References
- Agrawal, K. (2015). Investigating the determinants of Big data analytics (BDA) adoption in Asian emerging economies. Proceedings of the Americas Conference on Information Systems. AMCIS. https://doi.org/10.5465/AMBPP.2015.11290abstract
- Agrawal, K., Rakesh, N., & Inayat, U. (2019). Analysis of barriers in implementation of digital transformation of supply chain using interpretive structural modelling approach. Journal of Modelling in Management, 15(1), 297–317. https://doi.org/10.1108/JM2-03-2019-0066
- Al-Qirim, N., Ali, T., & Kamel, R. (2017). “Determinants of Big data adoption and success.” Pp. 88–92 in Proceedings of the International Conference on Algorithms, Computing and Systems - ICACS ’17. Jeju Island, Republic of Korea: ACM Press.
- Alsaad, A., Mohamad, R., & Ismail, N. A. (2019). The contingent role of dependency in predicting the intention to adopt B2B e-commerce. Information Technology for Development, 25(4), 686–714. https://doi.org/10.1080/02681102.2018.1476830
- Arunachalam, D., Kumar, N., & Kawalek, P. (2018). Understanding Big data analytics capabilities in supply chain management: unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416–436. https://www.sciencedirect.com/science/article/pii/S1366554516303799
- Awa, H.O., & Ojiabo, O.U. (2016). A model of adoption determinants of ERP within TOE framework. Information Technology & People., 29(4), 901–930. https://doi.org/10.1108/ITP-03-2015-0068
- Brinch, M. (2019). “Conceptualization and value creation of Big data in supply chain management: A business process perspective.”
- Chen, D., David, S., & Morgan, S. (2015). How the use of Big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39. https://doi.org/10.1080/07421222.2015.1138364
- Cheng, Y., Yunjuan, K., Xiutian, S., & Ciwei, D. (2018). Sustainable investment in a supply chain in the Big data era: An information updating approach. Sustainability, 10(2), 403. https://doi.org/10.3390/su10020403
- Clohessy, T., & Acton, A. (2019). Investigating the Influence of organizational factors on blockchain adoption: An innovation theory perspective. Industrial Management & Data Systems, 119(7), 1457–1491. https://doi.org/10.1108/IMDS-08-2018-0365
- Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017). Assessing business value of Big data analytics in European firms. Journal of Business Research, 70, 379–390. https://doi.org/10.1016/j.jbusres.2016.08.011
- Dey, P. (2020). Saudi public sector to remain highest spender on digital tech in 2020. IDC. https://www.argaam.com/en/article/articledetail/id/1344242 (17/07/2020).
- Ellis, S., Kimberly, K., Lorenzo, V., & Victoria, B. (2017). Digital transformation drives supply chain restructuring imperative. International data corporation.
- Feki, M. (2019). Big data analytics driven supply chain transformation. In K. Mezghani & W. Aloulou (Eds.), Business transformations in the era of digitalization (pp. 106–124). IGI Global. https://doi.org/10.4018/978-1-5225-7262-6.ch007
- Galea-Pace, S. (2020). How is Big data transforming the supply chain? Supply chain digital. Retrieved July 30, 2020 from https://www.supplychaindigital.com/technology/how-big-data-transforming-supply-chain
- Gangwar, H. (2018). Understanding the determinants of Big data adoption in India: An analysis of the manufacturing and services sectors. Information Resources Management Journal, 31(4), 1–22. https://doi.org/10.4018/IRMJ.2018100101
- Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065
- Hair, J.F., Ringle, C.M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
- Heckathorn, D.D. (2011). Comment: Snowball versus respondent‐driven sampling. Sociological Methodology, 41(1), 355–366. https://doi.org/10.1111/j.1467-9531.2011.01244.x
- Hong, W., & Zhu, K. (2006). Migrating to internet-based e-commerce: Factors affecting e-commerce adoption and migration at the firm level. Information & Management, 43(2), 204–221. https://doi.org/10.1016/j.im.2005.06.003
- Hsu, P.-F., Ray, S., & Yu-Yu, L.-H. (2014). Examining cloud computing adoption intention, pricing mechanism, and deployment model. International Journal of Information Management, 34(4), 474–488. https://doi.org/10.1016/j.ijinfomgt.2014.04.006
- Iacovou, C.L., Izak, B., & Albert, S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 19(4), 465. https://doi.org/10.2307/249629
- Jang, W., Soo-Sang, K., Sung-Won, J., & Gwang-Yong, G. (2019). “A study on the factors affecting intention to introduce Big data from smart factory perspective”. In R. Lee (Ed.), Big data, cloud computing, data science & engineering (Vol. 786), Pp. 129–56. Springer International Publishing.
