Exploring Customers’ Experiences with P2P Accommodations: Measurement Scale Development and Validation in the Chinese Market

Abstract

This study explored key dimensions of customers’ experiences with peer-to-peer (P2P) accommodations through Airbnb in China and developed a corresponding measurement scale. Thirty-four in-depth interviews were conducted with Airbnb customers. A pilot study and main survey were implemented to collect data. Exploratory factor analysis was performed, and five factors related to the customer experience were extracted: tangible and sensory experiences, host, cultural experience, interaction with peer guests, and location. Confirmatory factor analysis was performed using data gathered from the main survey. This study contributes to the literature by capturing customers’ holistic experiences in the Chinese market via empirical testing. Theoretical and practical implications are also discussed.

1. Introduction

The sharing economy has changed consumption patterns drastically; entrepreneurial individuals can now address consumer needs that were traditionally fulfilled by firms [1]. Sharing economy businesses, including peer-to-peer (P2P) accommodation services, have infiltrated tourism and hospitality. Firms such as Airbnb offer online platforms for everyday people to rent out their place of residence as a tourist accommodation. The popularity of these platforms has led P2P accommodations to grow exponentially over the past several years. As the most well-known P2P accommodation marketplace, Airbnb hosted over 5.6 million listings as of September 2020 [2].

This new accommodation form has aroused growing research interest, namely in why people choose P2P accommodations and the nature of customers’ experiences with them. Numerous scholars have explored related motivational factors and highlighted utilitarian and experiential features [3]. Scholars have also explored the construct of guests’ experiences with P2P accommodations [4]. However, most studies have involved Western cultural scenarios. Yet the customer experience is shaped by the context in which information is received; diverse social, cultural, and personal variables can apply [5]. For example, Western customers’ perceptions of their experiences may differ from those of Asian customers. Researchers have recently begun to investigate the effects of cultural traditions on guests’ experiences with P2P accommodations. Through a cross-cultural study of English and Chinese online platform reviews, Zhu et al. [6] found that Chinese customers favored family-like relationships with hosts whereas Western guests did not.

Despite exponential growth in China’s P2P accommodations as of late, operators are struggling to draw travelers away from commercial lodging. A survey by China State Information Center [7] indicated that 38% of travelers consider commercial hotels their sole accommodation choice; 35% prefer to stay in hotels rather than home rentals. These discrepancies may be attributed to low public awareness of P2P accommodations [7] and a lack of trust among strangers in China’s acquaintance-based society [8]. P2P accommodation operators thus continue to face challenges breaking into this potentially lucrative market—little is known about what Chinese customers value in their P2P accommodation experiences.

In addition, although scholars have developed various scales in hospitality, most focus on specific settings such as luxury hotels, economy hotels, or restaurants.

Table 1. Literature review of customer experience scale development in hospitality industry.

Author(s), Year	Research Field	Name of the Scale	Dimensions of the Scale
Knutson et al., 2009 [9]	hotels	guest’s hotel experience	environment, accessibility, driving benefit, incentive
Giritlioglu et al., 2014 [10]	Spa hotels	food and beverage service quality	assurance and employee knowledge, healthy and attractive food, empathy, tangibles, responsiveness of service delivery, reliability
Hung, 2015 [11]	Buddhism-themed hotels	normative expectations	reflection of Buddhism culture in the hotel environment and among the staff, ties with the Buddhism community, extent of Buddhism in the hotel design, worship/meditation considerations
Rauch et al., 2015 [12]	mid-scale hotels	service quality	service product, service delivery, service environment
Yang and Mattila, 2016 [13]	luxury hospitality industry	luxury hospitality values	functional value, hedonic value, symbolic value, and financial value
Ren et al., 2016 [14]	budget hotel	customer experience	tangible-sensorial experience, staff relational/interactional experience, aesthetic perception, location
Mody, Suess, and Lehto, 2019 [15]	hotels and Airbnb	accommodation experiencescape	entertainment, education, escapism, esthetics, serendipity, localness, communitas, personalization
Khan and Rahman, 2017 [16]	luxury hotel	hotel brand experience	hotel location, hotel stay and ambience, hotel staff competence, hotel website and social media, guest-to-guest experience
Kim et al., 2018 [17]	restaurant	customer perceptions of restaurant innovativeness	menu innovativeness, technology-based service innovativeness, experiential innovativeness, promotional innovativeness
Jia, 2019 [18]	restaurant	tourists’ meal experience	feeling, price, food, place, time, service
Lockwood and Pyun, 2020 [19]	upscale hotels	customers’ servicescape perceptions	aesthetic quality, functionality, atmosphere, spaciousness, and physiological conditions

presents a summary of the literature on customer experience scale development in hospitality. To the best of the authors’ knowledge, no scale is available to measure guests’ experiences with P2P accommodations, although customers’ expectations of these stays likely differ from those in hotels.

To address this knowledge gap, the current study aims to (1) empirically investigate the main components of customers’ experiences with P2P accommodations and identify which factors are most essential; and (2) develop a scale to measure customers’ experiences with P2P accommodations in detail. This paper first outlines the literature on the customer experience in hospitality, followed by an overview of studies on P2P accommodation experiences. Next, a mixed research method including in-depth interviews and surveys is described. Then, findings related to experience dimensions and the developed measurement scale are summarized. Finally, theoretical and practical insights shed light on the attributes of Chinese customers’ experiences with P2P accommodations.

2. Literature Review

2.1. Customer Experience

The customer experience has been a key concept in consumer behavior and marketing since the early 1980s. Instead of regarding customers as rational decision makers who only consider utilitarian values, Holbrook and Hirschman [20] presented an experiential view of consumption. They framed the consumption experience as subjective: it is rooted in one’s perceptions of an experience’s symbolic meanings as well as one’s responses to hedonic and esthetic criteria. Pine and Gilmore [21] expanded this experiential view by regarding experiences as a new economic offering. An increasing number of scholars have since embraced this perspective and recognized the value of customers’ emotions when interacting with products and service providers.

