1. bookVolume 37 (2021): Issue 1 (March 2021)
Journal Details
License
Format
Journal
eISSN
2001-7367
First Published
01 Oct 2013
Publication timeframe
4 times per year
Languages
English
access type Open Access

Building a Sample Frame of SMEs Using Patent, Search Engine, and Website Data

Published Online: 13 Mar 2021
Page range: 1 - 30
Received: 01 Sep 2019
Accepted: 01 Sep 2020
Journal Details
License
Format
Journal
eISSN
2001-7367
First Published
01 Oct 2013
Publication timeframe
4 times per year
Languages
English
Abstract

This research outlines the process of building a sample frame of US SMEs. The method starts with a list of patenting organizations and defines the boundaries of the population and subsequent frame using free to low-cost data sources, including search engines and websites. Generating high-quality data is of key importance throughout the process of building the frame and subsequent data collection; at the same time, there is too much data to curate by hand. Consequently, we turn to machine learning and other computational methods to apply a number of data matching, filtering, and cleaning routines. The results show that it is possible to generate a sample frame of innovative SMEs with reasonable accuracy for use in subsequent research: Our method provides data for 79% of the frame. We discuss implications for future work for researchers and NSIs alike and contend that the challenges associated with big data collections require not only new skillsets but also a new mode of collaboration.

Keywords

Anagnostopoulos, I., C. Anagnostopoulos, V. Loumos, and E. Kayafas. 2004. “Classifying Web Pages Employing a Probabilistic Neural Network.” IEE Proceedings – Software 151 (3): 139. DOI: https://doi.org/10.1049/ip-sen:20040121.10.1049/ip-sen:20040121Search in Google Scholar

Andrianantoandro, E., S. Basu, D.K. Karig, and R. Weiss. 2006. “Synthetic Biology: New Engineering Rules for An Emerging Discipline.” Molecular Systems Biology 2. DOI: https://doi.org/10.1038/msb4100073.10.1038/msb4100073168150516738572Search in Google Scholar

Arora, S.K., Y. Li, J. Youtie, and P. Shapira. 2015. “Using the Wayback Machine to Mine Websites in the Social Sciences: A Methodological Resource.” Journal of the Association for Information Science and Technology. DOI: https://doi.org/10.1002/asi.23503.10.1002/asi.23503Search in Google Scholar

Arora, S.K., A.L. Porter, J. Youtie, and P. Shapira. 2012. “Capturing New Developments in An Emerging Technology: An Updated Search Strategy for Identifying Nanotechnology Research Outputs.” Scientometrics 95 (1): 351–70. DOI: https://doi.org/10.1007/s11192-012-0903-6.10.1007/s11192-012-0903-6Search in Google Scholar

Atkinson, R.D., and M. Lind. 2018. Big is Beautiful: Debunking the Myth of Small Business. MIT Press.10.7551/mitpress/11537.001.0001Search in Google Scholar

Barcaroli, G., M. Scannapieco, and D. Summa. 2016. “On the Use of Internet As a Data Source for Official Statistics: A Strategy for Identifying Enterprises on the Web.” Rivista Italiana Di Economia, Demografia E Statistica 70 (4): 20–41.Search in Google Scholar

Baruch, Y., and B.C. Holtom. 2008. “Survey Response Rate Levels and Trends in Organizational Research.” Human Relations 61 (8): 1139–60. DOI: https://doi.org/10.1177/0018726708094863.10.1177/0018726708094863Search in Google Scholar

Berardi, G., A. Esuli, T. Fagni, and F. Sebastiani. 2015. “Classifying Websites by Industry Sector.” In Proceedings of the 30th Annual ACM Symposium on Applied Computing – SAC ’15, 1053–59. New York, New York, USA: ACM Press. DOI: https://doi.org/10.1145/2695664.2695722.10.1145/2695664.2695722Search in Google Scholar

