Understanding the Influence of the Users' Context in AmI

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Understanding the Influence of the Users' Context in AmI Anxo Cereijo Roibás Social Science Computer Review 2008; 26; 103 DOI: 10.1177/0894439307307699 The online version of this article can be found at: http://ssc.sagepub.com/cgi/content/abstract/26/1/103

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Understanding the Influence of the Users’ Context in AmI

Social Science Computer Review Volume 26 Number 1 February 2008 103-118 © 2008 Sage Publications 10.1177/0894439307307699 http://ssc.sagepub.com hosted at http://online.sagepub.com

Anxo Cereijo Roibás University of Brighton, UK

This article explores the use of ethnographies to understand the contextual influence on the user experience of ambient intelligent (AmI) systems in public environments. To do this, consolidated and experimental field studies to explore how users interact in real contexts with pervasive systems are discussed. The scope is to analyze, in a holistic way, how the system integrates into the users’ physical and social environment and can be responsive to their emotions and feelings. These studies, as part of a participatory design approach can potentially contribute to enhance the reliability and relevance of the results. Keywords: ambient deployments; context awareness; intelligent objects; pervasive interactive systems; privacy; security; trust; user experience

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ervasive computing envisions that users can, in any situation, select the most suitable interface to produce, or have access to, content and to share it with others. In an ideal scenario, the computer would even disappear from our awareness (Weiser, 1993). The scope is to provide seamless and pervasive access to information and networking applications in a smart, efficient, intuitive, usable, and user-friendly way, contributing to user empowerment and at the same time supporting human interactions. However, and especially when dealing with ambient intelligence (AmI), rather than being a simple means for communication, interfaces often become the focus of attention. In fact, the concept behind AmI is that information technology is unobtrusively embedded in the people’s surroundings through a distributed system of intelligent interfaces. To make them easy to live with and accessible to ordinary people, AmI systems should (ideally) be unobtrusive, grant interoperability with other interfaces such as mobile phones, allow a natural interaction, for example through smart agents that enable a multimodal interaction (gestures, voice, etc.), and provide contextualized services. These systems need to gain the confidence and trust of users through the enhancement of aspects such as security, control, reliability, privacy, and so on (Little, Briggs, & Coventry, 2005). In fact, enhancing environments and everyday objects around people, making them responsive to the users’ attention and actions, should entail, at a theoretical level, the facilitation of human contact, improving community and cultural standards, contributing to build knowledge and support decision skills for work, entertainment, citizenship, health, and general consumer choice. This offers opportunities for leisure, work, learning, and government, supporting social frameworks and modern lifestyles. However, to gain wide acceptance a certain balance is needed: The technology should enhance the quality of life but not be seeking domination. It should be reliable and controllable but

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nevertheless adaptive to human habits and changing contexts (Friedewald, Da Costa, Punie, Alahuhta, & Heinonen, 2005). Furthermore, there is a conflictive—and still unsolved— facet in AmI and pervasive computing paradigm in general, which involves an expected calm living nature and support of user empowerment. In fact, some authors argued for a significant shift from proactive computing to proactive people, where AmI technologies should be designed not to do things for people but to engage them more actively in what they currently do. According to this, rather than calm living it should promote engaged living, where technology is designed to enable people to do what they want, need, or never even considered before by acting in and on the environment. In other words, people—and not AmI systems—are expected to be constructive, creative, and in control of their interactions with the world – in more gratifying and innovative ways. Instead of augmenting the environment to reduce the need for humans to think for themselves about what to do, what to select, etc., and doing it for them, we should consider how ubicomp technologies can be designed to augment the human intellect so that people can perform ever greater feats, extending their ability to learn, make decisions, reason, create, solve complex problems and generate innovative ideas. (Rogers, 2006, pp. 404-421)

