Capital Structure Decisions in Swedish Biotechnology Firms: The Role of Intellectual Capital and Innovation Activities

Abstract

Biotechnology firms operate in a highly innovative and capital-intensive environment, characterized by high levels of R&D, long product development periods, significant regulations, and high levels of uncertainty. These firms rely heavily on intangible assets, such as intellectual capital and innovation. Consequently, intellectual capital and innovation activities play a crucial role in financial strategies and capital structure decisions. This study aims to examine how intellectual capital and innovation activity influence capital structure decisions of biotech firms in Sweden. In this paper, financial data of 1528 companies from 2012 to 2022 were analyzed. Using logistic regression modeling, the results showed that biotech firms with higher intellectual capital are more likely to issue equity whereas those with greater innovation activity tend to rely more on debt financing. These findings underscore the complexities of financial strategy in the biotech sector, emphasizing the need for flexible capital structure management. Moreover, policymakers should focus not only on equity availability but also on ensuring access to debt financing, as both are crucial for sustaining biotech innovation and growth.

1. Introduction

Most scholars seem to agree that the financing of firms with long product development time and large capital needs poses an extra challenge for the management of these firms, as the risk level is perceived as high by external financiers (Hall, 2010). Among these high-risk, but potentially high-reward firms, are biotechnology firms (C.-W. Lee, 2007; Zucker et al., 2002). In addition, biotechnology firms often also encounter financing problems because of their embryonic nature, that is lack of organizational structure and routines, especially in the early stages. Furthermore, it is often claimed that biotechnology firms lack collateral, business skills, and market presence (N. Lee & Lee, 2019).

While most biotechnology firms lack collateral, they often possess intangible assets, mainly intellectual capital in different forms (D’Amato, 2021; Pulic, 2004). Investing in human capital and other intangible assets is a way for biotechnology firms to create a competitive advantage and generate value (Shahhosseini, 2022). In comparison with other industries, the biotechnology sector is focused on creating a high level of innovation ability, that is, a lot of investments in research and development as well as patenting (Guarascio & Tamagni, 2019; Liu, 2022). The fact that biotechnology firms have some distinct characteristics that most other firms do not have, makes their financial strategy different, and there are several financing options available for biotech firms at various stages of their lifecycle (Cristian, 2019). In comparison with other types of firms, biotechnology firms rely more on funding from venture capital firms and business angels (Hopkins et al., 2013; Rossi et al., 2011). Furthermore, many industrialized countries around the world have developed financial schemes targeting biotechnology firms (Shin et al., 2019). Previous research on the financial strategies of biotechnology firms has led to conflicting results and many have used cross-sectional methods and data to compare biotechnology firms to other firms. Therefore, there is a need for a study using longitudinal panel data to explore and analyze the financial strategy of biotechnology firms.

The purpose of this paper is to analyze the relationship between intellectual capital, innovation activity, and financial strategy among Swedish biotechnology firms. Therefore, our research question is: Do intellectual capital and innovation activity determine capital structure decisions? Put differently, will innovation activity and intellectual capital be antecedents to the composition of debt and equity among Swedish biotechnology firms? To the best of our knowledge, this is the first paper that takes a longitudinal perspective on the financial strategies among biotechnology firms. The empirical data in this paper come from a database, Retriever Business, that collects annual reports from all Swedish companies. We have analyzed the financial data from 2012 to 2022 for all Swedish biotechnology companies, which means that the sample consisted of 4390 firms. The analysis was made using logistic regression modeling, and the results indicated that high levels of intellectual capital were negatively correlated to financial leverage. Furthermore, biotechnology firms with more innovation activities were more likely to issue debt rather than equity. These results are partially in line with previous research and highlight the importance of accessing equity for early-stage biotechnology firms in securing their financing and reducing the overall risk, but also that debt plays a very important role in the financing of biotechnology firms. The contribution of this paper is both practical and theoretical. It contributes to and extends the previous research and theories on financial strategy within the biotechnology sector. In addition, it provides insights into the management of the financing of biotechnology firms.

The remainder of the paper is organized as follows. Section 2 presents the literature review, which starts with a presentation of the financing in the biotechnology sector. The chapter then provides an overview of the research on how intellectual capital and innovation ability impact capital structure decisions among biotechnology firms. In Section 3, the empirical data are presented alongside the methods used to analyze the data. In Section 4, the results from the statistical analysis are presented. In Section 5, the conclusions of the study are presented, and the paper is finalized with a discussion of the implications and limitations in Section 6.

