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Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research

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Abstract

The Big Data era has descended on many communities, from governments and e-commerce to health organizations. Information systems designers face great opportunities and challenges in developing a holistic big data research approach for the new analytics savvy generation. In addition business intelligence is largely utilized in the business community and thus can leverage the opportunities from the abundant data and domain-specific analytics in many critical areas. The aim of this paper is to assess the relevance of these trends in the current business context through evidence-based documentation of current and emerging applications as well as their wider business implications. In this paper, we use BigML to examine how the two social information channels (i.e., friends-based opinion leaders-based social information) influence consumer purchase decisions on social commerce sites. We undertake an empirical study in which we integrate a framework and a theoretical model for big data analysis. We conduct an empirical study to demonstrate that big data analytics can be successfully combined with a theoretical model to produce more robust and effective consumer purchase decisions. The results offer important and interesting insights into IS research and practice.

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References

  1. Buchholtz, S., Bukowski, M., & Sniegocki, A. (2014). Big and open data in Europe: A growth engine or a missed opportunity. Warsaw, demosEuropa und Warsaw Institute for Economic Studies. Retrieved from http://www.bigopendata.eu/wp-content/uploads/2014/01/bodeurope2020fullreportsinglepage.pdf.

  2. Reich, B. H., & Benbasat, I. (2000). Factors that influence the social dimension of alignment between business and information technology objectives. MIS Quarterly, 24(1), 81–113.

    Article  Google Scholar 

  3. Biere, M. (2003). Business intelligence for the enterprise. Upper Saddle River, NJ: Prentice Hall Professional.

    Google Scholar 

  4. Williams, S., & Williams, N. (2010). The profit impact of business intelligence. Burlington, NJ: Morgan Kaufmann.

    Google Scholar 

  5. Wixom, B. H., Watson, H. J., & Werner, T. (2011). Developing an enterprise business intelligence capability: The Norfolk Southern Journey. MIS Quarterly Executive, 10(2), 61–71.

    Google Scholar 

  6. Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171–180.

    Article  Google Scholar 

  7. Barney, J. B. (2001). Resource-based theories of competitive advantage: A ten-year retrospective on the resource-based view. Journal of Management, 27(6), 643–650.

    Article  Google Scholar 

  8. Eisenhardt, K. M., & Santos, F. M. (2002). Knowledge-based view: A new theory of strategy. Handbook of strategy and management (Vol. 1, pp. 139–164). London: Sage.

    Google Scholar 

  9. Spender, J. C., & Grant, R. M. (1996). Knowledge and the firm: Overview. Strategic Management Journal, 17(S2), 5–9.

    Article  Google Scholar 

  10. Reynolds, P., & Yetton, P. (2015). Aligning business and IT strategies in multi-business organizations. Journal of Information Technology, 30, 101–118.

    Article  Google Scholar 

  11. Lohr, S. (2012). The age of big data. New York: New York Times.

    Google Scholar 

  12. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., & Barton, D. (2012). Big data. The Management Revolution. Harvard Business Review, 90(10), 61–67.

    Google Scholar 

  13. Freeland, C. (2012). Big data’s big impact. Accessed January 12.

  14. Wixom, B., Ariyachandra, T., Douglas, D., Goul, M., Gupta, B., Iyer, L., et al. (2014). The current state of business intelligence in academia: The arrival of big data. Communications of the Association for Information Systems, 34(1), 1.

    Google Scholar 

  15. Dumbill, E. (2012). What is big data? An introduction to the big data landscape. Sebastopol, CA: O’Reilly Media Inc.

    Google Scholar 

  16. Daniel, B. (2014). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology. doi:10.1111/bjet.12230.

    Google Scholar 

  17. Batra, S. (2014). Big data analytics and its reflections on the DIKW hierarchy. Review of Management, 4(1/2), 5–17.

    Google Scholar 

  18. Kapoor, B. (2010). Business intelligence and its use for humans. The Journal of Human Resource and Adult Learning, 6(2), 21–30.

    Google Scholar 

  19. Berner, M., Graupner, E., & Maedche, A. (2014). The information panopticon in the big data era. Journal of Organization Design, 3(1), 14–19.

    Article  Google Scholar 

  20. Laney, D., Hung, L. H., & Lapkin, A. (2013). Big data means big business. Financial Times.

  21. Fast Company (2014). The world’s top 10 most innovative companies in Big Data. https://www.fastcompany.com/most-innovative-companies/2014/industry/big-data.

  22. Wagner, D., Vollmar, G., & Wagner, H.-T. (2014). The impact of information technology on knowledge creation: An affordance approach to social media. Journal of Enterprise Information Management, 27(1), 31–44.

    Article  Google Scholar 

  23. Boulton, C. (2013). Much Hadoop about nothing. Wall Street Journal, 27(1), 31–44.

    Google Scholar 

  24. Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: The real-world use of big data. Somers, NY: IBM Global Business Services.

