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Despite the critical role of the policy environment in facilitating the advancement of building information modeling (BIM) as a systemic innovation to reshape traditional facility design, construction and operation processes, scant scholarly attention has been paid to systematically investigating how and why complex BIM policies are concretely and gradually implemented in different regional contexts from a dynamic policy diffusion perspective. This study aims to empirically investigate how different types of BIM policy instruments are dynamically implemented in heterogeneous regions over time and how the diffusion of BIM policies across different regions is comprehensively impacted by both internal efficiency needs and external legitimacy pressures.

This study employed a positivist research paradigm in which BIM policy data from 182 prefecture-level and above cities in China during 2011–2022 were analyzed with quantitative approaches for theory verification. Based on the content analysis of the evolutionary characteristics of the adopted BIM policy instruments in heterogeneous regions over time, the event history analysis (EHA) method was then used to further examine the mechanisms underlying the diffusion of BIM policies across different regions.

The content analysis results show that while environmental instruments (such as technological integration and goal planning) are the primary policy instruments currently adopted in China, recent years have also witnessed increasing adoptions of supply-side instruments (such as fiscal support and information support) and demand-side instruments (such as demonstration projects and tax incentives). After controlling for the impacts of regional fiscal and technical resources, the EHA results illustrate that BIM policy adoption positively relates to regional construction industry scale but negatively relates to regional industry productivity and that compared with public pressures from industry participants, vertical pressures from the central government and horizontal pressures from neighboring regions are more substantial drivers for policy adoption.

As an exploratory effort of using a dynamic policy diffusion perspective to systematically investigate how BIM policies are adopted in heterogeneous regional contexts to facilitate BIM advancement, this study not only characterizes the complexity and dynamics of BIM policies but also provides deepened understandings of the mechanisms underlying policy adoption in the conservative construction industry. The findings hold implications for how multifarious policy instruments can be more effectively and dynamically adopted to facilitate the advancement of BIM and related technologies as innovative solutions in the construction domain.

Despite its great potential to improve the sustainability of architectural, engineering, construction and facility management activities, the implementation of building information modeling (BIM) in many projects has failed to achieve expected benefits due to negative behavioral responses such as user resistance. This paper aims to characterize the complexity of end user's behavioral responses to BIM implementation in construction projects using a multidimensional perspective and examines how these responses are impacted by different levels of contextual factors.

By integrating technology acceptance, resistance and adoption literature, this paper theoretically proposes a research model to characterize the associations between different dimensions of behavioral responses and different levels of contextual factors. The model is then empirically tested with survey and interview data collected from BIM-based construction projects in China.

The empirical results not only validate the two-dimensional view of the behavioral responses (i.e. the dimension of support/resistance that ranges from aggressive resistance to enthusiastic support, and the dimension of actual use that ranges from non-use to high use) but also provide evidence for the prevalence of ambivalent responses such as supporting but lowly using and resisting but highly using. The empirical results also provide evidence that different levels of contextual factors generally play different roles in shaping the behavioral responses. Specifically, the dimension of support/resistance is more substantially impacted by the team-level factor while the dimension of actual use of BIM is more significantly associated with the project-level factor.

While previous research on BIM adoption or implementation behaviors has primarily focused on investigating users' response from single-dimension perspectives such as acceptance or non-acceptance, this study represents an exploratory effort of using a two-dimensional view to characterize the complexity and ambivalence of end users' behavioral responses to the implementation of innovative technologies such as BIM in construction projects. This study also contributes to deepened understandings of how these different dimensions of behavioral responses are intricately shaped by different levels (i.e. individual-, team- and project-levels) of contextual factors in construction projects which are characterized as temporary and inter-organizational.

This study aims to explore the factors influencing the intention to use e-democracy. The literature depicted conflicting results regarding such domain and especially in the Middle East, where the authors aim at establishing a solid view of the discipline.

A research model was developed based on the theory of planned behavior, technology acceptance model and unified theory for acceptance and use of technology-2. The model hypothesized that perceived public value (PPV), ease of use and enjoyment are major antecedents of attitudes, and subjective norms (SNs), perceived behavioral control (PBC) and attitudes are significant predictors of behavioral intentions. A quantitative cross-sectional design was used. A questionnaire was used to explore Jordanian citizens’ perceptions regarding the research model constructs. A sample of 302 Jordanian citizens filled the surveys based on their awareness of e-democracy concepts and their willingness to participate in the study. This study uses the structural equation modeling approach with partial least square as an analysis method.

Findings indicated that PPV, perceived ease of use and enjoyment jointly determine the attitudes of citizens toward e-democracy. Results also suggest that attitudes, PBC and SNs have a significant effect on citizen’s intention to use e-democracy. Finally, this research supported the role of enjoyment as the most significant determinant of citizen’s attitude toward using e-democracy.

This study is limited by its small sample size and newly developed Arabic instrument. Still, results imply that more research is needed to replicate and validate the instrument and support the role of the selected research constructs. Public institutions are required to make e-democracy applications enjoyable and easy to use. They also need to demonstrate its value to the public.

To the best of the authors’ knowledge, this study is one of the few studies to explore e-democracy and the first to conduct an empirical study (survey-based) in Jordan. The foundation of the study depended on three robust theories in the technology adoption theories.

This study aims to understand the relationship between politics and pandemics in shaping the characteristics and themes of people’s Tweets during the US 2020 presidential election. Additionally, the purpose is to detect misinformation and extremism, not only to help online social networks (OSN) to target such content more rapidly but also to provide a close to real-time picture of trending topics, misinformation, and extremism flowing on OSN. This could help authorities to identify the intents behind them and find out how and when they should address such content.

This study focuses on extracting and verifying knowledge from large-scale OSN data, at the intersection of the Coronavirus pandemic and the US 2020 presidential election. More specifically, this study makes manual, statistical and automatic inferences and extracts knowledge from over a million Tweets related to the two aforementioned major events. On the other hand, disinformation operations intensified in 2020 with the coincidence of the Coronavirus pandemic and presidential election. This study applies machine learning to detect misinformation and extreme opinions on OSN. Over one million Tweets have been collected by our server in real-time from the beginning of April 2020 to the end of January 2021, using six keywords, namely, Covid, Corona, Trump, Biden, Democrats and Republicans. These Tweets are inspected with regard to their topics, opinions, news, and political affiliation, along with misinformation and extremism.

Our analyses showed that the majority of these Tweets concern death tolls, testing, mask, drugs, vaccine, and travel bans. The second concern among these Tweets is reopening the economy and schools, unemployment, and stimulus bills. The third concern is related to the Coronavirus pandemic’s impacts on politics, voting, and misinformation. This highlights the topics that US voters on Twitter were most concerned about during this time period, among the multitude of other topics that politicians and news media were reporting or discussing. Automatic classification of these Tweets using a long short-term memory network revealed that Tweets containing misinformation formed between 0.5% and 1.1% of Coronavirus-related Tweets every month and Tweets containing extreme opinions formed between 0.5% and 3.1% of them every month, with its pick in October 2020, coinciding with the US presidential election month.

The originality of this study lies in establishing a framework to collect, process, and classify OSN data to detect misinformation and extremism and to provide a close to real-time picture of trending topics, misinformation, and extremism flowing on OSN.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.