The analytic orientation Tolmie et al. take towards understanding sensor data in terms of ‘orderly characteristics of the household’ is a distinct social science analytic rooted in a branch of sociology called ‘ethnomethodology’ [23]. Ethnomethodology has played a prominent role in the development of digital technology since the approach was adopted by Xerox PARC in the 1970s [51]. Its studies elaborate the methodological character of practical action and practical reasoning [6]. Of particular relevance here are the different orders of practical reasoning that were drawn upon in the doing of data work to make sense of and attribute meaning to the sensor data furnished by the connected shower or at least to representations of it. In this section, we group our findings in terms of the discrete orders of practical reasoning drawn on to contextualise the sensor data and elaborate human activity and practice. We thus unpack (a) how the sensor data was accounted for in terms of participants’ reasoning about their showering activities, product use, water use, and the impact of domestic infrastructure. We also consider (b) how participants’ responded to the future IoT service scenarios posited by the design team, before moving on in Section 4 to (c) unpack the methodological ways in which the sensor data was reflexively contextualised in interaction in the course of addressing both of the above considerations. Understanding the methodological ways in which the senor data was reflexively contextualised is particularly relevant to understanding the interactional accomplishment or production of context and the kinds of mechanisms that need to be designed into the IoT if it is to deliver on the promise of personalised context-aware services, a point we pick up in Section 5. First, however, we begin at the beginning with the observation that before anything else could happen, the field worker and our participants had to discriminate just who the data was about. If we look again at Fig. 3, we can see it provides insight into the total and average durations of showers and amount of water used in a household and even breaks this aggregate information down into specific instances of showering occurring on specific days at specific times using specific amounts of water, but it does not tell us specifically who generated the data. Let us start then by considering how participants discriminated who the data was about. The names in the edited interview extracts below are not the participant’s real ones.
Reasoning about showering activities
The following conversational extract makes visible the how participants drew on their understanding of showering activities to make sense of the data and discriminate who it was about.
Field worker: So, you’ve already started looking at this overview …
John: Yeah a little bit.
Field worker: what makes sense to you in looking at this initially?
John: It’s quite obvious whose shower is whose, because mine is about the third of the time of yours (looks at partner) if not a quarter.
Field worker: So you can begin to tell whose is whose here?
John: Easy.
Sarah: Yes, easy.
Field worker: How can you do that?
Sarah: My time.
John: Because Sarah takes longer. The only [odd] one is Saturday. Then I can still tell those two are me, because I went to the gym on Saturday and had a shower afterwards whereas Sarah was at work all day, so she’s just had her morning shower.
Field worker: So is there something about the order of this as well?
John: You can tell who’s got up first in the morning.
Field worker: So is that part of the routine?
Sarah: We sort of talk before going to bed who (looks at partner) …
John: Yeah, because my job starts at different times, so our routine will change.
The ‘obviousness’ of ‘whose shower is whose’ is for participants plain to see in the sensor data. However, it is notable that it is not at all obvious to the field worker, nor we suspect to other external parties, as discriminating who the data is about turns upon local knowledge of showering activities and what constitutes ‘the routine’ in ‘this’ house, which is nowhere encoded in the sensor data. It may be tempting to see the sensor data as surfacing and documenting the routine, e.g., in terms of a temporal order of showers, but this is misleading. The sensor data does not elaborate the routine but is rather accountable to it. Thus, we find that our participants are readily able to discriminate ‘whose shower is whose’ because they know who gets up first in the morning, how long they usually take to have a shower, and even what they do before getting into the shower. Some participants go straight from bed to shower, for example, whereas others first eat breakfast. It is the local activities and practices in which showering is embedded that constitute ‘the routine’, not the temporal order of showering itself (i.e., when showering occurs), and it is with reference to these local activities and practices that the data is held accountable. One participant continued with his routine of showering before his evening meal as he had done when serving in the Navy, for example, despite the fact that he retired from service over 40 years ago.
