Previous research assumes that poor quality of Open Government Data (OGD), OGD portals, and the services provided for OGD may result in reduced trust of citizens in OGD. However, studies that empirically test this assumption are scarce. Using the Information Systems (IS) Success Model as a theoretical basis, this study aims to examine the effects of data quality, system quality, and service quality on citizens’ trust in OGD. We used Structural Equation Modeling (SEM) to analyze the 200 responses to our online questionnaire. We found that trust in OGD can be predicted by citizens’ perceptions of OGD system quality and service quality. Furthermore, citizens’ perception of service quality positively influences their perceptions of data and system quality, whereas citizens’ perception of system quality positively influences their perception of data quality. This study is among the first that quantitatively examines the effects of data quality, service quality, and system quality on citizen's trust in OGD. It contributes to the scientific literature by providing an operationalization of elements of the IS Success Model in the context of OGD and by developing and applying a model of factors influencing citizen's trust in OGD. While previous research finds that perceived data quality is the most crucial driver for trust in OGD, our study finds that citizens’ perception of OGD service quality is a more important driver for trust in OGD. With regard to the practical contributions of this study, open data policymakers should be aware that citizens’ perceptions on data quality can be greatly improved when appropriate human services are provided (e.g., designated civil servants offering support or help to data users) in addition to the provision of OGD portal functionalities (e.g., data visualization and comparison tools).
{"title":"Citizens’ Trust in Open Government Data: A Quantitative Study about the Effects of Data Quality, System Quality and Service Quality","authors":"A. Purwanto, Anneke Zuiderwijk, M. Janssen","doi":"10.1145/3396956.3396958","DOIUrl":"https://doi.org/10.1145/3396956.3396958","url":null,"abstract":"Previous research assumes that poor quality of Open Government Data (OGD), OGD portals, and the services provided for OGD may result in reduced trust of citizens in OGD. However, studies that empirically test this assumption are scarce. Using the Information Systems (IS) Success Model as a theoretical basis, this study aims to examine the effects of data quality, system quality, and service quality on citizens’ trust in OGD. We used Structural Equation Modeling (SEM) to analyze the 200 responses to our online questionnaire. We found that trust in OGD can be predicted by citizens’ perceptions of OGD system quality and service quality. Furthermore, citizens’ perception of service quality positively influences their perceptions of data and system quality, whereas citizens’ perception of system quality positively influences their perception of data quality. This study is among the first that quantitatively examines the effects of data quality, service quality, and system quality on citizen's trust in OGD. It contributes to the scientific literature by providing an operationalization of elements of the IS Success Model in the context of OGD and by developing and applying a model of factors influencing citizen's trust in OGD. While previous research finds that perceived data quality is the most crucial driver for trust in OGD, our study finds that citizens’ perception of OGD service quality is a more important driver for trust in OGD. With regard to the practical contributions of this study, open data policymakers should be aware that citizens’ perceptions on data quality can be greatly improved when appropriate human services are provided (e.g., designated civil servants offering support or help to data users) in addition to the provision of OGD portal functionalities (e.g., data visualization and comparison tools).","PeriodicalId":118651,"journal":{"name":"The 21st Annual International Conference on Digital Government Research","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128696288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eduardo Cidres, André Vasconcelos, Filipe A. Leitao
As cloud computing is becoming more popular as a way to host services and to improve businesses, its adoption still remains to be a clearly defined process. Specifically, when the adoption is to be made within the public administration (where additional constraints apply when compared to the private sector). Legislation needs to be created, standards need to be developed, and public organizations need to be in synch with their cloud goals and approaches. This paper proposes a tool that supports the architecture assessment of cloud migration or adoption initiatives. A systematic literature review is conducted to gain a better understanding of the characteristics of a system that are important to consider when analyzing the cloud viability of a system. Considering the technical aspects of cloud adoption, this paper proposes several criteria linked to software qualities. With the criteria set established, weights are then defined according to their respective importance in the system, supporting the readiness level classification that a system has regarding cloud computing, based on multi-criteria decision analysis. The developed solution is then applied in a case study, assessing the usefulness and effectiveness of the proposed tool for the cloud adoption process.
