Small Bus Econ (2024) 62:833–855
https://doi.org/10.1007/s11187-023-00785-z
RESEARCH ARTICLE
Do intangible assets help SMEs in underdeveloped markets gain access to external finance?—the case of Vietnam
Chau Le · Bach Nguyen · Vinh Vo

Accepted: 7 May 2023 / Published online: 29 May 2023 © The Author(s) 2023
Abstract The credit frictions encountered by small and medium-sized enterprises (SMEs) have been widely examined in the entrepreneurship literature. Although theory suggests that asset tangibility helps increase firms’ borrowing capacity because it allows creditors to take possession of a firm’s assets more easily, this paper provides new evidence about the role of intangible assets in reducing credit frictions for SMEs. Using an extensive dataset of more than 155,852 SMEs in Vietnam and a multivariate probit model, we find that identifiable intangible assets improve firm access to debt and equity finance. Interestingly, it is found that the friction-reducing effect of intangibles is stronger on debt finance than on equity finance, suggesting nonequivalent distributional effects of intangible assets on firm capital structure. Moreover, firm age and size can moderate the association between intangibles and access to the two sources of external finance.
C. Le (*)
Lincoln International Business School, University
of Lincoln, Lincoln, UK
e-mail: cle@lincoln.ac.uk
B.		Nguyen
University of Exeter Business School, Rennes Drive, Exeter, UK
e-mail: b.nguyen@exeter.ac.uk
V. Vo
Institute of Business Research, University of Economics HCMC, Ho Chi Minh City, Vietnam
e-mail: vinhvx@ueh.edu.vn
Plain English Summary The transition from a manufacturing-based economy towards one driven by technology and innovation is making firms’ intellectual property and intangible assets important to their business models. We challenge the conventional presumption that asset intangibility is nonbankable, especially in less-developed financial markets. This study provides new evidence about the role of intangible assets in reducing credit friction for SMEs in Vietnam. Specifically, we find that intangible assets, especially those that are identifiable (e.g. software, databases, in-progress R&D, copyrights and the right to land use) are essential in determining firms’ ability to raise external finance. We also find that the friction-reducing effect of intangibles is stronger on debt than on equity finance. Thus, the principal implication of this study is that equity investors in less- developed financial markets do not fully appreciate the value of intangible assets, whereas banks and professional lenders take intangibles into consideration in their lending decisions.
Keywords Intangible assets · Financial constraints · External finance · SMEs · Vietnam
JEL Classification E22 · L21 · L26 · M21
1 Introduction
Credit frictions and financial constraints are recurring problems for small and medium-sized enterprises (SMEs) (Lim et al., 2020; Pissarides et al., 2003; Rao et al., 2021), especially in developing countries (Chen & Guariglia, 2013). One country where this is particularly the case is Vietnam. Although the country is currently transitioning its Soviet-style centrally planned economy to one that is characterised by a multisectoral market, its economic policy remains socialist in character. This means that the state plays a decisive role in directing economic development, with a long-term goal of developing socialism (Makino & Tsang, 2011). As a result, the financial system in Vietnam is intrinsically biased against the private sector (Pham & Talavera, 2018). Access to external finance is, therefore, a significant problem for Vietnamese private SMEs (Le et al., 2019; Nguyen & Canh, 2020).
There is substantial evidence that SMEs are subject to financial constraints. In particular, they have very limited access to formal sources of finance such as bank loans (Beck & Demirguc-Kunt, 2006). The largest single reason identified for SMEs’ failure to obtain credit from financial institutions is their lack of collateral or guarantors (Brassell & King, 2013). Extensive literature suggests that tangible assets help increase firms’ borrowing capacity because they are relatively easy for creditors to seize if the borrower defaults on the loan (Martina, 2015). The positive association between tangible assets and business financing has been widely confirmed in the literature (Campello & Giambona, 2013; Cole, 2013; Coleman et al., 2016; Scott, 1977). However, it is less clear what role intangible assets such as intellectual property might play in SMEs’ financing, especially in a developing financial market like Vietnam’s.
In the last few years, the global transition from a manufacturing-based economy towards one that is driven by technology and innovation has increased the role played by intellectual property and intangible assets in firms’ business models. The investments of entrepreneurs in intangibles have outstripped those made in tangibles in recent years, to the extent that intangible assets make up an increasing proportion of the value of enterprises (Brassell & Boschmans, 2019; Higón et al., 2017; Khattak & Ullah, 2021; Marrocu et al., 2012). Recent research shows that intangible assets have become critical drivers of firm values (Garanina et al., 2021; Lim et al., 2020). Examples are the patents and licenses of Apple and Pfizer, Amazon’s databases and Walmart’s unique market rights. For smaller firms, too, their investments in intangible assets serve as a signal of a their innovative capabilities and future growth, hence increasing their chances of attracting external funding such as venture capital or crowdfunding (Baum & Silverman, 2004). Therefore, Jarboe and Furrow (2008, p. 5) stress that: ‘Just as physical assets were used to finance the creation of more physical assets during the industrial age, intangible assets should be used to finance the creation of more intangible assets in the information age’.
Building on the emerging but increasingly prominent literature on the role of intangible assets (Lim et al., 2020), this paper sheds light on the credit frictionreducing effect of identifiable intangible assets for SMEs in Vietnam. Identifiable intangible assets are nonphysical but can be evaluated in monetary value and are reported in a firm’s financial statements, indicating that their values can be separately quantified, audited and traded in secondary financial markets (Lim et al., 2020). Some examples of identifiable intangible assets include trademarks, computer software, databases, mortgage servicing rights, licensing, franchise agreements and marketing rights. In this study, we only examine identifiable intangibles; hence, for the sake of simplicity, we refer to them as intangible assets/ intangibles throughout the paper. Specifically, we ask: Do intangible assets help Vietnamese SMEs obtain more debt and equity? If so, are SMEs with intangible assets more likely to attract debt lenders or equity investors? Are there specific groups of SMEs for which intangibles are particularly beneficial in improving their access to external finance?
Although the question of the role of intangibles on SME access to finance is a general one, it is interesting to look at it in the case of Vietnam. This is for three reasons. First, SMEs have been considered to be the growth engine of the country’s economy in the last decade. They account for 96% of Vietnam’s total active businesses, employ 47% of the country’s labour force and contribute up to 36% to the gross domestic product (OECD, 2021). However, SMEs in Vietnam are hampered by the country’s immature financial markets. While the market for debt has been liberalised and developed quite impressively in the last decade (Mateus & Hoang, 2021), the market for equity capital, especially that which serves SMEs, remains undersized (Nguyen et al., 2020). Entrepreneurs running SMEs therefore resort to informal sources such as friends and families when they need equity capital (Nguyen & Canh, 2020). It is thus interesting to examine whether intangibles help SMEs in Vietnam gain access to external finance in so unevenly developed a financial market. Second, initial evidence shows that banks in the developed countries appreciate intangibles and treat them as if they were tangible assets in their lending decision-making process (Lim et al., 2020). However, it is unclear whether banks and investors in Vietnam, where the institutions are less developed (e.g. lack of high-quality audits and weak copyright protection laws), appreciate intangibles or if they see them as highly risky assets. Third, we believe that the aforementioned business conditions are not specific to Vietnam but are typical of many developing economies. For example, prior studies show that SMEs in Thailand, Malaysia and Indonesia suffer from similar kinds of financial difficulties (Le & Shaffer, 2017). At the same time, businesses in these countries are also in the process of accumulating intangible assets (Wudhikarn & Pongpatcharatorntep, 2022). The study of Vietnam can serve as a template for evaluating the importance of intangible assets and their influence on business financing in other emerging markets and transition economies.
For our analyses, we use a dataset of more than 155,852 SMEs in Vietnam from 2008 to 2016. We apply a multivariate probit model to take the simultaneously determined process of debt and equity financing decisions into account. Our results clearly show the importance of intangible assets to helping Vietnamese SMEs gain access to both debt and equity. Furthermore, we find that the positive effects of intangibles on access to finance are heterogeneous, depending on (i) financing sources (debt versus equity) and (ii) firm characteristics (age and size).
The paper proceeds as follows. Section 2 reviews the literature on financial constraints in the context of SMEs and the role of intangible assets in business financing. We develop our hypotheses from that literature review. Section 3 provides descriptive statistics of the data sample and the testing approach. Key findings are discussed in Section 4, while Section 5 concludes the study with some policy remarks.
2		Literature review and hypothesis development
2.1		Financing constraints in the context of SMEs
SMEs are unlikely to be publicly traded (or incorporated) or to have their financial statements audited and widely reported in the press. This limits the sources of financing available to them (Berger & Udell, 1998). For example, their scale of operations debar them from raising funds publicly via the stock markets. The supply of bank credit and other sources of debt financing to SMEs is also constrained compared with that of larger firms because debt financing is heavily dependent on borrowers’ performance and requires stricter screening, contracting and monitoring. Informational opacity and their ‘liabilities of newness and smallness’ prevent SMEs from building reputations and credibly conveying their quality (Berger & Udell, 1998).
In addition, SMEs are less likely to generate sufficient profit and cash to meet lenders’ assessment criteria about their debt repayment capacity (Rao et al., 2021). Lenders therefore usually rely on collaterals, which are pledged as a signalling device to reduce asymmetric information and screening costs (Haselmann & Wachtel, 2007; Le & Nguyen, 2019; Wieneke & Gries, 2011). However, the assets used by SMEs as collaterals are more likely to be illiquid since they are more firm-specific or even locationspecific, and may involve incomplete contracts (Bras- sell & King, 2013; Caviggioli et al., 2020). This financing obstacle is even more severe for SMEs in developing countries where the financial environment typically involves more opaque information and weak enforcement; therefore, collateralisation appears to be a prerequisite for SMEs to access bank loans (Duarte et al., 2017). Indeed, Beck et al. (2006), using the World Business Environment Survey, find that a high requirement for collateral is one of the most important factors impeding SMEs’ motivation to apply for bank loans.
In the context of our analysis—Vietnam—the problems of financing constraints associated with SMEs are more prominent due to the country’s postsocialist political ideology. The socialist-oriented market economy is aimed at developing a multisectoral market economy in which the state sector plays a decisive role in directing economic development (Makino & Tsang, 2011). Because of the socialist ideology, the financial system in Vietnam is intrinsically biased against the private sector. Therefore, the lack of access to external finance is a significant problem for Vietnamese private SMEs (Le et al., 2019; Nguyen & Canh, 2020). This country-specific factor, combined with the informational asymmetries typical of emerging markets, heavily constrains domestic SMEs from gaining sufficient access to external finance. These severe financial constraints may delay firm investments and hamper firm growth (Anwar & Nguyen, 2011). In such a difficult situation, should Vietnamese SMEs focus entirely on building tangible assets, which could serve as collateral for obtaining external finance? Is there any friction-reducing effect associated with intangible assets at all? We discuss these issues in detail in the next section.
2.2		The association between intangible assets and external finance
While the importance of tangible assets to alleviating financial friction has been confirmed by the extensive literature (Hart & Moore, 1998; Kiyotaki & Moore, 1997), the evidence for the role that intangible assets play in business finance is inconsistent (Caillaud & Duchêne, 2011). This is understandable, given that intangible assets (usually referred to as knowledge assets or intellectual capital) are generally defined in terms of negatives: they lack physical substance, are non-monetary in nature and they lack identifiability. Specifically, OECD (2011) classifies three major categories of intangible assets: (i) computerised information such as software and databases; (ii) innovative property such as R&D results, copyrights, designs and trademarks; and (iii) economic competencies such as brand equity, firm-specific human capital, networks and organisational know-how.
While intangible assets and intellectual property are critical to the growth aspirations of SMEs, especially in the knowledge-based economy (Barnes & McClure, 2009), their lack of concrete form, their inalienable features and the absence of functioning markets for them make their evaluation extremely challenging. As a result, the capital market might misallocate resources for investment because it prioritises tangible assets over intangibles as collaterals. This issue is particularly pronounced in debt-financing because lenders usually view intangible assets as entailing higher risks (than the physical or financial assets) due to their volatile salvage value upon liquidation. This makes them a less attractive option for lenders who are mindful that they may have to recover loan losses in the event of default (Thornhill & Gellatly, 2005).
