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This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

Joint ventures (JVs) serve as a viable tool in mitigating some of the challenges posed to the effective delivery of construction projects. However, JVs are highly susceptible to failure in most developing countries. Therefore, this study seeks to unravel the critical factors influencing the failure of JVs in the South African construction industry.

A quantitative approach was adopted for the study using a well-structured questionnaire as the instrument for data collection. Respondents for the study were built environment professionals in Gauteng province in South Africa. Data elicited from respondents were analyzed using a four-pronged process which included descriptive statistics, one sample t-test, exploratory factor analysis and confirmatory factor analysis.

Resulting from the analysis conducted, four critical components emerged as the major factors influencing the failure of JVs in the South African construction industry, which are inefficient financial framework, divergent organizational culture, poor project governance and inadequacies from project stakeholders.

The outcome of this study presents a roadmap for stakeholders in the construction industry with the requisite knowledge of the critical factors leading to the failure of JVs, consequently providing a clear path for the successful delivery of JV mandates.

Evidence from literature suggests that several studies have been conducted on the various aspects of JVs in the South African construction industry; however, none has focused on the leading factors attributed to the failure of JVs. Also, the findings of this study cultivate a good theoretical platform for future studies on JVs.

This paper aims to examine the question, “How do firm-level, home-country and host-country environmental performance (EP) affect the outward foreign direct investment (OFDI) of Chinese multinational enterprises (MNEs)?”

The authors examine the relationships between EP and OFDI propensity and between EP and OFDI intensity using a sample of 359 Chinese firms in industries with a significant environmental footprint between 2009 and 2019 (2,002 firm-year observations) and a Heckman two-stage model.

This study shows that the propensity for OFDI by Chinese MNEs is significantly and positively related to the firm’s prior EP and the country-level EP of China. However, the amount of FDI invested is significantly and positively related to the firm’s prior EP and negatively related to the EP of the host country.

The findings suggest that FDI in a country by an MNE is determined by a combination of firm-level EP, home-country EP and host-country EP. This study finds that the decision to undertake FDI (propensity) and the decision about how much to invest (intensity) are determined by different factors. The propensity for FDI is determined by the home-country EP and firm-level EP. However, the intensity of FDI is determined by a combination of the host country EP and firm-level EP. A limitation is that this study only examines MNEs in China, so the findings may not apply to other countries.

This paper shows that MNEs’ EP is positively related to the propensity and intensity of their OFDI decisions. However, this paper shows that the home-country and host-country EP may also play an important role in determining the propensity or intensity of OFDI.

Studies showed that digital technology applications in the construction industry are low, especially in many developing nations. Construction incentivisation (CI) is one of the long-standing principles adopted to enhance project performance. There is a paucity of studies concerning CI to improve digital technology applications. Thus, this research investigated the relevance and perceived hindrances that may hinder the implementation of CI from promoting digital technologies and proffer ways to improve digital technology applications in the construction sector.

In Nigeria’s context, this research is exploratory. Twenty-four semi-structured virtual interviews were conducted in Lagos and Abuja, Nigeria, with knowledgeable participants that indicated interest and were interviewed. The engaged interviewees were drawn from government agencies, academicians in construction consultancy, Internet and communication technology experts, construction contracting firms and construction consulting firms. The collected data were coded and analysed through a thematic method.

Digitalisation of the industry via CI may face some hindrances. The perceived issues that may hinder CI implementation were classified into most severe, severe and fairly severe in Nigeria’s construction industry context. Findings proffer feasible policy solutions that can mitigate these issues and improve digital technology applications in the industry via the CI.

This study covered the relevance and perceived issues that may hinder the implementation of the CI to improve digital technology applications in the industry. Also, the study proffers policy solutions to enhance digital technology applications in the industry via the CI concept.

Findings from this research will support and offer a valuable understanding of the relevance of the “incentivisation concept” to improve digital technology applications in the Nigerian-built environment. Other developing countries with low applications of digital technology in construction may consider the suggested policy solutions from this research. Also, this study will stir policymakers and construction practitioners to support policies tailored towards improving digital technology applications in construction.

This research contributes by exploring the effectiveness of the CI concept and informing construction practitioners and policymakers on how to improve digital technology applications in the Nigerian construction industry.

Although total quality management (TQM) is an innovative management philosophy, how it connects innovative behaviour (INB) to innovation performance (INP) has gone unnoticed. Also, the external factors (technological turbulence [TUR], competitive intensity [CMP], market dynamism [MKD] and government regulation [GOV]) under which the INB–TQM connection may grow are yet to be understood. In spite of the various evolutions that have occurred in the banking industry, there remains a necessity to enhance the quality of service offered to clients. This paper aims to address these issues in the total quality management literature.

