MS, Kent State University, 2014, College of Education, Health and Human Services / School of Foundations, Leadership and Administration

The objective of this research is to investigate the core financial analytical competencies that hospitality managers must possess. Similar measurement was employed in both the industry practitioners' and the educators' survey, as the aim was to explore the importance and use of revenue and cost management competencies among manager participants and compare the findings with those obtained from the educators' survey. The results of this study showed that cost control, followed by revenue management, budgeting, forecasting, pricing, and asset management, are the essential financial competencies of hotel managers. The perception gap was found between hospitality managers and educators on the importance of these financial competencies. The findings of this study could provide suggestions for hospitality education and industry training programs.

Committee: Ning-Kuang Chuang (Committee Chair); Amy Gregory (Committee Member); Aviad Israeli (Committee Member) Subjects: Management

Doctor of Philosophy, The Ohio State University, 2014, Business Administration

The leakage of an organization's proprietary, digital assets to unauthorized parties can be a catastrophic event for any organization. The magnitude of these events have been recently underscored by the Target data breach, in which 70 million consumer credit card accounts were compromised, and financial costs are expected to exceed $1 billion. Digital assets have steadily progressed beyond low-value data and information, and into high-value knowledge-based domains. Failures to protect these latter types of digital assets can have even greater implications for firms or even macroeconomic conditions. Using the Target event as an illustrative motivation, we highlight the importance of two relatively-unexplored topics within the domain of digital asset protections - (1) vendor management, and (2) worker adherence to standard, well-codified procedures and technologies. We explicitly consider each of these topics through the separate empirical efforts detailed in this dissertation. Our first empirical effort examines the effects of sourcing and location decisions on the confidentiality of digital assets. We frame our study within a product-development dyad, with a proprietary, digital asset being shared between partners. We treat confidentiality as a performance dimension that is influenced by each organization accessing the asset. Specifically, we empirically investigate the realm of electronic video game development and the illegal distribution activities of these products. We employ a series of web-crawling data collection programs to compile an extensive secondary dataset covering the legitimate development activities for the industry. We then harvest data from the archives of a major, black-market distribution channel, and leverage these data to derive a novel, product-level measure of asset confidentiality. We examine the interacting factors of industrial clustering (agglomeration) and national property rights legislations in affecting this confidentiality m (open full item for complete abstract)

Committee: John Gray (Advisor); Kenneth Boyer (Advisor); James Hill (Committee Member); Elliot Bendoly (Committee Member) Subjects: Business Administration

PhD, University of Cincinnati, 2010, Engineering and Applied Science: Civil Engineering

Wastewater utilities in the United States face an aging workforce, higher consumer expectations, stricter environmental regulations, security concerns, and an aging infrastructure. As a result, many utilities have turned to Asset Management for better decision making to prioritize their needs. According to numerous studies that were conducted in the past decade, most notably the USEPA Clean Water and Drinking Water Infrastructure GAP Analysis Report and the ASCE Report Card, wastewater utilities will need to invest approximately 390 billion in capital infrastructure over the next two decades. Meanwhile, the field of Asset Management is emerging to improve the decision making process to renew, replace, or rehabilitate the infrastructure. Asset management can be defined as set of activities, guidelines, and decision tools that seek to minimize the life cycle costs of capital and O and M spending while maintaining an acceptable minimum level of service (USEPA 2006). This research provides a road map for the implementation of asset management in wastewater utilities with a strong focus on the critical tools that are needed to identify, quantify, and manage risk associated with the structural failure of sewers. The two components of the Business Risk Exposure; namely the probability and consequences of failure were thoroughly evaluated. Criticality matrices for linear assets were developed using expert opinion. A GIS based criticality tool was developed to identify the most critical assets. The GIS model was developed to eliminate biases and establish a systematic methodology to quantify the impact of failure of an asset. Subsequently, maps were generated showing the critical sewers that the utility needs to focus its efforts on to reduce its risk exposure. Probability curves of sewer failure were developed using historical data extracted from repair history performed between 1997 and 2009. Closed Circuit Television (CCTV) condition assessment methodologies are the basi (open full item for complete abstract)

Committee: Ossama Salem PhD (Committee Chair); Lawrence Gales PhD (Committee Member); Makram Suidan PhD (Committee Member); Heng Wei PhD (Committee Member) Subjects: Sanitation

