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This study proposes low-carbon technology (LCT) solutions from the perspective of incremental cost-effectiveness and public satisfaction based on calculating carbon emissions and economic costs.

According to the citation frequency, 11 indicators of low-carbon neighborhood (LCN) were selected so as to construct the low-carbon renewal potential evaluation model. Five neighborhoods were selected to evaluate low-carbon renewal potential based on the driving-pressure-state-impact-response (DPSIR). Moreover, the neighborhoods with the highest renewal potential were selected for further analysis. Then, the feasibility decision was carried out among seven typical LCTs based on the value engineering (VE) method. Finally, the TOPSIS method was applied to calculate the public satisfaction and demand so as to get the priorities of these LCTs. Through comprehensive analysis, the final LCT solutions could be carried out.

Our practice proves that the evaluation model combined with the decision-making methods can provide scientific decision-making support for the LCT solutions. Some LCTs perform consistently across different neighborhoods by comparing VE results and TOPSIS rankings. The solar photovoltaic (PV) (T3) has high value and significant attention which gives it a top priority for development, while the energy-efficient windows and doors (T2) have relatively low value.

There is a lack of research that considers the economic cost, low-carbon efficiency and public satisfaction when proposing LCT solutions for neighborhood renewal projects. Faced with the problem, we practice the decision-making from two dimensions, that is, the “feasibility decision with VE” and the “priorities decision with TOPSIS.” In this way, a balance between incremental cost-effectiveness and public satisfaction is achieved, and LCT solutions are proposed.

With the continuous increase in Web services, efficient identification of Web services that meet developers’ needs and understanding their relationships remains a challenge. Previous research has improved recommendation effectiveness by using correlations between Web services through graph neural networks (GNNs), while it has not fully leveraged service descriptions, limiting the depth and diversity of learning. To this end, a Web services recommendation method called LLMSARec, based on Large Language Model and semantic alignment, is proposed. This study aims to extract potential semantic information from services and learn deeper relationships between services.

This method consists of two core modules: profile generation and maximizing mutual information. The profile generation module uses LLM to analyze the descriptions of services, infer and construct service profiles. Concurrently, it uses LLM as text encoders to encode inferred service profiles for enhanced service representation learning. The maximizing mutual information model aims to align the semantic features of the services text inferred by LLM with structural semantic features of the services captured by GNNs, thus achieving a more comprehensive representation of services. The aligned representation serves as an input for the model to identify services with superior matching accuracy, thereby enhancing the service recommendation capability.

Experimental comparisons and analyses were conducted on the Programmable Web platform data set, and the results demonstrated that the effectiveness of Web service recommendations can be significantly improved by using LLMSARec.

In this study, the authors propose a Web service recommendation approach based on Large Language Model and semantic alignment. By extracting latent semantic information from services and effectively aligning semantic features with structural features, new representations can be generated to significantly enhance recommendation accuracy.

This systematic review aims to comprehensively explore the existing literature on the use of corporate communication within the realm of social media.

A total of 136 peer-reviewed journal articles are explored and analysed using both performance and bibliometric analysis.

This review identifies five main findings: (1) trends in corporate social media research that highlight the growth trajectory of research on social media use for corporate disclosure, (2) geographical coverage of studies indicating the concentration of research in certain regions, such as the USA, followed by China and the UK, with notable gaps in others, such as developing countries, (3) theoretical frameworks employed demonstrate that various theoretical frameworks are utilized, although a significant portion of the studies do not specify any theoretical underpinning, (4) social media platforms studied, confirming Twitter to be the most studied channel followed by Facebook and (5) thematic analysis of articles on disclosure type that categorized the articles using bibliometric analysis into five themes of disclosure: general disclosure, corporate social responsibility-related information, financial information, CEO announcements and strategic news communication. A subsequent cross-theme analysis classifies disclosure determinants and consequences of corporate social media usage.

Through a comprehensive and systematic analysis of existing research, this review offers novel insights into the current state of corporate communication on social media. It consolidates current knowledge, highlights under-explored areas in the existing literature and proposes new directions and potential avenues for future research.

The purpose of this study is to explore the relationship between environmental, social and governance (ESG) performance and tone management in the annual report. This is based on the notion that managers, driven by personal interests, may use their ESG accomplishments by using an abnormal positive tone to enhance their reputation or career prospects.

Using panel data from Chinese listed companies from 2010 to 2022, this study first investigates the relationship between ESG performance and abnormal tone management. The study then uncovers this relationship is mediated through the mechanisms of equity-based incentive and analyst coverage. The conclusions of this paper hold even after a series of robustness tests, such as propensity score matching, Heckman two-stage method and two-stage least squares with instrumental variables.

