A Competency Framework for Electric Vehicle Maintenance Technicians: Addressing the Environmental, Social, and Governance (ESG) Imperatives of the BEV Industry

Abstract

The fast expanding market of battery electric vehicles (BEVs) demands industry-specific competence requirements for maintenance technicians. We have therefore generated a knowledge structure of BEV maintenance through a literature review and expert consensus. Consensus was achieved following a Delphi study of 15 industry experts through three rounds of refining a broad initial list of competencies. The resulting framework consists of four core competency categories (Professional Knowledge, Professional Skills, Professional Attitude, and Personal Qualities), which are further divided into a total of 24 subcategories and 106 specific indicators that define the boundary of professional skill as well as core skill essentials. This approved tool can be used strategically for workforce grooming, curriculum design for training, and performance assessment in BEV maintenance to ensure that technical workforce capabilities are in line with sustainable mobility targets of the industry.

1. Introduction

1.1. Background and Motivation

With rapid technological advancements and the world’s increasing shift towards sustainable transportation, the market for battery electric vehicles (BEVs) is burgeoning. This transformation requires new technical skills in the field of maintenance (some of the traditional classics are not enough for high-voltage and more complex systems in BEVs [1]). It is increasingly at the heart of global plans for decarbonization and sustainable mobility, and is being propelled by the Environmental, Social, and Governance (ESG) agenda and the Sustainable Development Goals (SDGs), including SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities) [2]. The use of BEV fleets has rapidly increased the competency level demanded from maintenance technicians—including skills to manage high-voltage systems, battery management, and more ESG-aligned practices (GENRE, 2022) [2]. For instance, hybrid electric vehicles (HEVs) still have internal combustion engines of some form in addition to electric drive trains, so the maintenance required is generally an extension of regular automotive experience. In BEVs, however, the internal combustion engine is completely displaced, so the electric systems need some special knowledge from engineers working exclusively on high-voltage electric and energy storage systems [3]. It is in this respect that the elaboration of a holistic service portfolio—based on a well-structured competence framework—is a key enabler towards safe, efficient, and reliable preventive maintenance of BEVs. However, existing maintenance practices and competencies, which are designed for internal combustion engine vehicles or hybrids, do not sufficiently take into consideration the specific needs of fully electric vehicles (such as high-voltage battery systems, advanced diagnostics, and safety procedures specific to BEVs). Moreover, Environmental, Social and Governance (ESG) mandates are increasingly driving the incorporation of sustainable maintenance practices into technician education [4]. To fill these gaps, we suggest a dedicated competency framework that denotes a clear benchmark for the specific need of BEV maintenance. This will enable industry stakeholders to strategically develop vocational training content as well as help incorporate global ESG-driven goals [5].

1.2. Qualitative Literature Overview

Tremendous progress has been made in battery electric vehicles (BEVs) over the last several years with advances in technology and the evolution of global sustainability requirements. Fundamental works such as the main study of Pelegov and Pontes [1] offer an overall market description of EV battery trends, while Popescu et al. [6] as well as Gebhardt et al. [7] take a critical look at sustainability issues in circular supply chains of EV batteries.

The field of BEV maintenance workforce development is still in its infancy. Frameworks of social innovation and studies on talent management [8] highlight clear gaps in certain areas, in particular high-voltage system competences and the integration of ESG considerations in technician training. Nonetheless, the current literature has hardly provided any holistic competency models comprising technical and soft skills corresponding to sustainability objectives [9,10].

This subarea of study underscores the urgent need for a holistic and ESG-oriented competency framework specific to BEV maintenance technicians, which the present study fills by systematically developing and validating a competency framework via expert consensus.

1.3. ESG and Green Energy Context

Environmental, Social, and Governance (ESG) principles have become integral to the modern energy landscape [6,11]. The BEV industry is a key player in the global green energy transition, reducing greenhouse gas emissions and fostering sustainable mobility [12]. It situates the competency framework within the broader ESG context, recognizing that the maintenance practices of BEVs contribute to overall environmental performance and corporate sustainability [13]. By aligning technical standards with green energy goals, the framework supports initiatives that enhance operational safety, reduce waste, and promote responsible energy use [14]. Electric vehicle maintenance technicians are at the frontline of this transition, ensuring that BEVs operate safely and efficiently. Their role extends beyond routine repairs; it involves the management of high-voltage systems, adherence to rigorous safety protocols, and the adoption of new diagnostic technologies [15]. This section outlines the evolving responsibilities of these technicians, highlighting the need for specialized training and a well-defined set of competencies that address both technical proficiency and critical soft skills [16].

