• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3133-3139
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• 2023

Neutron imaging technology as a means of non-destructive detection of materials is complementary to X-ray imaging. Silicon photomultiplier (SiPM), a new type of optical readout device, has overcome some shortcomings of traditional photomultiplier tube (PMT), such as high-power consumption, large volume, high price, uneven gain response, and inability to work in strong magnetic fields. Its application in the field of neutron detection will be an irresistible general trend. In this paper, a thermal neutron imaging detector based on ⁶LiF/ZnS scintillation screen and SiPM array readout was developed. The design of the detector geometry was optimized by geant4 Monte Carlo simulation software. The optimized detector was evaluated with a step wedge sample. The results show that the detector prototype with a 48 mm × 48 mm sensitive area can achieve about 38% detection efficiency and 0.26 mm position resolution when using a 300 ㎛ thick ⁶LiF/ZnS scintillation screen and a 2 mm thick Bk7 optical guide coupled with SiPM array, and has good neutron imaging capability. It provides effective data support for developing high-performance imaging detectors applied to the China Spallation Neutron Source (CSNS).

• Journal of the Korean Society of Food Culture
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• v.23 no.2
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• pp.184-193
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• 2008

The purpose of this study was to determine the detailed customer satisfaction and dissatisfaction factors of family restaurants in Korea, and to then classify the factors into 3 groups, inlcuding core service, quality service, and basic service. ‘Core service’ represents the critical factors that generate both satisfaction and dissatisfaction; ‘quality service’ generates only satisfaction; and ‘basic service’ generates only dissatisfaction. This categorization is based on Herzberg’s motivation-hygiene theory (1976) as well as Cadotte & Turgeon (1988). Based on the characteristics of the three groups, differentiation strategies in managing customer service were suggested to the family restaurant managers. A qualitative research method, termed the critical incident technique (CIT), was used in the study. This method helps researchers find new factors or attributes by grouping key issues from the anecdotes (critical incidents) and then categorizing common factors from the key issues. This research categorized key satisfiers and dissatisfiers into 33 factors, which were from 402 critical incidents described by 261 respondents. Eleven factors (response to service failures, food taste and quality, attention paid to customers, coupon/mileage point/discount card, customer’s ordinary requests, waiting, food diversity, food price, facility sanitation, checking out, customer’s special requests) were classified into core service, which required maximum management not regarding the level of customer satisfaction. Six factors (employee attitude, event, education and explanation, complementary food, customer’s mistakes, attention paid to children) were classified into quality service, which required differentiation strategy management. Finally, nine factors (speed of food service, employee’s mistakes, food sanitation, atmosphere and interior, seating, forcing orders, parking, other customers, reservations) were classified into basic service, which required minimum management at the level of the industry standards.

• International Area Studies Review
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• v.15 no.1
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• pp.31-58
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• 2011

The purpose of this paper is to analyze the effects of real and monetary disturbances and economic interactions between two large countries, and to examine how wage indexation affects the transmission of real and monetary disturbances and affects the fiscal and monetary policies of a large country. A two large country model is built, and is theoretically analyzed. We conducted an empirical investigation to apply theoretical findings to the Japanese and US economic interactions in response to real and monetary disturbances originating in one or the other country. Empirical evidence on Japan-USA economic interactions shows that Japan is much more affected by the US economic policy than the USA is affected by the Japanese economic policy. The economic impacts of real and monetary disturbances on the Japanese and US economies are smaller when the Japanese and US wage indexing parameters are lower.

• Journal of Distribution Science
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• v.22 no.3
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• pp.33-47
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• 2024

Purpose: Most emerging-market countries are concerned about the technology boom, which is accompanied by an increase in revenue from online sales and services. This finding has been demonstrated during the COVID-19 pandemic; however, is this tendency continuing in the new normal, and what factors are driving the increase in consumer decisions? The purpose of this research is to investigate how the decision to utilize online services will be affected in the new normal as well as propose a new research approach in this field. Research Design, Methodology and Approach: By following a deductive research method associated with positivist philosophy, a survey in South Vietnam with 426 respondents using a convenience sampling method was conducted. The reliability of the measurement scales was examined by using the SPSS program. The SmartPLS programme was utilised to assess the measurement and structural models as well as test hypotheses by using partial least squares structural equation modelling. Results: According to the research findings, decision-making has been impacted by social influences, perceived usefulness, perceived ease of use, perceived trust, perceived price, and perceived convenience. Conclusions: The research results also bring significant contributions not only in practice in providing management implications but also in theory. The research model has also demonstrated the feasibility of employing the stimuli-organism-response framework and combining the theory of perceived risk with the technology acceptance model via the explanation of decision-making.

