• The Journal of Industrial Distribution & Business
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• v.9 no.2
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• pp.27-38
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• 2018

Purpose - This study centers on field surveys aimed at deriving the customer satisfaction factors of customer support centers that are positioned between suppliers and consumers in the supply chain. They consists of manufacturing, sales, distribution, consumption and collection, and that are in charge of core functions for suppliers' customer satisfaction management and consumers' satisfaction with consuming activities. Research design, data, and methodology - The customer satisfaction factors of customer support centers were derived through literature review and expert opinion surveys, and a questionnaire was developed through a process of the refinement of variables using pilot tests and 330 questionnaire sheets were distributed. The questionnaire sheets were collected and opinions in them were analyzed using fuzzy AHP methodology. Results - Three factors, which are turnover intentions, motivation, and job satisfaction, were derived as customer satisfaction factors of customer support centers, and the ranking relationships of these three factors were analyzed. In addition, the ranking relationships among six execution variables of turnover intentions, 10 execution variables of motivation, and 10 execution variables of job satisfaction were analyzed using fuzzy AHP methodology to obtain quite significant results. Based on the results of this study, three implications in the three strategic aspects and an implication in the academic aspects are presented. Conclusions - Motivation and job satisfaction, job satisfaction and turnover intentions, and motivation and turnover intentions are not formed by independent or different factors or environments. They are in the same context with each other (maintaining high correlations) and are in the relationships of virtuous circles in which they complement each other.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.207-218
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• 2020

Recently, due to the increase of online reviews and the development of analysis technology, the interest and demand for online review analysis continues to increase. However, previous studies have not considered the emotions contained in each vocabulary may differ from one reviewer to another. Therefore, this study first classifies the customer group according to the customer's grade, and presents the result of analyzing the difference by performing review analysis for each customer group. We found that the price factor had a significant influence on the evaluation of products for customers with high ratings. On the contrary, in the case of low-grade customers, the degree of correspondence between the contents introduced in the mall and the actual product significantly influenced the evaluation of the product. We expect that the proposed methodology can be effectively used to establish differentiated marketing strategies by identifying factors that affect product evaluation by customer group.

• The Journal of Industrial Distribution & Business
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• v.15 no.7
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• pp.1-9
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• 2024

Purpose: This research adopts the SERVQUAL and LSQ frameworks to examine the correlation between return reverse logistics service quality of the JD platform and customer satisfaction, as well as the linkage between consumer satisfaction and repurchase intention. Research design, data and methodology: A comprehensive literature review on both domestic and international logistics service quality has been conducted. Considering the unique aspects of JD's return reverse logistics services, an evaluation framework with 5 dimensions and 21 indicators is formulated, including communication, information, return process, empathy, and convenience. A conceptual model exploring the influence of JD's reverse logistics service quality on customer repurchase intention is developed, proposing six hypotheses. For this investigation, 358 valid questionnaires were collected, processed, and analyzed using SPSS 22.0. The structural equation modeling was conducted and validated through AMOS 21.0 software. Results: Following a thorough analysis of data, it reveals that: (1) Information quality, return process quality, and empathy significantly enhance customer satisfaction. (2) Customer satisfaction positively impacts repurchase intention. Conclusion: Based on these findings, three strategic recommendations are offered for e-commerce platforms with in-house logistics systems. The research also discusses limitations and future research directions.

• The Research Journal of the Costume Culture
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• v.15 no.6
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• pp.1023-1037
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• 2007

The focus of this study was on service recovery process of domestic internet fashion shopping mall, the purposes of this study were to extract perceived interaction effort and service justice with the recovery factors according to service failure by literature review, and to empirically examine the effect this variables on customer satisfaction with complaint handling and loyalty. The questionnaires was administered to 256 internet shopping mall customer, who has experiences of dissatisfaction and complaining behavior after buying fashion products. The data was analyzed by Cronbach's a, confirmatory factor analysis, correlation analysis, and structural equation modeling using LISREL 8.30 program. The results were as follows. First, perceived interaction partly affected serviced justice consumer. Interaction effort on the part of consumer negatively affected interactional justice, but didn't affected distributive justice and procedural justice. However interaction effort on the part of shopping mall positively affected all justice. Second, distributive, procedural and interactive justice positively affected customer satisfaction with complaint handling and loyalty. Finally, customer satisfaction with complaint handling positively affected customer loyalty. The implications of the research and directions for future researchers were discussed.

