Journal of Innovation & Knowledge最新文献

Although co-working spaces have attracted significant attention as a type of platform organisation in the sharing economy, little is known about why some spaces faced with an analogical environment demonstrate obvious differences in recovery abilities after sudden shocks, such as the one caused by the COVID-19 pandemic. To understand this novel phenomenon, we collected data from the Chengdu high-tech zone and employed grounded theory and fuzzy set qualitative comparative analysis (fsQCA) to identify the possible causal configurations for high levels of resilience in small and medium-sized platform organisations. Our findings reveal that (1) there are five antecedent configurations that multi-morphologically lead to the same outcome; (2) compared with other antecedent conditions, networking ability and diverse value co-creation with customers play more important roles in the formation of a high level of co-working spaces’ resilience; (3) under certain circumstances, a substitutional relationship exists between industrial experience and communication ability; and (4) when the endogenous factors of organisations (e.g. their abilities and resources) coincide with complex external environments, organisational resilience is expressed. In this manner, our study contributes to the organisational resilience literature and research on co-working spaces. Some practical suggestions for the sustainable development of co-working spaces are also provided.

Digital technology changes the innovation mode, and the innovative factors among innovation subjects will be digitally empowered. The government plays a very important role in determining the allocation of innovative factors. Therefore, it is necessary to clarify the main source of power for the high-quality development of the manufacturing industry in the future, so as to help the government to use digital technology to adjust the innovative factors allocation appropriately. We used systematic dynamic model to portray the innovation mechanism of manufacturing industry and set relevant parameters based on previous literature to predict the variation of innovation under different scenarios. It was found that government expenditure on science and technology can significantly improve innovation performance, followed by information expenditure, while government expenditure on education needs to reach a certain scale before it can play a positive role. However, after passing a certain threshold, education has a significantly positive role in promoting innovation.Therefore, when the government adjusts the structure of expenditures, it should ensure long-term and stable education expenditure, give full play to the role of innovative factors such as talents. It should also ensure the construction and maintenance of information infrastructure, so as to enhance the industry-university-research collaborative innovation in the digital era and promote high-quality development of manufacturing industry.

Data heterogeneity, particularly noted in fields such as genetics, has been identified as a key feature of big data, posing significant challenges to innovation in knowledge and information. This paper focuses on characterizing and understanding the so-called "curse of heterogeneity" in gene identification for low infant birth weight from a statistical learning perspective. Owing to the computational and analytical advantages of expectile regression in handling heterogeneity, this paper proposes a flexible, regularized, partially linear additive expectile regression model for high-dimensional heterogeneous data. Unlike most existing works that assume Gaussian or sub-Gaussian error distributions, we adopt a more realistic, less stringent assumption that the errors have only finite moments. Additionally, we derive a two-step algorithm to address the reduced optimization problem and demonstrate that our method, with a probability approaching one, achieves optimal estimation accuracy. Furthermore, we demonstrate that the proposed algorithm converges at least linearly, ensuring the practical applicability of our method. Monte Carlo simulations reveal that our method's resulting estimator performs well in terms of estimation accuracy, model selection, and heterogeneity identification. Empirical analysis in gene trait expression further underscores the potential for guiding public health interventions.

This paper extends the fast-growing research stream on gamification in corporate training by examining the impact of gamification on employees’ knowledge retention, knowledge sharing, and job performance. A mixed-methods approach was employed. Quantitative data from surveys and qualitative insights from experimental research were combined to ascertain the effectiveness of gamification techniques in corporate training. The sample consisted of 110 employees and business owners who attended gamified training sessions powered by the Discovery-Innovation-Growth platform. Participants were aged 18 to 64 years and had diverse educational backgrounds and job positions. They were also based in different European countries (Spain, France, Germany, United Kingdom, Italy, Finland, and Romania). The sample characteristics thus ensured comprehensive coverage of different employee profiles. Gamification significantly enhanced knowledge retention and job performance. Gamification techniques such as points, badges, and leaderboards positively influenced employee engagement, which in turn boosted the ability to retain and apply knowledge effectively at work. Social interaction mediated the relationship between gamification techniques and knowledge sharing, highlighting the role of collaborative learning environments. These insights offer valuable guidance for corporate instructors and human resource (HR) professionals aiming to develop engaging and personalized training programs that meet diverse employee needs and learning preferences. This study contributes to the theoretical framework of gamification in adult learning and organizational development, paving the way for future research in this dynamic field.

In organizations, the success of innovation efforts in improving performance relies heavily on entrepreneurial passion and the perceived importance of innovation. Understanding the link between deliberate practice and innovation broadens our current knowledge, emphasizing entrepreneurial passion as a driving force and the perceived importance of innovation as a crucial amplifying factor for performance. This study posits that deliberate innovation practice enhances innovation performance by fostering entrepreneurial passion, which is further influenced by the perceived importance of innovation. Grounded in self-determination theory and social cognition theory, this research collected data through a multisource, three-wave approach from software company employees. Utilizing SmartPLS (v 4.0) for measurement and structural model evaluation, the findings reveal that deliberate innovation practice positively impacts innovation performance. Entrepreneurial passion serves as a mediator between deliberate innovation practice and innovation performance, with perceived innovation importance strengthening this effect. This study demonstrates that the impact of deliberate innovation practice on performance is more pronounced when entrepreneurial passion mediates, especially at higher levels of perceived innovation importance. The findings offer essential theoretical insights and practical implications.