• Journal of the Korean Applied Science and Technology
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• v.40 no.4
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• pp.724-732
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• 2023

In this study, we tried to analyze the characteristics of color cosmetics information search and the major information of interest in the color cosmetics market after COVID-19 shown in the text mining analysis results by collecting data on online interest information of consumers in the color cosmetics market after COVID-19. In the empirical analysis, text mining was performed on all documents such as news, blogs, cafes, and web pages, including the word "color cosmetics". As a result of the analysis, online information searches for color cosmetics after COVID-19 were mainly focused on purchase information, information on skin and mask-related makeup methods, and major topics such as interest brands and event information. As a result, post-COVID-19 color cosmetics buyers will become more sensitive to purchase information such as product value, safety, price benefits, and store information through active online information search, so a response strategy is required.

• The Korean Journal of Food And Nutrition
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• v.37 no.3
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• pp.162-170
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• 2024

The domestic swine industry is currently facing a threat due to the recent increase in pork imports. This study aims to determine what factors influence consumers' intention to consume imported pork and suggest measures to support the domestic pork industry. To achieve this, we analyzed data from the Korea Rural Economic Institute's Food Consumption Behavior Survey using a binary logistic regression model. The results revealed that a higher intention to consume imported pork is linked to a higher intention to consume imported rice, purchasing meat online, frequent purchases of HMR, and procuring U.S. beef, especially among urban residents. On the other hand, a lower intention to consume imported pork is associated with a higher awareness of animal welfare certification, frequently dining out, and older age. Based on these findings, we propose the following response measures for the domestic swine industry: implementing educational programs, marketing, and advertising specifically targeting urban residents to improve their perception of domestic agricultural products; enhancing price competitiveness through distribution optimization; and developing policies to promote the use of domestic pork as an ingredient in processed foods.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.253-259
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• 2024

From the consumer's perspective, eco-friendly consumption is still a topic that can cause various psychological discomforts, and psychological discomfort can lower the intention to consume eco-friendly products through negative consumer psychological mechanisms. This study analyzed the influence of psychological discomfort regarding eco-friendly consumption on people's willingness to pay additional prices for eco-friendly products. In addition, we examined the moderating effect of consumers' involvement in the product family in this relationship. As a result of a statistical analysis based on consumer response data obtained from an online survey conducted with 407 American consumers, the level of people's psychological discomfort with eco-friendly consumption is directly related to their willingness to pay additional prices for eco-friendly products. Although it did not have a significant effect, it was confirmed that the influence of psychological discomfort on willingness to pay premium price was significantly different depending on the consumer's level of involvement.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.169-178
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• 2013

How do Korean and Chinese consumers to use counterfeit behavior? Began to research the question. Among consumers in Korea and China, Counterfeiting Usage and counterfeit attitude, counterfeit satisfaction and dissatisfaction factors were investigated. Differences in perception and for each what was confirmed. The results of this study are as follows. First, it showed significant difference for the Korean and Chinese consumers prefer to counterfeit brand, high preference, Louis Vuitton for consumers, while consumers in China also showed high preference for Chanel. Second, Korean and Chinese consumers prefer to counterfeit brand clothing and shoes there was a significant difference. In particular, The North Face brand of high preference, while Korean consumers, Chinese consumers a higher preference for the Converse brand. Third, the Korean consumer counterfeit compared to the Chinese consumer attitudes to higher moral awareness is interpreted. Fourth, South Korea and China all counterfeit consumer satisfaction factors showed the highest response rate of the price will be cheaper. Finally, South Korea and China all counterfeit consumer dissatisfaction factors showed the highest response rate of quality is not good enough. Future through an in-depth understanding of Korea and China of counterfeit consumer behavior, these findings will be useful to formulate a campaign strategy, to reduce the use of counterfeit.