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Data science: a game changer for science and innovation

• Regular Paper
• Open access
• Published: 19 April 2021
• Volume 11 , pages 263–278, ( 2021 )

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• Valerio Grossi 1 ,
• Fosca Giannotti 1 ,
• Dino Pedreschi 2 ,
• Paolo Manghi 3 ,
• Pasquale Pagano 3 &
• Massimiliano Assante 3  

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This paper shows data science’s potential for disruptive innovation in science, industry, policy, and people’s lives. We present how data science impacts science and society at large in the coming years, including ethical problems in managing human behavior data and considering the quantitative expectations of data science economic impact. We introduce concepts such as open science and e-infrastructure as useful tools for supporting ethical data science and training new generations of data scientists. Finally, this work outlines SoBigData Research Infrastructure as an easy-to-access platform for executing complex data science processes. The services proposed by SoBigData are aimed at using data science to understand the complexity of our contemporary, globally interconnected society.

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1 Introduction: from data to knowledge

Data science is an interdisciplinary and pervasive paradigm where different theories and models are combined to transform data into knowledge (and value). Experiments and analyses over massive datasets are functional not only to the validation of existing theories and models but also to the data-driven discovery of patterns emerging from data, which can help scientists in the design of better theories and models, yielding a deeper understanding of the complexity of the social, economic, biological, technological, cultural, and natural phenomenon. The products of data science are the result of re-interpreting available data for analysis goals that differ from the original reasons motivating data collection. All these aspects are producing a change in the scientific method, in research and in the way our society makes decisions [ 2 ].

Data science emerges to concurring facts: (i) the advent of big data that provides the critical mass of actual examples to learn from, (ii) the advances in data analysis and learning techniques that can produce predictive models and behavioral patterns from big data, and (iii) the advances in high-performance computing infrastructures that make it possible to ingest and manage big data and perform complex analysis [ 16 ].

Paper organization Section 2 discusses how data science impacts our science and society at large in the coming years. Section 3 outlines the main issues related to the ethical problems in studying human behaviors that data science introduces. In Sect.  4 , we show how concepts such as open science and e-infrastructure are effective tools for supporting, disseminating ethical uses of the data, and training new generations of data scientists. We will illustrate the importance of an open data science with examples provided later in the paper. Finally, we show some use cases of data science through thematic environments that bind the datasets with social mining methods.

2 Data science for society, science, industry and business

Data science as an ecosystem: on the left, the figure shows the main components enabling data science (data, analytical methods, and infrastructures). On the right, we can find the impact of data science into society, science, and business. All the activities related to data science should be done under rigid ethical principles

The quality of business decision making, government administration, and scientific research can potentially be improved by analyzing data. Data science offers important insights into many complicated issues, in many instances, with remarkable accuracy and timeliness.

The data science pipeline starts with raw data and transforms them into data used for analytics. The next step is to transform these data into knowledge through analytical methods and then provide results and evaluation measures

As shown in Fig.  1 , data science is an ecosystem where the following scientific, technological, and socioeconomic factors interact:

Data Availability of data and access to data sources;

Analytics & computing infrastructures Availability of high performance analytical processing and open-source analytics;

Skills Availability of highly and rightly skilled data scientists and engineers;

Ethical & legal aspects Availability of regulatory environments for data ownership and usage, data protection and privacy, security, liability, cybercrime, and intellectual property rights;

Applications Business and market ready applications;

Social aspects Focus on major societal global challenges.

Data science envisioned as the intersection between data mining, big data analytics, artificial intelligence, statistical modeling, and complex systems is capable of monitoring data quality and analytical processes results transparently. If we want data science to face the global challenges and become a determinant factor of sustainable development, it is necessary to push towards an open global ecosystem for science, industrial, and societal innovation [ 48 ]. We need to build an ecosystem of socioeconomic activities, where each new idea, product, and service create opportunities for further purposes, and products. An open data strategy, innovation, interoperability, and suitable intellectual property rights can catalyze such an ecosystem and boost economic growth and sustainable development. This strategy also requires a “networked thinking” and a participatory, inclusive approach.

Data are relevant in almost all the scientific disciplines, and a data-dominated science could lead to the solution of problems currently considered hard or impossible to tackle. It is impossible to cover all the scientific sectors where a data-driven revolution is ongoing; here, we shall only provide just a few examples.

The Sloan Digital Sky Survey Footnote 1 has become a central resource for astronomers over the world. Astronomy is being transformed from the one where taking pictures of the sky was a large part of an astronomer’s job, to the one where the images are already in a database, and the astronomer’s task is to find interesting objects and phenomenon in the database. In biological sciences, data are stored in public repositories. There is an entire discipline of bioinformatics that is devoted to the analysis of such data. Footnote 2 Data-centric approaches based on personal behaviors can also support medical applications analyzing data at both human behavior levels and lower molecular ones. For example, integrating genome data of medical reactions with the habits of the users, enabling a computational drug science for high-precision personalized medicine. In humans, as in other organisms, most cellular components exert their functions through interactions with other cellular components. The totality of these interactions (representing the human “interactome”) is a network with hundreds of thousand nodes and a much larger number of links. A disease is rarely a consequence of an abnormality in a single gene. Instead, the disease phenotype is a reflection of various pathological processes that interact in a complex network. Network-based approaches can have multiple biological and clinical applications, especially in revealing the mechanisms behind complex diseases [ 6 ].

Now, we illustrate the typical data science pipeline [ 50 ]. People, machines, systems, factories, organizations, communities, and societies produce data. Data are collected in every aspect of our life, when: we submit a tax declaration; a customer orders an item online; a social media user posts a comment; a X-ray machine is used to take a picture; a traveler sends a review on a restaurant; a sensor in a supply chain sends an alert; or a scientist conducts an experiment. This huge and heterogeneous quantity of data needs to be extracted, loaded, understood, transformed, and in many cases, anonymized before they may be used for analysis. Analysis results include routines, automated decisions, predictions, and recommendations, and outcomes that need to be interpreted to produce actions and feedback. Furthermore, this scenario must also consider ethical problems in managing social data. Figure 2 depicts the data science pipeline. Footnote 3 Ethical aspects are important in the application of data science in several sectors, and they are addressed in Sect.  3 .

2.1 Impact on society

Data science is an opportunity for improving our society and boosting social progress. It can support policymaking; it offers novel ways to produce high-quality and high-precision statistical information and empower citizens with self-awareness tools. Furthermore, it can help to promote ethical uses of big data.

Modern cities are perfect environments densely traversed by large data flows. Using traffic monitoring systems, environmental sensors, GPS individual traces, and social information, we can organize cities as a collective sharing of resources that need to be optimized, continuously monitored, and promptly adjusted when needed. It is easy to understand the potentiality of data science by introducing terms such as urban planning , public transportation , reduction of energy consumption , ecological sustainability, safety , and management of mass events. These terms represent only the front line of topics that can benefit from the awareness that big data might provide to the city stakeholders [ 22 , 27 , 29 ]. Several methods allowing human mobility analysis and prediction are available in the literature: MyWay [ 47 ] exploits individual systematic behaviors to predict future human movements by combining individual and collective learned models. Carpooling [ 22 ] is based on mobility data from travelers in a given territory and constructs a network of potential carpooling users, by exploiting topological properties, highlighting sub-populations with higher chances to create a carpooling community and the propensity of users to be either drivers or passengers in a shared car. Event attendance prediction [ 13 ] analyzes users’ call habits and classifies people into behavioral categories, dividing them among residents, commuters, and visitors and allows to observe the variety of behaviors of city users and the attendance in big events in cities.

Electric mobility is expected to gain importance for the world. The impact of a complete switch to electric mobility is still under investigation, and what appears to be critical is the intensity of flows due to charge (and fast recharge) systems that may challenge the stability of the power network. To avoid instabilities regarding the charging infrastructure, an accurate prediction of power flows associated with mobility is needed. The use of personal mobility data can estimate the mobility flow and simulate the impact of different charging behavioral patterns to predict power flows and optimize the position of the charging infrastructures [ 25 , 49 ]. Lorini et al. [ 26 ] is an example of an urban flood prediction that integrates data provided by CEM system Footnote 4 and Twitter data. Twitter data are processed using massive multilingual approaches for classification. The model is a supervised model which requires a careful data collection and validation of ground truth about confirmed floods from multiple sources.

Another example of data science for society can be found in the development of applications with functions aimed directly at the individual. In this context, concepts such as personal data stores and personal data analytics are aimed at implementing a new deal on personal data, providing a user-centric view where data are collected, integrated and analyzed at the individual level, and providing the user with better awareness of own behavioral, health, and consumer profiles. Within this user-centric perspective, there is room for an even broader market of business applications, such as high-precision real-time targeted marketing, e.g., self-organizing decision making to preserve desired global properties, and sustainability of the transportation or the healthcare system. Such contexts emphasize two essential aspects of data science: the need for creativeness to exploit and combine the several data sources in novel ways and the need to give awareness and control of the personal data to the users that generate them, to sustain a transparent, trust-based, crowd-sourced data ecosystem [ 19 ].

The impact of online social networks in our society has changed the mechanisms behind information spreading and news production. The transformation of media ecosystems and news consumption are having consequences in several fields. A relevant example is the impact of misinformation on society, as for the Brexit referendum when the massive diffusion of fake news has been considered one of the most relevant factors of the outcome of this political event. Examples of achievements are provided by the results regarding the influence of external news media on polarization in online social networks. These achievements indicate that users are highly polarized towards news sources, i.e., they cite (and tend to cite) sources that they identify as ideologically similar to them. Other results regard echo chambers and the role of social media users: there is a strong correlation between the orientation of the content produced and consumed. In other words, an opinion “echoes” back to the user when others are sharing it in the “chamber” (i.e., the social network around the user) [ 36 ]. Other results worth mentioning regard efforts devoted to uncovering spam and bot activities in stock microblogs on Twitter: taking inspiration from biological DNA, the idea is to model the online users’ behavior through strings of characters representing sequences of online users’ actions. As a result of the following papers, [ 11 , 12 ] report that 71% of suspicious users were classified as bots; furthermore, 37% of them also got suspended by Twitter few months after our investigation. Several approaches can be found in the literature. However, they generally display some limitations. Some of them work only on some of the features of the diffusion of misinformation (bot detections, segregation of users due to their opinions or other social analysis), or there is a lack of comprehensive frameworks for interpreting results. While the former case is somehow due to the innovation of the research field and it is explainable, the latter showcases a more fundamental need, as, without strict statistical validation, it is hard to state which are the crucial elements that permit a well-grounded description of a system. For avoiding fake news diffusion, we can state that building a comprehensive fake news dataset providing all information about publishers, shared contents, and the engagements of users over space and time, together with their profile stories, can help the development of innovative and effective learning models. Both unsupervised and supervised methods will work together to identify misleading information. Multidisciplinary teams made up of journalists, linguists, and behavioral scientists and similar will be needed to identify what amounts to information warfare campaigns. Cyberwarfare and information warfare will be two of the biggest threats the world will face in the 21st Century.

Social sensing methods collect data produced by digital citizens, by either opportunistic or participatory crowd-sensing, depending on users’ awareness of their involvement. These approaches present a variety of technological and ethical challenges. An example is represented by Twitter Monitor [ 10 ], that is crowd-sensing tool designed to access Twitter streams through the Twitter Streaming API. It allows launching parallel listening for collecting different sets of data. Twitter Monitor represents a tool for creating services for listening campaigns regarding relevant events such as political elections, natural and human-made disasters, popular national events, etc. [ 11 ]. This campaign can be carried out, specifying keywords, accounts, and geographical areas of interest.

Nowcasting Footnote 5 financial and economic indicators focus on the potential of data science as a proxy for well-being and socioeconomic applications. The development of innovative research methods has demonstrated that poverty indicators can be approximated by social and behavioral mobility metrics extracted from mobile phone data and GPS data [ 34 ]; and the Gross Domestic Product can be accurately nowcasted by using retail supermarket market data [ 18 ]. Furthermore, nowcasting of demographic aspects of territory based on Twitter data [ 1 ] can support official statistics, through the estimation of location, occupation, and semantics. Networks are a convenient way to represent the complex interaction among the elements of a large system. In economics, networks are gaining increasing attention because the underlying topology of a networked system affects the aggregate output, the propagation of shocks, or financial distress; or the topology allows us to learn something about a node by looking at the properties of its neighbors. Among the most investigated financial and economic networks, we cite a work that analyzes the interbank systems, the payment networks between firms, the banks-firms bipartite networks, and the trading network between investors [ 37 ]. Another interesting phenomenon is the advent of blockchain technology that has led to the innovation of bitcoin crypto-currency [ 31 ].

Data science is an excellent opportunity for policy, data journalism, and marketing. The online media arena is now available as a real-time experimenting society for understanding social mechanisms, like harassment, discrimination, hate, and fake news. In our vision, the use of data science approaches is necessary for better governance. These new approaches integrate and change the Official Statistics representing a cheaper and more timely manner of computing them. The impact of data science-driven applications can be particularly significant when the applications help to build new infrastructures or new services for the population.

The availability of massive data portraying soccer performance has facilitated recent advances in soccer analytics. Rossi et al. [ 42 ] proposed an innovative machine learning approach to the forecasting of non-contact injuries for professional soccer players. In [ 3 ], we can find the definition of quantitative measures of pressing in defensive phases in soccer. Pappalardo et al. [ 33 ] outlined the automatic and data-driven evaluation of performance in soccer, a ranking system for soccer teams. Sports data science is attracting much interest and is now leading to the release of a large and public dataset of sports events.

