Understanding How Residents’ Emotional Solidarity with Airbnb Visitors Influences Perceptions of Their Impact on a Community: The Moderating Role of Prior Experience Staying at an Airbnb

The Growth of Airbnb

Airbnb’s exponential growth has attracted interest from local and regional tourism planners and academia alike. While researchers from across a variety of disciplines have investigated the social, cultural, environmental, and economic impacts of Airbnb, and the peer-to-peer accommodation industry at large, our understanding of the implications of Airbnb in residential neighborhoods is still in nascent stages. Studies have ranged from topics including, but not limited to, understanding why travelers choose Airbnb—from both a consumer and supplier perspective (J. Kim, Yoon, and Zo 2015; Lampinen and Cheshire 2016; Möhlmann 2015) traveler experience of Airbnb (see, e.g., Mody, Suess, and Lehto 2017; Mody, Suess, and Lehto 2019a, 2019b; Mody, Hanks, and Dogru 2019), economic impacts of Airbnb on a destination (see, e.g., Levendis and Dicle 2016), pricing on Airbnb (see, e.g., Dogru and Pekin 2017; D. Wang and Nicolau 2017), changes in supply and demand of Airbnb (see, e.g., Dogru, Mody, and Suess 2016, 2019, 2020; Haywood et al. 2017), and demand dynamics (see, e.g., Dogru, Mody, and Suess 2016; Nunkoo and Ramkissoon 2011), racial discrimination and Airbnb (see, e.g., Edelman, Luca, and Svirsky 2017), impacts on the industry employment (Dogru et al. 2020), and the effects of Airbnb on gentrification and the regulation of Airbnb (see, e.g., Kaplan and Nadler 2015; Miller 2014; Mody, Suess, and Dogru 2019). However, to date, little empirical evidence exists to make generalizable assertions of residents’ perceptions of Airbnb.

Moreover, Airbnb has been portrayed as making neighborhoods significantly less safe where hosts are operating (Gutiérrez et al. 2017; Guttentag 2015). Stories in the media highlight myriad resident complaints pertaining to Airbnb and the potential threat to residents’ safety-related to “stranger-danger” (“Airbnb Has Come to a Vermont Town and Some Residents Are Worried,” 2017); increases in crime and vandalism; increased traffic hazards (“Illegal Hotels,” 2017); noise from groups and parties (“Nashville Residents Grapple with Their Own Airbnb Challenges,” 2017); disorderly conduct by Airbnb guests toward their hosts’ neighbors (Burdeau 2016). While this discourse certainly stems from documented incidents, it may disproportionately highlight problems and misrepresent how residents feel, at large, about Airbnb. On the contrary, promoting pervasive concerns about Airbnb by residents may be unfounded, according to recent empirical research and statistics evidencing that non-hosting residents who are confirmed neighbors of Airbnb hosts report that they are supportive of Airbnb and that the benefits from Airbnb visitors, including the economic and social impacts on neighborhoods, have a more substantial direct effect on their support for Airbnb than the perceived negative impacts from Airbnb (Mody, Suess, and Dogru 2019). Even residents with children living in their household have expressed general support for Airbnb in their neighborhoods and indicated more of a sense of feeling safe than not, living among Airbnb hosts (Suess, Woosnam, and Erul 2020). In need of further empirical evidence to inform researchers and community planners, in the present study, we propose that the cognitive appraisal theory and bottom-up spillover theory of subjective well-being provide a relevant contextual theory with which to frame an examination of how residents’ emotional solidarity and emotional responses influence their perceptions of the impacts visitors staying at Airbnbs have on the community’s well-being and, ultimately, their personal quality of life.

Emotional Solidarity

The roots of emotional solidarity originate from the late works of the classical sociologist, Emile Durkheim. Fascinated with the social facts comprising the Australian Aboriginal religion, Durkheim and Swain ([1912] 2008) sought to explain how an intimate emotional bond such as solidarity develops. In a basic sense, emotional solidarity is considered a perceived sense of cohesion and integration that develops from shared actions, common beliefs, and interaction between individuals (Durkheim and Swain [1912] 2008). Applied in a tourism context, researchers throughout the last decade have considered not only how solidarity develops between residents and tourists within destinations (Woosnam 2011b; Woosnam and Aleshinloye 2013; Woosnam, Norman, and Ying 2009) but also how solidarity contributes to the explanation of residents’ perceptions of tourism (Hasani, Moghavvemi, and Hamzah 2016; Lai and Hitchcock 2017; Li and Wan 2017; Moghavvemi et al. 2017; Woosnam 2012).

Woosnam (2012) initially examined the relationship between solidarity and residents’ perceptions of various impacts from tourism. The three factors of emotional solidarity (i.e., welcoming nature, emotional closeness, and sympathetic understanding) explained roughly one-third of the variance in two tourism impact factors (i.e., support for tourism development and contributions to the community). In addition, Lai and Hitchcock (2017) considered the same constructs and found that two of the emotional solidarity factors (i.e., excluding emotional closeness) significantly explained support for tourism development and contributions to the community. Interestingly, emotional closeness was found to be a significant (negative) predictor of a third construct, annoyances to the community. Further, three studies (Hasani, Moghavvemi, and Hamzah 2016; Li and Wan 2017; Moghavvemi et al. 2017) focused on the role of emotional solidarity in explaining residents’ attitudes regarding tourism impacts as well as their support for tourism development. Whereas Moghavvemi et al. (2017) found each of the emotional solidarity factors significantly explained residents’ attitudes toward tourism development, and in turn, support for tourism development, Hasani, Moghavvemi, and Hamzah (2016) reported that only welcoming nature explained the two outcome constructs. Li and Wan (2017) reported finding two of the emotional solidarity factors (i.e., excluding sympathetic understanding), each of which significantly explained perceived positive festival impacts and support for festival development.

However, there are noted gaps in the tourism literature surrounding emotional solidarity. As Moghavvemi et al. (2017) suggested, “Future research should also be undertaken to examine how emotional solidarity affects residents’ social well-being and quality of life” (p. 251). Given the ability of emotional solidarity to explain residents’ support for tourism, well-being, and quality of life, it stands to reason that the construct (as a measure of residents’ extant relationships with visitors to their community) may serve instrumental in understanding how residents perceive the impacts from visitors staying at Airbnbs on their community’s well-being and personal quality of life. However, minimal research has addressed how residents perceive the impacts of increased visitors within their neighborhoods and communities in the context of peer-to-peer accommodations (Jordan and Moore 2017). Few better opportunities exist for residents and tourists to come into contact and interact with one another than in instances where lodging and provision of meals can occur at a residential level (Wegmann and Jiao 2017). We hypothesize:

Hypothesis 1: Residents indicating higher emotional solidarity with visitors will perceive a more positive impact from Airbnb visitors to their community’s well-being.

Resident’s Emotions

Another gap is that emotional solidarity has rarely, if ever, been considered either in tandem with or as an antecedent to an individual’s elicited emotional responses. Residents’ perceptions of visitors may influence their cognitive information processing and emotions. Emotion refers to the mental state that results from processing or appraising of personally relevant information (Roseman 1984). Positive and negative emotions are two dominant dimensions that have been applied in studies of affective structure (D. Watson, Clark, and Tellegen 1988). Positive affect/emotion refers to the extent to which a person feels enthusiastic, active, and pleasant, while negative affect/emotion includes a variety of aversive emotional states, such as anger, disgust, guilt, fear, and nervousness.

Cognitive Appraisal Theory

The cognitive appraisal theory highlights that “it is not the specific events or physical circumstances that produce emotions, but rather the unique psychological appraisal made by the person evaluating and interpreting the events and circumstances” (Bagozzi, Gopinath, and Nyer 1999, p. 185). According to Zeelenberg and Pieters (2004), individuals evoke emotions after they appraise an event. In this regard, an individual’s emotions are a function of her or his subjective evaluations of a situation or an event on certain appraisal dimensions (L. Watson and Spence 2007). Because of differences in the appraisal processes among individuals, variations in their emotional responses to the same event are expected (Roseman, Spindel, and Jose 1990; N. Schwarz 1990).

