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    Title: 政府資料治理的知與行:內部觀點評估架構之建構與驗證
    An Assessment Framework of Government Data Governance from the Perspective of Internal Stakeholders
    Authors: 李洛維
    Lee, Lo-Wei
    Contributors: 朱斌妤
    Chu, Pin-Yu
    李洛維
    Lee, Lo-Wei
    Keywords: 資料治理
    智慧政府
    資料生命週期
    資料治理成熟度
    資料治理評估架構
    data governance
    smart government
    data lifecycle
    government data governance maturity model
    government data governance assessment framework
    Date: 2024
    Issue Date: 2024-06-03 11:46:38 (UTC+8)
    Abstract: 在以智慧政府為施政主軸的今日,各國政府數位服務的觸角已延伸至政治、經濟、社會等多元面向,同時也對民眾造成更深遠的影響。在這些智慧科技背後,資料居於關鍵核心地位,藉由一套完整的資料治理策略、標準化作業程序以及評估架構,將可更有效協助政府機關提升智慧政府的成效,以在未來面對更多挑戰。
    政府推動資料治理成效的良窳,除應於資料層面了解資料治理的內涵之外,在組織層面應探究機關本身的立場與其他機關的互動,在個人層面則應分析公務同仁對資料治理的知覺與行為等因素。基此,本研究首先於資料層面,透過文獻回顧建構「政府機關資料生命週期導向之資料治理成熟度評估架構」,以幫助政府機關更深入分析其資料治理在應然面與實然面的落差,並兼採組織與個人的觀點,建構出「政府機關資料治理知覺與行為理論模型」;其次,本研究依據上述架構與模型,透過問卷調查法針對個人層面進行資料治理知覺與行為之量化資料蒐集,並透過偏最小平方法進行實證,試圖辨明影響內部利害關係人推動資料治理的關鍵因素;第三,本研究選定重要智慧政府個案,以深度訪談進行質化資料蒐集,從組織層面探究由於政府機關資料治理成熟度與業務屬性之落差所導致推展資料治理行動的差異,以及隨之而來所面臨的困難、挑戰與解決方案。
    基對我國內政部及所屬機關蒐集彙整而得之研究成果,本研究分別回應了資料、個人、組織三個層面的研究問題,並與前期相關研究做出對話,呈現出因時空背景變遷對我國政府資料治理所造成的變化。最後,本研究提出三點實務建議與未來研究方向,除補足當前之研究缺口,亦期望藉由探明政府內部資料治理知覺與行為等變數間的關聯性,以有助於智慧政府成效提升及公共價值之實現。
    Smart government is currently the main focus of governance, with digital services of various governments touching upon multiple aspects such as politics, economy, and society, causing a profound impact on the public. Because data plays a crucial role, establishing a comprehensive data governance strategy, standard operating procedures, and evaluation framework can effectively help government agencies enhance the effectiveness of smart government, enabling them to face future challenges.
    The effectiveness of government data governance relies on understanding the essence of data governance at the data level, exploring the interaction with other agencies at the organizational level, and analyzing the factors of public servants' perception and behavior towards data governance at the individual level. Based on the above, firstly, this study constructs a “government data governance maturity model” through literature review at the data level, which helps government agencies to deeply analyze the gap between the normative and actual aspects of data governance. Additionally, this study also builds a “government data governance perception and behavior model” based on organizational and individual perspectives. Secondly, based on the aforementioned framework and model, this study employs a questionnaire survey method at the individual level to collect quantitative data on data governance perception and behavior. The partial least squares method is then utilized for validation, aiming to elucidate the key factors influencing internal stakeholders in promoting data governance. Thirdly, this study selects significant smart government cases and utilizes in-depth interviews at the organizational level for qualitative data collection, aims to explore the differences in data governance behaviors resulting from the gap between government agency data governance maturity level. Furthermore, this study investigates the difficulties, challenges, and potential solutions faced in this context.
    Based on the research outcomes, this study addresses research questions at the data, individual, and organizational levels. Also, this study engages in dialogue with prior relevant studies, illustrating the changes in government data governance in our country due to temporal and spatial background variations. Finally, this study presents three practical recommendations and future research directions to address current research gaps, aims to understand the relationship between variables such as government internal data governance perception and behavior, with the goal of enhancing the effectiveness of smart government and achieving public value.
    Reference: 內政部(2020)。智慧內政服務整合計畫,2023 年 9 月2日,取自:https://www.moi.gov.tw/News_Content.aspx?n=13876&s=214507
    內政部(2023)。內政部113年度施政計畫,2023 年 9 月10日,取自:https://ws.moi.gov.tw/Download.ashx?u=LzAwMS9VcGxvYWQvNDAwL3JlbGZpbGUvODk5OS80NC9lNGJjZGVhMS05MzdhLTRlYzMtYjliYS1hYjMyODAwNGM4ZTcucGRm&n=MTEz5bm05bqm5pa95pS%2f6KiI55Wr77yI6I2J5qGI54mI77yJLnBkZg%3d%3d
    內政部消防署(2020)。緊急醫療救護智能平臺-救急救難一站通推動計畫4年中程計畫,2024 年 1 月15日,取自:https://www.nfa.gov.tw/cht/index.php?act=download&ids=12482
    內政部統計處(2022a)。響應服務型智慧政府,跨域黑客松開創新局,2023 年 11 月17日,取自:https://ws.moi.gov.tw/Download.ashx?u=LzAwMS9VcGxvYWQvNDAwL3JlbGZpbGUvMC8xNjExMS84NmI2YTEyZS0xY2NmLTRkMmUtYWVlMi01Y2I2YTc0NDIzOTkucGRm&n=MTEx5bm05bCI6aGM5YiG5p6Q77yN6Z%2b%2f5oeJ5pyN5YuZ5Z6L5pm65oWn5pS%2f5bqcIOi3qOWfn%2bm7keWuouadvumWi%2bWJteaWsOWxgC5wZGY%3d
    內政部統計處(2022b)。地址編碼之建置、維運與應用,2023 年 12 月27日,取自:https://ws.moi.gov.tw/Download.ashx?u=LzAwMS9VcGxvYWQvNDAwL3JlbGZpbGUvMC8xNjcyNi9lZTk3ZTFjYS04ZWFmLTRlNTQtOGI0Yy1jYTkwMzg3MjEzNDUucGRm&n=5Zyw5Z2A57eo56K85LmL5bu6572u44CB57at6YGL6IiH5oeJ55SoKOWumueovykucGRm
    內政部建築研究所(2023)。112年度建築防火科技與智慧應用研發前瞻精進計畫,2023 年 12 月21日,取自:https://www.abri.gov.tw/cp.aspx?n=18838
    朱斌妤(2021)。因應開放資料後的政府資料治理策略與績效(編號:MOST 107-2410-H-004-122-MY3)。行政院國家科學及技術委員會。
    朱斌妤、李洛維(2022)。看不見的幕後英雄─淺談政府資料治理。載於陳敦源 、朱斌妤、蕭乃沂、黃東益、廖洲棚、曾憲立(編),政府數位轉型:一本必讀的入門書(頁223-236)。五南。ISBN:978-626-317-953-0
    李洛維、朱斌妤(2019)。公部門資料治理的發展與挑戰。T&D飛訊,253,1-26。
    李洛維、朱斌妤(2021)。推動服務型智慧政府的核心引擎:資料治理的挑戰與對策。文官制度,13(2),115-151。
    李洛維、朱斌妤、曾憲立(2023)。推動政府資料治理的關鍵因素:內部利害關係人角度的因果模型。公共行政學報,64,35-77。
    胡龍騰、曾冠球、張鎧如(2021)。後新冠時代的智慧政府發展趨勢與策略(編號:NDC-MIS-109-001)。行政院國家發展委員會。
    張鐙文、莊文忠、胡龍騰、曾冠球(2019)。電子化跨域治理成效指標之 設計與衡量:主觀性測量指標的應用與比較。東吳政治學報,37(1),1-54。
    黃東益、黃宗賢(2023)。當政府開放,「後臺」準備好了嗎? 開放政府革新下的組織變革策略。行政暨政策學報,77,1-32。
    楊東謀、吳怡融(2019)。台灣政府開放資料推行之近況調查與探討。教育資料與圖書館學,56(1),7-44。
    楊東謀、吳孟家(2022)。政府機關推行開放資料之影響因素探討:量化研究與多群組比較分析。圖書資訊學刊,20(1),131-171。
    廖洲棚、廖興中、黃心怡(2018)。開放政府服務策略研析調查:政府資料開放應用模式評估與民眾參與公共政策意願調查(編號:NDC-MIS-106-003)。行政院國家發展委員會。
    蕭乃沂、朱斌妤(2022)。數位發展與文官制度調適:以資料治理為例。文官制度,14(1),1-24。
    國發會(2016)。第五階段電子化政府計畫:數位政府。2024 年 5 月7 日,取自:https://ws.ndc.gov.tw/Download.ashx?u=LzAwMS9hZG1pbmlzdHJhdG9yLzEwL3JlbGZpbGUvNTU2Ni84MjA3L2M0ODQ2NWZkLTIwNTMtNDExMy1iMDhjLTMwNDIzZDkyZTE3NC5wZGY%3D&n=44CM56ys5LqU6ZqO5q616Zu75a2Q5YyW5pS%2F5bqc6KiI55WrKDEwNuW5tC0xMDnlubQpLeaVuOS9jeaUv%2BW6nOOAjS5wZGY%3D&icon=..pdf
    國發會(2020)。服務型智慧政府2.0推動計畫(110年至114年)。2023 年 4 月20 日,取自:https://www.moeaic.gov.tw/download-file.jsp;jsessionid=4B5AD6DCCBE73BE165EB704E72D22485?id=k4iXhg290zQ%3D&do=OD
    Abraham, R., Schneider, J., & vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, 424-438. https://doi.org/10.1016/j.ijinfomgt.2019.07.008
    ACRL (2000). Information Literacy Competency Standards for Higher Education. The University of Arizona. Retrieved May 22, 2023, from https://repository.arizona.edu/bitstream/handle/10150/105645/standards.pdf?sequence=1&i
    Adam, I. O., & Alhassan, M. D. (2022). The mediating role of ICT regulation on the effects of ICT access and ICT use on e-participation: Evidence from structural equation modelling and necessary condition analysis. African Journal of Science, Technology, Innovation and Development, 14(5), 1161-1172. https://doi.org/10.1080/20421338.2021.1937815
