• Asia Marketing Journal
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• v.26 no.2
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• pp.129-144
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• 2024

This research is on how people's trust in human-like AI-based service will influence customer engagement (CE). This study will discuss the relationship between trust and CE and explore how people's trust in AI affects CE when they lack knowledge of the company/brand. Items from the philosophical study of trust were extracted to build a scale suitable for trust in AI. The scale's reliability was ensured, and six components of trust in AI were merged into three dimensions: trust based on Quality Assurance, Risk-taking, and Corporate Social Responsibility. Trust based on quality assurance and risk-taking is verified to positively impact customer engagement, and the feelings about AI-based service fully mediate between all three dimensions of trust in AI and CE. The new trust scale for human-like AI-based services on social media sheds light on further research. The relationship between trust in AI and CE provides a theoretical basis for subsequent research.

• Asia pacific journal of information systems
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• v.8 no.3
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• pp.51-73
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• 1998

This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.

• Information Systems Review
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• v.26 no.2
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• pp.155-167
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• 2024

This study recognized the importance of joint research in the field of artificial intelligence and analyzed the characteristics of the industry-academic-research technological cooperation ecosystem focusing on patents from the perspective of the Techno-Economic Segment (TES). To this end, economic entities such as companies, universities, and research institutes within the ecosystem were identified for 7,062 joint research projects out of 113,289 artificial intelligence patents over the past 10 years filed in IP5 countries since 2012. Next, this study identified the topics of technological cooperation and the characteristics of cooperation. As a result of the analysis, technological cooperation is increasing, and the frequency of all types of cooperation was high in industry-to-industry (40%) and industry-to-university (25.2%) relationships. Here, this study confirmed that the role of universities is being strengthened, with an increase in the ratio of companies with strengths in funding and analytical data, industry and universities with excellent research personnel (9.8%), and cooperation between universities (1.9%). In addition, as a result of identifying collaborative patent research areas of interest and collaborative relationships through topic modeling and network analysis, overall similar research interests were derived regardless of the type of cooperation, and applications such as autonomous driving, edge computing, cloud, marketing, and consumer behavior analysis were derived. It was confirmed that the scope of research was expanding, collaborating entities were becoming more diverse, and a large-scale network including Chinese-centered universities was emerging.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.97-117
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• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.175-181
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• 2024

The paper examines the impact of digital media art on urban creative products, analyzing opportunities and challenges in the digital era. It emphasizes the development of urban cultural and creative products, highlighting their significance and future growth potential. The digital media era provides unprecedented innovation opportunities, utilizing advanced tools for efficient design, production, and marketing. Trends like personalization, customization, AI, and big data offer new expressions and market prospects. Cultural products evolve in design, marketing, and sales channels due to digital media, with tools like social media and e-commerce platforms opening new promotion avenues. Case studies illustrate digital media's role in driving innovation and enhancing user experiences. The paper addresses challenges in market competition, copyright, and technological renewal, while recognizing opportunities from AI and big data. The creative industries must adapt and innovate to remain relevant. Looking ahead, urban creative products will evolve under digitalization, relying on digital means to attract consumers and enhance brand value. Cultural products, beyond economic entities, disseminate urban culture and creative spirit. In the digital era, urban creative products demonstrate potential and necessity, prompting a reevaluation of digital technology's role. Through continuous innovation, this field contributes to cultural and economic levels, impacting urban characteristics and heritage. Urban creative products play an increasingly vital role in the global cultural and creative economy.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.173-182
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• 2020

The purpose of this paper is to study the influence of new service methods of "artificial intelligence (AI) + medical cosmetology", a new service means, on customers' purchase intentions. To AI medical beauty APP sales as an empirical study. This paper designed Likert seven scale to investigate, using SPSS 24.0 statistical analysis software and AMOS24.0 structural equation software to analyze the survey data. The analysis method uses reliability analysis, validity analysis, and construct equation model analysis. Through empirical research, the following results can be found, 1. The system quality of AI medical beauty app will have a positive impact on perceived usefulness and perceived ease of use. 2. The information quality of AI medical beauty app will have a positive impact on perceived ease of use and perceived usefulness. 3. The service quality of AI medical beauty app will have a positive impact on perceived ease of use and perceived usefulness 4. Consumers' perceived ease of use has a positive impact on perceived usefulness and purchase intention. 5. The usefulness of consumers' notification has a positive effect on purchase intention.

• Korean Journal of Artificial Intelligence
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• v.7 no.1
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• pp.23-59
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• 2019

The purpose of this study is to develop a measure of service quality in the traditional market by examining previous research on the service quality of the traditional market studied so far. After defining basic concepts through definition of traditional market and existing studies, 5 categories of configuration items for SERVQUAL measurement in traditional market were made up based on existing researches related to definition of service quality and service quality of traditional market. A survey was conducted on the items that fit the intention of this study and various statistical analyzes were conducted. Statistical analysis was performed using SPSS 22.0 and AMOS 22.0. The reliability of the items was measured by the reliability test, and the predictability and accuracy of the items were examined. The validity of the measured variables was verified through confirmatory factor analysis. Reliability, empathy, responsiveness, certainty, and tangibility were the most important factors in this study. Responsiveness factors include communication, time reduction, real time, promptness. Assurance factors include the assurance of delivery, prompt answers, product knowledge items. Tangibility factors include, convenient device systems, location information, presence as a fact, and as a result, the latest modern items are adopted. The quality of service in the traditional market developed in this study was found to be good in reliability and validity test. Confirmatory factor analysis result using structural equation model also met the conformity index standard. If service satisfaction is measured based on this research, basic data can be presented to policy makers who implement policies on traditional markets to make the right decisions. In addition, it will be able to provide traditional market operators with operational strategy and marketing data. In the future, based on the traditional market service quality scale developed in this study, it is necessary to grasp the factors to be continuously managed to improve the service quality of the traditional market, user satisfaction, and intention to use.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.287-295
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• 2017

Since Google's AlphaGo defeated a world champion of Go players in 2016, there have been many interests in the deep learning. In the financial sector, a Robo-Advisor using deep learning gains a significant attention, which builds and manages portfolios of financial instruments for investors.In this paper, we have proposed the a deep learning algorithm geared toward identification and forecast of the KOSPI index direction,and we also have compared the accuracy of the prediction.In an application of forecasting the financial market index direction, we have shown that the Robo-Advisor using deep learning has a significant effect on finance industry. The Robo-Advisor collects a massive data such as earnings statements, news reports and regulatory filings, analyzes those and recommends investors how to view market trends and identify the best time to purchase financial assets. On the other hand, the Robo-Advisor allows businesses to learn more about their customers, develop better marketing strategies, increase sales and decrease costs.

• Food Science and Industry
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• v.54 no.3
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• pp.124-131
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• 2021

In the 4th industrial revolution, Artificial Intelligence (AI), big data, Internet of Things (IoT) are already around us, making our society hyper-connected and blurring the lines between the digital and biological spheres. We witness drastic changes not only in the food industry, but also in economy, society and our life as a whole. Technologies bring industrial reorganization and greater changes at the system level and the food industry is not exceptional. Human demand for foods continues to grow and the very nature of the food industry remains unchanged, but its production, distribution and marketing face unprecedent innovations. Passing through the global pandemic, the food industry has been evolved into 'contact-free', as the safety become our top priority. Amid the gradual shift to technology-oriented society, the smarter food safety management skills and tools are being adopted in many countries exerting greater efforts to enhance traceability and to upgrade AI-powered safety management system.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.528-537
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• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.