Mastering Innovation in China: Insights from History on China’s Journey towards Innovation


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Starting from scratch, the country has shown the world an example of how a backward player can grow into a world leader in the technology sector. With its heavy investment in research and development, China is now not only getting closer to the technological frontier in conventional areas such as electronics, machinery and automobiles, but is also driving technological innovations in emerging areas such as robotics, artificial intelligence, space technology and e-commerce.

The following data and information provide evidence to the rise of China's technological capabilities thus far. In , the total number of researchers in China surpassed that of the US, a country which is commonly recognized as the world's technology front-runner.

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China has since then, for six consecutive years, ranked first in the world for its number of researchers. For the eighth consecutive year in , the number of applications for intellectual property rights in China ranked first globally. In , 4. Accordingly, China has become a major global originator of intellectual property.


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By the end of , the world's highest number of invention patent applications were made in China for the eighth consecutive year; the number of international patent applications submitted through the patent cooperation treaty PCT ranked second in the world; the number of trademark registration applications reached 7. In , it allocated The average annual growth rate from to was 20 percent, exceeding the annualized growth rate of GDP calculated at current prices in the same period, according to a July NBS report.

Funding for basic research in was By the end of , there were key laboratories, national engineering research centers and national engineering laboratories in operation. Since the beginning of the 21st century, China has launched the Tiangong space laboratory and the Shenzhou 11 spacecraft, and the BeiDou Navigation Satellite System has begun to form a global network.

China Innovation! New Technological Inventions And Advancement In China

The fourth generation of stealth fighters and large surface vessels has surpassed the international advanced level and has been put into service. China has also made remarkable achievements in the fields of jumbo domestic aircraft, high-speed railways, third-generation nuclear generators and new-energy vehicles. It was the world's first project of its kind. Jiaolong, China's manned deep-sea submersible, in successfully completed the world's first 7,meter dive into the western Pacific Ocean's Mariana Trench, the deepest known natural trench on Earth.

China has filed 54, patent applications for satellite navigations in , ranking first in the world. Over the course of the meeting, our disjointed observations and ideas about e-commerce trends began to coalesce into a larger view of the future, and by the end, we had agreed on a vision.

Our strategic imperative was to make sure that the platform provided all the resources, or access to the resources, that an online business would need to succeed, and hence supported the evolution of the ecosystem. The ecosystem we built was simple at first: We linked buyers and sellers of goods. As technology advanced, more business functions moved online—including established ones, such as advertising, marketing, logistics, and finance, and emerging ones, such as affiliate marketing, product recommenders, and social media influencers.

Alibaba today is not just an online commerce company. It is what you get if you take all functions associated with retail and coordinate them online into a sprawling, data-driven network of sellers, marketers, service providers, logistics companies, and manufacturers. In other words, Alibaba does what Amazon, eBay, PayPal, Google, FedEx, wholesalers, and a good portion of manufacturers do in the United States, with a healthy helping of financial services for garnish.

Why has so much value and market power emerged so quickly? Because of new capabilities in network coordination and data intelligence that all these companies put to use. The ecosystems they steward are vastly more economically efficient and customer-centric than traditional industries. These firms follow an approach I call smart business, and I believe it represents the dominant business logic of the future. Smart business emerges when all players involved in achieving a common business goal—retailing, for example, or ride sharing—are coordinated in an online network and use machine-learning technology to efficiently leverage data in real time.

This tech-enabled model, in which most operational decisions are made by machines, allows companies to adapt dynamically and rapidly to changing market conditions and customer preferences, gaining tremendous competitive advantage over traditional businesses. Ample computing power and digital data are the fuel for machine learning, of course. The more data and the more iterations the algorithmic engine goes through, the better its output gets. Data scientists come up with probabilistic prediction models for specific actions, and then the algorithm churns through loads of data to produce better decisions in real time with every iteration.

These prediction models become the basis for most business decisions. Thus machine learning is more than a technological innovation; it will transform the way business is conducted as human decision making is increasingly replaced by algorithmic output. Ant Microloans provides a striking example of what this future will look like. When Alibaba launched Ant, in , the typical loan given by large banks in China was in the millions of dollars.

Banks were reluctant to service companies that lacked any kind of credit history or even adequate documentation of their business activities. As a consequence, tens of millions of businesses in China were having real difficulties securing the money necessary to grow their operations. At Alibaba, we realized we had the ingredient for creating a high-functioning, scalable, and profitable SME lending business: the huge amount of transaction data generated by the many small businesses using our platform.

In , we bundled this lending operation together with Alipay, our very successful payments business, to create Ant Financial Services. We gave the new venture that name to capture the idea that we were empowering all the little but industrious, antlike companies. How is this possible? When faced with potential borrowers, lending institutions need answer only three basic questions: Should we lend to them, how much should we lend, and at what interest rate? Once sellers on our platforms gave us authorization to analyze their data, we were well positioned to answer those questions.

