• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.9-21
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• 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.613-623
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• 2021

This study aims to identify the effect of blue ocean leadership style on strategic decision making and it also aims to examine the mediating role of organizational politic and moderating role of emotional intelligence in the Government Link Companies (GLCs) in Malaysia. In order to achieve the objective of the study, a research framework had been developed to establish a relationship among the variables of the study based on resource-based view theory. Questionnaire method was used to collect the data form middle to top level employees in GLCs. All the items in the study's variables were assessed using the 5-point Likert scale. A stratified random sampling technique was used to identify the sample for this study. Data was derived from 135 middle to top level employees, which were involved in decision making process. The data was analyzed using the SPSS and the SmartPLS 3.0 software. This supplemented the theory surrounding blue ocean leadership styles and strategic decision making. The study also identified several avenues for further research by using different research methods and examining the impact of strategic decision making in different contexts.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.271-281
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• 2022

The objectives of the research are to study the effects of emotional intelligence (EI), reward management (RM), and occupational health and safety (OHS), on employee performance (EP) within a Thai motor service and repair firm. Starting in January 2022 through the end of March 2022, the researchers used simple random sampling techniques to select 88 employees for the case study. The research instrument was a questionnaire with an IOC value between 0.67-1.00 and a reliability value α of 0.78. Survey participants were asked to contribute their opinions to a five-level opinion survey which was hosted on Google Forms. Descriptive statistics analysis (mean and standard deviation) and multiple linear regression analysis were done using SPSS for Windows version 21. The results showed that employee opinions concerning EI, RM, OHS, and EP were at a high level, with the three hypotheses testing showing statistical significance (p ≤ 0.01). The decision coefficients (R²) all revealed relationship strength with RM = 0.861, OHS = 0.853, and EI = 0.731.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.659-676
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• 2018

Purpose: Although emotional intelligence(EI) is seemingly related to the service-oriented citizenship behaviors(OCB) of contact employees who are directly interacting with customers, there are only a few studies examining the relationship between the two. This study attempts to provide empirical evidence for a link between EI and service-oriented OCB. In addition, when EI affects service-oriented OCB, this present study identifies which emotional labor strategy between surface acting and deep acting is to be chosen. Methods: The data of EI, service-oriented OCB, surface acting, and deep acting were collected from 142 sales employees who provided services in four large domestic department stores. Hierarchical regression analyses were performed to verify empirically the main effects between EI and service-oriented OCB and mediating effects of emotional labor strategy. Results: The results show that EI is significantly related to service-oriented OCB in the current sample. Results also indicate that the deep acting rather than surface acting reveals a mediating effect in the process of EI affecting service-oriented OCB. Conclusion: The results of this study shows that EI which has a conceptual basis for OCB, can be extended to service-oriented OCB, The results also contribute to expanding the understanding of the relationship between EI and service-oriented OCB by testing the effect of EI on service-oriented OCB through deep acting. Theoretical and practical implications are reviewed, and limitations of the study and suggestions for future research are addressed.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.263-270
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• 2014

In cloud environment, business intelligence data warehouse is used for decision making and enterprise policy. But if new system is added in cloud environment, much cost and time is needed due to heterogenous characteristics in data integration. This paper suggests a data cleaning system for business intelligence in cloud environment. The proposed system minimizes the effect of local system when it integrates distributed system using XMDR-DAI. And this system provides standardized information to generate information of data warehouse in real time. Also the proposed system saves cost and time by integrating the data without a change of existed system. And it can improve quality of information by generating coherent information through data extraction and cleaning work in real time.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.23-43
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• 2022

In order to apply AI (Artificial Intelligence) in various industries, interest in algorithm selection is increasing. Algorithm selection is largely determined by the experience of a data scientist. However, in the case of an inexperienced data scientist, an algorithm is selected through meta-learning based on dataset characteristics. However, since the selection process is a black box, it was not possible to know on what basis the existing algorithm recommendation was derived. Accordingly, this study uses k-means cluster analysis to classify types according to data set characteristics, and to explore suitable classification algorithms and methods for resolving class imbalance. As a result of this study, four types were derived, and an appropriate class imbalance resolution method and classification algorithm were recommended according to the data set type.

• The Journal of Bigdata
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• v.8 no.2
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• pp.201-222
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• 2023

As the e-Commerce gets increased based on the platform, a lot of small and medium sized sellers have tried to develop the more effective strategies to maximize the profit. In order to increase the profitability, it is quite important to make the strategic decisions based on the range of promotion, discount rate and categories of products. This research aims to develop the business intelligence application which can help sellers of e-Commerce platform make better decisions. To decide whether or not to promote, it is needed to predict the level of increase in sales after promotion. I n this research, we have applied the various machine learning algorithm such as MLP(Multi Layer Perceptron), Gradient Boosting Regression, Random Forest, and Linear Regression. Because of the complexity of data structure and distinctive characteristics of product categories, Random Forest and MLP showed the best performance. It seems possible to apply the proposed approach in this research in support the small and medium sized sellers to react on the market changes and to make the reasonable decisions based on the data, not their own experience.

