• Korean Journal of Food Science and Technology
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• v.54 no.3
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• pp.334-342
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• 2022

To meet the needs of Muslim consumers, sausages were prepared using Halal-certified chicken thighs and different amounts of biji powder (0, 20, 30, 40, and 50%), and then the properties of the sausages were compared. As the biji powder levels increased, both the moisture content and the pH of the sausages significantly decreased, whereas their fiber content increased. As the biji powder levels increased, the free radical scavenging effect (DPPH, ABTS) and water holding capacity also increased, and the textural properties also improved. When Muslim consumers evaluated the sensory attributes of the sausages prepared in this experiment and those currently on the market, the ones prepared in this experiment were preferred over the market products. Based on the above results, chicken sausage for Muslim consumers could be successfully produced, and their overall quality and antioxidant effects could be improved by the addition of biji powder (up to 30%).

• Journal of Technology Innovation
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• v.23 no.3
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• pp.397-432
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• 2015

This paper investigates the effects of technology collaboration between the main supplier and buyer on buyer's new product development under dynamic environment. Based on 428 Korean manufacturing firms, we conducted regression analysis. The technology collaboration between the main supplier and buyer is adopted as a independent variable and quality, cost and lead time performance of new product development projects are used as dependents variables. Environment dynamic is also used as a moderate variables. We found that the in general, technology collaboration is positively associated with the performance of buyers' new product development, but in the high degree of dynamic environment, technology collaboration is negatively associated with the performance of buyers' new product development unlike our expectation. Thus, we divide our sample into two groups; shipbuilding industry with the low degree of environment dynamic and electronic and IT device industry with the high degree of environment dynamic and conducted a post hoc analysis. As a result, in ship building industry, the technology collaboration is significant to improve NPD projects performance, while in electronic and IT device industry, the technology collaboration with a main supplier is not significant as well as coefficient is negative. In that, under the highly dynamic condition with the fast change of technology and products obsolescence the NPD collaboration with the main supplier does not works unlike a stable environment. This implies that the NPD attributes of buyer are different by their environmental factor and the fit between given environmental feature and the collaboration synergy is critical factor for improving the effect of NPD collaboration between supplier and buyer.

• Food Science and Preservation
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• v.9 no.3
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• pp.271-276
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• 2002

To develop a wrapping film, which suppresses the microbial decay through the storage and prolongs the selflife of fruits and vegetables, the antimicrobial packaging films were prepared and applied to the preservation of strtwberries and cucumbers. Low density polyethylene(LDPE) film of 50㎛ thickness was faricated with 1% of Scutellariae baicalensis extract. The LDPE film impregnated with Scutellariae baicalensis extract showed antimicrobial activity on the disk test against Bacillus cereus, Escherchia coli and Fusarium sp.. The antimicrobial film changed the color and light transmittance, but did not affect heat shrinkage, mechanical tensile strength and wattability. Strawberries and cucumbers were separately wrapped with packaging films in the state of closely-adhered packaging as well as modified atmosphere packaging(MAP). The wrapped strawberries and cucumbers were stored for 21 days at 5$\^{C}$ and for 40 days at l0$\^{C}$, respectively. For the packaged strawberries and cucumbers at 5$\^{C}$ and 10$\^{C}$, the LDPE film impregnated with Scutellariae baicalensis extract showed the reduced growth of total aerobic bacteria, molds and yeasts and did not give any negative effect on other quality attributes during storage in comparison with conttrol film without any additive.

• Food Science of Animal Resources
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• v.32 no.5
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• pp.644-651
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• 2012

We have investigated the effect of lard fraction extracted with supercritical carbon dioxide (SC-$CO_2$) on the flavor enhancement of maillard reaction product (MRP) based meat flavors. MRP based meat flavors were prepared with low glutamic acid (Glu) hydrolyzed wheat gluten (NaCl concentration: 7.61%(w/v)), ribose, cysteine, garlic juice powder, protease-digested Lentinus edodes powder and lard fractions extracted with SC-$CO_2$. Lard was extracted with SC-$CO_2$ at each of three temperatures (40, 60, and $80^{\circ}C$) and at each of four pressures (30, 40, 50, and 60 MPa). Obtained lard SC-$CO_2$ fractions and MRP based meat flavors with those fractions were analyzed for their total yield, aroma pattern by SMart nose system, and sensorial properties. The extraction yield had no difference as temperature increased from $40^{\circ}C$ to $60^{\circ}C$ and even decreased at $80^{\circ}C$. However, increase in pressure level at $40^{\circ}C$ drastically increased the extraction yield. The aroma patterns of raw lard and lard SC-$CO_2$ fractions with 30 MPa were significantly discriminated from those of SC-$CO_2$ lard fractions extracted with higher pressure by SMart nose system. Aroma pattern of MRP based meat flavors with higher pressure extracted lard fractions also showed significant difference through pattern analysis by the SMart nose system. The MRP based meat flavor with lard SC-$CO_2$ fractions at 50 and 60 MPa were described as less sulfuric, less pungent, and more balanced in roasted meat and sweet attributes from sensory evaluation.

