Pub Date : 2025-01-14DOI: 10.1016/j.giq.2024.102002
Anouk Decuypere, Anne Van de Vijver
Governments are increasingly using AI for their decision making. Research on citizen perceptions highlight the context-dependent nature of their fairness assessment, rendering administrations unsure about how to implement AI so that citizens support these procedures. The survey experiments in this study, conducted in a pilot and a main study, (Npilot = 232; Nmain study = 2366) focuses on a high-risk decision-making context, i.e., selection of citizens for fraud detection. In the scenarios, we manipulated the proportion of the selection made by AI, based on information from past fraudsters, versus civil servants, who work based on their experience. In addition, we tested the effect of transparency (and explanation) statements and its impact on procedural fairness scores. We found that a higher proportion of AI in the selection for fraud audits was perceived as more procedurally fair, mostly through increased scores on bias suppression and consistency. However, participants' general attitude toward AI and trust in the administration explained more variance than the experimental manipulation. Transparency (explanations) had no impact.
{"title":"AI: Friend or foe of fairness perceptions of the tax administration? A survey experiment on citizens' procedural fairness perceptions","authors":"Anouk Decuypere, Anne Van de Vijver","doi":"10.1016/j.giq.2024.102002","DOIUrl":"10.1016/j.giq.2024.102002","url":null,"abstract":"<div><div>Governments are increasingly using AI for their decision making. Research on citizen perceptions highlight the context-dependent nature of their fairness assessment, rendering administrations unsure about how to implement AI so that citizens support these procedures. The survey experiments in this study, conducted in a pilot and a main study, (N<sub>pilot</sub> = 232; N<sub>main study</sub> = 2366) focuses on a high-risk decision-making context, i.e., selection of citizens for fraud detection. In the scenarios, we manipulated the proportion of the selection made by AI, based on information from past fraudsters, versus civil servants, who work based on their experience. In addition, we tested the effect of transparency (and explanation) statements and its impact on procedural fairness scores. We found that a higher proportion of AI in the selection for fraud audits was perceived as more procedurally fair, mostly through increased scores on bias suppression and consistency. However, participants' general attitude toward AI and trust in the administration explained more variance than the experimental manipulation. Transparency (explanations) had no impact.</div></div>","PeriodicalId":48258,"journal":{"name":"Government Information Quarterly","volume":"42 1","pages":"Article 102002"},"PeriodicalIF":7.8,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10DOI: 10.1016/j.giq.2024.102006
Helen K. Liu , MuhChyun Tang , Antoine Serge J. Collard
With the increasing attention paid to artificial intelligence (AI) and crowd intelligence (CI) in government, their connections still need to be explored. This study explores the dynamic relationship between AI and CI that constitutes hybrid intelligence for the public sector. Thus, we adopt a bibliometric analysis to identify trends, emerging themes, topics, and interconnections between these two streams of literature. Our review illustrates the intersection between AI and CI, revealing that AI designs can improve efficiency from CI inputs. Meanwhile, AI advancement depends on the quality of CI data. Furthermore, our review highlights key domains such as smart cities (Internet of Things), personnel design, social media, and governance through cases. Based on these illustrated cases, we conceptualize a hybrid intelligence spectrum, ranging from “engagement” to “efficiency,” with crowd intelligence anchoring the former through its emphasis on public participation and AI anchoring the latter through its focus on automation and optimization. Hybrid intelligence, encompassing various forms, occupies the middle ground to balance maximizing public engagement and achieving computational efficiency. Additionally, we elaborate on components of hybrid intelligence designs regarding input (conscious crowds and unconscious crowds), process (algorithmic management and artificial discretion), and outcome (user-focus benefits and non-user-focus outputs). Finally, we recommend prioritizing questions related to the design, regulation, and governance of hybrid intelligence for the public sector.
