Tess Neal Collection
Tess Neal is an Assistant Professor of Psychology in the ASU New College of Interdisciplinary Arts and Sciences and is a founding faculty member of the Program on Law and Behavioral Science. Dr. Neal has published one edited book and more than two dozen peer-reviewed publications in such journals as PLOS ONE; Psychology, Public Policy, and Law; and Criminal Justice and Behavior.
Neal is the recipient of the 2016 Saleem Shah Award for Early Career Excellence in Psychology and Law, co-awarded by the American Psychology-Law Society and the American Academy of Forensic Psychology. She was named a 2016 "Rising Star" by the Association for Psychological Science, a designation that recognizes outstanding psychological scientists in the earliest stages of their research career post-PhD "whose innovative work has already advanced the field and signals great potential for their continued contributions." She directs the ASU Clinical and Legal Judgment Lab.
- 2 English
- 2 Text
- 2 Public
The majority of trust research has focused on the benefits trust can have for individual actors, institutions, and organizations. This “optimistic bias” is particularly evident in work focused on institutional trust, where concepts such as procedural justice, shared values, and moral responsibility have gained prominence. But trust in institutions may not be exclusively good. We reveal implications for the “dark side” of institutional trust by reviewing relevant theories and empirical research that can contribute to a more holistic understanding. We frame our discussion by suggesting there may be a “Goldilocks principle” of institutional trust, where trust that is too low ...
- Neal, Tess M.S., Shockley, Ellie, Schilke, Oliver
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Using confirmatory factor analyses and multiple indicators per construct, we examined a number of theoretically derived factor structures pertaining to numerous trust-relevant constructs (from 9 to12) across four institutional contexts (police, local governance, natural resources, state governance) and multiple participant-types (college students via an online survey, community residents as part of a city’s budget engagement activity, a random sample of rural landowners, and a national sample of adult Americans via an Amazon Mechanical Turk study). Across studies, a number of common findings emerged. First, the best fitting models in each study maintained separate factors for each trust-relevant construct. Furthermore, post ...
- PytlikZillig, Lisa M., Hamm, Joseph A., Shockley, Ellie, et al.
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