Innovation adopter types

The Innovation adoption theory (IAT) is capable of providing a rationale to categorize travelers at a number of adoption stages in adopting a tourism service or product, from the pre-adoption stage (acquire initial knowledge, produce an attitude, and decide to adopt or not) to the post-adoption stage (seek to strengthen decisions on adoption decisions or retract) [14], which helps to divide travelers into pre-and-post adopters. The current study, therefore, divides travelers into pre-adopters (potential users who are aware of this innovation and will use it) and post-adopters (current users who have adopted this innovation).

Besides, the IAT can provide a useful perspective to extract and understand motivations because consumers’ innovation adoption can shape their motivations [14]. De Larrea et al. [17] illustrated that the beliefs that users hold for repeat usage are not likely to be the same beliefs that yield the initial adoption. Depending on the users’ experience, different factors can exert different effects on the intention and use of innovative services or products [18]. Besides, knowledge derived from past behavior can shape the users’ behavioral intentions [19]. Previous literature has indeed linked individuals at different innovation adoption processes with motivational differences [5]. Guttentag et al. [5] indicate that Airbnb users are more strongly motivated by practical motivations than experiential motivations. Maghrifani et al. [20] reveal that repeat travelers have stronger interaction-seeking motivations while potential travelers have stronger novelty- and assurance-seeking motivations. The current study, therefore, explores the motivations for using shared accommodations—fits with the IAT that illustrates potential and current users’ different motivations for product choices.

Motivations of using shared accommodations

Motivation is a term that explains the reason why individuals prefer to participate in a particular activity [8]. With the rapid development of shared accommodations in the tourism industry, contributions have emerged to travelers’ motivations to use shared properties [2, 5]. Researchers have tried to use economic, social, functional, and experiential motivations to explain the variations between potential customers and experienced customers [5, 19].

For potential customers, shared accommodations not only save money (refer to economic motivations), but also provide an opportunity to gain shared accommodation ethos and philosophy, which enable travelers to perceive an added value of this industry [21]. Besides, potential customers are more willing to accept others’ recommendations (refer to social motivations generated by learning and experiencing the social environment based on individuals’ social and cultural needs) to save time and make a better selection [22], which is likely to yield tourism market homogeneity in travelers’ preferences.

Experienced customers tend to be more confident in booking shared rooms according to their prior knowledge of both experiential and functional aspects formed by their actual experience of using it [19]. Specifically, experiential motivations (e.g., novelty experiences, interactions, shared accommodation ethos, etc.) are treated as the major motivating force for transactions in the sharing economy [23]. Functional motivations (e.g., better location, better household facilities, larger space, etc.) play a role in enhancing the service performance level [24].

In this sense, potential customers are easily motivated by social and economic motivations, but experienced customers are attracted by functional and experiential motivations. It may infer that those social and economic motivations group the homogeneity of the tourism market and functional and experiential motivations divide the heterogeneity of the tourism market [5].

Research design and data analysis

This research studies the motivations for shared accommodations with data collected from two questionnaires distributed to pre-adopters and post-adopters respectively. Particularly, questionnaires were designed in English to keep the originality of measurements, followed by back translating the questionnaires into Chinese by two academic scholars [29]. This study was approved by Xi’an Jiaotong-Liverpool University ethics committee and all respondents were provided informed consent on the first page of the online questionnaire. A pilot data collection was completed in April 2018. The questionnaires were first developed based on the existing studies and a few adjustments were made as some further insights were obtained from six pilot interviews (three pre-adopter and three post-adopters). The interview questions consisted of three sections: a) introductory questions, e.g., introduce yourself and this trip; b) motivation questions, including a variety of motivations c) demographic questions, including age, gender, educational background, etc.

SPSS.20 is used for conducting factor analysis, cluster analysis and discriminant analysis [5]. First, descriptive analysis is used to conduct an overview of travelers’ responses to survey questions. Second, an exploratory factor analysis by using the direct oblimin oblique rotation and principal axis factoring extraction is conducted for the 22 motivations, in order to identify the main motivation factors. Third, for remaining some meaningful variance that is lost from the factor analysis [30], the cluster analysis is used to aggregate the diverse groups of travelers based on 22 motivational items rated on a 6-level Likert scale (1 = strongly disagree, 6 = strongly agree), for the reason that even number Likert scale is capable of obtaining clearer attitudes or behaviors of respondents rather than ambiguous options [5, 26]. This study conducts the hierarchical cluster analysis to determine the optimal number of clusters by observing the significant change points via the agglomeration coefficients tables, followed by conducting a k-means cluster analysis to segment motivation-based travelers by inputting the optimal cluster number [5]. Last, a discriminant analysis was employed to confirm the validity of cluster solutions.

