Information Systems and e-Business Management

, Volume 17, Issue 2–4, pp 285–318 | Cite as

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

  • Abhishek Behl
  • Pankaj Dutta
  • Stefan Lessmann
  • Yogesh K. DwivediEmail author
  • Samarjit Kar
Original Article


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.


Big data analytics Interpretive structural modelling Fuzzy MICMAC Analytical network process E-commerce Start-ups 


Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.


  1. Agarwal R, Dhar V (2014) Editorial—big data, data science, and analytics: the opportunity and challenge for IS research. Inf Syst Res 25:443–448CrossRefGoogle Scholar
  2. Agrawal D, Das S, El Abbadi A (2011) Big data and cloud computing: current state and future opportunities. In: Proceedings of the 14th international conference on extending database technology. ACM Press, pp 530–533Google Scholar
  3. Akter S, Wamba SF (2016) Big data analytics in E-commerce: a systematic review and agenda for future research. Electron Mark 26(2):173–194CrossRefGoogle Scholar
  4. Attri R, Dev N, Sharma V (2013) Interpretive structural modelling (ISM) approach: an overview. Res J Manag Sci 2(2):3–8Google Scholar
  5. Austin LM, Burns JR (1985) Management science: an aid for managerial decision making. Macmillan Publishing Company, LondonGoogle Scholar
  6. Barrett M, Davidson E, Prabhu J, Vargo SL (2015) Service innovation in the digital age. MIS Q 39(1):135–154CrossRefGoogle Scholar
  7. Barton D, Court D (2012) Making advanced analytics work for you. Harvard Bus Rev 90(10):78–83Google Scholar
  8. Barve A, Kanda A, Shankar R (2007) Analysis of interaction among the barriers of third party logistics. Int J Agile Syst Manag 2(1):109–129CrossRefGoogle Scholar
  9. Behl A, Singh M, Venkatesh VG (2016) Enablers and barriers of mobile banking opportunities in rural India: a strategic analysis. Int J Bus Excell 10(2):209–239CrossRefGoogle Scholar
  10. Benedettini O, Neely A (2012) Complexity in services: an interpretative framework. In: 23rd annual conference of the production and operations management society (POMS), pp 1–11Google Scholar
  11. Beulke D (2011) Big data impacts data management: the 5 vs of big data. Accessed 23 Mar 2018
  12. Bhosale VA, Kant R (2016) An integrated ISM fuzzy MICMAC approach for modelling the supply chain knowledge flow enablers. Int J Prod Res 54(24):7374–7399CrossRefGoogle Scholar
  13. Boja C, Pocovnicu A, Batagan L (2012) Distributed parallel architecture for “big data”. Inform Econ 16:116–127Google Scholar
  14. Brown B, Chul M, Manyika J (2011) Are you ready for the era of ‘big data’? McKinsey Q 4:24–35Google Scholar
  15. Chang RM, Kauffman RJ, Kwon Y (2014) Understanding the paradigm shift to computational social science in the presence of big data. Decis Support Syst 63:67–80CrossRefGoogle Scholar
  16. Chen L, Nath R (2018) Business analytics maturity of firms: an examination of the relationships between managerial perception of IT, business analytics maturity and success. Inf Syst Manag 35(1):62–77CrossRefGoogle Scholar
  17. Constantiou ID, Kallinikos J (2015) New games, new rules: big data and the changing context of strategy. J Inf Technol 30(1):44–57CrossRefGoogle Scholar
  18. Csutora R, Buckley JJ (2001) Fuzzy hierarchical analysis: the Lambda-Max method. Fuzzy Sets Syst 120(2):181–195CrossRefGoogle Scholar
  19. Davenport TH (2012) The human side of big data and high-performance analytics. International Institute for Analytics, pp 1–13Google Scholar
  20. Davenport TH (2013) Analytics 3.0. Harv Bus Rev 91:64–72Google Scholar
  21. Devaraj S, Fan M, Kohli R (2002) Antecedents of B2C channel satisfaction and preference: validating e-commerce metrics. Inf Syst Res 13:316–333CrossRefGoogle Scholar
