Skip to main content

Factors Influencing the Intention to Adopt Big Data in Small Medium Enterprises

  • Conference paper
  • First Online:
International Conference on Information Systems and Intelligent Applications (ICISIA 2022)

Abstract

Making a data-driven decision is not just the forte of big business. Even small and medium businesses can benefit from big data. These days, companies are making adjustments to their business model to incorporate big data. Therefore, companies want to reap these fruits, big data set helps analyse and reveal trends, patterns, and correlations. as to whether the company is connected to the Internet or not, they need information that helps them to grow and prosper in their work, and here comes the role of big data. In the current research, the researcher dis-cusses the factors that help to adopt big data in SMEs in Palestine. The researcher approached quantitative statistical analysis and (TOE) theory was adopted to build the study model. The measurement tool, which is the questionnaire, was built to collect data. The study consisted of 310. The SmartPLS program was used to test the hypotheses. The results indicated that there is significant relationship between technological, organizational, and environmental factors and the intention to adopt big data in SMEs in Palestine, except the governmental support.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang S, Wang H (2020) Big data for small and medium-sized enterprises (SME): a knowledge management model. J Knowl Manag 24(4):881–897. https://doi.org/10.1108/JKM-02-2020-0081/FULL/PDF

    Article  Google Scholar 

  2. de Vasconcelos JB, Rocha Á (2019) Business analytics and big data. Int J Inf Manag 46:250–251. https://doi.org/10.1016/J.IJINFOMGT.2019.03.001

    Article  Google Scholar 

  3. Mandal S (2018) An examination of the importance of big data analytics in supply chain agility development: a dynamic capability perspective. Manag Res Rev 41(10):1201–1219. https://doi.org/10.1108/MRR-11-2017-0400

    Article  Google Scholar 

  4. Alfoqahaa S (2018) Critical success factors of small and medium-sized enterprises in Palestine. J Res Mark Entrep 20(2):170–188. https://doi.org/10.1108/JRME-05-2016-0014/FULL/PDF

    Article  Google Scholar 

  5. O’Connor C, Kelly S (2017) Facilitating knowledge management through filtered big data: SME competitiveness in an agri-food sector. J Knowl Manag 21(1):156–179. https://doi.org/10.1108/JKM-08-2016-0357/FULL/PDF

    Article  Google Scholar 

  6. Park JH, Kim MK, Paik JH (2015) The factors of technology, organization and environment influencing the adoption and usage of big data in Korean firms. In: 26th European regional conference of the international telecommunications society “What next for European telecommunications?”

    Google Scholar 

  7. Miah SJ, Vu HQ, Gammack J, McGrath M (2017) A big data analytics method for tourist behaviour analysis. Inf Manag 54(6):771–785. https://doi.org/10.1016/J.IM.2016.11.011

    Article  Google Scholar 

  8. Kwon O, Lee N, Shin B (2014) Data quality management, data usage experience and acquisition intention of big data analytics. Int J Inf Manag 34(3):387–394. https://doi.org/10.1016/j.ijinfomgt.2014.02.002

    Article  Google Scholar 

  9. Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manag 35(2):137–144. https://doi.org/10.1016/J.IJINFOMGT.2014.10.007

    Article  Google Scholar 

  10. Faizi S, Sałabun W, Rashid T, Watróbski J, Zafar S (2017) Group decision-making for hesitant fuzzy sets based on characteristic objects method. Symmetry 9(8):136. https://doi.org/10.3390/SYM9080136

    Article  MathSciNet  MATH  Google Scholar 

  11. Coleman S, Göb R, Manco G, Pievatolo A, Tort-Martorell X, Reis MS (2016) How can SMEs benefit from big data? Challenges and a path forward. Qual Reliab Eng Int 32(6):2151–2164. https://doi.org/10.1002/QRE.2008

    Article  Google Scholar 

  12. Sen D, Ozturk M, Vayvay O (2016) An overview of big data for growth in SMEs. Procedia - Soc. Behav. Sci. 235:159–167. https://doi.org/10.1016/J.SBSPRO.2016.11.011

    Article  Google Scholar 

  13. Maroufkhani P, Wagner R, Wan Ismail WK, Baroto MB, Nourani M (2019) Big data analytics and firm performance: a systematic review. Information 10(7). https://doi.org/10.3390/INFO10070226.

  14. Tien EL, Ali NM, Miskon S, Ahmad N, Abdullah NS (2020) Big data analytics adoption model for Malaysian SMEs, pp 45–53 (2020). https://doi.org/10.1007/978-3-030-33582-3_5.

  15. Zomaya AY, Sakr S (2017) Handbook of big data technologies, pp 1–895. https://doi.org/10.1007/978-3-319-49340-4.

  16. Grant D, Yeo B (2018) A global perspective on tech investment, financing, and ICT on manufacturing and service industry performance. Int J Inf Manag 43:130–145. https://doi.org/10.1016/J.IJINFOMGT.2018.06.007

    Article  Google Scholar 

  17. Mikalef P, Pappas IO, Krogstie J, Giannakos M (2018) Big data analytics capabilities: a systematic literature review and research agenda. Inf Syst E-Bus Manag 16(3):547–578. https://doi.org/10.1007/s10257-017-0362-y

    Article  Google Scholar 

  18. Li G, Yang X, Jun W, Tao Y (2018) A theoretical credit reporting system based on big data concept: a case study of humen textile garment enterprises. In: ACM international conference proceeding series, pp 22–26. https://doi.org/10.1145/3206157.3206163.

