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This study identifies a methodology to explore the issues of enterprise innovation ecosystem health (EIEH).

First, this study constructs the indicator system of EIEH based on the research objective; second, the dynamic vertical projection method (DVPM) and entropy weight method are proposed to analyze the status and influencing factors of EIEH; finally, the future development of EIEH is analyzed using GM (1,1).

In terms of methodology, the DVPM can effectively analyze EIEH, which can not only analyze the development status and potential of EIEH every year but also analyze the comprehensive state of EIEH for many years. In terms of practice, the value and grade of EIEH in China have been gradually increasing from 2016 to 2020, but the overall development is unbalanced, and five key factors affecting EIEH have been identified. The EIEH in China is predicted to steadily grow from 2021 to 2025.

The analytical method employed in this study can effectively analyze EIEH, which provides a new analytical perspective for the evaluation of EIEH and enriches the research content of the enterprise innovation ecosystem (EIE). By analyzing the results, we can gain a comprehensive understanding of the state of different EIEs, enabling each EIE to design tailored remedial measures to enhance EIEH and achieve sustainable development.

This paper aims to identify key factors influencing the development of advanced manufacturing clusters and propose governance pathways for their digital innovation ecosystems.

A quantitative analysis of the Tai-Xin Integrated Economic Zone in China is conducted using data collected through a questionnaire survey. An evaluation index for the development level of advanced manufacturing clusters is constructed, and a structural equation model is used to identify key influencing factors and governance pathways.

This paper reveals that factors such as industrial foundation, technological innovation capability, social institution environment and government policies have a significant positive impact on the development of digital innovation ecosystem in advanced manufacturing clusters. It constructs a governance model for the digital innovation ecosystem and proposes three major pathways: integration of heterogeneous innovation resources, enhancement of digital capabilities, and fostering digital collaborative governance. The crucial role of digital technology in improving data processing efficiency, optimizing resource allocation and promoting collaboration among entities is emphasized. These pathways can optimize resource allocation, boosting the competitiveness and innovation capacity of clusters.

By incorporating advanced manufacturing clusters into the digital innovation ecosystem framework, this paper enriches theoretical research on both fronts. It offers specific governance pathways and policy recommendations, providing valuable references and guidance for promoting the digital transformation and ecosystem construction of manufacturing clusters.

The paper aims to examine the digital economy's influence on China's regional innovation and development. It focuses on direct effects and spatial spillover across regions, and the mediating role of human capital. This analysis is vital for policy and strategic planning in the digital era.

This study uses panel data from 30 Chinese provinces (2004–2019) and uses the entropy method to quantify the digital economy's development. It investigates its impact on regional innovation using a dynamic spatial Durbin model (SDM) and mediation effect model, assessing direct effects, spatial spillover and human capital's mediating role. Various control variables are included for comprehensive analysis.

Findings show the digital economy significantly boosts regional innovation, acting as a growth driver. However, impacts vary regionally, with the central region gaining more than the eastern and western areas. Spatial spillover effects are mixed, showing negative short-term and positive long-term impacts under different weight matrices. Human capital is crucial for fostering innovation through the digital economy.

The paper offers unique insights into the spatial dynamics of the digital economy's impact on regional innovation in China. It advances understanding of the digital economy's role in regional development using innovative methods like the entropy method and dynamic SDM. Highlighting human capital as a key mediating factor enriches discussions on digital economy strategies for regional innovation.

The paper aims to understand the stages of the social innovation process and how it can be used for sustainable development.

This single case study used multiple sources, especially data from extensive field visits and selective in-depth interviews. Direct observation, web content analysis, journals, annual reports and news articles were also used.

The findings show that BRAC institute of skills development (BISD) adopted a unique formula for the social innovation process: problem identification, new idea, design prototype, pilot, perfect and scale up. This study also finds that BISD has a significant socio-economic impact in Bangladesh. The impacts of BISD are associated with several United Nations (UN) Sustainable Development Goals (SDGs), including SDG 1 on poverty, SDG 4 on inclusive learning, SDG 5 on gender equality and women empowerment, SDG 8 on decent work and economic growth and SDG 16 on social inclusion.

The discussions of this study ultimately pave a clear roadmap for policymakers, practitioners and academics to improve mechanisms for sustainable development through social innovations in emerging countries.

This paper provides a practical application of the social inclusive innovation process theory by which vocational training institutes can scale their sustainable impact. More knowledge is needed on how organisations can implement social innovation projects in emerging countries. This paper provides exploratory evidence to fill this gap. It demands a promising area of interest to investigate further research on the compatibility of social innovation in skills development programmes to gear up the status of an underprivileged community.

The aim of this study is to investigate technological and innovation orientation contribution to the development and sustainability of the industrial sector.

The authors gathered the perspectives of many experts who were aware enough of their company’s technical and innovation orientations to participate in this study to understand how technology and innovation orientations may affect sustainability and development. These people included the company managers, accounting department heads, IT department workers and employees in the innovation department. This was accomplished by distributing a thorough questionnaire intended to gather their perspectives.

