Search Results (88)

Small- and medium-sized enterprises (SMEs) face significant challenges in adopting robotic solutions due to limited financial resources, insufficient technical expertise, and uncertainty regarding operational and economic outcomes. Existing automation approaches are often technologydriven and provide limited support for systematic decisionmaking. This study proposes the Agile Robotics Implementation Model (ARIM), an iterative framework integrating Lean Manufacturing, Lean Robotics, and Lean Startup principles. ARIM combines process assessment, key performance indicator (KPI)-based evaluation, and iterative experimentation within the Robotic Startup Cycle, supported by a decision-support software tool. The framework was developed using a Design Science Research (DSR) approach and validated through an industrial case study. Results demonstrate strong agreement between predicted and realized KPI values. The implemented solution achieved a 24.5% return on investment (ROI), with a payback period of approximately 2.1 years, reduced labor demand by 3900 h, and improved productivity, ergonomics, and quality. The findings indicate that ARIM supports reliable and data-driven robotics implementation in the studied SMEs; broader transferability requires validation across multiple cases. Full article

Although museums are increasingly integrating large language models (LLMs) into their operations, current applications remain largely confined to narrow tasks such as chatbots and document management. While the emergence of agentic AI offers more advanced, autonomous capabilities, existing frameworks are primarily designed for general-purpose environments. Consequently, they provide limited guidance for museums, which function as complex socio-technical systems governed by strict institutional mandates, ethical responsibilities, and operational constraints. Due to these complexities, the practical deployment of agentic AI in real-world museum settings remains highly challenging. At present, a system-level architecture for agentic AI tailored specifically to the unique requirements of museums is notably absent. To address this gap, this study employs the Design Science Research (DSR) paradigm to propose the Intelligent Museum Agent Framework (IMAF). The proposed framework extends an existing general agent architecture by incorporating a bifurcated module for contexts to systematically enforce institutional and operational constraints. Additionally, it features an integrated memory structure grounded in cultural heritage data to ensure factual reliability. Ultimately, the IMAF provides a conceptual systems integration guideline for museums exploring domain-constrained autonomy and offers a basis for future implementation and empirical validation. Full article

Reality (XR) technologies hold significant potential for transforming higher education by enabling immersive, embodied, and data-informed learning experiences within smart campus ecosystems. However, adoption remains uneven, as many implementations are driven by technological availability rather than by educational psychology models, formative assessment logic, accessibility requirements, or sustainable implementation criteria. This study proposes a theoretically grounded set of guidelines for inclusive XR learning environments in higher education, following a Design Science Research methodology in which the guidelines are conceived as a design artifact derived from an interdisciplinary knowledge base. The study covers the design and development phase of the DSR cycle; the evaluation phase, comprising expert review and field deployment, constitutes the planned immediate next step. The literature base integrates recent systematic reviews and empirical studies on cognitive load, multimedia learning, self-regulated learning in immersive environments, Universal Design for Learning 3.0, and learning analytics. The resulting artifact comprises an Integrative Educational Psychology Model, organized across three mutually informing analytical layers connecting constructs such as presence, agency, embodiment, cognitive load, self-regulation, and learner variability, and ten design guidelines linking pedagogical alignment, UX/UI design, accessibility, feedback, analytics, and scalability. Two hypothetical illustrative use cases demonstrate how the framework could be operationalized across individual and collaborative learning scenarios. The proposed framework offers psychologically grounded and practically applicable guidance for educators, instructional designers, and developers seeking to create pedagogically meaningful and inclusive XR environments. Empirical evaluation will be necessary to confirm its effectiveness. Full article

During the purchase or renovation of housing, a limitation exists in the finish design process for construction projects, as buyers cannot switch between various finish options for their apartments. Instead, they must depend on visual graphics or physical samples to imagine the appearance of these finishes in their home. Combining Building Information Modeling (BIM) with Virtual Reality (VR) presents a promising way to overcome this challenge. This study introduces a BIM-VR tool and a methodological framework designed to support decision-making when selecting finishes, with a focus on cost, waste reduction, and overall project perception. The research method adapts the Design Science Research (DSR) technique for developing an artifact through four key steps: (1) identifying the problem the artifact aims to solve; (2) defining the artifact’s solution goals; (3) designing and developing the artifact; (4) evaluating and fine-tuning the artifact. The tool developed allows for immersive walkthroughs within a digital replica of a residential building, along with the ability to customize and measure finishes. The artifact was assessed through a questionnaire administered to 53 potential residential end users, including potential apartment buyers and individuals interested in residential customization processes. The findings indicate a mean perceived-improvement score of 87.7 out of 100 for the BIM-VR method, reflecting participants’ favorable comparison between the BIM-VR and traditional finish-selection approaches. This innovation benefits the construction industry by enhancing stakeholder communication and supporting a faster design process in building finishes selection. Full article

