Evaluating and Selecting a Property Management System

In his study - Evaluating and Selecting a Property Management System - by Galen Collins, Assistant Professor, School of Hotel and Restaurant Management, Northern Arizona University, Assistant Professor Collins states briefly at the outset: “Computerizing a property requires a game plan. Many have selected a Property Management System without much forethought and have been unhappy with the final results. The author discusses the major factors that must be taken into consideration in the selection of a PMS, based on his personal experience.” Although, this article was written in the year 1988 and some information contained may be dated, there are many salient points to consider. “Technological advances have encouraged many hospitality operators to rethink how information should be processed, stored, retrieved, and analyzed,” offers Collins. “Research has led to the implementation of various cost-effective applications addressing almost every phase of operations,” he says in introducing the computer technology germane to many PMS functions. Professor Collins talks about the Request for Proposal, its conditions and its relevance in negotiating a PMS system. The author also wants the system buyer to be aware [not necessarily beware] of vendor recommendations, and not to rely solely on them. Exercising forethought will help in avoiding the drawback of purchasing an inadequate PMS system. Remember, the vendor is there first and foremost to sell you a system. This doesn’t necessarily mean that the adjectives unreliable and unethical are on the table, but do be advised. Professor Collins presents a graphic outline for the Weighted Average Approach to Scoring Vendor Evaluations. Among the elements to be considered in evaluating a PMS system, and there are several analyzed in this essay, Professor Collins advises that a perspective buyer not overlook the service factor when choosing a PMS system. Service is an important element to contemplate. “In a hotel environment, the special emphasis should be on service. System downtime can be costly and aggravating and will happen periodically,” Collins warns. Professor Collins also examines the topic of PMS system environment; of which the importance of such a factor should not be underestimated. “The design of the computer system should be based on the physical layout of the property and the projected workloads. The heart of the system, housed in a protected, isolated area, can support work stations strategically located throughout the property,” Professor Collins provides. A Property Profile Description is outlined in Table 1. The author would also point out that ease-of-operation is another significant factor to think about. “A user-friendly software package allows the user to easily move through the program without encountering frustrating obstacles,” says Collins. “Programs that require users to memorize abstract abbreviations, codes, and information to carry out standard routines should be avoided,” he counsels.

Effect of child care subsidies on price and quality of care for low -income families

This dissertation examines the effect of regulations, resource and referral agencies, and subsidies on price and quality of care in child care centers. This research is based on a carefully developed conceptual framework that incorporates the factors affecting the demand and supply of child care. The first step in developing this framework is sketching out the structural equations. The structural equations help us understand the underlying behavior of individuals and firms making a decision. The exogenous variables are vector of attributes relating to family characteristics, child characteristics, regulations, subsidy, community characteristics and prices of inputs. Based on the structural equations, reduced form equations are estimated to find the effect of each of the exogenous variables on each of the endogenous variables. Reduced form equations help us answer public policy questions. The sample for this study is from the 1990 Profile of Child Care Settings (PCCS) data in which 2,089 center based programs were interviewed.^ Child/Staff Ratio (Group Level). Results indicate that among subsidies, only the state subsidy per child in poverty has a significant effect on the child/staff ratio at the group level. Presence of resource and referral agencies also increase the child/staff ratio at the group level. Also when the maximum center group size regulation for 25-36 months becomes more stringent, the child/staff ratio at the group level decreases.^ Child/Staff Ratio (Center Level). When the regulations for the maximum child/staff ratio for age groups 13-24 months and 37-60 months become lax, the child/staff ratio for the center increases. As the regulation for maximum group size for infants becomes stringent, the child/staff ratio decreases. An interesting finding is that as the regulations for maximum group size for age groups 13-24 months and 25-36 months become stringent, the child/staff ratio for the center increases. Another significant finding is that when a center is located in a rural area the child/staff ratio is significantly lower.^ Center Weighted Average Hourly Fees. Maximum group size regulations for age groups 25-36 months and 37-60 months have a negative effect on center hourly fee. Maximum child staff regulations for age groups 13-24 months and 37-60 months have a negative effect on center hourly fee. Maximum child staff regulations for age groups 0-12 months and 25-36 months have a positive effect on center hourly fee. Findings also indicate that the center average hourly price is lower when there is a resource and referral agency present. Cost adjusted prekindergarten funds and JOBS child care subsidies have a negative effect on average hourly fee. Cost adjusted social services block grant and state subsidy per child in poverty have a positive effect on the average hourly price. A major finding of this dissertation is the interaction of subsidy and regulatory variables.^ Another major finding is that child/staff ratio at the group level is lower when there is an interaction between geographic location and nature of center sponsorship. ^

Effect of child care subsidies on price and quality of care for low -income families

This dissertation examines the effect of regulations, resource and referral agencies, and subsidies on price and quality of care in child care centers. This research is based on a carefully developed conceptual framework that incorporates the factors affecting the demand and supply of child care. The first step in developing this framework is sketching out the structural equations. The structural equations help us understand the underlying behavior of individuals and firms making a decision. The exogenous variables are vector of attributes relating to family characteristics, child characteristics, regulations, subsidy, community characteristics and prices of inputs. Based on the structural equations, reduced form equations are estimated to find the effect of each of the exogenous variables on each of the endogenous variables. Reduced form equations help us answer public policy questions. The sample for this study is from the 1990 Profile of Child Care Settings (PCCS) data in which 2,089 center based programs were interviewed. Child/Staff Ratio (Group Level): Results indicate that among subsidies, only the state subsidy per child in poverty has a significant effect on the child/staff ratio at the group level. Presence of resource and referral agencies also increase the child/staff ratio at the group level. Also when the maximum center group size regulation for 25-36 months becomes more stringent, the child/staff ratio at the group level decreases. Child/Staff Ratio (Center Level): When the regulations for the maximum child/staff ratio for age groups 13-24 months and 37-60 months become lax, the child/staff ratio for the center increases. As the regulation for maximum group size for infants becomes stringent, the child/staff ratio decreases. An interesting finding is that as the regulations for maximum group size for age groups 13-24 months and 25-36 months become stringent, the child/staff ratio for the center increases. Another significant finding is that when a center is located in a rural area the child/staff ratio is significantly lower. Center Weighted Average Hourly Fees: Maximum group size regulations for age groups 25-36 months and 37-60 months have a negative effect on center hourly fee. Maximum child staff regulations for age groups 13-24 months and 37-60 months have a negative effect on center hourly fee. Maximum child staff regulations for age groups 0-12 months and 25-36 months have a positive effect on center hourly fee. Findings also indicate that the center average hourly price is lower when there is a resource and referral agency present. Cost adjusted prekindergarten funds and JOBS child care subsidies have a negative effect on average hourly fee. Cost adjusted social services block grant and state subsidy per child in poverty have a positive effect on the average hourly price. A major finding of this dissertation is the interaction of subsidy and regulatory variables. Another major finding is that child/staff ratio at the group level is lower when there is an interaction between geographic location and nature of center sponsorship.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^