Questionnaire design

One questionnaire for post-adopters and one questionnaire for pre-adopters were distributed online respectively. A screening question of “Have you ever used shared accommodations?” was used to distribute two different questionnaires to respondents. Each questionnaire is composed of two parts: Section 1 contains 22 items based on motivations for shared accommodations and Section 2 includes demographic questions. The motivation questions were the same as those while demographic questions were slightly different for pre-and-post adopters. Post-adopters were required to fill in the questionnaire according to their recent shared accommodation experience in the past 12 months and pre-adopters needed to answer the questions based on their expectations of shared accommodations in the coming 12 months.

In this exploratory research, the motivational scale items were organized with a particular concern for the research objectives, which were primarily based on previously observed motivations in the existing shared accommodation literature [2, 3]. The 22-item Likert questions (See S1 Table) were rooted in the comparison of the traveler heterogeneity between pre-adoption and post-adoption stages.

Data collection

The data collection focused on travelers living in the city of Shanghai, China. The rationale is as follows: firstly, the traffic in Shanghai is convenient, including numerous trains going to a number of domestic cities and two international airports that enable travelers to fly across the world; secondly, this new accommodation mode has spread quickly in Shanghai, therefore, travelers living in Shanghai could be more active and tend to engage in shared properties by having numerous opportunities to acquire shared economy philosophy and ethos as the first-tier city [1].

Respondents were recruited to complete an online survey via wjx.cn (the largest online panel data service in China, akin to Qualtrics) from December 2018 to March 2019 and these respondents could receive compensation for their responses from wjx.cn. The data collection service of wjx.cn includes providing reliable data and recruiting respondents under a number of criteria. Before recruiting particular respondents, researchers could set specific criteria of equal-sized pre-and-post adopters of shared accommodations, then wjx.cn would contact respondents via emails in their large-scale respondents’ database until a specified number of questionnaires were collected.

A total number of 904 responses were collected for this study, 840 of them were valid responses after incomplete questionnaires were discarded (92.9%). Among the invalid responses, 13 were from post-adopters and 51 were from pre-adopters. Consequently, valid responses were received from the purposive sampling approach with a quota frame based on the equal distribution of pre-adopters (who intended to first use shared accommodations in the next 12 months) and post-adopters (who had used shared properties during the previous 12 months), including 420 respondents (50%) of each sub-sample respectively. The underlying reasons for conducting the purposive sampling with equal-sized groups tend to yield a more reliable result of comparison that can evaluate motivations for each group and look at the overall travelers’ conditions [31].

Descriptive statistics for pre-and-post adopters’ motivations

Pre-adopters and post-adopters display similar characteristics (See S2 Table), in this context, the majority of respondents are female and in younger age groups, which matches the situation of the shared accommodation industry. Shanghai is one of the largest tourist sources in China because of Shanghai residents’ strong consumption ability and high-income level. The number of shared accommodation orders of Shanghai residents during the May Day holiday in 2023 reached 5.17 times that of the May Day holiday in 2019, indicating a strong recovery in the tourism market. Moreover, Shanghai residents have the highest consumption of shared accommodations, with an average overnight price of nearly 850 yuan, dominated by the generation after the 90s and 00s that mainly consisted of new users, accounting for over 72% of the shared accommodation consumption [32].

