情感(语言学)
营销
业务
独创性
感恩
符号(数学)
价值(数学)
补偿(心理学)
心理学
社会心理学
计算机科学
数学分析
数学
沟通
机器学习
创造力
作者
Ana Isabel Lopes,Edward C. Malthouse,Nathalie Dens,Patrick De Pelsmacker
出处
期刊:Journal of Service Management
[Emerald Publishing Limited]
日期:2024-02-08
卷期号:35 (6): 22-41
标识
DOI:10.1108/josm-05-2023-0219
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI