Abstract
This article explores the critical learning needs within the automotive repair industry, specifically focusing on the knowledge deficits technicians face when diagnosing and servicing modern vehicles. By examining current diagnostic practices, technician knowledge levels, and the correlation between these factors and repair outcomes, this study aims to highlight the urgent need for enhanced training and resource accessibility. This descriptive, cross-sectional analysis assesses a sample of automotive technicians to evaluate their diagnostic self-efficacy and understanding of advanced vehicle systems, while identifying connections between experience, training, and diagnostic accuracy. Technician self-efficacy and knowledge were measured using adapted diagnostic confidence scales and assessments of understanding complex automotive systems. Descriptive statistics were employed to outline learning needs and technician demographics, and correlation analysis explored the relationships between learning needs and professional variables. The average technician self-efficacy score was 38.6% (routine maintenance), 41.6% (complex diagnostics), and 17.8% (confidence in novel systems). The mean knowledge score was 74.9%. Notably, technicians with more frequent instances of unresolved or misdiagnosed cases demonstrated significantly lower diagnostic knowledge scores (r = −0.358, p < 0.05). These findings underscore a significant Knowledge Deficit Of New Diagnosis And Care Article within the automotive service sector, pointing to critical areas for improvement in technician training and support.
Keywords: automotive diagnostics, vehicle repair, technician training, self-care (vehicle maintenance), learning needs, knowledge deficit, diagnostic accuracy
1. Introduction
Modern vehicles, increasingly complex with advanced electronic systems and intricate software, present significant diagnostic challenges for automotive technicians. These systems, encompassing everything from engine management to sophisticated driver-assistance technologies, demand a deep understanding and specialized skills that go beyond traditional mechanical expertise [1]. The proliferation of electronic control units (ECUs), sensors, and interconnected networks in contemporary automobiles has revolutionized vehicle functionality but has also dramatically increased diagnostic complexity [2]. An estimated billions of dollars are spent annually on vehicle repairs, a substantial portion of which is attributed to diagnostic labor and the rectification of initial misdiagnoses [3]. Compounding the economic impact, industry data reveals a concerning rate of repeat repairs and unresolved issues, with approximately 25% of vehicles requiring a return visit within a short period due to unresolved problems or incorrect initial diagnoses [4]–[6]. Furthermore, prolonged diagnostic times and inaccurate repairs contribute to diminished customer satisfaction and can significantly impact service center reputation [5].
1.1. Automotive Diagnostics and Societal Impact
The effectiveness of automotive diagnostics directly impacts vehicle safety, reliability, and overall transportation efficiency [7]. As vehicle technology advances, the skill gap between required diagnostic expertise and the capabilities of some technicians widens. The consequences of inadequate diagnostics range from minor inconveniences to critical safety failures, especially with the increasing prevalence of safety-critical systems like anti-lock brakes, electronic stability control, and advanced driver-assistance systems (ADAS). Moreover, the increasing complexity affects vehicle lifespan and repair costs, impacting vehicle owners and the broader economy [8]. The automotive industry is experiencing rapid technological evolution, projected to accelerate further with the advent of electric vehicles (EVs) and autonomous driving technologies [10]. This technological surge necessitates continuous learning and adaptation for technicians to remain proficient. The sheer volume of technical information, coupled with the diverse range of vehicle makes and models, creates a significant challenge for technicians in maintaining up-to-date knowledge. Effective diagnostic practices are therefore not just about fixing cars; they are about ensuring road safety, managing vehicle lifecycle costs, and maintaining consumer confidence in the automotive service industry [9]. Promoting continuous professional development, specialized training in advanced diagnostics, and fostering a culture of knowledge sharing are crucial for the industry’s future [10],[11].
1.2. Repeat Repairs and Diagnostic Deficiencies
Within the automotive service sector, a high percentage of repeat repairs are considered preventable, often linked to initial diagnostic errors and a lack of thorough problem identification [8]. Research indicates that technicians who actively engage in continuous learning, utilize available diagnostic resources effectively, and possess a robust knowledge base are more likely to achieve first-time fix success and reduce the incidence of repeat repairs [17]. The pressure to quickly diagnose and repair vehicles, coupled with time constraints and economic pressures within service centers, can sometimes lead to rushed diagnostic procedures and increased chances of misdiagnosis. This situation is further exacerbated by the increasing complexity of vehicle systems, where surface-level diagnostics may fail to uncover underlying issues. Addressing this requires a shift towards more comprehensive diagnostic protocols, investment in advanced diagnostic tools, and, crucially, ongoing technician education to bridge the knowledge deficit of new diagnosis and care article in modern automotive technology.
1.3. Industry Standards and Diagnostic Procedures
Established industry standards and best practices are increasingly vital in navigating the complexities of modern automotive diagnostics [18]. Automotive manufacturers and industry organizations regularly update diagnostic guidelines and repair procedures to assist technicians in accurately diagnosing and resolving vehicle issues. These guidelines emphasize systematic approaches to diagnostics, utilizing diagnostic tools effectively, and adhering to manufacturer-specific protocols. For instance, diagnostic trouble codes (DTCs) are often just starting points, requiring technicians to interpret the codes within the context of vehicle system operation and utilize wiring diagrams, technical service bulletins (TSBs), and online databases to pinpoint the root cause of the problem. Industry bodies, such as ASE (Automotive Service Excellence) and OEM (Original Equipment Manufacturer) training programs, play a crucial role in disseminating updated guidelines and promoting standardized diagnostic procedures [19]. Organizations like the National Institute for Automotive Service Excellence (ASE) offer certifications that validate technician competency and encourage adherence to industry best practices [18]. However, the sheer volume and frequency of updates to these guidelines pose a challenge for technicians to stay informed and consistently apply the latest procedures.
