Airbnb as a disruptive innovation
As Guttentag (2015) described, the process of disruptive innovation appears to apply very directly to Airbnb. As is characteristic of disruptive innovations, Airbnb seemingly underperforms in comparison with traditional accommodations when considering traditional tourism accommodation performance attributes, as identified in the hotel choice literature. For example, hotel rooms are cleaned daily by professional staff, whereas Airbnb spaces generally are cleaned by the host according to his or her own standards; hotels offer the security of a private, locked room, whereas Airbnb guests often will have an unlicensed stranger present in the same residence; many hotels have brand reputations that assure a certain standard of quality, whereas Airbnb accommodations are only indirectly affiliated with the company; hotel reservations can be made easily and quickly, whereas Airbnb guests often must undertake a more prolonged process of communication with the host; hotels often offer amenities like restaurants, room service, fitness facilities, business centres, and meetings rooms, which generally will not be available in Airbnb accommodations; hotels very rarely cancel guests’ reservations and generally permit no-fee cancellations up until shortly before a stay, whereas cancellations by Airbnb hosts seem more common and are more problematic, plus guest cancellation policies are set by the host and are generally quite strict; hotel guests have 24/7 access to on-site professional staff in the event of any unexpected problems, whereas Airbnb guests must rely on a host who likely has many other responsibilities and may not even be present; hotels provide 24/7 front desk service accustomed to handling late night and early morning check-ins and check-outs, whereas Airbnb guests and hosts must make their own arrangements for key transfers and other steps associated with checking in and out; and, finally, hotels tend to be located in downtown and/or tourist areas, whereas Airbnb accommodations are much more widely dispersed in residential neighbourhoods (although some tourists will perceive this characteristic as an advantage rather than a disadvantage).
Even though Airbnb seemingly underperforms according to many key traditional accommodation attributes, for some consumers traditional accommodations may offer a “performance oversupply” regarding such attributes, meaning these consumers’ demands have been exceeded. These individuals therefore may consider a different product with inferior performance, if bolstered by an alternative set of benefits. Indeed, as is typical of disruptive innovations, Airbnb accommodations tend to be cheaper than hotels (Guttentag, 2015; Haywood et al., 2016; Hockenson, 2013) (although some recent research has questioned this common perception (Bird, 2016; Lane & Woodworth, 2016)). Moreover, Airbnb accommodations may provide for a more unique and authentic experience, they may offer useful household amenities (e.g., a refrigerator, washing machine, and dryer) not typically available in traditional accommodations, and they may offer more space than traditional accommodations. In other words, Airbnb offers a new value proposition that will appeal to some consumers. Yglesias (2012), writing in Slate, encapsulated these concepts of performance oversupply and alternative value proposition quite nicely in describing his experience using Airbnb in Buenos Aires:
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[The owner] was able to offer a local mobile phone and helpful restaurant recommendations, arrange a reasonably priced taxi to the airport for us, and even participate in a little mutually advantageous black-market currency exchange. It was cheaper than a hotel, and in exchange we gave up services we didn’t really need. We’re perfectly capable of making our own bed in the morning, and access to a normal refrigerator and kitchen is in practice more valuable than bellhop service.
Similarly, Sim (2015), writing in Medium, explained:
I don’t stay at an Airbnb place for the service because there isn’t any ... I consider Airbnb when the rental rate is at least 50% or $100 USD lower for a comparable living space, which nowadays is most of the time ... [H]otels have more comfortable beds, better linens, and bathrooms, whereas most apartments have better entertainment systems, internet connectivity, and laundry facilities (I typically will only rent apartments that have at least a washing machine in the apartment itself.) ... Services like housekeeping and a facility for me to exercise aren’t worth more than an additional $30/day in most markets because I can do without or find alternatives easily.
Consistent with the process of disruptive innovation, Airbnb enjoyed only marginal popularity during its first several years of operations. Early users presumably consisted of both low-end adopters (who otherwise would have stayed in cheap traditional accommodations) and new market adopters (who otherwise would have not taken the trip or would have sought free accommodation). Nonetheless, as Airbnb’s popularity has exploded over the last few years, it seems reasonable to contend that Airbnb is now attracting “mainstream” consumers. This expansion has surely been advanced by Airbnb’s previously described performance improvements (e.g., identify verification features, a 24/7 telephone hotline, and Instant booking), allowing it to satisfy the demands of an increasing number of consumers.
