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The implementation of agritourism in the U.S. has increased in recent years. Agritourism provides social and economic benefits to rural communities, leads small-scale farms to achieve financial viability, and offers clientele an entertaining opportunity to meet local producers. Although multiple resources exist to educate individuals interested in pursuing agritourism business ventures, related materials cannot be generalized to Mississippi due to inapplicable data. This study aimed to gain knowledge about Mississippi’s agritourism clientele by administering surveys to attendees of the 2021 Mississippi State Fair. The survey instrument was amended from a previous study conducted by Nasers and Retallick (2009) in Iowa. Although most of the results corresponded with findings in similar studies, numerous recommendations were made that were specific to Mississippi farmers and agritourism operators.

The purpose of this study was to examine differences of recalled events from a selected university student population. Eyewitness testimony is frequently used to convict defendants each year. Many of these convictions are based solely on eyewitness accounts. While much has been written on the reliability of eyewitness testimony, little is known about demographic characteristic differences that may exist. A videotaped event was shown to a sample of college students who were then asked to complete a questionnaire based on what they watched. There were significant differences found in the respondents‟ accuracy in recalling events of the video according to demographic characteristics of the sample.

The primary purpose of this thesis is a description of some of the major changes which the population of Texas has undergone, particularly in the first half of the twentieth century. Other approaches are possible. For example, it is both possible and important to develop the relationship of population change to social problems. However, it is not the purpose of this thesis to investigate these relationships. It is the purpose here to view the population problem in almost entirely a factual sense, basing observations and interpretations on strictly demographic data.

<p>  </p> <p>Many species of turtle are facing serious declines from multiple anthropogenic causes. One such turtle native to the United States, the Blanding’s Turtle (<em>Emydoidea blandingii</em>), has faced declines in nearly every sector of their range, yet few populations have known recorded demographics, such as abundance, density, sex ratio, and body sizes. To inform managers of these crucial demographic measures of three spatially adjacent populations on Camp Grayling, MI, I implemented a capture-mark-recapture study from 2021 to 2022. I calculated Lincoln-Petersen Index and ran constant, time-varying, and behavioral response models for both full and conditional likelihoods in Program MARK. All best fit models estimated populations ≥30. The three populations sampled at Camp Grayling were low in abundance, densities, and biomass compared to some more well-known populations. Adult body weight means were 1456 g and 1223 g for males and females, respectively. Mean carapace lengths for males was 22.42 cm and females were 20.11 cm. The males of these populations were significantly larger than the mean found across the Blanding’ Turtles range. There is evidence of recruitment within these populations. A long-term research plan should be implemented and focus on investigating connectedness of these populations, as well as building data for population viability analyses. Additionally, immediate conservation action should take place to protect this vulnerable species.</p>

Several researchers have attempted to understand the tourism expenditure patterns from the traveler's perspective (Cai, Hong, and Morrision, 1995; Dardis, et al., 1981; Prais and Houthakker,1971; Sheldon and Mak , 1987; Jang, et al.,2003). However, an examination of the previous studies indicates that only limited understanding of the traveler's expenditure patterns has been provided either because of only anecdotal evidence in the studies, or because of their failure to examine the impacts of the factors affecting expenditure patterns. This study examined the effects of socio-demographic, travel-related, and psychographic variables on travel expenditures. The expenditure patterns included lodging, meals and restaurants, attractions and festivals, entertainment, shopping, transportation, and total expenditures. From a theoretical perspective, this study contributes to the body of literature in relation to travel expenditure by examining the variables under each of the three constructs identified in predicting travel expenditures. The results of the study provide a more comprehensive and holistic picture in the search of travel expenditures based on multiple independent variables. This study found that travel-related variables (i.e. number of adult(s) and length of stay) were the most influential variables affecting tourism expenditures per person per day. From a practical standpoint, this study sheds light by providing information about how the traveler's characteristic effects travel expenditure patterns and destination marketers may use this information to better segment their target market, allocate their marketing dollars more effectively, and tailor their products to compete for tourist's dollars. Since consumer dollars and tourism organizations' marketing budgets are limited, this study may provide information which will help tourism marketers to develop better strategic marketing tools to satisfy and fulfill those tourists' needs and understand certain reasons behind their spending patterns.

