A study on the impact of host's personalized offline services and platform ease of use on shared homestay consumers' purchase intention

Abstract

전통적인 경제 모델과 달리 공유 경제 모델에 있어 "공급"과 "소비"의 관계에서 가장 큰 변화는 공급자가 비표준화 된 개인 공급자가 되어 다양한 개인화 서비스를 실현할 수 있다는 것이다. 반면에 개인 서비스 제공 업체에 대한 신뢰 문제도 있을 수 있다. 본연구에서는 에어비앤비를 연구대상으로 공유 홈스테이 모델에 따른 개인화 서비스와 편의성이 소비자의 구매 의도에 어떤 영향을 미치는지와, 플랫폼 신뢰를 형성하는 소비자의 혁신적 성향의 조절역할을 조사한다. 연구결과, 공급자의 개인화 서비스와 편의성은 공유 경제 플랫폼에 대한 소비자의 신뢰와 지각된 가치에 대한 매개 효과를 통해 소비자의 최종 구매 의도에 긍정적인 영향을 미쳤다. 또한 소비자의 혁신적 성향은 지각된 가치와 플랫폼 신뢰에 영향을 미치는 개인화 서비스과정에서 긍정적인 조절역할을 하였다.

Different from previous studies, this study focuses on accommodation providers' personalization services and platform convenience variables, identifying how these prior factors affect perceived value and trust in accommodation services on a shared homestay platform, and how consumers' innovation plays a role in the process. Through this, we would like to identify the mechanism of interaction between accommodation service providers and consumers mediated by the shared homestay platform and present implications for a more customer-centered platform operation strategy. This study has an extended meaning for prior research, empirically confirming that the increase in personalized offline service quality of personalized hosts in shared economic models has a positive impact on perceived value and platform trust of consumers. At the same time, we confirm that under the shared economy model, consumers' innovation propensity plays an important positive role in regulating their perceived value aspects as well as their confidence in the platform.

I. Introduction

Currently, there are many for-profit companies around the world that share homestays. Typical examples include Airbnb in the U.S. and Tujia in China. Among them, Airbnb is a purely shared service platform that provides only P2P services. In addition to providing P2P services, Tugia is an integrated model of shared services and existing services that provide standardized products operated on the platform. The success of these companies is not only because the sharing economy provides lower-priced products and services, but also because of the new interactions between suppliers and consumers formed under the shared economy model. As Matofska (2015) reported, the basis of a shared economy is not commodity prices, but relationships between social groups.

Starting with the most fundamental differences between the shared economy model and the existing model, this study studies the impact of these differences on consumers' purchasing decisions. The main difference between a shared economy and a traditional economy is that most of the providers of shared products and services are individuals. In traditional capitalist models, suppliers tend to offer standardized goods rather than personalized goods for greater profit, but with the advent of the shared economy, suppliers' quantities and diversity increase significantly at relatively low prices, allowing various businesses to provide nonstandardized personalized services.

Shared homestays, for example, unlike standardized room service in existing hotels, accommodations are provided by different individuals, so more personalized services can be provided to customers, such as cooking, pet companionship, smoking, and preparing special items. Ahn Sung-sook (2018) argues that the shared economy model is an on-demand model that meets individual needs. Kang Ji-won (2017) believes that guests can not only filter their special needs on more specific conditions in the app, but also communicate with landlords, such as leaving messages on the platform and sending e-mails, so that personalized services can improve consumers' perceived value. And research by Woo Kyung-jin (2010) showed that personalized services in the online travel and lodging industries have a positive impact on consumers' perceived value. Based on this prior study, this study noted that various personalized services provided by individual home providers on shared homestay platforms are one of the important variables that increase perceived value and trust of shared homestay consumers and ultimately increase purchase intent.

In a recent study on the shared economy, many scholars are paying attention to the importance of consumers' Innovation tendency in the process of influencing their intention to participate in the shared economy. According to Jeon Jong-geun (2017)'s study, consumer innovation plays a role in reducing perceived risk for car sharing, and Choi Chul-jae (2018) also argued that customer innovation is controlled in the process of influencing loyalty through customer satisfaction and trust.

