Roberto Garelli

The Didactic Challenge of Accounting Information Systems and ERPs for Business Schools: A Proposal for the Italian Universities

The strong integration triggered by the adoption of Enterprise Resource Planning Systems (ERPs) impacts many processes and tasks; the way accounting data are collected, elaborated, communicated and used often changes even dramatically when an ERP system is adopted. This design-oriented paper stems from multi-year didactic experiences in the Italian context, where the Business Economics courses still poorly take into account the emerging educational needs stemming from this scenario: in most Italian universities, in fact, subjects such as accounting, administration, controlling and auditing are taught according to traditional approaches, which tend to see these activities as scarcely integrated with both the operations and the strategic management processes. This paper proposes a framework for innovating the educational strategies for basic and advanced courses related to accounting, administration, controlling and auditing, in order to fill the identified gap between real-world demand and higher education supply in this context.

Mapping Financial Performances in Italian ICT-Related Firms via Self-organizing Maps

In this work, we explore the application of machine learning models (MLM) to the analysis of firms’ performance. To such aim, we consider a bunch of financial indicators on firms operating in the Information and Communication Technology (ICT) sector, with attention to enterprises providing ICT related-services. The rationale is to highlight the potential of MLM to exploit the complexity of financial data, and to offer a handy way to visualize the related information. In fact, instead of performing classical analysis, we discuss how to apply to those indicators Self-Organizing Maps-SOMs—that are well suited to manage high dimensional and complex datasets to extract their relevant features. It emerges that SOMs are useful in clustering companies depending on multi-dimensional criteria and in analysing hidden relations in companies’ performances.

Unsupervised neural networks for the analysis of business performance at infra-city level

The goal of this paper is using Neural Networks (NN) to analyze business performance and support small territories development policies. The contribution of the work to the existing literature may be basically summarized as follows: we are focusing on the application of an unsupervised neural network (namely: on Self-Organizing Maps—SOM) to discover firms clusters on micro-territories inside city’s boundaries, and to exploit possible development policies at local level. Although since early ’90 of the past century NN have been widely employed to evaluate firms performance, to the best of our knowledge the use of SOM of that specific task is much less documented. Moreover, the main novelty of the paper relies on the attention to data at “microscopic” level: data processing in an infra-city perspective, in fact, has been neglected till now, although recent studies demonstrate that inequalities in economic and well-being conditions of people are higher among neighbourhoods of the same city rather than among different cities or regions. The performance analysis of a large set (7000 environ) of companies settled in Genova, Italy permits to test our research method and to design further applications to a large spectrum of territorial surveys regarding both economic and social well-being conditions.