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Considering the role of analysts in disseminating information, the paper explains the idiosyncratic volatility puzzle of China's stock market. As the largest developing country, China's research can provide meaningful reference for the research of financial markets in other new countries.

From the perspective of behavior, establishing a direct link between individual investor attention and stock price overvaluation.

The authors find that there is a significant idiosyncratic volatility puzzle in China's stock market. Due to the role of mispricing, individual investor attention significantly enhances the idiosyncratic volatility effect, that is, as individual investor attention increases, the greater the idiosyncratic volatility, the lower the expected return. Attention can explain the idiosyncratic volatility puzzle in China's stock market. In addition, due to the role of information production and dissemination, securities analysts can reduce the degree of market information asymmetry and enhance the transparency of market information.

China is the second largest economy in the world, and few scholars analyze it from the perspective of investors' attention. The authors believe this paper has the potential in contributing to the academia.

Ethiopia has enacted laws on transparency and disclosure of information in state-owned enterprises (SOEs). However, these laws are not strict enough, with the transparency and disclosure practices disappointing in the country. Thus, this study aims to investigate the legal framework governing transparency and disclosure in SOEs.

This study uses doctrinal, qualitative and comparative approaches. Domestic legal texts are appraised based on the organization for economic co-operation and development Guideline on Corporate Governance of State-owned Enterprises, the World Bank Toolkit on Corporate Governance of State-owned Enterprises and best national practices. This approach has been further corroborated by qualitative analysis of the basic principles of transparency and disclosure.

The finding reveals that the laws on transparency and disclosure do not comply with global practices and are inadequate to ensure transparency and discourse in SOEs. They fail to establish appropriate disclosure frameworks and practices at the SOE and state-ownership entity levels. They also indiscriminately subject enterprises to multiple auditing functions and conflicting responsibilities.

To the author’s knowledge, this study is the first legal literature on transparency and disclosure in Ethiopian SOEs. This study assists the state as owner in reforming the laws and uplifting SOEs from their current unpleasant condition. It can also become a reference for future research.

This paper aims to explore the potential effect of key audit matters (KAM) on the audit report lag (ARL). In particular, it aims to discover whether the number of KAMs reported by an audit firm in Jordan is related to the length of its ARL.

The authors analysed data from the first three years of KAM reporting in Jordan (2017–2019) for 194 public listed Jordanian companies to examine the relation between the number of KAMs and the ARL, taking into account several control variables related to the Jordanian context.

This study found that there is no statistically significant relation between the number of KAMs reported by Jordanian audit firms and their ARLs, suggesting that the KAM reporting in Jordan is somewhat superficial, with the selection of what is actually reported as a KAM not directly related to the efforts needed to deal with its concerns. However, this study also found statistically significant positive relations between the ARL and each of audit fees, audit firm size, the issuance of a qualified audit opinion and company leverage and a statistically significant negative relation between the ARL and company profitability.

This is one of the very few studies to cover the potential relation between KAM reporting and the ARL. In a developing country context characterised by limited demand for an external audit of high quality, this study finds that auditors may decouple on their reporting of KAMs by not actually making significant efforts to deal with them.

In this study, the authors argue that the private information obtained and transmitted by institutions during the corporate visits can alleviate the degree of information asymmetry between firms and investors, so institutional visits may influence investors' heterogeneous beliefs. Therefore, the authors investigated whether and how institutional investors' corporate visits affect investors' heterogeneous beliefs.

This study examines whether and how institutional investors' corporate visits affect investors' heterogeneous beliefs using the data of A-share companies from the Shenzhen Stock Exchange (SZSE) during 2013–2019. Using empirical research method, this study designs and conducts an empirical research according to empirical research's basic norms.

The authors find that institutional visits effectively decrease investors' heterogeneous beliefs, especially institutional investors. Meanwhile, institutional site visits and sell-side institutional visits have a more significant negative effect on investors' heterogeneous beliefs. The findings remain after robustness tests with the alternative variable, instrumental variable, propensity score matching and quantile regression methods.

