Improving the Business Plan Evaluation Process: the Role of Intangibles

Abstract

One of the main objectives of the European MUSING project is to design and test software tools in order to support the activities of small and medium sized businesses. In this paper we examine financial risk management and, more specifically, the self-assessment of business plans. The role of intangible assets is discussed, and we report on how intangible assets can be collected, how they can be represented, taking into account their semantic relationships, and how they can be used to build an analytical tool for business plans. The basic technology embedded in the tool is the construction of classification trees, a well-known technique in inductive learning. We show how using knowledge of intangible assets can improve the construction of the classifier, as proved by the testing carried out so far.

Key Words:

• Bayesian causal map
• classification trees
• intangible assets
• qualitative analysis

Additional information

Notes on contributors

Franco Turini

Franco Turini Graduated with an honors degree in Information Science, in 1973, from the University of Pisa. He is a full Professor in the Department of Computer Science at the University of Pisa. In 1978/1980, he was a visiting scientist at Carnegie-Mellon University (Pittsburgh) and later at the IBM Research Center in San Jose, California. In 1992/1993, he was visiting Professor at the University of Utah. He is a member of the IEEE and ACM. His research interests include design, implementation, and the formal semantics of programming languages, logic programming and deductive databases, and knowledge discovery. He has been coordinator of several national and international research projects. He has been involved in the organisation and served on the scientific committee of various conferences in the area of logic programming, artificial intelligence, and data mining.

Miriam Baglioni

Miriam Baglioni Graduated in Computer Science with honors, in 2001, from the University of Pisa. Her thesis was entitled “DMQL: a proposal for a Data Mining Query Language”. She received her PhD in Computer Science, in July 2005 from the University of Pisa. Her thesis was entitled “Representation and exploitation of domain knowledge in a KDD environment”. She currently works at the Computer Science Department — University of Pisa as a researcher and developer in the field of Data Mining, Knowledge Discovery, and Ontologies.

Andrea Bellandi

Andrea Bellandi Graduated in Computer Science in 2004, from the University of Pisa. His thesis was entitled “Type of Services and Web Services Discovery”. The thesis focused on a semantic description of Web Services using ontologies. Since January 2005, he has been a PhD student in Computer Science and Engineering at IMT Lucca. Since January 2006, he has been collaborating with the Department of Computer Science of Pisa on research and development in KDD & data base fields, particularly the study of ontologies and Bayesian Networks in Support of Knowledge extraction in Business Intelligence.

Barbara Furletti

Barbara Furletti Graduated in Computer Science in 2003, from the University of Pisa. Her thesis was entitled “Logical Environment for spatio-temporal clustering”. Since 2003 she has collaborated with the Department of Computer Science of Pisa on research and development in KDD & data base fields. Since January 2006, she has been a PhD student in Computer Science and Engineering at IMT Lucca. Her interests include data mining, in particular its application to economic and business issues and Ontology development and application. She is a member of IEEE.

Chiara Pratesi

Chiara Pratesi Graduated in Business Economics from the University of Pisa in 2004. She completed a post-graduate Masters in Auditing and Internal Control at the University of Pisa. The thesis was entitled “XBRL standard and the SME financial-administrative consulting. The WINS project (Web-based services for self-assessment of enterprise)”. Since 2005 she has worked at Metaware S.p.A, ICT SME, on research and development in balance sheets, intangible assets analysis, and economical-financial analysis for merger and acquisition operations. Her interests include intangible capital analysis and measurement, and company evaluation methodologies.