Engaging customers is a critical requirement for sharing economy platforms (SEPs) to sustain and grow their user base. Although the interactions between users who consume the service (customers) and those who provide it (peer service providers) are the primary source of SEPs’ economic value, little is known about the role of interactivity in driving customer engagement. This research links these two important concepts by theorizing and empirically testing the influence of different dimensions of interactivity (two-way communication, participation, joint problem-solving) on customer engagement (cognitive engagement, emotional engagement, behavioral engagement) in SEPs.

The purpose of this study is to explore the value structure of sharing economy for consumers, and to construct a model of consumers' continuoususe intentionin the sharing economy. Firstly, based on the characteristics of sharing economy platform technology, this paper explores the composition of platform value (time adaptability, location adaptability, content accessibility) through qualitative analysis. Then through quantitative analysis, this paper explores the influence of platform value on consumers' perception of emotional value and economic value. Finally, on this basis, this paper studies the impact of consumer value of sharing economy on consumer behavior, and compares and analyzes the impact of consumer value of sharing economy on consumers under different product types. In short, this paper aims to study the value creation of sharing economy from the perspective of consumption value, and shape its future development direction.

A sharing economy has emerged through today’s trust-building mechanisms, and a sharing economy is called a future economic model through a positive future market prospect. In this context, while the overseas sharing economic business is becoming a global trend, the domestic sharing economic business is busy following the global trend. The purpose of this study is to investigate the development direction of sharing economic business in Korea. First, the sharing economic cases of 50 oversea and domestic businesses were analyzed by time series analysis. Next, a cross-country analysis to analyze the business distribution and KCERN's sharing economic model through sharing economic cube model was conducted. Finally, profit model analysis through business case study and the relationship between the derived factors were investigated. As a result of the analysis, this study found comparative trends between overseas and domestic including differences in cultural and institutional environments and profit models. This study suggested directions for domestic sharing economy business.

Collaborative apparel consumption is proposed as more sustainable alternative to conven-tional consumption. The purpose of this study is the exploration of consumers’ motives to participate in collaborative apparel consumption. Findings suggest that consumers’ inten-tion to participate in collaborative apparel consumption is mainly influenced by financial benefits, convenience and sustainability awareness.