However, the past two decades have witnessed spectacular development in information technology. This has driven the transformation to a knowledgebased and digital economy (Ribeiro-Soriano et al., 2020). In this informational era, the power of intangible assets such as intellectual property in firms’ business models has increased dramatically. According to Bryan et al. (2017), a hefty proportion of the profits of the largest companies around the world is driven by intangible assets. Firms that are intangibility-intensive in the high technology, telecommunications, healthcare and biotechnology industries are more innovative and profitable (Orhangazi, 2019). There are four distinct features that help intangible assets generate ‘claims to uniqueness, authenticity, particularity, and speciality’ (Orhangazi, 2019, p. 1261). First, intangible assets such as brand names, trademarks, patents and copyrights generate monopoly rents for their owners (Zeller, 2007). For example, patents give pharmaceutical firms monopoly rights in the production of their products and many of these products are not substitutable. Second, intellectual property increases barriers to entry in certain industries, such as high-technologies and telecommunications, creating ‘winner-takes-all’ features for these markets (as exemplified by Google, Facebook, Microsoft and Airbnb). Third, intellectual property investment gives firms pricing power in competitive markets. Finally, intangible assets generate artificial scarcity for products whose cost of reproduction tends to zero, allowing their owners to set the price well above the cost of reproduction.
In this context, intangible assets drive competitive differentiation and may send a signal to stakeholders, especially finance providers, about innovative business models and growth potentials (Abeysekera, 2019; Odasso et al., 2015; Ruckman & McCarthy, 2017). This consequently improves firms’ ability to attract financial resources. According to Anderson and Prezas (1999), intangible assets can generate substantial cash flow, which is the primary determinant of a lender’s credit assessment and decision making. Using microdata on patent applications filed by startups in the USA, Farre-Mensa et al. (2016) find that patent assets act as a catalyst that sets early-stage firms on a growth path by facilitating their access to capital. Hochberg et al. (2018) add further evidence that patent rights are important for reducing credit friction in innovation financing, finding that thicker trading in the secondary market for patents can facilitate lending to startups that have risky projects. Mann (2018) also shows that the pledgeability of patents for secured debts has become a common mechanism for mitigating credit frictions, supporting the value of patent collateral for financing innovation among public firms. Also, Lim et al. (2020) provide convincing evidence for the role of intangible assets in improving entrepreneurs’ borrowing capacity.
The mechanisms underpinning the link between intangible assets and firm access to debt finance similarly apply to equity finance. Equity is acknowledged as being better suited than debt finance for funding intangible assets. Equity investors tend to view intellectual property as providing competitive advantages such that it acts as a prerequisite for their involvement (Brassell & King, 2013). In developed markets, equity investors are proficient at valuing intangible assets and innovative business models. Anecdotal evidence shows that private equity firms, venture capitalists and business angels who are engaged in startup finance focus heavily on how the technology and innovation associated with intangible assets feature in the business model (Croce et al., 2020; De Clercq et al., 2012; Lefebvre et al., 2020). In recent years, crowdfunding, including peer-to-peer lending, has emerged as a prominent new source of alternative finance for entrepreneurial ventures in the digitalised society (Centobelli et al., 2016; Kaartemo, 2017). Research shows that crowd funders are more likely to target intangible-rich and innovation-based enterprises (Bruton et al., 2015) and that intellectual capital is one of the key drivers of successful equity crowdfunding campaigns (Vrontis et al., 2020). Studying the signalling of funding seekers, Walthoff-Borm et al. (2018) find that firms listed on equity crowdfunding platforms have more intangible assets and growth potential.
In sum, we expect that in the current knowledgebased economy, intangible assets increasingly possess critical features, including informational asymmetry reduction and signalling effects of cash-generation potential. These characteristics give an advantage to SMEs with more intangibles when it comes to accessing external financing compared to those with less or without intangible assets. As such, we propose the following hypothesis:
Hypothesis H1: Firms with more intangible assets have better access to debt and equity finance than firms with fewer intangible assets.
2.3		The relative importance of intangibles on equity finance and debt finance
In the preceding section, we have argued that intangible assets may grant SMEs more access to external finance. However, the literature suggests that the effects of intangibles on equity finance and debt finance are not equivalent in that intangibles attract more equity than debt finance. There are three main reasons that equity financiers differ from debt lenders in having a strong interest in the intangible assets of an entrepreneurial business. First, for equity investors, intangible assets serve as concrete evidence of sustainable competitive advantage and growth potential, which are the key parameters of their financing decisions (Brassell & King, 2013). The interest of equity financiers in intangibles is reasonable since SMEs are naturally small and young, and thereby lack the tangible assets and track records that might prove their performance. In this case, intangibles serve as an essential signal of future performance and growth potential, which equity providers view as essential criteria for funding decisions (Labidi & Gajewski, 2019).
Second, debt lenders such as banks are less willing to carefully scrutinise the value of intangible assets such as intellectual property due to the substantial costs of doing so (Bönte & Nielen, 2011; Brassell & Boschmans, 2019; Sakakibara, 2010). Also, the cash flow patterns of intangible and real assets differ in terms of timing and risk. On average, real asset investment starts generating cash flow earlier than intangible assets; however, once successful, intangibles can be expected to generate higher cash flow than the comparable real assets (Anderson & Prezas, 1999). While equity financiers are the final claimers of firm’s values (which are potentially generated by the intangibles), debt leaders are not. For this reason, debt lenders are less willing to take risks (i.e. wait longer time) than equity financiers, leading to the situation in which intangibles are less attractive to debt lenders.
Third, lenders such as banks are willing to grant loans collateralised by intangibles only if they are confident that the value attributed to intangibles will be recoverable in the event of default. However, there is substantial difficulty in evaluating intangible assets, not least because of their volatility. This may be due to the absence of agreed assessment methods or because their evaluation is context dependent, which makes them highly firm specific (Anderson & Prezas, 1999). This reduces the chances of fully recovering their value in the event of default, making them less attractive to debt lenders (VanderPal, 2019).
The dominant role of intangible assets in equity over debt financing is justifiable in the context of developed countries that have market-based financial systems. However, the situation may be different in the emerging economies. Demirgüç-Kunt and Mak- simovic (1999) describe how the equity markets and banks have developed along different lines in many nations to provide systematically distinct financing arrangements. For the bank-based financial markets in many developing countries, bank loans and other types of credit offered by financial institutions continue to be entrepreneurs’ most important source of external financing. Mateus and Hoang (2021) show that while the market for debt in Vietnam has liberalised and developed fairly significantly over the past decades, the equity financial markets, especially those that serve SMEs, remain undersized. For example, venture capital, crowdfunding and angel financing are not well developed and are inaccessible to most Vietnamese SMEs (Scheela & Dinh, 2004; Scheela et al., 2018). Prior research shows that informal finance (i.e. money sourced from families, relatives and friends) is the essential source of equity capital for SMEs in Vietnam (Nguyen & Canh, 2020) and the developing countries generally (Kislat, 2015). These investors are less likely to be well trained in finance and may not fully comprehend the value of intangible assets. To mitigate risk, they are eager to make financial decisions based on physical assets. Banks that are involved in debt financing, on the other hand, value intangible assets because they understand their inherent value and know how to trade intangibles in the event of business default. Lim et al. (2020) provide convincing evidence that identifiable intangible assets give good support to debt financing because they function in the same way as collateralisable tangible assets. The authors argue that although intangible assets are more difficult to use, their identifiability and capacity for being separately valued means they can still have substantial value when in the hands of different owners.
In light of these arguments, it is expected that in Vietnam debt lenders have stronger motivations than (informal) equity financiers to fund intangible-rich SMEs. As such, we propose the following hypothesis:
Hypothesis H2: The positive effects of intangible assets are stronger on debt finance than equity finance in countries with underdeveloped equity markets for SMEs such as Vietnam.
2.4		Age and size as moderators for the effect of intangibles
There is increasing evidence that SMEs contribute significantly to overall innovation, including new patents, inventions, discoveries and other types of intellectual assets (Cucculelli & Bettinelli, 2015). Given the importance of intangible assets to firm access to external finance, we explore if there are any specific groups of SMEs for which intangibles are more beneficial. Specifically, we argue that firms that are smaller and younger benefit from having intangibles in the sense that they are more likely to use them to obtain external finance compared to their larger and older counterparts.
It is noteworthy that SMEs in Vietnam, and indeed in the developing countries generally, are mostly new startups (Du & Mickiewicz, 2016; Nguyen & Canh, 2020). In this initial stage of doing business, a majority number of firms is unable to build up substantial tangible assets, which could serve as collateral for obtaining bank loans or as signals of growth potential to attract equity finance. In such circumstances, smaller and younger firms with intangible assets unsurprisingly stand out as superior candidates in the competition for external funding. The reason is that intangible resources, by virtue of their inherent inimitability, are critical sources of competitive advantage (Newbert, 2007). Thus, the possession of intangible resources represents an important factor in an SME’s ability to pursue strategies that result in positive economic outcomes (Anderson & Eshima, 2013). This potential of future performance, in turn, becomes one of the essential criteria of the funding decisions made by both equity financiers and debt lenders.
Also, in the face of technological changes, aged and large firms usually find it difficult to manage innovative activities due to the inflexibility and organisational inertia characteristic of incumbents, whereas young firms are more willing to leverage the novel knowledge associated with intangible assets to innovate and rise to market dominance (Hill & Rothaer- mel, 2003). Given that intangible assets tend to have greater strategic significance (Newbert, 2007), smaller and younger firms are able to use their intangible possessions to pursue more business opportunities. Therefore, investment in intangible assets is believed to improve SMEs’ access to funding opportunities that they would otherwise fail to obtain by reason of their liabilities of smallness and youngness. Larger and older firms, on the other hand, have typically established a track record of performance, and their fixed assets have also reached a value that could feasibly serve as collaterals. Therefore, the frictionreduction role played by intangibles in these firms becomes less prominent. In addition, since larger and older firms can obtain external finance using their pledgeable tangible assets and they can also use their performance track record as a measure of a reduction of informational asymmetry, they are probably less willing to disclose information about their intangible assets and intellectual property to third parties (Labidi & Gajewski, 2019), including lenders and investors. Therefore, we propose the following hypotheses:
Hypothesis H3: The positive association between intangible assets and firm access to debt finance is stronger for SMEs that are smaller and younger.
Hypothesis H4: The positive relationship between intangible assets and firm access to equity finance is stronger for SMEs that are smaller and younger.
3		Methodology and data
3.1		Data sample
We use a comprehensive firm-level dataset of 230,080 SMEs located in Ho Chi Minh City, Vietnam. The firms were registered between 2008 and 2016 and made reports to the General Department of Taxation during that period. We classify enterprises based on the Vietnam Enterprise Law and the definition adopted by World Bank. Accordingly, microenterprises have no more than 10 employees, small enterprises have between 10 and 50 employees, and firms with 50 to 300 employees are classified as medium enterprises. The reported data includes year of establishment, year of closure, classification of industry, location and detailed financial statements. We eliminate firms with less than three continuous years of reported data because this allows us to evaluate the evolution of financial performance. We also drop from the sample firms with abnormal asset value, that is, a negative value of total assets. Firms in agriculture, public administration, education and healthcare sectors are excluded from our sample since their production conditions are quite different from the ones in the other business sectors. Thus, the final sample consists of 155,852 firms. The number of firms varies from year to year, resulting in an unbalanced panel with 752,784 firm-year observations.
Table 1 summarises the panel structure by year and industry using the Vietnam Standard Industrial Classification System (VSIC). It is observable from the statistics that 50% of our sample concentrates on wholesale and retail trade (around 28.1%) and administrative and support services (around 24.9%) with only a handful of firms working in the water supply/management and mining industries (0.02% and 0.03%, respectively). Sample distribution is also uneven in time, with more than 30% of firms being observed in 2014 and 2015 (around 15% each). Only 4.7% of firms were in existence in 2016.
3.2		Model setting and definition of variables
We are interested in estimating the impact of intangible assets on the financing capacity of SMEs. The primary dependent variables of interest in this study are SMEs’ access to debt and equity. Although the

