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Dissertation of Ph.D.
"The Comparison of Forecasting Methods in Services Industry and An Early Warning Model to Demand Fluctuations For Hospitality Businesses" Advisor: Ass.Prof. Ali Argun Karacabey, 219p.

ABSTRACT

In this study, it's aimed to search to able to forecast the crises which arise from demand fluctuations, and to generate a systematic and dynamic process as a early warning model in the hospitality businesses. So the hospitality business is specific phenomena, an integrated forecasting approach should be more suitable for hospitality business. This integration has two components; quantitative forecasting and judgmental forecasting adjustment.

In the quantitative forecasting process, it was applied to 149 monthly series (arrivals and nights) of a five star hotel in Ankara via MA, Simple, Double, Winter's Exponential Smoothing and ARIMA. The results of these methods were compared by some error measurements. So Winter's Multiplicative have had minimum error measurements, it was selected for the next step.

At the other side of integration, two Delphi based inquiry panels was realized: The Variables Determination Panel and The Environmental Monitoring Panel. The first Panel was realized to determine variables which effect the demand for a five star hotel in Ankara. In the second panel, a questionnaire was answered for next month changes into generated scenarios from 25 factors by 27 specialists and professionals in hospitality and tourism. The opinions of second group panelists were used to adjust Winter's Multiplicative forecasts via an AHP based approach.

This process shows that, if forecast and forecast adjustment process is applied into hotels monthly, it will be an early warning model for crises arised demand fluctuations in hospitality business. The signals from the early warning will be valuable input for operational and managerial decision processes. The most important characteristic of model is elasticity for change and development.

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Dissertation of M.sc. "The Effects of Physical Surrondings on Service Buying Decisions: An Empirical Study on Food & Beverage Businesses", Hacettepe University (Ankara/Turkey) Graduate Studies on Business Administration, Tourism Management Programme, 1996, Adv: Prof. Dr. Dogan Yasar Ayhan
Turkce izlemek icin tiklayin Add your link here			Dissertation of Ph.D. "The Comparison of Forecasting Methods in Services Industry and An Early Warning Model to Demand Fluctuations For Hospitality Businesses" Advisor: Ass.Prof. Ali Argun Karacabey, 219p. ABSTRACT In this study, it's aimed to search to able to forecast the crises which arise from demand fluctuations, and to generate a systematic and dynamic process as a early warning model in the hospitality businesses. So the hospitality business is specific phenomena, an integrated forecasting approach should be more suitable for hospitality business. This integration has two components; quantitative forecasting and judgmental forecasting adjustment. In the quantitative forecasting process, it was applied to 149 monthly series (arrivals and nights) of a five star hotel in Ankara via MA, Simple, Double, Winter's Exponential Smoothing and ARIMA. The results of these methods were compared by some error measurements. So Winter's Multiplicative have had minimum error measurements, it was selected for the next step. At the other side of integration, two Delphi based inquiry panels was realized: The Variables Determination Panel and The Environmental Monitoring Panel. The first Panel was realized to determine variables which effect the demand for a five star hotel in Ankara. In the second panel, a questionnaire was answered for next month changes into generated scenarios from 25 factors by 27 specialists and professionals in hospitality and tourism. The opinions of second group panelists were used to adjust Winter's Multiplicative forecasts via an AHP based approach. This process shows that, if forecast and forecast adjustment process is applied into hotels monthly, it will be an early warning model for crises arised demand fluctuations in hospitality business. The signals from the early warning will be valuable input for operational and managerial decision processes. The most important characteristic of model is elasticity for change and development.