The Operational Value of Social Media Information

Production and Operations Management

Volumn 27, Issue 10, 2018, Pages 1749-1769

  Cui, Ruomeng ^a   Gallino, Santiago ^b   Moreno, Antonio ^c   Zhang, Dennis J ^d  

^a INDIANA UNIVERSITY   (United States)

^b DARTMOUTH COLLEGE   (United States)

^c NORTHWESTERN UNIVERSITY   (United States)

^d WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

Author keywords

machine learning; sales forecast; social media

Indexed keywords

EID: 85018660517     PISSN: 10591478     EISSN: 19375956     Source Type: Journal    
DOI: 10.1111/poms.12707     Document Type: Article

References (56)

1. Allon, G., D. J. Zhang. 2015. Managing service systems in the presence of social networks. Available at SSRN, 2673137.
2. Ang, E., S. Kwasnick, M. Bayati, E. L. Plambeck, M. Aratow. 2015. Accurate emergency department wait time prediction. Manuf. Serv. Oper. Manag. 18(1): 141–156.
3. Aral, S. 2011. Commentary-identifying social influence: A comment on opinion leadership and social contagion in new product diffusion. Market. Sci. 30(2): 217–223.
4. Banerjee, A., A. G. Chandrasekhar, E. Duo, M. O. Jackson. 2012. The diffusion of microfinance. Technical report, National Bureau of Economic Research.
5. Barber, B. M., T. Odean. 2008. All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Rev. Finan. Stud. 21(2): 785–818.
6. Bassamboo, A., R. Cui, A. Moreno. 2015. The wisdom of crowds in operations: Forecasting using prediction markets. Available at SSRN, 2679663.
7. Bell R. M., Y. Koren. 2007. Lessons from the net ix prize challenge. ACM SIGKDD Explor. Newsl. 9(2): 75–79.
8. Bikhchandani, S., D. Hirshleifer, I. Welch. 1992. A theory of fads, fashion, custom, and cultural change as informational cascades. J. Polit. Econ. 100(5): 992–1026.
9. Bollen, J., H. Mao, X. Zeng. 2011. Twitter mood predicts the stock market. J. Comput. Sci. 2(1): 1–8.
10. Breiman, L. 2001. Random forests. Mach. Learn. 45(1): 5–32.
11. Cai, H., Y. Chen, H. Fang. 2007. Observational learning: Evidence from a randomized natural field experiment. National Bureau of Economic Research, w13516.
12. Candogan, O., K. Bimpikis, A. Ozdaglar. 2012. Optimal pricing in networks with externalities. Oper. Res. 60(4): 883–905.
13. Chen, L., H. L. Lee. 2009. Information sharing and order variability control under a generalized demand model. Management Sci. 55(5): 781–797.
14. Chen, Y., J. Xie. 2008. Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Sci. 54(3): 477–491.
15. Chen, Y., Q. Wang, J. Xie. 2011. Online social interactions: A natural experiment on word of mouth versus observational learning. J. Market. Res. 48(2): 238–254.
16. Chen, H., P. De, Y. J. Hu, B-H. Hwang. 2014. Wisdom of crowds: The value of stock opinions transmitted through social media. Rev. Financ. Stud. 27(5): 1367–1403.
17. Cortes, C., V. Vapnik. 1995. Support-vector networks. Mach. Learn. 20(3): 273–297.
18. Cui, R., G. Allon, A. Bassamboo, J. A. Van Mieghem. 2015. Information sharing in supply chains: An empirical and theoretical valuation. Management Sci. 61(11): 2803–2824.
19. Da, Z., J. Engelberg, P. Gao. 2011. In search of attention. J. Finance 66(5): 1461–1499.
20. Devitt, A., K. Ahmad. 2007. Sentiment polarity identification in financial news: A cohesion-based approach. In ACL.
21. Ferreira, K. J., B. H. A. Lee, D. Simchi-Levi. 2015. Analytics for an online retailer: Demand forecasting and price optimization. Manuf. Serv. Oper. Manag. 18(1): 69–88.
22. Friedman, J., T. Hastie, R. Tibshirani. 2001. The Elements of Statistical Learning, volume 1. Springer series in statistics Springer, Berlin.
23. Gaur, V., A. Giloni, S. Seshadri. 2005. Information sharing in a supply chain under arma demand. Management Sci. 51(6): 961–969.
24. Gaur, V., S. Kesavan, A. Raman, M. L. Fisher. 2007. Estimating demand uncertainty using judgmental forecasts. Manuf. Serv. Oper. Manag. 9(4): 480–491.
25. Goh, K.-Y., C.-S. Heng, Z. Lin. 2013. Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content. Inf. Syst. Res. 24(1): 88–107.
26. Graves, S. C. 1986. A tactical planning model for a job shop. Oper. Res. 34(4): 522–533.
27. Graves, S. H. C. Meal, S. Dasu, Y. Qiu, S. Axsäter, C. Schneeweiss, E. Silver. 1986. Two-stage production planning in a dynamic environment. Multi-Stage Prod. Plann. Inventory Control 266: 9–43.