The development of China's capital market is imperfect, resulting in a strong speculative atmosphere. So, investors' irrational investment behaviors occur from time to time, leading to sizeable heterogeneous beliefs in China's capital market, which increases the risk of investment and is not conducive to the discovery of corporate value and the efficient allocation of resources. Therefore, exploring the factors influencing heterogeneous beliefs and finding ways to alleviate heterogeneous beliefs can reduce the proportion of speculative investors and promote the healthy development of China's capital market.

This study aims to determine museum experiences by analyzing the TripAdvisor reviews of the museum visitors in Gaziantep and offer suggestions for improving the visitors' experiences by taking museum curators' opinions.

The case study method was used as one of the qualitative approaches in the study. The research comprises two stages. TripAdvisor reviews about five museums in Gaziantep were analyzed in the first stage, and museum curators' opinions on the visitor complaint reasons and solution suggestions were discussed in the second stage.

The study showed that satisfying or non-satisfying experience factors might differ according to visitors, museum curators and both visitors and museum curators. Therefore, each museum curator should effectively manage every component of visitor experience factors by its target audience.

Although this study has some valuable findings and contributes to the literature, it also has limitations. The study's sample consists of five museums in Gaziantep. Further studies can be carried out on a larger population and sample. The data for determining visitor experiences, the first stage of the research, were obtained only from an online platform (TripAdvisor).

The proposed model provides a holistic perspective on evaluating and managing visitor experience. There may be structural problems (small size of the museum area, lack of parking spaces and elevators, etc.) with the museum beyond the manager's control. Also, the lack of information and communication (limited concept, lack of artifacts, etc.) causes criticisms of the museum.

This paper contributes to the museum management literature with a model for enriching the quality of the museum experience and increasing the museum's attractiveness. The study showed that museums contribute to the visitor's experience in all dimensions. While visitors thought museums primarily contribute to object experience, museum curators thought museums contribute more to visitors' cognitive experience and other experience elements.

The present study analyzed the visitor comments and the opinions of museum curators from a holistic perspective, considering the internal and external factors that are effective in the visitor experience, and revealed the reasons for the visitors' negative experience and the solution suggestions toward the improvement of the visitor experience.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

The aim of this paper is to empirically investigate whether social contacts can mediate the way in which current unemployment impacts future unemployment.

We use 2006–2017 data from the Household Income and Labour Dynamics in Australia (HILDA) survey and a dynamic random-effects model to describe the evolution of individual unemployment status over time.

Once controlled for the local context where individuals live and create friendships, we find that above-average social contacts reduce unemployment persistence. However, social contacts seem to be slightly less effective in deprived neighborhoods. These findings are consistent with the idea that individuals obtain information about job opportunities through a network of social contacts, and unemployment may lead to a decay of social capital, making it more difficult to find employment in future periods. Our results also show that neighborhood deprivation increases individual unemployment risk, while above-average neighborhood cohesion reduces the probability of unemployment in deprived neighborhoods.

Although many studies have been published on unemployment persistence, to the best of the author's knowledge, this is the first study quantifying the impact of social contacts on unemployment persistence. The study also offers fresh empirical evidence on the impact of neighborhood characteristics on unemployment risk.

Construction is a risky industry. Therefore, organizations are seeking ways towards improving their safety performance. Among these, the integration of technology into health and safety leads to enhanced safety performance. Considering the benefits observed in using technology in safety, this study aims to explore digital technologies' use and potential benefits in construction health and safety.

An extensive bibliometrics analysis was conducted to reveal which technologies are at the forefront of others and how these technologies are used in safety operations. The study used two different databases, Web of Science (WoS) and Scopus, to scan the literature in a systemic way.

