دانلود مقاله ISI انگلیسی شماره 29125
عنوان فارسی مقاله

تجزیه و تحلیل ویژگی خستگی در توسعه محصول با استفاده از شبکه های بیزی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
29125 2011 7 صفحه PDF سفارش دهید 5295 کلمه
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عنوان انگلیسی
Feature fatigue analysis in product development using Bayesian networks

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

Journal : Expert Systems with Applications, Volume 38, Issue 8, August 2011, Pages 10631–10637

کلمات کلیدی
تجزیه و تحلیل ویژگی خستگی - شبکه های بیزی - ترجیحات مشتری - توسعه محصول -
پیش نمایش مقاله
پیش نمایش مقاله تجزیه و تحلیل ویژگی خستگی در توسعه محصول با استفاده از شبکه های بیزی

چکیده انگلیسی

The construct of “feature fatigue” represents the phenomenon of customer’s inconsistent satisfaction: customers prefer to choose products with more features and capacities initially, but once actually worked with a product they will find the complex ones are too hard to use. Clearly, customer’s dissatisfaction after use will have a negative effect on company’s long-term revenue, and the inconsistence is a big challenge for firm’s product development. Researchers have proposed some methods to “defeat” feature fatigue, however, most recent research just analyzes features one by one and ignore the relationships among them. Another problem is that the uncertain nature of customer preferences has not been paid enough attention. To solve these problems, a probability based methodology for feature fatigue analysis is proposed, in which Bayesian network techniques are used to represent the uncertain customer preferences for capacity and usability. And in this method, sensitivity analysis is implemented to identify the key features that affect feature fatigue most, and the relationships among features are analyzed using Bayesian network inference. An example is given to illustrate the usage of the proposed method in product development process. Research highlights ► A probability based methodology is proposed for feature fatigue analysis. Bayesian network techniques are used to represent customer preferences. Key features that affect feature fatigue are identified using sensitivity analysis. Relationships among features are analyzed using Bayesian network inference.

