A typology of user misbehaviours in the sharing economy context

Abstract

Purpose

This paper aims to examine the types of user misbehaviours in the sharing economy (SE) context. SE offers a fruitful study setting due to the scope of potential misbehaviour and the expanded role of consumers.

Design/methodology/approach

The study drew on online archival data from the AirbnbHell.com website, where people share their stories about their Airbnb-related negative experiences. The authors reviewed 405 hosts’, guests’ and neighbours’ stories and coded the identified forms of misbehaviours into categories. The typology thus developed was validated in the context of the Uber Rides service.

Findings

User misbehaviours in the SE context can be distinguished based on the domain in which the user role is violated and the nature of violated norms. These two conceptual distinctions delineate a four-fold typology of user misbehaviours: illegal, unprofessional, unbefitting and uncivil behaviours.

Research limitations/implications

The trustworthiness of the stories could not be assessed.

Practical implications

The presented typology can be used as a mapping tool that facilitates detection of the full scope of misbehaviours and as a managerial tool that provides ideas for effective management of misbehaviours that correspond to each category.

Originality/value

The paper presents the first empirically derived comprehensive typology of user misbehaviours in SE settings. This typology enables classification of a broad set of misbehaviours, including previously overlooked unprofessional behaviours carried out by peer-service providers. The study also puts forward a revised definition of consumer misbehaviours that encompasses the impact of misbehaviours on parties not directly involved in the SE-mediated exchange.

Citation

Golf-Papez, M. and Culiberg, B. (2023), "A typology of user misbehaviours in the sharing economy context", European Journal of Marketing, Vol. 57 No. 13, pp. 111-151. https://doi.org/10.1108/EJM-08-2021-0583

Introduction

I live next to an illegal short-term rental Airbnb […]. The pictures of the property are fake […]. The guests threw beer cans at my house, broke my fence trying to climb over it to get a ball, and had loud parties into the early morning hours […]. When a real customer is disgusted with the property and does not want to stay because it is not clean, nor meets any of the description, the host does not give them their money back. He has thrown guests out because he has others checking in […]. When I contacted Airbnb, they told me to speak to the owner. He has been very nasty to me. — A neighbour to an Airbnb-rented home sharing her experience on Airbnbhell.com

This opening vignette illustrates how some people misbehave within exchange settings, potentially causing problems for marketers, firms, other consumers (Fullerton and Punj, 2004) and other parties not directly involved in the exchange. Existing marketing research mobilises a variety of terms to refer to such misconduct (e.g. dysfunctional customer behaviour (Daunt and Harris, 2012), deviant behaviour (Dootson et al., 2016) and problematic customer behaviour (Hibbert et al., 2012), and in this paper, we refer to it as consumer [1] misbehaviours. Predominantly studied in the context of the hospitality industry (Daunt and Harris, 2012; Gursoy et al., 2017; Harris and Reynolds, 2004), consumer misbehaviours are a widespread phenomenon appearing across different service settings (Fullerton and Punj, 2004). They also commonly appear in the context of sharing economy (SE) services, where more than half (55%) of European consumers using the service and more than one-tenth (14%) of their peers providing a service report that they have experienced at least one form of consumer misbehaviour whilst using the service (European Commission, 2017). Frequently reported forms of misbehaviour include peer consumers providing poor-quality goods/services and misrepresenting their goods/services, along with various payment issues (European Commission, 2017).

To grasp the diversity of consumer misbehaviours, scholars have offered several classifications, primarily categorising misbehaviours based on their nature or target (Berry and Seiders, 2008; Daunt and Harris, 2012; Freestone and Mitchell, 2004; Fullerton and Punj, 2004; Greer, 2015; Harris and Reynolds, 2004). Notwithstanding the insightfulness of these typologies, most of them capture misbehaving in purely offline settings (for an exception, see Freestone and Mitchell, 2004), lack empirical grounding (for an exception, see Daunt and Harris, 2012; Greer, 2015; Harris and Reynolds, 2004) or do not guide managers in detecting and/or mitigating a particular type of misbehaviour (Bitner et al., 1994). At the same time, being developed from the perspective of the customers and/or employees in traditional settings (Bitner et al., 1994; Harris and Reynolds, 2004), current typologies may not fully represent the diversity and nature of misbehaviours in the context of the SE, where, as illustrated by the opening vignette, the service is provided by peers and misbehaviours are easily observed and felt by third parties not involved in the exchange (e.g. neighbours). An empirical and systematic investigation of misbehaviours in the SE context has the potential to not only extend prior SE research with the first typology of misbehaviours in that context but also to help in addressing some of the shortcomings of prior typologies, as well as contributing towards the development of further comprehensive and nuanced typologies of consumer misbehaviours (Lugosi, 2019).

SE can be defined as “a socioeconomic system that allows peers to grant temporary access to their underutilized physical and human assets through online platforms” (Gerwe and Silva, 2020, p. 71). It is based on a triadic, platform-based relationship consisting of a firm or service enabler (e.g. Airbnb, Uber) which acts as an intermediary between the providers of a good or service and the consumers who demand those underutilised goods and services (Kumar et al., 2018). By meeting a range of consumer needs, including accommodation and transportation, the SE represents a viable alternative to established firms in traditional industries such as hotels and taxi companies (Wirtz et al., 2019). However, there is a misalignment between the rules governing traditional services and the new SE platforms, leading to irregularities and issues with accountability (Kathan et al., 2016). There are several reasons why SE represents a fruitful avenue for studying consumer misbehaviours. Firstly, there is a higher likelihood of consumer misbehaviours taking place since SE transactions include multiple parties and “multiple points of encounters throughout the transactions” (Moon et al., 2019, p. 239), and that assets are typically used without service personnel supervision (Schaefers et al., 2016). Secondly, investigating consumer misbehaviours in the SE context allows us to capture both consumer misbehaviours that occur in more traditional settings, such as in the accommodation sector, and new or modified forms of misbehaviours that are facilitated or enabled by the distinguishing characteristics of the SE. One such characteristic of the SE is that the transactions are typically mediated by internet-based platforms (Benoit et al., 2017; Eckhardt et al., 2019), which allows for an exploration of both online and offline forms of misbehaviour within the context of one service. Another distinct characteristic of the SE that shapes the phenomenon of consumer misbehaviours is the expanded role of consumers, with some taking on institutional roles that are typically carried out by firm service employees (Eckhardt et al., 2019). These roles include not only the provision of a service but also service recovery, as users as peer service providers often have to act as resolution managers. A greater awareness of the full scope of user misbehaviours that need to be managed would likely facilitate a discussion on how such misbehaviours should be addressed and by whom. This conversation is especially important in light of the documented economic, material and psychological costs (Berry and Seiders, 2008; Fisk et al., 2010; Fullerton and Punj, 1993; Harris and Reynolds, 2003) resulting from (un)managed consumer misbehaviours.

To collect empirical evidence on the full scope of misbehaviours in SE settings and offer a theoretically and practically useful way to classify these, this paper sets out to answer the following research question:

RQ1.

What are the different kinds of user misbehaviours manifested in the SE context?

In addition, we ask:

RQ2.

How can a greater understanding of the different types of user misbehaviours in the SE context inform the strategies used to mitigate such misbehaviours?

By addressing these questions in the context of Airbnb and Uber, our paper puts forward the first empirically derived comprehensive typology of user misbehaviours in an SE setting. Building on prior consumer misbehaviour literature that frames misbehaviours as a violation of social norms (Daunt and Harris, 2012; Denegri-Knott, 2006; Fullerton and Punj, 2004), this paper adds support to previous research that illustrates the relevance and insightfulness of framing (dysfunctional) service transactions (Hibbert et al., 2012; Solomon et al., 1985) or value-co-destruction (Järvi et al., 2020) in terms of the violation of or deviation from the norms and expectations associated with a particular consumer role. Our consideration of new potential perpetrators (i.e. users as peer service providers) and targets (i.e. peer service providers and unparticipating third parties) and misbehaviours in both online and offline realms allowed the identification of a more diverse set of user misbehaviours. Our typology of four different types of user misbehaviours provides insights into previously unidentified categories of misbehaviours (e.g. unprofessional behaviours carried out by peer-providers of a service and uncivil behaviours affecting unparticipating parties) and outlines the reported financial or material, psychological and physical impacts associated with each category of misbehaviour. Being developed on the basis of a clear classification system (cf. Bitner et al., 1994) and validated in the context of two different SE services (Airbnb and Uber), the typology offers different SE stakeholders (e.g. consumers, marketers and policymakers) both a theoretically and practically useful approach to classify misbehaviours. Finally, our contribution lies in a revised definition of consumer misbehaviours which acknowledges their potential to impact parties not directly included in the exchange and recognises diminished well-being as one of the potential outcomes.

The paper begins with a review of the literature on the various types of consumer misbehaviours, both in general and in the context of the SE. After the presentation of the methods used in this study, which included analysing Airbnb users’ experiences with misbehaviours, we present the four-fold typology of user misbehaviours in the context of Airbnb and illustrate the transferability of the typology to the context of Uber Rides.

We conclude by outlining our theoretical contributions and implications for practice and future research.

Consumer misbehaviour

Consumer misbehaviour refers to “any act by a customer in an online or offline environment that deprives the firm, its employees, or other customers of resources, safety, image, or an otherwise successful experience” (Fombelle et al., 2019, p. 387). Conceptualised under different names, including deviant customer behaviour (Harris and Daunt, 2011), aberrant consumer behaviour (Fullerton and Punj, 1993) and dysfunctional consumer participation (Hibbert et al., 2012), consumer misbehaviour is predominantly defined as a violation of norms of conduct in exchange situations (Fisk et al., 2010). These norms are typically formed through rules, customs, manners and laws (Moschis and Cox, 1989). Separating illegal misbehaviours from deviant ones, Lugosi (2019) suggested that for a behaviour to be called deviant, it has to diverge from usual or accepted social, cultural and/or ethical standards of behaviour (i.e. norms). Taking an institutional reading of misbehaviour, Plé and Demangeot (2020) argued that a behaviour can be perceived as deviant when it is in conflict with the institutional arrangements under which the exchange is taking place. Different interpretations of institutional arrangements may lead to a particular behaviour being perceived as deviant by some consumers and non-deviant by others (Plé and Demangeot, 2020). A similar situation arises when looking at misbehaviours from the perspective of role theory, wherein consumer misbehaviour could be understood as a lack of alignment between the perceived and expected behaviours in a particular role (Solomon et al., 1985; Turner, 2006). This approach – broadly adopted also in our paper – suggests that consumers enter market-mediated exchanges with the expectations about their own role and the role of the other party (e.g. employee and another consumer) (Solomon et al., 1985) and that particular behaviour could be considered disruptive (or experienced negatively) when there is a departure from the expected role behaviour (Fullerton and Punj, 2004; Solomon et al., 1985). Studying consumer misbehaviours from the perspective of role theory may be particularly relevant in the SE, which “could be said to disrupt who does what in exchanges” (Öberg, 2021, p. 1), and in which the participants adopt roles that they have not traditionally been expected to perform, such as peer service providers acting as pricing managers (Öberg, 2021).

Far from being an inconsequential phenomenon, consumer misbehaviours can have negative value outcomes for those misbehaving, as well as other participants in the SE (Plé and Demangeot, 2020). Such misbehaviours represent a considerable financial, psychological and physical cost to organisations, their employees and other customers (Fombelle et al., 2019; Harris and Reynolds, 2003). Although Fisk et al. (2010) claimed that there are also positive consequences of misbehaviour, such as increased job opportunities and fostering a positive self-image among consumers who observe but do not engage in misbehaviours, the general consensus is that the consequences of the problematic behaviours can be quite daunting, transferring into reduced profit margins for firms and higher prices for consumers (Harris, 2008), negative consumer experiences (Fullerton and Punj, 1993) and reduced morale and motivation among frontline employees (Harris and Reynolds, 2003).

