TY - JOUR
T1 - Is webcare good for business? A study of the effect of managerial response strategies to online reviews on hotel bookings
AU - Lopes, Ana Isabel
AU - Malthouse, Edward C.
AU - Dens, Nathalie
AU - De Pelsmacker, Patrick
N1 - Publisher Copyright:
© 2024, Ana Isabel Lopes, Edward C. Malthouse, Nathalie Dens and Patrick De Pelsmacker.
PY - 2024
Y1 - 2024
N2 - Purpose: Engaging in webcare, i.e. responding to online reviews, can positively affect consumer attitudes, intentions and behavior. Research is often scarce or inconsistent regarding the effects of specific webcare strategies on business performance. Therefore, this study tests whether and how several webcare strategies affect hotel bookings. Design/methodology/approach: We apply machine learning classifiers to secondary data (webcare messages) to classify webcare variables to be included in a regression analysis looking at the effect of these strategies on hotel bookings while controlling for possible confounds such as seasonality and hotel-specific effects. Findings: The strategies that have a positive effect on bookings are directing reviewers to a private channel, being defensive, offering compensation and having managers sign the response. Webcare strategies to be avoided are apologies, merely asking for more information, inviting customers for another visit and adding informal non-verbal cues. Strategies that do not appear to affect future bookings are expressing gratitude, personalizing and having staff members (rather than managers) sign webcare. Practical implications: These findings help managers optimize their webcare strategy for better business results and develop automated webcare. Originality/value: We look into several commonly used and studied webcare strategies that affect actual business outcomes, being that most previous research studies are experimental or look into a very limited set of strategies.
AB - Purpose: Engaging in webcare, i.e. responding to online reviews, can positively affect consumer attitudes, intentions and behavior. Research is often scarce or inconsistent regarding the effects of specific webcare strategies on business performance. Therefore, this study tests whether and how several webcare strategies affect hotel bookings. Design/methodology/approach: We apply machine learning classifiers to secondary data (webcare messages) to classify webcare variables to be included in a regression analysis looking at the effect of these strategies on hotel bookings while controlling for possible confounds such as seasonality and hotel-specific effects. Findings: The strategies that have a positive effect on bookings are directing reviewers to a private channel, being defensive, offering compensation and having managers sign the response. Webcare strategies to be avoided are apologies, merely asking for more information, inviting customers for another visit and adding informal non-verbal cues. Strategies that do not appear to affect future bookings are expressing gratitude, personalizing and having staff members (rather than managers) sign webcare. Practical implications: These findings help managers optimize their webcare strategy for better business results and develop automated webcare. Originality/value: We look into several commonly used and studied webcare strategies that affect actual business outcomes, being that most previous research studies are experimental or look into a very limited set of strategies.
KW - eWOM
KW - Hotel bookings
KW - Managerial responses
KW - Online reviews
KW - Webcare
UR - http://www.scopus.com/inward/record.url?scp=85184436343&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184436343&partnerID=8YFLogxK
U2 - 10.1108/JOSM-05-2023-0219
DO - 10.1108/JOSM-05-2023-0219
M3 - Article
AN - SCOPUS:85184436343
SN - 1757-5818
VL - 35
SP - 22
EP - 41
JO - Journal of Service Management
JF - Journal of Service Management
IS - 6
ER -