Table 1 reveals that pre-adopters are more likely to seek loadings with cheaper prices and more distinctive shared accommodation characteristics, with higher mean of E1 (4.133), E2 (4.088), E3 (4.002) and E4 (3.338) than post-adopters. The desire for shared accommodation ethos was included in the perceived value of shared properties, which can be distinguished from traditional hotels [7]. Moreover, social connections related to travel parties and word-of-mouth communication motivated pre-adopters more than post-adopters, with a greater mean of S1 (3.093), S2 (3.769) and S4 (4.233) than post-adopters. A possible explanation is that potential users without shared accommodation experiences are more likely influenced by others’ suggestions and recommendations. In addition, much attention is paid to the role of hosts that share local tips and travel suggestions with shared accommodation users [21]. The agreement on functional attributes is stronger in post-adopters because they are clearer in which kind of shared properties they prefer and which functional attributes dominated more in their selections, with greater mean of F1 (4.817), F3 (4.669), F4 (4.193), F5 (3.979), F6 (4.541) than pre-adopters. Last, the agreement on experiential benefits is stronger in post-adopters for they have had actual experiences in using shared properties, with higher mean of Ex2 (4.405), Ex3 (4.610), Ex4 (4.895), Ex5 (4.712), Ex6 (4.712), Ex7 (4.348) and Ex8 (4.893). Compared with potential users who answered the experiential questions based on their expectations of shared accommodations, the past experience of using shared accommodations tends to be helpful for them in segmenting shared loadings and selecting their ideal rooms more easily.

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Table 1. Descriptive statistics for pre-and-post adopters’ motivations.

https://doi.org/10.1371/journal.pone.0286868.t001

Cluster analysis results

In the present study, the multicollinearity problem was first assessed to ensure that no variables had correlations over 0.9 [33]. The two-stage cluster analysis was used widely in tourism and hospitality studies, a hierarchical cluster analysis was first conducted to obtain the optimal cluster number, followed by a k-means cluster analysis applied to effectively group respondents [5]. Moreover, the changes within clusters were examined subsequently [30]. The 22 motivations, including the removed five motivations in factor analysis, were inserted in cluster analysis. The rationale is that some meaningful variance is lost from the factor analysis, which means that factor-cluster analysis cannot reflect the original data of respondents when compressing variables into factors [30]. To make the interpretation easier, the original 22 factors along with the factor analysis result are used to explain the results of the cluster analysis.

For pre-adopters, the agglomeration coefficient obtained from the hierarchical cluster approach indicates that the most significant increase of agglomeration coefficient is from stage 418 to 417 (n = 420), presenting that the optimal cluster number is three. As shown in Table 2, a three-solution cluster emerges. The first cluster (n = 142, 33.8%), labelled “Recommendation Seekers”, holds less obvious motivations while this group of travelers are relatively easily attracted to use shared properties by recommendations of friends or travel companions and cheaper accommodation costs. The second cluster (n = 122, 29.0%) is termed “Experiential seekers”, as they hold the most favorable motivations for experiential benefits. Travelers from the last cluster (n = 156, 37.2%) reveal a rather favorable motivation for practical benefits, including lower prices and other functional benefits, thus they are termed “Practical seekers”.

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Table 2. Cluster analysis results for pre-adopters.

https://doi.org/10.1371/journal.pone.0286868.t002

Pre-adopters are more homogeneous because they are only aware of shared accommodations and are interested in using this tourism accommodation option. However, their knowledge of how to use shared accommodations properly (e.g., competencies of booking shared properties, preferences of shared properties and competencies of interaction with the hosts), their perceptions of shared accommodation attributes and their understanding of the shared accommodation industry are very limited [34]. Moreover, pre-adopters’ needs and desires are homogenized so that travelers were expected to prefer standard services or products of high quality and low price as compared to more customized high price services or products [35]. The rationale is that pre-adopters’ knowledge acquirement still stays at the initial level (awareness of knowledge), which restricts their perceptions of shared accommodation attributes. The limited knowledge of shared accommodation attributes makes it less possible for them to know their own preferences in selecting shared properties among a variety of loading listings [7]. After travelers have adopted the innovation service, their understanding of how to use the innovation properly and how the innovation works can be developed [14].

For post-adopters, the Agglomeration Schedule indicates that the most significant increase in agglomeration coefficient is from stage 415 to 414 (n = 420), which suggests that the optimal cluster number is six. Based on travelers’ motivations to select shared accommodations, the six clusters can be named Ethos travelers, Interactive travelers, Recommendation Seekers, Money savers, Novelty & Happiness seekers and Residual travelers (See Table 3). Particularly, Ethos travelers were strongly motivated by shared accommodation ethos, which means that they have developed a deeper understanding and general acceptance of shared accommodations and have already realized its practical and experiential benefits. According to the significant strong agreement in interaction benefits, social connections, cheaper price and novelty & enjoyment respectively, travelers are segmented as Interactive Travelers, Recommendation Seekers, Money Savers and Novelty & Happiness Seekers. The sixth cluster, Residual Travelers, is the remained group with the smallest number of travelers. They were less motivated by shared accommodation ethos, which yields less agreement in other motivations because of the limited interest in shared accommodation philosophy.

