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.
The implementation of smart grids-one of the urgent goals to meet international policy expectations for energy efficiency and CO 2 reduction targets-is not a technological issue alone, as it also requires social acceptance by various stakeholders (Wolsink, Renew Sustain Energy Rev, 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., Strategic Manag J 22:845-857, 2001; Porter, Competitive advantage, 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, Ind Corp Chang 11:529-555, 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.,
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