Recent Submissions

  • How Does Industrial Concentration Prepare an Economy for Business Cycle Change?

    Walker, Aaron; Business (Augusta University Libraries, 2020-05-04)
    This item presents the abstract for an oral presentation at the 21st Annual Phi Kappa Phi Student Research and Fine Arts Conference.
  • Evaluating the Relationships between Consumer Personality Dimensions and Online Purchase Intentions

    Lawrence, Danae L.; Department of Management and Marketing (Augusta University, 2019-12)
  • Racial Segregation as a Social Determinant of Health Outcomes: Evidence from Counties in the State of Georgia

    Lee, Divesia; Department of Finance and Economics, Department of English & Foreign Languages (Augusta University, 2019-05)
    Social determinants of health account for about 50 percent of health outcomes- more than any other category, yet is the most understudied, therefore warranting further investigation. We contend that within social determinants of health, analysis of racial segregation is of importance. Racial segregation is a structural form of racism, where people of similar race live in communities apart from people of other races. Prior studies have used a dissimilarity index to measure racial segregation and its impact on health outcomes, and has suggested that racial residential segregation has a negative impact on health outcomes, but none of these studies have focused on county level data or the State of Georgia in particular. Using a dataset from the Robert Wood Johnson Foundation, supplemented by other public health and demographic data for all counties in Georgia, we use regression analysis to model the relationship between segregation and various health outcomes. A variety of social determinants of health were analyzed ranging from factors of economic stability, neighborhood and physical environment, and education, to aspects of the healthcare system. Initial results suggest that racial segregation relates to health outcomes, but it depends on the health outcomes being measured. Conclusions are pending further quantitative analysis.
  • Forecasting Hotel Occupancy Rates in Augusta: Can Google Trends Improve Forecasts?

    Callison, Jamie; Department of Management and Marketing (Augusta University, 2018-05)
    This project will develop models in an attempt to develop better forecasts of hotel occupancy for the market in Augusta, Georgiaby utilizing historical occupancy data and Google trends data. Using the historical data from the years 2012 through 2015, a series of five univariate modelswill be made with differing forecasting equations to forecast the year 2016. The forecast for theyear 2016 will be compared to actual occupancy data from 2016 to measure for errors. The models will then be re-estimated with additional keywords that will be chosen on the basis that they will be commonly used to search for and book hotels. Some terms will be specific to Augusta and others will be general for booking hotels. With those terms, an index will be created to weigh the terms according to their relevance throughout the year, according to Google trends. With the addition of the keywords, the newforecasts will be compared to actual occupancy data from 2016. Errors of the univariate models and the models utilizing Google trends data will be compared to determine the accuracy of the two forecasting techniques.
  • Are NFL teams getting the most out of their wins? The Efficiency of Year End Revenues of Ten NFL Teams

    Gonzales, Savanna; Department of Management and Marketing (Augusta University, 2018-05)
    Major sports have taken over many prominent industries in today's world. With the economic impact of athletics comes its evolution from a spectator event into a business. Each team in the major leagues is now not only pressured to produce winsbut as a business, they must also bring in revenue. This means that efficiencyof funds is a vital goal of team managers and financial specialists. Thisresearch projectexaminesthe effects of various factors on year-end revenues for the top ten most valuable teams in the National Football League. Through the use of a DEA model that analyzes such inputs as income, team record and stand out players we areable to determine how efficiently each team is performing based on their revenues, or the output. Of the 10 teams studied, 5 were deemed efficient while 5 were deemed inefficient.Teams that didnot see successful revenue reports were analyzed based on their weaknesses and offered recommendations on which to improve where efficient teams were used a comparison. Ultimately, the goal of this research is to identify factors to improve revenue efficiency across the league as a whole by looking at the top performing teams (or best practices).
  • Marketing Downtown Augusta: Leader's Perceptions of Safety and Cleanliness in Downtown Augusta

    Long, William; Department of Management and Marketing (Augusta University, 2017-12)
  • Cash Flow Pattern Analysis of Fraud and Non-Fraud Firms: A Comparison and Contrast

    Runger, Shannon; Knox School of Accountancy (Augusta University, 2016-05)
    Companies may exhibit one of eight possible cash flow patterns on their Statement of Cash Flows. By pair-matching 30 firms that were known to have issued fraudulent financial statements with 30 non-fraud firms of similar size and industry, a comparison and contrast of the cash flow patterns can be made and the results analyzed. In my research, I examine and analyze the cash flow patterns of fraud and non-fraud firms as reported on the Statement of Cash Flows to determine whether or not the patterns provide some indication of fraudulent activity. I hypothesize that the fraud firms would be more likely to show a cash flow pattern during the year prior to fraud that indicated the firm was struggling and that alternatively, the pattern during the fraud year would be one that indicates a firm is stable and profitable. My findings not only do not support this hypothesis, they also indicate that this method of cash flow pattern analysis does not provide a reliable indication or prediction of fraudulent activity.