Tourism E-commerce Research Articles

Configuring the value-versus-attachment combinations in determining consumer purchase intention in tourism e-commerce live streaming: a fsQCA approach

ABSTRACT Based on complexity theory and applying the method of fuzzy-set qualitative comparative analysis (fsQCA), this study tested and identified the configuration effects of perceived values and virtual place attachment on purchase intention in tourism living streaming. Analyses on a sample of 583 experienced tourism live streaming audience members revealed six configurations, which can be summarized in three typical combinational paths: i.e. ‘emotional value-place identity’, ‘convenience – place identity’ and ‘price – place attachment’. Emotional transmission, price, convenience (perceived usefulness and perceived ease of use), place dependence and place identity form various combination paths through different configurations. The study provides management and marketing implications for streamers or tourism enterprise managers to design and implement effective tourism live streaming programmes.

Tourism e-commerce live streaming: Identifying and testing a value-based marketing framework from the live streamer perspective

Live streaming has become an important means for tourism product marketing in the internet age. However, from value-based marketing perspective, the value attributes and factors of tourism e-commerce live streaming (TEcLS) have not been clearly identified. Using a mixed-method approach and following value-based marketing theory, this study aims to identify a value-based marketing framework of TEcLS and empirically validated the framework. In Study 1, 11 basic live streaming attributes in 4 categories of value-based marketing of TEcLS were identified based on the analysis of TEcLS video data. In Study 2, a three-order factorial model, conceptualizing the value-based marketing framework of TEcLS, was identified and tested through two rounds of questionnaire surveys. This study provides new insights on value-based tourism marketing through live streaming as a technology-enabled tourism marketing tool from the live streamer perspective.

Construction of Tourism E-Commerce Platform Based on Artificial Intelligence Algorithm.

In the late twentieth century, with the rapid development of the Internet, e-commerce has emerged rapidly, which has changed the way people travel around the world. The greatest advantages of e-commerce are the flow of information and data and the importance of traveling freely to experience the mind and body in different fields. Tourism is an important part of the development of e-commerce, but the development of e-commerce tourism lags behind. To solve the current situation of the backward development of tourism e-commerce, this article studies the construction of a tourism e-commerce platform based on an artificial intelligence algorithm. By introducing modern information technology, based on a cloud computing platform, big data analysis, K-means, and other key technologies, this article solves the current situation of the development of an e-commerce platform. It also analyzes the construction methods of traditional cloud platforms and modern cloud platforms through comparative analysis and solves the construction methods suitable for artificial intelligence tourism. At the same time, combined with the actual situation of tourism, this article selects the appropriate networking method based on the analysis of the advantages and disadvantages of wired and wireless coverage methods and economics to complete the project design. Its purpose is to ensure that the work meets the specific construction needs and build an artificial intelligence-based smart tourism big data analysis model. It promotes the development of tourism e-commerce industry. It saves costs and improves efficiency for travel service providers. Then, according to the actual situation of tourism, it conducts demand analysis from the perspectives of tourists, scenic spots, service providers, tourism administrative agencies, etc. Experiments show that, through the practical application of the artificial intelligence tourism mobile e-commerce platform in this article, it can be seen that the artificial intelligence tourism mobile e-commerce platform designed in this article can meet the needs of customers for shopping-related tourism commodities. Tourists of attractions have increased by 3.54%, and the economy of tourist destinations has increased by 4.2%.

Research on the Design of Rural Tourism E-commerce System Based on Big Data Technology

The traditional rural tourism industry has some problems, such as incomplete tourism information, unable to share all data, serious data loss, and the decline of tourism service quality. In recent years, with the support of big data and e-commerce technology, many villages have vigorously developed tourism e-commerce, which has brought huge economic benefits to rural residents and improved their living environment to a certain extent. This paper briefly expounds the concept and characteristics of big data, analyzes the needs of rural tourism e-commerce on the basis of being familiar with big data related technologies, develops a rural tourism e-commerce system (hereinafter referred to as RTES), and explains the design of the system in detail from the system architecture design, database design and system function structure design. The application of the system shows that the tourism e-commerce designed and developed in this paper is guaranteed in practicality and security, and reflects professionalism and comprehensiveness in service. It can not only recommend and book the best tourist routes for customers, but also provide support for managers and facilitate management.

