A Study on the role of Online Brand Community as an IMC Tool (통합적 마케팅커뮤니케이션 도구로써 온라인 브랜드 커뮤니티의 역할)
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- Management & Information Systems Review
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- v.29 no.4
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- pp.123-142
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- 2010
This study suggest that firms can use online brand communities as an IMC tool to achieve high brand loyalty through marketer-controlled or loyal customer-controlled brand contacts. In this perspective, the online brand community as a marketing communication tool can help the firm in eliciting favorable responses from customers. This study finds that an online brand community, as a critical marketing promotion tool, helps a firm elicit favorable relationship with customers and build strong brand loyalty. In particular, this study suggests several important theoretical and managerial implications. First, this study confirm that "advertising usefulness" is the most powerful and important factor that affects cgerial 's positive emotionomehile "sales promotion usefulness" impacmehin "interactivity" but dies not impacmhin "cgerial iexperience"ltyevent usefulness" impacmehin "cgerial iexperience"but dies not impacmhin "interactivity." In addition, "cgerial iexperience" signifn "itly impacmehin "cgerial -to-cgerial iinteractivity." This indicates that online environment provides participapacmwith a fun and exciting environment. In that sense, enhancing the online brand community experiencemwould be a critical factor for building strong brand. Thi", mword of mouth can play a riclly important role in making many cgerial s to trust brand and to enhance online brand community loyalty. Web users are becoming web authoore owning and creating content limited only by their imaginations.
The problem of choosing the right product that will best fit a consumer's taste and preferences extends to the field of electronic commerce. However, e-commerce has been able to create a technological proxy for the social filtering process, known as online recommender systems (RSs). RSs aid users in filtering products and decisions on matters relating to personal taste. RSs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products and services online. However, most previous research on RSs has focused on the accuracy of the algorithms, with little emphasis on user interface and perspectives. This study identified transparency and feedback as possible ways to effectively evaluate RSs from the user's perspective. Thus, this research focused on examining and identifying the roles of transparency and feedback in recommender systems and how they affect users' attitudes toward the system. Results of the study showed that both transparency and feedback positively and significantly affected perceived trust, perceived value of the process, and perceived enjoyment. Furthermore, we found that perceived trust, perceived value of the process, and perceived enjoyment positively and directly affected users' intentions to use/reuse a recommender system.
The popularity of communities on the internet has captured the attention of marketing scholars and practitioners. By adapting to the culture of the internet, however, and providing consumer with the ability to interact with one another in addition to the company, businesses can build new and deeper relationships with customers. The economic potential of online communities has been discussed with much hope in the many popular papers. In contrast to this enthusiastic prognostications, empirical and practical evidence regarding the economic potential of the online community has shown a little different conclusion. To date, even communities with high levels of membership and vibrant social arenas have failed to build financial viability. In this perspective, this study investigates the role of various kinds of influencing factors to online community loyalty and basically suggests the framework that explains the process of building purchase loyalty. Even though the importance of building loyalty in an online environment has been emphasized from the marketing theorists and practitioners, there is no sufficient research conclusion about what is the process of building purchase loyalty and the most powerful factors that influence to it. In this study, the process of building purchase loyalty is divided into three levels; characteristics of community site such as content superiority, site vividness, navigation easiness, and customerization, the mediating variables such as self congruency, consumer experience, and consumer to consumer interactivity, and finally various factors about online community loyalty such as visit loyalty, affect, trust, and purchase loyalty are those things. And the findings of this research are as follows. First, consumer-to-consumer interactivity is an important factor to online community purchase loyalty and other loyalty factors. This means, in order to interact with other people more actively, many participants in online community have the willingness to buy some kinds of products such as music, content, avatar, and etc. From this perspective, marketers of online community have to create some online environments in order that consumers can easily interact with other consumers and make some site environments in order that consumer can feel experience in this site is interesting and self congruency is higher than at other community sites. It has been argued that giving consumers a good experience is vital in cyber space, and websites create an active (rather than passive) customer by their