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Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

The purpose of this study is to examine the moderating effect of playing devious games on the relationship between budget‐emphasis in performance evaluation and attitudes towards the budgetary process.

A survey questionnaire was selected. A hierarchical regression was used to analyze the data gathered from 216 Taiwanese managers in listed companies.

The results indicate that playing devious games to obtain extra budget requests moderated the relationships of budget‐emphasis in performance evaluation and attitudes towards the budgetary process. The relationships were strong and positive when playing devious games was low, and weak when playing devious games was high.

The study depends on manager questionnaire responses and thus variations in emotional responses are a concern. In addition, it remains problematic to question managers regarding their engagement in dysfunctional behaviors because managers may not wish to publicize their gaming strategies.

Managers playing devious games to obtain extra budget requests is a significant factor to be considered in attitudes towards the budgetary process. Superiors should communicate with these managers to better understand the purposes of the budget proposals before deciding how to allocate budget resources.

The findings should be useful to superiors in understanding how managerial game‐playing behaviors impact on the relationship between budget‐emphasis in performance evaluation and attitudes towards the budgetary process.

This study aims to discuss the impact that environmental consciousness and green intellectual capital had on competitive advantage. The study adopted green intellectual capital as an intervening variable to gain insight into how environmental consciousness indirectly affects competitive advantage.

A questionnaire survey on the environmental consciousness and intellectual capital management of Taiwan's manufacturing firms was conducted, and 227 samples were analyzed. The research model investigates the relevant relationships among the constructs by using a structural equation modelling approach.

Research results reveal that environmental consciousness had an indirect impact on competitive advantage through investment in green intellectual capital. It was thus known that green intellectual capital is a mediator of the relationship between environmental consciousness and competitive advantage.

This paper may serve as a reference for firms mapping out future environmental policies and provide an input of various perspectives and arguments into the discipline of green management.

The purpose of this paper is to investigate the influence of moral philosophy on the ethical beliefs of auditors. The paper argues that an individual's moral philosophy is the key factor in how one views ethical issues and largely determines the ethical choices one makes. The paper also seeks to discover the influence of personal values on the reasoning processes associated with ethics and to explore whether the personal value preferences of auditors, as a manifestation of their moral philosophy, influence their ethical beliefs and (presumably) their subsequent actions.

The authors designed a survey instrument to assess the value preferences, moral philosophies, and reactions of practicing auditors to judgment dilemmas. They then employed structural equation modeling to examine the sensitivity of auditors to the competence and integrity of clients with the aim of gaining insight into the ethical beliefs of auditors in general.

These results show that value preferences alone fail as predictors of ethical beliefs. Instead, personal values have an indirect influence on ethical beliefs via moral philosophy. Moreover, auditors strongly motivated by values based on self‐enhancement were negatively associated with idealism in ethics and positively associated with relativism. Therefore, it can be concluded that idealist auditors were more likely to condemn the actions of clients that violated moral norms, while relativist auditors were more permissive.

The results identify the role of moral philosophy as a mediator for the personal values and ethical beliefs of auditors, shedding light on how personal values can influence ethical sensitivity.

This study aims to investigate the relationships among budget emphasis, budget planning models, and performance, to determine whether an emphasis on the budget has indirect effects on performance, in the presence of other budget planning characteristics as mediators.

A questionnaire survey was conducted and structural equation modeling was used to test the proposed models among the constructs and related hypotheses.

The results indicate that while budget planning models entirely mediate the influence of budget emphasis on the performance of management and the organization, they partially mediate the influence of budget emphasis on budget satisfaction. In addition, it is determined that differentiation strategies have a significantly positive influence on budget emphasis, budget planning models and performance.

The results of this study provide a reference for organizations in the design of budgeting systems. During the design process, budget planning models should consider the degree of emphasis an organization places on the budget.

