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This study aims to investigate the factors influencing liquidity creation in banks, particularly focusing on the role of bank governance. Using a unique panel data set, it compares Islamic and conventional banks to discern governance’s impact on liquidity creation, offering insights for policymakers and bank managers.

Quantitative analysis is used on a panel data set to assess liquidity creation determinants in banks. A governance index is constructed, analyzing metrics such as risk management, audit committee effectiveness and Shariah board presence. Regression models identify significant relationships between governance factors and liquidity creation.

This study reveals a positive relationship between governance index and liquidity creation, especially in banks with better performance, higher credit risk, smaller size and lower equity, particularly in low-inflation environments. Specific governance practices significantly impact liquidity creation, alongside a positive relationship with Tier1 ratio, supporting the risk absorption hypothesis.

This research offers empirical evidence on the relationship between bank governance and liquidity creation, highlighting its significance for both Islamic and conventional banks. It provides valuable insights for policymakers and bank managers aiming to enhance banking sector stability and efficiency.

Social media played an irreplaceable role in young people’s online social life and information consumption during the COVID-19 pandemic. This research focuses on the impact of excessive information on social media about COVID-19 vaccines on Generation Z's (Gen Z) associated psychological states and long-term vaccine advocacy.

The research conducted structural equation modeling analysis with online survey data from 409 Gen Z citizens in the UK.

The findings suggest that excessive information increased Gen Z social media users' ambivalence and conspiracy beliefs around COVID-19 vaccines, which, in turn, reduced their long-term vaccine advocacy in terms of vaccine acceptance, vaccination intention and vaccine promotion. Importantly, Gen Z’s confidence in government and in the healthcare systems during COVID-19 was effective in helping them overcome the detrimental effects of conspiracy beliefs and ambivalence about long-term vaccine advocacy, respectively.

This research reveals the “dark side” of social media use in the post-pandemic period and highlights the significant roles played by social institutions in mitigating the detrimental effects of Gen Z’s support in social decisions. Beyond the context of COVID-19, this research has important implications for facilitating the civic engagement of Gen Z and boosting their confidence in social institutions in terms of social cohesion.

Highlights in academic papers serve as condensed summaries of the author’s key work, allowing readers to quickly grasp the paper’s focus. However, many journals do not currently offer highlights for their articles. To address this gap, some scholars have explored using supervised learning methods to extract highlights from academic papers. A significant challenge in this approach is the need for substantial amounts of training data.

This study examines the effectiveness of prompt-based learning for generating highlights. We develop task-specific prompt templates, populate them with paper abstracts and use them as input for language models. We employ both locally inferable pre-trained models, such as GPT-2 and T5, and the ChatGPT model accessed via API.

By evaluating the model’s performance across three datasets, we find that the ChatGPT model performed comparably to traditional supervised learning methods, even in the absence of training samples. Introducing a small number of training samples further enhanced the model’s performance. We also investigate the impact of prompt template content on model performance, revealing that ChatGPT’s effectiveness on specific tasks is highly contingent on the information embedded in the prompts.

This study advances the field of automatic highlights generation by pioneering the application of prompt learning. We employ several mainstream pre-trained language models, including the widely used ChatGPT, to facilitate text generation. A key advantage of our method is its ability to generate highlights without the need for training on domain-specific corpora, thereby broadening its applicability.

Because the success of m-payment services depends on the enablers and barriers that affect user satisfaction, the present research explores the effects of perceived value and sacrifices on users' satisfaction with m-payment services. The predicted relationships among perceived value, perceived sacrifices, users' satisfaction, continuance intention, word-of-mouth (WOM), shopping effectiveness, quality of life (QOL) and stickiness were established based on the mobile technology acceptance model (MTAM) and the value-based adoption model (VAM).

A representative data sample of 430 Egyptian banking clients was analyzed to test the hypotheses using partial least squares-structural equation modeling (PLS-SEM).

The findings revealed that all perceived value constructs significantly positively affect users' satisfaction. Moreover, all perceived sacrifice constructs significantly negatively affect users' satisfaction. Users' satisfaction, in turn, has a significant positive effect on continuance intention, WOM, shopping effectiveness, QOL and stickiness with m-payment services.

