Singular Design Foresight: A Foundational Method for Auditable Anticipation and Decision Closure

PurposeThis study introduces the Digital Maturity Assessment Model (DMAM), a model tailored to assess the digital maturity of SMEs, tracing its development from addressing business challenges to establishing a comparative analysis framework grounded in Resource Dependence Theory (RDT).Design/methodology/approachDMAM is based on positivist philosophy and objectivist epistemology, supported by Design Science Research (DSR) and Capability Maturity Model Integration (CMMI). The methodology involves iterative development, from problem identification to creating a practical solution for assessing SMEs' digital maturity and guiding digitalization efforts.FindingsDMAM offers a clear and specific methodology, distinguishing itself by addressing the unique needs of SMEs, particularly resource-dependent ones. The model’s development fills critical gaps in existing literature and provides a practical artifact for SMEs' digitalization.Originality/valueDMAM is original in its focus on the specific needs of resource-dependent SMEs, offering actionable recommendations and addressing shortcomings in existing models. It serves as a foundational framework for SMEs' digital transformation, making a significant contribution to the digital maturity assessment literature.

Reviewed by: Thinking about Causes: From Greek Philosophy to Modern Physics Francesca di Poppa Peter Machamer and Gereon Wolters, editors. Thinking about Causes: From Greek Philosophy to Modern Physics. Pittsburgh: University of Pittsburgh Press, 2007. Pp. vii + 318. Cloth, $75.00 This book contains sixteen essays, presented at the seventh Pittsburgh-Konstanz Colloquium in 2005. It includes historical topics, ranging from ancient Greek thought to late nineteenth- and early twentieth-century philosophy, and contemporary topics, including causal pluralism, epiphenomenalism, and causality in disciplines as different as physics and economics. The concept of causation has been elaborated in many ways, with many different philosophical functions, including its problematic relations to the concept of explanation. The essays cover a variety subjects, and the results are quite disparate. McCord Adams's discussion of medieval philosophers on sacramental causation and Norton's challenge to "causal fundamentalism" in contemporary physics are both about "causes," but their concepts of cause are only tangentially related. Despite its stated aim of bringing "unusual aspects of the history of causality to contemporary attention," the book offers no hint of a historical narrative (although a few of the essays do). Even so, I find this book a valuable addition to a philosophical library. Most of the essays are interesting, although some suffer from lack of detail, probably because of editorial constraints (among the exceptions are Inwood's "Moral Causes" and Shapiro's and Sober's "Epiphenomenalism: The Dos and the Don'ts"). One of the most interesting essays is Marilyn McCord Adams's "Powerless Causes." Medieval philosophers attempted to use the conceptual apparatus of causation to explain how sacramental rituals have some real power to produce grace, in cooperation with divine agency. The attempts collapsed, McCord Adams shows, because of difficulties in making a supernatural event (grace in the soul) even partially dependent on the efficacy of created causes. Throughout the discussion, McCord Adams shows the interconnection, and sometimes confusion, between the causal and the explanatory role of sacraments. It is an interesting, clearly written and fascinating overview of a complex debate, which took place in terms quite different from debates about causation in contemporary metaphysics or philosophy of science. In "From Scholasticism to Modern Physics—and Back?", Robert Schnepf argues that Descartes opens the door to a new argument for occasionalism by introducing a notion of cause that does the work of both formal and efficient causation. He does so by introducing what Schnepf calls the "epistemic approach": causes must meet the epistemic criterion of being clearly and distinctly perceived in their necessary relations to their effects. This is problematic because, as Malebranche remarks, no such necessity can be perceived except between an omnipotent will and its effects. Hence, the only real efficient cause is God. I wish that Schnepf had been more generous with textual quotes and references, and I found his discussion of the conflation between formal and efficient cause insufficient. However, the summary of the Scholastic background and the discussion of the Cartesian metaphysical shift between the Regulae and his more mature philosophy are interesting, and Schnepf's case for how Descartes helped revive occasionalism is persuasive. Both Laura Snyder's "Freedom from Necessity" and Paolo Parrini's "Mill on Causation and Historical Turn in Philosophy of Science" start from a reading of Mill's theory of causation [End Page 243] in the context of his anti-intuitionism and his political struggle for individual liberty against the tyranny of custom. Snyder concludes with an invitation to include historical considerations in interpretation, under penalty of reading an "artificial construction" rather than the actual philosopher. Parrini argues that the textual evidence is certainly compatible with Snyder's conclusion that Mill's discussion of causation was influenced by his political agenda, but it is also compatible with a different interpretation, i.e. that a political agenda initially influenced Mill's discussion of causation, which later took an autonomous turn. Parrini then warns against the excesses of the "historical turn": there is room for evaluating a philosopher's ideas outside of their immediate background, especially if we are to compare them to alternative systems or examine their internal coherence. Both discussions of Mill's concept of causation are very interesting...