Specifically, Gentile et al. [22] stated that “The customer experience originates from a set of interactions between a customer and a product, a company, or part of its organization, which provoke a reaction. This experience is strictly personal and implies the customers’ involvement at different levels (rational, emotional, sensorial, physical, and spiritual)”. The customer experience has also been defined as “the internal and subjective response customers have to any direct or indirect contact with a company” [23]. In tourism, individuals’ travel experiences can be influenced by the host–guest relationship; these interactions may be essential to one’s trip memories [24]. More broadly, Gupta and Vajic [25] explained that “… an experience occurs when a customer has any sensation or knowledge acquisition resulting from some level of interaction with different elements of a context created by the service provider”. Carù and Cova [26] echoed this view by contending that companies do not sell experiences; rather, they provide artifacts and contexts which are conducive to experiences, and can be employed by consumers to co-create their unique experiences.

Besides the experiential and contextual nature of the customer experience, a more holistic view has been proposed. Creating memorable experiences is of vital importance for companies. However, the customer experience is inherently holistic: the customer is personally involved in every provider interaction [27]. Knutson et al. [28] proposed a model in which the customer experience spans three stages: (1) pre-experience (i.e., anything occurring prior to the actual experience); (2) participation (i.e., experiential involvement); and (3) post-experience (i.e., the aftermath of participation). Verhoef et al. [29] (p. 32) similarly argued that “… the customer experience construct is holistic in nature and involves the customer’s cognitive, affective, emotional, social and physical responses to the retailer. … The customer experience encompasses the total experience, including the search, purchase, consumption, and after-sale phases of the experience, and may involve multiple retail channels”.

As the customer experience is a multi-dimensional construct, scholars have paid close attention to its components. Schmitt [30] proposed five experience domains—sense, feel, think, act, and relate—respectively, associated with sensory experiences, affective experiences, creative cognitive experiences, physical experiences, and social-identity experiences. Similarly, Gentile et al. [22] stated that the customer experience contains sensorial, emotional, cognitive, pragmatic, lifestyle, and relational components. Numerous studies have been conducted in hospitality to examine dimensions of the customer experience. Knutson et al. [9] developed an 18-item experience index consisting of four factors: environment, accessibility, driving benefit, and incentive. Walls et al. [31] explored customers’ experiences with luxury hotels and discovered that guests’ experiences were affected by the physical environment and human interaction.

In this study, the authors frame the customer experience as a holistic concept comprising product aspects and the overall consumption process. Such experiences also involve subjective responses to experiential elements on cognitive, sensory, emotional, behavioral, and spiritual levels. Lastly, service providers and customers mutually shape the customer experience.

2.2. P2P Accommodations and Experiences

Academic research on P2P accommodations has recently been expanded. In a systematic review of relevant studies, Prayag and Ozanne [32] revealed seven key themes: conceptual development, regulation, macro-level impacts, regime response, host behavior, guest/host experience, and marketing issues. Guttentag [33] later surveyed the literature on Airbnb and identified six thematic categories: Airbnb guests, Airbnb hosts, Airbnb supply and its impacts on destinations, Airbnb regulations, Airbnb’s impact on the tourism sector, and the Airbnb company.

Among the major research streams, customers’ experiences with P2P accommodations are especially popular. Several studies have sought to address related experiential dimensions, which can be roughly classified under two themes: the accommodation and the host [33]. For instance, Tussyadiah and Zach’s [34] analysis of Airbnb guest reviews in the U.S. city of Portland, Oregon, highlighted five accommodation attributes that customers value most: service, facility, location, feeling welcome, and the comfort of home. They also emphasized the importance of direct guest–host relations in social accommodation settings. Ultimately, guests’ reviews that focused on feeling welcome or on an accommodation’s location were associated with higher ratings. Cheng and Jin [4] also investigated the attributes influencing Airbnb customers’ experiences by examining online reviews; they discerned three key dimensions (i.e., location, amenities, and host). Sthapit and Jiménez-Barreto [35] identified several dimensions tied to Airbnb customers’ memorable experiences as well: social interactions with the host, the host’s attitude, and the accommodation’s location.

Along with dimensions related to the host (e.g., feeling welcome, host’s attitude, guest–host relationship) and accommodation (e.g., location, amenities, homelike atmosphere), researchers have proposed other dimensions of P2P accommodation users’ holistic experiences. Some scholars considered users’ perceptions of the attributes of P2P accommodation platforms such as Couchsurfing and Airbnb. Several service attributes were found to influence customers’ platform usage, such as visual-based trust [36], transaction experiences [37], web responsiveness and efficiency [38], and up-to-date and reliable information [39].

P2P accommodations can afford travelers a more authentic experience than hotels. These rentals allow visitors to live like locals, interact with nearby residents and communities, and potentially stay in “non-tourist” areas [40]. Being immersed in authentic culture is one of the most appealing features of P2P accommodations [41]: staying in a local rental provides tourists chances to encounter residents [42], become involved in the daily life of local people [40], and interact with the culture [43]. Social contact has also been regarded as a key motivator for staying in P2P accommodations. Thus far, however, most studies have been limited to guests’ contact with their host and local community. Lin et al. [44] and Lyu et al. [42] both highlighted the roles of fellow customers’ interactions in P2P accommodation experiences. They noted that customers were willing to share information online, make friends through casual conversation, take part in host-organized activities, and even travel together.

The preceding review suggests that several experiential dimensions/attributes drive guests to choose P2P accommodations: (1) the physical environment (i.e., location, amenities, and facilities); (2) service quality (i.e., from the host and booking platform); (3) the guest–host relationship; (4) local authentic experiences; and (5) interactions with other guests. Most relevant studies have considered experiential dimensions from a macro view. A more nuanced investigation [4] and empirical testing [38] of these experiential dimensions remain needed. A clearer understanding of the relative importance of various attributes can help service providers allocate resources more efficiently and tailor their offerings to satisfy customers’ needs [45].

Furthermore, Cheng and Jin [4] pointed out that some findings on the core experiential dimensions of P2P accommodations are contradictory. P2P accommodation guests are not homogenous. Their expectations and perceptions of their experiences can differ based on their desire for social interaction [3] as well as situational factors such as customer involvement and travel party composition [15]. Cheng and Zhang [46] highlighted the roles of cultural differences and tradition in guest–host contact in P2P accommodations. Zhang et al. [8] further explored Chinese customers’ experiences with P2P accommodations and found these guests’ experiences to potentially vary from those of Westerners. To date, though, most work on P2P accommodations has been conducted by American researchers focusing on the United States [47]. Comparatively, Asia is home to 60% of the world’s millennials and hosts a large portion of the target market for P2P accommodations [48]. Belarmino and Koh [47] thus called for further investigation of Asia’s P2P accommodation market through a holistic lens by considering phenomena such as repurchase behavior [49]. The current study aims to fill this research void by developing a measurement scale to capture P2P accommodation guests’ overall experiences in the Chinese market. This scale development process is also intended to reveal the relative importance of experiential dimensions.

