tourism e commerce

What is e-tourism and how is it changing travel?

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We live in a digital world nowadays, or an ‘e’ world as some may like to put in. We have concepts such as ‘e-business’, ‘e-commerce’, ‘e-marketing’ and ‘e-service’, so it seems it was only time before the idea of ‘e-tourism’ emerged. But what exactly is e-tourism, how does it work and why is it important? Read on to find out…

What is e-tourism?

Research and development, reservation and bookings, marketing and promotion, the tourist experience, is smart tourism e-tourism, the benefits of e-tourism, the disadvantages of e-tourism, how is e-tourism changing travel, e-tourism- further reading.

E-tourism is all about the introduction of digitalisation into the tourism industry. This manifests itself in many different ways. We see e-tourism before, during and after a holiday or trip itself – and actually there is a lot of e-tourism that goes on behind the scenes, so we don’t actually ‘see’ it at all!

Dimitrious Buhalis is known as an expert in the field of e-tourism and he defines it as the digitization of all processes and value chains in the tourism, travel, hospitality and catering sectors that allow organizations to maximize their efficiency and effectiveness.

This digitalization, over the years, has changed the way that the tourism industry works and in turn has altered the structure of the tourism industry , often for the better- making it more efficient and productive. And this is not unique to the tourism industry by any means, our whole world has been becoming increasingly digitalised for many years now. In fact, we have become so reliant on the digital aspects of our lives that the functioning of the contemporary tourism system and its future seem unthinkable without the technological innovation that we have today!

Examples of e-tourism

E-tourism is ingrained throughout the tourism system, from the booking process right through to the tourist experience and everywhere in between. It would be impossible to discuss every way that technology is used in the tourism industry (ok, well perhaps not impossible, but I would be here a very long time!). Below I have outlined some of the most common ways that that e-tourism occurs.

E-tourism is used to a large extent during the research and development stages of a tourism product or service. There are a wealth of digital resources at the disposal of tourism industry stakeholders , which enables them to collect large amounts of data and research their (potential) customers. In turn, this helps organisations in the travel and tourism industry to better understand their customers and therefore to better satisfy their needs and desires.

Likewise, recent years have seen many options for the tourists themselves to research their travel choices to a greater extent than they have previously been able to. Reading blogs, looking at travel pictures on Instagram, scouring Pinterest… when it comes to heading off on a city break or relaxing beach vacay, tourists often turn to the internet as a source of location inspiration- this is also evidence of e-tourism.

Central reservation systems have come a long way in the past couple of decades. First introduced in the 1960s by airlines, central reservation systems were quickly adopted by hotels and other businesses operating in the travel and tourism industry. Most recently these have been further developed to allow the tourist to play a key role in the booking process by linking their reservation systems to popular online booking platforms such as Expedia or Syscanner as well as in-house developed booking systems.

Nowadays, pretty much everything can be booked online. Tourists don’t need to make a trip into town specifically to visit a travel agent, and sit there while they look through brochures and databases to find a trip that ticks every box for them- tourists can do it for themselves! There is far more freedom and independence now, as consumers are part of the process from the start. Bookings and changes can be made at the tap of a button or the click of a link. This not only makes the process simpler and easier for the tourist, but it also helps the business to operate faster and more efficiently, reducing overhead costs and maximising productivity.

Some years ago the likes of travel agencies and tourist boards would focus their marketing efforts on printed advertising such as posters, brochures and flyers… but those days are long gone now. Whilst there will always be a place for physical advertising of this type, travel and tourism organisations now have a wealth of valuable data at their fingertips that they can use to inform their marketing.

As we live more of our lives online (think shopping, researching, connecting with our friends on social media etc), the organisations that want to sell us their products and/or services are more informed to do so than they have ever been before. Adverts can be targeted to specific customers based on location, age and other relevant demographics. It can also be based around the user’s online activity- yes, if you begin to research ecotourism holidays it is likely that you may begin to be shown adverts about eco lodges in the Gambia or ecotourism in Costa Rica ! Whilst there are certainly some ethical questions about how much of our data is used by organisations for advertising purposes, there is no disputing that the organisations of today have a big foot up in comparison to their counterparts from a decade or two ago!

In addition to this, we have new platforms where marketing can take place. Social media platforms such as Instagram or Facebook allow for both large companies and individuals to promote products, services or places. As I explain in my article about Instatourism , these social media platforms can be powerful tools for the purposes of marketing. And more and more people are working in the field too- many argue that the growth of travel influencers around the world has changed the marketing industry forever!

Technology has also enhanced the travel sector in many ways. More efficient aircraft, trains, cars etc have enabled us to travel further and faster than ever before. They typically create less damage to the environment too, with more environmentally friendly initiatives being researched and implemented such as bio fuels and hybrid models.

Travel is easier for the consumer these days too. No longer do we need to carry around our pocket-sized road maps, or get stressed out when we can’t read directions- all we need nowadays is a 4G connection and a navigation app! There are plenty of other apps that help us travel too, from train apps with timetables to flight comparison sites and more.

There are many ways that e-tourism has helped to enhance the tourist experience and to make the tourism industry more efficient. From having your room service brought to you by a robot, to checking a menu in a restaurant using a QR code, to downloading an app in a theme park that shows queue times for the rides to having an audio programme give you information on your phone as you walk through a museum. E-tourism is everywhere we look!

Is virtual tourism e-tourism?

Virtual tourism is an example of e-tourism in practice. It is essentially a hybrid concept- it combines both the notions of virtual reality and tourism. In essence, virtual tourism facilitates a tourism experience, without actually having to travel anywhere. Virtual tourism takes many different forms and comes in vary degrees of technological capability.

In its simplest form, virtual tourism may comprise of a video of a tourism destination. The ‘tourist’ watches the video, utilising their hearing and sight senses. More sophisticated forms of virtual tourism include being immersed in an environment through use of a headset or simulator. It may involve use of various props, users may be required to wear gloves and there may be additional sensations such as movement (like in a rollercoaster simulator), feeling (for example if the user is sprayed with water ) and smell. You can read a detailed article about the virtual tourism industry here.

Smart tourism and e-tourism are commonly interlinked, however smart tourism is not always an example of e-tourism. Smart tourism is all about tourism that is designed in a ‘smart’ way- the intention is to promote productivity and make the tourism industry efficient. Oftentimes this does require the use of digitalisation, or technology, hence making it a form of e-tourism, but this isn’t always the case 100% of the time. You can read all about the concept of smart tourism here.

Ultimately, e-tourism is a good thing. The use of technology in the tourism industry has helped to make it more efficient, run more smoothly (with less risk of human error) and making it more productive. This generally means that consumers (or tourists) are more satisfied with their tourism experience and that the organisations involved have increased profit margins and lower overheads.

E-tourism has introduced us to a whole new way of thinking and has helped to expose us to invaluable developments in the travel and tourism industry- it has helped to make parts of the industry more environmentally friendly, it has helped to have more effective marketing and product development and it has helped us to embrace new forms of tourism too, such as smart tourism and virtual tourism.

However, as is the case with any form of tourism, there are some negative impacts of e-tourism too. The use of technology sometimes takes away the ‘human’ aspect- customer service from a robot will never replace the smiles and conversations that a real person would bring to the situation. And using technology to a large extent may reduce the number of jobs in the tourism industry too, which can have a negative economic impact on the host community. Furthermore, technology can go wrong- a booking system that is down or a website that doesn’t work properly can cause loss of money and business, for example.

Ultimately, e-tourism is all about making the tourism industry more efficient through the use of technology. As I have outlined in this article, there are many ways that this can be done and the benefits of this can be far reaching. From the perspective of the tourism industry, the digitalisation of travel and tourism can help to enhance business prospects- income, productivity, performance etc. And from the perspective of the tourist it can help to make their tourism experience more enjoyable.

If you have found this article interesting, then I am sure that you will enjoy these too!

• What is smart tourism and why is it so BIG?
• Virtual tourism explained: What, why and where
• What is sustainable tourism and why does it matter?
• What is ecotourism and why is it so important?
• Niche tourism: What, why and where

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ORIGINAL RESEARCH article

The effects of tourism e-commerce live streaming features on consumer purchase intention: the mediating roles of flow experience and trust.

• 1 Library, Zhejiang Gongshang University, Hangzhou, China
• 2 Publicity Department, China Jiliang University, Hangzhou, China
• 3 School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou, China

Given that tourism e-commerce live streaming has become an important driver of tourism development after the outbreak of Covid-19 but limited attention has been paid to this area, this study examines the impacts of tourism e-commerce live streaming features (interactivity, authenticity, and entertainment) on the consumers’ purchase intention from the perspectives of consumers’ flow experience and trust based on the SOR theory. The authors collected survey data from 357 tourism e-commerce live streaming consumers and used the structural equation model to test the research model. The results reveal that interactivity and authenticity positively affect tourism e-commerce live streaming consumers’ purchase intention, but entertainment does not influence purchase intention positively; interactivity, authenticity, and entertainment each positively affects tourism e-commerce live streaming consumers’ flow experience and trust; both flow experience and trust positively affect tourism e-commerce live streaming consumers’ purchase intention; both flow experience and trust have mediating effects on the relationships between tourism e-commerce live streaming features and consumers’ purchase intention. This study extends existing theoretical research on tourism e-commerce live streaming and provides some managerial implications for tourism enterprises and streamers.

Introduction

With the rapid development of digital technology, e-commerce live streaming has become a new business model ( Wongkitrungrueng and Assarut, 2020 ; Sun et al., 2022 ). After the outbreak of COVID-19 pandemic in early 2020, the large-scale travel restrictions further pushed up the popularity of e-commerce live streaming. In China, the scale of e-commerce live streaming users was 464 million in 2021, with an increase of 75.79 million than in December 2020 ( CNNIC, 2022 ). In the field of tourism, the information needs and consumption habits of tourists are changing due to the impact of COVID-19. Different sectors of tourism industry applied high tech or live streaming approach to respond to COVID-19. For example, facial recognition and smart cameras were used in gambling industry ( Liu et al., 2021b ), digital technology was applied in hospitality industry in Macao ( Liu et al., 2021c ), and live streaming was used in tourism industry ( Liu et al., 2022 ). Tourism e-commerce live streaming is developing rapidly ( Deng et al., 2021 ; Xie et al., 2022 ). In China, online travel agents (OTAs), such as Ctrip, Mafengwo, and Tuniu, are accelerating the development of live streaming and exploring the business model innovation of “tourism + live streaming” through e-commerce live streaming and virtual tourism, such as the “Boss Live Session” activity initiated by Ctrip.

At the same time, e-commerce live streaming research has become a research hotspot ( Ang et al., 2018 ; Zhang et al., 2020 , 2022 ). E-commerce live streaming attracts consumers through instant interaction and vivid product display ( Tong, 2017 ; Ang et al., 2018 ; Liu et al., 2020c ). E-commerce live streaming can deliver richer information to consumers than posts that mainly convey product information through text and pictures( Yu and Zheng, 2022 ).Serving the more detailed and vertical needs of consumers, e-commerce live streaming attracts potential consumers, improves the conversion rate, and generates faster sales ( Hu and Chaudhry, 2020 ). It can both improve the conversion rate of both physical and virtual stores, and expose the brand to the public ( Xue and Liu, 2022 ). Previous studies have shown that e-commerce live streaming consumer’ purchase intentions can be influenced by live streaming strategy ( Zhang et al., 2020 ), IT affordances ( Sun et al., 2019 ), interaction ( Li et al., 2021 ; Zhang et al., 2021b ), and social presence ( Ang et al., 2018 ; Chen and Liao, 2022 ). However, little research has been devoted to studying the impacts of e-commerce live streaming features on consumer’s purchase intention systematically, and there are still very few studies on tourism e-commerce live streaming ( Deng et al., 2021 ; Qiu et al., 2021 ; Lin et al., 2022 ).

Tourism e-commerce live streaming promotes the marketing of tourism industry, taps the online consumption potential of tourists, and achieves the synergistic development of tourism online and offline ( Zhang et al., 2021b ; Xie et al., 2022 ). Despite the growing popularity of tourism live streaming, little research has been devoted to studying the impacts of tourism e-commerce live streaming features on consumer’s purchase intention ( Lv et al., 2022 ). Based on a review of studies pertaining to e-commerce live streaming, we proposed three core features of this form of communication: interactivity ( Xue et al., 2020 ; Kang et al., 2021 ), authenticity ( Tong, 2017 ), and entertainment ( Chen and Lin, 2018 ). The tourism e-commerce streamer interacts with the consumers, and the consumers can also interact with each other through pop-ups or other forms, forming an open virtual community centered on the streamer. Compared with the traditional tourism e-commerce marketing model in which consumers initiate the consultations, the interaction in the e-commerce live streaming is intuitive, instantaneous, and interactive, changing from traditional passive service to active guidance and creating a more realistic tourism shopping scenario. At the same time, tourism streamers show tourism products and exchange the information of the products through live streaming, helping tourism consumers establish an authentic perception of the tourism products. That is, tourism e-commerce live streaming creates a face-to-face shopping scenario in comparison with traditional tourism e-commerce. Thus, the perceived authenticity of tourism products is stronger, which helps to enhance the consumer’s trust ( Jiménez-Barreto et al., 2020 ). Another feature of tourism e-commerce live streaming is entertainment. Compared with e-commerce, the entertainment in e-commerce live streaming comes not only from the perception of the shopping experience, but also from the live streaming content and participation process, which is more conducive to the consumer’s flow experience. However, little research has been devoted to regarding interactivity, authenticity, and entertainment as the features of e-commerce live streaming to study their impacts on consumer’s flow experience and trust in an empirical study, especially in the field of tourism.

