• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.7-16
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• 2024

This study aims to explore driving factors of Artificial Intelligence application for city marketing strategy with perspectives of millennials and generation Z. This study proposed the following research questions: i) how perceived place branding factor, public service factor, affective factor, immersive experience factor, cognitive factor, cost benefit factor, social networking factor, and promotional value factor affect attitude toward AI application for city marketing; and ii) how attitude affect satisfaction and prospect toward AI application for city marketing? This study conducted an online survey with the assistance of a well-known research agency and applied factor and regression analysis to test hypotheses. The results found that effects of place branding, cognitive, social networking, and promotional value affect attitude significantly in the case of millennials, while effects of public service, affective, cost benefit, social networking, and promotional value affect attitude significantly in the case of generation Z. The results found that effects of attitude on satisfaction and prospect of AI showed significance. The results provide implications and different aspects for AI application of city marketing strategy with perspectives by generations, while millennials and generation Z perceived effects of promotional value as the most significant factor for AI application of city marketing strategy.

• Asia Marketing Journal
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• v.1 no.1
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• pp.4.1-4.25
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• 1998

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.227-239
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• 2023

Online marketing is a rapidly growing industry that heavily depends on digital technologies and data analysis to effectively reach and engage consumers. For that, artificial intelligence (AI) has emerged as a crucial tool for online marketers, enabling marketers to analyze extensive consumer data and automate decision-making processes. The purpose of this study was to investigate the ethical implications of using AI in online marketing, focusing on its impact on consumer privacy and decision-making. AI has created new possibilities for personalized marketing but raises concerns about the collection and use of consumer data, transparency and accountability of decision-making, and the impact on consumer autonomy and privacy. In this study, we reviewed the relevant literature and case studies to assess the potential risks and make recommendations for improving consumer protection. The findings provide insights into ethical considerations and offer a roadmap for balancing the advantages of AI in online marketing with the protection of consumer rights. Companies should consider these ethical issues when implementing AI in their marketing strategies. In this study, we explored the concerns and provided insights into the challenges posed by AI in online marketing, such as the collection and use of consumer data, transparency, and accountability of decision-making, and the impact on consumer autonomy and privacy.

• Journal of Wellbeing Management and Applied Psychology
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• v.2 no.1
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• pp.1-8
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• 2019

"In the future, 80% of doctors will be replaced by advanced technology." It has been talked about for a long time. When I first heard this story, people said it was ridiculous. But now that AlphaGo has won the Go match against Lee Se-dol, and many global companies have come up with a variety of services and products based on artificial intelligence, the story has become no more than ridiculous. In other words, it is beginning to come true. Artificial intelligence technology is already widely used in manufacturing and service industries. This spread of artificial intelligence is sure to usher in an era of great change in our future. And it is safe to say that it is the "medical world" where the biggest changes will be made. So how on earth does artificial intelligence replace medical personnel? If replaced, where would you stand out? In order to understand this, we must first be familiar with deep learning, which is the basis of medical artificial intelligence. And as the fourth industrial revolution gradually approaches reality, various occupational groups are becoming meaningless, as in the preceding industrial revolution, and in this paper we will learn about the impact of this situation on the medical community.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.18-28
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.1-9
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• 2017

Recently, the Q-Learning algorithm, which is one kind of reinforcement learning, is mainly used to implement artificial intelligence system in combination with deep learning. Many research is going on to improve the performance of Q-Learning. Therefore, purpose of theory try to improve the performance of Q-Learning algorithm. This Theory apply Cross Entropy Error to the loss function of Q-Learning algorithm. Since the mean squared error used in Q-Learning is difficult to measure the exact error rate, the Cross Entropy Error, known to be highly accurate, is applied to the loss function. Experimental results show that the success rate of the Mean Squared Error used in the existing reinforcement learning was about 12% and the Cross Entropy Error used in the deep learning was about 36%. The success rate was shown.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.567-582
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• 2014

The purpose of this study is to qualitatively identify the typologies and characteristics of the big data marketing strategy in major companies that are taking advantage of the big data business in Korea. Big data means piles accumulated from converging platforms such as computing infrastructures, smart devices, social networking and new media, and big data is also an analytic technique itself. Numerous enterprises have grown conscious that big data can be a most significant resource or capability since the issue of big data recently surfaced abruptly in Korea. Companies will be obliged to design their own implementing plans for big data marketing and to customize their own analytic skills in the new era of big data, which will fundamentally transform how businesses operate and how they engage with customers, suppliers, partners and employees. This research employed a Q-study, which is a methodology, model, and theory used in 'subjectivity' research to interpret professional panels' perceptions or opinions through in-depth interviews. This method includes a series of q-sorting analysis processes, proposing 40 stimuli statements (q-sample) compressed out of about 60 (q-population) and explaining the big data marketing model derived from in-depth interviews with 20 marketing managers who belong to major companies(q-sorters). As a result, this study makes fundamental contributions to proposing new findings and insights for small and medium-size enterprises (SMEs) and policy makers that need guidelines or direction for future big data business.

• 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.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.29-37
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• 2017

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the Gradient Descent Optimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.65-76
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• 2021

This study aims at investigating the effect of product innovation accounting on the market success of instant food and convenience food businesses in Thailand with customer response capability as the mediator. In addition, it examines the effects of management accounting systems, marketing intelligence, and technology orientation on product innovation accounting. The sample for this study is 258 instant food and convenience food businesses in Thailand. To test the research relationships, a structural equation model is used. The results of this study show that product innovation accounting has a significant effect on both customer response capability and market success. Similarly, customer response capability significantly leads to market success while it mediates the product innovation accounting-market success relationship. Testing the antecedents of the research relationships, management accounting system, marketing intelligence, and technology orientation potentially affect product innovation accounting. Accordingly, product innovation accounting is a key source of competitive advantage. Product innovation accounting must be recognized by company management as a strategic tool for competing in markets and environments. They must invest their resources and capabilities to create and develop product innovation accounting principles, as well as encouraging their staff to implement and use these principles in the workplace.