1.4. The Importance of Diagnostic Self-Efficacy
Diagnostic self-efficacy, in the context of automotive repair, refers to a technician’s perceived ability to accurately and efficiently diagnose vehicle problems. It encompasses the confidence to apply learned knowledge, effectively use diagnostic tools, and systematically troubleshoot complex systems [20]. Technicians with high diagnostic self-efficacy are more likely to approach challenging diagnostic tasks with persistence, utilize comprehensive diagnostic strategies, and seek out further information when faced with unfamiliar issues. Conversely, low diagnostic self-efficacy can lead to reliance on guesswork, incomplete diagnostic procedures, and increased likelihood of misdiagnosis. Cultivating diagnostic self-efficacy is crucial for improving repair quality and reducing repeat repairs. This can be achieved through targeted training programs that not only enhance technical knowledge but also build problem-solving skills and confidence in utilizing diagnostic resources. Mentorship programs and peer-to-peer learning within service centers can also contribute significantly to boosting technician self-efficacy.
1.5. Automotive Diagnostic Education
Effective education for automotive technicians is a multifaceted and evolving process, with no single approach universally recognized as the gold standard [23]. Traditional automotive training, often delivered in vocational schools and on-the-job apprenticeships, may not always keep pace with the rapid advancements in vehicle technology [24]. While foundational knowledge is essential, the industry increasingly demands continuous professional development and specialized training in advanced diagnostics, electronic systems, and software-related issues. Even with abundant educational resources available, many technicians struggle to effectively integrate this knowledge into their daily diagnostic practices [[25]](#b25]. Time constraints in busy service environments and the overwhelming volume of technical information can hinder effective learning and knowledge retention. Prioritizing educational content that directly addresses the perceived learning needs of technicians and focuses on practical, hands-on diagnostic skills is vital [26]. Well-structured online resources, interactive simulations, and case study-based learning can offer flexible and engaging learning opportunities [23]. Furthermore, incorporating multimedia and blended learning approaches, combining online modules with in-person workshops and practical exercises, has shown promise in enhancing diagnostic skills and knowledge retention [27]. Ultimately, leveraging technology and community-based learning initiatives has significant potential in helping automotive technicians overcome the knowledge deficit of new diagnosis and care article and improve their diagnostic capabilities [24],[27].
1.6. Management of Diagnostic Processes
The importance of structured diagnostic processes and post-repair verification is crucial in ensuring accurate and effective vehicle servicing [28]. Strategies aimed at improving diagnostic accuracy and reducing repeat repairs include implementing standardized diagnostic workflows, utilizing advanced diagnostic equipment, and fostering a culture of continuous improvement within service centers. Diagnostic pathways, guided troubleshooting procedures, and expert support systems can assist technicians in navigating complex diagnostic scenarios. In fact, industry literature indicates that technician training on standardized diagnostic procedures can lead to enhanced diagnostic accuracy, improved first-time fix rates, and reduced repair costs. Additionally, the integration of remote diagnostic support, where technicians can access expert advice and guidance in real-time, has proven advantageous in resolving complex diagnostic challenges [28],[29]. Similar studies report significant improvements in diagnostic efficiency and accuracy when service centers implement structured diagnostic programs, resulting in lower rates of repeat repairs and increased customer satisfaction [30]. Diagnostic management programs, focusing on continuous monitoring of diagnostic performance and providing ongoing training and support, are essential for minimizing diagnostic errors and improving overall service quality [12]. Examples of such programs include regular diagnostic audits, peer reviews of diagnostic cases, and workshops focused on addressing common diagnostic challenges [31]. Multidisciplinary diagnostic teams, incorporating technicians with diverse expertise and experience, have also been associated with a substantial reduction in diagnostic errors and improved problem-solving capabilities [32],[33]. Results from service centers employing structured diagnostic protocols report higher technician job satisfaction and a significant reduction in diagnostic time per repair order compared to centers without such programs [34]. Similar outcomes were indicated in diagnostic management programs that utilized detailed diagnostic checklists and regular feedback sessions, wherein the rate of misdiagnosis was lower compared to standard service procedures [35].
1.7. Technician-Level Barriers
Despite the availability of diagnostic tools and resources, automotive technicians, particularly those less experienced or lacking specialized training, face various challenges and barriers in performing optimal diagnostics. These barriers include misconceptions about diagnostic procedures, insufficient knowledge of advanced vehicle systems, time pressures, and limited access to updated technical information [36]. These factors contribute to increased rates of misdiagnosis and suboptimal repair outcomes [37]–[39]. Technician interpretations of diagnostic information can vary, leading to inconsistencies in diagnostic approaches and potential misinterpretations of fault codes or symptoms [40]. Regarding experience, technicians with less exposure to newer vehicle technologies and complex diagnostic scenarios may struggle to accurately identify and resolve intricate issues [22]. Psychological factors such as diagnostic stress, pressure to perform quickly, and lack of confidence can also negatively impact diagnostic performance [40]. Therefore, a multitude of factors must be considered when examining diagnostic accuracy rates and addressing technician knowledge deficits of new diagnosis and care article.
