Section: New Results

Designing and Evaluating User Experience and Methods for Open Innovation

From Usability to User Experience: an HCI Review

Participants : Dominique Scapin, Bernard Senach, Brigitte Trousse, Marc Pallot.

Through an extensive review of the literature, a paper [28] attempted to characterize a rather novel and popular view on human-computer interaction: User Experience (UX). After introducing its polysemous nature, this paper describes the origins of UX, its scope, underlying concepts and components, as well as its various definitions. Then, UX methods are surveyed and classified, distinguishing processes, frameworks, and specific methods. The paper identifies a set of issues about the needs for increased UX maturity. Even though UX can still be viewed as an extension of usability, its future may correspond to a paradigm evolution rather than simply a buzz word. The evolution is not drastic, but it adds complexity (including new measurements) by considering a few more user areas than traditional usability.

Evaluation of our Methods for Idea Generation Process

Participants : Anne-Laure Negri, Caroline Tiffon, Brigitte Trousse, Bernard Senach.

In 2011 we proposed a methodology coupling two methods [25] (GenIoT a generative method based on probes (fake sensors and/or actuators) and ALoHa! a bodystorming method for designing service concepts in the specific paradigm of the Internet of Things (IoT). In the frame of the European project ELLIOT - Experiential Living Lab for Internet Of Things -, ICT Usage Lab (cf. section  8.1.8 ) aims at co-creating “green” services, i.e. services based on air quality and noise measurement.

Both IoT ideation methods Aloha! and GenIoT were used for the co-creation of health related services (cf. section  ). The participants of the methods were Environment and Health professional. Results were very different than the workshops run with citizen in the frame of the mobility scenario in ELLIOT (see 2011 AxIS activity report). Comparison of these workshops shows that hybrid approaches –i.e. co-creation approaches mixing both real and virtual meetings are not working as well as pure face to face or pure online approaches. Moreover, GenIoT method seems to be more effective with citizen than with professional. Aloha! is effective in both groups but more efficient with professionals. However the participant experience of Aloha! is higher in the case of citizen (mainly because professionals are not used to practice creative thinking methods and do not appreciate to go out of their comfort zone).

Leading People Behavior Changes: Mining Evolutive Data

Participants : Brigitte Trousse, Yves Lechevallier, Guillaume Pilot, Carole Goffart, Bernard Senach.

The ECOFFICES project (cf. [62] , [22] and section  8.1.3 ) was for AxIS project team our first step towards eco-behavior study. It provided us a very rich context to study how to analyse the evolution of the energy consuming of employees during an energy challenge. A qualitative analysis from questionnaires (before and after the challenge) has been done as well quantitative analysis. The data set for quantitative data is composed of heterogeneous data issued from around 400 sensors (temperature, presence, behavior in terms of opening doors, windows, bonus, malus, etc.). We made different studies related to data preprocessing and data analysis.

In our first study [64] , we cleaned the data set and selected reliable data for data analysis (only temperature of various equipments, user presence and bonus/malus points). We decided not to work with aggregated variables such as the initial ponderation (defined by partners) for the various bonus-malus rules and the energy consuming at the office level. We decided to use (office, day) as statistical unit (i.e. 9995 units) with a vectoral representation. Finally we realized that the three initial periods (before the challenge, during and after the challenge) on 379 days (2011-2012) should be in fact decomposed in five periods, due to the fact the first and the last periods were split into two subperiods (with and without heat). For the analysis, we apply for each (office,day) a first analysis on a vectorial representation of temperature with the MND method (cf. section focuslab) in order to identify the best partition of these. The MND method uses euclidean distance between each value of the vectorial representation and the prototypes are defined by the means. Second we did a clustering of these units based on bonus and malus and finally we made the correspondence between these two partitions. Three classes for (office, day) are obtained. The interpretation in terms of team relied difficult but we proposed various conclusions for a winner for managing a specific bonus, or in managing ambient temperature or in behavioral change.