{"title":"Hybrid intelligence for the public sector: A bibliometric analysis of artificial intelligence and crowd intelligence","authors":"Helen K. Liu , MuhChyun Tang , Antoine Serge J. Collard","doi":"10.1016/j.giq.2024.102006","DOIUrl":"10.1016/j.giq.2024.102006","url":null,"abstract":"<div><div>With the increasing attention paid to artificial intelligence (AI) and crowd intelligence (CI) in government, their connections still need to be explored. This study explores the dynamic relationship between AI and CI that constitutes hybrid intelligence for the public sector. Thus, we adopt a bibliometric analysis to identify trends, emerging themes, topics, and interconnections between these two streams of literature. Our review illustrates the intersection between AI and CI, revealing that AI designs can improve efficiency from CI inputs. Meanwhile, AI advancement depends on the quality of CI data. Furthermore, our review highlights key domains such as smart cities (Internet of Things), personnel design, social media, and governance through cases. Based on these illustrated cases, we conceptualize a hybrid intelligence spectrum, ranging from “engagement” to “efficiency,” with crowd intelligence anchoring the former through its emphasis on public participation and AI anchoring the latter through its focus on automation and optimization. Hybrid intelligence, encompassing various forms, occupies the middle ground to balance maximizing public engagement and achieving computational efficiency. Additionally, we elaborate on components of hybrid intelligence designs regarding input (conscious crowds and unconscious crowds), process (algorithmic management and artificial discretion), and outcome (user-focus benefits and non-user-focus outputs). Finally, we recommend prioritizing questions related to the design, regulation, and governance of hybrid intelligence for the public sector.</div></div>","PeriodicalId":48258,"journal":{"name":"Government Information Quarterly","volume":"42 1","pages":"Article 102006"},"PeriodicalIF":7.8,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-08DOI: 10.1016/j.giq.2024.102005
Simon Dechamps, Anthony Simonofski, Corentin Burnay
Putting citizens as the cornerstone of a policymaking or service design process is usually referred to as citizen-centricity and is often considered a key practice in the context of digital government transformation. Nevertheless, the lack of a common comprehension of what citizen-centricity entails leads to practical and theoretical difficulties, among which the confusion generated by the multiple heterogeneous definitions and the difficulty of measuring the level of citizen-centricity of a digital initiative, to cite only two. As an answer, this study characterizes citizen-centricity by suggesting a typology grounded in theory and practice. It does so by surveying the recent scientific literature using a systematic literature review of 58 studies, combined with 14 qualitative interviews with public agents. The key contribution from our citizen-centricity typology is threefold. First, by emphasizing four understandings of citizen-centricity, sometimes referring to an end-result, a design process, a governance mode, or a way of identifying the user, we demonstrate that the concept has the potential to encompass a multitude of disparate realities. Furthermore, it provides a crucial lens through which to comprehend the concept, thereby facilitating alignment between stakeholders engaged in the pursuit of citizen-centricity. Second, we identify the characteristics given by the literature and practitioners for each understanding. Finally, we suggest that the four understandings of citizen-centricity cannot be sequenced, even iteratively, since they interact continuously. These contributions should guide future research and facilitate communication between research and practice about this concept.
