R&D investment and financing choices: A comprehensive perspective

Research Policy 39 (2010) 1148–1159

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Research Policy journal homepage: www.elsevier.com/locate/respol

R&D investment and financing choices: A comprehensive perspective Taiyuan Wang a,∗ , Stewart Thornhill b a b

IE Business School, Maria de Molina 6, Entreplanta, Madrid, 28006, Spain Richard Ivey School of Business, The University of Western Ontario, 1151 Richmond Street, London, ON, N6A 3K7, Canada

a r t i c l e

i n f o

Article history: Received 24 February 2009 Received in revised form 20 February 2010 Accepted 7 July 2010 Available online 13 August 2010 Keywords: Financing choice R&D investment Intervention barriers Appropriation discrepancy

a b s t r a c t We posit that the effects of R&D investment on financing choices depend on the degree of intervention barriers and appropriation discrepancy between capital providers and the firm. Based on these two contingencies, we categorize financing instruments into four types: common equity (common stock), convertible securities (preferred stock and convertible debt), transactional debt (corporate bonds), and relational debt (bank and commercial loans). From the experiences of 39 petroleum firms during the period 1976–2005, we found R&D investment has a positive effect on the use of common equity, a Ushaped effect on the use of convertible securities, and an inverted U-shaped effect on the use of relational debt to raise capital. These effects are sustained over several years. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Strategic investments often require large capital expenditures that are beyond firms’ normal operating cash flows. The supply of external capital is uncertain and may be limited (Folta and Janney, 2004), and thus access to external capital may be a source of competitive advantage (Barney, 1991). This strategic attribute of financing has attracted increasing attention in the management literature (David et al., 2008). Researchers have made extensive efforts to examine the difference between debt and equity financing (Balakrishnan and Fox, 1993; Baldwin and Johnson, 1996; O’Brien, 2003; Simerly and Li, 2000; Thornhill and Gellatly, 2005; Titman and Wessels, 1988; Vicente-Lorente, 2001). Debt and equity can be considered different forms of governance (Williamson, 1988). From management’s perspective, debt is less intrusive because debt investors can take control over the firm’s assets only if the firm has defaulted or violated the debt contract (Balakrishnan and Fox, 1993; Vicente-Lorente, 2001). In contrast, shareholders can monitor and intervene in the conduct of management through the board of directors (Balakrishnan and Fox, 1993; Vicente-Lorente, 2001). Mainly due to agency costs and tax effects, debt financing is less expensive than equity financing (Myers and Majluf, 1984; Shyam-Sunder and Myers, 1999). Debt financing also reduces overinvestment, which helps managers gain compensation and power but creates no value for shareholders (Amihud and Lev,

∗ Corresponding author. Tel.: +34 91 745 24 18; fax: +34 91 745 21 48. E-mail addresses: [email protected] (T. Wang), [email protected] (S. Thornhill). 0048-7333/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.respol.2010.07.004

1981). Too much debt, however, may cause financial distress and increase default risk (Wruck, 1990). Therefore, how much debt a firm raises reflects its risk-return preferences and strategic decisions (Covin and Slevin, 1988; Miller and Friesen, 1982). To explain how firms choose between debt and equity financing, scholars have considered strategic factors (Barton and Gordon, 1987, 1988; Bettis, 1983; Kochhar and Hitt, 1998). One of the most important strategic factors is R&D investment (Balakrishnan and Fox, 1993; O’Brien, 2003; Vicente-Lorente, 2001), which generates firm specific assets (Christensen, 1995; Helfat, 1994). According to transaction cost economics (TCE), firm-specific assets have lower resale value than do general assets because, by definition, general assets can be more easily redeployed (Williamson, 1988). Since debt investors may not have access to the firm’s proprietary information, they are unable to monitor and impede risky decisions that may lead to liquidation or reorganization (Williamson, 1988).1 As a result, they have to seek protection by selecting firms with fewer specific assets or requiring higher interest rates for firms with more specific assets (Balakrishnan and Fox, 1993; Titman and Wessels, 1988; Vicente-Lorente, 2001). They may also refuse to lend to firms with high levels of specific assets, which generally do not serve as collateral, even if such firms are willing to pay high interest

1 In case of liquidation, the firm ceases operations and sells its assets. In case of reorganization, the firm continues its operations but undergoes restructuring. For example, a common practice for reorganization in the U.S. is that the firm’s debt holders give up their debt claims and become shareholders (Chapter 11). Then they sell the firm or parts of it to another company. In either case (liquidation or reorganization), a market approach will be employed, and thus the rationale of TCE applies.

