Skip to main content

Advertisement

Log in

Do Companies Adopt Big Data as Determinants of Sustainability: Evidence from Manufacturing Companies in Jordan

  • Original Research
  • Published:
Global Journal of Flexible Systems Management Aims and scope Submit manuscript

Abstract

Information and communication technology make it easier for managers to gather customer data quickly and efficiently. However, managing, analysing, and utilizing the vast amount of data for sustainability decision are not easy. Therefore, this study aims to examine the readiness of manufacturing firms in adopting big data analytics in sustainable development. Moreover, this study employed the Partial Least Square Structural Equation Modelling (PLS-SEM) technique and analyses the data collected from 172 respondents working in different organizations in Amman and Jordan. The results reveal that there is a significant relationship between top management support and competitive pressures and intentions to adopt big data analytics. However, the moderating influence of perceived risk on the relationship between intention and actual use of big data has not been proved. The study provides fresh findings on determinants of intention to adopt big data analytics, actual use, and moderating role of perceived risk within the model to develop sustainability. Furthermore, the study has a number of theoretical and practical implications. Our main findings provide a deeper understanding of the enablers of BDA adoption through the development of a framework that includes direct and moderating constructs, as well as recommendations to practitioners on how to enhance BDA adoption based on eight BDA enablers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Availability of Data and Materials

The participants in the survey presented in this paper were anonymous, and only their organizational positions were indicated in the results. The data used in the current study can be provided on request.

References

  • Acerbi, F., Sassanelli, C., Terzi, S., & Taisch, M. (2021). A Systematic literature review on data and information required for circular manufacturing strategies adoption. Sustainability, 13(4), 2047.

    Article  Google Scholar 

  • Alsaad, A., Mohamad, R., & Ismail, N. A. (2017). The moderating role of trust in business to business electronic commerce (B2B EC) adoption. Computers in Human Behavior, 68, 157–169.

    Article  Google Scholar 

  • Appolloni, A., Jabbour, C. J. C., D’Adamo, I., Gastaldi, M., & Settembre-Blundo, D. (2022). Green recovery in the mature manufacturing industry: The role of the green-circular premium and sustainability certification in innovative efforts. Ecological Economics, 193, 107311.

    Article  Google Scholar 

  • Arrigo, E., Liberati, C., & Mariani, P. (2021). Social media data and users’ preferences: A statistical analysis to support marketing communication. Big Data Research, 24, 100189.

    Article  Google Scholar 

  • Bai, X., Zhang, F., Li, J., Guo, T., Aziz, A., Jin, A., & Xia, F. (2021). Educational big data: Predictions, applications and challenges. Big Data Research, 26, 100270.

    Article  Google Scholar 

  • Behl, A., Dutta, P., Lessmann, S., Dwivedi, Y. K., & Kar, S. (2019). A conceptual framework for the adoption of big data analytics by e-commerce startups: A case-based approach. Information Systems and E-Business Management, 17(2), 285–318.

    Article  Google Scholar 

  • Bose, R., & Luo, X. R. (2012). Green IT adoption: A process management approach. International Journal of Accounting & Information Management, 20(1), 63–77.

    Article  Google Scholar 

  • Cabrera-Sánchez, J.-P., & Villarejo-Ramos, A. F. (2020). Factors affecting the adoption of big data analytics in companies. Revista De Administração De Empresas, 59, 415–429.

    Article  Google Scholar 

  • Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39.

    Article  Google Scholar 

  • Dwivedi, A., Agrawal, D., Jha, A., Gastaldi, M., Kumar, S., & Idiano, P. (2021). Addressing the challenges to sustainable initiatives in value chain flexibility: Implications for sustainable development goals. Global Journal of Flexible Systems Management, 22(s2), 179–197. https://doi.org/10.1007/s40171-021-00288-4

    Article  Google Scholar 

  • Esteves, J., & Curto, J. (2013). A risk and benefits behavioral model to assess intentions to adopt big data. Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management and Organisational Learning: ICICKM 2013.

  • Faizi, R., El Fkihi, S., El Afia, A., & Chiheb, R. (2017). Extracting business value from big data. in Proceedings of the 29th International Business Information Management Association (IBIMA), pp. 3–4.

