The Joint Diffusion of a Digital Platform and its Complementary Goods: The Effects of Product Ratings and Observational Learning

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The Joint Diffusion of an Open Digital Platform and its
Complementary Goods: The effects of Product Rating
and Observational Learning
Meisam Hejazi Nia 1 , Norris Bruce 2
1PhD Student, University of Texas at Dallas, meisam.hejazinia@utdallas.edu ,
2Associate Professor of Marketing, University of Texas at Dallas, norris.bruce@utdallas.edu
August 22, 2014
Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 1 / 27

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Open Source
Distributed Innovation System
Volunteer Contributions (Online)
Intrinsic (joy, autonomy, etc.)
Extrinsic (Skills, Reputation, etc.)
No Pricing Mechanism (Driver of diﬀusion?)
Compete or Complement?
Network Eﬀects (Direct and Indirect?)

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User Generated Content In the Open Source
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Self Governed Community
Users: Rate (Vote), Use, Relevance
Developers: Review Process (Academia)
Distributed Innovation
Poetry and Democracy
Pragmatism, License, and Transparency
Frequent New Release Chaos
Complement Heterogeneity

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Research Questions
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......
To what extent do complementary products influence the OSS platforms’
adoption?
.
......
What are the effects of product ratings, observational learning on the OSS
complement’s diffusion?
.
......
What are the relative importance of indirect and direct network effects on
the OSS platforms’ diffusion?
.
......
What is the effect of governing process (review process) on the OSS
platforms’ diffusion?
.
......What is the effect of new releases on the OSS platforms’ diffusion?
.
......
What are the effects of license choices (GPL, BSD, Firefox Public) and
developers reward motives on plug-in adoption?

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This Study
Open Source and Open Platform
Complementary Goods
User Generated Content, Community,
and Product Innovation
Joint Diﬀusion

Fierefox Platform and its User Community
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Why Add-ons are Important?
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“ According to a post on the Mozilla Add-Ons Blog, 85% of
Firefox 4 users have at least one add-on installed ...The figure
doesn’t include .... that users haven’t actively chosen to
install....”
— techcrunch.com
.
......
“We are a great example of company whose growth was
primarily driven by Mozilla Add-ons. Firefox gave us a great
platform to build on. ”
— Garrett Camp, Founder CEO of StumbleUpon
.
......
“How does that compare with other
browsers?BrowserFramepoints to aChromium developer blog
postthat reveals that as of December 2010 about 1/3 of
Chrome users had at least one extension installed.”
— readwrite.com
.
......
“Why are add-ons important?
They are core to theMozillas
missionto offer choice
innovation
Customizationis a key
differentiator for Firefox.
Huge momentum
versusInternet
Explorer,Safari(no official
site), andOpera
Add-ons have become a
platform for innovation and
experimentation....
We believe that Firefox users
who have installed add-ons
tend to be more loyal
”
— Mozilla.com

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Mozilla’s Community
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“ [To attract a large, engaged volunteer base],
you must start oﬀ with something that people
are passionate about. It’s surprising that it’s
piece of software....[Poetry and Pragmatics]”
— Asa Dotzler, Mozilla’s Director of Community
Development
.
......
“A volunteer called me one day and said ’I
think we should take out an ad in the New
York Times’. I said ’Well, that costs too much
money,...,He said, maybe people can pay to have
their name in the ad’, so we worked together
and created this project ”
— Marketing Director of Firefox
.
......
“AMO Editors is a Mozilla community
dedicated to guard the security and reliability of
add-ons listed on AMO [Add-on Mozilla
Organization]. As part of Add-on review
process, editors review the code ...”
— Mozilla.com
Mozilla Community

