Business Analytics is one of the vital processes that must be incorpo-rated into any business.It supports decision-makers in analyzing and predicting future trends based on facts(Data-driven decisions),especially when...Business Analytics is one of the vital processes that must be incorpo-rated into any business.It supports decision-makers in analyzing and predicting future trends based on facts(Data-driven decisions),especially when dealing with a massive amount of business data.Decision Trees are essential for business ana-lytics to predict business opportunities and future trends that can retain corpora-tions’competitive advantage and survival and improve their business value.This research proposes a tree-based predictive model for business analytics.The model is developed based on ranking business features and gradient-boosted trees.For validation purposes,the model is tested on a real-world dataset of Universal Bank to predict personal loan acceptance.It is validated based on Accuracy,Precision,Recall,and F-score.The experimentfindings show that the proposed model can predict personal loan acceptance efficiently and effectively with better accuracy than the traditional tree-based models.The model can also deal with a massive amount of business data and support corporations’decision-making process.展开更多
This pioneering research represents a unique and singular study conducted within the United States, with a specific focus on non-technical graduate students pursuing degrees in business analytics. The primary impetus ...This pioneering research represents a unique and singular study conducted within the United States, with a specific focus on non-technical graduate students pursuing degrees in business analytics. The primary impetus behind this study stems from the escalating demand for data-driven professionals, the diverse academic backgrounds of students, the imperative for adaptable pedagogical methods, the ever-evolving landscape of curriculum designs, and the overarching commitment to fostering educational equity. To investigate these multifaceted dynamics, we employed a data collection method that included the distribution of an online survey on platforms such as LinkedIn. Our survey reached and engaged 74 graduate students actively pursuing degrees in Business Analytics within the United States. This comprehensive research is the first and only one of its kind conducted in this context, and it serves as a vanguard exploration into the challenges and influences that shape the learning journey of Python among non-technical graduate Business Analytics students. The analytical insights derived from this research underscore the pivotal role of hands-on learning strategies, exemplified by practice exercises and assignments. Moreover, the study highlights the positive and constructive influence of collaboration and peer support in the process of learning Python. These invaluable findings significantly augment the existing body of knowledge in the field of business analytics. Furthermore, they offer an essential resource for educators and institutions seeking to optimize the educational experiences of non-technical students as they acquire essential Python skills.展开更多
Bus rapid transit systems(BRT)have been an indispensable public transportation pillar,especially in densely populated regions.Accurate insight into the BRT network’s utilization is vital in BRT resource allocation pl...Bus rapid transit systems(BRT)have been an indispensable public transportation pillar,especially in densely populated regions.Accurate insight into the BRT network’s utilization is vital in BRT resource allocation planning contexts.This research focuses on how operators can utilize passengers’smart card data to develop origin-destination(OD)matrix-based business analytics.This research proposes a hybrid approach combining trip chaining,direct pairing,mode estimation methods,and visual analytics development.The novel approach is robust in handling incomplete smart card data transactions to generate origindestination matrices and corresponding visual analytics as decision support systems for the BRT operators.As a case study,we applied and validated the proposed analytics to more than 20.6 million smart card transactions in one of the largest global BRT systems in Jakarta,Indonesia.展开更多
Business analytics presents significant opportunities for enhancing strategic decision-making(SDM),which is crucial for organizational competitiveness.However,there is a knowledge gap in understanding the interactions...Business analytics presents significant opportunities for enhancing strategic decision-making(SDM),which is crucial for organizational competitiveness.However,there is a knowledge gap in understanding the interactions among environmental dynamism,business analytics use,environmental scanning,and rational and intuitive SDM.This paper addresses this gap by leveraging the information processing view and analyzing 218 survey responses using partial least squares(PLS)path modeling.It reveals that environmental dynamism influences business analytics use and environmental scanning.Business analytics use positively impacts rational SDM but negatively affects intuitive SDM.Environmental scanning partially mediates the relationship between business analytics use and rational SDM,and there is an inverse correlation between rational and intuitive SDM.This research introduces a novel theoretical framework,enriching the information processing view,and deepens understanding of how strategic information processing capabilities influence SDM.It also provides practical insights for organizations using business analytics to improve SDM processes in uncertain environments.展开更多