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Table 3. Cluster analysis results for post-adopters.

https://doi.org/10.1371/journal.pone.0286868.t003

Significant differences between travelers at the pre-adoption and post-adoption stages are found by comparing pre-adopters and post-adopters. Travelers’ cluster becomes plural and different after adoption, increasing from three to six clusters. Compared with pre-adopters, post-adopters reveal higher knowledge acquirement of shared accommodations, as they have access to three types of knowledge influencing the innovation adoption decisions. Before the actual involvement in shared accommodations, travelers’ knowledge significantly influences their decision-making. With an awareness-knowledge of innovation, travelers would make their decisions, represent their initial exposure to innovation and understand how innovation works [14]. In this regard, pre-adopters’ motivations are more homogenous, because they prefer standard service or products of high quality and low price, as compared to more customized high-price services or products [35], for they still stay at the awareness level of innovation knowledge. Compared with pre-adopters, post-adopters’ perceptions of attributes of shared accommodations, along with their preferences for different types of shared properties, emerge gradually as their growth experiences of shared accommodations. Pre-adopters who hold less experiential and functional knowledge of shared accommodations tend to rely more on recommendations of friends or travel companions. Therefore, post-adopters are more heterogeneous in motivation-based segmentation because they prefer different attributes of shared accommodation and their in-depth understanding of the tourism accommodation mode after experiencing the benefits of shared properties. However, pre-adopters are more homogenous because they are more likely to adopt shared properties with higher quality and lower prices [15].

A better understanding and differentiation among segments of post-adopters is crucial to promote travelers’ repeat participation. Six segments of post-adopters differ significantly in various motivations (See Table 4). In terms of personal demographics, six clusters are different in age, job and household composition at 1% significance level. Furthermore, with regard to travel characteristics and shared accommodation usage profiles, the six segments differ significantly in sources and types of shared accommodations, but not in travel purposes. A possible explanation is that the travel purpose of the majority of travelers is leisure (94.0%) rather than business or VRF (visiting relatives and friends).

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Table 4. Profiles of post-adopters’ clusters.

https://doi.org/10.1371/journal.pone.0286868.t004

The discriminant analysis results comprise one and five discriminant functions for pre-adopters and post-adopters respectively (See Table 5), which are in combination with different segments. In addition, 97.4% of the original cases are correctly classified as pre-adopters and 88.8% are correctly classified as post-adopters, which is regarded as relatively strong support for the cluster analysis results.

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Table 5. The overall discriminant analysis results.

https://doi.org/10.1371/journal.pone.0286868.t005

Discussion

Pre-adopters tend to be homogeneous, which can be explained by the findings of previous research that pre-adopters differ from post-adopters in terms of prior knowledge in decision-making [19, 36]. Firstly, Pre-adopters’ understanding of the heterogeneity of various types of shared accommodations provided by hosts to travelers is limited. Additionally, the credibility of rooms obtained from social connections could act as crucial information for pre-adopters in booking tourism accommodations, owing to their psychological uncertainty about staying at shared accommodations [19]. Finally, the results can be explained by the unique characteristics of motivations for pre-adopters, who are easily attracted by economic and social connection benefits [5, 28]. Thus, conducting the market segmentation becomes less useful than offering a lower price and word-of-mouth recommendations from their social connections.

There is still no explicit analysis offered on the issue of post-adopters’ willingness to afford extra expenses for some additional service they prefer or simply seek lower prices. The present findings reveal that post-adopters who have used this unique tourism accommodation usually have a stronger perception of its values in both functional and experiential aspects. This is because they tend to hear about more innovative information and realize greater shared accommodation values, service performance and so forth [37]. In other words, motivations for using shared properties are much clearer and in more detail, which echoes six clusters derived from different aspects of post-adopters’ preferences. Therefore, cheaper price is a valid strategy for money-savers, while for the rest five clusters, their willingness to afford extra expenses for some additional service is stronger.

