• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.24 no.1
• /
• pp.16-24
• /
• 2016

Despite rapid growing in domestic duty free industry, weight of income from airport duty free area is relatively decreasing because of rapid progress in downtown duty free area. The purpose of this research was investigating the relationship between airport duty free servicescape and its effects on customer satisfaction and image for duty free shop, in order to maintain own competitive advantages. In pursuing above, previous studies related to servicescape, service value, customer satisfaction, and image were examined for literature review. Based on this previous studies, research model were constructed. Hypothesis was verified by effect. Data from 305 samples was employed for final survey. The main results show that functionality, attraction and convenience were meaningful factors to effect perceived servicescape. On the other hand, cleanness and comfort had few or no influence on servicescape. The perceived servicescape affected on service value and customer satisfaction. Service value had positive effects on customer satisfaction which was discovered to affected on image for duty free shop.

• Korea Trade Review
• /
• v.44 no.6
• /
• pp.151-168
• /
• 2019

This study aims to investigate the determinants that can influence customer satisfaction and loyalty. Compared to some of the determinants that have been investigated by other scholars before, this study also includes a new approach to mobile food-ordering that has only appeared within the Chinese fast-food industry in recent years. It is very important to test if the convenience of mobile ordering services can influence customer satisfaction, as more and more customers are using mobile apps to order food, especially in the 4th Industrial Revolution Era. Research data mostly was collected from customers who visited one of five famous Western fast-food restaurants (KFC, McDonald's, Pizza Hut, Subway, and Burger King) in China. The results show that not only price, service quality, food quality, and the physical environment can significantly affect customer satisfaction but also the convenience of mobile ordering services, a new determinant accompanied by the fast development of IoT technology.

• Information Systems Review
• /
• v.25 no.1
• /
• pp.111-128
• /
• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• The Journal of Information Systems
• /
• v.27 no.1
• /
• pp.89-110
• /
• 2018

Purpose There is much information in customer reviews, but finding key information in many texts is not easy. Business decision makers need a model to solve this problem. In this study we propose a multi-topic sentiment analysis approach using Latent Dirichlet Allocation (LDA) for user-generated contents (UGC). Design/methodology/approach In this paper, we collected a total of 104,039 hotel reviews in seven of the world's top tourist destinations from TripAdvisor (www.tripadvisor.com) and extracted 30 topics related to the hotel from all customer reviews using the LDA model. Six major dimensions (value, cleanliness, rooms, service, location, and sleep quality) were selected from the 30 extracted topics. To analyze data, we employed R language. Findings This study contributes to propose a lexicon-based sentiment analysis approach for the keywords-embedded sentences related to the six dimensions within a review. The performance of the proposed model was evaluated by comparing the sentiment analysis results of each topic with the real attribute ratings provided by the platform. The results show its outperformance, with a high ratio of accuracy and recall. Through our proposed model, it is expected to analyze the customers' sentiments over different topics for those reviews with an absence of the detailed attribute ratings.

• Journal of Information Technology Applications and Management
• /
• v.14 no.4
• /
• pp.97-120
• /
• 2007

Development of information technology and Internet brought big changes in information society. Quantity of information increased rapidly and various types of information were presented through diverse channels. This change brought an impact in electronic commerce environment. A large number of products are transacted in online market. And various search functions and product information are presented for supporting customer's decision making. This study examined the effect of external information on purchase decision-making in electronic commerce environment. An experiment was conducted to see the customer product review, unit sales, etc. on purchase decision-making process in online shopping mall based electronic commerce. As a result of study, external information referring to number of purchasing, positive product review, and reliability of information has a positive effect on purchase-decision. The significance of the study can be found in that it defined 1) external information has an effect on decision-making, 2) positiveness and reliability of product information showed that they have an influence on customer, and 3) when self opinion and other person's opinion are different, one is not satisfied with decision making process. The results of the study can be of practical use in the design and implementation of online shopping mall in electronic commerce.

• The Journal of Industrial Distribution & Business
• /
• v.15 no.4
• /
• pp.11-18
• /
• 2024

Purpose: The primary purpose of this research is to investigate the multidisciplinary effect of food delivery apps (FDAs) in urban hotels in the wake of the lockdown due to Covid-19 pandemic. Specifically, the study aims: To explore and scrutinize the primary shifts in customer behavior and preferences in modern urban hotels, and to explore and scrutinize the primary shifts in customer behavior and preferences in modern urban hotels. Research design, data and methodology: This study conducted a systematic literature review to gather evidence of the FDA's effect on customer behavior and the hospitality industry during the Covid-19 pandemic. Complying with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) principles guarantees a structured and transparent method to search the literature and its analysis. Results: The result based on the systematic review has indicated that the booming business of food delivery at home companies and changing consumer tastes prove the FDA's growing circuit in the hotel industry, thus demonstrating their ability and power to adapt to changing trends. Conclusions: Therefore, this study concludes that using FDA's platform, future hospitality managers have to focus on agility in operations, innovation, and technology integration to keep up with changing consumer trends and market conditions.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.139-161
• /
• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Information Systems Review
• /
• v.16 no.3
• /
• pp.1-23
• /
• 2014

This research uses the Analytical Hiararchy Process (AHP) to scientifically and systematically calculate the weightings of attributes as well as dimensions considered for assessing an informational website. The present paper aims at observing and using the computed weightings to comparatively examine the perceptions of customer users and business users. We use the 3C-D-T (i.e., Content, Community, Commerce, Design, and Technology) framework to conduct a case study where we review and assess restaurant websites and calculate attribute weightings on these websites. Data used for website review was collected in two phases. Data in the first phase was collected from customer users, and data in the second phase was from business users who had registered in the same websites. Users were instructed to perform a pairwise comparison of the relative importance of website attributes. Our data analysis revealed that the customer users and business users demonstrated different views on the relative importance of the individual attributes. Based on the findings, we suggested that business users of restaurants should adapt their views to the customers' views to minimize perceptional differences, thereby increasing customer satisfaction and accomplishing successful business outcomes.

• Information Systems Review
• /
• v.24 no.3
• /
• pp.1-21
• /
• 2022

This research investigates how a service provider's response(s) to negative customer reviews influences the success of accommodation services in the context of online accommodation reservation platforms. Specifically, we attempt to comprehend the important role of attentive and instant responses to users' negative review comments in fostering future success by analyzing panel data on 856 motels registered in the largest accommodation reservation platform in Korea. The results present that response volume (Attentiveness) and faster responses (Timeliness) are positively associated with success. We further find that the two review-response strategies have a positive interaction effect on success. Moreover, we show that the effect of review responses is strengthened when the reputation of motels drops. The key findings of this research offer a set of practical guidelines for accommodation owners to achieve business success by effectively managing customer reviews and claims

• The Journal of the Korea Contents Association
• /
• v.16 no.2
• /
• pp.410-419
• /
• 2016

The purpose of this study is to examine how hotel customer's artistic experience affect the customer's satisfaction and customer's loyalty to the hotel brand. A total of 237 effective questionnaires for empirical analyses data were obtained from the hotel customers who had the experiences of appreciating on purpose or accidently the artworks displayed inside hotel. Trough the literature review it was suggested that four factors of artistic experience encompassed educational experience, escapist experience, emotional experience, esthetic experience. To test the proposed hypotheses, exploratory factor analyses and hierarchical regression analyses were conducted. The results showed that all four factors influenced customer's satisfaction and loyalty significantly. And artistic experience was confirmed to be mediated by customer's satisfaction on customer's loyalty. Based on the results, the meaningful implications and limitations were discussed.