Consumers of e-learning market differ from those of other markets in that they are replaced in a specific time scale. For example, e-learning contents aimed at highschool senior students cannot be consumed by a specific consumer over the designated period of time. Hence e-learning service providers need to attract new groups of students every year. Due to lack of information on products designed for continuously emerging consumers, the consumers face difficulties in making rational decisions in a short time period. Increased uncertainty of product purchase leads customers to herding behaviors to obtain information of the product from others and imitate them. Taking into consideration of these features of e-learning market, this study will focus on the online herding behavior in purchasing e-learning contents. There is no definite concept for e-learning. However, it is being discussed in a wide range of perspectives from educational engineering to management to e-business etc. Based upon the existing studies, we identify two main view-points regarding e-learning. The first defines e-learning as a concept that includes existing terminologies, such as CBT (Computer Based Training), WBT (Web Based Training), and IBT (Internet Based Training). In this view, e-learning utilizes IT in order to support professors and a part of or entire education systems. In the second perspective, e-learning is defined as the usage of Internet technology to deliver diverse intelligence and achievement enhancing solutions. In other words, only the educations that are done through the Internet and network can be classified as e-learning. We take the second definition of e-learning for our working definition. The main goal of this study is to investigate what factors affect consumer intention to purchase e-learning contents and to identify the differential impact of the factors between consumers with purchase experience and those without the experience. To accomplish the goal of this study, it focuses on herding behavior and perceived usefulness as antecedents to behavioral intention. The proposed research model in the study extends the Technology Acceptance Model by adding herding behavior and usability to take into account the unique characteristics of e-learning content market and e-learning systems use, respectively. The current study also includes consumer experience with e-learning content purchase because the previous experience is believed to affect purchasing intention when consumers buy experience goods or services. Previous studies on e-learning did not consider the characteristics of e-learning contents market and the differential impact of consumer experience on the relationship between the antecedents and behavioral intention, which is the target of this study. This study employs a survey method to empirically test the proposed research model. A survey questionnaire was developed and distributed to 629 informants. 528 responses were collected, which consist of potential customer group (n = 133) and experienced customer group (n = 395). The data were analyzed using PLS method, a structural equation modeling method. Overall, both herding behavior and perceived usefulness influence consumer intention to purchase e-learning contents. In detail, in the case of potential customer group, herding behavior has stronger effect on purchase intention than does perceived usefulness. However, in the case of shopping-experienced customer group, perceived usefulness has stronger effect than does herding behavior. In sum, the results of the analysis show that with regard to purchasing experience, perceived usefulness and herding behavior had differential effects upon the purchase of e-learning contents. As a follow-up analysis, the interaction effects of the number of purchase transaction and herding behavior/perceived usefulness on purchase intention were investigated. The results show that there are no interaction effects. This study contributes to the literature in a couple of ways. From a theoretical perspective, this study examined and showed evidence that the characteristics of e-learning market such as continuous renewal of consumers and thus high uncertainty and individual experiences are important factors to be considered when the purchase intention of e-learning content is studied. This study can be used as a basis for future studies on e-learning success. From a practical perspective, this study provides several important implications on what types of marketing strategies e-learning companies need to build. The bottom lines of these strategies include target group attraction, word-of-mouth management, enhancement of web site usability quality, etc. The limitations of this study are also discussed for future studies.
Fair Competition policy in aviation field has been discussed since open skies policy began in 1970s. This issue has been also the main topic in the ICAO's Worldwide Air Transport Conference, the Air Transport Symposium, etc. ICAO defines competition as the existent or potential rivalry between two or more operators, carriers or groups, striving for advantages in the same market based on different prices, qualities and services. In a broader sense, the definition includes more various meanings; reasonable, fair, effective, and unrestricted competitions. Nowadays, competition laws and regulations to air transportation have been applied more frequently and the issues varies from antitrust immunity, mergers and alliances, abuse of dominant positions, capacity dumping and predatory pricing, sales and marketing, to airport charges and fees, state aid and loan guarantees. Now, the competition among the airlines or nations in aviation industry is changing to cooperation level. A lot of airlines try to survive by various cooperation methods. Therefore the policy of Korean aviation industry should be developed, taking so-called "the viewpoint of national aviation industry ecosystem" into consideration and Korean government should prepare a policy of fair competition to cope with it. First, in the process of open skies policy with neighboring countries such as China, Japan and the Middle East, it is necessary to apply the fair competition act and prepare laws and regulations to implement it. Second, the standards of effective ownership and control of air transportation business should be reviewed. Third, in preparation for aviation agreements and liberalization, the Korean aviation industry needs to study and review competition and cooperation issues through the analysis of strict aviation market structure for airlines and airport operations. Fourth, it is necessary to create a fair air transportation environment for the development of air transportation and competitiveness through preemptive policies such as the approval of mergers, acquisitions, JV and the ripple effects analysis.
Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf
This study was examined the pollinating activity and the economical effect according to numbers released of Apis mellifera in the
Today, Mobile Instant Message (MIM) has become a communication means which is commonly used by many people as the technology on smart phones has been enhanced. Among the services, KakaoGame creates much profits continuously by using its representative Kakao platform. However, even though the number of users of KakaoGame increases and the characteristics of the users are more diversified, there are few researches on the relationship between the characteristics of the SNG users and the continuous use of the game. Since the social capital that is formed by the SNG users with the acquaintances create the sense of belonging, its role is being emphasized under the environment of social network. In addition, game user's concerns about the information privacy may decrease the trust on a game APP, and it also caused to threaten about the game system. Therefore, this study was designed to examine the structural relationships among SNG users' social capital, concerns about the information privacy, flow, SNG reuse intention and recommendation intention. The results from this study are as follow. First of all, the participants' bridging social capital had a positive effect on the flow of an SNG, but the bonding social capital had a negative effect on the flow of an SNG. In addition, awareness of information privacy concern had a negative effects on the flow of an SNG, but control of information privacy concern had a positive effect on the flow of an SNG. Lastly, the flow of an SNG had a positive effect on the reuse intention and recommendation intention of an SNG. Also, reuse intention of an SNG had a positive effect on the recommendation intention. Based on the results from this study, academic and practical implications can be drawn. First, This study focused on KakaoTalk which has both of the closed and open characteristics of an SNS and it was found that the SNG user's social capital might be a factor influencing each user's behaviors through the user's flow experiences in SNG. Second, this study extends the scope of prior researches by empirically analysing the relationship between the concerns about the SNG user's information privacy and flow of an SNG. Finally, the results of this research can provide practical guidelines to develop effective marketing strategies considering them for SNG companies.
Recently, as the non-face-to-face environment has developed due to COVID-19 and environmental pollution, the importance of online digital healthcare is increasing, and venture start-ups and activities such as health care, telemedicine, and digital treatments are also actively underway. This study conducted the impact on the acceptability of digital healthcare smartwatches with an integrated approach of the expanded integrated technology acceptance model (UTAUT2) and the behavioral inference model (BRT). The most advanced integrated technology acceptance model for innovative technology acceptance research was used to identify major factors such as utility expectations, social effects, convenience, price barriers, lack of alternatives, and behavioral intentions. For the study, about 410 responses from ordinary people in their teens to 60s across the country were collected, and based on this, the hypothesis was verified using structural equations after testing reliability and validity of the data. SPSS 23 and AMOS 23 were used for research analysis. Studies have shown that personal innovation has a significant impact on the reasons for acceptance (use value, social impact, convenience of use), attitude, and non-use (price barriers, lack of alternatives, and barriers to use). These results are the same as the results of previous studies that confirmed the influence of the main value of innovative ICT on user acceptance intention. In addition, the reason for acceptance had a significant effect on attitude, but the effect of the reason for non-acceptance was not significant. It can be analyzed that consumers are interested in new ICT products and new services, but purchase them more carefully and selectively. This study has evolved from the acceptance analysis of general-purpose consumer innovation technology to the acceptance analysis of consumer value in smartwatch digital healthcare, which is a new and important area in the future. Industrially, it can contribute to the product's purchase and marketing. It is hoped that this study will contribute to increasing research in the digital healthcare sector, which will play an important role in our lives in the future, and that it will develop into in-depth factors that are more suitable for consumer value through integrated approach models and integrated analysis of consumer acceptance and non-acceptance.
Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.
Overview of Research: Product availability is one of important competences of store to fulfill consumer needs. If stock-outs which means a product what consumer wants to buy is not available occurs, consumer will face decision-making uncertainty that leads to consumer's negative responses such as consumer dissatisfaction on store. Stockouts was much studied in the field of academia as well as practice in other countries. However, stock-outs has not been researched at all in Marketing and/or Distribution area in Korea. The main objectives of this study are to find out determinants of consumer responses such as Substitute, Delay, and Leave(SDL) when consumer encounters out-of-stock situation and then to examine the effects of these factors on consumer responses. Specifically, this study focuses on situational characteristics(e.g., purchase urgency and surprise), store characteristics (e.g., product assortment and store convenience), and consumer characteristics (e.g., brand loyalty and store loyalty). Then, this study empirically investigates relationships these factors with consumers behaviors such as product substitution, purchase delay, and store switching.
