2. Materials and Methods
2.1. Research Design and Objectives
The increasing demand for battery electric vehicles (BEVs) has transformed the automotive industry and created the need for a skilled workforce capable of supporting this transition. As governments and corporations worldwide push for carbon neutrality, BEV sales personnel play a crucial role in educating consumers, addressing technological concerns, and promoting sustainable mobility. However, existing research lacks a structured framework that defines the core competencies required for BEV sales professionals. This study aims to bridge this gap by identifying and validating key competency indicators for effective BEV sales, customer engagement, and strategic business development.
This research incorporates digital innovation and strategic management principles into BEV sales training to ensure that the competency framework aligns with global sustainability efforts. It supports SDG 9 (Industry, Innovation, and Infrastructure) by enhancing workforce readiness for clean energy transitions and SDG 4 (Quality Education) by developing structured training programs tailored to the evolving BEV market. Furthermore, by equipping sales personnel with the necessary expertise, the study contributes to SDG 11 (Sustainable Cities and Communities), facilitating more significant adoption of sustainable transportation solutions.
2.2. Research Methodology
This study employs the Delphi Method to establish a consensus among experts regarding the core competencies required for BEV sales personnel. The Delphi panel comprises senior managers, industry professionals with extensive sales experience, and academic experts specializing in automotive sales and green mobility. Through multiple structured surveys and expert feedback rounds, the study refines competency indicators and assesses their importance in the real-world sales environment. To validate expert consensus, Kolmogorov-Smirnov (K-S) and Kruskal-Wallis (K-W) statistical analyses are conducted to ensure reliability and consistency in the findings.
In addition to expert input, the study conducts an extensive literature review covering BEV technology, sales strategies, consumer behavior, and government policies related to electric vehicle adoption. By integrating theoretical research and industry expertise insights, the study develops a robust competency framework applicable to BEV sales training, workforce development, and strategic business planning.
2.3. Research Process and Implementation
The research follows a structured approach, beginning with a literature review to establish foundational knowledge and identify existing competency models in automotive sales. This is followed by expert consultations and Delphi surveys, where multiple rounds of data collection refine the competency indicators. Experts provide feedback on the technical knowledge, customer engagement skills, and sales strategies required for BEV professionals. Each survey round helps to improve and validate the competency framework, ensuring its relevance to the industry.
Once expert consensus is reached, the study applies statistical analysis to evaluate the consistency and significance of the identified competencies. By examining the level of agreement among experts, the research ensures that the competency indicators are practical, relevant, and adaptable to the evolving BEV market. The final competency framework is a foundation for training programs, sales performance evaluation, and recruitment strategies for BEV sales personnel.
2.4. Contribution to Digital Innovation and Sustainable Business Strategies
This research integrates digital innovation and strategic management into the competency framework, aligning with the MDPI special track “Integrating Digital Innovation and Strategic Management for a Sustainable Business Landscape.” As the automotive industry shifts towards digital sales models, BEV sales professionals must develop expertise in AI-powered customer analytics, online sales platforms, and data-driven marketing strategies. This study emphasizes the need for continuous learning, adaptability, and digital proficiency, ensuring that BEV sales personnel remain competitive in an increasingly technology-driven market.
By enhancing workforce training and competency-based education, this research supports the broader goals of Sustainability, digital transformation, and business innovation. The findings will serve as a valuable resource for automotive companies, policymakers, and academic institutions, enabling them to develop effective training programs, optimize sales strategies, and drive sustainable business growth. Through the integration of digital tools, strategic workforce planning, and sustainability-focused sales techniques, this study provides practical solutions for advancing BEV adoption and building a skilled workforce for the future of sustainable mobility.
2.5. Research Scope
This study focuses on the core competencies required for BEV sales personnel, covering technical knowledge and essential sales skills. The research explores BEV structural principles, vehicle models, power performance, operational features, and proper handling of charging stations and equipment. Additionally, sales personnel must develop expertise in customer relationship management, communication, automotive insurance, vehicle delivery procedures, and after-sales services. A study conducted by Power (2019) in Taiwan’s automotive market identified six key factors influencing customer satisfaction in new car sales, with sales personnel and delivery processes ranking among the most critical. The findings suggest that BEV sales professionals play a central role in shaping customer perceptions, reinforcing the need for structured competency training.
