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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.

Digitalisation is reshaping the learning process, shifting it towards personalisation and online learning, and fuelling the growth of massive open online courses (MOOCs) and micro-credentials. Despite being a popular global trend, limited studies have looked at micro-credentials and their impact on learners' behavioural outcomes. The purpose of this study is to investigate the impact of programme design factors on learning experience, as well as the inter-relationships between programme design, learning experience and behavioural responses (e.g. engagement and willingness to pay more (WTPM)) towards micro-credentials. In addition, the study aims to investigate learning enjoyment as a moderator.

A survey questionnaire was used to collect data from 354 respondents who are working adults living in the major economic states in Malaysia. Data analysis was performed using the analysis of a moment structures (AMOS) statistical software and SPSS (Statistical Package for the Social Sciences) PROCESS macro.

The results show the significance of programme design factors (i.e., flexibility, system quality and content quality) in determining the learning experience. The learning experience is found as a mediator in the relationship between programme design factors and learner engagement and WTPM. In addition, the moderation assessment confirms that enjoyment during learning strengthens the relationship between experience and behavioural responses.

This study is amongst a few selected studies that focus on engagement in and WTPM for micro-credentials. In addition, it emphasises the mediating role of the learning experience and the moderating role of enjoyment in understanding the impact of programme design on learners' experiences and behavioural responses.

The purpose of the present study is to examine and validate a research model encompassing power distance belief, need for uniqueness, self-monitoring, self-construal, social adjustive attitude, and value-expressive attitude as the causal antecedents for conspicuous luxury consumption.

Survey data was collected from a large consumer panel. The study applied a combinatory partial least squares structural equation modelling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approach in examining the research hypotheses and proposition.

The results indicated the effects of power distance belief, need for uniqueness, self-monitoring, and self-construal on social adjustive attitude and value-expressive attitude, which in turn determine conspicuous luxury consumption. In addition, the fsQCA findings revealed three configurations that articulate the complex interactions of the causal antecedents.

This study enriches the literature by providing a comprehensive understanding of the psychological motivation factors that drive conspicuous luxury consumption and assists luxury retailers in developing targeted strategies to appeal to consumers.

This study aims to investigate the predictive role of team personality composition in facilitating shared leadership through team member exchange (TMX), while also to examine the moderating effect of organizational culture.

The authors conducted a two-stage online survey and selected the customer service teams, claims teams and financial teams of 26 Chinese insurance companies as the research samples. The authors finally obtained validated questionnaires from 107 teams with 457 members. The hypothesized relationships were tested using SPSS 25.0 and Mplus.

The results indicate that both team relationship-oriented and task-oriented personality composition have significant positive effects on shared leadership with team-member exchange serving as a full mediator for both paths. As a boundary condition, organizational culture (i.e. including internal integration values and external adaptation values) has a moderating effect on the influence of TMX on shared leadership.

The study investigates the predictive role of team personality composition on shared leadership, which complements the empirical studies of shared leadership antecedents in the literature. Drawing on social exchange perspective, the authors find out that TMX serves as a mediator between team personality composition and shared leadership. The authors also identify the moderating effect of organizational culture on the emergence of shared leadership. The research emphasizes the contextual boundary condition in this process.

This study aims to present a novel topology optimization method for effectively minimizing the frequency response over a given frequency interval considering anisotropic features and fiber angles simultaneously.

The variable thickness sheet (VTS) method is used to obtain a free material distribution under the specified volume constraint. The anisotropic equivalent stiffness matrix based on the material fiber angles is considered in the orthotropic material properties model, which ensures a sufficiently large design space to minimize the frequency response. To lessen the computational burden, the quasi-static Ritz vector (QSRV) method is integrated to approximate the structural response.

Compared to considering only one element, the optimization process simultaneously considers the spatially-varying fiber angles and the material distribution, allowing for a broader design space to minimize the frequency response of additive manufacturing (AM) structures. The orthotropic properties play an important role in determining optimal material distribution of the structure. Moreover, the QSRV method makes the frequency response analysis more efficient.

The anisotropic stiffness and spatially-varying angles of the fiber materials induced by the layer-by-layer printing process of carbon fiber reinforced plastics (CFRP) are simultaneously considered to further minimize the frequency response of AM structures, which improves the performance of AM-CFRP structures.

This study aims to explore the use of modified graphene (MG) in copper wire drawing lubricants to enhance their friction-reducing and anti-wear capabilities.