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Fugini, M., Finocchi, J., & Locatelli, P. (2021). A big data analytics architecture for smart cities and smart companies. Big Data Research, 24, 100192.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s40171-017-0159-3

    Article  Google Scholar 

  • Gu, V. C., Cao, Q., & Duan, W. (2012). Unified modeling language (UML) IT adoption—A holistic model of organizational capabilities perspective. Decision Support Systems, 54(1), 257–269.

    Article  Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (Eight). Cengage Learning.

    Google Scholar 

  • Hajiheydari, N., Delgosha, M. S., Wang, Y., & Olya, H. (2021). Exploring the paths to big data analytics implementation success in banking and financial service: an integrated approach. Industrial Management and Data Systems, 121(12), 2498–2529.

    Article  Google Scholar 

  • Ikram, M., Sroufe, R., Awan, U., & Abid, N. (2021). Enabling progress in developing economies: A novel hybrid decision-making model for green technology planning. Sustainability, 14(1), 258.

    Article  Google Scholar 

  • Im, I., Kim, Y., & Han, H.-J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information and Management, 45(1), 1–9.

    Article  Google Scholar 

  • Johnson, M., Jain, R., Ethne, P. B., Deborah, S., Jessica, S., Johnson, M., Brennan-tonetta, P., & Silver, D. (2021). Impact of big data and artificial intelligence on industry: Developing a workforce roadmap for a data driven economy. Global Journal of Flexible Systems Management, 22(3), 197–217. https://doi.org/10.1007/s40171-021-00272-y

    Article  Google Scholar 

  • Kamioka, T., & Tapanainen, T. (2014). Organizational use of big data and competitive advantage–Exploration of antecedents. in Proceedings of Pacific Asia Conference on Information Systems, 372.

  • Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387–394.

    Article  Google Scholar 

  • Lian, J.-W., Yen, D. C., & Wang, Y.-T. (2014). An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital. International Journal of Information Management, 34(1), 28–36.

    Article  Google Scholar 

  • Low, C., Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial Management and Data Systems, 111(7), 1006–1023.

    Article  Google Scholar 

  • Mageto, J. (2021). Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains.

  • Mahesh, D. D., Vijayapala, S., & Dasanayaka, S. (2018). Factors Affecting the Intention to Adopt Big Data Technology: A Study Based on Financial Services Industry of Sri Lanka. Moratuwa Engineering Research Conference (MERCon), 2018, 420–425.

    Article  Google Scholar 

  • Malhotra, N. K. (2010). Marketing research: An applied orientation (10th ed.). Pearson Education.

  • Mandal, S. (2018). An examination of the importance of big data analytics in supply chain agility development: A dynamic capability perspective. Management Research Review, 21(10), 1201–1219.

    Article  Google Scholar 

  • Margaritis, I., Madas, M., & Vlachopoulou, M. (2022). Big data applications in food supply chain management: A conceptual framework. Sustainability, 14(7), 4035.

    Article  Google Scholar 

  • Maroufkhani, P., Iranmanesh, M., & Ghobakhloo, M. (2022). Determinants of big data analytics adoption in small and medium- sized enterprises (SMEs). Industrial Management and Data Systems. https://doi.org/10.1108/IMDS-11-2021-0695

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.

    Google Scholar 

  • Mourtzis, D., Vlachou, E., & Milas, N. (2016). Industrial big data as a result of IoT adoption in manufacturing. Procedia Cirp, 55, 290–295.

    Article  Google Scholar 

  • Müller, O., Fay, M., & Vom Brocke, J. (2018). The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems, 35(2), 488–509.

    Article  Google Scholar 

  • Paley, N. (2017). Leadership strategies in the age of big data, algorithms, and analytics (1st ed.). Boca Raton: CRC Press.

    Book  Google Scholar 

  • Pan, M.-J., & Jang, W.-Y. (2008). Determinants of the adoption of enterprise resource planning within the technology-organization-environment framework: Taiwan’s communications industry. Journal of Computer Information Systems, 48(3), 94–102.

    Google Scholar 

  • Paul, S. K. (2020). Strategies for managing the impacts of disruptions during COVID-19: An example of toilet paper. Global Journal of Flexible Systems Management, 21(3), 283–293. https://doi.org/10.1007/s40171-020-00248-4

    Article  Google Scholar 

  • Qin, X. (2012). Making use of the big data: next generation of algorithm trading. in International Conference on Artificial Intelligence and Computational Intelligence, pp. 34–41.

