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Cybersecurity is a present and growing concern that needs to be addressed with both behavioral and design-oriented research. Public cloud providers such as Amazon Web Services and federal funding agencies such as the National Science Foundation have invested billions of dollars into developing high-performance computing resources accessible to users through configurable virtual machine (VM) images. This approach offers users the flexibility of changing and updating their environment for their computational needs. Despite the substantial benefits, users often introduce thousands of vulnerabilities by installing open-source software packages and misconfiguring file systems. Given the scale of vulnerabilities, security personnel struggle to identify and prioritize vulnerable assets for remediation. In this research, we designed a novel unsupervised deep learning-based Multi-View Combinatorial-Attentive Autoencoder (MV-CAAE) to capture multi-dimensional vulnerability data and automatically identify groups of similar vulnerable compute instances to help facilitate the development of targeted remediation strategies. We rigorously evaluated the proposed MV-CAAE against state-of-the-art methods in three technical clustering experiments. Experiment results indicate that the MV-CAAE achieves V-measure scores (metric of cluster quality) 8 percent-48 percent higher than benchmark methods. We demonstrated the practical value through a comprehensive case study by clustering vulnerable VMs and gathering qualitative feedback from experienced security professionals through semi-structured interviews. The results indicated that clustering vulnerable assets can help prioritize vulnerable instances for remediation and enhance decision-making tasks. The present design-research work also contributes to our theoretical knowledge of cyber-defense. [ABSTRACT FROM AUTHOR]

Despite tremendous recent progress, extant artificial intelligence (AI) still falls short of matching human learning in effectiveness and efficiency. One fundamental disparity is that humans possess a wealth of prior knowledge, while AI lacks the essential commonsense knowledge required for learning tasks. Guided by schema theory, we employ the design science research methodology to introduce a novel knowledge-aware learning framework to harness the knowledge-based processes in human learning. Unlike existing pre-trained large language models (LLMs) and knowledge-aware approaches that treat knowledge in considerably different ways from humans, our theoretically grounded framework closely mimics how humans acquire, represent, activate, and utilize knowledge. The extensive evaluations in the context of text analytics tasks demonstrate that our design achieves comparable performance to the state-of-the-art LLMs and enhances model generalizability and learning efficiency. This study takes a step forward by bringing cognitive science into building cognitively plausible AI and human-AI collaboration research. [ABSTRACT FROM AUTHOR]

A more equal and sustainable digital future depends on the inclusion of digitally marginalized communities in the socioeconomic opportunities created by digital technologies. Digital inclusion is a complex process that involves all stages of digital innovation, including development, adoption, use, and maintenance. However, past research has largely approached digital inclusion as an adoption and use challenge. In this paper, we develop a view of digital inclusion as a design challenge. We focus on the activities of requirements elicitation (RE) as a critical element of the design process and draw on a design-based interpretive study involving the design of two mobile apps for agricultural communities in India and China. We analyze how the conditions of digital inequality underlying the digital marginalization of these communities affect their sensemaking as they participate in RE activities. We conceptualize these challenges as limitations on the emergence of technology affordances. Our findings reveal various shifts, or translations, in the emerging affordances, which enabled the RE activities to be more generative and consequently more inclusive. These affordance translations manifested along three main dimensions: specificity, temporality, and collectivity. We discuss the implications of these findings for the inclusion of marginalized communities in the design of new technologies. [ABSTRACT FROM AUTHOR]

Cyberattacks have been increasing in volume and intensity, necessitating proactive measures. Cybersecurity risk management frameworks are deployed to provide actionable intelligence to mitigate potential threats by analyzing the available cybersecurity data. Existing frameworks, such as MITRE ATT&CK, provide timely mitigation strategies against attacker capabilities yet do not account for hacker data when developing cyber threat intelligence. Therefore, we developed a novel information technology artifact, ATT&CK-Link, which incorporates a novel transformer and multi-teacher knowledge distillation design, to link hacker threats to this broadly used framework. Here, we illustrated how hospital systems can use this framework to proactively protect their cyberinfrastructure against hacker threats. Our ATT&CK-Link framework has practical implications for cybersecurity professionals, who can implement our framework to generate strategic, operational, and tactical cyber threat intelligence. ATT&CK-Link also contributes to the information systems knowledge base by providing design principles to pursue targeted cybersecurity analytics, risk management, and broader text analytics research through simultaneous multi-modal (e.g., text and code) distillation and classification. [ABSTRACT FROM AUTHOR]

In contrast with the accelerating trend of boundary-spanning (horizontal) technological innovation, the current Cooperative Patent Classification (CPC) scheme applies a hierarchical (vertical) structure to innovation output in terms of patents. For this reason, we argue that the CPC can be complemented with dynamic technological innovation system (TIS) discovery through machine learning that accounts for horizontal relationships across seemingly disparate technologies. Using a design science approach, we propose a framework to discover boundary-spanning TISs by leveraging the textual information from millions of patents. We validate our framework in terms of the ability of discovered relationships to predict future innovation quantity and quality in different technology classes. Our novel TIS-based innovation metrics that leverage patenting activity in related technology classes are significantly associated with future innovation intensity in focal technologies. We conduct experiments with machine learning models to further tease out the predictive utility of our TIS discovery framework. [ABSTRACT FROM AUTHOR]

Changing customer requirements and turbulent markets require suitably adapted capabilities to proactively cope with rapid changes. Agile manufacturing emerged in the late 1990s as a competitive concept to respond to these business challenges. While several seminal research works address agility in terms of concept definition, drivers and strategic capabilities, there is a lack of implementation frameworks of agile manufacturing. The current paper is concerned with the question of how to improve the agility of manufacturing companies. To address this question, a framework is iteratively developed with the aim of supporting decision makers in planning and implementing agility improvement projects. To this end, Design Science Research (DSR) methodology is used in conjunction with CIMO-logic (Context, Intervention, Mechanism, Outcome). The iterative development of the framework relies heavily on insights from case studies and from practitioners in the broad manufacturing sector. The paper provides evidence of the validity of the framework as well as outlining further research perspectives. [ABSTRACT FROM AUTHOR]