Industry code	2008	2009	2010	2011	2012	2013	2014	2015	2016	Total	Percent	
B	25	37	35	32	26	21	17	11	2	206	0.03%	
C	48	96	141	228	277	309	345	338	64	1846	0.25%	
D	4470	5827	5914	5360	4628	4129	3765	3470	734	38,297	5.09%	
E	12	27	27	29	24	21	22	20	3	185	0.02%	
F	2829	3945	4069	3697	3260	2949	2629	2410	548	26,336	3.50%	
G	13,824	20,389	27,434	28,934	28,280	28,553	28,677	27,410	7904	211,405	28.08%	
H	783	1148	1829	2825	3632	4378	4959	5019	1530	26,103	3.47%	
I	2107	3251	3962	4229	4339	4485	4555	4469	1559	32,956	4.38%	
J	189	292	585	895	1150	1360	1498	1440	381	7790	1.03%	
K	204	272	364	538	684	926	1070	1047	315	5420	0.72%	
L	1705	2270	2675	2649	2531	2398	2318	2202	702	19,450	2.58%	
M	4190	5938	7845	10,355	12,467	14,587	16,028	16,061	4928	92,399	12.27%	
N	10,346	13,406	16,306	20,634	24,594	28,569	31,609	31,711	10,215	187,390	24.89%	
R	971	1376	1842	2676	3474	4137	4740	4822	1865	25,903	3.44%	
S	3733	5247	6484	8503	10,287	12,032	13,142	13,228	4442	77,098	10.24%	
Total	45,436	63,521	79,512	91,584	99,653	108,854	115,374	113,658	35,192	752,784	100%	
Percent	6.04%	8.44%	10.56%	12.17%	13.24%	14.46%	15.33%	15.10%	4.67%	100%		

