The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism

Abstract The importance of big data analytics–enabled dynamic capability has been at the forefront of research for information systems management, operations management, and strategic management community. Prior studies have reported on the influence of big data analytics–enabled dynamic capability (BDA) for improved organizational agility and organizational performance, but there has been a paucity of literature regarding the role of big data analytics–enabled dynamic capability in untangling the supply chain ambidexterity dilemma and organizational performance. To address these research gaps, this paper draws on the dynamic capability view of the organization under the contingent effect of environmental dynamism. We tested our research hypotheses using 281 surveys, gathered using a pre-tested questionnaire. Our results suggest that BDA has positive effects on improving supply chain agility (SCAG), supply chain adaptability (SCAD) and performance measures (cost performance and operational performance). However, we noted that hypotheses regarding the moderating effect of environmental dynamism (ED) on the paths joining BDA and SCAG/SCAD were not supported. To address these unexpected results, we conducted post hoc analysis to explain the rationale behind the insignificant moderating effects of ED on the paths joining BDA and SCAG/SCAD. We found that the effects of BDA on SCAG/SCAD were higher under intermediate levels of environmental dynamism but comparatively weak when the environmental dynamism is low or high. Hence, we can argue that big data analytics can help enhance supply chain agility, supply chain adaptability, and organizational performance, but these effects are contingent upon the level of environmental dynamism. Moreover, a non-linear, inverse U-shaped moderating effect of environmental dynamism exists. Collectively, these findings provide a theory-based understanding of the organizational level of usage of big data analytics and its effects on supply chain agility, supply chain adaptability, and organizational performance. Moreover, they further shape our understanding of how big data analytics–enabled dynamic capabilities yield differential results under the moderating effect of environmental dynamism. Hence, we believe that our results will be useful for managers who are highly optimistic about the usage of these emerging technologies and their effects on supply chain characteristics. Finally, we have outlined our study limitations and offered numerous research directions.

[1]  Constantin Blome,et al.  Dynamic supply chain capabilities , 2018, International Journal of Operations & Production Management.

[2]  Terry S. Overton,et al.  Estimating Nonresponse Bias in Mail Surveys , 1977 .

[3]  S. Vickery,et al.  The effects of internal versus external integration practices on time-based performance and overall firm performance , 2004 .

[4]  A. Gunasekaran,et al.  Supply chain agility, adaptability and alignment: empirical evidence from the Indian auto components industry , 2018 .

[5]  Christopher S. Tang,et al.  The Value of Information Sharing in a Two-Level Supply Chain , 2000 .

[6]  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.

[7]  David W. Gerbing,et al.  An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment , 1988 .

[8]  Henri Schildt,et al.  Big data and organizational design – the brave new world of algorithmic management and computer augmented transparency , 2017 .

[9]  M. Cao,et al.  Supply chain collaboration: Impact on collaborative advantage and firm performance , 2011 .

[10]  Joseph F. Hair,et al.  On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012) , 2014 .

[11]  Prasanna Tambe.  Big Data Investment, Skills, and Firm Value , 2014, Manag. Sci..

[12]  Sharon E. DeGroote,et al.  The impact of IT on supply chain agility and firm performance: An empirical investigation , 2013, Int. J. Inf. Manag..

[13]  Indre Maurer,et al.  How Dynamic Capabilities Affect the Effectiveness and Efficiency of Operating Routines Under High and Low Levels of Environmental Dynamism , 2015 .

[14]  Tobias Schoenherr,et al.  The Roles of Supply Chain Intelligence and Adaptability in New Product Launch Success , 2015, Decis. Sci..

[15]  D. Teece,et al.  DYNAMIC CAPABILITIES AND STRATEGIC MANAGEMENT , 1997 .

[16]  L. Cronbach.  Coefficient alpha and the internal structure of tests , 1951 .

[17]  Allan Afuah,et al.  Dynamic Boundaries of the Firm: Are Firms Better Off Being Vertically Integrated in the Face of a Technological Change? , 2001 .

[18]  C. Fornell,et al.  Evaluating Structural Equation Models with Unobservable Variables and Measurement Error , 1981 .

[19]  Benjamin T. Hazen,et al.  Enterprise architecture: A competence-based approach to achieving agility and firm performance , 2017 .

[20]  H.L. Lee,et al.  Aligning Supply Chain Strategies with Product Uncertainties , 2002, IEEE Engineering Management Review.

[21]  Katherine N. Lemon,et al.  The WOW factor: Creating value through win-back offers to reacquire lost customers , 2007 .

[22]  M. Christopher.  The Agile Supply Chain : Competing in Volatile Markets , 2000 .

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

[24]  Michael J. Braunscheidel,et al.  The organizational antecedents of a firm’s supply chain agility for risk mitigation and response , 2009 .

