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International Journal of Business Analytics最新文献

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Applications of System Dynamics and Big Data to Oil and Gas Production Dynamics in the Permian Basin 系统动力学和大数据在二叠纪盆地油气生产动力学中的应用
IF 1.1
International Journal of Business Analytics Pub Date : 2022-01-01 DOI: 10.4018/ijban.314223
J. Burns, Pinyarat Sirisomboonsuk
{"title":"Applications of System Dynamics and Big Data to Oil and Gas Production Dynamics in the Permian Basin","authors":"J. Burns, Pinyarat Sirisomboonsuk","doi":"10.4018/ijban.314223","DOIUrl":"https://doi.org/10.4018/ijban.314223","url":null,"abstract":"In this paper, the authors create, justify, and document a system dynamics model of the oil and gas production within the Permian Basin of Texas. Then the researchers show how to fit the model to historical time series data (big data). The authors use the model to better understand the process structure, the production dynamics, and to explore the deleterious consequences of limited pipeline capacity in the Permian Basin. The model is also employed to better understand how to increase revenues derived from the basin. From this model, numerous suggestions are made as to how to improve the overall revenue and profitability coming from the Permian Basin. The model's ultimate purposes and its associated big data are to foster a basic appreciation of the causality inherent in the ‘system' and how basic model parameters affect and influence measures of model performance.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44526927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Energy Management in Manufacturing 制造业的能源管理
IF 1.1
International Journal of Business Analytics Pub Date : 2022-01-01 DOI: 10.4018/ijban.314224
Mehrnaz Khalaj Hedayati, Dara G. Schniederjans
{"title":"Energy Management in Manufacturing","authors":"Mehrnaz Khalaj Hedayati, Dara G. Schniederjans","doi":"10.4018/ijban.314224","DOIUrl":"https://doi.org/10.4018/ijban.314224","url":null,"abstract":"In order to reduce the growing negative impact of CO2 emissions, manufacturing firms have begun to refocus efforts on energy management. Several studies have focused on drivers and inhibitors of energy management but few regarding manufacturing energy management maturity. This study investigates both drivers and the role of knowledge management on manufacturing energy management maturity. Using multivariate analyses, questionnaire data from manufacturing personnel throughout the United States is utilized to assess these relationships. The results provide the support that economic followed by organizational and corporate social responsibility (CSR) positively impact knowledge management practices within organizations. Additionally, this study provides support that knowledge management practices within U.S. manufacturing organizations have a positive association with environmental management maturity. Findings contribute to theory and practical knowledge by highlighting the configurational effects of knowledge management and energy management maturity.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49323846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Combined Multi-Criteria Decision-Making Framework for Process-Based Digitalisation Opportunity and Priority Assessment (DOPA) 基于过程的数字化机会和优先级评估（DOPA）的多准则组合决策框架
IF 1.1
International Journal of Business Analytics Pub Date : 2022-01-01 DOI: 10.4018/ijban.298018
Nihan Yıldırım, Birden Tuluğ Siyahi, Oğuz Özbek, Imran Ahioğlu, Almira Selin Kahya
{"title":"A Combined Multi-Criteria Decision-Making Framework for Process-Based Digitalisation Opportunity and Priority Assessment (DOPA)","authors":"Nihan Yıldırım, Birden Tuluğ Siyahi, Oğuz Özbek, Imran Ahioğlu, Almira Selin Kahya","doi":"10.4018/ijban.298018","DOIUrl":"https://doi.org/10.4018/ijban.298018","url":null,"abstract":"With the introduction of Industry 4.0 and supporting technologies, both service and manufacturing companies faced external and internal pressure for \"going digital\". In many cases, companies cannot decide on the digitalisation initiative due to preliminary groundwork to justify the required investment. For digitalisation priority setting under uncertain benefits, available digital technology selection methods lack the focus on process needs and do not fully utilise quality management tools in the Multi Criteria Decision Making (MCDM) framework. In this context, this study aims to propose a novel, context-independent, and process-based Digital Opportunity Priority Assessment (DOPA) methodology. The proposed approach utilizes critical to quality measures (CTQs), the causes with potential adversary effects as alternatives, and the importance, frequency, and digital control level of CTQs as the criteria in TOPSIS. AHP and Fuzzy AHP validate CTQ importance criteria. The study also presents a real industry application to validate the proposed model.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43447505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Prediction of Bike Share Demand by Machine Learning 基于机器学习的自行车共享需求预测
IF 1.1
International Journal of Business Analytics Pub Date : 2022-01-01 DOI: 10.4018/ijban.288513
Tae You Kim, M. Park, J. Shin, Sung-Baik Oh