There are several research projects addressing different aspects of pervasive interactive systems and AmI (Arreymbi & Gachanga, 2006). Many of them are situated either in the area of mobile and pervasive games (Capra, Radenkovic, & Benford, 2005), in the experimental interactive arts, or with regard to health applications (Riva, 2003). However, most of the current research in AmI deals with how the environment is able to identify and model users’ activities, rather than how the user is willing, able, or likes to communicate with intelligent things and/or the environment or have them communicate with the user (Nijholt, Rist, & Tuinenbrejier, 2004). What’s more, the majority of the empirical studies regarding the multimodal aspect of the interaction focus only on certain interactive aspects such as the visual (Pham, Schneider, & Goose, 2000; Zheng & Wang, 2005) or the audio interaction (Scheible & Ojala, 2005), or the digital content in general (Goularte et al., 2004), and only a few pay sufficient attention to the problem of interacting with distributed, anonymous and even “invisible” interfaces that may lead users to feel lost (Tolmie et al., 2002) and sensing systems (Benford et al., 2005). However, they generally disregard the influence of the context during the interaction process (Intille et al., 2005). Another key aspect that needs further exploration regards the design of social interfaces, particularly when considering AmI systems as social actors. Addressing the social aspect of the interaction implies to take into account users’ social needs such as emotions, personality, affiliation, friendship, and so on (Takahashi et al., 2000) as well as their perception of their environment. This issue is particularly tricky when addressing AmI systems in public spaces. Research and development should be driven by a better understanding of the overall human activity (Greenfield, 2006). Yet the scope of the current project is to do an holistic analysis of the UX (user experience) to explore the “soft” aspects of the interaction process in specific contexts such as users’ emotions and feelings and, at the same time, to understand which sort of “intelligence” and


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interaction modalities are required in these environments to provide the user with a high quality of experience (Streitz, Magerkurth, Prante, & Röcker, 2005).

The Relevance of the Usage Environment A critical UX issue in AmI systems that needs to be identified is the role of the environment. Several studies in the area support the idea of the system behaving as a social actor that creates a relationship with the user (e.g., through embodied agents) (Reeves & Nass, 1996). Although such a system’s behavior would solve some intrinsic problems such as trust, it also raises a quandary about how AmI’s social intelligence (if any) should be.1 When people establish a relationship with their physical and social environment, this leaves traces in people and vice versa entailing a process of mutual adaptation. The social environment and therefore “social AmI systems” are expected to be smart enough to be responsive and, at the same time, able to evolve according to the user preferences, behavior, and personality and in relation to the changing the user’s context. This implies, on one hand, to resolve the technical challenge of sensing the environment and the presence of the user, his or her needs and feelings during the course of the day (biosensing). On the other, it entails understanding the semantics of the communication itself. For example, whether this social intelligence needs to manifest in an anthropomorphic way (e.g., reproaching or praising or even apologizing) and, specifically, how this interaction takes into account the users’ perception of their context empowering their relationship with it (if, for example, the user is concerned about thefts while using his or her camera in a slum, the system can reassure him or her by highlighting the presence of a nearby police officer). Because of the high number and the dynamic nature of the entities involved, this last point is probably one of the most complex. However, a social actor that relates to the user according to his or her context could improve AmI weaknesses related to trust, security, identity (and context), and privacy. According to this, to design social ubiquitous intelligent systems in public places it is imperative to understand the contextual impact on the user and involves the consideration of all other objects in the domain. Somehow designing such interactive systems can be compared to producing a play in theatre. It needs to know and take into account holistically the whole environment in the stage (light, acoustic, furniture, etc.) (Laurel, 1991). In fact, three main elements need to be taken into consideration to design appropriate ubiquitous systems: the characteristics of the users, the attributes of the interfaces, and the properties of the context. The level of usability and accessibility are important factors when evaluating the quality of mobile and pervasive interactive intelligent systems. However, because of the fragile equilibrium between the user, the environment, and these technologies, it is also crucial to assess the user’s overall experience with the system. This includes getting information about their emotions and feelings. McCarthy and Wright (2004) argued the user experience must take into consideration the emotional, intellectual, and sensual aspects of our interactions with technology. “Today we don’t just use technology, we live with it.” Much more deeply then ever before we are aware that interacting with technology involves us emotionally, intellectually and sensually. So people who design,


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use, and evaluate interactive systems need to be able to understand and analyze people’s felt experience with technology. Therefore, to identify the users’ experience in specific contexts, cognitive psychology and human factors research need to be complemented with disciplines such as ethnography to provide adequate solutions to AmI-related systems.