Research on Early Warning of Customer Churn Based on Mutual Information and Integrated Learning—Taking Ctrip as an Example

With the increasing competition pressure among tourism e-commerce platforms, how to reduce customer churn to the greatest extent is of great significance to tourism e-commerce platforms. Based on this, this paper takes Ctrip's hotel customer-related data as an example, and first uses a supervised feature selection method based on mutual information to select features that have an important impact on customer churn. Then, by using the cross-validation method, combined with evaluation indicators such as accuracy rate, F1-sorce, AUC, etc., select the best model set from Logistic regression, Support Vector Machine, Decision Tree, Random Forest, GBDT, XGBoost, and LightGBM. A subset of the optimal models, and then the optimal model fusion is performed. The empirical results show that the multi-model fusion has higher accuracy and stability. In addition, based on the model fusion results, this paper obtains the importance ranking of customer personal characteristics. Finally, this paper puts forward relevant suggestions on how to accurately manage Ctrip and reduce the customer churn rate.

Multi-view Graph Attention Network for Travel Recommendation

As an e-commerce feature, the recommender system can enhance the consumer shopping experience and create huge benefits for businesses. The e-tourism has become one of the largest service industries with the application and popularity of recommender systems. Many studies have confirmed that the travel product recommendation is widely different from traditional recommendations. Due to the financial and time costs, travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books, movies and food). In addition, choosing the appropriate travel product will be influenced by many factors, such as departure, destination and price. To tackle this challenging problem, we propose a MV-GAN (short for Multi-View Graph Attention Network for travel recommendation) model. It enriches user and product semantics through both metapath-guided neighbors aggregation and multi-view fusion in heterogeneous travel product recommendation graph. In particular, we design node-level and path-level attention networks for learning user and product representations from every single view. To collaboratively integrate multiple types of relationships in different views, a view-level attention mechanism is proposed to aggregate the node representations and obtain global user and product representations. We evaluate the proposed method on a public dataset and a dataset constructed from a large tourism e-commerce website in China. Extensive experiments not only validate the effectiveness of MV-GAN, but also show its potentially good interpretability.

SWOT Research on the Development of Rural Tourism E-Commerce System under the Background of Big Data Era

Economic development has gradually made tourism one of the most common options for people’s leisure and entertainment. The fast-paced life and heavy pressure in the city have made the relaxed and comfortable rural tourism sought after by many people. In our country, many villages have rich tourist attractions and unique cultural heritage, but they have not been fully developed due to information obstruction. Now, the advancement of Internet technology has made e-commerce, an emerging trade method, more closely related to rural tourism. Based on the background of big data, this paper has launched a research on the SWOT analysis of the development of rural tourism e-commerce system. Starting from the advantages, disadvantages, opportunities, and threats of the tourism e-commerce system, this paper discusses the necessity of combining rural tourism and e-commerce platforms in the Internet era, combining various internal and external conditions. From the perspective of the e-commerce system, all kinds of special features in the countryside can be transformed into product selling points on the platform to attract tourists, and the promotion of tourism revenue through the platform can further promote the continuous improvement of the rural tourism industry and form a virtuous circle. According to our data from the China Tourism Administration and related tourism statistics, the number of rural tourists in my country has increased from 630 million in 2011 to 3.13 billion in 2019, and the increase in rural tourism revenue has also reached 4.3 times. In order to prevent the rural backward marketing strategy from hindering the development of rural tourism, the introduction of a tourism e-commerce platform is a very necessary choice.

A multi-task learning approach for improving travel recommendation with keywords generation

Travel recommendation is very critical to helping users quickly find products or services that they are interested in. The key to travel recommender systems is learning user shopping intentions, which are expressed through various supervision signals, such as the clicked products and their titles. Existing travel recommendation methods commonly infer user intentions from click behaviors on travel products. However, remarkable keywords in the product title, such as departure, destination, travel time, hotel, and transportation are paid less attention. To this end, we hypothesize that modeling click sequences and product keywords in title jointly would result in a more holistic representation of a product and towards more accurate recommendations. Thus, we propose a TRKG (short for Travel Recommendation with Keywords Generation) model, which fulfills the travel recommendation and keywords generation tasks simultaneously. To generate explainable outputs, unlike most previous approaches that regard the product title as a hidden feature vector, TRKG regards keywords in the product title as an additional supervision signal. Meanwhile, TRKG integrates the long-term and short-term user preferences in the travel recommendation component and the keywords generation component. To evaluate the proposed model, we constructed datasets from a large tourism e-commerce website in China. Extensive experiments demonstrate that the proposed method yields significant improvements over state-of-the-art methods.

