• 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.

• Asia Marketing Journal
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• v.14 no.2
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• pp.39-63
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• 2012

Previous studies of reward programs have generally focused on designing the best programs for consumers and suggested that consumers' perception of the value of reward programs can vary according to the type of reward program (e.g., hedonic vs. utilitarian and direct vs. indirect) and its timing (e.g., immediate vs. delayed). These studies have typically assumed that consumers' preference for reward programs has a positive effect on brand loyalty. However, Dowling and Uncles (1997) pointed out that this preference does not necessarily foster brand loyalty. In this regard, the present study verifies this assumption by examining the effects of consumers' perception of the value of reward programs on their brand loyalty. Although reward programs are widely used by online shopping malls, most studies have examined the conditions under which consumers are most likely to value loyalty programs in the context of offline shopping. In the context of online shopping, however, consumers' preferences may have little effect on their brand loyalty because they have more opportunities for comparing diverse reward programs offered by many online shopping malls. That is, in online shopping, finding attractive reward programs may require little effort on the part of consumers, who are likely to switch to other online shopping malls. Accordingly, this study empirically examines whether consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. Meanwhile, consumers seek utilitarian and/or hedonic value from their online shopping activity(Jones et al., 2006; Barbin et al., 1994). They visit online shopping malls to buy something necessary (utilitarian value) and/or enjoy the process of shopping itself (hedonic value). In this sense, reward programs may reinforce utilitarian as well as hedonic value, and their effect may vary according to the type of reward (utilitarian vs. hedonic). According to Chaudhuri and Holbrook (2001), consumers' perception of the value of a brand can influence their brand loyalty through brand trust and affect. Utilitarian value influences brand loyalty through brand trust, whereas hedonic value influences it through brand affect. This indicates that the effect of this perception on brand trust or affect may be moderated by the type of reward program. Specifically, this perception may have a greater effect on brand trust for utilitarian reward programs than for hedonic ones, whereas the opposite may be true for brand affect. Given the above discussion, the present study is conducted with three objectives in order to provide practical implications for online shopping malls to strategically use reward program for establishing profitable relationship with customers. First, the present study examines whether reward programs can be an effective marketing tool for increasing brand loyalty in the context of online shopping. Second, it investigates the paths through which consumers' perception of the value of reward programs influences their brand loyalty. Third, it analyzes the effects of this perception on brand trust and affect by considering the type of reward program as a moderator. This study suggests and empirically analyzes a new research model for examining how consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. The model postulates the following 10 hypotheses about the structural relationships between five constructs: (H1) Consumers' perception of the value of reward programs has a positive effect on their program loyalty; (H2) Program loyalty has a positive effect on brand loyalty; (H3) Consumers' perception of the value of reward programs has a positive effect on their brand trust; (H4) Consumers' perception of the value of reward programs has a positive effect on their brand affect; (H5) Brand trust has a positive effect on program loyalty; (H6) Brand affect has a positive effect on program loyalty; (H7) Brand trust has a positive effect on brand loyalty; (H8) Brand affect has a positive effect on brand loyalty; (H9) Consumers' perception of the value of reward programs is more likely to influence their brand trust for utilitarian reward programs than for hedonic ones; and (H10) Consumers' perception of the value of reward programs is more likely to influence their brand affect for hedonic reward programs than for utilitarian ones. To test the hypotheses, we considered a sample of 220 undergraduate students in Korea (male:113). We randomly assigned these participants to one of two groups based on the type of reward program (utilitarian: transportation card, hedonic: movie ticket). We instructed the participants to imagine that they were offered these reward programs while visiting an online shopping mall. We then asked them to answer some questions about their perception of the value of the reward programs, program loyalty, brand loyalty, brand trust, and brand affect, in that order. We also asked some questions about their demographic backgrounds and then debriefed them. We employed the structural equation modeling (SEM) method with AMOS 18.0. The results provide support for some hypotheses (H1, H3, H4, H7, H8, and H9) while providing no support for others (H2, H5, H6, H10) (see Figure 1). Noteworthy is that the path proposed by previous studies, "value perception → program loyalty → brand loyalty," was not significant in the context of online shopping, whereas this study's proposed path, "value perception → brand trust/brand affect → brand loyalty," was significant. In addition, the results indicate that the type of reward program moderated the relationship between consumers' value perception and brand trust but not the relationship between their value perception and brand affect. These results have some important implications. First, this study is one of the first to examine how consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. In particular, the results indicate that the proposed path, "value perception → brand trust/brand affect → brand loyalty," can better explain the effects of reward programs on brand loyalty than existing paths. Furthermore, these results suggest that online shopping malls should place greater emphasis on the type of reward program when devising reward programs. To foster brand loyalty, they should reinforce the type of shopping value that consumers emphasize by providing them with appropriate reward programs. If consumers prefer utilitarian value to hedonic value, then online shopping malls should offer utilitarian reward programs and vice versa.