1.4. Problem Statement

Despite the growth of the BEV market, existing competency frameworks primarily cater to conventional internal combustion engine systems. There is a notable gap in specialized frameworks designed to meet the unique challenges of BEV maintenance, including high-voltage battery management, advanced charging systems, and comprehensive diagnostic procedures [17]. Ref. [18] addresses this gap by developing a validated competency framework explicitly tailored for BEV maintenance technicians.

1.5. Methodology Overview

A three-round Delphi process was carried out to refine the competency framework [19]. Initially, open-ended questionnaires were distributed to gather diverse expert insights, which were then synthesized into a structured instrument [4,20]. In subsequent rounds, a panel of 15 experts rated the importance of each competency using a five-point Likert scale. Statistical analyses—including descriptive statistics and nonparametric tests such as the Kolmogorov–Smirnov and Kruskal–Wallis tests—were employed to assess consensus and validate the reliability of the framework [21,22]. Competencies related to digital skills and diagnostic tools are critical in contemporary BEV maintenance. Technicians must proficiently use digital diagnostic software, interpret system fault codes, conduct firmware updates, and leverage data-driven tools effectively to ensure accurate diagnostics, timely maintenance, and adherence to technical standards.

1.6. Significance and Contributions

This study is one of the first to propose a comprehensive competency framework specifically tailored for BEV maintenance technicians, thereby filling a notable gap in the existing literature [23]. It also uniquely integrates ESG (Environmental, Social, and Governance) considerations into the competency model, aligning maintenance training with industry sustainability goals. The resulting framework is structured into 4 core competency dimensions, 24 sub-dimensions, and 106 specific indicators. This hierarchical structure supports effective talent selection, targeted training, and systematic performance evaluation in the BEV maintenance industry. By addressing the unique demands of BEV systems, the framework contributes to academic research [24] and practical industry applications, fostering safer and more sustainable maintenance practices in an era of rapid technological change [25]. The main contributions of this work are as follows:

• A four-dimension framework (Knowledge, Skills, Attitude, Traits) with 24 sub-dimensions and 106 indicators, validated via Delphi consensus.
• Integration of ESG and SDG targets (SDG 7, 9, 11) directly into technician competencies, ensuring alignment with global sustainability goals.
• Comparative analysis with existing frameworks (e.g., Villegas et al. 2018 [16]) demonstrating superior coverage of both technical and soft skill requirements.
• A pilot case study applying the framework in a Taiwanese BEV service center (Section 5), illustrating practical feasibility.

1.7. Relation to the Existing Literature

The competency structure constructed in this study supports those of previous research on automotive maintenance focusing on combining established technical and soft skills [26]. But what differentiates our model is the explicit inclusion of ESG and linkage to the Sustainable Development Goals (SDGs), notably SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 11 (Sustainable Cities and Communities).

While previous frameworks tend to focus solely on technical skills [27], we have expanded the focus to include what could be considered sustainability paired attitudes and personality traits, aiming to enhance workforce readiness in preparing for the environmental and social challenges associated with routine BEV maintenance. This kind of holistic approach has significant advantages when fostering responsible maintenance behavior, though it is more complex to measure soft skills, and this limitation is commonly shared by similar research studies [28].

In conclusion, our results contribute to the literature on how technical excellence and sustainability-oriented competencies may be jointly accomplished by the emerging BEV maintenance workforce.