To fill these gaps, based on the stimulus-organism-response (SOR) model, we used tourism e-commerce live streaming features (interactivity, authenticity, and entertainment) as stimulus variables (S), flow experience and trust as organism variables (O), and tourism consumers’ purchase intention as the response variable (R), to explore the influence mechanism of tourism e-commerce live streaming features on tourism consumers’ purchase intention. We aim to enrich and deepen the research on the formation mechanism of tourism consumers’ purchase intention in the context of tourism e-commerce live streaming theoretically, and practically provide guidance to enhance tourism consumers’ purchase intention and help to realize the integrated development of tourism industry online and offline.

Theoretical background and hypothesis development

To study the influence of the external environment on individual behavior, the stimulus-organism-response (SOR) theoretical model was proposed in the field of environmental psychology ( Mehrabian and Russell, 1974 ). In this context, stimulus (S) refers to external environmental factors that can act on an individual’s cognition and emotion (O) and ultimately elicit a behavioral response (R). A few studies has applied the SOR model in the research of e-commerce live streaming consumers. For example, Xu et al. (2020) employed the SOR framework to investigate contextual and environmental stimuli effects (streamer attractiveness, para-social interactions, and information quality) from a e-commerce live streaming context on viewer’s cognitive and emotional states (cognitive assimilation and arousal) and their subsequent responses (hedonic consumption, impulsive consumption, and social sharing); Guo J. et al. (2021) applied the SOR framework to examine the impact of live streaming feature on the consumers’ cross-border purchase intention from the perspectives of consumers’ overall perceived value and overall perceived uncertainty. However, Xu et al. (2020) did not pay attention to the roles of the e-commerce live streaming features, while Guo J. et al. (2021) regarded the live streaming feature as a concept and did not subdivide the live streaming feature. In addition, since tourism products have special features (e.g., high unit price, low purchase frequency, intangibility, and non-transferability) that are different from general products ( Xie et al., 2022 ), it is necessary to study on tourism e-commerce live streaming and the consumers’ psychology. In this study, tourism e-commerce live streaming features (interactivity, authenticity, and entertainment) were selected to assess the contextual and environmental stimuli, flow experience and trust were selected to assess the internal states of tourism consumers, and tourism customers’ purchase intention were selected to assess their responses. The research model is shown in Figure 1 .

Figure 1 . Research model. * , additional analysis is conducted to examine the mediating effect of organism.

The effects of tourism e-commerce live streaming features

Interactivity means that consumers can communicate and exchange information with the information source, emphasizing the two-way communication. When watching tourism e-commerce live streaming, viewers can consult and give gifts to the streamer, express their opinions, and communicate with other viewers through pop-ups. The streamers also actively communicate with their viewers in addition to presenting the products ( Liu et al., 2020c ; Wang and Liu, 2022 ). In the process of live streaming, the frequent interactions between the streamer and consumers make consumers feel temporarily detached from reality, forget about worries, and have a sense of immersion ( Liu et al., 2020a ). The interactive communication between streamers and consumers generates an interactive feedback signal to customers, which can produce a powerful psychological implication to customers and increase their trust in the streamers ( Chen et al., 2021 ). A high level of interaction between streamers and consumers can lead to cognitive and emotional changes of consumers, enhance consumers’ understanding of the streamers and products, and thus increase trust, which ultimately influences consumers’ purchase intentions ( Hou et al., 2020 ; Zhang et al., 2021a ). Liu et al. (2021a) emphasized the importance of interactivity in tourism live streaming. As such, we believe that tourism e-commerce live streaming with strong interactivity can stimulate consumers’ flow experience, trust, and purchase intention. Based on this, this paper proposes the following hypotheses:

H1a : Interactivity of tourism e-commerce live streaming positively affects consumers’ flow experience.

H1b : Interactivity of tourism e-commerce live streaming positively affects consumers’ trust.

H1c : Interactivity of tourism e-commerce live streaming positively affects consumers’ purchase intention.

Authenticity refers to the individual’s evaluation of the truthfulness of the information received. In the traditional tourism marketing, there is a risk of excessive embellishment, lens switching, or image manipulation of pictures ( Zhang et al., 2021a ). Customers cannot see the real products ( Lu et al., 2016 ), making them vulnerable to be cheated by inauthentic and beautified information and may hinder trust-building ( Escobar-Rodríguez and Bonsón-Fernández, 2017 ; Guo L. et al., 2021 ). In tourism e-commerce live streaming, the live streaming process is live and instant, without camera switching. It is a complete presentation of the whole tourism scene and products, with a strong sense of live immersion. In the process of live streaming, the streamers give real descriptions and effective evaluations of the products and offer purchase suggestions, which increase customers’ interest in watching the live streaming ( Li et al., 2021 ). Therefore, tourism e-commerce live streaming with authenticity will attract consumers and bring them into a specific scenario, thus creating a positive emotional experience for consumers. Tong (2017) emphasized that the authenticity of a webcast enhanced customer engagement and trust. Zhang et al. (2021a) showed that live streaming authenticity not only had a positive impact on consumer perceptions, but also influenced consumers’ purchase intentions. Liu et al. (2021a) argued that authenticity was crucial in tourism live streaming. As such, we believe that tourism e-commerce live streaming with strong authenticity can stimulate consumers’ flow experience, trust, and purchase intention. Based on this, this paper proposes the following hypotheses:

H2a : Authenticity of tourism e-commerce live streaming positively affects consumers’ flow experience.

H2b : Authenticity of tourism e-commerce live streaming positively affects consumers’ trust.

H2c : Authenticity of tourism e-commerce live streaming positively affects consumers’ purchase intention.

Entertainment refers to the degree of pleasure felt by consumers during the process of watching live streaming, with the aim of satisfying consumers’ pleasure psychology. Viewers tend to use media to relieve stress for entertainment ( Chen and Lin, 2018 ). To a large extent, consumers participate in consumption for the purpose of personal relaxation and stress relief ( Wang et al., 2020 ). Entertainment is reflected in the lively and interesting topics started by the streamer, and a series of entertaining activities held by the shopping platform or the streamer, such as regular lottery, virtual red envelope distribution, and thumb-up, etc. ( Liu et al., 2020c ). Meanwhile, the creative pop-up messages posted by the viewers and the hover animation of the live streaming window also increase the entertainment of the e-commerce live streaming ( Yu and Xu, 2017 ). Entertainment in live streaming can significantly influence consumers’ flow experience, perceived value, and usage attitude ( Chen and Lin, 2018 ; Cao et al., 2022 ), and also increase the emotional connection between the streamer and consumers ( Hilvert-Bruce et al., 2018 ). Previous studies showed that entertainment had a significant effect on tourist trust ( Pujiastuti et al., 2017 ), social media brand trust ( Zhang et al., 2022 ), and purchase intention ( Ma et al., 2022 ). From this, it can be hypothesized that tourism e-commerce live streaming with strong entertainment can stimulate consumers’ flow experience, trust, and purchase intention. Based on this, this paper proposes the following hypotheses:

H3a : Entertainment of tourism e-commerce live streaming positively affects consumers’ flow experience.

H3b : Entertainment of tourism e-commerce live streaming positively affects consumers’ trust.

H3c : Entertainment of tourism e-commerce live streaming positively affects consumers’ purchase intention.

The effect of flow experience

In the online context, flow leads users to become completely engaged in online tasks and interested to continue these activities. As the consumer experience quality is higher, the perceived value is higher and consumers are more willing to participate ( Prentice et al., 2019 ; Chen et al., 2022b ). E-commerce live streaming enables consumers to enjoy a sense of freedom, control and participation, and a better consumption experience, which can lead to consumers’ willingness to purchase ( Feng and Lu, 2020 ). Flow experience represents an intense involvement that leads to high psychological engagement such as satisfaction and loyalty for virtual world users ( Barker, 2016 ). Gao and Bai (2014) noted that flow experience affected consumers’ behavioral intention, such as the likelihood to purchase from the website. The online students’ flow experience has a significant relationship with continuous intention ( Zhao and Khan, 2022 ). In social commerce, consumers who have experienced flow are likely to participate in social commerce activities ( Zhang et al., 2014 ), which affects consumers’ purchase intention ( Xu et al., 2022 ). From this, it can be presumed that tourism e-commerce live streaming consumers with a stronger flow experience are more likely to generate purchase intentions. Based on this, this paper proposes the following hypothesis:

H4: Flow experience positively affects tourism e-commerce live streaming consumers’ purchase intention.

The effect of trust

Perceived trust refers to the degree of consumers’ trust in the tourism e-commerce live streaming streamer and the products recommended by the streamer. Consumers tend to make purchase decisions in a short period of time and with limited rationality because of perceived trust ( Liu and Shi, 2020 ; Liu et al., 2021e ). In e-commerce live streaming, trust helps to reduce various transaction costs ( Feng and Lu, 2020 ), and reduce consumers’ perceived risk and uncertainty about the streamers and products, makeing consumers actively participate in online transactions ( Liu et al., 2020c ; Guo J. et al., 2021 ). Prior studies demonstrated that trust had an important effect on consumer behavior ( Nadeem et al., 2020 ; Guo J. et al., 2021 ). Alkhalifah (2022) confirmed that trust in social commerce influenced behavior intention. Dong et al. (2022) demonstrated that live-streaming e-commerce with high-quality would increase consumers’ green trust and, thus, strengthen green purchase intention. In the context of tourism, tourist trust is widely accepted to play an important role in influencing their behavior intentions ( Iranmanesh et al., 2018 ; Han et al., 2021 ). It is difficult for consumers to make purchase decisions in tourism e-commerce live streaming because of high uncertainty and perceived risk, but perceived trust can help consumers reduce their decision costs and thus generate purchase intentions ( Lu and Chen, 2021 ). From this, it can be hypothesized that consumers with stronger trust in tourism e-commerce live streaming are more likely to generate purchase intention. Based on this, this paper proposes the following hypothesis:

H5 : Trust positively affects tourism e-commerce live streaming consumers’ purchase intention.

The mediating role of flow experience

Frequent interactions in e-commerce live streaming make consumers temporarily detach from reality and immerse themselves in the live streaming environment, forgetting their worries and generating a flow experience ( Liu et al., 2020a ). The streamer displays the product realistically and evaluate it effectively, give purchase suggestions, and increase customers’ interest in the product when watching the live broadcast ( Li et al., 2021 ). Entertainment in e-commerce live streaming can significantly influence consumers’ flow experience, perceived value, and attitude ( Chen and Lin, 2018 ). Consumers’ flow experience has an impact on attitudes, and when consumers are immersed in the live streaming environment, they want to participate unconsciously and are stimulated by the streamer to purchase ( Huang et al., 2021 ). Arghashi and Yuksel (2022) demonstrated that consumers’ flow experience mediated the relationship of interactivity and trust in AR apps. From this, it can be hypothesized that consumers obtain flow experience by watching tourism e-commerce live streaming and generate purchase intention under the influence of flow experience. Based on this, this paper proposes the following hypotheses:

H6a : Flow experience has a mediating effect between interactivity and purchase intention in tourism e-commerce live streaming.

H6b : Flow experience has a mediating effect between authenticity and purchase intention in tourism e-commerce live streaming.

H6c : Flow experience has a mediating effect between entertainment and purchase intention in tourism e-commerce live streaming.

The mediating role of trust

In e-commerce live streaming, interactivity can form an intimate relationship between the streamer and consumers and increase consumers’ perceived trust ( Wei et al., 2022 ). Authenticity can enhance viewers’ understanding of the products, reduce perceived risk, and promote trust ( Tong, 2017 ). Entertainment can increase consumers’ curiosity about the streamer and the product, and enhance their desire to participate in the live streaming, which leads to positive evaluation of the product and the streamer ( Wongkitrungrueng and Assarut, 2020 ). Perceived trust is an important factor to maintain loyalty and is the foundation of online shopping. Trust comes from the daily interaction between streamers and viewers, the professional competence of streamers, etc. ( Zhang et al., 2021a ). According to Alalwan et al. (2019) and Kim and Park (2013) , trust mediates the relationships between s-commerce dimensions and consumers’ value co-creation, and between the characteristics of s-commerce and purchase intention. Liu et al. (2021d) confirmed that social support had a direct positive effect on s-commerce purchase intention, and that social trust partially mediated the relationship. From this, it can be hypothesized that consumers generate trust by watching tourism e-commerce live streaming and generate purchase intention under the influence of trust. Based on this, this paper proposes the following hypotheses:

H7a : Trust has a mediating effect between interactivity and purchase intention in tourism e-commerce live streaming.

H7b : Trust has a mediating effect between authenticity and purchase intention in tourism e-commerce live streaming.

H7c : Trust has a mediating effect between entertainment and purchase intention in tourism e-commerce live streaming.

Methodology

Questionnaire design and measurement.