{"title":"Cloud Calculator: A cloud assessment tool for the Public Administration","authors":"Eduardo Cidres, André Vasconcelos, Filipe A. Leitao","doi":"10.1145/3396956.3396964","DOIUrl":"https://doi.org/10.1145/3396956.3396964","url":null,"abstract":"As cloud computing is becoming more popular as a way to host services and to improve businesses, its adoption still remains to be a clearly defined process. Specifically, when the adoption is to be made within the public administration (where additional constraints apply when compared to the private sector). Legislation needs to be created, standards need to be developed, and public organizations need to be in synch with their cloud goals and approaches. This paper proposes a tool that supports the architecture assessment of cloud migration or adoption initiatives. A systematic literature review is conducted to gain a better understanding of the characteristics of a system that are important to consider when analyzing the cloud viability of a system. Considering the technical aspects of cloud adoption, this paper proposes several criteria linked to software qualities. With the criteria set established, weights are then defined according to their respective importance in the system, supporting the readiness level classification that a system has regarding cloud computing, based on multi-criteria decision analysis. The developed solution is then applied in a case study, assessing the usefulness and effectiveness of the proposed tool for the cloud adoption process.","PeriodicalId":118651,"journal":{"name":"The 21st Annual International Conference on Digital Government Research","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126541732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study aimed to investigate the underlying factors that play an important role in improving citizens’ intention to use e-government services called Single Portal of Interactive Public Services (SPIPS) in Uzbekistan. To that end, a theoretical model known as Unified Model of E-government Adoption (UMEGA) was employed. A survey was conducted for 216 respondents in Uzbekistan to measure six constructs from UMEGA: (1) performance expectancy, (2) effort expectancy, (3) social influence, (4) perceived risk, (5) facilitating conditions and (6) attitude Reliability and validity test results indicated adequate consistency and validity. Results from structural equation model (SEM) indicated that performance expectancy had the greatest influence (β=0.745, p < 0.001) on intention to use e-government in Uzbekistan.
本研究旨在调查在提高乌兹别克斯坦公民使用电子政务服务意向方面发挥重要作用的潜在因素，该服务被称为交互式公共服务单一门户(SPIPS)。为此，采用了电子政务统一模型(UMEGA)这一理论模型。对乌兹别克斯坦216名被调查者进行了问卷调查，测量了UMEGA的六个构念:(1)绩效期望、(2)努力期望、(3)社会影响、(4)感知风险、(5)便利条件和(6)态度。信度和效度检验结果显示一致性和效度良好。结构方程模型(SEM)的结果表明，绩效预期对乌兹别克斯坦电子政务使用意愿的影响最大(β=0.745, p < 0.001)。
{"title":"E-Government Adoption in Uzbekistan: Empirical validation of the Unified Model of Electronic Government Acceptance (UMEGA)","authors":"Shokhrukh Avazov, Seohyun Lee","doi":"10.1145/3396956.3397008","DOIUrl":"https://doi.org/10.1145/3396956.3397008","url":null,"abstract":"This study aimed to investigate the underlying factors that play an important role in improving citizens’ intention to use e-government services called Single Portal of Interactive Public Services (SPIPS) in Uzbekistan. To that end, a theoretical model known as Unified Model of E-government Adoption (UMEGA) was employed. A survey was conducted for 216 respondents in Uzbekistan to measure six constructs from UMEGA: (1) performance expectancy, (2) effort expectancy, (3) social influence, (4) perceived risk, (5) facilitating conditions and (6) attitude Reliability and validity test results indicated adequate consistency and validity. Results from structural equation model (SEM) indicated that performance expectancy had the greatest influence (β=0.745, p < 0.001) on intention to use e-government in Uzbekistan.","PeriodicalId":118651,"journal":{"name":"The 21st Annual International Conference on Digital Government Research","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130881388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Data-driven Society: Balancing Prosperity and Security","authors":"Hun-Yeong Kwon, Ki-Yeong Min, M. Reiterer","doi":"10.1145/3406820","DOIUrl":"https://doi.org/10.1145/3406820","url":null,"abstract":"","PeriodicalId":118651,"journal":{"name":"The 21st Annual International Conference on Digital Government Research","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130444531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leonidas G. Anthopoulos, Wookjoon Sung, Soon Ae Chun
{"title":"Session details: Smart Cities: Intelligent Innovation and Transformation","authors":"Leonidas G. Anthopoulos, Wookjoon Sung, Soon Ae Chun","doi":"10.1145/3406817","DOIUrl":"https://doi.org/10.1145/3406817","url":null,"abstract":"","PeriodicalId":118651,"journal":{"name":"The 21st Annual International Conference on Digital Government Research","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128019053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reading news articles is essential and critical for understanding the local, nation-wide, and global emerging and developing events, as well as understanding the citizens’ demands and critics’ opinions. However, with the explosion of social media as news channels, citizens and groups of professionals share news and opinions, which has been the territory of trained journalists, adding more news to process. News often comes with multimedia objects, and suffers from integrity issues, especially with the unreliable or false claims, so-called fake news or altered or alternative facts. These quantity, diversity, and integrity pose significant challenges in the information age, not only for the decision-makers, including policymakers, business leaders but also for individual citizens. This study focuses on how the machine learning classification algorithms could help the news classifications in different categories to easily access the needed category of news and to filter out the noisy and harmful news.
