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This study holistically and systematically consolidates the available research on digital reading to reveal the research trends of the past 20 years. Moreover, it explores the thematic evolution, hotspots and developmental characteristics of digital reading. This study, therefore, has the potential to serve as a research guide to researchers and educators in relevant fields.

The authors applied a bibliometric approach using Derwent Data Analyzer and VOSviewer to retrieve 2,456 publications for 2003–2022 from the Web of Science (WoS) database.

The results revealed that most studies' participants were university students and the experimental methods and questionnaires were preferred in digital reading researches. Among the influential countries or regions, institutions, journals and authors, the United States of America, University of London, Electronic Library and Chen, respectively, accounted for the greatest number of publications. Moreover, the authors identified the developmental characteristics and research trends in the field of digital reading by analyzing the evolution of keywords from 2003–2017 to 2018–2022 and the most frequently cited papers by year. “E-books,” “reading comprehension” and “literacy” were the primary research topics. In addition, “attention,” “motivation,” “cognitive load,” “dyslexia,” “engagement,” “eye-tracking,” “eye movement,” “systematic analysis,” “meta-analysis,” “smartphone” and “mobile reading/learning” were potential new research hotspots.

This study provides valuable insights into the current status, research direction, thematic evolution and developmental characteristics in the field of digital reading. Therefore, it has implications for publishers, researchers, librarians, educators and teachers in the digital reading field.

This study aims to reduce the weight of the door, improve the operating efficiency and ensure the safety of vehicle operation.

Based on traditional aluminium alloy doors, a new type of honeycomb composite material was developed. Tests were conducted to determine the honeycomb compression resistance, honeycomb and skin shear performance, plate bending, thermal conductivity and environmental protection. Eight doors were developed based on the full-side open structure, and static strength and stiffness analyses were performed simultaneously. To solve door vibration problems, modal analysis and test were carried out.

The test results showed that the weight of the door was reduced by more than 40% whilst ensuring the strength and stiffness of the vehicle. The first–sixth-order test mode of the door was increased by more than 14% compared with existing aluminium alloy doors.

A new type of honeycomb composite material was used in this study. The test results showed that the weight of the door was reduced by more than 40% whilst ensuring the strength and stiffness of the vehicle. The 1st-to-6th order test mode of the door was increased by more than 14% compared with the existing aluminium alloy door.

This research is motivated by the increasing need for international interactions during the gradual recovery of the tourism industry. By recognizing the paucity of research on cultural closeness and accommodation categories, this research aims to illuminate the influencing mechanisms of psychological closeness and travelers’ willingness to book an accommodation-sharing property while booking an accommodation.

The authors employ a mixed-methods approach, including an experiment and semistructured interviews.

Results show that hosts’ higher cultural identity congruence leads to travelers’ higher willingness to book an accommodation-sharing property. Psychological closeness mediates the positive effect of cultural identity congruence on travelers’ willingness to book. The authors further explore the moderating role of room types (entire room vs. private room) and find that the mediation effect is stronger for booking an entire room.

The current research underlines the importance of cultural identity congruence and accommodation type on travelers’ willingness to book an accommodation-sharing property and psychological closeness.

One of the primary challenges of conducting information encountering (IE) research is the difficulty in capturing people's IE experiences. The purpose of this paper is to develop a general description framework of IE experiences as guidance for participants to record diary entries in diary studies of IE.

340 descriptions of IE incidents were collected from 18 previous IE studies as secondary data. A thematic analysis of the secondary data engendered a general description framework of IE experiences composed of 9 main themes and 31 sub-themes. The framework was then applied in a diary study to investigate Generation Z's online IE behavior.

The nine main themes of the framework, including “environment”, “foreground activity”, “stimulus noticed”, “reaction to stimulus”, “content examined”, “interaction with encountered information”, “value of experience”, “pre-encountering emotional state”, and “post-encountering emotional state”, were used to create a diary questionnaire for collecting IE incidents. The sub-themes were refined and organized into a coding scheme for the content analysis of the incidents collected. The diary study collected 255 valid IE incidents which were analyzed based on three phases, that is, pre-encountering, encountering, and post-encountering.

The value of this study consists in its methodological contributions. First, it makes creative use of secondary data accumulated in the literature and derives from the thematic analysis a general framework which people follow to describe their IE experiences. Second, it demonstrates the great potential of diaries for data collection in IE research through the successful application of the general description framework of IE experiences in a diary study. Third, the diary questionnaire created based on the framework provides sufficient guidance in eliciting complete and detailed IE incidents.

Extant research on the crowding-out effects of government subsidies on the positive role of firm innovation resources or activity remains limited. This paper aims to investigate the crowding-out effects of subsidies on the utilization of technological capabilities and also the contingency mechanisms of market-oriented economy based on the resource-based view (RBV), given the co-existence of the subsidies and technological capabilities for firm innovation in transitional economy.

This paper used panel data of 115 Chinese high-tech firms from 2002 to 2010. Fixed-effects model was used to test several hypotheses.

This paper empirically demonstrates that the subsidies crowd out the utilization of firms’ technological capabilities for invention outcomes in the near-term. Furthermore, this paper finds that the crowding-out effects are weaker when firms have high export intensity or are located in provinces with high market-oriented systems.

The findings of this paper apply to Chinese firms. Future research could test their generalizability to different samples and other transitional economies.

This paper highlights the crowding-out effects of the subsidies, revealing that high-tech firms should balance the direct effects and crowding-out effects of the subsidies.

This paper highlights the neglected interactions between the subsidies and technological capabilities based on RBV and provides a more nuanced understanding of the crowding-out effects of the subsidies in transitional economy.

