یک سیستم منطق محور فازی برای ارزیابی سطح اعتماد کسبوکار به مصرفکننده (B2C) در تجارت الکترونیک
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|3413||2005||6 صفحه PDF||12 صفحه WORD|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 28, Issue 4, May 2005, Pages 623–628
The purpose of this paper is to present an application of fuzzy logic to human reasoning about electronic commerce (e-commerce) transactions. This paper uncovers some of the hidden relationships between critical factors such as security, familiarity, design, and competitiveness. We analyze the effect of these factors on human decision process and how they affect the Business-to-Consumer (B2C) outcome when they are used collectively. This research provides a toolset for B2C vendors to access and evaluate a user's transaction decision process, and also an assisted reasoning tool for the online user.
During online shopping, a user often relies on common sense and applies vague and ambiguous terms when making a buying decision. Online customer normally develops in his/her mind some sort of ambiguity, given the choice of similar alternative products and services (Mohanty & Bhasker, 2005). Decisions to buy or not to buy online are often based on users' human intuitions common sense and experience, rather than on the availability of clear, concise and accurate data. Fuzzy logic is used for reasoning about inherently vague concepts (Lukasiewicz, 1970), such as ‘online shopping is convenient’, where level of convenience is open to interpretation. The purpose of this research is therefore to apply the fuzzy logic to human reasoning where we specifically focus on the reasoning processes behind e-commerce transactions. Fuzzy systems allow the encoding of knowledge in a form that can be used to reflect the way humans think about a complex problem such as online shopping. A human usually think in imprecise terms such as high and low, fast and slow, and heavy and light (Black, 1937). Fuzzy expert system model imprecise information, by attempting to capture knowledge in a similar fashion to the way in which it is considered to be represented in the human mind, and therefore improves cognitive modelling of a problem (Cox, 1994). As a result, fuzzy logic is leading to new and human-like, intelligent systems that might be used to understand the thought processes behind any B2C transactions. The rationale for using fuzzy logic systems to uncover vague decision process because it is well suited for modeling human decision-making. Human decision-making is complex, and can be based on simultaneous evaluation of many facets such as fear, experience, privacy, intuition and so forth. Though many factors influence the decision process of B2C transactions such as ease-of-use, pricing, convenience, and security (Akhter et al., 2003), the perception of an influencing feature is more important than the actual level of the feature itself. For example if the perceived security level is higher than its actual implementation then that will contribute positively to the level of B2C outcome. There may be cases where the reverse is true as well, but for such cases a high level of persuasion will be needed to alter the perception level. This research had adopted a fuzzy logic approach and utilized a mathematical research toolset known as Matlab fuzzy logic toolbox® to provide a means of coping with the ambiguity and vagueness that are often present in determining a transaction level in e-commerce. To build a fuzzy expert system for B2C e-commerce that is based on fuzzy logic, this research has captured, organised and used human expert knowledge (acquired by surveys and interviews). This research proposed to organise knowledge in terms of its logical groupings such as security, familiarity, design layout, competitiveness and trust. This paper is organized as follows. Section 2 discusses research methodology that demonstrates the value of qualitative technique for inquiry and analysis of data. Data collection and analysis explained in Section 3. Section 4 covered the rules providing a measure for Trust and B2C levels. Section 5 explained the analysis of different factors influencing Trust. Section 6 visualizes the Trust level and Section 7 draws our conclusions.
نتیجه گیری انگلیسی
The e-commerce has given increased choices to consumers due to the growth in the number of online Websites offering products with many variations. In our paper, a tool is defined to assist consumers and vendors to analyse the level of perceived trust in a specific Website. The consumers can broadly be categorized into two groups, namely those who are technically critical of a site and capable of measuring its security features and those who are not. This survey can be used to step by step follow the instructions and based on actual level of a feature decide its contribution in a category and consequently derive a total value of a factor say Security. Hence the survey can make a buying decision more solid, based on actual appearances of various features. An added advantage would be to feed this data to the FIS for Trust and B2C and the user could compare his/her buying decision with that of others based on the outcome of the fuzzy expert system. For those who are not necessary technically inclined this survey will assist them in trying to gauge the presence of a feature, say security seals and attach to it a certain contribution (i.e. yes=2 no=0 and don't know=1). After all the requirements for Trust is completed the FIS for Trust could be used to provide a perceived level that the user could compare with his own. Similarly the B2C level could both be guessed by the non-technical user and also computed based on input valued and presented to the user for comparisons. Now since this category of users is non-technical the perceived B2C level could also be compared with previous users who were technically inclined and presented as a possible different B2C level to reflect upon. This procedure would assist even the non-technical user to make an informed online purchase. The vendor would benefit from the survey data that is aggregated over time and is used to amend or refine existing rule-sets. Since the data would be accumulated over time the responses would be a blend from both technical and non-technical users. Hence the actual occurrence of a feature would be replaced by its perceived equivalence. Since the existence of a feature is only relevant to the user if it can be acknowledged, and if it can not then the vendor must seriously reconsider inclusion of this aspect on the website. In addition the vendor can use the survey data to ascertain the Trust level of the site as per user's perception and rectify if needed if this is not obvious or is having a negative impact on the Trust level. Furthermore a measure of the competitiveness is directly deductible from this survey and could be used to retain or increase market share. Lastly as the usage of the survey procedure matures (possibly by providing incentives as discounts on a completed transaction) the Fuzzy Inference Systems could be modified and adjusted where necessary. One limitation of the constructed FIS of this study is that all premises in the antecedent part of a rule have been connected with AND operation where OR operations could also be deployed. The implication and aggregation from the rule would then be significantly different.