# Poster child and guinea pig – insights from a structured literature review on Airbnb

David Dann (Karlsruhe Institute of Technology, Karlsruhe, Germany)
Timm Teubner (TU Berlin, Berlin, Germany)
Christof Weinhardt (Karlsruhe Institute of Technology, Karlsruhe, Germany)

ISSN: 0959-6119

Publication date: 14 January 2019

## Abstract

### Purpose

A growing body of research from various domains has investigated Airbnb, a two-sided market platform for peer-based accommodation sharing. The authors suggest that it is due time to take a step back and assess the current state of affairs. This paper aims to conflate and synthesize research on Airbnb.

### Design/methodology/approach

To facilitate research on Airbnb and its underlying principles in electronic commerce, the authors present a structured literature review on Airbnb.

### Findings

The findings are based on 118 articles from the fields of tourism, information and management, law and economics between 2013 and 2018. Based on this broad basis, the authors find that: research on Airbnb is highly diverse in terms of domains, methods and scope; motives for using Airbnb are manifold (e.g. financial, social and environmental); trust and reputation are considered crucial by almost all scholars; the platform’s variety is reflected in prices; and the majority of work is based on surveys and empirical data while experiments are scarce.

### Practical implications

Based on the present assessment of studied topics, domains, methods and combinations thereof, the authors suggest that research should move toward building atop of a common ground of data structures and vocabulary, and that attention should focus on the identified gaps and hitherto scarcely used combinations. The set of under-represented areas includes cross-cultural investigations, field experiments and audit studies, the consideration of dynamic processes (e.g. based on panel data), Airbnb’s “experiences” and automated pricing algorithms and the rating distribution’s skewness.

### Originality/value

This study provides a comprehensive overview of work on the accommodation sharing platform Airbnb, to the best of the auhtors’ knowledge, representing the first systematic literature review. The authors hope that researchers and practitioners alike will find this review useful as a reference for future research on Airbnb and as a guide for the development of innovative applications based on the platform’s peculiarities and paradigms in electronic commerce practice. From a practical perspective, the general tenor suggests that hotel and tourism operators may benefit from: focusing on their core advantages over Airbnb and differentiating features and aligning their marketing communication with their users’ aspirations.

## Keywords

#### Citation

Dann, D., Teubner, T. and Weinhardt, C. (2019), "Poster child and guinea pig – insights from a structured literature review on Airbnb", International Journal of Contemporary Hospitality Management, Vol. 31 No. 1, pp. 427-473. https://doi.org/10.1108/IJCHM-03-2018-0186

### Publisher

:

Emerald Publishing Limited

## 1. Introduction

#### 3.1.3 Consumer types.

In view of the broad spectrum of products and applications, it is not surprising that Airbnb users differ in many ways. Guttentag et al. (2018), for instance, consider different types of guests based on the motives interaction, home benefits, novelty, sharing economy ethos and local authenticity. They find that people are attracted to Airbnb’s practical (e.g. cost savings, convenient location, household amenities) rather than its experiential attributes (e.g. excitement, novelty, uniqueness). The authors cluster respondents into the five motivational segments money savers, home seekers, collaborative consumers, pragmatic novelty seekers, and interactive novelty seekers, where home seekers represent the largest group. Users from this cluster are usually older, more experienced, tend to book longer trips, entire homes, and travel as larger parties and significantly more likely with children. Pragmatic novelty seekers, in contrast, are more likely to rent entire homes while collaborative consumers tend to rent co-used accommodations. Further, they tend to have Airbnb experience both as guests and hosts. From a platform’s perspective, such user typologies enable a better understanding of its users and afford apposite targeting and marketing approaches.

In addition, Airbnb users differ from non-users in general. With regard to personality traits, they score higher on conscientiousness, extroversion, agreeableness, and openness (Pezenka et al., 2017, p. 776). Other studies report differences with regard to consumer-object relationships (Varma et al., 2016; Festila and Dueholm Müller, 2017; Poon and Huang, 2017). While some consumers prefer a personal experience, expressed through a reflection of the host’s personality in the accommodation, others favor a cleaner, more hotel-like experience. In terms of expectations regarding personalization and further aspects such as serendipity, localness, and communities, Airbnb surpasses traditional hotel offers (Mody et al., 2017). Furthermore, compared to users who book traditional hotels, Airbnb users are found to put less importance on factors such as security or housekeeping (Festila and Dueholm Müller, 2017).

#### 3.1.4 Host types.

Also from the hosts’ perspective, there are found different clusters (e.g. global citizen, local expert, personable, established, creative) which differ in self-presentation, communication behavior, pricing, and hosting frequency (Tussyadiah, 2016b). While all archetypes are distinguished by their textual self-description, important factors such as acceptance rates, response rates, and guest evaluations are comparable across clusters. However, “established” hosts exhibit slightly lower response rates, higher response time, lower prices, and higher rating scores.

Importantly, Airbnb hosts exhibit different behaviors depending on whether they leverage the platform in a professional or non-professional way. In several cities, professional hosts (i.e. those who offer more than one listing) account for more than half of all available listings (Table AI). For instance, professional hosts generate higher revenues and have higher occupancy rates (Li et al., 2015; Gibbs et al., 2017a). Likewise, professional hosts are early adopters, their listings tend to be entire apartments rather than private rooms, and they are more likely to be located within cities (Ke, 2017b). On the listing’s page, professional hosts use the self-description section for listing advertising purposes rather than for describing themselves personally. Importantly, professional hosts are perceived as being more trustworthy (Tussyadiah and Park, 2018).

Airbnb hots also differ from those on other accommodation sharing platforms. Couchsurfing hosts consider a prospective guest’s online representation as a supportive and friendship-forming tool while Airbnb hosts focus on risk assessment (Jung and Lee, 2017).

### 3.2 Reputation systems and trust

A central and ongoing challenge for Airbnb is the creation and maintenance of trust between its users (Gebbia, 2016). The platform provides different IT artifacts for signaling user reputation, where both hosts and guests can leverage these cues to manage their reputation and to establish trust (Fuller et al., 2007; Jøsang, 2007; Bente et al., 2012; Xie and Mao, 2017). Such means include star ratings (Zervas et al., 2015; Teubner et al., 2017), mutual text reviews (Abramova et al., 2015; Bridges and Vásquez, 2016), personal self-descriptions (Tussyadiah, 2016b; Ma et al., 2017a), profile images (Teubner et al., 2014; Ert et al., 2016; Fagerstrøm et al., 2017), identity verification and insurances (Teubner and Hawlitschek, 2018) and social connections, displaying how users are connected to others, directly or through mutual friends, usually based on their Facebook contacts (Airbnb, 2011). Importantly, hosts on Airbnb may also inherit the user’s general trust in the platform (Han et al., 2016). In the following, we consider the most widely studied categories of trust and reputation management.

#### 3.2.1 Star ratings.

Airbnb’s prominent star rating system can be seen as a form of experience assessment. After a completed transaction, guests are prompted to rate their host on a scale of 1 to 5 stars along the sub-dimensions accuracy, communication, cleanliness, location, check in, and value. The resulting average rating score (rounded to the half unit) represents an essential parameter for prospective user interaction. Airbnb displays this cumulative score only for hosts with at least three ratings (Airbnb, 2016b) and more than half of all listings (54.6 per cent) have not reached star rating visibility yet (Ke, 2017b). Moreover, guests are rated by their hosts as well. This assessment comprises an up- or down vote of whether they can recommend the guest to other hosts, a text review and star ratings for cleanliness, communication, and compliance with house rules.

Overall, the distribution of (the hosts’) rating scores is subject to a distinct skewness where ratings tend to be on the positive side (see Table AI). Based on more than 600,000 Airbnb listings worldwide, Zervas et al. (2015) report that almost 95 per cent of all listings exhibit an average rating score of 4.5 or 5.0 stars and that hardly any listings have ratings of 3.5 stars or less. Moreover, they find a stronger positive bias in Airbnb reviews than for the same listings when also listed on TripAdvisor. Similar findings are reported by most other studies (Ert et al., 2016; Ke, 2017b). Further, little to no differences in the star rating distribution is found when differentiated by room type (i.e. entire home, private room, shared room). Moreover, comparing Airbnb and Booking.com in five European cities, “a consistent gap of approximately 20 per cent in the average score per city in favor of Airbnb listings” is found (Ert et al., 2016, p. 66). These results echo earlier research on rating systems, showing that over 98 per cent of all exchanged ratings were 5.0 stars (Slee, 2013). Teubner and Glaser (2018) discuss several potential reasons for this skewness and consider survivorship processes where, in fact, low-rated listings exhibit increased churn rates.

Gutt and Kundisch (2016) focus on the rating sub-dimension value. They show that – compared to the overall rating – the value sub-dimension can offer additional insights for potential guests as it puts a listing’s quality in perspective of price. In contrast, Fradkin et al. (2018) argue that the high share of positive ratings is not explained by the system itself, but by the high quality standards most hosts meet. This hypothesis is corroborated by two experiments and internal data from Airbnb, indicating that the positivity bias may be caused by:

• Airbnb’s efforts of verifying user identities and actively fostering high-quality user profiles;

• Airbnb’s explicitly selective ranking algorithm that ranks down unfit hosts (e.g. based on listing quality, location relevance, reviews, host response time and guest and host preferences; Grbovic, 2017); and

• a natural selection process, where low-quality listings receive negative reviews, hence are not booked again, and in consequence drop out of the market.

Interestingly, review scores appear to be subject to spatial influences within a city, where more central areas typically exhibit better scores (Cummins, 2017).

