RESUMO
BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the Infodemic, i.e., the importance of the spread of false information related to the epidemic, has been highlighted. However, while there is a growing interest in this phenomenon, studies on the topic discovery, data collection, and data preparation phases of the information analysis process have been lacking. OBJECTIVE: Since the epidemic is unprecedented and has not ended to this day, we aimed to examine the existing Infodemic-related literature from January 2019 to December 2022. METHODS: We have systematically searched ScienceDirect and IEEE Xplore databases with some search limitations. From the searched literature we selected titles, abstracts and keywords, and limitations sections. We conducted an extensive structured literature search and analysis by filtering the literature and sorting out the available information. RESULTS: A total of 47 papers ended up meeting the requirements of this review. Researchers in all of these literatures encountered different challenges, most of which were focused on the data collection step, with few challenges encountered in the data preparation phase and almost none in the topic discovery section. The challenges were mainly divided into the points of how to collect data quickly, how to get the required data samples, how to filter the data, what to do if the data set is too small, how to pick the right classifier and how to deal with topic drift and diversity. In addition, researchers have proposed partial solutions to the challenges, and we have also proposed possible solutions. CONCLUSIONS: This review found that Infodemic is a rapidly growing research area that attracts the interest of researchers from different disciplines. The number of studies in this field has increased significantly in recent years, with researchers from different countries, including the United States, India, and China. Infodemic topic discovery, data collection, and data preparation are not easy, and each step faces different challenges. While there is some research in this emerging field, there are still many challenges that need to be addressed. These findings highlight the need for more articles to address these issues and fill these gaps.
RESUMO
The sustainable development of infrastructure construction projects heavily depends on favorable cooperation of all parties and ethical code of conduct, while Un-ethical pro-organizational behavior (UPB) may undermine the mutual efforts and cause serious consequences. UPB has aroused wide interest of researchers, but what may trigger construction employees to engage in UPB at team-level has not been elucidated completely. With information asymmetry and huge uncertainty, the behaviors of employees in temporary project teams are marked by environmental and personal characters. The study discusses the influences of collective moral judgement focus on self (CMJS) and Machiavellianism on UPB. Through a moderated mediation analysis conducted on a set of survey data from Chinese construction projects, the empirical results of the two-level hierarchical linear model indicate that CMJS positively impacts UPB directly, and meanwhile Machiavellianism acts as a partial mediator in the relationship between CMJS and UPB. The findings also reveal that performance-avoidance goal orientation (PAGO) and motivation to learn (MTL) moderate and strengthen the relationship between Machiavellianism and UPB. The study offers practical suggestions for both project managers and policymakers of construction projects.
