Thesis advisor: Philip Strahan / In my first essay, I study how the rise of non-bank loan investment from CLOs, mutual funds, and hedge funds influenced contracting relationships between firms and their senior lenders. Contrary to common perception that non-bank investors diluted the incentive for banks to monitor firms, I find evidence that bank underwriters embraced tighter contracts to mitigate agency and holdout problems associated with less-informed and dispersed non-bank investors. While recent studies show that non-bank loan investors lowered the cost and expanded the availability of capital ex ante, I conclude that tighter contracts also assigned stronger control rights to lenders and imposed higher renegotiation costs to firms ex post. In my second essay, we examine the drivers of M&A activity in bankruptcy. M&A in bankruptcy is counter-cyclical, and is more likely when the costs of financing a reorganization are greater than financing costs to a potential acquirer. Consistent with a senior creditor liquidation bias, the greater use of secured debt leads to more sales in bankruptcy - but, this result holds only for sales that preserve going concern value. We also show that overall creditor recovery rates are higher, and unsecured creditor recoveries and post-bankruptcy survival rates are not different, when bankrupt firms sell businesses as going concerns. Finally, in my third essay, we examine whether corporate credit rating analysts are rewarded based on ratings accuracy or bias. Overall, accurate analysts are more likely to be promoted. However, analysts who disproportionately downgrade firms compared to the corresponding S&P rating are less likely to be promoted despite being more accurate than analysts who disproportionately upgrade firms. Further, analysts whose rating decisions lead to significantly negative announcement returns are also less likely to be promoted. We conclude that Moody's rewards accurate analysts but punishes analysts for negative bias. / Thesis (PhD) — Boston College, 2015. / Submitted to: Boston College. Carroll School of Management. / Discipline: Finance.

The purpose of this study was first to determine factors associated with intellectual reputation, specifically among selected biomedical departments worldwide within the university setting. Second, the study aimed to examine intellectual reputation in relationship to doctoral graduates’ productivity in the biomedical sciences and in relationship to organizational biomedical advancement and productivity. Third, the study aimed to visualize a spatial relationship between intellectual reputation and local organizational biomedical advancement and productivity in the United States and the United Kingdom. Finally, a simulated research-based model was proposed for understanding hospital productivity. The study used quantitative analysis in order to achieve these goals. The Geographic Information System (GIS) and Geocommons were used to visualize possible relationship between universities and hospitals in different regions. The findings from this study suggest that the university’s research intensity, having a Nobel Laureate on staff, Hirsch Index of the most prominent researcher on staff, scientific patent, scientific publications, and affiliation with multiple countries are good predictors of intellectual reputation. Correlation analysis suggests that university intellectual reputation is associated with doctoral graduates’ productivity. When examining the relationship between the university and hospitals, university intellectual reputation was positively correlated with hospital biomedical advancement, r= .445, p =0.001. Hospital productivity was significantly correlated with university intellectual reputation, r= .322, p =0.001. University intellectual reputation was significantly correlated with hospital capacity to absorb knowledge (r= 0.211, p =0.005) and knowledge spillover (r=.242, p =0.001). Regression analysis reveals that hospital capacity to absorb knowledge and knowledge spillover are good predictors of hospital biomedical advancement, F (2, 176) = 52.637, p = 0.001. Hospital capacity to absorb knowledge, affiliation with a university, intellectual reputation of the affiliated university, and distance between the hospital and the affiliated university were shown to be good predictors to hospital productivity, F (4, 106) = 11.115, p = 0.001. Visual examination of the hospitals suggests that when the universities publish at a large quantity, this tends to influence the hospitals within the area to publish a large quantity as well. Additionally, hospitals that are more productive tend to cluster around universities with higher intellectual reputation. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

The determinants that affect the financial performance of an insurance company are complicated due to the intangible nature of insurance products and the lack of transparency in the market. Consequently, the financial performance of insurance companies is important to various stakeholders such as policyholders, insurance intermediaries and policymakers. This study aims to investigate the determinants of financial performance of insurance companies based on their financial strength rating performance. The empirical data are drawn from A.M. Best Insurance Report Online: Non- US Database. The sample consists of 57 insurers in the United Kingdom over the period of 2006 to 2010. The analyses include eight firm-specific variables, which are leverage, profitability, liquidity, size, reinsurance, growth, type of business and organisational form. Rating transition matrices and regression models are employed in this study. Rating transition analysis demonstrates a significant degree of rating changes, as reflected in the rating fluctuations. Based on the empirical results, this study establishes that profitability, liquidity, size and organisational form are statistically significant determinants of financial performance of insurance companies in the United Kingdom. This study recommends an alternative to measure the size of an insurance company, which is based on the gross premium written. In addition, this study provides insights into the effects of the global financial crisis on the financial performance of the insurance companies.

