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Enhancement of electric vehicles’ market competitiveness using fuzzy quality function deployment

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  • Babar, Abdul Haseeb Khan
  • Ali, Yousaf

Abstract

The rising level of carbon emissions has plunged the world into global warming. These emissions originate from different sectors however, one of the major contributions comes from the transportation sector. In order to tackle this problem, greener modes of transportation like Electric Vehicles (EV) are introduced as an alternative option. Currently, different types of EVs are available in the market, with Hybrid Electric Vehicles (HEV) being the most popular type of EV in developing countries. Nevertheless, a lot of customers are still preferring Conventional Vehicles (CV) over EVs, which is inevitably damaging the market share of EVs and creating problems in their wider acceptance. Thus, the aim of this study is to solve two problems. First, to identify the factors which make CVs more appealing to the customers. Secondly, how these shortcomings can be overcome for HEVs. For this purpose, the relationship of different parameters with CVs is evaluated using the multiple regression method, which is then incorporated into the Fuzzy Quality Function Deployment (FQFD) model to find the best solution for adding those parameters to HEVs. The analysis resulted in the identification of affordability, reliability, variety and fuel consumption as the key contributors that made CVs, a more attractive option to the customers. Furthermore, local manufacturing was identified as the best solution for improving the quality of HEVs and make them market competitive in developing countries. The practical applications of this research, along with the contextual analysis of the developing countries are the principal novelties of the study.

Suggested Citation

  • Babar, Abdul Haseeb Khan & Ali, Yousaf, 2021. "Enhancement of electric vehicles’ market competitiveness using fuzzy quality function deployment," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:tefoso:v:167:y:2021:i:c:s0040162521001700
    DOI: 10.1016/j.techfore.2021.120738
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