Improve Knowledge Transfer Research Articles

Over the last few decades, regenerative agriculture has emerged as a promising approach to redefine sustainability in agricultural practices. It aims to restore ecosystems, improve soil health and enhance biodiversity while maintaining agricultural productivity. This review analyses the primary social, economic and environmental motivations behind the adoption of regenerative agriculture. Additionally, it examines the roles various stakeholders play in either adopting, accelerating or hindering the implementation of regenerative agricultural practices. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 49 articles published between 2014 and 2025 were selected. The sustainability pillars (environmental, economic and social) were used to systematize the core findings. Key results indicate that environmental motivations are key drivers for adopting regenerative agriculture, though economic and social factors also play a role. Regenerative agriculture is identified as a significant contributor to sustainability; however, key challenges include the transition period, lack of incentives and inadequate regulations. These barriers are particularly challenging for farmers attempting to shift to regenerative practices. This review underscores the need for policy reforms, integration of regenerative agriculture into incentive and subsidy schemes and improved knowledge transfer to farmers about the long-term benefits of regenerative practices. Additionally, ensuring the replicability of regenerative agriculture models in countries where small- and medium-sized farming is predominant is crucial for enhancing the scalability of these practices.

The Farmer Field School (FFS) is a participatory extension approach widely implemented in developing countries and recognised as an effective method for disseminating and adopting agricultural technologies. However, few studies have evaluated the specific contribution of the FFS Farmer Field Day (FFD) component to vegetable production technologies adoption in Homa Bay Town Sub-County, Kenya. This study assessed the effect of the FFS Farmer Field Day on farmers’ adoption of vegetable production technologies. An ex-post-facto research design was used to analyse the influence of FFS Farmer Field Day participation on technology adoption. Primary data were collected from 120 vegetable farmers belonging to 10 FFS groups, while 10 extension officers served as the key informants. Stratified random sampling was applied, and data were analysed using the Statistical Package for Social Sciences (SPSS) Version 25. Descriptive statistics (frequencies and percentages) were used to determine the relationship between FFS Farmer Field Day participation and the adoption of selected vegetable production technologies. The findings revealed that FFS Farmer Field Day participation significantly enhanced the adoption of nursery establishment (97.5%), transplanting (86.8%), compost manure (85.1%), mulching (75.2%), and farmyard manure application (70.2%). The study concludes that the FFS Farmer Field Day is a crucial platform for promoting technology adoption among smallholder vegetable farmers. It recommends that the Ministry of Agriculture and Livestock Development and development partners integrate regular FFS Farmer Field Days into agricultural extension programs to improve knowledge transfer and uptake of sustainable production technologies

Reinforcement Learning (RL) has been considered a promising method to enable the automation of contact-rich manipulation tasks, which can increase capabilities for industrial automation. RL facilitates autonomous agents’ learning to solve environments with complex dynamics with little human intervention, making it easier to implement control strategies for contact-rich tasks compared to traditional control approaches. Further, RL-based robotic control has the potential to transfer policies between task variations, significantly improving scalability compared to existing methods. However, RL is currently inviable for wider adoption due to its relatively high implementation costs and safety issues, so current research has been focused on addressing these issues. This paper comprehensively reviewed recently developed techniques to improve cost and safety for RL in contact-rich robotic manipulation. Techniques were organized by their approach, and their impact was analysed. It was found that current research efforts have significantly improved the cost and safety of RL-based control for contact-rich tasks, but further improvements can be made by progressing research towards improving knowledge transfer between tasks, improving inter-robot policy transfer and facilitating real-world and continual RL. The identified directions for further research set the stage for future developments in more versatile and cost-effective RL-based control for contact-rich robotic manipulation in future industrial automation applications.

Abstract Purpose of Review Developmental Coordination Disorder (DCD/Dyspraxia) is a frequently overlooked and misunderstood neurodevelopmental disorder. Numerous studies have sought to index the rates of awareness of DCD and highlight a broad-spanning need for increased awareness to foster timely intervention and support for children with DCD. Therefore, we provide a semi-systematic review of studies that capture DCD awareness as well as a detailed discussion of pitfalls, successes, and future directions in raising awareness about DCD. Recent Findings Consistent with past research, recent evidence shows a broad-spanning lack of awareness among educators, parents and caregivers, clinicians and healthcare professionals. We provide an overview of these results and detailed discussion on the pitfalls, ongoing efforts, and productive future directions for raising DCD awareness. Summary Previous and recent efforts to promote DCD awareness have not yet reached their goal. Initiatives to improve knowledge transfer, training, resources, and research surrounding DCD must be continued in parallel with added efforts to connect to other fields, bridge resources and movements, and improve policy and funding opportunities in the field of DCD.

