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. 2023 Oct 19;18(10):e0292394.
doi: 10.1371/journal.pone.0292394. eCollection 2023.

Webcast marketing platform optimization via 6G R&D and the impact on brand content creation

Hui Wen  1
Affiliations

Webcast marketing platform optimization via 6G R&D and the impact on brand content creation

Hui Wen. PLoS One. .

Abstract

This work aims to investigate the development and management of cosmetics webcast marketing platforms, offering novel approaches for building and sustaining commercial brands. Firstly, an analysis of the current utilization of cosmetics webcast marketing platforms is conducted, identifying operational challenges associated with these platforms. Secondly, optimization strategies are proposed to address the identified issues by leveraging advancements in 6th Generation (6G) communication technology. Subsequently, a conceptual framework is established, employing big data interaction to examine the influence of webcast marketing platform experiences on brand fit. Multiple hypotheses are formulated to explore the relationship between platform experiences and brand fit. Finally, empirical analysis is performed within the context of the 5th Generation (5G) Mobile Communication Technology and extended to incorporate the 6G Mobile Communication Technology landscape. The results of the validation indicate the following: (1) the content generated by the webcast marketing platform has a positive impact on brand fit (β = 0.46, p<0.01; β = 0.31, p<0.05); (2) in the 6G network environment, a webcast marketing platform with high traffic transmission rates may enhance brand fit (β = 0.51, p<0.001); (3) the content generated by the webcast marketing platform exhibits significant positive regulatory effects on information-based and co-generated content (β = 0.42, p<0.01; β = 0.02, p<0.001). The findings of this work offer valuable insights for other scholars and researchers seeking to optimize webcast marketing platforms.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Cosmetics marketing process.
Fig 2
Fig 2. Framework of the cognitive matching theory.
Fig 3
Fig 3. Conceptual model of the impact of content marketing on brand fit behavior under webcasting.
Fig 4
Fig 4. Big data system architecture.
Fig 5
Fig 5. The theoretical model of the influence of content marketing on brand fit behavior under live webcast.
Fig 6
Fig 6
Model assumption path coefficients and results (a. First test; b. Second test). In Fig 6 *, **, and *** denote " p<0.05, p<0.01, and p<0.001, respectively.
Fig 7
Fig 7
Comparison of forecasted live broadcast sales and target sales (a. Studio-a; b. Studio-b).
Fig 8
Fig 8. Test results of independent samples of study enterprises (L- prefix represents Levene test; ROE denotes the Return on Equity; EPS refers to Earnings Per Share; ROE-Y means that the variances are equal; ROE-N means that the variances are not equal).
Fig 9
Fig 9. The sample test results of the research samples A and B after matching.
See this image and copyright information in PMC

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