Finally, data science has unveiled a shift from population statistics to interlinked entities statistics, connected by mutual interactions. This change of perspective reveals universal patterns underlying complex social, economic, technological, and biological systems. It is helpful to understand the dynamics of how opinions, epidemics, or innovations spread in our society, as well as the mechanisms behind complex systemic diseases, such as cancer and metabolic disorders revealing hidden relationships between them. Considering diffusive models and dynamic networks, NDlib [ 40 ] is a Python package for the description, simulation, and observation of diffusion processes in complex networks. It collects diffusive models from epidemics and opinion dynamics and allows a scientist to compare simulation over synthetic systems. For community discovery, two tools are available for studying the structure of a community and understand its habits: Demon [ 9 ] extracts ego networks (i.e., the set of nodes connected to an ego node) and identifies the real communities by adopting a democratic, bottom-up merging approach of such structures. Tiles [ 41 ] is dedicated to dynamic network data and extracts overlapping communities and tracks their evolution in time following an online iterative procedure.

2.2 Impact on industry and business

Data science can create an ecosystem of novel data-driven business opportunities. As a general trend across all sectors, massive quantities of data will be made accessible to everybody, allowing entrepreneurs to recognize and to rank shortcomings in business processes, to spot potential threads and win-win situations. Ideally, every citizen could establish from these patterns new business ideas. Co-creation enables data scientists to design innovative products and services. The value of joining different datasets is much larger than the sum of the value of the separated datasets by sharing data of various nature and provenance.

The gains from data science are expected across all sectors, from industry and production to services and retail. In this context, we cite several macro-areas where data science applications are especially promising. In energy and environment , the digitization of the energy systems (from production to distribution) enables the acquisition of real-time, high-resolution data. Coupled with other data sources, such as weather data, usage patterns, and market data (accompanied by advanced analytics), efficiency levels can be increased immensely. The positive impact to the environment is also enhanced by geospatial data that help to understand how our planet and its climate are changing and to confront major issues such as global warming, preservation of the species, the role and effects of human activities.

The manufacturing and production sector with the growing investments into Industry 4.0 and smart factories with sensor-equipped machinery that are both intelligent and networked (see internet of things . Cyber-physical systems ) will be one of the major producers of data in the world. The application of data science into this sector will bring efficiency gains and predictive maintenance. Entirely new business models are expected since the mass production of individualized products becomes possible where consumers may have direct access to influence and control.

As already stated in Sect.  2.1 , data science will contribute to increasing efficiency in public administrations processes and healthcare. In the physical and the cyber-domain, security will be enhanced. From financial fraud to public security, data science will contribute to establishing a framework that enables a safe and secure digital economy. Big data exploitation will open up opportunities for innovative, self-organizing ways of managing logistical business processes. Deliveries could be based on predictive monitoring, using data from stores, semantic product memories, internet forums, and weather forecasts, leading to both economic and environmental savings. Let us also consider the impact of personalized services for creating real experiences for tourists. The analysis of real-time and context-aware data (with the help of historical and cultural heritage data) will provide customized information to each tourist, and it will contribute to the better and more efficient management of the whole tourism value chain.

3 Data science ethics

Data science creates great opportunities but also new risks. The use of advanced tools for data analysis could expose sensitive knowledge of individual persons and could invade their privacy. Data science approaches require access to digital records of personal activities that contain potentially sensitive information. Personal information can be used to discriminate people based on their presumed characteristics. Data-driven algorithms yield classification and prediction models of behavioral traits of individuals, such as credit score, insurance risk, health status, personal preferences, and religious, ethnic, or political orientation, based on personal data disseminated in the digital environment by users (with or often without their awareness). The achievements of data science are the result of re-interpreting available data for analysis goals that differ from the original reasons motivating data collection. For example, mobile phone call records are initially collected by telecom operators for billing and operational aims, but they can be used for accurate and timely demography and human mobility analysis at a country or regional scale. This re-purposing of data clearly shows the importance of legal compliance and data ethics technologies and safeguards to protect privacy and anonymity; to secure data; to engage users; to avoid discrimination and misuse; to account for transparency; and to the purpose of seizing the opportunities of data science while controlling the associated risks.

Several aspects should be considered to avoid to harm individual privacy. Ethical elements should include the: (i) monitoring of the compliance of experiments, research protocols, and applications with ethical and juridical standards; (ii) developing of big data analytics and social mining tools with value-sensitive design and privacy-by-design methodologies; (iii) boosting of excellence and international competitiveness of Europe’s big data research in safe and fair use of big data for research. It is essential to highlight that data scientists using personal and social data also through infrastructures have the responsibility to get acquainted with the fundamental ethical aspects relating to becoming a “data controller.” This aspect has to be considered to define courses for informing and training data scientists about the responsibilities, the possibilities, and the boundaries they have in data manipulation.

Recalling Fig.  2 , it is crucial to inject into the data science pipeline the ethical values of fairness : how to avoid unfair and discriminatory decisions; accuracy : how to provide reliable information; confidentiality : how to protect the privacy of the involved people and transparency : how to make models and decisions comprehensible to all stakeholders. This value-sensitive design has to be aimed at boosting widespread social acceptance of data science, without inhibiting its power. Finally, it is essential to consider also the impact of the General Data Protection Regulation (GDPR) on (i) companies’ duties and how these European companies should comply with the limits in data manipulation the Regulation requires; and on (ii) researchers’ duties and to highlight articles and recitals which specifically mention and explain how research is intended in GDPR’s legal system.

The relationship between big and open data and how they relate to the broad concept of open government

We complete this section with another important aspect related to open data, i.e., accessible public data that people, companies, and organizations can use to launch new ventures, analyze patterns and trends, make data-driven decisions, and solve complex problems. All the definitions of open data include two features: (i) the data must be publicly available for anyone to use, and (ii) data must be licensed in a way that allows for its reuse. All over the world, initiatives are launched to make data open by government agencies and public organizations; listing them is impossible, but an UN initiative has to be mentioned. Global Pulse Footnote 6 meant to implement the vision for a future in which big data is harnessed safely and responsibly as a public good.

Figure 3 shows the relationships between open data and big data. Currently, the problem is not only that government agencies (and some business companies) are collecting personal data about us, but also that we do not know what data are being collected and we do not have access to the information about ourselves. As reported by the World Economic forum in 2013, it is crucial to understand the value of personal data to let the users make informed decisions. A new branch of philosophy and ethics is emerging to handle personal data related issues. On the one hand, in all cases where the data might be used for the social good (i.e., medical research, improvement of public transports, contrasting epidemics), and understanding the personal data value means to correctly evaluate the balance between public benefits and personal loss of protection. On the other hand, when data are aimed to be used for commercial purposes, the value mentioned above might instead translate into simple pricing of personal information that the user might sell to a company for its business. In this context, discrimination discovery consists of searching for a-priori unknown contexts of suspect discrimination against protected-by-law social groups, by analyzing datasets of historical decision records. Machine learning and data mining approaches may be affected by discrimination rules, and these rules may be deeply hidden within obscure artificial intelligence models. Thus, discrimination discovery consists of understanding whether a predictive model makes direct or indirect discrimination. DCube [ 43 ] is a tool for data-driven discrimination discovery, a library of methods on fairness analysis.

It is important to evaluate how a mining model or algorithm takes its decision. The growing field of methods for explainable machine learning provides and continuously expands a set of comprehensive tool-kits [ 21 ]. For example, X-Lib is a library containing state-of-the-art explanation methods organized within a hierarchical structure and wrapped in a similar fashion way such that they can be easily accessed and used from different users. The library provides support for explaining classification on tabular data and images and for explaining the logic of complex decision systems. X-Lib collects, among the others, the following collection of explanation methods: LIME [ 38 ], Anchor [ 39 ], DeepExplain that includes Saliency maps [ 44 ], Gradient * Input, Integrated Gradients, and DeepLIFT [ 46 ]. Saliency method is a library containing code for SmoothGrad [ 45 ], as well as implementations of several other saliency techniques: Vanilla Gradients, Guided Backpropogation, and Grad-CAM. Another improvement in this context is the use of robotics and AI in data preparation, curation, and in detecting bias in data, information and knowledge as well as in the misuse and abuse of these assets when it comes to legal, privacy, and ethical issues and when it comes to transparency and trust. We cannot rely on human beings to do these tasks. We need to exploit the power of robotics and AI to help provide the protections required. Data and information lawyers will play a key role in legal and privacy issues, ethical use of these assets, and the problem of bias in both algorithms and the data, information, and knowledge used to develop analytics solutions. Finally, we can state that data science can help to fill the gap between legislators and technology.

4 Big data ecosystem: the role of research infrastructures

Research infrastructures (RIs) play a crucial role in the advent and development of data science. A social mining experiment exploits the main components of data science depicted in Fig.  1 (i.e., data, infrastructures, analytical methods) to enable multidisciplinary scientists and innovators to extract knowledge and to make the experiment reusable by the scientific community, innovators providing an impact on science and society.

Resources such as data and methods help domain and data scientists to transform research or an innovation question into a responsible data-driven analytical process. This process is executed onto the platform, thus supporting experiments that yield scientific output, policy recommendations, or innovative proofs-of-concept. Furthermore, an operational ethical board’s stewardship is a critical factor in the success of a RI.

An infrastructure typically offers easy-to-use means to define complex analytical processes and workflows , thus bridging the gap between domain experts and analytical technology. In many instances, domain experts may become a reference for their scientific communities, thus facilitating new users engagement within the RI activities. As a collateral feedback effect, experiments will generate new relevant data, methods, and workflows that can be integrated into the platform by data scientists, contributing to the resource expansion of the RI. An experiment designed in a node of the RI and executed on the platform returns its results to the entire RI community.

Well defined thematic environments amplify new experiments achievements towards the vertical scientific communities (and potential stakeholders) by activating appropriate dissemination channels.

4.1 The SoBigData Research Infrastructure

The SoBigData Research Infrastructure Footnote 7 is an ecosystem of human and digital resources, comprising data scientists, analytics, and processes. As shown in Fig.  4 , SoBigData is designed to enable multidisciplinary scientists and innovators to realize social mining experiments and to make them reusable by the scientific communities. All the components have been introduced for implementing data science from raw data management to knowledge extraction, with particular attention to legal and ethical aspects as reported in Fig.  1 . SoBigData supports data science serving a cross-disciplinary community of data scientists studying all the elements of societal complexity from a data- and model-driven perspective.

Currently, SoBigData includes scientific, industrial, and other stakeholders. In particular, our stakeholders are data analysts and researchers (35.6%), followed by companies (33.3%) and policy and lawmakers (20%). The following sections provide a short but comprehensive overview of the services provided by SoBigData RI with special attention on supporting ethical and open data science [ 15 , 16 ].

4.1.1 Resources, facilities, and access opportunities

Over the past decade, Europe has developed world-leading expertise in building and operating e-infrastructures. They are large-scale, federated and distributed online research environments through which researchers can share access to scientific resources (including data, instruments, computing, and communications), regardless of their location. They are meant to support unprecedented scales of international collaboration in science, both within and across disciplines, investing in economy-of-scale and common behavior, policies, best practices, and standards. They shape up a common environment where scientists can create , validate , assess , compare , and share their digital results of science, such as research data and research methods, by using a common “digital laboratory” consisting of agreed-on services and tools.

The SoBigData Research Infrastructure: an ecosystem of human and digital resources, comprising data scientists, analytical methods, and processes. SoBigData enables multidisciplinary scientists and innovators to carry out experiments and to make them reusable by the community

However, the implementation of workflows, possibly following Open Science principles of reproducibility and transparency, is hindered by a multitude of real-world problems. One of the most prominent is that e-infrastructures available to research communities today are far from being well-designed and consistent digital laboratories, neatly designed to share and reuse resources according to common policies, data models, standards, language platforms, and APIs. They are instead “patchworks of systems,” assembling online tools, services, and data sources and evolving to match the requirements of the scientific process, to include new solutions. The degree of heterogeneity excludes the adoption of uniform workflow management systems, standard service-oriented approaches, routine monitoring and accounting methods. The realization of scientific workflows is typically realized by writing ad hoc code, manipulating data on desktops, alternating the execution of online web services, sharing software libraries implementing research methods in different languages, desktop tools, web-accessible execution engines (e.g., Taverna, Knime, Galaxy).

The SoBigData e-infrastructure is based on D4Science services, which provides researchers and practitioners with a working environment where open science practices are transparently promoted, and data science practices can be implemented by minimizing the technological integration cost highlighted above.

D4Science is a deployed instance of the gCube Footnote 8 technology [ 4 ], a software conceived to facilitate the integration of web services, code, and applications as resources of different types in a common framework, which in turn enables the construction of Virtual Research Environments (VREs) [ 7 ] as combinations of such resources (Fig.  5 ). As there is no common framework that can be trusted enough, sustained enough, to convince resource providers that converging to it would be a worthwhile effort, D4Science implements a “system of systems.” In such a framework, resources are integrated with minimal cost, to gain in scalability, performance, accounting, provenance tracking, seamless integration with other resources, visibility to all scientists. The principle is that the cost of “participation” to the framework is on the infrastructure rather than on resource providers. The infrastructure provides the necessary bridges to include and combine resources that would otherwise be incompatible.

D4Science: resources from external systems, virtual research environments, and communities

More specifically, via D4Science, SoBigData scientists can integrate and share resources such as datasets, research methods, web services via APIs, and web applications via Portlets. Resources can then be integrated, combined, and accessed via VREs, intended as web-based working environments tailored to support the needs of their designated communities, each working on a research question. Research methods are integrated as executable code, implementing WPS APIs in different programming languages (e.g., Java, Python, R, Knime, Galaxy), which can be executed via the Data Miner analytics platform in parallel, transparently to the users, over powerful and extensible clusters, and via simple VRE user interfaces. Scientists using Data Miner in the context of a VRE can select and execute the available methods and share the results with other scientists, who can repeat or reproduce the experiment with a simple click.

