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The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.

External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.

In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.

However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。

The purpose of this study is to investigate the influence of brand equity on corporate financial performance across various institutional factors in China, encompassing macro (regional economic development and product market development), meso (industry uncertainty), and micro (CEO overseas experience) levels.

Using archival data related to Chinese listed companies, this study employs standard error combined with fixed effect regression for model estimation to empirically evaluate the impact of brand equity on financial performance (Tobin’s q) and its boundary effects.

This study reveals that in China, the influence of brand equity on Tobin’s q isn’t significant. However, when considering institutional factors across various levels, its impact becomes significant. Specifically, the positive effect of brand equity on Tobin’s q in China is more pronounced in regions with higher economic or product market development, industries with high uncertainty, or when the CEO has overseas experience.

This study enriches the brand-related marketing literature in China and highlights the potential underperformance of brand equity within this context. Furthermore, this study advances the integration of resource-based view with institutional theory by combining brand equity with institutional factors at the macro-, meso-, and micro-level in China.

This study focuses on brand performance in China, the largest emerging market, emphasizing the importance of integrating brand equity with diverse institutional factors to amplify its beneficial influence on financial performance.

The study employs two independent experimental studies to collect data. It focuses on the matching effect between advertising appeals and product types. The Elaboration Likelihood Model serves as the theoretical framework for understanding the cognitive processing involved in consumers' responses to these advertising appeals and product combinations.

This paper aims to investigate the impact of advertising appeals on consumers' intentions to purchase organic food. We explored the interaction between advertising appeals (egoistic vs altruistic) and product types (virtue vs vice) and purchase intention. The goal is to provide insights that can enhance the advertising effectiveness of organic food manufacturers and retailers.

The analysis reveals significant effects on consumers' purchase intentions based on the matching of advertising appeals with product types. Specifically, when egoistic appeals align with virtuous products, there is an improvement in consumers' purchase intentions. When altruistic appeals match vice products, a positive impact on purchase intention is observed. The results suggest that the matching of advertising appeals with product types enhances processing fluency, contributing to increased purchase intention.

This research contributes to the field by providing nuanced insights into the interplay between advertising appeals and product types within the context of organic food. The findings highlight the importance of considering the synergy between egoistic appeals and virtuous products, as well as altruistic appeals and vice products. This understanding can be strategically employed by organic food manufacturers and retailers to optimize their advertising strategies, thereby improving their overall effectiveness in influencing consumers' purchase intentions.

Studies on social entrepreneurial intention have been increasing in recent years due to the growing concerns of young people on social and sustainability issues. Although social entrepreneurship is gaining momentum in emerging economies, only limited studies explore antecedents of social entrepreneurial intention in these countries, particularly in Southeast Asia. This paper aims to integrate the concept of entrepreneurship competence with the theory of planned behavior and examine the interrelationship between antecedents of this theory.

A survey of university students was used to validate the theory of planned behavior in explaining social entrepreneurial intention. Samples were selected using purposive sampling involving university students who had been exposed to social entrepreneurship incubators. The 372 valid responses were analyzed using partial least square structural equation modeling.

This study confirms the applicability of the theory of planned behavior to explain social entrepreneurial intention, even though not all attitudes toward social entrepreneurship orientation were significant. Out of five orientations, only attitudes toward social innovation significantly directed social entrepreneurial intention. This study validates that subjective norms and perceived behavioral control determine attitudes toward social entrepreneurship orientation. The roles of education and prior experience in providing entrepreneurship competence also positively shape individuals’ attitudes toward social entrepreneurship orientation and perceived behavioral control.

Although perceived behavioral control and entrepreneurial self-efficacy are frequently interchangeable, these concepts are independent. This study does not include social entrepreneurial self-efficacy by only examining the effect of entrepreneurship competence on perceived behavioral control. Data were limited to university students who have been exposed to social entrepreneurship incubators, which may not fully reflect the tendency of general Indonesian youths to become social entrepreneurs.

As entrepreneurship competence is essential in stimulating social entrepreneurial intention through the formation of attitudes toward social innovation and perceived behavioral control, the government and the universities can collaborate in establishing a supportive social entrepreneurship ecosystem within and outside the universities. This ecosystem may equip youths with essential knowledge and resources as well as complement subjective norms.

Studies of social entrepreneurial intention have mostly applied psychological theories; however, they rarely consider the impact of entrepreneurship education as the driver of social entrepreneurial intention. Studies of social entrepreneurial intention from psychological and educational perspectives tend to run in parallel. This study integrates entrepreneurship competence into the theory of planned behavior to explain social entrepreneurial intention. In addition, while social entrepreneurship is believed to consist of multiple orientations, not many studies have deliberated on this. This study reflects these multiple social entrepreneurship orientations in the attitudes toward social entrepreneurship.

The acceptance and perception of people regarding 3D bioprinted meat are considered as primary concerns but have not been widely evaluated. This study aimed to determine how biospheric, altruistic, egoistic, ecological worldviews, awareness of consequences, social norms and personal norms affect the consumption intention of 3D bioprinted meat as a future food source.

The values-beliefs-norms theory grounded this study. An online survey was conducted with 600 valid respondents for analysis utilizing the structural equation modeling method.

It was found that the ecological worldview had the highest significance, and biospheric and egoistic values positively impacted individuals’ ecological worldview. The awareness of consequences and social norms was also seen to directly influence personal norms, leading to consumption intention. However, it was determined that altruistic values toward an ecological worldview had no significant effect, as an individual's moral values are not affected by other people's well-being.

This study was able to assess and discover the positive consumption intention among Filipinos, highlighting societal norms and pro-environmental behavior. The findings may help manufacturers market 3D bioprinted meat effectively and aid studies on environmentalism, social movements and consumer behavior, leading to acceptance of the development and proliferation of cultured meats.

There have been no studies on cultured meats such as 3D bioprinted meat in the Philippines. The current study was able to fully assess the pro-environmental behavior among Filipinos and intention for 3D bioprinted meat against the generic behavioral assessment among related studies. Comparison was presented based on the findings.