Abstract
The large-scale generation of electricity is a major contributor to increasing levels of greenhouse gas emissions, putting pressure on the industry to reduce its environmental impacts. Electricity utility companies are looking to two strategies to help make this happen: the smart grid and demand-side management. Viewing the challenge from an IS perspective, this study attempts to answer the question: do smart grid information systems and technologies make a difference in utilities’ efforts to promote energy efficiency? Drawing on organizational information processing theory and extending it by integrating the concept of information waste, two competing hypotheses are developed and tested using hierarchical regression and data from 543 U.S. electricity utilities. The model, incorporating four levels of metering devices, is found to explain a high portion of the variance in energy efficiency effects of demand-side management programs. This suggests that there are IS-enabled information processing capacities within smart meters that have a significant influence on utilities’ EDM effectiveness. However, the results also point to the potential for both positive and negative effects. Implications of these findings for practice and future research directions are discussed.
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Notes
The term “utility” is used to denote organizations that provide electricity to end customers. These organizations may or may not be vertically integrated across the electricity supply chain and may be private firms, cooperatives or government-owned.
Most recent report available at the time the analysis was completed.
In the Report, these devices are reported as AMI meters; however, to avoid confusion between the total AMI infrastructure and the metering devices, this paper refers to them as smart meters.
In the Report, the effects of net metering in terms of reducing electricity consumption are isolated from the energy efficiency effects of EDM, so this does not affect the results.
References
Abrahamse, W., & Steg, L. (2009). How do socio-demographic and psychological factors relate to households' direct and indirect energy use and savings? Journal of Economic Psychology, 30, 711–720.
Achenbach, J. (2010). The 21st century grid: Can we fix the infrastructure that powers our lives? National Geographic Magazine, (July 2010), http://ngm.nationalgeographic.com/print/2010/07/power-grid/achenbach-text. Accessed April 28, 2011.
Adebanjo, D. (2009). Understanding demand management challenges in intermediary food trading: a case study. Supply Chain Management: An International Journal, 14(3), 224–233.
Anderson, C. K., & Carroll, B. (2007). Demand management: beyond revenue management. Journal of Revenue and Pricing Management, 6, 260–263.
Auffhammer, M., Blumstein, C., & Fowlie, M. (2008). Demand-side management and energy efficiency revisited. The Energy Journal, 29(3), 91–104.
Bhatt, G., Emdad, A., Roberts, N., & Grover, V. (2010). Building and leveraging information in dynamic environments: the role of IT infrastructure flexibility as enabler of organizational responsiveness and competitive advantage. Information Management, 47, 341–349.
Brandes, O. M. (2005). At a watershed: ecological governance and sustainable water management in Canada. Journal of Environmental Law and Practice, 16(1), 79–97.
Buliung, R. N., Soltys, K., Bui, R., Habel, C., & Lanyon, R. (2010). Catching a ride on the information super-highway: toward an understanding of internet-based carpool formation and use. Transportation, 37, 849–873.
Canever, M. D., Van Trijp, H. C. M., & Beers, G. (2008). The emergent demand chain management: key features and illustration from the beef business. Supply Chain Management: An International Journal, 13(2), 104–115.
Chenhall, R. H., & Moers, F. (2007). The issue of endogeneity within theory-based, quantitative management accounting research. The European Accounting Review, 16(1), 173–195.
Clastres, C. (2011). Smart grids: another step toward competition, energy security and climate change objectives. Energy Policy, 39, 5399–5408.
Corbett, J. (2011). Demand management in the smart grid: An information processing perspective. In Proceedings of the Seventeenth Americas Conference on Information Systems, Detroit, MI.
Corbett, J. (2012). Leaders and lemmings: Organizational responses to smart grid transformation. In Proceedings of the Eighteenth Americas Conference on Information Systems, Seattle, WA.
Curtis, M., & Khare, A. (2004). Energy conservation in electric utilities: an opportunity for restorative economics at SaskPower. Technovation, 24, 395–402.
Daft, R. L., & Weick, K. E. (1984). Toward a model of organizations as interpretation systems. Academy of Management Review, 9(2), 284–291.
Edmunds, A., & Morris, A. (2000). The problem of information overload in business organizations: a review of literature. International Journal of Information Management, 20(1), 17–28.
Elliot, S. (2011). Transdisciplinary perspectives on environmental sustainability: a resource base and framework for IT-enabled business transformation. MIS Quarterly, 35(1), 197–236.
Energy Information Association (2011). Annual electric power industry report instructions. http://www.eia.doe.gov/cneaf/electricity/page/forms.html. Accessed April 6, 2011.
Environmental Protection Agency (2011). Inventory of U.S. Greenhouse gas emissions and sinks:1990-2009: Executive summary http://www.epa.gov/climatechange/emissions/downloads11/US-GHG-Inventory-2011-Executive-Summary.pdf. Accessed December 12, 2011.
