Search results

The purpose of the study is to investigate strategies for enhancing pollution oversight by local governments while reducing government-enterprise collusion (GEC) levels. Additionally, the factors influencing pollution control efforts at incineration plants are explored. Potential approaches to improving them and for effectively reducing waste incineration pollution are suggested.

The authors examined the most effective methods for mitigating incineration-related pollution and preventing collusion and developed a differential game model involving interactions between local governments and incineration plants. The findings of this work have significant policy implications for central governments worldwide seeking to regulate waste incineration practices.

The results indicate that, first, elevating environmental assessment standards can incentivize local governments to improve their oversight efforts. Second, collusion between incineration plants and local governments can be deterred by transferring benefits from the plants to the local government, while increased supervision by the central government and the enforcement of penalties for collusion can also mitigate collusion. Third, both central and local governments can bolster their supervisory and penalty mechanisms for instances of excessive pollution, encouraging incineration plants to invest more in pollution control. Finally, when the central government finds it challenging to detect excessive incineration-related pollution, enhancing rewards and penalties at the local government level can be a viable alternative.

This study stands out by considering the dynamic nature of pollutants. A differential game model is constructed which captures the evolving dynamics between local governments and incineration plants, offering insights regarding the prevention of collusion from a dynamic perspective. The findings may provide a valuable reference for governments as they develop and enforce regulations while motivating incineration plants to actively engage in reducing waste-incineration pollution.

The study aims to investigate the effects of intellectual capital and its constituents on the performance of listed companies operating in China's construction sector. The study also intends to examine the moderating role of digital transformation.

Hypotheses will be tested using Modified Value-Added Intellectual Capital (MVAIC). The sample will be comprised of 93 Shenzhen and Shanghai A-share listed companies within the construction industry from the period of 2015–2021. Multiple regression analysis was employed to investigate the influence of intellectual capital, its components and digital transformation on the performance of construction firms.

The study's results reveal that the performance of construction firms greatly depends on intellectual capital and its components. Furthermore, digital transformation plays a vital moderating role between intellectual capital and its components and construction firm performance.

This study addresses a critical inquiry on how construction managers can employ intellectual capital to enhance the performance of firms during digital transformation. Additionally, this research bridges this gap by guiding construction managers to concentrate on their external surroundings when examining firm performance.

By focusing on the predictors influencing construction firms' performance, this study contributes to the existing corpus of knowledge. This study employs resource orchestration theory (ROT) to determine how the different components of intellectual capital impact the performance of construction firms, with digital transformation acting as a moderating variable. This research will be valuable to researchers, construction industry professionals and policymakers.

The purpose of this study is to examine whether service innovation capability can affect firm performance in the architecture, engineering and construction (AEC) context, and, if so, how.

This study developed a theoretical framework illustrating the performance impacts of service innovation capability through the business model in the AEC sector. An empirical study was conducted to test the hypotheses using 374 valid questionnaires using structure equation model (SEM).

The results verify that service innovation capability positively influences firm performance mediated by the business model. As to the direct effect, service innovation capability is positively associated with firm performance.

This study highlights how service innovation capability affects performance and reveals the underlying mechanism.

Market sentiment has shown to influence housing prices in the global north, but in emerging economies, the nexus is rare to chance on in the current state of science for policy direction. More importantly in the recent decade where policymakers are yet to conclude on the myriad of factors confronting the housing market in sub-Saharan Africa inhibiting affordability. This paper therefore examines the impact of market sentiment on house prices in South Africa.

The study used the Autoregressive Distributed Lag (ARDL) approach with quarterly data spanning from 2005Q1 to 2020Q4.

In all, it was established that market sentiment plays a minimal role in the property market in South Africa. But there was enough evidence of cointegration from the bound test between sentiment and house prices. Nevertheless, the lag values of sentiment pointed to a rise in house prices. Exchange rate volatilities and inflation had a statistically significant effect on prices in both the long and short term, respectively.

Policymakers could still monitor market sentiment in the housing market due to the strong chemistry between house prices and sentiment, as evidenced from the bound test, but focus on economic fundamentals as the main policy tool for house price reduction.

The findings and the creation of the sentiment index make an invaluable contribution to the paper and add to the paucity of literature on the study of market sentiment in the housing market.

To propose a new investment-income valuation model by real options approach (ROA) for old community renewal (OCR) projects, which could help the government attract private capital's participation.

The new model is proposed by identifying the types of options private capital has in the OCR project, selecting the option model most suitable for private capital investment decisions, improving the valuation model through the triangular fuzzy numbers to take into account the uncertainty and flexibility, and demonstrating the feasibility of the calculation model through an actual OCR project case.

The new model can valuate OCR projects more accurately based on considering uncertainty and flexibility, compared with conventional methods that often underestimate the value of OCR projects.

The investment-income of OCR projects shall be re-valuated from the lens of real options, which could help reveal more real benefits beyond the capital growth of OCR projects, enable the government to attract private capital's investment in OCR, and alleviate government fiscal pressure.

The proposed OCR-oriented investment-income valuation model systematically analyzes the applicability of real option value (ROV) to OCR projects, innovatively integrates the ROV and the net present value (NPV) as expanded net present value (ENPV), and accurately evaluate real benefits in comparison with existing models. Furthermore, the newly proposed model holds the potential to be transferred to various social welfare projects as a tool to attract private capital's participation.

While spontaneous and voluntary knowledge contribution in online communities promotes value co-creation, dysfunctional knowledge behaviors hamper the effectiveness and development of such communities. The study conceptualizes physicians' proactive knowledge sharing and knowledge withholding behaviors in physician-driven online health communities (OHCs) and integrates the theories of role identity as well as communal and exchange relationships to understand the root causes and motivations behind these two types of knowledge behaviors.