Recently, scholars have begun to use scenario-based choice experiments on the effectiveness of different reputation cues showing, for instance, that the availability of a review score increases a listing’s chances of being booked (Ert et al., 2016). In a similar manner, the number of positive reviews is identified as a strong trust-enhancing cue, instrumental for shaping consumers’ booking decisions (Abramova et al., 2017). In fact, the effect of star rating availability is supported by:

• results from a trust game experiment among actual Airbnb users; and

• transaction data from the platform itself (Abrahao et al., 2017).

The availability of a high star rating increases others’ willingness to trust and it can particularly counter the detrimental trust effects of high social distance.

According to Abramova et al. (2017), beyond review score, also the number of reviews represents an important driver for guest’s rental decisions. Several studies use this number as a proxy for a listing’s popularity and hence its performance (Lee et al., 2015; Ke, 2017a, 2017b; Liang et al., 2017). What is frequently reported is that Airbnb listings exhibit a richer-get-richer phenomenon, where “listings with more existing reviews will have more new reviews” (Ke, 2017b, p. 9). Last, listings with Superhost status are found to receive more ratings within a given time frame, and that these are higher on average (Liang et al., 2017).

#### 3.2.2 Text reviews and self-descriptions.

Beyond star ratings, users on Airbnb rely on text-based elements to build trust. First, users can provide a textual self-description on their profile, that is, provide some personal information such as occupation, hobbies, or life motto (Ma et al., 2017a). Airbnb suggests providing a description of at least 50 words highlighting why a user decided to join the “community,” their interests, or anything else they believe a prospective interaction partner would want to know (Airbnb, 2017b). Second, along with numerical star ratings, prior transaction partners describe their experiences with each other by means of text reviews. These typically entail statements about the host/guest, travel purpose, and the apartment and its surrounding, representing valuable information for potential future guests/hosts (Bridges and Vásquez, 2016; Bae et al., 2017). The implications emanating from text-based online representation are considerable. Both self-descriptions and text reviews are actively used to reduce perceptions of risk and to prevent users from misunderstanding and unfounded expectations (Jung and Lee, 2017).

Afforded by the availability of textual profile information, recent research has applied text analysis and natural language processing to decipher meaning and implications of such text-based elements and to understand Airbnb’s users in greater detail. Tussyadiah (2016b) uses word co-occurrence in the textual self-descriptions to identify and differentiate clusters of hosts. Categories are style of self-presentation, pricing, and activity patterns. Similarly, Ma et al. (2017a) find hosts’ self-descriptions to refer to different themes (i.e. origin/residence 69 per cent, work/education 60 per cent, interests and tastes 58 per cent, hospitality 53 per cent, travel 48 per cent, relationships 28 per cent, personality 27 per cent, life motto and values 8 per cent) and that a hosts’ trustworthiness increases:

• with the length of self-descriptions; and

• for profiles that disclose information particularly referring to work, origin, hospitality, and personal interests.

Text reviews have been subject to in-depth investigations too. Bridges and Vásquez (2016), for instance, explore linguistic patterns in Airbnb reviews, by and large reflecting the high proportion of positive star ratings also in these written evaluations (93 per cent of the analyzed text reviews were classified as positive). Interestingly, 79.5 per cent of all guest reviews mention the host by name (other studies report similar proportions; Alsudais, 2017). Of the 7 per cent not-entirely positive reviews, three out of four came from guests, typically referring to issues with comfort (48 per cent), communication (21 per cent), or cleanliness (15 per cent). The authors suggest that negative experiences are communicated by means of subtle or “lukewarm” cues, for instance by explicitly not writing or emphasizing something.

Importantly, not every user submits a review after each transaction; different sources report review rates between 31 per cent and 72 per cent (Cox, 2017). In this vein, Bae et al. (2017) find that divergence between expectation and trip experience (regardless of whether in a positive or in a negative way) increases the likelihood of review provision. Similarly, the authors show that users perceive reviews as more credible for small social distance between themselves and the host. Review credibility, in turn, supports approval, ultimately resulting in increased booking intentions.

Analyzing word co-occurrence within text reviews, Tussyadiah and Zach (2016) identify five recurrent themes (referring to service, facility, location, feeling welcome, and comfort). Linking these themes to a listing’s overall rating, the authors find that the “location” and the “feel welcome” themes are associated with higher ratings, while signal words from the “service” theme are associated with lower ratings (Tussyadiah and Zach, 2016). Similarly, Brochado et al. (2017) distinguish eight themes (stay, host, home, place, location, apartment, room, city). Interestingly, they find that this categorization is robust for different cultures (i.e. India, Portugal, US). Similarly, Camilleri and Neuhofer (2017) identify welcoming, expressing feelings, evaluating location and accommodation, helping and interacting, recommending, and thanking as key components. Similar to the positivity bias in ratings, the positive-to-negative word ratio of 14 million English Airbnb reviews is more than twice as high when compared to a benchmark of Yelp reviews (Ke, 2017b).

#### 3.2.3 Profile images.

The intuitive judgement of other people on the basis of their visual appearance represents an innate human behavior. Based on two discrete-choice experiments, Ert et al. (2016) show that this intuitive evaluation process leads to a preferred selection of hosts with trustworthy profile images. In a similar manner, Fagerstrøm et al. (2017) find an increased likelihood to rent from hosts with positive or neutral facial expressions. Negative expressions or the absence of images, in contrast, are not even compensated by lower prices or higher ratings, demonstrating the paramount importance of visual, particularly facial cues. Interestingly, women are more affected by facial expressions than men and similarity seeking is found to govern trusting decisions and transactions on Airbnb, where higher social distance (i.e. lower similarity) is associated with lower levels of trust and fewer transactions (Abrahao et al., 2017). Due to the fact that not only hosts but also guests have to market themselves on Airbnb, they are also subject to photo evaluation. Focusing on the decision of hosts to accept or reject booking requests, Karlsson et al. (2017) find that women, elderly people, and users with trustworthy photos are more likely to be granted permission to book.

### 3.3 Prices and pricing

Financial motives represent one of the key factors for buyers on peer-to-peer marketplaces (Bucher et al., 2016). For many cities such as Berlin, Airbnb listings are roughly 30 per cent less expensive than hotel rooms (BATO, 2016). It is hence not surprising that prices and pricing strategies are of particular relevance for Airbnb which is also reflected in distinct price differences between cities (see Table AI and Figure A1 in the Appendix). Several papers have explored determinants of listing prices, usually based on hedonic pricing models (Rosen, 1974). Such models assume that any valuable amenity (e.g. a whirlpool) or other competitive advantages (e.g. favorable location) will sooner or later be reflected by the price. While this relation is well-established for brick-and-mortar factors in the hospitality and tourism literature (Wang and Nicolau, 2017), the success of Airbnb now begs the question whether these relations transfer to the C2C context and whether additional, soft factors such as a host’s reputation and personal branding yield tangible economic value as well. And indeed, based on interviews with hosts, Ikkala and Lampinen (2014, 2015) find that hosts monetize their reputational capital in the form of demanding higher prices.

Several empirical studies have since then supported the notion that higher rating scores, along with other reputational signals (e.g. verified identification, duration of platform membership, Superhost status, number of Facebook friends) in fact translate into price markups (Edelman and Luca, 2014; Abramova et al., 2017; Chen and Xie, 2017; Gibbs et al., 2017a; Liang et al., 2017; Teubner et al., 2017; Wang and Nicolau, 2017). For instance, Teubner et al. (2017) find that an additional star is reflected in a 20$markup for a typical stay at a typical accommodation (2 persons, 2 nights). Moreover, the attainable price is also driven by the host’s level of professionalism. Li et al. (2015) show that professional hosts: • generate 16.9 per cent more in daily revenues, • exhibit 15.5 per cent higher occupancy rates, and • are less likely to exit the market. Furthermore, with the Superhost badge, Airbnb provides an own attestation of host superiority (e.g. high response rate, positive evaluations, sufficient number of bookings, few cancelations; Liang et al., 2017). The authors show that guests accept this badge as an indicator of quality and are willing to pay more for a Superhost’s accommodation as compared to hosts without the badge. Moreover, the results of Gutt and Herrmann (2015) suggest that pricing is subject to the visual presence of reputational capital, for instance, expressed through the star rating. As outlined above, Airbnb displays the accumulated star rating scores only for listings with three or more ratings. A longitudinal assessment reveals that once a listing surpasses this threshold, hosts monetize their ratings’ reputational capital, where “rating visibility causes hosts to increase their prices by an average of 2.69 €” (Gutt and Herrmann, 2015, p. 7). Similarly, panel data suggests that hosts react to receiving additional reviews, ID verification, and superhost status by increasing prices slightly (i.e. by 0.5 per cent to 1.6 per cent) (Neumann and Gutt, 2017). In contrast to models based on empirical data, choice experiments are used to study the influence of information cues and profile images on user decisions and willingness to pay. Abramova et al. (2017), for instance, compare trust-enhancing cues and estimate the marginal willingness-to-pay showing that, for instance, consumers are willing to pay €27.76 extra for a listing with 15 positive reviews. Also, visual information conveyed through profile images has a significant impact on listing prices, as its absence or angry facial expressions are found to be compensated neither by low prices nor high ratings (Fagerstrøm et al., 2017). Ert et al. (2016) find that more trustworthy hosts (based on perceptions of their photo) yield higher willingness to pay. Also, they report a significant positive influence of the apartment photos’ visual appeal on price, which is consistent with another study’s finding that an increase of$2,455 in annual revenues due to high apartment photo quality (Zhang et al., 2016).

