De-biasing the reporting bias in social media analytics

Production and Operations Management

Volumn 25, Issue 5, 2016, Pages 849-865

  Chen, Hongyu ^a   Zheng, Zhiqiang ^b   Ceran, Yasin ^c  

^a CALIFORNIA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF TEXAS AT DALLAS   (United States)

^c SANTA CLARA UNIVERSITY   (United States)

Author keywords

BB NBD; inverse probability weighting; reporting bias; social media analytics; user generated content

Indexed keywords

EID: 84947794925     PISSN: 10591478     EISSN: 19375956     Source Type: Journal    
DOI: 10.1111/poms.12509     Document Type: Article

References (42)

1. Allen, S. G., 1981. An empirical model of work attendance. Rev. Econ. Statist. 63 (1): 77-87.
2. Barmby, T. A., C. D. Orme, J. G. Treble,. 1991. Worker absenteeism: An analysis using microdata. Econ. J. 101 (405): 214-229.
3. Bhattacharya, B., 1997. Is your brand loyalty too much, too little, or just right? Explaining deviations in loyalty from the Dirichlet norm. Int. J. Res. Mark. 14: 421-435.
4. Chatfield, C., G. J. Goodhardt,. 1970. The Beta-Binomial model for consumer purchasing behavior. J. Roy. Statist. Soc. Ser. C 19 (3): 240-250.
5. Chen, P., S. Dhanasobhon, M. Smith,. 2008. All reviews are not created equal: The disaggregate impact of reviews and reviewers at Amazon.com. Working paper, Temple University, Philadelphia, PA.
6. Chevalier, J. A., D. Mayzlin,. 2006. The effect of word of mouth on sales: Online book reviews. J. Mark. Res. 43 (3): 345-354.
7. Clemons, E. K., G. G. Gao, L. M. Hitt,. 2006. When online reviews meet hyper differentiation: A study of the craft beer industry. J. Manag. Inform. Systems. 23 (2): 149-171.
8. Dellarocas, C., C. A. Wood,. 2008. The sound of silence in online feedback: Estimating trading risks in the presence of reporting bias. Management Sci. 54 (3): 460-476.
9. Du, R., W. Kamakura, C. Mena,. 2007. Size and share of customer wallet. J. Mark. 71 (4): 94-113.
10. Duan, W., B. Gu, A. B. Whinston,. 2008. Do online reviews matter? An empirical investigation of panel data. Decis. Support Syst. 45 (4): 1007-1016.
11. Etzion, H., N. Awad,. 2007. Pump up the volume? Examining the relationship between number of online reviews and sales: Is more necessarily better? ICIS 2007 Proceedings. Paper 120.
12. Fader, P. S., B. G. S. Hardie,. 2000. A note on modeling underreported Poisson counts. J. Appl. Statist. 27 (8): 953-964.
13. Fader, P. S., D. C. Schmittlein,. 1993. Excess behavioral loyalty for high-share brands: Deviations from the Dirichletmodel for repeat purchasing. J. Mark. Res. 30 (4): 478-493.
14. Forman, C., A. Ghose, B. Wiesenfeld,. 2008. Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Inform. Syst. Res. 19 (3): 291-313.
15. Gao, G., B. Greenwood, J. McCullough, R. Agarwal,. 2012. A digital soapbox? The information value of online physician ratings. American Society for Health Economists 4th Biennial Conference.
16. Gartner, M., 1976. Endogenous bandwagon and underdog effects in a rational choice model. Public Choice 25 (1): 83-89.
17. Ghose, A., P. G. Ipeirotis,. 2011. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Trans. Knowl. Data Engrg. 23 (10): 1498-1512.
18. Godes, D., J. C. Silva,. 2012. Sequential and temporal dynamics of online opinion. Mark. Sci. 31 (3): 448-473.