    Ahmad, N., Waqas, M., & Zhang, X. (2021). Public sector employee perspective towards adoption of e-government in Pakistan: A proposed research agenda [Virtual Conference]. 2020 ASIS&T Asia-Pacific Regional Conference, December 12-13, 2020, Wuhan, China. https://doi.org/10.2478/dim-2020-0029
    Ahn, M. J., & Chen, Y.-C. (2022). Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Government Information Quarterly, 39(2), 101664. https://doi.org/10.1016/j.giq.2021.101664
    Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
    Akinnuwesi, B. A., Uzoka, F.-M. E., Fashoto, S. G., Mbunge, E., Odumabo, A., Amusa, O. O., Okpeku, M., & Owolabi, O. (2022). A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19. Sustainable Operations and Computers, 3, 118-135. https://doi.org/10.1016/j.susoc.2021.12.001
    Al Hadwer, A., Tavana, M., Gillis, D., & Rezania, D. (2021). A systematic review of organizational factors impacting cloud-based technology adoption using technology-organization-environment framework. Internet of Things, 15, 100407. https://doi.org/10.1016/j.iot.2021.100407
    Al-Busaidy, M., & Weerakkody, V. (2009). E-government diffusion in Oman: A public sector employees' perspective. Transforming Government People Process and Policy, 3(4), 375-393. https://doi.org/10.1108/17506160910997883
    Al-Rahmi, M. W., Uddin, M., Alkhalaf, S., Al-Dhlan, K. A., Cifuentes-Faura, J., Al-Rahmi, A. M., & Al-Adwana, A. S. (2022). Validation of an Integrated IS Success Model in the study of e-government. Mobile Information Systems, 2022(5), 1-16. https://doi.org/10.1155/2022/8909724
    Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2016). A Conceptual Framework for Designing Data Governance for Cloud Computing. Procedia Computer Science, 94, 160-167. https://doi.org/10.1016/j.procs.2016.08.025
    Al-Ruithe, M., & Benkhelifa, E. (2017). Analysis and classification of barriers and critical success factors for implementing a cloud data governance strategy. Procedia Computer Science, 113, 223-232. https://doi.org/10.1016/j.procs.2017.08.352
    Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2019). A systematic literature review of data governance and cloud data governance. Personal and Ubiquitous Computing, 23, 839-859. https://doi.org/10.1007/s00779-017-1104-3
    Al-Ruithe, M., & Benkhelifa, E. (2020). Determining the enabling factors for implementing cloud data governance in the Saudi public sector by structural equation modelling. Future Generation Computer Systems, 107, 1061-1076. https://doi.org/10.1016/j.future.2017.12.057
    AlGhamdi, S., Win, K. T., & Vlahu-Gjorgievska, E. (2022). Employees' intentions toward complying with information security controls in Saudi Arabia's public organisations. Government Information Quarterly, 39(4), 101721. https://doi.org/10.1016/j.giq.2022.101721
    Alhassan, I., Sammon, D., & Daly, M. (2019). Critical success factors for data governance: A theory building approach. Information Systems Management, 36(2), 98-110. https://doi.org/10.1080/10580530.2019.1589670
    Alruwaie, M., El-Haddadeh, R., & Weerakkody, V. (2020). Citizens' continuous use of eGovernment services: The role of self-efficacy, outcome expectations and satisfaction. Government Information Quarterly, 37(3), 101485. https://doi.org/10.1016/j.giq.2020.101485
    Alzahrani, L., Al-Karaghouli, W., & Weerakkody, V. (2017). Analysing the critical factors influencing trust in e-government adoption from citizens’ perspective: A systematic review and a conceptual framework. International Business Review, 26(1), 164-175. https://doi.org/10.1016/j.ibusrev.2016.06.004
    Amanbek, Y., Balgayev, I., Batyrkhanov, K., & Tan, M. (2020). Adoption of e-Government in the Republic of Kazakhstan. Journal of Open Innovation: Technology, Market, and Complexity, 6(3), 46. https://doi.org/10.3390/joitmc6030046
    Amron, M. T., Ibrahim, R., Bakar, N. A. A., & Chuprat, S. (2019). Acceptance of cloud computing in the Malaysian public sector: A proposed model. International Journal of Engineering Business Management, 11, 1-9. https://doi.org/10.1177/1847979019880709
    Andersen, K. V., & Henriksen, H. Z. (2006). E-government maturity models: extension of the Layne and Lee model. Government Information Quarterly, 23(2), 236-248. https://doi.org/10.1016/j.giq.2005.11.008
    Arkorful, V. E., Lugu, B. K., Shuliang, Z., & Charway, S. M. (2023). Investigating COVID-19 Vaccine uptake intention using an integrated model of protection motivation theory and an extended version of the theory of planned behavior. Health Communication. https://doi.org/10.1080/10410236.2023.2201730
    Artyushina, A. (2020). Is civic data governance the key to democratic smart cities? The role of the urban data trust in Sidewalk Toronto. Telematics and Informatics, 55, 101456. https://doi.org/10.1016/j.tele.2020.101456
    Attard, J., Orlandi, F., Scerri, S., & Auer, S. (2015). A systematic review of open government data initiatives. Government Information Quarterly, 32(4), 399-418. https://doi.org/10.1016/j.giq.2015.07.006
    Awa, H. O., Ojiabo, O. U., & Emecheta, B. C. (2015). Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science & Technology Policy Management, 6(1), 76-94. https://doi.org/10.1108/JSTPM-04-2014-0012
    Azamela, J. C., Tang, Z., Ackah, O., & Awozum, S. (2022). Assessing the antecedents of e-Government adoption: A case of the Ghanaian public sector. SAGE Open, 12(2), 1-13. https://doi.org/10.1177/21582440221101040
    Basloom, R. S., Mohamad, M. H. S., & Md Auzair, S. (2022). Applicability of public sector reform initiatives of the Yemeni government from the integrated TOE-DOI framework. International Journal of Innovation Studies, 6(4), 286-302. https://doi.org/10.1016/j.ijis.2022.08.005
    Bélanger, F., & Carter, L. (2008). Trust and risk in e-government adoption. The Journal of Strategic Information Systems, 17(2), 165-176. https://doi.org/10.1016/j.jsis.2007.12.002
    Blazquez, D., & Domenech, J. (2018). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change, 130, 99-113. https://doi.org/10.1016/j.techfore.2017.07.027
    Bonina, C., & Eaton, B. (2020). Cultivating open government data platform ecosystems through governance: Lessons from Buenos Aires, Mexico City and Montevideo. Government Information Quarterly, 37(3), 101479. https://doi.org/10.1016/j.giq.2020.101479
    Cahyono, T. A., & Susanto, T. D. (2019). Acceptance factors and user design of mobile e-Government website (Study case e-government website in Indonesia). Procedia Computer Science, 161, 90-98. https://doi.org/10.1016/j.procs.2019.11.103
    Çaldağ, M. T., Gökalp, E., & Alkış, N. (2019). Analyzing determinants of open government based technologies and applications adoption in the context of organizations [Conference presentation]. International Conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government (EEE), August 1st, Las Vegas, Nevada.
