A data mining approach to business modelling

According to the extant literature, business models may be considered as “cognitive” devices, and their adoption requires the development of a deep level of knowledge about customers, suppliers and competitors. Recent experimental studies show that the adoption of data-mining tools create a positive interaction with business models, empowering the strategic performance capabilities that drives the achievement of competitive advantage. The present paper aims to improve the studies about the interaction between data-mining tools and business model design, by discussing whether the adoption of a data-mining in a real context may be enabled or hindered by organizational heterogeneity related to factors such as the level of authority, level of experience, managerial and technical skills, educational background, and so forth. The tool adopted in the case study, the Structured Neural Network, is particularly suitable in support of strategic management, since it stimulates the convergence of personal knowledge and beliefs towards the exploitation of the key concepts and the cause-and-effect relations needed for the design of the business model. Furthermore it provide a fact-based test for its robustness. The results provide both scientific and practical implications.