Deep Collaboration Research Articles

Purpose This paper reviews whether China’s Guangdong-Hong Kong-Macao Greater Bay Area (GBA) can be developed into a sustainable innovation technology and finance hub through analysing the policy measures and actions taken by the governments to deal with the challenges in the plan implementation. Design/methodology/approach The study employs a qualitative framework based on policy document reviews, including the GBA Development Plan (2021-2035) and cross-border case studies of Hong Kong and Shenzhen. A comparative approach evaluates the GBA’s progress with those of global best practices in sustainable urban development and innovation ecosystems in Japan and the USA. Findings The GBA has made significant achievements in sustainable innovation, driven by strong policy support for R&D investment and cross-border collaboration. However, some challenges such as regulatory harmonisation and economic disparities amongst cities are identified. The case studies of Hong Kong and Shenzhen reveal that Hong Kong is one of the world’s leading finance centres, serving as a gateway between Mainland China and the world. In contrast, Shenzhen has emerged as a pioneer in high-tech industries, advanced manufacturing, and sustainable urban growth. Deeper collaboration between the governments and the communities — the market, private enterprises, and professional communities — is required to ensure long-term policy coherence, collaborative governance, and inclusive growth, in securing the GBA’s position as a 21st-century leader in finance, technology, and sustainability. Originality/value The paper provides a comparative review of the development of the GBA through analysing the two cases of Hong Kong and Shenzhen, as well as a comparison with Tokyo, New York and San Francisco Bay Areas.

ABSTRACT This study explores the impact of the digital economy on urban innovation quality, using data from 265 Chinese cities over the period 2012–2023. The results show that digital economy initiatives significantly enhance urban innovation quality, with pilot zones outperforming non‐pilot zones by 15%. The mechanism analysis emphasises the mediating role of collaborative innovation. Compared to the breadth of collaborative innovation, the digital economy has a relatively small effect on the depth of collaborative innovation; the depth of collaboration plays a more significant role in improving innovation quality. Further analysis shows that the digital economy's impact on innovation quality is stronger in regions with higher internet penetration and advanced industrial structures. In cities with higher internet penetration, innovation quality improves through deeper collaboration, whilst in cities with lower penetration, it improves mainly through broader networks. In cities with advanced industrial structures, the digital economy boosts both the breadth and depth of collaboration, enhancing innovation quality. This study enhances our understanding of how the digital economy drives urban innovation by unravelling the complex mechanisms behind collaborative innovation.

As AI systems become more integrated into society, the relationship between humans and AI is shifting from simple automation to co-creative collaboration. This evolution is particularly important in education, where human intuition and imagination can combine with AI’s computational power to enable innovative forms of learning and teaching. This study is grounded in the #ppAI6 model, a framework that describes six levels of creative engagement with AI in educational contexts, ranging from passive consumption to active, participatory co-creation of knowledge. The model highlights progression from initial interactions with AI tools to transformative educational experiences that involve deep collaboration between humans and AI. In this study, we explore how educators and learners can engage in deeper, more transformative interactions with AI technologies. The #ppAI6 model categorizes these levels of engagement as follows: level 1 involves passive consumption of AI-generated content, while level 6 represents expansive, participatory co-creation of knowledge. This model provides a lens through which we investigate how educational tools and practices can move beyond basic interactions to foster higher-order creativity. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting the levels of creative engagement with AI tools in education. This review synthesizes existing literature on various levels of engagement, such as interactive consumption through Intelligent Tutoring Systems (ITS), and shifts focus to the exploration and design of higher-order forms of creative engagement. The findings highlight varied levels of engagement across both learners and educators. For learners, a total of four studies were found at level 2 (interactive consumption). Two studies were found that looked at level 3 (individual content creation). Four studies focused on collaborative content creation at level 4. No studies were observed at level 5, and only one study was found at level 6. These findings show a lack of development in AI tools for more creative involvement. For teachers, AI tools mainly support levels two and three, facilitating personalized content creation and performance analysis with limited examples of higher-level creative engagement and indicating areas for improvement in supportive collaborative teaching practices. The review found that two studies focused on level 2 (interactive consumption) for teachers. In addition, four studies were identified at level 3 (individual content creation). Only one study was found at level 5 (participatory co-creation), and no studies were found at level 6. In practical terms, the review suggests that educators need professional development focused on building AI literacy, enabling them to recognize and leverage the different levels of creative engagement that AI tools offer.

