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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":"17 1","pages":"19 - 38"},"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":"31 1","pages":"1 - 18"},"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":"34 1","pages":"141 - 151"},"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":"58 1","pages":"123 - 139"},"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":"39 1","pages":"29 - 50"},"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":"39 1","pages":"8 - 28"},"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":"225 1","pages":"101 - 122"},"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
A Combined Approach For Collaborative Filtering Based Recommender Systems with Matrix Factorisation and Outlier Detection 基于矩阵分解和离群点检测的协同过滤推荐系统
Journal of Business Analytics Pub Date : 2021-07-03 DOI: 10.1080/2573234X.2021.1947752
V. P, V. G, K. S. Joseph
{"title":"A Combined Approach For Collaborative Filtering Based Recommender Systems with Matrix Factorisation and Outlier Detection","authors":"V. P, V. G, K. S. Joseph","doi":"10.1080/2573234X.2021.1947752","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1947752","url":null,"abstract":"ABSTRACT Recommender system is a data sifting tool that can recommend items that can be of interest to the user. Collaborative filtering (CF) makes recommendations based on the ratings the users give to items. But noisy or inaccurate ratings reduce the quality of the recommendations. In spite of extensive studies carried on CF-based recommenders, a robust recommender to handle outlier in dataset is a challenging problem. In this study, a Factor wise Matrix Factorisation model (FWMF) is proposed for the prediction of item rating in recommender systems. To further strengthen the proposed FWMF model, a meta learning model that combines density-based outlier detection and bagging outlier detection is proposed to detect outliers. The outliers predicted are eliminated, and a comparative analysis is carried with FWMF to find the effect of outliers in making recommendations. The experiments were analysed with various error metrics conducted on benchmark dataset show that the proposed outlier extent recommendation model outperforms the conventional CF-based systems.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"17 1","pages":"111 - 124"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74204149","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
Revenue characterisation with Singular Spectrum Analysis 用奇异频谱分析描述收入特征
Journal of Business Analytics Pub Date : 2021-07-03 DOI: 10.1080/2573234X.2021.1970483
R. Sambasivan
{"title":"Revenue characterisation with Singular Spectrum Analysis","authors":"R. Sambasivan","doi":"10.1080/2573234X.2021.1970483","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1970483","url":null,"abstract":"ABSTRACT In this work, a method to characterise the daily sales revenue for an online store is presented. Daily sales revenue is a time series. The developed characterisation identifies the major sources of variation in the time series. Such a characterisation can be used for purposes such as developing structural forecasting models and extracting insights that can be leveraged for business and operations planning. In this work, this characterisation is developed using a technique called Singular Spectrum Analysis. Achieving good results with Singular Spectrum Analysis requires the judicious selection of an algorithm parameter called the window length. A framework to select this parameter is provided. Literature survey revealed that applications of Singular Spectrum Analysis to business data are limited. To the best of found knowledge from the literature survey, Singular Spectrum Analysis has not been applied to retail revenue stream analysis.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"27 1","pages":"140 - 154"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78546384","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
Success Factors Affecting the Intention to Use Business Analytics: An Empirical Study 影响商业分析意向的成功因素:实证研究
Journal of Business Analytics Pub Date : 2021-06-20 DOI: 10.1080/2573234X.2021.1943017
Hokey Min, Hey-Young Joo, Seok-Beom Choi
{"title":"Success Factors Affecting the Intention to Use Business Analytics: An Empirical Study","authors":"Hokey Min, Hey-Young Joo, Seok-Beom Choi","doi":"10.1080/2573234X.2021.1943017","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1943017","url":null,"abstract":"ABSTRACT In the era of knowledge-based economy, the firm’s ability to derive actionable insights from big data can be a game changer. Such ability can be developed and nurtured by utilising BA which is designed to help business executives and policy makers make well-thought and informed decisions. To have a clear picture of what will lead to the serious consideration of BA as a business intelligence tool, this paper identifies contextual variables that include privacy, security, risk concerns, information technology (IT) capability and perceived value that may significantly influence the firm’s intention to use BA. This paper conducted confirmatory factor analyses and used the structural equation model to determine what either motivate or inhibit the BA adoption Through a series of hypothesis testing, we discovered that higher security and risk concerns along with a lack of IT capability became important deterrents to BA adoption. Also, we found that firms which recognised the value of BA were more inclined to adopt BA than the others. This paper is one of the first attempts to develop practical guidelines for the potential adopters of BA based on the empirical study of BA practices among the Korean firms.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"94 1","pages":"77 - 90"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72661368","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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