• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5229-5252
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• 2016

With proliferation of diverse network access technologies, users demands are also increasing and service providers are offering a Quality of Service (QoS) to satisfy their customers. In roaming, a mobile node (MN) traverses number of available networks in the heterogeneous wireless networks environment and a single operator is not capable to fulfill the demands of user. It is crucial task for MN for selecting a best network from the list of networks at any time anywhere. A MN undergoes a network selection situation frequently when it is becoming away from the home network. Multiple Attribute Group Decision (MAGD) method will be one of the best ways for selecting target network in heterogeneous wireless networks (4G). MAGD network selection process is predominantly dependent on two steps, i.e., attribute weight, decision maker's (DM's) weight and aggregation of opinion of DMs. This paper proposes Multi-Attribute Intuitionistic Fuzzy Group Decision Method (MAIFGDM) using TOPSIS for the selection of the suitable candidate network. It is scalable and is able to handle any number of networks with large set of attributes. This is a method of lower complexity and is useful for real time applications. It gives more accurate result because it uses Intuitionistic Fuzzy Sets (IFS) with an additional parameter intuitionistic fuzzy index or hesitant degree. MAIFGDM is simulated in MATLAB for its evaluation. A comparative study of MAIFDGM is also made with TOPSIS and Fuzzy-TOPSIS in respect to decision delay. It is observed that MAIFDGM have low values of decision time in comparison to TOPSIS and Fuzzy-TOPSIS methods.

• Journal of Distribution Science
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• v.9 no.4
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• pp.5-13
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• 2011

This study provides analysis of the effectiveness of domestic marketing strategies of the Korean coffee shop "Caffe Bene". It bases its evaluation on statistical outputs of 'choice attributes,' "market segmentation," demographic characteristics," and "satisfaction differences." The results are summarized in four points. First, five choice attributes were extracted from factor analysis: price, atmosphere, comfort, taste, and location; these are related to coffee shop selection behavior. Based on these five factors, cluster analysis was conducted, with statistical results classifying customers into three major groups: atmosphere oriented; comfort oriented; and taste oriented. Second, discriminant analysis tested cluster analysis and showed two discriminant functions: location and atmosphere. Third, cross-tabulation analysis based on demographic characteristics showed distinctive demographic characteristics within the three groups. Atmosphere oriented group, early-20s, as women of all ages was found to be 'walking down the street 'and 'through acquaintances' in many cases, as the cognitive path, and mostly found the store through 'outdoor advertising', and 'introduction'. Comfort oriented group was mainly women who are students in their early twenties or professionals, and appeared as a group to be very loyal because of high recommendation to other customers compared to other groups. Taste oriented group, unlike the other group, was mainly late-20s' college graduates, and was confirmed, as low loyalty, with lower recommendation activity. Fourth, to analyze satisfaction differences, one-way ANOVA was conducted. It shows that groups which show high satisfaction in the five main factors also show high menu satisfaction and high overall satisfaction. This results show that segmented marketing strategies are necessary because customers are considering price, atmosphere, comfort, taste, location when they choose coffee shop and demographics show different attributes based on segmented groups. For example, atmosphere oriented group is satisfied with shop interior and comfort while dissatisfied with price because most of the customers in this group are early 20s and do not have great financial capability. Thus, price discounting marketing strategies based on individual situations through CRM system is critical. Comfort oriented group shows high satisfaction level about location and shop comfort. Also, in this group, there are many early 20s female customers, students, and self-employed people. This group customers show high word of mouth tendency, hence providing positive brand image to the customers would be important. In case of taste oriented group, while the scores of taste and location are high, word of mouth score is low. This group is mainly composed of educated and professional many late 20s customers, therefore, menu differentiation, increasing quality of coffee taste and price discrimination is critical to increase customers' satisfaction. However, it is hard to generalize the results of study to other coffee shop brand, because this study have researched only one domestic coffee shop, Caffe Bene. Thus if future study expand the scope of locations, brands, and occupations, the results of the study would provide more generalizable results. Finally, research of customer satisfactions of menu, trust, loyalty, and switching cost would be critical in the future study.

