ISSN 2512-6660 (Online) ISSN 1478-9906 (Print)
CN 10-1192/N
CN 10-1192/N
In the context of rapid globalization and technological innovation, this study introduces a novel evaluation standard system designed for the modern service industry. Utilizing the DPSIR (driving forces, pressures, states, impacts, responses) framework, this study categorizes pertinent characteristics to elucidate their roles within the service sector, enhancing understanding of the complex dynamics between environmental factors and human activities. Meanwhile, the application of the entropy weight method significantly reduces subjectivity, thereby improving the reliability and effectiveness of assessment outcomes by quantifying the informational contribution of various indicators. Furthermore, incorporating the technique for order preference by similarity to ideal solution (TOPSIS), analysis fosters a robust index system for assessing the developmental levels of China’s life service industry. This study’s innovative approach and its practical implications mark a significant leap in strategic decision-making and understanding of the modern service industry, offering a novel and adaptable tool for industry analysis.
As one of the three pillars of the tourism industry, hotel sales are influenced by a variety of factors. Particularly, with the exponential growth of the internet, user-generated images, text, and data presented by hotels impact hotel sales to varying degrees. This study attempts to explore how different factors affect hotel industry sales. Firstly, it examines review images, text data, and hotel base attribute data; secondly, it employs a deep learning-based approach to analyze the different types of data; finally, it uses random forest to calculate feature importance values and analyzes them based on different star ratings variance. The results show that image-text consistency influences all types of hotel sales. Furthermore, the consistency of image text also affects all hotel sales, and there are differences in the factors influencing sales across hotel types. The findings can be used to provide valuable advice to hotel managers in the sales field.
With the gradual advancement of digital transformation in the tourism industry, exploiting implicit information of tourism demand for prediction has gradually become mainstream in this research field. Among these works, the research on unidirectional implicit information is well-developed, whereas studies on interactive implicit information are scarce. Therefore, to further enhance the performance of tourism forecasting, this study employs a LangChain-based interactive context sentiment analysis model in the realm of feature engineering. By incorporating the sentiment tendencies found in online tourism reviews into tourism demand forecasting research, the model’s inferential capabilities are significantly improved. In terms of model processing, a new tourism demand prediction fusion model, EMD-STGCN-GRU-LSTM-Transformer (abbreviated as EST-Net), has been developed to address the unique spatio-temporal characteristics and imbalance of tourism data, thereby enhancing the model’s ability to accurately extract spatio-temporal sequences. Additionally, the PCA method is utilized to aggregate multiple key indicators of sentiment attention and natural environmental factors, constructing a tourism prediction indicator system to further correct the overall framework bias.
Generative AI technology, represented by ChatGPT, has sparked profound changes in a variety of fields, and undoubtedly generative AI will drive continuous reforms and innovation in the business and experience of the travel and hospitality sectors. This article mainly explores the application of generative AI in the tourism industry based on existing literature, analyzes the constraints that may exist when it is applied in the field of tourism and hospitality, and explores future directions and opportunities for combining tourism, hospitality, and generative AI. This paper hopes to inspire scholars and practitioners to apply generative AI to tourism services, marketing and management, to explore smarter, more humane and more convenient tourism development modes and new business models, achieving high-quality development of tourism.
The debate surrounding the relationship between financial structure and systemic financial risk has been ongoing. This controversy arises from a lack of consideration for national resource endowments, which serve as the foundation for the development of industrial structure. Insufficient research has been conducted on the interplay between financial structure, industrial structure, and systemic financial risk. In light of this, the primary objective of this study is to underscore the crucial role of industrial structure in elucidating the interaction between financial structure and systemic financial risk. The research findings highlight that financial structure indirectly influences systemic financial risk through its impact on industrial structure. There exists significant heterogeneity in the transmission effect of industrial structure, with a more pronounced effect observed in areas characterized by high levels of economic development and belonging to mature clusters. Furthermore, the transmission effect of industrial structure is influenced by efficient market and effective government. The improvement of the efficient market can promote the upgrading of the industrial structure while reducing the level of potential risk. Excessive government intervention will reduce the transmission effect of the industrial structure.
Stunting has a significant impact on children’s health and, in the long term, negatively affects productivity and GDP by 2–3%. Therefore, it is crucial to reduce stunting rates through regional mapping based on their capacity to address stunting and by evaluating relevant indicators. A multi-criteria decision making (MCDM) approach, utilizing principal component analysis (PCA) and Entropy for weighting, and MARCOS, COPRAS, and WASPAS for ranking, can be applied. The weighting results from PCA and Entropy indicate that access to drinking water (C11) and households receiving food assistance (C10) are the largest contributing factors, while the smallest contributors are poverty rate (C7) and Gini ratio (C6). Using PCA weights across all MCDM methods, DKI Jakarta (A11) emerges as the best-performing region, while Papua (A34) ranks the worst. When Entropy weights are applied, DKI Jakarta (A11) ranks first in MARCOS and WASPAS, while South Kalimantan (A22) ranks best in COPRAS. Papua (A34), however, remains the worst performer across all methods. This study concludes that the ranking results from PCA and Entropy weighting methods are identical, showing a strong correlation. This provides policymakers with confidence in assessing each province’s capacity to address stunting, highlighting that Papua (A34) demonstrates relatively poor performance in managing stunting.
Under the dual carbon goal and the national energy security strategy, improving the ESG development level of China’s energy enterprises is undoubtedly an important issue at present. Based on relevant theories of ESG development, this study constructs a model of influencing factors of ESG development of energy enterprises in China on the basis of summarizing factors that will have an impact on ESG development of energy enterprises. Then, combined with 501 effective studies obtained by questionnaire survey, it analyzes the interrelationship and action mechanism among various influencing factors. The results show that government factors have the greatest impact on the ESG development level of China’s energy enterprises, followed by enterprises, investors, the third-party rating agencies and financial institutions. The results of this study will play a certain reference role in China’s energy green transformation, ensuring national energy security, and promoting the sustainable development of energy enterprises.
A novel location-queuing problem for blood collection facilities against MPHEs is studied in this paper. The decision variables to be determined include the opening plan of fixed blood collection rooms, the location of mobile blood collecting vehicles, and the number of service desks within facilities. This problem is formulated as a bi-objective multi-period integer nonlinear programming model, incorporating unique features that distinguish it from previous studies, such as pandemic risk, blood donation behavior, and the heterogeneity of blood collection facilities. The objectives are to minimize the total system cost and maximize donor satisfaction. To solve this problem, an improved multi-objective grey wolf optimization (IMOGWO) algorithm, which incorporates chaotic mapping and adaptive convergence factors, is proposed. Real data from Chongqing, China, is utilized to demonstrate the applicability of the model and the effectiveness of IMOGWO. Using evaluation metrics such as the C metric (CM), number of Pareto frontier (NPF), maximum spread (MS), spacing (SP), mean ideal distance (MID) and computation time (CPU time), numerical experiments demonstrate that the proposed IMOGWO outperforms non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization(MOPSO), multi-objective whale optimization (MOWOA), multi-objective chimp optimization (MOChOA), and multi-objective grey wolf optimization (MOGWO).