{"title":"Citizen-centricity in digital government: A theoretical and empirical typology","authors":"Simon Dechamps, Anthony Simonofski, Corentin Burnay","doi":"10.1016/j.giq.2024.102005","DOIUrl":"10.1016/j.giq.2024.102005","url":null,"abstract":"<div><div>Putting citizens as the cornerstone of a policymaking or service design process is usually referred to as citizen-centricity and is often considered a key practice in the context of digital government transformation. Nevertheless, the lack of a common comprehension of what citizen-centricity entails leads to practical and theoretical difficulties, among which the confusion generated by the multiple heterogeneous definitions and the difficulty of measuring the level of citizen-centricity of a digital initiative, to cite only two. As an answer, this study characterizes citizen-centricity by suggesting a typology grounded in theory and practice. It does so by surveying the recent scientific literature using a systematic literature review of 58 studies, combined with 14 qualitative interviews with public agents. The key contribution from our citizen-centricity typology is threefold. First, by emphasizing four understandings of citizen-centricity, sometimes referring to an end-result, a design process, a governance mode, or a way of identifying the user, we demonstrate that the concept has the potential to encompass a multitude of disparate realities. Furthermore, it provides a crucial lens through which to comprehend the concept, thereby facilitating alignment between stakeholders engaged in the pursuit of citizen-centricity. Second, we identify the characteristics given by the literature and practitioners for each understanding. Finally, we suggest that the four understandings of citizen-centricity cannot be sequenced, even iteratively, since they interact continuously. These contributions should guide future research and facilitate communication between research and practice about this concept.</div></div>","PeriodicalId":48258,"journal":{"name":"Government Information Quarterly","volume":"42 1","pages":"Article 102005"},"PeriodicalIF":7.8,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.giq.2024.102001
Heidi Hietala , Tero Päivärinta
Digitalization drives societal transformation to reform existing practices in the evolving environment. Central to this transformation is the creation of interoperable digital public services across diverse organizations, increasingly guided by human-centric principles and life-event orientation. This paper explores the complex process of achieving digital service innovation, emphasizing the need for inter-organizational balancing between radical transformation and efficiency through collective ambidexterity, where multiple ecosystem actors coordinate to balance innovation and efficiency simultaneously. While previous research predominantly studied ambidexterity at the organizational level, focusing on its antecedents, mechanisms, and outcomes, our study extends this inquiry to the broader ecosystem. Via a single-case study, we investigate how collective ambidexterity can be governed in a large-scale digital service ecosystem. To address the research question, we developed a multi-level conceptual model of governing mechanisms, antecedents, and outcomes of collective ambidexterity across three analytical levels: the ecosystem, organization group, and organization. Our theoretical contribution is twofold. First, we enhance conceptual clarity on collective ambidexterity and show how Modes of Collaboration (MoC) can facilitate innovation and efficiency of human-centric digital services throughout the three levels of governance. Second, the resulting governance model emphasizes the need to connect centralized, decentralized, and group-level governance strategies for developing digital services—to achieve and govern collective ambidexterity in the development of digital services in the public sector.
{"title":"Governing collective ambidexterity: Antecedents, mechanisms, and outcomes in digital service ecosystems","authors":"Heidi Hietala , Tero Päivärinta","doi":"10.1016/j.giq.2024.102001","DOIUrl":"10.1016/j.giq.2024.102001","url":null,"abstract":"<div><div>Digitalization drives societal transformation to reform existing practices in the evolving environment. Central to this transformation is the creation of interoperable digital public services across diverse organizations, increasingly guided by human-centric principles and life-event orientation. This paper explores the complex process of achieving digital service innovation, emphasizing the need for inter-organizational balancing between radical transformation and efficiency through collective ambidexterity, where multiple ecosystem actors coordinate to balance innovation and efficiency simultaneously. While previous research predominantly studied ambidexterity at the organizational level, focusing on its antecedents, mechanisms, and outcomes, our study extends this inquiry to the broader ecosystem. Via a single-case study, we investigate how collective ambidexterity can be governed in a large-scale digital service ecosystem. To address the research question, we developed a multi-level conceptual model of governing mechanisms, antecedents, and outcomes of collective ambidexterity across three analytical levels: the ecosystem, organization group, and organization. Our theoretical contribution is twofold. First, we enhance conceptual clarity on collective ambidexterity and show how Modes of Collaboration (MoC) can facilitate innovation and efficiency of human-centric digital services throughout the three levels of governance. Second, the resulting governance model emphasizes the need to connect centralized, decentralized, and group-level governance strategies for developing digital services—to achieve and govern collective ambidexterity in the development of digital services in the public sector.</div></div>","PeriodicalId":48258,"journal":{"name":"Government Information Quarterly","volume":"42 1","pages":"Article 102001"},"PeriodicalIF":7.8,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-27DOI: 10.1016/j.giq.2024.102004
Zhichao Ba , Leilei Liu , Yikun Xia
Scholarly citation has been extensively utilized to explore scholars' citation behaviors and elucidate their citation decision-making during academic writing. Likewise, policy citation serves as an invaluable instrument for scrutinizing policymakers' policy adoption decisions during policy formulation. This study offers a fine-grained portrayal and measurement of policy citation features, providing insights into policy adoption decisions from a novel perspective of citation choice. Specifically, each policy citation is conceptualized as a multidimensional feature collection consisting of 26 interpretable citation features (comprising 51 measurements) pertinent to the adoption of specific policies. These features are classified into three main categories: citation authority, citation proximity, and citation continuity. Utilizing large-scale information and communications technology (ICT) policies in China as empirical data, we conduct a series of logistic regressions and Random Forest-based classification experiments to quantitatively evaluate the importance of each constructed citation feature on policymakers' adoption decisions. Our empirical results reveal that policymakers' adoption of specific policies is predominantly influenced by citation proximity, followed by citation authority and citation continuity. Notably, central and local policymakers exhibit distinct adoption patterns; the former tends to prioritize policy continuity in their decision-making, whereas the latter lean towards adopting high-impact policies and learning from policy adoption pioneers. Moreover, the impact of policies is intricately entwined with their citation patterns, with high-cited policies often spearheading policy innovations, while low-cited policies tend to follow and imitate.
