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Innovation ambidexterity is crucial for fostering growth and gaining a competitive advantage in small and medium enterprises (SMEs). Previous research indicates that achieving a balance between exploration and exploitation is a multifaceted phenomenon occurring across various levels. This paper aims to examine the influence of individual, organizational and institutional factors on the ambidextrous innovation of Chinese niche leaders using a configurational perspective.

This study uses secondary data collected from 69 Chinese niche leaders in the new equipment manufacturing industry. The authors use fuzzy-set qualitative comparative analysis to investigate how owner openness, age, digitization, the formal institutional environment and the informal institutional environment jointly influence innovation ambidexterity.

By using fuzzy set analysis, this study categorizes combinations of interdependent factors that promote innovation ambidexterity. In particular, the authors pinpoint three configurations that foster high innovation ambidexterity and two configurations that lack such high levels of innovation ambidexterity. The analysis results suggest that innovation paradoxes in SMEs are linked to a nested system comprising leadership, organizational factors and the institutional environment.

This study elucidates the mechanism of innovation ambidexterity through a configurational perspective. This research proposes and validates a framework that enables SMEs to develop ambidextrous innovation capabilities, thereby integrating organizational ambidexterity theory and shedding light on the intricately complex nature of innovation ambidexterity.

There is a wealth of value hidden in regional cultural heritage, but its preservation status is not optimistic. This study introduces a method that focuses on the inherent cultural value of regional cultural heritage to preserve it by value construction and release.

Based on the great value of regional cultural heritage due to spatial adjacency and temporal continuity, this paper focuses on its inherent cultural value to explore the preservation path and chooses Shichahai cultural heritage digital resources for a case study. This paper draws lessons from the narrative method of ancient Chinese historiography, constructs a cultural space and tells cultural stories. A linked data organization model for digital resources is created to construct a conceptual cultural space. Then, the space is materialized by linked dataset creation. The authors tell cultural stories discovered from the space, which are presented by various user interfaces using visualization technologies.

A cultural space promotes the development of a fine-grained description of regional cultural heritage and aids in relationship discovery to enhance the value construction ability. Additionally, storytelling via interactive user interfaces is helpful in the utilization and dissemination of knowledge extracted from a cultural space and enhances the value release of regional cultural heritage. In this way, a path with the inherent cultural value of regional cultural heritage as the core is established, and preservation is achieved.

This study focuses on the inherent cultural value of regional cultural heritage and proposes a new path to preserve these resources. This approach will enrich research on the preservation of regional cultural heritage and contribute to the construction and release of its cultural value.

Does the history of the bureaucratic system, along with the establishment of the Great Wall during the Ming and Qing dynasties (1368–1911), affect firm behavior across the borderlands of the Great Wall?

The Ming and Qing dynasties built a centralized administrative system in the borderlands on the south side of the Great Wall, in contrast to the “feudal lordship” system on the north side. Employing a regression discontinuity analysis framework with the Great Wall as a geographical discontinuity, we examine the long-run effects of the Great Wall on firms’ earnings management.

Using a large sample of nonlisted firms in the central core frontier region, we show that the earnings management of firms in the region south of the Great Wall is significantly curtailed compared with firms in the north of it, and this effect is more pronounced for non-SOEs. Our findings are robust to a battery of tests to account for alternative explanations.

Overall, by emphasizing the role of institutions, like legal system, shaped in history on firms’ earnings management, this study sheds new light on institutional determinants of firms’ behaviors in earnings information disclosure.

First, we enrich our understanding of the institutional determinants of firms’ financial reporting outcomes. Second, our findings shed new light on the long-term effects of historical ruling styles on modern corporate behavior.

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

This chapter critically evaluates whether football can attain recognition as a national sport in China. Article No. 11, released by the Chinese government in 2015, aimed to develop a new national strategy centralised on the sport of football to foster consumption and enhance national soft power. Consequently, this also means encouraging Chinese football fans to support the national football team. Comparing the significance of local football clubs and the national football team to Chinese football fans is deemed meaningless and unable to generate useful information to comprehend Chinese people's attitudes towards local and national communities. Through literature comparisons with established Chinese national sports such as Chinese martial arts, badminton and table tennis, the discussion reveals that football currently falls short of meeting the general criteria of invention and popularity to be considered a Chinese national sport. In the specific Chinese context, it also proves that football fails to meet the criterion of politics, hindering its identification as a national sport. Consequently, the chapter rebuts the assumption and advocates for the validity of comparing how fans assess their fandom for local and national football teams.

In the past 10 years, the scale of running events in China has increased dramatically, and the forms of running events have also become rich and diverse. Running is not only a social phenomenon but also a historical and cultural phenomenon as an organic part of human culture with its own sociological values in China. This chapter offers insight into the development of Chinese running culture and how this has emerged from ancient and modern Chinese running cultures based on Foucault's disciplinary power theory, biopower and the technologies of the self. This chapter argues that running culture in China constructs the subjectivity of the Chinese runners under the joint action of the technologies of power and the technologies of the self. The findings acknowledge how Chinese Runners present and express themselves by showing a ‘sense of presence’. Runners illustrate the implicit or explicit meaning and value of a particular way of life through running. Runners regard running as the technology of the self for self-expression and self-creation so that individuals can control their bodies and soul, thoughts, behaviours and ways of existence. Emerging technologies of power provide possibilities for the production of running culture in China, and the current policy under the technologies of power meets the needs of runners. In Chinese running culture, power was not oppressive but productive.

In many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.

The proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.

Using the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.

First of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.

This article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

This paper investigates the ways digital media applications in rural areas have transformed the influence of social networks (SN) on farmers' adoption of various climate change mitigation measures (CCMM), and explores the key mechanisms behind this transformation.

The study analyzes data from 1,002 farmers’ surveys. First, a logit model is used to measure the impact of SN on the adoption of different types of CCMM. Then, the interaction term between digital media usage (DMU) and SN is introduced to analyze the moderating effect of digital media on the impact of SN. Finally, a conditional process model is used to explore the mediating mechanism of agricultural socialization services (ASS) and the validity of information acquisition (VIA).

The results reveal that: (1) SN significantly promotes the adoption of CCMM and the marginal effect of this impact varies with different kinds of technologies. (2) DMU reinforces the effectiveness of SN in promoting farmers' adoption of CCMM. (3) The key mechanisms of the process in (2) are the ASS and the VIA.

This study shows that in the context of DMU, SN’s promotion effect on farmers' adoption of CCMM is strengthened.

The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.