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In the context of the severe global challenges posed by climate and environmental issues, this paper aims to explore the connection between female Chief Executive Officers (CEOs) and the level of sustainable development in companies. This study aims to investigate the impact of female CEOs on corporate ESG performance and provide a detailed analysis of the underlying mechanisms.

This study uses a sample of listed companies from 2010 to 2021, as reported by Bloomberg. This study uses logit regression models to test hypotheses and conduct robustness tests using the generalized method of moments, propensity score matching and heckman two statge tests.

The research findings indicate that female CEOs can enhance a company’s ESG performance, primarily by elevating the level of green innovation and engaging in more philanthropic activities. When environmental uncertainty is high, the risk-averse attitude of female CEOs may diminish the enhancement of ESG performance. However, granting a higher proportion of equity to female CEOs incentivizes risk-taking, thereby strengthening the improvement of ESG performance. Further analysis reveals that the impact of female CEOs on ESG performance is more significant in non-state-owned enterprises, high-pollution industries, and companies with low financing constraints.

The authors have shown that two key ways in which female CEOs enhance a company’s ESG performance are by increasing the level of green innovation and assuming more social responsibility. Nonetheless, this remains a shortcoming of this work, opening a door for future research to examine and enrich. There may be other possible mechanisms explaining the influence of female CEOs on corporate ESG performance. More research is warranted about the CEO’s additional traits, which were not considered in this study but may have an impact on a company’s ESG performance. Finally, while the analysis has delved into the moderating effects of external factors such as environmental uncertainty and CEO ownership on the influence of female CEOs on corporate ESG performance, there is room for exploring whether other factors also play a moderating role in future studies.

First, the findings of this study highlight the beneficial societal and economic effects of choosing female CEOs. The inclination to take on social responsibility and care for the environment are both higher among female CEOs. Furthermore, the authors have also discovered that female CEOs possess unique advantages in promoting corporate sustainability and enhancing ESG standards. This can contribute to breaking down stereotypes about gender roles in the workplace. Finally, this research shows that organizational heterogeneity and market risks have an impact on female CEOs’ capacity to improve company ESG performance.

A significant innovation of this paper lies in its unique focus on the connection between female CEOs and corporate ESG performance, along with the underlying mechanisms. Against the backdrop of sustainable development, the paper integrates social gender theory, upper echelon theory and agency theory into a comprehensive framework, shedding light on the influence of female CEOs on ESG performance and the associated mechanisms.

Educational data mining (EDM) discovers significant patterns from educational data and thus can help understand the relations between learners and their educational settings. However, most previous data mining techniques focus on prediction of learning performance of learners without integrating learning patterns identification techniques.

This study proposes a new framework for identifying learning patterns and predicting learning performance. Two modules, the learning patterns identification module and the deep learning prediction models (DNN), are integrated into this framework to identify the difference of learning performance and predicting learning performance from profiles of students.

Experimental results from survey data indicate that the proposed identifying learning patterns module could facilitate identifying valuable difference (change) patterns from student’s profiles. The proposed learning performance prediction module which adapts DNN also performs better than traditional machine techniques in prediction performance metrics.

To our best knowledge, the framework is the only educational system in the literature for identifying learning patterns and predicting learning performance.

This study aims to empirically investigate how knowledge paradox affects collaboration performance. Knowledge paradox, which arises from the simultaneous need for knowledge sharing and protection, is common in interorganizational collaboration. Using the ambidexterity perspective, this paper aims to reexamine the effect of the knowledge paradox on collaborative performance to explore the moderating roles of structural and contextual ambidexterity.

This study used a sample of 153 firms involved in vertical and horizontal collaboration, collected via questionnaires. Hypotheses were tested using hierarchical regression analysis.

This study demonstrates that the stronger the knowledge paradox is, the higher the potential for value creation. Thus, knowledge paradox has a positive impact on collaborative performance. The functions of structural ambidexterity and contextual ambidexterity strengthen this positive relationship.

This paper not only expands the theoretical application of the knowledge paradox and ambidexterity theory in the context of interorganizational relationships but also provides significant managerial implications. By comprehending the dynamics of the knowledge paradox and the role of ambidexterity, managers can make well-informed decisions to enhance their collaborative performance.

The scholarly discourse concerning the significance of relational embeddedness within multinational corporation (MNC) subsidiaries has reached a stage of maturity, albeit with fragmentation. In light of this, this paper aims to delineate hot topics that can serve as a promising research trajectory for future investigations into the phenomenon of relational embeddedness in MNC subsidiaries.

Through a systematic literature review, the authors examined 66 articles published between 1998 and 2022, sourced from two prominent databases: Scopus and Web of Science. To ensure the rigor of the investigation, the authors specifically focused on articles published in journals accredited with a minimum two-star rating according to the ABS (2021) criteria.