Consumer misbehaviour in the sharing economy

Knowledge of consumer misbehaviour is fragmented and limited in SE settings, with studies addressing this issue starting to emerge only recently. Schaefers et al. (2016) discussed two unique aspects of consumer misbehaviour in the SE domain. The first particularity relates to SE exchanges involving assets being shared successively and by different customers, which can lead to inappropriate handling, damage or overuse of the accessed good. The second particularity refers to the occurrence of indirect misbehaviour, which occurs in the absence of others as a result of limited supervision by service personnel. Encountering such misbehaviours was found to be contagious (Schaefers et al., 2016).

Another literature stream touches on consumer misbehaviours in the SE from the perspective of value co-destruction, which is defined as “an interactional process between service systems” (e.g. firms and customers, employees) “that results in a decline in at least one of the systems’ well‐being” (Plé and Chumpitaz Cáceres, 2010, p. 431). In this domain, different examples of hosts and guests’ misbehaviours have been identified by Buhalis et al. (2020), such as damage to property, sexual harassment and reduced safety and security. Sthapit (2019) identified two sources of value co-destruction in the context of Airbnb, namely, the bad behaviour of hosts and poor Airbnb customer service. Camilleri and Neuhofer (2017), on the other hand, examined various host-social practices that can lead to value formation or value destruction. While these studies hint at the link between misbehaviours and value destruction, they do not specifically focus on the types of misbehaviours that are enacted and typically examine the process of value destruction from the perspective of one party. For instance, focusing exclusively on the providers of Uber service, Sthapit and Björk (2019) reported various forms of drivers’ bad behaviours, including offensive language and overcharging.

In terms of the consequences of misbehaviours, Plé and Demangeot (2020) elaborated on the effects of the deviant behaviours of the actors in the SE at different levels, pointing out that combined deviant tourist behaviours enacted at the micro-user level can affect actors at another level, i.e. macro municipality level, leading the latter to adapt their behaviours to counter the effects of aggregated deviance and impose regulation. A more focused approach was adopted by Lu et al. (2020), who showed that the style of host-guest interaction influences guest’s satisfaction and switching intentions after experiencing service failure in the context of home-sharing. Similarly, focusing on service recovery aspects rather than on types of misbehaviours, Moon et al. (2019) examined the complaint management mechanisms and attribution of responsibility in the context of negative Airbnb experiences.

Typologies of consumer misbehaviours

Scholars have sifted through a plethora of misbehaviours to provide categorisations of consumer misbehaviour by considering different classification bases, methodological approaches and research contexts (for an overview of typologies, see Appendix 1).

Prior research predominantly classifies misbehaviours in terms of the nature of the act or the targets of misbehaviours (Fombelle et al., 2019; Greer, 2015). Early typologies, mostly conceptual in spirit, are particularly concerned with categorising misbehaviours based on the nature of the act. One of the first typologies was proposed by Moschis and Cox (1989), who made the distinction between normative vs deviant behaviour in terms of how desirable such behaviour was for society and regulated vs nonregulated behaviour, reflecting the demands placed upon members of society. Although part of their typology also included desirable behaviours, the part that referred to deviant behaviours distinguished between negligent behaviours, such as product misuse and criminal/fraudulent behaviour, like shoplifting. Focusing on problematic behaviours, one of the more recognised, yet anecdotal, typologies is Lovelock’s (1994) eight types of jaycustomers in retail services, namely, thief, vandal, belligerent, family feuder, deadbeat and rule-breaker. Later studies empirically confirmed several of these categories and added additional ones. The most common types of misbehaviours, found across the board were rule breaking, verbal and physical abuse (Bitner et al., 1994; Boo et al., 2013; Gursoy et al., 2017; Harris and Reynolds, 2004). Additionally, the typology by Bitner et al. (1994), developed in hotel, restaurant and airline service encounter settings, also included drunkenness and uncooperative customers, similarly to Boo et al. (2013), who later identified inconsiderate (e.g. noisy) and crude (e.g. drunk) behaviours in a range of services. Harris and Reynolds (2004) complemented these findings with a qualitative study in the hospitality sector which came up with additional types of misbehaviours/jaycustomers based on how overt/covert and financially motivated/unmotivated the misbehaviours were. Their classification includes compensation letter writers, undesirable customers, service workers, vindictive customers and sexual predators. Another, more recent, typology from the hospitality sector was put forward by Gursoy et al. (2017), which added inattentive parents with naughty children, outlandish requesters, hysterical shouters, poor hygiene manners and ignorant customers to the list of previously identified misbehaviours. While such typologies hint at the diversity of actual forms of consumer misbehaviours, it would be difficult to claim that they represent a comprehensive list of misbehaviours in a certain setting.

The other approach to classifying misbehaviours includes considering who is the target of misbehaviour, with employees, customers and company being most commonly mentioned. The differences among such typologies occur due to further fragmentation of the aforementioned categories or the addition of new categories. For example, Fullerton and Punj (2004) discussed three different company-related targets: merchandise (e.g. theft), the marketer’s financial assets (e.g. insurance fraud) and the marketer’s physical or electronic premises (e.g. spreading viruses). All of their categories are then matched with different dimensions, including the nature of the act, type and degree of disruption and reactions by the providers and other consumers (Fullerton and Punj, 2004). This classification is insightful, yet still needs to be empirically verified. On the other hand, Fombelle et al. (2019) followed the logic of the three targets (i.e. company, employee and customer) with the goal of identifying prevention strategies. In the context of tourism and group travel, Tsaur et al. (2019) discussed two additional targets, namely, the tourist site environment and operation of the tour group. Greer (2015), on the other hand, relied on company and employees as targets and developed three superordinate categories of consumers’ defective co-creation behaviour in professional services. In addition to goods-related misbehaviour (i.e. property abuse and fraud) and interpersonal misbehaviour (i.e. verbal abuse and physical aggression), Greer (2015) identified a category of relational misbehaviour which includes two new forms of consumer misbehaviour: underparticipation and overparticipation. Underparticipation includes behaviours where consumers refuse to adequately engage with the service provider or to provide time, effort or money for the service delivery. In contrast, in case of overparticipation, consumers interact unnecessarily or excessively with service providers or the personal interaction has social or romantic overtones (Greer, 2015). Some references to the latter can be found in previously mentioned uncooperative customers (Bitner et al., 1994), outlandish requesters (Gursoy et al., 2017) and sexual predators (Harris and Reynolds, 2004).

Although existing categorisations of consumer misbehaviour provide valuable insights into this phenomenon, they cannot fully capture the nature and complexity of consumer misbehaviours in the SE that stems from the particularities of who is involved in the market exchanges and how the interactions take place. Firstly, the SE market includes a larger pool of potential perpetrators of misbehaviours, as we need to consider not only end users but also peer providers of SE services. In contrast to the dyadic exchanges that occur between a company and its customers on traditional markets, SE exchanges are triadic in nature and include the platform providers, peer service providers and customers (Benoit et al., 2017). Since SE platforms as service enablers are not directly involved in the service delivery process, misbehaviours in the SE context would seem to be less easily observed by the platforms and more easily disputed than in traditional settings. The SE characteristic that the service commonly takes place without any or with limited service personnel supervision increases the likelihood of misbehaviours and poses unique and additional challenges for their detection and mitigation (Schaefers et al., 2016).

In addition to more potential perpetrators, the SE context also presents a broader range of potential targets of misbehaviours. While prior studies defined either employees or customers as targets (Greer, 2015; Harris and Reynolds, 2004), recent research demonstrates the broader influence of misbehaviours on unparticipating parties, such as neighbours who are disturbed by Airbnb users (Buhalis et al., 2020; Gurran et al., 2020; Stergiou and Farmaki, 2020). As such, considering the views of unparticipating parties may be necessary to obtain a comprehensive picture of user misbehaviours in the SE.

Finally, in terms of place of consumer interactions, prior categorisations (see Appendix 1 for an overview) encapsulate misbehaviours that stem from direct in-person interactions, which take place in traditional hospitality and service settings (Boo et al., 2013; Greer, 2015; Harris and Reynolds, 2004). However, in the SE, interactions are more complex and fluid – while interactions between employees of platforms and customers occur online (e.g. via the Airbnb.co.uk website), interactions between peer-service providers and customers are carried out both online (e.g. via chat on the Airbnb website prior to/during/after stay) and offline (e.g. in the accommodation during the stay). Exploring misbehaviours in the context of the SE allows us to embrace the seamless interwovenness of online and offline exchanges (Plé and Demangeot, 2020). Taken all together, the unique features of the complex SE market call for a novel and more comprehensive categorisation of consumer misbehaviours, a call that this study aims to answer.

Research context and approach

To collect the empirical evidence of the full scope of user misbehaviours in the context of the SE, we selected Airbnb as our research context. With more than 6.6 million active listings across more than 220 countries and regions (Airbnb, 2023), Airbnb is the largest online marketplace for lodging. The sheer number of users (4 million hosts and 900 million total guests) suggests that it is likely a fruitful ground for consumer misbehaviours to take place. This observation is confirmed by media reports (Carville, 2021) and prior research (Moon et al., 2019), which documented various forms of misbehaviours that occur within Airbnb-mediated exchanges. The expected variety of misbehaviours was one of the key reasons for selecting Airbnb as our research context.

Our study drew on online archival data. This is online data related to user misbehaviour that was created on behalf of organisations (i.e. Airbnb) and individuals (i.e. Airbnb users and neighbours) for their own purposes (Fischer and Parmentier, 2010). To identify disparate forms of user misbehaviours, we examined the AirbnbHell.com website. AirbnbHell.com is a third-party website where people share their Airbnb-related negative experiences in the form of stories. Started by a dissatisfied Airbnb host in 2013, the website features “thousands of stories” with the aim to “warn other potential hosts and guests about the dangers and risks associated with using the Airbnb service” (AirbnbHell, 2021). The site, successfully used in prior research on critical Airbnb-related negative incidents (Moon et al., 2019), was selected for several reasons. Firstly, it allowed us to get insights into what users (rather than researchers) perceive to be problematic and/or dysfunctional forms of behaviour (i.e. consumer misbehaviours). This is aligned with the perspective of role theory, which suggests that it is the fellow “actors” that define whether a particular role has been properly enacted (Solomon et al., 1985). Secondly, the site allowed us to capture the perspectives of different stakeholders – guests, hosts, as well as neighbours as external parties that may be affected by misbehaviours. These perspectives might be less biased than the ones shared through official platform-owned channels (Mikołajewska-Zając, 2018), as stories are published anonymously and typically unobserved by the other party or Airbnb. Finally, the site visited by “tens of thousands of unique visitors every month” (AirbnbHell, 2021), is active both in terms of the number of posts being published daily and the data richness in terms of how descriptive these posts are.

Our sample included all stories that were published in the AirbnbHell sub-sections “Guest stories”, “Host stories” and “Neighbors” in the period of 1 September 2019 to 31 December 2020. In total, we examined 405 stories of negative Airbnb-related experiences. All stories were written in English and are quoted here as written. In addition to stories, as shown in Table 1, our data sources included Airbnb governance policies such as the Community Standards, Terms of Use [2] and country-specific legislation [3]. We used the documents to familiarise ourselves with Airbnb’s expectations of acceptable user conduct and with separating legal and illegal misbehaviours.

