A multiple regression model for predicting reference desk staffing requirements

Abstract

PurposeThe 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/approachA 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.FindingsThe 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/valueIt 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.