2. Materials and Methods

2.1. Research Design

We adopted a mixed-methods research design to develop and validate a competency framework tailored to electric small-passenger car maintenance technicians. The overall approach integrated qualitative and quantitative techniques, beginning with an exhaustive review of the academic literature, industry reports, and international and domestic standards related to electric vehicle maintenance. The initial phase focused on identifying and categorizing relevant competency areas, while the subsequent phases were devoted to expert consensus-building. The framework is hierarchically organized into three layers: core dimensions, sub-dimensions, and specific indicators. This structured design not only facilitates a comprehensive evaluation of both technical and non-technical skills but also ensures that the final model reflects the complex requirements of the evolving BEV maintenance industry. By employing a rigorous Delphi method, the research design provided a systematic procedure for refining and validating the competencies, thereby enhancing the credibility and practical applicability of the framework.

2.2. Development of the Initial Instrument

The initial instrument was developed through a systematic literature review and relevant industry standards. Multiple databases and sources were consulted to gather information on competency requirements, safety protocols, technical skills, and regulatory standards in electric vehicle maintenance. The preliminary set of competency items were organized into four broad dimensions: Professional Knowledge, Professional Skills, Professional Attitude, and Personal Traits. These dimensions were subdivided into 24 sub-dimensions, yielding 106 specific indicators. Each indicator was designed to capture nuanced aspects of technician performance, ranging from high-voltage battery management to effective communication and teamwork. This comprehensive inventory was intended to serve as the foundation for the subsequent expert validation process, ensuring that both widely recognized and emerging competencies were included in the model.

2.3. Expert Selection and Content Validity

A rigorous two-stage expert review process was conducted to ensure the instrument’s content validity. Five experts with diverse expertise—spanning organizational behavior, technical education, engineering, and EV maintenance—were invited to review the draft instrument. Their feedback focused on the proposed indicators’ clarity, relevance, and comprehensiveness. Suggestions from this preliminary review were systematically incorporated, resulting in a revised questionnaire that addressed any ambiguities or redundancies. Subsequently, a panel of 15 experts was purposively selected based on their extensive knowledge and practical experience in the field. This panel was divided into three groups, with five experts in each group: education and training directors, senior technical supervisors, and veteran maintenance technicians. The diverse composition of the panel ensured that the instrument reflected a balanced perspective from both academic and industry viewpoints, thereby reinforcing the framework’s robustness and applicability in real-world settings.

2.4. Geographic Limitation of Experts

Our Delphi panel primarily included experts from the USA and Taiwan. While these regions are actively engaged in BEV deployment and maintenance practices, the geographic concentration may introduce regional biases in the identified competencies. Future research should consider including experts from other regions such as Europe, Africa, or other parts of Asia to increase the global applicability and relevance of the competency framework.

2.5. Delphi Process

A structured three-round Delphi process was employed to refine and validate the competency framework. In the first round, open-ended questionnaires were distributed to all panel members to elicit broad and unfiltered insights regarding the initial set of competencies. The qualitative responses were meticulously analyzed and synthesized to generate a more structured questionnaire version. In the second round, the revised instrument was administered using a five-point Likert scale, allowing experts to rate the importance and relevance of each indicator quantitatively. Descriptive statistical methods, including the calculation of means, modes, and standard deviations, were used to summarize the responses. In addition, nonparametric tests such as the Kolmogorov–Smirnov test for distribution analysis and the Kruskal–Wallis test for inter-group comparisons were conducted to assess the level of consensus across different expert groups. The third round involved further refinement based on statistical feedback and expert comments from the second round. Experts were provided with the aggregated results and invited to reassess their ratings, fostering convergence towards a consensus. This iterative process continued until a satisfactory level of agreement was achieved across all dimensions, sub-dimensions, and indicators.

2.6. Delphi Method Conflict Resolution

During each Delphi round, aggregated anonymous feedback highlighting areas of consensus and disagreement was provided to experts. If significant divergence was noted, participants were asked to review conflicting points in subsequent rounds and provide justifications or reconsider their positions. This iterative feedback loop effectively reduced initial disagreements, resulting in high consensus by the final round.

2.7. Data Analysis

Data analysis was performed using descriptive and inferential statistical techniques to ensure the robustness of the consensus reached among experts. For each round of the Delphi process, the mean, mode, and standard deviation of ratings were calculated to assess the central tendency and variability of the responses. The Kolmogorov–Smirnov test was employed to examine the normality of the distribution of responses, which provided insight into the consistency of expert ratings. Additionally, the Kruskal–Wallis test was used to compare the ratings among the different groups of experts, ensuring that there were no significant discrepancies based on professional background. These statistical methods collectively enabled a rigorous evaluation of the reliability and validity of the competency framework. The quantitative analysis confirmed the consensus on critical competencies and highlighted specific areas where expert opinions required further discussion and adjustment.

