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This study aims to provide insight into the usage of metaverse technologies in libraries, examining their impact on library service user engagement and assisting in unique promotion and marketing techniques for the metaverse setting.

This study used a literature review approach to synthesize current scholarly material on metaverse technologies and their practical applicability in the library context. It gathered academic literature from Emerald, Web of Science, Scopus and Google Scholar databases to construct a strong foundation for the study from journal articles, which were retrieved using the researcher’s “search string.”

Metaverse technologies are reshaping the idea of smart libraries, offering innovative approaches to interact with information resources and delivering users progressively immersive and dynamic learning experiences. This in-depth investigation adds to a better understanding of the emerging link between metaverse technology and library professionals. It bridges the gap between libraries and users, transcending geographical boundaries and enabling libraries to serve a global audience. It anticipates metaverse-infused academic libraries welcoming new methods of engaging with their users with interactive capabilities to create a unique environment to explore library resources in an interactive marketing approach.

The metaverse is not only the future but also the future of libraries. This research fills the gap by advancing the literature on cutting-edge library technologies and empowers users to become reflective and well-informed information producers. It also helps library stakeholders engage with their audience in new and impactful ways and explore innovative marketing strategies tailored to the metaverse environment.

Exposure to indoor air pollutants poses a significant health risk, contributing to various ailments such as respiratory and cardiovascular diseases. These unhealthy consequences are specifically alarming for athletes during exercise due to their higher respiratory rate. Therefore, studying, predicting and curtailing exposure to indoor air contaminants during athletic activities is essential for fitness facilities. The objective of this study is to develop a neural network model designed for predicting optimal (in terms of health) occupancy intervals using monitored indoor air quality (IAQ) data.

This research study presents an innovative approach employing a long short-term memory (LSTM) recurrent neural network (RNN) to determine optimal occupancy intervals for ensuring the safety and well-being of occupants. The dataset was collected over a 3-month monitoring campaign, encompassing 15 meteorological and indoor environmental parameters monitored. All the parameters were monitored in 5-min intervals, resulting in a total of 77,520 data points. The dataset collection parameters included the building’s ventilation methods as well as the level of occupancy. Initial preprocessing involved computing the correlation matrix and identifying highly correlated variables to serve as inputs for the LSTM network model.

The findings underscore the efficacy of the proposed artificial intelligence model in forecasting indoor conditions, yielding highly specific predicted time slots. Using the training dataset and established threshold values, the model effectively identifies benign periods for occupancy. Validation of the predicted time slots is conducted utilizing features chosen from the correlation matrix and their corresponding standard ranges. Essentially, this process determines the ratio of recommended to non-recommended timing intervals.

Humans do not have the capacity to process this data and make such a relevant decision, though the complexity of the parameters of IAQ imposes significant barriers to human decision-making, artificial intelligence and machine learning systems, which are different. Present research utilizing multilayer perceptron (MLP) and LSTM algorithms for evaluating indoor air pollution levels lacks the capability to predict specific time slots. This study aims to fill this gap in evaluation methodologies. Therefore, the utilized LSTM-RNN model can provide a day-ahead prediction of indoor air pollutants, making its competency far beyond the human being’s and regular sensors' capacities.

The research in this paper aims to investigate the development of Library and Information Science in Chinese universities. Specifically, it focuses on understanding the spatial and temporal aspects of subject knowledge output and providing a more comprehensive explanation of the imbalance in subject research.

This study applies the bibliometric method to analyze 131,112 papers published by 51 universities in mainland China from 1977 to 2021, as recorded in the Chinese Social Sciences Citation Index (CSSCI). The study classifies the evolution trends of the discipline and quantifies the published article data of the universities using the index of published articles. Additionally, it examines the development status, structural situation, influencing factors and prospects of universities in different categories and regions.

The field of Library and Information Science is gaining momentum in Chinese universities, but there are significant differences in its development. While the relative gap among universities in a regional context is diminishing, the absolute gap in the category perspective is increasing. The development of Library and Information Science is influenced by various factors, including the academic environment, geographical position, scientific research projects and academic traditions. The uneven development of the discipline is maintained in the short term.

