Unlocking the drivers of big data analytics value in firms

Although big data analytics (BDA) is considered the next “frontier” in data science by creating potential business opportunities, the way to extract those opportunities is unclear. This paper aims to understand the antecedents of BDA value at a firm level. The authors performed a study using a mixed methodology approach. First, by carrying out a Delphi study to explore and rank the antecedents affecting the creation of BDA value. Based on the Delphi results, we propose an empirically validated model supported by a survey conducted on 175 European firms to explain the antecedents of BDA sustained value. The results show that the proposed model explains 62% of BDA sustained value at the firm level, where the most critical contributor is BDA use. We provide directions for managers to support their decisions on BDA strategy definition and refinement. For academics, we extend BDA value literature and outline some potential research opportunities.

[1]  Chia-Chien Hsu,et al.  The Delphi Technique: Making Sense of Consensus , 2007 .

[2]  Sunil Erevelles,et al.  Big Data consumer analytics and the transformation of marketing , 2016 .

[3]  B. Menguc,et al.  Creating a firm-level dynamic capability through capitalizing on market orientation and innovativeness , 2006 .

[4]  P. Drnevich,et al.  Clarifying the conditions and limits of the contributions of ordinary and dynamic capabilities to relative firm performance , 2011 .

[5]  Marleen Huysman,et al.  Debating big data: A literature review on realizing value from big data , 2017, J. Strateg. Inf. Syst..

[6]  Kenneth L. Kraemer,et al.  Review: Information Technology and Organizational Performance: An Integrative Model of IT Business Value , 2004, MIS Q..

[7]  Detmar W. Straub,et al.  A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example , 2005, Commun. Assoc. Inf. Syst..

[8]  Tiago Oliveira,et al.  An empirical analysis to assess the determinants of SaaS diffusion in firms , 2016, Comput. Hum. Behav..

[9]  Lakshmi S. Iyer,et al.  Big Data & Analytics for Societal Impact: Recent Research and Trends , 2018, Information Systems Frontiers.

[10]  Arnold Picot,et al.  Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda , 2015, J. Strateg. Inf. Syst..

[11]  Jennifer Jie Xu,et al.  Business Intelligence in Blogs: Understanding Consumer Interactions and Communities , 2012, MIS Q..

[12]  Oliver Schilke.  On the Contingent Value of Dynamic Capabilities for Competitive Advantage: The Nonlinear Moderating Effect of Environmental Dynamism , 2014 .

[13]  Shahriar Akter,et al.  Quality dominant logic in big data analytics and firm performance , 2018, Bus. Process. Manag. J..

[14]  Stanley F. Slater,et al.  Intelligence generation and superior customer value , 2000 .

[15]  Rudolf R. Sinkovics,et al.  The Use of Partial Least Squares Path Modeling in International Marketing , 2009 .

[16]  Elizabeth M. Daniel,et al.  The future of inter-organisational system linkages: findings of an international Delphi study , 2005, Eur. J. Inf. Syst..

[17]  R. Brislin.  Back-Translation for Cross-Cultural Research , 1970 .

[18]  Shahriar Akter,et al.  How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study , 2015 .

[19]  Mark Keil,et al.  Understanding the most critical skills for managing IT projects: A Delphi study of IT project managers , 2013, Inf. Manag..

[20]  William Yeoh,et al.  Managing the Implementation of Business Intelligence Systems: A Critical Success Factors Framework , 2008, Int. J. Enterp. Inf. Syst..

[21]  Peter J. Sher,et al.  Information Technology as a Facilitator of Enhancing Dynamic Capability through Knowledge Management , 2020 .

[22]  Gregory J. Skulmoski,et al.  Journal of Information Technology Education the Delphi Method for Graduate Research , 2022 .

[23]  Roger H. L. Chiang,et al.  Big Data Research in Information Systems: Toward an Inclusive Research Agenda , 2016, J. Assoc. Inf. Syst..

[24]  Shahriar Akter,et al.  Big data analytics and firm performance: Effects of dynamic capabilities , 2017 .

[25]  A. V. D. Ven,et al.  Group Techniques for Program Planning , 1975 .

[26]  Aleš Popovič,et al.  Towards business intelligence systems success: Effects of maturity and culture on analytical decision making , 2012, Decis. Support Syst..

[27]  Bongsik Shin,et al.  Data quality management, data usage experience and acquisition intention of big data analytics , 2014, Int. J. Inf. Manag..

[28]  Yolande E. Chan,et al.  A Delphi study of knowledge management systems: Scope and requirements , 2007, Inf. Manag..

[29]  N. Dalkey,et al.  An Experimental Application of the Delphi Method to the Use of Experts , 1963 .

[30]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

[31]  Wynne W. Chin.  The partial least squares approach for structural equation modeling. , 1998 .

[32]  Rameshwar Dubey,et al.  Impact of big data & predictive analytics capability on supply chain sustainability , 2018 .

[33]  Peter C. Verhoef,et al.  Creating Value with Big Data Analytics: Making Smarter Marketing Decisions , 2016 .

[34]  Paul A. Pavlou,et al.  Understanding the Elusive Black Box of Dynamic Capabilities , 2011, Decis. Sci..

[35]  Qian Huang,et al.  Developing Organizational Agility through IT Capability and KM Capability: The Moderating Effects of Organizational Climate , 2013, PACIS.

