B. Satrio
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Governments are increasingly deploying open data portals and platforms as a technological innovation to empower citizens by providing access to data. Yet, engagement with these portals remains low, suggesting that current approaches may not adequately map the issues surrounding the adoption of these tools. Research on open data has been conducted to overcome the technical and institutional barriers to adopting open data portals and platforms. However, there is a void in the literature about research on the citizens’ motivations that support or inhibit their adoption. This study addresses this gap by drawing on the Self-Concordance Model, a motivational theory that explores the alignment of an individual’s goals and values, to explain citizens’ motivations better. Through an integrative literature review, we conceptualized citizens’ motivational factors, linked them with corresponding barriers, and organized them into a taxonomy that reflects their role across different stages of the adoption process. Our analysis reveals that identified and intrinsic motivations play distinct roles in both pre-adoption and post-adoption phases, suggesting that tailored design strategies targeting these motivations could effectively initiate and sustain citizen engagement. This study advances open data research by connecting motivation and use of motivation theory to map the citizens’ behavioral dynamics underlying their adoption. Our proposed taxonomy provides a foundation for future research into motivation-driven strategies in designing open data portals and platforms’ interventions to increase citizens’ engagement.
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Governments are increasingly deploying open data portals and platforms as a technological innovation to empower citizens by providing access to data. Yet, engagement with these portals remains low, suggesting that current approaches may not adequately map the issues surrounding the adoption of these tools. Research on open data has been conducted to overcome the technical and institutional barriers to adopting open data portals and platforms. However, there is a void in the literature about research on the citizens’ motivations that support or inhibit their adoption. This study addresses this gap by drawing on the Self-Concordance Model, a motivational theory that explores the alignment of an individual’s goals and values, to explain citizens’ motivations better. Through an integrative literature review, we conceptualized citizens’ motivational factors, linked them with corresponding barriers, and organized them into a taxonomy that reflects their role across different stages of the adoption process. Our analysis reveals that identified and intrinsic motivations play distinct roles in both pre-adoption and post-adoption phases, suggesting that tailored design strategies targeting these motivations could effectively initiate and sustain citizen engagement. This study advances open data research by connecting motivation and use of motivation theory to map the citizens’ behavioral dynamics underlying their adoption. Our proposed taxonomy provides a foundation for future research into motivation-driven strategies in designing open data portals and platforms’ interventions to increase citizens’ engagement.
Measuring User Engagement in Open Data Platforms
A Multi-Layer Framework of Behavioral Indicators
Existing research on open data engagement has predominantly focused on the supply-side indicators or on the subjective demand-side evaluations, while objective-based analysis of the demand-side engagement remains underexplored. Drawing on the platform ecosystem perspective, this paper takes a different approach by examining open data platform engagement as an indicator of value creation through observable behavioral interactions, thereby positioning our study on the demand-side and within an objective-based evaluation. Through interviews with 15 open data platform stakeholders, we identify opportunities to measure engagement using behavioral indicators derived from platform use. Building on these findings, we develop a multi-layer framework of behavioral indicators to measure engagement with open data platforms. The framework consists of four layers which includes: system-level indicators, behavioral metrics, interactive signals, and contextual factors. We present a proof-of-concept implementation that operationalizes the proposed framework using platform logs, providing guidance for open data platform designers and policymakers to evaluate engagement. The paper contributes to the open data evaluation literature by reconceptualizing engagement as an observable, value-creating behavior in open data platforms. In this way, the demand-side is taken into account, and the actual behavior is measured, which data can complement the subjective data. Overall, the study demonstrates how integrating objective measures with subjective evaluation can support a more comprehensive and value-oriented engagement measurement of open data platforms.
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Existing research on open data engagement has predominantly focused on the supply-side indicators or on the subjective demand-side evaluations, while objective-based analysis of the demand-side engagement remains underexplored. Drawing on the platform ecosystem perspective, this paper takes a different approach by examining open data platform engagement as an indicator of value creation through observable behavioral interactions, thereby positioning our study on the demand-side and within an objective-based evaluation. Through interviews with 15 open data platform stakeholders, we identify opportunities to measure engagement using behavioral indicators derived from platform use. Building on these findings, we develop a multi-layer framework of behavioral indicators to measure engagement with open data platforms. The framework consists of four layers which includes: system-level indicators, behavioral metrics, interactive signals, and contextual factors. We present a proof-of-concept implementation that operationalizes the proposed framework using platform logs, providing guidance for open data platform designers and policymakers to evaluate engagement. The paper contributes to the open data evaluation literature by reconceptualizing engagement as an observable, value-creating behavior in open data platforms. In this way, the demand-side is taken into account, and the actual behavior is measured, which data can complement the subjective data. Overall, the study demonstrates how integrating objective measures with subjective evaluation can support a more comprehensive and value-oriented engagement measurement of open data platforms.