The core challenge of Knowledge Base Question Answering (KBQA), as a bridge between natural language and structured knowledge, is to accurately map complex semantic queries into Graph Query Language (GQL). Compared with the traditional Text-to-SQL task, KBQA faces a dual challenge: the structural differences between GQL and SQL and the lack of high-order subgraph information in multi-hop inference of knowledge graphs. While existing approaches such as ChatKBQA have made progress, the limitation of subgraph scalability severely constrains multi-hop query performance. To this end, this study proposes Knowledge Graph Multi-hop Perceptron (KGMP) - a retrieval-generation framework fine-tuned based on open-source large language models, whose innovativeness is reflected in three aspects: 1. Dynamic Graph Traversal Mechanism: Through an iterative subgraph expansion strategy, KGMP effectively achieves dynamic traversal of problem oriented graphs with progressive reasoning. 2. Structured Interaction Protocol: Based on SparQL syntax, KGMP designs a lightweight interaction instruction set to build an efficient communication interface between LLM and knowledge graph. 3. Graph Structure Optimization Technique: Develop subgraph reordering algorithms and pruning strategies based on the reranker model to ensure that the subgraphs input to the LLM are both compact and semantically complete. By integrating KGMP as a retrieval module into the ChatKBQA framework and providing it with optimised multi-hop subgraph input, the experimental results show a performance improvement of 6.2% and 5.3% on the WebQSP and CWQ datasets, respectively. This study provides a new technical paradigm for deep collaboration between LLM and knowledge graph.

The core challenge of Knowledge Base Question Answering (KBQA), as a bridge between natural language and structured knowledge, is to accurately map complex semantic queries into Graph Query Language (GQL). Compared with the traditional Text-to-SQL task, KBQA faces a dual challenge: the structural differences between GQL and SQL and the lack of high-order subgraph information in multi-hop inference of knowledge graphs. While existing approaches such as ChatKBQA have made progress, the limitation of subgraph scalability severely constrains multi-hop query performance. To this end, this study proposes Knowledge Graph Multi-hop Perceptron (KGMP) - a retrieval-generation framework fine-tuned based on open-source large language models, whose innovativeness is reflected in three aspects: 1. Dynamic Graph Traversal Mechanism: Through an iterative subgraph expansion strategy, KGMP effectively achieves dynamic traversal of problem oriented graphs with progressive reasoning. 2. Structured Interaction Protocol: Based on SparQL syntax, KGMP designs a lightweight interaction instruction set to build an efficient communication interface between LLM and knowledge graph. 3. Graph Structure Optimization Technique: Develop subgraph reordering algorithms and pruning strategies based on the reranker model to ensure that the subgraphs input to the LLM are both compact and semantically complete. By integrating KGMP as a retrieval module into the ChatKBQA framework and providing it with optimised multi-hop subgraph input, the experimental results show a performance improvement of 6.2% and 5.3% on the WebQSP and CWQ datasets, respectively. This study provides a new technical paradigm for deep collaboration between LLM and knowledge graph.