3. Methodology

In this study, the customer experience is taken as a second-order, multi-dimensional construct. When lower-order dimensions constitute a general concept, a formative higher-order construct emerges [50]. The present study is exploratory and investigates customers’ P2P accommodation experiences using a formative measurement approach. The customer experience in this case comprises several indicators, with dimension representing an irreplaceable part of the construct. Both quantitative and qualitative methodologies (i.e., in-depth interviews and a survey, respectively) were adopted to develop a formative scale. The authors then assumed a formative approach to scale conceptualization and development.

3.1. Step 1—In-Depth Interviews

Several experiential attributes of P2P accommodations were initially drawn from the literature and served as interview stimuli. Interviews were conducted to explore the factors influencing Chinese guests’ experiences with P2P accommodations. All interviews followed a semi-structured format. Participants were chosen via convenience sampling based on two criteria: (1) Chinese adult travelers residing in China who (2) had used P2P accommodations within the past six months. Interviewees’ demographic characteristics (e.g., age, gender, income level, occupation) were considered to ensure participant diversity. The number of interviews was not set a priori; interviews continued until the point of saturation (i.e., when no new relevant information emerged). Thirty-four guests were interviewed in total.

The researchers contacted interviewees by phone and made an appointment for a face-to-face interview. Each interview lasted around 30 min on average. Interviews progressed based on the interview protocol, beginning with participants’ most recent experience with P2P accommodations. Open-ended questions were asked to elicit factors that motivated interviewees to choose P2P accommodations before, during, and after travel; evaluations of each interviewee’s overall experience; and their intentions to use and recommend P2P accommodations in the future.

Interviews were held in Chinese. The researcher collected notes and audio records throughout the interview process after obtaining participants’ consent. Each audio recording was transcribed shortly after the interviews, and transcriptions were analyzed using the inductive method suggested by Strauss and Corbin [51]. The text was read line by line to identify salient information categories through an open coding procedure. Axial coding was then performed to discover underlying uniformities in the original set of categories and to formulate a smaller set of higher-level concepts. Seven experiential themes emerged from the coding process: physical environment, location, sensory perceptions, service quality, guest–host relations, interactions with peer guests, and local cultural experiences.

The authors’ approach to scale development followed Churchill’s (1979) [52] widely cited paradigm. Churchill’s procedure has been deemed the most appropriate way to develop a reliable and valid multi-item instrument [53]. Figure 1 depicts the scale development process.

The researchers transcribed the data gathered through in-depth interviews. Grounded theory was used to analyze textual data. Initially, texts were reviewed and decoded in the original language (i.e., Chinese) before being translated into English for the purpose of citing verbatim responses in this manuscript. Two authors performed independent open coding to identify experience-related elements. During this process, the two researchers frequently compared their independent work. Any discrepancies were discussed until a consensus was reached. The researchers next proceeded with axial coding to associate identified elements. Coding results were then sent to the third author to assess coding accuracy. The three researchers attended a final meeting to confirm code labels. To enhance validity, two experts familiar with this research area were invited to serve as peer reviewers and to appraise the final product to ensure content accuracy [54].

The in-depth interviews with 34 respondents generated 30 items related to customers’ experiences with P2P accommodations: the physical environment (seven items), location (three items), sensory perceptions (four items), service quality (four items), guest–host relations (six items), interactions with peer guests (three items), and local cultural experiences (three items). These dimensions embodied cognitive, sensory, emotional, behavioral, and spiritual dimensions; for example, the physical environment and location represented guests’ cognitive experiences, whereas guest–host relations denoted guests’ behavioral and emotional experiences.

Table 2. Interview results of customers’ experiences with P2P accommodations—Dimensions and items (N = 34).

Dimensions	Sample Quotes
1. Physical environment
✧ Cleanliness and tidiness	Clean, tidy, hygienic
✧ Room size	Spacious room, suite room, entire house; nice for family stay
✧ Facilities	Well-furnished, kitchen, washing machine, speedy WIFI, recreational facilities
✧ Washing supplies	“The washing supplies were good-quality branded products”
✧ Safety	“The room key used a password, which made me feel safe”
✧ Interior design and decoration	Stylish and unique; exquisite design
✧ Reliability	“The room design was the same as shown online”; “The room was not as spacious as shown online”
2. Location
✧ Convenience	Located in a central area; close to train station/attractions; convenient transportation and access
✧ Surrounding environment	Natural and quiet surroundings
✧ Nearby facilities	Supermarket or local market nearby; local food restaurants
3. Sensory perceptions
✧ Homelike feelings	Feel at home; warm, homelike feelings
✧ Atmosphere with literature and art	Atmosphere with literature and art; full of artistic ambiance
✧ Lighting	“Soft lighting makes me feel cozy and relaxed”
✧ Smell	“The room is equipped with an aroma diffuser, and it was turned on before we arrived”
4. Service quality
✧ Daily room cleaning	“There was no service staff to clean the room for us, and we needed to take the garbage out ourselves”
✧ Pick-up service	Pick-up service is provided
✧ Friendly service staff	“The service staff were very friendly and polite; they smiled and greeted us whenever encountered”
✧ Responsive	Service staff are conscientious and responsive
5. Guest–host relations
✧ Reliable	“Our flight was delayed, and the host waited for us until midnight”; “We booked the room online successfully, but the host said no room was available when we arrived”
✧ Approachable	“The host didn’t appear”; “The host lived upstairs and greeted us every day”
✧ Welcoming	Warmly welcomed by the host; “The host prepared lemon pie for us as a welcome dessert”
✧ Eager to help	The host gives travel advice and recommends restaurants; the host helps us book tickets
✧ Interaction	“We chatted with the host and shared personal stories”; “The host took us to the local market”
✧ Care	The host cares about the guests; the host takes care of guests like family
6. Interaction with peer guests
✧ Communication	Review other guests’ comments; ask for other guests’ advice
✧ Activities	Enjoy a barbecue; cook meals together; outdoor activities
✧ Sharing	Chat and share travel experiences; share personal stories and make friends
7. Local cultural experiences
✧ Local food	The host cooks local food for the guests
✧ Local people	Live with local people; the host speaks the local dialect
✧ Room design	The room contains local cultural elements

lists the specific dimensions and associated items pertaining to customers’ experiences with P2P accommodations along with sample quotes.

