Alright, so it's my pleasure to introduce
Prof. Feng Zhu, who is a professor at Harvard
Business School he is an expert in digital markets
especially those in which platforms bring together
different types of agents so for example Uber which
brings together consumers on the one hand and
drivers on the other. As we all know I think markets
like these are subject to substantial network
effects and these create economies of scale and
therefore they're prone to tipping. So, in markets like
these we oftentimes will see one or two dominant
firms. But professors Zhu's work doesn't stop there
right he looks under the hood and he identifies the
factors that explain whether platforms succeed
or fail and an important contribution of his is
he doesn't just assume that a platform is plopped down
in a market and that it inherits all the
characteristics of that rather, he envisions it he
asks sort of what are the strategic decisions that a
platform can make that affect a lot of the
attributes that faces for example the strengths of
its network effects whether consumers find it
easy or difficult to multi-home so on and so
forth.
Extrapolating a bit without attribution to him
a lot of those choices I think have clear
implication for antitrust and what side of the line
firms are on, as they make choices. And that makes
today's talk especially relevant for us so please
join me in welcoming Prof. Feng Zhu.
[Audience Applauds]
Thanks so much Jeff for the introduction and also
for the invitation. So, it's a really a great pleasure
to be here and so as Jeff mentioned I've been doing
the research around the platforms for a number of
years so it's really great to have the chance to
share some of my latest research with you guys and
also get us some feedback because much of the work
now some of the work at least is still very much
working progress. So feel free to interrupt between
the talk by raising your hand and I'd be happy to
hear your thoughts on some of the work I have done.
And also for this talk I'd like to kind of take the
opportunity to present multiple research projects
related to this idea around the platform
performance. And many of these projects that have
working papers on my own website so if you want to
know more about individual papers feel free to email
me or go to my website to download these papers.
So many people will say that today we're living in
the age of platforms because as Jeff also
mentioned it.
So like examples like Uber, Upwork, Airbnb, iOS,
Android, EBay, Care.com these are all kind of
platforms we interact for many people even on the
daily basis.
So a network clustering can also put more kind of
structure world describing platforms so when we're
thinking about platforms we're really thinking
about like market intermediaries right.
So market intermediaries a middle man here connecting
different groups of customers right there
should be different groups of customers and as a
result of existence on this intermediary right
these groups of customers can interact directly with
each other right so in the case of Uber because of
existent Uber riders can interact with drivers and
they interact directly with each other and
it turns out that if you look across all different kinds
of platform markets and you suddenly start to
realize actually the performance of these
platforms can vary significantly.
So for instance Uber even though it's like a public
company you would think it's a dominant platform
with very high valuation like $68 billion dollars
but the Uber still losing money today.
So I will often tell students like one
interesting example is that okay if you go on to
the street, give away hamburgers for free
you can practically kill McDonald's but then
questions is whether this business model is a good
business model or not right or is Uber like
doing that right yeah but if you think about other
sharing economy companies when we talk about sharing
economy companies two examples would typically
mentioned would be Uber or an Airbnb right but Airbnb
right is a private company whose value around $40
billion dollars but if you talk Airbnb it turns out
that Airbnb is extremely profitable.
Why one sharing economy company is losing so much
money and is actually expecting to lose more
money going forward but the other one is actually
very profitable, and the TaskRabbit that you would
argue is also kind of sharing economy company to
some extent right and was value at around $120
million dollars and later on got acquired by IKEA.
And, Groupon used to be valued at $13 billion
dollars at IPO, today if you look at its valuation
it's only like 10% of it $1.4 billion today right.
And we all know for our own kind of conversations
with friends or reading from newspapers like many
platforms startups failed to scale up.
Lots of platform companies failed even though you
think there would be network effects right for
these businesses. And also many incumbent platforms
such as like Uber they actually struggling
essentially to maintain their market positions to
kind of generate positive returns for their
businesses.
So much of my research right in the past few
years were trying to understand like why do
some platforms perform so much better, so
profitable and thrive, while other platforms couldn't
do that and I think this work in the end helps us
understand like whether some of the platform
markets are evolving towards winner takes all
but others are not and whether some platform
markets are more contestable than others
so this has implications on barrier to entry whether
some of the markets have high barrier to entry or
not. And most of my work in the end focuses very much
on the essentially the structure of the market
because when we think about the platform
markets, we're really thinking about essentially
a network right, platform being in the middle
connecting consumers to kind of a service
providers or app developers on the other
side, essentially building a tools idea network.
It turns out that many of the network properties,
characteristics actually would be able to affect
the performance of these platform significantly
So, in this talk I'd like to kind of work
through just a few network properties we have
identify in our research, me and my collaborators.
And it turns out that these five kind of
factors right, they have actually very strong
influences on platform performances. So the first
one is actually kind of common to economists is
around the network effect but here I'd like to kind
of also emphasize is not just the presence of
network effects it's actually the strength of
network effects that matter significantly in
driving the competitive dynamics on network
structures right. So when we think about network
effects right typically in platform setting there
could be like three types of network effects, one is
Direct Network Effects.
Direct Network Effects arise when the platform
enable like basically communication across
different groups of agents for instance in the case
of Facebook, WeChat, because you can chat with
others right so there is a direct network effects in
sense that you are a utility for adopting that
particular platform will increase as a result of
other participants on platform right and another
common one would be this Indirect Network Effects.
This takes place between like essentially
consumers and service providers so the reason we
like iOS or Android it's because of number of apps
on top of this platforms and the reason some of the
app developers are interested developing apps
for a particular platform is because there is a
large installed base of these platforms right and
the third network effect is Data Network Effects
become increasingly important because of the
race of big data and also machine learning right so
part of the reason why Google has such a big
market share not only in the U.S.
but in many countries is because Google actually
leverages these Data Network Effects or many
effects is not really because of Indirect
Network Effects in a sense.
Oh, the reason I want to kind of browse Google is
because I love ads served on Google, that's not the
reason right and so the idea here is that the more
people using Google to search for a particular
item right and Google could capture that consumers
behavior right, linking your search query with a
link you clicked on and you that kind of result to
refine its algorithm to kind of make the algorithm
even better. So as a result when the next person is
using Google to search for a particular query, Google
can do a better job in displaying in the result
right so the Data Network Effect actually allows
Google to continuously to improve the quality of its
search engine right so this also generates this
positive feedback loop.
