Governments around the world are releasing open data with the aim of stimulating innovation, driving economic development, and advancing social good. Despite significant investment in data infrastructure and technology, many open data initiatives struggle to effectively engage stakeholders or deliver on their intended outcomes. Thus, this dissertation targets the research question: How can a human-centered open government data ecosystem be modeled to ensure that stakeholders are engaged and find data? It also explores which stakeholders should be engaged and what methods are most effective in engaging them. To address the research questions, this dissertation adopts a design science methodology and develops the Model of Open Data Engagement (MODE) through iterative design and evaluation. The process began with a meta-analysis of open data literature to identify stakeholder roles and engagement mechanisms, followed by a review of existing ecosystem models. These insights informed an initial model, which was then refined through comparative analysis of four case studies: the United States, the United Arab Emirates, Sierra Leone, and the City of Los Angeles. A global survey was then conducted to test and refine the proposed model, which was further evaluated using the Indian case study. The final MODE framework presents a structured interaction between three primary stakeholder groups (i.e., data providers, data consumers, and data influencers) through specific engagement activities that are most effective for related interactions. These engagement pathways are further linked to distinct desired outcomes, achieving the tangible benefits of open data use. Theoretically, this dissertation advances open data research by providing a structured, stakeholder-centered model that links engagement activities to expected outcomes. It addresses gaps in earlier models by incorporating the interplay between stakeholder roles, motivations, and methods of engagement. Practically, the model serves as a diagnostic and design tool for governments aiming to build or strengthen open data ecosystems. By aligning engagement strategies with stakeholder needs, MODE supports more inclusive, responsive, and sustainable open data initiatives.
","abstract_html":"<p>Governments around the world are releasing open data with the aim of stimulating innovation, driving economic development, and advancing social good. Despite significant investment in data infrastructure and technology, many open data initiatives struggle to effectively engage stakeholders or deliver on their intended outcomes. Thus, this dissertation targets the research question: How can a human-centered open government data ecosystem be modeled to ensure that stakeholders are engaged and find data? It also explores which stakeholders should be engaged and what methods are most effective in engaging them. To address the research questions, this dissertation adopts a design science methodology and develops the Model of Open Data Engagement (MODE) through iterative design and evaluation. The process began with a meta-analysis of open data literature to identify stakeholder roles and engagement mechanisms, followed by a review of existing ecosystem models. These insights informed an initial model, which was then refined through comparative analysis of four case studies: the United States, the United Arab Emirates, Sierra Leone, and the City of Los Angeles. A global survey was then conducted to test and refine the proposed model, which was further evaluated using the Indian case study. The final MODE framework presents a structured interaction between three primary stakeholder groups (i.e., data providers, data consumers, and data influencers) through specific engagement activities that are most effective for related interactions. These engagement pathways are further linked to distinct desired outcomes, achieving the tangible benefits of open data use. Theoretically, this dissertation advances open data research by providing a structured, stakeholder-centered model that links engagement activities to expected outcomes. It addresses gaps in earlier models by incorporating the interplay between stakeholder roles, motivations, and methods of engagement. Practically, the model serves as a diagnostic and design tool for governments aiming to build or strengthen open data ecosystems. By aligning engagement strategies with stakeholder needs, MODE supports more inclusive, responsive, and sustainable open data initiatives.</p>","abstract_has_math":false,"creators":["Holm, Jeanne"],"institution":null,"degree_name":"Information Systems and Technology, PhD","degree_level":"Open Access Dissertation","degree_discipline":"Center for Information Systems and Technology","degree_department":null,"school":null,"contributors":["Samir Chatterjee","Itamar Shabtai"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-01-01T08:00:00Z","date_published":"2025-01-01T08:00:00Z","updated_at":"2026-07-24T01:41:17Z","subjects":["Data science","Ecosystem","Government","Open data","Transparency","Political Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/1048","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Samir Chatterjee","Itamar Shabtai"]},{"key":"dc:creator","label":"Author","values":["Holm, Jeanne"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2026-05-11T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Center for Information Systems and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Information