In order to ensure the reliability and validity of the questionnaire, this paper adopted the mature scale, and made appropriate modifications according to the characteristics of tourism e-commerce live streaming. All constructs were measured by Likert five-point scale, i.e., one means “strongly disagree” and five means “strongly agree,” and the larger the number, the higher the degree of agreement. The measurement of interactivity mainly referred to Liu et al. (2020a) and Wei et al. (2022) . Items of authenticity referred to Tong (2017) . The scale for entertainment was adapted from Chen and Lin (2018) and Lv et al. (2022) .The measurement of flow experience mainly referred to Chen and Lin (2018) . Items of trust referred to McKnight et al. (2002) and Chen et al. (2022c) . The scale for purchase intention was adapted from Liu et al. (2013) , Chen et al. (2017) , and Liu et al. (2020b) . The questionnaires were sent to experts in the field of tourism e-commerce live streaming for review. The initial questionnaire was formed after modification according to the experts’ suggestions. The initial questionnaires were sent to 50 respondents for pre-survey, and the final questionnaire was formed after modification based on the pre-survey results.

Data collection and sample description

Questionnaires were distributed online and offline to avoid homologous deviation. The questionnaires were distributed online through the Wenjuanxing app, which is a professional online survey, evaluation and voting platform with nearly 50 million users in China ( Liu et al., 2021f ). The link of the questionnaire on Wenjuanxing app was shared through WeChat and QQ to expand the coverage of samples. Meanwhile, offline questionnaires were distributed to respondents by paper-based questionnaires. We selected individuals who had watched tourism e-commerce live streaming by the screening question (“Have you had the experience of watching tourism e-commerce live streaming in the past?”). Those people who had not watched tourism e-commerce live streaming were excluded. A total of 462 questionnaires were received, and 357 valid questionnaires were obtained by excluding invalid questionnaires with incomplete answers, illogical answers, and <1 min of online filling time, with an effective rate of 77.27%. Since the data for this study were obtained from both online and offline sources, there might be differences between the data obtained from the two sources. We tested the sample differences through a one-way ANOVA by summing the scores of all question items of each questionnaire. The ANOVA results show a value of p  > 0.05, which indicates that there is no significant difference between the two groups of samples collected based on different routes. Therefore, the two groups of samples can be used as a whole sample.

The descriptive statistics of our survey samples are shown in Table 1 . In terms of gender, there are more females than males, with 162 males (45.38%) and 195 females (54.62%). In terms of age, the group of 18–24 years old accounts for the largest proportion, and the next largest percentage is in the group of 25–30 years old. In terms of education level, there are more samples with bachelor degree or above. In terms of monthly income, those with monthly income of 5,000–10,000 yuan accounts for the largest proportion. In terms of online shopping experience, most of the samples have more than 3 years of online shopping experience. Overall, the samples in this study are representative of the tourism e-commerce live streaming consumers.

Table 1 . Descriptive statistics of the study samples ( N  = 357).

Data analysis results

Reliability analysis.

Reliability reflects the stability and consistency of a scale. The greater is the reliability of a scale, the smaller is its standard error of measurement. In the Likert scale method, Cronbach’s alpha coefficient is the commonly used reliability test indicator. As can be seen from Table 2 , the Cronbach’s alpha value for each construct in this study is above 0.7. This shows that the scale of this study has good reliability.

Table 2 . Reliability analysis results.

Validity analysis

Validity consists of convergent and discriminant validity. Convergent validity refers to a high degree of correlation between items, and discriminant validity refers to a low degree of correlation or the significant differences between constructs. Convergent validity is measured by the factor loading of each item, the composite reliability (CR) of the construct, and the average variance extracted (AVE) of the construct. It requires that factor loadings are preferably >0.5, combined reliability (CR) values are >0.6, and average variance extracted (AVE) values are >0.5 ( Fornell and Larcker, 1981 ). According to Table 3 , the factor loading of each item is >0.6, CR values are all above 0.7, and AVE values are >0.5. Therefore, the scale of this study has good convergent validity.

Table 3 . Convergent validity analysis results.

The discriminant validity of the scale is good if the square root of the AVE value of each construct is greater than the correlation coefficient between the constructs ( Fornell and Larcker, 1981 ). The numbers on the diagonal in Table 4 are the square roots of the AVE values. It can be seen that the square root of each construct’s AVE value is greater than the correlation coefficient between its corresponding constructs. This shows that the discriminant validity of the scale in this study is good.

Table 4 . Discriminant validity analysis results.

Common method bias and multicollinearity test

This study used a questionnaire method to collect data from the same subjects, so there was a possibility that the problem of common method bias may arise. In order to effectively control the generation of common method bias, Podsakoff et al. (2003) suggested the methods of ex ante procedural prevention and ex post statistical testing. In terms of ex ante prevention, the purpose of this study was stated in the first part of the questionnaire. We emphasized the anonymous completion of the questionnaire, avoided semantically ambiguous measurement questions, and selected consumers of tourism e-commerce live streaming in different provinces and cities. From the ex post statistical testing aspect, this study used the Harman one-way method to test the common method bias. The exploratory factor analysis was conducted by principal component analysis on all measured question items of the constructs of interactivity, authenticity, entertainment, flow experience, trust, and purchase intention without rotation. The results showed that the first principal component explained 29.773% of the total variance, which was less than the critical value of 50% ( Podsakoff et al., 2003 ; Chen et al., 2022a ). It can be seen that the common method bias problem in this study is not serious.

Multicollinearity refers to the inaccuracy of model estimation due to the presence of highly correlated relationships among the independent constructs in a linear regression model. Variance inflation factor (VIF) is one of the indicators to test for multicollinearity. In this study, the multicollinearity problem of the model was tested, and the results showed that none of the VIF values in this study was higher than 10. Therefore, there is no multicollinearity problem in this study.

Hypothesis testing

We conducted structural equation modeling to verify the hypotheses. The indexes and evaluation criteria for evaluating the model fit ( Wu, 2010 ) are shown in Table 5 . The comparison shows that all the fit indicators meet the requirements, indicating that the model of this study has a good fit.

Table 5 . Fitting of the study model.

Table 6 demonstrates the path coefficients and hypotheses results in this study. Interactivity ( β  = 0.238, p  < 0.001), authenticity ( β  = 0.205, p  < 0.001), and entertainment ( β  = 0.154, p  < 0.01) all positively influenced flow experience. Therefore, H1a, H2a, and H3a are supported. Interactivity ( β  = 0.132, p  < 0.05), authenticity ( β  = 0.135, p  < 0.05), and entertainment ( β  = 0.142, p  < 0.05) all positively influenced trust. Therefore, H1b, H2b, and H3b are supported. Interactivity ( β  = 0.191, p  < 0.001), authenticity ( β  = 0.153, p  < 0.01), flow experience ( β  = 0.255, p  < 0.001), and trust ( β  = 0.223, p  < 0.001) all positively influenced purchase intention. Therefore, H1c, H2c, H4, and H5 are supported. The results of the data analysis show that entertainment does not influence purchase intention positively ( β  = 0.092, p  > 0.05). Therefore, H3c is not supported. The results are shown in Figure 2 .

Table 6 . Structural equation model validation results.

Figure 2 . Path coefficient test results. * p  < 0.05, ** p  < 0.01, and *** p  < 0.001. n.s., not significant.

In addition, a bootstrapping procedure with 5,000 samples was used to examine mediating effects ( Shi et al., 2011 ; Zhou et al., 2015 ). The results are shown in Table 7 . The effect of interactivity on purchase intention through the mediating effect of flow experience is 0.064 with 95% confidence interval excluding 0. The mediating effect is significant. Flow experience also mediates the both effects of authenticity and entertainment on purchase intention. The effect of interactivity on purchase intention through the mediating effect of trust is 0.031 with 95% confidence interval excluding 0. The mediating effect is significant. Trust also mediates the both effects of authenticity and entertainment on purchase intention. Therefore, H6a–H6c and H7a–H7c are supported.

Table 7 . The mediation effects test analysis results.

Conclusion and implications

Discussion and conclusion.

This paper applied the S–O–R model to the study of consumers’ purchase intention in tourism e-commerce live streaming, focused on the influences of the tourism e-commerce live streaming features on consumers’ purchase intention, and analyzed the antecedent variables and paths of tourism consumers’ purchase intention through flow experience and trust. The following conclusions are drawn from the empirical research and analysis:

First, interactivity and authenticity of the tourism e-commerce live streaming features have positive effects on consumers’ purchase intention, but entertainment has insignificant effect on consumers’ purchase intention. Compared with the traditional online tourism marketing, tourism e-commerce live streaming is more immersive, as the streamer presents the tourism products to the consumers visually. The streamer introduces the tourism products, conducts live experience, and shares the experience. Tourism e-commerce live streaming creates a face-to-face shopping atmosphere, so that consumers can directly understand the advantages and disadvantages of tourism products. Tourism e-commerce streamers attract and retain consumers through real-time interaction and real product display, thus increasing the conversion rate ( Li et al., 2021 ; Zhang et al., 2021a ).The effect of entertainment on consumers’ purchase intention is not significant, which may be because when viewers watch tourism e-commerce live streaming for entertainment purposes, viewers will only stay in the viewing part and cannot directly generate purchase intention.

Second, the flow experience has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. The research results show that the interactivity, authenticity, and entertainment of tourism e-commerce live streaming positively affect flow experience, flow experience positively affects consumers’ purchase intention, and flow experience has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. The interactivity of tourism e-commerce live streaming allows consumers to communicate with the streamer and other consumers in both directions and then immerse themselves in the live streaming environment. The authenticity of tourism e-commerce live streaming can increase consumers’ interest in the products. The entertainment of tourism e-commerce live streaming can meet the pleasure psychology of consumers. The flow experience of tourism consumers makes them want to participate in the live streaming unconsciously and generate purchase intention under the stimulation and guidance of the streamer, which is consistent with the conclusions of Huang et al. (2021) .

Finally, trust has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. The research results show that the interactivity, authenticity, and entertainment of tourism e-commerce live streaming positively affect trust, trust positively affects consumers’ purchase intention, and trust has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. There are risks of pictures being embellished in traditional online tourism marketing, so tourism consumers are often skeptical of tourism marketing. The interactivity of tourism e-commerce live streaming strengthens the emotional communication between consumers and streamers and brings the psychological distance closer. According to social exchange theory, consumers are more willing to trust the products recommended by streamers, thus increasing their purchase intentions ( Cropanzano and Mitchell, 2005 ; Wei et al., 2022 ). In addition, the authenticity of tourism e-commerce live streaming weakens the risks of camera switching and excessive picture embellishment. Viewers can see the full information of live streaming scenes and products realistically, and every move of the streamer can be captured by viewers, increasing the credibility of online shopping. Therefore, the credibility of the information source positively affects consumers’ willingness to purchase ( Tong, 2017 ). Finally, the entertainment of tourism e-commerce live streaming can increase consumers’ curiosity of the product and desire to participate, which leads to positive evaluation of the product and the streamer ( Wongkitrungrueng and Assarut, 2020 ), thus increasing consumers’ purchase intentions.

Research implications

Firstly, we have identified three unique features of tourism e-commerce live streaming features, namely interactivity, authenticity, and entertainment. Moreover, we studied the consumers’ purchase intention through the three features of the new media form. This provides a fresh perspective for the quantitative studies of tourism e-commerce live streaming. Previous studies have shown that e-commerce live streaming consumer’ purchase intentions can be influenced by live streaming strategy ( Zhang et al., 2020 ), interaction ( Li et al., 2021 ; Zhang et al., 2021b ), and social presence ( Ang et al., 2018 ; Chen and Liao, 2022 ). However, previous studies have mostly studied the impact of one feature of e-commerce live streaming on consumer’ purchase intention, without systematic and comprehensive studies, and they have not connected live streaming features with consumers’ perception ( Sun et al., 2019 ; Deng et al., 2021 ; Qiu et al., 2021 ; Lin et al., 2022 ). This study confirmed that interactivity and authenticity of the tourism e-commerce live streaming features have positive effects on consumers’ purchase intention, but entertainment has insignificant effect on consumers’ purchase intention. It enriches the research content of tourism e-commerce live streaming.

Secondly, we offered theoretical insight into consumers’ purchase intention by employing the SOR model to tourism e-commerce live streaming research. A few studies have applied SOR model in the research of e-commerce live streaming ( Xu et al., 2020 ; Guo J. et al., 2021 ), whereas, they have not paid attention to the tourism e-commerce live streaming. Since tourism products have special features (e.g., high unit price, low purchase frequency, intangibility, and non-transferability) that are different from general products ( Xie et al., 2022 ), it is necessary to study on tourism e-commerce live streaming and the consumers’ psychology. The effectiveness of the SOR model in tourism e-commerce live streaming was confirmed, which provides a more profound and thorough understanding of the formation of tourism consumers’ purchase intention.

Finally, this study also examined the mediating effects of flow experience and trust on the relationship between tourism e-commerce live streaming features and consumers’ purchase intention, which contributes to research related to consumers’ purchase intention in live streaming commerce ( Lu and Chen, 2021 ). To the best of our knowledge, in the existing literature, no research has examined the direct and mediating effect of flow experience in e-commerce live streaming. A few studies have provided empirical evidence for the positive effect of trust on e-commerce live streaming consumers’ purchase intention ( Tong, 2017 ; Dong et al., 2022 ), whereas, they have not paid attention to the mediating effect of trust. The results of this study indicated that flow experience and trust partially mediates the impact of tourism e-commerce live streaming features on consumers’ purchase intention. It enriches the research content of emotional and cognitive reactions in tourism e-commerce live streaming.

