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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.

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 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.

In social media, crisis information susceptible of generating different emotions could be spread at exponential pace via multilevel super-spreaders. This study aims to interpret the multi-level emotion propagation in natural disaster events by analyzing information diffusion capacity and emotional guiding ability of super-spreaders in different levels of hierarchy.

We collected 47,042 original microblogs and 120,697 forwarding data on Weibo about the “7.20 Henan Rainstorm” event for empirical analysis. Emotion analysis and emotion network analysis were used to screen emotional information and identify super-spreaders. The number of followers is considered as the basis for classifying super-spreaders into five levels.

Official media and ordinary users can become the super-spreaders with different advantages, creating a new emotion propagation environment. The number of followers becomes a valid basis for classifying the hierarchy levels of super-spreaders. The higher the level of users, the easier they are to become super-spreaders. And there is a strong correlation between the hierarchy level of super-spreaders and their role in emotion propagation.

This study has important significance for understanding the mode of social emotion propagation and making decisions in maintaining social harmony.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0192.

Drawing on self-determination theory (SDT), this study conceptualizes and empirically examines how and when green visionary leadership (GVL) stimulates employees’ green innovation (GI) in the workplace.

We collected 237 supervisor-employee matched multi-wave data from multiple organizations in China. The data were tested and analyzed using path analyses.

A significant relationship was found between GVL and GI, which was mediated by employees’ green passion (GP). Additionally, green value orientation (GVO) moderated both the direct relationship between GVL and GP and the indirect effect of GVL on employee GI via GP. Employees with stronger GVO were more likely to translate GVL into higher GI through increased GP.

Organizations should strengthen employees’ belief in green development, prioritize the recruitment, selection and training of leaders with GVL qualities, and foster a GVO among employees. This can enhance employees’ GP and ultimately drive GI within the organization.

This study develops an integrative model of GVL as central for organizational sustainability, focusing on GI. We apply SDT and identify GP as a mediator between GVL and GI, thus contributing to the understanding of psychological processes in GVL effectiveness. We also explore GVO as a boundary condition, providing insights into when GVL promotes GI. This advances research on leadership and green management literatures.

To systematically characterize and objectively evaluate basic railway safety management capability, creating a closed-loop management approach which allows continuous improvement and optimization.

A basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards, railway safety rules and regulations and existing safety data from railway transport enterprises is presented. The system comprises a guideline layer including safety committee formation, work safety responsibility, safety management organization and safety rules and regulations as its components, along with an index layer consisting of 12 quantifiable indexes. Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.

The case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.

Construction of an evaluation index system that is quantifiable, generalizable and accessible, accurately reflects the main aspects of railway transportation enterprises’ basic safety management capability and provides interoperability across various railway transportation enterprises. The application of the game theoretic combination weighting method to derive composite weights which combine experts’ subjective evaluations with the objectivity of data.

There is a clear correspondence between different types of high performance work systems (HPWS), economic rents and market entry strategies. This correspondence is manifested through the strategic vertical fit and internal synergetic horizontal fit within HPWS, leading to a “core-periphery” structure in human resource management practice configurations. This study aims to depict the “core-periphery” structure within specific HPWSs by identifying human resource (HR) practice configurations and by analyzing the matching mechanisms and structural characteristics among different HR practices based on the interaction between vertical and horizontal fits.

This study uses a sample of 277 enterprises in East China and employs the ability-motivation-opportunity (AMO) framework and fuzzy-set qualitative comparative analysis (fsQCA) to identify and depict the “core-periphery” structure within HPWS. By integrating fsQCA with t-test, the study explores the interaction between vertical and horizontal fits within HPWS based on economic rent formation mechanisms.

Seven specific HR practice configurations, shown to effectively enhance organizational performance, have been identified and systematically classified into three typologies of HPWS, based on their distinct underlying mechanisms of horizontal and vertical fit.

The data used in this study were sourced from manufacturing enterprises. Future research can enhance generalizability by expanding the dataset and examining whether efficient HPWS exist across different industries.

The findings of this study emphasize the crucial roles of interaction and complementarity among HR practices within HPWS. By extracting and synthesizing HR practice configurations from empirical data, this study provides practical insights for constructing effective HPWS in real-world settings.

First, this study incorporates both vertical and horizontal fits of HPWS into a unified analytical framework, emphasizing their interactive mechanisms and providing a more nuanced understanding of how these fits operate together. This contributes to the existing literature by validating and expanding the understanding of how specific HR practice configurations align with corporate strategies. Second, by adopting a configurational perspective, this study is able to conduct an in-depth exploration of the “core-periphery” structure within HPWS, thereby challenging the widely held assumption within the AMO framework that all HR practices carry equal significance within a specific HPWS. Third, the empirical analyses identify three specific types of HPWS that can be applicable to different new product strategies from a dynamic perspective, providing practical references for companies to select appropriate HPWS types based on their characteristics.

There is a dearth of knowledge regarding how user dependency offers valuable resources to develop the intellectual capital of social streaming apps (SSAs) companies. This study aims to integrate major conceptual components of the UandD model, identify contextualized goal-oriented SSA dependency and empirically evaluate their interrelated user-dependency relationships in the SSA context.

A mixed-methods approach was utilized in this study. First, user gratifications were elicited through a qualitative approach, considering the exploratory stage of the SSA phenomenon. Second, statistical methods were applied to investigate and extract the sub-dimensions of SSA dependency. At last, a research model was developed grounded on the UandD model and empirically validated using the quantitative approach.

The results validated the gratification-dependency-attitude-behavior relationships hypothesized by the UandD framework in SSA. The role of user-SSA dependency in enhancing intellectual capital in the social media industry has been highlighted in this study.

This research not only provides an opportunity for the UandD model to realize its theoretical potential as envisioned by scholars but also contributes to the scholarship on social streaming apps and media dependency theory by conceptualizing goal-oriented dependency in SSAs.

The research results will guide digital media practitioners to a more nuanced understanding of the relationships between their users and modern digital media apps and thus empower the practitioners to better manage their intellectual capital based on the facilitation of their users’ dependency.

This work is one of the pioneers in contextualizing the UandD model in the SSA field, refining and measuring the SSA dependency and its distinct subdimensions and employing mixed-methods to offer a comprehensive understanding of how user dependency boosts intellectual capital in the SSA industry.