The delayed and modest uptake of evidence-based treatments following cardiovascular clinical trials highlights the need for greater attention to implementation early in the development and testing of treatments. However, implementation science is not well understood and is often an afterthought following phase 3 trials. In this review, we describe the goals, frameworks, and methods of implementation science, along with common multilevel barriers and facilitators of implementation. We propose that some of the approaches used for implementation well after a trial has ended can be incorporated into the design of phase 3 trials to foster early post-trial implementation. Approaches include, but are not limited to, engaging broad stakeholders including patients, clinicians, and decision-makers in trial advisory boards; using less restrictive eligibility criteria that ensure both internal validity and generalizability; having trial protocols reviewed by regulators; integrating trial execution with the health care system; evaluating and addressing barriers and facilitators to deployment of the intervention; and undertaking cost-effectiveness and cost utility analyses across jurisdictions. We provide case examples to highlight concepts and to guide end-of-trial implementation. • Cardiovascular disease remains inadequately treated, highlighting the need for greater attention to implementation science. • Trialists and clinicians could harness key principles of implementation science to foster better end-of-trial uptake. • Phase 3 trials could use less restrictive eligibility criteria, engage broad stakeholders in trial design, use existing health care systems to execute the trial, evaluate barriers and facilitators to implementation, and improve workflow processes within the trial. • When trial results are positive, trial sites could be engaged in early implementation research and deployment of the intervention. [ABSTRACT FROM AUTHOR]

Socially shared regulation of learning (SSRL) is a crucial process for groups of learners to successfully collaborate. Detecting and supporting SSRL is a challenge, especially in real time, but hybrid intelligence approaches such as Artificial Intelligence (AI) agents may make this possible. Leveraging the concept of trigger events which invite SSRL, we present a design of an AI agent, MAI, which can detect SSRL and prompt students to raise their group‐level metacognitive awareness with the aim of facilitating SSRL. We present the methodology we used to design MAI, drawing on the Echeloned DSR (eDSR) Methodological Framework and making use of the Wizard of Oz prototyping paradigm. We likewise present empirical results evaluating our initial prototype of MAI, using lexical alignment between speakers as a quantitative measure of the effect of MAI's prompts on facilitating SSRL, the Partner Model Questionnaire as a quantitative measure of perceptions of MAI, and interviews as qualitative context for these perceptions. We found that the first prototype of MAI did not facilitate SSRL as hoped, possibly owing to mixed perceptions of MAI's reliability and lack of clarity about MAI's role in the collaborative learning task. From these findings, we offer revised prompts for the next iteration of prototyping this agent and a refined set of design requirements for future development of metacognitive AI agents for supporting SSRL. Practitioner notes What is already known about this topic Socially Shared Regulation of Learning (SSRL) is recognized as a critical component for the success of collaborative learning, emphasizing the importance of group‐level regulatory processes in achieving shared goals, enacting strategies and monitoring learning progress. Supporting SSRL in face‐to‐face collaborative learning environments presents challenges, including the complexity of coordinating and synchronizing individual contributions and regulatory actions within a group context. What this paper adds This paper introduces the design of Metacognitive Artificial Intelligence (MAI), a novel AI system aimed at enhancing Human‐AI collaboration for supporting and augmenting SSRL processes. Through empirical research, the study offers lessons learned and design considerations for developing artificial agents on facilitating and enhancing SSRL among learners, demonstrating how AI can play a pivotal role in collaborative learning environments. The findings highlight the critical importance of multidisciplinary knowledge in the design of multi‐agent interfaces (MAI) that provide real‐time, adaptive support for group metacognitive processes and decision‐making. Implications for practice and/or policy Educational technologists can utilize the proposed design principles in the development and integration of MAI tools to enhance SSRL. Educators can incorporate the principles of MAI and our relevant findings into their teaching strategies to actively foster and support socially shared regulation of learning among students. Policymakers should consider revising educational frameworks to include the use of AI technologies that support SSRL strategies in collaborative learning. What is already known about this topic Socially Shared Regulation of Learning (SSRL) is recognized as a critical component for the success of collaborative learning, emphasizing the importance of group‐level regulatory processes in achieving shared goals, enacting strategies and monitoring learning progress. Supporting SSRL in face‐to‐face collaborative learning environments presents challenges, including the complexity of coordinating and synchronizing individual contributions and regulatory actions within a group context. What this paper adds This paper introduces the design of Metacognitive Artificial Intelligence (MAI), a novel AI system aimed at enhancing Human‐AI collaboration for supporting and augmenting SSRL processes. Through empirical research, the study offers lessons learned and design considerations for developing artificial agents on facilitating and enhancing SSRL among learners, demonstrating how AI can play a pivotal role in collaborative learning environments. The findings highlight the critical importance of multidisciplinary knowledge in the design of multi‐agent interfaces (MAI) that provide real‐time, adaptive support for group metacognitive processes and decision‐making. Implications for practice and/or policy Educational technologists can utilize the proposed design principles in the development and integration of MAI tools to enhance SSRL. Educators can incorporate the principles of MAI and our relevant findings into their teaching strategies to actively foster and support socially shared regulation of learning among students. Policymakers should consider revising educational frameworks to include the use of AI technologies that support SSRL strategies in collaborative learning. [ABSTRACT FROM AUTHOR]

Over the past ~ 25 years, chemoinformatics has evolved as a scientific discipline, with a strong foundation in pharmaceutical research and scientific roots that can be traced back to the late 1950s. It covers a wide methodological spectrum and is perhaps best positioned in the greater context of chemical information science. Herein, the chemoinformatics discipline is delineated, characteristic (and partly problematic) features are discussed, and a global view of the field is provided, emphasizing key developments. [ABSTRACT FROM AUTHOR]