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Fig. 1. Financing instruments: intervention barriers and appropriation discrepancy.

rates (Stiglitz and Weiss, 1981).2 Consequently, firms with high R&D investment will face difficulty raising debt and have to rely on equity financing (Folta and Janney, 2004; O’Brien, 2003). We contend this effect of R&D investment on firm financing claimed by TCE needs further investigation. First, access to proprietary information is a necessary but not sufficient condition for capital providers to intervene in the conduct of management (Williamson, 1988), which often determines firm performance and asset appropriation. For example, holders of preferred stocks have access to the firm’s proprietary information, but may be excluded from voting or have little power to influence management even if they have voting rights (Emanuel, 1983). Second, TCE assumes information asymmetry between the firm and its debt investors (Williamson, 1988). This assumption seems questionable for private loan providers (e.g., commercial banks), who often have access to the firm’s proprietary information and are able to monitor the use of funds, resulting in successful intervention in the conduct of management (Boot, 2000; David et al., 2008; Degryse and Ongena, 2001). Third, TCE views financing as a transaction between the firm and its capital providers, whose returns essentially depend on the firm’s success in the long-run (Stiglitz and Weiss, 1981; Williamson, 1988). R&D develops firm specific assets and capabilities (Helfat, 1994, 1997) and thus may lead to superior performance (Dierickx and Cool, 1989; Ravenscraft and Scherer, 1982; Zahra and Covin, 1995), but TCE does not consider this positive role of R&D investment. To address these issues, we examine the effects of R&D investment on financing choices by taking a more comprehensive view. We categorize financing instruments into four groups based on the degree of intervention barriers and appropriation discrepancy between capital providers and the firm (Fig. 1). Intervention barriers indicate the extent to which capital providers are impeded from monitoring and intervening in the conduct of management. Such barriers are essentially the results of information asymmetry between the firm and its capital providers and contractual designs of the financial instruments (e.g., voting rights of preferred stockholders). Appropriation discrepancy captures the extent to which returns of capital providers are separated from the firm’s residual value. This two-by-two categorization helps clarify the boundary of TCE (Williamson, 1988) and incorporate the mechanism through which R&D investment improves firm performance in the long-run (Ravenscraft and Scherer, 1982; Zahra and Covin, 1995), which may affect not only firms’ financing choices but also capital providers’ investment decisions.

2

We thank an anonymous reviewer for raising this point.

2. Theory and hypotheses 2.1. Financing instruments: A refined categorization Firms, especially large corporations, often raise capital by issuing a variety of instruments. Major financing instruments include common stock, preferred stock, bank loans, commercial loans, corporate bonds, and convertible debt (Hovakimian et al., 2001). As shown in Fig. 1, Quadrant I of the matrix describes a situation in which intervention barriers and appropriation discrepancy between capital providers and the firm are low. Common equity fits well in this quadrant. Common shareholders have access to the firm’s proprietary information and can intervene in the firm’s management through the board of directors (Williamson, 1988). Returns to common shareholders are essentially the residual value of the firm, resulting in no appropriation discrepancy (Williamson, 1988). In sharp contrast with Quadrant I, Quadrant III illustrates a condition where both intervention barriers and appropriation discrepancy are high. Corporate bonds fit well in this cell. Holders of corporate bonds have no access to the firm’s proprietary information (Boot, 2000; David et al., 2008; Degryse and Ongena, 2001). Their benefits are predetermined interest payments, which are isolated from the firm’s residual value that belongs to common shareholders (Williamson, 1988). Following prior research (Boot, 2000; David et al., 2008; Degryse and Ongena, 2001), we categorize corporate bonds as transactional debt. Between common equity and transactional debt, some financing instruments exhibit mixed levels of intervention barriers and appropriation discrepancy. Quadrant II specifies high intervention barriers and low appropriation discrepancy. Convertible securities such as convertible debt and preferred stock fit this quadrant well. Holders of such securities generally have an option to convert their securities into the firm’s common stock (Emanuel, 1983; Ingersoll, 1977). As such, their returns are indirectly aligned with the firm’s residual value, resulting in a low level of appropriation discrepancy. Holders of convertible debt have no access to the firm’s proprietary information and thus cannot intervene in the firms’ management (Williamson, 1988). Preferred stockholders generally have access to the firm’s proprietary information by attending shareholder meetings, but may not have voting rights. Even if their voting rights are not excluded, their influence on management tends to be limited given their minority status (Emanuel, 1983). Quadrant IV describes a situation in which intervention barriers are low while appropriation discrepancy is high. Recent research has acknowledged that private loan providers such as banks have access to the firm’s proprietary information and are able to intervene with the firm’s operations (Boot, 2000; David et al., 2008; Degryse and Ongena, 2001). They also secure funds by collateralizing the firm’s assets

