• Journal of Information Technology Applications and Management
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• v.23 no.4
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• pp.101-116
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• 2016

In this paper, we suggested a model that reflects the role played by the consumer's regulatory focus (promotion focus and prevention focus) as determinant factors of trust in the Web site. Also, we considered the moderating effects of consumers' purchasing experience toward online shopping. We conducted a two-month survey of 230 individuals using online shopping sites for hypotheses testing. The study results are summarized as follows. Firstly, promotion focused consumers showed higher trust to a web site compared to prevention focused consumers. Secondly, the moderating effect of purchasing experience between regulatory focus and consumer trust to a web site is statistically significant. The effect of a prevention focused consumers on consumer trust to a web site is stronger when purchasing experience toward online shopping is high compared to low. Based on these findings, this study presents practical and academic implications of the research.

• The Journal of Information Systems
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• v.21 no.3
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• pp.117-136
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• 2012

Given to the prevalence of social commerce such as Groupon, Ticketmonster, and Coupang, it has become critical to understand customer purchasing behavior in social commerce environments. When consumers make purchasing decisions in social commerce, they often act impulsively. This is because social commerce is a deal-of-the-day website that features discounted gift certificates usable at local companies. However, the vast majority of social commerce research has viewed consumer decision-making as a rational process. This study develops a theoretical framework to investigate key drivers of customer's impulsive purchasing in social commerce. This study identifies flow, positive emotion, negative emotion, social commerce attractiveness, and discounted price as the key antecedents of impulsive purchasing. Data collected from 164 users who had prior purchasing experiences with social commerce were empirically tested against the research model using partial least squares analysis. The analysis results indicate that flow plays an important role in facilitating customers' impulsive purchasing in social commerce environments. Moreover, the findings show the exact roles of positive emotion, negative emotion, social commerce attractiveness, and discounted price on consumer's impulsive purchasing.

• Korean Institute of Interior Design Journal
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• v.19 no.3
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• pp.154-161
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• 2010

Domestic distribution environment in Korea has rapidly and qualitatively been developed due to the open of distribution market. However, the function of commercial establishment goes beyond the place value that simple transaction of capacity takes place, and it is recognized as life groove space enhancing the satisfaction of costumers' purchasing pattern. For this reason, there has been the need to change the purchasing environment of commercial space. The change factors of commercial space contributed to the establishment of consumption identity formed by change of consumers' lifestyle and the purchasing pattern. It is necessary to see the change factors of the purchasing pattern as the conversion of distribution environment in terms of supplier and as the change of consumers' lifestyle in terms of consumers. In reality, however, the commercial facility ill-imitated the advanced model and qualitatively was developed. In this situation, the change of the purchasing pattern according to the conversion of facility is needed as an early stage. The purpose of this study is to analyze the overflow supply of commercial facility. Also It will discuss the plan characteristics of commercial space conversion of facility conforming to the change of the purchasing pattern that is predicted in the lifestyle on a conversion stage of facility.

• Journal of the Korea Safety Management & Science
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• v.21 no.4
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• pp.7-15
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• 2019

This paper proposes the FMEA-based model to avoid backdoor transactions when purchasers select suppliers for products and services. In the model, backdoor transactions consist of two categories: backdoor selling and maverick buying. Both of which influence negative effects on cost savings due to not only uncompetitive advantage but also unusable purchasing leverage by unethical and misbehavior of purchase requestors. For the risk evaluation based on FMEA, three and five risk types of backdoor selling and maverick buying are identified respectively. Current risk priority numbers(RPN) based on those risk types are calculated by three categories: occurrence, detection and severity. Six risk mitigation strategies and fourteen mitigation tactics are identified to improve current RPN. In order to validate the model, questionnaires are collected from fifteen companies and statistically analyzed. The analysis result shows that the model reduces backdoor transaction risks and has no differences in reduction of backdoor transaction risks regardless of the type of purchasing organization units and existence of purchasing procedures in the organization.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.449-455
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• 2002

