Study Overview
This study investigates the psychological preparedness of traditional auto repair shop owners in Taiwan for the transition to electric vehicles (EVs). As the automotive industry rapidly shifts towards electrification, these businesses face significant pressures to adapt their models, skills, and strategies. This interactive report summarizes the key findings from a mixed-methods study involving interviews and questionnaires with shop owners.
Core Research Question:
What shop-level characteristics and owner attributes influence the psychological preparedness for EV-related transformation among traditional auto repair shops in Taiwan?
The study aims to identify demographic and structural factors associated with varying levels of readiness, from non-response to active adaptation. The findings are intended to inform targeted policies and support mechanisms for these businesses. Note: The results presented here are based on an initial sample of 34 respondents, with a larger data collection effort underway.
Who Participated? (Sample Demographics)
This section provides an overview of the characteristics of the 34 traditional auto repair shop owners and their businesses that participated in the initial phase of this study. Understanding the sample helps contextualize the findings on EV adaptation readiness.
Shop Region Distribution
Shop Size (Number of Employees)
Owner's Age
51.6 years (Avg)
Standard Deviation: 7.0 years
Owner's Experience
22.8 years (Avg)
Standard Deviation: 7.9 years
Note: Data based on N=34 valid responses collected May 9-19, 2025 (preliminary sample).
How Ready Are They? (EV Readiness Levels)
Psychological readiness for EV transformation was measured on a 4-point scale. This section visualizes the distribution of these readiness levels among the surveyed shop owners, providing a snapshot of the current state of preparedness in the industry.
Distribution of EV Readiness Levels (Counts)
Distribution of EV Readiness Levels (Percentage)
Readiness Levels Defined:
- Level 1: No intention to make any EV-related changes.
- Level 2: Acknowledges the trend and is considering preparation.
- Level 3: Actively monitoring EV development and exploring partnerships.
- Level 4: Has already engaged in training or equipment upgrades.
What Factors Influence Readiness?
This section delves into the core findings of the study, examining which shop and owner characteristics significantly influence psychological preparedness for EV transformation. The results are based on an Ordered Probit regression model.
Research Hypotheses Status:
H1: Older shop owners tend to be more passive in responding to the EV transition.
Status: Partially Supported (Borderline Significant)
The model indicates a tendency for older owners to be more conservative (p=0.096).
H2: Smaller-scale repair shops are more likely to adopt a passive stance toward EV-related changes.
Status: Supported
Smaller shops show significantly less willingness to transform (p=0.016).
H3: The education level of the shop owner is not significantly related to whether they respond passively or proactively to the EV transition.
Status: Partially Supported (Borderline Significant, positive trend)
The model suggests a slight positive trend with higher education, but it's borderline (p=0.053). The original hypothesis stated "not significantly related", so this finding is nuanced.
Ordered Probit Model Estimation Results
The table below shows the coefficients from the model. A negative coefficient suggests the variable is associated with lower readiness (more passive), while a positive coefficient suggests higher readiness (more proactive). Hover over a variable name for its interpretation based on the study.
| Variable | Coefficient (β) | Std. Error | p-value | Significance |
|---|
Conceptual Framework
The study's conceptual framework outlines the hypothesized relationships between various shop and owner characteristics and their psychological preparedness for EV transformation. This framework guided the data collection and analysis.
Factors Influencing Psychological Preparedness for EV Transformation
Shop Profile
(Region, Shop Age, Size)
Owner Profile
(Age, Education)
Technical Factors
(Experience)
Psychological Preparedness for EV Transformation (y)
(Ordinal Scale: No Plan to Actively Acting)
This diagram illustrates the key variable categories hypothesized to influence a shop's readiness to adapt to EVs.
Discussion and Implications
The preliminary findings from this study offer valuable insights into the challenges and drivers of EV adaptation among traditional auto repair shops in Taiwan. This section discusses the interpretation of these findings and their potential implications for policy and industry practice.
Interpretation of Key Findings:
- Owner's Age: Older shop owners showing a tendency towards more passive responses (borderline significant) might be attributed to factors like risk aversion as they approach retirement or a perceived shorter timeframe to recoup new investments in EV technology and training.
- Shop Size: Smaller shops are significantly less prepared. This is likely due to limitations in capital for new equipment and training, as well as potentially fewer human resources to dedicate to learning and implementing new EV service protocols.
- Owner's Education: The borderline significant positive trend with higher education suggests that while formal education might not be a primary driver, it could play a supporting role, perhaps by enhancing owners' ability to research and understand new technologies or business models. However, practical, hands-on technical experience (also borderline significant and positive) appears crucial.
- Owner's Experience: More years of technical experience showed a borderline significant positive association with readiness. This could indicate that experienced owners have a better grasp of automotive technological shifts and may be more confident in their ability to adapt, or they might have built more resilient businesses capable of investing in change.
Implications:
For Policymakers:
Targeted support is crucial. This could include financial incentives or subsidies specifically for small and medium-sized shops to invest in EV diagnostic tools and safety equipment. Offering accessible, subsidized on-the-job training programs tailored to the needs of experienced technicians in older age brackets could also be beneficial.
For Industry Stakeholders:
Collaboration is key. Industry associations, technical and vocational schools, and EV manufacturers should work together to develop standardized training curricula and certification programs. Creating networks for knowledge sharing and technical support among traditional shops could help accelerate technology adoption.
For Researchers:
This study provides preliminary empirical evidence. Future research should leverage the full dataset (once >300 responses are collected) for more robust analysis. Longitudinal studies tracking shops' adaptation over time would offer deeper insights into the transition process. Expanding the research to include qualitative case studies of successfully adapted shops could provide practical examples and best practices.
Methodology Snapshot
This study employed a mixed-methods research design, combining qualitative insights from in-depth interviews with quantitative data from a structured questionnaire. The aim was to comprehensively understand the factors influencing EV adaptation readiness.
Approach
- Cross-sectional survey design.
- Target Population: Traditional auto repair shop owners in Taiwan.
- Sampling: Purposive sampling across northern, central, and southern regions, including shops of varying sizes and ages.
- Data Collection (Preliminary): 34 valid responses via face-to-face interviews and online surveys (May 9-19, 2025). Full data collection aims for >300 responses.
Variables & Model
Dependent Variable: Psychological preparedness for EV transformation (4-point ordinal scale).
Independent Variables: Shop region, shop age, shop size, owner's age, owner's education level, owner's years of technical experience.
Estimation Model: Ordered Probit Regression, suitable for ordinal dependent variables. This model estimates how explanatory variables influence the likelihood of an owner being in one readiness category versus another.
Simplified Ordered Probit Model Logic
The model estimates thresholds (τ₁, τ₂, τ₃) on the latent scale that define these observed categories.
Others
The advertisement will be published in PIP magazine in June, and questionnaire 1 is used to invite maintenance shops to participate in the activity of filling out the questionnaire. Questionnaire 2 was to be filled in by randomly selected maintenance shops. Software, these are some records of my use of Eviews and Python.