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.
This paper discusses masking as a technique for preserving the privacy of business processes. Being sensitive by nature, business processes capture an organization’s know how with regard to who does what, when, where, and why. From a modeling perspective, a business process encompasses either activities or data artifacts where the latter are this paper’s focus. Due to the loosely coupled nature of data artifacts, applying masking to some critical data artifacts could impact the continuity of their business processes calling for a close coordination between what is referred to as a data artifact’s life cycle and transactional cycle. Both cycles encompass states that track the completion progress of a data artifact at run time. For coordination purposes during masking, exchanges of messages between life and transactional cycles take place. Experiments demonstrating the synergy between these cycles and hence, exchanges of messages in real-time are also presented in the paper.
Digital transformation (DT) remains conceptually fragmented and operationally inconsistent despite substantial scholarly attention and organizational investment. Existing research often isolates themes such as technology adoption, customer experience, data governance, or business model renewal, limiting the development of a coherent theoretical architecture. This study addresses these gaps by constructing and validating an integrative framework of forty-one strategic imperatives structured across seven dimensions: Competition, Customer, Data, Innovation, Regulation, Technology, and Value. A sequential mixed-method design combines a Semi-Structured Literature Review (SSLR) of 135 articles with expert validation interviews, ensuring both theoretical rigor and practical relevance. The SSLR identifies forty imperatives, while expert insights refine ambiguous constructs, elevate under-represented areas such as regulatory alignment and data governance, and introduce the additional imperative of Embedding Data Ethics. The integrated findings reveal that DT unfolds through interdependent socio-technical and strategic capabilities rather than isolated initiatives. Several imperatives—such as ecosystem orchestration, operational excellence Differentiation, and digital growth—are strongly supported in the literature, whereas others, including customer obsession, regulatory sandboxing, and ethical governance, emerge as under-theorized yet practically decisive. The resulting framework advances DT scholarship by strengthening conceptual clarity, integrating siloed research domains, and establishing a multidimensional foundation for future theory building. For practitioners, it provides actionable guidance for aligning strategic intent with operational implementation and mitigating the risks associated with fragmented transformation efforts.
Enterprise Resource Planning (ERP) systems are increasingly implemented by higher education institutions (HEIs) to support administrative and financial processes. However, ERP implementation in HEIs remains challenging due to decentralized governance structures, diverse stakeholder interests, and limited alignment between system functionalities and institutional practices. This study examines stakeholder-related factors influencing ERP implementation and adoption in German HEIs. It is based on an in-depth case study at a large public university and is complemented by survey data from nine additional HEIs. The findings identify five factors that consistently shape stakeholder perceptions and adoption processes: (1) project team composition and resource allocation, (2) structured change management, (3) alignment of ERP functionalities with institutional processes, (4) continuous training and support, and (5) long-term leadership commitment. The analysis highlights that early and sustained stakeholder involvement is associated with higher perceived alignment and acceptance of ERP systems, while predominantly top-down implementation approaches tend to intensify resistance and usability concerns. By focusing on stakeholder perspectives, this study contributes empirical insights into ERP implementation challenges in German HEIs and offers practice-oriented implications for managing stakeholder involvement during ERP adoption in highly decentralized public institutions.
The increasing complexity and heterogeneity of data in Delegated Broker Business insurance models challenge insurers’ ability to ensure data quality, traceability, and operational reliability. While the CRISP-DM is widely adopted, its limited support for data governance and multi-source integration constrains its use in modern, data-intensive environments. This paper proposes an extended, data-centric framework that adapts CRISP-DM to the realities of delegated broker business ecosystems. The framework integrates principles from Agile, Lean, and DataOps, embedding quality assurance and governance across six iterative phases: Business Understanding, Data Understanding, Data Preparation, Dashboard Development, Validation, and Sustainability. It prioritises the standardisation and transformation of heterogeneous data into reliable, analysis-ready assets. A real-world implementation within an insurer operating in the French market demonstrates the framework’s efficacy, transforming a manual, fragmented process into an automated business intelligence pipeline. This transformation yielded significant improvements in data consistency, a substantial reduction in data dimensionality, and enhanced operational alignment. Governance artefacts, including a data cleaning rulebook and residual risk matrix, enables continuous monitoring and refinement. The study confirms the framework successfully bridges data engineering, governance, and business intelligence, offering a scalable methodology to build trustworthy foundations for decision-making and future AI adoption within the insurance sector and other data-intensive industries.
Service-oriented microenterprises often struggle with manual order handling, fragmented customer records, and inefficient delivery coordination, which limit operational efficiency and service quality. This study presents the design, deployment, and evaluation of a cloud-based order tracking system adopted by over 500 small and microenterprises in the carpet cleaning industry. Leveraging Software-as-a-Service (SaaS) architecture and open interoperability standards, the system streamlines manual processes, improves route optimization, and enhances customer communication. To assess its real-world impact, detailed operational data were analyzed from 50 early-adopting firms with sufficiently complete pre-implementation and post-implementation records. The findings suggested improvements in order processing time, notification practices, delivery-related operations, and overall service coordination, while customer satisfaction was described based on post-implementation survey feedback. Applying the Technology–Organization–Environment (TOE) framework, this research explores the key drivers and barriers to adoption in resource-constrained service firms. Findings suggest that small service businesses can achieve enterprise-grade automation through scalable, modular, and cost-effective cloud solutions, demonstrating the broader potential of digital transformation in microenterprise service sectors.
