• Information Systems Review
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• v.14 no.1
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• pp.85-103
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• 2012

ISMP is a method of writing clearly the user requirements in the RFP(Request for Proposal) of the IS development projects. Unlike the conventional methods of RFP preparation that describe the user requirements of target systems in a rather superficial manner, ISMP systematically identifies the businesses needs and the status of information technology, analyzes in detail the user requirements, and defines in detail the specific functions of the target systems. By increasing the clarity of RFP, the scale and complexity of related businesses can be calculated accurately, many responding companies can prepare proposals clearly, and the level of fairness during the evaluation of many proposals can be improved, as well. Above all though, the problems that are posed as chronic challenges in this field, i.e., the misunderstanding and conflicts between the users and developers, excessive burden on developers, etc. can be resolved. This study is a case study that analyzes the execution process, execution accomplishment, problems, and the success factors of two pilot projects that introduced ISMP for the first time. ISMP performance procedures of actual site were verified, and how the user needs in the request for quote are described was examined. The satisfaction levels of ISMP RFP for quote were found to be high as compared to the conventional RFP. Although occurred were some problems such as RFP preparation difficulties, increased workload, etc. due to the lack of understanding and execution experience of ISMP, in overall, also occurred were some positive effects such as the establishment of the scope of target systems, improved information sharing and cooperation between the users and the developers, seamless communication between issuing customer corporations and IT service companies, reduction of changes in user requirements, etc. As a result of conducting action research type in-depth interviews on the persons in charge of actual work, factors were derived as ISMP success factors: prior consensus on the need for ISMP, the acquisition of execution resources resulting from the support of CEO and CIO, and the selection of specification level of the user requirements. The results of this study will provide useful site information to the corporations that are considering adopting ISMP and IT service firms, and present meaningful suggestions on the future study directions to researchers in the field of IT service competitive advantages.

• Asia Marketing Journal
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• v.13 no.3
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• pp.55-77
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• 2011

This study investigate the interaction effects of bundle price discount framing and message framing on consumer's attitude of bundle component. Although each effect of bundle price discount framing and message framing has been explored individually, few attempts have been made to invest them jointly. This study tests the interaction effects of bundle price discount framing and message framing on consumer's evaluation of bundle component. Moreover, this research focuses on consumer's evaluation of individual bundle component while the existing research on bundling primarily focused on consumer's evaluation of the bundle. Prior research suggests that consumers are sensitive to the framing of prices and discounts in the presentation of the bundle offer. For example, there is considerable evidence that partitioning or consolidating the prices of a bundle can influence the attractiveness of the bundle offer. Similarly, there is evidence that an equivalent price reduction to the overall bundle, one of the individual products in the bundle, or distributed among the individual products in the bundle can alter the perceived attractiveness of the offer (e.g. Chakravarti, Krish, Paul, and Srivastava 2002; Hamilton and Srivastava 2008; Janiszewski and Cunha 2004; Johnson, Herrmann and Bauer 1999; ; Morwitz, Greenleaf, and Johnson 1998; Yadav 1994; 1995). In line with these earlier research, this research suggests that the bundle type can influence the consumer's evaluation of bundle component. There are two types of bundle - mixed-leader bundle and mixed-joint bundle. In mixed-leader bundling, the price of one of the two products is discounted when the other product is purchased at the regular price. In mixed-joint bundling, a single price is set when the two product are purchased jointly. This study supposes that the teeth whitening product is the leader product in a mixed-leader bundle. So bundle price discount framing is manipulated such as "Buy the teeth whitening product (regular price \80,000) and get 50% discount on the functional toothpaste(regular price \40,000), special set price \100,000" or "Buy the functional toothpaste and the teeth whitening product as a set and get discount for the set, special set price \60,000". Message framing is manipulated through the product claims described in an advertising bill. The positive framing presents that "Over 95% of users achieved the expected 2-3 shades of improvement in two weeks" where as the negative framing presents "less than 5% of users did not achieve the expected 2-3 shades of improvement in two weeks". This study uses hypothetical brand name of the teeth whitening product and the functional toothpaste This study is based on a 2x2 factorial design with bundle discount framing (mixed-leader bundle vs. mixed-joint bundle) and massage framing (positive vs. negative). The dependant variables are consumer's perceived quality and attitude of the teeth whitening product The data reveals that two dependant variables are correlated, so the data is analyzed with two-way MANOVA. This research explores the significant interaction effect of bundle discount framing and message framing on consumer's perceived quality and attitude of the teeth whitening product. When the message framing is positive, consumer's perceived quality and attitude of the teeth whitening product is higher in mixed-leader bundle than mixed-joint bundle condition. However, when the message framing is negative, consumer's evaluation is higher in mixed-joint bundle than mixed-leader bundle. The author explains this result by stating that consumers are less likely to use heuristics such as price-quality association and value discounting hypothesis(Raghubir 2004) in the negative message framing condition. Additionally, consumer's perceived risk of the teeth whitening product in the negative message framing condition can be more reduced by the bundle partner(e.g. the toothpaste) in mixed-joint bundle than mixed-leader bundle. Based on the results, marketing managers are advised to use different bundle type based on message framing of their product.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.