The influence of perceived value factors on electric vehicle purchasing intention: The moderating effect of the Lebanese economic crisis

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Type of the article: Research Article

Abstract
In the wake of Lebanon’s economic crisis, the energy crisis, and the resulting high oil-product prices, electric vehicle technology is a lifeline for eco-conscious consumers, promising energy independence and lasting savings. This paper aims to evaluate the effect of perceived value factors on electric vehicle purchasing intention. It evaluates the moderating effect of the Lebanese economic crisis on the relationship between constructs. The study employed a quantitative, deductive approach. Data were collected from 124 potential car buyers via an online questionnaire in 2025, selected using purposive sampling. A pre-screening questionnaire reinforced the actual purchasing intention as an inclusion criterion. The results show that maintenance and technical performance constitute the most influential direct determinant of purchase intention (β = 2.084; p < 0.001), followed by the direct effect of the economic crisis (β = 1.724). These findings underscore that consumers prioritize reliability and functionality. Financial incentives have a significant effect (β = 1.219), while media awareness has a more variable positive effect (β = 1.275). Environmental awareness has a more moderate but still significant direct effect (β = 0.520). Analysis of moderating effects reveals that the economic crisis strongly amplifies the impact of financial incentives (β = 0.860) and strengthens the influence of environmental and media awareness, while mitigating the effect of technical performance (β = -0.619). The intention to purchase electric vehicles is primarily shaped by technical and economic considerations, whereas environmental and informational dimensions play a conditional role, strongly dependent on the crisis context, thereby offering clear guidelines for marketing positioning and supporting public policies.

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    • Figure 1. Conceptual framework
    • Figure 2. Structural model
    • Figure 3. Economic Crisis (Garvin slope)
    • Table 1. Demographics
    • Table 2. Operational criteria
    • Table 3. Descriptive statistics
    • Table 4. Correlations
    • Table 5. Factor coefficients
    • Table 6. Regression weights
    • Table A1. Demographics
    • Table A2. Items
    • Conceptualization
      Mahmoud Edelby, Riad Alchami, Ghalia Nassreddine
    • Data curation
      Mahmoud Edelby, Riad Alchami, Ghalia Nassreddine
    • Formal Analysis
      Mahmoud Edelby, Riad Alchami, Ghalia Nassreddine, Mazen Massoud
    • Investigation
      Mahmoud Edelby, Ghalia Nassreddine, Mazen Massoud
    • Methodology
      Mahmoud Edelby, Ghalia Nassreddine, Mazen Massoud
    • Project administration
      Mahmoud Edelby, Mazen Massoud
    • Resources
      Mahmoud Edelby
    • Supervision
      Mahmoud Edelby
    • Validation
      Mahmoud Edelby, Riad Alchami, Ghalia Nassreddine, Mazen Massoud
    • Writing – original draft
      Mahmoud Edelby, Riad Alchami, Ghalia Nassreddine, Mazen Massoud
    • Writing – review & editing
      Mahmoud Edelby, Riad Alchami
    • Software
      Riad Alchami, Ghalia Nassreddine, Mazen Massoud
    • Visualization
      Riad Alchami, Ghalia Nassreddine, Mazen Massoud