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To make governing more accountable, accessible, effective, and transparent, governments across the world utilize the internet and other information and communication technologies (ICT) to enhance the quality and range of public services provided to citizens and other stakeholders (Olphert and Damodaran, 2007; Krishnan et al., 2013; Porwol et al., 2016; Das et al., 2017; Ashaye and Irani, 2019). The pertinence of ICT infrastructure to network readiness and societal progress has been previously discussed (Ifinedo and Usoro, 2009; Shin, 2010). The expansion of social media, open standards, and open source applications has revolutionized civil society participation and engagement in public administration (Porwol et al., 2016, 2018). Prior research has discussed e-government diffusion and maturity across the world (e.g., Azad et al., 2010; Lee et al., 2011; Ifinedo and Singh, 2011; Zhao et al., 2014; Das et al., 2017). While e-government broadly describes the use of IT as providing public services to citizens in a country or region, it is different from e-participation, a concept that is described “as the process of engaging citizens through ICTs in policy, decision-making, and service design and delivery in order to make it participatory, inclusive, and deliberative” (UNDESA, 2013). Macintosh (2004) described e-participation as the use of information and communication technologies to broaden and deepen political participation by enabling citizens to connect with one another and with their elected representatives. E-government is not the same as e-participation (Lee et al., 2011; Gulati et al., 2014). Governments could use IT resources to reach out to citizens and govern them; this reality differs from e-participation, which encapsulates the interaction between civil society and a government’s decision- and policy-making processes (Sæbø et al., 2008; Krishnan et al, 2013; 2017). As noted in UNPAN’s (2018) report, e-participation emphasizes citizen engagement in governance and “can serve as a catalyst for citizen engagement and in achieving the objectives of the 2030 Agenda” (p. 33). In fact, active participation and citizen engagement can be exercised through e-participation (Phang and Kankanhalli, 2008).
Research on e-participation is still evolving and our current study intends to add to the growing body of work focused on drivers of e-participation progress across the world. According to Krishnan et al. (2017), e-participation research can be classified into three streams: a) studies that are conceptual in nature (e.g., Phang and Kankanhalli, 2008; Sæbø et al., 2008; Porwol et al., 2016); b) studies that examine the demand-side of e-participation (i.e., citizens’ perspective) rather than the supply-side (i.e., governments’ perspective) with data sourced from a particular country or geographical region (e.g., Lau et al., 2008; Fedotova et al., 2012); c) studies that benchmark e-participation initiatives (e.g., Holgersson and Karlsson, 2014; Zolotov et al., 2018). Another area of interest is studies that examine e-participation progress globally and compare development across national contexts (Åström et al., 2012; Gulati et al., 2014; Krishnan et al, 2017). Our study is designed to contribute to growing the aspect of research focused on global and regional perspectives. Many previous studies have used cross-sectional data for analysis. For example, Gulati et al. (2014) used cross-national data to compare e-participation development across national contexts at a particular point in time. It is worth noting there are weaknesses in the use of cross-sectional data for such studies as the development or evolution of e-participation cannot be identified in a cross-sectional study (Wooldridge, 2010). Panel data or longitudinal data analysis that uses measurements of the same phenomenon over time tends to produce better insights (Gujarati and Porter, 2009; Wooldridge, 2010; Das et al., 2017). Indeed, Das et al. (2017) provided examples where certain variables were found to be significant only in cross-sectional analysis and not in panel data analysis.