Our analysis consisted of several steps. Firstly, we closely read each story (for an example of a story, see Appendix 2) and identified all the different forms of user misbehaviours – behaviours that disrupted their own or others’ experience (Fombelle et al., 2019; Plé and Demangeot, 2020). Overall, we identified 265 unique forms of misbehaviours. For each form, we identified the location and time of misbehaviour, the perpetrator and target of misbehaviour. We also inspected whether this misbehaviour is allowed by Airbnb and/or legislation. After this “emic” level of analysis (Belk et al., 2012), we focused on comparing and contrasting misbehaviours and sorting them into like categories. The coding was informed but not dictated by the prior literature. In looking for emergent patterns in the data, we observed the usefulness of thinking about the forms of misbehaviours from the perspective of role theory (Solomon et al., 1985; Turner, 2006), which suggested paying attention to the roles consumers play in market-mediated exchanges and the norms/expectations associated with a particular consumer role. Such a lens allowed us to devise two classification bases (presented in the Findings section). Through further analysis, we observed that the misbehaviours could be classified into well-distinguished and manageable categories (Sandberg and Alvesson, 2020) by crossing two pre-identified classification bases and creating a cross-classification matrix (Patton, 2015). Our matrix of four categories of user misbehaviours was shaped by going back and forth between the raw data and our logical constructions (emergent categories). We stopped collecting new data once it did not change our categorisation of misbehaviours. An overview of the coding process is provided in Appendix 2.

Our typology of user misbehaviours in an Airbnb context captured 93% of the identified individual forms of misbehaviours. Two researchers double-coded all extracted individual forms (i.e. 265) into four categories of misbehaviours, achieving an intercoder reliability of 84%. All discrepancies in coding were resolved through a discussion and close re-reading of the stories within which a particular form of misbehaviour was embedded. Nineteen forms of misbehaviours (7%) remained uncategorised as the stories did not provide enough context to sort the misbehaviours into only one category or in any category at all. The typology of user misbehaviours is presented in the following section.

After developing a typology of user misbehaviours, we conducted an additional analysis of the data. To obtain a richer understanding of the types of misbehaviours, we analysed the impacts that were reported for each concrete form of misbehaviour. We re-examined all 405 Airbnb stories and, using the language and terminology of the authors of the stories, recorded the impacts that were unambiguously connected with a concrete form of misbehaviour. Our data set included 617 reported impacts [4]. For each impact, we identified the person who reported the impact (e.g. guest, host and neighbour), the person that was affected by the impact (e.g. guest, host and neighbour), the type of impact [5] (e.g. psychological, physical, financial or material) and its magnitude or perceived severity of harm (e.g. mild, moderate and severe). We then examined the similarities and differences of impacts (and their characteristics) among the four identified types of misbehaviours and used the findings to depict a richer portrait of each of type of misbehaviour.

To investigate the transferability of the typology to another SE context, we conducted an additional study, examining the forms of misbehaviours in the context of personal transportation/mobility and its most visible representative: Uber Rides. We reviewed a total of 471 stories of negative incidents reported by riders and drivers and published on the subreddits r/uber and r/uberdrivers, Trustpilot Uber page (UK) and RideGuru Forum page (see Table 1 for more details about our data sample). These data sources were selected because they provided a rich insight into what Uber users deemed to be problematic behaviours. Out of all the stories, we extracted 103 unique forms of misbehaviours and, for each form, identified the location and time of misbehaviour, its perpetrator and target. After familiarising ourselves with Uber governance policies such as the Community Standards and Rides Terms, we closely read each story and classified the identified forms into four types of misbehaviours discovered in the context of Airbnb. The classification was first done independently by two researchers, who then met to discuss and resolve any discrepancies in coding. The results of this scrutiny study are outlined in the section that follows the presentation of the typology below.

Findings

Study 1: a typology of user misbehaviours in the sharing economy context

Our investigation revealed different kinds of user misbehaviours carried out by Airbnb hosts and/or guests. Examples include using the Airbnb service under a false name, committing sexual harassment, damaging other’s property and leaving fake reviews. Through the analysis of individual forms of misbehaviours, we identified two classification bases. Firstly, with regard to violating the rules (i.e. norms) and expectations of how a person in a particular role should behave, user misbehaviours can be distinguished based on the domain of role violation. In this sense, user misbehaviour can either violate the expectations of conduct carried out as a member of society (i.e. a citizen) [6] or a service provider (i.e. host), user of a peer-provided service (i.e. guest) and user of the Airbnb platform (i.e. guest, host and others). While the former roles are “anchored in society at large” (Turner, 2006, p. 245) and apply across organisational boundaries, the latter ones are anchored in the pertinent exchange settings, in our case, in the context of accommodation-sharing and use of online services (platforms). The second basis for classification refers to the nature of violated norms. In this respect, user misbehaviour can violate either the formal or informal norms. Whereas formal norms reflect the written rules of conduct established by some authority (e.g. government and Airbnb), informal norms represent the rules of conduct that stem from people’s interactions and are not clearly stated and specified.

Crossing one conceptual basis (i.e. the domain of role violation) with another (i.e. the nature of violated norms) delineates a four-fold typology of user misbehaviours in the context of Airbnb:

1. illegal user behaviours;

2. unprofessional user behaviours;

3. unbefitting user behaviours; and

4. uncivil user behaviours.

These four types are illustrated with examples in Figure 1 and Appendix 4 and outlined below. In the sections that follow, we link evidence to the data sources, which are presented in abbreviated form for readability (e.g. N1 stands for story number 1 published in the AirbnbHell sub-section on neighbour stories; see Appendix 3 for details).

Illegal user behaviours

Illegal user behaviours are misbehaviours that are prohibited by law and violate the expectations about how consumers should conduct themselves as members of a particular society. Our data set included examples of illegal misbehaviours that were carried out by Airbnb users and are intentionally or unintentionally directed against a person or an animal, directed against another person’s property or directed against another person’s or platform’s financial assets.

The first category of illegal user behaviours relates to misbehaviours against a person. Examples include a host rejecting a booking request due to the guest’s race (G2), a guest receiving an unwanted kiss from the host (G3) and guests being held against their will by an Airbnb host (G1). This category captures various acts of violence, including a guest shooting and killing another guest within Airbnb-rented accommodation, as evidenced by the following host’s story:

I had a one guest for a one-night booking the week prior to July 27th. This person had a verified Airbnb account that included one five-star review […] I was notified by a neighbor around 1:30 AM that something seemed wrong with the amount of cars at my property and traffic in and out […]. The shooting happened shortly after that, before the police even made it there, leaving one 18-year-old man dead in my driveway. This one guest rental was a 200-300 person party of underage people and dangerous criminals. (H8)

While a fatal shooting represents a misbehaviour that is primarily directed against another person in the exchange setting, our data set also included experiences of potentially illegal user behaviour directed against the perpetrator him- or herself. Drug abuse on the part of the host (G6) and guest (H3) is one such misbehaviour. Furthermore, some illegal misbehaviour seems to be oriented towards animals, as it can be witnessed from a story of a host who reported that the guest “actually kicked the dog in the chest” (H10).

Rather than being directed against the self, another person or animal, some misbehaviours interfere with another person’s property. In this regard, several of our analysed Airbnb stories mentioned that something had been stolen. For instance, a guest reported that everyone staying at a particular Airbnb rental “had all their electronics (three iPads, one computer and one smartwatch) and chargers missing” (G4) and one of the hosts reported that the guests took his “expensive Canon camera 50 mm f/1.2 lens and an ironing machine” (H1). Another host reported how the guest organised a garage sale of his items from his property, leaving it completely empty. In his words: “[b]y the time my cleaners got to the room, the only thing that was left was the lock and forks” (H9). A different form of property-related illegal user behaviour involves causing deliberate damage to the property. Such acts of vandalism, carried out by guests, can be illustrated by the example of a host who reported that his guests caused severe damage to his property: my “house was riddled with bullet holes in the walls, broken furniture” and “feces and urine on the walls and floors” (H2). Such misbehaviours could result in both material and financial damage.

Some illegal misbehaviours carried out in the context of Airbnb seem to be oriented against the financial assets of another user or the platform. For instance, consider the example of online scams, where (potential) Airbnb users are persuaded to entrust money to a person who pretends to be an Airbnb host:

I made a reservation and paid for an apartment to a host (not sure if he gave his real name though because of what is happening now), and he said I should pay into a third-party account which was supposedly for Airbnb as they were the ones who would receive the money on his behalf. I paid a deposit and two months’ payment as he said the minimum stay was two months. I was sent a link which generated an invoice. I have a copy of the invoice with the details of the account I was supposed to transfer the money into. After payment, we were supposed to receive an email with a contract attached; this never materialized until today. We were supposed to be checking in today but instead the host with whom we have been talking to has just decided to block us on his phone after receiving our money and never provided us with the service. The link is no longer working. (G5)

Illegal user behaviours were carried out by both guests and hosts, and in the period before the stay, during the stay and after the stay. This category captured 10.9% of all identified distinct forms of user misbehaviours. Illegal behaviours were associated with various psychological impacts (corresponding to 45% of all reported impacts within this category), financial or material impacts (corresponding to 34% of all reported impacts within this category) and physical ones (corresponding to 13% of reported impacts within this category). For instance, users reported how experiencing illegal behaviours made them feel “scared for [their] life” (I1), “very frightened” (I2) and “really worried” (I3). The reported financial or material impacts included “irreparable damage” (I4), “$50,000 in damages” (I5) and ruined furniture for almost EUR 20,000 (I6). Finally, the reported physical impacts included a loss of life (I5) and a broken nose (I6). In comparison with the three other types of misbehaviours, illegal behaviours seemed to have the strongest and most severe impacts.

Unprofessional user behaviours

Misbehaviours that are legal but do not conform to explicitly stated expectations of how users of Airbnb should behave are in this paper referred to as unprofessional user behaviours. The label “unprofessional” (Cambridge University Press, 2023) suggests that the corresponding misbehaviours do not show the standard of behaviour or skill that is expected of a consumer in their role as a user of the Airbnb platform, an Airbnb guest and/or an Airbnb host. These expectations of appropriate behaviour are presented in Airbnb’s Community Standards and Policies (e.g. Review Policy) and throughout their Webpage, where Airbnb provides instructions and resources about how consumers should use the online platform and how they should “navigate hosting and travelling” (Airbnb, 2021b). The forms of (un)acceptable behaviour are also put forward by hosts who prepare the “House Rules” that “guests have to agree to before booking” (Airbnb, 2021c). The category of unprofessional user behaviours captured 40.4% of all distinct forms of user misbehaviours identified from the sampled Airbnb stories. These misbehaviours break the commitments established between Airbnb and their users and between hosts and guests. Our data set included unprofessional misbehaviours that are (un)intentionally directed against the user experience, directed against a person and directed against another person’s or platform’s financial assets.

Some unprofessional user behaviours are directed against the experience of using Airbnb. These misbehaviours include underparticipation, where users fail to provide the necessary resources (i.e. time, effort and information) for successful Airbnb experiences (Greer, 2015) and misrepresentation, where users impact the Airbnb experience by providing false information about themselves, their intentions and spaces. With regard to underparticipation, our data set included examples of user unresponsiveness. In violation of Airbnb Community Standards, which directly advise against being unresponsive, some guests and hosts fail to respond in a timely or satisfactory manner to another user’s enquiries (e.g. questions, requests and calls for help) before, during and after the stay. A guest, for instance, shared how she locked herself in the room and had to “yell […] for help” as “[a]ll attempts to contact the owners – phone calls, texts – were ignored” (G12). A different form of misbehaving by underparticipation includes hosts providing an uninhabitable environment. For instance, several Airbnb stories reported the issue of hosts not keeping their spaces clean; guests mentioned that their accommodation was “infested with rodents” (G13) and “bedbugs” (G14) and that there was “thick, black mold in the shower cubicle, by the windows and bed” (G26).