This paper proposes a new quantitative index of discipline development, the university publication index. This index allows for an examination of the temporal and spatial trends of discipline development using domestic universities as the subject of research. The paper presents an overview of discipline development through four aspects: academic participation practice, discipline governance mechanisms, education and teaching systems and discourse construction within the discipline. The theoretical support provided by this study can help facilitate innovative development in the discipline.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

This paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what extent does user feedback affect influential users in changing from one archetype to another?

Based on a sample of 13,840 influential users drawn from the Covid-19 community on Zhihu, the archetypes of influential users were derived from their ongoing participation behavior in the community using the Gaussian mixture model. Additionally, user feedback characteristics such as relevance and volume from 222,965 commenters who contributed 546,344 comments were analyzed using the multinomial logistic regression model to investigate the archetype change of influential users.

Findings suggest that influential users could be clustered into three distinctive archetypes: touch-and-go influential users, proactive influential users and super influential users. Moreover, feedback variables have various impacts on the influential user archetype change, including a shift toward creating higher-quality content and fostering increased interaction, a shift toward generating lower-quality content and decreased interaction but improved speed and having mixed effects due to differences in information processing among these archetypes.

This study expands the existing knowledge of influential users and proposes practical approaches to cultivate them further.

Corporate leadership and environmental, social and governance (ESG) performance are closely intertwined, as effective corporate leadership can facilitate the achievement of strong ESG performance. Thus, the purpose of the study is to investigate the impact of corporate board leadership on the ESG performance of listed firms.

The sample has been taken from the listed firms of the Nifty 500 index spanning the period of 10 years from 2012 to 2022. Dynamic panel data estimations are applied through a fixed effect model.

The findings of this study revealed that board size, board independence and board qualification have a significant positive influence on ESG performance. It is evident that good corporate governance practices can positively influence ESG performance by fostering accountability, transparency and ethical behavior, as well as better integrating ESG considerations into their decision-making processes and ensuring that ESG issues are prioritized at the highest levels of management. Further findings also revealed that chief executive officer (CEO) duality has a significant negative relationship with ESG performance, which goes against the belief of stakeholder theory.

It has practical implications for policymakers, as they can enact new regulations pertaining to the CEO’s position in the organizations to make corporate governance responsible for improved sustainability and ESG performance.

There are very few studies analyzing the impact of corporate board structure on ESG performance related to emerging markets. Thus, this study contributes to that literature by using the methodology GMM panel data for the first time as per our knowledge

The principal purposes of the research are to empirically investigate three forms of perceived overload on social media and shed light on their associations with users’ passive usage intention by contemplating the mediating influence of social network exhaustion and discontented feelings.

This study employed a cross-sectional methodology to collect statistical data (N = 679) from WeChat users in mainland China. Primitive analysis, confirmatory factor analysis and structural equation modeling were employed to test the corresponding hypotheses.

The findings reveal that three dimensions of perceived overload influence social network exhaustion positively. In addition, communication overload and system feature overload exert positive impacts on the discontented feeling. Furthermore, it is uncovered that social network exhaustion and discontented feeling are related to passive usage intention positively.

Theoretically, this paper offers a conceptual framework to explicate passive usage intention through elucidating social network exhaustion and the discontented feeling that arises from perceived overload in contemporary social media-mediated environments. Practically, the current research has certain realistic implications for WeChat users and SNS operators.

Probing what triggers people’s passive usage intention of social media has been an emerging theme in recent years, yet there is a dearth of discourse that delves into the antecedents of WeChat users’ passive usage intention. The results obtained from the study have enhanced the understanding of the adverse consequences associated with the utilization of social media in mainland China.