This study finds a positive correlation between ESG performance and the presence of abnormal positive tone in annual reports. Furthermore, the mechanistic analysis reveals that managers in companies with strong ESG performance are motivated to use an overly positive tone, largely due to their vested interests in equity-based compensation. Moreover, in an effort to alleviate the pressure stemming from heightened financial analyst coverage and enhance the impression conveyed through analysts' reports, managers with superior ESG performance also tend to inflate the tone within their annual reports.

This study provides significant insights into the ongoing dialogue surrounding ESG-related equity incentives, which incentivize managerial manipulation of stock prices through the use of abnormal positive tone. The findings call upon investors to exercise greater vigilance in examining narrative information in annual reports, as abnormally positive tones may not always faithfully represent performance but rather reflect managerial self-interest.

There is an emphasis on the importance of robust oversight mechanisms within corporate governance bodies to curb the manipulation of tone for managers’ personal gain.

This study enhances the theoretical foundation of ESG studies, offering a holistic perspective on the intricate interplay among ESG performance, managerial behavior and financial markets, with potential implications for researchers, investors and regulators.

This research examined the effect of information literacy (IL) on academic engagement (AE), cognitive engagement (CE) and academic performance among business students in Pakistan.

A cross-sectional survey was conducted to collect data from business students, recruited through a proportionate stratified convenient sampling technique, of the top 13 business institutions in Pakistan. The questionnaire was personally administered by visiting each institution with permission for data collection. A total of 554 responses were received and analyzed using the partial least squire-structural equation modeling approach.

The results exhibited that these business students perceived themselves as information literate. Furthermore, IL of business students appeared to predict positively their AE, CE and academic performance.

These results provided empirical and pragmatic insights for business educators, business librarians and accreditation bodies about IL effectiveness in academia. These findings may also inform policy and practice for IL instruction programs being carried out in business-related educational institutions not only in Pakistan but also in other countries of South Asia as they share similar characteristics.

This research would be a great contribution to the existing literature on IL, especially in the academic context as the interrelationship between IL, AE, CE and academic performance has not been investigated so far.

This paper addresses the limitations of current graph neural network-based recommendation systems, which often neglect the integration of side information and the modeling of complex high-order interactions among nodes. The research motivation stems from the need to enhance recommendation performance by effectively utilizing all available data. We propose a novel method called MSHCN, which leverages hypergraph neural networks to integrate side information and model complex interactions, thereby improving user and item representations.

The MSHCN method employs a hypergraph structure to incorporate various types of side information, including social relationships among users and item attributes, which are essential for enriching user and item representations. The k-means clustering algorithm is utilized to create item-associated hypergraphs, while sentiment analysis on user reviews refines the modeling of user interests. Additionally, hypergraphs are constructed for user-user and item-item interactions based on interaction similarity. MSHCN also incorporates contrastive learning as an auxiliary task to enhance the representation learning process.

Extensive experiments demonstrate that MSHCN significantly outperforms existing recommendation models, particularly in its ability to capture and utilize side information and high-order interactions. This results in superior user and item representations and improved recommendation performance.

The novelty of MSHCN lies in its use of a hypergraph structure to integrate diverse side information and model intricate high-order interactions. The incorporation of contrastive learning as an auxiliary task sets it apart from other hypergraph-based models, providing a significant enhancement in recommendation accuracy.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

This paper explored the socioeconomic factors affecting green cities development. Extant literature have highlighted green cities as a major path towards sustainability in the construction industry but very little is known on the socioeconomic aspect of green cities and its bid in promoting sustainability in the construction industry; hence, the premise of this study which highlights the socioeconomic factors affecting green cities development in Ghana.

A comprehensive literature review was conducted to identify the socioeconomic factors affecting green cities. A quantitative research strategy was adopted to collect primary data from respondents who have the requisite understanding and knowledge in green cities using questionnaires. The data gathered was then analysed using descriptive statistics and exploratory factor analysis viz principal component analysis.

The socioeconomic factors affecting green city development comprised: Green support mechanisms (i.e. innovation and technology, green city planning (urban planning), stakeholder engagement, awareness, city planning (transportation) and environmental regulations); green inhibitors (i.e. population, culture, housing and policy implementation); green market and finance (i.e. digital finance, green market mechanism, green investment finance, risks and uncertainties, income levels of clients). It was evident that socioeconomic factors are significant to the development of green cities in Ghana and hence policy makers and various stakeholders should prioritize socioeconomic factors in the bid to achieve sustainability through green cities in the construction industry.

This paper presents a foremost and comprehensive study on the socioeconomic factors affecting green cities in Ghana. The study results showed that even though the path to sustainability in green cities has pivoted mainly on environmental factors, socioeconomic factors are also significant to green city development, hence, policy makers and the construction industry should keenly consider the socioeconomic factors affecting green city development in the bid towards sustainability for cities.