Abstract
Increased similarity with one’s friends’ choices in a social network leads a user to engage further with the social network. Participation is modelled based on user utility derived both from participating in preferred events and from joint participation with friends. The model implies that users will participate more as they learn that they share more interests with their friends. These implications are tested using panel data from an online video gaming network in which users can learn the characteristics of friends’ recent game play behaviour. The focal user’s time on the platform increases substantially as friend’s choices become more similar to the focal user’s behaviour. These results are robust to multiple possible sources of endogeneity.
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The quit date is inferred from the last gaming session unless censored by the last week in the sample.
Ma et al., (2015) similarly use genre in their study of the role of network homophily and influence on the purchase of ringtones.
Due to compatibility issues during this early rollout period, sample includes only a few PlayStation or Wii users.
These measures entail a number of modeling choices. Alternatives to duration of time spent by user i in category a could be the number of gaming sessions or the number of game titles played. The distance measure need not have been strictly Euclidean and could have been, for example absolute value of the difference in values. And each category of age-appropriateness, platform, and genre, need not have been weighted equally.
The median G2 size was just four friends.
Attenuation bias results from measurement error. Even measurement errors that are independent from the variables of interest will affect the calculation of the coefficients. The OLS estimate, \({\hat{\beta}}^{OLS}=\mathit{\operatorname{cov}}\left(y,x\right)/\mathit{\operatorname{var}}(x)\) is replaced by \({\hat{\beta}}^{OLS}=\mathit{\operatorname{cov}}\left(y,x+\epsilon \right)/\mathit{\operatorname{var}}\left(x+\epsilon \right)\). The measurement error, ϵ, does not affect the numerator but inflates the denominator.
Valid instruments replace x + ϵ with first stage estimates, proj(x + ϵ| z), which will be free of the measurement error. Thus, \({\hat{\beta}}^{IV}=\mathit{\operatorname{cov}}\left(y, proj\left(x+\epsilon |z\right)\right)/\mathit{\operatorname{var}}\left( proj\left(x+\epsilon |z\right)\right)\) has both an unbiased numerator and an unbiased denominator.
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Appendix
Appendix
First stage regressions for Table 3
Focal-G1 Usage | Focal-G1 Genre | Focal-G1 Platform | Focal-G1 ESRB | |
|---|---|---|---|---|
Focal-G2 Usage Distance | 0.465*** | −0.009*** | −0.001 | −0.007*** |
(0.004) | (0.001) | (0.002) | (0.002) | |
Focal-G2 Genre Distance | −0.040*** | 0.327*** | 0.008*** | 0.031*** |
(0.003) | (0.003) | (0.003) | (0.003) | |
Focal-G2 Platform Distance | 0.009*** | −0.001 | 0.261*** | 0.002 |
(0.002) | (0.002) | (0.003) | (0.002) | |
Focal-G2 ESRB Distance | −0.012*** | 0.027*** | 0.007** | 0.319*** |
(0.002) | (0.002) | (0.003) | (0.003) | |
G1 Size (1000 s) | 1.276*** | −0.082 | 0.328*** | 0.236*** |
(0.225) | (0.054) | (0.094) | (0.063) | |
G1 Density | 0.014*** | 0.007** | 0.014*** | 0.014*** |
(0.003) | (0.003) | (0.004) | (0.003) | |
G2 Size (1000 s) | 0.020*** | −0.007*** | −0.016*** | −0.010*** |
(0.001) | (0.001) | (0.001) | (0.001) | |
User×week Observations | 1,259,559 | 1,259,559 | 1,259,559 | 1,259,559 |
Number of user fixed effects | 87,332 | 87,332 | 87,332 | 87,332 |
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Ward, M.R. Network engagement from learning friends’ preferences: evidence from a video gaming social network. Electron Markets 32, 1239–1255 (2022). https://doi.org/10.1007/s12525-022-00583-7
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DOI: https://doi.org/10.1007/s12525-022-00583-7