Abstract

The purpose of this quantitative correlational study was to examine the predictive relationship between perceptions of organizational culture: (a) group culture, (b) developmental culture, (c) hierarchical culture, and (d) rational culture, and the level of trust in human-robot interaction among agricultural employees within the Western, Midwestern, and Eastern United States. The Competing Values Framework and Three Factor Model of Human-Robot Trust served as the theoretical foundations for this study. Four research questions were used to investigate the predictive relationship between each organizational culture type and human-robot trust. A fifth overarching question about the four organizational culture types combined and human-robot trust. Multiple regression analysis was used to analyze the results. Data were collected from a sample of 87 employees from various agricultural organizations within the United States by using the online Agricultural Human-Robot Interaction Survey, which consisted of the Organizational Culture Profile and the Trust Perception Scale-HRI. Study results indicated that hierarchical culture significantly predicted the level of human-robot trust, t(82) = 2.349, p =.021, and that the four organizational cultures together are statistically significant in contributing to the level of human-robot trust F(4, 82) = 2.811, p = .031. Results further indicated that organizational culture explains 12.1% of the variance in the level of human-robot trust, R2= 0.121. This empirical evidence demonstrates that organizational culture is predictive of the level of human-robot trust among agricultural employees.

Details

Title
Organizational Culture and Trust Within Agricultural Human-Robot Teams
Author
Baylis, LaTanya Cherie
Publication year
2020
Publisher
ProQuest Dissertations & Theses
ISBN
9798684695759
Source type
Dissertation or Thesis
Language of publication
English
ProQuest document ID
2459643625
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.