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With the rapid development of social media, many organizations have begun to attach importance to social media platforms. This research studies the management and the use of social media in academic music libraries, taking the Center for Chinese Music Studies of the Chinese University of Hong Kong (CCMS) as a case study.

We conducted a sentiment analysis of posts on Facebook’s public page to analyze the reaction to the posts with some exploratory analysis, including the communication trend and relevant factors that affect user interaction.

Our results show that the Facebook channel for the library has a good publicity effect and active interaction, but the number of posts and interactions has a downward trend. Therefore, the library needs to pay more attention to the management of the Facebook channel and take adequate measures to improve the quality of posts to increase interaction.

Few studies have analyzed existing data directly collected from social media by programming based on sentiment analysis and natural language processing technology to explore potential methods to promote music libraries, especially in East Asia, and about traditional music.

Existing literature demonstrates the important role of information transparency in enterprise development and market surveillance. However, little empirical research has examined the information transparency effect in supply chain management. This study aims to fill this gap by exploring the significant role of information transparency on supply chain financing and its mechanism, taking trade credit as the starting point.

From the data set comprising 3,880 Chinese firms with A-shares listed on the Shenzhen and Shanghai Stock Exchanges from 2011 to 2020, we obtain the basic picture of information transparency and trade credit. Panel fixed effects regression is used to test the hypotheses concerning the antecedents to trade credit.

The empirical results show that: first, information transparency can significantly support corporate access to trade credit and is found to facilitate financing by mitigating perceived risk. Second, among companies with higher levels of financing constraints, weaker market power and more concentration of suppliers, information transparency promotes trade credit more markedly. Third, the outbreak of COVID-19 causes a substantial increase in uncertainty and risk in external circumstances and then the effect of information transparency is weakened. Fourth, the contribution to trade credit is likely to be stronger for disclosures containing management transparency elements compared to single financial transparency.

To the best of our knowledge, this study is one of the first to explore the positive role of information transparency to supply chain financing, which to a certain extent makes up for the lack of information transparency research in the supply chain. It provides new ideas for enterprises to obtain trade credit financing and promote the improvement of supervision departments’ disclosure policies.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

Background: This paper examines the impact of coronavirus COVID-19 outbreak on employees’ mental health, specifically psychological distress and depression. It aims at identifying the main stressors during and post COVID-19, examining the main moderating factors which may mitigate or aggravate the impact of COVID-19 on employees’ mental health and finally to suggest recommendations from a human resource management perspective to mitigate COVID-19’s impact on employees’ mental health.

Methods: This paper is a literature review. The search for articles was made in Google scholar, Web of Science and Semantic scholar. We used a combination of terms related to coronavirus OR COVID-19, workplace and mental health. Due to the paucity of studies on the COVID-19 impact on employees’ mental health, we had to draw on studies on recent epidemics.

Results: The identified literature reports a negative impact of COVID-19 on individual’s mental health. Stressors include perception of safety, threat and risk of contagion, infobesity versus the unknown, quarantine and confinement, stigma and social exclusion as well as financial loss and job insecurity. Furthermore, three dimensions of moderating factors have been identified: organizational, institutional and individual factors. In addition, a list of recommendations has been presented to mitigate the impact of COVID-19 on the employee’s mental health, during and after the outbreak, from a human resource management perspective.

Conclusions: Coronavirus is new and is in a rapid progress while writing this paper. Most of current research are biomedical focusing on individuals’ physical health. In this context, mental health issues seem overlooked. This paper helps to broaden the scope of research on workplace mental health, by examining the impact of a complex new pandemic: COVID-19 on employees’ mental health, from social sciences perceptive, mobilizing psychology and human resource management.

“Metaverse” has become a buzzword in the Chinese stock market. However, it remains unclear whether a firm's metaverse-related announcements will elicit positive stock market reactions. Whether and how stakeholder reactions are influenced by a firm's metaverse-related readiness also needs to be further explored. This study aims to discuss the aforementioned objective.

The authors derived a set of factors based on readiness theory and business ecosystem literature and extend them into the context of the metaverse. The authors used a sample of 642 Chinese listed firms in 2021 to investigate the hypotheses through the event study.

The study’s findings show that metaverse coverage induces a positive stock market reaction, but it is subject to three moderating effects. The authors introduce the novel concepts of IT readiness, ecosystem readiness and digital infrastructure readiness as the moderators. Stakeholders perceive metaverse announcements as overhyped, and stock prices do not fluctuate significantly after a metaverse announcement when the listed firms are not ready to embrace the metaverse.

This study is one of the first that introduces the event study method into the metaverse research, and it reveals that different levels of readiness influence stakeholders' evaluations and reactions to corporate metaverse coverage. This provides empirical evidence on metaverse development in China from the stock market's perspective.

The purpose of this study is to designed a robot odometry based on three dimensional (3D) laser point cloud data, inertial measurement unit (IMU) data and real-time kinematic (RTK) data in underground spatial features and gravity fluctuations environment. This method improves the mapping accuracy in two types of underground space: multi-layer space and large-scale scenarios.