Practical implications

First of all, the positive roles of tourism e-commerce live streaming’s features on consumers’ perceptions point to the need for enterprises and streamers to invest resources in amplifying these three features when designing the live streaming. In terms of interactivity, the streamer should interact with consumers, enliven the atmosphere of the live streaming room, and make detailed and accurate answers to the questions raised by consumers. As one example, the streamer can design interactive lucky draws at different stages of the live-streaming process ( Lv et al., 2022 ). In terms of authenticity, streamers should show the products in all aspects, strengthen the authenticity of the products and the consumers’ sense of live immersion, and create the feeling of offline shopping for consumers. Streamers can also effectively evaluate the products based on his or her own experience and provide consumers with purchase suggestions. In terms of entertainment, streamers can post some interesting content, discuss interesting entertainment topics, and hold a series of entertaining activities. For example, streamers can introduce tourism products in the form of sitcoms, or conduct role-play related to the tourism live streaming theme or destinations ( Lv et al., 2022 ; Xie et al., 2022 ).

In addition, the flow experience has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. The research results show that flow experience positively affects consumers’ purchase intention, and flow experience has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. Therefore, the tourism e-commerce live streaming platform and streamers should enhance consumers’ flow experience in order to increase their purchase intention. First of all, the tourism e-commerce live streaming platform and streamers should seek to create attractive content that meets consumers’ expectations ( Xu et al., 2019 ), and further compel them to continue watching and purchase. Additionally, emotional connections with the consumers through friendly words, passionate and immersive explanations and interactions are recommended strategies for streamers. Finally, when a consumer expresses confusion about the live streaming content, the streamer should give an accurate answer in time to satisfy the consumer’s curiosity about the tourism product. These methods enhance the consumer’s flow experience, thus increasing consumers’ purchase intentions.

Finally, trust has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. The research results show that trust positively affects consumers’ purchase intention, and trust has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. Therefore, the tourism e-commerce live streaming platform and streamers should enhance consumers’ trust in order to increase their purchase intention. First of all, a clear and comprehensive introduction of the products will help the consumers to enhance their perception of the consumption experience ( Wongkitrungrueng et al., 2020 ), especially with tourism products. In addition, reliable streamers facilitate consumers’ trust ( Ma et al., 2022 ). In order to assemble a group of high-quality streamers, organizations should establish strict recruitment standards. Finally, streamers should strictly control the quality of products according to their own expertise and eliminate unqualified products into the live streaming room, so as to enhance consumers’ trust and promote the formation of purchase intention.

Limitations and further research

There are still some limitations in the study. Firstly, the features of tourism e-commerce live streaming are multifaceted, so future research can expand the features of tourism e-commerce live streaming by introducing factors such as the ease use of platform and platform usefulness to explore consumers’ psychology and behavior. Secondly, the features of tourism e-commerce live streaming affect consumers’ cognitive and emotional responses, but whether there are other mediating and moderating constructs in the influence mechanism need further investigation in the future. Finally, this study used a self-report questionnaire to collect data, and respondents might be influenced by various factors such as emotions and the environment. Therefore, the study results might be biased. In the future, multiple measurement methods can be tried to measure the constructs more accurately.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

XL contributed conception and design of the study, performed the statistical analysis, wrote the first draft, and revised the manuscript. LZ and QC organized the data collection. XL, LZ, and QC polished the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by the Special Project of Zhejiang Provincial Social Science Foundation (21GXSZ063YBM), Soft Science Research Program of Zhejiang Province (2021C35045), Scientific Research Project of Zhejiang Education Department (Y202248666), Research Project on Higher Education of Zhejiang Gongshang University (Xgy22015), and the Key Project of Discipline Construction and Management of Zhejiang Gongshang University (2022).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: tourism e-commerce, live streaming, interactivity, authenticity, entertainment, flow experience, trust, purchase intention

Citation: Liu X, Zhang L and Chen Q (2022) The effects of tourism e-commerce live streaming features on consumer purchase intention: The mediating roles of flow experience and trust. Front. Psychol . 13:995129. doi: 10.3389/fpsyg.2022.995129

Received: 15 July 2022; Accepted: 09 August 2022; Published: 26 August 2022.

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Copyright © 2022 Liu, Zhang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xiaoli Liu, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

E-Commerce and Tourism

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• Introduction

Tourism IT System Trends

Has e-commerce passed its prime or is it just resting? While business and stock market expectations have not been fulfilled, online transactions in the travel and tourism industry are continuously increasing despite tough economic problems in this arena and fewer travelers overall. This industry is the leading application in the B2C (business-to-consumer) arena. Whereas other industries are displaying a stronger hold to traditional processes, the tourism industry is witnessing an acceptance of e-commerce to the extent that the entire industry structure is changing. The Web is used not only for information gathering, but also for ordering services. A new type of user is emerging, one who acts as his or her own travel agent and builds a personalized travel package.

In 2003 more than 64 million Americans—30% of the U.S. adult population—used the Internet to look for information about destinations or to check prices and schedules. And two- thirds of them—42 million—booked travel via the Internet, an 8% gain over 2002, according to the Travel Industry Association of America (www.tia.org). In the same period European online travel sales increased by 44%, reaching over $14 billion, according to the Danish Center for Regional and Tourism Research (www.crt.dk). One survey predicts that by 2007, 30% of all B2C transactions in the German-speaking European countries will be enacted via the Internet [ 9 ], while other market research institutes have made predictions ranging on either side of this figure. All of these statistics are problematic in that researchers used different variables and measurement methods, with some researchers distinguishing between e-business and e-commerce and some not. But even considering this lack of standardization, all statistics for the travel domain point upward.

In addition, terms such as e-commerce and e-business fall short in encapsulating tourism: such terms are transaction- and business-oriented and ignore the fact that the Web is also a medium of curiosity, of creating communities, or just having fun—all of which may or may not result in business being conducted. The tourism product in particular has to do with emotional experiences; it is not just business. The travel and tourism industry as a global (and a globalization) industry demonstrates the following features:

• Travel and tourism represent approximately 11% of the worldwide GDP, according to the World Travel & Tourism Council.
• The World Tourism Organization predicts one billion international arrivals in the year 2010. On average, tourism is expected to grow faster than other economic sectors.
• As an umbrella industry, it relates to many sectors such as culture or sports. Over 30 different industrial components have been identified that serve travelers, which explains the industry’s heterogeneity.
• Due to its SME structure (especially when taking a destination point of view) it has great importance for regional development. For example, the E.U. hotel and restaurant sector accounts for more than 1.3 million enterprises, or 8.5% of all European enterprises. The majority of these enterprises are small, with 1 to 9 employees.
• The supply and demand sides form a worldwide network, where both production and distribution are based on cooperation.
• The product is perishable and complex; for example, an unsold hotel bed represents lost income. The supplier risk of loss can be reduced if information access is available.
• The tourism product itself is a bundle of basic products. To support the rather complex bundling, products must have well-defined interfaces with respect to consumer needs, prices, and distribution channels.

Tourism is an information-based business, the product is a “confidence good,” and an a priori comprehensive assessment of its qualities is impossible. Tourists must leave their daily environment to consume the product. At the moment of decision making, only an abstract model of the product is available, based on information acquired through multiple channels, such as television, brochures, word-of-mouth, or the Web. Tourism products require information gathering on both the consumer and supply sides—and thus entail high information search costs. Such informational market imperfections lead to the establishment of comparably long information and value chains.

Figure 1 differentiates between the supply and demand sides and the respective intermediaries. The nodes indicate the relevant types of players in the field, and links mark the relationships as well as the information flow, with only the most relevant links shown. We designate suppliers like hotels or restaurants, mostly SMEs, as “primary.” With respect to a functional differentiation, these companies are on the same level as the big players like airlines. Tour operators can be seen as product aggregators, and travel agents act as information brokers, providing the final consumer with the relevant information and booking facilities. CRS/GDS (central reservation systems/global distribution systems), stemming from the airline reservation systems developed in the 1960s, also include products such as packaged holidays, or other means of transport. Whereas the intermediaries on the right side can be seen as the professional connection between supply and demand (mainly based on the electronic infrastructure and functionality of CRS/GDS), the left side is relevant for the management, planning, and branding of a destination. These national, regional, and local tourism organizations are normally publicly funded, act on behalf of all suppliers within a destination, and are not engaged in the booking process. The upstream flow of Figure 1 consists of product information, whereas the downstream flow reports on market behavior, mostly represented in terms of statistical aggregates. Both information flows create a tourist information network linking all market participants and reflecting the economic relationships between them.

The Web is changing the needs of consumers, who are increasingly less loyal, take more frequent vacations of shorter duration, and take less time between choosing and consuming a tourism product. The Web is also forging new ways to satisfy consumer needs, as it allows for an “informatization” of the entire tourism value chain—resulting in numerous value-generating strategies [ 11 ]:

• Value extraction. Examples of this strategy, which increases efficiency and reduces costs, include process automation and client outsourcing, such as self-check-in of hotel guests or airline passengers.
• Value capture. Data mining for forecast or yield management is an example of this strategy, in which client and sales information supports marketing goals.
• Value addition. This strategy involves a linear combination of products and services to create richer product bundles. One example is the linkage of mobile services and existing Web sites, to advise tourists during their travel.
• Value creation. The focus here is on network effects, involving, for example, tourists participating in service definition and destination planning.

With such strategies, not only are processes changed, but new services can be designed, extending the range of options to customize and configure products. Customization describes the process of individualizing products or services based on IT-enabled mass customization. Configuration refers to the bundling of different product or service components to integrated offerings. Companies combine their core products with layers of additional services.

The Web is changing the needs of consumers, who are increasingly less loyal, take more frequent vacations of shorter duration, and take less time between choosing and consuming a tourism product.

Given the dynamics of the sector and the very competitive e-market, nearly all stakeholders have implemented their strategies. Tourism has also become the playing field for new entrants, either startups or companies from the media and IT sectors. Since tourism is an information-based business, it fits well with their respective background. One can observe a trend toward further specialization and an ongoing deconstruction of the value chain, paralleled by an integration of players and products. Companies compete and cooperate simultaneously, and boundaries within the industry are blurring. Each market player is affected:

• Tourists are addressed by more players, and they play a more active role in specifying their services, such as by using reverse-auction sites.
• Travel agents see a diminishing power in the sales channel, prompting them to put more emphasis on consulting and more complex products.
• Internet travel sites are providing new market functionality and technology, focusing on personalized intelligent tools for travelers (we will describe the recommendation functionalities).
• Destination management organizations are developing cooperation models within destinations. Here they will occupy a new role as consolidator and aggregator.
• Based on mass-customization and flexible configurations, tour operators will blur the boundaries between the individual and packaged tour. For example, the Italian operator Costa Crociere has developed a personalized cruise builder.
• CRS/GDS demonstrate an “Intel inside” marketing strategy by linking to major tourist Web sites to increase transaction volume. They also move into direct sales for the retail segment.
• Suppliers will increasingly form alliances and support electronic direct sales, increasing price competition as well as price differentiation. They will also redefine customer processes such as electronic ticketing or automated check-in.

Such developments are leading to an evolution of the market best described as an ongoing interplay between concentration (as in the U.S. with the major online travel sites such as Expedia, Orbitz, or Travelocity) and the simultaneous entrance of new players. The increased complexity associated with this evolution calls for technical innovations to generate superior consumer services such as transparent access, market overview, and price comparisons.

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The emerging business scenario is based on flexible network structures and increasing consumer integration. If one adds the tourist life cycle, taking into consideration the mobility of travelers, one can link the respective tourist phases with company processes (see Figure 2 ).

Processes obviously cross company borders, leading to distributed B2B2C applications, supporting both company cooperation as well as mobile communication with consumers. Technology based on a common pervasive infrastructure will become transparent, or invisible to the consumer, and information will be available at home, work, and during travel. In such a scenario IT systems should:

• Support heterogeneous data formats and business functions as well as distributed data sources. Such systems must account for different types of participating entities, with their functional differences;
• Be scalable and open with regard to geographical and functional extensions. They will support the entire consumer life cycle and all business phases;
• Enable full autonomy of the respective participants but enhance cooperative behavior, providing sophisticated tools for suppliers as well as dynamic network configurations;
• Integrate mobile and fixed services, enabling multichannel access to services provided by the various players;
• Support attentive user interfaces and personalization through extensive exploitation of user modeling, taking into consideration user behavior and cognition as well as emotional aspects.

The research and development activities crossing travel and tourism applications have addressed these themes, producing some remarkable results. Quite naturally, many such activities follow an AI-based approach, using horizontal technologies that can be exploited in applications such as travel planning and scheduling, visitor guidance systems, individual pricing, reverse auctions, or workflow management for supporting cooperative marketplaces. Some of the technologies expected to have a major impact include the following:

Information extraction. Tourist information portals are still largely based on unstructured information. A critical problem in developing distributed systems involves accessing information formatted for human use and transforming it into a structured data format, such as XML. This problem is tackled by wrapping techniques. Such techniques provide highly accurate extraction rules that adapt to structural site changes, ensuring the correct extraction of data [ 5 , 6 ].

