بکارگیری سیستم هوشمند پیشنهاد بر اساس محاسبه ی ذهنی و شبکه های عصبی مصنوعی در بهره برداری از هوش تجاری برای پیشنهادات محصول/خدمات (P/S) تحصیل در خارج

Successfully designing and positioning product/service will lead most enterprises to have the chance to keep the competitive advantages, and consumers also will have clear and strong preferences for product/service (P/S) during competitive environment. At Taiwan, study abroad service which being provided by travel agencies can be viewed as a new market during the recent years. Most travel agencies intend to develop the novel techniques and mine the useful business intelligence (BI) for improving their quality of P/S or adjusting their strategies of marketing. In this article, a recommendation expert system (ES) based on mental accounting and artificial neural networks (ANNs) is proposed to address the issue of BI mining. The travel agencies can obtain BI relating to consumers’ decision-making, consumers’ responses for different mental accounting and the recommended features for P/S by inputting referenced information into such recommendation ES. Finally, we also take an illustrative example owing to a local travel agency at Taiwan to demonstrate the feasibility and rationality of the proposed architecture.

The managerial significance of behavioral decision theory, Simonson (1993) concludes as “In some situations, consumers do have clear and strong preferences for particular product or service characteristics. In such cases, none of the (behavioral science) manipulations are expected to affect purchase decisions. However, most enterprises can increase their sales significantly by supplementing the voice of the customer with a better understanding of the various “irrational” influences on purchase decisions and translating that knowledge into specific sales, positioning, pricing, and communications tactics.” If the enterprise can successfully design and position their product/service (P/S), consumers will have clear and strong preferences for their P/S during the competitive environment, then the enterprise can have the chance to win most of the battle. Reviewing the relating tourism studies, many scholars studying the effect of previous trip experience on choice behavior have found that past trip experience may impact an individual’s choice decision-making (Kando and Summers, 1971, Mazursky, 1989 and Schreyer et al., 1984). Schreyer et al. (1984) developed the Experience Use History (EUH) theory that suggests previous participation in recreational activities may be utilized as an indicator of the amounts and types of information a person can draw on to make decisions with regards to leisure behavior. They further asserted that EUH may serve as an indicator of motivations for visiting. Beaulieu and Schreyer (1984) expressed the significance of users’ experience on choice behavior more directly. They stated that one of the most important factors affecting choice behavior should be the amount and type of experience a person has with an activity. The transnational sightseeing traveling is day by day vigorous arises along with globalization development or tourism trend. In order to improve the quality of life, the majority people will travel regard as the free time or achieve one of the life style leisure goals. The traveling expectations may be affected with respect to the different personnel characteristics, environment and economy.

In this study, we illustrate a case owing to Taiwanese travel agency’s BI mining applications to demonstrate the feasibility and rationality of the proposed recommendation ES based on the mental accounting and artificial neural networks to address study abroad service. Three important concluding remarks or conclusions can be made as: (1)Including those factors based on the mental accounting into modeling the correlation among motivations, features and decision-making will enhance the rationality and accuracy of recommendation ES. (2) A self-adjustment ES based on the ANNs is proposed to address the real management applications. The core architecture can re-train according to data warehouse update or the real requirements. (3) As for the study abroad service, a recommendation ES is proposed and the same architecture can be applied into many different industries due to the flexibility, rationality and feasibility of the proposed architecture. Until now, this local travel agency at Taiwan had kept to apply this recommendation ES into mining useful BI for the subsequent study abroad projects. And, the managers found out the successful rate (it is defined as the ratio of study abroad without any quarrels or complains) of their study abroad project almost reached 85% (=17/20). This result denotes the quality and goodwill of this travel agency can be enhanced and it also verify the steady effect for our proposed architecture.