Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment

With big data analytics growing rapidly in popularity, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. Drawing on the resource‐based view, the dynamic capabilities view, and on recent literature on big data analytics, this study examines the indirect relationship between a big data analytics capability (BDAC) and two types of innovation capabilities: incremental and radical. 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 incremental and radical innovation capabilities. To test their proposed research model, the authors used survey data from 175 chief information officers and IT managers working in Greek firms. By means of partial least squares structural equation modelling, the results confirm the authors’ assumptions regarding the indirect effect that BDACs have on innovation capabilities. Specifically, they find that dynamic capabilities fully mediate the effect on both incremental and radical innovation capabilities. In addition, under conditions of high environmental heterogeneity, the impact of BDACs on dynamic capabilities and, in sequence, incremental innovation capability is enhanced, while under conditions of high environmental dynamism the effect of dynamic capabilities on incremental innovation capabilities is amplified.     

Big Data‐Savvy Teams’ Skills,Big Data‐Driven Actions and Business Performance

Prior studies on big data analytics have emphasized the importance of specific big data skills and capabilities for organizational success; however, they have largely neglected to investigate the use of cross‐functional teams’ skills and links to the role played by relevant data‐driven actions and business performance. Drawing on the resource‐based view (RBV) of the firm and on unique data collected from 240 big data experts working in global agrifood networks, we examine the links between the use of big data‐savvy (BDS) teams’ skills, big data‐driven (BDD) actions and business performance. BDS teams depend on multi‐disciplinary skills (e.g. computing, mathematics, statistics, machine learning and business domain knowledge) that help them turn their traditional business operations into modern data‐driven insights (e.g. knowing real‐time price changes and customer preferences), leading to BDD actions that enhance business performance. Our results, raised from structural equation modelling, indicate that BDS teams’ skills that produce valuable insights are the key determinants for BDD actions, which ultimately contribute to business performance. We further demonstrate that those organizations that emphasize BDD actions perform better compared to those that do not focus on such applications and relevant insights.     

Big Data and Predictive Analytics in Fire Risk Using Weather Data

The objective of this article is to study the impact of weather on the damage caused by fire incidents across the United States. The article uses two sets of big data—-fire incidents data from the National Fire Incident Reporting System (NFIRS) and weather data from the National Oceanic and Atmospheric Administration (NOAA)—to obtain a single comprehensive data set for prediction and analysis of fire risk. In the article, the loss is referred to as “Total Percent Loss,” a metric that is calculated based on the content and property loss incurred by an owner over the total value of content and property. Gradient boosting tree (GBT), a machine learning algorithm, is implemented on the processed data to predict the losses due to fire incidents. An R² value of 0.933 and mean squared error (MSE) of 124.641 out of 10,000 signify the extent of high predictive accuracy obtained by implementing the GBT model. In addition to this, an excellent predictive performance demonstrated by the GBT model is further validated by a strong fitting between the predicted loss and the actual loss for the test data set, with an R² value of 0.97. While analyzing the influence of each input variable on the output, it is observed that the state in which a fire incident takes place plays a major role in determining fire risk. This article provides useful insights to fire managers and researchers in the form of a detailed framework of big data and predictive analytics for effective management of fire risk.     

Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations: A Configurational Perspective

Big data analytics (BDA) is beneficial for organizations, yet implementing BDA to leverage profitability is fundamental challenge confronting practitioners. Although prior research has explored the impact that BDA has on business growth, there is a lack of research that explains the full complexity of BDA implementations. Examination of how and under what conditions BDA achieves organizational performance from a holistic perspective is absent from the existing literature. Extending the theoretical perspective from the traditional views (e.g. resource‐based theory) to configuration theory, the authors have developed a conceptual model of BDA success that aims to investigate how BDA capabilities interact with complementary organizational resources and organizational capabilities in multiple configuration solutions leading to higher quality of care in healthcare organizations. To test this model, the authors use fuzzy‐set qualitative comparative analysis to analyse multi‐source data acquired from a survey and databases maintained by the Centres for Medicare & Medicaid Services. The findings suggest that BDA, when given alone, is not sufficient in achieving the outcome, but is a synergy effect in which BDA capabilities and analytical personnel's skills together with organizational resources and capabilities as supportive role can improve average excess readmission rates and patient satisfaction in healthcare organizations.     