Table 1 Total number of SMEs in the data sample by year and industry

Industrial classification is based on Vietnam Standard Industrial Classification System such that B: mining; C: manufacturing; D: electricity, gas, steam and air conditioning supply; E: water supply and management; F: construction; G: wholesale and retail trade; H: transportation and storage; I: accommodation and food service activities; J: information and communication; K: financial services; L: real estate activities, M: professional, scientific and technical services; N: administrative and support services; R: arts, entertainment and recreation; S: other services

concept of ‘access to finance’ is generally defined from the literature as the availability of a supply of reasonable quality financial services at reasonable costs, it is very challenging to measure from practical observations (Bae et al., 2012). This is because it is difficult to accurately distinguish between involuntary and voluntary exclusion from using financial services. Accordingly, firms do not obtain external finance may belong to one of two groups: (i) their applications were turned down due to insufficient income, discrimination or bad credit, or (ii) they had no need for external finance. As a result, most research focuses on the ‘use of financial services’ as a proxy for access to finance (Bae et al., 2012). Although ‘access’ is not always equal to ‘use’, it is a necessary condition to use (Bae et al., 2012), especially in the context of SMEs financing. Pham and Talavera (2018) show that due to limited access to formal financial markets, Vietnamese SMEs had to seek and use informal loans from family and friends and private lenders as principal sources of external finance. Following the literature, we construct the following two dummy variables to represent the use of debt and equity finance as proxies for dependent variables.
3.2.1		Dependent variables
Debt finance (Debt) is proxied by a dummy variable, which takes the value of 1 if a firm uses debt finance in the form of either formal or informal credit, and 0 otherwise. A firm acquires debt finance in year t if it reports short-term or long-term debts in the balance sheet, and interest expenses in the income statement for the same period.
Equity finance (Equity) is a dummy variable that takes the value of 1 if a firm acquires more equity in the period, and 0 otherwise. From the financial statements, we observe that firms acquire more equity finance in the financial year t if the increase in equity is higher than the increase in profit after tax in the same period. That means we exclude retained earnings from equity investment since retained earnings are considered to be internal finance.
3.2.2		Independent variable
Intangible assets (Intangibles) is measured by the value of intangible assets (in logarithm form). Previous studies use the term ‘intangible assets’ to indicate several types 
of capital and resources; these include goodwill capital (Mueller & Supina, 2002), human and relational capital (Mansion & Bausch, 2020), capabilities associated with top managers (Anderson & Eshima, 2013) and intellectual property (Cucculelli & Bettinelli, 2015; Orhangazi, 2019). In this study, following Lim et al. (2020), we employ the concept of identifiable intangibles, which have their values separately quantified and reported in the balance sheets. SMEs in our data sample are firms that registered and reported to the General Department of Taxation; therefore, their financial statements strictly follow the accounting regulations for SMEs published by the Ministry of Finance under the Circular 133/2016/ TT-BTC. Under this regulation, reporting of intangible assets must be compliant to Vietnamese Accounting Standard (VAS) 04 such that firms report intangible assets only if the values of these assets are separately identifiable and measurable (in monetary terms) with an initial value (i.e. historical cost) of at least VND 30 million (equivalent to US$1319). In addition, intangible assets are considered identifiable when enterprises can lease, sell or exchange them to acquire concrete future economic benefits (e.g. turnover increase, saved costs or other forms of financial returns) and their initial value must be determined on a reliable basis. Intangible assets reported by these firms include the right to use land for a definite term, computer software, copyrights and patents, aquatic resource exploitation permits, ecommerce websites and distribution rights.
3.2.3		Control variables
Tangible assets (Tangibles) is measured by the ratio of long-term tangible assets to total assets. Tangibles serve as collaterals for debt financing (especially bank loans), and are a proxy for informational asymmetry reduction between the lenders and the borrowers (Du et al., 2015). The higher the proportion of tangible assets, the easier it is for SMEs to obtain debt finance.
Firm size (Size) is proxied by the natural logarithm of firm total assets measured in million VND. It is widely accepted that bigger firms are less financially constrained and have better opportunities to access external finance (Carreira & Silva, 2010).
Firm age (Age) is measured by the number of years between a firm starting operations and the reported year. Like firm size, firm age is an important determinant of success in obtaining external finance (Anderson & Eshima, 2013). Older firms with performance track records, wider networks and established positions in their local markets may find it easier to gain access to finance (Nguyen & Canh, 2020).
Profit growth (Growth) is a dummy variable that takes the value of 1 if a firm has experienced growth in total pre-tax profit compared to the previous period. It shows how efficiently a firm employs its assets to generate income and acquire more business opportunities. For lenders and investors, it is an essential indicator of the firm’s competence and efficiency, and strongly influences their funding decisions (Nguyen & Canh, 2020). A growth in profitability brings about capital resources that might directly influence the levels of external finance (Chittenden et al., 1996; Fu et al., 2002).
Cash flow (Cash) is the value of cash and cash equivalents (in logarithm form). Following the conventional literature, we take into account the role of cash flow in accessing external finance (Anderson & Prezas, 1999; Chen & Guariglia, 2013). In general, firms with a higher level of cash flow are less likely to seek external finance than firms with lower levels. However, a higher level of cash flow increases the borrowing capacity. As such, this variable may be associated with debt and equity finance in both directions.
Credit risk score (Z-score) is Altman’s Z-score, developed for private firms (Altman et al., 2017). Altman’s Z-score model has been widely used in the academic literature as well as in the financial industry (e.g. commercial banks) to predict a firm’s financial distress and to assess the credit risk of a borrower (Altman, 2018). The Z-score model was developed for public firms in Altman (1968), and was then revised for private firms such that
Z′ = 0.717 ∙X1 + 0.847 ∙X2 + 3.107 ∙X3 + 0.420 ∙X4 + 0.998 ∙X5
where X1 = Working capital/Total assets; X2 = Retained earnings/Total assets; X3 = EBIT/Total assets; X4 = Book value of equity/Book value of total liabilities; X5 = Sales/Total assets. In the literature, the credit score has been widely used to examine SMEs’ access to finance (Dietsch & Petey, 2002; Terdpao- pong & Mihret, 2011).
We control for industrial effect by including in the model dummy variables representing different business sectors. The SMEs in our data sample belong to 15 sub-industrial categories based on the VSCI (see
Table 1). We group firms into six industry sectors according to their economic activities by constructing six dummy variables that represent manufacturing industry (sector 1), wholesale and retail trade (sector 2), financial services (sector 3), information technology (sector 4), professional and technical support services (sector 5), and others (sector 6).
3.2.4		Model setting and estimation method
Given the binary nature of our dependent variables, we apply a bivariate probit model to jointly test the probability of using debt and equity finance. The bivariate selection mechanism was developed as follows:
z1∗it = 𝜶1i + 𝜷1Intangiblesit-1 + 𝜹1Ωit + 𝜺1it (1)
z2∗it = 𝜶2i + 𝜷2Intangiblesit-1 + 𝜹2Ωit + 𝜺2it	(2)
where:

