• Information Systems Review
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• v.10 no.1
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• pp.135-164
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• 2008

This research is to offer a structured yet practical ex-ante evaluation methodology for IT investment. Benchmarking the best practices of four Korean organizations, we try to integrate core processes, relevant measures, and evaluation dimensions into a consistent and wholesome body of evaluating methodology. The best practices we considered encompass a wide range of business enterprises, including for-profit, non-profit, service-oriented, and manufacturing entities. The proposed methodology consists of three stages; the first stage checks the validity of investments by looking into comprehensiveness of planning, willingness to accomplish, justifiable grounds for the investments, overlapping investments, and obstructing risks; the second do so by putting an IT investment into economic, strategic, and technological perspectives; and the last third would produce a unified quantity that summarizes outcome of the previous stages. Incorporating the proven knowledge, guidelines, and quantifying tools, the methodology could make a valuable reference model for IT evaluation practitioners who have been bedeviled by having to going through such ex-ante evaluations.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.113-132
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• 2007

Price rigidity involves prices that do not change with the regularity predicted by standard economic theory. It is of long-standing interest for firms, industries and the economy as a whole. However, due to the difficulty of measuring price rigidity and price adjustments directly, only a few studies have attempted to provide empirical evidence for explanatory theories from Economics and Marketing. This paper proposes and validates a research model to examine different theories of price rigidity and to predict what variables can explain the observed empirical regularities and variations in price adjustment patterns of Internet-based retailers. I specify and test a model using more than 3 million daily observations on 385 books, 118 DVDs and 154 CDs, sold by 22 Internet-based retailers that were collected over a 676-day period from March 2003 to February 2005. I obtained a number of interesting findings from the estimation of our logit model. First, quality seems to play a role-I find that both price levels as proxies for store quality, and information on the quality of a product consumers have, affect online price rigidity. Second, greater competition(i.e., less industry concentration) leads to less price rigidity(i.e., more price changes) on the Internet. I also find that Internet-based sellers more frequently change the prices of popular products, and the sellers with broader product coverage change prices less frequently, which seem due to economic forces faced by these Internet-based sellers. To the best of my knowledge, this research is the first to empirically assess price rigidity patterns for multiple industries in Internet-based retailing, and attempt to explain the variation in these patterns. I found that price changes are more likely to be driven by quality, competitive and economic considerations. These results speak to both the IS and economics literatures. To the IS literature these results suggest we take economic considerations into account in more sophisticated ways. The existence and variation in price rigidity argue that simplistic assumptions about frictionless and completely flexible digital prices do not capture the richness of pricing behavior on the Internet. The quality, competitive and economic forces identified in this model suggest promising directions for future theoretical and empirical work on their role in these technologically changing markets. To the economics literature these results offer new evidence on the sources of price rigidity, which can then be incorporated into the development of models of pricing at the firm, industry and even macro-economic level of analysis. It also suggests that there is much to be learned through interdisciplinary research between the IS, economics and related business disciplines.

• KIISE Transactions on Computing Practices
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• v.23 no.6
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• pp.359-370
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• 2017

In overseas-hospital construction, the digital hospital is a trend that is based on the developments of the information and communication technologies, state-of-the-art medical equipment, smart health, and telemedicine. Along with the increasing proportion of IT, this has resulted in the spreading of the concept throughout city-like hospitals and their transformation into digital hospitals. In the hospital-construction business, IT is a key element that will link the modernization of the mechanical, electrical, and equipment systems, construction, and medical equipment for efficiency maximization through integration. The purpose of this study is the analysis of the market-expansion success factors through the construction of a success-story-based, IT-driven overseas-hospital business. The digital-hospital concept and the development process are analyzed through a literature review, and the success factors are analyzed in terms of the cost, time, and quality that are proposed in the project-management body of knowledge. The main contributions of this study regarding the success factors are as follows: First, a cost-side need exists regarding the establishment of strategic-value engineering in terms of increasing the value from the perspectives of the IT and operational infrastructures; second, in terms of the construction time, all of the hospital systems must comply with the established deadlines for the integrated test and commissioning; and lastly, in terms of quality, it is important to ensure that the System Integration digital-hospital services are delivered according to the user requirements.