The modern engineering workplace is undergoing a quiet but profound transformation. For the first time in history, four distinct generations—Baby Boomers, Generation X, Millennials, and Generation Z—are working together on complex engineering projects, often under tight deadlines and high technological pressure. Each generation brings unique strengths to the table: Boomers offer deep institutional knowledge, Gen Xers bring structured problem-solving, Millennials contribute collaborative agility, and Gen Z thrives on digital innovation. However, These strengths are often accompanied by misalignments in communication preferences, leadership expectations, and attitudes toward technology and workflow management. This research explores how engineering project managers can successfully navigate and leverage these generational differences to enhance project outcomes. The study focuses on identifying specific project management strategies that foster collaboration, reduce friction, and improve overall team performance in multigenerational engineering environments. Through a comprehensive literature review, coupled with data from surveys and interviews with engineering professionals across age groups, the research uncovers common patterns of intergenerational conflict, communication breakdowns, and mismatched work styles. The findings reveal that flexible leadership approaches—particularly those that adapt to generational needs—can significantly improve knowledge transfer, communication efficiency, and team cohesion. Strategies such as establishing cross-generational mentoring programs, using mixed-mode communication protocols, and tailoring feedback styles are found to be particularly effective. Furthermore, project performance metrics—such as on-time delivery, task ownership, and innovation quality— improve in teams that embrace rather than suppress generational diversity

Purpose The assimilation of external knowledge by gatekeepers plays a pivotal role in rejuvenating corporate knowledge, thus fostering organizational success. This study aims to delve into the function of gatekeepers in disseminating knowledge and enhancing knowledge transfer performance through a motivational lens. It investigates whether gatekeepers inherently disseminate knowledge or if certain motivations drive their actions. Particularly, this research centers on the influence of autonomous motivation (intrinsic acceptance) and controlled motivation (external pressure) on knowledge transfer performance, asserting that understanding both motivations is crucial for grasping the intricacies of the behavior-performance nexus. Design/methodology/approach A structured questionnaire survey was used to select a total of 24 firms of three different sizes, large, medium and small, in the provinces of Hunan, Guangdong, Shandong and Zhejiang, China, to conduct a survey of knowledge gatekeepers, and 321 valid questionnaires were finally obtained. The study analyzed the proposed hypotheses through structural equation modeling. Findings The controlled motivation of gatekeepers negatively affects knowledge transfer performance, while autonomous motivation has a positive impact. Additionally, this study unveils that work effort serves as a mediator in the correlation between motivation for knowledge dissemination and knowledge transfer performance. Furthermore, the capability for knowledge transfer positively moderates the link between work effort and knowledge transfer performance. Remarkably, the mediating effect of work effort becomes more pronounced when gatekeepers possess higher knowledge transfer capability. Originality/value This research contributes to the knowledge gatekeeping theory and the literature on knowledge transfer by examining the connection between knowledge gatekeepers’ motivation to disseminate knowledge and knowledge transfer performance. It challenges the conventional belief that gatekeepers consistently share knowledge and provides new perspectives on the mechanisms linking motivation and performance. By doing so, this study helps organizations boost employees’ job autonomy and improve knowledge transfer performance.

Purpose: The nutritional quality of the food supply is influenced by actions from the government, the food industry, and universities. Considering the potential of registered dietitians (RDs) to transfer nutrition knowledge, this study aimed to assess the positivity of the relationships between RDs and these actors to improve knowledge transfer initiatives for the benefit of food nutritional quality.Methods: Using a descriptive and correlation research design, 68 RDs, members of the Ordre des diététistes-nutritionnistes du Québec (dietitians in the Province of Québec, Canada), evaluated their relationships with the government, the food industry, and universities in a web-based survey.Results: Overall, RDs rated their relationships with universities more positively than with government or industry, which were similarly rated (p < 0.001). More specifically, RDs working in clinical nutrition rated all dimensions of their relationships with the actors in general less favourably than RDs in other sectors (p < 0.05).Conclusions: To support the successful transfer of knowledge regarding the nutritional quality of foods, these findings suggest that RDs should be encouraged to attempt to develop more positive relationships with government and industry, while maintaining their positive relationships with universities. Future research could further examine the clinical RD subgroup as well as the reasons for these results by conducting in-depth interviews or group discussions.