In addition to technical expertise, this study examines marketing strategies used in BEV sales, including product positioning, pricing models, promotional strategy, and distribution channels. The effectiveness of these strategies in driving BEV market growth is analyzed to understand how sales personnel can enhance consumer engagement. Furthermore, the study investigates customer demand analysis, exploring how BEV sales professionals assess consumer preferences, budget constraints, and driving needs to offer tailored product recommendations. Understanding market trends and competitive dynamics is also a key focus, as the research evaluates industry shifts and competitive positioning to help sales personnel develop more effective sales approaches.
Sales data analysis is another critical aspect of this study, assessing how BEV sales professionals leverage digital tools to track market performance, compare competing brands, and identify consumer purchasing patterns. By integrating data-driven decision-making, the research highlights how sales professionals can refine their sales strategies based on real-time insights. Lastly, customer relationship management (CRM) is explored in-depth, including customer feedback collection, complaint handling, and after-sales support, ensuring that BEV sales personnel are equipped to maintain strong, long-term customer relationships.
2.6. Research Limitations
This study employs the Delphi Method to gather insights from BEV sales experts, which introduces certain limitations. The reliance on expert opinions means that responses may be influenced by subjective judgment, personal experience, or emotional bias, potentially limiting the accuracy of the findings. Since experts must rely on their industry knowledge when completing surveys, their assessments may not fully reflect the real-world complexities of BEV sales operations.
Another limitation arises from the sampling method, as the Delphi panel comprises a select group of industry and academic professionals, including senior automotive sales managers, dealership executives, and experienced sales personnel. While these individuals provide valuable insights, the limited sample size restricts the generalizability of the findings. The research conclusions are, therefore, applicable within the specific context of the experts surveyed but may require further validation through broader industry-wide studies.
Despite these limitations, the study offers a structured competency framework as a foundation for training programs, recruitment strategies, and professional development initiatives in the BEV sector. This research advances sustainable mobility by addressing technical and strategic competencies, equipping sales personnel with the skills needed to support the growing BEV market and align with global sustainability goals.
2.7. Delphi Method
2.7.1. Introduction to the Delphi Method
The Delphi Method is a structured research technique designed to gather expert opinions and build consensus on complex issues that lack sufficient quantitative data. Unlike traditional group discussions or brainstorming sessions, the Delphi Method relies on multiple rounds of anonymous surveys, allowing experts to provide feedback independently without being influenced by dominant personalities or group pressure. This iterative approach refines expert opinions over successive rounds until a consensus is reached, making it widely used in decision-making, policy formulation, and forecasting studies.
2.7.2. Characteristics and Application of the Delphi Method
Initially developed by the RAND Corporation in the 1960s for military and technological forecasting, the Delphi Method has since been adapted for various fields, including business, healthcare, education, and engineering. Unlike face-to-face meetings, this method is conducted through written questionnaires, often distributed via email or digital platforms. The process allows experts to respond independently, review summarized feedback from the previous round, and refine their opinions in subsequent iterations.
The success of the Delphi Method depends on selecting experts with deep knowledge and extensive practical experience in the research field. These experts must represent diverse perspectives, ensuring the results capture a broad and balanced view. Additionally, the Delphi process eliminates interpersonal conflicts and social biases, allowing for a more objective analysis of the research topic. This structured communication method ensures that every expert’s opinion is valued equally, leading to collective decision-making based on expertise rather than influence.
2.7.3. Strengths and Limitations of the Delphi Method
The Delphi Method offers several advantages, making it a valuable tool for predictive analysis and expert-driven research. One of its key strengths is anonymity, which prevents dominant voices from overshadowing other participants and reduces the risk of bias. Experts can express their views freely without fear of criticism or pressure. Additionally, since the process is conducted remotely, it eliminates logistical challenges such as scheduling conflicts and geographical constraints. This flexibility allows experts to provide well-thought-out responses at their convenience.
However, the Delphi Method is not without limitations. The selection of experts is crucial, and a poorly chosen panel can lead to unreliable results. Additionally, the process requires multiple rounds, making it time-consuming. Experts may also revise their responses based on group feedback rather than their original perspective, potentially introducing groupthink or reducing the diversity of opinions. Furthermore, since reactions are based on subjective expertise rather than empirical data, findings may still carry an element of personal judgment. Despite these challenges, the Delphi Method remains widely accepted for structuring expert consensus on emerging or complex topics.
2.7.4. Delphi Method Implementation Process
Implementing the Delphi Method follows a structured, multi-step process to ensure validity, reliability, and expert consensus. First, the research topic is defined, and relevant literature is reviewed to establish a foundation for questionnaire design. Next, experts are selected based on their qualifications, experience, and knowledge. These experts remain anonymous to one another throughout the study, ensuring independent assessments.
