PurposeDespite 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.Design/methodology/approachBy 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.FindingsThe 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.Originality/valueWhile 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.

Social network analysis (SNA) has gained increasing academic attention in the construction domain over the past two decades due to its capability to characterize the complexity and dynamics of interindividual and interorganizational interactions. To date, however, scant attempt has been made to develop an integrated framework to systematically review the diversified network research at different levels in this domain and to quantitatively characterize the evolution of related research interests and research instruments. This study aims to fill this gap by conducting a bibliometric-qualitative review based on 106 papers published from 1997 to 2020. Keyword cooccurrence analysis is employed to reveal the research foci, identify the research trends, and develop a comprehensive categorization framework, which classifies related research based on two interrelated dimensions: the type of network node (individual and organization) and the levelof network analysis (project level, corporate level, and industry level). The framework then facilitates further content analysis in terms of research topics, research designs, and research instruments. The results provide evidence that the research foci in this domain are generally moving towards addressing the complexity and dynamics of project-related relations at more diversified levels, in terms of not only research topics but also research instruments. Future research can be enriched by investigating the multiple types of dynamic interproject relationships, adopting state-of-the-art methodologies for network data collection and triangulation, and employing multiple SNA constructs and inferential statistical methods to reveal how complex networks coevolve and interact with actors’ behaviors as well as project and organizational outcomes.