• Corrosion Science and Technology
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• v.7 no.4
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• pp.208-211
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• 2008

Safety operation of main pipelines is primarily provided by anticorrosive monitoring. Anticorrosive monitoring of oil pipeline transportation objects is based on results of complex corrosion inspections, analysis of basic data including design data, definition of a corrosion residual rate and diagnostic of general equipment's technical condition. All the abovementioned arrangements are regulated by normative documents. For diagnostics of corrosion-technical condition of oil pipeline transportation objects one presently uses different methods such as in-line inspection using devices with ultrasonic, magnetic or another detector, acoustic-emission diagnostics, electrometric survey, general external corrosion diagnostics and cameral processing of obtained data. Results of a complex of diagnostics give a possibility: $\cdot$ to arrange a pipeline's sectors according to a degree of corrosion danger; $\cdot$ to check up true condition of pipeline's metal; $\cdot$ to estimate technical condition and working ability of a system of anticorrosive protection. However such a control of corrosion technical condition of a main pipeline creates the appearance of estimation of a true degree of protection of an object if values of protective potential with resistive component are taken into consideration only. So in addition to corrosive technical diagnostics one must define a true residual corrosion rate taking into account protective action of electrochemical protection and true protection of a pipeline one must at times. Realized anticorrosive monitoring enables to take a reasonable decision about further operation of objects according to objects' residual life, variation of operation parameters, repair and dismantlement of objects.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.897-911
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• 2021

This study aims to analyze the important role of business analytics capability, information quality, and innovation capability in influencing organization agility and organization performance during the Covid-19 pandemic. Data was collected from 76 companies from various sectors in Indonesia. Structural Equation Model-Partial Least Square (SEM-PLS) analysis was conducted to analyze the relationship between variables and test a series of hypotheses. Importance-Performance Matrix Analysis (IPMA), a useful analysis approach in PLS-SEM, is used, which extends the results of the estimated path coefficient (importance) by adding a dimension that considers the average values of the latent variable scores (performance). The IPMA approach examines not only the performance of an item but also the importance of that item. The results show that business analytics capability has a significant effect on information quality and innovation capability which then affects organization agility. Organizational performance is influenced by organizational agility. IPMA results show that organizational agility has the highest level of impact on organizational performance. This study will assist companies in planning business analytics, improving information quality, increasing innovation capability, and ultimately increasing agility and performance during the Covid-19 pandemic. This study will add to existing knowledge about previous literature, especially in the Covid-19 pandemic situation.

• The Journal of Bigdata
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• v.4 no.1
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• pp.73-81
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• 2019

With the increase of education data, there have been many studies on the application of various analytics to improve students' performance and educational environments over the past decade. This paper first introduces the cases of universities that successfully utilize the analysis results and, more specifically, examines which data and analytical techniques are used for each analysis purpose. Based on the findings, the limitations of the current analytics and the direction of future analysis are discussed.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.226-247
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• 2024

Most studies on recommender systems to evaluate recommendation performances focus on offline evaluation methods utilizing past customer transaction records. However, evaluating recommendation performance through real-world stimulation becomes challenging. Moreover, such methods cannot evaluate the duration of the recommendation effect. This study measures the personalized recommendation (stimulus) effect when the product recommendation to customers leads to actual purchases and evaluates the duration of the stimulus personalized recommendation effect leading to purchases. The results revealed a 4.58% improvement in recommendation performance in the online environment compared with that in the offline environment. Furthermore, there is little difference in recommendation performance in offline experiments by period, whereas the recommendation performance declines with time in online experiments.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.33-57
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• 2017

This study aims to introduce an emerging prescriptive analytics method and suggest its efficient application to a category-based service system. Prescriptive analytics method provides the whole process of analysis and available alternatives as well as the results of analysis. To simulate the process of optimization, large scale journal articles have been collected and categorized by classification scheme. In the process of applying the concept of prescriptive analytics to a real system, we have fused a dynamic automatic-categorization method for large scale documents and intellectual structure analysis method for scholarly subject fields. The test result shows that some optimized scenarios can be generated efficiently and utilized effectively for reorganizing the classification-based service system.