So, when I was a doctorate student right at that time
most of the papers around platforms actually studied
models for economics basically the idea is that
two platforms would compete and then derive
some statistics right but it turns out that much of
the work right essentially around that time right was
the focus on the existence of networks effects
regardless whether it's theoretical work or
empirical work right so people have documented
direct across a variety of these markets right VCR,
DVD, video game consoles there exists significant
network effect right but the problem is that
knowing like the presence of network effect itself
it's insufficient in helping us really
understand like different competitive dynamics
across different markets or different market
structures across different markets right.
So take like for instance a video game console as one
example, you actually observe that the market
leadership switches every few years right versus
like a Google for instance right it has been a
dominant player for a long period of time right, so in
other markets like in the DVD or Hi-Def DVD market
it's really winner takes all right Blu-ray suddenly
became the dominant standard.
So how would we explain like these factors across
the market dynamics why some markets evolve
towards a situation where multiple kind of players
can co-exists right and the market leadership can
change while others are evolving towards like
monopoly like market structure.
So at that time I basically worked with my
advisor Marco Iansiti and we published paper
basically looking at suppose a theoretical
model with some empirical evidence supporting the
theoretical model, basically we tried to
model the competition between an entrant with a
high quality essentially platform in a sense the
entrant platform is perceived to offer greater
utility to customers. And, also incumbent with
greater installed base so that's incumbent advantage
and I will allow for indirect network effect in
this market and tried to develop essentially
a dynamic model tried to predict the evolution of
this market. And what we discovered is that if we
allow significant network effect the strength of
indirect network effect here we use a parameter E
to capture that can actually help predict
essentially the entrance essentially equilibrium
market share in the long term.
So when the strength is extremely high it turns
out like as intuition would suggest that the
market will move towards a monopoly situation, where
the incumbent eventually become a monopoly.
And the entrant will have zero percent market share
over the years but when the strength is below that
thresh hold it turns out that the entrant
eventually will still become the dominant player
in the market if you just allow two players and when
E is a moderate so it's not moderately strong and
it turns out the entrant will eventually become a
monopoly platform in the end but when E is below
like one right, in between zero to one, we have a way
to measure this E right it turns out that the market
will evolve towards a oligopolistic structure
where the entrant actually would have a higher market
share.
So in other words even with network effect the
entrants may not be deter from entering the market
depending on its strengths and the entrants may still
take over the market leadership.
So, empirical if you look at if you compare these
two markets you will see why?
So, we're actually empirically look at this
video game console market and estimated its networks
strength of indirect network effect we actually
found that actually network effects in that
market is not that strong it's significantly but is
not that strong.
Part of the reason is that this market is really hit
driven in a sense that average video game console
owner only buys like maybe six to seven video game
titles per console.
So as a result if a new comer can come up with
like six to seven hit titles the new comer can
actually have a chance to put up a good battle
against incumbent.
But if you at like the VHS and the Betamax market so
some other academic studies have also
estimated that the network effects there, the network
effects actually significantly stronger and
part of the reason is that average household per
month will rent like four to five movie titles.
So as a result the dependence on a variety of
movie titles is much more higher compared with the
video game case, video game console case.
So as result the market could quickly tip towards
this incumbent with installed base advantage,
this is also the case for High-Def DVD players like
Blu-ray and the HD-DVD.
So I also later on learned a case the case
actually also helps to illustrate one very key
point is that network effects right even though
we tried to provide empirical estimation and
show a particular number for a particular market it
turns out that the firms strategies can also
influence the strength of network effect in that
particular market.
So, let me give you just one example from a case I
learned on Fasten.
So Fasten is actually a startup in Boston trying
to challenge Uber or Lyft in the Boston market it's
a ride sharing company.
The business model for Fasten is somewhat
different from Uber in that Fasten only takes a
$1 instead of 20-25 percent charge by Uber or
Lyft, $1 per trip because Fasten takes less,
so in the end Fasten could charge less on the
consumer rider side and still give driver more
money, no?
Initially because Fasten has a smaller installed
base as a result in order to get a Fasten car the
rider may have to wait a little bit longer but you
will pay less.
So, it turns out it's extremely popular in
Boston for a while and lots of drivers of course
would like to multi-home and sign up on Fasten as
well because unless Uber can lower the fee of their
entire schedule.
Fasten expanded in to Texas, Austin and claim
that it's actually the first profitable company
in the ride sharing market.
So but one year later it was...
Fasten started in 2015 and they became profitable in
2017 according to Fasten.
But suddenly Fasten quit the market in 2018
and part of the reason was actually do to this
pooling service introduced by Uber.
So if you think about the pooling service it
actually adds another layer of network effects
into the market.
So essentially the idea here is that for a pooling
rider I need to essentially match you with
another rider going the same direction around the
same time.
So the installed base becomes extremely
important, so there is essentially some kind of
direct network effect being introduced by Uber.
So with the stronger network effects Fasten
couldn't match their service because the
installed base is much smaller so in the end
Fasten actually decided to kind of essentially get
acquire by another company in Russia and they
eventually decided to leave the Boston market.
So now the strength of network effects may not
stay stable in a market so as a result it's actually
very useful to be diligent for companies to be
diligent like pay attention to the how the
network effects may change or may evolve over time.
So then later on I sort of started to think after
teaching the Fasten case I started think about
essentially the differences between Uber
and Airbnb because why Uber still losing so much money
and why Airbnb is so profitable?
So one of the factors I identified in my research
is this network structure issue the clustering factor.
So if you think about the Uber network, it's really
a network with lots of local clusters in a sense
that if you are in DC as a rider you probably only
care about the number of drivers in DC. You don't
really care about the number of drivers in
San Francisco unless you travel so frequently to
San Francisco.
And if you're a driver in DC of course you only care
about the number of users in DC mostly, unless there
are lots of travelers coming into the DC area.
And as a result so the Uber network has this
local cluster feature where some
interconnections because of this mobile kind of
users essentially some people may travel from one
city to another.
Then, if you look at the Airbnb network it's
completely the opposite in a sense that if you are
the host, Airbnb host in DC you're not interested
attracting local residents nobody is going to stay at
the Airbnb host in a local city.
