An artificial neural network analysis of the satisfaction of hospital building maintenance services

Modern hospital buildings are very large, complex, sophisticated, and costly structures. The productivity of the hospital building users is influenced by the building performance. Maintenance services are conducted on the buildings to ensure optimum building performance and users’ satisfaction. However, many instances, have been recorded dissatisfactions among the hospital building users with respect to the performance of the maintenance services. This research investigated the satisfaction of private hospital building users with respect to maintenance services through a survey questionnaire that included ten satisfaction of maintenance services. The data revealed that the users are most satisfied with the standard of workmanship, cleanliness of the maintained area, communication with users during maintenance works, and degree of politeness and kindness of the maintenance workers. Using the nine/ten as the independent variables, an artificial neural network model to predict the satisfaction of users with maintenance services was presented. The model revealed that cleanliness and tidiness of the maintained area and the quality of maintenance works are the main predictors of the total maintenance service delivery of the maintenance organisations.