Showing papers on "Business analytics published in 2018"
TL;DR: The historical development, architectural design and component functionalities of big data analytics, including analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability and traceability are examined.
941 citations
TL;DR: A big data analytics-enabled transformation model based on practice-based view is developed, which reveals the causal relationships among big data Analytics capabilities, IT-enabled Transformation practices, benefit dimensions, and business values and provides practical insights for managers.
315 citations
TL;DR: The results of an econometric study that analyzes the direction, sign, and magnitude of the relationship between BDA and firm performance based on objective measurements of BDA assets find that live BDA Assets are associated with an average of 3–7 percent improvement in firm productivity.
Abstract: The emergence of big data has stimulated enormous investments into business analytics solutions, but large-scale and reliable empirical evidence about the business value of big data and analytics (...
242 citations
TL;DR: This editorial discusses that in order to reach digital transformation and the creation of sustainable societies, first, none of the actors in the society can be seen in isolation, instead the authors need to improve their understanding of their interactions and interrelations that lead to knowledge, innovation, and value creation.
Abstract: The digitalization process and its outcomes in the 21st century accelerate transformation and the creation of sustainable societies. Our decisions, actions and even existence in the digital world generate data, which offer tremendous opportunities for revising current business methods and practices, thus there is a critical need for novel theories embracing big data analytics ecosystems. Building upon the rapidly developing research on digital technologies and the strengths that information systems discipline brings in the area, we conceptualize big data and business analytics ecosystems and propose a model that portraits how big data and business analytics ecosystems can pave the way towards digital transformation and sustainable societies, that is the Digital Transformation and Sustainability (DTS) model. This editorial discusses that in order to reach digital transformation and the creation of sustainable societies, first, none of the actors in the society can be seen in isolation, instead we need to improve our understanding of their interactions and interrelations that lead to knowledge, innovation, and value creation. Second, we gain deeper insight on which capabilities need to be developed to harness the potential of big data analytics. Our suggestions in this paper, coupled with the five research contributions included in the special issue, seek to offer a broader foundation for paving the way towards digital transformation and sustainable societies
235 citations
TL;DR: This study develops and validates the concept of Data Analytics Competency as a five multidimensional formative index and empirically examines its impact on firm decision making performance and reveals that all dimensions of data analytics competency significantly improve decision quality.
Abstract: The concept of Data Analytics (DA) competency has been conceptualized and validated.The impact of DA competency on decision making performance is empirically examined.All dimensions of DA competency significantly improve decision quality.All dimensions, except bigness of data, significantly increase decision efficiency. This study develops and validates the concept of Data Analytics Competency as a five multidimensional formative index (i.e., data quality, bigness of data, analytical skills, domain knowledge, and tools sophistication) and empirically examines its impact on firm decision making performance (i.e., decision quality and decision efficiency). The findings based on an empirical analysis of survey data from 151 Information Technology managers and data analysts demonstrate a large, significant, positive relationship between data analytics competency and firm decision making performance. The results reveal that all dimensions of data analytics competency significantly improve decision quality. Furthermore, interestingly, all dimensions, except bigness of data, significantly increase decision efficiency. This is the first known empirical study to conceptualize, operationalize and validate the concept of data analytics competency and to study its impact on decision making performance. The validity of the data analytics competency construct as conceived and operationalized, suggests the potential for future research evaluating its relationships with possible antecedents and consequences. For practitioners, the results provide important guidelines for increasing firm decision making performance through the use of data analytics.
173 citations
Posted Content•
TL;DR: In this paper, the authors provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning and highlight the value of customized architectures by proposing a novel deep-embedded network.
Abstract: Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional machine learning. However, our research into the existing body of literature reveals a scarcity of research works utilizing deep learning in our discipline. Accordingly, the objectives of this overview article are as follows: (1) we review research on deep learning for business analytics from an operational point of view. (2) We motivate why researchers and practitioners from business analytics should utilize deep neural networks and review potential use cases, necessary requirements, and benefits. (3) We investigate the added value to operations research in different case studies with real data from entrepreneurial undertakings. All such cases demonstrate improvements in operational performance over traditional machine learning and thus direct value gains. (4) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning. (5) Our computational experiments find that default, out-of-the-box architectures are often suboptimal and thus highlight the value of customized architectures by proposing a novel deep-embedded network.
