Academic journal article Issues in Informing Science & Information Technology

Warranty and the Risk of Misinforming: Evaluation of the Degree of Acceptance

Academic journal article Issues in Informing Science & Information Technology

Warranty and the Risk of Misinforming: Evaluation of the Degree of Acceptance

Article excerpt


This paper is an extension of our previous findings presented in Christozov, Chukova, and Mateev (2006, 2007). We address an open problem posted earlier related to the evaluation of, so called, degree of acceptance of a product by a client. Here, we propose an approach and illustrate a useful procedure for evaluating this degree of acceptance. So, in this paper we provide a detailed outline of an approach for quantifying the risk caused by information asymmetry by briefly summarizing the ideas shared in Christozov, Chukova, and Mateev (2006, 2007) and elaborating on the evaluation of the degree of acceptance.

The phenomenon of information asymmetry between two parties occurs when one of the parties has better understanding and is better informed on the subject of communication than the other one. There are several aspects of information asymmetry that have attracted the interest of researchers. The concept of information misbalance originates in Arrow (1963). His ideas were further developed by Akerlof (1970) in his paper "The Market for 'Lemon's", where the term "information asymmetry" was firstly introduced. Akerlof investigated the influence of asymmetric information on the market value of a commodity and his ideas initiated studies on the impact and usage of the information asymmetry to improve the influence in business relationships. Slovac (1993) studied the asymmetric impact of negative and positive information on the social trust, known as principle of Information Asymmetry or Trust Asymmetry. White and Eiser, 2005 continue this line of research. The role of information asymmetry as a source of misinterpretation, which results in misinforming and/or misleading in a sales/purchase process and might lead to wrong purchase decisions has never been studied at the level it deserves. Some authors (Hseih, Lai, & Shi, 2006) consider the impact of information asymmetry on the success in business transactions, but they do not go beyond recommendations on how to improve the information process. Christozov, Chukova, and Mateev (2006, 2007) proposed a model to quantify the risk of misinforming, caused by information asymmetry and the current paper extends their study.

The outline of this paper is as follows. The next section provides a brief description of the model for quantifying and evaluating the risk of misinforming. It emphasizes on the new approach of collecting data and evaluating the degree of acceptance. Following that we provide an illustrative example. We conclude with a few ideas for further research.

Quantitative Measure of the Risk of Misinforming

Next, we extend the model for quantifying the risk of misinforming, proposed in Christozov, Chukova, and Mateev (2007) study. One of the main difficulties in quantifying the risk of misinforming is that the risk is subjective, i.e., one and the same message containing information on the product of interest, may convey correct information to some customers and misinform others. This misinformation can have different degrees and consequences for the individual customers within a given group of customers. A message describing the product may inform some of the customers correctly regarding several properties or features of the product, as well as abilities of the product to solve for a particular task or category of tasks, and at the same time, it can misinform them regarding some other features or tasks. Our model aims to allow measuring the risk of misinforming at the task level. In order to simplify the model, we will model only the risk of misinforming between a single producer/seller to a group of customers/buyers regarding a single product. In order to quantify the risk of misinforming, caused by the information asymmetry, we need to identify and measure the factors, which influence this risk

Description of the Product

We denote the product by D. …

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