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This paper aims to analyze the research highlights, evolutionary process and future research directions in the field of tourism forecasting.

This study used CiteSpace to conduct a bibliometric analysis of 1,213 tourism forecasting articles.

The results show that tourism forecasting research has experienced three stages. The institutional collaboration includes transnational collaboration and domestic institutional collaboration. Collaboration between countries still needs to be strengthened. The authors’ collaboration is mainly based on on-campus collaboration. Articles with high co-citation are primarily published in core tourism journals and other relevant publications. The research content mainly pertains to tourism demand, revenue management, hotel demand and tourist volumes. Ex ante forecasting during the COVID-19 pandemic has broadened existing tourism forecasting research. The future forecasting research focuses on the rational use of big data, improving the accuracy of models and enhancing the credibility of forecasting results.

This paper uses CiteSpace to analyze tourism forecasting articles to obtain future research trends, which supplements existing research and provides directions for future research.

本文旨在分析旅游预测领域的研究重点、演化过程和未来的研究方向。

本研究使用 CiteSpace 软件对 1213 篇旅游预测文章进行了文 献计量学分析。

结果表明, 旅游预测研究经历三个阶段。机构合作包含国际机构合作和 国内机构合作, 需要持续加强国家之间的合作, 作者之间的合作多以校内合作为 主。高引用文章不仅发表在旅游领域的核心期刊还发表在其他专业的核心期刊上。 旅游预测研究的主要内容为旅游需求、收入管理、酒店需求和游客量。新冠疫情 期间的事前预测拓宽了现有的旅游预测研究。未来预测的研究重点在于合理利用 大数据, 提高模型的准确定以及提高预测结果的可信度。

本文使用 CiteSpace 分析旅游预测文章得到未来研究趋势, 既是对 现有研究的补充, 又为今后的研究提供方向。

Este artículo pretende analizar los aspectos más destacados de la investigación, el proceso evolutivo y las futuras orientaciones de la investigación en el campo de la previsión turística.

Este estudio utilizó CiteSpace para realizar un análisis bibliométrico de 1213 artículos sobre previsión turística.

Los resultados muestran que la investigación sobre previsión turística ha experimentado tres etapas. La colaboración institucional incluye la colaboración transnacional y la colaboración institucional nacional. La colaboración entre países aún debe reforzarse. La colaboración entre autores se basa principalmente en la colaboración dentro del campus. Los artículos con una alta cocitación se publican principalmente en las principales revistas de turismo y en otras publicaciones relevantes. El contenido de la investigación se refiere principalmente a la demanda turística, el revenue management, la demanda hotelera y los volúmenes turísticos. La previsión previa y durante la pandemia de la COVID-19 ha ampliado la investigación existente sobre previsión turística. La futura investigación sobre previsiones se centra en el uso racional de los big data, la mejora de la precisión de los modelos y el aumento de la credibilidad de los resultados de las previsiones.

Este artículo utiliza CiteSpace para analizar artículos de previsión turística con el fin de obtener futuras tendencias de investigación, lo que complementa la investigación existente y proporciona orientaciones para futuras investigaciones.

The normalization of COVID-19 disease prevention has presented a major opportunity for the transformation and upgrade of cultural and tourism products in Taiwan. This paper aims to explore the attractiveness of Taiwanese military dependents’ villages, known as juancun, two studies were conducted to test the path relationships of juancun experiencescape, authenticity, memorability and revisit intention.

This research conducted two studies by using a mixed sampling of snowball sampling and purposive sampling for the questionnaire survey. Based on the 397 valid questionnaires retained in Study 1, a cultural heritage site experiencescape was developed and validated; in Study 2, 393 valid questionnaires were collected, and structural equation modeling was conducted to test hypotheses that were developed drawing on the stimulus–organism–response model and theory of buyer behavior.

It is revealed that cultural heritage site experiencescape include natural and cultural, social, sensory and functional factors and that the experiencescape affects authenticity positively; “existential authenticity–memorability” mediates the relationship of experiencescape and revisit intention, whereas co-creation behavior has the positive moderating effects on the relationship of experiencescape and authenticity.

The research developed an experiencescape scale for cultural heritage sites and described the managerial implications for destination management organizations regarding product development and service design.

