• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.50-59
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• 2006

The risk of fault reactivation in the Gippsland Basin was calculated using the FAST (Fault Analysis Seal Technology) technique, which determines fault reactivation risk by estimating the increase in pore pressure required to cause reactivation within the present-day stress field. The stress regime in the Gippsland Basin is on the boundary between strike-slip and reverse faulting: maximum horizontal stress $({\sim}\;40.5\;Mpa/km)$ > vertical stress (21 Mpa/km) ${\sim}$ minimum horizontal stress (20 MPa/km). Pore pressure is hydrostatic above the Campanian Volcanics of the Golden Beach Subgroup. The NW-SE maximum horizontal stress orientation $(139^{\circ}N)$ determined herein is broadly consistent with previous estimates, and verifies a NW-SE maximum horizontal stress orientation in the Gippsland Basin. Fault reactivation risk in the Gippsland Basin was calculated using two fault strength scenarios; cohesionless faults $(C=0;{\mu}=0.65)$ and healed faults $(C=5.4;\;{\mu}=0.78)$. The orientations of faults with relatively high and relatively low reactivation potential are almost identical for healed and cohesionless fault strength scenarios. High-angle faults striking NE-SW are unlikely to reactivate in the current stress regime. High-angle faults oriented SSE-NNW and ENE-WSW have the highest fault reactivation risk. Additionally, low-angle faults (thrust faults) striking NE-SW have a relatively high risk of reactivation. The highest reactivation risk for optimally oriented faults corresponds to an estimated pore pressure increase (Delta-P) of 3.8 MPa $({\sim}548\;psi)$ for cohesionless faults and 15.6 MPa $({\sim}2262\;psi)$ for healed faults. The absolute values of pore pressure increase obtained from fault reactivation analysis presented in this paper are subject to large errors because of uncertainties in the geomechanical model (in situ stress and rock strength data). In particular, the maximum horizontal stress magnitude and fault strength data are poorly constrained. Therefore, fault reactivation analysis cannot be used to directly measure the maximum allowable pore pressure increase within a reservoir. We argue that fault reactivation analysis of this type can only be used for assessing the relative risk of fault reactivation and not to determine the maximum allowable pore pressure increase a fault can withstand prior to reactivation.

• Journal of Distribution Research
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• v.16 no.1
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• pp.95-115
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• 2011

