2) Internal sources traffic vs external sources traffic: as network of a platform increases, its value increases. So the platform should become a destination where users want to spend time. Measuring traffic source (direct vs indirect) could help to analyze whether users considering a platform more valuable (direct traffic is higher) over time.

Picture 16: Amazon Number of Prime Members.

Source: Marketplace Pulse Research, 2020.

Picture 17:Amazon Number of Sellers with Over $100'000 in Sales.

Source: Amazon Quarterly Results, 2020.

According to the statistical data (Picture 18, 19, 20), provided by SEMrush and Similar Web, it could be seen that organic growth not only exceeds (except for the year 2018), but also has an increasing trend. It means, that Amazon's customers are more likely to go for purchases directly to the amazon.com and value the company more through the years of company's development. What is more, direct visiting of company's site is the main search and marketing channel for company's consumers and accounts for around 58% of all existing channels.

So, from the above analyses of the Amazon's network effect, we could conclude that it is not simply present, but has an increasing trend. So, H₀ is approved for this factor.

Picture 18: Amazon Organic vs Paid Traffic as of 2015-2018 years.

Source: SEMrush Study, 2018.

Picture 19: Organic VS Paid Overview of Amazon.com as of 2019 year.

Source: Similar Web, 2019.

Picture 20: Overview of Marketing Channels for Amazon.com as of 2019 year.

Source: Similar Web, 2019.

- Spillover effect

Qualitative spillover effect for the platforms is usually expressed in a way of transparency and availability of information increase. For the Amazon company, growth of transparency and available information is the case. Company offers a catalogue of more than 350 millions of products available for a purchase, with all product characteristics and features description. There is not any company with traditional business model that could offer such a wide variety of information for its consumers, because in this sense, for traditional BM there are limitations in terms of inventory, production, storage, and logistics management. So wide range of information and different products available on amazon.com allows customers to get easy and convenient access to that information and compare products.

The proof of the qualitative spillover effect for the Amazon is the statistical data that claims that 68% of online shoppers compare products they find on retailers' brand sites to what's on Amazon. (EPiServer, 2020). Users use amazon.com as additional trustworthy source of information, which positively influencing companies loyalty, ranks and, as a result, revenues. Spillover effects for the Amazon also could be traced in a form of platform specific features - ratings and reviews. These features that are usually specific for digital platforms are in some sort communication between users of ecosystem. By good reviews and ratings, buyers give additional information to other buyers that the product quality is good, or that the seller is polite and professional. For example, according to the Statista, positive reviews are one of the main drivers for decision to purchase. (Picture 15, Statista, 2020). By bad ratings and reviews, buyers give additional information to sellers on their weak points that should be improved.

It's a huge incentive for sellers to improve their ratings, as higher Amazon Star rating generates higher conversion, sales and sessions. According to the research made by Eleanor Adams on Amazon, when Amazon star rating changes from an average rating (3.5) to a good rating (4.5), it increases conversion rate by 10%, session by 40% and sales by $18'000 per week (which accounts for $950'000 annual increase). As a result, such interactions between users generate higher number of transactions within ecosystem and higher revenues for the company, while sellers earn more and buyers get better products or services.

Sometimes researches also specify quantitative spillover effect, but as it is quite hard to measure, it is usually expressed in a form of economic impact assessment. One of the most recent analyses on consumer surplus, Broadband India Forum and WIK, have tried to quantify a spillover effect from the usage of Rich Internet Applications. The results of the study estimates that use of such services allows customers to save on average 803.9 minutes per week, which, according to average annual income in India in 2017, account for USD 98 billions consumer surplus in 2017. So, H₀ is approved for this factor.

Chapter 3. Outcomes and recommendations

3.1 Key findings on factors driving business model adaptation to a new MSP BM in retail industry

Digital revolution has given a rise to a new form of business model - multisided platform, which is also known as digital platform. Due to that fact, lots of new companies using platform as their business model have appeared. As for today, digital companies surrounds us in every aspect of our life and in every industry. Prosperity of platform based companies in the market performance and among customers has pushed traditional companies to adapt to platforms reality. That is why consideration of what is so special in platforms and what factors drive traditional companies to adapt is a relevant topic for the research.

After the analyses of literature review on the topic, we reveal key factors driving that adaptation in the retail industry and have tried to estimate their presence on the example of one of the hugest and most well-known e-commerce retail company - Amazon.

