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The study aims to provide concrete service remediation and enhancement for LLM developers such as getting user forgiveness and breaking through perceived bottlenecks. It also aims to improve the efficiency of app users' usage decisions.

This paper takes the user reviews of the app stores in 21 countries and 10 languages as the research data, extracts the potential factors by LDA model, exploratively takes the misalignment between user ratings and textual emotions as user forgiveness and perceived bottleneck and uses the Word2vec-SVM model to analyze the sentiment. Finally, attributions are made based on empathy.

The results show that AI-based LLMs are more likely to cause bias in user ratings and textual content than regular APPs. Functional and economic remedies are effective in awakening empathy and forgiveness, while empathic remedies are effective in reducing perceived bottlenecks. Interestingly, empathetic users are “pickier”. Further social network analysis reveals that problem solving timeliness, software flexibility, model updating and special data (voice and image) analysis capabilities are beneficial in breaking perceived bottlenecks. Besides, heterogeneity analysis show that eastern users are more sensitive to the price factor and are more likely to generate forgiveness through economic remedy, and there is a dual interaction between basic attributes and extra boosts in the East and West.

The “gap” between negative (positive) user reviews and ratings, that is consumer forgiveness and perceived bottlenecks, is identified in unstructured text; the study finds that empathy helps to awaken user forgiveness and understanding, while it is limited to bottleneck breakthroughs; the dataset includes a wide range of countries and regions, findings are tested in a cross-language and cross-cultural perspective, which makes the study more robust, and the heterogeneity of users' cultural backgrounds is also analyzed.

This work examines a repairable machining system’s reliability by considering multiple failure scenarios, including individual component failures, hardware and software malfunctions, failures resulting from shared causes and failures caused by human error. When a system is susceptible to several modes of failure, the primary goal is to forecast availability and other reliability metrics as well as to calculate the expected profit of the repairable machining system.

The process of recovering after a system failure involves inspecting the system and fixing any malfunctions that may have occurred. The repair procedures for all kinds of faults are taken to follow a general distribution to represent real-time circumstances. We develop a non-Markovian stochastic model representing different system states that reveal working, failed, degraded, repair and delayed repair states. Laplace transformation and the supplementary variable technique are used to assess the transient states of the system.

Analytical expressions for system performance indices such as availability, reliability and cost-benefit analysis are derived. The transient probabilities when the system experiences in different states such as failed, degraded and delayed states are computed. The results obtained are validated using Mathematica software by performing a numerical illustration on setting default values of unknown parameters. This ensures the accuracy and reliability indices of the analytical predictions.

By methodically examining the system in its several states, we will be able to spot possible problems and offer efficient fixes for recovery. The system administrators would check to see if a minor or major repair is needed, or if a replacement is occasionally taken into consideration to prevent recurring repairs.

Previous studies have rarely integrated the financing modes of a capital-constrained manufacturer with the choices of online sales strategies. To address this gap, the authors study how a manufacturer selects optimal financing modes under different sales strategies in three dual-channel supply chains.

This paper considers three sales strategies, namely, combining a traditional retailer channel with one of the direct selling, reselling and agency selling channels, and two common financing modes, namely, bank financing and retailer financing. The authors obtain equilibrium outcomes of the manufacturer and traditional retailer and then provide the conditions for them to select optimal financing modes under three sales strategies.

The results indicate that the manufacturer’s financing decisions rely on the initial capital and interest rates, and the manufacturer selects retailer financing only if the initial capital is relatively larger. In terms of financing mode options, the retailer financing mode is more beneficial for the manufacturer under the three sales strategies. From the perspective of sales strategies, the direct selling model is more beneficial. In addition, the higher the consumer acceptance of the online channel, the more profits the manufacturer obtains.

This paper provides suggestions on how the capital-constrained manufacturer chooses financing modes and sales strategies.

This paper integrates the financing mode and different sales strategies to investigate the manufacturer’s optimal operational decisions. These sales strategies allow us to investigate the manufacturer’s optimal financing modes in the presence of both different financing modes and sales strategies.

The purpose of this study is to solve the problem that existing researches ignore the long-term and staged nature of digital transformation, failing to conduct specific discussions for different stages. It responded the call by constructing a three-stage evolutionary model to analyze the impact of digital transformation at different stages on the sustainable performance of manufacturing enterprises. The moderating effect of core technology capabilities is also explored, guided by the theory of assimilation innovation.

Based on the panel data of Chinese listed manufacturing companies from 2012 to 2020, this study empirically investigate the impact of digital transformation (digital process, digital operation and digital ecology) on sustainability performance (economic performance and environmental performance).