• Culinary science and hospitality research
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• v.17 no.2
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• pp.1-17
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• 2011

The purpose of this study is to make an evaluation on menu selection and satisfaction levels of the tourists who visit Korean restaurants in Busan. This study aims to propose general, strategic plans for improving the menu quality management of Korean restaurants in the future and find out measures to make restaurant users satisfied and revisit. A survey was conducted on ordinary people who had visited Korean restaurants in tourist hotels in Busan. A total of 310 copies of questionnaire were distributed to them from September 1 to 30, 2008. The SPSS/PC+ for Window 12.0 was used for data processing and analysis. As a result, it was found that selection attributes of Korean restaurant menu had an effect on customer satisfaction and revisit intention, and the most influential factors were health and menu designs. These days, people are getting more interested in keeping in shape with the Well-being trend. Such a modern trend is an important factor when selecting menu items. Therefore, when menu management is considered, health-related factors need to be considered more than anything else. In addition, various and unique menu items need to be designed to draw people's curiosity and make customers revisit.

• Journal of Service Research and Studies
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• v.11 no.2
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• pp.21-36
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• 2021

This study investigated the means-end chain theory more concretely through the APT hard laddering technique. This is carrying out a questionnaire survey targeting users by hair salon type, and the items drawn from the qualitative laddering technique are applied. The technique is a comparative analysis of each attribute, consequences, and value item by analyzing each step's questions. The results are as follows. First, hairdresser's ability, acceptance of individual-customized opinions, and cheap price were the most mentioned items in the selection attributes. As for the consequences items, image transformation, neatness, novelty, and psychological stability were drawn in order. The items indicated as important among the value items were satisfaction, followed by happiness, confidence, beauty, and bond. Second, the remarkable selection attributes, irrelevant of hair salon type, was revealed as hairdresser's ability and the key values pursued when using a hair salon were drawn as satisfaction, confidence, and beauty. From this result, it was found that meeting the desire of consumers using hair salons can be linked with ultimately pursued values. It was also verified that partial differences were shown by hair salon type and this meant that consumers' desire and expected benefits were different by hair salon type. Although this study drew value perception through comparison with hair salon types based on the means-end chain theory, it was confirmed that the most important selection attribute was hairdresser's ability and they select and use hair salons to gain satisfaction and confidence.

• Journal of the East Asian Society of Dietary Life
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• v.22 no.2
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• pp.315-321
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• 2012

In this study, the product attributes that give customers the estimated benefits and products that can predict the customer's choice conjoint analysis techniques to identify the restaurant affinity markets a new dining concept was to develop. Questionnaire for this study of 400 non-response is negative and insincere characters, except for the final analysis, the questionnaire Part 309 was the target. Conjoint model used in this study Pearson's R is 0.928 ($p$<0.000), Kendall's tau is 0.750 ($p$<0.000) with an orthogonal plan was well suited for profiling attributes are extracted 16. Part of the relative importance of the value of the property to determine the result of analyzing the properties that are most important at the level of the respondents of the induct (38.46%), and followed by price (30.52%), Atmosphere (18.28%), and Exclusive space (12.73%) was followed. Portion of the property value for each analysis among industry preference for the Italian food was highest, a nature-friendly interior atmosphere had the highest affinity Average per price at 10,000 won~30,000 won or less than the amount of affinity was higher location of the restaurant alone, showed that space preferred. Through simulation in a virtual seafood restaurant nature-friendly image, average price per person ranging from 10,000 to 30,000won at an exclusive restaurant was most preferred.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.87-98
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• 2017

