• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.545-556
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• 2021

The financial technology sector is now growing rapidly all over the world, and it has improved the banking system efficiency and customer experience. This research study attempts explicitly to explore the consumer acceptance attitude of FinTech and its products in Pakistan. Technology Acceptance Model was used to assess the entire variable associated with the consumer attitude to adopt new technology. Based on a survey conducted from Pakistan data and by employing the multiple regression analysis, this study proves that the risk involved in FinTech products and services results in less usage of financial technology. The findings of the study also show that the risk should be reduced if banks and other institutes that are involved in financial transactions online must provide security. Moreover, customers are not willing to pay an extra amount for using financial technology. It argues that usefulness helps to change the attitude of banking customers to use financial technology. The attitudes of the customers have a positive relationship with the adoption of financial technology. These results also help guide financial institutions to enhance the adoption of FinTech products. User attitudes must be changed by providing users with more security, less risky applications, and cost-effective products.

• Journal of Distribution Science
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• v.19 no.4
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• pp.5-13
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• 2021

Purpose: The sporting field is one of the most lucrative industries that most producers would want to share and drive-in sales towards its direction. The purpose of the present study is to evaluate how sports psychology has become a useful discipline in enhancing consumer purchase intentions. Research design, data, and methodology: This study employs a qualitative coding method to analyze and interpret the data obtained with a PRISMA declaration for analytical purposes. Using Web QDA (Qualitative Data Analysis) online tools, the current study coded the data obtained. Results: According to the prior studies, marketers should go the extra mile of looking for what sports customers are looking for. They understand that one way to increase the customers' willingness to purchase their products is by looking into the specific things that the customers look for and enjoy in sports. Conclusions: After all, the present study concludes that most marketers need to apply the concepts of sports psychology to understand consumer purchase intentions in particular retail stores. Consumers are likely to be influenced by their peers or groups to make decisions driven towards purchasing given sports apparel and the retail store to purchase a product.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.3
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• pp.397-417
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• 2021

As review text contains the experience and opinions of the customers, analyzing review text helps to understand the subject. Existing studies either only used sentiment analysis on online restaurant reviews to identify the customers' assessment on different features of the restaurant or network analysis to figure out the customers' preference. In this study, we conducted both sentiment analysis and network analysis on the review text of the restaurants with high star ratings and those with low star ratings. We compared the review text of the two groups to distinguish the difference of the two and identify what makes great restaurants great.

• Journal of Distribution Science
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• v.21 no.5
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• pp.1-9
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• 2023

Purpose: Marketing and especially distribution channels has proved challenging for small and medium-sized enterprises (SMEs) worldwide due to its exorbitant cost. The objective of this study is to access the role and impact of digital marketing as a new distribution channel in companies located in Kosovo. Research design, data and methodology: To achieve the objective of the research, data was collected from 64 respondents/participants working in different organizations and sectors. Results: The result of the data collected showed that digital marketing plays a huge role and an effective medium in distribution of products and services, and helps in boosting sales of companies. The results showed that this form of marketing helps with retention of customers and cost effectiveness. Conclusions: The managerial implication of this study is that it is believed that customers in the topical conversation region are impacted by Kosovo businesses and their online marketing initiatives. The results of this study suggest that marketers and managers should take advantage of social media in order to accomplish the study's main objective. However, for enhanced productivity and high-level effectiveness of Digital marketing in Kosovo, there should be proper sensitization on the available digital marketing options and how it can be done.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.371-372
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• 2022

Korean customers purcahse some products directoly from overseas for saving the money. However, in refunding or exchanging the products from overseas, customers may complain due to different regulations and language barriers. Mutual purchasing relationships is a prerequisite for establishing trust with customers and online retailers need various activities to gain the trust of consumers. This research has a purpose of investigating the relationship between trust and satisfaction according to the quality of reverse logistics service.

• The Journal of Industrial Distribution & Business
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• v.15 no.3
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• pp.11-20
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• 2024

Purpose: This study focuses on the Chinese smartphone market to estimate product attributes influencing Chinese customers' preference for developing new smartphones through conjoint analysis. Research design, data and methodology: The online questionnaire survey is processed among Chinese potential smartphone customers. Conjoint analysis including traditional conjoint analysis (TCA) and choice-based conjoint analysis (CBCA), is used to analyze the useful data of 500. Results: Results indicate that price is the most important predictor while screen size is the least for Chinese customers' preference whether the method is TCA or CBCA. However, the importance of brand, capacity, CPU, and screen design is different. Moreover, based on each smartphone attribute level's utility, the new products with the best combinations are different compared with both methods. Finally, the predicted market shares of the top 3 products are the same with maximum utility rule model between TCA and CBCA. However, when considering with the new best combined product, they are significantly different. Conclusions: Managers should recognize the differences between TCA and CBCA and select the best method to develop new smartphones for sustainable business in the Chinese competitive market based on the important attributes of price, brand, capacity, CPU, screen design, and size.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.4
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• pp.763-774
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• 2016

Companies have recently become interested in the importance of long-term relationships with customers because business-based marketing ideas of the past have evolved into long-term relationship-based marketing. Establishing a relationship with customers to a company is not a simple method to form a market of consumers and provider; it is now understood as an important factor directly connected to the survival of a company. This study is to help Korean fashion brands in China build an efficient strategy for sales promotion and loyal customers through the analysis of the effect of familiarity with Korean fashion brands on satisfaction and brand loyalty in a rapidly growing Chinese fashion market. An online questionnaire covering Korean fashion brands in China was completed by 377 Chinese male and female customers aged 20 to 39 years old from March 20 to March 27, 2014. Data analysis was performed by factor analysis and path analysis using SPSS 20.0 and AMOS. Both direct experiences and indirect experiences influenced brand familiarity. It showed that brand familiarity had a significant direct effect and an indirect effect through satisfaction on brand loyalty. A competitive advantage in the present Chinese fashion market requires that loyalty builds and that brand loyalty increases by creating a long-term relationship with customers when familiarity about the brand is induced.

• Knowledge Management Research
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• v.20 no.1
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• pp.45-75
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• 2019

Recently, the growth of beauty industry such as plastic surgery and beauty is continued every year in Korea. With the increased interest in appearance based on the improvement of life standard and the development of media, people's perception of cosmetic plastic surgery is changing. Now, as the service for consumer satisfaction based on their desire, the perception of plastic surgery medical service is changed to the high value-added industry with the high growth potential. Thus, this study aims to suggest the strategies for providing the medical service that could satisfy customers, by drawing the factors cognized as important when customers aim to get the cosmetic plastic surgery, and then additionally analyzing the relationships of those factors. On top of performing the topic modeling based on customers' comments data of social commerce related to cosmetic plastic surgery, this study also conducted the network analysis for visualizing the relations of each keywords. The drawn main factors were divided by applying the sub-categories of the SERVQUAL theory, and the additional characteristics of plastic surgery were shown by referring the relevant previous researches. Moreover, the interview with the cosmetic plastic surgery specialists (plastic surgeons) and customers who actually received the plastic surgery, helped the understanding of the interpretation of each factor and the actual relevant phenomenons. The significance of this study is to draw and discuss the main factors that should be observed by Korean cosmetic plastic surgery medical institutes, by mining and analyzing the opinions of customers interested in the cosmetic plastic surgery and procedure with the use of topic modeling. In other words, the quality of medical service of cosmetic plastic surgery could be improved by presenting the key factors that could be considered by the cosmetic plastic surgery medical service suppliers and also the actual strategies.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.