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One of the most important challenges confronting enterprise managers is that of controlling employees' social cyberloafing. The use of enterprise social media entails opportunities for cyberloafing. However, previous research on how enterprise social media use affects cyberloafing is rather limited. Using the job demands-resources (JD-R) model, this paper proposes a research model to investigate the relationship between enterprise social media usage and employees' social cyberloafing behavior.

Structural equation modeling was performed to test the research model and hypotheses. Surveys were conducted in an online platform in China, generating 510 employees' data for analysis.

First, both public social media and private social media used for work-related and social-related purposes have a positive effect on employees' job engagement. Further, job engagement has a negative effect on employees' social cyberloafing. Second, the use of public social media for work-related and social-related purposes has no effect on employees' emotional exhaustion. However, work-related private social media usage has a negative effect on employees' emotional exhaustion, and social-related private social media usage has a positive effect on employees' emotional exhaustion. Further, employees' emotional exhaustion has a positive effect on employees' social cyberloafing. Third, there are significant differences in the effects of enterprise social media on employees' social cyberloafing between male and female employees.

First, this paper contributes to the social cyberloafing literature by establishing a relationship between enterprise social media usage and social cyberloafing in relation to the dual influence mechanism. Second, it contributes to the JD-R model by clarifying how the use of enterprise social media with different motivations affects social cyberloafing through a mediation mechanism, namely, an enabling mechanism and a burden mechanism. Third, this paper also contributes to the social cyberloafing literature by revealing the boundary condition, namely gender, between enterprise social media use and employees' social cyberloafing.

An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

This paper aims to use an ionic liquid (IL, [HMIM]PF₆) to improve the lubrication performance of liquid metal (LM) as a lithium grease additive and to expand the application range of LM.

In this paper, the different mass ratios of [HMIM]PF₆/LM mixtures were added into the lithium grease on a four-ball tribo-meter to investigate the effects of its tribological behavior. Scanning electron mircoscope/energy dispersive spectroscopy and X-ray photoelectron spectroscopy were used to reveal the anti-wear and friction-reducing mechanism of the additives.

When the load was used at 461 N, the average coefficient of friction (ACOF) and average wear scar diameter (AWSD) of steel ball Lubricated with grease with an optimal ratio of 2:3 ([HMIM]PF₆/LM) were reduced by 32.8% and 30.5%, respectively. Friction and wear mechanisms are ascribed to friction-induced additive components that can simultaneously form a composite lubrication film consisting of FePO₄, FeF₃, Ga₂O₃, In₂O₃ and SnO₂.

Compared with the pure lithium-based grease, when [HMIM]PF₆/LM was added with an optimal ratio of 2:3, the ACOF and AWSD were reduced by 12.4% from 0.097 to 0.085 and 23.8% from 552.117 µm to 420.590 µm under 392 N, respectively. When at 461 N, the ACOF and AWSD of steel ball were reduced by 32.8% from 0.122 to 0.082 and 30.5% from 715.714 µm to 497.472 µm, respectively. It was shown that the simultaneous addition of LM and [HMIM]PF₆ can form a composite lubrication film consisting of FePO₄, FeF₃, Ga₂O₃, In₂O₃ and SnO₂.

In this paper, [HMIM]P F₆ is added with LM simultaneously to improve the lubrication properties of lithium grease, and expand the application scope of LM.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0017/

The purpose of this paper is to explore the operating characteristics of gallium-based liquid metals (GLMs) by directly adding them as lubricants in real mechanical equipment.

This paper conducts an analysis of the rotor-bearing system under GLM lubrication using a constructed test rig, focusing on vibration signals, surface characteristics of the friction pair, contact resistance and temperature rise features.

The study reveals that GLM can effectively improve the lubrication condition of the tribo-pair, leading to a more stable vibration signal in the system. Surface analysis demonstrates that GLM can protect the sample surface from wear, and phase separation occurs during the experimental process. Test results of contact resistance indicate that, in addition to enhancing the interfacial conductivity, GLM also generates a fluid dynamic pressure effect. The high thermal conductivity and anti-wear effects of GLM can reduce the temperature rise of the tribo-pair, but precautions should be taken to prevent oxidation and the loss of its fluidity.

The overall operating characteristics of the rotor-bearing system under GLM lubrication were investigated to provide new ideas for the lubrication of the rotor-bearing system.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0067/

This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments.

A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms.

The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments.

This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio.

Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing.

This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

The paper aims to study the effect of sintering temperature on the microstructure, shear strength and ratcheting fatigue life of nanosilver sintered lap shear joint. In addition, the Gerber model is used to predict the ratcheting fatigue lives of nanosilver sintered lap shear joints at different sintering temperatures.

In this paper, the nanosilver sintered lap shear joints were prepared at three sintering temperatures of 250 °C, 280 °C and 310 °C. The bonding quality was characterized by scanning electron microscopy, X-ray diffraction, transmission electron microscope and shear tests, and the long-term reliability was studied by conducting ratcheting fatigue tests. In addition, three modified models based on Basquin equation were used to predict the ratcheting fatigue life of nanosilver sintered lap shear joint and their accuracies were evaluated.

When the sintering temperature is 250°C, the nanosilver sintered lap shear joint shows the porosity of 22.9 ± 1.6 %, and the shear strength of 22.3 ± 2.4 MPa. Raising the sintering temperature enhances silver crystallite size, strengthens sintering necks, thus improves shear strength and ratcheting fatigue life in joints. In addition, the ratcheting fatigue lives of the joints sintered at different temperatures are effectively predicted by three equivalent force models, and the Gerber model shows the highest life prediction accuracy.