An IMU–Laser–RTK fusion mapping algorithm based on Iterative Kalman Filter was proposed, and the observation equation and Jacobian matrix were derived. Aiming at the problem of inaccurate gravity estimation, the optimization of gravity is transformed into the optimization of SO(3), which avoids the problem of gravity over-parameterization.

Compared with the optimization method, the computational cost is reduced. Without relying on the wheel speed odometer, the robot synchronization localization and 3D environment modeling for multi-layer space are realized. The performance of the proposed algorithm is tested and compared in two types of underground space, and the robustness and accuracy in multi-layer space and large-scale scenarios are verified. The results show that the root mean square error of the proposed algorithm is 0.061 m, which achieves higher accuracy than other algorithms.

Based on the problem of large loop and low feature scale, this algorithm can better complete the map loop and self-positioning, and its root mean square error is more than double compared with other methods. The method proposed in this paper can better complete the autonomous positioning of the robot in the underground space with hierarchical feature degradation, and at the same time, an accurate 3D map can be constructed for subsequent research.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

The application of circular economy (CE) has received wide coverage in the built environment, including public-private partnership (PPP) infrastructure projects, in recent times. However, current studies and practical implementation of CE are largely associated with construction demolition, waste and recycling management. Few studies exist on circular models and success factors of public infrastructures developed within the PPP contracts. Thus, the main objective of this article is to identify the models and key success factors associated with CE implementation in PPP infrastructure projects.

A systematic review of the literature was undertaken in this study using forty-two (42) peer-reviewed journal articles from Scopus, Web of Science, Google Scholar and PubMed.

The results show that environmental factors, sustainable economic growth, effective stakeholder management, sufficient funding, utilization of low-carbon materials, effective supply chain and procurement strategies facilitate the implementation of CE in PPP infrastructure projects. Key CE business models are centered around the extension of project life cycle value, circular inputs and recycling and reuse of projects.

Although the study presents relevant findings and gaps for further investigations, it has a limited sample size of 42 papers, which is expected to increase as CE gain more prominence in PPP infrastructure management in future.

The findings are relevant for decision-making by PPP practitioners to attain the social, economic and environmental benefits of transitioning to circular infrastructure management.

This study contributes to articulating the key models and measures toward sustainable CE in public infrastructure development.

The main objective of this study is to evaluate the impact of Persian Scientific Papers (PSPs) on Persian Wikipedia by studying Wikipedia's citations to these papers.

The present study is applied research, which has been performed by the web-mining method, such as downloading web pages, extracting information (references), identifying papers, detecting peer-review journals and calculating the frequency rates. The statistical population included 10,000 Persian Wikipedia Pages (PWPs) that were analyzed in two rounds with a six-month interval.

The number of pages containing the Persian references section was 3,994 and 4,063 out of the 10,000 pages extracted in the first and second rounds. The ratio of pages that cited scientific sources (58 and 67 pages) to the pages extracted from the PWP was equal to 0.58 and 0.67%. The ratio of pages that cited scientific sources to pages with Persian references in each round was equal to 1.45 and 1.64%. The number of references extracted from the PWP in each round equaled 30,441 and 35,891. Eight titles from reputable Persian journals had received at least three citations from Wikipedia.

The present study has determined the extent of interaction between science and society (knowledge flow) in the form of citations from Wikipedia articles to articles in peer-reviewed journals. The study of this issue in Persian Wikipedia in more than 2000 Persian peer-reviewed journals shows the originality of the present paper. Studying citation reliability in a collaborative and openly editable platform is another originality of the work.

Motivated by the real-world practice that the boom of the online selling induces a higher product return as well, selecting which online channel mode indicates who takes ownership over the product and thus bears the loss of the product return. This paper aims to seek the optimal online channel modes for the two members in a platform supply chain in the presence of product returns.

This study aims to develop a platform supply chain that consists of one platform company and one supplier. Along with an offline distribution channel, the supplier can choose two alternative online selling modes (i.e. the reselling and agency modes) to sell its product through the online marketplace. This paper applies Stackelberg game to derive the equilibrium with different business scenarios and selects the optimal online channel modes for two parties, respectively. Moreover, this paper extends to a different supply chain with a reverse channel leadership and a different product return policy for testing the robustness.

Several interesting and important results are derived in this paper. Firstly, it is found that the relative pricing are largely relied on the costs of two channels. Secondly, the platform supply chain may benefit from a pure channel rather than the dual-channel when this channel enjoys a relatively low cost and/or a sufficiently high consumer preference. Then, the platform and the supplier act contradictorily when selecting their optimal online channel modes. To be specific, the platform motivates to choose the online reselling mode when both the commission rate and the slotting fee are relatively low, whereas the supplier is likely to select the online agency mode under this circumstance. Finally, a win-win situation in regards to the optimal online channel mode for two parties is achievable with numerical experiments.

Based on the analytical studies, the results derived in the authors’ work can provide managerial insights to assist the supplier and the platform company in determining the operational decision and selecting the optimal online channel mode to deal with consumer returns. In addition, appropriate commission rate along with slotting fee will make both parties achieve a win-win situation in determining their optimal online channel mode.

To the authors’ best knowledge, this paper makes the first move to determine the optimal online channel mode in the content of consumer returns and study how it is affected by different product return policies.