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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.

Rice is the main cereal in Senegal. Despite efforts to improve the sector, consumers still prefer imported rice. Only one previous study conducted by the authors analyzed these preferences using a sensory analysis approach (Mané et al., 2021). This initial study showed that local rice can compete with imported rice if processing is improved. Based on these results, this study aims to identify the physicochemical parameters responsible for the sensory quality identified in Senegalese consumers.

In this context, the physicochemical and cooking properties of 12 rice samples were analyzed and the correlations between these physicochemical and sensory properties were studied.

The results showed that imported rice had a higher 1000-kernel weight, grain length and transparency values, whereas local rice had higher water uptake, swelling ratios, gelatinization temperature and iron and magnesium contents. Correlations have shown that positive descriptors such as “beautiful,” “white color,” “good taste,” “fragrant,” “fine grains,” “typical rice odor,” well-cooked” and “scattered” were correlated with varietal and technological criteria such as high 1000-grain weight, grain length, whiteness, transparency and absence of impurities in rice. In contrast, negative sensory descriptors such as “pasty” and “sticky texture” were associated with water uptake ratio, gelatinization temperature, rice breakage and cooking time.

These results show how to improve the quality of new rice varieties in the country based on the physicochemical parameters associated with the positive sensory properties cited above by consumers.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

This study aims to systematically review the literature in the fields of liquidity, informational efficiency and default risk. The authors outline the key research streams and provide possible pathways for future research.

The study adopts bibliographic mapping to identify the most influential studies in the research fields of liquidity, informational efficiency and default risk from 1984 to 2021.

The study identifies four key research themes that include efficiency and transparency of markets; corporate yield spreads; market interactions: bonds, stocks and cryptocurrencies; and corporate governance. By assessing publications published from 2018 to 2021, the authors also document seven key emerging research trends: cross markets, managerial learning and corporate governance, state ownership and government subsidies, international evidence, machine learning (FinTech approaches), environmental themes and financial crisis. Drawing on these emerging trends, the authors highlight the opportunities for future research.

Keyword searches have limitations since some studies might be overlooked if they do not match the specified search criteria, even though their relevance to the topic is under investigation. Adopt the R project to expand this review by incorporating more literature from other databases, such as the Scopus database could be a possible solution.

The four key research streams contribute to a comprehensive understanding of liquidity, informational efficiency and default risk. The emerging trends integrate existing knowledge and leave the chance for innovative research to expand the research frontier.

This study fulfills the systematic literature review streams in the fields of liquidity, informational efficiency and default risk, and provides fruitful opportunities for future research.

Given its growing economic potential and social impact, this study aims to understand the motivations and concerns regarding metaverse usage. It identifies user needs and risks around the metaverse grounded on uses and gratifications theory and perceived risk theory.

The authors analyzed user reviews and rating data from Roblox, a representative modern metaverse platform. They applied BERTopic modeling to extract topics from reviews, identifying key motivations and risk aspects related to metaverse usage. They further constructed an explanatory model to assess how those affect user satisfaction and changes in these effects over time.

This study discovered that gratifications like entertainment, escapism, social interaction and avatar-based self-expression significantly influence user satisfaction in the metaverse. It also highlighted that users find satisfaction in self-expression and self-actualization through creating virtual spaces, items and video content. However, factors such as identity theft, fraud and child safety were identified as potential detriments to satisfaction. These influences fluctuated over time, indicating the dynamic nature of user needs and risk perceptions.

The novelty of this study lies in its dual application of the uses and gratifications theory and perceived risk theory to the metaverse. It provides a novel perspective on user motivations and concerns, shedding light on the distinct elements driving user satisfaction within the metaverse. This study unravels the metaverse’s unique capacity to assimilate features from established digital media while offering a distinctive user-generated experience. This research offers valuable insights for academics and practitioners in digital media and marketing.

This research pioneers the application of both uses and gratifications and perceived risk theories to understand factors influencing metaverse satisfaction. By establishing a comprehensive framework, it explores the metaverse’s unique value as a user-content creation platform, while encompassing existing digital platform characteristics. This study enriches the academic literature on the metaverse and offers invaluable insights for both metaverse platforms and brand marketers.