    Cenfetelli, R., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33(4), 689-707. https://doi.org/10.2307/20650323
    Chacón, S. R. P., Vilchez, J. L. R., Berrios, J. A. C., Ibañez, C. A. R., & Mauricio, D. S. (2021). Increasing e-government adoption by emphasizing environmental sustainability: an extended case study in Peru. Transforming Government: People, Process and Policy, 15(4), 550-565. https://doi.org/10.1108/TG-10-2020-0305
    Charalabidis, Y., Loukis, E., & Alexopoulos, C. (2014). Evaluating second generation open government data infrastructures using value models. 47th Hawaii International Conference on System Sciences, Waikoloa, HI, USA. https://doi.org/10.1109/HICSS.2014.267
    Chatfield, A. T., & Reddick, C. G. (2019). A framework for Internet of Things-enabled smart government: A case of IoT cybersecurity policies and use cases in U.S. federal government. Government Information Quarterly, 36(2), 346-357. https://doi.org/10.1016/j.giq.2018.09.007
    Chen, J. V., Jubilado, R. J. M., Capistrano, E. P. S., & Yen, D. C. (2015). Factors affecting online tax filing – An application of the IS Success Model and trust theory. Computers in Human Behavior, 43, 251-262. https://doi.org/10.1016/j.chb.2014.11.017
    Chin, W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management and Data Systems, 120(12), 2161-2209. https://doi.org/10.1108/IMDS-10-2019-0529
    Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Eds.), Modern Methods for Business Research (pp. 295-336). Lawrence Erlbaum Associates.
    Choi, J.-C., & Song, C. (2020). Factors explaining why some citizens engage in E-participation, while others do not. Government Information Quarterly, 37(4), 101524. https://doi.org/10.1016/j.giq.2020.101524
    Chu, P.-Y., Hsiao, N., Lee, F.-W., & Chen, C.-W. (2004). Exploring success factors for Taiwan’s government electronic tendering system: Behavioral perspectives from end users. Government Information Quarterly, 21(2), 219-234. https://doi.org/10.1016/j.giq.2004.01.005
    Chung, H.-Y., Lee, G.-G., & Kuo, R.-Z. (2016). Determinants of public servants’ intention to adopt e-government learning. Review of Public Personnel Administration, 36(4), 396-411. https://doi.org/10.1177/0734371X15590482
    Claudia, I., Alexandra, P., & Octav-Ionut, M. (2012). The Influence Of Perceived Risk On Conumers’ Intention To Buy Online: A Meta-Analysis Of Empirical Results. Romanian Economic Business Review, 6(1), 162-174. Retrieved Jan 21, 2024, from http://www.rebe.rau.ro/RePEc/rau/jisomg/SP12/JISOM-SP12-A15.pdf
    CMMI Institute (2019). Data Management Maturity Model. Retrieved April 21, 2023, from https://stage.cmmiinstitute.com/getattachment/cb35800b-720f-4afe-93bf-86ccefb1fb17/attachment.aspx
    Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd Ed.). Lawrence Erlbaum Associates, Inc.
    Cronemberger, F. A., & Gil-Garcia, J. R. (2022). Characterizing stewardship and stakeholder inclusion in data analytics efforts: the collaborative approach of Kansas City, Missouri. Transforming Government: People, Process and Policy, 16(4), 405-417. https://doi.org/10.1108/TG-05-2022-0065
    DAMA International (2017). DAMA DMBOK v2 Wheel Images. Retrieved May 5, 2023, from https://www.dama.org/cpages/dmbok-2-wheel-images
    Danila, R., & Abdullah, A. (2014). User's Satisfaction on E-government Services: An Integrated Model. Procedia - Social and Behavioral Sciences, 164, 575-582. https://doi.org/10.1016/j.sbspro.2014.11.148
    DataFlux (2007). The data governance maturity model. Establishing the people, policies and technology that manage enterprise data. DataFlux Corporation LLC. Retrieved May 7, 2023, from https://www.fstech.co.uk/fst/whitepapers/The_Data_Governance_Maturity_Model.pdf
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technologies. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
    Dawes, S. S., & Helbig, N. (2010). Information strategies for open government: Challenges and prospects for deriving public value from government transparency. Electronic Government, 50-60. http://dx.doi.org/10.1007/978-3-642-14799-9_5
    Dawes, S. S., Vidiasova, L., & Parkhimovich, O. (2016). Planning and designing open government data programs: An ecosystem approach. Government Information Quarterly, 33(1), 15-27. https://doi.org/10.1016/j.giq.2016.01.003
    Defitri, S. Y., Bahari, A., Handra, H., & Febrianto, R. (2020). Determinant factors of e-government implementation and public accountability: TOE framework approach. Public Policy and Administration, 19(4), 31-57. Retrieved May 19, 2023, from https://ojs.mruni.eu/ojs/public-policy-and-administration/article/view/5273/5292
    DeLone, W. H., & McLean, E. R. (1992). Information system success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95. http://dx.doi.org/10.1287/isre.3.1.60
    DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information system success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748
    Deng, H., Karunasena, K., & Xu, W. (2018). Evaluating the performance of e-government in developing countries: A public value perspective. Internet Research, 28(1), 169-190. https://doi.org/10.1108/IntR-10-2016-0296
    DGI (2023). Definitions of Data Governance. The Data Governance Institute. Retrieved May 5, 2023, from https://datagovernance.com/the-data-governance-basics/definitions-of-data-governance/
    Diamantopoulos, A., & Winklhofer, H. M. (2001). Index Construction with Formative Indicators: An Alternative to Scale Development. Journal of Marketing Research, 38(2), 269-277. https://doi.org/10.1509/jmkr.38.2.269.18845
    van Donge, W., Bharosa, N., & Janssen, M. F. H. W. A. (2022). Data-driven government: Cross-case comparison of data stewardship in data ecosystems. Government Information Quarterly, 39(2), 101642. https://doi.org/10.1016/j.giq.2021.101642
    Drakopulos, L., Havice, E., & Campbell, L. (2022). Architecture, agency and ocean data science initiatives: Data-driven transformation of oceans governance. Earth System Governance, 12, 100140. https://doi.org/10.1016/j.esg.2022.100140
    Dutta, A., Roy, R., & Seetharaman, P. (2022). An assimilation maturity model for IT governance and auditing. Information & Management, 59(1). https://doi.org/10.1016/j.im.2021.103569
    Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M. (2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211-230. https://doi.org/10.1016/j.giq.2017.03.001
    Dzandu, M. D. (2023). Antecedent, behaviour, and consequence (a-b-c) of deploying the contact tracing app in response to COVID-19: Evidence from Europe. Technological Forecasting and Social Change, 187, 122217. https://doi.org/10.1016/j.techfore.2022.122217
    Eke, D. O., Bernard, A., Bjaalie, J. G., Chavarriaga, R., Hanakawa, T., Hannan, A. J., Hill, S. L., Martone, M. E., McMahon, A., Ruebel, O., Crook, S., Thiels, E., & Pestilli, F. (2022). International data governance for neuroscience. Neuron, 110(4), 600-612. https://doi.org/10.1016/j.neuron.2021.11.017
    Elmansori, M. M., & Ishak, Z. (2021). Factors influencing e-government services adoption in Libya: an empirical study. Electronic Government, 17(4), 494-511. https://dx.doi.org/10.1504/EG.2022.10035772
    European Commission (2020a). Data governance and data policies at the European Commission. European Commission. Retrieved May 10, 2023, from https://commission.europa.eu/publications/data-governance-and-data-policies-european-commission_en
    European Commission (2020b). Commission Staff Working Document Impact Assessment Report: Accompanying the document “Proposal for a Regulation of the European Parliament and of the Council on European Data Governance (Data Governance Act).” Retrieved December 11, 2023, from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52020PC0767
    Faundeen, J. L., Burley, T. E., Carlino, J. A., Govoni, D. L., Henkel, H. S., Holl, S. L., Hutchison, V, B., Martín, E., Montgomery, E. T., Ladino, C. C., Tessler, S., & Zolly, L. S. (2013). The United States Geological Survey Science Data Lifecycle Model. USGS. Retrieved May 9, 2023, from https://pubs.usgs.gov/of/2013/1265/pdf/of2013-1265.pdf
    Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451-474. https://doi.org/10.1016/S1071-5819(03)00111-3
    Filgueiras, F., & Lui, L. (2023). Designing data governance in Brazil: an institutional analysis. Policy Design and Practice, 6(1), https://doi.org/10.1080/25741292.2022.2065065
    Firican, G. (2011). Stanford data governance maturity model. Lights on Data, Retrieved July 19, 2022, from https://www.lightsondata.com/data-governance-maturity-models-stanford/
    Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley, USA.