Dijital Pazarlama Yöneticilerinin Bakış Açısıyla En Uygun Otel Seçimi: Analitik Hiyerarşi Prosesi (AHP) ve PROMETHEE Yaklaşımı

The widespread use of the Internet has enabled the rapid development of e-commerce in tourism. Online experience sharing platforms, which have become important by providing various opportunities for tourists to make their holiday decisions, have become one of the most visited websites in the world. In addition, online travel agencies offer opportunities to their potential guests during the holiday decision process by sharing their guest experiences regarding reservations made on their websites. The positive and negative opinions of the tourists about their experiences enable certain ranking lists to be formed in both experience sharing platforms and online travel agencies. During the process of making their holiday decisions, potential guests create the highest visitor and booking traffic in businesses that rank high in the relevant ranking lists. In this study, it is aimed to answer the decision problem of tourists in choosing a hotel by using multi-criteria decision-making methods. For this, criteria weight values were calculated using AHP (Analytical Hierarchy Process) method over seven criteria determined for the first seven alternative hotels among the top-rankedhotels in the list of Istanbul hotels on the Booking.com website and with the PROMETHEE method, the order of preference was determined at a very good decision level with 89 % measurement quality. As a result of the expert opinion among the criteria determined in the research, it was determined that the criterion with the highest weight value was the cleaning criterion. Among the seven alternatives determined, Hotel5 was determined to be the most suitable choice. In the light of the information obtained in the study, it was concluded that the reason for the emergence of the differences regarding the rankings of alternative hotels was due to the dynamic factors applied by Booking.com online travel agency in hotel rankings.

Evaluation of Tourism E-Commerce User Satisfaction

According to the UNWTO, within 4 to 5 years, the proportion of tourism e-commerce in e-commerce will reach 20%-25%. The purpose of this paper is to improve the inadequacy of tourism e-commerce in customer experience, to conduct customer e-commerce satisfaction surveys, and to draw customers' dissatisfaction with tourism e-commerce. The experimental results show that the overall customer satisfaction is 2.6128. According to the division of the scale vector, the overall satisfaction of the travel e-commerce customers is generally level. The first-level fuzzy comprehensive evaluation is 0.0967, 0.1696, 0.3366, 0.2469, 0.502. According to the principle of maximum membership degree, the evaluation grades of R3 and R5 in the first-level fuzzy comprehensive evaluation are “unsatisfactory,” that is, the tourism-supporting services and contract-performance services become the main factors affecting customer satisfaction. In order to improve customer satisfaction, the tourism e-commerce platform should strengthen the management of tourism-supporting services and contract-fulfillment services.

Understanding consumer behavior in the multimedia context: incorporating gamification in VR-enhanced web system for tourism e-commerce.

This study aims to investigate how gamification and virtual reality (VR)-enhanced web services can be integrated to influence consumer behavior in the context of tourism e-commerce. A gamified VR-enhanced tourism web system (VRTWS) was designed and developed for this investigation, while a research framework with 12 hypotheses was proposed and empirically tested by adopting PLS-SEM approach to analyze 208 valid data collected from survey. The results reveal that both Enjoyment and Activation in Gamification significantly and positively affected Media Richness. Additionally, Media Richness significantly and positively affected both Usefulness and Ease of Use in using VR technology with gamification. Also, a user’s Perceived Value is not only positively affected by Usefulness and Ease of Use but also Interactivity and Immersion in a gamified VRTWS. Immersion was found to be positively affected by Presence. Through the positive effect on Satisfaction, user’s Perceived Value had positive effect on the Intention toward adoption. The proposed gamified VRTWS and the study results with implications are expected to be referenced by the researchers and practitioners for managing incorporation of gamification into designing, developing, and managing their VR-enhanced service in tourism e-commerce.