This paper introduces Educational Conceptual Model Design a new research methodology to develop conceptual models in the field of educational technology. It is based on principles from Conceptual Design Methodology, Conceptual Modeling Methodology, Design-Based Research, and Design Science Research. It is a systematic methodology structured in four phases: problem definition, conceptual foundation, model development, and knowledge contribution. This methodology is designed to be streamlined and grounded in theory, focusing on consistency, replicability, and alignment with theoretical rigor. The methodology is particularly suited to respond to rapid advancements in technology and continuously evolving educational challenges, including the integration of emerging tools such as Artificial Intelligence in Education. Its main characteristic is that it offers a transition from problem identification to practical models with a special emphasis on the design process. This approach reduces the complexity of developing a model while maintaining its applicability across different educational technology areas, which can range from adaptive learning systems to data-driven learning analytics. The paper concludes by discussing the methodology’s contributions to theory and practice, its limitations, and potential future directions, including scalability, cross-disciplinary integration, and the incorporation of disruptive technologies. This methodology serves as a foundational framework for researchers and practitioners aiming to design innovative forward-thinking solutions that meet the dynamic needs of modern education.

Informatization in the field of management of the enterprise’s economic activity is carried out with the aim of increasing the productivity of employees by reducing the cost of products and services; as well as improving the qualifications and professional knowledge of specialists. The possibility of substantiating managerial decisions regarding the choice of information technologies using the methods of linguistic vector evaluations, fuzzy logical conclusion, and fuzzy relationship of preferences is considered. The method of applying the methods of linguistic vector evaluations and the fuzzy logical conclusion of evaluating possible values of management decisions when determining the expediency of introducing information technologies into the activities of economic entities in conditions of uncertainty (crisis) is presented. It was determined that the application of this method, with a simpler mathematical apparatus, allows to evaluate the possible variants of the values of management decisions with qualitative input parameters in crisis conditions. This technique allows you to predict the results of the enterprise for the case of quantitative information in relation to a number of input parameters, however, the mathematical apparatus for processing input information is quite complex. Therefore, it will be logical to check the possibility of applying other methods of the theory of fuzzy logic to solve the problems of evaluating the results of management decisions with qualitative input parameters

Strategic planning in industrial supply chains involves long-term decisions on the allocation of scarce resources under conditions of structural uncertainty. These decisions are characterised by competing objectives, limited reversibility and substantial managerial responsibility. While recent research highlights the potential of artificial intelligence, and in particular generative AI, to support complex decision-making, existing contributions largely remain conceptual and provide limited guidance on how such capabilities can be operationalised in governance-sensitive strategic contexts. This paper addresses this gap by developing a governance-aware decision-support framework for the use of generative AI in strategic supply chain planning. Adopting a design science research approach, the study conceptualises generative AI as an organisational capability that supports, but does not replace, managerial judgement. Three interrelated artefacts are developed: an operative decision framework for strategic planning, a capability-to-decision mapping that links AI and generative AI capabilities to concrete decision functions and a governance-oriented implementation design that ensures responsibility, transparency and accountability. The proposed framework explicitly separates analytical evaluation, scenario exploration and final decision-making. Generative AI is positioned as an exploratory capability that expands the decision space by generating alternative scenarios and making implicit assumptions explicit, while normative trade-offs and resource allocation decisions remain firmly under managerial control. An illustrative, literature-based industrial use case demonstrates how the framework can support strategic decisions under resource scarcity without undermining governance requirements. The paper contributes to the literature by operationalising capability-based perspectives on AI for strategic planning and by introducing scarcity-aware decision support as a unifying lens. For practitioners, it provides a structured and transferable reference model for the responsible use of generative AI in strategic supply chain planning.

