The Joint Diffusion of a Digital Platform and its Complementary Goods: The Effects of Product...

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The Joint Diffusion of an Open Digital Platform and itsComplementary 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?)

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 2 / 27

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User Generated Content In the Open Source

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Self Governed Community

Users: Rate (Vote), Use, RelevanceDevelopers: Review Process (Academia)

Distributed Innovation

Poetry and DemocracyPragmatism, License, and TransparencyFrequent New Release Chaos

Complement Heterogeneity

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 3 / 27

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Research Questions

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To what extent do complementary products influence the OSS platforms’adoption?

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What are the effects of product ratings, observational learning on the OSScomplement’s diffusion?

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What are the relative importance of indirect and direct network effects onthe OSS platforms’ diffusion?

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What is the effect of governing process (review process) on the OSSplatforms’ diffusion?

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......What is the effect of new releases on the OSS platforms’ diffusion?

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What are the effects of license choices (GPL, BSD, Firefox Public) anddevelopers reward motives on plug-in adoption?

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 4 / 27

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This Study

Open Source and Open Platform

Complementary Goods

User Generated Content, Community,and Product Innovation

Joint Diffusion

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 5 / 27

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% ofFirefox 4 users have at least one add-on installed ...The figuredoesn’t include .... that users haven’t actively chosen toinstall....”

— techcrunch.com

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“We are a great example of company whose growth wasprimarily driven by Mozilla Add-ons. Firefox gave us a greatplatform to build on. ”

— Garrett Camp, Founder CEO of StumbleUpon

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“How does that compare with otherbrowsers?BrowserFramepoints to aChromium developer blogpostthat reveals that as of December 2010 about 1/3 ofChrome users had at least one extension installed.”

— readwrite.com

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“Why are add-ons important?

They are core to theMozillasmissionto offer choiceinnovation

Customizationis a keydifferentiator for Firefox.Huge momentumversusInternetExplorer,Safari(no officialsite), andOpera

Add-ons have become aplatform for innovation andexperimentation....

We believe that Firefox userswho have installed add-onstend to be more loyal

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— Mozilla.com

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 7 / 27

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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 peopleare passionate about. It’s surprising that it’spiece of software....[Poetry and Pragmatics]”

— Asa Dotzler, Mozilla’s Director of Community

Development

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“A volunteer called me one day and said ’Ithink we should take out an ad in the NewYork Times’. I said ’Well, that costs too muchmoney,...,He said, maybe people can pay to havetheir name in the ad’, so we worked togetherand created this project ”

— Marketing Director of Firefox

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“AMO Editors is a Mozilla communitydedicated to guard the security and reliability ofadd-ons listed on AMO [Add-on MozillaOrganization]. As part of Add-on reviewprocess, editors review the code ...”

— Mozilla.com

Mozilla Community

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 8 / 27

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Position of This Research in Literature

Diffusion NetworkEffects

OSS UGC

This study * * * *

Bass (1969), Mahajan et al (1995), Xie et al. (1997), Horskey andSimon (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)

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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.

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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.

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Chavalier and Mayzlin (2006), Moe and Trusov (2011), Dellarocas etal. (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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Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 9 / 27

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Mozilla Firefox: Open Two Sided Market

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 10 / 27

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Overview

.Data..

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Unbalanced panel data on 52 Firefox Add-ons, and Firefox, IE andChrome

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

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 11 / 27

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Basic Statistics

Mean SD Min Max

Platform Firefox Daily Users (M) 229 16.00 185.00 262.00FF Add-ons created 128.00 192.00 4.00 2,418.00Google Chrome Daily Users (M) 189.00 119.00 20.00 423.00Microsoft IE Daily Users (M) 354.00 47.00 240.00 437.00AMO Editor’s Contributions (W) 1,444.00 442.00 794.00 2,620.00AMO nomination Queue 362.00 220.00 80.00 949.00

Add-on Downloads (K) 7.64 18.50 11.79 283.44Daily Users (M) 0.88 1.96 1E-6 16.97Rating Valence (Free launch) 4.28 0.50 1.00 5.00Rating Variance 1.46 0.76 0.48 4.20New Version of Add-on 0.02 0.12 0.00 1.00Length 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 themore popular ones

OSS facilitates manifold add-ons creation

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 12 / 27

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Frequent releases of OSS

Proprietary Software new Releases: payment required, and long leadtime

Open Source: agile frequent market feedback, frequent releases(as Reymond (1999) ”Release early and Release Often” suggests)

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 13 / 27

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Platform’s Diffusion

.Model..

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ypt = mt + ϵt

t = 1..T , ϵit ∼ N(0,V )

∆mt = (pt + qmtMt

)(Mt −mt) + ωt

ωt ∼ N(0,W ),ωt⊥ϵt

pt = p0 + Ztρ+ XtβMt = M0 + Atκ

t:Time index (days)

yt :Obsereved cumulative number ofFirefox 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 publishedAdd-ons

{β, q, κ, ρ,V ,W }: Parameters Vector

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 14 / 27

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Complements’ Diffusion

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yjt = njt + ϵjt

t = sj ..T , ϵjt ∼ N(0,Vj)

∆njt = (pjt + qjt(1− δj)njt−1

αjmt−1)

×(αjmt−1 − njt−1)− δjnjt−1 + ωjt

ωjt ∼ N(0,Wj), ωjt⊥ϵjt

pjt = p0j + p1jPVt + AVjt

AVjt = γt−τ

qjt = q0t + q1jRTVjt + q2jOLjt + q3jSTAVGjt

t:Time index (days), with launcheddate sj , unbalanced panel

yjt :Add-on observed cumulativedownloads (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