5G-Advanced (3GPP Release 18) architectural changes include multi-access edge computing (MEC) architectural changes, network automation, and non-public networks (NPNs). It is important to note that even though these advancements provide substantial performance advantages, they destroy fixed-perimeter security models, providing a distributed attack surface. The use of current security assessment strategies, which are usually non-fluid and isolated, is inadequate to offer the required runtime security health assurance needed in such fluid environments. This study presents a new security assurance framework (SAF) that would be used to provide ongoing evidence-based protection on core, edge, and private network domains. This framework employs a four-layer architecture, including monitoring, analytics (LM), policy engine, and enforcement, to convert security periodically audited to a dynamic threat-control-metric evidence chain. A 96% attack detection rate and a 99.8% reduction in response time (with a mean of 20.1 s) are proven by validation on an emulated 5G-Advanced testbed (approximating Release 18 features using Open5GS (v2.7.2 Rel-17, community developed, Seoul, Republic of Korea and custom extensions) based on a design science research (DSR) paradigm. Although the overhead (13% CPU, 21.4% memory) is manageable, the findings prove that all-time, multi-domain assurance is crucial to the healthy functioning of 5G-Advanced and is a key roadmap to autonomous 6G security. Full article

The transition towards circular practices in the construction sector requires integrated approaches addressing both material resource procurement and the fragmentation of local stakeholder networks. This study presents the ACT-FLOW Framework, a multi-level approach enabling territorial circular processes by integrating actor networks and material flows. The framework comprises three interconnected levels (strategies, processes, and indicators) supporting the definition, implementation, and evaluation of circular practices across building and territorial scales with an iterative refinement phase. Methodologically, it was developed through a Design Science Research approach (DSR) articulated into four steps: (1) define the scope and boundaries; (2) develop a knowledge base; (3) structure the framework and its components; and (4) validate and apply it to a real case, the Circular Design Polito Lab, a research infrastructure currently under development by the Politecnico di Torino (Italy). The results demonstrate how the framework supports stakeholder coordination, structures circular workflows, and enhances circular performance monitoring. The primary limitation is that the case study has not yet been realized; consequently, it is not feasible to conduct an ex post but only an ex ante evaluation of the results. Future research will assess the framework’s capacity to foster ecosystemic conditions for circular construction practices through its longitudinal application across project phases. Full article

The AS/EN9100 standard represents the primary quality management framework governing aerospace supply chains. However, its implementation remains challenging for small and medium-sized enterprises (SMEs) due to limited resources, fragmented processes, and insufficient integration of digital support mechanisms. Existing studies primarily focus on standard interpretation, certification outcomes, or isolated implementation practices, while lacking a structured process–chronological implementation architecture suitable for SME environments. This study develops and empirically validates a digitally supported AS/EN9100 implementation framework using a Design Science Research (DSR) approach combined with Action Research principles. The proposed framework transforms the traditional clause-based interpretation of the standard into a coordinated implementation architecture integrating process management principles, risk-based thinking, and a digital support layer. The framework was validated in a real organizational environment through implementation. The integrated digital support environment also improved the coordination of responsibilities, monitoring of implementation milestones, and management of documentation workflows. From a systems perspective, the study conceptualizes quality management implementation as a socio-technical transformation process rather than a compliance-driven activity. The contribution of the study lies in the development of a transferable organizational and process innovation artifact that integrates process structuring, digital coordination, and adaptive management principles into a unified implementation framework for regulated SME environments. Full article