Analysis of intersection crashes is a significant area in traffic safety research. This study contributes to the area by identifying traffic-geometric characteristics and driver demographics that affect different types of crashes at signalized intersections. A simple methodology to estimate crash frequency at intersections based on the size of the intersection is also developed herein. First phase of this thesis used the crash frequency data from 1,335 signalized intersections obtained from six jurisdictions in Florida, namely, Brevard, Seminole, Dade, Orange, and Hillsborough Counties and the City of Orlando. Using these data a simple methodology has been developed to identify the expected number of crashes by type and severity at signalized intersections. Intersection size, based on the total number of lanes, was used as a factor that was simple to identify and a representative of many geometric and traffic characteristics of an intersection. The results from the analysis showed that crash frequency generally increased with the increased size of intersections but the rates of increase differed for different intersection types (i.e., Four-legged intersection with both streets two-way, Four-legged intersection with at least one street one-way, and T-intersections). The results also showed that the dominant type of crashes differed at these intersection types and severity of crashes was higher at the intersections with more conflict points and larger differential in speed limits between major and minor roads. The analysis may potentially be useful for traffic engineers for evaluating safety at signalized intersections in a simple and efficient manner. The findings in this analysis provide strong evidence that the patterns of crashes by type and severity vary with the size and type of intersections. Thus, in future analysis of crashes at intersections, the size and type of intersections should be considered to account for the effects of intersection characteristics on crash frequency. In the second phase, data (crash and intersection characteristics) obtained from individual jurisdictions are linked to the Department of Highway Safety and Motor Vehicles (DHSMV) database to include characteristics of the at-fault drivers involved in crashes. These crashes are analyzed using contingency tables and binary logistic regression models. This study categorizes crashes into three major types based on relative initial movement direction of the involved vehicles. These crash types are, 1) Initial movement in same direction (IMSD) crashes. This crash type includes rear end and sideswipe crashes because the involved vehicles for these crashes would be traveling in the same direction prior to the crash. 2) Initial movement in opposite direction (IMOD) crashes comprising left-turn and head on crashes. 3) Initial movement in perpendicular direction (IMPD) crashes, which include angle and right-turn crashes. Vehicles involved in these crashes would be traveling on different roadways that constitute the intersection. Using the crash, intersection, and at-fault driver characteristics for all crashes as inputs, three logistic regression models are developed. In the logistic regression analyses total number of through lanes at an intersection is used as a surrogate measure to AADT per lane and also intersection type is introduced as a 'predictor' of crash type. The binary logistic regression analyses indicated, among other results, that at intersections with one-way roads, adverse weather conditions, older drivers and/or female drivers increase the likelihood of being at-fault at IMOD crashes. Similar factors associated with other groups of crashes (i.e., IMSD and IMPD) are also identified. These findings from the study may be used to develop specialized training programs by zooming in onto problematic intersections/maneuvers.

With healthcare organizations struggling to remain competitive and financially stable in a market where minimizing costs is a priority, hospital administrators feel the sense of urgency when it comes to keeping patients satisfied with services in order to expand volume and market share. The Emergency Department is considered the front door of a healthcare organization, and keeping its visitors satisfied in order to guarantee a future visit or a referral to a friend or family member is a must. While patient input on the services received in a healthcare facility is essential to improving quality of care, the costs associated with measuring, collecting and analyzing their feedback are remarkable. This research focuses on developing a linear regression model to predict patient satisfaction in the ED using surrogate measures related to patient's socio-demographic characteristics and visit characteristics. With a model of this kind, healthcare administrators can potentially eliminate survey costs while still being able to determine where the hospital stands in the eyes of the patient. Three modeling approaches were used to develop a multiple regression equation. Modeling approach 1 used monthly patient satisfaction scores as the dependent variable collected by a third-party survey organization. The goal of this model was to predict monthly patient satisfaction scores. Modeling approach 2 used patient satisfaction scores collected by the discharge registrar prior to the patient leaving the ED. The goal of this model was to predict patient satisfaction scores on a patient-by-patient basis. Modeling approach 3 used patient satisfaction scores collected by a third-party survey organization. The goal of this modeling approach was to predict patient satisfaction scores on a patient-by-patient basis. Each modeling approach developed in this study used its own survey tool. Though this study had limitations when it came to developing the models and validating the findings, results are very promising. Analysis shows that predicting average patient satisfaction scores on a monthly basis gives the most accurate results, with socio-demographic characteristics and visit characteristics explaining 96% of variation in monthly average patient satisfaction scores. Other model indicators, such as normality of residuals, predicted error, mean square error, and predicted R-square show that the model fits the data very well and has strong predictive ability. Models that attempted to predict patient satisfaction on a patient-by-patient basis appeared to be ineffective, with very large predicted errors and prediction intervals and low predictive ability.

Purpose: The purpose of this study was to examine the role of social media presence in thriving in and increasing brand awareness in the fashion retail industry of Sweden. The study investigates how social media usage affects brand awareness in the fashion industry of Sweden. The study also explored how respondents’ demographics influence the retail fashion brands' awareness campaigns/programs using various social media platforms in the fashion industry of Sweden. Method: This study has used exploratory research to examine the role of social media presence in increasing brand awareness in the Swedish fashion industry. The study used a qualitative method involving semi-structured interviews with the marketing, communication, and social media experts from the fashion retail brands of Sweden. The Swedish retail fashion industry was chosen as it is known for its minimalistic, functionality, and clean lines. The sampled retail fashion brands are chosen due to the nearby geographical presence of the researcher’s locality. Another factor that focused to interview the marketing, communication, and social media experts of these retail businesses was that they handle most of the social media activities within their businesses. Research findings: The study, through interviews with the digital marketers and social media experts of these brands as well as a review of related literature, concluded that social media marketing has a significant influence on fashion retail companies' efforts to build brand awareness. These retail businesses have benefited in terms of strong brand awareness, whether it be by simply having a social media platform presence or investing in social media paid ads. Most interviewees claimed to have seen an uptick in sales, and unquestionably a rise in customer interest. The study further concluded that demographics play a vital role in the brands' awareness campaigns/programs using various social media platforms in the fashion industry of Sweden. Theoretical Implications (Contributions): In the context of building brand awareness for fashion retail companies, this study has confirmed and added to existing marketing research. Based on the research's conclusions, it was concluded that social media marketing has a more positive impact on a company's exposure than other types of advertising. Additionally, the study's findings demonstrate the effectiveness of using free advertisements based on pages in social media networks like Facebook, and Instagram to engage users and keep them constantly aware of a brand's products online. Practical Implications: This study has positive ramifications for aspiring entrepreneurs who place a high priority on social media platforms. Additionally, it takes up less time and costs less money because it offers a selection of paid advertisements where you can choose how much money to spend. Other than that, having a free account and engaging in self-promotional activities like posting or keeping your online platform current with trends is a useful way to gradually increase the number of followers and customers while simultaneously raising brand awareness.