194 M Tanninen, T-K Lehtonen, and M Ruckenstein
Still, people did not deem it enough to be rewarded only after reaching the
goals specified in policies. Instead, the focus groups revealed that, despite
any inaccuracies, people’s personal data has innate value regardless of their
activity status, and a policy’s terms and conditions should be attractive
enough for them to give out their personal information. Clearly, people can
regard their data as a form of currency with meaningful purchasing power,
echoing demands made by technology developers to combat informational
asymmetries. For instance, Lanier (2013) argues that as commercial agents
profit from digital traces, a portion of their gains should be distributed to
the data subjects as remuneration for providing their data. This view resonated with how Matti, a paramedic in his 40s, approached the matter.
I don’t think people like the idea of being monitored, or, at least, I don’t
like it. But if you got some support and guidance for, say, exercising – or
could there be a discount for the gym, a personal trainer or dietician
services [included in the policy]? I don’t like the idea that in return for
being stalked and monitored and being subjected to data collection and
data distribution, I would get just a [premium] discount.
In the focus groups, people not only assessed existing practices but also went
further. They began to imagine ‘good’ and ‘bad’ deals with insurance companies and to think about their own bargaining power. For instance, Marjo,
a 45-year-old university lecturer who did not yet have a behaviour-based
policy said that she ‘could maybe take the smart features as a freebie if the
insurance price remained the same.’ Another interviewee, Eero, a chef in
his 50s reflected that if he ‘got a great deal with some [wellbeing] service
provider,’ he might allow the insurance company to gather his data. Thus,
customers expected something in return for their personal data, even when
they were not conforming to the activity or health goals set by the policies.
An especially striking finding in the interviews was that, in a world of
digital services, consumers appear to value especially highly connection
with, and help from real-life experts. As Matti’s statement above exemplifies, people were interested in receiving guidance from medical professionals, dieticians, and personal trainers who could help them interpret their
data and plan health interventions based on it. Only on some occasions did
customers feel that it would be sufficient to have their data interpreted by a
robot or an artificial intelligence application – a finding that must be a disappointment, considering the insurance companies’ ambitions for the data
economy of the near future (Grundstrom 2020). Instead of a novel, largely
automated circulation of information that would enable cutting labour
costs for insurance companies, our focus groups appear to imagine that the
new data circuits will create more personalised services based on human
interpretation and interaction.
The fairness of the (current) trade-offs between the data, rewards, and
services was reasoned about in varying ways. Some felt that the exchange
Still, people did not deem it enough to be rewarded only after reaching the
goals specified in policies. Instead, the focus groups revealed that, despite
any inaccuracies, people’s personal data has innate value regardless of their
activity status, and a policy’s terms and conditions should be attractive
enough for them to give out their personal information. Clearly, people can
regard their data as a form of currency with meaningful purchasing power,
echoing demands made by technology developers to combat informational
asymmetries. For instance, Lanier (2013) argues that as commercial agents
profit from digital traces, a portion of their gains should be distributed to
the data subjects as remuneration for providing their data. This view resonated with how Matti, a paramedic in his 40s, approached the matter.
I don’t think people like the idea of being monitored, or, at least, I don’t
like it. But if you got some support and guidance for, say, exercising – or
could there be a discount for the gym, a personal trainer or dietician
services [included in the policy]? I don’t like the idea that in return for
being stalked and monitored and being subjected to data collection and
data distribution, I would get just a [premium] discount.
In the focus groups, people not only assessed existing practices but also went
further. They began to imagine ‘good’ and ‘bad’ deals with insurance companies and to think about their own bargaining power. For instance, Marjo,
a 45-year-old university lecturer who did not yet have a behaviour-based
policy said that she ‘could maybe take the smart features as a freebie if the
insurance price remained the same.’ Another interviewee, Eero, a chef in
his 50s reflected that if he ‘got a great deal with some [wellbeing] service
provider,’ he might allow the insurance company to gather his data. Thus,
customers expected something in return for their personal data, even when
they were not conforming to the activity or health goals set by the policies.
An especially striking finding in the interviews was that, in a world of
digital services, consumers appear to value especially highly connection
with, and help from real-life experts. As Matti’s statement above exemplifies, people were interested in receiving guidance from medical professionals, dieticians, and personal trainers who could help them interpret their
data and plan health interventions based on it. Only on some occasions did
customers feel that it would be sufficient to have their data interpreted by a
robot or an artificial intelligence application – a finding that must be a disappointment, considering the insurance companies’ ambitions for the data
economy of the near future (Grundstrom 2020). Instead of a novel, largely
automated circulation of information that would enable cutting labour
costs for insurance companies, our focus groups appear to imagine that the
new data circuits will create more personalised services based on human
interpretation and interaction.
The fairness of the (current) trade-offs between the data, rewards, and
services was reasoned about in varying ways. Some felt that the exchange