D4Science VREs are equipped with core services supporting data analysis and collaboration among its users: ( i ) a shared workspace to store and organize any version of a research artifact; ( ii ) a social networking area to have discussions on any topic (including working version and released artifacts) and be informed on happenings; ( iii ) a Data Miner analytics platform to execute processing tasks (research methods) either natively provided by VRE users or borrowed from other VREs to be applied to VRE users’ cases and datasets; and iv ) a catalogue-based publishing platform to make the existence of a certain artifact public and disseminated. Scientists operating within VREs use such facilities continuously and transparently track the record of their research activities (actions, authorship, provenance), as well as products and links between them (lineage) resulting from every phase of the research life cycle, thus facilitating publishing of science according to Open Science principles of transparency and reproducibility [ 5 ].

Today, SoBigData integrates the resources in Table  1 . By means of such resources, SoBigData scientists have created VREs to deliver the so-called SoBigData exploratories : Explainable Machine Learning , Sports Data Science , Migration Studies , Societal Debates , Well-being & Economy , and City of Citizens . Each exploratory includes the resources required to perform Data science workflows in a controlled and shared environment. Resources range from data to methods, described more in detail in the following, together with their exploitation within the exploratories.

All the resources and instruments integrate into SoBigData RI are structured in such a way as to operate within the confines of the current data protection law with the focus on General Data Protection Regulation (GDPR) and ethical analysis of the fundamental values involved in social mining and AI. Each item into the catalogue has specific fields for managing ethical issues (e.g., if a dataset contains personal info) and fields for describing and managing intellectual properties.

4.1.2 Data resources: social mining and big data ecosystem

SoBigData RI defines policies supporting users in the collection, description, preservation, and sharing of their data sets. It implements data science making such data available for collaborative research by adopting various strategies, ranging from sharing the open data sets with the scientific community at large, to share the data with disclosure restriction allowing data access within secure environments.

Several big data sets are available through SoBigData RI including network graphs from mobile phone call data; networks crawled from many online social networks, including Facebook and Flickr, transaction micro-data from diverse retailers, query logs both from search engines and e-commerce, society-wide mobile phone call data records, GPS tracks from personal navigation devices, survey data about customer satisfaction or market research, extensive web archives, billions of tweets, and data from location-aware social networks.

4.1.3 Data science through SoBigData exploratories

Exploratories are thematic environments built on top of the SoBigData RI. An exploratory binds datasets with social mining methods providing the research context for supporting specific data science applications by: (i) providing the scientific context for performing the application. This context can be considered a container for binding specific methods, applications, services, and datasets; (ii) stimulating communities on the effectiveness of the analytical process related to the analysis, promoting scientific dissemination, result sharing, and reproducibility. The use of exploratories promotes the effectiveness of the data science trough research infrastructure services. The following sections report a short description of the six SoBigData exploratories. Figure 6 shows the main thematic areas covered by each exploratory. Due to its nature, Explainable Machine Learning exploratory can be applied to each sector where a black-box machine learning approach is used. The list of exploratories (and the data and methods inside them) are updated continuously and continue to grow over time. Footnote 9

SoBigData covers six thematic areas listed horizontally. Each exploratory covers more than one thematic area

City of citizens. This exploratory aims to collect data science applications and methods related to geo-referenced data. The latter describes the movements of citizens in a city, a territory, or an entire region. There are several studies and different methods that employ a wide variety of data sources to build models about the mobility of people and city characteristics in the scientific literature [ 30 , 32 ]. Like ecosystems, cities are open systems that live and develop utilizing flows of energy, matter, and information. What distinguishes a city from a colony is the human component (i.e., the process of transformation by cultural and technological evolution). Through this combination, cities are evolutionary systems that develop and co-evolve continuously with their inhabitants [ 24 ]. Cities are kaleidoscopes of information generated by a myriad of digital devices weaved into the urban fabric. The inclusion of tracking technologies in personal devices enabled the analysis of large sets of mobility data like GPS traces and call detail records.

Data science applied to human mobility is one of the critical topics investigated in SoBigData thanks to the decennial experience of partners in European projects. The study of human mobility led to the integration into the SoBigData of unique Global Positioning System (GPS) and call detail record (CDR) datasets of people and vehicle movements, and geo-referenced social network data as well as several mobility services: O/D (origin-destination) matrix computation, Urban Mobility Atlas Footnote 10 (a visual interface to city mobility patterns), GeoTopics Footnote 11 (for exploring patterns of urban activity from Foursquare), and predictive models: MyWay Footnote 12 (trajectory prediction), TripBuilder Footnote 13 (tourists to build personalized tours of a city). In human mobility, research questions come from geographers, urbanists, complexity scientists, data scientists, policymakers, and Big Data providers, as well as innovators aiming to provide applications for any service for the smart city ecosystem. The idea is to investigate the impact of political events on the well-being of citizens. This exploratory supports the development of “happiness” and “peace” indicators through text mining/opinion mining pipeline on repositories of online news. These indicators reveal that the level of crime of a territory can be well approximated by analyzing the news related to that territory. Generally, we study the impact of the economy on well-being and vice versa, e.g., also considering the propagation of shocks of financial distress in an economic or financial system crucially depends on the topology of the network interconnecting the different elements.

Well-being and economy. This exploratory tests the hypothesis that well-being is correlated to the business performance of companies. The idea is to combine statistical methods and traditional economic data (typically at low-frequency) with high-frequency data from non-traditional sources, such as, i.e., web, supermarkets, for now-casting economic, socioeconomic and well-being indicators. These indicators allow us to study and measure real-life costs by studying price variation and socioeconomic status inference. Furthermore, this activity supports studies on the correlation between people’s well-being and their social and mobility data. In this context, some basic hypothesis can be summarized as: (i) there are curves of age- and gender-based segregation distribution in boards of companies, which are characteristic to mean credit risk of companies in a region; (ii) low mean credit risk of companies in a region has a positive correlation to well-being; (iii) systemic risk correlates highly with well-being indices at a national level. The final aim is to provide a set of guidelines to national governments, methods, and indices for decision making on regulations affecting companies to improve well-being in the country, also considering effective policies to reduce operational risks such as credit risk, and external threats of companies [ 17 ].

Big Data, analyzed through the lenses of data science, provides means to understand our complex socioeconomic and financial systems. On the one hand, this offers new opportunities to measure the patterns of well-being and poverty at a local and global scale, empowering governments and policymakers with the unprecedented opportunity to nowcast relevant economic quantities and compare different countries, regions, and cities. On the other hand, this allows us to investigate the network underlying the complex systems of economy and finance, and it affects the aggregate output, the propagation of shocks or financial distress and systemic risk.

Societal debates. This exploratory employs data science approaches to answer research questions such as who is participating in public debates? What is the “big picture” response from citizens to a policy, election, referendum, or other political events? This kind of analysis allows scientists, policymakers, and citizens to understand the online discussion surrounding polarized debates [ 14 ]. The personal perception of online discussions on social media is often biased by the so-called filter bubble, in which automatic curation of content and relationships between users negatively affects the diversity of opinions available to them. Making a complete analysis of online polarized debates enables the citizens to be better informed and prepared for political outcomes. By analyzing content and conversations on social media and newspaper articles, data scientists study public debates and also assess public sentiment around debated topics, opinion diffusion dynamics, echo chambers formation and polarized discussions, fake news analysis, and propaganda bots. Misinformation is often the result of a distorted perception of concepts that, although unrelated, suddenly appear together in the same narrative. Understanding the details of this process at an early stage may help to prevent the birth and the diffusion of fake news. The misinformation fight includes the development of dynamical models of misinformation diffusion (possibly in contrast to the spread of mainstream news) as well as models of how attention cycles are accelerated and amplified by the infrastructures of online media.

Another important topic covered by this exploratory concerns the analysis of how social bots activity affects fake news diffusion. Determining whether a human or a bot controls a user account is a complex task. To the best of our knowledge, the only openly accessible solution to detect social bots is Botometer, an API that allows us to interact with an underlying machine learning system. Although Botometer has been proven to be entirely accurate in detecting social bots, it has limitations due to the Twitter API features: hence, an algorithm overcoming the barriers of current recipes is needed.

The resources related to Societal Debates exploratory, especially in the domain of media ecology and the fight against misinformation online, provide easy-to-use services to public bodies, media outlets, and social/political scientists. Furthermore, SoBigData supports new simulation models and experimental processes to validate in vivo the algorithms for fighting misinformation, curbing the pathological acceleration and amplification of online attention cycles, breaking the bubbles, and explore alternative media and information ecosystems.

Migration studies. Data science is also useful to understand the migration phenomenon. Knowledge about the number of immigrants living in a particular region is crucial to devise policies that maximize the benefits for both locals and immigrants. These numbers can vary rapidly in space and time, especially in periods of crisis such as wars or natural disasters.

This exploratory provides a set of data and tools for trying to answer some questions about migration flows. Through this exploratory, a data scientist studies economic models of migration and can observe how migrants choose their destination countries. A scientist can discover what is the meaning of “opportunities” that a country provides to migrants, and whether there are correlations between the number of incoming migrants and opportunities in the host countries [ 8 ]. Furthermore, this exploratory tries to understand how public perception of migration is changing using an opinion mining analysis. For example, social network analysis enables us to analyze the migrant’s social network and discover the structure of the social network for people who decided to start a new life in a different country [ 28 ].

Finally, we can also evaluate current integration indices based on official statistics and survey data, which can be complemented by Big Data sources. This exploratory aims to build combined integration indexes that take into account multiple data sources to evaluate integration on various levels. Such integration includes mobile phone data to understand patterns of communication between immigrants and natives; social network data to assess sentiment towards immigrants and immigration; professional network data (such as LinkedIn) to understand labor market integration, and local data to understand to what extent moving across borders is associated with a change in the cultural norms of the migrants. These indexes are fundamental to evaluate the overall social and economic effects of immigration. The new integration indexes can be applied with various space and time resolutions (small area methods) to obtain a complete image of integration, and complement official index.

Sports data science. The proliferation of new sensing technologies that provide high-fidelity data streams extracted from every game, is changing the way scientists, fans and practitioners conceive sports performance. The combination of these (big) data with the tools of data science provides the possibility to unveil complex models underlying sports performance and enables to perform many challenging tasks: from automatic tactical analysis to data-driven performance ranking; game outcome prediction, and injury forecasting. The idea is to foster research on sports data science in several directions. The application of explainable AI and deep learning techniques can be hugely beneficial to sports data science. For example, by using adversarial learning, we can modify the training plans of players that are associated with high injury risk and develop training plans that maximize the fitness of players (minimizing their injury risk). The use of gaming, simulation, and modeling is another set of tools that can be used by coaching staff to test tactics that can be employed against a competitor. Furthermore, by using deep learning on time series, we can forecast the evolution of the performance of players and search for young talents.

This exploratory examines the factors influencing sports success and how to build simulation tools for boosting both individual and collective performance. Furthermore, this exploratory describes performances employing data, statistics, and models, allowing coaches, fans, and practitioners to understand (and boost) sports performance [ 42 ].

Explainable machine learning. Artificial Intelligence, increasingly based on Big Data analytics, is a disruptive technology of our times. This exploratory provides a forum for studying effects of AI on the future society. In this context, SoBigData studies the future of labor and the workforce, also through data- and model-driven analysis, simulations, and the development of methods that construct human understandable explanations of AI black-box models [ 20 ].

Black box systems for automated decision making map a user’s features into a class that predicts the behavioral traits of individuals, such as credit risk, health status, without exposing the reasons why. Most of the time, the internal reasoning of these algorithms is obscure even to their developers. For this reason, the last decade has witnessed the rise of a black box society. This exploratory is developing a set of techniques and tools which allow data analysts to understand why an algorithm produce a decision. These approaches are designed not for discovering a lack of transparency but also for discovering possible biases inherited by the algorithms from human prejudices and artefacts hidden in the training data (which may lead to unfair or wrong decisions) [ 35 ].

5 Conclusions: individual and collective intelligence

The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s [ 23 ]. Since 2012, every day 2.5 exabytes (2.5 \(\times \) 10 \(^18\) bytes) of data were created; as of 2014, every day 2.3 zettabytes (2.3 \(\times \) 10 \(^21\) bytes) of data were generated by Super-power high-tech Corporation worldwide. Soon zettabytes of useful public and private data will be widely and openly available. In the next years, smart applications such as smart grids, smart logistics, smart factories, and smart cities will be widely deployed across the continent and beyond. Ubiquitous broadband access, mobile technology, social media, services, and internet of think on billions of devices will have contributed to the explosion of generated data to a total global estimate of 40 zettabytes.

In this work, we have introduced data science as a new challenge and opportunity for the next years. In this context, we have tried to summarize in a concise way several aspects related to data science applications and their impacts on society, considering both the new services available and the new job perspectives. We have also introduced issues in managing data representing human behavior and showed how difficult it is to preserve personal information and privacy. With the introduction of SoBigData RI and exploratories, we have provided virtual environments where it is possible to understand the potentiality of data science in different research contexts.

Concluding, we can state that social dilemmas occur when there is a conflict between the individual and public interest. Such problems also appear in the ecosystem of distributed AI systems (based on data science tools) and humans, with additional difficulties due: on the one hand, to the relative rigidity of the trained AI systems and the necessity of achieving social benefit, and, on the other hand, to the necessity of keeping individuals interested. What are the principles and solutions for individual versus social optimization using AI, and how can an optimum balance be achieved? The answer is still open, but these complex systems have to work on fulfilling collective goals, and requirements, with the challenge that human needs change over time and move from one context to another. Every AI system should operate within an ethical and social framework in understandable, verifiable, and justifiable way. Such systems must, in any case, work within the bounds of the rule of law, incorporating protection of fundamental rights into the AI infrastructure. In other words, the challenge is to develop mechanisms that will result in the system converging to an equilibrium that complies with European values and social objectives (e.g., social inclusion) but without unnecessary losses of efficiency.

