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In the era of digital intelligence, individuals are increasingly interacting with digital information in their daily lives and work, and a growing phenomenon known as digital hoarding is becoming more prevalent. Prior research suggests that humanities researchers have unique and longstanding information interaction and management practices in the digital scholarship context. This study therefore aims to understand how digital hoarding manifests in humanities researchers’ behavior, identify the influencing factors associated with it, and explore how they perceive and respond to digital hoarding behavior.

Qualitative research methods enable us to acquire a rich insight and nuanced understanding of digital hoarding practices. In this study, semi-structured interviews were conducted with 20 humanities researchers who were pre-screened for a high propensity for digital hoarding. Thematic analyses were then used to analyze the interview data.

Three main characteristics of digital hoarding were identified. Further, the research paradigm, digital affordance, and personality traits and habits, collectively influencing the emergence and development of digital hoarding behaviors, were examined. The subtle influence of traditional Chinese culture was encountered. Interestingly, this study found that humanists perceive digital hoarding as a positive expectation (associated with inspiration, aesthetic pursuit, and uncertainty avoidance). Meanwhile, humanists' problematic perception of this behavior is more widely observed — they experience what we conceptualize as an “expectation-perception” gap. Three specific information behaviors related to avoidance were identified as aggravating factors for digital hoarding.

The findings deepen the understanding of digital hoarding behaviors and personal information management among humanities researchers within the LIS field, and implications for humanities researchers, digital scholarship service providers, and digital tool developers are discussed.

The purpose of this paper is to investigate and examine the factors contributing to consumers’ mobile shopping continuance intention (CI) of food and non-food items via smartphones and other mobile terminals.

An integrated model was proposed on the basis of the technology acceptance model (TAM) and expectation confirmation model (ECM), focussing on perceived value (PV). The survey responses of 203 Chinese mobile shoppers (m-shoppers) were analysed using structural equation modelling with the partial least squares approach.

The results indicated that perceived usefulness does not motivate all user groups. Furthermore, satisfaction and perceived ease of use significantly impacted different user groups. For online food m-shoppers, value for money (VM) was the most important factor influencing satisfaction and CI. However, perceived usefulness only affected CI for non-food m-shoppers.

Marketers can improve users’ CI by enhancing VM and maximising effectiveness and enjoyment while minimising prices. Moreover, in determining strategies for different users, marketers should identify the behavioural differences among all groups and those between the two classified groups.

This is one of the studies attempting to explain Chinese mobile shopping consumers’ CI, but especially through an integrated model based on TAM and ECM with PV on food and non-food m-commerce perspective. It offers several implications for researchers and practitioners and contributes to the literature of technology acceptance and post-adoption behaviour in m-commerce.

Impulse buying has been the focus of attention in the marketing. With the rise of online shopping, online impulse buying phenomenon becomes increasingly serious. Whereas, the impulse buying behavior in an online environment is rarely discussed in relevant literature. The purpose of this paper is to explore the impact of the type of product and time pressure on consumer online impulse buying intention; this is a relatively new issue of marketing academia in China.

In this paper, the experimental methodology was adopted to explore the impact of consumer online impulse buying tendencies, the departure from the type of product and the time pressure.

Results show that low-involvement feeling products stimulate consumer online impulse buying tendencies. Simultaneously, there is an interaction effect between time pressure and product type, which is, under the influence of time pressure, the enhancement of low-involvement feeling products on consumer online impulse buying tendency is more significant.

This study discusses the interaction between time pressure and product type on consumers’ online impulse buying tendency, which has not been studied before. While discussing the impact of product types on consumers’ impulse buying tendency on the internet, this paper considers the impact of time pressure on consumers’ impulsive buying tendency, and applies the term of time pressure, a psychological research term, to the field of marketing research, so as to make the cross-links between disciplines closer.

The outbreak of COVID-19 pandemic has posed severe challenges to infrastructure construction in China. Particularly, the complex technology and high process uncertainty of deep foundation pit construction make its safety risk identification a challenging issue of general concern. To address these challenges, Building Information Modeling (BIM) can be used as an important tool to enhance communication and decision-making among stakeholders during the pandemic. The purpose of this study is to propose a knowledge management and BIM-integrated safety risk identification method for deep foundation pit construction to improve the management efficiency of project participants.

This paper proposes a risk identification method that integrates BIM and knowledge management for deep foundation pit construction. In the framework of knowledge management, the topological relationships between objects in BIM are extracted and visualized in the form of knowledge mapping. After that, formal expressions of codes are established to realize the structured processing of specification provisions and special construction requirements. A comprehensive plug-in for deep foundation pit construction is designed based on the BIM software.

The proposed method was verified by taking a sub-project in deep foundation pit project construction as an example. The result showed the new method can make full use of the existing specification and special engineering requirements knowledge. In addition, the developed visual BIM plug-in proves the feasibility and applicability of the proposed method, which can help to increase the risk identification efficiency and refinement.

The deep foundation pit safety risk identification is challenged by the confusion of deep foundation pit construction safety knowledge and the complexity of the BIM model. By establishing the standardized expression of normative knowledge and special construction requirements, the efficiency and refinement of risk identification are improved while ensuring the comprehensiveness of results. Moreover, the topology-based risk identification method focuses on the project objects and their relations in the way of network, eliminating the problem of low efficiency from the direct BIM-based risk identification method due to massive data.

