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This study aims to explore the consumer perceptions of cognition and intention to visit pilot zone of international medical tourism as emerging, developed medical tourism destinations.

Using a survey-based quantitative method, based on a survey of 439 tourists who have cross-border travel experience, the partial least squares approach was performed to test the hypotheses.

The results show that internal factors had a stronger influence on destination image compared to external factors. Among different factors, preferential policies had the greatest impact on intention to visit. Perceived quality had a stronger effect on intention to visit than preference. Geographical distance had a varied effect, with those furthest away in Northeast China showing greater intention to visit compared to closer regions.

This study explores the impact of multidimensional destination perception on medical tourists’ behavioural intention in emerging destinations by integrating the push-pull theory and theory of planned behaviour and tests how geographical distance affects intention to visit emerging destinations. Using China international medical tourism pilot area as a typical case of medical tourism emerging destinations for empirical analysis. This research offers guidance for branding and marketing strategies, contributes to a deeper understanding of medical tourists’ destination choices, enriches the theoretical explanation of emerging destination choice in medical tourism and provides valuable insights for destination recovery.

In China, supply chain finance (SCF) has gradually emerged as a new service for the retail industry. This case systematically discusses how JD conducts product design and risk control of supply chain finance and related financial services, and analyze the impact of supply chain finance on JD's retail operations. The case also analyzes the relationship between JD supply chain finance and traditional financial institutions, and explore the future development of retail supply chain finance.

This paper aims to examine the unique Chinese context by analyzing the city labels (e.g. smart city and eco city) used by Chinese local governments at or above the provincial capital level to represent themselves (adopted city labels) and the developmental pathways they actually pursued (adopted developmental pathways).

The authors compared the city brand choices to those anticipated based on their geographic and economic contexts (predicted city labels and developmental pathways) as well as the directives outlined in national planning documents (imposed city labels and developmental pathways). The authors identified ten main categories of city labels used to designate themselves and establish the frequency of their use based on municipal plan documents, economic and geographic data and national plan documents and policy reports, respectively.

The authors discovered that both local economic development and geographic factors, as well as top-down administrative influences, significantly impact city branding strategies in the 38 Chinese cities studied. When these models fall short in predicting adopted city labels and pathways, it is often because cities favor a service-oriented reputation over a manufacturing-focused one, and they prefer diverse, multifaceted industrial images to uniform ones.

The originality and value of this paper lie in its contribution to the academic literature on city branding by developing a predictive model for brand development at the municipal level, with explicit attention to the national-local nexus. The paper’s approach differs from existing research in the first cluster of city branding by not addressing issues of stakeholder involvement or adoption and implementation processes. Additionally, the paper’s focus on the political power dynamics at the national level and urban governance details at the municipal level provides a unique perspective on the topic. Overall, this paper provides a valuable contribution to the field of city branding by expanding the understanding of brand development and its impact on the socioeconomic environment.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.