Introduction The idiom “you are what you own” has been considerably transformed into “you are what you can access”. The shift from ownership to access, the results of endless hyper-consumption, and the change in value mindsets initiated a new phenomenon, which is Sharing Economy (SE). SE has grown rapidly and refers to an entirely new business model, socio-economic ecosystem, and context for sharing the access to goods and services in technology-enabled peer-to-peer (P2P) ecosystems or consumer-to-consumer (C2C) engagement platforms. According to Botsman and Rogers (2010), SE is a result of the linkage between offline and online world, which was triggered by the society to overcome natural resources constraints. Resources in SE can be tangible (e.g., cars and homes) and intangible (e.g., expert local knowledge and labor). SE allows the sustainable use of idle resources, and it enables sellers to create new and flexible opportunities to market to consumers who experience personalized and even customized products and services at lower prices (Yang, Song, Chen, & Xia, 2017). SE opened up new horizons for a considerable number of new players across industries from a supply perspective by broadening the options for supply, which also remedies the response to peak demand. SE has given a rise to the humanization of consumer-supplier relationship in tourism and hospitality (TH), and sharing has become a mainstream practice in this context. The recent shift of customers‟ willingness to share accommodation with a host as opposed to using a private hotel room has many implications for TH (Lu & Kandampully, 2016). For example, Airbnb has become one of the most prominent competitors in hotel industry, and it enables people to lease or rent short-term accommodation including vacation rentals, apartment rentals, homestays, and even experiences via instant booking. From cash-strapped travelers to high-end business travelers, Airbnb has revolutionized the TH service in a new form of contractual relationship and gained a well-grounded popularity. Some scholars, on the other hand, argued that SE is a “fundamentally different business model” which could make it a new marketplace instead of a direct competitor in hotel industry. From this point of view, Airbnb do not compete or pose a challenge to traditional TH services but extend the concept of TH (Lu & Kandampully, 2016). Hotel industry has reactively responded to the direct, indirect, and induced effects of Airbnb to economy, and Airbnb‟s impact on hotel industry have recently been researched by several scholars (Mody, Suess, & Lehto, 2017; Priporas, Stylos, Rahimi, & Vedanthachari, 2017; Zervas, Proserpio, & Byers, 2017). Customer engagement in TH has been empirically found to enhance customers‟ service brand evaluation, brand trust, and brand loyalty (So, King, Sparks, & Wang, 2016). Guests attach great importance to motivational drivers, more meaningful “beyond-purchase” social interactions and unique experiences in authentic settings, which give rise to customer engagement beyond the service encounter. Disruptive innovation theory also states that products or services that offer alternative benefits compared to conventional attributes can transform a market and attain a critical mass, which can be observed in Airbnb‟s story (Young, Corsun, & Xie, 2017). Airbnb is successfully promoting the mottos of “Belong Anywhere” and “Don’t Go There. Live There” to their guests. The feelings of trust and belonging were negatively changed by mass production and noncustom travel experiences, and people sometimes forgot the meaning of community due to high tendency of prestige and advertising. Consumers‟ changing attitudes towards utilization and accessibility compared to ownership created an indirect need for intimate connection between people, namely human connection. Then, social concerns upon products and services gave a rise to mass-customized product and service expectations of consumers. This is where Airbnb‟s value proposition comes into play. First, it creates not only financial but also personal rewards through a “personal concierge” and a “home away from home” experience. Second, Airbnb is not a simple transaction, rather it is deemed to be a lifetime experience. So “guest experience” is at the heart of Airbnb‟s strategic position. From the “experience” point of view, SE has also opened up new rooms for service research. Service in the context of Airbnb is considered as an experience, rather than a utilitarian relation. Also, service quality has always been a critical factor in highly-competitive service industries like TH. Service quality perception is multi-faceted, and the studies focusing on it are rather limited, especially in hotel industry. These studies highlighted the complexities associated with evaluating service quality and the contribution of service process delivery on service outcomes, which results in the perception of service quality. Therefore, perceived service quality can be influenced by different internal processes and interpersonal variables. In order to study service quality in Airbnb, the types of settings in this context are to be noted. There are two main types of hosting via Airbnb: (1) remote hospitality, which refers to hosting situations in which the host does not physically share the place with the guest (e.g. booking the entire place), and (2) on-site hospitality, where the host is physically present and sharing the apartment with the guest. Mainly, on-site hospitality is an important part of the sociability within the host–guest relationship. Priporas et al. (2017) studied service quality in the context of remote hospitality, and we decided to respond to their relevant call for future research on the other type of Airbnb accommodation, which is on-site hospitality referring to “Shared Rooms” and “Private Rooms” in Airbnb‟s listings. “Shared Rooms” refer to an exact communal experience with the host, and guests sleep in a space that is shared with others and share the entire space with other people. “Private Rooms” refer to privacy, to some extent, in which guests (i) value a local connection (ii) have their own private room for sleeping and (iii) may share some spaces with others. We do expect that human connection and experience gap can be better researched with on-site hospitality existing in “Shared Rooms” and “Private Rooms”. This is because hosts design their services to create and build a relationship with their guests, leading to superior guest experiences and the so-called positive moment-of-truth. In addition to the online storytelling on hosts‟ home pages, the most important moment-of-truth is created during the guests‟ stay at the host‟s place; thus, the host plays a major role in the customer‟s perception of service and the subsequent review of the experience (Lu & Kandampully, 2016). Considering the previously mentioned “experience gap” in the literature, our research question and relevant sub-questions are as follows: • What are the antecedents of perceived service quality in Airbnb on-site hospitality? o How well does SERVQUAL suffice for measuring perceived service quality in this context? o How well the cognitive and attitudinal factors expand this measurement? o What are the additional perceived service quality factors that can be derived from guests‟ online textual reviews to remedy the “experience gap”? Literature review Cheng (2016) conducted a systematic review of SE by using co-citation and content analysis of papers, and the findings reveal three distinct research areas of SE: (1) SE‟s business models and its impacts, (2) Nature of SE, and (3) SE‟s sustainability development. Moreover, two unique areas, specifically in TH, were identified: (1) SE‟s impacts on destinations and TH services and (2) SE‟s impacts on tourists. The comparison of both literatures has revealed limited expansion in TH literature despite the fact that TH are at the frontier of SE (Cheng, 2016). Pesonen and Tussyadiah (2017) conducted cluster analysis to identify user profiles corresponding to consumer motivations for using TH services of SE. They concluded that a consumer group uses TH services of SE to make their trips more convenient, while another group uses these services mostly for social reasons. Yang et al. (2017) studied the loyalty in SE