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This paper aims to study the impact of the interaction effects between live-streaming marketing and clothing type on consumers' intention to purchase clothing, and the mediating effect of internalization and identification on the relationship between them.

This paper conducts a scenario experiment to 486 consumers who had experience in purchasing clothing on the live-streaming platform and employs the analysis of variance, structural equation model and multivariate regression model.

Our findings reveal that professional live-streaming marketing (PLSM) can better stimulate consumers' intention to purchase formal clothing than entertainment live-streaming marketing (ELSM) does. Compared with PLSM, ELSM can better stimulate consumers' intention to purchase casual clothing. When PLSM promotes formal clothing, it triggers the internalization mechanism of consumers, so as to improve their purchase intention. When ELSM promotes casual clothing, it triggers consumers' identification mechanism, so as to improve their purchase intention.

This paper helps to identify the differences in the impact of different types of live-streaming marketing on consumers' intention to purchase different types of clothing, as well as the mediating role of internalization and identification mechanisms. This paper provides a theoretical reference for clothing firms to strategically select the appropriate type of live-streaming marketing.

This paper aims to explore the impact of green finance on the heterogeneity of enterprise green technology innovation and the underlying mechanism between them.

Using the data of China's A-share listed enterprises from 2008 to 2020 and the fixed effect model, the authors empirically explore the relationship and mechanism between green finance and green technology innovation by constructing the green finance index while considering both the quality and quantity of innovation.

The study suggests that green finance is positively related to the quality and quantity of enterprise green technology innovation, while green finance is more effective in stimulating the quality of green technology innovation than quantity. In addition, alleviating financial mismatch and improving the quality of environmental information disclosure are core mechanisms during the process of green finance facilitating green technology innovation. Furthermore, green finance exerts a more positive effect on the quality and quantity of green technology innovation with large-size enterprises, heavily polluting industries and enterprises in the eastern region.

This paper enriches the literature on green finance and green technology innovation and provides practical significance for green finance implementation.

Nano graphitic-carbon nitride (g-C₃N₄) is an emerging lubrication technology with excellent performance and significant potential for future applications. This study aims to investigate the effect of nano g-C₃N₄ as a lubricant additive on the wear performance of bearing steel disk.

Various mass fractions of g-C₃N₄ were introduced into the base oil. Combining tribological testing, rheological testing and surface analysis methods, the anti-wear properties and lubrication mechanisms were analyzed.

Transmission electron microscopy images revealed that the size of the nanoparticles of g-C₃N₄ ranges from 10 to 100 nm. Phase analysis of the g-C₃N₄ sample was conducted using X-ray diffraction. Further, 1.0% mass fraction of g-C₃N₄ in the base oil provides excellent anti-wear and friction-reducing performance. Compared to the base oil alone, it reduces the average friction coefficient by 63.8% and decreases the wear rate by 43.1%, significantly reducing the depth and width of the wear scar. Energy-dispersive X-ray spectroscopy and scanning electron microscope analysis revealed that the oil sample containing nano g-C₃N₄ can form a lubricating film on the sliding surface of bearing steel after wear, which enhances the lubricating properties of the base oil.

The synergistic effect of the base oil and nanoparticles reduces friction and wear and is expected to extend the service life of bearing steel. These findings suggest that incorporating nano g-C₃N₄ as a lubricant additive offers significant potential for improving the performance of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0456/

The review paper aims to understand the development of workplace ostracism as a field in organizational studies from 2000 to the present. The study provides a comprehensive synthesis of the current state of the domain by exploring its antecedents, consequences, underlying mechanisms and buffering mechanisms.

The present study analyses 134 published peer-reviewed empirical and non-empirical articles retrieved from the Scopus database. A systematic literature review and bibliometric analyses (using VOS viewer) have been used to gain insights into the development and trends within the field. Bibliometric analyses involved science mapping techniques such as co-citation analysis, co-occurrence of keywords and bibliographic coupling. Combining these three techniques, the study aimed to provide a comprehensive overview of the workplace ostracism research domain's historical, current and future landscape.

In the present study, through descriptive analyses, the authors uncovered publishing trends, productive journals, countries and industries that contribute to this research field. The systematic review enabled the showcasing of the current landscape of workplace ostracism. The bibliometric analyses shed light on major authors, influential articles, prominent journals and significant keywords in workplace ostracism.