A panel review was conducted next to assess the face validity of items related to the constructs under development. Five panel members raised concerns about items related to the customer experience. One member suggested deleting items related to service staff, claiming that P2P accommodations should not have such staff. Because this study only considered Airbnb properties operated by a host, rather than by professional real-estate operators, staff-related items were omitted. Another member suggested revising items to ensure that each item captured only one aspect of the P2P customer experience. For instance, the item “The room is spacious and cozy” was divided into two items: “The room is spacious” and “The room makes me feel cozy”. Finalized items taken from the literature and/or interviews appear in

Table 3. Customer experience items developed from the literature and/or interviews.

Items Derived from the Literature and/or Interviews		Literature (Examples)	Interview
1	The exterior of the property is visually appealing.	Bitner (1992) [55], Clemes et al. (2011) [56]	√
2	The design of the room is visually appealing.	Wu and Liang (2009) [57]; Clemes et al. (2011) [56]; Zhu et al. (2019a) [58]	√
3	The room is clean and sanitary.	Wu and Liang (2009) [57]	√
4	The room is spacious.	Lyu et al. (2019) [42]; Guttentag et al. (2018) [3]	√
5	The layout of the room feels good.	Bitner (1992) [55]	√
6	The room facilities (e.g., TV and air-conditioning) are in good condition.	Clemes et al. (2011) [56]; Lyu et al. (2019) [42]; Zhu et al. (2019a) [58]	√
7	The room is quiet.	Bitner (1992) [55]; Clemes et al. (2011) [56]	√
8	The use of the free WIFI is smooth.	Ren et al. (2016) [14]	√
9	The area surrounding the property is good.	Knutson et al. (2009) [9]; Zhu et al. (2019a) [58]	√
10	The location is convenient.	Tussyadiah and Zach (2017) [34];Guttentag et al. (2018) [3]	√
11	There are multiple choices for living facilities nearby.	Zhu et al. (2019a) [58]	√
12	The lighting makes me feel comfortable.	Wu and Liang (2009) [57]; Clemes et al. (2011) [56]	√
13	The room smells good.	Bitner (1992) [55]	√
14	I feel relaxed staying at the property.	Lyu et al. (2019) [42]; Sim et al. (2006) [59]	√
15	I feel cozy staying at the property.	Tussyadiah and Zach (2017) [34]; Guttentag et al. (2018) [3]	√
16	I feel safe staying at the property.	Lyu et al. (2019) [42]; Guttentag and Smith (2017) [49]; Tussyadiah and Park (2018) [60]	√
17	It is smooth to make a reservation through Airbnb.	Guttentag and Smith (2017) [49]	√
18	The room is the same as shown online.	Tussyadiah and Park (2018) [60]	√
19	I feel comfortable making a reservation on Airbnb.	Guttentag and Smith (2017) [49]; Tussyadiah and Pesonen (2018) [61]; Tussyadiah and Park (2018) [60]	√
20	The host contacts me on his/her own.	-	√
21	The host can provide information I need.	Tussyadiah and Zach (2017) [34]; Wiles and Crawford (2017) [62]	√
22	My check-in process is smooth.	Guttentag and Smith (2017); Zhu et al. (2019a) [58]	√
23	The food offered by the host tastes good.	Wiles and Crawford (2017); Zhu et al. (2019a) [58]	√
24	The service provided by the host caters to my needs.	Tussyadiah and Zach (2016) [34]	√
25	The host is present during my stay.	Lyu et al. (2019) [42]; Moon et al. (2019) [63]	√
26	The host is hospitable.	Tussyadiah and Zach (2016) [34]	√
27	The host is eager to help.	Tussyadiah and Zach (2016) [34]	√
28	I enjoy communicating with the host.	Moon et al. (2019) [63]	√
29	The host genuinely cares about me.	Tussyadiah and Zach (2017) [34]; Moon et al. (2019) [63]	√
30	Reviews of the property posted on Airbnb are useful.	Liang et al. (2017) [37]	√
31	I interact with other guests at this property.	Lin et al. (2019) [44]	√
32	I share information with other guests.	Lin et al. (2019) [44]	√
33	I enjoy interacting with other guests.	Wu and Liang (2009) [57]; Lin et al. (2019) [44]	√
34	The room design contains local cultural elements.	McIntosh and Siggs (2005) [64]	√
35	I feel involved in the local community when staying at this property.	Tussyadiah and Pesonen (2016) [61]	√
36	The food provided by the host enables me to learn more about local cuisine.	Tussyadiah and Pesonen (2016) [61]; Guttentag et al. (2018) [3]	√
37	Living with the local people helps me experience local culture and customs.	Tussyadiah and Pesonen (2016) [61]; Guttentag et al. (2018) [3]	√

.

3.2. Step 2—Questionnaire Development

Based on items adapted from the literature review and interviews, a questionnaire was designed to collect quantitative data. The questionnaire was drafted in English and then submitted to an expert panel for feedback on the content, structure, format, and wording of items. The finalized questionnaire included two parts: one section about respondents’ demographic information and another in which respondents evaluated experiential items. All items were measured on a 7-point Likert scale anchored by 1 = “very unsatisfied” and 7 = “very satisfied”.

The final version of the instrument was written in Chinese because the target audience for this study consisted of Chinese customers. One of the researchers translated the questionnaire into Chinese. Fluent bilingual experts studying either tourism or hospitality management then reviewed the translation. Before finalizing the questionnaire, back-translation was conducted to reduce translation bias, as suggested by Van de Vijver and Hambleton [65]. Several native Chinese speakers who were proficient in English were invited to verify the accuracy of translation and then translate items back into English. Based on a comparison between the original English survey and the retranslated versions, questions that were less accurately translated were modified. This process ensured the accuracy of the multi-language survey instrument [66] The instrument’s appropriateness was re-evaluated after a pilot test in accordance with respondents’ comments.

3.3. Step 3—Pilot Study

Per Churchill’s [52] scale development procedures, researchers must conduct a pilot study once sample items have been generated. The aim of a pilot study is to collect data and purify the developed measure. Exploratory factor analysis (EFA) and a reliability test were performed to identify dimensions of the P2P customer experience.