In the systematic review, the authors delineated four principal themes addressed in the literature concerning subsidiaries and relational embeddedness. Within these themes, the authors identified five underexplored research avenues that hold promise for future studies on relational embeddedness within the context of subsidiaries: (a) the question of the dark side of relational embeddedness, (b) the development of a global construct for relational embeddedness, (c) understanding how the social factors of relational embeddedness relate to each other, (d) the gains that local partners have in developing relational embeddedness with subsidiaries of foreign MNCs and how this relationship is moderated by the institutional environment and (e) the impact of internal.

While this study drew upon two major databases, future researchers are encouraged to explore alternative repositories to ensure the thoroughness of the findings. Another limitation of this study pertains to the chosen set of keywords, which did not encompass literature on innovation collaboration or knowledge flows within foreign subsidiaries. These areas are interconnected with the knowledge management literature and relational embeddedness, warranting attention in future investigations.

The managerial insights cater to two distinct cohorts: multinational subsidiary managers, equipping them with insights into leveraging relational strategies effectively and managers of partner companies, facilitating informed decision-making in optimizing access to subsidiary knowledge and resources.

In addition to facilitating the consolidation of fragmented literature, this study has identified five theoretical gaps that remain insufficiently explored within research utilizing the relational embeddedness framework in the context of MNC subsidiaries. Consequently, this research serves as an inaugural step for future investigations, elucidating specific avenues ripe for further exploration in the field.

This study develops a comprehensive research model and investigates the significant factors affecting positive marketing outcomes in the Metaverse through perceived social benefits and trust.

The authors propose a new research model based on social exchange theory (SET) and examine the impact of cost and reward factors. Using 327 survey samples collected from current Metaverse users in South Korea, dual-stage analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and an artificial neural network (ANN) were employed to test the study’s hypotheses.

The results showed that perceived social benefit and trust had significant mediating effects on marketing outcomes, such as loyalty to the seller, product/service attitude, and purchase intention. All antecedents, except perceived performance risk, had a crucial impact on the two mediators. The most interesting finding of this study is the positive influence of knowledge-seeking efforts on perceived social benefits.

This study is the first empirical research to examine the effectiveness of marketing in the Metaverse. It also proposes a new theoretical model based on SET to investigate users’ behavioral intentions regarding marketing in the Metaverse, and confirms its explanatory power. Moreover, the results of this study also offer suggestions to brands on how to market to consumers in the Metaverse.

Online games are widely adopted electronic applications that facilitate flow experiences, which is a highly enjoyable experience for players, thus motivating further engagement in online gameplay. During gameplay, players set gaming goals, and they must make cognitive efforts to achieve these goals. However, we do not know how goal-setting and cognitive gaming elements (game complexity and game familiarity) create flow, indicating a research gap. To fill this gap, we use the cognitive gaming elements in the literature and the theoretical elements of goal-setting theory to build a model.

Conducting a large-scale online survey, we collect 3,491 responses from online game players and use structural equation modeling for data analysis.

We find that challenging goals, game complexity, game familiarity and telepresence are positively linked to player-perceived flow, explaining 45% of the variance. The new finding is that challenging goals can strengthen the link between game complexity and flow. We also find that telepresence can strengthen the link between game familiarity and flow.

Our study provides the novel insight that gaming goals and cognitive gaming elements can generate player-perceived flow. This insight can help game makers design gaming elements to accommodate players' cognitive efforts to achieve in-game goals, thus creating flow and effectively increasing players' game engagement.

This study aims to enhance the effectiveness of knowledge markets and overall knowledge management (KM) practices within organisations. By addressing the challenge of internal knowledge stickiness, it seeks to demonstrate how machine learning and AI approaches, specifically a text-based AI method for personality assessment and regression trees for behavioural analysis, can automate and personalise knowledge market incentivisation mechanisms.

The research employs a novel approach by integrating machine learning methodologies to overcome the limitations of traditional statistical methods. A natural language processing (NLP)-based AI tool is used to assess employees’ personalities, and regression tree analysis is applied to predict and categorise behavioural patterns in knowledge-sharing contexts. This approach is designed to capture the complex interplay between individual personality traits and environmental factors, which traditional methods often fail to adequately address.

Cognitive style was confirmed as a key predictor of knowledge-sharing, with extrinsic motivators outweighing intrinsic ones in market-based platforms. These findings underscore the significance of diverse combinations of environmental and individual factors in promoting knowledge sharing, offering key insights that can inform the automatic design of personalised interventions for community managers of such platforms.

This research stands out as it is the first to empirically explore the interaction between the individual and the environment in shaping actual knowledge-sharing behaviours, using advanced methodologies. The increased automation in the process extends the practical contribution of this study, enabling a more efficient, automated assessment process, and thus making critical theoretical and practical advancements in understanding and enhancing knowledge-sharing behaviours.

Based on the theory of embodied cognition, this study uses the Mehrabian–Rusell model to explore the influence of tourists’ body involvement during sports vacations on their post-trip behavioral intention, as well as the regulatory role of tourism involvement in this process.

