Value co-creation for smart villages:the institutionalization of regional service ecosystems

Abstract Building a versatile portfolio of public and private digital-enabled services is vital in rural and sparsely populated regions, where traditional market mechanisms alone cannot guarantee the availability of essential services. However, contemporary services tend to build on prevalent institutions, often governed by decisions based on market mechanisms, such as economies of scale — service-by-service and village-by-village. A shift is suggested towards networks of smart villages co-creating value as regional service ecosystems. We draw from institutional theory and employ the Service-dominant logic (SDL) framework in investigating value co-creation in rural villages in Sweden. Analyzing 53 laddering interviews, we derive scripts encoding institutional principles for innovating bundles of digital-enabled services. The study brings forth novel insight for e-government research and practice, and the SDL discourse therein, on outlining required institutional practices and institutional work to counteract plain market mechanisms for governing value co-creation on smart rural service portfolios

Towards real-time learning for edge-cloud continuum with vehicular computing

Abstract Sensor-driven IoT systems are well-known for their capacity to accelerate massive amounts of data in a comparatively short period of time. To have any use, the information delivery and decision making based on the data require efficient learning models together with dynamically deployed computing and network resources. The current cloud and high-performance computing infrastructures, as well as modern edge computing systems especially in the 5G and beyond networks, can be addressed to resolve these challenges. However, there are several application areas especially in vehicular and urban computing, where just harnessing more computational power does not solve computational and real-time requirements of the modern sensing systems that operate in mobile and context-dependent environments. For now, the mathematical challenges of distributed computing and real-time learning algorithms have not been profoundly addressed in the context of the IoT and real-world sensing applications. Data-driven systems also require giving full attention to information delivery, data management, data cleaning, and sensor fusion technologies that need to be equally distributed and real-time competent as the learning algorithms themselves. New software-defined computing and networking approaches and architectures are required to orchestrate the numerous connected resources dynamically, controllably, and securely along with the evolving needs. The key challenge here is to uniform collaboration between different aspects of the system, from data processing and delivery to the algorithms and learning models, not forgetting the computational capacity and networking capabilities, all this in real-time with real-world applications

DECAS:a modern data-driven decision theory for big data and analytics

Abstract Decisions continue to be important to researchers, organizations and societies. However, decision research requires re-orientation to attain the future of data-driven decision making, accommodating such emerging topics and information technologies as big data, analytics, machine learning, and automated decisions. Accordingly, there is a dire need for re-forming decision theories to encompass the new phenomena. This paper proposes a modern data-driven decision theory, DECAS, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics (machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality; and (3) meaningful integration of the classical decision making elements with data and analytics can lead to more informed, and possibly better, decisions. This paper elaborates the DECAS theory and clarifies the idea in relation to examples of data-driven decisions

Digital ambidexterity in the public sector:empirical evidence of a bias in balancing practices

Abstract Purpose:The purpose of this study is to explore and theorize on balancing practices (BP) for digital ambidexterity in the public sector. Design/methodology/approach: The research is designed as an interpretative case study of a large Swedish authority, involving data collection in the form of interviews and internal documents. The method of analysis involves both theorizing on the findings from a previous framework for digital innovation and deriving design implications for ambidextrous governance. Findings: The findings show that all identified BP except one (shadow innovation) is directed toward an increased emphasis on efficiency (exploitation) rather than innovation (exploration). With the increased demand for innovation capabilities in the public sector, this is identified as a problem. Research limitations/implications: The limitations identified are related to the choice in the method of an interpretative case study, with issues of transferability and empirical generalizability as the main concerns. The implications for research are related to a need for additional studies into the enactment of digital ambidexterity, where the findings offer insight and inspiration for continued research. Practical implications: The study shows that managers and executives involved in the design and imposition of governance within the public sector need to take the design recommendations for digital ambidexterity into consideration. Social implications: The study offers two main implications for practice. First, policymakers need to take the conceptual distinction of efficiency and innovation into account when designing policies for the digital government. Second, existing funding practices need to be re-designed to better facilitate innovation. Originality/value: This is the first study directed toward enhancing the insight into BP for digital ambidexterity in the public sector. The study has so far resulted in both a localized shift in policy and new directions for research. With the public sector facing needs for increased innovation capabilities, the study offers a first step toward understanding how this is currently counteracted through governance design

Efficiency creep and shadow innovation:enacting ambidextrous IT Governance in the public sector

Abstract The current push towards increased innovation within the public sector calls for new approaches to IT Governance. However, recent findings highlight the aim to avoid trade-offs between innovation and efficiency through organisational ambidexterity. This paper reports a case study of ambidextrous IT Governance in two large government agencies. According to the findings, ambidextrous IT Governance is enacted through two separate but interrelated mechanisms that emerge simultaneously. In terms of exploitation, the “efficiency creep” mechanism creates a bias for efficiency — rather than innovation-oriented investments. In terms of exploration, the “shadow innovation” mechanism involves unsanctioned innovation activities. These two mechanisms interplay, in the enactment of ambidextrous IT Governance. The contribution of this study lies in theorising about how ambidextrous IT Governance is enacted in public sector organisations, and how efficiency creep and shadow innovation influence each other. This contribution aids future research and practice on public sector innovation and IT Governance

