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In-game win probability models for Canadian football 加拿大足球游戏中的获胜概率模型
Journal of Business Analytics Pub Date : 2021-12-18 DOI: 10.1080/2573234X.2021.2015252
S. Hill
{"title":"In-game win probability models for Canadian football","authors":"S. Hill","doi":"10.1080/2573234X.2021.2015252","DOIUrl":"https://doi.org/10.1080/2573234X.2021.2015252","url":null,"abstract":"ABSTRACT This article presents in-game win probability models for Canadian football. Play-by-play and wagering data for games from the Canadian Football League for the 2015 to 2019 seasons is used to create logistic regression and gradient boosting models. Models with and without the effect of pregame spread and total (over/under) data are presented and discussed. The resulting win probability models are well-calibrated and can be used to support in-game decision-making, review coaching decisions, estimate the magnitude of team “comebacks”, and potentially identify in-game wagering opportunities. An R Shiny application is provided to allow for estimation of in-game win probability for user-provided game state inputs. Opportunities for future work are identified and described.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82034173","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
Detecting temporal workarounds in business processes – A deep-learning-based method for analysing event log data 检测业务流程中的临时变通方法——一种基于深度学习的分析事件日志数据的方法
Journal of Business Analytics Pub Date : 2021-12-07 DOI: 10.1080/2573234X.2021.1978337
Sven Weinzierl, Verena Wolf, Tobias Pauli, D. Beverungen, Martin Matzner
{"title":"Detecting temporal workarounds in business processes – A deep-learning-based method for analysing event log data","authors":"Sven Weinzierl, Verena Wolf, Tobias Pauli, D. Beverungen, Martin Matzner","doi":"10.1080/2573234X.2021.1978337","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1978337","url":null,"abstract":"ABSTRACT Business process management distinguishes the actual “as-is” and a prescribed “to-be” state of a process. In practice, many different causes trigger a process’s drifting away from its to-be state. For instance, employees may “workaround” the proposed systems to increase their effectiveness or efficiency in day-to-day work. So far, ethnography or critical incident techniques are used to identify how and why workarounds emerge. We design a deep-learning-based method that helps detect different workaround types in event logs. Our method tracks indications of potential workarounds in the early stages of their emergence among deviating behaviour. Our evaluation based on four real-life event logs reveals that our method performs well and works best for business processes with fewer variations and a higher number of different activities. The proposed method is one of the first information technology artefacts to bridge the boundaries between the complementing research disciplines of organisational routines and business processes management.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79830638","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: 9
Optimal trimming proportion in regression analysis for non-normal distributions 非正态分布回归分析中的最优修剪比例
Journal of Business Analytics Pub Date : 2021-11-29 DOI: 10.1080/2573234X.2021.2007803
Amit Mitra, Pankush Kalgotra
{"title":"Optimal trimming proportion in regression analysis for non-normal distributions","authors":"Amit Mitra, Pankush Kalgotra","doi":"10.1080/2573234X.2021.2007803","DOIUrl":"https://doi.org/10.1080/2573234X.2021.2007803","url":null,"abstract":"ABSTRACT Regression analysis is a widely used modelling tool in business decision making. However, proper application of this methodology requires that certain assumptions, associated with the model, be satisfied. The assumption we focus on is the normality of the response variable, which is directly related to the assumption of normality of the error component. In a variety of fields in business, such as finance, marketing, information systems, operations, and healthcare, the selected dependent variable does not inherently have a normal distribution. In the regression context, where the model parameters and independent variables are assumed to be constant, the distribution of the random error component thus influences the distribution of the dependent variable. Here, we study the impact of symmetric and asymmetric error distributions on the performance of the estimated model parameters. To create robust estimates, through a process of trimming the response variable, we study the effectiveness of the trimmed estimators with respect to the ordinary least squares estimator (OLS) via a simulation procedure. Accordingly, to minimise the ratio of the mean squared error of the trimmed estimator to that of the OLS, a recommendation is developed for the optimal trimming proportion.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89816712","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
Using efficiency frontiers to visualise suppliers’ performance capabilities: moving beyond supplier rationalisation 利用效率边界可视化供应商绩效能力:超越供应商合理化
Journal of Business Analytics Pub Date : 2021-11-26 DOI: 10.1080/2573234X.2021.1999179
Osman T. Aydas, Anthony D. Ross, Hamieda Parker, Sepideh Alavi
