Purpose: In the digital economy era, digital platforms are vital infrastructure for innovation subjects to perform digital innovation activities. Achieving efficient and smooth knowledge transfer between innovation subjects through digital platforms has become a novel research subject. This study aims to examine the knowledge transfer mechanism of digital platforms in the digital innovation ecosystem through modeling and simulation to offer a theoretical basis for digital innovation subjects to acquire digital value through knowledge-sharing and thus augment their competitive advantage.Design/methodology/approach: This study explores the optimal symbiotic interaction rate between different users based on the classic susceptible-infected-removed (SIR) model. Additionally, it constructs a knowledge transfer mechanism model for digital platforms in the digital innovation ecosystem by combining the theories of communication dynamics and symbiosis. Finally, Matrix Laboratory (MATLAB) software is used for the model and numerical simulation.Findings: The results demonstrate that (1) the evolutionary path of the symbiotic model is key to digital platforms' knowledge transfer in the digital innovation ecosystem. In the symbiotic model, the knowledge transfer path of digital platforms is “independent symbiosis—biased symbiosis (user benefit)—reciprocal symbiosis,” aligning with the overall interests of the digital innovation ecosystem. (2) Digital platforms' knowledge transfer effects within the digital innovation ecosystem show significant differences. The most effective knowledge transfer model for digital platforms is reciprocal symbiosis, whereas the least effective is parochial symbiosis (platform benefit). (3) The symbiotic rate has a significant positive impact on the evolutionary dynamics of knowledge transfer on digital platforms, especially in the reciprocal symbiosis model.Originality/value: This study's results aid digital innovators in achieving efficient knowledge transfer through digital platforms and identify how symbiotic relationships affect the knowledge transfer process across the ecosystem. Accordingly, the authors propose targeted recommendations to promote the efficiency of knowledge transfer on digital platforms.

With the rapid development of sensor and communication technologies, a large amount of spatiotemporal traffic data has been accumulated, presenting the characteristics of big data. The potential information and regularity of traffic state evolution can be extracted from the huge traffic flow time series data and applied to intelligent transportation systems. This study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network. Transfer entropy algorithm is utilized to detect the spatiotemporal causality of network traffic states based on the extensive traffic time series data, which could measure the amount and direction of information transmission. A combination of Gaussian kernel density estimation and sliding window approach is proposed to calculate the transfer entropy and construct dynamic spatiotemporal causality graphs based on the causality significance test. The indexes of affected coefficient, influence coefficient, input degree, and output degree are defined to evaluate the causal interaction of traffic states among different road segments and identify the critical roads and potential bottlenecks of the existing road network. Experimental results based on real-world traffic sensor data indicate that the structures of traffic causality graphs are time-varying; the traffic cause-effect interaction among different road segments during the peak time is more significant than that during the nonpeak time; and the critical road segments can be identified, which are mainly located at the intersections of arterial roads, undertaking the convergence and dispersion of large traffic flows.

Purpose: In the era of the digital economy, the digital innovation ecosystem is an important vehicle to alleviate enterprises' resource and capability constraints and thus improve their digital innovation performance. Embedding digital innovation ecosystems for survival and development opportunities has become a new strategic choice for enterprises. However, how digital innovation ecosystem embeddedness affects the digital innovation performance of complementary enterprises has not yet been fully revealed. This study examines whether digital innovation ecosystem embeddedness affects the digital innovation performance of complementary enterprises through ambidextrous capabilities (exploration and exploitation) and the moderating role of strategic flexibility. Design/methodology/approach: A field survey was conducted in China, collecting survey data from 578 complementary enterprises in advanced manufacturing industries embedded in digital innovation ecosystems. This study applies multiple regression analysis to verify the relevant hypotheses. Findings: The results confirmed that (1) digital innovation ecosystem embeddedness has a significant positive effect on complementary enterprises' digital innovation performance; (2) Ambidextrous capabilities play a partial mediating role in the relationship between digital innovation ecosystem embeddedness and complementary enterprises' digital innovation performance. (3) Strategic flexibility positively moderates the effect of digital innovation ecosystem embeddedness on ambidextrous capabilities and digital innovation performance. Practical implications: The findings, intended to guide enterprises that complement the digital innovation ecosystem to achieve digital innovation and performance improvement, highlight the importance of eco-embedded strategies, ambidextrous capabilities and strategic flexibility. Originality/value: The finding enriches antecedent research on digital innovation performance and provides practical insights for firms to embed themselves in digital innovation ecosystems to improve performance.

In the context of online shopping, commodity information and consumer reviews are main factors that will affect purchasing behavior. Started from the preference of commodity information browsing and the inherent property of online reviews, this paper focuses onthe browsing data for statistical analysis and the interval distribution of consumer reviews based on the real data of 360 buy which is the domestic large-scale B2C commerce website in China. Researches find that commodity information browsing time distribution on Internet is fragment and can be depicted by the fat tail effect. It also demonstrated that user's browsing patterns are related to the type of information and the displaying of the information, which means that the pieces of the picture and the length of the titles affecting the clicks rate. Reviews on the interval distribution can be depicted by the power-law function and there is a monotonically increasing relationship between power-exponent and the customers’ concerns with the corresponding commodity, the higher the exponent, the higher the degree of consumer attention. The finding obtained some basic rules of the browsing mode and review model, which is of important significance for future research.

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