Relevant bibliographies by topics / Data Innovation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Innovation'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Data Innovation"

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Gao, Xueyuan, and Hua Feng. "Data-Driven Business Innovation Processes: Evidence from Authorized Data Flows in China." Systems 12, no. 8 (2024): 280. http://dx.doi.org/10.3390/systems12080280.

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The importance of data in current societal activities cannot be overstated, yet we know little about data governance and application. Using the Chinese Government Data Empowerment Initiative, this paper examines the process of data-driven business innovation. Using the staggered DiD model, we found that government data points effectively facilitate firms’ product innovation, with higher-quality data correlating with better innovation performance. Government-authorized data points aid firms in accumulating experience in applying and managing data, thereby enhancing their production performance.
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Bourouaha, Abdelhammid, and Samir Baha Eddine Maliki. "Innovation in Mena region: descriptive analysis among firms of Mena region." Review of Finance and Markets 07, no. 01 (2020): 36–62. https://doi.org/10.5281/zenodo.6818251.

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Nowadays with rapid changes in the business world, companies in the developed countries are increasingly including innovation as one of their strategies to ensure business expansion and profitability. This study examined the innovativeness of the firms in Mena region. The study utilized Business Environment and Enterprise Performance Survey (BEEPS) of the Mena region.  The finding shows that the maximum of the innovations is in firms level follows to low investment, risks and expenditures. Innovation in country level takes the second level after firm level follows the high levels of risks
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Xiong, Xiong, Jin Zhang, Xi Jin, and Xu Feng. "Review on Financial Innovations in Big Data Era." Journal of Systems Science and Information 4, no. 6 (2016): 489–504. http://dx.doi.org/10.21078/jssi-2016-489-16.

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AbstractThe rise of Big Data brings the financial innovation opportunities as well as challenges. This paper reviews different fields of big-data-based financial innovations as well as the scientific discoveries and theoretical breakthroughs of risk analysis with respect to these financial innovations. Based on the current research status, several key problems are put forward and their relative solutions are discussed. The three mean aspects are listed as the pricing and risk measuring for data-driven financial innovation products or services; the changes that data-driven financial innovation
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Jensen, Paul H., and Elizabeth Webster. "Australian Innovation Data." Australian Economic Review 41, no. 3 (2008): 323–29. http://dx.doi.org/10.1111/j.1467-8462.2008.00516.x.

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Heikkilä, Marikka, Jukka Heikkilä, and Farhan Ahmad. "Data-Driven Business Model Innovation in Europe: Ethical Data Practices and Ecosystem Involvement." Systems 13, no. 3 (2025): 164. https://doi.org/10.3390/systems13030164.

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Creating, sharing, and using data are expected to lead to the development of new innovative business models. This study investigates the interplay between business model change, ethical data practices, and participation in data ecosystems in fostering data-driven innovation. Using survey data from 1200 European companies, analyzed through partial least squares structural equation modeling (PLS-SEM), the findings reveal that while firms recognize the potential benefits of data, business model change alone is insufficient to drive innovation. Instead, active engagement in data ecosystems and adh
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Nursyamsiah, Siti, and Saiful Hidayat. "PENGARUH SUMBER PENGETAHUAN EKTERNAL TERHADAP TYPE INOVASI : SEBUAH TINJAUAN BERBASIS PELAKU PASAR DAN SCIENCE BASED ACTORS (STUDI EMPIRIS PADA UMKM DI YOGYAKARTA)." Holistic Journal of Management Research 3, no. 1 (2020): 47–62. http://dx.doi.org/10.33019/hjmr.v3i1.1826.

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The research aims to determine the influence of external knowledge sources based on market actors and science actors on the types of innovations including product innovation, process innovation, market innovation and organizational innovation in innovating SMEs food and beverage at D. I Yogyakarta. This research is based on 60 samples obtained from the dissemination of questionnaires directly and through Google form. To analyzed data, this study utilized a logistic regression model with SPSS 23. The findings show that the external knowledge source of market base actors has significant effect o
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Pinkovetskaia, Iuliia S., Natalya R. Aleksandrova, Tatyana V. Treskova, and Daniela S. Veas Iniesta. "Present Innovation Policy: Russian Regions Data." Cuestiones Políticas 39, no. 69 (2021): 146–63. http://dx.doi.org/10.46398/cuestpol.3969.08.