Food service is the major part of franchise business in Korea, accounting for 69.9% of the brands in the market. As the food service industry becomes mature, many franchisees have struggled to survive in the market. In general, consumers have higher levels of expectation toward service quality of franchised outlets compared that of (non-franchised) independent ones. They also tend to believe that franchisees deliver standardized service at the uniform food price, regardless of their locations. Such beliefs seem to be important reasons that consumers prefer franchised outlets to independent ones. Nevertheless, few studies examined the impact of qualify features of franchisees on customer satisfaction so far. To this end, this study examined the characteristics of various quality features of franchisees in the food service industry, regarding their relationship with customer satisfaction and dissatisfaction. The quality perception of heavy-users was also compared with that of light-users in order to find insights for developing differentiated marketing strategy for the two segments. Customer satisfaction has been understood as a one-dimensional construct while there are recent studies that insist two-dimensional nature of the construct. In this regard, Kano et al. (1984) suggested to categorize quality features of a product or service into five types, based on their relation to customer satisfaction and dissatisfaction: Must-be quality, Attractive quality, One-dimensional quality, Indifferent quality, and Reverse quality. According to the Kano model, customers are more dissatisfied when Must-be quality(M) are not fulfilled, but their satisfaction does not arise above neutral no matter how fully the quality fulfilled. In comparison, customers are more satisfied with a full provision of Attactive quality(A) but manage to accept its dysfunction. One-dimensional quality(O) results in satisfaction when fulfilled and dissatisfaction when not fulfilled. For Indifferent quality(I), its presence or absence influences neither customer satisfaction nor dissatisfaction. Lastly, Reverse quality(R) refers to the features whose high degree of achievement results in customer dissatisfaction rather than satisfaction. Meanwhile, the basic guidelines of the Kano model have a limitation in that the quality type of each feature is simply determined by calculating the mode statistics. In order to overcome such limitation, the relative importance of each feature on customer satisfaction (Better value; b) and dissatisfaction (Worse value; w) were calculated following the formulas below (Timko, 1993). The Better value indicates how much customer satisfaction is increased by providing the quality feature in question. In contrast, the Worse value indicates how much customer dissatisfaction is decreased by providing the quality feature. Better = (A + O)/(A+O+M+I) Worse = (O+M)/(A+O+M+I)(-1) An on-line survey was performed in order to understand the nature of quality features of franchisees in the food service industry by applying the Kano Model. A total of twenty quality features (refer to the Table 2) were identified as the result of literature review in franchise business and a pre-test with fifty college students in Seoul. The potential respondents of our main survey was limited to the customers who have visited more than two restaurants/stores of the same franchise brand. Survey invitation e-mails were sent out to the panels of a market research company and a total of 257 responses were used for analysis. Following the guidelines of Kano model, each of the twenty quality features was classified into one of the five types based on customers' responses to a set of questions: "(1) how do you feel if the following quality feature is fulfilled in the franchise restaurant that you visit," and "(2) how do you feel if the following quality feature is not fulfilled in the franchise restaurant that you visit." The analyses revealed that customers' dissatisfaction with franchisees is commonly associated with the poor level of cleanliness of the store (w=-0.872), kindness of the staffs(w=-0.890), conveniences such as parking lot and restroom(w=-0.669), and expertise of the staffs(w=-0.492). Such quality features were categorized as Must-be quality in this study. While standardization or uniformity across franchisees has been emphasized in franchise business, this study found that consumers are interested only in uniformity of price across franchisees(w=-0.608), but not interested in standardizations of menu items, interior designs, customer service procedures, and food tastes. Customers appeared to be more satisfied when the franchise brand has promotional events such as giveaways(b=0.767), good accessibility(b=0.699), customer loyalty programs(b=0.659), award winning history(b=0.641), and outlets in the overseas market(b=0.506). The results are summarized in a matrix form in Table 1. Better(b) and Worse(w) index indicate relative importance of each quality feature on customer satisfaction and dissatisfaction, respectively. Meanwhile, there were differences in perceiving the quality features between light users and heavy users of any specific franchise brand in the food service industry. Expertise of the staffs was labeled as Must-be quality for heavy users but Indifferent quality for light users. Light users seemed indifferent to overseas expansion of the brand and offering new menu items on a regular basis, while heavy users appeared to perceive them as Attractive quality. Such difference may come from their different levels of involvement when they eat out. The results are shown in Table 2. The findings of this study help practitioners understand the quality features they need to focus on to strengthen the competitive power in the food service market. Above all, removing the factors that cause customer dissatisfaction seems to be the most critical for franchisees. To retain loyal customers of the franchise brand, it is also recommended for franchisor to invest resources in the development of new menu items as well as training programs for the staffs. Lastly, if resources allow, promotional events, loyalty programs, overseas expansion, award-winning history can be considered as tools for attracting more customers to the business.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.193-221
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• 2017

