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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]

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]

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]

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]

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.

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]

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.)

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]

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]

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 Tourism Review is the property of Emerald Publishing Limited 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.)

The food lifestyle has evolved from the regional differences that were previously shaped by different geographic locations to the pluralistic complexity of food styles that are now influenced by different user consciousnesses. The field of food design has assumed a greater social responsibility, while also facing the challenge of sustainable development. Consequently, it is of paramount importance to devise or refine the methodology of food design innovation. This study makes a significant contribution to the field of sustainable food design by combining design methodology with sociological concepts such as "Field and Habitus" to construct the "F-U-BVF" food lifestyle analysis framework. This study examines the applicability of the F-U-BVF analytical framework, exemplified by tofu, through the refinement and translation of user needs, among other methods. The objective is to guide users toward sustainable lifestyles based on a design methodology, thereby creating multiple values conducive to food sustainability. At the same time, the deconstruction of the structural relationship, process, and methodology of sustainable food design based on the F-U-BVF analytical framework is intended to break through the barriers of traditional design science. This is to provide a new systematic research paradigm for sustainable food design, as well as to provide a new perspective on the experience and interactive relationship between people and food. Finally, it is also to provide effective food design innovation solutions for relevant multistakeholders. This is of great significance for the development of sustainable food design. [ABSTRACT FROM AUTHOR]

The connections between universal rights to water supply, energy security, and food supply stand out as a challenge that requires project evaluation models that can capture the complex dynamics and interdependencies of these resources. This study proposes the elaboration of a nexus evaluation model (NEM) for projects related to the water–energy–food nexus (WEFN) from the perspective of sustainability, Industry 4.0, and the Sustainable Development Goals (SDGs). The model considers the three dimensions of sustainability—economic, environmental, and social; the three structuring factors of Industry 4.0—physical, biological, and digital; and the 17 SDGs proposed by the United Nations. A Design Science Research (DSR) approach was adopted in which the design and development of the model, and demonstration and evaluation phases, were supported by a group of experts. The model was applied to three different projects focused on sustainable technological innovation in energy and agriculture, with the results presented in the RGB color scale represented numerically as a number from 0 to 255. The results demonstrated that, in the relationship between nexus and sustainability, the projects presented scores between 162 and 217 for the environmental dimension, between 158 and 202 for the economic dimension and between 170 and 212 for the social dimension. In the nexus and Industry 4.0 relationship, the projects obtained scores ranging from 9 to 94 in the biological factor, from 13 to 141 in the digital factor, and from 13 to 141 in the physical factor. In the nexus and SDG relationship, scores ranged from 214 to 244 for water, from 195 to 255 for energy, and from 30 to 255 for food. These results from the model were consistent with the reality of the projects being evaluated, demonstrating a greater alignment of the projects with the dimensions of sustainability and the SDGs than with the factors of Industry 4.0. The proposal of the model contributes to broaden the understanding of how projects related to the nexus can be evaluated considering multiple contemporary dimensions. [ABSTRACT FROM AUTHOR]

Designers' work processes are shaped by a four-phase 'discover, define, develop, and deliver' model that alternates between divergent and convergent thinking. We suggest consideration of this conceptual scaffold in 'design sprint' workshops for graduate students in the life sciences and in design to promote creativity, interdisciplinary collaboration, and knowledge cocreation. Designers' work processes are shaped by a four-phase 'discover, define, develop, and deliver' model that transitions between divergent and convergent thinking. We suggest consideration of this conceptual scaffold in 'design sprint' workshops for graduate students in the life sciences and in design to promote creativity, interdisciplinary collaboration, and knowledge cocreation. [ABSTRACT FROM AUTHOR]

