• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.186-200
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• 2023

This study aims to improve the usability of the white cane, which is walking equipment that most local visually impaired people use and carry when going out, and to contribute to the prevention of safety accidents and the walking rights of visually impaired people by providing improvement and resolution measures for the problems identified. Also, this study is a study on the visually impaired, primarily targeting the 1st to 2nd degree visually impaired people, who cannot go out on their own without walking equipment such as a white cane, corresponding to 20% among approximately 250,000 blind and low vision people in the Korean population. In the study process, the concept has been developed from the user's point of view in order that the white cane becomes a real help in the walking step of the visually impaired and the improvement of usability of the white cane, the main walking equipment for the visually impaired, are done by problem identification through the Double Diamond Model of Design Thinking (Empathize → Define → Ideate → Prototype → Test (verify)). As a result of the investigation in the process of Empathy, a total of five issues was synthesized, including an increase in the proportion of the visually impaired people, an insufficient workforce situation to help all the visually impaired, an improvement and advancement of assistive devices essential for the visually impaired, problems of damage, illegal occupation, demolition, maintenance about braille blocks, making braille block paradigms for the visually impaired and for everyone. In Ideate and Prototype steps, situations derived from brainstorming were grouped and the relationship were made through the KJ method, and specific situations and major causes were organized to establish the direction of the concept. The derived solutions and major functions are defined in four categories, and representative situations requiring solutions and major functions are organized into two user scenarios. Ideas were visualized by arranging the virtual Persona and Customer Journey Map according to the situation and producing a prototype through 3D modeling. Finally, in the evaluation, the final concept derived is a device such a smart cane for guidance for the visually impaired as ① a smart cane emphasizing portability + ② compatibility with other electronic devices + ③ a product with safety and convenience.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.5
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• pp.29-43
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• 2016

The Lean Startup, a methodology for minimizing failure rate of startups, has been receiving attention since its publication in 2011. Although it has been receiving enormous attention as an effective methodology of startups' growth and the emergence of unicorn companies, it is undeniable that the theoretical research and cases on this topic have not been fully accumulated in Korea. Progress of management theory has been made when combining the theory and case studies. In this paper, we thus excavated the 'Cardoc' case, which has applied the lean startup concept to the entire process of service and customer development from the inception of its product design. The following are the findings of the case. First, for the successful application of lean startup, it is essential that all team members to understand the lean startup concept and are willing to apply it thoroughly to the business management. Second, the prompt launching of MVP(Minimum Viable Product) is more important than table discussion. Third, it is crucial to select the appropriate key metrics and analytic tools for effective learning. Fourth, startup must scale up promptly as soon as it verifies the product-market fit through the BML(Build-Measure-Learn) iteration cycle. Fifth, all new business expansion should be lean. Cardoc is currently testing new MVPs in order to move onto the next scale-up process with huge investments in newly added segments. This study is meaningful in that it elaborates the representative case of a Korean startup that has applied the lean startup strategy under the circumstance of insufficient discussion of Korean startup cases in comparison with growing attention both in concept development and case accumulation abroad. We hope that this paper can be a stepping stone for future relevant research on the implementation of lean startup methodology in Korea.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Journal of KIISE:Software and Applications
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• v.28 no.2
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• pp.179-191
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• 2001

Comparison shopping is a merchant brokering process that finds the best price for the desired product from several Web-based online stores. To get a scalable comparison shopper, we need an agent that automatically constructs a simple information extraction procedure, called a wrapper, for each semi-structured store. Automatic construction of wrappers for HTML-based Web stores is difficult because HTML only defines how information is to be displayed, not what it means, and different stores employ different ways of manipulating customer queries and different presentation formats for displaying product descriptions. Wrapper induction has been suggested as a promising strategy for overcoming this heterogeneity. However, previous scalable comparison-shoppers such as ShopBot rely on a strong bias in the product descriptions, and as a result, many stores that do not confirm to this bias were unable to be recognized. This paper proposes a more scalable comparison-shopping agent named MORPHEUS. MORPHEUS presents a simple but robust inductive learning algorithm that antomatically constructs wrappers. The main idea of the proposed algorithm is to recognize the position and the structure of a product description unit by finding the most frequent pattern from the sequence of logical line information in output HTML pages. MORPHEUS successfully constructs correct wtappers for most stores by weakening a bias assumed in previous systems. It also tolerates some noises that might be present in production descriptions such as missing attributes. MORPHEUS generates the wrappers rapidly by excluding the pre-processing phase of removing redundant fragments in a page such as a header, a tailer, and advertisements. Eventually, MORPHEUS provides a framework from which a customized comparison-shopping agent can be organized for a user by facilitating the dynamic addition of new stores.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.167-175
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• 2020

