural Model

The research model undergoes a structural model test, examining the relationship between latent variables. Hypothesis testing is conducted by examining the significance score between constructs, which relies on bootstrapping standard error to compute empirical "t values" and "p values.". The study's structural model is assessed using the significance of relationships and the coefficient of determination (R2 value).

This study uses a 5% significance level in marketing research to test the significance of path coefficients. Empirical t values and p values are used to assess these levels, with a p value smaller than 0.05.

Adjusted R-squared penalizes the inclusion of unnecessary variables, increasing the value only if new variables significantly improve the model's performance. The researcher used Adjusted R square in this study to measure the model's predictive power in their study.

In this present research, trust in government, reciprocity, social interaction, & identification are the variables for measuring social capital. As per the Table 3 & Figure 2, the social capital (reciprocity, social interaction, trust in government, & identification) adjusted R square value for “intention to use” e-participation is 0.74. This demonstrates that 74 percent of the variation is explained by this study model in the “intention to use” e-participation.

Table 3 Path Coefficients of the Inner Model - Main Effects
Dependent Variable	Independent Variable	β	T Statistics	Adjusted R-square
Intention to       Use	Identification	0.11*	3.15	0.74
	Reciprocity	0.30*	6.26
	Social Interaction	0.29*	7.28
	Trust in government	0.37*	8.15
*p value< 0.05

Further, on considering particular variables of social capital, the beta value of identification is 0.11, t-value is 3.15 and P-value regarding “intention to use” e-participation is 0.000. Thus, this shows that “intention to use” in regard to E-participation is significantly impacted by identification variable.  H2 is therefore supported. Hence, Identification positively influences “Intention to Use” e-participation. The identification has significant positive impact on “intention to use” e-participation.

Similarly, the beta value of reciprocity is 0.30, t-value is 6.26 and p-value towards “intention to use” e-participation is 0.000. Thus, this shows that “intention to use” in regard to E-participation is significantly impacted by reciprocity variable. Thus, H3 is supported. Hence, the reciprocity positively influences “Intention to Use” e-participation. It has significant positive impact on “intention to use” e-participation.

Likewise, the beta value of social interaction is 0.29, t-value is 7.28 and p-value towards “intention to use” e-participation is 0.000. Thus, this shows that “intention to use” in regard to E-participation is significantly impacted by social interaction variable. Thus, H4 is supported. Hence, reciprocity positively influences “intention to use” e-participation.

Lastly, the beta value of trust-in-government is 0.37, t-value is 8.15 and p-value towards “intention to use” e-participation is 0.000. Thus, this indicates that “intention to use” in regard to E-participation is significantly impacted by trust-in-government variable. Thus, H5 is supported. Hence, trust-in-government positively influences citizen`s “intention to use” e-participation.

Result indicated that a higher significant influence on “intention to use” e-participation is found for trust in government (β=0.37*, t=8.15, p<0.05), which is followed by reciprocity (β=0.30*, t= 6.26, p<0.05), social interaction (β=0.29*, t=7.28, p<0.05), and identification (β =0.11*, t =3.15, p <0.025).

In conclusion, every social capital variable has a strong favourable influence on citizens' “intention to use” e-participation. Thus, it is clear that citizens' “intention to use” e-participation is significantly positively impacted by social capital.

Discussion

This research examines social capital through four variables: identification, reciprocity, social interaction, and trust in government. This reflects that although users of e-participation technologies are strangers to one another, they may utilize e-participation technologies to seek similar goals, which could give the impression that others are supporting the same community objectives. This result is consistent with other studies. Chang & Chuang, (2011) found that an individual's feeling of identification had a favourable impact on employees’ intention to share knowledge  in an industry setting.  In light of actual use of e-participation in online communities, (Naranjo-Zolotov, et al., 2019a)  found that a netizen`s feeling of identification towards the community had a favourable effect. Furthermore, reciprocity significantly influenced “intention to use” e-participation. This indicates that despite the lack of familiarity of citizens with one another, they would like to use e-participation for a shared objective, which could result in developing a sense of cooperation among the community as a whole. In simple words, the willingness for e-participation usage is higher among members of the community if the effort they invested on the e-participation platform is likely to be returned. Finally, this result is consistent with another studies. Sauk et al., (2013) results confirmed  that both employees' implicit and explicit intentions to share knowledge were significantly strengthened by reciprocity. Further, Chang & Chuang, (2011) mentioned that in virtual communities, the practice of sharing knowledge was significantly and favorably impacted by reciprocity. But Naranjo-Zolotov, et al., (2019b) has reported in their study a non-significance of reciprocity in regard of usage behavior of e participation.  Likewise, social interaction has significantly influenced the “intention to use” e-participation. This outcome is aligned with past studies. Researchers discovered that interaction favorably affected continuous intention to use online social networks & on the knowledge sharing behavior respectively (Chang & Chuang, 2011; Lin & Lu, 2011a).  Lastly, Trust in government is significantly influenced “intention to use” e-participation. This means that the willingness of the populace to comply, accept, cooperate & support innovative governmental policies & initiatives is reflected in their level of faith in the government. That is, individuals who have a higher level of trust in the government are more willing to contribute a larger quantity of ideas on e- participation platforms. Furthermore, when citizens have confidence in the government, they are more inclined to exhibit a higher level of interest with governmental affairs and in turn, are more inclined to utilize e-participation platforms. Long-term implementation of e-participation can potentially enhance people' trust in local governing bodies, hence indirectly promoting social capital within the participatory community members (Warren et al., 2014).