### 3.4 Economic impacts and media coverage

#### 3.4.1 Economic impacts.

Having established an alternative mode of consumption, it comes as no surprise that Airbnb has affected the “traditional” hotel industry. Arguably, compared to a hotel, a stay at an Airbnb host differs entirely in terms of comfort and overall experience and that hence, Airbnb is not likely to substitute hotels altogether. Recent studies estimate that a 1 per cent increase in Airbnb inventory results in a 0.05 per cent decrease of hotel revenues (Zervas et al., 2017), and that additional Airbnb supply has a negative effect on hotel performance (Xie and Kwok, 2017). In contrast, others do not find significant effects of the number of Airbnb listings on hotel sales performance (Blal et al., 2018). Yet, major hotel and lodging associations consider Airbnb as a threat that is already causing price and revenue cuts – especially during peak times (Benner, 2017). This particularly affects mid-price hotels, for which Airbnb listings are considered a suitable substitute (Guttentag and Smith, 2017).

Beyond the hotel industry, it is suggested that the emergence of Airbnb has also affected local housing markets. While there is currently limited evidence how specifically peer-based accommodation sharing affects prices and availability, there are indications of cyclic dynamics insofar as that rental price increases are highest in areas with already large numbers of Airbnb listings (Schäfer and Braun, 2016), causing further regular apartments to be converted into short-term offers. This consequently puts increasing pressure on the housing market – an effect that has been reported for Boston (Horn and Merante, 2017), Barcelona (Llop, 2016), and Sydney (Gurran and Phibbs, 2017). While for popular neighborhoods in Berlin, a relation between the presence of Airbnb and rental price growth is found (Schäfer and Braun, 2016), none is observed for the city of Hamburg (Brauckmann, 2017). Besides increasing rent levels and the potential effects of gentrification, the increasing frequency of short-term renting raises questions also for those who live in airbnbified cities and neighborhoods, including noise, pollution, traffic, nuisance, and waste management (Gurran and Phibbs, 2017).

#### 3.4.2 Media coverage.

In view of Airbnb’s meteoric success and such impacts on entire industries, cities, and residents, the platform’s development was accompanied by ample media coverage and several studies have portrayed this process. Between 2009 and 2013, Airbnb’s presence in common mainstream publications passed through three different phases. While first attempts to understand and locate the platform in existing categories turned out to be inappropriate, Airbnb was then described as a distinct phenomenon, and eventually acknowledged as an iconic business model (Mikhalkina and Cabantous, 2015). Similarly, the tech blogging community captured Airbnb’s development between 2011 and 2014 as a two-stage process. The first step was to establish a two-sided market with respective network- and lock-in effects and thereafter, a process of augmenting the platform through incremental improvements for consumers and providers followed (Constantiou et al., 2016).

### 3.5 Legal and regulatory aspects

The character of Airbnb’s (and its hosts’) business model raises a variety of legal questions and sometimes conflicts with applicable national law. These questions can roughly be structured along the categories housing, taxation, consumer protection, regulation, and liability.

#### 3.5.1 Housing.

First and foremost, short-term rental is legally restricted in many cities. Consider Berlin as an example. Here, like in most other European capitals, the vacation rental business is flourishing. In 2016, 600,000 guests booked a stay, representing an annual increase of around five percent (Airbnb, 2016c). In the same manner, the number of hosts has grown by around eleven percent. These figures are politically explosive insofar as they suggest that the city’s misappropriation act has had little effect (BATO, 2016; Schäfer and Braun, 2016). Since May 2014, hosts are not allowed to rent out entire apartments or houses repeatedly on a short-term basis (with few exceptions). In other European cities, Airbnb’s strategy of lobbying and acting upon legislation has been quite successful. For instance, Airbnb agreed on a time limit for renting holiday homes in Amsterdam. Residents are allowed to (fully) rent out their apartments for a maximum of 60 days per year. In London, this limit is 90 days. Here, Airbnb has agreed to block all orders that exceed this number. In other cities with a vibrant tourism industry such as Barcelona, the dispute between Airbnb and municipals is far from being settled (Interian, 2016; Llop, 2016; Gutiérrez et al., 2017; Santolli, 2017).

#### 3.5.2 Taxation.

Next, there exist concerns with regard to taxation, comprising mainly two aspects. First, many cities charge tourism taxes, typically in the range of 5-10 per cent of the room rate. Private Airbnb hosts usually do not pay this as they are not registered as hotel or tourism operators. In many cities (mainly in the USA), Airbnb was hence forced to cooperate with the fiscal authorities and now transfers the tax directly, a model which is argued for by legal scholars (Lee, 2016). Second, rental income is subject to personal taxation. However, the literature suggests that many private hosts ignore the tax relevance of this type of income (Cleveland, 2016).

#### 3.5.3 Consumer protection and regulation.

Another conflict is rooted in the fact that hosts offer products and services that may substitute those of hotels but do not face equally strict regulation, for instance, with regard to hygiene and fire inspection standards (Benner, 2017). This raises questions of consumer protection and competition fairness. In this regard, Airbnb argues that the “self-regulating community” of hosts and guests, supported by the platform’s reputation systems, actually can complement, if not replace governmental monitoring and/or regulatory processes (Stabrowski, 2017). Moreover, Airbnb claims to perform background checks on hosts for criminal records and sex offender registries (Airbnb, 2017a; Levin, 2017). As a contrast, Airbnbhell.com offers a platform for guests and hosts to share their negative experiences to raise awareness about its risks and to prevent other people from using it (AirbnbHell, 2017).

The debate on unfair competition between the highly regulated hotel industry and Airbnb revolves around seven key issues which public institutions should consider to prevent pressure on the housing market and to counteract touristification, including taxation schemes, the control of visitor streams, information ownership, safety, consumer protection, fair competition, and the housing market in general (Oskam and Boswijk, 2016). There exists no one-size-fits-all vision for handling short-term rentals in the future. Regulatory measures are intended to protect interests of both visitors and locals and thereby require businesses and hosts to comply. For instance, an analysis of written submissions to the city of Sydney revealed that noise, traffic, parking, and waste, but also a general feeling of unease bothers local residents where short-term rentals penetrate residential areas (Gurran and Phibbs, 2017). Local institutions are required to frame regulation on the basis of individual indicators such as visitor numbers, frequency and type of incidents, collected taxes, and the evolution of housing prices – and it is argued that individual and novel regulation is better suited to govern Airbnb than the existing and one-size-fits-all approaches (Jonas, 2015; Lines, 2015; Oskam and Boswijk, 2016). It is proposed that Airbnb may actively support such efforts by committing to self-regulatory measures such as providing transparency about operated properties and visitors (Quattrone et al., 2016).

#### 3.5.4 Liability.

Furthermore, questions of liability particularly apply to hosts. First, a guest may (intendedly or not) cause damage to the host’s property. For such cases, Airbnb offers a 1-million dollar insurance (Airbnb, 2015a) but it is questioned that adequate coverage can be claimed in case it is actually needed (Lieber, 2014; Dobbins, 2017). Of course, financial compensation will not be able to cover damage or loss of objects of sentimental value. Perhaps even more importantly, liability is largely unclear for cases in which a guest comes to harm, for instance, due to technical deficiencies of the apartment (Booth and Newling, 2016). In particular, Airbnb hosts are considered as landlords (rather than innkeepers) by some jurisdictions in which case they are not held liable for injuries that occur on their property (Loucks, 2015).

#### 3.5.5 Discrimination.

Last, for peer-based sharing platforms, the general tenet is that more disclosed information leads to better assessments, reduced uncertainty, and hence the facilitation of transactions among strangers. Nevertheless, Airbnb’s striving to facilitate the creation of trust may result in further, unexpected consequences. One observation is that African-American hosts are forced to charge lower prices (Edelman and Luca, 2014). A subsequent study demonstrated that also guest profiles with distinctively African-American names are denied 16 per cent more often when requesting a stay than potential guests with distinctively “white” names (Edelman et al., 2017). Here, rejection rates did not depend on the host’s age, experience, location, listing price, or usage (occasional/professional). Moreover, even the host’s own ethnicity did not affect rejection rates, suggesting that in-group thinking or homophily are not at work. In an effort of preemptive obedience, Airbnb prompted its hosts to commit to a non-discrimination statement (Airbnb, 2016a; Benner, 2016). In view of Airbnb’s general terms of use, it is argued that users are forced to sign the included arbitration agreements which prevent legal enforcement against cases of racial discrimination and that, further, without regulatory adaptions, such waivers threaten civil rights enforcement (Toto, 2017). Another suggestion is to expand the instant booking option which lets guests book without further host approval (Edelman et al., 2017). As of today, however, only about 25 per cent of all listings offer the instant booking option (see Table I). It was also discussed to reduce the prominence of profile photos which do, however, play an unchanged paramount role.

## 4. Correlation of themes, methods, foci and domains

We now analyze the different themes, methods, foci, and domains of the reviewed studies in greater detail. Considering the general occurrence of different themes, the role of profile photos has experienced rather little research attention (9 studies) while user types and their motives to use Airbnb have been subject to much more extensive investigation (45 studies). Moreover, we consider the occurrence of combinations of these factors to derive first insights into possible research gaps. To do so, we use make use of correlation analysis for the outlined measures (see Figure 1).