services from relational benefits perspective and concluded that confidence and social benefits have significant and positive effects on commitment in SE services. Molz (2014) introduced the term „Network Hospitality‟, which is relatively new and rooted in old traditions of welcoming strangers. Airbnb represents just one of many types of network hospitality, and in Airbnb, trust is almost shaped based on peer reviews, not solely on one-to-one peer interactions. The online review information becomes the basis for members‟ reputation in the network. The information accumulated on Airbnb‟s online platform helps both parties to establish their reputation, as well as publicizing their personalities, thereby facilitating the process of finding the best match. Moreover, there are hundreds of people working in Airbnb‟s customer service, trust, and safety departments who are devoted to ensuring the intimacy provision of trusted services. Airbnb requires all hosts to abide by their “Hospitality Standards”, which include expected levels of cleanliness, commitment, and communication. The flexibility, reliability, and consistency of Airbnb‟s service providers help them to build and maintain the relationship Airbnb enjoys with their guests and hosts (Lu & Kandampully, 2016; Zervas et al., 2017). Pine and Gilmore (1998) predicted the rise of experience in their seminal study, referring to the “experience economy” and also stating “As goods and services become commoditized, the customer experiences that companies create will matter most.” They called this as “Staging Experiences”. Also, there exists evidence in literature that providers are shifting their focus from product- and service-oriented to design of quality experiences. In terms of the glamour of SE in TH, a “more unique experience” is deemed to be second only to better pricing. Airbnb may eventually address all elements of the accommodation experience, from travel reservations to ticketing for local attractions. Consumers are looking for local authenticity in their travels. Psychological authenticity refers to emotional genuineness, self-attunement, and psychological depth (Walls, Okumus, Wang, & Kwun, 2011). If TH industry is to surpass its SE competition in terms of guest experience, it should leverage an expanded experience economy paradigm that incorporates additional dimensions (Mody et al., 2017). Authentic host-guest experiences probably only exist between like-minded and privileged members who possess high cultural capital (Cheng, 2016). With that, Walls et al. (2011) have suggested the need for researchers to identify specific dimensions “that exist in both our everyday and tourist experiences”. Both in Airbnb and traditional TH, guest satisfaction and likelihood to reuse are driven by similar factors such as quality and utility of services, trust to the host, and economic value. There are several models for measuring service quality, including SERVQUAL (Parasuraman, Zeithaml, & Berry, 1988) and SERVPERF (Cronin & Taylor, 1992). Service quality literature received widespread attention after the seminal work by Parasuraman et al. (1988) as they proposed the gap model and developed SERVQUAL (an attribute-based technique) as a tool for measuring service quality. According to SERVQUAL, service quality consists of five dimensions measured by a total of 22 items. The proposed five service quality dimensions are tangibles, reliability, responsiveness, assurance, and empathy. SERVQUAL basically requires measures of expectations and performance, and service quality is calculated from subtractions between these two components (i.e., performance [P] - expectations [E]). Regarding service quality in TH, Akbaba (2006) utilized SERVQUAL for business hotels, and Priporas et al. (2017) inquired SERVQUAL‟s applicability in Airbnb context with promising results. The major distinction between two research directions (i.e. hotels vs. Airbnb) is that even though guests expect similar core services such as clean rooms and comfortable beds, different attributes support the competitive advantage of hotels and Airbnb. While conveniences offered by hotels are unparalleled by Airbnb accommodation, the latter appeal to consumers driven by experiential and social motivations (Pesonen & Tussyadiah, 2017). Research model This research aims to identify the antecedents of perceived service quality of guests‟ in Airbnb on-site hospitality context. Our research model is presented in Figure 1, and it is subject to enhancement through the analytics of guest reviews. A survey will be developed to test the proposed research model. The items of constructs will be mainly derived from extant literature and enriched with the linguistic and textual analysis of reviews. Firstly, factors shaping expectation are predicted as per the literature and preliminary analysis of random guest reviews: (i) host‟s reputation capital (e.g., ratings and reviews), (ii) host‟s photos, (iii) guest‟s past accommodation experience, and (iv) word of mouth. Secondly, SERVQUAL part in the model is the same as proposed by Parasuraman et al. (1988). Last but not least, Airbnb is deemed to promote global geographical imaginaries (e.g., collaboration, social equity, solidarity, community, trust, reciprocity, altruism, autonomy, intimacy, and authenticity) to justify their business model (O‟Regan & Choe, 2017). Finally, regarding the guest review analysis, Airbnb has a detailed review mechanism, and we have gathered the publicly available reviews that are up to 500 words. We have observed cognitive and attitudinal dimensions within reviews through text analytics and grouped those in the research model as follows: • Intimacy: The emergence of intimacy as a commercial value in TH industry has been researched. (e.g., How well people know each other? How people occupy space together? How people share private information, family pictures, furniture choice etc.?) (Prager, 1997) • Authenticity: We focus on the existential authenticity (i.e., being one‟s true self or being true to one‟s essential nature) from guests‟ perceptions (e.g., Is Airbnb like ‘living the local life’?) (Lalicic & Weismayer, 2017) • Commitment: It refers to the consistent behavior of Airbnb hosts in terms of social and cost components. (e.g., How well hosts abide by Airbnb policies and procedures? Do hosts have ongoing effectiveness of service?) (Lu & Kandampully, 2016) • Privacy: It refers to the psychological zone to disclose personal and cultural values. Informational and physical privacy threats are important in Airbnb context (Lutz, Hoffmann, Bucher, & Fieseler, 2017). • Security: It refers to the state of being free from danger or threat. According to Yang and Ahn (2016), security in Airbnb‟s services is a more powerful antecedent of attitude toward Airbnb than significant dimensions of motivation toward SE, such as enjoyment and reputation. With that we will only elaborate on interpersonal security in Airbnb (i.e., between host and guest, not between guest and Airbnb). Conclusion SE is a fairly new and multi-disciplined field that covers open rooms for research, and specifically, Airbnb is one of the most prominent businesses in this context. The literature review presented underlies the infancy of well-grounded studies covering service quality perceptions of customers in SE. Seeking for additional dimensions from Airbnb guests‟ reviews is a novel research approach in studying customer engagement, and those dimensions shall be included in the research model. This research has certain limitations. Our perceived service quality conceptualization requires empirical validation to establish the boundaries of the construct. The guest reviews in Airbnb are subject to data quality issues. Also, reviews should contain substantial amount of words up to a certain threshold. Data collection from emerging world regions is rather tough since Airbnb is not widespread across those regions. Thus, US and European countries will be firstly taken into account, where the use of Airbnb is quite common. The study is expected to provide useful insights for TH practitioners and managers. It can underlie the factors that trigger customer engagement in this context. Cognitive/attitudinal factors are foreseen as the differentiators, which stand as the basis for service design and delivery.