  • Raguseo, E. (2018). Big data technologies: An empirical investigation on their adoption, benefits and risks for companies. International Journal of Information Management, 38(1), 187–195.

    Article  Google Scholar 

  • Raut, R. D., Kumar, S., 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. https://doi.org/10.1016/j.jclepro.2019.03.181

    Article  Google Scholar 

  • Ringle, Christian M., Wende, Sven, & Becker, J.-M. (2015). SmartPLS 3. http://www.smartpls.com.

  • Rogers, G. F. C. (1983). The nature of engineering: a philosophy of technology. Macmillan International Higher Education.

  • Ryabchikov, M. Y., & Ryabchikova, E. S. (2022). Big data-driven assessment of proposals to improve enterprise flexibility through control options untested in practice. Global Journal of Flexible Systems Management, 23(1), 43–74. https://doi.org/10.1007/s40171-021-00287-5

    Article  Google Scholar 

  • Settembre-blundo, D., González-Sánchez, R., Medina-salgado, S., & García-Muiña, F. E. (2021). Flexibility and resilience in corporate decision making: A new sustainability-based risk management system in uncertain times. Global Journal of Flexible Systems Management, 22(December), 107–132. https://doi.org/10.1007/s40171-021-00277-7

    Article  Google Scholar 

  • Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., & Hu, Y. (2019). Investigating the adoption of big data analytics in healthcare: The moderating role of resistance to change. Journal of Big Data, 6(1), 1–20.

    Article  Google Scholar 

  • Sherer, S. A., Meyerhoefer, C. D., & Peng, L. (2016). Information & Management Applying institutional theory to the adoption of electronic health records in the US. Information and Management, 53(5), 570–580. https://doi.org/10.1016/j.im.2016.01.002

    Article  Google Scholar 

  • Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263–286.

    Article  Google Scholar 

  • Soon, K. W. K., Lee, C. A., & Boursier, P. (2016). A study of the determinants affecting adoption of big data using integrated Technology Acceptance Model (TAM) and diffusion of innovation (DOI) in Malaysia. International Journal of Applied Business and Economic Research, 14(1), 17–47.

    Google Scholar 

  • Sun, S., Hall, D. J., & Cegielski, C. G. (2020). Organizational intention to adopt big data in the B2B context: An integrated view. Industrial Marketing Management, 86, 109–121.

    Article  Google Scholar 

  • Sushil. (2015). Valuation of Flexibility. Global Journal of Flexible Systems Management, 16(3), 219–220.

    Article  Google Scholar 

  • Thacker, M., Shah, L., & Shah, M. (2022). Society sync–Digitalize society management systems with artificial intelligence technologies. Intelligent Systems with Applications, 14, 200069.

    Article  Google Scholar 

  • Tsourela, M., & Roumeliotis, M. (2015). The moderating role of technology readiness, gender, and sex in consumer acceptance and actual use of Technology-based services. The Journal of High Technology Management Research, 26(2), 124–136.

    Article  Google Scholar 

  • Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, pp. 157–178.

  • Verma, S., & Chaurasia, S. (2019). Understanding the determinants of big data analytics adoption. Information Resources Management Journal (IRMJ), 32(3), 1–26.

    Article  Google Scholar 

  • Villarejo Ramos, Á. F., & Cabrera-Sánchez, J.-P. (2019). Factors affecting the adoption of big data analytics in companies. Revista De Administração De Empresas, 59(6), 413–427.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zomaya, A. Y., & Sakr, S. (2017). Handbook of big data technologies. Springer. https://doi.org/10.1007/978-3-319-49340-4

    Book  Google Scholar 

Download references

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Muhammad Ikram.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics Approval

The data used in this study did not involve any human or animal subjects.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jum’a, L., Ikram, M., Alkalha, Z. et al. Do Companies Adopt Big Data as Determinants of Sustainability: Evidence from Manufacturing Companies in Jordan. Glob J Flex Syst Manag 23, 479–494 (2022). https://doi.org/10.1007/s40171-022-00313-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40171-022-00313-0

Keywords

Navigation