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The purpose of this study is to investigate the effects of different bullet screen types (functional vs social) on the continuous watching intention of consumers, as well as the influence mechanism. In addition, this study analyses the moderating role of consumer motivational orientation on the above effects.

First, objective data were obtained through the crawler to test the proposed hypotheses. An objective data analysis (417 group sample) was conducted to analyse the relationship between the percentage of social bullet screens and consumers sustained growth ratio to indirectly test the primary effect. Second, a questionnaire survey was conducted to test (176 questionnaires) the mediating role of perceived social crowding. Finally, a simulated online contextual experiment (340 participants) is conducted to explore the moderating role of consumer motivational orientation.

First, functional bullet screens produce higher continuous watching intention and lower perceived social crowding than social bullet screens. Second, perceived social crowding mediates the relationship between bullet screen type and continuous watching intention. Third, consumers' motivational orientation type (task-motivated vs recreation-motivated) moderates the relationship among bullet screen type, perceived social crowding and continuous watching intention.

The results of this study shed light on the differential impact of different types of bullet screens (functional and social) on consumers' continuous watching intentions, which makes up for the lack of research on the content of bullet screens in the field of livestreaming. Meanwhile, compared with the previous positive psychological research perspective, this study explores the intermediate mechanism of bullet screen type on consumers' continuous watching intention through a negative psychological perspective, which helps e-commerce companies and streamers better understand the differential impact of different bullet screens. Finally, this study explores the joint influence effect of bullet screen and consumer motivation type, which fills the theoretical research gap of consumer motivation orientation type in the category of live-streaming.

To improve the localization accuracy of the magnetically controlled capsule endoscope (MCCE), a new localization method, based on the magnetic dipole model, is proposed, where the anti-disturbance permanent magnet (APM) is used as the source of stable magnetic field, thus reducing the interference of the geomagnetic field or the electric conductor magnetic field in the system.

The coupling magnetic force model between the APM and the capsule endoscope is established to obtain the magnetic force relationship and magnetic induction intensity. Along the three axes, magnetic induction intensity data are collected by a 3 × 3 sensor array composed of nine magnetic field intensity sensors, while the data are uploaded to the main computer by the STM32F103C8T6 control board over a ESP8266 WIFI module connection. Next, the axial magnetic induction intensity data are decoupled to obtain the measurement trajectory, whereas the error function is established based on the calculated trajectory parameters. Finally, the Levenberg–Marquardt (L-M) algorithm is used to solve the position information of the MCCE.

Experiments show that the average localization error of an MCCE in a straight and circular bend tube is 4.76 mm, whereas in a U-bend tube, it is 6.82 mm.

The optimized simulation value in the linear and bending environment is in good agreement with the experimental value, which verifies the accuracy of the MCCE localization system based on magnetic field sensor array, exhibiting good performance in localization and position tracking while providing a theoretical basis for the subsequent research.

The adoption of emerging information and communication technologies in construction project teams has engendered numerous virtual spaces, characterized by communication visibility and content persistence. As a result, the knowledge exchanged in these virtual spaces serves as a team’s digital resources. However, the extant literature mostly takes a process-based approach to examine the impact of knowledge sharing, thus failing to fully comprehend the process of converting digital resources into performance, resulting in a gap in the literature.

This study employs team resource-based theory to construct a theoretical model and develop hypotheses. Specifically, knowledge integration capability and team efficacy are hypothesized as two types of critical capabilities that mediate the links between knowledge sharing (quantity and quality) in virtual spaces and management performance. Data from 128 middle and senior construction project managers were collected to test the proposed theoretical model.

The results suggest that relationships between knowledge sharing (quantity and quality) and project management performance are both mediated by knowledge integration capability. Moreover, team efficacy could only partially translate knowledge sharing quantity into performance and couldn’t transform knowledge sharing quality into performance. Besides, knowledge integration is found to strengthen the link between knowledge sharing quantity and performance but weaken the relationship between knowledge sharing quality and performance.

This study explores how knowledge shared in virtual spaces could be leveraged for improving management performance in construction project teams. The findings in this study enhance the understanding of knowledge sharing in digital environments and afford important insights into transforming digital resources into performance within construction project teams.

This study aims to explore how the application of digital technology and information technology can help firms improve their innovation performance and examines the mediating mechanisms of supply chain agility and supply chain integration.

This study conducted a questionnaire survey of 320 business managers in an automotive cluster in China and analyzed the collected data using structural equations.

Digital technology applications (DTA) have a positive impact on innovation performance, while supply chain agility and integration mediate this impact. In addition, information technology applications (ITA) also has a positive impact on innovation performance, while supply chain agility and integration mediate between the two. Supply chain agility (SCA) and supply chain integration (SCI) significantly enhance the positive impact of technology adoption on firms' innovation performance.

This study confirms the impact of digital technology and information technology applications on innovation performance and explores the mediating role played by supply chain agility and integration.

Live-streaming platforms emphasize dynamic social interaction and fan engagement. Users integrate into the live-streaming community through continuous social learning activities, such as sending bullet comments, reviewing comments and interacting with celebrities. However, comprehensive research on the transactional intricacies of live-streaming e-commerce from the perspectives of community and learning is still lacking.

