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«Warranty and the Risk of Misinforming: Evaluation of the Degree of Acceptance1 Dimitar Christozov Stefanka Chukova American University in Victoria ...»

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Issues in Informing Science and Information Technology Volume 5, 2008

Warranty and the Risk of Misinforming: Evaluation

of the Degree of Acceptance1

Dimitar Christozov Stefanka Chukova

American University in Victoria University of

Bulgaria, Blagoevgrad, Wellington, Wellington,

Bulgaria New Zealand dgc@aubg.bg stefanka@vuw.ac.nz Plamen Mateev, University of Sofia “St. Kliment Ohridski”, Sofia, Bulgaria pmat@fmi.uni-sofia.bg Abstract Nowadays the product warranty plays increasingly important role in any business transaction. It is a valuable attribute of the product and it is used extensively in marketing and advertising. It is a tool to enforce the competing power of the producer on the market place. There is a vast literature on the product warranty related to the product malfunctioning. In this study we address another type of warranty - the warranty of misinforming, which of great importance in the light of indirect business communication, as in e-commerce. Here, we extend previous studies, aiming to provide a more realistic model for quantifying the risk of misinforming caused by information asymme- try. We propose an approach for evaluating the degree of acceptance of the product with respect to individual tasks, which previously was assumed to be a known constant.

Keywords: information asymmetry, misinforming, warranty, risk, degree of acceptance.

1 This research is partially supported by NFSI-BG, Grant No VU-MI-105/2005 Introduction 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 de- tailed outline of an approach for quantifying the risk caused by information asymmetry by briefly summarizing the ideas shared in Chris- tozov, Chukova, and Mateev (2006, Material published as part of this publication, either on-line or in print, is copyrighted by the Informing Science Institute. 2007) and elaborating on the evaluation Permission to make digital or paper copy of part or all of these of the degree of acceptance.

works for personal or

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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. Each product D is represented by a set of L attributes/characteristics.





This set of L attributes/characteristics, noted by C = {Cl}, l = 1, 2, …, L, describes the structure of the product and, in these study, these attributes/characteristics are assumed to be independent.

Description of the Group of Buyers (Customers) and Group of Tasks Description of the group of buyers (customers) The group of buyers is denoted by B = {bj}, j=1, 2, …, n, where bj represents the jth buyer. Each buyer j has a set of tasks that s/he needs to solve.

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Description of the Tasks (Problems) Let Aj = {aij}, i = 1, 2, …, kj, be the set of kj tasks the jth buyer needs to solve using the product D. The set of all tasks A = {UA }, j = 1, 2,..., n, can be structured according to the existence of j tasks that are common for many, not necessarily all, buyers. Each common task specifies a particular category within the set of tasks A. Let Ai*, i = 1,2,..., k denote the ith category of tasks for the set of buyers B. We assume that there are tasks common for all buyers, e.g., surfing the Internet. Different buyers have different needs with respect to this task and different degree of acceptance. For example, some buyers will use Internet to download movies and other large files; others will use Internet only for shopping, which does not require transferring of large data sets, etc.

The “Internet surfing” is a category and “downloading movies” is a specific task from this category. And of course, different buyers have different measures of what is acceptable performance k in using Internet. Then, A* = is the set of all different tasks and, of course, A* = A. Set A UA * i i =1 describes the set of tasks as a union of buyers’ tasks, whereas A* is the same set of tasks from task category point of view, (for details, see Christozov, Chukova, and Mateev (2007)). For completeness, we assume that all buyers need to solve for all tasks, but for some of these tasks the buyer’s need to solve is equal to zero.

Note that for some of the tasks the buyer needs to solve using the product may not be among the tasks the product is design for. Therefore, the probability that the product is suitable to solve for such tasks is equal to zero.

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For example, let the product D is a personal computer (PC) and the attributes of interest are the CPU, RAM, HDD, VRAM and the Ethernet, i.e., C = {C1 =CPU, C2 =RAM, C3 =HDD, C4=VRAM, C5 =Ethernet}, with L = 5. Further, for the task category “word-processing”, we can

–  –  –

The need of a buyer to solve for a task Assume that, the need of the jth buyer to solve for a task from category Ai* is nij, where 0 ≤ nij ≤ 1.

If nij = 0, this means that the jth buyer does not need to solve for task Ai*, while nij=1 means that bj definitely needs to solve for a task from category Ai*. It is reasonable to assume that if nij = 0, then qij=0, i.e., if the buyer j does not need to solve for aij, then every product is acceptable with respect to aij. Therefore, the jth buyer can be described through the set bj={(aij, qij, nij)| aij ∈ Ai*, i=1, 2,…,k, 0 ≤ nij, qij, ≤ 1). We assume that for bj, j=1, 2, …, n, all tasks in A* are presented in his description, but for some of these tasks the corresponding need is equal to zero.

