Abstract

Platform workers can typically not take their ratings from one platform to another. This creates lock-in as building up reputation anew can come at prohibitively high cost. A system of portable reputation may mitigate this problem but poses several new challenges and questions. This study reports the results of an online experiment among 180 actual clients of five gig economy platforms to disentangle the importance of two dimensions of worker reputation: (1) contextual fit (i.e., the ratings’ origin from the same or another platform) and (2) contentual fit (i.e., the ratings’ origin from the same or a different job type). By and large, previous work has demonstrated the potential of imported ratings for trust-building but usually confounded these two dimensions. Our results provide a more nuanced picture and suggest that there exist two important boundary conditions for reputation portability: While imported ratings can have an effect on trust, they may only do so for matching job types and in the absence of within-platform ratings.

Details

Title
The role of contextual and contentual signals for online trust: Evidence from a crowd work experiment
Author
Corten, Rense 1 ; Kas, Judith 2 ; Teubner, Timm 3   VIAFID ORCID Logo  ; Arets, Martijn 4 

 Utrecht University, Department of Sociology, Utrecht, the Netherlands (GRID:grid.5477.1) (ISNI:0000 0001 2034 6234) 
 Utrecht, Netherlands (GRID:grid.5477.1) 
 Einstein Center Digital Future, TU Berlin, Trust in Digital Services, Berlin, Germany (GRID:grid.6734.6) (ISNI:0000 0001 2292 8254) 
 Utrecht, Netherlands (GRID:grid.6734.6) 
Pages
41
Publication year
2023
Publication date
Dec 2023
Publisher
Springer Nature B.V.
ISSN
10196781
e-ISSN
14228890
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2850407800
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.