In the evolving digital economy, the challenge of sharing data while ensuring data sovereignty has become increasingly critical. Data trusts have been proposed as a governance model built around independent, fiduciary stewardship of data exchange between providers and consumers. Despite the concept’s potential, research in this domain remains nascent and fragmented across disciplines. Through a structured, topic-centered literature review, we address three research questions on the conceptualization of data trusts, the roles within the ecosystem, and outstanding research gaps. Our key contributions are threefold. First, we conceptualize data trusts through a consolidated definition, position them among adjacent data intermediaries, and derive a typology of four archetypes along primary objective and locus of governance authority. Second, we consolidate the fragmented role vocabulary into a minimal five-role model. Third, we propose a theory-informed research agenda that identifies nine gap categories and outlines directions for future work.
The rapid advancement of Artificial Intelligence (AI) has introduced transformative changes in Higher Education Institutions (HEIs), particularly in the deployment of Management Information Systems (MIS). The primary aim of this study is to examine how managerial support, technical capability, and other organizational factors interact with technological drivers and government involvement to shape AI adoption within HEIs. Guided by the “Technology–Organization–Environment (TOE) and Diffusion of Innovation (DOI) frameworks”, the study employs a quantitative, survey-based methodology. Data were collected from 334 respondents across Saudi Arabian HEIs actively engaged in AI initiatives. Structural Equation Modeling (SEM) was applied to test hypothesized relationships among constructs, including Managerial Capability (MC), Technological Drivers (TD), Government Involvement (GI), and AI Adoption (AIA). The results reveal that technological drivers, particularly digital platforms, big data utilization, and trust, security, are significant enablers of managerial capability, which subsequently exerts a strong positive influence on both AI adoption and government involvement. In contrast, traditional enablers such as managerial support, technical capability, organizational readiness, and change management did not demonstrate direct effects, suggesting their role may be mediated through integrated technological infrastructures. Furthermore, government involvement emerged as a critical external facilitator, significantly enhancing AI adoption outcomes. This study enriches literature by clarifying the interdependent pathways through which technology, management, and institutional frameworks coalesce to enable AI adoption in HEIs. These findings also highlight the importance of aligning technological investments with managerial competencies while leveraging supportive government policies, offering actionable insights for policymakers, administrators, and institutional leaders aiming to accelerate AI integration in higher education.
For short-lifecycle products, online reviews of earlier versions become less informative. In response, digital platforms hosting these products adopt governance mechanisms that keep reviews timely and aligned with the current versions. In the mobile app context, one such mechanism conceals prior reviews whenever an app is updated and excludes them when deriving review-based measures of reputation. The concern is that by increasing search frictions, the mechanism may depress demand for some apps but not others. In this paper, we investigate how this concealing governance mechanism influences app demand. Using an instrumental variable approach, our results reveal asymmetrical impacts. We find that top-ranked free “superstar apps” tend to benefit from updating. Paid “non-superstar apps” are negatively impacted and the effect is primarily driven by higher-priced apps, while free ones only suffer if their prior review-based reputation was high. Our findings highlight how a platform governance mechanism results in redistributing demand. They also offer platforms and developers guidance on the implications of review-concealing policies.
This study probes the factors influencing citizen adoption of the Ghana Citizen Mobile Apps for Public Services (GCMA-PS). Since the unveiling of the GCMA-PS, no study has empirically validated the adoption of this m-government app form, which is a momentous research gap that this study bridges. The study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating three citizen-centric constructs, technology literacy, information literacy, and awareness creation, to capture contextual issues facing m-governance diffusion. This study employed a quantitative survey design, collecting data from 602 Ghanaian citizens, and the responses were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) in Smart PLS. The results indicate that performance expectancy, effort expectancy, and social influence significantly influence citizens’ intentions to adopt the GCMA-PS. Also, awareness creation is a significant direct predictor, while technology literacy positively influences both behavioral intention and the UTAUT core constructs of performance and effort expectancy. This study establishes that technology literacy and awareness creation are critical determinants of m-government adoption in developing economies, guiding policymakers to prioritize targeted awareness campaigns and digital literacy investment.
