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on Knowledge Management and Knowledge Economy |
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Issue of 2025–08–18
four papers chosen by Laura Nicola-Gavrila, Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Moreno, William Fernando |
| Abstract: | Representing and reasoning with complex, uncertain, context-dependent, and value-laden knowledge remains a fundamental challenge in Artificial Intelligence (AI) and Knowledge Representation (KR). Existing frameworks often struggle to integrate diverse knowledge types, make underlying assumptions explicit, handle normative constraints, or provide robust justifications for inferences. This preprint introduces the Conditional Reasoning Framework (CRF) and its Orthogonal Knowledge Graph (OKG) as a novel computational and conceptual architecture designed to address these limitations. The CRF operationalizes conditional necessity through a quantifiable, counterfactual test derived from a generalization of J.L. Mackie's INUS condition, enabling context-dependent reasoning within the graph-based OKG. Its design is grounded in the novel Theory of Minimal Axiom Systems (TOMAS), which posits that meaningful representation requires at least two orthogonal (conceptually independent) foundational axioms; TOMAS provides a philosophical justification for the CRF's emphasis on axiom orthogonality and explicit context (W). Furthermore, the framework incorporates expectation calculus for handling uncertainty and integrates the "ought implies can" principle as a fundamental constraint for normative reasoning. By offering a principled method for structuring knowledge, analyzing dependencies (including diagnosing model limitations by identifying failures of expected necessary conditions), and integrating descriptive and prescriptive information, the CRF/OKG provides a promising foundation for developing more robust, transparent, and ethically-aware AI systems. |
| Date: | 2025–05–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:zwpnv_v9 |
| By: | Katharina Brennig (Paderborn University); Christian Bartelheimer (Georg-August-Universität Göttingen); Bernd Löhr (Paderborn University); Daniel Beverungen (Paderborn University); Oliver Müller (Paderborn University) |
| Abstract: | Knowledge-intensive processes (KIPs) are complex, strategic core processes that drive organizational competitive advantage. These processes rely on explicit and tacit knowledge. While explicit knowledge can be codified and leveraged---often through technologies such as process mining---tacit knowledge remains embedded in individual process participants, limiting knowledge transfer and organizational learning. Process mining, a data-driven approach to analyze process data, works best for standard processes that are managed for consistency, costs, and time but is insufficiently equipped to enhance KIPs, which depend on dynamic, experience-based decision-making. We present findings from a 39-month Action Design Research (ADR) project to conceptualize a new class of IT artifacts that enable process mining for KIPs. This class of IT artifacts integrates richer process-related information, facilitating knowledge transfer by allowing participants to learn from similar process instances and engage in socialization. We propose five theory-ingrained design principles that guide the development of such systems and examine their role in fostering knowledge creation within organizations. Our research bridges critical gaps between business process management and knowledge management, offering theoretical and managerial insights. For practitioners, our findings provide a foundation for improving knowledge-intensive processes, ultimately upgrading strategic decision-making and organizational performance. |
| Keywords: | Organizational Knowledge Creation, Business Process Management, Process Mining, Knowledge-Intensive Processes, Action Design Research |
| JEL: | M15 D83 O33 L86 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:dispap:148 |
| By: | Searle, Nicola; Ganglmair, Bernhard; Borghi, Maurizio |
| Abstract: | A robust Research & Innovation (R&I) ecosystem is essential for progress, economic resilience, and addressing complex challenges. At the heart of this ecosystem, knowledge fuels innovation and further discovery. However, knowledge leakage (the loss of valuable information) can disrupt this cycle. This poses a challenge for what is known as Trusted Research & Innovation (TRI), a framework designed to strengthen research security, protect national interests, and build resilient research systems. Despite its significance, the challenges of TRI remain poorly understood. This report investigates knowledge leakage. It begins with an overview of the TRI context, focusing on policymaking, and then reviews the literature on knowledge leakage and related concepts. An exploratory data analysis examines novel empirical data to better understand the extent of knowledge leakage and how it impacts economic areas of defence, economic, and national security importance. The data analysis finds that industries deemed important for economic and national security (the UK's 'sensitive economic areas') have an 18% higher incidence of leakage than those that are not. |
| Keywords: | knowledge leakage, research security, theft of IP, economic security, national security, Trusted Research & Innovation |
| JEL: | F52 O25 O33 O34 O38 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:321868 |
| By: | Agbleke, Divine Swerwzie |
| Abstract: | Abstract This study develops and tests an empirical model for quantifying Indigenous Knowledge within firms. Indigenous Knowledge is conceptualized as the cognitive and educational capacity embedded in an organization’s workforce—an intangible asset that has garnered increasing scholarly interest. The paper contributes to this discourse by proposing a measurable framework and examining its determinants. The stock of indigenous knowledge is operationalized through a weighted average of cumulative years of education, normalized by the number of employees with comparable qualifications. Using multiple regression analysis, the study evaluates the impact of training practices, organizational structures, and performance indicators on the average stock of indigenous knowledge across firms in the finance and insurance sectors. Findings reveal that access to formal documents by heads of departments, the nurturing of innovative thinking, and the implementation of effective—particularly standardized—training programs significantly enhance indigenous knowledge. Equally, an exclusive focus on productivity is associated with a reduction in knowledge stock. However, a significant interaction between productivity and incremental sales revenue suggests that the negative effects of productivity can be offset when accompanied by improved commercial performance. The study has both theoretical and practical implications. It advances the empirical measurement of indigenous knowledge in organizational contexts and provides actionable guidance for firms. Key recommendations include improving access to formal knowledge systems, integrating innovation into operational practices, designing standardized and effective training programs, and monitoring the interaction between organizational culture and structural mechanisms. Keywords: Indigenous Knowledge Knowledge Quantification Standardized Training Human Capital Firm Performance Knowledge Management |
| Date: | 2025–08–04 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:ex59m_v1 |