Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.     

Business value of big data analytics: A systems-theoretic approach and empirical test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.     

The promising future of healthcare services: When big data analytics meets wearable technology

As policy-makers and business practitioners across the globe expend extraordinary effort toward the field of e-health, the thriving development of healthcare-wearable technology is creating great opportunities and posing a remarkable future for healthcare services. This paper employs a game theory model to investigate the dynamics of wearable device market. We extend the two-dimensional product differentiation model by incorporating consumer diversity, consumer density, and firms’ big data analytics (BDA) investment strategy. Our model reveals that with differentiated consumer densities firms are more likely to engage in quality competition and the firm that invests in BDA can achieve higher profits. Furthermore, the overall quality of biomedical and healthcare services can be improved under various market conditions. Our findings provide practical guidance to wearable device manufacturers on optimizing competition strategies and offer insights to social planners on potential policy-making to promote better healthcare services.     

Using big data analytics to extract disease surveillance information from point of care diagnostic machines

This paper explains a novel approach for knowledge discovery from data generated by Point of Care (POC) devices. A very important element of this type of knowledge extraction is that the POC generated data would never be identifiable, thereby protecting the rights and the anonymity of the individual, whilst still allowing for vital population-level evidence to be obtained. This paper also reveals a real-world implementation of the novel approach in a big data analytics system. Using Internet of Things (IoT) enabled POC devices and the big data analytics system, the data can be collected, stored, and analyzed in batch and real-time modes to provide a detailed picture of a healthcare system as well to identify high-risk populations and their locations. In addition, the system offers benefits to national health authorities in forms of optimized resource allocation (from allocating consumables to finding the best location for new labs) thus supports efficient and timely decision-making processes.     

Closing the loop: Empirical evidence for a positive feedback model of IT business value creation

Researchers have established that information technology (IT) can improve firms’ productivity. Whether improved productivity leads to additional investment in IT, however, remains largely uninvestigated. In this paper, we consider whether the relationship between productivity and subsequent IT investment might be positive, negative, or ad hoc, and hypothesize that this relationship is positive. We analyze seven years of panel data from 1236 healthcare firms and present empirical evidence supporting our hypothesis. When our finding is combined with extant research, it becomes reasonable to propose that unidirectional causality does not fully describe the process of IT business value creation. Instead, we argue that existing static models of IT business value with unidirectional causality can be recast as dynamic models that explicitly incorporate multiple time periods and a positive feedback relationship to more accurately capture the complexity of this process. The creation of IT business value can thus be understood as a positive feedback model where IT investment in a given time period builds the stock of IT inputs, where those IT inputs then impact productivity, and where productivity leads to IT investment in a future time period, beginning the cycle anew.     

Actualizing big data analytics affordances: A revelatory case study

Drawing on a revelatory case study, we identify four big data analytics (BDA) actualization mechanisms: (1) enhancing, (2) constructing, (3) coordinating, and (4) integrating, which manifest in actions on three socio-technical system levels, i.e., the structure, actor, and technology levels. We investigate the actualization of four BDA affordances at an automotive manufacturing company, i.e., establishing customer-centric marketing, provisioning vehicle-data-driven services, data-driven vehicle developing, and optimizing production processes. This study introduces a theoretical perspective to BDA research that explains how organizational actions contribute to actualizing BDA affordances. We further provide practical implications that can help guide practitioners in BDA adoption.     

Orchestrating big data analytics capability for sustainability: A study of air pollution management in China

Under rapid urbanization, cities are facing many societal challenges that impede sustainability. Big data analytics (BDA) gives cities unprecedented potential to address these issues. As BDA is still a new concept, there is limited knowledge on how to apply BDA in a sustainability context. Thus, this study investigates a case using BDA for sustainability, adopting the resource orchestration perspective. A process model is generated, which provides novel insights into three aspects: data resource orchestration, BDA capability development, and big data value creation. This study benefits both researchers and practitioners by contributing to theoretical developments as well as by providing practical insights.     

Exploiting mean field analysis to model performances of big data architectures

Big data processing systems are characterized by a relevant number of components that are used in parallel to run multiple instances of the same tasks in order to achieve the needed performance levels in applications characterized by huge amounts of data. Such a number of components depend on the dimension of the involved data, so that new resources (e.g., processing or storage servers) are usually added as the working database grows. A reliable performance evaluation of these systems is at the same time crucial, in order to enable administrators and developers to keep the pace with data growth, and extremely difficult, due to the intrinsic complexity of these architectures. Notwithstanding, the available literature does not yet offer sufficient experiences, nor significant methodologies, in such a direction.This paper presents a novel modeling approach, based on mean field analysis, a set of methods for approximate inference of probabilistic models, derived from statistical physics, for performance evaluation of big data systems. This approach, by containing the excessive state space growth characterizing more traditional modeling methodologies, also requires a significantly reduced effort with respect to simulation based ones.     