This result is aligned with other previous research studies. Result demonstrated that active e-participation is positively & significantly correlated with trust in the government. In another study, Lee believed that, in the context of e-government, e-participation in agenda-setting would be positively correlated with trust in the government (Lee & Kim, 2018). As per Choi & Song, (2020), a higher probability of e-participation is correlated with a greater degree of trust towards the government, which helps to explain what makes some persons engage in e-participation but not others. But the result of this current study contradicts the study of (Naranjo-Zolotov et al., 2019b) discovered that there was no relationship between the “intention to use” e-participation & citizens' trust towards the government.

Conclusion

This current study revealed that all the four variables of social capital (Identification, reciprocity, social interaction, and trust in government) have positive significant impact on “intention to use” e-participation which reflects that citizen`s trust in government signals that as the government is ultimately responsible for putting the results of consultation or decision-oriented processes into practice, it is the government who will be responsive towards the citizens for their requirements and act in their best interests. Also, trust in government reflects citizens’ willingness to comply, cooperate, adopt, and support government policies and innovative programs. Further, identification indicated that citizens feel identified with the community i.e. there is a feeling of belonging of the citizens to a community. They are more willing to use e- participation or use it more frequently to contribute to that community. Similarly, social interaction reflects that Even though e-participation might include numerous of individuals who are strangers to one another, people can use the online social networks that are already in place to promote their efforts about e-participation technologies that are supported by local governments for consultation and decision-making. Lastly, reciprocity signals that a participatory community's members perceive one another as helpful, despite their lack of familiarity with one another. They contribute to e-participation towards a shared objective, which fosters a sense of reciprocity within the community as a whole. The results can assist the public sector in developing long-term strategies to encourage and disseminate e-participation among people. Specifically, highlighting the potential benefits for the community and praising the contributions received by participants and how those contributions had a positive effect on the community.

Implications

This study's findings underscore the significant influence of social capital dimensions—identification, reciprocity, social contact, and faith in government—on people' intentions to utilize e-participation platforms. These insights hold considerable ramifications for politicians, governmental entities, and organizations advocating digital governance.

Theoretical Implications

The research contributes to the literature on e-participation by presenting empirical insights on how various dimensions of social capital contribute to citizens' use of digital participation mechanisms. It confirms the hypothesis that trust in government is critical to enforcing compliance, cooperation, and acceptance of digital governance projects. In addition, identification and social interaction highlight the social and psychological aspects of e-participation, affirming that civic engagement is strengthened by community belongingness and network effects. Reciprocity also supports the argument that cooperation among individuals creates a collaborative and inclusive e-participation setting.

Managerial Implications

The research emphasizes for public managers and digital governance strategists the requirement of building trust and strengthening community identification to improve the acceptance of e-participation platforms. Government departments should guarantee that citizen comments are obviously included into policy and follow open decision-making procedures. Using internet social networks can also greatly increase awareness and involvement, therefore motivating more people to take part. Strategies should also include highlighting success stories and showing how citizen contributions have resulted in significant legislative changes, therefore highlighting the advantages of e-participation.

Practical Contributions

Governments should actively work on enhancing public trust by maintaining transparency in e-governance projects, ensuring data security, and responding effectively to citizen concerns. Strengthening community identity through online forums, discussion groups, and targeted campaigns can make citizens feel more connected and motivated to participate in digital platforms. Governments can integrate e-participation features with existing social media platforms to facilitate discourse, collaboration, and information sharing. Acknowledging and rewarding active contributors can enhance reciprocity, reinforcing the perception that individual participation leads to tangible benefits for the community. Conducting workshops and digital literacy programs to educate citizens about the benefits of e-participation and how they can effectively engage with government initiatives.

These approaches help government agencies build a more inclusive, interactive, and participatory digital governance ecosystem, hence increasing citizen involvement and improving the results of policies.

Acknowledgments

We would like to express our sincere gratitude to all the individuals and organizations who contributed to the successful completion of this study. Special thanks to our colleagues and mentors for their valuable insights and constructive feedback throughout the research process. We also extend our appreciation to the participants who generously took the time to share their perspectives and experiences, making this research possible. Lastly, we are grateful to our family and friends for their unwavering encouragement and support.

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Received: 16-May-2025, Manuscript No. AMSJ-25-15932; Editor assigned: 17-May-2025, PreQC No. AMSJ-25-15932(PQ); Reviewed: 18-Jun-2025, QC No. AMSJ-25-15932; Revised: 06-Jul-2025, Manuscript No. AMSJ-25-15932(R); Published: 12-Jul-2025