Context: The complexity of large-scale, software-intensive systems demands collaborative efforts across engineering disciplines to assure that all system properties are correctly implemented. Effective communication and knowledge sharing among the involved engineering are critical for fostering this collaboration. As a result, engineering artifacts, such as reference architectures, play a key role as boundary objects—shared objects used by different communities to reach a mutual understanding. Objective: This study aimed to understand how engineer should utilize reference architectures as boundary objects, and explored the challenges in using them in the co-engineering of system properties. Methodology and Methods: Semi-structured interviews were conducted with 10 experts from two engineering disciplines focusing on system properties of particular importance to contemporary large-scale software-intensive systems development. To analyze the data, an abductive thematic analysis approach was used. Results: Reference architectures can be used as promoters of synergy, change, and learning by the engineers involved in the co-engineering of system properties. However, related challenges with regard to organizational contexts were identified. These findings underscore the need for further investigations into the relationship between the role of reference architectures, power, and organizational theory.

This study investigates the role of University–Industry Collaboration (UIC) in enabling cross-industry knowledge sharing, focusing on the implications of structural social capital within these collaborations. Through a multiple case study involving three distinct UIC structures, we explore how the nature of these collaborations, whether research-focused or networking-focused, influences cross-industry knowledge sharing. Our findings reveal that research-focused UIC, characterized by formal and structured interactions, primarily leverages cognitive social capital to abstract and disseminate knowledge across industries. Conversely, networking-focused UIC, which operates through informal and less-structured channels, relies mainly on relational social capital to foster direct, trust-based knowledge sharing between industries. We identify key enablers and distinctions in these UIC structures, illustrating how they shape cross-industry knowledge sharing. Specifically, we propose a conceptual model, highlighting the mediating role of social capital dimensions and the moderating effects of interest and funding. This model offers new insights into the relationship between structural social capital and cross-industry knowledge sharing in UIC. From a managerial perspective, our study suggests that whether firms engaged in UIC can leverage UIC structures for cross-industry knowledge sharing largely comes down to their own strategical development and management of social capital. From a policy perspective, our study suggests that augmenting already existing policy toward cross-regional knowledge sharing by considering aspects of networking-focused UIC and cross-industrial knowledge sharing, could leverage gains from UIC.

An increase in size and complexity prompts research of autonomous control of connected cyber-physical systems (CPS) at the level of intents (high-level goals). Domain-specific solutions such as intent-based networking, and the next generation of supervisory control and data acquisition and manufacturing execution systems, were influenced by the concept of autonomic computing and Monitor-Analyze-Plan-Execute loops over shared knowledge to introduce such control. However, there is a dearth of knowledge concerning the architectural attributes required to enable autonomic computing at a domain-independent level. CPSs are also often contributed by multiple stakeholders, thereby forming systems of systems in which architectures are by necessity federated. Herein, we apply a systematic approach to develop a taxonomy of such attributes, considering the federated nature of architectures, the need to accommodate heterogeneity and the legacy systems commonly encountered in practice. The proposed taxonomy covers nine key dimensions present within different domains of autonomic computing: knowledge representation, learning, distribution, reactivity, environment observability, information acquisition automation, information analysis automation, decision selection automation, and action implementation automation. The utility of the taxonomy is further demonstrated through an application to architectures across domains and scales.

This study provides an evaluation of an architecture framework intended to support stakeholders in realizing trustworthy cyber-physical systems (CPS), referred to as the T-Framework. The framework explicitly addresses CPS complexity, including the fact that multiple trustworthiness aspects will need to be considered for contemporary CPS, from classical dependability aspects to ethical concerns involving artificial intelligence. In addition, this study also investigates the problems that are repeatedly encountered by the stakeholders involved in realizing trustworthy CPS. To achieve the goals of the study, the boundary object and knowledge boundary concepts from social sciences were used. These concepts are useful tools to examine how various involved stakeholders can cooperate on a project through the utilization of objects, even though they have different perspectives and conflicting interests. Focus groups were used as the methodological approach to gather feedback from various experts in CPS from industry and academia. Findings show that stakeholders repeatedly encounter problems when making trade-offs between trustworthiness attributes and system aspects, dealing with prioritization, and making final decisions. The findings further show that the T-Framework can potentially guide stakeholders in addressing these problems as a boundary object. Furthermore, based on the feedback from the participants, several aspects for improvements or additional consideration in the T-Framework were identified, including clarifications regarding the framework workflow and terminology.

Research background: Digital ecosystems in Europe are heterogenous organizations involving different economies, industries, and contexts. Among them, Digital Innovation Hubs (DIHs) are considered a policy-driven organization fostered by the European Commission to push companies’ digital transition through a wide portfolio of supporting services.

Purpose of the article: There are DIHs existing in all European economies, but literature needs more precise indications about their status and nature. The purpose is to study a distribution of DIHs and differences in portfolios of DIHs’ services across European economies. Therefore, the paper wants to deliver more precise data on effects on national and European policies. This is required to define their final role and scope in the complex dynamics of the digital transition, depending on regional context and heterogeneity of industries.

Methods: Data on 38 economies was collected from the S3 platform (on both existing and in preparation DIHs) and further verified by native speaking researchers using manual web scrapping of websites of DIHs identified from S3. To find potential similarities of digital ecosystems in different economies as emanated by the existence of DIHs, clusterization (Ward’s method and Euclidean distances) was applied according to the services offered. Economies were clustered according to the number of DIHs and the spread of DIHs intensity in different cities. The results were further analyzed according to the scope of the provided services.

Findings & value added: The applied clustering classified European economies in four different sets, according to the types of services offered by the DIHs. These sets are expression of the different digitalization statuses and strategies of the selected economies and, as such, the services a company can benefit from in a specific economy. Potential development-related reasons behind the data-driven clustering are then conjectured and reported, to guide companies and policy makers in their digitalization strategies.

