- School of Public Policy
- Technology Policy and Assessment Center
Philip Shapira is a Professor in the School of Public Policy at Georgia Institute of Technology and Professor of Management, Innovation and Policy with the Manchester Institute of Innovation Research, Alliance Manchester Business School, University of Manchester. His interests encompass science and technology policy, economic and regional development, innovation management and policy, industrial competitiveness, technology trajectories and assessment, innovation measurement, and policy evaluation. Prof. Shapira's current and recent research includes projects that examine nanotechnology research and innovation systems assessment, responsible research and innovation in synthetic biology, and next generation manufacturing and institutions for technology diffusion. Prof. Shapira is a director of the Georgia Tech Program in Science, Technology and Innovation Policy and the Georgia Manufacturing Survey. He is co-editor (with J. Edler, P. Cunningham, and A. Gök) of the Handbook of Innovation Policy Impact (Edward Elgar 2016) and (with R. Smits and S. Kuhlmann) of Innovation Policy: Theory and Practice. An International Handbook (Edward Elgar, 2010). Prof. Shapira is a Fellow of the American Association for the Advancement of Science and a Fellow of the Royal Society of Arts.
- Ph.D., University of California, Berkeley, City and Regional Planning
- M.A., University of California, Berkeley, Economics
- M.C.P., Massachusetts Institute of Technology, City Planning
- Dip.TP (Dist.), Gloucestershire College of Art and Design, U.K.
- Science, Technology, and Innovation Policy
- Asia (East)
- United States
- United States - Georgia
- Regional Development
- Emerging Technologies - Innovation
- Small and Midsize Enterprises
- Technology Management and Policy
- Innovation intermediaries at the convergence of digital technologies, sustainability, and governance: A case study of AI-enabled engineering biology
In: Technovation [Peer Reviewed]
Date: January 2024
We probe the missions and practices of innovation intermediaries involved in the convergence of digital technologies, focusing on the case of AI-enabled engineering biology (AI-EB). As in other areas of emerging digitalization, applications and commercialization in this convergent technology domain raise multiple societal, ethical, and sustainability questions. Through interviews with various stakeholders involved in the AI-EB innovation ecosystem, we explore the extent to which these innovation intermediaries, as pivotal actors in innovation ecosystem development, are embedding attention to societal and sustainability objectives and concerns as well as facilitating economic goals. Notwithstanding available frameworks for responsible innovation that encourage attention to societal and sustainability implications, we find that innovation intermediaries in the engineering biology domain continue to emphasize conventional scale-up and commercialization approaches. Insights for intermediary development, research management, and policy are explored.
- The digitalisation paradox of everyday scientific labour: How mundane knowledge work is amplified and diversified in the biosciences
In: Research Policy [Peer Reviewed]
Date: January 2023
This paper examines how automation and digitalisation influence the way everyday scientific work practices are organised and conducted. Drawing on a practice-based study of the field of synthetic biology, the paper uses ethnographic, interview and survey data to offer a sociomaterial and relational perspective of technological change. As automation and digitalisation are deployed in research settings, our results show the emergence and persistence of what we call ‘mundane knowledge work’, including practices of checking, sharing and standardising data; and preparing, repairing and supervising laboratory robots. While these are subsidiary practices that are often invisible in comparison to scientific outputs used to measure performance, we find that mundane knowledge work constitutes a fundamental part of automated and digitalised biosciences, shaping scientists' working time and responsibilities. Contrary to expectations of the removal of such work by automation and digitalisation, we show that mundane work around data and robots persists through ‘amplification’ and ‘diversification’ processes. We argue that the persistence of mundane knowledge work suggests a digitalization paradox in the context of everyday labour: while robotics and advanced data analytics aim at simplifying work processes, they also contribute to increasing their complexity in terms of number and diversity of tasks in creative, knowledge-intensive professions.
- Analyzing research outcomes and spillovers at a U.S. nanotechnology user facility
In: Journal of Nanoparticle Research [Peer Reviewed]
Date: November 2022
- Analyzing research outcomes and spillovers at a US nanotechnology user facility
In: Journal of Nanoparticle Research [Peer Reviewed]
Date: November 2022
This paper maps research outcomes and identifies spillover effects at a US University Research Center (URC) that offers user facilities for nanotechnology research. We use scientometric and network science approaches to analyze measures of topical orientation, productivity, impact, and collaboration applied to URC-related Web of Science abstract publications records. A focus is on the analysis of spillover effects on external organizations (i.e., non-affiliated users). Our findings suggest the URC’s network relies on external organizations acting as brokers, to provide access to the facilities to other external organizations. Analysis of heterophily indicates that collaboration among internal and external organizations is enhanced by the facilities, while articles written by a mix of co-authors affiliated with internal and external organizations are likely to be more cited. These results provide insights on how URCs with user facilities can create conditions for diverse collaboration and greater research impact.
