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EnglishOrganisations currently compete within contexts that require collaboration with other players (suppliers, cus- tomers, competitors), which is central to achieving sustainable competitive advantages. This new perspective, which is centred on relationships, has changed the way companies design and implement their competitive strategies, while also challenging traditional tools of strategy analysis. Artificial intelligence, particularly arti- ficial neural networks, can help address these challenges. This paper proposes an innovative application of the Auto-Contractive Map method, which is a deep non-supervised Artificial Neural Network algorithm that has already been widely applied to bio-medical, security, insurance, and financial studies, but has not yet been used in the domains of tourism and strategy. Our study demonstrates the effectiveness of this method, compared to other methods that have been applied to tourism studies. This method successfully addresses issues in the complex and dynamic competitive settings of tourism destinations, which are characterised by the inclusion of many stakeholders. Specifically, the Auto-Contractive Map method allows both scholars and practitioners to significantly advance formulations of collaborative strategies in a destination, at three levels: (i) defining priority areas of action, (ii) identifying relevant stakeholders and governance levels that have control over these areas, and (iii) profiling key features of the destination’s positioning, compared with its competitors.
Artificial intelligence to design collaborative strategy: An application to urban destinations, 2021-01.
| Autori: | Manuela De Carlo; Guido Ferilli; Francesca d'Angella; Massimo Buscema | |
| Data di pubblicazione: | gen-2021 | |
| Titolo: | Artificial intelligence to design collaborative strategy: An application to urban destinations | |
| Rivista: | ||
| Editore: | Elsevier | |
| Volume: | 129 | |
| Pagina iniziale: | 936 | |
| Pagina finale: | 949 | |
| Numero di pagine: | 12 | |
| Revisione (peer review): | esperti anonimi | |
| IF: | con ISI Impact Factor | |
| Rilevanza: | internazionale | |
| Lingua: | English | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.jbusres.2020.09.013 | |
| Settore Scientifico Disciplinare: | Settore SECS-P/07 - Economia Aziendale | |
| Parole Chiave: | Artificial neural networks (ANN) Auto-contractive map (Auto-CM) Collaboration Strategy design Tourism destination | |
| Abstract: | Organisations currently compete within contexts that require collaboration with other players (suppliers, cus- tomers, competitors), which is central to achieving sustainable competitive advantages. This new perspective, which is centred on relationships, has changed the way companies design and implement their competitive strategies, while also challenging traditional tools of strategy analysis. Artificial intelligence, particularly arti- ficial neural networks, can help address these challenges. This paper proposes an innovative application of the Auto-Contractive Map method, which is a deep non-supervised Artificial Neural Network algorithm that has already been widely applied to bio-medical, security, insurance, and financial studies, but has not yet been used in the domains of tourism and strategy. Our study demonstrates the effectiveness of this method, compared to other methods that have been applied to tourism studies. This method successfully addresses issues in the complex and dynamic competitive settings of tourism destinations, which are characterised by the inclusion of many stakeholders. Specifically, the Auto-Contractive Map method allows both scholars and practitioners to significantly advance formulations of collaborative strategies in a destination, at three levels: (i) defining priority areas of action, (ii) identifying relevant stakeholders and governance levels that have control over these areas, and (iii) profiling key features of the destination’s positioning, compared with its competitors. | |
| Numero degli autori: | 4 | |
| Supporto: | Online | |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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