Over the past few decades,with the development of automatic identification,data capture and storage technologies,people generate data much faster and collect data much bigger than ever before in business,science,engin...Over the past few decades,with the development of automatic identification,data capture and storage technologies,people generate data much faster and collect data much bigger than ever before in business,science,engineering,education and other areas.Big data has emerged as an important area of study for both practitioners and researchers.It has huge impacts on data-related problems.In this paper,we identify the key issues related to big data analytics and then investigate its applications specifically related to business problems.展开更多
Natural Language Processing(NLP),a branch of artificial intelligence,is gaining traction as a potent tool for business analytics.With the proliferation of unstructured textual data,businesses are actively seeking meth...Natural Language Processing(NLP),a branch of artificial intelligence,is gaining traction as a potent tool for business analytics.With the proliferation of unstructured textual data,businesses are actively seeking methodologies to distill valuable insights from vast textual repositories.The introduction of NLP in the realm of business analytics offers a transformative approach,automating traditional manual processes and fostering real-time,data-driven decisionmaking.From sentiment analysis to text summarization,NLP is facilitating businesses in deciphering consumer feedback,predicting market trends,and breaking down linguistic barriers in the age of globalization.This paper sheds light on the evolution of NLP techniques in business analytics,their applications,and the inherent challenges and opportunities they present.展开更多
Need of transformation of means of support of project financing for commercial banks is proved.The analysis and modeling of business processes of project management by the contextual chart and the chart of decompositi...Need of transformation of means of support of project financing for commercial banks is proved.The analysis and modeling of business processes of project management by the contextual chart and the chart of decomposition is carried out that allowed to describe the main stages of project financing.With use of tools of programming the business application of project management which will promote operational assessment on selection of introduced drafts is created.展开更多
Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns.The quantity and variety of computer data are growing exponentially for many reasons.For example,retail...Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns.The quantity and variety of computer data are growing exponentially for many reasons.For example,retailers are building vast databases of customer sales activity.Organizations are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and products.Challenges of big data include volume and variety in both structured and unstructured data.In this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional models.We studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision tree.We implemented naive Bayes and logistic regression classification models.Experimental results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.展开更多
Capturing potential travel demand is crucial for carriers to improve their market performance,especially in developing economies with an emerging middle class and increasing socioeconomic inclusion.However,the impact ...Capturing potential travel demand is crucial for carriers to improve their market performance,especially in developing economies with an emerging middle class and increasing socioeconomic inclusion.However,the impact of upward economic mobility on deregulated transport systems and how carriers can capitalize on this trend to increase revenues remain unclear,as this phenomenon is influenced by several confounding factors.This study aims to estimate and decompose the impact of the inclusiveness boom and bust in Brazil on its domestic intercity travel industry.By utilizing Instrumental Variables Least Absolute Shrinkage and Selection Operator(IV-LASSO)and Quantile Regression,our high-dimension sparse approach intends to estimate the effects of a set of economic mobility features on travel markets.We also employ a meta-machine learning approach based on Stacking Regression to assess the contribution of these features to revenue generation.Our findings suggest that airlines are more efficient than bus carriers at implementing market development strategies to achieve effective market inclusion.The customer retention rate for bus carriers is 32%lower,indicating the need to enhance demand management.Moreover,Stacking outperforms base machine learners in predicting revenues for both transport modes.Finally,an event study carried out for the economic downturn period shows a persistent adverse effect on demand and prices and identifies the moments when the machine learning models perform most poorly.展开更多
Web 3.0 technology will revolutionize the learning process,enabling data linking to connect learning resources and create ontologies for different areas of knowledge that enable‘smart searches.’Smart or semantic sea...Web 3.0 technology will revolutionize the learning process,enabling data linking to connect learning resources and create ontologies for different areas of knowledge that enable‘smart searches.’Smart or semantic searches perceive relationships among various pieces of information and present them to the learner.Connectivism has been proposed as a theory to guide learning in this new Web 3.0 environment.This paper discusses the relevance of connectivism and then develops an ontology for learning resources.The authors propose a hybrid similarity measure to evaluate the similarity among different learning resources.The paper presents a case study that was conducted to evaluate the proposed similarity measure on education data sets and demonstrates the effectiveness of the proposed methods.展开更多
Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for suc...Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for such territory risk classification.In this work,spatially constrained clustering is first applied to insurance loss data to form rating territories.The generalized linear model(GLM)and generalized linear mixed model(GLMM)are then proposed to derive the risk relativities of obtained clusters.Each basic rating unit within the same cluster,namely Forward Sortation Area(FSA),takes the same risk relativity value as its cluster.The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics,including RMSE,MAD,and Gini coefficients.The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process.展开更多
基金Taif University Researchers Supporting Project number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘Business Analytics is one of the vital processes that must be incorpo-rated into any business.It supports decision-makers in analyzing and predicting future trends based on facts(Data-driven decisions),especially when dealing with a massive amount of business data.Decision Trees are essential for business ana-lytics to predict business opportunities and future trends that can retain corpora-tions’competitive advantage and survival and improve their business value.This research proposes a tree-based predictive model for business analytics.The model is developed based on ranking business features and gradient-boosted trees.For validation purposes,the model is tested on a real-world dataset of Universal Bank to predict personal loan acceptance.It is validated based on Accuracy,Precision,Recall,and F-score.The experimentfindings show that the proposed model can predict personal loan acceptance efficiently and effectively with better accuracy than the traditional tree-based models.The model can also deal with a massive amount of business data and support corporations’decision-making process.
文摘This pioneering research represents a unique and singular study conducted within the United States, with a specific focus on non-technical graduate students pursuing degrees in business analytics. The primary impetus behind this study stems from the escalating demand for data-driven professionals, the diverse academic backgrounds of students, the imperative for adaptable pedagogical methods, the ever-evolving landscape of curriculum designs, and the overarching commitment to fostering educational equity. To investigate these multifaceted dynamics, we employed a data collection method that included the distribution of an online survey on platforms such as LinkedIn. Our survey reached and engaged 74 graduate students actively pursuing degrees in Business Analytics within the United States. This comprehensive research is the first and only one of its kind conducted in this context, and it serves as a vanguard exploration into the challenges and influences that shape the learning journey of Python among non-technical graduate Business Analytics students. The analytical insights derived from this research underscore the pivotal role of hands-on learning strategies, exemplified by practice exercises and assignments. Moreover, the study highlights the positive and constructive influence of collaboration and peer support in the process of learning Python. These invaluable findings significantly augment the existing body of knowledge in the field of business analytics. Furthermore, they offer an essential resource for educators and institutions seeking to optimize the educational experiences of non-technical students as they acquire essential Python skills.
基金supported by the P3MI Research Grant 2020,Institut Teknologi Bandung[grant number:3662/I1.B04/PL/2019].
文摘Bus rapid transit systems(BRT)have been an indispensable public transportation pillar,especially in densely populated regions.Accurate insight into the BRT network’s utilization is vital in BRT resource allocation planning contexts.This research focuses on how operators can utilize passengers’smart card data to develop origin-destination(OD)matrix-based business analytics.This research proposes a hybrid approach combining trip chaining,direct pairing,mode estimation methods,and visual analytics development.The novel approach is robust in handling incomplete smart card data transactions to generate origindestination matrices and corresponding visual analytics as decision support systems for the BRT operators.As a case study,we applied and validated the proposed analytics to more than 20.6 million smart card transactions in one of the largest global BRT systems in Jakarta,Indonesia.
文摘Business analytics presents significant opportunities for enhancing strategic decision-making(SDM),which is crucial for organizational competitiveness.However,there is a knowledge gap in understanding the interactions among environmental dynamism,business analytics use,environmental scanning,and rational and intuitive SDM.This paper addresses this gap by leveraging the information processing view and analyzing 218 survey responses using partial least squares(PLS)path modeling.It reveals that environmental dynamism influences business analytics use and environmental scanning.Business analytics use positively impacts rational SDM but negatively affects intuitive SDM.Environmental scanning partially mediates the relationship between business analytics use and rational SDM,and there is an inverse correlation between rational and intuitive SDM.This research introduces a novel theoretical framework,enriching the information processing view,and deepens understanding of how strategic information processing capabilities influence SDM.It also provides practical insights for organizations using business analytics to improve SDM processes in uncertain environments.