They're interested in basically people traveling
outside of DC from anywhere else and
similarly if you are like a local people in DC why
visit the Airbnb you probably care about Airbnb
properties outside of DC you're not interested in
staying at the same location.
So as a result Airbnb network is more globally
connected than Uber network, these things
would have implications to their defensibility, think
about the Uber network structure if companies
like Fasten wanted to enter and challenge Uber
all it needs to do is to concentrate on one market
and go there.
But if a company wants to challenge Airbnb it's
actually a lot harder so for any host in DC you
really have to announce to the rest of the world yes,
yes I'm host platform and if you travel to DC you
could stay at.
So as a result it's a lot costly for you to start a
company to essentially match the service by
Airbnb once Airbnb has already scaled up.
Later it turns out that if you look at across many
platform markets you can actually classified them
into one of these two groups.
So Uber like Uber, Groupon, TaskRabbit any
platform based on local matching will belong to
this kind of network structure.
And Upwork, EBay, Expedia these platforms are going
to belong to this kind of global connecting network
structure, that actually affect the defensibility
of the platforms.
So based on this insight my co-author and I also
did some literature review and found that it turns
out that the most existing work always implicit
assumes this kind of network structure around
the platforms.
So we decided to extend the leadership and
basically try to take a closer look at how this
network clustering may affect the incumbent
performance.
So we build a theoretical model, it's a game
theoretical model essentially we model the
competitive interactions between incumbent platform
which outputs across in multiple markets think of
an Uber it operates in multiple markets and the
one entrant think of Fasten seeking to enter one market.
And the market allows some connectivity in the sense
that factual buyers are mobile in a sense that
okay I live in Boston but I travel to DC I could use
Uber here. And so that's the idea and if the
entrant would invest to build awareness of its offering.
So the model is very simple but it turns out
that it has some interesting kind of
predictions, why is that?
We found that even if there is no cost for the
entrant to build awareness basically even if the
advertising is free it turns out, we found that
the entrant doesn't want to build awareness among
all people in that city.
The reason is that if you do that it will trigger
competitive reaction from the incumbent and this is
actually is not good it's actually less optimal
compared to the situation where you just reach a
certain, you build awareness on a small
fraction of the market participants.
So this matches perfectly with the Fasten case
because when inquire with the founders they said
well we never thought about taking over the
market share from Uber we only just want to kind of
target a small segment of the Boston market and make
some money that actually matches with the
strategies very well.
And we also discovered that having more mobile
buyers should make it easier for the entrant to
enter actually.
So the reason is that to say there are lots of
people traveling from San Francisco or DC to Boston
and these people they have no idea about Fasten
because Fasten is only local to the Boston market.
So these people are essentially captive to the
Uber network, you're arriving in Boston you
only use Uber application.
So as a result if Uber started to launch a war
against the Fasten in the Boston market, is going to
kind of reduce the price equally also for all these
travelers who actually to who Uber actually has a
monopoly power to some extent.
So is actually suboptimal as a result for Uber to
fight aggressively against a Fasten if there are a
lots of travelers from outside Boston, drive in Boston.
So as a result this interconnectivity actually
would make it easier for the entrant to enter the
market because it reduces the incentive for the
incumbent to fight.
So in the case of Fasten actually even though Uber
from the very beginning were aware of the
existence of Fasten.
Uber actually didn't reduce its price to fight
against a Fasten, it did broke Uber accounts for
all Fasten founders basically but it didn't do
much beyond that.
And we also discover that our model showed stronger
network effects in general help deter entry so is in
a favor of the incumbent and benefit Uber.
So these is indeed the case after the Uber pool
service became very popular Fasten had to
leave the market.
So after this exercise I was actually amaze in a
sense that Fasten was so strategic, I really
understood these dynamics including like they knew
it's time to basically exit, including that even
its decision to exit was actually somewhat optimal
according to our model result.
So the certain factor, if there is a question feel
free to raise your hand.
So the certain factor we looked very close at is
this idea of multi-homing, in a sense that most of
drivers they use kind of multiple apps they're are
on Uber, they're on Lyft, they're on like in New
York they have other platforms like a Juno and
other platforms.
And actually many times consumers also have
multiple apps installed so I mean, I guess many of us
have both Uber, Lyft installed.
So this actually has implications for
competitive dynamics in a sense that it basically
makes it more difficult essentially for basically
one platform to dominate the market in a sense that
if switching cost or adaption cost for
additional platform is so trivial that the new
platform essentially can enter and pay a very small
price to incentivize customers or service
providers to overcome the switching or adaption cost
to adopt the new platform.
So Fasten was leveraging that see this is one ad
made by Fasten in Boston actually on Facebook
essentially.
So Fasten basically shows that a clear message
saying that different apps same driver so regardless
you use Uber app or Fasten app so the left color is
Uber color the right color is Fasten color.
So but in the end you get same custom driver but you
pay less that was the ad.
So the idea is to leverage multi-homing to enter
here, so if you look at other platforms like this
food delivery networks, so lots of restaurants are
working with multiple platforms to deliver food
and lots of customers are interested in using
multiple platforms to order food as well.
So I worked on multiple research projects around
the multi-homing so in the interest of time I would
just focus on the first one it's also a paper
we're still revising for our journal.
So this paper was motivated by this kind of
intuition that even though it turns out that in many
of the markets we saw like this two sided
multi-homing in a sense both consumers and service
providers are they're multi-homing they adopting
kind of multiple platforms.
But an issue it turns out that most papers would
tend to assume that only one side is multi-homing
because in the leadership there is this implicit
assumption that if one side is multi-homing then
there is no need for the other side to multi-home
because without multi-home you can access all service
providers in one platform already.
So but it doesn't really match with the empirical
phenomena and also in the literature most of the
papers were to focus on how platforms use prices
to influence multi-homing but in reality platforms
use a variety of strategies to influence
this multi-homing tendencies.
So here are just some examples when Microsoft
was trying to grow its Windows phone system many
years ago Microsoft was reported to offer like
$100k or more to many popular app developers
asking them oh why don't you kind port your
application from iOS or Android to Windows phone
system?