In the context of the present study, when residents appraise an increase in visitors from an obtrusive venue (i.e., peer-to-peer accommodations) in a positive fashion, that positive appraisal may then elicit positive emotions. In contrast, when the negative impacts of an event are salient to residents, they are likely to experience negative emotions (R. Schwarz 2002). While Ouyang, Gursoy, and Sharma (2017) acknowledged the connection in a tourism context, they did not examine the relationship between solidarity and emotions. In a residential neighborhood, peer-to-peer accommodations, such as Airbnb hosts, generate increased visitors to a neighborhood. Thus, residents’ emotional solidarity may significantly influence how they process emotions related to the visitor influx. Such a connection makes plausible sense given Durkheim put forth the notion that emotions are what aid in sustaining the solidarity of an individual’s experience (Durkheim and Swain 2008). Carrying this idea forward, it is probable that higher levels of residents’ emotional solidarity with visitors would be significantly related to their positive emotional responses and/or reduced negative emotional responses toward their neighboring Airbnb hosts. As such, the following is hypothesized:

Hypothesis 2: Residents’ higher emotional solidarity with visitors will be related to more positive emotions toward Airbnb hosts in their neighborhood.

Hypothesis 3: Residents’ higher emotional solidarity with visitors will be related to a reduction in negative emotions toward Airbnb hosts in their neighborhood.

Influence of Residents’ Emotions on Perceptions of Community Well-being

Emotions are considered critical in understanding the underlying well-being of consumers in marketing and hospitality literature (Bitner 1992) because emotions can explain variations in individuals’ responses beyond and above rational cognitions (Bigné, Mattila, and Andreu 2008; Lashley, Morrison, and Randall 2005; Lee and Shea 2015; Liu, Sparks, and Coghlan 2016). For this reason, emotions have also received greater attention from tourism scholars in recent years. For example, Ouyang, Gursoy, and Sharma (2017) examined relationships between residents’ perceptions of the impacts from tourism on a community and their positive and negative emotions. Findings of which suggest that residents’ perceptions of the various impacts of tourism on a community are likely to predict their positive and negative emotions, which, in turn, play a critical role in whether or not they express support for tourism development. From this tacit perspective, the current study aims to address the influence of residents’ emotions (positive and negative) on their perceptions of the impacts associated with increased visitors from Airbnb on their community’s well-being by focusing on the internal appraisal process where cognition and emotion determine residents’ perceptions. We hypothesize:

Hypothesis 4: Higher levels of positive emotions toward Airbnb hosts lead to more positive perceived impacts on community well-being from Airbnb visitors.

Hypothesis 5: Higher levels of negative emotions toward hosting residents lead to less positive perceived impact on perceived community well-being from Airbnb visitors.

Community Well-Being

In effect, how residents perceive the community’s well-being to be affected as a result of increased visitors is influenced by their positive or negative emotions. Positive emotions (and the less negative emotions) influence a greater perceived improvement to the community’s well-being and how visitors affect residents’ personal quality of life (psychologically speaking).

In considering the nature of an assessment of a community’s well-being, we looked first to the extensive tourism literature on community quality of life (for a thorough review, see Uysal et al. 2016). Many studies measure subjective well-being through a series of individual life domains (Andrews and Withey 1976; Campbell 1976). In particular, residents’ perceptions of tourism’s impacts have affected their sense of well-being in four primary domains: their material, community, and emotional life and their health and safety (K. Kim, Uysal, and Sirgy 2013). Cummins (1996) grouped 173 different life domains derived from the early literature and identified seven of the most utilized: material well-being, health, productivity, intimacy, safety, community, and emotional well-being. These seven domains were accepted and applied not only in the general quality of life research, but also in tourism research, specifically (e.g., see H. Kim, Woo, and Uysal 2015; K. Kim, Uysal, and Sirgy 2013; Moscardo et al. 2013). Most of the researchers utilized seven domains and the specific measurement items on each domain according to the particularity of the case and research questions they explored (Andereck and Nyaupane 2011).

Other studies have adopted multidimensional approaches to studying various impacts from tourism on a community across eight domains, including economic health (e.g., community infrastructure and standard of living); social resources (e.g., public service provisions, social support network); environmental resources (e.g., unspoiled nature and protection of natural resources); healthy culture (e.g., neighborhood conditions, physical amenities, community commitment); political factors (e.g., confidence in local institutions, power in influencing local institutions); and subjective well-being of local residents (e.g., community living experiences and community satisfaction) (Andereck and Nyaupane 2011; Grzeskowiak, Sirgy, and Widgery 2003; McGehee and Andereck 2004; Sirgy and Cornwell 2001). Other widely used domains include health and family life (e.g., see Cummins 1996; K. Kim, Uysal, and Sirgy 2013; McCabe and Johnson 2013; Nawijn and Mitas 2012; Woo, Kim, and Uysal 2015). Andereck and Nyaupane (2011) grouped indicators from the Total Quality of Life (TQL) scale into eight domains: community well-being, urban issues, way of life, community pride and awareness, natural/cultural preservation, economic strength, recreation amenities, and crime and substance abuse. Relatedly, Andereck, and Nyaupane’s (2011) domains were then divided into two groups, material and nonmaterial domains, by Woo, Kim, and Uysal (2015).

K. Kim, Uysal, and Sirgy (2013) recognized that residents’ sense of well-being in multiple life domains affects their overall well-being and should be studied with principles of reciprocity applied to residents and regional tourism development. However, much research is needed to understand better how impacts from tourism on a community affect residents’ perceptions of changes to their community’s well-being (Uysal et al. 2016). As noted in the introduction, one of Airbnb’s key propositions to the travel market is that staying in peer-to-peer accommodations facilitates an experience in a local neighborhood. As we consider how Airbnb facilitates hosts who generate increased visitors to their communities and affect the neighboring non–peer-to-peer accommodation hosting residents, we are interested in the assessment of community well-being dimensions. Thus, we visit the concept of bottom-up spillover theory of subjective well-being (Andrews and Withey 1976; Campbell 1976).

The bottom-up spillover theory of subjective well-being postulates that effects within individual well-being domains accumulate and vertically spill over to superordinate domains (e.g., overall well-being). In this regard, the subjective well-being of local residents is considered the pinnacle of a quality of life hierarchy and associated with overarching community well-being, a higher order of various well-being subdimensions (Neal, Uysal, and Sirgy 2007). In effect, those subdimensions that contribute to the improvement of community well-being help enhance residents’ personal quality of life

Bottom-up Spillover Theory of Subjective Well-Being

The basic premise of bottom-up spillover theory in this study is that community well-being domains and sub-domains are functionally related to residents’ personal quality of life. In essence, individual quality of life would be the ultimate outcome, influenced by well-being domains according to a specific context of the case. In the present study’s context, residents’ community well-being is related to increased visitors to their neighborhood as a result of Airbnb hosts and the impact they have on various community well-being domains (e.g., community well-being, family/emotional well-being, social well-being, cultural well-being, and economic well-being, environmental well-being) (Neal, Uysal, and Sirgy 2007).

At the most basic level, researchers make specific reference to quality of life using specific psychological constructs such as subjective well-being, happiness, life satisfaction, perceived quality of life, domain satisfaction hedonic well-being, and positive and negative affect (Uysal et al. 2016). The concept of quality of life was first applied to the travel and tourism context by MacCannell (1973). Since then, research has identified that tourism contributes to both benefits and costs to a community (García, Vázquez, and Macías 2015). While individual studies are too numerous to list, one can argue that impacts from tourism and increased visitors to the community affect many stakeholders. One of the most important stakeholder groups is community residents.

