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July 4, 2026Journal of Marketing Analytics0 citationsOpen Access

Modelling multistage purchase intentions in influencer marketing: an ordinal regression and machine learning approach

MČMarián ČvirikSKSilvia Komara

Key Points

  • This article aims to explain the impact of influencer marketing determinants on multistage purchase intentions among young adults.
  • Utilized an ordinal regression model to analyze purchase intentions across three phases: interest, information search, and desire.
  • Applied machine learning models and SHAP-based interpretation to assess the determinants' predictive relevance.
  • Focused primarily on young adults as the target demographic for influencer marketing.
  • Identified key determinants (credibility, perceived influence, engagement) significantly impacted purchase intentions across the three phases.
  • Demonstrated that machine learning approaches provided a nuanced understanding of behavioral intention transitions.
  • Validated consistency of findings between inferential and predictive methods.

Abstract

Abstract With the growing importance of social networks and digitalisation, influencer marketing is becoming a key tool for reaching young consumers in particular, gradually replacing traditional one-way communication channels. Despite the abundance of empirical studies in this area, the problem of fragmentation of knowledge and reliance on isolated constructs or linear models that do not reflect the sequence of the decision-making process persists. Moreover, purchase intention is often studied in the literature only as a unidimensional category. The aim of this article is to comprehensively explain the impact of key determinants of influencer marketing—credibility, perceived influence and engagement—on purchase intention. The primary research focused on young adults as a key segment affected by digitalization and the main consumers of influencer marketing. Based on the principles of the AIDA model, the study deconstructs purchase intention in more detail into three specific phases: (i) interest in the product, (ii) search for more information and (iii) desire for the product. From a methodological perspective, the article fills an existing knowledge gap focused on multistage purchase intentions by using empirical research and an ordinal regression model supplemented by machine learning models and SHAP-based interpretation. This approach allows for a more precise understanding of the transitions between different levels of behavioural intentions while also assessing the predictive relevance of the analysed determinants and the consistency of findings across inferential and predictive approaches.

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Cite This Study

Čvirik et al. (2026) studied this question.

synapsesocial.com/papers/6a48a6b689561a0c2d78ea64https://doi.org/10.1057/s41270-026-00507-w
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