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Toward a Model for Human Open Government Data (OGD) Interaction and an Application for OGD Literacy Taxonomy: A User-centered Perspective

Xiao, Fanghui (2023) Toward a Model for Human Open Government Data (OGD) Interaction and an Application for OGD Literacy Taxonomy: A User-centered Perspective. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Acknowledging the value of transparency and accountability, the development of Open Government Data (OGD) and its portals have been rapidly proliferating around the world. Consequently, massive amounts of government data, from federal to state to local levels, are available via various OGD portals. Also, the emphasis of OGD projects has gradually shifted from a publisher-centered paradigm to a user-centered paradigm, as laws and regulations caught up with policies that make these resources more widely accessible to the public. Thereby, improving data use has become the new major aim of OGD projects. However, extant studies show that users often experience difficulties in finding, understanding, and using government data. Low-level data literacy of individuals was also identified as a major obstacle to using OGD. Within the still-emerging field of human data interaction (HDI), very few studies focus on how users interact with OGD and the fundamental OGD literacy capabilities. Therefore, motivated by the existing challenges of interacting with OGD and the corresponding research gaps in HDI and OGD literacy, this dissertation aims to take a user-centered perspective, relating the relevant models and previous research studies in the two areas, HDI and OGD, to empirically probe into OGD user online interactive behaviors and then to develop a model for human OGD interaction (H-OGD-I). This dissertation also aims to examine contextualized user challenges of interacting with OGD, pinpoint user literacy challenges and platform design barriers to identify the fundamental OGD literacy capabilities that enable users to use OGD based on the proposed H-OGD-I model, and finally develop a taxonomy for OGD literacy capabilities.

This dissertation focuses on the users of local-level rather than federal- or state-level OGD portals. Previous studies claimed that the OGD is mainly collected at local-level, thus, supporting local-level OGD is to support the success of OGD as a whole. Also, a local-level OGD portal is critical because of its closer connection with local organizations, neighborhoods, and communities, which more directly impacts a citizen’s daily life and neighborhood. Accordingly, the users from three local-level OGD portals were studied in this dissertation: the city of Philadelphia (OpenDataPhilly), the Western Pennsylvania Regional Data Center (WPRDC), and the city of Boston (Analyze Boston). The targeted users are the non-expert end users who have experience interacting with OGD. An end user refers to an individual who uses the OGD directly rather than consuming the effects of the OGD application, e.g., by using transportation apps. To achieve the objectives and answer the research questions of this dissertation, five sub-studies with mixed research method design were conducted, including Study 1: observing users’ OGD accessing behavioral patterns, Study 2: identifying users’ individual OGD behaviors, Study 3: exploring users’ cascading OGD behaviors, Study 4: examining user challenges in each H-OGD-I stage, and Study 5: investigate fundamental OGD literacy capabilities.

This dissertation first contributes to the HDI field by providing a model for H-OGD-I with a deep insight into user OGD behaviors. This new H-OGD-I model accounts for the stages and user behaviors when interacting with OGD, and therefore it advances the understanding of the complexity of OGD user behaviors. Also, the newly discovered behaviors in the stages of Sensemaking and Sharing make this H-OGD-I model more comprehensive. In addition, this model is expected to be generalized to structured research data due to the similarity of the infrastructure of structured data, which may inspire researchers in the field of research data management to understand their users’ behaviors. Furthermore, this H-OGD-I model can assist OGD interface designers in defining more effective user interactions that help users find, acquire, and make sense of data, as well as facilitate librarians and instructors to develop OGD literacy capabilities. Additionally, the second main contribution – the taxonomy of OGD literacy capabilities was developed based on the H-OGD-I model and two empirical studies, which explored and identified the fundamental capabilities that are needed in each stage of OGD interaction. This taxonomy can contribute to guiding the design of education programs, namely, data literacy-related curricula or workshops, to enhance users’ OGD literacy. Given few research studies exist focusing on OGD literacy, this OGD literacy taxonomy can contribute to filling this research gap in data literacy.

Overall, this dissertation provides a holistic view and a deep insight into human OGD interaction (H-OGD-I model) and offers a corresponding application of OGD literacy capabilities (taxonomy). The comprehensive H-OGD-I model makes a theoretical contribution to the field of HDI and the subsequent taxonomy of OGD literacy capabilities makes a practical contribution, which advocates for improving people’s data literacy skills.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Xiao, Fanghuifax2@pitt.edufax20000-0001-9518-5068
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairHe, Daqingdah44@pitt.edudah44
Committee MemberBiehl, JacobBIEHL@pitt.eduBIEHL
Committee MemberMattern, Eleanor "Nora"emm225@pitt.eduemm225
Committee MemberWalker, Daviddavid.walker@pitt.edudavid.walker
Date: 10 January 2023
Date Type: Publication
Defense Date: 21 November 2022
Approval Date: 10 January 2023
Submission Date: 29 November 2022
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 198
Institution: University of Pittsburgh
Schools and Programs: School of Computing and Information > Library and Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: human data interaction, user behaviors, user-centered design, data literacy, open government data, open data
Date Deposited: 10 Jan 2023 16:16
Last Modified: 10 Jan 2023 16:16


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