• Journal of Information Technology Services
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• v.15 no.3
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• pp.15-31
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• 2016

Sales Force Automation (SFA) is a strategic information system and one of the components of operational CRM system. SFA supports salespeople's activities such as selection of potential customers, creative value proposition, after-sales services, etc. SFA is increasingly used in many companies because it has the advantages to raise the salespeople's productivity by developing forecasting ability, value proposition ability, after sales service ability etc. Many researches have shown that implementation of SFA leads to the increase of salepeople performance, organizational performance, and quality of customer relationship. However, Some prior studies have discussed on the SFA implementation failure and pointed out that one of important causes of this failure is salespeople's resistance to SFA. Although many researches explain SFA acceptance phenomenon using Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB), these researches didn't deeply investigate the salespeople's resistance to SFA. Therefore, this study focuses on the factors influencing salespeople's resistance to SFA and the relationships among these factors. This study identified three factors (salespeople's perceived loss of power, perceived loss of autonomy, and perceived time and effort waste) influencing salespeople's resistance to SFA. The hypotheses testing results showed that salespeople's perceived loss of power and perceived time and effort waste significantly increased salespeople's resistance to SFA. And salespeople's perceived loss of power plays a mediating role between perceived loss of autonomy/perceived time and effort waste and salespeople's resistance to SFA. At the end of the paper, theoretical and managerial implications of this study and the limitations and future research directions are discussed.

• Information Systems Review
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• v.14 no.1
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• pp.37-65
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• 2012

This research aims to design and analyze a ubiquitous customer relationship management system based on near field communication which can be applied to stores in off-line environment. The existing customer relationship management system has been used mainly for stores in off-line environment to issue a royalty card, to stamp a seal on the purchase goods, and to manage the history of customers' visits and purchases. But, the existing system has two weaknesses; it makes difficult for a store manger not only to acquire a wealth of customer data but also to systematically manage the acquired data. In particular, the effectiveness and efficiency of the royalty card are questioned when a customer makes purchases in the store in that the customer frequently does not carry it or loses it. So, this research suggests a ubiquitous customer relationship management system where a tag for near field communications is attached to a store in off-line environment; a store manager can collect and manage easily customer's dada and customers can seamlessly acquire store's information. To do this, this research conducts the followings. First, we review the previous researches of customer relationship management to examine the concept of ubiquitous customer relationship management. Second, from the examination, we draw the factors to be considered in ubiquitous customer relationship management system based on near field communication. Third, we propose a scenario where the suggested system is used. Fourth, we analyze the participants' values and the process that will be used for the suggested system. Finally, we discuss the possibility of the application of this system to real business environment from various viewpoints.

• International Journal of Contents
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• v.8 no.2
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• pp.67-74
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• 2012

Technological substitution is the process by which a radical technology replaces the dominant technology in an industry. The processes of diffusion and substitution have been modeled extensively (Technology & innovation, 2010). However, the formulation of classical quantitative models encompasses only part of the theoretical space. These models impose many simplified constraints to the achievement of analytical resolution. The interior design organization needs to establish a set of technical system requirements by describing the scope of the accessibility needs of the organization against current technology use. Because of complicated design resources and ongoing advances in design technologies, design systems face the challenge of prioritizing new technologies for supporting. The problem is small design organization administration often displays a lack of concern toward the evaluation of technology integration. In this paper, I will identify the influence of a design organization's technology, and predict how future technology will inform, support, and potentially hinder productivity, culture, and work satisfaction within a design organization in the industry. In addition, I will use current design organizational behavior and leadership models to support my predictions. Finally, I will examine a proven approach to assist designers with evaluating technology integration in interior design organization. The goal is to develop a high quality, professional development scorecards for the evaluation. I will conduct both the evaluation of technology integration and CRM performance evaluation is recommended to assess the effectiveness of technology integration. Therefore, the evaluation of integration technologies oriented design hold the promise of solving the organization application integration challenge. The evaluation of integration technology is a significant pattern for processing such a vision. The careful selection of an integration technology for this purpose is crucial in contributing toward the success of such an interior design organization endeavor.

• (The)Study of the Eastern Classic
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• no.69
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• pp.115-146
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• 2017

This paper is about the research on ontology design for a digital archive of seonsaengan(先生案) of the Joseon Period. Seonsaengan is the register of staff officials at each government office, along with their personal information and records of their transfer from one office to another, in addition to their DOBs, family clan, etc. A total of 176 types of registers are known to be kept at libraries and museums in the country. This paper intends to engage in the ontology design of 47 cases of such registers preserved at the Jangseogak Archives of the Academy of Korean Studies (AKS) with a focus on their content and structure including the names of the relevant government offices and posts assumed by the officials, etc. The work for the ontology design was done with a focus on the officials, the offices they belong to, and records about their transfers kept in the registers. The ontology design categorized relevant resources into classes according to the attributes common to the individuals. Each individual has defined a semantic postposition word that can explicitly express the relationship with other individuals. As for the classes, they were divided into eight categories, i.e. registers, figures, offices, official posts, state examination, records, and concepts. For design of relationships and attributes, terms and phrases such as Dublin Core, Europeana Data Mode, CIDOC-CRM, data model for database of those who passed the exam in the past, which are already designed and used, were referred to. Where terms and phrases designed in existing data models are used, the work used Namespace of the relevant data model. The writer defined the relationships where necessary. The designed ontology shows an exemplary implementation of the Myeongneung seonsaengan(明陵先生案). The work gave consideration to expected effects of information entered when a single registered is expanded to plural registers, along with ways to use it. The ontology design is not one made based on the review of all of the 176 registers. The model needs to be improved each time relevant information is obtained. The aim of such efforts is the systematic arrangement of information contained in the registers. It should be remembered that information arranged in this manner may be rearranged with the aid of databases or archives existing currently or to be built in the future. It is expected that the pieces of information entered through the ontology design will be used as data showing how government offices were operated and what their personnel system was like, along with politics, economy, society, and culture of the Joseon Period, in linkage with databases already established.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.