  22. Dinev T, Hart P (2006) An extended privacy calculus model for E-commerce transactions. Inf Syst Res 17:61–80CrossRefGoogle Scholar
  23. Dubey R, Ali SS (2014) Identification of flexible manufacturing system dimensions and their interrelationship using total interpretive structural modelling and fuzzy MICMAC analysis. Glob J Flex Syst Manag 15(2):131–143CrossRefGoogle Scholar
  24. Dubey R, Gunasekaran A, Childe SJ, Wamba SF, Papadopoulos T (2016) The impact of big data on world-class sustainable manufacturing. Int J Adv Manuf Technol 84(1):631–645CrossRefGoogle Scholar
  25. Ernst & Young (2016) Blockchain and the future of audit. Retrieved from EY. Accessed 17 Aug 2018
  26. Esteves J, Curto J (2013) A risk and benefits behavioral model to assess intentions to adopt big data. J Intell Stud Bus 3(3):37–46Google Scholar
  27. Farris DR, Sage AP (1975) On the use of interpretive structural modeling for worth assessment. Comput Electr Eng 2(2):149–174CrossRefGoogle Scholar
  28. Fisher D, DeLine R, Czerwinski M, Drucker S (2012) Interactions with big data analytics. Interactions 19(1):50CrossRefGoogle Scholar
  29. Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manag 35(2):137–144CrossRefGoogle Scholar
  30. George G, Haas MR, Pentland A (2014) Big data and management. Acad Manag J 57:321–326CrossRefGoogle Scholar
  31. Goes PB (2014) Big data and IS research. MIS Q 38:3–8Google Scholar
  32. Gorane SJ, Kant R (2013) Modelling the SCM enablers: an integrated ISM-fuzzy MICMAC approach. Asia Pac J Mark Logist 25(2):263–286CrossRefGoogle Scholar
  33. Gubela R, Lessmann S, Haupt J, Baumann A, Radmer T, Gebert F (2017) Revenue uplift modeling. In: Proceedings of the 38th international conference on information systems (ICIS‘2017), Seoul, South Korea. AISGoogle Scholar
  34. Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of “big data” on cloud computing: review and open research issues. Inf Syst 47:98–115CrossRefGoogle Scholar
  35. Hsinchun C, Chiang RHL, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(1):1165–1188Google Scholar
  36. Jagadish HV, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, Shahabi C (2014) Big data and its technical challenges. Commun ACM 57(7):86–94CrossRefGoogle Scholar
  37. Jao J (2013) Why big data is a must in ecommerce. Accessed 27 Aug 2017
  38. Jones TM, Harrison JS, Felps W (2018) How applying instrumental stakeholder theory can provide sustainable competitive advantage. Acad Manag Rev 43(3):371–391CrossRefGoogle Scholar
  39. Kambatla K, Kollias G, Kumar V, Grama A (2014) Trends in big data analytics. J Parallel Distrib Comput 74(7):2561–2573CrossRefGoogle Scholar
  40. Kandasamy WBV (2007) Elementary fuzzy matrix, theory and fuzzy models for social scientists. Ann Arbor, MI: ProQuest Information and Learning (University of Microfilm International)Google Scholar
  41. Kauffman RJ, Srivastava J, Vayghan J (2012) Business and data analytics: new innovations for the management of E-commerce. Electron Commer Res Appl 11(2):85–88CrossRefGoogle Scholar
  42. Khajouei H, Kazemi M, Moosavirad SH (2017) Ranking information security controls by using fuzzy analytic hierarchy process. Inf Syst e-Bus Manag 15(1):1–19CrossRefGoogle Scholar
  43. Koirala P (2012) What is big data analytics and its application in E-commerce? Accessed 10 Jan 2018
  44. Koutsabasis P, Stavrakis M, Viorres N, Darzentas JS, Spyrou T, Darzentas J (2008) A descriptive reference framework for the personalisation of e-business applications. Electron Commer Res 8(3):173–192CrossRefGoogle Scholar
  45. 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):387–394CrossRefGoogle Scholar
  46. Loukis E, Arvanitis S, Kyriakou N (2017) An empirical investigation of the effects of firm characteristics on the propensity to adopt cloud computing. Inf Syst e-Bus Manag 15(4):963–988CrossRefGoogle Scholar
