Does management capacity increase organizational performance? An empirical analysis of public housing agencies

Since the 1990s, scholars have paid special attention to public management’s role in theory and research under the assumption that effective management is one of the primary means for achieving superior performance. To some extent, this was influenced by popular business writings of the 1980s as well as the reinventing literature of the 1990s. A number of case studies but limited quantitative research papers have been published showing that management matters in the performance of public organizations. ^ My study examined whether or not management capacity increased organizational performance using quantitative techniques. The specific research problem analyzed was whether significant differences existed between high and average performing public housing agencies on select criteria identified in the Government Performance Project (GPP) management capacity model, and whether this model could predict outcome performance measures in a statistically significant manner, while controlling for exogenous influences. My model included two of four GPP management subsystems (human resources and information technology), integration and alignment of subsystems, and an overall managing for results framework. It also included environmental and client control variables that were hypothesized to affect performance independent of management action. ^ Descriptive results of survey responses showed high performing agencies with better scores on most high performance dimensions of individual criteria, suggesting support for the model; however, quantitative analysis found limited statistically significant differences between high and average performers and limited predictive power of the model. My analysis led to the following major conclusions: past performance was the strongest predictor of present performance; high unionization hurt performance; and budget related criterion mattered more for high performance than other model factors. As to the specific research question, management capacity may be necessary but it is not sufficient to increase performance. ^ The research suggested managers may benefit by implementing best practices identified through the GPP model. The usefulness of the model could be improved by adding direct service delivery to the model, which may also improve its predictive power. Finally, there are abundant tested concepts and tools designed to improve system performance that are available for practitioners designed to improve management subsystem support of direct service delivery.^

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Improving publicly funded human services: Incorporating capacity building into the contracting relationship between Children's Services Councils and nonprofit organizations

This qualitative two-site case study examined the capacity building practices that Children’s Services Councils (CSCs), independent units of local government, provide to nonprofit organizations (NPOs) contracted to deliver human services. The contracting literature is replete with recommendations for government to provide capacity building to contracted NPOs, yet there is a dearth of scholarship on this topic. The study’s purpose was to increase the understanding of capacity building provided in a local government contracting setting. Data collection consisted primarily of in-depth interviews and focus groups with 73 staff from two CSCs and 28 contracted NPOs. Interview data were supplemented by participant observation and review of secondary data. The study analyzed capacity building needs, practices, influencing factors, and outcomes. The study identified NPO capacity building needs in: documentation and reporting, financial management, program monitoring and evaluation, participant recruitment and retention, and program quality. Additionally, sixteen different types of CSC capacity building practices were identified. Results indicated that three major factors impacted CSC capacity building: CSC capacity building goals, the relationship between the CSC and NPOs, and the level of NPO participation. Study results also provided insight into the dynamics of the CSC capacity building process, including unique problems, challenges, and opportunities as well as necessary resources. The results indicated that the CSCs’ relational contracting approach facilitated CSC capacity building and that CSC contract managers were central players in the process. The study provided evidence that local government agencies can serve as effective builders of NPO capacity. Additionally, results indicated that much of what is known about capacity building can be applied in this previously unstudied capacity building setting. Finally, the study laid the groundwork for future development of a model for capacity building in a local government contracting setting.

Disaster Risk Management Disparity in the Caribbean: Evidence from Barbados, Dominican Republic, Jamaica and Trinidad and Tobago