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 15 / 27

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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

αjmt−1)

×(αjmt−1 − njt−1)− δjnjt−1 + ωjt

ωjt ∼ N(0,Wj)

pjt = p0j + p1jPVt + AVjt

AVjt = γt−τ

qjt = q0t + q1jRTVjt + q2jOLjt + q3jSTAVGjt

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)

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 16 / 27

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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 toestimate underlying state of platform

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q, κ, rho,V ,W |mt : Gibbs, Block M-H Mode sampler algorithm for plat-form: Improve Acceptance Rate

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Parallel Sampler:njt |mt , αj , pjt , qjt , δj ,Vj ,Wj :EKFFBS recursion to estimate un-derlying state of Complements

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αj , pjt , qjt , δj ,Vj ,Wj |njt ,mt : Gibbs, Block M-HMode sampler algorithm for each Complement

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Hierarchical Bayesian, MCMC, Gibbs Samplerfor all add-ons’ parameters(N-IG conjugate)

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 17 / 27

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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 predictionfit (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..

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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

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 18 / 27

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Model Performance

Model Description Deviance informationCriteria (DIC)

Penalty Term(pD) Log Likelihood

1 No Churn 393,181,425 196,887,137 296,4252 No Version Carry Over 393,249,002 196,921,381 296,8813 No AMO effect on Platform 393,258,460 196,926,790 297,5604 Interaction Model

(PVjtAVjt ,RTVjtOLjt ,RTVjtSTAVGjt)393,224,624 196,921,662 309,350

5 Unexplained immitation factor 393,183,354 196,889,620 297,9426 Unexplained innovation factor 393,229,107 196,907,050 292,4967 Unexplained Churn 393,265,094 196,298,559 297,3098 No accumulative add-on cre-

ation393,220,649 196,903,764 293,439

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

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 19 / 27

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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 SquareError (MSE)

Firefox Platform 1.2-e-04 2.04e-05Auto Pager 0.0016 4.71e-06Google Translator 0.0012 3.34e-06

Low MAD and MSE for one step ahead forecast indicates proper fit of the model

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 20 / 27

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Parameters’ Distributions: Heterogeneity

(1) Low relevance of the innovations in OSS (2) Positive influences of frequent new releases

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 21 / 27

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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-02Total 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-03CompetitionChrome ρ1 -4.91E-05 1.51E06 -5.17E-05 -4.73E-05Internet Explorer ρ2 -5.66E-04 1.52E-06 -5.68E-04 -5.64E-04AMOTotal AMO Contributions ρ3 3.42E-05 1.51E-06 3.16E-05 3.61E-05Nomination Queue Length ρ4 3.52E-05 1.51E-06 3.26E-05 3.70E-05

Internal Market ForceUnobserved 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-02State 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)

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 22 / 27

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Add-ons’ Parameter Vector Estimates

Mean SD 2.5th 97.5th

Relevance Factor αj 0.0142 0.0019 0.0104 0.0179Churn Factor δj 0.0174 0.0021 0.0132 0.0215

External Market Forces:Unobserved p0 0.0087 0.0018 0.0051 0.0123Add-on New Release p1 0.0047 0.001 0.0026 0.0067Platform New Release p2 0.0059 0.0012 0.0035 0.0083

Internal Market Forces:Unobserved q0 0.0057 0.0014 0.0030 0.0085Rating Variance q1 0.0131 0.0018 0.0096 0.0167Observational Learning q2 0.0054 0.0016 0.0022 0.0086Rating valence q3 7 0.0043 0.0014 0.0016 0.0070

Variance:Observation Equation vj 0.0002 1.56E-05 0.0002 0.0002State 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

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 23 / 27

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New releases’ and AMO contributions’ Endogeneity

.Model..

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yjt = mt + vtZ 1t = µt + υt

mt = mt−1 + (p0 + µtp1 + Z 2t p2+

qmt−1

M0+Atκ)(M −mt−1) + wt

µt = γ1 + γ2µt−1 + ςt

µt = γ1 +2 µt−1 + γeZ2t + ςt

(wt

υt

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vt ∼ N(0,V )

ςt ∼ N(0, ψ)

Estimate Mean STD 2.5% 97.5%

Corr(wt , υt) 0.01 0.13 -0.20 0.22Σ21 5e-4 0.01 -0.01 0.01

Corr(wt , υt) -2e-3 0.12 -0.20 0.19Σ21 -4e-5 5e-3 -0.01 0.01

Method: Condition onAMO contribution, then ondiffusion (EKF, FFBS)

AMO contribution: Notsignificant correlation anddiagonal elements ofvar-covar matrix

Smoothed New Releases:Not significant correlationand diagonal elements ofvar-cover matrix

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 24 / 27

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Findings’ Summary and Conclusion

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......Complementary Goods Extend the Market Size of the platform (Mozilla)

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Learning signals (OL, STVar, St) increase the adoption of thecomplements (add-ons, plug-ins)

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......Review process is important for OSS platform’s diffusion

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Licenses and developers commercial incentives do not have any effect onthe diffusion of complements

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......Frequent releases have positive effect on the diffusion of complements

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 25 / 27

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Managerial Take Aways

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Open platform would need either to internalize the review process, oraffect it more softly?

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An open platform can measure the relevance of each of the add-ons for itsincentive mechanism design

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An open platform should not be too much concerned about licenses andcommercial incentives of the developers

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An open platform should motivate frequent releases (as Reymond (1999)”Release early and Release Often” suggests)

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 26 / 27

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Thank You

Hejazi Nia, Bruce (UTD) The Joint Diffusion of Open Digital Platform and its Complementary GoodsAugust 22, 2014 27 / 27