For better understanding user behavior, especially exploring what factors would motivate user engagement in sharing economy and whether there are some differences between people behaviors in sharing economy and conventional economy, this research developed a conceptual framework of user engagement (UE) in sharing economy on the basis of customer engagement and related literature and tested it through empirical analysis.

Marketing in the sharing economy The shift on the enhanced complexity of customers‟ needs has created a new business model termed as the sharing economy emerging through the traditional B2B2C sector, and substituted with micro-entrepreneurs who act as service providers (Kumar, Lahiri, and Dogan, n.d.). The importance of the sharing economy is based on the fact that in a short period of time it has managed to disrupt well-established fields (i.e.: taxi and accommodation industry), through the provision of low-cost convenience without the ownership responsibility (Eckhardt & Bardhi, 2015). In general, the sharing economy service providers are not responsible for marketing and promotional aspects since this is an aspect taken care of the service enablers (i.e.: Airbnb). In the sharing economy, marketing needs to focus on the development of early adopters, meaning younger generations (Laciana & Rovere, 2011), since they are the largest generational cohort, and are expected to remain the largest one for the forthcoming decades (Fry, 2016), whilst they have a considerably lower spending capacity than older people (Henderson, 2016). Therefore, younger generations select cost-efficient options and engage in utility-based brand switching (Kumar et al., n.d.). As a result, sharing economy marketing strategies mainly focus on apps or websites where their existing customers may visit (McAlone, 2016). Moreover, multigenerational marketing is considered as a rational segmentation strategy for service enablers (Eckhardt & Bardhi, 2015), since the older a generation is the lower the general adoption rates in sharing economy marketing (Hall & Krueger, 2015). Still, the complex decision-making of consumers in sharing economy‟s marketing is affected by several factors such as price and quality issues and the associated risks (Pappas, 2017). Despite the importance of sharing economy in modern business, the literature is silent on the complexity of aspects affecting the related marketing activities. The paper examines the complexity of marketing activities formulation examining peer-to-peer (P2P) accommodation holidaymakers in Athens, Greece. It specifically evaluates the impact of risks, price and quality issues, and social aspects, on P2P accommodation marketing activities, also including the socio-demographics of age and income. The research contribution is in both, theoretical and methodological domains. In terms of literature the study provides an understanding of the complexity formulation of marketing activities, with special reference to the sharing economy. Methodologically-wise, the research implements fuzzy-set Qualitative Comparative Analysis (fsQCA), which is considered new to the study of tourism and hospitality (Pappas & Papatheodorou, 2017). It further compares fsQCA suitability with regression, which is the dominant correlational mode of analysis. Study tenets In service industry research the term „tenet‟ is used to describe testable precepts able to identify complex conditions (Papatheodorou & Pappas, 2017). This study has formulated six tenets: (T1) The same attribute has the ability to determine a different decision for marketing activities depending on its configuration with other attributes (T2) A complex configuration with at least two simple conditions can leads to an outcome condition that can have a consistently high score (Recipe principle) (T3) Complex configurations can influence the marketing activities for P2P holidaymakers (T4) When the combinations differ on the simple conditions of configurations, they can influence in a positive or negative manner the marketing activities for P2P holidaymakers (T5) Sufficient marketing activities do not always result in a high outcome score (Equifinality principle), and (T6) When the Y scores are high, a given recipe for the marketing activities is not relevant for all cases. Complexity in tourism Complexity theory focuses on complex systems with nonlinear dynamics, characterised by self-organisation, emergence, and evolution (Arévalo & Espinosa, 2015). The theory is used to evaluate the nonparametric, and dynamic processes of complex phenomena in several different disciplines (Olya & Al-ansi, 2018). Tourism complexity is based on several conflicting elements, such as the translocal relationships and multilocality, the heterogeneity of actors, the places and governance globalisation, and the extreme diversity of operations (Darbellay & Stock, 2012). Moreover, tourism deals with complex policies involving multiple actors, and a perpetually changing multi-level coordination in a local, national and international level (Lai, Hsu, and Wearing 2016). The degree of behavioural complexity renders Newtonian (linear) thinking inadequate and highlights the necessity for nonparametric (nonlinear) research (Laws & Prideaux, 2005). Method The study was held in Athens, Greece in adult P2P accommodation holidaymakers. Following the study of Pappas (2017), structured questionnaires were distributed to the P2P rentals, asking from the holidaymakers to fill them in during their stay. As Akis, Peristianis and Warner (1996) suggest, the study‟s sample size should have a minimum of 95 percent level of confidence and a maximum of 5 percent statistical error, whilst the most conservative response of 50/50 (meaning half of the respondents would express positive views and the other half negative ones) was adopted. For N>20, t-table defines cumulative probability (Z) in 1.96 level. Following Akis et al. (1996), the sample size calculation is: Rounded to 400 In total, 712 useful responses were collected, generating a statistical error of 3.67 per cent. The questionnaire consists of 24 Likert scale statements adopted from previous research, including two socio-demographic questions (age; income). The study employed fsQCA for the evaluation of complex configurations. fsQCA is considered a mixed method since it employs quantitative testing and qualitative inductive reasoning, and it is able to examine the potential complex relationships that have a bearing upon the outcome of interest, and identifies combinations of binary sets generated from its predictors. Since the research also estimated negated sets (presence or absence of a simple condition), the symbol “~” was used for the indication of an attributional absence. Research calibration was made by using 42 randomly selected individual cases. For the evaluation of the marketing activities „f_ma‟ affecting holidaymakers, the calibrated fuzzy-sets used were „f_a‟ for age, „f_i‟ for income, „f_r‟ for risks, „f_sa‟ for social aspects, „f_pi‟ for price issues, and „f_qi‟ for quality issues. Results Three sufficient configurations emerged from the research. More specifically, the first solution (f_a*f_i*~f_r*~f_sa*f_pi*f_qi) concerns the price-quality nexus, the second configuration (f_a*~f_i*f_r*~f_sa*f_pi*~f_qi) deals with price sensitivity, and the third one (f_a*~f_i*f_r*f_sa*~f_pi*f_qi) focuses on social interaction. The generated solutions for marketing activities are presented in the table below. The results indicate that all four simple conditions appear in at least one solution (T1), whilst at least two simple conditions are included in each sufficient configuration (T2). Moreover, the findings suggest that the solutions focus on: (i) price-quality nexus (ii) price sensitivity, and (iii) social interaction (T3). In addition, none of the simple conditions appears in all configurations (T4), and three different solutions seem to lead to the same outcome (T5). Finally, the coverage varies from .429 to .453, meaning that none of the solutions applies in all cases (T6). As a result, the findings confirm the six tenets of the study. fsQCA versus regression The study used a structural equation model for the examination of linear relationships, and implemented Confirmatory Factor Analysis (CFA), since the sum of the examined items is based on previous analytic research. Due to the large sample (N=712) χ2/df instead of χ2 was selected, since it is considered as a better estimate of goodness (Chen & Chai, 2007). Following Kline (2010), the research estimated the four most important fit indices: χ2=634.921, df=352, χ2/df=1.803 (acceptable value 0≤χ2/df≤2 [Schermelleh-Engel, Moosbrugger & Müller, 2003]), CFI=.902 (acceptable value is when CFI is close to 1.0 [Weston and Gore 2006]), SRMR=.782 (acceptable value is when SRMR<.8 [Hu & Bentler, 1999]), and RMSEA=.475 (acceptable value is when RMSEA<.5 [Browne & Cudeck, 1993]). In factor analysis, all values less than .4 were suppressed (minimum acceptable value .4 [Norman & Streiner, 2008]) in an effort to evaluate higher coefficients. In all constructs, the Average Variance Explained (AVE) was higher than .5 (minimum acceptable .5 [Kim, 2014]), and the convergent validity (CR) higher than .7 (minimum acceptable value: .7 [Huang, Wang, Wu, & Wang, 2013]). The Figure below explains the study‟s endogenous variables. The comparison of fsQCA with regression highlights that the latter cannot encapsulate the full range of alternative combinations, in restricts the presence/absence of a construct or socio-demographic in one outcome, whilst the row coverage in all sufficient configurations (also showcasing high consistency) is higher than the overall R2 (.393). As a result, fsQCA seems to be more efficient than regression concerning the examination of marketing activities on P2P holidaymakers, since it better presents the influence of the constructs under examination. Managerial implications The study offers a number of managerial implications. For starters, through the use of fsQCA, traditional accommodation providers and destinations can better understand complexity aspects of consumer trends, being able to sufficiently reposition their marketing activities. Moreover, fsQCA can assist on the clarification of the factors affecting marketing complexity in tourism and hospitality, and better promote and advertise the products and services in reference. The understanding of complex marketing patterns, can further lead to the formulation of competitive advantages and strengthen the competitiveness of the enterprises engaged in a destination, as well as the destination itself. In addition, destinations can better comprehend the complex evolution of sharing economy and build upon its strengths, aving the opportunity to formulate a cooperative market towards traditional establishments and P2P rentals. Limitations Despite the theoretical and methodological contribution of the study, several limitations need to be highlighted. The main limitation derives from the study‟s main strength, which is the limited application of fsQCA in tourism and hospitality. Much further use of fsQCA in the field could reveal its full potential. Another limitation deals with the examination of other groups, such as the holidaymakers selecting traditional establishments for their stay, P2P stakeholders, and destination authorities. Since different groups of respondents may produce different outcomes, any generalisation of the findings should be made with caution. Finally, if the research is repeated in some other destination or in later time, the focus of the generated complex configurations may alter. Therefore, the results should be carefully interpreted.