Focusing on the behavior characteristics of the reference group represented by online celebrities and fans in the live-streaming environment, this study utilized social learning as the theoretical basis to examine how reference groups affect consumer purchase intention through a series of intermediary effects. An empirical investigation and machine learning algorithms were utilized to explore and verify the hypothesized model.

The results show that: (1) reference groups’ behavior positively stimulated social presence and enhanced consumer purchase intention through the chain-mediating effect of social presence and trust in online celebrities; (2) celebrity characteristics (professionalism, attractiveness and interactivity) positively impacted consumer trust; (3) in addition, machine learning algorithms substantiated that reference groups’ behavior, social presence, trust and celebrity characteristics had a remarkably robust predictive effect on purchase intention.

These findings hold theoretical implications for understanding how the social community affects consumers’ purchase intention in the live-streaming context and practical significance for marketing strategies toward live-streaming e-commerce.

This study aims to realize the multipurpose use of inorganic materials in adsorption treatment of pigment wastewater and preparation of core-modified Color Index Pigment Red 57:1 (C.I. Pigment Red 57:1, PR 57:1).

In this paper, the inorganic materials (sepiolite and SiO₂·nH₂O) were used in both PR 57:1 production wastewater treatment and its core-modification. The inorganic material firstly adsorbed 3-hydroxy-2-naphthoic acid (bon acid) in the pigment wastewater to reduce chemical oxygen demand. Then, the inorganic material adsorbed with bon acid was reused to prepare core-modified PR 57:1.

In the pigment wastewater adsorption experiment, it was found that under pH = 3, the adsorption percentage of bon acid by inorganic material can reached up to 46.00%. The pigment characterization results showed that the core-modified PR 57:1 had a core-shell structure. Under UV light irradiation for 1 h, the core-modified PR 57:1 prepared with sepiolite and SiO₂·nH₂O showed total color difference ΔE value of 1.43 and 2.05, respectively, which was lower than that of unmodified PR 57:1 (ΔE = 2.89). In addition, the transmittance of pigment water suspension test results showed that the core-modified PR 57:1 showed better water dispersibility.

This paper attempts to develop a synergistic strategy based on the multipurpose use of inorganic materials in adsorption treatment of pigment wastewater and preparation of core-modified PR 57:1.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

This study aims to propose a research framework grounded in the technology acceptance model (TAM) to assess the new service experiences in the medical industry through examining the intention to use online-to-offline (O2O) medical platforms. It focuses on the connections between immediacy of connection, context-based affordability, reliability and perceived convenience and perceived usefulness. This study also looks at how these determinants affect users’ intentions to use O2O medical platforms, with a particular emphasis on the moderating effect of customer online reviews.

This study was a quantitative research endeavour grounded in the TAM model that analysed usage intention of O2O medical platforms. Conducted in Sarawak, within the Borneo region, a total of 251 rows of data were collected by questionnaire and underwent initial analysis using SPSS, followed by a more in-depth two-stage partial least squares structural equation model analysis conducted with SmartPLS4.

According to this study’s findings, perceived usefulness and perceived convenience are significantly impacted by the proposed predictors – immediacy of connection, context-based affordability and reliability. Furthermore, intention to use O2O medical platforms was discovered to have a positive and significant impact from perceived usefulness and perceived convenience. It is noteworthy that the association among perceived convenience and perceived usefulness – which leads to usage intention of O2O medical platforms – did not show the moderating effect of customer online reviews.

The findings suggested that context-based affordability, immediacy of connection and reliability need to be focused on by the information and communication technology developers, policymakers and medical professionals, as these variables can influence perceived convenience and usefulness, which will further impact the intention to use O2O medical platforms.

The formulated research framework holds significance in comprehending the usage intention of O2O medical platforms among consumers in Sarawak. O2O medical platforms have experienced global growth; however, limited studies were found in the Malaysian context. Hence, the objective of this study is to examine the intention to use O2O medical platforms in a relatively understudied area, specifically in Sarawak, an island located in Borneo, Malaysia.

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/

Despite the increasing importance of boundary-spanning behaviors in construction projects, the research on how leader anger expressions impact employees’ boundary-spanning behaviors remains unclear. This study aims to investigate the impact of leader anger expressions on employees’ boundary-spanning behaviors in construction projects while exploring the mediating effect of work hope and the moderating effect of power distance orientation through the lens of social information processing theory.

The empirical data were collected from a questionnaire survey of 235 employees in construction projects, and the hypotheses were tested using the PROCESS program developed by Hayes.

This study demonstrated that leader anger expressions negatively affect employees’ work hope, and work hope mediates the relationship between leader anger expressions and employees’ boundary-spanning behaviors. Besides, the stronger the employees’ power distance orientation, the stronger the negative relationship between leader anger expressions and employees’ work hope, and the above mediating effect was also stronger.

The use of cross-sectional data was unable to entirely infer a causal relationship between leader anger expressions and employees’ boundary-spanning behaviors. Additionally, the reliance on self-reported data inevitably introduced some common method bias.

Applying social information processing theory, this paper enriches the discourse on factors influencing employees’ boundary-spanning behaviors in construction projects. It also offers valuable insights for leaders in construction projects to manage their teams effectively.