مقاله انگلیسی رایگان در مورد هم آفرینی ارزش در خرید زنده گردشگری – الزویر 2024

Abstract

Live streaming has been increasingly integrated into e-commerce platforms to sell tourism offerings, yet its value-adding mechanism for enhancing customer shopping experiences remains underexplored. From a value co-creation perspective, this study investigates how customers can co-create value by leveraging the interactive potential in tourism live shopping, and how such customer co-creation behavior can be fostered through the attributes and actions of tourism live streamers, drawing on source credibility and social presence theories. The empirical findings indicate that the credibility traits of tourism live streamers, characterized by trustworthiness, expertise, and attractiveness, are instrumental in fostering customer co-creation behaviors, which can in turn elevate customer perceived value of their tourism live shopping experiences. Additionally, the study reveals that the level of social presence during tourism live shopping sessions may positively moderate the influence of streamer credibility traits on customer co-creation behaviors.

Introduction

Live-streamed shopping, or live shopping, merges live broadcasting with e-commerce, offering a more dynamic and interactive alternative to traditional TV and web-based online shopping (Wongkitrungrueng & Assarut, 2020). This approach features live streamers who actively demonstrate products and communicate with potential customers in a real-time manner to facilitate sales (Chen & Zhang, 2023). By integrating socialization, entertainment, and instant purchasing, live-streamed shopping provides a superior shopping experience, making it a significant experiential marketing channel across diverse product categories (Bai et al., 2024).

The same trend has also been witnessed in the hospitality and tourism industry. Numerous tourism sectors, including hotels, restaurants, attractions, and destination marketing organizations, now adopt live streaming for communication, promotion, and direct sales (Lin et al., 2022). In such tourism live shopping events, tourism organizations collaborate with live streamers, including travel influencers, celebrities, and professional sellers, to promote and sell tourism offerings (Xu et al., 2021). Streaming viewers, namely those potential travelers, can engage directly with the streamers, participating in real-time interactions and making on-the-spot purchases as needed (Xu et al., 2021). Extant research has underscored the potential economic, emotional, social, and knowledge-based benefits of tourism live e-commerce, highlighting its role as a promising digital tourism marketing channel (Wang and Guo, 2024, Xie et al., 2022).

Results

4.1. Sample profile
Table 2 outlines the demographic profile of the survey respondents. The predominant group consisted of female young adults, aged between 18 and 35 years. A large portion of these respondents were single and held a college degree. Over half of the participants reported an annual income below 10,000 RMB (around 1,500 EUR). In terms of preferred platforms for tourism live shopping, Douyin emerged as the most popular, followed by Taobao and Flyzoo, both Alibaba affiliates, and then Ctrip. As for the types of tourism offerings purchased through live shopping, hotels were the most common choice, succeeded by restaurants and tourist attractions. Purchases of event tickets, tour packages, and transportation services via live shopping were less frequent.

4.2. Measurement validity
Before hypotheses testing, we conducted preliminary analyses to assess measurement validity. First, in our analysis, we employed the Maximum Likelihood with Robust Standard Errors (MLR) method, a parameter estimation that does not require strict multivariate normality (Mueller & Hancock, 2008). Second, to address common method bias, we implemented both procedural and statistical controls. Procedurally, we made the online survey anonymous, clearly stated the study’s purpose in the introduction, kept the survey questions brief and included attention-check questions. Statistically, Harmon’s single-factor test showed that the largest factor accounted for 36.3 % of the total variance, below the 50 % threshold suggested by Podsakoff (2003). Also, the Common Latent Factor (CLF) test indicated that the differences in standardized regression weights for all items in models with and without a CLF were smaller than 0.2, in line with the guidelines by MacKenzie and Podsakoff (2012). Hence, common method bias was not deemed a serious concern.