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Position of This Research in Literature
Diﬀusion Network
Eﬀects
OSS UGC
This study * * * *
Bass (1969), Mahajan et al (1995), Xie et al. (1997), Horskey and
Simon (1983), Van den Bulte and Lilien (1997),Libai et al (2009),
Norton and Bass (1987), Kalish (1985), Vankatesh et al. (2004),
Lenk and Rao (1990), Putsis and Srinivasan (1999)
* − − −
Ransbotham et al. (2012), Zhang et al. (2012), Shankar and Bayus
(2001), Srinivasan et al. (2004,2005), Tellis et. al (2009), Bayus et.
al (1997), Nair. et al (2004), Church and Gandal (2012, 1993, 1992),
Katz and Shapiro (1992,1986,1985),Ferrell et al. (2006,1986),etc.
− * − ∗
Mallapragada et al. (2012), Kumar et al.(2011), Borah and Tel-
lis (2014), Gandal et. al. (2011,2002,1995), Burns and Stalker
(1961), Damanpour(1991), Ettlie et. al. (1984), Levin et al.
(1987), Candy and Tellis (2000), Moorman (2011), Lerner and Tirole
(2002), Spithoven et al. (2013), Spithoven et al. (2013), Boudreau,
K. (2010),Lattemann and Sieglitz (2005), Preece and Shneiderman
(2009),Ke and Zhang (2009,2010) etc.
− − ∗ ∗
Chavalier and Mayzlin (2006), Moe and Trusov (2011), Dellarocas et
al. (2007), Chen et al. (2010), Sun (2012), Clemons et al. (2006),
Godes and Silva (2012), Zhao et al. (2012), Moe and Schweidel
(2012), Duan et al. (2008), Bikhchandani et al. (1998, 2001, 2008),
etc.
− − − *

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Mozilla Firefox: Open Two Sided Market

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Overview
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Data
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Unbalanced panel data on 52 Firefox Add-ons, and Firefox, IE and
Chrome
Daily data for six years 2008-2013 (Tmax = 1686 days)
.
Methodology
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53 Bass diﬀusion models (Discrete-NL State Space)
HB Extended Kalman Filter
.
Results
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OL and rating valence and variance increase the demand for Add-ons
Add-ons increase the market size of the Firefox platform
Slow add-on review process can diminish the platforms’ success
OSSP’s (i.e. Chrome and Firefox) compete (not complement)
New releases of add-on and platform have positive inﬂuences

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Basic Statistics
Mean SD Min Max
Platform Firefox Daily Users (M) 229 16.00 185.00 262.00
FF Add-ons created 128.00 192.00 4.00 2,418.00
Google Chrome Daily Users (M) 189.00 119.00 20.00 423.00
Microsoft IE Daily Users (M) 354.00 47.00 240.00 437.00
AMO Editor’s Contributions (W) 1,444.00 442.00 794.00 2,620.00
AMO nomination Queue 362.00 220.00 80.00 949.00
Add-on Downloads (K) 7.64 18.50 11.79 283.44
Daily Users (M) 0.88 1.96 1E-6 16.97
Rating Valence (Free launch) 4.28 0.50 1.00 5.00
Rating Variance 1.46 0.76 0.48 4.20
New Version of Add-on 0.02 0.12 0.00 1.00
Length of time series (D) 13,219.00 457.00 260.00 1686.00
According to Firefox report on Jan 2010 Bookmarks(6%),
Appearance(17%), and Download Management(6%) Add-ons are the
more popular ones
OSS facilitates manifold add-ons creation

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Frequent releases of OSS
Proprietary Software new Releases: payment required, and long lead
time
Open Source: agile frequent market feedback, frequent releases
(as Reymond (1999) ”Release early and Release Often” suggests)

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Platform’s Diﬀusion
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Model
..
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ypt = mt + ϵt
t = 1..T , ϵit ∼ N(0, V )
∆mt = (pt + q mt
Mt
)(Mt − mt) + ωt
ωt ∼ N(0, W ),ωt⊥ϵt
pt = p0 + Ztρ + Xtβ
Mt = M0 + Atκ
t:
Time index (days)
yt:
Obsereved cumulative number of
Firefox users (10M)
mt:
Latent cumulative number of Fire-
fox users
Xt:
Vector of Competitors’ users (i.e.
Chrome and MS IE) (10M)
Zt:
Nomination queue and AMO con-
tributions vector
At:
Cumulative number of published
Add-ons
{β, q, κ, ρ, V , W }: Parameters Vector

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Complements’ Diffusion
.
Model
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yjt = njt + ϵjt
t = sj ..T , ϵjt ∼ N(0, Vj )
∆njt = (pjt + qjt(1 − δj )
njt−1
αj mt−1
)
×(αj mt−1 − njt−1) − δj njt−1 + ωjt
ωjt ∼ N(0, Wj ), ωjt⊥ϵjt
pjt = p0j + p1j PVt + AVjt
AVjt = γt−τ
qjt = q0t + q1j RTVjt + q2j OLjt + q3j STAVGjt
t:
Time index (days), with launched
date sj , unbalanced panel
yjt:
Add-on observed cumulative
downloads (K)
njt:
Add-on latent diffusion state
mt:
Platform latent diffusion state
αj :
Endogenized Market Size parame-
ter
{αj , pjt, qjt, δjt, Vj , Wj }: Parameters Vector

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Complements’ Diﬀusion
.
Model
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yjt = njt + ϵjt
t = sj ..T , ϵjt ∼ N(0, Vj )
∆njt = (pjt + qjt(1 − δj )
njt−1
αj mt−1
)
×(αj mt−1 − njt−1) − δj njt−1 + ωjt
ωjt ∼ N(0, Wj )
pjt = p0j + p1j PVt + AVjt
AVjt = γt−τ
qjt = q0t + q1j RTVjt + q2j OLjt + q3j STAVGjt
pjt:
Innovation factor
qjt:
Imitation factor
AVjt, PVt:
Smoothed new release(version)
shock
RTVjt:
Rating variance
OLjt:
Daily users (OL)
STAVGjt:
Discrete Rating Valence
δj :
Disadoption rate (Churn)

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Estimation: MCMC Sampler
Start with diﬀused uninformative prior
Structural restrictions on market sizes
..mt|q, κ, ρ, V , W : EKFFBS recursion to
estimate underlying state of platform
.
q, κ, rho, V , W |mt: Gibbs, Block M-
H Mode sampler algorithm for plat-
form: Improve Acceptance Rate
.
Parallel Sampler:njt|mt, αj , pjt, qjt, δj , Vj , Wj :
EKFFBS recursion to estimate un-
derlying state of Complements
.
αj , pjt, qjt, δj , Vj , Wj |njt, mt: Gibbs, Block M-H
Mode sampler algorithm for each Complement
.
Hierarchical Bayesian, MCMC, Gibbs Sampler
for all add-ons’ parameters(N-IG conjugate)

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Why Stochastic Time Varying Method, Kalman Filter?
.
EKF: Discretized
..
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Robust to Process and Measurement Error, characterise uncertainty
Does not require Analytical Solution; Efficient, general and flexible
Granular data, mollifies interval bias, better in/out sample prediction
fit (Xie et al. 1997)
.
OLS Inference Problem
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Biased Estimates (Putsis and Srinivasan 1999)
No Direct way to find Standard Errors
.
MLE Inference Problem
..
......
Requires closed form solution
Downward biased Standard Errors (Van den Bulte and Lilien 1997)
.
NLLS
..
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Requires closed form solution
Not robust to specification errors

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Model Performance
Model Description Deviance information
Criteria (DIC)
Penalty Term(pD) Log Likelihood
1 No Churn 393,181,425 196,887,137 296,425
2 No Version Carry Over 393,249,002 196,921,381 296,881
3 No AMO eﬀect on Platform 393,258,460 196,926,790 297,560
4 Interaction Model
(PVjtAVjt, RTVjtOLjt, RTVjtSTAVGjt)
393,224,624 196,921,662 309,350
5 Unexplained immitation factor 393,183,354 196,889,620 297,942
6 Unexplained innovation factor 393,229,107 196,907,050 292,496
7 Unexplained Churn 393,265,094 196,298,559 297,309
8 No accumulative add-on cre-
ation
393,220,649 196,903,764 293,439
9 Unexplained relevance factor 393,338,158 196,971,387 302,309
* Proposed Model 393,029,826 196,694,177 179,264
DIC favors the proposed model

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Model Performance: A Step Ahead Forecast
One step ahead forecast indicates model ability to capture the phenomena
Example Add-on Mean Abso-
lute Deviation
(MAD)
Mean Square
Error (MSE)
Firefox Platform 1.2-e-04 2.04e-05
Auto Pager 0.0016 4.71e-06
Google Translator 0.0012 3.34e-06
Low MAD and MSE for one step ahead forecast indicates proper ﬁt of the model

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Parameters’ Distributions: Heterogeneity
(1) Low relevance of the innovations in OSS (2) Positive inﬂuences of frequent new releases

