MFIS, Kent State University, 2021, College of the Arts / School of Fashion

Fashion renting, a form of collaborative consumption, is an emerging sustainable business model that extends the product life cycle and minimizes waste garments. Fashion renting has been the topic of interest for multiple studies, and as a result, the current fashion discipline possesses a substantial understanding of the factors that encourage consumers to engage in fashion renting. A limitation, however, is that relatively little attention has been placed on consumers who do not engage in fashion renting. This is an important gap in research especially considering the fact that understanding non-users of products or services often lead to valuable and distinct implications. The purpose of this study, therefore, is to investigate both the user and non-user groups of fashion renting services to obtain a better understanding of the phenomenon. Specifically, two separate focus group interviews for users and non-users are conducted. The interview results are then converted into a survey questionnaire and distributed via Amazon Mechanical Turk. Factor Analysis and MANOVA are conducted to explore whether different sets of factors drive consumers' decision to use or not use fashion renting services. The findings of this study provide meaningful results for fashion researchers as well as practitioners.

Committee: Jonghan Hyun, Ph.D. (Advisor); Jihyun Kim-Vick, Ph.D. (Committee Member); Aryn Karpinski, Ph.D. (Committee Member) Subjects: Behavioral Sciences; Business Administration; Entrepreneurship; Marketing; Sustainability; Textile Research

Master of Arts, Miami University, 2020, Economics

Airbnb is a two-sided platform that connects individuals seeking short-term lodging to property owners renting out their homes. For many property owners, it is possible that revenues from a short-term rental listing will surpass those of a long-term lease agreement. Therefore, it is likely that property owners are incentivized to evict their long-term tenants to list their properties on Airbnb. In Cincinnati, OH, the city council voted in April 2019 to enact a short-term rental policy to combat this potential issue among others. We leverage the policy intervention and study the effects of the Airbnb market on evictions in Cincinnati utilizing both Regression Discontinuity in Time and Difference-in-Difference specifications. We found that the policy led to a subsequent decrease in the total number of listings, multi-listers, and the percent of entire-unit listings. If property owners are evicting their long-term tenants to list their properties on Airbnb on a grand scale, one would expect that this dip in Airbnb activity after April 2019 would lead to fewer evictions. However, we found no significant variation in evictions during this time period. Therefore, there is little evidence of a significant relationship between Airbnb usage and evictions in Cincinnati, OH based on the data we have at this time.

Committee: Mark Tremblay Ph.D (Advisor); Charles Moul Ph.D (Committee Member); Austin Smith Ph.D (Committee Member) Subjects: Economics

PhD, University of Cincinnati, 2020, Design, Architecture, Art and Planning: Regional Development Planning

Ridehailing presents an opportunity for disadvantaged populations to gain access to opportunities otherwise inaccessible without a car, and recent research suggests they are doing just that. Yet the specifics of ridehail use among the disadvantaged remain somewhat foggy. Specifically, what types of trips are common, how much do individuals spend, and what barriers (if any) are present? Previous ridehail equity studies have been limited in scope due to either small sample sizes or limited detail of trip-level data. These limitations have left gaps in knowledge. To fill those gaps, this work asks and answers three questions about ridehail use by riders in Austin's disadvantaged neighborhoods. First, how often and for what purposes do riders in disadvantaged neighborhoods use ridehailing? Second, do riders in disadvantaged neighborhoods encounter biases when using ridehailing? Third, what is the price/income sensitivity for riders in disadvantaged neighborhoods? In answering these three questions, this research begins to better understand a broader, foundational question: what explains the use of ridehailing by riders in disadvantaged neighborhoods? A data set rich in trip-level detail provided by RideAustin is used to answer these questions. This data set consists of ridehail variables not yet seen in the academic literature, thereby permitting several novel findings. It is demonstrated that the disadvantaged, by various measures, took the greatest share of RideAustin trips per capita. Logit models predict many of those trips by the disadvantaged were for functional, rather than social, purposes. Ordinary least squares models estimate population density to be negatively associated with RideAustin use. While transit service frequency is predicted to have a positive association. Riders in low income, more racially/ethnically diverse neighborhoods are more likely to be rated 1 star than riders in other neighborhoods. Likewise, riders in those same neighborhoods are (open full item for complete abstract)

