Nowadays, the current application of artificial intelligence (AI) to financial context is opening a new field of study, named financial intelligence, in which the implementation of AI-based solutions as “financial brain” aims at assisting in complex decision-making (DM) processes as wealth and risk management, financial security, financial consulting, and blockchain. For venture capitalist organizations (VCOs), this aspect becomes even more critical, since different actors (shareholders, bondholders, management, suppliers, customers) with different DM behaviors are involved. One last layer of complexity is the potential variation of behaviors performed by managers even in presence of fixed organizational goals. The aim of this study is twofold: a general analysis of the debate on implementing AI in DM processes is introduced, and a proposal for modeling financial AI-based services is presented. A set of qualitative methods based on the application of cultural psychology is presented for modeling financial DM processes of all actors involved in the process, machines as well as individuals and organizations. The integration of some design thinking techniques with strategic organizational counseling supports the modeling of a hierarchy of selective criteria of fund-seekers and the creation of an innovative value proposition accordingly with goals of VCOs to be represented and supported in AI-based systems. Implications suggest that human/AI integration in the field can be implemented by developing systems where AI can be conceived in two distinct functions: (a) automation: treating Big Data from the market defined by management of VCO; and (b) support: creating alert systems that are coherent with ordered weighted decisional criteria of VCO.
Making investment decisions is usually considered a challenging task for investors because it is a process based on risky, complex, and consequential choices (Shanmuganathan, 2020). When it comes to Investments in human capital (IHC), such as startups fundings, the aspect of decision-making (DM) becomes even more critical since the outcome of the DM process is not completely predictable. Indeed, it has to take into consideration the will, goals, and motivations of each human actor involved: those who invest as well as those who seek investments. We define this specific DM process as multi-actor DM (MADM) since not a group is making decisions but different actors, or groups of different actors, who – starting from non-coinciding objectives – need to reach a mutual agreement and converge toward a common goal for the success of the investment. This review aims to give insights on psychological contributions to the study of complex DM processes that deal with IHC to provide scholars and practitioners with a theoretical framework and a tool for describing the complex socio-ecological systems involved in the DM processes. For this purpose, we discuss in the paper how the third generation of activity theory (Leont’ev, 1974, 1978;Engeström, 1987, 2001) could be used as an appropriate model to explain the specificities of MADM construct, focusing on the particular case of startup funding. Design thinking techniques will be proposed as a methodology to create a bridge between different activity systems.
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