Application of Big Data Technology in the Impact of Tourism E-Commerce on Tourism Planning

With the improvement of the material standard of living, the demand of the people for spiritual culture continues to increase. In terms of tourism, people have gradually shifted from simple tourism needs to integrated tourism needs. Tourism has become an effective way for people to expand their horizons and enrich their spiritual world. Tourism is one of the first industries to apply network technology. After long-term exploration and innovation, tourism e-commerce has developed rapidly. Coupled with the advent of the era of big data (EBD), the concept of customized tourism has gradually entered people’s lives. This paper mainly introduces the research on the impact of e-commerce tourism on custom tourism in the EBD and intends to provide some ideas and directions for the good development of custom tourism. This paper proposes a research strategy on the impact of tourism e-commerce on customized tourism in the EBD, including related theoretical research methods, random forest algorithms, support vector machine classification algorithms, and Bayesian estimation algorithms, which are used to customize tourism e-commerce in the EBD, and research experiment on the impact of tourism. The experimental results show that 79.84% of customers are willing to purchase related products again after experiencing travel customization services using big data technology. By using the various characteristics of tourism big data for data mining and classification, it provides users with personalized travel search services. At the same time, big data technology can provide basic technical support for customized tourism development, which shows that it can also provide customized services for users.

Mapping Hotspots and Emerging Trends of Tourism E-Commerce: A Multidisciplinary Perspective

Tourism e-commerce is a multidisciplinary research area that involves tourism management and e-commerce. This paper provides a review of 1960 scientific studies published over the past two decades (1999-2018). This study presents a multidisciplinary comparative study, namely, a systematic review of tourism e-commerce, aiming to provide a reference guide for researchers. Under the methodological framework of a domain analysis, this paper analysed the scientific outputs and visualized the co-cited references, burst keywords, keyword co-occurrences and topic characteristics of journals. The results show that the tourism management discipline represents the main body of tourism e-commerce research and that this research demonstrates a rapid growth trend consistent with overall research. There are two main clusters of representative co-cited references in the tourism management discipline. However, in the e-commerce discipline, the distribution of co-cited references is scattered, lacking obvious cluster characteristics. The keyword co-occurrences in both disciplines present four themes, including network and information technology, consumer behaviour and social media as common themes. Tourism management journals can be divided into two categories, while e-commerce journals can be divided into three categories. Overall, the findings of this study can benefit researchers and practitioners.

Critical Success Factors Associated to Tourism e-Commerce: Study of Peruvian Tourism Operators

The incorporation of information and communication technologies (ICT) has generated new opportunities and innovation in business models, such as electronic commerce (EC). Despite the benefits that the EC offers PYMEs, its adoption is low. Several authors have argued that many factors condition the adoption of EC in developing countries. This study proposes a model of factors associated with the adoption of EC by tourism operators based on factors categorized into organizational, individual, environmental, and technological factors. In this study, a structural equation modeling and confirmatory factor analysis tools were used to analyze the data. The data collected from 116 participants (69%) males and 31% females, managers of tourism operators). The results reveal that 11 factors influence the adoption of EC. Also, the operators currently using EC consider that the most influential factor are related with organizational factors operators that have not implement EC value factors involving skills, knowledge and experience in technology. This study can be used to establish policies on ICT adoption in tourism PYMEs.

An Empirical Study on the Impact of Online Travel Consumers' Brand Loyalty: The Mediating Effect of Flow Experience

In recent years, virtual reality (VR) technology has developed vigorously, and how to make good use of virtual reality technology to create virtual tourism has become an important topic for tourism ecommerce. Previous studies have shown that creating a good online experience for consumers is an effective way for e-commerce to establish consumer brand loyalty, but there are few literatures focusing on the special group of online travel consumers. Based on the flow theory, the article uses Chinese online travel consumers as a sample to conduct empirical research, focusing on exploring the relationship between consumer perception of VR technology and brand loyalty, and testing the mediating effect of flow experience. The results show that: (1) the perceived usefulness and ease of use of VR technology have a significant positive impact on consumers' online experience; (2)the flow experience of travel consumers has a significant positive impact on their brand loyalty; (3) the perceived usefulness and ease of use of VR technology have a significant positive impact on consumers' brand loyalty;(4)the flow experience has a mediating effect between the perception of VR technology and brand loyalty of travel consumers.