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Innovation policies are currently one of the main axes for improving the efficiency of national economies in developed and developing countries. The innovation policy includes a comprehensive system of activities. In Russia, increasing the specific weight of commercial entities engaged in innovative activities is among the most important objectives for the short term. The aim of our study was to evaluate the use of innovation based on taking into account the specific weight of organizations that perform innovations in the total number of organizations operating in the regions of Russia. Our st
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Rhoden, Imke, Daniel Weller, and Ann-Katrin Voit. "Spatio-Temporal Dynamics of European Innovation—An Exploratory Approach via Multivariate Functional Data Cluster Analysis." Journal of Open Innovation: Technology, Market, and Complexity 8, no. 1 (2022): 6. http://dx.doi.org/10.3390/joitmc8010006.

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We apply a functional data approach for mixture model-based multivariate innovation clustering to identify different regional innovation portfolios in Europe, considering patterns of specialization among innovation types. We combine patent registration data and other innovation and economic data across 225 regions, 13 years, and eight patent classes. The approach allows us to form several regional clusters according to their specific innovation types and captures spatio-temporal dynamics too subtle for most other clustering methods. Consistent with the literature on innovation systems, our ana
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Maruapey, M. Husein. "URGENSI TATA KELOLA INOVASI PEREKAMAN DATA DAN DOKUMEN KEPENDUDUKAN." JURNAL GOVERNANSI 5, no. 1 (2019): 54. http://dx.doi.org/10.30997/jgs.v5i1.1705.

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Managing innovation involves many complex activities from organizations, stakeholders, and knowledge. Innovation is essential in improving the efficiency of the public sector. So the importance of governance innovation will provide the consequences of any failure or success of the innovation itself. Governance innovation also reflects the managing innovation in a way that is innovative as one strategy innovation process, so that innovation can show results and benefits. The focus of this research is the investigation phenomenon of governance innovations data and documents civil registration re
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Avolio, G., E. Blasi, C. Cicatiello, and S. Franco. "The drivers of innovation diffusion in agriculture: evidence from Italian census data." Journal on Chain and Network Science 14, no. 3 (2014): 231–45. http://dx.doi.org/10.3920/jcns2014.x009.

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Innovation is a key issue in the discussion about the links between agriculture, food production and sustainability. Indeed, the creation, adoption and exploitation of innovations can interact with all three dimensions of sustainability – environment, society and economy. Despite the increasing support for innovation practices in the agrifood sector from institutions and public policies, innovation in this sector has spread quite slowly. Indeed, the diffusion of innovations strongly depends on the social, institutional and productive system behind the technological/structural features of the f
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Dissertations / Theses on the topic "Data Innovation"

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Plevoets, Christina, and Rodrigo Fernandes. "Exploring the role of Big Data and Analytics : Creating Data-Driven Innovation." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13463.

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Purpose: The purpose of this thesis is to understand the role of big data in the innovation process and specify the conditions or scenarios where big data and innovation can join forces. Research design, approach and method: The theoretical framework used is based on the innovation framework with big data from Kusiak (2015) and it is validated by three propositions: 1) Big data analytics (BDA) gives the possibility to discover new insights, such as increase profitability, expansion of customer base and market growth, hence it is a contributor to innovation. 2) Firms are investing or plan to in
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Eros, Emily J. (Emily Jean). "Transportation data as disruptive innovation in Mexico City." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90096.

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Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 76-84).<br>Despite the popularity of big data and smart city initiatives in rich countries, relatively few city governments in the Global South possess even basic information about public transportation routes and operations within their jurisdictions. The growing ubiquity of affordable mobile phones and internet-capable devices has enabled some developing cities to begin collecting and compiling these data
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Öhman, Gerth. "Idé och innovation." Licentiate thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-16513.