Information integration. Wrappers can be built atop structured or semistructured information sources. This sets the stage for systems that answer queries based on the extraction and combination of data fetched from multiple wrappers [ 4 ]. Tourism-related information sources represent a perfect application for such technologies. For example, Theaterloc is an information integration application that lets users retrieve information about theaters and restaurants in the U.S. from five distinct online sources [ 1 ]. The core components of this application are a mediator that exploits AI planning technologies, a domain model (containing a unifying ontology), and a set of axioms describing mapping relationships between the integrated data view and the sources. When queries are posed, the system reasons about the domain model and source descriptions in order to build a plan for retrieving and integrating data.

Information presentation. Tourism, particularly cultural heritage, is a privileged application domain for intelligent information presentation techniques [ 10 ]. Natural language technologies have been used to build contextual presentations and speech and gesture recognition. Also, animated characters support an augmented interactivity involving users in the appreciation of their cultural heritage. Applications have been developed where the exhibit and the information presentation are blended. For example, the user, monitored by a set of sensors, can activate personalized windows on a mobile device to receive information regarding a given museum exhibit. In addition, unsolicited suggestions about supplemental topics or objects can be delivered.

Recommendations. Recommender systems suggest products and provide consumer information to facilitate the decision process. In tourism, some notable applications focus on destination selection and travel products bundling [ 2 , 8 ]. In these applications the user is asked explicitly about his needs and constraints. These systems, combining content-based filtering technologies, interactive query management, and variations of the collaborative-filtering approach or case-based reasoning, rank suggestions extracted from structured catalogs. Tourism recommendation poses peculiar requirements related to the complexity and intangibility of the travel product. Recommendations must refer to a variety of products, such as locations, attractions, accommodations, and flights, in order to provide a meaningful picture of the proposed travel.

Semantic Web. The Semantic Web vision, or the idea of having Web data defined and linked so it can also be used by machines for automation, integration, and reuse across various applications, provides a unifying view of the previous technologies. In tourism, this technology may have a major impact (see, for example, the European project [ 3 ]). The industry provides a challenging test bed for peer-to-peer semantic Web services, based on the integration of the semantic Web with peer-to-peer Web services. For instance, services for finding or integrating information providers eventually need to directly exploit resources present at other nodes without intervention of any central server, where nodes may join and be integrated in an ad-hoc manner [ 7 ].

Mobility. Travelers expect to get access to services and information from various devices, whenever and wherever they need it. Typical mobile applications can be found in the following areas: airlines, hotels and restaurants, transportation, city guides, traffic and weather conditions, and other services like translation or currency conversion. Mobile terminals create new and enhanced ways to support tourists while on tour. While the new technologies promise benefits and added value, they also raise challenges concerning usability, accessibility over different devices, trustworthiness, and interactivity. The challenge is a context-sensitive, personalized, and effective model of interaction that accounts for the constraints of ubiquitous access. Here, sophisticated user models developed in the tourism domain, such as the recommendation systems described previously, may help surmount these obstacles.

Travel and tourism have illustrated how e-commerce may change the structure of an industry, and in the process create new business opportunities. The deployment of more specialized services, flexible network configurations, and further consumer integration will lead to smart marketplaces that integrate all stakeholders. The underlying pervasive IT scenario enables as well as enforces this development, demonstrating that tourism is an interesting field of application as well as research. As such it may also be of interest for other industries to learn from this development and to understand emerging e-marketplaces.

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Please note you do not have access to teaching notes, tourism e-commerce live streaming: the effects of live streamer authenticity on purchase intention.

Tourism Review

ISSN : 1660-5373

Article publication date: 24 October 2023

This paper aims to explore the impact of live streamer authenticity (LSA) on purchase intention in tourism e-commerce live streaming, with a focus on boundary conditions and underlying mechanisms.

Design/methodology/approach

The data collected from 451 participants were analyzed using structural equation modeling.

This paper found that four dimensions of LSA – sincerity, truthfulness endorsement, expertise and uniqueness – positively influenced purchase intention, while visibility did not. In addition, sincerity, truthfulness endorsement and uniqueness had an indirect influence on purchase intention through flow experience, while sincerity, truthfulness endorsement, expertise and uniqueness had an indirect effect through perceived trust. Furthermore, self-construal moderated the effect of sincerity and truthfulness endorsement on purchase intention, with the positive effect being stronger for the dependent self-construal.

Originality/value

To the best of the authors’ knowledge, it is the first study to examine LSA dimensions and their consequences. This paper not only provides a better and more detailed understanding of the complexity of LSA but also contributes to the development of authenticity theory by responding to individual authenticity studies.

本文旨在研究旅游直播电商中主播真实性对旅游购买意向的影响, 及其内在机制和边界条件。

本文采用问卷调查的方法收集451名参与者的数据。然后用结构方程模型（SEM）对收集的数据进行分析。

本文发现, 主播真实性的四个维度–真诚性、真实背书、专业性和独特性–对购买意愿有积极影响, 而主播的透明性没有影响。此外, 真诚性、真实背书和独特性通过心流体验对购买意愿产生间接影响, 此外真诚性、真实背书、专业性和独特性还通过感知信任产生间接影响。此外, 自我建构调节了真诚性和真实背书对购买意向的影响。对于依赖型自我建构, 真诚性和真实背书对购买意愿的影响更强。

本文是最早探讨主播真实性维度及其后果影响的研究之一。本文不仅对主播真实性的复杂性有了更好更详细的了解, 而且响应了对个体真实性研究的呼吁, 为真实性理论的发展做出了贡献。

Este artículo explora el impacto de la autenticidad del streamer en la intención de compra en la retransmisión en directo de comercio electrónico turístico, centrándose en las limitaciones y los procesos subyacentes.

Diseño/metodología/enfoque

Se analizaron los datos recopilados de 451 participantes utilizando un modelo de ecuaciones estructurales (SEM).

Este trabajo halló que cuatro dimensiones de la autenticidad de los streamers en directo -la sinceridad, el respaldo a la veracidad, la pericia y la singularidad- influían positivamente en la intención de compra, mientras que la visibilidad no lo hacía. Además, la sinceridad, el respaldo de veracidad y la singularidad influyeron indirectamente en la intención de compra a través de la experiencia de flujo, mientras que la sinceridad, el respaldo de veracidad, la pericia y la singularidad tuvieron un efecto indirecto a través de la confianza percibida. Además, la autoconstrucción moderó el efecto de la sinceridad y el respaldo de veracidad sobre la intención de compra, siendo el efecto positivo más fuerte para la autoconstrucción dependiente.

Originalidad/valor

Es el primer estudio que examina las dimensiones de la autenticidad de los retransmisores en directo y sus consecuencias. Este trabajo no sólo proporciona una comprensión mejor y más detallada de la complejidad de la autenticidad de los streamers en directo, sino que también contribuye al desarrollo de la teoría de la autenticidad al responder a los estudios sobre autenticidad individual.

• Purchase intention
• Self-construal
• Perceived trust
• Flow experience
• Live streamer authenticity
• Autenticidad de los streamers en directo
• Intención de compra
• Experiencia de flujo
• Confianza percibida
• Autoconstrucción

Liu, Y. and Sun, X. (2023), "Tourism e-commerce live streaming: the effects of live streamer authenticity on purchase intention", Tourism Review , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/TR-04-2023-0245

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The Top Travel E-Commerce Websites in 2021

• by Fiona Su
• Sep 15, 2021

Top e-commerce travel websites leverage a headless CMS and e-commerce microservices to respond directly to their customers.

The travel industry popularized the importance and benefits of customer convenience, loyalty and rewards programs, competitive pricing and price comparisons, diverse search filtering, and streamlined checkout experiences.

The best travel e-commerce websites include Booking.com, Tripadvisor, Uber, and Airbnb, each of which uses an internally designed headless and modular architecture.

Licensing fabric allows travel e-commerce websites to reduce development overhead without negatively impacting the customer experience.

E-commerce changed the way travel providers and online travel agencies (OTAs) served consumers. The 1960s launch of SABRE, the world’s first computerized airline reservation system , led to the 1996 release of travel e-commerce website Travelocity.

Today, online sales and travel e-commerce websites contribute to 66% of the revenue brought in by the global travel and tourism market. Altogether, the global market size of e-commerce travel exceeds $517.8 billion .

Headless commerce allows travel e-commerce websites to continually innovate and respond to customers’ changing needs, especially as the industry responds to the COVID-19 pandemic. By using headless CMS solutions, the best travel e-commerce websites may continue to iterate, innovate, and grow their market share.

Features of Travel E‑Commerce Websites

The travel industry popularized many of the features so common across e-commerce websites. Let’s look at some of the typical features you’ve come to expect from the best travel e-commerce websites.

A summary of common features leveraged by the best e-commerce travel websites is illustrated in the table below.

Customer convenience

How did consumers book tickets, airfare, or accommodations before the internet? Travel arrangements required extensive planning via phone or in-person meetings. Without the assistance of travel agencies, consumers were required to coordinate plans with multiple points of contact — cabs or rental cars, airlines, and hotels.

E-commerce travel websites simplify this process . Instead of painstakingly coordinating an itinerary between multiple sources, consumers may plan, price shop, and purchase fares and lodging from or through one specific vendor.

Loyalty programs and incentives

Frequent flier programs are believed to have been introduced by Texas International Airlines in 1979 before being embraced further by American and United Airlines in 1981. In 2017, 91% of OTAs maintained a loyalty program .

Travel e-commerce websites use data analytics and insights to personalize offerings from intricate partner networks . Customers who participate in these programs earn anything from free or discounted fares, special rates on rentals and accommodations, and even discounted liquor, flowers, or health insurance .

Competitive pricing

Around the time travel e-commerce websites began to take off, airlines realized unsold seats could be sold at discounted rates by consolidators and auction houses . This appealed to flexible customers who preferred low fares over minimizing flight times and layovers.

Some e-commerce travel companies eventually adopted dynamic pricing to automatically adjust the cost of fares, lodging, and reservations. Leveraging personalization allows travel sites to “ be more competitive ” and respond to real-time market conditions while offering customers the ability to search for and choose offers that fit their budgets and needs.

Search filtering

Planning for travel is highly personal — no two customers’ journeys are the same. Travel e-commerce websites provide customizable search filtering designed to provide real-time information based on a customer’s requests, preferences, and needs.

Some companies, like Kayak, go the extra mile by providing charts to compare median prices based on historical data, destination, and travel dates. Responsive and useful search filters allow customers to easily find the results they’re looking for when planning their travel itinerary, guiding them further along the sales funnel.

Simplified checkout

Airline and travel e-commerce websites have some of the highest cart abandonment rates in e-commerce, at 87.87% and 81.31%, respectively. (For comparison, the average cart abandonment rate of online stores is 69.8% .)

OTAs and travel companies understand that cart abandonment stems from inefficient user interfaces and too much choice. By keeping the checkout process simple and limiting choices to the best offers, e-commerce travel websites may improve conversions and reduce abandonment.

Top Travel E-Commerce Websites

E-commerce travel websites excel at using headless commerce to prioritize the customer experience. The top four travel and tourism companies ranked by traffic , as of August 1, 2021, are listed below.

Booking.com

The home page of Booking.com, spotlighting its upfront search feature and options to make a wide variety of reservations.

• Customer convenience: Customers may book and reserve a variety of travel arrangements
• Loyalty programs and incentives: Yes, the Booking.com Genius program provides free lifetime access to discounts and rewards
• Competitive pricing: Guarantees the best available rates
• Search filtering: Extensive search filters for a wide variety of reservations
• Simplified checkout: No registration of prepayment is required; immediate confirmation

Since its launch in 1996, OTA Booking.com has continually iterated on its architecture and design to facilitate a convenient customer experience. Website visitors are immediately presented with a search box that recommends popular nearby attractions and distinctly highlights selected travel dates.

Site navigation is simple, too. Booking.com offers customers the option to book accommodations, flights, and car rentals, as well as tickets to attractions or and taxi reservations.

Reservations are instantly confirmed and free of booking and administrative fees. In many cases, Booking.com claims reservations may be canceled free of charge. The travel e-commerce giant also offers price-matching and guarantees the best available rates .

Booking.com is powered by an internally developed CMS . Though Booking.com’s internal architecture allows for scalability, its downside — especially when compared to headless CMS’s like fabric XM — is that developers must continually maintain the software and custom-build additions to the framework.

Tripadvisor

The Tripadvisor home page, with easy access to travel research and bookings and suggestions for local attractions and “experiences.”

• Customer convenience: Customers may quickly find, research, and book a wide variety of travel experiences
• Loyalty programs and incentives:  Yes, for an annual feed, Tripadvisor Plus offers deals on hotel reservations and discounts on attractions
• Competitive pricing:  Compares average rates from a wide variety of OTAs and booking websites
• Search filtering:  Specific filters for each type of accommodation being researched and booked
• Simplified checkout:  The checkout process is dependent on the specific partner site chosen

Tripadvisor, which bills itself as “ the world’s largest travel guidance platform ,” aims to help customers plan for and book travel arrangements. Eighty percent of Tripadvisor visitors take longer than four weeks to complete their purchase and spend 29% longer performing research than visitors to other sites.

Though Tripadvisor claims it is a “booking supplier” vs. OTA, customers may book hotels, vacation rentals, flights, rental cars, cruises, and even restaurant reservations. However, Tripadvisor emphasizes its user-generated “934 million reviews and opinions of nearly 8 million businesses over the sole ability to book travel arrangements.