Background: Implementation strategies are key to enhancing the translation of new innovations but there is a need to systematically design and tailor strategies to match the targeted implementation context and address determinants. There are increasing methods to inform the development and tailoring of implementation strategies to maximize their usability, feasibility, and appropriateness in new settings such as the Cognitive Walkthrough for Implementation Strategies (CWIS) approach. The aim of the current project is to apply the CWIS approach to inform the redesign of a multifaceted selection-quality implementation toolkit entitled Adoption of Curricular supports Toolkit: Systematic Measurement of Appropriateness and Readiness for Translation in Schools (ACT SMARTS) for use in middle and high schools. Methods: We systematically applied CWIS as the second part of a community-partnered iterative redesign of ACT SMARTS for schools to evaluate the usability and inform further toolkit redesign areas. We conducted three CWIS user testing sessions with key end users of school district administrators (n = 3), school principals (n = 6), and educators (n = 6). Results: Our CWIS application revealed that end users found ACT SMARTS acceptable and relevant but anticipate usability issues engaging in the ACT SMARTS process. Results informed the identification of eleven usability issues and corresponding redesign solutions to enhance the usability of ACT SMARTS for use in middle and high schools. Conclusions: Results indicated the utility of CWIS in assessing implementation strategy usability in service of informing strategy modification as part of our broader redesign to improve alignment with end user, end recipient, and setting needs. Recommendations regarding the use of this participatory approach are discussed. [ABSTRACT FROM AUTHOR]

Any behaviour that deviates from the social norms of a wider society and is viewed negatively by society as a whole is considered to be a behavioural problem. In office environments, there is a significant prevalence of behaviour problems, and they have a long-lasting impact on work productivity. Intervention provision is a current component of behavioural problem-solving strategies from which people may benefit through smart environments. While many interventions are presently employed in both smart and non-smart environments, it is unknown how well they work in relation to user preferences. Additionally, change levels are not the foundation of the present intervention methods. The study looked into how providing interventions can help smart environment users to promote good behaviour. The study used the Design Science Research (DSR) Methodology's rigour cycle to deploy the Generic User Model and the Positive Behaviour Change (PBC) model. The PBC Model was examined through an artificial-summative evaluation. The results of the artificial-summative evaluation showed that change-based interventions can be effective for reducing inappropriate behaviour and increasing positive behaviour, and that input from user models can give intervention users personalised effects. The implication of the results from this study is that intervention providers do not only need to focus on modifying negative behaviours, but also strengthening and sustaining good behaviours to motivate continuous future occurrence. [ABSTRACT FROM AUTHOR]

In an era where Information and Communication Technologies (ICT) redefine the boundaries of competition and collaboration, the concept of coopetition — simultaneous competition and cooperation — emerges as a strategic imperative for small and medium-sized enterprises (SMEs). This study presents the design and development of an Industrial Internet of Things (IIoT) artefact designed to enable competition among SMEs. Bringing the Service-Dominant Logic (S-D logic) foundations to IIoT, a Design Science Research (DSR) approach was employed. This approach facilitated the integration of design theory with practical problem-solving, creating the Coopetition IIoT-based System. The primary goal of this solution is to augment the dynamics of coopetition networks, with a particular focus on SMEs. The practical evaluation of the Coopetition IIoT-based System is assessed through a prototype evaluation by experts representing twenty-four manufacturing stone SMEs, a crucial sector in the Portuguese economy. The feedback received was highly positive, indicating a positive evaluation rate of 78.9%. This favourable response highlights the Coopetition IIoT-based System's proficiency in fostering simultaneous competition and cooperation throughout the lifecycle of business opportunities in SME networks, thereby underscoring its potential as a facilitator of effective competition. Theoretically, this research enriches the application of S-D logic in coopetition networks and advances state-of-the-art IIoT systems. Practically, the Coopetition IIoT-based System demonstrates significant potential in boosting the competitiveness of SMEs in developed economies. Nonetheless, the ultimate efficacy of such IIoT systems will be best determined through real-world application and evaluation. Future research should concentrate on the real-case deployment and assessment of Coopetition IIoT-based Systems within coopetition networks. [ABSTRACT FROM AUTHOR]

This research presents a novel microlevel (i.e., project-level) construction productivity framework aimed at measuring and benchmarking productivity consistently within and between projects, work packages, and activities. Using a design science research approach, the framework was rigorously developed with data collected from UK-based construction projects and refined through several cycles of implementation and industry engagement. The framework is useful in providing new insights into project performance, helping understand process variability, setting benchmarks, and conducting data analytics for improved decision making. This framework can be used for industrywide measurement both within and across subsectors of construction in the UK and internationally. [ABSTRACT FROM AUTHOR]

The development of the Drag-Free and Attitude Control System is crucial for space-based gravitational wave detectors. The Drag-Free and Attitude Control System includes for spacecraft attitude control, drag-free control, moving optical subassemblies pointing control, and test masses electrostatic control during multiple phases of the mission lifetime. An accurate model of the spacecraft dynamics model is not only a precondition for the control design of Drag-Free and Attitude Control System, but also can serve as a valuable reference to assess the noise budget of gravitational wave detectors. This paper presents a generic, nonlinear mathematical model that describes the multi-body dynamics for the gravitational wave detectors. The model fully considers the couplings between the bodies involved, with a particular focus on the impact of the moving optical subassemblies on the spacecraft attitude. Additionally, a corresponding reduced model for the control design of the science mode is divided. Both models are validated numerically by Monte Carlo simulations and motion analysis, respectively. • Introduce a spacecraft dynamics model that fully describes the motion coupling. • Simscape simulation has validated the accuracy of the dynamics model. • The spacecraft dynamics model is applicable to multiple phases of motion. • Monte Carlo simulations have validated the applicability to different parameters. [ABSTRACT FROM AUTHOR]

