Exploring the relationship between supplier development, big data analytics capability, and firm performance

Extant research shows that big data analytics (BDA) capability is often employed as a part of organizational resources to enhance firm performance. Drawing upon the resource-based view, dynamic capabilities, and contingency theory, this study endeavors to examine the alignment between BDA capability and a specific type of procurement strategies (i.e., supplier development) and its impact on firm performance. The study extends the BDA capability research by investigating the direct impact of BDA capability on supplier development and firm performance, respectively, and by exploring both mediating and moderating effects on the relationship between supplier development and firm performance. The main results show that a firm’s BDA capability has not only a direct positive significant impact on supplier development, but also a direct positive significant impact on its business performance. More importantly, the results indicate strong moderating and mediating effects of BDA capability on supplier development, which in turn affects the improvement of firm performance. Theoretical and managerial implications along with future research directions are provided in the end.

[1]  J. Barney,et al.  The resource-based view of the firm: Ten years after 1991 , 2001 .

[2]  Ajai S. Gaur,et al.  Buyer-Supplier Partnership Quality and Supply Chain Performance: Moderating Role of Risks, and Environmental Uncertainty , 2011 .

[3]  H. Chan,et al.  The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan , 2011 .

[4]  David N. Burt,et al.  World Class Supply Management The Key To Supply Chain Management , 2002 .

[5]  Cristóbal Sánchez-Rodríguez,et al.  The effect of supplier development initiatives on purchasing performance: a structural model , 2005 .

[6]  Bin Zhou,et al.  Lean principles, practices, and impacts: a study on small and medium-sized enterprises (SMEs) , 2016, Ann. Oper. Res..

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

[8]  William J. Doll,et al.  The Measurement of End-User Computing Satisfaction , 1988, MIS Q..

[9]  Peter Trkman,et al.  International Journal of Information Management , 2022 .

[10]  Zongwei Luo,et al.  A bibliographic study on big data: concepts, trends and challenges , 2017, Bus. Process. Manag. J..

[11]  J. Barney Purchasing, Supply Chain Management and Sustained Competitive Advantage: The Relevance of Resource-based Theory , 2012 .

[12]  James Durbin,et al.  Errors in variables , 1954 .

[13]  Nishikant Mishra,et al.  Big data cloud computing framework for low carbon supplier selection in the beef supply chain , 2018, Journal of Cleaner Production.

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

[15]  Ning Zhang,et al.  An optimization model for green supply chain management by using a big data analytic approach , 2017 .

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

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

[18]  Benny Tjahjono,et al.  Big data analytics in supply chain management: trends and related research , 2014 .

[19]  Benjamin T. Hazen,et al.  Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda , 2016, Comput. Ind. Eng..

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

[21]  Petros Ieromonachou,et al.  Big data analytics in supply chain management: A state-of-the-art literature review , 2017, Comput. Oper. Res..

[22]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[23]  N. Venkatraman,et al.  The Concept of Fit in Strategy Research: Towards Verbal and Statistical Correspondence , 2018 .

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

[25]  A. Carr,et al.  Communication methods, information sharing, supplier development and performance , 2007 .

[26]  Desirée Knoppen,et al.  Purchasing: Can we bridge the gap between strategy and daily reality? , 2015 .

[27]  Sneha Kumari,et al.  Role of Big Data in Decision Making , 2018 .

[28]  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..

[29]  D. Luzzini,et al.  Cinderella purchasing transformation: linking purchasing status to purchasing practices and business performance , 2016 .

[30]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[31]  Tsan-Ming Choi,et al.  Optimal apparel supplier selection with forecast updates under carbon emission taxation scheme , 2013, Comput. Oper. Res..

[32]  David J. Ketchen,et al.  Achieving Research Design Excellence Through the Pursuit of Perfection: Toward Strong Theoretical Calibration , 2018 .

[33]  Govindan Kannan,et al.  Incorporating risk measures in closed-loop supply chain network design , 2014 .

[34]  Andreas Größler,et al.  An empirical model of the relationships between manufacturing capabilities , 2006 .

[35]  De-Min Wu,et al.  Alternative Tests of Independence between Stochastic Regressors and Disturbances , 1973 .

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

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

[38]  Mohan M. Kumaraswamy,et al.  Predicting purchasing performance: The role of supplier development programs , 2003 .

[39]  Michael N. Katehakis,et al.  On computing optimal (Q,r) replenishment policies under quantity discounts , 2012, Ann. Oper. Res..

[40]  Wendy L. Tate,et al.  Services Supply Management: The Next Frontier for Improved Organizational Performance , 2007 .

[41]  Donald R. Jones,et al.  Conceptualizing the Dynamic Strategic Alignment Competency , 2011, J. Assoc. Inf. Syst..

[42]  P. K. Bagchi,et al.  Purchasing Development in Small and Medium-Sized Enterprises (SMEs) , 2009 .

[43]  M. F. Acar,et al.  The relationships between corporate social responsibility, environmental supplier development, and firm performance , 2016 .

[44]  D. Lambert,et al.  Supply Chain Management: Implementation Issues and Research Opportunities , 1998 .

