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- Gama, Fábio, Ass. Professor, 1980-, et al.
(författare)
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Ideation and Machine Learning : Problem Finding in Disruptive Innovation
- 2024
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Konferensbidrag (refereegranskat)abstract
- Disruptive innovation is widely recognised as a bold enterprise. Risks and uncertainties drive incumbent firms to seek alternative solutions to find disruptive ideas. Machine learning emerges as a powerful tool to reduce uncertainties while processing vast amounts and types of information. However, incumbents encounter immense difficulty in codifying tacit knowledge into effective algorithms and often end up with incremental or tactical outcomes despite bold aspirations. Using the literature on problem finding, we explore the development process of machine learning for ideation. Our action research conducted on a healthcare firm provides theoretical and managerial contributions. First, this study suggests that ideation for disruptive innovation benefits from machine learning by facilitating a heuristic search in which a group of actors evaluate plausible hypotheses rather than seek logically accurate conclusions. Previous studies on ideation stress directional search. Second, we propose an ideation process centred on problem formulation to identify disruptive innovation based on its inherent characteristics (e.g., radical functionality and discontinuous technical standard). Third, we discuss the challenges of adopting algorithm-based systems in the ideation — a process well known for being fuzzy.
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| 4. |
- Irgang dos Santos, Luís Fernando, Doctoral Candidate in Innovation Sciences, 1989-, et al.
(författare)
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Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections : a Systematic Review
- 2023
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Ingår i: Journal of Healthcare Informatics Research. - Cham : Springer. - 2509-4971 .- 2509-498X. ; 7, s. 1-41
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Forskningsöversikt (övrigt vetenskapligt/konstnärligt)abstract
- Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions. © 2023, The Author(s).
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| 5. |
- Sjöberg, Jeanette, 1976-, et al.
(författare)
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Promoting Life-Long Learning Through Flexible Educational Format for Professionals Within AI, Design and Innovation Management
- 2023
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Ingår i: Design, Learning, and Innovation. - Cham : Springer. - 9783031313912 - 9783031313929 ; , s. 38-47
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Konferensbidrag (refereegranskat)abstract
- In recent years, the concept of lifelong learning has been emphasized in relation to higher education, with a bearing idea of the possibility for the individual for a continuous, self-motivated pursuit of gaining knowledge for both personal and professional reasons, provided by higher education institutions (HEI:s). But how can this actually be done in practice? In this paper we present an ongoing project called MAISTR, which is a collaboration between Swedish HEI:s and industry with the aim of providing a number of flexible courses within the subjects of Artificial intelligence (AI), Design, and Innovation management, for professionals. Our aim is to describe how the project is setup to create new learning opportunities, including the development process and co-creation with industry, the core structure and the pedagogical design. Furthermore, we would like to discuss both challenges and opportunities that come with this kind of project, as well as reflecting on early stage outcomes. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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| 7. |
- Björkdahl, Joakim, 1975, et al.
(författare)
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Explaining business model innovation processes: A problem formulation and problem solving perspective
- 2022
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Ingår i: Industrial Marketing Management. - New York : Elsevier BV. - 0019-8501 .- 1873-2062. ; 105, s. 223-239
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Tidskriftsartikel (refereegranskat)abstract
- This study explains the business model innovation processes in industrial firms. Drawing on three case studies of leading business-to-business firms shifting from product-based to service-based business models, it introduces problems as a theoretical concept to explain business model innovation processes. We show how formulating and solving problems guide the search for a viable business model and why some problem formulation and solving activities lead firms to shift between backward-looking and forward-looking searches. The decision to shift to a forward-looking search is triggered by the perception of failure to continue with an established way of working, while the shift to a backward-looking search is based on the perception of high alternative costs. We contribute to the business model innovation and servitization literature by theorizing the process of business model innovation and providing implications for managers.
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| 8. |
- Gharaie, Amirhossein, et al.
(författare)
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Challenges of Data-driven Service Development in Manufacturing Industries – a Review
- 2022
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Ingår i: Sustainable Business Model Challenges : Economic Recovery and Digital Transformation. - Roma : LUMSA University. - 9791221011883 ; , s. 419-428
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Konferensbidrag (refereegranskat)abstract
- This literature review aims to identify the existing challenges of data-driven service development in manufacturing industries, and a general approach to manage the challenges. The three primary categories are technological, ecosystem- and business model-related. Those are identified and categorized through the lens of data-driven service taxonomy framework. Digital twin was identified as one overarching approach with the potential to solve some of the identified challenges. Future research can focus on exploring the different level of importance of the existing challenges.
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- Holmén, Magnus, 1967-, et al.
(författare)
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Conclusions
- 2021. - 1
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Ingår i: Technological Change and Industrial Transformation. - Abingdon : Routledge. - 9780429423550 - 9781138390034 ; , s. 232-244
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Bokkapitel (refereegranskat)abstract
- Chapter 12, “Conclusions”, conceptualizes industrial transformation as consisting of qualitative changes in the structure of inter-firm activities and relations. The authors stress that industrial transformation consists of uncertain endogenous processes of qualitative state changes, limiting the role of increasing returns. However, uncertainty can be managed or reduced by increasing control. By distinguishing the book’s chapters into research, design and development (RD&D), production and distribution, the authors show how digitalization increases control and becomes a powerful driver of industrial transformation. The authors compare the seminal work of Allyn Young (1928) to explain how demand changes, digitalization and industrial transformation coevolve cumulatively.
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