Koforidua Technical University

PurposeCreativity fosters innovativeness, competitiveness and sustained success. This study aims to evaluate the mediating role of employee creativity in the nexus of human resource management (HRM) practice and organizational ambidexterity. Design/methodology/approachData were collected from 973 employees of 75 health care institutions comprising 48 (indigenously-owned) and 27 (internationally-owned). The positivist paradigm was adopted. Construct validity and reliability were established using confirmatory factor analysis. The study hypotheses and proposed model were evaluated using structural equation modeling FindingsThe study’s results show that ability-enhancing practices and organizational ambidexterity were positively related. Motivation-enhancing practices and organizational ambidexterity were positively related. Opportunity-enhancing practices and organizational ambidexterity were nonsignificantly related. Employee creativity mediates the ability-enhancing practices and organizational ambidexterity relationship. Similarly, employee creativity mediates the motivation-enhancing practices and organizational ambidexterity relationship. Nonetheless, employee creativity did not mediate the opportunity-enhancing practices and organizational ambidexterity relationship. Research limitations/implicationsThe generalizability of the findings will be constrained due to the research’s health care focus and cross-sectional data. Practical implicationsThe findings of the study would provide valuable insight to stakeholder’s, policy makers and management of health care institutions in developing a supportive work environment that promote the generation of originality and novelty in ideas to ensure institutional competitiveness, competencies and innovativeness. Originality/valueBy evidencing empirically that employee creativity mediates the ability-enhancing practices, motivation-enhancing practices and organizational ambidexterity relationship, the study extends the literature on employee creativity, HRM practices and organizational ambidexterity.

Abstract This study presents a systematic review of artificial intelligence applications, particularly machine learning and deep learning models, for okra leaf disease detection and diagnosis. Due to the limited number of okra-specific studies, a related study on leaf diseases of other crops was included for comparative analysis. Following the PRISMA framework, 28 peer-reviewed articles published between 2015 and 2025 were examined. The results show that convolutional neural networks dominate the current study, accounting for nearly 70% of all reviewed models. Architectures such as MobileNet, ResNet, and InceptionV3 consistently achieved accuracies above 90%. Okra-focused studies reported performance ranging from 87% to 98.63% (mean = 94.5%) but were constrained by small or imbalanced datasets. In contrast, studies on other crops achieved accuracies between 77% and 99.84% (mean = 96%), supported by substantially larger datasets. The review further identifies limited adoption of explainable AI, vision transformers, and federated learning approaches. Key study gaps include dataset scale, environmental integration, real-world validation, and reproducibility. The study recommends developing large-scale okra-specific datasets, integrating agronomic variables, deploying lightweight, mobile-ready architectures, and strengthening the adoption of explainable and federated learning frameworks to enable scalable, field-deployable diagnostic systems.

Entrepreneurship education (EE) is a key economic driver aimed at stimulating economic growth, technology, innovation and business in Ghana. However, its impact on Business Startup Initiatives (BSIs) remains inadequately explored within Ghanaian Technical Universities (GTUs). In this paper, we investigated the influence of EE and Entrepreneurship courses (EC) on BSIs among business students in GTUs using the social cognitive career theory (SCCT). A survey method was used to collect data from a random sample of 320 business students in the TUs. The PLS-SEM path analysis on EE and EC had a significant positive influence on BSIs in idea generation, business plans, career choices, goals, and innovation. This study establishes the importance of EE and EC in enhancing BSIs among TU business students. This research encourages the TU management to focus on EE and the EC approaches in initiating startups. The SCCT used contributed significantly to the BSI dimensions.

IntroductionThe integration of artificial intelligence (AI) in the construction industry holds immense potential to enhance productivity, improve decision-making, and increase operational efficiency. However, in many resource-constrained environments, the adoption of AI technologies remains slow owing to infrastructural limitations, skills deficits, regulatory uncertainties, and institutional challenges. This study investigates strategic approaches to AI adoption within the construction industry of resource-constrained environments, using Ghana as a representative case.MethodsA quantitative research design was employed, involving 239 responses from construction professionals. The data was analysed using exploratory factor analysis (EFA) to identify and validate the strategic approaches.ResultsThe analysis revealed a three-cluster structure comprising strategic collaboration and governance, operational integration and safety, and workforce development and technical infrastructure. These clusters offer a context-specific approach that captures the multifaceted requirements for AI adoption in resource-constrained construction environments.DiscussionRather than the application of EFA, the contribution of this study lies in the empirically validated framework it produces which clarifies how AI adoption strategies interrelate in the construction industry of Ghana. The study contributes to the growing literature on digital transformation in construction by offering practical, evidence-based guidance to industry leaders, policymakers, and researchers seeking to drive AI adoption in resource-constrained environments.

Climate change impacts have intensified globally, yet empirical evidence on climate risks affecting resilient building performance in Ghana remains limited. This study identifies and evaluates climate risks in Ghana's building sector using fuzzy synthetic evaluation (FSE) to establish a quantitative risk hierarchy for building-level decision-making. Primary data were collected through expert surveys and analyzed using descriptive statistics and FSE methodology, selected for its capacity to handle subjective assessments in contexts where historical climate data for buildings are scarce. The analysis revealed three primary risk categories with distinct criticality levels: Urban Thermal and Extreme Weather Risks (criticality index 4.15), Environmental and Atmospheric Stressors (4.07), and Secondary Climate Impact Risks (3.72). Urban Thermal and Extreme Weather Risks emerged as most critical, driven by urban heat island effects, extreme temperature fluctuations, and intensified storm events that directly compromise building envelope integrity and indoor thermal comfort. Environmental and Atmospheric Stressors including air quality deterioration, humidity extremes, and solar radiation intensification ranked second, affecting material durability and occupant health. Secondary Climate Impact Risks, while lower in direct severity, demonstrated substantial compounding effects through water scarcity, ecosystem degradation, and infrastructure interdependencies that amplify building system vulnerabilities. The findings provide construction stakeholders with an evidence-based risk hierarchy for prioritizing climate adaptation investments in tropical developing contexts. Practitioners can use these insights to devise targeted mitigation strategies aligned with Ghana's NDCs, SDG 11 (Sustainable Cities), and SDG 13 (Climate Action), while the FSE framework offers a replicable methodology for similar assessments across West African building sectors.