• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.377-389
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• 2021

This study aims at investigating the effects of big accounting data on the sustainable business growth of listed firms in Thailand. In addition, it examines the mediating effects of accounting information quality and decision-making effectiveness and the moderating effects of digital innovation on the research relationships. The study's useful samples are the 289 listed Thai companies. To examine the research relationships, the structural equation model and multiple regression analysis are used in this study. According to the results of this study, big accounting data has a significant effect on accounting information quality, decision-making effectiveness, and sustainable business growth. Next, accounting information quality significantly affects decision-making effectiveness and sustainable business growth. Similarly, decision-making effectiveness significantly affects sustainable business growth. Both accounting information quality and decision-making effectiveness mediate the big accounting data-sustainable business growth relationships. Lastly, digital innovation moderates the effects of accounting information quality and decision-making effectiveness on sustainable business growth. Accordingly, In conclusion, big accounting data has emerged as a key source of sustainable competitive advantage. As a result, to succeed in competitive environments, businesses must have a thorough understanding of big accounting data.

• Journal of Information Technology Applications and Management
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• v.28 no.1
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• pp.13-31
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• 2021

The purpose of this study is to find the factors that influence big data characteristics on decision satisfaction and utilization behavior, analyze the extent of their influence, and derive differences from existing studies. To summarize the results of this study, First, the study found that among the three categories that classify the characteristics of big data, qualitative attributes such as representation, purpose, interpretability, and innovation in the value innovation category greatly enhance decision confidence and decision effectiveness of decision makers who make decisions using big data. Second, the study found that, among the three categories that classify the characteristics of big data, the individuality properties belonging to the social impact category improve decision confidence and decision effectiveness of decision makers who use big data to make decisions. However, collectivity and bias characteristics have been shown to increase decision confidence, but not the effectiveness of decision making. Third, the study found that among the three categories that classify the characteristics of big data, the attributes of inclusiveness, realism, etc. in the integrity category greatly improve decision confidence and decision effectiveness of decision makers who make decisions using big data. Fourth, it was analyzed that using big data in organizational decision making has a positive impact on the behavior of big data users when the decision-making confidence and finally, decision-making effect of decision-makers increases.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.355-364
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• 2005

Previous researches on subsequence matching have been focused on how to make indexes in order to speed up the matching time, and do not take into account the effectiveness issues of subsequence matching methods. This paper considers the effectiveness of subsequence matching methods and proposes two metrics for effectiveness evaluations of subsequence matching algorithms. We have applied the proposed metrics to Korean stock data and five known matching algorithms. The analysis on the empirical data shows that two methods (i.e., the method supporting normalization, and the method supporting scaling and shifting) outperform the others in terms of the effectiveness of subsequence matching.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3197-3206
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• 2011

The objective of this study is to provide the guide for producing data for effective analysis of weapons systems and munitions. Therefore, the prototype from the study of creating the effectiveness data of surface to surface Artillery system under different battle environments was developed in this paper. For this purpose, mathmatical method which is in use of calculating Lethal Area and EFC(Expected Fractional Casualty) is analyzed and applied to the prototype for generating effectiveness data of surface to surface Artillery system. To validate the suggested prototype, we did the following actions: we select the source data corresponding to JMEM(Joint Munition Effectiveness Manuals) data and apply our prototype to the source data, and then match the results to check validity.

• Journal of Distribution Science
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• v.18 no.7
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• pp.49-62
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• 2020

Purpose: The purpose of this study is to examine under what conditions suggestion programs conducted by organizations actually increase individual perception of their work effectiveness. Specifically, this study looks into the effects of cooperation perception and relational demography of employee on work effectiveness of suggestion programs. It does this by focusing on the interaction effect of organizational commitment. Research design, data and methodology: Data was collected from 1,872 participants who took part in the suggestion program of HCCP 6th DATA. This data was subjected to multiple regression analysis. Results: a) higher employee cooperation perception enhances work effectiveness of suggestion program, but relational difference of knowledge diversity between team members has no effect on work efficiency; b) Positive effect of cooperation perception, and difference in education level on work effectiveness become greater as commitment increases. However, organizational commitment decreases the positive effect of difference in organizational tenure on work effectiveness by suggestion program. Conclusion: The results point to the importance of broadening the current conceptual models of employee work effectiveness of suggestion program to include relational demography, as well as the utility of conducting additional cross-level research on suggestion programs.