Unlike previous studies, this study focuses on accommodation provider personalized services and platform ease variables, identifying how these antecedents affect perceived value and trust in accommodation services on shared homestay platforms, and studying how consumer innovation tendencies play a role in the process. Through this, we aim to identify the mechanism of interaction between accommodation service providers and consumers connected through shared homestay platforms and present implications for a more customer-centered platform operation strategy.

II. Theoretical background

1. Host's Personalized Offline Services

According to Woo Kyung-jin (2010), personalized services are for companies to identify individual customers' needs and provide customized products, services, and information. Kang Ji-won (2017) demonstrated that personalized services affect perceived benefits, perceived risks, and brand immersion through research, claiming that consumers can communicate with product or service providers in advance using online tools.

You Haihua (2016) considered shared homestay host personalized offline services relative to hotel standardized services and argued that there are three characteristics: 1)personality-accommodation hosts are all individuals; 2)unique-different buildings or facilities; 3)consumer personal needs satisfaction. Su Tao (2010) argued that personalized services in the lodging industry mainly have five differences in service differences, owner's activities, interaction with customers, emotional exchange, and more basic service provision than standardized services.

This study conducts a host-related survey to consumers that provides real-world accommodation services on a shared homestay platform. With reference to prior research, we introduce key selection criteria related to offline real-world accommodation services.

2. Consumer's Innovation tendency

Midgley and Dowling (1978) defined innovative tendencies as sensitive and innate to new ideas and introduced the concept of innate innovation associated with them. This has greatly influenced the study of various decisions and behaviors of consumers in future studies. Hirschman (1980) pointed out that seeking novelty is an important variable that influences consumers to adopt new products.

Joseph and Vyas (1984) used the term open-processing Novelty and interpreted innovative tendencies in a cognitive style, describing concepts related to individual intelligence, perception, and attitude characteristics. He also argued that cognitive style influences how consumers react to new products, new emotions, new experiences, and new communications. In other words, people with a cognitive style of "open mind" have seen themselves more open to new experiences and argue that this is why they always seek novelty.

Looking at other studies of onsumer's Innovation tendency, Hirschman (1980) understood them as "somewhat different" that all consumers have, and attributed them to different attitudes toward new ideas or things. They pointed out that consumers' innovative tendencies are influenced by individual psychological characteristics and lifestyles. In addition, Shim Kwan-seop (2019) demonstrated that consumer innovation has a regulatory effect in the process in which shared accommodation consumption value and consumption risk affect the intention of participating in shared accommodation supply.

3. Perceived Value

With regard to perceived values, scholars have conducted many studies based on social sciences such as psychology, sociology, and anthropology, and have presented slightly different concepts of perceived values according to their definitions (Chang and Dibb, 2012). However, most studies show that value is approached by dividing it into two main areas: value measurement from an economic and functional perspective and value measurement from a cognitive and emotional perspective (Kim Hae-joong, 2016).

From an economic and functional perspective, Zeithaml (1988) measured perceived value using the ratio between the benefit and value received by the customer from the product or service and its sacrifice (Fornell et al., 1996). Monroe (1990) argued that perceived value equals the sum of transaction value and acquired value. In terms of perception and sentiment, Kotler and Philip (2007) argued that, in addition to converting the functional and economic uses of a product to monetary value, it can be extended to psychological values that exceed quality and price.

Zeithaml (1988) argued that perceived value has two dimensions: expectation and actuality. In other words, benefits, affordability, cost efficiency, and differences between reality and customer expectations. This work seeks to combine the above-mentioned prior studies to measure perceived value through the above four dimensions.

