• Iberoamérica
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• v.22 no.1
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• pp.73-109
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• 2020

In the tourism industry, emotional labor-related problems are highly likely because workers cannot avoid frequent face-to-face contacts with customers. Emotional labor, however, is not always recognized in the same pattern by every worker in similar situations. As can be see in this research, emotional labor appears to vary depending on individual characteristics and sociocultural and economic conditions. In fact, there are so many factors affecting emotional labor, and the level of influence concerning these factors can vary depending on when and where they are. Psychological and mental pains and stress from emotional labor depend on a number of factors. The expression of emotional labor, depending on age, length of employment and gender, shows a clear difference. In particular, for those who are planning to participate in international labor migration to the United States in the future, emotional labor was often not recognized as a serious problem or rather a positive social and cultural experience. In fact, there are various types of tourism workers in Oaxaca, and their experiences vary greatly depending on individual and economic and sociocultural environments. The emotional labor of the workers in the tourism sector of Oaxaca also has various forms depending on various conditions. Positive or negative perceptions and consequences of emotional labor vary depending on the circumstances of the individual and working conditions. In other words, socioeconomic conditions, individual personality or experiences, sociocultural characteristics, and autonomy in the workplace, have a significant impact. As a result, the forms and effects of emotional labor expressed and understood by tourism workers vary considerably and may vary depending on the situations. Considering these problems, the way emotional labor emerges is greatly influenced by sociocultural or personal factors as well as economic factors. In addition, rather than considering the existence and seriousness of emotional labor as given depending on the nature of a particular industry or work, it should also be recognized that the expression of emotional labor differ considerably from individual to individual. Also, the seriousness of problems caused by emotional labor can be diversified. In this sense, it is necessary to clearly understand the meanings of the dynamism, diversity and multidimensionality of emotional labor from a new perspective.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.

• Journal of The Korean Association For Science Education
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• v.43 no.1
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• pp.17-28
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• 2023

In line with the entry into the digital-based intelligent information society, the science curriculum emphasizes the cultivation of scientific competencies, and computer-based test (CBT) is drawing attention for assessment of competencies. CBT has advantages to develop items that have high fidelity, and to establish a feedback system by accumulating results into the database. However, it is necessary to solve the problems of improving validity of assessment results, lowering measurement efficiency, and increasing management factors. To examine students' responses to the introduction of the new assessment tools in the process of transitioning from paper-based test (PBT) to CBT, in this study, we analyzed the results of the PBT and the CBT conducted in 2021 National Assessment of Educational Achievement (NAEA). In particular, we sought to find the effects on student achievement when only the mode of assessment was changed without change of items, and the effect on student achievement when the items were composed including technology enhanced features that take advantage of CBT. This study is derived through the analysis of the results of 7,137 third-grade middle school students taking one among the three kinds of assessments, which were the PBT or two kinds of CBT. After the assessment, the percentage of correct answers and the item discriminations were collected for each group, and expert opinions on characteristics of response were collected through the expert council involving 8 science teachers with experience in NAEA. According to the results, there was no significant difference between students' achievement results in the PBT and the CBT-M, which means simple mode conversion type of CBT, so it could be explained that the mode effect did not appear. However, it was confirmed that the percentage of correct answers for the construct response items was somewhat high in the CBT, and this result was analyzed to be related to the convenience of the response. On the other hand, there were the items with a difference of more than 10%p from the correct answer rate of similar items, among the items to which technology enhanced functions were applied following the introduction of CBT. According to the analysis of response rate of options, these results could be explained that the students' level of understanding could be more closely grasped through the innovative items developed through the technology enhanced function. Based on the results, we discussed some guidance to be considered when introducing CBT and developing items through CBT, and presented implications.

• Journal of Practical Agriculture & Fisheries Research
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• v.3 no.1
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• pp.142-157
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• 2001

