Organizational learning depends on how knowledge is acquired, shared, and integrated across organizational units, yet the allocation of learning capabilities often overlooks differences in how employees interpret information and develop shared understanding. This study develops a mathematical decision-support model for allocating organizational learning capability (OLC) dimensions across organizational units by incorporating sensemaking into resource allocation decisions. Drawing on organizational learning and sensemaking theories, a binary optimization model was formulated to maximize the weighted contribution of selected learning capability items under unit-level budget limits, an organization-wide budget constraint, and minimum learning capability requirements. Sensemaking was incorporated through a unit-specific effectiveness coefficient and a minimum capacity constraint representing collective interpretation and shared understanding. A structured scoring procedure was specified to estimate the effectiveness coefficients, and the model was solved using LINGO (a commercial optimization software package) for a hypothetical organization comprising five units. Sensitivity analyses were conducted by varying sensemaking coefficients, minimum capacity requirements, and the total budget. The computational results produced different allocations of learning capability dimensions across the five units. Changes in sensemaking coefficients altered selected allocations, particularly in Unit 2, while tighter minimum sensemaking requirements affected Unit 4. Budget reductions also changed the selected dimensions, whereas budget increases left the allocation unchanged under the examined conditions. These findings indicate that incorporating sensemaking into learning capability allocation can account for differences in organizational contexts and identify how resource constraints shape knowledge management priorities. The model provides a structured basis for comparing unit-specific learning investments and examining alternative allocation decisions, although its effectiveness in organizational practice requires empirical validation.
Artificial intelligence (AI) and automation are reshaping skill requirements in the knowledge economy, placing increasing pressure on organizations to reconsider how employees acquire and update their competencies. Although research on AI-related workforce development continues to expand, the relationships among technological skills, organizational learning, and sector-specific upskilling strategies remain insufficiently integrated across disciplines. This study examines recent research trends in workforce upskilling and identifies the principal competencies and organizational responses associated with AI-driven transformation. A systematic literature review was conducted using EBSCO Discovery Service (a cross-database discovery platform), covering peer-reviewed publications from January 2022 to December 2025. Publication distributions, keyword frequencies, and recurring research themes were examined to characterize the development of the field. The analysis identified five interconnected areas: foundational digital literacy, AI-specific technical competencies, human-centered skills, organizational learning systems, and sector-specific adaptation strategies. The reviewed literature also showed a growing emphasis on continuous learning, AI-supported training, and the integration of skill development into organizational practices, although approaches differed across industries and institutional contexts. These findings indicate that workforce adaptation to AI depends on the interaction between individual competencies and organizational learning arrangements rather than technical training alone. By bringing together research from management, education, economics, and related fields, this review provides an integrated account of upskilling priorities and identifies directions for research on knowledge development, organizational adaptation, and workforce transformation.
Digital watermarking plays an important role in protecting digital content, verifying ownership, and supporting the trustworthy distribution of digital information. However, maintaining watermark robustness without compromising visual quality remains a challenge, particularly when embedding strength is selected through empirical adjustment. This study investigates an adaptive wavelet-domain watermarking method that uses genetic algorithm (GA) optimization to determine embedding energy for digital image protection. The watermark was encrypted using the Data Encryption Standard (DES) and embedded into selected wavelet subbands. A GA was employed to adjust the embedding strength according to an image-distortion criterion, while watermark quality and extraction performance were evaluated using relative error (RE) and other image-quality measures. The proposed method was tested against cropping, rotation, scaling, Joint Photographic Experts Group (JPEG) compression, filtering, Gaussian noise, and other distortions, and its performance was compared with manually selected embedding energy and a least significant bit (LSB) method. The results showed that the GA-based method achieved watermark extraction rates of 87.2% after 25% cropping, 93.4% after 15$^{\circ}$ rotation, and 96.3% under JPEG compression at a quality factor of 50. Under these conditions, the corresponding extraction rates for manually selected embedding energy were 83.1%, 89.2%, and 91.8%, respectively. The proposed method also maintained low visual distortion in the reported imperceptibility test. These findings indicate that GA-based parameter selection is a feasible alternative to manual adjustment in wavelet-domain watermarking. The method provides a technical approach to improving the resilience of embedded ownership information against common image-processing operations, with potential applications in digital content protection and copyright verification.
