Adoption of big data analytics in medium-large supply chain firms in Saudi Arabia
-
DOIhttp://dx.doi.org/10.21511/kpm.06(1).2022.06
-
Article InfoVolume 6 2022, Issue #1, pp. 62-74
- Cited by
- 723 Views
-
201 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Big Data Analytics (BDA) is one of the most digital innovations for supporting supply chain firms’ activities. Empirically, multiple benefits of BDA in Supply Chain Management (SCM) have been demonstrated. The study aimed to investigate the relationship between technical, organizational, and environmental factors and supply chain firms’ performance using the Technology-Organization-Environment (TOE) framework and the Diffusion of Innovation (DOI) theory. This study was conducted at medium-large supply chain firms in Saudi Arabia, the sample size reached 700 firms recognized by Saudi Arabia’s Ministry of Commerce and Industry in different domains. In this study, a questionnaire was used to collect primary data. The collected data are analyzed using SPSS version 26.0. SPSS is used to describe respondents’ demographic profiles. The percentage of respondents to the questionnaire reached 57%. In addition, to test hypotheses and accomplish research goals, PLS-SEM version 3.0 is used to examine the relationship between independent and dependent variables. From the PLS results, the study reported that complexity (β = 0.097, t = 2.817), security (β = 0.222, t = 3.486), IT expertise (β = 0.108, t = 1.993), and external support (β = 0.211, t = 3.468) were positively related to firm’s performance; in contrast, relative advantage (β = –0.006, t = 0.200), compatibility (β = –0.020, t = 0.314), top management support (β = –0.046, t = 0.386), organizational resources (β = –0.065, t = 1.179), competitive pressure (β = –0.011, t = 0.199), and privacy (β = –0.05, t = 0.872) were negatively related to firm’s performance.
- Keywords
-
References59
-
Tables2
-
Figures0
-
- Table 1. Results of Outer Loading, Cronbach’s Alpha, CR, and AVE for composite variables
- Table 2. Path coefficient of variables
-
- Abawajy, J. (2015). Comprehensive analysis of big data variety landscape. International Journal of Parallel, Emergent and Distributed Systems, 30(1), 5-14.
- Agrawal, K. P. (2015). Investigating the determinants of big data analytics (BDA) adoption in emerging economies. Academy of Management Proceedings, 1, 11210-11290.
- 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.
- Arunachalam, D., Kumar, N., & Kawalek, J. 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.
- Asiaei, A., & Rahim, N. Z. A. (2019). A multifaceted framework for adoption of cloud computing in Malaysian SMEs. Journal of Science and Technology Policy Management, 10(3), 708-750.
- Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2019). Big data adoption: State of the art and research challenges. Information Processing & Management, 56(6), 102095.
- Boonsiritomachai, W., McGrath, M., & Burgess, S. (2014). A research framework for the adoption of Business Intelligence by Small and Medium-sized enterprises. In Proceedings of the 27th Annual Conference on Small Enterprise Association of Australia & New Zealand (pp. 1-22). SEAANZ.
- Chandra, S., & Kumar, K. N. (2018). Exploring factors influencing organizational adoption of augmented reality in e-commerce: Empirical analysis using technology organization environment model. Journal of Electronic Commerce Research, 19(3), 237-265.
- Chehbi-Gamoura, S., Derrouiche, R., Damand, D., & Barth, M. (2020). Insights from Big Data Analytics in Supply Chain Management: An All-Inclusive Literature Review Using the SCOR Model. Production Planning and Control, 31(5), 355-382.
- Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1883.
- Cruz-Jesus, F., Pinheiro, A., & Oliveira, T. (2019). Understanding CRM adoption stages: empirical analysis building on the TOE framework. Computers in Industry, 109, 1-13.
- Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly: Management Information Systems, 13(3), 319-339.
- Emani, C. K., Cullot, N., & Nicolle, C. (2015). Understandable big data: a survey. Computer Science Review, 17, 70-81.
- Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2018). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 294, 567-592.
- 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.
- Galea-Pace, S. (2020). How Is Big Data Transforming the Supply Chain? Supply Chain Digital.
- Gangwar, H. (2018). Understanding the determinants of big data adoption in India: An analysis of the manufacturing and services sectors. Information Resources Management Journal (IRMJ), 31(4), 1-22.
- Govindan, K., Rajeev, A., Padhi, S. S., & Pati, R. K. (2020). Supply Chain Sustainability and Performance of Firms: A Meta-Analysis of the Literature. Transportation Research Part E: Logistics and Transportation Review, 137, 101923.
- Grover, P., & Kar, A. K. (2017). Big data analytics: A review on theoretical contributions and tools used in literature. Global Journal of Flexible, Systems Management, 18(3), 203-229.
- Hung, S. Y., Huang, Y. W., Lin, C. C., Chen, K. C., & Tarn, J. M. (2016). Factors influencing business intelligence systems implementation success in the enterprises. PACIS 2016 Proceedings, 297.
- Isma’ili, A., Li, M., Shen, J., & He, Q. (2016). Cloud computing adoption determinants: an analysis of Australian SMEs. PACIS 2016 Proceedings, 209.
- Jang, W. J., Kim, S. S., Jung, S. W., & Gim, G. Y. (2018, July). A study on the factors affecting intention to introduce Big data from smart factory perspective. In 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science Engineering (pp. 129-156). Springer, Cham.
