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The emotion regulation research has drawn considerable attention from academicians and scholars in the contemporary world. As a result, the publications that are specifically dedicated to emotion regulation research are rapidly escalating. Therefore, this study aims to conduct a bibliometric analysis of research articles that have been published in the field of “emotion regulation.” The study primarily examines the growth and development of scholarly publications, seminal studies, influential authors, productive journals, research production and collaboration among countries, emerging research themes, research hotspots and thematic evolution of emotion regulation research.

The Web of Science Core Collection database was used to gather the study’s data, which was then analysed using VOSviewer and Bibliometrix, Biblioshiney open-source package of the R language environment.

The study’s results reveal that the research on emotion regulation has grown significantly over the last three decades. Notably, Emotion and Frontiers in Psychology are the most dominant and productive journals in the field of emotion regulation research. The most prominent author in the area of emotion regulation is identified as James Gross, followed by Gratz, Wang and Tull. The USA is at the forefront of research on emotion regulation and has collaborated with most of the developed countries like Germany, England and Canada. The keyword analysis revealed that the most potential research areas in the field of emotion regulation are functional magnetic resonance imaging, amygdala, post-traumatic stress disorder, borderline personality disorder, alexithymia, emotion dysregulation, depression, anxiety, functional connectivity, neuroimaging, mindfulness, self-regulation, resilience and coping. The thematic evolution reflects that the research on emotion regulation has recently focused on issues including Covid-19, non-suicidal self-injury, psychological distress, intimate partner violence and mental health.

The results of this study highlighted the current knowledge gaps in emotion regulation research and suggested areas for further investigation. The present study could be useful for researchers, academicians, planners, publishers and universities engaged in emotion regulation research.

Applying the internationalization process model (IPM) and the strategic fit perspective, this research aims to test the effects of firm age on Chinese firms’ outward foreign direct investment (OFDI) in developing and developed countries.

Using data on some Chinese firms, this study applied the zero-inflated negative binomial model and Heckman two-stage model to do the analyses.

This research found that firm age has different effects on Chinese firms’ OFDI in developed and developing countries. State ownership and industry munificence independently and jointly can moderate these effects.

This study contributes to the IPM and solves the theoretical conflict about the firm age–OFDI relationship.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

This study aims to identify the decision-making process involved in the purchase of organic wine from consumer values to attitudes to behavioural intention towards organic wine via the value–attitude–behaviour (VAB) model. Involvement in wine is also taken into consideration.

The data were collected using a snowball sampling method and a closed-ended questionnaire. A total of 209 responses were analysed. Linear regression and PROCESS Macro on SPSS were used to perform data analysis.

Both biospheric-altruistic values and egoistic values are positively associated with attitudes towards organic wine. Attitude is found to mediate the relationship between biospheric-altruistic/egoistic values and behavioural intention. Egoistic values are found to significantly predict behavioural intention in the organic wine purchase context. Involvement was found to moderate the relationship between egoistic values and attitudinal loyalty.

This study identifies the decision-making hierarchy from consumer values to attitudes to behavioural intention, theoretically confirming the robustness of the VAB model in the organic wine consumption context. It also makes a practical contribution by indicating the marketing emphasis of organic wine and segmenting potential consumers according to their values and levels of wine involvement.

This paper investigates the influence of political connections on sustainability disclosure in the context of China's Regulation 18.

The study employs a quasi-experimental approach, utilizing difference-in-difference (DiD) analysis, dynamic DiD and propensity score matching to analyze the effects of politically connected independent directors on sustainability disclosure following the implementation of Regulation 18.

Companies with politically connected independent directors show an improvement in sustainability disclosures after Regulation 18. This effect is stronger for firms facing high political pressure or lacking alternative political power. Additionally, the increase in value from sustainability disclosures compensates for the loss of politically connected independent directors, indicating a positive value impact of sustainability disclosures.

This study provides novel insights into the corporate disclosure policy in China by investigating the impact of politically connected directors on sustainability disclosure. Additionally, it sheds light on the limitations of political power and its substitution effects within companies.

This study explores the influence of cultural heritage tourism perception on China's tourism image. It analyzes the role of the spiritual bond established between overseas Chinese youth and the motherland during their visit to the cultural heritage sites in China. This study constructs a theoretical model with 350 overseas Chinese youth as samples based on the identity theory, Stimulus-Organism-Response (S-O-R) theory, and Howard-Sheth model. The results show that cultural heritage tourism perception directly and positively promotes cultural identity among overseas Chinese youth. It is also indirectly and positively associated with their cultural identity through enhancing the tourism image. Cultural intelligence plays a positive moderating role between cultural heritage tourism perception and cultural identity. The results provide significant implications for developing cultural heritage tourism and cultural communication.