{"title":"Multidimensional policy citation features: Insights into policymakers' policy adoption decision-making","authors":"Zhichao Ba , Leilei Liu , Yikun Xia","doi":"10.1016/j.giq.2024.102004","DOIUrl":"10.1016/j.giq.2024.102004","url":null,"abstract":"<div><div>Scholarly citation has been extensively utilized to explore scholars' citation behaviors and elucidate their citation decision-making during academic writing. Likewise, policy citation serves as an invaluable instrument for scrutinizing policymakers' policy adoption decisions during policy formulation. This study offers a fine-grained portrayal and measurement of policy citation features, providing insights into policy adoption decisions from a novel perspective of citation choice. Specifically, each policy citation is conceptualized as a multidimensional feature collection consisting of 26 interpretable citation features (comprising 51 measurements) pertinent to the adoption of specific policies. These features are classified into three main categories: citation authority, citation proximity, and citation continuity. Utilizing large-scale information and communications technology (ICT) policies in China as empirical data, we conduct a series of logistic regressions and Random Forest-based classification experiments to quantitatively evaluate the importance of each constructed citation feature on policymakers' adoption decisions. Our empirical results reveal that policymakers' adoption of specific policies is predominantly influenced by citation proximity, followed by citation authority and citation continuity. Notably, central and local policymakers exhibit distinct adoption patterns; the former tends to prioritize policy continuity in their decision-making, whereas the latter lean towards adopting high-impact policies and learning from policy adoption pioneers. Moreover, the impact of policies is intricately entwined with their citation patterns, with high-cited policies often spearheading policy innovations, while low-cited policies tend to follow and imitate.</div></div>","PeriodicalId":48258,"journal":{"name":"Government Information Quarterly","volume":"42 1","pages":"Article 102004"},"PeriodicalIF":7.8,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-24DOI: 10.1016/j.giq.2024.102000
Simone Busetti, Francesco Maria Scanni
The escalating threat of cyber risks has propelled cybersecurity policy to the forefront of governmental agendas worldwide. Incident reporting, a cornerstone of cybersecurity legislation, may facilitate swift responses to cyberattacks and foster a learning process for policy enhancement. Despite its widespread adoption, there are no analyses on its efficacy, implementation, and avenues for improvement. This article provides a theory-based evaluation of incident reporting using the methods of realist synthesis and process tracing. We develop a program theory of incident reporting hypothesizing its dual role as a fire alarm and a catalyst for policy learning. The program theory is tested by drawing upon a range of literature and official documents, supplemented by insights from the Italian context through interviews with key informants. The evaluation reveals mixed findings. While incident reporting effectively serves as a fire alarm, particularly for organizations with limited cybersecurity capacity, challenges persist due to capacity gaps and a reluctance to report incidents. The link between incident reporting and policy learning remains tenuous, with evidence of inertia hindering the implementation of more radical changes. Policy recommendations include streamlining internal communications, combining rapid and in-depth reporting, fostering data-sharing agreements, ensuring dedicated communication of lessons from central cyber actors, and streamlining organizational procedures for implementing changes.
