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Veteran business owners are essential contributors to American society and the U. S. economy. Statistics showed a looming drawdown of military personnel and comparatively higher unemployment rates than the civilian population, which led to a growing interest in assisting veterans with entrepreneurship. Studies show that military service has a strong association with entrepreneurship. Few studies have identified key factors of veteran business ownership and action-oriented questions on how or why veteran entrepreneurs find their way to business ownership. There are calls in the literature to answer the question of whether entrepreneurial competencies can influence entrepreneurial intentions. Veterans are often faced with the challenge of building a second career following separation from the military. There is limited research about what factors may motivate and support their transition to self-employment or how they fare compared to nonveteran employees. Furthermore, there are no studies that examine the role of resilience in the entrepreneurial process related to American Veteran Entrepreneurs. The purpose of the study is to determine if resilience and entrepreneurial competencies influence veteran entrepreneurial intention to start a business. This study examines the relationship between entrepreneurial competencies and entrepreneurial intentions among Veterans.
Prescription drug expenditures and utilization are the fastest and most widely varying expenditures within Medicaid programs across US states. The passage of the Affordable Care Act (ACA) in 2010 and the subsequent Medicaid state expansions resulted in very large coverage gains among several demographics at the state level. A number of studies prior to ACA highlighting determinants of health utilization and expenditures have been identified and studied discreetly, however, the relationships among these determinants, and the latent constructs of policy, access, enabling health system and predisposing characteristics have not been tested concurrently in relation to drug expenditures
Data from the Centers for Medicare and Medicaid Services ( CMS), US Department of Labor , Department of Education, and state Medicaid programs were merged to create a balanced panel data (n=350 observations and 53 variables over a seven ( 7) period from 2009 to 2015); and was analyzed using random effects (RE) panel regression analysis to estimate a model for drug expenditure across US state Medicaid programs.
Based on Andersen’s Behavioral Model of Health Services, and using a Structure Equation Modeling the study also examined the relationships between and tested the hypothesized effects of policy, access and predisposing factors on State Medicaid expenditures. Findings on effects of cost containment policies, ACA expansion, access to health care facilities and demographic distribution within Medicaid and an econometric model that estimates state drug expenditures are included followed by discussions, limitations and future directions for research
There has been a considerable interest in the development and installation of the building facade system using a dynamic motion which is called a kinetic facade system. Since the kinetic facade system can respond to the change of the external weather conditions, this can play a key role in saving building energy consumption and satisfying occupant’s thermal and visual comforts. Although the building application of the kinetic facade systems continues to increase because of its benefits, the features of the kinetic system cause relatively high installation costs compared to a conventional fixed facade system. Therefore, evaluating a performance of the kinetic facade system in the early design stages is becoming more important. However the current process for evaluating the performance using a simulation tool is complicated and time-consuming process because the dynamic motion of the kinetic system. It takes a significant amount of time due to the repetitive simulation process associated with complex geometry and dynamic movements. Therefore, most studies on kinetic performance have limitations in grasping the range of time related to performance and specificity of kinetic movement. Thus, this research suggested a prediction methodology using a regression model for a customized kinetic façade system. The regression model allowed user to compare the performance of the kinetic facade system without a simulation process. Also, it can be used to predict an optimal angle of a kinetic motion at a specific point in time.
There has been a considerable interest in the development and installation of the building facade system using a dynamic motion which is called a kinetic facade system. Since the kinetic facade system can respond to the change of the external weather conditions, this can play a key role in saving building energy consumption and satisfying occupant’s thermal and visual comforts. Although the building application of the kinetic facade systems continues to increase because of its benefits, the features of the kinetic system cause relatively high installation costs compared to a conventional fixed facade system. Therefore, evaluating a performance of the kinetic facade system in the early design stages is becoming more important.
However the current process for evaluating the performance using a simulation tool is complicated and time-consuming process because the dynamic motion of the kinetic system. It takes a significant amount of time due to the repetitive simulation process associated with complex geometry and dynamic movements. Therefore, most studies on kinetic performance have limitations in grasping the range of time related to performance and specificity of kinetic movement.
Thus, this research suggested a prediction methodology using a regression model for a customized kinetic façade system. The regression model allowed user to compare the performance of the kinetic facade system without a simulation process. Also, it can be used to predict an optimal angle of a kinetic motion at a specific point in time.
NUBIA A. CASTILLO DE VALLE. How does family firm status moderate the relationship between organizational readiness for change and organizational resilience in times of crisis? (Under the direction of DR. TORSTEN M. PIEPER)
The literature on organizational resilience shows that there has been little research about organizational resilience drivers. This study aimed to empirically explore if organizational readiness for change, precisely the three dimensions of organizational readiness for change, as determinants of organizational resilience. And how firms’ structure moderates that relationship in the context of change (adoption or usage of technology) in times of COVID-19. SMART-PLS is a statistical technique that has become popular in business and social sciences. The PLS-SEM measurement model was used to assess the reliability and validity of the instrument in this study. The result suggests that psychometrics are reliable and evidence of rational validity. This research is important because it will influence organizational resilience research, and it will inform managers practitioners on how to prepare for disruption and build resilient organizations. The data was sourced via a survey by Qualtrics for a total sample of 160 companies divided into 80 family firms and 80 non-family firms. The target responders were leaders of those organizations. The results suggested that only management support and change efficacy have a direct relationship with organizational resilience. Since this is an empirical cross-sectional study, causality is not inferred and not able to be generalized. Appropriateness was not significant. The moderations variables were not significant. This study suggests that two dimensions of organizational readiness for change (management support and change efficacy) could predict organizational resilience. Keywords: PLS-SEM, Organizational resilience, COVID-19, Firm Structure, Organizational readiness for change.