2.8. Ethical Considerations and Timeline

Participation in the Delphi process was entirely voluntary, and all responses were treated with strict confidentiality. Informed consent was obtained from each expert before their involvement, and all data were anonymized to prevent any potential bias or conflict of interest. The study followed institutional ethical guidelines and received approval from the appropriate review board. The entire process, from the initial literature review to the final round of the Delphi study, was carried out between February and June 2023. This timeline ensured that sufficient time was allocated for iterative feedback, thorough analysis, and the refinement of the competency framework, ultimately contributing to a robust and validated instrument for the BEV maintenance industry.

3. Results

3.1. Expert Content Validity and Instrument Revision

The initial competency framework, derived from an extensive literature review and an analysis of domestic and international industry standards, comprised 106 detailed indicators organized into 4 core dimensions and 24 sub-dimensions. An initial content validity assessment was conducted with five domain experts representing diverse fields such as organizational behavior, technical education, engineering, and electric vehicle (EV) maintenance. Their feedback focused on several critical areas:

• Clarity and Precision: Many indicators required more precise wording to delineate technical specifications. For example, experts recommended that the description of high-voltage battery system configurations be more detailed to ensure that technicians understand the necessary safety protocols.
• Redundancy Elimination: Certain items, particularly within the Personal Traits dimension, exhibited overlap. For instance, the “Organizational Innovation Planning” indicator was found redundant compared to similar items addressing professional innovation.
• Addition of Specific Details: Experts suggested incorporating additional sub-indicators to capture nuanced competencies, such as accurately interpreting diagnostic data and adhering to standardized emergency response procedures.

Based on this input, systematic revisions were made to enhance clarity, reduce redundancy, and ensure that the framework comprehensively covered all necessary competencies.

Table 1. Summary of expert content validity feedback and revisions.

Expert	Feedback/Suggestion	Revision Made
Expert A	Specify configuration details for high-voltage battery systems.	Revised indicator A-1-1 to “Ability to identify and understand high-voltage battery assembly configuration”.
Expert B	Remove redundant soft skill items.	Deleted indicator D-2 “Organizational Innovation Planning”.
Expert D	Includes detailed functionalities for DC/DC transformers.	Added indicator A-3-4, “Understanding DC/DC transformer functionality”.
Expert E	Enhance clarity in battery management indicators.	Revised indicator A-6-1 to “Ability to accurately assess battery module status information”.

This table summarizes the key expert feedback and corresponding revisions made to enhance the competency framework.

summarizes the key modifications implemented after the expert review. The initial expert panel included only four specialists specifically selected for their extensive and highly relevant experience in BEV technology and maintenance. Utilizing a small expert group in the exploratory stage is common practice in Delphi studies, particularly in highly specialized fields, as it allows for targeted and deep insights that inform the subsequent Delphi rounds involving larger expert groups. This approach is consistent with the methodological recommendations for preliminary expert panels in the Delphi research literature.

3.2. Delphi Process Results

The revised competency instrument was further validated through a structured three-round Delphi process involving a panel of 15 experts divided equally among education and training directors, senior technical supervisors, and experienced maintenance technicians. This iterative process was designed to refine the indicators and measure consensus on the importance and clarity of each item.

3.2.1. Round 1 Results

In the first Delphi round, experts provided ratings on all 106 indicators using a five-point Likert scale. Analysis of these responses revealed a strong consensus on technical competencies, particularly within the Professional Skills dimension, which achieved a perfect mean score of 5.00 (SD = 0.000). However, the Personal Traits dimension showed significant variability (mean = 3.80, SD = 1.146), suggesting that the evaluation of soft skills was more subjective. In addition to quantitative ratings, qualitative comments were collected to capture further nuances and suggestions for improvement.

Table 2. Round 1 statistical analysis of competency dimensions.

