Leveraging Corporate Assets and Talent to Attract Investors in Japan: A Country with an Innovation System Centered on Large Companies

Abstract

Drug discovery and development require significant costs and time, making investment acquisition crucial. However, there are few biopharmaceutical startups with high valuations in Japan. Unlike other countries, entrepreneurship in Japan is relatively inactive, and startups have a minimal presence in the drug-discovery field. Instead, in Japan’s innovation system, research and development (R&D) has been led by large incumbent companies, which are believed to have a wealth of promising assets and talent. This study tested the hypothesis that biopharmaceutical startups leveraging these assets and talent might be more attractive to investors by regression analysis using a dataset of Japanese unlisted biopharmaceutical startups. The results demonstrated that Japanese biopharmaceutical startups showed significantly higher valuations and total funding amounts if they were corporate spin-offs (CSOs). Additionally, they achieved significantly higher valuations and total funding amounts if their R&D lead persons had corporate backgrounds. These findings suggest that in Japan’s innovation system, which is centered on large companies, CSOs and startups leveraging R&D talent with corporate experience may be more appealing to investors.

1. Introduction

Drug discovery and development are highly research and development (R&D) intensive (Pisano 2006). Startups play a crucial role in bridging new technologies with product development (Audretsch 2003; Christensen 1997) and have significantly contributed to new drug development (Kneller 2010; Okuyama 2023a, 2024a). In Japan, startups are uncommon, and all recently approved drugs have been created by large incumbent pharmaceutical companies, resulting in weak international competitiveness in the pharmaceutical industry (Okuyama 2023d). Drug discovery and development require significant costs (Wouters et al. 2020) and time (Shareef et al. 2024); therefore, biopharmaceutical startups must acquire sufficient investments to survive and grow. Three-fourths of unlisted biopharmaceutical startups in Japan have a valuation of less than JPY 5 billion, and none exceed JPY 25 billion as of May 2023 (Okuyama 2023b). Accordingly, nurturing promising biopharmaceutical startups to attract investors is an urgent issue in Japan.

Since World War II, Japan has maintained an innovation system in which large companies lead R&D with their own central laboratories (Odagiri 1999; Nishimura 2003). Companies’ proportion of R&D expenditure in Japan is larger than that in Europe and the US (National Institute of Science and Technology Policy 2023). Large pharmaceutical companies have developed basic pharmaceutical technologies in-house (Okuyama 2023d), and new drug R&D processes have been completed by a single pharmaceutical company in Japan (Okada 2023). Additionally, extremely low job mobility in Japan (Ono 2010) hampers the translation of academic research into application. In 2016, less than 0.1% of university researchers transitioned from universities to companies, and less than 1% of corporate researchers transitioned from companies to universities in Japan (Prime Minister of Japan and His Cabinet 2019). Academic entrepreneurship is also inactive in Japan owing to low entrepreneurial awareness (Global Entrepreneurship Monitor 2022) and scarcity of investment money (Cabinet Secretariat and Ministry of Economy, Trade and Industry 2021). Alternatively, lifetime employment has long been commonplace in major Japanese companies, and research personnel with graduate degrees have developed strong R&D capabilities, from basic to applied, through lifelong work in the corporate R&D sector (Ono 2010). Major pharmaceutical companies often hold promising internal technology seeds, but face limited investment owing to changes in R&D strategies and portfolio management (Schuhmacher et al. 2016). Additionally, the shift toward open innovation in Japanese companies is lagging (New Energy and Industrial Technology Development Organization 2018). Considering these issues, it is predicted that many promising technology seeds and talents lie dormant within the R&D sections of major pharmaceutical companies in Japan. Therefore, it is hypothesized that biopharmaceutical startups that are corporate spin-offs (CSOs) from major pharmaceutical companies possess high-quality technological seeds and talents, making them more attractive to investors in the Japanese pharmaceutical industry. This study tested the hypothesis that in Japan, where large corporations have traditionally led R&D and are expected to internally nurture promising technological seeds and talents, corporate spin-offs (CSOs) leveraging these assets, as well as biopharmaceutical startups leveraging these talents, are more successful in attracting investors for drug discovery and development than university startups or de novo startups.

