Wenshuai Wu

The intersection between digital technologies and tourism emerges as a prominent area of research within the tourism industry. This is primarily driven by the immense complexity of digital tourism itself as a multifaceted social, economic, and cultural phenomenon. This paper reveals the future implications of tourism in the context of digital intelligence. To gain insights into the latest applications of digital tourism, this work reviews 278 studies spanning from 2012 to 2024. A systematic literature analysis method is proposed to identify the research hotspots and trends. Through value co-creation theory and Schumpeter’s innovation theory, five key trajectories are suggested, as follows: in-depth application of digital technologies in tourism, spawning new scenarios, new forms and new modes of digital tourism, and deep integration of digital economy and tourism real economy. This state-of-the-art review will help to reflect the efforts towards promoting the smart development of digital technologies in tourism.

The reform of the teaching plan for the core course “big data technology and applications” in the digital economy major has become a globally recognized challenge. Qualitative data, fuzzy and uncertain information, a complex decision-making environment, and various impact factors create significant difficulties in formulating effective responses to teaching reform plans. Therefore, the objective of this paper is to develop a precision evaluation technology for teaching reform plans in the core course “big data technology and applications”, to address the challenges of uncertainty and fuzziness in complex decision-making environments. In this study, an extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method based on a probabilistic uncertain linguistic term set (PULTS), which is presented for teaching reform plan evaluation. The extended probabilistic uncertain linguistic VIKOR method can effectively and accurately capture the fuzziness and uncertainty of complex decision-making processes. In addition, PULTS is integrated into the VIKOR method to express decision-makers’ fuzzy language preference information in terms of probability. A case study is conducted to verify and test the extended method, and the research results demonstrate that it is highly effective for decision-making regarding teaching reform plans to foster the high-quality development of education, especially in uncertain and fuzzy environments. Furthermore, parameter and comparative analyses verify the effectiveness of the extended method. Finally, the paper outlines directions for future research.

In the context of the construction of new liberal arts, the integration and intersection of disciplines have become a new trend in the development of higher education. How to promote the teaching reform of big data technology and application courses in the new liberal arts construction scenario has become an important issue in enhancing students’ digital talent literacy and social adaptability. In this study, an extended probabilistic linguistic TODIM (an acronym in Portuguese for interactive multi-criteria decision making) with probabilistic linguistic entropy weight and Hamming distance is presented for teaching reform plan evaluation for the core course “big data technology and applications” in the digital economy major. Firstly, probabilistic linguistic entropy weight, based on the entropy of the additive linguistic term set, is applied to generate weight information. Secondly, parameter sensitivity analysis is carried out to prove the stabilization and effectiveness of the extended TODIM approach. Thirdly, this extended approach can integrate the psychological factors and cognitive behaviors of decision-makers for effectively responding to education management in the new liberal arts construction scenario. Finally, a case study on teaching reform plan evaluation is carried out, and a comparative analysis with different criteria weights and different methods is conducted to verify the extended approach. The results indicate that the extended approach can provide an effective technical tool for scientific decision-making, especially in the teaching reform plan evaluation scenario in order to promote high-quality development of education.

Talent training quality is an important field within higher education research. Innovating the talent training mode and deepening educational reform programs are both of great significance for enhancing the quality of postgraduate innovation and entrepreneurship education in universities. In this study, Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) I and II methods are extended with the probability linguistic term set (PLTS) to accurately express and quantitatively evaluate the reform scheme of postgraduate innovation and entrepreneurship education talent training mode under the big data environment. First, probabilistic linguistic PROMETHEE I and II methods are presented for quantitatively evaluating the reform scheme of postgraduate innovation and entrepreneurship education talent training, which have the advantages of good effectiveness and feasibility. Second, the PLTS is imported into the evaluation methods and applied to accurately depict qualitative information about the index data of the reform scheme effect by the degree of probability. Third, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with PLTS is proposed to perform a comparative study and conduct visual analysis to verify the effectiveness of the extended probabilistic linguistic PROMETHEE I and II methods. Fourth, an empirical example illustrates the specific evaluation process, verifies the feasibility of the extended methods, and explains the effectiveness of the results. The research findings indicate that the proposed method to reform scheme evaluation can lead to better decision quality, especially in a complex fuzzy and uncertain decision-making environment.

There is no well‐built theory on credit risk measurement and decision analysis for financial big data, and an effective and scientific evaluation system for them has not been formed. A review of them can contribute to grasping the abovementioned topics, understanding current issues, analyzing research problems, mastering research challenges, and predicting future research directions. Besides, this paper points out four research directions of credit risk measurement and decision analysis for financial big data. Moreover, this paper can provide some guidance directions and insights for practitioners, researchers, financial institutions, and government departments who have an interest in complex decision‐making in big data.

The substantial importance and complexity of oil spill emergency management have been confirmed by the massive, disastrous oil spill accidents that have occurred in recent years. The goal of this study is to research and develop an effective new model to accurately respond to oil spill emergencies for the purpose of disaster risk reduction. In this model, firstly, grey relational analysis is used to treat the variable and complex environment of an oil spill. Secondly, a revised grey relational analysis method based on the concept of angle between two vectors is presented to optimize assessment accuracy by measuring the similarity between the reference series and alternative series. Thirdly, by incorporating expected utility theory, a revised grey relational analysis for facilitating consensus is developed for multicriteria group decision-making. Finally, an empirical study on oil spill emergency management is discussed to demonstrate the feasibility and effectiveness of the proposed model for oil spill emergency management in a complex environment to reduce disaster risk.

Arrow’s impossibility theorem stated that no single group decision making (GDM) method is perfect, in other words, different GDM methods can produce different or even conflicting rankings. So, 1) how to evaluate GDM methods and 2) how to reconcile different or even conflicting rankings are two important and difficult problems in GDM process, which have not been fully studied. This paper aims to develop and propose a group decision-making consensus recognition model, named GDMCRM, to address these two problems in the evaluation of GDM methods under a multi-criteria environment in order to identify and achieve optimal group consensus. In this model, the ordinal and cardinal GDM methods are both implemented and studied in the process of evaluating the GDM methods. What’s more, this proposed model can reconcile different or even conflicting rankings generated by the eight GDM methods, based on empirical research on two real-life datasets: financial data of 12 urban commercial banks and annual report data of seven listed oil companies. The results indicate the proposed model not only can largely satisfy the group preferences of multiple stakeholders, but can also identify the best compromise solution from the opinion of all the participants involved in the group decision process.