Our algorithms can look at transaction data to assess how well a business is doing, how competitive its offerings are in the market, whether its partners have high credit ratings, and so on.

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Ant uses that data to compare good borrowers those who repay on time with bad ones those who do not to isolate traits common in both groups. Those traits are then used to calculate credit scores. All lending institutions do this in some fashion, of course, but at Ant the analysis is done automatically on all borrowers and on all their behavioral data in real time.

At the same time, the algorithms that calculate the scores are themselves evolving in real time, improving the quality of decision making with each iteration. The algorithms might, for example, analyze the frequency, length, and type of communications instant messaging, e-mail, or other methods common in China to assess relationship quality. This work requires both a deep understanding of the business and expertise in machine-learning algorithms.

Consider again Ant Financial. If a seller deemed to have poor credit pays back its loan on time or a seller with excellent credit catastrophically defaults, the algorithm clearly needs tweaking. Engineers can quickly and easily check their assumptions. Which parameters should be added or removed? Which kinds of user behavior should be given more weight?


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  8. To become a smart business, your firm must enable as many operating decisions as possible to be made by machines fueled by live data rather than by humans supported by their own data analysis. Transforming decision making in this way is a four-step process. Ant was fortunate to have access to plenty of data on potential borrowers to answer the questions inherent in its lending business.

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    For many businesses, the data capture process will be more challenging. But live data is essential to creating the feedback loops that are the basis of machine learning. Consider the bike rental business. Start-ups in China have leveraged mobile telephony, the internet of things in the form of smart bike locks , and existing mobile payment and credit systems to datafy the entire rental process. Renting a bike traditionally involved going to a rental location, leaving a deposit, having someone give you a bike, using the bike, returning it, and then paying for the rental by cash or credit card.

    Several rival Chinese companies put all of this online by integrating various new technologies with existing ones. A crucial innovation was the combination of QR codes and electronic locks that cleverly automated the checkout process. By opening the bike-sharing app, a rider can see available bicycles and reserve one nearby. Once the rider arrives at the bicycle, he or she uses the app to scan a QR code on the bicycle. Assuming that the person has money in his or her account and meets the rental criteria, the QR code will open the electronic bike lock.

    When the bike is returned, closing the lock completes the transaction. The process is simple, intuitive, and usually takes only several seconds. Datafying the rental process greatly improves the consumer experience. On the basis of live data, companies dispatch trucks to move bikes to where users want them. They can also alert regular users to the availability of bikes nearby.

    Thanks in large part to these innovations, the cost of bike rentals in China has fallen to just a few cents per hour. Most businesses that seek to be more data-driven typically collect and analyze information in order to create a causal model.

    The model then isolates the critical data points from the mass of information available. That is not how smart businesses use data. Instead, they capture all information generated during exchanges and communications with customers and other network members as the business operates and then let the algorithms figure out what data is relevant. In a smart business, all activities—not just knowledge management and customer relations—are configured using software so that decisions affecting them can be automated.

    This does not mean that a firm needs to buy or build ERP software or its equivalent to manage its business—quite the opposite. Traditional software makes processes and decision flows more rigid and often becomes a straitjacket. In contrast, the dominant logic for smart business is reactivity in real time. The first step is to build a model of how humans currently make decisions and find ways to replicate the simpler elements of that process using software—which is not always easy, given that many human decisions are built on common sense or even subconscious neurological activity.

    The growth of Taobao, the domestic retailing website of Alibaba Group, is driven by continuous softwaring of the retailing process.

    Mastering Innovation in China: Insights from History on China’s Journey towards Innovation Mastering Innovation in China: Insights from History on China’s Journey towards Innovation
    Mastering Innovation in China: Insights from History on China’s Journey towards Innovation Mastering Innovation in China: Insights from History on China’s Journey towards Innovation
    Mastering Innovation in China: Insights from History on China’s Journey towards Innovation Mastering Innovation in China: Insights from History on China’s Journey towards Innovation
    Mastering Innovation in China: Insights from History on China’s Journey towards Innovation Mastering Innovation in China: Insights from History on China’s Journey towards Innovation
    Mastering Innovation in China: Insights from History on China’s Journey towards Innovation Mastering Innovation in China: Insights from History on China’s Journey towards Innovation
    Mastering Innovation in China: Insights from History on China’s Journey towards Innovation Mastering Innovation in China: Insights from History on China’s Journey towards Innovation
    Mastering Innovation in China: Insights from History on China’s Journey towards Innovation Mastering Innovation in China: Insights from History on China’s Journey towards Innovation
    Mastering Innovation in China: Insights from History on China’s Journey towards Innovation Mastering Innovation in China: Insights from History on China’s Journey towards Innovation
    Mastering Innovation in China: Insights from History on China’s Journey towards Innovation Mastering Innovation in China: Insights from History on China’s Journey towards Innovation

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