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Proenvironmental hotels and hotels with green initiatives are emerging as a method to address environmental issues and respond to tourists’ environmental concerns. To better understand what can encourage reservations in proenvironmental hotels, this study aims to investigate the connection between the performing arts watching experience and the preference for such a hotel.

Five scenario-based experiments were conducted. A total of 1,024 participants for the five studies were recruited with the help of Credamo, a commonly used Chinese data collection platform.

The results indicated that viewing performing arts could increase tourists’ preferences for proenvironmental hotels. This phenomenon occurred due to the fact that performing arts watching experience can induce a psychological state of self-transcendence in individuals, which, in turn, can raise their levels of altruism, and ultimately lead to proenvironmental hotel choices. This effect will not occur, however, when people watch performing arts with either an extrinsic motivation or in an analytical state.

The findings of this study provide hotel managers with a novel approach to market the proenvironmental attributes of their hotels and to promote tourists’ proenvironmental behaviors.

This study proposes performing arts viewing experiences as a novel way to encourage proenvironmental hotel choice. To the best of the authors’ knowledge, this is the first study to explore the impact of the performing arts watching experience on tourist behavior.

The article aims to elucidate how embracing Tropicália's conceptual framework can foster a more fluid and adaptive approach to organizing, transcending traditional boundaries and embracing diversity, innovation and creativity. The analysis encompasses various facets of organizational dynamics, including holdership, professional praxis, organizational ambiance, knowledge dissemination and diversity promotion. By examining Tropicália's reverberations in these areas, this article seeks to provide insights and perspectives that can contribute to the literature on organizational theory and practice, offering a rejuvenated and contemporaneous approach to the art of organizing.

This article explores the conceptual architecture of Tropicália, a Brazilian cultural and artistic movement, and its potential impact on contemporary organizational structures. By embracing Tropicália's essence, organizations can cultivate an adaptable and diverse ethos, free from traditional constraints. This analysis encompasses holdership as sustenance, professional praxis, organizational ambiance, knowledge dissemination and diversity promotion. Tropicália's potential to foster engagement, fuel innovation and shape an inclusive culture is examined. This article contributes a contemporary perspective to organizational theory, emphasizing the importance of integrating Tropicália's intellectual fabric for navigating the modern business landscape and fostering creativity and innovation.

The findings of this study highlight the potential impact of Tropicália on contemporary organizational practices. By embracing Tropicália's conceptual framework, organizations can foster a more fluid and adaptive approach to organizing, transcending traditional boundaries and embracing diversity, innovation and creativity. Tropicália's immersive and transformative esthetic experiences can create dynamic and inclusive organizational environments that encourage individual agency and stakeholder engagement. The analysis encompasses implications for holdership and management practices, organizational culture, collaboration and knowledge sharing, diversity and inclusion, innovation and creativity. Tropicália has the potential to foster employee engagement, drive innovation and create a more inclusive and adaptive organizational culture.

This article provides originality and value by exploring the potential ramifications of Tropicália on contemporary organizational esthetics. It offers a fresh and contemporary perspective on the art of organizing by drawing upon the unique conceptual framework of Tropicália. By embracing the principles of Tropicália, organizations can cultivate an organizational ethos that goes beyond traditional boundaries, fostering adaptability, diversity and innovation. The analysis encompasses aspects of organizational practices, including holdership, professional praxis, organizational culture and diversity and inclusiveness. The findings contribute to the existing literature on organizational theory and praxis, offering a rejuvenated perspective on organizing in the modern business landscape.

This study aims to provide the main contents of the revision of the 2023 OECD Guidelines for Multinational Enterprises and suggest implications for the Korean government and multinational enterprises.

Following the brief history of the revision of OECD Guidelines for Multinational Enterprises, this study reviews and evaluates major substantive and procedural revisions of the 2023 OECD Guidelines, and then suggests countermeasures for Korean government and businesses.