2. Literature Review

2.1. Financing in Biotechnology Industry

The biotechnology industry has experienced remarkable growth over the past few decades. This growth is driven by advancements in technology, increasing demand for innovative healthcare solutions, and the rising importance of sustainable practice across various sectors such as agriculture and energy (Kearney, 2019). Biotechnology is a key driver for economic growth and innovation worldwide. Leading biotechnology hubs, such as those in the United States (Boston, San Francisco), Europe (Germany, the UK), and Asia (China, Japan, South Korea), demonstrate the industry’s potential when supported by strong institutional frameworks, a venture capital ecosystem, and collaborative networks (Chiaroni & Chiesa, 2006; Dorocki, 2014; Sopoligová & Pavelková, 2017). The biotechnology industry is marked by high capital intensity, strict regulatory processes, and significant reliance on venture capital to ensure that biotechnology firms have access to financial resources. The reliance on external financing becomes more critical due to the high cost and risks associated with research development. Previous studies (Harada et al., 2021; Lo & Thakor, 2022) have indicated that the costs of bringing a drug to market have escalated while the success rate for new treatments has declined, making it crucial for biotech firms to seek external capital to sustain their R&D efforts.

Venture capital remains a primary financing source for biotech firms, particularly in the early stages of development. However, the nature of venture capital investment in the biotech sector is complicated, as investors often seek exit strategies that align with their financial objectives. For instance, (Jeppsson, 2018) highlights that a corporate financial manager in biotech must manage a mix of investors with varying goals and timelines, which complicates the capital structure necessary for long-term success. Additionally, the relationship between financing entities and research firms can be fraught with conflicts of interest. In addition to venture capital, the hybrid financing model, which combines various funding mechanisms such as debt, equity, and alternative financing sources to support the unique needs of biotech firms, is increasingly utilized in biotech firms to optimize their capital structure. This approach allows companies to leverage the benefits of both equity and debt, thereby reducing the overall cost of capital and enhancing financial flexibility (Zhang et al., 2021). Strategic alliances become another critical financing source for biotech firms. Collaborations with established companies, research institutes, and other biotech firms can provide access to additional resources, expertise, and funding opportunities (Fernald et al., 2015; Lamrani, 2023).

In Sweden, the biotech sector has seen significant growth, supported by a strong emphasis on innovation and collaboration between academia and industry. Swedish biotech firms often leverage venture capital as a primary financing source, which is critical for fostering innovation and scaling operations (Rossi et al., 2011; Sohn & Kang, 2015). The relationship between venture capital and biotech firms is particularly vital, as it not only provides necessary funding but also enhances the firms’ capabilities through strategic guidance and networking opportunities (Bertoni et al., 2010; Zheng et al., 2010). The presence of venture capital has grown in recent years, improving access to early-stage and growth capital. Moreover, private investment is another significant source, especially during the startup phase. Many biotech firms begin with private capital from founders, parent companies, or external investors (Nilsson, 2001). Additionally, the financing sources in the biotech sector have been shaped by public sources. Government initiatives, such as a Swedish innovation agency (Vinnova), play a significant role in fostering innovation through strategic funding programs (Staack, 2019). The Swedish biotech sector earns significant benefits from public funding. The OECD reported that public research funding in Sweden is strategically allocated based on research performance, which incentivizes biotech firms to engage in high-quality research (OECD, 2023).

Despite the promising growth and investment opportunities in the biotech sector, many challenges remain. Many biotech firms have struggled with financial sustainability due to high research and development costs and lengthy timelines required for commercialization. Thus, strategic financial management is essential, requiring a balanced capital structure with equity and debt.

2.2. Intellectual Capital and Biotech Finance

The concept of intellectual capital encompasses various components which include human capital, structural capital, and relational capital (Harrison & Sullivan, 2000; Pulic, 2004; Tovstiga & Tulugurova, 2009). These components collectively contribute to firm competitiveness and performance (W. Y. Wang & Chang, 2005). Although intellectual capital has an important impact on firm performance, firms with a high proportion of intangible assets such as intellectual capital face difficulties in issuing debt. This is because these assets cannot be easily resold or repurposed at a lower price in the event of financial distress, making them less attractive as collateral for lenders (D’Amato, 2021). Moreover, intellectual capital is riskier and more difficult to value as compared to tangible assets. Previous research has shown that firms with more intellectual capital may have lower financial leverage. The same study (D’Amato, 2021) highlighted that the relationship between intellectual capital and financial leverage is moderated by firm risk and profitability indicating that firms with significantly high levels of intellectual capital may prefer to issue equity rather than debt to mitigate the risk. This finding aligned with another study (Jin & Xu, 2022) which revealed that agriculture firms with higher intellectual capital tend to have lower financial leverage.