COVID-19疫情防控的常态化为台湾的文化和旅游产品升级转型提供了巨大的契机。为了明确台湾眷村的旅游吸引力, 本研究操作两个研究设计以检定眷村体验景观、真实性、难忘性及重游意图之间的路径关系。

本研究操作的两个研究设计皆采用滚雪球抽样和目的性抽样的混合抽样方法进行问卷调查。基于研究设计一回收的397份有效问卷, 发展和验证了眷村文化遗产地体验景观量表; 运用研究设计二回收的393份有效问卷, 操作结构方程模型分析以检定基于刺激-机体-反应理论和买方行为理论发展的假设。

本研究揭示了文化遗产地体验景观包含自然与文化、社会、感官、功能性因素; 体验景观正向影响真实性; “存在主义真实性-难忘性”中介调节体验景观和重游意图的关系; 共创行为正向调节体验景观与真实性的关系。

本研究发展了文化遗产地体验景观量表, 并就产品发展和服务设计为旅游目的地管理组织提供了实务建议。

Esta investigación llevó a cabo dos estudios mediante un muestreo mixto de bola de nieve y muestreo intencionado para la encuesta por cuestionario. A partir de los 397 cuestionarios válidos retenidos en el Estudio 1, se desarrolló y validó el experiencescape del patrimonio cultural; en el Estudio 2, se recogieron 393 cuestionarios válidos y se realizó un modelado de ecuaciones estructurales para probar las hipótesis que se desarrollaron a partir del modelo estímulo-organismo-respuesta y la teoría del comportamiento del comprador.

La normalización de la prevención de la enfermedad COVID-19 ha supuesto una gran oportunidad para la transformación y mejora de los productos culturales y turísticos de Taiwán. Para explorar el atractivo de las aldeas de los familiares de militares en Taiwan, conocidos como juancun, se realizaron dos estudios para probar las relaciones de trayectoria del experiencescape del juancun, la autenticidad, la memorabilidad y la intención de volver a visitarlo.

Se revela que el experiencescape del patrimonio cultural de la aldea militar incluye factores naturales y culturales, sociales, sensoriales y funcionales, y que el experiencescape afecta positivamente a la autenticidad; la “autenticidad existencial-memorabilidad” media en la relación del experiencescape y la intención de volver a visitarlo, mientras que el comportamiento de cocreación tiene los efectos moderadores positivos en la relación del experiencescape y la autenticidad.

La investigación desarrolló una escala de experiencescape para el patrimonio cultural del sitio y describió las implicaciones gerenciales para las organizaciones de gestión de destinos en relación con el desarrollo de productos y el diseño de servicios.

This paper establishes a quality model for automation assembly of range hood impeller based on generalized grey relational degree, it improves the debugging efficiency of the newly developed assembly workstation.

First, spot check the trial production impellers and obtain three indexes that reflect the assembly quality of the impellers. Then, analyze the parameters that affect the assembly quality of the impeller using grey relational analysis (GRA), establish a model for the assembly quality of the range hood impeller based on the generalized grey relational degree and identify the main parameters. After that, analyze the transmission structure of automation assembly workstation, identify the reasons that affect parameters and propose improvement plans. Finally, a trial production is conducted on the automation assembly workstation after adopting the improved plan to verify the quality model of impeller automation assembly.

The research shows that compared to manual assembly, the automation assembly quality of the impeller using GRA model has been improved, shortening the debugging cycle of the newly developed assembly workstation.

The newly developed automation equipment will have some problems in the trial production stage, which often rely on the experience of engineers for debugging. In this paper, the automation assembly quality model of range hood impeller based on GRA is established, which can not only ensure the quality of finished impeller but also shorten the debugging cycle of the equipment. In addition, GRA can be widely used in the commissioning of other automation equipment.

This study has developed a set of impeller automation assembly workstation. The debugging method in the trial production stage is beneficial to shorten the trial production time and improve the economic benefits.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

While a substantial amount of research has been conducted in western societies exploring public attitudes toward police (ATP) among immigrants in recent decades, the question of how recently arrived immigrants view the police in Asian societies has been largely overlooked. This study aims to explore Southeast Asian immigrants' ATP in Taiwan and how assimilation, discrimination, affirmation, procedural justice, bifocal lenses and contact experiences – viewed simultaneously – impact their perceptions.

Using a combination of convenience and snowball sampling methods, a total of 579 completed survey responses were collected in Taiwan with a response rate of 89%. Structural Equation Modeling (SEM) was used to examine key factors that impact immigrants' attitudes toward the Taiwanese police.