{"title":"Prediction of Bike Share Demand by Machine Learning","authors":"Tae You Kim, M. Park, J. Shin, Sung-Baik Oh","doi":"10.4018/ijban.288513","DOIUrl":"https://doi.org/10.4018/ijban.288513","url":null,"abstract":"In the fourth industrial revolution period, multinational companies and start-ups have applied a sharing economy concept to their business and have attempted to better serve customer demand by integrating demand prediction results into their business operations. For survival amongst today’s fierce competition, companies need to upgrade their prediction model to better predict customer demand in a more accurate manner. This study explores a new feature for bike share demand prediction models that resulted in an improved RMSLE score. By applying this new feature, the number of daily vehicle accidents reported in the Washington, D.C. area, to the Random Forest, XGBoost, and LightGBM models, the RMSLE score results improved. Many previous studies have primarily focused on feature engineering and regression techniques within given dataset. However, this study is meaningful because it focuses more on finding a new feature from an external data source.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45784054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Impact of Credit Financing on the Ordering Policy for Imperfect Quality Items With Learning and Shortages 信贷融资对学习型和短缺型不合格品订购政策的影响
IF 1.1
International Journal of Business Analytics Pub Date : 2022-01-01 DOI: 10.4018/ijban.304829
M. Jayaswal, Isha Sangal, M. Mittal
{"title":"Impact of Credit Financing on the Ordering Policy for Imperfect Quality Items With Learning and Shortages","authors":"M. Jayaswal, Isha Sangal, M. Mittal","doi":"10.4018/ijban.304829","DOIUrl":"https://doi.org/10.4018/ijban.304829","url":null,"abstract":"The paper develops an order quantity model with trade credit plus shortages under learning effects for deteriorating imperfect quality products. Generally, when the lot has imperfect items, the inspection of a lot is necessary to improve the quality of the lot. In this article, the seller provides a defective lot to his buyer under credit financing scheme, and after that buyer separates the whole lot under the screening process into two categories, one is defective and the other is non-defective items. The buyer sells out defective items at a low price as compared to non-defective items. It is assumed that customers' demand of good quality items is greater than the inspection rate for the whole lot to neglect the shortages situation. After keeping all points together, the buyer optimized his total profit concerning order quantity and shortage. A suitable numerical example and a sensitivity analysis have been provided for the validity of this model. The aim and utility of this paper have been presented in the conclusion section.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43611197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data Envelopment Analysis and Analytics Software for Optimizing Building Energy Efficiency 优化建筑能源效率的数据包络分析和分析软件
IF 1.1
International Journal of Business Analytics Pub Date : 2022-01-01 DOI: 10.4018/ijban.290404
Z. Radovilsky, P. Taneja, P. Sahay
{"title":"Data Envelopment Analysis and Analytics Software for Optimizing Building Energy Efficiency","authors":"Z. Radovilsky, P. Taneja, P. Sahay","doi":"10.4018/ijban.290404","DOIUrl":"https://doi.org/10.4018/ijban.290404","url":null,"abstract":"This research was motivated by the need to identify the most effective Data Envelopment Analysis (DEA) model and associated data analytics software for measuring, comparing, and optimizing building energy efficiency. By analyzing literature sources, the authors identified several gaps in the existing DEA approaches that were resolved in this research. In particular, the authors introduced energy efficiency indices like energy consumption per square foot and per occupant as a part of DEA models’ outputs. They also utilized inverse and min-max normalized output variables to resolve the issue of undesirable outputs in the DEA models. The evaluation of these models was done by utilizing various data analytics software including Python, R, Matlab, and Excel. The authors identified that the CCR DEA model with inverse output variables provided the most reliable energy efficiency scores, and the Python’s PyDEA package produces the most consistent efficiency scores while running the CCR model.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47482812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Stock Market Responses to the COVID-19 Health Crisis 股市对COVID-19健康危机的反应
IF 1.1
International Journal of Business Analytics Pub Date : 2022-01-01 DOI: 10.4018/ijban.303114
Abdul Wajid, Kanishka Gupta
{"title":"Stock Market Responses to the COVID-19 Health Crisis","authors":"Abdul Wajid, Kanishka Gupta","doi":"10.4018/ijban.303114","DOIUrl":"https://doi.org/10.4018/ijban.303114","url":null,"abstract":"The outbreak of the novel COVID-19 pandemic emerged as a major black swan event which has caused shock waves and severely hurt the sentiments of market participants. The pandemic has raised uncertainties and risks all over the world, impacting substantially the world's 20 largest economies. While the stock markets' intense reaction to the official news of the pandemic is well known, the reaction of largest world economies during the initial phases of the outbreak until 11th March 2020 is not very well established. Therefore, the present study investigates how stock markets in world's 20 largest economies have reacted to major events and press releases associated with disease from the beginning of the pandemic (i.e., 31st December 2020 till 11th March 2020). The results of the study suggest that the declaration of the novel COVID-19 as a pandemic was the most devastating event for stock markets. This was confirmed by using various parametric and non-parametric tests. In addition, the last event was further analyzed by observing CARs of various indices individually.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43204062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Business Analytics in Sport Talent Acquisition. Methods, Experiences, and Open Research Opportunities 体育人才获取中的商业分析。方法、经验和开放的研究机会
