Web-Based Training in the U.S Lodging Industry

Menu analysis is the gathering and processing of key pieces of information to make it more manageable and understand- able. Ultimately, menu analysis allows managers to make more informed decisions about prices, costs, and items to be included on a menu. The author discusses If labor as well as food casts need to be included in menu analysis and if managers need to categorize menu items differently when doing menu analysis based on customer eating patterns.

Best Management Practices in Green Lodging Defined and Explained

Best management practices in green lodging are sustainable or “green” business strategies designed to enhance the lodging product from the perspective of owners, operators and guests. For guests, these practices should enhance their experience while for owners and operators, generate positive returns on investments. Best management practices in green lodging typically starts with a clear understanding of each lodging firm’s role in society, its impact on the environment and strategies developed to mitigate negative environmental externalities generated from the production of lodging goods and services. Negative externalities of hotel operations manifest themselves in energy and water usage, waste generation and air pollution. Hence, best management practices in green lodging are dynamic, cost effective, innovative, stakeholder driven and environmentally sound technical and behavioral solutions that attempt to ameliorate or eliminate the negative environmental externalities associated with lodging operations, while simultaneously generate positive returns on green investments. Thus, best management practices in green lodging should reduce lodging firms’ operating costs, increase guest satisfaction, reduce or eliminate the negative environmental impacts associated with hotel operations while simultaneously enhance business operations.

Gimme' A Break: Offering Sabbaticals as an Optional Leave Benefit in the Lodging Industry

ABSTRACT The purpose of this study is to investigate the extended leave programs offered by lodging companies in the United States and to suggest a model that could be used in the lodging industry. This model mirrors successful sabbatical leave programs offered by leading companies featured in the annual report, 100 Best Companies to Work For (from this point forward, referred to as 100 Best), published on-line by Fortune Magazine, 2013 (CNN, 2013). While extended leave programs are not entirely lacking in the industry, our research discovered that such leave systems are rare. According to the companies investigated that offer a sabbatical leave program, this benefit offers highly sought after time away from work for top performing employees at the management and higher levels. The benefits reported include happier employees who have increased feelings of company loyalty, job satisfaction, and overall better attitudes. The sponsoring companies stated that those who take part in such leave contribute at a higher level upon their return, bringing fresh ideas and a renewed commitment to the company’s success.

Predicting Lodging Manager Annual Salary Based on Metropolitan Statistical Area Attributes and Lodging Industry Performance: Exploring the Concept

Using multiple regression analysis, lodging managers’ annual mean salaries in 143 Metropolitan Statistical Areas (MSA) within the U.S. were analyzed to identify what relationships existed with variables related to general MSA characteristics, along with the lodging industry’s size and performance. By examining the relationship between these variables, the authors predict the long-term possibility of predicting lodging industry managers’ salaries. These predictions may have an impact on financial performance of an individual lodging property or organization. Through this paper, this concept was applied and explored within U.S. MSAs. These findings may have value for a variety of stakeholders, including human resources practitioners, the hospitality education community, and individuals considering lodging management careers.

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.