User engagement and the participatory design approach are well-established in information systems research for many years, and several studies have investigated the challenges of user engagement in the innovation processes. The majority of these studies have discussed participatory design activities – specifically user engagement –in an organizational context. From this perspective, user engagement within an organization employs (relatively) mature technology, but the users are exclusively employees with certain levels of expertise and commitment. Therefore, the full spectrum of users’ perspectives is widely neglected. Accordingly, the purpose of this thesis is to investigate and discuss how the process of voluntary user engagement in real-life contexts (in this study, living labs) is shaped when the innovations are not yet mature. The objective is to propose a framework that addresses issues of sustainable user engagement and commitment by including the users’ perspectives.  To this end, the following research questions are further explored:

RQ1: What aspects of innovation have an impact on the process of user engagement?

RQ2: What aspects of the engagement context have an impact on the process of user engagement?

RQ3: What aspects related to the users themselves have an impact on the process of user engagement?

In order to meet the purpose of this study, the living lab was used as the context of participatory design activities in three different studied cases. The first living lab case was called “USEMP” and concerned testing and evaluation of a digital innovation with voluntary users. The second living lab case, “UNaLab”, incorporated ten European cities, aiming to develop nature-based solutions to problems in these cities following a living lab approach. The third living lab case, “U4IoT”, was designed to facilitate the engagement of five European Large-Scale Pilots with (current and future) users throughout the use and adoption of the Internet of things (IoT).

This thesis is based on a qualitative interpretive case study approach. Beyond conducting two rounds of literature review, this research used multiple data collection methods within the context of the studied living lab cases. These included two rounds of semi-structured interviews with the living lab and innovation experts (24 interviews), four international workshops with 62 participants, and two rounds of open-ended questionnaires with 41 participants. A high-level analysis of the results from the three cases was also conducted through qualitative data coding, in which the results of all appended papers were reinterpreted, reorganized, synthesized and presented.

This study contributes to the research on participatory design in the information systems research field by focusing on voluntary user engagement in living labs when the innovation is not yet mature. In so doing, this dissertation provides the Plan–Act–Reflect user engagement framework, which investigates the issues of user engagement and incorporates the perspectives of both users and innovation and living lab experts. The analysis of the results illustrated that user engagement in the living lab context is not a linear process with pre-determined entry and exit points. Instead, it is an iterative process characterized by complex interplay between different engagement phases, including cognitive engagement (plan), realize engagement (act), and engagement commitment (reflect). The results of this study could help participatory design practitioners, living lab organizers, project planners and decision makers on a larger scale – such as that of urban living labs – to understand not only how to engage users in the innovation processes but also how to keep them engaged. This may be accomplished through every part of the process, from user preparation to implementation to testing and adoption of innovations.

The emergence and pervasiveness of digital technologies are changing many aspects of our lives, including what and how we innovate. Industries and societies are competing to embrace this wave of digitalization by developing the right infrastructures and ecosystems for innovation. Similarly, innovation managers and entrepreneurs are using digital technologies to develop novel products, services, processes, business models, etc. One of the major consequences of digitalization is the massive amounts of machine-readable data generated through digital interactions. But this is not only a consequence, it is also a driver for other innovations to emerge. Employing analytical techniques on data to extract useful patterns and insights enables different aspects of innovation. During the last decade, scholars within digital innovation have started to explore this relationship between analytics and innovation, a phenomenon referred to as data-driven innovation (DDI). Most theories to date view analytics as variable that affects innovation in performative terms and treats it as a black-box. However, if the innovation managers and entrepreneurs are to manage and navigate DDI, and for the investors, funders and policymakers to take informed decisions, they need a better understanding of how DDI outcomes (i.e. market offerings such as products and services) are shaped and how they emerge from a process perspective.

This dissertation explores this research gap by addressing two research questions: “What characterizes data-driven innovation outcomes?” and “How do data-driven innovations emerge in federated networks?” A federated network is a type of – increasingly common – contemporary innovation structure that is also enabled by digital technology. The dissertation is based on a compilation of five articles addressing these questions. The overall research approach follows a multiple case study design and the empirical investigation takes place in two case sites corresponding to two EU-funded projects.

As a result, a classification taxonomy is developed for data-driven digital services. This taxonomy contributes to the conceptualization of DDI outcomes grounded on static and dynamic characteristics. In addition, a DDI process framework is proposed that highlights the importance of exploration, the temporal relationship between data acquisition and innovation development, and the various factors that influence the process along with examples of their contextual manifestations. Finally, social and cognitive interactions within federated networks of DDI are explored to reveal that the innovation teams rely on data-driven representations to facilitate various stakeholders’ engagement and contribution throughout the process. These representations eventually stabilize into boundary objects that retain the factual integrity of the data and analytical models but are also flexible for contextual interpretation and use. These findings contribute to the current discourse within digital innovation by introducing the lens of data analytics to conceptualize a specific type of digital artifacts, and well as providing a rich descriptive account of an extended digital innovation process. They also contribute to the discourse on data-driven innovation by providing an empirical account of DDI from a process viewpoint.