The digital wave is reshaping the ecosystem of vocational education, where deep collaboration between vocational colleges and enterprises has become a critical pathway for cultivating highly adaptable technical and skilled talents. Efficient information communication mechanisms now serve as core variables determining the effectiveness of such cooperation. This study focuses on vocational college teachers, enterprise managers, and internship students engaged in school-enterprise collaboration. Following the procedural grounded-theory paradigm, in-depth interviews, non-participatory observations, and digital document analyses were conducted to construct a "technical support-information interaction-goal alignment-effect feedback loop" theoretical model. The findings reveal that while digital technologies significantly enhance information transmission efficiency and resource-sharing breadth, they also introduce new barriers, such as information overload, delayed feedback, and system heterogeneity. The root causes lie in schools' and enterprises' divergent organizational goals and insufficient technological adaptability, as well as the absence of institutional incentives. Based on these insights, a three-dimensional (institutions-technology-culture) synergy optimization path is proposed, and the "demand-technology-institution" linkage mechanism is further refined, offering a transferable communication governance framework for the digital transformation of vocational education.

With the development of the digital economy and the increasing demand for informatization in the water engineering industry, the cultivation of water engineering professionals faces new challenges. This paper analyzes the current development trends in the water engineering industry and the impact of the digital economy on the talent competency structure, proposing a multidisciplinary talent training model centered on university-industry collaboration. By constructing a multidisciplinary curriculum system, strengthening practical teaching, promoting deep collaboration between universities and enterprises, and applying digital technologies, this study explores pathways to cultivate talent that meets the demands of the new era, providing a reference for the reform of higher education in water engineering.

Calls to Indigenize the curriculum have been occurring and, indeed, increasing across Canadian universities since the release in 2015 of the calls to action of the Truth and Reconciliation Commission (TRC, 2015). The present article documents the emergence at two universities of a support program for Indigenous curriculum, in the form of digital Indigenous Learning Bundles. The Indigenous Learning Bundles model was first conceptualized in 2018 by Kanien’kehá:ka (Mohawk) scholar Kahente Horn-Miller of Carleton University, in Ottawa, Canada, and was later adopted in 2021 by an Indigenous-led curriculum development team at Western University. Both projects involve deep collaborations between Indigenous and non-Indigenous people and strive to unite people around the ethical inclusion of Indigenous knowledge in a post-secondary setting. The bundles involve the co-creation of a collection of Indigenous-led digital teaching resources that prioritize local Indigenous knowledges and ethics in the making and delivering of learning opportunities in classrooms. The present paper draws on Indigenous approaches to scholarship in teaching and learning to document the development of these unique Indigenous Learning Bundles. Using aspects of case study and self-study research, the authors review and analyze project documents and their own experiences of the project to offer up six core tenets of Indigenous Learning Bundles work. They suggest that such work should uphold Indigenous ethics and intellectual sovereignty; privilege local Indigenous community voices and knowledges; operate in conciliable spaces outside Euro-Western academic governance and disciplinary structures; engage people collaboratively in the development process; rely on ongoing instructor supports to facilitate the teaching in classrooms; and require ongoing institutional support to be sustained into the future. The authors discuss the strengths and limitations of Indigenous bundles work in general and make recommendations for educators and universities wishing to explore similar Indigenous curriculum projects.

This article explores how to use animation methods for visual ethnographic research and embraces the post-qualitative inquiry (PQI) trend during the research process, by chronicling our team's research journey with the enigmatic Xianniang ritual of the Miao people in China. Faced with a fundamental impasse – a strict prohibition against filming that created a crisis of ‘non-presence’ – we turned to documentary animation as a method of inquiry. Through deep collaboration with a cultural holder who became a co-creator, the animation process itself evolved from a representational tool into a dynamic, multi-agential ‘apparatus for knowledge-becoming.’ This practical, generative experience led us to an unexpected yet profound encounter with PQI. This paper, structured as a narrative of this methodological and theoretical adventure, argues that documentary animation is not merely an extension of visual ethnography. Instead, it serves as a crucial medium capable of deeply integrating PQI theory with ethnographic practice. By embracing rupture, incompletion, and co-creation, it activates new pathways for knowledge production and cross-cultural dialogue, ultimately reshaping our understanding of reality, research, and knowledge itself.