{"title":"Evaluating incident reporting in cybersecurity. From threat detection to policy learning","authors":"Simone Busetti, Francesco Maria Scanni","doi":"10.1016/j.giq.2024.102000","DOIUrl":"10.1016/j.giq.2024.102000","url":null,"abstract":"<div><div>The escalating threat of cyber risks has propelled cybersecurity policy to the forefront of governmental agendas worldwide. Incident reporting, a cornerstone of cybersecurity legislation, may facilitate swift responses to cyberattacks and foster a learning process for policy enhancement. Despite its widespread adoption, there are no analyses on its efficacy, implementation, and avenues for improvement. This article provides a theory-based evaluation of incident reporting using the methods of realist synthesis and process tracing. We develop a program theory of incident reporting hypothesizing its dual role as a fire alarm and a catalyst for policy learning. The program theory is tested by drawing upon a range of literature and official documents, supplemented by insights from the Italian context through interviews with key informants. The evaluation reveals mixed findings. While incident reporting effectively serves as a fire alarm, particularly for organizations with limited cybersecurity capacity, challenges persist due to capacity gaps and a reluctance to report incidents. The link between incident reporting and policy learning remains tenuous, with evidence of inertia hindering the implementation of more radical changes. Policy recommendations include streamlining internal communications, combining rapid and in-depth reporting, fostering data-sharing agreements, ensuring dedicated communication of lessons from central cyber actors, and streamlining organizational procedures for implementing changes.</div></div>","PeriodicalId":48258,"journal":{"name":"Government Information Quarterly","volume":"42 1","pages":"Article 102000"},"PeriodicalIF":7.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-19DOI: 10.1016/j.giq.2024.101988
Tony Busker , Sunil Choenni , Mortaza S. Bargh
There are many good reasons to use synthetic data instead of real data for research purposes. These reasons may range from the business sensitiveness of real data to increased cost of collecting real data in accordance with GDPR requirements. In this paper, we elaborate upon the potentials of the Large Language Model GPT as a tool to generate synthetic data for analytical purposes when there is no real-data available or accessible. Primarily, we show that by varying the scope of probes adequately, we can generate data of different granularities. To show this, we generated stereotypical data with three levels of granularity by posing more than 18,500 probes to GPT. In total, we generated stereotypical data for eight different views, which can be categorized in three view types corresponding to the three levels of granularity. Secondarily, we show that by varying the scope of probes one can create meaningful information. To show this, we performed a so-called similarity analysis on the generated stereotypical data. We used data visualizations, e.g. heatmaps, to show the views and categories within the views that are similar and those that are at odd with each other. We elaborate upon the application areas of the insight gained about such similarities and differences. Furthermore, we discuss several other types of analysis that can be performed on the generated stereotypical data.