Redistributed Manufacturing and the Impact of Big Data: A Consumer Goods Perspective

Abstract

Digitalization and the growth of big data promise greater customization as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasize mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with new manufacturing technologies can facilitate redistributed manufacturing (RDM). Through analysis of 24 consumer goods industry cases using primary and secondary data, we investigated evolving manufacturing configurations, their underlying drivers, the role of big data applications, and their impact on the redistribution of manufacturing. We find some applications of RDM concepts, although in other cases existing manufacturing configurations are leveraged for high volume consumer goods products through big data analytics and market segmentation. The analysis indicates that the framework put forward in the paper has broader value in organizing thinking about emerging interrelationships between big data and manufacturing.     

Competitive Dynamics between Multinational Enterprises and Local Internet Platform Companies in the Virtual Market in China

We adopt the dynamic capability perspective and the institutional view as the guiding theoretical lenses to explain the relative performance of foreign internet platform companies (IPCs) operating in China. Based on data obtained from 51 interviews a multiple‐case‐study approach is adopted, with representative matched cases between foreign IPCs, including Google, eBay, Amazon and Groupon, and local IPCs. The findings highlight the unique characteristics of the IPCs and the Chinese context that challenge assumptions prevailing in the literature of the applicability of firm‐specific advantages in determining a sustainable competitive advantage. We highlight the dynamic capabilities of the firm, such as flexibility and experimentation, in contributing to sustainable competitive advantage. Further, rather than focusing on firm‐specific resources, we find that the active agency of the firm can approach institutions as resources through external links with diversified institutional players, which is crucial for multinational enterprise IPCs to develop sustainable competitive advantage. Drawing on the findings we present a number of propositions and implications for theory and practice.     

Contingencies versus External Pressure: Professionalization in Boards of Firms Affiliated to Family Business Groups in Late‐Industrializing Countries

We examine the antecedents of professionalization in boards of firms affiliated to family business groups, increasingly recognized in the literature as the dominant form of big business organization in many late‐industrializing countries. Dimensions of board professionalization that we include in our study are board size, ratio of salaried executives and outsider presence. We compare predictions on board composition derived from contingency, institutional and power perspectives. Turkish family business groups, considered as an archetypal example of this form of organization, provide the empirical setting for the study, with data on 299 firms affiliated to ten different family business groups. Our results provide greater support for institutional and power perspectives, showing that, relative to internal and external complexity facing affiliate firms, institutional pressures and the presence of joint venture partners better predict board professionalization.     

Top Management Team Heterogeneity,Strategic Change and Operational Performance*

This study examines the role of top management team (TMT) heterogeneity in facilitating strategic change. Based on the upper echelons literature, we argue that heterogeneous management teams are better able to handle the simultaneous and conflicting demands of refocusing the organization strategically and keeping up operational performance. We expect this to be true only for teams that are heterogeneous with respect to factors directly related to job requirements, however. Data were collected from 92 full TMTs of hospitals in Spain that were confronted with institutional pressures that challenged their current strategies. In support of our hypotheses, the results show job‐related TMT heterogeneity moderates the relation between strategic change and operational performance. No moderating effect is found for non‐job‐related TMT heterogeneity.     

The Mirror Effect: Corporate Social Responsibility,Corporate Social Irresponsibility and Firm Performance in Coordinated Market Economies and Liberal Market Economies

We investigate the classic management debate of agency versus institutional pressures through the application of the varieties of capitalism literature. In particular, we examine corporate social responsibility (CSR), corporate social irresponsibility (CSiR) and their relationships with firm performance in two types of capitalist systems: coordinated market economies (CMEs) and liberal market economies (LMEs). We note that while the CSR literature has tended to develop a balanced view on the influence of agency and institutional pressures, the CSiR literature has tended to emphasize the influence of agency. The latter appears to be a result of the fundamental attribution bias, where irresponsible corporate behaviours are attributed to individual managers or organizations, rather than the institutional environment. Our results, which include five years of data across 16 countries, show significantly greater CSR and significantly lower CSiR in CMEs compared with LMEs. Further, we find a positive relationship between CSR and firm performance in CMEs but not LMEs, and a negative relationship between CSiR and firm performance in LMEs but not CMEs. Overall, our results demonstrate the influence of the institutional environment, suggesting that corporate behaviours mirror the external environment.     