- Jin, D., & Kim, H. J. (2018). Integrated understanding of Big data, Big data analysis, and business intelligence: A case study of logistics. Sustainability, 10(10), 3778. https://doi.org/10.3390/su10103778
- Kearns, G.S., & Sabherwal, R. (2007). Strategic alignment between business and information technology: A knowledge-based view of behaviors, outcome, and consequences. Journal of Management Information Systems, 23(3), 129–162. https://doi.org/10.2753/MIS0742-1222230306
- Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of Big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. The International Journal of Logistics Management, 29(2), 676–703. https://doi.org/10.1108/IJLM-06-2017-0153
- Liang, Saraf, Hu & Xue. (2007). Assimilation of enterprise systems: The effect of institutional pressures and the mediating role of top management. MIS Quarterly, 31(1), 59. https://doi.org/10.2307/25148781
- Low, C., Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems, 111(7), 1006–1023. https://doi.org/10.1108/02635571111161262
- Madhlangobe, W. (2018). Assessment of factors influencing intent-to-use Big data analytics in an organization: A survey study. [Doctoral dissertation]. Nova Southeastern University. NSUWorks, College of Engineering and Computing. (1054). https://nsuworks.nova.edu/gscis_etd/1054
- Maduku, D.K., Mpinganjira, M., & Duh, H. (2016). Understanding mobile marketing adoption intention by South African SMEs: A multi-perspective framework. International Journal of Information Management, 36(5), 711–723. https://doi.org/10.1016/j.ijinfomgt.2016.04.018
- Malaka, I., & Brown, I. (2015). Challenges to the organisational adoption of big data analytics: A case study in the South African Telecommunications Industry. In Proceedings of the 2015 Annual Research Conference on South African Institute of Computer Scientists and Information Technologists, pp. 1–9, Stellenbosch, South Africa.
- McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68. Retrieved May 25, 2020 from https://hbr.org/2012/10/big-data-the-management-revolution
- Mezghani, K. (2018). Effects of personal innovativeness on IS Managers' Intentions to switch toward cloud ERP in Saudi SMEs. The Electronic Journal Information Systems Evaluation, 21(1), 46–61. Retrieved June 30, 2020 from http://www.ejise.com/volume21/issue1Q46
- Mezghani, K., & Ayadi, F. (2016). Factors explaining IS Managers’ attitudes toward cloud computing adoption. International Journal of Technology and Human Interaction, 12(1), 1–20. https://doi.org/10.4018/IJTHI.2016010101
- Mikalef, P., Ilias, O., John, K., & Michail, G. (2018). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and E-Business Management, 16(3), 547–578. https://doi.org/10.1007/s10257-017-0362-y
- Mishra, D., Angappa, G., Thanos, P., & Stephen, J. (2018). Big data and supply chain management: A review and bibliometric analysis. Annals of Operations Research, 270(1–2), 313–336. https://doi.org/10.1007/s10479-016-2236-y
- Nambisan, S., Mike, W., & Maryann, F. (2019). The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research Policy, 48(8), 103773. https://doi.org/10.1016/j.respol.2019.03.018
- Nguyen, T., Li, Z., Virginia, S., Petros, I., & Yong, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254–264. https://doi.org/10.1016/j.cor.2017.07.004
- Oliveira, T., & Martins, M.F. (2010). Understanding e-business adoption across industries in European countries. Industrial Management & Data Systems, 110(9), 1337–1354. https://doi.org/10.1108/02635571011087428
- Petersen, T., & Nguyen, T. (2017). “Technology adoption in Norway: Organizational assimilation of Big data.”
- Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., & Podsakoff, N.P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. https://doi.org/10.1037/0021-9010.88.5.879
- Premkumar, G., & Roberts, M. (1999). Adoption of new information technologies in rural small businesses. Omega, 27(4), 467–484. https://doi.org/10.1016/S0305-0483(98)00071-1
- Rogers, E.M. (2010). Diffusion of innovations. Simon and Schuster.
- Salleh, K.A., & Janczewski, L. (2016). Adoption of Big data solutions: A study on its security determinants using Sec-TOE framework. CONF-IRM 2016 Proceedings. 66. Retrieved June 01, 2020 from http://aisel.aisnet.org/confirm2016/66
- Sam, K.M., & Chatwin, C.R. (2018). “Understanding adoption of Big data analytics in China: From organizational users perspective.” Pp. 507–510 In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). Bangkok: IEEE.
- Schoenherr, T. (2016). Mobile devices and applications for supply chain management: Process, contingency, and performance effects. Transportation Journal, 55(4), 4. https://doi.org/10.5325/transportationj.55.4.0333
- Schüll, A., & Maslan, N. 2018. “On the adoption of Big data analytics: Interdependencies of contextual factors:” in Proceedings of the 20th International Conference on Enterprise Information Systems. Funchal, Madeira, Portugal: SCITEPRESS - Science and Technology Publications. Pp. 425–31.