The purpose of this paper is to investigate the relationship between green management and environmental performance. This was accomplished by considering each operational element, including the supply and acquisition of upstream materials, research and development, manufacturing and packaging, marketing, promotion and education, and recycling activities.

The paper constructs an integral model of the green value chain to reveal the extent to which Taiwanese manufacturing industries are adopting green value chain management and implementing environmentally conscious business practices. Survey data were collected from 118 Taiwanese manufacturers.

The findings indicate a positive relationship between green value chain management and environmental performance. The results suggest that when firms only implement green management in particular areas the effect is insignificant; however, a comprehensive implementation can result in an overall improvement in environmental performance.

This paper may serve as a reference for firms mapping out future environmental policies and provide an input of various perspectives and arguments into the discipline of green management.

The purpose of this paper is to investigate the relationship between individual trust of students in computer supported semi-virtual collaboration groups and student’s performance in school.

Longitudinal questionnaires and interviews are conducted during the case study. By analyzing the data from the questionnaires and the grade earned by the students, the sample students are ranked with respect to the trust level and individual performance. Furthermore, the Wilcoxon signed-rank test is used to compare individual trust level and performance in the computer supported semi-virtual collaborative environment.

The distribution of an individual’s trust level is roughly consistent with the distribution of the individual’s performance in the collaboration. Besides, the relationship between a student’s trust level and the student’s performance is positively correlated.

This study integrates the issues of trust, school performance, and collaboration in an educational context. Furthermore, the conclusions drawn from this paper extend the literature of multiple disciplines including education, management, and psychology.

The conclusions could apply in the fields of education and management since the analysis revealed the relationship between an individual’s trust level and their performance.

This study contributes to the field of trust and collaboration research with a link to trust development and performance. The study also provides an insight into how to successfully improve the performance of student semi-virtual collaboration groups.

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.

For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.

The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).

From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.

The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

This paper aims to discuss the key factors affecting the quality characteristics, such as the number of solder balls, the spread distance of residual underfill and the completion time of the underfilling.

The Taguchi method is applied to configure the orthogonal table and schedule and execute the experiment. In addition, principal components analysis is used to obtain the points. Then, based on gray relational analysis and the technique for order preference by similarity to ideal solution, the closeness between each quality characteristic and the ideal solution is adopted as the basis for evaluating the quality characteristics.

The optimal parameter combination is proposed, which includes 4 dispensing (11 mg/dispensing), a “half flow” interval state, 80°C preheating module PCB board and an L-shaped dispensing path and verification testing is performed.

For vehicles and handheld electronic products, solder joints that connect electronic components to printed circuit boards may be cracked due to collision, vibration or falling. Consequently, solder balls are closely surrounded and protected by the underfill to improve joint strength and resist external force factors, such as collision and vibration. This paper addresses the defects caused during the second reflow process of a vehicle electronic communication module after the underfilling process.

This study investigates how consumers’ perceptions of brand prominence and credibility are influenced by companies’ disclosure of transparency and quality of environmental, social and governance (ESG) information. It also examines how regulatory focus influences the relationship between purchase intention and brand credibility.

The study employs a quantitative approach, utilizing an online survey of 464 Taiwanese consumers. The data collected is analyzed using structural equation modeling (SEM) to evaluate the direct and indirect effects of perceived ESG information transparency and quality on brand prominence, brand credibility and purchase intentions.

The findings reveal that perceived transparency and quality of ESG information significantly enhance brand credibility and prominence. Consequently, increased brand credibility and prominence have a significant positive impact on consumer purchase intentions. Additionally, the study identifies a moderation effect, wherein a prevention regulatory focus strengthens the influence of brand credibility on purchase intentions.

This study contributes to the literature on brand management and consumer behavior by integrating regulatory focus theory with the impacts of ESG information. It provides new insights into how ESG-related communications influence consumer behavior, highlighting the importance of transparent and high-quality ESG data in fostering consumer trust and positive brand evaluations.