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Journal of Business Analytics最新文献

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Business analytics capability, organisational value and competitive advantage 业务分析能力，组织价值和竞争优势
Journal of Business Analytics Pub Date : 2019-07-03 DOI: 10.1080/2573234X.2019.1649991
M. O’Neill, A. Brabazon
{"title":"Business analytics capability, organisational value and competitive advantage","authors":"M. O’Neill, A. Brabazon","doi":"10.1080/2573234X.2019.1649991","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1649991","url":null,"abstract":"ABSTRACT Business Analytics makes the assumption that given a sufficient set of analytics capabilities exist within an organisation, the existence of these capabilities will result in the generation of organisational value and/or competitive advantage. Taken further, do enhanced capability levels lead to enhanced impact for organisations? Capability in this study is grounded in the four pillars of Governance, Culture, Technology and People from the Cosic, Shanks and Maynard capability framework. We set out to undertake the first empirical investigation to measure if there is a positive relationship between Business Analytics capability levels as defined by Cosic, Shanks and Maynard, and the generation of value and competitive advantage for organisations, and do enhanced capability levels lead to enhanced impact. Data gathered from a survey of 64 senior analytics professionals from 17 sectors provides evidence to support that a strong and statistically significant correlation exists between higher capability levels and the ability to generate enhanced organisational value and competitive advantage. Additionally, a revised definition of Business Analytics is proposed, given that Business Analytics should give rise to organisational value and/or competitive advantage and that for this to occur the necessary capabilities must be in place.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"18 1","pages":"160 - 173"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83926078","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: 12
The origins of business analytics and implications for the information systems field 商业分析的起源及其对信息系统领域的影响
Journal of Business Analytics Pub Date : 2019-07-03 DOI: 10.1080/2573234X.2019.1693912
N. Hassan
{"title":"The origins of business analytics and implications for the information systems field","authors":"N. Hassan","doi":"10.1080/2573234X.2019.1693912","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1693912","url":null,"abstract":"ABSTRACT Like many other disciplines, the information systems (IS) community has embraced big data analytics and data science. However, in the rush to exploit the popularity of this latest trend, the areas of big data analytics and data science that are most relevant to the IS field are not made clear. While many consider data analytics as an evolution of decision support systems (DSS), that is, as a technology that needs to be managed or enhanced, this essay traces the complex origins and philosophy of analytics instead back to Luhn’s text analytics in the late 1950s, Naur’s Computing as a Human Activity and his datalogy, Tukey’s Future of Data Analysis of the 1960s, and Codd’s relational database schema in the 1970s, well before big data analytics and data science became industry buzzwords. Many of what is now considered mainstream thinking in big data analytics and data science can be traced back to these visionaries. This essay examines the implications of the complex origins of data analytics and data science for the IS field, specifically on how those different discourses impact future research and practice.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"5 1","pages":"118 - 133"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81370719","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
An evidence-based management framework for business analytics 基于证据的业务分析管理框架
Journal of Business Analytics Pub Date : 2019-01-02 DOI: 10.1080/2573234X.2019.1609341
K. Scheibe, Sree Nilakanta, C. Ragsdale, Bob Younie
{"title":"An evidence-based management framework for business analytics","authors":"K. Scheibe, Sree Nilakanta, C. Ragsdale, Bob Younie","doi":"10.1080/2573234X.2019.1609341","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1609341","url":null,"abstract":"ABSTRACT It is said that knowledge is power, yet often, decision makers ignore information that ought to be considered. The phenomenon known as Semmelweis reflex occurs when new knowledge is rejected because it contradicts established norms. The goal of evidence-based management (EBMgt) is to help overcome Semmelweis reflex by integrating evaluated external evidence with stakeholder preference, practitioner experiences, and context. This evaluated external evidence is the product of scientific research. In this paper, we demonstrate an EBMgt business analytics model that uses computer simulation to provide scientific evidence to help decision makers evaluate equipment replacement problems, specifically the parallel machine replacement problem. The business analytics application is demonstrated in the form of a fleet management problem for a state transportation agency. The resulting analysis uses real-world data allowing decision makers to unfreeze their current system, move to a new state, and re-freeze a new system.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"154 1","pages":"47 - 62"},"PeriodicalIF":0.0,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75167244","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