This study on risk and disaster management capacities of four Caribbean countries: Barbados, the Dominican Republic, Jamaica, and Trinidad and Tobago, examines three main dimensions: 1) the impact of natural disasters from 1900 to 2010 (number of events, number of people killed, total number affected, and damage in US$); 2) institutional assessments of disaster risk management disparity; and 3) the 2010 Inter-American Bank for Development (IADB) Disaster Risk and Risk Management indicators for the countries under study. The results show high consistency among the different sources examined, pointing out the need to extend the IADB measurements to the rest of the Caribbean countries. Indexes and indicators constitute a comparison measure vis-à-vis existing benchmarks in order to anticipate a capacity to deal with adverse events and their consequences; however, the indexes and indicators could only be tested against the occurrence of a real event. Therefore, the need exists to establish a sustainable and comprehensive evaluation system after important disasters to assess a country‘s performance, verify the indicators, and gain feedback on measurement systems and methodologies. There is diversity in emergency and preparedness for disasters in the four countries under study. The nature of the event (hurricanes, earthquakes, floods, and seismic activity), especially its frequency and the intensity of the damage experienced, is related to how each has designed its risk and disaster management policies and programs to face natural disasters. Vulnerabilities to disaster risks have been increasing, among other factors, because of uncontrolled urbanization, demographic density and poverty increase, social and economic marginalization, and lack of building code enforcement. The four countries under study have shown improvements in risk management capabilities, yet they are far from being completed prepared. Barbados‘ risk management performance is superior, in comparison, to the majority of the countries of the region. However, is still far in achieving high performance levels and sustainability in risk management, primarily when it has the highest gap between potential macroeconomic and financial losses and the ability to face them. The Dominican Republic has shown steady risk performance up to 2008, but two remaining areas for improvement are hazard monitoring and early warning systems. Jamaica has made uneven advances between 1990 and 2008, requiring significant improvements to achieve high performance levels and sustainability in risk management, as well as macroeconomic mitigation infrastructure. Trinidad and Tobago has the lowest risk management score of the 15 countries in the Latin American and Caribbean region as assessed by the IADB study in 2010, yet it has experienced an important vulnerability reduction. In sum, the results confirmed the high disaster risk management disparity in the Caribbean region.

Disater Risk Management Disparity in the Caribbean: Evidence from Barbados, Dominican Republic, Jamaica and Trinidad and Tobago

This study on risk and disaster management capacities of four Caribbean countries: Barbados, the Dominican Republic, Jamaica, and Trinidad and Tobago, examines three main dimensions: 1) the impact of natural disasters from 1900 to 2010 (number of events, number of people killed, total number affected, and damage in US$); 2) institutional assessments of disaster risk management disparity; and 3) the 2010 Inter-American Bank for Development (IADB) Disaster Risk and Risk Management indicators for the countries under study. The results show high consistency among the different sources examined, pointing out the need to extend the IADB measurements to the rest of the Caribbean countries. Indexes and indicators constitute a comparison measure vis-à-vis existing benchmarks in order to anticipate a capacity to deal with adverse events and their consequences; however, the indexes and indicators could only be tested against the occurrence of a real event. Therefore, the need exists to establish a sustainable and comprehensive evaluation system after important disasters to assess a country’s performance, verify the indicators, and gain feedback on measurement systems and methodologies. There is diversity in emergency and preparedness for disasters in the four countries under study. The nature of the event (hurricanes, earthquakes, floods, and seismic activity), especially its frequency and the intensity of the damage experienced, is related to how each has designed its risk and disaster management policies and programs to face natural disasters. Vulnerabilities to disaster risks have been increasing, among other factors, because of uncontrolled urbanization, demographic density and poverty increase, social and economic marginalization, and lack of building code enforcement. The four countries under study have shown improvements in risk management capabilities, yet they are far from being completed prepared. Barbados’ risk management performance is superior, in comparison, to the majority of the countries of the region. However, is still far in achieving high performance levels and sustainability in risk management, primarily when it has the highest gap between potential macroeconomic and financial losses and the ability to face them. The Dominican Republic has shown steady risk performance up to 2008, but two remaining areas for improvement are hazard monitoring and early warning systems. Jamaica has made uneven advances between 1990 and 2008, requiring significant improvements to achieve high performance levels and sustainability in risk management, as well as macroeconomic mitigation infrastructure. Trinidad and Tobago has the lowest risk management score of the 15 countries in the Latin American and Caribbean region as assessed by the IADB study in 2010, yet it has experienced an important vulnerability reduction. In sum, the results confirmed the high disaster risk management disparity in the Caribbean region.