[36]  Maria R. Lee,et al.  Leveraging Big Data and Business Analytics , 2013, IT Prof..

[37]  Adrian B. Ryans.  Estimating Consumer Preferences for a New Durable Brand in an Established Product Class , 1974 .

[38]  Vasant Dhar,et al.  Editorial - Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research , 2014, Inf. Syst. Res..

[39]  Varun Grover,et al.  Types of Information Technology Capabilities and Their Role in Competitive Advantage: An Empirical Study , 2005, J. Manag. Inf. Syst..

[40]  Lars Mathiassen,et al.  The post mortem paradox: a Delphi study of IT specialist perceptions , 2008, Eur. J. Inf. Syst..

[41]  Manto Gotsi,et al.  The role of GPS-enabled information in transforming operational decision making: an exploratory study , 2014, Eur. J. Inf. Syst..

[42]  T. Oliveira,et al.  Assessing business value of Big Data Analytics in European firms , 2017 .

[43]  Guy Paré,et al.  A systematic assessment of rigor in information systems ranking-type Delphi studies , 2013, Inf. Manag..

[44]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

[45]  Yi-Hui Chiang,et al.  Using a combined AHP and PLS path modelling on blog site evaluation in Taiwan , 2013, Comput. Hum. Behav..

[46]  Roy C. Schmidt,et al.  MANAGING DELPHI SURVEYS USING NONPARAMETRIC STATISTICAL TECHNIQUES , 1997 .

[47]  J. Alberto Espinosa,et al.  Big Data: Issues and Challenges Moving Forward , 2013, 2013 46th Hawaii International Conference on System Sciences.

[48]  Lior Fink,et al.  Exploring the perceived business value of the flexibility enabled by information technology infrastructure , 2009, Inf. Manag..

[49]  Enver Yücesan,et al.  The impact of ERP on supply chain management: Exploratory findings from a European Delphi study , 2003, Eur. J. Oper. Res..

[50]  Suzanne D. Pawlowski,et al.  The Delphi method as a research tool: an example, design considerations and applications , 2004, Inf. Manag..

[51]  Shahriar Akter,et al.  How to improve firm performance using big data analytics capability and business strategy alignment , 2016 .

[52]  Kenneth L. Kraemer,et al.  Post-Adoption Variations in Usage and Value of E-Business by Organizations: Cross-Country Evidence from the Retail Industry , 2005, Inf. Syst. Res..

[53]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[54]  C. Powell.  The Delphi technique: myths and realities. , 2003, Journal of advanced nursing.

[55]  Kenneth L. Kraemer,et al.  Fact or Fiction? A Sensemaking Perspective on the Reality Behind Executives' Perceptions of IT Business Value , 2007, J. Manag. Inf. Syst..

[56]  Yi Wang,et al.  IT capability and organizational performance: the roles of business process agility and environmental factors , 2014, Eur. J. Inf. Syst..

[57]  Gene Rowe,et al.  The Delphi technique: Past, present, and future prospects — Introduction to the special issue☆ , 2011 .

[58]  Shu-Hui Chuang,et al.  A resource-based perspective on knowledge management capability and competitive advantage: an empirical investigation , 2004 .

[59]  Paul P. Tallon,et al.  Competing Perspectives on the Link Between Strategic Information Technology Alignment and Organizational Agility: Insights from a Mediation Model , 2011, MIS Q..

[60]  Morgan Swink,et al.  How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management , 2015, J. Manag. Inf. Syst..

[61]  Anna Sidorova,et al.  Business intelligence success: The roles of BI capabilities and decision environments , 2013, Inf. Manag..

[62]  Dursun Delen,et al.  The analytics paradigm in business research , 2018, Journal of Business Research.

[63]  Z. Irani,et al.  Critical analysis of Big Data challenges and analytical methods , 2017 .

[64]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[65]  Steve G. Sutton,et al.  Enhancing the Business Value of Business Intelligence: The Role of Shared Knowledge and Assimilation , 2013, J. Inf. Syst..

[66]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[67]  M. Dolores Gallego,et al.  Designing a forecasting analysis to understand the diffusion of open source software in the year 2010 , 2008 .

[68]  France Bélanger,et al.  An organizational perspective on m-business: usage factors and value determination , 2014, Eur. J. Inf. Syst..

[69]  Yini Lin,et al.  Exploring the role of dynamic capabilities in firm performance under the resource-based view framework , 2014 .

[70]  Peter Trkman,et al.  Business analytics in supply chains - The contingent effect of business process maturity , 2012, Expert Syst. Appl..

[71]  Murray Turoff,et al.  The Delphi Method: Techniques and Applications , 1976 .

[72]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[73]  Rana Tassabehji,et al.  The impact of big data analytics on firms’ high value business performance , 2016, Information Systems Frontiers.

[74]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[75]  Robert D. Galliers,et al.  Towards an understanding of the role of business intelligence systems in organisational knowing , 2016, Inf. Syst. J..

[76]  Dominic Barton,et al.  Making advanced analytics work for you. , 2012, Harvard business review.

[77]  Robbie T. Nakatsu,et al.  A comparative study of important risk factors involved in offshore and domestic outsourcing of software development projects: A two-panel Delphi study , 2009, Inf. Manag..