In the biomedical sector, a typical science-based industry that heavily utilizes academic science and technology for product development, scientists with high research-publication performance are often productive in generating patents (Subramanian et al. 2013; Zucker et al. 1998, 2002) and actively involved in commercializing their discoveries (Maine et al. 2014; Stuart and Ding 2006). University startups founded by these highly productive researchers attract numerous resources (Stuart and Ding 2006) and are more likely to reach an initial public offering (IPO) (Fuller and Rothaermel 2012). The Small Business Innovation Research (SBIR) program has produced high-performance biopharmaceutical startups in the US (Toole and Czarnitzki 2007). However, there is a lack of research analyzing the attractiveness of CSOs that leverage corporate technological seeds and biopharmaceutical startups that leverage corporate R&D talents. Recent studies investigate the success factors of spin-offs in the biomedical field; however, they do not analyze the characteristics of startups (Makino et al. 2018; Yashiro et al. 2024). Some literature reports the superior performance of CSOs compared to other types of startups, but do not focus on science-based industries (Modina et al. 2023; Wennberg et al. 2011; Zahra et al. 2007). Additionally, the above-mentioned literature analyzes Western countries, where the innovation system differs from that of Japan. To the best of the author’s knowledge, there are no reports quantitatively analyzing the relationship between the origin of startups and their valuation in the biomedical field, particularly in Japan, which has a unique innovation system, making this study’s novelty high.

In this paper, hypotheses are introduced in the section titled “Hypothesis”, based on the characteristics of pharmaceutical R&D and the innovation system in Japan, where large corporations lead R&D. Next, the data and methodology used to test these hypotheses are described in the section titled “Methods”. The statistics and the results of regression analysis are presented in the section titled “Results”. Finally, in the section titled “Discussion and Conclusions”, strategies for fostering promising biopharmaceutical startups in Japan and countries with innovation systems similar to Japan’s are discussed. The limitations of this study are also addressed.

2. Hypothesis

Drug discovery and development have a low project success rate (Sun et al. 2022) and require significant time (Shareef et al. 2024) and expense (Wouters et al. 2020). Therefore, valuation methods that consider future uncertainties have been developed (Fujiwara 2014; Casault et al. 2014). The probability of success in a new drug project is influenced by various factors, including scientific (reliability of action mechanism and pharmacokinetics) and clinical aspects (reliability of clinical efficacy, side effect margin, and target patient population) (Cook et al. 2014; de Visser et al. 2020). In the US and Belgian biotechnology startups, companies with a diverse range of talent or the ability to recruit such talent receive higher valuations (McMillan and Thomas 2005; Baeyens et al. 2006). By contrast, in Japan, a single pharmaceutical company often manages the entire process of a pharmaceutical project, from initial research to clinical development (Okuyama 2023d; Okada 2023). Consequently, a wide range of knowledge and expertise, from fundamental science to clinical application, is accumulated within the company. Considering this, along with the difficulty in assembling necessary talent due to the low job mobility (Ono 2010), investors likely view CSOs as biopharmaceutical startups with the most diverse capabilities necessary for new drug development in Japan. CSOs may also have an advantage in forming partnerships with pharmaceutical companies in Japan. Biopharmaceutical startups often form alliances with pharmaceutical companies during clinical development (Bianchi et al. 2011; Schuhmacher et al. 2013). Partnerships enhance valuation (Zheng et al. 2010; Fujiwara 2014; Helen and Lehtimaki 2023); however, in Japan, startups are not active (Okuyama 2023d), and partnerships between academia and pharmaceutical companies are far from routine (Mallapaty 2019). Therefore, a CSO with connections to the parent company is likely to have higher expectations from investors. Thus, the following hypothesis is posited.

H1. 

Japanese unlisted biopharmaceutical startups have a higher likelihood of acquiring (a) higher valuation and (b) more total funding if they are CSOs, rather than if they are not.

