Examining the adoption of Big data analytics in supply chain management under competitive pressure: evidence from Saudi Arabia

ABSTRACT Grounded in the Technology-Organisation-Environment (TOE) framework, this study identifies the main factors affecting the intention to adopt Big Data Analytics (BDA) in supply chain management (SCM) for firms based in Saudi Arabia. This study focuses on identifying and analysing the role of competitive pressure as a contextual variable that can moderate the effects of these factors on the adoption intention. A survey of 220 IT managers revealed that compatibility, relative advantage, and top management support are positively perceived factors as they foster the firms’ intentions to adopt BDA in SCM. Their effects on intentions were positively moderated by competitive pressure as a contextual variable. However, BDA complexity and organisational readiness were not supported as influencing firms’ intentions to adopt BDA. The statistical analyses also indicated that the effects of complexity and organisational readiness on intentions are not significantly moderated by competitive pressure. This study contributes to the literature by emphasising the interaction between TOE factors, instead of considering them separately. It also offers guidance to managers aiming to adopt and use BDA in SCM.

[1]  K. Mezghani,et al.  Effects of Personal Innovativeness on IS Managers' Intentions to Switch Toward Cloud ERP in Saudi SMEs , 2018 .

[2]  Mike Wright,et al.  The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes , 2019, Research Policy.

[3]  Hema Date,et al.  Understanding determinants of cloud computing adoption using an integrated TAM-TOE model , 2015, J. Enterp. Inf. Manag..

[4]  Pei-Fang Hsu,et al.  International Journal of Information Management , 2014 .

[5]  Ray Y. Zhong,et al.  Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives , 2016, Comput. Ind. Eng..

[6]  Lech J. Janczewski,et al.  Adoption of Big Data Solutions: A study on its security determinants using Sec-TOE Framework , 2016, CONF-IRM.

[7]  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.

[8]  Kenneth L. Kraemer,et al.  Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies , 2006, Eur. J. Inf. Syst..

[9]  Vincenzo Esposito Vinzi,et al.  PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement , 2010 .

[10]  Daniel K. Maduku,et al.  Understanding mobile marketing adoption intention by South African SMEs: A multi-perspective framework , 2016, Int. J. Inf. Manag..

[11]  C. Dong,et al.  Sustainable Investment in a Supply Chain in the Big Data Era: An Information Updating Approach , 2018 .

[12]  Anke Schüll,et al.  On the Adoption of Big Data Analytics: Interdependencies of Contextual Factors , 2018, ICEIS.

[13]  Yu Min Wang,et al.  Understanding the determinants of RFID adoption in the manufacturing industry , 2010 .

[14]  Hart O. Awa,et al.  A model of adoption determinants of ERP within T-O-E framework , 2016, Inf. Technol. People.

[15]  Mary C. Whitton,et al.  Using innovation diffusion theory to guide collaboration technology evaluation: work in progress , 2001, Proceedings Tenth IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE 2001.

[16]  Gaby Odekerken-Schröder,et al.  Using PLS path modeling for assessing hierarchial construct models: guidelines and impirical illustration , 2009 .

[17]  Izak Benbasat,et al.  Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology , 1995, MIS Q..

[18]  Mustafizur Rahman,et al.  Big data and its impact on digitized supply chain management , 2017 .

[19]  V. E. Vinzi,et al.  A global Goodness – of – Fit index for PLS structural equation modelling 1 , 2004 .

[20]  Kevin Zhu,et al.  Migrating to internet-based e-commerce: Factors affecting e-commerce adoption and migration at the firm level , 2006, Inf. Manag..

[21]  Hsin-Chieh Wu,et al.  Determinants of RFID adoption intention: Evidence from Taiwanese retail chains , 2010, Inf. Manag..

[22]  Qing Hu,et al.  Assimilation of Enterprise Systems: The Effect of Institutional Pressures and the Mediating Role of Top Management , 2007, MIS Q..

[23]  Gregory Vial,et al.  Understanding digital transformation: A review and a research agenda , 2019, J. Strateg. Inf. Syst..

[24]  Casey G. Cegielski,et al.  Organizational intention to adopt big data in the B2B context: An integrated view , 2020 .

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

[26]  Yong Wang,et al.  The moderating effect of the business strategic orientation on eCommerce adoption: Evidence from UK family run SMEs , 2009, J. Strateg. Inf. Syst..

[27]  Kalyan Agrawal,et al.  Investigating the determinants of Big Data Analytics (BDA) adoption in Asian emerging economies , 2015, AMCIS.

