Purpose: This paper reviews existing research to understand when, how and with what results robotic process automation (RPA) is implemented by organizations.Design/methodology/approach: The authors have identified 84 sources across eight databases and have analyzed them through the lens of a context–intervention–mechanism–outcomes framework (CIMO). The CIMO analysis maps the contextual drivers, intervention approaches and value related outcomes associated with RPA implementation.Findings: The result of the analysis is the identification of four mechanisms explaining the approach organizations take to implement RPA: digitizing business processes, performing knowledge work together with humans, replacing outsourcing with RPA robots and developing a new business model. Therefore, in this paper, in order to reduce RPA literature fragmentation, the authors take into account the digital transformation (DT) perspective, by considering RPA as one example of digital technology.Practical implications: This study sensitize organizational adopters to the different mechanisms they can deploy to conduct RPA implementations to achieve different desired outcomes in response to different drivers. Moreover, having a clear picture of the key enablers and associated barriers to the realization of these alternative paths serve as a useful map to guide the implementation process.Originality/value: The findings contribute to DT research by conceptualizing these mechanisms through which organizations deploy automation tools—such as RPA.

By considering the recently proposed definitions and metrics, oral healthcare quality management (OHQM) emerges as a distinct field in the wider healthcare area. The goal of this paper is to systematically review quality management initiatives (QMIs) implementation by dental clinics. The research methodology approach is a review of 72 sources that have been analyzed using the Context–Intervention–Mechanism–Outcome Framework (CIMO). The analysis identifies five mechanisms that explain how quality management initiatives are implemented by dental clinics. The simplest QMIs implementations are related to (1) overall quality. The next ones, in terms of complexity, are related to (2) patient satisfaction, (3) service quality, (4) internal processes improvement, and (5) business outcomes. This paper is the first attempt to provide a critical review of this topic and represents an important advancement by providing a theoretical framework that explains how quality management is implemented by practitioners in this field. The results can be used by scholars for advancing their studies related to this emerging research area and by healthcare managers in order to better implement their quality management initiatives.

Scholars have become interested in understanding what drives academics to pursue an entrepreneurial path, but less attention is paid to the individual-level factors that are considered the best predictors of entrepreneurial intentions among academics (AEI). Using a configurational approach, this study investigates the individual factors that influence AEI by confirming whether and under what conditions the antecedents can explain the outcomes. Fuzzy-set comparative qualitative analysis (fsQCA) was performed to identify the causal paths that influence AEI. The results reveal multiple configurations of conditions that influence AEI. The findings show that the attitude toward entrepreneurship is a core condition that explains high AEI. This effect is obtained when a positive attitude among academics is supported by having higher self-efficacy and a mastery goal orientation. In addition, academic status (i.e., job position, job experience) can play an important role in this equation. Also, motivational factors (i.e., performance goal orientation, performance avoidance goal orientation) can lead to low AEI. This is one of the first studies to apply a configurational approach in examining different causal paths to AEI in a developing-country context. This paper uses academic status, self-efficacy, goal orientation, attitude toward entrepreneurship, and academic output as conditions to identify multiple unique pathways that influence AEI.