The first round of the Delphi survey consists of an open-ended questionnaire where experts provide their initial opinions. Responses are then aggregated, analyzed, and summarized into structured statements. In subsequent rounds, experts receive a revised questionnaire containing statistical summaries and feedback from previous rounds, allowing them to refine their views. This process repeats until a consensus is reached, typically after three or four rounds. The final step involves analyzing the agreed-upon indicators and interpreting their implications for the research objectives.
2.7.5. Statistical Analysis and Data Processing
To evaluate expert responses, the study employs descriptive statistics, including mean scores and standard deviation calculations, to assess the importance of different competency indicators. Higher mean scores indicate more substantial agreement on a given competency, while lower standard deviation values suggest higher consistency among expert opinions. The study also applies Kolmogorov-Smirnov (K-S) and Kruskal-Wallis (K-W) tests to ensure statistical significance and consistency across rounds. If consensus is not reached, additional refinements are made until the results become stable.
By employing a rigorous data analysis process, the Delphi Method ensures that the research findings are credible and applicable to real-world scenarios. The final consensus is a validated competency framework, providing practical insights for training programs, workforce development, and strategic decision-making in the BEV industry. Through expert-driven evaluations and statistical validation, this study establishes a comprehensive competency model that aligns with sustainable business strategies and digital innovation in automotive sales.
2.8. Expert Panel Selection and Delphi Method Implementation
This study aims to establish core competency indicators for BEV (Battery Electric Vehicle) sales personnel by leveraging the Delphi Method, which facilitates expert consensus through iterative feedback. The study was conducted in two stages: expert review for questionnaire validation and Delphi survey rounds to refine and confirm competency indicators.
Five experts were invited to review the questionnaire for validity and content accuracy in the initial stage. This panel comprised three academic scholars specializing in competency research and automotive sales education, along with two senior managers from the automotive industry in
Table 1 with extensive experience in sales management and workforce training. The experts provided critical insights and modifications to ensure that the questionnaire comprehensively covered the necessary competencies for BEV sales professionals, balancing theoretical frameworks with industry applications.
The Delphi survey was conducted in three rounds following the expert review, allowing experts to assess and refine the proposed competency indicators iteratively. This process ensured that the final competency framework was statistically validated, industry-relevant, and applicable to real-world BEV sales operations.
2.9. Delphi Expert Panel Composition
To ensure a comprehensive and representative expert panel, this study included 15 experts from different professional backgrounds in
Table 2 within the automotive sales industry in
Table 3. The panel was divided into three groups based on their roles and expertise:
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BEV Sales Consultants and Assistants (5 Experts)
Professionals with at least two years of BEV sales experience specializing in imported or domestic electric vehicle models.
Their input provided practical insights into the day-to-day competencies required for frontline BEV sales personnel, including customer engagement, technical knowledge, and sales execution strategies.
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Sales Supervisors and Team Leaders (5 Experts)
Mid-level managers with over four years of experience in automotive sales and workforce training.
Their role focused on identifying critical competencies for managing sales teams, training new sales representatives, and overseeing customer satisfaction metrics.
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Senior Sales Directors and Dealership Managers (5 Experts)
High-level executives with over ten years experience in BEV sales operations, dealership management, and strategic business planning.
Their expertise contributed to defining long-term competency frameworks, digital transformation strategies, and sustainable business development in BEV sales.
2.10. Delphi Survey Process and Expert Consensus Development
The first round of the Delphi survey focused on identifying core competency dimensions and sub-dimensions, incorporating expert feedback to refine the questionnaire structure. In the second round, experts were provided with statistical summaries and qualitative feedback from the first round, allowing them to reassess and refine their evaluations. The third round aimed to achieve statistical convergence, with experts reaching a consensus on the final set of core competencies.
To ensure the reliability and significance of expert responses, mean scores, and standard deviation analyses were applied. Higher mean scores indicated more remarkable agreement on the importance of specific competencies, while lower standard deviation values confirmed consistency among expert opinions. Additionally, Kolmogorov-Smirnov (K-S) and Kruskal-Wallis (K-W) statistical tests were conducted to validate the level of consensus achieved in each round.
2.11. Finalized Competency Framework for BEV Sales Personnel
As a result of this structured Delphi process, the study successfully developed a comprehensive competency framework for BEV sales personnel, integrating technical expertise, customer relationship management, marketing strategies, and data-driven sales analysis. The framework is a practical guideline for recruitment, workforce training, and sales performance evaluation, ensuring that BEV sales professionals are well-equipped to navigate the rapidly evolving electric vehicle market.