- Building a Bottom-Up Bioeconomy
In: Issues in Science and Technology
- Commercializing Emerging Technologies through Networks: Insights from Strategies of UK Nanotechnology Small and Midsize Enterprises
In: Journal of Technology Transfer [Peer Reviewed]
- Policy interactions with research trajectories: The case of cyber-physical convergence in manufacturing and industrials
In: Technological Forecasting and Social Change [Peer Reviewed]
- Mapping technological innovation dynamics in artificial intelligence domains: Evidence from a global patent analysis
In: PLoS ONE [Peer Reviewed]
Date: December 2021
Artificial intelligence (AI) is emerging as a technology at the center of many political, economic, and societal debates. This paper formulates a new AI patent search strategy and applies this to provide a landscape analysis of AI innovation dynamics and technology evolution. The paper uses patent analyses, network analyses, and source path link count algorithms to examine AI spatial and temporal trends, cooperation features, cross-organization knowledge flow and technological routes. Results indicate a growing yet concentrated, non-collaborative and multi-path development and protection profile for AI patenting, with cross-organization knowledge flows based mainly on interorganizational knowledge citation links.
- Building the bioeconomy: A targeted assessment approach to identifying biobased technologies, challenges and opportunities in the UK
Date: August 2023
We explore opportunities, challenges, and strategies to translate and responsibly scale innovative biobased technologies to build more sustainable bioeconomies. The pandemic and other recent disruptions have increased exposure to issues of resilience and regional imbalance and raised attention to pathways that could shift production and consumption regimes based more on local biobased resources and dispersed production. The paper reviews potential biobased technologies strategies and then identifies promising and feasible options with a focus on the United Kingdom. Initial landscape and bibliometric analyses identified 50 potential existing and emerging potential biobased technologies. These technologies were assessed for their ability to fulfil requirements related to biobased production, national applicability, and economic, societal, and environmental benefits, leading to identification of 18 promising biobased production technologies. Through further analysis and focus group discussion with industrial, governmental, academic, agricultural, and social stakeholders, three technology clusters were identified for targeted assessment, drawing on cellulose-, lignin-, and seaweed-feedstocks. Case studies for each of these clusters were developed, addressing conversations around sustainable management and the use of biomass feedstocks, and associated environmental, social, and economic challenges. These cases are presented with discussion of insights and implications for policy. The approach presented in the paper is put forward as a scalable assessment method which can be useful in prompting, informing, and advancing discussion and deliberation on opportunities and challenges for biobased transformations.
- Applications and Societal Implications of Artificial Intelligence in Manufacturing: A Systematic Review
Date: July 2023
This paper undertakes a systematic review of relevant extant literature to consider the potential societal implications of the growth of AI in manufacturing. We analyze the extensive range of AI applications in this domain, such as interfirm logistics coordination, firm procurement management, predictive maintenance, and shop-floor monitoring and control of processes, machinery, and workers. Additionally, we explore the uncertain societal implications of industrial AI, including its impact on the workforce, job upskilling and deskilling, cybersecurity vulnerability, and environmental consequences. After building a typology of AI applications in manufacturing, we highlight the diverse possibilities for AI's implementation at different scales and application types. We discuss the importance of considering AI's implications both for individual firms and for society at large, encompassing economic prosperity, equity, environmental health, and community safety and security. The study finds that there is a predominantly optimistic outlook in prior literature regarding AI's impact on firms, but that there is substantial debate and contention about adverse effects and the nature of AI's societal implications. The paper draws analogies to historical cases and other examples to provide a contextual perspective on potential societal effects of industrial AI. Ultimately, beneficial integration of AI in manufacturing will depend on the choices and priorities of various stakeholders, including firms and their managers and owners, technology developers, civil society organizations, and governments. A broad and balanced awareness of opportunities and risks among stakeholders is vital not only for successful and safe technical implementation but also to construct a socially beneficial and sustainable future for manufacturing in the age of AI.
- Large-Scale Text Analysis Using Generative Language Models: A Case Study in Discovering Public Value Expressions in AI Patents
Date: May 2023
Labeling data is essential for training text classifiers but is often difficult to accomplish accurately, especially for complex and abstract concepts. Seeking an improved method, this paper employs a novel approach using a generative language model (GPT-4) to produce labels and rationales for large-scale text analysis. We apply this approach to the task of discovering public value expressions in US AI patents. We collect a database comprising 154,934 patent documents using an advanced Boolean query submitted to InnovationQ+. The results are merged with full patent text from the USPTO, resulting in 5.4 million sentences. We design a framework for identifying and labeling public value expressions in these AI patent sentences. A prompt for GPT-4 is developed which includes definitions, guidelines, examples, and rationales for text classification. We evaluate the quality of the labels and rationales produced by GPT-4 using BLEU scores and topic modeling and find that they are accurate, diverse, and faithful. These rationales also serve as a chain-of-thought for the model, a transparent mechanism for human verification, and support for human annotators to overcome cognitive limitations. We conclude that GPT-4 achieved a high-level of recognition of public value theory from our framework, which it also uses to discover unseen public value expressions. We use the labels produced by GPT-4 to train BERT-based classifiers and predict sentences on the entire database, achieving high F1 scores for the 3-class (0.85) and 2-class classification (0.91) tasks. We discuss the implications of our approach for conducting large-scale text analyses with complex and abstract concepts and suggest that, with careful framework design and interactive human oversight, generative language models can offer significant advantages in quality and in reduced time and costs for producing labels and rationales.