In our second study [34] in collaboration with Francisco de A.T. De Carvalho, our goal was to improve the interpretation task at the office and team level by applying AxIS advanced methods. To do this, we applied our hard clustering method presented in [34] on this dataset where each office was characterized by two different representations:

  • Interval representation: each office is characterized by a vector of intervals corresponding to the average, minimum and maximum of daily temperatures on the three temperature sensors during these five periods. Then the office is represented by a vector of 15 intervals and the distance used is Hausdorff distance. This classification is consistent with the partition into three classes obtained during the ECOFFICES project. The class obtained with nine eco-responsible ecoffices is the same. However, other offices are divided into two classes according to the type of heating used during the winter period. The classical method divided these offices into two clusters, one of which contains the offices using the radiators during the winter period.

  • Sequential representation: Each office is characterized by a vector of 9 measures, the min, max and average of daily temperatures of the three sensors in these five periods. The values ​​are ordinate versus time and the distance used is Urso and Vichi distance (adequate for curves). The results of this approach are quite different from the classical approach results. These results required more effort for their interpretation in collaboration by specialists.

Leading People Behavior Changes: a Literature Review

Participants : Bernard Senach, Anne-Laure Negri.

Our research towards eco-behavior study started with the ECOFFICES project (cf. sections  6.5.3 and 8.1.3 for more details) was recently complemented with a litterature review aiming at a deeper understanding of breaks and levers to eco behavior adoption. A first work was focused on the so-called "modal change problem", compiling methods and tools aimed at supporting people to use public transportation system rather than their personal car. A second work was initiated to get a better understanding of the role that users interface could play in encouraging people to adopt a specific behavior. This work is still in progress.

Eco mobility : prompting people to adopt public transportation mode rather than their personal car.

The first review of work conducted in the fields of Persuasive Communication, Commitment, Nudges and Persuasive Technology showed that behavioral change is a process with many steps requiring to support each step with specific means. For instance, if mass communication can support the public awareness of a problem, information is not sufficient to convince people to really change their behavior. It is necessary to push them to act and numerous well-known influence techniques are nowadays available. All recent technological development (geo localization, mobile devices, social networks) can provide very effective support for behavioral changes as far as they rely on design principles identified by research in Persuasive Technology. A presentation was done on this topic for GreenCode Forum [67] (see the video on youtube).

Future of Internet and User-Open Innovation for Smart Cities

Participants : Marc Pallot, Brigitte Trousse, Bernard Senach.

We pursued our work on this topic and contributed to a white paper [59] which is one of the main outcomes of the FIREBALL project [cf. section ), a Coordination Action within the 7th Framework Programme for ICT, running in the period 2010-2012. The aim of this project was to bring together communities and stakeholders who are active in three areas, namely: research and experimentation on the Future Internet (FIRE); open and user-driven innovation in Living Labs; and urban development for smarter cities. The goal was to develop a common vision on how the different methodologies and concepts in these areas can be aligned for cities as playgrounds of open and user driven innovation related to the Future Internet.

The white paper addresses several aspects that are critical for understanding the ‘smart city’ concept and the current progress in this area. Based on cases studies and foresight reports we aim to shed light on how the concept of smart city is currently adopted by European Cities and what the ambitions and expectations are in using this concept. It investigates the drivers and bottlenecks that influence the transformation towards a “smart city”. Underlying approaches to smart cities are discussed, both in terms of the strategies and planning approaches. From this point of view, this paper explores the conditions that must be established to stimulate the transformation towards smart cities, and the resources that are available or should be made available such as investments in broadband networks and in smart applications, as well as in the capabilities to innovate. This also points to the changing structures and processes of innovation and city development. Interestingly, we see a tendency towards more decentralized and bottom-up approaches to planning and innovation. Innovation ecosystems are characterized by a combination of top down and bottom up initiatives, leading to networking and collaboration among stakeholders, which eventually extend to real innovation communities. Increasingly, citizens, advanced businesses and local governments act as proactive catalysers of innovation, shaping cities as “agents of change”.