Doctor of Philosophy, The Ohio State University, 2004, Economics

This dissertation is composed of two empirical studies on risk management. The first part is an empirical study on income risk and portfolio choice of investors. Recent theoretical work has shown that uninsurable labor income risk likely reduces the share of risky asset investment. Little empirical work has been done to examine this effect. This empirical study on the issue has three novel features. First, the long labor income history in NLSY79 is used to estimate the labor income risk. Second, the study distinguishes between permanent and transitory labor income risk, and estimates them for individuals. Third, I explicitly consider human capital as a component of the portfolio. Human capital is treated as a risk-free asset and estimated using signal extraction technique to labor income data. The study finds strong empirical support for the theory that labor income risk significantly reduces the share of risky assets in the portfolio of an investor. Furthermore, as economic theory suggests, permanent income risk has a significant effect on portfolio choice while transitory income risk has little effect. The second part of the dissertation is an empirical study on the interest rate risk management of banks. Using a rolling sample of bank holding companies from 1986 to 2002, the study investigates how banks adjust their balance sheet maturity structure according to their perception of current and future interest rate changes. Banks tend to lengthen the maturity of net assets when the yield curve is steeply sloped and shorten it when they expect the interest rate to increase in the future. To account for the off-balance-sheet activity effect on interest rate risk exposure, the sample is divided into those with high and low interest rate derivative activities. For banks with little off-balance-sheet interest rate derivative activities, the cross-sectional variation in their responsiveness of maturity structure to interest rate changes explains the stock market risk and (open full item for complete abstract)

Committee: Pok-sang Lam (Advisor) Subjects:

Master of Education (MEd), Bowling Green State University, 2007, Career and Technology Education/Technology

Digital information is becoming increasingly prevalent and libraries must develop new strategies to effectively archive electronic materials. Therefore, the purpose of this study was to identify and synthesize best practices of digital asset management of electronic texts in academic research libraries. The study was a “snapshot” of major topics related to digital asset management. Minimal research has been conducted to identify best practices in this field when compared to the amount of research that has been conducted for the print domain. The main issues that are seen in existing literature are digital materials selection, funding, digital rights management (DRM), and information authenticity. Several major trends also arose from the literature, including digital library architecture, metadata standards and search strategies, and bibliographic management software. A modified Delphi study was used to gather qualitative data from librarians at several institutions within the Association of Research Libraries (ARL). Eight librarians from several ARL institutions participated in two E-mail questionnaires. Participants were selected by identifying research-extensive libraries on the ARL website and selecting potential participants based on their job titles. Data was analyzed by identifying major themes and patterns that arose in the individual responses. Participants all possessed baccalaureate degrees, but had varied educational backgrounds, representing fields such as computer science, history, humanities, and natural sciences. Most of the participants also held advanced degrees in library science. Each library represented by the participants had a unique organizational structure and used different systems and strategies. There was significant discussion about the use of proprietary versus open-source software, content management systems, and other specific digital asset management strategies. Participants revealed that each library has a specialization and fulfills (open full item for complete abstract)

Committee: Paul Cesarini (Advisor) Subjects: Library Science

MS, University of Cincinnati, 2024, Engineering and Applied Science: Civil Engineering

Traffic sign condition assessment is a critically important responsibility for transportation agencies. This thesis presents an innovative framework to address this crucial task by leveraging mobile sensors, a machine vision camera, and GPS for real-time traffic sign condition assessment. An Artificial Intelligence (AI) application, integrated into a Robotic Operating System (ROS) framework, processes image and GPS data streams. The system deploys the YOLOv8 computer vision algorithm for traffic sign detection and segmentation, supported by DeepOCSort algorithm for object tracking. This sophisticated system segments traffic signs from other objects, enabling shape-based and color-based damage assessment. Also, distance and speed from the GPS is employed to calculate the stopping sight distance to identify severe obstructions. The study successfully demonstrated the efficacy of this approach by integrating these components into a seamless framework capable of real-time segmentation, classification, and geo-referencing of damaged traffic signs at regular traffic speeds. The XGboost machine learning algorithm, chosen for its accuracy and real-time prediction time, was used for the second-stage damage classification. This work validates the value and effectiveness of AI and mobile sensor technology in traffic sign condition assessment, making it a scalable, cost-effective solution for transportation agencies.