In contrast to failing to provide resources, some users misbehave by misrepresenting them, as well as their intentions for using the Airbnb service. With regard to guests, a misbehaviour by misrepresenting intentions includes using an Airbnb accommodation for organising an event or party without the host’s approval:

An Airbnb guest held an unauthorized party during the pandemic lockdown. We never allow parties, even before they became illegal. This guest said she was coming alone. The police estimated that there were at least 100 people in the two-bedroom home when they arrived. (H4)

Hosts, on the other hand, misrepresent their intentions by offering “[e]xperiences that are merely transactions” and “not a place for others to belong” (Airbnb, 2021d). An Airbnb guest reported that they decided to try Airbnb “because [they] wanted the experience of living as a local would” and they “wanted to stay in places that had some character” (G8). After staying in several Airbnb accommodations, the guest noticed that the hosts rarely live in the rented-out accommodations and that many of the hosts “are far more interested in making additional income to pay their bills than they are in providing a valuable, guest and customer service oriented, hospitable experience” (G8). While not misbehaving by not living in their Airbnb accommodation, hosts misbehave by offering places that are characterless and lack a home-like atmosphere. Additional examples of misbehaving by misrepresenting include providing an inaccurate location for the accommodation (G9), providing inaccurate pictures of the place (G11) and not providing the promised basic amenities, such as toilet paper (G15) or soap (G7). Misrepresentation does not qualify as unprofessional behaviour only when it is completed but also when it is encouraged. In this regard, several users reported that hosts offered an inducement in exchange for positive reviews. For instance, a guest reported that when she confronted her host about an issue with cleanliness, she was not offered an apology but rather “a bottle of champagne […] as a bribe to not put a negative review on Airbnb” (G12). This practice, encouraging others to misrepresent their experience, is in violation of Airbnb’s Review Policy, which prohibits any sort of incentivisation in an attempt to influence reviews, as well as the publication of reviews that do not reflect actual experience (Airbnb, 2021e).

While guests can use reviews to flag actual misbehaviours to the hosts and future Airbnb guests, some users misbehave through posting reviews that include commentary about other users’ social, political or religious views, assumptions about other users’ personalities and reviews that are not objective or accurate. These violations are captured in the sub-category of unprofessional behaviours that are directed against a person, in this context, against another Airbnb user. Several stories described the experience of users who felt that they were not accurately represented in the posted review. “[Hosts’] review on me was completely dishonest in an attempt to protect themselves”, reported one of the guests (G12). Another one stated:

[…] “the host left me a bad review […] saying that he would not recommend our family to any host, which is unfair as despite all that happened, we left the apartment far cleaner than we found it and broke no rules” (G26).

Our data set also included several Airbnb-prohibited misbehaviours that seemed to be primarily directed against the financial assets. Some of these misbehaviours are directed against Airbnb’s own financial assets. For instance, a guest reported that the host “offered to deal in cash instead of taking payment through Airbnb” (G16). Other misbehaviours are directed against the financial assets of other Airbnb users. Consider the experience of a guest who was asked to pay an extra charge after making a reservation:

The host provided only three rooms for nine people instead of our original request of four rooms under a charge of 1000 euro and insisted on charging an additional 200 euro for a fourth room […]. We tried our best to comply by paying for the extra fee to settle down, as there were old people and a small kid in our group and everyone was exhausted after a whole day’s travel. (G18)

The above quote illustrates how some unprofessional behaviours lead to negative financial consequences. Other examples of financial or material impacts (corresponding to 15% of all reported impacts within the category) associated with unprofessional behaviours include receiving only partial refunds (I7) and various unexpected costs, such as extra costs for the internet (I8). In addition to being affected financially or materially, users reported various forms of psychological impacts (68%), including feeling upset (I9), “unsafe and uncomfortable” (I10) and helpless (I11), and some forms of physical impacts (7%) such as “waking up with bug bites on [the] neck (I12)”. Most of the identified impacts associated with unprofessional behaviours seem to be moderate in strength/severity.

Unbefitting user behaviours

Unbefitting user behaviours are misbehaviours of guests and hosts that, like unprofessional behaviours, violate the expectations about how consumers should behave in the role of a service provider and a service/platform user. While not befitting to the roles of Airbnb host, guest and user of the platform, unbefitting behaviours violate the standards of behaviour that are not codified into community standards, policies and house rules, as is the case with unprofessional behaviours. In contrast to unprofessional behaviours, unbefitting ones are enforced by the approval or disapproval of participants in Airbnb-mediated exchanges. Overall, the category of unbefitting behaviours captured 32.1% of identified forms of misbehaviours and included misbehaviours that are directed against the user experience, directed against a person and directed against other person’s property.

Misbehaviours that affect the experience of using Airbnb include two sub-categories: “overparticipation” and “underparticipation” (Greer, 2015). Requests to overparticipate in the Airbnb experience can be witnessed from the reports of guests who described how the hosts made unreasonable requests such as “to mind and socialise” their dog (G20), to “feed [their] chicken” (G20) and to stop “closing [all] the doors because it made noise” (G21). On the other hand, some misbehaviours resembled underparticipation, where users failed to provide the necessary resources for successful Airbnb experiences, such as time, effort and information (Greer, 2015). A tenant, for instance, reported that their landlord did not inform them about the arrival of Airbnb guests: My landlord has turned two rooms of the house I’ve lived in for nine years into an Airbnb. Last night three dudes checked into one room. Nobody in the house had any notice about these strangers (N2). The issue of uncooperative Airbnb hosts can also be seen from Airbnb stories that describe how hosts ignored guests’ requests and complaints, such as: “We were promised AC repairs the day we reported them within 24 hours but they [sic] never happened” (G22). A lack of action on the host’s side was echoed by the experience of another guest who felt that the host was “dismissive of [their] complaints, insulting their intelligence with nonsensical responses” (G23). The guest wrote:

Once inside and settled, a lot of issues were noticed that became red flags: the rooms upstairs were extremely hot, regardless of if the temperature controlling the central A/C was set at the lowest setting of 68. I advised the host of the problem, and made the following suggestions to remedy: install a portable A/C unit, install a window A/C unit, or program the thermostat to go lower than 68 (if possible). The response received was literally a screenshot of some computer screen showing the temperature setting of 68 and stating to me it was comfortable. (G23)

The lack of regard for user’s comfort and underparticipation on the host’s side could also be seen from stories of guests who reported that their stays were negatively impacted by hosts scheduling non-urgent work and repairs on the accommodation while the guests were staying there. Consider the following experience of one of the Airbnb guests:

The first thing [the host] told me was her cesspit would be emptied the day after our arrival. We would have to sit in her garden with years’ worth of poo being emptied before us. The cesspit was also uncomfortably close to the caravan. We booked the caravan months in advance so she could have booked this at any other time but didn’t. (G24)

Some unbefitting behaviours are less related to users’ actions and more to their style of communication. One of the most common unbefitting behaviours from our data set was aggressive communication. This misbehaviour, directed against a person who is directly associated with the Airbnb service, comes in the form of shouting and yelling. A guest, for instance, reported how the host “started to shout at [them] after [they] questioned his service” (G18). The aggressive communication style was not directed only towards other Airbnb users but also towards Airbnb employees. For instance, a guest admitted to swearing at Airbnb customer support representatives (G10) and insulting them by calling them “buffoons, imbeciles, morons, and idiots” (G27).

Finally, some unbefitting user behaviours are directed against other Airbnb users’ property. Examples include guests throwing bath towels on the floor (H5), ruining rugs by not cleaning them after vomiting (H6), leaving indoor furniture and electrical items outside in the garden (H7) and using kitchenware as ashtrays (H7).

Such user misbehaviours seem to most often affect users psychologically. To be specific, psychological impacts represented 74% of all reported impacts of unbefitting behaviours and included feeling that something is not fair (I13), feeling resentful (I14) and feeling surprised and baffled (I15). In total, 15% of reported unbefitting behaviour-associated impacts related to financial or material consequences, with the targets mentioning how they had to, in response to unbefitting misbehaviour, bear various unexpected costs (I16). Only 5% of the reported impacts in this context referred to physical harm, such as getting a backache from sleeping on a cheap bed (I17) or hitting one’s head on a low ceiling (I18). Most of the reported impacts were moderate in magnitude/severity to harm.

Uncivil user behaviours

Uncivil user behaviours are misbehaviours that, like illegal ones, violate the expectations of how people should conduct themselves in their role as members of a society. In contrast to illegal misbehaviours, however, uncivil ones are not prohibited by explicit rules such as laws but rather are enforced by the approval and disapproval of people who use the Airbnb service (i.e. guests and hosts) and people who are affected by the service (i.e. neighbours). Closely corresponding to what Smith et al. (2010) call “everyday incivilities”, these misbehaviours are labelled as uncivil as they seem to lack virtues attached to what is regarded as being civil or behaving civilly in the role of a citizen (Coupet, 2020).

Our data set included misbehaviours that were (un)intentionally directed against a person (i.e. other Airbnb users and people affected by the Airbnb service) and against their property. We identified two sub-categories of misbehaviours that were primarily affecting other people: verbal incivilities and noise-related incivilities. Verbal incivilities take the form of inappropriate language, such as swearing and making sexually suggestive comments in public. For instance, a neighbour reported that Airbnb guests cursed at him (N3), and another neighbour witnessed Airbnb guests “catcalling at all the old ladies that walk the street for their workout” (N5). The second sub-category of uncivil misbehaviours directed against another person includes noise-related incivilities where Airbnb users are involved in producing unwanted sound that can cause distress, annoyance or disturbance to unwilling listeners. Such noise-related incivilities were frequently reported in accommodation where the space was shared with other people and/or in the vicinity of the Airbnb accommodation. For instance, a guest complained that during his stay, the host was “exceptionally loud, talking on speaker phone” (G25) and another complained that the host “had loud, domineering sex in a room next to [hers]” (G19). Noise-related disturbances were also commonly reported by neighbours. Consider the following neighbour’s experience of loud guests in an Airbnb apartment:

For nearly two years, my life has been completely disrupted by an Airbnb next to my apartment. This is a mirror image of my apartment, and while I live alone, mostly, up to 14 people have been accommodated next door. The floors of this place are tiled, so all sound is amplified. I have listened to countless nights trying to sleep, through drunken, drugged behaviour, people roaring and screaming, night and early morning. (N4)

The second sub-category of uncivil user behaviours includes misbehaviours that are directed against another person’s property. Our data set included various examples of misbehaviours that included elements of trespassing, where Airbnb users intentionally or unintentionally performed an uncivil act on a neighbour’s land or property. For instance, a neighbour reported that “[the guest] parked in [their] driveway and attempted to enter [his] home” (N6). Other neighbours shared that guests urinated in their backyards (N7) or left trash, such as beer cans, there (N8).