In the biotechnology sector, most firms prioritize innovation and a strong science base, and intellectual capital serves as a critical driver for value creation. Unlike other industries, biotechnology firms rely heavily on intangible assets e.g., patents, research expertise, and innovative capabilities to secure their competitive advantages and future growth. For instance, Taiwan’s biotechnology industry has established a substantive relationship between intellectual capital and knowledge productivity, underscoring the importance of skilled personnel in driving innovation and productivity (Huang & Jim Wu, 2010). The findings suggest that a well-developed human capital base is essential for fostering creativity and enhancing the overall performance of biotech startups. Biotechnology firms with robust patent portfolios may leverage these intangible assets to attract financing, impacting the capital structure. In a study by (Kiskis et al., 2016) it was highlighted that biotech firms in Arizona effectively capitalize their patents which can increase their market value and secure their funding through equity or debt issuance. Another study (Anghel et al., 2018) argued that biotechnology companies with substantial intellectual capital tend to have better financial performance, which may lead them to prefer equity financing over debt issuance.

The reliance on intellectual capital significantly influences firms’ financing decisions, which aligns with several key financial and economic theories. The pecking order suggests that firms prefer internal funding over external funding due to the cost associated with asymmetric information. For instance, firms with significant intellectual capital may face higher information asymmetry, which can lead to increased costs of equity and debt, as investors may perceive them as riskier investments (Orens et al., 2009; Prabowo, 2017). For biotechnology firms, high levels of intellectual capital lead to higher uncertainty in tangible outcomes. Issuing equity may become a strategic choice to avoid the risk of financial distress associated with debt. Moreover, signaling theory highlights the role of equity issuance in signaling to the market as firms may disclose information to signal quality to the investor and communicate confidence in leveraging intellectual capital as a signal of potential growth and innovation capacity (Güneysu et al., 2020; Shahhosseini, 2022). Biotechnology firms possessing a lot of intellectual capital such as valuable patents, a strong research team, and cutting-edge innovations are more attractive to investors. The intellectual capital signals high growth potential and lower perceived risk, making equity issuance a more attractive financing option. Therefore, this study posits that a high level of intellectual capital has an impact on biotechnology firms’ financial decisions. The hypothesis in this study was formulated as follows:

Hypothesis 1 (H₁).

Biotechnology firms with higher intellectual capital are more likely to issue equity as compared to debt.

2.3. Innovation Activity and Biotech Finance

Innovation plays a pivotal role in driving firm growth, particularly through product development processes and patenting. Firms that consistently engage in innovation activities, such as R&D and patenting are more likely to achieve sustainable growth. For example, in the pharmaceutical sector, patenting has been shown to lead to increased growth, indicating the importance of innovation in leveraging R&D investments effectively (Guarascio & Tamagni, 2019). There is a positive correlation between innovation activities, particularly those measured by patents, and firm performance. The number of patents granted is a significant indicator of a firm’s innovative capabilities which positively influence performance metrics such as return on assets (ROA) and sales growth (Artz et al., 2010). This aligns with findings from (Liu, 2022), which demonstrated that an increase in the number of patents is positively related to a company’s profitability, emphasizing that innovation activities are beneficial for financial outcomes. However, one of the primary challenges innovative firms are facing is the inherent risk associated with innovation activities. The characteristics of innovation, especially the risk of failure, create significant obstacles to obtaining external financing (Golej, 2016). In the biotechnology industry, new ventures and small to medium-sized enterprises often rely heavily on research and development (R&D) which often entails high uncertainty and risk. The intensity of R&D activities not only enhances innovation capabilities but also influences financial strategies. The financial strategies of biotechnology firms are shaped by their innovation activities. For instance, many SMEs in the biotechnology industry engage in partnerships and alliances with larger pharmaceutical companies to leverage their marketing and distribution capabilities (Fernald et al., 2015; Schoonmaker & Rau, 2014). These collaborations can provide essential funding and resources, allowing biotech firms to focus on their core competencies in R&D while mitigating financial risks associated with high innovation costs. Furthermore, venture capital plays a significant role in supporting innovation within the biotech industry, as it provides the necessary capital for R&D activities (Sohn & Kang, 2015). The high-risk nature of R&D in biotechnology firms means that firms must carefully manage their leverage to avoid financial distress (Bruneo et al., 2024). The uncertainty and the high risk make traditional debt financing less attractive, as it imposes fixed repayment obligations that can strain cash flows during periods of low cash flow. Conversely, equity financing allows firms to raise capital without the immediate pressure of repayment, thus aligning better with the long-term nature of biotech innovation (Brown & Floros, 2012; Ye, 2022). Moreover, some studies have shown that firms with higher patent activity tend to attract more equity financing, as patents serve as quality signals to investors, indicating the potential for future profitability and innovation (Hoenen et al., 2014; Hottenrott et al., 2016; Hsu & Ziedonis, 2013). Therefore, this study posits that high innovation activity has an impact on biotechnology firms’ financial decisions. The hypothesis in this study was formulated as follows:

Hypothesis 2 (H₂).