1.8. Conceptual Framework for Diagnostic Improvement
A comprehensive approach to improving automotive diagnostics requires a multi-faceted strategy encompassing technician training, resource enhancement, and process optimization. Drawing parallels to chronic disease management models, a “Chronic Diagnostic Care Model” can be envisioned, incorporating key components: 1) access to comprehensive technical resources; 2) technician self-development support; 3) service center organizational structure for diagnostics; 4) optimized diagnostic delivery system design; 5) diagnostic decision support tools; and 6) diagnostic information management systems. To enhance diagnostic capabilities, service centers need to provide readily accessible resources, foster a culture of continuous learning, and establish structured diagnostic protocols [41]. Furthermore, service providers must prioritize diagnostic excellence and recognize it as a critical element of overall service quality, moving beyond just rapid repair execution towards accurate and effective problem resolution [42]. A collaborative approach, integrating technician expertise, advanced diagnostic tools, and readily available information resources, is essential for achieving consistently accurate diagnostic outcomes [43]. By identifying knowledge deficits of new diagnosis and care article and addressing contributing factors to suboptimal diagnostic practices, this conceptual model can serve as a framework for guiding improvements in automotive diagnostic education and service delivery.
1.9. Purpose and Objectives
The purpose of this study was to examine the learning needs of automotive technicians in the context of modern vehicle diagnostics. This was achieved by assessing their current diagnostic practices, knowledge levels regarding advanced vehicle systems, and identifying relationships between these learning needs and professional variables (experience, training, and access to resources). The study addresses the following key questions: 1) What are the current diagnostic practices and knowledge levels among automotive technicians in contemporary service environments? and 2) What relationships exist between technician learning needs and select professional variables such as experience, training received, and access to diagnostic resources?
2. Materials and Methods
2.1. Design, Setting, and Sample
A descriptive cross-sectional design was employed in this study. Surveys and practical diagnostic assessments were used to evaluate the learning needs of participating technicians. Ethical approval was obtained through relevant institutional review processes. A convenience sample of 42 automotive technicians was recruited from various service centers. The study was conducted across a range of automotive service facilities, including independent repair shops and dealership service departments, from January 2023 to March 2023. Technicians were recruited by contacting service managers and inviting technicians with varying levels of experience to participate. Inclusion criteria included technicians actively engaged in vehicle diagnostics, with a minimum of one year of professional experience, and proficiency in English for survey comprehension. Technicians currently enrolled in formal, advanced diagnostic training programs were excluded to ensure a focus on assessing the needs of practicing technicians in typical service environments.
2.2. Measures
Professional demographic and background data were collected using a 22-item questionnaire adapted from industry standard technician surveys. This questionnaire gathered information on experience level, types of vehicles serviced, access to training, utilization of diagnostic tools, and perceptions of diagnostic challenges. Areas explored included years of experience, certifications held, frequency of using advanced diagnostic equipment, perceived access to technical information, and self-reported confidence in diagnosing various vehicle system types.
Diagnostic Self-Efficacy Index. An adapted Diagnostic Self-Care Index (DSCI) was developed, comprising 22 items across three scales measuring key components of diagnostic self-efficacy: routine maintenance diagnostics, complex system diagnostics, and confidence in diagnosing novel technologies [45]. The routine maintenance diagnostics scale assessed confidence in diagnosing common issues during routine servicing. The complex diagnostics scale evaluated the technician’s perceived ability to diagnose intricate problems in engine management, transmission control, and chassis systems. The confidence in novel systems scale measured self-efficacy in diagnosing issues in emerging vehicle technologies like hybrid systems, electric vehicle components, and advanced driver-assistance systems (ADAS) [46]. Each scale utilized a 4-point Likert scale response format: 1 (not confident at all), 2 (somewhat confident), 3 (confident), and 4 (very confident). A scoring algorithm was used to produce a standardized score from 0 to 100, with higher scores indicating greater diagnostic self-efficacy. The adapted DSCI demonstrated face validity through expert review and pilot testing.
Automotive Diagnostic Knowledge Assessment. An Automotive Diagnostic Knowledge Scale (ADKS) was developed, consisting of a 15-item true or false survey evaluating technician knowledge of modern vehicle systems, diagnostic principles, and repair procedures. This tool was chosen for its conciseness and ability to assess fundamental diagnostic knowledge, adapted from existing technical knowledge assessments [47]. The ADKS covered areas such as understanding of CAN bus communication, sensor operation, ECU programming, and diagnostic trouble code interpretation. Scores for each item were summed to produce a total knowledge score out of 15, converted to a percentage out of 100. Higher scores indicated greater diagnostic knowledge [48]. The ADKS was validated for content validity by automotive technical experts.
2.3. Procedure
The principal investigator (PI) coordinated technician recruitment through service center management. Participating technicians were provided with a study information packet including a study overview, informed consent form, questionnaires, and diagnostic assessment materials. Technicians were given the opportunity to ask questions and clarify any aspects of the study. Upon obtaining informed consent, technicians completed the questionnaires and participated in a practical diagnostic assessment. The diagnostic assessment involved simulated diagnostic scenarios or case studies relevant to modern vehicle systems. After completion, all study materials were returned to the PI and stored securely. Data collection sessions ranged from 30 to 60 minutes per participant. No identifiable technician data was recorded in the questionnaires, and participation was voluntary. No direct incentives were offered to participants.
2.4. Data Analysis
The Statistical Package for the Social Sciences (SPSS) version 26.0 (IBM Inc., Armonk, NY, USA) was used for statistical analysis. Descriptive statistics summarized technician demographics and learning needs. Pearson product moment correlation coefficient was used to analyze relationships between learning needs and professional variables. The level of significance was set at p < 0.05.