z1it
z2it

1ifz1∗it > 0
0 otherwise, 1ifz2∗it > 0
0 otherwise,
Intangiblesit-1 is the key variable of our analysis and represents the value of intangible assets (in logarithm) reported in the firm balance sheet in period t - 1; Ωit is a set of control variables including tangible assets, firm size, firm age, profit growth, cash flow, credit risk score and industry dummy variables; 𝜺1it and 𝜺2it are error terms assumed to be jointly normally distributed with zero means, unit variances and correlation 𝜌.
The dependent variables z1itandz2it are dummy variables representing the use of debt finance and an increase in equity finance in period t, as explained in Section 3.2.1. The equity finance category does not include retained earnings or other sources of internal funding. The two choices of external finance are assumed to be interrelated, as articulated in the trade-off and pecking order theories of capital structure (Hovakimian et al., 2004; Myers, 1977; Myers & Majluf, 1984). In a discrete choice context with correlated decisions, bivariate (for two equations) or multivariate (for three or more equations) probit models are commonly used to address a jointly determined process, allowing us to estimate the interrelatedness (error covariance) of the two decisions under consideration (Greene, 2003). We estimate Eqs. (1) and (2) using simulated maximum likelihood (SML) and the Geweke–Hajivassiliou–Keane (GHK) smooth recursive conditioning simulator, as suggested by Cappel- lari and Jenkins (2003).
3.3		Descriptive statistics of variables and distribution of data sample
In this section, we present key statistics relating to the variables to paint a general picture of the financing situations and development of intangible assets of SMEs in Vietnam. Table 2 summarises the definitions, mean values and standard deviations of all variables for the total sample, for the sub-sample of firms that accesses debt finance only, the sub-sample of firms that accesses equity finance only, and the sub-sample of firms that can access both debt and equity. As may be observed from Table 2, only 14.1% of analysed firms use debt financing, whereas 38.4% of entrepreneurs rely on equity from external sources rather than selfgenerated funds during the analysed period. In order to have a more insightful picture of accessibility to external finance, we summarize the distribution of data samples regarding the use of debt and equity by year in Fig. 1. It appears from the graph that the proportion of firms without access to external finance is on an upward trend over time. Around 45.14% of firms had no access to external finance in 2009, but the figure consistently increases year-to-year until the reported figure is 58.53% in 2016. The period of 2009–2014 saw a declining trend in the proportion of entrepreneurs that could access debt finance. This is consistent with the timeline of the global financial crisis (GFC) and the European debt crisis, when credit supply conditions tightened because of banks’ reduced ability and willingness to extend credit and provide new loans (Vasilescu, 2014). In terms of equity finance, we observe a downward trend in the number of firms that could access equity finance from 2013 to 2016, with 2015 and 2016 appearing to be years when it was challenging for SMEs to raise equity finance. The proportion of firms that could access both sources of external finance is marginal (around 5%).
Regarding the independent variable, the average value of intangible assets (in logarithm) is reported as 8.925, but there is a remarkable difference between the two groups (i.e. leveraged firms vs. unleveraged
Dependent variables
Debt finance (Debt)	Dummy variable that takes the value of	0.141
1 if a firm reports short-term and/or	(0.348)
long-term debts in the balance sheet, and interest expenses in the income statement for the same period, and 0 otherwise	
Equity finance (Equity)	Dummy variable that takes the value of 1	0.384
if the firm acquires more equity (exclud- (0.486) ing retained earnings) in the period, and
0 otherwise	
Independent variable
Intangible assets (Intangibles)	Value of intangible assets (in logarithm)	8.925 16.918	7.225	16.793
(9.686) (7.801)	(9.236)	(7.983)	
Control variables
Tangible assets (Tangibles)	Long-term tangible assets to total assets	0.072	0.134	0.058	0.145
(0.430) (0.221)	(0.567)	(0.218)	
Firm size (Size)	Natural logarithm of firm’s total assets	21.465 22.951	21.193	22.850
measured in million VND	(1.669) (1.385)	(1.508)	(1.397)	
Firm age (Age)	The number of years since firm started its	4.147	5.775	3.602	6.594
operation to the reported year	(3.666) (3.756)	(3.811)	(3.937)	
Profit growth (Growth)	Dummy variable that takes the value of 1	0.378	0.453	0.251	0.435
when firm experiences a growth in total	(0.484) (0.498)	(0.434)	(0.496)
probit before tax this year compared to previous period	
Cash flow (Cash)	Cash and cash equivalents (in logarithm) 20.124 20.470	20.194	20.518
(1.759) (1.700)	(1.671)	(1.695)	
Credit risk score (Z-score)	Alman’s Zʹ-score for private firms	0.113	0.0003	1.907	0.050
(17.386) (0.046)	(194.68)	(8.635)	
Table 2 Variable definition and descriptive statistics
Definition
Total Debt (only) Equity (only) Debt + Equity

We report mean value of each variable and standard deviation in parentheses. Altman’s Zʹ-score was developed for private firms by Altman et al. (2017) such that: Z′ = 0.717 ∙ X1 + 0.847 ∙ X2 + 3.107 ∙ X3 + 0.420 ∙ X4 + 0.998 ∙ X5 , where X1 = Working capital/ Total assets; X2 = Retained earnings/Total assets; X3 = EBIT/Total assets; X4 = Book value of equity/Book value of total liabilities; X5 = Sales/Total assets

firms). The mean value for firms with access to debt only is double that of firms with equity only (16.918 compared to 7.225), reflecting the relative importance of intangible assets to the borrowing capacity of SMEs. Firms that can access both debt and equity finance reported an average intangible asset value of 16.793, which is equivalent to that of firms with only debt. The average ratio of tangible assets to total assets is also higher for firms that can access debt, but the highest figure is identified for those that can access both sources of external finance. For other control variables such as firm size, age, growth and cash flow, higher mean values are also observed for the sample of leveraged firms. In addition, firms that can access debt show a lower value of Altman’s Z-score. This is consistent with practical observations that lenders are more likely to offer loans to less risky customers (i.e. bigger and older firms with more cash and growth potential) that have a lower probability of default.
4		Empirical results
4.1		Regression results
Table 3 reports the estimated results using a bivariate probit model based on the joint distribution of two variables (debt and equity). This method allows us

No ex-^inance	45.14%	46.51%	46.12%	47.59%	50.64%	52.86%	56.55%	58.53%
Debt	12.13%	10.22%	9.31%	7.73%	7.69%	7.56%	8.78%	8.55%
Equity	37.86%	37.77%	38.83%	38.79%	36.81%	35.19%	29.89%	27.95%
Debt+Equity	4.87%	5.49%	5.74%	5.89%	4.86%	4.39%	4.78%	4.98%

No ex-^inance Debt Equity	Debt+Equity
Fig. 1 Distribution of data sample: access vs. non-access to external finance by year

to theoretically address the simultaneity of debt and equity financing decisions. In all specifications, the correlation (rho) between the error terms ( 𝜀1it and 𝜀2it) of the underlying stochastic utility function associated with debt and equity finance is significant at 1%, indicating that a bivariate probit model is more appropriate than two univariate probit models. We report the marginal effects of intangibles and interactive variables (Intangibles × Age and Intangible × Size) in both debt and equity equations in Table 4. Marginal effects of intangibles were calculated following the bivariate probit estimations (1) and (2) reported in Table 3. The average marginal effect of each variable was predicted on the joint probability distribution of z1itandz2it based on the approach of Greene (2003). For interaction terms, we predict the full interaction effects by computing the cross derivative of the expected value of the dependent variables as suggested by Norton et al. (2004). According to Ai and Norton (2003) and Norton et al. (2004), the magnitude of the interaction effect in nonlinear models does not equal the marginal effect of the interaction term; therefore, the test for the statistical significance of the interaction effect must be based on the estimated cross-partial derivative, not on the coefficient of the interaction term.
Marginal effects of Intangibles in Debt and Equity equations were calculated following bivariate probit estimations (1) and (2) reported in Table 3. Average marginal effect of each variable was predicted on the joint probability distribution of z1itandz2it based on the approach of Green (2003). As we estimated bivariate probit via multivariate probit suggested by Cap- pellari and Jenkins (2003) using Stata 17, predictions following mvprobit are obtained using the command mvppred. For interaction terms, we calculate the marginal effects of interaction terms (Intangibles × Size; Intangibles × Age) following bivariate probit estimations (3)–(6). Then we estimate the full interaction effects (TotalME) of interaction terms which equal the cross-partial derivative of the expected value of dependent variables z1it andz2it based on the approach suggested by Norton et al. (2004)
As observable from Table 3, the coefficients associated with the variable Intangibles are positive and statistically significant in both debt and equity equations, suggesting that intangible-rich firms have better

