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.
Artificial intelligence (AI) has been a major driver of economic growth in the last decade. Many organizations adopt AI to improve performance, but evidence shows that these benefits are primarily realized by large companies, leaving behind small- and medium-sized enterprises (SMEs). This study investigated the level of digitalization maturity as a technological foundation for AI readiness in Swedish manufacturing SMEs and examined its relation to organizational performance, focusing on revenue and operating profit (earnings before interest and taxes). Using a sample of 246 established SMEs undergoing digital transformation, descriptive statistics were calculated, and correlation and regression analyses were conducted. The results show substantial variation in digitalization maturity across firms and industries. Correlation analysis indicates that digitalization maturity is positively associated with performance, particularly in terms of revenue. Regression models show that higher digitalization maturity levels are associated with increases in revenue per employee, while the effects on profit per employee are weaker and less consistent. This suggests that in SMEs, performance improvements from digital capability development, which enables AI adoption, primarily emerge through revenue growth and productivity gains, whereas profitability effects may take longer to materialize. The study is limited by its cross-sectional design and the use of survey-based measures of digitalization maturity focused only on the technological perspective, which restrict causal interpretation. To determine the long-term impact of AI adoption and digital capability development on financial performance, future research should follow these companies over time. This study provides insights for SME managers and policymakers, emphasizing the importance of investing early in digital capabilities that enable efficient operations and future AI use.
Robots are entering the workplace at a rapid pace across various industrial sectors. The increasing presence of software robots is also affecting white-collar workers and managers. This study targets those software robots that are very accessible and that will therefore affect millions of jobs worldwide based on desktop automation, namely through a dedicated technology called robotic process automation (RPA). Although this RPA technology is receiving much attention from consultants and technical scholars, little is known about how white-collar employees would react to RPA and what their concerns would be. Based on two vignette analyses including text coding of semi-structured interviews and statistical ANOVA-based testing, this study examines employees’ acceptability and well-being across diverse RPA situations and explains these perceptions by psychological theories. The first panel focuses on RPA in general while the second panel focuses on RPA in the RPA-intensive field of human resources. The resulting RPA adoption model focuses on explaining and predicting which situations and which strategic motivations can lead to less fear or concerns for RPA, and thus higher acceptability of RPA. Overall, our data suggest that white-collar employees’ well-being is significantly higher when RPA replaces one’s routine job tasks and when RPA-robotization is implemented for intrinsic reasons, while a more varied picture appears when RPA replaces complex job tasks. The latter is especially crucial when RPA is getting more integrated with artificial intelligence, calling for balanced process automation. Our findings help organizations make strategic choices about their framing of RPA implementation and provide guidance to facilitate their technological changes.
Although Enterprise Resource Planning (ERP) systems have proven valuable and necessary for many organizations, implementing such systems requires significant investment and effort. To ensure fruitful outcomes, researchers and practitioners rely on the framework of Critical success factors (CSFs) as a reference for their strategies. In this framework, user training is typically considered one of the most crucial factors. Although various studies have discussed different aspects of training, few have attempted to categorize and transform these findings into actionable guidelines. Through a systematic literature review of the research works published since 2013 across 3 databases. The study identified the overlooked research themes and categorized factors that influence learning motivation, behaviors, and training transfer, as well as currently available tools and training practices. It is discovered that 3 training dimensions and 6 training practices were emphasized. Despite extensive academic focus on certain methods, there is a disparity between industry and research, where some methods are widely studied but lack adoption and vice versa. This study is one of the few dedicated to investigating literature trends on this topic using a systematic literature review method. It provides guidelines for practitioners in designing ERP training strategies and provides a basis for future research using empirical data to validate training dimensions and practices further.
