Thesis (Ph. D.)--Ohio State University, 1966. / Includes vita. Includes bibliographical references (p. 139-142). Available online via OhioLINK's ETD Center

published_or_final_version / Real Estate and Construction / Master / Master of Science in Real Estate and Construction

The Hong Kong Special Administrative Region Government is definitely a typical example of a tradition bureaucracy which translates policies into actions. It relies on neutral and career civil servants working in the Weberian hierarchy to deliver public goods and services. The principal-agent theory may be used to study the contractual and hierarchical relationships in the public bureaucracy. It postulates that agents have conflicts of interests and information advantages over their principals and make use of opportunities to manipulate their principals and the political process for gains. Hence, an appropriate manpower strategy, either by external labour market or internal labour market, is necessary to ensure agents choose those courses of actions that produce desirable outcomes so as to achieve organizational objectives and operational efficiency. The Hong Kong Housing Authority (“HKHA”), as a statutory body, is responsible for formulating public housing policies in Hong Kong whereas the Housing Department, as the executive arm of the HKHA and one of sixty-one government departments, is responsible for its implementation with the objectives of the Government’s Long Term Housing Strategy. In late 1990s, the Government embarked many public management reforms in which the fundamental ways in delivering public goods and services have been altered. Since 1998, the HKHA has introduced many substantial reforms in its core businesses, including an aggressive manpower strategy of employing contract staff and ceasing the recruitment of civil servants. The HKHA’s original long-term manpower strategy which mainly relies on the civil service fits the Internal Labour Market model very well and promotion serves as an effective mechanism to achieve organizational efficiency. However, the employment of contract staff which introduced a number of efficiency features from the external labour market fails to appreciate the importance of promotion, and operational inefficiency was found as evidenced by various documentary sources and analyses from interviewees. The findings of this study are consistent with the Hypotheses and it is recommended that the HKHA explore alternative human resource management policies and practices, in additional to the existing ones, to cope with the rising public expectations and the increasing challenges for the years ahead. / published_or_final_version / Politics and Public Administration / Master / Master of Public Administration

There is an increasing trend in self-service check-in (SSCI) utilization. SSCI would impact the passenger flow and baggage flow in airport operation because of the reduction of check-in processing time and the change of passenger arrival time to the airport. This dissertation is using the simulation model to simulate the passenger flow and a demand analysis model to calculate the baggage flow under various SSCI utilization rate for the assessment of the impacts on airport operation. The simulation results suggest that the higher the SSCI utilization rate, the shorter the queue length and waiting time at the check-in desks. However, the bottleneck would be potentially relocated to the downstream passenger security check and emigration process. Nevertheless, the increasing SSCI utilization provides room for the reduction of check-in desk without compromising the service level provided to passenger. On the other hand, the baggage system demand analysis is based on the modified arrival profile with addressing later passenger arrival time due to perception of shorter in-airport time. The results show that the later the passenger arriving at airport, the less the make-up devices required for the baggage handling system, which reduces the operation cost and delays the need for baggage hall expansion. Increasing SSCI utilization would bring both opportunities and challenges to the airport operation and this dissertation provides a study framework to assess the impacts on airport operation which could be applied by the airport operators. Airport operator could develop a more accurate and realistic simulation model and demand analysis model with real data collected from regular surveys and use them to forecast the service level and facility requirement with the change of SSCI utilization. As a result, airport operator can have a better facility planning to achieve cost-effectiveness and provide satisfactory service level to passenger. / published_or_final_version / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