Although teachers' instruction has been extensively studied, there is a dearth of research investigating how teachers design lessons and integrate new information into their existing lessons. This study utilized a case study approach to examine how teachers engaged in a nanoscale science professional development experience integrated new science content into their lesson plans. Five experienced science teachers were tracked as they conducted science research and developed new lessons that integrated their research into their science instruction. The findings showed that these teachers had to cross borders between science and education to design lessons, and three priorities framed their lesson planning processes: understandability, engaging students, and relevant real-world applications. For teachers whose students were assessed with end-of-grade high-takes testing, fitting the new information into the tested material was essential. The study provides insight into this border-crossing process and has implications for the design of programs that implement research experiences for teachers. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neuro-developmental disorder that results in narrow interest and impairments in communication and social relationships. Usually, unknown contexts generate anxiety and frustration in persons with ASD, and great impediment in accessing services, including health ones. This seriously compromises prevention and treatment interventions in different medical areas. Technology offers many opportunities for persons with ASD and can be used to act as a mediator, helping to manage communication and interpersonal relationships and to familiarize with the environment, especially in new contexts. Specifically, we used digital tools to reduce stress and anxiety while teaching adequate dental care to children with ASD (MyDentist project). To generalize the R&D approach, we followed in the context of the MyDentist project for its adoption for research and development of inclusive software in e-health. We analyze our design and development process using the three-cycle model proposed by Hevner: the relevance cycle, which connects design with the environment; the rigor cycle, which connects design with the grounding theory; and the design cycle, which builds and evaluates artifacts. We present the results obtained in the project and the lessons learned. Lessons are formalized as guidelines that are further validated by a focus group including experts and stakeholders. A set of guidelines which can drive software development and research in similar contexts. Our research will benefit e-health researchers to perform rigorous and relevant Design Science Research. It will also inform software developers of e-health solutions and healthcare professionals. [ABSTRACT FROM AUTHOR]

Today's Information Systems (IS) design research projects pursue digital innovation to conquer complex societal challenges. Many of these projects reach out beyond disciplinary and organisational boundaries, as evident in interdisciplinary consortia and academia‐industry collaboration. The design activities in each project differ based on contextual requirements and the team's underlying design logic. As diversity increases, shared understanding is essential for project success. Established design research methodologies need complementary tools to support design researchers in configuring their design activities and representing them faithfully, dimensions that contribute to a shared understanding. This article presents Baustein as an instance of such design tools. Baustein is tailorable to the contextual requirements of each design research project, comprising an ensemble of card‐deck, ready‐made configurations, and a manual. To ensure theoretical and practical relevance, the design of Baustein is based on primary empirical data (workshop and interviews with 16 IS design researchers) and a literature analysis of 99 published IS design research projects. We demonstrate its proof‐of‐value through three main evaluation episodes, altogether involving over 110 IS design researchers. With Baustein, design research teams can balance the trade‐off between creative messiness and standardised configurations of design activities. [ABSTRACT FROM AUTHOR]

Using the design science methodology, we develop an automated grading bot using robotic process automation (RPA) software. This grader demonstrates RPA's capability to enhance the efficiency and accuracy of grading structured query language (SQL) queries. Validation of the RPA grader demonstrates that it can grade assignments and/or exams in 30 to 60 minutes, a task that would take a human grader approximately 50 hours. In contrast to human graders, who misgraded 5 to 8 percent of queries, the RPA grader graded all queries correctly. Notably, it correctly evaluated 188 distinct correct solutions submitted by students to a single question. To facilitate broad adoption, we provide instructions and the necessary code for implementing the RPA grader. We validate that professors and teaching assistants can successfully implement the RPA grader even with no previous bot building experience. [ABSTRACT FROM AUTHOR]

Purpose: This research investigates the systematic reuse of service concept elements within modular service design, aiming to offer actionable insights into effective conceptualization of services and extending methodological underpinnings to enhance the approach of service design. Design/methodology/approach: Employing a design science research approach, this study investigates the intentional and targeted reuse of service concept elements for modular service design. It develops four general design principles and applies them in a real-world context to demonstrate and evaluate the purposeful integration of service concept elements. Findings: This research reveals the efficacy of reusing service concept elements for modular service design, highlighting the benefits of this approach in conceptualizing new services. It theorizes generalizable design knowledge by formalizing four design principles that allow to underpin the reuse of service concept elements. Originality/value: This research contributes to service design literature by providing actionable insights into the systematic reuse of service concept elements, particularly within the framework of modular service design. We develop and test general design principles and, specifically, apply them for analytics-based digital services. [ABSTRACT FROM AUTHOR]

Small and medium-scale enterprises (SMEs) need a platform that actively enables collaboration with research institutions and consultants as SMEs lack the financial resources to conduct independent research. Such a platform will require a verifiable manipulation-free system to enable, execute, and record collaboration activities and to track reputations among the organizations and individuals that use the platform. Blockchain provides an opportunity to build such a collaborative platform by enabling the verifiable recording of the results of the collaborations, aggregating the resulting reputation of the collaborating parties, and offering tokenized incentives to reward positive contributions to the platform. Cryptocurrencies from which blockchain tokens are derived are volatile, thereby reducing business organizations' interest in blockchain applications. Hence, there is a need to design a self-sustaining valuable token model that incentivizes user behaviours that positively contribute to the platform. This paper explores the application of game theory in analyzing token-based economic interactions between various groups of users in an implemented blockchain-based collaboration platform to design and simulate a token distribution system that provides a fair reward mechanism for users while also providing a dynamic pricing model for the utility value provided by platform tokens. To achieve this objective, we adopted the design science research method, a running case of a blockchain collaboration platform that enables research collaboration, and extensive form games in game theory, first to analyze and simulate token outcomes of users of the collaboration platform. Secondly, the research used a logarithmic model to show the dynamic utility pricing property of the developed token model where the self-sustainability of the token is backed by the availability of an internal resource within the platform. Thirdly, we applied a qualitative approach to analyze potential risks in the designed token model and proposed risk mitigation strategies. Thus, the resulting models and their simulations, such as token distribution models and a dynamic token utility model, as well as the identified token risks and their mitigation strategies, represent the main contributions of this work. [ABSTRACT FROM AUTHOR]

In large-scale emergencies and disasters, real-time situation awareness around health resources can be lifesaving. Understanding the situation at hand, the pressing needs and the actions needed often using information systems that require manual data entry. Relying on humans to enter data manually during surge response is both error-prone and an impediment to the critical requirement of real-time situation awareness. This paper explores the potential of Ultra-High Frequency Radio Frequency Identification (UHF RFID) technology to automate the process of manual data entry followed in providing situation awareness related to critical care resources during surge response. Using design science, an RFID-based prototype was developed to identify and track intensive care resources in real-time. The system was tested in a simulated hospital ward environment to understand deployment challenges and a dashboard was developed to reflect real-time data from multiple sources simultaneously. When properly deployed, UHF RFID can be a viable approach to automating critical care reporting during surge response. Automating routine processes can enhance data quality and minimise workload on health professionals leading to better patient care and evidence based decision-making. [ABSTRACT FROM AUTHOR]