The findings from this study provide valuable insights for automotive manufacturers, dealership managers, and policymakers, supporting the strategic development of BEV sales teams in alignment with sustainability goals and digital transformation trends.
2.12. Experiment Implementation
This study was conducted in two phases: expert content validity review and Delphi survey rounds to refine the core competency indicators for BEV sales personnel.
2.12.1. Expert Content Validity Review
The initial Delphi questionnaire draft was sent to five experts on February 26, 2024, for content validation. Experts were asked to assess the questionnaire’s clarity, relevance, and comprehensiveness. After receiving feedback by March 15, 2024, the responses were analyzed, and necessary modifications were made to develop the final Delphi survey.
2.12.2. Delphi Survey Process
Delphi panel members were selected after consulting with the research advisor after the expert review. After obtaining their consent, the Delphi survey was conducted in three iterative rounds between March and April 2024. Each round involved distributing the questionnaire, collecting responses, and performing statistical analysis on mode, mean, and standard deviation to measure consensus. Each iteration refined the competency framework based on expert feedback until a stable consensus was reached. The final results formed the validated competency indicators for BEV sales personnel in
Table 4 and
Table 5.
2.13. Statistical Analysis and Data Processing
2.13.1. Data Analysis Approach
This study conducted a three-round Delphi survey using SPSS 20.0 statistical software to analyze the collected data. The analysis focused on calculating the arithmetic mean, mode, and standard deviation to assess the degree of consensus among experts. The reliability and validity of the questionnaire were examined to ensure the accuracy and consistency of the results. To quantify expert opinions, a five-point Likert scale was used, ranging from “very important” (5 points) to “very unimportant” (1 point). Statistical tests, including Kolmogorov-Smirnov (K-S) one-sample test and Kruskal-Wallis (K-W) one-way ANOVA, were applied to verify the consistency of expert responses across different competency indicators.
2.13.2. Arithmetic Mean (M) and Importance Evaluation
The arithmetic mean (M) was used to measure the central tendency of expert ratings, reflecting the perceived importance of each competency indicator. Higher mean values indicate a greater extent, while lower values suggest reduced relevance. Following statistical guidelines, a mean score above 4.5 was classified as “very important,” scores between 3.5 and 4.5 as “important,” and scores below 3.5 as “not important”. This study identified the most critical competencies for BEV sales personnel by ranking indicators based on their mean scores.
2.13.3. Standard Deviation (SD) and Consensus Measurement
Standard deviation was used to determine the variability of expert responses. A higher SD indicates a more significant divergence in expert opinions, whereas a lower SD suggests a strong consensus on a given competency indicator. The progression from the second to the third round of the Delphi survey was analyzed to assess whether expert opinions became more stable over time. A decreasing SD value across rounds signified increasing agreement and validation of the competency indicators.
2.13.4. Mode (Mo) and Expert Agreement
The mode representing the most frequently chosen response for each competency indicator was analyzed to determine the most widely accepted expert opinion. If the mode value for a given competency consistently appeared at the higher end of the Likert scale, it confirmed the broad expert agreement on the indicator’s importance. This measure helped further validate the prioritization of key competencies.
2.13.5. Kolmogorov-Smirnov (K-S) Test for Expert Consistency
To ensure the statistical reliability of the Delphi results, the Kolmogorov-Smirnov (K-S) one-sample test was performed. This test examined whether expert ratings exhibited a uniform distribution, indicating consensus. The indicator was deemed inconsistent and excluded from the final competency framework if the p-value was more significant than 0.05 (p > 0.05). Conversely, if the p-value was below 0.05 (p < 0.05), it confirmed a substantial level of expert agreement on that competency indicator.
2.13.6. Kruskal-Wallis (K-W) Test for Group Consistency
The Kruskal-Wallis (K-W) one-way ANOVA was conducted to assess whether expert opinions varied significantly across professional backgrounds. If the p-value exceeded 0.05 (p > 0.05), it indicated no significant difference between expert groups, validating a unified competency framework. However, if the p-value was below 0.05 (p < 0.05), it suggested a lack of consensus among expert groups, and the competency indicator in question was considered for removal.
Through these rigorous statistical validation techniques, this study ensured that the final competency indicators for BEV sales personnel were both statistically sound and practically relevant. The findings provide a structured and reliable competency framework, supporting training programs, performance evaluations, and strategic workforce development in the BEV sector.