[25]  Amir Pezeshkan,et al.  An empirical assessment of the dynamic capabilities–performance relationship , 2016 .

[26]  Cheryl Burke Jarvis,et al.  A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research , 2003 .

[27]  Ying Lu,et al.  Understanding the Link Between Information Technology Capability and Organizational Agility: An Empirical Examination , 2011, MIS Q..

[28]  Baofeng Huo,et al.  The impact of operations and supply chain strategies on integration and performance , 2017 .

[29]  Angappa Gunasekaran,et al.  Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture , 2019, British Journal of Management.

[30]  A. Oke,et al.  Antecedents of supply chain visibility in retail supply chains: A resource-based theory perspective , 2007 .

[31]  Elena Tappia,et al.  Assessing efficiency and innovation in the 3PL industry: an empirical analysis , 2017 .

[32]  M. Tushman,et al.  Ambidexterity as a Dynamic Capability: Resolving the Innovator's Dilemma , 2007 .

[33]  Constantin Blome,et al.  The performance impact of supply chain agility and supply chain adaptability: the moderating effect of product complexity , 2015 .

[34]  Martin Christopher,et al.  “Supply Chain 2.0”: managing supply chains in the era of turbulence , 2011 .

[35]  Barney Tan,et al.  IT-enabled operational agility: An interdependencies perspective , 2017, Inf. Manag..

[36]  Andy Neely,et al.  The forces that shape organisational performance measurement systems:: An interdisciplinary review , 1999 .

[37]  M. Taisch,et al.  The value of Big Data in servitization , 2015 .

[38]  Fujun Lai,et al.  Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research , 2012 .

[39]  Juan A. Marin-Garcia,et al.  Triple-A and competitive advantage in supply chains: Empirical research in developed countries , 2018, International Journal of Production Economics.

[40]  Jung P. Shim,et al.  An exploratory study of radio frequency identification (RFID) adoption in the healthcare industry , 2007, Eur. J. Inf. Syst..

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

[42]  Ned Kock,et al.  A Note on How to Conduct a Factor-Based PLS-SEM Analysis , 2015, Int. J. e Collab..

[43]  Nada R. Sanders,et al.  The Emerging Role of the Third‐Party Logistics Provider (3PL) as an Orchestrator , 2011 .

[44]  Rajdeep Grewal,et al.  Information Technology Competencies, Organizational Agility, and Firm Performance: Enabling and Facilitating Roles , 2013, Inf. Syst. Res..

[45]  Elizabeth M. Daniel,et al.  The role of emergent information technologies and systems in enabling supply chain agility , 2005, Int. J. Inf. Manag..

[46]  David J. Ketchen,et al.  Addressing Common Method Variance: Guidelines for Survey Research on Information Technology, Operations, and Supply Chain Management , 2011, IEEE Transactions on Engineering Management.

[47]  P. H. Friesen,et al.  Strategy‐making and environment: The third link , 1983 .

[48]  Nagesh N. Murthy,et al.  Achieving supply chain agility through IT integration and flexibility , 2008 .

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

[50]  A. Gunasekaran,et al.  Big data analytics capability in supply chain agility , 2019, Management Decision.

[51]  Ralf Wilden,et al.  Dynamic capabilities and performance : strategy, structure and environment , 2013 .

[52]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[53]  D. Teece.  The Foundations of Enterprise Performance: Dynamic and Ordinary Capabilities in an (Economic) Theory of Firms , 2014 .

[54]  G. Hult,et al.  Bridging organization theory and supply chain management: The case of best value supply chains , 2007 .

[55]  Divesh Ojha,et al.  Transformational leadership and supply chain ambidexterity: Mediating role of supply chain organizational learning and moderating role of uncertainty , 2018 .

[56]  Li Ling-yee,et al.  Marketing resources and performance of exhibitor firms in trade shows: A contingent resource perspective , 2007 .

[57]  Carl Marcus Wallenburg.  INNOVATION IN LOGISTICS OUTSOURCING RELATIONSHIPS: PROACTIVE IMPROVEMENT BY LOGISTICS SERVICE PROVIDERS AS A DRIVER OF CUSTOMER LOYALTY , 2009 .

[58]  Christopher S. Tang.  Perspectives in supply chain risk management , 2006 .

[59]  D. Teece,et al.  Introduction: On the nature and scope of dynamic capabilities , 2010 .

[60]  Christopher A. Voss,et al.  Contingency research in operations management practices , 2008 .

[61]  Benjamin T. Hazen,et al.  A Trail Guide to Publishing Success: Tips on Writing Influential Conceptual, Qualitative, and Survey Research , 2014 .

[62]  Nagesh N. Murthy,et al.  The antecedents of supply chain agility of a firm: Scale development and model testing , 2006 .