This paper aims to study the application of reinforcement learning (RL) in the control of an output-constrained flapping-wing micro aerial vehicle (FWMAV) with system uncertainty.

A six-degrees-of-freedom hummingbird model is used without consideration of the inertial effects of the wings. A RL algorithm based on actor–critic framework is applied, which consists of an actor network with unknown policy gradient and a critic network with unknown value function. Considering the good performance of neural network (NN) in fitting nonlinearity and its optimum characteristics, an actor–critic NN optimization algorithm is designed, in which the actor and critic NNs are used to generate a policy and approximate the cost functions, respectively. In addition, to ensure the safe and stable flight of the FWMAV, a barrier Lyapunov function is used to make the flight states constrained in predefined regions. Based on the Lyapunov stability theory, the stability of the system is analyzed, and finally, the feasibility of RL in the control of a FWMAV is verified through simulation.

The proposed RL control scheme works well in ensuring the trajectory tracking of the FWMAV in the presence of output constraint and system uncertainty.

A novel RL algorithm based on actor–critic framework is applied to the control of a FWMAV with system uncertainty. For the stable and safe flight of the FWMAV, the output constraint problem is considered and solved by barrier Lyapunov function-based control.

The success of online health communities (OHCs) depends on maintaining long-term relationships with physicians and preventing churn. Even so, the reasons for physician churn are poorly understood. In this study, an empirical model was proposed from a social influence perspective to explore the effects of online social influence and offline social influence on physician churn, as well as the moderating effect of their online returns.

The empirical data of 4,145 physicians from a Chinese OHC, and probit regression models were employed to verify the proposed theoretical model.

The results suggest that physicians' churn intention is influenced by online and offline social influences, and the offline social influence is more powerful. Physicians' reputational and economic returns could weaken the effect of online social influence on churn intention. However, physicians' economic returns could strengthen the effect of offline social influence on churn intention.

This research study is the first attempt to explore physician churn and divides the social influence into online and offline social influences according to the source of social relationship. The findings contribute to the literature on e-Health, user churn and social influence and provide management implications for OHC managers.

The purpose of this paper is to report on the findings and lessons that were learned from the many cases of internet fraud complaints that are gathered by the Federal Trade Commission in the USA. The implications that are contained in the behavior of the complaints and trends identified are critical for addressing all types of online criminal activities in the increasing world of cybercrimes.

Data for this paper are extracted from the Consumer Sentinel Network Data Handbook covering a period of 13 years. Using the raw data, the occurrences were plotted and trends (number of cycles, time between cycles, and leading state and lagging states) were identified. Descriptive statistics covering the 13 years were examined and discussed. Using 20002 as the base year, the rate of growth of each of the states were ranked annually and were tested for stability and predictability using non-parametric approaches.

The key findings are indigenous to the occurrences of online fraud complaints in the USA. However, as the leading nation with the best database on fraud complaints, the findings are mission critical to fraud prevention across the globe. Specifically, this study found that since 2002, there have been four distinct cycles. Each cycle is clearly noticeable because there is a rapid growth in the number of crime complaints in the beginning half of the period followed by a slowing down period. However, the speed of change from one cycle to the next is steeper and the time gap is faster. While it is true that from the perspective of all the 50 states, the ranks appear to change every year and thus are not stable. However, the majority of the changes are relatively small so their relative positions by rank are still predictable.

The paper extends existing understanding of online fraud occurrences in the USA. The findings are timely and based on longitudinal data that span over a decade. The four new cycles identified are critical to the body of knowledge. The observed change in time from one cycle to another and its changes from a mathematic to a geometric one are also new. The findings are invaluable to persons working in law enforcement related occupations (auditors, lawyers, forensic experts, among others) and who must deal with the increasing problem of online fraud and cybercrimes.

Tingting Qi's chapter titled, “Moving toward Decentralization? Changing Education Governance in China After 1985,” provides the historical and policy context for the volume. This chapter integrates the post-1978 Chinese educational reforms into the socioeconomic context of China. The special contribution of this chapter is that it explores the complexity of educational decentralization in China through an in-depth analysis of changes in education finance, administration, and curriculum. Qi reviews prior studies, government documents, laws, and regulations related to Chinese education reform since 1978 within the context of education decentralization in China. Qi also demonstrates that China's educational policy reforms are moving China toward “centralized decentralization” because decentralization is driven by a common, centralized national goal of economic modernization. The chapter presents evidence that “centralized decentralization” is a strategic maneuver that maintains centralized control while providing the reform legitimacy of decentralization. By focusing on decentralization as the core of Chinese educational policy reforms, this chapter situates the following chapters within the social, cultural, and political context of post-1978 China.

The proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the collective Internet agenda. Unfortunately, as bot creators use more sophisticated approaches to avoid being discovered, it has become increasingly difficult to distinguish social bots from legitimate users. Therefore, this paper proposes a novel social bot detection mechanism to adapt to new and different kinds of bots.

This paper proposes a research framework to enhance the generalization of social bot detection from two dimensions: feature extraction and detection approaches. First, 36 features are extracted from four views for social bot detection. Then, this paper analyzes the feature contribution in different kinds of social bots, and the features with stronger generalization are proposed. Finally, this paper introduces outlier detection approaches to enhance the ever-changing social bot detection.

The experimental results show that the more important features can be more effectively generalized to different social bot detection tasks. Compared with the traditional binary-class classifier, the proposed outlier detection approaches can better adapt to the ever-changing social bots with a performance of 89.23 per cent measured using the F1 score.

Based on the visual interpretation of the feature contribution, the features with stronger generalization in different detection tasks are found. The outlier detection approaches are first introduced to enhance the detection of ever-changing social bots.