In recent years, the digital transformation has completely changed the demand/offering interaction in the travel industry (Шейх, A., & Суюнчалиева, 2019), as well as largely affecting the customer journey. In this direction, “big social data” and user-generated content have become key sources of well-timed and rich knowledge (Bello-Orgaz et al., 2016; Nguyen & Jung, 2017), supporting data driven decision approaches addressed the managing of complex relationships. Properly, “big social data” management underpins a value cocreation approach on tourism experience design, providing predictive modeling analysis to contribute to the theory on co-design in the tourism market. Based on this theoretical framework, the paper suggests how to apply “big social data” in the tourist experience codesign (Wang & Alasuutari, 2017; Moscardo, 2017), providing an increased value for the visitors and a better decision making approach on the part of managing institutions, site managers or enterprises (Zhang, 2018; Ardito et al., 2019). Hence, the study deals with the support of big social data to the theory on tourism co-design experience. In this respect, the field analysis concentrated specifically on user-generated content regarding the Pompeii Archaeological Site (P.A.S.), to trace valuable insights for the tourist experience.

Digital transformation and tourist experience co-design: big social data for planning cultural tour

Giuseppe Festa;
2020-01-01

Abstract

In recent years, the digital transformation has completely changed the demand/offering interaction in the travel industry (Шейх, A., & Суюнчалиева, 2019), as well as largely affecting the customer journey. In this direction, “big social data” and user-generated content have become key sources of well-timed and rich knowledge (Bello-Orgaz et al., 2016; Nguyen & Jung, 2017), supporting data driven decision approaches addressed the managing of complex relationships. Properly, “big social data” management underpins a value cocreation approach on tourism experience design, providing predictive modeling analysis to contribute to the theory on co-design in the tourism market. Based on this theoretical framework, the paper suggests how to apply “big social data” in the tourist experience codesign (Wang & Alasuutari, 2017; Moscardo, 2017), providing an increased value for the visitors and a better decision making approach on the part of managing institutions, site managers or enterprises (Zhang, 2018; Ardito et al., 2019). Hence, the study deals with the support of big social data to the theory on tourism co-design experience. In this respect, the field analysis concentrated specifically on user-generated content regarding the Pompeii Archaeological Site (P.A.S.), to trace valuable insights for the tourist experience.
2020
9788409203109
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11575/137086
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact