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An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet

  • Anindya Ghose

    ()

    (Stern School of Business, New York University, New York, New York 10012)

  • Sang Pil Han

    ()

    (Stern School of Business, New York University, New York, New York 10012)

Registered author(s):

    We quantify how user mobile Internet usage relates to unique characteristics of the mobile Internet. In particular, we focus on examining how the mobile-phone-based content generation behavior of users relates to content usage behavior. The key objective is to analyze whether there is a positive or negative interdependence between the two activities. We use a unique panel data set that consists of individual-level mobile Internet usage data that encompass individual multimedia content generation and usage behavior. We combine this knowledge with data on user calling patterns, such as duration, frequency, and locations from where calls are placed, to construct their social network and to compute their geographical mobility. We build an individual-level simultaneous equation panel data model that controls for the different sources of endogeneity of the social network. We find that there is a negative and statistically significant temporal interdependence between content generation and usage. This finding implies that an increase in content usage in the previous period has a negative impact on content generation in the current period and vice versa. The marginal effect of this interdependence is stronger on content usage (up to 8.7%) than on content generation (up to 4.3%). The extent of geographical mobility of users has a positive effect on their mobile Internet activities. Users more frequently engage in content usage compared to content generation when they are traveling. In addition, the variance of user mobility has a stronger impact on their mobile Internet activities than does the mean. We also find that the social network has a strong positive effect on user behavior in the mobile Internet. These analyses unpack the mechanisms that stimulate user behavior on the mobile Internet. Implications for shaping user mobile Internet usage behavior are discussed. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors.

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    File URL: http://dx.doi.org/10.1287/mnsc.1110.1350
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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 57 (2011)
    Issue (Month): 9 (September)
    Pages: 1671-1691

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    Handle: RePEc:inm:ormnsc:v:57:y:2011:i:9:p:1671-1691
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    1. Michael R. Baye & J. Rupert J. Gatti & Paul Kattuman & John Morgan, 2006. "Clicks, Discontinuities, and Firm Demand Online," Working Papers 2006-21, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    2. Verbeek, Marno, 1990. "On the estimation of a fixed effects model with selectivity bias," Economics Letters, Elsevier, vol. 34(3), pages 267-270, November.
    3. Lahiri, Kajal & Schmidt, Peter, 1978. "On the Estimation of Triangular Structural Systems," Econometrica, Econometric Society, vol. 46(5), pages 1217-21, September.
    4. Jacoby, Jacob & Szybillo, George J & Berning, Carol Kohn, 1976. " Time and Consumer Behavior: An Interdisciplinary Overview," Journal of Consumer Research, Oxford University Press, vol. 2(4), pages 320-39, March.
    5. Anindya Ghose & Sang Pil Han, 2009. "A Dynamic Structural Model of User Learning in Mobile Media Content," Working Papers 09-24, NET Institute, revised Oct 2009.
    6. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
    7. Mark Stewart, 2006. "Maximum simulated likelihood estimation of random-effects dynamic probit models with autocorrelated errors," Stata Journal, StataCorp LP, vol. 6(2), pages 256-272, June.
    8. Stewart, Mark, 2006. "The Inter-related Dynamics of Unemployment and Low-Wage Employment," The Warwick Economics Research Paper Series (TWERPS) 741, University of Warwick, Department of Economics.
    9. William Greene, 2007. "Fixed and Random Effects Models for Count Data," Working Papers 07-15, New York University, Leonard N. Stern School of Business, Department of Economics.
    10. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-26, November.
    11. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    12. Arellano, M, 1987. "Computing Robust Standard Errors for Within-Groups Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 49(4), pages 431-34, November.
    13. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
    14. Sungjoon Nam & Puneet Manchanda & Pradeep K. Chintagunta, 2010. "The Effect of Signal Quality and Contiguous Word of Mouth on Customer Acquisition for a Video-on-Demand Service," Marketing Science, INFORMS, vol. 29(4), pages 690-700, 07-08.
    15. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    16. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "The Persistence of Marketing Effects on Sales," Marketing Science, INFORMS, vol. 14(1), pages 1-21.
    17. Wesley Hartmann & Puneet Manchanda & Harikesh Nair & Matthew Bothner & Peter Dodds & David Godes & Kartik Hosanagar & Catherine Tucker, 2008. "Modeling social interactions: Identification, empirical methods and policy implications," Marketing Letters, Springer, vol. 19(3), pages 287-304, December.
    18. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 531-542.
    19. Zabel, Jeffrey E., 1992. "Estimating fixed and random effects models with selectivity," Economics Letters, Elsevier, vol. 40(3), pages 269-272, November.
    20. Puhani, Patrick A, 2000. " The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
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