Airbnb has been increasingly gaining popularity since 2008 due to its low prices and direct interactions with the local community. This paper employed a general linear model (GLM) and a geographically weighted regression (GWR) model to identify the key factors affecting Airbnb listing prices using data sets of 794 samples of Airbnb listings of business units in Metro Nashville, Tennessee. The results showed that the GWR model performs better than the GLM in terms of accuracy and affected variable selections. Statistically significant differences varied across regions in Metro Nashville. The coefficients illustrate a decreasing trend while there is an increase in the distance from the listed units to the convention center, which indicates that Airbnb listing prices are more sensitive to the distance from the convention center in the central area than in other areas. These findings can also provide implications for stakeholders such as Airbnb hosts to gain a better understanding of the market situation and formulate a suitable pricing strategy.

This study examines the advantages and disadvantages of basic, intermediate, and advanced methods for visitor use forecasting where seasonality and limited data are characteristics of the estimation problem. The monthly use rates at the Milwaukee County Zoo, Wisconsin are used to illustrate the seasonal time series techniques. Forecasting methods include the Naive 1, Naive 2, single moving average (SMA) with the classical decomposition procedure, single exponential smoothing (SES), double exponential smoothing (DES), Winter's, and the seasonal autoregressive integrated moving average (SARIMA). The variation in visitor rates over the years makes the visitation trend for the Milwaukee County Zoo appealing in this empirical application. The series ranges from January 1981 through December 1999, a total of 228 months. The last 12, 24, or 60 months of those data are excluded from the original analysis, and used to evaluate the various methods. SARIMA and SMA with the classical decomposition procedure are found to be roughly equivalent in performance, as judged by modified mean absolute percentage error (MAPE) and modified root mean square percentage error (RMSPE) values of a longer estimation period with shorter period ahead forecasts. This study also finds that the SMA with classical decomposition method is more accurate than other techniques when a shorter estimation period with longer period ahead forecasts are included. While this study may not speak to all users of leisure related data, it serves as a comparative reference for those who seek guidance in deciding among a set of forecasting tools.

Food waste mitigation is a critical means of addressing current and future economic, social and environmental concerns, to ensure enough food to feed the world’s growing population in a sustainable way. U.S. federal, state, and local governments have recognized the importance of food waste issues. A variety of policies associated with food waste management, food reduction, and food donation are set to allow and incentivize individuals, organizations and businesses to reduce their food waste. This study focuses on investigating current food waste prevention and diversion activities using data drawn from two U.S. waste reduction recognition programs, the U.S. Food Waste Challenge (FWC) and the EPA Food Recovery Challenge (FRC). Participants involved in these two programs follow the EPA Food Recovery Hierarchy to prevent and divert their food waste. Descriptive statistics, crosstabs, and content analysis were used to analyze the data. The results indicated that a majority of participants were in the food store category, and that Region 9 had the highest participation rate; the top three food waste prevention and diversion activities were food donation, source reduction, and collaboration/partnership. Different strategies used in different categories by FRC awardees were identified. The results of this study can be used to identify changes and strategies needed in practices, programs, and policies. The outcomes of the study provide a clear picture regarding what has been done and what needs to be done more in the future.

Current research investigating the accommodation experience in the sharing economy in China is limited, especially from a cross-cultural perspective. To fill this gap, this study examined the accommodation experience of Airbnb guests using text-mining techniques and compared the accommodation experience perception between two culturally different groups: domestic Chinese and foreign English-speaking Airbnb guests. The results showed that the two groups shared eight common dimensions, including “Convenience/Location”, “Amenities”, “Feel at home”, “Check-in/out”, “Experience”, “Availability/Transportation”, “Host”, and “Style/Decoration”. However, there are differences in the relative importance of each dimension of accommodation experience between the domestic and foreign Airbnb guests. For example, the foreign guests more often mentioned homeliness, location/convenience, and availability/transportation, while the domestic guests showed greater interest in check-in procedures and style/decoration. Additionally, the two groups have several unique dimensions. The dimensions unique to foreign guests are “Recommendation” and “Booking flexibility”, while the dimensions unique to domestic guests are “Revisit” and “Cleanliness”. This study provides both theoretical and practical implications for peer-to-peer accommodation hosts and platforms. For example, Airbnb hosts can improve the satisfaction of Airbnb guests by improving several common extracted topics (e.g., amenities quality and host response) and the fact that foreign guests care more about homeliness, while domestic guests pay more attention to the check-in process and house design and decoration.