Grouping episodes into semantically related categories is necessary for better mnemonic structure. However, the effect of grouping on memory of subordinate details was not clearly understood. In an fMRI study, we tested whether attending superordinate during semantic association disrupts or enhances subordinate episodic details. In each cycle of the experiment, five cue words were presented sequentially with two related detail words placed underneath for each cue. Participants were asked whether they could imagine a category that includes the previously shown cue words in each cycle, and their confidence on retrieval was rated. Participants were asked to perform cued recall tests on presented detail words after the session. Behavioral data showed that reaction times for categorization tasks decreased and confidence levels increased in the third trial of each cycle, thus this trial was considered to be an important insight where a semantic category was believed to be successfully established. Critically, the accuracy of recalling detail words presented immediately prior to third trials was lower than those of followed trials, indicating that subordinate details were disrupted during categorization. General linear model analysis of the trial immediately prior to the completion of categorization, specifically the second trial, revealed significant activation in the temporal gyrus and inferior frontal gyrus, areas of semantic memory networks. Representative Similarity Analysis revealed that the activation patterns of the third trials were more consistent than those of the second trials in the temporal gyrus, inferior frontal gyrus, and hippocampus. Our research demonstrates that semantic grouping can cause memories of subordinate details to fade, suggesting that semantic retrieval during categorization affects the quality of related episodic memory.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.3
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• pp.1-12
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• 2016

This study aimed to interpret the fundamental cause of road-kill occurrences and analyzed spatial characteristics of the road-kill locations from Route 44 in Seorak National Park, Korea. Logistic regression analysis was utilized for backward elimination on variables. Seorak National Park Service has constructed GIS-data of 81 road-kill occurrences from 2008 to 2013 and these data were assigned as dependent variables in this study. Considered as independent variables from previous studies and field surveys, vegetation age-class, distance to streams, coverage of fences and retaining walls, and distance to building sites were assigned as road-kill impact factors. The coverage of fences and retaining walls(-1.0135) was shown as the most influential factor whereas vegetation age-class(0.0001) was the least influential among all of the significant factor estimates. Accordingly, the rate of road-kill occurrence can increase as the distance to building sites and stream becomes closer and vegetation age-class becomes higher. The predictive accuracy of road-kill occurrence was shown to be 72.2% as a result of analysis, assuming as partial causes of road-kill occurrences reflecting spatial characteristics. This study can be regarded as beneficial to provide objective basis for spatial decision making including road-kill occurrence mitigation policies and plans in the future.

• Journal of Distribution Science
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• v.9 no.3
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• pp.15-23
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• 2011

This study examined the effects educational service elements have on the satisfaction of educational service; it also analyzed how such satisfaction influenced educational institutions' registration renewal and word-of-mouth communications about the schools. The key factors of educational services included the following four components: physical environment, education programs, instructors, and tuition fee. An investigation into how the factors of educational services in private educational institutions may affect consumer satisfaction was an additional goal of this study. Based on the cause-and-effect analysis of the above constructs, the degree of influence that each of the attributes and the component factors have on consumers' educational service selection, as well as the consumers' satisfaction, were explored. An empirical analysis was conducted on students who were taking classes in cram schools; the analysis was also conducted on the students' parents. The results indicated that the hypotheses regarding the elements of educational service, educational program, and the instructor factor were supported. Furthermore, a hypothesis concerning the effects educational service satisfaction has on re-registration and word of mouth was supported, as well. Upon aggregating the above study results, it was concluded that when determining the effects of satisfaction on educational service, the educational program, and the instructor factor played a crucial role, while physical surroundings and tuition did not. When addressing the influence of the educational services factors on educational services satisfaction, education programs and instructors had a positive effect, whereas physical environment and tuition fee had no statistically significant impact. According to the influence of educational services satisfaction on registration renewal and word-of-mouth effects, educational services satisfaction was found to be a positive influential factor for both. In addition, it was found that satisfaction of educational service had a positive effect on renewing enrollment and forming word-of-mouth recommendations. Although the research seems simple, some theoretical implications can be drawn, as follows: First, one interpretation of the findings that the programs' and instructors' factors had a positive effect on educational services satisfaction but the physical environment and tuition fee factors did not have a significant effect suggests that the greatest influence factors in educational services are educational programs and instructors. Second, increased educational services satisfaction may result in consumer behavior changing, resulting in more registration renewals and more favorable word-of-mouth comments. Third, increased educational services satisfaction may result in enhancing consumer responses, resulting in more registration renewals and more favorable word-of-mouth opinions. Further research directions should include developing an integrated model that will allow for variation over time, before and after purchases, in relation to consumers' choices of educational services.

