• Management & Information Systems Review
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• v.33 no.3
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• pp.129-154
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• 2014

We are making products and services that we didn't easily think or even imagine when young and living using them. Consumer needs become more diversified, markets and technologies are being constantly changed, and corporate global competitiveness is getting intensified. Now, companies that don't change nor create new values cannot survive. The cycle of new products is faster, and therefore corporations should improve previous products continuously or create new values according to such a trend. This era requires 'creativity' for companies to overcome uncertainty and survive. Corporate creativity is not easily activated as financial and physical compensation unlike the work ability or performance. When there is an environment that people with various abilities, experiences and backgrounds can express their opinions freely, it becomes possible. Despite the importance, the studies have focused on creativity education so far instead of the viewpoint of corporate business administration. In addition, most studies have dealt with job characteristics and employees' personality factors rather than the process that is connected to actual performance. In other words, the studies on environmental factors that can lead motivation as situational characteristics of the employee's company in a current non-monetary and non-physical way are very insufficient. Therefore, this study aims to comprehensively examine the process of creating creative performance in the environment inside the company that the employee could approach most closely from the corporate deinstitutional perspective. As the major variables, job characteristics and supervisor's ethical leadership were selected, and the process that is connected to the creative performance was explained by means of motivation necessary to understand creativity of the organization and creative process engagement(CPE). The summary of the verification results is following. First, job characteristics and supervisor's attitude(ethical leadership) had positive effects on intrinsic motivation and creativity process engagement. Second, intrinsic motivation and creativity process engagement had positive effects on creative performance. Third, intrinsic motivation and creativity process engagement had mediated the relationship between job characteristics, supervisor's attitude and intrinsic motivation, creativity process engagement

• Management & Information Systems Review
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• v.32 no.5
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• pp.69-88
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• 2013

This study aimed to analyze the effects that the exchange relationship among employees engaged in the wedding industry in Busan, Ulsan and Gyeongnam area exerted on job satisfaction and voluntary service intentions. For this purpose, a theoretical model was presented based on previous studies and the relationship among variables was analyzed based on the analysis results through a survey. There have recently been great difficulties in the whole wedding industry due to the global economic depression and the low birthrate. Moreover, it is hard to optimize the future business environment. In particular, the low birthrate has weakened the demand base of the wedding industry. As a result, it is expected that the competition among companies will be more fierce in the wedding industry. Employees' voluntary service intention become much more important to overcome difficulties in this industry. Employees' voluntary service intentions can be possible when their job satisfaction is achieved. Many studies report the result that this job satisfaction can naturally be achieved when the high quality exchange relationship among a leader, team members (co-workers) and customers is formed. It is viewed that employees' voluntary service intentions should be induced through job satisfaction to improve the profitability through the efficient operation and management of the system. A leader of the organization should make an effort to form the high-quality exchange relationship with members, support so that team members(coworkers) and other employees can get along together and the teamwork can be improved and induce them to improve a high level of service quality with friendly customer services. In spite of the significance of the study discussed above, there are the following limitations in this study. There is a limit to a certain extent in generalizing study conclusions because the study was limited to the Busan, Ulsan and Gyeongnam area. If practical surveys which full- and part-time employees are included in the composition of samples are conducted for wedding suppliers in the country in the future, the quality of study will be able to improve.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

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$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of Global Scholars of Marketing Science
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• v.19 no.4
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• pp.44-56
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• 2009