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and specifying restrictive covenants that enable them to be kept informed of the firm’s financial condition (Titman and Wessels, 1988). Returns to private loans still carry preset interest terms, which are separated from the residual value of the firm. Following prior research (Boot, 2000; David et al., 2008; Degryse and Ongena, 2001), we label these loans as relational debt. 2.2. Hypothesis development Common equity does not force firms to pay regular interests. Reduced financial obligations are very important for firms that invest heavily in R&D. By its nature, R&D investment is risky (Miller and Bromiley, 1990). Innovation helps to achieve superior performance through developing new products/processes (Roper et al., 2008; Teece, 1986), which are subject to both market (Miller and Droge, 1986) and technology uncertainties (Song et al., 2005). Technology uncertainty makes it difficult to know whether R&D expenditures will lead to successful new products and/or processes (Miller and Bromiley, 1990). Market uncertainty makes it difficult to determine whether customers’ tastes and competitors’ actions will affect the value of new products and/or processes (Miller and Droge, 1986). As a result, firms with high levels of R&D expose their businesses to risk (Miller and Friesen, 1982). Reduced financial obligations achieved by financing through common equity may help such firms buffer their failures in R&D projects. Otherwise they may face the hazard of bankruptcy, which is the highest concern of most decision makers (March and Shapira, 1987). Meanwhile, R&D investment is resource-consuming. Researchers have pointed out that having slack resources is critical for R&D (Nohria and Gulati, 1996). Reduced financial obligations from common equity financing increase discretionary slack (Bourgeois, 1981), which may be supportive of R&D investment. Common stockholders’ benefits are the residual value of the firm (Williamson, 1988), which may be improved through R&D investment. R&D investment helps build firm-specific resources and capabilities (Helfat, 1994, 1997), which are strategic in nature and thus can lead to performance superiority (Ravenscraft and Scherer, 1982; Zahra and Covin, 1995). A by-product of R&D investment is absorptive capacity, which helps to identify, assimilate, and commercialize new information and knowledge (Cohen and Levinthal, 1990). Firms with absorptive capacity may be more efficient and effective in further innovation, resulting in superior performance (Cohen and Levinthal, 1990). Although risks involved in R&D may expose holders of common stock to losses, they are able to mitigate this unsystematic risk through portfolio selection (Bettis, 1983). The foregoing discussion suggests that common equity is an ideal financing instrument for both the R&D intensive firm and its common stock investors. Hypothesis 1. There is a positive relationship between R&D investment and financing through common equity. R&D investment is generally resource consuming (Nohria and Gulati, 1996). As such, a firm with a high level of R&D investment may need to reduce other financial obligations so as to mitigate its risk of financial distress. Because interest expense can represent a major financial obligation, we posit that R&D-intensive firms may try to limit the use of debt. Although R&D investment may lead to superior financial performance (Ravenscraft and Scherer, 1982; Zahra and Covin, 1995), which can generate future cash flows that make debt financing relatively cheaper, firms generally place more emphasis on factors that threaten their survival (March and Shapira, 1987). Therefore, from a firm’s perspective, a high level of R&D investment may force it to reduce its reliance on debt financing. TCE suggests that asset-specificity of R&D investment may also hinder firms’ access to debt financing (Williamson, 1988). R&D