Description of the Seller Next, we focus on the description of the Seller as a party in this communication process. The Seller sells the product D, which is capable of solving tasks from Ai*. Let pi = p(Ai*) be the probability that D is capable to solve for Ai*. If p(Ai*)=0, then D is not suitable at all for solving for this category, whereas, if p(Ai*)=1, D is an excellent choice for solving for Ai*, i.e., D meets any high level of buyer’s need, related to the task Ai*. Further, as a part of its marketing policy, the Seller sends a message to the group of buyers describing the properties/qualities/features of D.

The content of this message is based on the sellers’ evaluation of p(Ai*) and it usually has no information about and cannot take into account the value of {qij}, j=1,2,…,n. In this communication, the information asymmetry is due to the difference between the expertise of the seller and the buyer regarding the product D. The usage of a specialized terminology in the message may increase the level of information asymmetry. Based on this message and his/her background, the buyer j assesses the probability pij = p ( aij ) that the product D is suitable for solving for task aij.

ˆ ˆ ˆ If nij = 0 and/or qij = 0, then there is no need to estimate p (aij ).

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An Illustrative Example Survey To illustrate the ideas described in the previous sections, we conducted a survey and collected information needed to evaluate the risk caused by information asymmetry and to analyze its impact on the choice of the product warranty.

In our survey the product D is a personal computer (PC) with a configuration given in the Appendix. This configuration is an updated version of the PC configuration used in Christozov, Chukova, and Mateev (2007). The survey consists of two parts. The first part addresses the assessment of the degree of acceptance of the attributes/components of the product, while the second part is a repetition of the survey used in Christozov, Chukova, and Mateev (2007).

The two parts of the survey were offered to respondents in two, one-hour apart, stages. Firstly, the respondents were asked to fill in Form 1, which addresses the degree of acceptance and how often they need to solve for a particular task. After approximately one hour, the second part of the survey was given out. The time gap between the first and the second part of the survey was introduced because we wanted to reduce the dependence between the information used to evaluate the degree of acceptance for the different PC components and the configuration of the particular PC given in the second part of the survey.

The survey was conducted on two independent groups of respondents. The first part of the survey (Form 1) was identical for both groups. The number of respondents of the two groups was 53 and

10. The second part was divided into two forms. The first form of the second part of the survey offers a choice of two warranty policies: one for malfunctioning and one for misunderstanding, whereas the second form of the second part of the survey extends the list choices by an additional one, which is a mixture between the two policies offered in the first form of the second part of the survey. Each of the groups of respondents was given only one of the forms of the second part of the survey. The responses for Form 1 for both groups were collected in one dataset, while the data from the second part of the survey for the two groups were considered separately.

Results of the Survey The two forms of the survey are given in the Appendix. In the next section we present a summary of the data and their analysis, followed by recommendations regarding the best choice of the product warranty. A comparison between our findings and Christozov, Chukova, and Mateev (2007) findings is also provided.

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Further, in the assessment of the risk of misinforming, we use the values of the degree of acceptance of the product with respect to the tasks as given in Table 2. In Christozov, Chukova, Mateev (2007) it was assumed that the degrees of acceptance with respect to different tasks are identical, equal to q = 0.5. In this study, as shown in Table 2, these values are task-dependent, but they still do not take into account the distinctiveness of the buyers.

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Interpretation The estimated degrees of acceptance, shown in Table 2, are lower than the assumed value of qij = q = 0.5 for i=1,2,…,n, and i = 1, 2, …, kj in Christozov, Chukova, Mateev (2007). Of course, these, lower degrees of acceptance affect the level of the risk of misinforming. As expected, and it was confirmed by our analysis, it led to the reduction of the risks. For, example, 0.213 vs. 0.296 for the overall adjusted buyers’ risk and 0.438 vs. 0.492 for overall unadjusted buyers’ risk in the case of three forms of warranty. This effect was expected, because a lower degree of acceptance

672 Christozov, Chukova, Mateev

means, roughly speaking, lower requirements towards the quality of the product, which of course, leads to less number of wrong purchase decisions and respectively lower risk of misinforming.

The risk for the seller shows to be high enough to deserve attention. For this level of risk, because of the domination of optimists, the seller has to offer warranty of the third type. Also, it is clear that a small proportion of respondents recognize their inability to judge properly the qualities of the product. These observations coincide with the findings in Christozov, Chukova, Mateev (2007).

Conclusion and Future Work



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