III. Hypothesis and Study Model

1. Derive Hypotheses

Kang Ji-won (2017) empirically demonstrated that personalized services have a relatively large impact on perceived usefulness formation in his research on dining apps. In addition, it has been shown that the personalized service of the app and perceived benefits are related to the influence of the process (+). Woo Kyung-jin (2010) also argued that the higher the use of personalized services on online tourism sites, the higher the perceived usefulness. James & Mona (2011) argued in their book "Service Management" that services that exceed the expectations of basic services significantly improve perceived value. On the shared homestay platform, accommodation service providers are expected to increase consumers' perceived value by providing personalized services such as cooking, pet companion, and smoking in accordance with consumer needs beyond basic accommodation services. In summing up prior studies, this study presents the following hypotheses assuming that personalized services have a positive impact on perceived value:

H1. The provider's personalized services will have a significant (+) impact on the perceived value of the shared economy platform.

Not many studies directly investigate the relationship between provider personalized services and platform trust. However, combining prior research, personalized services can be seen as differentiated services, such as customer-centered services, that meet the diverse needs of consumers and are differentiated from standardized services. Corbitt & Thanasankit (2003) saw one of the factors affecting trust as customer-centric services; Koufaris & Hampton (2004) saw customer orientation as a prerequisite for consumers to create online purchase trust. Johnson & Grayson (2005) also suggested that consumer confidence in service providers affects product effectiveness. At the same time, the shared economy platform is used as an intermediary platform for the services of providers and is determined by the consumer confidence platform for service providers, so the following hypothesis is presented in this study.

H2. Supplier personalized services will have a significant (+) impact on shared economy platform trust.

Patwardhan (2005) reported that with respect to platform ease and perceived value, consumers' perceived value is influenced by factors such as information and interaction. Anderson (1992) noted that one of the determinants of perceived value involves whether the purchasing process is complex. The ease of use of shared economic platforms can be seen as implying the ease in the process of consumers accessing and trading information easily. In addition, according to Ahn Jung-seok (2015), the impact relationship of ease of service on perceived value shows that ease of transaction has the greatest influence on perceived value, followed by decision-making and accessibility. Based on these prior studies, this work assumes that the ease of use of the platform will have a positive bearing on the perceived value and presents the following hypotheses.

H3. Platform ease will have a significant (+) impact on the perceived value of consumers.

With respect to platform ease and confidence in the platform, McKnight & Chervany (2001) argues that structural guarantees of the web and situational normality can also promote confidence in online consumption. Wang & Emurian (2005) suggested that the good structure and content of the platform would facilitate the creation of consumer confidence. Sillence (2006) argued that the visual perception of the platform, the rationality of the structure, and whether the content is sufficient affect trust. Furthermore, according to Shin Sang-yoon (2019), decision-making and transaction-ability of mobile apps have a positive impact on trust. In a similar study, Kim Jong-taek (2012) demonstrated that ease affects trust definitions in research on financial instruments. Combining these prior findings, this work derives the following hypotheses:

H4. Platform ease will have a significant (+) impact on the consumer's platform confidence.

On a shared economy platform, offline service providers provide personalized services, which have a significant impact on perceived value and platform trust in the consumption process. Looking at prior research, several studies have reported that consumer innovation tendencies play a role in regulating. According to Kang Bong-hee (2012), consumer innovation has a controlling effect in the relationship in which marketing stimulation affects purchases. Jung Sung-kwang (2012) argued that consumer innovation has a controlling effect in forming satisfaction of kiosk users. In addition, according to Kim Hong-young (2018), consumers' innovation creates positive interactions in the evaluation of fresh products provided by companies, which further strengthens consumers' intention to purchase them. Choi Chul-jae (2018) argued that customer innovation can be applied to online shopping situations, and that the impact of service quality on trust varies depending on customer innovation. As customer innovation affects customer satisfaction and trust in online services, we have identified the difference in customer satisfaction and trust influence based on customer innovation (Hua Dai et al., 2015). Taken together, the following hypotheses are derived in this work:

H5. The impact of personalisation services on the perceived value of shared economy platform services will vary depending on innovation propensity.

H6. The impact of personalisation services on shared economic platform confidence will depend on innovation propensity.