Biotechnology firms with higher innovation activity are more likely to issue equity as compared to debt.

3. Materials and Methods

3.1. Data

The sample of this study was obtained from Retriever Business, a Swedish database that collects information from the annual reports of all Swedish companies. We included 1528 companies classified in the biotechnology, research, and development industry that are listed in the database from 2012 to 2022. Companies were selected based on the availability of the reports during the observation periods. We have an unbalanced panel dataset consisting of 15,284 observations, due to some panel members having missing values in some observation points.

3.2. Measures

3.2.1. Dependent Variable

We measure the dependent variable, capital structure (CAPSTRUC), as 1 if the net equity issuance is higher than the net debt issuance and 0 otherwise. This follows previous studies by (Chang et al., 2006; Rajaiya, 2023) which employed logistic regression with binary variables (1 for net equity issuance and 0 for net debt issuance) to investigate the capital structure decisions. This approach allows us to model the probability of a firm choosing equity over debt given the set of predictors. Net equity issuance is calculated as follows:

$$Net Equity Issuance={\displaystyle \frac{{total equity}_t-{total equity}_{t-1}}{{total assets}_t}}$$

Net debt issuance is measured as follows:

$$Net Debt Issuance={\displaystyle \frac{{total debt}_t-{total debt}_{t-1}}{{total assets}_t}}$$

3.2.2. Independent Variables

Value-added intellectual coefficient (VAIC). VAIC represents how much new value has been created per invested monetary unit in each resource. Earlier research by (D’Amato, 2021) used VAIC as a proxy of intellectual capital in investigating the determinants of firms’ capital structure. The VAIC concept was introduced by (Pulic, 2000, 2004) who suggested that a firm’s market value is influenced by both capitals employed (physical and financial capital) and the efficiency of its intellectual capital, which includes human and structural capital. The VAIC method measures intellectual capital indirectly by combining the efficiencies of capital employed (VACA), human capital (VAHU), and structural capital (STVA), offering insights into the efficiency of a firm’s tangible and intangible assets. A higher VAIC value reflects better resource utilization for value creation. VAIC is measured as follows:

VAIC = VACA + VAHU + STVA

VACA is a measure of the efficiency of capital employed and is calculated as:

$$\mathrm{V}\mathrm{A}\mathrm{C}\mathrm{A}={\displaystyle \frac{\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e} \mathrm{a}\mathrm{d}\mathrm{d}\mathrm{e}\mathrm{d}}{\mathrm{C}\mathrm{a}\mathrm{p}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{o}\mathrm{y}\mathrm{e}\mathrm{d}}}$$

VAHU represents the efficiency of human capital and is determined as:

$$\mathrm{V}\mathrm{A}\mathrm{H}\mathrm{U}={\displaystyle \frac{\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e} \mathrm{a}\mathrm{d}\mathrm{d}\mathrm{e}\mathrm{d}}{\mathrm{H}\mathrm{u}\mathrm{m}\mathrm{a}\mathrm{n} \mathrm{c}\mathrm{a}\mathrm{p}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}}}$$

STVA is an indicator of the efficiency of structural capital and is calculated as:

$$\mathrm{S}\mathrm{T}\mathrm{V}\mathrm{A}={\displaystyle \frac{\mathrm{S}\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l} \mathrm{c}\mathrm{a}\mathrm{p}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}}{\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e} \mathrm{a}\mathrm{d}\mathrm{d}\mathrm{e}\mathrm{d}}}$$

The added value is determined as:

• Value added = Net sales revenue − Cost of goods sold − Depreciation

Furthermore, capital employed, human capital, and structural capital are calculated as:

• Capital employed = Total assets − Intangible assets
• Human capital = Total expenditures on employees (wages, salary, etc.)
• Structural capital = Value added − Human capital

Innovation (INNOV). We measure innovation for one year by calculating the natural logarithm of the nominal value of the patents and licenses in that particular year (P. Wang, 2024). The Retriever database provides the nominal value of patents and licenses in Swedish Kronor. The formula is as follows:

$$INNOV=\ln (patents \& licenses)$$

3.2.3. Control Variables

Firm profitability (PROFIT) is measured as the percentage of a firm’s Earnings before Interest, Tax, and Depreciation to its Total Assets. Thus,

$$PROFIT={\displaystyle \frac{EBITDA}{Total Assets}}$$

Sales growth opportunities (GROWTH) indicates net sales growth between two periods. The variable is measured as follows:

$$GROWTH={\displaystyle \frac{{net sales}_t-{net sales}_{t-1}}{{net sales}_{t-1}}}$$

Asset tangibility (TANGIBILITY) is the proportion of a firm’s tangible assets to its total assets, as follows:

$$TANGIBILITY={\displaystyle \frac{Total Tangible Assets}{Total Assets}}$$

Size (SIZE) is measured as the natural logarithm of a firm’s total assets. $SIZE=\ln (total assets)$

Age (AGE) is a firm’s age since it was established until the data were generated on 17 August 2024.