• Journal of the Korean Society of Food Culture
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• v.27 no.3
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• pp.285-293
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• 2012

The purpose of this study was to determine the relationships among food quality, food awareness, customer satisfaction, and revisit intentions of Korean restaurants in Chinese University or College students in the Daegu and Gyeongbuk areas. A questionnaire developed from a literature review included a series of questions about the quality of Korean food, Korean food awareness, customer satisfaction, and revisit intentions. Analysis of the survey data was performed on 234 valid responses. Statistical analyses, including frequencies, factor analysis, reliability analysis and regression, were performed using the SPSS program. The results indicated that food quality perceived by Chinese students had a significant impact on customer satisfaction. On the contrary, food quality according to Korean food awareness by Chinese students did not have a significant impact on customer satisfaction. Further, customer satisfaction had a significant influence on revisit intentions, whereas customer satisfaction according to Korean food awareness did not have a significant effect. In conclusion, food quality is a significant factor in determining the success of the foodservice industry.

• Journal of Distribution Science
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• v.21 no.1
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• pp.13-22
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• 2023

Purpose: This study aims to investigate the linkages among cross-channel integration capability (CCI), showrooming, webrooming, and customer value of retail enterprises. From the literature review, this research proposes the research model on the direct impact of showrooming and webrooming on customer value as well as the indirect impact of cross-channel integration capability on customer value which is mediated by both showrooming and webrooming of retail enterprises. Research design, data, and methodology: By conducting a survey of 304 consumers in the five biggest retailers in Hanoi-Vietnam from mid-September 2021 to the end of November 2021, the PLS-SEM was used to test the hypotheses. Results: The research results reveal the favorable impact of (CCI) on improving showrooming and webrooming, and the important role of developing both showrooming and webrooming in bringing more value to the customer of retail enterprises. The findings also express that showrooming and webrooming acts mediating role in the favorable relationship between (CCI) and customer value of retailers. Conclusions: This research clarifies the positive impact of (CCI), showrooming, and webrooming on customer value. In addition, this study suggests practical implications for retail managers to provide more value for customers by enhancing (CCI) and developing both showrooming and webrooming.

• Korean Management Science Review
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• v.17 no.3
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• pp.19-30
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• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• Korean Management Science Review
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• v.24 no.2
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• pp.177-196
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• 2007

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as Association Rule, Frequency Matrix, and Rule Induction, this study suggests an integrative prediction model. Integrated models consist of four models: ASFM model which combines Association Rule(A) and Frequency Matrix(B), ASRI model which combines Association Rule(A) and Rule Induction(C), FMRI model which combines Frequency Matrix(B) and Rule Induction(C), and ASFMRI model which combines Association Rule(A), Frequency Matrix(B), and Rule Induction(C). The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1,000 transactions are selected for a specific item. Using this data set. it suggests an integrated model predicting whether a customer buys or not buys a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as Association Rule, Frequency Matrix, and Rule Induction.

• Journal of Distribution Science
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• v.18 no.10
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• pp.65-75
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• 2020

Purpose: This study aims to examine how to review contents of experiential and utilitarian products (e.g., skincare products) and how to affect review helpfulness by applying natural language processing techniques. Research design, data, and methodology: This study uses 69,633 online reviews generated for the products registered at Amazon.com by 13 Korean cosmetic firms. The authors identify key topics that emerge about consumers' use of skincare products such as skin type and skin trouble, by applying bigram analysis. The review content variables are included in the review helpfulness model, including other important determinants. Results: The estimation results support the positive effect of review extremity and content on the helpfulness. In particular, the reviewer's skin type information was recognized as highly useful when presented together as a basis for high-rated reviews. Moreover, the content related to skin issues positively affects review helpfulness. Conclusions: The positive relationship between extreme reviews and helpfulness of reviews challenges the findings from prior literature. This result implies that an in-depth study of the effect of product types on review helpfulness is needed. Furthermore, a positive effect of review content on helpfulness suggests that applying big data analytics can provide meaningful customer insights in the online retail industry.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.