There is a lot of prior research on the effect of perceived value on intention to purchase. Zeithaml (1988), primarily from the perspective of consumer psychology, demonstrated that the higher the perceived benefit of a product or service, the greater the perceived value, the greater the consumer's perceived value, the greater the consumer's willingness to purchase the product. Eggert and Ulaga (2002) saw the main cause of consumer purchases as perceived value, and Tam (2004) also argued that perceived value is more likely to cause consumer buying behavior than consumer satisfaction. Lin Tingting (2019) concluded that perceived value has a significant positive effect on the intention of purchasing clothes. An Daecheon (2015) study on eco-friendly cars also showed that perceived value has a beneficial definition impact on purchase intention. Therefore, the following hypotheses are proposed in this work.

H7. Perceived value will have a significant (+) impact on the purchase intention of shared economic platform services.

Prior research has shown that trust has a very positive effect on purchase intentions. Shin Sang-yoon (2019) confirmed that trust had a positive impact on purchase intention, and other studies also indicated that trust and purchase intention were positive effects. Jarvenpaa (2005) saw trust in online retailers as influencing online consumers' shopping attitudes, and Genfen (2003) considered trust to be the key to successful transactions, particularly in online trading environments, to help consumers identify sellers, reduce transaction complexity and promote purchasing intent. Therefore, in this study, the following research hypothesis is presented based on the above prior findings.

H8. Trust in shared economic platforms has a significant (+) impact on purchase intent.

2. Study Model

Fig. 1. Study Mode

3. Operational definition and measurement questions for variables

A summary of the manipulative definitions and measurement questions of variables in this work is shown in Table 1.

Table 1. Define and Measure Items

4. Investigation Method

This research survey was conducted on residents of shared homestay in Beijing, Shanghai and Seoul, over a period of two-month period. The survey was conducted on residents by requesting shared homestay hosts in three cities to place questionnaires in their accommodations. A total of 450 questionnaires were distributed, of which 416 valid questionnaires were retrieved, with a response rate of 92.44%. In detail, 165 in Beijing, 142 in Shanghai and 143 in Seoul were recovered, and 152 in Beijing, 133 in Shanghai and 131 in Seoul were used for analysis after removing invalid questionnaires. The number of questionnaires in Beijing, Shanghai and Seoul accounted for 36.5 percent, 32.0 percent and 31.5 percent of the total, respectively. The number of questionnaires retrieved in each region was different, but a similar proportion was maintained appropriate for each region. Table 1 shows the distribution of survey participants over gender, age, education level, and monthly income. The feasibility of the questionnaire was established only after conducting a preliminary survey, and the questionnaire measured 24 variables of six factors. The scale "1" represents a complete discrepancy and "5" uses a Likert scale that represents complete agreement.

Table 2. Statistical Characteristics of Sample

IV. Empirical Analysis

1. Validate reliability and validity

Reliability test. In this paper, SPSS 26.0 was used to test the reliability of the tested items, as shown in Table 3. The Cronbach's α coefficients of all measurements ranged from 0.910 to 0.948, and the combined reliability ranged from 0.851 to 0.935, both exceeding the required level of 0.7, indicating that the scale had a good reliability.

Table 3. Factor Analysis Results

Convergence validity analysis. The confirmatory factor analysis results were as follows: X2/ DF =2.172, GFI=0.902, AGFI=0.880, RMSEA=0.053, indicating that the measurement model has a sufficient model fitting. The standardized load of all items ranged from 0.773 to 0.921, and the AVE value exceeded 0.8, exceeding the minimum requirement of 0.5. This indicates that the scale has good combinatorial validity.

Table 4. Correlation Matrix

2. Structural Model

In this paper, AMOS23.0 was used to perform data fitting analysis on the model, and then SPSS26.0 was used to verify the moderating variables. The overall fitting degree of the model was shown in Table 5. In the main statistics of the overall model fitting, the X2/ DF value is between 1 and 3, CFI, GFI, NFI, RFI, IFI, TLI are all greater than 0.90, RMSEA is less than 0.08, it can be seen that the model's fitting degree meets the academic requirements, and the fitting degree is good.