The Use of Ethnographies Making environments and everyday objects smart technically implies, among other things, integrating small microelectronic processors and sensors into them. In this way, intelligent artefacts can explore their environment, communicate with the environment and other smart things, and interact with users helping them to achieve their tasks in new rewarding ways. However, “Although many concepts have already been tested out as prototypes in field trials, the repercussions of such extensive integration of computer technology into our everyday lives are difficult to predict” (Bohn, Coroama, Langheinrich, Mattern, & Rohs, 2004, p. 5). A number of different data-gathering technique—most of them from the ethnographic praxis—have been more or less successfully employed to understand the influence of the context of use in mobile and pervasive systems. At the same time, appropriate user studies can provide valuable information about people’s latent needs, expectations, and concerns regarding future technologies (evoking the Henry Ford quote: “If I had asked people what they wanted, they would have said faster horses.”). A traditional methodology for data collection in user-centered design projects consists of direct observation in the “workplace” (Kjeldskov & Stage, 2004). The strength of this approach is that it does not focus exclusively on tasks but analyzes in a holistic way users’ interaction in their real-world contexts. User centered design researchers have historically favored direct observation because it is a method that situates the researcher in the context in which technology use occurs (Hagen, Robertson, Kan, & Sadler, 2005). However, most of AmI systems are designed for individual use within a personal body space. Therefore, observing the interface actions of the user can be physically arduous (Kjeldskov et al., 2005; Mark, Christensen, & Shafae, 2003; Newcomb, Pashley, & Stasko, 2003). During this activity, it is also possible to map the movements of users in spaces of aggregation such as stations and parks. These maps can consider an average period of 2 hr and can be a source of information regarding the sociability processes in public spaces as well as the effect of technology in this phenomenon. Considering aspects such as the physical and social environment as well as the users’ threads of experience, such as emotions and feelings during their interaction with the system, potentially enhances the consistency and relevance of the results. In-the-field analysis can be a complex activity when the interaction happens in more private environments, such as the users’ home or when the space is not fixed to a specific place, but can be anywhere as it happens with pervasive communication systems. In these situations, users are usually moving and carrying out different activities at the same time, making the observation intrusive, interfering with the normal course of these tasks or, if distant, not reliable. In fact, such a research approach needs to balance the privacy and autonomy of participants with the need



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of gathering accurate data (Hagen et al., 2005). A good solution could be to complement in-situ evaluation techniques with traditional laboratory evaluations (Beck, Christiansen, Kjeldskov, Kolbe, & Stage, 2003). Data-gathering techniques involving the users directly are effective participatory design tools. Hagen (see Hagen et al., 2005) grouped them into three categories: • Mediated data collection where users either do the data collection, for example, selfreporting, diaries, cultural probes, experience clips and mobile probes, or use some technology that collects the data automatically as a consequence of using the device, for example use/data logs, or finally, wear mobile recording devices such as cameras or sensors, for example, video observation, use/data logs. In all three cases, the data collection happens in the users’ natural settings. • Simulations and enactments involve a form of pretending to allow the data collection. Simulations usually are held in labs and aim to imitate aspects of the use in the real contexts (e.g., lab tests, prototypes, emulators, simulators, scenarios, and heuristics). Enactments instead involve a sort of role-playing including visual imagery or storytelling (e.g., scenarios, role-playing, prototyping, and storyboarding). • Combinations where existing methods such as questionnaires, focus groups and interviews, and/or mediated data collection and/or simulations and enactments are combined to allow access to complementary data.