The study’s results highlight the significant positive relationship between technological and innovation orientation. Moreover, the study demonstrates that both technological and innovation orientation were found to positively impact the sustainability and development of the industrial sector.

This study provides practical insights for policymakers, industrial managers and innovation supporters in Jordan. Managers can use these insights to reassess technology adoption and innovation strategies. Additionally, investing in staff skills and technology readiness can boost efficiency, competitiveness and long-term growth.

To the best of the authors’ knowledge, this study is pioneering research to shed light on the connection between technological orientation, innovation orientation and sustainability and development in the industrial sector, providing valuable insights for policymakers and practitioners alike.

This study aims to explore how coworkers leverage epistemic objects and guanxi (a Chinese term defining relationships based on mutual dependence) network to promote knowledge integration of “who knows what” in the development of sustainable innovation.

This study used a mixed-methods research approach, including quantitative questionnaires, social network analyses and a qualitative ethnography, all of which were collected from a large enterprise in China.

Epistemic objects can promote knowledge integration of “who knows what” among coworkers during their innovation development process. In addition, structural holes in a coworker network will impede knowledge integration of “who knows what,” but guanxi can turn this impeding effect into a facilitating effect.

First, the focus on the role of epistemic objects in eliciting knowledge generates implications for creating employee identity and coordinating knowledge heterogeneity. Second, by demonstrating how epistemic objects trigger both affective and cognitive trust to promote knowledge integration of “who knows what,” the authors complement existing studies of knowledge management (KM). Third, by presenting how coworkers fill their structural holes in their collaborative innovation, the study reveals the nature of connecting the appropriate resources with the appropriate needs, which generates implications for social capital integration and innovation enhancement. Fourth, by showing how “structural hole controllers” become “structural hole fillers” under different conditions, the authors recognize the different ways in which brokers leverage their structural holes and highlight the unique role of Chinese guanxi culture in triggering a structural hole filling behavior, thereby contributing to the literature of structural hole theory and culture management. Fifth, by creating a full picture of how coworkers strategically leverage their knowledge of “who knows what” in the development of sustainable innovation, the authors identify the influential factor that stimulates innovation, adding to the literature on the interaction between KM and innovation. Sixth, the emphasis on the independent role of epistemic objects produces an implication for the interplay between object-control and human-control in innovation work.

This study supports organizational leaders to make optimal decisions in their innovation development process by suggesting them to invest in developing an integrated knowledge of “who knows what.” To achieve it, the authors suggest managers make good use of non-human artifacts to gain the identification with the knowledge of not only themselves but also the whole team, and award the “integrator” an honor for filling structural holes that may trigger the creation of more structural hole fillers. Furthermore, the focus on the independent role of epistemic objects as knowledge elicitors and trust triggers in innovation work generates another practical implication for managers to rethink the controlling role of objects and humans in the organization and modify their managerial practices accordingly.

By exploring how coworkers leverage epistemic objects and guanxi network to promote knowledge integration of “who knows what” in the development of sustainable innovation, this study reveals the role of object-control and human-control in facilitating knowledge practices for stimulating innovation, thereby contributing to the literature of KM and innovation.

The global COVID-19 epidemic has posed significant challenges to the development of innovation and entrepreneurship education in Chinese and foreign universities, and the application of artificial intelligence generated content (AIGC) technology has presented both opportunities and challenges to its development. The purpose of this study is to summarize advanced experiences and models of new engineering innovation and entrepreneurship education development in Chinese and foreign universities, as well as to analyze the influencing factors. Taking the sustainable development goals (SDGs) into account, this study qualitatively proposes enhancement paths and improvement suggestions based on the application of AIGC technology, providing a reference for promoting the sustainable development (SD) of innovation and entrepreneurship education in Chinese universities.

By using the qualitative comparative analysis (QCA), this paper studies the interaction mechanism between the influencing factors of innovation and entrepreneurship in universities under the background of SDGs. This paper selects 12 representative universities with different cultures and strengths. Meanwhile, this paper analyzes the content of 2,535 publications on innovation and entrepreneurship education and summarizes seven influencing factors as comparison criteria. Then, this paper codes, summarizes and uses configuration to assess the primary factors influencing the development of innovation and entrepreneurship in colleges and universities at home and abroad.

On the quality of new engineering innovation and entrepreneurship education, comprehensive, diverse influencing factors and upgrading paths are obtained. Furthermore, this research proposes that the SD of innovation and entrepreneurship education in universities should make effective use of “AI plus education” and actively construct practical and teaching platforms. Meanwhile, the ChatGPT is being used to strengthen the innovation and entrepreneurship curricular system and talent training mode. The research also makes recommendations for improving teachers’ ability to acquire intelligent tools and promotes three-way teaching modalities of “teacher-AI-student” by taking into account the influence of various aspects.

