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Work Space versus Work Place:
The Context Aware Internet of Things
Dimitrios Gkouskos, Malmö University, dimitrios.gkouskos@mah.se Nancy L Russo, Malmö University, nancy.russo@mah.se
Introduction
Ubiquitous computing environments enable workers to easily transition between work and non-work activities regardless of location. While this blurring of boundaries can be viewed as having a negative impact on work-life balance through the intrusion of work activities on home, family and leisure time (Clark, 2000; Davis, 2000), a positive aspect has also been found wherein technology, particularly the smartphone, enables workers to have more control of their schedules and to remain connected to family and friends during work time (Wajcman, et al., 2008). While technology has made this duality possible, we have not yet fully taken advantage of the potential of technology to facilitate the integration of these activities. “Currently the design of the ubiquitous computing environments for home and work are each dedicated to specific realms and often fail to take into consideration the requirements to integrate activities across these two life spaces” (Cousins & Varshney, 2009, p. 123). To better support workers in managing their transitions between work and non-work activities we suggest that context aware computing environments utilizing the Internet of Things may offer a viable solution.
Network-connected technologies such as smartphones, activity trackers, smart appliances, and sensors may be integrated to create an Internet of Things (IOT) ecosystem that offers a ubiquitous computing environment to support the user in a variety of daily activities. To provide the appropriate type of support for the user and enable positive user experiences, this IOT system should be able to determine the user’s context and take action accordingly. For example, a context-aware smartphone can filter and present information such as notifications, applications, and contacts most relevant to the user’s current situation. A home-based IOT system should be able to recognize that an incoming phone call is work-related and thus switch off the vacuum cleaner or mute the TV.
At one time context-aware computing referred almost exclusively to location-based triggers, with the implication being that location (possibly augmented with date/time) determined what activity the user was engaged in and therefore what information or support the user would need. However, with today’s mobile computing environments, the link between physical location and type of activity is no longer deterministic, and more nuanced understanding of context is required (Dourish, 2004). In recognition of this change in work context, the terms work space and work place have been defined to distinguish between the less useful notion of physical location in determining context (Gieryn, 2000) and the more fully developed context that is defined by the emergent and dynamic “meaning that these
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settings, actions and actors acquire at any given time from a subjective perspective” (Mancini et al., 2009).
This idea of context can be enriched with other information provided by IOT devices as well as information provided by the user. A richer understanding of context enables the IOT system to identify at a much more detailed level of granularity the specific needs of a user at a particular time. In this paper we explore the issue of context from the perspective of the user. Ultimately we want to understand how users’ perceive their context, particularly the work/non-work dichotomy and how this impacts the services that IOT-connected devices may provide to the user.
Previous Work on Context-Awareness
Much of the research related to context originates from a technological starting point, i.e. is produced by asking the question “how can technology sense context?” (a review is available in Perera et al., 2014). One of the most common ways to automatically detect context is by using smartphone GPS sensors and labelling locations as “home” or “work”. The system subsequently uses geolocation to determine the user’s context as work or home. As mentioned earlier, the changing nature of work tasks, along with technological advancements, have enabled varied work arrangements where employees have an increased level of flexibility regarding the location where work is done. Working remotely, having multiple workplaces, and having no formal workplace at all are just some examples of changing work practices. If work activities are no longer limited to one physical location (the work space), then relying on geolocation and labeling context as work/home is insufficient in capturing the reality of the situation. Activity recognition algorithms go beyond the location data available from sensors and GPS tracking software in mobile devices to customize the device’s behavior based on the user’s activity (application use patterns, for example) in addition to location, to provide messages and alerts at an optimal time, and to adapt the presentation mode such as screen brightness and font size (Lockhart et al., 2012).
While the importance of the user as an actor is recognized in much of the technology-driven work on context awareness (Perera et al., 2014), as well as in IoT context research (for instance in Cordeiro et al., 2016), the meaning that the user attributes to different contexts is insufficiently addressed. Semantic location services (Kim et al., 2011) utilize user feedback to complement machine-learned location sensing algorithms with user definitions of locations, which highlights both the value of knowing the user’s perception of his or her activity locations and the willingness of users to provide such information However, these services address only the location mapping and not other aspects of context. To provide state-of-the-art functionalities, context-aware office systems require some level of user-provided data such as personal preferences and biometric information (mood or stress level, for example) in combination with location sensor data, connected calendars, environmental parameters and activities (such as application usage) data (Röker, 2010).