Lessons learned systems (LLS) are knowledge management systems (KMS) that collect, verify, store, disseminate, and enable the reuse of experiential knowledge created during the execution of organizational activities. Despite this promise, many scholars have noted that many LLS fail to properly manage their intellectual capital. While behavioral and organizational factors contribute to the success or failure of any IT system, their design is also a critical consideration. Here, a systematic literature review is helpful for understanding the current state of knowledge on LLS design research though to the best of our knowledge, none exists as past reviews have focused on behavioral or organizational factors around the use of KMS. Towards this knowledge gap and to guide future research, we performed a systematic literature review of LLS design research across two decades, finding that most designs employ web technologies, are largely designed for use by managerially- and technically oriented users in the construction context, and only support explicit and declarative knowledge. Based on these findings, we recommend that future research be directed at improving the projectability of design knowledge, supporting the transfer of LL across communities, and improving support for tacit and procedural knowledge. [ABSTRACT FROM AUTHOR]

Increasingly, the manufacturers and third-party sellers sell their products online at large e-commerce platforms. A good forecast is necessary to avoid resource wastage in the form of over-production or underserved demand. Sensing the demand for a new product on e-commerce platforms in the presence of other competitive products requires techniques beyond traditional forecasting methods. Typical ecommerce platforms, being a two-sided marketplace, need to make a trade-off between showing very relevant and trusted products versus exploring new selections. As customers heavily rely upon organic search functionality for product discovery, the demand forecasting for products depends upon signals from search algorithms. In this paper, we propose an end-to-end Deep Learning (DL) based solution to estimate new product demand. Our design science research (DSR) approach involves the use of search ranking algorithm and its features to simulate the impressions of new products in the presence of existing products and vice versa. This approach helps to estimate demand using simulated search funnel metrics to bake in competitive characteristics of other products. [ABSTRACT FROM AUTHOR]

The multiplication of complex datasets in empirical social sciences calls for methods that can improve the design of complex datasets before the actual gathering of data. Yet mainstream scholars in related fields have rarely explored such methods. In this study, we introduce Interpretive Structural Modeling (ISM) as such a method. As a mixed method, ISM integrates Boolean algebra, matrix theory, and directed graph theory to impose a formal structure to qualitative understanding of a complex system. ISM's final output is a directed graph that can be visually and easily interpreted. We show that ISM can structure indicators graphically into a multilayered and multi-blocked model, thus uncovering hidden interactions among indicators. By doing so, ISM can reveal hidden and undesired redundancies and incoherencies among indicators within a complex dataset. Most critically, ISM achieves these goals without relying on statistical analysis and hence before the actual gathering of any data. Deploying ISM when designing complex datasets thus facilitates more rigorous conceptualization and understanding of complex social phenomena, steers us away from badly designed complex datasets, and saves precious resource. We use ISM to probe several complex datasets to demonstrate its potentials. [ABSTRACT FROM AUTHOR]

Advances in graphing technologies and learning sciences pedagogy have the potential to equitably support students when exploring complex systems depicting dynamic relationships across multiple disciplinary topics in Science, Technology, Engineering, and Mathematics (STEM). We report on the cumulative impact of science units designed in a Research Practice Partnership (RPP) that leveraged Knowledge Integration (KI) pedagogy to support middle school students to generalize insights to new graph representations and science topics. Teachers across 11 schools incorporated the graph-science units into their curriculum plans. We analyzed ~ 8000 responses to validated and reliable graph-science KI assessment items administered before the first year and after one, two, or three years of instruction aligned with KI pedagogy. With random coefficient, multi-level, mixed-effect regression modeling, we analyzed performance after one-, two-, and three-years of graph-science KI instruction. We also analyzed the growth trajectories of subgroups, i.e., multilingual learners. Data suggest two years of graph-science KI instruction is needed to make significant impacts on student learning and ameliorated the disparity between students with different native language fluencies. These results illustrate the value of technology-enhanced, pedagogically aligned K-12 science instruction that is designed to support connecting diverse graph data and science knowledge comprehensively and cumulatively. [ABSTRACT FROM AUTHOR]

This study proposes a process-mapping method based on the SPM to identify waste in office environments. Design Science Research was used to develop a method (M0) for mapping processes in office environments. This method was subjected to critical analysis by four experts in the field, who sent their contributions. Thus, a new method (M1) was generated, which was instantiated in a company in Brazil in the process of technical publications and user guides. As a result, in the practical application, it was possible to verify the efficiency of the proposed method, and the future flow of the analyzed flow allowed a reduction in the lead time by 75% and an increase in the rate of added value from 4.88% to 19.56%. The method can contribute to the identification of waste more complex than the usual VSM and thus improve office environments. This way, managers and process engineers can understand the activities in detail and propose improvements in these processes, generating greater speed and precision. This method proposal for administrative environments, aims to expand the detailing of waste, allowing greater precision in the identification of waste and, consequently, greater effectiveness in improving the process flow analyzed. [ABSTRACT FROM AUTHOR]

Purpose: Design Science (DS) is a relatively new paradigm for addressing complex real-world problems through the design and evaluation of artifacts. Its constituent methodologies are currently being discussed and established in numerous related research fields, such as information systems and management (Hevner et al., 2004). However, a DS methodology that describes the "how to" is largely lacking, not only in the field of OM but in general. The Theory of Constraints (TOC) and its underlying thinking processes (TP) have produced several novel artifacts for addressing ill-structured real-world operations problems (Dettmer, 1997; Goldratt, 1994), but they have not been analyzed from a DS research standpoint. The purpose of this research is to demonstrate how TOC's thinking process methodology can be used for conducting exploratory DS research in Operations and Supply Chain Management (OSCM). Design/methodology/approach: A case study of spare parts replenishment illustrates the use of TOC's thinking processes in DS to structure an initially unstructured problem context and to facilitate the design of a novel solution. Findings: TOC's thinking processes are an effective methodology for problem-solving DS research, enabling the development of novel solutions in initially unstructured and wicked problem situations. Combined with structured CIMO design logic TOC's thinking process offers a systematic method for exploring wicked problems, designing novel solutions, and demonstrating theoretical contributions. Research limitations/implications: The implication for research is that TOC's thinking process methodology can provide important elements of the lacking "how to" methodology for DS research, not only for the field of OM but in general for the field of management. Practical implications: The practical outcome of the research is a novel design for dynamic buffer-based replenishment that extends beyond organizational boundaries. Originality/value: This work shows how the thinking processes can be used in DS research to develop rigorous design propositions for ill-structured problems. [ABSTRACT FROM AUTHOR]

As the digital paradigm stimulates changes in various areas, seaports, which are fundamental to logistics and the global supply chain, are also undergoing a digital revolution, evolving into smart ports. Smart gates are essential components in this transformation, playing a vital role in increasing port efficiency. In the context of smart gates, the aim of this study is to understand how process management can serve as a catalyst for digital transformation, promoting efficiency in traffic flow and logistics. To achieve this objective, the design science research (DSR) methodology was followed, which allowed for the integration of information from several sources of requirement, encompassing both theoretical and practical aspects. The practical component took place at one of Portugal's largest container terminals, which allowed for the integration of information from various sources. As a result, this study presents the conceptual definition of a smart gate in terms of processes, main technologies, and key performance indicators that will support the monitoring and improvement of future operations. The results provide theoretical and practical contributions: on a practical level, they present a real application of the transformation towards a smart gate, serving as a model for other ports in their digitalization; on a theoretical level, they enrich the literature with a methodology for digitalizing maritime road gates, showing how the use of process management approaches, such as the BPMN, can increase operational efficiency in container terminals. [ABSTRACT FROM AUTHOR]

Science, technology, and innovation (STI) systems are fundamental to the economic development of any nation. However, their high hierarchy and centralization create inequities for the more dispersed regions to access their benefits. Traditional approaches to STI system decentralization have been through public control and investment policies, posing a challenge for emerging economies. Given these conditions, it is necessary to explore alternative approaches such as open innovation (OI), which can facilitate bringing the STI system to the regions by breaking its hierarchical structure; and organizational ecology (OE), which can contribute to the construction of ecosystemic appropriation of STI in the regions. The objective of this research is to propose a conceptual model that addresses the need to decentralize Colombia's STI system through an alternative approach to public policy governance, utilizing OI and OE. The methodology used for this research is Design Science Research (DSR), which allows for the creation of an artifact-type model, validated through the representational validation technique, supported by a cross-impact analysis matrix completed by 67% members of the subregional STI committees in the department of Caldas. The result is a conceptual model that integrates the components of Colombia's STI system, decentralizes them through OI factors, and ensures the ecosystemic appropriation of STI in the regions through OE factors. Model criteria, such as organizational readiness, collaborative capacity, absorptive capacities, intellectual capital, technological capital, and local niche, are presented as key elements in the decentralization of the STI system and the ecosystemic appropriation in the co-creation of a mutualistic STI system in Colombia's regions. The findings of the model represent an integrated model that unfolds sequentially; the first phase develops the decentralization through OI factors, and the second phase develops the ecosystemic appropriation from OE factors. This research contributes an integrated OI and OE model as an alternative to the traditional STI system decentralization approach from public policy governance and nation-region control, overcoming the hierarchical barrier of the system and granting ecosystemic appropriation of science, technology, and innovation in the regions. [ABSTRACT FROM AUTHOR]

Data transparency plays a critical role in understanding IoT privacy practices and making informed decisions. To gain a comprehensive understanding of transparency in the IoT environment, a systematic literature review of 58 academic articles is conducted to investigate the progress and status of existing data transparency studies from a design perspective. Data transparency was identified as a signifier to bridge the connection between user behavior and privacy risks. The level of transparency achieved was shaped by users' privacy perceptions, which in turn influenced their privacy behavior. GUI-based transparency design has been widely used in IoT, but it is not sufficient to provide users with accessible, understandable, and unified transparency information. A conceptual transparency design is proposed based on the extracted design opportunities and practices. This paper provides an important resource on transparency issues in the IoT environment, and will benefit the design and computer science communities. [ABSTRACT FROM AUTHOR]

For a long time, decision-making in auditing was limited to a risk-oriented recommendation and consisted of the rigorous analysis of a sample of data. The new trend in the audit decision process focuses on the use of decision support systems (DSSs) founded on data analytics (DA) to better concentrate on in-depth analysis. This study examines how DA can improve the audit decision-making approach in the banking sector. We show that DA techniques can improve the quality of audit decision-making within banks and highlight the advantages associated with mastering these techniques, which results in a more effective and efficient audit of digital banking transactions. We propose an artifact-based data analytics-driven decision support system (DA-DSS) for an automatic fraud detection system supported by DA. The proposed DA-DSS artifact with a random forest classifier at its core is a promising innovation in the field of electronic transaction fraud detection. The results show that the random forest classifier can accurately classify the data generated by this artifact with an accuracy varying from 88 to 93% using transaction data collected from 2021 to 2022. Other classifiers including k-nearest neighbors (KNN) are also used, with a classification rate ranging from 66 to 88% for the same transaction datasets. These results show that the proposed DA-DSS with random forest can significantly improve auditing by reducing the time required for data analysis and increasing the results' accuracy. Management can use the proposed artifact to enhance and speed up the decision-making process within their organization. [ABSTRACT FROM AUTHOR]

This paper presents the conceptual design of the HumanEnerg Hotspot, an agile toolkit aimed at addressing the human energy crisis in the context of Industry 5.0. The toolkit has been developed using a blend of Design Science Research (DSR) and Human-Centered Design (HCD) methodologies, enabling a comprehensive human-centered problem identification and solution-seeking approach. The toolkit includes a variety of strategies, techniques, frameworks, and resource recommendations for industry use and has been designed to be easily adaptable for use in diverse industry settings. The toolkit is intended to support the European Union's goal for industry to influence society through a human-centric approach to Industry 5.0 by prioritizing human energy reinforcement and creating a more resilient and productive workforce. The toolkit provides a valuable resource for employees and managers alike and offers a promising solution for addressing the human energy crisis in the era of Industry 5.0. [ABSTRACT FROM AUTHOR]

