• Cross-Cultural Studies
• /
• v.52
• /
• pp.165-194
• /
• 2018

Employing Fairclough's critical discourse analysis (CDA), the purpose of this study was to analyze linguistically significant features, intertextuality, and sociocultural practice focusing on selected editorials of The Global Times, $Hu{\acute{a}}nqi{\acute{u}}$ Shíbào on the 2018 United States-China Trade War. The editorial titled "With the strong will of 'the War to Resist America and Aid Chosun,' let us go through the trade war against America" focused on the use of 'war' related vocabulary in the frame of 'war.' First, "Trade War" and "War to Resist America and Aid Chosun" are examples that reveal metaphors and a war frame. Second, "Strategy" is used positively for China but negatively towards America. Third, various war related words are used. Fourth, cases of allusion illustrate war. Intertextuality in terms of discourse practice pertains to two findings. First, The Global Times, $Hu{\acute{a}}nqi{\acute{u}}$ Shíbào repeatedly uses the phrase 'equivalent revenge.' That is because the expression enables China to justify their counterattack and such war that China may wage can be interpreted as just counterattack much like a self-defense mechanism. Second, the expression, 'the counterattack is not intended but it is not fearful' is repeated in several editorials of the newspaper. The reasons are the following: 1) it is used to appeal to the public, 2) by invoking the feeling of fear, the public should be understand why they should unite, and 3) the expression, "it is not fearful" is used to preserve China's global image and "the counterattack is not intended" is used to signal China's will to America. The whole expression is a good example of intertextuality that repetitively illustrates the intended meaning of China in nine editorials in the newspaper within three months, March 23-June 17, 2018. Finally, sociocultural practice is manipulated through the editorial for disseminating the Chinese government's hegemonic ideology. First, it is clear that the core national project, "China Manufacturing 2025" cannot be abandoned. Second, by calling for "War to Resist America and Aid Chosun" the editorial is manipulated to condemn and intimidate America, avoid dissent of the people, appeal to the people, and empower the government. Third, China somehow wants to open up the possibility of negotiation with the United Sates.

• Asia Marketing Journal
• /
• v.15 no.4
• /
• pp.131-159
• /
• 2014

Corporate rebranding has been evident in the qualitative corporate rebranding studies as an imposed organizational change that induces mixed reactions and ambivalent attitudes among consumers. Corporate rebranding for the established and familiar corporate brands leads to more ambivalent attitudes as these companies represent larger targets for disparaging information. Consumers are found to hold both positive and negative reactions toward companies and brands that they are familiar with. Nevertheless, the imposed change assumption and ambivalent attitude, in particular corporate rebranding, have never been widely explored in the quantitative corporate rebranding studies. This paper aims to provide a comprehensive empirical examination of the ambivalence towards rebrandingrebranded brand attitude-purchase intention relationships. The author proposes that corporate rebranding for familiar corporate brands is a double-edged sword that not only raises the expectation for better performance, but also induces conflicted and ambivalent attitudes among consumers. These consumers' ambivalent attitudes are influenced by both the parent brands-related and general attitude factors which further affect their rebranded brand attitude and purchase intention. A total of 156 useable questionnaires were collected from Malaysian working adults; and two established Malaysian airfreight operators were utilized as the focal parent brands. The study found a significant impact of prior parent brand attitudes on ambivalence towards rebranding (ATR). The parent brand attitudes served as anchors in influencing how new information was processed (Mazaheri et al., 2011; Sherif & Hovland, 1961) and closely related to behavioral intention (Prislin & Quellete, 1996). The ambivalent attitudes experienced were higher when individuals held both positive and negative reactions toward the parent brands. Consumers also held higher ambivalent attitudes when they preferred one of the parent brands; while disliked the other brand. The study also found significant relationships between the lead brand and the rebranded brand attitude; and between the partner brands and ATR. The familiar but controversial partner brand contributed significantly to the ambivalent attitudes experienced; while the more established lead brand had significant impact on the rebranded brand attitude. The lead and partner brands, though both familiar, represented different meanings to consumers. The author attributed these results to the prior parent brand attitudes, the skepticism and their general ambivalence toward the corporate rebranding. Both general attitude factors (i.e. skepticism and general ambivalence towards rebranding) were found to have significant positive impacts on ATR. Skeptical individuals questioned the possibility of a successful rebranding (Chang, 2011) and were more careful with their evaluations toward 'too god to be true' or 'made in heaven' pair of companies. The embedded general ambivalent attitudes that people held toward rebranding could be triggered from the associative network by the ambiguous situation (Prislin & Quellete, 1996). In addition, the ambivalent rebranded brand attitude was found to lower down purchase intention, supporting Hanze (2001), Lavine (2001) and van Harreveld et al. (2009)'s studies. Ambivalent individuals were found to prefer delay decision making by choosing around the mid-ranged points in 'willingness to buy' scale. The study provides several marketing implications. Ambivalence management is proven to be important to corporate rebranding to minimize the ambivalent attitudes experienced. This could be done by carefully controlling the parent brands-related and general attitude factors. The high ambivalent individuals are less confident with their own conflicted attitudes and are motivated to get rid of the psychological discomfort caused by these conflicted attitudes (Bell & Esses, 2002; Lau-Gesk, 2005; van Harreveld et al., 2009). They tend to process information more deeply (Jonas et al., 1997; Maio et al., 2000; Wood et al., 1985) and pay more attention to message that provides convincible arguments. Providing strong, favorable and convincible message is hence effective in alleviating consumers' ambivalent attitudes. In addition, brand name heuristic could be utilized because the rebranding strategy sends important signal to consumers about the changes that happen or going to happen. The ambivalent individuals will pay attention to both brand name heuristic and rebranding message in their effort to alleviate the psychological discomfort caused by ambivalent attitudes. The findings also provide insights to Malaysian and airline operators for a better planning and implementation of corporate rebranding exercise.

• Journal of Intelligence and Information Systems
• /
• v.16 no.3
• /
• pp.77-97
• /
• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.73-85
• /
• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.