AI-Enabled Business Models in Legal Services: From Traditional Law Firms to Next-Generation Law Companies?

What will happen to law firms and the legal profession when the use of artificial intelligence (AI) becomes prevalent in legal services? We address this question by considering three related levels of analysis: tasks, business models, and organizations. First, we review AI’s technical capabilities in relation to tasks, to identify contexts where it is likely to replace or augment humans. AI is capable of doing some, but not all, legal tasks better than lawyers and is augmented by multidisciplinary human inputs. Second, we identify new business models for creating value in legal services by applying AI. These differ from law firms’ traditional legal advisory business model, because they require technological (non-human) assets and multidisciplinary human inputs. Third, we analyze the organizational structure that complements the old and new business models: the professional partnership (P2) is well-adapted to delivering the legal advisory business model, but the centralized management, access to outside capital, and employee incentives offered by the corporate form appear better to complement the new AI-enabled business models. Some law firms are experimenting with pursuing new and old business models in parallel. However, differences in complements create conflicts when business models are combined. These conflicts are partially externalized via contracting and segregated and realigned via vertical integration. Our analysis suggests that law firm experimentation with aligning different business models to distinct organizational entities, along with ethical concerns, will affect the extent to which the legal profession will become ‘hybrid professionals’

Toward a taxonomy of entrepreneurship education research literature: A bibliometric mapping and visualization

The retrospective amount of research literature dedicated to entrepreneurship education (EE) is overwhelming, which makes producing an overview difficult. However, advanced bibliometric mapping and clustering techniques can help visualize and structure complex research literature. Thus, the objective of this mapping study is to systematically explore and cluster the EE research literature to deliver a taxonomic scheme that can serve as a basis for future research. The analyzed data, which were drawn from the Web of Science and Scopus, consist of 1773 peer-reviewed documents published between 1975 and 2014. On the one hand, this taxonomy should create stronger ties to educational research; on the other, it can foster international research collaboration to boost both interdisciplinary EE and its impact on a global basis. This work reinforces our understanding of current EE research by identifying and distilling the most powerful intellectual relationships among its contributions and contributors. Consequently, this study addresses not only the academic community but also entrepreneurship educators and policymakers in an effort to boost entrepreneurial spirit, design effective policy instruments, and, ultimately, improve societal welfare.ISSN:1747-938