Insights on electric vehicle adoption: Does attitude play a mediating role?

  • Received October 25, 2021;
    Accepted February 8, 2022;
    Published February 17, 2022
  • Author(s)
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  • Article Info
    Volume 18 2022, Issue #1, pp. 104-116
  • Cited by
    3 articles

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

Vehicles are classified as a mobile source of pollution worldwide. This problem is compounded in countries like India, where the population is enormous, and the number of automobiles increases quickly. To overcome this issue, governments and individuals must adopt electric vehicles and maximize the use of eco-friendly vehicles. However, the adoption of electric vehicles in India is gradual. One of the reasons is the attitude towards traditional and electric vehicles. This study’s primary objective is to determine how attitude influences the adoption of electric vehicles. The topic is vital since the attitude provided by numerous studies influences the intention to buy anything. This study considered one dependent variable (electric vehicle adoption) and one mediating variable (attitude) along with five independent variables. The data collection method was straightforward, and the sample size was 366 respondents. Exploratory factor analysis (EFA), structural equation modeling (SEM), and mediation analysis were used to analyze the data. All adopted constructs were trustworthy, with average variance extracted exceeding 0.55, composite reliability exceeding 0.75, and factor loadings exceeding 0.70 for most. The model fit indices were also found to be significant on several parameters. Among all other variables, only financial incentives affect electric vehicle adoption. In other circumstances, opinions did not influence customer uptake of electric vehicles.

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    • Figure 1. Proposed research model
    • Figure 2. Model fit
    • Figure 3. Structural model
    • Table 1. Demographic profile
    • Table 2. KMO and Bartlett’s test
    • Table 3. Reliability and validity
    • Table 4. Discriminant validity (Fornell-Larcker criterion)
    • Table 5. CFA results indicators
    • Table 6. Regression path coefficients results
    • Table 7. Mediation analysis
    • Data curation
      Imran Ali
    • Formal Analysis
      Imran Ali
    • Investigation
      Imran Ali
    • Methodology
      Imran Ali
    • Software
      Imran Ali
    • Writing – original draft
      Imran Ali
    • Conceptualization
      Mohammad Naushad
    • Funding acquisition
      Mohammad Naushad
    • Project administration
      Mohammad Naushad
    • Resources
      Mohammad Naushad
    • Supervision
      Mohammad Naushad
    • Validation
      Mohammad Naushad
    • Visualization
      Mohammad Naushad
    • Writing – review & editing
      Mohammad Naushad