Perceptions of the Beach Users: A Case Study of the Coastal Areas of North Cyprus Towards Establishment of a "Carrying Capacity"

Within the main elements of economic sustainability, socio-cultural sustainability, and environmental sustainability, the criteria of 'carrying capacity’ have ben emphasized through residents’ perception analysis to explore practical methods towards the application and implementation of such criteria. As data analysis revealed, the main tourist resources in the case of North Cyprus –the coast and the beach- have a certain capacity to sustain the impact and pressure of tourism. Despite the significance of the indigenous environment and with respect to the residents’ perception of optimum carrying capacity levels, this issue has not been given a due consideration. This has resulted in a process of coastal development which bypasses any measure ore application of a standard to harmonize the degree of physical development and the capacity of the beach. The main objective of this paper is to establish the concept of ‘carrying capacity’ as the means to achieve the reconciliation of environmental impacts with tourism development. The study concludes that, if carrying capacity measurement and its implementation are not incorporated into the planning decision as a clear policy, there will be grave negative consequences for those resources attracting visitors.

Improving resource management in virtualized data centers using application performance models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity.^ We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. ^ This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.^

The Use of Thermal Capacity in Measuring the Effectiveness of Meals on Wheels Transport Containers

The Meals on Wheels (MOW) program is designed to help combat hunger in persons needing assistance. MOW has a duty not only to provide food but also to ensure that it reaches eligible clients safely. Given the population that MOW serves, transporting food safely takes on increased importance. This experiment focused on the major food safety issue of maintaining temperature integrity through the use of transport containers. For containers that did not contain electric heating elements, several factors influenced how fast the food temperature fell. Those factors included the U-value and size of the container as well as how many meals were in the container. As predicted, the smaller the U-value, the longer it took the temperature to fall. Larger containers did better at maintaining food temperatures, provided they were fully loaded. In general, fully loaded small and medium containers were better at maintaining food temperatures than larger containers loaded with the same number of meals.

Fair and QoS-oriented resource management in heterogeneous networks

In this paper, a heterogeneous network composed of femtocells deployed within a macrocell network is considered, and a quality-of-service (QoS)-oriented fairness metric which captures important characteristics of tiered network architectures is proposed. Using homogeneous Poisson processes, the sum capacities in such networks are expressed in closed form for co-channel, dedicated channel, and hybrid resource allocation methods. Then a resource splitting strategy that simultaneously considers capacity maximization, fairness constraints, and QoS constraints is proposed. Detailed computer simulations utilizing 3GPP simulation assumptions show that a hybrid allocation strategy with a well-designed resource split ratio enjoys the best cell-edge user performance, with minimal degradation in the sum throughput of macrocell users when compared with that of co-channel operation.

Managing small-scale commercial fisheries for adaptive capacity: Insights from dynamic social-ecological drivers of change in Monterey Bay

Globally, small-scale fisheries (SSFs) are driven by climate, governance, and market factors of social-ecological change, presenting both challenges and opportunities. The ability of small-scale fishermen and buyers to adapt to changing conditions allows participants to survive economic or environmental disturbances and to benefit from optimal conditions. This study presented here identifies key large-scale factors that drive SSFs in California to shift focus among targets and that dictate long-term trends in landings. We use Elinor Ostrom’s Social-Ecological System (SES) framework to apply an interdisciplinary approach when identifying potential factors and when understanding the complex dynamics of these fisheries. We analyzed the interactions among Monterey Bay SSFs over the past four decades since the passage of the Magnuson Stevens Fisheries Conservation and Management Act of 1976. In this region, the Pacific sardine (Sardinops sagax), northern anchovy (Engraulis mordax), and market squid (Loligo opalescens) fisheries comprise a tightly linked system where shifting focus among fisheries is a key element to adaptive capacity and reduced social and ecological vulnerability. Using a cluster analysis of landings, we identified four modes from 1974 to 2012 that were dominated by squid, sardine, anchovy, or lacked any dominance, enabling us to identify external drivers attributed to a change in fishery dominance during seven distinct transition points. Overall, we show that market and climate factors drive the transitions among dominance modes. Governance phases most dictated long-term trends in landings and are best viewed as a response to changes in perceived biomass and thus a proxy for biomass. Our findings suggest that globally, small-scale fishery managers should consider enabling shifts in effort among fisheries and retaining existing flexibility, as adaptive capacity is a critical determinant for social and ecological resilience.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.