The user’s experience with a recommender system is significantly shaped by the dynamics of user-algorithm interactions. These interactions are often evaluated using interaction qualities, such as controllability, trust, and autonomy, to gauge their impact. As part of our effort to systematically categorize these evaluations, we explored the suitability of the interaction qualities framework as proposed by Lenz, Dieffenbach and Hassenzahl. During this examination, we uncovered four challenges within the framework itself, and an additional external challenge. In studies examining the interaction between user control options and interaction qualities, interdependencies between concepts, inconsistent terminology, and the entity perspective (is it a user’s trust or a system’s trustworthiness) often hinder a systematic inventory of the findings. Additionally, our discussion underscored the crucial role of the decision context in evaluating the relation of algorithmic affordances and interaction qualities. We propose dimensions of decision contexts (such as ‘reversibility of the decision’, or ‘time pressure’). They could aid in establishing a systematic three-way relationship between context attributes, attributes of user control mechanisms, and experiential goals, and as such they warrant further research. In sum, while the interaction qualities framework serves as a foundational structure for organizing research on evaluating the impact of algorithmic affordances, challenges related to interdependencies and context-specific influences remain. These challenges necessitate further investigation and subsequent refinement and expansion of the framework.
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Introduction: Sensor-feedback systems can be used to support people after stroke during independent practice of gait. The main aim of the study was to describe the user-centred approach to (re)design the user interface of the sensor feedback system “Stappy” for people after stroke, and share the deliverables and key observations from this process. Methods: The user-centred approach was structured around four phases (the discovery, definition, development and delivery phase) which were fundamental to the design process. Fifteen participants with cognitive and/or physical limitations participated (10 women, 2/3 older than 65). Prototypes were evaluated in multiple test rounds, consisting of 2–7 individual test sessions. Results: Seven deliverables were created: a list of design requirements, a personae, a user flow, a low-, medium- and high-fidelity prototype and the character “Stappy”. The first six deliverables were necessary tools to design the user interface, whereas the character was a solution resulting from this design process. Key observations related to “readability and contrast of visual information”, “understanding and remembering information”, “physical limitations” were confirmed by and “empathy” was additionally derived from the design process. Conclusions: The study offers a structured methodology resulting in deliverables and key observations, which can be used to (re)design meaningful user interfaces for people after stroke. Additionally, the study provides a technique that may promote “empathy” through the creation of the character Stappy. The description may provide guidance for health care professionals, researchers or designers in future user interface design projects in which existing products are redesigned for people after stroke.
As interactive systems become increasingly complex and entwined with the environment, technology is becoming more and more invisible. This means that much of the technology that people come across every day goes unnoticed and that the (potential) workings of ambient systems are not always clearly communicated to the user. The projects discussed in this paper are aimed at increasing public understanding of the existence, workings and potential of screens and ambient technology by visualizing its potential. To address issues and implications of visibility and system transparency, this paper presents work in progress as example cases for engaging people in ambient monitoring and public screening. This includes exploring desired scenarios for ambient monitoring with users as diverse as elderly people or tourists and an interactive tool for mapping public screens.
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
De afgelopen twee decennia is er veel meer aandacht ontstaan bij onderzoekers en beleidsmakers voor het begrip co-creatie. Bijna altijd wordt de rol van co-creatie als positief en essentieel gezien in een proces waarin maatschappelijke of publieke uitdagingen worden onderzocht en opgelost (zogenaamde sociale innovatie). Het meeste onderzoek naar deze twee begrippen is kwalitatief van aard en gebaseerd op ‘case studies’.In zijn promotieonderzoek kijkt Peter Broekema naar de rol van co-creatie binnen sociale innovatie in Europese samenwerkingsprojecten. In zijn eerste artikel heeft hij de begrippen co-creatie en sociale innovatie tussen 1995 en 2018 binnen de EU geanalyseerd en geconcludeerd dat beide begrippen steeds breder gebruikt worden en samen met het begrip impact zijn getransformeerd tot een beleidsparadigma.In het tweede artikel keek Peter Broekema hoe beide begrippen doorwerken in specifieke subsidieoproepen en hoe consortia deze begrippen toepassen en samenwerken. Hierbij bleek dat er weliswaar verschillende typen consortia bestaan, maar dat zij geen specifieke co-creatiestrategie hadden.In zijn laatste twee artikelen zal hij gedetailleerd kijken naar een aantal EU projecten en vaststellen hoe de samenwerking is verlopen en hoe tevreden de verschillende partners zijn met het resultaat. Peter Broekema maakt hiervoor gebruik van projecten waarin hij zelf participeert (ACCOMPLISSH, INEDIT en SHIINE).EU beleidsparadigma van sociale innovatie in combinatie met co-creatie en impact. Co-creatie vindt vaak binnen eigen type stakehodlers plaatsAbstractSocial innovation and co-creation are both relatively new concepts, that have been studied by scholars for roughly twenty years and are still heavily contested. The former emerged as a response to the more technologically focused concept of innovation and the latter originally solely described the collaboration of end-users in the development of new products, processes or services. Between 2010-2015, both concepts have been adapted and started to be used more widely by for example EU policymakers in their effort to tackle so called ‘grand societal challenges’. Within this narrative – which could be called co-creation for social innovation, it is almost a prerequisite that partners – especially citizens - from different backgrounds and sectors actively work together towards specific societal challenges. Relevance and aimHowever, the exact contribution of co-creation to social innovation projects is still unclear. Most