nature. Some researchers have tried to pin down the positive experience, with limited success and less empirical support. Web sites can provide a cognitively stimulating experience for the user. We define the online community experience as playfulness based on the past studies. Playfulness is created by the excitement generated through a website's content and measured using three descriptors Marketers can promote using and visiting online communities, which deliver a superior web experience, to influence their customers' attitudes and actions, encouraging high involvement with those communities. Specially, we suggest that transcendent customer experiences(TCEs) which have aspects of flow and/or peak experience, can generate lasting shifts in beliefs and attitudes including subjective self-transformation and facilitate strong consumer's ties to a online community. And we find that website success is closely related to positive website experiences: consumers will spend more time on the site, interacting with other users. As we can see figure 2, visit loyalty and consumer affect toward the online community site didn't directly influence to purchase loyalty. This implies that there may be a little different situations here in online community site compared to online shopping mall studies that shows close relations between revisit intention and purchase intention. There are so many alternative sites on web, consumers do not want to spend money to buy content and etc. In this sense, marketers of community websites must know consumers' affect toward online community site is not a last goal and important factor to influnece consumers' purchase. Third, building good content environment can be a really important marketing tool to create a competitive advantage in cyberspace. For example, Cyworld, Korea's number one community site shows distinctive superiority in the consumer evaluations of content characteristics such as content superiority, site vividness, and customerization. Particularly, comsumer evaluation about customerization was remarkably higher than the other sites. In this point, we can conclude that providing comsumers with good, unique and highly customized content will be urgent and important task directly and indirectly impacting to self congruency, consumer experience, c-to-c interactivity, and various loyalty factors of online community. By creating enjoyable, useful, and unique online community environments, online community portals such as Daum, Naver, and Cyworld are able to build customer loyalty to a degree that many of today's online marketer can only dream of these loyalty, in turn, generates strong economic returns. Another way to build good online community site is to provide consumers with an interactive, fun, experience-oriented or experiential Web site. Elements that can make a dot.com's Web site experiential include graphics, 3-D images, animation, video and audio capabilities. In addition, chat rooms and real-time customer service applications (which link site visitors directly to other visitors, or with company support personnel, respectively) are also being used to make web sites more interactive. Researchers note that online communities are increasingly incorporating such applications in their Web sites, in order to make consumers' online shopping experience more similar to that of an offline store. That is, if consumers are able to experience sensory stimulation (e.g. via 3-D images and audio sound), interact with other consumers (e.g., via chat rooms), and interact with sales or support people (e.g. via a real-time chat interface or e-mail), then they are likely to have a more positive dot.com experience, and develop a more positive image toward the online company itself). Analysts caution, however, that, while high quality graphics, animation and the like may create a fun experience for consumers, when heavily used, they can slow site navigation, resulting in frustrated consumers, who may never return to a site. Consequently, some analysts suggest that, at least with current technology, the rule-of-thumb is that less is more. That is, while graphics etc. can draw consumers to a site, they should be kept to a minimum, so as not to impact negatively on consumers' overall site experience.
Purpose: Prior studies rarely investigated the effects of the Word of Mouse (WoM) information on consumers' motivation and willingness to purchase a product. Furthermore, few scholars have studied how word-of-mouth information works and they fail to find consistent results. Research design, data and methodology: This study uses a multivariate regression model to investigate the influence of WoM on consumer attitudes and consumer decision-making. It categorizes the quality of WoM into source level and acceptance level, to analyze its influence from a new perspective. A total of 400 surveys were completed, resulting in 336 usable questionnaires for analysis. It was collected in 14 cities from all regions in China. This study constructs a theoretical model of WoM influence on consumers' purchase willingness based on a systematic review of the related literature on WoM quality, perceived value, customer trust, and consumers' purchase willingness. Results: Empirical results reveal that the Internet WoM (consumer's source level and acceptance level) indirectly affects consumer behavior by influencing consumer attitudes. Conclusions: This study provides practical significance and value for merchants to develop better WoM marketing and to establish the reliability of WoM websites. Companies should consider online WoM from the perspective of consumers, thereby improving existing marketing strategies.