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A consumer-level perspective
Although the concept of disruptive innovation seems to apply very directly to Airbnb, the disruptive innovation literature offers only limited insight into understanding why a consumer would choose to use an innovation like Airbnb. The disruptive innovation literature includes a mixture of conceptual thinking (e.g., Christensen & Raynor, 2003; Govindarajan & Kopalle, 2006), empirical research (e.g., Hüsig et al., 2005; Keller & Hüsig, 2009), and debate (e.g., Lepore, 2014; Tellis, 2006), but it has been mostly restricted to an industry-level perspective focusing on characteristics and dynamics of the disrupted industry, the firms involved, and the products involved. This perspective is understandable, as disruptive innovation is essentially a business theory describing why some companies fail, and Christensen’s two major books (Christensen, 1997; Christensen & Raynor, 2003) are directed very explicitly at business managers. A basic understanding of the demand side of disruptive innovation can be extrapolated from Christensen’s (1997) description of disruptive products as offering an alternative package of benefits, generally centered on being cheaper, simpler, smaller and/or more convenient. It is essentially a Lancastrian approach of decomposing products into collections of attributes that are weighed against each other (Lancaster, 1966), with adopters choosing an innovation due to its unique collection of attributes (Adner, 2002; Christensen, 1997). Also, Christensen (1997) noted that once demand on a key performance attribute is satisfied, consumers focus on secondary attributes (like preferring a smaller disk drive once the performance is sufficient). Nevertheless, such insights only provide a general understanding of the demand side of disruptive innovation.
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The most concentrated look at disruptive innovation demand comes from Adner (2002), who modelled demand for computer disk drives (Christensen’s primary product example) and demonstrated the particular importance of unit price in propagating disruptive innovation 43 demand. Adner noted that consumers differ according to factors like budget constraints, planned uses for a product, and access to substitutes, which in turn lead to variations in how consumers value different product attributes, their willingness to pay for performance improvements, and their minimum performance thresholds. Nonetheless, as performance levels become very high, this market heterogeneity is reduced because most consumers have become satisfied with product performance and the characteristics that previously distinguished consumers become decreasingly relevant. In turn, as the marginal utility from performance enhancements diminishes, unit price, rather than a price-performance ratio, becomes increasingly important in encouraging adoption. As Adner summarized, “Critical to a disruptive outcome is the price at which the invader offers its product. The attacking firm must have the incentive and ability to offer its technically inferior, yet nonetheless satisfactory product at a sufficiently lower unit price to consumers than its rival” (p. 686).
Innovation attributes
The characteristics defining an innovation will naturally impact the speed and extent of its adoption. Rogers (2003) summarized that adoption is influenced by five key attributes, which are relative advantage (the perception that an innovation is better than its predecessor), compatibility (the perception that an innovation is consistent with a potential adopter’s values, past experiences, and needs), complexity (the perception that an innovation is difficult to use or understand), trialability (whether an innovation can be tested), and observability (whether the results of innovation adoption can be viewed by others). Importantly, it is the perceived, rather than the objective, qualities of these attributes that are of critical importance (Greenhalgh Robert, 48 Bate, Macfarlane, & Kyriakidou, 2005; Rogers, 2003), which is somewhat distinct from the disruptive innovation literature that tends to implicitly focus on objective attribute performance.
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Numerous reviews of innovation diffusion research have highlighted the particular importance of relative advantage on innovation adoption (Arts, Frambach, & Bijmolt, 2011; Evanschitzky, Eisend, Calantone, & Jiang, 2012; Rogers, 2003; Tornatzky & Klein, 1982). Relative advantage essentially represents a comparative assessment of an innovation’s array of benefits and costs (Hall, 2005; Rogers, 2003). These benefits and costs can vary widely and may relate to, for example, financial implications (e.g., inexpensiveness or the opportunity for greater profit), functional attributes, social prestige, convenience, satisfaction, or immediacy of reward (Rogers, 2003). The notion of relative advantage captures much of the demand-side thinking related to disruptive innovation, but also takes a broader perspective by focusing on perceived instead of absolute performance, and by considering indirect advantages like social prestige that are not generally recognized in the disruptive innovation literature (Greenhalgh et al., 2005; Rogers, 2003).
For example, Christou and Kassianidis (2002) examined the relative advantage of online shopping for travel products by considering the perceived physical effort of in-store travel shopping, time pressure, and enjoyment. Le, Hollenhorst, Harris, McLaughlin, and Shook (2006) examined the relative advantage of environmentally friendly practices for Vietnamese hotels by considering cost savings, sales, and firm reputation. Park and Gretzel (2006) examined the relative advantage of travel search engines by considering factors including cost savings and efficiency. Finally, Smerecnik and Andersen (2011) examined the relative advantage of environmental sustainability innovations in North American hotels and ski resorts by considering factors including market advantage, customer satisfaction, and employee satisfaction.