The most significant substantive change of the 2023 revision is that expectations for environmental due diligence and disclosure obligations, including climate change and biodiversity, for multinational enterprises have been expanded and strengthened. Regarding procedural changes, the biggest change is the introduction of a basis rule for the National Contact Points for Responsible Business Conduct (NCPs for RBC) to judge each issue and a rule that the final statement must include follow-up details and deadlines, which is expected to strengthen the effectiveness of the NCP dispute resolution mechanism.

This study is the first academic paper to introduce major substantive and procedural revisions to the 2023 OECD Guidelines for Multinational Enterprises in Korea. This study also provides implications for the Korean government and companies following the 2023 revised OECD Guidelines for Multinational Enterprises as follows. First, the Korean government must establish a public–private partnership to closely communicate to prevent Korean companies from being harmed by failing to meet strengthening international Environment, Social and Governance (ESG) standards. In addition, Korean government should actively participate in ESG-related international forums, including the OECD, and strive to reflect the needs and interests of Korean companies. Second, the Korean NCP should strengthen its activities to prevent potential damage by expanding education and promotions for Korean businesses on related overseas legislative trends and NCP dispute case studies so that Korean companies can effectively deal with the strengthened ESG standards. Third, Korean multinational enterprises should preemptively establish an advanced ESG management system to seize new opportunities in the global supply chain previously concentrated in China and India in the process of reorganizing global supply chains according to the trend of strengthening ESG standards and the US value alliance strategy.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

Human brands in the music industry use self-presentation tactics on social media to manage audience impressions. This practice has led to many posts asking followers to adopt behaviors favoring the human brand. However, its effectiveness in leveraging relevant performance metrics for musicians outside social media, such as popularity in specialized media, music sales and number of contracted concerts, needs further exploration. This study aims to reveal the effect of impression management tactics conveyed on social media on the market performance of musicians’ human brands.

Secondary data research classifies 5,940 social media posts from 11 music artists into self-presentation tactics (self-promotion, exemplification, supplication and ingratiation). It shows their predictions on three market performance metrics in an annual balanced panel study.

Impression management tactics via posts on social media are mostly self-promotion, improving the musicians’ market performance by increasing the number of contracted concerts. Conversely, ingratiation generated the most positive effect on the musician’s popularity but reduced music sales. Besides lowering the musicians’ popularity, exemplification also reduced the number of contracted concerts, while the supplication had no significant effect.

To the best of the authors’ knowledge, the research is the first to use social media postings of musicians’ official human brand profiles based on self-presentation typologies as a complete impression management tool. Furthermore, it is the first to test the effects of these posts on market performance metrics (i.e. outside of social media) in a longitudinal study.

This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.

Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.

Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.

In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

This paper aims to investigate the antecedents of recombinant innovation from the perspective of ego–network dynamics, and further disentangle whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under heterogeneous knowledge base.

This paper uses 1,801 patent data in China’s biotechnology field as a sample and adopts fixed effects regression model to examine the effects of ego–network dynamics on recombinant innovation and further uses the Wald tests to discern which ego–network dynamic is more conducive to recombinant innovation under heterogeneous knowledge base.

The empirical results indicate that ego–network dynamics have a positive impact on recombinant innovation. Specifically, for firms with high knowledge breadth and high knowledge depth as well as high knowledge breadth and low knowledge depth, ego–network stability is more conducive to recombinant innovation. By contrast, for firms with low knowledge breadth and high knowledge depth, recombinant innovation benefits more from ego–network expansion. As for firms with low knowledge breadth and low knowledge depth, both ego–network stability and ego–network expansion can promote recombinant innovation, while the effects are not significant.

This research may enlighten managers to choose suitable ego–network dynamics strategies for recombinant innovation based on their knowledge base.

This research not only contributes to the literature on recombinant innovation by revealing the impact of different ego–network dynamics on recombinant innovation but also contributes to network dynamics theory by exploring whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under a heterogeneous knowledge base.

Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.