Committee: Munir Nazzal Ph.D. (Committee Chair); Ali Minai Ph.D. (Committee Member); Lei Wang Ph.D. (Committee Member) Subjects: Civil Engineering

MS, University of Cincinnati, 2023, Engineering and Applied Science: Civil Engineering

Potholes represent a significant engineering challenge and a hazard to road safety, necessitating prompt and effective repair strategies. Given the increasing popularity of Cold Mix Patching Materials (CMPMs) for fixing potholes during winter, especially in cold regions, it is crucial to establish standardized specifications for CMPMs. This study addresses two critical needs in pavement engineering and road maintenance: the establishment of baseline specification parameters for CMPMs and the development of a real-time automated pothole detection system. The performance of cold asphalt mixtures (CMPMs) was assessed through various laboratory tests, including boiling test, indirect tensile strength (ITS), torque test, workability test, tackiness test, and Hamburg Wheel Tracking Device (HWTD) tests. These tests revealed that key factors influencing the performance of CMPMs are aggregate absorption ratios, binder content, and the impact of antistripping agents. The study demonstrates that the combination of low absorption aggregates and an optimal amount of binder improves the ability of patching materials to resist moisture and enhances their mechanical properties. This, in turn, increases the longevity and durability of the materials. Furthermore, in-situ evaluations underscored the variability in material performance due to location-specific factors, emphasizing the importance of taking into account the location in which the CMPMs will be used before selecting the material. Moreover, the findings of the study reveal the significance of considering the freshness of the CMPMs before utilizing it. Additionally, assessing the stockpiled mixture showed that stockpiling CMPMs drastically affects the mixture adhesion and moisture susceptibility resistance. Concurrently, the study successfully engineered an innovative, cost-effective automated system leveraging machine learning techniques to detect, localize, and estimate the surface area of potholes, achieving a mean averag (open full item for complete abstract)

Committee: Munir Nazzal Ph.D. (Committee Chair); Lei Wang Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Civil Engineering

MFA, Kent State University, 2021, College of Communication and Information / School of Visual Communication Design

When approaching a design problem, design practitioners need to analyze the situation and identify the many variables at play (context) to frame the problem and propose an informed solution. This thesis focuses on creating and implementing a modeled approach to understanding context by identifying and coding data gathered by the practitioners through data tagging to identify patterns and themes coming from multiple inputs. The primary research highlighted critical findings regarding the tagging process and the effects of a broad/narrow tagging approach and generated concepts like the "information density scale" and "tag quality." Furthermore, by applying carefully crafted research on alternative visual platforms, the research refined the types of tools that should be developed and provided information on the best practices to code and aggregate the data. In the end, this thesis created a prototype for a computer application that operates as a context-aware framework, based on solid conceptual notions that could help understand the context more systematically. This prototype provides an innovative way to understand contextual information to identify data patterns reliably and consistently; furthermore, it unlocks new ways to experience and understand the information and consequently how to use it to improve the design process.

Committee: Jessica Barness (Advisor); Gretchen Rinnert (Committee Member); Ken Visocky O’Grady (Committee Member) Subjects: Design

Doctor of Business Administration (D.B.A.), Franklin University, 2022, Business Administration

The global pandemic posed an uncertain economic environment that required holistic thinking to succeed, especially for asset managers. Due to the complexity, severity, and unprecedented nature of the environment, there was a need to possess skills beyond analytical intelligence that allowed for thriving in ambiguity and uncertainty. Emotional intelligence and resilience are two of the proposed skills applicable in this environment. High levels of emotional intelligence help individuals navigate ambiguity, read situations, and pose more holistic solutions. Resilience allows individuals to rebound from misinterpretations of indicators quickly. A non-experimental, correlational study quantitatively examined asset managers, gathering anonymous demographic, emotional intelligence, and resilience data. The researcher measured their self-reported emotional intelligence using the Self-Reported Emotional Intelligence Test (SREIT) and resilience using the Connor-Davidson Resilience Scale 10 (CD-RISC10), with a multi-part electronic assessment delivered using SurveyMonkey. Both instruments used a five-point Likert scale to measure the independent variables. An asset management industry-related professional organization circulated the email inviting study participation. Asset managers self-selected to participate. There was no segregation regarding age, gender, experience, years in the role, or educational background. Hypotheses posed a positive relationship between emotional intelligence and resilience. From a theoretical standpoint, the constructs of chaos and intentional change theories were used as lenses to view the study results. The study uncovered a modest correlation between emotional intelligence and resilience and concluded with implications and suggestions for further research.