Considering factors that drive adaptation of traditional BMs to MSP BMs in the retail, we could summarize our research by factors:

Higher level of data acquisition & data analyses: digital platform business model implies that all the transactions and other activities incorporated by suppliers and buyers occurs on the platform. That is why companies, such as Amazon, are able to obtain the history of all searches and clicks, products and preferences. As a result of platforms opportunities to, firstly, obtain and, then, to analyze such huge amounts of data, platforms are able to create `recommendations' or `customers who liked that, usually also likes …' . this features of platforms allow to generate huge flows of additional revenues. For the Amazon factor of higher level of data acquisition & analyses is present, as company's consumer recommendations generate around 35% of Amazon's total revenue. (McKinsey & Co, 2017).

Transfer of inventory risks from a retailor (platform) to a supplier: this factor is specific for the retail industry, as activities involving selling of physical products directly entails inventory risks. Online retailers as Amazon, being a platform, play a role of intermediary between byers and suppliers of physical products. By initiating only connection between two parties as a platform, avoiding products production, online retail platforms are able to transfer inventory risks to third-party sellers.

Actually, this factor is one of the most relevant for the retail industry, and one of the most interesting to be considered by the example of Amazon. Being brick-and mortal garage book store in the beginning of its foundation, Amazon has become one of the most successful e-commerce retail platforms. And every step that company has initiated to reduce its inventory risks and costs was a step towards platform BM. Firstly, company brought its business from offline to online, eliminating catalogues printing need ant centralizing merchants. Then, it initiated consignment program, which eliminated “out of stock” inventory risks. After that, Amazon created storage and manufacturing partnerships, transferring al its inventory activities to outsource. And the latest step, company has opened its marketplace to third-party sellers. So, amazon has made it possible for itself to enlarge catalogue of products offered to consumers without any additional inventory risks for the company with a help of fulfillment by merchants (sellers manage storage, delivery and other inventory activities by themselves). As a result of analyses, it could be seen that transferring inventory burden from platform to third-party sellers is present for Amazon.

Easier entrance to other fields: Amazon, as many others successful and well-known platforms is successful in obtaining huge amount of data on users interaction as was mentioned above. And such an asset could be valuable in multiple scenarios and in multiple fields. Valuable asset in terms of user data, combining with a success experience in one field, could easily be incorporated to other fields. As a result, we can see that Amazon, being successful in acquiring user data and experience in retail, now is present in electronics, entertainment industry and even on computer clouding services.

Easier entrance to other countries: these factor is quite debating. According to theoretical approach that could be attributed to this factor, and which was proposed in one of the latest researches on the MSPs, the easiness of entrance to different countries by platforms depends on the presence of external network effect between countries. (Stallkamp, M., Schotter, A., 2019). That means that each additional user of the platform in one country, adds value for users in other countries. The Amazon case implies presence of external network effect within a country rather than between countries. It means that Amazon consumers from USA would gain more value if USA supplier join the platform. As the delivery will be faster and shipping costs will be lower than if supplier from another country join the platform. What is more, estimation of company performance in other countries has shown quite low revenues and no significant changes in growth. (Statista, 2020). So, in case of Amazon, the presence of such factor as easier entrance to other countries cannot be approved by the data found.

Consumer Lock-in is Created with Horizontal Integration: platforms are usually present in several fields simultaneously, as was mentioned above. It means that they are horizontally diversified - offer several services to their users. As a result, customers of a platform are usually “locked in” its platform. They use one channel (one platform for all services they need). Consumers gain from such a lock-in as its convenient for them to eliminate switching between channels. For platforms it is gaining of constant customers and increase in revenues. For amazon, such a lock-in of customers is present through its Amazon Prime subscription that provide additional services as delivery in the day of order, video and music streaming, TV shows, etc. for members. According to CIRP research, Amazon Prime members retention is quite close to ideal: 93% continue their subscription after one year of membership, 98% - after two years and 64% - after a trial period. (CIRP, 2019). That means that Amazon customers that have tried Amazon's “lock-in”, are actually locked in it after the trial - they do not need and, moreover, do not want to use anything else. According to the Statista, 35% of Amazon buyers name Prime eligibility (all additional services and perks offered for Amazon Prime members) as the main factor that drives their purchases on the platform. (Statista, 2020). So, it could be concluded that consumer lock-in created with horizontal integration is present for the Amazon. And it influences consumer retention and company's revenues in a positive way.