- Smaoui Hachicha, Z., & Mezghani, K. (2018). Understanding intentions to switch toward cloud computing at firms’ level: A multiple case study in Tunisia. Journal of Global Information Management, 26(1), 136–165. https://doi.org/10.4018/JGIM.2018010108
- Sonnenwald, D.H., Maglaughlin, K.L., & Whitton, M.C. (2001). Using innovation diffusion theory to guide collaboration technology evaluation. IEEE 10th International Workshop on Enabling Technologies, Work in progress, Infrastructure for Collaborative Enterprises, Cambridge, MA.
- Sun, S., Cegielski, C.G., Jia, L., & Hall, D.J. (2018). Understanding the factors affecting the organizational adoption of big data. Journal of Computer Information Systems, 58(3), 193–203. https://doi.org/10.1080/08874417.2016.1222891
- Sun, S., Hall, D.J., & Cegielski, C.G. (2019). Organizational intention to adopt Big data in the B2B context: An integrated view. Industrial Marketing Management, 86, 109–121. https://doi.org/10.1016/j.indmarman.2019.09.003
- Tahiduzzaman, M., Rahman, M., Dey, S. K., Rahman, M. S., & Akash, S. M. (2017). Big data and its impact on digitized supply chain management. IJRDO - Journal of Business Management (ISSN: 2455–6661), 3(9), 196–208. Retrieved May 20, 2020, from https://www.ijrdo.org/index.php/bm/article/view/1477
- Tenenhaus, M., Amato, S., & Esposito Vinzi, V. (2004). A global Goodness-of-Fit index for PLS structural equation modelling. In: XLII SIS Scientific Meeting. Padova, CLEUP, pp. 739–742.
- Tornatzky, L.G., & Fleischer, M. (1990). The Processes of technological innovation. Mass: Lexington Books, Lexington.
- Tornatzky, L.G., & Klein, K.J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28–45. https://doi.org/10.1109/TEM.1982.6447463
- Tsai, M.C., Lee, W., & Wu, H.C. (2010). Determinants of RFID adoption intention: Evidence from Taiwanese retail chains. Information & Management, 47(5–6), 255–261. https://doi.org/10.1016/j.im.2010.05.001
- Usman, U.M.Z., Ahmad, M.N., & Zakaria, N.H. (2019). The determinants of adoption of cloud-based ERP of Nigerian’s SMES manufacturing sector using toe framework and. International Journal of Enterprise Information Systems (IJEIS), 15(3), 27–43. https://doi.org/Theory
- Venkatesh, V., & Bala, H. (2012). Adoption and impacts of interorganizational business process standards: Role of partnering synergy. Information Systems Research, 23(4), 1131–1157. https://doi.org/10.1287/isre.1110.0404
- Verhoef, P.C., Thijs, B., Yakov, B., Abhi, B., John, Q., Nicolai, F., & Michael, H. (2019). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901.
- Verma, S., & Chaurasia, S. (2019). Understanding the determinants of Big data analytics adoption. Information Resources Management Journal (IRMJ), 32(3), 1–26. https://doi.org/10.4018/IRMJ.2019070101
- Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
- Vinzi, V.E., Trinchera, L., & Amato, S. 2010. PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: concepts, methods and applications pp. 47–82). Berlin: Springer-Verlag.
- Wang, Y., & Ahmed, P.K. (2009). The moderating effect of the business strategic orientation on Ecommerce adoption: Evidence from UK family run SMEs. The Journal of Strategic Information Systems, 18(1), 16–30. https://doi.org/10.1016/j.jsis.2008.11.001
- Wang, Y.M., Wang, Y.S., & Yang, Y.F. (2010). Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting and Social Change, 77(5), 803–815. https://doi.org/10.1016/j.techfore.2010.03.006
- Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195. https://doi.org/10.2307/20650284
- Zhong, R., Stephen, T., George, Q., & Shulin, L. (2016). Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 101, 572–591. https://doi.org/10.1016/j.cie.2016.07.013
- Zhu, K., Dong, S., Xu, S.X., & Kraemer, K.L. (2006). Innovation diffusion in global contexts: Determinants of post-adoption digital transformation of European companies. European Journal of Information Systems, 15(6), 601–616. https://doi.org/10.1057/palgrave.ejis.3000650
- Zhu, S., Song, J., Hazen, B.T., Lee, K., & Cegielski, C. (2018). How supply chain analytics enables operational supply chain transparency: An organizational information processing theory perspective. International Journal of Physical Distribution & Logistics Management, 48(1), 47–68. https://doi.org/10.1108/IJPDLM-11-2017-0341