Drug discovery and development are R&D-intensive, with valuation heavily emphasizing R&D, particularly in the early stages (Pukthuanthong 2006; de Visser et al. 2020). R&D investment is positively correlated with market equity in the early phase of biotechnology startups (Joos and Zhdanov 2008). Therefore, the role of the person leading R&D is crucial in early phases, such as prior to the IPO. For instance, the presence of “star” scientists on the staff of US biotechnology startups is important for valuation (Pukthuanthong 2006). In Japan, researcher mobility between companies and universities is extremely low (Prime Minister of Japan and His Cabinet 2019) and, in contrast to the US, Japanese academics conduct relatively little applied research and remain isolated from industry (Ranga et al. 2017). Instead, corporate researchers are sent to university research labs and transfer cutting-edge research from universities to companies in Japan’s pharmaceutical industry (Zucker and Darby 2001). Therefore, in Japan, it is believed that those who lead R&D in biopharmaceutical startups are more likely to meet investors’ expectations if they come from the corporate sector, possessing both the scientific and clinical knowledge necessary for drug development, rather than academia, where they tend to have a bias towards science in terms of knowledge and experience. Therefore, the following hypothesis is posited.

H2. 

Japanese unlisted biopharmaceutical startups have a higher likelihood of acquiring (a) higher valuation and (b) more total funding when their R&D lead is from a corporate background, rather than when they are not.

3. Methods

3.1. Database and Samples

The “STARTUP DB” by For Startups, Inc. is one of Japan’s largest startup databases, containing information on about 18,000 startups. The information is gathered from various sources, including databases, websites, press releases, university announcements, and participation in pitch and matching events. For this study, information registered in the STARTUP DB as of 1 May 2023 was utilized as the data source. Obtaining comprehensive information on unlisted biopharmaceutical startups in Japan has been challenging, resulting in a limited number of studies analyzing the factors that influence their valuation. This study addresses this gap by utilizing the database that systematically compiles extensive information on unlisted technology startups in Japan, including those in the biopharmaceutical sector. This approach enables robust statistical analyses, offering insights that extend beyond the scope of traditional case studies and represent a significant contribution of this research.

In the STARTUP DB, details of the products and services offered by each startup are tagged as “service information”. The assumed stock price is calculated by dividing the recent fundraising amount by the issued number of shares, and then multiplying it by the total number of shares issued by the company, to determine the valuation of each company. All startups tagged with “drug discovery” were selected, and among them, companies that met both conditions of being unlisted and having valuation information were extracted. The analysis included 82 companies, excluding startups not related to drug discovery and development, based on database and website information. Among the 82 companies, the variable “Previous startup management experience” was missing for three companies, and the variable “R&D lead from industry” was missing for two other companies. The remaining 79 and 77 companies were used for the analysis in Model 1a and 2a (where the startup being a CSO was used as the independent variable) and Model 1b and 2b (where the R&D lead possessing R&D experience within the company was used as the independent variable), respectively.

3.2. Dependent Variables

Valuation was used as the dependent variable in Model 1. In the STARTUP DB, valuation is calculated based on the most recent fundraising activities. The study used the total amount of funding raised as the dependent variable to measure the longer-term expectations of startups (Model 2).

3.3. Independent Variables

The independent variables were defined as follows: (a) the startup being a CSO, and (b) the R&D lead possessing R&D experience within the company. Parhankangas and Arenius (2003) defined CSO as a new business formation based on the business ideas developed within the parent firm being taken into a self-standing firm. This study used the definition, among the many others, to select variable (a), focusing on whether startups inheriting assets and talent from pharmaceutical companies are highly valued by investors. The variable (a) was determined by the founding history of the startups identified from their websites. Specifically, companies that were clearly stated to be spin-offs from a particular company on their corporate websites were selected as CSOs. Nine companies met the criteria. Literature and websites were searched using these company names to confirm that they were indeed spin-offs using inherited assets from their parent firms (Appendix A). Of the nine CSOs, seven were from major Japanese pharmaceutical companies (Takeda, Eisai, KyowaHakko). Asahi Kasei, a large chemical company with significant pharmaceutical investments, and RaQualia, formerly Pfizer’s Japan lab, comprised the other two. Thus, all parent companies engage in large-scale pharmaceutical R&D. Of the remaining 70 companies, 60 were identified as university spin-offs from STARTUP DB and corporate websites. For the remaining 10, six developed their own assets, two used university technologies, and one acquired assets from an unrelated company. An additional company was confirmed to use self-developed assets through an interview article. Thus, the selection of CSO in this study was considered reasonable.