{"title":"Developing Machine Learning Models to Automate News Classification","authors":"R. Singh, Soon Ae Chun, V. Atluri","doi":"10.1145/3396956.3397001","DOIUrl":"https://doi.org/10.1145/3396956.3397001","url":null,"abstract":"Reading news articles is essential and critical for understanding the local, nation-wide, and global emerging and developing events, as well as understanding the citizens’ demands and critics’ opinions. However, with the explosion of social media as news channels, citizens and groups of professionals share news and opinions, which has been the territory of trained journalists, adding more news to process. News often comes with multimedia objects, and suffers from integrity issues, especially with the unreliable or false claims, so-called fake news or altered or alternative facts. These quantity, diversity, and integrity pose significant challenges in the information age, not only for the decision-makers, including policymakers, business leaders but also for individual citizens. This study focuses on how the machine learning classification algorithms could help the news classifications in different categories to easily access the needed category of news and to filter out the noisy and harmful news.","PeriodicalId":118651,"journal":{"name":"The 21st Annual International Conference on Digital Government Research","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132925737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the topic of artificial intelligence in government has become a major area of study. Governments have been eager to adopt artificial intelligence for a number of purposes, including for the prediction of risk in social services. Child protection services are exploring predictive analytics for the initial screening of cases. While research identifies data quality issues as a major barrier, little is known about the characteristics of these issues in child protection, their relationship to organizational memory contained in administrative data, and their impact on the ability of an organization to adopt these technologies. This study gained insight into the socio-technical limitations of duplicate records when trying to bring organizational memory to bear in predictive decision support by interviewing and observing staff use of information technology systems. The study's findings suggest that record duplication in case management systems in child protection could pose a significant challenge to the introduction of artificial intelligence technologies such as predictive analytics for decision assistance. There is a need to address foundational information management and system issues before artificial intelligence approaches such as this can be introduced in the child protection sector.
{"title":"Artificial Intelligence and Organizational Memory in Government: The Experience of Record Duplication in the Child Welfare Sector in Canada","authors":"Thomas M. Vogl","doi":"10.1145/3396956.3396971","DOIUrl":"https://doi.org/10.1145/3396956.3396971","url":null,"abstract":"In recent years, the topic of artificial intelligence in government has become a major area of study. Governments have been eager to adopt artificial intelligence for a number of purposes, including for the prediction of risk in social services. Child protection services are exploring predictive analytics for the initial screening of cases. While research identifies data quality issues as a major barrier, little is known about the characteristics of these issues in child protection, their relationship to organizational memory contained in administrative data, and their impact on the ability of an organization to adopt these technologies. This study gained insight into the socio-technical limitations of duplicate records when trying to bring organizational memory to bear in predictive decision support by interviewing and observing staff use of information technology systems. The study's findings suggest that record duplication in case management systems in child protection could pose a significant challenge to the introduction of artificial intelligence technologies such as predictive analytics for decision assistance. There is a need to address foundational information management and system issues before artificial intelligence approaches such as this can be introduced in the child protection sector.","PeriodicalId":118651,"journal":{"name":"The 21st Annual International Conference on Digital Government Research","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121164877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we analyze the impact of innovative EBT (electronic benefit transfer) system that adopted in SNAP service delivery on SNAP payment error rate. As Clinton administration pursued public service innovation as part of New Public Management reform, the way of delivering SNAP was changed from paper coupon to EBT. To examine this impact, we have set three different types of dependent variables. The first dependent variable is combined payment error rate which is a sum of overpayment error rate and underpayment error rate. The second and third dependent variable that used in this study is overpayment error rate and underpayment error rate. In panel fixed effect model, EBT variable is significant when underpayment error rate is a dependent variable. In other words, according to empirical results from panel fixed effect model, the EBT system is significant in reducing official errors and mistakes. Contrary to this result, electronic system does not show any significant effect on overpayment error rate that closely related to the level of transparency. We did additional hierarchical regression analysis to draw determinants of overpayment error rate. Among the variables we include in hierarchical model, SR variable turned out to be the most important factor that affect overpayment error rate. According to third stage of hierarchical model, in the case of transparency, the back-up systems and social actor's attitude toward transparency are more important.