Table 3 Intangible assets and firms’	access to debt and equity finance (coefficients)	
	(1)	(2)	(3)	(4)	(5)	(6)	
VARIABLES	Debt	Equity	Debt	Equity	Debt	Equity	
Intangibles	0.0253***	0.0077***	0.0538***	0.0429***	0.0290***	0.0133***	
	(0.0003)	(0.0002)	(0.0041)	(0.0027)	(0.0004)	(0.0004)	
Tangibles	0.0383***	0.0149***	0.0380***	0.0129**	0.0391***	0.0180***	
	(0.0037)	(0.0055)	(0.0037)	(0.0054)	(0.0037)	(0.0055)	
Intangibles × Size			- 0.0013***	- 0.0016***			
			(0.0002)	(0.0001)			
Intangibles × Age					- 0.0008***	- 0.0011***	
					(0.0001)	(0.0001)	
Size	0.3872***	- 0.0699***	0.4052***	- 0.0517***	0.3890***	- 0.0668***	
	(0.0020)	(0.0016)	(0.0033)	(0.0021)	(0.0020)	(0.0016)	
Age	0.0088***	0.0425***	0.0093***	0.0434***	0.0200***	0.0565***	
	(0.0007)	(0.0006)	(0.0007)	(0.0006)	(0.0012)	(0.0009)	
Cash	- 0.0923***	0.0637***	- 0.0920***	0.0631***	- 0.0916***	0.0645***	
	(0.0015)	(0.0013)	(0.0015)	(0.0013)	(0.0015)	(0.0013)	
Growth	0.0182***	- 0.3300***	0.0185***	- 0.3297***	0.0183***	- 0.3301***	
	(0.0046)	(0.0037)	(0.0046)	(0.0037)	(0.0046)	(0.0037)	
Z-Score	- 0.0048*	0.0024	- 0.0048*	0.0025	- 0.0047*	0.0026*	
	(0.0025)	(0.0015)	(0.0025)	(0.0015)	(0.0025)	(0.0015)	
Y2010	- 0.0860***	0.0607***	- 0.0862***	0.0605***	- 0.0857***	0.0616***	
	(0.0102)	(0.0088)	(0.0102)	(0.0088)	(0.0102)	(0.0088)	
Y2011	- 0.1498***	0.1112***	- 0.1502***	0.1107***	- 0.1496***	0.1120***	
	(0.0099)	(0.0085)	(0.0099)	(0.0085)	(0.0099)	(0.0085)	
Y2012	- 0.2612***	0.1343***	- 0.2621***	0.1331***	- 0.2610***	0.1352***	
	(0.0098)	(0.0083)	(0.0098)	(0.0083)	(0.0099)	(0.0083)	
Y2013	- 0.3392***	0.0219***	- 0.3404***	0.0200**	- 0.3381***	0.0243***	
	(0.0098)	(0.0083)	(0.0098)	(0.0083)	(0.0098)	(0.0083)	
Y2014	- 0.3418***	- 0.0128	- 0.3429***	- 0.0141*	- 0.3409***	- 0.0101	
	(0.0098)	(0.0083)	(0.0098)	(0.0083)	(0.0098)	(0.0083)	
Y2015	- 0.3146***	- 0.0442***	- 0.3159***	- 0.0454***	- 0.3148***	- 0.0432***	
	(0.0097)	(0.0082)	(0.0097)	(0.0082)	(0.0097)	(0.0082)	
Y2016	- 0.3322***	- 0.0126	- 0.3344***	- 0.0146	- 0.3344***	- 0.0148	
	(0.0127)	(0.0104)	(0.0127)	(0.0104)	(0.0127)	(0.0104)	
Constant	- 7.8634***	- 0.3183***	- 8.2640***	- 0.6947***	- 7.9642***	-0.4579***	
	(0.0402)	(0.0293)	(0.0703)	(0.0414)	(0.0414)	(0.0300)	
Industry	YES		YES		YES		
Rho	0.0177***	(0.0026)	0.0175***	(0.0026)	0.0171***	(0.0026)	
Wald chi2	91,642.56		91,100.79		91,858.51		
Prob	0.000		0.000		0.000		
Observations	493,010		493,010		493,010		
Estimation is obtained from multivariate probit models via bivariate probit following the approach proposed by Cappellari and Jenkins (2003). Coefficients are reported followed by robust standard errors in parentheses. ***, ** and * denote significance at 1%, 5% and 10%, respectively
Table 4 Marginal effects of Intangibles and Tangibles	Mean	Std. error	[95% conf. interval]	
variables	Debt equation					
	Intangibles	0.00547	0.000004	0.00546	0.00548	
	Intangibles × Size	- 0.00028	0.000000	- 0.00028	- 0.00028	
	TotalME_Intangibles × Size	0.00337	0.000001	0.00337	0.00337	
	Intangibles × Age	- 0.00016	0.000000	- 0.00016	- 0.00016	
	TotalME_ Intangibles × Age	- 0.00006	0.000000	- 0.00006	- 0.00006	
	Equity equation					
	Intangibles	0.00167	0.000001	0.00166	0.00167	
	Intangibles × Size	- 0.00035	0.000000	- 0.00035	- 0.00035	
	TotalME_Intangibles × Size	- 0.00054	0.000000	- 0.00054	- 0.00053	
	Intangibles × Age	- 0.00025	0.000000	- 0.00025	- 0.00025	
	TotalME_Intangibles × Age	- 0.00013	0.000000	- 0.00013	- 0.00013	