Regarding variable (b), this study identified the individual responsible for leading R&D in the startup, irrespective of their formal position within the company. The analysis includes the CTO if present; otherwise, the CEO, Chief Scientific Officer, or head of R&D was identified as the R&D lead based on descriptions from the startup’s website. The background of the R&D lead was investigated via the startup’s website and web searches to determine whether they possess R&D experience within the company. In all cases in which the background of the R&D lead with corporate experience was identified, except for one, the R&D lead had more than ten years of experience at the company. The R&D lead had six years of experience in one case; however, excluding the case did not affect the significance between the dependent and independent variables.

3.4. Control Variables

Entrepreneurs, firms, and investors’ characteristics and market conditions influence the startups’ valuation and fundraising (Berre and Pendeven 2023). Regarding entrepreneur characteristics, their previous startup management experience (Sievers et al. 2013) and educational background (Hsu 2007) positively influence fundraising. Regarding firm characteristics, the number of patents (Hottenrott et al. 2016), product development stage (Yang et al. 2009), and business model (Streletzki and Reinhard 2013) positively influence fundraising. These factors were added as control variables in the analysis. Additionally, because the fundraising amount may also be influenced by the time since establishment, the number of years since establishment was included as a control variable. While firm characteristics, such as country and sector, could also have an impact (Berre and Pendeven 2023), this study focused specifically on pharmaceuticals in Japan; therefore, these factors were not included in the analysis.

The CEO’s previous startup management experience and educational backgrounds were identified from the startup’s website and other web sources. For three CEOs without confirmed PhDs or MDs, their twenty years of research experience in companies’ labs led to the assumption they had PhD-level expertise, and they were included as PhD holders in the analysis. The product-development stage was identified by examining the R&D pipeline of each startup, based on information from their websites and the STARTUP DB. The business model was identified based on information from the startups’ websites and the STARTUP DB. The number of patents and the date of establishment were determined using the information provided by the STARTUP DB.

3.5. Multiple Regression

Verification was conducted by multiple linear regression analysis using IBM SPSS Statistics ver.29. No significant correlations were observed between the independent and any of the control variables; therefore, multicollinearity was not deemed to be a concern.

4. Results

Table 1. Summary statistics and definition of variables.

Variable	Definition	Mean	S.D.	N
Dependent variables
1. Valuation	Valuation of a startup as of 1 May 2023, in million Yen	4227.74	4893.25	82
2. Total funding amount	Total funding amount of a startup from its inception, in million Yen	1982.44	2338.64	82
Independent variables
3. Spin-off	Dummy = 1 if a startup is spun-off from a company	0.11	0.31	82
4. R&D lead from industry	Dummy = 1 if the R&D lead of a startup has previous R&D experience in an industry	0.46	0.50	79
Control variables
5. Previous startup management experience	Dummy = 1 if a CEO of a startup has previous experience to establish a startup or work in a management team of a startup	0.19	0.39	79
6. Scientific degree of CEO	Dummy = 1 if a CEO holds an M.D. or a Ph.D. degree	0.65	0.48	82
7. Business degree of CEO	Dummy = 1 if a CEO holds an MBA degree	0.16	0.37	82
8. Number of patents	Number of patents filed by a startup	4.05	7.46	82
9. Product development stage	Dummy = 1 if a startup has pipeline under clinical trial	0.29	0.46	82
10. Business model: Internal drug discovery	Dummy = 1 if a startup has its internal drug discovery program	0.73	0.45	82
11. Business model: Provision of platform technology	Dummy = 1 if a startup provides a technology platform used for drug discovery based on its own technology	0.30	0.46	82
12. Business model: Contract service	Dummy = 1 if a startup conducts contract service related to drug discovery	0.21	0.41	82
13. Company age	Number of years since the establishment of a startup	7.79	6.21	82

summarizes the descriptive statistics and variable definitions.

Table 2. Correlation tables of independent and control variables when (a) independent variable was spin-off (n = 79) and (b) independent variable was R&D lead from industry (n = 77).

(a)
	Spin-Off	Previous Startup Management Experience	Scientific Degree of CEO	Business Degree of CEO	Number of Patents	Product Development Stage	Business Model: Internal Drug Discovery	Business Model: Provision of Platform Technology	Business Model: Contract Service	Company Age
Spin-off	1.0000
Previous startup management experience	−0.0720	1.0000
Scientific degree of CEO	0.0158	0.0214	1.0000
Business degree of CEO	0.0703	0.0649	−0.1288	1.0000
Number of patents	−0.0946	0.0815	−0.2233	−0.0114	1.0000
Product development stage	0.1963	−0.0391	−0.2586	0.4873	0.2682	1.0000
Business model: Internal drug discovery	0.1256	−0.0009	0.0933	0.0950	0.0353	0.2729	1.0000
Business model: Provision of platform technology	−0.0727	0.2951	−0.0079	−0.1363	−0.2178	−0.3903	−0.2683	1.0000
Business model: Contract service	−0.0816	−0.0030	−0.0217	0.0500	0.1920	−0.0590	−0.4097	−0.2752	1.0000
Company age	0.0616	−0.0032	−0.1490	0.1442	0.5673	0.1952	−0.2050	−0.2348	0.3426	1.0000
(b)
	R&D Lead from Industry	Previous Startup Management Experience	Scientific Degree of CEO	Business Degree of CEO	Number of Patents	Product Development Stage	Business Model: Internal Drug Discovery	Business Model: Provision of Platform Technology	Business Model: Contract Service	Company Age
R&D lead from industry	1.0000
Previous startup management experience	0.0982	1.0000
Scientific degree of CEO	0.0886	−0.0064	1.0000
Business degree of CEO	−0.0438	0.0760	−0.1345	1.0000
Number of patents	0.0566	0.1064	−0.2051	−0.0089	1.0000
Product development stage	0.0438	−0.0264	−0.2694	0.4839	0.2763	1.0000
Business model: Internal drug discovery	−0.0979	−0.0279	0.0613	0.0912	0.0633	0.2707	1.0000
Business model: Provision of platform technology	−0.0124	0.2644	−0.0343	−0.1345	−0.2037	−0.3923	−0.3048	1.0000
Business model: Contract service	0.0649	0.0232	0.0178	0.0599	0.1682	−0.0478	−0.3817	−0.2602	1.0000
Company age	0.0193	0.0228	−0.1167	0.1556	0.5573	0.2129	−0.1690	−0.2180	0.3077	1.0000

illustrates the correlation matrix of independent and control variables to determine the relationship between each variable.

Table 3. Multiple linear regression analysis. Standard errors are in parentheses. *** p < 0.001, ** p < 0.01, * p < 0.05.

Variables	Dependent Variable
	1. Valuation		2. Total Funding Amount
	Model 1a	Model 1b	Model 2a	Model 2b
No control variables
3. Spin-off	5200.55 ** (1639.43)		2036.04 * (799.58)
4. R&D lead from industry		3385.18 ** (1061.28)		1629.02 ** (502.87)
Control variables included
3. Spin-off	5301.20 *** (1487.16)		1981.22 ** (745.66)
4. R&D lead from industry		3268.91 *** (948.15)		1513.67 ** (459.77)
5. Previous startup management experience	−1795.89 (1237.27)	−2597.62 * (1286.64)	−696.81 (620.37)	−1024.32 (623.91)
6. Scientific degree of CEO	−578.57 (1008.22)	−915.65 (1046.06)	−73.02 (505.52)	−179.99 (507.25)
7. Business degree of CEO	39.74 (1502.32)	71.73 (1528.59)	−739.50 (753.27)	−677.44 (741.23)
8. Number of patent	299.38 *** (80.77)	227.58 ** (80.46)	128.68 ** (40.50)	100.52 * (39.02)
9. Product development stage	3736.44 ** (1353.71)	4179.06 ** (1363.97)	1976.77 ** (678.75)	2147.71 ** (661.41)
10. Business model: Internal drug discovery	−132.72 (1309.71)	831.46 (1346.00)	−163.40 (656.69)	281.94 (652.69)
11. Business model: Provision of platform technology	1629.54 (1292.42)	1866.34 (1323.99)	417.78 (648.02)	549.51 (642.02)
12. Business model: Contract service	2201.67 (1424.83)	2011.76 (1453.73)	684.14 (714.41)	561.22 (704.93)
13. Company age	−238.04 (131.43)	−138.68 (132.44)	−79.57 (65.90)	−46.00 (64.22)
Adj R-2	0.35	0.35	0.28	0.32
N	79	77	79	77

presents the multiple regression analysis results.