The dual processes of cognition and affect impact consumers’ choice of sharing economy-based experiential tourism. Prior knowledge of sharing economy technologies and favourable attitude toward sharing economy impact consumers’ intention to adopt a peer-to-peer experiential tourist service. Data from 150 respondents and OLS and OLogit analyses supported the hypothesis.

Ever since the notion of a sharing economy was highlighted by Time Magazine as one of the ten ideas that will one day change the world, there has been a significant increase in scholarly attention dedicated to investigating the impact sharing economies will have on individuals, organizations and society as a whole. Particularly, sharing economy has revolutionized the landscape of the tourism industry through Airbnb (Fang, Ye, & Law, 2016). Academic research has focused on studying consumption practices and behaviors from a recipient perspective, but relatively little attention has been given to understand what impacts sharing economy has, from a sharer point of view (Fagerstrøm, Pawar, Sigurdsson, Foxall, & Yani-de-Soriano, 2017). Specifically, the existing literature has yet to explore in what ways and to what extent sharers are engaged with sharing economy platforms, and explore what consequences accrue from engaging in sharing economy activities. As a result, this paper seeks to fill this gap by proposing a framework drawing on self-determination theory (SDT), in conjunction with a tourism well-being perspective to examine how sharers’ perception of extrinsic rewards indirectly influences their well-being through the mediating role of engagement in tourism sharing economy activities. Data from Airbnb hosts in London were analyzed through PLS. The findings show that hosts’ engagement with Airbnb fully mediates the relationship between extrinsic rewards and their wellbeing. In other words, extrinsic rewards from using the sharing platform have a positive effect on sharers’ engagement in sharing economy activities (Tussyadiah & Pesonen, 2016; Guttentag, 2015). In turn, engagement in sharing economy activities has been found to have a positive effect on the sharers’ well-being (Kim, Uysal, & Sirgy, 2013; Ganju, Pavlou, & Banker, 2016). The paper has some important managerial implications.