The findings suggest that procedural justice and assimilation are two robust and direct predictors of immigrants' attitudes toward Taiwanese police. Immigrants from Southeast Asian countries who perceive that they have been treated fairly by Taiwan police tended to report more positive ATP. Likewise, higher levels of assimilation boosted confidence in the police. In addition, both nationality and marital status had a significant impact on perceptions of the police.

This pioneering study examines immigrants' ATP among four groups of Southeast Asians in Taiwan —namely, immigrants from Indonesia, Vietnam, Thailand and the Philippines. The use of SEM strengthens the robustness of the findings derived from this study.

The purpose of this study is to extend Bippus and Young’s (2005) study and examine the effectiveness of the “I-you,” “I,” “You,” “We,” “But” and Question-based “Why” statements from Winer’s (2021) verbal coding program of conflict management using Bandura’s (1977) social learning theory (SLT).

Mixed methods were used using 175 university students from Texas and New York. A cross-sectional convenience sampling approach was conducted. Survey data was collected using Qualtrics.

Descriptive results demonstrated that the “We” statement was the most passive, the “I-you” statement was the most assertive and the “But,” “I,” “You” and Question statements were perceived to be aggressive. In addition, assertive “I-You” statements were perceived to be more effective in resolving the conflict and maintaining a relationship, whereas aggressive statements were less likely to resolve the conflict and maintain the relationship. Qualitative themes also support the “I-You” statement as the most assertive, while the “But,” “You” and “I” statements were found to be the most aggressive statements.

Implications and applications are discussed to stimulate future research among researchers and practitioners when addressing conflict. Being aware of the verbal statements that de-escalate conflict may be helpful in solving conflict in interpersonal, family and professional relationships. Future trainings can adopt effective verbal statements to resolve conflict when experiencing anger issues. Future research can continue to investigate verbal communication statements using SLT to help practitioners and managers address conflict in interpersonal relationships.

This study examines verbal statements in relation to communication styles and conflict management.

This article aims to investigate the financial constraints and nonlinearity of farm size growth.

Farm size growth is measured with land, labor and output using data from the Farm Accountancy Data Network (FADN) for Hungary and Slovenia. A dynamic panel model is applied to assess financial constraints and nonlinearity of farm size growth.

Results show that, except for land in Slovenia and output in Hungary, liquidity constraints are less important for farm size growth than endogenous factors based on farm size growth expectations and steady farm size restructuring. Smaller farms are growing faster than larger ones. The hypothesis that a higher level of subsidies would increase farm size is not supported for Hungary. When farms reach a certain size, the land area of the largest farms increases. Farm debts in Hungary are linked with land growth and in Slovenia with output growth.

Further research on the impact of liquidity constraints and subsidies can be conducted at a disaggregate farm-type level to examine whether there is variability in the underlying interlinkages at the farm-type specialization level.

The implication that farm size growth is dependent on initial size and that smaller farms are growing faster than bigger ones indicates that it is not necessary to favor the fastest growing smaller farms thus supports the application of a non-discriminatory farm size policy for observing farm size structural changes.

The dynamic panel econometric model that incorporates cash flow as a measure of financial constraints provides insight into farm size growth in cross-country comparison in relation to potential farm liquidity constraints, farm debt and the nonlinearity of farm size, which information is of relevance to policy makers and practitioners.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

As perceived corporate social responsibility (CSR) has become one of the long-term sustainable development strategies for many companies, this paper investigates the interrelationships between sustainable values (SVs), perceived CSR and customer behaviour in the restaurant industry.

To gather data needed to meet the study aims, we conducted an online survey of restaurant patrons in China, and used partial least squares structural equation modelling (PLS-SEM) to analyse the proposed models and test the hypotheses.

The obtained findings confirm that the three studied SV dimensions (equality, respect for nature and shared responsibility) have positive effects on perceived CSR, which in turn significantly affects word of mouth (WOM). Moreover, perceived CSR mediates the relationship between the three SVs and WOM but has no impact on the relationship between SVs and revisit intention.

The results of this study have practical implications for managers in the restaurant industry. Restaurant managers can prioritize their CSR efforts based on the relative importance of SV dimensions, which is useful in generating positive WOM.

Even though the importance of CSR is widely recognised, it is insufficiently studied from the perspective of restaurant customers. This research not only addresses this gap, but also expands the current understanding of SVs and their impact on CSR.