The tourism research has repeatedly and robustly demonstrated that residents respond consistently to community aspects that they perceive to be positively or negatively affected from tourism (García, Vázquez, and Macías 2015), which dynamically change their quality of life (Allen et al. 1988; Almeida-García et al. 2016; Deccio and Baloglu 2002; García, Vázquez, and Macías 2015; K. Kim, Uysal, and Sirgy 2013; Ko and Stewart 2002; Látková and Vogt 2012; Nunkoo and Ramkissoon 2011; Perdue, Long, and Allen 1990). Indeed, the tourism literature is rife with studies exploring the nuances of quality of life constructs (e.g., see Andereck and Nyaupane 2011; Cummins 1996; H. Kim, Woo, and Uysal 2015; K. Kim, Uysal, and Sirgy 2013; McCabe and Johnson 2013; Nawijn and Mitas 2012); however, the tourism literature lacks measures that relate to the residents’ subjective, holistic experience of a community and its effect on their personal quality of life. Therefore, our study is designed to capture perceptions of visitors’ impacts on community well-being and relate these variables to a measure of personal quality of life. Based on this, and in the context of increased visitors from Airbnb in residential neighborhoods, our study hypothesizes that:

Hypothesis 6: Higher levels of perceived community well-being resulting from Airbnb visitors lead to a perceived improvement of personal quality of life.

Previous Experience with Airbnb

Research in hospitality and tourism has identified the importance of understanding attitudinal differences between individuals with and without experience related to staying at peer-to-peer accommodations (Mody, Suess, and Lehto 2017). In fact, the psychological mechanisms underlying accrued experiences that shape continuous attitudes and behavioral intentions have been well established (Jin et al. 2015; Mody, Suess, and Lehto’s (2017). Attitudes would be inherent to both experienced and inexperienced individuals and potentially alter how they respond to given changes in their environment. In this respect, residents who have prior experience staying at an Airbnb as a guest themselves may feel differently about Airbnb hosts in their neighborhood and perceive the impacts that Airbnb visitors have on the community’s well-being and their personal quality of life differently. Thus, the authors further hypothesize:

Hypothesis 7a: Residents with prior experience staying at an Airbnb will have higher emotional solidarity with visitors than residents with no prior experience staying at an Airbnb.

Hypothesis 7b: Residents with prior experience staying at an Airbnb will have more positive emotions toward Airbnb hosts in their neighborhood than residents with no prior experience staying at an Airbnb.

Hypothesis 7c: Residents with prior experience staying at an Airbnb will have less negative emotions toward Airbnb hosts in their neighborhood than residents with no prior experience staying at an Airbnb.

Hypothesis 7d: Residents with prior experience staying at an Airbnb will perceive more positive impacts from Airbnb visitors on the community’s well-being than residents without prior experience staying at an Airbnb.

Hypothesis 7e: Residents with prior experience staying at an Airbnb will perceive a more positive affect on their personal quality of life than residents without prior experience staying at an Airbnb.

Hypothesis 8a: For residents with prior experience staying at an Airbnb, higher emotional solidarity with visitors will have a stronger positive effect on their perceptions of Airbnb visitors’ impact on the community’s well-being than for residents without prior experience staying at an Airbnb.

Hypothesis 8b: For residents with prior experience stating at an Airbnb, higher emotional solidarity with visitors will have a stronger positive effect on their positive emotions toward Airbnb hosts in their neighborhood than for residents without prior experience staying at an Airbnb.

Hypothesis 8c: For residents with prior experience staying at an Airbnb, higher emotional solidarity with visitors will have a stronger effect in reducing their negative emotions toward Airbnb hosts in their neighborhood than for residents without prior experience staying at an Airbnb.

Hypothesis 8d: For residents with prior experience staying at an Airbnb, higher positive emotions toward Airbnb hosts in their neighborhood will have a stronger positive effect on their perceptions of Airbnb visitors’ impact on the community’s well-being than for residents without prior experience staying at an Airbnb.

Hypothesis 8e: For residents with prior experience staying at an Airbnb, (higher) negative emotions toward Airbnb hosts will have a weaker (negative) effect on their perceptions of Airbnb visitors’ impact on the community’s well-being than for residents with no prior experience staying at an Airbnb.

Hypothesis 8f: For residents with prior experience staying at an Airbnb, higher perceived positive impacts to the community’s well-being from Airbnb visitors will have a stronger positive effect on their personal quality of life than for residents without prior experience staying at an Airbnb.

Data Collection

Data for the study were collected by the online research company Qualtrics™. Samples were drawn from a nation-wide panel. Qualtrics sends a survey link to its panel participants without revealing information about the study prior to beginning the survey, minimizing self-selection bias. In addition, Qualtrics randomizes survey assignment across respondents who answer periodic refinement questions, thus enabling better targeting and further minimizing self-selection bias. In addition, this helps to ensure that nonresponse is more of a random event than a systematic event (see http://www.websm.org). The online survey collected responses from residents who were screened to indicate they had never been an Airbnb host themselves. A total of 467 usable responses were collected. Since the purpose of the study was to determine residents’ emotional solidarity, their emotional responses and their perceptions of the impact of Airbnb visitors on their community’s well-being and personal QOL dimensions, the authors separately surveyed individuals who were (1) not an Airbnb host themselves and (2) aware of at least one of their neighbors who was an Airbnb host. Considering residents’ previous experience staying at an Airbnb as a moderator of their emotional solidarity, emotions, and perceptions of Airbnb’s impact on their community’s well-being and personal quality of life, data were collected from 209 residents who had previously stayed at an Airbnb themselves and 256 residents who had never stayed at an Airbnb. The sample is representative of 45 of 50 states in the United States.

Survey Development

The constructs examined in the present study (Figure 1) were operationalized using indicators from scales previously validated in extant literature. Table 2 presents each of the items measured by the questionnaire. In the first section of the survey, 6 of the 10 items comprising the Emotional Solidarity Scale (adapted from Moghavvemi et al. 2017) were measured on a 7-point Likert-type scale (1 = strongly disagree and 7 = strongly agree) asking residents to indicate their levels of solidarity with visitors to the neighborhood. The six items were unmodified in wording and appeared exactly the same way as in Moghavvemi et al. (2017). In the second section of the survey, 11 emotional response statements representing the nature of the resident–Airbnb host relationship in the neighborhood were included, adapted from studies that have measured emotional responses of residents related to tourism development (Ouyang, Gursoy, and Sharma 2017; Richins 1997). Specifically, negative emotions (e.g., frustrated, irritated, angry, nervous, worried) and positive emotions (e.g., warm-hearted, happy, pleased, excited, thrilled, enthusiastic) were measured by asking the respondent to indicate a level of agreement with the emotional descriptor on a 7-point Likert-type scale (1 = strongly disagree and 7 = strongly agree) associated with the presence of Airbnb hosts in their neighborhood. The only modifications made to these items were the object of each phrase (i.e., changing “tourists” from Ouyang, Gursoy, and Sharma [2017] to “Airbnb hosts”).

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Figure 1.

Model of residents’ emotional solidarity, emotions, community well-being, and personal quality of life.

Next, respondents were asked to indicate whether 27 items pertaining to the dimensions of well-being—economic well-being, social well-being, cultural well-being, emotional well-being, environmental well-being, and community well-being, adapted from (Woo, Kim, and Uysal 2015)—would improve or worsen because of an increase of visitors to the neighborhood as a result of Airbnb visitors. Items were measured on a 7-point Likert-type scale (1 = worsen, and 7 = improve). The only modification of this scale was the context of the root question that was presented to participants. For instance, participants were asked to respond to the questions given a hypothetical scenario of visitors increasing due to Airbnb hosts in the neighborhood (versus tourism in general as Woo, Kim, and Uysal 2015 conceived). In addition, respondents were asked to indicate the extent to which they either agreed or disagreed on a 7-point Likert-type scale (1 = strongly disagree and 7 = strongly agree) with a statement about the propensity of Airbnb visitors in their neighborhood to improve their personal quality of life. The single-item measure was adapted to capture a critical quality of life–related pinnacle outcome. Finally, demographic questions including age, gender, ethnicity, education, income, household, housing area, neighborhood type, and residence location were asked.