So there would be like a same app in Windows phone
system and very recently you hear from the news
that many of the merchants on the Alibaba platform
started to complaint that Alibaba is kind of
implicitly trying to push them to adopt to work
exclusively with Alibaba not with its competitors
like JD or PDD.
So the thought is that if you started to work with
Alibaba's competitors, Alibaba can adjust its
algorithm so that when people search for a
particular product your product won't show up on
the first few pages which could significantly
influence the sales of these merchants.
We also heard stories that EBay actually sued Amazon
saying that Amazon kind of had attempted to lure the
top sellers on EBay's market place asking them
sell on Amazon's market places by exploiting
essentially EBay's internal messaging systems.
So firms strategies turns out can have significant
influence on the multi-homing tendencies,
they change essentially the competitive dynamics.
So the research project we worked specifically on was
looking at the competition between Groupon and The
Living Social before they are merged.
So it turns out that in 2010, so Groupon's action
made some changes to its counter so previously when
you go to Groupon, Groupon is going to give you like
a clear message on how many deals have been
bought for a particular offer so here 471 people
have purchased this particular offer and the
deal is on.
But this was before Groupon's IPO and it turns
out that many analysts started to use this deal
counter to infer Groupon's profitability because you
know like how many people have purchased, you know
the price, you know roughly the cost, etc. And
so as a result Groupon had to actually revise its
statement for IPO several times so Groupon was upset
about this so Groupon said okay now I'm going to
change my counter so that instead of telling you the
exact number I'm going to give you some kind of
vague threshold and also going to play with the
number so instead of showing the actual number
I'm going to display a number that has been
reduced by a random percentage.
So as a result like even if it says over 50 bought
you have no idea whether it means 471 or like 51.
So then in the end you have no clue like the
profitability of the company but in the end
what happens is that this has spillover effects for
its rivals Living Social because before this
counter change many people were saying other
competitor can easy look at this information and
know what are the popular merchants and they could
just approach the same merchants so that they
could offer deal on Living Social later on or offer
deal on some other platforms but after the
counter change so it will be very difficult for you
to know oh which deal is the very popular which
deal is the worst like as our effort to poach.
So we did some empirical analysis we did discover
after this policy change with the deal counter
Living Social actually copied a few deals from
Groupon but there is also interesting that after
Living Social started to copy few deals Living
Social also expanded its effort to source new deals
and as a result it's actually was worth it
enhancing in a sense that the variety of deals in
the market increased because Living Social
started to look for its own deals.
And of course this operation is very costly
like previously you could copy now you have to do
discovery, discovery is usually more costly but at
the same time we also observe there is a seesaw
effect in a sense that when there is less
multi-homing the merchant decide in a sense that
okay the few merchants are offering deals on both
platforms now specially these popular ones and the
two platforms become multi-differentiated
actually then we saw actually more multi-homing
on the consumer side it turns out as a consumer
because the user so differentiated now and
more willing to sign up for deal offers on both
platforms.
So as a result Living Social even though like
financially it has incur more cost but it's the
position on the consumer side it's actually
becoming stronger after this so in a sense the
more consumers are willing to visit Living Social now.
So as a result there is multi-homing on both sides
it's actually more difficult essentially for
the incumbent to drive a competitor out of the
market because of this kind of seesaw dynamics.
So in order to actually drive a competitor outside
the market you need to really fight on both sides
making sure this multi-homing tendency is
reduced on both sides of the market so that will be
a managerial takeaway from this research.
So there is a force factor we also worked a lot of times
and thinking a lot on this idea around the
disintermediation so the idea of disintermediation
is that platforms they are essentially middlemen
intermediaries in air balling matching but once
you're match with a service provider the
question is do we really have to stay on the
platform afterwards or not?
Could you leave the platform?
So there is a, you could also describe this
disintermediation problem as a leakage issue
essentially.
So they might be able to essentially connect
directly with each other afterwards without the
help from the platforms but I started thinking
about this idea actually when I first started at
the Harvard MBA program in 2013.
So we had this case about this hundred year old
company called Li & Fung.
And Li & Fung some of you may have heard of this
company is based in Hong Kong and is company known
for connecting retailers in the U.S.
like H&M, Gap, with manufactures in Asia
mostly in China for instance if H&M needs to
make some cloths they could, Li & Fung can help
you identify the right manufacturer with the
right capacity and they could also do all kinds of
monitoring to make sure that the practice follows
the standards and Li & Fung also make sure that
the cloth are going to be shipped on time to the
U.S. retailer.
So this company has been doing extremely well so at
the time I was teaching this case and Li & Fung
probably was performing probably at its peak but
afterwards it turns out that Li & Fung performance
started to deteriorate and the company today
actually lost probably more than 80% of its
market value afterwards.
So I started to investigate what's going
with Li & Fung why hundred year old company couldn't
kind of thrive today?
So then I discover all these examples in a sense
that, oh Walmart initially signed like a multiyear
contract to work with Li & Fung but after a few years
Walmart discover actually I could just work with
these manufactures directly why do I have to
pay a huge commission to Li & Fung to coordinate
all the work.
So this is partly due to the fact Walmart started
to know these manufactures extremely well also partly
due to the fact that during because we're in
the digital age as a result these is actually a
lot more information about Chinese manufacturers
today you can Google and look for its reputation,
look for all the complaints about a
particular manufacturer.
So as a result you could, you could without the help of
Li & Fung you could start to have some trust in some
manufacturers.
Then I thought as I started to study more
platforms and I discover that this issue of
disintermediation is a big problem for many
platforms.
So for instance for Airbnb, so if you have
traveled to a city before and stayed with an Airbnb
host the next time you travel to that city you
probably can just reach out to that host directly
and ask him whether you can book the room without
going to Airbnb and pay the 15-20 percent
commission for the nights.
And EBay is also struggling with this
issue, TaskRabbit also has this issue because if you
use TaskRabbit to hire for instance some cleaner or
plumber or people who can assemble furniture in your
house and then after the first interaction you probably
would have all the contact information about that
person and then the next time you want to kind of
have someone do the same service, conditioning on
the first time interaction was positive you probably
won't go to TaskRabbit again and paid the commission.
And the reality that the problem with this platform
is that if you have positive experience hiring
a person the next time you will just interact with
that person directly.