Open Data Portals Engagement
A Cross-Country Analysis of Game Elements
Despite their pivotal role in promoting transparency, open data portals often struggle to engage citizens, functioning instead as static ‘data graveyards’. While external activities, such as hackathons, can raise awareness, they do not directly cultivate sustained engagement within the portals. One promising approach to leverage citizens’ engagement motivation is the integration of game elements to transform passive data access into interactive gamified experiences. However, despite its potential, there is limited research on gamified citizens’ motivation to engage with open data portals. This paper examines how static and dynamic game elements are implemented across 31 open data portals. Lastly, we use the Self-Concordance Model to discuss the alignment between motivation, personal values, and game elements. Our findings reveal that most portals incorporate ‘discovery’ elements into their dataset-searching features, subtly gamifying exploration. Additionally, portals emphasising external activities, such as hackathons and events, often lack integrated social features, suggesting a trade-off between external engagement and sustained in-portal interaction. These findings challenge the assumption that open data engagement relies primarily on external initiatives, emphasising in-portal gamification instead. This study provides recommendations for policymakers to engage with users within open data portals.
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Despite their pivotal role in promoting transparency, open data portals often struggle to engage citizens, functioning instead as static ‘data graveyards’. While external activities, such as hackathons, can raise awareness, they do not directly cultivate sustained engagement within the portals. One promising approach to leverage citizens’ engagement motivation is the integration of game elements to transform passive data access into interactive gamified experiences. However, despite its potential, there is limited research on gamified citizens’ motivation to engage with open data portals. This paper examines how static and dynamic game elements are implemented across 31 open data portals. Lastly, we use the Self-Concordance Model to discuss the alignment between motivation, personal values, and game elements. Our findings reveal that most portals incorporate ‘discovery’ elements into their dataset-searching features, subtly gamifying exploration. Additionally, portals emphasising external activities, such as hackathons and events, often lack integrated social features, suggesting a trade-off between external engagement and sustained in-portal interaction. These findings challenge the assumption that open data engagement relies primarily on external initiatives, emphasising in-portal gamification instead. This study provides recommendations for policymakers to engage with users within open data portals.
Game elements enabling citizens’ engagement
An integrative literature review into elements, motivations, drivers and barriers
Gamification may foster citizen relationships with the government but can also result in a disinterest in participation. In the context of digital government, we do not know which game elements specifically contribute to citizen engagement. In this paper, we conduct an integrative study, drawing from existing literature on citizen engagement through gamification. We examined the citizens’ motivation to engage with the government, linked it with game elements explored in the literature, and finally exhibited how these elements could support or inhibit citizens’ motivation. Using self-concordance and civic engagement models, we investigated gamification focusing on individual experiences, both personally and as citizens. The findings of this research show a link of static game elements with external and introjected forms of motivation. In contrast, dynamic game elements seem more aligned with intrinsic and identified motivation. We develop a taxonomy outlining these relationships, enabling further research on game elements and their impact on citizen engagement over time.
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Gamification may foster citizen relationships with the government but can also result in a disinterest in participation. In the context of digital government, we do not know which game elements specifically contribute to citizen engagement. In this paper, we conduct an integrative study, drawing from existing literature on citizen engagement through gamification. We examined the citizens’ motivation to engage with the government, linked it with game elements explored in the literature, and finally exhibited how these elements could support or inhibit citizens’ motivation. Using self-concordance and civic engagement models, we investigated gamification focusing on individual experiences, both personally and as citizens. The findings of this research show a link of static game elements with external and introjected forms of motivation. In contrast, dynamic game elements seem more aligned with intrinsic and identified motivation. We develop a taxonomy outlining these relationships, enabling further research on game elements and their impact on citizen engagement over time.