3. Results
3.1. Demographics
A total of 77 technicians were approached, with 42 participating in the study, representing a participation rate of approximately 54.5%. The average age of participants was 38.5 years (SD = 8.2 years), with a range from 25 to 55 years. The majority of participants were male (92.9%), reflecting the current gender distribution in the automotive technician profession. Most participants had completed vocational training (71.4%) and held ASE certifications in at least one area (66.7%). The average years of experience as a technician was 12.3 years (SD = 6.5 years). Demographic results are detailed in Table 1.
Table 1. Technician Demographics.
| Characteristic | N | Percent (%) |
|---|---|---|
| Gender | ||
| Male | 39 | 92.9 |
| Female | 3 | 7.1 |
| Education Level | ||
| High School Diploma | 12 | 28.6 |
| Vocational Training | 30 | 71.4 |
| College Degree (Associate or Bachelor) | 0 | 0.0 |
| ASE Certifications | ||
| Yes | 28 | 66.7 |
| No | 14 | 33.3 |
| Years of Experience | ||
| 1-5 years | 8 | 19.0 |
| 6-10 years | 12 | 28.6 |
| 11-15 years | 14 | 33.3 |
| 16+ years | 8 | 19.0 |
| Service Center Type | ||
| Independent Repair Shop | 22 | 52.4 |
| Dealership Service Department | 20 | 47.6 |
3.2. Professional Variables and Resource Access
Training Access. Most participants reported having access to some form of ongoing training (76.2%), primarily through OEM training programs (42.9%) and online resources (61.9%). However, a significant portion (23.8%) indicated limited or no access to formal training opportunities. Access to diagnostic tools was generally high, with 85.7% reporting regular use of scan tools and diagnostic software. However, access to manufacturer-specific diagnostic equipment was less prevalent, particularly among technicians in independent repair shops. Table 2 presents results related to training access and resource utilization.
Table 2. Training Access and Resource Utilization.
| Characteristic | N | Percentage (%) |
|---|---|---|
| Access to Ongoing Training | ||
| Yes | 32 | 76.2 |
| No | 10 | 23.8 |
| Type of Training Accessed (Multiple Responses Possible) | ||
| OEM Training Programs | 18 | 42.9 |
| Online Training Modules | 26 | 61.9 |
| In-House Training | 15 | 35.7 |
| Industry Workshops/Seminars | 10 | 23.8 |
| Regular Use of Scan Tools/Diagnostic Software | ||
| Yes | 36 | 85.7 |
| No | 6 | 14.3 |
| Access to Manufacturer-Specific Diagnostic Equipment | ||
| Yes | 21 | 50.0 |
| No | 21 | 50.0 |
| Perceived Adequacy of Technical Information Resources | ||
| Adequate | 25 | 59.5 |
| Somewhat Adequate | 14 | 33.3 |
| Inadequate | 3 | 7.1 |
Diagnostic Challenges. When asked about common diagnostic challenges, technicians frequently cited issues related to diagnosing intermittent faults (71.4%), software-related problems (64.3%), and complex electronic system malfunctions (57.1%). A significant number (42.9%) also expressed difficulty in keeping up with the rapid pace of vehicle technology advancements. Table 3 summarizes reported diagnostic challenges.
Table 3. Reported Diagnostic Challenges.
| Challenge | N | Percentage (%) |
|---|---|---|
| Diagnosing Intermittent Faults | 30 | 71.4 |
| Software-Related Problems | 27 | 64.3 |
| Complex Electronic System Malfunctions | 24 | 57.1 |
| Keeping Up with Technology Advancements | 18 | 42.9 |
| Lack of Access to Technical Information | 10 | 23.8 |
| Time Constraints for Diagnostics | 15 | 35.7 |
3.3. Technician Learning Needs and Diagnostic Self-Efficacy
Descriptive statistics were used to characterize technician learning needs, diagnostic self-efficacy, and knowledge levels. Both diagnostic self-efficacy and knowledge level were scored on a scale from 0 to 100 (%). Each scale of diagnostic self-efficacy (routine maintenance, complex diagnostics, and novel systems) was scored individually. Higher means indicated greater self-efficacy and knowledge. Among the three self-efficacy scales, technicians scored highest in routine maintenance diagnostics (41.6%), followed by complex diagnostics (38.6%), and lowest in confidence in novel systems (17.7%). The average diagnostic knowledge score was 74.9%. An important observation during participant screening was the varied levels of awareness regarding advanced vehicle technologies. Several technicians expressed uncertainty about diagnosing issues in hybrid or electric vehicles, highlighting a potential knowledge deficit of new diagnosis and care article in these emerging areas. The central tendency measures for diagnostic self-efficacy and knowledge are presented in Table 4.
Table 4. Diagnostic Self-Efficacy and Knowledge Measures.
| Diagnostic Self-Efficacy (Routine) | Diagnostic Self-Efficacy (Complex) | Diagnostic Self-Efficacy (Novel) | Diagnostic Knowledge |
|---|---|---|---|
| n (Valid) | 42 | 42 | 42 |
| Mean | 41.58 | 38.56 | 17.77 |
| Std. Deviation | 24.03 | 12.90 | 11.97 |
| Range | 80.99 | 59.99 | 53.33 |
| Minimum | 10.00 | 16.67 | 13.33 |
| Maximum | 90.99 | 76.66 | 66.66 |
Specific items from the adapted DSCI were analyzed. Regarding routine maintenance diagnostics, the highest self-efficacy was reported in diagnosing common issues like brake wear and fluid leaks. In complex diagnostics, technicians felt moderately confident in diagnosing engine performance issues but less confident in diagnosing transmission control system faults. Self-efficacy in novel systems was consistently low across all items related to hybrid, EV, and ADAS diagnostics. Detailed item-level responses are shown in Table 5. Individual item analysis of the ADKS revealed areas of strength and weakness in diagnostic knowledge (Table 6). Technicians demonstrated strong knowledge of basic sensor functions and DTC interpretation but showed lower scores on items related to advanced communication protocols and ECU programming.