access to external finance than firms with fewer or no intangible assets. This finding thus fully supports hypothesis H1.
Hypothesis H2 theorises the relative attractiveness of intangible assets to debt financers and equity investors. To test this hypothesis, we compare the marginal effects of intangibles in debt and equity equations: 54.7 × 10-4 and 16.7 × 10-4 , respectively. We can interpret these figures as follows. On average, for any 100% increase in the value of intangible assets (i.e. 1 unit increase in Intangibles variable), the probability for firm to use debt finance increases by 0.547 percentage points whereas the probability for firm to use equity finance increases by only 0.167 percentage points, holding all other variables constant. Although the magnitude of the variation in the two effects is economically humble, they are statistically significantly different from each other. Although this finding runs contrary to the extant literature, it is sensible in the context of Vietnam’s less-developed equity market and supports the study’s hypothesis H2.
To explain the result, we need to take into account the nature of the equity market for SMEs in Vietnam. Unlike large firms, Vietnamese SMEs are not listed in stock markets which, according to the efficient market hypothesis, could reflect intrinsic value of firms (Mal- kiel, 2005). Moreover, the alternative equity markets are insufficiently developed to be accessible to the majority of Vietnamese SMEs (Scheela & Dinh, 2004; Scheela et al., 2018). Prior research indicates that informal finance in the form of money borrowed from family, relatives and friends is the most important source of equity financing for SMEs in Vietnam (Nguyen & Canh, 2020).
These unprofessional and network-based investors may not fully appreciate the value of intangible assets (Mateus & Hoang, 2021; Thornhill & Gellatly, 2005), which they treat as a complement to rather than a substitute for asset tangibility. However, the banks in Vietnam will consider the values of intangibles when making their lending decisions. There are probably two reasons for this. First, the intangible assets in our study are separately identifiable, valuable and potentially collateralisable, and are instrumental in generating cash flows (i.e. priced, audited and reported in balance sheets). Hence, banks can assess their value at low to no cost. As such, they may be treated like tangible assets in the lending decision-making processes (Lim et al., 2020). Second, the secondary markets for trading intangible assets (e.g. trademarks, franchises, copyrights) is starting to develop in Vietnam (Quan et al., 2020). Therefore, banks may find it feasible to liquidate the intangible assets that firms have pledged as collateral in the event of payment default. For these very specific characteristics of a developing financial market, the effect of intangibles is stronger on debt finance than equity finance in the case of Vietnamese SMEs.
Turning to the moderating effect of firm age and size, it may be seen from Table 3 that the coefficients associated with the interaction terms between intangibles and firm size/age are negative and statistically significant in all specifications. This finding indicates that the effect of intangible assets on access to both debt and equity is stronger for smaller and younger firms. In a dynamic and technology-intensive economic landscape, nascent and small firms are more flexible and willing to rely on intellectual capital when becoming involved in innovation activities, whereas larger and older firms with established track records and fixed assets are less likely to do so (Petruzzelli et al., 2018). As a result, older and large firms are unlikely to leverage their intellectual assets to gain access to external finance. Conversely, smaller and younger firms, which are relatively free of bureaucracy and which have the autonomy to explore innovative solutions, are preferred by lenders and investors when they have more intangibles (Petruzzelli et al., 2018). As such, hypotheses H3 and H4 are supported.
Regarding firm-specific factors, the estimated results show significant positive coefficients for firms’ size and age to debt financing, implying that access to bank loans is easier for bigger and older firms as they are perceived by lenders to be less risky. This is highly consistent with the extensive literature on SMEs’ financial constraints (Ullah, 2020). Firm age also positively affects equity financing, but firm size surprisingly shows a negative effect on access to equity. One possible explanation for this finding is that smaller firms are keen to grow by acquiring and investing more equity, whereas bigger entrepreneurs are more likely to generate profit, preferring to use their internally generated funds via retained earnings rather than external sources of equity finance. Interestingly, the cash variable has a positive effect on equity financing but negatively impacts firm accessibility to debt finance. The level of cash holding contains information about a firm’s investment opportunities (Ferrando & Mulier, 2015), hence sending a good signal to equity investors and incentivising them to provide funding. Although this finding runs contrary to the literature, it lends support to Lim et al. (2020) who argue that firms that generate substantial cash may prefer to be debt-free so that its managers are not subject to scrutinisation by lenders. Growth in profit boosts the probability of accessing debt finance because in Vietnam’s conservative credit environment, profit is considered to be the factor that most affects borrowing capacity and the likelihood of repaying loans. However, it seems that entrepreneurs with increasing profits are more likely to use internally generated funds (i.e. retained earnings), which is consistent with our argument about the effect of firm size on equity finance. Lastly, the effect of the Z-score is significant for debt finance because this represents the credit risk assessment employed by banks and financial institutions in their lending decisions. However, its effect on equity financing is negligible.
4.2		Robustness check
We conducted two robustness checks. First, we split the sample into two periods 2008–2011 and 2012–2016 to test whether the findings are different during and after the GFC. This also enables us to examine the relative importance of intangible assets in the economic conditions of an era that is becoming increasingly digitalised and knowledge-based. During 2010/2011, the Vietnamese government introduced several measures aimed at enabling firms to cope with the uncertainty generated by the GFC and at improving the competitiveness of the domestic private sector. These included financial support schemes for firms to engage in R&D activities, and training schemes geared to transforming firms’ business models from labour intensive to knowledge intensive (Haaker et al., 2021; Vu & Tran, 2020). The changing economic and institutional conditions inevitably led to the increasing importance of intangible assets. For example, Quan et al. (2020) evidently show that since 2010, intangibles have made up a large share of the total assets of Vietnamese firms. Based on their arguments, we use a split-sample method to examine the increasing importance of intangibles to Vietnamese SMEs.
The regression results are presented in Appendix, Table 5. It can be seen that intangibles helped SMEs obtain debt and equity finance both during and after the GFC. This finding reveals that banks in Vietnam began to accept intangible assets as collateral some time ago. In terms of the moderating effects of firm age and size, the findings are quite consistent with the full sample analysis. Specifically, intangibles are essential to smaller and younger firms when they seek debt and equity finance.
Second, we employ a multinominal logit model to estimate the effect of intangibles on three groups of firms: (i) those with access to debt finance only, (ii) those with access to equity finance only and (iii) those with access to both sources of external finance. Even though this approach fails to take into account the simultaneously determined process of debt and equity financing decisions, it provides us with a nuanced understanding of the effects of intangibles on access to debt and equity finance when they are examined individually and collectively. The results are presented in Table 6 in the Appendix. They show that intangibles have a significant positive effect on both debt and equity finance, with higher coefficients being observed in debt than in equity equations. In terms of the moderating effects, the findings are consistent with the main results; that is, smaller and younger firms gain more debt and equity from intangibles than their larger and older counterparts.
5		Discussion and conclusion
While intangible assets have been proved to be major contributors to business value in the current knowledge-based economy, it is widely perceived that investment in intangible assets adds to the financial constraints faced by SMEs due their low liquidity nature (Anderson & Prezas, 1999). Moreover, SMEs’ financing obstacle is even more severe in less developed markets where information asymmetry is notoriously high, hindering their investment in developing intangible assets (Labidi & Gajewski, 2019) and consequently, exacerbating SMEs’ financing woes. Is asset intangibility harmful to SMEs access to external finance in the context of developing countries? To answer this question, we examine a sample of more than 155,852 Vietnamese SMEs. Our findings stand in sharp contrast to the presumption that intangible assets are not constructive for SMEs to obtain external finance. In contrast, we show that, in the context of Vietnam—a less developed financial market—SMEs having more intangible assets have better access to external finance, including equity and debt.
By theorising and to empirically show that intangibles contribute to SMEs’ access to external finance in the context of Vietnam, we make three contributions to the extant literature. First, we add to the literature examining SMEs’ access to external finance a factor of asset intangibility. In the literature, the role of intangible assets in financing has not been examined systematically due to the presumption that asset intangibility may be harmful to SMEs in their process of seeking external finance. In this study, we challenge this conventional presumption that asset intangibility is nonbankable owing to its uncertain returns and low redeployability. Specifically, we provide empirical evidence about the role of intangible assets in helping SMEs gain access to both debt and equity finance. This finding adds value to a strand of literature linking intangibles to firm performance (Anderson & Eshima, 2013; Cuc- culelli & Bettinelli, 2015; Demmou & Franco, 2021; VanderPal, 2019). Building on this, we demonstrate that intangibles are also linked to improved access to capital resources, which could be one of the key mechanisms that strengthens the competitive advantages of SMEs and helps them achieve better performance. Our findings thus echo Quan et al. (2020) who, also in the context of Vietnam, estimate that intangible assets increasingly make up a large share of companies’ total assets, and that intangible assets exert a positive impact on improving company capability of obtaining external sources of fund.
Second, our study contributes to the strand of literature investigating the relativity of access to equity and debt finance in the context of SMEs. Specifically, we show that, in the context of Vietnam, the effect of intangible assets is stronger for SMEs to gain access to debt finance than for them to obtain equity finance. This result nuances the extant literature’s understanding of intangibles. Previous studies in the field of entrepreneurial finance document that equity investors are more likely to target business models and growth potentials associated with intangibles, whereas debt holders focus more on asset collateralisation, which is generally believed to be more problematic when the assets are intangible (Casamatta & Haritchabalet, 2014; Lim et al., 2020). Our findings, obtained from examining a developing financial market, add to the literature by showing that when the equity markets are underdeveloped, investors are less likely to be professional; as such, they may not appreciate the value of intangible assets. Banks, however, have professional evaluation tools and an understanding of the secondary market for intangibles, and are therefore more likely to accept intangibles as collaterals. We argue that the stronger impact of intangible assets on SMEs’ obtaining debt finance versus equity finance is valid only in less developed countries, where the debt markets are more mature than the equity markets (Fink et al., 2009; Mateus & Hoang, 2021).
Third, we contribute to the literature examining the role played by firm-level characteristics in determining their access to external finance (Anderson & Eshima, 2013; Carreira & Silva, 2010; Lee et al., 2015; Mulier et al., 2016; Öztürk & Mrkaic, 2014). To do this, we propose a framework to test the moderating effects of firm size and age on the association between intangible assets and SMEs’ access to external capital resources. We find that younger and smaller SMEs could benefit more from having more intangibles when they try to seek external finance. This demonstrates that intangible assets may substitute for tangible assets and serve as a signal of SMEs’ capability that helps reduce informational asymmetry. This finding is consistent with a strand of the well-established literature that argues for the importance of internal resources, especially intangible ones, to the investment, performance and growth of SMEs (Cucculelli & Bettinelli, 2015; Mansion & Bausch, 2020; Mueller & Supina, 2002).
This study comes with some important practical implications for SMEs. First, SMEs should be aware that the conventional presumption about the harm of asset intangibility on firm ability to obtain external finance is inappropriate. In fact, intangibles enhance firm access to external finance. Therefore, SMEs should be confident in investing in improving their intangible assets. This strategy not only help firms become more competitive in the knowledgebased and digitalised economy but also allows them to obtain more external capital resources. Second, SMEs in developing countries with limited access to equity funding might rely on intangible assets to obtain more bank loans. It is evident that banks in Vietnam do consider SMEs having intangibles as reliable and trustworthy to make lending decisions. Third, SMEs should develop their intangible assets when they are still young and small. This may serve as a strong signal to external funders that the firms are capable of and full of growth potential, leading to them providing firms with more finance.
Moreover, the study offers some implications for policymakers. Specifically, our findings highlight the increasing role of knowledge-based capital in business financing structure. This emphasises the importance of policy interventions for intangible- backed financing, such as a legislative framework for intellectual rights, secondary market transactions, and accounting standards and valuations. Specifically, robust approaches and clear guidelines need to be introduced to facilitate intangible asset classification and valuation. The enforceability of intellectual property rights and rights transfer should be enhanced to improve investors’ confidence in engaging in intangibles financing (Besley, 1995). For example, investors should be certain about how they can obtain effective controls over intangible assets and how asset value can be realised in the case of default. Asset-backed and alternative financing techniques should be adapted for intellectual property and intangible assets.
This study is not without limitations that should be acknowledged in order to provide potential avenues for future research. First, due to data limitations (non-incorporated SMEs), we are still unable to identify reliable values of external finance and must use a set of dummy variables. This largely reduces the amount of information available for obtaining an in-depth analysis of the topic. Also, our analysis may suffer from potential sample selection biases in the sense that firms that did not obtain external finance may be because they had no demand. Due to the limited availability of variables, we have not successfully found a set of satisfactory instrument variables to address this issue. Third, the dataset employed in this study is country specific. Vietnam was selected for the suitability of its empirical context to the research questions under investigation. However, one of the main weaknesses of a country-specific research design is that we only observe within-country effects, which may be influenced by social embeddedness, culture, institutional systems and business traditions (Nguyen & Canh, 2020; Suzuki et al., 2014). In addition, our data sample contains a very small proportion of SMEs that reported intangible assets in their financial statements (around 5.1%). Although the test shows that the positive effect of intangible effect on firm’s accessibility on external finance is statistically significant, the estimated marginal effect is too small to gauge an economic significance. Therefore, future research should retest our findings’ validity using a multi-country dataset with more extended survey periods.
Appendix
Table 5 Robustness test with time-period sub-sample regression
	Period: 2008-2011	Period: 2012-2016	
	(1)
Debt	(2) Equity	(3) Debt	(4) Equity	(5) Debt	(6) Equity	(7) Debt	(8) Equity	(9) Debt	(10) Equity	(11)
Debt	(12) Equity	
Intangibles	0.0244***	0.0085***	0.0771***	0.0333***	0.0266***	0.0151***	0.0252***	0.0074***	0.0431***	0.0429***	0.0303***	0.0134***	
	(0.0005)	(0.0004)	(0.0072)	(0.0050)	(0.0008)	(0.0006)	(0.0003)	(0.0003)	(0.0050)	(0.0033)	(0.0006)	(0.0004)	
Tangibles	0.0154***	0.0277***	0.0152***	0.0274***	0.0156***	0.0302***	0.1178***	0.0089	0.1169***	0.0055	0.1206***	0.0134*	
	(0.0042)	(0.0082)	(0.0042)	(0.0082)	(0.0042)	(0.0081)	(0.0074)	(0.0078)	(0.0074)	(0.0079)	(0.0074)	(0.0076)	
Intangibles x Size			-0.0024***	-0.0011***					-0.0008***■	-0.0016***			
			(0.0003)	(0.0002)					(0.0002)	(0.0002)			
Intangibles x Age					-0.0006***	-0.0016***					-0.0010***	-0.0011***	
					(0.0001)	(0.0001)					(0.0001)	(0.0001)	
Size	0.3797***	-0.1176***	0.4152***	-0.1032***	0.3809***	-0.1134***	0.3927***	-0.0495***	0.4036***■	-0.0322***	0.3953***	-0.0464***	
	(0.0035)	(0.0030)	(0.0060)	(0.0042)	(0.0036)	(0.0030)	(0.0025)	(0.0019)	(0.0040)	(0.0025)	(0.0025)	(0.0019)	
Age	0.0043***	0.0519***	0.0054***	0.0527***	0.0129***	0.0739***	0.0104***	0.0393***	0.0107***	0.0402***	0.0242***	0.0525***	
	(0.0013)	(0.0011)	(0.0013)	(0.0011)	(0.0026)	(0.0019)	(0.0008)	(0.0006)	(0.0008)	(0.0007)	(0.0014)	(0.0010)	
Cash	-0.0922***	0.0768***	-0.0921***	0.0763***	-0.0919***	0.0776***	-0.0914***	0.0587***	-0.0912***	0.0581***	-0.0905***	0.0596***	
	(0.0027)	(0.0025)	(0.0027)	(0.0025)	(0.0027)	(0.0025)	(0.0018)	(0.0016)	(0.0018)	(0.0016)	(0.0018)	(0.0016)	
Growth	0.0308***	-0.3178***	0.0316***	-0.3175***	0.0309***	-0.3177***	0.0118**	-0.3354***	0.0120**■	-0.3351***	0.0119**	-0.3356***	
	(0.0079)	(0.0067)	(0.0079)	(0.0067)	(0.0079)	(0.0067)	(0.0057)	(0.0045)	(0.0057)	(0.0045)	(0.0057)	(0.0045)	
Z-score	-0.0009	0.0012	-0.0009	0.0012	-0.0009	0.0014	-0.0171**	0.0029	-0.0171**	0.0030	-0.0170**	0.0029	
	(0.0033)	(0.0026)	(0.0033)	(0.0026)	(0.0034)	(0.0026)	(0.0084)	(0.0019)	(0.0084)	(0.0019)	(0.0084)	(0.0019)	
Constant	-7.6645***	0.4088***	-8.4404***	0.1123	-7.8107***	0.2915***	-8.2860***	-0.5122***	-8.6069***■	-1.0184***	-8.5036***	-0.8021***	
	(0.0665)	(0.0514)	(0.1251)	(0.0790)	(0.0687)	(0.0530)	(0.0503)	(0.0351)	(0.0867)	(0.0494)	(0.0532)	(0.0371)	
Year	Yes		Yes		Yes		Yes		Yes			Yes	
Industry	Yes		Yes		Yes		Yes		Yes			Yes	
Wald chi2	29056.88		28710.14	29210.79	62131.92	61933.17	62215.84	
Prob.	0.000		0.000	0.000	0.000	0.000		0.000	
Observations	152,688		152,688	152,688	340,322	340,322	340,322	