Interestingly, data science can play a vital role in enhancing desirable behaviors in the system, e.g., by supporting coordination and cooperation that is, more often than not, crucial to achieving any meaningful improvements. Our ultimate goal is to build the blueprint of a sociotechnical system in which AI not only cooperates with humans but, if necessary, helps them to learn how to collaborate, as well as other desirable behaviors. In this context, it is also essential to understand how to achieve robustness of the human and AI ecosystems in respect of various types of malicious behaviors, such as abuse of power and exploitation of AI technical weaknesses.

We conclude by paraphrasing Stephen Hawking in his Brief Answers to the Big Questions: the availability of data on its own will not take humanity to the future, but its intelligent and creative use will.

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Grossi, V., Giannotti, F., Pedreschi, D. et al. Data science: a game changer for science and innovation. Int J Data Sci Anal 11 , 263–278 (2021). https://doi.org/10.1007/s41060-020-00240-2

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• Published: 28 November 2023

Does the Belt and Road Initiative promote international innovation cooperation?

• Weiwei Xiao 1 ,
• Qihang Xue 2 , 3 &
• Xing Yi 1  

Humanities and Social Sciences Communications volume  10 , Article number:  880 ( 2023 ) Cite this article

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• Development studies

International innovation cooperation is crucial to the enhancement of national technological innovation capabilities in the context of globalization. Does the Belt and Road Initiative (BRI), as a major international initiative, promote innovation cooperation between China and BRI partner countries? Taking the top 80 global countries in innovation capability as the research sample, this paper uses the DID method to answer this question. The empirical results show that the BRI indeed promoted innovation cooperation between them to a certain extent. Specifically, it significantly increased the proportion of their cooperative patents in China’s total patents, and the promotion effect was more obvious for countries with better economic foundations. Furthermore, the mechanism tests indicate that shortening the institutional distance, strengthening the exchange of scientific and technological talents, and stimulating cultural differences were important mechanisms promoting their innovation cooperation. Although the BRI did not significantly increase the proportion of cooperative patents in BRI partner countries’ total patents, it effectively improved their innovation foundations and capabilities.

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Introduction.

Innovation is essential for sustainable economic growth (Lucas, 1988 ; Romer, 1986 ). Moreover, innovation cooperation can further increase knowledge diversity and stimulate creativity (Fleming et al., 2007 ; Reagans and Zuckerman, 2001 ) to provide an inexhaustible impetus for the prosperity and development of a country or region. At present, the world’s innovation landscape is constantly reshaping, emerging countries are continuously improving their innovation capabilities, and the demand for innovation cooperation between countries is also increasing. In the context of globalization, key innovation elements such as talent, knowledge, and technology are rapidly flowing around the world, providing a good opportunity for international innovation cooperation. Many studies document that innovation cooperation between countries can not only improve the value and quality of innovation results (Bercovitz and Feldman, 2011 ; Singh and Fleming, 2010 ) but also broaden the channels for underdeveloped countries to achieve technological catch-up and surpass their targets through international knowledge spillovers (Giuliani et al., 2016 ). Strengthening international innovation cooperation and actively integrating into the global science and technology innovation network are undoubtedly of great significance for enhancing the innovation strength of countries, especially developing countries, and for thereby promoting healthy and sustainable economic development.

The Belt and Road Initiative (hereafter referred to as the BRI) provides a broader space and platform for innovation cooperation between countries. The BRI is a road of innovation. In 2016, the Special Plan for Promoting the Scientific and Technological Innovation Cooperation of the Belt and Road Construction was jointly issued by the Ministry of Science and Technology and three other ministries of China. It aims to strengthen the exchange of scientific and technological talent between countries, build an innovation cooperation platform, and formulate and implement targeted scientific and technological innovation cooperation policies to promote sustainable development and common prosperity. In 2019, China, Thailand, Russia, etc., jointly signed the Cooperation Initiative on the Silk Road of Innovation , promising to further deepen pragmatic cooperation in the field of scientific and technological innovation. Furthermore, the Chinese government has actively called on enterprises, universities, research institutions, and other innovative entities to fully participate in international innovation cooperation. For instance, the Chinese Academy of Sciences launched science and education development projects in developing countries since 2013 to cultivate more than 5000 professional and technical talents for countries along the Belt and Road routes. Technological innovation cooperation is the core content and important driving force of BRI construction. Therefore, it has important theoretical value and practical significance for examining the BRI’s economic and social effects from the perspective of international innovation cooperation and exploring the interactive relationship between China and BRI partner countries (i.e., countries that have signed BRI cooperation documents).

Since the BRI was proposed, it has attracted widespread attention from scholars. They have carried out systematic research from the perspectives of foreign investment (Herrero and Xu, 2017 ; Wiederer, 2018 ), energy efficiency (Peng et al., 2021 ), export trade (Ramasamy and Yeung, 2019 ), financial cooperation (Sun and Hou, 2019 ), global value chain optimization (Dai and Song, 2021 ), etc., and achieved valuable research results. However, although the BRI has provided many conveniences for innovation cooperation among countries, there are few studies concerned with whether it can promote international innovation cooperation, and the motivation and internal mechanisms behind the question.

Based on the above analysis, the relationship between the BRI and international innovation cooperation is explored next. The top 80 global countries in innovation capacity are taken as the research sample. Whether the country officially signed BRI cooperation documents with China is regarded as a quasi-natural experiment. From the perspectives of the initiator (China) and the partners (BRI partner countries), we use the difference-in-differences (DID) method to test whether the BRI can promote innovation cooperation between them. The empirical results show that the BRI indeed promoted innovation cooperation to a certain extent. The promotion effect is more obvious for partner countries with better economic foundations. Specifically, it significantly increased the proportion of cooperative patents in China’s total patents, indicating that the Chinese government is more motivated and willing to have more resources to cooperate with BRI partner countries. Furthermore, the mechanism tests show that shortening institutional distance, strengthening the exchange of scientific and technological talent, and stimulating cultural differences are important mechanisms promoting innovation cooperation. Although the BRI did not significantly increase the proportion of cooperative patents in partner countries’ total patents, it effectively improved their innovation foundations and capabilities.

Our marginal contributions are as follows: first, investigating the BRI’s economic and social effects from the perspective of international innovation cooperation broadens the research scope of the BRI; second, the impact of the BRI on the initiator and partners is discussed, and the impact differences between them are compared. Moreover, the reasons behind it are explained. Third, the mechanisms of the BRI affecting innovation cooperation are explored from the aspects of institutional distance, cultural distance, and the exchange of scientific and technological talent. The positive effect of the BRI on the innovation foundations and capabilities of partner countries is examined.

The remainder of the paper is organized as follows. Section “Literature review and research hypotheses” gives the literature review and research hypotheses. Section “Research design” describes the research design, mainly introducing the model design, variables, and data sources. Section “Empirical results” analyzes the empirical results. Section “Mechanism analysis” tests the impacting mechanisms. Section “Further analysis: The BRI and BRI partner countries' innovation capabilities” further explores the impact of the BRI on BRI partner countries’ innovation capabilities. Section “Discussion” provides further discussion. The last section summarizes the main conclusion and policy implications.

Literature review and research hypotheses

Literature review.

There are mainly two strands of literature closely related to this study. The first strand of literature examines the socio-economic effects of the BRI. Some of them mainly focus on the impact of the BRI on China’s economic development. They found that it significantly increases China’s potential for outward FDI and exports (Shao, 2020 ; Yu et al., 2020 ), enhances the technological innovation capabilities of domestic enterprises through market competition and capacity utilization (Wu and Si, 2022 ), creates more job opportunities for China (Liao et al., 2021 ), and improves the quality of economic growth in Chinese cities (Kong et al., 2021 ). Additionally, some also found that the BRI causes more “hostility” to China’s overseas investments (Jin et al., 2021 ) and an increase in carbon emissions (Xiao et al., 2023 ).

Other literature focuses on the impact of the BRI on the partner countries and other economies. They found that it improves the business environment of the BRI partner countries (Chen et al., 2020 ), and significantly enhances their international trade status and exports (Ramasamy and Yeung, 2019 ), thereby promoting their actual income and economic performance (Bird et al., 2020 ; Yang et al., 2020 ; Ma, 2022 ). Additionally, the BRI also brings closer communication between countries (Dang and Zhao, 2020 ), and improves trade integration level (Han et al., 2018 ) and energy efficiency (Peng et al., 2021 ). However, some scholars argue some negative impacts of the BRI on other countries. For instance, Overholt ( 2015 ) considered China’s expansion of outward FDI aims at transferring excess production capacity overseas; Howard and Howard ( 2016 ) deemed that China transfers some industries with high pollution and high-energy consumption to the partner countries; Bruni ( 2019 ) showed that the BRI exacerbates partner countries’ income inequality.

The second strand of literature explores the influencing factors of innovation cooperation. Innovation cooperation refers to the joint acquisition or creation of knowledge and technology, which can enhance the absorption capacity and innovation performance of innovation entities (Fleming et al., 2007 ; Mendi et al., 2020 ). Hence, scholars mainly explore the influencing factors of innovation cooperation from the perspectives of R&D costs, external environment, geographical location, and partner types. They found that when R&D costs and innovation difficulty are high, innovation entities tend to prefer innovation cooperation (Becker and Dietz, 2004 ; Marchi et al., 2022 ). Enterprises located in technology parks and those with low environmental uncertainty are more likely to choose innovation cooperation (Urriago et al., 2016 ; Liu et al., 2023 ). Faria et al. ( 2010 ) and Liu et al. ( 2023 ) emphasized the importance of partners and found that different partners have a significant impact on the probability and outcome of innovation cooperation.

International innovation cooperation is also influenced by institutional and cultural distances between countries. Among them, institutional distance refers to the differences in laws, regulations, policy environments, and market rules between different countries (Song et al., 2011 ), while cultural distance refers to the differences in values, thinking patterns, customs, and beliefs between different countries (Estrin et al., 2010 ). Scholars showed that both formal and informal institutional distances have a significant impact on innovation performance and cooperation (Wang and Chuang, 2020 ; Wang et al., 2023 ), and cultural distance has become a key factor affecting innovation transfer among multinational enterprises (Ansari et al., 2014 ). Jensen and Szulanski ( 2004 ) and Alofan et al. ( 2020 ) argued that the increase in cultural distance between countries significantly suppresses knowledge spillovers and technology transfer, thus exerting a negative impact on innovation cooperation.

To sum up, although many scholars have investigated the social and economic effects of the BRI on various countries, few have paid attention to its impact on innovation cooperation among countries. Meanwhile, in the investigation of the influencing factors of innovation cooperation, there is also little literature on the impact of major international initiatives on innovation cooperation between countries. In view of this, this study empirically explores the impact of the BRI on innovation cooperation between countries and the mechanisms, so as to expand and supplement the aforementioned two strands of literature.

Research hypotheses

Technological innovation cooperation is an important force in promoting the BRI toward high-quality development. Chinese President Xi Jinping reiterated at the 2nd Belt and Road Forum for International Cooperation that the Technology and Innovation Cooperation Action Plan is one of the most critical parts of the Belt and Road Initiative. Additionally, with increasing scientific and technological innovation strength, China has the basic conditions to participate in and lead international scientific and technological innovation cooperation. Statistics indicate that in 2019, the number of PCT international patent inventions in China exceeded 58,000, second only to the United States; the number of published scientific papers in China was first in the world, and the number of high-quality international scientific papers was second in the world. China also pays high attention to international scientific and technological innovation cooperation. In 2017, the number of international coauthored papers in China was 97,400, accounting for 27.0% of the total number of published scientific papers. China’s international scientific and technological innovation continues to increase. It can be said that the BRI has opened up space for scientific and technological innovation cooperation between China and other countries and deepened cooperation through political mutual trust, talent exchanges, cultural tolerance, etc., to jointly solve the technical problems of economic development.

First, the BRI can promote macro-policy coordination, reduce policy barriers, and shorten institutional distances between China and BRI partner countries, thereby promoting their cooperation in science and technology innovation. Generally, the expansion of institutional distance has a negative impact on innovation cooperation (Li et al., 2014 ). Specifically, (i) large institutional distance increases the cost of cooperation between parties. They need to spend more time and energy to understand and adapt to each other’s institutional environment, thereby increasing the difficulty and uncertainty of innovation cooperation (Banalieva and Dhanaraj, 2013 ). (ii) Large institutional distance makes it difficult to form uniform rules and procedures within the innovation group, and the innovation output is also likely to deviate from the policy orientation and market preference of the host countries (Li et al., 2014 ). (iii) Enterprises are one of the most important subjects of technological innovation, and their transnational commercial activities are also the main carriers of innovation cooperation. However, multinational companies prefer to conduct investment and business cooperation in countries with similar systems to reduce transaction costs and investment risks, thereby obtaining higher economic returns (Wang et al., 2016 ). The BRI effectively shortens the institutional distance between China and BRI partner countries. Policy communication and mutual trust are the BRI’s important elements. China and partner countries jointly formulated cooperation plans and maintained communication about development strategies and implementation policies, providing institutional guarantees for cooperation and exchanges.

Second, as a national-level strategic agreement, one of the goals of the BRI is to jointly establish a community of common destiny with cultural tolerance. McKercher and Chow ( 2001 ) showed that people are more willing to engage in cultural and knowledge-related activities in areas with larger cultural differences. Furthermore, compared to cultural convergence, cultural divergence is more effective in promoting innovation cooperation, which can provide more exchanges and collisions of ideas (Vaara et al., 2012 ). However, due to the trust and communication barriers brought about by cultural distance (Beugelsdijk et al., 2014 ), it is difficult to play a role in promoting innovation cooperation. The BRI can effectively break these barriers. Various innovative entities cooperate under the guidance of the government and are endorsed by national credibility, breaking the trust barriers caused by cultural differences. With increasingly close cultural exchanges, talent exchanges, and academic exchanges, the cooperative relationship under different cultural backgrounds has also been further consolidated to give play to the positive impact of cultural differences on innovation cooperation.