Cultural probes (Figure 1) aim to get inspirational responses to understand beliefs, desires, aesthetic preferences, and cultural concerns of users without observing them directly. This technique that was initially used by Gaver in industrial design to find new feature design (Gaver, Dunne, & Pacenti, 1999) and has been recently exported to Human Computer Interaction (HCI) (Hulkko, Mattelmäki, Virtanen, & Keinonen, 2004). However, the intrinsic unstructured nature of the data collected here makes interpretation of results very difficult. Automated data-collection techniques such as use/data logs are the less intrusive and demanding technique for the user. However, it might pose serious ethical issues if privacy aspects are not properly managed and, at the same time, the technical sophistication of the system required can make this research tool rather expensive. An example of click-stream data logs consists in a small camera and a microphone embedded in the handheld to collect audio-visual data during its use (Engestrom, 1999). In-situ evaluation techniques have been used in several projects regarding the design of interactive systems in public or semipublic environments, such as the evaluation of ambient displays at work and in the university (Mankoff et al., 2003), to evaluate ambient displays for the deaf that visualizes peripheral sound in the office (Ho-Ching, Mankoff, & Landay, 2003), to evaluate a sound system to provide awareness to office staff about events taking place at their desks (Mynatt, Back, Want, Baer, & Ellis, 1998), and to evaluate a system of interactive office door displays that had the function of electronic Post-It notes to leave messages to the office occupant when they are not there (Cheverst, Dix, Fitton, & Rouncefield, 2003). There is an interesting example of data gathering considering older users in private environments such as their homes. It regards the evaluation of a Computer Supported Collaborative Care ambient display aimed to help day-by-day the local members


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Figure 1 Cultural Probes Pack and Detail of the Maps

of an elder’s care network by using augmented digital picture frame to give information about elders. This evaluation combined heuristic evaluation with an in-home Wizard of Oz evaluation and aimed to analyze eldercare from the entire support network’s viewpoint, taking into account the social context beyond the family and close friends. It also considered other people peripherally involved in the elder’s care to observe possible problems in the eldercare process such as miscommunication, misunderstanding, unequilibrated distribution of responsibilities, distrust, unmet care needs, and so on. The concept was to focus on the older users instead of the caring process as a whole. In this way crucial issues related to the use of the supporting technology such as emotion, trust, and privacy might easily arise (Consolvo et al., 2004). Research team members collected the data used in the displays by calling the elders and their parents or caregivers several times each day; the evaluators then updated the displays remotely using a Web-based tool. However, this practice created in many older users a perception of being oversurveyed and thus not always provided sincere or accurate information. Simulations and enactments are very useful when the usage contexts make particularly difficult the mediated data collection because of highly private, technical, or legal issues (e.g., military environments) or when the system is at a very experimental level. Simulations using proof of concept mock-ups or explorative prototypes in labs have been used largely to evaluate the usability and accessibility of interactive systems. Although they might provide valuable information about the UX with a certain interface, they tend to disregard the contextual and emotional aspect of the interaction. Moreover, they can only be used when the conceptual model of the system reaches an adequate level of maturity as they presume the use of a sort of prototype.



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Theatre Workshops: Personas and Scenarios The above-described techniques in combination with focus groups and questionnaires are useful not only to identify the main users’ requirements but also to write the scripts for the scenarios of use. Theatrical performances based on scenarios are a valuable technique to collect data and assess requirements and other crucial information such as users’ feelings and emotions because the user’s self-esteem is not directly in play, so inhibiting factors like embarrassment or shyness are not elicited. Moreover, users do not get the sensation of being under examination. The use of drama (Stato & Salvador, 1999) can be an effective tool in participatory design as it facilitates the dialogue between designers and users. According to Newell (Newell, Gregor, & Alm, 2006), it can cross boundaries of technical language and knowledge, allowing elderly potential users to be involved effectively in the process of design at the preprototyping stage. Live play gives the audience the possibility of interacting directly with the actors, providing feedback about the feasibility and realism of the situation played; however, in the case of budget restrictions the use of video can be an effective alternative. Several personas need to be identified to represent the main typologies of users of these pervasive systems.2 After this, the scripts for the scenarios can be created from the stories (experiential narratives) that participants bring to the focus groups (Howard, Carroll, Vetere, Murphy, & Peck, 2002). Each scenario corresponds to a prospected UX with pervasive multimedia systems for each user profile. The scenarios can be subsequently assessed using the technique of dramatizations (theatre). In this way, users and designers can discuss the feasibility of the different elements of the system proposed. This methodology has proved to be useful not only to confirm their reliability and relevance but also to achieve a high engagement of the users during the design process (Newell et al., 2006).

