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Can China catch-up with the United States technologically by mobilizing its bureaucracy and assigning ambitious targets to local governments? We analyzed an original dataset of 4.6 million patents filed in China from 1990 through 2014 and paired this with a new, rigorous measure of patent novelty that approximates the quality of innovation. In 2006, China's central government launched a national campaign to promote indigenous innovation and introduced bureaucratic targets for patents. Our analysis finds evidence that these targets, combined with political competition, pushed local governments to "game the numbers" by channeling relatively more effort toward boosting non-novel—possibly junk—patents over novel patents. Nationally, this is reflected in a surge of aggregate patents paired with a falling ratio of novel patents. China's innovation drive is susceptible to manipulation and waste—it is enormous in scale but low in productivity. [ABSTRACT FROM AUTHOR]

Research Summary: Non‐practicing entities (NPEs) are firms that accumulate and acquire patents but do not further develop or implement the patented inventions (known as patent trolling). NPEs seek to receive royalties or profits through out‐of‐court settlements in patent infringement cases. We examine how firms targeted by NPEs in NPE‐initiated litigations (i.e., target firms) shift their innovation strategies and trajectories in response to heightened litigation risks. We theorize and show that after the initial lawsuit, target firms draw more upon their in‐house technologies to reduce the legal ground for further lawsuits. Furthermore, nontarget firms in related technology areas shift their innovation activities away from those of target firms under high NPE litigation risks. These effects are more pronounced with higher innovation costs and under more competitive product markets. Managerial Summary: Non‐practicing entities (NPEs) are known as patent trolls that accumulate and acquire patents but do not further develop or implement these patented inventions. These patent trolls aim to obtain royalties or profits through out‐of‐court settlements in patent infringement cases. We investigate how firms targeted by patent trolls in litigations (i.e., target firms) change their innovation strategies and trajectories to deal with increased NPE litigation risks. After the initial lawsuit, we find that these target firms use their in‐house technologies more to reduce the legal ground for future lawsuits. Moreover, nontarget firms in related technology areas move their innovation activities away from those of target firms under high litigation risks. These effects are stronger when innovation costs are higher and under more competitive product markets. View Video S1 [ABSTRACT FROM AUTHOR]

Innovation activities create substantial firm value, but they are difficult to manage owing to agency risk, which is commonly thought to result in shirking, and hence underinvestment in innovation. However, agency risk can also create inefficient allocation of resources among innovation activities, on which the literature has provided limited understanding. We examine an important outcome created by agency risk—that agents pursue quantity of innovation at the expense of novelty—and investigate how it is influenced by corporate and public governance. We theorize that improved corporate governance tools, including better alignment of agents' private incentives and stronger monitoring, and high-quality public governance reduce such agency risk in state-owned enterprises (SOEs). Furthermore, higher-quality public governance enhances the functioning of corporate governance tools in further reducing such agency risk in innovation. We test our theory by examining SOEs in China that responded to the state's pro-innovation policies relying disproportionately on quantifiable outcomes (e.g., patent counts) for assessing innovation performance. Our difference-in-differences estimates provide overall support for our hypotheses. These findings provide new insights on how agency risk affects innovation by distinguishing the consequences for quantity and novelty of innovation and for how conventional corporate governance tools shaping innovation depend on public governance. [ABSTRACT FROM AUTHOR]

Research Summary: Researchers increasingly use unstructured text data to construct quantitative variables for analysis. This goal has traditionally been achieved using keyword‐based approaches, which require researchers to specify a dictionary of keywords mapped to the theoretical concepts of interest. However, recent machine learning (ML) tools for text classification and natural language processing can be used to construct quantitative variables and to classify unstructured text documents. In this paper, we demonstrate how to employ ML tools for this purpose and discuss one application for identifying artificial intelligence (AI) technologies in patents. We compare and contrast various ML methods with the keyword‐based approach, demonstrating the advantages of the ML approach. We also leverage the classification outcomes generated by ML models to demonstrate general patterns of AI technological innovation development. Managerial Summary: Text‐based documents offer a wealth of information for researchers and business analysts. However, researchers often need to find a way to classify these documents to use in subsequent research projects. In this paper, we demonstrate how supervised ML methods can be used to automate the process of classifying textual documents into pre‐defined categories or groups. We provide an overview of when such techniques may be used in comparison to other methods, and the considerations and tradeoffs associated with each method. We apply these methods to identify AI‐based technologies from all patents in the United States, based on patent abstract text. This allows us to show interesting patterns of AI innovation development in the United States. We also provide the code and data used in this paper for future research. [ABSTRACT FROM AUTHOR]

There is growing interest in management and organizational research to study the relocation of knowledge workers, defined as a move by the knowledge worker to a different place of work. Relocation has been well studied as a potential source of losses or gains in human and social capital. However, our understanding of whether and how it disrupts a scientist's innovation activities is limited. Relocation could disrupt innovation activities in the new workplace by making it difficult for a scientist to coordinate work with prior collaborators with whom the scientist has relational experience and forcing the scientist to work with new collaborators. In this study, we develop a conceptual framework assessing the effectiveness of the scientists' research and development (R&D) experience to counter these disruptions arising from relocation and develop valuable patented innovations. We hypothesize that both the scientist's relational experience and working with new collaborators decrease the value of innovations the scientist creates after relocation. Scientist R&D experience, however, is double-edged in nature: It leads to less valuable innovations prior to relocation but facilitates the creation of more valuable innovations after it. Our theory suggests that this is because R&D experience facilitates the scientist's adaptation to the new context and helps coordinate her or his activities in new collaborations. Nevertheless, R&D experience is less effective in sustaining the efficacy of relational experience with prior collaborators after relocation. Using a longitudinal dataset from the knowledge-intensive genomics industry, we find support for our hypotheses. This study yields important managerial and policy implications. [ABSTRACT FROM AUTHOR]

For years, a red-shaded map of China's intellectual property regime would show the strongest IP protection in the major cities--indicated by deep, 100 percent red, and the weakest--barely 10 percent [...]