Based on this study’s research objectives, target respondents met the following criteria: (1) Chinese adult travelers residing in China who (2) had used P2P accommodations within the past six months at the time the survey was conducted. Snowball sampling was used for the pilot study; specifically, questionnaires were distributed online (via WeChat) from one friend/colleague to another who met participant selection requirements. Invited participants’ characteristics (e.g., gender, age, income) were controlled in this phase. EFA was then carried out, after which the number of measurement items was reduced accordingly and the questionnaire was revised. Finalized questionnaires were later disseminated for the main survey.

In accordance with the literature review, interviews with Airbnb customers, and panel members’ feedback, an initial questionnaire was developed for the pilot study in Chinese and English. One screening question, thirty-seven questions related to experiential attributes, and ten demographic questions were included. In the pilot study, 200 questionnaires were distributed via WeChat to Airbnb customers in four first-tier cities: Beijing, Shanghai, Guangzhou, and Shenzhen. In total, 165 questionnaires were returned, 154 of which were usable. Among the 154 respondents, 36.4% were men and 63.6% were women; 71.5% were below 27 years old; and 92.8% possessed a high school diploma or above.

Questionnaires were designed on Wenjuanxing (sojump.com, accessed on 5 January 2020), a renowned P2P platform widely used in academic research in China. This platform enabled the researchers to verify the completeness of all items and response options. Participants could not submit their questionnaire unless all required items had been answered; therefore, none of the usable questionnaires contained missing values for items related to various aspects of the experience construct. Some values were missing for demographic information: 26 missing values for area of residence and 35 for travel destination. Because the travel destination question was irrelevant to subsequent analysis, this item was deleted.

To explore the dimensions of customers’ experiences with Airbnb accommodations, EFA was applied with varimax rotation. The Kaiser-Meyer-Olkin measure of sampling adequacy equaled 0.926 (p < 0.05), and Bartlett’s test of sphericity was significant at p < 0.001, indicating the suitability of data for factor analysis. The EFA results revealed five factors (30 items) with eigenvalues above 1.0; these factors explained 79.507% of the total variance. Communalities ranged from 0.563 to 0.934, suggesting that the variance of original variables was explained by the five identified factors. All factor loadings exceeded 0.5. Cronbach’s alpha values for the five factors were above 0.8, indicating good internal consistency.

Table 4. EFA results of dimensions of the customer experience.

Dimensions and Items	Cronbach’s Alpha	Communalities	Factor Loading	Item-to-Total Correlation	Eigenvalue	Variance Explained %
1 Tangible—Sensory experience	0.963				15.360	51.199
Exterior design		0.667	0.671	0.750
Interior design		0.839	0.865	0.763
Cleanliness		0.760	0.803	0.750
Spaciousness		0.602	0.669	0.704
Layout		0.793	0.793	0.796
Facilities		0.697	0.743	0.751
Quietness		0.563	0.600	0.673
WIFI		0.567	0.591	0.635
Lighting		0.760	0.721	0.759
Smell		0.718	0.751	0.757
Relaxing feeling		0.801	0.753	0.784
Cozy feeling		0.762	0.754	0.771
2 Host	0.946				3.340	11.133
Contacts guest on his/her own		0.564	0.607	0.646
Information provision		0.660	0.589	0.731
Check-in service		0.590	0.511	0.726
Presence during stay		0.684	0.777	0.542
Hospitality		0.889	0.885	0.648
Eager to help		0.934	0.917	0.680
Enjoyable communication		0.913	0.888	0.718
Cares about guest		0.792	0.805	0.668
3 Cultural experience	0.948				2.575	8.583
Cultural elements		0.730	0.600	0.770
Feel involved in local community		0.863	0.744	0.775
Learn more about local food		0.811	0.731	0.719
Learn more about local culture		0.881	0.776	0.759
4 Interaction with peer guests	0.962				1.360	4.532
Interact with peer guests		0.921	0.899	0.530
Share information		0.885	0.886	0.501
Enjoy interactions		0.868	0.869	0.520
5 Location	0.898				1.218	4.060
Good surrounding environment		0.714	0.636	0.615
Convenient location		0.837	0.834	0.515
Living facilities		0.769	0.800	0.533

presents detailed EFA results for the P2P customer experience construct.

The first and most essential factor incorporated the tangible and sensory experience dimensions, explaining 51.199% of the total variance with an eigenvalue of 15.360. This factor consisted of 12 items, covering the most basic attributes of Airbnb properties: exterior design, interior design, cleanliness, spaciousness, layout, facilities, quietness, Wi-Fi, lighting, smell, relaxing feeling, and cozy feeling. This factor was labeled “Tangible and sensory experience”.

The second factor included services provided by the host and guest–host interaction. This factor encompassed eight items: host contact (with the guest) on his/her own, information provision, check-in service, presence during guest’s stay, hospitality, eager to help, pleasant communication, and caring about guests. This factor explained 11.133% of the total variance with an eigenvalue of 3.340. The factor was named “Host”.

The third factor reflected guests’ feelings about the cultural authenticity of their Airbnb accommodations. Four items related to cultural experience loaded on this factor: cultural elements, feeling involved in the local community, learning more about local food, and learning more about local culture and customs. This factor explained 8.583% of the total variance, exhibited an eigenvalue of 2.575, and was labeled “Cultural experience”.

The fourth factor pertained to interactions with other Airbnb guests. Three items related to guest interaction loaded on this factor: interact with other guests, share information, and enjoy interactions. This factor explained 4.060% of the total variance, had an eigenvalue of 1.218, and was called “Interaction with peer guests”.

The last factor was related to the location of the Airbnb property, including three items: good surrounding environment, convenient location, and nearby living facilities. This factor explained 4.532% of the total variance, and the eigenvalue was 1.36. The factor was labeled “Location”. In total, seven items were deleted (i.e., items #24, #17, #30, #16, #19, #18, and #23) due to low factor loadings or failure to load clearly on a specific factor. Among the deleted items, #17, #18, and #19 each concerned guests’ usage of the Airbnb platform. Although these items were not associated with a factor identified in this study, the booking platform’s influence on the customer experience should be explored further.