But if you have a negative experience the first time
the next time you will go to another platform to
hire a person.
So regardless whether the experience is positive or
negative the platform will not have that customer
again that's a problem and the same if you hire a
nanny on Care.com so do we really go back to Care.com
every time you want to hire the same person like
probably not.
And HomeTry used to be a very popular company doing
like this kind offline kind of matching
essentially for you to hire someone to clean the
home and actually died because of this
intermediation because they found that they spent
a lot of money trying to scale up but the people
come and they leave afterwards.
So essentially you're paying money for first
interaction so which was very stressful for the
company and later on I learned the case about
this company Upwork it's an online labor market place.
So the idea here is that if you want someone to
kind of write a piece of software program you can
go on Upwork and hire someone mostly outside the U.S.
at a lower rate if you want someone to copy edit
a paper you can find a copy editor there and my Ph.D.
students even told me that you can hire someone to
write the presentation, I did not do that.
So but you can find a variety of services there.
So another case about this company and also discover
that the company has been kind of essentially
fighting with disintermediation issue
and have tried essentially lots of weapons to rid of
disintermediation.
So later on a Ph.D.
student on my grade school, she basically made
some arrangement so she actually worked with
online kind of freelancer platform so she went there
and over the summer she basically investigated
disintermediation issue for that particular
platform and she also elaborated some further
experiments to show to actually kind of support
some of her policies here.
So one of the key hypothesis in her
presentation research was that, it turns out that
for a present business to viable and the typical
kind of a textbook recommendation would be
the platform needs to build lots of trust so
that the two sides can interact with each other.
So without trust nobody is going to hire an Uber
driver essentially you need to have some degree
of trust.
So they way platforms builds trust is like
reputation systems, background checks, I.D.
verification, sometimes in the online labor market
space you do skill test in a sense if you claim you
can kind of translate English into French then I
give you a test to make sure you do know French.
So essentially the platforms in order to grow
their business to enable transaction to make people
feel comfortable transacting each other
they kind of tried to do a lot to build the trust but
if you started to build so much trust then value
creation of the platform in terms of facilitating
the transactions decrease significantly in a sense
if I know that this programmer has been doing
perfect job for the past hundred transactions for a
different client then why do I need to actually do
all the work on the platform and then pay the
commission and in order to get some like services
such as insurance potential refund if the
job doesn't work out, if there is a dispute the
platform can intervene.
So these services are become less valuable to me.
So as a result I have lots of incentives to take the
transactions off the platform to deal with this
service provider directly, directly.
So she did find that in the platform she worked
with as platforms started to increase this value
creation in matching the two sides and you actually
observe more disintermediation as a
result.
People after being assure that the other side is
very reliable very high quality yes and they need
to hire that person but I'm more willing to
actually work with this person off the platform
rather than on the platform to avoid paying
the service fee.
So then it becomes a dilemma for many platforms
in terms of how they actually thrive in a sense
that ok you can pay lots of money to build like
some installed base to make your platform very
popular but the question is how do you retain this
people specially the good ones, the good ones are
more likely to be hired to go off the platform
so that becomes a very difficult issue.
So after writing this paper we also started to
kind of look around for solutions.
So it turns out that yeah actually some solutions
platforms can use to mitigate the
disintermediation issue.
So one example is this platform called Thumbtack,
Thumbtack is also a platform basically for you
to look for some helpers like plumbers for
instance, someone to clean your house.
So their business model is more about charging a
commission on a particular task, their business model
is based on ok charging a fee while the two sides
are matched essentially before you actually
interact with each other the first time before I
tell you the contact information of the other
party the service provider will have to pay.
Essentially Thumbtack will reach out to the service
provider, here is a like a description of the task
then you pay a fee in order to get contact
information of the other side.
So that was the business model but again this
business model is actually works for the first
transaction but it doesn't work for repeated
transactions when the plumber actually did his
or her first project in a home then afterwards
Thumbtack won't be able to prevent disintermediation.
So Upwork later on started to kind of have this
basically a new pricing structure to encourage
people to stay on the platform.
So for the first interaction Upwork is
going to charge like ten percent but if you work
repeatedly then the accumulative amount for
the same payer exceeds like say $500 then the
commission is going to be lower, exceeds some other
amounts the commission is going to drop even further.
But the problem of course is that once you build the
sufficient trust specially from this repeated
transaction do you really want to pay even if it's
just two percent?
Do we really want to pay that?
So that's another basically a dilemma.
So we searched also further for additional
solutions because we feel like these solutions are
probably not ideal ones.
So I came across this Chinese equivalent of
Upwork so this company has also been struggling with
disintermediation for ten years.
So it turns out that disintermediation is even
more prominent in the Chinese market because
people are very kind of sensitive to the price and
ZBJ's platform was charging twenty percent
the commission and then the company actually did
some kind of a survey of its service providers and
also users and it discover actually more than 90
percent of the transactions were
disintermediated.
So the company capture the ten percent essentially of
the value it created.
So it was a big headache because the company has
been looking for solutions for ten years and in the
end it actually discovered one action that worked
really well for the company.
So ZBJ did a lots of data analytics of all the
transactions on ZBJ and it discovered that many kind
of clients, many users are using the platform to
design logos because when it comes to design logos
you really need some creativity so why they
don't have that kind of creativity you want to
outsource it to professionals they design
logos for you.
And this platform is actually very well known
for local design and it turns out that after
people design logo on the platform the next thing
many clients do is to do business registration they
start a new company.
Now turns out that in China because business
registrations have to deal with the government the
process is extremely complicated and very slow
and offline, you usually have to hire a law firm
from the offline market and that law firm tends to
charge lots of money.
So ZBJ said ok we could probably provide this
service ourselves so the company started to design
its IT system to connect with government websites.
So that the company can actually automatically
file for registration and also company develop lots
of tools to kind of help the clients to estimate
the likelihood of success because some of the logos
if it's a duplicate of some other logos and that
is going to get it rejected.
So because this is an IT company and it uses a
digital solution to enable this process the company
was able to charge a lower rate than this offline law
firms.
And within one or two years the company
essentially became the largest trademark
registration company in China, the whole China
it's the largest one and for that service alone the
company generated enough profit to be thriving.