A first insight from this pertains to the relations between surveys, empirical work, studies on motives, and the foci on providers/consumers. We see that motives are usually assessed by means of surveys (r = 0.64) and hardly by empirical analysis (−0.53). Also, surveys usually focus on consumers (0.67) and hardly on providers (−0.63). In contrast, research on providers is usually based on empirical work (0.46), a method which is, however, underrepresented for the consumer perspective (−0.36). In consequence, research into the providers’ motives to adopt and use Airbnb represents a natural opportunity for future work – especially for the domain of tourism as this domain focusses particularly on consumers (0.31). Next, Figure 1 reveals that studies on legal and regulatory aspects (which tend to be published in law outlets, 0.55) are often conceptual (0.50). Thus, data-driven research may further inform Airbnb-related debates in jurisprudence.

With regard to timely developments, we see that the domain of tourism is taking over recently (0.31), while conceptual work appears to die off (−0.27). Furthermore, we see that empirical work is mainly conducted on pricing strategies and prices (0.37), and in particular so for US/Canada-based samples (0.34).

## 5. Discussion and conclusion

We have taken a look at the research landscape around Airbnb and some fundamental data on the listings supplied through the platform. Airbnb represents a poster child of the platform economy and likewise serves as a guinea pig in a variety of contemporary studies in the domains of Information and Management, Tourism/Travel/Hospitality, Law, and Economics. Along the dimensions of user motivation, trust and reputation, prices, economic and media impacts and legal and regulatory aspects, we provide a structured overview on this timely and emerging field of research. We argue that studying Airbnb represents a highly worthwhile endeavor as the platform represents more than a simple booking portal, but rather a blueprint for an entire category of novel business models – setting de facto standards for web design, trust and reputation management, and for the social and behavioral norms of user interaction – also outside the domain of travel and tourism. From this analysis there emerges a set of overarching stylized facts and implications for both research and practice.

### 5.1 Theoretical implications

First, research on Airbnb with regard to quantity, related fields, used methods, scope, and research questions has experienced tremendous growth over the past years. While earlier work mainly considered user types and motivations, we now see research on reputation mechanisms, user interface design, trust, legal assessments, prices and pricing, geographic aspects, text sentiment, the platform’s overall inventory and its impact on established industries. However, not all aspects and methods (and combinations thereof) are equally represented (see Section 4). Future work should hence consider to focus the identified gaps and hitherto scarcely used combinations (e.g. of method and focus).

Second, for platform business models, trust is considered as crucial by scholars, users, and platform providers. A broad variety of mechanisms and artifacts are implemented by the platform and a plethora of studies confirm their effectiveness for creating trust and facilitating transactions. Specifically, positive effects are found for higher star ratings scores (Lee et al., 2015; Ert et al., 2016; Zhang et al., 2016; Abrahao et al., 2017; Fagerstrøm et al., 2017; Ke, 2017b; Sanchez-Vazquez et al., 2017; Fradkin et al., 2018), larger numbers of ratings (Li et al., 2015; Zhang et al., 2016; Abrahao et al., 2017; Abramova et al., 2017; Ke, 2017b), the presence of text reviews (Ikkala and Lampinen, 2014; Bae et al., 2017; Sanchez-Vazquez et al., 2017; Fradkin et al., 2018; Liang et al., 2018a, 2018b), profile images (Ert et al., 2016; Fagerstrøm et al., 2017; Karlsson et al., 2017; Liang et al., 2017), personal information (Ikkala and Lampinen, 2014; Ma et al., 2017a), and other subordinate factors such as the Superhost badge (Ke, 2017b; Liang et al., 2017), ID verification (Abramova et al., 2017; Jung and Lee, 2017), or favorable room presentation (Zhang et al., 2016; Abramova et al., 2017; Jung and Lee, 2017; Liang et al., 2017). While most of these studies conceptualize trust as an undifferentiated construct, little was it studied in its multi-dimensionality (e.g. ability, benevolence, integrity) or multi-referentiality (e.g. platform, peers) in the context of Airbnb (Hawlitschek et al., 2016b). As Airbnb, however, deliberately “designs for trust” (Gebbia, 2016), an even more nuanced conceptualization of trust may yield further insights into trust-related entanglements on Airbnb specifically – but also on peer-to-peer platforms in general.

Third, as this literature review has revealed, a large share of work on Airbnb is based on empirical data. For such studies, we observe that the website InsideAirbnb.com is increasingly used as a viable resource, providing data on listings, reviews, and calendars. It appears quite likely that much of the upcoming research will do so too as InsideAirbnb.com, as a data repository, alleviates researchers from the technical burdens of implementing web scrapers and allows for better comparability across results. Research should move toward building atop of a common ground of data structure and vocabulary.

### 5.2 Practical implications

The present research also yields several managerial implications for the traditional hotel industry. For instance, it becomes evident that managers in the hotel industry can no longer ignore the presence of Airbnb and the type of service it provides to its users (Blal et al., 2018). Instead, hotels should actively differentiate their offers from Airbnb and emphasize their own strengths. This includes, for instance, strengthening loyalty programs, for which Airbnb does not offer a substitute (Young et al., 2017). In addition, hotels can leverage economies of scale to provide access to assets that are hardly being offered by private hosts (open spaces, gastronomic facilities, gyms, etc.) and services (concierge, maintenance) (Akbar and Tracogna, 2018). This is well in line with hotels’ higher family friendliness (Mao and Lyu, 2017; Poon and Huang, 2017) and aspects such as instant booking and confirmation, the absence of minimum stay durations, and more reliable room availability throughout the year (Gunter and Önder, 2017). Eventually, with regard to Airbnb, hotels’ marketing should particular stress their superiority with regard to process risks, safety, and security (Yang and Ahn, 2016; Mao and Lyu, 2017; Poon and Huang, 2017; Young et al., 2017; Malazizi et al., 2018).

Another recurrent theme in the literature are motives for using Airbnb and we find that such motives are manifold. Financial (Tussyadiah, 2016a; Guttentag and Smith, 2017; Liang et al., 2018a, 2018b) and social reasons (Möhlmann, 2015; Tussyadiah, 2015; Guttentag et al., 2018), trust (Tussyadiah, 2015; Mittendorf and Ostermann, 2017; Wang and Heng, 2017), and risk-related factors (Lampinen and Cheshire, 2016; Varma et al., 2016; Jung and Lee, 2017) emerge as the most important motives but there are indices of other factors too, including sustainability (Hamari et al., 2016) and authenticity (Guttentag and Smith, 2017; Guttentag et al., 2018). Importantly, not only consumers but hosts as well are found to be motivated both by economic and social motives. Interestingly, while most studies identify financial motives as predominant, Airbnb’s marketing does not address this direction at all. Apparently, the platform attempts to create an image of a social travelers’ community in which money does not play a role at all. Such “sharewashing” practices have recently been discussed as misleading users and the public and it may well be that the disavowal of most users’ economic motives harms rather than benefits Airbnb as a company (Troncoso, 2014; Hawlitschek et al., 2018). One practical implication standing to reason for Airbnb but also for other platform operators is that they may want to revisit their users’ motives, their marketing communication, and possible discrepancies between them.

### 5.3 Limitations

Like any research, the present study is not without limitations. One aspect may concern the literature screening process. Given that there does not exist a natural and clear-cut criterion for what constitutes a sufficiently Airbnb-related paper, this process may be vulnerable to some selection bias. Nevertheless, error sensitivity may be rather low as exclusion criteria were thoroughly discussed among authors. Moreover, given the rapid development of the platform, regulation, and Airbnb-related research, this review must be considered as a snapshot in time. Nevertheless, we are positive that it may help to identify research lacunas.

### 5.4 Future research

Airbnb’s worldwide presence is reflected in a large spectrum of cultural habits, products, and prices – which all interact. Investigating actual Airbnb data reveals that there exist significant differences with regard to market penetration, prices, and more intricate aspects such as reputation scores (see Table AI and Figure A1 in the Appendix). For “local” studies on Airbnb, it is hence important to keep in mind that rash generalizations to other cities and regions, let alone for the entire platform, may not be justified. A striking observation in this regard is the low number of studies that deliberately consider and compare samples of different origins (Brochado et al., 2017; Rahimi et al., 2016). Future research hence should examine socio-demographic, regional, and cultural aspects in greater depth, for instance, with regard to the effect of cultural norms on the roles of user motives, prices, trust building, and reputation (e.g. across Western/Eastern societies).

Moreover, only few studies have set out to conduct experiments on provider-consumer interactions and trust (Abrahao et al., 2017) or actual field experiments directly on the platform (Edelman and Luca, 2014; Edelman et al., 2017). Despite the technical and ethical intricacies of audit studies, this approach represents a highly promising path for future work as it reveals insights into actual, that is, non-hypothetical user behavior.

The open data repository InsideAirbnb.com provides monthly panel-like data for an increasing number of cities which open up the possibility of studying dynamic aspects, for instance, with regard to the evolvement of prices, transaction volumes, or user reputation (Teubner and Glaser, 2018). This opportunity should be seized by future work.

Beyond these currently discussed topics, several other, less obvious research gaps come to mind. First, two of the most recent developments include experiences and the platform’s automated pricing algorithm (Hill, 2015; Gibbs et al., 2017b). With the introduction of experiences in late 2016, Airbnb attempts to tap into an additional business potential, where locals offer (i.e. sell) guided tours, workshops, and other activities – positioning Airbnb as a wholesale tourism company. Research on such peer-based tourism services is, however, scarce. Moreover, Airbnb’s automated pricing tool represents by and large terra incognita.