The category of uncivil user behaviours captured 9.4% of all identified distinctive forms of misbehaviours. These misbehaviours were most commonly reported by the neighbours and referred to the time of the guests’ stay. Reported impacts associated with the uncivil behaviours were psychological (71%), physical (10%) and financial or material (8%) in nature. To illustrate this, psychological impacts included feeling disrespected (I6), feeling scared (I19) and feeling unhappy (I14). Being hit by a beer can (I20) is an example of a physical impact associated with uncivil behaviours. On the other hand, various unexpected costs, such as those for security cameras and lights in the neighbourhood (I21), represent reported financial costs of the uncivil behaviours. Such behaviours seem to cause harm that is moderate in magnitude.

Study 2: Transferability of the typology to Uber context

Our analysis of Uber’s misbehaviours suggests that the typology of misbehaviours developed in the context of accommodation sharing (Airbnb) applies to other contexts, namely, to the context of personal mobility or personal transportation services. Two researchers double-coded the concrete forms of Uber misbehaviours into four pre-identified types of misbehaviours. The researchers were able to categorise 85% of the identified forms – 15% of the forms lacked context to be categorised into only one type or any type of misbehaviour. The researchers achieved 72% agreement on the classification of misbehaviours – all differences in coding were resolved through a discussion, re-reading of the stories and examination of Uber governance policies, especially their Community Guidelines.

Slightly more than one-tenth of the identified concrete forms of Uber misbehaviours (13%) were classified as illegal misbehaviours. Some of these misbehaviours closely correspond in nature to misbehaviours witnessed in Airbnb settings. For instance, in the context of misbehaviours directed against a person, a passenger reported that “the driver seemed to be on drugs” (R1) and the driver shared his experience of a passenger demonstrating race-based hostility, calling him “a nigger” and “a slave” (D1). Users also reported several misbehaviours that were directed against another person’s property, such as a driver stealing the passenger’s keys (R²). Some of the illegal misbehaviours were unique to Uber, such as the experience of the rider who shared how their driver drove dangerously, “run[ning] the stop sign and almost get[tting] T-boned” (R3).

The category of unprofessional behaviours captured 43% of all forms of misbehaving in an Uber context. A large share of these were directed against the user experience. For instance, engaging in some sort of misrepresentation, some riders use their accounts to order Uber for their children (D2) and drivers complete the trip with non-approved vehicles (R4). Breaking Uber’s rule of no (suggestive) flirting and corresponding to overparticipation, some Uber users overshare intimate details (R5). In addition to sub-categories of misbehaviours that were previously identified in the context of unprofessional Airbnb user behaviours (i.e. directed against the user experience, against a person and against another person’s/platform’s financial assets), we also identified unprofessional behaviours that were directed against a property, including misbehaviours such as the rider vomiting in the car due to excessive alcohol consumption (D3) and the rider ripping off the phone charging station from the headrest (D4). All these misbehaviours break the rules of user conduct codified in the Uber Community Guidelines and therefore correspond to unprofessional user behaviours.

The category of unbefitting behaviours captured 38% of identified Uber misbehaviours. Similarly, as in the context of Airbnb, this category included various forms of misbehaviours directed against the user experience (e.g. a driver underparticipating by “act[ing] like it’s an inconvenience to open their trunks” to store riders’ groceries or bags [R6]) and a rider overparticipating by adjusting the driver’s seat to get more room for the seat in the back [D6], directed against a person (e.g. a rider criticising the driver for taking a long time to see a jaywalker in the middle of the night [D5]) and directed against property (e.g. making a mess in the car while eating [D6]). Such misbehaviours violate the expectations of user conduct in the role of a rider or a passenger, yet they are not codified into the Uber Community Standards and other governance policies.

Finally, our data set of Uber misbehaviours included several examples (6% of all identified forms) of uncivil behaviour. Most of these examples correspond to different types of verbal incivilities that were also witnessed in the context of Airbnb. A driver, for instance, reported how his passengers regularly roll the windows down to “catcall, scream, or yell derogatory things to people on the street” (D7). Similar misbehaviour has also been carried out by drivers and reported by the riders. In addition to behaving uncivilly towards people on the street, some users reported verbal incivilities that were oriented towards the staff working at drive-through restaurants (D6). Similarly, as with Airbnb misbehaviours, some Uber-related uncivil behaviours seem to be directed against property. While in the case of Airbnb, this property was of a personal nature (e.g. neighbour’s garden), in the case of Uber, uncivil behaviours also occurred on public property. A driver, for instance, reported how one of his riders threw a drink can out of the window and into the street (D7).

In conclusion, our analysis of misbehaviours in the context of Uber suggests that while the context of personal mobility offers some unique forms of misbehaving, these forms may be reliably classified under the categories of illegal, unprofessional, unbefitting and uncivil user behaviours.

Discussion

Theoretical contributions

This paper set out to examine the types of user misbehaviours in the SE context. Based on our analysis, we developed a typology that offers a theoretically and practically useful way to organise and categorise the diverse forms of consumer misbehaviours that have been reported. In Sandberg and Alvesson’s (2020) terminology for varied styles of theorising, we put forward an “ordering theory” of consumer misbehaviours that can help us grasp the diverse range of misbehaviours. Specifically, we identify four categories of misbehaviours, which we label illegal, unprofessional, unbefitting and uncivil. The four categories are set apart by two classification bases, i.e. the nature of the violated norms (i.e. formal vs informal) and the domain of role violation (i.e. user as a citizen vs user as a peer-service provider/user of a peer-provided service/platform user). The contributions of this study are as follows.

To the best of our knowledge, this is the first typology of consumer misbehaviours in a SE setting. The value of the developed typology is that it is empirically derived (cf. Fombelle et al., 2019; Harris and Reynolds, 2004) and that it has the potential to better capture the variety of consumer misbehaviours. The four major categories of misbehaviours encapsulate both the misbehaviours that occur in traditional settings (e.g. firm-directed misbehaviours) and misbehaviours that are enabled by the particularities of the SE (e.g. peer service providers’ misbehaviours and uncivil behaviours) that existing typologies (Bitner et al., 1994; Boo et al., 2013) do not capture. Furthermore, in contrast to prior typologies that are mostly developed in the context of the offline use of services (Bitner et al., 1994; e.g. Greer, 2015; Harris and Reynolds, 2004) and in correspondence to the finding that SE requires offline and online service interactions to get the business done (Cheng et al., 2018), our typology includes online and offline misbehaviours in an integrated fashion. In comparison with prior typologies that do not specify the basis on which misbehaviours have been classified (Bitner et al., 1994; Boo et al., 2013), our typology specifies two new bases on which the misbehaviours can be distinguished: the domain of role violation and the nature of the violated norms. The bases indicate who is in charge of regulating misbehaviour (i.e. society or business) and how the misbehaviour can be regulated (formally or informally), providing theoretical and practical guidance for addressing existing and novel misbehaviours. The typology is applicable to a variety of SE service contexts, as it does not rigidly prescribe the actual forms of misbehaviours that belong under unprofessional, uncivil and unbefitting behaviours. For instance, unprofessional behaviours in the context of Uber include several misbehaviours that we observed in Airbnb settings (e.g. writing false reviews and peer service provider not responding to user enquiries) as well as additional Uber-specific misbehaviours such as the driver not keeping their eyes on the road (Uber, 2023). The versatile and holistic nature of our typology arises from our research approach, which is based on the affected parties perceiving something as misbehaviour rather than researchers labelling a behaviour as such (cf. Fombelle et al., 2019; Fullerton and Punj, 2004). Such an approach highlights the diversity of forms that are perceived as problematic/dysfunctional by consumers and hints at the importance of firms and policymakers in setting the boundaries on what it seems like “boundless” phenomenon. It also invites scholars to consider all stakeholders of consumer misbehaviours. Our work further supports prior research that shows how SE-exchanges impact not only parties participating in the exchange but also external parties such as local residents affected by home-sharing (Buhalis et al., 2020; Plé and Demangeot, 2020) and pedestrians affected by ride-sharing electric scooters (Sikka et al., 2019) and cars (Stanton et al., 2019). While these uninvolved parties have largely been disregarded in previous definitions and typologies of consumer misbehaviour, we argue that they need to be acknowledged by scholars and practitioners alike, not only to provide a more nuanced understanding of misbehaviour but also to carve out appropriate measures to mitigate misbehaviours that affect them. This observation calls for a revision of the definition of consumer misbehaviour as follows: consumer misbehaviour is any act by a consumer in an online or offline environment that deprives the firm, its employees, other consumers or any other indirectly affected non-participating individuals of resources, safety, image, well-being or an otherwise successful experience (Fombelle et al., 2019, p. 387). This revised definition extends prior conceptualisations of consumer misbehaviours by recognising a potential target of misbehaviours (i.e. indirectly affected non-participating individuals) and outcome (i.e. negatively impacted well-being) that are overlooked in prior definitions. Rather than affecting the service experience, some forms of misbehaviours in the SE context affect the quality of everyday life. We now briefly discuss each of the major categories of misbehaviours identified in this study.

Unprofessional behaviour stems from the unique nature of being a participant in SE-mediated exchanges wherein, for instance, hosts are simultaneously users of the Airbnb platform and providers of accommodation services who are required to follow Airbnb’s written expectations about acceptable behaviour. Failing to do so results in unprofessional behaviour, where consumers misbehave by not providing the expected level of service. As previous studies have focused on either users of services (Harris and Reynolds, 2004) or employees in traditional markets (e.g. hospitality, professional services and financial services; Bitner et al., 1994), they could not capture this particular type of misbehaviour. In this category, we can find forms of misbehaviours that have been identified in previous typologies, such as underparticipation (Greer, 2015). However, we are able to categorise an additional novel form, namely, misrepresentation, which is manifested in providing false information about users themselves, their intentions and spaces. Identified earlier as a form of value co-destruction, i.e. puffery and over-exaggeration (Buhalis et al., 2020), these misbehaviours are primarily executed by the service providers. Their actions may be close to previously studied employee misbehaviours (Marquardt et al., 2021; Sims, 2002), with an important distinction that in the context of SE, these are actually peer-service providers – separate entities not used by the platform. Accordingly, compared to employees, their behaviour is less scrutinised and their misbehaviour may not always be observed by the platform. Due to their impact on service delivery and quality, platforms aim to “professionalise” the work of peer providers by specifically stating what is considered to be (un)acceptable behaviour.

The category of unbefitting behaviour is associated with another special feature of the SE market: informality. Often framed in terms of personal relationships, host-guest interactions (Makkar et al., 2020) are less formal than between employees and consumers in traditional markets. The providers of SE services are also more likely to operate informally because they are not always subject to the same legal and safety regulations as traditional service providers (Williams and Horodnic, 2017). Since the relationships are more informal and not so heavily regulated (Guttentag, 2015), the peer-to-peer community may establish unwritten rules which determine what represents appropriate behaviours. Unbefitting behaviour arises when consumers, in their role as service providers or service/platform users, do not abide by these unwritten rules. While some forms of unbefitting behaviour have been identified in previous categorisations, such as verbal abuse (Bitner et al., 1994; Boo et al., 2013), “overparticipation” and “underparticipation” (Greer, 2015), others, such as unbefitting behaviours directed at property (e.g. throwing bath towels on the floor), have not been considered, as they may have been perceived as a usual negative part of business operations in traditional settings. Contrary to traditional services where a consumer interacts with a company, the interaction in the SE is typically between peers who, compared to employees, may be more sensitive about how their property is being treated. Moreover, the impacts of unbefitting behaviours have been shown to be moderately severe, which is similar to unprofessional behaviours. When evaluating the impact of misbehaviour in the domain of the platform, the user does not seem to distinguish whether the violated rules were formal or informal, meaning that both need to be considered when regulating misbehaviour.

