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This study aims to identify a value co-creation framework for live streaming through tourism scenes (LStTS). It also clarifies the value attributes of LStTS and makes an empirical test.

The study used a mixed-method approach. In Study 1, a total of 12,216 pieces of viewers’ comments and ten web news reports were coded and analyzed employing a grounded theory approach. In Study 2, data were collected from 587 Douyin e-commerce users. Exploratory factor analysis and partial least squares structural equation modeling were used to test the value co-creation framework of LStTS.

In Study 1, six value attributes in three categories were identified based on a content analysis of viewers’ comments. In Study 2, a three-order factorial model of value co-creation in LStTS was identified and tested.

Our study is limited by the preponderance of female respondents in the sample and the unique nature of the research context.

Merchants and streamers should consider whether there is a fit between the merchandise and the tourism scene when selecting the tourism scene for live streaming marketing; they can select novel and beautiful natural tourism scenes to attract viewers. Detailed and comprehensive product information should be provided in the process of live streaming marketing and sharing with consumers.

The novelty of our study lies in the provision of a new value co-creation framework in LStTS, which offers a theoretical basis for analyzing the value of the tourism scene in live streaming marketing.

Drawing on the shopping goal theory, this study sets the following research questions. Research question 1: How do keyword characteristics of e-commerce platforms affect seller performance? Research question 2: Do these effects differ according to product type (utilitarian vs hedonic)?

Empirical analysis was conducted using a dataset of 24,907 keywords collected from 30 product categories on Amazon. Multi-level modeling was used to distinguish between product level and keyword level.

Regression analysis showed that keyword readability, customer characteristic keywords and brand characteristic keywords positively affect seller sales. The influence of these keyword characteristics on seller performance varied significantly across different product types.

The exploratory results of this study expanded existing studies by adding keyword characteristics to the factors that determine e-commerce platform seller performance and expanded the literature that mainly examines search engine keyword characteristics. In addition, by newly utilizing the LIWC (Linguistic Inquiry and Word Count) methodology, this study provided a novel contribution to the literature on keyword characteristics.