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Assessing the quest of SMEs in pivoting for new technological ventures: comparing the patenting indexes of seven developed cities

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Abstract

The role of science and patenting is often viewed as the focus of SMEs in post catch-up conditions, as they seek to pivot from performing low value-added activities to new technological ventures in searching for new niches. SMEs—particularly those in city areas—are incentivized to commit to research that is linked to scientific knowledge and patenting activities. This study is interested to explore whether these SMEs upgrade, file patents and commit to long term upgrading. Their performances are benchmarked to SMEs of San Jose—the largest city in Silicon Valley (the highly cited Marshallian industrial cluster). We have configured an extraction process for bulk patenting data and architected a sorting procedure to derive a list of relevant indexes from patents assigned to the bottom 40 (and 60) percent of the total assignees of a region. We discovered that SMEs in Taipei, Seoul, Singapore, Tel Aviv, Hong Kong and Dublin had indeed upgraded to adopt emerging (science-based) technologies and appropriate them—not only to supply for existing market needs, but also to build their competitive edge for future endeavours. While many cities are found to be relatively ahead in producing technologies that are classified as long cycle and science-based, Taipei saw a rising number of SMEs committed to science amid the majority which invested in non-science technologies. Taipei nonetheless stands out as the city which bears many characteristics of what a Marshallian cluster is like. This study sheds new light on the technological pursuit of SMEs in post catch-up conditions.

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Fig. 1

Source: Authors’ Design

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Source: KISTEP (2017) and Choi (2017)

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Notes

  1. For the cases of Taiwan and South Korea, the SMEs were once the supporting industries in the colonial era.

  2. Infrastructure in cities facilitates the provision of knowledge accessibility for clients, geographical proximity to research partners (resources) and diverse skilled workforces and networks (Collinson & Gregson, 2003; Teirlinck, 2018).

  3. Tel Aviv is the business capital of Israel.

  4. The governments of post catch-up economies are generally found obligated to devise various financial assistance programs (e.g. state endowed venture capital fund) to support firms (the SMEs in particular) which invest in industries that are deemed productive. Doh and Kim (2014 p. 1562) highlighted the financial support mechanism for SMEs in Korea.

  5. . Forms of entry barriers are diverse, and include IPR protection in the incumbent markets against the products by new firms (Shin et al., 2016).

  6. Some cities (such as Hong Kong) have migrated many of their manufacturing operations to lower cost economies (such as Mainland China) and pivoted their economies to focus on particular servicing industries (such as financial hub or tourism spot).

  7. Among the post catch-up economies, there are only two capable of populating local firms (in different regional industrial avenues) and which acquired a routine to advance indigenous technology capabilities to spawn short cycle technologies (see Hobday, 1995; Lee, 2013 p. 54).

  8. Greater Taiwan is home to about 7 million people while greater Dublin has about 2 million population. Singapore and Hong Kong have 5–7 million population respectively.

  9. See comparative view in Strange et al., (2006), and Lin et al., (2010).

  10. It is noted that the economies of the selected cities have a high tendency to patent their innovations in the USPTO (Schmoch, 2009).

  11. Besides the selected six cities, for comparative purposes we are also interested in other productive cities which are active in patenting. For inclusivity, we applied the following criteria to ensure that there are two representative cities for each country in our samples (with an exception for the cases of China and the US). The criteria are: (1) Two cities from each country by rank of GDP per capita; (2) For cities in developed economies, we targeted those with 1000 patents or above; and (3) For cities in developing economies, we targeted those with 100 patents or above. We selected the most productive 5 cities for the cases of China and the US, as they are relatively large in terms of both the size of their economy and patent counts.

  12. It is not uncommon in the literature to use percentiles to split data into defined categories. However, there is no definite (or consensus) percentile fixed to represent a certain group in a study. Agrawal et al. (2014), for example, used the 75th percentile in the list of number of patent inventors across the technology class-year distribution to profile small firms of a region. Nonetheless, a cut-off is required to filter and cross-check (such as done in this study) the extracted data in a profiling exercise to ensure that the targeted cases are covered in the sample. This study borrows the concept of B40 in profiling the bottom 40 percent of household income (SMECorp, 2019). The concept is used by the Department of Statistics Malaysia (DOSM, 2020) to demarcate income level groups into bottom 40, middle 40 and top 20 in the national population census. The percentile is also found useful in profiling business enterprises by their level of revenue.

  13. Those SMEs not in the patenting list are excluded in this study.

  14. Reported in Table 5. We however acknowledged the limitation of this study. There is possibly no perfect approach to search and profile SMEs exhaustively, which includes our setting and subsequent treatment of data. We thus acknowledge that our construct and series of data treatment could generate random (unexpected) measurement errors.

  15. This echoes the view of Clancy et al., (2001 p. 18) and Doh and Kim (2014 p. 1558).

  16. We believe such context is relevant to the cases of other selected cities too, as they share many similar technological development trajectories (appropriated short cycle technologies—ICT during catch-up period) and policy measures in the post catch-up phase of economic development (see Lee, 2013 pp. 72–100; Shiu et al., 2014). San Jose as the benchmark city in this study may not share similar priorities. There are other approaches to assess if firms of a city diversify and venture into new technologies—for example, Relatedness index (see Boschma et al., 2015) derived from 3-digit patent classes is among those commonly used. We decided not to adopt such an approach in this study as our city cases targeting B40 assignees are performing at a relatively low base. The indexes eventually derived from a low frequency of patenting can be misleading.

  17. The agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) required all member states (including the developing ones) to fulfil the obligations by 2005. The selection of 2007–2009 as the period of study (with consideration of time lag from the focal year) coincides with our interest to learn the patent counts resulting from a common (obligated) routine.

  18. We limit the x-axes to 8000 patents in Fig. 3 and Fig. 4 and share of 0.1 in Appendix 1 Fig. 14a and b, as not doing so would compress our observations of the majority.

  19. We used the Herfindahl–Hirschman Index (HHI) to derive the concentration ratio. Concentration is used to assess whether the local patents are dominated by a few established firms. We plotted the 1-HHI ratio to review whether the local patents are evenly owned among the local firms.

  20. Data is mined from Patseer’s patent database on 26 June 2022.

  21. Dataset for B60 will be provided upon request as we have overwhelmed this paper with many tables and figures.

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Acknowledgements

The authors would like to acknowledge the support by Laboratory Program for Korean Studies through the Ministry of Education of the Republic of Korea and Korean Studies Promotion Service of the Academy of Korean Studies (AKS-2018-LAB-1250001). The support from the National Science and Technology Council of Taiwan (111-2410-H-007-027-MY2) is also acknowledged. The authors also express appreciation to the participants of the 10th Global Tech Mining Conference for feedback on an earlier draft of this paper. The third author acknowledges the funding from the Basic Research Program of the National Research University Higher School of Economics and the Russian Academic Excellence Project 5–100.

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Authors and Affiliations

  1. Institute of Technology Management, National Tsing Hua University, 101, Sec.2, Kuang Fu Road, Hsinchu, 30013, Taiwan

    Chan-Yuan Wong & Jeffrey Sheu

  2. SARChI Industrial Development, University of Johannesburg, Johannesburg, South Africa

    Chan-Yuan Wong

  3. Department of Economics, Seoul National University, Gwanak-Ro 1, Seoul, 08826, Korea

    Keun Lee

  4. CIFAR, Toronto, Canada

    Keun Lee

  5. Institute for Statistical Studies and Economics of Knowledge of the National Research University Higher School of Economics, Moscow, Russia

    Keun Lee

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Correspondence to Chan-Yuan Wong.

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Appendices

Appendix 1

See Fig. 14.

Fig. 14
figure 14

a Plot between Knowledge Localization and Share of World Patents of the Selected Cities. b Plot between De-centralization and Share of World Patents of the Selected Cities. Highlighted lines in a and b denote sample average of the 87 cities. The count of world patents is derived from the total of 87 cities. Plots for other cities are filtered to better show better the positions of selected cities

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Appendix 2

See Fig. 15.

Fig. 15
figure 15figure 15

a 3-year Moving Average of Knowledge Localization of B40 in Selected Cities, 2000–2018. b 3-year Moving Average of Technological Diversification of B40 in Selected Cities, 2000–2018. c 3-year Moving Average of Cycle Time of B40 in Selected Cities, 2000–2018. d 3-year Moving Average of Science-based Linkage of B40 in Selected Cities, 2000–2018. e 3-year Moving Average of Originality of B40 in Selected Cities, 2000–2018

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Wong, CY., Sheu, J. & Lee, K. Assessing the quest of SMEs in pivoting for new technological ventures: comparing the patenting indexes of seven developed cities. Scientometrics 128, 4029–4064 (2023). https://doi.org/10.1007/s11192-023-04729-y

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