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.

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.

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.