Loyalty Programs: loyalty programs are usually central for ecosystems like Amazon. Actually, Amazon Prime membership program, mentioned in the paragraph above, is a good example of such a loyalty program. It offers a scope of additional services to its subscribers in exchange to a monthly fee. Amazon Prime offers video and music streaming services, faster delivery of purchased products, TV shows and channels subscription and many other additional offers. According to the statistical data, number of Amazon Prime members for the year 2019 surpasses 150 millions, while for the year 2017 it accounted for 100 millions members. (Statista, 2020). Revenue generated only by Prime subscription accounts for over 5% of total company's revenue for the year 2019. While total percentage of Prime revenue from the total company's revenue is even higher, as Amazon Prime subscribers spend on average 2,3 times higher amounts of money than regular Amazon buyers. (Statista, 2020). To sum up, loyalty programs as a central factor for ecosystem is present for the Amazon. This form of subscription generate a great portion of company's revenues.

Higher level of scalability: if traditional business models scale by selling greater amounts by lower fixed costs due to greater amounts, platforms scale in the sense of shift of production to third-party suppliers as it was mentioned above in the paragraph dedicated to inventory risks transfer. And this is the case for the Amazon. To see the scale of the company, lets consider some figures for the Amazon: more than 2.5 millions of sellers, 150 millions of Amazon Prime members, 350 millions of products available on the marketplace. Not only figures for these parameters are spectacular but their growth mentioned in the Table 6. in the second chapter also. To sum up, higher level of scalability as a factor that could drive traditional BM for platform adaptation is present for the Amazon company.

Lower transaction costs: platforms totally transform the logic of transaction costs, or costs associated with performing business services or providing products. This is because platforms actually do not perform transactions themselves, but rather initiate and coordinate them. As a result, costs of transactions are transferred to other parties. This is actually one of the main reasons that makes platforms “less capital intensive, easier to scale and more profitable in the long-term”. (Hдnninem, M., et al, 2017 ). Amazon's main strategy in lowering its traditional costs was actually the first step towards its platform BM - company has moved to the online presence only, which eliminates needs for costs of maintaining and managing its physical facilities. As a result, lower transaction costs are present for Amazon as a platform business model.

Positive network effect: a network effect is one of the hugest advantages of platform BM over traditional ones. A network effect is a positive externality of platforms existence, it is not present in traditional pipeline business models. The network effect implies that when there is an increase in one side of a platform user base (e.g. suppliers of products), it brings more value for each participant of other side of a user base (e.g. buyers of products) and vice versa. According to the theoretical approaches defined in the second paragraph of this research to estimate the presence of the network effect, we have analyzed numbers for organic users and organic traffic for the Amazon company. As figures for both of defined parameters have an increasing trend, it means that the network effect is present for the Amazon company, because numbers for loyal participants on both sides of the platform have been increasing and the time spent on the platform has been estimated more valuably by its users. ( Statista, 2020).

Spillover effect: spillover effect is usually expressed in the increase in transparency and availability of information. And for the Amazon company this is the case. Company provides a catalogue of more than 350 millions of products with their descriptions and characteristics. According to the statistical data, 68% of US online shoppers use amazon.com to compare products they find on other retail brand sites. (EPiServer, 2020). It means that online shoppers use the Amazon as the additional source of information about products. What is more, Amazon's review and rankings initiated features are also additional sources of transparency and information availability for the company. They provide information signals for both buyers and sellers. (products with good reviews could be more trusted for further purchases - information to other buyers, bad ranking implies that there was something wrong with a product or services provided - information to sellers). According to the statistical data, positive product reviews were estimated as the main driver for purchases by 57% of Amazon shoppers. (Picture 15, Statista, 2020).

3.2 Limitations and further research on the topic

To come to conclusion, the topic of participation in multisided platforms is not only interesting due to the fact that in the modern world platforms have become a companies of the first need that meet our daily requirements as transportation, socialization, financial services, information search and products purchasing but also is increasingly relevant as digitalization has transformed many industries and has changed consumer preferences.

In our work we focus on analyzation of factors that drive traditional business models to adapt to this new reality of digital platforms in the retail industry. To achieve this, first we analyze evolution of definitions, concepts and theoretical frameworks in the existing scientific research on the topic. We have tried to summarize and logically develop the evolution of theoretical basement despite differences and discrepancies in connotations. But, as multisided platforms itself is quite a new topic, there is no clear logic in the development of theoretical frameworks in existing literature review, that could be regarded as a limitation of this research.