Human digital twins are computational models of the human actions involved in interacting and operating technical artifacts. Such models provide a conceptual and practical tool for artificial intelligence designers when they seek to replace human work with intelligent machines. Indeed, digital twins have long served as models of technical and cyber-physical processes. Human digital twins have such models as their foundations but also include models of human actions. As a result, human digital twin models enable technology designers to model how people interact with intelligent technical artifacts. Yet, development of human digital twins is associated with certain conceptual problems. To clarify the basic idea, we constructed a human digital twin for Minsky's M-Machine. The abstract conceptual structure of this machine and its generality allowed us to analyze the general properties of human digital twins, their design, and their use as tools in designing intelligent technologies. [ABSTRACT FROM AUTHOR]

Mobile learning applications are being developed to support the teaching and learning process. The lack of an accepted methodology for developing these applications motivated the establishment of a design science research process for developing mobile learning applications. Design science methodology from the information systems domain was inherited and modified by consolidating common practices found in the development of mobile learning applications. The proposed methodology consolidates a set of logically related processes to support the development of mobile learning applications with ease and ensure quality results. Expert review of the proposed methodology was conducted, and the results indicate it can positively contribute to the development of mobile learning applications. [ABSTRACT FROM AUTHOR]

An editorial is presented on the reasons why papers are accepted or rejected in scholarly publishing with an emphasis on how to turn a rejection into a revision. The article breaks down a paper into three sections—the introduction, the conclusion, and then the middle—with insight into each section. Additionally, the article looks at paper preparation based on the type of paper including design science papers and theory papers.

The benefits of teacher-facing dashboards are incontestable, yet their evidence is finite in terms of long-term use, meaningful usability, and maturity level. Thus, this paper uses design science research and critical theory to design and develop TEADASH to support teachers in making decisions on teaching and learning. Three cycles of design science research and multiple small loops were implemented to develop the dashboard. The tool was then deployed and evaluated in real time with the authentic courses. Five courses from two Swedish universities were included in this study. The co-design with teachers is crucial to the applicability of this dashboard, while letting teachers use the tool during their courses is more important to help them to recognize the features they actually use and the tool's usefulness for their teaching practices. TEADASH can address the prior matters, align with the learning design, and meet teachers' needs. The technical and co-design aspects, as well as the advantages and challenges of applying TEADASH in practice, are also discussed here. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study examines the continuing expansion of the Internet of Things (IoT) and its possible development as a source of audit evidence for both internal and external audits. In other words, the auditor checks whether a measure (metric) related to classifying and prioritizing requests and/or incidents is available in the enterprise's current incident reporting system. This approach uses Design Science Research and is based on the use of auditing standards and frameworks to drive the design of audit plans using metrics available on or derived from the enterprise IoT. This study extends the research on enterprise IoT and its potential application to the enterprise audit process. It provides a framework that illustrates how both internal and external auditors can reasonably assure the provenance and validity of enterprise IoT data while also evaluating the security and controls surrounding its production. [ABSTRACT FROM AUTHOR]

The amount of household waste produced worldwide is increasing every year. In combination with other actions to reduce waste production and improve waste management, community engagement and community‐focused programs are needed to motivate the public to change their behavior in such a way that reduces their waste generation and increases the accuracy of waste sorting. It is also helpful for people to become more waste literate to empower them to be part of the solution. The Home Waste Audit (HWA) is a community science activity designed to increase waste literacy and reduce household waste. In the HWA, participants record their waste for a set period of time, research their local waste streams, and complete surveys to share perceptions of household waste habits. Here, we present data from a HWA conducted in 2021 as a case study. Before the audit, 66% of participants underestimated their weekly waste generation. Throughout the HWA, weekly waste count among households decreased by 31%. Participants found purchasing items with less/no packaging and avoiding single‐use plastics challenging. Easier changes included learning which items can/cannot be recycled and repurposing waste items. Several changes to waste habits were maintained 1 year after participation. These results demonstrate that the HWA is an effective tool for individuals to be a part of the solution by learning about local waste streams, reducing waste production, and accurately managing their household waste. Plain Language Summary: To tackle growing global waste generation, outreach programs can engage the public to reduce their household waste production and improve their individual waste management. Here, we introduce the Home Waste Audit (HWA), a community science activity designed to increase understanding about how to sort household waste and provide motivation for participants to decrease the amount of waste they generate and throw away each week. We present data from our 2021 HWA as a case study. In 2021, participants underestimated their weekly waste before the audit and collectively decreased both their weekly recycling and landfill waste throughout the activity. Participants found that buying items with less or no packaging and avoiding single‐use plastics was very challenging. However, they found it easier to learn about waste sorting guidelines and reuse items that would otherwise be thrown away. The results of this work demonstrate that the HWA is an effective way for the public to learn information about waste, pollution, and their local waste sorting guidelines and initiate both short and long‐term behavioral changes to reduce and manage their household waste effectively. Collectively, the HWA can help reduce the amount of waste sent to landfills and mismanaged waste in our communities. Key Points: The Home Waste Audit is a community science activity designed to increase participants waste literacy and reduce household wasteParticipants recorded their waste for 4 weeks while learning about local waste streams and taking action to decrease their weekly wasteParticipants decreased their waste by 31% and made long‐term behavioral changes to reduce household waste [ABSTRACT FROM AUTHOR]

In the context of crowd innovation and the generative design driven by big language models, the exploration of personalized requirements has become a key in significantly improving product innovation, concept feasibility, and design interaction efficiency. To mine a large number of vague and unexpressed implicit requirements of personalized products, a domain knowledge graph-based method is proposed in this research. First, based on the classical theory of design science, the characteristics and categories of personalized implicit requirements are analyzed, and the theoretical basis of implicit requirement mining is formed. Next, in order to improve the practicability and construction efficiency of the domain knowledge graph, a more informative ontology is constructed, and better-performing natural language processing (NLP) models are proposed. Then, a multi-category personalized implicit requirement mining method based on a knowledge graph is proposed. Finally, a platform was developed based on the technical solution proposed in this study, and an example verification was conducted in the field of electromechanical engineering. The efficiency improvement of the training model proposed in the research was analyzed, and the practicality of implicit requirement mining methods are discussed. [ABSTRACT FROM AUTHOR]