Beręsewicz, M., R. Lehtonen, F. Reis, L. Di Consiglio, and M. Karlberg. 2018. “An Overview of Methods for Treating Selectivity in Big Data Sources.” Eurostat Statistical Working Papers. Luxembourg. DOI: https://doi.org/10.2785/312232 (accessed July 2020).Search in Google Scholar

Birch, D.G.W. 1987. Job Creation in America: How Our Smallest Companies Put the Most People to Work. University of Illinois at Urbana-Champaign.Search in Google Scholar

Blazquez, D., and J. Domenech. 2018. “Big Data Sources and Methods for Social and Economic Analyses.” Technological Forecasting and Social Change 130 (May): 99–113. DOI: https://doi.org/10.1016/j.techfore.2017.07.027.10.1016/j.techfore.2017.07.027Search in Google Scholar

Blazquez, D., J. Domenech, and A. Debón. 2018. “Do Corporate Websites’ Changes Reflect Firms’ Survival?” Online Information Review 42 (6): 956–70. DOI: https://doi.org/10.1108/OIR-11-2016-0321.10.1108/OIR-11-2016-0321Search in Google Scholar

Broder, A. 2002. “A Taxonomy of Web Search.” ACM SIGIR Forum 36 (2): 3–10. DOI: https://doi.org/10.1145/792550.792552.10.1145/792550.792552Search in Google Scholar

Bruni, R., and G. Bianchi. 2020. “Website Categorization: A Formal Approach and Robustness Analysis in the Case of e-Commerce Detection.” Expert Systems With Applications 142: 113001. DOI: https://doi.org/10.1016/j.eswa.2019.113001.10.1016/j.eswa.2019.113001Search in Google Scholar

Buelens, B., P. Daas, J. Burger, M. Puts, and J. Van Den Brakel. 2014. “Selectivity of Big Data.” Available at: https://www.researchgate.net/profile/Bart_Buelens/publication/261436243_Selectivity_of_Big_data/links/00b49539806fa06b08000000/Selectivity-of-Big-data.pdf.Search in Google Scholar

Cohen, W.M., R.R. Nelson, and J.P. Walsh. 2000. “Protecting Their Intellectual Assets: Appropriability Conditions and Why U.S. Manufacturing Firms Patent (or Not).” 7552. Available at: http://www.nber.org/papers/w7552 (accessed February 2020).10.3386/w7552Search in Google Scholar

Connelly, R., C.J. Playford, V. Gayle, and C. Dibben. 2016. “The Role of Administrative Data in the Big Data Revolution in Social Science Research.” Social Science Research 59: 1–12. DOI: https://doi.org/10.1016/j.ssresearch.2016.04.015.10.1016/j.ssresearch.2016.04.01527480367Search in Google Scholar

Cortes, C., and V. Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20 (3): 273–97. DOI: https://doi.org/10.1007/BF00994018.10.1007/BF00994018Search in Google Scholar

Coughlin, S.M. 2006. “Is the Patent Paradox a Result a Large Firm Perspective-Differential Value of Small Firm Patents Over Time Explains the Patent Paradox.” Santa Clara Computer & High Tech. LJ 23: 371. Available at: https://digitalcommons.law.scu.edu/cgi/viewcontent.cgi?referer=https://scholar.google.com/&httpsredir=1&-article=1429&context=chtlj (accessed March 2019).Search in Google Scholar

Cruz, R.M.O., L.G. Hafemann, R. Sabourin, and G.D.C. Cavalcanti. 2018. “DESlib: A Dynamic Ensemble Selection Library in Python.” Available at: http://arxiv.org/abs/1802.04967 (accessed March 2020).Search in Google Scholar

Demirel, P., and M. Mazzucato. 2012. “Innovation and Firm Growth: Is R&D Worth It?” Industry & Innovation 19 (1): 45–62. DOI: https://doi.org/10.1080/13662716. 2012.649057.Search in Google Scholar