28. Hameed, B. 2011. Social media usage exploding amongst fortune 500 companies. Available at http://socialtimes.com (accessed date November 24, 2013).
29. Heath, D. C., P. L. Jackson. 1994. Modeling the evolution of demand forecasts ith application to safety stock analysis in production/distribution systems. IIE Trans. 26(3): 17–30.
30. Huang, T., J. A. Van Mieghem. 2014. Clickstream data and inventory management: Model and empirical analysis. Prod. Oper. Manag. 23(3): 333–347.
31. Ifrach, B., C. Maglaras, M. Scarsini. 2011. Monopoly pricing in the presence of social learning.
32. James, G., D. Witten, T. Hastie, R. Tibshirani. 2013. An Introduction to Statistical Learning. Springer, New York.
33. Jing, B. 2011. Social learning and dynamic pricing of durable goods. Market. Sci. 30(5): 851–865.
34. Kesavan, S., V. Gaur, A. Raman. 2010. Do inventory and gross margin data improve sales forecasts for us public retailers? Management Sci. 56(9): 1519–1533.
35. Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2, August 20–25, 1995, Montreal, Quebec, Canada, pp. 137–1143.
36. Kremer, M., B. Moritz, E. Siemsen. 2011. Demand forecasting behavior: System neglect and change detection. Management Sci. 57(10): 1827–1843.
37. Li, X., L. Wu. 2014. Herding and social media word-of-mouth: Evidence from groupon. Working paper, Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2264411
38. Louppe, G., L. Wehenkel, A. Sutera, P. Geurts. 2013. Understanding variable importances in forests of randomized trees. In Adv. Neural Inf. Process. Syst. 431–439.
39. Luca, M. 2011. Reviews, reputation, and revenue: The case of yelp. com. Technical report, Harvard Business School.
40. McMahan, H. B., G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin, Chikkerur, S., D. Liu, M. Wattenberg, A. M. Hrafnkelsson, T. Boulos, J. Kubica. 2013. Ad click prediction: a view from the trenches. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 11–14, 2013, Chicago, Illinois, USA, 1222–1230.
41. Moe, W. W, P. S. Fader. 2002. Fast-track: Article using advance purchase orders to forecast new product sales. Market. Sci. 21(3): 347–364.
42. Moretti, E. 2011. Social learning and peer effects in consumption: Evidence from movie sales. Rev. Econ. Stud. 78(1): 356–393.
43. Nichols, D. R., J. J. Tsay. 1979. Security price reactions to long-range executive earnings forecasts. J. Acc. Res. 17(1): 140–155.
44. Ord, K., R. Fildes. 2012. Principles of Business Forecasting. Cengage Learning, Boston, MA.
45. Osadchiy, N., V. Gaur, S. Seshadri. 2013. Sales forecasting with financial indicators and experts' input. Prod. Oper. Manag. 22(5): 1056–1076.
46. Pang, B., L. Lee. 2008. Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2): 1–135.
47. Penman, S. H. 1980. An empirical investigation of the voluntary disclosure of corporate earnings forecasts. J. Acc. Res. 18(1): 132–160.
48. Resnik, P., et al. 1999. Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. (JAIR) 11: 95–130.
49. Rudin, C., G.-Y. Vahn. 2015. The big data newsvendor: Practical insights from machine learning. Available at SSRN 2559116.
50. Socher, R., B. Huval, C. D. Manning, A. Y. Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics, pp. 1201–1211.
51. Stone, M. 1977. An asymptotic equivalence of choice of model by cross-validation and akaike's criterion. J. Roy. Stat. Soc. Ser. B (Methodol.) 44(1): 44–47.
52. Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. Ser. B (Methodol.) 58(1): 267–288.
53. Vahl, A. 2013. Facebook news feed updates: How marketers should respond to story bump. Available at http://www.socialmediaexaminer.com/story-bump (accessed date December 25, 2013).
54. Wang, H., D. Can, A. Kazemzadeh, F. Bar, S. Narayanan. 2012. A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. Proceedings of the ACL 2012 System Demonstrations, Association for Computational Linguistics, pp. 115–120.
55. Ye, S., G. Aydin, S. Hu. 2014. Sponsored search marketing: Dynamic pricing and advertising for an online retailer. Management Sci. 61(6): 1255–1274.
56. Zhang, D. J., G. Allon, J. A. Van Mieghem. 2015. Does social interaction improve service quality? Field evidence from massive open online education.