So then the company said ok so now giving that I
have this service and this service is generating lots
of revenue I actually don't have to charge
commission now.
So the company basically reduced the commission
rate to zero and so there won't be any
disintermediation now because if it's zero
percent commission, everybody wants to bring
their terminal transactions online so
that they can showcase essentially how many
projects I have completed essentially as a way to
attract new clients or new service providers.
So lots of people basically brought even
their offline projects through the platform which
gave the platform even more sales leads for this
trademark service the company is running.
So in the end the company became a unicorn is about
to go IPO this year and so after the company had this
success the company also replicated this strategies
in kind of in different verticals so the company
also started to provide accounting services for
small enterprises, etc. so for these service
providers on the platform if you need like some
accounting services the platform can provide it.
So from this example once we could see that one
solution to deal with disintermediation is to
essentially avoid this commission fee essentially
and generate lots of revenue selling
complementary services.
So later on I started to realize this was also one
of the reasons why Alibaba won the battle against
EBay in China.
So EBay actually went into Chinese market before
Alibaba with its acquisition of a local
market leader essentially there but EBay's business
model is about charging a commission fee essentially
so in the US market it charges 10-15 percent, in
Chinese market EBay decided to charge
essentially more or less the same amount of
commission.
And when Alibaba came into the market and Alibaba
started to say ok given we're the follower we're
not going to charge you anything we just offer the
service Taobao, initially we offer completely for
free to buyers and sellers.
And so when you charge a commission fee one thing
you need to do is you don't want buyers and
seller to communicate before the transaction is over.
So on EBay it turns out it's very difficult to buy
a product because EBay won't allow the two sides
to talk but Alibaba came in with no transaction fees.
So Alibaba actually encourage people to talk
so many buyers and sellers found that it's just far
more convenient essentially to just kind
of be able to talk to each other, discuss the
product, and discuss the negotiated price before
the transaction is over.
So many people decided to switch and within four
years Alibaba had basically the market
leadership with an 80 percent market share EBay
had to quit the Chinese market.
But then people started to question this business
model it's like giving away free hamburger right
you kill the EBay so then how would you survive given
that you're not making any money but it turns out
that once you build a huge market place you then find
other ways to generate the profits.
So today actually Alibaba's Taobao still is
free to buyers and sellers, the Taobao
platform generates most of revenues from advertising
so because there are so many merchants there and
when consumers search for a particular product is
not clear which merchants to list the first but if
you want to be listed first you need to pay
essentially advertising fee essentially.
So this is essentially the Google model the search
engine is completely free but most of the money will
come from advertisers.
So as a result one way to deal with
disintermediation from this research is to start
thinking about getting rid of the commission fee
maybe generate money from other sources.
So another network strategy we discovered is
about the bridging strategy so the idea is
that if you have built one successful network if you
have built one successful network one way to grow
further is by bridging a current network with new
networks to each other so as if in the end like they
behave just like just a single big network.
And it turns out that this strategy is extremely
effective in the platform setting because much of
the kind of assets or resources of a particular
platform is around the installed base or data.
And installed bases and the data are arguably very
valuable in multiple scenarios across multiple
markets.
So I learned a case about this fintech startup so now
it's actually the largest fintech company in
the world now called Ant Financial which is a
spinoff from Alibaba.
So this company started like a basically -- started
as a result of this Alibaba's market place one
is Taobao which is C-to-C market and TMall which is
B-to-C market.
So Alibaba at that time was already very
successful like attracting like millions of sellers
connecting with millions of buyers but in China
because of the low penetration of the credit
cards Alibaba had to develop the payment method
by itself called Alipay.
And later on Alibaba also opened up Alipay not just
to merchant or buyers on Alibaba platform also to
other kind of online platforms or even offline
merchants.
So Alibaba also decided to create a separate unit
called Ant Financial to manage this payment
service. From there because Ant Financial collected so
much data as a result of this payment tool because
it knows like for instance how many times people go
to theaters, it knows when you travel, whether you
fly business class or economy class, it knows
like what kind of products you buy, how expensive
these products are?
So as a result from this data, Ant Financial was
able to create credit scores to individuals and
also to all the merchants on Alibaba network in a
sense that Ant Financial knows like for instance
how many units are you selling and how much
inventory you have etc., on the platform.
So once you have the credit score, around that
time like most of the Chinese citizens they do
not have a credit score the infrastructure is not
as develop as in the US but once you have the
credit score you could actually issue loans
because you can evaluate risk very accurate.
So the company started this online banking
service called MYBank and MYBank was able to kind of
essentially issue loans to all the merchants on
Alibaba because these merchants contributed lots
of data essentially to this company to evaluate
their performance and Alibaba's Ant Financial
also could issue loans to consumers based on you
past purchasing behaviors and these loans actually
most of them are very kind of small loans like two
thousand dollars, three thousand dollars and
traditionally it's almost impossible for you to get
these kind of loans from the banks because banks
are not interested in customers like asking for
small loans because in order for bank to kind of
issue a loan they have to do so much paperwork to
figure out how many house properties you have,
whether you own a car or not.
So if you request for a small loan if they justify
all these transactions cost there is just no data
analytics, Ant Financial was able to basically kind
of help these merchants or individual consumers in China.
And then because in Alipay many users are moving
their money from their bank accounts to Alipay
account so that you can make purchases so lots of
money essentially would be seating in the Alipay account.
So Alibaba also started this money market fund
called Yu'e Bao which allows customers,
consumers to actually kind of invest in this fund and
within actually four years this is already the
largest money market fund in the world.
So as you can see that in the end Ant Financial is
almost like another platform essentially
linking kind of consumers with not just the
merchants here but also all kinds of merchants
outside Alibaba's echo system.
So this two networks are being bridge together and
essentially co-grow, co-evolve together.
It sounds like as much as you are talking about the
network structures being indeterminate of market
structure business success, you keep coming
back to kind of the differentiated business
models being an equally significant variable in
terms of the way the market structure evolves
and in terms whether business will succeed?
Yeah, I completely agree, yes.
Totally, so network structures in this case
I would say are very indulgenes, indulgenes
is not like you see oh this is a platform market
that's a network structure and there is another
platform market there it is like a network
structure has to be different.