The aim of this article is two-fold. First, it offers a unique account of San Min, the prototype of the current Chinese educational principle proposed by Yan Fu (1854–1921) that aimed at improving people’s physical, intellectual and moral capacities. This system of educational thinking has received only marginal attention in Anglophone research literature. Second, given the influence of Yan Fu’s interpretation and promulgation of Herbert Spencer’s educational philosophy during that period, it investigates the extent to which San Min is derived from Spencer’s educational thought (the “Spencerian Triad”). This article focusses on how Yan Fu adapted the ideas of San Min from Spencer’s account.

This article considers Yan Fu’s principle of San Min in relation to Spencer’s educational triad through a close reading and comparison of key primary texts (including Yan Fu’s original writing). It explores the similarities and differences between each account of education’s goals and its proposed means of attainment.

Yan Fu’s principle of San Min is shown to have been adapted from the Spencerian Triad. However, using the theory of Social Organism, Yan Fu re-interpreted Spencer’s individual liberty as liberty for the nation. While Spencer’s goal was to empower individuals, Yan Fu aimed to serve collective independence, wealth and power.

This article addresses oversights concerning San Min’s Western origins in the Spencerian Triad and its influence on Chinese education under Yan Fu’s sway. It is significant because San Min is still at the core of the current Chinese educational policy.

Improving productivity and efficiency has always been crucial for industrial companies to remain competitive. In recent years, the topic of environmental impact has become increasingly important. Published research indicates that environmental and economic goals can enforce or rival each other. However, few papers have been published that address the interaction and integration of these two goals.

In this paper, we identify both, synergies and trade-offs based on a systematic review incorporating 66 publications issued between 1992 and 2021. We analyze, quantify and cluster examples of conjunctions of ecological and economic measures and thereby develop a framework for the combined improvement of performance and environmental compatibility.

Our findings indicate an increased significance of a combined consideration of these two dimensions of sustainability. We found that cases where enforcing synergies between economic and ecological effects were identified are by far more frequent than reports on trade-offs. For the individual categories, cost savings are uniformly considered as the most important economic aspect while, energy savings appear to be marginally more relevant than waste reduction in terms of environmental aspects.

No previous literature review provides a comparable graphical treatment of synergies and trade-offs between cost savings and ecological effects. For the first time, identified measures were classified in a 3 × 3 table considering type and principle.

Criticality cognitions regarding the same workplace event often differ between leaders and employees. Nevertheless, its consequences on employee work outcomes remain unknown. In this study, we draw on cognitive dissonance theory to examine how and why leader–employee differences in cognitions of workplace event criticality impact employee job-related outcomes.

Wu used multilevel polynomial regression analyses from a time-lagged, multi-source field study with 145 leader–employee dyads to test our proposed model.

Leader–employee differences in cognitions of workplace event criticality can bring both benefits and perils to employees. Specifically, such differences can cause employee rumination, which in turn leads to an increase in both employee voice and fatigue.

This study contributes to the event and cognitive discrepancy literature in four ways. First, prior event studies largely adopted a singular employee perspective for investigation (e.g. Chen et al., 2021; Lin et al., 2021). By examining the impacts of event criticality from the dual perspective of leaders and employees, we attain a more comprehensive understanding of the implications of workplace events in organizational life. Second, extant studies have predominantly focused on the dark side of cognitive discrepancy (e.g. Bashshur et al., 2011; Erdogan et al., 2004; Grandey et al., 2013). Our study reveals that leader–employee differences in criticality cognitions can have both a bright and a dark side on employee outcomes, offering a more balanced and dialectical view of the consequences of cognitive discrepancy. Third, drawing on cognitive dissonance theory, we introduce employee rumination as an underlying mechanism to explain the impacts of leader–employee differences in criticality cognitions on employee voice and fatigue. Finally, while prior cognitive dissonance research has primarily employed an intrapersonal perspective (e.g. Sivanathan et al., 2008; Pugh et al., 2011; Grandey et al., 2013), our study adopts an interpersonal lens and underscores that interpersonal differences in cognitions can also serve as an example of cognitive discrepancy to instigate internal dissonance processes. By doing so, we enrich our understanding of cognitive dissonance theory.