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we're here with chunchun who is an postdoc researcher with the Mac Institute for innovation management he's
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here to talk about his research looking into bilateral rating systems in online marketplaces welcome thank you for
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inviting me it's a great honor to be here to share my research with you can you tell us first about your work what
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are you trying to study so our paper is essentially trying to understand the impact of the rating
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systems on those online marketplaces so notice that a recent years the digital innovations in especially the rapid
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development of the mobile Internet of servers as well as the smart forms they have really changed people's everyday
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life so one of the greatest example is the sharing economies so the companies like uber and airbnb is so these
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companies works like a platforms that connects millions of online individual customers to millions of all fly
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individual service providers three very simple applications on their smartphones
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so notice that on the demand side of these platforms the count of the customers now is able to make orders at
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any time in any place and on the supply side the individual service providers now they can't decide when to work and
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how long to work on the platform it is because of these new technologies that removes their physical obstacles between
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two parties but on the other hand the psychological obstacles namely that the trust between the two parties is still
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there notice that the concept of trust is not a new is not a new concept but it and
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never play a important role as in the current economy context and the reason is because so the transactions that's
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going to happen on these platforms is between two or two parties of completely strangers and notice that because of
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these high liquidity of both parties people the customers and the service providers they are less likely to
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encounter with each other for more than once so what I mean is for example you are less likely to stay in the
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same accommodation listed on the Airbnb website and the reason is because probably you you want to change a
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different place to visit in your next of application right and even if your take
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the ubers to go to office every day still it is less likely that you meet the same driver so exactly because of
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this high liquidity of both side and also almost negligible entering cost of the supply side this create a big issues
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in establishing the trust and notice that the customers it is very difficult for the customer to judge how serious
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the service provides provider fears about his own business activity and the willingness to continue provide service
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in the long run okay and the rating system has been proved that it is an effective tool to
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establish the trust between two parties it is natural that people want to have reassurance from other users so that
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they can make sure that they didn't waste their money with the money on the bad products or better service it is the
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rating system that makes the waste of crowds available that brings the people who's completely stranger people's
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together to conduct business activities and our papers what we really are trying
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to understand is how this impact of these rating systems on the three parties of the game which is the
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platforms the service providers and also the the customers so what are your papers key takeaways and were there any
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conclusions that surprised you so we're still in the process of analyzing our
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model but we have I think we have already some interesting results to share with you so we first in the first
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stage we consider a platform without a rating system versus a unilateral rating system in a sense that only the
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customers is able to submit their ratings to the service providers and in a next stage we're going to analyze the
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bilateral rating system where both parties they can read each other after the transactions so currently what we
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found is that the rating systems will have different impact on the free party of the game the the service providers
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customers and the platform so from the less will talk about this one by one so first are from the perspective of the
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plat platforms we found that it is always their best interest to implement this rating
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systems and the underlying intuition behind is because by allowing the customers to read the service providers
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it makes the service providers have more motivations to exert more effort because
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now their effort is more transparent and observable through the ratings so these
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kind of make the customers feel a little bit happy because they get some extra utilities and this kind of in turn makes
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the other platforms more flexible in redesign is pricing strategy so for example right now the platform's is able
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to increase the price a little bit and also increase the fee charged by the a charge of from via the service providers
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a little bit so that's kind of both a boost of the revenue of their platform
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ok so that's for the platforms so then when we turn to the service providers
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one would attempt to think that these should get worse after the implementations of the rating systems
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and this is also what we think initially because simply because first they have to work harder because their effort is
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now observable and a second as the reason we just mentioned the platforms now going to increase the fee a little
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bit so it sounds like that the service provider they are in create double loss and it is true that for the year revenue
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per order they do get a decrease but this is not the whole story this is only part of the story and because we know
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that on average now the service providers there are making more effort so he's gonna attract more customers to
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use this platform to book service so that's indeed what we found so under some circumstances the transaction
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volume gonna increase so great such that is going to exceed the the loss of the revenue promoter so in that case the