This is the first study to examine several levels of m-payment outcomes, including m-payment, consumer and bank outcomes, based on the integration of MTAM and VAM models.

The systematic literature review aims to highlight the transformative influence of mobile technology in reshaping library services for Library and Information Science (LIS) professionals and users.

The selection of relevant literature utilized the PRISMA-20 guidelines and a systematic literature review, wherein 39 articles were meticulously examined from a pool of 198. The inclusion and exclusion criteria were applied to articles retrieved from the Scopus database and Google Scholar, focusing on data from 2018 to 2023 and excluding all the literature except journal articles.

The results demonstrate a significant awareness among LIS professionals and users regarding adopting mobile-based library services. Nonetheless, the adoption of mobile technology in libraries faces impediments from internal and external factors. These challenges encompass the need for coherent policies and practical issues such as insufficient financial resources, limitations in infrastructure, unpredictable electricity and Internet connectivity. The study also highlights concerns related to data security, the digital divide, a shortage of IT experts and inadequate staffing. The findings suggest that libraries must navigate this evolving landscape to provide improved and more accessible services to their users.

This study addresses a gap in the existing research by contributing to the literature on using mobile technology for accessing library services and resources. The findings aim to assist LIS professionals in developing plans, policies and strategies for implementing mobile-based library services.

The results of this study contribute to improving and expanding current mobile-based services in libraries, ensuring that information and resources are universally accessible “on the go environment.”

In a reflection of individuals’ interest in maintaining a healthy lifestyle, nutraceutical restaurants, which provide health benefits, have emerged on the market. However, little is known about the underlying factors and mechanisms of the consumption of nutraceutical foods. Therefore, grounded in an extended cognitive–affective–conative model, this study examines the role of dining motivations and restaurant attributes in explaining an integrated model that focuses on customers’ loyalty formation process.

Data were collected from 893 Chinese customers who had recently dined at traditional Chinese medicine restaurants. Using partial least squares structural equation modeling, this study examined the structural relationships among dining motivations, restaurant attributes, customers’ perceived benefits, overall satisfaction, place attachment and revisit intentions.

The results of a confirmatory factor analysis revealed that two main motivations (i.e. health and traditional culture) and five restaurant attributes (i.e. food, service staff food recommendation, service staff service quality, furniture and tableware and atmosphere) are stimuli that influence customers’ perceived benefits. This study also revealed that four types of perceived benefits (functional, emotional, social and epistemic) significantly affect overall satisfaction and place attachment, which determine revisit intentions.

The findings of this study help identify not only important dining motivations and restaurant attributes but also their roles in loyalty formation with respect to traditional Chinese medicine restaurants. Thus, the results contribute to an improved understanding of how to satisfy customers’ dining motivations and efficiently manage restaurant attributes to strengthen customer loyalty.

This study is among the first to simultaneously examine the effects of customer motivation and restaurant attributes and to explain how they affect customer loyalty behaviors at traditional Chinese medicine restaurants.

International trade transactions, extracted from customs declarations, include several fields, among which the product description and the product category are the most important. The product category, also referred to as the Harmonised System Code (HS code), serves as a pivotal component for determining tax rates and administrative purposes. A predictive tool designed for product categories or HS codes becomes an important resource aiding traders in their decision to choose a suitable code. This tool is instrumental in preventing misclassification arising from the ambiguities present in product nomenclature, thus mitigating the challenges associated with code interpretation. Moreover, deploying this tool would streamline the validation process for government officers dealing with extensive transactions, optimising their workload and enhancing tax revenue collection within this domain.

This study introduces a methodology focused on the generation of sentence embeddings for trade transactions, employing Sentence BERT (SBERT) framework in conjunction with the Multiple Negative Ranking (MNR) Loss function following a contrastive learning paradigm. The procedure involves the construction of pairwise samples, including anchors and positive transactions. The proposed method is evaluated using two publicly available real-world datasets, specifically the India Import 2016 and United States Import 2018 datasets, to fine-tune the SBERT model. Several configurations involving pooling strategies, loss functions, and training parameters are explored within the experimental setup. The acquired representations serve as inputs for traditional machine learning algorithms employed in predicting the product categories within trade transactions.