3.4. Step 4—Main Survey

A formal survey was conducted to examine the scale’s construct validity and reliability. Quota sampling was adopted to obtain a representative sample. Specifically, individuals residing in the four largest cities in China (Beijing, Shanghai, Guangzhou, and Shenzhen) were selected as the target population. Given the large sample size for the main survey, the collaborating survey company (Wenjuanxing) distributed questionnaires according to the researchers’ sampling requirements. This company hosts a large active participant pool. Participants were randomly selected online based on the given criteria. Invitations were sent directly to potential participants, and all participants were compensated.

3.4.1. Demographic Profile of Main Survey

The final questionnaire used in the main survey included one screening question, thirty measurement items, and nine demographic questions. Data for the formal survey were collected in June and July 2019. In total, 785 questionnaires were completed, 519 of which contained no missing data.

Table 5. Geographic distribution of respondents’ resident areas.

Residence Area	Number	Percentage
Beijing	55	10.60%
Shanghai	150	28.90%
Guangzhou	175	33.70%
Shenzhen	139	26.80%
Total	519	100%

displays respondents’ areas of residence. Travelers from the top four Chinese cities represented a large proportion of Airbnb users, covering the northern, central, and southern regions of the country. These data were sufficiently diverse and deemed appropriate for data processing.

Respondents’ demographic profiles demonstrated fair diversity in gender, age, marital status, occupation, education level, and annual income (

Table 6. Demographic profiles of questionnaire respondents (N = 519).

Demographic Variables	Description	No.	Percentage %
Gender	Male	249	48.0
	Female	270	52.0
Age	18–22	59	11.4
	23–27	149	28.7
	28–32	120	23.1
	33–37	110	21.2
	38–42	45	8.7
	43–47	24	4.6
	>47	12	2.3
Marital status	Married	212	40.8
	Single	296	57.0
	Other	11	2.1
Occupation	College student	33	6.4
	Manufacturing worker	16	3.1
	Sales	49	9.4
	Marketing/PR	23	4.4
	Service staff	17	3.3
	Administrator	42	8.1
	HR staff	24	4.6
	Finance staff	45	8.7
	Office clerk	29	5.6
	Technician	64	12.3
	Company managerial staff	55	10.6
	Teacher	24	4.6
	Consultant	6	1.2
	Professionals	23	4.4
	Other	69	13.3
Education	Middle school or below	25	4.8
	High school	39	7.5
	Vocational school	26	5.0
	High school diploma	119	22.9
	Undergraduate	262	50.5
	Postgraduate or above	48	9.2
Annual income (RMB)	No income	32	6.2
	<30,001	56	10.8
	30,001–60,000	119	22.9
	60,001–90,000	81	15.6
	90,001–120,000	92	17.7
	120,001–150,000	66	12.7
	150,001–180,000	26	5.0
	>180,000	47	9.1

). Roughly half (48%) respondents were men, and 52% were women. Most were millennials, with 73% between 23 and 37 years old. As might be expected among relatively young respondents, 57% were single. Respondents were mostly well educated, with 82.6% having earned at least a high school diploma.

3.4.2. Travel- and Accommodation-Related Information

Respondents were asked to provide travel-related information about their most recent Airbnb stay, including their travel purpose, length of stay, and property rate (

Table 7. Travel- and accommodation-related information obtained from the main survey.

Items	Descriptions	No.	Percentage
Travel purpose	Business	64	12.3
	Tourism	283	54.5
	Family leisure	146	28.1
	Other	26	5
Length of stay	1–2 nights	322	62
	3–4 nights	136	26.2
	5–6 nights	46	8.9
	7–8 nights	11	2.1
	>8 nights	4	0.8
Room rate (RMB)	<101	34	6.6
	101–200	143	27.6
	201–300	123	23.7
	301–400	90	17.3
	401–500	68	13.1
	501–600	26	5
	601–700	9	1.7
	>700	26	5

). Most respondents were traveling alone or with friends, 28.1% were traveling with family, and 12.3% were traveling for business purposes. Many respondents (62%) stayed at an Airbnb property for one or two nights; about one-quarter (26.2%) stayed for 3–4 nights. Over half of the respondents chose to stay at less expensive Airbnb properties. Roughly half (51.3%) stayed at an Airbnb property costing between RMB100 and RMB300 per night.

3.4.3. Measurement Model of Customer Experience

Confirmatory factor analysis (CFA) was carried out in AMOS 21 to verify factors extracted from EFA in the pilot study and to confirm the latent constructs based on observed variables [67]. The maximum likelihood was used for model estimation. The following indices were checked to determine the measurement model’s goodness-of-fit: the minimum discrepancy (CMIN), normally referred to as Chi-square (χ²); degrees of freedom (df); p-value; χ²/df; goodness-of-fit index (GFI); comparative fit index (CFI); Tucker–Lewis index (TLI); and root mean square error of approximation (RMSEA). Although there are no definite standards or cut-off points for these indices, researchers have provided guidelines: Byrne [67] and Hair et al. [68] suggested ranges are shown in

Table 8. Suggested acceptable ranges of model fit indices.

Indices	Good	Acceptable
χ2/df	<3.0	<5.0
GFI	>0.95	>0.90
CFI	>0.95	>0.90
TLI	>0.95	>0.90
RMSEA	<0.05	<0.08

.

The measurement model for the customer experience was subject to first-order CFA to assess the relationships between observed and latent variables (i.e., factors under a higher-level construct). In particular, a first-order CFA was calculated to evaluate the 30 items and five latent variables under the customer experience construct: “Tangible and sensory experience” (Tang), “Host” (Host), “Cultural experience” (Cult), “Interaction with peer guests” (Inte), and “Location” (Loca).

Table 9. Model estimates of customer experience.