{"title":"Using efficiency frontiers to visualise suppliers’ performance capabilities: moving beyond supplier rationalisation","authors":"Osman T. Aydas, Anthony D. Ross, Hamieda Parker, Sepideh Alavi","doi":"10.1080/2573234X.2021.1999179","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1999179","url":null,"abstract":"ABSTRACT This paper offers a framework for analysis to benefit buying firms as they evaluate current and prospective suppliers, and to assist supplying organisations in becoming more competitive. It explores the notion of performance improvement frontiers for suppliers, in the context of developing suppliers rather than rationalising or pruning them. Dual-efficiency (strengths and weaknesses) frontiers are constructed using inverted efficiency techniques. Unilateral and bilateral approaches to the construction of these frontiers are examined. It is found that certain information content of bilaterally determined DEA assurance ranges can serve as a compromise between the buyer’s ideal performance priorities and a supplier’s capability-based priorities. For this reason, it represents a reasonable and jointly determined set of performance expectations for buyers to recommend to the supplier set. For the suppliers themselves, the bilateral ranges contribute a prioritised behavioural focus to develop or improve their capabilities on specific performance attributes.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74939232","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
The winding road of requesting healthcare data for analytics purposes: using the one-interview mental model method for improving services of health data governance and big data request processes 为分析目的请求医疗数据的曲折之路:使用一次访谈心理模型方法改进医疗数据治理和大数据请求流程的服务
Journal of Business Analytics Pub Date : 2021-10-28 DOI: 10.1080/2573234X.2021.1992305
Kanupriya Singh, I. Jahnke, A. Mosa, P. Calyam
{"title":"The winding road of requesting healthcare data for analytics purposes: using the one-interview mental model method for improving services of health data governance and big data request processes","authors":"Kanupriya Singh, I. Jahnke, A. Mosa, P. Calyam","doi":"10.1080/2573234X.2021.1992305","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1992305","url":null,"abstract":"ABSTRACT Medical schools store large sets of patient data. The data is important for the analysis of trends and patterns in healthcare practice. However, obtaining access to the data can be problematic due to the data protection mechanisms. In this study, we investigate the current practices from the lens of both the data requester and the data provider. Results reveal discrepancies between how the provider organises the data governance process, how the process is presented to the data requester, and the data requester’s perception of satisfactory user experience. This study provides a simple one interview mental model method approach for data governance services to reveal potential problems in the process. This is a quick and effective method for data providers to help uncover the challenges and to provide foundations for future fully automated (human out of the loop) systems for data accessibility in healthcare organisations.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89476063","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
Evaluation of machine learning applications using real-world EHR data for predicting diabetes-related long-term complications 利用真实世界的电子病历数据评估机器学习应用，以预测糖尿病相关的长期并发症
Journal of Business Analytics Pub Date : 2021-09-21 DOI: 10.1080/2573234X.2021.1979901
A. Mosa, Chalermpon Thongmotai, Humayera Islam, Tanmoy Paul, K. S. M. T. Hossain, Vasanthi Mandhadi
{"title":"Evaluation of machine learning applications using real-world EHR data for predicting diabetes-related long-term complications","authors":"A. Mosa, Chalermpon Thongmotai, Humayera Islam, Tanmoy Paul, K. S. M. T. Hossain, Vasanthi Mandhadi","doi":"10.1080/2573234X.2021.1979901","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1979901","url":null,"abstract":"ABSTRACT The biggest concern about diabetes-related complications is that they are unrecognised in the early stages but can be immutable and devastating with time. Identifying the population at high risk of developing such complications can help intervene in preventative care at an early stage. This study aims to present a data-driven approach to predict the patients at higher risk for diabetes-related complications using real-world data. We used comorbid diagnostic features from the electronic health records called “Cerner Health Facts EMR Data” to build machine learning-based prediction models for three diabetes-related long-term complications: (a) eye diseases, (b) kidney diseases, and (c) neuropathy. Our developed pipeline was able to generate highly accurate models for predictions. We deduced from the F1-scores that applying the class balancing techniques improved the overall performance of the models, and SVM with oversampling technique was the most consistent classifier for all three cohorts.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86542544","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
Data ownership revisited: clarifying data accountabilities in times of big data and analytics 重新审视数据所有权:在大数据和分析时代澄清数据责任
Journal of Business Analytics Pub Date : 2021-08-04 DOI: 10.1080/2573234X.2021.1945961
Martin Fadler, Christine Legner
{"title":"Data ownership revisited: clarifying data accountabilities in times of big data and analytics","authors":"Martin Fadler, Christine Legner","doi":"10.1080/2573234X.2021.1945961","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1945961","url":null,"abstract":"ABSTRACT Today, a myriad of data is generated via connected devices and digital applications. In order to benefit from these data, companies have to develop their capabilities related to big data and analytics (BDA). A critical factor that is often cited concerning the “soft” aspects of BDA is data ownership, i.e., clarifying the fundamental rights and responsibilities for data. IS research has investigated data ownership for operational systems and data warehouses, where the purpose of data processing is known. In the BDA context, defining accountabilities for data is more challenging because data are stored in data lakes and used for previously unknown purposes. Based on four case studies, we identify ownership principles and three distinct types: data, data platform, and data product ownership. Our research answers fundamental questions about how data management changes with BDA and lays the foundation for future research on data and analytics governance.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80728402","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: 7