The sintered silver bondline is suffering a complex stress state. The study only takes the shear stress into consideration. The tensile stress and the combination of shear stress and tensile stress can to be considered in the future study.

The paper provides the experimental and theoretical support for robust bonding and long-term reliability of sintered silver structure.

The introduced model can predict the ratcheting fatigue lives of the joints sintered at different temperatures, which shows a potential in engineering applications.

The study revealed the relationship between the sintering temperature and the microstructure, the shear strength and the ratcheting fatigue life of the joint. In addition, the Gerber model can predict the ratcheting fatigue life accurately at different sintering temperatures.

This study aims to deliver a new perspective on how the interaction of independent and cooperative innovation affects firm digitization. Based on resource constraint theory, this study argues that the aforementioned interaction negatively affects firm digitization. The moderating role of managerial discretion is also discussed in light of the principles of the awareness-motivation-capability (AMC) framework.

The proposed hypotheses are empirically tested using a negative binomial modeling approach. The data used are from A-share listed companies in China’s Shanghai and Shenzhen stock markets from 2006 to 2020.

This study suggests that the interaction of independent innovation and cooperative innovation negatively impacts digitization. In addition, this study argues that environmental discretion and organizational discretion weaken the negative impact of the mentioned interaction on digitization. However, additional discretion in the Chinese context has no effect on above relationships.

This study explores the impact of the interaction of independent and cooperative innovation on digitization and incorporates managerial discretion into this framework based on the AMC framework.

The purpose of this paper is to explore the high-quality development (HQD) strategy of Chinese mineral resource enterprises, which is important for Chinese mineral resource enterprises to improve the efficiency and benefit of resource utilization, reduce the intensity of resource and energy consumption and gradually form resource-saving and environment-friendly enterprises.

This study establishes an evaluation index system with four dimensions: economy, environment, society and management innovation. The entropy value method assigns weights to them and then uses the system dynamics (SD) model for case simulation.

The results of the SD simulation conclude that the fulfillment of social responsibility and the implementation of management innovation can accelerate the realization of HQD of mineral resource enterprises; profitability plays a crucial role in economic indicators; the improvement of energy-saving volume has the most significant impact on environmental benefits; the social contribution is the key element to measure social indicators; and the sales rate of core products has the most significant impact on the benefits of management innovation.

Based on the few studies on the evaluation of the development strategy of mineral resource enterprises, this study establishes an evaluation index system that considers the interactions between indicators, combines the entropy value method with SD and uses the SD model to comprehensively and systematically analyze the impact and degree of each factor on the HQD of mineral resource enterprises.

By integrating the Uses and Gratifications Theory and Flow Theory, this research seeks to untwine the veiled effects of interactive commerce enhancements (ICEs), specifically haptic imagery and social presence, in promoting user immersion and sustaining social commerce (SC) users' usage intention, considering the moderating role of autotelic personality.

The research utilized purposive sampling of Malaysian SC app users with recent transactions. A dual-source data collection approach, encompassing offline and online channels, was employed to ensure a broad and diverse respondent pool. Partial least squares–structural equation modeling was chosen for its adeptness in analyzing complex relationships in predictive studies.

The findings revealed the significant positive effects of haptic imagery and social presence on user immersion and continuance intention within SC apps. Social presence and immersion were found to mediate the proposed paths. Additionally, autotelic personality traits were identified as moderators, influencing the strength of these relationships.

This research makes a unique contribution by addressing critical gaps in SC environments, extending the concept of ICEs, understanding the impacts of underlying mediators and pioneering the examination of autotelic personality traits' moderating effects. It introduces a fresh perspective on how individual differences impact user engagement. This groundbreaking study benefits social media and interactive marketing literature by comprehensively understanding how ICEs elevate SC, fostering innovation and heightened engagement.

Conceptual and empirical research over the past 20 years has moved the social media (SM) literature beyond the embryotic stage to a well-developed academic discipline. As the lead article in the special issue in the Journal of Research in Interactive Marketing on Cutting-Edge Research in Social Media and Interactive Marketing, this review and agenda article has two key goals: (1) to review key SM and interactive marketing research over the past three years and (2) to identify the next wave of high priority challenges and research opportunities.

Given the “cutting-edge” research focus of the special issue, this review and research agenda paper focused on articles published in 25 key marketing journals between January 2021 and March 2024. Initially, the search request was for articles with “social media, social selling, social commerce” located in the article title, author-selected key words and journal-selected keywords. Later, we conducted searches based on terminology from articles presented in the final review. In total, over 1,000 articles were reviewed across the 25 journals, plus additional ones that were cited in those journals that were not on the initial list.

Our review uncovered eight key content areas: (1) data sources, methodology and scale development; (2) emergent SM technologies; (3) artificial intelligence; (4) virtual reality; (5) sales and sales management; (6) consumer welfare; (7) influencer marketing; and (8) social commerce. Table I provides a summer of key articles and research findings for each of the content areas.

As a literature review and research agenda article, this paper is one of the most extensive to date on SM marketing, and particularly with regard to emergent research over the past three years. Recommendations for future research are integrated through the paper and summarized in Figure 2.

Consumer welfare is one of the eight emergent content areas uncovered in the literature review. Specific focus is on SM privacy, misinformation, mental health and misbehavior.