• Educational Technology International
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• v.17 no.1
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• pp.117-150
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• 2016

The purpose of this study was to develop design guidelines on the learning analytics which can help to promote students' self-regulated learning (SRL) strategies in MOOCs learning environments. First of all, to develop the first draft of design guidelines, relevant literature review and case analysis on current MOOCs platforms such as edX, K-MOOC, Coursera, Khan Academy and FutureLearn were conducted. Then, to validate the design guidelines, expert reviews (validation questionnaires and in-depth interviews) and learner evaluation (in-depth interviews) were conducted. Through the recursive validation, the design guidelines were finalized. Overall, the final version of design guidelines on learning analytics to facilitate SRL strategies was suggested. The final design guidelines consist of 15 items in 10 categories related to the information analyzed based on individual student's learning behaviors and activities on MOOCs environments. Moreover, the results of interview also revealed that the social comparisons, learning progress reports, and personalization might contribute to the improvements of their SRL competences. This study has an implication that MOOCs could offer a higher success or completion rate to students with low SRL skills by taking advantage of the information on learning analytics

• Asia pacific journal of information systems
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• v.30 no.4
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• pp.847-878
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• 2020

In the era of disruptive change, a data-driven approach is vital to Human Resource Management (HRM) of any leading organization, for it is used to gain a competitive advantage. HR analytics (HRA) has emerged as innovative technologies since advanced analytics, i.e., predictive or prescriptive analytics, were widely used in the High Performing Organizations (HPOs). Therefore, many organizations elevate themselves to become HPOs through Data Science on the "people side." This paper proposes a systematic literature review using the Literature Weighted Scoring (LWS) to develop a conceptual framework based on three adoption theories, which are the Technology-Organization-Environment (TOE), Diffusion of Innovation (DOI), and Unified Theory of Acceptance and Use of Technology (UTAUT). The results show that a total of 13 theory-derived factors are determined as influential factors affecting HRA adoption, and the top three factors are "Quantitative Self-Efficacy," "Top Management Support," and "Data Availability." The conceptual framework with hypotheses is proposed to provide a foundation for further studies on organizational HRA adoption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2799-2823
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• 2019

With the help of learning analytics, MOOCs have wider potential to succeed in learning through promoting self-regulated learning (SRL). The current study aims to apply and validate visualization design guidelines for a MOOC dashboard to enhance such SRL capabilities based on learning analytics. To achieve the research objective, a MOOC dashboard prototype, LM-Dashboard, was designed and developed, reflecting the visualization design guidelines to promote SRL. Then, both expert and learner participants evaluated LM-Dashboard through iterations to validate the visualization design guidelines and perceived SRL effectiveness. The results of expert and learner evaluations indicated that most of the visualization design guidelines on LM-Dashboard were valid and some perceived SRL aspects such as monitoring a student's learning progress and assessing their achievements with time management were beneficial. However, some features on LM-Dashboard should be improved to enhance SRL aspects related to achieving their learning goals with persistence. The findings suggest that it is necessary to offer appropriate feedback or tips as well as to visualize learner behaviors and activities in an intuitive and efficient way for the successful cycle of SRL. Consequently, this study contributes to establishing a basis for the visual design of a MOOC dashboard for optimizing each learner's SRL.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.442-452
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• 2022

This study was conducted to understand research trends of HR Analytics (HRA) in Korea and to suggest future research directions. First, a comparative analysis was conducted by classifying six areas of recruitment on-board, work environment, performance evaluation, retention, and exit/retirement building on the employee life cycle framework. The results indicate that first, the distribution of detailed research topics in Korean HRA research has similar to that of international research. Second, Korean HRA studies related to employee training and development function are insufficient. Third, the scope and the method of machine learning are becoming enriched. Finally Korean HRA studies are still in the technical domain and toward entering the predictive analysis domain.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.46-51
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• 2015

The explosive growth of data and the rapidly changing technical social evolution new analysis paradigm for predicting and reacting the future the past and present ig data. Prescriptive analysis has a fundamental difference because can support specific behaviors and results according to user's goals with defin researchers establish judgments and activities achiev the goals. However research methods not widely implemented and even the terminology, Prescriptive analysis, is still unfamiliar. This paper thus propose an infrastructure in the prescriptive analysis field with key considerations for enhancing capability of researchers through a case study based on InSciTe Advisory developed with scientific big data. InSciTe Advisory system s developed in 2013, and offers a prescriptive analytics report which contains various As-Is analysis results and To-Be analysis results 5W1H methodology. InSciTe Advisory therefore shows possibility strategy aims to reach a target role model group. Through the availability and reliability of the measurement model the evaluation results obtained relative advantage of 118.8% compared to Elsevier SciVal.