This research uses the QCA research method, which analyzes not only influencing factors on the SD of innovation and entrepreneurship education but also explores the interaction mechanisms among factors. Furthermore, the research incorporates SDGs and AIGC technology application scenarios into the field of domestic innovation and entrepreneurship education, which will be helpful in SDGs of innovation and entrepreneurship education on both theoretical and practical levels.

This chapter delves into sustainability-related competences in innovation management for further education as part of the European lifelong learning initiative. Despite extensive research in primary and secondary education, adult education often remains overlooked. Competence-based learning is a favored approach to integrating knowledge, skills, and attitudes across various domains. Through thematic analysis and deductive coding, the study examines 72 innovation management-related further education programs conducted in Baden-Württemberg, Germany, between June and December 2022. The findings unequivocally demonstrate the inclusion of sustainability competences in innovation-related further education. Notably, Strategic Competence emerges as the most prevalent, while Normative Competence appears least frequently. This research significantly advances the convergence of further education in innovation management, responsible management education, and competence-based learning, emphasizing the importance of sustainability competences in adult learning contexts. By shedding light on this underexplored domain, the study prompts further exploration and development of sustainable educational practices for lifelong learning.

This chapter elucidates the significance of innovation in fostering green entrepreneurship and cultivating a resilient, eco-friendly economy. It underscores the three categories of innovation available to green entrepreneurs: product innovation, process innovation, and business model innovation. These avenues empower green entrepreneurs to craft sustainable products and services, enhance operational efficiency, and establish novel markets for eco-friendly goods and services. This chapter also explores green entrepreneurs’ challenges, including lack of funding, limited market demand, and regulatory barriers, provides strategies to overcome these challenges, and discusses the role of public–private partnerships (PPPs) and cross-sector collaboration in promoting green entrepreneurship and sustainable development. It also highlights the benefits of these collaborations, such as access to funding and resources, technical expertise, market development, networks, collaboration, and shared knowledge and expertise. Finally, this chapter emphasizes that green entrepreneurship can be supported through partnerships that combine the strengths and resources of multiple sectors, such as the government, private industry, non-profits, and academia. Ultimately, this chapter provides a roadmap for green entrepreneurs to overcome challenges and leverage collaborations to create sustainable products and services, improve efficiency, and develop new markets for sustainable goods and services.

The study aims to analyse the race towards green development and United Nations sustainable development goals (SDGs) in the cases of Huawei and Shell. Both companies are the leaders in their respective industries. Huawei is an example case study representing the information and communications technology (ICT) industry whereas Shell is an example case study representing the oil and gas industry. The research analyses of the races in achieving UN SDGs were undertaken based on the innovation diffusion framework with the use of machine learning algorithms trained to extract data on sustainability activities and initiatives.

The research analyses the two case studies of Huawei and Shell. The research was undertaken through the steps of training machine learning algorithms, industry benchmarking and evaluating the performance of the race. The analyses regarding the activities and initiatives of Huawei and Shell in contributing towards SDGs are based on the data in the past 10 years (Years 2010–2019) using machine learning to extract data on sustainability activities and initiatives. In the case of Huawei, 313 sustainability reports were fed to the unsupervised machine learning algorithms revealing 15,101 sustainability actions and initiatives related to UN SDGs in the ICT industry. In the case of Shell, 2,015 sustainability reports were fed to the unsupervised machine learning algorithms revealing 47,365 sustainability actions and initiatives related to UN SDGs in the oil and gas industry.

The analyses of findings revealed that Huawei and Shell performed very well in progressing towards the UN SDGs. Huawei had strong performance in the ICT industry with regard to SDGs No. 3, 4, 7, 8, 11, 12 and 16 while Shell had strong performance in the oil and gas industry with regard to SDGs No. 3, 4, 6, 7, 8, 12 and 16. Both companies had placed a focus on achieving SDG 12 responsible consumption and production, SDG 7 affordable and clean energy and SDG 4 quality education. The synthesised business model innovations of Huawei and Shell had shown their environmental, social and governance strategies – Huawei’s 2030 vision for green development and Shell’s 2050 vision for net zero emissions.

The five pillars of people, planet, prosperity, peace and partnership according to the UN 2030 agenda for sustainable development have shown the way a company operates to promote sustainable eco-systems. The extent to which both Huawei and Shell link corporate strategies to the UN SDGs has reflected their implementation progress. Furthermore, the business model innovations of Huawei and Shell provides a useful framework which can be applied to encourage other companies/organisations in various industries to undertake ESG activities in practice.

The main contribution of this research is the application of machine learning algorithms and the innovation diffusion model in analysing the SDGs performance. The study applies the innovation diffusion framework to explore strategic actions and initiatives of Huawei and Shell in transitioning towards sustainability. The use of machine learning algorithms has identified their sustainability approach in achieving the UN SDGs.