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Present thesis is to deepen the current knowledge about the concept and development of ideas of innovators and the potential small businesses in rural areas has to adopt innovation and new technology. The thesis is limited tostudying the process itself and the progression from concept and development of ideas for invention and innovation to market and customer. The research questions include: What motivates an inventor to innovate? Howto practice innovation in small companies? How are innovators approach toproblem solving? What opportunities exist for small businesses in rural areas toadopt in
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Bartoloni, Eleonora. "Innovation, profitability and financial provision : a panel data investigation." Thesis, University of Warwick, 2011. http://wrap.warwick.ac.uk/35162/.

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URBANIAK, ROBERT, and NICLAS ZACHRISSON. "POTENTIALEN AV BIG DATA I FRONT END OF INNOVATION." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192493.

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The aim of this study was to explore the potential of Big Data in the Front End of Innovation and develop theory through conceptual work. Specifically research questions were aimed at the potential of Big Data in opportunity identification, opportunity analysis, idea generation and idea selection. When conducting the study, it was found that some vagueness and opposing opinions are present regarding the use of the term Big Data and its definition. The findings of this study focus on what Big Data offers organizations increased abilities in analyzing external environments including competitors,
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Seaux, Julien. "Migration and innovation : an analysis based on patent data." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0318.

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Cette thèse a pour but d’analyser la migration des inventeurs en étudiant leur contribution à l’innovation de leur pays de destination ainsi que leur pays d’origine, en contrôlant par leurs caractéristiques individuelles telles que leur genre, éducation, expérience, mobilité interentreprise et d’autres. De plus, nous décomposons les flux de migrants par canaux d’entrer dans le pays de destination, tel que le canal de l’éducation, multinational ou si l’inventeur change d’entreprise, et analysons la sélection des migrants et le gap de productivité entre les natifs et les migrants. Aussi, dans ce
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Mankevich, Vasili. "Managing Innovation Networks : Exploring Coopetition Dynamics in Innovation Ecosystems." Thesis, Umeå universitet, Institutionen för informatik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-90139.

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Companies increasingly rely on external partners when starting their innovation initiatives. Emergent innovation ecosystems of heterogeneous actors proved to be successful in leveraging combined competence for the creation of the new ventures. However, constantly changing environment of simultaneous competition and cooperation – coopetition, presents a challenge for the ecosystem management. Drawing on the network orchestration and coopetition research, I analyze management practices and coopetition dynamics in the digital creative industry in Northern Sweden. Based on the analysis, I offer tw
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Bremström, Maria, and Susanne Stipic. "Capitalising on Big Data from Space : How Novel Data Utilisation Can Drive Business Model Innovation." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264180.

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Business model innovation has in recent year become more important for firms looking to gain competitive advantage on dynamic markets. Additionally, incorporating data into a firm’s business model has been shown to lead to improved performance. This development has led to interest in the connection between data utilisation and business model innovation. This thesis provides an in-depth case study of a Swedish space firm active within the satellite industry. The firm operates within an increasingly dynamic market, and ongoing disruptions in the form of new market entrants and rapid technologica
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Cammarano, Antonello. "Five dimensions of innovation: Studying technological strategies through patent data." Doctoral thesis, Universita degli studi di Salerno, 2016. http://hdl.handle.net/10556/2188.

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2014 - 2015<br>Technological innovation is the key driver for technological progress and firms’ economic growth. Since firms pursue different innovation approaches, they achieve different innovation performances. Actually, a firm’s ability to develop technologies and products is strongly conditioned by its stock of knowledge, expertise and technology from prior R&D. The R&D conducted by companies is an investment activity whose output is the firm’s knowledge stock. This asset positively contributes to the firms’ future financial performance and, then, to its market value. The higher the
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Pillu, Hugo. "Knowledge flows through patent citation data." Phd thesis, Ecole Centrale Paris, 2009. http://tel.archives-ouvertes.fr/tel-00458678.

