INFORMATION TECHNOLOGY PROJECTS IN NONPROFIT UNIVERSITY: A PROJECT GOVERNANCE PERSPECTIVE

The universities rely on the Information Technology (IT) projects to support and enhance their core strategic objectives of teaching, research, and administration. The researcher’s literature review found that the level of IT funding and resources in the universities is not adequate to meet the IT demands. The universities received more IT project requests than they could execute. As such, universities must selectively fund the IT projects. The objectives of the IT projects in the universities vary. An IT project which benefits the teaching functions may not benefit the administrative functions. As such, the selection of an IT project is challenging in the universities. To aid with the IT decision making, many universities in the United States of America (USA) have formed the IT Governance (ITG) processes. ITG is an IT decision making and accountability framework whose purpose is to align the IT efforts in an organization with its strategic objectives, realize the value of the IT investments, meet the expected performance criteria, and manage the risks and the resources (Weil & Ross, 2004). ITG in the universities is relatively new, and it is not well known how the ITG processes are aiding the nonprofit universities in selecting the right IT projects, and managing the performance of these IT projects. This research adds to the body of knowledge regarding the IT project selection under the governance structure, the maturity of the IT projects, and the IT project performance in the nonprofit universities. The case study research methodology was chosen for this exploratory research. The convenience sampling was done to choose the cases from two large, research universities with decentralized colleges, and two small, centralized universities. The data were collected on nine IT projects from these four universities using the interviews and the university documents. The multi-case analysis was complemented by the Qualitative Comparative Analysis (QCA) to systematically analyze how the IT conditions lead to an outcome. This research found that the IT projects were selected in the centralized universities in a more informed manner. ITG was more authoritative in the small centralized universities; the ITG committees were formed by including the key decision makers, the decision-making roles, and responsibilities were better defined, and the frequency of ITG communication was higher. In the centralized universities, the business units and colleges brought the IT requests to ITG committees; which in turn prioritized the IT requests and allocated the funds and the resources to the IT projects. ITG committee members in the centralized universities had a higher awareness of the university-wide IT needs, and the IT projects tended to align with the strategic objectives. On the other hand, the decentralized colleges and business units in the large universities were influential and often bypassed the ITG processes. The decentralized units often chose the “pet” IT projects, and executed them within a silo, without bringing them to the attention of the ITG committees. While these IT projects met the departmental objectives, they did not always align with the university’s strategic objectives. This research found that the IT project maturity in the university could be increased by following the project management methodologies. The IT project management maturity was found higher in the IT projects executed by the centralized university, where a full-time project manager was assigned to manage the project, and the project manager had a higher expertise in the project management. The IT project executed under the guidance of the Project Management Office (PMO) has exhibited a higher project management maturity, as the PMO set the standards and controls for the project. The IT projects managed by the decentralized colleges by a part-time project manager with lower project management expertise have exhibited a lower project management maturity. The IT projects in the decentralized colleges were often managed by the business, or technical leads, who often lacked the project management expertise. This research found that higher the IT project management maturity, the better is the project performance. The IT projects with a higher maturity had a lower project delay, lower number of missed requirements, and lower number of IT system errors. This research found that the quality of IT decision in the university could be improved by centralizing the IT decision-making processes. The IT project management maturity could be improved by following the project management methodologies. The stakeholder management and communication were found critical for the success of the IT projects in the university. It is hoped that the findings from this research would help the university leaders make the strategic IT decisions, and the university’s IT project managers make the IT project decisions.

UNDERSTANDING CUSTOMER CHOICES IN SERVICE OUTSOURCING AND REVENUE MANAGEMENT

This dissertation investigates customer behavior modeling in service outsourcing and revenue management in the service sector (i.e., airline and hotel industries). In particular, it focuses on a common theme of improving firms’ strategic decisions through the understanding of customer preferences. Decisions concerning degrees of outsourcing, such as firms’ capacity choices, are important to performance outcomes. These choices are especially important in high-customer-contact services (e.g., airline industry) because of the characteristics of services: simultaneity of consumption and production, and intangibility and perishability of the offering. Essay 1 estimates how outsourcing affects customer choices and market share in the airline industry, and consequently the revenue implications from outsourcing. However, outsourcing decisions are typically endogenous. A firm may choose whether to outsource or not based on what a firm expects to be the best outcome. Essay 2 contributes to the literature by proposing a structural model which could capture a firm’s profit-maximizing decision-making behavior in a market. This makes possible the prediction of consequences (i.e., performance outcomes) of future strategic moves. Another emerging area in service operations management is revenue management. Choice-based revenue systems incorporate discrete choice models into traditional revenue management algorithms. To successfully implement a choice-based revenue system, it is necessary to estimate customer preferences as a valid input to optimization algorithms. The third essay investigates how to estimate customer preferences when part of the market is consistently unobserved. This issue is especially prominent in choice-based revenue management systems. Normally a firm only has its own observed purchases, while those customers who purchase from competitors or do not make purchases are unobserved. Most current estimation procedures depend on unrealistic assumptions about customer arriving. This study proposes a new estimation methodology, which does not require any prior knowledge about the customer arrival process and allows for arbitrary demand distributions. Compared with previous methods, this model performs superior when the true demand is highly variable.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.