This study enriches the existing literature by offering a comprehensive research framework for workplace ostracism. It goes beyond that by presenting significant bibliographic insights by applying bibliometric analyses. Furthermore, this study identifies and emphasizes future research directions using the theory, characteristics, construct and methodologies framework, aiming to expand the knowledge base and understanding of this topic.

This paper aims to provide a full introduction, new classification, comparison and investigation of the challenges as well as applications of layerless 3D printing, which is one of the industry 4.0 pioneers.

Given the significance and novelty of uniform 3D printing, more than 250 publications were collected and reviewed in an unbiased and clear manner.

As a result, the majority of uniform parts printed in polymer form are known up to this point. In a novel division for better researchers’ comprehension, uniform printing systems were classified into three categories: oxygen inhibition (OI), liquid lubrication (LL) and photon penetration (PP), and each was thoroughly investigated. Furthermore, these three approaches were evaluated in terms of printing speed, precision and accuracy, manufacturing scale and cost.

The parameters of each approach were compared independently, and then a practical comparison was conducted among these three approaches. Finally, a variety of technologies, opportunities, challenges and advantages of each significant method, as well as a future outlook for layerless rapid prototyping, are presented.

This study aims to test the relationship between virtual knowledge sharing (VKS) and team effectiveness (TE) during the COVID-19 pandemic. The study also explores the moderating role of collaborative technologies.

This is a cross-sectional study conducted in the service sector of India. A purposive sample of 321 knowledge workers from National Capital Region of India was used. Questionnaires were distributed to knowledge workers working in a virtual environment. The hypotheses were tested with confirmatory factor analysis and structural equation modeling (SEM) using partial least square-SEM.

The study reveals that, amid the COVID-19 pandemic, virtual knowledge sharing (VKS) positively affects team effectiveness (TE). Furthermore, the impact of VKS on TE is contingent upon the utilization of collaborative technologies.

The study contributes to the existing literature by exploring the impact of VKS on TE during the COVID-19 pandemic and the importance of collaborative technologies in facilitating virtual team collaboration, which has practical implications for organizations seeking to enhance TE in virtual environments.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

Small- and medium-sized enterprises (SMEs) increasingly implement digitalization in uncertain business environments. However, a dearth exists in the entrepreneurship literature for understanding the decision-making logic of digitalization as a management issue. Drawing on the effectuation theory, this study aims to explore the relationships between effectuation dimensions and SMEs’ digitalization.

Using quantitative data collected from 345 Chinese SMEs through questionnaires, the authors conducted the principal component analysis and hierarchical linear regression analysis.

The results highlight significant positive relationships between the four effectuation elements – experimentation, affordable loss, flexibility and precommitment – and SMEs’ digitalization. Moreover, this research considers the environmental conditions as moderators and reveals that environmental dynamism and complexity associated with high uncertainty negatively moderate the effects of effectuation on SMEs’ digitalization.

SMEs embarking on digitalization should constantly experiment to determine optimal strategies while contemplating their affordable losses. Flexibility should also be maintained to discard unproductive tactics and redirect to other viable options. Additionally, precommitments can reduce the risk that SMEs encounter in digitalization process. While the effectuation principles consolidate the likelihood of a successful digitalization, this research recommends that entrepreneurs should carefully consider their possible application in uncertain environments.

This study contributes to the entrepreneurship literature by theoretically clarifying the decision-making mechanism of digitalization and extends the application of effectuation to this context by illuminating the influences of effectuation principles on SMEs’ digital transformation. The identification of negative moderating effects of environmental uncertainty also augments an academic criticism about uncertainty creating the conditions for effectuation.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

This study aims to develop a random polygon garment pattern generator and a drape simulation system to automate the garment design process.

Garments were categorized into four groups based on the geometric features of the human body. Garment patterns in each group consisted of basic points, and the patterns were automatically placed, sewn and simulated around the body in three-dimensional space. Additional pattern manipulation functions were developed to modify the shapes of patterns by adding darts and cuts, either manually or randomly.

Users can produce new designs they had not considered before using the random manipulation functions. Since the three-dimensional simulation process is automated, users can focus solely on the design process.

Garments composed of multiple layers were not considered.

This system differs from existing clothing computer-aided design systems in that even users lacking prior knowledge of garment design can generate various examples. It can help users understand the relationship between 2D patterns and 3D garments without the need for a pattern drafting process.