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The purposes of this dissertation are (1) to define the various components of Just-In-Time/Total Quality Control (JIT/TQC), (2) to assess the differences between JIT and non-JIT firms with respect to measures of human resource development and management, and (3) to assess the relationships between the management commitment and employee involvement components of JIT/TQC and measures of manufacturing performance. A conceptual model is developed depicting the relationships among the major components of JIT/TQC and both management commitment and employee involvement are hypothesized to be critical to a successful implementation and improved manufacturing performance. The criteria for the Malcolm Baldrige National Quality Award are examined and category four, Human Resource Development and Management, is found to cover the same activities as the combined employee involvement and management commitment components of JIT/TQC. A survey is constructed to collect data similar to that required for the Human Resource Development and Management category of the Baldrige application. In addition, for those firms that indicate they have started a JIT/TQC implementation, data is collected for current measures of manufacturing performance and measures prior to the implementation of JIT/TQC. Surveys are sent to Directors of Manufacturing in companies with SIC code 36 (electrical and electronic equipment). Chi square and Hotelling's T$\sp2$ tests are used to test for differences between JIT and non-JIT firms with respect to measures of management commitment to workers and employee involvement. Statistically significant differences are found in the use of teams, suggestion systems, recognition programs, multi-function workers, and in the types of training provided. The results of multiple regression analysis indicate significant relationships between changes in manufacturing performance, in the areas of quality, flexibility, and cost, and variables representing the categories of timing of JIT/TQC implementation, employee involvement, and management commitment. Overall, the research results support the relationships hypothesized in the conceptual model.

The applications of artificial intelligence (AI) and natural language processing (NLP) methods have enabled managers and researchers to process, interpret, and extract value from text efficiently. These systems offer a lens through which to understand consumer behavior, monitor the dynamics of markets and brands. Recent advancements, including interpretable AI, variational autoencoders and the most recent transformer-based large language models (LLMs), have significantly altered the research landscape. These models, with their robust capabilities for language representation and generation, introduce both new opportunities and challenges in converting raw textual data into actionable business insights. In line with this research trend, this dissertation aims to compile and adapt three of my works on interpretable and generative models for insights from the textual data. The first essay explores the development of interpretable machine learning algorithm for the automatic extraction of corpus-level concepts from textual data without the need for human-defined guidance or labeled concepts. Through a case study involving online purchase journey data, we demonstrates the ability of the model to identify and quantify the importance of customer review concepts correlated with purchase conversion, providing external validation of its efficacy as an exploratory tool. The second essay presents the use of Variational AutoEncoder that transforms unstructured patent text into a structured, spatial representation of firms' innovation activities. By learning a disentangled vector space of patents, the model offers interpretable insights into firms' AI-based intellectual property portfolios. Through applications across three decades of patents, this chapter showcases the model’s utility in visualizing technology landscapes, engineering intuitive features from text, and augmenting patent applications to reduce rejection risks, thereby illustrating the transformative potential of generative AI in analyzing unstructured corporate data. The third essay explores the capabilities and limitations of large language models (LLMs) in simulating human preferences and reasoning in the context of misinformation. By adopting the dual process theory, this study investigates the extent to which LLMs can mimic human discernment in the accuracy estimation and the sharing of political news headlines. We argue that while current LLMs struggle to replicate complex cognitive reasoning behaviors, the integration of psychological theories offers a pathway to align language agents more closely with human reasoning processes. Broadly, this dissertation addresses the issues of how to align the NLP systems with human intentions for the task of language understanding, language representation, agent development in information systems research, and highlights potential avenues for future research in these areas.