• Journal of Information Processing Systems
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• 제15권6호
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• pp.1489-1502
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• 2019

This work introduces a novel unweighted combination method (UCSS) for business failure perdition (BFP). With considering features of BFP in the age of big data, UCSS integrates the quantitative and qualitative analysis by utilizing soft set theory (SS). We adopt the conventional expert system (ES) as the basic qualitative classifier, the logistic regression model (LR) and the support vector machine (SVM) as basic quantitative classifiers. Unlike other traditional combination methods, we employ soft set theory to integrate the results of each basic classifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify the performance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing the equal weight approach (CMEW), neural network algorithm (CMNN), rough set and D-S evidence theory (CMRD), and the receiver operating characteristic curve (ROC) and SS (CFBSS) as benchmarks. The superior performance of UCSS has been verified by the empirical experiments.

• Asia pacific journal of information systems
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• 제29권1호
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• pp.35-49
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• 2019

The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML. The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.

• 대한산업공학회지
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• 제40권3호
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• pp.342-357
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• 2014

The present study analyzes the relationship between research and development (R&D) inputs and performance of a national technology innovation R&D program using successive binary Logistic regression models based on a typical R&D logic model. In particular, this study focuses on to answer the following three main questions; (1) "To what extent, do the R&D inputs have an effect on the performance creation?"; (2) "Is an obvious relationship verified between the immediate predecessor and its successor performance?"; and (3) "Is there a difference in the performance creation between R&D government subsidy recipient types and between R&D collaboration types?" Methodologically, binary Logistic regression models are established successively considering the "Success-Failure" binary data characteristic regarding the performance creation. An empirical analysis is presented analyzing the sample n = 2,178 R&D projects completed. This study's major findings are as follows. First, the R&D inputs have a statistically significant relationship only with the short-term, technical output, "Patent Registration." Second, strong dependencies are identified between the immediate predecessor and its successor performance. Third, the success probability of the performance creation is statistically significantly different between the R&D types aforementioned. Specifically, compared with "Large Company", "Small and Medium-Sized Enterprise (SMS)" shows a greater success probability of "Sales" and "New Employment." Meanwhile, "R&D Collaboration" achieves a larger success probability of "Patent Registration" and "Sales."

• The Journal of Asian Finance, Economics and Business
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• 제9권2호
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• pp.177-183
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• 2022

The COVID-19 pandemic has hurt the economy and negatively impacted all enterprises' financial performance. The COVID-19 pandemic has put a strain on global manufacturing capacity and supply chains, and it is also the pandemic that has given up new opportunities for the logistics industry to develop as e-commerce has developed. By analyzing the financial performance of logistic firms listed on the Vietnam Stock Exchange, this study tries to quantify those consequences. A total of 114 logistic companies were included in the study's sample. The Wilcoxon Signed Rank Test was performed to test the difference between some ratios in 2019 and 2020. This study found that the financial performance of 114 logistic firms listed on the Vietnam stock exchange has not improved. The data show that during the COVID-19 pandemic, the leverage ratio increased while the profitability and efficiency ratios decreased. The liquidity ratio did not show any significant differences. On the contrary, these businesses' performance, such as returns on assets, receivable turnover, and leverage, has decreased. The COVID-19 had a global impact on supply chains, therefore export activity and international transportation were badly hampered, with only a few domestic logistic enterprises growing.

• 유통과학연구
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• 제21권5호
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• pp.31-41
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• 2023

Purpose: Electric vehicle (EV) technology started in 2015 in Thailand. The Thai Government has indicated that 30% of all cars produced in Thailand by 2025 will be EVs. Using EVs in Thailand will reduce road pollution and increase energy efficiency, especially in major cities. Hence, the adoption of EVs in the country has been promoted. This study pointed out that social influence, facilitating conditions, perceived enjoyment, environmental concern, attitude, and perceived behavioral control are key factors affecting the behavioral intention to adopt EVs among logistic and distribution firms in Thailand. Research design, data, and methodology: 500 top management, middle management and purchasing managers of logistic and distribution firms in Thailand are surveyed. The study employed judgmental, convenience, and snowball sampling. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) are the main statistical tools for data analysis. Results: The results show that all determinants impact customers' willingness to adopt EVs, except perceived enjoyment and environmental control. Conclusions: The study proposes to promote the incentives by decreasing electricity prices and endorsing EVs purchase to accelerate the adoption of EVs in Thailand. Therefore, future policies should focus on behavioral intention toward EVs amongst logistic and distribution firms for enhancing the future of mobility in Thailand.