Decision-making in venture finance and public funding occurs under conditions of extreme uncertainty, incomplete information, and high institutional accountability. Although digital and algorithmic tools are increasingly adopted to support such decisions, evaluation processes remain opaque, inconsistent, and difficult to justify ex post. Insights are presented in this research derived from a longitudinal design-science research program conducted through VentureSense, which functioned as an applied research environment for studying digital decision systems across private investment and public funding contexts. Using a socio-technical Information Systems perspective, recurring structural limitations were identified across multiple decision-support systems, including venture evaluation, public grant assessment, contextual intelligence, and trust verification frameworks. Rather than proposing new predictive algorithms, a layered design framework is advanced for explainable digital decision systems that integrates evaluation logic, human-system interaction, workflow governance, and institutional accountability. The findings contribute to Information Systems research by reframing explainability as a core design property of decision systems and by offering analytically grounded design principles applicable to both private and public funding institutions.

This paper presents a comprehensive fuzzy-logical model for assessing Level of Protection Mechanism Maturity (LPMM) of socio-economic systems under conditions of high uncertainty, wartime pressures and accelerated transformation. The approach builds on Mamdani-type fuzzy inference and formalizes heterogeneous economic and institutional signals as linguistic variables in order to reduce information loss and to avoid strong distributional assumptions. The construct includes three input dimensions selected for their integrative role in the protective mechanism: Financial Stability (FS), Level of Adaptability to External Threats (LETA), and Social and Institutional Security (SIS), which jointly determine an integral output indicator LPMM. Each input variable is described by two terms (low/high) and mapped to generalized bell-shaped membership functions μ(x)=gbellmf(x,[a,b,c]), allowing flexible curvature and data-sensitive calibration. With ω = 3 inputs and z = 2 terms per input, the rule base contains r = 2³ = 8 if-then rules, ensuring structural completeness and transparent expert interpretation. The model is implemented in Matlab using Fuzzy Logic Toolbox (version R2021A) with a standard defuzzification workflow. Empirical verification for 2021–2025 demonstrates high sensitivity of the integral indicator: a clear LPMM decline in 2022 reflects the effect of external shocks and crisis factors, whereas consistent growth in 2023–2025 indicates gradual recovery and improved adaptation capacity. These results support the practical use of the framework for monitoring resilience, comparing scenarios, ranking policy options, and designing targeted interventions across financial, security, and institutional domains. Because knowledge is encoded as a rule base, the model can be updated as new risks or structural changes emerge, and integrated into automated monitoring and risk-forecasting pipelines. The framework strengthens methodological rigor by combining quantitative inputs with qualitative judgments in a single semantic space, and by providing interpretable outputs suitable for stakeholder communication. It is recommended for use in state design of economic security policy and strategic planning where fast decisions are made under incomplete information.

ABSTRACTWhat is the relationship between legitimation and institutionalization? We take a fresh look at a more complex and nuanced landscape than has previously been documented. Our approach is to view legitimation and institutionalization as separate, though related, processes. We engage in theory building to develop a typology suggesting four different ways in which the social construction of reality can be achieved: instantiating (mutual causal emergence, coming into being), realizing (legitimating toward institutionalization), aspiring (legitimating from institutionalization) and missing (mutual causal suppression, unrealized potential). Our typology contributes a new foundational framework for the sociology of knowledge for better explicating socially constructed reality, not only in periods of stability but also in the uncertainty of liquid times and states.

