Listening to online reviews: A mixed-methods investigation of customer experience in the sharing economy
Introduction
The past five years have witnessed the rapid growth of the sharing economy, which has encouraged the utilization of idle resources through the temporary transfer of usage rights [1]. Sparked by the growth of Airbnb and Uber, sales revenue arising from the sharing economy is projected to increase to 335 billion dollars by 2025, and companies operating in the sharing economy are expected to grow by 2133% in 12 years.1 In the United States, 83% of people are familiar with sharing services, and 56.5 million people have used sharing services.2 In China, the transaction scale of the sharing economy in 2019 reached 3282.8 billion yuan, and the number of participants was approximately 800 million.3 The sharing economy has been considered a “creative disruption” because its innovative business model connects strangers through collaborative consumption and value cocreation with the help of IT platforms [2]. In particular, the increasing connectivity via IT platforms stimulates customer demand, and service providers are pursuing new growth opportunities to attract and retain customers.
Peer-to-peer (P2P) accommodations, a business model of the sharing economy widely used in the accommodation sector, have gained popularity because customers can obtain a sense of community belongingness and experiences authentic to local customs in offline hospitality services through online bookings [3,4]. P2P accommodation involves a person renting his/her houses or rooms to another person in exchange for money, which is enabled by online platforms [5]. Unlike traditional hotels, two attractive features of P2P accommodations lie in social interaction and the low cost of quality accommodation [4]. Indeed, the emergence of P2P accommodations has transformed hospitality services and poses significant challenges to the hotel industry [2]. It is worth conducting an in-depth investigation into customer experience and behavior in the P2P accommodation sector.
Previous studies have focused on customers' participation motivation, customer experience, and use/purchase intention in P2P accommodations [6,7]. Evidence has shown that hedonic (adventure, gratification, sharing, and friend-seeking) and utilitarian motivations (e.g., cost savings, resource efficiency, and information acquisition) drive customers to participate in accommodation-sharing services [6]. Studies investigating customer experience have reported that customers value diverse service or product attributes at various accommodation-sharing levels [3,8]. Prior literature has demonstrated that customer trust, formed by host attributes, listing characteristics, and contextual elements, is a crucial factor affecting customer purchases [6,7]. Nevertheless, there is limited literature that gauges the antecedents of customer loyalty from the attitudinal and behavioral dimensions in choosing P2P accommodations. The mediation process through which customer experience affects customer loyalty is also underinvestigated. Due to the exchange of money in P2P accommodations, customers usually compare the encounter experience with their expectations and judge the value from what they “gave” and “gained” in this transaction [4]. Perceived value (e.g., value for money) is critical organism feedback because it mirrors the overall assessment of the customer encounter experience [9]. In addition, evidence has shown that “value” is a salient topic in P2P accommodations [9], which is driven by customer experience and at the same time shapes customer loyalty [10]. Accordingly, we aim to examine the mediating role of perceived value between customer experience and customer loyalty in the sharing economy context.
Customer experience, depicting how customers respond to staged encounters under a specific condition, derives from a set of interactions between a customer and the living environment, service providers, and other guests [11]. Experience clues shape customer experience, including functional and mechanic clues that are emitted by things and human clues that are emitted by people [12]. Customer experience involves the physical environment, defined as the tangible components of the physical experience (functional and mechanic clues), and human interaction, defined as intangible elements sensed from people (humanic clues) [13]. Previous research has posited that the physical environment and human interaction are the two manifestations that shape the customer experience in accommodation services [13,14]. A positive association between customer experience involving the physical environment and human interaction dimensions and perceived value has been found [[13], [14], [15]]. Nevertheless, the literature comparing the influence of these two dimensions on customer value has yielded mixed results [10,13]. Some scholars have found that the physical environment appears to be stronger than human interaction in affecting emotive and cognitive value [13]. However, other researchers have refuted this idea, arguing instead that customers value host hospitality and social interaction more than the physical environment in the accommodation-sharing economy [3,4]. These contradictory results call for further investigation on which dimension of customer experience exerts a more decisive influence on perceived value in P2P accommodations.