The systemic analysis of several studies showed that the digital technologies use in construction are still a niche theme and need more assessment. The study provided that sensors and wireless technology are of utmost importance in terms of construction safety. Moreover, the study revealed that artificial intelligence, machine learning, building information modeling (BIM), sensors and wireless technologies are trending technologies compared to unmanned aerial vehicles, serious games and the Internet of things. On the other hand, the study provided that the technologies are even more effective with integrated use like in the case of BIM and sensors or unmanned aerial vehicles. It was observed that the use of these technologies varies with respect to studies conducted in different countries. The study further revealed that the studies conducted on this topic are mostly published in some selected journals and international collaboration efforts in terms of researching the topic have been observed.

This study provides an extensive analysis of WoS and Scopus databases and an in-depth review of the use of digital technologies in construction safety. The review consists of the most recent studies showing the benefits of using such technologies and showing the usage on a systemic level from which both scientists and practitioners can benefit to devise new strategies in technology usage.

The purpose of this study was to develop a suitable module for digital conservation of traditional knowledge of medicinal plants (MPs) used by tribal communities living in the Anuppur district of Madhya Pradesh, Central India.

The research used a qualitative approach to gather the data of MPs through the use of literature review and field survey. Based on the acquired data, a prototype digital learning system was constructed and assessed. This study used digital learning technologies to assess the requirements for transmitting traditional knowledge of important MPs used by tribal communities so that people can absorb and conserve them.

Over time, the focus on the digital conservation of traditional MP’s knowledge has progressively increased globally. Despite the rise in this field of study, information technology methods to preserve and distribute traditional knowledge of MPs have remained a few. When adopting digital learning to maintain traditional knowledge of MPs, it was discovered that it would be necessary to engage with relevant knowledge keepers, use multimedia, and provide content in local languages.

This study helps in conservation of important MP species that are having biologically important therapeutic compounds meant for treating various ailments. Older generations of various tribal communities mainly hold traditional knowledge of important MPs, and unless it is preserved, it will perish along with its caretakers.

It is worth looking at a digital platform that can help future generations to maintain traditional knowledge of MPs, as it is a dynamic and ever-changing, it must involve a digital tool for its future conservation. Current methods for maintaining traditional knowledge of MPs were ineffective and constrained by space and time.

The purpose of this paper is to propose a decision support framework that can be used by decision-makers to identify the most convenient disruption analysis (DA) methods for megaprojects and their stakeholders.

The framework was initially developed by conducting a comprehensive literature review to obtain extensive knowledge about disruption management and megaprojects. Focus group discussion (FGD) sessions with the participation of the construction practitioners were then organized to validate and strengthen the findings of the literature review. Consequently, 17 selection factors were identified and categorized as requirement, ability and outcome. Lastly, the most convenient DA methods for megaprojects were identified by performing integrated fuzzy analytical hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) analysis. Additionally, consistency analysis was also conducted to verify the reliability of the results.

The results revealed that the measured mile method is the most appropriate DA method for megaprojects. In case the measured mile method cannot be adopted due to various technical and contractual reasons, the decision-makers are proposed to consider program analysis, work or trade sampling, earned value analysis and control chart method, respectively. Second, the selection factors such as “Comprehensible analysis procedure,” “Existing knowledge and experience about a particular DA method,” “Ability to resolve greater number of disruption events,” “Ability to resolve complex disruption events,” “Ability to exclude factors that are not under the owner's responsibility” and “General acceptance by practitioners, courts, and arbitration, etc.” were given the top priority by the experts, highlighting the critical aspects of the DA methods.

Disruption claims in megaprojects are very critical for the contractors to compensate for the losses stemming from disruption events. Although the effective use of DA methods maximizes the accuracy and reliability of disruption claims, decision-makers can barely implement these methods adequately since past studies neglect to present extensive knowledge about the most convenient DA methods for megaprojects. Thus, developing a decision support framework for the selection of DA methods, this study is the earliest attempt that examines the mechanisms and inherent differences of DA methods. Additionally, owing to the robustness and versatility of this research approach, the research approach could be replicated also for future studies focusing on other project-based industries since disruption is also a challenging issue for many other industries.