Graphene was modified using oleic and stearic acids to improve its dispersibility in lubricants. Various concentrations of MG were then introduced into a copper wire drawing lubricant to investigate their tribological performance. Wear mechanisms were evaluated with scanning electron microscopy, optical microscopy, Raman spectroscopy and energy dispersive spectroscopy (EDS).

The best concentration of MG is 1.5 Wt.%, at which the copper wire drawing oil exhibits a friction coefficient and wear rate of 0.085 and 2.11 × 10⁻⁶ mm³/Nm, respectively, representing decreases of 22.7% and 47.6% compared to the base oil. It was further found that the addition of 1.5 Wt.% MG to a copper wire drawing fluid with a water content of 70% resulted in a 30.3% reduction in friction coefficient compared to the base oil. Raman spectroscopy and EDS analysis confirmed that the MG tribo-film formed on the worn copper disc effectively minimized friction and wear.

This study analyzes the tribological performance of different concentrations of MG in copper wire drawing oils, establishing a basis for the application of MG in copper wire drawing fluids.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0399/

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

This study aims to investigate the affordances of service robots (SRs) in hotels and their effects on frontline employees (FLEs).

Purposive and referral samplings methods were used to conduct 28 semistructured interviews with hotel FLEs, and the transcribed manuscript was analyzed based on grounded theory.

The study identifies six dimensions of SR affordances: physical, sensory, task, safety, social and emotional affordances. The main effects of SR affordances on FLEs involve reducing work stress and mental fatigue and increasing positive emotions in the psychological aspects of FLEs. In terms of behavioral aspects, shifts in task priorities and enhancements in SR usage behaviors were observed. Accordingly, a mechanistic framework was revealed through which SR affordances influence FLEs via direct and indirect interactions between FLEs and SRs.

This paper expands robotics research from a supply-side perspective and is one of the few studies to investigate SR affordances in the field of hospitality research. Findings of this study provide practical guidelines for designing and implementing SRs to support hotel FLEs in their daily work.

本研究旨在调查酒店中服务机器人（SR）的可供性及其对一线员工（FLEs）的影响。

本研究采用目的性和推荐抽样方法, 对酒店一线员工进行了28次半结构化访谈, 并根据扎根理论对转录的手稿进行了分析。

本研究确定了服务机器人的六个可供性维度：物理、感官、任务、安全、社会和情感可供性。服务机器人可供性对一线员工的主要影响包括减少工作压力和心理疲劳, 以及在心理方面增加积极情绪。在行为方面, 观察到任务优先级的变化和服务机器人使用行为的增强。因此, 研究揭示了一种机制框架, 通过一线员工与服务机器人的直接和间接互动, 服务机器人可供性影响一线员工。

本文从供给侧视角扩展了机器人研究, 是少数几篇研究酒店业中服务机器人可供性的研究之一。本研究结果为设计和实施服务机器人以支持酒店一线员工的日常工作提供了实践指南。

This study aims to assess the social network and perceived social support among community corrections offenders in China and compare the relationship between different sub-dimensions of social support and offenders’ social adaptation.

A descriptive design combined with self-administered questionnaires was used. A total of 54 valid responses were analysed, with participants displaying the following demographic characteristics: male offenders outnumbered female offenders, probationers constituted the highest proportion among all community corrections offenders and educational levels were generally low. Descriptive statistics and correlation analysis were conducted. The Lubben Social Network Scale, Interpersonal Support Evaluation List Scale and Person at Re-education-centre Social Adaptation Scale were used to measure the variables.

Participants exhibited generally good levels of social support, with moderately extensive social networks and perceived support. All sub-dimensions showed significant positive correlations with social adaptation, with the highest correlation for appraisal support (r = 0.743, p < 0.01), followed by instrumental support (r = 0.714, p < 0.01), emotional support (r = 0.687, p < 0.01), informational support (r = 0.685, P < 0.01) and social networks (r = 0.426, p < 0.01).

This study expands the understanding of community corrections offenders in China by focusing on the underexplored Sichuan Province, using empirical methods for data collection. It addresses a gap in previous research by examining both structural and functional support for offenders rather than focusing solely on one aspect. The study uses two scales, rarely used in Chinese research, to measure structural and functional support, thereby enhancing research on social support for offenders. Practically, it explores the correlation between social support and re-entry, offering valuable insights for improving offenders’ reintegration into society.

These results underscore the critical role of social networks and perceived social support in reintegrating Chinese community corrections offenders. Distinct from prior research, this study used a more comprehensive scale, less commonly used by Chinese scholars, to enable detailed measurement and analysis, highlighting the significance of both structural and functional support in informing practice in related fields.