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Platform Parameter Vector Estimates
Mean SD 2.5th 97.5th
Market Size coefficients:
Intercept M0 1.54E-02 1.52E-06 1.54E-02 1.54E-02
Total Add-ons Created κ 3.60E-02 1.51E-06 3.60E-02 3.60E-02
External Market Forces:
Unobserved Innovation Force p0 1.76E-03 1.52E-06 1.76E-03 1.76E-03
Competition
Chrome ρ1 -4.91E-05 1.51E06 -5.17E-05 -4.73E-05
Internet Explorer ρ2 -5.66E-04 1.52E-06 -5.68E-04 -5.64E-04
AMO
Total AMO Contributions ρ3 3.42E-05 1.51E-06 3.16E-05 3.61E-05
Nomination Queue Length ρ4 3.52E-05 1.51E-06 3.26E-05 3.70E-05
Internal Market Force
Unobserved Imitation Force q0 1.27E-08 1.902E-09 -2.03E-09 2.77E-08
Variances:
Observation equation vp 1.44E-02 4.30E-03 8.52E-03 2.30E-02
State Equation wp 1.12E-01 8.35E-03 9.75E-02 1.25E-01
Significant indirect network effects (NE), but insignificant direct NE’s
Negative effects of slow review process
Competition between OSS platforms (Chrome and Firefox)

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Add-ons’ Parameter Vector Estimates
Mean SD 2.5th 97.5th
Relevance Factor αj 0.0142 0.0019 0.0104 0.0179
Churn Factor δj 0.0174 0.0021 0.0132 0.0215
External Market Forces:
Unobserved p0 0.0087 0.0018 0.0051 0.0123
Add-on New Release p1 0.0047 0.001 0.0026 0.0067
Platform New Release p2 0.0059 0.0012 0.0035 0.0083
Internal Market Forces:
Unobserved q0 0.0057 0.0014 0.0030 0.0085
Rating Variance q1 0.0131 0.0018 0.0096 0.0167
Observational Learning q2 0.0054 0.0016 0.0022 0.0086
Rating valence q3 7 0.0043 0.0014 0.0016 0.0070
Variance:
Observation Equation vj 0.0002 1.56E-05 0.0002 0.0002
State Equation wj 0.0002 1.74E-05 0.0002 0.0003
Positive effects of new frequent releases
Positive effects of quality signals
No effects of licenses and incentive types

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New releases’ and AMO contributions’ Endogeneity
.
Model
..
......
yjt = mt + vt
Z1
t = µt + υt
mt = mt−1 + (p0 + µtp1 + Z2
t p2+
qmt−1
M0+At κ )(M − mt−1) + wt
µt = γ1 + γ2µt−1 + ςt
µt = γ1 +2 µt−1 + γeZ2
t + ςt
(
wt
υt
)
∼ MVN(0, Σ)
vt ∼ N(0, V )
ςt ∼ N(0, ψ)
Estimate Mean STD 2.5% 97.5%
Corr(wt, υt) 0.01 0.13 -0.20 0.22
Σ21 5e-4 0.01 -0.01 0.01
Corr(wt, υt) -2e-3 0.12 -0.20 0.19
Σ21 -4e-5 5e-3 -0.01 0.01
Method: Condition on
AMO contribution, then on
diffusion (EKF, FFBS)
AMO contribution: Not
significant correlation and
diagonal elements of
var-covar matrix
Smoothed New Releases:
Not significant correlation
and diagonal elements of
var-cover matrix

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Findings’ Summary and Conclusion
.
......Complementary Goods Extend the Market Size of the platform (Mozilla)
.
......
Learning signals (OL, STVar, St) increase the adoption of the
complements (add-ons, plug-ins)
.
......Review process is important for OSS platform’s diffusion
.
......
Licenses and developers commercial incentives do not have any effect on
the diffusion of complements
.
......Frequent releases have positive effect on the diffusion of complements

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Managerial Take Aways
.
......
Open platform would need either to internalize the review process, or
aﬀect it more softly?
.
......
An open platform can measure the relevance of each of the add-ons for its
incentive mechanism design
.
......
An open platform should not be too much concerned about licenses and
commercial incentives of the developers
.
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An open platform should motivate frequent releases (as Reymond (1999)
”Release early and Release Often” suggests)

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The Joint Diffusion of a Digital Platform and its Complementary Goods: The Effects of Product Ratings and Observational Learning

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The Joint Diffusion of a Digital Platform and its Complementary Goods: The Effects of Product Ratings and Observational Learning