Some misbehaviours might have broader societal implications that go beyond pure market exchanges. Extending previous categorisations of misbehaviours that mainly considered misbehaviours from the business perspective (Greer, 2015; Harris and Reynolds, 2004), our work considers them also from the broader social perspective. If consumers violate formal rules in their role as citizens, they engage in illegal behaviour, which is not a novel finding, as these misbehaviours have long been recognised by scholars (Lovelock and Wirtz, 2016; Moschis and Cox, 1989). Our study, however, illustrates that the formal rules come in different forms and that there is a value in distinguishing between misbehaviours that break company policies and misbehaviours that break laws (cf. Bitner et al., 1994). On the other hand, some users break unwritten rules and thus engage in what we label as uncivil behaviour. These misbehaviours, negatively impacting individuals who are not directly involved in the market-mediated exchange (i.e. neighbours), support the view of Buhalis et al. (2020) who, providing examples such as noise pollution and parking problems, framed local residents as a relevant stakeholder of value co-destruction in SE. Separating uncivil behaviours from unbefitting ones gives us a more nuanced understanding of the phenomenon of misbehaviours, considering that prior typologies either do not incorporate these misbehaviours or treat them uniformly as a single category, such as verbal abuse and noise (Bitner et al., 1994; Boo et al., 2013). However, making a distinction between noise-related misbehaviours targeted at other users within the exchange and unsuspecting observers outside the exchange brings attention to the importance of unwritten rules in the market and societal domains, as it raises the question of how these unwritten rules are determined and communicated about, as they are not universal and may differ among people from different backgrounds and cultural contexts.

Managerial implications

The findings of this study have implications for companies, peer service providers and public policymakers who have a vested interest in managing misbehaviours, especially in the context of the SE. Firstly, companies can use the presented typology as a mapping tool that allows them to assign each specific form of misbehaviour, current or potential, in one of the four categories. Such a mapping exercise would facilitate the discussion around what is considered to be illegal, unprofessional, unbefitting and uncivil misbehaviour in the specific context of the company and how and by whom each category of misbehaviour should be addressed. For instance, by mapping misbehaviours, an SE platform might identify an example of behaviour that is currently classified as unbefitting (e.g. being dismissive to guests’ complaints) but should, due to the potential negative impact on service quality, be prohibited by a written rule and so become an example of an unprofessional behaviour. On the other hand, the company might agree with a particular form of misbehaving (e.g. a guest throwing a towel on the floor) being classified as unbefitting behaviour and thus as a behaviour that the company does not wish to specifically prohibit in their policies and community standards but rather to allow peer-service providers to manage it at their own discretion. In sum, using this typology as a mapping tool facilitates the discussion as to what misbehaviours are and how they will be tolerated and actively managed by the SE platform.

Besides being a mapping tool that provides an overview of the full scope of the misbehaviours in a particular context, the typology also provides ideas for the effective detection and mitigation of misbehaviours that correspond to each category. To curb unprofessional and unbefitting behaviours and avoid disrupted and negative customer experiences, efforts should be invested into clarifying the roles of being a peer service provider and user of a peer-provided service. In this regard, it might be especially important to educate users about what constitutes optimal participative behaviour (as opposed to underparticipative and overparticipative behaviour) (Greer, 2015). While SE platforms and peer-service providers might want to socialise users by explicitly specifying the role expectations in the community standards and house rules, respectively, and thus expanding the pool of behaviours that are considered to be unprofessional, it is important to acknowledge that some service users (guests in our case) might feel resentful towards the platform or person specifying the expectations (Evans et al., 2008). Establishing the conduct expectations in a more tacit, informal way (e.g. by publicly sharing the best hosting practices and so encouraging learning by observation) would present an alternative, complementary way through which role expectations can be communicated. While SE platforms play an active role in developing the expectations of how users should behave in their role of peer-service provider, user of the service and user of the platform, they are taking a backseat when it comes to defining expectations of user conduct in the role of a citizen. In this domain, the conduct expectations are defined by society and governmental institutions, and SE firms’ responsibility lies in helping their users to become aware of these expectations. One good example of a management tactic in this spirit is the Airbnb Responsible Hosting Webpage, which lists information about the relevant tax, property and safety legislation (to prevent illegal misbehaviours), as well as concrete suggestions how to act towards neighbours (to prevent uncivil misbehaviours).

User socialisation into each role needs to be accompanied by sanctions that follow when a user violates conduct expectations. In the case of illegal and unprofessional behaviours, the sanctions for misbehaving will be formal and vertical in nature, imposed top-down from the state (government) and platform, respectively. For instance, Airbnb hosts may lose their special Superhost status if they behave unprofessionally by not maintaining a 90% response rate or higher, where the response rate refers to the percentage of new enquiries and reservation requests responded to within 24 h in the period of 30 days (Airbnb, 2021f). On the other hand, unbefitting and uncivil behaviours are sanctioned by the actions of the people who are involved in a particular service exchange or are affected by this exchange. The literature on social control suggests that there is a variety of informal, verbal and nonverbal sanctions that people use to show to another person that they disapprove of their counter-normative behaviour, including giving an angry look, making a comment to another bystander or personally insulting the user (Chekroun and Brauer, 2002). In the context of the SE, such informal, peer-to-peer sanctions may be delivered through reputation mechanisms like consumer reviews. The public criticism that occurs in the form of reviews likely helps in curbing unbefitting behaviours as it allows violators to recognise their wrongdoing, deters others from similar violations and reinforces a shared sense of commitment to the norms (Billingham and Parr, 2020). On the other hand, SE platforms should develop tools (such as the Airbnb Neighbourhood support form available on airbnb.com/neighbours) that allow non-participating parties, such as neighbours, to share their experiences of being affected by SE-mediated exchanges. The perspective of third-parties could also be obtained by regularly monitoring public sites where people share their negative experiences with service providers (e.g. pages like AirbnbHell). While unlikely to be directly involved in managing uncivil misbehaviours, platforms need to be aware of what are the norms, appropriate conducts or routines in a particular society/local environment (e.g. what it means to be a good neighbour or a good co-passenger) to decide which uncivil behaviours could negatively impact the platform’s brand image if unmanaged. Such uncivil misbehaviours should become formally prohibited and transformed into unprofessional ones.

Limitations and future research opportunities

This work has several limitations that could serve as fruitful future research opportunities. Firstly, while AirbnbHell stories provided insights into what consumers (as opposed to researchers) consider to be problematic and/or dysfunctional forms of behaviour, it is important to acknowledge that we could not assess the trustworthiness of the stories. It is possible that some of the identified individual forms of misbehaviours did not actually occur or were presented in an exaggerated way. To estimate the prevalence of different forms of misbehaviour, future research should thus consider using a different research method, such as a survey. Secondly, while Airbnb and AirbnbHell proved to be a fruitful grounds to explore misbehaviours in the context of SE, we need to acknowledge that Airbnb represents just one type of SE service, the one where people typically share their own assets as opposed to firm-owning the assets as is the case with bike-sharing services. Since such assets are increasing likely to be owned by the SE firms themselves (Eckhardt et al., 2019) or managed by other firms (e.g. property management firms in the case of Airbnb hosts with multiple listings), future research should examine how knowledge about who is the owner of the assets influences the likelihood of users misbehaving and the nature of misbehaving. An additional limitation of our data source is that it provides only a limited view of the misbehaviours that occur while using the platform itself (e.g. hacking, cyberbullying and doxing). While we were able to capture online misbehaviours such as scams, posting fake or inaccurate reviews and making transactions outside of official payment systems, further study with a focus on the use of the platforms (e.g. Airbnb.co.uk website, Uber mobile app) might uncover new instances of illegal, unprofessional, unbefitting and uncivil behaviours. Testing the usefulness of this typology in other research contexts beyond the SE would also present a well-needed extension of the current work.

This study adds further support to the growing number of studies that frame (dysfunctional) service transactions (Hibbert et al., 2012; Solomon et al., 1985) or value-co-destruction (Järvi et al., 2020) in terms of the deviation of expected behaviours associated with a particular role. One opportunity for future research lies in exploring user motivations for these deviations in the context of each of the four categories of user misbehaviours (i.e. illegal, unprofessional, unbefitting and uncivil conduct). In terms of role theory, these deviations may result from interacting parties having different role definitions, one party stepping out of their role (Solomon et al., 1985) or one party experiencing an intra-role conflict, having to respond to conflicting expectations from the service provider, peers and non-participating individuals (Turner, 2006). Another potential reason for misbehaviour may include the user’s low commitment to conform to expected role behaviour and future research should examine the factors that encourage and discourage users from engaging in misbehaviours that are currently poorly understood (i.e. unprofessional, unbefitting and uncivil). On the other hand, it would be relevant to examine whether misbehaviours are contagious from one role to another and, thus whether users who misbehave in the role of service providers also misbehave in the role of users of a (peer-provided) service? Answering such questions would present a step forward towards a more well-rounded understanding of the phenomenon of consumer misbehaviours in the context of the SE and beyond.

Figures

Figure 1.

Types of user misbehaviours in the context of Airbnb

Figure A1.

An example of an uncategorised story and the coding process

Figure A2.

Overview of the coding process

Table 1.

An overview of the data set

Research stage	Data type	Data source	Examples of data sources	Sub-purpose
Study 1: Developing the typology of user misbehaviours in the context of Airbnb	Archival data	AirbnbHell stories (published in the period 1/9/2019 to 31/12/2020)	405 stories: 87 stories published by hosts, 300 stories published by guests, 18 stories published by neighbours	To identify the different forms of misbehaviours in the context of Airbnb To illustrate the different forms of misbehaviours with the reported impacts
	Supplementary archival data (see note below)	Airbnb user conduct governance documents (available in the period of data collection from 01/1/2021 to 31/05/2021)	Airbnb Community Standards, terms of service, privacy policy, non-discrimination policy	To understand what behaviour is (not) allowed by Airbnb
		Legislation (country-specific) (available in the period of data collection from 01/1/2021 to 31/05/2021)	gov.uk (e.g. sections Your rights and the law, Discrimination, Antisocial behaviour), Legislation gov.uk (e.g. Clean Neighbourhoods and Environment Act 2005, Theft Act 1968)	To understand what behaviour is legal/illegal in a particular country
Study 2: Validating the typology of user misbehaviours in the context of Uber Rides	Archival data	Stories published on the subreddit Uber (published in the period from 20.10.2022 to 25.11.2022), on the subreddit Uberdrivers (published in the period from 1.11.2022 to 25.11.2022), on the Uber Trustpilot (UK) page (published in the period 25.10.2022 to 25.11.2022) and available on the first page of RideGuru Forum (in the period 1.11.2022 to 25.11.2022)	471 stories: 191 stories published by drivers and 280 stories published by riders	To check the transferability of typology to another SE context
	Supplementary archival data (see note below)	Uber user conduct governance documents (available in the period of data collection from 1.11.2022 to 25.11.2022)	Uber’s Community Guidelines (UK site), Rider Terms	To understand what behaviour is (not) allowed by Uber
		Legislation (country-specific) (available in the period of data collection from 1.11.2022 to 25.11.2022)	Gov.uk (e.g. sections Child car seats, Littering, Antisocial behaviour), Crown Prosecution Service (e.g. section driving offences)	To understand what behaviour is legal/illegal in a particular country

Note:

For practical reasons and given that published stories do not allow us to make assumptions about when a particular misbehaviour actually occurred (versus was mentioned in a story), we reviewed the policies and legislation that were available online at the time of data collection

Source: Authors’ own work

Table A1.