Dennis Jr., W.J. 2003. “Raising Response Rates in Mail Surveys of Small Business Owners: Results of An Experiment.” Journal of Small Business Management 41 (3): 278–95. DOI: https://doi.org/10.1111/1540-627X.00082.10.1111/1540-627X.00082Search in Google Scholar

Einav, L., and J. Levin. 2014. “Economics in the Age of Big Data.” Science 346 (6210): 1243089–1243089. DOI: https://doi.org/10.1126/science.1243089.10.1126/science.124308925378629Search in Google Scholar

ESSnet Big Data. 2020. “WPC Enterprise Characteristics – ESSnet Big Data.” Available at: https://webgate.ec.europa.eu/fpfis/mwikis/essnetbigdata/index.php/WPC_Enterprise_characteristics (accessed March 2020).Search in Google Scholar

Fan, R.-E., K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. 2008. “LIBLINEAR: A Library for Large Linear Classification.” Journal of Machine Learning Research 9: 1871–74. DOI: https://dl.acm.org/doi/pdf/10.5555/1390681.1442794.Search in Google Scholar

Flanegin, F., S. Racic, and D. Rudd. 2011. “Accuracy and Cost of U.S. Financial Data.” Journal of Applied Business Research (JABR) 25 (6). DOI: https://doi.org/10.19030/-jabr.v25i6.994.Search in Google Scholar

Fortune. 2019a. “Fortune 500 j Fortune.” Available at: https://fortune.com/fortune500/ (accessed March 2020).Search in Google Scholar

Fortune. 2019b. “Fortune 500 j Fortune.” Available at: https://fortune.com/fortune500/ (accessed March 2020).Search in Google Scholar

Giesen, D., M. Vella, C.F. Brady, P. Brown, D. Ravindra, and A. Vaasen-Otten. 2018. “Response Burden Management for Establishment Surveys at Four National Statistical Institutes.” Journal of Official Statistics 34 (2): 397–418. DOI: https://doi.org/10.2478/jos-2018-0018.10.2478/jos-2018-0018Search in Google Scholar

Glover, E.J, K. Tsioutsiouliklis, S. Lawrence, D.M. Pennock, and G.W. Flake. 2002. “Using Web Structure for Classifying and Describing Web Pages.” In Proceedings of the Eleventh International Conference on World Wide Web – WWW ’02, 562. New York, New York, USA: ACM Press. DOI: https://doi.org/10.1145/511446.511520.10.1145/511446.511520Search in Google Scholar

Graber, M.A., and M. Weckmann. 2002. “Pharmaceutical Company Internet Sites As Sources of Information About Antidepressant Medications.” CNS Drugs 16 (6): 419–23. DOI: https://doi.org/10.2165/00023210-200216060-00005.10.2165/00023210-200216060-0000512027787Search in Google Scholar

Groves, R.M., and B.A. Harris-Kojetin, Eds. 2017. “Innovations in Federal Statistics.” Washington, D.C.: National Academies Press. DOI: https://doi.org/10.17226/24652.10.17226/2465228426187Search in Google Scholar

Hastie, T., S. Rosset, J. Zhu, and H. Zou. 2009. “Multi-Class AdaBoost.” Statistics and Its Interface 2 (3): 349–60. DOI: https://doi.org/10.4310/SII.2009.v2.n3.a8.10.4310/SII.2009.v2.n3.a8Search in Google Scholar

Hicks, D., and D. Hegde. 2005. “Highly Innovative Small Firms in the Markets for Technology.” Research Policy 34 (5): 703–16. DOI: https://doi.org/10.1016/j.respol.2005.03.008.10.1016/j.respol.2005.03.008Search in Google Scholar

Hu, X., and R. Rousseau. 2015. “From a Word to a World: The Current Situation in the Interdisciplinary Field of Synthetic Biology.” PeerJ 3 (January): E728. DOI: https://doi.org/10.7717/peerj.728.10.7717/peerj.728431206825650074Search in Google Scholar