research on co-creation has been focussing on the involvement of end-users in the development of products, processes and services. In general, scholars conclude that the involvement of end-users is effective and leads to a higher level of customer satisfaction. Only recently, research into the involvement of citizens in social innovation projects has started to emerge. However, the majority of research on co-creation for social innovation has been focusing on collaborations between two types of partners in the quadruple helix (citizens, governments, enterprises and universities). Because of this, it is still unclear what co-creation in social innovation projects with more different type of partners entails exactly. More importantly however, is that most research has been based on national case studies in which partners from different sectors collaborate in a familiar ‘national’ setting. Normally institutional and/or cultural contexts influence co-creation (for example the ‘poldermodel’in the Netherlands or the more confrontational model in France), so by looking at projects in a central EU and different local contexts it becomes clear how context effects co-creation for social innovation.Therefore this project will analyse a number of international co-creation projects that aim for social innovation with different types of stakeholders in a European and multi-stakeholder setting.With this research we will find out what people in different contexts believe is co-creation and social innovation, how this process works in different contexts and how co-creation contributes to social innovation.Research question and - sub questionsThe project will answer the following question: “What is the added value of co-creation in European funded collaboration projects that aim for social innovation?” To answer the main question, the research has been subdivided into four sub questions:1) What is the assumed added value of co-creation for social innovation?2) How is the added value of co-creation for social innovation being expressed ex ante and ex post in EU projects that aim specifically for social innovation by co-creation?3) How do partners and stakeholders envision the co-creation process beforehand and continuously shape this process in EU projects to maximise social innovation?4) How do partners and stakeholders regard the added value of co-creation for social innovation in EU projects that that aim for social innovation?Key conceptsThe research will focus on the interplay between the two main concepts a) co-creation and b) social innovation. For now, we are using the following working definitions:a) co-creation is a non-linear process that involves multiple actors and stakeholders in the ideation, implementation and assessment of products, services, policies and systems with the aim of improving their efficiency and effectiveness, and the satisfaction of those who take part in the process.b) social innovation is the invention, development and implementation of new ideas with the purpose to (immediately) relieve and (eventually) solve social problems, which are in the long run directed at the social inclusion of individuals, groups or communities.It is clear that both definitions are quite opaque, but also distinguish roughly the same phases (ideation/invention, development, implementation and assessment) and also distinguish different levels (products/services, policies and systems). Both concepts will be studied within the policy framework of the EU, in which a specific value to both concepts has been attributed, mostly because policymakers regard co-creation with universities and end-users almost as a prerequisite for social innovation. Based on preliminary research, EU policies seem to define social innovation in close reation with ‘societal impact’, which could defined as: “the long lasting effect of an activity on society, because it is aimed at solving social problems”, and therefore in this specific context social innovation seems to encompasses societal impact. For now, I will use this working definition of social innovation and will closely look at the entanglement with impact in the first outlined paper.MethodologyIn general, I will use a qualitative mixed method approach and grounded theory to answer the main research question (mRQ). In order to better understand the added value of co-creation for social innovation in an EU policy setting, the research will:SubRQ1) start with an analysis of academic literature on co-creation and social impact. This analysis will be followed by and confronted with an analysis of EU policy documents. SubRQ2) use a qualitative data analysis at nineteen EU funded projects to understand how co-creation is envisoned within social innovation projects by using the quintuple helix approach (knowledge flows between partners and stakeholders in an EU setting) and the proposed social innovation journey model. By contrasting the findings from the QDA phase of the project with other research on social innovation we will be able to find arachetypes of social innovation in relation with the (perceived) added value of co-creation within social innovation. SubRQ3) These archetypes will be used to understand the process of co-creation for social innovation by looking closely at behavioural interactions within two social innovation projects. This close examination will be carried out by carrying out interviews with key stakeholders and partners and participant observation.SubRQ4) The archetypes will also be used to understand the perceived added value by looking closely at behavioural interactions within two social innovation projects. This close examination will be carried out by carrying out interviews with key stakeholders and partners and participant observation.ImpactThe project will contribute to a better understanding of the relationship between co-creation and social innovation on different levels:a) Theoretical: the research will analyse the concepts of co-creation and social innovation in relation to each other by looking at the origins of the concepts, the adaptation in different fields and the uptake within EU policies;b) Methodological: a model will be developed to study and understand the non-lineair process of co-creation within social innovation, by focusing on social innovation pathways and social innovation strategies within a quintuple helix setting (i) academia, ii) enterprises and iii) governments that work together to improve iv) society in an v) EU setting);c) Empirical: the project will (for the first time) collect data on behavioural interactions and the satisfaction levels of these interactions between stakeholders and partners in an EU project.d) Societal: the results of the research could be used to optimize the support for social innovation projects and also for the development of specific funding calls.
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
