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A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

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Abstract

E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Big data has acted like a catalyst in their growth story. Big data analytics (BDA) has attracted e-commerce firms to invest in the tools and gain cutting edge over their competitors. The process of adoption of these BDA tools by e-commerce start-ups has been an area of interest as successful adoption would lead to better results. The present study aims to develop an interpretive structural model (ISM) which would act as a framework for efficient implementation of BDA. The study uses hybrid multi criteria decision making processes to develop the framework and test the same using a real-life case study. Systematic review of literature and discussion with experts resulted in exploring 11 enablers of adoption of BDA tools. Primary data collection was done from industry experts to develop an ISM framework and fuzzy MICMAC analysis is used to categorize the enablers of the adoption process. The framework is then tested by using a case study. Thematic clustering is performed to develop a simple ISM framework followed by fuzzy analytical network process (ANP) to discuss the association and ranking of enablers. The results indicate that access to relevant data forms the base of the framework and would act as the strongest enabler in the adoption process while the company rates technical skillset of employees as the most important enabler. It was also found that there is a positive correlation between the ranking of enablers emerging out of ISM and ANP. The framework helps in simplifying the strategies any e-commerce company would follow to adopt BDA in future.

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Authors and Affiliations

  1. Shailesh J Mehta School of Management, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India

    Abhishek Behl & Pankaj Dutta

  2. Chair of Information Systems, School of Business and Economics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany

    Stefan Lessmann

  3. School of Management, Swansea University, Fabian Way, Swansea, SA1 8EN, UK

    Yogesh K. Dwivedi

  4. Department of Mathematics, National Institute of Technology Durgapur, Durgapur, West Bengal, 713209, India

    Samarjit Kar

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  1. Abhishek Behl
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Correspondence to Yogesh K. Dwivedi.

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Appendix

Appendix

See Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 and 16.

Table 1 SSIM matrix of enablers
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Table 2 Reachability matrix of the primary enablers
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Table 3 Final reachability matrix with transitivity (*)
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Table 4 Level for developing diagraph
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Table 5 Binary direct relationship matrix
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Table 6 Fuzzy relationship matrix
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Table 7 Fuzzy direct reachability matrix
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Table 8 Stabilized fuzzy matrix with ranks of enablers
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Table 9 Thematic clustering of enablers
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Table 10 Rule for obtaining output from fuzzy triangular matrix
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Table 11 Linguistic scale and corresponding triangular fuzzy numbers.
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Table 12 SSIM matrix of enablers
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Table 13 Fuzzy pairwise comparison table
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Table 14 Final crisp values for one evaluator
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Table 15 Final weights and crisp integrated weights for themes
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Table 16 Final weight for each enabler and their ranking
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Behl, A., Dutta, P., Lessmann, S. et al. A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach. Inf Syst E-Bus Manage 17, 285–318 (2019). https://doi.org/10.1007/s10257-019-00452-5

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