Second, what is common to many platforms is that users need to market themselves based on their online reputation and/or personal brand (Harris and Rae, 2011; Yannopoulou, 2013; Tussyadiah, 2016b; Dann et al., 2018). With each platform specializing on one particular type of product or service, users handle distinct reputation scores for an increasing number of platforms (Dakhlia et al., 2016). The ever-growing relevance of Airbnb and other peer-based platforms prompts the idea of leveraging one’s reputation from one context in other contexts as well, that is, on different platforms. This poses the question of whether (and if so, how) reputation is actually transferable between platforms (Teubner et al., 2019). Instead of starting all over again with zero reviews and no reputation, new Airbnb users could refer to their existing ratings on other platforms. This notion of reputation portability is identified as an important lever to address issues of platform competition (EU, 2017, p. 93).

Third, while the rating distribution skewness toward positive ratings on Airbnb is regarded as common knowledge and many studies have described this distribution (Zervas et al., 2015), little is known about the root causes for its occurrence. There exist several conjectures, for instance, on social interactions among hosts and guests, under-reporting of negative experiences, non-anonymity and publicity of reviews, mid- and long-term selection, or strategic reasons in view of future transactions (Dambrine et al., 2015; Bridges and Vásquez, 2016). Empirical work addressing these suppositions, however, is yet scarce.

## 6. Conclusion

Studying Airbnb is due even beyond the questions and issues directly related to the platform as it serves as a template for various other ventures, reflected in many startups’ claim to represent “the Airbnb of …” (Horton and Zeckhauser, 2016). In addition, many of the mechanisms and design elements used by Airbnb (e.g. star ratings, text reviews, profile images) are being used by most other platforms too. Given the increasing importance of two-sided markets, platform business models, the associated economic, social, and regulatory upheavals, and Airbnb’s function as a poster child and role model make it worth studying all the more. In summary, our study provides an overview of work on the accommodation sharing platform Airbnb. The prevalence of personal host-guest interactions – both online and, importantly, also offline – distinguishes the platform from traditional accommodation markets (i.e. hotels) and charges the used IT design elements and mechanisms with particular social and economic meaning. As a highly diverse and steadily growing community of scholars investigates the phenomena surrounding Airbnb, this review can only represent a first step in view of the necessity to sort, structure, and review the vast amount of literature. We hope that researchers and practitioners alike will find this review useful as a reference for future research and as a guide for the development of innovative applications based on the platform’s peculiarities and paradigms in e-commerce practice.

## Figures

#### Figure 1.

Correlation table of publication properties (n = 118; cut-off | r | < 0.25)