The Evaluation of Online Travel E-commerce on Customer Perceptions in China: A Case Study of CTRIP Company

This paper examines the development trend of online travel in China by using a case study method ─ CTRIP Company, to illustrate and analyse the application of E-commerce in tourism and travel setting. Additionally, it utilizes questionnaire to collect primary data. The results show high-educated people are the leading online travel customers. There are a rising number of on-line travelers and most of them are self-guide traveler. Otherwise, customers who are over 45-year-old and travel for business prefer choosing travel agencies for their itinerary. On the other hand, the data displays that security and price are the top two significant factors with which on-line travelers are concerned. Finally, it will propose several recommendations to improve E-tourism development level in China.

Neural Attentive Travel package Recommendation via exploiting long-term and short-term behaviors

Travel package recommendation is a critical task in the tourism e-commerce recommender systems. Recently, an increasing number of studies proposed various travel package recommendation algorithms to improve Online Travel Agencies (OTAs) service, such as collaborative filtering-based, matrix factorization-based and neural network-based methods. Despite their value, however, the main challenges that incorporating complex descriptive information of the travel packages and capturing complicated users’ long-term preferences for fine-grained travel package recommendation are still not fully resolved. In terms of these issues, this paper propose a novel model named Neural Attentive Travel package Recommendation (NATR) for tourism e-commerce by combining users’ long-term preferences with short-term preferences. Specifically, NATR mainly contains two core modules, namely, travel package encoder and user encoder. The travel package encoder module is developed to learn a unified travel package representation by an attentive multi-view learning approach including word-level and view-level attention mechanisms. The user encoder module is designed to study long-term and short-term preference of the user by Bidirectional Long Short-Term Memory (Bi-LSTM) neural networks with package-level attention mechanism. In addition, we further adopt a gated fusion approach to coalesce these two kinds of preferences for learning high-quality the user’s representation. Extensive experiments are conducted on a real-life tourism e-commerce dataset, the results demonstrate the proposed model yields significant performance advantages over several competitive methods. Further analyses from different attention weights provide insights of attentive multi-view learning and gated fusion network, respectively.

Research on the Adoption of Mobile Hotel Reservation Based on TTF and UTAUT

It is of great significance for the promotion and popularization of tourism ecommerce to study the factors influencing the adoption of mobile hotel reservation. From the perspective of user experience, taking TTF and UTAUT as the theoretical framework, four external variables including situational awareness, user experience, perceived risk and perceived cost are added to build a mobile hotel reservation adoption model. Based on the fact that senior college students have more travel needs, this paper takes college students as an example to make an empirical analysis. The results show that: task technology matching, situational awareness, effort expectation and performance expectation have a significant impact on user experience, and then affect user behavior intention; social impact, perceived risk and perceived cost also have a significant impact on user behavior intention, among which social impact has the strongest impact on college students' mobile hotel reservation intention. Combined with the analysis of empirical results, some suggestions are put forward for the development and operation promotion of mobile hotel reservation application.

Prediction on VLBP-based online tourism E-commerce market

With the improvement of economic living standard and the development of Internet, the demand of online tourism is also growing. The increasing number of tourists and the upgrading of consumption stimulate the growth of the transaction scale of the online travel market. With the demand of accurate development of e-commerce market, a reasonable quantitative mathematical model is an effective means of resource integration. In this paper, VLBP is used to analyse the development of China’s online tourism market by establishing a nonlinear relationship model between online tourism market transaction volume and various influencing factors to predict the future market transaction volume, so as to provide quantitative reference for the development of China’s online tourism e-commerce marketing.

RETRACTED: A study on the development of the tourism e-commerce platform in small and medium-sized cities based on SWOT analysis – A case study of Ma’anshan city

The tourism e-commerce platform is a new business model of tourism. By summarizing the domestic studies and the survey of the market of tourism e-commerce platform in Ma’anshan, this paper analyzes the problems involved in Ma’anshan’s tourism e-commerce platform. And with the help of SWOT analysis, the development of tourism e-commerce platform shall adopt a reversible strategic positioning and some specific useful measures to develop Ma’anshan’s tourism e-commerce platforms.