An overview of typologies of consumer misbehaviour

Author(s)	Classification basis	Types of misbehaviour	Research approach	Research context
Moschis and Cox (1989)	Demands placed upon its members, desirability of behaviour	negligent, criminal/fraudulent	Conceptual	Not specified
Lovelock (1994)	Nature of behaviour	thief, vandal, belligerent, family feuder, deadbeats and rule breakers	Conceptual	Services
Bitner et al. (1994)	Not defined	drunkenness, verbal and physical abuse, breaking company policies or laws and uncooperative customers	Empirical (interviews with employees)	Hotel, restaurant and airline service
Fullerton and Punj (2004)	Target, the nature of behaviour, type and degree of disruption and reactions by others	consumer misbehaviour directed against: (A) marketer employees, (B) other consumers in the exchange setting, (C) merchandise and services, (D) marketers’ financial assets and (E) marketers’ physical or electronic premises	Conceptual	Not specified
Harris and Reynolds (2004)	Overtness, financial motivation	compensation letter writers, undesirable customers, property abusers, service workers, vindictive customers, oral abusers, physical abusers and sexual predators	Empirical (interviews with employees and customers)	Hospitality
Boo et al. (2013)	Not defined	grungy (e.g. unhygienic behaviour), inconsiderate (e.g. noise), rule breaking (e.g. smoking), crude (e.g. drunk), violent or physical abuse (e.g. fighting) and verbal abuse (e.g. profanity)	Empirical (interviews of individuals about others)	Services
Greer (2015)	Target of misbehaviours	goods-related misbehaviour (property abuse and fraudulence), interpersonal misbehaviour (verbal abuse, physical aggression) and relational misbehaviour (underparticipation; overparticipation)	Empirical (interviews with service providers)	Professional services
Gursoy et al. (2017)	Not defined	inattentive parents with naughty kids, oral abusers, outlandish requesters, hysterical shouters, poor hygiene manners, service rule breakers and ignorant customers	Empirical (netnography)	Hospitality
Tsaur et al. (2019)	Target of misbehaviours	aimed at group operation, tour leaders, tour members, the tourism environment; and tourism organisations	Empirical (interviews with tour members and leaders)	Tourism and group travel
Fombelle et al. (2019)	Target of misbehaviours	firm-directed deviance, employee-directed deviance and customer-directed deviance	Conceptual	Not specified

Source: Authors’ own work

Table A2.

Characterising each form: illustrative example

Concrete form of misbehaviour	Who carries out the misbehaviour?	Who is the target of misbehaviour?	When did the misbehaviour occur?	Where did the misbehaviour occur?
Host providing accommodation that is too hot (to sleep in)	Host	Guest	During stay	Airbnb accommodation
Host falsely listing air-conditioning as an amenity	Host	Guest	Before stay	Airbnb website
Host not informing the guest that accommodation does not have a (working) air-conditioner	Host	Guest	Before stay	n/a

Table A3.

Categorising misbehaviours: illustrative example

Concrete form of misbehaviour	Domain of role violation	Nature of violated norm	The “location” of norm inscription	Type of misbehaviour
Host providing accommodation that is too hot (to sleep in)	User as a peer-service provider	Unclear	Airbnb Community Standards prohibit providing uninhabitable spaces, but room temperature is not listed as an example of problematic behaviour, and it is not clear whether 93 degrees (Fahrenheit) would make the space uninhabitable	Unclear
Host falsely listing air-conditioning as an amenity	User as a peer-service provider, user of the Airbnb platform	Formal	Airbnb Community Policy prohibits inaccurate listings Airbnb Community Standards prohibit misrepresentation of spaces	Unprofessional
Host not informing the guest that accommodation does not have a (working) air-conditioner	User as a peer-service provider	Formal	Airbnb Community Standards prohibit breaking commitments	Unprofessional

Source: Authors’ own work

Table A4.

Data sources referenced in the findings section

Title of the story	AirbnbHell Section	Reference code	Link	Date of post
Held against my will by an Airbnb host	Guest stories	G1	www.airbnbhell.com/held-against-my-will-by-an-airbnb-host/	17/10/2019
Rejected by a host because I’m from Taiwan?	Guest stories	G2	www.airbnbhell.com/rejected-host-because-im-from-taiwan/	09/02/2020
Sexually assaulted at owner-occupied Airbnb	Guest stories	G3	www.airbnbhell.com/sexually-assaulted-at-owner-occupied-airbnb/	15/12/2019
Airbnb puts lives at risk when everyone has the keys	Guest stories	G4	www.airbnbhell.com/airbnb-puts-lives-at-risk-when-everyone-has-the-keys/	28/11/2019
Airbnb nightmare: another guest scammed	Guest stories	G5	www.airbnbhell.com/airbnb-nightmare-another-guest-scammed/	04/01/2020
Most terrifying Airbnb experience, have not received anything	Guest stories	G6	www.airbnbhell.com/most-terrifying-airbnb-experience-havent-received-anything/	16/12/2019
The Airbnb Amityville Horror in Holbrook	Guest stories	G7	www.airbnbhell.com/airbnb-amityville-horror-in-holbrook/	13/09/2019
Not impressed with Airbnb experiences overall	Guest stories	G8	www.airbnbhell.com/not-impressed-with-airbnb-experiences-overall/	13/05/2020
Classic bait and switch in districts of Algiers	Guest stories	G9	www.airbnbhell.com/classic-bait-and-switch-in-districts-of-algiers/	21/09/2019
Forced out of Airbnb cabin in the woods	Guest stories	G10	www.airbnbhell.com/forced-out-of-airbnb-cabin-in-the-woods/	21/06/2019
Airbnb in Miami Beach not what I expected	Guest stories	G11	www.airbnbhell.com/airbnb-miami-beach-not-expected/	19/06/2019
Locked in small, disgusting Airbnb room	Guest stories	G12	www.airbnbhell.com/locked-in-small-disgusting-airbnb-room/	04/11/2019
Complete dump misrepresented on Airbnb	Guest stories	G13	www.airbnbhell.com/complete-dump-misrepresented-on-airbnb/	15/12/2019
Death trap with bedbugs and health concerns	Guest stories	G14	www.airbnbhell.com/death-trap-with-bedbugs-and-health-concerns/	28/06/2019
Disgusting suite, silence on refund from Airbnb	Guest stories	G15	www.airbnbhell.com/disgusting-suite-silence-on-refund-from-airbnb/	16/06/2019
Last second cancellation after 4-h wait for host	Guest stories	G16	www.airbnbhell.com/last-second-cancellation-after-four-hour-wait-for-host/	29/01/2020
Bad Airbnb service for family in Slovenia	Guest stories	G18	www.airbnbhell.com/bad-airbnb-service-for-family-in-slovenia/	28/07/2019
Female guests flee from sex stalking host	Guest stories	G19	www.airbnbhell.com/female-guests-flee-from-sex-stalking-host/	06/10/2019
Airbnb nightmare you would not wish on anyone	Guest stories	G20	www.airbnbhell.com/airbnb-nightmare-you-wouldnt-wish-on-anyone/	29/03/2020
Airbnb from hell: Unionville Nightmare	Guest stories	G21	www.airbnbhell.com/airbnb-from-hell-unionville-nightmare/	06/07/2019
Water damage is the least of this Airbnb’s Problems	Guest stories	G22	www.airbnbhell.com/water-damage-the-least-airbnbs-problems/	27/06/2019
The worst Airbnb experience (so far) in Jersey City	Guest stories	G23	www.airbnbhell.com/worst-airbnb-experience-jersey-city/	11/09/2019
Where’s Airbnb’s policy with a mouse in the caravan	Guest stories	G24	www.airbnbhell.com/where-airbnb-policy-with-a-mouse-in-the-caravan/	22/08/2019
Horrible landlord now a Airbnb host	Guest stories	G25	www.airbnbhell.com/horrible-landlord-now-airbnb-host/	19/08/2019
Host enters at night, scaring family with small children	Guest stories	G26	www.airbnbhell.com/host-enters-at-night-scaring-family-with-small-children/	29/07/2019
Stranded in Singapore after customer service fiasco	Guest stories	G27	www.airbnbhell.com/stranded-in-singapore-after-customer-service-fiasco/	20/07/2019
Host guarantee means nothing to Airbnb	Host stories	H1	www.airbnbhell.com/host-guarantee-means-nothing-to-airbnb/	08/09/2019
Shooting inside and outside my Airbnb home	Host stories	H2	www.airbnbhell.com/shooting-inside-and-outside-my-airbnb-home/	06/11/2019
Nightmare guest gets 13-year Airbnb host banned	Host stories	H3	www.airbnbhell.com/nightmare-guest-gets-13-year-airbnb-host-banned/	31/10/2019
More than $10K in damages, Airbnb paid $510	Host stories	H4	www.airbnbhell.com/more-than-10k-in-damages-airbnb-paid-510/	31/05/2020
Airbnb invades privacy and preys on the poor	Host stories	H5	www.airbnbhell.com/airbnb-invades-privacy-and-preys-on-the-poor/	04/01/2020
Guest from hell bringing unknown guests in	Host stories	H6	www.airbnbhell.com/guest-from-hell-bringing-unknown-guests-in/	19/02/2020
Home trashed by Airbnb guests and no customer support	Host stories	H7	www.airbnbhell.com/home-trashed-by-airbnb-guest-and-no-customer-support/	12/08/2019
Airbnb left me with a dead body, $50,000 in damages	Host stories	H8	www.airbnbhell.com/airbnb-left-me-with-a-dead-body-50000-in-damages/	12/10/2019
Solution to Airbnb guests damaging properties	Host stories	H9	www.airbnbhell.com/solution-to-airbnb-guests-damaging-properties/	01/02/2020
Airbnb guest attacked my dog, deleted from Airbnb	Host stories	H10	www.airbnbhell.com/airbnb-guest-attacked-my-dog-deleted-from-airbnb/	17/12/2019
One Airbnb guest brought a gun, killing four people	Neighbour stories	N1	www.airbnbhell.com/one-airbnb-guest-brought-gun-killing-four-people/	4/11/2019
Landlord exploits long-term guests on Airbnb	Neighbour stories	N2	www.airbnbhell.com/landlord-exploits-long-term-guests-on-airbnb/	12/04/2019
Airbnb party house makes resident consider moving	Neighbour stories	N3	www.airbnbhell.com/airbnb-party-house-makes-resident-consider-moving/	25/05/2020
Airbnb provides noisy and unruly neighbours	Neighbour stories	N4	www.airbnbhell.com/airbnb-provides-noisy-and-unruly-neighbours/	05/05/2019
My Airbnb neighbour hell begins today	Neighbour stories	N5	www.airbnbhell.com/my-airbnb-neighbor-hell-begins-today/	05/03/2019
Why are Airbnb services even allowed	Neighbour stories	N6	www.airbnbhell.com/why-are-airbnb-services-even-allowed/	12/08/2019
Drunk Airbnb guest wandering the neighbourhood	Neighbour stories	N7	www.airbnbhell.com/drunk-airbnb-guest-wandering-the-neighborhood/	06/01/2020
The love shack…just groovy, Airbnb	Neighbour stories	N8	www.airbnbhell.com/love-shack-just-groovy-airbnb/	22/04/2019
Assaulted in an Airbnb and banned for life?	Guest stories	I1	www.airbnbhell.com/assaulted-in-airbnb-banned-for-life/	07/03/2020
Host enters at night, scaring family with small children	Guest stories	I2	www.airbnbhell.com/host-enters-at-night-scaring-family-with-small-children/	29/07/2019
Bad Airbnb service for family in Slovenia	Guest stories	I3	www.airbnbhell.com/bad-airbnb-service-for-family-in-slovenia/	28/07/2019
Hell against my will by an Airbnb host	Guest stories	I4	www.airbnbhell.com/held-against-my-will-by-an-airbnb-host/	17/10/2019
Airbnb left me with a dead body, $50,000 in damages	Host stories	I5 I7	www.airbnbhell.com/airbnb-left-me-with-a-dead-body-50000-in-damages/	12/10/2019
Airbnb guest attacked my dog, deleted from Airbnb	Host stories	I6	www.airbnbhell.com/airbnb-guest-attacked-my-dog-deleted-from-airbnb/	17/12/2019
Airbnb business relies on creating deception	Guest stories	I7	www.airbnbhell.com/airbnb-business-relies-on-creating-deception/	22/08/2019
Amsperience Treeland Wormerveer listing in Amsterdam	Guest stories	I8	www.airbnbhell.com/amsperience-wormerveer-airbnb-listing-in-amsterdam/	09/10/2019
Forced out of Airbnb cabin in the woods	Guest stories	I9	www.airbnbhell.com/forced-out-of-airbnb-cabin-in-the-woods/	21/06/2019
Airbnb condones lying and scamming hosts	Guest stories	I10	www.airbnbhell.com/airbnb-condones-lying-and-scamming-hosts/	31/10/2019
Stranded in LA after Airbnb nightmare	Guest stories	I11	www.airbnbhell.com/stranded-in-la-after-airbnb-nightmare/	20/06/2019
Airbnb has my money and would not refund me	Guest stories	I12	www.airbnbhell.com/airbnb-has-my-money-and-wont-refund-me/	17/08/2019
Landlord exploits long-term guests on Airbnb	Neighbour stories	I13	www.airbnbhell.com/landlord-exploits-long-term-guests-on-airbnb/	12/04/2019
San Juan del Sur, Nicaraguan Hell Vacation	Guest stories	I14	www.airbnbhell.com/san-juan-del-sur-nicaraguan-hell-vacation/	14/10/2019
Someone’s trash could be your Airbnb furniture	Guest stories	I15	www.airbnbhell.com/someones-trash-could-be-airbnb-furniture/	03/10/2019
Guest life ban for complaining about racism	Guest stories	I16	www.airbnbhell.com/guest-life-ban-for-complaining-about-racism/	31/01/2020
The worst Airbnb experience (so far) in Jersey City	Guest stories	I17	www.airbnbhell.com/worst-airbnb-experience-jersey-city/	11/11/2019
Death trap with bedbugs and health concerns	Guest stories	I18	www.airbnbhell.com/death-trap-with-bedbugs-and-health-concerns/	28/6/2019
Airbnb host guarantee scam: no payment for damages	Host stories	I19	www.airbnbhell.com/airbnb-host-guarantee-scam-no-payment-for-damages/	21/12/2019
Airbnb party house makes resident consider moving	Neighbour stories	I20	www.airbnbhell.com/airbnb-party-house-makes-resident-consider-moving/	25/5/2020
The Love Shack … Just Groovy, Airbnb	Neighbour stories	I21	www.airbnbhell.com/love-shack-just-groovy-airbnb/	22/04/2019
Midnight trip driver on drugs and more weird things	Rider stories	R1	www.reddit.com/r/uber/comments/yv8ezg/midnight_trip_driver_on_drugs_and_more_weird/	14/11/2022
Uber rider confrontation with driver in Washington, D.C.	Driver stories	D1	www.reddit.com/r/uberdrivers/comments/z3nt3r/uber_rider_confrontation_with_driver_in/	24/11/2022
Uber driver will not return my house…	Rider stories	R2	www.uk.trustpilot.com/reviews/6379d04fb84cc27618f13266	20/11/2022
Scary ride	Rider stories	R3	www.uk.trustpilot.com/reviews/637b3c0a252cba2c02ded256	21/11/2022
Can a parent order Uber or Lyft for a teenager? My kid?	Driver stories	D2	https://ride.guru/lounge/p/can-a-parent-order-uber-or-lyft-for-a-teenager-my-kids	2018
Not always the vehicle shown	Rider stories	R4	www.uk.trustpilot.com/reviews/637ba74b252cba2c02df5526	21/11/2022
Driver bringing up sex	Rider stories	R5	www.reddit.com/r/uber/comments/yyyodv/driver_bringing_up_sex/	19/11/2022
How many drunks have puked in your car?	Driver stories	D3	https://ride.guru/lounge/p/how-many-drunks-have-puked-in-your-car-how-much-did-you-make-from-cleanup-fees	2019
PAX’s review shouldn’t count if they vandalised my car	Driver stories	D4	www.reddit.com/r/uberdrivers/comments/z2zlgi/paxs_review_shouldnt_count_if_they_vandalized_my/	24/11/2022
Scary ride	Driver stories	R6	www.uk.trustpilot.com/reviews/637b3c0a252cba2c02ded256	22/11/2022
Bruh I’m pissed	Driver stories	D5	https://ride.guru/lounge/p/as-a-driver-what-were-some-of-your-moments-when-you-had-to-say-im-just-the-driver	01/11/2022
Just did my first one-star to a customer. Justified or not?	Driver stories	D6	www.reddit.com/r/uberdrivers/comments/z1gwox/just_did_my_first_onestar_to_a_customer_justified/	22/11/2022
Why are the windows locked	Driver stories	D7	www.reddit.com/r/uberdrivers/comments/9ju630/why_are_the_windows_locked/	02/11/2022

