Purpose-This paper aims to explore how Living Labs might be evaluated, building on the current efforts of the European Network of Living Lab (ENoLL) to encourage new members, and complementing their existing criteria with elements from business model development strategies-specifically the Business Model Canvas (BMC) (Osterwalder and Pigneur, 2010). Design/methodology/approach-First, it is explored how Living Labs have emerged, at the intersection of transition management, open innovation and collaborative consumption. It is then suggested that the BMC could be a complementary tool in Living Lab evaluation. Findings-This tool helped identify three important elements missing from current ENoLL evaluation criteria: identification of the cost structure, customer segments and the revenue stream. The case study of an Energy Living Lab created in Western Switzerland is used to reflect on the strengths and weaknesses of different evaluation criteria; this paper is then concluded with some ideas on how future research might contribute to further strengthening Living Lab evaluation process towards long-term "sustainability". Originality/value-This article will be of value for ENoLL to refine their evaluation criteria for the next "wave" of application. It could as well help living labs to reflect on how to keep a living lab alive.
A sustainable neighbourhood was built Switzerland by one of the leaders in this field. Half of the 400 apartments have been equipped with smart meters delivering big data on energy consumption (electricity, water, heating…). The company would like to know if it is possible to link socio-demographic typology of residents with energy consumption patterns. To answer this question we present in this article a multimethod approach combining qualitative analysis, frequently used in marketing (multiple correspondence analyses), and quantitative analysis from applied statistics to answer this question. First, we have conducted a survey among the residents of the sustainable neighbourhood to gather socio-demographic data, and then we have proposed a marketing typology of residents. In parallel, we have analysed load curves with statistical models (clustering factors, hermano beta models, coincidence factors, som, expert practice) to see if there are patterns of energy consumption and to determine groups of similar load curves. Then we have compared the discrepancies in the composition of the groups between both methods. This study is based on a single case study generating a new research hypothesis: the typology of residents based on socio-demographic data can be linked to energy consumption pattern of a household.
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