Doctor of Philosophy, Case Western Reserve University, 2022, Organizational Behavior

Interorganizational project structures, typified in the construction industry, are highly equivocal and interdependent contexts that require coordination amid pervasive conflict. Although organizations are typically considered the unit of analysis in interorganizational coordination, it is the individual organizational representatives who are responsible for the ‘on the ground' coordinative efforts. In a two-year-long ethnography of an interorganizational construction project, I explored how individual organizational representatives used artifacts and their associated sensate experience to manage conflict and resolve felt tension in order to coordinate. The theorized process model I developed shows how the most productive interactions among organizational representatives involved creating an artifactual experience, comprising interaction with an artifact that evoked a meaningful aesthetic experience. In project team meetings, organizational representatives would use artifacts such as building plans, models, and prototypes to create an artifactual interaction comprising four interactive dimensions of revealing, engaging with, commentating on, and proximally positioning to the artifact. In so doing, the bodily senses of the organizational representatives were engaged in an aesthetic experience. Through the synchrony in attention and shared aesthetic experience, project team members experienced increases in collective energy that cut through the felt tension that would emerge in team meetings. Rather than resolving interorganizational conflicts, the collective energy helped project team members to co-construct a salient line of discourse for the team, which took the form of a decision or provisional agreement that became the next concrete step in a path forward. My findings on experience-as-coordination-mechanism suggest that embodied forms of knowledge such as felt tension, aesthetic experience, and collective energy are important for understanding interorganizational (open full item for complete abstract)

Committee: John Paul Stephens (Committee Chair); Ronald Fry (Committee Member); Hans Hansen (Committee Member); Richard Boland Jr. (Committee Member); Diana Bilimoria (Committee Member) Subjects: Aesthetics; Behavioral Sciences; Management; Organizational Behavior

Doctor of Philosophy, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects: Education

Doctor of Philosophy, The Ohio State University, 1968, Graduate School

Committee: Not Provided (Other) Subjects: Education

Doctor of Philosophy, The Ohio State University, 1966, Graduate School

Committee: Not Provided (Other) Subjects: Education

Doctor of Philosophy, The Ohio State University, 2016, Food, Agricultural and Biological Engineering

In recent years, the digital world has experienced an explosion in the magnitude of data being captured and recorded in various industry fields. Accordingly, big data management has emerged to analyze and extract value out of the collected data. The traditional construction industry is also experiencing an increase in data generation and storage. However, its potential and ability for adopting big data techniques have not been adequately studied. This research investigates the trends of utilizing big data techniques in the construction research community, which eventually will impact construction practice. For this purpose, the application of 26 popular big data analysis techniques in six different construction research areas (represented by 30 prestigious construction journals) was reviewed. Trends, applications, and their associations in each of the six research areas were analyzed. Then, a more in-depth analysis was performed for two of the research areas including construction project management and computation and analytics in construction to map the associations and trends between different construction research subjects and selected analytical techniques. In the next step, the results from trend and subject analysis were used to identify a promising technique, Artificial Neural Network (ANN), for studying two construction-related subjects, including prediction of concrete properties and prediction of soil erosion quantity in highway slopes. This research also compared the performance and applicability of ANN against eight predictive modeling techniques commonly used by other industries in predicting the compressive strength of environmentally friendly concrete. The results of this research provide a comprehensive analysis of the current status of applying big data analytics techniques in construction research, including trends, frequencies, and usage distribution in six different construction-related research areas, and demonstrate the applicability an (open full item for complete abstract)

Committee: Qian Chen Dr. (Advisor) Subjects: Civil Engineering; Comparative Literature; Computer Science