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An aspect of paramount importance in this regard is that smart grid
business models meet consumer expectations. Especially along with
increasing market liberalization energy firms need to understand and
react upon consumer preferences. Thus, an investigation of consumer
preferences and conclusions of how those might affect business
models in the field is of interest. However, we still only poorly
understand consumer preferences in the field of smart grids and how
those preferences differ across different consumer typologies and
different countries. Thus we ask, what are customer preferences in
the field of smart grids and how do customer preferences differ
between different customer types and across countries?
From earlier research we learned that not only technology but business models are relevant for the establishment and further diffusion of clean technology in general (e.g. Boehnke, 2007; DISTRES, 2009; Frantzis et al., 2008; Loock, 2010a; Schoettl & Lehmann-Ortega, 2010; Wüstenhagen & Boehnke, 2008). However, when it comes to smart grids we have only found limited research that indicates which business model configurations exist and which of these different consumer types would prefer (Forsa, 2010; Kaufmann, 2010; Kranz, 2010).
For a thorough evaluation of the benefit of certain business model configuration a deep understanding of customer preferences is a precondition. A suitable analytic frame for such investigation is the concept of customer value, which exactly discusses the interface between customer preferences and a firms offering, hence it’s business model. In particular customer value has been identified as an important object of a firm’s approach of economic value creating (Belz & Bieger, 2006; Parasuraman, 1997; Slater, 1997; Woodruff, 1997). “Customer value is a customer's perceived preference for and evaluation of those product attributes, attribute performances, and consequences arising from use that facilitate (or block) achieving the customer's goals and purposes in use situations” (Woodruff, 1997: 142).
We conducted an online consumer survey on smart grids for four European countries (Germany, Switzerland, Austria and Lichtenstein). After recruitment of cunsumers by online and print media, by leaflets and by inserts to the electricity bill of a regional energy provider we got a sample of 837 probands. A hierarchical clustering based on Ward's method on SPSS was used to identify three clusters. The analysis is based on questions relating the advantages and reservations of using smart meters. We characterized each customer type according to socio-economic aspects.
In line with previous research (Forsa 2010) we detected a high number (around 2/3) of customers who do not have any prior knowledge and have never heard about smart meters. Different results, however, were obtained from Germany where only about 1/3 of respondents stated to have never heard about smart meters. Another interesting outcome is the fact, that the expected advantages of the usage of smart meters greatly outweigh the concerns by almost all respondents. In line with this wie detected a high willingness to pay for a smart meter by one third of the consumers. With help of a cluster analysis we furthermore assigned customers to three clusters, each with customers that have different amounts of concerns and expect different amounts of advantages. Surprising differences about their willingness to pay for smart meters and their attitude to the consumption of green energy could be established. Those results have implications for further research on social acceptance of smart grids and managerial business model design for smart grid products and services.
Smart Grid, Customer Value, Business Model
|project||IMPROSUME - The Impact of Prosumers in a Smart Grid based Energy Market|
|date of presentation||22-6-2011|
|event||34th IAEE International Conference (Stockholm)|
|citation||Loock, M., & Curtius, H. C. (2011). Customer value of smart grids: Empirical evidence from a cross-European-country study and implications for business models. Presented at 34th IAEE International Conference, Stockholm.|