  19. Davenport TH, Dyché J (2013) Big data in big companies. Baylor Bus Rev 32(1):20–21. http://search.proquest.com/docview/1467720121?accountid=10067%5Cnhttp://sfx.lib.nccu.edu.tw/sfxlcl41?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&genre=article&sid=ProQ:ProQ:abiglobal&atitle=VIEW/REVIEW:+BIG+DATA+IN+BIG+COMPANIES&title=Bay

    Google Scholar 

  20. Kapoor KK, Dwivedi YK, Williams MD (2014) Examining the role of three sets of innovation attributes for determining adoption of the interbank mobile payment service. Inf Syst Front 17(5):1039–1056. https://doi.org/10.1007/S10796-014-9484-7

    Article  Google Scholar 

  21. Gu VC, Cao Q, Duan W (2012) Unified Modeling Language (UML) IT adoption — a holistic model of organizational capabilities perspective. Decis Support Syst 54(1):257–269. https://doi.org/10.1016/J.DSS.2012.05.034

    Article  Google Scholar 

  22. Kandil AM, Ragheb MA, Ragab AA, Farouk M (2018) Examining the effect of toe model on cloud computing adoption in Egypt. Bus Manag Rev 9(9):113–123

    Google Scholar 

  23. Alshamaila Y, Papagiannidis S, Li F (2013) Cloud computing adoption by SMEs in the north east of England: a multi-perspective framework. J Enterp Inf Manag 26(3):250–275. https://doi.org/10.1108/17410391311325225

    Article  Google Scholar 

  24. Laurell C, Sandström C, Berthold A, Larsson D (2019) Exploring barriers to adoption of Virtual Reality through Social Media Analytics and Machine Learning – an assessment of technology, network, price and trialability. J Bus Res 100:469–474. https://doi.org/10.1016/J.JBUSRES.2019.01.017

    Article  Google Scholar 

  25. Rogers EM, Singhal A, Quinlan MM (2014) Diffusion of innovations. In: An integrated approach to communication theory and research. Routledge, pp 432–448

    Google Scholar 

  26. Sun S, Cegielski CG, Jia L, Hall DJ (2018) Understanding the factors affecting the organizational adoption of big data. J Comput Inf Syst 58(3):193–203. https://doi.org/10.1080/08874417.2016.1222891

    Article  Google Scholar 

  27. Xu W, Ou P, Fan W (2017) Antecedents of ERP assimilation and its impact on ERP value: a TOE-based model and empirical test. Inf Syst Front 19(1):13–30. https://doi.org/10.1007/S10796-015-9583-0/TABLES/6

    Article  Google Scholar 

  28. Lautenbach P, Johnston K, Adeniran-Ogundipe T (2017) Factors influencing business intelligence and analytics usage extent in South African organisations. S Afr J Bus Manag 48(3):23–33

    Google Scholar 

  29. Anderson JC, Gerbing DW (1988) Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull 103(3):411

    Article  Google Scholar 

  30. Hair M, Hult JF, Ringle GTM, Sarstedt CM (2017) A Primer on partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks, p 165

    MATH  Google Scholar 

  31. Ngah AH, Gabarre S, Eneizan B, Asri N (2021) Mediated and moderated model of the willingness to pay for halal transportation. J Islam Mark 12(8):1425–1445. https://doi.org/10.1108/JIMA-10-2019-0199

    Article  Google Scholar 

  32. Hair JF, Hult GTM, Ringle CM, Sarstedt M (2014) A Primer on partial least squares structural equation modeling (PLS-SEM). Eur J Tour Res 6(2):211–213

    MATH  Google Scholar 

  33. Verma S, Chaurasia S (2019) Understanding the determinants of big data analytics adoption. Inf Resour Manag J 32(3):1–26. https://doi.org/10.4018/IRMJ.2019070101

    Article  Google Scholar 

  34. Verma S, Bhattacharyya SS, Kumar S (2018) An extension of the technology acceptance model in the big data analytics system implementation environment. Inf Process Manag 54(5):791–806. https://doi.org/10.1016/j.ipm.2018.01.004

    Article  Google Scholar 

  35. Sam KM, Chatwin CR (2019) Understanding adoption of big data analytics in china: from organizational users perspective. In: IEEE international conference on industrial engineering and engineering management, December 2019, vol 2019, pp 507–510. https://doi.org/10.1109/IEEM.2018.8607652.

  36. Wang L, Yang M, Pathan ZH, Salam S, Shahzad K, Zeng J (2018) Analysis of influencing factors of big data adoption in Chinese enterprises using DANP technique. Sustainability 10(11). https://doi.org/10.3390/su10113956.

  37. Jang W-J, Kim S-S, Jung S-W, Gim G-Y (2019) A study on the factors affecting intention to introduce big data from smart factory perspective, vol 786

    Google Scholar 

  38. Mangla SK, Raut R, Narwane VS, Zhang Z, Priyadarshinee P (2020) Mediating effect of big data analytics on project performance of small and medium enterprises. J Enterp Inf Manag. https://doi.org/10.1108/JEIM-12-2019-0394.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ahmed F. S. Abulehia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abulehia, A.F.S., Khairudin, N., Sharif, M.H.M. (2023). Factors Influencing the Intention to Adopt Big Data in Small Medium Enterprises. In: Al-Emran, M., Al-Sharafi, M.A., Shaalan, K. (eds) International Conference on Information Systems and Intelligent Applications. ICISIA 2022. Lecture Notes in Networks and Systems, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-031-16865-9_12

Download citation

Publish with us

Policies and ethics