Celebrating Impact: The Research and Scholarship of President Katia Passerini: Other Featured Work

Among many functionalities, Generative Artificial Intelligence (GenAI) can model the topology and semantics of user-supplied datasets – a functionality required to evaluate learning levels through mind maps. Since GenAI evolves by orchestrated changes to the underlying algorithms and, organically, by learning, we need to understand this evolution’s speed and reliability. We conducted two experiments tasking ChatGPT with scoring mind maps drawn by 113 undergraduate students describing their motivation and deterrence towards entrepreneurship. The results show that we should not fall into the “hype” curve typical of the beginning of any emerging technology. While the pace of learning in GenAI is unprecedented, caution is necessary when rechecking data and analytical techniques.

This article articulates the nuanced challenges of integrating Generative AI (GenAI) into educational settings, aiming to dispel overly simplistic narratives driven by unwarranted enthusiasm or unfounded apprehensions. It introduces a conceptual framework for the application of GenAI within higher education, delineating four key strategies that leverage the dual roles of educators—as both creators and designers—while positioning GenAI as either a facilitative agent (creature) or a utilitarian tool. The identified strategies—Interactive Co-Creation, Adaptive Design, Learning Scaffold, and Efficient Structuring—underscore GenAI’s potential to revolutionize teaching methodologies by enabling personalized education, enhancing content quality, and expediting course development processes. Emphasizing GenAI’s capacity to cater to diverse student needs, simplify educational content creation, and foster engaging learning environments, the model provides educators with a roadmap for integrating GenAI into their instructional practices to harness the full potential of educational technology.

As digital technologies continue to accelerate the complexity and diversity of customer demands to an unprecedented degree, companies small and large are pursuing business innovation strategies to cope with the challenge. This empirical study focuses on understanding the factors that contribute to business improvement, and specifically on-demand service innovation. The study results show that digital entrepreneurship, a precursor of digital transformation, plays a key role in on-demand service innovation through variables such as entrepreneurial orientation and digital orientation.

Researchers have long been fascinated with the phenomenon of lurking and free riding in knowledge sharing. This interest has led to the investigation of which factors drive decisions to contribute to a knowledge exchange as opposed to only exploiting the information in such exchange. Many studies have specifically focused on identifying the extrinsic and intrinsic motivational drivers for knowledge sharing in communities of practice by administering user surveys on behavioral intention, expectations, and satisfaction with the community. Our analysis is different from prior studies in that it does not look at expectations of reciprocity and other individual characteristics. Rather, it extracts and analyzes interaction data and, then, it groups such data based on factors like geographical location and related cultural background. This study adopts known models of national culture and relates them to social interactions using a large dataset mined from an online community of practice.

The question of how knowledge assets are utilized in the context of online communities is the primary impetus of this research. Using a multilevel approach, this paper investigates factors that influence the use of knowledge in an online question and answer platform (OQA). It focuses on three levels including informational, individual, and community, and reviews interactions across each level. The study tests the multilevel model with data from StackOverflow.com, a renowned online community for programmers to exchange knowledge assets, especially questions and answers about coding issues. Traditional hierarchical regression analysis proved insufficient to explicate the complexity associated with human decision-making processes with respect to asset utilization. However, a machine learning technique with a Chi-square automatic interaction detection algorithm provided a richer understanding of the relative importance of factors and their thresholds for influencing knowledge asset use.

In this paper, we focus our attention back to the persistent question on whether psychological traits can help identify and characterize entrepreneurs and, in particular, these new digital entrepreneurs. Are they unique offspring of millennials and digital natives, or are they business as usual? We use the Five Factor Model (FFM) of personality and psycholinguistic text analysis of interviews with entrepreneurs at a business incubator to analyze startup performance. We find an increase in model predictive power when we distinguish digital entrepreneurs from traditional entrepreneurs.

The dynamic knowledge creation process, traditionally described and applied in the context of large firms, is receiving growing attention in entrepreneurship research as a model to identify ways to decrease mortality rates in the "valley of death," the stage of entrepreneurship infamously known for the high percentage of startup failures. Prior innovation literature associates entrepreneurship activities with the socialization phase of Nonaka’s dynamic theory of knowledge creation (also known as the SECI model). However, linguistic analysis of interviews with entrepreneurs indicates that internalization (i.e., the creation of tacit knowledge from explicit assets) plays an important role in new ventures activities. In this study, we distinguish between internalization in the current venture from internalization during the entrepreneur’s prior employment. We find a strong relationship between references to the past and internalization that is mediated by negative emotions, which may have motivated the entrepreneur to leave prior employment and launch a new venture. We also find a strong relationship between an entrepreneur’s references to the past and the number of employees in the venture, indicative of the importance of an entrepreneur’s prior knowledge internalization to the current venture.