{"title":"The Impact of Changing Public Service Delivery on Inappropriate Payment Error: From Analogue Paper Coupon to Digitalized Electronic Benefit Transfer System","authors":"Sabinne Lee, Kwangho Jung","doi":"10.1145/3396956.3398255","DOIUrl":"https://doi.org/10.1145/3396956.3398255","url":null,"abstract":"In this study, we analyze the impact of innovative EBT (electronic benefit transfer) system that adopted in SNAP service delivery on SNAP payment error rate. As Clinton administration pursued public service innovation as part of New Public Management reform, the way of delivering SNAP was changed from paper coupon to EBT. To examine this impact, we have set three different types of dependent variables. The first dependent variable is combined payment error rate which is a sum of overpayment error rate and underpayment error rate. The second and third dependent variable that used in this study is overpayment error rate and underpayment error rate. In panel fixed effect model, EBT variable is significant when underpayment error rate is a dependent variable. In other words, according to empirical results from panel fixed effect model, the EBT system is significant in reducing official errors and mistakes. Contrary to this result, electronic system does not show any significant effect on overpayment error rate that closely related to the level of transparency. We did additional hierarchical regression analysis to draw determinants of overpayment error rate. Among the variables we include in hierarchical model, SR variable turned out to be the most important factor that affect overpayment error rate. According to third stage of hierarchical model, in the case of transparency, the back-up systems and social actor's attitude toward transparency are more important.","PeriodicalId":118651,"journal":{"name":"The 21st Annual International Conference on Digital Government Research","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125193199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Taxpayers may be interested in overpayment and which group of taxpayers he or she belongs to. Government officials may be concerned with underpaying taxpayers for auditing purposes and group taxpayers in the rapidly changing society. Machine learning and data mining techniques have been applied to provide solutions to these taxation related queries. Classification algorithms allow predicting the tax bracket based on the taxpayers' attributes. The regression model allows to predict the tax estimate so that the overpayment or underpayment can be determined. Clustering algorithms group taxpayers so that they can be compared to the past year tax brackets. Finally, feature selection allows finding salient attributes to predict the tax and tax bracket. In this article, New York State's Open Tax Data is used to demonstrate the machine learning and data mining algorithms and identify issues of using them. Furthermore, various visualization techniques are to present the discovered information to both taxpayers and government officials.
{"title":"Open Government Data for Machine Learning Tax Recommendation","authors":"Teryn Cha","doi":"10.1145/3396956.3397002","DOIUrl":"https://doi.org/10.1145/3396956.3397002","url":null,"abstract":"Taxpayers may be interested in overpayment and which group of taxpayers he or she belongs to. Government officials may be concerned with underpaying taxpayers for auditing purposes and group taxpayers in the rapidly changing society. Machine learning and data mining techniques have been applied to provide solutions to these taxation related queries. Classification algorithms allow predicting the tax bracket based on the taxpayers' attributes. The regression model allows to predict the tax estimate so that the overpayment or underpayment can be determined. Clustering algorithms group taxpayers so that they can be compared to the past year tax brackets. Finally, feature selection allows finding salient attributes to predict the tax and tax bracket. In this article, New York State's Open Tax Data is used to demonstrate the machine learning and data mining algorithms and identify issues of using them. Furthermore, various visualization techniques are to present the discovered information to both taxpayers and government officials.","PeriodicalId":118651,"journal":{"name":"The 21st Annual International Conference on Digital Government Research","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128385385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander Ronzhyn, M. Wimmer, G. Pereira, C. Alexopoulos
The increasing use of disruptive technologies in the public sector pushes toward the next stage of the digital government evolution: Government 3.0. This new stage is characterised by the focus on data-driven and evidence-based decision and policy making, where disruptive technologies are deployed. Along this, the engagement of citizens and other stakeholders both in data provision and co-creation continues to be crucial. One way to increase citizen participation is through the introduction of gamification elements into the digital government services. In this paper, the authors review literature and projects on gamification as a tool for improving citizen participation rates in Government 3.0. Based on this, avenues for further research are outlined in the area, taking into consideration a) the knowledge collected from recent EU-funded projects involving gamification and b) the opinions of experts in the domain, which were collected along interactive workshops. Finally, five research needs are identified and outlined for gamification in digital government.
{"title":"Gamification in Public Service Provisioning: Investigation of Research Needs","authors":"Alexander Ronzhyn, M. Wimmer, G. Pereira, C. Alexopoulos","doi":"10.1145/3396956.3398256","DOIUrl":"https://doi.org/10.1145/3396956.3398256","url":null,"abstract":"The increasing use of disruptive technologies in the public sector pushes toward the next stage of the digital government evolution: Government 3.0. This new stage is characterised by the focus on data-driven and evidence-based decision and policy making, where disruptive technologies are deployed. Along this, the engagement of citizens and other stakeholders both in data provision and co-creation continues to be crucial. One way to increase citizen participation is through the introduction of gamification elements into the digital government services. In this paper, the authors review literature and projects on gamification as a tool for improving citizen participation rates in Government 3.0. Based on this, avenues for further research are outlined in the area, taking into consideration a) the knowledge collected from recent EU-funded projects involving gamification and b) the opinions of experts in the domain, which were collected along interactive workshops. Finally, five research needs are identified and outlined for gamification in digital government.","PeriodicalId":118651,"journal":{"name":"The 21st Annual International Conference on Digital Government Research","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125256473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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