TY  - GEN
AB  - From a strategy perspective, the growth of social media accelerates the need for tourism organisations to constantly re-appraise their competitive strategies. This study contributes theoretically to the tourism performance literature by validating a new approach to examining the determinants of hotel performance. Drawing from and extending prior hotel determinants studies, this study uses artificial neural network model with ten input variables to investigate the relationships among user generated online reviews, hotel characteristics, and Revpar. The sample includes 235 Swiss hotels for the period 2008-2010, with 59,688 positive reviews from 69 online sources. The empirical findings reveal four hidden nodes that have a significant impact on RevPar. Three of these have negative impacts: room quality, positive regional review, hotel regional reputation, and regional room star rating has a positive impact. Further, the findings imply that there may be boundaries to reputational benefits for Swiss hotels.
AD  - Kent Business School, University of Kent, Canterbury, Kent, CT2 7PE, UK
AD  - Kent Business School, University of Kent, Medway Campus, Kent, ME4 4AG, UK
AD  - Faculdade de Economia, Universidade de Coimbra, 3004-512, Coimbra, Portugal
AD  - University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
AU  - Phillips, Paul
AU  - Zigan, Krystin
AU  - Santos Silva, Maria Manuela
AU  - Schegg, Roland
DA  - 2015-10
DO  - 10.1016/j.tourman.2015.01.028
DO  - DOI
ID  - 1048
JF  - Tourism Management
KW  - Economie/gestion
KW  - artificial neural network
KW  - determinants of performance
KW  - hotel
KW  - online reviews
KW  - Switzerland
KW  - tourism
KW  - user-generated content
L1  - https://arodes.hes-so.ch/record/1048/files/schegg_interactiveeffects_2015.pdf
L2  - https://arodes.hes-so.ch/record/1048/files/schegg_interactiveeffects_2015.pdf
L4  - https://arodes.hes-so.ch/record/1048/files/schegg_interactiveeffects_2015.pdf
LA  - eng
LK  - https://arodes.hes-so.ch/record/1048/files/schegg_interactiveeffects_2015.pdf
N2  - From a strategy perspective, the growth of social media accelerates the need for tourism organisations to constantly re-appraise their competitive strategies. This study contributes theoretically to the tourism performance literature by validating a new approach to examining the determinants of hotel performance. Drawing from and extending prior hotel determinants studies, this study uses artificial neural network model with ten input variables to investigate the relationships among user generated online reviews, hotel characteristics, and Revpar. The sample includes 235 Swiss hotels for the period 2008-2010, with 59,688 positive reviews from 69 online sources. The empirical findings reveal four hidden nodes that have a significant impact on RevPar. Three of these have negative impacts: room quality, positive regional review, hotel regional reputation, and regional room star rating has a positive impact. Further, the findings imply that there may be boundaries to reputational benefits for Swiss hotels.
PY  - 2015-10
SN  - 0261-5177
T1  - The interactive effects of online reviews on the determinants of Swiss hotel performance :a neural network analysis
TI  - The interactive effects of online reviews on the determinants of Swiss hotel performance :a neural network analysis
UR  - https://arodes.hes-so.ch/record/1048/files/schegg_interactiveeffects_2015.pdf
VL  - October 2015, no. 50, pp. 130-141
Y1  - 2015-10
ER  -