دانلود مقاله ISI انگلیسی شماره 687
عنوان فارسی مقاله

از کلیک کردن تا جلب توجه: رویکرد هوش کسب و کار (هوش تجاری) به محاسبه احتمال مورد توجه قرار دادن مصرف کنندگان

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
687 2012 9 صفحه PDF سفارش دهید 6600 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
From clicking to consideration: A business intelligence approach to estimating consumers' consideration probabilities
منبع

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

Journal : Decision Support Systems, Available online 6 November 2012

کلمات کلیدی
تجارت الکترونیکی - هوش کسب و کار - بازاریابی آنلاین - احتمال جلب توجه - فیلترینگ مشارکتی - مدل کلاس نهان -
پیش نمایش مقاله
پیش نمایش مقاله از کلیک کردن تا جلب توجه: رویکرد هوش کسب و کار (هوش تجاری) به محاسبه احتمال مورد توجه قرار دادن مصرف کنندگان

چکیده انگلیسی

With rapid advances in e-commerce applications and technologies, finding the chance that a product falls into a consumer's consideration set after being inspected (i.e., consideration probability, CP) becomes an important issue of recommendation services and marketing strategies for both academia and practitioners. This paper proposes a novel business intelligence (BI) approach (namely, the two-step estimation approach, TEA) to estimating CPs with a two-step procedure: one is to introduce partial belongings of consumers to the latent classes with both positive and negative preferences (tastes); the other step is to generate CPs based on the degrees of partial belongings in a weighted probability manner. Experiment results from different online shopping scenarios reveal that TEA is effective and outperforms the traditional latent class model.

مقدمه انگلیسی

Considering the costs of search, consumers are often unable to evaluate all products before making purchase decisions [21], [28] and [47]. Therefore, they tend to adopt a consider-then-choose process in which a consumer first selects a small group of products as a consideration set (also known as choice set or evoked set) and then chooses one of them to purchase [20], [39], [56], [58], [59] and [63]. For example, when shopping online, a consumer first inspects and selects some promising products into a shopping list from recommendations provided by consumer decision support systems (CDSSs, such as search engines or recommender systems), then deeply evaluates these selected products in a comparison matrix (a special type of decision aids that allow consumers to sort products by any attribute in an “products attributes” matrix) to choose the favorite one [16], [17], [21] and [63]. The set of products added into the shopping list (comparison matrix) can be viewed as the consideration set which is the output of the first stage (consideration stage) and the input of the second stage (choice stage) [7], [21] and [36]. Compared to the process that directly chooses a product from all available ones, the consider-then-choose process is deemed typical and even more rational [23]. Thus, it becomes a primary focus of attention for e-sellers and e-marketplaces to estimating the probability that a product falls into a consumer's consideration set after being inspected, namely, consideration probability (CP) [15], [30] and [39]. Compared with traditional brick-and-mortar stores where the behavior of inspecting is hard to observe and record, e-marketplaces are able to easily trace consumers' clicking behavior which can be seen as a strong signal of inspecting in online shopping [39]. Therefore, “click” and “inspect” are used interchangeably unless otherwise indicated in this paper. Fig. 1 illustrates the consider-then-choose process.

نتیجه گیری انگلیسی

In this paper, we have developed a novel approach, namely the two-step estimation approach (TEA), to estimate the consumer's consideration probabilities (CPs), which is important to consumers as well as to e-sellers and e-marketplaces. The approach has extended the traditional latent class model (LCM) by reflecting partial belongings of consumers to classes, and considering the customers' preferences (tastes) in a both positive and negative manner. Subsequently, it has generated CPs using the partial belonging degrees in light of weighted probability. Moreover, experiments have been conducted with varying parameters, revealing that TEA outperformed LCM significantly, especially in the cases where consumers inspected or considered more products.

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