Committee: Kathy Richie (Committee Chair); Bethany Poore (Committee Member); Susan Campbell (Committee Member) Subjects: Business Administration; Psychology

Master of Science in Civil Engineering, University of Toledo, 2021, Civil Engineering

The size and complexity of transportation infrastructures are increasing with the increase of demand. Government agencies have the arduous task of planning, constructing, and maintaining them while providing safety and efficient level of service to the public. Municipalities across the country are searching for ways to assist planners to assess infrastructure's performance, safety condition, prepare asset management plans and evaluate the effect on transportation system of whole region while effectively communicating among stakeholders and demonstrating accountability and transparency to the public. The City of Columbus, Ohio is the 14th largest city in the United States. The City is poised for the utilization of smart tools and technologies to leverage its infrastructure with autonomous and connected vehicles, parking optimization technologies, and smart mobility apps, etc. A prototype visualization tool is created, which utilizes various ArcGIS products and capabilities to enable a multitude of data including infrastructure assets, traffic safety, 311, demographic, and travel data gathered from mobile devices to be integrated and displayed geospatially as well as graphically for municipal asset and safety management, planning, and decision-making. Through interactive maps and infographics, this prototype captures and easily shows information that is relevant and that matters to the people being served. This study demonstrates data visualization examples that employ data from multiple sources throughout the City and adjacent region that can be leveraged to optimize smart technologies needed to support asset management, community quality of life and traffic safety improvements as part of the City's Vision Zero initiative, among other things.

Committee: Eddie Y. Chou (Advisor); Bhuiyan Alam (Committee Member); Liangbo Hu (Committee Member) Subjects: Geography; Transportation; Transportation Planning

Doctor of Philosophy, Case Western Reserve University, 2020, Civil Engineering

Water Distribution Systems (WDSs) are the most essential civil infrastructure systems for the functioning of communities. Economic prosperity and social wellbeing of modern society depend on reliable, robust, and resilient WDSs. Unfortunately, a majority of water pipelines in the United States (U.S.) are in service beyond their intended design life and have experienced a high failure rate. Aged metallic pipelines are susceptible to failure due to corrosion deterioration, traffic loading, excessive water pressure, seismic loading, and other factors. These failures result in enormous direct and indirect economic and societal consequences. Moreover, water pipe failures often lead to cascading consequences to other interconnecting infrastructure, especially road networks. In the face of these frequent failures, water utilities are struggling to maintain their assets with limited budget and resource constraints. There is a clear need to develop a comprehensive framework to assess and mitigate the risk posed by combined external loading (e.g., water pressure, traffic loading, earthquakes) and corrosion deterioration to WDSs. This research presents a risk-based asset management decision-support framework for WDSs subjected to combined corrosion and external loading, considering both component-level and system-level risk. A critical literature review of existing literature on various aspects of asset management is performed to establish the knowledge gap between current practice and theory in WDS research. Various fragility models of pipelines and systems are developed considering the effect of corrosion deterioration. Uncertainties involved in fragility and restoration models are accounted for using the probabilistic approaches. The seismic restoration process is modeled considering the repair activities overtimes. Next, a framework is proposed that incorporates topological and hydraulic reliability model, seismic functionality and resilience model, scenario-based seismi (open full item for complete abstract)

Committee: Yue Li Dr. (Advisor); Wojbor Woyczynski Dr. (Committee Member); Xiong Yu Dr. (Committee Member); Christian Carloni Dr. (Committee Member) Subjects: Civil Engineering

Master of Science, University of Toledo, 2017, Civil Engineering

In the United States, aging of the transportation infrastructures increased the need of M&R activities significantly. Further, as the consequence of economic and population growth, increasing travel demand accelerated the rate of deterioration along with the congestion and poor safety. At most of the highway agencies, budget allocation decisions are often based on the historical proportions or empirical relations. However, the public awareness and involvement in the sustainable development demand the accountability of such decision- making. Compounded with the budget constraints and rise in the highway improvement costs, highway agencies have a need of efficient and most cost-effective decision-making. Thus, development of a data-driven tool as a senior decision support system is essential for short-term as well as long-term planning, specifically, in a limited available resource scenario such as of now. This thesis presents the development and implementation of a trade-off analysis tool in Transportation Asset Management (TAM). The tool is capable of budget allocation and optimal treatment policy determination, based on the budget versus asset performance plots. For a given budget level, the corresponding performance of the asset network is determined using the linear programming optimization with the objective of average network condition maximization. The optimization model essentially consists of an asset deterioration model. The deterioration rates are predicted based on the historical condition data using the Markov model. At the network level, the macroscopic model is adopted where each decision variables represents a specific proportion of the asset network. For each decision variable, future condition prediction models are developed. An annual budget scenario can be analyzed with the flexibility to vary the treatment unit costs, allowable treatments, treatment average life, the budget allocation between asset types, and the span of the analysis period. (open full item for complete abstract)

