Supporting the operational performance management of public service systems during slow-onset disasters
| dc.contributor.author | Pamukcu, Duygu | en |
| dc.contributor.committeechair | Zobel, Christopher W. | en |
| dc.contributor.committeemember | Balcik, Burcu | en |
| dc.contributor.committeemember | Russell, Roberta S. | en |
| dc.contributor.committeemember | Zhang, Yang | en |
| dc.contributor.committeemember | Ragsdale, Cliff T. | en |
| dc.contributor.department | Business, Business Information Technology | en |
| dc.date.accessioned | 2023-01-24T09:00:33Z | en |
| dc.date.available | 2023-01-24T09:00:33Z | en |
| dc.date.issued | 2023-01-23 | en |
| dc.description.abstract | Disasters impact different communities differently due to pre-existing vulnerabilities and inequalities, which diversify amounts and types of public service needs. Understanding the varying needs of communities that rely on government services helps decision-makers allocate limited resources properly during crises to maintain effective, efficient, and equitable service provision across the region. This dissertation includes three independent studies which commonly investigate how service operations can be successfully managed to maintain the operational performance goals of public organizations during slow-onset disasters. The first study focuses on the volatility in the service needs of citizens from a public system during a long-term disaster. The study proposes a time series approach for predicting demand volatility patterns to manage service productivity. This chapter explores the longitudinal impacts of long-term disasters for better service performance management since the timely and accurate prediction of deviations from the expected service demand is vital for utilizing limited resources. The study further discusses the differential impacts of such disasters across locations of socio-economically diverse populations to emphasize the need to consider the diverse needs of people for efficient and effective service provision. The second study builds upon the discussions in the first study and discusses static and dynamic risk factors of slow-onset disasters to reveal how these factors diversify the service needs of communities and impact the corresponding service response performance of public systems during the disaster. The study performs time series analyses to test the impact of capacity adjustments and dynamic disaster risk features on service performance, considering service response time as the performance indicator. The third study focuses on efficient and equitable capacity management and prioritization strategies of an information technology-based public system that experiences significant changes in service demand during disasters. The study presents a mathematical model quantifying the relative service efficiencies associated with service requests from an input-output-based standpoint to uncover the inefficiencies in response performance to different service categories. The paper discusses the opportunities for managing service efficiency and equity within and between service departments by rearranging available capacities and prioritization strategies during emergencies. | en |
| dc.description.abstractgeneral | Disasters impact different communities differently due to pre-existing vulnerabilities and inequalities, which diversify amounts and types of public service needs. Understanding the varying needs of communities that rely on government services helps decision-makers allocate limited resources properly during crises to maintain effective, efficient, and equitable service provision across the region. The three independent studies of the dissertation commonly investigate how service operations can be successfully managed to maintain the operational performance goals of public organizations during slow-onset disasters (e.g., climate change, drought, pandemic). The first study focuses on the variability in the service needs of citizens from a public system during a long-term disaster. The study explores the longitudinal impacts of disasters for better service performance management since the timely and accurate prediction of demand variability is important for resource management. The study further discusses the different impacts of such disasters across locations of socio-economically diverse populations to emphasize the need to consider the diverse needs of people for efficient and effective service provision. The second study discusses disaster risk factors of slow-onset disasters to reveal how these factors affect the service needs of communities and impact the corresponding service response performance of public systems. The study tests the impact of capacity adjustments and disaster risk factors on service performance. The third study focuses on efficient and equitable capacity management and prioritization strategies of a public system that experiences significant changes in service demand during disasters. The study quantifies the relative service efficiencies associated with service requests to uncover the inefficiencies in response performance to different service categories. The paper discusses the opportunities for managing service efficiency and equity within and between service departments. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:36409 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/113375 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution-NonCommercial 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | en |
| dc.subject | Public Service System | en |
| dc.subject | Disaster Operations Management | en |
| dc.subject | Performance measurement | en |
| dc.subject | Efficiency | en |
| dc.subject | Equity | en |
| dc.subject | Productivity | en |
| dc.subject | Capacity Planning | en |
| dc.subject | 311 | en |
| dc.title | Supporting the operational performance management of public service systems during slow-onset disasters | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Business, Business Information Technology | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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