Navigating influence: Unraveling the impact of micro-influencer attributes on consumer choices in the Chinese social media
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DOIhttp://dx.doi.org/10.21511/im.20(2).2024.13
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Article InfoVolume 20 2024, Issue #2, pp. 152-168
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Creative Commons Attribution 4.0 International License
This study aims to explore the relationship between consumer purchasing behavior and key micro-influencer attributes, including knowledge, entertainment value, credibility, and transparency, within the context of Chinese social media platforms. The paper adopts a quantitative approach, employing partial least squares structural equation modeling (PLS-SEM) to analyze the intricate relationships among latent variables. The respondents comprise active users of major Chinese social media platforms, such as Weibo and Xiaohongshu. For primary data collection, 329 respondents were surveyed online, utilizing a convenient sampling method as part of non-probability sampling. Data collection spanned four weeks, and participants were given the option to respond in either English or Mandarin. The findings suggest significant associations between consumer purchasing behavior and micro-influencer attributes. Specifically, knowledge, entertainment value, credibility, and transparency exhibit varying degrees of influence on consumer behavior within the Chinese social media landscape. The p-value for H1, H2, H3, and H7 appeared as 0.000 and shows that these are the highly significant relations, whereas the p-value for H3 (0.019), for H5 (0.001), and for H6 (0.028) shows that these relations play a moderate role in the proposed model. Elucidating the role of key attributes provides valuable insights for marketers and businesses seeking to leverage micro-influencer marketing strategies effectively in this rapidly evolving digital landscape.
- Keywords
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JEL Classification (Paper profile tab)M31, M37
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References66
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Tables8
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Figures3
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- Figure 1. Conceptual model
- Figure 2. Estimation model
- Figure 3. Structural model
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- Table 1. Cronbach’s alpha statistics
- Table 2. Reliability statistics
- Table 3. Fornell-Larcker criterion
- Table 4. Model fit
- Table 5. R-statistics
- Table 6. F-statistics
- Table 7. Path analysis results
- Table A1. Research variables
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