In comparison to other travel e-commerce websites, Tripadvisor is structured to encourage travel “experiences.” Through a combination of research and suggestions, customers receive guidance intended to maximize their travel journeys.

The Tripadvisor website operates through a combination of microservices , achieving a modular and event-driven commerce architecture. . However, unlike with third-party e-commerce microservices that scale on their own, engineers must put effort into mitigating “cross-team overhead.”

The Uber homepage offers access to the ride request form front-and-center, with the ability to request an immediate pickup or schedule one for a later time.

• Customer convenience:  Quick and simple ride request/scheduling with a visible breakdown of the total estimated fare
• Loyalty programs and incentives:  Yes, Uber offers the free-to-join Uber Rewards program, which rewards customers with points for booking rides or ordering food via Uber Eats; points may be exchanged for a variety of perks and benefits
• Competitive pricing:  Provides nice estimates, but no comparison
• Search filtering:  Serves over 10,000 cities and 600 airports globally, but rides aren’t always available on-demand
• Simplified checkout:  Requires sign-up before requesting rides

Uber was founded in 2009 as a global ridesharing company that disrupted the taxi market. Since its founding, the travel e-commerce company has grown to be the world’s largest ridesharing company with 1.51 billion trips in Q2 2021.

After creating or logging into an account, riders may request a ride through the Uber website or its smartphone app . Customers may either request an immediate pickup or schedule one for later though, notably, service isn’t always available depending on the user’s location and the requested pickup time.

Uber’s travel e-commerce website is powered by multiple microservices . This headless and modular architecture allows Uber to develop independent services in different languages, depending on the need, while still integrating those microservices into the larger overall platform.

With this setup, Uber can continually integrate and improve its architecture to best meet the needs of its drivers and riders.

The main Airbnb page, with an accessible and highly visible search tool to quickly and easily present available listings according to the required parameters.

• Customer convenience:  Easy to search for, sort, and review for relevant listings
• Loyalty programs and incentives:  No, the Airbnb Superguest program has long been talked about but has no set launch date
• Competitive pricing:  Individual rates are determined by Airbnb hosts but may be more affordable compared to traditional lodgings
• Search filtering:  A wide variety of filters allow customers to narrow down results based on needs and preferences
• Simplified checkout:  Requires an account sign-up before making reservations

E-commerce travel giant Airbnb was founded in San Francisco in 2007 . Since then, it has expanded to over 220 countries and 100,000 cities, earning hosts an annual average of $9,600.

Airbnb allows customers to search through offered stays and experiences . A versatile set of filters helps users narrow down their search to find accommodations that meet their specific requirements. Customers are also presented with reviews, ratings, and the cost of each offer. In addition, each offer displays specific details about the property, similar to typical real estate or apartment listings.

In 2017, Airbnb began transitioning to a service-oriented architecture built on a Thrift IDL-centered framework . As a result, Airbnb developers could prioritize resilience to improve and support server stability without compromising service quality.

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The effects of tourism e-commerce live streaming features on consumer purchase intention: The mediating roles of flow experience and trust

Affiliations.

• 1 Library, Zhejiang Gongshang University, Hangzhou, China.
• 2 Publicity Department, China Jiliang University, Hangzhou, China.
• 3 School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou, China.
• PMID: 36092030
• PMCID: PMC9462463
• DOI: 10.3389/fpsyg.2022.995129

Given that tourism e-commerce live streaming has become an important driver of tourism development after the outbreak of Covid-19 but limited attention has been paid to this area, this study examines the impacts of tourism e-commerce live streaming features (interactivity, authenticity, and entertainment) on the consumers' purchase intention from the perspectives of consumers' flow experience and trust based on the SOR theory. The authors collected survey data from 357 tourism e-commerce live streaming consumers and used the structural equation model to test the research model. The results reveal that interactivity and authenticity positively affect tourism e-commerce live streaming consumers' purchase intention, but entertainment does not influence purchase intention positively; interactivity, authenticity, and entertainment each positively affects tourism e-commerce live streaming consumers' flow experience and trust; both flow experience and trust positively affect tourism e-commerce live streaming consumers' purchase intention; both flow experience and trust have mediating effects on the relationships between tourism e-commerce live streaming features and consumers' purchase intention. This study extends existing theoretical research on tourism e-commerce live streaming and provides some managerial implications for tourism enterprises and streamers.

Keywords: authenticity; entertainment; flow experience; interactivity; live streaming; purchase intention; tourism e-commerce; trust.

Copyright © 2022 Liu, Zhang and Chen.

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The effects of tourism e-commerce live streaming features on consumer purchase intention: The mediating roles of flow experience and trust

1 Library, Zhejiang Gongshang University, Hangzhou, China

2 Publicity Department, China Jiliang University, Hangzhou, China

3 School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou, China

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Given that tourism e-commerce live streaming has become an important driver of tourism development after the outbreak of Covid-19 but limited attention has been paid to this area, this study examines the impacts of tourism e-commerce live streaming features (interactivity, authenticity, and entertainment) on the consumers’ purchase intention from the perspectives of consumers’ flow experience and trust based on the SOR theory. The authors collected survey data from 357 tourism e-commerce live streaming consumers and used the structural equation model to test the research model. The results reveal that interactivity and authenticity positively affect tourism e-commerce live streaming consumers’ purchase intention, but entertainment does not influence purchase intention positively; interactivity, authenticity, and entertainment each positively affects tourism e-commerce live streaming consumers’ flow experience and trust; both flow experience and trust positively affect tourism e-commerce live streaming consumers’ purchase intention; both flow experience and trust have mediating effects on the relationships between tourism e-commerce live streaming features and consumers’ purchase intention. This study extends existing theoretical research on tourism e-commerce live streaming and provides some managerial implications for tourism enterprises and streamers.

Introduction

With the rapid development of digital technology, e-commerce live streaming has become a new business model ( Wongkitrungrueng and Assarut, 2020 ; Sun et al., 2022 ). After the outbreak of COVID-19 pandemic in early 2020, the large-scale travel restrictions further pushed up the popularity of e-commerce live streaming. In China, the scale of e-commerce live streaming users was 464 million in 2021, with an increase of 75.79 million than in December 2020 ( CNNIC, 2022 ). In the field of tourism, the information needs and consumption habits of tourists are changing due to the impact of COVID-19. Different sectors of tourism industry applied high tech or live streaming approach to respond to COVID-19. For example, facial recognition and smart cameras were used in gambling industry ( Liu et al., 2021b ), digital technology was applied in hospitality industry in Macao ( Liu et al., 2021c ), and live streaming was used in tourism industry ( Liu et al., 2022 ). Tourism e-commerce live streaming is developing rapidly ( Deng et al., 2021 ; Xie et al., 2022 ). In China, online travel agents (OTAs), such as Ctrip, Mafengwo, and Tuniu, are accelerating the development of live streaming and exploring the business model innovation of “tourism + live streaming” through e-commerce live streaming and virtual tourism, such as the “Boss Live Session” activity initiated by Ctrip.

At the same time, e-commerce live streaming research has become a research hotspot ( Ang et al., 2018 ; Zhang et al., 2020 , 2022 ). E-commerce live streaming attracts consumers through instant interaction and vivid product display ( Tong, 2017 ; Ang et al., 2018 ; Liu et al., 2020c ). E-commerce live streaming can deliver richer information to consumers than posts that mainly convey product information through text and pictures( Yu and Zheng, 2022 ).Serving the more detailed and vertical needs of consumers, e-commerce live streaming attracts potential consumers, improves the conversion rate, and generates faster sales ( Hu and Chaudhry, 2020 ). It can both improve the conversion rate of both physical and virtual stores, and expose the brand to the public ( Xue and Liu, 2022 ). Previous studies have shown that e-commerce live streaming consumer’ purchase intentions can be influenced by live streaming strategy ( Zhang et al., 2020 ), IT affordances ( Sun et al., 2019 ), interaction ( Li et al., 2021 ; Zhang et al., 2021b ), and social presence ( Ang et al., 2018 ; Chen and Liao, 2022 ). However, little research has been devoted to studying the impacts of e-commerce live streaming features on consumer’s purchase intention systematically, and there are still very few studies on tourism e-commerce live streaming ( Deng et al., 2021 ; Qiu et al., 2021 ; Lin et al., 2022 ).

Tourism e-commerce live streaming promotes the marketing of tourism industry, taps the online consumption potential of tourists, and achieves the synergistic development of tourism online and offline ( Zhang et al., 2021b ; Xie et al., 2022 ). Despite the growing popularity of tourism live streaming, little research has been devoted to studying the impacts of tourism e-commerce live streaming features on consumer’s purchase intention ( Lv et al., 2022 ). Based on a review of studies pertaining to e-commerce live streaming, we proposed three core features of this form of communication: interactivity ( Xue et al., 2020 ; Kang et al., 2021 ), authenticity ( Tong, 2017 ), and entertainment ( Chen and Lin, 2018 ). The tourism e-commerce streamer interacts with the consumers, and the consumers can also interact with each other through pop-ups or other forms, forming an open virtual community centered on the streamer. Compared with the traditional tourism e-commerce marketing model in which consumers initiate the consultations, the interaction in the e-commerce live streaming is intuitive, instantaneous, and interactive, changing from traditional passive service to active guidance and creating a more realistic tourism shopping scenario. At the same time, tourism streamers show tourism products and exchange the information of the products through live streaming, helping tourism consumers establish an authentic perception of the tourism products. That is, tourism e-commerce live streaming creates a face-to-face shopping scenario in comparison with traditional tourism e-commerce. Thus, the perceived authenticity of tourism products is stronger, which helps to enhance the consumer’s trust ( Jiménez-Barreto et al., 2020 ). Another feature of tourism e-commerce live streaming is entertainment. Compared with e-commerce, the entertainment in e-commerce live streaming comes not only from the perception of the shopping experience, but also from the live streaming content and participation process, which is more conducive to the consumer’s flow experience. However, little research has been devoted to regarding interactivity, authenticity, and entertainment as the features of e-commerce live streaming to study their impacts on consumer’s flow experience and trust in an empirical study, especially in the field of tourism.

To fill these gaps, based on the stimulus-organism-response (SOR) model, we used tourism e-commerce live streaming features (interactivity, authenticity, and entertainment) as stimulus variables (S), flow experience and trust as organism variables (O), and tourism consumers’ purchase intention as the response variable (R), to explore the influence mechanism of tourism e-commerce live streaming features on tourism consumers’ purchase intention. We aim to enrich and deepen the research on the formation mechanism of tourism consumers’ purchase intention in the context of tourism e-commerce live streaming theoretically, and practically provide guidance to enhance tourism consumers’ purchase intention and help to realize the integrated development of tourism industry online and offline.

Theoretical background and hypothesis development

To study the influence of the external environment on individual behavior, the stimulus-organism-response (SOR) theoretical model was proposed in the field of environmental psychology ( Mehrabian and Russell, 1974 ). In this context, stimulus (S) refers to external environmental factors that can act on an individual’s cognition and emotion (O) and ultimately elicit a behavioral response (R). A few studies has applied the SOR model in the research of e-commerce live streaming consumers. For example, Xu et al. (2020) employed the SOR framework to investigate contextual and environmental stimuli effects (streamer attractiveness, para-social interactions, and information quality) from a e-commerce live streaming context on viewer’s cognitive and emotional states (cognitive assimilation and arousal) and their subsequent responses (hedonic consumption, impulsive consumption, and social sharing); Guo J. et al. (2021) applied the SOR framework to examine the impact of live streaming feature on the consumers’ cross-border purchase intention from the perspectives of consumers’ overall perceived value and overall perceived uncertainty. However, Xu et al. (2020) did not pay attention to the roles of the e-commerce live streaming features, while Guo J. et al. (2021) regarded the live streaming feature as a concept and did not subdivide the live streaming feature. In addition, since tourism products have special features (e.g., high unit price, low purchase frequency, intangibility, and non-transferability) that are different from general products ( Xie et al., 2022 ), it is necessary to study on tourism e-commerce live streaming and the consumers’ psychology. In this study, tourism e-commerce live streaming features (interactivity, authenticity, and entertainment) were selected to assess the contextual and environmental stimuli, flow experience and trust were selected to assess the internal states of tourism consumers, and tourism customers’ purchase intention were selected to assess their responses. The research model is shown in Figure 1 .

Research model. * , additional analysis is conducted to examine the mediating effect of organism.

The effects of tourism e-commerce live streaming features

Interactivity means that consumers can communicate and exchange information with the information source, emphasizing the two-way communication. When watching tourism e-commerce live streaming, viewers can consult and give gifts to the streamer, express their opinions, and communicate with other viewers through pop-ups. The streamers also actively communicate with their viewers in addition to presenting the products ( Liu et al., 2020c ; Wang and Liu, 2022 ). In the process of live streaming, the frequent interactions between the streamer and consumers make consumers feel temporarily detached from reality, forget about worries, and have a sense of immersion ( Liu et al., 2020a ). The interactive communication between streamers and consumers generates an interactive feedback signal to customers, which can produce a powerful psychological implication to customers and increase their trust in the streamers ( Chen et al., 2021 ). A high level of interaction between streamers and consumers can lead to cognitive and emotional changes of consumers, enhance consumers’ understanding of the streamers and products, and thus increase trust, which ultimately influences consumers’ purchase intentions ( Hou et al., 2020 ; Zhang et al., 2021a ). Liu et al. (2021a) emphasized the importance of interactivity in tourism live streaming. As such, we believe that tourism e-commerce live streaming with strong interactivity can stimulate consumers’ flow experience, trust, and purchase intention. Based on this, this paper proposes the following hypotheses:

H1a : Interactivity of tourism e-commerce live streaming positively affects consumers’ flow experience.