We find then in looking at the sensor data that in addition to discriminating who gets up and does what first, participants make the data accountable to routine features of everyday life such as eating an evening meal, going to the gym on Saturday, or going to work. Work was drawn on by many participants to reason about shower duration as this was constrained by time, the distance they had to travel on any particular occasion, and whether or not their partner had to satisfy similar demands. With regard to this latter point, it becomes visible that just when showering takes place and for just how long is a negotiated (and even contested) matter done with respect to other members of the home and their needs. It might thus be said that ‘the routine’ is also dynamic, a point underscored by Sarah and John who ‘talk before going to bed’ about use of the shower as Jon’s job routinely ‘starts at different times’. The dynamic and negotiated character of ‘the routine’ is further reflected in irregular temporal patterns of showering, which were made accountable in terms of part-time working, weekends, leisure activities, cleaning the bathroom, looking after the baby, or the grandchildren during school holidays. Showering is not only embedded in and accountable to an array of dynamic and negotiated local practices that constitute ‘the routine’ in ‘this’ house then; ‘the routine’ is also temporally variable and temporally distributed. This means that whatever constitutes ‘the routine’ in any home does not necessarily happen at the same time or even roughly the same time every day or every week. Indeed, as with looking after the grandchildren during school holidays, it may only happen a handful of times a year. Nevertheless, garnering insight into everyday life demonstrably turns on understanding ‘the routine’. Problematically, however, ‘the routine’ is not a property of sensor data nor is it elaborated by it. Indeed, the converse holds true: ‘the routine’ demonstrably elaborates sensor data.
Reasoning about product use
While the time series graphs clearly ‘tell’ us that something has happened—that the shower has been turned on or off, the head moved around, or that personal cleaning products have been removed from or returned to the scales—they do not ‘say’ what has been done. Discriminating this also requires the doing of data work as can be seen in the following extract where scale data is being examined.
Field worker: So does this relate to your usual routine?
Elaine: Yes, shampoo conditioner and shower gel, which I took off because I couldn’t reach for it all the time so I just put it back at the end.
Field worker: OK. Can you go into more detail about what exactly is going on in the time between the products coming off the shelf and …
Elaine: So shampoo, you wash your hair, rinse it out, then conditioner. Conditioner is something that you have to keep in your hair for a longer time. So usually I put conditioner on and then do the rest of my shower routine. So something that uses shower gel, for example, or shaving or just, I brush my teeth in that time as well …
Field worker: Cool.
Elaine: because its something that – hair conditioner, the longer you leave it, the longer it stays in, the nicer your hair is.
This extract makes it visible that while the smart shelf graph displays three products going on and off it, and that two items were taken off for a short time and a third for much longer, there is no way of telling by looking at the graph alone what the products are or what is being done with them. To understand that, we have to appeal to members’ personal showering practices, which again are nowhere documented in the data, and how they use particular products. When we do so, we find that product use is embedded in ‘shower routines’, such as leaving conditioner in while one gets on with other things in the bathroom. Showering routines are highly personal and individual. How and when one person uses shower gel, shampoo, or conditioner, for example, is different to how and when another uses them, if they use them at all (e.g., a balding participant only uses shower gel) and their use combines with other activities and products in the bathroom. As one participant described their showering routine, ‘I like do everything in the shower, I even brush my teeth in the shower!’ Nonetheless, and despite enormous variation in individual showering routines, participants often described an orderliness to product use (e.g., wash hair, add conditioner and leave, wash body, do teeth, and remove conditioner), and this includes the orderly placement of products for ease of use during showering as Elaine describes for example above.