{"title":"Exploiting GPT for synthetic data generation: An empirical study","authors":"Tony Busker , Sunil Choenni , Mortaza S. Bargh","doi":"10.1016/j.giq.2024.101988","DOIUrl":"10.1016/j.giq.2024.101988","url":null,"abstract":"<div><div>There are many good reasons to use synthetic data instead of real data for research purposes. These reasons may range from the business sensitiveness of real data to increased cost of collecting real data in accordance with GDPR requirements. In this paper, we elaborate upon the potentials of the Large Language Model GPT as a tool to generate synthetic data for analytical purposes when there is no real-data available or accessible. Primarily, we show that by varying the scope of probes adequately, we can generate data of different granularities. To show this, we generated stereotypical data with three levels of granularity by posing more than 18,500 probes to GPT. In total, we generated stereotypical data for eight different views, which can be categorized in three view types corresponding to the three levels of granularity. Secondarily, we show that by varying the scope of probes one can create meaningful information. To show this, we performed a so-called similarity analysis on the generated stereotypical data. We used data visualizations, e.g. heatmaps, to show the views and categories within the views that are similar and those that are at odd with each other. We elaborate upon the application areas of the insight gained about such similarities and differences. Furthermore, we discuss several other types of analysis that can be performed on the generated stereotypical data.</div></div>","PeriodicalId":48258,"journal":{"name":"Government Information Quarterly","volume":"42 1","pages":"Article 101988"},"PeriodicalIF":7.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-17DOI: 10.1016/j.giq.2024.101996
Claire Ingram Bogusz , Johan Magnusson , Mattias Rost
Extant research on public sector digital transformation has emphasised the process of deliberate digital technology use by public organizations in pursuit of efficiency and innovation. Studies of the unintended or contrarian uses associated with digital technologies have been scarce. This study explores a case in which parents of schoolchildren in the City of Stockholm react to the perceived poor usability of a learning management system through citizen “hacktivism”. The parents developed a challenger app on top of an existing platform, to which the city reacted by trying to obstruct development work, both technically and through litigation. We interpret this as a case of digital transformation reconfiguration through boundary object tuning, legal tuning and digital transformation tuning. These lead to, respectively, reconfiguration of 1) the site of transparency and engagement, 2) the boundaries of responsibility and ownership and 3) the locus of control over public services. We contribute to the public sector digital transformation literature by offering tuning as a way to understand (re)configuration of the non-linear and dialectical and materially embedded process of digital transformation. We also empirically explore the phenomenon of citizen hacktivism, offering insights into associated processes and effects.
{"title":"Leave it to the parents: How hacktivism-as-tuning reconfigures public sector digital transformation","authors":"Claire Ingram Bogusz , Johan Magnusson , Mattias Rost","doi":"10.1016/j.giq.2024.101996","DOIUrl":"10.1016/j.giq.2024.101996","url":null,"abstract":"<div><div>Extant research on public sector digital transformation has emphasised the process of deliberate digital technology use by public organizations in pursuit of efficiency and innovation. Studies of the unintended or contrarian uses associated with digital technologies have been scarce. This study explores a case in which parents of schoolchildren in the City of Stockholm react to the perceived poor usability of a learning management system through citizen “hacktivism”. The parents developed a challenger app on top of an existing platform, to which the city reacted by trying to obstruct development work, both technically and through litigation. We interpret this as a case of digital transformation reconfiguration through <em>boundary object tuning</em>, <em>legal tuning</em> and <em>digital transformation tuning</em>. These lead to, respectively, reconfiguration of 1) the site of transparency and engagement, 2) the boundaries of responsibility and ownership and 3) the locus of control over public services. We contribute to the public sector digital transformation literature by offering tuning as a way to understand (re)configuration of the non-linear and dialectical and materially embedded process of digital transformation. We also empirically explore the phenomenon of citizen hacktivism, offering insights into associated processes and effects.</div></div>","PeriodicalId":48258,"journal":{"name":"Government Information Quarterly","volume":"42 1","pages":"Article 101996"},"PeriodicalIF":7.8,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/j.giq.2024.101995
Houcai Wang , Zhenya Robin Tang , Li Xiong , Xiaoyu Wang , Lei Zhu
Citizens proactively engage in public deliberation during emergencies, which is pivotal for the success of emergency management. Drawing on the net valence model, the current manuscript investigates the antecedents for citizens' engagement in mobile government social media during emergencies. Using an online payment survey service provider, data were acquired from 740 subscribers to mobile government social media in mainland China. The research findings show that source credibility and perceived transparency, but not mobility, increased perceived benefits, which further increased citizens' engagement during emergencies. The findings also demonstrate that privacy risk and perceived Internet censorship increased perceived risk; however, perceived risk did not affect citizens' engagement during emergencies. These findings can inform future research on public participation with mobile government social media in emergencies and provide insights for emergency management practitioners.