The findings indicate that digital operations and digital ecology significantly improve economic performance and environmental performance. Furthermore, the core technological capacity of the enterprise serves to modify the positive correlation between digital transformation at each stage and sustainable performance to some extent. In other words, when an enterprise is equipped with the requisite technological capacity, the digital transformation at each stage accelerates both economic performance and environmental performance, which in turn is conducive to an improvement in the enterprise’s sustainable development performance.

The findings contribute to the theoretical framework of digital transformation and sustainable development in all stages of enterprises. Furthermore, they provide guidance for achieving sustainable development through the implementation of digital transformation and the enhancement of core technological capacity.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

With burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.

We consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.

Numerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.

Drawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

To propose a new investment-income valuation model by real options approach (ROA) for old community renewal (OCR) projects, which could help the government attract private capital's participation.

The new model is proposed by identifying the types of options private capital has in the OCR project, selecting the option model most suitable for private capital investment decisions, improving the valuation model through the triangular fuzzy numbers to take into account the uncertainty and flexibility, and demonstrating the feasibility of the calculation model through an actual OCR project case.

The new model can valuate OCR projects more accurately based on considering uncertainty and flexibility, compared with conventional methods that often underestimate the value of OCR projects.

The investment-income of OCR projects shall be re-valuated from the lens of real options, which could help reveal more real benefits beyond the capital growth of OCR projects, enable the government to attract private capital's investment in OCR, and alleviate government fiscal pressure.

The proposed OCR-oriented investment-income valuation model systematically analyzes the applicability of real option value (ROV) to OCR projects, innovatively integrates the ROV and the net present value (NPV) as expanded net present value (ENPV), and accurately evaluate real benefits in comparison with existing models. Furthermore, the newly proposed model holds the potential to be transferred to various social welfare projects as a tool to attract private capital's participation.

This paper investigates whether China's R&D tax deduction policy triggers firms to manipulate their R&D expenditures upward.

This paper employs the ratio of actual tax savings as a proxy for the benefits of the R&D tax deduction policy based on manually collected and systematically cross-checked data. The relationship between tax benefits and abnormal R&D spending is estimated in a sample of Chinese A-share listed companies for the period 2007–2018.

The findings suggest that tax deductions lead to positive abnormal R&D spending and that this deviation in R&D spending may be attributed to firms' upward R&D manipulation for tax avoidance. The results also indicate that this behavior is more significant for the period after the policy revision, in non-HNTEs (high and new technology enterprises), and in firms with a high ratio of R&D expenses.

It is difficult to establish a sophisticated and unified model to identify the specific strategy of upward R&D manipulation that firms use to obtain tax benefits.

Managers should take into account upward R&D manipulation when designing governance mechanisms. Policymakers in developing countries may further pursue preferential tax policies that cover every stage of innovation activities gradually; the local provincial governments need to leverage their proximity and flexibility advantages to develop a tax collection and administration system.

This study contributes to the understanding of the complex effect of R&D tax incentives and helps more fully illuminate firms' upward R&D manipulation behavior from the perspective of tax planning strategies, which are underexplored in previous research.

The academic background of tourism marketing dates back to the 1960s. There had been a slight increase in its capacity until the early 1990s. However, since then, it has boomed, reaching thousands of scientific journal articles and tens of scientific books published only in English each year. Therefore, this study aims to present how tourism marketing has progressed academically within the past 60 years over four waves and how this progress may move forward as the next wave.

A bibliometric analysis grounds this study, which characterizes the past and present of tourism marketing research and anticipates the future. Content analysis, such as word clouds and social network analysis, was adopted to identify topic clusters and their connections. A total of 9,239 articles published between 1969 and 2024 were extracted from Scopus. Software packages such as VOSviewer were used to determine connections within topics.

The authors have assessed the findings broadly. Four waves were from the late 1960s to the first quarter of 2000. In spite of the limited size of papers at the early stage, the last wave saw a boom and a diversified number and category of subjects studied. In each wave, new subjects were added to broaden the picture.

The discussion of findings is based only on those scientific papers published in English since 1969 but excludes the whole list of textbooks. Subsequent research should also consider all papers and textbooks released in different languages to have the broadest picture assessment worldwide.

The study conveys various suggestions for industry practitioners and policymakers to focus on carefully assessing trends in marketing tourism services and how these may be shaped shortly. This may help practitioners and policymakers redesign their services and marketing strategies in light of future developments.

