Small and medium-sized enterprises (SMEs) that aim to harness data analytics often look to cloud-based solutions to reduce initial invest and to ensure scalability. To support that endeavor, this study examines individual-level adoption dynamics of cloud analytics in the specific organizational context of SMEs. A composite model was developed which integrates the socio-technical variables of the technology-organization-environment framework into the technology acceptance model. Data was collected from employees of SMEs operating in Germany (n = 368) using an online questionnaire and was analyzed using partial least squares structural equation modeling. The proposed model was found to explain 78
Semi-automated operational environments, where human operators and automated systems coexist, pose significant challenges for process discovery approaches, which typically target either fully manual or fully automated workflows. This paper presents a comprehensive guideline for manual process discovery specifically tailored to semi-automated contexts. Building on established methods (document analysis, direct observation, structured interviews, and collaborative workshops), we adapt and extend each method to capture both human-driven activities and machine-generated events. The resulting artifact comprises four modular guideline packages, offering step-by-step procedures, guiding questions, and optional tooling to uncover control flow, data flow, resource assignments, and system interactions. Use cases in an Industrial Internet of Things (IIoT) setting illustrate how these guidelines can be applied to complex, data-intensive scenarios, though the approach remains domain-agnostic and can be transferred to other semi-automated environments. The development followed the Design Science Research (DSR) cycle, incorporating iterative refinement and validation via expert workshops, surveys, and real-world use cases. Our evaluation confirms that the proposed guidelines enhance the completeness, accuracy, and efficiency of manual process discovery in hybrid human–machine landscapes.
E-government intermediaries facilitate individuals’ access to and utilization of e-government services by bridging the gap between disadvantaged populations and e-government platforms. While these intermediaries help reduce bureaucratic barriers and the digital divide, their types, functions, and ecosystem dynamics remain underexplored. This study systematically reviews e-government intermediaries to enhance conceptual and practical understanding by identifying their types and functions. Despite technological advancements that may eliminate some traditional intermediaries, digital inequalities persist, and intermediaries remain essential. They are unlikely to disappear, but rather to evolve through reintermediation. Using PRISMA as a methodological framework, this study systematically analyzes literature from Web of Science (WoS) and Scopus databases to identify research gaps. The findings explore the functions and types of e-government intermediaries, concluding with future trends and new research directions. This study contributes to the e-government literature by providing a structured review and offering insights for both scholars examining e-government intermediaries and practitioners who interact with them or use them to bridge the digital divide.
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.