{"title":"What determinants influence citizens' engagement with mobile government social media during emergencies? A net valence model","authors":"Houcai Wang , Zhenya Robin Tang , Li Xiong , Xiaoyu Wang , Lei Zhu","doi":"10.1016/j.giq.2024.101995","DOIUrl":"10.1016/j.giq.2024.101995","url":null,"abstract":"<div><div>Citizens proactively engage in public deliberation during emergencies, which is pivotal for the success of emergency management. Drawing on the net valence model, the current manuscript investigates the antecedents for citizens' engagement in mobile government social media during emergencies. Using an online payment survey service provider, data were acquired from 740 subscribers to mobile government social media in mainland China. The research findings show that source credibility and perceived transparency, but not mobility, increased perceived benefits, which further increased citizens' engagement during emergencies. The findings also demonstrate that privacy risk and perceived Internet censorship increased perceived risk; however, perceived risk did not affect citizens' engagement during emergencies. These findings can inform future research on public participation with mobile government social media in emergencies and provide insights for emergency management practitioners.</div></div>","PeriodicalId":48258,"journal":{"name":"Government Information Quarterly","volume":"42 1","pages":"Article 101995"},"PeriodicalIF":7.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ongoing conflict in Ukraine has triggered a humanitarian crisis, leading to a substantial increase in refugees. This situation presents a significant challenge for European countries, emphasizing the urgent need for effective refugee management strategies. Hence, effective decision-making is needed for the public sector to create a better livelihood for refugees. In this study, we propose using the concept of intelligence defined by Herbert Simon for effective refugee management. Following the Design Science Research Methodology, we utilize 58 semi-structured stakeholder interviews within Switzerland to identify problems and define design goals that facilitate intelligence in refugee management. Based on the design goals, we developed R2G – “Refugees to Government”, an application that utilizes community data and state-of-the-art NLP, including a chatbot interface, to offer an interactive dashboard for identifying refugee needs. The chatbot allows policymakers to interact with refugee data through dynamic, conversational queries, enabling real-time identification of refugee needs and providing data-driven intelligence. Our assessment of R2G, facilitated through 28 semi-structured interviews, resulted in four design principles for data-driven intelligence in refugee management: community-driven insight, spatial-temporal knowledge, multilingual data synthesis and visualization, and interactive data querying through chatbots. Additionally, we provide policy recommendations emphasizing the ethical use of community data, the integration of advanced NLP techniques in government processes, and the need for shifting governmental roles towards data analytics.
{"title":"Data-driven intelligence in crisis: The case of Ukrainian refugee management","authors":"Kilian Sprenkamp , Mateusz Dolata , Gerhard Schwabe , Liudmila Zavolokina","doi":"10.1016/j.giq.2024.101978","DOIUrl":"10.1016/j.giq.2024.101978","url":null,"abstract":"<div><div>The ongoing conflict in Ukraine has triggered a humanitarian crisis, leading to a substantial increase in refugees. This situation presents a significant challenge for European countries, emphasizing the urgent need for effective refugee management strategies. Hence, effective decision-making is needed for the public sector to create a better livelihood for refugees. In this study, we propose using the concept of intelligence defined by Herbert Simon for effective refugee management. Following the Design Science Research Methodology, we utilize 58 semi-structured stakeholder interviews within Switzerland to identify problems and define design goals that facilitate intelligence in refugee management. Based on the design goals, we developed R2G – “Refugees to Government”, an application that utilizes community data and state-of-the-art NLP, including a chatbot interface, to offer an interactive dashboard for identifying refugee needs. The chatbot allows policymakers to interact with refugee data through dynamic, conversational queries, enabling real-time identification of refugee needs and providing data-driven intelligence. Our assessment of R2G, facilitated through 28 semi-structured interviews, resulted in four design principles for data-driven intelligence in refugee management: community-driven insight, spatial-temporal knowledge, multilingual data synthesis and visualization, and interactive data querying through chatbots. Additionally, we provide policy recommendations emphasizing the ethical use of community data, the integration of advanced NLP techniques in government processes, and the need for shifting governmental roles towards data analytics.</div></div>","PeriodicalId":48258,"journal":{"name":"Government Information Quarterly","volume":"42 1","pages":"Article 101978"},"PeriodicalIF":7.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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