文摘Over the past few decades,with the development of automatic identification,data capture and storage technologies,people generate data much faster and collect data much bigger than ever before in business,science,engineering,education and other areas.Big data has emerged as an important area of study for both practitioners and researchers.It has huge impacts on data-related problems.In this paper,we identify the key issues related to big data analytics and then investigate its applications specifically related to business problems.
文摘Natural Language Processing(NLP),a branch of artificial intelligence,is gaining traction as a potent tool for business analytics.With the proliferation of unstructured textual data,businesses are actively seeking methodologies to distill valuable insights from vast textual repositories.The introduction of NLP in the realm of business analytics offers a transformative approach,automating traditional manual processes and fostering real-time,data-driven decisionmaking.From sentiment analysis to text summarization,NLP is facilitating businesses in deciphering consumer feedback,predicting market trends,and breaking down linguistic barriers in the age of globalization.This paper sheds light on the evolution of NLP techniques in business analytics,their applications,and the inherent challenges and opportunities they present.
文摘Need of transformation of means of support of project financing for commercial banks is proved.The analysis and modeling of business processes of project management by the contextual chart and the chart of decomposition is carried out that allowed to describe the main stages of project financing.With use of tools of programming the business application of project management which will promote operational assessment on selection of introduced drafts is created.
文摘Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns.The quantity and variety of computer data are growing exponentially for many reasons.For example,retailers are building vast databases of customer sales activity.Organizations are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and products.Challenges of big data include volume and variety in both structured and unstructured data.In this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional models.We studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision tree.We implemented naive Bayes and logistic regression classification models.Experimental results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.
基金São Paulo Research Foundation(FAPESP)-grants n.2015/19444-1 and 2020/01616-0National Council for Scientific and Technological Development(CNPq)-grants n.301654/2013-1,n.301344/2017-5.
文摘Capturing potential travel demand is crucial for carriers to improve their market performance,especially in developing economies with an emerging middle class and increasing socioeconomic inclusion.However,the impact of upward economic mobility on deregulated transport systems and how carriers can capitalize on this trend to increase revenues remain unclear,as this phenomenon is influenced by several confounding factors.This study aims to estimate and decompose the impact of the inclusiveness boom and bust in Brazil on its domestic intercity travel industry.By utilizing Instrumental Variables Least Absolute Shrinkage and Selection Operator(IV-LASSO)and Quantile Regression,our high-dimension sparse approach intends to estimate the effects of a set of economic mobility features on travel markets.We also employ a meta-machine learning approach based on Stacking Regression to assess the contribution of these features to revenue generation.Our findings suggest that airlines are more efficient than bus carriers at implementing market development strategies to achieve effective market inclusion.The customer retention rate for bus carriers is 32%lower,indicating the need to enhance demand management.Moreover,Stacking outperforms base machine learners in predicting revenues for both transport modes.Finally,an event study carried out for the economic downturn period shows a persistent adverse effect on demand and prices and identifies the moments when the machine learning models perform most poorly.
文摘Web 3.0 technology will revolutionize the learning process,enabling data linking to connect learning resources and create ontologies for different areas of knowledge that enable‘smart searches.’Smart or semantic searches perceive relationships among various pieces of information and present them to the learner.Connectivism has been proposed as a theory to guide learning in this new Web 3.0 environment.This paper discusses the relevance of connectivism and then develops an ontology for learning resources.The authors propose a hybrid similarity measure to evaluate the similarity among different learning resources.The paper presents a case study that was conducted to evaluate the proposed similarity measure on education data sets and demonstrates the effectiveness of the proposed methods.
文摘Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for such territory risk classification.In this work,spatially constrained clustering is first applied to insurance loss data to form rating territories.The generalized linear model(GLM)and generalized linear mixed model(GLMM)are then proposed to derive the risk relativities of obtained clusters.Each basic rating unit within the same cluster,namely Forward Sortation Area(FSA),takes the same risk relativity value as its cluster.The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics,including RMSE,MAD,and Gini coefficients.The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process.