But it turns out that firm strategies, also external
factors can trigger network structures
significantly and that has important implications for
the competitiveness in that market.
So again so in this case it's a delivery strategy
in bridging the networks and making sure that these
two networks can support each other essentially
Ant Financial becomes very successful because so much
data are coming from Alibaba's platform.
And Taobao or Zhima become more successful because
Ant Financial started to kind of enable its
transactions, issuing loans so that the people
can purchase more and startups can actually
build up inventory accordingly.
So these two networks can co-evolve but then
we started to think okay maybe if that's the case
lots of positive feedbacks you can identify in this
two networks.
So is this like a winner takes all, should we worry
that ok then in the end a monopoly is going to
emerge in this market place?
It turns out not, it turns out that because of
network bridging other giants can also leverage a
network bridging getting into like for example
payment services.
So Tencent which has this very popular kind of
application called WeChat so it's like kind of
Facebook of China, quite similar but because
Facebook is also blocked in China so WeChat has
almost like a monopoly where is close to like
probably 80-90 percent of penetration pretty much
all Chinese are on WeChat.
So because people are chatting so much and also
there are opportunities for people to essentially
kind of transfer money to each other so if we kind
of go to eat together I want to kind of transfer
some money to you or during the Chinese
New Year it's kind of a cultural that people
actually send the red pockets to each other
essentially with some money.
So WeChat also has this feature built in to it,
based on the social network.
So as a result WeChat also successful essentially
another network bridging started to grow another
payment services.
So today like WeChat is also very big having like
basically in terms of market share it's probably
almost at the same level as Alipay now depending on
the data source essentially equally
popular because the WeChat product itself it's
extremely popular so as a result even though you
might think oh lots network effects, positive
feedback groups, the market is not going to be
competitive but it turns out the other giants can
also leverage this network bridging in entering this
market becomes and even competitor.
So today actually many Chinese would be on both
WeChat and Alipay because they use these two
products in different scenarios.
If you want to transfer money in WeChat then you
probably need to use WeChat Pay but if you want
to buy on Alibaba's platform, you have to use
Alipay because Taobao/TMall doesn't
except WeChat.
So as you can see that there are like a lot of
factors, lots of factors here I mentioned the five
but presumably as I continue to do research
and other scholars continue to do research we
could identify more factors influencing
essentially the competitiveness of
platform markets and also the performance of these
platform companies.
Could you speak a little bit to the degree in which
the actual industry may limit the way that
platforms can compete?
So for example with Uber you have this local market
structure because it would be inefficient for cars to
drive across different states, where as in the
sale of products on Amazon it's easier to transport
materials across different regions?
Totally, yes.
So, this is to do with the clustering story in a
sense whether they matching or eventually the
service or transaction has to take place locally so
within a geographical region or within some kind
of a region.
Some kind of network cluster or where their
products can move freely from one area to another.
Certainly markets factors would change this and then
you see in addition to market factors
technologies can also change this.
So think about like driverless car
technologies you think that okay right now like
an Uber, like Lyft, these are like two sided
networks you can think must be some kind of
network effect even though they may not necessarily
be very strong between riders and drivers.
But think about the war when we have driverless
car technologies then essentially one side is
gone, like driver side is gone.
So there is no network effects in the end because
any new entrant even like a BMW or Toyota as long as
you can manufacture cars, you can just or driverless
cars, you can just deploy like maybe thousands of
cars, they just drive alone you can enter the
market that way.
So because there is no network effect anymore
so technology can actually kill network effects but
the story didn't end here but then companies can
actually regenerate the network effects again
because once you are in the driverless car you're
not going to pay attention to the road situation
anymore you started to kind of care about for
instance on the content provided in the car maybe
a very big screen with lots of YouTube videos
etc. Then content delivery can generate the different
kinds of network effects because you maybe want to
kind of go to like for instance Google car
because Google has YouTube and YouTube is exclusive
available for Google car.
And then you're going to be like I want to finish a
movie so I use Google to find one and then maybe
like Apple has like kind of a car services so like
oh iTune services exclusively offered in
Apple driverless car then in that case actually that
allows these competitors to reintroduce network
effects in the market to drive kind of essentially
platform competition essential they become a
platform again but it's with a different set of
players is not with Uber it's all this content providers.
So both these market structures technology as
they evolve they could kind of make the situation
even more exciting.
So that's why I predict I could still have my job
for many years to come yeah, and just to
summarize what are some of the implications?
So not all platform businesses are viable or
sustainable because of all of these network structure
issues and sometimes you just cannot overcome some
of the challenges and even with significant network
effects it's not always the platform being used as
necessarily winner takes all, winner takes most,
you have to take a holistic view of all these
factors to determine whether the platform is
actually going to become a monopoly like platform.
And the entry barrier can be low when the incumbent
or the entrant can leverage multi-home
network clustering or network bridging.
So you really have to think about all these
factors to think about whether some markets are
contestable or not and I made have seem to pay
close attention to it turns out that these
factors I mentioned none of them are specific or
fixed for a particular platform market, firm's
strategy, government regulations, all these
technology kind of advancements, they could
all change these network properties.
So as a result we have to kind of pay close
attention to these external factors and then
re-evaluate the situation from time to time.
So that concludes my talk and thank you so much feel
free to email me.
Thank you.
[Applause]
Any questions for him?
So, if we can go back to the first example, first
issue with strength of network effects I'm
interested can you talk a little bit more about
specifically the examples of the video game console,
and DVD or VHS, Betamax, DVD, Blu-ray and all that?
I'm interested if you've done any thinking about
whether the important factor in the strength of
network effects is the price of acquiring the
platform versus the later acquisitions like the
price of the video game consoles versus the video games.
If there is something important about the
differentiation in those prices or if has more to
do with the number of options available versus
the number of options you're actually going
take, can you talk a little bit more about the
strength of network effects and what the
important factors are in that?
Surprise is usually a very important in terms of
influencing multi-homing tendencies.
So if the prices are low consumers are more likely
to multi-home so for instance like Disney also
introduces its streaming services to rival against
Netflix.
It turns out that if you look at the Disney price
less than $10/month and Netflix subscription price
also less than $10/month, they are relative low so
you would expect that in that case lots of
consumers may subscribe to both services.