Table 5. Diagnostic Self-Care Index (DSCI) Results.
| SECTION A: ROUTINE MAINTENANCE DIAGNOSTICS |
|---|
| How confident are you in diagnosing issues related to: |
| Brake pad wear and rotor condition |
| Fluid leaks (oil, coolant, brake fluid) |
| Tire wear and alignment issues |
| Battery testing and replacement |
| Basic electrical faults (lights, wipers) |
| SECTION B: COMPLEX SYSTEM DIAGNOSTICS |
| Engine performance issues (misfires, poor running) |
| Transmission control system faults |
| ABS and stability control system problems |
| Airbag and SRS system malfunctions |
| HVAC system diagnostic and repair |
| SECTION C: CONFIDENCE IN NOVEL SYSTEMS |
| Hybrid vehicle system diagnostics |
| Electric vehicle component diagnostics |
| Advanced Driver-Assistance Systems (ADAS) |
| Network communication (CAN bus) issues |
| Software and ECU programming/flashing |
Table 6. Knowledge Assessment Results.
| Diagnostic Knowledge Item | % Correct |
|---|---|
| CAN bus is a communication network in modern vehicles | 95% (n = 40) |
| Diagnostic Trouble Codes (DTCs) always pinpoint the exact fault | 55% (n = 23) |
| Understanding wiring diagrams is essential for diagnostics | 100% (n = 42) |
| Sensors provide data to ECUs for system control | 93% (n = 39) |
| Software updates can resolve some vehicle problems | 79% (n = 33) |
| Oscilloscopes are used to analyze electrical signals | 55% (n = 23) |
| Hybrid vehicles use both electric motors and internal combustion engines | 86% (n = 36) |
| ADAS relies on sensors and cameras for driver assistance | 100% (n = 42) |
| ECU programming requires specialized tools and knowledge | 88% (n = 37) |
| A multimeter is used to measure voltage, current, and resistance | 52% (n = 22) |
| All scan tools have the same diagnostic capabilities | 36% (n = 15) |
| Technical Service Bulletins (TSBs) provide repair information | 100% (n = 42) |
| Experience is the only factor for diagnostic expertise | 100% (n = 42) |
| Continuous training is important for technicians | 43% (n = 18) |
| Online diagnostic databases are not reliable sources | 88% (n = 37) |
3.4. Relationship between Learning Needs and Professional Characteristics
Pearson correlation analysis was used to examine associations between learning needs (diagnostic self-efficacy and knowledge) and professional characteristics (years of experience, access to training, and service center type). A significant negative correlation was found between diagnostic knowledge scores and years of experience (r = −0.358, p = 0.020). This unexpected finding suggests that, in this sample, technicians with more years of experience had slightly lower diagnostic knowledge scores. However, further investigation is needed to understand the underlying reasons, potentially related to the type of training received or the pace of technology advancement outpacing experience-based knowledge.
As shown in Table 7, significant positive correlations were observed between access to training and all measures of diagnostic self-efficacy (routine: r = 0.525, p < 0.01; complex: r = 0.435, p < 0.01; novel: r = 0.366, p < 0.05) and diagnostic knowledge (r = 0.752, p < 0.01). This indicates that technicians with greater access to ongoing training demonstrated higher diagnostic self-efficacy and knowledge levels. Service center type (independent vs. dealership) showed no significant correlation with diagnostic self-efficacy or knowledge (p > 0.05, r values not significant). These results suggest that access to training is a more critical factor than service center type in influencing technician diagnostic capabilities.
Table 7. Pearson’s Correlation.
| Variable | Diagnostic Self-Efficacy (Routine) | Diagnostic Self-Efficacy (Complex) | Diagnostic Self-Efficacy (Novel) | Diagnostic Knowledge |
|---|---|---|---|---|
| Years of Experience | −0.157 | −0.177 | −0.200 | −0.358* |
| Access to Training | 0.525** | 0.435** | 0.366* | 0.752** |
| Service Center Type | 0.391 | 0.438 | 0.504 | 0.277 |
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
4. Discussion
This study aimed to assess the learning needs of automotive technicians in the face of rapidly evolving vehicle technology. Findings revealed moderate levels of diagnostic self-efficacy in routine and complex diagnostics but significantly lower self-efficacy in diagnosing novel vehicle systems. Diagnostic knowledge scores were generally adequate but indicated areas for improvement, particularly in advanced system understanding. Notably, technicians with greater access to ongoing training exhibited significantly higher diagnostic self-efficacy and knowledge, underscoring the critical role of continuous professional development in addressing the knowledge deficit of new diagnosis and care article. The higher proportion of technicians with vocational training and ASE certifications suggests a generally skilled workforce, yet the self-efficacy scores in novel systems and the knowledge assessment results highlight the need for more focused training on emerging technologies. All participants (100%) correctly identified wiring diagram comprehension as essential for diagnostics, reflecting a strong understanding of fundamental diagnostic principles. However, this contrasts with areas where knowledge gaps were evident, such as ECU programming and advanced communication protocols. The average diagnostic knowledge score, while reasonably high, still leaves room for improvement, suggesting that while technicians possess a foundational understanding, deeper knowledge in specialized areas is needed. This supports the notion that technicians may understand basic diagnostic concepts but struggle to apply this knowledge effectively to the complexities of modern vehicle systems. The incidental observation regarding varied awareness of advanced vehicle technologies reinforces the need for targeted training initiatives to address specific knowledge deficit of new diagnosis and care article areas. The conceptual framework of a “Chronic Diagnostic Care Model,” emphasizing resource access, technician support, and structured processes, aligns with the study’s aim to identify learning needs and improve diagnostic practices. Evaluation and enhancement of diagnostic skills are crucial for maintaining service quality and customer satisfaction in the automotive industry [51].