• Korean Journal of Soil Science and Fertilizer
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• v.12 no.4
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• pp.189-213
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• 1980

1. Aiming at supply of basic informations on tree species siting and forest fertilization by understanding of soil properties that are demanded by each tree species through studies of forest soil's morphological, physical and chemical properties in relation to tree growth in our country, the necessary data have been collected in the last 10 years, are quantified according to quantification theory and are analyzed in accordance with multi-variate analysis. 2. Test species, larch and the Korean white pine, are plantable in extensive areas from mid to north in the temperate zone and are the two most recommended reforestation tree species in Korea. However, their respective site demands are not known and they have been in confusion or considered demanding the same site during reforestation. When the Korean white pine is planted in larch sites, it has shown relatively good growth. But, when larch is planted in the Korean white pine site it can be hardly said that the larch growth is good. To understand on such a difference soil factors have been studied so as to see how the soil's morphological, physical and chemical factors affect tree growth helped with the electronic computer. 3. All the stands examined are man-made mature forests. From 294 larch plots and 259 white pine plots dominant trees are cut as samples and through stem analysis site index is determined. For each site index soil profiles are made in the related forest-land for analysis. Soil samples are taken from each profile horizon and forest-land productivity classification tables are worked out through physical and chemical analysis of the soil samples for each tree species for the study of relationships between physical, chemical and the combined physical/chemical properties of soil and tree growth. 4. In the study of relationships between physical properties of soil and tree growth it is found out that larch growth is influenced by the following factors in the order of deposit form, soil depth, soil moisture, altitude, relief, soil type, depth of A-horizon, soil consistency content of organic matter soil texture bed rock gravel content aspect and slope. For the Korean white pine the influencing factors' order is soil type, soil consistency bed rock aspect depth of A-horizon soil moisture altitude relief deposit form soil depth soil texture gravel content and slope. 5. In the study of relationships between chemical properties of soil and tree growth it is found out that larch growth is influenced by the following factors in the order of base saturation organic matter CaO C/N ratio, effective $P_2O_5$ PH.exchangeable $K_2O$ T-N MgO C E C Total Base and Na. For the Korean white pine the influencing factors' order is effective $P_2O_5$ Total Base T-N Na C/N ratio PH CaO base saturation organic matter exchangeable $K_2O$ C E C and MgO. 6. In the study of relationships between the combined physical and chemical properties of soil and tree growth it is found out that larch growth is influenced by the following factors in the order of soil depth deposit form soil moisture PH relief soil type altitude T-N soil consistency effective $P_2O_5$ soil texture depth of A-horizon Total Base exchangeable $K_2O$ and base saturation. For the Korean white pine the influencing factors' order is soil type soil consistency aspect effective $P_2O_5$ depth of A-horizon exchangeable $K_2O$ soil moisture Total Base altitude soil depth base saturation relief T-N C/N ratio and deposit from. 7. In the multiple regression of forest soil's physical properties larch's correlation coefficient is 0.9272 and for the Korean white pine it is 0.8996. With chemical properties larch has 0.7474 and the Korean white pine has 0.7365. So, the soil's physical properties are found out more closely related with tree growth than chemical properties. However, this seems due to inadequate expression of soil's chemical factors and it is proved that the chemical properties are not less important than the physical properties. In the multiple regression of the combined physical and chemical properties consisting of important morphological and physical factors as well as chemical factors of forest soils larch's multiple correlation coefficient is found out to be 0.9434 and for the Korean white pine it is 0.9103 leading to the highest correlation. 8. As shown in the partial correlation coefficients larch needs deeper soil depth than the Korean white pine and in the deposit form colluvial and creeping soils are demanded by the larch. Adequately moist to too moist should be soil moisture and PH should be from 5.5 to 6.1 for the larch. Demands of T-N soil texture and soil nutrients are higher for the larch than the Korean white pine. Thus, soil depth, deposit form, relief soil moisture PH N altitude and soil texture are good indicators for species sitings with larch and the Korean white pine while soil type and soil consistency are indicative only limitedly of species sitings due to their wide variation as plantation environments. For larch siting soil depth deposit form relief soil moisture PH soil type N and soil texture are indicators of good growth and for Korean white pine they are soil type soil consistency effective $P_2O_5$ and exchangeable $K_2O$, which is demanded more by the Korean white pine than larch generally. 9. Physical properties of soil has been known as affecting tree growth to greatest extent so far. However, as a result of this study it is proved through computer analysis that chemical properties of soil are not less important factors for tree growth than chemical properties and site demands for larch and the Korean white pine that have been uncertain So far could be clarified.

