The Politics of Road Transport Insurance, 2022-2023

Kester, J.;Adam, Z.

Description

This dataset contains a set of semi-structured interviews with (motor) insurers, insurance stakeholders, and stakeholders in transport or law working closely with insurers. The interviews, across multiple countries, were based on semi-structured questions around how current and future mobility developments and innovations - Electric Vehicles, Autonomous Vehicles, Mobility Data, Micro-Mobility and Shared Mobility - affect insures, and how insurers in turn affect these shifts in our mobility. Questions were asked about the most important mobility challenges that insurers witnessed; how these mobility developments affect them from a underwriting, busines, legal, claims and pricing perspective; how insurers are adapting to these development (in terms of collaborations, lobby, learning, etc.); and whether insurers should have an explicit role to play in the mobility transition.Interviewees (N=52) either consented writtenly through Oxfords consent form (stored) or verbally (on record) to be quoted anonymously. Those who did not agree explicitly to anonymous quotation have been excluded from data archiving (n=13). Data thus comprises of 39 transcripts in Word format (totalling less than 3MB) with insurers or stakeholders in transport or law working closely with (motor) insurers in the United Kingdom, Netherlands or Germany. We've further included 1 semi-structured questionairre in Word format to reference the semi-structured questions asked to stakeholders; a data table with an anonymized overview of the interviewees in Excel format; and an blank consent form shared with interviewees.All transcripts have been through a round of anonymisation: removal of any direct (names, companies, age, profesional history) and indirect indentifiers (references to people/meetings, etc), with stronger anonymisation the more unique the organisation (as more identifiable). At any time, use of quotes should be anonymously attributed to general branch/sector!

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.4

FAIR Score

73%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

UK Data Service

Assigned Domain

Subfield

Automotive Engineering

Field

Engineering

Domain

Physical Sciences

Confidence Score

54%

Source

Scholar Data Model

Normalization Factors

FT

57.69

CTw

1.00

MTw

1.00