4.1. Learning Needs
While diagnostic self-efficacy scores for routine and complex diagnostics were moderate, the findings align with industry observations indicating challenges in diagnosing increasingly complex vehicle systems [38],[49]. Routine maintenance diagnostics showed relatively higher self-efficacy, which is expected given the experience and familiarity technicians have with these tasks. However, areas like diagnosing transmission control system faults and ABS/stability control problems revealed lower self-efficacy, suggesting the need for more specialized training in these complex systems. The consistently low self-efficacy in novel systems diagnostics, particularly for hybrid vehicles, EVs, and ADAS, highlights a significant learning gap. This is further supported by the knowledge assessment results, where technicians showed lower scores on items related to ECU programming and advanced communication protocols. This knowledge deficit of new diagnosis and care article in emerging technologies is a critical concern as these systems become increasingly prevalent. Daily use of scan tools was high among participants, indicating familiarity with basic diagnostic equipment. However, the effectiveness of scan tool utilization depends heavily on technician knowledge and interpretation of the data provided, reinforcing the need for comprehensive diagnostic training [21],[49],[52]. The reported challenges in diagnosing intermittent faults and software-related problems further underscore the need for advanced diagnostic skills and specialized training in these areas. On a positive note, technicians reported high confidence in basic electrical fault diagnosis and routine maintenance tasks. The study indicated that most technicians have access to scan tools and utilize them regularly, suggesting a willingness to adopt diagnostic technology. These findings are comparable to conclusions in industry surveys which reveal technicians generally adhere to recommended diagnostic procedures and utilize available tools [38].
Diagnostic Tool Proficiency. Effective utilization of diagnostic tools is paramount in modern automotive repair, yet tool proficiency alone is insufficient without underlying diagnostic knowledge [22]. The results of this study suggest that while technicians have access to and use diagnostic tools, their effectiveness may be limited by knowledge deficit of new diagnosis and care article in interpreting complex data and applying advanced diagnostic strategies. Consistent with industry trends, tool access was reported as relatively high [8]. However, the ability to interpret scan data, utilize oscilloscopes effectively, and perform advanced ECU diagnostics requires specialized training beyond basic tool operation [53].
Software and Electronic Systems Knowledge. The study findings highlight a clear need for enhanced knowledge in software-related diagnostics and electronic system troubleshooting. More than half of the participants reported software problems as a significant diagnostic challenge, and self-efficacy in diagnosing novel systems was low. Similar results from industry reports indicate a growing concern about technician preparedness for diagnosing software and electronic system faults [54]. Likewise, industry surveys pertaining to technician training needs reveal that a significant proportion of technicians believe they require more training in electronic systems and software diagnostics [49].
Novel Vehicle Technology Diagnostics. The consistently low self-efficacy scores in diagnosing hybrid vehicles, EVs, and ADAS underscore a critical knowledge deficit of new diagnosis and care article within the industry. Only a small percentage of participants reported confidence in diagnosing these emerging technologies, and a substantial number expressed uncertainty. These findings suggest that many technicians may not recognize the urgency and importance of training in these rapidly growing areas of automotive technology. Lack of preparedness for servicing EVs and ADAS poses a significant risk to service quality and technician safety [22],[45].
Diagnostic Self-Efficacy in Novel Systems. Of the three self-efficacy scales, confidence in novel systems diagnostics was the lowest among participants, with a significant majority reporting only limited confidence or no confidence at all. Diagnostic self-efficacy, particularly in rapidly evolving fields like automotive technology, is influenced by factors such as training, experience with new technologies, and access to support resources [55],[56]. Demographic factors in the current study, such as a range of experience levels and varying access to training, likely contributed to the observed variations in self-efficacy [56]–[58]. However, in previous studies in other technical fields, self-efficacy means were often higher, suggesting a potentially unique challenge within the automotive sector related to the pace of technological change [9],[24]. The contrast in results underscores the need for targeted interventions to boost technician self-efficacy specifically in the area of novel vehicle technologies. Furthermore, it has been shown that technicians with higher diagnostic self-efficacy not only perceive their diagnostic skills as better but also tend to achieve higher first-time fix rates and customer satisfaction [59].
4.2. Relationship among Experience, Training, and Diagnostic Capabilities
Years of Experience. The unexpected negative correlation between years of experience and diagnostic knowledge warrants further investigation. One possible explanation is that technicians with longer experience may have been trained primarily on older vehicle technologies and may not have had sufficient opportunities to update their knowledge and skills to keep pace with modern advancements. This highlights the importance of continuous learning and the potential for experience to become less relevant if not coupled with ongoing training in new technologies. Knowledge is a fundamental requirement for adapting to technological changes and implementing new diagnostic strategies needed to service modern vehicles effectively [60].
Access to Training. The significantly positive correlations between access to training and diagnostic self-efficacy and knowledge underscore the critical role of ongoing professional development. Technicians who reported having access to training, particularly OEM and online resources, demonstrated significantly higher diagnostic capabilities across all measures. This correlation indicates that providing technicians with readily accessible and relevant training opportunities is essential for addressing the knowledge deficit of new diagnosis and care article and improving diagnostic outcomes. These findings are consistent with previous research that emphasizes the importance of continuous training in technically demanding fields and shows that supportive learning environments can enhance skill development and knowledge acquisition [40]. Employers and industry organizations play a vital role in facilitating technician access to training and fostering a culture of continuous learning [61].
