Stakeholders have recognized a need to employ and advance renewable-energy technologies. Renewable energy plays a crucial role in sustaining governments’ energy resources security, mitigating environmental risks associated with extensive use of fossil fuels, and offering long-lasting energy resources for industrial, commercial, and residential needs for electrical power. This study focused on providing a better understanding of solar photovoltaic (PV) adoption at the residential level. Although many researchers investigated solar energy adoption in a search for what may motivate individuals to invest in such a technology, previous studies of solar energy adoption did not address spatial and nonspatial factors simultaneously. Geographic information system (GIS) technologies have emerged as powerful tools to study geographic characteristics that can be tested statistically and displayed on an interactive map to support decision makers. This dissertation investigated residential solar energy adoption by operationalizing homeowners’ spatial and nonspatial factors. The dissertation features two artifacts that were created as part of the research. First, it includes an algorithm to build a predictive model, technically evaluating the model by applying it to a dataset of Los Angeles County as a case study. Second, this dissertation entailed developing a spatial decision-support system as an instantiation of the output model in the first artifact. The system is an interactive GIS-based web application that supports decision makers (e.g., policymakers, solar firms, utility companies and nonprofit organizations) in their decision-making process. A combined qualitative and quantitative methodological approach was used to evaluate the application’s usability and user experience.
","abstract_html":"<p>Stakeholders have recognized a need to employ and advance renewable-energy technologies. Renewable energy plays a crucial role in sustaining governments’ energy resources security, mitigating environmental risks associated with extensive use of fossil fuels, and offering long-lasting energy resources for industrial, commercial, and residential needs for electrical power. This study focused on providing a better understanding of solar photovoltaic (PV) adoption at the residential level. Although many researchers investigated solar energy adoption in a search for what may motivate individuals to invest in such a technology, previous studies of solar energy adoption did not address spatial and nonspatial factors simultaneously. Geographic information system (GIS) technologies have emerged as powerful tools to study geographic characteristics that can be tested statistically and displayed on an interactive map to support decision makers. This dissertation investigated residential solar energy adoption by operationalizing homeowners’ spatial and nonspatial factors. The dissertation features two artifacts that were created as part of the research. First, it includes an algorithm to build a predictive model, technically evaluating the model by applying it to a dataset of Los Angeles County as a case study. Second, this dissertation entailed developing a spatial decision-support system as an instantiation of the output model in the first artifact. The system is an interactive GIS-based web application that supports decision makers (e.g., policymakers, solar firms, utility companies and nonprofit organizations) in their decision-making process. A combined qualitative and quantitative methodological approach was used to evaluate the application’s usability and user experience.</p>","abstract_has_math":false,"creators":["Alzahrani, Ahmed Omar"],"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":["Lorne Olfman","June Hilton"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-01-01T08:00:00Z","date_published":"2020-01-01T08:00:00Z","updated_at":"2026-07-24T01:40:22Z","subjects":["big data","DSR","Energy Informatics","GIS","solar photovoltaic","Spatial analysis"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/635","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lorne Olfman","June Hilton"]},{"key":"dc:creator","label":"Author","values":["Alzahrani, Ahmed Omar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-01-01T08:00:00Z"]},{"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":["big data","DSR","Energy Informatics","GIS","solar photovoltaic","Spatial analysis"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/635"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Stakeholders have recognized a need to employ and advance renewable-energy technologies. Renewable energy plays a crucial role in sustaining governments’ energy resources security, mitigating environmental risks associated with extensive use of fossil fuels, and offering long-lasting energy resources for industrial, commercial, and residential needs for electrical power. This study focused on providing a better understanding of solar photovoltaic (PV) adoption at the residential level. Although many researchers investigated solar energy adoption in a search for what may motivate individuals to invest in such a technology, previous studies of solar energy adoption did not address spatial and nonspatial factors simultaneously. Geographic information system (GIS) technologies have emerged as powerful tools to study geographic characteristics that can be tested statistically and displayed on an interactive map to support decision makers. This dissertation investigated residential solar energy adoption by operationalizing homeowners’ spatial and nonspatial factors. The dissertation features two artifacts that were created as part of the research. First, it includes an algorithm to build a predictive model, technically evaluating the model by applying it to a dataset of Los Angeles County as a case study. Second, this dissertation entailed developing a spatial decision-support system as an instantiation of the output model in the first artifact. The system is an interactive GIS-based web application that supports decision makers (e.g., policymakers, solar firms, utility companies and nonprofit organizations) in their decision-making process. A combined qualitative and quantitative methodological approach was used to evaluate the application’s usability and user experience.
"]},{"key":"dc:title","label":"Title","values":["Adoption of Residential Solar Energy: Exploratory Study Approach and Spatial Decision Support System"]}]}],"canonical_facts":{"dc:contributor":["Lorne Olfman","June Hilton"],"dc:creator":["Alzahrani, Ahmed Omar"],"dc:date.available":["2023-01-01T08:00:00Z"],"dc:description.abstract":["Stakeholders have recognized a need to employ and advance renewable-energy technologies. Renewable energy plays a crucial role in sustaining governments’ energy resources security, mitigating environmental risks associated with extensive use of fossil fuels, and offering long-lasting energy resources for industrial, commercial, and residential needs for electrical power. This study focused on providing a better understanding of solar photovoltaic (PV) adoption at the residential level. Although many researchers investigated solar energy adoption in a search for what may motivate individuals to invest in such a technology, previous studies of solar energy adoption did not address spatial and nonspatial factors simultaneously. Geographic information system (GIS) technologies have emerged as powerful tools to study geographic characteristics that can be tested statistically and displayed on an interactive map to support decision makers. This dissertation investigated residential solar energy adoption by operationalizing homeowners’ spatial and nonspatial factors. The dissertation features two artifacts that were created as part of the research. First, it includes an algorithm to build a predictive model, technically evaluating the model by applying it to a dataset of Los Angeles County as a case study. Second, this dissertation entailed developing a spatial decision-support system as an instantiation of the output model in the first artifact. The system is an interactive GIS-based web application that supports decision makers (e.g., policymakers, solar firms, utility companies and nonprofit organizations) in their decision-making process. A combined qualitative and quantitative methodological approach was used to evaluate the application’s usability and user experience.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/635"],"dc:subject":["big data","DSR","Energy Informatics","GIS","solar photovoltaic","Spatial analysis"],"dc:title":["Adoption of Residential Solar Energy: Exploratory Study Approach and Spatial Decision Support System"],"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":"2026-07-24T01:40:22Z"}