Artificial intelligence (AI) and digital platforms are increasingly influencing how universities manage experiential learning, interdisciplinary collaboration, and innovation-oriented educational activities. Challenge-based capstone and graduation projects play an important role in this context because they connect technical learning with teamwork, stakeholder engagement, project management, and applied innovation. However, many universities still rely on fragmented and highly manual coordination processes, which can limit scalability, transparency, and effective alignment between project requirements and participant capabilities. This study presents Smart Innovation Hub, an AI-enabled information system developed to support challenge-based innovation and capstone-project coordination in higher education. The platform brings together challenge intake, participant profiling, AI-supported recommendations, mentor coordination, workflow governance, and human review within a shared educational innovation environment. The system operationalizes an Innovation Bridge ecosystem model that connects students, faculty mentors, research centers, and external partners through a data-supported coordination framework. A Design Science Research (DSR) methodology guided the development and pilot evaluation of the platform within a public university environment. The pilot evaluation relied on several evidence sources, including platform logs, coordinator records, stakeholder surveys, milestone documentation, and partner feedback collected during implementation activities. Early pilot observations suggested an approximate 60% reduction in average team-formation cycle time, together with positive stakeholder perceptions regarding workflow usability and recommendation quality. These findings should be interpreted as preliminary implementation indicators within a single-institution pilot environment. The study contributes an AI-enabled educational innovation ecosystem architecture, a hybrid semantic-structured recommendation framework for challenge-based coordination, and a structured workflow model that integrates explainability and human oversight into educational innovation management. The findings further suggest that AI-enabled information systems may improve the transparency and coordination of challenge-based innovation workflows while preserving institutional governance and human decision-making. Full article

Digital platforms are increasingly presented as instruments for sustainable tourism governance, yet destinations often remain data-rich and governance-poor: digital traces are dispersed across actors, indicators are weakly standardised and communities frequently lack meaningful access to the information that shapes destination decisions. This article addresses this problem through the conceptual design and preliminary formative evaluation of ORVE (Optimisation of Resources and Valorisation of Experiences), a destination-level platform designed to connect tourists and residents, companies and institutions and Destination Management Organisations (DMOs) through a circular data ecosystem, understood as feedback loops across stakeholder levels. Methodologically, the study adopts Design Science Research (DSR). It operationalises problem identification, definition of solution objectives, artefact design and development, preliminary demonstration and formative evaluation, while recognising that full-scale causal evaluation remains a future research stage. The empirical component draws on a real-world pre-test with 12 tourism companies mediated by Biosphere Portugal, two Biosphere-administered pilot-company surveys involving 58 and 52 companies and scenario-based testing by 14 student groups involving more than 60 final-year students from Tourism and Tourism and Hospitality Management programmes. These sources are interpreted as exploratory and formative evidence rather than as a representative adoption study or a causal impact evaluation. The results suggest perceived usefulness for structuring sustainability information, supporting indicator monitoring and informing decision making, while also revealing operational constraints related to usability, data-entry flexibility, privacy communication, validation mechanisms, data availability in micro and small enterprises and the need for close onboarding support. The article contributes a refined platform architecture, a governance requirements matrix, design principles, an operationalisation roadmap and an evaluation protocol for sustainable tourism platform governance. Full article

Despite the widespread adoption of educational technologies, a critical gap remains in Generative Artificial Intelligence (GenAI) tools that are both pedagogically grounded and responsive to the practical needs of teachers and students. This study examines the use of the Scaffolding Cognitive Artificial Intelligence (SCAI) chatbot to support students’ pre-class preparation and classroom engagement in secondary-school physics. The SCAI chatbot was designed by applying scaffolding theory and aligning chatbot interactions with the educational curriculum, with the aim of providing adaptive explanations, formative questioning, and teacher-facing preparation analytics. A Design Science Research (DSR) approach with a mixed-methods design was employed. Quantitative data were collected through pre-class preparation tests, while system-generated log data captured student interaction patterns. Qualitative data were obtained through semi-structured interviews with both teachers and students. The findings suggest that SCAI-supported preparation was associated with higher student preparation and more active engagement when compared with textbook-based preparation. Interview and log data further indicated that the chatbot helped simplify difficult concepts, support students’ confidence, and provide teachers with useful information about students’ progress before classroom instruction. However, because the study was conducted in a limited educational context and the intervention combined AI-supported interaction, scaffolding, flipped preparation, and teacher analytics, the findings should be interpreted as exploratory evidence of a meaningful association rather than definitive proof of causation. Overall, the study contributes to understanding how a scaffolding-based GenAI chatbot can support pre-class preparation and teacher-informed instructional planning. Full article

