The Study on the Factors of film's Processing Fluency Inducing Film's Preference Fluency (영화의 선호도 유창성에 영향을 미치는 영화의 처리 유창성 요인에 관한 연구)
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- Asia Marketing Journal
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- v.13 no.4
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- pp.29-54
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- 2012
Recently, as film has been admitted as the artistic merit and higher value-added business, the past studies on film have been lively carried on various fields. Especially, the film business is thought to be value-added business and has explored the causes of influencing spectators. However, there are no researches enough to explain what induces the choice and diffusion of the film. The film is not only hedonic products but also typical example of experiential products. So how to process film formation plays important roles in explaining the procedures of forming preference on the film and the movies spread more widely. But the great part of study of films has been concentrated on exploring hedonic factors of influencing spectator's choice. Until now there is not enough study for the relationship between experiential information processing and film preference. To explain film's preference, our study focuses on preference fluency and processing fluency that can provide an insight for our question about the relationship. In this article, to explain the procedures of processing experiential information and forming preference on the film, our study focuses on finding the relationship between film's processing fluency and film's preference fluency and explores the factors that affect film's processing fluency. To achieve the goal of this study, we distinguish factors of film's conceptual fluency from factors of film's perceptual fluency and explore the paths from the factors to film's preference fluency. The factors which have effects on perceptual fluency are hypothesized to be distinction of image expression, distinction of sound expression, correspondence between actors' image and their role. The factors which have effect on conceptual fluency are supposed to be well-organized story, suitability of lines expression. The experiments in which students were sampled at 'C' university were conducted in 2010 (december). Data collection was proceeded through questionnaires. We test the hypothesized model by using structural equation modeling(Amos 17.0). The fit indices for the model are as follows : x2=416.266(df=213, p=0.00), GFI=0.855, AGFI=0.812, RMSEA =0.069, IFI=O.925, CFI=0.920, TLI=0.905. According to the guidelines, there is evidence that our measurement model fits data. The results of empirical study are as follows. The path from film's perceptual fluency to film's preference fluency is supported(estimate: 0.223, C.R: 2.641). The path from film's conceptual fluency to film's preference fluency is supported(estimate: 0.397, C.R: 4.863). The path from distinction of image expression to film's perceptual fluency is not supported(estimate: 0.113, C.R: 1.665). The path from distinction of sound expression to film's perceptual fluency is supported (estimate: 0.190, C.R: 2.042). The path from correspondence between actors' image and their role to film's perceptual fluency is supported(estimate: 0.686, C.R: 5.566). The path from well-organized story to film's conceptual fluency is supported(estimate: 0.396, C.R: 4.023). The path from suitability of lines expression to film's conceptual fluency is supported(estimate: 0.536, C.R: 5.441). Concludingly, our study explored the influencing factors of film's processing fluency inducing film's Preference fluency. First, film's perceptual fluency and film's conceptual fluency have positive effects on film's preference fluency. Second, distinction of image expression is not significant on film's perceptual fluency, but distinction of sound expression and image's correspondence of actors' image and their role have positive effects on film's perceptual fluency. Lastly, well-organized story and suitability of lines expression have positive effects on film's conceptual fluency.
Brand experience has received much attention from considerable marketing research. When consumers consume and use brands, they are exposed to various specific brand-related stimuli. These brand-related stimuli include brand identity and brand communications(e.g., colors, shapes, designs, slogans, mascots, brand characters) components. Brakus, Schmitt, and Zarantonello(2009) conceptualized brand experience as subjective and internal consumer responses evoked by brand-related stimuli. They demonstrated that brand experience can be broken down into four dimensions(sensory, affective, intellectual, and behavioral). Because experiences result from stimulations and lead to pleasurable outcomes, we expect consumers to want to repeat theses experiences. That is, brand experiences, stored in consumer memory, should affect brand loyalty. Consumers with positive experiences should be more likely to buy a brand again and less likely to buy an alternative brand(Fournier 1998; Oliver 1997). Brand attachment, one of dimensions of the consumer-brand relationship, is defined as an emotional bond to the specific brand(Thomson, MacInnis, and Park 2005). Brand attachment is target-specific bond between the consumer and the specific brand. Thus, strong attachment is attended by a rich set of schema that link the brand to the consumer. Previous researches propose that brand attachments should affect consumers' commitment to the brand. Brand experience differs from affective construct such as brand attachment. Brand attachment is based on interaction between a consumer and the brand. In contrast, brand experience occurs whenever there is a direct and indirect interaction with the brand. Furthermore, brand experience is not an emotional relationship concept. Brakus et al.(2009) suggest that brand experience may result in brand attachment. This study aims to distinguish brand experience dimensions and investigate the effects of brand experience on brand attachment and brand commitment. We test research problems with data from 265 customers having brand experiences in various product categories by using multiple regression and structural equation model. The empirical results can be summarized as follows. First, the paths from affective, behavior, and intellectual experience to the brand attachment were found to be positively significant whereas the effect of sensory experience to brand attachment was not supported. In the consumer literature, sensory experiences for consumers are often equated with aesthetic pleasure. Over time, these pleasure experiences can affect consumer satisfaction. However, sensory pleasures are not linked to attachment such as consumers' strong emotional bond(i.e., hot affect). These empirical results confirms the results of previous studies. Second, brand attachment including passion and connection influences brand commitment positively but affection does not influence brand commitment. In marketing context, consumers with brand attachment have intention to have a willingness to stay with the relationship. The results also imply that consumers' emotional attachment is characterized by a set of brand experience dimensions and consumers who are emotionally attached to the brand are committed. The findings of this research contribute to develop differences between brand experience and brand attachment and to provide practical implications on the brand experience management. Recently, many brand managers have focused on short-term view. According to this study, we suggest that effective brand experience management requires taking a long-term view of marketing decisions.