• The Journal of Economics, Marketing and Management
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• v.11 no.6
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• pp.1-8
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• 2023

Purpose: In the era of the Fourth Industrial Revolution, individuals equipped with fundamental understanding and practical skills in artificial intelligence (AI) are essential. This study aimed to validate the effectiveness of AI education for enhancing AI literacy among business major student. Research design, data and methodology: Data for analyzing the effectiveness of the AI Fundamental Education Program for business major students were collected through surveys conducted at the beginning and end of the semester. Structural equation modeling was employed to perform basic statistical analyses regarding gender, grade, and prior software (SW) education duration. To validate the effectiveness of AI education, seven variables - AI interest, AI perception, data analysis/utilization, AI projects, AI literacy, AI self-efficacy, and AI learning persistence - were defined and derived. Results: All seven operationally defined variables showed statistically significant positive changes. The average differences were observed as follows: 0.47 for AI interest, 0.32 for AI perception, 0.37 for data analysis/utilization, 0.27 for AI projects, 0.25 for AI literacy, 0.39 for AI self-efficacy, and 0.41 for AI learning persistence. Statistically, AI interest exhibited the most substantial average difference. Conclusions: Through this study, the applied AI education was confirmed to enhance learners' overall competencies in AI, proving its utility and effectiveness in AI literacy education for business major students. Future research endeavors should build upon these results, focusing on ongoing studies related to AI education programs tailored to learners from diverse academic backgrounds and conducting continuous efficacy evaluations.

• IE interfaces
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• v.25 no.1
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• pp.13-20
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• 2012

Recently, the collaboration among small and medium-sized enterprises(SMEs) has been recognized as an effective competitive tool. As several systems have been developed to boost the collaboration, it is necessary to analyze the effectiveness of the systems in terms of their contribution to enhance operational performance of SMEs through objective and quantitative validation. In particular, the analysis for SMEs rather than large-scaled enterprises has not received much attention due to lack of relevant information and difficulty of collecting data. This paper presents a framework for analyzing the effectiveness of the collaboration support system, called i-manufacturing hub, which has been implemented by Korean government. Identification of influential factors to the effectiveness of collaboration hub, and constructing necessary hypotheses are proposed. To overcome the difficulty in data collection only by means of surveys through subjective questionnaires, we exploit system log data that are generated while SMEs use the system. As an initial phase to analyze the effectiveness through hypothesis validation, we discuss several interesting observations and challenges in the direction of enhancing collaboration among SMEs for better operational performance improvement and more participation in the collaboration hub.

• Journal of Information Management
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• v.38 no.1
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• pp.1-19
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• 2007

This study investigates the effect of data fusion on the retrieval effectiveness by performing an experiment combining multiple representations of Web documents. The types of document representation combined in the study include content terms, links, anchor text, and URL. The experimental results showed that the data fusion technique combining document representation methods in Web environment did not bring any significant improvement in retrieval effectiveness.

• Journal of Korean Academy of Nursing Administration
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• v.16 no.3
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• pp.267-275
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• 2010

Purpose: This study conducted to identify the effect of learning organization construction and learning orientation on organizational effectiveness among hospital nurses. Method: Data was collected from convenient sample of 296 nurses who worked for the major hospitals in Seoul, Gyeonggi-do and Gangwoen-do. The self-reported questionnaire was used to assess the general characteristics, the level of the learning organization construction, learning orientation and organizational effectiveness. The data were analyzed using descriptive statistics, pearson's correlation coefficient and multiple regression. Result: The mean score of learning organization construction was 3.61(${\pm}.32$), learning orientation got 3.26(${\pm}.39$), and organizational effectiveness obtained 3.38(${\pm}.42$). The learning organization construction affects of organizational effectiveness by 44.18% and learning orientation by 37.43%. Conclusion: This finding indicates that learning organization construction and learning orientation affects the nurses' organizational effectiveness in hospital.

• Ocean Systems Engineering
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• v.5 no.3
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• pp.161-179
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• 2015

An emergency response system (ERS) for vessel oil spills is a complex and dynamic system comprising a number of subsystems and activities. Failures may occur during the emergency response operations, this has negative impacts on the effectiveness of the ERS. Of the classes of problems in analyzing failures, the lack of quantitative data is fundamental. In fact, most of the empirical data collected via questionnaire survey is subjective in nature and is inevitably associated with uncertainties caused by the human being's inability to provide complete judgement. In addition, incomplete information and/or vagueness of the meaning about the failures add difficulties in evaluating the effectiveness of the system. Therefore this paper proposes a framework to evaluate the ERS effectiveness by using the combination of fuzzy reasoning and evidential synthesis approaches. Based on analyzing the procedure of ERS for oil spills, the failures in the system could be identified, using Analytic Hierarchy Process(AHP)to determine the relative weight of identified failures. Fuzzy reasoning combined with evidential synthesis is applied to evaluate the effectiveness of ERS for oil spills under uncertainties last. The proposed method is capable of dealing with uncertainties in data including ignorance and vagueness which traditional methods cannot effectively handle. A case study is used to illustrate the application of the proposed method.