Comparison of cities along the dimensions Airbnb density (#listings per 1,000 population) and median price ($US) ## Table I. Literature overview Authors(Year) Motivesand types Reputation and trust Text Photos Prices Economic impact Legal and regulation Approach Consumers Providers Method Sample Origin Domain Varma et al. (2016) × × Investigation of importance of different factors for Airbnb users and non-users. While many factors exhibit similar importance, differences are found in the importance of security, cleaning, loyalty programs, and recommendations. Executives of large hotels do not fear Airbnb, smaller businesses do × Survey, Interviews 347, 12 202 US cities TOURISM Lutz and Newlands (2018) × Characteristic distinction of guests who frequently stay in a shared room and those who prefer to stay in an entire home × Survey, Empirical 659, 500 5 US Cities INFMAN Bae et al. (2017) × × Before a trip, consumers purchase intention is influenced by social distance, credibility of reviews, review breadth, information usefulness, and adoption of reviews. After a trip, perceived information discrepancy influences travelers’ willingness to share their trip experience × Survey 411 South Korea INFMAN Chen and Chang (2018) × × × Evaluation of rating, rating volume, review, information quality, and media richness on intention. Perceived value and satisfaction are determinants of intention to buy. Rating volume, review, information quality, and media richness are important precursors × Survey 280 INFMAN Dann et al. (2018) × × × Proposal of research model and scenario-based online experiment design for explaining guests’ intention to book by their perceived social and economic value and how those are reflected in hosts’ user representation × Survey INFMAN Guttentag and Smith (2017) × Airbnb consumers consider the platform as a substitute for especially mid-range hotels. In terms of traditional hotel attributes, Airbnb consumers have high expectations of the service × Survey 844 Canada TOURISM Guttentag et al. (2018) × Consumers are mostly attracted by Airbnb’s practical and experiential attributes. Motives: Interaction, home benefits, novelty, sharing economy ethos, local authenticity. Cluster analysis identifies Money Savers, Home Seekers, Collaborative Consumers, Pragmatic Novelty Seekers, and Interactive Novelty Seekers × Survey 844 Canada TOURISM Hamari et al. (2016) × Participation in collaborative consumption (CC) is driven by the user motives sustainability, enjoyment, and economic benefits, partly mediated through attitude (toward CC) × Survey 168 Worldwide INFMAN Hawlitschek et al. (2016a, 2016b) × Differentiation and evaluation of motives for and against peer-to-peer sharing, differentiated for providers and consumers. Development of measurement model. Main drivers include enjoyment in sharing, social factors, economics benefits. Deterrents include process risk, privacy and effort concerns, and independence through ownership × × Survey 61, 605 Germany INFMAN Karlsson et al. (2017) × × Exploration of drivers for host decisions of accepting or rejecting guests. Refusing a guest is a common behavior of hosts in peer-to-peer networks. The decision is affected by both trip-related attributes and (guests’) personal characteristics × Survey 192 Australia TOURISM Lalicic and Weismayer (2018) × Analysis of influencing factors of consumers’ Airbnb loyalty. While social and authentic experiences are antecedents of consumers’ loyalty to Airbnb, perceived economic benefits and perceived reduce risk exhibit no significant impact × Survey 557 Worldwide TOURISM Lee and Kim (2018) × The effect of hedonic and utilitarian values on satisfaction and loyalty of Airbnb users. Airbnb users’ hedonic value has a positive impact on satisfaction and loyalty, while utilitarian value influences only on satisfaction. Product involvement is a moderator × Survey 511 US TOURISM Liang et al. (2018a) × Relationship between satisfaction, trust and switching intention, repurchase intention in the context of Airbnb. Trust was found as a mediator between transaction-based satisfaction and repurchase intention. Trust in Airbnb did not affect trust in hosts × Survey 395 Canada, US TOURISM Liang et al. (2018b) × Survey on consumer repurchase intention, which is found to be affected by perceived value, perceived risk, electronic word of mouth, perceived authenticity, and price sensitivity × Survey 395 Canada, US TOURISM Liu and Mattila (2017) × 2 (high vs low power) x 2 (belongingness vs uniqueness appeal) design to measure click through and purchase intentions for an Airbnb advertisement. The high power framing exhibits higher purchase intentions for the uniqueness ad, the low power framing exhibits higher purchase intentions for the belongingness ad × Survey 139 US TOURISM Malazizi et al. (2018) × Survey on Airbnb hosts reveals that financial, safety, and security risk negatively influence hosts’ satisfaction. Financial, safety, security, and political risks negatively influence continuance intention to use; and psychological risk increases satisfaction, continuance intention to use, and intention to recommend. Satisfaction positively affects continuance intention to use and intention to recommend × Survey 221 Cyprus ECON Mao and Lyu (2017) × Investigation of repurchase intention based on the TPB. Attitude and subjective norms emerge as determinants of repurchase intention while PBC does not. Perceived value and risk impact customer attitude × Survey 624 US TOURISM Mittendorf and Ostermann (2017) × Trust is a positive and perceived risk a negative direct antecedent of hosts’ willingness to accept a customer on Airbnb. Business travelers are perceived to be more trustworthy than private travelers × Survey 53 INFMAN Mody et al. (2017) × Comparative assessment of hotels and Airbnb. Serendipity, localness, communities, and personalization represent valuable experience economy constructs. Airbnb appears to outperform the hotel industry in the provision of all experience dimensions × Survey 630 US TOURISM Möhlmann (2015) × Survey on the determinants of using a sharing option again: Satisfaction intention to use (again) are driven by utility, trust, cost savings, and familiarity × Survey 187 Germany INFMAN Pezenka et al. (2017) × Comparison of Airbnb users’ personalities with Airbnb-nonusers reveals that, based on the Big Five personality traits, Airbnb users score significantly higher on openness, extraversion, agreeableness, and conscientiousness × Survey 1,426 Worldwide TOURISM Poon and Huang (2017) × Effects of traveler personality and trip properties on intention to use Airbnb. Airbnb users and non-users express few differences in their demographics and perceived importance of accommodation attributes × Survey 248 Hong Kong TOURISM Priporas et al. (2017) × Convenience and assurance are critical contributors to the measurement of service quality in remote Airbnb lodgings × Survey 202 Worldwide TOURISM Stollery and Jun (2017) × South Korean Airbnb guests perceive monetary savings, hedonic benefits, and novelty as antecedents and psychological risk as a deterrent of perceived value × Survey 410 South Korea INFMAN Teubner and Flath (2019) × Investigation on how privacy concerns affect a (potential) provider’s intentions to share accommodation via different channels. Privacy concerns are largest for “intermediate” audiences (sufficiently large, still personal connection, e.g. social media platforms) × Survey 237 Germany INFMAN Tussyadiah (2015) × Exploratory study on drivers and deterrents of collaborative consumption in travel. Drivers are societal aspects of sustainability and community, and economic benefits. Deterrents are lack of trust, lack of efficacy with regard to technology, lack of economic benefits × Survey 754 US TOURISM Tussyadiah (2016a) × Exploration of satisfaction drivers with P2P accommodation: Enjoyment, monetary benefits, accommodation amenities. Social benefits influence guest satisfaction for the private room category but not for entire homes/apartments × Survey 644 US TOURISM Yang and Ahn (2016) × Enjoyment, reputation, and perceived security are found to be antecedents of attitude toward Airbnb. Sustainability and economic benefit exhibit no significant influence. Attitude toward Airbnb positively affects loyalty toward Airbnb × Survey 294 South Korea INFMAN Young et al. (2017) × × Investigation of driving factors of P2P usage and influencing factors in preferring a P2P option over a hotel. P2P usage is motivated by leisure travel. Price, location, dwelling size, trip length and size of the tour group are the most influential factors × Survey 788 Denver TOURISM Festila and Dueholm Müller (2017) × Analysis of consumer-object relationships. Identification of four types of Airbnb users: Outgoing, Pragmatic, Friend, Experience Seeker × Interviews 13 INFMAN Ikkala and Lampinen (2014) × × × Interviews with hosts. These divert reputational capital into rental price and sometimes price their property below market level to choose their exchange partners from a wider pool of candidates × Interviews 11 Helsinki INFMAN Ikkala and Lampinen (2015) × Exploration of motives for hosts to participate in hospitality-exchange services. Main motives are financial and social reasons. While the presence of money often drives hosts to participate in Airbnb hosting, social factors tend to gain in importance over time × Interviews 11 Helsinki INFMAN Jung and Lee (2017) × × Interviews with Airbnb and Couchsurfing hosts on the first phase of transaction initiation. While Couchsurfing hosts use these mainly for socializing, Airbnb hosts use them for risk assessment and reduction × Interviews 12 Seoul INFMAN Lampinen and Cheshire (2016) × Airbnb hosts see monetary benefits as a gateway for further social exchange and interpersonal interaction × Interviews 12 San Francisco INFMAN Wang and Heng (2017) × Airbnb hosts have non-economic motivation to bypass the platform, and they are able to overcome trust barriers through leveraging the unbundling of intermediary functions × Interviews 10 China INFMAN So et al. (2018) × Motivations and constraints of Airbnb consumers: Findings from a mixed-methods approach × Interview, Survey 8, 519 US TOURISM Tussyadiah and Park (2018) × × Study on how Airbnb hosts present themselves online (well-traveled or of a certain profession). Well-traveled hosts are perceived more trustworthy and guests exhibit a higher willingness to book × × Empirical, Survey 31,119. 