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.
Virtual communities function as large-scale knowledge interaction systems in which users jointly produce, exchange, and validate knowledge resources. However, not all interactions contribute positively to system performance. This study examined how different forms of value co-destruction behavior degrade knowledge interaction processes and user-level value outcomes in virtual communities. Drawing on survey data from 530 users of firm-hosted virtual communities, a structural equation modeling approach was employed to analyze the effects of five negative interaction behaviors—irresponsible behavior, knowledge hiding, avoidance, conflict, and negative information interaction—on three dimensions of user value: practical, entertainment, and social value. The results indicate that avoidance, conflict, and negative information interaction significantly reduce practical value by impairing knowledge accessibility and information reliability. Knowledge hiding, avoidance, and conflict significantly reduce entertainment and social value by weakening interaction quality and relational embeddedness. Interestingly, irresponsible behavior increases individual entertainment and social value while simultaneously posing systemic risks to collective knowledge quality. These findings suggest that value co-destruction is not merely a behavioral problem but a systemic phenomenon that degrades knowledge flow efficiency, information quality, and collaborative stability in digital knowledge ecosystems. The study contributes to knowledge engineering research by identifying key failure mechanisms in knowledge interaction systems and offers governance implications for designing resilient and sustainable online knowledge platforms.
In the context of technological development and digitalization of agriculture, mobile applications are playing an increasingly essential role in the management of small farms located in countries with fragmented agricultural structures. The aim of this research is to evaluate the most widely adopted mobile applications for monitoring and managing agricultural activities in areas with high agricultural potential such as Myzeqe, Korça, and Saranda in Albania. In order to achieve an impartial and sustainable assessment, multi-criteria decision-making (MCDM) methods integrated with fuzzy logic helped address the uncertainties and subjectivity in the evaluation process. The fuzzy CRiteria Importance through Intercriteria Correlation (CRITIC) method was employed to objectively determine the weights of the criteria based on the variability and contradiction between them. The fuzzy Combined Compromise Solution (CoCoSo) method was then adopted to rank the mobile applications. As revealed from the findings in this study, the most highly-rated criteria by experts, i.e., criterion C1-Ease of use and criterion C6-Integration with other technologies had the highest weight. The least rated criterion by experts was criterion C7-Technical support and training. AgriApp (A7) was the mobile application identified with the best performance. The contribution of this research lied in the building of a structured and objective framework to evaluate mobile technologies applied in agriculture, thus enabling more informed decisions for their adoption at the local and regional level.
The extent to which emotional perception shapes the acquisition, analysis, and presentation of knowledge within human–machine communicative interaction remains insufficiently understood. In this study, the principles of emotion artificial intelligentce (AI) (also referred to as affective computing) were integrated with trust as a socio-technical construct to investigate the mediating role of emotional expression in cognitive processing. A mixed-methods design was adopted, drawing on structured questionnaires and open-ended responses collected from 50 participants over a five-year period. Statistical modelling revealed that system quality significantly enhanced perceived ease of use when emotional signals were effectively encoded and decoded by both humans and machines. Trust was found to exert a positive influence on perceived usefulness, credibility, and user satisfaction, although it did not directly predict behavioural intention. In contrast, perceived ease of use demonstrated a strong positive association with intention in emotion-driven contexts, thereby rendering human–machine interaction more engaging, reliable, and trustworthy. These findings indicate that the tension between emotional and rational dimensions of higher cognitive processes within knowledge systems is shaped less by individual reluctance than by systemic and institutional determinants. The contribution of this work lies in the development of a conceptual framework for emotion-aware knowledge presentation, offering design implications for intelligent systems in education, public administration, business applications, and conversational AI. By demonstrating how emotion-aware mechanisms enhance both cognitive efficiency and affective engagement, the study advances understanding of human–machine cooperation and provides actionable guidance for the construction of more adaptive and trustworthy knowledge systems.
The macroeconomic performance of nations provides valuable insights into the knowledge economy and the governance structures that sustain its development. This study formalizes a framework for evaluating knowledge flows and innovation capacity through multi-criteria decision analysis (MCDA) using open World Bank data. The analysis employs the Logarithmic Decomposition of Criteria Importance (LODECI) method in conjunction with the Preference Selection Index (PSI) to determine objective weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) is applied to rank the G7 countries and Türkiye in 2023. Knowledge flows, as represented by exports and foreign direct investment (FDI), serve as proxies for cross-border knowledge exchange, while inflation, unemployment, and economic growth are assessed within a reproducible, policy-driven framework. The weighting procedure assigns the greatest aggregate importance to inflation and the least to unemployment. The resulting rankings place the United States first, followed by Japan in second place, Türkiye fourth, and the United Kingdom last. The analysis further highlights how factors such as price stability, external openness, and investment dynamics shape national knowledge creation, diffusion, and organizational learning processes. By focusing on the utilization of open data, explicit knowledge representation, and transparent multi-criteria methodologies, the proposed framework strengthens digital knowledge infrastructures and facilitates actionable cross-country benchmarking. The findings have important policy implications, particularly in understanding how national macroeconomic variables influence innovation capacity. The framework is designed to be extensible, allowing for future adaptation to evaluate additional indicators, such as R&D intensity, high-tech export shares, and patenting activity. Furthermore, the approach is structured to support replication across various regions and timeframes, ensuring its broad applicability and scalability.
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