19. Goodhardt, G. J., A. S. C. Ehrenberg, C. Chatfield,. 1984. The Dirichlet: A comprehensive model of buying behavior. J. Roy. Statist. Soc. Ser. A. 147 (5): 621-655.
20. Heckman, J. J., 1979. Sample selection bias as a specification error. Econometrica 47 (1): 153-161.
21. Hu, N., J. Zhang, P. A. Pavlou,. 2009. Overcoming the J-shaped distribution of product reviews. Commun. ACM 52 (10): 144-147.
22. Jabr, W., Z. Zheng,. 2014. Know yourself and know your enemy: An analysis of firm recommendations and consumer reviews in a competitive environment. MIS Q. 38 (3): 635-654.
23. Johnson, L., S. Kotz, A. Kemp,. 1992. Univariate Discrete Distributions, 2nd edn. John Wiley & Sons, New York.
24. Kamakura, W. A., M. Wedel,. 2003. List augmentation with model based multiple imputation: A case study using a mixed outcome factor model. Stat. Neerl. 57 (1): 46-57.
25. Li, X., L. M. Hitt,. 2008. Self-selection and information role of online product reviews. Inform. Syst. Res. 19 (4): 456-474.
26. Liu, Y., 2006. Word of mouth for movies: Its dynamics and impact on box office revenue. J. Mark. 70 (3): 74-89.
27. McAllister, I., D. T. Studlar,. 1991. Bandwagon, underdog, or projection? Opinion polls and electoral choice in Britain, 1979-1987. J. Polit. 53 (03): 720-741.
28. Moe, W. W., D. A. Schweidel,. 2012. Online product opinions: Incidence, evaluation, and evolution. Mark. Sci. 31 (3): 372-386.
29. Morrison, D. G., D. C. Schmittlein,. 1981. Predicting future random events based on past performance. Management Sci. 27 (9): 1006-1023.
30. Morrison, D. G., D. C. Schmittlein,. 1988. Generalizing the NBD model for customer purchases: What are the implications and is it worth the effort? J. Bus. Econ. Statist. 6 (2): 145-159.
31. Mudambi, S. M., D. Schuff,. 2010. What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Q. 34 (1): 185-200.
32. Nielsen News. 2009. Nielsen Global Online Consumer Survey of April 2009.
33. Rossi, E. P., 2014. Even the rich can make themselves poor: A critical examination of IV methods in marketing applications. Mark. Sci. 33 (5): 655-672.
34. Schlosser, A. E., 2005. Posting versus lurking: Communicating in a multiple audience context. J. Consum. Res. 32 (2): 260-265.
35. Schmittlein, D. C., A. C. Bemmaor, D. G. Morrison,. 1985. Why does the NBD model work? Robustness in representing product purchases, brand purchases and imperfectly recorded purchases. Mark. Sci. 4 (3): 255-266.
36. Schweidel, D. A., G. Knox,. 2013. Incorporating direct marketing activity into latent attrition models. Mark. Sci. 32 (3): 471-487.
37. Theil, H., 1966. Applied Economic Forecasting. North-HoIland Publishing, Amsterdam.
38. Winkelmann, R., 1996. Markov chain Monte Carlo analysis of underreported count data with an application to worker absenteeism. Empirical Econ. 21 (4): 575-587.
39. Winkelmann, R., 2008. Econometric Analysis of Count Data, 5th edn. Springer, Berlin.
40. Wooldridge, J., 2010. Econometric Analysis of Cross Section and Panel Data, 2nd edn. MIT Press, Cambridge, MA.
41. Ying, Y., F. Feinberg, M. Wedel,. 2006. Leveraging missing ratings to improve online recommendation systems. J. Mark. Res. 43 (3): 355-365.
42. Zheng, Z., P. Fader, B. Padmanabhan,. 2012. From business intelligence to competitive intelligence: Inferring competitive measures using augmented site-centric data. Inform. Syst. Res. 23 (3): 698-720.