The article presents an approach to the formation of strategic development guidelines for agribusiness in Ukraine under the conditions of institutional transformation caused by war, decentralization, and regulatory reforms. The relevance of the research stems from the need for post-war recovery of the agricultural sector and the enhancement of its resilience to internal and external challenges. The study proposes a strategic planning model based on the forecasting of institutional changes, which integrates regional characteristics, the potential for cooperation with territorial communities, and the scope of state support mechanisms. The methodology is grounded in simplex modeling and the use of integrated performance indicators, including the internal rate of return (IRR), net present value (NPV), and a partnership interaction index. The model was applied to evaluate four development scenarios for the period 2024–2028: “institutional breakthrough,” “gradual development,” “regional imbalance,” and “institutional instability.” The empirical section includes a comparative analysis of an agribusiness platform in Vinnytsia region (Ukraine) and agricultural cooperatives in Lublin Voivodeship (Poland), highlighting the importance of institutional stability and cooperation with local authorities. The study identifies six priority directions for enhancing strategic management in Ukraine’s agrarian sector: institutional adaptation of strategies, deeper collaboration with local communities, regional diversification of planning tools, integration of innovation and digitalization, targeted public support, and decentralized financial mechanisms. A sensitivity analysis demonstrates the relative vulnerability of each scenario to fluctuations in institutional conditions and investment risks. The results may be applied in national agricultural policy design, the formation of corporate strategies by agroholdings, and the development of regional support programs.

This study, based on engineering education accreditation standards, addresses the supply–demand imbalance in China’s cybersecurity talent cultivation by constructing a sustainable “education-industry-society” collaborative model. Through case studies at Huaihua University and other institutions, employing methods such as literature analysis, field research, and empirical investigation, we systematically explore reform pathways for an innovative cybersecurity talent development system. The research proposes a “three-platform, four-module” practical teaching framework, where the coordinated operation of the basic skills training platform, comprehensive ability development platform, and innovation enhancement platform significantly improves students’ engineering competencies (practical courses account for 41.6% of the curriculum). Findings demonstrate that eight industry-academia practice bases established through deep collaboration effectively align teaching content with industry needs, substantially enhancing students’ innovative and practical abilities (172 national awards, 649 provincial awards). Additionally, the multi-dimensional evaluation mechanism developed in this study enables a comprehensive assessment of students’ professional skills, practical capabilities, and innovative thinking. These reforms have increased the employment rate of cybersecurity graduates to over 90%, providing a replicable solution to China’s talent shortage. The research outcomes offer valuable insights for discipline development under engineering education accreditation and contribute to implementing sustainable development concepts in higher education.

The Ministry of Education's supply-Demand Matching Employment Education Project is a key measure to deepen the integration of industry and education and solve the structural contradiction between supply and demand of talents. This article takes the "Third Phase of Shanghai Lingli Intelligent Technology Co., LTD. Employment and Internship Base Project" (initiated in 2024) jointly carried out by Nantong Institute of Technology and a leading domestic intelligent technology enterprise (Shanghai Lingli Intelligent Technology Co., LTD.) as a practical case, systematically sorting out the project implementation path, collaborative mechanism, educational achievements and experience reflections. The project has established an integrated education ecosystem of "technology empowerment - industrial practice - direct employment" through four core modules: co-construction of courses, development of practical training platforms, dual-mentor system, and targeted employment delivery. During the two years of implementation, a total of 200 suitable talents have been cultivated, and the efficiency of the enterprise's technological achievement transformation has been significantly improved. Research shows that the employment education model of deep collaboration between schools and enterprises can effectively bridge the gap between educational supply and industrial demand, providing a replicable paradigm for the cultivation of new engineering talents in the intelligent era.

