The role of information governance in big data analytics driven innovation

The age of big data analytics is now here, with companies increasingly investing in big data initiatives to foster innovation and outperform competition. Nevertheless, while researchers and practitioners started to examine the shifts that these technologies entail and their overall business value, it is still unclear whether and under what conditions they drive innovation. To address this gap, this study draws on the resource-based view (RBV) of the firm and information governance theory to explore the interplay between a firm’s big data analytics capabilities (BDACs) and their information governance practices in shaping innovation capabilities. We argue that a firm’s BDAC helps enhance two distinct types of innovative capabilities, incremental and radical capabilities, and that information governance positively moderates this relationship. To examine our research model, we analyzed survey data collected from 175 IT and business managers. Results from partial least squares structural equation modelling analysis reveal that BDACs have a positive and significant effect on both incremental and radical innovative capabilities. Our analysis also highlights the important role of information governance, as it positively moderates the relationship between BDAC’s and a firm’s radical innovative capability, while there is a nonsignificant moderating effect for incremental innovation capabilities. Finally, we examine the effect of environmental uncertainty conditions in our model and find that information governance and BDACs have amplified effects under conditions of high environmental dynamism.     

The synergistic effects of IT-enabled resources on organizational capabilities and firm performance

Computing the value of IT investments and clarifying how the portfolio of IT/IS resources affect a firm's performance and sustainable competitive advantage are critical issues today. We attempted to develop an effective measurement technique and use organizational theory to discover the strategic role of IT-enabled resources in the firm's competitive agenda. Based on a resource-based view of the firm, we proposed a way to evaluate the synergistic effect of such resources on the firm's capabilities, as they, influence the firms’ strategic objectives and improve its financial performance. The technological, human, and organizational resources work together to generate sub-additive cost and super-additive value synergies. Operations, R&D, and marketing capabilities allow firms to implement a business strategy that reflects its customer needs. A survey was conducted to check our framework. Our findings should provide valuable decision guides for practitioners when choosing a portfolio of IT/IS resources for implementing business strategies.     

Big data analytics capabilities: a systematic literature review and research agenda

With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited attention paid to the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies, and to have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains. The research framework is grounded on past empirical work on IT business value research, and builds on the resource-based view and dynamic capabilities view of the firm. By identifying the main areas of focus for BDA and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of competitive advantage. To this end, we identify gaps in the extant literature and propose six future research themes.     

Analytics Capabilities and the Decision to Invest in Analytics

The increasing use of data-driven decision making and big data is leading organizations to invest in analytics software and services. However, little is known about the type of analytics capabilities within IT that are required and whether there is a common progression or development model of analytics capabilities. Also unknown is how the level of analytics capabilities and other factors influence a firm’s decision to invest in analytics. The purpose of this research is to explore the relationships between levels of distinct analytics capabilities and to understand how they and other factors influence the analytics investment decision. The findings suggest that there is a distinct progression in the development of analytics capabilities, and that firm size is associated with increased capability. The results suggest that firms more likely to invest in analytics have higher current levels of specific analytics capabilities, are larger, and are located in less-competitive industries.     

Information technology as a facilitator for enhancing dynamic capabilities through knowledge management

Research on dynamic capabilities is an emerging field: it studies the activities of firms during turbulent administrative environments. Current management interests are also focused on knowledge management as a major determinant of business excellence and competitive advantage. Our motivation for this paper was to answer the research question: Does knowledge management (KM) contribute to the enhancement of dynamic capabilities and thus to the enhancement of business excellence and competitive advantage? Our effort examined the use of KM in enhancing dynamic capabilities. Based on results from a survey of major Taiwanese firms, we tested a set of hypotheses with regression models. Empirical findings suggest that management of both endogenous and exogenous knowledge through IT applications significantly enhances dynamic capabilities.     

