Data underlying the master thesis: Multiparty Computation. Identifying the Consumers' Willingness to Share Sensitive Automotive Data on MPC-enabled Data Marketplaces: A Discrete Choice Modelling Approach
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Datacite citation style:
van Aalst, Cornelis Pieter Christian (2021): Data underlying the master thesis: Multiparty Computation. Identifying the Consumers' Willingness to Share Sensitive Automotive Data on MPC-enabled Data Marketplaces: A Discrete Choice Modelling Approach. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/16543125.v1Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
licenceCC BY 4.0
The main raw dataset within the .RAR-file contains stated choice data from consumers about MPC-enabled data marketplaces. This dataset may be used to determine the consumers' willingness to contribute automotive (GPS) data on MPC-enabled data marketplaces. Van Aalst (2021, ch. 7) draws conclusions and recommendations for future research.
Furthermore, read the README.txt file very carefully to see in which way you can use these data in Discrete Choice Modeling (DCM) in Rstudio to retrieve the data-sharing factor estimates. These data can be utilized in predicting consumers' willingness to share automotive GPS data in similar data marketplaces.
- 2021-08-31 first online, published, posted
formatFiletypes: .csv raw data .tsv stated choice design format .dat cleaned and formatted input data for DCM models .IPYNB python notebooks to clean the data .R estimation models .csv output data .sav demographics analyses .png visualisations
organizationsTU Delft Technology, Policy and Management, Department of Engineering Systems and Services