• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.78-79
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• 2011

Tourism demand is expected to continue to rise, actively respond to these demands and customer needs, optimize and differentiated consumer-based content development and delivery that incorporates principals of the need for mediation, is increasing. Smartphone and internet represented SNS collects information based on customer and customer needs by providing differentiated content to meet the cost and the total satisfaction of all tourism is needed to build Platform Company. Platform Company maximize the utilization of natural maritime resources, marine tourism to meet customer demand, profitable and stable income, including job creation are contributing to.

• Journal of Information Technology Applications and Management
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• v.23 no.4
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• pp.55-70
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• 2016

Recently, development of indoor positioning system and IoT such as beacon makes it possible to collect and analyze each customer's shopping behavior in off-line shopping malls. In this study, we propose a realtime brand recommendation scheme based on each customer's brand visiting history for off-line shopping mall with indoor positioning system. The proposed scheme, which apply collaborative filtering to off-line shopping mall, is composed of training and apply process. The training process is designed to make the base brand network (BBN) using historical transaction data. Then, the scheme yields recommended brands for shopping customers based on their behaviors and BBN in the apply process. In order to verify the performance of the proposed scheme, simulation was conducted using purchase history data from a department store in Korea. Then, the results was compared to the previous scheme. Experimental results showd that the proposed scheme performs brand recommendation effectively in off-line shopping mall.

• Journal of Distribution Science
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• v.17 no.9
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• pp.93-102
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• 2019

Purpose - Capturing the shifting consumer behavior perspective on online transportation network performance in Indonesia, this study aims to empirically examine the impact of electronic customer relationship management (e-CRM) and e-service quality on customer e-satisfaction and e-loyalty. Research design, data, and methodology - A quantitative approach was applied, and then we determined the respondents who met the predetermined criterion by using purposive sampling method. In total, 167 online transportation customer in Indonesia participated in this electronic questionnaire survey. To tested the collected data, Partial Least Square (PLS) - (SEM) analytical tools were employed. Results and Findings - There are five hypotheses proposed in this study and state that only one hypothesis is rejected, The dominant relationship between variables in the hypothesis is shown in the variable relationship of e-service quality on e-satisfaction. CRM, Service Quality, Satisfaction and Loyalty implemented comprehensively in cyberspace provides a clear picture for academics but also for practitioners who are struggling in the service industry that specifically appoints online transportation business. The findings of this research provide both managerial and theoretical implications to maintain customer e-loyalty in online transportation network business environment in Indonesia.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.101-109
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• 2021

Cybercrimes are the biggest threat that can influence the future of e-commerce, particularly in difficult times such as the COVID-19 pandemic. This pandemic has resulted in noticeable changes in e-customer behaviour represented in three types: spending rates, types of goods bought, and the number of purchasing times. Moreover, the percentage of cybercrime in many countries, including Saudi Arabia, has increased during the pandemic. The increase in the number of cybercrimes during the COVID-19 crisis and the changes in consumer behaviour shows that there is an urgent need to conduct research on the factors that have led to this. This study will explore the most significant factors that have an effect on the unexpected change of customer behaviour and cybercrime perceived risk during the COVID-19 pandemic in Saudi Arabia. The finding of the study will hopefully contribute to attempts in finding safer methods for shopping online during COVID-19 and similar crisis.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.30-36
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• 2023

In today's worldwide environment, information systems (IS) usage is growing swiftly. As a result, it now affects every aspect of life and serves as a general growth tool for individuals, groups, and governments. information system success (ISS) is affected by customer satisfaction and their acceptance of using these services. In addition, this issue will be a critical thing for SMEs, especially in Saudi Arabia. SMEs have a shortage and lack IT experience and resources. The research's question is What are the ISS that will improve customer satisfaction and SME performance in Saudi Arabia. Through an online survey, The data on how Saudi SMEs succeed in IS was acquired. Citizens and residents users in Saudi Arabia, representing a range of ages and educational backgrounds. In the IS success factors evaluation, which assessed the degree of agreeability and disagreeability of specific statements related to the six dimensions based on the empirical data, it was found that the users agreed with the majority of the claims. For users, usability is the most important feature. This study discovered that enhancing the system's overall user experience might lead to higher overall satisfaction.

• Information Systems Review
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• v.13 no.1
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• pp.75-96
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• 2011

This research uses coolhunting to predict the future trend of e-Commerce industry. Coolhunting is a method to take Cool Trends which are the future trend through social network analysis for discovering the trendsetter and its collective intelligence. Coolhunting is generally carried out by social network analysis while this research uses NodeXL of social network analysis tools. We designed industrial network research model for relation among e-Commerce corporation, product, the types of customer service and customer service employee to discover the Cool Trends of e-Commerce industry. According to the result of this research, e-Commerce industrial network was being changed from chaos to collective intelligence form. As a analysis result for network influences, we found that Cool Trends of e-Commerce industry invigorate social commerce industry through the collective intelligence focusing intelligence VIP, Excellence, grade of Administrating for women customers(trendsetter) and it promotes semantic consumption from customers and purchasing power will be concentrated on cosmetic, beauty, perfume product categories in social commerce. We propose the strategic direction for e-Commerce corporation and hope that domestic e-Commerce corporation continues to grow and high-quality services are provided for customers.

• Journal of Korean Society for Quality Management
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• v.44 no.2
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• pp.451-466
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• 2016

Purpose: This study was to identify customers' demands in railway services system and then to seek the way to satisfy the customer expectations. Methods: We suggest a Quality Function Deployment(QFD)-based approach comprised of three stages. In first stage, SERVPERF survey was carried out to assess current positions of customer expectations in the market. Then, factor analysis was incorporated into SERVPERF to classify customer expectations for the house of quality. In the second stage, the analytic network process was used to prioritize the importance of the customer attributes. Finally, QFD was performed utilizing customer attributes and their weights. Results: The QFD identified the most important customer expectations as: accident prevention, swift reaction to accident, on-time arrivals and departures of the train. It also shows that driving capability, equipment for safety, and training for disaster are the most critical technical requirements. Conclusion: The results are useful for identifying the customers' demands in railway services systems, and they can contribute to the service quality and customer satisfaction.

• The Journal of Information Systems
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• v.27 no.3
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• pp.63-80
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• 2018

Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.172-175
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• 2003

The enterprise find a solution to the problems such as a reduction of manufacturing period, accurate analysis for customer demand, improvement for customer service and rise of manufacture accomplishment. Internet is a good solution to such problems. Internet offers WWW(World Wide Web), remote control, file transfer and e-mail service. Among the services, WWW takes large portion because of convenient GUI, easy information search and unlimited information registration. Remote Monitoring Server(RMS) system that uses network service is constructed for chip mounter. Hardware base consists of RMS, chip mounter and C/S(Customer Service) server. Software includes DBMS and various modules in server home page. This provide product number, bad product number, trouble code, content and countermeasure in real-time information module, user information in setup module, detailed error information in fault diagnosis module, fault history in fault history module and customer information in customer service management module.