In franchise business, exclusive sales territory (sometimes EST in table) protection is a very important issue from an economic, social and political point of view. It affects the growth and survival of both franchisor and franchisee and often raises issues of social and political conflicts. When franchisee is not familiar with related laws and regulations, franchisor has high chance to utilize it. Exclusive sales territory protection by the manufacturer and distributors (wholesalers or retailers) means sales area restriction by which only certain distributors have right to sell products or services. The distributor, who has been granted exclusive sales territories, can protect its own territory, whereas he may be prohibited from entering in other regions. Even though exclusive sales territory is a quite critical problem in franchise business, there is not much rigorous research about the reason, results, evaluation, and future direction based on empirical data. This paper tries to address this problem not only from logical and nomological validity, but from empirical validation. While we purse an empirical analysis, we take into account the difficulties of real data collection and statistical analysis techniques. We use a set of disclosure document data collected by Korea Fair Trade Commission, instead of conventional survey method which is usually criticized for its measurement error. Existing theories about exclusive sales territory can be summarized into two groups as shown in the table below. The first one is about the effectiveness of exclusive sales territory from both franchisor and franchisee point of view. In fact, output of exclusive sales territory can be positive for franchisors but negative for franchisees. Also, it can be positive in terms of sales but negative in terms of profit. Therefore, variables and viewpoints should be set properly. The other one is about the motive or reason why exclusive sales territory is protected. The reasons can be classified into four groups - industry characteristics, franchise systems characteristics, capability to maintain exclusive sales territory, and strategic decision. Within four groups of reasons, there are more specific variables and theories as below. Based on these theories, we develop nine hypotheses which are briefly shown in the last table below with the results. In order to validate the hypothesis, data is collected from government (FTC) homepage which is open source. The sample consists of 1,896 franchisors and it contains about three year operation data, from 2006 to 2008. Within the samples, 627 have exclusive sales territory protection policy and the one with exclusive sales territory policy is not evenly distributed over 19 representative industries. Additional data are also collected from another government agency homepage, like Statistics Korea. Also, we combine data from various secondary sources to create meaningful variables as shown in the table below. All variables are dichotomized by mean or median split if they are not inherently dichotomized by its definition, since each hypothesis is composed by multiple variables and there is no solid statistical technique to incorporate all these conditions to test the hypotheses. This paper uses a simple chi-square test because hypotheses and theories are built upon quite specific conditions such as industry type, economic condition, company history and various strategic purposes. It is almost impossible to find all those samples to satisfy them and it can't be manipulated in experimental settings. However, more advanced statistical techniques are very good on clean data without exogenous variables, but not good with real complex data. The chi-square test is applied in a way that samples are grouped into four with two criteria, whether they use exclusive sales territory protection or not, and whether they satisfy conditions of each hypothesis. So the proportion of sample franchisors which satisfy conditions and protect exclusive sales territory, does significantly exceed the proportion of samples that satisfy condition and do not protect. In fact, chi-square test is equivalent with the Poisson regression which allows more flexible application. As results, only three hypotheses are accepted. When attitude toward the risk is high so loyalty fee is determined according to sales performance, EST protection makes poor results as expected. And when franchisor protects EST in order to recruit franchisee easily, EST protection makes better results. Also, when EST protection is to improve the efficiency of franchise system as a whole, it shows better performances. High efficiency is achieved as EST prohibits the free riding of franchisee who exploits other's marketing efforts, and it encourages proper investments and distributes franchisee into multiple regions evenly. Other hypotheses are not supported in the results of significance testing. Exclusive sales territory should be protected from proper motives and administered for mutual benefits. Legal restrictions driven by the government agency like FTC could be misused and cause mis-understandings. So there need more careful monitoring on real practices and more rigorous studies by both academicians and practitioners.
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.
shows the research model of this study. To accomplish above-mentioned research objectives, the following ten hypotheses were proposed and verified : is the estimation results of l\1NL model, and
shows the marginal effects for each determinant to consumer's responses(SDL). Significant statistical results were as follows. Purchase urgency, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty were turned out to be significant determinants to influence consumer alternative behaviors in case of out-of-stock situation. Specifically, first, product substitution behavior was triggered by purchase urgency, surprise, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty. Second, purchase delay behavior was led by purchase urgency, purchase quantities, and brand loyalty. Third, store switching behavior was influenced by purchase urgency, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty. Finally, when out-of-stock situation occurs, store convenience and salesperson service did not have significant effects on consumer alternative responses.
A New Exploratory Research on Franchisor's Provision of Exclusive Territories
(가맹본부의 배타적 영업지역보호에 대한 탐색적 연구)
Intelligent Brand Positioning Visualization System Based on Web Search Traffic Information : Focusing on Tablet PC
(웹검색 트래픽 정보를 활용한 지능형 브랜드 포지셔닝 시스템 : 태블릿 PC 사례를 중심으로)
이메일무단수집거부
이용약관
제 1 장 총칙
제 2 장 이용계약의 체결
제 3 장 계약 당사자의 의무
제 4 장 서비스의 이용
제 5 장 계약 해지 및 이용 제한
제 6 장 손해배상 및 기타사항
Detail Search
Image Search
(β)