Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages

Forecasting tourism demand by fuzzy time series models

PurposeThis study aims to adapt a neural network based fuzzy time series model to improve Taiwan's tourism demand forecasting.Design/methodology/approachFuzzy sets are for modeling imprecise data and neural networks are for establishing non‐linear relationships among fuzzy sets. A neural network based fuzzy time series model is adapted as the forecasting model. Both in‐sample estimation and out‐of‐sample forecasting are performed.FindingsThis study outperforms previous studies undertaken during the SARS events of 2002‐2003.Research limitations/implicationsThe forecasting model only takes the observation of one previous time period into consideration. Subsequent studies can extend the model to consider previous time periods by establishing fuzzy relationships.Originality/valueNon‐linear data is complicated to forecast, and it is even more difficult to forecast nonlinear data with shocks. The forecasting model in this study outperforms other studies in forecasting the nonlinear tourism demands during the SARS event of November 2002 to June 2003.</jats:sec

Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan

Real effective exchange rate, Neural networks, Prices, Tourism demand,

Internal and external drivers for quality certification in the service industry: Do they have different impacts on success?

This paper presents the results of a study of hotels that are certified for quality management to identify the reasons for seeking quality certification. The authors analyse whether internal or external drivers for seeking certification have different impacts on benefits and the use of quality tools in the hotel industry. The analysis groups hotels according to the importance of their internal reasons for certification, and uses cluster analysis to identify the significant differences between groups of hotels. The findings for the 32 hotels analysed show that hotels that pursued certification for internal reasons develop better quality tools and have increased levels of benefit