Impulse purchasing is defined as an immediate purchase with no pre-shopping intentions. Previous studies of impulse buying have focused primarily on factors linked to marketing mix variables, situational factors, and consumer demographics and traits. In previous studies, marketing mix variables such as product category, product type, and atmospheric factors including advertising, coupons, sales events, promotional stimuli at the point of sale, and media format have been used to evaluate product information. Some authors have also focused on situational factors surrounding the consumer. Factors such as the availability of credit card usage, time available, transportability of the products, and the presence and number of shopping companions were found to have a positive impact on impulse buying and/or impulse tendency. Research has also been conducted to evaluate the effects of individual characteristics such as the age, gender, and educational level of the consumer, as well as perceived crowding, stimulation, and the need for touch, on impulse purchasing. In summary, previous studies have found that all products can be purchased impulsively (Vohs and Faber, 2007), that situational factors affect and/or at least facilitate impulse purchasing behavior, and that various individual traits are closely linked to impulse buying. The recent introduction of new distribution channels such as home shopping channels, discount stores, and Internet stores that are open 24 hours a day increases the probability of impulse purchasing. However, previous literature has focused predominantly on situational and marketing variables and thus studies that consider critical consumer characteristics are still lacking. To fill this gap in the literature, the present study builds on this third tradition of research and focuses on individual trait variables, which have rarely been studied. More specifically, the current study investigates whether impulse buying tendency has a positive impact on impulse buying behavior, and evaluates how consumer characteristics such as the need for cognitive closure (NFCC), psychological wellbeing, and susceptibility to interpersonal influences affect the tendency of consumers towards impulse buying. The survey results reveal that while consumer affective impulsivity has a strong positive impact on impulse buying behavior, cognitive impulsivity has no impact on impulse buying behavior. Furthermore, affective impulse buying tendency is driven by sub-components of NFCC such as decisiveness and discomfort with ambiguity, psychological wellbeing constructs such as environmental control and purpose in life, and by normative and informational influences. In addition, cognitive impulse tendency is driven by sub-components of NFCC such as decisiveness, discomfort with ambiguity, and close-mindedness, and the psychological wellbeing constructs of environmental control, as well as normative and informational influences. The present study has significant theoretical implications. First, affective impulsivity has a strong impact on impulse purchase behavior. Previous studies based on affectivity and flow theories proposed that low to moderate levels of impulsivity are driven by reduced self-control or a failure of self-regulatory mechanisms. The present study confirms the above proposition. Second, the present study also contributes to the literature by confirming that impulse buying tendency can be viewed as a two-dimensional concept with both affective and cognitive dimensions, and illustrates that impulse purchase behavior is explained mainly by affective impulsivity, not by cognitive impulsivity. Third, the current study accommodates new constructs such as psychological wellbeing and NFCC as potential influencing factors in the research model, thereby contributing to the existing literature. Fourth, by incorporating multi-dimensional concepts such as psychological wellbeing and NFCC, more diverse aspects of consumer information processing can be evaluated. Fifth, the current study also extends the existing literature by confirming the two competing routes of normative and informational influences. Normative influence occurs when individuals conform to the expectations of others or to enhance his/her self-image. Whereas informational influence occurs when individuals search for information from knowledgeable others or making inferences based upon observations of the behavior of others. The present study shows that these two competing routes of social influence can be attributed to different sources of influence power. The current study also has many practical implications. First, it suggests that people with affective impulsivity may be primary targets to whom companies should pay closer attention. Cultivating a more amenable and mood-elevating shopping environment will appeal to this segment. Second, the present results demonstrate that NFCC is closely related to the cognitive dimension of impulsivity. These people are driven by careless thoughts, not by feelings or excitement. Rational advertising at the point of purchase will attract these customers. Third, people susceptible to normative influences are another potential target market. Retailers and manufacturers could appeal to this segment by advertising their products and/or services as products that can be used to identify with or conform to the expectations of others in the aspiration group. However, retailers should avoid targeting people susceptible to informational influences as a segment market. These people are engaged in an extensive information search relevant to their purchase, and therefore more elaborate, long-term rational advertising messages, which can be internalized into these consumers' thought processes, will appeal to this segment. The current findings should be interpreted with caution for several reasons. The study used a small convenience sample, and only investigated behavior in two dimensions. Accordingly, future studies should incorporate a sample with more diverse characteristics and measure different aspects of behavior. Future studies should also investigate personality traits closely related to affectivity theories. Trait variables such as sensory curiosity, interpersonal curiosity, and atmospheric responsiveness are interesting areas for future investigation.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.1
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• pp.9-16
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• 2018

Landscape architecture is an indispensable professional service in building sustainable land and urban environments. The landscape architecture industry is closely related to the promotion of the health and welfare of the people, urban revitalization and residential environment improvement as well as job creation. Despite various public interest values of landscape architecture, the growth engine of the landscape architecture industry, which is supposed to improve the quality of landscape services, has stagnated. In 2015, the Landscape Architecture Promotion Act was enacted to provide a landscape architectural promotion facility and complex system to support revitalization through the integration of the landscape architecture industry. The purpose of this study is to suggest an improvement plan to enhance the effectiveness of the landscape architectural promotion facility and complex system. The results of the analysis are as follows: First, workers and experts in landscape architecture recognized the need for policies and projects to promote the landscape architecture industry. Second, the industrial types suitable for the landscape architectural promotion facility were landscape design, landscape maintenance and management, and landscape construction industry. Meanwhile the industrial types suitable for a landscape architectural promotion complex were landscape trees and landscape facilities production and distribution. Third, the expected effect of the designation of the landscape architectural facility was 'the increase of the business opportunity through the expansion of the network'. On the other hand, that of the landscape architectural promotion complex was 'the activation of various information sharing'. Fourth, 'the size of the local government landscape architecture industry and the capacity to cultivate' was the most important among the designation criteria of the landscape architectural promotion facility. As for that of the landscape architectural promotion complex, the 'feasibility of promotion plan' was the most crucial. Fifth, 'tax benefit and deductible exemption' was considered as a necessary support method for the activation of the landscape architectural promotion facility, and 'maintenance and management fee support' was recognized in the case of the landscape architectural promotion complex.