Source: Authors’ own work

Table A5.

Additional illustrative examples of forms of user misbehaviour in the context of airbnb

Type of misbehaviour	Concrete form of misbehaviour	Sub-category of misbehaviour	Illustrative examples	Data Source	Who carried out the misbehaviour?	The time of misbehaving
Illegal user behaviours	Giving an unwanted kiss	Directed against a person	“One night when I knew the owners were out, I came downstairs to make some food and work on my laptop. The female owner came home while I was cooking, and had clearly been drinking … I was doing my best to exit the conversation and go back to my bedroom when the female owner got into my personal space, said “I’m going to kiss you now,” and before anything even registered, she grabbed my face with both her hands and kissed me full-on on the lips, then began crying. I made an escape to my room, very shaken”	Link	Guest	During stay
	Driving guests drunk	Directed against a person	“He drives guests drunk. it is illegal for him to drive, unless a breathalyzer is installed in his car”	Link	Host	During stay
	Kicking a dog	Directed against an animal	“One of them kicked my dog who was just standing beside me not giving much a s&%t about what’s going on. Like he actually kicked a dog in the chest”	Link	Guest	During stay
	Treating others differently because of their race or national origin	Directed against a person	“I hear someone opening the bedroom door … it was the maid … she explained [that] she must visit the apartment every morning to ‘see if everything is okay’ … [as] we were not white Americans”. The host’s exact words, if memory serve me, were: ‘I don’t want any Spanish, blacks or anyone from the streets in the apartment” … [She] explained that her Airbnb listing was [intentionally] in German … She preferred only Germanic guests: from Switzerland, Austria, Germany, Northern Italy … [by regularly checking on us she] simply wanted to make sure that nothing was stolen	Link	Host	During stay
	Not installing a smoke detector in Airbnb rental	Directed against another person’s property Directed against a person	“no smoke detectors”	Link	Host	Before stay During stay
Unprofessional user behaviours	Writing a false review	Directed against user experience Directed against a person	“My guest was unable to find the house, and cancelled the reservation … Airbnb allowed a guest that was never in my house to comment on it. And so, the guest give me one-star ratings for everything. The guest rated the house as very dirty – without ever being inside”	Link	Host	Before stay
	Not cleaning the accommodation between the guests	Directed against user experience	“The cleaner arrived an hour later, four hours from my initial call. The cleaner was shocked when she viewed the sheets and promised to inform the owners. She also agreed that the floors had not been vacuumed, the toilet had certainly not been cleaned or flushed, and the basin had not been wiped – it was disgusting, with old soap and toothpaste left. The shower had not been wiped clean. This room had obviously been missed”	Link	Host	During stay
	Not responding to the guests’ enquiries	Directed against user experience	“[T]he host was responsive when we asked questions before booking. When we arrived at the complex and called the host, his number was disconnected’	Link	Host	During stay
	Not providing basic amenities	Directed against user experience	“After getting inside it was clear that the apartment hadn’t been cleaned: no sheets on beds, no toilet paper, trash bags, shampoo or other listed amenities”	Link	Host	During stay
	Failing to pay	Directed against another person’s or platform’s financial assets	“The next day I got a message from Airbnb saying “payment is delayed.” The Guest was on “free” night number two. Two days later, after chasing Airbnb, I got a message saying payment could not be collected. The guest was on “free” night four … On the fifth day, after all the failed attempts to get money from Airbnb, the guest told me they would transfer the money via bank transfer … She sent me a screenshot of the bank transfer and confirmation number. The money never actually went through and the guest left on day six”	Link	Guest	During stay After stay
	Bringing in additional, unregistered guests	Directed against another person’s or platform’s financial assets	“I entered the bedroom (of which the door was wide open to the hallway) to find a strange person asleep/passed out on the bed (not in the bed – on the bed). I immediately called the guest that was registered and asked, “What is going on? Who is the person that is in the unit?” The guest stated that his ‘friend’ was drunk, and had nowhere to stay. He let him stay at my place and was taking a hotel room for the night. I informed the guest that under no circumstances were unregistered guests allowed in the units and that this ‘friend’ had to leave”	Link	Guest	During stay
Unbefitting user behaviours	Not informing the guest that there is a cat in the accommodation	Directed against user experience Directed against a person	“Two of the guests are allergic to cats and the landlord didn’t mention it in the ad”	Link	Host	Before stay
	Micromanaging the water consumption	Directed against user experience	“The host was super creepy. She kept micromanaging how much water we used in the kitchen, … and yelled at me about how much water I used while taking a bath”	Link	Host	During stay
	Eating guest’s food	Directed against other person’s property	“[my] food was eaten [by the host]”	Link	Host	During stay
	Not repairing creaking doors	Directed against user experience	“Every door also squeaked. I asked my host why they didn’t oil the hinges and she said she wanted it that way so she knew what was going on”	Link	Host	During stay
	Not cleaning the food after eating	Directed against other person’s property	“There was rotten food everywhere”	Link	Guest	During stay
Uncivil user behaviours	Attempting to use neighbour’s pool	Directed against other person’s property	“I have a pool so several renters tried telling me that my pool was the community pool and I had to let them use it because they were ‘paying for it’”	Link	Guest	During stay
	Throwing a beer can at the neighbour	Directed against a person	“I had beer cans thrown at me”	Link	Guest	During stay
	Guests not socially distancing during the pandemic	Directed against a person	“We had a family move into out village … They are raucous and have no respect for social distancing”	Link	Guest	During stay

Source: Authors’ own work

Notes

1. The terms consumer and customer are used interchangeably in this paper.

2. AirbnbHell stories typically mention the location of the Airbnb-rented accommodation. When the location was not provided, we used the Terms of Service for European Users as a reference point.

3. When the location was not provided, we registered particular misbehaviour as legal or illegal based on the UK laws (see Table 1, for example, of data sources).

4. At least one impact was reported/coded for 72% of unique forms of misbehaviours.

5. Some of the reported impacts within each category remained unclassified due to the lack of context.

6. We use the term “citizen” in its broadest and depoliticised sense. In this sense, a good citizen refers to any person in society who acts responsibly. Examples of responsible acts include obeying the law, paying taxes, driving carefully and behaving oneself socially by minimising offence to others (Pykett et al., 2010).