Robust standard errors in parentheses. ***, ** and * denote significance at 1%, 5% and 10%, respectively
2Springer
Table 6 Robustness test with multinominal probit regression
	(1)
Debt	(2) Equity	(3)
Debt+Equity	(4) Debt	(5) Equity	(6) Debt+Equity	(7) Debt	(8) Equity	(9) Debt+Equity	
Intangibles	0.0408***	0.0107***	0.0592***	0.1602***	0.0477***	0.2262***	0.0505***	0.0199***	0.0774***	
	(0.0013)	(0.0009)	(0.0015)	(0.0095)	(0.0059)	(0.0121)	(0.0013)	(0.0009)	(0.0017)	
Tangibles	0.5228***	0.1296	0.5255***	0.4989***	0.1167	0.5018***	0.5532***	0.1545	0.5565***	
	(0.1242)	(0.1076)	(0.1268)	(0.1226)	(0.1071)	(0.1253)	(0.1247)	(0.1089)	(0.1277)	
Intangibles χ Size				-0.0053***	-0.0017***	-0.0075***				
				(0.0004)	(0.0003)	(0.0005)				
Intangibles χ Age							-0.0021***	-0.0019***	-0.0035***	
							(0.0002)	(0.0001)	(0.0002)	
Size	0.6968***	-0.1123***	0.5248***	0.7677***	-0.0970***	0.6366***	0.7011***	-0.1082***	0.5317***	
	(0.0046)	(0.0033)	(0.0054)	(0.0072)	(0.0043)	(0.0095)	(0.0046)	(0.0033)	(0.0055)	
Age	0.0211***	0.0750***	0.0750***	0.0229***	0.0758***	0.0773***	0,0494***	0.0954***	0.1287***	
	(0.0015)	(0.0010)	(0.0017)	(0.0015)	(0.0010)	(0.0017)	(0.0030)	(0.0016)	(0.0036)	
Cash	-0.1225***	0.1254***	-0.0701***	-0.1214***	0.1250***	-0.0687***	-0.1204***	0.1269***	-0.0668***	
	(0.0038)	(0.0034)	(0.0045)	(0.0037)	(0.0035)	(0.0044)	(0.0038)	(0.0035)	(0.0045)	
Growth	-0.0495***	-0.5883***	-0.3848***	-0.0476***	-0.5882***	-0.3825***	-0.0498***	-0.5891***	-0.3842***	
	(0.0101)	(0.0068)	(0.0122)	(0.0101)	(0.0068)	(0.0122)	(0.0101)	(0.0068)	(0.0122)	
Z-score	-0.0058	0.0044	-0.0055	-0.0058	0.0044	-0.0054	-0.0056	0.0045	-0.0051	
	(0.0038)	(0.0031)	(0.0037)	(0.0037)	(0.0031)	(0.0037)	(0.0038)	(0.0031)	(0.0037)	
Y2010	-0.1992***	0.0561***	0.0456	-0.1991***	0.0561***	0.0456	-0.1975***	0.0574***	0.0480*	
	(0.0216)	(0.0163)	(0.0281)	(0.0216)	(0.0163)	(0.0281)	(0.0217)	(0.0163)	(0.0282)	
Y2011	-0.3310***	0.1203***	0.0580**	-0.3317***	0.1199***	0.0572**	-0.3294***	0.1212***	0.0608**	
	(0.0213)	(0.0158)	(0.0271)	(0.0212)	(0.0158)	(0.0271)	(0.0213)	(0.0158)	(0.0272)	
Y2012	-0.5975***	0.1219***	-0.0188	-0.5991***	0.1209***	-0.0208	-0.5958***	0.1229***	-0.0155	
	(0.0216)	(0.0155)	(0.0267)	(0.0215)	(0.0155)	(0.0267)	(0.0216)	(0.0155)	(0.0268)	
Y2013	-0.7082***	-0.0522***	-0.3424***	-0.7107***	-0.0539***	-0.3456***	-0.7035***	-0.0482***	-0.3343***	
	(0.0215)	(0.0157)	(0.0273)	(0.0214)	(0.0157)	(0.0273)	(0.0215)	(0.0157)	(0.0274)	
Y2014	-0.7180***	-0.1095***	-0.4228***	-0.7191***	-0.1102***	-0.4246***	-0.7138***	-0.1052***	-0.4156***	
	(0.0210)	(0.0153)	(0.0273)	(0.0210)	(0.0153)	(0.0272)	(0.0211)	(0.0154)	(0.0273)	
Y2015	-0.6595***	-0.1576***	-0.4423***	-0.6609***	-0.1580***	-0.4447***	-0.6589***	-0.1561***	-0.4399***	
	(0.0206)	(0.0152)	(0.0270)	(0.0205)	(0.0152)	(0.0270)	(0.0206)	(0.0153)	(0.0270)	
Y2016	-0.7141***	-0.1174***	-0.4019***	-0.7188***	-0.1182***	-0.4089***	-0.7187***	-0.1203***	-0.4095***	
	(0.0275)	(0.0192)	(0.0349)	(0.0275)	(0.0192)	(0.0349)	(0.0275)	(0.0192)	(0.0349)	
Constant	-14.9222***	-0.9335***	-13.2823***	-16.5148***	-1.2510***	-15.7827***	-15.1766***	-1.1371***	-13.7593***	
	(0.1154)	(0.0626)	(0.1335)	(0.1614)	(0.0737)	(0.2171)	(0.1219)	(0.0654)	(0.1411)	
Industry		Yes			Yes			Yes		
Wald chi2		84605.72			82977.89			84823.1		
Prob.		0.000			0.000			0.000		
Observations		493,010			493,010			493,010		
Robust standard errors in parentheses. ***, ** and * denote significance at 1%, 5% and 10%, respectively
Acknowledgements This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number [502.02-2018.24].
Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativeco mmons. org/licen ses/by/4. 0/.
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