• Journal of Korean Society of Forest Science
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• v.53 no.1
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• pp.1-26
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• 1981

1. Aiming at supply of basic informations on tree species siting and forest fertilization by understanding of soil properties that are demanded by each tree species through studies of forest soil's morphological, physical and chemical properties in relation to tree growth in our country, the necessary data have been collected in the last 10 years, are quantified according to quantification theory and are analyzed in sccordance with multi-variate analysis. 2. Test species, japanese larch (Larix leptolepis Gord) and the Korean white pine, (pinus koraiensis S et Z.) are plantable in extensive areas from mid to north in the temperate forest zone and are the two most recommended reforestation tree species in Korea. However, their respective site demands are little known and they have been in confusion or considered demanding the same site during reforestation. When the Korean white pine is planted in larch sites, it has shown relatively good growth, but, when Japanese larch is planted in Korean white pine site it can be hardly said that the Japanese Larch growth is good. To understand on such a difference soil factors have been studied so as to see how th soil's morphological, physical and chemical factors affect tree growth helped with the electronic computer. 3. All the stands examined are man-made mature forests. From 294 Japanese larch plots and 259 Korean white pine plots dominant trees are cut as samples and through stem analysis site index is determined. For each site index soil profiles are made in the related forest-land for analysis. Soil samples are taken from each profile horizon and forest-land productivity classification tables are worked out through physical and chemical analyses of the soil samples for each tree species for the study of relationships between physical, chemical and the combined physical/properties of soil and tree growth. 4. In the study of relationships between physical properties of soil and tree growth it is found out that Japanese larch growth is influenced by the following factors in the decreasing order of weight deposit form, soil depth, soil moisture, altitude, relief, soil type, depth a A-horizon, soil consistency, content of organic matter, soil texture, bed rock, gravel content, aspect and slope. For the Korean white pine the influencing factors' order is soil type, soil consistency, bed rock, aspect, depth of A-horizon, soil moisture, altitude, relief, deposit form, soil depth, soil texture, gravel content and slope. 5. In the study of relationships between chemical properties of soil and tree growth it is found out that Japanese larch growth is influenced by the following factors in the order of base saturation, organic matter, CaO, C/N ratio, effective $P_2O_5$, PH, exchangeable, $K_2O$, T-N, MgO, CEC, Total Base and Na. For the Korean white pine the influencing factors' order is effective $P_2O_5$, Total Base, T-N, Na, C/N ratio, PH, CaO, base saturation, organic matter, exchangeable $K_2O$, CEC and MgO. 6. In the study of relationships between the combined physical and chemical properties of soil and tree growth it is found out that Japanese larch growth is influenced by the following factors in the order of soil depth, deposit form, soil moisture, PH, relief, soil type altitude, T-N, soil consistency, effective $P_2O_5$, soil texture, depth of A-horizon, Total Base, exchangeable $K_2O$ and base saturation. For the Korean white pine the influencing factors' order is soil type, soil consistency, aspect, effective $P_2O_5$, depth of A-horizon, exchangeable $K_2O$, soil moisture, Total Base, altitude, soil depth, base saturation, relief, T-N, C/N ratio and deposit form. 7. In the multiple correlation of forest soil's physical properties larch's correlation coefficient for Japanese Larch is 0.9272 and for Korean white pine, 0.8996. With chemical properties larch has 0.7474 and Korean white pine has 0.7365. So, the soil's physical properties are found out more closely related with tree growth than chemical properties. However, this seems due to inadequate expression of soil's chemical factors and it is proved that the chemical properities are not less important than the physical properties. In the multiple correlation of the combined physical and chemical properties consisting of important morphological and physical factors as well as chemical factors of forest soils larch's multiple correlation coefficient is found out to be 0.9434 and for Korean white pine it is 0.9103 leading to the highest correlation. 8. As shown in the partial correlation coefficients Japanese larch needs deeper soil depth than Korean white pine and in the deposit form of colluvial and creeping soils are demanded by the larch. Moderately moist to not moist should be soil moisture and PH should be from 5.5 to 6.1 for the larch. Demands of T-N, soil texture and soil nutrients are higher for the larch than the Korean white pine. Thus, soil depth, deposit form, relief, soil moisture, PH, N, altitude and soil texture are good indicators for species sitings with larch and the Korean white pine while soil type and soil consistency are indicative only limitedly of species sitings due to their wide variations as plantation environments. For the larch siting soil depth, deposit form, relief, soil moisture, pH, soil type, N and soil texture are indicators of good growth and for the Korean white pine they are soil type, soil consistency, effective $P_2O_5$ and exchangeable $K_2O$. In soil nutrients larch has been found out demanding more than the Korean white pine except $K_2O$, which is demanded more by the Korean white pine than Japanese larch generally. 9. Physical properties of soil has been known as affecting tree growth to the greatest extent so far. However, as a result of this study it is proved through computer analysis that chemical properties of soil are not less important factors for tree growth than chemical properties and site demands for the Japanese larch and the Korean white pine that have been uncertain so far could be clarified.