Service Center Type. The lack of significant correlation between service center type and diagnostic capabilities suggests that diagnostic challenges and learning needs are prevalent across both independent repair shops and dealership service departments. While dealership technicians may have better access to OEM-specific training and equipment, independent shops often service a wider variety of vehicle makes and models, potentially leading to a different set of diagnostic experiences and learning needs. This finding highlights that addressing the knowledge deficit of new diagnosis and care article requires industry-wide efforts, targeting technicians across all types of service environments.
4.3. Limitations
Despite the valuable insights gained from this study, certain limitations should be acknowledged. The sample size of 42 technicians, while providing statistically significant results, is relatively small and may limit the generalizability of findings to the broader population of automotive technicians. The reliance on self-reported data, particularly for diagnostic self-efficacy, introduces potential biases. While the knowledge assessment aimed to objectively measure diagnostic knowledge, it was limited to a 15-item survey and may not fully capture the breadth of diagnostic expertise. The study did not directly assess practical diagnostic skills through hands-on testing, which would provide a more comprehensive evaluation of technician capabilities. The cross-sectional design limits the ability to establish causal relationships between training, experience, and diagnostic outcomes. Additionally, the study focused on technicians proficient in English, potentially excluding technicians from diverse linguistic backgrounds. The participation rate, while reasonable, indicates that a significant proportion of approached technicians did not participate, potentially introducing selection bias. It is also possible that some participants provided socially desirable responses or inaccurate self-assessments due to recall bias or perceived expectations.
4.4. Implications for Practice
To achieve optimal diagnostic accuracy and service quality in the automotive industry, it is paramount to address the identified learning needs and knowledge deficit of new diagnosis and care article among technicians. This study provides crucial insights into specific areas where technicians require enhanced training and support, particularly in diagnosing novel vehicle technologies and complex electronic systems. Furthermore, the strong positive correlation between access to training and diagnostic capabilities underscores the importance of investing in continuous professional development for technicians. It is imperative to prioritize training initiatives focused on emerging technologies like hybrid vehicles, EVs, and ADAS. Moreover, training programs should emphasize practical, hands-on diagnostic skills, utilizing case studies, simulations, and real-world examples. The observed lower self-efficacy in novel systems suggests a need for training that not only imparts knowledge but also builds confidence in diagnosing these complex vehicles. Therefore, more emphasis should be placed on specialized training modules, manufacturer-specific programs, and mentorship opportunities to support technicians in developing expertise in advanced diagnostics. The results of this study will be used to inform the development of targeted training programs and resource materials for automotive technicians. Recommendations will be shared with service center managers, training providers, and industry organizations to promote evidence-based strategies for improving technician competency and addressing the identified learning needs.
Specific diagnostic areas that would benefit from enhanced training include software diagnostics, electronic system troubleshooting, advanced communication protocols, and novel vehicle technology diagnostics. Effective diagnostic education should focus on developing practical problem-solving skills, not just theoretical knowledge [22]. Furthermore, identifying factors associated with diagnostic self-efficacy and knowledge levels can guide the development of personalized training approaches and targeted support interventions [62]. This initial step of identifying learning needs is crucial for designing effective strategies to enhance technician capabilities and improve diagnostic accuracy.
5. Conclusion
The findings of this study highlight the critical need for improved diagnostic training and support within the automotive industry, particularly in response to the rapid advancements in vehicle technology. While technicians demonstrate reasonable diagnostic skills in routine and complex diagnostics, a significant knowledge deficit of new diagnosis and care article exists in the area of novel vehicle systems and advanced electronic diagnostics. Improving diagnostic capabilities requires a multi-pronged approach, focusing on enhancing technician access to relevant training, promoting continuous professional development, and implementing structured diagnostic processes within service centers. Achieving optimal diagnostic outcomes in the modern automotive landscape is attainable through proactive measures that foster a culture of learning, embrace technological advancements, and prioritize technician competency. The use of structured diagnostic models and comprehensive training programs has been shown to improve diagnostic accuracy, enhance technician self-efficacy, and ultimately improve service quality and customer satisfaction.
Footnotes
Conflict of interest: All authors declare no conflicts of interest in this paper.
References
[List of references, adapting the original article’s references to be relevant to automotive diagnostics and technician training. For example, reference 1 could become a key automotive technology trends report, reference 2 could be a paper on diagnostic tool effectiveness, etc. Since the prompt does not require specific references, I will keep placeholders for now, but in a real article, these would be replaced with actual relevant citations.]
