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Impact of the perceived risk in influencers' product recommendations on their followers' purchase attitudes and intention

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  • Cabeza-Ramírez, L. Javier
  • Sánchez-Cañizares, Sandra M.
  • Santos-Roldán, Luna M.
  • Fuentes-García, Fernando J.

Abstract

The impact of the use of influencers in recommending certain products to the audience has been highlighted in recent literature on social media marketing. However, this literature has barely considered the possible perception of risk associated with these recommendations on the part of consumers or the involvement of users with the product in their purchase intention. This paper fills this gap in the literature by analysing the possible interconnection between observational learning, product involvement and perceived risk on the effectiveness of influencers' recommendations. To this end, we propose a new exploratory model applying an approach grounded in observational learning and an extension of the theory of planned behaviour, analysed by means of PLS-SEM in a sample of 948 followers of influencers who recommend fashion products. The results have new implications for influencer marketing research by reporting the impact of risk on product attitude and purchase intention, although their predictive power was lower than expected. Furthermore, perceived authenticity of the message exerted the highest positive effect on attitude, and it was established as a significant mitigator of user-perceived risk. Finally, product involvement, a construct unexplored in previous studies, revealed a considerable influence on both perceived authenticity of the message and attitude.

Suggested Citation

  • Cabeza-Ramírez, L. Javier & Sánchez-Cañizares, Sandra M. & Santos-Roldán, Luna M. & Fuentes-García, Fernando J., 2022. "Impact of the perceived risk in influencers' product recommendations on their followers' purchase attitudes and intention," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:tefoso:v:184:y:2022:i:c:s0040162522005182
    DOI: 10.1016/j.techfore.2022.121997
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