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

[64]  Paul E. Spector,et al.  Common Method Issues: An Introduction to the Feature Topic in Organizational Research Methods , 2010 .

[65]  I. Chen,et al.  Towards a theory of supply chain management: the constructs and measurements , 2004 .

[66]  H. Winklhofer,et al.  Index Construction with Formative Indicators: An Alternative to Scale Development , 2001 .

[67]  Detmar W. Straub,et al.  Common Beliefs and Reality About PLS , 2014 .

[68]  Sang M. Lee,et al.  Ambidextrous supply chain as a dynamic capability: building a resilient supply chain , 2016 .

[69]  David J. Teece,et al.  Dynamic Capabilities: Routines versus Entrepreneurial Action , 2012 .

[70]  Selim Zaim,et al.  Business analytics and firm performance: The mediating role of business process performance , 2019, Journal of Business Research.

[71]  Morgan Swink,et al.  An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective , 2018 .

[72]  Constantin Blome,et al.  Ambidextrous Governance in Supply Chains: The Impact on Innovation and Cost Performance , 2013 .

[73]  Constantin Blome,et al.  Antecedents and enablers of supply chain agility and its effect on performance: a dynamic capabilities perspective , 2013 .

[74]  S. Zahra,et al.  Entrepreneurship and Dynamic Capabilities: A Review, Model and Research Agenda , 2006 .

[75]  Kathleen M. Eisenhardt,et al.  CROSSROADS - Microfoundations of Performance: Balancing Efficiency and Flexibility in Dynamic Environments , 2010, Organ. Sci..

[76]  Amir Pezeshkan,et al.  Dynamic Capabilities and Organizational Performance: A Meta‐Analytic Evaluation and Extension , 2016 .

[77]  Eric T. Bradlow,et al.  The Role of Big Data and Predictive Analytics in Retailing , 2017 .

[78]  Cyril R. H. Foropon,et al.  Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain , 2019, International Journal of Production Economics.

[79]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[80]  Benjamin T. Hazen,et al.  Big data and predictive analytics for supply chain and organizational performance , 2017 .

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

[82]  Mihalis Giannakis,et al.  A multi-agent based system with big data processing for enhanced supply chain agility , 2016, J. Enterp. Inf. Manag..

[83]  E. B. Andersen,et al.  Modern factor analysis , 1961 .

[84]  Chad W. Autry,et al.  A contingent resource-based perspective of supply chain resilience and robustness , 2014 .

[85]  Roger G. Schroeder,et al.  Perceptual measures of performance: Fact or fiction? , 2004 .

[86]  Zhongsheng Hua,et al.  The Impact of IT Capabilities on Firm Performance: The Mediating Roles of Absorptive Capacity and Supply Chain Agility , 2013, Decis. Support Syst..

[87]  Shahriar Akter,et al.  Big data analytics in E-commerce: a systematic review and agenda for future research , 2016, Electronic Markets.

[88]  Zi-Lin He,et al.  Exploration vs. Exploitation: An Empirical Test of the Ambidexterity Hypothesis , 2004, Organ. Sci..

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

[90]  K. B. Hendricks,et al.  An Empirical Analysis of the Effect of Supply Chain Disruptions on Long‐Run Stock Price Performance and Equity Risk of the Firm , 2005 .

[91]  Maria Caridi,et al.  Do virtuality and complexity affect supply chain visibility , 2010 .

[92]  Xavier Brusset,et al.  Does supply chain visibility enhance agility , 2016 .

[93]  Eric T. G. Wang,et al.  The strategic value of supply chain visibility: increasing the ability to reconfigure , 2010, Eur. J. Inf. Syst..

[94]  P. Humphreys,et al.  Reducing supply chain environmental uncertainty through e-intermediation: An organisation theory perspective , 2008 .

[95]  Angappa Gunasekaran,et al.  Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view , 2018, Production Planning & Control.

[96]  P. M. Podsakoff,et al.  Self-Reports in Organizational Research: Problems and Prospects , 1986 .

[97]  Gino Marchet,et al.  Value creation models in the 3PL industry: what 3PL providers do to cope with shipper requirements , 2017 .

[98]  Luu Trong Tuan.  Organisational ambidexterity and supply chain agility: the mediating role of external knowledge sharing and moderating role of competitive intelligence , 2016 .

[99]  S. Winter.  Understanding dynamic capabilities , 2003 .

[100]  S. Fawcett,et al.  Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management , 2013 .

[101]  Gah-Yi Vahn,et al.  BUSINESS ANALYTICS IN THE AGE OF BIG DATA , 2014 .

[102]  Jie Yang,et al.  Supply chain agility: Securing performance for Chinese manufacturers , 2014 .

[103]  S. Kendrick,et al.  The Use of Third-Party Logistics Services by Large American Manufacturers, the 2002 Survey , 2002, Transportation Journal.