An overview of air quality analysis by big data techniques: Monitoring,forecasting, and traceability

With the rapid development of economy and the frequent occurrence of air pollution incidents, the problem of air pollution has become a hot issue of concern to the whole people. The air quality big data is generally characterized by multi-source heterogeneity, dynamic mutability, and spatial–temporal correlation, which usually uses big data technology for air quality analysis after data fusion. In recent years, various models and algorithms using big data techniques have been proposed. To summarize these methodologies of air quality study, in this paper, we first classify air quality monitoring by big data techniques into three categories, consisting of the spatial model, temporal model and spatial–temporal model. Second, we summarize the typical methods by big data techniques that are needed in air quality forecasting into three folds, which are statistical forecasting model, deep neural network model, and hybrid model, presenting representative scenarios in some folds. Third, we analyze and compare some representative air pollution traceability methods in detail, classifying them into two categories: traditional model combined with big data techniques and data-driven model. Finally, we provide an outlook on the future of air quality analysis with some promising and challenging ideas.     

Firm-level benefits of IT-enabled resources: A conceptual extension and an empirical assessment

While the business value of IT (BVIT) is central to the IS discipline, only recently a possible chain of causation from IT assets (i.e., fungible, widely available, commodity-like, technology-based products) to firm performance has been conceptually specified. Furthermore, little empirical evidence exists regarding IT assets’ business value. In light of this paucity, this paper makes several contributions to IS research and practice. First, it advances the BVIT literature by empirically testing a model that traces a path from IT assets through IT-enabled resources to firm performance. Second, it extends the BVIT and resource-based view (RBV) literatures by explicating and testing the impact of a firm’s external environment on its IT-enabled resources. Third, it builds on recent literature to argue for, and test, two distinct forms of firm-level outcome: operational and strategic benefits. Finally, the paper contributes to managers’ and IS practitioners’ knowledge by demonstrating the transformative capacity of IT assets on the strategic potential of organizational resources. Empirically, the paper develops and employs valid and reliable scales to test the research model using survey data on IT-enabled customer service departments. The findings demonstrate that when an IT asset is combined with an organizational resource, the extent of synergy borne out of the resulting relationship can positively impact the strategic potential of the ensuing IT-enabled resource. This IT-enabled resource, in turn, is positively associated with firm-level benefits. Further, the external environment is shown to exert a positive effect on the strategic potential of outside-in IT-enabled resources. In sum, this paper offers several important conceptual and empirical contributions to a stream of research that is at the core of the IS discipline.     

EHR application portfolio and hospital performance: Effects across hospitals with varying administrative scale and clinical complexity

Electronic Health Record (EHR) has the potential to transform the work required to create and deliver healthcare services. This has triggered large-scale adoption across hospitals. However, whether all hospitals obtain a similar effect from their EHR implementations remains an important question because there are significant differences differences in characteristics of hospitals adopting these systems. To examine differences in effects across hospitals, we examine whether the impacts of EHR applications are contingent on work domains, by assessing performance effects across hospitals with varying administrative scale and clinical complexity. Because EHRs constitute a suite of applications with different functionalities, examining the effects of different sets of applications is challenging due to nonlinear interdependencies between applications. Therefore, we use an archetype approach, identifying synergistic EHR (EHRSYN) archetype as an ideal portfolio—a conceptual anchor for a hospital’s EHR portfolio. We test contingencies by combining this technology archetype with the work domains—administrative scale and clinical complexity. We test our hypotheses using empirical data from 137 hospitals in California, hypothesizing the differences in effects of EHRs on financial, operational, and clinical performances across hospitals with different administrative scales (size) and clinical complexities (case-mix) of work. While hospitals gain the most by implementing a portfolio close to the EHRSYN archetype, our nuanced models reveal that the benefits of such portfolios increase for large hospitals and are greater for hospitals treating less complex cases. These findings underscore how variations in applications used and work domains demarcate boundaries related to the performance effects of EHRs. We present a detailed discussion of the theoretical contributions and practical implications of our findings.     