The application of post-occupancy evaluation (POE) to address the quality of built spaces is widely recognized. This paper deals with the artifact 'Como Você Mora? – CVM' (How Do You Live?) – BIM', which incorporates questions from the POE CVM in BIM. The hypothesis raised was that POE data incorporated into BIM assists in understanding and analyzing the data designers obtained, for it provides clearer and more contextualized answers in the information model. The objective of the paper is to demonstrate the potential of incorporating a POE into the BIM information model, with the POE questionnaire CVM as a case study. To achieve the proposed goal, the work steps are based on Design Science Research, and are divided into (1) problem identification, (2) definition of expected results, (3) development, (4) demonstration, (5) evaluation, and (6) communication. In this paper, the first three steps of the method are presented. The contribution is made by the methodological advance in the area of POE over previous research, seeking artifacts that assist the realization of more efficient projects in terms of housing quality assessment. Results show the potential of incorporating CVM POE into BIM, to facilitate data analysis and looking for patterns of responses by project typology, through the color map in floor plans, as well as the possibility of concentrating all project information permanently linked to a single BIM model. This can in turn avoid the loss of information we obtained in the POE. [ABSTRACT FROM AUTHOR]

Purpose: Business process training is a crucial activity in the business process management lifecycle, performed whenever an organization needs to train workers about how to carry out their activities according to defined processes, after significant process changes, or whenever new workers come on board. Due to their motivational character, serious games have been understood as an unconventional alternative to support training in organizational processes. Still, methodologies to design serious digital games specifically for business process training are missing in the literature. This research paper presents a method – Play Your Process for Training (PYP4Training) – for designing digital games for business process training. Design/methodology/approach: The research is guided by design science research methodologies and comprises the adaptation of Play Your Process (PYP), a method for designing business process-based digital games (BPBDG). PYP activities and supporting tools were shaped to cope with the specific requirements of BPBDG design for process training purposes, bringing to light a new method: PYP4Training. Findings: PYP4Training was evaluated by designing a BPBDG for training a heavy equipment maintenance process in a multinational mining company. The game was evaluated by process owners and actors who reported a positive perception of the game as an option for process training. However, there is still space for improving trainees' engagement. Originality/value: The research proposes an innovative way for business process training using digital games. Nevertheless, literature shows a lack of systematic procedures to build such games and results about their use. [ABSTRACT FROM AUTHOR]

Much of the literature that examines critical science agency (CSA) focuses on how students enact their CSA to support knowledge construction and agentic action. Few studies, however, address how science curricula can be specifically designed to support students in exercising their CSA. In this study, I examine features of a community-based science (CBS) curricular design that engages students in justice-oriented science learning to advance their CSA. More specifically, I analyze the design and structure of an environmental science elective course to investigate features of CBS curricular design that support students in exercising their CSA, including: (1) leveraging learning goals to create community change, (2) developing students' toolkits, (3) cultivating spaces for advocacy and critical hope, and (4) critical and ongoing reflection. The findings suggest that science curriculum can be purposefully designed to assist students in exercising their CSA through generative learning experiences that empower them as community change agents. As we move toward more equity and justice-centered science learning, I recommend that future science curricula take community-based science approaches to design, structuring learning around students' CSA by attending to how formal science learning can be used as an avenue to support community change. [ABSTRACT FROM AUTHOR]

The heavy dependency of cryptography methods on mathematical theory and computational science necessitates the designing of a computationally secure cryptographic algorithm which is hard to break by an adversary. Matrix methods are intensively used in cryptography for their robustness and adaptability in the encryption of long messages but time complexity is a big concern while dealing with matrices of volumetric size. Further, the key generation function may limit the size of key domain which compromises the perfect security. Our work addresses these hindrances in priority to give an algorithm which is perfectly secure. This work contributes towards the matrix methods in cryptography; the two main attributes of a secured communication namely, true randomness of key selection and a liberty to extend the dimension of key space. [ABSTRACT FROM AUTHOR]

The present study developed a Detection Android cybercrime Model (DACM), deploying the design science approach to detect different Android-related cybercrimes. The developed model consists of five stages: problem identification and data collection, data preprocessing and feature extraction, model selection and training, model evaluation and validation, and model deployment and monitoring. Compared to the existing cybercrime detection models on the Android, the developed DACM is comprehensive and covers all the existing detection phases. It provides a robust and effective way to spot cybercrime in the Android ecosystem by following Machine Learning (ML) technology. The model covers all the detection stages that are normally included in similar models, so it provides an integrated and holistic approach to combating cybercrime. [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.