Encoding trade transactions utilising SBERT with MNR loss facilitates the creation of enhanced embeddings that exhibit improved representational capacity. These fixed-length embeddings serve as adaptable inputs for training machine learning models, including support vector machine (SVM) and random forest, intended for downstream tasks of HS code classification. Empirical evidence supports the superior performance of our proposed approach compared to fine-tuning transformer-based models in the domain of trade transaction classification.

Our approach generates more representative sentence embeddings by creating the network architectures from scratch with the SBERT framework. Instead of exploiting a data augmentation method generally used in contrastive learning for measuring the similarity between the samples, we arranged positive samples following a supervised paradigm and determined loss through distance learning metrics. This process involves continuous updating of the Siamese or bi-encoder network to produce embeddings derived from commodity transactions. This strategy aims to ensure that similar concepts of transactions within the same class converge closer within the feature embedding space, thereby improving the performance of downstream tasks.

This research applies the stimulus-organism-behavior-consequence framework to explore how blockchain-enabled traceability influences trust in organic food producers and retailers, which impacts consumers’ purchase behaviors and subsequent outcomes.

Using a purposive sample of 5,326 Vietnamese consumers, multiple linear and polynomial regression with response surface analysis were employed to examine the hypotheses.

Blockchain-enabled traceability significantly enhances trust in both producers and retailers, which congruently and incongruently influences organic food purchase behaviors. This behavior also drives consumers’ word-of-mouth and repurchase intentions. Serial mediation analysis confirms blockchain’s impact through trust and purchase behaviors.

Stakeholders should adopt blockchain to boost transparency and trust, which increases consumer engagement. Policymakers can support this transition through regulations and incentives to enhance food security and sustainability.

This study expands on blockchain research by applying the stimulus-organism-behavior-consequence framework in the organic food supply chain, showing how blockchain-enhanced trust synergistically affects consumers’ purchase behaviors, word-of-mouth and repurchase intentions.

Axleboxes are an important structure that connects the wheelset with the bogie frame. Inside axlebox bogies have lower wheelset yaw angle stiffness and better curve-passing performance. The purpose of this paper to study the differences in the wear evolution law and the influencing factors of the two types of metro vehicles.

This paper established the dynamic model and wear model of both outside axlebox and inside axlebox metro vehicles to research the wheel wear evolution law of the two types of vehicles. The curve passing performance of two vehicles is analyzed. The effect of key parameters on wheel wear is studied, including the lateral distance of the axlebox, the longitudinal stiffness of the rotary arm node, the lateral stiffness of the rotary arm node and the wheel profiles.

The results showed that the model of inside axlebox metro vehicles improved vehicle safety and curve-passing performance. At the same time, inside axlebox metro vehicles reduce wheel wear of the wheel tread area and wheel flange area. When the S1002 wheel tread profile matched with the vehicle parameters, the wheel wear is minimized.

This paper established a dynamic model for inside axleboxes metro vehicles, then used a wheel wear model to analyze the evolution of wheel wear and the key influencing factors of the inside axleboxes metro vehicles.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0256/

This study aims to apply the theory of consumption value to analyze the utilization of Metaverse technologies within hospitality and tourism while examining the factors that impact consumer intentions to use the Metaverse.

This paper aims to consider an extensive study spanning the period October 2021 to March 2023 was conducted to understand the shifts in an individual's intention to use Metaverse technologies in hospitality.

The findings of this study confirm that individual attitudes to the Metaverse and trust in Metaverse technologies significantly impact their intention to use the Metaverse.

The study aims to provide fresh insights into how individuals perceive Metaverse technologies in the context of choosing hotels and resorts, which enriches the understanding of consumer behaviors around Metaverse technology in hospitality.

This study aims to consider not only tourist intentions to use the Metaverse but also how diverse consumption values impact user attitudes, an area currently underresearched.