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 -