In the field of dentistry, there existed relatively few emergency patients or patients who need intensive care and thus had low medical dispute rates. However, these days, there is a general tendency of increased medical disputes. Although many medical disputes are caused by medical accidents of the dentists, because dental assistants are also lawfully involved in practicing dentistry, there is a possibility of medical disputes or medical accidents caused by dental assistants. Therefore, the role of the dental assistants cannot be ignored. This study consists of a survey given to dental hygienists currently working in general hospitals, dental hospitals and private dental clinics. Following is the results of the analysis of 275 respondents' backgrounds, medical disputes rates including patients' complaints, their understanding of medical regulations and their general understanding of overall dental practice and medical disputes. 1. 251 of 274(91.6%) respondents doubted the risk of medical accident and dispute. 2. 81(29.5%) dental hygienist experienced complaint from patients. They have been working in the private dental clinic, the rate of this experience was high. 3. 349 case of 1805(19.3%) the complaints by patients, highest percentage among its category, were those regarding dental fees and poor service. 4. 129 case of 1805(7.1%) patients' complaints, highest percentage among it's subcategory, were those regarding the absence of explanations of precautions or request of agreements before dental treatment. 5. 252 of 267 (94.4%) dental hygienists chart after a scaling treatment. However, only 55(20.7%) dental hygienists chart the fact of explaining the precautions. 6. 6(2.2%) dental hygienists do not inspect patients' medical history, if patients don't mention it. 7. 104 of 274(38.0%) dental hygienists responded to be capable of administering first aid treatment. 8. 115(41.8%) dental hygienists have a first aid kit and equipment. 9. In case of medical dispute, 268(97.8%) dental hygienists respond that, charting plays a big role in resolving the dispute. 10. In case of medical dispute, 272(93.3%) dental hygienists respond that, explanation and agreement before treatment have an important role in settlement of dispute 11. Only 160(58.4%) dental hygienists responded correct answer that the duration of keeping medical records is 10 years. 12. 124(45.3%) respondents thought that it is legal for a dental hygienist to take a panoramic dental X-ray, 71(25.9%) respondents thought that it is legal practice cervical resin treatment by dental hygienist, and 37(13.5%) respondents thought that it is legal extract primary teeth by dental hygienist. 13. 24(18.76%) respondents thought that it doesn't matter to tell patient's state to others 14. 272(99.27%) responded that receiving education for the prevention of medical disputes was needed and of them, 61.0% thought it was urgent. 15. 186(64.2%) has never had classes regarding the prevention of medical disputes while in school and 212(77.4%) has not had the same type of classes after graduating from school. 16. 256(93.4%) responded that there will be even more of an increased number of medical disputes. Among them, 83.3% of respondents though that due to the increased opportunity of acquiring information through the internet and mass media. The study shows that 29.5 percentage of dental hygienists have experienced the medical disputes and complaints and they are lack of recognition of medical regulations and dental hygienist's official duty. So, there is a big potential of the percentage to increase. Therefore, the correct understanding of explaining precautions and requesting agreement before dental treatments and performing them are mandatory. Moreover, classes regarding the prevention and counterplans of medical disputes need to be widely offered.
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.