China has become one of the most important players in the landscape of higher education worldwide. The nation is home to the largest tertiary sector in the world, is the leading sender of international students, the third largest receiver of international students, and its government has aggressively pushed internationalization policies at its top universities. Policymakers and educational stakeholders in China have been implementing these strategies in order to chase world-class status for the nation’s universities. While the world-class university concept is ubiquitous across the globe, there has been no agreed upon definition for these elite institutions. In China, though, rankings have been adopted to make sense of this elite status. This dissertation explores the impact that university rankings have had on the Chinese higher education system. There has been considerable research on university rankings in China, but some gaps remain. Studies have explored Chinese universities’ ambitions for world-class status, but rankings are often marginalized within these studies. Studies on the impact of university rankings have mostly focused on their connection to Chinese international students, as league tables have key tools in decision-making for this population. Conversely, research that has focused on domestic students has emphasized geographic biases in university admissions and affluence advantages in the system, and usually has not engaged with global or local rankings. To fill these gaps, my study centers university rankings within the intersection of the local and global settings. I used two original datasets to engage this exploration of how university rankings impact Chinese universities. First, I interviewed 48 faculty and staff members from the elite spectrum of the Chinese higher education sector. Through the interviews, I investigated how the concept of the world-class university relates to university rankings in China. I confirmed that these league tables have provided a concrete, commensurate indicator for decision-makers to make sense of the global higher education hierarchy, with specific cut-offs to be considered world-class. Further, I examined the intersection between global ranking ambitions of Chinese universities coupled with stringent control from the central government through the striving model. I found that while international forces have had considerable impact on these institutions, local characteristics are highly filtered through a Chinese domestic lens, as governmental distinction has dominated the focus of elite universities. Concurrently, I surveyed over 900 students from across Chinese universities in an exploration of ranking familiarity and knowledge. Through multivariate analysis, I found that students from affluent classes, elite universities, and those with study abroad ambitions were all more familiar with rankings. However, in an actual test of knowledge, I discovered that elite university students actually did worse in regards to global ranking knowledge, while the associations to affluence and study abroad ambition disappeared. The findings in this research have centered rankings in a comparative perspective of higher education in China and the lessons learned can be adapted to future studies in other societies or sectors.

Networks have been widely used to represent and analyze a system of connected elements. Online social media networks, as a result of the expansion of the Internet and increased need of communication, have become an increasingly important part of people's lives. This thesis focuses on the statistical analysis of network data motivated by problems in online social media. It discusses problems arising from both explicit network data and implicit network data. Explicit network data are data where network structures are observable, implicit network data are those that do not have a network structure but occur under the influence of an underlying network. For the explicit network data analysis, we develop a novel method of recovering a fundamental characteristic -- network degree distributions -- under sampling. We formulate the problem of estimating degree distribution as an inverse problem. We show that this problem is ill-conditioned for many sampling methods in practice, and accordingly propose a constrained, penalized weighted least-squares approach to solve this problem. We demonstrate the ability of our method to accurately reconstruct the degree distributions from simulated network data and real world social network data. We also propose practical usage of the estimates relevant to marketing and advertising. For the implicit network data analysis, we look at review data from the popular review websites. Motivated by articles from the popular press and the research community which publicized that the average rating for top review sites is above 4 out of 5 stars, we study the phenomena of review rating trends and convergence using restaurant review data from TripAdvisor. We analyze the trend on different levels -- a rough analysis of the characteristics of the ratings, and a subtler statistical modeling with ordinal logistic regressions. Taking into account the implicit network underlying the review data, we suggest the upward trend observed in restaurant review ratings may be explained by social influence on an individual's perception of qualities. We use the intensity of review postings as an indicator of how popular a restaurant is and to test to what extent the increase in review intensity explains increases in average rating. After that, we consider a more nuanced approach to the joint modeling of ratings and review intensity which would allow for interaction between the two, rather than intensity serving only as an explanatory variable to ratings. Specifically, a state-space model is used to test the interaction between review intensity and review ratings.

