Community-Driven Governance: Between Complex Education and Flipped Inclusion - Research Perspectives for an Eco-Sustainable Economic Pedagogy

Community-Driven Governance: Between Complex Education and Flipped Inclusion - Research Perspectives for an Eco-Sustainable Economic Pedagogy

Maria Carbone, Alessia Sozio, Tonia De Giuseppe
Copyright: © 2024 |Pages: 8
DOI: 10.4018/979-8-3693-3003-6.ch013
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Abstract

Technological progress and, in particular, the spread of artificial intelligence, constitutes in the context an element of innovation no longer only at the level of production but also at the level of administration and control. This chapter explores the potential and risks that directors are faced with as a result of the entry of artificial intelligence into the boardroom, proposing to investigate whether in the corporate governance of small and medium-sized companies it is possible to act pedagogically to form an organizational culture devoted to inclusion through the application of cooperative learning methodologies that allow, in fact, to achieve greater inclusiveness among the individuals working there. The project proposes to apply the methodology of flipped inclusion, and more specifically Cohen's method of complex instruction because through the creation of heterogeneous teamwork and interdependence of roles, the resources of all group members can be enhanced.
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Introduction

The transformation of economic activities due to artificial intelligence (AI) technologies has been underway for some time but has become particularly pronounced in recent years. This evolution has prompted experts in corporate law and business organizations to globally confront the effects of this technological revolution. AI is not only opening up new opportunities but also posing unprecedented interpretative problems and innovative regulatory demands on legislators and supervisory authorities. Many believe AI is central to the “fourth industrial revolution,” irrevocably changing daily life.

More interconnected societies will base decision-making processes on big data analysis; firms in competitive markets will access resources at lower costs, and goods and services can be purchased from anywhere through automated services. The organization and management of modern enterprises are undergoing a metamorphosis (Rossi, 2012), leading to a modified genetic code of corporate governance. This is characterized by a renewed balance between inter-organic dynamics and positioning in a globalized, digital market. Accessibility and interoperability among communication platforms support technological democratization, fostering shared value and well-being.

Corporate governance, understood as the institutional framework regulating corporate decision-making, is in flux. Questions abound regarding the impact of new AI technologies on corporate structures, particularly governance itself. AI-driven disruptive innovation benefits corporate governance in various ways (Sickle et al., 2018). Companies utilize new technologies not just for production and distribution but also for management and control, enhancing market performance and decision-making processes internally.

Entrusting “machines” with corporate compliance could refocus administrators on business while mitigating conflicts of interest inherent in human decision-makers. Terms like “roboboard,” “corp-tech,” and “self-driving corporations” indicate an impending dehumanization of the corporate legal person (Abriani, 2020). The risk is that corporations dominating markets through digital interaction, enabled by big data analytics, may prevail (Mancuso Hobey, 2020).

This debate is ongoing, influenced by regulatory developments and behavioral changes among governance actors, aimed at sustainable value creation. Major market players, aware of the importance of innovation and change, are reviewing business models and internal organization, redefining strategic goals, strengthening culture and engagement, and balancing recruitment with retraining. A shift from platform governance to community-driven governance is evident. The largest economic players use AI technologies not only to facilitate economic exchanges but also to enable stakeholder participation in corporate assets. This practice, implemented through feedback (Scarchillo, 2019), involves a process where system action results impact the system itself, influencing future behavior.

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