Experimental Prototypes The scenarios above provide the basis for identification of possible implementation settings and verify the system requirements. Once potential interfaces and applications were determined, assessing their quality in the field required the development of proof of concept mock-ups. To have a realistic feedback about the users’ experience with the system, the Low-Fi prototypes representing AmI systems and applications should be tested in the field. In fact, when the evaluation considers the real context of use, it provides crucial information about how the physical and social environment can influence the use of the system. For example, displaying personal information in public displays may raise concerns about the management of the privacy or even embarrassment in crowded areas (Russell, Drews, & Sue, 2002).

Methodology for the Design of Scenarios in Mobile Awareness Systems As the scope of the current project was to understand the reciprocal influence between users and their environments for AmI systems in public spaces, the methodology


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mainly focused in collecting qualitative (or “soft”) data rather than quantitative statistics. In other words, critical incident analysis, content analysis, and discourse analysis prevailed and a few longer and more in-depth sessions took place on numerous but briefer occasions. The project focused on users with ages ranging from 18 to 55 years, and busintainment has been selected as the principal area of application of AmI for the selected category of users. Methodologically this work systematically made use of users’ field study with the aim of achieving an understanding of the research context, inspired by “Quick-and-Dirt— Ethnography” (Hughes et al., 1994). This included study of documentation, ethnographic data collection and analysis from studies of user panels, observation, focus groups, interviews and mapping of users’ movements, and experimental techniques such as enactments as part of a user-centered approach. This work has followed the following steps: 1. Study of documentation regarding existing technologies and theories about models of interaction and successful interactive experiences 2. Exploration of usage scenarios. This phase used ethnographic research such as interviews, focus groups (Williams & Robson, 2004), questionnaires, observation (video recording in the field and video-data analysis), mapping of users’ movements, and questionnaires with 24 representatives of the target group of users. 3. Validation of scenarios. As part of the participatory design process, user representatives have been systematically involved also in the assessment of the experiential scenarios hypothesized. To do this, dramatizations such as theatre have been organized (Carmichael, Newell, Dickinson, & Morgan, 2005) making use of the practice of pretending in the field.

The first phase consisted of two initial focus group sessions with some representatives of the users. These workshops aimed to get the users’ view about specific concerns with AmI in busitainment. Each session involved 12 participants living in London, U.K., and users were distributed into each one of the sessions according to two age categories (18 to 30 and 31 to 55) with a broad mix of cultural and educational backgrounds, occupation, and lifestyle). Participants were asked to bring real and fictional stories (experiential narratives) regarding the subject to be shared during the session. This phase also included naturalistic observation (photo/video recording in the field and data analysis) and mapping of user movements. Observations were undertaken for a period of 3 months in the city of London. The observer collected the data in pictures, videos, and hand-written notes. During this activity, it was also possible to map the movements of users in spaces of aggregation such as stations and squares. These maps considered an average period of 2 hr and were a valuable source of information regarding the sociability practices in public spaces (how people meet) as well as the role of technology (mobile phones mainly) in these processes. The discussion and the storytelling activities during the focus groups have been very useful in the creation of the scripts for the scenarios. In fact, the quotes presented below outline some critical AmI issues in busintainment situations. It is peculiar that—although not stated in the focus groups’ brief—most of the attendees explicitly deemed mobile phones as core interfaces for AmI systems on the move: “[When on the move] I’d like my mobile to inform me about ‘important’ things, such as reminding me to buy milk while I am walking near the