Committee: Eddie Chou Dr. (Committee Chair); Liangbo Hu Dr. (Committee Member); Habib Kaake Dr. (Committee Member) Subjects: Civil Engineering

Bachelor of Business Administration (BBA), Ohio University, 2017, Business Administration

Building on theoretical research from university endowments and behaviors; this study introduces a statistical analysis of endowment returns with respect to size, performance, and asset allocation. The findings from observations of reported endowment returns and asset allocations indicate that a strong relationship exists between endowment performance and allocation strategy and the endowment's total assets under management. Specifically, this analysis shows that endowment returns shrink with size, become more allocated to traditional investments as size decreases, and vary less with industry standard benchmark indexes as size decreases.

Committee: Andrew Fodor (Advisor) Subjects: Business Administration; Finance

Doctor of Philosophy, University of Toledo, 2014, Civil Engineering

The aging transportation network and significantly constrained budget environment in the U.S. have prompted many transportation agencies to search for optimal asset preservation strategies. This dissertation presents the development and implementation of a network-level, multiple-year optimization process for managing multiple transportation assets. The optimal preservation strategy is determined in three stages. At the first stage, the relative importance of different asset types is estimated using the analytic hierarchy process (AHP) method to include consideration of various factors such as asset value, asset condition, and safety. A linear programming model is established to determine the optimal budget allocation and treatment policy considering multiple asset types simultaneously. At the second stage, work plans for each asset component are generated based on the network-level optimization results and using project selection models based on AHP method and multiple attribute utility theory (MAUT). At the third stage, the selected projects are further optimized considering the geographic locations by project coordination models based on constraint programming and integer programming. The objective of project coordination is to maximize the total number of the projects that can be combined to achieve cost savings, by rescheduling some projects subject to budget constraints and project timing constraints. The main advantages of the optimization models developed in this study are the ability to generate the optimal budget allocation among different asset types and various rehabilitation treatments, forecast future asset condition, generate candidate project lists for a large scale transportation network, and coordinate multiple asset projects in nearby locations. A set of example problems were solved using the four-lane divided highway network in District 2 of Ohio. Pavement and bridges were the two types of assets considered. The results show that t (open full item for complete abstract)

Committee: Eddie Chou (Committee Chair); Defne Apul (Committee Member); Liangbo Hu (Committee Member); George Murnen (Committee Member); Hongyan Zhang (Committee Member) Subjects: Engineering; Operations Research; Transportation

Master of Science in Engineering, Youngstown State University, 2009, Department of Civil/Environmental and Chemical Engineering

Water system information has traditionally been represented on physical map sheets, map books, ledger books and index cards. Because of the level of detail required, a water system map could be hundreds or thousands of pages long. The map, ledger books and index cards can be extremely unwieldy and present difficulty in trying to find specific information.A Geographic Information System (GIS) is a computer mapping and analysis system that allows features to be displayed graphically in layers using a coordinate system and linked to attribute data. By linking information electronically, data can be sorted, combined, and analyzed in ways that would have been impossible previously. This thesis illustrates the potential usefulness of a GIS applied to manage the assets of a water distribution system, with specific application to the City of Youngstown Water Distribution System. The downtown area of Youngstown, Ohio was chosen for a pilot study. Geodatabase layers and attribute tables were developed for water lines and hydrants. Several applications of the GIS for distribution system maintenance were demonstrated. The ability to obtain global position system (GPS) coordinates and to incorporate into the GIS was also shown to be possible. This study shows that, using existing personnel and available technology, a GIS can be implemented for asset management at the daily operations level and also at the budgeting and planning level. The initial cost for equipment totals $48,000 and can be implemented for daily operations immediately with no increase in personnel costs with a project payback time of 50 weeks.

Committee: Scott Martin PhD (Advisor); Shakir Husain PhD (Committee Member); Hans Tritico PhD (Committee Member) Subjects: Civil Engineering; Environmental Engineering