In a workplace, workers exhaust their resources due to workplace adversity or acquire resources through job-related passion. The purpose of this study is to verify the factors that affect the conversion of workers to entrepreneurs and through what paths entrepreneurial intention is generated. To this end, the effects of workplace adversity and job-related passion on entrepreneurial intention were explored with workers in Korea. Also, by empirically analyzing the effects of workplace adversity and job-related passion on entrepreneurial intention through the mediation of job-related creativity, this author attempted to derive the factors and implications associated with the conversion of workers to entrepreneurs. Analyzing 333 workers' data acquired through online surveys with the statistical packages of SPSS and AMOS, this study has gained the following results. First, workplace adversity is found to have positive(+) effects on entrepreneurial intention. This implies that workplace adversity that is negatively regarded is rather a crucial variable that increases entrepreneurial intention. Second, workplace adversity has positive(+) effects on job-related creativity. It means that job-related creativity is an effective factor to overcome workplace adversity. Third, job-related passion is found to have positive(+) effects on entrepreneurial intention. The passion to concentrate on the resources secured is an important factor to elevate entrepreneurial intention. Fourth, job-related passion is verified to have positive(+) effects on job-related creativity. It implies that creative methods can be effective in achieving the goal. Fifth, job-related creativity is found to have positive(+) effects on entrepreneurial intention. Creativity is an intention or action that precedes starting up a business, and it is judged that high job-related creativity reflects high expectation about the possibility of success in starting up a business. Sixth, job-related creativity is found to have mediating effects in correlation between workplace adversity and entrepreneurial intention. Seventh, job-related creativity is found to have mediating effects in correlation between job-related passion and entrepreneurial intention. This means that job-related creativity is an effective factor to alleviate the adversity of workers and increase job-related passion in the process of becoming entrepreneurs. Academically, there were few previous studies related to the adversity of workers in Korea. As this study targets office workers, it can be said that it is a differentiated study extending the range of subjects. Also, practically, it has been learned that negative workplace adversity, too, is an important variable that affect entrepreneurial intention positively. This is practically meaningful in terms of office workers' career management because even in adverse situations that are negative, starting up a business through work experiences may work as an alternative.
This study conducted a classification of small-scale biological habitats created in Seoul to analyze and synthesize location characteristics, habitat structure, biological habitat functions, and threat factors of representative sites, as well as derive creation and management problems according to the ecological characteristics. The aim was to suggest improvement measures and management items. Data collected through a field survey was used to categorize 39 locations, and 8 representative sites were selected by dividing them into location, water system, and size as classification criteria for typification. Due to the characteristics of each type, the site was created in an area where amphibian movement was disadvantageous due to low or disconnected connectivity with the hinterland forest, and the water supply was unstable in securing a constant flow and maintaining a constant water depth. The habitat structure has a small area, an artificial habitat structure that is unfavorable for amphibians, having the possibility of sediment inflow, and damage to the revetment area. The biological habitat function is a lack of wetland plants and the distribution of naturalized grasses, and threats include the establishment of hiking trails and decks in the surrounding area. Artificial disturbances occur adjacent to facilities. When creating habitats according to the characteristics of each type, it was necessary to review the possibility of an artificial water supply and introduce a water system with a continuous flow in order to connect the hinterland forest for amphibian movement and locate it in a place where water supply is possible. The habitat structure should be as large as possible, or several small-scale habitats should be connected to create a natural waterfront structure. In addition, additional wetland plants should be introduced to provide shelter for amphibians, and facilities such as walking paths should be installed in areas other than migration routes to prevent artificial disturbances. After construction, the management plan is to maintain various water depths for amphibians to inhabit and spawn, stabilize slopes due to sediment inflow, repair damage to revetments, and remove organic matter deposits to secure natural grasses and open water. Artificial management should be minimized. This study proposed improvement measures to improve the function of biological habitats through the analysis of problems with previously applied techniques, and based on this, in the future, small-scale biological habitat spaces suitable for the urban environment can be created for local governments that want to create small-scale biological habitat spaces, including Seoul City. It is significant in that it can provide management plans.
Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.
Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.
Nowadays, it is common that most consumers are purchasing goods in e-stores. The e-stores eager to attract, revisit, retain, and finally convert them into loyal customers. The e-store marketers have planned and executed numerous marketing efforts. As one of the marketing activities, e-store managers attempt to build web sites that meet customers' functional and psychological needs. A wide array of studies has been done to identify factors that could affect customers' response of web sites. Majority of studies were conducted to verify technology-related and functional variables of the website which facilitate transactions and enhance customer responses such as purchase intention and website loyalty. However, there has been little research on the external cues of website and psychological variables of consumer that could have positive influences on customer response. The purpose of this study is to investigate the influence of e-store personality on e-store loyalty through mediating variables such as e-store identification, e-store trust, and e-store engagement. The authors of this study develop the model and set up the six main hypotheses and a set of sub-hypotheses based on a literature review, shown in
Consumers differ in the way they make a purchase. An audio mania would willingly make a bold, yet serious, decision to buy a top-of-the-line home theater system, while he is not interested in replacing his two-decade-old shabby car. On the contrary, an automobile enthusiast wouldn't mind spending forty thousand dollars to buy a new Jaguar convertible, yet cares little about his junky component system. It is product involvement that helps us explain such differences among individuals in the purchase style. Product involvement refers to the extent to which a product is perceived to be important to a consumer (Zaichkowsky, 2001). Product involvement is an important factor that strongly influences consumer's purchase decision-making process, and thus has been of prime interest to consumer behavior researchers. Furthermore, researchers found that involvement is closely related to perceived risk (Dholakia, 2001). While abundant research exists addressing how product involvement relates to overall perceived risk, little attention has been paid to the relationship between involvement and different types of perceived risk in an electronic commerce setting. Given that perceived risk can be a substantial barrier to the online purchase (Jarvenpaa, 2000), research addressing such an issue will offer useful implications on what specific types of perceived risk an online firm should focus on mitigating if it is to increase sales to a fullest potential. Meanwhile, past research has focused on such consumer responses as information search and dissemination as a consequence of involvement, neglecting other behavioral responses like online merchant selection. For one example, will a consumer seriously considering the purchase of a pricey Guzzi bag perceive a great degree of risk associated with online buying and therefore choose to buy it from a digital storefront rather than from an online marketplace to mitigate risk? Will a consumer require greater trust on the part of the online merchant when the perceived risk of online buying is rather high? We intend to find answers to these research questions through an empirical study. This paper explores the impact of enduring product involvement and perceived risks on required trust level, and further on online merchant choice. For the purpose of the research, five types or components of perceived risk are taken into consideration, including financial, performance, delivery, psychological, and social risks. A research model has been built around the constructs under consideration, and 12 hypotheses have been developed based on the research model to examine the relationships between enduring involvement and five components of perceived risk, between five components of perceived risk and required trust level, between enduring involvement and required trust level, and finally between required trust level and preference toward an e-tailer. To attain our research objectives, we conducted an empirical analysis consisting of two phases of data collection: a pilot test and main survey. The pilot test was conducted using 25 college students to ensure that the questionnaire items are clear and straightforward. Then the main survey was conducted using 295 college students at a major university for nine days between December 13, 2010 and December 21, 2010. The measures employed to test the model included eight constructs: (1) enduring involvement, (2) financial risk, (3) performance risk, (4) delivery risk, (5) psychological risk, (6) social risk, (7) required trust level, (8) preference toward an e-tailer. The statistical package, SPSS 17.0, was used to test the internal consistency among the items within the individual measures. Based on the Cronbach's
In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.
