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This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

Given the evolving market integration, this study aims to explore the connectedness of 12 real estate investment trusts (REITs) during the COVID-19 period.

The connectedness of 12 REITs was examined by considering three sample periods: full period, COVID peak period and COVID recovery period by using the quantile vector autoregressive (VAR) approach.

The findings ascertain that REIT markets are sensitive to COVID, revealing significant connectedness during each sample period. The USA and The Netherlands are the major shock transmitters; thus, these countries are relatively better options for the predictive behavior of the rest of the REIT markets. In contrast, Hong Kong and Japan are the least favorable REIT markets with higher shock-receiving potential.

The study recommends implications for real estate industry agents and investors to evaluate and anticipate the direction of return connectedness at each phase of the pandemic, such that they can incorporate those global REITs less vulnerable to unplanned crises. Apart from these implications, the study is limited to the global REIT markets and only focused on the period of COVID-19, excluding the concept of other financial and health crises.

This study uses a novel approach of the quantile-based VAR to determine the connectedness among REITs. Furthermore, the present work is a pioneer study because it is targeting different time periods of the pandemic. Additionally, the outcomes of the study are valuable for investors, policymakers and portfolio managers to formulate future development strategies and consolidate REITs during the period of crisis.

The purpose of this paper is to study the insulation structure optimization method of multiwinding high-frequency transformer (HFT).

This paper takes 100 kW, 10 kHz multiwinding HFT as the research object. First, the distribution of electric field strength within the core window of multiwinding HFT with different winding configurations is simulated by the electrostatic field finite element method. The symmetrical hybrid winding structure with minimum electric field strength is selected as the insulation design. To reduce the electric field strength at the end region of the winding, the electrostatic ring and angle ring are designed based on the response surface method.

The optimal results show that the maximum electric field strength can be reduced by 15.4%, and the low voltage stress can be achieved.

The above research provides guidance and basis for the optimal design of insulation structure of multiwinding HFT.

The new ways to complete financial transactions have been developed by setting up mobile payment (m-payment) platforms and such platforms to access banking in the financial mainstream can transact as never before. But, does m-payment have veiled consequences? To seek an answer, the research was undertaken to explore the dark sides of m-payment for consumers by extending the theory of innovation resistance (IR) and by measuring non-adoption intention (NAI).

Three hundred individuals using popular online m-payment apps such as Paytm, PhonePe, Amazon Pay and Google Pay were surveyed for the primary data. IBM AMOS based structural equation modelling (SEM) was used to analyse the data.

Each m-payment transaction leaves a digital record, making some vulnerable consumers concerned about privacy threats. Lack of global standards prevents consumers from participating in the m-payment system properly until common interfaces are established based on up-to-date standards. Self-compassion (SC) characteristics such as anxiety, efficacy, fatigue, wait-and-see tendencies and the excessive choice of technology effect contribute to the non-adoption of m-payment.

This study proposes a threat model and empirically explores the dark sides of m-payment. In addition, it also unveils the moderator's role of SC in building the structural relationship between IR and NAI.

The purpose of this paper is to propose a model that examines the relationships among the five dimensions of the unified theory of acceptance and use of technology (UTAUT) toward the overall behavioral intentions (BIs); to use cashless payment systems in Thailand, which are practically based on the basic models and theories of consumer behavior such as the theory of reasoned action (TRA), theory of planned behavior (TPB) and technology acceptance model (TAM); and to explain the indirect effects between UTAUT and BIs to use cashless payment systems mediating by perceived risk and trust.

A total of 708 respondents, who have had an experience with a cashless payment system in Thailand, were selected using a stage sampling method. The data obtained from the participants were analyzed using a structural equation modelling approach.

The results of this paper reveal that UTAUT model, perceived risk and trust have all significant influences on BIs to use a cashless payment system. This suggests that consumers in Thailand adopt to specific financial technological innovation if they perceive that the risk is low and they can trust the system, especially if it is associated with a reliable online banking network.