Power Analysis

For a model with five latent variables and 36 observed variables (anticipated effect size = 0.3, statistical power level = 0.8, α = 0.05), a minimum sample size of 150 was required to detect an effect, while 452 is the recommended sample size (Soper 2017). In this regard, the present study’s overall sample size is 103% of the minimum sample size needed for hypothesis testing of the overall model (467/452). For the group comparisons, the sample sizes (n = 209 and n = 256) exceed the minimum sample size required to detect effects.

Data Analysis

As the first step in analyzing the data, descriptive statistics and distributions were assessed using Stata 15. Next, a series of t tests were performed to compare differences among construct mean scores of residents with and without prior experience staying at an Airbnb as a guest. Following the assessment of relative construct performance, a confirmatory factor analysis (CFA) was conducted on the model constructs. Structural equation model (SEM) analysis provided estimates for the two samples, including residents with prior experience staying at an Airbnb and residents with no prior experience staying at an Airbnb. The CFA employed common method bias and convergent and discriminant validity tests.

In the next stage of analyses, structural equation modeling (SEM) techniques tested the models’ hypotheses (i.e., hypotheses 1–6) (Figure 1). The five dimensions (e.g., economic well-being, emotional well-being, cultural well-being, community well-being, and environmental well-being) comprising community well-being were modeled as a second-order construct. Residents’ perceptions of the increase in visitors to their neighborhood from Airbnb and impact on their personal quality of life were included in the model as a single-indicator construct. The dimension of emotional solidarity with visitors was posited to add explanatory power to the constructs of residents’ positive and negative emotions associated with Airbnb hosts operating in their neighborhood, which predict the dependent variable of the perceived impact of Airbnb visitors on community well-being, and, ultimately, personal quality of life. Measurement invariance was confirmed. Following the SEM procedures, pairwise parameter comparison tests for hypotheses 7a-f and 8a-f were performed. Finally, an alternative model (Figure 2) without the construct of emotional solidarity was tested.

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Figure 2.

Alternative model: without residents’ emotional solidarity with visitors.

Sample Profile

The profiles of residents with and without prior experience staying at an Airbnb are presented (Table 1). Results of multiple χ² tests indicated that these two samples differed significantly (p < 0.05) across respondents’ age, income, education, ethnicity, neighborhood setting, number of Airbnb hosts operating in the neighborhood, and attitude toward the density of Airbnb hosts. Table 1 indicates that residents with prior experience staying at an Airbnb as a guest were generally middle-aged, higher income, more educated, and living in urban or suburban and single-family homes. Despite the sample being moderately skewed toward females, the respondent profile is generally consistent with that of the US general population. In addition, the 467 respondents represent 45 US states, including 271 different cities and urban, suburban, and rural areas across the country; the sample’s geographic distribution demonstrated representativeness of the US general population.

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Table 1.

Sample Profile.

	Residents with Prior Experience Staying at an Airbnb(n = 209)		Residents with No Prior Experience Staying at an Airbnb(n = 254)		χ2 Value (df)
	Frequency	Percentage	Frequency	Percentage
Gender					.466 ns (1)
Male	64	30.62	71	27.73
Female	145	69.38	85	33.20
Age (years)					92.23 ** (66)
18–25	4	1.91	57	22.27
26–34	62	29.67	80	31.25
35–54	66	31.58	32	12.50
55–64	30	14.35	34	13.28
65 or older	18	8.61	38	14.84
Income (yearly)					19.52** (7)
<$15,000	25	11.96	48	18.75
$15,000 to <$30,000	39	18.66	43	16.80
$30,000 to <$45,000	33	15.79	50	19.53
$45,000 to <$60,000	25	11.96	30	11.72
$60,000 to <$75,000	28	13.40	26	10.16
$75,000 to <$90,000	17	8.13	10	3.91
$90,000 to <$120,000	32	15.31	21	8.20
>$120,000	25	11.96	48	18.75
Education					19.49** (4)
Grade school	0	0.00	1	0.39
High school	31	14.83	57	22.27
Some college	49	23.44	92	35.94
College	95	45.45	78	30.47
Graduate school	34	16.27	28	10.94
Ethnicity					11.005** (5)
White/Caucasian	21	10.05	187	73.05
Black/African American	16	7.66	40	15.63
Asian	1	0.48	11	4.30
American Indian/Alaskan Native	14	6.70	3	1.17
Multiracial	3	1.44	7	2.73
None of these	21	10.05	8	3.13
Hispanic background					2.737 ns (1)
Yes	31	14.83	25	9.77
No	178	85.17	230	89.84
Neighborhood type					9.917 ns (13)
City block	21	10.05	41	16.02
Company town	4	1.91	3	1.17
Gated community	12	5.74	7	2.73
Housing estate	5	2.39	6	2.34
Intentional community	1	0.48	1	0.39
Multifamily residential	24	11.48	27	10.55
Single-family residential	99	47.37	111	43.36
Private community	8	3.83	8	3.13
Public housing	3	1.44	7	2.73
Retirement community	11	5.26	2	0.78
Apartment	8	3.83	21	8.20
Condominium	5	2.39	13	5.08
Planned Community	4	1.91	5	1.95
Other	4	1.91	4	1.56
Neighborhood setting					8.179** (2)
Urban	65	31.10	66	25.78
Suburban	110	52.63	122	47.66
Rural	30	14.35	64	25.00
Household				0.00
Single adult living alone or with other adults	82	39.23	77	30.08	6.198 ns (3)
Single adult living with children or dependents	24	11.48	46	17.97
Married couple living without children or dependents	45	21.53	58	22.66
Married couple living with children or dependents	58	27.75	75	29.30
Airbnb hosts in the neighborhood
1-5	151	72.25	221	86.33	20.060*** (4)
6-10	27	12.92	19	7.42
11-15	17	8.13	4	1.56
16-20	7	3.35	3	1.17
>20	6	2.87	9	3.52
Airbnb hosts in the neighborhood (attitudinal)					55.733*** (24)
Far too many	4	1.91	14	5.47
Moderately too many	9	4.31	15	5.86
Slightly too many	20	9.57	17	6.64
Neither too many nor too few	139	66.51	164	64.06
Slightly too few	22	10.53	16	6.25
Moderately too few	9	4.31	15	5.86
Far too few	6	2.87	15	5.86
Airbnb stays (as guests)
1	48	22.97
2	49	23.44
3	33	15.79
4	26	12.44
5	24	11.48
≥6	28	13.40

*

p < 0.05; **p < 0.01; ***p < 0.001; ^nsp > 0.05.

A large majority of residents across both groups (72.25% and 86.33%) indicated that they were aware of at least 1 and up to 5 active Airbnb hosts in their neighborhoods. Relatedly, more than half of the respondents (66.51% and 64.06%) felt that there were neither too many nor too few Airbnb hosts operating in their neighborhood, with only a small percentage (15.79% and 17.97%) indicating they felt there were too many Airbnb hosts in their neighborhoods. For the group of residents with previous experience staying at an Airbnb as a guest, 39.71% reported that they had spent four or more nights in an Airbnb.

These statistics highlight that the sample is representative of a general resident who does not seem to be either averse to or overly biased toward Airbnb (as demonstrated by a majority of residents indicating they did not use Airbnb extensively and only a minor sentiment of too many hosts operating in the respondents’ neighborhoods).

Table 2 presents the summary statistics for the items used to measure the various constructs of the model in the survey in addition to including literature sources from which the measures were adopted. The Cronbach’s α ranged from 0.836 to 0.945, above 0.70, as recommended by Hair et al. (2010) and Nunnally and Bernstein (1994). Notably, means on items were higher (and lower for negative emotions items) for the residents with prior experience staying at an Airbnb.