In the context of Continual Semantic Segmentation (CSS), replay-based methods tend to achieve better performance than knowledge distillation-based ones, as the former utilizes additional data to transfer old knowledge. However, this advantage is at the cost of necessitating additional space for storing the generative model and extra time for continual training. To address this predicament, we propose a novel CSS framework, namely Adversarial Attack-based Knowledge Retention (AAKR). The AKKR framework generates specific adversarial samples by adding images, and uses them to retain old knowledge. Specifically, we leverage adversarial attacks to generate adversarial images for incremental samples. By imposing additional constraints within these attacks, we enhance the transfer of old knowledge, thereby reinforcing the understanding of previously learned information. Furthermore, we design an attack probability module that adjusts adversarial attack directions based on training feedback. This module effectively encourages the new model to learn old knowledge from poorly protected classes, significantly improving knowledge transfer effectiveness. Our comprehensive experiments demonstrate the efficacy of AAKR, and showcase that AAKR surpasses state-of-the-art competitors on benchmark datasets.

Objectives/Goals: This literature review examines the current landscapes of plain language summaries (PLS), which are used to make research accessible to nonexpert audiences. It aims to identify gaps in their implementation by focusing on challenges related to consistency, accessibility, and quality across fields. Methods/Study Population: A systematic search was conducted using databases such as PubMed and Google Scholar, employing key search terms like “plain language summaries,” “scientific communication,” “health literacy,” “patient education,” “knowledge translation,” “accessibility in research,” “public engagement,” “lay-person,” and “lay summaries.” Literature from multiple sources (pharmaceutical companies/industry, nonprofit organizations, private–public partnerships, and government) was compared to assess the gaps in current PLS best practices. Results/Anticipated Results: Search results yielded 95 articles. Of those, 37 articles fit the criteria, highlighting critical gaps in PLS implementation for clinical research. Preliminary findings suggest a lack of standards and guidelines, as well as a need for more research on the effectiveness of PLS for improving knowledge transfer and patient engagement. Key limitations were identified for investigator-initiated trials (IITs). A best practice table, comparing recommendations from each group of sources, was developed with suggestions for writing effective PLS. While there is consensus on some principles (i.e., importance of simplicity), differences emerge regarding optimal length and the use of layperson glossaries and graphics. The table aims to serve as a guide for creating effective and standardized PLS across fields. Discussion/Significance of Impact: There are limited PLS best practice resources tailored for IITs. These findings could lead to more practical tools and a streamlined approach to enhance communication strategies for lay audiences. This would benefit trial participants and community members who rely on this information and bridge the gap between scientific communities and the public.

The research intention would be to identify critical gamification elements that most effectively improve knowledge transfer in modern academia. Data was pooled from 587 students pursuing MBA degrees in the District of Erode, Tamil Nadu, India. The researcher used the exploratory factor analysis technique to comprehend the core association of the eighteen recognized variables. Based on the findings that these eighteen variables are categorized into four distinct areas, the researcher then labeled as per the ability to summarise the fundamental aspects of motivation and engagement, critical thinking and real-world application, knowledge retention, and performance, interest, and focus. Moreover, cronbach’s alpha was inclusively used to assess the variables’ reliability. Finally, the study concluded that this methodology is highly beneficial in knowledge retention, enhances participant curiosity to explore, and improves connection with the course material.

This study explores transdisciplinary knowledge creation through communities of practice (CoPs) to enhance boundary crossings. Research on CoPs is yet to focus on people with no concern or interest in CoPs, who are ignored as “outsiders.” However, these individuals could be candidates for the future and involving them and using their knowledge would be effective for solving complex social problems. We review transdisciplinary research and argue for the usefulness of building transdisciplinary communities of practice (TDCoPs) to enhance boundary crossings and involve disinterested people. Furthermore, transformative boundary objects (TBOs) are effective in promoting learning that alters stereotypes and prejudices (transformative learning). We conducted case studies focusing on “municipal meister” institutions in Japan. We searched for 22 municipalities in which such institutions existed and selected four successful municipalities to build case studies. All the cases built CoPs to improve knowledge transfer and attempted to involve local residents and craftspeople in their events. We also discuss the identification of successful points in building TDCoPs, how to develop CoPs for TDCoPs, and how to use TBOs. This study contributes to the theory of CoPs and boundary objects, and suggests practical implications for learning and other purposes, such as the grand challenge.