Finally, the BRI provides a good opportunity for the exchange of scientific and technological talent between countries, thereby laying a talent foundation for their innovation cooperation. Talent is the core determinant of innovation activities and capabilities. Talent exchange can enhance communication and trust and increase the knowledge stock of innovative entities (Johnson, 2010 ), thus promoting innovation cooperation. People-to-people communication is the core to the long-term sustainability of the BRI, and talent exchange is one of the most important areas. The BRI provides more convenient conditions and policy support for talent exchange. For instance, in July 2016, the Ministry of Education of China announced that China would subsidize 10,000 new students from countries along the Belt and Road routes to study in China or other countries each year for the next 5 years and send 2500 students to study in countries along the Belt and Road routes each year in the next 3 years.

Based on the above analysis, the following four research hypotheses are proposed:

Hypothesis 1: The BRI can promote innovation cooperation between China and BRI partner countries.

Hypothesis 2: The BRI can shorten the institutional distance, thereby promoting innovation cooperation.

Hypothesis 3: The BRI can give full play to the positive effect of cultural distance on innovation cooperation.

Hypothesis 4: The BRI can enhance talent exchange, thereby promoting innovation cooperation.

Research design

The top 80 global countries in innovation capability released by the World Intellectual Property Organization are selected as the research sample herein. The reasons are that on the one hand, the GDP of these countries accounts for more than 87% of the world’s total, and their total number of multinational cooperative invention patents accounts for more than 97% of the world, while the number of multinational cooperative patents in the remaining countries is extremely small, especially the number of cooperative patents with China, which is almost zero; on the other hand, the sample countries cover Asia, Europe, North America, Oceania, Africa and other regions, including developed and developing countries, which can fully reflect the innovation cooperation between countries. The BRI was formally proposed in 2013. Considering the availability of the required data, we finally selected the 5 years before and after the BRI was proposed as the sample period, i.e., 2008–2018. Then, since this paper studies the level of innovation cooperation between China and other countries, China and Hong Kong are excluded. Due to missing data, Montenegro, Serbia, and Bulgaria are also removed. Ultimately, 825 country-year observations were obtained.

Model setting

To effectively identify whether participation in the BRI (i.e., signing BRI cooperation documents with China) can promote international innovation cooperation, the DID method is employed to evaluate the effect of the BRI based on the literature (Yu et al., 2020 ; Jiang et al., 2021 ; Lee and Wang, 2022 ). This method has been widely recognized and applied because it can better identify causal effects according to the existence of a policy and the difference-in-differences before and after the policy (Yu et al., 2020 ). Since the BRI partner countries and China signed BRI cooperation documents in different years, the following staggered DID model is established:

where c and t represent the country and the year, respectively; Treat is a dummy variable that distinguishes the treatment group from the control group. When a country formally signed BRI cooperation documents with China, it equals 1; otherwise, it equals 0. Post is a time dummy variable. When a country signed the cooperation documents in and after the year, it equals 1; otherwise, it equals 0. X ct denotes a series of control variables at the national level, including economic level, industrial structure, labor structure, urbanization level, FDI level, etc.; μ t and ω c represent the year and country fixed effects, respectively; σ ct is the random disturbance term. Due to the existence of multicollinearity, the treatment variable (Treat) and country fixed effects, time variable (Post), and year fixed effects cannot be included in the model at the same time, so they are omitted.

Variables and data sources

Explained variable.

The level of international innovation cooperation. Patents can directly reflect innovation achievements and can be used to accurately and effectively measure the national innovation level (Popp, 2006 ). Hence, following Giuliani et al. ( 2016 ), we adopt the number of joint patents between countries to measure the level of innovation cooperation between them. The patent data are derived from WIPO’s PCT international patent database, which collects relevant information about PCT international patent applications from countries around the world. This database can effectively avoid the data deviation of a single national patent office and the data duplication statistics of different patent offices. We use the advanced search method to manually collect the number of patents jointly invented by China and other countries from 2008 to 2018 and the total number of invention patents of each country from 2008 to 2018 according to the nationality of the patent inventor. Furthermore, to reveal the impact of the BRI on innovation cooperation in a more detailed and accurate manner, following Giuliani et al. ( 2016 ), we divide the number of joint patents by the total number of invention patents in China and other countries and then multiply it by 100 to construct the innovation cooperation level variables HOMIC and HOSIC. The two variables have two advantages. On the one hand, compared with the absolute value of cooperative patent data, the relative value is more accurate and effective because it is not easily affected by the total number of national innovation patents, national innovation capabilities, economic level, and other factors. On the other hand, the national innovation cooperation level is measured from both parties: China and other countries. It is possible to more systematically and comprehensively examine the impact of the BRI on innovation cooperation between them.

Core explanatory variable: Treat ×Pos t

The staggered DID model is used herein. Treat × Post is the interaction item of the treatment variable (Treat) and the time variable (Post). Therefore, when country c officially signed BRI cooperation documents with China in year t , Treat × Post equals 1; otherwise, it equals 0.

Control variables

Following the existing studies (Yu et al., 2020 ; Nugent and Lu, 2021 ; Jin et al., 2021 ), we select the following five national-level control variables to ensure the validity of the empirical results: (i) economic scale (PGDP), expressed by GDP per capita; (ii) industrial structure (ADV), expressed by the proportion of manufacturing added value in GDP; (iii) human capital level (LABOR), expressed by the proportion of people aged 15–64 in the total population; (iv) urbanization level (URBAN), expressed by the proportion of the urban population in the total population; and (v) openness level (OPEN), expressed by the amount of FDI. The above data are all derived from the World Bank Open Data. Table 1 gives their descriptive statistics.

Empirical results

Baseline regression.

Table 2 reports the baseline regression results. Specifically, Column (1) shows the impact of the BRI on the level of innovation cooperation for China (hereafter referred to as HOMIC). The estimated coefficient of Treat  ×  Post is significantly positive at the 1% level, indicating that the BRI indeed significantly improved the HOMIC, i.e., the proportion of the number of cooperative patents between China and the BRI partner countries in China’s total patents; Column (2) reports the impact of the BRI on the level of innovation cooperation for the BRI partner countries (hereafter referred to as HOSIC). The estimated coefficient of Treat  ×  Post is not significant, indicating that the BRI did not significantly promote the HOSIC, i.e., the proportion of the number of cooperative patents between China and the BRI partner countries in the BRI partner countries’ total patents. Furthermore, considering the lag of the policy and the delay of patent applications, the coefficient of the core explanatory variable Treat × Post is re-estimated by taking its lag term. Columns (3) and (4) report the regression results, showing that the BRI only significantly increased the HOMIC but had no significant impact on the HOSIC. The reason is that for HOMIC, as the BRI’s initiator, China naturally has stronger motives and incentives to promote its cooperation with the signatories of the BRI (Nugent and Lu, 2021 ). Under the guidance of the Chinese government, Chinese companies favor the signatories of the BRI when investing or cooperating with other countries (Yu et al., 2020 ) to improve HOMIC; for HOSIC, as of 2018, most of the countries that have signed BRI cooperation documents with China are developing or underdeveloped countries with weak economic and innovation foundations. A large amount of resource input is the basis and prerequisite for innovative activities (Huang et al., 2019 ). Therefore, their weak economic and innovation foundations become the most important factors restricting the improvement of the level of cross-border innovation cooperation (Manso, 2011 ). This may also be an important reason why their level of innovation cooperation with other countries is not significantly sensitive to external policy shocks.

To verify the above conjecture, the difference-in-differences-in-differences (DDD) model is established to test whether the BRI promoted innovation cooperation between China and countries with a solid economic foundation. In this regard, a dummy variable Income is set to measure the economic basis. Specifically, following Bolarinwa and Akinlo ( 2021 ) and using data from the World Bank, a country with a per capita national income greater than or equal to US$3896 is defined as a country with a solid economic foundation, and its Income equals 1; otherwise, it is defined as a country with a weak economic foundation, and its Income equals 0. The DDD model is built as follows (Qi et al., 2021 ):

If the estimated coefficient of Treat × Post × Income is significantly positive, the promoting effect of the BRI on international innovation cooperation with a solid economic foundation country is more significant. Columns (5) and (6) of Table 2 report the regression results. In Column (5), the estimated coefficient of Treat × Post × Income is significantly positive, indicating that as the countries’ economic level increased, the BRI became increasingly effective in promoting HOMIC; in Column (6), the explained variable is HOSIC, and the estimated coefficient of Treat × Post × Income is also positive but not significant. The above results further illustrate that the effect of the BRI on promoting innovative cooperation is more pronounced in regions with better economic development foundations.

Parallel trend tests

The DID model is employed to test the impact of the BRI on innovation cooperation between China and BRI partner countries. An important prerequisite for using this model is that the parallel trend assumption holds prior to the BRI. According to the existing studies (Du and Zhang, 2018 ; Moser and Voena, 2009 ), the interaction term between the dummy variable of signing BRI cooperation documents (Treat) and the year dummy variable (Year) is introduced in the model, and the year before the BRI is taken as the benchmark for regression testing (as shown in Table 3 ). pre and post represent the interaction items before and after the BRI, respectively. Additionally, to prevent the existence of autocorrelation factors in time series in different countries in different years during a long time window, standard errors are clustered at the country and year joint level (Bo, 2020 ). Table 3 shows that for HOMIC, the coefficients of the interaction terms before the BRI are not significant and negative, and the coefficients of the interaction terms after the BRI are significantly positive; for HOSIC, the coefficients of the interaction terms before and after the BRI are not significant. This shows that the parallel trend assumption holds, proving the validity of the model.

Robustness tests

The previous empirical results show that the BRI indeed promoted innovation cooperation between China and BRI partner countries, which is mainly reflected in its significant increase in the number of cooperative patents as a proportion of China’s total patents. However, the proportion of cooperative patents in BRI partner countries’ total patents did not increase significantly. To further ensure the robustness and reliability of the empirical conclusion, we perform a series of robustness tests, specifically redefining the BRI partner countries, removing the special years and countries, eliminating the interference of other policies, carrying out placebo tests and using the PSM-DID method.

Redefining BRI partner countries

In the above baseline regression, the countries that have officially signed BRI documents with China are defined as BRI partner countries, and the policy shock time is defined as the year when the cooperation documents were signed. However, some scholars define the countries along the Belt and Road routes as BRI partner countries and set the time of policy shock to 2013 (Kong et al., 2021 ; Jiang et al., 2021 ). The reasons that the BRI was formally proposed by China in 2013 to establish friendly and win‒win bilateral and multilateral mechanisms with countries around the world. When the BRI was put forward, the countries along it were the first to be affected, and the Chinese government and enterprises also took the lead in cooperation and exchanges with them (Kong et al., 2021 ). Therefore, we redefine the countries along the Belt and Road routes as the treatment group and set the policy shock time to 2013. Columns (1) and (2) of Table 4 report the regression results. For HOMIC, the estimated coefficient of the new core explanatory variable (Treat × Post2) is significantly positive at the 1% level; however, for HOSIC, the estimated coefficient of Treat × Post2 is still not significant, which is consistent with the previous empirical conclusion.

Removing the special years

In the baseline regression, BRI partner countries are those formally signing cooperation documents with China. However, when the BRI was proposed, although some countries had not formally signed cooperation documents with China, they may have been affected by the BRI (Li and Li, 2020 ). To avoid its influence on the empirical conclusion, 2013 (the year when the proposal was proposed) and 2014 (considering the policy lag) are removed sequentially for reregression. The results of Columns (3)–(6) in Table 4 show that the BRI still significantly promoted HOMIC but did not obviously promote HOSIC, again supporting the results of the baseline regression.

Removing the special countries

The number of patents jointly invented by countries and China is taken as the research object to examine the impact of the BRI on international innovation cooperation. However, the number of joint invention patents of some countries with China during the entire sample period is always zero. The possible reasons are that these countries have a weak innovation foundation, their total innovation output has always been at a very low level, or the channel for innovation cooperation between them and China has not been opened due to the intervention of other factors. Including these countries in the research sample may cause certain biases. In this regard, countries with zero patents in cooperation with China from 2008 to 2018 are removed from the regression. The results of Columns (7) and (8) in Table 4 show that the significance and sign of the estimated coefficients of Treat×Post are basically consistent with the baseline regression.

Eliminating the interference of other policies

When examining the impact of the BRI on innovation cooperation between China and BRI partner countries, it is necessary to avoid the influence of other policies in the same period (Kong et al., 2021 ). The China–Central and Eastern Europe ‘16 + 1’ cooperation framework established by China and 16 Central and Eastern European countries in 2012 may interfere with the impact of the BRI (Dai and Song, 2021 ). On the one hand, this cooperation system was established in 2012, only one year after the proposal of the BRI. On the other hand, Central and Eastern Europe is one of the regions closest to the BRI. Both the China–Central and Eastern Europe ‘16 + 1’ and the BRI emphasized in-depth cooperation in technological innovation, infrastructure construction, and investment. In addition, the distribution of the 16 Central and Eastern European countries in the treatment group and the control group is also different. It is necessary to remove them for regression to eliminate the possible interference caused by the China–Central and Eastern Europe ‘16 + 1’ cooperation framework. Columns (9) and (10) of Table 4 report the regression results. We can see that the results are consistent with the above baseline analysis. This also means that after eliminating the possible interference of the China–Central and Eastern Europe ‘16 + 1’ cooperation framework, the research conclusion is still robust.

