The interface of wildlife and nature tourism

Doctor of PhilosophyDepartment of Horticulture and Natural ResourcesRyan L SharpThe relationship between tourism and wildlife is complex and multifaceted, with impacts on both the environment and human well-being. This dissertation will investigate the intersection of tourism and wildlife, focusing on three main aspects: the impact of outdoor recreation on wildlife, the potential of virtual nature tourism to decrease impacts on wildlife while still providing health benefits to participants, and the balance between access and protection for both humans and wildlife. This dissertation focuses on the relationship between wildlife tourism and its impact on both participants and wildlife. The author examines several factors that can shape the development of wildlife tourism, such as the format of the tourism, the beliefs and motivations of the participants, and the effects on both wildlife and participants. Virtual nature tourism will be explored as a potential solution to decrease the impact on sensitive wildlife while increasing access to learn about and observe wildlife in their natural habitat. This can include wildlife webcams and guided tours that can be viewed remotely, such as WildEarth safaris. Virtual nature tourism can be beneficial for people who cannot travel to experience nature in person due to time, financial, or health limitations, while still providing health benefits. The author notes that outdoor recreation tourism can have both health benefits for participants and negative impacts on wildlife. In order to balance these factors, the author suggests that understanding the patterns of wildlife behavior and human recreation is crucial in developing regulations and educational programs that ensure both wildlife and tourism can thrive. The author also explores the dynamics between wildlife and protected area tourists and how decisions made by park managers affect the balance between conservation and recreation. The author suggests that protected areas can benefit from a zoning approach that caters to different types of tourists and their preferences. Overall, the author argues that a better understanding of the relationship between wildlife tourism and its impact on both participants and wildlife is crucial for informed management decisions that benefit both. Documenting the variations in benefits and impacts on humans and wildlife will inform management decisions that will allow for a range of access and protection. This dissertation aims to contribute to the larger debate and ongoing efforts towards a sustainable balance between tourism and wildlife conservation

Detecting consumer emotions on social networking websites

The social networking environment goes beyond connecting friends. It also connects customers with companies and vice versa. Customers share their experience with friends, followers, and companies and these experiences carry sentiments and emotions thereby creating big data. There is an ocean of data that is available for companies to extract and make meaning out of it by applying to different business contexts such as consumer feedback analysis and marketing & communications. For companies to benefit from consumer emotion data, they must make use of computational methods that can save time and work consumed by traditional consumer research methods such as questionnaires and interviews. The objective of this research is to explore existing literatures on detecting consumer emotions from social networking data. The author carried out a systematic literature review on research articles from three bibliographic databases with the intent to find out social networking data extraction process, dataset sizes, computational methods used, consumer sentiments, emotions studied, limitations and its application in a managerial context. To further understand consumer emotion detection, a case study in the form of a Twitter marketing campaign was conducted to emulate the process of consumer emotion detection on a company that is selling stress management products and services. The results indicate that most companies use Twitter networking platform to carry out consumer emotion analysis. The dataset sizes range from small to very large. The studies have used variety of computational methods, some with accuracies to measure the performance. These methods have been applied in various industries such as travel, restaurant, healthcare, and finance to name a few. Managerial applications include marketing, supply chain, feedback analysis, product development, and customer satisfaction. There are few limitations that were identified from using these methods. The case study results and discussion with the case company CIO communicated the potential for the use of some of the methods for consumer behavior research. The valuable feedback from the CIO revealed that by customizing existing methods, their company can create new tools and methods to understand their customers by providing better recommendations and customize their offerings to individual customers

Comparison of subjective and physiological stress levels in home and office work environments

Work stress is a major problem to individuals and society, with prolonged periods of stress often leading to health issues and reduced productivity. COVID-19 has increased the incidence of individuals working in a mixture of home and office-based environments, with each location presenting its own stressors. Identification of stress levels in each environment will allow individuals to better plan how to mitigate stress and boost productivity. In this project, differences in stress levels are predicted in each work environment from individuals’ physiological responses and subjectively reported stress and productivity. Initial work on the project focused upon development of a system for the detection of dementia-related difficulties through the wearable-based tracking of physiological indicators. As such, a review of the available commercial and laboratory devices available for tracking physiological indicators of dementia-related difficulties was conducted. Furthermore, no publicly available physiological dataset for predicting difficulties in dementia currently exists. However, a review of the methods for collecting such a dataset and the impact of COVID-19 found that it is impractical and potentially unethical to conduct an experiment with people with dementia during the pandemic. As such, a pivot in research was necessitated. Comparing the stress levels of individuals working in home and office environments was selected. A data collection experiment was then performed with 13 academics working in combinations of home and office environments. Descriptive statistical features were then extracted from both the physiological and questionnaire data, with the relationships between attributes and features calculated using various advanced data analytics and statistical approaches. The resultant correlation coefficients and statistical summaries of stress were used to evaluate relationships between stress and work environment at different times of day, different days of the week, and while performing different activities. A bagged tree machine learning model was trained over the data, achieving 99.3% accuracy when evaluated using 10-fold cross validation. When tested on the purely unseen instances it achieved 56% accuracy corresponding to inter-class stress classification, however a testing accuracy of 73.7% was achieved using principal component analysis for dimensionality reduction and the dataset is balanced using Synthetic Minority Oversampling Technique