The objective of this systematic review and meta-analysis is to assess and synthesize the global evidence on the level of nurses' knowledge and its determinants regarding the prevention of surgical site infections. This systematic review and meta-analysis were conducted following strict methodological guidelines to ensure accuracy and reliability. Adhering to the 2020 PRISMA checklist, a systematic review and meta-analysis sought to establish the pooled proportion of nurse's knowledge and its determinants regarding surgical site infection prevention globally. MeSH terms and keywords were included in the search. Data extraction, quality assessment, and analysis followed established protocols. Heterogeneity and publication bias was assessed using STATA version 17.0. A total of seventeen observational studies, with sample sizes ranging from 30 to 515 participants, were included in the final analysis in a global context. In this systematic review and meta-analysis, the pooled proportion of nurses with good knowledge of surgical site infection prevention is 62% (95% CI: 50-74%) when assessed using a dichotomous scale. However, when knowledge is measured using a three-point Likert scale, the pooled proportion of those with good knowledge drops to 46% (95% CI: 21-72%), with an additional 27% (95% CI: 16-38%) demonstrating fair or moderate knowledge. This systematic review and meta-analysis is the first to synthesize data on nurses' knowledge of surgical site infection (SSI) prevention. The findings reveal poor knowledge levels, highlighting the need for targeted educational interventions globally. While the pooled odds ratio is not statistically significant, training, longer service years, and higher education improve SSI prevention knowledge by enhancing critical thinking, boosting confidence, and fostering adherence to evidence-based practices. Future research should focus on identifying factors influencing nurses' knowledge, particularly through longitudinal and interventional studies. Policymakers should incorporate international guidelines such as those recommended by the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) into nursing curricula, supported by robust assessment tools and educator training, to improve knowledge transfer and implementation of best practices.

Abstract Since 2017, Digital Twins (DTs) have gained prominence in academic research, with researchers actively conceptualising, prototyping, and implementing DT applications across disciplines. The transformative potential of DTs has also attracted significant private sector investment, leading to substantial advancements in their development. However, their adoption in politics and public administration remains limited. While governments fund extensive DT research, their application in governance is often seen as a long-term prospect rather than an immediate priority, hindering their integration into decision-making and policy implementation. This study bridges the gap between theoretical discussions and practical adoption of DTs in governance. Using the Technology Readiness Level (TRL) and Technology Acceptance Model (TAM) frameworks, we analyse key barriers to adoption, including technological immaturity, limited institutional readiness, and scepticism regarding practical utility. Our research combines a systematic literature review of DT use cases with a case study of Germany, a country characterised by its federal governance structure, strict data privacy regulations, and strong digital innovation agenda. Our findings show that while DTs are widely conceptualised and prototyped in research, their use in governance remains scarce, particularly within federal ministries. Institutional inertia, data privacy concerns, and fragmented governance structures further constrain adoption. We conclude by emphasising the need for targeted pilot projects, clearer governance frameworks, and improved knowledge transfer to integrate DTs into policy planning, crisis management, and data-driven decision-making.

Active Domain Adaptation (ADA) improves knowledge transfer efficiency from the labeled source domain to the unlabeled target domain by selecting a few target sample labels. However, most existing active sampling methods ignore the local uncertainty of neighbors in the target domain,making it easier to pick out anomalous samples that are detrimental to the model. To address this problem, we present a new approach to active domain adaptation called Local Uncertainty Energy Transfer (LUET), which integrates active learning of local uncertainty confusion and energy transfer alignment constraints into a unified framework. First, in the active learning module, the uncertainty difficult and representative samples from the target domain are selected through local uncertainty energy selection and entropy-weighted class confusion selection. And the active learning strategy based on local uncertainty energy will avoid selecting anomalous samples in the target domain. Second, for the discrimination issue caused by domain shift, we use a global and local energy-transfer alignment constraint module to eliminate the domain gap and improve accuracy. Finally, we used negative log-likelihood loss for supervised learning of source domains and query samples. With the introduction of sample-based energy metrics, the active learning strategy is more closely with the domain alignment. Experiments on multiple domain-adaptive datasets have demonstrated that our LUET can achieve outstanding results and outperform existing state-of-the-art approaches.