A social evaluation of the perceived goodness of explainability in machine learning 对机器学习中可解释性的感知良善的社会评价
Journal of Business Analytics Pub Date : 2021-07-25 DOI: 10.1080/2573234X.2021.1952913
Jonas Wanner, L. Herm, K. Heinrich, Christian Janiesch
{"title":"A social evaluation of the perceived goodness of explainability in machine learning","authors":"Jonas Wanner, L. Herm, K. Heinrich, Christian Janiesch","doi":"10.1080/2573234X.2021.1952913","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1952913","url":null,"abstract":"ABSTRACT Machine learning in decision support systems already outperforms pre-existing statistical methods. However, their predictions face challenges as calculations are often complex and not all model predictions are traceable. In fact, many well-performing models are black boxes to the user who– consequently– cannot interpret and understand the rationale behind a model’s prediction. Explainable artificial intelligence has emerged as a field of study to counteract this. However, current research often neglects the human factor. Against this backdrop, we derived and examined factors that influence the goodness of a model’s explainability in a social evaluation of end users. We implemented six common ML algorithms for four different benchmark datasets in a two-factor factorial design and asked potential end users to rate different factors in a survey. Our results show that the perceived goodness of explainability is moderated by the problem type and strongly correlates with trustworthiness as the most important factor.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76498456","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: 6
Induction of a sentiment dictionary for financial analyst communication: a data-driven approach balancing machine learning and human intuition 金融分析师沟通情感词典的归纳:一种平衡机器学习和人类直觉的数据驱动方法
Journal of Business Analytics Pub Date : 2021-07-19 DOI: 10.1080/2573234X.2021.1955022
Matthias Palmer, J. Roeder, Jan Muntermann
{"title":"Induction of a sentiment dictionary for financial analyst communication: a data-driven approach balancing machine learning and human intuition","authors":"Matthias Palmer, J. Roeder, Jan Muntermann","doi":"10.1080/2573234X.2021.1955022","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1955022","url":null,"abstract":"ABSTRACT While sentiment dictionaries are easy to apply and provide reproducible results, they often exhibit inferior classification performance compared to machine learning approaches trained for specific application domains. Nevertheless, both approaches typically require manual data analysis. This paper develops a domain-specific dictionary using regularised linear models drawing from textual reports of financial analysts. The first evaluation step demonstrates that the developed financial analyst dictionary can explain cumulative abnormal stock returns related to earnings events more accurately compared to other finance-related dictionaries and sentiment classifiers. In a second step, the approaches are compared using manually annotated sentiment. The financial analyst dictionary is more accurate than other dictionary-based approaches, although it cannot compete with a pre-trained deep learning sentiment classifier. While we show that the proposed approach is suited for texts of financial analysts, it can be applied to other use cases. The approach realises context specificity while reducing extensive manual data analysis.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75638498","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: 5
Establishing and theorising data analytics governance: a descriptive framework and a VSM-based view 建立和理论化数据分析治理:一个描述性框架和一个基于vsm的视图
Journal of Business Analytics Pub Date : 2021-07-16 DOI: 10.1080/2573234X.2021.1955021
J. Baijens, Tim Huygh, R. Helms
{"title":"Establishing and theorising data analytics governance: a descriptive framework and a VSM-based view","authors":"J. Baijens, Tim Huygh, R. Helms","doi":"10.1080/2573234X.2021.1955021","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1955021","url":null,"abstract":"ABSTRACT The rise of big data has led to many new opportunities for organisations to create value from data. However, an increasing dependence on data also poses many challenges for organisations. To overcome these challenges, organisations have to establish data analytics governance. Leading IT and information governance literature shows that governance can be implemented through mechanisms. The data analytics literature is not very abundant in describing specific governance mechanisms. Hence, there is a need to identify and describe specific data analytics governance mechanisms. To this end, a preliminary framework based on literature was developed and validated using a multiple case study design. This resulted in an extended descriptive framework that can aide managers in implementing data analytics governance. Furthermore, we draw on viable system model (VSM) theory to make a theoretical contribution by discussing how data analytics governance can contnue to fulfil its purpose of creating (business) value from data.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76967427","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: 8
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