Doctor of Philosophy, The Ohio State University, 2022, Business Administration

This dissertation addresses the relative importance of price and non-price criteria in sourcing decisions from three distinct perspectives. Each essay is motivated by the same problem: that organizations tend to unintentionally overweight cost minimization objectives in their sourcing decisions. In the first of three essays, I show that excessively price-based decision-making is a widespread problem in sourcing. To do this, I combined two sources of data on contract awards by the US federal government. I applied coarsened exact matching to identify cases where contracts were awarded using different criteria in similar situations. I then used logistic regression to show that when non-price criteria are weighted more heavily, the same contractor is more likely to receive awards for similar work in the future. This relationship is absent when there is a requirement for the decision-maker to provide written justification for the use of the more price-based approach, allowing me to infer a solution to the problem identified. In the second essay, I investigate whether the procurement profession's identity influences the relative importance of price in supplier selection decisions. I first conducted a series of semi-structured interviews with current practitioners, eliciting their comments on: their level of identification with the procurement profession; procurement's group image; others' perceptions of procurement's group image; and, procurement's status within their organization. Drawing from the observed variation in responses, I designed and conducted a scenario-based experiment. I find that strong identification with the procurement profession can contribute to more price-based sourcing decisions. In the third essay, I expand my focus from procurement professionals to a broader set of professions that commonly contribute to sourcing decisions: supply management, engineering, and marketing. Seeking to understand how these different perspectives influence (open full item for complete abstract)

Committee: John Gray (Advisor); James Hill (Advisor); Christian Blanco (Committee Member) Subjects: Business Administration; Management; Operations Research

Doctor of Philosophy, The Ohio State University, 2019, Computer Science and Engineering

Machine learning is becoming an integral part of everyday life. Therefore, development of a high performance genre of machine learning algorithms is becoming increasingly significant from the perspectives of performance, efficiency, and optimization. The current solution is to use machine learning frameworks such as TensorFlow, PyTorch and CNTK, which enable us to utilize specialized architectures such as multi-core CPUs, GPUs, TPUs and FPGAs. However, many machine learning frameworks facilitate high productivity, but are not designed for high performance. There is a significant gap in the performance achievable by these frameworks and the peak compute capability of the current architectures. In order for machine learning algorithms to be accelerated for large-scale data, it is essential to develop architecture-aware machine learning algorithms. Since many machine learning algorithms are very computationally demanding, parallelization has garnered considerable interest. In order to achieve high performance, data locality optimization is extremely critical, since the cost of data movement from memory is significantly higher than the cost of performing arithmetic/logic operations on current processors. However, the design and implementation of new algorithms in machine learning has been largely driven by a focus on computational complexity. In this dissertation, the parallelization of three extensively used machine learning algorithms, Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and Word2Vec, is addressed by a focus on minimizing the data movement overhead through the memory hierarchy, using techniques such as 2D-tiling and rearrangement of data computation. While developing each parallel algorithm, a systematic analysis of data access patterns and data movements of the algorithm is performed and suitable algorithmic adaptations and parallelization strategies are developed for both multi-core CPU and GPU platforms. Experimental resul (open full item for complete abstract)

Committee: P. Sadayappan (Advisor); Srinivasan Parthasarathy (Committee Member); Eric Fosler-Lussier (Committee Member) Subjects: Computer Science

Master of Science (MS), Wright State University, 2014, Computer Science

Vector-based lexical semantics is a powerful technique that still has many undiscovered applications. In this thesis I apply a vector-space lexical-semantic model newly developed by Mikolov et. al. trained on skip-grams to the lexical hypothesis in personality psychology. The method produces interpretable dimensions that are consistent across several sets of descriptive personality words. The dimensions include ones for conflict and positive and negative evaluation. However they are more descriptive of word usage semantics than of the characteristics of the thing described and thus do not include a recognizable component of the 5 factor model in their first 14 dimensions. They do include a component that seems to indicate the degree to which the word applies to people that could be useful in identifying personality words in English.

Committee: Michael Raymer Ph.D. (Advisor); Travis Doom Ph.D. (Committee Member); Gary Burns Ph.D. (Committee Member) Subjects: Computer Science; Modern Language; Personality; Personality Psychology; Psychology