Information Processing and Demand Response Systems Effectiveness: A Conceptual Study

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dc.contributor.authorSangeeth L.R., Silpa
dc.contributor.authorK. Mathew, Saji
dc.date.accessioned2017-12-28T02:15:10Z
dc.date.available2017-12-28T02:15:10Z
dc.date.issued2018-01-03
dc.description.abstractThis paper studies the effectiveness of demand response (DR) programs based on information processing theory. Following information processing theory, we propose a theoretical model which examines the fit between information processing needs and information processing capacity in an energy informatics framework. We analyze nature of tasks in DR programs and classify them into generic tasks categories based on the complexity of tasks. Our model further analyzes information processing capacity of DR within an automatic metering infrastructure (AMI) system and identifies four constituents of information processing capacity. Further, we extend task-technology fit and information processing theory to posit six propositions that explore the fit between elements of information processing capacity and needs and how the fit will impact DR outcomes. Our model contributes to the information system research connecting information and utility sector to administer the effectiveness of demand response systems that ultimately enhances environment sustainability.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2018.661
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50550
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTheory and Information Systems
dc.subjectAutomatic metering infrastructure, demand response, energy informatics, organization information processing theory.
dc.titleInformation Processing and Demand Response Systems Effectiveness: A Conceptual Study
dc.typeConference Paper
dc.type.dcmiText

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