Drawing from Huffcutt, Conway, Roth, and Stone’s (2001) taxonomy of employment-interview constructs, we hypothesized that asynchronous web-based video employment interviews would be associated with job performance and organizational tenure using a crowd-sourced sample of 75 employed professionals. We found that composite interview ratings and construct ratings of mental capability, knowledge and skills, applied social skills, and conscientiousness were significantly related to self-rated job performance. We also found that construct ratings of knowledge and skills and applied social skills were significantly associated with self-reported organizational tenure. Implications for web-based video employment-interview research and practice are discussed.

The purpose of this study was to assess the extent to which selected variables are related to scores on the Utah State University Faculty Evaluation Questionnaire (USU FEQ). The variables examined were size of class, level of class, college under whose auspices a class is offered and whether the class is required or elective. In addition, the relationship between total mean score and responses to question 23, a percentage ranking of instructors, was assessed. The fall quarter, 1975, FEQ results were used in this assessment. Employing the same procedures as had been followed in previous quarters, 315 courses were evaluated. Courses at all levels (100-700) and within all colleges of the University were sampled. Through multiple regression analysis, it was found that 87% of the variance in total mean scores was explained by the selected variables. Question 23 was the outstanding contributor; therefore, the analysis was repeated with this variable removed. As a result, 21% of the variance was explained by the remaining variables. Question 23 was highly related to total mean score. Reasonably accurate prediction of instructors ratings can be made from a knowledge of responses to this question. A small negative correlation was found between sample size and total mean score. Instructors of larger classes tended to rate lower than instructors of smaller classes. A small contribution to the explained variance was made by the variable of class size. This contribution has little practical significance. Instructors of different colleges received different average ratings. Those colleges most closely related to high total mean scores were Education, Family Life and Humanities, Arts and Social Sciences. Those colleges receiving lowest ratings were Engineering and Science. Knowledge of the college under whose auspices a course exists improves prediction of instructors ratings on the USU FEQ. Whether a course was required or elective and the level of a course had little relation to the ratings instructors received. The outcomes of this investigation may be limited by any one or a combination of the following: The results of this study were obtained from fall quarter evaluations. Ratings may relate to the quarter in which the course was evaluated. The USU population was the only university used in the sampling. Results, therefore, will be generalizable to USU alone. This study has been concerned with environmental variables, i.e., class size, level, college, required vs. elective. Student, class, or teacher characteristics have not been considered. Significant relationships may exist in these areas. Within University policy, professors are allowed to choose the quarter in which they will be evaluated. Therefore, some volunteer effects may be present in this study. However, extra encouragement to evaluate fall quarter was given to teachers to help control for these effects. From this study of the relationship between selected variables and total mean scores, the following may be concluded; Question 23 could be used to obtain a quick, easy estimate of student ratings of an instructor. The significant differences between ratings of instructors in different colleges receive presents an area of concern. When comparing instructors from different colleges, precautions may need to be taken. Differential norms for the colleges could be considered. The size of an instructor's class is of no significant concern when interpreting FEQ results. The results of this study show no basis for the employment of differential norms for differing class sizes. No basis for considering whether a course is required or elective and level of a course when interpreting questionnaire results is given. From the above summary and conclusions the following recommendations are made; The college under whose auspices an instructor teaches should be considered when interpreting FEQ results. College norm groups should be considered for inclusion on results printouts. The significantly higher ratings received by instructors in the College of Education should be investigated. The following questions arise; A. Do students rate instructors higher if instructors give them higher grades? (Grades received in Education are higher.) B. Are the students in the college more empathetic to their instructors and, therefore, more lenient in their ratings? C. Is the course content less demanding or more interesting so as to receive higher student ratings? D. Are, in fact, the instructors in the College of Education better teachers? If so, what are the characteristics that make them better? These questions and others should be researched. 3. Since relationships may vary according to the particular aspect of teaching performance that the student is asked to rate (Clark & Keller, 1954), a factor analytic study including the selected variables of this study and all of the questions on the USU FEQ may be profitable. 4. Scores on the USU FEQ may be related to the department in which the course is taught. Correlations might be determined for the departments which have a large enough N. 5. Determination of whether students' subjective criteria in rating faculty match the faculty members' goals in teaching may disclose some valuable information about the USU FEQ. 6. The benefits from student evaluation of instruction can only exist to the extent that ratings represent valid appraisals of classroom instruction. Research must be conducted in answer to the question, "Is the USU Faculty Evaluation Questionnaire a valid instrument?"