• Korean Security Journal
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• no.62
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• pp.135-158
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• 2020

In casinos, security personnel who manage the safety of customers and employees play a very important role. In particular, there is a high percentage of female employees in casinos, and because the ratio of female and male employees is similar, the probability of female customers or female employees experiencing accidents may be similar to or higher than that of males. Women's security agents who handle women's case accidents can provide female customers and employees with a security service that only women can do. However, most of the agents doing security work at casinos are male, and the proportion of women is very low. Therefore, this research is about employees who are currently working as women in casinos and conducted qualitative research to find out about various experiences they experienced while working in the casino. A total of five study participants were interviewed three times to analyze and categorize the data collected. The first question is the professor's recommendation, his personal information search and his acquaintance's recommendation. The second question, the factors behind the necessary skills at work, are various athletic skills, good physical conditions and foreign language skills. In the third question, the satisfaction factors of the task are the scarcity value of the work, the satisfaction of the pay, the suitability of the individual and the expectation of the future, and the unsatisfactory factors of the work are the risk of the work, the stress on the customer, the discrimination against the sex, the gaze around, the tiredness of the shift work. In the fourth question, factors on the need for female casino security agents are providing differentiated services to female customers, protecting female employees and providing opportunities for women in related majors. The results of this study were interviewed by an expert of more than 20 years in the casino security business, and female casino security agents said that since it is a necessary requirement, they should seek a direction for development through institutional and cognitive improvement.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.123-134
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• 2010

Considering the benefits for both consumers and suppliers, firms are taking advantage of the Internet as a medium to communicate with and sell products to their consumers. This trend makes the online shopping environment a growing field for both researchers and practitioners. This paper contributes by testing a model of online consumer behavior with websites varying in levels of hedonism. Unlike past studies, we included all three types of emotions (arousal, pleasure, and dominance) and flow into the model. In this study, we assumed that website interfaces, such as background colors, music, and fonts impact the three types of emotions at the initial exposure to the site (Mazaheri, Richard, and Laroche, 2011). In turn, these emotions influence flow and consumers' perceptions of the site atmospherics-perception of site informativeness, effectiveness, and entertainment. This assumption is consistent with Zajonc (1980) who argued that affective reactions are independent of perceptual and cognitive operations and can influence responses. We, then, propose that the perceptions of site atmospherics along with flow, influence customers' attitudes toward the website and toward the product, site involvement, and purchase intentions. In addition, we studied the moderating impact of the level of hedonism of websites on all the relationship in the model. Thus, the path coefficients were compared between "high" and "low" hedonic websites. We used 39 real websites from 12 product categories (8 services and 4 physical goods) to test the model. Among them, 20 were perceived as high hedonic and 19 as low hedonic by the respondents. The result of EQS 6.1 support the overall model: $\chi^2$=1787 (df=504), CFI=.994; RMSEA=.031. All the hypotheses were significant. In addition, the results of multi-groups analyses reveal several non-invariant structural paths between high and low hedonic website groups. The findings supported the model regarding the influence of the three types of emotions on customers' perceptions of site atmospherics, flow, and other customer behavior variables. It was found that pleasure strongly influenced site attitudes and perceptions of site entertainment. Arousal positively impacted the other two types of emotions, perceptions of site informativeness, and site involvement. Additionally, the influence of arousal on flow was found to be highly significant. The results suggested a strong association between dominance and customers' perceptions of site effectiveness. Dominance was also found to be associated with site attitudes and flow. Moreover, the findings suggested that site involvement and attitudes toward the product are the most important antecedents of purchase intentions. Site informativeness and flow also significantly influenced purchase intentions. The results of multi-group analysis supported the moderating impacts of hedonism of the websites. Compared to low (high) hedonic sites, the impacts of utilitarian (hedonic) attributes on other variables were stronger in high (low) hedonic websites. Among the three types of emotions, dominance (controlling feelings) effects were stronger in high hedonic sites and pleasure effects were stronger in low hedonic sites. Moreover, the impact of site informativeness was stronger for high hedonic websites compared to their low-hedonic counterparts. On the other hand, the influence of effectiveness of information on perceptions of site informativeness and the impact of site involvement on product attitudes were stronger for low hedonic websites than for high hedonic ones.

• Journal of Navigation and Port Research
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• v.39 no.1
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• pp.45-53
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• 2015

These days Container Terminals are focusing on increasing the quantity of containers and shipping lines choose Terminals by referring to the key elements of a terminal to perform the overall operation the fastest such as the location of the terminal, discharging ability, keeping environment, and other elements related to shipping in general. Container terminal is able to offer On-Dock service has become an important factor for shipping lines to choose that terminal. In this paper, we propose an algorithm for On-Dock system work algorithm, the algorithm Empty container exports, Full Container algorithm and The aim of our study focus on both container's gate out time and search for the effective terminal operation which is using the general On-Dock system through several algorithm like container batch priority, gate in and out job priority and empty container yard equipment allocation rule based on the automatic allocation method and manual allocation scheme for container. Gathering these information, it gives the priority and yard location of gate-out containers to control. That is, by selecting an optimum algorithm container, container terminals Empty reduces the container taken out time, it is possible to minimize unnecessary re-handling of the yard container can be enhanced with respect to the efficiency of the equipment. Operations and operating results of the Non On-Dock and On-Dock system is operated by the out work operations (scenarios) forms that are operating in the real Gwangyang Container Terminal derived results. Gwangyang Container terminal and apply the On-Dock system, Non On-Dock can be taken out this time, about 5 minutes more quickly when applying the system. when managing export orders for berths where On-Dock service is needed, ball containers are allocated and for import cargoes, D/O is managed and after carryout, return management, container damage, cleaning, fixing and controlling services are supported hence the berth service can be strengthened and container terminal business can grow.