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Table 2.

Summary Statistics for Measurement Items.

Constructs and Measurement Items		Residents with Prior Experience Staying at an Airbnb		Residents with No Prior Experience Staying at an Airbnb
		Mean(n = 209)	SD	Mean(n = 254)	SD
Emotional Solidarity	“Please indicate how much you agree or disagree with the following statements about your feelings towards visitors in your neighborhood.” (Measured on a seven-point Likert-type scale: 1 = strongly disagree 7 = strongly agree) (Moghavvemi et al. 2017)
	I am proud to have visitors come to my neighborhood	5.716	1.224	5.336	1.445
	I feel the neighborhood benefits from having visitors	5.509	1.383	5.168	1.470
	I feel close to some visitors I have met in my neighborhood	5.725	1.144	5.294	1.507
	I have made friends with some visitors in my neighborhood	5.149	1.554	4.898	1.627
	I identify with visitors in my neighborhood	5.197	1.408	4.807	1.556
	I have a lot in common with visitors in my neighborhood	5.182	1.395	4.792	1.513
Emotional Responses	“To what extent do you either agree or disagree with the way Airbnb hosts in your neighborhood makes you feel, in general?” (measured on a seven-point Likert-type scale: 1 = strongly disagree 7 = strongly agree) (Ouyang et al. 2017; Richins 1997)
	I am frustrated about Airbnb hosts	2.788	1.576	3.246	1.578
	I am irritated about Airbnb hosts	2.841	1.605	3.332	1.655
	I am angry about Airbnb hosts	2.990	1.653	3.426	1.638
	I am nervous about Airbnb hosts	3.009	1.612	3.554	1.677
	I am worried about Airbnb hosts	3.894	1.647	3.445	1.647
	I am warm-hearted about Airbnb hosts	4.894	1.382	4.417	1.48
	I am happy about Airbnb hosts	4.923	1.465	4.566	1.49
	I am pleased about Airbnb hosts	4.846	1.449	4.410	1.51
	I am excited about Airbnb hosts	4.774	1.378	4.72	1.48
	I am thrilled about Airbnb hosts	4.417	1.536	4.45	1.51
	I am enthusiastic about Airbnb hosts	4.566	1.529	4.46	1.64
Community Well-being	“If the number of visitors to your neighborhood increases from Airbnb, do you believe the following will improve or worsen” (measured on a seven-point Likert-type scale: 1 = much worse to 7 = improve) (Woo et al., 2015).
	Economic well-being
	Strong and diverse economy	5.096	1.224	4.666	1.249
	Tax revenue (sales tax/bed tax)	4.903	1.293	4.517	1.267
	The value of my house and/or land	5.120	1.292	4.488	1.416
	Emotional well-being
	Opportunities to interact with visitors	5.211	1.152	4.703	1.310
	Opportunities to make new friends	5.129	1.183	4.823	1.351
	The relationship between residents and visitors	4.889	1.289	4.450	1.361
	Family relationships	4.144	1.461	3/890	1.412
	Community well-being
	Community pride	4.951	1.254	4.580	1.307
	A feeling of belonging to my community	4.913	1.312	4.505	1.315
	Desirability of living in the community	4.819	1.288	4.482	1.422
	Enjoyment of living in the community	4.889	1.308	4.509	1.356
	The image of my community to others	4.961	1.195	4.556	1.326
	Cultural Well-being
	Awareness of natural and cultural heritage	4.865	1.266	4.501	1.303
	Opportunities to participate in the local culture	4.947	1.236	4.562	1.284
	An understanding of different cultures	5.043	1.286	4.665	1.265
	Environmental Well-being
	Quality of roads, bridges, and utility services	4.770	1.251	4.392	1.31
	Neighborhood/housing appearance	4.995	1.353	4.427	1.43
	Proper zoning/land use	4.644	1.288	4.145	1.37
	Noise pollution	4.403	1.544	4.396	1.51
	Litter control	4.427	1.546	4.146	1.52
	The prevention of crowding and congestion	4.241	1.360	4.014	1.491
	Social Well-being
	Stores and restaurants owned by local residents	5.110	1.230	4.773	1.329
	Plenty of retail shops and restaurants	5.403	1.267	4.733	1.285
	Quality recreation opportunities	5.014	1.272	4.596	1.338
	Family activities	4.772	1.348	4.149	1.420
	Feeling safe	5.024	1.312	4.521	1.279
Personal Quality of Life	“If the number of visitors to your neighborhood increases from Airbnb, do you believe the following will improve or worsen?” (measured on a seven-point Likert-type scale: 1 = much worse to 7 = improve)
	My Personal Quality of Life	4.802	1.331	4.309	1.301

Comparing Construct Means: Residents’ Prior Experience Staying at an Airbnb as a Guest

Results of the t tests for mean comparisons between the samples of residents with and without prior experience staying at an Airbnb as a guest are presented in Table 3. Mean scores represent an average score of the items included in the measurement of the construct. Residents who had prior experience staying at an Airbnb as a guest had significantly higher levels of emotional solidarity with visitors, thus confirming hypotheses 7a–7e. Residents who had prior experience staying at an Airbnb as a guest also reported stronger positive emotions toward Airbnb hosts, expressed less negative emotions toward Airbnb hosts, and perceived more positively the impacts from Airbnb visitors on the community’s well-being and their personal quality of life.

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Table 3.

Performance on Dimensions: Residents with and without Prior Experience Staying at an Airbnb.

Dimensions	Mean:Residents with Prior Experience Staying at an Airbnb	Mean:Residents with No Prior Experience Staying at an Airbnb	Difference	t
Emotional Solidarity	5.32	4.97	0.345	2.97 ** (hypothesis 7a)
Positive Emotions	3.53	3.03	0.504	4.30*** (hypothesis 7b)
Negative Emotions	2.91	3.40	−0.496	−3.69 ** (hypothesis 7c)
Community Well-being	4.92	4.52	0.400	3.69*** (hypothesis 7d)
Personal Quality of Life	4.80	4.31	0.493	4.01*** (hypothesis 7e)

**

p < 0.01; ***p < 0.001.

CFA Results

As the first step in the CFA, common method bias was tested using a latent variable approach outlined in Podsakoff et al. (2003). A single unmeasured first-order factor (i.e., common factor) was added to a second CFA with all of the measures as indicators. Next, standardized regression weights for all loadings across the two models were compared. Significant differences were not found that would indicate common method bias.

The CFA results are presented in Table 4. The sample of residents who had prior experience staying at an Airbnb as a guest indicated an acceptable fit to the data: χ²/df = 1848/866; comparative fit index (CFI) = 0.919; Tucker–Lewis index (TLI) = 0.911; root mean square error of approximation (RMSEA) = 0.063; standardized root mean square residual (SRMR) = 0.040. Results of the sample of residents who had no prior experience staying at an Airbnb as a guest also indicated an acceptable fit to the data: χ²/df = 1848/866; CFI = 0.899; TLI = 0.887; RMSEA = 0.078; SRMR = 0.062. In addition, all items on constructs indicated high reliability—all items loaded on to model constructs with significant (p < 0.001) standardized factor loadings (0.707–0.903 for the residents who had prior experience staying at an Airbnb and for residents who had no prior experience staying at an Airbnb, 0.659–0.903), indicating convergent validity. The average variances extracted (AVEs) for the constructs were higher than 0.50 as recommended by Hair et al. (2010) and Nunnally and Bernstein (1994) (e.g., 0.624–0.786 for the residents who had prior experience staying at an Airbnb and from 0.574–0.725 for the residents who never stayed at an Airbnb), providing additional evidence of convergent validity. Further, the square roots of the AVE for all constructs across both samples were greater than interconstruct correlations, demonstrating discriminant validity. Univariate skewness values for the variables ranged from −1.37 to 0.814, and kurtosis values ranged from −1.178 to 1.612. From a multivariate perspective, Mardia’s normalized estimate of multivariate kurtosis was found to be 111.475, indicating significant positive kurtosis and that the data are multivariate non-normal. Thus, the authors used the bootstrapping procedure with maximum likelihood estimation to address the issue of non-normality (Byrne 2016).