Measurement Model for CE			Item Statement	T-Value	Std FL	CR	AVE
Tangible and sensorial experience						0.96	0.69
CE1	<---	Tang	Exterior design	N/A	0.783
CE2	<---	Tang	Interior design	12.734	0.884
CE3	<---	Tang	Cleanliness	11.92	0.842
CE4	<---	Tang	Spaciousness	10.679	0.775
CE5	<---	Tang	Layout	12.662	0.88
CE6	<---	Tang	Facilities	11.579	0.824
CE7	<---	Tang	Quietness	9.665	0.715
CE8	<---	Tang	WIFI	9.608	0.712
CE12	<---	Tang	Lighting	12.203	0.857
CE13	<---	Tang	Smell	12.172	0.855
CE14	<---	Tang	Relaxing feeling	12.713	0.883
CE15	<---	Tang	Cozy feeling	12.511	0.873
Host						0.92	0.59
CE16	<---	Host	Contacts guest on his/her own	N/A	0.748
CE17	<---	Host	Information provision	10.924	0.843
CE18	<---	Host	Check-in service	11.081	0.854
CE19	<---	Host	Presence during stay	10.71	0.829
CE20	<---	Host	Hospitality	8.128	0.648
CE21	<---	Host	Eager to help	9.856	0.771
CE22	<---	Host	Enjoyable communication	9.912	0.775
CE23	<---	Host	Cares about guest	8.723	0.691
Cultural experience
CE27	<---	Cult	Cultural elements	N/A	0.976	0.93	0.77
CE28	<---	Cult	Feel involved in local community	40.006	0.98
CE29	<---	Cult	Learn more about local food	20.831	0.879
CE30	<---	Cult	Learn more about local culture	9.65	0.624
Interaction with peer guests						0.85	0.66
CE24	<---	Inte	Interact with peer guests	N/A	0.491
CE25	<---	Inte	Share information	6.645	0.863
CE26	<---	Inte	Enjoy interactions	6.952	1.003
Location						0.9	0.76
CE9	<---	Loca	Good surrounding environment	N/A	0.777
CE10	<---	Loca	Convenient location	12.62	0.929
CE11	<---	Loca	Living facilities	12.324	0.901

Note: Std Fl = Standardized factor loadings; CR = Composite reliability; AVE = Average variance extracted; N/A = not applicable.

presents model estimates of the customer experience, and

Table 10. Goodness-of-fit of CFA results of customer experience.

Indicator	χ2	df	χ2/df	GFI	TLI	CFI	IFI	RMSEA
Value	1623.957	395	4.111	0.818	0.891	0.901	0.902	0.078

lists the goodness-of-fit indices. As indicated, χ²/df < 5; RMSEA < 0.08; and GFI, TLI, CFI, and incremental fit index (IFI) were considered acceptable. The five latent variables loaded well on the proposed model and were significantly correlated, revealing an acceptable model structure.

3.4.4. Reliability and Validity of Customer Experience Measurement Scale

Reliability refers to the degree of consistency of items under a latent variable [68]. A variable’s internal consistency can be evaluated with numerous diagnostic measures, such as the item-to-total correlation and the reliability coefficient or Cronbach’s Alpha [68]. Validity is used to measure a scale’s adequacy in measuring a specific variable (i.e., whether the construct measures what it is intended to measure) [69]. Convergent and discriminant validity are the most common forms of validity. Convergent validity refers to “the degree to which two measures of the same concepts are correlated” [54]. Discriminant validity can be evaluated by examining whether the average variance extracted (AVE) for each construct is higher than the shared variances of that construct.

The construct reliability of the proposed multi-item scale was measured using Cronbach’s alpha. In this study, all alpha coefficients ranged from 0.792 to 0.940, exceeding the cut-off value of 0.7 to suggest a reliable instrument. The correlation coefficient of each dimension of the customer experience appears in

Table 11. Correlation matrix of five dimensions of customer experience.

	Customer Experience	Tang	Host	Cult	Inte	Loca
Customer experience	1
Tang	0.949 **	1
Host	0.923 **	0.801 **	1
Cult	0.872 **	0.758 **	0.787 **	1
Inte	0.823 **	0.710 **	0.736 **	0.760 **	1
Loca	0.833 **	0.786 **	0.716 **	0.660 **	0.621 **	1
AVE		0.69	0.59	0.77	0.66	0.76

** Significant at p < 0.01.

along with coefficients among all dimensions and AVE values. The five questionnaire dimensions were significantly related, with correlation coefficients spanning between 0.621 and 0.801. The correlation coefficients of customer experience dimensions ranged from 0.823 to 0.949, indicating that the customer experience construct exhibited good validity. Additionally, the AVE values of the five dimensions of the customer experience were each above 0.5, implying good convergent validity. The AVE for each construct was greater than the squared correlation coefficients between constructs; therefore, the discriminant validity was acceptable.

4. Theoretical Implications

Given dynamic growth in the P2P accommodation market, it is essential to understand what contributes to customers’ overall experiences and what these guests value most. Until now, little valid measurement scales were available to assess the P2P accommodation experience. Although many customer experience scales have been developed in the hospitality industry, such instruments are largely limited to traditional hotel settings (e.g., luxury hotels, economy hotels, and restaurants). Cultural differences can also affect guests’ experiences [46]. More investigation is needed regarding Asian guests’ experiences with P2P accommodations [47]. Hence, the current study addressed this gap by developing an experience-focused measurement scale to capture Chinese guests’ holistic experiences with P2P accommodations and to reveal the relative importance of experiential dimensions.

This study has tapped into the newly established hospitality sector of P2P accommodations to explore customers’ experiences with such accommodations in the Chinese market. The research adopted an experiential approach to examine attributes that could influence Chinese customers’ perceptions of P2P accommodations. Findings contribute to the tourism and hospitality literature by delineating experiential dimensions of P2P accommodations and their relative importance in customers’ experiences. This study also presents a measurement scale to assess such experiences empirically. To the authors’ best knowledge, this work could be the first to capture tourists’ holistic experiences with P2P accommodations in the Chinese market. The developed dimensions and measurement scale of the customer experience expand customer experience research related to P2P accommodations and provide a theoretical framework for measuring the customer experience.

In sum, although researchers have explored the experiential dimensions identified in this study within different cultural contexts, most, if not all, prior studies simply addressed such dimensions on a macro level. Scholars rarely empirically indicated dimensions’ specific meanings or which aspects were more essential. The present study adopted a mixed methodology to capture Chinese P2P guests’ holistic experiences more thoroughly. Results showed that these customers held distinctive perceptions of what constitutes a “good experience”. Compared with Westerners, Chinese guests possessed higher expectations regarding homelike feelings and a stronger desire to cultivate family-like relationships with hosts and guests.

Methodologically, most studies on P2P accommodation experiences have relied on online review analysis that tends to be overwhelmingly positive [33]. In response to Zervas et al.’s [1] call for P2P accommodation studies that do not involve consumer reviews, the present authors integrated quantitative and qualitative approaches (i.e., in-depth interviews and surveys) to capture Chinese Airbnb users’ perceptions of their stays.