Millions of users on the Internet have fallen into phishing website traps. Detection tools are designed to warn users against such attacks, but often fail to achieve this purpose. One crucial reason behind this is that users rarely have a chance to interact and build a relationship with a detection tool that stealthily runs at the backend. A warning message on a rarely seen interface from such a tool hardly inspires users' trust in its authenticity and accuracy. In this study, we propose an ontology-based intelligent interface personalization (OBIIP) design for the warning interfaces of phishing website detection tools. We first constructed an ontology of warning interface elements (OWIE), which is a comprehensive knowledgebase for warning interface design. We then used OWIE in the design and creation of an OBIIP prototype and assessed it in a laboratory experiment and an online experiment. The results show the significant value of OBIIP in improving users' performance in terms of self-protection against website phishing attacks and building a stronger relationship with the detection tool in terms of trust in and use of the tool. Millions of users on the Internet have fallen into phishing website traps. Detection tools are designed to warn users against such attacks but often fail to achieve this purpose due to usability issues. To address these issues and increase user self-protection against such attacks, we propose an ontology-based intelligent interface personalization (OBIIP) design for the warning interfaces of phishing website detection tools. Our design involves two phases: proof-of-concept and proof-of-value. The proof-of-concept phase consists of developing an ontology of warning interface elements (OWIE) based on the ontology approach in design science, expert feedback, and inputs from multiple populations through three rounds of surveys with 1,297 participants. OWIE is then used in the design and creation of an OBIIP prototype. The proof-of-value phase involves a controlled laboratory experiment (with 596 participants) to assess OBIIP's value in terms of users' self-protection performance as well as a posthoc online data collection (with 191 participants) and analysis to reveal the role of the design element categories in users' trust and perceived personalization in OBIIP. The assessment results show the significant value of OBIIP in improving self-protection performance as well as the pervasive impact of OBIIP in improving users' relationship with the security tool in terms of trust in and use of the tool. This work also identifies categories of design elements that matter in the OBIIP process. History: Suprateek Sarker, Senior Editor; David (Jingjun) Xu, Associate Editor. Funding: This work was partially supported by the U.S. National Science Foundation [Grant CNS-1049497]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2021.0065. [ABSTRACT FROM AUTHOR]

Abstract Human digital twins are computational models of the human actions involved in interacting and operating technical artifacts. Such models provide a conceptual and practical tool for artificial intelligence designers when they seek to replace human work with intelligent machines. Indeed, digital twins have long served as models of technical and cyber-physical processes. Human digital twins have such models as their foundations but also include models of human actions. As a result, human digital twin models enable technology designers to model how people interact with intelligent technical artifacts. Yet, development of human digital twins is associated with certain conceptual problems. To clarify the basic idea, we constructed a human digital twin for Minsky’s M-Machine. The abstract conceptual structure of this machine and its generality allowed us to analyze the general properties of human digital twins, their design, and their use as tools in designing intelligent technologies.

Supply chain visibility (SCV) has been gaining recognition in recent years as a key factor for achieving analytical capabilities and improving supply chain performance. However, levels of SCV implementation lag behind current technological advances. This research was motivated by the lack of visibility in inbound logistics, which limits the possibility of managing deviation, in particular concerning changes in arrival time of incoming goods, in large industrial firms. We addressed this problem by adopting a design science approach. In particular, we followed context–intervention–mechanism–outcome (CIMO) logic to map and analyse material and information flows. The problems areas were successively translated via business and functional requirements into technological solutions. We evaluated alternative technologies using controlled experiments that mimicked real-life situations. This study provides guidance for manufacturing companies aiming to enhance deviation management and predictive capabilities by improving visibility in their inbound logistics and potentially extending visibility to other areas, such as internal and outbound flows. [ABSTRACT FROM AUTHOR]

As video-sharing sites emerge as a critical part of the social media landscape, video viewership prediction becomes essential for content creators and businesses to optimize influence and marketing outreach with minimum budgets. Although deep learning champions viewership prediction, it lacks interpretability, which is required by regulators and is fundamental to the prioritization of the video production process and promoting trust in algorithms. Existing interpretable predictive models face the challenges of imprecise interpretation and negligence of unstructured data. Following the design-science paradigm, we propose a novel Precise Wide-and-Deep Learning (PrecWD) to accurately predict viewership with unstructured video data and well-established features while precisely interpreting feature effects. PrecWD's prediction outperforms benchmarks in two case studies and achieves superior interpretability in two user studies. We contribute to IS knowledge base by enabling precise interpretability in video-based predictive analytics and contribute nascent design theory with generalizable model design principles. Our system is deployable to improve video-based social media presence. [ABSTRACT FROM AUTHOR]

While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context. [ABSTRACT FROM AUTHOR]

Unstructured multimedia data (text and audio) provides unprecedented opportunities to derive actionable decision-making in the financial industry, in areas such as portfolio and risk management. However, due to formidable methodological challenges, the promise of business value from unstructured multimedia data has not materialized. In this study, we use a design science approach to develop DeepVoice, a novel nonverbal predictive analysis system for financial risk prediction, in the setting of quarterly earnings conference calls. DeepVoice forecasts financial risk by leveraging not only what managers say (verbal linguistic cues) but also how managers say it (vocal cues) during the earnings conference calls. The design of DeepVoice addresses several challenges associated with the analysis of nonverbal communication. We also propose a two-stage deep learning model to effectively integrate managers’ sequential vocal and verbal cues. Using a unique dataset of 6,047 earnings call samples (audio recordings and textual transcripts) of S&P 500 firms across four years, we show that DeepVoice yields remarkably lower risk forecast errors than that achieved by previous efforts. The improvement can also translate into nontrivial economic gains in options trading. The theoretical and practical implications of analyzing vocal cues are discussed. [ABSTRACT FROM AUTHOR]