IMPROSUME - The Impact of Prosumers in a Smart Grid based Energy Market

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.

The implementation of smart grids - one of the urgent goals to meet international policy expectations for energy efficiency and CO2 reduction targets - is not a technological issue alone, as it also requires social acceptance by various stakeholders (Wolsink 2011). It is of particular interest that smart grid products and services provide value to the customer. On the one hand, customer value of smart grid technologies is crucial to customer acceptance. On the other hand, as customer value is a key driver for economic value creation and competitive advantage (DeSarbo et al. 2001; Porter 1985), it is also important for companies and investors and thus will affect market acceptance of smart grid technologies. In the literature, business models address the bridge between customers and company needs and serve as mediators between technology and economic success by providing a value proposition to customers and a revenue model for companies (Chesbrough and Rosenbloom 2002). However, we know from the literature that a one-size-fits-all business model may not lead to the best results as it might fail to address heterogeneous customer value perceptions (DeSarbo et al. 2001; Morris et al. 2005; Ruiz et al. 2007; Wiedmann et al. 2009). Thus, different business models providing different customer value propositions need to be developed to fit the different market segments in an optimal way. On the basis of a cross-European country study, we explore three generic B2C customer segments for smart grid products and services based on different value perceptions (Supporters, Ambiguous and Skeptics). Based on the segmentation we conceptually derive four generic business model designs with different customer value propositions best suited for approaching those segments (Saver, Smart+, Trader, Smart Camouflage). Implications for energy policy, research and smart grid management are derived from the findings.

Implementing smart metering is an important field for energy policy to successfully meet energy efficiency targets. From an integrated social acceptance and customerperceived value theory perspective we model the importance of customer value of smart metering in this regard. We further shape the model on a choice-based conjoint experiment with Swiss private electricity customers. The study finds that overall customers perceive a positive value from smart metering and are willing to pay for it. Further, based on a cluster analysis of customers' value perceptions, we identify four customer segments, each with a distinct value perception profile for smart metering. We find that energy policy and management should integrate a solid understanding of customer value for smart metering in their initiatives and consider different smart metering market segments within their measures.