Artificial intelligence (AI) is a powerful technology with a range of capabilities, which are beginning to become apparent in all industries nowadays. The increased popularity of AI in the construction industry, however, is rather limited in comparison to other industry sectors. Moreover, despite AI being a hot topic in built environment research, there are limited review studies that investigate the reasons for the low-level AI adoption in the construction industry. This study aims to reduce this gap by identifying the adoption challenges of AI, along with the opportunities offered, for the construction industry. To achieve the aim, the study adopts a systematic literature review approach using the PRISMA protocol. In addition, the systematic review of the literature focuses on the planning, design, and construction stages of the construction project lifecycle. The results of the review reveal that (a) AI is particularly beneficial in the planning stage as the success of construction projects depends on accurate events, risks, and cost forecasting; (b) the major opportunity in adopting AI is to reduce the time spent on repetitive tasks by using big data analytics and improving the work processes; and (c) the biggest challenge to incorporate AI on a construction site is the fragmented nature of the industry, which has resulted in issues of data acquisition and retention. The findings of the study inform a range of parties that operate in the construction industry concerning the opportunities and challenges of AI adaptability and help increase the market acceptance of AI practices.

Artificial intelligence (AI) is a powerful technology that can be utilized throughout a construction project lifecycle. Transition to incorporate AI technologies in the construction industry has been delayed due to the lack of know-how and research. There is also a knowledge gap regarding how the public perceives AI technologies, their areas of application, prospects, and constraints in the construction industry. This study aims to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. This study adopted social media analytics, along with sentiment and content analyses of Twitter messages (n = 7906), as the methodological approach. The results revealed that: (a) robotics, internet-of-things, and machine learning are the most popular AI technologies in Australia; (b) Australian public sentiments toward AI are mostly positive, whilst some negative perceptions exist; (c) there are distinctive views on the opportunities and constraints of AI among the Australian states/territories; (d) timesaving, innovation, and digitalization are the most common AI prospects; and (e) project risk, security of data, and lack of capabilities are the most common AI constraints. This study is the first to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. The findings inform the construction industry on public perceptions and prospects and constraints of AI adoption. In addition, it advocates the search for finding the most efficient means to utilize AI technologies. The study helps public perceptions and prospects and constraints of AI adoption to be factored in construction industry technology adoption.

Due to hectic city lives and the growing health concerns in light of the global pandemic, mountain tourism has become increasingly popular worldwide, which has increased the related research. Based on traditional bibliometric laws, such as those authored by Price, Bradford, Lotka, and Zipf, this study acquired 1413 mountain tourism journal articles via bibliometric analysis and identified the most influential journal articles, researchers, and countries in mountain tourism research as indexed in the Web of Science (WoS) database during 2010–2020. The keyword analysis revealed mountain tourism’s emerging research topics, including climate change, sustainable development, sustainability, sustainable tourism, protected areas, rural tourism, and conservation. The most influential journal was Sustainability in the mountain tourism. The research results showed that China, the U.S., and Romania produced the most significant mountain tourism articles indexed in the WoS. Most developed countries in Europe had the highest average and average normalized citations, which indicated that they may have more influence in this field as compared to other countries. Some developing countries, such as India, Nepal, and China, had higher citations, average citations, and/or average normalized citations than other countries. The main research trend was the sustainable development aspect of mountain-based tourism during the COVID-19 pandemic. We identified the research gap in WoS; although there is some research shedding light on tourism via bibliometrics, mountain tourism bibliometric analysis and science mapping via VOSViewer is scarce. The paper summarizes the critical aspects of the current discussion of sustainable mountain tourism, such as transport and coopetition (i.e., combing with cooperation and competition) in mountain tourism areas. The results indicated that government agencies and destination managers need to strike a balance between sustainable mountain tourism development and environment and natural landscape conservation after COVID-19.

PurposeThis paper aims to explore the factors that affect housing prices as per Chinese articles indexed in the Chinese Science Citation Database (CSCD). There were different foci regarding what drove housing prices in China in Chinese articles, and international journal articles in English. As most previous English articles only threw light on international research, it motivated the researchers to systematically review Chinese literature’s factors that affected housing prices in China.Design/methodology/approachThis paper reviewed housing price research articles indexed in the two largest Chinese academic research databases: the CSCD and China Knowledge Infrastructure Engineering Database (CNKI.NET). It systematically collected the data and adopted descriptive analysis techniques and synthesis.FindingsThis research reviewed the literature published from 2015 to 2020 and revealed some unique factors affecting China's housing prices. For example, research focused on administrative aspects such as macroeconomic regulation and control (often known as macro control). Authors of Chinese articles suggested that the two-child policy affected housing prices, which differed from that in the English journal articles. The research results implied that researchers should read top Chinese journals on top of good international journals when they study China's real estate market in the future.Research limitations/implicationsBecause the domestic real estate market started late, domestic real estate transaction data and real estate-related statistics are more difficult to obtain. The research is mostly based on the relationship between supply and demand, government policy and individual consumer factors, and the sample has a short time span.Practical implicationsAs China is a planned economy country, administrative factors are one main factor that affects the housing price. There were a significant number of articles in Chinese that considered this factor to be the main driver of the real estate price. It included government investment and macro-control, i.e. direct government intervention to cool down the overheated economy. Yet, there are few English articles that threw light on this factor including the commodity housing supply and government behaviour that affect housing price. The second-child policy, which is unique in China, also played an important role in the determination of the housing price. In the articles indexed in CNKI, the second-child rate, willingness to have a second child or having a second child were mentioned in the Chinese articles but not the English ones.Social implicationsIn this paper, the economic, social, administrative and environmental factors were summarised, which basically covered all the factors affecting housing prices. The administrative factors were a special group of factors that affect the housing price because of the country's planned economic system. Secondly, it provided useful information to real estate development enterprises in China. To make a correct investment and management decision, real estate development enterprises must understand the actual situation and possible problems of the industry. In this study, we analysed the research literature on the real estate industry in China for the period from 2015 to 2020 one by one and determined the influencing factors of the housing price, which provided references for effective cost control. Thirdly, it allows the public to understand and grasp the real estate industry. As the housing price has been continuously increasing, the public pays increasing attention to the real estate industry. Through the literature analysis of the impact of real estate prices, this paper revealed the elements of house price expenses, which makes it convenient for ordinary people to understand the real estate industry.Originality/valueThis study allows foreigners who do not know Chinese to know more about factors that drove housing prices from the Chinese perspective. It also provides insights to overseas developers who wish to enter the property market in China. The results can be generalised to other non-English-speaking real estate research.

The green leadership (GL) concept has significantly gained popularity over the last decade. Consequently, more research has been conducted on this emerging leadership concept, emphasizing leadership styles that promote the green environment so that sustainable goals can be achieved. In the present research, leaders’ emotional intelligence (EI) is positioned as a mediating variable between GL and employees’ green organizational citizenship behavior (GOCB). The data of this research comprised managerial and non-managerial staff from the manufacturing and service industries. A PLS-SEM was used to evaluate the relationship between the various factors among 422 employees. The empirical findings indicated that GL and GOCB had a favorable and robust relationship. The results of the study also suggested that a leader’s EI mediates the influence of green leadership on their employees’ green organizational citizenship behavior. Green leadership is essential in creating sustainable environmental behaviors among employees. It can strengthen leaders’ EI, which successively helps them to garner positivity and foster an environment of mutual harmony and cooperation in the workplace to support pro-environmental policies. Overall, our study contributes to and advances previous studies and shows that green leadership plays a critical role in influencing a leader’s own EI which, in turn, predicts the green OCB of their employees in the workplace.