The rapid advancement of information technology has encouraged higher education institutions to improve the efficiency of managing Research and Community Service (PPM) activities. This study aims to design and develop a web-based PPM Management Information System prototype for LPPM Universitas Timor. Using the Research and Development (R&D) method, the study involves need analysis, system design, development, testing, and evaluation of the prototype. The system is designed to facilitate proposal management, status tracking, evaluation processes, and reporting. The research findings indicate that the prototype significantly enhances the efficiency of PPM management by providing a user-friendly interface and automated notification features. Testing with end-users, including lecturers and LPPM administrators, yielded positive feedback on ease of use and process transparency. However, improvements are needed in areas such as system performance when handling large documents and the addition of email-based reminder features. In conclusion, the developed prototype serves as a foundational framework for building more comprehensive PPM management systems in the future, supporting efficiency, productivity, and academic objectives at Universitas Timor.

Purpose This study aims to formalize the operational perspective in business process management (BPM) by developing an ontology-based approach to integrating passive resources, mainly operational equipment, into process modeling and execution. The research addresses the current gap in BPM techniques for comprehensively modeling and tracking passive resources essential to real-world processes and manual tasks. Design/methodology/approach The study employs the design science research methodology (DSRM) (Peffers et al., 2007) to create an artifact that addresses the identified problem. For this, we develop a meta-model and ontology for the operational perspective following established ontology creation guidelines (Noy, 2001). Based on a systematic literature review, we identify existing approaches and gaps to create a prototypical implementation based on the ontology using resource description framework (RDF) to showcase the practical application in a craft business context. Findings This research presents a novel ontology-based framework that formalizes the operational perspective in process management, specifically focusing on the role of passive resources. The developed meta-model and ontology facilitate the structured integration of operational equipment into BPM, improving transparency, resource allocation and process execution. A prototypical implementation using IoT technologies showcases the framework’s feasibility in supporting passive resource management within craft business contexts, including tool allocation and real-time process adaptation. Originality/value This research bridges a critical gap between BPM and asset management, offering a foundational framework for integrating passive resources into process management. The ontology-based approach facilitates the explicit modeling of operational equipment and materials, paving the way for enhanced BPMN extensions. Future research directions include expanding the ontology’s scope, developing AI-driven resource allocation and investigating applications in human-centric and knowledge-intensive processes. This work underscores the significance of incorporating operational equipment management within BPM to achieve greater process efficiency and adaptability.

Electroencephalography (EEG) provides a non-invasive window into neural dynamics at high temporal resolution and plays a pivotal role in clinical neuroscience research. Despite this potential, prevailing computational approaches to EEG analysis remain largely confined to task-specific classification objectives or coarse-grained pattern recognition, offering limited support for clinically meaningful interpretation. To address these limitations, we introduce NeuroNarrator, the first generalist EEG-to-text foundation model designed to translate electrophysiological segments into precise clinical narratives. A cornerstone of this framework is the curation of NeuroCorpus-160K, the first harmonized largescale resource pairing over 160,000 EEG segments with structured, clinically grounded natural-language descriptions. Our architecture first aligns temporal EEG waveforms with spatial topographic maps via a rigorous contrastive objective, establishing spectro-spatially grounded representations. Building on this grounding, we condition a Large Language Model through a state-space-inspired formulation that integrates historical temporal and spectral context to support coherent clinical narrative generation. This approach establishes a principled bridge between continuous signal dynamics and discrete clinical language, enabling interpretable narrative generation that facilitates expert interpretation and supports clinical reporting workflows. Extensive evaluations across diverse benchmarks and zero-shot transfer tasks highlight NeuroNarrator's capacity to integrate temporal, spectral, and spatial dynamics, positioning it as a foundational framework for time-frequency-aware, open-ended clinical interpretation of electrophysiological data.