Big Data Enabled Organizational Transformation: The Effect of Inertia in Adoption and Diffusion

Big data and analytics have been credited with being a revolution that will radically transform the way firms operate and conduct business. Nevertheless, the process of adopting and diffusing big data analytics, as well as actions taken in response to generated insight, necessitate organizational transformation. Nevertheless, as with any form of organizational transformation, there are multiple inhibiting factors that threaten successful change. The purpose of this study is to examine the inertial forces that can hamper the value of big data analytics throughout this process. We draw on a multiple case study approach of 27 firms to examine this question. Our findings suggest that inertia is present in different forms, including economic, political, socio-cognitive, negative psychology, and socio-technical. The ways in which firms attempt to mitigate these forces of inertia is elaborated on, and best practices are presented. We conclude the paper by discussing the implications that these findings have for both research and practice.

[1]  Michael D. Myers,et al.  The qualitative interview in IS research: Examining the craft , 2007, Inf. Organ..

[2]  Joey F. George,et al.  Toward the development of a big data analytics capability , 2016, Inf. Manag..

[3]  Michail N. Giannakos,et al.  Big data analytics capabilities: a systematic literature review and research agenda , 2017, Information Systems and e-Business Management.

[4]  Elena Karahanna,et al.  Shackled to the Status Quo: The Inhibiting Effects of Incumbent System Habit, Switching Costs, and Inertia on New System Acceptance , 2012, MIS Q..

[5]  T. Amburgey,et al.  Organizational Inertia and Momentum: A Dynamic Model Of Strategic Change , 1991 .

[6]  Patrick Mikalef,et al.  Big Data Analytics Capability: Antecedents and Business Value , 2017, PACIS.

[7]  Frantz Rowe,et al.  Socio-Technical inertia, Dynamic Capabilities and Environmental uncertainty: Senior Management Views and Implications for Organizational Transformation , 2017, ECIS.

[8]  W. P. Barnett,et al.  The Red Queen, Success Bias, and Organizational Inertia , 2006, Manag. Sci..

[9]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[10]  Shirley Gregor,et al.  The Nature of Theory in Information Systems , 2006, MIS Q..

[11]  Atreyi Kankanhalli,et al.  Investigating user resistance to information systems implementation: a status quo bias perspective , 2009 .

[12]  Kalle Lyytinen,et al.  Explaining information systems change: a punctuated socio-technical change model , 2008, Eur. J. Inf. Syst..

[13]  Frantz Rowe,et al.  Strategizing information systems-enabled organizational transformation: A transdisciplinary review and new directions , 2012, J. Strateg. Inf. Syst..

[14]  Ioanna D. Constantiou,et al.  New games, new rules: big data and the changing context of strategy , 2015, J. Inf. Technol..

[15]  Michail N. Giannakos,et al.  Big Data and Strategy: A research Framework , 2016, MCIS.

[16]  Ying Liu.  Big Data and Predictive Business Analytics , 2014 .

[17]  J. Manyika,et al.  Are you ready for the era of ‘big data’? , 2010 .

[18]  Shahriar Akter,et al.  Modelling quality dynamics, business value and firm performance in a big data analytics environment , 2017, Int. J. Prod. Res..

[19]  Richard Vidgen,et al.  Management challenges in creating value from business analytics , 2017, Eur. J. Oper. Res..

[20]  Detmar W. Straub,et al.  Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs , 1999, MIS Q..

[21]  A. Gunasekaran,et al.  Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .

[22]  Georg Schreyögg,et al.  Organizational Path Dependence: Opening the Black Box , 2009 .

[23]  M. Hannan,et al.  Structural Inertia and Organizational Change , 1984 .

[24]  Izak Benbasat,et al.  The Case Research Strategy in Studies of Information Systems , 1987, MIS Q..

[25]  Detmar W. Straub,et al.  Validation in Information Systems Research: A State-of-the-Art Assessment , 2001, MIS Q..

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

[27]  M. Janssen,et al.  Factors influencing big data decision-making quality , 2017 .

[28]  Michael Prescott.  Big data and competitive advantage at Nielsen , 2014 .

[29]  Markus C. Becker,et al.  Adaptation and inertia in dynamic environments , 2016 .

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

[31]  Elena Pessot,et al.  Cultivating business model agility through focused capabilities: A multiple case study , 2017 .

[32]  Robert K. Yin,et al.  Case Study Research and Applications: Design and Methods , 2017 .

[33]  Patrick Mikalef,et al.  Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA , 2017 .

[34]  Michail N. Giannakos,et al.  Information Governance in the Big Data Era: Aligning Organizational Capabilities , 2018, HICSS.

[35]  Steffi Haag.  Organizational Inertia as Barrier to Firms' IT Adoption - Multidimensional Scale Development and Validation Research-in-Progress , 2014 .