A dynamic capabilities perspective of IS project portfolio management

Organizations use information systems project portfolio management (IS PMM) to reconfigure their IS resources and capabilities to match changing market and economic conditions. IS PPM can therefore be characterised as a dynamic capability. We investigate how firms developed and adapted IS PPM to match the turbulent recessionary conditions witnessed after 2008–2009. This study contributes to an understanding of IS PPM by identifying the constituent dynamic capabilities and providing empirical examples of adaptation. To our knowledge, the study is the first to apply the notion of second order dynamic capabilities to the IS domain and also makes an important contribution to the more general concept of dynamic capabilities by providing empirical evidence and theoretical justification of the increased detailed, centrally controlled and analytical nature of IS PPM dynamic capabilities in recessionary conditions.     

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.     

Examining the role of information technology in cultivating firms’ dynamic marketing capabilities

Most prior research has investigated an organization's dynamic capabilities in general and overlooked their effect on critical business functions. Our study considered the role of IT in improving firm's dynamic marketing capabilities. We developed a model consisting of market orientation, IT infrastructure capabilities, and the use of IT in customer relationship management (CRM). With data collected from 135 manufacturing and service firms in Taiwan, our results supported most of our hypotheses. Our results showed important direct effects of a firm's market orientation, use of IT to support CRM, and the functionality of IT infrastructure capabilities on its dynamic marketing capabilities.     

An integrated big data analytics-enabled transformation model: Application to health care

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. This model was then tested in healthcare setting. By analyzing big data implementation cases, we sought to understand how big data analytics capabilities transform organizational practices, thereby generating potential benefits. In addition to conceptually defining four big data analytics capabilities, the model offers a strategic view of big data analytics. Three significant path-to-value chains were identified for healthcare organizations by applying the model, which provides practical insights for managers.     

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.     

Toward the development of a big data analytics capability

The era of big data has begun such that organizations in all industries have been heavily investing in big data initiatives. We know from prior studies that investments alone do not generate competitive advantage; instead, firms need to create capabilities that rival firms find hard to match. Drawing on the resource-based theory of the firm and recent work in big data, this study (1) identifies various resources that in combination build a big data analytics (BDA) capability, (2) creates an instrument to measure BDA capability of the firm, and (3) tests the relationship between BDA capability and firm performance. Results empirically validate the proposed theoretical framework of this study and provide evidence that BDA capability leads to superior firm performance.     

Supply chain management information systems capabilities. An exploratory study of electronics manufacturers

Supply Chain Management Information Systems (SCM IS) play an increasingly critical role in the ability of firms to reduce costs and increase the responsiveness of their supply chain. This paper develops an empirically supported model of the organizational capabilities enabled by SCM IS. The model integrates and enriches theoretical and empirical studies of competitive strategy, supply chain management, and interorganizational information systems. Evidence from an exploratory case study of three large firms in the electronics manufacturing industry is examined to build a better-supported theory of SCM IS capabilities. The findings suggest the organizational capabilities enabled by SCM IS can be conceptualized as the level of support provided for: operational efficiency; operational flexibility; internal planning and analysis; and external planning and analysis. The theoretical model furthers an understanding of SCM IS capabilities and is sufficiently developed to permit operationalization for future studies evaluating the effectiveness of SCM IS.     

Business intelligence and analytics use,innovation ambidexterity,and firm performance: A dynamic capabilities perspective

To survive in a dynamic and hyper-competitive business environment, firms are compelled to simultaneously introduce incremental and radical innovations. While it is recognised that business intelligence and analytics (BI&A) can support innovation and provide organisational value, the literature provides a limited understanding of its impact on balancing different innovation activities and ensuring performance gains. In this study, we examine the relationship between BI&A use, innovation ambidexterity, and firm performance by relying on the process theory of IS value creation as well as the dynamic capabilities perspective. We test our model using data collected from medium- and large-sized firms in Slovenia, applying partial least squares modelling. The results support the notion that BI&A use is positively associated with successful balancing between explorative and exploitative innovation activities, which in turn enhances firm performance. Our results also indicate that innovation ambidexterity is enhanced in two ways: indirectly through interaction with the firm’s absorptive capacity, and directly by increasing the possibilities of faster experimentation with offerings of products or services and improved predictability of the value of new products or services.     

Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?