Food businesses, especially those facing supply chain challenges, operate within a global environment characterized by uncertainty. The effects of the COVID-19 pandemic have underscored substantial losses in this sector. Our study aims to develop a mixed-formative-reflexive construct model that evaluates the impact of relational practices and resilience on the performance of enterprises within the Romanian food chain during crises. To select the sample, we employed a proportional stratified sampling method. The survey design included establishing the demographic profile of respondents, constructing items, and identifying latent variables specific to the proposed construct model. Data collection was conducted through an online questionnaire, resulting in 298 valid responses and a response rate of 35.06 %. Hypothesis testing and validation of the proposed construct model were performed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The research findings indicate that 56.9 % of the variance in food supply chain resilience can be attributed to the combined effects of proactive, reactive, and network design capabilities. The practical implications of the research suggest a need to reconsider planning strategies during crises. This involves reassessing risks, designing or redesigning products, and improving communication and collaboration with customers, suppliers, and other stakeholders. Therefore, enhancing relational practices and resilience can significantly and directly improve the performance of the food supply chain.

This study critically evaluates the contributions of the Journal of Innovation & Knowledge (JIK) toward advancing research aligned with the Sustainable Development Goals (SDGs), employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol for systematic literature review. We meticulously examine JIK’s interdisciplinary research patterns, thematic focuses, and topic evolution, emphasizing the journal’s engagement with SDG 9 (Industry, Innovation, and Infrastructure), SDG 8 (Decent Work and Economic Growth), and SDG 4 (Quality Education). By utilizing advanced techniques such as co-citation mapping and BERTopic modeling, this study identifies six major topics within the JIK, aligning with specific SDGs: “EcoTech Revolution” highlights ‘green technology,’ “Entrepreneurial Innovation” focuses on ‘innovation management,’ “Economy 4.0” centers on ‘digital transformation,’ “Effectual Innovation” involves ‘technological innovation capability,’ “Collaborative Economy” addresses ‘sharing economy sustainability,’ and “Green Innovation” explores ‘eco-innovation.’ These topics underscore JIK’s role in advancing interdisciplinary research across key areas such as technology, entrepreneurship, the digital economy, practical innovation applications, collaborative consumption models, and environmental sustainability. Our findings highlight gaps, particularly in addressing SDG 5 (Gender Equality) and SDG 15 (Life on Land), presenting opportunities for JIK to diversify its research agenda toward a more inclusive sustainability discourse. Contributions from a global array of authors underscore JIK’s international impact and its role in fostering diverse sustainability perspectives.

In a world that has rapidly transformed through the advent of artificial intelligence (AI), our systematic review, guided by the PRISMA protocol, investigates a decade of AI research, revealing insights into its evolution and impact. Our study, examining 3,767 articles, has drawn considerable attention, as evidenced by an impressive 63,577 citations, underscoring the scholarly community's profound engagement. Our study reveals a collaborative landscape with 18,189 contributing authors, reflecting a robust network of researchers advancing AI and machine learning applications. Review categories focus on systematic reviews and bibliometric analyses, indicating an increasing emphasis on comprehensive literature synthesis and quantitative analysis. The findings also suggest an opportunity to explore emerging methodologies such as topic modeling and meta-analysis. We dissect the state of the art presented in these reviews, finding themes throughout the broad scholarly discourse through thematic clustering and BERTopic modeling. Categorization of study articles across fields of research indicates dominance in Information and Computing Sciences, followed by Biomedical and Clinical Sciences. Subject categories reveal interconnected clusters across various sectors, notably in healthcare, engineering, business intelligence, and computational technologies. Semantic analysis via BERTopic revealed nineteen clusters mapped to themes such as AI in health innovations, AI for sustainable development, AI and deep learning, AI in education, and ethical considerations. Future research directions are suggested, emphasizing the need for intersectional bias mitigation, holistic health approaches, AI's role in environmental sustainability, and the ethical deployment of generative AI.

This study is to understand the current status of online sustainability reporting (SR) practices by Irish universities. The paper reveals that online SR is in its infancy at the sample universities and there is significant room for improvement. Irish universities have initiated using web-based SR to inform their stakeholder groups, but they tend to report symbolic contents rather than substantive ones. The findings indicate that online SR practice by Irish universities is for the purpose of gaining legitimacy rather than satisfying the information needs of stakeholders. The assessment of online SR practices serves as a first step to help the universities use new technologies, in particular the internet, to improve the dialogue with stakeholders. This paper reminds universities that they should seek more than merely to meet the minimum acceptable level of online SR disclosure in society. The study will be of interest to university administrators, policymakers, regulators, and other stakeholder groups of universities.

The widespread application of data technologies such as big data, artificial intelligence, and the Internet of Things plays an important role in promoting agricultural technology innovation. This article uses the 2022 survey data of farmers' economic behaviour in Hubei Province, China to construct an analytical framework and indicator system for the impact of digital technology adoption on farmers' grain production technology innovation. The relationship between digital technology adoption and farmers' grain production technology innovation is explored using binomial logistic regression models and multiple linear regression models, And use regression decomposition method to calculate the degree of impact of digital technology adoption on farmers' grain production technology innovation. The research has drawn the following conclusions: firstly, digital technology is conducive to enhancing technological innovation in grain production; The second is that innovation in grain production technology is the result of multiple factors such as digital technology, agricultural machinery and technology promotion, farmers' income, and skill training working together; The barriers and limitations of digital technology on innovation in grain production technology can be overcome by improving the service level of agricultural machinery and promoting agricultural technology training. This article believes that enhancing the innovation level of grain production technology requires attention to the adoption of digital technology and adopting diversified measures to enhance the innovation level of grain production technology amongst farmers.