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Dans cette thèse, nous analysons les différents aspects des externalités de connaissance et la façon dont les citations de brevet peuvent être utilisées comme un indicateur de ces flux. La première partie de cette thèse examine la littérature traditionnelle sur les externalités de connaissance, et cela d'un point de vue à la fois qualitatif et quantitatif (la forme quantitative est réalisée grâce à une méta-analyse). Nous insistons sur les conséquences résultant de l'utilisation de différents canaux de mesure de ces externalités, précisément nous nous attardons sur les hypothèses sous-jacentes
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Books on the topic "Data Innovation"

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Andersen, Michael Moesgaard, and Torben Pedersen. Data-Driven Innovation. Routledge, 2021. http://dx.doi.org/10.4324/9781003041702.

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Sharma, Neha, Amlan Chakrabarti, Valentina Emilia Balas, and Alfred M. Bruckstein, eds. Data Management, Analytics and Innovation. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2934-1.

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Sharma, Neha, Amlan Chakrabarti, Valentina Emilia Balas, and Alfred M. Bruckstein, eds. Data Management, Analytics and Innovation. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-2937-2.

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Goswami, Saptarsi, Inderjit Singh Barara, Amol Goje, C. Mohan, and Alfred M. Bruckstein, eds. Data Management, Analytics and Innovation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-2600-6.

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Sharma, Neha, Amlan Chakrabarti, and Valentina Emilia Balas, eds. Data Management, Analytics and Innovation. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9364-8.

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Sharma, Neha, Amlan Chakrabarti, Valentina Emilia Balas, and Jan Martinovic, eds. Data Management, Analytics and Innovation. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-5616-6.

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Sharma, Neha, Amlan Chakrabarti, Valentina Emilia Balas, and Jan Martinovic, eds. Data Management, Analytics and Innovation. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-5619-7.

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Balas, Valentina Emilia, Neha Sharma, and Amlan Chakrabarti, eds. Data Management, Analytics and Innovation. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1274-8.

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Balas, Valentina Emilia, Neha Sharma, and Amlan Chakrabarti, eds. Data Management, Analytics and Innovation. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1402-5.

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Sharma, Neha, Amlan Chakrabarti, and Valentina Emilia Balas, eds. Data Management, Analytics and Innovation. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-32-9949-8.

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Book chapters on the topic "Data Innovation"

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Curley, Martin, and Bror Salmelin. "Data-Driven Innovation." In Open Innovation 2.0. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62878-3_12.

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Treder, Martin. "Data for Innovation." In The Chief Data Officer Management Handbook. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6115-6_23.

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Alonso, Daniel. "Data Innovation Spaces." In The Elements of Big Data Value. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68176-0_9.

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AbstractWithin the European Big Data Ecosystem, cross-organisational and cross-sectorial experimentation and innovation environments play a central role. European Innovation Spaces (or i-Spaces for short) are the main elements to ensure that research on big data value technologies and novel applications can be quickly tested, piloted and exploited for the benefit of all stakeholders. In particular, i-Spaces enable stakeholders to develop new businesses facilitated by advanced Big Data Value (BDV) technologies, applications and business models, bringing together all blocks, actors and functiona
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Andersen, Michael Moesgaard, and Torben Pedersen. "Firms and innovation." In Data-Driven Innovation. Routledge, 2021. http://dx.doi.org/10.4324/9781003041702-3.

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Al-Mubaraki, Hanadi Mubarak, Ali Husain Muhammad, and Michael Busler. "Data Collection." In Innovation and Entrepreneurship. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13683-7_3.

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Al-Mubaraki, Hanadi Mubarak, Ali Husain Muhammad, and Michael Busler. "Data Analysis." In Innovation and Entrepreneurship. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13683-7_4.

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Hayashi, Teruaki, and Yukio Ohsawa. "Data-Interactive Innovations as the Heart of Market of Data (MoDAT)." In Understanding Innovation. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06145-5_2.