Big data analytics (BDA) and the Internet of Things (IoT) tools are considered crucial investments for firms to distinguish themselves among competitors. Drawing on a strategic management perspective, this study proposes that BDA and IoT capabilities can create significant value in business processes if supported by a good level of data quality, which will lead to a better competitive advantage. Responses are collected from 618 European and American firms that use IoT and BDA applications. Partial least squares results reveal that better data quality is needed to unlock the value of IoT and BDA capabilities.     

Utilizing big data analytics for information systems research: challenges,promises and guidelines

This essay discusses the use of big data analytics (BDA) as a strategy of enquiry for advancing information systems (IS) research. In broad terms, we understand BDA as the statistical modelling of large, diverse, and dynamic data sets of user-generated content and digital traces. BDA, as a new paradigm for utilising big data sources and advanced analytics, has already found its way into some social science disciplines. Sociology and economics are two examples that have successfully harnessed BDA for scientific enquiry. Often, BDA draws on methodologies and tools that are unfamiliar for some IS researchers (e.g., predictive modelling, natural language processing). Following the phases of a typical research process, this article is set out to dissect BDA’s challenges and promises for IS research, and illustrates them by means of an exemplary study about predicting the helpfulness of 1.3 million online customer reviews. In order to assist IS researchers in planning, executing, and interpreting their own studies, and evaluating the studies of others, we propose an initial set of guidelines for conducting rigorous BDA studies in IS.     

Manufacturing big data ecosystem: A systematic literature review

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to real-time big data analytics and cybersecurity.     

The role of IS capabilities in delivering sustainable improvements to competitive positioning

It has long been argued that organizations have struggled to achieve business benefits, and in particular sustainable competitive advantage, from their IT investments. In this paper we draw upon resource-based theory to explore how the effective deployment of IS capabilities might deliver sustainable improvements to an organization’s competitive positioning. In so doing, this research makes a significant departure from the enterprise-level orientation of prior studies, by focusing upon the role of IS capabilities in leveraging sustainable improvements to competitive positioning from individual IS initiatives. Based upon the responses to a quantitative and qualitative survey of practicing managers, it has been shown that an organization’s ability to leverage and sustain improvements in its competitive positioning, from IS initiatives, are directly dependent upon its ability to effectively apply an appropriate portfolio of IS capabilities. Moreover, it has been shown that sustainable improvements in competitive positioning are most likely in circumstances in which the successful outcome of an IS initiative is dependent upon ‘outside-in’ and ‘spanning’ capabilities, which are both lacking in transparency and difficult to replicate.     

Strategic business value from big data analytics: An empirical analysis of the mediating effects of value creation mechanisms

Big data are a prominent source of value capable of generating competitive advantage and superior business performance. This paper represents the first empirical investigation of the theoretical model proposed by Grover et al. (2018), considering the mediating effects of four value creation mechanisms on the relationship between big data analytics capabilities (BDAC) and four value targets. The four value creation mechanisms investigated (the source of the value being pursued) are transparency, access, discovery, and proactive adaptation, while the four value targets (the impacts of the value creation process) are organization performance, business process improvement, customer experience and market enhancement, and product and service innovation. The proposed empirical validation of Grover et al.’s (2018) model adopts an econometric analysis applied to data gathered through a survey involving 256 BDA experts. The results reveal that transparency mediates the relationship for all the value targets, while access and proactive adaptation mediate only in case of some value targets, and discovery does not have any mediating effect. Theoretical and practical implications are discussed at the end of the paper.     

Mining marketing maps for business alliances

A business can strengthen its competitive advantage and increase its market share by forming a strategic alliance. With the help of alliances, businesses can bring to bear significant resources beyond the capabilities of the individual co-operating firms. Thus how to effectively evaluate and select alliance partners is an important task for businesses because a successful corporation partner selection can therefore reduce the possible risk and avoid failure results on business alliance. This paper proposes the Apriori algorithm as a methodology of association rules for data mining, which is implemented for mining marketing map knowledge from customers. Knowledge extraction from marketing maps is illustrated as knowledge patterns and rules in order to propose suggestions for business alliances and possible co-operation solutions. Finally, this study suggests that integration of different research factors, variables, theories, and methods for investigating this research topic of business alliance could improve research results and scope.