Artificial Intelligence (AI), along with other innovative technologies, is a game changer with the power to completely transform ERP systems, revolutionizing how organizations manage operations, make decisions, and plan strategically. The rapid and forceful infiltration of AI into organizational systems has led to a surge in research on AI-powered ERP systems. However, there is still a need for more comprehensive and systematic studies to fully understand this phenomenon. This paper is a systematic literature review that synthesizes findings from academic and grey literature, exploring the application of all three categories of AI technologies: generative, analytics, and automation in business organizations. The review outlines the evolution of research, spotlights current trends, and identifies gaps and future research directions for the AI-driven ERP landscape. The review synthesizes 183 academic publications and 35 industry documents. Bibliometric analysis revealed an 11.3
A key challenge for companies engaged in business model innovation is to prototype and evaluate new business models quantitatively. The complexity of this challenge is compounded when companies operate in business ecosystems, due to the need for joint value creation and mutual value capture. However, extant evaluation approaches, such as spreadsheets, are insufficient for addressing the complex, multilateral dynamics inherent to ecosystems. Though research indicates that system dynamics simulation represents a promising approach to overcoming this gap, its application in ecosystem settings remains an unexplored area of research. Therefore, we develop an actor-based modeling framework to leverage system dynamics for business model prototyping and evaluation from an ecosystem perspective. Furthermore, we present nine good design practices to guide effectively a business model’s system dynamics modeling process in the context of a business model innovation project. Our findings are derived from a two-and-a-half-year action design research project conducted in collaboration with two German industrial companies. Our research contributes to the existing body of knowledge on business model innovation, particularly regarding quantitative business model prototyping and evaluation prior to market implementation.
This study investigates the development of a data-centric process model tailored to address the specific challenges associated with the manufacturing of Micro-electro-mechanical systems (MEMS)-based sensors. These challenges include managing complex data flows, sensitivity to environmental factors, and the need for high precision in production. MEMS-based sensor production is a critical area in advanced manufacturing, yet conventional data science solution lifecycles provide only limited explicit guidance for the real-time adaptivity and tightly coupled calibration processes required in this context. The research aims to create an interdisciplinary solution for real-time anomaly detection, adaptive calibration, and context-sensitive feature selection. Using the Design Science Research (DSR) methodology, the study integrates design requirements, design principles, and design features into a nine-phase process model. The development of the artefact involves collaboration across roles, such as, domain experts, data scientists, data engineers, and project managers, to ensure practical applicability in industrial settings. Validated through three case studies: wafer map clustering, adaptive calibration, and smart feature selection, the proposed process model significantly reduces production bottlenecks, enhances accuracy, and optimizes resource allocation. Overall, the study contributes a novel, structured process model that addresses critical demands in MEMS manufacturing and illustrates how a DSR-grounded, domain-specific lifecycle can be instantiated in practice, while providing design knowledge that may inform tailored AI process models in other high-stakes, sensor-intensive production contexts.
The rapid acceleration of digital transformation (DX) is fundamentally reshaping competitive landscapes, making accurate assessment of DX maturity paramount for e-business success. Organizations are increasingly leveraging intelligent technologies like the Internet of Things (IoT) in order to enhance their e-business capabilities thereby achieve a competitive advantage. However, existing DX maturity frameworks often suffer from significant limitations including restricted accessibility, insufficient granularity, reliance on domain experts, subjective assessments, and high effort requirements. This study develops a novel, hierarchical DX Maturity Index (DX-MI), specifically designed as a self-diagnostic tool to address these critical gaps. Developed through design science research, the DX-MI is grounded in an evidence-based approach that emphasizes objective, quantifiable metrics and systematically weighted measurement instruments, with weights assigned using the Analytical Hierarchy Process (AHP). This framework ensures accurate and reliable evaluations with minimal expert intervention, enabling businesses to obtain actionable insights for strategic e-business transformation. Case study validation across diverse companies demonstrates that the DX-MI provides reliable, practical measurement. It offers clear pathways for managers to strategically optimize their DX investments and enhance e-business performance, thereby serving as a valuable tool for organizational development and competitive positioning.
Robotic Process Automation (RPA) is often marketed as a lightweight and easy-to-implement solution for process automation. However, this portrayal can be misleading. Our study reveals that RPA implementation shares many complexities with more comprehensive automation technologies, requiring strategic planning and organizational commitment. Through a systematic literature review following PRISMA and Kitchenham guidelines, we identify 12 organizational challenges to RPA implementation using the TOE framework and derive 7 sequential technical, organizational, and strategic conditions for its successful adoption. Four key insights include: RPA implementation (1) demands deep technical knowledge of both RPA tools and the existing digital infrastructure; (2) requires specific organizational and process-related capabilities; (3) involves multiple actors with distinct roles across 2022pre- and post-implementation phases; and (4) depends on clear policies and governance strategies. These findings highlight the importance of moving beyond simplistic narratives of RPA adoption and recognizing the multifaceted conditions that shape its success. Our review also identifies research gaps, including the need for studies on designing governance frameworks, aligning automation technologies with different process types, and supporting hybrid human-automation solutions. The role of RPA enthusiasts in driving adoption also emerges as a critical but underexplored area. This study contributes a nuanced understanding of RPA implementation challenges and offers practical insights for organizations navigating process automation. It serves as a resource for both researchers and practitioners aiming to better prepare for the technical, organizational, and strategic demands of RPA.
