The Effects of Online Service Quality on Consumer Satisfaction and Loyalty Intention -About Booking and Issuing Air Tickets on Website- (온라인 서비스 품질이 고객만족 및 충성의도에 미치는 영향 -항공권 예약.발권 웹사이트를 중심으로-)
- Park, Jong-Gee;Ko, Do-Eun;Lee, Seung-Chang
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- Journal of Distribution Research
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- v.15 no.3
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- pp.71-110
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- 2010
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1. Introduction Today Internet is recognized as an important way for the transaction of products and services. According to the data surveyed by the National Statistical Office, the on-line transaction in 2007 for a year, 15.7656 trillion, shows a 17.1%(2.3060 trillion won) increase over last year, of these, the amount of B2C has been increased 12.0%(10.2258 trillion won). Like this, because the entry barrier of on-line market of Korea is low, many retailers could easily enter into the market. So the bigger its scale is, but on the other hand, the tougher its competition is. Particularly due to the Internet and innovation of IT, the existing market has been changed into the perfect competitive market(Srinivasan, Rolph & Kishore, 2002). In the early years of on-line business, they think that the main reason for success is a moderate price, they are awakened to its importance of on-line service quality with tough competition. If it's not sure whether customers can be provided with what they want, they can use the Web sites, perhaps they can trust their products that had been already bought or not, they have a doubt its viability(Parasuraman, Zeithaml & Malhotra, 2005). Customers can directly reserve and issue their air tickets irrespective of place and time at the Web sites of travel agencies or airlines, but its empirical studies about these Web sites for reserving and issuing air tickets are insufficient. Therefore this study goes on for following specific objects. First object is to measure service quality and service recovery of Web sites for reserving and issuing air tickets. Second is to look into whether above on-line service quality and on-line service recovery have an impact on overall service quality. Third is to seek for the relation with overall service quality and customer satisfaction, then this customer satisfaction and loyalty intention. 2. Theoretical Background 2.1 On-line Service Quality Barnes & Vidgen(2000; 2001a; 2001b; 2002) had invented the tool to measure Web sites' quality four times(called WebQual). The WebQual 1.0, Step one invented a measuring item for information quality based on QFD, and this had been verified by students of UK business school. The Web Qual 2.0, Step two invented for interaction quality, and had been judged by customers of on-line bookshop. The WebQual 3.0, Step three invented by consolidating the WebQual 1.0 for information quality and the WebQual2.0 for interactionquality. It includes 3-quality-dimension, information quality, interaction quality, site design, and had been assessed and confirmed by auction sites(e-bay, Amazon, QXL). Furtheron, through the former empirical studies, the authors changed sites quality into usability by judging that usability is a concept how customers interact with or perceive Web sites and It is used widely for accessing Web sites. By this process, WebQual 4.0 was invented, and is consist of 3-quality-dimension; information quality, interaction quality, usability, 22 items. However, because WebQual 4.0 is focusing on technical part, it's usable at the Website's design part, on the other hand, it's not usable at the Web site's pleasant experience part. Parasuraman, Zeithaml & Malhorta(2002; 2005) had invented the measure for measuring on-line service quality in 2002 and 2005. The study in 2002 divided on-line service quality into 5 dimensions. But these were not well-organized, so there needed to be studied again totally. So Parasuraman, Zeithaml & Malhorta(2005) re-worked out the study about on-line service quality measure base on 2002's study and invented E-S-QUAL. After they invented preliminary measure for on-line service quality, they made up a question for customers who had purchased at amazon.com and walmart.com and reassessed this measure. And they perfected an invention of E-S-QUAL consists of 4 dimensions, 22 items of efficiency, system availability, fulfillment, privacy. Efficiency measures assess to sites and usability and others, system availability measures accurate technical function of sites and others, fulfillment measures promptness of delivering products and sufficient goods and others and privacy measures the degree of protection of data about their customers and so on. 2.2 Service Recovery Service industries tend to minimize the losses by coping with service failure promptly. This responses of service providers to service failure mean service recovery(Kelly & Davis, 1994). Bitner(1990) went on his study from customers' view about service providers' behavior for customers to recognize their satisfaction/dissatisfaction at service point. According to them, to manage service failure successfully, exact recognition of service problem, an apology, sufficient description about service failure and some tangible compensation are important. Parasuraman, Zeithaml & Malhorta(2005) approached the service recovery from how to measure, rather than how to manage, and moved to on-line market not to off-line, then invented E-RecS-QUAL which is a measuring tool about on-line service recovery. 2.3 Customer Satisfaction The definition of customer satisfaction can be divided into two points of view. First, they approached customer satisfaction from outcome of comsumer. Howard & Sheth(1969) defined satisfaction as 'a cognitive condition feeling being rewarded properly or improperly for their sacrifice.' and Westbrook & Reilly(1983) also defined customer satisfaction/dissatisfaction as 'a psychological reaction to the behavior pattern of shopping and purchasing, the display condition of retail store, outcome of purchased goods and service as well as whole market.' Second, they approached customer satisfaction from process. Engel & Blackwell(1982) defined satisfaction as 'an assessment of a consistency in chosen alternative proposal and their belief they had with them.' Tse & Wilton(1988) defined customer satisfaction as 'a customers' reaction to discordance between advance expectation and ex post facto outcome.' That is, this point of view that customer satisfaction is process is the important factor that comparing and assessing process what they expect and outcome of consumer. Unlike outcome-oriented approach, process-oriented approach has many advantages. As process-oriented approach deals with customers' whole expenditure experience, it checks up main process by measuring one by one each factor which is essential role at each step. And this approach enables us to check perceptual/psychological process formed customer satisfaction. Because of these advantages, now many studies are adopting this process-oriented approach(Yi, 1995). 2.4 Loyalty Intention Loyalty has been studied by dividing into behavioral approaches, attitudinal approaches and complex approaches(Dekimpe et al., 1997). In the early years of study, they defined loyalty focusing on behavioral concept, behavioral approaches regard customer loyalty as "a tendency to purchase periodically within a certain period of time at specific retail store." But the loyalty of behavioral approaches focuses on only outcome of customer behavior, so there are someone to point the limits that customers' decision-making situation or process were neglected(Enis & Paul, 1970; Raj, 1982; Lee, 2002). So the attitudinal approaches were suggested. The attitudinal approaches consider loyalty contains all the cognitive, emotional, voluntary factors(Oliver, 1997), define the customer loyalty as "friendly behaviors for specific retail stores." However these attitudinal approaches can explain that how the customer loyalty form and change, but cannot say positively whether it is moved to real purchasing in the future or not. This is a kind of shortcoming(Oh, 1995). 3. Research Design 3.1 Research Model Based on the objects of this study, the research model derived is
. 3.2 Hypotheses 3.2.1 The Hypothesis of On-line Service Quality and Overall Service QualityThe relation between on-line service quality and overall service quality I-1. Efficiency of on-line service quality may have a significant effect on overall service quality. I-2. System availability of on-line service quality may have a significant effect on overall service quality. I-3. Fulfillment of on-line service quality may have a significant effect on overall service quality. I-4. Privacy of on-line service quality may have a significant effect on overall service quality. 3.2.2 The Hypothesis of On-line Service Recovery and Overall Service Quality The relation between on-line service recovery and overall service quality II-1. Responsiveness of on-line service recovery may have a significant effect on overall service quality. II-2. Compensation of on-line service recovery may have a significant effect on overall service quality. II-3. Contact of on-line service recovery may have a significant effect on overall service quality. 3.2.3 The Hypothesis of Overall Service Quality and Customer Satisfaction The relation between overall service quality and customer satisfaction III-1. Overall service quality may have a significant effect on customer satisfaction. 3.2.4 The Hypothesis of Customer Satisfaction and Loyalty Intention The relation between customer satisfaction and loyalty intention IV-1. Customer satisfaction may have a significant effect on loyalty intention. 3.2.5 The Hypothesis of a Mediation Variable Wolfinbarger & Gilly(2003) and Parasuraman, Zeithaml & Malhotra(2005) had made clear that each dimension of service quality has a significant effect on overall service quality. Add to this, the authors analyzed empirically that each dimension of on-line service quality has a positive effect on customer satisfaction. With that viewpoint, this study would examine if overall service quality mediates between on-line service quality and each dimension of customer satisfaction, keeping on looking into the relation between on-line service quality and overall service quality, overall service quality and customer satisfaction. And as this study understands that each dimension of on-line service recovery also has an effect on overall service quality, this would examine if overall service quality also mediates between on-line service recovery and each dimension of customer satisfaction. Therefore these hypotheses followed are set up to examine if overall service quality plays its role as the mediation variable. The relation between on-line service quality and customer satisfaction V-1. Overall service quality may mediate the effects of efficiency of on-line service quality on customer satisfaction. V-2. Overall service quality may mediate the effects of system availability of on-line service quality on customer satisfaction. V-3. Overall service quality may mediate the effects of fulfillment of on-line service quality on customer satisfaction. V-4. Overall service quality may mediate the effects of privacy of on-line service quality on customer satisfaction. The relation between on-line service recovery and customer satisfaction VI-1. Overall service quality may mediate the effects of responsiveness of on-line service recovery on customer satisfaction. VI-2. Overall service quality may mediate the effects of compensation of on-line service recovery on customer satisfaction. VI-3. Overall service quality may mediate the effects of contact of on-line service recovery on customer satisfaction. 4. Empirical Analysis 4.1 Research design and the characters of data This empirical study aimed at customers who ever purchased air ticket at the Web sites for reservation and issue. Total 430 questionnaires were distributed, and 400 were collected. After surveying with the final questionnaire, the frequency test was performed about variables of sex, age which is demographic factors for analyzing general characters of sample data. Sex of data is consist of 146 of male(42.7%) and 196 of female(57.3%), so portion of female is a little higher. Age is composed of 11 of 10s(3.2%), 199 of 20s(58.2%), 105 of 30s(30.7%), 22 of 40s(6.4%), 5 of 50s(1.5%). The reason that portions of 20s and 30s are higher can be supposed that they use the Internet frequently and purchase air ticket directly. 4.2 Assessment of measuring scales This study used the internal consistency analysis to measure reliability, and then used the Cronbach' $\alpha$ to assess this. As a result of reliability test, Cronbach'$\alpha$ value of every component shows more than 0.6, it is found that reliance of the measured variables are ensured. After reliability test, the explorative factor analysis was performed. the factor sampling was performed by the Principal Component Analysis(PCA), the factor rotation was performed by the Varimax which is good for verifying mutual independence between factors. By the result of the initial factor analysis, items blocking construct validity were removed, and the result of the final factor analysis performed for verifying construct validity is followed above. 4.3 Hypothesis Testing 4.3.1 Hypothesis Testing by the Regression Analysis(SPSS) 4.3.2 Analysis of Mediation Effect To verify mediation effect of overall service quality ofand , this study used the phased analysis method proposed by Baron & Kenny(1986) generally used. As shows, Step 1 and Step 2 are significant, and mediation variable has a significant effect on dependent variables and so does independent variables at Step 3, too. And there needs to prove the partial mediation effect, independent variable's estimate ability at Step 3(Standardized coefficient
$\beta$ eta : efficiency=.164, system availability=.074, fulfillment=.108, privacy=.107) is smaller than its estimate ability at Step 2(Standardized coefficient$\beta$ eta : efficiency=.409, system availability=.227, fulfillment=.386, privacy=.237), so it was proved that overall service quality played a role as the partial mediation between on-line service quality and satisfaction. Asshows, Step 1 and Step 2 are significant, and mediation variable has a significant effect on dependent variables and so does independent variables at Step 3, too. And there needs to prove the partial mediation effect, independent variable's estimate ability at Step 3(Standardized coefficient
$\beta$ eta : responsiveness=.164, compensation=.117, contact=.113) is smaller than its estimate ability at Step 2(Standardized coefficient$\beta$ eta : responsiveness=.409, compensation=.386, contact=.237), so it was proved that overall service quality played a role as the partial mediation between on-line service recovery and satisfaction. Verified results on the basis of empirical analysis are followed. First, as the result of, it shows that all were chosen, so on-line service quality has a positive effect on overall service quality. Especially fulfillment of overall service quality has the most effect, and then efficiency, system availability, privacy in order. Second, as the result of , it shows that all were chosen, so on-line service recovery has a positive effect on overall service quality. Especially responsiveness of overall service quality has the most effect, and then contact, compensation in order. Third, as the result of and , it shows that and all were chosen, so overall service quality has a positive effect on customer satisfaction, customer satisfaction has a positive effect on loyalty intention. Fourth, as the result of and , it shows that and all were chosen, so overall service quality plays a role as the partial mediation between on-line service quality and customer satisfaction, on-line service recovery and customer satisfaction. 5. Conclusion This study measured and analyzed service quality and service recovery of the Web sites that customers made a reservation and issued their air tickets, and by improving customer satisfaction through the result, this study put its final goal to grope how to keep loyalty customers. On the basis of the result of empirical analysis, suggestion points of this study are followed. First, this study regarded E-S-QUAL that measures on-line service quality and E-RecS-QUAL that measures on-line service recovery as variables, so it overcame the limit of existing studies that used modified SERVQUAL to measure service quality of the Web sites. Second, it shows that fulfillment and efficiency of on-line service quality have the most significant effect on overall service quality. Therefore the Web sites of reserving and issuing air tickets should try harder to elevate efficiency and fulfillment. Third, privacy of on-line service quality has the least significant effect on overall service quality, but this may be caused by un-assurance of customers whether the Web sites protect safely their confidential information or not. So they need to notify customers of this fact clearly. Fourth, there are many cases that customers don't recognize the importance of on-line service recovery, but if they would think that On-line service recovery has an effect on customer satisfaction and loyalty intention, as its importance is very significant they should prepare for that. Fifth, because overall service quality has a positive effect on customer satisfaction and loyalty intention, they should try harder to elevate service quality and service recovery of the Web sites of reserving and issuing air tickets to maximize customer satisfaction and to secure loyalty customers. Sixth, it is found that overall service quality plays a role as the partial mediation, but now there are rarely existing studies about this, so there need to be more studies about this. The Research on Recommender for New Customers Using Collaborative Filtering and Social Network Analysis (협력필터링과 사회연결망을 이용한 신규고객 추천방법에 대한 연구)
- Shin, Chang-Hoon;Lee, Ji-Won;Yang, Han-Na;Choi, Il Young
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- Journal of Intelligence and Information Systems
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- v.18 no.4
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- pp.19-42
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- 2012
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Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.
Development of Yóukè Mining System with Yóukè's Travel Demand and Insight Based on Web Search Traffic Information (웹검색 트래픽 정보를 활용한 유커 인바운드 여행 수요 예측 모형 및 유커마이닝 시스템 개발)
- Choi, Youji;Park, Do-Hyung
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- Journal of Intelligence and Information Systems
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- v.23 no.3
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- pp.155-175
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- 2017
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As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.
Relationship Between Usage Needs Satisfaction and Commitment to Apparel Brand Communities: Moderator Effect of Apparel Brand Image (의류 브랜드 커뮤니티의 이용욕구 충족과 커뮤니티 몰입의 관계: 의류 브랜드 이미지의 조절효과)
- Hong, Hee-Sook;Ryu, Sung-Min;Moon, Chul-Woo
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- Journal of Global Scholars of Marketing Science
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- v.17 no.4
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- pp.51-89
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- 2007
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INTRODUCTION Due to the high broadband internet penetration rate and its group-oriented culture, various types of online communities operate in Korea. This study use 'Uses and Gratification Approach, and argue that members' usage-needs satisfaction with brand community is an important factor for promoting community commitment. Based on previous studies identifying the effect of brand image on consumers' responses to various marketing stimuli, this study hypothesizes that brand image can be a moderate variable affecting the relationship between usage-needs satisfaction with brand community and members' commitment to brand community. This study analyzes the influence of usage-needs satisfaction on brand community commitment and how apparel brand image affects the relationships between usage-needs satisfactions and community commitments. The hypotheses of this study are proposed as follows. H1-3: The usage-needs satisfaction of apparel brand community (interest, transaction, relationship needs) influences emotional (H1), continuous (H2), and normative (H3) commitments to apparel brand communities. H4-6: Apparel brand image has a moderating effect on the relationship between usage-needs satisfaction and emotional (H4), continuous (H5), and normative (H6) commitments to apparel brand communities. METHODS Brand communities founded by non-company affiliates were excluded and emphasis was placed instead on communities created by apparel brand companies. Among casual apparel brands registered in 6 Korean portal sites in August 2003, a total of 9 casual apparel brand online communities were chosen, depending on the level of community activity and apparel brand image. Data from 317 community members were analyzed by exploratory factor analysis, moderated regression analysis, ANOVA, and scheffe test. Among 317 respondents answered an online html-type questionnaire, 80.5% were between 16 to 25 years old. There were a total of 150 respondents from apparel brand communities(n=3) recording higher-than-average brand image scores (Mean > 3.75) and a total of 162 respondents from apparel brand communities(n=6) recording lower-than-average brand image scores(Mean < 3.75). In this study, brand community commitment was measured by a 5-point Likert scale: emotional, continuous and normative commitment. The degree of usage-needs satisfaction (interest, transaction, relationship needs) was measured on a 5-point Likert scale. The level of brand image was measured by a 5-point Likert scale: strength, favorability, and uniqueness of brand associations. RESULTS In the results of exploratory factor analysis, the three usage-needs satisfactions with brand community were classified as interest, transaction, and relationship needs. Brand community commitment was also divided into the multi-dimensional factors: emotional, continuous, and normative commitments. The regression analysis (using a stepwise method) was used to test the influence of 3 independent variables (interest-needs satisfaction, transaction-needs, and relationship-needs satisfactions) on the 3 dependent variables (emotional, continuous and normative commitments). The three types of usage-needs satisfactions are positively associated with the three types of commitments to apparel brand communities. Therefore, hypothesis 1, 2, and 3 were significantly supported. Moderating effects of apparel brand image on the relationship between usage-needs satisfaction and brand community commitments were tested by moderated regression analysis. The statistics result showed that the influence of transaction-needs on emotional commitment was significantly moderated by apparel brand image. In addition, apparel brand image had moderating effects on the relationship between relationship-needs satisfaction and emotional, continuous and normative commitments to apparel brand communities. However, there were not significant moderate effects of apparel brand image on the relationships between interest-needs satisfaction and 3 types of commitments (emotional, continuous and normative commitments) to apparel brand communities. In addition, the influences of transaction-needs satisfaction on 2 types of commitments (continuous and normative commitments) were not significantly moderated by apparel brand image. Therefore, hypothesis 4, 5 and 6 were partially supported. To explain the moderating effects of apparel brand image, four cross-tabulated groups were made by averages of usage-needs satisfaction (interest-needs satisfaction avg. M=3.09, transaction-needs satisfaction avg. M=3.46, relationship-needs satisfaction M=1.62) and the average apparel brand image (M=3.75). The average scores of commitments in each classified group are presented in Tables and Figures. There were significant differences among four groups. As can be seen from the results of scheffe test on the tables, emotional commitment in community group with high brand image was higher than one in community group with low brand image when transaction-needs satisfaction was high. However, when transaction-needs