The basic understanding of the UTAUT model that influences BIs to use cashless payment systems has been the focus of this current paper. This paper empirically examined the overall direct and indirect influences of UTAUT model and perceived risk, trust and BI to use. This current paper also expands the UTAUT theory by exploring several dimensions (i.e. performance expectancy, effort expectancy and social influence). Research findings reveal that effort expectancy can reduce perceived risk and increase trust in Thailand's cashless payment systems. This can generate more customer interest and engagement, as well as provide insights into customers' intentions in using a cashless payment system.

Consumers worldwide are increasingly adopting mobile payments. However, despite its global popularity, this innovation did not diffuse among American consumers extensively. As of 2019, less than 30% of smartphone users in the USA engaged in mobile payments. The purpose of this paper is to investigate drivers of mobile payment adoption in the USA and to explore the mediating factors of consumers’ attitudes toward mobile payments.

Data were collected through Amazon M-Turk in the USA with 222 final respondents. Participants received an incentive to participate in this study. The measurement and structural models were evaluated by the component-based partial least squares (PLS) approach using Smart-PLS 3 software.

The study shows several factors affecting consumers’ attitudes toward mobile payments. Specifically, relative advantage, compatibility, observability, perceived security and convenience influence attitudes toward mobile payments and, consequently, behavioral intent to use mobile payment technology. This investigation contributes to the ongoing inquiry on the adoption of mobile payment technology in the USA. It offers specific insights for managers on how to increase mobile payment adoption. The study establishes a modified diffusion of innovations (DOI) model of consumer attitudes toward the use of mobile payments.

The objective of this study is to focus mainly on consumers in the USA, and it limits the generalizability of this study.

This study contributes to the current research on mobile payment adoption. Based on theoretical considerations, the authors derived a research model specifying critical drivers of an individual’s intentions to use mobile payments. Specifically, we establish a modified DOI-based model of consumer attitudes towards mobile payments. It shows several contributing factors affecting consumers’ attitudes toward mobile payments. The paper has important managerial implications aimed at the increase of the diffusion of mobile payments in the USA.

As an Internet fashion brand, HSTYLE has developed into an Internet enterprise with annual sales of 1.5 billion RMB within 10 years, establishing its position as the top industry performer in China. This case studies HSTYLES' innovation in business model and organizational management. HSTYLE's workgroups have achieved the balance of responsibilities and rights in a small team of three members at minimum, while mobilizing the enthusiasm and initiative of the line managers with the support of public service sector. At the same time, HSTYLE enriches its brand style, establishes a fashion cloud platform, and integrates individual and organizational consumers into its existing fashion design, manufacturing and sales system.

The purpose of this paper is to investigate the monitoring of the friction condition of mechanical seals.

Acoustic emission signals from the friction of the seal end face were obtained, and their bispectral characteristics were extracted. The variation of non-Gaussian information with the degree of friction was investigated, and by combining bispectral characteristics with information entropy, a bispectral entropy index was established to represent the friction level of the seal end face.

In the start-up stage, the characteristic frequency amplitude of the micro-convex body contact is obvious, the friction of the end face is abnormal, the complexity of the system increases in a short time and the bispectral entropy rises continuously in a short time. In the stable operation stage, the characteristic frequency amplitude of the micro-convex body contact varies with the intensity of the seal face friction, the seal face friction is stable and the bispectral entropy fluctuates up and down for a period of time.

The bispectral analysis method is applied to the seal friction monitoring, the seal frequency domain characteristics are extracted, the micro-convex body contact characteristic frequency is defined and the bispectral entropy characteristic index is proposed, which provides a certain theoretical basis for the mechanical seal friction monitoring.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0242/

The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends, hotspots, and directions for future research.

The data source for this paper is the Web of Science Core Collection, and 7,154 publications and related information have been derived. We use recognized bibliometric indicators to evaluate publications and visually analyze them through scientific mapping tools (VOS Viewer and CiteSpace).

The analysis results show that China is the most productive and influential country/region. East Asia countries have strong cooperation with each other and also have cooperation with other countries. The study shows that risk management has been involved in various fields such as credit, supply chain, health emergency and disaster especially in the background of COVID-19. We also found that machine learning, especially deep learning, has been playing an increasingly important role in risk management due to its excellent performance.

This paper focuses on studying risk management in East Asia, exploring its publication's fundamental information, citation and cooperation networks, hotspots, and research trends. It provides some reference value for scholars who are interested or further research in this field.