301 14 US cities TOURISM Teubner (2017) × Social network analysis based on US-based Airbnb transactions/ reviews. Hosts and guests form a complex transactional network (giant component) × × Empirical, SNA 100,572, 2.7M 44 cities worldwide WP Ke (2017a) × Linking Airbnb listings to US Census data suggests that income represents a major driver for people to host on Airbnb. Entire home listings tend to be located in areas with higher income and receive more reviews × Empirical 211,124 US WP Ke (2017b) × × × Quantitative description of Airbnb based on large-scale data set including room types, rating distributions (number, valence), word analysis, host types (e.g. multi-listers), and review growth × Empirical 2.3M Worldwide WP Li et al. (2015) × × Linear regression of properties managed by professional and non-professional hosts. Properties of professional hosts have higher revenues (16.9%), higher occupancy rates (15.5%), and are less likely to exit the market (13.6%). Non-professional hosts are less likely to offer different rates across different dates based on underlying demand (e.g. due to major holidays or conventions) × Empirical 24,845 Chicago WP Tussyadiah (2016b) × × × Cluster analysis of Airbnb hosts based on their profile information. Identified archetypes are Global Citizen, Local Expert, Personable, Established, and Creative × Empirical 12,785 NYC TOURISM Guttentag (2015) × × × Consideration of Airbnb’s development through the lens of disruptive innovation theory. Motivations for using Airbnb include cost-savings, household amenities, and the potential for more authentic local experiences × Conceptual TOURISM Kim et al. (2015) × × Conceptual model of service platforms as trusted third parties for reducing (perceived) risks. The proposed model for consumers’ intention includes antecedents to trust, relative advantages, and perceived risk × Conceptual INFMAN Yannopoulou (2013) × Airbnb as a platform for user-generated brands with three emerging themes: access to private sphere, meaningful interpersonal interactions, and authenticity Conceptual INFMAN Neumann and Gutt (2017) × × Theoretical model for setting optimal listing prices. Panel data analysis suggests that hosts react to receiving additional reviews, ID verification, and superhost status by increasing prices slightly (i.e. by 0.5% to 1.6%) × Theoretical Model, Empirical 143,405 8 US cities INFMAN Abramova et al. (2017) × × Conjoint choice analysis; Effects of trust cues on choice likelihoods/WTP equivalences; Star ratings (5 stars) with 1, 5, 15 (27.76€) reviews, ID verification (17.72€), and verified apartment photo (12.57€) have greatest impact, whereas host/guest similarity and social network have much lower or no impact × Survey 450 Germany INFMAN Fagerstrøm et al. (2017) × × Negative facial expression/absence of facial image reduces likelihood to rent. Reverse effect for neutral/positive facial expressions. Absence of facial image/angry facial expression cannot be compensated for by low prices or high rating × Survey 139 INFMAN Mauri et al. (2018) × × Personal reputation (e.g. ratings, photos) explains almost 40% of popularity variation of Airbnb listings (conceptualized as rating score, number of ratings, number of times saved to wishlist, superhost) × Survey 502 Italy, UK TOURISM Abrahao et al. (2017) × Online experiment (trust game) among Airbnb users recruited through the platform. Reputation systems increase trust between dissimilar users (i.e. high social distance). Actual market data suggests that transactions between users with high social distance are only facilitated by high host reputation × Online Experiment, Empirical 8,906, 1M US ECON Qiu and Abrahao (2018) × × Online investment game with Airbnb users reveals that going from 4 to 5 stars as a host is equivalent to having 10 more reviews. Yet, the relative effectiveness of ratings and number of reviews differ on the reputation’s differentiation power × Online Experiment 5,277 US INFMAN Fradkin et al. (2018) × Two field experiments on Airbnb’s reputation system. First, Airbnb guests are offered a$25 coupon to submit a review. Second, a simultaneous review system is implemented and tested. Both tweaks make the reputation system more informative × × Field Experiment 558,959, 15,470 + 15,759 WP
Ert et al. (2016) × × × Hedonic price regression on Airbnb listings indicates that more trustworthy photos lead to a higher prices and increased chances to purchase, whereas review scores do not exhibit sufficient variance. Review scores affect guests’ decisions when varied experimentally × Empirical, Survey 175, 566, 270 Stockholm, Israel, Israel TOURISM
Chen and Xie (2017) × × Hedonic pricing approach. Functional characteristics of Airbnb listings were significantly associated to the price of the listings, and that three of five behavioral attributes of hosts were statistically significant × Empirical 5,779 Austin TOURISM
Dai et al. (2017) × × Development of tool for transforming text reviews into a star rating (1 to 5 stars) using ordinary text processing and data mining methods × Empirical 68,276 Boston INFMAN
Edelman and Luca (2014) × × × Quantitative analysis of NYC-based listings and hosts using hedonic price models. Indication of “racial discrimination” against Afro-American hosts on Airbnb who charge approximately 12% less than other hosts for equivalent listings × Empirical 3,752 NYC WP
Fradkin (2015) × Study on the efficiency of Airbnb’s search algorithm based on internal data. Removal of frictions is expected to lead to 102% of additional matches. A personalized ranking algorithm would increase matching rates by up to 10% × × Empirical 569,864 US WP
Gunter (2018) × Examination of the relative importance of the 4 criteria for obtaining Superhost status. Star rating > reliable cancelation behavior > responsiveness > sufficient Airbnb demand. Commercial Airbnb providers are more likely to receive Superhost status × Empirical 17,356, 16,696 San Francisco TOURISM
Gutt and Herrmann (2015) × × Difference-in-difference analysis of listings with and without visible star rating. Hosts with visible star rating price their listing 2.60€ higher than hosts of comparable listings without visible star rating × Empirical 14,871 NYC INFMAN
Gutt and Kundisch (2016) × × Empirical analysis of the review sub-dimension “value” on listing price shows that guests react to price changes with awarding lower ratings particularly in the value dimension × Empirical 14,859 NYC INFMAN
Lee et al. (2015) × × Linear regression exploration of features associated with room sales: Price, minimum stay, amenities, host responsiveness, wish list, number of reviews, membership seniority. Not so critical for room sales: Overall rating, number of references × Empirical 4,178 5 US cities INFMAN
Liang et al. (2017) × × Investigation of superhost badges. Guests are willing to pay more and submit higher ratings for listings provided by superhosts × Empirical 3,830 Hong Kong TOURISM
Martin-Fuentes et al. (2018) × × Application of a Support Vector Machine to classify listings on Airbnb within the 1-5 Hotel-star categories. The classifier is trained with hotel data from Booking.com × Empirical NA Worldwide TOURISM
Sanchez-Vazquez et al. (2017) × Introduction of a recommender system for Airbnb listings Empirical 15,000 NYC, London INFMAN
Teubner and Glaser (2018) × Development of Airbnb rating scores over time. Lower rated listings exhibit higher dropout rates. Overall rating score skewness is also governed by other phenomena (e.g. regression to the mean, law of large numbers) × Empirical 43,288 Berlin INFMAN
Teubner et al. (2017) × × × Hedonic price regression models: Signals such as the hosts’ rating scores, duration of membership, and Superhost status provide economic value. Also, conventional signals such as accommodation photographs consistently translate into price premiums × Empirical 13,884 86 German cities ECON
Xie and Mao (2017) × × Analysis of the impacts of quality and quantity attributes of Airbnb hosts on listing performance. Host quality attributes significantly influence listing performance through cue-based trust × Empirical 5,805 Austin TOURISM
Zervas et al. (2015) × The majority of Airbnb properties (∼95%) have 4.5 or 5.0 star ratings and almost none have less than 3.5 stars. Comparison of listing available both on Airbnb and TripAdvisor reveals that highest ratings are more common on Airbnb × Empirical 226,594 Worldwide WP
Grbovic (2017) × Brief description of techniques used for search ranking at Airbnb, which is based on listing quality, location relevance, reviews, host response time and guest and host preferences and past booking history × Conceptual INFMAN
Roelofsen and Minca (2018) × Conceptualization of how Airbnb uses both platform design mechanics and an influencing public representation to establish a self-regulated, biopolitics-controlled community × Conceptual ECON
Abramova et al. (2015) × Exploration and evaluation of different strategies for host in response to negative text reviews. The strategies confession, apology, and denial can improve future guests’ trusting beliefs. If subject of criticism is beyond the host’s control, denial does not increase trust, whereas confession and excuse still have positive effects × Survey 320 Germany INFMAN
Edelman et al. (2017) × × Field experiment on Airbnb. Guests with distinctively African-American names are 16% less likely to be accepted than guests with distinctively White names × Field Experiment 6,400 5 US cities ECON
Ma et al. (2017a) × Empirical analysis of topics Airbnb hosts reveal in self-description. Hosts most frequently write about Origin/Residence, Work/Study, and Interests/Tastes. Survey on how trustworthy self-descriptions shows that perceived trustworthiness increases with profile length and number of topics mentioned. Choice experiment shows that perceived trustworthiness is a predictor of guests’ host choice × Empirical, Survey 40,005, 1,200, 355 12 US cities INFMAN
Alsudais (2017) × Manual labeling of Airbnb reviews; 85% of reviews include a reference to a host (either by paraphrasing or by name) × Empirical 1,024 3 US cities INFMAN
Bridges and Vásquez (2016) × Analysis of 21200 text reviews (for guests and hosts). These comprise a very restricted set of words and are mostly positive (93%). Further analysis reveals that less-than-positive experiences are communicated using subtle cues, for instance, by information that is excluded × × Empirical 400 4 US cities TOURISM
Brochado et al. (2017) × Semantic and relational text review analysis. A concept map comprising eight themes (stay, host, place, location, apartment, room, city, home) reveals that all themes are culturally universal, that is, can be observed across India, Portugal, and the US × × Empirical 1,776 India, Portugal, US TOURISM
Camilleri and Neuhofer (2017) × Qualitative thematic analysis of text reviews from Airbnb listings in Malta, identifying six key components: Welcoming, expressing feelings, evaluating location and accommodation, helping and interacting, recommending, and thanking × Empirical 850 Malta TOURISM
Johnson and Neuhofer (2017) × Exploration of value co-creation experiences in Jamaica based on qualitative online content analysis of text reviews × × Empirical 942 Jamaica TOURISM
Ma et al. (2017b) × Development of a computational framework to predict perceived trustworthiness of host profile texts. Host profiles were assessed by AMT workers with regard to trustworthiness. Examples for positive features are words from the categories “positive emotion” and “social.” × Empirical 4,180/ 450 12 US cities INFMAN
Phua (2018) × Analysis and conceptualization of negative reviews about Airbnb on business review platforms. Most of the negative reviews (27%) criticize problems with accessing a competent customer service agent. Others complain last-minute cancelation by hosts (21%), pricing/fee structure (14%) or misrepresentation (13%) × Empirical 664 TOURISM
von Hoffen et al. (2017) × Comparison of sentiment scores of Airbnb reviews with those of #airbnb-tweeds. Overall, reviews exhibit more positive sentiment. Identification of positive/negative words in positive/negative reviews Empirical 20,000 Washington, Berlin INFMAN
Gunter and Önder (2017) × × Regression analysis of Airbnb listings demand in Vienna reveals a price-inelastic demand structure. Listing size, number of photos, and host responsiveness positively drive demand. Listing price, distance to city center, and host’s response time have negative impact × Empirical 7,864 Vienna TOURISM
Kakar et al. (2016) × × Hispanic and Asian hosts exhibit 9.6% and 9.3% lower listing prices as compared to White hosts. No significant impact of gender and sexual orientation on price listings × Empirical 715 San Francisco WP
Rahimi et al. (2016) × Color scheme and interior ornateness analysis of Airbnb listings from cities around the world. Color schemes are found to be rather similar, whereas ornateness varies considerably depending on city, but also for different neighborhoods within cities × Empirical 48,651, 2,095 6 US cities, 2 RU cities, 2 Asian cities ECON