As the market for coffee chains becomes fierce, it is important for coffee chains to establish an enduring relationship with customers and encourage them to revisit their stores. Thus, it becomes important to understand consumers' repurchase decision-making mechanisms in the context of coffee chains. Customer satisfaction, perceived switching costs, and regret were considered as the main factors of customers' repurchase intentions. Especially, the effect of regret experience of coffee chains on consumers' repurchase decision was examined. In addition, coffee quality, physical environment quality, service encounters performance, and brand trust were considered as attributes of coffee shop selection, and their effects on customer satisfaction and perceived switching cost were investigated. The results of the study showed that customer satisfaction and perceived switching costs had a positive effect on repurchase intention, while regret had a negative effect on repurchase intention. Coffee quality and physical environmental quality had no significant effect on customer satisfaction and perceived conversion cost. Service encounter performance had a significant impact on perceived switching costs alone. Brand trust had a significant impact on both customer satisfaction and perceived switching cost.

• Knowledge Management Research
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• v.22 no.3
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• pp.55-73
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• 2021

The sharing economy is considered as a collaborative consumption which enables customers to share unused resources. This study investigated the key factors affecting consumer loyalty in the context of sharing accommodation. Emotions, perceived value and self-image consistency were posited as key antecedents of enhancing customer loyalty. Authentic experience, home amenities, and price fairness were also considered as Airbnb's selection attributes. Airbnb was selected a survey target because it is the largest company in the domain of shared accommodation market. The research model was analyzed for 294 Airbnb customer through structural equation models. Additionally, this paper examine Airbnb customers' experiences by topic modelling method posted on the Naver blog. Based on the understanding of the key factors affecting customer loyalty to sharing accommodation, the analysis results contribute to establish effective marketing and operation strategies by enhancing customer experience.

• Journal of the Korean Society of Food Culture
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• v.24 no.2
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• pp.181-190
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• 2009

The purpose of this study was to investigate individuals' perceptions toward green tea and green tea cafes according to their usage of green tea cafes, gender, and marital status. The survey was conducted online and completed by 200 respondents who had been to green tea cafes and by 200 respondents who had not. The visitors of green tea cafes had more positive perceptions regarding the atmosphere, familiar taste, good quality of raw foods, freshness of foods, good service, excellent taste, and menu variety of green tea cafes. Males perceived green tea as 'good for health' more than the females. There was no difference in the perception of green tea cafes by marital status. Regarding a popularization plan for green tea cafes, the customers felt more need for a tea sommelier than the non-customers, and females versus males felt stronger about having a tea sommelier that would provide opportunities to taste various tea products and develop a green tea menu that considered nutrition and health. Unmarried respondents felt a greater necessity for having a place with a comfortable environment, tasting a variety of tea products, and having a chance to experience tea culture. When selecting a green tea cafe, the quality of the food and cleanliness of the cafe were considered to be most important. The implications of the data are discussed.

• The Journal of Industrial Distribution & Business
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• v.14 no.1
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• pp.21-28
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• 2023

Purpose: This study aims to analysis the difference by Michelin rating in customer satisfaction of restaurant listed in the Korea Michelin Guide. There are opinions that the Michelin Guide's rating system and evaluation criteria are somewhat ambiguous. Research design, data, and methodology: This study collected 145 actual online reviews published on TripAdvisor to examine how the effect of the content attributes of reviews on consumer satisfaction varies according to the Michelin grade. Based on this, two studies were conducted. Study 1 examined the effect of strong and weak positive reviews on consumer satisfaction according to the rating. Study 2 examined the effect of image information on consumer satisfaction. Results: The results revealed that the lower the Michelin rating, the more positive review had a significant effect on consumer satisfaction. The higher the rating, the more image information had an effect on consumer satisfaction. Expectations for Michelin three-star restaurants are higher than those of two-star restaurants, so customers are more likely to be used negatively when writing reviews. Conclusions: Accurate information on Michelin selection criteria should be delivered so as not to form high expectations and not to disappoint. For consumers to be satisfied with the name Michelin, the standards should be stricter.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.