University competence centres (UCCs) are created to enhance university-industry collaboration and knowledge sharing among collaborating partners. This study investigates the organisation of knowledge sharing among firms in UCCs through a qualitative case study of UCCs with or without a focus on research in their activities. Data collection was done through interviews and observations over a period of 24 months. While the findings indicate that both types of UCCs are non-neutral, they also reveal several different characteristics that appear primarily based on a strong tie either to the first (education) or the second (research) mission of academia. Although both types of UCCs act to build a common meaning among participating organisations, the focus on the first or the second mission leads to this meaning is primarily being constructed in the firm-to-firm or university-to-firm interfaces, respectively. Whereas cross-industry knowledge sharing is emphasised by both types of centres, it is thus more strongly emphasised by UCCs without a focus on research as it helps to avoid harmful effects of knowledge spillovers. The focus on the first mission also appears able to sustain the organisation of knowledge ecosystems created by UCCs without a focus on research in a prefigurative form, which is otherwise typically transient. Furthermore, the challenges to sustainability are different, with centres focused on research being pre-occupied with funding issues, while centres not focused on research leveraging on others means to maintain the interest of industry. The findings contribute to innovation management research and practice by refining current understanding of processes and practices of university-industry collaboration, and how they contribute to facilitate (cross-industry) collaboration and knowledge transfer. Given that university-industry collaboration is often promoted in national innovation policies to create value for society as whole, our findings contribute towards enabling organisations, managers as well as governments to take more informed actions when engaging in such collaborations.

The introduction of more automation into our vehicles is increasing our ability to avoid or mitigate the effects of collisions. Early systems could brake when a likely collision was detected, while more advanced systems will be able to steer to avoid or reconfigure a collision during the same circumstances. This paper addresses how the post-impact motion of an impacted vehicle could be included in the decision-making process of severity minimisation motion planning. A framework is proposed that builds on previous work by the authors, combining models from motion planning, vehicle dynamics, and accident reconstruction. This framework can be configured for different contexts by adjusting its cost function according to relevant risks. Simulations of the unified system are carried out and analysed from the perspective of vehicle model complexity and collision parameters sensitivity. Additionally, effects are highlighted concerning different modelling decisions, with respect to vehicle dynamics models and collision models, that are important to consider in further research.

The transition from engineering student to early career engineer is often difficult as not all skills that constitute effective engineering practice are formally taught. Work placements are suggested as a solution by providing opportunities to learn skills that academia is unable to teach. However, academic requirements for skills such as research proficiency can be overlooked in a work placement environment, since they are often seen as of little value to engineers. Nevertheless, through interviews with master’s students that have conducted their thesis projects at a firm, their experience of boundary spanning to align academic and industrial requirements has been shown to prepare them for an (early) career in engineering by providing opportunities to learn informal professional skills. As the effect is moderated by the motivation of the individual firm for offering work placements, teachers need to consider this motivation when planning and preparing a student for such a work placement.

Commercial deployment of maritime autonomous surface ships (MASSs) is close to becoming a reality. Although MASSs are fully autonomous, the industry will still allow remote operations centre (ROC) operators to intervene if a MASS is facing an emergency the MASS cannot handle by itself. A human-centred design for the associated emergency response systems will require attention to the ROC operator workplace, but also, arguably, to the behaviour-shaping constraints on the engineers building these systems. There is thus a need for an engineer-centred design of engineering organisations, influenced by the current discourse on human factors. To contribute to the discourse, think-aloud protocol interviewing was conducted with well-informed maritime operators to elicit fundamental demands on cognition and collaboration by maritime autonomy emergency response systems. Based on the results, inferences were made regarding both design factors and methodological choices for future, early phase engineering of emergency response systems. Firstly, engineering firms have to improve their informal gathering and sharing of information through gatekeepers and/or organisational liaisons. To avoid a too cautious approach to accountability, this will have to include a closer integration of development and operations. Secondly, associated studies taking the typical approach of exposing relevant operators to new design concepts in scripted scenarios should include significant flexibility and less focus on realism.

Internal crowdsourcing utilizes a firm’s employees, of which many have a strong understanding of the domains in which the firm operates, for contributing with, developing and evaluating ideas. On the one hand, these employees can use their domain-specific knowledge to identify the value of what may seem a far-fetched solution to the average employee. On the other hand, previous research has shown that employees typically evaluate ideas in their domains less favorably if they do not align with ongoing exploitation activities. Hence, this study focuses on whether a higher degree of relevant domain-specific knowledge makes employees participating in internal crowdsourcing prefer exploitative solutions when evaluating ideas. An empirical study of an online platform for firm-internal innovation in a multinational engineering company showed that employees who only infrequently participated in internal crowdsourcing mostly contributed to and evaluated ideas within their own domain. Employees who frequently participated also contributed to and evaluated ideas outside their own domains. By statistically analyzing group differences during idea evaluation, we show that employees participating infrequently favor exploitable solutions, whereas employees participating frequently are more uncertain. The former difference is only seen concerning ideas that require domain-specific knowledge to understand, but the latter is observed for all types of ideas. This study makes three substantial contributions. First, employees with domain-specific knowledge, through their preference for exploitative solutions, bias the outcome of internal crowdsourcing when idea evaluation requires domain-specific knowledge. Second, this bias is aggravated by the overall higher level of uncertainty displayed by employees participating frequently in internal crowdsourcing and thereby tend to reach out to other domains. Third, in order to mitigate this, bias management can build engagement in internal crowdsourcing through idea challenges that do not require domain-specific knowledge and consider avoiding employees with a strongly associated domain knowledge for idea evaluation.