Greenwashing in food marketing undermines consumer trust and impedes Sustainable Development Goal 12 (SDG 12). While prior research has established linkages between greenwashing perception, green skepticism, and purchase intention, no publicly deployed decision support tool has been developed for practitioner use. This study applies Design Science Research (DSR) methodology to translate validated behavioral models into a deployable decision support system rather than re-testing established relationships. We present the development, deployment, and evaluation of the Greenwashing Advertising Message Assessment (GWAMA), a web-based DSR artifact grounded in a validated Stimulus–Organism–Response (S-O-R) structural equation model. GWAMA integrates factor-loading-weighted composite scoring with SEM-derived parameters to generate real-time greenwashing risk diagnostics for food advertising messages. Usability was evaluated with 150 Thai food industry professionals using a Technology Acceptance Model (TAM) instrument applied to the live system. Results provide indicative evidence of stakeholder acceptance, with high perceived usefulness, ease of use, and intention to use. This study contributes by demonstrating how validated behavioral models can be translated into a publicly deployable decision support artifact, with practical implications for sustainable marketing governance and SDG 12 implementation in emerging economies. Full article

The integration of artificial intelligence (AI) in education has made significant advancements in personalized learning and adaptive instruction. However, current systems remain limited by three critical gaps: (a) fragmented architectures that decouple technical performance from ethical governance, (b) the treatment of fairness and accountability as external constraints rather than embedded design principles, and (c) reliance on single-modality data that inadequately represents complex learning environments. These restrictions hinder scalability and limit the capacity of AI systems to deliver equitable, transparent, and context-aware educational experiences. This study aims to address these challenges by designing and validating an ethics-aware, multi-agent conceptual framework for adaptive education in which personalization and responsible AI are co-developed as integrated system properties. The proposed architecture uses five coordinated agents: perception, pedagogy, assessment, feedback, and ethics monitoring. These five agents share one knowledge layer containing learner profiles, domain models, competency structures, interaction histories, and machine-readable policy rules. A four-stage feedback loop comprises: (a) outcome aggregation, (b) system evaluation and validation, (c) teacher review and intervention, and (d) agent update and policy refinement. It enables real-time adaptation, teacher oversight, and iterative system improvement. Adopting a design science research (DSR) methodology and mixed-methods evaluation across functional, pedagogical, ethical, and system-level dimensions, the proposed framework is expected to demonstrate improved learner modeling accuracy, enhanced knowledge tracing, and more robust multimodal engagement analysis compared to centralized and single-modality approaches. Based on design science evaluation against established benchmarks and component-level validation in a simulated learning management system (LMS), this theoretical framework is projected to improve learner modeling accuracy, enhance knowledge tracing, and enable more robust multimodal engagement analysis compared with centralized and single-modality approaches. These projections constitute theoretically derived hypothesis and remain subject to empirical validation in live deployment studies. This study’s theoretical contribution lies in demonstrating that ethics-by-design and adaptive personalization are architecturally compatible and mutually reinforcing design principles. Full article

The widespread adoption of Generative Artificial Intelligence (GenAI) in regulated enterprises is currently hindered by the “Static Alignment Trap”: the inability of traditional safety methods, such as Reinforcement Learning from Human Feedback (RLHF), to adapt to rapidly shifting compliance landscapes without costly retraining. This paper introduces and evaluates the Agreement Validation Interface (AVI), a modular governance architecture that functions as a deterministic middleware layer. By decoupling governance from the core inference engine, AVI implements Dynamic Bilateral Alignment (DBA), enforcing policy constraints at both the input and output stages through vector-based semantic retrieval. Adopting a Design Science Research (DSR) methodology, we validated the system against the FinanceBench financial benchmark ($N=150$ queries, three repeated runs, 450 total observations) and a proprietary Russian-language provocative content dataset developed internally at MWS AI ($N=201$ queries; not publicly available). The empirical results demonstrate that the architecture achieves an 83.2% Large Language Model (LLM)-judge compliance rate (95% confidence interval, CI: 79.4–87.1%), statistically significantly exceeding the unfiltered baseline of 63.7% ($\Delta =+19.5$ percentage points (pp), $t=4.02$, $p=0.002$). The vector-based input filter achieves perfect detection performance (Precision $=1.000$, Recall $=1.000$, F1 $=1.000$). Cross-domain validation on 201 Russian-language provocative queries confirms generalizability (Recall $=0.985$, LLM compliance among triggered queries $=0.977$). The operational Time-to-Compliance for enforcing new rules was reduced from hours (model fine-tuning) to under five seconds (vector indexing). These findings suggest that enterprise AI safety requires an architectural shift from model-centric training to system-centric control, complemented by system-prompt-level anti-inference governance. We conclude that AVI offers a scalable, cost-effective, and statistically validated framework for auditable AI compliance, independent of the underlying model provider. Full article

