This research project aims to exploit the data available from a new generation of sensors and devices to develop new economic models. To do this we will carry out the following:
Firstly, we will build on existing research to develop a prototype home-hub that builds upon an extended domestic router capable of capturing a diverse collection of information into the home of 5 members of the project. Existing approaches to home automating have often proffered the use of home hubs as central points of control (e.g. Intel Home or Microsoft HomeOs) within their visions of a future home. However, we believe that the home hub is much more likely to arrive in the home setting in a piecemeal fashion. Consequently, we propose to adopt an evolutionary approach, to supplement the existing technologies in the home based on existing Digital Economy research in the Homework project. To supplement this quantitative data we will use ethnographic methods including technology tours, diary studies, experience sampling as well as a range of in-situ studies structured around unpacking the everday life for each artefact in the intranet of things.
Secondly, we will combine grounded theory and econometric techniques to analyse the data and build a series of theoretical models. Such models will be a simplification of the real world, but will supply testable predictions. We will analyse this data for patterns of use, an example illustrates the point. Consider the activity 'making tea'. This consists of a number of sub-activities including; putting the tea bag into the pot, filling the kettle, boiling the kettle, pouring water onto tea, allowing the tea to steep, deciding when the tea is strong enough, pouring tea into cup, adding milk, adding sugar etc. Sensors on the tea pot, kettle, milk jug, cup etc will provide quantitative data on for example, times of use, frequency of use, timings on use, proxies for distances travelled etc. Continuing with the analogy, the qualitative ethnographic data may provide context on when tea is made, who else is present, what other activities are co-incident etc. Together we will be able to develop a functional form of the activity 'making tea'.
Thirdly, we will test the theoretical models using conjoint analysis. Conjoint analysis requires the user to make choices from a series of trade-offs which in turn identifies the relative importance and sensitivities of the variables and their relationship to some monetary value (price). Returning to our previous example, this will enable us to identify which of the activities are most important to the tea-making activity to make more or less tea or different teas, operationalising the latent need for more resources. Each cluster will be operationalised into an algorithm for development into a software app.
Fourthly, the algorithms will be developed into offerings which will then be placed on the H.A.T. to 'sell back' such offerings, simulating a market system using the Dropletpay payment platform. We will use the facilities of the International Institute of Product and Service Innovation (IIPSI) within WMG, to develop a 'H.A.T.'-fest. Here, developers, investors and SMEs will create new offerings based on the commodification of the personal data set ,applying the algorithms created to embed in their offerings, thereby creating a multi-sided market for new offerings to serve the home occupant, landlord or building manager as buyers of such offerings. The structure, conduct and performance of such offerings on the market platform will be then monitored to test its sustainability.
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