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  • Association for Information Systems AIS Electronic Library (AISeL)

    PACIS 2014 Proceedings Pacific Asia Conference on Information Systems(PACIS)

    2014

    GAINING COMPETITIVE ADVANTAGE FROM ANALYTICS THROUGH THE MEDIATION OF DECISION-MAKING EFFECTIVENESS: AN EMPIRICAL STUDY OF UK MANUFACTURING COMPANIES Guangming Cao University of Bedfordshire, Luton, United Kingdom, [email protected]

    Yanqing Duan University of Bedfordshire Luton, United Kingdom, [email protected]

    Follow this and additional works at: http://aisel.aisnet.org/pacis2014

    This material is brought to you by the Pacific Asia Conference on Information Systems (PACIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in PACIS 2014 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact [email protected]

    Recommended Citation Cao, Guangming and Duan, Yanqing, "GAINING COMPETITIVE ADVANTAGE FROM ANALYTICS THROUGH THE MEDIATION OF DECISION-MAKING EFFECTIVENESS: AN EMPIRICAL STUDY OF UK MANUFACTURING COMPANIES" (2014). PACIS 2014 Proceedings. 377. http://aisel.aisnet.org/pacis2014/377

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  • GAINING COMPETITIVE ADVANTAGE FROM ANALYTICS

    THROUGH THE MEDIATION OF DECISION-MAKING

    EFFECTIVENESS: AN EMPIRICAL STUDY OF UK

    MANUFACTURING COMPANIES

    Cao, Guangming, University of Bedfordshire, Luton, UK, [email protected]

    Duan, Yanqing, University of Bedfordshire, Luton, UK, [email protected]

    Abstract

    While it is widely believed that analytics capabilities enable a company to identify valuable insights

    from big data thereby to improve the effectiveness of decision-making and to gain competitive

    advantage, little empirical research has been undertaken to investigate the mechanisms through

    which analytics capabilities improve decision-making effectiveness and organisational

    competitiveness. This paper aims to reduce this research gap in the literature. Drawing on the

    resource-based view (RBV), it develops a research model to specify the interrelationships among

    information processing capabilities, resource heterogeneity, decision-making effectiveness, and

    competitive advantage. It then empirically tests the proposed model using structural equation

    modelling based on 232 responses collected from UK manufacturing companies. This paper has made

    several contributions to the research and practice of business analytics and decision-making. First, it

    advances the literatures by conceptually developing and empirically testing a path model linking

    information processing capabilities to decision-making effectiveness and competitive advantage.

    Second, it advances our knowledge by clarifying and testing the mediating role of decision-making

    effectiveness in affecting the relationship between information processing capabilities and competitive

    advantage. Third, it extends the RBV and decision-making literatures by explicating and testing the

    mediating role of resource heterogeneity in affecting the relationship between information processing

    capabilities and competitive advantage, and between information processing capabilities and

    decision-making effectiveness. Finally, this paper contributes to managers’ and business analytics

    practitioners’ knowledge by demonstrating the importance of improving decision-making

    effectiveness in gaining competitive advantage.

    Keywords: Information processing capability, Decision making effectiveness, Resource heterogeneity,

    Competitive advantage, Mediating role, Questionnaire survey

    mailto:[email protected] mailto:[email protected]

  • 1 INTRODUCTION

    In order to compete successfully in the context of digitalisation, it is essential for contemporary

    business organisations to be able to address the unprecedented challenges posed by big data that is

    characterised by high volume, high velocity of generation and transmission, and a large variety of data

    and file type. While a number of business analytics (BA) studies (Davenport et al. 2001, Davenport et

    al. 2010, Davenport 2013, Kiron and Shockley 2011, Kiron et al. 2012, Lavalle et al. 2011) suggested

    that companies with advanced analytic capabilities can derive valuable insights from big data, thereby

    to make effective decisions and to gain competitive advantage; “many companies are still struggling

    to figure out how, where and when to use analytics” (Kiron et al. 2012, p.17), or “unsure how to

    proceed” (Barton and Court 2012, p.97), or “struggling to achieve a worthwhile return” (Marchand

    and Peppard 2013, p.105).

    Since BA is still emerging as an important area of research, there are only a few empirical studies in

    the literature. To help business organisations understand the challenges and opportunities associated

    with the use of BA, MIT Sloan Management Review conducted three large scale questionnaire surveys

    consecutively; two of them were jointly with IBM Institute for Business Value and one with SAA

    Institute Inc. (Kiron and Shockley 2011, Kiron et al. 2012, Lavalle et al. 2011). These surveys were

    typically based on responses collected from more than 2,500 executives, managers, and analysts

    working across more than 30 industries and 100 countries. The finding from the 2010 survey

    indicated that there is an emerging performance gap between companies that use BA and those that do

    not (Lavalle et al. 2011), and this divide becomes larger and rapidly widening based on the findings of

    the 2011 survey (Kiron and Shockley 2011). The findings from the latest survey suggested that

    companies using BA to compete and innovate are characterised by believing data being a core asset to

    enhance business operations, effective use of data for faster results, and supports for analytics by

    senior managers; consequently, they are much more likely to be able to make effective decisions and

    create a competitive advantage (Kiron et al. 2012).

    These studies have highlighted the importance of using BA to compete and innovate, together with a

    number of recommendations made for leveraging analytic capabilities to improve organisational

    performance. However, they did not reveal the specific mechanisms through which for example

    analytic capabilities are related to effective decision-making and competitive advantage as no

    inferential analyse was conducted; nor did other BA studies since little empirical research exists in

    this emerging research area based on our literature review.

    This paper therefore aims to reduce this research gap by focusing on examining the interrelationships

    among analytics capabilities, decision-making effectiveness, and competitive advantage. Drawing on

    the resource-based view (RBV) and relevant empirical studies in other research areas, this paper will

    develop a path model to specify the links among these variables. It then empirically tests the proposed

    path model using structural equation modelling based on data collected from 232 UK manufacturing

    companies. The structure of the paper is as follows. The next section presents the conceptual research

    model and hypotheses. The subsequent section describes the instrument development and the data

    collection processes and reports on the empirical results. The final section discusses the results and

    implications.

    2 THEORETICAL DEVELOPMENT

    2.1 Key Concepts Defined

    Before developing our theoretical model, we clarify two key concepts to be used in this research:

    information processing capabilities and decision-making effectiveness. Information processing

    capabilities/capacities were used by Galbraith (1974) without definition in 1974 to outline the

    information processing view of organisational design. These terms were adopted by Tushman and

    Nadler (1978, p.614) in 1978 to further develop the information processing view of organisational

    design, while information processing was defined as “the gathering, interpreting, and synthesis of

    information in the context of organizational decision making”. Building upon the information

  • processing view (Galbraith 1974), Premkumar et al. (2005, p.266), defined information processing

    capability as “the level of IT support for various activities” to refer to similar notions when they

    examined the fit between information processing needs and information processing capability and its

    effect on performance in an inter-organisational supply chain context