Committee: Carla Chifos Ph.D. (Committee Chair); David Edelman Ph.D. (Committee Member); Raymond Geddes Ph.D. (Committee Member); Conrad Kickert Ph.D. (Committee Member) Subjects: Urban Planning

Master of Arts, Miami University, 2020, Economics

This paper estimates the amount of a particular type of fraud that occurs in the short-term rental market. The type of fraud examined can only be committed by those who are multi-listers, meaning that the host needs to have two or more properties available online to have the ability to commit this crime. To identify systematic fraud from daily Airbnb data, we examine the difference between single and multi-listers to determine if there is a statistically significant difference in the amount of same day bookings. The fraud being examined relies on making the guest switch units the day they are supposed to begin their stay due to a last-minute excuse. There can be underlying differences in the types of units offered by single listers and multi-listers that can cause one of the groups to have a higher proportion of same day bookings than the others. After controlling for these differences, we see that single listings have a lower proportion in comparison to multi-listers. This pattern suggests that widespread Airbnb fraud could be occurring.

Committee: Mark Tremblay (Advisor); George Davis (Committee Member); Ejindu Ume (Committee Member) Subjects: Economics

MDES, University of Cincinnati, 2020, Design, Architecture, Art and Planning: Design

People have a lack of information about open spaces near their fixed places of stay. The most common way to get information about unknown spaces is a recommendation from people around. However, this narrow network cannot lead to activating public space. This study aims to solve the underused Public Open Spaces (POSs) through a network design with benchmarking the Sharing Business apps such as Uber, Yelp, and Airbnb. Users can explore their surrounding area filtered by selected keywords and get recommendations described from previous visitors. The design of the search and review functions made intuitive and straightforward to make the process easier than other business apps to engage the users for further use. This project structured in three phases. In the first phase, the author had to studies three different fields, which are public space visitors' perspectives, the current service structure of the Sharing Economy apps, and the actual usage of the POS in the study area. For the second phase, created the possible users based on the first step's studies, ideated the functions which they needed to understand their neighborhood area and designed the user scenarios of workflow with different functions. In the final phase, the author finalized the wireframe concept and digitized the idea to design an app prototype that simulated as a real application. By gathered validate results of participants, this study confirmed the possibility of the idea could contribute to activating the underused POSs.

Committee: Emily Verba Fischer M.F.A. (Committee Chair); Heekyoung Jung Ph.D. (Committee Member); Vikas Mehta Ph.D. (Committee Member) Subjects: Design

Doctor of Philosophy, The Ohio State University, 2020, Consumer Sciences

The recent emergence of P2P commerce that facilitates the redistribution of services has been commonly known as the sharing economy. Inspired by the ideology of the sharing economy, many emerging initiatives are flourishing in hospitality and tourism services. Sharing businesses such as Airbnb appeal to consumers for its relative low-cost accommodations and opportunity to directly interact with the local community. Following the online transaction, authentic guest-host experiences distinguish the sharing economy markets from the early P2P markets. The use of hosts and guests' profile information in Airbnb provide more personal information as a means of identity verification with intention to increase a sense of personal contact and facilitate the process of trust building. The growing popularity of Airbnb can be largely attributed to the flexibility of diverse service components compared to traditional standardized hotel services. Additionally, Airbnb provides the opportunity for guests to seek cultural immersion experiences in local communities. A large stream of literature on sharing economy has primarily focused on its business models, consumer motivations, consumer decision making, and regulation issues. However, research relevant to understanding the effectiveness of service attributes in the sharing economy is lacking. In this respect, this study aims to fill this gap by focusing on the case of Airbnb and exploring the impact of service attributes on consumer behavior. A set of three experimental studies were conducted to examine the influence of service attributes on booking intention, willingness to pay more, intention to spread word-of-mouth. Results indicated that core, hedonic, and cultural service attributes are important predictors of behavioral intentions. Especially, the effectiveness of service attributes on behavioral intentions are operated through expected enjoyment, experiential value, and experiential quality. The findings of the research provid (open full item for complete abstract)

Committee: Jay Kandampully (Advisor); Milos Bujisic (Committee Member); Stephanie Q. Liu (Committee Member) Subjects: Management; Marketing