  47. Mahrt M, Scharkow M (2013) The value of big data in digital media research. J Broadcast Electron Media 57(1):20–33CrossRefGoogle Scholar
  48. Maity M, Dass M (2014) Consumer decision-making across modern and traditional channels: E-commerce, m-commerce, in-store. Decis Support Syst 61:34–46CrossRefGoogle Scholar
  49. Mandal A, Deshmukh SG (1994) Vendor selection using interpretive structural modelling (ISM). Int J Oper Prod Manag 14(6):52–59CrossRefGoogle Scholar
  50. Martin KE (2015) Ethical issues in the big data industry. MIS Q Executive 14:67–85Google Scholar
  51. Maru P (2014) Big data analytics: taking a deeper dive. Retrieved from Accessed 23 Sept 2018
  52. Manyika J (2011) Big data: The next frontier for innovation, competition, and productivity. Accessed 17 June 218
  53. 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):547–578CrossRefGoogle Scholar
  54. Minelli M, Chambers M, Dhiraj A (2012) Big data, big analytics: emerging business intelligence and analytic trends for today’s businesses. Wiley, New YorkGoogle Scholar
  55. Mithas S, Lee MR, Earley S, Murugesan S, Djavanshir R (2013) Leveraging big data and business analytics. IT Prof 15(6):18–20CrossRefGoogle Scholar
  56. Mohanty S, Jagadeesh M, Srivatsa H (2013) Big data imperatives: enterprise ‘big data’ warehouse, ‘BI’ implementations and analytics. Apress, New YorkCrossRefGoogle Scholar
  57. Morgado EM, Reinhard N, Watson RT (1999) Adding value to key issues research through Q-sorts and interpretive structured modeling. Commun AIS 1(1):3Google Scholar
  58. Nelson RR, Todd PA, Wixom BH (2005) Antecedents of information and system quality: an empirical examination within the context of data warehousing. J Manag Inf Syst 21:199–235CrossRefGoogle Scholar
  59. Ngai EWT, Xiu L, Chau DCK (2009) Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst Appl 36(2):2592–2602CrossRefGoogle Scholar
  60. Ngai EW, Gunasekaran A, Wamba SF, Akter S, Dubey R (2017) Big data analytics in electronic markets. Electron Mark 27(3):243–245CrossRefGoogle Scholar
  61. Ohlhorst FJ (2012) Big data analytics: turning big data into big money. Wiley, New YorkCrossRefGoogle Scholar
  62. Olszak CM (2016) Toward better understanding and use of Business Intelligence in organizations. Inf Syst Manag 33(2):105–123CrossRefGoogle Scholar
  63. Opricovic S, Tzeng GH (2003) Defuzzification within a multicriteria decision model. Int J Uncertain Fuzziness Knowl Based Syst 11(5):635–652CrossRefGoogle Scholar
  64. Provost F, Fawcett T (2013) Data science and its relationship to big data and data-driven decision making. Big Data 1(1):51–59CrossRefGoogle Scholar
  65. Qi L, Xu X, Zhang X, Dou W, Hu C, Zhou Y, Yu J (2016) Structural balance theory-based E-commerce recommendation over big rating data. IEEE Trans Big Data 4(3):301–312CrossRefGoogle Scholar
  66. Ramamurthy K, Sen A, Sinha AP (2008) Data warehousing infusion and organizational effectiveness. IEEE Trans Syst Man Cybernetics-Part A Syst Humans 38(4):976–994CrossRefGoogle Scholar
  67. Ramaswamy S (2013) What the companies winning at big data do differently. Bloomberg, June. Accessed 1 Apr 2018
  68. Riggins FJ, Wamba SF (2015) Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In: 2015 48th Hawaii international conference on system sciences (HICSS). IEEE, pp 1531–1540Google Scholar
  69. Saaty TL (1988) What is the analytic hierarchy process? In: Mitra G, Greenberg HJ, Lootsma FA, Rijkaert MJ, Zimmermann HJ (eds) Mathematical models for decision support. Springer, Berlin, pp 109–121CrossRefGoogle Scholar
  70. Sachdeva N, Singh O, Kapur PK (2015) Modeling critical success factors for adoption of big data analytics project: an ISM-MICMAC based analysis. Commun Dependability Q Manag 18(4):93–110Google Scholar