• The Journal of Industrial Distribution & Business
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• v.11 no.6
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• pp.41-53
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• 2020

Purpose: The purpose of this study is to review the previous studies on the characteristics of the image search service provided by using artificial intelligence, the social impact characteristics, and the moderating effect of perceived anthropomorphism, and conduct empirical analysis to identify the constituent factors affecting purchase intention. To clarify. Through this, I tried to present theoretical and practical implications. Research design, data, and methodology: Research design was that characteristics of image search service (ubiquity and information quality) and social impact characteristics (subjective norms, electronic word of mouth marketing) are affected by mediation of satisfaction and flow, therefore, control of perceived anthropomorphism have an effect on purchase intention to increase. For analysis, research conducted literature review, and developed questionnaires, so that EM firm which is a specialized research institute has collected data. This was conducted on 410 people between the 20s and 50s who have mobile shopping experiences. SPSS Statistics 23 and AMOS 23 had been used to perform necessary analysis such as exploratory factor analysis, reliability analysis, feasibility analysis, and structural equation modeling based on this data. Results: first, ubiquity, information quality and subjective norms were found to have a positive effect on purchase intention through satisfaction and flow parameters. Second, satisfaction and flow were found to have a mediating effect between ubiquity, information quality, and subjective norms and purchase intentions. However, there was no mediating effect between eWOM information and purchase intention. Third, perceived anthropomorphism was found to have a moderating effect between information quality and satisfaction, and it was found that there was no moderating effect on the relationship between information quality and flow. Conclusions: The information quality of image search services using artificial intelligence has a positive effect on satisfaction, and it has been found that there is a positive moderate effect of perceived anthropomorphism in this relationship, which may be an academic contribution to the distribution science utilizing artificial intelligence. Therefore, it is possible to propose a distribution strategy that improves purchase intention by utilizing image search service and anthropomorphism in practical business and providing a more enjoyable immersive experience to customers.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.183-189
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• 2019

After the fourth industrial revolution, a business that utilizes Artificial Intelligence (AI) is expanding centered around IT industries and it is expected that the quality of AI services will improve. This study aims to examine changes in marketing through the advance and development of AI and to establish and apply marketing strategies to respond to future market changes. Based on existing data, the development of AI technology was examined and looked into changes in marketing and counter strategies through cases overseas and South Korea. Artificial Intelligence technology has a close impact on our lives, changing our lives, and thus changing consumer patterns, perceptions, and consumer culture. In the future, innovative changes in AI technologies will require government policies, the vision of the corporation, and it is necessary to establish longer-term success strategies. Collaboration between companies and industries is also important.

• Journal of Distribution Science
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• v.15 no.12
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• pp.85-94
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• 2017

Purpose - Owing to economic development and rapid globalization, the number of people traveling abroad has increased dramatically in recent years. For instance, according to data from World Tourism Organization, approximately 1,724 million tourists traveled abroad in 2016. This phenomenon has resulted in a change for domestic markets, as they no longer serve only domestic customers but also serve foreign customers as well. Therefore, intercultural service encounters between services providers and customers from diverse cultural backgrounds are becoming more frequent. Especially in the field of retailing, salesperson's customer oriented selling behavior is particularly important for the successful interactions. However, it is hard to find some factors that can improve salesperson's customer oriented selling behavior in intercultural sales encounters. Research design, data, and methodology - A quantitative survey methodology was utilized to collect data on 312 salespeople at duty-free shops located on Jeju Island, Korea. As a tourism-based region, Jeju Island has attracted a large number of foreign tourists since being designated as an international free city in 2002. Owing to this phenomenon, intercultural sales encounters between salespersons and customers from different cultures have become commonplace. Compared to other salespeople, salespeople working in duty-free shops have more frequent intercultural interactions, as over 90% of their total customers are from foreign countries. Additionally, regular professional training programs for salespeople help cultivate cultural intelligence. Data analysis was conducted using SPSS 20. Results - This paper explores the role of empathy and cultural intelligence in intercultural sales encounters using a theoretical model incorporating the causal relationships between empathy(cognitive empathy and emotional empathy) and customer oriented selling behavior, as well as the moderating effects of cultural intelligence in these relationships. Conclusions - This study is almost the first to explore the influence of empathy and cultural intelligence in intercultural sales encounters. Thus, this study provides a meaningful contribution to the application of empathy and cultural intelligence in the retailing field and will draw the attention of personal distribution practicers and researchers to the importance of empathy and cultural intelligence. Additionally, this study has useful managerial implications for employee selection, training, and development in retailing firms engaged in intercultural sales encounters.