The Automation of Management and Business Science

Artificial intelligence and the conduct of literature reviews

“…These debates resonate with scholars in Information Systems (IS), who ponder which role AI and automation can play in theory development (Tremblay et al, 2018) and in combining data-driven and theory-driven research (Maass et al, 2018). With this commentary, we join the discussion which has been resumed recently by Johnson et al (2019) in the business disciplines. The authors observe that across this multi-disciplinary discourse, two dominant narratives have emerged.…”

“…Overall, this step tends to be time-consuming with many mechanical tasks potentially lending themselves to automation (Carver et al, 2013; Johnson et al, 2019). The need for automation and AI-support is particularly salient when considering the rapid growth of research output (Larsen et al, 2019) and the inefficiency of investing valuable time of academic experts to complete repetitive and mechanical tasks.…”

“…After all, it is important to remember that new assemblages of constructs and relationships are not a sufficient condition for a strong theoretical contribution. As pointed out by Johnson et al (2019), it is the “why” associated with the relationships, the underlying theoretical rationale (Whetten, 1989), which is critical and one of the open challenges for AI-based theory development in the near future. In contrast to the creative and unstructured endeavor of theory development, the process of aggregating evidence from primary studies in order to test hypotheses and theories (most notably in a meta-analysis) can largely be supported by AI-based tools using the extant methodological literature as guidance.…”

“…The potential of artificial intelligence (AI) to augment and partially automate research has sparked vivid debates in many scientific disciplines, including the health sciences (Adams et al, 2013; Tsafnat et al, 2014), biology (King et al, 2009), and management (Johnson et al, 2019). In particular, the concept of automated science is raising intriguing questions related to the future of research in disciplines that require “high-level abstract thinking, intricate knowledge of methodologies and epistemology, and persuasive writing capabilities” (Johnson et al, 2019: 292). These debates resonate with scholars in Information Systems (IS), who ponder which role AI and automation can play in theory development (Tremblay et al, 2018) and in combining data-driven and theory-driven research (Maass et al, 2018).…”

The anatomy of an award-winning meta-analysis: Recommendations for authors, reviewers, and readers of meta-analytic reviews

“…We have shown that some of the newer techniques affected Stahl et al findings, which proved sensitive to outliers and whether a fixed-or random-effects model was used. As for the near future, Marshall and Wallace (2019), as well as Johnson, Bauer and Niederman (2017), argued that we will see increased adoption of machine-learning systems in literature search and screening, which already exist but tend to be in the domain of wellfunded health topics such as immunization (Begert, Granek, Irwin, & Brogly, 2020). Machine learning is a response to the ''torrential volume of unstructured published evidence has rendered existing (rigorous, but manual) approaches to evidence synthesis increasingly costly and impractical'' (Johnson et al, 2017: 8).…”