• Journal of Hospice and Palliative Care
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• v.1 no.1
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• pp.30-38
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• 1998

Background : Hepatocellular carcinomoma is the 3rd most common malignancy and the 2nd most common cause of death in Korea. The prediction of life-expectancy in terminal cancer patients is a major problem for patients, families, and physicians. We would like to investigate the prognostic factors of hepatocellular carcinoma, and therefore contribute to the prediction of the survival time of patients with hepatocellular carcinoma. Methods : A total of 91 patients(male 73, female 18) with hepatocellular carcinoma who were admitted to the hospital between January and lune 1995 were entered into the study, and data were collected prospectively on 28 clinical parameters through medical obligation record. We surveyed an obligation and local district office records, and confirmed the surivival of patients till July, 1996. Using Cox-proportional hazard model, give the significant variables related to survival. These determined prognostic factors. Life regressional analysis was used, there were calculated predicted survival day based on combinations of the significant prognostic factors. Results : 1) Out of 91 patients, 73 were male, and 18 were female. The mean age was $56.7{\pm}10.6$ ears. During the study, except for 16 patients who could not follow up, out of 75 patients, the number of deaths was 57(76%) and the number of survivals was 18(24%). 2) Out of the 28 clinical parameters, the prognostic factors related to reduced survival rate were prothrombin time<40%(relative risk:10.8), weight loss(RR:4.4), past history of hypertension (RR:3.2), ascites(RR:2.8), hypocalcemia(RR:2.5)(P<0.001). 3) Out of five factors, the survival day is 1.7 in all of five, $4.2{\sim}10.0$ in four, $10.4{\sim}41.9$ in three, $29.5{\sim}118.1$ in two, $124.0{\sim}296.6$ in one, 724.0 in none. Conclusion : In hepatocellular carcinoma we found that the prognostic factors related to reduce survival rate were prolonged prothrombin time(<40%), weight loss, past history of hypertension, ascites, and hypocalcemia(<8.7mg/dl). The five prognostic factors enabled the prediction of life-expectancy in patients with hepatocellular carcinoma and may assist in managing patients with hepatocellular carcinomal.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.27-57
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• 2008