Partial domain adaptation (PDA) is a challenging task in real-world machine learning scenarios. It aims to transfer knowledge from a labeled source domain to a related unlabeled target domain, where the support set of the source label distribution subsumes the target one. Previous PDA works managed to correct the label distribution shift by weighting samples in the source domain. However, the simple reweighing technique cannot explore the latent structure and sufficiently use the labeled data, and then models are prone to over-fitting on the source domain. In this work, we propose a novel importance sampling-based shift correction (IS2C) method, where new labeled data are sampled from a built sampling domain, whose label distribution is supposed to be the same as the target domain, to characterize the latent structure and enhance the generalization ability of the model. We provide theoretical guarantees for IS2C by proving that the generalization error can be sufficiently dominated by IS2C. In particular, by implementing sampling with the mixture distribution, the extent of shift between source and sampling domains can be connected to generalization error, which provides an interpretable way to build IS2C. To improve knowledge transfer, an optimal transport-based independence criterion is proposed for conditional distribution alignment, where the computation of the criterion can be adjusted to reduce the complexity from $\mathcal {O}(n^{3})$ to $\mathcal {O}(n^{2})$ in realistic PDA scenarios. Extensive experiments on PDA benchmarks validate the theoretical results and demonstrate the effectiveness of our IS2C over existing methods.

With the advancement of science, technology, and innovation, the mechanism for transforming academic research into commercially viable solutions has become a key area. This study aims to explore how science, technology, and innovation can collaborate to promote the commercialization of academic achievements, and to reveal their roles and obstacles in this process. Through case analysis of stakeholders such as academia, business, and technology transfer offices, combined with semi-structured interviews and non participatory observations, this study delves into the main mechanisms of knowledge transfer, how innovation drives this process, and the challenges that exist. Research has found that knowledge transfer is achieved through three mechanisms: formal collaboration, informal networks and open innovation, as well as knowledge absorption and adaptation. In addition, technological innovation, especially artificial intelligence, data analysis, and blockchain technology, has become a catalyst for accelerating the transformation of academic research into commercial applications. However, the inconsistency of expectations between academia and business and intellectual property issues remain the main obstacles in knowledge transfer. This study provides a theoretical basis for cooperation between academia and industry, and offers practical suggestions for improving knowledge transfer mechanisms.

Electro-organic chemistry presents a promising frontier in drug discovery and early development, facilitating novel reactivity aligned with green chemistry principles. Despite this, electrochemistry is not widely used as a synthesis and manufacturing tool in drug discovery or development. This overview seeks to identify key areas that require additional research to make synthetic electrochemistry more accessible to chemists in drug discovery and early development and provide potential solutions. This includes expanding the reaction scope, simplifying rapid scale-up, developing electrode materials, and improving knowledge transfer to aid reproducibility and increase the awareness of electrochemistry. The integration of electro-organic synthesis into drug discovery and development holds the potential to enable efficient, sustainable routes toward future medicines faster than ever.

