PurposeThe purpose of this study is to develop a mathematical model that can be used to forecast the number of individuals who enter the library as well as the number of patrons that seek assistance at the reference desk of the library. An accurate estimate of demand at the reference desk is valuable for effective staffing decisions.Design/methodology/approachAn exponential smoothing model (Winter's model) was developed for forecasting. Data were gathered at the University of Tennessee at Chattanooga for an entire year. Using these data, an exponential smoothing model was formulated for forecasting the number of patrons seeking assistance. Since the data showed no trend but the presence of two seasonality factors, one for the week‐of‐the‐semester effect and one for the day‐of‐the‐week effect, the Winter's method appeared to be best suited. The Winter's method develops a formula from the data and allows the formula to be continuously fine‐tuned as new observations come in day after day.FindingsThe modified Winter's exponential smoothing model proved to be a good predictor of the number of patrons seeking assistance. In spite of large natural random variability present in the data, the actual values seem to follow the forecasts very closely.Originality/valueIt is vital to be able to forecast the number of clients at the reference desk that seek assistance per day. The modified exponential smoothing model is a valuable tool for such forecasting.
Purpose -The purpose of this study is to develop a multiple regression model that can be used to predict the number of patrons that seek assistance at the reference desk of the library. This will facilitate the scheduling of the reference desk librarians. Design/methodology/approach -A multiple regression model is developed, where the dependent variable in the regression model is the number of patrons that seek assistance at the reference desk of the library and the predictor variables (independent variables) are the door count and the semester under study. Data were gathered at the University of Tennessee at Chattanooga for an entire year. Using these data, a multiple regression model was formulated and tested for significance. Then, the model was used for forecasting the required staff at the reference desk for a period for which data was available.Findings -The regression model, with the addition of daily variations, proved to be a good predictor of the number of patrons seeking assistance. Hence, the staffing need was estimated. Overall, the regression model with the added daily index proved to be a very good predictor. Originality/value -It is crucial to be able to predict the number of clients at the reference desk that seek assistance per day. With the use of a sample of data, it was possible to predict the number of clients seeking assistance at the reference desk.
This study grew out of a need to assess reference desk data and determine two items: the number of questions handled for the last three years and the times when the desk should be double staffed. Analysis of the data by chi-square standardized residuals identified the days of the week, and the times during the day of heavy and light use. Data sort identified the heavy/light use weeks, months, and semesters. Descriptive analysis also established the variability and the range of data. When comparing data from week to week for the random sample years with the data collected by every hour for every day of the week for the non-random sample years, this revealed a very similar pattern. Heavy days, weeks, hours, and months fell into a similar pattern from semester to semester and from year to year. Other academic libraries can follow this model and apply it to their work environment after adjusting for their academic calendar and user behavior.
Purpose -The purpose of this study is to develop a mathematical model that can be used to forecast the demand for the inter-library loan (ILL) requests. Accurate estimates of demand are valuable for assisting researchers in their research endeavors. Design/methodology/approach -Data were gathered at the University of Tennessee at Chattanooga for a period of 48 months from July 2008 to the end of June of 2012. Using these data, a centered moving average with seasonal variation model was formulated for forecasting the demand for the inter-library loan. These forecasts were then compared with the actual values to determine the accuracy of prediction. Findings -Centered moving average with seasonal variation model proved to be a good predictor of the demand for the inter-library loans. The model proved to be a very good forecasting tool as the actual values seem to follow the forecasts very closely. Originality/value -It is very important to be able to forecast the demand for the inter-library loans. Researchers constantly demand material for their research and librarians try to fulfill their demands. If the demand can be forecast with some degree of accuracy, the process can be expedited.
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