So the result it may be quite easy for Disney to
enter this market place even though you would
think Netflix has lots of customers installed base
already, lots of content already because of the low
price for both platforms.
In terms of network effects, so I'm also doing
some research some more thinking about essentially
what really kind of determines essentially the
strength of the network effects.
What are some of the factors we can look at and
the result through empirical analysis we
could know that oh network effects is strong on
network effects wake.
So one factor as you mentioned is essentially
the need for variety, that's actually very
important.
So if you don't care about variety say like an on
Uber network I don't really care who is driving
the car or what kind of car you have, then I only
care about just my wait time then the network
effect cannot be very strong because a new comer
can just deploy, dump enough money to hire
enough drivers to offer service in a local city to
get started.
So as a result the barrier entry cannot be very high,
well another thing is also whether this market is a hit
driven or is a long term market.
If everybody's preference concentrate on just the
few hits in that market's place so that Airbnb
everyone just want to stay in like a small number of
properties we don't really care about the variety
then that's also the network effects in that
case is also going to be relatively low.
Because a new comer could just get like a few hits
and then you could start your business.
The barrier to entry is not going to be very
strong but the good thing for Airbnb is that it
turns out that people's preferences are very
diverse because when you travel to a place yeah you
probably want to stay in the downtown area, someone
will want to stay in suburb.
So they have like different constraints as a
result that they prefer different kinds of
properties so in that case the variety actually
matters a lot in that case as well.
So these are the factors we could use to think
about the strength of network effects here.
So in example I showed it's really because like
in the DVD or the VHS, Betamax case consumers are
actually renting lots of titles per months.
So the variety becomes very important so around
that time like the close of the rental stores,
rental businesses also enhanced it in a sense if
you operate a rental store you also need to decide
like how to allocate your shelf spaces in a sense
maybe 50% will be VHS tapes, 50% will be Betamax
tapes, like it turns out that as the market starts
to evolve the choice of this rental store owners
is a function essentially of the market share of
these two standards and actually amplifies this
network effects in the end because rental stores
owners after realizing, oh VHS is actually about to
take off then I should just forget about the
Betamax standard and desire to also influence
consumers choice of which kind of machine to get in
the end in a local market, so all these things should
be taken into consideration.
I was wondering if you comment at all about
alternative strategies to prevent multi-homing,
I was wondering if you could give us your thoughts on
alternative strategies to prevent multi-homing and
you brought up the one example in China where
basically they enter this space that was super
complicated as a way to sort of enabling the
network, are there other strategies?
Yes, certainly so part of the reason why lots of
Amazon merchants actually I mean some of them must
be multi-homing but it turns out that Amazon can
reduce multi-homing, actually slow this
fulfilled by the Amazon Services because you
provide this logistics support for all the
merchants.
Merchants send all inventory to your
warehouse and then you would ship them and you
handle all the returns.
So if you look at Amazon's pricing structure any
merchants even if you're not selling products on
Amazon you can use its warehouse because you
create economy of scale in the shipping part.
And so they welcome you but on the other hand if
you're not selling on Amazon actually Amazon has
increase the fee structure for you basically you have
to pay more.
So that actually so as a result if you're operating
on EBay and you try to leverage Amazon's
fulfillment services the cost structure is a lot
higher compared to if you operate on Amazon and use
Amazon's fulfillment services, fulfilled by
Amazon.
So that actually reduce incentive for the
merchants once you started the two years FBA,
fulfilled by Amazon Services to multi-home on
EBay that was one strategy.
And also this Prime thing also that deals with
consumer multi-homing so today when I buy, look for
some products I no longer go through Google I go
through all the products availability.
Oh this on Amazon, this is the price on Walmart, this
is the price if you're a Prime member a natural
starting point to go to would be actually the
Amazon website.
So this also reduces multi-homing tendencies,
so Amazon actually manage to reduce multi-homing
essentially on both sides of the market both on the
merchant side and also the consumer side.
So as we saw from this Groupon versus Living
Social example this strategy actually is going
to be effective whether it'll be actually very
effective that probably contributes to the fact
that Amazon is valued so much today.
So early you were talking about network bridging and
based on these large companies that essentially
amassed a certain amount of data and that certain
small markets are able to itemize for money
transfers, credit scores, things like that.
Is there anything unique about how those markets
operate in and as far as entry is concern so
someone who is looking to enter into those markets
that itemize from the business behemoths you
know is there anything unique about entry or
should we think about those markets as operating
in the same way as frankly any other market?
What kinds of factors are you thinking about like in
terms of uniqueness?
For example would you have to enter into like in some
other market that the behemoth is playing in,
are your strategies effective against the
behemoth essentially has you research showed
anything interesting or unique about this market?
Totally, so when there is a possibility for bigger
players to leverage a network bridging to grow
the opportunity for small player is actually very
limited because in terms of network bridging the
idea is that across multiple networks there is
actually synergy across this multiple networks.
So for a new comer, for an new startup is actually
very very difficult to survive in a competitive
environment like this because you're only
operating in one product line but the incumbent has
a portfolio of products that are interconnected
with each other.
So as a result most of the time when we observe this
kind of market competition we're are really looking
at the competition between the giants among the
giants.
So in terms of payment services another example
is like for instance Grab, Grab is basically Uber
equivalent in South East Asia.
They are also thinking about leveraging this ride
sharing network to add this payment feature as
well as a way to enter the payment services area
because I mean somehow the payment service I
predicted that in the future like it's going to
get more and more crowded because this is actually
one of the fundamental need of everybody in a
sense that you have to pay basically everyday
multiple times.
So this is actually very attractable market both in
terms of enlarging the installed base and also
collecting lots of data to enable any other kind of
services.
So as a result in that space is going to be very
competitive but there is actually not so easy for
startup to enter very easy once you are facing the
competitors based on this network bridging.
But clearly even like in other settings like in the
case of Uber you see that the startup can still
leverage some other network properties like a
multi-homing, network clustering to enter
successfully.
In New York for instance in addition to Uber and
Lyft I heard there are actually lots of startups
of the ride sharing services there.
Ok, we probably have time for one more question.
Alright, thank you everyone for joining us.
[Applause]
Thank you.