Comparison of influence of selected viral advertising attributes on shopping behavior of Millennials – empirical study


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The study aims to evaluate the impact of selected factors of viral campaigns on Millennials customers’ consumer behavior. This goal was achieved in two steps: in the first step, the authors determined the impact of selected attributes on purchasing behavior in general, and in the second step, they compared the impact of the selected research campaigns – the guerrilla campaign of the company 4KA and the viral campaign of the company ABSOLUT. The inputs to the analyses were obtained through answers from 360 respondents, which completed the questionnaire on a sample of Millennials customers generation (1975–2000) – social generation, which collaborate and cooperate, expect technology to simply work for adventure and passionate about values (Smith, Nichols, 2015). The survey part of the questionnaire consisted of 8 attributes (Novelty, Relevance, Aesthetics, Clarity, Humor, Emotion arousal, Surprise, Design, Purchase intention). Data were collected based on participants’ availability and their will to participate in the questionnaire and quota selection. The PLS PM method was used to assess the impact, and the bootstrap-based parametric method was used to assess the difference in the impact. One of the most important findings is that attributes such as Novelty, Relevance, Humor, and Surprise significantly affect purchasing behavior. Concerning the company 4KA, significant impacts were seen in Relevance and Surprise, and with the company ABSOLUT, significant impacts were seen in Relevance, Humor, and Surprise. When analyzing the difference in the impact, there were no significant differences between the campaigns.

This article is one of the partial outputs under the scientific research grant VEGA 1/0694/20, VEGA 1/0609/19.

view full abstract hide full abstract
    • Figure 1. Distribution of age of the examined sample
    • Figure 2. Factor loadings PLS PM model
    • Figure 3. coefficient scheme PLS PM model whole, 4KA, ABSOLUT
    • Table 1. Latent research variables – references
    • Table 2. Identification characteristics of the sample
    • Table 3. Descriptive statistics and Confirmatory Factor Analysis
    • Table 4. PLS PM conditions
    • Table 5. PLS PM model output – whole
    • Table 6. PLS PM model output – 4KA
    • Table 7. PLS PM model output – ABSOLUT
    • Table 8. PLS PM estimate differences test
    • Conceptualization
      Martin Mudrik, Richard Fedorko
    • Data curation
      Martin Mudrik, Beata Gavurova
    • Formal Analysis
      Martin Mudrik, Martin Rigelsky
    • Validation
      Martin Mudrik, Martin Rigelsky, Beata Gavurova, Radovan Bačik, Richard Fedorko
    • Writing – original draft
      Martin Mudrik, Martin Rigelsky
    • Writing – review & editing
      Martin Mudrik, Martin Rigelsky, Richard Fedorko
    • Methodology
      Martin Rigelsky, Beata Gavurova, Radovan Bačik
    • Software
      Martin Rigelsky
    • Resources
      Beata Gavurova
    • Funding acquisition
      Radovan Bačik, Richard Fedorko