Division of motor vehicle (DMV) offices serve a wide swath of Americans in all states and can therefore serve as excellent vehicles to study the quality of public services in the country. However, relatively little attention has been devoted in the academic literature to studying operations in DMV offices, especially as it relates to service quality and productivity. In an attempt to address the same, this paper aims to present the results of a study of DMV offices across the USA through a nationwide survey about vehicle titling and registration services, that received response from 31 of the 50 states and District of Columbia.
The authors use a mixed methods approach – a sequential unequal weight mixed methods approach starting with a quantitative analysis of DMV operational data followed by a qualitative case study approach. The primary data collected for this study were with a nationwide survey of the highest DMV office in each state, conducted through the American Association of Motor Vehicle Administrators. Out of the 50 states, 31 states and District of Columbia responded to the survey. In addition to descriptive statistical analysis performed to glean nationwide findings, Data Envelopment Analysis was used to determine efficiency of operations. Finally, extensive in-person interviews with senior managers of DMV offices in Ohio and Indiana were conducted to get more in-depth information for case studies and identification of best practices.
States exhibit significant variations in labor and capital productivity and based on Data Envelopment Analysis, Texas and Minnesota DMVs are the most efficient in terms of using their labor and capital inputs to maximize the number of transactional services rendered. The authors also find that while operational performance of vehicle titling and registration services is monitored by most DMV offices across the nation, assessment of customer satisfaction received much less attention. Among the states that do well on both are Indiana and Ohio; the case studies presented based on interviews with their officials that also identify best practices.
This research was limited to the USA as are its findings. Additionally, it focuses only on vehicle titling and registration at DMV offices because that represents the bulk of services performed by a DMV and the output is standard across all states. Nonetheless, a future study should be extended to other DMV services.
Given the finding that assessment of customer satisfaction is not widely practiced in DMV offices, DMV officials should address this by putting appropriate systems in place. Additionally, practitioners and state officials can use the findings of this study to develop best practices for their operations and also determine the most appropriate ways to structure the provision of those services that result in enhanced efficiencies and customer satisfaction.
DMV services are among the most widely used services offered by the government in the USA and the overall size and scope of services provided by them across the country is immense. Thus, any improvements in productivity and service quality has significant implications in terms of improving public satisfaction with government services.
To the best of our knowledge, this represents the first nationwide comparative study of DMV offices in the USA that focuses on service quality and analyzes productivity across the states. Additionally, the case study provided at the end of the paper identifies best practices from two states that have received national recognition for service quality which could be adopted by all DMV offices across the USA. The findings also conform/strengthen numerous hypotheses espoused in existing models and theories from service operations literature by providing evidence in their favor.
Martin, J., Bhadury, J., Cordeiro, J., Waite, M. and Amoako-Gyampah, K. (2018), "Service operations in DMV (division of motor vehicles) offices of the USA - a comparative study", Management Research Review, Vol. 41 No. 4, pp. 504-523. https://doi.org/10.1108/MRR-02-2017-0060Download as .RIS
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