This study was conducted to get the basic information for promoting farm machinery productivity by surveying the regular maintenance and repair status of major farm machinery such as power tiller, farm tractor, rice transplanter and combine harvester. The survey was carried out through 9 provinces including Cheju province by direct visiting farmers with prepared questionnaire. The results of this study can be summarized as follows : 1. The average farming carrier of the surveyed farmers was 25.3 years, and 21-30 years of farming carrier showed the highest portion as 40.7%. The average carrier of using farm machinery was 9.4 years, and that was 14.9 years for power tiller, 8.3 years for farm tractor, 9.0 years for rice transplanter, 7.9 years for combine harvester, 7.5 years for mini tiller, 9.7 years for power sprayer, and 8.2 years for binder etc. 2. The regular maintenance for farm machinery was conducted mainly at repair shop (49.5%) or dealer agency (12.0%) as 61.5%, and 34.9% of farmers conducted the regular maintenance by themselves at their house. 3. The reasons for not-fully recognizing operation manual and insufficient before-, during-, after-maintenance of farm machinery were insufficient time for them (45.8%), troublesome (22.9%), unknown maintenance method (16.3%), unknown the necessity for maintenance (12.4%), and others (2.6%) in order. 4. For the annual exchange of engine oil, 3.2 times is necessary but actually 1.7 times was exchanged for power tiller, 4.3 times is necessary but actually 1.9 times was exchanged for farm tractor, 2.7 times is necessary but actually 1.7 times was exchanged for rice transplanter, 2.2 times is necessary but actually 2.3 times was exchanged combine harvester. 5. For the annual cleanness or exchange of fuel filter, 3.2 times is necessary but actually 1.1 times was done for power tiller, 4.3 times is necessary but actually 1.6 times was done for farm tractor, 2.7 times is necessary but actually 1.7 times was done for rice transplanter, 1.9 times is necessary but actually 0.8 times was done for combine harvester. 6. For the annual cleanness or exchange of air filter, 3.2 times is necessary but actually 1.4 times was done for power tiller, 4.2 times is necessary but actually 2.4 times was done for farm tractor, 2.6 times is necessary but actually 1.6 times was done for rice transplanter, 3.9 times is necessary but actually 7.0 times was done for combine harvester. 7. For the experience of breakdown related to maintenance, 5.3% of farmers experienced breakdown due to the insufficient exchange of engine oil, 7.7% of farmers experienced breakdown due to the insufficient cleanness or exchange of fuel filter, and 2.9% of farmers experienced breakdown due to the insufficient cleanness or exchange of air filter. 8. Most farmers (76.1%) recognized the necessity for agricultural machinery training or education, and most farmers preferred about one week for the training period, simple or ease maintenance for the training level, agricultural technical center or agricultural machinery manufacturer for the training agency. 9. Complete recognition of operation manual and sufficient before-, during-, and after-maintenance for farm machinery can minimize the breakdown as well as conduct suitable period farming, enlarge the endurance, prevent the safety accidents, and promote productivity of farm machinery. Therefore, these can be accomplished by the thorough training or education for agricultural machinery.

• Asia Marketing Journal
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• v.15 no.4
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• pp.61-101
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• 2014

This study investigates when and how disagreements in online customer ratings prompt more favorable product evaluations. Among the three metrics of volume, valence, and variance that feature in the research on online customer ratings, volume and valence have exhibited consistently positive patterns in their effects on product sales or evaluations (e.g., Dellarocas, Zhang, and Awad 2007; Liu 2006). Ratings variance, or the degree of disagreement among reviewers, however, has shown rather mixed results, with some studies reporting positive effects on product sales (e.g., Clement, Proppe, and Rott 2007) while others finding negative effects on product evaluations (e.g., Zhu and Zhang 2010). This study aims to resolve these contradictory findings by introducing preference heterogeneity as a possible moderator and causal attribution as a mediator to account for the moderating effect. The main proposition of this study is that when preference heterogeneity is perceived as high, a disagreement in ratings is attributed more to reviewers' different preferences than to unreliable product quality, which in turn prompts better quality evaluations of a product. Because disagreements mostly result from differences in reviewers' tastes or the low reliability of a product's quality (Mizerski 1982; Sen and Lerman 2007), a greater level of attribution to reviewer tastes can mitigate the negative effect of disagreement on product evaluations. Specifically, if consumers infer that reviewers' heterogeneous preferences result in subjectively different experiences and thereby highly diverse ratings, they would not disregard the overall quality of a product. However, if consumers infer that reviewers' preferences are quite homogeneous and thus the low reliability of the product quality contributes to such disagreements, they would discount the overall product quality. Therefore, consumers would respond more favorably to disagreements in ratings when preference heterogeneity is perceived as high rather than low. This study furthermore extends this prediction to the various levels of average ratings. The heuristicsystematic processing model so far indicates that the engagement in effortful systematic processing occurs only when sufficient motivation is present (Hann et al. 2007; Maheswaran and Chaiken 1991; Martin and Davies 1998). One of the key factors affecting this motivation is the aspiration level of the decision maker. Only under conditions that meet or exceed his aspiration level does he tend to engage in systematic processing (Patzelt and Shepherd 2008; Stephanous and Sage 1987). Therefore, systematic causal attribution processing regarding ratings variance is likely more activated when the average rating is high enough to meet the aspiration level than when it is too low to meet it. Considering that the interaction between ratings variance and preference heterogeneity occurs through the mediation of causal attribution, this greater activation of causal attribution in high versus low average ratings would lead to more pronounced interaction between ratings variance and preference heterogeneity in high versus low average ratings. Overall, this study proposes that the interaction between ratings variance and preference heterogeneity is more pronounced when the average rating is high as compared to when it is low. Two laboratory studies lend support to these predictions. Study 1 reveals that participants exposed to a high-preference heterogeneity book title (i.e., a novel) attributed disagreement in ratings more to reviewers' tastes, and thereby more favorably evaluated books with such ratings, compared to those exposed to a low-preference heterogeneity title (i.e., an English listening practice book). Study 2 then extended these findings to the various levels of average ratings and found that this greater preference for disagreement options under high preference heterogeneity is more pronounced when the average rating is high compared to when it is low. This study makes an important theoretical contribution to the online customer ratings literature by showing that preference heterogeneity serves as a key moderator of the effect of ratings variance on product evaluations and that causal attribution acts as a mediator of this moderation effect. A more comprehensive picture of the interplay among ratings variance, preference heterogeneity, and average ratings is also provided by revealing that the interaction between ratings variance and preference heterogeneity varies as a function of the average rating. In addition, this work provides some significant managerial implications for marketers in terms of how they manage word of mouth. Because a lack of consensus creates some uncertainty and anxiety over the given information, consumers experience a psychological burden regarding their choice of a product when ratings show disagreement. The results of this study offer a way to address this problem. By explicitly clarifying that there are many more differences in tastes among reviewers than expected, marketers can allow consumers to speculate that differing tastes of reviewers rather than an uncertain or poor product quality contribute to such conflicts in ratings. Thus, when fierce disagreements are observed in the WOM arena, marketers are advised to communicate to consumers that diverse, rather than uniform, tastes govern reviews and evaluations of products.

• Journal of Dental Rehabilitation and Applied Science
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• v.39 no.3
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• pp.105-118
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• 2023

Purpose: This study aimed to gain better understanding of the importance of dental prosthesis order platform services and to identify the essential elements for their enhancement and wider adoption among dental professionals. Materials and Methods: A survey was conducted to assess the perspectives of dentists, dental technicians, dental hygienists, and dental industry professionals toward dental prosthesis ordering and associated platform services (a total of 53 respondents). The questionnaire was devised after an expert review and assessed for reliability using Cronbach's alpha coefficient. Factor analysis revealed that 57 factors across five categories accounted for 88.417% of the total variance. The survey was administered through an online questionnaire platform, and data analysis was conducted using a statistical software, employing one-way analysis of variance and Tukey's honestly significant difference test (α = 0.05). Results: The essential elements identified were accurate information input, effective communication, delivery of distortion-free impressions, convenience in data transmission and storage, development of stable and affordable platform services (P < 0.05). Furthermore, significant differences were observed in the importance of these items based on age, dental profession, and career experience (P < 0.05). Conclusion: The dental prosthesis ordering platform services, the requirements of dental personnel were stability, economic efficiency, and ease of transmitting and storing prosthesis data. The findings can serve as important indicators for the development and improvement of dental prosthesis order platform services.

• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.65-77
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• 2023