Imdaejeong Wonlim is located on the verge of Sangsa Village in Sapyeong-ri, Daepyeong-myeon, Hwasun-gun Gyeongsangnam-do toward Northwest. It was planned by Sa-ae, Minjuhyeon in 1862 on the basis of Gobanwon built by Nam Eongi in 16th century against the backdrop of Mt. Bongjeong and facing Sapyeong Stream. As water flows from west to east in the shape of crane, this area is a propitious site standing for prosperity and happiness. This area shows a distinct feature of Wonlim surrounding the Imdaejeong with multi layers as consisting of 5 districts - front yard where landmark stone with engraved letters of 'Janggujiso of Master Sa-ea' and junipers are harmoniously arranged, internal garden of upper pavilion ranging from a pavilion to square pond with a little island in the middle, Sugyeongwon of under pavilionu consisting of 2 ponds with a painting of three taoist hermits, forest of Mt. Bonggeong and external garden including Sapyeong Stream and farmland. According to documentation and the results of on-site investigation, it is certainly proved that Imdaejeong Wonlim was motivated by Byeoseo Wonlim which realized the idea of 'going back to hometown after resignation' following the motives of Janggujiso, a hideout aimed to accomplish the ideology, 'training mind and fostering innate nature,' on the peaceful site surrounded by water and mountain, as well as motives of Sesimcheo(洗心處) to be unified with morality of Mother Nature, etc. In addition, it implies various imaginary landscapes such as Pihangji, Eupcheongdang, square pond with an island and painting of three Taoist hermits based on a notion that 'the further scent flies away, the fresher it becomes,' which is originated from Aelyeonseol(愛蓮說). In terms of technique of natural landscape treatment, divers techniques are found in Imdaejeong Wonlim such as distant view of Mt. Bongjeong, pulling view with an intention of transparent beauty of moonlight, circle view of natural and cultural sceneries on every side, borrowed scenary of pastoral rural life adopted as an opposite view, looked view of Sulyundaero, over looked view of pond, static view in pavilion and paths, close view of water space such as stream and pond, mushroom-and-umbrella like view of Imdaejeong, vista of pond surrounded by willows, imaginary view of engraved letters meaning 'widen knowledge by studying objectives' and selected view to comprise sunrise and sunset at the same time. In the beginning of construction, various plants seemed to be planted, albeit different from now, such as Ginkgo biloba, Phyllostachys spp., Salix spp., Pinus densiflora, Abies holophylla, Morus bombycis, Juglans mandschurica, Paulownia coreana, Prunus mume, Nelumbo nucifera, etc. Generally, it reflected dignity of Confucianism or beared aspect of semantic landscape implying Taoist taste and idea of Phoenix wishing a prosperity in the future. Furthermore, a diversity of planting methods were pursued for such as liner planting for the periphery of pond, bosquet planting and circle planting adopted around the pavilion, spot planting using green trees, solitary planting of monumentally planted Paulownia coreana and opposite planting presenting the Abies holophylla into yin and yang.
. This model is composed of four paths such as dimensions of e-store personality. First of all, hypothesis 1 is partially supported because sub-hypothesis 1-1 and 1-2 are supported, whereas sub-hypothesis 1-3, 1-4, and 1-5 are rejected. Specifically, it reveals that warmth and sophistication dimensions in e-store personality have positive influence on e-store identification, however, activity, progressiveness, and strictness does not have any significant relationship on e-store identification. Secondly, hypothesis 2 was supported. Therefore, it can be said that e-store identification has a positive impact on e-store trust. Thirdly, hypothesis 3 is also supported. Hence, there is a positive relationship between e-store identification and e-store engagement. Fourthly, hypothesis 4 is supported too. e-store identification has a positive influence on e-store loyalty. Fifthly, hypothesis 5 is also accepted. This indicates that e-store trust is a precedent variable which positively affects e-store loyalty. Lastly, it reveals that e-store engagement has a positive impact on e-store loyalty. Therefore, hypothesis 6 is supported. The findings of the study imply that some dimensions of e-store personality have a positive influence on e-store identification, and that e-store identification has direct and indirect influence on e-store loyalty through e-store trust and e-store engagement positively. These results also suggest that the e-store identification in e-store personality is a precedent variable which positively affects e-store loyalty directly and indirectly through e-store trust and engagement as a mediating variable. Therefore, e-store marketers need to implement website strategy based on e-store personality, e-store identification, e-store trust, and e-store engagement to meet customers' psychological needs and enhance e-store loyalty. Finally, the limitations and future study directions based on this study are discussed.
How Enduring Product Involvement and Perceived Risk Affect Consumers' Online Merchant Selection Process: The 'Required Trust Level' Perspective
(지속적 관여도 및 인지된 위험이 소비자의 온라인 상인선택 프로세스에 미치는 영향에 관한 연구: 요구신뢰 수준 개념을 중심으로)
Major Class Recommendation System based on Deep learning using Network Analysis
(네트워크 분석을 활용한 딥러닝 기반 전공과목 추천 시스템)
Location and Construction Characteristics of Imdaejeong Wonlim based on Documentation
(기문(記文)을 중심으로 고찰한 임대정원림(臨對亭園林)의 입지 및 조영 특성)
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