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Dourish outlines a second way to view context, rooted in phenomenology, where “context is a feature of interaction” (Dourish, 2004). In the phenomenological view, context is not just information, but rather something that is contextually relevant to the user’s activity and current interest. As such, the question of relevance is central to context-awareness. We therefore want to study the relationship between location and activities, with a focus on the user’s perception of the relevance of the location in performing the activity. Exploring Context: A Pilot Study
Drawing from the ethnographic tradition, it is apparent that in order to assess what is relevant to the user, we must observe situated action and obtain feedback from the user. By asking the user to record activities at particular times or to respond questions produced by the system we – and the systems we build – can learn a particular user’s pattern of behavior and thus be able to provide support that is tailored to the user’s context.
In this study we aim to explore the user’s perception of context and identify relevant context cues that can better capture current work practices. In order to explore the applicability and limitations of the home/work dichotomy, we will use an adaptation of the experience sampling method (ESM) to determine the user’s perception of context. ESM (Larson & Csikszentmihalyi, 1983) is a well-known, widely used method for capturing self-reports of momentary user experience. The method works by asking users in random or predetermined intervals to answer a few questions about the experiences they have recently had.
In the pilot study, we presented the user with a short questionnaire via their smartphone periodically throughout the day (using the Paco app for IOS and Android phones). The questions included location (work/home/school/other), activity (user specified), and the strength of the relationship between activity and location. In the first half of the pilot study the relationship variable was a simple binary response – is your activity dependent on your location? In the second half, users were provided with a Likert scale to describe the strength of the dependency between the activity and the location. The primary purpose of this pilot study was to evaluate the feasibility of using the experience sampling method and smartphone application for this type of data collection.
Pilot Study Results
For two weeks 8 participants responded to the short survey described above, resulting in 123 usable data points. Participants were colleagues and friends, and not intended to be a representative sample. This short pilot study confirmed that the experience sampling method via smartphone app was feasible. One problem identified was the ease of ‘double reporting’ where a respondent entered the same response multiple times (by accidentally clicking ‘submit’ twice, for example). This required duplicate entries to be removed from the data. Some modifications were made to the questions based on participant feedback.
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Participants reported 35 instances of location at ‘work’, 61 instances of location at ‘home’, and 27 instances of location as ‘other’. Approximately one third of the ‘other’ locations were described as ‘in transit’, indicating that this might be another major location category to include in future studies. Nearly all of the activities reported at the ‘work’ location were obviously work-related. The only exceptions in this sample data were ‘leaving’, ‘socializing’ and ‘drinking coffee’. The ‘home’ and ‘in transit’ locations in particular encompassed a variety of tasks – we call these activity-diverse locations. There are 25 instances (41%) where the participant labelled their location as ‘home’ and their activity as work-related. Whereas many of the ‘in transit’ location activities were described as waiting (for a bus or plane, for example), or traveling, there were also instances of leisure activities and work activities taking place while commuting. Since it is likely that the user’s need for technological support in these contexts will be somewhat different from the context of working at in the office, relevant features of these environments should be explored in more detail in future studies.
In the majority of the responses, the participants viewed the location as relevant for the activity. This relationship was strongest with ‘other’ locations and weakest with ‘work’. When given only a binary option (the location is relevant: yes/no), the location was identified as relevant in 78% of the responses. However, when the participants were given a 5-point Likert scale with which to indicate the strength of the relevance, the location was identified as relevant or very relevant in only 59% of the responses. We suspect that this change is related to increased attention given to the issue of relevance. The determination of relevance will be addressed in more detail in the next phase of the study.
Conclusion
Future work will expand the data collection to the general population via Sony’s Citizen Science platform. This platform allows any smartphone user to participate in the study. The Citizen Science application obtains the user’s permission to capture data via short questionnaires sent randomly throughout the day to the user’s smartphone. The questionnaire will be expanded to include an open-ended question asking why the user gave the reported relevance score (assessing the importance of the location for doing the activity). This information will give more insight into the determination of which context cues (beyond location) are important for users. For example, is it access to technology or to certain individuals, or some other factors about a location that makes it conducive – or even necessary – for a particular activity? A third phase of data collection will be to conduct semi-structured interviews with a subset of the participants in order to get a more holistic, in-depth understanding of perceptions of context and relevant context cues.
These results will contribute not only to our understanding of the context of work today, but eventually the development of IOT environments that can modify device performance, services, data, and interactions to provide a supportive work place regardless of location.
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Acknowledgments. This work was partially financed by the Knowledge Foundation through the Internet of Things and People research profile.
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