دانلود مقاله ISI انگلیسی شماره 29483
عنوان فارسی مقاله

ترکیب نظریه مجموعه راف با تجزیه و تحلیل تقاضای سفر

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
29483 2003 7 صفحه PDF سفارش دهید 4090 کلمه
خرید مقاله
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عنوان انگلیسی
Incorporating the rough sets theory into travel demand analysis
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Tourism Management, Volume 24, Issue 5, October 2003, Pages 511–517

کلمات کلیدی
مجموعه های دقیق - تقاضای گردشگری - حجم تجارت - ارزش مداوم گسسته -
پیش نمایش مقاله
پیش نمایش مقاله ترکیب نظریه مجموعه راف با تجزیه و تحلیل تقاضای سفر

چکیده انگلیسی

This research integrates the rough sets theory into tourism demand analysis. Originated from the area of Artificial Intelligence, the rough sets theory was introduced to reveal important structures and to classify objects. A rough sets approach can capture useful information from a set of raw hybrid data and discover knowledge from the data in a form of decision rules. This makes the rough sets approach a useful classification and pattern recognition technique. Because of its ability to accommodate hybrid data and its algorithms without rigorous theoretical and statistical assumptions, the theory could complement the orthodox demand framework. This paper introduces a new rough sets approach for deriving rules from an Information Table of tourist arrivals. The induced rules were able to forecast change in demand with 87% accuracy.

مقدمه انگلیسی

As tourism becomes the largest industry in the world, its impact on the global economy is widely recognized. To keep pace with the rapid flow of tourists, a destination needs to plan the utilization of its resources. One of the key and preliminary elements in the planning process is to study the demand for tourist arrivals in terms of both volume and determinants. To understand the relationship between tourist arrivals and their determining factors, most of the existing causal studies of tourism demand apply economic models that use mathematical functions. These models are established on the basis of many statistical assumptions and limitations. To a certain extent, the traditional models serve the purpose of acquiring new knowledge. These models, however, do not provide sufficient predictive capability when it comes to problems involving interactions among many interdependent variables with unknown probability distribution. In other words, these models are unable to perform consistently well in situations where the exogenous variables correlate with each other, and when distributions of the samples of variables do not meet the required independent and identical distribution (iid) condition. Moreover, these forecasting models have very much been exploited and only marginal improvements might be expected from their continued use. This paper presents a new approach that applies the rough sets theory to form a model for tourism demand. The rough sets theory was originally introduced by Pawlak (1982) to reveal important structures in a data set and to classify objects. Unlike a conventional data analysis, which uses statistical inferential technique, the rough sets approach is based on data-mining techniques to discover knowledge. Originating from Artificial Intelligence, a subdivision of Computer Science, the rough sets approach has been found successful in pattern recognition and object classification in medical and financial fields (Slowinski & Zopounidis, 1995; Tanaka & Maeda, 1998). The theory has been incorporated into tourism and hospitality research by Law and Au (1998) and Law and Au (2000), and Au and Law (2000). Through the rough sets approach, the relationship of a decision (dependent) variable and a set of condition (independent) variables in terms of decision rules could be modeled. Most importantly, these decision rules can represent data in both numeric and non-numeric forms. This makes the rough sets approach a very useful classification and pattern recognition technique. However, to date, no published work has ever linked the rough sets theory with relationship modeling and forecasting in tourism demand analysis. This study makes an attempt to bridge this gap. The objective of this research is to induce patterns in a form of decision rules, which are able to distinguish between the classes of arrival volume (in terms of percentage change) based upon differences in the factors that affect tourist arrivals. The aim is to understand how demand is affected by changes in demand determinants. The remaining part of this paper is organized as follows: Section 2 presents the methodology of the research and concepts of the rough sets theory. Section 3 depicts the empirical results and model performance and Section 4 is concluded with discussions on implications and suggestions for future research within the tourism industry.

نتیجه گیری انگلیسی

This paper has provided a new methodology for deriving rules from a tourism demand IT. The new approach is potentially useful for industry practitioners and policy makers in many ways. First, unlike many conventional models which are founded on many statistical assumptions, the only assumption made under this approach is that the values of attributes could be categorized. Second, compared to complex mathematical equations, the ultimate knowledge represented is in a form of rules, and these rules are much more intuitive and straightforward to comprehend. The rough sets theory can accommodate attributes with nominal values without prior transformation to numeric ones, which very often creates unnecessary loss of information. The results obtained in this study are based on economic variables used in conventional demand theory. The study can be extended to include other important attributes that could enhance the understanding of any relationship between tourism demand and its determinants as well as to improve the predictability of tourism demand. These variables not only consist of demographic variables but also other psychographic variables such as those described in travel motivation theory, and theories of consumer behavior and destination choice. To facilitate this future research, researchers should also look into the measurement issues of the non-economic variables.

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