• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.95-105
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• 2013

Recently, SME's Collaboration activities have become one of a vital factor for sustaining competitive edge. This is because of the rapidly changing and competitive market environment, and also to leverage performance by overcoming obstacles of having limited internal resources. Discussing about the effects and relationships of the firm's collaboration activities and its outputs are not new. However, as ICT and various technologies have been diffused into the traditional industries, boundaries and practice capabilities within the industries are becoming ambiguous. Thus contents of the products/services and their development methods are also go and come over the industries. Although many researchers suggested the relations of SME's collaboration activities and innovation performances, most of the previous literatures are focusing on broad perspectives of firm's environmental factors rather than considering various SME's idiosyncrasy factors such as their major product and customer types at once. Therefore, the purpose of this paper is to analyze how SME(Small Medium Enterprise)'s external collaboration activities by their idiosyncrasy act as an input to types of innovation performance. In order to analyze collaboration effects in detail, we defined factors that can represent the SME's business environment - Perceived importance of using external resources, Perceived importance of external partnership, Collaboration and Collaboration levels of Major Product types, Customer types and lastly the Firm Sizes. We have also specifically divided the performance of innovation types as product innovation and process innovation based on existing research. In this study, the empirical analysis is based on Probit Regression Model to observe the correlations with the impact of each SME's business environment and their activities. For the empirical data, 497 samples were collected which, this sample data was extracted from the 'Korean Open Innovation Survey' performed by ETRI(Korean Electronics Telecommunications Research Institute) in 2010. As a result, empirical test results indicated that the impact of collaboration varies depend on the innovation types (Product and Process Innovation). The Impact of the collaboration level for the product innovation tend to be more effective when SMEs are developing for a final product, targeting on for individual customers (B2C). But on the other hand, the analysis result of the Process innovation tend to be higher than the product innovation, when SMEs are developing raw materials for their partners or to other firms targeting on for manufacturing industries(B2B). Also perceived importance of using external resources has effected to both product and process innovation performance. But Perceived importance of external partnership was statistically insignificant. Interesting finding was that the service product has negative effects on for the process innovation performance. And Relationship between size of the firms and their external collaboration activities with their performance of the innovations indicated that the bigger firms(over 100 of employees) tend to have better for both product and process innovations. Finally, implications of the results can be suggested as performance of innovation can be varied depends on firm's unique business idiosyncrasy as well as levels of external collaboration activities. The Implication of this research can be considered for firms in selecting an appropriate strategy as well as for policy makers.

• Journal of Distribution Research
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• v.15 no.1
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• pp.25-47
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• 2010

CS is one of the major concerns of managers in the world because it is well known to be a key medium construct for firms' superior outcome. One of the major agents for CS management is retailers. Firms try to manage not only employees but also retailers to promote CS behaviors. And so diverse incentives are used to promote their CS behaviors under diverse channel setting such as multi-channel. However in spite of the rising needs there has been scarce studies on the optimal incentive structure for a manufacturer to offer competing retailers at the multi-channel. In this paper, we try to find better way for a manufacturer to promote the competing retailers' CS behaviors. We investigated how to promote the retailers' CS behavior via game-theoretic modeling. Especially, we focus on the possible incentive, CS bonus type reward introduced in the studies of Hauser, Simester, and Wernerfelt(1994) and Chu and Desai(1995). We build up a multi stage complete information game and derive a subgame perfect equilibrium using backward induction. Stages of the game are as following. (Stage 1) Manufacturer sets wholesale price(w) and CS bonus($\eta$). (Stage 2) Both retailers in competition set CS effort level($e_i$) and retail price($p_i$) simultaneously. (Stage 3) Consumers make purchasing decisions based on the manufacturer's initial reputation and retailers' CS efforts.

• Korean journal of food and cookery science
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• v.16 no.5
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• pp.437-444
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• 2000

The purpose of this study were to identify customer expectation, perception and satisfaction of foodservice quality to analyze the influencing factors on foodservice quality and finally to provide basic information for the improvement of foodservice quality. Among expectation scores of food quality attributes, ‘hygiene of food(3.27)’received the highest score. In expectation scores of service quality attributes,‘hygiene of tableware(3.40)’was the most important. Satisfaction of ‘appropriate 1 portion size(-0.11)’was the highest scored, while‘dealing with complaints on meals(-0.70)’was the most dissatisfied one. Satisfaction was highly correlated with‘providing preferred menu(r = -0.62)’of food quality expectation. It was highly correlated with‘dealing with complaints on meals(r = -0.61)’of service quality expectation. Expectation and perception of foodservice as 2.25 and 2.90 out of 5, respectively, which suggests that foodservice needs to be improved. The attributes identified in Quadrant A, which was labelled‘focus here’and supposed to indicate the areas of high expectation but in low perception, was hygiene of carts holding used trays. The results of expectation and perception analysis indicated the areas that the attention of management should be given to improve quality of foodservice. The stepwize regression analysis suggested that foodservice expectation and perception explaints around 56.7% of the variation in general customer satisfaction.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Archives of design research
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• v.19 no.2 s.64
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• pp.53-62
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• 2006