- Kamble, S. S., & Gunasekaran, A. (2019). Big data-driven supply chain performance measurement system: a review and framework for implementation. International Journal of Production Research, 58(1), 65-86.
- Kandil, A. M. N. A., Ragheb, M. A., Ragab, A. A., & Farouk, M. (2018). Examining the effect of TOE model on cloud computing adoption in Egypt. The Business & Management Review, 9(4), 113-123.
- 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. International Journal of Logistics Management, 29(2), 676-703.
- Maduku, D., 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.
- Malaka, I., & Brown, I. (2015). Challenges to the Organisational Adoption of Big Data Analytics: A Case Study in the South African Telecommunications Industry. SAICSIT 15: The 2015 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists (pp. 1-27).
- Maroufkhani, P., Tseng, M. L., Iranmanesh, M., Ismail, W. K. W., & Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54, 102190.
- Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276.
- Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2018). Big Data and Supply Chain A., Management: A Review and Bibliometric Analysis. Annals of Operations Research, 270(1-2), 313-336.
- Nam, D. W., Kang, D. W., & Kim, S. (2015). Process of big data analysis adoption: Defining big data as a new IS innovation and examining factors affecting the process. In 2015 48th Hawaii International Conference on System Sciences (pp. 4792-4801). IEEE.
- Nambisan, S., Wright, M., & Feldman, M. (2019). The Digital Transformation of Innovation and Entrepreneurship: Progress, Challenges and Key Themes. Research Policy, 48(8), 103773.
- Nguyen, T., & Petersen, T. E. (2017). Technology adoption in Norway: organizational assimilation of big data (Master’s Thesis).
- Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review. Computers and Operations Research, 98, 254-264.
- Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at the firm level. The Electronic Journal Information Systems Evaluation, 14(1), 110-121.
- Palanisamy, V., & Thirunavukarasu, R. (2017). Implications of big data analytics in developing healthcare frameworks – A review. Journal of King Saud University Computer and Information Sciences, 31(4), 415-425.
- Premkumar, G., & Roberts, M. (1999). Adoption of new information technologies in rural small businesses. Omega, 27(4), 467-484.
- Priyadarshinee, P., Raut, R. D., Jha, M. K., & Kamble, S. S. (2017). A cloud computing adoption in Indian SMEs: Scale development and validation approach. The Journal of High Technology Management Research, 28(2), 221-245.
- Queiroz, M. M., & Pereira, S. C. F. (2019). Intention to adopt big data in supply chain management: A Brazilian perspective. Revista de Administração de Empresas, 59(6), 389-401.
- Ramanathan, U., Subramanian, N., & Parrott, G. (2017). Role of Social Media in Retail Network Operations and Marketing to Enhance Customer Satisfaction. International Journal of Operations and Production Management, 37(1), 105-123.
- Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of Cleaner Production, 224, 10-24.
- Rogers, E. M. (1995). Lessons for guidelines from the Diffusion of innovations. Joint Commission Journal on Quality and Patient Safety, 21(7), 324-328.
- 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.
- Santos, T. F., & Leite, M. S. A. (2018). Performance Measurement System Based on Supply Chain Operations Reference Model: Review and Proposal. In G. P. Moynihan (Ed.), Contemporary Issues and Research in Operations Management.
- 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 (ICEIS), 1, 425-431.
- Scupola, A. (2009). SMEs’e-commerce adoption: perspectives from Denmark and Australia. Journal of Enterprise Information Management, 22(1-2), 152-166.
- 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.
- Tahiduzzaman, Md., Rahman, M., Dey, S. K., Rahman, Md S., & Akash, S. M. (2017). Big Data and Its Impact on Digitized Supply Chain Management. IJRDO-Journal of Business Management, 3(9), 196-208.
- Thamir, H. A., Mezghani, K., & Alsadi, A. K. (2020). Examining the adoption of Big data analytics in supply chain management under competitive pressure: evidence from Saudi Arabia. Journal of Decision Systems, 30(2-3), 300-320.
- Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation. Lexington Books.
- 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.
- Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77-84
- Wamba, S. F., Ngai, E. W. T., Riggins, F., & Akter, S. (2017). Guest editorial: Transforming operations and production management using big data and business analytics: future research directions. International Journal of Operations & Production Management, 37(1), 2-9.
- Wang, C., Li, X., Zhou, X., Wang, A., & Nedjah, N. (2016). Soft Computing in Big Data Intelligent Transportation Systems. Applied Soft Computing Journal, 38, 1099-1108.
- 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.
- Wright, L. T., Robin, R., Stone, M., & Aravopoulou, E. (2019). Adoption of Big Data Technology for Innovation in B2B Marketing. Journal of Business-to-Business Marketing, 26(3-4), 281-93.
- Yadegaridehkordi, E., Nilashi, M., Nasir, M. H. N. B. M., & Ibrahim, O. (2018). Predicting determinants of hotel success and development using structural equation modelling (SEM)ANFIS method. Tourism Management, 137(1), 199-210.
- Zhu, S., Song, J., Hazen, B.T., Lee, K., & Cegielski, C. (2018). How supply chain analytics enables operational supply chain transparency: An organisational information processing theory perspective. International Journal of Physical Distribution & Logistics Management, 48(1), 47-68.