Organisational change is regarded as the process of transforming structure and function of the organisations to adapt operations to external business environment with full of uncertainty. The purpose of this paper is to assess organisational change project based six box model as the organisational diagnose technique. A case study is undertaken in a high-tech enterprise in China, Lezhichen Technology, regarding their change initiative by project for further improvement and deeper collaborations with clients. It presents the change overview includes stakeholder analysis, drivers and constraints, cost and benefit analysis together with the change assessment and evaluation. The findings indicate that the outcome of organisational change project is linked with the contributions of both strategic organisational and certain human resource practices. Understanding the potential challenges and strategies can support managers implement the organisational change project in a more effective approach.

In the context of the “double carbon” goals and energy transformation, the integration of energy and transportation has emerged as a crucial trend in their coordinated development. Wind power prediction serves as the cornerstone technology for ensuring efficient operations within this integrated framework. This paper introduces a wind power prediction methodology based on an NGBoost–GRU fusion model and devises an innovative dynamic charging optimization strategy for electric vehicles (EVs) through deep collaboration. By integrating the dynamic feature extraction capabilities of GRU for time series data with the strengths of NGBoost in modeling nonlinear relationships and quantifying uncertainties, the proposed approach achieves enhanced performance. Specifically, the dual GRU fusion strategy effectively mitigates error accumulation and leverages spatial clustering to boost data homogeneity. These advancements collectively lead to a significant improvement in the prediction accuracy and reliability of wind power generation. Experiments on the dataset of a wind farm in Gansu Province demonstrate that the model achieves excellent performance, with an RMSE of 36.09 kW and an MAE of 29.96 kW at the 12 h prediction horizon. Based on this predictive capability, a “wind-power-charging collaborative optimization framework” is developed. This framework not only significantly enhances the local consumption rate of wind power but also effectively cuts users’ charging costs by approximately 18.7%, achieving a peak-shaving effect on grid load. As a result, it substantially improves the economic efficiency and stability of system operation. Overall, this study offers novel insights and robust support for optimizing the operation of integrated energy systems.

In the absence of published reflections on generative peer relationships in transactional analysis (TA), the authors examine the underlying forces that allow such dyads either to flourish and be creatively fertile or to be pulled apart. This article offers companionship to those who have experienced the uncanny meeting that initiates a deep collaboration, within which individuals must navigate the undercurrents that accompany the pleasures of deep, playful, intellectual conversations. The science of nonlinear systems offers insight into phenomena associated with dyads, such as synchronization and serendipity. Complex behavior emerges as closeness/distance, confluence/differentiation, and cooperation/competition are negotiated. The marker that sets apart the necessary confluence of a creative dyad from unhealthy symbiosis is the inclusion of all ego states and the flexibility of movement in and out of roles.

This article summarizes the research creation activities as part of Singing Storytellers in 2014, and subsequently the development of knowledge dissemination outputs through deep collaboration with scholars and artists. Over the past decade, the field of research creation has brought together academics and practitioners across institutes of higher education, the arts sector, and the creative industry in Canada and internationally. My work as a documentary filmmaker and sound artist has expanded to a curatorial practice that centers community engagement, in no small part due to the connections with artists and scholars, training opportunities, and mentorship from which I benefited during this project while I was a graduate student. While collaborations between the musicians and scholars involved in Singing Storytellers have thrived since and resulted in multiple initiatives, this article reflects on the initial collaboration that brought a novel approach to the Sunjata epic performance accompanied by poetic translation to the public through multimedia. Here, I reflect on the process of filming, editing, and presenting The Sunjata Story – Glimpse of a Mande Epic drawing on methods from visual anthropology and ethnomusicology. I conclude with a consideration of how acts of reciprocity can create more sustainable relationships between artists and scholars – a practice I now incorporate into my own scholarship today.