The purpose of this paper is to provide directional and policy references to develop a higher level of service quality and consumer-oriented e-commerce platform. This paper has established a model of consumer behavior of Chinese fresh agricultural e-commerce using customer satisfaction theory and cognitive value theory, and used survey and SPS23.0 to verify hypothesis. Studies have shown that when consumers consume fresh agricultural products, product quality, logistics and distribution service quality, interactive quality of e-commerce platform, and product price and cognitive value have a positive effect on consumer behavior. This study is meaningful in the study of consumer behavior of fresh agricultural e-commerce, and in the case of fresh agricultural e-commerce companies, consumer behavior can be understood. In the model constructed in this paper, the relationship between each influencing factor and consumer behavior is considered comprehensively, but the possible relationship between fine molecular factors has not been studied and analyzed. In the future learning process, it is necessary to make clear the characteristics and particularity of the industry, think about its influencing factors comprehensively and make in-depth analysis.

• Journal of Distribution Science
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• v.11 no.1
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• pp.55-63
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• 2013

Purpose - As the members of the baby boomer generation have retired in earnest, the start-up market has received more attention than ever before. According to recent statistical data, an increasing number of entrepreneurs are in their fifties. There has been a continuous increase in promotional materials on small business issues published by start-ups. This means that senior start-ups have increased in number. A number of support systems have been established for youth start-ups, but there are few government support policies in place for the senior start-up market. Thus, this study suggests a number of constructive alternatives from the perspective of government policy and marketing strategy for entrepreneurs, in order to generate competitiveness in the start-up process, through examining the current state of the senior start-up and by diagnosing extant problems. Research design, data, methodology - This study gives a number of options regarding the government's support policies and the securing of competitiveness in order to vitalize senior business start-ups. As for the government's support policies, funding support policy, publicizing business start-up policies, and operating systematic mentoring policies before retirement have all been covered. In particular, in order for senior business start-ups to become competitive, development through mutual relations with diverse policies is urgently needed. The aging population is becoming an issue in Korea, so businesses for the aged, and the creation of jobs for these people, will become a social issue. Senior business start-ups are playing an important role in expanding enterprise productivity, in addition to enhancing national competitiveness. Expanding senior business start-ups is important, because they also serve to expand the national infrastructure. Productivity increase through continuous expansion is thus recommended. Results - In order to expand the competitiveness of business start-ups, marketing-related observations and learning in regard to customers are necessary for the baby boomer generation, and competitiveness for seniors is urgently needed. Conclusions - Studies on the business start-up policies for the domestic baby boomer generation are almost non-existent, and systematic studies on small businesses are necessary. Only the government is providing statistical studies for small businesses, and such research remains at a general level for entrepreneurs. Therefore, a support system that can actually assist entrepreneurs is essential. Continuous business start-up studies with respect to the baby boomers should be vitalized, to invigorate studies on competition. In order to supplement and strengthen foundational support, senior business start-ups must develop various competitive capabilities with a focus on the customer. The government and the various stakeholder agencies and organizations involved with start-up businesses must find ways to offer support to founders. Such support should include access to knowledge and legal and consultancy services in order to incubate the rapid increase in start-ups founded by seniors. Government support projects should be expanded to meet this end.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.25-53
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• 2009

The purpose of this paper is to analysis sectoral patterns of technological innovation in Korean manufacturing sector. Pavitt(1984) put forward a well-known taxonomy that industries three groups of industries characterized by markedly different innovative modes, namely science-based, production-intensive and supplier-dominated industries. Using Pavitt's taxonomy as a framework, we try to explain similarities and differences among sectors in the sources and impact of innovations. Based on a sample of 2,371 firms in manufacturing industry, this paper investigated its relevance to explain the sources and directions of innovative activities in Korean industries. Empirical study shows that in supplier dominated firms most process innovations come from suppliers of equipment and materials. In science-based firms product innovation is produced internally, based on the rapid development of the underlying sciences in the universities and research institutes. It also shows that production-intensive firms have a positive association between innovativeness and customer collaboration. This explanation has implications for our understanding of the sources and directions of technical changes, the formation of technological advantages at the level of both region and country.