Product use is also and obviously tied to the use of showering equipment. However, the relationship between product and equipment is not visible in the data. It is not as simple as shower on, shampoo on, rinse, and shower off, for example. That the shower had been turned on and the shower head moved did not necessarily mean people had gotten into the shower and begun their ablutions. Participants might have to wait for the water to reach the desired temperature, and they often adjusted the shower head to suit their personal preference and the activities they were engaged in (e.g., washing their hair or not). By the same token, if the shower had been turned off, it did not necessarily mean that an individual had gotten out but, as part of their showering activity, they might be washing while saving water before turning the shower back on to rinse. Some of our participant’s even ‘pottered about’ outside the shower for several minutes (e.g., brushing their teeth while conditioning the hair) before getting back in to finish showering. It is also the case that the use of products was embedded in broader domestic routines. Participants with long hair, for example, only washed it on certain days of the week when they had time to treat it properly. Thus, discriminating what has been done during showering turns on understanding personal showering practices and shower routines, implicating the highly individualised and orderly uses of products and their in-use relationship to bathroom equipment and the broader round of domestic routines that enable participants to discriminate who the data is about in the first place.
Reasoning about water use
Particular showering routines and the broader domestic routines in which they are embedded were also invoked to account for water use and to reason about why the data had the shape that it has, which was often quite ‘surprising’ to participants at first glance.
Helen: It surprised me how many litres …
Tom: Yeah you don’t want to see litres do you.
Helen: I mean 300 litres of water (pointing at the Tuesday on the screen).
Tom: Shhh, don’t say it out loud!
Field worker: That is a lot considering …
Helen: I took a shower for two today didn’t I?
Tom: 21 minutes. I mean, I don’t think it’s a lot necessarily – I think it’s in line, isn’t it? (Pointing to his partner’s other showers on the shower overview).
Tom: I mean (points at Friday) 18 minutes is 269 litres, 21 minutes is 299. Its in line, its just a bit longer that’s all.
Despite the initial reaction to how much water was used in the course of showering, as Tom makes perspicuous, water use was quickly made accountable to the norm, that is, the local norm, the norm for you or me ‘in this house’, not some general norm (e.g., 45 l for a 5 min shower in the UK). Our participants rendered what at first appeared to be ‘a lot’ of water into an amount that is ‘in line’ with normal usage by comparing particular instances of showering with one another. Nonetheless, anomalies, such as taking a noticeably longer or shorter shower, became accountable matters that prompted explanation (e.g., having a stressful day or being in a rush).
In articulating the reasons for their data’s appearance, our participants also invoked the weather as a determining factor in their choice of temperature on any occasion of showering. If the weather was warm, which at the time of the study it was, the shower temperature was generally cooler than it would be in winter. Water use was also accounted for by one participant in terms of environmental considerations and what they perceived as a moral responsibility to reduce water consumption. Our other participants were also concerned with reducing the amount of water they used. However, for them, it was on the grounds of cost. As noted above, out of the 6 households participating in the study, 5 had water meters and pay for the amount of water they use. Most were concerned to manage their water consumption then, and one even went so far as to ‘gamify’ showering with her friends, using an egg timer to keep her showers under 4 min. So seasonal variations in temperature and moral and economic concerns all shape the data and are built into ‘the routine’, though again these matters are absent from the sensor data.
Reasoning about domestic infrastructure
One final issue was frequently invoked to account for how the data comes to have the shape that it has. When discussing the graphs, household members would bring to account various physical features of their showers. One of the first topics that came into question was the amount of pressure their shower produces and its impact on the water flow graph.
Paul: I would like more pressure but unfortunately we can’t achieve that.
Field worker: Is that down to the boiler?
Paul: No, its down to the builders of the house putting in 15 millimetre pipe and not 22 millimetre pipe.
While some participants revealed that their shower systems were quite powerful and required adjustment to a comfortable setting, others, like Paul, revealed that theirs did not go any more powerful than the reading displayed. Readings were defined by the limits of what pressure could be provided as opposed to any preference these particular users may have had. Graph readings were also recognised as being impacted by other systems in the house that used water, an example being a dip in the graph line which was recognised as the toilet being flushed, as opposed to the sink or washing machine being used. It thus became apparent that the domestic infrastructure connected to the water supply underpinned participants’ accounts of the water flow graphs and their features.
