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The purpose of this study is to examine the effect of collaborative innovation networks on new product development (NPD) performance in small and medium-sized enterprises (SMEs). It also investigates the mediating role of business model innovation and moderating role of collaboration experience and external information technology (IT) capability in the above relationship.

To test the research hypotheses about the relationships above, survey data were collected from 209 Chinese manufacturing SMEs. Multiple hierarchical regressions analysis was used to examine the hypotheses.

Results reveal that collaborative innovation networks have positive impacts on NPD performance in SMEs. Moreover, business model innovation mediates and collaboration experience and external IT capability positively moderate the relationship between collaborative innovation networks and NPD performance in SMEs.

This study paints a more complete picture of the relationship between collaborative innovation networks and NPD performance in SMEs and advances the understanding of how and when SMEs enhance their NPD performance through collaborative innovation networks.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Firms that see new product development (NPD) as key to survival and growth can optimize its impact through formation of collaborative networks. Working with relevant external partners facilitates access to the key resources, knowledge and talent needed. Scope exists to enhance the process further through a focus on business model innovation together with previous collaboration experience and external IT capabilities.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Computational aesthetics involves the application of computer-based methods to analyse and understand various phenomena. Given the significance of university library websites in the advancement of science and knowledge, it is essential to consider their aesthetic qualities in conjunction with their web visibility, particularly regarding search engine optimisation (SEO). This study aims to investigate the relationship between visual complexity as an important aspect of the aesthetics of a website and its SEO rankings of top university library websites.

This study employed an analytical survey research design, analysing 35 library websites affiliated with top universities as ranked by Times Higher Education in 2023. Visual complexity was assessed using Python Athec programming, a tool specifically developed for computational aesthetic analysis in social science research. Additionally, the AIOSEO online smart tool was utilised to extract SEO scores. Data analysis was conducted using SPSS and Excel.

The study found no significant correlation between visual complexity and SEO rankings, with a significance level of 0.125 indicated by linear regression and correlation analyses. This suggests that top university library websites may not require extensive SEO optimisation due to their established credibility and branding. Notably, even those with lower SEO rankings continue to attract international users.

This research is distinguished by its innovative use of Python programming to measure user experience in the context of aesthetics. It offers new insights into the field of computational aesthetics for the managers of libraries.

In this paper, a new grey Cosine New Structured Grey Model (CNSGM(1,N)) prediction power model is constructed for the small-sample modeling and prediction problem with complex nonlinearity and insignificant volatility.

Firstly, the weight of some relevant factors is determined by the grey comprehensive correlation degree, and the data are preprocessed. Secondly, according to the principle of “new information priority” and the volatility characteristics of the sequence growth rate, the ideas of damping accumulation power index and trigonometric function are integrated into the New Structured Grey Model (NSGM(1,N)) model. Finally, the non-structural parameters are optimized by the genetic algorithm, and the structural parameters are calculated by the least squares method, so a new CNSGM(1,N) predictive power model is constructed.

Under the principle of “new information priority,” through the combination with the genetic algorithm, the traditional first-order accumulation generation is transformed into damping accumulation generation, and the trigonometric function with the idea of integer is introduced to further simulate the phenomenon that the volatility is not obvious in the real system. It is applied to the simulation and prediction of China’s carbon dioxide emissions, and compared with other comparison models; it is found that the model has a better simulation effect and excellent performance.

The main contribution of this paper is to propose a new grey CNSGM(1,N) prediction power model, which can not only be applied to complex nonlinear cases but also reflect the differences between the old and new data and can reflect the volatility characteristics of the characteristic behavior sequence of the system.

With the rise of esports, research on the perceived fit between esports sponsors and events remains limited. This study uses the Elaboration Likelihood Model (ELM) to investigate how the perceived fit between sponsors and esports events effects brand awareness, consumer attitudes and purchasing behavior, aiming to offer insights for more effective marketing strategies.

Data were collected from 245 participants during the Taipei Game Show 2024, using purposive sampling of individuals aged 18 and above. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS 4.0.1.6 to examine the relationships among perceived fit, brand awareness, consumer attitudes and purchasing behavior, and to investigate the mediating effects.

The results discovered that brand awareness, perceived fit and consumer attitudes had significant positive effects on purchasing behavior, explaining 75% of its variance. Additionally, perceived fit positively affected both brand awareness and consumer attitude. Mediating effect showed that both brand awareness and consumer attitude play significant mediating roles between perceived fit and purchasing behavior, with consumer attitude having a stronger mediating effect.

This study highlights to the limited body of research on esports sponsorships by demonstrating that perceived sponsor-event fit is crucial for enhancing brand awareness, advancing positive consumer attitudes and driving purchasing behavior. The ELM framework highlights the importance of central and peripheral routes in influencing consumer decisions, offering strategies for companies to optimize sponsorship effectiveness and improve brand competitiveness.

This study aims to provide a comprehensive analysis of two-sided matching (TSM) research, an interdisciplinary field that integrates both theoretical and practical perspectives. By examining 756 research articles from the Web of Science database, this paper seeks to identify key trends, collaboration patterns and emerging research topics within the TSM domain.