In the rapidly evolving socio-economic landscape of India, nurturing leadership among youth is vital for national progress. The National Cadet Corps (NCC), India’s largest uniformed youth organization, has played a pivotal role in shaping disciplined, responsible, and motivated individuals since its inception in 1948. This study examines the role of NCC in fostering leadership qualities among university and school-going youth based on secondary data drawn from government reports, policy documents, academic literature, and media sources. Through an analytical lens, the paper explores how NCC’s structured training modules ranging from drills and camps to adventure and social service activities in still core leadership competencies such as decision-making, communication, time management, teamwork, and national consciousness. It also draws connections between the NCC experience and transformational leadership theories, highlighting the experiential and value-driven learning that cadets undergo. The paper further discusses challenges such as uneven state-wise participation, resource disparities, and limited institutional incentives. It concludes with recommendations for policy integration, academic credit recognition, and deeper collaboration with national youth missions. By showcasing the transformative potential of NCC training, this study reaffirms the organization’s relevance in aligning youth development with the nation’s vision for Atmanirbhar Bharat and the Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education) and SDG 16 (Peace, Justice, and Strong Institutions).

This study employs text mining techniques to conduct a systematic quantitative analysis of cybersecurity-related scientific publications and technological research. It aims to break through the limitations of traditional unidirectional evolutionary research, reveal the knowledge evolution rules between scientific theories and technical practices in this field, and provide valuable references and decision-making support for optimizing the collaborative innovation ecosystem. Firstly, we took academic papers and patent research on cybersecurity from 2005 to 2024 as the research objects and divided them into ten stages according to the time series. Subsequently, we identified scientific and technological topics and formed science–technology topics to assess their similarity. Then, we selected 3040 pairs of collaborative topic pairs and categorized them into three distinct groups: weak, moderate, and strong correlation. Finally, we constructed a science–technology topic association evolution atlas and analyzed the types of evolutionary pathways of topic associations and their mechanisms of action accordingly. The results demonstrate five evolutionary patterns in science–technology topic associations: division, merging, inheritance, co-occurrence, and independent development. Additionally, the science–technology topics demonstrate a high degree of collaboration, exhibiting a collaborative effect of “initial accumulation–fluctuating differentiation–deep collaboration”. Meanwhile, the correlation evolution of strongly related science–technology topics presents a symmetrical effect of “technology–science–technology” and “science–technology/technology–science”.

Given the intensifying global economic competition, the integration of industry and education has emerged as one of the key strategies to drive high-quality economic development in the region. The integration of industry and education can effectively address the disconnect between traditional education and practical demands, ensuring that talent cultivation aligns with market needs. Through collaboration with enterprises, universities can adjust their curriculum based on actual work scenarios, nurturing outstanding individuals with practical skills and professional qualities. Concurrently, enterprises can engage in the teaching process, co-cultivating specialized professionals with universities, thus enhancing their competitiveness and innovation capabilities. With the progression of time, more and more universities and enterprises are beginning to explore integrated approaches, fostering deep collaboration to bolster their competitiveness and innovation capacity, injecting strong impetus into the rapid development of regional economies. However, challenges and issues persist throughout the process, such as how to enhance cooperation efficiency and industry-university integration. Exploration and resolution of these aspects are necessary through continuous practice.

Addressing the employment challenges of undergraduate graduates from regular higher education institutions majoring in marketing, the issues in traditional planning pedagogy, and the impact of big data and artificial intelligence on the employment of marketing graduates, the Brand Planning Workshop course innovatively proposes the "IMTA" teaching model. This model emphasizes bidirectional empowerment between schools and enterprises and the dual-chain integration of industry and education. By incorporating real enterprise projects and encouraging students to participate in academic competitions , the course reconstructs its content with a focus on employment needs, strengthening students' practical skills. Additionally, the teaching team is restructured across disciplines to achieve deep collaboration between schools and enterprises in talent cultivation. The integration of big data and AI technologies enhances teaching effectiveness. This reform helps compensate for students' academic background shortcomings, improves their employment competitiveness, aligns talent cultivation with market demands, fosters the development of high-quality brand planning professionals, promotes diversified student growth, and achieves tight integration of the talent chain, professional chain, industrial chain, and innovation chain