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Table 4.

CFA Results.

Constructs and Measurement Items	Residents with Prior Experience Staying at an Airbnb			Residents with No Prior Experience Staying at an Airbnb
	Standardized Factor Loading a	Error Variance	AVE	Standardized Factor Loading a	Error Variance	AVE
First-order loadings
Emotional Solidarity (α = 0.919; ρ = 0.933; AVE = 0.584) b			0.647			0.659
I am proud to have visitors come to my neighborhood	0.767	0.033		0.732	0.032
I feel the neighborhood benefits from having visitors	0.727	0.036		0.717	0.033
I feel close to some visitors I have met in my neighborhood	0.878	0.020		0.903	0.015
I have made friends with some visitors in my neighborhood	0.871	0.021		0.864	0.019
I identify with visitors in my neighborhood	0.759	0.033		0.837	0.022
I have a lot in common with visitors in my neighborhood	0.814	0.027		0.802	0.025
Negative Emotions (α = 0.941; ρ = 0.942; AVE = 0.731) b			0.725			00.721
I am frustrated about Airbnb hosts	0.815	0.027		0.880	0.018
I am irritated about Airbnb hosts	0.868	0.021		0.847	0.021
I am angry about Airbnb hosts	0.852	0.022		0.827	0.023
I am nervous about Airbnb hosts	0.840	0.024		0.822	0.023
I am worried about Airbnb hosts	0.881	0.019		0.869	0.019
Positive Emotions (α = 0.947; ρ = 0.947; AVE = 0.749) b			0.778			0.720
I am warm-hearted about Airbnb hosts	0.893	0.016		0.817	0.023
I am happy about Airbnb hosts	0.903	0.015		0.827	0.022
I am pleased about Airbnb hosts	0.938	0.011		0.870	0.018
I am excited about Airbnb hosts	0.841	0.022		0.875	0.017
I am thrilled about Airbnb hosts	0.883	0.017		0.819	0.023
I am enthusiastic about Airbnb hosts	0.831	0.023		0.879	0.017
Second-order loadings
Economic Well-being (α = 0.818; ρ = 0.606; AVE = 0.821) b			0.614			0.575
Strong and diverse economy	0.826	0.026		0.792	0.027
Tax revenue (sales tax/bed tax)	0.725	0.036		0.659	0.038
The value of my house and/or land	0.797	0.030		0.814	0.025
Emotional Well-being (α = 0.882; ρ = 0.888; AVE = 0.666) b			0.683			0.614
Opportunities to interact with visitors	0.807	0.025		0.733	0.031
Opportunities to make new friends	0.847	0.021		0.769	0.027
The relationship between residents and visitors	0.847	0.021		0.854	0.019
Family Relationships	0.805	0.017		0.774	0.026
Community Well-being (α = 0.931; ρ = 0.929; AVE = 0.723) b			0.740			0.712
Community pride	0.832	0.023		0.852	0.019
A feeling of belonging to my community	0.871	0.018		0.834	0.021
Desirability of living in the community	0.853	0.020		0.860	0.018
Enjoyment of living in the community	0.864	0.019		0.828	0.022
The image of my community to others	0.880	0.017		0.843	0.020
Cultural Well-being (α = 0.900; ρ = 0.858; AVE = 0.669) b			0.786			0.725
Awareness of natural and cultural heritage	0.876	0.018		0.880	0.018
Opportunities to participate in the local culture	0.897	0.016		0.847	0.021
An understanding of different cultures	0.887	0.017		0.827	0.023
Environmental Well-being (α = 0.908; ρ = 0.905; AVE = 0.615) b			0.617			0.611
Quality of roads, bridges, and utility services	0.772	0.030		0.763	0.029
Neighborhood/housing appearance	0.808	0.027		0.816	0.024
Proper zoning/land use	0.822	0.025		0.820	0.023
Noise pollution	0.707	0.038		0.686	0.036
Litter control	0.794	0.029		0.800	0.025
Crowding and congestion	0.804	0.028		0.798	0.026
Social Well-being (α = 0.899; ρ = 0.896; AVE = 0.591) b			0.614			0.574
Amount of stores and restaurants owned by local residents	0.775	0.029		0.751	0.029
Quality of retail shops and restaurants	0.759	0.031		0.734	0.030
Quality of recreation opportunities	0.762	0.030		0.787	0.025
Family activities	0.793	0.027		0.721	0.032
Feeling safe	0.815	0.024		0.810	0.023
Fair prices for leisure, entertainment, and tourism	0.796	0.026		0.739	0.029

a

Entries are standardized values; all statistically significant (p < 0.01). Error variance entries are standardized.

b

α = Cronbach’s alpha of reliability; ρ = composite construct reliability; AVE = average variance extracted. The average variance estimates (AVEs) ranged between 0.574 and 0.778.

Results of the measurement invariance for the measurement model (CFA) indicated an acceptable fit to the data, establishing configural invariance. No substantial differences between additional fit indices (ΔCFI = 0.004, ΔTLI = 0, ΔRMSEA = 0, and ΔSRMR = 0.007) across the configural and metric-invariant models were found, confirming metric invariance.

SEM Results (with Emotional Solidarity)

The results of the structural model with the emotional solidarity construct are presented in Table 5. The widely-used fit indices (Hu and Bentler 1999) indicated an acceptable fit of the model to the data for the group of residents who had prior experience staying at an Airbnb (χ²/df = 2041.03/808 CFI = 0.915; TLI = 0.908; RMSEA = 0.073; SRMR = 0.073) and the group of residents who had no prior experience staying at an Airbnb (χ²/df = 1728/808; CFI = 0.889; TLI = 0.881; RMSEA = 0.079; SRMR = 0.113). The parameter estimates (Table 5) confirmed hypotheses 1, 2, 3, 5, and 6 in the group of residents who had prior experience staying at an Airbnb and hypotheses 1, 2, 3, 4, 5, and 6 in the group of residents who had no prior experience staying at an Airbnb. In the case of residents who had prior experience staying at an Airbnb as a guest, the relationship between negative emotions toward Airbnb hosts and perceived impact of Airbnb visitors on the community’s well-being was not significant, thus indicating a lack of support for hypothesis 3.

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Table 5.

Results of Structural Equation Model.

	Residents with prior Experience Staying at an Airbnb		Residents with No Prior Experience Staying at an Airbnb		Pairwise Comparison
Path	Estimate a (z score)	p Value b	Estimate a (z score)	p Value b	t Score	p Value b
Structural Model
Emotional Solidarity → Community Well-being (hypothesis 1)	0.322(4.93)	0.000	0.163(2.81)	0.003	1.082	0.065 (hypothesis 8a)
Emotional Solidarity → Positive Emotions (hypothesis 2)	0.652(70.70)	0.000	0.604(7.80)	0.000	0.480	0.621 (hypothesis 8b)
Emotional Solidarity → Negative Emotions (hypothesis 3)	−0.412(–4.57)	0.000	−0.302(–3.58)	0.000	0.913	0.361 (hypothesis 8c)
Positive Emotions → Community Well-being (hypothesis 4)	0.357(6.12)	0.000	0.239(4.04)	0.000	1.390	0.165 (hypothesis 8d)
Negative Emotions → Community Well-being (hypothesis 5)	−0.002(–0.06)	ns	−0.167(–3.60)	0.000	2.333	0.020 (hypothesis 8e)
Community Well-being → Personal Quality of Life (hypothesis 6)	1.173(14.39)	0.000	1.090(15.37)	0.000	0.780	0.453 (hypothesis 8f)

Note: Residents with prior experience staying at an Airbnb Model Fit: χ²/df = 2041.03/ 808; comparative fit index (CFI) = 0.915; Tucker–Lewis index (TLI) = 0.908; root mean square error of approximation (RMSEA) = 0.073; standardized root mean square residual (SRMR) = 0.073. Residents with no prior experience staying at an Airbnb Model Fit: χ²/df = 1728/808; CFI = 0.889; TLI = 0.881; RMSEA = 0.079; SRMR = 0.113.

a

Unstandardized estimates.

b

p value based on bias-corrected percentile bootstrap intervals.