The research utilizes bibliometric analysis combined with a structural topic model to analyze TSM-related articles published between January 1, 2000, and September 30, 2022. The study identifies leading subfields, journals, countries/regions and institutions based on publication volume, total citations and average citations per article. Interaction and collaboration patterns among these entities are examined through co-occurrence and coupling networks. Additionally, five major research topics are identified and explored using topic modeling and co-word networks. This hybrid knowledge mining approach better reveals the inherent structural changes in topic clusters. Topic distribution and network analysis are beneficial in capturing the attention allocation of different entities to knowledge.

The analysis reveals five prominent research topics in TSM: communication resource allocation, stable matching research, computing task assignment, TSM decision-making and market matching mechanism design. These topics represent the main directions of TSM research. The study also uncovers a shift in research focus from theoretical aspects to practical applications. Furthermore, the distribution of knowledge and interaction patterns among key entities align with the identified research trends.

This study offers a novel and detailed overview of TSM research highlighting significant trends and collaboration patterns within the field. By integrating bibliometric methods with structural topic modeling the study provides unique insights into the evolution of TSM research making it a valuable resource for both academic and professional communities.

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.

This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.

Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.

A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.

The purpose of this study is to analyze the dynamic changes of the arms trade network not only from the network structure but also the influence mechanism from the aspects of the economy, politics, security, strategy and transaction costs.

The study employs the Temporal Exponential Random Graph Model and the Separable Temporal Exponential Random Graph Model to analyze the endogenous network structure effect, the attribute effect and the exogenous network effect of 47 major arms trading countries from 2015 to 2020.

The results show that the international arms trade market is unevenly distributed, and there are great differences in military technology. There is a fixed hierarchical structure in the arms trade, but the rise of emerging countries is expected to break this situation. In international arms trade relations, economic forces dominate, followed by political, security and strategic factors.

Economic and political factors play an important role in the arms trade. Therefore, countries should strive to improve their economic strength and military technology. Also, countries should increase political mutual trust and gain a foothold in the industrial chain of arms production to enhance their military power.

The contribution of this paper is to analyze the special trade area of arms trade from a dynamic network perspective by incorporating economic, political, security, strategic and transaction cost factors together into the TERGM and STERGM models.

This paper attempts to combine the application of artificial intelligence in predicting and evaluating the classification of surrounding rock grades and provides guidance for subsequent support design and reinforcement support operations.

This paper discusses the use of BPNN as the primary tool, combined with three swarm bionic optimization algorithms (GA, PSO, GWO), to solve stability evaluation and grade prediction of surrounding rock in ultra-deep roadway excavation.

Taking the Great Wall ore group as the core and the Shanghaimiao mining area as the extension, the optimal model is applied to the classification of surrounding rock grade in ultra-deep roadway engineering. Prediction results show that the performance of BPNN models is excellent.

Due to the limitations of geological conditions and construction environment in deep coal mines, the period of roadway excavation is too long, resulting in less data collection.

The prediction results can provide guidance for the excavation method, support scheme correction and reinforcement support scheme design of deep coal mine roadway engineering.

It provides guidance for deep mining of coal mine (the premise of surrounding rock support stability), so as to ensure the economic and safety benefits of coal enterprises.

The neural network is applied to rock mechanics in a deep site for the first time, which is used to solve the prediction direction of surrounding rock grade evaluation. The index of the input layer is determined by combining the “three high and one disturbance” with the on-site construction situation, which is closer to the actual project. The swarm intelligent bionic algorithms are selected to optimize the hyperparameters of back propagation neural network, so as to improve the accuracy of the models. The classification and evaluation system of surrounding rock for the Great Wall ore group is constructed, which is the core of Shanghaimiao mining area in the northwest of China, guiding the dynamic adjustment of on-site excavation and support operations.

The emergence of live streaming commerce has injected promising impetus into rural development and attracted many rural streamers. This study aims to explore the influencing factors of rural streamers’ engagement intentions to help promote the sustainable development of rural live streaming commerce.

Grounded in the extended valence framework, this research employs a mixed-methods approach encompassing both qualitative and quantitative methodologies. In the qualitative phase, the authors conduct in-depth interviews with 15 rural streamers, employing data coding techniques to uncover underlying factors. Subsequently, in the quantitative phase, the authors analyze survey data from 282 rural streamers, subjecting hypotheses to validation through structural equation modeling.

The findings derived from the analysis of both interviews and questionnaires reveal that several platform qualities, including platform rural-aiding support, perceived effectiveness of dispute resolution, perceived interactivity and platform reputation, have a positive effect on trust in the platform and validate the extended valence framework in understanding rural streamers’ live streaming intention. In addition, ties with customers have a moderating effect. Specifically, the stronger the ties with customers, the stronger the positive effect of perceived benefits and the weaker the positive effect of trust in the platform on live streaming intention will be.

This study contributes to the rural live streaming commerce literature and trust research from the sellers’ perspective and provides practical implications for policymakers and live streaming platform managers on enhancing rural streamers’ participation.