Due to high electromagnetic torque at low speed, vernier machines are suitable for direct-drive applications such as electric vehicles and wind power generators. The purpose of this paper is to present an exact sub-domain model for analytically predicting the open-circuit magnetic field of permanent magnet vernier machine (PMVM) including tooth tips. The entire field domain is divided into five regions, viz. magnets, air gap, slot openings, slots, and flux-modulation pole slots (FMPs). The model accounts for the influence of interaction between PMs, FMPs and slots, and radial/parallel magnetization.

Magnetic field distributions for slot and air-gap, flux linkage, back-EMF and cogging torque waveforms are obtained from the analytical method and validated by finite element analysis (FEA).

It is found that the developed sub-domain model including tooth tips is very accurate and is applicable to PMVM having any combination of slots/FMPs/PMs.

The main contributions include: accurate sub-domain model for PMVM is proposed for open-circuit including tooth-tip which cannot be accounted for in literature; the model accounts the interaction between flux modulation pole (FMP) and slot; developed sub-domain model is accurate and applicable to any slot/FMP/PM combinations; and it has investigated the influence of FMP/slot opening width/height on cogging torque.

The purpose of this paper is to empirically explore whether and to what extent the changes in state corporate income tax rates affect corporate tax aggressiveness.

Using a differences-in-differences approach with dynamic treatment, the authors investigate the effect of staggered changes in state corporate income tax rates in the USA on corporate tax aggressiveness.

Firms become more aggressive in avoiding taxes following state tax increases but are insensitive to tax cuts. The effect of state tax increases on tax aggressiveness is weaker for firms with greater debt tax shields and marginal tax rates. Firms are more likely to shift their operations and relocate their headquarters out of states experiencing tax increases.

To the best of the authors' knowledge, this paper is the first to study the relation between state tax policy changes and corporate tax aggressiveness. This paper finds an asymmetrical pattern of corporate tax aggressiveness in response to state tax changes.

Machinery operating in a sand-dust environment is more susceptible to sand particles. The purpose of this paper is to investigate the impact of sand particle deposition rate, surface hardness and normal load on the tribological performance.

A predictive model to approximate the number of sand particles within the pin-on-disc contact surface is proposed. The efficacy of the model is validated through experimental method, which replicates a sand environment with two distinct particle deposition rates. Dry sliding friction experiments are also conducted using 45 carbon steel and H90 brass pins against GCr15 bearing steel discs.

When at high particle deposition rate [6.89 × 10^–5 g/(s·mm²)], the contact surfaces are separated by particles, resulting in an indirect metal contact. While at low deposition rate [6.08 × 10^–8 g/(s·mm²)], there is an alternating occurrence of direct and indirect metal contacts. In sand environment, the specific wear rate of 45 and H90 decreases by 50% and 33%, respectively, compared to non-sand environment when the applied load is 2.45 N. However, it is only 0.18% for 45 but remains significant at 25% for H90 at load of 9.8 N.

The predictive model and experimental method used in this paper are helpful for understanding the interaction between particles and sliding surfaces, thereby providing a solid foundation for material selection and load optimization of friction pairs influenced by sand-dust environments.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0155/

Highway traffic systems are complex and variable, and studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable bifurcation points can alleviate traffic congestion from a new perspective. Bifurcation analysis is used to explain the changes in system stability, identify the unstable bifurcation points of the system, and design feedback controllers to realize the control of the unstable bifurcation points of the traffic system. It helps to control the sudden changes in the stable behavior of the traffic system and helps to alleviate traffic congestion, which is of great practical significance.

In this paper, we improve the macroscopic traffic flow model by integrating severe weather factors such as rainfall, snowfall, and dust. We use traveling wave transform to convert it into a traffic flow stability model suitable for branching analysis, thus converting the traffic flow problem into a system stability analysis problem. First, this paper derives the existence conditions of the model Hopf bifurcation and saddle-node bifurcation for the improved macroscopic model, and finds the stability mutation point of the system. Secondly, the connection between the stability mutation points and bifurcation points of the traffic system is analyzed. Finally, for the unstable bifurcation point, a nonlinear system feedback controller is designed using Chebyshev polynomial approximation and stochastic feedback control method.

The Hopf bifurcation is delayed and completely eliminated without changing the equilibrium point of the system, thus controlling the abrupt behavior of the traffic system.

Currently there are fewer studies to explain the changes in the stability of the transportation system through bifurcation analysis, in this paper; we design a feedback controller for the unstable bifurcation point of the system to realize the control of the transportation system. It is a new research method that helps to control the sudden change of the stable behavior of the traffic system and helps to alleviate traffic congestion, which is of great practical significance.

The purpose of this paper is to propose a whole set of methods for underwater target detection, because most underwater objects have small samples, low quality underwater images problems such as detail loss, low contrast and color distortion, and verify the feasibility of the proposed methods through experiments.

The improved RGHS algorithm to enhance the original underwater target image is proposed, and then the YOLOv4 deep learning network for underwater small sample targets detection is improved based on the combination of traditional data expansion method and Mosaic algorithm, expanding the feature extraction capability with SPP (Spatial Pyramid Pooling) module after each feature extraction layer to extract richer feature information.

The experimental results, using the official dataset, reveal a 3.5% increase in average detection accuracy for three types of underwater biological targets compared to the traditional YOLOv4 algorithm. In underwater robot application testing, the proposed method achieves an impressive 94.73% average detection accuracy for the three types of underwater biological targets.

Underwater target detection is an important task for underwater robot application. However, most underwater targets have the characteristics of small samples, and the detection of small sample targets is a comprehensive problem because it is affected by the quality of underwater images. This paper provides a whole set of methods to solve the problems, which is of great significance to the application of underwater robot.