Industrial parks, consisting of manufacturers and associated services such as transport and warehousing, have played an important role in stimulating the economic and industrial development. Enterprises are able to share common facilities, services and infrastructures. It becomes a common practice for manufacturers to use third-party logistics to handle the flow of materials both within and outside the industrial park. This thesis proposed an Auto-ID enabled real-time decision support solution for fleet management in industrial parks. This research investigates a fleet management decision framework that is suitable for industrial parks, and discusses the design and development of Fleet Advanced Planning and Scheduling (FAPS) Shell Platform, which is an innovative framework for different stakeholders in collaborating decisions and operations in industrial parks. The proposed FAPS Shell Platform integrates Auto-ID and telecommunication devices, which are deployed in an operation environment to collect real-time information. Within the service platform, an Agent-based Information Service (AIS) is proposed and developed for managing heterogeneous data sources. The AIS accesses and integrates information from different data sources for supporting upper-level decisions. Finally, a hierarchical transportation planning and scheduling model is proposed for minimizing the use of vehicles in a flexible and practical manner. This research has made the following achievements and contributions. Firstly, the scalable and re-configurable FAPS Shell Platform infrastructure provides a common communication channel for industrial stakeholders in transportation decisions and operations collaboratively. The development of the platform is based on several core technologies to achieve seamless dual-way connectivity and interoperability among enterprise information systems (EISs), Auto-ID devices and other heterogeneous data sources. Secondly, an innovative heterogeneous data sources querying mechanism is designed to access different kinds of data sources, such as web services, databases, Auto-ID devices and EISs. In addition, the AIS provides a platform for users managing their different data sources with several visualization tools. With the use of AIS, comprehensive and real-time information can be gathered easily and those information are able to support upper-level decisions. Thirdly, an Auto-ID enabled operation environment solution is designed and deployed in the industrial park. Items and pallets are wrapped as a smart object (SO) and different types of device gateways are deployed in key value adding points. With the supporting of SOs and devices gateways, real-time operation data can be feedback to the backend server for monitoring and controlling purposes. Finally, a hierarchical transportation planning and scheduling model is proposed for synchronizing material flow between supply hubs and manufacturers. The model can improve the flexibility of the planning and scheduling results. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Ranking queries and similarity queries are elementary operations with many important applications. There are lots of research works investigating efficient evaluation of various ranking and similarity queries in databases over the past few decades. In this thesis, ranking and similarity queries on three interesting complex data types are studied, namely, multidimensional cube, object summary and tree. Efficient and effective solutions are proposed to solve their related applications. First, the evaluation of ranking queries on multidimensional cubes is studied. In exploratory data analysis, a relation can be considered as a multidimensional cube to investigate the relationship among its attributes. Given a relation with records that can be ranked, an interesting problem is to identify selection conditions for the relation, which result in sub-relations qualified by an input record and render the ranking of the input record as high as possible among the qualifying tuples. The ranking of the input record in a sub-relation measures the quality of the corresponding multidimensional cube of this sub-relation. In this thesis, a standing maximization problem, which aims to identify a multidimensional cube of high quality, is extensively studied. As an immediate consequence of its NP-hardness, three greedy methods are proposed to explore the search space only partially, while striving to identify sub-optimal solutions of high quality. Next, the efficient evaluation of ranking queries on object summaries is investigated. An object summary is a tree structure of tuples that summarizes the context of a particular data subject tuple. The object summary has been used as a model of keyword search in relational databases; where given a set of keywords, the objective is to identify the data subject tuples relevant to the keywords and their corresponding object summaries. However, a keyword search result may return a large number of object summaries, which brings in the issue of effectively and efficiently ranking them in order to present only the most important ones to the user. In this thesis, a model that ranks object summaries according to their relevance to a set of input thematic keywords is introduced. Efficient algorithms are proposed to answer the proposed thematic ranking query. Finally, the similarity join query on tree-structured data is studied. Treestructured data are ubiquitous nowadays and a number of applications require efficient management of such data. Given a large collection of tree-structured objects (e.g., XML documents), the similarity join finds the pairs of objects that are similar to each other, based on a similarity threshold and a tree edit distance measure. The state-of-the-art similarity join methods compare simpler approximations of the objects (e.g., strings), in order to prune pairs that cannot be part of the similarity join result based on distance bounds derived by the approximations. In this thesis, we propose a novel similarity join approach, which is based on the dynamic decomposition of the tree objects into subgraphs, according to the similarity threshold. Our technique avoids computing the exact distance between two tree objects, if the objects do not share at least one common subgraph. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Not available / text

Not available / text

Not available / text

<p> Small businesses are the backbones of the American economy and contribute nearly 46% of the U.S. gross domestic product. However, the failure rate for small businesses is high. Only half of all small businesses will be able to survive for 5 years, and only 20% of small business can survive 10 years. Failure is a common phenomenon among entrepreneurs of small businesses. This qualitative phenomenological study involved examining entrepreneurs&rsquo; perceptions of their failure experiences. The study involved exploring the impacts of failure on entrepreneurs, the valuable lessons that entrepreneurs have taken from their failure experiences, and how they have applied what they have learned into business practices. Ten participants were purposefully selected for an individual face-to-face interview. The researcher created and asked 12 open-ended interview questions during the interviews under the framework of 3 research questions. Through analysis of the data gathered from the interviewees, the findings revealed that failure has a tremendous impact on entrepreneurs&rsquo; finances, relationships, and emotions. The findings also included valuable lessons that entrepreneurs have learned from their failure experiences, including acquiring knowledge on business management, awareness of self-limitations, enhancing faith, and leading changes by setting examples and showing care to employees. The research also revealed entrepreneurs apply what they have learned from failure directly back into daily business practices. In the process, they had to accept their personal weaknesses by adapting to changes. Learning from failure is a continuous process. The lessons shared are critical to entrepreneurial growth, especially in leadership. What these entrepreneurs have learned and practiced is worth exploring in hopes of shedding light on entrepreneurial education.</p>