143 citations
TL;DR: Evidence from the findings suggests that knowledge-based interactions between the customers and the salesforce in those organizations form the core of knowledge co-creation, which can in turn adequately lead to evidence-based, effective and efficient decision making for better business returns.
136 citations
TL;DR: The findings show that market turbulence has negative universal effects and that agile manufacturing enablers are being progressively deployed and aided by BDBA to yield better competitive and business performance objectives.
Abstract: The purpose of this study was to examine the role of big data and business analytics (BDBA) in agile manufacturing practices. Literature has discussed the benefits and challenges related to the deployment of big data within operations and supply chains, but there has not been a study of the facilitating roles of BDBA in achieving an enhanced level of agile manufacturing practices. As a response to this gap, and drawing upon multiple qualitative case studies undertaken among four U.K. organizations, we present and validate a framework for the role of BDBA within agile manufacturing. The findings show that market turbulence has negative universal effects and that agile manufacturing enablers are being progressively deployed and aided by BDBA to yield better competitive and business performance objectives. Further, the level of intervention was found to differ across companies depending on the extent of deployment of BDBA, which accounts for variations in outcomes.
127 citations
19 Nov 2018
TL;DR: The focus of this article is how to transition from analytics to AI, with three eras of analytical focus detailed, with AI portrayed as a fourth era.
Abstract: Analytics have been employed by companies for several decades, but now many firms are interested in building their capabilities for artificial intelligence (AI). Many AI systems, however, are based...
120 citations
TL;DR: An ontology of big data analytics is proposed and presented and a big data Analytics service-oriented architecture (BASOA) is presented, and the proposed BASOA is viable for enhancing business intelligence and enterprise information systems.
Abstract: This article examines how to use big data analytics services to enhance business intelligence (BI). More specifically, this article proposes an ontology of big data analytics and presents a big data analytics service-oriented architecture (BASOA), and then applies BASOA to BI, where our surveyed data analysis shows that the proposed BASOA is viable for enhancing BI and enterprise information systems. This article also explores temporality, expectability, and relativity as the characteristics of intelligence in BI. These characteristics are what customers and decision makers expect from BI in terms of systems, products, and services of organizations. The proposed approach in this article might facilitate the research and development of business analytics, big data analytics, and BI as well as big data science and big data computing.
115 citations
23 Aug 2018
TL;DR: An overview of research challenges and opportunities for business analytics is provided to lay the groundwork for this new journal, which is destined to become the pinnacle for rigorous and relevant analytics research manuscripts.
Abstract: There are plenty of definitions proposed for business analytics – some of them focus on the scope/coverage/problem, some on the nature of the data, and some concentrate on the enabling methods and ...
TL;DR: The purpose in this paper is to illustrate how analytics, as a complement, rather than a successor, to the traditional research paradigm, can be used to address interesting emerging business research questions.
TL;DR: PA was largely aligned with HRM, however its development reflects the shifting focus of HR departments from supporting functional to strategic organisational requirements, and consideration of ethical issues was largely absent.
TL;DR: In this paper, big data analytics (BDA) has the potential to improve demand forecasting, communications and better manage supply chain resources, despite the recognized benefits and the increase of BDA research.
Abstract: Big Data Analytics (BDA) has the potential to improve demand forecasting, communications and better manage supply chain resources. Despite such recognised benefits and the increase of BDA research,...
TL;DR: A business analytics approach that mines customer visit segments from basket sales data that extracts knowledge that may support several decisions ranging from marketing campaigns per customer segment, redesign of a store's layout to product recommendations.
Abstract: Basket analytics is a powerful tool in the retail context for acquiring knowledge about consumer shopping habits and preferences. In this paper, we propose a business analytics approach that mines customer visit segments from basket sales data. We characterize a customer visit by the purchased product categories in the basket and identify the shopping intention or mission behind the visit e.g. a ‘breakfast’ visit to purchase cereal, milk, bread, cheese etc. We also suggest a semi-supervised feature selection approach that uses the product taxonomy as input and suggests customized categories as output. This approach is utilized to balance the product taxonomy tree that has a significant effect on the data mining results. We demonstrate the utility of our approach by applying it to a real case of a major European fast-moving consumer goods (FMCG) retailer. Apart from its theoretical contribution, the proposed approach extracts knowledge that may support several decisions ranging from marketing campaigns per customer segment, redesign of a store's layout to product recommendations.
