How Incumbents Beat Disruptors? Evidence from Hotels’ Responses to Home-sharing Rivals

Wei Chen (), Karen Xie (), Jianwei Liu () and Yong Liu ()
Additional contact information
Wei Chen: Eller College of Management, University of Arizona, Tucson, Arizona, 85721
Karen Xie: Daniels College of Business, University of Denver, Denver, Colorado, 80208
Jianwei Liu: School of Management, Harbin Institute of Technology, Harbin, China, 150001
Yong Liu: Eller College of Management, University of Arizona, Tucson, Arizona, 85721

No 19-11, Working Papers from NET Institute

Abstract: Growing research attention is paid to the disruption of sharing economy services (Airbnb, Uber, Lending Club, etc.) and how they cut into incumbent firms’ profit. Yet, the literature is silent on how incumbents respond to the rivalry and what are the performance outcomes if taking a defensive stance. In this paper, we investigate incumbent hotels’ responses to home sharing and how different reactions among hotels lead to distinct outcomes in customer satisfaction. Integrating casual inference and machine learning, we analyze large-scale, multidimensional data on hotels and home-sharing services in Beijing from March, 2015 to December, 2017 and three findings are gleaned. First, we find heterogeneous reactions of hotels, with their management responses to online guest reviews (reviews, hereafter) surging at higher-priced hotels while plunging at lower-priced ones compared with hotels that do not experience home sharing’s entry. The distinct response strategy (active vs passive) is likely due to different extent of decline in sales at these two types of hotels after home sharing’s entry. Second, hotels that are responsive to reviews experience a significant rise in customer satisfaction while the less responsive hotels do not. We show that this difference can be attributed to distinct response strategies of hotels and not their price segment (higher-priced or lower-priced). Third, utilizing state-of-the-art deep learning algorithms combined with topic modeling, we identify the theme-specific content features (topics and their sentiments) in reviews on both hotels and home sharing. Hotels that are responsive to reviews improve significantly on sentiments of two out of seven topics (i.e., cleanliness and service), which explains their performance gains when facing the disruption. And these two topics are the exact areas where home sharing outperforms hotels based on the review comparison. These suggest that responding to reviews allows hotel managers to not only bridge the gap between their property and home-sharing rivals but also differentiate from hotels less responsive - an interesting segmentation in the market when disruptors enter the game. This study makes the first attempt to investigate incumbent firms acting to sharing economy disruptors. Implications are made on how different types of hotels can and should react for improved performance.

Keywords: Incumbent business; sharing economy; management response; difference-in-differences; deep learning; convolutional neutral network; topic modeling (search for similar items in EconPapers)
JEL-codes: L8 M31 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2019-09
New Economics Papers: this item is included in nep-big and nep-pay
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