Japanese unlisted biopharmaceutical startups being CSOs showed a significant positive effect on both the startup’s valuation (Model 1a) and the total funding amount (Model 2a). Therefore, H1 was supported. The regression models without control variables show that in Japan, if a biopharmaceutical startup is a CSO, its valuation increases by 5201 million yen (MY) and the total funding amount increases by 2036 MY, compared to a non-CSO. A Japanese unlisted biopharmaceutical startup’s R&D lead with corporate background showed a significant positive effect on both the startup’s valuation (Model 1b) and total funding amount (Model 2b). Therefore, H2 was supported. The regression models without control variables show that in Japan, if the R&D lead of a biopharmaceutical startup has corporate experience, the valuation increases by 3385 MY and the total funding amount increases by 1629 MY. This effect size is smaller compared to being a CSO. With the regression models that include control variables, the significance between the independent and the dependent variable was maintained, and the regression coefficients took similar values, thereby confirming the validity of the model.

5. Discussion and Conclusions

This study suggests that spin-offs from large incumbent pharmaceutical companies are likely to receive high valuations from investors in Japan. Since drug discovery and development require significant time and costs (Shareef et al. 2024; Wouters et al. 2020), securing substantial investment is crucial to overcoming the “valley of death” in R&D (Okuyama 2023c). In Japan, biopharmaceutical startups with high valuations are rare (Okuyama 2023b), making their creation to attract investor interest a significant challenge. The results indicate that revitalizing CSOs could be a possible solution to this challenge. Unlike in Western countries, Japan’s R&D has been led by large companies rather than public institutions, leading to strong internal R&D capabilities. Leveraging the technology seeds and R&D talent of these companies is expected to generate startups that attract investors. Entrepreneurial activities in Japan are quite low (Global Entrepreneurship Monitor 2022) and initiatives, such as the Japanese version of SBIR and the policy for creating 1000 university startups (modeled after the U.S.), have not yielded significant results (Ministry of Economy, Trade and Industry 2020; Yamaguchi 2015). In Japan, R&D personnel with extensive knowledge and experience from basic research to commercialization are concentrated in major incumbent companies, and startups led by such personnel are well-received by investors.

Japan’s lifetime employment system results in low mobility among R&D personnel (Ono 2010; Prime Minister of Japan and His Cabinet 2019), limiting the involvement of talent from major pharmaceutical companies in startups. To increase startups that attract investors, the government needs to promote an environment where such personnel are comfortable leaving large companies to join startups possibly through bold tax incentives.

This study suggests that differences in national innovation systems may lead to variations in the types of startups that attract investors. In the US, startups lead drug discovery and development, while in Europe, both startups and large incumbent companies play this role; in Japan, large incumbent companies take the lead (Okuyama 2023d). In recent years, emerging countries have also been gaining prominence in global new drug development (Okuyama 2024b), making it important to consider how each country’s innovation system influences the evaluation of startups. This study adds new insights to this perspective.

One limitation of this study is that the valuations are calculated based on the most recent investment amounts, which may result in a comparison of companies at different fundraising stages. To address this limitation, this study used the total funding amount that the company received from its inception to the most recent point as a dependent variable along with the valuation. However, the establishment years of the companies analyzed range from 1998 to 2021; thus, the limitation of comparing companies at different stages needs to be taken into account when interpreting this study’s results. The amount of funding increases as a company’s fundraising history matures. To address this issue, this study includes company age as a control variable and no significant correlation was observed between total funding amount and company age.

Another limitation is that the factors that should be controlled in the regression may remain, such as partnerships, corporate parent, and university characteristics. Because of data limitations and concerns related to overfitting, these factors were not included in the analysis, which might affect the results.

The results of this study are preliminary findings derived from a small sample size. It will be necessary to generalize the theory further through expanding the number of cases and conducting analyses of other countries with similar innovation systems.