P-DOT: a model of computation for big data

In response to the high demand of big data analytics, several programming models on large and distributed cluster systems have been proposed and implemented, such as MapReduce, Dryad and Pregel. However, compared with high performance computing areas, the basis and principles of computation and communication behaviour of big data analytics is not well studied. In this paper, we review the current big data computational model DOT and DOT Advanced, and propose a more general and practical model p-DOT (p-phases DOT). p-DOT is not a simple extension, but with profound significance: for general aspects, any big data analytics job execution expressed in DOT model or bulk synchronous parallel model can be represented by it; for practical aspects, it considers I/O behaviour to evaluate performance overhead. Moreover, we provide a cost function of p-DOT implying that the optimal number of machines is near-linear to the square root of input size for a fixed algorithm and workload, and certify that the processing paradigm of p-DOT is scalable and fault-tolerant. Finally, we demonstrate the effectiveness of the model through several experiments.     

利用大数据实现电能计量装置运行状态质量评估

为解决当前采集系统计量工单漏报、误报、数量大、单一、准确度低等问题,极大减少基层运维人员现场工作量,消除人为因素干扰计量装置故障判断,提升电力计量装置运行质量的可靠性、稳定性,辅助决策计量装置的采购与调配。本文探索开展电力计量装置运行状态下的质量评估工作,运用大数据分析技术,将影响电能计量装置运行质量的数据从系统、现场、时间、空间、数量、标准等多维度多系统进行诊断分析,消除各个系统数据专业壁垒,对运行设备质量形成健康、亚健康、疾病、重病四级健康评价,实现计量装置故障快速精准定位、发现隐性缺陷,提供问题解决方案。转变运维方式,由以前的“事后救治”升级到“主动防控”,实现精准高效运维、精益化管理的目的。     

Big data and firm performance: The roles of market-directed capabilities and business strategy

Despite being the object of much interest, deep insights regarding why and when investments in big data resources enhance firm performance are lacking in the literature. Building on the resource-based view (RBV) and data provided by 301 senior marketing managers, this study reveals that big data resources primarily improve firm performance by enhancing the market-directed capabilities of the firm. In addition, the data indicate that firms pursuing a differentiation rather than cost-leadership strategy gain most from big data resource investments and that such resources account for 13 % of the variance in the performance of firms pursuing a differentiation strategy.     

From cacophony to harmony: A case study about the IS implementation process as an opportunity for organizational transformation at Sentara Healthcare

The cacophony of criticisms emanating from an organization facing an information technology-enabled transformation can be deafening and deleterious. This is especially true in healthcare in the US, where information systems investments are typically huge and often perceived by change resistant stakeholders as disruptive or even potentially life threatening. We describe how the IS implementation process itself contributed to organizational transformation in terms of changes in coordination, culture, and learning at a successful organization, Sentara Healthcare, which transformed the discordant cacophony of the change process into a harmonious implementation.     

Evolution of the impact of e-business technology on operational competence and firm profitability: A panel data investigation

This study examines the evolution of the impact of e-business technology on operational competence and profitability using a panel dataset of 154 Spanish firms. We find that (1) e-business technology has a positive effect on operational competence that decreases over time and (2) the firm’s proficiency in exploiting a portfolio of operational capabilities has a positive impact on profitability that becomes more substantial over time. The findings provide some insights on how the initial and subsequent IT investments affect operational competence and profitability over time. This study methodologically illustrates how to perform a partial least squares estimation using panel data.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

How information-sharing values influence the use of information systems: An investigation in the business intelligence systems context

Although the constituents of information systems (IS) success and their relationships have been well documented in the business value of information technology (IT) and strategic IS literature, our understanding of how information-sharing values affect the relationships among IS success dimensions is limited. In response, we conduct a quantitative study of 146 medium and large firms that have implemented a business intelligence system in their operations. Our results highlight that in the business intelligence systems context information-sharing values are not directly linked to IT-enabled information use, yet they act as significant moderators of information systems success dimensions relationships.     

Computational relaxed TP model transformation: restricting the computation to subspaces of the dynamic model

The tensor‐product (TP) model transformation is a recently proposed numerical method capable of transforming linear parameter varying state‐space models to the higher order singular value decomposition (HOSVD) based canonical form of polytopic models. It is also capable of generating various types of convex TP models, a type of polytop models, for linear matrix inequality based controller design. The crucial point of the TP model transformation is that its computational load exponentially explodes with the dimensionality of the parameter vector of the parameter‐varying state‐space model. In this paper we propose a modified TP model transformation that leads to considerable reduction of the computation. The key idea of the method is that instead of transforming the whole system matrix at once in the whole parameter space, we decompose the problem and perform the transformation element wise and restrict the computation to the subspace where the given element of the model varies. The modified TP model transformation can readily be executed in higher dimensional cases when the original TP model transformation fails. The effectiveness of the new method is illustrated with numerical examples. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society