• Information Systems Review
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• v.18 no.1
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• pp.141-155
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• 2016

This study identified terminologies related to the proximity and frequency of disaster by social network analysis (SNA) and text mining, and then expressed the outcome into a mind map. The termdocument matrix of text mining was utilized for the terminology proximity analysis, and the SNA closeness centrality was calculated to organically express the relationship of the terminologies through a mind map. By analyzing terminology proximity and selecting disaster response-related terminologies, this study identified the closest field among all the disaster response fields to disaster response and the core terms in each disaster response field. This disaster response terminology analysis could be utilized in future core term-based terminology standardization, disaster-related knowledge accumulation and research, and application of various response scenario compositions, among others.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Journal of Information Management
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• v.41 no.4
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• pp.227-249
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• 2010

Scholarly information has increased tremendously according to the development of IT, especially the Internet. However, simultaneously, people have to spend more time and exert more effort because of information overload. There have been many research efforts in the field of expert systems, data mining, and information retrieval, concerning a system that recommends user-expected information items through presumption. Recently, the hybrid system combining a content-based recommendation system and collaborative filtering or combining recommendation systems in other domains has been developed. In this paper we resolved the problem of the current recommendation system and suggested a new system combining collaborative filtering and Naive Bayes Classification. In this way, we resolved the over-specialization problem through collaborative filtering and lack of assessment information or recommendation of new contents through Naive Bayes Classification. For verification, we applied the new model in NDSL's paper service of KISTI, especially papers from journals about Sitology and Electronics, and witnessed high satisfaction from 4 experimental participants.

• Smart Media Journal
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• v.12 no.10
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• pp.55-62
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• 2023

Innovation and change are occurring rapidly in the agriculture and livestock industry, and new technologies such as smart bams are being introduced, and data that can be used to control equipment is being collected by utilizing various sensors. However, there are various challenges in the operation of bams, and virtual sensor technology is needed to solve these challenges. In this paper, we define various data items and sensor data types used in livestock farms, study cases that utilize virtual sensors in other fields, and implement and design a virtual sensor system for the final smart livestock farm. MBE and EVRMSE were used to evaluate the finalized system and analyze performance indicators. As a result of collecting and managing data using virtual sensors, there was no obvious difference in data values from physical sensors, showing satisfactory results. By utilizing the virtual sensor system in smart livestock farms, innovation and efficiency improvement can be expected in various areas such as livestock operation and livestock health status monitoring. This paper proposes an innovative method of data collection and management by utilizing virtual sensor technology in the field of smart livestock, and has obtained important results in verifying its performance. As a future research task, we would like to explore the connection of digital livestock using virtual sensors.

• Information Systems Review
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• v.25 no.4
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• pp.89-112
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• 2023

In March 2020, as it was declared a COVID-19 pandemic, various quarantine measures were taken. Accordingly, many changes have occurred in the tourism and hospitality industries. In particular, quarantine guidelines, such as the introduction of non-face-to-face services and social distancing, were implemented in the restaurant industry. For decades, research on restaurant attributes has emphasized the importance of three attributes: atmosphere, service quality, and food quality. Nevertheless, to the best of our knowledge, research on restaurant attributes considering the COVID-19 situation is insufficient. To respond to this call, this study attempted an exploratory approach to classify new restaurant attributes based on understanding environmental changes. This study considered 31,115 online reviews registered in Naverplace as an analysis unit, with 475 general restaurants located in Euljiro, Seoul. Further, we attempted to classify restaurant attributes by clustering words within online reviews through TF-IDF and LDA topic modeling techniques. As a result of the analysis, the factors of "prevention of infectious diseases" were derived as new attributes of restaurants in the context of COVID-19 situations, along with the atmosphere, service quality, and food quality. This study is of academic significance by expanding the literature of existing restaurant attributes in that it categorized the three attributes presented by existing restaurant attributes and further presented new attributes. Moreover, the analysis results have led to the formulation of practical recommendations, considering both the operational aspects of restaurants and policy implications.

• Information Systems Review
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• v.24 no.1
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• pp.1-19
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• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• Information Systems Review
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• v.19 no.3
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• pp.127-154
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• 2017

This study aims to identify various service innovation patterns in the service industry and understand the main differences among them. We attempt to create a new typology of service innovation by analyzing its patterns based on the four major dimensions of service innovation (i.e., service concept, service delivery, customer interaction, and technology). We then investigate whether firms pursuing different service innovation patterns significantly differ from one another in terms of their performance (high and low performance). Based on empirical data collected from 198 Korean firms in the knowledge-intensive business service sector, four major clusters composed of different service innovation dimensions are identified. These four clusters can be interpreted as specific service innovation patterns, including "technology based high customer interaction," "high technology based high service delivery," "service delivery and high customer interaction-integrated," and "strongly balanced" innovators. High firm performance does not depend on the individual service innovation dimension but on the specific configurations of such service dimensions. Customer interaction also has an important role in achieving innovation success and improving firm performance, while technology has a key role in enhancing firm performance. This study sheds new light on service innovation research by developing a new typology of service innovation, identifying four major clusters as service innovation patterns, and exploring the relationship between service innovation patterns and firm performance.