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- Ayele, Workneh Yilma, 1978-
(författare)
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A toolbox for idea generation and evaluation : Machine learning, data-driven, and contest-driven approaches to support idea generation
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Ideas are sources of creativity and innovation, and there is an increasing demand for innovation. For example, the start-up ecosystem has grown in both number and global spread. As a result, established companies need to monitor more start-ups than before and therefore need to find new ways to identify, screen, and collaborate with start-ups.The significance and abundance of data are also increasing due to the growing digital data generated from social media, sensors, scholarly literature, patents, different forms of documents published online, databases, product manuals, etc. Various data sources can be used to generate ideas, yet, in addition to bias, the size of the available digital data is a major challenge when it comes to manual analysis.Hence, human-machine interaction is essential for generating valuable ideas where machine learning and data-driven techniques generate patterns from data and serve human sense-making. However, the use of machine learning and data-driven approaches to generate ideas is a relatively new area. Moreover, it is also possible to stimulate innovation using contest-driven idea generation and evaluation. However, the measurement of contest-driven idea generation processes needs to be supported to manage the process better. In addition, post-contest challenges hinder the development of viable ideas. A mixed-method research methodology is applied to address these challenges.The results and contributions of this thesis can be viewed as a toolbox of idea-generation techniques, including a list of data-driven and machine learning techniques with corresponding data sources and models to support idea generation. In addition, the results include two models, one method and one framework, to better support data-driven and contest-driven idea generation. The beneficiaries of these artefacts are practitioners in data and knowledge engineering, data mining project managers, and innovation agents. Innovation agents include incubators, contest organizers, consultants, innovation accelerators, and industries.Future projects could develop a technical platform to explore and exploit unstructured data using machine learning, visual analytics, network analysis, and bibliometric for supporting idea generation and evaluation activities. It is possible to adapt and integrate methods included in the proposed toolbox in developer platforms to serve as part of an embedded idea management system. Future research could also adapt the framework to barriers that constrain the development required to elicit post-contest digital service. In addition, since the proposed artefacts consist of process models augmented with AI techniques, human-centred AI is a promising area of research that can contribute to the artefacts' further development and promote creativity.
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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. |
- 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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