Towards stakeholder governance on large e-government platforms:a case of Suomi.fi

Abstract E-government evolves towards large-scale software platforms that integrate access to and information exchange among public services. Governance of large-scale e-government platforms is challenging because of the large number of stakeholders with diverging needs, agendas, and changing service portfolios. This paper presents a revelatory case, the e-government platform Suomi.fi, its stakeholders and stakeholder interactions related to development and governance of the platform. Our stakeholder analysis of Suomi.fi identified 15 stakeholder interaction types and related issues regarded as important for governance of large-scale e-government platforms. The results contribute by addressing the importance of stakeholder identification and continuing governance beyond individual development and implementation projects. Such a large-scale platform involved additional stakeholder types of external influencers (including media, other countries, the European Union, third party software integrators) and other external platforms, compared to the project-centric stakeholder models. Hence, we argue for extended stakeholder governance models and practices for large-scale e-government platforms

Design objectives for evolvable knowledge graphs

Abstract Knowledge graphs (KGs) structure knowledge to enable the development of intelligent systems across several application domains. In industrial maintenance, comprehensive knowledge of the factory, machinery, and components is indispensable. This study defines the objectives for evolvable KGs, building upon our prior research, where we initially identified the problem in industrial maintenance. Our contributions include two main aspects: firstly, the categorization of learning within the KG construction process and the identification of design objectives for the KG process focusing on supporting industrial maintenance. The categorization highlights the specific requirements for KG design, emphasizing the importance of planning for maintenance and reuse

Ex-post evaluation of data-driven decisions:conceptualizing design objectives

Abstract This paper addresses a need for developing ex-post evaluation for data-driven decisions resulting from collaboration between humans and machines. As a first step of a design science project, we propose four design objectives for an ex-post evaluation solution, from the perspectives of both theory (concepts from the literature) and practice (through a case of industrial production planning): (1) incorporate multi-faceted decision evaluation criteria across the levels of environment, organization, and decision itself and (2) acknowledge temporal requirements of the decision contexts at hand, (3) define applicable mode(s) of collaboration between humans and machines to pursue collaborative rationality, and (4) enable a (potentially automated) feedback loop for learning from the (discrete or continuous) evaluations of past decisions. The design objectives contribute by supporting the development of solutions for the observed lack of ex-post methods for evaluating data-driven decisions to enhance human-machine collaboration in decision making. Our future research involves design and implementation efforts through on-going industry-academia cooperation

Knowledge graph construction and maintenance process:design challenges for industrial maintenance support

Abstract Knowledge graphs (KGs) structure knowledge to develop intelligent systems in several application domains. Industrial maintenance support requires knowledge and expertise on a variety of aspects of the factory, machinery, and components. However, the actual creation and maintenance process of KGs has remained unelaborated. We review the KG literature to integrate previous models into one process model also incorporating knowledge engineering principles within. The literature review and a subsequent case study together represent the problem and objectives definition phases of a design science project. The contributions include the integrated process model for KG creation and maintenance and the initially observed design challenges in the KG process operationalisation in a context of supporting industrial maintenance

Digital twin ecosystems:potential stakeholders and their requirements

Abstract Context: As industries are heading for digital transformation through Industry 4.0, the concept of Digital Twin (DT) — a software for digital transformation, has become popular. Many industries use DT for its advantages, such as predictive maintenance and real-time remote monitoring. Within DT domain, an emerging topic is the concept of an ecosystem—a digital platform that would create value for different stakeholders in an ecosystem of DT-driven products and services. The identification of potential stakeholders and their requirements provides valuable contributions to the development of healthy Digital Twin Ecosystems (DTE). However, current empirical knowledge of potential stakeholders and their requirements are limited. Objective/Methodology: Thus, the objective of this research was to explore potential stakeholders and their requirements. The research employed an empirical research methodology in which semi-structured interviews were conducted with DT professionals for data collection. Results: Data analysis of the study revealed 13 potential stakeholders who were categorized as primary (manufacturers, suppliers, subcontractors, and intelligent robots), secondary (maintenance service providers, platform integration service providers, tech companies, etc.), and tertiary (research organizations, third-party value-added service providers, cyber security firms, etc.). This study also presents the different requirements of these stakeholders in detail. Contribution: The study contributes to both research and industry by identifying possible stakeholders and their requirements. It contributes to the literature by adding new knowledge on DTEs and fills a research gap while contributing industry by providing ample knowledge to the industry’s practitioners that is useful in the development and maintenance of a healthy DTE