• Journal of Information Technology Applications and Management
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• 제23권4호
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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.

• 유통과학연구
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• 제17권9호
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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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• 제21권12호
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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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• 제23권8호
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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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• 제23권10호
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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.

• 경영정보학연구
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• 제13권1호
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• pp.75-96
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• 2011

본 연구는 e-Commerce 산업의 미래 트랜드를 예측하기 위해 Coolhunting을 실시하였다. Coolhunting은 사회네트워크 분석을 통해 트렌드세터를 발견하고 이들의 집단지성을 통해 미래 트랜드인 Cool Trends를 읽어내는 방법이다. Coolhunting은 사회네트워크분석을 통해 실시되는데 본 연구에서는 사회 네트워크 분석 Tool 중 NodeXL을 활용하였다. e-Commerce 산업의 Cool Trends를 발견하기 위해 e-Commerce 기업, 상품, 고객서비스 유형, 고객응대직원, 고객간의 산업네트워크 연구모형을 설계하였다. 연구모형을 통해 e-Commerce 산업의 흐름을 분석하고, 네트워크 영향력을 나타내는 사이중앙값과 페이지랭크값 분석을 통해 트렌드세터들의 특성을 파악하였다. 본 연구의 결과 e-Commerce 산업 네트워크는 혼돈형태에서 현재 집단지성형태의 네트워크로 변화되고 있었다. 네트워크 영향력에 대한 분석결과 e-Commerce 시장의 Cool Trends가 VIP, 우수, 관리등급의 여성고객(트렌드세터)들을 중심으로 집단지성을 통한 상거래인 소셜커머스 시장이 활성화 될 것이고, 소셜커머스에서는 소비자들에게 시맨틱한 소비를 촉진시키고, 상품군 중 화장품/미용기구/향수 상품군에서 고객 들의 구매력이 집중될 것이라는 것을 발견하였다. 본 연구결과를 통해 e-Commerce 기업이 취해야 할 전략적 방향성을 제언하였고, 국내 e-Commerce 기업들에게 있어서 지속적 성장이 이루어지고, 고객들에게 있어서는 양질의 서비스가 제공되기를 바란다.

• 품질경영학회지
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• 제44권2호
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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.

• 한국정보시스템학회지:정보시스템연구
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• 제27권3호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 통신소사이어티 추계학술대회논문집
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• pp.361-364
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• 2003

The customer premise facilities assume an important role in the telecommunication networks. The present technical standards for domestic customer cabling facilities are established with the multimedia environment reflected, but are insufficient for accommodation of multimedia services of the ultra high-speed information and communication networks In the present paper, the status of international standardization as well as technical standards in the U.S.A., Japan, other countries are reviewed and the domestic technical standard and emblem is investigated and analyzed in order to enhance customer cabling facilities in Korea. The problems with emblem are analyzed, and how to improve customer cabling telecommunication facilities is suggested based on the above.