Introduction In order to solve the information asymmetry and make full use of resources (unused goods, spare capacity and so on), the sharing economy, which based on the application of mobile internet technology and focused on the consumption of the right to use, is developing rapidly. Lack of trust is considered to be one of the important problems hindering the development of sharing economy. Möhlmann (2015) said that trust is a fundamental determinant of sharing economy development and ensuring shared satisfaction. At present, sharing economy enterprises have established product evaluation system and docked a third party credit rating system (i.e. Zhima Credit) to promote consumers’ trust. However, with most enterprises are becoming more and more consistent in evaluation and credit rating system, there is a new problem emerged: how to further deepen consumers’ trust in order to promote consumers’ intention to participate continuously (Zucker, 1986; Lewis & Weigert, 1985). Granovetter (1985) mentioned that trust is the product of social relationships. Botsman and Rogers (2011) made it clear that social networks play an important role in building trust in sharing economy. And people are always more willing to accept the opinions of people who have social relations with them (Lu, Zhao, & Wang, 2010). Hence, with the feature of participant of everyone in sharing economy, it is necessary to further strengthen consumers’ trust through the social interaction among consumers and between consumers and sharing platforms. Theoretical Development Sharing economy is based on LBS and other new technologies to realize the matching of supply and demand information across time and space. And to a certain extent, it solves the problem of information asymmetry in the era of e-commerce. Yet consumer have new requirements for product information. For example, consumers are more concerned about whether the utility of products and services can meet their needs (i.e. some consumers are more concerned about when to reach their destination than the performance of the car). But it is difficult for company to understand each everyone of consumers’ utility preference and publish product utility information based on these preferences (Xie & Gerstner, 2007). Because this kind of utility preference has the characteristic of timeliness and personalization. Therefore, consumers’ perceived uncertainty about the utility of the product will also affect the consumers’ trust. In summary, we then address these research questions:(1) can the promotion of trust between consumers and sharing platforms be achieved through the socializing of sharing platforms and then affect consumers’ intention to participate continuously; (2) if so, which type of privacy protection method and social information can promote consumer trust; and (3) does the impact of sharing platform socialization on consumer trust vary according to consumers’ concerns about the perceived utility uncertainty of the product. Research Design We then address these issues using experiment data. The first experiment was a 2 (social media: have, not have) x 2 (individual trust propensity: trust, not trust) between-subject design on the intention to trust sharing platform and participant continuously. Perceived utility uncertainty was added to this design as a moderator. Moreover, the second experiment was a 2 (social information type: hedonic, utilitarian) x 2 (degree of privacy protection: week, strong) between-subject design on the intention to trust sharing platform. Our scales come from existing scales and optimize it according to the characteristics of sharing economy (Schneider, 1999; Dhar & Wertenbroch, 2000; McKnight, Choudhury, & Kacmar, 2002). Results and Conclusion The results of the first experiment show that whether or not the social platform has no significant impact on the consumer trust if the individual tends to trust others. And for individuals who tends to do not trust others, they are more likely to trust socialized sharing platforms. The higher the consumer perceived utility uncertainty is, the higher (lower) trust of the consumer platform in the socialized (not socialized) sharing platform. For the second experiment results, in the context of sharing economy, the utilitarian information has a greater positive impact on the consumer trust than the hedonic information. In addition, the stronger the privacy protection of sharing platforms, the higher the trust of consumes. This study makes several academic contributions. First, we extend the topic of socialization to sharing economy context. Second, we add the perceived utility uncertainty as a moderator, and supplement the role of perceived uncertainty. This research also provides several practical implications. First, sharing platform can promote their consumers’ trust by establishing their own social platform. This platform can be used as a differentiated competitive strategy for sharing platform. Second, sharing platform should guide consumers to share utilitarian information (i.e. weather for specific place or real time traffic) to improve consumer satisfaction. Third, sharing platform can access consumer preference information through this social platform for further product development and classification.

