• Journal of Information Science Theory and Practice
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• 제9권1호
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• pp.54-67
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• 2021

This paper presents the ISRI (Information Systems Research Indicators) Web tool, publicly and freely available at isri.sciencesphere.org. Targeting Information Systems (IS) researchers, it compiles and organizes IS adoption and use theories/models, constructs, and indicators (measuring variables) available in the scientific literature. Aiming to support the IS theory development process, the purpose of ISRI is to gather and systematize information on research indicators to help researchers and practitioners' work. The tool currently covers eleven theories/models: DeLone and McLean's IS Success Model (D&M ISS); Diffusion of Innovations Theory (DOI); Motivational Model (MM); Social Cognitive Theory (SCT); Task-Technology Fit (TTF); Technology Acceptance Model (TAM); Technology-Organization-Environment Framework (TOE); Theory of Planned Behavior (TPB); Decomposed Theory of Planned Behavior (DTPB); Theory of Reasoned Action (TRA); and Unified Theory of Acceptance and Use of Technology (UTAUT). It also includes currently over 400 constructs, nearly 2,500 indicators, and about 60 application contexts related to the models. For the creation of the tool's database, nearly 580 references were used.

• Journal of information and communication convergence engineering
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• 제20권2호
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• pp.96-102
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• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• Asia pacific journal of information systems
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• 제12권2호
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• pp.69-98
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• 2002

With the development of the Internet, electronic commerce, electronic markets, and digital economy, new business paradigm and new ways of business have been emerging and developing. The development of right and robust business models for electronic markets is a key for e-business success. This paper reviews previous studies and successful cases for e-business models. This paper presents strategic factors such as the business value and the source of revenue, products and services, business processes and technologies, and the characteristics of markets and relationship with customers and partners as a framework for developing sustainable and robust business models.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권2호
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• pp.87-95
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• 2015

In this study, we pursue a new direction for system modeling by introducing the concept of granular models, which produce results in the form of information granules (such as intervals, fuzzy sets, and rough sets). We present a rationale and several key motivating arguments behind the use of granular models and discuss their underlying design processes. The development of the granular model includes optimal allocation of information granularity through optimizing the criteria of coverage and specificity. The emergence and construction of granular models of type-2 and type-n (in general) is discussed. It is shown that achieving a suitable coverage-specificity tradeoff (compromise) is essential for developing granular models.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.125-128
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• 1999

A linear time-invariant model can be described either by a parametric model or by a nonparametric model. Nonparametric models, for which a priori information is not necessary, are basically the response of the dynamic system such as impulse response model and frequency models. Parametric models, such as transfer function models, can be easily described by a small number of parameters. In this paper aiming to take benefit from both types of models, we will use linear-combination of basis fuctions in an impulse response using a few parameters. We will expand and generalize the Kautz functions as basis functions for dynamical system representations and we will consider estimation problem of transfer functions using Kautz function. And so we will present the influences of poles settings of Kautz function on the identification accuracy.

• 경영정보학연구
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• 제4권2호
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• pp.293-307
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• 2002

정보시스템 아웃소싱이 활성화되면서 아웃소싱 가치와 가격에 대한 관심이 고조되고 있다. 정보시스템 아웃 소싱은 개발 아웃소싱과 운영 아웃소싱으로 나눌 수 있다. 운영 아웃소싱 비용에 대한 연구는 최근에 시작되어 지속적인 추가 연구가 필요하다. 운영 아웃소싱 유형 중 대표적인 유형이 응용시스템 운영 아웃소싱(applicationsystems outsourcing)인데, 서비스 수준에 대한 복잡한 판단이 필요하기 때문에 정교한 비용산정 모형이 요구된다. 기존 연구에서는 여러 업종의 데이터를 수집하여 비용산정 모형을 구축하였기 때문에 업종의 고유한 특성을 반영하지 못하였으며, 따라서 산정 비용의 정확성이 높지 않다고 할 수 있다. 본 연구에서는 정보시스템의 중요성이 매우 높은 산업분야 중의 하나인 증권업종과 의료업종의 응용시스템 아웃소싱 비용 산정 모형을 도출한다. 업종의 특성을 반영하여 서비스 수준을 결정하는 요소를 정의하고, 증권업종의 비용 모델을 먼저 도출하였다. 의료업종과 증권업종이 서로 차별화되는지를 분석하였으며, 의료업종의 별도 비용 모델을 구축하여 업종간의 비용모델 차이가 존재함을 보였다.

• 경영과정보연구
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• 제4권
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• pp.67-88
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• 2000

The purpose of this study is to present models for evaluation and selection of DataBase Management Systems(DBMS) suppliers. The major concern of management is that most decision problems have multiple, usually conflicting, criteria. The fuzzified multiple-objective programming models are given to accomodate the aspiration level and satisfaction level of decision makers. The proposed models are classified into two types, that is, pre-emptive priority and interpolated non-membership function model. Numerical examples illustrating each type of model are presented and the implications of these models are discussed.

• International Journal of Internet, Broadcasting and Communication
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• 제11권2호
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• pp.19-26
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• 2019

For the last two decades, a number of information systems are developed for various aims, depending on business' needs. There are a lot of organizations in the world which are using information systems in their environment, such as telecommunications organizations, universities and banks. Using information system has become crucial for most of organizations regarding with increasing the performance of work procedures and improve productivity and efficiency in general. There are many different models that have been designed and validated to explain the effect of constructs on the adoption of technologies. The aim of this research is to review the literature on information systems adoption and to analyze the different types of models which are frequently applied by researchers in their efforts to examine the factors that estimate the adoption of technologies. The research explores information systems adoption literature that focuses on development models.

• Journal of Information Technology Applications and Management
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• 제11권1호
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• pp.39-51
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• 2004

Application systems outsourcing is an important part of IT outsourcing services. Application systems outsourcing costs is determined by service levels of outsourcers. Recent researches show there is a strong need to build industry-specific cost estimation models. In this study, an industry-specific application systems operation cost estimation model is suggested. We reviewed operation cost models of previous researches, and proposed a cost estimation model for security industry. Industry-specific service factors are defined and service levels are determined by Interviews with experts. The proposed model is tested and adjusted with empirical data. The new model shows more accurate prediction than previous general models. Future research will be needed to develop outsourcing cost estimation models for other industries and to refine cost models developed in this study.

• Asia pacific journal of information systems
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• 제26권1호
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• pp.66-79
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• 2016

The availability of detailed data on customers' online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the 'entropy' concept from information theory, while 'random forests' method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers' information search behaviors differ significantly across product categories.