University-Industry knowledge transfer plays a central role in converting academic research into technological and organizational innovation, but its success remains uneven in developing economies. This study investigates the factors associated with successful university-to-industry knowledge transfer in Iran and develops an empirical model for assessing such initiatives. Six explanatory constructs—Knowledge Destination, Knowledge Source, Knowledge Characteristics, University-Industry Distance, Transfer Mechanisms, and Government Dimension—were identified through a literature review and expert interviews. Survey data were then collected from 88 experts with experience in both academic and industrial settings and analyzed using partial least squares structural equation modeling (PLS-SEM). The results showed that Knowledge Characteristics, University-Industry Distance, Transfer Mechanisms, and Knowledge Destination had statistically significant positive relationships with Knowledge Transfer Success. In contrast, Knowledge Source and Government Dimension did not show statistically significant relationships with the outcome construct. The model explained 67% of the variance in Knowledge Transfer Success and demonstrated predictive relevance ($Q^2$ = 0.36). These findings indicate that successful knowledge transfer depends primarily on the nature and applicability of the knowledge, the distance between the participating organizations, the mechanisms used for transfer, and the receiving industry’s capacity and readiness. The proposed framework provides an empirically tested basis for diagnosing weaknesses in University-Industry knowledge transfer projects and supports more focused decisions by university administrators, industry managers, and innovation policymakers.
Stakeholder engagement plays an important role in sustainable tourism development by supporting inclusive governance, information sharing, and collaborative learning among tourism actors. However, limited empirical evidence exists regarding how stakeholder interaction, knowledge exchange, and participation mechanisms influence sustainable tourism outcomes within local governance systems in developing countries. This study investigates stakeholder engagement as a driver of sustainable tourism development in Ghana, with particular attention to the roles of digital participation and local governance structures within Metropolitan, Municipal, and District Assemblies (MMDAs). A quantitative descriptive research design was adopted, and structured questionnaires were administered to 365 respondents across six tourism-hosting communities within the Ahanta West Municipal Assembly using stratified and convenience sampling approaches. The collected data were analysed using descriptive statistics and one-way analysis of variance (ANOVA). The results showed that respondents regarded stakeholder engagement, digital participation mechanisms, and collaboration with cultural institutions as important elements of sustainable tourism development. The findings also showed significant differences across age groups and length of residence, indicating uneven experiences of participation and information access among stakeholders. Digital engagement tools received the highest level of support, suggesting growing interest in technology-supported participation mechanisms within tourism governance. The findings indicate that stakeholder engagement contributes to sustainable tourism development when participation structures support meaningful interaction, knowledge exchange, and equitable involvement among community actors. This study provides empirical evidence from local governance settings in Ghana and contributes to tourism governance literature by linking stakeholder engagement with knowledge-sharing practices and digital participation mechanisms, while offering practical guidance for the development of more inclusive and context-sensitive tourism governance systems.
Transaction data increasingly serves as a strategic knowledge source in data-driven decision support systems. However, many organizations still use transaction records primarily for operational purposes and do not systematically transform them into actionable knowledge that supports managerial decision-making. This study aims to investigate a knowledge discovery framework based on the Cross-Industry Standard Process for Data Mining (CRISP-DM) and the Frequent Pattern Growth (FP-Growth) algorithm for extracting transactional knowledge and supporting intelligent decision-making. A dataset containing 978 sales transactions and 157 active products was analyzed through six CRISP-DM stages, including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The FP-Growth algorithm was implemented to identify frequent itemsets and generate association rules. Twenty-one experimental scenarios involving different support and lift thresholds were conducted to determine an appropriate parameter configuration. The results showed that a minimum support value of 0.06 combined with a lift value of 2 produced a balanced set of association rules with strong business relevance. Stable purchasing patterns and contextual association patterns were identified. The extracted rules were integrated into a management information system to support product bundling and cross-selling functions. The results indicate that FP-Growth-based frequent pattern mining can transform transaction records into operational transactional knowledge with practical business value. This study demonstrates that integrating a complete CRISP-DM process with knowledge extraction and system deployment provides an effective pathway for developing data-driven marketing intelligence and decision-support mechanisms. The proposed framework provides a practical foundation for intelligent marketing systems and offers insights into knowledge discovery applications in transactional environments.
Knowledge interoperability across heterogeneous systems poses a fundamental challenge to the predominantly knowledge-driven world, where semantic consistency directly affects data integration and knowledge exchange. Ontology alignment plays a pivotal role in facilitating semantic interoperability by identifying meaningful correspondences among concepts exemplified in different ontologies. This study investigated a probabilistic framework for ontology correspondence discovery using Hidden Markov Models (HMMs). In the proposed approach, each ontology was transformed into an HMM through Resource Description Framework (RDF) triple extraction using SPARQL (an RDF query language standardized by W3C) queries, where concepts were represented as hidden states and relationships as observation symbols. Both transition probability matrices and observation probability matrices were computed to characterize structural and semantic information embedded within the ontology. Concept vectors were then generated and compared by cosine similarity to identify potential correspondences among ontology concepts. Experimental evaluation was conducted using benchmark datasets from the Ontology Alignment Evaluation Initiative (OAEI). The results revealed that the proposed HMM-based framework achieved high precision and satisfactory recall, particularly for ontologies containing intricate relational structures. In addition, probabilistic modeling could effectively accommodate semantic variability and incomplete annotations without relying heavily on lexical resources or complex structural heuristics. The framework in this paper provides a superior mechanism for supporting semantic interoperability and contributes to knowledge integration across heterogeneous information.