The development of a 3D digital cadastre is a key objective of Australia’s Cadastre 2034 strategy for modernising land information infrastructure. Jurisdictions across Australia are progressively transitioning from conventional 2D cadastral systems towards 3D cadastral models to better represent complex land and property rights, particularly in dense urban environments. In New South Wales (NSW), LandXML is currently the standard for digital cadastral lodgement. However, its limitations in supporting 3D spatial data representation have prompted investigation of alternative standards such as LandInfra and its InfraGML encoding. The aim of this study is to investigate how LandInfra handles existing cadastral information in New South Wales, Australia. In particular, this study is a technical and structural comparison of LandXML and InfraGML, examining data modelling workflows and geometric encoding. A hybrid research methodology integrating Design Science Research (DSR) and Case Study Research (CSR) was applied. Two representative cadastral plans—a standard deposited plan and a strata plan—were digitised using LISCAD 2025 v25.9.23.1 and AutoCAD Civil 3D 2026 V1 and subsequently modelled in both LandXML and InfraGML formats. Validation was conducted using KITModelViewer and schema validators, with comparative analysis of development cycle, modelling structure, usability, and workflow. This study demonstrates that InfraGML offers semantic richness and structural flexibility compared to LandXML within the scope of the examined case studies, although its practical adoption is constrained by limited commercial software support and may present adoption challenges for practitioners. The findings of this research suggest that LandInfra offers considerable potential for advancing the future development of 3D cadastre in Australia. In this context, InfraGML is positioned as a promising data standard for ongoing investigation and future research, rather than an immediate substitute for LandXML. Within the scope of this study, a fully operational 3D cadastral implementation is neither developed nor validated within existing legal or institutional frameworks, and complex 3D scenarios are not addressed. Future research should explore integration with CAD platforms, legislative implications of 3D survey features, complex volumetric cases, and formal 3D topological validation, and alternative modelling approaches, such as using Nested Parcels method and InfraJSON encoding. Full article

Singular Design Foresight (SDF) is proposed as a foundational methodological framework for advancing Design Foresight (DF) toward a more explicit, traceable, and evaluable scientific discipline. The framework formalizes DF as a structured cycle in which qualitative foresight inputs—such as signals, trends, and expert interpretations—are progressively transformed into analyzable representations that support decision closure under conditions of structural uncertainty. SDF combines an expert-defined conceptual universe with semantic projections to relate textual and contextual evidence to anticipatory constructs, enabling the generation of traceable indicators and structured configurations of viable futures. Within this architecture, the Stakeholder Viability Principle (SVP) functions as a filtering mechanism that delimits relevant futures according to continuity, agency, and axiological coherence, while Social Singularity captures context-specific critical transitions that shape when and why decision closure becomes necessary. The framework is organized in alignment with Design Science Research (DSR), adopting an evaluation logic centered on validity, utility, and attribution. Rather than presenting conclusive system-level validation, the article synthesizes summative evidence from previously published studies on semantic projections, singularity detection, and mixed expert–corpus foresight applications to support the plausibility, internal coherence, and operational feasibility of the proposed framework, while delimiting full integrated validation as a future research objective. SDF does not aim to provide deterministic prediction; instead, it enables auditable anticipatory representations and justified closure under uncertainty. In this sense, the framework is compatible with forecasting understood as the production of evaluable anticipations under explicit assumptions, while preserving the interpretive and situated character of strategic decision-making. Full article