Fairbank, J. F., Labianca, G., Steensma, H. K., & Metters, R. (2006). Information processing design choices, strategy, and risk management performance. Journal of Management Information Systems, 23(1), 293–319.
Fan, M., Stallaert, J., & Whinston, A. B. (2003). Decentralized mechanism design for supply chain organizations using an auction market. Information Systems Research, 14(1), 1–22.
Farhangi, H. (2010). The path of the smart grid. IEEE Power & Energy Magazine, 8(1), 18–28.
Fox-Penner, P. (2010). Smart power: Climate change, the smart grid, and the future of electric utilities. Washington, DC: Island Press.
Francis, M. (1998). Lean information and supply chain effectiveness. International Journal of Logistics Research and Applications: A Leading Journal of Supply Chain Management, 1(1), 93–108.
Galbraith, J. R. (1973). Designing complex organizations. Reading: Addison-Wesley.
Galbraith, J. R. (1977). Organization design. Reading: Addison-Wesley Publishing Company, Inc.
Hartway, R., Price, S., & Woo, C. K. (1999). Smart meter, customer choice and profitable time-of-use rate option. Energy, 24, 895–903.
Herter, K., McAuliffe, P., & Rosenfeld, A. (2007). An exploratory analysis of California residential customer response to critical peak pricing of electricity. Energy, 32, 25–34.
Hicks, B. J. (2007). Lean information management: understanding and eliminating waste. International Journal of Information Management, 27, 233–249.
Hirst, E. (1995). Electric utilities and energy efficiency. Oakridge National Laboratory Review, 28(2).
Hledik, R. (2009). How green is the smart grid? The Electricity Journal, 22(3), 29–41.
Huber, G. P. (1990). A theory of the effects of advanced information technologies on organizational design, intelligence, and decision making. Academy of Management Review, 15(1), 47–71.
Hult, G. T. M., Hurley, R. F., Giunipero, L. C., & Nichols, E. L., Jr. (2000). Organizational learning in global purchasing: a model and test of internal users and corporate buyers. Decision Sciences, 31(2), 293–325.
Hult, G. T. M., Ketchen, D. J., Jr., & Slater, S. F. (2004). Information processing, knowledge development, and strategic supply chain performance. Academy of Management Journal, 47(2), 241–253.
Hyndman, R. J., & Fan, S. (2009). Forecasting long-term peak half-hourly electricity demand for South Australia: Report for electricity supply industry planning council (South Australia) Clayton. Australia: Monash University.
Intergovernmental Panel on Climate Change. (2007). Climate change 2007: Mitigation. Contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press.
Iyer, A. V., Deshpande, V., & Wu, Z. (2003). A postponement model for demand management. Management Science, 49(8), 983–1002.
Jack, E. P., & Powers, T. L. (2009). A review and synthesis of demand management, capacity management and performance in health-care services. International Journal of Management Reviews, 11(2), 149–174.
Jenkin, T. A., Webster, J., & McShane, L. (2011). An agenda for ‘green’ information technology and systems research. Information and Organization, 21, 17–40.
Kharecha, P. A., Kutscher, C. F., Hansen, J. E., & Mazria, E. (2010). Options for near-term phaseout of CO2 emissions from coal use in the United States. Environmental Science & Technology, 44, 4050–4062.
Kranz, J., & Picot, A. (2012). Is it money or the environment? An empirical analysis of factors influencing consumers' intention to adopt the smart metering technology. In Proceedings of the Eighteenth Americas Conference on Information Systems, Seattle, WA.
Kundu, G. K., Manohar, B. M., & Bairi, J. (2011). IT support service: identification and categorisation of wastes. International Journal of Value Chain Management, 5(1), 68–91.
Lapide, L. (2006). Demand management revisted. The Journal of Business Forecasting, 25(3), 17–19.
Lee, C. C., & Grover, V. (1999). Exploring the mediation between environmental and structural attributes: the penetration of communication technologies in manufacturing organizations. Journal of Management Information Systems, 16(3), 187–217.
Mani, D., Barua, A., & Whinston, A. B. (2010). An empirical analysis of the impact of information capabilities design on business process outsourcing performance. MIS Quarterly, 34(1), 39–62.
Mason, I. G., Page, S. C., & Williamson, A. G. (2010). A 100 % renewable electricity generation system for New Zealand utilising hydro, wind, geothermal and biomass resources. Energy Policy, 38, 3973–3984.
McCollum, D., & Yang, C. (2009). Achieving deep reductions in US transport greenhouse gas emissions: scenario analysis and policy implications. Energy Policy, 37, 5580–5596.
