Abstract

The adoption of new technology into collaborative workflows has permeated every aspect of our personal and professional lives with the promise of performing work processes more efficiently and with greater capability. The continued rise of ubiquitous computing and heightened need for collaborative features suggest that a view of enabling technologies in a workflow should include the physical computing infrastructure, the collaborative interaction between humans and computers, and the informatics (i.e., collection and representation of data within the workflow). The development and integration of technology for collaborative workflows introduces many variables that are of great concern to companies, organization, and individuals. These variables include the costs of development, the switching cost associated with migrating from the current workflow to the technology-enhanced workflow, and details of how the technology-mediated workflow functions compare to the current workflow functions. There is, however, no consistent, generalizable approach to evaluate and compare an existing workflow with the enhanced technology-mediated workflow in a manner that identifies improvements and barriers in replicable qualitative and quantitative measures. In order to develop such a consistent, generalizable approach, this research investigates what necessary set of cross-disciplinary metrics and methodology is required to effectively evaluate technology-mediated collaborative workflow through an analysis of related works from four disciplines (Social Sciences, Organization and Behavioral Management, Industrial Engineering, and Human-Computer Interaction). The research introduces the Collaborative Space – Analysis Framework (CS-AF), a cross-disciplinary model and methodology designed to evaluate and compare collaborative workflows. The research includes testing the CS-AF model using two diverse empirical studies designed to evaluate a current-state workflow, compared to a technology-mediated workflow on five key collaborative areas (Context, Technology, Process, Attitude and Behavior, and Outcomes). The research incorporates the CS-AF model and methodology to test the effectiveness of the approach for capturing and analyzing essential quantitative and qualitative parameters of the collaborative workflows. The second empirical study tested hypertensive patients currently involved in clinical maintenance with regular outpatient monitoring. The test included 50 hypertension patients, selected based on matched-pairs for age and gender to test the workflow model in a 3-week trial. All participants were tested on an existing workflow (current-state), then the population was randomly split within pairs. The matched-pairs were assigned to one of two alternative workflows: 25 patients were introduced to a manual hypertension self-exam workflow (control group), and their matched-pair counterparts were introduced to technology-mediated hypertension self-exam workflow. All participants were tested on the existing workflow (current-state), followed by the introduction of an alternate workflow, and then tested a second time (pre-/ post-) with the same CS-AF procedure. The study incorporated the research findings from these two tests and a comparison between the workflows introduced using the CS-AF metrics. Findings from the two diverse empirical studies using the CS-AF (Graphic Communications sales order process, and Health Information Technology hypertension exam workflow) indicate that technology-mediated workflows do improve collaborative performance; however, adoption is not as pronounced as hypothesized. The research findings indicate that the lack of acceptance is due to non-technology factors, such as attitude and behavior, which play a significant role in adoption and need similar attention as technology innovation to drive true adoption and ultimately better collaborative performance. The research findings also indicate that the effectiveness of the CS-AF may have potential as a generalizable approach for evaluating technology-mediated collaborative workflow in a variety of unique domains.

Publication Date

4-19-2021

Document Type

Dissertation

Student Type

Graduate

Degree Name

Computing and Information Sciences (Ph.D.)

Department, Program, or Center

Computer Science (GCCIS)

Advisor

Pengcheng Shi

Advisor/Committee Member

Linlin Chen

Advisor/Committee Member

Pamela Grover

Campus

RIT – Main Campus

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