Kanani, P.H, and A.K. McCallum. 2012. “Selecting Actions for Resource-Bounded Information Extraction Using Reinforcement Learning.” In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining – WSDM ’12, 253. New York, New York, USA: ACM Press. DOI: https://doi.org/10.1145/2124295.2124328.10.1145/2124295.2124328Search in Google Scholar

Kaplan, R.M., D.A. Chambers, and R.E. Glasgow. 2014. “Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias.” Clinical and Translational Science 7 (4): 342–46. DOI: https://doi.org/10.1111/cts.12178.10.1111/cts.12178543981625043853Search in Google Scholar

King, G. 2007. “An Introduction to the Dataverse Network As An Infrastructure for Data Sharing.” Sociological Methods & Research 36 (2): 173–99. DOI: https://doi.org/10.1177/0049124107306660.10.1177/0049124107306660Search in Google Scholar

Kingma, D.P., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.” ArXiv Preprint ArXiv:1412.6980. Available at: http://arxiv.org/abs/1412.6980 (accessed February 2020).Search in Google Scholar

Kitchin, R. 2014. “Big Data, New Epistemologies and Paradigm Shifts.” Big Data & Society 1 (1): 205395171452848. DOI: https://doi.org/10.1177/2053951714528481.10.1177/2053951714528481Search in Google Scholar

Lane, J. 2016. “Big Data for Public Policy: The Quadruple Helix.” Journal of Policy Analysis and Management 35 (3): 708–15. DOI: https://doi.org/10.1002/pam.21921.10.1002/pam.21921Search in Google Scholar

Lane, J. And S. Bertuzzi. 2011. “Measuring the Results of Science Investments.” Science 331 (6018): 678–80. DOI: https://doi.org/10.1126/science.1201865.10.1126/science.120186521310987Search in Google Scholar

Lanjouw, J.O., and M. Schankerman. 2004. “Protecting Intellectual Property Rights: Are Small Firms Handicapped?” The Journal of Law and Economics 47 (1): 45–74. DOI: https://doi.org/10.1086/380476.10.1086/380476Search in Google Scholar

Lewandowski, D., and N. Höchstötter. 2008. “Web Searching: A Quality Measurement Perspective.” In Web Search: Multidisciplinary Perspectives, edited by Amanda Spink and Michael Zimmer, 309–40. Berlin, Heidelberg: Springer-Verlag. DOI: https://doi.org/10.1007/978-3-540-75829-7_16.10.1007/978-3-540-75829-7_16Search in Google Scholar

Lewandowski, D. 2015. “Evaluating the Retrieval Effectiveness of Web Search Engines Using a Representative Query Sample.” Journal of the Association for Information Science and Technology 66 (9): 1763–75. DOI: https://doi.org/10.1002/asi.23304.10.1002/asi.23304Search in Google Scholar

Lin, C., Y.-A. Huang, and R. Stockdale. 2011. “Developing a B2B Web Site Effectiveness Model for SMEs.” Internet Research 21 (3): 304–25. DOI: https://doi.org/10.1108/10662241111139327.10.1108/10662241111139327Search in Google Scholar

Lindič, J., M. Bavdaž, and H. Kovačič. 2012. “Higher Growth Through the Blue Ocean Strategy: Implications for Economic Policy.” Research Policy 41 (5): 928–38. DOI: https://doi.org/10.1016/j.respol.2012.02.010.10.1016/j.respol.2012.02.010Search in Google Scholar

Lundvall, B.-Å., and S. Borrás. 2005. “Science, Technology and Innovation Policy.” In The Oxford Handbook of Innovation, edited by Jan Fagerberg, David C. Mowery, and Richard R. Nelson: 599–631. Oxford University Press Oxford.Search in Google Scholar

MacFeely, S. 2019. “Big Data and Official Statistics.” In Big Data Governance and Perspectives in Knowledge Management, edited by Sheryl Kruger Strydom and Moses Strydom, 25–54. IGI Global. DOI: https://doi.org/10.4018/978-1-5225-7077-6.ch002.10.4018/978-1-5225-7077-6.ch002Search in Google Scholar