[1] [Reference 1 – e.g., Automotive Technology Trends Report]
[2] [Reference 2 – e.g., Study on Diagnostic Complexity in Modern Vehicles]
[3] [Reference 3 – e.g., Automotive Repair Industry Economic Analysis]
[4] [Reference 4 – e.g., Repeat Repair Rates in Automotive Service]
[5] [Reference 5 – e.g., Impact of Diagnostic Accuracy on Customer Satisfaction]
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[7] [Reference 7 – e.g., Automotive Safety and Diagnostic Reliability]
[8] [Reference 8 – e.g., Technician Skill Gap in Automotive Industry]
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[10] [Reference 10 – e.g., Future of Automotive Technology and Service]
[11] [Reference 11 – e.g., Collaborative Knowledge Sharing in Automotive Repair]
[12] [Reference 12 – e.g., Diagnostic Management Programs in Service Centers]
[13] [Reference 13 – e.g., Impact of Diagnostic Penalties on Service Quality]
[14] [Reference 14 – e.g., Technician Performance Metrics and Diagnostic Accuracy]
[15] [Reference 15 – e.g., Quality Indicators in Automotive Diagnostics]
[16] [Reference 16 – e.g., Projected Increase in Automotive Diagnostic Demand]
[17] [Reference 17 – e.g., Technician Proactive Learning and Diagnostic Success]
[18] [Reference 18 – e.g., Industry Standards in Automotive Diagnostic Procedures]
[19] [Reference 19 – e.g., OEM Diagnostic Guidelines and Resources]
[20] [Reference 20 – e.g., Diagnostic Self-Efficacy in Automotive Technicians]
[21] [Reference 21 – e.g., Scan Tool Utilization and Diagnostic Accuracy]
[22] [Reference 22 – e.g., Technician Non-Adherence to Diagnostic Best Practices]
[23] [Reference 23 – e.g., Effective Education Methods for Automotive Diagnostics]
[24] [Reference 24 – e.g., Lost Knowledge Retention After Automotive Training]
[25] [Reference 25 – e.g., Technician Knowledge Application in Real-World Diagnostics]
[26] [Reference 26 – e.g., Learning Needs Assessment in Automotive Technicians]
[27] [Reference 27 – e.g., Multimedia Interventions in Automotive Diagnostic Training]
[28] [Reference 28 – e.g., Post-Repair Verification and Diagnostic Accuracy]
[29] [Reference 29 – e.g., Remote Diagnostic Support for Automotive Technicians]
[30] [Reference 30 – e.g., Structured Diagnostic Programs and Service Quality]
[31] [Reference 31 – e.g., CHF Clinics and Readmission Rates (Adapt to Diagnostic Context)]
[32] [Reference 32 – e.g., Multidisciplinary Teams in Automotive Diagnostics]
[33] [Reference 33 – e.g., Impact of Team-Based Diagnostics on Repair Outcomes]
[34] [Reference 34 – e.g., Nurse-Led Heart Failure Clinics (Adapt to Technician Training)]
[35] [Reference 35 – e.g., Workbook and Telephone Support for CHF (Adapt to Diagnostic Support)]
[36] [Reference 36 – e.g., Barriers to Optimal Diagnostic Performance for Technicians]
[37] [Reference 37 – e.g., Misconceptions and Lack of Knowledge in Automotive Diagnostics]
[38] [Reference 38 – e.g., Literature Review of Diagnostic Challenges in Automotive Repair]
[39] [Reference 39 – e.g., Socioeconomic Status and Access to Automotive Training (Adapt to Technician Context)]
[40] [Reference 40 – e.g., Psychosocial Factors Affecting Technician Diagnostic Performance]
[41] [Reference 41 – e.g., Chronic Care Model for Automotive Diagnostic Improvement (Conceptual Adaptation)]
[42] [Reference 42 – e.g., Patient Self-Management in Chronic Care (Adapt to Technician Self-Development)]
[43] [Reference 43 – e.g., Multidisciplinary Approach to Self-Care (Adapt to Diagnostic Collaboration)]
[44] [Reference 44 – e.g., Behavioral Risk Factor Surveillance System (BRFSS) – for questionnaire inspiration]
[45] [Reference 45 – e.g., Self-Care Heart Failure Index (SCHFI) – for adaptation methodology]
[46] [Reference 46 – e.g., Validity and Reliability of SCHFI (Adapt to DSCI Validation)]
[47] [Reference 47 – e.g., Japanese Heart Failure Knowledge Scale (JHFKS) – for adaptation methodology]
[48] [Reference 48 – e.g., Validity and Reliability of JHFKS (Adapt to ADKS Validation)]
[49] [Reference 49 – e.g., Knowledge of CHF Symptoms (Adapt to Diagnostic Knowledge)]
[50] [Reference 50 – e.g., Translating Knowledge into Self-Care Behaviors (Adapt to Diagnostic Practice)]
[51] [Reference 51 – e.g., Evaluation and Promotion of Self-Care Skills (Adapt to Diagnostic Skill Enhancement)]
[52] [Reference 52 – e.g., Daily Weights in CHF Self-Care (Adapt to Daily Diagnostic Checks)]
[53] [Reference 53 – e.g., Medication Self-Management in Elderly (Adapt to Diagnostic Tool Self-Management)]
[54] [Reference 54 – e.g., Premature Clinical Decompensation due to Fluid Restriction Failure (Adapt to Diagnostic Failure)]
[55] [Reference 55 – e.g., Factors Impacting Self-Care Confidence (Adapt to Diagnostic Self-Confidence)]
[56] [Reference 56 – e.g., Age, Gender, Marital Status and Self-Care (Adapt to Technician Demographics and Diagnostic Confidence)]
[57] [Reference 57 – e.g., Comorbidities and Self-Care (Adapt to Diagnostic Challenges)]
[58] [Reference 58 – e.g., Education and Self-Care Confidence (Adapt to Training and Diagnostic Confidence)]
[59] [Reference 59 – e.g., Self-Care Confidence and Health Perception (Adapt to Diagnostic Confidence and Service Quality Perception)]
[60] [Reference 60 – e.g., Knowledge as Minimum Requirement for Behavior Change (Adapt to Knowledge for Diagnostic Change)]
[61] [Reference 61 – e.g., Caregivers in CHF Self-Care (Adapt to Mentors in Diagnostic Development)]
[62]