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
    Foy, M., Martyn, D., Daly, D., Byrne, A., Aguneche, C., & Brennan, R. (2022). Blockchain-based governance models for COVID-19 digital health certificates: A legal, technical, ethical and security requirements analysis. Procedia Computer Science, 198, 662-669. https://doi.org/10.1016/j.procs.2021.12.303
    Fu, J.-R., Farn, C.-K., & Chao, W.-P. (2006). Acceptance of electronic tax filing: A study of taxpayer intentions. Information & Management, 43(1), 109-126. https://doi.org/10.1016/j.im.2005.04.001
    Gamage, P. (2016). New development: Leveraging ‘big data’ analytics in the public sector. Public Money & Management, 36(5), 385-390. https://doi.org/10.1080/09540962.2016.1194087
    Gao, J., Sun, Y., Rameezdeen, R., & Chow, C. (2022). Understanding data governance requirements in IoT adoption for smart ports – a gap analysis. Maritime Policy & Management, 1-19. https://doi.org/10.1080/03088839.2022.2155318
    Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101-107. https://doi.org/10.1093/biomet/61.1.101
    Gelhaar, J., & Otto, B. (2020). Challenges in the Emergence of Data Ecosystems. Conference: Pacific Asia Conference on Information Systems (PACIS), Dubai. https://aisel.aisnet.org/pacis2020/175
    Gelhaar, J., Groß, T., & Otto, B. (2021). A Taxonomy for Data Ecosystems. Proceedings of the 54th Hawaii International Conference on System Sciences, January 5-8, Grand Wailea, Maui, Hawaii. http://hdl.handle.net/10125/71359
    Gesk, T. S., & Leyer, M. (2022). Artificial intelligence in public services: When and why citizens accept its usage. Government Information Quarterly, 39(3), 101704. https://doi.org/10.1016/j.giq.2022.101704
    Gil-Garcia, J. R. (2008). Using partial least squares in digital government research. In G. David Garson, & M. Khosrow-Pour (Eds.), Handbook of Research on Public Information Technology (pp. 239-253). IGI Global.
    Gil-Garcia, J. R., Dawes, S. S., & Pardo, T. A. (2018). Digital government and public management research: finding the crossroads. Public Management Review, 20(5), 633-646. https://doi.org/10.1080/14719037.2017.1327181
    Gil-Garcia, J. R., & Flores-Zúñiga, M. Á. (2020). Towards a comprehensive understanding of digital government success: Integrating implementation and adoption factors. Government Information Quarterly, 37(4), 101518. https://doi.org/10.1016/j.giq.2020.101518
    Gökalp, M. O., Gökalp, E., & Kayabay, K., Koçyiğit, A., & Eren, P. E. (2022). The development of the data science capability maturity model: a survey-based research. Online Information Review, 46(3), 547-567. https://doi.org/10.1108/OIR-10-2020-0469
    Gonzalez-Zapata, F., & Heeks, R. (2015). The multiple meanings of open government data: Understanding different stakeholders and their perspectives. Government Information Quarterly, 32(4), 441-452. https://doi.org/10.1016/j.giq.2015.09.001
    Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed, a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
    Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12. https://psycnet.apa.org/doi/10.1016/j.lrp.2013.01.001
    Hair, J. F., Sarstedt, M. & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566-584. https://doi.org/10.1108/EJM-10-2018-0665
    Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications, Inc.
    Haneem, F., Kama, N., Taskin, N., Pauleen, D., & Bakar, N. A. A. (2019). Determinants of master data management adoption by local government organizations: An empirical study. International Journal of Information Management, 45, 25-43. https://doi.org/10.1016/j.ijinfomgt.2018.10.007
    Harrison, T. M., & Luna-Reyes, L. F. (2020). Cultivating Trustworthy Artificial Intelligence in Digital Government. Social Science Computer Review, 40(2), 494-511. https://doi.org/10.1177/0894439320980122
    Hausladen, I., & Schosser, M. (2020). Towards a maturity model for big data analytics in airline network planning. Journal of Air Transport Management, 82, 101721. https://doi.org/10.1016/j.jairtraman.2019.101721
    Heeks, R. (2006). Benchmarking e-government: Improving the national and international measurement, evaluation and comparison of e-government. IDPM i-Government Working Paper, 18, 1-33. http://dx.doi.org/10.1016/B978-0-7506-8587-0.50017-2
    Henseler, J., Hubona, G., & Ray, P. (2016a). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. https://doi.org/10.1108/IMDS-09-2015-0382
    Henseler, J., Ringle, C. M., & Sarstedt, M. (2016b). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405-431. https://doi.org/10.1108/IMR-09-2014-0304
    Hoe, S. L. (2016). Defining a smart nation: the case of Singapore. Journal of Information, Communication and Ethics in Society, 14(4), 323-333. https://doi.org/10.1108/JICES-02-2016-0005
    Hossain, A., Quaresma, R., & Rahman, H. (2019). Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study. International Journal of Information Management, 44, 76-87. https://doi.org/10.1016/j.ijinfomgt.2018.09.016
    Hsieh, P.-J. (2015). Physicians' acceptance of electronic medical records exchange: An extension of the decomposed TPB model with institutional trust and perceived risk. International Journal of Medical Informatics, 84(1), 1-14. https://doi.org/10.1016/j.ijmedinf.2014.08.008
    Hujran, O., Al-Debei, M. M., Al-Adwan, A. S., Alarabiat, A., & Altarawneh, N. (2023). Examining the antecedents and outcomes of smart government usage: An integrated model. Government Information Quarterly, 40(1), 101783. https://doi.org/10.1016/j.giq.2022.101783
    Huang, H., Yao, H. A., Krisp, J. M., & Jiang, B. (2021). Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions. Computers, Environment and Urban Systems, 90, 101712. https://doi.org/10.1016/j.compenvurbsys.2021.101712
    Huang, S.-Y., Tang, K.-Z., Chang, C.-M., & Ke, C.-D. (2009). User acceptance of intergovernmental services: An example of electronic document management system. Government Information Quarterly, 26 (2), 387-397. https://doi.org/10.1016/j.giq.2008.07.003
    Hussein, R., Karim, N. S. A., & Selamat, M. H. (2007). The impact of technological factors on information systems success in the electronic‐government context. Business Process Management Journal, 13(5), 613-627. https://doi.org/10.1108/14637150710823110
    Hyytinen, A., Tuimala, J., & Hammar, M. (2022). Enhancing the adoption of digital public services: Evidence from a large-scale field experiment. Government Information Quarterly, 39(3), 101687. https://doi.org/10.1016/j.giq.2022.101687
    IBM. (2007). The IBM data governance council maturity model: Building a roadmap for effective data governance. Retrieved August 1, 2022, from http://hosteddocs.ittoolbox.com/TheFundimentals.PDF
    IBM. (2013). The fundamentals of data lifecycle management in the era of big data: How data lifecycle management complements a big data strategy. Retrieved May 5, 2023 from http://hosteddocs.ittoolbox.com/TheFundimentals.PDF
    Irawan, M. Z., Bastarianto, F. F., & Priyanto, S. (2022). Using an integrated model of TPB and TAM to analyze the pandemic impacts on the intention to use bicycles in the post-COVID-19 period. IATSS Research, 46(3), 380-387. https://doi.org/10.1016/j.iatssr.2022.05.001
    Isaac, O., Abdullah, Z., Aldholay, A. H., & Ameen, A. A. (2019). Antecedents and outcomes of internet usage within organisations in Yemen: An extension of the unified theory of acceptance and use of technology (UTAUT) model. Asia Pacific Management Review, 24(4), 335-354. https://doi.org/10.1016/j.apmrv.2018.12.003
    Jann, W., & Wegrich, K. (2007). Theories of the policy cycle. In F. Fischer, G. J. Miller, & M. S. Sidney (Eds.), Handbook of Public Policy Analysis: Theory, Politics, and Methods (1st ed., pp. 43-62). Boca Raton: CRC Press.