    Google Scholar 

  25. Zhang, D. (2013). Granularities and inconsistencies in big data analysis. International Journal of Software Engineering and Knowledge Engineering, 23(06), 887–893.

    Article  Google Scholar 

  26. Economist. (2010, Febraury 25). A different game: Information is transforming traditional businesses. The Economist.

  27. Lund, S. (2013). Game changers: Five opportunities for US growth and renewal. New York: McKinsey.

    Google Scholar 

  28. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al. (2011). Big data: The next frontier for innovation, competition, and productivity. New York: McKinsey Global Institute.

    Google Scholar 

  29. Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98.

    Google Scholar 

  30. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.

    Google Scholar 

  31. Bennett, M. (2014). Leverage the convergence of BI and Big Data in 2015 or miss out. Edmonds, WA: Martha Bennett’s Blog.

    Google Scholar 

  32. McNeely, C. L., & Hahm, J. O. (2014). The big (data) bang: Policy, prospects, and challenges. Review of Policy Research, 31(4), 304–310.

    Article  Google Scholar 

  33. Agrawal, D., Bernstein, P., Bertino, E., Davidson, S., Dayal, U., Franklin, M., et al. (2012). Challenges and opportunities with big data A community white paper developed by leading researchers across the United States. Washington, D.C.: Computing Research Association.

    Google Scholar 

  34. Zicari, R. V. (2014). Big Data: Challenges and opportunities. In R. Akerkar (Ed.), Big data computing (pp. 103–128). New York: CRC Press, Taylor & Francis Group.

    Google Scholar 

  35. Godes, D., Mayzlin, D., Chen, Y., Das, S., Dellarocas, C., Pfeiffer, B., et al. (2005). The firm’s management of social interactions. Marketing Letters, 16(3–4), 415–428.

    Google Scholar 

  36. Dellarocas, C., Zhang, X. M., & Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive marketing, 21(4), 23–45.

    Article  Google Scholar 

  37. Alshibly, H., & Chiong, R. (2015). Customer empowerment: Does it influence electronic government success? A citizen-centric perspective. Electronic Commerce Research and Applications, 14, 393–404.

    Article  Google Scholar 

  38. Davis, A., & Khazanchi, D. (2008). An empirical study of online word of mouth as a predictor for multi-product category e-commerce sales. Electronic Markets, 18(2), 130–141.

    Article  Google Scholar 

  39. Duan, W., Gu, B., & Whinston, A. B. (2009). Informational cascades and software adoption on the internet: An empirical investigation. MIS Quarterly, 33(1), 23–48.

    Google Scholar 

  40. Kostyra, D. S., Reiner, J., Natter, M., & Klapper, D. (2015). Decomposing the effects of online customer reviews on brand, price, and product attributes. International Journal of Research in Marketing, 33, 11–26.

    Article  Google Scholar 

  41. Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.

    Article  Google Scholar 

  42. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.

    Article  Google Scholar 

  43. Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter?—An empirical investigation of panel data. Decision Support Systems, 45(4), 1007–1016.

    Article  Google Scholar 

  44. Liang, T.-P., Ho, Y.-T., Li, Y.-W., & Turban, E. (2011). What drives social commerce: The role of social support and relationship quality. International Journal of Electronic Commerce, 16(2), 69–90.

    Article  Google Scholar 

  45. Straub, D., Limayem, M., & Karahanna-Evaristo, E. (1995). Measuring system usage: Implications for IS theory testing. Management Science, 41(8), 1328–1342.

    Article  Google Scholar 

  46. Boyer, J., Frank, B., Green, B., Harris, T., & Van De Vanter, K. (2010). A practical guide for achieving BI excellence. New York: IBM Corporation.

    Google Scholar 

  47. Balboni, F., & Cook, S. (2012). Analytics in the boardroom: Accelerating competitive advantage. Somers, NY: IBM Institute for Business Value.

    Google Scholar 

  48. Elbashir, M. Z., Collier, P. A., Sutton, S. G., Davern, M. J., & Leech, S. A. (2013). Enhancing the business value of business intelligence: The role of shared knowledge and assimilation. Journal of Information Systems, 27(2), 87–105.

    Article  Google Scholar 

  49. McGovern, T. (2015). Big Data Now.

  50. Association, C. R. (2012). Challenges and opportunities with big data. http://www.cra.org/ccc/files/docs/init/bigdatawhitepaper.pdf.

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Authors and Affiliations

  1. Department of Business Technology and Entrepreneurship, Faculty of Business and Law, Swinburne University of Technology, Melbourne, Australia

    Xuemei Tian & Libo Liu

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  1. Xuemei Tian
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Correspondence to Xuemei Tian.

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Tian, X., Liu, L. Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research. Electron Commer Res 17, 169–183 (2017). https://doi.org/10.1007/s10660-016-9242-7

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