Introduction Recent years have witnessed a rapid growth in sharing service businesses. In B2C sharing-service businesses such as Zipcar, customers share goods and services provided by a service firm with other customers and perform the roles played by service employees in traditional service businesses. Consequently, how well one customer carries out expected tasks influences the quality of service provided to other customers. Extant studies have emphasized the importance of a governance system to prevent such a social dilemma as the personal interest of an individual being pursued at the sacrifice of the interest of the community. However, few studies have empirically examined the effect of different design of a governance system. To fill this gap in the research, this study examines the framing effect of customer messages on customer intention to cooperate by complying with the request by the firm. Theoretical development For customers to be willing to cooperate with a firm, they have to be first motivated to do so. The framing effect of on customer motivation has been well demonstrated (Ganzach & Karsahi, 1995) in diverse service contexts such as healthcare (Christensen, Heckerling, Mackesy, Bernstein, & Elstein, 1991), education (Fryer Jr, Levitt, List, & Sadoff, 2012), and consumer behaviors (Ganzach & Karsahi, 1995; White, MacDonnell, & Dahl, 2011) In the context of service consumption, motivation is defined as the inner driver that triggers an individual to cooperate with the service providers (Tsai, Wu, & Huang, 2017). Whether messages were framed as a gain versus a loss exerted a significant impact on consumer motivation. In the consumer behavior contexts, consumer reactions to frames were mixed (Ganzach & Karsahi, 1995). In this study, we will examine the framing effect in the context of sharing service consumption. H1: In the B2C sharing service context, the framing of customer message (gain vs. loss) will affect customer motivation to comply with the request of the service firm. Customers tend to behave different depending on the value they pursue through consumption (Hwang & Griffiths, 2017). Even in the same consumption context, values of customer pursuit can vary widely. Hence, we intend to examine the moderating effect of customer value perception of sharing service on the effect of message framing on motivation. Studies showed that customers pursuing utilitarian values consider monetary savings and convenience as important, while customers pursuing symbolic value consider status and self-esteem as important and those pursing hedonic values consider entertainment and exploration as important (Rintamäki, Kanto, Kuusela, & Spence, 2006). The framing effect was shown to differ by the emphasized value of the product in the context of advertisement. A gain frame was more effective than a loss frame when the ad highlighting the hedonic attributes of a product while a loss frame was more effective when the ad stressing the utilitarian attributes of the product (Lin, 2007). Taken together, we conjecture that customers pursing different values will react differently to the same frame of messages and the level of motivation triggered by the same message frame will also differ. H2: Customer value perception of sharing service will moderate the framing effect of customer messages (gain vs. loss) on motivation. Specifically, customers pursuing utilitarian values will react more strongly to the messages framed as a loss (H2a), while customers pursuing hedonic or symbolic values will react more strongly to messages framed as a gain (H2b). The effect of motivation on customer behaviors and behavioral intentions have been well demonstrated (Ganzach & Karsahi, 1995). In the service context, customer cooperation behaviors induced by motivation significantly influence the efficiency of service operations (Mills & Morris, 1986). We propose that the motivation enhanced by customer messaged influence customer willingness to cooperate. H3: In the B2C sharing service context, motivation affects customer willingness to cooperate. Methodology Data will be collected from American consumers who have used a car sharing service at least once in the past one year through an online scenario-based survey using a 2 (message frames: gain vs. loss) x 3 (values of sharing service: utilitarian vs. hedonic vs. symbolic) between-subject experimental design. Hypotheses will be tested by an analysis of variance and a regression analysis. Implication The findings of this study will help P2P service firms better design customer messages in inducing customer cooperation and how to customize the design by customers’ value perceptions of sharing service.

Introduction In order to solve the information asymmetry and make full use of resources (unused goods, spare capacity and so on), the sharing economy, which based on the application of mobile internet technology and focused on the consumption of the right to use, is developing rapidly. Lack of trust is considered to be one of the important problems hindering the development of sharing economy. Möhlmann (2015) said that trust is a fundamental determinant of sharing economy development and ensuring shared satisfaction. At present, sharing economy enterprises have established product evaluation system and docked a third party credit rating system (i.e. Zhima Credit) to promote consumers’ trust. However, with most enterprises are becoming more and more consistent in evaluation and credit rating system, there is a new problem emerged: how to further deepen consumers’ trust in order to promote consumers’ intention to participate continuously (Zucker, 1986; Lewis & Weigert, 1985). Granovetter (1985) mentioned that trust is the product of social relationships. Botsman and Rogers (2011) made it clear that social networks play an important role in building trust in sharing economy. And people are always more willing to accept the opinions of people who have social relations with them (Lu, Zhao, & Wang, 2010). Hence, with the feature of participant of everyone in sharing economy, it is necessary to further strengthen consumers’ trust through the social interaction among consumers and between consumers and sharing platforms. Theoretical Development Sharing economy is based on LBS and other new technologies to realize the matching of supply and demand information across time and space. And to a certain extent, it solves the problem of information asymmetry in the era of e-commerce. Yet consumer have new requirements for product information. For example, consumers are more concerned about whether the utility of products and services can meet their needs (i.e. some consumers are more concerned about when to reach their destination than the performance of the car). But it is difficult for company to understand each everyone of consumers’ utility preference and publish product utility information based on these preferences (Xie & Gerstner, 2007). Because this kind of utility preference has the characteristic of timeliness and personalization. Therefore, consumers’ perceived uncertainty about the utility of the product will also affect the consumers’ trust. In summary, we then address these research questions:(1) can the promotion of trust between consumers and sharing platforms be achieved through the socializing of sharing platforms and then affect consumers’ intention to participate continuously; (2) if so, which type of privacy protection method and social information can promote consumer trust; and (3) does the impact of sharing platform socialization on consumer trust vary according to consumers’ concerns about the perceived utility uncertainty of the product. Research Design We then address these issues using experiment data. The first experiment was a 2 (social media: have, not have) x 2 (individual trust propensity: trust, not trust) between-subject design on the intention to trust sharing platform and participant continuously. Perceived utility uncertainty was added to this design as a moderator. Moreover, the second experiment was a 2 (social information type: hedonic, utilitarian) x 2 (degree of privacy protection: week, strong) between-subject design on the intention to trust sharing platform. Our scales come from existing scales and optimize it according to the characteristics of sharing economy (Schneider, 1999; Dhar & Wertenbroch, 2000; McKnight, Choudhury, & Kacmar, 2002). Results and Conclusion The results of the first experiment show that whether or not the social platform has no significant impact on the consumer trust if the individual tends to trust others. And for individuals who tends to do not trust others, they are more likely to trust socialized sharing platforms. The higher the consumer perceived utility uncertainty is, the higher (lower) trust of the consumer platform in the socialized (not socialized) sharing platform. For the second experiment results, in the context of sharing economy, the utilitarian information has a greater positive impact on the consumer trust than the hedonic information. In addition, the stronger the privacy protection of sharing platforms, the higher the trust of consumes. This study makes several academic contributions. First, we extend the topic of socialization to sharing economy context. Second, we add the perceived utility uncertainty as a moderator, and supplement the role of perceived uncertainty. This research also provides several practical implications. First, sharing platform can promote their consumers’ trust by establishing their own social platform. This platform can be used as a differentiated competitive strategy for sharing platform. Second, sharing platform should guide consumers to share utilitarian information (i.e. weather for specific place or real time traffic) to improve consumer satisfaction. Third, sharing platform can access consumer preference information through this social platform for further product development and classification.