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supermarket, but I’d become mad if it decided for me.” This point has been shared by many of the attendees of both workshops and supports the concept that AmI systems need to provide personalized and customized solutions related to the users’ identity and context. In this way, they present an opportunity for users in terms of decision support system. Other interesting series of observations regarded the increase of efficiency that AmI systems could provide: “I’d like my mobile to give me pertinent information when and where I need it. This would save me time otherwise spent looking for it.” This aspect seems to be a natural consequence of the previous point. We have also collected serious concerns regarding the negative aspects of AmI. One of the main anxieties regarded the lessening of the user’s control during the dialogue with systems close to the “disappearing computer” paradigm, which is perceived by the users as being deficient of evident (or intuitive) interactive clues. “How do I know when and where the interaction is available?” “It seems to me that [in these systems] the ‘intangibility’ of the interaction is taken to the extreme, rendering in this way, the interaction even harder.” Most of the participants asserted that the use of mobile phones within intelligent pervasive systems would make them feel more in charge of the dichotomy cause–effect. “I’d feel weird ‘talking’ to walls, especially if there are other people around. I’d much prefer interacting with my mobile.” This appeal for a higher tangible nature of the interaction provided a crucial clue about the use of “material” widgets that could be physically connected (like Lego pieces) to get “combined” services (see scenario below). Numerous observations can be represented by the following quote: “I’d be quite worried about my privacy. What about if I don’t want my mobile to tell my friends that I can I am in town?” This issue is clearly related to the management of privacy particularly in the case of social awareness. Another diffused fear regarded the trust and the quality of the information. “How can we be sure that the information we are receiving is accurate or biased by marketing or other hidden purposes?” Security issues were the third main problem that were raised during the focus groups: “Somehow, these (awareness) systems would make me feel quite in charge of the situation, but at the same time I might feel quite vulnerable if I assume that the others around me could have information about myself!” These expectations and concerns were valuable sources for the elaboration of the scenarios in which the alleged strengths of AmI systems have been emphasized and its weaknesses mitigated. The scenarios provided the basis for the identification of possible implementation settings and the corroboration of the system requirements. Because this work focused on the nomadic and peripatetic aspects of users, home ambient interfaces have not been considered. Five personas have been created to represent the different typologies of users of these systems.3 Vanessa (Figure 2) embodies one of the main categories of users directly involved in this research. The scripts for the scenarios have been created from the stories shared during the focus groups, focusing on the different AmI UX issues highlighted above. Although some elements that have been included in the scenario are not novel individually, the intention was to re-create a complete view of how AmI interactive systems could be totally embedded in


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Figure 2 Summarized Description of One Persona Vanessa, a married 39-year-old consultant living in North London and moving regularly for work, is mad about martial arts, healthy living, socializing and music. Her preferred means of transporting is her bike as she is particularly concerned about the environment.

the personas’ daily lives (just recalling the famous William Gibson quote: “The future is here. It’s just not widely distributed yet.”). A summary of the script for one of the scenarios that focused on the tangibility of the interaction with AmI systems is shown below: It is a sunny Monday afternoon in London 2013, and Vanessa has 2 hours before her next work appointment in East London so she decides to take a break and cycle to the Olympic Park in Stratford. She loves its terrific cycle ways, and there is always a good chance to catch up with friends. As she enters the park, she is tagged and welcomed by a giant public screen (this has always embarrassed her). She stops by the screen and using her finger she designs a small square (which will be her interacting area; in this way, the whole public screen can be used contemporaneously by different users). Subsequently she takes out of her pocket her collection of widgets.4 Vanessa chooses the “weather,” “contacts,” and “maps,” puts them together, and draws them up the selected area in the public screen. Suddenly the screen displays a map of the area, showing the weather for the next few hours and the location of two of her friends in the park. She thinks about informing them as she is in the area, but she prefers to give them a surprise. Because she hasn’t exercised for 5 days, the system shows a nice trail that will allow her to burn 500 kcal in one hour which she finds very persuading. It is very warm, and the system highlights several fountains along the way so she can drink some water. This makes her feel pampered. Pointing her mobile phone toward the screen the map is downloaded in the handheld activating a routing systems that directs her toward her friends and to the trail. She feels very excited. On her way she spots the new concert hall, so she gets closer to it and shakes her “favorites” widget cube to upload the link with the events for the month. She will check them later at home from her computer. A vibration reassures her that the information has been successfully downloaded.

