Lodging capacity optimization: Application of an inventory model to China\u27s lodging industry

Since China decided to promote tourism in 1978, the rapidly increasing number of foreign visitors has continuously put pressure on China\u27s tourism infrastructure and service facilities. To keep up with the demand, China\u27s hotel industry has been expanding at a growth rate that is faster than that of foreign visitors. In 1990s, the overdevelopment became a problem confronting the China\u27s hotel industry. However, the hotel supply has continuously been increasing beyond the demand. Severe over-capacity is likely to occur in China\u27s hotel industry time series analysis and single-period inventory model, this dissertation estimates the optimal capacity for both China\u27s and Shanghai\u27s high-end hotel segment for future four years. The estimated optimal capacity is then compared with the expected room capacity to explore the possibility and magnitude of future over-capacity or under-capacity; Data required for this study are collected from Yearbook of China Tourism Statistics, China Hotel Industry Study, and other related publications from the National Tourism Administration of People\u27s Republic of China and the Shanghai Municipal Tourism Administrative Commission; The estimated optimal capacity for future four years suggests that both Shanghai\u27s and China\u27s high-end hotel segments are experiencing over-capacity, and will continue for approximate one and half years. After the demand catches up with the supply, both China\u27s and Shanghai\u27s high-end hotel segment needs to expand at a slightly higher rate. In addition, the forecasted future demand of both China\u27s and Shanghai\u27s three-star rated hotels also shows an upward trend in future four years; The results of the study are expected to provide investors, hospitality practitioners, and local government with a useful tool for scientific capacity planning to avoid unnecessary future under- or overdevelopment. It is also expected that this study will address the gap in the literatures of destination capacity optimization

A Review of Merger and Acquisition Wave Literature: Proposing Future Research in the Restaurant Industry

The purpose of this study is to identify research trends in Merger and Acquisition waves in the restaurant industry and propose future research directions by thoroughly reviewing existing Merger and Acquisition related literature. Merger and Acquisition has been extensively used as a strategic management tool for fast growth in the restaurant industry. However, there has been a very limited amount of literature that focuses on Merger & Acquisition in the restaurant industry. Particular, no known study has been identified that examined M&A wave and its determinants. A good understanding of determinants of M&A wave will help practitioners identify important factors that should be considered before making M&A decisions and predict the optimal timing for successful M&A transactions. This study examined literature on six U.S M&A waves and their determinants and summarized main explanatory factors examined, statistical methods, and theoretical frameworks. Inclusion of unique macroeconomic factors of the restaurant industry and the use of factor analysis are suggested for future research

Wine tourism and the senior market spending behaviors: A qualitative approach

Wine tourism has increased in popularity in the United States. Nevertheless, many wineries located in rural areas are not well known and have been understudied. On the other hand, the senior market has demonstrated to have great potential for tourism. However, studies regarding senior tourism behaviors including spending, are scant. This research aims to reduce this research gap by analyzing spending habits of senior population who visit rural wineries. By using a qualitative approach this study will be able to: a) Identify the characteristics and profile of senior travelers; b) Determine the underlaying reasons of senior visitors’ expenditure patterns; c) Provide theorical and practical implications related to rural wine tourism. Those implications will help business owners and policy makers develop strategies that promote the development of wine tourism in the country

An Analysis of Online Customer Complaints in Multiple Sectors of the Hotel Industry

The Internet has become a highly used tool for communication. Through the use of online communities consumers are able to view as well as leave comments regarding their experiences. These comments can either be positive or negative. By examining the customer complaining behavior, the purpose of this study is to identify the differences between negative online complaints against luxury hotels and mid-scale hotels, and to provide managers with some insights to help them respond properly to different online complaints. Using both qualitative and quantitative approaches, online complaints will be analyzed. The findings of this study are expected to help hotel managers properly respond to online complaints

Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance