The purpose of this study was to categorize the gap between coffee shop 'importance' (as perceived by customers before patronizing the coffee shop) and 'satisfaction' (perception of customers after patronizing the coffee shop) as positive or negative and to analyze the effect of these gaps on purchasing behavior. To do this, I used the gap between importance and satisfaction regarding the choice of a coffee shop as the explanatory variable and performed an empirical analysis of the direction and size of the effect of the gap on purchasing behavior (overall satisfaction, willingness-to-revisit) by applying the Ordered Probit Model (OPM). A previous study that used IPA to evaluate the effects of gaps estimated the direction and size of a quadrant but failed to analyze the effect of gaps on customers. In this study, I evaluated the effects of positive and negative gaps on customer satisfaction and willingness-to-revisit. Using OPM, I quantified the effect of positive and negative gaps on overall customer satisfaction and willingness-to-revisit. Per-head expenditure, frequency of visits, and coffee-purchasing place had the most positive effects on overall customer satisfaction. Frequency of visits, followed by per-head expenditure and then coffee-purchasing place, had the most positive impact on willingness-to-visit. Thus per-head expenditure and frequency of visits had the greatest positive effects on overall satisfaction and willingness-to-revisit. This finding implies that the higher the actual satisfaction (gap) of customers who spend KRW5,000 or more once or more per week at coffee shops is, the higher their overall satisfaction and willingness-to-revisit are. Despite the fact that economical efficiency had a significant effect on overall satisfaction and willingness-to-revisit, college and university students still use coffee shops and are willing to spend KRW5,000 because they do not only purchase coffee as a product itself, but use the coffee shop for other activities, such as working, meeting friends, or relaxing. College and university students also access the Internet in coffee shops via personal laptops, watch movies, and study; thus, coffee shops should provide their customers with the appropriate facilities and services. The fact that a positive gap for coffee shop brand had a positive effect on willingness-to-revisit implies that the higher the level of customer satisfaction, the greater the willingness-to-revisit. A negative gap for this factor, on the other hand, implies that the lower the level of customer satisfaction, the lower the willingness-to-revisit. Thus, the brand factor has a comparatively greater effect on satisfaction than the other factors evaluated in this study. Given that the domestic coffee culture is becoming more upscale and college/university students are sensitive to this trend, students are attentive to brands. In most upscale coffee shops in Korea, the outer wall is built out of glass that can be opened, the interiors are exotic with an open kitchen. These upscale coffee shops function as landmarks and match the taste of college/university students. Coffee shops in Korea have become a cultural brand. To make customers feel that coffee shops are upscale, good quality establishments and measures to provide better services in terms of brand factor should be instituted. The intensified competition among coffee shop brands in Korea as a result of the booming industry indicates that provision of additional services is needed to differentiate competitors. These customers can also use a scanner free of charge. Another strategy that can be used to boost brands could be to provide and operate a seminar room for seminars and group study. If coffee shops adopt these types of strategies, college/university students would be more likely to consider the expenses they incur worthwhile and, subsequently, they would be more likely to be satisfied with the brands of these coffee shops, with an associated increase in their willingness-to-revisit. Gender and study year had the most negative effects on overall satisfaction and willingness-to-revisit. Female students were more likely to be satisfied and be willing to return than male students, and third and fourth-year students were more likely to be satisfied and willing-to-return than first or second-year students. Students who drink coffee, read books, and use laptops alone at coffee shops are easily noticeable. High-grade students tend to visit coffee shops alone in order to use their time efficiently for self-development and to find jobs. The economical efficiency factor had the greatest effect on overall satisfaction and willingness-to-revisit in terms of a positive gap. The higher the actual satisfaction (gap) of students with the price of the coffee, the greater their overall satisfaction and willingness-to-revisit. Economical efficiency with a negative gap had a negative effect on willingness-to-revisit, which implies that a less negative gap will result in a greater willingness-to-revisit. Amid worsening market conditions, coffee shops located around colleges/universities are using strategies, such as a point or membership card, strategic alliances with credit-card companies, development of a set menu or seasonal menu, and free coffee-shot services to increase their competitive edge. Product power also had a negative effect in terms of a negative gap, which indicates that a higher negative gap will result in a lower willingness-to-revisit. Because there are many more customers that enjoy coffee in this decade, as compared to previous decades, the new generation of customers, namely college/university students, want various menu items in addition to coffee, and coffee shops should, therefore, add side menu items, such as waffles, rice cakes, cakes, sandwiches, and salads. For example, Starbucks Korea is making efforts to enhance product power by selling rice cakes flavored in strawberry, wormwood, and pumpkin, and providing coffee or cream free of charge. In summary, coffee shops should focus on increasing their economical efficiency, brand, and product power to enhance the satisfaction of college/university students. Because shops adjacent to colleges or universities enjoy a locational advantage, providing differentiated services in terms of economical efficiency, brand, and product power, is likely to increase customer satisfaction and return visits. Coffee shop brands should, therefore, be innovative and embrace change to meet their customers' desires. Because this study only targeted college/university students in Seoul, comparative studies targeting diverse regions and age groups are required to generalize the findings and recommendations of this study.
As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.
The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.
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 Quality The 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 of and , 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. 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 : 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.
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