Remedies against bias in analytics systems 针对分析系统偏见的补救措施
Journal of Business Analytics Pub Date : 2019-01-02 DOI: 10.1080/2573234X.2019.1633890
J. Edwards, E. Rodriguez
{"title":"Remedies against bias in analytics systems","authors":"J. Edwards, E. Rodriguez","doi":"10.1080/2573234X.2019.1633890","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1633890","url":null,"abstract":"ABSTRACT Advances in IT offer the possibility to develop ever more complex predictive and prescriptive systems based on analytics. Organizations are beginning to rely on the outputs from these systems without inspecting them, especially if they are embedded in the organization’s operational systems. This reliance could be misplaced unethical or even illegal if the systems contain bias. Data, algorithms and machine learning methods are all potentially subject to bias. In this article we explain the ways in which bias might arise in analytics systems, present some examples, and give some suggestions as to how to prevent or reduce it. We use a framework inspired by the work of Hammond, Keeney and Raiffa on psychological traps in human decision-making. Each of these traps “translates” into a potential type of bias for an analytics-based system. Fortunately, this means that remedies to reduce bias in human decision-making also translate into potential remedies for algorithmic systems.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"9 1","pages":"74 - 87"},"PeriodicalIF":0.0,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75257314","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
Predicting the point spread in professional basketball in real time: a data snapshot approach 实时预测职业篮球分差:数据快照方法
Journal of Business Analytics Pub Date : 2019-01-02 DOI: 10.1080/2573234X.2019.1625730
V. Kayhan, A. Watkins
{"title":"Predicting the point spread in professional basketball in real time: a data snapshot approach","authors":"V. Kayhan, A. Watkins","doi":"10.1080/2573234X.2019.1625730","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1625730","url":null,"abstract":"ABSTRACT Predicting the point spread of a professional basketball game is difficult but important for many stakeholders. We propose a new approach to predict the point spread in real time using in-game data. The approach uses a snapshot from the current game to identify historical games that have the same snapshot. After identifying these games, we predict the point spread of the current game using information obtained from the historical games. Using data obtained from six seasons of professional basketball games, we compare the prediction error of this approach to that of a deep learning technique, a long short-term memory network, and a general linear model. The proposed approach performs nearly the same as both models without the need for resource-intensive training. We discuss the robustness of this approach for making real-time predictions as games are underway. The findings have real-world implications for game enthusiasts, coaching staffs, and, most importantly, bettors.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"24 1","pages":"63 - 73"},"PeriodicalIF":0.0,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76735782","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
An empirical model of the decision to switch between electricity price contracts 电价合约转换决策的实证模型
Journal of Business Analytics Pub Date : 2019-01-02 DOI: 10.1080/2573234X.2019.1645575
Gauthier Lanot, Mattias Vesterberg
{"title":"An empirical model of the decision to switch between electricity price contracts","authors":"Gauthier Lanot, Mattias Vesterberg","doi":"10.1080/2573234X.2019.1645575","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1645575","url":null,"abstract":"ABSTRACT In this paper, we explore how sensitive the timing of switches between electricity contracts is to current and past prices. We present a model for time series of individual binary decisions which depends on the history of past and present prices. The model is based on the Bayesian learning procedure which is at the core of sequential decision-making. Given a-priori distributions of the information conditional on the state of the world, we show that the model captures dependence on past prices in a straightforward fashion. We estimate by maximum likelihood the parameters of the model on a sample of Swedish households who decide over time between competing electricity price contracts. The estimated parameters suggest that households do respond to prices by switching between contracts and that the response to price can be sizeable for alternative price processes. Importantly, the model structure implies that in general, the response to a price change will not be immediate but delayed.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"12 1","pages":"24 - 46"},"PeriodicalIF":0.0,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85449452","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
