A Socio-technical Investigation of the Smart Grid: Implications for Demand-side Activities of Electricity Service Providers
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Enabled by advanced communication and information technologies, the smart grid represents a major transformation for the electricity sector. Vast quantities of data and two-way communications abilities create the potential for a flexible, data-driven, multi-directional supply and consumption network well equipped to meet the challenges of the next century. For electricity service providers (“utilities”), the smart grid provides opportunities for improved business practices and new business models; however, a transformation of such magnitude is not without risks. Three related studies are conducted to explore the implications of the smart grid on utilities’ demand-side activities. An initial conceptual framework, based on organizational information processing theory, suggests that utilities’ performance depends on the fit between the information processing requirements and capacities associated with a given demand-side activity. Using secondary data and multiple regression analyses, the first study finds, consistent with OIPT, a positive relationship between utilities’ advanced meter deployments and demand-side management performance. However, it also finds that meters with only data collection capacities are associated with lower performance, suggesting the presence of information waste causing operational inefficiencies. In the second study, interviews with industry participants provide partial support for the initial conceptual model, new insights are gained with respect to information processing fit and information waste, and “big data” is identified as a central theme of the smart grid. To derive richer theoretical insights, the third study employs a grounded theory approach examining the experience of one successful utility in detail. Based on interviews and documentary data, the paradox of dynamic stability emerges as an essential enabler of utilities’ performance in the smart grid environment. Within this context, the frames of opportunity, control, and data limitation interact to support dynamic stability and contribute to innovation within tradition. The main contributions of this thesis include theoretical extensions to OIPT and the development of an emergent model of dynamic stability in relation to big data. The thesis also adds to the green IS literature and identifies important practical implications for utilities as they endeavour to bring the smart grid to reality.