This study constructs and analyses a research framework by analysing the replies of 260 executives in senior and intermediate positions across 21 quality-certified universal banks in Ghana, using the Smart PLS methodology.

TQM played a partial mediating role between INB and INP (variance accounted for = 46.85%, p = 0.000). TUR (β = 0.023, p = 0.000), CMP (β = 0.043, p = 0.000), MKD (β = 0.056, p = 0.000) and GOV (β = 0.068, p = 0.000) positively and significantly moderated the INB–TQM connection.

Future research may examine the proposed framework in various environments and sectors.

Practical insights for industry players in the sector are discussed.

To the best of the authors’ knowledge, this study is the first to show how innovation serves both as an antecedent and consequence of TQM. It is also the first to explicate the boundary conditions under which the INB–TQM relationship may flourish.

By building and examining an integral model, the principal objectives of this research are to systematically explore how indirect and direct network externalities lead to user loyalty toward WeChat through the mediating effect of perceived gratifications.

The data were collected through an online survey of 688 young people in Mainland China. To empirically assess the conceptual model, zero-order correlation analyses and structural equation modeling were carried out utilizing web-based data.

Path analysis results demonstrate that indirect network externalities and direct network externalities exert a significant impact on users' hedonic gratifications and utilitarian gratifications. Moreover, the study discovers the significant mediating influences of utilitarian gratifications on the association between indirect network externalities and user loyalty.

Theoretically, this article may extend the scope of diverse studies on the association between network externalities and perceived gratifications and offer fresh insights into how mobile social media could actually improve user loyalty through enhancing perceived values among younger generation. Practically, this research assists mobile social media practitioners in retaining users and gaining competitive advantages over rival applications.

Although the extraordinary growth of WeChat has successfully become the dominant media by which individuals develop interpersonal network and contact with others, the roles of perceived gratifications between network externalities and user loyalty toward WeChat have not yet been investigated in depth. These obtained outcomes not only enrich the existing literature regarding the relationship between network externalities and affective response, but also offer fresh insights to mobile social media designers, marketers and users.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

The purpose of this paper is to explain how enterprise resource planning (ERP) implementation evolves by cloud computing in different industries with different delivery models of cloud ERP. This paper also investigates infrastructure as a service (IaaS) as a delivery approach for cloud ERP. Case research on IaaS is rarely found in the literature. In addition, this paper intends to reveal how this transformation from on-premises to the cloud would influence the ERP implementation process.

A multiple-case study is conducted to identify the different deployed models of cloud ERP systems in the implementation projects. The influences of emerging cloud computing technology on ERP implementation are investigated by interviewing consultants related to the projects.

The findings illustrate that not only software as a service (SaaS) but also IaaS and platform as a service cloud computing services are widely applied in cloud ERP implementation. This study also indicates that certain technical limitations of cloud ERP might have a positive effect on the outcome of ERP implementation.

This study investigates how cloud computing influences ERP implementation from different aspects. The result identifies both SaaS and IaaS as two different approaches widely adopted in cloud ERP implementation. Besides, this study has discussed in-depth and analyzed these two cloud ERP paradigms in five factors, including functionality, performance, portability, security, cost and customization. The classification and suggestions are original to the literature.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This study examines the impact of debt maturity structure on stock price crash risk (SPCR) in Asian economies and the moderating effect of firm age on this relationship.

The study utilized annual data from 432 nonfinancial firms publicly listed in six Asian countries: China, Hong Kong, Japan, Singapore, Pakistan and India. The observation period covers 14 years, from 2007 to 2020. The sample was categorized into three groups: the entire sample and one group each for developing and developed Asian economies. A generalized least squares panel regression method was employed to test the research hypotheses.

The results suggest that long-term debt has a significant negative influence on SPCR in Asian economies, indicating that firms with high long-term debt experience lower future SPCR. Moreover, firm age negatively moderates this relationship, implying that older firms may experience a more pronounced reduction in SPCR due to high long-term debt. Finally, firms in developed Asian economies with high long-term debt are more effective in mitigating the risk of a significant drop in their stock prices than firms in developing Asian economies.

This study contributes to the literature in several ways. To the best of the researcher’s knowledge, this is the first of such efforts to investigate the relationship between debt maturity structure and crash risk in Asia. Additionally, it reveals that long-term debt influences SPCR directly and indirectly in Asia through the moderating role of firm age. Lastly, it is likely one of the first studies by a research team in Asia to compare the nonfinancial markets of developed and developing Asian countries.