Review of Results To address complex issues such as users’ understanding, emotions, and concerns in terms of trust, privacy, and so on (Ackerman, Darrell, & Weitzner, 2001), the data-gathering techniques presented in this article focused on users rather than on their tasks or objectives



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Figure 3 “Material” Widgets Resembling Lego Bricks

with the analyzed interfaces. This research shows how the physical and social contexts have a strong impact on attitudes and shows that context influences users’ emotions and feelings during the process in a positive or negative way and can persuade or discourage use (Kjeldskov et al., 2004). For example, during the in-the-field assessment of the proof of concept mock-ups, some users found it unsafe consulting a local guide in their mobile phones in a crowded street, as they were very concerned about theft. The analysis of documentation and study cases have been good sources of functional and data requirements; questionnaires provided good information about user requirements, and finally in-the-field observation and mapping of movements have been a very valuable technique in highlighting environmental and usability requirements as well as identifying general patterns of behaviors. Questionnaires showed a high users’ interest in AmI systems in public spaces but, because of the restrictive nature of this tool, they fell short of showing the preferred modalities and features of these systems. The focus groups revealed two main strengths of AmI systems in public spaces: satisfy specific users’ contextual needs and improve substantially the efficiency of information


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systems. However, some weaknesses such as a difficult management of the privacy, fragile security measures, scarce user control of the interaction process, and concerns about trusting the information provided have also clearly arisen. The impact of these issues on the users’ experience has been corroborated during the assessment of the scenarios. Once the scenarios were constructed, a validation session was organized consisting of in-situ theatre performances following the scripts of the scenarios. The plays were performed in public environments (mainly squares in the city) by some of the users while the others could comment on what they were watching. The advantage of these in-situ enactments is that they provided precious information of some contextual factors that had not been identified in the research process (e.g., some users found it odd to interact with a public display by gesticulating in public spaces). Although the preference of a certain tangibility of the interaction (as opposed to the invisible computing) was a recurring conflictive issue during the validation of the scenarios, one of the main lessons learned in these sessions was the users’ predilection for systems that promote proactive users encouraging and engaging them to do things rather than focused on proactive computing. In fact, scenarios where AmI technologies were designed to do things for people raised negative feedback from the attendees. Another critical point regarded the system’s behaving as a social actor. Developing a relationship with the user was perceived as positive only if “the computer’ is able to become convincing in terms of trust, security, and not least, understanding the user and of his or her context. Observations and mapping of movements provided qualitative information about how the physical and social context can influence the users’ interaction in public spaces. For example, it emerged how one of the main limitations of using mobile phones in AmI regards the inadequate locative sharing potential of these interfaces as they encourage their owners to temporarily disconnect from a social group (such a group of friends) during the time of its use.

Future Work The user studies illustrated here focus on several multidisciplinary and cross-disciplinary domains of pervasive interactive AmI systems in public spaces and raised the following issues that need to be explored further: socialibility, context awareness, users’ creativity, enhanced interactivity, and convergence. Beyond these areas, it is imperative to analyze how the new technological paradigms will affect the perceived quality of experience (PQoE) in AmI systems. These paradigms include hybrid artefacts, advanced interaction modalities, new forms of content and novel environments, intelligent environments, and immersive environments such as collaborative virtual environments and multiuser environments. In this sense, an examination of the contributions that disciplines such as the interactive arts, space technology, medicine, and games could give to this area might offer significant insights. However, to achieve satisfactory PQoE standards in these new territories, new immersive field research instruments need to be explored. A potential area where AmI technologies can be successful is in encouraging attitudinal and behavioral change (Fogg, 2003). AmI systems can provide a constant and pervasive