Dimension	Mode	Mean	Standard Deviation
Professional Knowledge	5	4.87	0.352
Professional Skills	5	5.00	0.000
Professional Attitude	5	4.27	0.884
Personal Traits	5	3.80	1.146

provides a detailed summary of the descriptive statistics for the four main dimensions.

3.2.2. Round 2 Results

Based on Round 1 feedback, the instrument was refined and redistributed in Round 2. In this round, experts again rated the indicators on a five-point Likert scale. The revised questionnaire showed notable improvement in consensus:

• Both the Professional Knowledge and Professional Skills dimensions achieved unanimous ratings (mean = 5.00, SD = 0.000).
• The variability in the Professional Attitude and Personal Traits dimensions decreased, with mean scores of 4.47 (SD = 0.640) and 4.07 (SD = 0.704), respectively.

These improvements indicate that the iterative feedback process effectively addressed earlier ambiguities.

Table 3. Round 2 statistical analysis of competency dimensions.

Dimension	Mode	Mean	Standard Deviation
Professional Knowledge	5	5.00	0.000
Professional Skills	5	5.00	0.000
Professional Attitude	5	4.47	0.640
Personal Traits	4	4.07	0.704

summarizes the key statistics for Round 2.

3.2.3. Round 3 Results

In the third and final Delphi round, experts reviewed the aggregated feedback and the revised instrument. The ratings further converged, leading to a high degree of consensus across all dimensions:

• Professional Knowledge and Professional Skills maintained perfect consensus (mean = 5.00, SD = 0.000).
• The Professional Attitude dimension improved slightly (mean = 4.53, SD = 0.516), while the Personal Traits dimension stabilized (mean = 4.20, SD = 0.561).

Table 4. Round 3 statistical analysis of competency dimensions.

Dimension	Mode	Mean	Standard Deviation
Professional Knowledge	5	5.00	0.000
Professional Skills	5	5.00	0.000
Professional Attitude	5	4.53	0.516
Personal Traits	4	4.20	0.561

displays the final statistical summary.

3.2.4. Comparison of Delphi Rounds

A detailed comparative analysis was performed to track the evolution of consensus over the Delphi rounds. This study revealed a significant reduction in the standard deviations, particularly in dimensions that initially exhibited higher variability. For example, the Personal Traits dimension’s standard deviation decreased from 1.146 in Round 1 to 0.704 in Round 2, demonstrating improved alignment among experts.

Table 5. Comparison of standard deviations between Round 1 and Round 2.

Dimension	Round 1 SD	Round 2 SD
Professional Knowledge	0.352	0.000
Professional Skills	0.000	0.000
Professional Attitude	0.884	0.640
Personal Traits	1.146	0.704

illustrates these changes.

3.3. Flowchart of the Delphi Process

To provide a visual summary of the systematic refinement process, Figure 1 presents a flowchart of the Delphi process. The flowchart outlines the sequential steps, beginning with the initial literature review and instrument development, followed by three rounds of expert consultation, iterative revisions based on quantitative and qualitative feedback, and culminating in the final validated competency framework. This figure serves as a clear roadmap of the methodology employed.

3.4. Final Competency Framework Summary

The final competency framework is structured into three hierarchical layers:

• Dimensions: Four core dimensions encapsulating the overall competency requirements: Professional Knowledge, Professional Skills, Professional Attitude, and Personal Traits.
• Sub-dimensions: Within these dimensions, 24 sub-dimensions were identified to target specific areas of expertise, such as battery management, charging system protocols, and diagnostic capabilities.
• Indicators: A total of 106 detailed indicators were validated through the Delphi process, offering precise performance measures for each competency area.

This comprehensive framework is intended to serve as a strategic tool for improving technician selection, targeted training programs, and performance evaluation in the BEV maintenance industry.

3.5. Reliability and Statistical Considerations

Throughout the Delphi process, a series of statistical analyses were conducted to ensure the reliability and validity of the framework. Descriptive statistics were computed for each indicator across all rounds, including means, modes, and standard deviations. Nonparametric tests, such as the Kolmogorov–Smirnov test for normality and the Kruskal–Wallis test for group comparisons, provided additional evidence of consensus. The observed decrease in standard deviations across rounds confirms that iterative feedback successfully narrowed the variance in expert opinions, thereby enhancing the robustness of the final competency framework.