    Janowski, T. (2015). Digital government evolution: From transformation to contextualization. Government Information Quarterly, 32(3), 221-236. https://doi.org/10.1016/j.giq.2015.07.001
    Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, adoption barriers and myths of open data and open government. Information Systems Management, 29(4), 258-268. https://doi.org/10.1080/10580530.2012.716740
    Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), 101493. https://doi.org/10.1016/j.giq.2020.101493
    Jones, D. T. (2018). Data governance framework strategic plan. City of Philadelphia Department of Behavioral Health and Intellectual disAbility Services. Retrieved April 27, 2023, from https://dbhids.org/wp-content/uploads/2019/02/DBHIDS-DG-Framework-Strategic-Plan-v2.03.pdf
    Jöreskog, K. G., & Wold, H. (1982). The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects. In Wold, H. & Jöreskog, K. G. (Eds), Systems under Indirect Observation Part I, (pp. 263-270). North-Holland, Amsterdam,
    Ju, J., Liu, L., & Feng, Y. (2018). Citizen-centered big data analysis-driven governance intelligence framework for smart cities. Telecommunications Policy, 42(10), 881-896. https://doi.org/10.1016/j.telpol.2018.01.003
    Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152. https://doi.org/10.1145/1629175.1629210
    Kim, H. Y., & Cho, J.-S. (2018). Data governance framework for big data implementation with NPS case analysis in Korea. Journal of Business and Retail Management Research, 12(3), 36-46. https://doi.org/10.24052/JBRMR/V12IS03/ART-04
    Kimathi, F. A., & Zhang, Y. (2019). Citizens’ acceptance of e-government service: Examining e-tax filing and payment system (ETFPS) in Tanzania. African Journal of Library, Archives and Information Science, 29(1), 45-62.
    Klievink, B., Romijn, B.-J., Cunningham, S., & de Bruijn, H. (2017). Big data in the public sector: Uncertainties and readiness. Information Systems Frontiers, 19, 267-283. https://doi.org/10.1016/j.jairtraman.2019.101721
    Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA Journal, 42(4), 303-312. https://doi.org/10.1177/0340035216672238
    König, P. D. (2021). Citizen-centered data governance in the smart city: From ethics to accountability. Sustainable Cities and Society, 75, 103308. https://doi.org/10.1016/j.scs.2021.103308
    Lacombe, I., & Jarboui, A. (2022). Governance and management of digital transformation projects: an exploratory approach in the financial sector. International Journal of Innovation Science. https://doi.org/10.1108/IJIS-02-2022-0034
    Leavitt, H. J. (1965). Applied organizational change in industry: Structural, technological and humanistic approaches. In J. G. March (Ed.), Handbook of Organizations (pp. 1144-1170). Routledge.
    Li, W. (2021). The role of trust and risk in citizens’ e-government services adoption: A perspective of the extended UTAUT Model. Sustainability, 13(14), 7671. https://doi.org/10.3390/su13147671
    Liang, J. K., Eccarius, T., & Lu, C. C. (2019). Investigating factors that affect the intention to use shared parking: A case study of Taipei City. Transportation Research Part A, 130, 799-812. https://doi.org/10.1016/j.tra.2019.10.006
    Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, Yet Forsaken? Introducing a Cross-Validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2), 362-392. https://doi.org/10.1111/deci.12445
    Liu, Z.-G., Li, X.-Y., & Jomaas, G. (2022). Effects of governmental data governance on urban fire risk: A city-wide analysis in China. International Journal of Disaster Risk Reduction, 78, 103138. https://doi.org/10.1016/j.ijdrr.2022.103138
    Lis, D., & Otto, B. (2020). Data Governance in Data Ecosystems – Insights from Organizations [Virtual Conference]. Conference: Americas Conference on Information Systems (AMCIS), August 15-17. https://aisel.aisnet.org/amcis2020/strategic_uses_it/strategic_uses_it/12
    Lnenicka, M., & Komarkova, J. (2019). Big and open linked data analytics ecosystem: Theoretical background and essential elements. Government Information Quarterly, 36(1), 129-144. https://doi.org/10.1016/j.giq.2018.11.004
    Lopes, K. M. G., Macadar, M. A., & Luciano, E. M. (2019). Key drivers for public value creation enhancing the adoption of electronic public services by citizens. International Journal of Public Sector Management, 32(5), 546-561. https://doi.org/0.1108/IJPSM-03-2018-0081
    Lu, N. L., & Nguyen, V. T. (2016) .Online tax filing - e-government service adoption case of Vietnam. Modern Economy, 7, 1498-1504. http://dx.doi.org/10.4236/me.2016.712135
    MacKenzie, S. B., Podsakoff, P. M., & Jarvis, C. B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90(4), 710-730. https://doi.org/10.1037/0021-9010.90.4.710
    MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293-334. https://doi.org/10.2307/23044045
    Madan, R., & Ashok, M. (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 40(1), 101774. https://doi.org/10.1016/j.giq.2022.101774
    Mao, Z., Wu, J., Qiao, Y., & Yao, H. (2022). Government data governance framework based on a data middle platform. Aslib Journal of Information Management, 74(2), 289-310. https://doi.org/10.1108/AJIM-03-2021-0068
    Mathai, N., McGill, T., & Toohey, D. (2022). Factors influencing consumer adoption of electronic health records. Journal of Computer Information Systems, 62(2), 267-277. https://doi.org/10.1080/08874417.2020.1802788
    McClure, C. R. (1994). Network literacy: A role for libraries? Information Technology and Libraries, 13(2), 115-125.