Many consumers enjoy the challenge of purchasing a brand that matches well with their own values and personalities (for example, Ko et al., 2008; Ko et al., 2006). Therefore, the personalities of consumers can impact on the final selection of a brand and its brand personality in two ways: first, the consumers may incline to purchase a brand or a product that reflects their own personalities; second, consumers tend to choose a company that has similar brand personalities to those brands that are being promoted. Therefore, the objectives of this study are following: 1. Is there any empirical relationship between a consumer's personality and the personality of a brand that he or she chooses? 2. Can a corporate brand be differentiated by the brand personality? In short, consumers are more likely to hold favorable attitudes towards those brands that match their own personality and will most probably purchase those brands matching well with their personality. For example, Matzler et al. (2006) found that extraversion and openness were positively related to hedonic product value; and that the personality traits directly (openness) and indirectly (extraversion, via hedonic value) influenced brand effects, which in turn droved attitudinal and purchase loyalty. Based on the above discussion, the following hypotheses are proposed: Hypothesis 1: the personality of a consumer is related to the brand personality of a product/corporate that he/she purchases. Kuksov (2007) and Wernerfelt (1990) argued that brands as a symbolic language allowed consumers to communicate their types to each other and postulated that consumers had a certain value of communicating their types to each other. Therefore, how brand meanings are established, and how a firm communicate with consumers about the meanings of the brand are interesting topics for research (for example, Escalas and Bettman, 2005; McCracken, 1989; Moon, 2007). Hence, the following hypothesis is proposed: Hypothesis 2: A corporate brand identity is differentiated by the brand personality. And there are significant differences among companies. A questionnaire was developed for collecting empirical measures of the Big-Five personality traits and brand personality variables. A survey was conducted to the online access panel members through the Internet during December 2007 in Korea. In total, 500 respondents completed the questionnaire, and considered as useable. Personality constructs were measured using the Five-factor Inventory (NEO-FFI) scale and a total of 30 items were actually utilized. Brand personality was measured using the five-dimension scale developed by Aaker (1997). A total of 17 items were actually utilized. The seven-point Likert-type scale was the format of responses, for example, from 1 indicating strongly disagreed to 7 for strongly agreed. The Analysis of Moment Structures (AMOS) was used for an empirical testing of the model, and the Maximum Likelihood Estimation (MLE) was applied to estimate numerical values for the components in the model. To diagnose the presence of distribution problems in the data and to gauge their effects on the parameter estimates, bootstapping method was used. The results of the hypothesis-1 test empirically show that there exit certain causality relationship between a consumer's personality and the brand personality of the consumer's choice. Thus, the consumer's personality has an impact on consumer's final selection of a brand that has a brand personality matches well with their own personalities. In other words, the consumers are inclined to purchase a brand that reflects their own personalities and tend to choose a company that has similar brand personalities to those of the brand being promoted. The results of this study further suggest that certain dimensions of the brand personality cause consumers to have preference to certain (corporate) brands. For example, the conscientiousness, neuroticism, and extraversion of the consumer personality have positively related to a selection of "ruggedness" characteristics of the brand personality. Consumers who possess that personality dimension seek for matching with certain brand personality dimensions. Results of the hypothesis-2 test show that the average "ruggedness" attributes of the brand personality differ significantly among Korean automobile manufacturers. However, the result of ANOVA also indicates that there are no significant differences in the mean values among manufacturers for the "sophistication," "excitement," "competence" and "sincerity" attributes of the corporate brand personality. The tight link between what a firm is and its corporate brand means that there is far less room for marketing communications than there is with products and brands. Consequently, successful corporate brand strategies must position the organization within the boundaries of what is acceptable, while at the same time differentiating the organization from its competitors.

• The korean journal of orthodontics
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• v.31 no.6 s.89
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• pp.549-558
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• 2001

This study was undertaken to demonstrate the forces in the maxillary alveolar bone generated by the activation of the maxillary posterior crossbite appliance In the treatment of posterior buccal crossbite caused by buccal ectopic eruption of the maxillary second molar. A photoelastic model was fabricated using a Photoelastic material (PL-3) to simulate alveolar bone and ivory-colored resin teeth. The model was observed throughout the anterior and posterior view in a circular polariscope and recorded photographically before and after activation of the maxillary posterior crossbite appliance. The following conclusions were reached from this investigation : 1. When the traction force was applied on the palatal surface of the second molar, stresses were concentrated at the buccal and palatal root apices and alveolar crest area. The axis of rotation of palatal root was at the root apex and that of the buccal root was at the root li4 area. In this result, palatal tipping and rotating force were generated. 2. When the traction force was applied on the buccal surface of the second molar, more stresses than loading on the palatal surface were observed in the palatal and buccal root apices. Furthermore, the heavier stresses creating an intrusive force and controlled tipping force were recorded below the buccal and palatal root apices below the palatal root surface. In addition, the axis of rotation of palatal root disappeared whereas the rotation axis of the buccal root moved to the root apex from the apical 1/4 area. 3. When the traction force was simultaneously applied on the maxillary right and left second molars, the stress intensity around the maxillary first molar root area was greater than the stress generated by the only buccal traction of the maxillary right or left second molar. As in above mentioned results, we should realize that force application on the palatal surface of second molars with the maxillary posterior crossbite appliance Produced rotation of the second molar and palatal traction, which nay cause occlusal Interference. That is to say, we have to escape the rotation and uncontrolled tipping creating occlusal interference when correcting buccal posterior crossbite. For this purpose, we recommend buccal traction rather than palatal traction force on the second molar.