Emil Lucian Crișan

Digital transformation (DT) is a major challenge for organisations. Digital maturity models (DMMs) and digital transformation maturity models (DTMMs) have been developed to help decision-makers in making decisions about DT. The terms DMM and DTMM are used interchangeably and have evolved in parallel, and this duality has generated confusion for both researchers and practitioners. The goal of this paper is to generate more prescriptive knowledge for understanding these maturity models related to digital phenomena. For this purpose, we review 60 papers that discuss DMMs and DTMMs using the context-intervention-mechanism-outcome (CIMO) theoretical framework of analysis. The results reveal four fundamental mechanisms that explain how maturity models in this field of study are constructed and used: models focused on the outcomes of digital transformation (DMMs), models centred on the process of digital transformation (DTMMs), mixed models (focusing on both outcomes and processes associated with DT), and strategic models, which evaluate the outcomes, process, and strategy of DT. Considering the differences between DMMs and DTMMs, the paper identifies theoretical and methodological gaps in current research while providing valuable results for researchers in developing new models, and for practitioners in selecting the most appropriate maturity model for their organisations in their DT path.

Throughout the article we reviewed the present literature on successful onboarding to identify determinants, measurement models, and outcomes associated with its success, ultimately offering pragmatic implementation techniques for companies to attain successful onboarding outcomes. We have applied a systematic approach to analyze relevant articles, categorizing determinants, measurement models, and outcomes into various classifications founded on their presence throughout the literature. Key findings include the identification of eleven determinants, four measurement mechanisms, and nine outcomes associated with successful onboarding. Integration of these factors provides organizations with practical strategies to enhance successful employee onboarding. This research enhances the current body of literature by synthesizing key determinants, measurement models, and outcomes associated with successful onboarding, offering a comprehensive framework for organizations to improve their onboarding processes.

Digital transformation is anticipated to be the fourth industrial revolution. Considering this phenomenon, some economic entities tend to group and collaborate using digital technologies, thus forming economic ecosystems. The notion of ecosystem is a relatively recent concept used in economic language, coming from biology. Because of this, the ecosystem and the specific types of ecosystems, such as business and digital, are not consistently defined in the literature. There are multiple conceptualizations of these concepts, which in practice leads to confusion and even misuse of the terminology. The main purpose of the paper is to revise the concepts related to the ecosystem construct, but also the most important types of ecosystems for the world economy, namely the business and digital ecosystems. The methodology used is the analysis of the main sources dealing with ecosystems, particularly digital and business. We have reviewed papers from the following journals: Technological Forecasting and Social Change, Journal of Business Research, Technovation, Digital Business, International Journal of Information Management, and Technology Innovation Management Review. It is important to note that the analysis of the articles was carried out from a managerial point of view, without adopting a technical perspective. Following this analysis, we made a comparison between the two types of ecosystems, highlighting their common features.

Onboarding, the process of integrating new employees into companies, involves a wide range of techniques and opinions in academic literature. This study explores the complex aspects of onboarding, analysing several terms such as "employee onboarding," "organizational socialization," "orientation," "assimilation," and "hazing." This research tries to clarify the conceptual landscape of employee integration by drawing from a thorough review of the literature. This study provides a structured classification of determinants that lead to successful onboarding, offering valuable insights into key elements that enhance staff integration. Organizations may enhance their onboarding procedures by comprehending these determinants and how they interact, creating a conducive environment for new employees, which can lead to successful onboarding.

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.

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.

Recent literature has focused increasingly on software ecosystems and digital platforms. Digital platforms, as the foundation that supports software ecosystems’ development, have reshaped the traditional business models by focusing more on new dimensions such as stakeholders instead of customers and suppliers, or value co-creation instead of value creation. Software ecosystems contribute significantly to how business is conducted around the world. By performing an overview of recent literature published in Web of Science top academic journals, our paper presents a bibliometric co-occurrence and co-authorship analysis of both software ecosystems and digital platforms. The results show that software ecosystems and digital platforms research collide based on the key words co-occurrence analysis under a certain degree, underlying the evolution of the two topics towards platform ecosystem and platform economy research in recent years.