Table 5. Model Fits

According to the model test results, personalized service has a significant positive impact on perceived value (β=0.36, t=6.927, P < 0.001), and H₁ holds. Personalization service has a significant positive effect on platform trust (β=0.31, t=5.523, P < 0.001), and H2 holds. Platform convenience had a significant positive effect on platform trust (β=0.39, t=7.173, P < 0.001), and H3 held. Platform convenience had a significant positive effect on perceived value (β=0.32, t=6.669, P < 0.001), and H4 held. Novelty orientation significantly moderated the positive effect of personalized service on perceived value (β=0.29, t=7.227, P < 0.001), and H5 held. Novelty orientation significantly moderates the positive impact of personalized services on platform trust (β=0.27, t=6.405, P < 0.001), and H5 holds. Perceived value had a significant positive effect on purchase intention (β=0.43, t=6.204, P < 0.001), and H7 held. Platform trust has a significant positive effect on purchase intention (β=0.29, t=4.638, P < 0.001), and H8 holds.

V. Discussion and Conclusion

1. Theoretical Implications

This study confirms that the ease of personalized offline services and platforms on shared homestay hosts has a significant positive impact on consumers' perceived value and trust in shared economic platforms. This paper has extended implications for prior research, and empirically confirms that the increase in personal host personalized offline service quality in the shared economy model has a positive impact on consumers' perceived value and platform confidence. At the same time, we confirm that under the shared economic model, consumers' innovative tendencies play an important positive role in their trust in the platform as well as in the perceived value aspect of consumers.

Table 6. Hypothesis Test Results

The results of this study are consistent with the conclusion that consumer innovation has a regulatory effect in Jeon Jong-geun's (2017) study of car shared intent determinants. Further studies have introduced psychological variations such as the Technology Readiness Index or personality, as well as innovative tendencies as individual variations. In addition, it seems necessary to introduce socio-psychological factors in future studies in view of the social concept of the shared economy creating desirable social values. For example, we might introduce variables such as consumer citizenship.

2. Practical Implications

The results of this study provide two practical implications for individuals and companies providing shared economy services. First, on shared homestay platforms, some hosts still offer only basic generalized accommodation services to lower sales prices. However, consumers want to satisfy their innovative tendencies when using shared homestay accommodation services, so better provision of services that meet the unique needs of consumers or have other characteristics can give them a relative competitive advantage and increase customer attraction competitiveness.

Second, in order to increase profits and reduce costs, many shared economic platforms must carefully consider the costs and benefits associated with developing standardized offline products and services that the platform itself operates directly. In order for both pure shared economic services and standardized services to continue to generate revenue, differentiated business strategies need to be implemented to meet consumer needs. Airbnb, which acts as a pure platform for personal hosts without a standardized offline product operated by itself, seems to be clearer in its management strategy than Tujia in China, which operates its own offline service and serves as a matchmaking platform for private hosts.

In addition, online platform operating companies should make efforts to increase the ease of use of the platform, and invest efforts in developing payment systems and operating systems to promote user convenience.

3. Limitations of this study

First of all, since the survey area is only limited to Beijing, Shanghai and Seoul, and the respondents are only limited to those who travel to these three cities, it cannot fully reflect all the users of home-share accommodation. At the same time, it only studied the shared home stay, but failed to fully study other sharing economy platforms. Secondly, in terms of service factors, only the landlord's personalized services and the consumer's innovation tendency are considered, but more other factors are not included.

4. Suggestions for future research

To suggest ways to increase the academic value of this study, we include: First, in addition to host personalization services and platform ease, there will be determinants that can increase the purchase intention of shared economic platforms. In order to further expand and improve the conceptual framework of this study, it is necessary to study additional economic factors of consumers, psychological factors of consumers, and price competitiveness. Second, the survey subjects of this study were limited to consumers of specific platforms in the shared homestay field, but it is necessary to add consumers of shared economic platforms in other industries in the future.