The authors collected survey data from 166 users from one of the largest physician-driven OHCs in China and applied the covariance-based structural equation modeling approach to test the hypotheses.

The findings suggest that (1) physicians' professional role identity had a positive indirect effect on proactive knowledge sharing behaviors through communal motivation, and work pressure weakened this indirect effect; and (2) professional role identity had a negative indirect impact on knowledge withholding behaviors through exchange motivation.

This study extends proactive knowledge sharing and knowledge withholding behaviors from the organizational management domain to the online environment, exploring the underlying causes and motivations behind both behaviors in the unique context of physician-driven OHCs. The findings offer practical suggestions for the effective management of OHC platforms, as well as policy implications that respond to the workforce shortage of healthcare providers, a crisis that is unfolding globally.

This study explores whether religion plays an important role in corporate poverty alleviation. Religious atmosphere affects managers' attitude towards corporate social responsibility (CSR) and then influences corporate poverty alleviation. This study first examines the impact of religious atmosphere on corporate poverty alleviation and then investigates whether formal institutions, such as law enforcement environments and ownership, influence the relationship between religious atmosphere and corporate poverty alleviation behavior.

In 2016, the Chinese government initiated a nationwide campaign aiming to eliminate poverty in China by 2020. The authors conduct empirical tests with data on Chinese listed firms from 2016 to 2020. The religious atmosphere is measured by the number of Buddhist monasteries and Taoist temples within a certain radius around Chinese listed firms' registered addresses. The authors adopt the ordinary least squares (OLS) method for regression and take the two-stage least squares (2SLS) method to address the endogeneity issue.

The results show a positive relationship between religious atmosphere and corporate poverty alleviation donations. Law enforcement attenuates the positive association between the religious atmosphere and corporate poverty alleviation donations. Religion and corporate poverty alleviation donations have a more positive association for non-state-owned enterprises (non-SOEs) than for state-owned enterprises (SOEs).

The authors' findings have important implications. First, this study inspires incorporating the ethical value of traditional culture, such as religion, into CSR. Second, the findings imply that informal institutions have a greater impact on corporate decision-making when formal institutions are weak, suggesting that informal institutions should be emphasized when promoting CSR in countries where formal institutions are relatively weak. The study investigates only religious influence on corporate poverty alleviation based on Buddhism and Taoism, but the authors do not examine the impacts of other religions. Future research may examine the relationships between other religions and corporate poverty alleviation in China.

This study illustrates the positive role played by religion in promoting CSR by relating religious atmosphere to corporate poverty alleviation. It fills the research gap between religion and CSR and also contributes to the literature on determinants of corporate poverty alleviation.

This study examines the impact of the US government customer concentration and product innovation in supplier firms. The US government customer concentration is defined as the proportion of sales made by a supplier firm to the US government as a major customer. To measure product innovation, the author uses two key metrics: the number of patents and the novelty of the patents. The results indicate that a supplier firm’s relationship with the US government, as measured by the tenure of the relationship, has a significant impact on product innovation. Furthermore, the author shows that changes in the composition of the US government, Senate can also affect the level of innovation in supplier firms.

This study employs the Compustat’s Segment Customer database and the National Bureau of Economic Research (NBER) Patent Citation database to gather information regarding patents granted by the United States Patent and Trademark Office (USPTO). The author also incorporates data from US Congressional committees from the 96th to 115th Congresses to assess the effect of changes in seniority of US senators on influential committees on the firm’s innovation. For a robustness test, the author utilizes a propensity score matched analysis.

The author demonstrates that a firm’s dependence on the US government as a customer channel for an extended period negatively impacts the firm’s innovation efficiency, as measured by the number of patents, citations and novelty of the patents. In addition, the author provides evidence that changes in the seniority of US senators on influential committees have a significant impact on firms located in the same state as the new senior senators. These firms decrease innovative efforts due to the political connections, resulting in lower levels of innovation. These findings are robust after controlling the endogeneity issues. In conclusion, this study contributes to the existing literature by offering insight into the relationship between customer concentration and firm innovation. The findings highlight the importance of considering the relationship between firms and their customer base in determining innovation outcomes.

This study demonstrates that a heavy reliance on the US government as a customer channel has a detrimental impact on a firm’s innovation efficiency. Furthermore, the author analyzes the exogenous shock of changes in the seniority of US senators on the relationship between customer dependence and innovation. By utilizing a propensity matched sample, the author addresses endogeneity concerns and provides robust evidence for empirical findings. In conclusion, this study sheds light on the complex relationship between customer dependence and firm innovation, particularly in the context of the US government as a sales channel.

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.

The purpose of this paper is to test the existence of stylized facts, such as the volatility clustering, heavy tails seen on financial series, long-term dependence and multifractality on the returns of four real estate indexes using different types of indexes: conventional and Islamic by comparing pre and during COVID-19 pandemic.

Firstly, the authors examined the characteristics of the indexes. Secondly, the authors estimated the parameters of the stable distribution. Then, the long memory is detected via the estimation of the Hurst exponents. Afterwards, the authors determine the graphs of the multifractal detrended fluctuation analysis (MF-DFA). Finally, the authors apply the WTMM method.

The results suggest that the real estate indexes are far from being efficient and that the lowest level of multifractality was observed for Islamic indexes.

The inefficiency behavior of real estate indexes gives us an idea about the prediction of the behavior of future returns in these markets on the basis of past informations. Similarly, market participants would do well to reassess their investment and risk management framework to mitigate new and somewhat higher levels of risk of their exposures during the turbulent period.

To the authors’ knowledge, this is the first real estate market study employing STL decomposition before applying the MF-DFA in the context of the COVID-19 crisis. Likewise, the study is the first investigation that focuses on these four indexes.