Based on the conservation of resource (COR) theory, this study explores employees’ learning agility (ELA) as an antecedent of knowledge sharing behaviour, specifically in the supply chain environment. However, such discretionary behaviour can be negatively affected by the prevalence of psychological contract breaches. According to COR theory, employees' resources (knowledge, ability and skills) act as motivational factors that employees strive to protect, retain and at the same time invest in favour of obtaining more resources. On the other hand, when resource loss weighs more than resource gain, an individual agitated with resource depletion will minimise resource loss by decreasing their effort for future displays of resources. A random sample of 418 participants from the public sector in the Fiji Islands yielded a sample of 418 participants. The proposed model was analysed through structural equation modeling (SEM) to determine its fit. The analysis supports the proposed theoretical framework, providing a new dimension for ELA as an unexplored phenomenon for knowledge sharing behaviour (KSB) in the supply chain. The study specifically draws the attention of policymakers on industry, innovation and infrastructure (SDG09), where immediate actions are needed to create resilient supply chain management through ELA. Research shows that agile employees can easily adapt to unexpected changes, actively participate in discussions and quickly contribute to innovative and creative solutions. KSB can be further developed through a culture of learning and sharing, rewards for KSB, psychological support and upholding its promised obligations through regular communication, establishing a more resilient supply chain management.

Based on Kansei Engineering, this study obtained consumers' emotional preferences aiming to enhance the emotional connection between consumers and clothing to extend the service life of clothing and realize sustainable clothing design.

Six Kansei word pairs that are the most important to consumers were identified through literature reviews, magazines, websites, card sorting of consumers and cluster analysis. Finally, the consumers scored the 32 product specimens through a 5-level rating semantic differential scale questionnaire of six Kansei word pairs. The researchers verified the consumers' emotional preferences through principal component analysis and established the relationship between Kansei words and design elements of color through partial least squares.

The study found consumers' emotional preferences: elegant, minimalist, formal, casual, mature, practical and distinctive style. Besides white, black, gray, blue, consumers will also like red and yellow-red in the future. The crucial findings of this study are to get recommended guidelines that consumers' emotional preferences match the corresponding design elements.

The study's findings can be used to style the design of men's plain-color shirts and guide online marketers and designers to design apparel that meets consumers' emotional needs to develop consumers' sustainability reliance on clothing. This study also explains the overall process and methodology for integrating consumer preferences and product design elements.

With the function to store individual’s data input, personal health record (PHR) enhanced the accessibility to personal health information. This study aims to assess the factors that impact the intention of Malaysian internet users to use PHR and create a modified technology acceptance model (TAM) for eHealth.

Multivariate statistical analysis was performed on a total of 216 responses using the partial least square technique based on the cross-sectional survey among Malaysian internet users.

Behavioral intention was positively associated to PHR. Subjective norm significantly influenced both attitude and intention to use, whereas trust and perceived usefulness significantly influenced attitude. There was no significant positive impact in the relationships between compatibility and perceived ease of use and intention to use; nevertheless, they positively influenced perceived usefulness. Attitude exhibited mediating influence between trust, perceived usefulness and subjective norm and intention to use. Nonetheless, perceived risk did not affect behavioral intention. Thus, PHR acceptance was well-justified by the modified TAM in evaluating eHealth acceptance.

The eHealth vendors can enhance their marketing and development strategies on related products.

Literatures and empirical evidence on eHealth are still scarce, especially in emerging markets. The role of attitude may not be well-researched in health-care context, therefore was included in this study’s modified TAM. Critical determinants, namely, trust and risk, were added to the model.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

Carbon reduction in the construction supply chain can critically affect the construction industry’s transition to an environmentally sustainable one. However, implementing carbon reduction in all parties is restricted because of the poor understanding of the drivers influencing the low-carbon construction supply chain (LCCSC). The purpose of this paper is to systematically identify the drivers of LCCSC, analyze their causality, and prioritize the importance of their management.

A decision-making analysis process was developed using an integrated decision-making trial and evaluation laboratory (DEMATEL)–analytical network process (ANP). First, the hierarchical drivers of the LCCSC were identified through a literature review. The DEMATEL method was subsequently applied to analyze the interactions between the drivers, including the direction and strength of impact. Finally, the ANP analysis was used to obtain the drivers’ weights; consequently, their priorities were established.

Various factors with complex interactions drive LCCSC. With respect to their influence relationships, incentive policy, regulatory policy, consumers’ low-carbon preference, market competition, supply chain performance, and managers’ low-carbon awareness have more significant center degrees and are cause drivers. Their strong correlations and influence on other drivers should be noticed. In terms of weights in the driver system, regulatory policy, consumers’ low-carbon preference, supply chain performance, and incentive policy are the key drivers of LCCSC and require primary attention. Other drivers, such as supply chain collaboration, employee motivation, and public participation, play a minor driving role with less management priority.

Despite some contributing studies with localized perspectives, the systematic analysis of LCCSC drivers is limited, especially considering their intricate interactions. This paper establishes the LCCSC driver system, explores the influence relationships among the drivers, and determines the key drivers. Hence, it contributes to the sustainable construction supply chain domain by enabling decision-makers and practitioners to systematically understand the drivers of LCCSC and gain management implications on priority issues with limited resources.