  71. Saxena JP, Sushil, Vrat P (1992) Scenario building: a critical study of energy conservation in the Indian cement industry. Technol Forecasting Social Change 41(2):121–146CrossRefGoogle Scholar
  72. Shareef MA, Dwivedi YK, Stamati T, Williams MD (2014) SQ mGov: a comprehensive service-quality paradigm for mobile government. Inf Syst Manag 31(2):126–142CrossRefGoogle Scholar
  73. Sharma R, Mithas S, Kankanhalli A (2014) Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. Eur J Inf Syst 23(4):433–441CrossRefGoogle Scholar
  74. Shin DH (2016) Demystifying big data: anatomy of big data developmental process. Telecommun Policy 40(9):837–854CrossRefGoogle Scholar
  75. Shukla M, Mattar L (2019) Next generation smart sustainable auditing systems using big data analytics: understanding the interaction of critical barriers. Comput Ind Eng 128:1015–1026CrossRefGoogle Scholar
  76. Sun Z, Sun L, Strang K (2016) Big data analytics services for enhancing business intelligence. J Comput Inf Syst 58(2):162–169Google Scholar
  77. Sushil (2012) Interpreting the interpretive structural model. Glob J Flex Syst Manag 13(2):87–106CrossRefGoogle Scholar
  78. Taylor L, Schroeder R, Meyer E (2014) Emerging practices and perspectives on big data analysis in economics: bigger and better or more of the same? Big Data Soc 1(2):20–37Google Scholar
  79. Valmohammadi C, Dashti S (2016) Using interpretive structural modeling and fuzzy analytical process to identify and prioritize the interactive barriers of e-commerce implementation. Inf Manag 53(2):157–168CrossRefGoogle Scholar
  80. Valmohammadi C, Servati A (2011) Performance measurement system implementation using Balanced Scorecard and statistical methods. Int J Product Perform Manag 60(5):493–511CrossRefGoogle Scholar
  81. Vossen G (2014) Big data as the new enabler in business and other intelligence. Vietnam J Comput Sci 1(1):3–14CrossRefGoogle Scholar
  82. Wamba SF, Akter S, Edwards A, Chopin G, Gnanzou D (2015) How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int J Prod Econ 165(2):234–246CrossRefGoogle Scholar
  83. Whalen P, Uslay C, Pascal VJ, Omura G, McAuley A, Kasouf CJ, Gilmore A (2016) Anatomy of competitive advantage: towards a contingency theory of entrepreneurial marketing. J Strateg Mark 24(1):5–19CrossRefGoogle Scholar
  84. Wixom BH, Yen B, Relich M (2013) Maximizing value from business analytics. MIS Q Executive 12(3):111–123Google Scholar
  85. Woudstra U, Berghout E, Tan CW, van Eekeren P, Dedene G (2017) Resource complementarity and IT economies of scale: mechanisms and empirical evidence. Inf Syst Manag 34(2):185–199CrossRefGoogle Scholar
  86. Yin RK (2013) Case study research: design and methods. Sage Publications, Beverley HillsGoogle Scholar
  87. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefGoogle Scholar
  88. Zhao D (2013) Frontiers of big data business analytics: patterns and cases in online marketing. Big data and business analytics, pp 43–68Google Scholar
  89. Zhao JL, Fan S, Hu D (2014) Business challenges and research directions of management analytics in the big data era. J Manag Anal 1(3):169–174Google Scholar
  90. Zhou ZH, Chawla NV, Jin Y, Williams GJ (2014) Big data opportunities and challenges: Discussions from data analytics perspectives. IEEE Comput Intell Mag 9(4):62–74CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Abhishek Behl
    • 1
  • Pankaj Dutta
    • 1
  • Stefan Lessmann
    • 2
  • Yogesh K. Dwivedi
    • 3
    Email author
  • Samarjit Kar
    • 4
  1. 1.Shailesh J Mehta School of ManagementIndian Institute of Technology BombayPowai, MumbaiIndia
  2. 2.Chair of Information Systems, School of Business and EconomicsHumboldt-Universität zu BerlinBerlinGermany
  3. 3.School of ManagementSwansea UniversitySwanseaUK
  4. 4.Department of MathematicsNational Institute of Technology DurgapurDurgapurIndia

Personalised recommendations