This study investigates the key factors that influence customer satisfaction when interacting with augmented reality shopping assistance applications (ARSAPs). ARSAPs grant consumers the capability to experience products in a virtually simulated user environment before product acquisition. With the development of mobile e-commerce due to breakthroughs in smartphone and augmented reality (AR) technologies, there is an increasing potential for these emergent AR mobile services, yet there is a need for further improvement.

This study initially explored the key satisfaction factors for ARSAPs by utilizing topic modeling of a collection of actual user reviews. These factors are subsequently revisited and complemented by existing literature, and finally verified through logistic regression analysis supported by sentiment analysis.

This study identified the key factors that influence customer satisfaction with ARSAPs, including visuality, sense of reality, credibility, format, completeness, understandability, relevance, flexibility, response time, reliability, availability, ease of use and privacy. In particular, two additional factors (i.e. visuality and sense of reality) were newly identified as important in the context of AR, despite their previous omissions in existing literature.

This study is the first to investigate the key factors that influence customer satisfaction with ARSAPs from users' perspectives, utilizing topic modeling of a large amount of real-world data on actual user feedback. By identifying new factors (i.e. visuality and sense of reality) that were not identified in previous literature, this study provides important academic implications for a broader understanding of AR and related technologies that are essential elements of the metaverse. This study also provides valuable insights for developers and companies in the e-commerce industry on how to optimize AR applications and develop more targeted and effective marketing strategies in this field.

Gallic acid is a substance that is widely found in nature. Initially, it was only used as a corrosion inhibitor to retard the rate of corrosion of metals. In recent years, with intensive research by scholars, the modification of coatings containing gallic acid has become a hot topic in the field of metal protection. This study aims to summarize the various preparation methods of gallic acid and its research progress in corrosion inhibitors and coatings, as well as related studies using quantum chemical methods to assess the predicted corrosion inhibition effects and to systematically describe the prospects and current status of gallic acid applications in the field of metal corrosion inhibition and protection.

First, the various methods of preparation of gallic acid in industry are understood. Second, the corrosion inhibition principles and research progress of gallic acid as a metal corrosion inhibitor are presented. Then, the corrosion inhibition principles and research progress of gallic acid involved in the synthesis and modification of various rust conversion coatings, nano-coatings and organic resin coatings are described. After that, studies related to the evaluation and prediction of gallic acid corrosion inhibition on metals by quantum chemical methods are presented. Finally, new research ideas on gallic acid in the field of corrosion inhibition and protection of metals are summarized.

Gallic acid can be used as a corrosion inhibitor or coating in metal protection.

There is a lack of research on the synergistic improvement of gallic acid and other substances.

The specific application of gallic acid in the field of metal protection was summarized, and the future research focus was put forward.

To the best of the authors’ knowledge, this paper systematically expounds on the research progress of gallic acid in the field of metal protection for the first time and provides new ideas and directions for future research.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

Cultivating loyal customers is a pressing concern for streamers. The present study investigates how to build interpersonal relationships with streamers and whether different interpersonal relationship factors lead to repurchase intention and WOM intention in live streaming commerce. The moderating effect of gender is also examined.

A self-administered questionnaire was completed by 429 live streaming commerce users in mainland China. Partial least squares structural equation modeling was used to test the research hypotheses.

The results indicate that all four streamer attributes (expertise, authenticity, attractiveness, and homophily) have a positive influence on swift guanxi, and swift guanxi is effective in predicting both calculative commitment and affective commitment. In addition, all interpersonal relationship factors (swift guanxi, calculative commitment, and affective commitment) significantly affect repurchase intentions, with only affective commitment being linked to WOM intention. Also, the moderating role of gender was confirmed in expertise – swift guanxi, attractiveness – swift guanxi, cognitive commitment – repurchase intention and affective commitment – repurchase intention linkages.

This paper contributes to the live streaming commerce literature by integrating swift guanxi, calculative commitment, and affective commitment to understand the repurchase intention and WOM intention from the relationship-building process perspective. In addition, this paper enriches the source credibility and source attractiveness models by identifying gender boundaries on the effectiveness of these models in predicting swift guanxi.