مقدمه انگلیسی

Intuition and past research suggest that customers usually buy products based on the number of provided features, and they are often seduced by extra features in the moment of purchasing (Kruger et al., 2007, Nowlis and Itamar Simonson, 1996 and Venkatesh and Mahajan, 1993). In today’s competitive environment, satisfying customer needs has become a great concern, and companies try their best to develop products with more features and capacities (Chen and Wang, 2007 and Jiao et al., 2006). However, many studies show that people are poor predictors of their own enjoyment and happiness, especially when they are facing products with too many features (Mandel & Nowlis, 2008). That means satisfaction and dissatisfaction in the moment of purchasing is not necessarily equal to customers’ experiences after using (Löfgren & Witell, 2008). Actually customers often overestimate the utility of extra features prior to purchasing, and after use, they will complain and even return products considering the problem of usability or mismatch with their expectations (Keijzers, den Ouden, & Lu, 2008). So just focusing on how to attract customers by high-feature products will not be helpful for seller’s long-term revenue. Many cases have been reported to show this problem. For example, a study points that 63% of mobile phone returns in UK has no hardware or software fault but the reported problems relate to usability like issues about the configuration of the handset (Keijzers et al., 2008). Another case is the BMW 7-Series cars, whose dashboard contains over 700 features. This kind of high capacity car is truly attractive in the first moment, but most of the owners are frustrated by the multi-function displays and multi-step options in the complicated system, and their dissatisfaction will affect BMW’s sale in a long term (Rust, Thompson, & Hamilton, 2006). To represent the phenomenon of customer’s inconsistent satisfaction, Thompson, Hamilton, and Rust (2005) used the construct of “feature fatigue” (FF). Based on some case studies, they indicate that capability and usability are two important factors to affect customer’s long-term satisfaction. When buying products such as a cell phone, even though customers know that too many features will lead to usability problem, they still tend to choose high-feature models because capability gets more attention in this moment. However, after working with a product actually, customers will find that usability becomes more important as products with more features are harder to use. So adding numerous features can increase the perceived capability of loaded product, but at the same time it will reduce the perceived usability. To examine how many features are suitable for a product, Thompson et al. (2005) propose an analytical model considering both before and after use, which can help manager to balance sales benefits and customer usability cost of adding features. To date, the problem of FF has been recognized as an important issue in many fields, and some research has been reported to explain or “defeat” FF. But there are still some limitation and shortcoming in previous research. Facing the problem as which feature should be added to a product considering FF, Thompson et al. (2005) just focus on the total number of features but ignore the difference between them. Some researchers have recognized that different feature has different impact on customer’s perceived capability and usability. Typically they try to divide features into different classes like hedonic and utilitarian ones (Gill, 2008 and Tsai and Liu, 2007), but the processes of evaluation and classification are usually one by one, ignoring the relationship analysis among features. In fact, as a product is combined with a set of functions and features, the same feature, no matter hedonic or utilitarian, will have different impact on FF in different combination. For example, if adding FM radio to a cell phone which contains MP3 player function, the perceived capacity may not increase significantly as adding the radio to a simple phone which has no entertainment function. As for usability, customers may not feel the product becomes harder to use if they have paid attention to learn how to use the MP3 player. But for simple phone users who have no related experiences, the FM radio will set the product to a higher complexity level. So when making feature adding decisions, the relationships among features contained in the product should be considered from both capacity and usability aspects. Furthermore, the uncertainty nature of customer preferences is a big challenge for the research of FF. When asking questions like “How do you feel the capacity of product A”, customers will give their answers depending on their own experience, feeling, context and even their mood at that time (Corney, 2000). As FF analysis is mainly based on customer preferences for capacity and usability (Rust et al., 2006), the uncertainties must be quantified and combined in the analysis process, to make sure that all decisions are taken on a rational basis. It is, of course, an extremely complex task because of the large number of features and their relationships to be considered and the large uncertainty associated with customer preferences for the whole product and each individual feature. To overcome the existing problems, we propose a probability based methodology to deal with the task of FF analysis in product development. In this method Bayesian network is used to represent the structure of a product. And we will describe how to build and use Bayesian network based on customers’ perceived capability and usability data. The proposed methodology will address the issues concerned with FF embedded with uncertainties of customer preferences and will help decision-makers to make intelligent decisions during the process of product development. The remainder of this paper proceeds as follows. In the next section, we elaborate on the recent literature on FF. In Section 3, the proposed method is introduced. And an example is presented to illustrate the usage of this method in Section 4. Finally, in Section 5, conclusions are given.

نتیجه گیری انگلیسی

In this paper, a probability based methodology was proposed to analyze the effect of FF on company’s long-term revenue. Bayesian network techniques were used to represent the uncertain customer preferences for capacity and usability. In order to construct a Bayesian network, customers were asked to evaluate the capacity and complexity (lack of usability) level of the whole product and each individual feature. Based on these evaluation data Bayesian networks were built and used for the FF analysis task. Sensitivity analysis was implemented based on entropy reduction to identify the key features that affect product’s FF most. Experts in the company could determine the capacity and complexity level of these key features from both marketing and engineering concerns. After that some candidate products were constructed, and their FF degree could be got through calculation based on the Bayesian networks. And using inference techniques the relationships among features were analyzed by comparing the prior and posterior probabilities. The method proposed in this paper can guide decision-makers in marketing and engineering to make the most influential decisions in the process of product development. As the application of probabilistic graphical models, the uncertainty problem of customer preferences for capability and complexity could be handled efficiently. If enough data are hard to get in some situations, the Bayesian network can be constructed through experts’ domain knowledge and the proposed method can still work for FF analysis. As a closing remark, we are aware of the limitations within this study. One is that to build a Bayesian network, the features contained in a product should be given first. If decision-makers want to delete/add one or some features, they should rebuild Bayesian network from the initial data gathering step. Another limitation is that the data used in this study just focuses on “present” customer preferences. In fact, customer preferences are dynamic and may vary drastically from time to time (Chen & Wang, 2008). So our future work is to be aimed at dealing with the problem of changeable features and dynamic customer preferences in the process of defeating FF.

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