Structural equation modeling (SEM) was used to test the hypotheses, mediating effects and moderating effects. The data were collected through an online survey of 631 visitors to sports tourism destinations in China.

Proprioception and kinesthesia in sports tourism activities can affect post-trip behavioral intention through body arousal and tourism satisfaction. Tourism involvement positively regulates the influence of body embeddedness and body arousal on tourism satisfaction. Furthermore, a “threshold effect” exists in the emotional effect of tourists’ body involvement.

To develop sports tourism, it is important to take the following steps: create multi-sensory stimulation to improve the physical participation of tourists in sports tourism activities, design sports resorts that cater to people of different age groups, evaluate tourists’ satisfaction and use their feedback to make continuous improvements, improve the basic convenience services offered at sports resorts, use social media to display the unique physical environment and others characteristics of sports destinations to expand popularity.

This study constructs a conceptual model of the influence mechanism of tourists’ body involvement on post-trip behavioral intention to present valuable insights that could help promote the sustainable development of sports tourism.

This study uses the pleasure–arousal–dominance (PAD) theory to explore how users’ emotional engagement with ChatGPT drives their continued adoption of ChatGPT and word-of-mouth (WOM) behaviour in the context of travel-related service.

This study obtained reliable data from 428 Chinese respondents who used ChatGPT for travel-related purposes. Structural equation modelling was used to test a series of hypotheses based on the PAD framework.

This study identifies three key features of human–artificial intelligence (AI) interaction, namely, service ubiquity, entertainment and anthropomorphism, which significantly influence users’ emotional responses, including pleasure, arousal and dominance. Dominance and pleasure are found to enhance emotional experiences, driving continued adoption and positive WOM recommendations for ChatGPT, whereas arousal influences WOM but does not affect continued adoption. The results also confirm that users’ perceived pleasure from interacting with ChatGPT has the strongest effect. These findings advance theoretical understanding by clarifying the emotional mechanisms underlying human–AI interactions in the tourism context.

This study examines the emerging trend of tourists’ continuous adoption of ChatGPT for travel-related services. The results highlight how different emotions in human–AI interaction influence long-term use of AI-powered tool for travel-related services.

本研究基于愉悦-兴奋-支配（PAD）理论, 探讨在旅行相关服务中, 用户对 ChatGPT 的情感投入如何推动其持续使用该技术, 并激发口碑行为。

本研究基于428名使用过ChatGPT进行旅游相关服务的中国受访者所提供的可靠数据, 运用结构方程模型（SEM）, 结合PAD框架, 对一系列假设进行了验证。

本研究确定了用户与人工智能互动的三个关键特征：服务无处不在、娱乐性和拟人化, 这些特征对用户的情感反应（包括愉悦感、兴奋感和主导感）产生了显著影响。研究发现, 主导性和愉悦感增强了用户的情感体验, 推动了对ChatGPT的持续采用和积极的口碑推荐（WOM）, 而兴奋感仅影响口碑推荐, 但不影响持续采用。研究结果还证实, 用户在与ChatGPT互动中感知到的愉悦感具有最强的影响力。这些发现阐明了旅游业中人与人工智能互动的情感机制, 推动了相关理论的深入理解。

本研究探讨了游客持续采用ChatGPT进行旅游相关服务的这一新兴趋势。研究结果突显了在人机交互中, 不同情感如何影响游客对人工智能驱动工具在旅游服务中的长期使用。

Propósito: El estudio emplea la teoría del placer-despertar-dominio (PAD) para explorar cómo el compromiso emocional de los usuarios con ChatGPT impulsa su adopción continuada de ChatGPT y el comportamiento boca a boca en el contexto de un servicio relacionado con los viajes.

Este estudio recopiló datos fiables de 428 participantes chinos que utilizaron ChatGPT para fines relacionados con viajes. Se empleó el modelado de ecuaciones estructurales para evaluar una serie de hipótesis fundamentadas en el marco PAD.

Este estudio identifica tres características clave de la interacción entre humanos e IA: la ubicuidad del servicio, el entretenimiento y el antropomorfismo, que influyen significativamente en las respuestas emocionales de los usuarios, como el placer, la excitación y la dominación. La dominación y el placer mejoran las experiencias emocionales, impulsando la adopción continuada y las recomendaciones boca a boca positivas de ChatGPT, mientras que la excitación solo influye en las recomendaciones boca a boca, sin afectar la adopción continuada. Los resultados también confirman que el placer percibido por los usuarios al interactuar con ChatGPT es el factor con mayor impacto. Estos hallazgos contribuyen a la comprensión teórica al esclarecer los mecanismos emocionales que subyacen en las interacciones entre humanos e IA en el contexto del turismo.

Este estudio analiza la tendencia emergente de la adopción sostenida de ChatGPT por parte de los turistas en el ámbito de los servicios de viaje. Los resultados subrayan cómo las diversas emociones generadas en la interacción humano-IA inciden en el uso prolongado de herramientas basadas en IA para servicios turísticos.

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.