SEM Results (without Emotional Solidarity)

The results of the alternative structural model, without emotional solidarity, are presented in Table 6. The widely used fit indices indicated an acceptable fit of the model to the data for the group of residents with prior experience staying at an Airbnb as a guest (χ²/df = 1284.08/586; CFI = 0.909; TLI = 0.903; RMSEA = 0.076; SRMR = 0.124) and the group of residents who had no prior experience staying at an Airbnb as a guest (χ²/df = 1560/586; CFI = 0.895; TLI = 0.886; RMSEA = 0.079; SRMR = 0.113).

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Table 6.

Results of Structural Equation Model (without Emotional Solidarity).

	Residents with Prior Experience Staying at an Airbnb		Residents with No Prior Experience Staying at an Airbnb
Path	Estimate a (z Score)	p Value b	Estimate a (z Score)	p Value b
Structural Model
Positive Emotions → Community Well-being (hypothesis 4)	0.498(8.53)	0.000	0.368(6.33)	0.000
Negative Emotions → Community Well-being (hypothesis 5)	−0.026(–0.54)	ns	−0.155(–3.07)	0.002
Community Well-being → Personal Quality of Life (hypothesis 6)	1.172(14.40)	0.000	0.977(16.79)	0.000

a

Unstandardized estimates.

b

p value based on bias-corrected percentile bootstrap intervals.

Equation-level testing using effect size measures (Durlak 2009) was conducted between the full and alternative models (Cohen’s f²) across the two groups of residents with and without prior experience staying at an Airbnb as a guest, to determine whether the emotional solidarity construct in the full model contributed additional explanatory power (Table 7). For residents with prior experience staying at an Airbnb, the addition of emotional solidarity in the full model added significant explanatory power to the outcomes of community well-being and personal quality of life, as evidenced by the medium-size effects determined by Cohen’s f² values. For residents with no prior experience staying at an Airbnb, the addition of emotional solidarity in the full model added a small level of explanatory power to the community well-being construct. These results indicate the salience of emotional solidarity with visitors, as a manifestation of previous experience with Airbnb, to the formation of residents’ perceptions toward the visitors and their impacts on a community.

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Table 7.

Equation Level Statistics.

	Residents with Prior Experience Staying at an Airbnb						Residents with No Prior Experience Staying at an Airbnb
	Model		Alternative Model		Effect Size		Model		Alternative Model		Effect Size
	VariancePredicted	mc2	VariancePredicted	mc2	Cohen’s f2	Size	VariancePredicted	mc2	VariancePredicted	mc2	Cohen’s f2	Size
Positive Emotions	0.476	0.313	n/a	n/a	n/a	n/a	0.362	0.236	n/a	n/a	n/a	n/a
Negative Emotions	0.196	0.119	n/a	n/a	n/a	n/a	0.095	0.049	n/a	n/a	n/a	n/a
Community Well-being	0.474	0.508	0.392	0.415	0.1890	Medium	0.515	0.322	0.623	0.249	0.1077	Small
Personal Quality of Life	1.287	0.728	1.186	0.686	0.1544	Medium	0.892	0.601	1.220	0.726	−0.3133	n/a
Overall R2,a	.929		.997				.924		.996

a

The overall R² is the coefficient of determination (CD).

Pairwise parameter comparison tests for the full model (presented in Table 5) indicated that estimates for two relationships were significantly different between the groups of residents; first, for residents with prior experience staying at an Airbnb as a guest, the positive effect of residents’ emotional solidarity with visitors on the perceived impact of Airbnb visitors on the community’s well-being was significantly higher than for residents with no prior Airbnb experience, supporting hypothesis 8a (p < 0.10). Second, for residents with prior experience staying at an Airbnb as a guest, (higher) negative emotions toward Airbnb hosts had a weaker (negative) effect on perceived impacts of Airbnb visitors on the community’s well-being than for residents with no prior experience staying at an Airbnb, supporting hypothesis 8e (p < 0.05).

In sum, the results of testing alternative models indicate that emotional solidarity with visitors does indeed enhance residents’ positive emotions and reduces their negative emotions toward Airbnb hosts. Further, our results suggest potentially different pathways from emotional solidarity with visitors to residents’ perceptions of the impacts Airbnb visitors have on their community’s well-being. The effect of negative emotions toward Airbnb hosts on perceived impacts from Airbnb visitors on community well-being was significant for residents who had no prior experience staying at an Airbnb, but not significant for residents with prior experience, suggesting that negative emotions toward Airbnb hosts is reduced when residents have prior experience with Airbnb as a guest themselves and through emotional solidarity with visitors. Our findings have both theoretical and practical implications and can be used to guide authorities on decisions related to understanding and regulating peer-to-peer accommodations and the benefits of increasing the number of visitors to communities. Table 8 presents the results of the various hypotheses tested in the present study.

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Table 8.

Summary of Hypothesis Testing.

Path	Residents with Prior Experience Staying at an Airbnb	Residents with No Prior Experience Staying at an Airbnb
Full model hypotheses (with Emotional Solidarity)
Emotional Solidarity → Community Well-being (hypothesis 1)	Supported	Supported
Emotional Solidarity → Positive Emotions (hypothesis 2)	Supported	Supported
Emotional Solidarity → Negative Emotions (hypothesis 3)	Supported	Supported
Positive Emotions → Community Well-being (hypothesis 4)	Supported	Supported
Negative Emotions → Community Well-being (hypothesis 5)	Not supported	Supported
Community Well-being → Personal Quality of Life (hypothesis 6)	Supported	Supported
Alternative model hypotheses (without Emotional Solidarity)
Positive Emotions → Community Well-being (hypothesis 4)	Supported	Supported
Negative Emotions → Community Well-being (hypothesis 5)	Not supported	Supported
Community Well-being → Personal Quality of Life (hypothesis 6)	Supported	Supported
Comparative Hypotheses: Residents with prior experience staying at an Airbnb vs. no prior experience staying at an Airbnb
Residents with prior experience staying at an Airbnb will have higher emotional solidarity with visitors than residents with no prior experience staying at an Airbnb. (hypothesis 7a)	Supported
Residents with prior experience staying at an Airbnb will have more positive emotions toward Airbnb hosts in their neighborhood than residents with no prior experience staying at an Airbnb. (hypothesis 7b)	Supported
Residents with prior experience staying at an Airbnb will have less negative emotions toward Airbnb hosts in their neighborhood than residents with no prior experience staying at an Airbnb. (hypothesis 7c)	Supported
Residents with prior experience staying at an Airbnb will perceive more positive impacts from Airbnb visitors on the community’s well-being than residents without prior experience staying at an Airbnb. (hypothesis 7d)	Supported
Residents with prior experience staying at an Airbnb will perceive a more positive affect on their personal quality of life than residents without prior experience staying at an Airbnb. (hypothesis 7e)	Supported
For residents with prior experience staying at an Airbnb, higher emotional solidarity with visitors will have a stronger positive effect on their perceptions of Airbnb visitors’ impact on the community’s well-being than for residents without prior experience staying at an Airbnb. (hypothesis 8a)	Supported
For residents with prior experience stating at an Airbnb, higher emotional solidarity with visitors will have a stronger positive effect on their positive emotions toward Airbnb hosts in their neighborhood than for residents without prior experience staying at an Airbnb. (hypothesis 8b)	Not supported
For residents with prior experience staying at an Airbnb, higher emotional solidarity with visitors will have a stronger effect in reducing their negative emotions toward Airbnb hosts in their neighborhood than for residents without prior experience staying at an Airbnb. (hypothesis 8c)	Not supported
For residents with prior experience staying at an Airbnb, higher positive emotions toward Airbnb hosts in their neighborhood will have a stronger positive effect on their perceptions of Airbnb visitors’ impact on the community’s well-being than for residents without prior experience staying at an Airbnb. (hypothesis 8d)	Not supported
For residents with prior experience staying at an Airbnb, (higher) negative emotions toward Airbnb hosts will have a weaker (negative) effect on their perceptions of Airbnb visitors’ impact on the community’s well-being than for residents with no prior experience staying at an Airbnb. (hypothesis 8e)	Supported
For residents with prior experience staying at an Airbnb, higher perceived positive impacts to the community’s well-being from Airbnb visitors will have a stronger positive affect on their personal quality of life than for residents without prior experience staying at an Airbnb. (hypothesis 8f)	Not supported