Achieving customer loyalty is an effective way for marketers to generate profit by retaining regular customers [16]. There are two types of customer loyalty: attitudinal loyalty and behavioral loyalty [17]. Attitudinal loyalty represents the commitment to the product, and behavioral loyalty refers to customers' repeated repurchases of a product [16]. Investigating the relationship between perceived value and customer loyalty has gained momentum, especially in experiential industries [14,15]. Most studies have focused on the perceived value-attitudinal loyalty link, and less attention has been given to the relationship between perceived value and behavioral loyalty [14]. In contrast to attitudinal loyalty, behavioral loyalty requires greater customer commitment and increased benefits [18]. In this study, we aim to investigate and compare the effects of perceived value on both attitudinal loyalty and behavioral loyalty.
Our research question is “what and how are the relationships among customer experience (i.e., physical environment and human interaction), perceived value, and customer loyalty (i.e., attitudinal loyalty and behavioral loyalty) in P2P accommodations?” This study is rooted in stimulus-organism-response (S-O-R) theory. The S-O-R framework emphasizes the role of an organism, which is triggered by environmental stimuli and stimulates a response [19]. The physical environment and human interaction dimensions are experience clues that can act as environmental stimuli during customer encounter experiences [13]. Perceived value is based on internal feedback because customers often make a value judgment that derives from their consumption experience. Customer loyalty, both attitudinal and behavioral loyalty, is the response formed by a customer's value evaluation of the experience. In addition, we also extend the S-O-R framework to compare the effects of different stimuli in affecting organism feedback and investigate whether organism feedback differs in influencing various responses.
To address our research questions, we integrate text mining with econometric analysis to systematically and deeply examine the relationship among customer experience, perceived value, and customer loyalty using 4166 listings and 40,459 customer reviews in Xiaozhu.com. Prior studies have investigated customer experience in P2P accommodations using text mining to extract customer opinions from the English context, including automatic cluster analysis and manual deduced analysis [3,8]. We provide a wealth of knowledge on dealing with unstructured data in the Chinese language using a mixed-methods approach of integrating latent Dirichlet allocation (LDA), machine learning algorithms, and dictionary-based sentiment analysis to summarize topic-specific sentiment. In terms of text mining, we not only efficiently process a large amount of text and make the machine accurately understand natural language but also conduct topic, entity, and relation extraction to obtain useful information [20]. First, to determine what dimensions the customer experience includes, we employ LDA to conduct topic modeling and identify the general topics. Second, we use machine learning algorithms to predict the topics at the sentence, review, and product levels. Third, we take advantage of dictionaries to perform sentiment analysis to extract topic-specific sentiment. To the best of our knowledge, this study is the first investigation that attempts to obtain topic-specific sentiments from unstructured text and link them to actual customer behavior toward P2P accommodation services using econometric analysis. We find that the perceived dimensions of the physical environment and human interaction shape customer experience, positively affecting perceived value. Interestingly, the physical environment is as crucial as human interaction for customer value judgment in P2P accommodations. We also find that perceived value is positively associated with attitudinal loyalty and behavioral loyalty. Notably, the positive effect of perceived value on attitudinal loyalty is more prominent than that on behavioral loyalty. Moreover, we provide strong evidence that the relationship between customer experience (i.e., physical environment and human interaction) and customer loyalty (i.e., attitudinal loyalty and behavioral loyalty) is mediated by perceived value.
