A primary objective of the theory of intellectual capital (IC) is to investigate the ways in which an organization’s resources might be used to generate value (Bai et al., 2024). Previous researchers have reached a consensus that Intellectual Capital (IC) is not a single-dimensional notion, but rather encompasses various levels, including the person, organisational, and network levels (Crupi et al. 2021). IC involves not only the knowledge held by individuals, but also extends beyond that. Within this context, a prevailing theoretical framework has been progressively developing that views IC as a theoretical concept comprised of three primary components (Nahapiet and Ghoshal, 1998): Human Capital (HC), Social or relational Capital (SC), Organizational or structural capital (OC). IC is crucial for establishing a competitive advantage as it improves a company’s financial performance, promotes innovation, manages knowledge, increases overall company value, and strengthens a company’s image (Chuang and Huang, 2018). Furthermore, IC is considered a key driver of firms’ value creation (Harrison and Sullivan, 2000; Sullivan 2000; Chen et al. 2005; Tseng and James Goo 2005). According to Weritz et al. (2024), the combined synergies of intellectual capital dimensions, as drivers of value creation in the digital age, can enhance the success of digital transformation, thereby boosting firm value.
The great majority of research at the firm level examining companies’ innovation potential and output has focused on a number of internal and external variables. One body of research identified intellectual capital as a critical component of innovation (Kianto, Sáenz, and Aramburu 2017; Lin et al. 2011; Subramaniam and Youndt 2005; Zhang et al. 2017). However, the majority of studies have either focused on skilled or managerial intellectual capital (McGuirk, Lenihan, and Hart 2015; Sun, Li, and Ghosal 2020). Artificial Intelligence (AI) has become a critical enabler of competitive advantage in organizations, facilitating the automation of processes, enhancement of decision-making, and the creation of new value propositions. AI technologies such as machine learning, natural language processing, and computer vision allow companies to analyze vast amounts of data and gain insights that were previously inaccessible, leading to improved efficiency and innovation (Russell & Norvig, 2020). Moreover, AI has reshaped how organizations manage knowledge, fostering a deeper integration of human capital, social capital, and structural capital to create more effective knowledge-sharing systems (Gama & Magistretti, 2023). As companies adopt AI-driven solutions, the role of intellectual capital in fostering innovation becomes even more pronounced, particularly in how human expertise interacts with AI systems to enhance creativity and problem-solving (Kaplan & Haenlein, 2019). However, the integration of AI into organizational structures also raises challenges regarding ethics, transparency, and the potential for job displacement, making it essential for firms to carefully manage this transition (Haefner, et al., 2021).
In order to understand the impact of digital technologies on the management and use of IC inside companies, this track’s goal is to provide an overview of the relationship between artificial intelligence (AI), IC and value creation by covering three major lines of research. The first relates to the potential of AI for creating firm value through competitive advantage represented by intellectual capital in terms of different knowledge resources. The second is about changing the organization’s mindset toward AI and new human resource practices. Lastly, the concluding major research avenue revolves around the novel paradigm of encouraging diversity for creativity in a variety of businesses.
First, from a strategic perspective, it is relevant to investigate intellectual capital dimensions as critical drivers for generating innovation and companies’ competitive advantage, particularly within AI. Its advent is revolutionizing the processes of value creation affecting the generation, dissemination, and utilization of knowledge within and among companies (Mariz-Pérez, Teijeiro-Álvarez, and García-Álvarez 2012).
Second, the adoption of innovative technologies affects companies and people across industries. AI is impacting the way employees carry out their work, the competencies they require, and the approach they utilize to generate value. Exploring the consequences of these changes on employee engagement, motivation, and performance is a thought-provoking endeavor (Mariz-Pérez, Teijeiro-Álvarez, and García-Álvarez 2012; Gan and Yusof 2019; Mostafa et al. 2021).
Finally, the rapid advancement of AI technologies necessitates an investigation into how these innovations impact organizational structures and leadership strategies. AI not only transforms routine tasks but also demands a rethinking of leadership and decision-making processes, as machines become more integrated into these roles. Investigating how organizations adapt their intellectual capital to leverage AI, particularly in strategic decision-making and innovation management, is crucial for sustaining competitive advantage (Haefner et al., 2021). Furthermore, as AI reshapes industry boundaries and introduces new competitive dynamics, understanding how organizations manage the intersection between human capital and AI-driven processes is essential to fostering sustainable growth (Malik et al., 2021).
This track is in line with the UN Sustainable Development Goals (SDGs) number 5, 9, and 10. Goal 5 promotes offering men and women the same chances in the workplace and an appropriate involvement at all levels, while Goal 9 attempts to assist technological advancement, research, and innovation, particularly in emerging nations, to increase market integration and increase accessibility to banking and financial services for small organizations and businesses. Reducing inequality inside and among countries is the aim of Goal 10, intending to advance and enhance people’s cultural, economic, and legal inclusion, regardless of age, gender, impairment, race, ethnicity, birthplace, religion, as well as other conditions.
Track main topics
The track intends to contribute to the developing discussion in the context of innovation as a result of the substantial interest that diversity has gained from academics, practitioners, and policymakers. We accept submissions of articles that use qualitative and/or quantitative empirical methodologies to explore a wide range of topics, including but not limited to:

• Intellectual Capital (IC), business model innovation and value creation;
• Artificial Intelligence strategies, IC, and value creation in different industries;
• IC and innovating human resource management practices;
• Impact of Artificial Intelligence on IC management and value creation;
• The effect of digital transformation on IC in SMEs;
• Drivers and barriers of IC for Artificial Intelligence;
• IC and startups’ innovation capability;
• The effect of Artificial Intelligence on inclusion and equality;
• IC diversity in management and corporate boards;
• Diversity and inclusion as driving force for Artificial Intelligence;
• The role of Artificial Intelligence and IC in Corporate Social Responsibility (CSR);
• IC and sustainable innovation;
• IC and social innovation;
• Artificial Intelligence challenges and threats to IC and value creation;
• Understanding artificial intelligence to prevent feeling overwhelmed;
• Artificial Intelligence and its contribution to economic, social, and environmental sustainability.

Valeria Schifilliti, University of Messina, Italy
Elvira Tiziana La Rocca, University of Messina, Italy
Francesco Fasano, University of Calabria, Italy
María Eugenia Sánchez Vidal, Polytechnic University of Cartagena, Spain