Positioning Organizational Culture Studies Between The Construction Industry And Other Industries

(Purpose) The main aim of this paper is to portray the overall position of construction related organizational culture (OC) studies amongst other OC related studies found. The amount, the extent, and the scope of the construction OC studies are investigated and their relationships and positions to other OC studies in other industries are described.(Methodology) This study follows a bibliometric research approach integrated with an unsupervised learning based analysis of selected core studies. The study uses both well-established bibliometric methods and novel unsupervised learning approach to uncover theoretical themes of OC studies based on citation patterns and semantic text. Accordingly, the construction related OC studies are to be identified.(Findings) Related to vast field of OC studies, the research of OC focused in the construction sector tend to be rare. The construction related OC studies have focused on topics, such as knowledge management, leadership, innovation, firm performance, and characterization of OC. The semi-qualitative machine aided methodology was found to support coarse qualitative analysis of the extensive dataset. Together with human interpretation the results seem to be credible.(Limitations) The semi-automatic methodology of the Bibliographic coupling (BC) and the Latent Dirichlet Allocation (LDA) creates the results without human effort. This may lead to the wrong interpretations without proper analysis of the results. Also the use of limited phrase search may exclude some relevant studies outside the research data. Although the impact is insignificant compared to the found data amount.(Originality) The research is based on vast dataset with somewhat new methodologies compared with the traditional literature studies. First, it was possible locate the construction OC studies in the broad scientific field of OC and to identify some interesting research clusters. Secondly, the methodology itself represents an interesting and modern way to do mixed-method interpretation of the dataset in the construction sector.