Results unveiled five key experiential dimensions: tangible and sensorial experience, host, cultural experience, interaction with peer guests, and location. These macro-level dimensions have been supported in the literature; however, the underlying meanings of each dimension partially vary from Western Airbnb users as detailed below.

First, tangible and sensory experiences comprised the most important factor influencing Chinese customers’ perceptions of their accommodation experiences. This result substantiates studies by Walls [70], Knutson et al. [9], and Guttentag et al. [3]. Instead of reserving standard rooms in branded chain hotels, many tourists—especially millennials—prefer to stay in accommodations with distinct architecture, interior design, and a homelike atmosphere [49]. Respondents in this study considered practical attributes (e.g., cleanliness, a quiet room, and Wi-Fi) as must-haves from Airbnb hosts, not simply appealing factors. Respondents also emphasized the importance of a homelike atmosphere, which can be delivered through interior décor, ambiance, and amenities (e.g., kitchen, washing machine, dining room). Chinese Airbnb guests wanted hosts to treat them as friends or even family, and guests expected to share living space with hosts. This need is distinct from that of Western Airbnb users who value personal space [6,8,58].

Second, the “Host” and “Interaction with peer guests” dimensions played major roles in guests’ experiences with P2P accommodations. This result echoes studies reporting that social relations greatly influence the guest experience [71]. Regarding host attributes, respondents mentioned expectations for professional services such as registration and advice sharing. Chinese Airbnb customers particularly stressed the affective aspects of hotel attributes: interviewees highly valued hosts’ caring behavior, which could help guests feel at home in accommodations. In the same vein, Zhu et al. [6] pointed out that Chinese customers expected close guest–host relationships. Their study also identified a positive relationship between guests’ homelike feelings and loyalty. Therefore, to better accommodate Chinese customers, P2P accommodation providers should aim to provide personalized guest interactions to promote a homelike experience and sense of belonging.

Aside from host–guest relations, interactions with peer guests represent another important component of Airbnb customers’ experiences. Cova et al. [72] and Lin et al. [44] came to similar conclusions. Tourists generally enjoy socializing and sharing experiences with other guests [44], and Chinese customers seem more willing to participate in shared activities while staying at P2P accommodations [58]. This dimension was especially prevalent in the current study, potentially due to the nature of Chinese culture in which people embrace collectivism and emphasize peer bonding [73].

Third, “cultural experience” was a notable component of guests’ P2P accommodation experiences as well. Rather than merely seeking rest and relaxation, tourists desire the inspiration that arises from meeting new people and experiencing different cultures [62]. As Pine and Gilmore [21] stated, tourists can achieve greater value through the educational dimension of the tourist experience when engaging with network-based hospitality. Acquiring knowledge and skills during travel enables tourists to better understand a destination’s local history and culture, which enhances their cultural experiences [74]. Furthermore, participating in local recreational activities, such as dinner parties, games, or cultural events, provides tourists with a sense of living like locals and enriches their perceptions of cultural authenticity [42,62]. P2P accommodation operators should focus on the cultural diversity of shared properties and potential experiential features that allow travelers to become immersed in local life [34].

Last but not least, location was tied to customers’ experiences with P2P accommodations, aligning with several previous studies [34]. Tourists largely prefer Airbnb properties situated either in central areas (i.e., near the city center and highways) or close to local attractions [4]. Local neighborhoods that typically do not receive much tourist expenditure may benefit from Airbnb users’ dispersed spending [40]. Wachsmuth and Weisler [75] pointed out that Airbnb listings tend to be geographically uneven, creating new rent gaps in culturally desirable neighborhoods.

5. Practical Implications

First, this study sheds light on Chinese market characteristics in relation to P2P accommodations. Survey results showed that most P2P accommodation users in China were millennials (i.e., younger than age 37; 84.4%). This proportion is consistent with that reported in CSIC’s “Chinese P2P Accommodation Development Report 2018”, in which millennials were found to account for 83% of all Chinese Airbnb users [7]. This trend is unsurprising, as young travelers born in the Internet technology era are more accustomed to online sharing behavior. Most respondents reported having only stayed at an Airbnb property for 1–2 nights, explaining that they preferred to experience different types of rooms with unique interior aesthetics. In addition, over 90% of respondents chose Airbnb properties costing less than RMB500 per night. Price was thus a highly motivating factor among P2P accommodation users. This finding coincides with previous Airbnb-related studies in which price was named a driving factor [3,61]. However, price alone will not necessarily encourage Airbnb reservations [76].

These findings provide fresh insight for P2P accommodation operators, including platforms and hosts. Web design and advertising campaigns targeting Chinese customers should amplify listings’ emotional sensorial feelings and affectionate moments among hosts and guests. By catering to Chinese customers’ distinct needs and desires, P2P accommodation hosts—especially Westerners—may communicate more effectively and build more harmonious relationships with Chinese guests. P2P accommodation platforms and hosts should strive to foster a homelike atmosphere when targeting Chinese guests. P2P accommodation operators should focus on the cultural diversity of shared properties and potential experiential features that allow travelers to become immersed in local life.

In 2018, China implemented the Rural Revitalization Strategy with the aim of accelerating development in rural areas [77]. P2P accommodations have since been introduced in many rural tourism destinations. Room listings within Chinese rural areas reached 5000 as of 2018 on Xiaozhu, a P2P accommodation platform [7]. Rural tourism has become quite popular among Chinese residents; according to a survey [7], 40.13% of respondents claimed that they travel to rural areas once a month to experience rural life. The home-sharing business model should be encouraged to further boost economic development in these regions. P2P platform operators may hence wish to collaborate with local tourism bureaus and homeowners to expand listings in rural tourism destinations.

6. Limitations and Future Research Directions

Similar to other studies, our research is not free from limitations; findings should be interpreted cautiously for numerous reasons. This study is the first to devise a scale measuring the customer experience in the P2P accommodation industry. Because customers’ experiences are subjective and likely to shift across scenarios, this model may not be generalizable to other cultural contexts. Future research should replicate our model in additional settings for cross-validation. Subsequent work could expand upon the model proposed in this study by including other variables, such as the perceived value of experiences [28], motivational factors behind booking P2P accommodations [3], and customer satisfaction [78]. These features should promote deeper exploration of the antecedents and consequences of customers’ experiences with P2P accommodations.