Managing big data in the retail industry of Singapore: Examining the impact on customer satisfaction and organizational performance

Much of the research on big data analytics has been centered on technical or system development. Research has been carried out on the usage of big data analytics to understand customer relationships and experience, amongst others. Still, there is a lack of research in the retail industry considering big data management, examining the impact on customer satisfaction and organizational performance in the retail sector. Retailers explore analytics to gain a unified picture of their customers and operations across the store or online channels and make strategic decisions contributing to the growth of the retail industry. Thereof, this study has been conducted by majorly focusing on the Singapore retail industry to clarify the feasibility of big data management analytics. Quantitative research method was employed involving 500 participants from the retail industry of Singapore. The results of the study stated that amongst the different big data analytics utilized within the retail industry of Singapore, social media analytics had been majorly answered by the participants. Future researchers can study about the upcoming retail trends in Singapore and how the effects of big data analysis changed in the past few years and deal with the unexpected future recessions in the retail industry within Singapore.     

大数据行为研究趋势:一个“时空关”的视角

本文从"时空关",即时间、空间、关联3个维度来分析近十余年大数据行为研究的进展,总结近年来本课题组的相关研究结果,并探讨未来发展趋势。各种时空大数据的广泛采集和应用取得了突破性的进展,推动了有关人类流动行为的研究;与此同时,社会网络和社会媒体大数据的发展把人们关联行为的研究推向新高度。流动性和关联性以及这两个方向的交融构成了大数据行为研究的基本范畴,而通过"时空关+"不同内容和场景形成了大数据应用的广泛格局。大数据行为研究面临的主要挑战是如何解决大数据分析中的内生性问题,未来的研究将更加重视通过随机实验、田野实验或者自然实验等方法来做因果推断。大数据行为研究的"时空关+"为探讨人类行为的基本规律以及各种应用场景提供了一个崭新的视角。     

History,Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations

Big data and analytics (BDA) are gaining momentum, particularly in the practitioner world. Research linking BDA to improved organizational performance seems scarce and widely dispersed though, with the majority focused on specific domains and/or macro‐level relationships. In order to synthesize past research and advance knowledge of the potential organizational value of BDA, the authors obtained a data set of 327 primary studies and 1252 secondary cited papers. This paper reviews this body of research, using three bibliometric methods. First, it elucidates its intellectual foundations via co‐citation analysis. Second, it visualizes the historical evolution of BDA and performance research and its substreams through algorithmic historiography. Third, it provides insights into the field's potential evolution via bibliographic coupling. The results reveal that the academic attention for the BDA–performance link has been increasing rapidly. The study uncovered ten research clusters that form the field's foundation. While research seems to have evolved following two main, isolated streams, the past decade has witnessed more cross‐disciplinary collaborations. Moreover, the study identified several research topics undergoing focused development, including financial and customer risk management, text mining and evolutionary algorithms. The review concludes with a discussion of the implications for different functional management domains and the gaps for both research and practice.     

Performance Metric Portfolios: A Framework and Empirical Analysis

The widespread adoption of supply chain management principles suggests that managers recognize the importance of evaluating operational decisions holistically. However, it is often difficult to link specific operational practices to strategic level outcomes and in turn to corporate financial results. This presents problems for both managers and academic researchers attempting to justify the often high cost of operational improvement initiatives in terms of objective accounting metrics. This study provides evidence that it is possible to demonstrate linkages between carefully chosen portfolios of tactical, strategic, and financial metrics. Survey data from 118 manufacturers are used to evaluate hypotheses linking multilevel metrics of performance across three well‐established strategic foci. We present portfolios of metrics drawn from the literature and from the Supply Chain Counciľs supply‐chain operations reference model and related design and customer chain models. Our analysis suggests that metric portfolios in which tactical metrics are designed to match strategic‐level metrics, based on alignment with a specific strategic focus, provide clearer mechanisms for understanding performance linkages.     

Rethinking Resilience Analytics

The concept of “resilience analytics” has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data‐driven analytic techniques, as well as the prevalence of high‐profile natural and man‐made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber–physical–social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise.     

