Browsing by Subject "Research and development"

Now showing 1 - 4 of 4

Essays on international trade
(2010-05) French, Scott Thomas; Corbae, Dean; Abrevaya, Jason; Freitas, Kripa; Ramondo, Natalia; Ruhl, Kim
This dissertation consists of three essays pertaining to the causes of the levels and composition of the international trade flows of nations, and the consequential implications for the levels of per capita income and welfare of their populations. The first of these documents a pattern of comparative advantage in product level, bilateral trade data that conventional quantitative trade models have difficulty explaining. It goes on to develop a theory of product level productivity differences based on endogenous differences in the allocation of research and development into product and process innovation across countries over time, and it shows that, when fitted to cross-country manufacturing wage data, the predicted product level technology distribution is consistent with the observed trade pattern. The second essay shows that the distribution of technology levels inferred in the first essay can help explain the inability of both ad-hoc and theoretically based gravity models of trade to account for the observed positive correlation between the percentage of manufacturing output that is traded and countries' per capita income. It derives a modified gravity equation based on a Ricardian model of trade with deterministic product level technology differences across countries. It then uses estimates from a product level gravity estimation to compute the component of this equation that differs from a conventional gravity equation in order to determine the extent to which the observed concentration of comparative advantage in a common set of products for low-income countries explains the small percentage of their output that is exported. The final essay shows that a simple model of firm profit maximization in the presence of sunk costs of entering the export market is broadly consistent with the observed persistence of exporting behavior in firm level data. It uses this simple model and moments from data on US manufacturing firms to estimate the value of the sunk export entry costs faced by these firms using an indirect inference strategy. These costs are shown to be substantial relative the revenue stream of a typical firm.
Inclusive learning with assistant chatbot in massive open online courses : examining students’ perceptions, utilizations, and expectations
(2024-05) Han, Songhee; Liu, Min, Ed. D.; Min Kyung Lee; Grace MyHyun Kim; Xiaofen Hamilton
This study examines students’ learning experiences with an assistant chatbot in professional development MOOCs designed for journalists. Utilizing a mixed-methods approach, it focuses on the students’ learning experience’s sub-domains, such as social presence, teaching and cognitive presence, self-regulation, ease of use, and behavioral intention. Employing the Community of Inquiry (CoI) framework and the Technology Acceptance Model (TAM), the study first assesses the impact of demographics like age, gender, region, and native language on these learning experiences. The study revealed that age and gender had no significant influence on learning experiences, while geo-cultural regions showed variations, particularly in social presence and, teaching and cognitive presence. Socioeconomic regions demonstrated more notable differences, especially between lower-middle and high-income areas. However, the native language did not significantly influence learning experiences. Second, structural equation modeling (SEM) validated several hypothesized relationships, highlighting the positive impact of self-regulation on various other learning domains. Interestingly, teaching and cognitive presence did not significantly influence behavioral intention, nor was there a significant relationship between behavioral intention and use time. Age and socioeconomic region factors were identified as full moderators, while gender was a partial moderator from multigroup SEM results. Third, an extensive analysis of student interactions with the chatbot was conducted using various data sources. This analysis revealed eight key topics of chatbot interactions and showed predominantly neutral sentiments in the chatbot text logs. However, survey and interview data indicated a generally positive perception of the chatbot, especially noting its operational effectiveness and ease of use. Sentiments varied across socioeconomic regions, with more positive feedback from lower-income regions, while those from higher-income regions had higher expectations. The study also observed differences in navigational patterns between chatbot users and non-users in the course. Chatbot users exhibited more diverse navigations, indicating deeper engagement with course materials and a higher completion rate. In contrast, non-users followed a more structured progression, mainly relying on the predefined course path. Finally, the study highlighted students’ expectations for the chatbot, emphasizing the need for improvements in response accuracy, diversity, and additional capabilities like multi-language support. The findings emphasize the role of demographic variables in shaping student interactions with chatbots in MOOCs and suggest that modifying chatbot responses for inclusiveness could be key in meeting diverse student needs. The implications include that adhering to Universal Design for Learning principles, empowered by current advancements in AI-based chatbot technology, and informed by the CoI and TAM, could better address the diverse needs in MOOCs, especially in chatbot-enhanced learning environments.
The role of software engineering process in research & development and prototyping organizations
(2010-08) Willis, Michael Brian, 1980-; Barber, K. Suzanne; Grazer, Thomas
Software Research and Development Organizations (or SRDs) have unique goals that differ from the goals of Production Software Organizations. SRDs focus on exploring the unknown, while Production Software Organizations focus on implementing solutions to known problems. These unique goals call for reevaluating the role of Software Engineering Process for SRDs. This paper presents six common Software Engineering Processes then analyzes their strengths and weaknesses for SRDs. The processes presented include: Waterfall, Rational Unified Process (RUP), Evolutionary Delivery Cycle (EDLC), Team Software Process (TSP), Agile Development and Extreme Programming (XP). The results indicate that an ideal software process for SRDs is iterative, emphasizes visual models, uses a simple organization structure, produces working software (with limited functionality) early in the lifecycle, exploits individual capabilities, minimizes artifacts, adapts to new discoveries and requirements, and utilizes collective code ownership among developers. The results also indicate that an ideal software process for SRDs does NOT define rigid personnel roles or rigid artifacts, is NOT metric-driven and does NOT implement pair programming. This paper justifies why SRDs require a unique software process, outlines the ideal SRD software process, and shows how to tailor existing software processes to meet the unique needs of SRDs.
Using science to innovate : explaining productivity in the pharmaceutical industry innovation activities
(2012-08) Stone, Alexandra Bella; Dukerich, Janet M.; Stolp, Chandler; Wilson, Robert; Osborne, Cynthia; Callan, Benedicte; Henderson, Andrew
Scientific and technological (S&T) advances underpin opportunities for innovation in the pharmaceutical industry. Government-funded research institutions and firms perform biomedical research to generate S&T advances and enable pharmaceutical innovation. Previous research found that the number of new drugs approved by the US Food and Drug Administration (FDA) has stagnated. The observed stagnation has been interpreted as a decline in the return on research investments. The apparent decline in productivity may be due to the increasing technological difficulty of using S&T advances to develop new drugs and the organizational complexity of incorporating S&T advances generated by government-funded research institutions and firms to develop a new drug. I apply theories of organizational learning to examine how the use of S&T advances to develop new drugs affects the productivity of drug development activities, measured as the time taken to complete early stage pre-clinical research and late stage clinical development activities. I have constructed a novel data set that maps the production and utilization of S&T advances in three phases of market-oriented drug development. By measuring productivity at the project level, I am able to model productivity as the time taken to complete a R&D project as a function of three factors: (1) the technological characteristics of the drug; (2) the use of components generated by other entities; and (3) the research capabilities of the innovating firm. These models enable me to identify technological and organizational factors that affect the efficiency with which S&T advances are transformed into new drugs. Analyses indicate that different technological and organizational factors affect the productivity of pre-clinical research and clinical development. While the time taken to complete a pre-clinical research project is largely determined by the complexity and innovativeness of the drug, the time taken to complete clinical development is a function of the firm's R&D previous experience. The time taken to complete the entire drug development project is determined by the complexity of pre-clinical research and the firm's R&D capabilities. The results are discussed in detail along with policy implications.