• Korean Management Science Review
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• v.25 no.1
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• pp.139-147
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• 2008

Conjoint Analysis is marketers' favorite methodology for finding out how buyers make trade-offs among competing products and suppliers. Thousands of applications of conjoint analysis have been carried out over the past three decades. The conjoint analysis has been so popular as a management decision tool due to the availability of a choice simulator. A conjoint simulator enables managers to perform 'what if' question accompanying the output of a conjoint study. Traditionally the First Choice Model (FCM) has been widely used as a choice simulator. The FCM is simple to do, easy to understand. In the FCM, the probability of an alternative is zero until its value is greater than others in the set. Once its value exceeds that threshold, however, it receives 100%. The LOGIT simulation model, which is also called as "Share of Preference", has been used commonly as an alternative of the FCM. In the model part worth utilities aren't required to be positive. Besides, it doesn't require part worth utilities computed under LOGIT model. The simulator can be used based on regression, monotone regression, linear programming, and so on. However, it is not free from the Independent from Irrelevant Alternatives (IIA) problem. This paper proposes the EBA (Elimination-By-Aspects) model as a useful conjoint-like method. One advantage of the EBA model is that it models choice in terms of the actual psychological processes that might be taking place. According to EBA, when choosing from choice objects, a person chooses one of the aspects that are effective for the objects and eliminates all objects which do not have this aspect. This process continues until only one alternative remains.

• Journal of Distribution Science
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• v.14 no.11
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• pp.61-73
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• 2016

Purpose - Marketing scholars have developed various types of mathematical models for describing marketing phenomenon, because there is no single model comprehensive enough to incorporate all the relevant marketing phenomena. This study tries to summarize the behavioral foundations and the mathematical derivations of the most widely used marketing models and discusses their strategic implications. This study selected four representative marketing models: multinomial logit(MNL) model, elimination-by-aspects(EBA) model, Hauser and Shugan model and Bass diffusion model. Especially, this study focuses on Hauser and Shugan(1983)'s Defender model and discusses the model's behavioral foundation and its implications. Research design, data, and methodology - Of the four selected model, the multinomial logit model is selected as the basic normative model and the other three models are described as descriptive models in contrast. Starting the discussion from the multinomial logit model, this study explains what important strategic variables are incorporated in each of the four models. The IIA(independence of irrelevant alternatives) axiom and Luce choice model is also discussed in relation to the multinomial logit model. The concept of 'efficient frontier' is discussed in relation to Hauser and Shugan's model. Graphs and tables are used to represent the key implications. No empirical study is included. Results - The analyses of the mathematical marketing models are shown to be very useful in understanding the essence of positioning strategy. The multinomial logit model implies the importance of increasing utility or consumer preference level. The EBA model implies the importance of lowering the inter-brand similarity and dominating the competitors. Hauser and Shugan model implies the importance of considering customer heterogeneity distribution in selecting the target market. Conclusions - It is shown that the concepts of 'efficient frontier' is useful in understanding the effectiveness of positioning strategy. Market positioning can be understood as occupying some place on the efficient frontier. The important strategic implications can be summarized as follows: Always try to increase customer preference by providing what they value, and differentiate from competing alternatives as much as possible. The best positioning strategy is to dominate all the competitors and the worst is to be dominated by the competitors.

• Journal of Distribution Science
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• v.11 no.5
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• pp.63-76
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• 2013

Purpose - For the past several decades, behavioral economics or behavioral decision theory has undergone rapid development. This study provides a critical review of the development of behavioral economics with a focus on what are deemed to be core theories in the field. Starting from the utility function proposed by Daniel Bernoulli in the 18th century, the development history of utility functions until the emergence of the prospect theory is thoroughly reviewed. Some of the experimental results violating the traditionally assumed utility function and supporting the prospect theory value function are summarized. The most representative principles of rational choice are transitivity, independence from irrelevant alternatives (IIA), and regularity. The development of behavioral economics has been triggered by finding counter-examples to these principles. Some of the choice behaviors discussed in this study as counter-examples to the traditional theories of rational choice are the St. Petersburg paradox; the Allais paradox; gambling behavior; and the various context effects including the similarity effect, attraction effect, and the compromise effect. The Elimination-by-Aspects (EBA) model, which was proposed as an explanation for the similarity effect, is discussed in detail as well. Based on the literature review and further analysis, this study summarizes the relationship between the context effects, prospect theory, and EBA model. Research design, data, and methodology - This study provides an extensive literature review on several important theories in the field of behavioral decision theory and adds some critical comments to the theories and the relationships among them. This study first reviews the development of utility functions. Daniel Bernoulli introduced the concept of utility function to solve the St. Petersburg paradox. In the mid-20th century, Herbert Simon proposed the "satisficing" heuristic and presented a value function with a shape different from traditional utility functions. This study highlights the strengths and weaknesses of several utility functions proposed until the emergence of the prospect theory value function. Results - This study posits that prospect theory and EBA model are the two most important theories in the field of behavioral decision theory. They can explain various choice behaviors that traditional utility maximization analysis has been unable to. The application of these models to various fields is further increasing nowadays. This study explains how prospect theory and the EBA model can be used to explain the context effects. Conclusions - The traditional economic theory relies on a single variable called "utility" in explaining consumer choice. However, this study argues that, in investigating consumer choice, several other variables should also be considered. These are the similarity among alternatives, an alternative's prototypicality within the category, the dominance relationship between alternatives, and the reference point in evaluating alternatives. Due to the development of behavioral economics, we are now closer to a more complete understanding of consumer choice behavior than in the past when we had only a single tool called utility.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.243-251
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• 1993

With the recent issuance of a dynamic side impact test regulation in the Federal Motor Vehicle Safety Standard in the United States of America, many aspects of occupant protection in side impact crashes have been under investigation. Many investigations of real world accidents, crash test results and simulation studies have established that in side impact crashes of passenger cars, thoracic and pelvic injuries of occupant are, large part, caused by occupants' impact against the interior side of the vehicle, primarily the door. This paper is concerned with the development of a lumped mass computer model, which simulates the interaction of a struck car door and an adjacent seated occupant in side impacr, based CTP code which has been successfully used in vehicle and occupant simulation. New model developments include elimination of influence of vehicle side structure stiffness in the occupant injury responses. The model was used to investigated the effect of various door padding characteristics on occupant responses to improve vehicle safety performance. The evaluation of different crush properties of door padding have also focused to understand of behavior of impacted occupant. Results from simulations, The effects of both material coefficients $C_{f}$ and p were illustrated in terms of occupant injury criteria TTI and pelvis.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.565-574
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• 2012

This study focuses on a consumer-based design process that overcomes practical drawbacks of the previously used design process. On the contrary of the existing method of attracting design attributes by designers' own insights, it present the PRETREE model that attracts the important design attributes of the products based on consumer preferences. The PRETREE model has the advantage that it does not require identifying design attributes a priori. For the wire-wireless telephones, this study presents the identifying process the important design attributes empirically using PRETREE Model. The PRETREE Model has been widely used in the fields of psychology, consumer science, economics and business administration. It might also be useful in the design field because it can identify the important design attributes objectively without designers' own insights.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.