After theoretical analysis, we move to the analytical part where we try to analyze the presence of factors driving traditional BMs adaptation to MSPs by the case study of the Amazon company. As presence of factors in the retail industry was considered on the example of one company, this work is limited to generalization of factor presence to other companies in this industry. The fact that factors analyzed were present for the Amazon does not imply that they would be present for other e-commerce retail companies.

Moreover, factors driving BM adaptation in this master thesis are quite hard to evaluate numerically and there are limited common tools for their estimation. Further researches on the topic could analyze them in a more detailed way and on wider samples.

To sum up, we can say that the main goal of the master thesis - analysis of factors that drive traditional business model adaptation to the reality of multisided platforms in global retail industry - was reached. And limitations of the research considered above could be regarded as prospects for further research on the topic.

Conclusion

Multisided platforms (MSPs) are becoming the topic of an increasing interests, as more and more companies in the modern world wants to incorporate this new business model into their businesses. Main reasons for such the increasing interests are the digitalization of all industries that we are surrounded by, the spread of the internet and activities performed through the internet, and, as a result, changes in consumers' preferences and purchasing behavior. To keep up with all this changes in the consumers' behavior, the global retail industry also shows a high interest and a desire to participate in the multisided platform business model.

The main goal of this master thesis was to identify the main factors driving the traditional business model adaptation to the multisided platform business model in the global retail industry. The chapter 1.1 was devoted to the definition of the MSP itself in the existing literature on the topic, and to the evolution of concepts and approaches of the topic. The main finding of the literature review is the fact that the topic of platform economy has not been researched significantly, so there are lots of gaps in the knowledge on this topic. What is more, the definition of the multisided platform is quite debating. After analyses of existing theories, and consideration of different approaches we have come to defining a platform as multisided market if, at any given time, it satisfies the following factors: 1) presence of at least two sides of participants, 2) value obtained on the one side, increasing with the number of participants on the other side, 3) the presence of an intermediary to provide a direct interaction between sides, 4) participants are permanent members of one of the sides, 5) a certain degree of affiliation for one of the sides, 6) provides an opportunity for the direct interaction between sides, 6) presence o chicken-and-egg dilemma. (Yablonsky, 2013).

In the chapter 1.2 we examined the multisided platform as a new business model. We pointed out shifts of focuses appearing while the company moves from a traditional business model to a platform business model. Changes of company's focuses include: 1) from the control to orchestrations of resources, 2) from internal to external optimization, 3) from customers' value to the ecosystem's value, 4) from the supply-side economies to the demand-side and some others.

The chapter 1.3 is devoted to the factors that could drive companies to adapt their traditional business models to the multisided platforms in the retail industry. Most of the factors, identified in the chapter 1.3 are general and could be attributed to any industry. That is why, in the next chapter we considered whether all the factors mentioned in 1.3 are present for the Amazon online retail platform.

The analytical part of the research begins in the paragraph 2.1, where we gave a short insight on what the Amazon company is, what products and services it currently offers, the history of the company, its main financial parameters and the description of Amazons business model and its path from the traditional brick-and-mortal company to one of the most prosperous digital platforms.

In the chapter 2.2 we analyzed the presence or the absence factors analyzed in the paragraph 1.3 on the case of Amazon company. Zero hypotheses implied that “i” factor is present for the Amazon company, while alternative hypotheses implied that “i” factor is not present for the Amazon company. The financial performance and secondary statistical data were analyze to accept or reject the hypotheses.

The third chapter of the master thesis was devoted to key findings of the research, its limitations and prospects for further research. We came to the conclusion that the following factors that could drive a traditional BM adaptation to the MSP BM in the retail industry were present for the Amazon company: the spillover effect, the positive network effect, lower transaction costs, the higher level of scalability, loyalty programs, the consumer lock-in created by horizontal integration, the easier entrance to other fields, the transfer of inventory risks, and higher levels of data acquisition & data analyses. While such factor as the easier entrance to other countries was failed to be proven on the Amazon's secondary data that was analyzed. After that we analyzed possible limitations of the research that also could be considered as prospects for further research.

As a result of the work, we could conclude that MSPs topic is quite debating, but relevant and interesting to consider. Digital platforms, such as Amazon are quit prosperous and well-known in the modern world. And it is always interesting to consider what stands besides such a successful story. In our work we achieved the goal that was set - we analyzed factors driving traditional BMs to adapt to MSPs reality by the example of the Amazon. But it is always important to consider both sides of the situation, there are factors that could challenge such an adaptation. They also were mentioned in this work. Companies always should consider both advantages and disadvantages, and decide what overweighs in their situation.

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