Mergel, I., R.K. Rethemeyer, and K. Isett. 2016. “Big Data in Public Affairs.” Public Administration Review 76 (6): 928–37. DOI: https://doi.org/10.1111/puar.12625.10.1111/puar.12625Search in Google Scholar

Mooney, R.J., and R. Bunescu. 2005. “Mining Knowledge from Text Using Information Extraction.” ACM SIGKDD Explorations Newsletter 7 (1): 3–10. DOI: https://doi.org/10.1145/1089815.1089817.10.1145/1089815.1089817Search in Google Scholar

Munari, F., and L. Toschi. 2014. “Running Ahead in the Nanotechnology Gold Rush. Strategic Patenting in Emerging Technologies.” Technological Forecasting and Social Change 83: 194–207. DOI: https://doi.org/10.1016/j.techfore.2013.07.002.10.1016/j.techfore.2013.07.002Search in Google Scholar

Nathan, M., A. Rosso, T. Gatten, P. Majmudar, and A. Mitchell. 2013. “Measuring the UK’s Digital Economy With Big Data.” National Institute of Economic and Social Research London. Available at: https://www.niesr.ac.uk/sites/default/files/publications/SI024_GI_NIESR_Google_Report12.pdf (accessed February 2020).Search in Google Scholar

National Academies of Sciences, Engineering, and Medicine. 2018. “Sampling and Estimation.” In Reengineering the Census Bureau’s Annual Economic Surveys, edited by K.G. Abraham, C.F. Citro, G.D. White, and N.K. Kirkendall: 91–118. Washington, D.C.: National Academies Press. DOI: https://doi.org/10.17226/25098.10.17226/25098Search in Google Scholar

OECD, and the Royal Society. 2010. Symposium on Opportunities and Challenges in the Emerging Field of Synthetic Biology. OECD Publishing. DOI: https://doi.org/10.1787/9789264086265-en.10.1787/9789264086265-enSearch in Google Scholar

Oldham, P., S. Hall, and G. Burton. 2012. “Synthetic Biology: Mapping the Scientific Landscape.” Edited by J.A. Gilbert. PLoS ONE 7 (4): E34368. DOI: https://doi.org/10.1371/journal.pone.0034368.10.1371/journal.pone.0034368333511822539946Search in Google Scholar

Oostrom, L., A. Walker, B. Staats, M. Slootbeek-van Laar, S. Azurduy, and B. Rooijakkers. 2016. “Measuring the Internet Economy in the Netherlands: A Big Data Analysis.” The Hague: Statistics Netherlands. Available at: https://www.nldigital.nl/wp-content/uploads/2016/10/measuring-the-internet-economy.pdf (accessed February 2020).Search in Google Scholar

PCAST. 2005. “The National Nanotechnology Initiative at Five Years.” Washington, D.C., U.S.A. Available at: https://www.nano.gov/sites/default/files/pub_resource/final_pcast_nano_report_for_web.pdf (accessed February 2019).Search in Google Scholar

Pedregosa, F., G. Varoquaux, A. Gramfort, V.M.B. Thirion, O. Grisel, M.Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–30. Available at: https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf (accessed February 2019).Search in Google Scholar

Pew. 2018. “Internet/Broadband Fact Sheet.” Available at: http://www.pewinternet.org/fact-sheet/internet-broadband/ (accessed February 2019).Search in Google Scholar

Popp, D. 2016. “Economic Analysis of Scientific Publications and Implications for Energy Research and Development.” Nature Energy 1 (4): 16020. DOI: https://doi.org/10.1038/nenergy.2016.20.10.1038/nenergy.2016.20Search in Google Scholar

Popp, D. 2017. “From Science to Technology: The Value of Knowledge from Different Energy Research Institutions.” Research Policy 46 (9): 1580–94. DOI: https://doi.org/10.1016/j.respol.2017.07.011.10.1016/j.respol.2017.07.011Search in Google Scholar

Qi, X., and B.D. Davison. 2007. “Web Page Classification.” ACM Computing Surveys 41(2): 1–31. DOI: https://doi.org/10.1145/1459352.1459357.10.1145/1459352.1459357Search in Google Scholar

Raimbault, B., J.-P. Cointet, and P.-B. Joly. 2016. “Mapping the Emergence of Synthetic Biology.” Edited by Mark Isalan. PLOS ONE 11 (9): E0161522. DOI: https://doi.org/10.1371/journal.pone.0161522.10.1371/journal.pone.0161522501777527611324Search in Google Scholar

Rajaraman, A., and J.D. Ullman. 2011. Mining of Massive Datasets. Cambridge University Press.Search in Google Scholar

Reimsbach-Kounatze, C. 2015. “The Proliferation of ‘Big Data’ And Implications for Official Statistics and Statistical Agencies: A Preliminary Analysis.” 245. OECD Digital Economy Papers. Paris. DOI: https://doi.org/10.1787/5js7t9wqzvg8-en.10.1787/5js7t9wqzvg8-enSearch in Google Scholar

Rothwell, R. 1989. “Small Firms, Innovation and Industrial Change.” Small Business Economics 1 (1): 51–64. DOI: https://doi.org/10.1007/BF00389916.10.1007/BF00389916Search in Google Scholar

Safavian, S.R., and D. Landgrebe. 1991. “A Survey of Decision Tree Classifier Methodology.” IEEE Transactions on Systems, Man, and Cybernetics 21 (3): 660–74. DOI: https://doi.org/10.1109/21.97458.10.1109/21.97458Search in Google Scholar

Särndal, C.-E., B. Swensson, and J. Wretman. 2003. Model Assisted Survey Sampling. New York: Springer-Verlag.Search in Google Scholar

Shapira, P., A. Gök, E. Klochikhin, and M. Sensier. 2013. “Probing ‘Green’ Industry Enterprises in the UK: A New Identification Approach.” Technological Forecasting and Social Change. DOI: https://doi.org/10.1016/j.techfore.2013.10.023.10.1016/j.techfore.2013.10.023Search in Google Scholar

Sicilia, M.-A., E. García-Barriocanal, and S. Sánchez-Alonso. 2017. “Community Curation in Open Dataset Repositories: Insights from Zenodo.” Procedia Computer Science 106: 54–60. DOI: https://doi.org/10.1016/j.procs.2017.03.009.10.1016/j.procs.2017.03.009Search in Google Scholar

Simon, H.A. 1996. The Sciences of the Artificial. 3rd ed. Cambridge, MA: MIT Press.Search in Google Scholar

Sullivan, I., A. DeHaven, and D. Mellor. 2019. “Open and Reproducible Research on Open Science Framework.” Current Protocols Essential Laboratory Techniques 18 (1). DOI: https://doi.org/10.1002/cpet.32.10.1002/cpet.32Search in Google Scholar

Talan, D.M. 2016. “Opportunities and Challenges for Using Big Administrative Data.” Available at: https://www.bls.gov/osmr/research-papers/2016/pdf/st160100.pdf (accessed February 2019).Search in Google Scholar

Tambe, P. 2014. “Big Data Investment, Skills, and Firm Value.” Management Science 60 (6): 1452–69. DOI: https://doi.org/10.1287/mnsc.2014.1899.10.1287/mnsc.2014.1899Search in Google Scholar

Teece, D.J., M. Peteraf, and S. Leih. 2016. “Dynamic Capabilities and Organizational Agility: Risk, Uncertainty, and Strategy in the Innovation Economy.” California Management Review 58 (4): 13–35. DOI: https://doi.org/10.1525/cmr.2016.58.4.13.10.1525/cmr.2016.58.4.13Search in Google Scholar

