Service research suggests homes are becoming increasingly connected as consumers automate and personalize new forms of service provision. Yet, large-scale empirical evidence on how and why consumers automate smart domestic products (SDPs) is lacking. To address this knowledge gap, we analyze 13,905 consumer-crafted, automated combinations of SDPs, totaling 1,144,094 installations, across 253 separate service providers on the web service IFTTT.com . An exploratory network analysis examines the topology of the network and an interpretive coding exercise reveals how consumers craft different styles of human-computer interaction to cocreate value. The results reveal that the SDP network is disassortative, is imbalanced, has a long-tailed degree distribution, and that popular services have high centrality across all product category combinations. We show that popular combinations of SDPs are primarily motivated by utilitarian value-seeking enacted through a preference for automated tasks outside of conscious attention, though more individualistic combinations are slightly more likely to be hedonistically inclined. We conclude by showing how these consumer-crafted forms of service provision within domestic environments reveal design redundancy and opportunities for service innovation.
Food insecurity is a persistent and pernicious problem in the UK. Due to logistical challenges, national food insecurity statistics are unmeasured by government bodies -and this lack of data leads to any local estimates that do exist being routinely questioned by policymakers. We demonstrate a data-driven approach to address this issue, deriving national estimates of food insecurity via combination of supervised machine learning with network analysis of user behaviour, extracted from the world's most popular peer-to-peer food sharing application (OLIO). Despite long-standing theoretical links between social graph topologies and physical neighbourhoods, prior research has not considered dimensions of geography, network interactions and behaviours in the digital/analogue space simultaneously. In addressing this oversight, we produce a browserbased, interactive and rapidly updateable visualisation, which can be used to analyse the spatial distribution of food insecurity across the UK, and provide new perspective for policy research.
CCS CONCEPTS• Computing methodologies → Machine learning approaches; • Applied computing → Sociology; • Human-centered computing → Geographic visualization.
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