Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/03C670F7-021D-419C-8E89-14BCBB64C7CA | ||
| Year | 2022 | ||
| Type of Item | Doctoral Dissertation | ||
| License |
|
||
| Bibliographic Citation | Ioanna Atsalaki, "Disruptive technology forecasting in tourism by fuzzy logic", Doctoral Dissertation, School of Production Engineering and Management, Technical University of Crete, Chania, Greece, 2022 https://doi.org/10.26233/heallink.tuc.93196 | ||
| Appears in Collections |
One of the main reasons businesses usually fail when (rapid or long-term) technological innovations alter markets is their inability to adapt fast and understand the effect of such innovations to existing and/or traditionally working business models. As disruptive technological innovations become the new norm, attributes of chosen disruptive technologies must be part of the enhanced (new) business model with the aim to benefit customers. This requires a radical departure from 'conventional thinking' to first identify major key factors that drive such technologies and to incorporate the effect of disruptive technologies into the new business model in almost real-time. The result is improved market status and competitiveness of companies and increased returns due to model flexibility and adjustability, as well as predictability. This thesis uses and implements a rule-based Fuzzy Inference System (FIS), an excellent tool to capture even random market phenomena. This is because of the FIS nonlinear universal approximation properties, ability to express human expert knowledge and experience by using fuzzy inference rules represented in “if-then” statements, and ability to handle, incorporate and account for accrued experimental data and a-priori knowledge before returning a solution. The proposed forecasting model is capable to model and predict the disruptiveness for a new technology itself. Results are promising; thus, this method provides a very promising support tool to companies allowing for them to face and overcome disruptive technologies threats (defending against a disruptive challenge) and to offer opportunities to their customers. Real data from Airbnb are applied to the model and the predictability success Is really satisfied.
Copyright © DIAS 2013