Tree-based methods for clustering time series using domain-relevant attributes 使用领域相关属性聚类时间序列的基于树的方法
Journal of Business Analytics Pub Date : 2019-01-02 DOI: 10.1080/2573234X.2019.1645574
Mahsa Ashouri, G. Shmueli, Chor-yiu Sin
{"title":"Tree-based methods for clustering time series using domain-relevant attributes","authors":"Mahsa Ashouri, G. Shmueli, Chor-yiu Sin","doi":"10.1080/2573234X.2019.1645574","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1645574","url":null,"abstract":"ABSTRACT We propose two methods for time-series clustering that capture temporal information (trend, seasonality, autocorrelation) and domain-relevant cross-sectional attributes. The methods are based on model-based partitioning (MOB) trees and can be used as automated yet transparent tools for clustering large collections of time series. We address the challenge of using common time-series models in MOB by instead utilising least squares regression. We propose two methods. The single-step method clusters series using trend, seasonality, lags and domain-relevant cross-sectional attributes. The two-step method first clusters by trend, seasonality and cross-sectional attributes, and then clusters the residuals by autocorrelation and domain-relevant attributes. Both methods produce clusters interpretable by domain experts. We illustrate our approach by considering one-step-ahead forecasting and compare to autoregressive integrated moving average (ARIMA) models for forecasting many Wikipedia pageviews time series. The tree-based approach produces forecasts on par with ARIMA, yet is significantly faster and more efficient, thereby suitable for large collections of time-series. The simple parametric forecasting models allow for interpretable time-series clusters.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"41 1","pages":"1 - 23"},"PeriodicalIF":0.0,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89638501","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: 11
Optimizing management of emergency gas leaks: a case study in business analytics 优化应急气体泄漏管理:业务分析中的案例研究
Journal of Business Analytics Pub Date : 2019-01-02 DOI: 10.1080/2573234X.2019.1638735
Charles Hadlock, S. Woolford
{"title":"Optimizing management of emergency gas leaks: a case study in business analytics","authors":"Charles Hadlock, S. Woolford","doi":"10.1080/2573234X.2019.1638735","DOIUrl":"https://doi.org/10.1080/2573234X.2019.1638735","url":null,"abstract":"ABSTRACT Managing the response to reported gas leaks is of significant importance to both utilities and regulators. This paper utilizes a business analytics approach to investigate strategies for managing gas leak response while balancing the objectives of both utilities and regulators. The approach integrates the translation of the business issue into a quantitative framework by which actual gas leak data can be analysed and modelled to measure the performance of different gas leak response strategies. An agent-based simulation model is utilized to provide a decision support platform that translates the analytic results into a visualization tool to assist stakeholders and decision makers in evaluating the impact of different response strategies. The paper highlights both the analytic methods and the related “soft skills” that must be managed in the business analytics context to ensure an outcome that is acceptable for all stakeholders.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"32 1","pages":"88 - 99"},"PeriodicalIF":0.0,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75703068","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
6 Fallstudien
Journal of Business Analytics Pub Date : 2019-01-01 DOI: 10.15358/9783800658725-217
Mischa Seiter
{"title":"6 Fallstudien","authors":"Mischa Seiter","doi":"10.15358/9783800658725-217","DOIUrl":"https://doi.org/10.15358/9783800658725-217","url":null,"abstract":"","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"102 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75692721","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
Literaturverzeichnis Literaturverzeichnis
Journal of Business Analytics Pub Date : 2019-01-01 DOI: 10.15358/9783800658725-241
Mischa Seiter
{"title":"Literaturverzeichnis","authors":"Mischa Seiter","doi":"10.15358/9783800658725-241","DOIUrl":"https://doi.org/10.15358/9783800658725-241","url":null,"abstract":"","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80824214","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
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