3.6. Example Clarification (High-Voltage Battery Management Skills)

Competencies involving high-voltage battery management include skills such as safely handling and replacing high-voltage battery packs, performing diagnostic checks on battery state-of-health (SOH), managing battery thermal systems, and adhering strictly to safety standards during maintenance procedures.

3.7. Clarification of Soft Skills

Teamwork refers to technicians effectively collaborating, especially during complex repairs and diagnostics, ensuring mutual safety and efficiency. Leadership involves experienced technicians guiding and mentoring others, facilitating adherence to best practices, and making informed decisions during critical operations. Adaptability to change emphasizes a technician’s ability to rapidly adjust to evolving technologies, new diagnostic tools, and updated maintenance protocols critical in the rapidly advancing BEV industry.

4. Discussion

4.1. Interpretation of Findings

The results demonstrate that the developed competency framework for electric small-passenger-car maintenance technicians is comprehensive and robust. The framework, organized into 4 dimensions—Professional Knowledge, Professional Skills, Professional Attitude, and Personal Traits—and further delineated into 24 sub-dimensions with 106 specific indicators, reflects a high consensus among industry experts. Quantitative data from the Delphi process reveal that technical competencies, particularly those related to high-voltage battery management, diagnostic testing, and emergency response procedures, consistently achieved high ratings with minimal variability. This high level of agreement underscores the critical importance of these technical skills in ensuring safe and efficient BEV maintenance.

Furthermore, while evaluating soft skills such as Professional Attitude and Personal Traits exhibited more significant variability in earlier rounds, the iterative Delphi process led to a substantial convergence of expert opinions. The reduction in standard deviations from Round 1 to Round 3 provides robust evidence that the iterative feedback significantly improved the clarity and relevance of these indicators. In addition, the framework integrates SDG-specific considerations by addressing aspects such as sustainability in maintenance practices and the promotion of environmental stewardship, aligning with global objectives such as SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities). The integration of these elements enhances the technical robustness of the framework and ensures that the competencies support a broader agenda for sustainable development in the electric vehicle sector.

4.2. Practical Implications

The validated competency framework has numerous practical applications for stakeholders in the BEV maintenance industry. First, it provides a critical tool for talent selection by establishing clear, measurable criteria that can be integrated into recruitment processes. Employers can use these indicators to assess candidate suitability in technical and interpersonal domains. Second, the framework informs the development of targeted training programs. Organizations can design comprehensive curricula that address specific gaps and enhance overall technician performance by clearly outlining the required competencies—from technical proficiencies in high-voltage systems to soft skills such as effective communication and teamwork.

Moreover, the framework supports systematic performance evaluation. Organizations can use the detailed indicators as benchmarks to monitor technician progress over time, identify areas for improvement, and implement remedial measures. Notably, the framework aligns with broader ESG objectives and sustainable development goals by promoting maintenance practices that contribute to environmental sustainability and energy efficiency. For instance, by ensuring that technicians are well-versed in high-voltage safety protocols and diagnostic practices, the framework supports efforts to minimize environmental risks and optimize resource usage in accordance with SDG 9 (Industry, Innovation, and Infrastructure). This strategic alignment helps organizations meet regulatory standards and market expectations while fostering a culture of continuous improvement and sustainable operations.

4.3. Comparison with Existing Studies

Compared to previous competency frameworks developed for automotive maintenance technicians, our results confirm many universally identified competencies, such as technical skills in diagnostics and high-voltage safety, and the importance of soft skills such as teamwork and adaptability. However, our study distinctly contributes by explicitly incorporating ESG-related competencies into the framework, a dimension that previous competency studies have rarely addressed directly. This unique ESG integration ensures the framework supports not only operational excellence but also aligns technician competencies with contemporary sustainability goals inherent to the electric vehicle industry. Thus, while our findings corroborate established industry knowledge, the explicit ESG dimension represents a significant innovation and contribution to the literature on BEV technician competency frameworks.