Background: The shortage of dental hygienists as assistant is a great concern to dental clinics, while dental hygienists are rather pursuing the role of oral hygiene control and preventive treatments which is the main role for dental hygienists in the United States. The dental hygienist and dental assistant system in the United States can be a reference in these discussions. Methods: Educational requirements for licensure and work areas for dental hygienists and dental assistants were investigated through the information provided by the American Dental Association (ADA), American Dental Hygienists Association, National Board Dental Hygiene Examination (NBDHE), Dental Assistants Association of America (ADAA), and Dental Assistants National Board (DANB). Results: In the United States, each state has different systems, but in general, dental hygienists obtain licenses after completing 2~3 years of associate degree programs in dental hygiene after obtaining basic learning skills, and mainly perform tasks related to patient screening procedures, oral hygiene management and preventive care. Dental assistants can take the license test after completing a training course of 9~11 months to obtain a dental assistant certification. Additional expanded work typically requires passing state qualification tests, completing a training program, obtaining a degree, or gaining clinical experience for a certain period of time, depending on the state Conclusion: The scope of work of dental hygienists designated by the Medical Engineer Act and the Enforcement Decree in Korea includes both the work of dental hygienists and dental assistants in the United States, and if a dental assistant system like the United States is introduced to address the current shortage of dental assistants, institutional supplementation such as adjustment of the scope of work and expansion of the role of dental hygienists in oral hygiene management and prevention work is needed and in-depth discussion is necessary.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.165-183
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• 2016

Over the past decade, there has been a rapid diffusion of technological devices and a rising number of various devices, resulting in an escalation of virtual reality technology. Technological market has rapidly been changed from smartphone to wearable devices based on virtual reality. Virtual reality can make users feel real situation through sensing interaction, voice, motion capture and so on. Facebook.com, Google, Samsung, LG, Sony and so on have investigated developing platform of virtual reality. the pricing of virtual reality devices also had decreased into 30% from their launched period. Thus market infrastructure in virtual reality have rapidly been developed to crease marketplace. However, most consumers recognize that virtual reality is not ease to purchase or use. That could not lead consumers to positive attitude for devices and purchase the related devices in the early market. Through previous studies related to virtual reality, there are few studies focusing on why the devices for virtual reality stayed in early stage in adoption & diffusion context in the market. Almost previous studies considered the reasons of hard adoption for innovative products in the viewpoints of Typology of Innovation Resistance, MIR(Management of Innovation Resistant), UTAUT & UTAUT2. However, product-based antecedents also important to increase user intention to purchase and use products in the technological market. In this study, we focus on user acceptance and resistance for increasing purchase and usage promotions of wearable devices related to virtual reality based on headgear products like Galaxy Gear. Especially, we added a variables like attitude confidence as a dimension for user resistance. The research questions of this study are follows. First, how attitude confidence and innovativeness resistance affect user intention to use? Second, What factors related to content and brand contexts can affect user intention to use? This research collected data from the participants who have experiences using virtual rality headgears aged between 20s to 50s located in South Korea. In order to collect data, this study used a pilot test and through making face-to-face interviews on three specialists, face validity and content validity were evaluated for the questionnaire validity. Cleansing the data, we dropped some outliers and data of irrelevant papers. Totally, 156 responses were used for testing the suggested hypotheses. Through collecting data, demographics and the relationships among variables were analyzed through conducting structural equation modeling by PLS. The data showed that the sex of respondents who have experience using social commerce sites (male=86(55.1%), female=70(44.9%). The ages of respondents are mostly from 20s (74.4%) to 30s (16.7%). 126 respondents (80.8%) have used virtual reality devices. The results of our model estimation are as follows. With the exception of Hypothesis 1 and 7, which deals with the two relationships between brand awareness to attitude confidence, and quality of content to perceived enjoyment, all of our hypotheses were supported. In compliance with our hypotheses, perceived ease of use (H2) and use innovativeness (H3) were supported with its positively influence for the attitude confidence. This finding indicates that the more ease of use and innovativeness for devices increased, the more users' attitude confidence increased. Perceived price (H4), enjoyment (H5), Quantity of contents (H6) significantly increase user resistance. However, perceived price positively affect user innovativeness resistance meanwhile perceived enjoyment and quantity of contents negatively affect user innovativeness resistance. In addition, aesthetic exterior (H6) was also positively associated with perceived price (p<0.01). Also projection quality (H8) can increase perceived enjoyment (p<0.05). Finally, attitude confidence (H10) increased user intention to use virtual reality devices. however user resistance (H11) negatively affect user intention to use virtual reality devices. The findings of this study show that attitude confidence and user innovativeness resistance differently influence customer intention for using virtual reality devices. There are two distinct characteristic of attitude confidence: perceived ease of use and user innovativeness. This study identified the antecedents of different roles of perceived price (aesthetic exterior) and perceived enjoyment (quality of contents & projection quality). The findings indicated that brand awareness and quality of contents for virtual reality is not formed within virtual reality market yet. Therefore, firms should developed brand awareness for their product in the virtual market to increase market share.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.