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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.

This research aims to investigate the disciplinary functions of depositors and subordinated debt holders within Indonesia's dual banking system, examining the impact of regulatory changes on market discipline.

The study employs a comprehensive analysis of the dual banking system in Indonesia over 15 years. Utilizing a non-public dataset from the Financial Services Authority and the Indonesia Deposit Insurance Corporation, the study employs propensity score matching and difference-in-differences analysis.

The findings reveal distinct patterns in the exercise of market discipline by depositors over different regulatory regimes. During the blanket guarantee regime (2002–2005), depositors lacked the incentive to monitor banks but resumed their disciplinary role under the limited guarantee regime (2005–2017). Islamic banks faced simultaneous market and regulatory discipline, with market discipline prevailing.

This study contributes to the literature by providing novel insights into the interplay between regulatory changes, market discipline and depositor behavior within Indonesia's dual banking system. The utilization of a comprehensive non-public dataset from regulatory authorities adds to the originality of the research.

The purpose of this paper is to analyze whether and how intergovernmental fiscal transfers (IFTs) affect technological innovation.

China’s provincial panel data from 2007 to 2019 are used in an empirical study to examine the effect of IFTs on technological innovation and the role of fiscal spending policy in the relationship between the two by using the spatial Durbin model.

Results show an evident spatial correlation for the effect of IFTs on technological innovation, indicating that IFTs have a significant negative influence on technological innovation in local and surrounding regions. IFTs also inhibit technological innovation by negatively affecting science and technology spending and education spending.

These findings can aid policymakers in advancing technological innovation by improving the system of fiscal transfers and optimizing the structure of fiscal spending.

Although the determinants of technological innovation have been analyzed, no studies have investigated the effect of IFTs on technological innovation. Thus, this paper aims to address this gap.

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.

To investigate the influence of vehicle operation speed, curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.

Taking a small-radius curve of a high-speed railway as the research object, field measurements were conducted to obtain track parameters and wheel–rail profiles. A coupled vehicle-track dynamics model was established. Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.

Key service parameters affecting wheel–rail creepage were identified, including the matching relationship between curve geometry and vehicle speed and rail profile parameters. The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis, leading to the establishment of parameter optimization criteria.

This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves. A response surface-based parameter-creepage relationship model was established, and a multi-parameter coordinated optimization strategy was proposed. The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.

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.”

Genre classification of songs based on lyrics is a challenging task even for humans, however, state-of-the-art natural language processing has recently offered advanced solutions to this task. The purpose of this study is to advance the understanding and application of natural language processing and deep learning in the domain of music genre classification, while also contributing to the broader themes of global knowledge and communication, and sustainable preservation of cultural heritage.

The main contribution of this study is the development and evaluation of various machine and deep learning models for song genre classification. Additionally, we investigated the effect of different word embeddings, including Global Vectors for Word Representation (GloVe) and Word2Vec, on the classification performance. The tested models range from benchmarks such as logistic regression, support vector machine and random forest, to more complex neural network architectures and transformer-based models, such as recurrent neural network, long short-term memory, bidirectional long short-term memory and bidirectional encoder representations from transformers (BERT).

The authors conducted experiments on both English and multilingual data sets for genre classification. The results show that the BERT model achieved the best accuracy on the English data set, whereas cross-lingual language model pretraining based on RoBERTa (XLM-RoBERTa) performed the best on the multilingual data set. This study found that songs in the metal genre were the most accurately labeled, as their text style and topics were the most distinct from other genres. On the contrary, songs from the pop and rock genres were more challenging to differentiate. This study also compared the impact of different word embeddings on the classification task and found that models with GloVe word embeddings outperformed Word2Vec and the learning embedding layer.

This study presents the implementation, testing and comparison of various machine and deep learning models for genre classification. The results demonstrate that transformer models, including BERT, robustly optimized BERT pretraining approach, distilled bidirectional encoder representations from transformers, bidirectional and auto-regressive transformers and XLM-RoBERTa, outperformed other models.

The Unified Payments Interface (UPI) has emerged as a transformative force within the digital payment landscape, revolutionising how individuals and businesses conduct financial transactions in India. This research paper embarks on a theoretical journey to elucidate the growth trajectory of UPI by delving into secondary data sources. Through meticulous analysis and synthesis of existing literature, regulatory reports and industry insights, this paper constructs a comprehensive narrative that traces the evolution of UPI from its inception to its current status as a cornerstone of the Indian digital payments ecosystem. The theoretical framework employed in this study draws upon concepts from innovation diffusion theory, network effects and technological adoption models to elucidate the factors driving the adoption and proliferation of UPI. By examining key milestones, regulatory interventions, technological advancements and market dynamics, this paper unveils the intricate interplay of various stakeholders, including banks, fintech firms, government bodies and consumers, in shaping the trajectory of UPI growth. Furthermore, this research delves into the utility of UPI as a payment infrastructure by exploring its implications for financial inclusion, efficiency, security and economic development. By facilitating seamless, real-time and interoperable transactions, UPI has democratised access to financial services, empowered small businesses and catalysed the transition towards a less-cash economy. This paper fills a critical gap in the literature by providing a holistic and theoretically grounded analysis of UPI's evolution and its broader implications. It contributes valuable insights into the mechanisms driving digital payment innovations and their socio-economic impacts, thereby informing policy decisions to foster inclusive and sustainable financial ecosystems.

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.