Placebo tests

To further test the reliability of the empirical conclusion, a false treatment group is constructed for placebo tests. The countries that did not participate in the BRI at the end of 2018 are regarded as the false treatment group, and 2013 is used as the external shock year (Dai and Song, 2021 ). Since the level of innovation cooperation of nonpartner countries cannot be affected by the BRI, if they are used as the treatment group and the regression estimation results are also significantly positive, then the BRI did not have a real impact on innovation cooperation. Columns (11) and (12) of Table 4 report the regression results. The estimated coefficients of Treat × Post are no longer significantly positive, which supports the baseline regression conclusion. It is worth mentioning that for HOMIC, the estimated coefficient of Treat × Post is significantly negative at the 1% level. A possible explanation is that after the BRI was put forward, the Chinese government and enterprises preferred BRI partner countries when cooperating with other countries. In contrast, under the premise of limited resources and projects, innovative cooperation activities between China and nonpartner countries may be squeezed out (Li and Li, 2020 ). Therefore, the estimated coefficient of Treat × Post is significantly negative.

PSM-DID method

To further alleviate the endogeneity problem caused by sample selection bias, the method of combining propensity score matching and difference-in-differences (PSM-DID) is used for regression. One advantage of this method is to ensure that the basic characteristics of the treatment group and the control group were not significantly different before the policy was implemented (Jiang et al., 2021 ; Liu et al., 2020 ). Columns (13) and (14) of Table 4 report the regression results. We can see that the results are consistent with the previous baseline regression results.

Mechanism analysis

The previous results confirm that the BRI indeed significantly promoted innovation cooperation between China and BRI partner countries. Specifically, it increased the number of joint invention patents as a proportion of China’s total invention patents. Therefore, what are the specific mechanisms? Next, the mechanisms are analyzed from three aspects: institutional distance, cultural distance, and talent exchange.

Institutional distance

The BRI may shorten the institutional distance between two countries, thereby promoting innovation cooperation. Li et al. ( 2014 ) showed that a large institutional distance has a negative impact on innovation cooperation between countries. The reasons are that the expansion of institutional distance not only increases the cost and difficulty of cooperation (Banalieva and Dhanaraj, 2013 ) but also makes innovation output deviate from market demand (Li et al., 2014 ), making it difficult to obtain expected economic returns. The BRI may effectively shorten the institutional distance between China and BRI partner countries. Officially signing BRI cooperation documents means that the consensus between the two parties is further deepened at the national strategic level. They will continue to communicate and adjust policies to provide a better institutional environment and cooperation platform for more comprehensive cooperation.

Therefore, based on relevant studies (Guo and Tu, 2021 ; Wu and Pan, 2019 ), worldwide governance indicators (WGI) are used to calculate the institutional distance between countries. The specific formula is as follows:

where I Kt and I c kt represent the scores of China and country c on institutional dimension K , respectively; V IK represents the variance of the scores of all sample countries; and K represents the six dimensions of WGI. The data are derived from the World Bank WGI. A greater SD means a longer institutional distance. Columns (1) and (2) of Table 5 report the regression results. We can see that the estimated coefficient of the core explanatory variable (Treat × Post) is significantly negative regardless of whether the control variables are added. This indicates that after signing the BRI, the institutional distance between China and partner countries has been significantly shortened, providing a good institutional environment for innovation cooperation.

Cultural distance

The BRI can better bring into play the promotion effect of cultural distance on innovation cooperation, thereby improving HOMIC. Regarding the impact of cultural distance on innovation cooperation, the academic community has not yet reached an agreement. Some have argued that a large cultural distance makes it difficult for innovative entities with different cultural backgrounds to establish reliable trust relationships (Beugelsdijk et al., 2014 ) and even cause cultural conflicts, which is not conducive to the maintenance of cooperative relationships (Chang et al., 2012 ). Other studies have opposed this view. They considered that the differences in thinking and the complementarity of knowledge brought about by cultural distance are more likely to stimulate innovative inspiration and thinking (Vaara et al., 2012 ), which is conducive to improving the level of innovation cooperation. Following Liu et al. ( 2021 ), we use the KSI index provided by Kogut and Singh ( 1988 ) to measure cultural distance to test the impact of cultural distance on innovation cooperation. The specific formula is as follows:

where C K and C ck represent the scores of China and country c on cultural dimension K , respectively; V CK represents the variance of the scores of all sample countries; and K represents the six dimensions of the KSI index. The data are derived from the Geert Hofstede website. In addition, considering that our research data are panel data, the interaction term between the cultural distance variable and time variable is constructed to form panel data (Nunn and Qian, 2014 ). First, the full sample data are used to test the impact of cultural distance on international innovation cooperation (Column (3) in Table 5 ). The results show that the coefficient of cultural distance is positive but not significant, indicating that cultural distance promoted innovation cooperation among countries, which is also consistent with the relevant literature (Vaara et al., 2012 ). Cultural differences can improve the level of innovation cooperation, but there are currently some obstacles preventing the promotion effect. To test whether the BRI can break these obstacles and better play the role of cultural distance in promoting innovation cooperation, subsample regression is performed according to whether or not to participate in the BRI. Columns (4) and (5) of Table 5 report the regression results. They show the results with nonpartner and partner countries as the research sample. We find that under the premise that other variables remain the same, cultural distance is significantly positive in the sample of BRI partner countries, showing that the BRI enhanced trust and cultural exchanges among partner countries and broke the natural barriers of cultural distance so that the promoting effect of cultural diversity on innovation cooperation between countries is revealed. The reasons are that the BRI is strategic cooperation at the national level, which can provide a trust guarantee for cross-border cooperation with government credibility, build a solid bridge for cooperation between innovative entities under different cultural backgrounds, and break the barriers to cooperation caused by cultural distance to better play its role in promoting innovation cooperation between countries.

Talent exchange

The BRI can also promote the exchange of talent between countries, thereby enhancing the level of innovation cooperation between them. Talent is the most fundamental factor determining the innovation capability of an enterprise, region, and country (Mannasoo et al., 2018 ; David and Kamel, 2009 ). International talent exchange can obviously promote the dissemination of technology and knowledge among different countries and lay a talent foundation for innovative cooperation between countries. Therefore, to test whether the BRI significantly promoted the exchange of talent between China and BRI partner countries, the number of Chinese students studying abroad and the number of foreign students in China as proxy variables of talent exchange (denoted as Talex1 and Talex2, respectively) are selected for regression. Columns (6) and (7) of Table 5 report the regression results. We can see that after joining the BRI, the number of personnel exchanges between China and partner countries increased significantly. Talent is the foundation of innovation, and exchanges and cooperation between talent can significantly improve the level of innovation cooperation among countries.

Further analysis: The BRI and BRI partner countries’ innovation capabilities

The above results show that the BRI significantly promoted HOMIC but did not significantly promote HOSIC. The possible reason is that the innovation foundations of some BRI partner countries are relatively weak. Did the BRI help increase the innovation expenditure of BRI partner countries and improve their innovation capabilities? This problem needs to be further analyzed. Next, we empirically explore the impact of the BRI on the innovation capabilities of BRI partner countries.

The previous results show that for BRI partner countries, the BRI did not significantly promote the ratio of the number of cooperative patents to partner countries’ total patents. The possible reason is that the innovation foundations of these countries are relatively weak. The promotion effect of the BRI may be mainly reflected in improving their innovation foundations and innovation capabilities. To test this conjecture, we empirically test whether partner countries’ innovation capabilities significantly improved after joining the BRI. Existing studies (Du et al., 2019 ; Kimpimäki et al., 2021 ) have shown that the total number of international innovation patents, the number of scientific and technical journal papers, and the ratio of R&D expenditure to GDP can be used to effectively measure a country’s innovation foundation and capabilities. Columns (1)–(7) of Table 6 report the regression results. We can see that after considering innovation output and the lag of policy implementation, the total number of invention patents and the number of scientific and technical journal papers in the partner countries significantly improved compared to before joining the BRI. Meanwhile, the BRI also significantly increased the proportion of BRI partner countries’ R&D expenditures in GDP, indicating that this promotion effect is timely without lag. These results show that after joining the BRI, the partner countries significantly improved their innovation input and output (the number of patents and scientific and technical papers), which means that the innovation capacities and foundations of these countries significantly improved. The BRI realized the exchange and integration of commodities, technology, capital and personnel between China and BRI partner countries, significantly increased the investment and innovation cooperation activities of Chinese enterprises and scientific research institutions in BRI partner countries (Jiang et al., 2021 ; Jin et al., 2021 ), and promoted innovation investment and technological progress in underdeveloped countries. The above results show that the BRI had obvious positive externalities. It not only promoted innovation cooperation but also had a significant role in promoting the innovation foundations and innovation capabilities of BRI partner countries.

Our results show that the BRI can facilitate innovative cooperation among countries participating in the BRI. China, as the BRI initiator, will give more innovation cooperation opportunities to BRI partner countries. Under the call of the government, Chinese enterprises have deeply cooperated with BRI partner countries’ enterprises, accelerating technology transfer and knowledge spillover. This is also a significant reason why the BRI significantly increased the proportion of cooperative patents to China’s total patents. We further analyze how the BRI affected innovation cooperation among countries. The BRI weakened the institutional barriers between countries and achieved policy interoperability through the signing of bilateral or multilateral agreements. Moreover, the exchange of talent has increased, and the cultural distance has shortened through cultural exchanges, academic exchanges, etc. All these factors have played a significant role in promoting innovation cooperation among them. For countries participating in the BRI, the total number of international innovation patents, the number of scientific and technical journal papers, and the ratio of R&D expenditure to GDP all increased to varying degrees after joining the BRI. This shows that the BRI improved partner countries’ innovation foundations and capabilities. In general, the BRI can bring about a significant boost to the innovation level of China and partner countries.

However, BRI initiators will face many challenges and constraints in innovative cooperation. Some scholars have argued that the BRI may exacerbate income inequality in partner countries (Bruni, 2019 ), induce corruption (Kelly et al., 2016 ), and arouse the resentment of citizens. Meanwhile, it would also bring more troubles to Chinese companies’ overseas investments (Jin et al., 2021 ). In fact, there are indeed many risks in the advancement of the BRI. First, political risk has always been the key to affecting the BRI. Countries along the Belt and Road have different political systems. It is difficult to achieve complete political communication and trust. Moreover, some of them have serious internal political dissent. China’s foreign policy is difficult to maintain stably and continuously. This would bring great risks and challenges to the overseas investment and innovation cooperation of BRI partner countries. Second, it is difficult to coordinate the interests of countries along the Belt and Road, and the economic risks are relatively high. Many major countries in the world have corresponding diplomatic plans in the areas along the Belt and Road. These diplomatic plans and China’s BRI may diverge, so it is necessary to properly handle the issues of strategic balance and interest coordination. Moreover, innovation cooperation projects generally need large capital investments with long cycles and are easily affected by economic fluctuations. Finally, the economic foundations of the countries along the Belt and Road are quite different, and some innovative cooperation projects do not have high economic benefits. Some countries’ economic foundations are very weak. Before innovative cooperation, they need to be aided in the construction of infrastructure, which will increase the cost of innovative cooperation. For countries with higher levels of innovation, cooperation with countries with low levels of innovation has difficulty achieving the expected results, and the benefits obtained are also relatively small. This will make domestic enterprises and scientific research institutions less motivated in innovation cooperation.

Although we have fully discussed how the BRI affects innovation cooperation among countries from both theoretical and empirical perspectives, there are still some deficiencies and areas that can be expanded. First, we use the number of patents coinvented between countries to measure their innovation cooperation, but there are many types of innovation cooperation, such as jointly establishing R&D institutions and R&D funds and jointly publishing academic papers. Therefore, we can further analyze the heterogeneous impact of the BRI on different types of innovation cooperation and different types of patents in the future. Second, due to data limitations, we use 80 countries and regions around the world as research samples, ignoring the impact of the BRI on other countries in the world. If obtaining data, we can then conduct a worldwide full-sample analysis.

Conclusions and policy implications

Conclusions.

Examining the economic and social effects of the BRI from the perspective of international innovation cooperation is of important theoretical value and practical significance. In this paper, 80 countries and regions during the period from 2008 to 2018 are selected to conduct a theoretical analysis and empirical tests on whether the BRI can promote innovation cooperation between China and BRI partner countries. The main research conclusions are as follows:

First, the BRI promoted innovative cooperation among countries to a certain extent. The BRI significantly increased the proportion of cooperative patents to China’s total patents but did not increase the proportion of cooperative patents to the partner countries’ total patents. Additionally, the effect of the BRI is more obvious in promoting innovative cooperation between China and countries with a higher economic development level.

Second, we explore the specific mechanisms by which the BRI affected innovation cooperation. The BRI shortened the institutional distance between countries, providing a better institutional environment and cooperation platform for innovative cooperation. Moreover, it strengthened the exchange of scientific and technological talent, laying a talent foundation for innovative cooperation. Finally, it also inspired the promotion effect of cultural differences on innovation.

Third, for BRI partner countries, the BRI improved their innovation foundation and capabilities. Specifically, their number of international innovation patents, the number of scientific and technical journal papers, and the ratio of R&D expenditure to GDP all increased significantly compared with those before joining the BRI.

Policy implications

This research has important implications for advancing the construction of the BRI and deepening trust and cooperation between countries:

First, various innovation entities should take full advantage of the development opportunities brought about by the BRI and actively respond to policy guidance and calls to strengthen innovation cooperation and improve their own scientific and technological innovation capabilities. Innovation cooperation is an important measure to enhance a country’s innovation strength in the context of globalization. Enterprises, universities, scientific research institutions and other innovation entities should actively grasp the policy dividends to strengthen talent exchanges and innovation cooperation with other countries and continue to acquire new external knowledge and advanced experience to improve the abilities of independent innovation.

