Competing for Stakeholders: Three Essays on Business Sustainability

Literature Review. Competing for stakeholders toward a better understanding of Business Sustainability. Identifying Stakeholders' Multiple utility source: a first step for value Co-creation. The model in action: a real example of value co-creation in an Italian SME

Large-Scale Kernel Methods for Independence Testing

Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible framework for fully nonparametric hypothesis tests of independence, which can capture any type of departure from independence, including nonlinear associations and multivariate interactions. However, these approaches come with an at least quadratic computational cost in the number of observations, which can be prohibitive in many applications. Arguably, it is exactly in such large-scale datasets that capturing any type of dependence is of interest, so striking a favourable tradeoff between computational efficiency and test performance for kernel independence tests would have a direct impact on their applicability in practice. In this contribution, we provide an extensive study of the use of large-scale kernel approximations in the context of independence testing, contrasting block-based, Nystrom and random Fourier feature approaches. Through a variety of synthetic data experiments, it is demonstrated that our novel large scale methods give comparable performance with existing methods whilst using significantly less computation time and memory.Comment: 29 pages, 6 figure

Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data

Microbial identification is a central issue in microbiology, in particular in the fields of infectious diseases diagnosis and industrial quality control. The concept of species is tightly linked to the concept of biological and clinical classification where the proximity between species is generally measured in terms of evolutionary distances and/or clinical phenotypes. Surprisingly, the information provided by this well-known hierarchical structure is rarely used by machine learning-based automatic microbial identification systems. Structured machine learning methods were recently proposed for taking into account the structure embedded in a hierarchy and using it as additional a priori information, and could therefore allow to improve microbial identification systems. We test and compare several state-of-the-art machine learning methods for microbial identification on a new Matrix-Assisted Laser Desorption/Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) dataset. We include in the benchmark standard and structured methods, that leverage the knowledge of the underlying hierarchical structure in the learning process. Our results show that although some methods perform better than others, structured methods do not consistently perform better than their "flat" counterparts. We postulate that this is partly due to the fact that standard methods already reach a high level of accuracy in this context, and that they mainly confuse species close to each other in the tree, a case where using the known hierarchy is not helpful

Classifications of innovations: Survey and future directions

The purpose of this paper is to focus on similarity and/or heterogeneity of taxonomies of innovation present in the economic fields to show as the economic literature uses different names to indicate the same type of technical change and innovation, and the same name for different types of innovation. This ambiguity of classification makes it impossible to compare the various studies; moreover the numerous typologies existing in the economics of innovation, technometrics, economics of technical change, management of technology, etc., have hindered the development of knowledge in these fields. The research presents also new directions on the classification of innovation that try to overcome these problems.Classifications, Taxonomy, Technical change, Product, Innovation Patterns, Management of Technology, Economics of innovation