    Mensah, I. K. (2019). Predictors of Electronic Government Services Adoption: The African Students’ Perspective in China. International Journal of Public Administration, 42(12), 997-1009. https://doi.org/10.1080/01900692.2019.1572621
    Mensah, I. K., & Mi, J. (2019). Computer self-efficacy and e-government service adoption: The moderating role of age as a demographic factor. International Journal of Public Administration, 42(2), 158-167. https://doi.org/10.1080/01900692.2017.1405980
    Mensah, I. K. (2020). Impact of government capacity and e-government performance on the adoption of e-government services. International Journal of Public Administration, 43(4), 303-311. https://doi.org/10.1080/01900692.2019.1628059
    Mensah, I. K., Luo, C., & Thani, X. C. (2022). The moderating impact of technical support and internet self-efficacy on the adoption of electronic government services. International Journal of Public Administration, 45(14), 1039-1052. https://doi.org/10.1080/01900692.2021.1961150
    Mergel, I., Rethemeyer, R. K. & Isett, K. (2016). Big data in public affairs. Public Administration Review, 76(6), 928-937. https://doi.org/10.1111/puar.12625
    Meyers, M., Niech, C., & Eggers, W. D. (2015). Anticipate, sense, and respond: Connected government and the internet of things. Retrieved April 7, 2023, from https://www2.deloitte.com/content/dam/Deloitte/tr/Documents/technology/iot-public-sector.pdf
    Mhina, J. R. A., Md Johar, M. G., & Alkawaz, M. H. (2019). The influence of perceived confidentiality risks and attitude on Tanzania government employees’ intention to adopt web 2.0 and social media for work-related purposes. International Journal of Public Administration, 42(7), 558-571. https://doi.org/10.1080/01900692.2018.1491596
    Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Fjørtoft, S. O., Torvatn, H. Y., Gupta, M., & Niehaves, B. (2022). Enabling AI capabilities in government agencies: A study of determinants for European municipalities. Government Information Quarterly, 39(4), 101596. https://doi.org/10.1016/j.giq.2021.101596
    Mital, M., Chang, V., Choudhary, P., Papa, A., & Pani, A. K. (2018). Adoption of Internet of Things in India: A test of competing models using a structured equation modeling approach. Technological Forecasting & Social Change, 136, 339-346. https://doi.org/10.1016/j.techfore.2017.03.001
    Moon, M. J. (2020). Shifting from old open government to new open government: Four critical dimensions and case illustrations. Public Performance & Management Review, 43(3), 535-559, https://doi.org/10.1080/15309576.2019.1691024
    Myeong, S., Kim, Y., & Ahn, M. J. (2021). Smart city strategies - Technology push or culture pull? A case study exploration of Gimpo and Namyangju, South Korea. Smart Cities, 4(1), 41-53. https://doi.org/10.3390/smartcities4010003
    Nam, T. (2022). Do the right thing right! Understanding the hopes and hypes of data-based policy. Government Information Quarterly, 37(3), 101491. https://doi.org/10.1016/j.giq.2020.101491
    Neumann, O., Guirguis, K., & Steiner, R. (2022). Exploring artificial intelligence adoption in public organizations: A comparative case study. Public Management Review, 26(1), 114-141. https://doi.org/10.1080/14719037.2022.2048685
    Nielsen, O. B., Persson, J. S., & Madsen, S. (2018). Why governing data is difficult: Findings from Danish local government. In: Elbanna A, Dwivedi YK, Bunker D, et al. (eds.) Smart Working, Living and Organising (pp. 15-29). Midtown Manhattan, New York City: Springer International Publishing. https://doi.org/10.1007/978-3-030-04315-5_2
    Nunnally, J. (1978). Psychometric methods (2nd ed.). McGraw-Hill.
    OECD (2019). The Path to Becoming a Data‑Driven Public Sector. OECD Digital Government Studies. OECD Publishing, Paris. Retrieved May 25, 2023, from https://doi.org/10.1787/059814a7-en
    OECD (2020a). The OECD digital government policy framework: Six dimensions of a digital government. OECD Publishing, Paris. Retrieved May 29, 2023, from https://doi.org/10.1787/14e1c5e8-en-fr
    OECD (2020b). The OECD 2019 Open Useful Reusable Data (OURDATA) Index. OECD Publishing, Paris. Retrieved May 31, 2023 from https://www.oecd.org/governance/digital-government/ourdata-index-policy-paper-2020.pdf
    OECD (2022). Going Digital to Advance Data Governance for Growth and Well-being. OECD Publishing, Paris. Retrieved May 24, from https://doi.org/10.1787/e3d783b0-en
    Okongwu, U., Morimoto, R., & Lauras, M. (2013). The maturity of supply chain sustainability disclosure from a continuous improvement perspective. International Journal of Productivity and Performance Management, 62(8), 827-855. https://doi.org/10.1108/IJPPM-02-2013-0032
    Olaitan, O., Herselman, M., & Wayi, N. (2019). A data governance maturity evaluation model for government departments of the Eastern Cape province, South Africa. South African Journal of Information Management, 21(1), a996. https://doi.org/10.4102/sajim.v21i1.996
    OMB (2021). Federal Data Strategy 2021 Action Plan. Office of Management and Budget, the CDO Council and the General Services Administration. Retrieved May 30, 2023, from https://strategy.data.gov/2021/action-plan/
    van Ooijen, C., Ubaldi, B., & Welby, B. (2019). A data-driven public sector: Enabling the strategic use of data for productive, inclusive and trustworthy governance. OECD Working Papers on Public Governance, 33. OECD Publishing, Paris, https://doi.org/10.1787/09ab162c-en
    Ospina, M. L. C., & Pinzón, B. H. D. (2018). Theoretical perspectives on usage of e-government services: A literature review. Conference: Twenty-fourth Americas Conference on Information Systems (AMCIS), August 16-18, New Orleans, LA. https://aisel.aisnet.org/amcis2018/eGovernment/Presentations/7/
    Otto, B. (2011). Organizing Data Governance: Findings from the Telecommunications Industry and Consequences for Large Service Providers. Communications of the Association for Information Systems, 29(1), 45-66. https://doi.org/10.17705/1CAIS.02903
    Otto, B. (2015). Quality and value of the data resource in large enterprises. Information Systems Management, 32(3), 234-251. https://doi.org/10.1080/10580530.2015.1044344
    Ozen, O., Pourmousa, H., & Alıpour, N. (2018). Investigation of the critical factors affecting e-government acceptance: A systematic review and a conceptual model. Innovative Journal of Business and Management, 7(3), 77-84. Retrieved May 17, 2023, from https://www.forskerforum.no/wp-content/uploads/2019/06/%C3%96zen-2018-2.pdf
    Ozkan, S., & Kanat, I. E. (2011). e-Government adoption model based on theory of planned behavior: Empirical validation. Government Information Quarterly, 28(4), 503-513. https://doi.org/10.1016/j.giq.2010.10.007
    Panagiotopoulos, P., Bowen, F., & Brooker, P. (2017). The value of social media data: Integrating crowd capabilities in evidence-based policy. Government Information Quarterly, 34(4), 601-612. https://doi.org/10.1016/j.giq.2017.10.009
    Park, S., & Gil-Garcia, J. R. (2022). Open data innovation: Visualizations and process redesign as a way to bridge the transparency-accountability gap. Government Information Quarterly, 39(1), 101456. https://doi.org/10.1016/j.giq.2020.101456
    Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2020). Big Data and AI - A transformational shift for government: So, what next for research? Public Policy and Administration, 35(1), 24-44. https://doi.org/10.1177/0952076718780537
    Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623-656. https://doi.org/10.2307/25148814
    Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236-263. http://dx.doi.org/10.1057/ejis.2008.15
    Petter, S., & McLean, E. (2009). A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level. Information & Management, 46(3), 159-166. https://doi.org/10.1016/j.im.2008.12.006
    Petter, S., DeLone, W., & McLean, E. (2014). Information Systems Success: The Quest for the Independent Variables. Journal of Management Information Systems, 29(4), 7-62. https://doi.org/10.2753/MIS0742-1222290401
    PIC (2016). Performance Improvement Council’s data quality working group guide: Data quality maturity model. Retrieved May 10, 2023, from https://dokumen.tips/documents/performance-improvement-councils-data-quality-quality-maturity-model-and.html?page=1
    Pirannejad, A., & Ingrams, A. (2023). Open government maturity models: A global comparison. Social Science Computer Review, 41(4), 1140-1165. https://doi.org/10.1177/08944393211063107
    Puron-Cid, G., & Villaseñor-García, E. A. (2023). Applying neural networks analysis to assess digital government evolution. Government Information Quarterly, 40(3), 101811. https://doi.org/10.1016/j.giq.2023.101811
    Rahi, S. (2023). What drives citizens to get the COVID-19 vaccine? The integration of protection motivation theory and theory of planned behavior. Journal of Social Marketing, 13(2). https://doi.org/10.1108/JSOCM-05-2022-0100
    Rahmafitria, F., Suryadi, K., Oktadiana, H., Putro, H. P. H., & Rosyidie, A. (2021). Applying knowledge, social concern and perceived risk in planned behavior theory for tourism in the Covid-19 pandemic. Tourism Review, 76(4), 809-828. https://doi.org/10.1108/TR-11-2020-0542
    Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Williams, J. (2012). Theories and theoretical models for examining the adoption of e-government services. e-Service Journal, 8(2), 26-56. https://doi.org/10.2979/eservicej.8.2.26
    Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Weerakkody, V. (2015). Investigating success of an e-government initiative: Validation of an integrated IS success model. Information Systems Frontiers, 17, 127-142. https://doi.org/10.1007/s10796-014-9504-7
    Rana, N. P., Williams, M. D., Dwivedi, Y. K., Lal, B., & Williams, M. D., & Clement, M. (2017). Citizens’ adoption of an electronic government system: Towards a unified view. Information Systems Frontiers, 19, 549-568. https://doi.org/10.1007/s10796-015-9613-y
    Rantala, S., Swallow, B., Paloniemi, R., & Raitanen, E. (2020). Governance of forests and governance of forest information: Interlinkages in the age of open and digital data. Forest Policy and Economics, 113, 102123. https://doi.org/10.1016/j.forpol.2020.102123
    Rehman, A., & Hashim, F. (2018). Corporate Governance Maturity and Its Related Measurement Framework. Conference: 5th International Conference on Accounting Studies (ICAS 2018), Penang, Malaysia.