TL;DR: This research extends the model to business analytics, to identify elements of analytics technology assets and business analytics capability and to understand the mechanism of business value creation using multiple case studies.
TL;DR: A survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries is presented in this paper.
Abstract: High performance computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show that hybrid environments are the natural path to get the best of the on-premise and cloud resources—steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This article brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.
09 Jul 2018
TL;DR: The analysis of the antecedents or determinant factors of the adoption of business analytics (BA) is an important research topic as discussed by the authors, drawing on the arguments of the resource based view and dynamic cap...
Abstract: The analysis of the antecedents or determinant factors of the adoption of business analytics (BA) is an important research topic. Drawing on the arguments of the resource based view and dynamic cap...
TL;DR: An approach to conducting workforce analytics that is designed to improve strategy execution and organizational effectiveness through the application of systems diagnostics is introduced.
Abstract: In this article, I introduce an approach to conducting workforce analytics that is designed to improve strategy execution and organizational effectiveness through the application of systems diagnostics What differentiates the approach are two analytic steps that precede the analyses that are typical of workforce analytics today: competitive advantage analytics and enterprise analytics Conducting these two additional steps enables the analyst to identify the critical business issues that are the biggest problems for senior business leaders, and to determine if structural issues coming from the organization design and culture are at play First conducting those analyses best enables traditional workforce analytics to provide insights the organization's leadership views as truly valuable
Book•
28 Jun 2018
TL;DR: Practical Guide to Logistic Regression as mentioned in this paper covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable, which can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fishe
Abstract: Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fishe
TL;DR: Based on 170 samples from firm-level survey, the nomological linkage from IT competence to CRM performance is analyzed and the results show data management capability fully mediates between IT competence and BA use, while customer response capability partially mediating between BA use andCRM performance.
TL;DR: This synthesis organizes audit research, thereby offering guidelines regarding possible future research about approaches for more complex and data driven analytics in the engagement and identifying gaps.
TL;DR: This research work is to find meaningful indicators or metrics in a learning context and to study the inter-relationships between these metrics using the concepts of Learning Analytics and Educational Data Mining, thereby, analyzing the effects of different features on student’s performance using Disposition analysis.
Abstract: Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research work is to find meaningful indicators or metrics in a learning context and to study the inter-relationships between these metrics using the concepts of Learning Analytics and Educational Data Mining, thereby, analyzing the effects of different features on student's performance using Disposition analysis. In this project, K-means clustering data mining technique is used to obtain clusters which are further mapped to find the important features of a learning context. Relationships between these features are identified to assess the student's performance.
TL;DR: A focused study on market state modeling and analysis for online P2P lending, which proposes two enhanced sequential models by extending the Bayesian hidden Markov model (BHMM), and demonstrates several motivating applications enabled by the models, such as bidding prediction and herding detection.
Abstract: Online peer-to-peer (P2P) lending is an emerging wealth-management service for individuals, which allows lenders to directly bid and invest on the listings created by borrowers without going through any traditional financial intermediaries. As a nonbank financial platform, online P2P lending tends to have both high volatility and liquidity. Therefore, it is of significant importance to discern the hidden market states of the listings (e.g., hot and cold), which open venues for enhancing business analytics and investment decision making. However, the problem of market state modeling remains pretty open due to many technical and domain challenges, such as the dynamic and sequential characteristics of listings. To that end, in this paper, we present a focused study on market state modeling and analysis for online P2P lending. Specifically, we first propose two enhanced sequential models by extending the Bayesian hidden Markov model (BHMM), namely listing-BHMM (L-BHMM) and listing and marketing-BHMM (LM-BHMM), for learning the latent semantics between listings’ market states and lenders’ bidding behaviors. Particularly, L-BHMM is a straightforward model that only considers the local observations of a listing itself, while LM-BHMM considers not only the listing information but also the global information of current market (e.g., the competitive and complementary relations among listings). Furthermore, we demonstrate several motivating applications enabled by our models, such as bidding prediction and herding detection. Finally, we construct extensive experiments on two real-world data sets and make some deep analysis on bidding behaviors, which clearly validate the effectiveness of our models in terms of different applications and also reveal some interesting business findings.