Zhang et al. (2016) × × Comparison of listing performance, based on their representation on Airbnb. On average, listings with verified (i.e. implying high quality) room photos are 9% more frequently booked, yielding additional 2,455 in yearly earnings × Empirical 17,826 7 US cities INFMAN Benítez-Aurioles (2018) × Investigation of the (counter-intuitive) negative correlation of price with flexible cancelation policies or instant book availability × Empirical 497,509 44 cities worldwide TOURISM Dudás et al. (2017) × Multi-band geographic visualization of Airbnb listings in Budapest (different colors for different properties: price, attractiveness, distance). No significant correlation between a listing’s price and its location in Budapest in found × Empirical NA Budapest ECON Gibbs et al. (2017a) × Hedonic price regression models on metropolitan areas in Canada. Economic value can be obtained by particular host and listing attributes × Empirical 15,716 5 Canadian cities TOURISM Gibbs et al. (2017b) × Empirical analysis of five Canadian metropolitan areas shows that across these markets the majority (52.2%) of Airbnb providers do not utilize dynamical pricing. Price fluctuation is more common for professional hosts, hosts with greater experience, entire homes, and listings in high demand markets × Empirical 39,837 5 Canadian cities TOURISM Gutiérrez et al. (2017) × × Airbnb accommodation distribution in Barcelona reveals a center-periphery pattern and capitalization of proximity to tourist attractions more than the hotel sector. Empirical data analysis identifies touristic areas that experience high pressure from Airbnb × Empirical 14,539 Barcelona TOURISM Lécuyer et al. (2017) × Implementation of an Airbnb crawler, accessing listings in high frequency to derive insights on occupancy rates and revenues; by and large corroborating claims by Airbnb and prior studies (e.g. in 2014, 6% of hosts owned ≥ 3 listings but accounted for 37% of all revenues) × Empirical NA NYC INFMAN Wang and Nicolau (2017) × Empirical analysis of Airbnb data from 33 cities. Not only listing attributes but also host attributes have a significant effect on the price × Empirical 180,533 33 cities worldwide TOURISM Xie and Kwok (2017) × × Empirical data analysis from 2008 to 2011. Airbnb supply (i.e. #listings) leads to a decline of local hotels’ financial performance. Yet, increasing price difference between hotels and Airbnb listings and higher price dispersion of Airbnb listings can mitigate this effect × Empirical 1,482 Austin TOURISM Hill (2015) × Description of how Airbnb has developed, introduced, and improved its smart pricing algorithm × Conceptual INFMAN Adamiak (2018) × Description of Airbnb supply in European cities × Empirical 737k 432 EU cities TOURISM Blal et al. (2018) × Average prices of Airbnb rentals positively influence hotel sales performance patterns while average satisfaction of Airbnb users negatively affects them. Airbnb’s effect on hotel sales performance patterns varies across different hotel segments. Yet, the total number of Airbnb listings is found to have a non-significant effect on hotel sales performance × Empirical 101 San Francisco TOURISM Cheng and Foley (2018) × Analysis of online newspaper comments made in response to an article reporting Airbnb’s new anti-discrimination policy (text-mining and co-stakeholder analysis) × × Empirical 217 TOURISM Constantiou et al. (2016) × Re-telling the Airbnb story based on blog entry analysis. Two phases in Airbnb’s platform development are identified: 1) Creation of a network of users and 2) platform augmentation Empirical 813 INFMAN Fang et al. (2016) × Empirical analysis of Airbnb/ sharing economy on tourism industry employment, suggesting that a) the entry of Airbnb benefits the entire tourism industry by generating new jobs as more tourists come due to the lower accommodation cost but b) low-end hotels are threatened and hence marginal effects decrease with increasing sharing economy size × Empirical 657 Idaho TOURISM Gurran and Phibbs (2017) × × Analysis of written submissions to a NSW inquiry on short-term renting and Airbnb inventory for the city of Sydney. Noise, nuisance, traffic, parking, and waste management issues arise when short-term holiday rentals penetrate residential areas × Empirical NA Sydney ECON Horn and Merante (2017) × Investigation of the impact of Airbnb density on rental prices (tract-wise) for the city of Boston. An increase in Airbnb listings by one standard deviation is associated with an increase in asking rents of 0.4% × Empirical 832 Boston ECON Schäfer and Braun (2016) × × Empirical analysis reveals that 0.3% of accommodations in Berlin violate the misuse prohibition law (Zweckentfremdungsverbot). Considering individual neighborhoods, this number can be higher (up to 7.04% for Mitte), which indicates that there is more of a problem with individual sub-markets than a problem for the whole city × Empirical 11,495 Berlin ECON Wegmann and Jiao (2017) × × Regression analysis for Airbnb penetration in different US cities. Derivation of four principles for cities: Usage of web scraping to obtain data; booking patterns vary and necessitate micro-geographic regulation; need of enforcement; differentiation between host types × Empirical 19,337 5 US cities ECON Zervas et al. (2017) × Empirical analysis of Airbnb’s entry into the US state Texas. Lower-priced hotels are most affected by the rise of Airbnb × Empirical 294,383 Texas INFMAN Akbar and Tracogna (2018) × Consideration of Airbnb as a hybrid form of governance (including both market and hierarchy arrangements) based on transaction cost theory and platform economics Conceptual TOURISM Lee (2016) × × The paper explores how short-term rentals affect prices and supply of affordable housing in Los Angeles, and how municipal policymakers can best regulate Airbnb, arguing for a 14% occupancy tax on any unit listed on the platform × Conceptual LAW Llop (2016) × × Analysis of Airbnb’s negative influence on the city of Barcelona and it’s corresponding regulatory measures × Conceptual ECON Mikhalkina and Cabantous (2015) × Analysis of Airbnb’s media coverage in six mainstream business publications between 2009 and 2013 reveals a three-staged process: From the first attempts to classify Airbnb into existing categories, over the approach of describing Airbnb as a separate phenomenon, up to the final acknowledgement of Airbnb as an iconic business model Conceptual INFMAN Brauckmann (2017) × Combination of official statistical data with a geo-information system reveals no definitive impact of the sharing economy on housing markets × Empirical NA Hamburg TOURISM Quattrone et al. (2016) × Identification of disparities in different municipal regulation of Airbnb. Utilization of public available Airbnb data to identify areas that benefit from its presence. Propose to introduce a municipal regulated transferable sharing right × Empirical 17,825 London INFMAN Oskam and Boswijk (2016) × Outline of Airbnb’s further development in the next years and Delphi panel discussion on the impact this development can have on the tourism sector, hotel industry, and municipal government goals Delphi Panel 31 Amsterdam TOURISM Interian (2016) × Legal/regulatory analysis of issues between Airbnb and US cities. It is argued that Airbnb should be held liable for ensuring basic compliance by using measures similar to what has been implemented in many European cities × Conceptual LAW Jonas (2015) × Examination of New York City’s current regulation of common sharing scenarios. Instead of trying to squeeze companies within this domain into existing regulations, the city should develop individual sharing economy-specific regulations × Conceptual LAW Lines (2015) × The paper explores options for Arizona municipalities to regulate Airbnb, where two alternatives are outlined. First, existing regulation may be used to govern Airbnb. Alternatively, it is argued that a new system addressing Airbnb’s unique operations, benefits, and problems should be implemented × Conceptual LAW O’Regan and Choe (2017) × Analysis of Airbnb from the perspective of cultural capitalism. Examination of Airbnb’s impact on cultural, economic, political, and consumer-related contexts × Conceptual TOURISM Santolli (2017) × Legal/regulatory analysis of issues between Airbnb and the city of Barcelona where officials have used an unenforceable, ineffective de jure ban on Airbnb × Conceptual LAW Stabrowski (2017) × Socio-economic effects and legal and regulatory implications of Airbnb; Identification of opportunities to align goals from Airbnb and local governments through “platform cooperativism” (e.g. by distributing annual dividends to all city residents) × Conceptual ECON Toto (2017) × Legal analysis on the role of lass action waivers and arbitration agreements in Airbnb’s terms of use, which prevent legal enforcement against cases of racial discrimination. It is argued that without regulatory adaptions, such waivers threaten civil rights enforcement × Conceptual LAW ## Table AI. City summary Region, City #T #Listings Density Last data #Hosts #R/L Price (US) Score Inst.B. Ent.Apt Prf.Lst
NA Asheville 1 864 9.7 04/2016 643 32.1 99 95.32 0.19 0.62 0.43
Austin 3 9,663 10.2 03/2017 7,492 13.9 160 95.85 0.23 0.69 0.36
Boston 3 4,870 7.2 10/2017 2,705 24.8 140 93.37 0.35 0.62 0.60
Chicago 2 5,207 1.9 05/2017 3,532 25.4 99 95.04 0.29 0.59 0.48
Denver 2 3,918 5.7 11/2017 3,030 32.8 100 96.63 0.44 0.68 0.42
Los Angeles 11 31,253 7.9 05/2017 20,810 20.8 100 94.21 0.27 0.58 0.51
Montreal 2 10,619 6.1 05/2016 8,368 9.2 55.2 92.39 0.14 0.60 0.35
Nashville 4 5,332 7.8 09/2017 3,425 31.9 149 96.53 0.49 0.76 0.51
New Orleans 27 5,215 13.3 03/2018 3,050 36.3 132 95.71 0.56 0.83 0.59
New York City 35 48,852 5.7 03/2018 40,530 18.5 100 93.56 0.31 0.49 0.32
Oakland 2 1,718 4.1 05/2016 1,427 15.6 98 93.96 0.11 0.56 0.34
Portland 29 4,738 7.4 02/2018 3,793 49.5 90 96.81 0.41 0.66 0.37
Quebec City 9 2,297 4.3 09/2017 1,662 22.7 64 93.41 0.41 0.63 0.45
San Diego 2 6,608 4.7 07/2016 4,300 14.1 135 94.38 0.18 0.66 0.52
San Francisco 29 4,804 5.6 03/2018 3,346 49.9 150 95.79 0.36 0.58 0.51
Santa Cruz 1 814 3.0 10/2015 616 27.2 150 94.86 0.10 0.65 0.40
Seattle 2 3,818 5.4 01/2016 2,751 22.2 100 94.54 0.15 0.67 0.43
Toronto 7 12,714 4.5 06/2017 9,152 16.0 76.8 94.00 0.21 0.62 0.44
Vancouver 4 6,651 10.3 10/2017 5,050 22.5 92 94.13 0.26 0.68 0.41
Victoria 1 1,691 19.7 08/2016 1,256 18.5 80 94.53 0.21 0.67 0.44
Washington DC 3 7,788 11.4 05/2017 5,820 19.5 125 94.72 0.30 0.68 0.40
AS Hong Kong 1 6,474 0.9 08/2016 3,334 12.7 70.59 88.44 0.25 0.50 0.61
AU Melbourne 8 14,305 3.7 04/2017 10,506 16.3 81.9 94.02 0.29 0.57 0.41
Northern Rivers 1 2,350 7.9 04/2016 1,703 10.6 114.27 93.85 0.13 0.65 0.47
Sydney 12 32,830 8.1 01/2018 25,221 10.3 104.52 93.28 0.34 0.61 0.36
Tasmania 18 4,459 8.7 02/2018 3,038 31.2 116.22 95.54 0.51 0.76 0.48
EU Amsterdam 28 18,547 22.6 12/2017 15,907 18.2 141.6 94.41 0.20 0.79 0.24
Antwerp 2 1,227 2.5 05/2017 968 21.6 76.7 92.33 0.26 0.70 0.36
Athens 2 5,127 7.7 05/2017 3,535 24.2 47.2 94.23 0.51 0.83 0.54
Barcelona 21 18,531 11.5 02/2018 10,909 27.6 69.62 90.76 0.45 0.47 0.60
Berlin 19 20,576 5.9 05/2017 17,810 12.9 53.1 93.39 0.17 0.50 0.25
Brussels 2 6,192 5.4 05/2017 4,623 18.0 64.9 91.45 0.25 0.65 0.39
Copenhagen 2 20,545 26.9 06/2017 19,079 10.7 105.92 94.39 0.15 0.81 0.16
Dublin 3 6,729 12.8 02/2017 4,756 21.0 93.22 91.99 0.26 0.47 0.47
Edinburgh 10 9,638 19.5 09/2017 7,175 26.9 94.47 94.69 0.35 0.57 0.42
Geneva 19 3,060 15.7 01/2018 2,361 15.5 96.305 93.42 0.28 0.66 0.41
London 6 53,904 6.1 03/2017 37,642 12.5 93.8 91.71 0.23 0.50 0.45
Madrid 6 16,313 5.2 01/2018 9,838 27.7 69.62 92.43 0.48 0.63 0.57
Malaga 1 4,853 8.5 11/2017 2,386 20.2 69.62 91.00 0.57 0.76 0.70
Mallorca 2 14,858 17.1 03/2017 6,323 7.4 118 91.88 0.36 0.87 0.71
Manchester 1 865 1.6 04/2016 560 17.2 62.98 91.18 0.17 0.41 0.52
Paris 28 59,945 26.7 03/2018 51,683 16.3 88.5 92.49 0.27 0.87 0.25
Rome 1 25,275 8.8 05/2017 14,100 22.6 82.6 91.99 0.45 0.60 0.65
Trentino 1 1,847 1.7 10/2015 1,275 3.1 82.6 91.39 0.13 0.77 0.55
Venice 2 6,027 22.8 05/2017 2,860 35.8 129.8 91.16 0.50 0.75 0.73
Vienna 22 9,201 5.2 09/2017 6,522 20.8 64.9 93.84 0.36 0.67 0.44
543,112
Notes:

#T = number of available snapshots (months); Density = Listings per 1,000 capita; #R/L = number of reviews per listing; Inst.B. = Instant Booking; Ent.Apt = Entire Apartment; Prf.Lst = Professional Listing (i.e. host offers more than one); NA = North America; AS = Asia; AU = Australia; EU = Europe

Source: Data from InsideAirbnb.com

Table AI

Figure A1

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## Corresponding author

Timm Teubner can be contacted at: teubner@tu-berlin.de