The current study, therefore, explores the motivation evolution process from pre-adopters to post-adopters, which is vital to understanding how market segments change from three to six. Regarding price, shared accommodations are considered a cost-effective alternative to traditional accommodation options [38]. Pre-adopters tend to use social connection information sources to make booking decisions, offering more convenience to book rooms with greater credibility in providing both practical and experiential benefits. For post-adopters, shared accommodation characteristics could distinguish actual travelers effectively because of their growing level of knowledge. Therefore, their willingness to pay for the shared accommodation up-gradation increases. In this sense, six segments appear in post-adopters, which satisfy their preferences in more aspects. Post-adopters are classified in more detail, which can be employed to reduce competition by separating market segments. Among them, money savers and recommendation travelers are practical seekers; experiential seekers are classified as interaction, ethos and novelty seekers.; travelers who have fewer motivations for repeat usage are considered as well.

Theoretical implications

This study illustrates the importance of studying pre-adopters. Previous literature has indeed linked individuals at different innovation adoption processes with motivational and profile differences [5, 39], including a number of practical and experiential motivations [5] and different educational backgrounds, social status, degree of uncertainty as well as social networking [39]. This study offers a comprehensive classification of traveler types according to time of adoption, rooted in the Innovation Adoption theory represented by Rogers [14] and therefore divides travelers into pre-adopters and post-adopters. This study mainly focuses on the motivations of pre-adopters (refer to potential travelers) and post-adopters (refer to actual travelers). This study extends the issue that pre-adopters are different from post-adopters in using shared properties because it is still not clear if the findings from tourism and wider fields could be appropriate to the shared accommodation industry. Therefore, an important contribution of this study to the tourism market is emphasizing the motivation and market segments for pre-adopters.

This research provides the first comparative look at travelers’ heterogeneity for shared accommodations at the different adoption stages. Pre-adopters are likely to be more homogeneous with a high level of price-performance, as the factors that distinguished travelers previously become decreasingly relevant. However, in order to target post-adopters properly and reduce market competition, it is crucial to conduct the tourism segmentation among heterogeneous travelers.

This study also explores how the motivations of pre-adopters evolve into motivations of post-adopters in shared accommodations. The research contribution is bringing out a new research direction and method. That is, in the remaining tourism market segmentation studies, it’s necessary to discuss the evolution process from pre-to-post adopters. In this sense, this study provides a brand-new understanding of the motivation evolution process, which is also an innovative methodology for future tourism segmentation studies with similar research questions.

Practical implications

The study serves to help highlight overall marketing insights, to not only encourage more post-experience travelers to re-engage in the shared accommodation experience, but also to highlight to the pre-experience potential users of shared accommodations as to the other benefits—i.e. enhancing the marketing to broaden the appeal. Specifically, this research offers valuable guidelines for addressing the needs and preferences of specific target groups. The results reveal the importance of differentiated strategies for pre-and-post adopters to meet their motivations, improving their experiences, and thus leading to higher satisfaction levels.

Our findings provide a strong indication of the misalignment between provider expectations and consumer intentions, suggesting that further data-driven insights could increase the matching quality of the platform. We deem such an approach worthwhile because it might create awareness and point to blind spots, resulting in proactive interventions and improvements to make the sharing economy more equitable and enjoyable. Shared accommodations and similar home-sharing services providers are able to propound clear guidance of different accommodation types and appeal to hosts to foster positive experiences and customer satisfaction by framing their listings in a more targeted fashion.

Limitations

Despite the positive results of this study, limitations still exist: first of all, the respondents are located in Shanghai, so it is necessary to enlarge the sample size in order to have an overall understanding of the entire shared accommodation industry in China. In addition, 22 motivations are provided in this study for travelers to use shared accommodations, while constraints are required to be considered in future studies. Finally, motivations are likely to be changed over time [40]. A better understanding of the changes in motivation-based segmentation for travelers at different adoption phases is important to make effective tourism market strategies.

Future research

As a follow-up to this research that collected valuable data at the pre-COVID-19 stage, we plan to have a significant look at travelers’ motivation-based segmentation at the post-COVID-19 stage in order to make the updated tourism marketing strategies. Second, the constraints of travelers will be considered carefully. Last, it would be worth collecting pre-adopters’ planned travel expenses and post-adopters’ realized travel expenditures, and analyzing the proportion of shared accommodation expenses on overall travel expenditure.