4.4. Organizational Advantages

Organizations adopting this competency framework benefit through improved recruitment processes, clearer performance evaluations, targeted professional training, and alignment with sustainability objectives. Specifically, businesses can better identify skill gaps, design tailored training curricula, enhance technician efficiency and safety, and reinforce corporate ESG commitments, all of which contribute directly to competitive advantage and operational excellence.

4.5. Limitations of the Research

Despite the strengths and comprehensive nature of the competency framework, several limitations should be acknowledged. Firstly, the expert panel was relatively small (15 experts) and drawn from a limited number of organizations, which may constrain the generalizability of the findings across different regions or industry sectors. A more significant, diverse sample could provide additional insights, particularly regarding the more subjective soft skill indicators. Secondly, the Delphi method inherently relies on expert judgment, which introduces a degree of subjectivity—especially in evaluating personal traits and professional attitudes. Although statistical measures were applied to mitigate bias, these assessments may reflect individual perspectives influenced by specific organizational cultures or regional practices.

Furthermore, the rapid pace of technological innovation in the BEV industry implies that the framework may require periodic updates to remain current with emerging technologies and maintenance practices. Finally, this study did not incorporate empirical field validation or integrate objective performance data, both of which would further substantiate the practical applicability of the framework. Future studies could address these limitations by including a broader range of experts and conducting longitudinal field evaluations.

4.6. Future Research Directions

Building upon the findings and limitations, several future research directions are recommended. Future research should broaden the expert panel by incorporating international professionals and practitioners from a wider array of organizations, thereby enhancing the generalizability of the results. Additionally, incorporating objective performance metrics—such as on-site performance data, safety records, and longitudinal studies—would provide a more robust validation of the competency framework and offer insights into its practical impact on maintenance practices.

Further studies should also explore the dynamic integration of ESG considerations into the competency framework. This includes developing quantitative measures to assess how technician competencies influence environmental outcomes and aligning these measures with relevant Sustainable Development Goals (SDGs), such as SDG 7, SDG 9, and SDG 11. Moreover, research into adaptive frameworks that can be periodically updated to reflect rapid technological advances in BEV systems is essential. Finally, longitudinal studies examining the impact of competency-based training programs on technician performance, safety outcomes, and organizational sustainability will further validate the framework and support continuous improvement efforts.

4.7. Future Field Validation Plans

Although our competency framework has been validated through expert consensus, empirical field validation remains necessary to confirm its practical effectiveness. Future research will involve collaborating with EV service centers and educational institutions to pilot the implementation of this framework, thereby evaluating its impact on technician performance, safety outcomes, and operational efficiency in real-world contexts.

4.8. Final Remarks

In summary, this article contributed to a rigorously validated competency framework that addresses the complex requirements of BEV maintenance. The framework’s hierarchical structure—encompassing dimensions, sub-dimensions, and specific indicators—captures technical and soft skills essential for safe and efficient maintenance operations. Through a systematic Delphi process, expert consensus was achieved, reinforcing the reliability and applicability of the framework. Notably, the framework aligns with broader ESG and SDG objectives by promoting sustainable maintenance practices that support energy efficiency and environmental stewardship.

This comprehensive framework is a strategic tool for talent selection, targeted training, and systematic performance evaluation in the BEV maintenance industry. Bridging the gap between technical proficiency and sustainable practices contributes to the advancement of sustainable mobility and helps organizations meet regulatory and market demands. Future research and periodic updates will be essential to maintain the framework’s relevance in rapid technological and industry changes, ensuring its continued role as a foundation for sustainable and high-performance maintenance operations.

5. Conclusions

5.1. Summary of Findings

The research has successfully developed a comprehensive and rigorously validated competency framework tailored for electric small-passenger-car maintenance technicians operating within the rapidly evolving battery electric vehicle (BEV) industry. The framework is structured hierarchically into 4 principal dimensions—Professional Knowledge, Professional Skills, Professional Attitude, and Personal Traits—further delineated into 24 sub-dimensions and a total of 106 detailed indicators. The Delphi process, conducted over three iterative rounds, achieved a high level of consensus among a diverse panel of experts. Technical competencies such as high-voltage battery management, diagnostic testing, and emergency response procedures received near-unanimous ratings, underscoring their critical importance for operational safety and efficiency. Additionally, while the evaluation of soft skills showed more significant variability in earlier rounds, the iterative refinement process resulted in a robust convergence of expert opinions. These findings confirm the validity of the proposed framework and emphasize its relevance in addressing the specific demands of BEV maintenance in a sustainable and technologically advanced context. Organizations can leverage this framework strategically for training, evaluation, and recruitment, supporting both operational excellence and ESG commitments. Given rapid technological advances, periodic updates and empirical field validation are recommended to maintain relevance and practical effectiveness.