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presence and, therefore, long-term influence on the users. However, as discussed before, their impact relies on the degree they enable users to engage with the information, by monitoring, understanding, interpreting, and acting on it, rather than the system acting on their behalf. Persuasive technologies are intended to provide dynamic information about a behavioural process that will encourage people from doing or not doing something, by being alerted and/or made aware of the consequences of what they are about to do. Moreover, designing a device to be solely in the control of the users (and their social group) enables them to be the owners of the collected data. This circumvents the need to be centrally concerned with privacy issues, allowing the focus of the research to be more oriented towards considering how best to design dynamically updated information to support cognitive and social change. (Rogers, 2006, pp. 404-421)

Conclusion Considering ambient or ubiquitous technology as smart social actors implies understanding how the development of social relationships between users and the environment can be supported. In this sense, designing human characteristics in the system is not as crucial as building unobtrusive stable relationships with the users based on trust and, at the same time, able to safeguard his or her privacy. However, social intelligence involves understanding user’s needs and feelings and, at the same time, his or her perception of the context. In fact, credible social relationships imply a responsive behavior to the user’s actions, needs, and feelings in a specific context to provide contextualized (e.g., encouraging the user to drink water while doing sports or on an excessive warm day). Traditional data-gathering and evaluation techniques based on cognitive psychology focus on the human–machine interaction system disregarding a crucial aspect in the process: the context of the user. The physical and social context might have a strong impact in the PQoE of AmI interfaces in public spaces: It influences in a positive or negative way the users’ emotions and feelings toward the interaction process, persuading or discouraging its use. This article aims to understand which research tools can be more appropriate when considering users interacting with mobile system— in the specific area of busitainment— that provide awareness about their physical and social surroundings. The challenge is how to facilitate users to benefit from the advances of ICT in terms of exchange of information with their setting and, at the same time, integrate and enhance the new communities of mobile users arising all over the world. This work presents an overview of diverse ethnographic praxis intended to know the users and understand how the usage scenarios can influence the quality of their experience when interacting with mobile communication systems. These ethno-methodologies study the users and their context in the field living labs, focus groups, and on-the-field enactments. This approach is part of a participatory design process aimed to create relevant future scenarios and applications for mobile awareness systems.


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Notes 1. Social intelligence is a person’s ability to “get along with people in general, social technique or ease in society, knowledge of social matters, susceptibility to stimuli from other members of a group as well as insight into the temporary moods and underlying personality traits of strangers” (Vernon, 1933). 2. A persona is a description of a user archetype that can serve as a guide in the design process. The personas can be synthesised during the focus groups and aim to represent the individuals that embody the characteristics of a target user population. 3. A persona is a description of a user archetype that can serve as a guide in the design process. Three personas have been synthesised from the focus groups in Phase 1 and aim to represent the individuals that embody the characteristics of a target user population. 4. Each tangible widget can have a specific shape and represents a different application (contacts, maps, Instant Messaging, weather, transports, agenda, calendar, etc.), and they can be assembled as Lego bricks (see Figure 1). When touching a mobile phone or the selected area of the public screen with a widget, the application automatically opens, and when assembling different widgets, the result is a combined application. If assembling two widgets of the same category (e.g., agenda, contacts, or favorites) belonging to different persons, then they can share information.

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Anxo Cereijo Roibás is senior lecturer at the University of Brighton. He teaches interaction design for wireless devices at the MSc in Digital Television and interactive and lectures in multi-channel identity. He has worked as human interface engineer at the Mobile Internet Services Provider, HiuGO SpA and has been interaction design consultant for Omnitel-Vodafone. He can be contacted by e-mail at [email protected].


Understanding the Influence of the Users' Context in AmI

Understanding the Influence of the Users' Context in AmI

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