[104]  Vijay Gurbaxani,et al.  A process oriented framework for assessing the business value of information technology , 1995, DATB.

[105]  A. Roth,et al.  The effect of an ambidextrous supply chain strategy on combinative competitive capabilities and business performance , 2010 .

[106]  Juan A. Marin-Garcia,et al.  A Triple-A supply chain measurement model: validation and analysis , 2018, International Journal of Physical Distribution & Logistics Management.

[107]  Varun Grover,et al.  Shaping Agility through Digital Options: Reconceptualizing the Role of Information Technology in Contemporary Firms , 2003, MIS Q..

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

[109]  Tor Bøe-Lillegraven,et al.  Untangling the Ambidexterity Dilemma Through Big Data Analytics , 2014 .

[110]  Pervaiz Akhtar,et al.  The Internet of Things, dynamic data and information processing capabilities, and operational agility , 2017, Technological Forecasting and Social Change.

[111]  M. Fisher.  What is the Right Supply Chain for Your Product , 1997 .

[112]  Benjamin T. Hazen,et al.  Examining the effect of external pressures and organizational culture on shaping performance measurement systems (PMS) for sustainability benchmarking: Some empirical findings , 2017 .

[113]  J. Birkinshaw,et al.  Organizational Ambidexterity: Antecedents, Outcomes, and Moderators , 2008 .

[114]  T. Schoenherr,et al.  Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential , 2015 .

[115]  Ralph L. Rosnow,et al.  Essentials of Behavioral Research: Methods and Data Analysis , 1984 .

[116]  Oliver Schilke,et al.  Second-Order Dynamic Capabilities: How Do They Matter? , 2014 .

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

[118]  Maria Nieves Perez-Arostegui,et al.  Supply chain flexibility in dynamic environments: The enabling role of operational absorptive capacity and organisational learning , 2018 .

[119]  D. Dillman.  Mail and internet surveys: The tailored design method, 2nd ed. , 2007 .

[120]  Ahad Zare Ravasan,et al.  The role of business analytics capabilities in bolstering firms' agility and performance , 2019, Int. J. Inf. Manag..

[121]  Georg Schreyögg,et al.  How dynamic can organizational capabilities be? Towards a dual-process model of capability dynamization , 2007 .

[122]  U. Juettner,et al.  Supply chain resilience in the global financial crisis: an empirical study , 2011 .

[123]  M. Lindell,et al.  Accounting for common method variance in cross-sectional research designs. , 2001, The Journal of applied psychology.

[124]  Lei-Yu Wu,et al.  Applicability of the resource-based and dynamic-capability views under environmental volatility , 2010 .

[125]  Scott B. MacKenzie,et al.  Working memory: theories, models, and controversies. , 2012, Annual review of psychology.

[126]  A. Neely,et al.  Measuring performance in a changing business environment , 2003 .

[127]  Daniel A. Levinthal.  Organizational Capabilities in Complex Worlds , 2001 .

[128]  R. Adams,et al.  Data supply chain (DSC): research synthesis and future directions , 2018, Int. J. Prod. Res..

[129]  K. A. Chatha,et al.  Impact of supply chain risk on agility performance: Mediating role of supply chain integration , 2018, International Journal of Production Economics.

[130]  Kenneth W. Green,et al.  Triple‐A supply chain performance , 2012 .

[131]  P. Lawrence,et al.  Differentiation and Integration in Complex Organizations , 1967 .

[132]  M. Tushman,et al.  Ambidextrous Organizations: Managing Evolutionary and Revolutionary Change , 1996 .

[133]  D. Teece.  Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance , 2007 .

[134]  Ned Kock,et al.  From composites to factors: Bridging the gap between PLS and covariance‐based structural equation modelling , 2018, Inf. Syst. J..

[135]  Kathleen M. Eisenhardt,et al.  DYNAMIC CAPABILITIES, WHAT ARE THEY? , 2000 .

[136]  M. C. Holcomb,et al.  Performance outcomes of supply chain agility: When should you be agile? , 2015 .

[137]  Helen Lisbeth Walker,et al.  A decision theory perspective on complexity in performance measurement and management , 2018 .

[138]  Mie Augier,et al.  Dynamic Capabilities and the Role of Managers in Business Strategy and Economic Performance , 2009, Organ. Sci..

[139]  V. Daniel R. Guide,et al.  Notes from the Editors: Redefining some methodological criteria for the journal ☆ , 2015 .

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

[141]  M. C. Holcomb,et al.  An exploration of the strategic antecedents of firm supply chain agility: The role of a firm's orientations , 2016 .

[142]  Hau L. Lee.  The triple-A supply chain. , 2004, Harvard business review.

[143]  Angappa Gunasekaran,et al.  A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry , 2014 .