Do environmental management systems improve business performance in an international setting?

With the worldwide increase in the adoption of environmental management systems (EMSs), some research has emerged that evaluates the reasons why facilities adopt them. However, there is little information about how these motivations extend to different international settings, and the link between the comprehensiveness of an EMS and business performance has yet to be demonstrated. While both institutional pressures and resources and capabilities may encourage EMS adoption and improved business performance, questions remain about whether organizations that are motivated mainly by their resources and capabilities benefit to the same extent as organizations that are driven to adopt an EMS mainly because of institutional pressures. We analyze these relationships using OECD survey data from manufacturing facilities operating in Canada, Germany, Hungary, and the United States. Our results show that facilities that are motivated to adopt more comprehensive EMSs because of their complementary resources and capabilities, such as export orientation, employee commitment and environmental R&D, (as opposed to institutional pressures) observe greater overall facility-level business performance.     

A Strategy Tripod Perspective on Knowledge Creation Capability

Drawing on the strategy tripod perspective, in this study we examine how the performance effect of knowledge creation capability is contingent on key industrial and institutional variables. We find that technological turbulence, competitive intensity and government support all positively moderate the relationship between knowledge creation capability and firm performance, while dysfunctional competition has a negative moderating impact. This study provides a more fine‐grained analysis on the performance implications of knowledge creation capability. Moreover, it represents one of the first attempts to empirically test the interactive effects of the three legs of the strategy tripod (the industry‐based, resource‐based and institution‐based views) in one study and supports the importance of integrating the three legs to better understand the complex phenomenon.     

双向部分透明供应链的大数据投资决策与激励

针对供应链中需求大数据的分散投资与决策激励问题,考虑单个供应商和制造商均能通过大数据投资预测需求,且彼此可共享部分大数据。建立基于上下游间双向部分透明的大数据投资决策模型,揭示双向透明对于大数据应用价值与投资激励的影响,并设计契约合作机制解决大数据投资的激励失调问题。研究发现：双向透明总是对供应商有利,当逆向透明度低时,正向部分透明可能对制造商最有利,当正向透明度低时,促进双向透明可能对所有参与者均有利;正向透明而逆向不透明有利于提高大数据投资的可行性;制造商只存在大数据投资激励不足,而供应商在双向透明度低时还存在大数据投资激励过度;投资补偿契约能协调各参与者的大数据投资激励,且可提升系统投资利润5-49%。     

战略和愿景提升中小企业创新能力机制研究

本文首先根据企业调研和问卷分析的结果,对阻碍中小企业创新能力提升的内在原因进行了分析,发现创新过程中的战略不协调是阻碍中小企业创新的重要内因.接着,本文对创新能力内涵和影响创新能力的战略因素方面进行了文献综述.然后采用文献研究和案例研究结合的方法,对前人的理论进行了分析总结,将昆药、海尔等案例进行了分析,提出通过战略和愿景提升中小企业创新能力的三条机制:一,清晰的在组织层面上共享的愿景通过指引创新资源配置方向来实现创新能力的提升;二,战略和组织结构的协调通过影响创新资源的配置和组织学习的方式来影响核,心能力的构建;三,战略和文化的协调通过影响组织学习的方式来影响核心能力的形成和积累.最后,本文针对中小企业创新中普遍存在的问题,提出了一些建议.     

Performance Decline and Turnaround in Public Organizations: A Theoretical and Empirical Analysis*

Public sector performance is currently a significant issue for management practice and policy, and especially the turnaround of those organizations delivering less than acceptable results. Theories of organizational failure and turnaround derive largely from the business sector and require adaptation to the public service. The performance of public organizations is more complex to measure, is related to institutional norms, and the idea of ‘failure’ is problematic. Empirical findings from a real‐time, longitudinal study of poorly performing English local authorities are used to develop an initial theory of performance failure and turnaround suited to public organizations. The paper argues that the typical performance of public organizations over time is cyclical. Where cognition and leadership capability are absent, organizations fail to self‐initiate turnaround. In this situation authoritative external intervention is necessary. The strategies applied are principally concerned with building a leadership capability that engages senior politicians and managers in order to overcome inertia and collective action problems. The theory is presented in the form of seven propositions that provide a basis for further research across the public sector.