• Journal of Information Processing Systems
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• 제12권1호
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• 경영과학
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• 제34권1호
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• pp.27-45
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• 2017

The purpose of this study is to empirically analyze whether open innovation activities are significant and which methods are more effective in developing new businesses. Based on the latest technological innovation survey data of the Science and Technology Policy Institute, the results were analyzed by binary logistic regression analysis. As a result of the analysis, we confirmed that open innovation activities have a positive effect on the performance of developing new businesses. In the open innovation activities, Recruitment (invitation) of specialist in related fields, Business alliance technical agreement, Dispatch of personnel, M&A, Acquisitions identify related field trends showed more influence in order. It would be beneficial to improve the performance of developing new businesses with a low probability of success if utilize more effective innovation activities in developing new business in enterprises or organizations throughout this study.

• 한국국방경영분석학회지
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• 제35권1호
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• pp.83-93
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• 2009

미래전은 정밀 타격과 같은 첨단 군 기술에 의한 효과 중심의 마비전이다. 이러한 미래전에서 작전 주도권과 집중력 강화를 지원하기 위한 새로운 개념의 군수 역할이 요구되고 있으며 전투원 및 전투부대 중심의 집중군수가 대안으로 제시되고 있다. 이와 같은 집중 군수를 실현하기 위해서는 전 제대의 자산을 실시간으로 파악 할 수 있는 자산가시화가 선행되어야 하지만 현재 우리 군에서 사용하고 있는 군수정보체계는 제한적인 정보만을 제공하고 있다. 본 연구에서는 웹 기반 2종 및 4종 군수품 관리 시스템의 구현 방안을 제시하였다. 제시된 대안 체계 시제는 전투원 및 전투부대 중심으로 군수가 지원될 수 있도록 전투부대 중심의 업무 프로세스 통합과 군수 기능간 동기화를 추구하였다. 또한 집중 군수의 필수 요소인 자산 가시화를 제공함으로써 이를 통한 신속하고 효율적인 군수 지원이 가능함을 확인하였다.

• 한국정보시스템학회지:정보시스템연구
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• 제22권4호
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• pp.49-69
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• 2013

Recently, enterprises have reinforced security control in order to prevent infringement of personal information and abuse of customer information by insiders. However, the reinforcement of security control by enterprises makes it difficult for internal users to perform business by using a business information system. There is, therefore, a need for research on various fields, which makes it possible to establish an appropriate security control policy while minimizing an impact on business. The present research verifies and analyzes an impact on difficulty in business of internal users using customer information, which is caused by security control performed by display restriction on customer information identifiers. The present research is intended to academically develop a technique for statistically analyzing an impact degree and a causal relationship between security control and an impact on business, which is a dichotomous variable, and to practically contribute to the establishment of an efficient security policy in consideration of an impact on business when an enterprise applies security control. A research target was internal business information systems of domestic securities enterprises, data was collected by questionnaire, and verification/analysis was performed by logistic regression analysis.

• 한국전자거래학회지
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• 제22권1호
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• pp.37-49
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• 2017

병원 진료의 효율성과 진료 품질의 극대화를 위해 진료예약을 시행하고 있다. 그러나 예약일에 방문하지 않는 예약부도로 인해 의료서비스 자원이 낭비되고 다른 환자의 진료기회를 박탈하는 등 현대 병원 운영에서 주요한 이슈의 하나로 떠오르고 있다. 이러한 예약부도의 원인을 분석하기 위해 미국의 한 보훈병원의 5만 건 진료예약 데이터를 대상으로 예약부도 요인의 중요도와 유의성을 검정하였다. 랜덤 포레스트와 로지스틱 회귀분석 결과, 예약대기시간, 방문율, 내원거리, 예약부도율, 환자 나이, 중증도, 질병의 복합도, 만성통증, 우울증, 약물의존 등이 주요한 원인으로 파악되었다. 예약대기시간과 방문율, 내원거리, 예약부도율은 SMS 사전 통지를 강화하고 사후 전화 상담을 통해 점진적으로 개선 가능할 것으로 판단되며 기타 요인에 대해서는 환자 그룹별로 차별화된 대응방안을 구성하는 것을 고려해 볼 수 있다.