    Ur Rehman, I. H., Turi, J. A., Rosak-Szyrocka, J., Alam, M. N., & Pilař, L. (2023). The role of awareness in appraising the success of e-government systems. Cogent Business & Management, 10(1). https://doi.org/10.1080/23311975.2023.2186739
    Rivera, S., Loarte, N., Raymundo, C., & Dominguez, F. (2017). Data governance maturity model for micro financial organizations in Peru. In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017).
    Rodríguez-Priegoa, N., & Porcu, L. (2022). Challenges in times of a pandemic: what drives and hinders the adoption of location-based applications? Economic Research-Ekonomska Istraživanja, 35(1), 438-457. https://doi.org/10.1080/1331677X.2021.1902364
    Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). The Free Press.
    Ruijer, E., Grimmelikhuijsen, S., & Meijer, A. (2017). Open data for democracy: Developing a theoretical framework for open data use. Government Information Quarterly, 34(1), 45-52. https://doi.org/10.1016/j.giq.2017.01.001
    Ruijer, E. (2021). Designing and implementing data collaboratives: A governance perspective. Government Information Quarterly, 38(4), 101612. https://doi.org/10.1016/j.giq.2021.101612
    Samuel, M., Doctor, G., Christian, P., & Baradi, M. (2020). Drivers and barriers to e-government adoption in Indian cities. Journal of Urban Management, 9(4), 408-417. https://doi.org/10.1016/j.jum.2020.05.002
    Saxena, S., & Janssen, M. (2017). Examining open government data (OGD) usage in India through UTAUT framework. Foresight, 19(4), 421-436. http://dx.doi.org/10.1108/FS-02-2017-0003
    Saxena, S. (2018). Role of “perceived risks” in adopting mobile government (m-government) services in India. Foresight, 20(2), 190-205. https://doi.org/10.1108/FS-08-2017-0040
    Scott, M., DeLone, W., & Golden, W. (2011). IT quality and egovernment net benefits: A citizen perspective. Conference: 19th European Conference on Information Systems, June 9-11, Helsinki, Finland. http://aisel.aisnet.org/ecis2011/87
    Seddon, P. B. (1997). A respecfication and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240-253.
    Seddig, D., Maskileyson, D., Davidov, E., Ajzen, I., & Schmidt, P. (2022). Correlates of COVID-19 vaccination intentions: Attitudes, institutional trust, fear, conspiracy beliefs, and vaccine skepticism. Social Science & Medicine, 302, 114981. https://doi.org/10.1016/j.socscimed.2022.114981
    Setyowati, M. S., Utami, N. D., & Saragih, A. H. (2023). Strategic factors in implementing blockchain technology in Indonesia's value-added tax system. Technology in Society, 72, 102169. https://doi.org/10.1016/j.techsoc.2022.102169
    Shah, S. I. H., Peristeras, V., & Magnisalis, I. (2021). DaLiF: a data lifecycle framework for data-driven governments. Journal of Big Data, 8(1), 1-44. https://doi.org/10.1186/s40537-021-00481-3
    Shameli-Sendi, A. (2020). An efficient security data-driven approach for implementing risk assessment. Journal of Information Security and Applications, 54, 102593. https://doi.org/10.1016/j.jisa.2020.102593
    Shareef, M. A., Kumar, V., Kumar, U., & Dwivedi, Y. K. (2011). e-Government Adoption Model (GAM): Differing service maturity levels. Government Information Quarterly, 28(1), 17-35. https://doi.org/10.1016/j.giq.2010.05.006
    Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2023). Predictive model assessment and selection in composite-based modeling using PLS-SEM: extensions and guidelines for using CVPAT. European Journal of Marketing, 57(6), 1662-1677. https://doi.org/10.1108/EJM-08-2020-0636
    Shin, D.-H. (2013). User centric cloud service model in public sectors: Policy implications of cloud services. Government Information Quarterly, 30(2), 194-203. https://doi.org/10.1016/j.giq.2012.06.012
    Shmueli, G., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2016). The Elephant in the Room: Predictive Performance of PLS Models. Journal of Business Research, 69(10), 4552-4564. https://doi.org/10.1016/j.jbusres.2016.03.049
    Singh, H., Grover, P., Kar, A. K., & Ilavarasan, P. V. (2020). Review of performance assessment frameworks of e-government projects. Transforming Government People Process and Policy, 14(1), 31-64. https://doi.org/10.1108/TG-02-2019-0011
    Soguel, N., & Luta, N. (2021). On the road towards IPSAS with a maturity model a Swiss case study. International Journal of Public Sector Management, 34(4), 425-440. https://doi.org/10.1108/IJPSM-09-2020-0235
    Solli-Sæther, H., & Gottschalk, P. (2010). The modeling process for stage models. Journal of Organizational Computing and Electronic Commerce, 20(3), 279-293. https://doi.org/10.1080/10919392.2010.494535
    Spruit, M., & Pietzka, K. (2015). MD3M: The master data management maturity model. Computers in Human Behavior, 51(B), 1068-1076. https://doi.org/10.1016/j.chb.2014.09.030
    Stefanovic, D., Marjanovic, U., Delić, M., Culibrk, D., & Lalic, B. (2016). Assessing the success of e-government systems: An employee perspective. Information & Management, 53(6), 717-726. https://doi.org/10.1016/j.im.2016.02.007
    Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36(2), 111-147. https://www.jstor.org/stable/2984809
    Surbakti, F. P., Wang, W., Indulska, M., & Sadiq, S. (2020). Factors influencing effective use of big data: a research framework. Information and Management, 57(1), 103146. https://doi.org/10.1016/j.im.2019.02.001
    Talukder, M. S., Shen, L., Talukder, M. F. H., & Bao, Y. (2019). Determinants of user acceptance and use of open government data (OGD): An empirical investigation in Bangladesh. Technology in Society, 56, 147-156. https://doi.org/10.1016/j.techsoc.2018.09.013
    Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://www.jstor.org/stable/23011007
    Thomas, M. A., Cipolla, J., Lambert, B., & Carter, L. (2019). Data management maturity assessment of public sector agencies. Government Information Quarterly, 36(4), 101401. https://doi.org/10.1016/j.giq.2019.101401
    Thompson, N., Ravindran, R., & Nicosia, S. (2015). Government data does not mean data governance: Lessons learned from a public sector application audit. Government Information Quarterly, 32(3), 316-322. https://doi.org/10.1016/j.giq.2015.05.001
    Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
    UN (2020). E-government survey 2020: Digital government in the decade of action for sustainable development. Retrieved May 2, 2023, from https://digitallibrary.un.org/record/3884686/files/2020_UN_E-Government_Survey_%28Full_Report%29.pdf
    UNESCAP (2009). What Is Good Governance? UNESCAP: Bangkok, Thailand, 2009. Retrieved September 7, 2023, from https://www.unescap.org/sites/default/files/good-governance.pdf
    Veeramootoo, N., Nunkoo, R., & Dwivedie, Y. K. (2018). What determines success of an e-government service? Validation of an integrative model of e-filing continuance usage. Government Information Quarterly, 35(2), 161-174. https://doi.org/10.1016/j.giq.2018.03.004
    Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
    Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
    Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
    Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
    de Vries, H., Tummers, L., & Bekkers, V. (2018). The Diffusion and Adoption of Public Sector Innovations: A Meta-Synthesis of the Literature. Perspectives on Public Management and Governance, 1(3), 159-176. https://doi.org/10.1093/ppmgov/gvy001
    Wang, C.-S., Lin, S.-L., Chou, T.-H., & Li, B.-Y. (2019). An integrated data analytics process to optimize data governance of non-profit organization. Computers in Human Behavior, 101, 495-505. https://doi.org/10.1016/j.chb.2018.10.015
    Wang, H. J., & Lo. J. (2016). Adoption of open government data among government agencies. Government Information Quarterly, 33(1), 80-88. https://doi.org/10.1016/j.giq.2015.11.004
    Wang, Y.-S., & Liao, Y.-W. (2008). Assessing eGovernment systems success: A validation of the DeLone and McLean model of information systems success. Government Information Quarterly, 25(4), 717-733. https://doi.org/10.1016/j.giq.2007.06.002
    Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13. https://doi.org/10.1016/j.techfore.2015.12.019
    Warkentin, M., Gefen, D., Pavlou, P. A., & Rose, G. M. (2002), Encouraging citizen adoption of e-government by building trust. Electronic Markets, 12(3), 157-162. http://dx.doi.org/10.1080/101967802320245929
    Warkentin, M., Sharma, S., Gefen, D., Rose, G. M., & Pavlou, P. (2018). Social identity and trust in internet-based voting adoption. Government Information Quarterly, 35(2), 195-209. https://doi.org/10.1016/j.giq.2018.03.007
    Weerakkody, V., Irani, Z., Lee, H., Hindi, N., & Osman, I. (2016). Are U.K. citizens satisfied with e-government services? Identifying and testing antecedents of satisfaction. Information Systems Management, 33(4), 331-343. https://doi.org/10.1080/10580530.2016.1220216
    Weerakkody, V., Kapoor, K., Balta, M. E., Irani, Z., & Dwivedi, Y. K. (2017a). Factors influencing user acceptance of public sector big open data. Production Planning & Control, 28(11-12), 891-905. https://doi.org/10.1080/09537287.2017.1336802
    Weerakkody, V., Irani, Z., Kapoor, K., Sivarajah, U., & Dwivedi, Y. K. (2017b). Open data and its usability: An empirical view from the citizen’s perspective. Information Systems Frontiers, 19(2), 285-300. https://doi.org/10.1007/s10796-016-9679-1
    Willis, K. S., & Nold, C. (2022). Sense and the city: An emotion data framework for smart city governance. Journal of Urban Management, 11(2), 142-152. https://doi.org/10.1016/j.jum.2022.05.009
    Wirtz, B. W., & Müller, W. M. (2019). An integrated artificial intelligence framework for public management. Public Management Review, 21(7), 1076-1100. https://doi.org/10.1080/14719037.2018.1549268
    Wirtz, B. W., Piehler, R., Thomas, M.-J., & Daiser, P. (2016). Resistance of public personnel to open government: A cognitive theory view of implementation barriers towards open government data. Public Management Review, 18(9), 1335-1364. https://doi.org/10.1080/14719037.2015.1103889
    Wirtz, B. W., Weyerer, J. C., & Rösch, M. (2018). Citizen and open government: An empirical analysis of antecedents of open government data. International Journal of Public Administration, 41(4), 308-320, https://doi.org/10.1080/10.1080/01900692.2016.1263659
    Xianjun, Q., Minghong, C., & Xiaoli, L. (2019). User Acceptance Model of Government Microblog and Its Empirical Study. Procedia Computer Science, 162, 940-945. https://doi.org/10.1016/j.procs.2019.12.071
    Xie, Q., Song, W., Peng, X., & Shabbir, M. (2017). Predictors for e-government adoption: integrating TAM, TPB, trust and perceived risk. The Electronic Library, 35(1), 2-20. https://doi.org/10.1108/EL-08-2015-0141
    Xiong, L., Wang, H., & Wang, C. (2022). Predicting mobile government service continuance: A two-stage structural equation modeling-artificial neural network approach. Government Information Quarterly, 39(1), 101654. https://doi.org/10.1016/j.giq.2021.101654
    Yang, T.-M., Wu, Y.-J. (2016). Examining the socio-technical determinants influencing government agencies' open data publication: A study in Taiwan. Government Information Quarterly, 33(3), 378-392. https://doi.org/10.1016/j.giq.2016.05.003
    Yang, T.-M., Wu, Y.-J. (2022). To use or not to use? Exploring the factors influencing professional reusers’ intention to adopt and utilize governmental open data in Taiwan. Journal of Educational Media & Library Sciences, 59(2), 101-135. https://doi.org/10.6120/JoEMLS.202207_59(2).0001.RS.BE
    Yang, Z., Sun, J., Zhang, Y., & Wang, Y. (2015). Understanding SaaS adoption from the perspective of organizational users: A tripod readiness model. Computers in Human Behavior, 45, 254-264. https://doi.org/10.1016/j.chb.2014.12.022
    Zahid, H., & Din, B. H. (2019). Determinants of intention to adopt e-Government services in Pakistan: An imperative for sustainable development. Resources, 8(3), 128. https://doi.org/10.3390/resources8030128
    Zaman, U., Zahid, H., Habibullah, M. S., & Din, B. H. (2021). Adoption of big data analytics (BDA) technologies in disaster management: A decomposed theory of planned behavior (DTPB) Approach. Cogent Business & Management, 8(1), 181253. https://doi.org/10.1080/23311975.2021.1880253
    Zhang, Q., Sun, X., & Zhang, M. (2022). Data Matters: A strategic action framework for data governance. Information & Management, 59(4), 103642. https://doi.org/10.1016/j.im.2022.103642
    Zhao, Y., & Fan, B. (2021). Understanding the key factors and configurational paths of the open government data performance: Based on fuzzy-set qualitative comparative analysis. Government Information Quarterly, 38(3), 101580. https://doi.org/10.1016/j.giq.2021.101580
    Ziemba, E., Papaj, T., Żelazny, R., & Jadamus-Hacura, M. (2016). Factors influencing the success of e-government. The Journal of Computer Information Systems, 56(2), 156-167. https://doi.org/10.1080/08874417.2016.1117378
    Zolotov, M. N., Naranjo, M., Oliveira, T., & Casteleyn, S. (2018). E-participation adoption models research in the last 17 years: A weight and meta-analytical review. Computers in Human Behavior, 81, 350-365. https://doi.org/10.1016/j.chb.2017.12.031
    Zorrilla, M., & Yebenes, J. (2022). A reference framework for the implementation of data governance systems for industry 4.0. Computer Standards & Interfaces, 81, 103595. https://doi.org/10.1016/j.csi.2021.103595
    Zuiderwijk, A., Janssen, M., & Dwivedi, Y. K. (2015). Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Information Quarterly, 32(4), 429-440. https://doi.org/10.1016/j.giq.2015.09.005
    Zurkowski, P. G. (1974). The Information Service Environment Relationships and Priorities. Retrieved May 17, 2023, from https://files.eric.ed.gov/fulltext/ED100391.pdf
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