This study involved administering two rating forms (student self-rating on commitment and student rating of teacher rapport) to approximately 1,400 secondary students taught by 12 different teachers at two different high school Latter-day Saint (LDS) released time seminaries along the Wasatch Front in Utah. Seminaries and Institutes of Religion (S&I) function within the Church Educational System (CES) of the LDS Church, providing religious education for secondary students between the ages of 14-18. The purpose of this study was to explore relationships between student, teacher, and course characteristics on student ratings of teacher rapport and to explore a possible relationship between student self-assessments on their own commitment to learning with student ratings on their rapport with their teacher. Evidence suggests that teacher characteristics such as the teacher's age and experience have little to no impact on student ratings of teacher rapport. Female students tended to rate their teacher more favorably on rapport than male students, although practical significance was minimal. Younger students reported greater interest in seminary and higher-grade expectancy. They also tended to rate themselves higher on commitment. A statistically significant difference was found for teacher rapport scores between two groups based on the order of test administration. Group 1--self-first (student self-rating before student rating of teacher rapport) reported higher levels of rapport than group 2--comparison (student rating of teacher rapport prior to student self-rating). Students tended to rate their teacher more favorably after completing a self-rating on commitment. Practical significance between study groups was minimal because findings were small. Further research is suggested based on these findings to seek more understanding regarding the relationship between student self-evaluations and student ratings of their teacher.

Numerous efforts to assess teaching excellence have been attempted, but systematic research has produced limited results at best. This study expanded upon recent studies focusing on how students' perceptions and attitudes can be used to identify the best course environments and the qualities of teaching excellence. This is especially critical considering that most previous empirical research has been conducted at the university level, while community colleges have been mostly overlooked. Thus, little is know about community college students' perceptions of teaching excellence. To assess their views of teaching excellence, a questionnaire was given to students from one community college to identify the underlying factors that are most central to teaching excellence (research question one). While some of the perceptions of community college students were similar to perceptions documented previously with university students, some perceptual differences were revealed. Confirmatory factor analysis (CFA) was used to evaluate the goodness of fit when used with community college students of the eight original dimensions representing the factor structure similar to that of Herbert Marsh's SEEQ (research question two). For research question three, a second confirmatory factor analysis was employed to assess goodness of fit using the modified 12-dimension version of the survey instrument. The CFA suggested at least a marginal or reasonable fit of the two proposed factor models with community college students. Finally, based on inconsistent findings of previous research, a fourth research question investigated whether demographic factors influence students' perceptions of courses and teaching excellence. A multiple regression analysis of six demographic variables suggested that five variables (e.g., Age, Gender, Ethnicity, Reason for Attendance, Employment and Semester Hours completed) had some impact as to how students respond to certain items that make up the 12 teaching excellence dimensions. The R2 values representing the teaching excellence dimensions ranged from .01 to .034. While many of dimensions had demographic predictor variables that were shown to be statistically significant, as effect sizes were small the practical significance of the results is probably minimal at best. A discussion of the results, limitations, implications for future practice and research are discussed in Chapter Five of this study.

<p>This study aims to investigate the relationship between systematic risk and credit ratings. The systematic risk, frequently measured by beta, is an important consideration for both investors and corporations. Therefore it is interesting to examine if indications about the systematic risk could be gained by looking at credit ratings, especially on the Nordic market, where credit ratings are seemingly growing in importance. Consequently, the following research hypothesis is posed;<em> We intend to establish a relationship between market risk (Beta) and credit ratings for firms in the Nordic countries.</em></p><p><em> </em></p><p>In order to confirm or deny the research hypothesis, theories from peer reviewed databases were collected. These were divided into three sections; background theories, hypotheses about credit ratings and a literature review. The background theories consisted of two classical financial theories, the Capital Asset Pricing Model and the Efficient Market Hypothesis, which are the foundation upon which the research field have progressed. The hypotheses is specifically designed to explain the relationship between credit ratings and either systematic risk or stock price. The literature review contains information about studies which did not contribute to theory building, but produced results interesting in the research area.</p><p> </p><p>The actual sample in the thesis consisted of the 58 credit rated companies on the Nordic stock market. These companies were rated by Moody’s and/or Standard & Poor’s, the two largest credit rating agencies in the world. As a measure of the systematic risk, betas for each of the companies were calculated. To investigate the relationship between these variables a regression analysis was performed, as well as one sample T-test using the software SPSS.</p><p> </p><p>The result revealed a moderate relationship between beta and credit risk, a relationship which was not statistically significant on the five percent level. Our results suggest that credit ratings contain some information about companies’ systematic risk, a finding that might be useful for market participants.</p><p> </p>