Academic Journal

Segmenting Hotel Guests' Behavior: Insights for Developing Effective Water Conservation Programs.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Segmenting Hotel Guests' Behavior: Insights for Developing Effective Water Conservation Programs.
Συγγραφείς: Sancho‐Esper, Franco M., Sellers‐Rubio, Ricardo, Casado‐Diaz, Ana Belen, Campayo‐Sanchez, Fernando, Rodriguez‐Sanchez, Carla, Sanchez, Carolina
Πηγή: Business Strategy & the Environment (John Wiley & Sons, Inc); Mar2025, Vol. 34 Issue 3, p3544-3560, 17p
Θεματικοί όροι: Water conservation, Gaussian mixture models, Ecotourism, Hotel guests, Consumers
Περίληψη: This research underscores the need to modify tourists' behaviors to ensure sustainable water usage in high‐demand destinations. In‐room water conservation of 681 hotel guests was analyzed based on habit, effort, and enjoyment theory (HEET). The analysis reveals four distinct guest clusters based on a combination of cognitive, affective, automated behavior, and consumer identity variables. Water conservation behaviors and attitudes ranged from those of "Environmentally conscious tourists," who exhibited strong conservation efforts and low hedonic motivations, to those of "Pleasure‐seeking tourists," who prioritized pleasure over sustainability. The segmentation analysis used the Gaussian finite mixture model for clustering, providing insights into how to tailor social marketing interventions to promote water conservation behavior among guests. This novel segmentation approach fills a gap in the literature by considering non‐cognitive and habitual behaviors. It also provides a practical framework for designing more effective environmental strategies in tourism, particularly in water‐stressed areas. [ABSTRACT FROM AUTHOR]
Copyright of Business Strategy & the Environment (John Wiley & Sons, Inc) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
Περιγραφή
ISSN:09644733
DOI:10.1002/bse.4160