E-mail has indeed developed from 'a means of instant communication' to an indispensable part of online marketing. Therefore, companies need to implement consistent customer management. Communication with customers and marketing through e-mail is a powerful way of communication and adapting one-to-one marketing strategies to customer trends, habits and taste preferences. Since setting accurate targets is especially important in the fashion industry, e-mail marketing is the most effective way to communicate with customers and one-to-one marketing constitutes a very important strategy. In this study, I will analyze this powerful one-on-one marketing tool, particularly actual e-mail messages sent by an Internet Shopping Mall from June 12 to July 30, 2005, examine the effect of these messages on sales growth and analyze actual feedback received. Regarding e-mail read rates broken down by age and gender, 1 found that females in their late twenties recorded the highest rate at 21.66% and their contribution to sales growth was recorded at 3.5% From actual sales records, found that 28.10% of total sales were attributable to people in their late twenties, showing that the age group that reads e-mails the most also buys the most. Regarding feedback by e-mail title, e-mails from the 'Casual' category seemed to be the most effective, in that most of these e-mails were read. Also, messages sent on Tuesdays were read the most, according to the feedback analysis by weekday. Section e-mails were read more often than regular e-mails. Regarding the view rate according to the time e-mails were sent, messages sent to females in their late twenties at two o'clock in the afternoon were read by 20.93% of recipients, recording the highest read rate. By offering informative content and practical tips, visitors will be attracted to the site and generate site traffic. Therefore, we can conclude that sending e-mail messages can greatly contribute to sales growth and e-mail marketing is very effective. Also, in order to make e-mail campaigns more effective and improve marketing results, we need to analyze actual results and apply our findings in future e-mail campaigns. With this, we get successful marketing results.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.67-88
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• 2019

Webtoon is a Korean-style digital comics platform that distributes comics content produced using the characteristic elements of the Internet in a form that can be consumed online. With the recent rapid growth of the webtoon industry and the exponential increase in the supply of webtoon content, the need for effective webtoon content recommendation measures is growing. Webtoons are digital content products that combine pictorial, literary and digital elements. Therefore, webtoons stimulate consumer sentiment by making readers have fun and engaging and empathizing with the situations in which webtoons are produced. In this context, it can be expected that the sentiment that webtoons evoke to consumers will serve as an important criterion for consumers' choice of webtoons. However, there is a lack of research to improve webtoons' recommendation performance by utilizing consumer sentiment. This study is aimed at developing consumer sentiment pattern maps that can support effective recommendations of webtoon content, focusing on consumer sentiments that have not been fully discussed previously. Metadata and consumer sentiments data were collected for 200 works serviced on the Korean webtoon platform 'Naver Webtoon' to conduct this study. 488 sentiment terms were collected for 127 works, excluding those that did not meet the purpose of the analysis. Next, similar or duplicate terms were combined or abstracted in accordance with the bottom-up approach. As a result, we have built webtoons specialized sentiment-index, which are reduced to a total of 63 emotive adjectives. By performing exploratory factor analysis on the constructed sentiment-index, we have derived three important dimensions for classifying webtoon types. The exploratory factor analysis was performed through the Principal Component Analysis (PCA) using varimax factor rotation. The three dimensions were named 'Immersion', 'Touch' and 'Irritant' respectively. Based on this, K-Means clustering was performed and the entire webtoons were classified into four types. Each type was named 'Snack', 'Drama', 'Irritant', and 'Romance'. For each type of webtoon, we wrote webtoon-sentiment 2-Mode network graphs and looked at the characteristics of the sentiment pattern appearing for each type. In addition, through profiling analysis, we were able to derive meaningful strategic implications for each type of webtoon. First, The 'Snack' cluster is a collection of webtoons that are fast-paced and highly entertaining. Many consumers are interested in these webtoons, but they don't rate them well. Also, consumers mostly use simple expressions of sentiment when talking about these webtoons. Webtoons belonging to 'Snack' are expected to appeal to modern people who want to consume content easily and quickly during short travel time, such as commuting time. Secondly, webtoons belonging to 'Drama' are expected to evoke realistic and everyday sentiments rather than exaggerated and light comic ones. When consumers talk about webtoons belonging to a 'Drama' cluster in online, they are found to express a variety of sentiments. It is appropriate to establish an OSMU(One source multi-use) strategy to extend these webtoons to other content such as movies and TV series. Third, the sentiment pattern map of 'Irritant' shows the sentiments that discourage customer interest by stimulating discomfort. Webtoons that evoke these sentiments are hard to get public attention. Artists should pay attention to these sentiments that cause inconvenience to consumers in creating webtoons. Finally, Webtoons belonging to 'Romance' do not evoke a variety of consumer sentiments, but they are interpreted as touching consumers. They are expected to be consumed as 'healing content' targeted at consumers with high levels of stress or mental fatigue in their lives. The results of this study are meaningful in that it identifies the applicability of consumer sentiment in the areas of recommendation and classification of webtoons, and provides guidelines to help members of webtoons' ecosystem better understand consumers and formulate strategies.

• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.42-50
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• 2021

Along with the rapid growth of the food service industry, food safety requirements and hygiene are increasing in importance in restaurants and hotels. Accordingly, there is a need for quick and practical monitoring techniques to determine hygiene status in the field. In this study, we investigated 5 domestic 5-star hotels specifically, personal hygiene (hands of workers), cooking utensils (knife, cutting board, food storage container, slicing machine blade, ice-maker scoop) and other facilities (refrigerator handle, sink). In addition, we examined the hygiene management status of customer contact points (tongs for buffet, etc.) to derive the correlation between the ATP values as a, a verification method. As a result of our five-hotel survey, we found that cooking utensils and personal hygiene were relatively sanitary compared to other inspection items (cookware 92.2%, personal hygiene 91.4%, facilities and equipment 76.19%, customer contact items 88.6%). According to our ATP-based mothod, kitchen utensils (51 ± 45 RLU/25㎠) were relatively clean compared to other with facilities and equipment (167 ± 123 RLU/25㎠). In the present study, we also evaluated the usefulness of the ATP bioluminescence method for monitoring surface hygiene at hotel restaurants. After correlation analysis of surveillance of hygienic status points and ATP assay, most results showed negative and high correlation (-0.64--0.89). Our ATP assay (92 ± 67 RLU/25㎠) of each item after cleaning showed signigicantly reduced results compared to the ATP assay (1020 ± 1254 RLU/25㎠) for normal status, thereby indicating its suitability as a tool to verify the validity of cleaning. By our results, ATP bioluminescence could be used as an effective tool for visual numerical evaluation of invisible contaminants.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.27-42
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• 2020

Startup accelerators have emerged as new investment entities that help early startups, which are not easy to survive continuously due to lack of funds, commercialization capabilities, and experiences. As their positive performance on early startups and the ecosystem has been proven, the number of early startups which want to receive their investment is also increasing. However, they are vaguely preparing to attract accelerators' investment because they do not have any information on what factors the accelerators consider important. In addition, researches on startup accelerators are also at an early level, so there are no remarkable prior studies on factors that decide on investment. Therefore, this study aims to help startups prepare for investment attraction by looking at what factors are important for accelerators to invest, and to provide meaningful implications to academia. In the preceding study, we derived five upper level categories, 26 lower level accelerators' investment determinants through the qualitative meta-synthesis method, secondary data analysis, observation on US accelerators and in-depth interviews. In this study, we want to derive important implications by deriving priorities of the accelerators' investment determinants. Therefore, we used AHP that are evaluated as the suitable methodology for deriving importance and priority. The analysis results show that accelerators value market-related factors most. This means that startups that are subject to investment by accelerators are early-stage startups, and many companies have not fully developed their products or services. Therefore, market-related factors that can be evaluated objectively seem to be more important than products (or services) that are still ambiguous. Next, it was found that the factors related to the internal workforce of startups are more important. Since accelerators want to develop their businesses together with start-ups and team members through mentoring, ease of collaboration with them is very important, which seems to be important. The overall priority analysis results of the 26 investment determinants show that 'customer needs' and 'founders and team members' understanding of customers and markets' (0.62) are important and high priority factors. The results also show that startup accelerators consider the customer-centered perspective very important. And among the factors related to startups, the most prominent factor was the founder's openness and execution ability. Therefore, it can be confirmed that accelerators consider the ease of collaboration with these startups very important.

• Journal of Korea Port Economic Association
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• v.38 no.1
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• pp.1-14
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• 2022

The change in the management environment of the logistics industry in the era of global competition is becoming an era in which customers choose companies. Differentiation from competitors through the provision of products and services suitable for customers As customers' choices change depending on their superiority, companies are constantly striving to receive or retain customers' choices. Ultimately, this competitive structure can be seen as the importance of long-term relationship building. Therefore, in this study, we examined how factors related to transaction characteristics performed by logistics companies for customer satisfaction in the transaction relationship between cargo companies and shippers affect performance and long-term transaction intentions. First, we derived the factors of logistics service, cost, logistics infrastructure, and company competency, which are transaction characteristics factors of a logistics company that must be specifically realized for customer satisfaction in transactions between logistics companies. Second, we analyzed how the transaction characteristics factors of a logistics company affect the company's performance, and finally, how the company's performance factors affect long-term transaction intentions. As a result of empirical analysis, there were no statistically significant results on the relationship between transaction characteristics and performance of logistics companies, which can be attributed to the small size of the logistics companies that were the sample. In other words, logistics companies that do not have sufficient capacity to provide services at low prices have no choice but to engage in constant bleeding competition. It can be seen that it reflects the characteristics of the industry. On the other hand, the relationship between corporate performance factors and long-term transaction intention was found to have a positive relationship. The higher the level of partnership with logistics companies and visible financial performance is, the higher the transaction will be in the future, and the more the transaction volume will be gradually increased. And even if it costs a little more, it can be seen that the intention to continue trading is greatly expressed.