The summary of the descriptive statistics of the data is shown in

Table 1. Summary statistics of the data.

Variable	Mean	Std.Dev	Minimum	Maximum
VAIC	13.4213	48.9466	−296.9945	696.1298
INNOV	0.6721	2.0464	0	13.2343
PROFIT	0.0379	0.9071	−61.3181	7.6
GROWTH	0.9198	14.0931	−1	848.1667
TANGIBILITY	0.0817	0.1572	−0.0638	0.9862
SIZE	7.8513	1.8302	5.0039	16.5468
AGE	20.0124	10.4900	2	50

. The sample indicates a wide range of VAIC with a minimum value of −296.9945 and a maximum of 696.1298. This shows the heterogeneity of biotech companies in Sweden in managing their intellectual asset efficiently. The high dispersion in profitability and growth opportunities indicates that the industry is characterized by both high-risk high-reward firms and more stable players. As expected, the low average tangibility highlights the sector’s reliance on intangible assets rather than physical ones. The average firm in the sample has total assets of approximately USD 260,000, with a standard deviation of 1.75 indicating a moderate level of variability in the size of firms. The age of the biotech firms included in the sample ranges from 2 years to 50 years.

3.3. Statistical Analysis

We tested the hypotheses using the logistic regression model and performed the analysis in the Stata 17 program. Logistic regression was selected because the dependent variable, representing the firm’s financing strategy, is binary: equity issuance (coded as 1) or debt issuance (coded as 0). This method is ideal for analyzing situations where the outcome variable is dichotomous, in this case, the likelihood of opting for equity rather than debt, based on a set of predictor variables. Previous studies such as (Muzir, 2011; Pirogova et al., 2019) used this statistical method in examining companies’ capital structure decisions. Logistic regression captures the relationships between the independent variables such as VAIC, innovation, profitability, and other firm characteristics and financing strategy as the dependent variable. The independent variables are assumed to affect the relative probability of choosing equity-based financing over debt-based financing, making logistic regression well-suited to modeling changes in odds ratios as influenced by independent variables. The logistic regression equation is as follows:

$$P\left(CAPSTRUCT=1\right)={\displaystyle \frac{1}{1+ e^{-(\beta 0 + \beta 1VAIC + \beta 2INNOV + \beta 3PROFIT + \beta 4GROWTH + \beta 5TANGIBILITY + \beta 6SIZE + \beta 7AGE)}}}$$

Here: P (CAPSTRUCT = 1) is the probability that a firm is issuing equity as a source of financing instead of issuing debt; β0 is the intercept; β1, β2,…, β8 are the coefficients for each respective independent variable; e is the base of the natural logarithm.

To test the presence of multicollinearity among independent variables, we calculated the Variance Inflation Factors (VIFs). The VIF scores were all below 10, which indicates no multicollinearity problems. The correlation matrix between variables is shown in

Table 2. Correlation matrix.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
(1) CAPSTRUC	1.000
(2) VAIC	0.021	1.000
(3) INNOV	−0.028	−0.020	1.000
(4) PROFIT	0.028	0.032	−0.103	1.000
(5) GROWTH	0.014	−0.004	0.005	−0.026	1.000
(6) TANGIBILITY	0.017	−0.010	−0.058	0.005	−0.001	1.000
(7) SIZE	0.019	0.012	0.330	0.018	0.013	0.107	1.000
(8) AGE	−0.021	0.014	−0.043	0.014	−0.015	0.123	0.189	1.000

.

4. Results and Analysis

In our logistic regression model, the base scenario represents a firm issuing debt, meaning the model estimates the probability of a firm choosing equity over debt as a financing source. We performed the Hausman test to choose between fixed effects and random effects in our estimation model, and the results show that the random effects model provides a more consistent and unbiased estimate (

Table A1. Hausman test results.

	----- Coefficients -----
	(b)	(B)	(b-B)	sqrt(diag(V_b-V_B))
	fe	re	Difference	Std. err.
VAIC	−0.0005	−0.0006	0.0001	0.0002
INNOV	−1.5838	−0.0630	−1.5207	1.1405
profit	9.9555	4.2063	5.7492	2.0980
growth	0.4245	0.4291	−0.0045	0.0182
tangibility	21.1176	16.7566	4.3610	7.8885
RD	8.9402	2.6330	6.3071	14.0143
size	−9.8076	−4.3127	−5.4948	2.6554
b = consistent under H0 and Ha; obtained from xtlogit.
	B = Inconsistent under Ha, efficient under H0; obtained from xtlogit.
Test of H0: Difference in coefficients not systematic
chi2(6)  = (b-B) ' [(V_b-V_B)^(−1)](b-B)
= 9.48
Prob > chi2 = 0.1485

). The logistic regression estimation results are shown in

Table 3. Logistic regression estimation results.

Variable	Base Scenario = Issuing Debt
	Coefficient	Odds Ratio	SE	P > |z|	[95% Conf. Interval]
Const	0.2191	1.2450	0.0927	0.018	0.0373	0.4010
VAIC	0.0008	1.0008	0.0003	0.021	0.0001	0.0015
INNOV	−0.0328	0.9677	0.0087	0.000	−0.0500	−0.0156
PROFIT	0.1391	1.1492	0.0322	0.000	0.0759	0.2023
GROWTH	0.0065	1.0065	0.0021	0.002	0.0022	0.0107
TANGIBILITY	0.2262	1.2539	0.1095	0.039	0.0116	0.4409
SIZE	0.0365	1.0372	0.0105	0.001	0.0158	0.0573
AGE	−0.0075	0.9925	0.0017	0.000	−0.0108	−0.0041
Log-likelihood	−9943.2253
Pseudo R2	0.0036
AIC	19,904.45
BIC	19,972.87

. The VAIC, which represents the efficiency of a firm utilizing its intellectual capital and financial capital to create value, was found to be a significant predictor of capital structure strategy. Firms with higher value-added intellectual capital are more likely to issue equity as their major financing alternative. Hypothesis 1, which posited that intellectual capital efficiency (VAIC) positively influences the likelihood of issuing equity over debt, is supported. The odds ratio for intellectual capital (VAIC) is 1.0008, indicating that an increase in intellectual capital efficiency is associated with a small but statistically significant increase in the likelihood of issuing equity over debt. While the effect size is modest, this finding suggests that biotech firms with stronger intellectual capital resources are perceived as more attractive to equity investors. The findings indicate that intellectual capital efficiency significantly influences firms’ capital structure choices. Firms with high intellectual capital are often characterized by their ability to leverage intangible assets, such as patents and proprietary technologies, which can enhance their attractiveness to equity investors. Intellectual capital positively moderates the market value of equity (Milanda et al., 2022). Habib and Dalwai (2024) highlighted that effective management of intellectual capital can lead to improved firm performance which in turn influenced the financial decisions. This seems particularly relevant for biotechnology firms, where the ability to innovate and manage intellectual resources effectively may enhance profitability and reduce reliance on debt financing. The positive correlation between intellectual capital and firm performance reinforces the notion that biotechnology firms with higher intellectual capital are better positioned to attract equity investment, as they can demonstrate a sustainable competitive advantage through innovation and knowledge management (Chiu & Chen, 2017). Moreover, sufficient disclosure of intellectual capital allows firms to reduce their cost of equity capital (Salvi et al., 2020). The ability to raise capital at a lower cost incentivizes firms with higher intellectual capital efficiency to issue equity instead of debt.

From the equity investors’ point of view, intellectual capital is viewed as a signal of long-term growth opportunities (Ezeoha & Botha, 2012). Thus, equity investors see firms with higher intellectual capital as more attractive.

Conversely, Hypothesis 2, predicting that firms with higher innovation activity are more likely to issue equity, is not supported, as our results show a negative relationship, suggesting that biotech firms with strong innovation activity prefer debt financing. The variable is statistically significant at a 99% confidence level with an odds ratio of 0.9677 for innovation activity (INNOV). This suggests that a one-unit increase in innovation intensity reduces the odds of issuing equity by approximately 3.23%. Firms with higher innovation activity tend to prefer debt financing over equity, likely to avoid ownership dilution or because patents act as collateral for loans. The economic significance of this finding is notable, as it implies that biotech firms with active innovation pipelines do not necessarily favor equity financing, despite the high-risk nature of their operations. This phenomenon can be attributed to several interrelated factors. Firstly, biotechnology firms are characterized by R&D expenditure, which is crucial for their innovation processes. Biotechnology firms with significant R&D investment may be more inclined to rely on debt financing, especially when their patents provide collateral to lenders, as it allows them to retain control over their operations and intellectual property which is valuable in this industry (Azim Khan, 2024). The reliance on debt can be seen as a strategic choice to avoid the dilution of ownership that typically happens with equity financing, especially in an industry where maintaining competitive advantage is crucial (Bruneo et al., 2024). Furthermore, the rationale behind this behavior is that debt issuance can serve as a signal of firm quality and stability, which is particularly important in the high-risk biotech sector. Firms that face high agency costs may prefer debt as a means to convey credible signals about their financial health and operational capabilities. Additionally, the dynamics of capital structure decisions in biotech firms are also influenced by external market conditions. For instance, during periods of high market sentiment, firms may experience pressure to issue equity, but those with strong innovation capabilities might still prioritize debt to fund their projects while managing the risks associated with equity dilution (Maung, 2014). Moreover, the cyclical behavior of debt and equity finance suggests that firms adapt their financing strategies based on prevailing economic conditions, which can affect their capital structure decisions (Covas & Den Haan, 2012; Covas & Haan, 2011). In addition, the market conditions and the relative costs of capital influence the choice between debt and equity. (Brown et al., 2017) indicates that firms tend to issue debt when market conditions are favorable, as the cost of debt may be lower than that of equity during certain periods. Biotech firms, which often experience fluctuating stock prices and investor sentiment, may find that debt financing is more accessible and less sensitive to market volatility compared to equity financing (Thakor et al., 2017).

The preference for debt financing among highly innovative biotech firms can be explained through some theoretical lenses. Pecking order theory posits that firms prefer internal financing first, followed by debt, and finally equity as a last choice (Myers & Majluf, 1984). The reluctance of innovation-intensive firms to issue equity is particularly consistent with this theory, as these firms often face severe asymmetric information problems due to the uncertain and long-term nature of their R&D investments. Moreover, if a firm holds valuable patents, it may expect higher future revenue streams. Thus, profits can cover debt repayments. A firm is more comfortable issuing debt rather than diluting ownership through equity issuance. This aligns with (Harasheh et al., 2024) who found that the value of patents is positively associated with debt, meaning that firms with more valuable patents tend to have higher debt levels. Furthermore, agency cost theory (Jensen, 1986; Jensen & Meckling, 1976) provides another perspective on this financing preference. Firms with intensive R&D activities tend to have high agency costs due to the difficulty of monitoring research expenditures and the uncertain time horizon for commercial returns. As a result, issuing debt can act as a disciplinary mechanism, ensuring that managers allocate resources efficiently. While high agency costs and asymmetric information make equity financing less attractive for biotech firms, patents serve as collateral, reducing lender risk and making debt a more viable financing option. This explains why firms with more valuable patents exhibit a higher preference for debt issuance, consistent with (Harasheh et al., 2024). Empirical research from (Margaritis & Psillaki, 2010) has shown that firms with high agency costs often resort to debt financing to impose financial discipline. In this context, Swedish biotech firms may favor debt issuance as a governance tool to manage agency costs, even when equity financing is available.

In addition to the main explanatory variables, all of the control variables also showed a significant relationship with the firms’ financing choices. Biotech firms with higher profitability are more likely to prefer equity over debt. (Mun & Jang, 2017) suggested that profitable firms tend to issue equity because they have better access to the equity market which makes it easier to issue equity financing. Sales growth opportunities were positively associated with equity issuance. High-growth firms are often associated with risky investment projects, which increases the barrier to accessing debt financing under information asymmetry (Segarra-Blasco & Teruel, 2009). This also explains the positive relationship between tangibility and equity issuance. According to the pecking order theory, higher tangible assets reduce the information asymmetry and ease the financing of equity. Furthermore, we documented a higher likelihood of issuing equity for larger firms. Large firms have better access to cheaper equity financing than smaller firms (Jang & Kim, 2009). Our result is aligned with (John, 2005; Majumdar, 2014) that as firms grow older, firms have better access to the debt market. The plausible explanation is that older firms often exhibit more financial stability, which makes them more appealing for the creditor’s valuation.

We performed a performance assessment to check the predictive power of the estimation model as shown in

Table 4. Model performance assessment results.

Sensitivity	Pr (+|D)	99.32%
Specificity	Pr (−|~D)	0.72%
Positive predictive value	Pr (D|+)	59.15%
Negative predictive value	Pr (~D|−)	42.34%
False + rate for true ~D	Pr (+|~D)	99.28%
False − rate for true D	Pr (−|D)	0.68%
False + rate for classified +	Pr (~D|+)	40.85%
False − rate for classified −	Pr (D|−)	57.66%
Correctly classified		59.04%

, as it is not possible to do the Hosmer–Lemeshow test for panel data series. The predicted outcomes (0 or 1) are compared to the actual observed values of the dependent variable to create a classification table, which includes True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). From this, key performance metrics such as accuracy (proportion of correct predictions), sensitivity (ability to identify actual positives), specificity (ability to identify actual negatives), and precision (proportion of correct positive predictions) are calculated to evaluate the model’s predictive power. The model correctly identifies 99.32% of true positives, showing that it has good sensitivity. The model is not strong in correctly identifying the true negatives, with only 0.72% predictive power. Nevertheless, the estimation model reflects a reasonable performance by correctly classifying 59.04% of observations.

5. Conclusions

This study contributes to the literature on capital structure decisions in the biotechnology sector by addressing a critical gap, which is how Swedish biotech firms navigate capital structure decisions despite their reliance on intellectual capital and innovation activities. while previous research has broadly examined financial strategies, the unique characteristics of biotech firms such as high risk, research and development intensive, and long commercialization timelines require a more understanding of capital structure choices. By empirically analyzing a decade of financial data, our findings provide new insights into how Swedish biotech firms manage and structure their financing to sustain innovation and growth. Biotech firms’ financing decisions are shaped by their reliance on intellectual capital and innovation activity, requiring a mix of financial strategies. Our results indicated that biotech firms with high intellectual capital are more likely to issue equity rather than debt. This preference reflects the challenges associated with collateralizing intangible assets like intellectual capital, making equity a more viable and attractive option. Additionally, intellectual capital may enhance firm valuation and reduce perceived risk from equity investors, thereby improving access to equity markets. On the other hand, biotech firms with high innovation activity exhibited a stronger reliance on debt financing. Despite the risks associated with debt, it remains a viable financing option for biotech firms. For example, biotech firms with strong patent portfolios may secure debt financing more easily. Additionally, they may prefer debt to retain control over proprietary technologies and mitigate ownership dilution. These findings underscore the complexities of biotech capital structure decisions and highlight the importance of a balanced financing approach that considers both equity and debt as viable sources of funding.

6. Implications and Limitations

This study provides both theoretical and managerial implications as follows. The findings in the paper extend the pecking order framework and the signaling theory to the biotechnology industry. As mentioned above, the biotechnology industry has certain characteristics that make the financial strategy more complex than in many other industries. The results indicate that the financial strategy, the choice between debt and equity, in biotechnology firms is more complex than previously suggested. This implies that the management of the financial strategy in the growing biotechnology firms should be flexible and consider taking advantage of a changing financial environment.

Additionally, our study also provides some advice for policymakers. Previously, a lot of the focus for policymakers has been on developing new methods for increasing the supply of equity for biotechnology firms. However, our findings highlighted the equally critical role of debt financing in supporting biotech firms’ financing. It is essential for policymakers to develop strategies that improve access to debt financing. One key challenge is the rapid transformation of the banking landscape, where the closure of local bank branches has been shown to negatively affect firms with intangible assets, such as biotechnology firms (Ho & Berggren, 2025). To mitigate this, policymakers should consider measures to prevent banking deserts, which are communities or municipalities without any bank branches, as these deserts have been shown to be detrimental to entrepreneurs and growing firms. Potential solutions may include promoting government-backed loan programs, offering loan guarantees for biotech firms, or fostering partnerships between financial institutions and the biotech sector to facilitate more tailored lending options

There are some limitations of this study that should be considered. While the biotechnology industry operates globally and shares common characteristics across different countries, this study focuses on Swedish biotech firms. Swedish biotech firms benefit from a strong research ecosystem and a well-developed venture capital market. These national characteristics may influence financial strategies and capital structure decisions. Thus, some caution is recommended before generalizing the results to other contexts. Future research could explore cross-country comparisons to assess the extent to which these findings hold in different financial ecosystems. Additionally, firm-specific parameters such as size, lifecycle stage, and managerial decision-making processes may also shape financing choices beyond the scope of the variables analyzed. While this study includes several key firm characteristics as control variables, it does not capture all possible factors influencing capital structure, such as managerial risk preferences, strategic alliances, or regulatory constraints. Future research could further explore these aspects to refine our understanding of financial strategies in biotech firms. Moreover, in our study, the low pseudo-R-squared value suggests that while the predictors are statistically significant in influencing the firm’s financing strategy, there may be other unobserved factors influencing these decisions that are not included in the model. This is a limitation that could potentially affect the model’s ability to fully capture the complexity of firms’ financing decisions. Future research can include other factors such as macroeconomic factors to get a more comprehensive overview of capital structure decisions. The model exhibits a weakness in correctly identifying firms that issue debt, with a low specificity. This could be due to the dataset containing significantly more firms that issue equity than those that issue debt, the model may become biased toward predicting equity. We suggest future studies to incorporate additional explanatory variables or explore alternative statistical techniques to improve predictive performance. Despite these limitations, our study offers valuable insights into Swedish biotech firms’ capital structure, leveraging a longitudinal dataset to examine intellectual capital and innovation. Future research can extend this analysis across countries and incorporate additional variables to enhance understanding of biotech financial decisions. our findings may serve as a foundation for comparative studies in other economies.