Exploring Customers’ Feedback on the Technological and Operational Changes at the Departments of Motor Vehicles Due to Covid-19 Pandemic

Boniphace Kutela, Raynard Tom Magehema () and Rafael Mwekh’iga ()
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Boniphace Kutela: Roadway Safety Texas A&M Transportation Institute, 701 North Post Oak Road, Houston, TX 77024, USA
Raynard Tom Magehema: ��Department of Civil Engineering, Ardhi University, P.O. Box 35176 Dar es Salaam, Tanzania
Rafael Mwekh’iga: ��Department of Civil Engineering, Ardhi University, P.O. Box 35176 Dar es Salaam, Tanzania

International Journal of Innovation and Technology Management (IJITM), 2023, vol. 20, issue 06, 1-23

Abstract: In Early 2020, after COVID-19 was declared a pandemic, the Center for Disease Control and Prevention (CDC) provided guidelines to combat the spread of COVID-19. As a result, Departments of Motor Vehicles (DMVs) introduced several technological changes and modified operation protocols. This study explored the changes of customer feedback resulting from the changes in DMVs operations across the United States. A total of 24164 Google reviews were collected from 69 cities in 43 States. Sentiment analysis, text network, and logistic regression were applied to evaluate the changes of the feedback before and after the lockdown period. It was found that the post-lockdown reviews had higher positive polarity and low negative polarity than pre-lockdown reviews. Furthermore, positive feedback topics, such as excellent service and short waiting time, increased significantly during the post-lockdown era. Keywords that showed a higher likelihood of higher ratings include appointment, wait time, and minutes. Conversely, lower ratings were associated with keywords — customer services, hours, and people, among others. Further, the logistic regression showed that the odds of high ratings increased nearly three times when COVID-19-related keywords were mentioned. Several recommendations to improve operations are presented in this study. It is expected that the findings will benefit DMV operators and policymakers across the United States.

Keywords: COVID-19; Department of Motor Vehicles; text mining; sentiment analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219877023420038

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