5.2. Practical Implications and Contributions

The practical implications of this competency framework are multifaceted. Primarily, it serves as a strategic tool for talent selection, enabling organizations to benchmark and assess prospective maintenance technicians against clearly defined technical and interpersonal criteria. Furthermore, the framework offers a foundational basis for designing and implementing targeted training programs. By detailing specific competencies—from technical skills in handling high-voltage systems to soft skills such as effective communication and teamwork—the framework enables training curricula to be tailored to address identified competency gaps. Additionally, the framework provides a structured approach to performance evaluation, allowing organizations to systematically monitor technician progress, identify areas for improvement, and implement remedial actions. These applications are especially critical in the context of the BEV industry, where operational safety, efficiency, and sustainability are paramount.

5.3. Integration with Sustainable Development Goals

A distinctive contribution is the alignment of the competency framework with the global Sustainable Development Goals (SDGs). In particular, the framework supports SDG 7 (Affordable and Clean Energy) by ensuring that maintenance practices promote energy efficiency and safety in BEV operations. It also contributes to SDG 9 (Industry, Innovation, and Infrastructure) by fostering continuous improvement and technological innovation in maintenance processes. SDG 11 (Sustainable Cities and Communities) enhances the reliability and sustainability of urban mobility services. Furthermore, by embedding sustainability principles within the training and performance evaluation processes, the framework aids organizations in meeting broader environmental, social, and governance (ESG) objectives, thereby promoting responsible resource management and sustainable operational practices. This integration strengthens the technical underpinnings of BEV maintenance and ensures that the industry’s evolution contributes positively to global sustainability efforts.

5.4. Limitations and Future Research Directions

Despite the robust methodology and significant contributions, several limitations warrant discussion. The expert panel, although diverse, was relatively small (15 experts) and primarily drawn from selected organizations, which may limit the generalizability of the findings across different geographic regions and industry segments. Additionally, the inherent subjectivity in the Delphi method, particularly in the evaluation of soft skills and personal traits, may introduce bias despite the statistical measures applied. Moreover, the rapid pace of technological advancements in the BEV sector necessitates periodic updates to the competency framework to ensure continued relevance. Future research should aim to perform the following:

• Broaden the expert panel to incorporate a wider, more international range of perspectives.
• Integrate objective performance metrics and conduct field validations to substantiate the practical applicability of the framework further.
• Explore longitudinal studies that assess the impact of competency-based training programs on technician performance, safety outcomes, and overall organizational sustainability.
• Develop adaptive protocols for regularly updating the framework in line with emerging BEV technologies and evolving maintenance practices.

5.5. Final Remarks

In conclusion, the research presents a meticulously constructed competency framework that meets the technical and operational needs of BEV maintenance technicians and aligns with global sustainability and ESG imperatives. The hierarchical structure of the framework—comprising dimensions, sub-dimensions, and specific indicators—provides a comprehensive and practical tool for talent selection, training development, and performance evaluation in the BEV maintenance industry. By ensuring that maintenance practices are both efficient and sustainable, the framework contributes significantly to advancing sustainable mobility and supports critical SDGs such as SDG 7, SDG 9, and SDG 11. Future updates and expanded research efforts will be essential to maintain the framework’s relevance in the face of continuous technological and industry changes, ensuring its role as a cornerstone for sustainable and high-performance maintenance operations.

5.6. Recommended Update Period

Due to the rapid technological and regulatory changes within the BEV industry, we recommend the competency framework be reviewed and updated approximately every two years. Regular updates will ensure continued alignment with evolving industry standards, emerging technologies, and changing workforce requirements.