Second, China and the BRI partner countries should maintain close communication and adjust and improve the interconnection blueprint in a timely manner. The ultimate goal of the BRI is win‒win cooperation. When major changes occur, China and the BRI partner countries should communicate in a timely manner, fully trust, and adjust corresponding development plans and strategies in time to truly achieve mutual benefits and win‒win results. Additionally, in the face of some countries or organizations’ misunderstandings about the BRI, China and partner countries should continue to advance the blueprint for interconnection based on the basic principles of achieving shared growth through discussion, contribution and collaboration and take concrete actions and cooperation results to provide clarification.

Finally, China should avoid possible risks in the construction of the BRI and establish long-term cooperation mechanisms with other countries. To date, the number of countries or regions that have signed BRI cooperation documents with China has reached 144, covering Asia, Europe, North America, Oceania, Africa, and other global regions. Some are developed countries, and some are developing countries. There are major differences in political systems, customs, cultures, and ideologies among countries. Some frictions and risks inevitably occur in the construction of the BRI. China and relevant countries should further strengthen mutual trust and pragmatic cooperation to establish a long-term cooperation framework.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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This research was supported by the National Social Science Fund of China (Grant No. 20&ZD057).

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Technology and the Innovation Economy

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Darrell m. west darrell m. west senior fellow - center for technology innovation , douglas dillon chair in governmental studies.

October 19, 2011

• 19 min read

Executive Summary

Innovation and entrepreneurship are crucial for long-term economic development. Over the years, America’s well-being has been furthered by science and technology. Fears set off by the Soviet Union’s 1957 launch of its Sputnik satellite initiated a wave of U.S. investment in science, engineering, aerospace, and technology. Both public and private sector investment created jobs, built industries, fueled innovation, and propelled the U.S. to leadership in a number of different fields.

In this paper, I focus on ways technology enables innovation and creates economic prosperity. I review the range of new advances in education, health care, and communications, and make policy recommendations designed to encourage an innovation economy. By adopting policies such as a permanent research and development tax credit, more effective university knowledge commercialization, improving STEM worker training, reasonable immigration reform, and regional economic clusters, we can build an innovation economy and sustain our long-term prosperity.

The Link to Economic Prosperity

Researchers have found a link between technology innovation and national economic prosperity. For example, a study of 120 nations between 1980 and 2006 undertaken by Christine Qiang estimated that each 10 percentage point increase in broadband penetration adds 1.3 percent to a high income country’s gross domestic product and 1.21 percent for low to middle-income nations. [i]

In addition, Taylor Reynolds has analyzed the role of communication infrastructure investment in economic recoveries among OECD countries and found that nearly all view technology development as crucial to their economic stimulus packages. [ii] He demonstrates that there is a strong connection between telecommunication investment and economic growth, especially following recessions. These kinds of investments help countries create jobs and lay the groundwork for long-term economic development.

As a result, many nations around the world are investing in digital infrastructure as a way to jump-start economies weakened by the recent financial collapse. The decline in stock market valuations, rise in unemployment, and reduction in overall economic growth has highlighted the need to target financial resources and develop national priorities. In conditions of economic scarcity, countries no longer have the luxury of being passive and reactive. Instead, they must be proactive and forward-looking, and think clearly about how to create the basis for sustainable economic recoveries.

Not surprisingly, given its long-term potential, a number of countries have identified information technology as a crucial infrastructure need for national development. Broadband is viewed in many places as a way to stimulate economic development, social connections, and civic engagement. National leaders understand that cross-cutting technology speeds innovation in areas such as health care, education, communications, and social networking. When combined with organizational changes, digital technology can generate powerful new efficiencies and economies of scale. [iii]

People Understand Importance of Innovation, But Doubt U.S. Future

Despite the importance of the connection between technology innovation and economic prosperity, public opinion surveys reveal interesting results in people’s views about innovation. A 2009 Newsweek -Intel Global Innovation Survey interviewed 4,800 adults in the United States, China, United Kingdom, and Germany.  Researchers found that “two-thirds of respondents believe innovation will be more important than ever to the U.S. economy over the next 30 years.” [iv]   People understand the basic point that innovation has been key to past prosperity and is vital moving forward.

The survey also found interesting differences between Americans and the Chinese in what they think is important to future advances. According to the survey, “Americans are focused on improving math and science education, while Chinese are more concerned about developing creative problem-solving and business skills.” [v] Apparently, people from the respective nations have different fears about their current innovation training and what is necessary for future innovation.

However, there is a remarkable divergence between Americans and Chinese in assessments of the contemporary situation. Americans are remarkably pessimistic about their own future.  When asked how the U.S. was doing in 2009, only 41 percent of Americans thought our country was ahead of China on innovation compared to 81 percent of Chinese who felt the U.S. was ahead. [vi] Americans worried that their country was falling behind on innovation while other countries were moving forward.

There are objective reasons behind this American pessimism. There are too few Americans studying the traditional STEM fields of science, technology, engineering, and math. Due to our immigration policy, it is difficult for foreign students who are educated in the United States to stay here, get jobs, and contribute to American innovation the way many immigrants have done in the U.S. previously. [vii]   With our current debt and budget deficit levels, Americans worry about our long-term ability to invest in education and research in the way we did in the past and produce positive results.

An analysis of patents granted shows that our country’s long-term dominance has come to an end.  In 1999, American scientists were granted 90,000 patents, compared to 70,000 for those from all other countries. [viii]   By 2009, though, non-U.S. innovators earned more patents (around 96,000) compared to Americans (93,000). This represented the first time in recent years where non-Americans had garnered more patents. [ix]

The United States spends only 2.8 percent of its federal budget on national research and development as a percentage of GDP. This is less than the 4.3 percent spent by the government in Sweden, 3.1 percent by Japan, and 3.0 percent by South Korea, but higher than that of Germany (2.5 percent), France (2.2 percent), Canada (1.9 percent), or England (1.9 percent). Europe as a whole devotes 1.9 percent to research and development, while industrialized nations spend around 2.3 percent. [x]

If one adds together all the science and technology workers in the United States as a percentage of the workplace, 33 percent of American employees have science or technology positions. This is slightly less than the 34 percent figure for the Netherlands and Germany, but higher than the 28 percent in France and Canada,. [xi]

The productivity in this area has fueled considerable demand for those with science and engineering expertise, and it has been difficult for the United States to produce sufficient knowledge workers. [xii]   Thirty-eight percent of Korean students now earn degrees in science and engineering, compared to 33 percent for Germany, 28 percent for France, 27 percent for England, and 26 percent for Japan. The United States has fallen behind in this area.  Despite great demand for this kind of training, only 16 percent of American graduates have backgrounds in science and engineering. [xiii]

In America, the private sector surpassed the federal government in 1980 in terms of the amount of money spent on research and development. By 2003, commercial companies provided 68 percent of the $283 billion spent on research and development, compared to 27 percent from the federal government. Of this total, $113 billion came from the federal government, while $170 came from the private sector. According to information from the National Science Board, the percentage of research and development spending coming from the federal government has dropped from around 63 percent in the early 1960s to 27 percent today, while that of the private sector increased from 30 to 68 percent. [xiv]

The Need for a Clear Focus on Innovation

In moving forward, it is clear that information technology enables innovation in a variety of policy areas.  According to Philip Bond, the president of TechAmerica, “each tech job supports three jobs in other sectors of the economy.” And in information technology, he says, there are five jobs for each IT position. [xv]

Faster broadband and wireless speeds also enable people to take advantage of new digital tools such as GIS mapping, telemedicine, virtual reality, online games, supercomputing, video on demand, and video conferencing.  New developments in health information technology and mobile health, such as emailing X-rays and other medical tests, require high-speed broadband. And distance learning, civic engagement, and smart energy grids require sufficient bandwidth. [xvi]

High-speed broadband allows physicians to share digital images with colleagues in other geographic areas.  Schools are able to extend distance learning to under-served populations. Smart electric grids produce greater efficiency in monitoring energy consumption and contribute to more environment-friendly policies.  Video conferencing facilities save government and businesses large amounts of money on their travel budgets. New digital platforms across a variety of policy domains spur utilization and innovation, and bring additional people, businesses, and services into the digital revolution.

In the education area, better technology infrastructure enables personalized learning and real-time assessment. Imagine schools where students master vital skills and critical thinking in a personalized and collaborative manner, teachers assess pupils in real-time, and social media and digital libraries connect learners to a wide range of informational resources. Teachers take on the role of coaches, students learn at their own pace, technology tracks student progress, and schools are judged based on the outcomes they produce. Rather than be limited to six hours a day for half the year, this kind of education moves toward 24/7 engagement and learning fulltime. [xvii]  

These represent just a few of the examples where innovation is taking place. Technology fosters innovation, creates jobs, and boost long-term economic prosperity. By improving communication and creating opportunities for data-sharing and collaboration, information technology represents an infrastructure issue as important as bridges, highways, dams, and buildings.

Getting Serious about Innovation Policy

To stimulate innovation, we need a number of policy actions. Right now, the United States does not have a coherent or comprehensive innovation strategy. Unlike other nations, who think systematically about these matters, we make policy in a piecemeal fashion and focus on short versus long-term objectives. This limits the efficiency and effectiveness of our national efforts. There are a number of areas that we need to address.

Research and Development Tax Credits : An example of our national short-sightedness is the research and development tax credit.  Members of Congress have extended this many times in recent years, but they generally do this on an annual basis.  Rather than extend this credit over a long period of time, they renew it episodically and never on a predictable schedule.

This makes it difficult for companies to plan investments and pursue consistent strategies over time. Due to political uncertainties and institutional politics, we end up creating inefficiencies linked to the vagaries of federal policymaking. [xviii] While companies in other countries invest and deduct on a more predictable schedule, we shoot ourselves in the foot through a short-sighted perspective.  Bond notes that “23 countries now offer a more generous and stable credit” than the United States. [xix]

Commercializing University Knowledge : Universities represent a crucial linchpin in efforts to build an innovation economy.  They are extraordinary knowledge generators, but must do a better job of transferring technology and commercializing knowledge. University licensing offices must speed up their review process in order to encourage the formation of businesses. Universities should think more seriously about innovation metrics so they allocate resources efficiently and create the proper incentives.

Right now, many places count the number of patents and licensing agreements without much attention to the businesses created, products that are marketed, or revenue that is generated. They should make sure their resources and incentives are aligned with metrics that encourage technology transfer and commercialization. [xx]

STEM Workforce Training and Development : The United States is facing a crisis in STEM training and workforce development. There are many dimensions of this challenge, but one of the most important concerns is the low number of college students graduating with degrees in science, technology, engineering, and math. Few American students are developing proficiency in these subjects, which is hindering the country’s economic future. Past American prosperity has been propelled by advances in the STEM fields.   Skills in these areas helped the country win the space race and the Cold War and we need them now as we transition to a technology driven economy.

To deal with this problem, President Barack Obama’s Council of Advisors on Science and Technology (PCAST) has produced an official report that calls for the creation of a Master Teachers Corps. Among other recommendations, the report emphasizes two actions: 1) hiring 100,000 new STEM teachers and 2) paying higher salaries to the top 5 percent of STEM teachers. [xxi]   However, in an era of budget cutbacks and attacks on teacher unions, it has been difficult to build support for raising teacher salaries in general and adopting differential pay in particular.

In his 2011 State of the Union, the President restated his commitment to putting education at the forefront of the national agenda, emphasizing the need for quality teachers, investment in STEM education programs, and a “bold restructuring” of federal education funding. He called for identifying effective teachers and creating reward systems to retain top-performing individuals.

It is vital to address these issues because basic facts about STEM teaching and competency are not well known.  Failing schools not only harm students, they weaken the overall economy. With the U.S. facing a crisis of massive proportions in terms of its ability to innovate and create jobs, it is imperative that we transform STEM teaching to prepare students for the future economy. Real emphasis should be placed on teacher investment because research has shown that teachers are the primary factor in ensuring student growth and achievement.

An Einstein Strategy for Immigration Reform : We need reasonable immigration reform. One of our most important challenges is a new narrative defining immigration as a brain gain that improves economic competitiveness and national innovation. A focus on brains and competitiveness would help America overcome past deficiencies in immigration policy and enable our country to move forward into the 21 st century. It is a way to become more strategic about promoting our long-term economy and achieving important national objectives. [xxii]

We need to think about immigration policy along the lines of an “Einstein Principle.” In this perspective, national leaders would elevate brains, talent, and special skills to a higher plane in order to attract more individuals with the potential to enhance American innovation and competitiveness. The goal is to boost the national economy, and bring individuals to America with the potential to make significant contributions.  This would increase the odds for prosperity down the road. It has been estimated that “over 50,000 workers with advanced degrees leave the country for better opportunities elsewhere.” [xxiii]

O-1 Genius Visas : In order to boost American innovation, current policy contains a provision for a visa “brains” program. The so-called “genius” visa known as O-1 allows the government to authorize visas for those having “extraordinary abilities in the arts, science, education, business, and sports.” In 2008, around 9,000 genius visas were granted, up from 6,500 in 2004.  The idea behind this program is to focus on talented people and encourage them to come to the United States. It is consistent with what national leaders have done in past eras, where we encouraged those with special talents to migrate to our nation.

However, this program has been small and entry passes have gone to individuals such as professional basketball player Dirk Nowitzki of Germany and various members of the Merce Cunningham and Bill T. Jones/Arnie Zane dance companies. [xxiv] While these people clearly have special talents, it is important to extend this program in new ways and target people who create jobs and further American innovation.  This would help the United States compete more effectively.

EB-5 Job Creation Visas : There is a little-known EB-5 visa program that offers temporary visas to foreigners who invest at least half a million dollars in American locales officially designated as “distressed areas.” If their financial investment leads to the creation of 10 or more jobs, the temporary visa automatically becomes a permanent green card.  Without much media attention, there were 945 immigrants in 2008 who provided over $400 million through this program. [xxv] On a per capita basis, these benefits make the program one of the most successful economic development initiatives in the federal government.

