Artificial Intelligence Big Data and the Human Mind: A Study on The Effects of New Technologies on Students' Decision-making Process

Abstract

Recent developments in the fields of machine learning and data management are drastically changing the way companies and individuals consume information necessary for decision-making. However, the effects that exposure to these new techniques has on the human decision-making process is still unclear, in spite of the benefits that wider knowledge on the topic could bring to businesses. The aim of this thesis is to advance the current state of research on the matter, by investigating the effects that taking courses on Big Data or Artificial Intelligence has on students enrolled in master’s degrees at Bocconi University or Copenhagen Business School. A review of the current literature led to the identification of three tendencies part of the students’ decision-making process worth considering: their reliance on data rather than opinions, their preference for internal rather than external innovation, that is to say Not-Invented-Here Syndrome, and their propensity for delegating a task to a machine rather than doing it in person. The students were surveyed before and after half of them took part in courses concerning Big Data or Artificial Intelligence, testing their attitudes with vignette scenarios. Their responses were then analysed with a differences-in-difference approach. The results show that exposure to Big Data and Artificial Intelligence increases data literacy and causes the development of a unique framework for approaching different decision-making situations. On the other hand, there seem to be no significant effects on Not-Invented-Here Syndrome and on the tendency to delegate to machines, as these phenomena are not altered by the attendance of courses on Big Data and Artificial Intelligence. The implications of these outcomes are then discussed, showing how they can provide useful information and outlining the positive effects of the courses considered for students and companies alike. In particular, the courses are shown to increase students’ ability to use data without creating a bias for automation or a blind trust in machines and software, as respondents remain aware of the importance of human work. The final part of the thesis presents the findings’ implications for business and academia, along with interesting related topics that future research may consider worth investigating.