satisfaction was low, there was not any difference between the community group with high brand image and community group with low brand image regarding emotional commitment to apparel brand communities. It means that emotional commitment didn't increase significantly without high satisfaction of transaction-needs, despite the high apparel brand image. In addition, when apparel brand image was low, increase in transaction-needs did not lead to the increase in emotional commitment. Therefore, the significant relationship between transaction-needs satisfaction and emotional commitment was found in only brand communities with high apparel brand image, and the moderating effect of apparel brand image on this relationship between two variables was found in the communities with high satisfaction of transaction-needs only. Statistics results showed that the level of emotional commitment is related to the satisfaction level of transaction-needs, while overall response is related to the level of apparel brand image. We also found that the role of apparel brand image as a moderating factor was limited by the level of transaction-needs satisfaction. In addition, relationship-needs satisfaction brought significant increase in emotional commitment in both community groups (high and low levels of brand image), and the effect of apparel brand image on emotional commitment was significant in both community groups (high and low levels of relationship-needs satisfaction). Especially, the effect of brand image was greater when the level of relationship-needs satisfaction was high. in contrast, increase in emotional commitment responding to increase in relationship-needs satisfaction was greater when apparel brand image is high. The significant influences of relationship-needs satisfaction on community commitments (continuous and normative commitments) were found regardless of apparel brand image(in both community groups with low and high brand image). However, the effects of apparel brand image on continuous and normative commitments were found in only community group with high satisfaction level of relationship-needs. In the case of communities with low satisfaction levels of relationship needs, apparel brand image marginally increases continuous and normative commitments. Therefore, we could not find the moderating effect of apparel brand image on the relationship between relationship-needs satisfaction and continuous and normative commitments in community groups with low satisfaction levels of relationship needs, CONCLUSIONS AND IMPLICATIONS From the results of this study, we draw several conclusions; First, the increases in usage-needs satisfactions through apparel brand communities result in the increases in commitments to apparel brand communities, wheres the degrees of such relationship depends on the level of apparel brand image. That is, apparel brand image is a moderating factor strengthening the relationship between usage-needs satisfaction and commitment to apparel brand communities. In addition, the effect of apparel brand image differs, depending on the level and types of community usage-needs satisfactions. Therefore, marketers of apparel brand companies must determine the appropriate usage-needs, depending on the type of commitment they wish to increase and the level of their apparel brand image, to promote member's commitments to apparel brand communities. Especially, relationship-needs satisfaction was very important factor for increasing emotional, continuous and normative commitments to communities. However the level of relationship-needs satisfaction was lower than interest-needs and transaction-needs. satisfaction. According to previous study on apparel brand communities, relationship-need satisfaction was strongly related to member's intention of participation in their communities. Therefore, marketers need to develope various strategies in order to increase the relationship- needs as well as interest and transaction needs. In addition, despite continuous commitment was higher than emotional and normative commitments, all types of commitments to apparel brand communities had scores lower than 3.0 that was mid point in 5-point scale. A Korean study reported that the level of members' commitment to apparel brand community influenced customers' identification with a brand and brand purchasing behavior. Therefore, marketers should try to increase members' usage-needs satisfaction and apparel brand image as the necessary conditions for bringing about community commitments. Second, marketers should understand that they should keep in mind that increasing the level of community usage needs (transaction and relationship) is most effective in raising commitment when the level of apparel brand image is high, and that increasing usage needs (transaction needs) satisfaction in communities with low brand image might not be as effective as anticipated. Therefore, apparel companies with desirable brand image such as luxury designer goods firms need to create formal online brand communities (as opposed to informal communities with rudimentary online contents) to satisfy transaction and relationship needs systematically. It will create brand equity through consumers' increased emotional, continuous and normative commitments. Even though apparel brand is very famous, emotional commitment to apparel brand communities cannot be easily increased without transaction-needs satisfaction. Therefore famous fashion brand companies should focus on developing various marketing strategies to increase transaction-needs satisfaction.
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