[28]  Trevor Clohessy,et al.  Investigating the influence of organizational factors on blockchain adoption , 2019, Ind. Manag. Data Syst..

[29]  Chinyao Low,et al.  Understanding the determinants of cloud computing adoption , 2011, Ind. Manag. Data Syst..

[30]  Noor Azizi Ismail,et al.  The contingent role of dependency in predicting the intention to adopt B2B e-commerce , 2018, Inf. Technol. Dev..

[31]  Rajiv Sabherwal,et al.  Strategic Alignment Between Business and Information Technology: A Knowledge-Based View of Behaviors, Outcome, and Consequences , 2006, J. Manag. Inf. Syst..

[32]  Truc Nguyen,et al.  Technology adoption in Norway : organizational assimilation of big data , 2017 .

[33]  Surabhi Verma,et al.  Understanding the Determinants of Big Data Analytics Adoption , 2019, Inf. Resour. Manag. J..

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

[35]  Karim Mezghani,et al.  Factors Explaining IS Managers Attitudes toward Cloud Computing Adoption , 2016, Int. J. Technol. Hum. Interact..

[36]  G. Premkumar,et al.  Adoption of new information technologies in rural small businesses , 1999 .

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

[38]  Karim Mezghani,et al.  Understanding Intentions to Switch Toward Cloud Computing at Firms' Level: A Multiple Case Study in Tunisia , 2018, J. Glob. Inf. Manag..

[39]  Viswanath Venkatesh,et al.  Adoption and Impacts of Interorganizational Business Process Standards: Role of Partnering Synergy , 2012, Inf. Syst. Res..

[40]  Mohammad Nazir Ahmad,et al.  The Determinants of Adoption of Cloud-Based ERP of Nigerian's SMES Manufacturing Sector Using Toe Framework and Doi Theory , 2019, Int. J. Enterp. Inf. Syst..

[41]  Hyun-Jung Kim,et al.  Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics , 2018, Sustainability.

[42]  Irwin Brown,et al.  Challenges to the Organisational Adoption of Big Data Analytics: A Case Study in the South African Telecommunications Industry , 2015, SAICSIT '15.

[43]  Mondher Feki,et al.  Big Data Analytics Driven Supply Chain Transformation , 2019, Advances in E-Business Research.

[44]  Douglas D. Heckathorn,et al.  Comment: Snowball versus Respondent-Driven Sampling , 2011 .

[45]  Wynne W. Chin,et al.  Handbook of Partial Least Squares , 2010 .

[46]  Fabio Casati,et al.  Business Process , 2004, The Practical Handbook of Internet Computing.

[47]  Tiago Oliveira,et al.  Understanding e-business adoption across industries in European countries , 2010, Ind. Manag. Data Syst..

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

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

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

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

[52]  Kin Meng Sam,et al.  Understanding Adoption of Big Data Analytics in China: From Organizational Users Perspective , 2018, 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[53]  Inayat Ullah,et al.  Analysis of barriers in implementation of digital transformation of supply chain using interpretive structural modelling approach , 2019, Journal of Modelling in Management.

[54]  Hemlata Gangwar,et al.  Understanding the Determinants of Big Data Adoption in India: An Analysis of the Manufacturing and Services Sectors , 2018, Inf. Resour. Manag. J..

[55]  E. Rogers Diffusion of Innovations , 1962 .

[56]  L. G. Tornatzky,et al.  Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings , 1982, IEEE Transactions on Engineering Management.

[57]  Kamel Rouibah,et al.  Determinants of Big Data Adoption and Success , 2017, ICACS.

[58]  Sung-Won Jung,et al.  A Study on the Factors Affecting Intention to Introduce Big Data from Smart Factory Perspective , 2018, Big Data, Cloud Computing, Data Science & Engineering.

[59]  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.

[60]  Benjamin T. Hazen,et al.  How supply chain analytics enables operational supply chain transparency , 2018 .

[61]  M. Fleischer,et al.  processes of technological innovation , 1990 .

[62]  Y. Lai,et al.  Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation , 2018 .

[63]  Thijs Broekhuizen,et al.  Digital transformation: A multidisciplinary reflection and research agenda , 2021, Journal of Business Research.

[64]  T. Schoenherr,et al.  Mobile Devices and Applications for Supply Chain Management: Process, Contingency, and Performance Effects , 2016 .

[65]  Dianne Hall,et al.  Understanding the Factors Affecting the Organizational Adoption of Big Data , 2018, J. Comput. Inf. Syst..