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Cloud-based big data analytics for customer insight-driven design innovation in SMEs

Author

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  • Liu, Ying
  • Soroka, Anthony
  • Han, Liangxiu
  • Jian, Jin
  • Tang, Min

Abstract

Fast development of IT and ICT facilitate customers to post a large volume of their concerns and expectation online, which are widely accepted to be a valuable resource for product designers. However, it is found that only a small number of small and medium-sized enterprises (SMEs) have capabilities to leverage customer online insights for design innovation, which often demonstrate a significant share in national economies growth. To discover the beneath reasons regarding the barrier that prevent them to make effective utilization, in this study, as a concrete example, manufacturing SMEs in the South Wales and Greater Manchester industrial areas of the UK are focused and their potential motivations for using and knowledge of big data-based customer analytics are investigated. An exploratory survey was conducted in terms of the type of customer data they have, the storage approaches, the volume of customer data, etc. Next, a carefully devised exploratory study was undertaken to understand how SMEs perceive the relations between customer data and product design, how about their expectations from big customer data analytics and what really challenges SMEs to exploit the value of big customer data. Besides, a demonstration platform is developed to present SMEs an automatic process of analysing customer online reviews and the capacity on customer insights acquisition and strategic decision making. Finally, findings from two focus groups indicate the different managerial and technical considerations required for SMEs considering implementing big data and customer analytics. This study encourages SMEs to welcome big customer data and suggests that a cloud-based approach may be the most appropriate way of giving access to big data analytics techniques.

Suggested Citation

  • Liu, Ying & Soroka, Anthony & Han, Liangxiu & Jian, Jin & Tang, Min, 2020. "Cloud-based big data analytics for customer insight-driven design innovation in SMEs," International Journal of Information Management, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:ininma:v:51:y:2020:i:c:s0268401219305183
    DOI: 10.1016/j.ijinfomgt.2019.11.002
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    Citations

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    Cited by:

    1. Shivam Gupta & Théo Justy & Shampy Kamboj & Ajay Kumar & Eivind Kristoffersen, 2021. "Big data and firm marketing performance: Findings from knowledge-based view," Post-Print hal-03609916, HAL.
    2. Perdana, Arif & Lee, Hwee Hoon & Koh, SzeKee & Arisandi, Desi, 2022. "Data analytics in small and mid-size enterprises: Enablers and inhibitors for business value and firm performance," International Journal of Accounting Information Systems, Elsevier, vol. 44(C).
    3. Maya Vachkova & Arsalan Ghouri & Haidy Ashour & Normalisa Binti Md Isa & Gregory Barnes, 2023. "Big data and predictive analytics and Malaysian micro-, small and medium businesses," SN Business & Economics, Springer, vol. 3(8), pages 1-28, August.
    4. Jianmin Song & Senmao Xia & Demetris Vrontis & Arun Sukumar & Bing Liao & Qi Li & Kun Tian & Nengzhi Yao, 2022. "The Source of SMEs’ Competitive Performance in COVID-19: Matching Big Data Analytics Capability to Business Models," Information Systems Frontiers, Springer, vol. 24(4), pages 1167-1187, August.

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