This dissertation features financial market innovation and product market innovation. Two essays feature return predictability in commodity futures, which have been financialized during the past two decades. One essay studies the relation between CEO’s external job market tournament and product innovation in the stock market. The first essay identifies a trend factor that exploits the short-, intermediate-, and long-run moving averages of settlement price in commodity futures markets. The trend factor generates statistically and economically large returns during the sample period 2004-2019. It outperforms the well-known momentum factor by more than five times the Sharpe ratio and less downside risk. The trend factor cannot be explained by existing factor models and is priced cross-sectionally. Then we find that the trend factor can be explained by funding liquidity measured by TED spread. Overall, the results indicate that there are significant economic gains from using the information on historical prices in commodity futures markets. The second essay uses machine learning tools to study the serial dependence (lead-lag relations) of commodity futures returns. We use LASSO to select the predictors because the number of predictors is large relative to the number of observations. We find significant full-sample and out-of-sample predictability. In the full sample, we find that LASSO can identify a sparse set of predictors that either come from economically linked commodities or are likely driven by excessive speculative trading. The out-of-sample forecasts based on LASSO generate statistically and economically large gains. When we use more complex machine learning models such as regression trees and neural networks to forecast commodity futures returns, the out-of-sample performance is worse than LASSO portfolios, suggesting that nonlinearities and interactions do not appear substantial in the data. We also find that index trading due to financialization drives the excess comovement among commodity futures. The third essay examines how the tournament-like progression in the CEO labor market influences corporate innovation strategies. By exploiting a text-based proxy for product innovation based on product descriptions from 10-Ks, we find a significant positive relation between industry tournament incentives (ITIs) and product innovation. We then explore the trade-off effects of ITIs on product innovation created through long-term patenting technologies and short-term “routine” product development. We discover that ITIs strengthen routine product development activities but decrease patent-based innovation. Further analyses show that the effect of ITIs on product innovation is stronger when the product market is more competitive and when CEO characteristics indicate a higher probability of winning the tournament prize.
Maternity leave policies in the United States have begun to shift in recent decades, often offering women more maternity leave and parental bonding time than in the past. Whereas women were once expected to leave the workforce after the birth of a child, modern women often return to the workforce to continue their careers, prompting organizations to align their benefits to needs of expecting and/or new mothers. Using a human capital lens, this dissertation aimed to understand the experiences and perceptions of women who took longer than the 12 weeks of maternity leave protected by FMLA. Utilizing qualitative methodology, semi-structed interviews were conducted with women who took longer than 12 weeks of leave upon the birth of their child. The findings from this project suggest women do experience their leaves as ‘long’ and women often describe their leave as compared to others in their social network. The women in the study often found themselves navigating policies alone, completing the planning for their leave alone, and being contacted while on leave for business questions. The findings in this study have various practical, theoretical, and methodological implications.
The correct folding of proteins after synthesis and stress-promoted denaturation is critical for cell viability in all organisms. The Hsp70 molecular chaperone is a key player in proteostasis, deciding which proteins are foldable and which are too badly damaged and need to be targeted for degradation. Hsp70 plays an important role as a drive of cancer, stabilizing key mutated oncoproteins such as HER2, p53, RNR, SHR and MUC1. This importance of Hsp70 in basic cell functions as well as human illness prompted us to examine novel ways to characterize Hsp70 genetic and physical interactors. In this thesis, we decided to tackle three main roadblocks in studying chaperone interactions; 1) purification of chaperone complexes at native stoichiometry in mammalian cells, 2) understanding the roles of co-chaperones in cancer 3) teasing apart bridged vs direct chaperone interactions. To solve the issue of native stoichiometry purification, we have utilized CRISPR-Cas9 genome engineering to insert epitope tags into the N-terminus of Hsp70 in mammalian cells. This tagged chaperone is present as the only Hsp70 in cells, is stable without the use of any selectable marker and allows expression of Hsp70 at native levels. To understand co-chaperone function in cancer, we used a novel chemogenomic screening technology on WT and DNAJA1 knockout HAP1 cells. In doing so, we have uncovered a dependence of a large proportion of approved oncology drugs on DNAJA1 status. Finally, we have used cross-linking mass spectrometry to define for the first time the direct interactors of Hsp70 in yeast. Our data reveals a wealth of information of fundamental Hsp70 function including discovery of active Hsp70 dimers, client binding throughout Hsp70 and a huge number of novel PTM-associated Hsp70 interactions. Overall, aside from gaining fundamental insight into the workings of Hsp70, this thesis provides a roadmap and tools for the chaperone community to explore novel biologically relevant Hsp70 interactions.
