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- Habibipour, Abdolrasoul, 1979-
(författare)
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User engagement in Living Labs : Issues and concerns
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Osman, Ahmed M. Shahat
(författare)
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Smart Cities and Big Data Analytics : A Data-Driven Decision-Making Perspective
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The phenomenon of digitalization has led to the emergence of a new term—big data. Big data refers to the vast volumes of digital data characterized by its volume, velocity, variety, veracity, and value. The accumulation of enormous amounts of digital data has encouraged academics to develop appropriate technologies and algorithms to manage and analyze these data in order to leverage the embedded relationships within the data to support decision-making. This approach has revolutionized the organizational strategies of most business areas by digitally transforming business operations and decision-making processes.A “smart city” is a new concept that depends primarily on digitization and big data analysis. The aim of a smart city is to tackle the challenges of ever-increasing urbanization by utilizing atypical approaches. The utilization of big data analysis in smart cities has been investigated thoroughly in the literature from various aspects, such as those related to recommended technologies and the domains of applications. A smart city is a compound system with multi-domain attributes in which the citizens represent key participants in decision-making. However, harnessing big data analysis to support decision-making in the smart city context is rarely approached in academia. The infrequency of this type of research was sufficient to motivate this interesting research. Two research questions drive this thesis: RQ1: What are the challenges of utilizing big data analytics (BDA) to enable decision-making in smart cities? RQ2: What are the design principles of the BDA framework in the context of smart cities? To address these research questions, numerous research methods were applied, including a systematic literature review, design science research, use case, and case study. In addition, internationally acknowledged information systems databases were searched to collect quality scholarly articles and conference proceedings: ACM Digital Library, IEEE, SCOPUS, Springer Link, INSPEC, INSPEC, and Web of Science. A freely published dataset for experimental purposes on Yelp (www.yelp.com) was used for the use case experiment. Lastly, the case study was based on data from a national Egyptian digital transformation project called Nafeza.The research findings revealed the need to introduce an inventive framework for exploiting big data analysis in smart city applications. The main contribution of this research is the proposal of a novel framework for utilizing big data analytics in smart cities. The proposed framework, the Smart Cities Data Analytics Panel (SCDAP), is a domain-independent big data analysis framework. It compiles the relevant design principles mentioned in the literature, particularly those that are distinctive to smart cities. The design principles of SCDAP are founded on the literature review, use case, and case study methodologies and are the main contribution of this research.As the four papers that formed the foundation of this thesis combine theoretical and practical research, the contributions of this research can be of direct benefit to academic researchers in this field and practitioners of smart city projects.
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| 3. |
- Padyab, Ali
(författare)
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Exploring Impacts of Secondary Information Use on Individual Privacy
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Information collected from individuals via online social networks and Internet of things devices can be used by institutions and service providers for different business purposes to tailor and customize their services, which is defined as secondary use of information. Although the literature on secondary use is well developed, prior studies have largely focused on direct use of information such as those instances of information use that do not stem from data mining. Advances in data mining and information-processing techniques facilitate discovery of customers’ and users’ behaviors and needs. Research shows that individuals’ behavior can be inferred with high accuracy from their shared information, which may in turn jeopardize privacy. A recent scandal of Cambridge Analytica using about 87 million Facebook profiles to target those users with customized micro-targeted political ads has created public outrage and raised criticisms of secondary use. Given this background, the purpose of this thesis is to explore impacts of organizations’ and service providers’ secondary use of personal information in order to draw conclusions related to how individuals’ attitudes are formed and what role secondary use plays in managing privacy.This research investigates user awareness and attitudes towards potential secondary uses of information. To pursue this, a multi-method qualitative approach using a descriptive questionnaire with 1000 European citizens and a total of 10 focus groups with 43 participants was employed. A qualitative content analysis using both inductive and deductive approaches was conducted to analyze the results. The conceptual framework employed in this thesis was genres of disclosure.The research results suggest that user awareness of the potential for indirect personal information disclosure was relatively low. It was consequently found that participant attitudes toward privacy and disclosure shifted from affective to cognitive when they experienced firsthand the potential inferences that could be made from their own data. Generally, the participant users only considered their direct disclosure of information; through observing potential indirect inferences about their own shared contents and information, however, the participants became more aware of potential infringements on their privacy.The study contributes to information privacy and information systems literature by raising understanding of the impacts of secondary use, in particular its effects on individual privacy management. In addition, this thesis suggests that information privacy is affected differently by direct and indirect uses. Its contribution to information privacy research is to complement previous methodological approaches by suggesting that if users are made aware of indirect inferences that can be made from their content, negative affective responses decrease while cognitive reactions increase through the processing of information related to their disclosure genres. The reason is that indirect use of information inhibits the negotiation of information privacy boundaries and creating unresolved tensions within those boundaries. Cognitive awareness of inferences made to individual information significantly affects the privacy decision-making process. The implication is that there is a need for more dynamic privacy awareness mechanisms that can empower users by providing them with increased awareness of the indirect information they are sharing.
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| 4. |
- Rizk, Aya, 1988-
(författare)
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Data-driven Innovation : An exploration of outcomes and processes within federated networks
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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