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The chapter provides empirical research results on the peculiarities of social innovation and the specific features that its business model must support. It concludes by proposing a Social Innovation Business Model Canvas and steps towards Social Innovation typologies.

The research is based on the results of a comparative analysis of 25 business case studies and 32 biographies conducted within the SIMPACT research framework. We then implemented a process of reverse engineering to uncover the business models behind the cases which facilitated the creation of a typology for different social innovation business models. Reverse engineering is the application of tools and processes used to study new business ventures in comparison with existing ones. As such, it sheds further light on the broad characteristics of social business models and their value creation mechanisms. The evidence coming from the cases were analyzed within a new business model and clustered to identify a typology of business models of social innovations.

The main SIMPACT findings, resulting from the reverse engineering process and upon which our discussion is based, can be seen in the following distinguishing characteristics of SI business models. SI business models are: configured around finding complementarity between antagonistic assets and seemingly conflicting logics; often structured around a divergence in the allocation of cost, use, and benefit leading to multiple value propositions; modeled on multiactor/multisided business strategies, and developed as frugal solutions and through actions of bricolage. Four typologies of social innovation were identified: beneficiary as actor, beneficiary as customer, beneficiary as user, and community-asset-based models.

While much attention has been placed on for-profit business models, there is little literature on social/not-for-profit business models. This chapter can add to this gap by providing substantial empirical evidence.

Practitioners in the field of social innovation, particularly the growing intermediary sector, could integrate the findings of the research in their work.

The work is also leading to the construction of a future business toolbox for social innovation, which will be even more useful for incubators, accelerators, and supporting structures.

Research presented in this chapter is the result of an extensive comparative analysis across all of Europe, including examples of failure, and the first to propose a typology of SI Business Models.

The main objective of this article is to propose an interpretive model that attempts to decipher a product's values in terms of functionality, usability and meaning. This model can support companies in better integrating these values in their product offering and in defining the most adequate innovation strategies that they can adopt.

The authors conducted an empirical analysis on more than 450 products from the Italian furniture industry. Moreover, using an interpretative model 50 product signs were mapped (materials, surfaces, colours, etc.) for each product. The obtained database was analyzed via the principal component analysis (PCA) statistical technique with the intent to identify dominant product languages. In fact, interpreting a product language as a set of product signs, the article describes an objective process able to identify dominant product languages as combinations of different product signs.

The interpretive model described in this article represents a first result in itself. In addition, by mapping the dynamics of dominant product languages, it has been demonstrated that they evolve differently in relation to several product typologies. In turn, the possibility of “brokering” dominant product languages from one product typology to another and from one industry to another has been verified.

First of all, this model can support companies in the identification of emerging trends and, consequently, allows them to develop product semantic forecasts. In addition, the analysis of dominant product languages over time can also allow a company to propose combinations of product signs typical of past periods. Finally, the identification of dominant product languages can also allow companies to analyze the state‐of‐the‐art of the industry and, consequently, identify different ways to propose innovation to the market.

Most of conducted researches related to product languages have shown primarily a qualitative‐based approach, in which the observations are made by a restricted set of design experts on a subset of representative products. In contrast with the current literature in this research field, this article describes an objective process that is able to identify dominant product languages.