The impact of COVID-19 risk perceptions on intentions to consume energy beverages: The mediation role of a healthy lifestyle and sustainable consumption

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The COVID-19 pandemic has produced tremendous socioeconomic upheaval worldwide, affecting people’s purchasing habits and intentions. This study assesses the relationship between COVID-19 risk perceptions and intentions to consume energy drinks. Furthermore, it analyzes the role of a healthy lifestyle and sustainable consumption in mediating this relationship. A survey approach was used to obtain the data. An online questionnaire (400 samples) was distributed through social media to Palestinian citizens and residents (students, employees, free professionals, laborers, and others). The study used a 5-point Likert scale. Data analysis used descriptive statistics (measures of central tendency and dispersion). PLS was utilized to investigate the mediation effect, whereas SPSS was used to analyze the data and test the hypotheses. Risk perception was assessed using seven variables: fear, conduct, awareness and knowledge, trust and confidence, healthy lifestyle, sustainable consumption, and intention to use energy beverages. The findings indicate that COVID-19 risk perception affects the propensity to consume energy beverages (B = 3.692; p ˂ 0.001). In addition, the results show that COVID-19 risk perception has a significant relationship with a healthy lifestyle and sustainable consumption (B = 3.358; p ˂ 0.001; B = 3.571; p ˂ 0.001). The findings also highlighted a partial mediation of healthy lifestyle and sustainable consumption in the association between COVID-19 risk perception and desire to use energy beverages.

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    • Figure 1. Conceptual model
    • Figure 2. Mediation effect
    • Table 1. Internal consistency coefficients (Cronbach’s Alpha)
    • Table 2. Means and standard deviations of independent variables
    • Table 3. Hypothesis 1 testing
    • Table 4. Analysis of variance for H1 (ANOVA)
    • Table 5. Coefficients for H1
    • Table 6. Hypothesis 2 testing
    • Table 7. Analysis of variance for H2 (ANOVA)
    • Table 8. Coefficients for H2
    • Table 9. Hypothesis 3 testing
    • Table 10. Analysis of variance for H3 (ANOVA)
    • Table 11. Coefficients for H3
    • Table 12. Hypothesis 4 testing
    • Table 13. Coefficients for H4
    • Table 14. Hypothesis 5 testing
    • Table 15. Coefficients for H5
    • Table 16. Path analysis results using Smart PLS3
    • Conceptualization
      Iyyad Zahran
    • Methodology
      Iyyad Zahran, Younes Megdadi, Ahmad Albloush
    • Software
      Iyyad Zahran
    • Writing – original draft
      Iyyad Zahran
    • Data curation
      Younes Megdadi
    • Project administration
      Younes Megdadi
    • Supervision
      Younes Megdadi
    • Writing – review & editing
      Younes Megdadi, Ahmad Albloush
    • Formal Analysis
      Ahmad Albloush
    • Resources
      Ahmad Albloush