Advertising Strategy in the Presence of Reviews: An Empirical Analysis

Hollenbeck, Brett; Moorthy, Sridhar; Proserpio, Davide Μ.

doi:10.1287/mksc.2019.1180

Cited by 57 publications

(22 citation statements)

References 45 publications

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“…Similar techniques based on heuristic and checks have been used in different settings, i.e., matching advertising spending and TripAdvisor hotel reviews(Hollenbeck et al 2019).…”

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A first look at online reputation on Airbnb, where every stay is above average

2020

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Judging by the millions of reviews left by guests on the Airbnb platform, this trusted community marketplace for accommodations is fulfilling its mission of matching travelers with hosts having room to spare remarkably well. Based on our analysis of ratings we collected for millions of properties listed on Airbnb worldwide, we find that nearly 95% of Airbnb properties boast an average star-rating of either 4.5 or 5 stars (the maximum); virtually none have less than a 3.5 star-rating. We contrast this with the ratings of roughly 700,000 hotels, B&Bs, and vacation rentals worldwide that we collected from TripAdvisor. We find that hotel and B&B average ratings are much lower-3.8 and 4.1 stars, respectively-with much more variance across reviews. TripAdvisor vacation rental ratings are more similar to Airbnb ratings, but only about 85% of properties have an average rating of 4.5 or 5 stars. We then consider properties cross-listed on both platforms. For these properties, we find that even though the average ratings on Airbnb and TripAdvisor are more similar than hotels and B&Bs, proportionally more properties receive the highest ratings (4.5 stars and above) on Airbnb than on TripAdvisor. Moreover, there is only a weak correlation in the ratings of individual cross-listed properties across the two platforms. Finally, we show that these differences are consistent when considering data from two different time periods: 2015 and 2018.

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“…Similar techniques based on heuristic and checks have been used in different settings, i.e., matching advertising spending and TripAdvisor hotel reviews(Hollenbeck et al 2019).…”

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confidence: 99%

A first look at online reputation on Airbnb, where every stay is above average

2020

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“…Practices related to reputation management merit more attention as they have been relatively overlooked in the literature of OCR systems (but see Hollenbeck et al 2017, Proserpio andZervas 2017). The limited number of studies that focus on evaluated organizations look into how hotels and restaurants receive and react to OCRs, but rarely investigate organizations' proactive role on OCR systems.…”

Section: Discussionmentioning

confidence: 99%

“…This is more so the case because critical reviews are considered to be more credible, altruistic, and crucial as well as more closely examined and responded to by businesses (Levy et al 2012). Moreover, Hollenbeck et al (2017) show that small, independent hotels are especially vulnerable, while larger, chained-brand hotels have the chain's brand name to protect themselves against negative reviews.…”

Section: Ocrs and Reputation Managementmentioning

confidence: 99%

“…This holds in particular for smaller organizations, who lack the financial and human resources for marketing and managing reputations which large firms possess (Huang-Horowitz 2015). A study by Hollenbeck et al (2017) demonstrates that advertisement spending in the hotel industry has decreased because OCR systems have developed into an alternative way of managing reputation. Indeed, they find that the higher the rating of a hotel on Tripadvisor, the less it spends on advertising.…”

Section: Ocrs and Reputation Managementmentioning

confidence: 99%

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From reactivity to reputation management: online consumer review systems in the restaurant industry

Kim

Velthuis

2021

Journal of Cultural Economy

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The rapid growth of online consumer review (OCR) systems such as Tripadvisor has greatly reconfigured the operating environment for numerous businesses and organizations. As OCRs become a crucial source of information for consumer decision-making, we pose a twofold question: how do restaurants perceive OCRs and how do they respond to being evaluated on them? In answering this question, we distinguish between different types of organizational responses: staff management, goal setting, operational practices and reputation management. We base our study on in-depth interviews with mid-price restaurants in Amsterdam. Our findings show that reactivity related to staff management, goal setting and operational practices is limited and highly deliberate. Instead, reputational responses are more extensive. That is, restaurants respond to and even appropriate OCRs in order to promote themselves, to signal professionalism, and to limit the reputational damage of negative reviews. On the basis of these findings, we argue that more attention be paid to the agency of evaluated entities and that OCR systems be theorized more as a multisided platform with a hybrid functionality of both valuation and marketing.

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“… 9 Many previous studies have used average star ratings as a proxy for quality, including Dellarocas, Gao, and Narayan (2010), McDevitt (2014), Zervas, Proserpio, and Byers (2017), De los Santos and Wildenbeest (2017), Hollenbeck, Proserpio, and Moorthy (2017), and Luca and Luca (2017). Glaeser et al (2016) also cite the use of TripAdvisor reviews to measure firm quality as a promising prospect for future research. …”

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Online Reputation Mechanisms and the Decreasing Value of Chain Affiliation

Hollenbeck¹

2018

Journal of Marketing Research

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This article investigates the value of business format franchising and how it is changing in response to a large increase in consumer information provided by online reputation mechanisms. Theory has suggested that much of the value of chain affiliation to firms comes from the ability of chain partners to use the same name, imagery, logo, and marketing to create a common brand reputation and signal specific qualities in settings with asymmetric information between buyers and sellers. As more information becomes available, consumers should rely less on branding for quality signals, and firms’ ability to extend reputations across heterogeneous outlets should decrease. To examine this empirically, the author combines a large panel of hotel revenues with millions of online reviews from multiple platforms. Chain-affiliated hotels earn substantially higher revenues than equivalent independent hotels, but this premium has declined by over 50% from 2000 to 2015. This can be largely attributed to an increase in online reputation mechanisms, and this effect is largest for low-quality and small-market firms. Measures of the information content of online reviews show that as information has increased, independent hotel revenue has grown substantially more than chain hotel revenue. This result should be viewed as descriptive, with attempts to come to near causality including the use of machine learning to derive latent dimensions of firm quality from the text of online reviews. Finally, the correlation between firm revenue and chain-wide reputation is decreasing, whereas the correlation with individual hotel reputation is increasing.

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Advertising Strategy in the Presence of Reviews: An Empirical Analysis

Abstract: Workshop, for their helpful comments. Sridhar Moorthy thanks the Social Sciences and Humanities Research Council of Canada, and Brett Hollenbeck thanks the Morrison Center for Marketing and Data Analytics, for their generous support of this research.

Cited by 57 publications

References 45 publications

A first look at online reputation on Airbnb, where every stay is above average

A first look at online reputation on Airbnb, where every stay is above average

From reactivity to reputation management: online consumer review systems in the restaurant industry

Online Reputation Mechanisms and the Decreasing Value of Chain Affiliation

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