Melville, N. (2010). Information systems innovation for environmental sustainability. MIS Quarterly, 34(1), 1–21.
Melville, N., & Ramirez, R. (2008). Information technology innovation diffusion: an information requirements paradigm. Information Systems Journal, 18, 247–273.
Mithas, S., & Krishnan, M. S. (2009). From association to causation via a potential outcomes approach. Information Systems Research, 20(2), 295–313.
Mittal, N., & Nault, B. R. (2009). Investments in information technology: indirect effects and information technology intensity. Information Systems Research, 20(1), 140–154.
Moore, S. (2008). Are all meter data management systems created equal? Energy Pulse, http://www.energypulse.net/centers/article/article_display.cfm?a_id=1844. Accessed March 1, 2011.
Premkumar, G., Ramamurthy, K., & Saunders, C. S. (2005). Information processing view of organizations: an exploratory examination of fit in the context of interorganizational relationships. Journal of Management Information Systems, 22(1), 257–294.
Ramchurn, S. D., Vytelingum, P., Rogers, A., & Jennings, N. R. (2012). Putting the ‘smarts’ into the smart grid. Communications of the Association for Computing Machinery, 55(4), 86–97.
Sorrell, S. (2009). Jevons' Paradox revisted: the evidence for backfire from improved energy efficiency. Energy Policy, 37, 1456–1469.
Stromback, J., Dromacque, C., & Yassin, M. H. (2011). The potential of smart meter enabled programs to increase energy and systems efficiency: a mass pilot comparison. Helskini: VaasaETT Global Energy Think Tank.
Strueker, J., & Dinther, C. (2012). Demand response in smart grids: Research opportunities for the IS discipline. In Proceedings of the Eighteenth Americas Conference on Information Systems, Seattle.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (Fifth edition). Boston: Pearson Education, Inc.
Taylor, D. H., & Fearne, A. (2009). Demand management in fresh food value chains: a framework for analysis and improvement. Supply Chain Management: An International Journal, 14(5), 379–392.
The Climate Group (2008). Smart 2020: Enabling the low carbon economy in the information age. http://www.smart2020.org/_assets/files/02_Smart2020Report.pdf. Accessed January 24, 2012.
Trautmann, G., Turkulainen, V., Hartmann, E., & Bals, L. (2009). Integration in the global sourcing organization: an information processing perspective. Journal of Supply Chain Management, 45(2), 57–74.
Trkman, P., McCormack, K., de Oliveira, M. P. V., & Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), 318–327.
Tushman, M. L., & Nadler, D. A. (1978). Information processing as an integrating concept in organizational design. The Academy of Management Review, 3(3), 613–624.
Valocchi, M., Schurr, A., Juliano, J., & Nelson, E. (2007). Plugging in the consumer: Innovating utility business models for the future. http://www-935.ibm.com/services/us/gbs/bus/pdf/ibv_g510-7872-00_plugging_in.pdf. Accessed June 13, 2011.
Watson, R. T., & Boudreau, M.-C. (2011). Energy informatics. Athens: Green ePress.
Watson, R. T., Boudreau, M.-C., & Chen, A. (2010a). Information systems and environmentally sustainable development: energy informatics and new directions for the IS community. MIS Quarterly, 34(1), 1–16.
Watson, R. T., Boudreau, M.-C., & Li, S. (2010b). Telematics at UPS: energy infomatics in action. MISQ Executive, 9(1), 203–213.
Watson, R. T., Corbett, J., Boudreau, M.-C., & Webster, J. (2012). An information strategy for environmental sustainability. Communications of the Association for Computing Machinery, 55(7).
Womack, J. P., & Jones, D. T. (1996). Lean thinking: Banish waste and create wealth within your corporation. London: Simon and Schuster.
Womack, J. P., Jones, D. T., & Roos, D. (1990). The machine that changed the world. New York: Rawson Associates.
Wunderlich, P., Veit, D., & Sarker, S. (2012). Adoption of information systems in the electricity sector: The issue of smart metering. In Proceedings of the Eighteenth Americas Conference on Information Systems, Seattle, WA.
Zarghami, M. (2010). Urban water management using fuzzy-probabilistic multi-objective programming with dynamic efficiency. Water Resource Management, 24, 4491–4504.
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The author would like to thank Jane Webster, Brent Gallupe, Chialin Chen, the anonymous reviewers and special issue Editors for their comments on previous versions of the manuscript, and Peng Hu for his research assistance. This research was supported by a grant from the Social Sciences and Humanities Research Council of Canada.
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Corbett, J. Using information systems to improve energy efficiency: Do smart meters make a difference?. Inf Syst Front 15, 747–760 (2013). https://doi.org/10.1007/s10796-013-9414-0
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DOI: https://doi.org/10.1007/s10796-013-9414-0