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Andersen, Michael Moesgaard, and Torben Pedersen. "Human behavior." In Data-Driven Innovation. Routledge, 2021. http://dx.doi.org/10.4324/9781003041702-2.

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Andersen, Michael Moesgaard, and Torben Pedersen. "Crafting a growth strategy based on innovation." In Data-Driven Innovation. Routledge, 2021. http://dx.doi.org/10.4324/9781003041702-6.

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Andersen, Michael Moesgaard, and Torben Pedersen. "Searching for accelerated growth in a data-driven world." In Data-Driven Innovation. Routledge, 2021. http://dx.doi.org/10.4324/9781003041702-1.

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Conference papers on the topic "Data Innovation"

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Hodgson, Barry. "Bridging the Data Skills Gap: The Role of the UK’s National Innovation Centre for Data and its Data Innovation Bootcamp." In 2024 IEEE 20th International Conference on e-Science (e-Science). IEEE, 2024. http://dx.doi.org/10.1109/e-science62913.2024.10678712.

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Bijedić, Nina, Dražena Gašpar, and Azra Smajić. "ENHANCING DATA LITERACY BY IMPROVING STUDENTS' UNDERSTANDING OF DATA AND DATA ANALYSIS." In 17th annual International Conference of Education, Research and Innovation. IATED, 2024. https://doi.org/10.21125/iceri.2024.0723.

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Cui, Mengjie, Yangyang Li, Xiuling He, and Ruijie Zhou. "Data-driven Analysis and Evaluation of Pedagogical Innovation." In 2024 4th International Conference on Educational Technology (ICET). IEEE, 2024. https://doi.org/10.1109/icet62460.2024.10867986.

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Mora, Jimmy Ugalde, and Linnette Roldán Morales. "Student Innovation in University Data Management Through SIDEM." In 2024 IEEE 6th International Conference on BioInspired Processing (BIP). IEEE, 2024. https://doi.org/10.1109/bip63158.2024.10885386.

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Yu, Youshan. "Data-driven Innovation or Innovation-driven Data Generation?" In 2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). IEEE, 2023. http://dx.doi.org/10.1109/ice/itmc58018.2023.10332261.

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Marsh, James Barney. "Data, knowledge, innovation." In OCEANS 2011 - SPAIN. IEEE, 2011. http://dx.doi.org/10.1109/oceans-spain.2011.6003561.

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Berkhout, Guus, and Patrick van der Duin. "Mobile data innovation." In the 6th international conference. ACM Press, 2004. http://dx.doi.org/10.1145/1052220.1052297.

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Fu, Xin, and Hernán Asorey. "Data-Driven Product Innovation." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. http://dx.doi.org/10.1145/2783258.2789994.

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Mossberger, Karen, Kellen Gracey, Kevin DeSouza, and Joshua M. Uebelherr. "Data, tools, & innovation." In dg.o 2015: 16th Annual International Digital Government Research Conference. ACM, 2015. http://dx.doi.org/10.1145/2757401.2757441.

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Yan, Wangcheng, Wenjun Zhou, Paolo Letizia, and Bogdan Bichescu. "Crowdsourcing Data Science for Innovation." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.164.

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Reports on the topic "Data Innovation"

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Liberman, Babe, Jenny Bradbury, Andrew Vollavanh, and Sana Karim. Innovating for Student Success: District-Led Innovation Showcase. Digital Promise, 2025. https://doi.org/10.51388/20.500.12265/255.

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Student success systems are evidence-based approaches that leverage data, relationships, and student-centered mindsets to systematically address achievement patterns and meet individual student needs. The District-Led Innovation Showcase examines the technology and data systems element, highlighting how schools and districts are developing custom data solutions to address gaps in commercially available products for operationalizing student success systems. District-Led Innovations (DLIs) are solutions that range from simple spreadsheet trackers to sophisticated integrated platforms. The Showca
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Crespi, Gustavo, and Ezequiel Tacsir. Effects of Innovation on Employment in Latin America. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0009072.

