Graduation Semester and Year
Summer 2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Social Work
Department
Social Work
First Advisor
Rebecca Mauldin
Second Advisor
Anne Nordberg
Third Advisor
Katherine Sanchez
Fourth Advisor
Rachel Voth Schrag
Fifth Advisor
Thomas Valente
Abstract
Technological innovations, such as artificial intelligence, have the potential to mitigate health disparities in healthcare, but lower uptake of these innovations in low resource healthcare settings, due to limited resources and staff shortages, may lead to missed opportunities. However, low-cost implementation strategies may facilitate the uptake of these innovations and increase their accessibility and adoption in this setting. The Network Model of Diffusion of Innovations suggests the identification of opinion leaders and the study of advice networks as strategies that may help enhance the uptake of innovations. Specifically, this framework suggests the use of a sociometric or network approach to identify opinion leaders who can utilize their influence and advice to accelerate the diffusion and adoption of an innovation. However, non-sociometric opinion leader identification techniques require less time, resources, or expertise than the sociometric approach. Therefore, this study had four aims around the study of advice networks, advice connections, and opinion leader identification methods in a low resource healthcare setting. The first two aims of this study were to describe and compare the network characteristics of three different types of advice seeking networks (i.e., general, technological, and artificial intelligence). The third aim was to identify opinion leaders using theoretically based identification techniques (i.e. sociometric, self-identification, staff selection, positional, and self-selection) and compare the outcomes of the different approaches to those of a sociometric approach. Finally, the fourth aim was to identify factors associated with advice seeking ties between network members. This study recruited 121 participants from December 2023 to January 2024 from a network of seven low resource primary care clinics in north Texas. Network characteristics were calculated and visualizations created to compare the different types of advice seeking networks. Phi correlations were run to compare opinion leader identification methods and the degree to which these methods correlate with the
sociometric approach. Finally, a series of exponential random graph models were used to identify factors associated with advice seeking connections within the three advice networks (i.e., general, technological, artificial intelligence) in one of the seven clinics. Results showed that advice seeking networks had different structures depending on the type of advice that was sought. For example, employees sought general advice from more people than they did for technological or artificial intelligence advice. In addition, correlation analyses revealed three opinion leader identification methods (i.e., self-identification, positional, and staff selection) that were significantly correlated with the sociometric approach for the identification of opinion leaders. Finally, findings revealed network structural effects (i.e., transitivity) and individual level factors (i.e., education, years employed, role, and age) that were significantly associated with the likelihood of advice seeking connections in this setting. These findings may help low resource community clinics increase their uptake of technological innovations by identifying cost and time efficient opinion leader identification techniques that are significantly correlated with the sociometric approach and that may identify influential members in the network. In addition, findings may help during the recruitment of opinion leaders through non-sociometric approaches by revealing employee characteristics that can be used as predictors of an employee’s levels of influence within the network.
Keywords
Social network analysis, Implementation, Health, Opinion leader, Technology, Artificial intelligence, Innovation, Advice, Network, Diffusion of innovations
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Kunz Lomelin, Alan, "Implementation of Technological Innovations: An Identification and Examination of Opinion Leaders and Advice Networks in Primary Care" (2024). Social Work Dissertations. 137.
https://mavmatrix.uta.edu/socialwork_dissertations/137