Strategy as a matter of beliefs: the recorded music industry reinventing itself by rethinking itself

Managerial and organizational cognition studies the ways cognitions of managers in groups, organizations and industries shape their strategies and actions. Cognitions refer to simplified representations of managers’ internal and external environments, necessary to cope with the rich, ambiguous information requirements that characterize strategy making. Despite the important achievements in the field, many unresolved puzzles remain as to this process, particular as to the cognitive factors that condition actors in framing a response to a discontinuity, how actors can change their models in the face of a discontinuity, and the reciprocal relation between cognition and action. I leverage on the recent case of the recorded music industry in the face of the digital technology to study these issues, through a strategy-oriented study of the way early response to the discontinuity was constructed and of the subsequent evolution of this response. Through a longitudinal historical and cognitive analysis of actions and cognitions at both the industry and firm-level during the period in which the response took place (1999-2010), I gain important insights on the way historical beliefs in the industry shaped early response to the digital disruption, on the role of outsiders in promoting change through renewed vision about important issues, and on the reciprocal relationship between cognitive and strategic change.

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.