Relative advantage represents the most straightforward explanation for innovation adoption, but countless superior innovations are never widely adopted, as numerous other factors influence adoption as well (Rogers, 2003). In addition to relative advantage, innovations will be more appealing if they are consistent with an adopter’s values and beliefs, if they are associated with positive past experiences, and if they closely align with existing needs (Rogers, 2003). Previous reviews of the innovation diffusion literature have highlighted that, along with relative advantage, compatibility plays an especially important role in adoption (Arts et al., 2011; Tornatzky & Klein, 1982). Because the three stated dimensions of compatibility – values/beliefs, previous experience, and needs – are somewhat diverse, compatibility can take many different forms and in some cases is conceptualized very similarly to relative advantage. Looking back at the four tourism studies referenced above, Christou and Kassianidis’s (2002) study of online travel shopping measured compatibility in terms of time pressure and enjoyment – two of the same items they used to examine relative advantage; Le et al.’s (2006) study of Vietnamese hotels measured compatibility in terms of employee support; Park and Gretzel’s (2006) study of travel search engines measured compatibility in terms of consistency with how one likes to plan trips and one’s travel planning needs, and a factor analysis ended up combining the compatibility items with the relative advantage items into a single factor named “perceived usefulness;” and Smerecnik and Andersen’s (2011) study of hotels and ski resorts measured compatibility in terms of alignment with current procedures and employee practices.
Innovation adoption also is influenced by complexity, as innovations that are easier to use will be adopted more readily. Complexity dissuades adoption because potential adopters may be daunted by the expected learning costs required to begin using an innovation (Arts et al., 2011; Rogers, 2003; Wood & Moreau, 2006). As was mentioned earlier, Tussyadiah (2015) found 50 perceived lack of efficacy to be the primary obstacle for PSR non-adopters. Likewise, trialability and observability will influence innovation adoption, as innovations that can be tested on a limited basis and have results that are visible to others will be adopted more readily. Airbnb, of course, cannot truly be “tested” on a limited basis, as there is a strict dichotomous division between use and non-use. This characteristic is typical of many tourism products (Gratzer, Werthner, & Winiwarter, 2004), and service innovations more generally (De Brentani, 1991), as they are intangible “confidence goods” that are produced and consumed simultaneously and therefore do not allow for testing prior to purchase. Nevertheless, Rogers (2003) noted that later adopters can use the experiences of earlier adopters as proxy tests, and the public reviews of Airbnb accommodations may assist this process. Likewise, Airbnb has limited observability, as potential adopters cannot readily observe other Airbnb guests using the service, but again, Airbnb reviews may help mitigate this issue. Although complexity, trialability, and observability are key innovation attributes (and the importance of online reviews will be revisited in the later discussion of communication), they are not especially germane to the present study because they do not directly represent motivations, which are this study’s focus. As Arts et al. (2011) noted, relative advantage and compatibility relate directly to a product’s benefits, whereas trialability and observability relate to the assessment of such benefits. Arts et al. do not mention complexity, but that concept is also somewhat distinct, as complexity is more of a barrier to adoption than a reason for adoption.
Personal innovativeness
Innovation adoption decisions are not only influenced by attributes of the innovation itself, but also by characteristics of the potential adopter. “Innovativeness” is the term used to describe how 51 early an individual tends to be in adopting innovations, and it is often examined through the use of chronological adopter segments (Rogers, 2003). These segments generally follow those initially proposed by Rogers (1958) – “innovators,” “early adopters,” “the early majority,” “the late majority,” and “laggards” – and research suggests earlier adopters tend to exhibit certain characteristics, including more education, higher social status, greater acceptance of uncertainty, and being more networked (Rogers, 2003). Rogers’ (1958) divisions are based on standard deviations from the mean in a normal curve, as adoption of (successfully diffused) innovations tends to follow a normal bell curve (or S-curve if adoption is considered cumulatively), in which adoption is initially slow, accelerates in the middle, and then eventually slows down again (Rogers, 2003).
Nonetheless, innovativeness is also often viewed as a continuum-based personality trait, so the standardized approach has received criticism, most notably from Midgley and Dowling (1978), for equating an abstract construct (innovativeness) with an operational measure (time of adoption) (Roehrich, 2004). Consequently, innovativeness is frequently measured with more trait-oriented behavioural and attitudinal scales (Roehrich, 2004). Innovativeness can be examined on either a global or product dimension level, and past findings generally recommend the latter, as the global perspective seems too broad to maintain predictive validity regarding behaviour at the product level (Gatignon & Robertson, 1985; Goldsmith, Freiden, & Eastman 1995; Roehrich, 2004). For example, an individual who quickly adopts automobile-related innovations probably would not be particularly likely to quickly adopt an innovation like Airbnb.
Innovativeness has not received much explicit attention from tourism researchers, but a handful of studies have applied the concept to the field. Most notably, Tussyadiah (2015) found PSR users exhibited a higher level of tourism innovativeness than non-users, but similar levels of technology innovativeness. Also, Litvin, Kar, and Goldsmith (2001) found that individuals exhibiting higher levels of tourism innovativeness viewed themselves differently from their less innovative counterparts; Lee, Qu, and Kim (2007) found Korean travellers’ technology innovativeness was positively related to intentions to search and make purchases from a travel website; San Martín and Herrero (2012) found that travellers’ technology innovativeness was positively related to intentions to purchase rural tourism accommodations online; and Couture, Arcand, Sénécal, and Ouelletlane (2015) found travellers’ tourism innovativeness was positively related to various interactions with Quebec’s official tourism website.
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