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This study examines the impact of process and product innovation on employment growth and composition in Argentina, Chile, Costa Rica, and Uruguay using micro data from innovation surveys. Based on the model put forward by Harrison et al. (1998), employment growth is related to process innovations and to the growth of sales separately due to innovative and unchanged products. Results show that compensation effects are pervasive and that the introduction of new products is associated with employment growth at the firm level. No evidence of displacement effects due to the introduction of product
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Stucchi, Rodolfo, David Giuliodori, and Sheila De Elejalde. Employment Generation, Firm Size and Innovation: Microeconometric Evidence from Argentina. Inter-American Development Bank, 2011. http://dx.doi.org/10.18235/0008968.

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This paper provides evidence about the relationship between innovation and employment in Argentina. In particular, it quantifies the impact of different types of innovations (process or product innovations) on employment growth and skill composition (skilled-unskilled labor) and the impact of different innovation strategies (buy or make) on employment growth, and analyzes whether these impacts depend on firm size or technology intensity. To answer these questions a model proposed in Harrison, Jaumandreu, Mairesse, and Peters (2008) was estimated using an IV approach with data from the Innovati
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Warren, James A., and Ronald F. Boisvert. Building the Materials Innovation Infrastructure: Data and Standards. National Institute of Standards and Technology, 2012. http://dx.doi.org/10.6028/nist.ir.7898.

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Jaffe, Adam, and Karen Palmer. Environmental Regulation and Innovation: A Panel Data Study. National Bureau of Economic Research, 1996. http://dx.doi.org/10.3386/w5545.

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Navas-Alemán, Lizbeth. Innovation and Competitiveness in Mining Value Chains: The Case of Brazil. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003813.

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Mining companies have mirrored other large multinational companies in setting up global value chains (GVCs), sourcing their inputs and services from an ever-larger number of highly capable suppliers in developing countries, such as those in resource-rich Latin America. However, recent empirical studies on the mining GVC in that region suggest that even innovative local suppliers find it difficult to exploit their innovations in local and foreign markets. Using a conceptual framework that combines literature on innovation and GVCs, this study analyzed how global/regional- and firm-level factors
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Miranda, Javier, and Nikolas Zolas. Measuring the Impact of Household Innovation using Administrative Data. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w25259.

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Majeed, Omer, Jonathan Hambur, and Robert Breunig. Do Monetary Policy and Economic Conditions Impact Innovation? Evidence from Australian Administrative Data. Reserve Bank of Australia, 2024. http://dx.doi.org/10.47688/rdp2024-01.

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Recent papers have argued that monetary policy and economic conditions can influence the amount of innovative activity in the economy, and therefore productivity and living standards in the future. This paper examines whether this is the case for Australia, a small open economy that tends to import innovation from overseas. We find that contractionary (expansionary) monetary policy reduces (increases) aggregate research and development (R&amp;D) spending, and that lower (higher) R&amp;D spending reduces (increases) future productivity. However, using firm-level data and a broader survey measur
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Gallego, Juan Miguel, Rodrigo Taborda, and Hernando Gutiérrez. Innovation and Productivity in the Colombian Service Industry. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0006955.

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This paper presents the results of a study on the ways that innovation and productivity are connected in the Colombian manufacturing and service industry. Using data from the Manufacturing Innovation Survey (2007-2008) and the Service Innovation Survey (2008-2009), the paper addresses two main questions: first, whether patterns of innovations differ among manufacturing and services industries, and second, whether service firms innovate, and, if so, whether their innovation approach differs from that of manufacturing. The main findings are (1) that service firms engage in process innovation, bo
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Abdulla, Sara, and Husanjot Chahal. Voices of Innovation. Center for Security and Emerging Technology, 2023. http://dx.doi.org/10.51593/20220022.

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This data brief identifies the most influential AI researchers in the United States between 2010 and 2021 via three metrics: number of AI publications, citations, and AI h-index. It examines their demographic profiles, career trajectories, and research collaboration rates, finding that most are men in the later stages of their career, largely concentrated in 10 elite universities and companies, and that nearly 70 percent of America’s top AI researchers were born abroad.
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