Theoretical Implications

Theoretical contributions in the way of emotional solidarity exist for this research. Not only is this one of the first research studies to consider emotional solidarity in the context of visitors to peer-to-peer accommodations in a residential neighborhood, but it also is the first time the construct has been utilized as an antecedent of emotional responses among residents. Such findings build on Suess, Woosnam, and Erul’s (2019) study in which residents’ emotional solidarity with visitors explained the greater sense of feeling safe related to Airbnb visitors in their neighborhood. As such, both studies point to amending Woosnam’s (2011) model to include emotional responses as an outcome of emotional solidarity or as a mediator of the relationship between emotional solidarity and an outcome variable such as perceived improvement to well-being and quality of life (as in the current study). Ultimately, considering emotional solidarity within this study is a step toward understanding relationships residents have with the visitors to their neighborhood, and how that contributes to the operation of home-sharing businesses in communities.

The present study also makes significant theoretical contributions to the modeling of visitors’ impact on community well-being. In view of the suggestion by K. Kim, Uysal, and Sirgy (2013) to rethink measurement of community well-being with various subdomains within the community. The present research sought to create a holistic model of community well-being by examining the economic, social, environmental, cultural, and emotional community indicators that potentially change when there is an increase of visitors to a residential neighborhood. Such a model demonstrates a novel contribution to the literature surrounding community well-being and quality of life (per the extensive literature review conducted by Uysal et al. 2016) and lays the groundwork for future research incorporating relationships between residents and visitors in the context of peer-to-peer accommodations attracting more visitors to residential areas. Increased visitors from Airbnb and their impact on residents’ community well-being is multifaceted and complex, and if well-being and personal quality of life are impacted negatively, residents will have greater pushback against not only peer-to-peer accommodation hosts in their neighborhood (consistent with Garau-Vadell, Gutierrez-Tano, and Diaz-Armas 2018 suggests) but also tourism in general (K. Kim, Uysal, and Sirgy 2013; Roehl 1999; Suess, Baloglu, and Busser 2018; Uysal et al. 2016).

Practical Implications

The significant growth of the peer-to-peer accommodation industry necessitates a comprehensive examination at the neighborhood level and residents’ sentiments toward existing host operations, which is useful for considering future regulation or expansion. The confirmation that residents perceived the impacts from Airbnb visitors on their community’s well-being and personal quality of life to be mostly positive demonstrates tolerant resident attitudes, which is consistent with Nunkoo and So (2016), Mody, Suess, and Dogru (2019), and Suess, Woosnam, and Erul (2020). That residents perceive positive impacts from increased visitors to their community is also, in part, a result of the growing popularity of Airbnb and the substantial percentage of the population with prior experience staying at an Airbnb as a guest. Prior experience may have some effect on a resident’s understanding and tolerance of Airbnb hosts operating in their neighborhood.

Thus, the hotel industry, regulators, and destination marketing organizations can better weigh the costs and benefits of Airbnb and other peer-to-peer accommodation hosts in residential neighborhoods beyond popular media discourse and the anti–home sharing campaigns led by hotel corporations losing market share. Indeed, there is an ongoing conflict between commercial lodging and peer-to-peer accommodation networks, and one that merits further empirical research that represents residents’ sentiments. Resident support or lack thereof can be a key factor in regulatory standards for peer-to-peer accommodations. Residents’ positive emotions toward their Airbnb hosting neighbors indicate a greater likelihood that Airbnb and other such peer-to-peer accommodations will not receive extensive lobbying related to their regulation and, thus, give greater confidence to the sustainability of the sharing economy (as suggested by Nieuwland and van Melik 2020).

Relatedly, the relationship between residents’ negative emotions toward Airbnb hosts and perceptions of Airbnb visitors’ impact on a community suggests that regulators should carefully consider a resident microgeographic analysis when considering regulation that is based on factors including housing density and housing types. It is important to consider in which groups negative emotions toward hosts exist, such as from residents living in closer proximity to their Airbnb hosts neighbors such as in multi-unit housing types. Investigating differences in housing types that can affect and be affected by decisions about Airbnb can help build consensus about how certain neighborhoods can be regulated in the context of the density, alleviating some of the conflicts surrounding the general regulation of the sharing economy and allowing hosts to operate in neighborhoods that would otherwise benefit from increased visitors and in which residents are receptive to Airbnb. This is particularly important given that the impacts on a community from Airbnb visitors are not perceived as negatively as the media and other popular discourse frequently portrays them to be. In fact, the present study provides evidence to support broad emotional solidarity among residents and Airbnb visitors that is highly relevant to Airbnb (the company) and other platforms promoting visitor–resident interaction and authentic neighborhood experiences for travelers who book peer-to-peer accommodations listed on their network.

Limitations and Future Research

The study’s findings must be viewed with consideration for certain theoretical and empirical limitations and considerations for future research. First, given the potential for systematically different perceptions between urban, suburban, and rural residents, one could argue that modeling the responses of these three groups, separately, would avoid confounding the effects of visitors on residents from differences in proximity (i.e., visitors’ proximity to residents may be different because of urban density). While sample size limitations precluded such modeling, particularly for the rural resident sample (n = 104), the authors’ joint modeling of the rural, suburban, and urban samples is consistent with existing research in the tourism and community realm that has combined the evaluations of residents from different communities related to tourism development and increased visitors.

Second, the authors found a significant relationship between residents’ emotional solidarity with visitors and their positive emotional responses associated with Airbnb hosts, which indicate predominantly positive sentiments. One must acknowledge that there are likely to be subsegments of residents who comprise mixed emotions (e.g., a segment of residents in certain neighborhood or household types may be more fearful of Airbnb visitors in their neighborhood and concerned about personal safety). In addition, the relationships between the perceived impact of Airbnb visitors on community well-being as well as personal well-being would also subsequently differ across certain subsegments. In such a case of heterogeneity, extrapolation of the results to the general residential population is somewhat tenuous. The authors suggest that future research concerning the modeling of emotions and sentiments toward Airbnb hosts and neighborhood visitors should not only incorporate valence of experiences with these two groups but also be based on larger samples to capture the dynamics underlying different sociodemographic and psychographic/behavioral segments of residents.

Third, future research on modeling residents’ emotions could also include other antecedents of response formation, such as the sources of information to which individuals are exposed, accumulated personal Airbnb experience from travel, and/or sociodemographic characteristics (Beerli and Martin 2004; Suárez 2011). Relatedly, the role of community satisfaction is affecting residents’ perceived impacts of tourism on a community that has been previously explored on community attributes (Woo, Kim, and Uysal 2015) and in the context of medical tourism (Suess, Baloglu, and Busser 2018.). Additionally, the framework pertaining to residents’ attitudes (Cui and Ryan 2011) identifies place-based and situational factors as potential moderators of the relationship between attitudinal and behavioral intentions of support for tourism. These various relationships can be explicitly modeled, applied to advocacy for Airbnb, and/or support for its regulation in future research.