H1b : Interactivity of tourism e-commerce live streaming positively affects consumers’ trust.

H1c : Interactivity of tourism e-commerce live streaming positively affects consumers’ purchase intention.

Authenticity refers to the individual’s evaluation of the truthfulness of the information received. In the traditional tourism marketing, there is a risk of excessive embellishment, lens switching, or image manipulation of pictures ( Zhang et al., 2021a ). Customers cannot see the real products ( Lu et al., 2016 ), making them vulnerable to be cheated by inauthentic and beautified information and may hinder trust-building ( Escobar-Rodríguez and Bonsón-Fernández, 2017 ; Guo L. et al., 2021 ). In tourism e-commerce live streaming, the live streaming process is live and instant, without camera switching. It is a complete presentation of the whole tourism scene and products, with a strong sense of live immersion. In the process of live streaming, the streamers give real descriptions and effective evaluations of the products and offer purchase suggestions, which increase customers’ interest in watching the live streaming ( Li et al., 2021 ). Therefore, tourism e-commerce live streaming with authenticity will attract consumers and bring them into a specific scenario, thus creating a positive emotional experience for consumers. Tong (2017) emphasized that the authenticity of a webcast enhanced customer engagement and trust. Zhang et al. (2021a) showed that live streaming authenticity not only had a positive impact on consumer perceptions, but also influenced consumers’ purchase intentions. Liu et al. (2021a) argued that authenticity was crucial in tourism live streaming. As such, we believe that tourism e-commerce live streaming with strong authenticity can stimulate consumers’ flow experience, trust, and purchase intention. Based on this, this paper proposes the following hypotheses:

H2a : Authenticity of tourism e-commerce live streaming positively affects consumers’ flow experience.

H2b : Authenticity of tourism e-commerce live streaming positively affects consumers’ trust.

H2c : Authenticity of tourism e-commerce live streaming positively affects consumers’ purchase intention.

Entertainment refers to the degree of pleasure felt by consumers during the process of watching live streaming, with the aim of satisfying consumers’ pleasure psychology. Viewers tend to use media to relieve stress for entertainment ( Chen and Lin, 2018 ). To a large extent, consumers participate in consumption for the purpose of personal relaxation and stress relief ( Wang et al., 2020 ). Entertainment is reflected in the lively and interesting topics started by the streamer, and a series of entertaining activities held by the shopping platform or the streamer, such as regular lottery, virtual red envelope distribution, and thumb-up, etc. ( Liu et al., 2020c ). Meanwhile, the creative pop-up messages posted by the viewers and the hover animation of the live streaming window also increase the entertainment of the e-commerce live streaming ( Yu and Xu, 2017 ). Entertainment in live streaming can significantly influence consumers’ flow experience, perceived value, and usage attitude ( Chen and Lin, 2018 ; Cao et al., 2022 ), and also increase the emotional connection between the streamer and consumers ( Hilvert-Bruce et al., 2018 ). Previous studies showed that entertainment had a significant effect on tourist trust ( Pujiastuti et al., 2017 ), social media brand trust ( Zhang et al., 2022 ), and purchase intention ( Ma et al., 2022 ). From this, it can be hypothesized that tourism e-commerce live streaming with strong entertainment can stimulate consumers’ flow experience, trust, and purchase intention. Based on this, this paper proposes the following hypotheses:

H3a : Entertainment of tourism e-commerce live streaming positively affects consumers’ flow experience.

H3b : Entertainment of tourism e-commerce live streaming positively affects consumers’ trust.

H3c : Entertainment of tourism e-commerce live streaming positively affects consumers’ purchase intention.

The effect of flow experience

In the online context, flow leads users to become completely engaged in online tasks and interested to continue these activities. As the consumer experience quality is higher, the perceived value is higher and consumers are more willing to participate ( Prentice et al., 2019 ; Chen et al., 2022b ). E-commerce live streaming enables consumers to enjoy a sense of freedom, control and participation, and a better consumption experience, which can lead to consumers’ willingness to purchase ( Feng and Lu, 2020 ). Flow experience represents an intense involvement that leads to high psychological engagement such as satisfaction and loyalty for virtual world users ( Barker, 2016 ). Gao and Bai (2014) noted that flow experience affected consumers’ behavioral intention, such as the likelihood to purchase from the website. The online students’ flow experience has a significant relationship with continuous intention ( Zhao and Khan, 2022 ). In social commerce, consumers who have experienced flow are likely to participate in social commerce activities ( Zhang et al., 2014 ), which affects consumers’ purchase intention ( Xu et al., 2022 ). From this, it can be presumed that tourism e-commerce live streaming consumers with a stronger flow experience are more likely to generate purchase intentions. Based on this, this paper proposes the following hypothesis:

H4: Flow experience positively affects tourism e-commerce live streaming consumers’ purchase intention.

The effect of trust

Perceived trust refers to the degree of consumers’ trust in the tourism e-commerce live streaming streamer and the products recommended by the streamer. Consumers tend to make purchase decisions in a short period of time and with limited rationality because of perceived trust ( Liu and Shi, 2020 ; Liu et al., 2021e ). In e-commerce live streaming, trust helps to reduce various transaction costs ( Feng and Lu, 2020 ), and reduce consumers’ perceived risk and uncertainty about the streamers and products, makeing consumers actively participate in online transactions ( Liu et al., 2020c ; Guo J. et al., 2021 ). Prior studies demonstrated that trust had an important effect on consumer behavior ( Nadeem et al., 2020 ; Guo J. et al., 2021 ). Alkhalifah (2022) confirmed that trust in social commerce influenced behavior intention. Dong et al. (2022) demonstrated that live-streaming e-commerce with high-quality would increase consumers’ green trust and, thus, strengthen green purchase intention. In the context of tourism, tourist trust is widely accepted to play an important role in influencing their behavior intentions ( Iranmanesh et al., 2018 ; Han et al., 2021 ). It is difficult for consumers to make purchase decisions in tourism e-commerce live streaming because of high uncertainty and perceived risk, but perceived trust can help consumers reduce their decision costs and thus generate purchase intentions ( Lu and Chen, 2021 ). From this, it can be hypothesized that consumers with stronger trust in tourism e-commerce live streaming are more likely to generate purchase intention. Based on this, this paper proposes the following hypothesis:

H5 : Trust positively affects tourism e-commerce live streaming consumers’ purchase intention.

The mediating role of flow experience

Frequent interactions in e-commerce live streaming make consumers temporarily detach from reality and immerse themselves in the live streaming environment, forgetting their worries and generating a flow experience ( Liu et al., 2020a ). The streamer displays the product realistically and evaluate it effectively, give purchase suggestions, and increase customers’ interest in the product when watching the live broadcast ( Li et al., 2021 ). Entertainment in e-commerce live streaming can significantly influence consumers’ flow experience, perceived value, and attitude ( Chen and Lin, 2018 ). Consumers’ flow experience has an impact on attitudes, and when consumers are immersed in the live streaming environment, they want to participate unconsciously and are stimulated by the streamer to purchase ( Huang et al., 2021 ). Arghashi and Yuksel (2022) demonstrated that consumers’ flow experience mediated the relationship of interactivity and trust in AR apps. From this, it can be hypothesized that consumers obtain flow experience by watching tourism e-commerce live streaming and generate purchase intention under the influence of flow experience. Based on this, this paper proposes the following hypotheses:

H6a : Flow experience has a mediating effect between interactivity and purchase intention in tourism e-commerce live streaming.

H6b : Flow experience has a mediating effect between authenticity and purchase intention in tourism e-commerce live streaming.

H6c : Flow experience has a mediating effect between entertainment and purchase intention in tourism e-commerce live streaming.

The mediating role of trust

In e-commerce live streaming, interactivity can form an intimate relationship between the streamer and consumers and increase consumers’ perceived trust ( Wei et al., 2022 ). Authenticity can enhance viewers’ understanding of the products, reduce perceived risk, and promote trust ( Tong, 2017 ). Entertainment can increase consumers’ curiosity about the streamer and the product, and enhance their desire to participate in the live streaming, which leads to positive evaluation of the product and the streamer ( Wongkitrungrueng and Assarut, 2020 ). Perceived trust is an important factor to maintain loyalty and is the foundation of online shopping. Trust comes from the daily interaction between streamers and viewers, the professional competence of streamers, etc. ( Zhang et al., 2021a ). According to Alalwan et al. (2019) and Kim and Park (2013) , trust mediates the relationships between s-commerce dimensions and consumers’ value co-creation, and between the characteristics of s-commerce and purchase intention. Liu et al. (2021d) confirmed that social support had a direct positive effect on s-commerce purchase intention, and that social trust partially mediated the relationship. From this, it can be hypothesized that consumers generate trust by watching tourism e-commerce live streaming and generate purchase intention under the influence of trust. Based on this, this paper proposes the following hypotheses:

H7a : Trust has a mediating effect between interactivity and purchase intention in tourism e-commerce live streaming.

H7b : Trust has a mediating effect between authenticity and purchase intention in tourism e-commerce live streaming.

H7c : Trust has a mediating effect between entertainment and purchase intention in tourism e-commerce live streaming.

Methodology

Questionnaire design and measurement.

In order to ensure the reliability and validity of the questionnaire, this paper adopted the mature scale, and made appropriate modifications according to the characteristics of tourism e-commerce live streaming. All constructs were measured by Likert five-point scale, i.e., one means “strongly disagree” and five means “strongly agree,” and the larger the number, the higher the degree of agreement. The measurement of interactivity mainly referred to Liu et al. (2020a) and Wei et al. (2022) . Items of authenticity referred to Tong (2017) . The scale for entertainment was adapted from Chen and Lin (2018) and Lv et al. (2022) .The measurement of flow experience mainly referred to Chen and Lin (2018) . Items of trust referred to McKnight et al. (2002) and Chen et al. (2022c) . The scale for purchase intention was adapted from Liu et al. (2013) , Chen et al. (2017) , and Liu et al. (2020b) . The questionnaires were sent to experts in the field of tourism e-commerce live streaming for review. The initial questionnaire was formed after modification according to the experts’ suggestions. The initial questionnaires were sent to 50 respondents for pre-survey, and the final questionnaire was formed after modification based on the pre-survey results.

Data collection and sample description

Questionnaires were distributed online and offline to avoid homologous deviation. The questionnaires were distributed online through the Wenjuanxing app, which is a professional online survey, evaluation and voting platform with nearly 50 million users in China ( Liu et al., 2021f ). The link of the questionnaire on Wenjuanxing app was shared through WeChat and QQ to expand the coverage of samples. Meanwhile, offline questionnaires were distributed to respondents by paper-based questionnaires. We selected individuals who had watched tourism e-commerce live streaming by the screening question (“Have you had the experience of watching tourism e-commerce live streaming in the past?”). Those people who had not watched tourism e-commerce live streaming were excluded. A total of 462 questionnaires were received, and 357 valid questionnaires were obtained by excluding invalid questionnaires with incomplete answers, illogical answers, and <1 min of online filling time, with an effective rate of 77.27%. Since the data for this study were obtained from both online and offline sources, there might be differences between the data obtained from the two sources. We tested the sample differences through a one-way ANOVA by summing the scores of all question items of each questionnaire. The ANOVA results show a value of p  > 0.05, which indicates that there is no significant difference between the two groups of samples collected based on different routes. Therefore, the two groups of samples can be used as a whole sample.

The descriptive statistics of our survey samples are shown in Table 1 . In terms of gender, there are more females than males, with 162 males (45.38%) and 195 females (54.62%). In terms of age, the group of 18–24 years old accounts for the largest proportion, and the next largest percentage is in the group of 25–30 years old. In terms of education level, there are more samples with bachelor degree or above. In terms of monthly income, those with monthly income of 5,000–10,000 yuan accounts for the largest proportion. In terms of online shopping experience, most of the samples have more than 3 years of online shopping experience. Overall, the samples in this study are representative of the tourism e-commerce live streaming consumers.

Descriptive statistics of the study samples ( N  = 357).

Data analysis results

Reliability analysis.

Reliability reflects the stability and consistency of a scale. The greater is the reliability of a scale, the smaller is its standard error of measurement. In the Likert scale method, Cronbach’s alpha coefficient is the commonly used reliability test indicator. As can be seen from Table 2 , the Cronbach’s alpha value for each construct in this study is above 0.7. This shows that the scale of this study has good reliability.

Reliability analysis results.

Validity analysis

Validity consists of convergent and discriminant validity. Convergent validity refers to a high degree of correlation between items, and discriminant validity refers to a low degree of correlation or the significant differences between constructs. Convergent validity is measured by the factor loading of each item, the composite reliability (CR) of the construct, and the average variance extracted (AVE) of the construct. It requires that factor loadings are preferably >0.5, combined reliability (CR) values are >0.6, and average variance extracted (AVE) values are >0.5 ( Fornell and Larcker, 1981 ). According to Table 3 , the factor loading of each item is >0.6, CR values are all above 0.7, and AVE values are >0.5. Therefore, the scale of this study has good convergent validity.

Convergent validity analysis results.

