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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.

The purpose of this paper is two-fold: first, to introduce an innovative model of financial order book, less simplified than the existing literature and still able to replicate all statistical features of true markets; second, to simulate realistically the effects of policies aimed to reduce the instability of financial markets.

The paper is based on an agent-based model and the applied methodology is the computational simulation.

The policymaker can actively reduce the instability of financial market by means of policies aimed to increase the heterogeneity of investors, both in terms of the behavioral attitude for market participation and of the differentiation of opinions; favor investors who show insensibility with regard to market information; limit the allowed number of counterparts for any market order; reduce the time validity of orders; and maintain flexibility and efficient bargaining, reduce transaction costs and avoid Tobin taxes.

In future research, an opinion dynamics engine within a clustered community network will be embedded.

Indeed, the obtained results are policy rules which could be immediately applied.

The order book model contained in the paper is completely new, innovative and original. Innovativeness is based on a reduced number of simplifying assumptions. The realism of the presented mechanism is higher than in other existing models. The value of the model is high because of several factors. From the scientific point of view, it constitutes the reliable framework on which many other papers will be based: it is the core center of future research. From the policymaker’s point of view, it represents a credible tool for policy hypotheses testing.