[45]  Fiona Xiaoying Ji,et al.  Impact of Lean Supply Chain Management on Operational Performance: A Study of Small Manufacturing Companies , 2015 .

[46]  A. Hayes Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach , 2013 .

[47]  Roger G. Schroeder,et al.  A FRAMEWORK FOR QUALITY MANAGEMENT RESEARCH AND AN ASSOCIATED MEASUREMENT INSTRUMENT , 1994 .

[48]  Joseph A. Cote,et al.  Lack of method variance in self-reported affect and perceptions at work: Reality or artifact? , 1989 .

[49]  K. Tan,et al.  Buyer‐supplier relationships: The impact of supplier selection and buyer‐supplier engagement on relationship and firm performance , 2006 .

[50]  Rajeev Sharma,et al.  Estimating the effect of common method variance: the method-method pair technique with an illustration from TAM research , 2009 .

[51]  E. Hartmann,et al.  Top and bottom line relevance of purchasing and supply management , 2012 .

[52]  K. Tan,et al.  Supplier Selection and Assessment: Their Impact on Business Performance , 2002 .

[53]  Nezih Altay,et al.  Big data in humanitarian supply chain networks: a resource dependence perspective , 2016, Annals of Operations Research.

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

[55]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

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

[57]  Mehmet Kabak,et al.  A holistic evaluation of the e-procurement website by using a hybrid MCDM methodology , 2013, Electron. Gov. an Int. J..

[58]  Zahir Irani,et al.  Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector , 2016, Annals of Operations Research.

[59]  Angappa Gunasekaran,et al.  Big Data and supply chain management: a review and bibliometric analysis , 2018, Ann. Oper. Res..

[60]  D. A. Kenny,et al.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.

[62]  S. Seuring,et al.  Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management , 2017 .

[63]  Joseph L. Cavinato,et al.  Fitting Purchasing to the Strategic Firm: Frameworks, Processes, and Values , 1990 .

[64]  T. Cheng,et al.  The impact of supplier development on buyer competitive advantage: A path analytic model , 2012 .

[65]  J. Hausman Specification tests in econometrics , 1978 .

[66]  Michael N. Katehakis,et al.  On optimal bidding and inventory control in sequential procurement auctions: the multi period case , 2014, Ann. Oper. Res..

[67]  Niraj Kumar,et al.  Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice , 2017, Transportation Research Part E: Logistics and Transportation Review.

[68]  Angappa Gunasekaran,et al.  Distribution network design with big data: model and analysis , 2018, Ann. Oper. Res..

[69]  F. Rozemeijer Purchasing myopia revisited again , 2008 .

[70]  Jim Shi,et al.  Optimal replenishment rate for inventory systems with compound Poisson demands and lost sales: a direct treatment of time-average cost , 2014, Annals of Operations Research.

[71]  Harpreet Kaur,et al.  Heuristic modeling for sustainable procurement and logistics in a supply chain using big data , 2017, Comput. Oper. Res..

[72]  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.

[73]  C. Watts,et al.  Supplier Development Programs: An Empirical Analysis , 1993 .

[74]  Qing Cao,et al.  The impact of alignment between virtual enterprise and information technology on business performance in an agile manufacturing environment , 2005 .

[75]  Daniel R. Krause,et al.  Critical elements of supplier development The buying-firm perspective , 1997 .

[76]  Giacomo Marzi,et al.  Big data and dynamic capabilities: a bibliometric analysis and systematic literature review , 2019, Management Decision.

[77]  Barbara B. Flynn,et al.  Editorial: Survey Research Design in Supply Chain Management: The Need for Evolution in Our Expectations , 2017 .

[78]  Sachchidanand Singh,et al.  Big Data analytics , 2012 .

[79]  P. Humphreys,et al.  The impact of supplier development on buyer-supplier performance , 2004 .

[80]  R. Hensley A review of operations management studies using scale development techniques , 1999 .

[81]  Prasanta Kumar Dey,et al.  Strategic supplier performance evaluation: a case-based action research of a UK manufacturing organisation , 2015 .

[82]  José Carlos da Silva Freitas Junior,et al.  The effect of data strategy on competitive advantage , 2020 .

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

[84]  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.

[85]  Gilvan C. Souza,et al.  Supply Chain Analytics , 2016 .

[86]  Vicky Ching Gu,et al.  The effects of organizational culture and environmental pressures on IT project performance: A moderation perspective , 2014 .

[87]  Gi Mun Kim,et al.  Investigating the Value of Sociomaterialism in Conceptualizing IT Capability of a Firm , 2012, J. Manag. Inf. Syst..

[88]  Firm Resources and Sustained Competitive Advantage , 1991 .

[89]  Robert B. Handfield,et al.  Measuring the benefits of ERP on supply management maturity model: a “big data” method , 2015 .

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

[91]  Sunil Tiwari,et al.  Big data analytics in supply chain management between 2010 and 2016: Insights to industries , 2018, Comput. Ind. Eng..

[92]  T. Davenport Competing on analytics. , 2006, Harvard business review.

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

[94]  J. Barney Firm Resources and Sustained Competitive Advantage , 1991 .