This is a great way to tie U.S. immigration policy to job creation. If a goal of national policy is to encourage investment and job creation, targeted visas of this sort are very effective.  Such programs explicitly link new immigration with concrete economic investment. They also generate needed foreign capital ($500,000) for poor geographic areas. There is public accountability for this policy program because entry visas are granted on a temporary basis and become permanent only AFTER at least 10 jobs have been created.  This kind of visa program is the ultimate in targeting and quality control. Unless the money is invested and leads to new jobs, the newcomer is not allowed to stay in the United States.

H-1B Worker Visas : Right now, only 15 percent of annual visas are set aside for employment purposes.  Of these, some go to seasonal agricultural workers, while a small number of H-1B visas (65,000) are reserved for “specialty occupations” such as scientists, engineers, and technological experts. Individuals who are admitted with this work permit can stay for up to six years, and are able to apply for a green card if their employer is willing to sponsor their application.

The number reserved for scientists and engineers is drastically below the figure allowed between 1999 and 2004. In that interval, the federal government set aside up to 195,000 visas each year for H-1B entry.  The idea was that scientific innovators were so important for long-term economic development that we needed to boost the number set aside for those specialty professions.

Today, most of the current allocation of 65,000 visas run out within a few months of the start of the government’s fiscal year in October.  Even in the recession-plagued period of 2009, visa applications exceeded the supply within the first three months of the fiscal year. American companies were responsible for 49 percent of the H-1B visa requests in 2009, up from 43 percent in 2008. The companies which were awarded the largest number of these visas included firms such as Wipro (1,964), Microsoft (1,318), Intel (723), IBM India (695), Patri Americas (609), Larsen & Toubro Infotech (602), Ernst & Young (481), Infosys technologies (440), UST Global (344), and Deloitte Consulting (328). [xxvi]

High-skill visas need to be expanded back to 195,000 because at its current level, that program represents only six and a half percent of the million work permits granted each year by the United States. That percentage is woefully inadequate in terms of the supply needed. Entry programs such as the H-1B, O-1, and L-1 visa programs grant temporary visas for a period of a few years to workers with special talents needed by American employers. They enable U.S. companies to attract top people to domestic industries, and represent a great way to encourage innovation and entrepreneurship.

Regional Economic Clusters : We need regional economic clusters that take advantage of innovation-rich geographic niches. There are several examples of successful and geographically-based clusters such as Silicon Valley, Boston’s Route 128, and the Research Triangle in North Carolina. In each of these areas, there is a combination of creative talent associated with terrific universities, access to venture capital, and state laws that promote innovation through tax policy and/or infrastructure development.

Research has demonstrated that these innovation clusters generate positive economic results. According to a Brookings report by Mark Muro and Bruce Katz, “it is now broadly affirmed that strong clusters foster innovation through dense knowledge flows and spillovers; strengthen entrepreneurship by boosting new enterprise formation and start-up survival, enhance productivity, income-levels, and employment growth in industries, and positively influence regional economic performance.” [xxvii]

The question is how to promote such clusters in other geographic areas. There clearly are other places with the underlying conditions that foster technology innovation. But Muro and Katz caution that political leaders can’t force clusters that don’t already exist and that they should let the private sector lead in encouraging cluster formation. It is important to leverage existing resources and take advantage of workforce development programs, banking rules, educational institutions, and tax policies. [xxviii]

[i] Christine Zhen-Wei Qiang, “Telecommunications and Economic Growth,” Washington, D.C.:  World Bank, unpublished paper.

[ii] Taylor Reynolds, “The Role of Communication Infrastructure Investment in Economic Recovery,” Working Party on Communication Infrastructures and Services Policy, OECD, March, 2009.

[iii] Erik Brynjolfsson and Adam Saunders, Wired for Innovation, Cambridge, Massachusetts:  MIT Press, 2009.

[iv] Daniel McGinn, “The Decline of Western Innovation:  Why America is Falling Behind and How to Fix It,” The Daily Beast, November 15, 2009.

[v] Daniel McGinn, “The Decline of Western Innovation:  Why America is Falling Behind and How to Fix It,” The Daily Beast, November 15, 2009.

[vi] Daniel McGinn, “The Decline of Western Innovation:  Why America is Falling Behind and How to Fix It,” The Daily Beast, November 15, 2009.

[vii] Darrell West, Brain Gain:  Rethinking U.S. Immigration Policy, Washington, D.C.:  Brookings Institution Press, 2010.

[viii] Darrell M. West, Biotechnology Policy Across National Boundaries, New York:  Palgrave/Macmillan, 2007.

[ix] Michael Arndt, “Ben Franklin, Where Are You?” Business Week, January 4, 2010, p. 29.

[x] Organisation for Economic Co-Operation and Development, Science and Technology Statistical Compendium, 2004.

[xi] Organisation for Economic Co-Operation and Development, Science and Technology Statistical Compendium, 2004.

[xii] Darrell West, Brain Gain:  Rethinking U.S. Immigration Policy, Washington, D.C.:  Brookings Institution Press, 2010.

[xiii] Organisation for Economic Co-Operation and Development, Science and Technology Statistical Compendium, 2004.

[xiv] National Science Board, “Science and Engineering Indictors 2004,” Washington, D.C.:  National Science Foundation, 2004, p. 0-4.

[xv] Philip Bond, “Tech Provides Map for Nation’s Future,” Politico, September 18, 2011.

[xvi] Darrell West, “An International Look at High-Speed Broadband,” Washington, D.C.:  Brookings Institution, February, 2010.

[xvii] Darrell West, “Using Technology to Personalize Learning and Assess Students in Real-Time,” Washington, D.C.:  Brookings Institution, October 6, 2011.

[xviii] Martin Baily, Bruce Katz, and Darrell West, “Building a Long-Term Strategy for Growth through Innovation,” Washington, D.C.:  Brookings Institution, May, 2011.

[xix] Philip Bond, “Tech Provides Map for Nation’s Future,” Politico, September 18, 2011.

[xx] Martin Baily, Bruce Katz, and Darrell West, “Building a Long-Term Strategy for Growth through Innovation,” Washington, D.C.:  Brookings Institution, May, 2011.

[xxi] President’s Council of Advisors on Science and Technology, “Prepare and Inspire:  K-12 Education in Science, Technology, Engineering, and Math for America’s Future,” September, 2010.

[xxii] Richard Herman and Robert Smith, Immigrant, Inc.:  Why Immigrant Entrepreneurs Are Driving the New Economy and How They Will Save the American Worker, Hoboken, New Jersey:  John Wiley & Sons, 2010.

[xxiii] Center for Public Policy Innovation, “Restoring U.S. Competitiveness:  Navigating a Path Forward Through Innovation and Entrepreneurship,” Washington, D.C., September 7, 2011.

[xxiv] Moira Herbst, “Geniuses at the Gate,” Business Week, June 8, 2009, p. 14.

[xxv] Lisa Lerer, “Invest $500,000, Score a U.S. Visa,” CNNMoney.com.

[xxvi] Moira Herbst, “Still Wanted:  Foreign Talent—And Visas,” Business Week, December 21, 2009, p. 76.

[xxvii] Mark Muro and Bruce Katz, “The New ‘Cluster Moment’:  How Regional Innovation Clusters Can Foster the Next Economy,” Washington, D.C.:  Brookings Institution, September 21, 2010.

[xxviii] Mark Muro and Bruce Katz, “The New ‘Cluster Moment’:  How Regional Innovation Clusters Can Foster the Next Economy,” Washington, D.C.:  Brookings Institution, September 21, 2010.

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Research to innovation & impact: What it takes and why it is worth the investment

By upali nanda assoc. aia posted 2 days ago.

By Upali Nanda, PhD, Assoc. AIA, EDAC, ACHE, Global Practice Director, Research & Partner, HKS

In 2006, HKS hired Dr. Debajyoti Pati, an experienced PHD, as “Director of Research.” This was a highly surprising move at the time—the title was uncommon in the architecture industry. Even in the years after, only a handful of architecture firms—and one art firm—have a research director role. Before Pati’s hiring, Big R “Research” was primarily the domain of academia. And firms did minimal—small r "research" to inform their projects. Healthcare as a typology was just starting to embrace the idea that design could be evidence-informed, which was first written about in 20001.  

Over the 25 years since, medium to large firms have increasingly employed design researchers, with many firms now boasting in-house research and consulting departments that include PhD researchers, clinicians, strategists, economists, engineers, and design thinkers.  

The trend has been driven by a growing demand for authentic and unbiased research. In 2008, HKS established CADRE, a 501c3 not-for-profit organization that promotes open-sourced research developed through industry and academic coalitions. How did the evolution of architecture into a research fueled industry come about?  

Research as an Investment 

In the design service industry, R&D is never as prominent as in manufacturing and technology. However, innovative design solutions are considered research by the IRS, and, with proper qualifications, architecture firms can receive R&D tax credits for such activities. With research now an accepted component of architectural practice, firms are turning their attention to the potential benefits of R&D investment.  

The industry is facing an explosion of innovation driven by billions of dollars in R&D spending. Most of this innovation is narrowly focused, related to technologies that improve process speed and quality of existing services offered by AEC providers. Meanwhile, broader challenges of climate change, systemic inequity, mental health, and evolving work culture are colliding with those of aging infrastructure and an uncertain policy and economic climate. Thus, beyond developing technology that optimizes existing architectural processes, research is now vital to provide strategic context to fuel innovation. 

A Research Roadmap 

Research can provide a roadmap for developing new technologies for existing services and understanding emerging market opportunities. It can also help develop new services and tools and improve the measurement of design performance in three primary ways. 

1. Foresight. Understanding “which way the winds are blowing” with foresight reports that look at market trends and are informed by the socio-economic and political climate.  

Example: HKS and CADRE’s Clinic 20XX study assessed the state of primary care in healthcare, drivers of change, trends responding to the drivers, and design implications. The post-COVID study produced design strategies for today’s ever-changing healthcare climate. 

Image: HKS and CADRE’s Clinic 20XX 7 key principles

More recently a partnership with the Center for Brainhealth and Brain Capital Alliance resulted in framing an entire design opportunity space of brain healthy workplaces , brain healthy housing and brain healthy cities .

Image: HKS, the Center for Brainhealth and Brain Capital Alliance defined factors of a Brain Healthy workplace

2. Insight. Informing projects on increasingly tight deadlines and budgets with timely insight and evidence that can help prioritize resources. 

Example: HKS developed a design diagnostic tool now used by all its healthcare design teams to methodically assess a current state to inform the planning process. Demographic assessment, market analyses, climate analyses, deep community engagement and methodical stakeholder interviews are now part of an intentional design process at HKS.

Image: One of HKS’s design diagnostic tools to aid healthcare design teams based on research

3. Impact. Measuring project outcomes is a task the architecture industry hasn’t traditionally embraced. Despite growing interest, clients rarely request post occupancy assessments. However, measuring outcomes is essential to break the cycle of design service commoditization. The resulting data allows us to better understand ways to achieve design excellence and to translate it to important outcomes such as health and well-being.  

Example: A recent HKS longitudinal study with university partner UC San Diego found that by integrating health and wellbeing principles in design, the university saw not just a decrease in energy use and an increase in environmental quality. The study also saw a reduction in self-reported student depression and an increase in health and wellbeing behaviors.  

Although findings of any study are rarely completely generalizable, the complexity of architectural design projects necessitates an investment in qualitative and quantitative data and competent analyses to advance innovative design.  

Realizing the importance of this approach, HKS has also made an ongoing investment in our living labs program that pairs researchers with design studios to advance experimentation and innovation testing and to develop new technologies, materials, and designs. The program includes measuring performance and well-being outcomes.  

Readying for Adaptation & Innovation 

Despite the frequent misuse and misunderstanding of the term “innovation,” its essence is rooted in the ability to adapt to a swiftly changing environment by providing insights about both gradual and transformative changes in technology, society, and the markets the AEC industry serves. Research is fundamentally necessary to foster this innovation, and organizations are implementing internal programs, R&D processes, and governance structures to nurture it.  

At HKS, research and innovation initiatives are championed by top executives, including the CEO, President, and key sector leaders, who recognize the value of these investments. Additionally, internal programs like the research incubator/accelerator offer all staff the chance to dedicate time and resources to industry-relevant, timely, research-driven topics. The objective is to deliver tangible benefits in various forms.  

Image: HKS design strategies  for workplace brain health

These endeavors have yielded diverse outcomes, such as new reports, prototype designs, and tools. Notably, the widely circulated AIA Resilience Design Toolkit originated from HKS’s incubator program, spurred by a client inquiry that challenged conventional perspectives.  

Architecture firms must ensure their work doesn’t just chase the latest technology, but that it resonates with broader market and societal needs and aspirations. This requires active participation from architects, interior designers, urban planners, and design strategists in a dialogue with clients and stakeholders that is grounded in research. By actively engaging in R&D activities, the profession can more effectively contribute to solving client problems, creating Intellectual Property (IP) that can be scaled to provide broad impact and economic value, building research experience to create a learning organization, and sparking insights that shape the future, within, throughout, and beyond the built environment. 

Acknowledgements

Dr. Michael O Neil, Julie Hiromoto, and the HKS research team  

_____________________________________

Dr. Upali Nanda oversees a range of innovation practices that work within, through and beyond the built environment for meaningful impact. Prior to her current role she served as the global research director for the firm and as the Executive Director for the non-profit Center for Advanced Design Research and Education. Dr. Nanda teaches as Associate Professor of Practice at the Taubman School of Architecture and Urban Planning at University of Michigan and serves on the board of the Academy for Neuroscience for Architecture. Her award-winning research around health and wellbeing, neuroscience and architecture, sensthetics, point of decision design, and outcome-driven design has been widely published. She has won various research and innovation awards including the 2018 Women in Architecture Innovator Award.

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