Section snippets
The S-O-R framework
The S-O-R framework indicates that the link between environmental stimuli and behavioral responses is mediated by internal emotional feedback, such as pleasure and arousal [19]. The framework is applied in social commerce to explain the psychological processes of customers who encounter environmental stimuli in making purchase decisions [21]. In experiential marketing, the S-O-R model highlights the vital importance of experience clues related to product or service providers, determining
Dataset and samples
To identify what attributes of P2P accommodations customers value, we collected data from Xiaozhu.com, a leading Chinese online short-term rental. First, Xiaozhu.com, established in 2012, has recently experienced remarkable growth as an online accommodation-sharing platform in China. It now offers over 800,000 listings in more than 700 cities with approximately 50 million active users.4 Second, given the explosive popularity of the sharing
The influence of customer experience on perceived value
Table 6 presents the standardized regressions for perceived value and customer loyalty. Model 1 regarding the effects of customer experience on perceived value supports Hypotheses 1a and 1b. Specifically, the perceived dimensions of the PE (α1=0.276, p < 0.001) and HI (α2=0.248, p < 0.001) are positively associated with perceived value. The findings are in line with previous studies arguing that host hospitality and property characteristics are distinctive features of customer experience,
Findings
Based on the LDA model, we extract thirteen topics, including attitudinal loyalty (i.e., “repurchase intentions” and “the willingness to recommend”), perceived value (i.e., “enjoyment and pleasure” and “value for money”), PE (i.e., “aesthetics and comfort”, “amenities”, “surroundings”, “location and transportation”, and “safety facilities”), and HI dimensions (i.e., “reliability”, “hospitality and caring”, “professionalism”, and “social interaction”). By calling traditional machine learning
Conclusion
This research deepens our understanding of customer experience in the sharing economy by uncovering the subdimensions of HI and the PE. Our findings support the positive customer experience-perceived value-customer loyalty link. Moreover, we demonstrate no substantial difference between the PE and HI in affecting perceived value for Chinese guests. In addition, we find that the positive effect of perceived value on attitudinal loyalty is stronger than that on behavioral loyalty.
By integrating
Funding
This work was supported by the National Natural Science Foundation of China (Grant Number: 71874131) and Key Projects of Philosophy and Social Sciences Research at Ministry of Education of China (Grant Number: 20JZD024).
Declaration of Competing Interest
None.
Fuzhen Liu is PhD candidate in the Faculty of Business, The Hong Kong Polytechnic University. Her research interests include information system, sharing economy, online reviews, operations management, and social network. She received her M.S. degree in e-commerce from Wuhan University and now is pursuing her Doctor of Philosophy at The Hong Kong Polytechnic University. She has published papers in the Journal of Knowledge Management.
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Fuzhen Liu is PhD candidate in the Faculty of Business, The Hong Kong Polytechnic University. Her research interests include information system, sharing economy, online reviews, operations management, and social network. She received her M.S. degree in e-commerce from Wuhan University and now is pursuing her Doctor of Philosophy at The Hong Kong Polytechnic University. She has published papers in the Journal of Knowledge Management.
Kee-Hung Lai (Mike Lai) is a Professor at the Faculty of Business, The Hong Kong Polytechnic University. Dr. Lai has research interest in management information systems with publications appearing at Communications of the ACM, Communications of the AIS, Decision Support Systems, European Journal of Information Systems, Information & Management, Journal of Management Information Systems, etc.
Jiang Wu is a Professor and Associate Dean of the School of Information Management of Wuhan University, Wuhan, China. He obtained his Ph.D. from Huazhong University of Science and Technology. His research focuses on social networks, electronic commerce, business analytics, and informetrics. His research has appeared in journals such as the Journal of Informetrics, Scientometrics, Journal of the Association for Information Science and Technology, Journal of Knowledge Management, and Decision Support Systems.
Wenjing Duan is currently an Associate Professor of Information Systems & Technology Management at the School of Business, The George Washington University, Washington, D. C. Her research interests glide the intersections between Information Systems, Economics, and Marketing. Among her primary research interests are the social and economic impact of online consumer-generated content and social media, online communities and online social network, information systems and digital marketing, and healthcare and IT. She has published in MIS Quarterly, Information Systems Research, Communications of ACM, Journal of Retailing, Decision Support Systems, among others.