• Journal of Global Scholars of Marketing Science
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• v.17 no.4
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• pp.247-269
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• 2007

In recent years, one of the major reasons for the fierce competition amongst firms is that they strive to increase their own market shares and customer acquisition rate in the same market with similar and apparently undifferentiated products in terms of quality and perceived benefit. Because of this change in recent marketing environment, the differentiated after-sales service and diversified promotion strategies have become more important to gain competitive advantage. Price promotion is the favorite strategy that most retailers use to achieve short-term sales increase, induce consumer's brand switch, in troduce new product into market, and so forth. However, if marketers apply or copy an identical price promotion strategy without considering the characteristic differences in product and consumer preference, it will cause serious problems because discounted price itself could make people skeptical about product quality, and the changes of perceived value might appear differently depending on other factors such as consumer involvement or brand attitude. Previous studies showed that price promotion would certainly increase sales, and the discounted price compared to regular price would enhance the consumer's perceived values. On the other hand, discounted price itself could make people depreciate or skeptical about product quality, and reduce the consumers' positivity bias because consumers might be unsure whether the current price promotion is the retailer's best price offer. Moreover, we cannot say that discounted price absolutely enhances the consumer's perceived values regardless of product category and purchase situations. That is, the factors that affect consumers' value perceptions and buying behavior are so diverse in reality that the results of studies on the same dependent variable come out differently depending on what variable was used or how experiment conditions were designed. Majority of previous researches on the effect of price-comparison advertising have used consumers' buying behavior as dependent variable. In order to figure out consumers' buying behavior theoretically, analysis of value perceptions which influence buying intentions is needed. In addition, they did not combined the independent variables such as brand loyalty and price discount rate together. For this reason, this paper tried to examine the moderating effect of brand loyalty on relationship between the different levels of discounting rate and buyers' value perception. And we provided with theoretical and managerial implications that marketers need to consider such variables as product attributes, brand loyalty, and consumer involvement at the same time, and then establish a differentiated pricing strategy case by case in order to enhance consumer's perceived values properl. Three research concepts were used in our study and each concept based on past researches was defined. The perceived acquisition value in this study was defined as the perceived net gains associated with the products or services acquired. That is, the perceived acquisition value of the product will be positively influenced by the benefits buyers believe they are getting by acquiring and using the product, and negatively influenced by the money given up to acquire the product. And the perceived transaction value was defined as the perception of psychological satisfaction or pleasure obtained from taking advantage of the financial terms of the price deal. Lastly, the brand loyalty was defined as favorable attitude towards a purchased product. Thus, a consumer loyal to a brand has an emotional attachment to the brand or firm. Repeat purchasers continue to buy the same brand even though they do not have an emotional attachment to it. We assumed that if the degree of brand loyalty is high, the perceived acquisition value and the perceived transaction value will increase when higher discount rate is provided. But we found that there are no significant differences in values between two different discount rates as a result of empirical analysis. It means that price reduction did not affect consumer's brand choice significantly because the perceived sacrifice decreased only a little, and customers are satisfied with product's benefits when brand loyalty is high. From the result, we confirmed that consumers with high degree of brand loyalty to a specific product are less sensitive to price change. Thus, using price promotion strategy to merely expect sale increase is not recommendable. Instead of discounting price, marketers need to strengthen consumers' brand loyalty and maintain the skimming strategy. On the contrary, when the degree of brand loyalty is low, the perceived acquisition value and the perceived transaction value decreased significantly when higher discount rate is provided. Generally brands that are considered inferior might be able to draw attention away from the quality of the product by making consumers focus more on the sacrifice component of price. But considering the fact that consumers with low degree of brand loyalty are known to be unsatisfied with product's benefits and have relatively negative brand attitude, bigger price reduction offered in experiment condition of this paper made consumers depreciate product's quality and benefit more and more, and consumer's psychological perceived sacrifice increased while perceived values decreased accordingly. We infer that, in the case of inferior brand, a drastic price-cut or frequent price promotion may increase consumers' uncertainty about overall components of product. Therefore, it appears that reinforcing the augmented product such as after-sale service, delivery and giving credit which is one of the levels consisting of product would be more effective in reality. This will be better rather than competing with product that holds high brand loyalty by reducing sale price. Although this study tried to examine the moderating effect of brand loyalty on relationship between the different levels of discounting rate and buyers' value perception, there are several limitations. This study was conducted in controlled conditions where the high involvement product and two different levels of discount rate were applied. Given the presence of low involvement product, when both pieces of information are available, it is likely that the results we have reported here may have been different. Thus, this research results explain only the specific situation. Second, the sample selected in this study was university students in their twenties, so we cannot say that the results are firmly effective to all generations. Future research that manipulates the level of discount along with the consumer involvement might lead to a more robust understanding of the effects various discount rate. And, we used a cellular phone as a product stimulus, so it would be very interesting to analyze the result when the product stimulus is an intangible product such as service. It could be also valuable to analyze whether the change of perceived value affects consumers' final buying behavior positively or negatively.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.