This paper considers that advanced planning and scheduling (APS) in manufacturing and the efficient purchasing where each customer order has its due date and multi-suppliers exit We present a Make-To­Order Supply Chan (MTOSC) model of efficient purchasing process from multi-suppliers and APS with outsourcing in a supply chain, which requires the absolute due date and minimized total cost. Our research has included two states. One is for efficient purchasing from suppliers: (a) selection of suppliers for required parts; (b) optimum part lead­time of selected suppliers. Supplier selection process has received considerable attention in the business­management literature. Determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions usually is complex and unstructured. These influence factors can be divided into quantitative and qualitative factors. In the first level, linguistic values are used to assess the ratings for the qualitative factors such as profitability, relationship closeness and quality. In the second level a MTOSC model determines the solutions (supplier selection and order quantity) by considering quantitative factors such as part unit price, supplier's lead-time, and storage cost, etc. The other is for APS: (a) selection of the best machine for each operation; (b) deciding sequence of operations; (c) picking out the operations to be outsourcing; and (d) minimizing makespan under the due date of each customer's order. To solve the model, a genetic algorithm (GA)-based heuristic approach is developed. From the numerical experiments, GA­based approach could efficiently solve the proposed model, and show the best process plan and schedule for all customers' orders.

• Journal of the Korean Applied Science and Technology
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• v.38 no.6
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• pp.1502-1511
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• 2021

In order to analyze the phenomenon that occurs as the human brand domain of the golf advertising model expands, this study conducted a causal relationship (SEM) of 521 people using a mobile survey (URL) according to a sampling plan from July 1 to November 15, 2021. was applied and analyzed. First, the hypothesis was adopted with a statistically significant positive (+) effect on the fit of the human brand of the golf advertising model. Second, the hypothesis of suitability was adopted with a statistically significant positive (+) effect on purchasing behavior. Third, the human brand of the golf advertising model was hypothesized to have a statistically significant positive (+) effect on purchasing behavior. Finally, as a result of calculating the indirect effect through bootstrapping, it can be seen that the indirect effect of suitability in the relationship between human brand and purchasing behavior is statistically significant.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.1
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• pp.42-48
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• 2002

This paper addresses the necessity of integration between buyer and suppliers for effective implementation of Just-In-Time purchasing in a multi-item environment. An integrated inventory model of facilitating multiple shipments in small lots is developed. Also, an iterative solution procedure is developed to simultaneously find the order(contract) interval for each item and number of shipments between buyer and suppliers. We show by example that when the integrated policy is adopted by both buyer and suppliers in a cooperative manner, both parties can benefit.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• The Journal of Fisheries Business Administration
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• v.48 no.3
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• pp.47-59
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• 2017

Fish mortality is the most important success factor in aquaculture management. To order fingerlings considering the effect of mortality is a important problem in aquaculture farm. This study is aimed to decision the number and size of fry in aquaculture farm. This study build the mathematical model that finds the value of decision variable to minimize total cost that sums up the fingerling purchasing cost, aquaculture farm operating cost and feeding cost under mortality constraint. The proposed mathematical model involve biological and economical variables: (1) number of fingerlings (2) fish growth rate (3) mortality (4) price of a fry (5) feeding cost, and (6) possible order period. Numerical simulation model presented here in. The objective of numerical simulation is to provide for decision makers to analyse and comprehend the proposed model. When extensive biological and cost data become available, the proposed model can be widely applied to yield more accurate results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.153-164
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• 2019

Since the appearance of smartphones, the smartphone market has been in fierce completion by new technologies and marketing trends. The smartphone market is now somewhat saturated, and the manufacturers are trying to improve their position in the market through the repurchase of existing customers and the influx of competitors. At the same time, customers have their own purchasing criteria for smartphones. Therefore, manufacturers need to determine new technology and marketing trends based on customer purchasing trends and usage characteristics. The aim of this study is to analyze the quality attributes of smartphones. We conducted a survey on 220 respondents, and divided the respondents into several groups by purchasing trends and usage characteristic through cluster analysis. The groups are analyzed and compared based on the Kano model for the quality attributes of smartphone. The analysis result are as follows. Firstly, purchasing trends divide responders into groups that prefers high-end premium smartphones and those that take into account practicality in terms of purchasing trends. Secondly, usage characteristic divide responders into three groups: those with clear usage pattern, those who prefer ease of use, and the rest, and we find out that those with clear usage pattern are important customer in viral marketing. Lastly, Kano analysis is revealed the 'Slow/hi-speed camera', 'Private mode', 'Widget', 'Health care' are attractive quality attributes.