1. |
- Sanchez Preciado, Deycy Janeth, 1975-
(author)
-
Developing Technology Transfer Processes in rural contexts : The case of Cauca in Colombia
- 2018
-
Doctoral thesis (other academic/artistic)abstract
- This doctoral thesis addresses the technology transfer process in rural developing economies. Technology transfer refers to the movement of physical artifacts and knowledge from a transferor (e.g. a university) to a recipient (e.g. a cooperative or a producer). Many rural developing economies depend on rural enterprises engaged in small-scale production. These enterprises usually have limited market reach, inadequate financial margins, and low value added products. In this context, technology transfer commonly features large information and knowledge asymmetry between the transferors and recipients, the recipients’ dependence on government financial support, and the recipients’ underdeveloped business skills. Despite the importance of technology transfer for production improvements by enterprises in rural economies, little is known about how the two sides interact when technologies to fit the small-scale production context are transferred. To address this knowledge gap, this thesis focuses on how rural enterprises adapt and use technologies that are collaboratively developed with universities with the support of governments and non-governmental organisations (NGOs). Empirically, the thesis analyses technology transfer aimed at improving silk, fish, and coffee production in Cauca, a region in Colombia. The thesis uses Situated Learning Theory, action research, and case study methodology. The thesis shows that i) intermediaries broker and facilitate (organise) the interaction between universities and cooperatives and rural enterprises; ii) there are seven features that enable technology transfer in rural developing economies and iii) ‘systems’ of technology transfer evolve in rural developing economies through analysis of problem formulation and problem solving as the mechanisms.
|
|
2. |
- Gama, Fábio, Ass. Professor, 1980-, et al.
(author)
-
Ideation and Machine Learning : Problem Finding in Disruptive Innovation
- 2022
-
Conference paper (peer-reviewed)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.
|
|