The discriminant validity of the scale is good if the square root of the AVE value of each construct is greater than the correlation coefficient between the constructs ( Fornell and Larcker, 1981 ). The numbers on the diagonal in Table 4 are the square roots of the AVE values. It can be seen that the square root of each construct’s AVE value is greater than the correlation coefficient between its corresponding constructs. This shows that the discriminant validity of the scale in this study is good.

Discriminant validity analysis results.

Common method bias and multicollinearity test

This study used a questionnaire method to collect data from the same subjects, so there was a possibility that the problem of common method bias may arise. In order to effectively control the generation of common method bias, Podsakoff et al. (2003) suggested the methods of ex ante procedural prevention and ex post statistical testing. In terms of ex ante prevention, the purpose of this study was stated in the first part of the questionnaire. We emphasized the anonymous completion of the questionnaire, avoided semantically ambiguous measurement questions, and selected consumers of tourism e-commerce live streaming in different provinces and cities. From the ex post statistical testing aspect, this study used the Harman one-way method to test the common method bias. The exploratory factor analysis was conducted by principal component analysis on all measured question items of the constructs of interactivity, authenticity, entertainment, flow experience, trust, and purchase intention without rotation. The results showed that the first principal component explained 29.773% of the total variance, which was less than the critical value of 50% ( Podsakoff et al., 2003 ; Chen et al., 2022a ). It can be seen that the common method bias problem in this study is not serious.

Multicollinearity refers to the inaccuracy of model estimation due to the presence of highly correlated relationships among the independent constructs in a linear regression model. Variance inflation factor (VIF) is one of the indicators to test for multicollinearity. In this study, the multicollinearity problem of the model was tested, and the results showed that none of the VIF values in this study was higher than 10. Therefore, there is no multicollinearity problem in this study.

Hypothesis testing

We conducted structural equation modeling to verify the hypotheses. The indexes and evaluation criteria for evaluating the model fit ( Wu, 2010 ) are shown in Table 5 . The comparison shows that all the fit indicators meet the requirements, indicating that the model of this study has a good fit.

Fitting of the study model.

Table 6 demonstrates the path coefficients and hypotheses results in this study. Interactivity ( β  = 0.238, p  < 0.001), authenticity ( β  = 0.205, p  < 0.001), and entertainment ( β  = 0.154, p  < 0.01) all positively influenced flow experience. Therefore, H1a, H2a, and H3a are supported. Interactivity ( β  = 0.132, p  < 0.05), authenticity ( β  = 0.135, p  < 0.05), and entertainment ( β  = 0.142, p  < 0.05) all positively influenced trust. Therefore, H1b, H2b, and H3b are supported. Interactivity ( β  = 0.191, p  < 0.001), authenticity ( β  = 0.153, p  < 0.01), flow experience ( β  = 0.255, p  < 0.001), and trust ( β  = 0.223, p  < 0.001) all positively influenced purchase intention. Therefore, H1c, H2c, H4, and H5 are supported. The results of the data analysis show that entertainment does not influence purchase intention positively ( β  = 0.092, p  > 0.05). Therefore, H3c is not supported. The results are shown in Figure 2 .

Structural equation model validation results.

Path coefficient test results. * p  < 0.05, ** p  < 0.01, and *** p  < 0.001. n.s., not significant.

In addition, a bootstrapping procedure with 5,000 samples was used to examine mediating effects ( Shi et al., 2011 ; Zhou et al., 2015 ). The results are shown in Table 7 . The effect of interactivity on purchase intention through the mediating effect of flow experience is 0.064 with 95% confidence interval excluding 0. The mediating effect is significant. Flow experience also mediates the both effects of authenticity and entertainment on purchase intention. The effect of interactivity on purchase intention through the mediating effect of trust is 0.031 with 95% confidence interval excluding 0. The mediating effect is significant. Trust also mediates the both effects of authenticity and entertainment on purchase intention. Therefore, H6a–H6c and H7a–H7c are supported.

The mediation effects test analysis results.

Conclusion and implications

Discussion and conclusion.

This paper applied the S–O–R model to the study of consumers’ purchase intention in tourism e-commerce live streaming, focused on the influences of the tourism e-commerce live streaming features on consumers’ purchase intention, and analyzed the antecedent variables and paths of tourism consumers’ purchase intention through flow experience and trust. The following conclusions are drawn from the empirical research and analysis:

First, interactivity and authenticity of the tourism e-commerce live streaming features have positive effects on consumers’ purchase intention, but entertainment has insignificant effect on consumers’ purchase intention. Compared with the traditional online tourism marketing, tourism e-commerce live streaming is more immersive, as the streamer presents the tourism products to the consumers visually. The streamer introduces the tourism products, conducts live experience, and shares the experience. Tourism e-commerce live streaming creates a face-to-face shopping atmosphere, so that consumers can directly understand the advantages and disadvantages of tourism products. Tourism e-commerce streamers attract and retain consumers through real-time interaction and real product display, thus increasing the conversion rate ( Li et al., 2021 ; Zhang et al., 2021a ).The effect of entertainment on consumers’ purchase intention is not significant, which may be because when viewers watch tourism e-commerce live streaming for entertainment purposes, viewers will only stay in the viewing part and cannot directly generate purchase intention.

Second, the flow experience has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. The research results show that the interactivity, authenticity, and entertainment of tourism e-commerce live streaming positively affect flow experience, flow experience positively affects consumers’ purchase intention, and flow experience has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. The interactivity of tourism e-commerce live streaming allows consumers to communicate with the streamer and other consumers in both directions and then immerse themselves in the live streaming environment. The authenticity of tourism e-commerce live streaming can increase consumers’ interest in the products. The entertainment of tourism e-commerce live streaming can meet the pleasure psychology of consumers. The flow experience of tourism consumers makes them want to participate in the live streaming unconsciously and generate purchase intention under the stimulation and guidance of the streamer, which is consistent with the conclusions of Huang et al. (2021) .

Finally, trust has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. The research results show that the interactivity, authenticity, and entertainment of tourism e-commerce live streaming positively affect trust, trust positively affects consumers’ purchase intention, and trust has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. There are risks of pictures being embellished in traditional online tourism marketing, so tourism consumers are often skeptical of tourism marketing. The interactivity of tourism e-commerce live streaming strengthens the emotional communication between consumers and streamers and brings the psychological distance closer. According to social exchange theory, consumers are more willing to trust the products recommended by streamers, thus increasing their purchase intentions ( Cropanzano and Mitchell, 2005 ; Wei et al., 2022 ). In addition, the authenticity of tourism e-commerce live streaming weakens the risks of camera switching and excessive picture embellishment. Viewers can see the full information of live streaming scenes and products realistically, and every move of the streamer can be captured by viewers, increasing the credibility of online shopping. Therefore, the credibility of the information source positively affects consumers’ willingness to purchase ( Tong, 2017 ). Finally, the entertainment of tourism e-commerce live streaming can increase consumers’ curiosity of the product and desire to participate, which leads to positive evaluation of the product and the streamer ( Wongkitrungrueng and Assarut, 2020 ), thus increasing consumers’ purchase intentions.

Research implications

Firstly, we have identified three unique features of tourism e-commerce live streaming features, namely interactivity, authenticity, and entertainment. Moreover, we studied the consumers’ purchase intention through the three features of the new media form. This provides a fresh perspective for the quantitative studies of tourism e-commerce live streaming. Previous studies have shown that e-commerce live streaming consumer’ purchase intentions can be influenced by live streaming strategy ( Zhang et al., 2020 ), interaction ( Li et al., 2021 ; Zhang et al., 2021b ), and social presence ( Ang et al., 2018 ; Chen and Liao, 2022 ). However, previous studies have mostly studied the impact of one feature of e-commerce live streaming on consumer’ purchase intention, without systematic and comprehensive studies, and they have not connected live streaming features with consumers’ perception ( Sun et al., 2019 ; Deng et al., 2021 ; Qiu et al., 2021 ; Lin et al., 2022 ). This study confirmed that interactivity and authenticity of the tourism e-commerce live streaming features have positive effects on consumers’ purchase intention, but entertainment has insignificant effect on consumers’ purchase intention. It enriches the research content of tourism e-commerce live streaming.

Secondly, we offered theoretical insight into consumers’ purchase intention by employing the SOR model to tourism e-commerce live streaming research. A few studies have applied SOR model in the research of e-commerce live streaming ( Xu et al., 2020 ; Guo J. et al., 2021 ), whereas, they have not paid attention to the tourism e-commerce live streaming. Since tourism products have special features (e.g., high unit price, low purchase frequency, intangibility, and non-transferability) that are different from general products ( Xie et al., 2022 ), it is necessary to study on tourism e-commerce live streaming and the consumers’ psychology. The effectiveness of the SOR model in tourism e-commerce live streaming was confirmed, which provides a more profound and thorough understanding of the formation of tourism consumers’ purchase intention.

Finally, this study also examined the mediating effects of flow experience and trust on the relationship between tourism e-commerce live streaming features and consumers’ purchase intention, which contributes to research related to consumers’ purchase intention in live streaming commerce ( Lu and Chen, 2021 ). To the best of our knowledge, in the existing literature, no research has examined the direct and mediating effect of flow experience in e-commerce live streaming. A few studies have provided empirical evidence for the positive effect of trust on e-commerce live streaming consumers’ purchase intention ( Tong, 2017 ; Dong et al., 2022 ), whereas, they have not paid attention to the mediating effect of trust. The results of this study indicated that flow experience and trust partially mediates the impact of tourism e-commerce live streaming features on consumers’ purchase intention. It enriches the research content of emotional and cognitive reactions in tourism e-commerce live streaming.

Practical implications

First of all, the positive roles of tourism e-commerce live streaming’s features on consumers’ perceptions point to the need for enterprises and streamers to invest resources in amplifying these three features when designing the live streaming. In terms of interactivity, the streamer should interact with consumers, enliven the atmosphere of the live streaming room, and make detailed and accurate answers to the questions raised by consumers. As one example, the streamer can design interactive lucky draws at different stages of the live-streaming process ( Lv et al., 2022 ). In terms of authenticity, streamers should show the products in all aspects, strengthen the authenticity of the products and the consumers’ sense of live immersion, and create the feeling of offline shopping for consumers. Streamers can also effectively evaluate the products based on his or her own experience and provide consumers with purchase suggestions. In terms of entertainment, streamers can post some interesting content, discuss interesting entertainment topics, and hold a series of entertaining activities. For example, streamers can introduce tourism products in the form of sitcoms, or conduct role-play related to the tourism live streaming theme or destinations ( Lv et al., 2022 ; Xie et al., 2022 ).

In addition, the flow experience has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. The research results show that flow experience positively affects consumers’ purchase intention, and flow experience has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. Therefore, the tourism e-commerce live streaming platform and streamers should enhance consumers’ flow experience in order to increase their purchase intention. First of all, the tourism e-commerce live streaming platform and streamers should seek to create attractive content that meets consumers’ expectations ( Xu et al., 2019 ), and further compel them to continue watching and purchase. Additionally, emotional connections with the consumers through friendly words, passionate and immersive explanations and interactions are recommended strategies for streamers. Finally, when a consumer expresses confusion about the live streaming content, the streamer should give an accurate answer in time to satisfy the consumer’s curiosity about the tourism product. These methods enhance the consumer’s flow experience, thus increasing consumers’ purchase intentions.

Finally, trust has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. The research results show that trust positively affects consumers’ purchase intention, and trust has a mediating role between the tourism e-commerce live streaming features and consumers’ purchase intention. Therefore, the tourism e-commerce live streaming platform and streamers should enhance consumers’ trust in order to increase their purchase intention. First of all, a clear and comprehensive introduction of the products will help the consumers to enhance their perception of the consumption experience ( Wongkitrungrueng et al., 2020 ), especially with tourism products. In addition, reliable streamers facilitate consumers’ trust ( Ma et al., 2022 ). In order to assemble a group of high-quality streamers, organizations should establish strict recruitment standards. Finally, streamers should strictly control the quality of products according to their own expertise and eliminate unqualified products into the live streaming room, so as to enhance consumers’ trust and promote the formation of purchase intention.

Limitations and further research

There are still some limitations in the study. Firstly, the features of tourism e-commerce live streaming are multifaceted, so future research can expand the features of tourism e-commerce live streaming by introducing factors such as the ease use of platform and platform usefulness to explore consumers’ psychology and behavior. Secondly, the features of tourism e-commerce live streaming affect consumers’ cognitive and emotional responses, but whether there are other mediating and moderating constructs in the influence mechanism need further investigation in the future. Finally, this study used a self-report questionnaire to collect data, and respondents might be influenced by various factors such as emotions and the environment. Therefore, the study results might be biased. In the future, multiple measurement methods can be tried to measure the constructs more accurately.

Data availability statement

Author contributions.

XL contributed conception and design of the study, performed the statistical analysis, wrote the first draft, and revised the manuscript. LZ and QC organized the data collection. XL, LZ, and QC polished the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by the Special Project of Zhejiang Provincial Social Science Foundation (21GXSZ063YBM), Soft Science Research Program of Zhejiang Province (2021C35045), Scientific Research Project of Zhejiang Education Department (Y202248666), Research Project on Higher Education of Zhejiang Gongshang University (Xgy22015), and the Key Project of Discipline Construction and Management of Zhejiang Gongshang University (2022).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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