Trust signals in the babysitting industry in the sharing economy
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Date
2023-05
Department
University of Baltimore. Division of Science, Information Arts and Technologies
Program
University of Baltimore. Master of Science in Interaction Design and Information Architecture
Citation of Original Publication
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Attribution-NonCommercial-NoDerivs 3.0 United States
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by the University of Baltimore for non-commercial research and educational purposes.
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by the University of Baltimore for non-commercial research and educational purposes.
Abstract
This thesis will explore the meaning of trust, trust propensity, trust signals/cues, and other trust
builders. It will then relate how trust drives the sharing economy and is essential in the
babysitting industry. It will then fill in the gaps found in the research by testing the trust cues of
mutual friends and star ratings in the babysitting industry of the sharing economy. These trust
cues were tested through two surveys. The results were analyzed using t-tests, ANOVA
(Analysis of Variance), and post hoc tests. The findings showed that star ratings are a stronger
immediate trust cue than written reviews and that mutual friends substantially impact hiring
rates. It found that as the number of mutual friends increased, so did hiring rates. It also found
that mutual friends seem to have more strength than star ratings and that the presence of mutual
friends may affect users' willingness to pay.