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Game theoretic approach of a novel decision policy for customers based on big data

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Abstract

In recent days, big data based analysis in hotel industry become popular. Merchants are attracting clients using the accurate analysis of historic data and predicting the behavior of possible clients to perform proper marketing strategy. To study the principle of the game between clients and merchants, in this work, we propose a novel two-stage game theoretic approach of decision policy for clients when choosing the suitable hotel to stay among many candidates, the merchants will provide a non-cooperative game strategy to attract the attention of potential clients. Analysis of the non-cooperative game method based on big data has been given. Simulation results indicate that, by using our proposed novel method, the average price for clients to choose a satisfied hotel is reduced and the successful rate of stay is increased for merchants, which will bring the expected income to a higher level because of the sticky phenomena of users.

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References

  1. Adolph, M. (2014). Big data, its enablers and standards. PIK—Praxis der Information sverarbeitung und Kommunikation, 37(3), 197–204.

    Google Scholar 

  2. Asay, M. (2013). Q&A. Is open source sustainable? Technology innovation. Management Review, 3(1), 46–49.

    Google Scholar 

  3. Banerjee, S., Viswanathan, V., Raman, K., & Ying, H. (2013). Assessing prime-time for geotargeting with mobile big data. Journal of Marketing Analytics, 1(3), 174–183.

    Article  Google Scholar 

  4. Barton, D., & Court, D. (2012). Making advanced analytics work for you. Harvard Business Review, 90(10), 78–83.

    Google Scholar 

  5. Bennett, M. (2013). The financial industry business ontology: Best practice for big data. Journal of Banking Regulation, 14(3/4), 255–268.

    Article  Google Scholar 

  6. Bertot, J. C., Gorham, U., Jaeger, P. T., Sarin, L. C., & Choi, H. (2014). Big data, open government and e-government: Issues, policies and recommendations. Information Polity: The International Journal of Government & Democracy in the Information Age, 19(1), 5–16.

    Google Scholar 

  7. Bharadwaj, A., El Sawy, O. E., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482.

    Article  Google Scholar 

  8. Bone, S. A., Fombelle, P. W., Ray, K. R., & Lemon, K. N. (2014). How customer participation in B2B peer-to-peer problem-solving communities influences the need for traditional customer service. Journal of Service Research, 18(1), 23–38.

    Article  Google Scholar 

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

    Google Scholar 

  10. Chow-White, P. A., & Green, J. G. (2013). Data mining difference in the age of big data: Communication and the social shaping of genome technologies from 1998 to 2007. International Journal of Communication, 7, 28.

    Google Scholar 

  11. Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412–421.

    Article  Google Scholar 

  12. Dobre, C., & Xhafa, F. (2014). Parallel programming paradigms and frameworks in big data era. International Journal of Parallel Programming, 42(5), 710–738.

    Article  Google Scholar 

  13. Fanning, K., & Grant, R. (2013). Big data: implications for financial managers. Journal of Corporate Accounting & Finance, 24(5), 23–30.

    Article  Google Scholar 

  14. Gabel, T. J., & Tokarski, C. (2014). Big data and organization design. Journal of Organization Design, 3(1), 37–45.

    Article  Google Scholar 

  15. Galbraith, J. R. (2014). Organization design challenges resulting from big data. Journal of Organization Design, 3(1), 2–13.

    Article  Google Scholar 

  16. George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321–326.

  17. Reips, U. D., & Garaizar, P. (2011). Mining twitter: A source for psychological wisdom of the crowds. Behavior Research Methods, 43(3), 635–642.

    Article  Google Scholar 

  18. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 61–67.

    Google Scholar 

  19. Fan, W., & Bifet, A. (2013). Mining big data: Current status, and forecast to the future. ACM SIGKDD Explorations Newsletter, 14(2), 1–5.

    Article  Google Scholar 

  20. Schroeck, M., Smart, R., Romero-Morales, D., & Tufano, P. (2012). Analytics: The real-world use of big data: How innovative enterprises extract value from uncertain data. Cambridge: IBM Institute for Business Value.

    Google Scholar 

  21. Vossen, G. (2014). Big data as the new enabler in business and other intelligence. Journal of Computer Science, 1(1), 3–14.

    Google Scholar 

  22. Geethakumari, G., & Srivatsava, A. (2012). Big data analysis for implementation of enterprise data security. IRACST-International Journal of Computer Science, Information Technology and Security (IJCSITS), 2(4), 742–746.

    Google Scholar 

  23. Rajaraman, A., & Ullman, J. D. (2012). Mining of massive datasets. Cambridge: Cambridge University Press.

    Google Scholar 

  24. Begoli, E., Horey, J. (2012) Design principles for effective knowledge discovery from big data, software architecture (WICSA) and European conference on software architecture (ECSA) 2012 (pp. 215–218).

  25. Nardo, M., Petracco Giudici, M., & Naltsidis, M. (2015). Walking down wall street with a tablet: A survey of stock market predictions using the web. Journal of Economic Surveys. doi:10.1111/joes.12102.

    Google Scholar 

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Acknowledgements

This research was supported by the National Social Science Foundation of China (No.14AGL023). The work of Su Hu was jointly supported by the MOST Program of International S&T Cooperation (Grant No. 2016YFE0123200), National Natural Science Foundation of China (Grant No. 61471100/61101090/61571082), Science and Technology on Electronic Information Control Laboratory (Grant No. 162105003) and Fundamental Research Funds for the Central Universities (Grant No. ZYGX2015J012/ZYGX2014Z005). We would like to thank all the reviewers for their kind suggestions to this work.

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Shasha Liu and Bingjia Shao contribute the idea and the main algorithm of this paper, Yuan Gao and Su Hu perform the simulation of this manuscript. Weigui Zhou and Yi Li help improve the idea and writing of this work.

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Correspondence to Bingjia Shao or Yuan Gao.

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Liu, S., Shao, B., Gao, Y. et al. Game theoretic approach of a novel decision policy for customers based on big data. Electron Commer Res 18, 225–240 (2018). https://doi.org/10.1007/s10660-017-9259-6

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