Intangibles are concepts which cannot be directly measured; they describe mental images of concepts of the real world forming part of our everyday language. In business, intangible are the image of the company, the customer satisfaction, the loyalty, the human capital, the reputation of the company, the employee commitment, …

Company assets consist of a mix of tangible and intangible. The objective of a firm is building assets and maximizing future economic returns. But, the nature of company assets has certainly changed. The share of tangible assets steadily decline in the value in the market of industrial organizations. The ultimate driving force for any economy, even in the actual context, is determined by the demands of the people. For that reason, actions focusing on actual user valuation of supplied products (goods and services) are essential, specifically in the new economy sector, where customers change very quickly their minds and are aware of the concurrence.

This leads to the need of providing tools for measuring intangibles in an integrated business context, that is to provide a complete system for measurement of intangibles in a complex organization; particularly the customer satisfaction measurements constitute an important instrument for such analysis. The main objective underlying the design of this system is to tackle the problem from data collection to analytics interpretation. This requires methodological advances in the fields of:

• Data imputation, to fill in the incomplete data sources as it often happens in practice to carry out the operation,
• The statistical modelling approach and
• Performing in depth interpretation of the results, by means of a visual display of the significant results.

Particular emphasis will be devoted to the situations where heterogeneity is present in the data, where the usual assumption of homogeneity over the entire set of individuals is unrealistic. This is the usual case for example, where potential sources of heterogeneity can be expected among different subgroups defined by gender, groups of age, ethnicity, or level of education. In those cases, having a unique model for measuring the intangibles can be misleading. Hence automatic procedures for heterogeneity detection should be provided, to segment the population and to provide more reliable measurements and their comparison among segments.

Main Advisor at Universitat Politècnica de Catalunya (UPC)
Co-advisor at Aalborg Universitet (AAU)