This was also the case for the temperature graphs, where the time it took for the water to warm up on just this occasion that the graph displayed turned upon knowledge of their heating system. The efficiency of the boiler was frequently invoked to explain the shape of the curvature on the graph. Understanding routine adjustments to the boiler itself was also brought into account as a feature of temperature management, where, for some households, this provided the most effective means of making the shower hotter or cooler. Other infrastructural features that our cohort stressed as important was understanding what temperature the water was going to be at when it initially comes out of the shower head. They revealed to us that temperature readings were bound to routine temperature adjustment and caution was exercised if, for example, a central heating system was known to make the temperature of the water in the pipes initially warm but was subsequently followed by cold water. In other cases, whether a member had gone in the shower just before them was seen as a feature of the graph in that the pipes, on these occasions, had already been warmed up. Knowing how the domestic infrastructure affected the temperature of the water was key to understanding the graphs. Even the energy efficiency of the house was called on to account for temperature settings. Thus, in houses deemed ‘cold’ by participants, hot showers were routinely had ‘to warm up’. Again, none of this ‘insight’ is to be found in or is provided by the sensor data.
Responding to the service scenarios
Having reflected on their shower data, our participants were also asked to consider the three future scenarios that might motivate consumer adoption of connected shower services. One of these focused on the local use of sensor data to enable shower scheduling. The other two focused on transacting data with external parties in exchange for services that on the one hand enabled differential charging based on water consumption rates to promote water conservation and on the other provided personalised product offers. The scheduling service was dismissed as irrelevant by all of our participants. Not only did participants know and work around each other’s showering patterns, there was also the sense, as one participant put it, that scheduling showers ‘seems sort of controlling, a bit military’ and thus inappropriate to the mundane order of showering in domestic life. Differential charging received a more mixed reception. Some participants thought such a service might be useful if they received a default reduction in charges for installing a connected shower regardless of the amount of water used, whereas others were concerned that water companies might exploit their data to make more money through ‘time of day’ charging. The product offer scenario was similarly received. While participants could see that product offers might ‘save you money’, a core part of the underlying service model was seen as unviable.
Field worker: They sell you something as a service, so you have shampoo as a service, it’s a bit like having a milkman right? He comes around once a week when he knows your running low or whenever, drops the bottles off. You get the same sort of thing with your bathroom products.
Stuart: That will never work.
Field worker: Why do you reckon it wouldn’t work?
Stuart: I didn’t work for the milkman.
Participants were also wary that such personalised services might impact personal autonomy and freedom of choice, with product manufacturers leveraging the data to exercise ‘control’ over consumer purchasing behaviour.
Cutting across these considerations was common concern about transacting personal data in the first place, regardless of the service being offered. Participants were concerned about their showering activities being ‘readable all the time’, both locally by fellow household members, and the consequences this might have in making what goes in the bathroom accountable to others (a general concern that attaches to data sharing as highlighted by Tolmie et al. [54] and Tolmie and Crabtree [53]) and with respect to the consequences of making the data available to external parties. Participants were particularly concerned that their data would be open to reinterpretation by external parties and that it could have horizons of use that may be incongruent with their own. One participant suggested, for example, that governmental agencies could garner insight into how many people lived in a property by way of seeing how many showers were had each day. Participants were also concerned that their shower data might be combined with other data, e.g., from supermarket loyalty cards or other smart home systems, and be used to profile their homes and target them in some way. One participant invoked her Hive thermostat by way of example and how after installing it she started to receive emails comparing the heating of her house to other houses on her street.
Elaine: That was eye opener, because I thought it was just a thermostat that I was going to control but you get all this guff about your neighbours that are also using Hive and that your house is one degree hotter than theirs!
Participants were concerned by the ‘lack of control’ over what is done with their data by external parties and also the potential for the data to be ‘leaked’ or ‘hacked’. As one participant put it, ‘the more information you put out there, the more information can get away.’ Overall, considerable risks were attached to transacting data in exchange for personalised services, which speaks to the broader need to build transparency and control into autonomous consumer-oriented IoT services if they are to be widely adopted [36].