• 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.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• International Commerce and Information Review
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• v.18 no.3
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• pp.3-21
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• 2016

The thesis examines genealogical study of various aspects to overcome lots of problems which come by when we execute SWIFTNet TSU BPO. Practical implications regarding the innovation of electronic trade infrastructure are as follows. First, the shipping documents in the SWIFTNet TSU BPO are directly sent to an importer by an exporter after the baseline is confirmed. With this process itself, therefore, the bank cannot secure the account receivable. When initiating the SWIFTNet TSU BPO deal, it is needed to set regulations on the bank's account receivable security in the contract. Second, the SWIFTNet TSU BPO should also have an institutionally unified sharing platform with security, stability and convenience. It other words, it is needed to develop services which meet e-payment paradigm and international environments through continued analysis on market changes and flow. Third, the SWIFTNet TSU is useful in terms of promptness, reduction of risk in foreign exchange payment, cost reduction. Therefore, the SWIFT should be perfectly united and linked among the banks, importer and exporter to make the SWIFTNet TSU more convenient in countries around the world. Fourth, the SWIFT should be approached from the aspect of expansion of network and creation of a new business model through analysis on these problems with a worldwide perspective. At the same time, it is necessary to build a cooperative system to share information and promote comprehensive management for efficient operation.

• Journal of the Korean Geographical Society
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• v.50 no.3
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• pp.277-287
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• 2015

This paper addresses limitations of land-change modeling application in the context of REDD (Reducing Emissions from Deforestation and forest Degradation). REDD is an international conservation policy that aims to protect forests via carbon credit generation and trading. In REDD, carbon credits are generated only if there is measurable quantied carbon sequestration activities that are additional to business-as-usual (BAU). A "reference level" is defined as simulated baseline carbon emissions for the future under a BAU scenario, and predictive land-change modeling plays an important role in constructing reference levels. It is tested in this research how predictive accuracies of two land-change models, namely Geographic Emission Benchmark (GEB) and GEOMOD, vary with respect to different spatial scales: Xishuangbanna prefecture and Yunnan province. The accuracies are measured by Figure of Merit. In this Chinese case study, it turns out that GEB's better performance is mainly due to quantity (e.g., how many hectares of forest will be converted to agricultural land?) rather than spatial allocation (e.g., where will the conversion happen?). As both quantity and allocation are crucial in REDD reference level setting it appears to be fundamental to systematically analyze accuracies of quantity and allocation independently in pursuit of accurate reference levels.

• Information Systems Review
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• v.18 no.2
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• pp.59-77
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• 2016

Although various recommendation techniques have been applied to the e-commerce market, few studies compare the intent to use these techniques from the customer's perspective. In this paper, we conduct a comparative analysis of customers' intention to use five recommendation techniques widely adapted by online shopping malls and focus on the differences in purchasing electronic goods and apparel products. The recommendation techniques are as follows: best-seller recommendation, merchandiser recommendation, content-based recommendation, collaborative filtering recommendation, and social recommendation. Additionally, we examine which factors influence customer intent to use the recommendation services. Data were collected through a survey administered to 220 e-commerce users with prior experience with recommendation services. Collected data were examined using analysis of variance and regression analysis. Results indicate statistically significant differences in customers' intention to use recommendation services according to the recommendation technique. In particular, the best-seller recommendation technique is preferred when purchasing electronic goods, whereas the content-based recommendation technique is preferred for apparel purchases. Factors such as personal characteristics and personality, purchasing tendency, as well as perception of the product or recommendation service affect a customer's intention to use a recommendation service. However, the influence of these factors varies depending on the recommendation technique. This study provides guidelines for companies to adopt appropriate recommendation techniques according to product categories and personal characteristics of customers.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.563-568
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• 2007

This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.