Systems and Technology, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data science","Ecosystem","Government","Open data","Transparency","Political Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/1048"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Governments around the world are releasing open data with the aim of stimulating innovation, driving economic development, and advancing social good. Despite significant investment in data infrastructure and technology, many open data initiatives struggle to effectively engage stakeholders or deliver on their intended outcomes. Thus, this dissertation targets the research question: How can a human-centered open government data ecosystem be modeled to ensure that stakeholders are engaged and find data? It also explores which stakeholders should be engaged and what methods are most effective in engaging them. To address the research questions, this dissertation adopts a design science methodology and develops the Model of Open Data Engagement (MODE) through iterative design and evaluation. The process began with a meta-analysis of open data literature to identify stakeholder roles and engagement mechanisms, followed by a review of existing ecosystem models. These insights informed an initial model, which was then refined through comparative analysis of four case studies: the United States, the United Arab Emirates, Sierra Leone, and the City of Los Angeles. A global survey was then conducted to test and refine the proposed model, which was further evaluated using the Indian case study. The final MODE framework presents a structured interaction between three primary stakeholder groups (i.e., data providers, data consumers, and data influencers) through specific engagement activities that are most effective for related interactions. These engagement pathways are further linked to distinct desired outcomes, achieving the tangible benefits of open data use. Theoretically, this dissertation advances open data research by providing a structured, stakeholder-centered model that links engagement activities to expected outcomes. It addresses gaps in earlier models by incorporating the interplay between stakeholder roles, motivations, and methods of engagement. Practically, the model serves as a diagnostic and design tool for governments aiming to build or strengthen open data ecosystems. By aligning engagement strategies with stakeholder needs, MODE supports more inclusive, responsive, and sustainable open data initiatives.
"]},{"key":"dc:title","label":"Title","values":["Innovating Government the Open Way: Modeling the Open Data Ecosystem"]}]}],"canonical_facts":{"dc:contributor":["Samir Chatterjee","Itamar Shabtai"],"dc:creator":["Holm, Jeanne"],"dc:date.available":["2026-05-11T07:00:00Z"],"dc:description.abstract":["Governments around the world are releasing open data with the aim of stimulating innovation, driving economic development, and advancing social good. Despite significant investment in data infrastructure and technology, many open data initiatives struggle to effectively engage stakeholders or deliver on their intended outcomes. Thus, this dissertation targets the research question: How can a human-centered open government data ecosystem be modeled to ensure that stakeholders are engaged and find data? It also explores which stakeholders should be engaged and what methods are most effective in engaging them. To address the research questions, this dissertation adopts a design science methodology and develops the Model of Open Data Engagement (MODE) through iterative design and evaluation. The process began with a meta-analysis of open data literature to identify stakeholder roles and engagement mechanisms, followed by a review of existing ecosystem models. These insights informed an initial model, which was then refined through comparative analysis of four case studies: the United States, the United Arab Emirates, Sierra Leone, and the City of Los Angeles. A global survey was then conducted to test and refine the proposed model, which was further evaluated using the Indian case study. The final MODE framework presents a structured interaction between three primary stakeholder groups (i.e., data providers, data consumers, and data influencers) through specific engagement activities that are most effective for related interactions. These engagement pathways are further linked to distinct desired outcomes, achieving the tangible benefits of open data use. Theoretically, this dissertation advances open data research by providing a structured, stakeholder-centered model that links engagement activities to expected outcomes. It addresses gaps in earlier models by incorporating the interplay between stakeholder roles, motivations, and methods of engagement. Practically, the model serves as a diagnostic and design tool for governments aiming to build or strengthen open data ecosystems. By aligning engagement strategies with stakeholder needs, MODE supports more inclusive, responsive, and sustainable open data initiatives.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/1048"],"dc:subject":["Data science","Ecosystem","Government","Open data","Transparency","Political Science"],"dc:title":["Innovating Government the Open Way: Modeling the Open Data Ecosystem"],"thesis:degree_discipline":["Center for Information Systems and Technology"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Information Systems and Technology, PhD"]},"updated_at":"2026-07-24T01:41:17Z"}