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service provider is actually getting better due to the fact that they are being rated and the last part is for
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their customers so it is natural to think that after the implementation of the rating systems the customers are it
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should attract more customers to join this platform and however we found that it is actually not always the case and
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the reason is really deep on the pricing strategy of the platform and notice that the platform care about
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I care about two things one is the revenue per order the other is a transaction volume and you notice that
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usually there's a tension between these two factors so if you want to have a
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higher profit margin per order usually you cannot at the same time achieve the goal of having a high transaction volume
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right so okay so then what we found is that if the customers valuation about the service itself is at a pretty low
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level and then in that case the transaction volume plays a more important role versus Avandia the
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revenue per order to the platforms so in the in that case what the platform's
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gonna do is they're gonna they're gonna adopt a less aggressive pricing strategy
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such that it's gonna charge the fee from the server's a relatively lower and then
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it's gonna induce all the servers on the platform to exert some F some level of
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extra effort so that's got to make the customer feel happy so more customers
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gonna involve in these platforms and increase the transaction volume while on the other hand if the evaluation of the
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customers to the service itself is already pretty high so which means that they are already very satisfied with the
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service even if without any extra amount of effort by the service provider in this case we found that the revenue per
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order plays a more dominant role than the transaction volume so in this case the platform will adopt a very
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aggressive stress pricing strategy in a sense that is going to increase the fee charged from the service provider so
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that's gonna make only part of the service provider exert some extra amount of effort but
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the remaining service providers they were just produced the usual the the basic service without the actor effort
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and these kind of this amount of the service providers they're they just barely make a living on the platform so
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in this case the the customers the amount of customers that involving these platforms is actually lower than the
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system are they on the platform without a rating system that's that's our main
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findings what are some practical applications of your research okay based on our analysis first we know that
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okay it is it is always the best interest for the platform to implement such a rating systems and then a second
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in terms of his pricing strategies first we think that the platform should conduct some marketing research to
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gathers the informations about how the customers feel about the service itself so for example they need to know that
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what is the woodenness to pay off the customers to acquire this service and after they know this information they
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can identify which region of the the customers valuation falls in and then according to that information they know
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either they should adopt a aggressive a pricing strategy or a mild one okay so what sets your research apart from work
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in this area from prior work okay so for the previous or works and they can be described into two stream of
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literature's so the first string is they start at a perspective of the platforms
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they try to understand that the optimal pricing strategy of the platform but under the assumption that they they they
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think that the rating distribution or the effort distributions across the service they are pre pre given so they
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are not changed so this is the first string and a second string of the literature is they start from the
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precepts perspective of the service provider they try to understand the optimal decision of the service provider
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and under the assumption that the prices structure in the system is given and as
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you may have noticed that actually the the price decision of the platform and the decision of the service provider
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they are interrelated so they're going to affect each other and eventually they're going to reach an equilibrium so
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that that is our research we endure endure tonight both the a decision of the platform and also the decision of
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the service provider and also in the next stage were also endogenous the decision of the customers together so
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and solve for the equilibrium that's gonna give us a more complete picture about the system so how will you follow
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up your research okay yeah as we said that we're we're still are this is still
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a ongoing research so in the next stage we're gonna keep focused on analyzing the bilateral rating systems
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we're also the servers they can read back the customers and notice that lease
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is a very important feature in the platforms such that the customers there they are not just a purchase a physical
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product they actually purchase a service so in this case both the customers and the service providers they're going to
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interact with each other for some period of time it could be as short as a ride like uber or as long as couple of months
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like Airbnb so in this case that the service provider is also under the potential risks from the customer side
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because the customers misbehavior may may damage the the asset of the service provider like the service cars or house
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right so um and that's going to affect the server's future ability to provide a
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service so for the platforms like this where the customers they're buying service rather than a physical product a
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bilateral Reading System is needed and this is what we're going to focus in the
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next stage well thank you so much for joining us today okay thank you very much for inviting me
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you [Music]