TL;DR: In this paper, business performance analytics (BPA) entails the systematic use of data and analytical methods (mathematical, econometric and statistical) for performance measurement and management.
Abstract: Business Performance Analytics (BPA) entails the systematic use of data and analytical methods (mathematical, econometric and statistical) for performance measurement and management. Although poten...
TL;DR: This research presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and cataloging data to provide insights into its underlying mechanisms.
Abstract: The rapid accumulation of data in diverse forms and from various sources has been driving an increasing interest in big data and business analytics. Applications of a variety of analytical techniqu...
TL;DR: In this paper, a special issue addressed sustainable consumption and production (SCP) in business decision-making model by examining novel methods, practices, and opportunities, and concluded that the issues of SCP should be explored in different business contexts.
Abstract: This special issue addresses sustainable consumption and production (SCP) in business decision-making model by examining novel methods, practices, and opportunities. The articles present and analyze top-down sustainability efforts as well as bottom-up efforts on the models, customer perceptions, business decision-making models and proposed solution methods This editorial note summarizes the discussions on the business decision-making operational attributes, sustainable consumption and production practices, and on evaluation and practical methods A dominant finding is that the issues of SCP in business decision-making model should be explored in different business contexts.
TL;DR: The way that data and analytics are imagined shapes their incorporation and appropriation into practices and organisational structures – what I call here the data frontiers.
Abstract: It could be argued that the power of data is located in what they are used to reveal. Yet we have little understanding of the role played by the emerging industry of data analytics in the interpretation and use of big data. These data analytics companies act as intermediaries in the digital data revolution. Understanding the social influence of big data requires us to understand the role played by data analytics within organisations of different types. This particular article focuses very specifically upon the way in which data and data analytics are envisioned within the marketing rhetoric of the data analytics industry. It is argued that to understand the spread of data analytics and the adoption of certain analytic strategies, we first need to look at the projection of promises upon that data. The way that data and analytics are imagined shapes their incorporation and appropriation into practices and organisational structures – what I call here the data frontiers. This article draws upon a samp...
TL;DR: A data science toolbox for manufacturing prediction tasks is developed to bridge the gap between machine learning research and concrete practical needs and seeks to enhance the understanding of predictive modeling.
TL;DR: In this article, the authors explored the relationship between market orientation, innovation and market performance and examined the intervening role of IT infrastructure, business analytics (BA) capabilities and market turbulence in the proposed model.
Abstract: Market orientation (MO) (intelligence generation, intelligence dissemination and responsiveness) is known as one of the key concepts in marketing literature. Although prior research has widely focused on the meaning and application of MO, few attempts have been made to explore how market-oriented firms lead to innovation and market performance and what factors actually moderate this relationship. To fill this gap, the present study aims to explore the relationship between MO, innovation and market performance. This study also attempts to examine the intervening role of IT infrastructure, business analytics (BA) capabilities and market turbulence in the proposed model.,In this study, a questionnaire-based survey was undertaken to test the proposed hypotheses. To verify the proposed theoretical model, partial least squares (PLS)/structured equation modeling (SEM) was performed with 114 valid survey data.,Despite prior studies which postulated innovation performance as the final outcome of MO (Han et al., 1998; Song et al., 2015), this study focused on innovation performance as a mediating outcome which finally leads to market performance. The statistical results approve the putative relationship which means managers would be able to realize the paramount role of innovation as an integral part of achieving higher market performance. In addition, no support was found for the relationship between intelligence generation and responsiveness. This finding shows that not all obtained information can help managers in the decision-making process.,This study aims to enrich literature by developing a conceptual model to test the link between MO, innovation and market performance. The value of this study is to investigate the roles of flexible IT infrastructure, BA capabilities and market turbulence as the potential moderators in the proposed model. The results advance the understanding of the influence of BA capabilities on the link between intelligence dissemination and responsiveness. Findings also show innovation performance as remarkable and deemed valuable capability, leading to higher performance in marketing-related activities, particularly in highly turbulent markets.