ارزیابی تبلیغات در یک مدل تصمیم گیری سلسله مراتبی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی|
|2184||2013||23 صفحه PDF||22 صفحه WORD|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Annals of Tourism Research, Volume 40, January 2013, Pages 260–282
پاسخ تبلیغاتی دریک فرایند تصمیم گیری چند مرحله ای
نمونه ی سنجش ها
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نمونه ی سنجش ها
Many destination marketing organizations in the United States and elsewhere are facing budget retrenchment for tourism marketing, especially for advertising. This study evaluates a three-stage model using Random Coefficient Logit (RCL) approach which controls for correlations between different non-independent alternatives and considers heterogeneity within individual’s responses to advertising. The results of this study indicate that the proposed RCL model results in a significantly better fit as compared to traditional logit models, and indicates that tourism advertising significantly influences tourist decisions with several variables (age, income, distance and Internet access) moderating these decisions differently depending on decision stage and product type. These findings suggest that this approach provides a better foundation for assessing, and in turn, designing more effective advertising campaigns.
Tourism advertising is regarded as one of the most influential information sources for prospective and current visitors (Burke and Gitelson, 1990, Gretzel et al., 2000, Kim et al., 2005 and USTA, 2011). Recently, many tourism destination organizations (DMOs) in the United States and elsewhere have been challenged by state budget cuts which have led to strong pressure to defend funding for destination-specific tourism advertising (Papatheodorou et al., 2010, Ritchie et al., 2010, Spring, 2010 and USTA, 2011). Indeed, USTA, 2009 and USTA, 2011 reported that the average state tourism office budget in the United States for 2009 is $353 million, which represents a 3.5 percent decrease as compared to the previous fiscal year, and is the first time in the past five years that the growth of the annual tourism budget has declined. Kim McClelland, Chairman of the Utah Board of Tourism, in discussing the challenges facing tourism promotion said: “I think what will happen is we’ll have to spend the money even smarter than we have in the past … I think all the states across the country, I just have to believe, are dealing with similar budget challenges” (Gainesville.com, 2008). This economic situation facing travel agencies clearly demonstrates that the estimation of advertising effects on tourist behavior remains a crucial research challenge for tourism researchers (Shields, 2006 and USTA, 2011).
نتیجه گیری انگلیسی
This study considers for the first time the influence of advertising within a staged decision framework where the tourist first chooses whether or not to visit a destination, and second, decides to purchase products featured in an advertisement. As a “refinement” of the second decision, the purchase of specific types of advertised products is also considered (a third stage). Consequently, this article contributes to the tourism literature in a number of important ways. First, the implementation of a staged model allows for the identification of differential advertising influences depending on both the decisions on destinations and products (1st and 2nd decisions) and the product type (3rd decision). As part of analysis, the results of this study indicate that the influence of advertising differs significantly depending upon stage of the decision making process and upon the tourism products under consderation. Second, it is argued that the proposed model better reflects what happens in people’s mind when making decisions (first, where to go and then, what to buy) in that it attempts to better mimic the decision processes within an advertising context; as such, it enables the estimation of the differential impact of advertising. Thus, this model enables the destination marketing organization to consider important correlations that may exist between different decisions, and avoid the potential bias that could come from using different samples or from using a single sample with separate estimations (one for each decision).