consumption, has been attempted to replace with sharing economy which is consumer-to-consumer’s activity of obtaining, giving, or sharing the access to goods and services (Hamari et al., 2016). Research argued that the sharing economy develops based on information and communication technology (ICT) as it is an emerging economic-technological phenomenon, proliferation of using social networking site (SNS) (e.g. Instagram, Facebook, YouTube), and increased consumer awareness (Belk, 2014; Hamari et al., 2016). SNS refers to a digital environment that allows individual to create his/her space where sharing and constructing relationship with others are possible (Lin & Lu, 2011). Among the numbers of SNSs, Instagram is gaining notable attention as powerful marketing tool which may especially be important for fashion industry. Hutchins (2017) reported that number of its daily active users exceeds 400 million, and 90% of users are under 35 who may be familiar with the idea of the sharing economy. In terms of marketing, 53% of Instagram users are found to follow their favorite brands (Hutchins. 2017). According to Ryan and Deci (2000), consumer behavior of using information technology such as Instagram was influenced by extrinsic and intrinsic motivations. Although numerous researches have done with the sharing economy and SNS respectively, despite the recognized role of SNS in the sharing economy, comprehensive and empirical study of the sharing economy and SNS is very limited. Hence, the purpose of this research is to investigate consumer’s motivations to use Instagram for participating sharing economy by developing research framework based on the motivation theory (Ryan & Deci, 2000) perspective.

A free-market economic system supported by the progress of the Industrial Revolution 4.0 has given birth to a sharing economy with a disruptive business model. In many ways, this business model is more effective, efficient, and makes it easy for businesses and consumers. However, because disruptive innovation is not asymmetrical with the conventional business that sustains innovation, several regulatory issues arise because it is fundamentally very different and cannot be regulated by standard law. Disruptive innovation may create chaos if it is regulated by norms that are used to regulate conventional business. This research was conducted with a normative method, which examines various theories, principles, laws and regulations to get justification for how the law should govern. The findings of this study are: competition law must be designed pragmatically so that it can keep pace with changes in business models that are rapidly changing. For this reason, it is necessary to shift regulatory authority from the Government to business people to make self-regulation, as a rule, that was born from the agreement of the business actors themselves. Self-regulation is considered more effective in maintaining fair competition, so that the market will be more dynamic, and consumers will be more prosperous.

Purpose - Previous studies examined effects of sharing economy in the fields such as accommodation and automobile sector, while there are lack of researches in the field of skill-sharing economy. By classifying skill-sharing into general and special skill-sharing, this study explored effects of variables such as transaction utility, social utility, sustainability utility, emotional utility, economic utility, and trust utility, on attitudes, intention, satisfaction, and loyalty of demand (i.e., customers) and supply (i.e., providers) sides, potential, and actual customers. Research design, data, and methodology - Data were collected via both online and offline surveys. This study applied factor analysis and multiple regression analysis for findings. Results – Results show that utilities for general suppliers’ skill-sharing are significant than other cases. Among utilities, this study found that trust utility shows significant for the cases of special customers’, general suppliers’ and special suppliers’ potential skill-sharing. The results implies that trust is crucial in the transaction of the sharing economy. Conclusions – Enhanced managerial systems help resolve issues on the sharing economy. This study provides implications what are positive effects of skill-sharing economy and recommends proper establishment of the sharing economy.

Purpose - The purpose of this study is to grasp the concept, characteristics and application status of sharing economy, and to derive a research model based on sharing economic service, and to analyze factors and influences of consumers' intention to reuse of sharing economy. Research design, data, and methodology - The questionnaires were created to examine variables for practical and theoretical implications. After pilot survey, conducted for 24 days from March 10th to April 2st in 2017, total numbers were 377. But 330 copies were used for the analysis with IBM SPSS Statistics 23.0 and IBM SPSS AMOS 23.0. The structural equation model was applied for this. Results - First, sharing economic services remain at an early stage, but it is meaningful to identify the revenue mechanism of the business model of the sharing economic platform. Second, in this study, it is meaningful that we systematized the theoretical structure by examining existing studies on the characteristics of the sharing economic service and consumer characteristics, and by examining empirically. Third, Satisfaction and Reliability are related to the characteristics of Sharing Economic Service (Security, Convenience, Discount, Sharing, Social Interaction), Consumer Characteristics (Personal Innovation, Word-of-Mouth) It is meaningful to broaden the understanding of the factors by verifying the mediating effect. Fourth, the sharing economy business is meaningful in that it is a new consumption trend that changes the meaning of consumption to consumers. Gradually, more and more people are recalling that purchasing something is not consumption, but sharing and borrowing is also consumption. In other words, through the sharing economy, consumers can experience more products and services, have more choices, and are expected to have a positive impact on economic growth by increasing the utilization of idle resources. Conclusions - Currently, the sharing economy is growing rapidly all over the world. Therefore, in the subsequent study, it is necessary to compare Korea and China's sharing economy and study the cultural and social characteristics of Korea and China. In particular, I think that steady research is necessary for more precise and specific direction on the influence of the shared economy.