This study continues a recent study published by Kozak (2023) that has been elaborated, particularly from the industry perspective. The current study examines the academic profile of all subjects investigated in the case of tourism marketing, but mainly in an academic sense. Accordingly, this paper outlines the facts and trends researchers may follow throughout the research frame published in the past six decades.

旅游营销的学术背景可以追溯到20世纪60年代。在20世纪90年代初之前, 旅游营销的研究能力有所提升, 但增幅有限。然而, 自那时起, 旅游营销领域迅速发展, 每年仅用英文发表的期刊文章已达到数千篇, 出版的书籍也达到了数十本。因此, 本研究旨在展示过去60年内旅游营销研究在学术上的发展历程, 分为四个浪潮, 并探讨未来可能出现的下一个发展浪潮。

本研究以文献计量分析为基础, 描述了旅游营销研究的过去和现在, 并预测了未来的发展。采用内容分析方法, 如词云和社交网络分析, 确定了主题集群及其相互联系。从Scopus数据库中提取了1969年至2024年间发表的9,239篇文章, 并使用VOSviewer等软件包确定了主题之间的联系。

我们对研究结果进行了广泛的评估。从20世纪60年代末到2000年的第一季度, 共出现了四个浪潮。尽管早期阶段的论文数量有限, 但在最后一个浪潮中, 研究主题的数量和类别均呈现出爆炸式增长。在每个浪潮中, 均有新的研究主题被引入, 使得研究广度得以拓宽。

本研究的讨论结果仅基于自1969年以来用英文发表的科学论文, 不包括书籍。后续研究应考虑不同语言发布的所有论文和书籍, 以获得全球范围内最广泛的评估图景。

对行业从业者和政策制定者的建议集中在审慎评估旅游服务营销趋势及其未来可能的变化上。这可能有助于从业者和政策制定者在未来发展背景下重新设计其服务和营销策略。

本研究延续了Kozak（2023）最近发表的以行业视角为主的研究, 从学术角度全面考察了旅游营销领域内所有被研究主题的学术轮廓。具体而言, 本论文概述了过去六十年间已发表研究框架中的事实和趋势, 为研究人员提供可遵循的指导。

Los antecedentes académicos del marketing turístico se remontan a la década de 1960. Hasta principios de la década de 1990 se produjo un ligero aumento de su capacidad. Sin embargo, desde entonces ha experimentado un gran auge, alcanzando miles de artículos de revistas y decenas de libros publicados solo en inglés cada año. Por lo tanto, este estudio pretende presentar cómo ha progresado académicamente el marketing turístico en los últimos 60 años, a lo largo de cuatro oleadas, y cómo este progreso puede avanzar como la siguiente oleada en el futuro.

Un análisis bibliométrico fundamenta este estudio, en el que se caracteriza el pasado y el presente de la investigación en marketing turístico y se anticipa el futuro. Para identificar los grupos temáticos y sus conexiones se adoptaron análisis de contenido, como nubes de palabras y análisis de redes sociales. De Scopus se extrajeron nueve mil doscientos treinta y nueve artículos publicados entre 1969 y 2024. Se utilizaron paquetes de software, como VOSviewer, para determinar las conexiones dentro de los temas.

Hemos evaluado los resultados a grandes rasgos. Hubo cuatro oleadas, desde finales de los años sesenta hasta el primer trimestre de 2000. A pesar del limitado número de trabajos de las primeras oleadas, en la última se produjo un auge y una diversificación del número y la categoría de los temas estudiados. En cada oleada se añadieron nuevos temas para ampliar el panorama.

El análisis de los resultados se basa únicamente en los artículos científicos publicados en inglés desde 1969, pero excluye toda la lista de libros de texto. En futuras investigaciones deberían incluirse todos los artículos y libros de texto publicados en otros idiomas para ofrecer un panorama lo más amplio posible a nivel mundial.

Las recomendaciones para los profesionales del sector y los responsables políticos se centran en evaluar con detenimiento las tendencias en la comercialización de los servicios turísticos y en cómo pueden evolucionar en un futuro próximo. Esto puede ayudar a los profesionales y a los responsables políticos a rediseñar sus servicios y estrategias de marketing teniendo en cuenta la evolución futura.

Este estudio se basa en uno anterior, publicado recientemente por Kozak (2023), que se desarrolló específicamente desde la perspectiva de la industria. El presente estudio examina el perfil académico de todos los sujetos estudiados en el caso del marketing turístico, pero principalmente desde una perspectiva académica. En consecuencia, en este trabajo se esbozan los hechos y tendencias que los investigadores pueden seguir a lo largo del marco de investigación publicado en las últimas seis décadas.