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]

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]

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]

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]

Background: Despite success in HIV treatment, diagnosis and management of hypertension (HTN) and cardiovascular disease (CVD) remains suboptimal among people living with HIV (PLWH) in Botswana, with an overall HTN control of only 19% compared to 98% HIV viral suppressed. These gaps persist despite CVD primary care national guidelines and availability of free healthcare including antihypertensive medications. Our study aims to develop and test strategies to close the HTN care gap in PLWH, through integration into HIV care, leveraging the successful national HIV care and treatment program and strategies. Methods: The InterCARE trial is a cluster randomized controlled hybrid type 2 effectiveness-implementation trial at 14 sites designed to enroll 4652 adults living with HIV and HTN plus up to 2326 treatment partners. Primary outcomes included effectiveness (HTN control) and implementation outcomes using the Reach Effectiveness Adoption Implementation and Maintenance framework, with explanatory mixed methods used to understand variability in outcomes. InterCARE trial's main strategies include healthcare worker HTN and CVD care training plus long-term practice facilitation, electronic health record (EHR) documentation of key indicators and use of reminders, and use of treatment partners to provide social support to people living with HIV and HTN. InterCARE started with formative research to identify contextual factors influencing care gaps using the Consolidated Framework for Implementation Research. Results were used to adapt initial and develop additional implementation strategies to address barriers and leverage facilitators. The package was pilot tested in two clinics, with findings used to further adapt or add strategies for the clinical trial. Discussion: If successful, the InterCARE model can be scaled up to HIV clinics nationwide to improve diagnosis, management, and support in Botswana. The trial will provide insights for scale-up of HTN integration into HIV care in the region. Trial registration: ClinicalTrials.gov reference NCT05414526. Registered 18 May 2022, https://clinicaltrials.gov/study/NCT05414526?term=NCT05414526.&rank=1. [ABSTRACT FROM AUTHOR]

The introduction of the Industrial Internet of Things (IIoT) has led to major changes in the industry. Thanks to machine data, business process management methods and techniques could also be applied to them. However, one data source has so far remained untouched: The network data of the machines. In the business environment, process mining, for example, has already been carried out based on network data, but the IIoT, with its particular protocols such as OPC UA, has yet to be investigated. With the help of design science research and on the shoulders of CRISP-DM, we first develop a framework for process mining in the IIoT in this paper. We then apply the framework to real-world IIoT network traffic data and evaluate the outcome and performance of our approach in detail. We find tremendous potential in network traffic data but also limitations. Among other things, due to the dependence on process experts and the existence of case IDs. [ABSTRACT FROM AUTHOR]

As part of the special issue on Teacher Learning and Organizational Contexts, this paper explores the collective organizational sensemaking processes of middle school science teachers engaged in a design‐based research project focused on supporting science, technology, engineering, and mathematics (STEM) identity and interest development of middle school youth with the design of a STEM ecosystem. Most of the literature on STEM ecosystems focuses on the pathways available or not for youth to develop robust STEM identities and interests. Research on STEM teachers in STEM ecosystems generally focuses on teachers’ roles in brokering youth's experiences across learning settings and not on teacher learning. We fill this gap in the literature through an examination of how teachers came to understand STEM ecosystems as a means for supporting youth interest and learning in STEM and their role as educators within STEM ecosystems that include but extend beyond classroom practice. We used interaction analysis of video and audio data collected over two cycles of professional development to describe how designed activities supported STEM teachers to surface and attend to sources of ambiguity between the two learning environments. Our findings suggest that teachers understood the out‐of‐school environment vis‐à‐vis their organizational contexts—such as, how valued outcomes of school, like teaching vocabulary, were being addressed. Over time, teachers came to value out‐of‐school learning as distinct from their classroom and as a space that is “not school” that provided necessary learning opportunities for students. The teachers recognized the unique ways the out‐of‐school STEM setting surfaced students’ strengths in ways not always afforded by school. Implications for professional learning design for in‐service science teachers to work toward providing more cohesive STEM learning experiences for youth are discussed. [ABSTRACT FROM AUTHOR]