• Journal of Navigation and Port Research
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• v.29 no.5 s.101
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• pp.439-445
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• 2005

Recently, owing to the increasing demand on the simplification of arrival and departure procedures, IMO's (International Maritime Organization) Facilitation Committee (FAL) is carrying out the standardization project of arrival and departure formalities and clearance form. Also, many port authorities of developed countries are making active researches for the smooth flow and efficiency of the information inbound and outbound ships by way of simplifying their formalities or through electronic means. However, this standardization project cannot be done by one country but by mutual cooperation among related nations. And to carry out this task, the first thing to be done is to standardize the formalities and document form, and to integrate information. To this end, this study has reviewed the model cases of advanced ports of developed countries with regard to their simplification and standardization efforts. And also we have analyzed the formalities and clearance form of the three countries Korea, China, and Japan. And then for the solution of common problems of three countries, this paper has suggested an ebXML-based Global Port B2B framework. Through this framework, we can reuse and automate the necessary information on the arrival and departure of ships, consequently realizing simplification, and laying a foundation for the introduction of e-commerce to the port industry.

• Asia pacific journal of information systems
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• v.17 no.3
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• pp.1-24
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• 2007

With the development of information technology (IT), various information systems (IS) such as Web-based systems and mobile systems have appeared utilizing different technologies. However, recent studies on IS use and user satisfaction rarely account for technological differences among IS and environmental characteristics where IS are intended to be used. The purpose of this research is to investigate the determinants of the use of Web-based ticketing systems for cultural activities and to empirically validate their relationships. Environmental psychology suggests that human beings respond to external stimuli from environments with their emotions, and their emotional states influence human actions, e.g., IS use in this research. Applying environmental psychology to the use of Web-based systems in the culture and entertainment industry, we propose that web site characteristics first influence a user's internal state of mind (i.e., flow) and then the flow state influences the IS use. Studies related to the state of flow collectively affirm the key role played by the flow construct in shaping individual attitudes and behaviors toward IS. Users' flow states are captured by their shopping enjoyment, perceived behavioral control, and the level of concentration on the IS use. Referring to social presence theory, we have included such web site characteristics as content quality, context of web site, and community quality. In our research model, a second order construct is utilized to represent web site quality, because flow theory suggests that holistic experiences with web-based systems (rather than individual characteristics of the web site) are important in explaining the IS use. Further, we have included trust as another important factor influencing the IS use since business transactions on the web encompass higher uncertainty comparing to offline transactions. In order to test our hypotheses, we have conducted an online survey which results in 1,141 valid responses in the final sample. The data were collected from respondents who have experiences in Internet ticketing systems. Although it was a convenient sample, the sample represents a wide variety of user demographics. Validity and reliability of the research instrument were tested and research hypotheses were examined using PLS Graph 3.0. The results indicate that web site characteristics significantly influence the level of user concentration, user's enjoyment in shopping, and perceived behavioral control. Further, the use of Internet ticketing systems is influenced by users' flow states and trust in the web channel. User satisfaction is turned out to be affected by the use of Internet ticketing systems. Unlike extant research on the relationship between web site characteristics and its use, our study has found that, in the culture and entertainment industry, the impact of web site characteristics on IS use is mediated by a user's flow state. This finding has a practical implication that web site design should include as many features that enhance shopping enjoyment and concentration. Other practical implications of these findings and future research implications are also discussed.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.289-305
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• 2023

The rapid increase in the use of mobile devices is changing consumers' online shopping behavior. However, the difference in the effect on the conversion rate according to the time when consumers switch from a small screen to a large screen has not been sufficiently studied. In addition, the differences in the effect of device conversion on purchase performance according to the characteristics of each country's infrastructure have not been sufficiently studied. Against this background, this study aims to analyze whether the timing of switching from mobile devices to PC devices and the country's mobile Internet penetration rate are moderating the positive effect of device switching on purchase performance. For empirical analysis, Google Merchandise Store data was collected and 101,466 data from 130 countries were analyzed with a multilevel model. As a result of the analysis, consumers' device switching (i.e., mobile to PC) had a positive effect when it occurred in the middle of the consumer journey. However, it was analyzed that when device switching occurred at the later stage of the consumer journey, it had a negative effect on purchase performance. In addition, it was analyzed that the higher the mobile Internet penetration rate, the weaker the positive effect of consumer device conversion on purchase performance.