IF 1.1
International Journal of Business Analytics Pub Date : 2022-01-01 DOI: 10.4018/ijban.290406
R. D. L. Torre, Laura Calvet, David López-López, A. Juan, Sara Hatami
{"title":"Business Analytics in Sport Talent Acquisition. Methods, Experiences, and Open Research Opportunities","authors":"R. D. L. Torre, Laura Calvet, David López-López, A. Juan, Sara Hatami","doi":"10.4018/ijban.290406","DOIUrl":"https://doi.org/10.4018/ijban.290406","url":null,"abstract":"Recruitment of young talented players is a critical activity for most professional teams in different sports such as football, soccer, basketball, baseball, cycling, etc. In the past, the selection of the most promising players was done just by relying on the experts’ opinion, but without a systematic data support. Nowadays, the existence of large amounts of data and powerful analytical tools have raised the interest in making informed decisions based on data analysis and data-driven methods. Hence, most professional clubs are integrating data scientists to support managers with data-intensive methods and techniques that can identify the best candidates and predict their future evolution. This paper reviews existing work on the use of data analytics, artificial intelligence, and machine learning methods in talent acquisition. A numerical case study, based on real-life data, is also included to illustrate some of the potential applications of business analytics in sport talent acquisition. In addition, research trends, challenges, and open lines are also identified and discussed.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47404824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Socio-Demographic Impacts on the Personal Savings Portfolio Choice 社会人口学对个人储蓄组合选择的影响
IF 1.1
International Journal of Business Analytics Pub Date : 2022-01-01 DOI: 10.4018/ijban.288511
M. Buric, M. Raicevic, Ljiljana Kašćelan, Vladimir Kašćelan
{"title":"Socio-Demographic Impacts on the Personal Savings Portfolio Choice","authors":"M. Buric, M. Raicevic, Ljiljana Kašćelan, Vladimir Kašćelan","doi":"10.4018/ijban.288511","DOIUrl":"https://doi.org/10.4018/ijban.288511","url":null,"abstract":"Insufficiently developed financial system, poor standard of living and inappropriate education of citizens on the saving products, lead to low level of investment in the financial market of developing countries. In this paper special attention is paid to examining the socio-demographic profile of Montenegrin citizens that invest their funds in some of the offered form of savings, as well as examining main factors that restrict their investment. For this purpose, data collected through the survey of Montenegrin citizens were processed using Decision Tree method. Survey results have shown that there is a low level of savings, as well as that citizens prefer deposits and life insurance products rather than pension plans and debt securities. Also, the results indicate that the main causes of the current state of savings in Montenegro are low standard of living, citizens´ poor awareness and the financial system which causes the insufficiently attractive supply of savings.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47856979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Applying Machine Learning to the Development of Prediction Models for Bank Deposit Subscription 将机器学习应用于银行存款认购预测模型的开发
IF 1.1
International Journal of Business Analytics Pub Date : 2022-01-01 DOI: 10.4018/ijban.288514
Sipu Hou, Zongzhen Cai, Jiming Wu, Hongwei Du, Peng Xie
{"title":"Applying Machine Learning to the Development of Prediction Models for Bank Deposit Subscription","authors":"Sipu Hou, Zongzhen Cai, Jiming Wu, Hongwei Du, Peng Xie","doi":"10.4018/ijban.288514","DOIUrl":"https://doi.org/10.4018/ijban.288514","url":null,"abstract":"It is not easy for banks to sell their term-deposit products to new clients because many factors will affect customers’ purchasing decision and because banks may have difficulties to identify their target customers. To address this issue, we use different supervised machine learning algorithms to predict if a customer will subscribe a bank term deposit and then compare the performance of these prediction models. Specifically, the current paper employs these five algorithms: Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Neural Network. This paper thus contributes to the artificial intelligence and Big Data field with an important evidence of the best performed model for predicting bank term deposit subscription.","PeriodicalId":42590,"journal":{"name":"International Journal of Business Analytics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47370596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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