This paper presents exploratory research on consumer awareness and attitudesabout organic food, for which consumer demand continues to increase the paper also assesses consumers' organic food distribution channel preferences. By conducting a literature review, a case study has been carried out in order to glean customer behavior, market condition and typesof distribution channels, and development of specialty stores for organic foods. The early research indicates that consumer awareness and customer attitudes toward organic food are mostly positive however, organic food's high price, as well as a lack of organic food stores, cause a negative effect on consumers' purchase intention. Secondly, the U.S. organic food retail channel consists of such mainstream supermarket/grocery stores and leading natural and organic food supermarket chains as Whole Foods, Trader Joe's, and Sunflower Farmers Market. For the current retail distribution of organic food in Korea, off-line stores are composed of direct management stores and franchise chains. Most of the organic food retail distribution operates through the Internet shopping mall, and are commonly located at retail distribution centers as multi-channel, shop-in-shop stores. Moreover, unlike in the U.S., association and consumers' cooperatives (Co-Ops), and such other member-direct retail stores as Hansallim, iCOOP, Nature Dream,and online shopping malls, are all active in Korea. Thirdly, as a result of an analysis of the present state of the organic food retail channel, as well as building a case for organic food specialty stores, the distinctive featuresand rapid growth of such unique organic food stores as Whole Foods Market, or Trader Joe's successful downsizing strategies, as well as Sunflower Farmers Market low-price approach, show steady industry growth. Moreover, as a result of a case studyof such domestic representative organic food specialty stores as "Olga" and "Chorokmaeul," a similar management style to the United States' "Whole Foods Market" and "Trader Joe's," respectively, can be seen. Similar to the U.S. market, Korean organic food markets should also implement active retail distribution opportunities, allowing consumers to select from various diverse and differentiated choices. In order to accomplish this goal, it is necessary to prepare such measures as sustaining reasonable prices, securing various suppliers for unique products,and improving consumer trust through advertisement strategies that are suitable for each company's branding processes.
The purpose of this study is 1) to investigate the effects of Internet apparel store attributes, shopping values, Internet usage, and consumers' characteristics on consumers' Internet apparel purchasing behavior; and 2) to identify the influence of Internet apparel store attributes, Internet usage, and consumers' characteristics on the Internet apparel purchasing behavior of hedonic or utilitarian consumers. The data were collected via an online survey. A total of 4,254 Internet users participated in this survey. Only 589 out of the users' reponses were used as a sample for this research, because those users had previously experienced Internet apparel purchasing. Factor analysis revealed five dimensions of Internet apparel store attributes: site design/navigation, promotion programs, trust, product assortment/ information, and customer service. Based on the respondents' shopping value scores, we identified them as hedonic or utilitarian consumers. Through multiple regression analyses, site design/navigation and promotion programs among store attribute variables, hedonistic or utilitarian shopping values, the number of years on the Internet, income, and educational level were found to be significant predictors of Internet apparel shopping frequency. Among them, hedonistic shopping values were the best predictor of Internet apparel purchasing frequency. Based on the Internet apparel purchasers' shopping value, purchasers were divided into two groups. For hedonic Internet apparel shoppers, the number of years on the Internet, educational level, sex, age, and income were significant predictors of Internet apparel shopping behavior. On the other hand, promotion programs among store attribute variables was the only significant factor that affects utilitarian consumers' Internet apparel shopping behavior.
Credit card is means of payment used like cash in terms of function and its users have increased consistently. With development of Internet and electronic commerce a role as payment method of credit card has been growing. But as the risk which results from centralized information and online increases, credit card fraud is also growing. Card theft and loss are decreasing due to countermeasure of card companies and financial supervisory authorities, while card forge and identity theft are increasing. Recently because of frequent personal information leakage and deregulation of financial security following easy-to-use payment enforcement, customer's anxiety about card fraud is growing. And the increase of card fraud lowers trust on credit system as well as causes social costs. In this paper, the security problems of card operating system are addressed in depth and the measures such as immediate switch to IC card terminals, introduction of new security technology, supervision reinforcement of the authorities are proposed.
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
If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.
. 3.2 Hypotheses 3.2.1 The Hypothesis of On-line Service Quality and Overall Service Quality 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
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
Recommender system using BERT sentiment analysis
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