Ten Bosch, O., D. Windmeijer, A. Van Delden, and G. Van Den Heuvel. 2018. “Web Scraping Meets Survey Design: Combining Forces.” In Big Data Meets Survey Science Conference, Barcelona, Spain. Available at: https://www.bigsurv18.org/conf18/uploads/73/61/20180820_BigSurv_WebscrapingMeetsSurveyDesign.pdf (accessed February 2020).Search in Google Scholar

Toumey, C. 2009. “Plenty of Room, Plenty of History.” Nature Nanotechnology 4 (12): 783–84. DOI: https://doi.org/10.1038/nnano.2009.357.10.1038/nnano.2009.35719966818Search in Google Scholar

U.K. Office for National Statistics. 2020. “Business enterprise research and development, UK: 2019.” Available at: https://www.ons.gov.uk/economy/governmentpublicsectorandtaxes/researchanddevelopmentexpenditure/bulletins/businessenterpriseresearchanddevelopment/2019 (accessed February 2020).Search in Google Scholar

U.S. Census Bureau. 2020a. “About Annual Business Survey.” Available at: https://www.census.gov/programs-surveys/abs/about.html (accessed March 2020).Search in Google Scholar

U.S. Census Bureau. 2020b. “About this Survey.” Available at: https://www.census.gov/-programs-surveys/abs/about.html.Search in Google Scholar

U.S. Energy Information Administration. 2018. “Available Energy Explained.” Available at: https://www.eia.gov/energyexplained/renewable-sources/incentives.php (accessed February 2019).Search in Google Scholar

U.S. Small Business Administration. 2018. “Frequently Asked Questions.” Available at: https://www.sba.gov/sites/default/files/advocacy/Frequently-Asked-Questions-Small-Business-2018.pdf (accessed February 2019).Search in Google Scholar

USPTO. 2019. “PatentsView.” Available at: https://www.patentsview.org/download/(accessed March 2020).Search in Google Scholar

USPTO. 2020. “US Patent Activity, CY 1790 to Present.” Available at: https://www.uspto.gov/web/offices/ac/ido/oeip/taf/h_counts.htm (accessed March 2020).Search in Google Scholar

Van Delden, A., D. Windmeijer, and O. Ten Bosch. 2019a. “Finding Enterprise Websites.” In European Establishment Statistics Workshop. Bilbao, Spain. Available at: https://www.researchgate.net/profile/Arnout_Delden/publication/336995371_Finding_enterprise_websites/links/5dbebf29299bf1a47b0f5669/Finding-enterprise-websites.pdf (accessed February 2020).Search in Google Scholar

Van Delden, A., D. Windmeijer, and O. Ten Bosch. 2019b. “Searching for Business Websites.” Centraal Bureau Voor De Statistiek. Available at: https://www.cbs.nl/-/media/_pdf/2020/01/searching-for-business-websites.pdf (accessed February 2020).Search in Google Scholar

Van Doren, D., S. Koenigstein, and T. Reiss. 2013. “The Development of Synthetic Biology: A Patent Analysis.” Systems and Synthetic Biology 7 (4): 209–20. DOI: https://doi.org/10.1007/s11693-013-9121-7.10.1007/s11693-013-9121-7382481724255694Search in Google Scholar

Wang, F., and L. Vaughan. 2014. “Firm Web Visibility and Its Business Value.” Internet Research 24 (3): 292–312. DOI: https://doi.org/10.1108/IntR-01-2013-0016.10.1108/IntR-01-2013-0016Search in Google Scholar

Williams, C.K.I., and C.E. Rasmussen. 2006. Gaussian Processes for Machine Learning. Cambridge, MA: MIT Press.Search in Google Scholar

Young, L.J., M. Hyman, and B.R. Rater. 2018. “Exploring a Big Data Approach to Building a List Frame for Urban Agriculture: A Pilot Study in the City of Baltimore.” Journal of Official Statistics 34 (2): 323–40. DOI: https://doi.org/10.2478/jos-2018-0015.10.2478/jos-2018-0015Search in Google Scholar

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