Methods to Identify User Needs and Decision Mechanisms for the Adoption of Electric Vehicles

Ensslen, Axel; Kuehl, Niklas; Stryja, Carola; Jochem, Patrick · 2016 · Crossref

DOI: 10.3390/wevj8030673

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Summary

This paper addresses the critical need for understanding user acceptance and decision-making mechanisms regarding electric vehicle (EV) adoption. While EV supply and demand are growing, absolute numbers remain low, and existing literature lacks a comprehensive overview of methods used to identify customer needs. The authors aim to fill this gap by categorizing and evaluating five distinct data collection methods: experimental studies, interviews and focus groups, surveys, sales documentation analysis, and social media analytics. The study classifies these methods based on sample size and the degree of anonymity, providing case study results to illustrate their specific advantages and limitations. The authors review methodological approaches ranging from qualitative to quantitative and automated techniques. Interviews and focus groups offer deep, personal insights into decision processes but involve small samples. Surveys allow for statistical inference on larger populations, measuring stated or revealed preferences. Laboratory experiments enable the observation of causal links and physiological responses (e.g., emotional arousal) to simulated choice tasks, though they are limited by small, often student-based samples. Sales documentation analysis utilizes existing Customer Relationship Management (CRM) data to examine B2B decision structures. Finally, social media analytics leverages large-scale, publicly available data from platforms like Twitter to identify intrinsically expressed customer needs through machine learning algorithms. Case study results demonstrate the utility of these methods. A survey of 109 fleet managers in Germany revealed that EV purchase decisions differ significantly from internal combustion engine vehicle (ICEV) decisions. Specifically, "range" was considered earlier and more frequently for EVs, while "safety," "car size," and "usage costs" were prioritized later. "Design" was considered earlier for EVs, suggesting a role for public relations in adoption. Analysis of sales documentation for e-mobility product service systems showed that purchase decisions require substantial explanatory support, averaging 10 contacts and four months. Logistic regression indicated that positive purchase decisions were associated with higher organizational formalization and more proactive or persuasive contacts. Social media analysis of 600,000 tweets identified 330 consensus "need-tweets," with the majority (162) relating to charging infrastructure dissatisfaction, followed by cost (60) and car features (60). The significance of this work lies in its framework for selecting appropriate research methods based on sample size and anonymity requirements. The authors conclude that no single method is sufficient; rather, they complement each other. Qualitative methods are best for exploring diffuse needs, while surveys and sales data help test hypotheses and understand revealed preferences. Experimental studies provide causal insights into emotional drivers, and social media analytics offers scalable, real-time data on intrinsic user concerns. The findings suggest that future EV adoption strategies should address specific barriers like range anxiety and charging infrastructure, while leveraging the public relations value of EV design in organizational contexts.

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• acceptance adoption