Complex safety-critical systems are often characterised by heterogeneous knowledge representations, conflicting information sources, and inconsistent evaluation outcomes. To address these challenges, a knowledge-driven evaluation framework grounded in Extenics is developed. The framework begins with a conjugate analytical decomposition that restructures safety-related factors—personnel, equipment, environment, and management—into formally defined and comparable knowledge units, organised into a hierarchical structure comprising four primary dimensions and sixteen subordinate attributes. On this basis, matter-element models, together with classical and joint knowledge domains, are established to enable unified knowledge representation. A correlation-based reasoning mechanism is then constructed to quantify the degree of association between knowledge attributes and predefined safety states, allowing systematic treatment of contradictory and dynamically evolving information. The evaluation process is completed through weighted aggregation and the computation of extension superiority, yielding an integrated classification of system safety conditions. Application to a coal mine case using third-quarter operational data from 2024 demonstrates that the proposed framework can consistently identify overall safety levels and reveal structurally weak knowledge components. The study provides a generalisable and interpretable approach to knowledge modelling, conflict resolution, and differentiated risk governance in complex systems.
Entity–relation extraction constitutes a fundamental step in the construction of domain-specific knowledge graphs. In fault analysis of transmission systems, this task is complicated by extensive entity–relation overlap, nested structures, and strong semantic dependencies in technical texts. To address these challenges, an entity–relation joint extraction framework integrating reinforcement learning with a global pointer network (GPN) is developed (joint extraction model based on GPN and reinforcement learning, RL-BGPNet). A fault-oriented dataset is first established from helicopter transmission system maintenance manuals and related technical documents. Global semantic associations are then captured through a relation-aware attention mechanism, while parallel decoding is achieved using a GPN to accommodate overlapping and nested entities. The extraction of entity–relation triplets is further formulated as a multi-step decision process under a reinforcement learning paradigm, enabling coordinated optimization of entity recognition and relation classification and alleviating error accumulation caused by task interference. Experimental evaluations demonstrate that the proposed framework maintains stable performance under complex semantic conditions and exhibits satisfactory generalization, supporting its application to knowledge extraction and preliminary knowledge graph construction in the helicopter transmission system fault domain.
The strategic siting of a military airport constitutes a high-stakes planning problem characterized by complex trade-offs, long-term operational consequences, and pronounced uncertainty in expert judgment. In contrast to civilian airport planning, where economic efficiency and environmental externalities are typically prioritized, military airport location decisions are governed by additional requirements related to operational security, survivability, logistical resilience, and future capacity expansion. To address these challenges, a hybrid Multi-Criteria Decision-Making (MCDM) framework is proposed for the systematic evaluation and selection of military airport locations under uncertainty. Six core criteria and their associated sub-criteria, reflecting operational, strategic, technical, and infrastructural considerations, were identified through expert consultation and domain analysis. Criteria weights were derived using the Defining Interrelationships Between Ranked Criteria II (DIBR II) method and its Fuzzy, Grey, and Rough extensions, enabling the explicit modelling of vagueness, incompleteness, and ambiguity inherent in subjective assessments. Expert evaluations were aggregated using the Einstein Weighted Arithmetic Average (EWAA) operator, which accommodates heterogeneous levels of expertise and mitigates dominance bias. Alternative locations were subsequently ranked using the Weighted Aggregated Sum Product Assessment (WASPAS) method, allowing for flexible integration of additive and multiplicative aggregation schemes. The robustness of the obtained rankings was examined through a sensitivity analysis of the WASPAS aggregation parameter $\lambda$, confirming that variations in the aggregation structure do not alter the identification of the optimal and least-preferred alternatives. Furthermore, a comparative analysis with five established MCDM techniques revealed a high degree of rank correlation, thereby reinforcing the internal consistency and reliability of the proposed framework. The results demonstrate that the integration of uncertainty theories with advanced MCDM techniques provides a rigorous and adaptable decision-support tool for military infrastructure planning. Owing to its modular structure and methodological generality, the proposed framework can be readily adapted to diverse geographical settings, operational doctrines, and security environments, offering practical value for strategic decision-making in the defense sector.
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