PurposeA growing body of literature analyzes eco-innovation in the wine industry, specifically focusing on the internal and external factors that contribute to sustainable innovation in wineries. The purpose of this study is to classify wineries according to their eco-innovation strategy and investigate variations in innovation groups concerning networking and socio-demographic characteristics.Design/methodology/approachThe authors use an original firm-level survey conducted between June 2022 and January 2023 to study eco-innovation practices of 234 Hungarian winemakers using four groups of indicators: marketing, organizational, process and product innovations. The authors apply principal component analysis and cluster analysis to identify eco-innovation dimensions and group wineries. The authors also investigate the relationship between eco-innovation strategies, determinants and firm-specific characteristics.FindingsThe estimations identify two innovation modes: with Cluster 1 (characterized by innovative wineries) surpassing Cluster 2 (comprising less innovative wineries) in all aspects. Marketing and process innovations were more widespread than product and organizational innovations. The results confirm the importance of firm and manager characteristics, corporate organization and size, age and education of management influence innovation activity.Research limitations/implicationsThe cross-sectional nature of the survey provides a snapshot of the wine industry. To identify changing trends, it is beneficial to conduct follow-up research and ongoing monitoring for a more comprehensive understanding of the dynamics of eco-innovation over time. The study offers valuable information about the Hungarian wine sector, but generalization of results to other geographic areas or wine-producing regions should be careful. The unique characteristics of particular regions might have a different effect on the dynamics of eco-innovation. Comparative studies across different wine regions or countries may reveal regional variations in eco-innovation strategies and the impact of local contexts.Practical implicationsThe study highlights the priority of human elements, such as the commitment of owners and managers to sustainability in driving force for innovation. The ability of businesses to absorb new knowledge and the exchange of information within organizations is crucial in promoting innovation. Considering the results of the survey, managers should proactively strive to improve knowledge transfer and information sharing within their organizations and establish cooperative alliances with universities, research institutes, suppliers, industry bodies and associations and global consultancy firms. Such knowledge-based cooperation can counterweight the innovative inertia of the micro and small enterprises representing the Hungarian wine sector.Social implicationsTo address the issue of “innovative inertia” faced by micro and small businesses in the wine sector, policymakers may implement focused supports, such as educational initiatives and grants, to augment their understanding and promote sustainable innovation. They should initiate intra- and inter-cluster connections and cooperations. Incentives, such as tax reduction or financial support, may stimulate wineries to introduce eco-innovation practices. Policymakers could also streamline the process of obtaining funding or grants for wineries interested in investing in cutting-edge solutions, such as renewable energy or advanced cultivation technologies to accelerate the adoption of sustainable practices.Originality/valueThis study contributes to the eco-innovation literature by providing insights into the drivers and practices of wineries in Hungary. The results emphasize the significance of networking, sharing of knowledge and firm/manager-specific characteristics in influencing eco-innovation in the wine industry.

ABSTRACT Given the high turnover of care workers in residential aged care facilities, continuous training on infection prevention and control (IPC) remains essential even after the COVID-19 pandemic. This study examined the impact of a Telegram-based training on aged care providers’ IPC knowledge, attitudes, self-efficacy. A two-arm, pre-post quasi-experimental study with repeated measures at week 2 (T1) and week 4 (T2) was conducted for seventy-two (72) participants from 14 residential aged care facilities in Malaysia, with 60 completing the study (30 per arm). All instructional activities and data collection for both groups were delivered through Telegram. The intervention group received a 2-day synchronous workshop (3 h/day) alongside e-materials for a 10-unit IPC module. Primary outcomes were measured using a knowledge test, COVID-19 Elderly Caregiver Questionnaire (attitude component), and an adapted General Self-Efficacy Scale. Telegram’s acceptability was also assessed using an adapted Technology Acceptance Model questionnaire. Results showed that the intervention group had a faster knowledge transfer (91.3%–93.5%; T0-T2 = ∆22.9; p ≤ .002;), higher knowledge scores (∆7.2–16; p ≤ .002; large effect sizes 0.9–1.7), and a small-to-modest increments in self-efficacy (∆13.2; p = .002) and attitude (∆5.2; p = .045). Integrating synchronous sessions into an online program improved knowledge transfer, enhanced self-efficacy and promoted positive attitudes by enabling real-time interpersonal communication and instant feedback. Telegram proved effective for mobile learning in aged care settings with limited resources.

Science and technology parks are major investments that are majorly focused on realizing faster economic growth and innovation. The parks carry great prospects for regional economic diversification. However, over the years, Science and technology parks have encountered challenges that call for proactive management, sound policies and innovativeness. This paper used the Drivers, Pressures, State, Impacts and Response (DPSIR) approach to determine how Africa can leverage science and technology parks to accelerate growth. The study used secondary data from books, scholarly journals and technical reports. After the published sources were collected, an analytical framework was developed with three categories including drivers, pressures and impacts. The results of the study indicate that the main drivers of science and technology parks development are government policy, academic research, urge by countries to keep pace with technology and public private partnerships. It was also established that the parks had impacts such as promoting economic development and sustainability in urban growth as well as improving knowledge transfer from universities to industry. Governments have responded by coming up with policies that support the establishment of these cities. The study recommends that governments and other stakeholders need to invest more in science and technology parks through policies and public private partnerships to accelerate growth in Africa.