Page 293 - Emerging Trends and Innovations in Web-Based Applications and Technologies

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These constructs are interconnected and will guide the B. User Satisfaction:
evaluation of GarageLocator’s impact. Ø Overall satisfaction ratings collected via post-service
surveys.
2. Data Collection
The research model incorporates both qualitative and Ø Percentage of users rating their experience as “excellent”
quantitative data collection methods: or “very good.”
Ø User Surveys: To capture perceptions of convenience, Ø Frequency of repeat bookings by existing users.
satisfaction, and app usability.
C. Operational Efficiency:
Ø Garage Feedback: To evaluate operational benefits, Ø Reduction in average waiting times at garages due to
workload management, and service quality streamlined scheduling.
improvements.
Ø Improvement in garage workflow management,
Ø Platform Analytics: Data on user interactions, service measured by the number of daily services completed.
bookings, and feature usage.
Ø Reduction in no-show rates for scheduled appointments.
Ø Performance Metrics: Metrics such as average service D. Predictive Maintenance Effectiveness:
wait times, booking rates, and predictive maintenance Ø Accuracy of predictive maintenance notifications in
effectiveness.
preventing breakdowns.
3. Evaluation Metrics
The success of GarageLocator will be evaluated based on: Ø Percentage of users acting on predictive alerts to
schedule maintenance.
Ø Service Accessibility: Number of users accessing E. Platform Engagement:
nearby garages and scheduling appointments.
Ø Active user retention rates over time.
Ø User Satisfaction: Ratings and reviews collected from
users via surveys. Ø User participation in loyalty programs and rewards.
Ø Engagement with key features such as reviews, ratings,
Ø Operational Efficiency: Reduction in wait times and and cost estimations.
improved appointment scheduling for garages.
2. Data Collection Methods
Ø Adoption Rates: Number of active users and service Data for performance evaluation is collected from the
providers on the platform over time.
following sources:
4. Analytical Methods Ø Platform Analytics: User interactions, bookings, and
The research will employ the following analytical techniques:
feature usage statistics.
Ø Statistical Analysis: To evaluate user feedback and Ø User Surveys: Feedback on service quality, app
performance metrics.
usability, and satisfaction.
Ø Regression Models: To identify relationships between
GarageLocator features and user satisfaction or Ø Garage Reports: Metrics related to appointment
management and operational outcomes.
efficiency outcomes.
Ø Predictive Maintenance Logs: Records of alerts issued
Ø Comparative Analysis: To compare pre- and post- and their outcomes.
implementation metrics for both users and garages.
3. Analytical Techniques
By systematically evaluating the components of this research The following methods are used to analyze performance
model, the study will provide actionable insights into how data:
GarageLocator optimizes auto maintenance services,
enhances service accessibility, and contributes to the Ø Descriptive Statistics: Summarizes trends and usage
modernization of the automotive repair industry. patterns.
V. PERFORMANCE EVALUATION Ø Comparative Analysis: Compares pre- and post-
The performance evaluation of GarageLocator focuses on implementation metrics to assess improvements.
assessing its impact on key metrics related to service
accessibility, user satisfaction, and operational efficiency. Ø Regression Analysis: Identifies relationships between
app features and user satisfaction or operational
The evaluation is conducted using both qualitative and efficiency.
quantitative data collected from users and service providers.
The methodology and outcomes are outlined as follows: Ø Sentiment Analysis: Analyzes user feedback and
reviews for qualitative insights.
1. Evaluation Metrics
A. Service Accessibility: 4. Results Interpretation
Ø Average time taken by users to locate a garage and book The results of the performance evaluation are interpreted to
a service. identify:
Ø Percentage increase in the number of users accessing Ø Strengths: Features that drive the highest engagement
garages within a defined radius. and satisfaction.
Ø Distribution of services accessed via the platform (e.g., Ø Areas for Improvement: Features requiring refinement
repairs, maintenance, diagnostics). to meet user expectations.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 283