projects are often based on existing assets such as product lines, engineering facilities, and managerial resources (Helfat, 1994). Because such assets are likely to be immobile and firm-specific (Barney, 1991; Penrose, 1959), firms with related assets are better positioned to pursue particular R&D projects. Furthermore, R&D investment involves particular organizational processes and routines (Helfat, 1994), which are also firm specific (Teece et al., 1997). By definition, firm-specific assets have utility only for the firm (Balakrishnan and Fox, 1993), and thus have lower resale value than do general assets in the event of liquidation or reorganization (Williamson, 1988). To seek protection against these risks, debt investors may select firms with more general assets (Titman and Wessels, 1988), require higher interest premiums for firms that have more specific assets (Balakrishnan and Fox, 1993; O’Brien, 2003), or refuse to lend to firms with high levels of firm-specific assets that cannot serve as collateral (Stiglitz and Weiss, 1981). As a result, firms with more specific assets will have difficulty securing debt, suggesting a negative relationship between R&D investment and debt financing (Balakrishnan and Fox, 1993; Vicente-Lorente, 2001). This TCE argument assumes intervention barriers and appropriation discrepancy between the firm and its debt investors (Williamson, 1988). If debt holders can intervene in the conduct of management, they would be able to impede risky decisions or at least avoid risky firms (Stiglitz and Weiss, 1981). If debt holders own the firm’s residual value as common shareholders do, they would have an incentive to accept risk if the firm is expected to generate large, positive returns. As discussed previously, transactional debt exhibits high levels of intervention barriers and appropriation discrepancy between capital providers and the firm (Boot, 2000; David et al., 2008; Degryse and Ongena, 2001). Thus, the TCE argument applies well to transactional debt, suggesting a negative effect of R&D investment on the use of transactional debt to raise capital. Hypothesis 2. There is a negative relationship between R&D investment and financing through transactional debt. Similar to transactional debt, convertible securities also exhibit a high level of intervention barriers. Holders of convertible debt have limited access to the firm’s proprietary information and thus cannot intervene in the conduct of management (Williamson, 1988). Although holders of preferred stocks may have access to the firm’s proprietary information, they cannot significantly intervene in management due to their limited voting rights and power (Emanuel, 1983). Thus, holders of convertible securities are unlikely able to impede risky decisions or avoid risky firms. In the event of liquidation or reorganization, however, they can claim the firm’s liquidated assets prior to common shareholders. As such, they may have an aversion to specific assets that have lower resale value (Williamson, 1988), suggesting a negative relationship between R&D investment and financing through convertible securities. This argument, however, may not hold for firms with very high R&D investment. Besides preset interests or dividends (Ingersoll, 1977; Merton, 1974), holders of convertible securities possess an option that allows them to exchange their securities for the firm’s common stock. Exercising this option, holders of convertible securities become common shareholders, resulting in the elimination of appropriation discrepancy. The value of this option essentially depends on the value of common stock (Emanuel, 1983), which, as discussed previously, has a close association with R&D investment. As such, for firms that invest heavily in R&D, the value of their convertible securities is likely to be high and thus convertible securities may be attractive to capital providers. For such firms, financing through convertible securities is also valuable given that preset financial obligations of convertible securities are lower than those of ordinary debt (Ingersoll, 1977). Thus, the relationship between R&D investment and financing through con-

T. Wang, S. Thornhill / Research Policy 39 (2010) 1148–1159

vertible securities may become positive when R&D investment is high. Hypothesis 3. There is a U-shaped relationship between R&D investment and financing through convertible securities. Relational debt providers may prefer investing in firms that have a certain level of R&D investment. Returns of relational debt providers depend on continuous lending to successful firms (Boot, 2000; Degryse and Ongena, 2001; Stiglitz and Weiss, 1981). The “innovate or die” dilemma forces firms to emphasize innovation (Eisenhardt and Martin, 2000; Teece et al., 1997). Otherwise, they may be trapped in their own products, markets, and routines, losing competitive advantages to competitors (Ahuja and Lampert, 2001). Therefore, relational debt providers may want to avoid firms that do not innovate at all. Furthermore, firms that emphasize R&D investment need to maintain financing flexibility, which can be realized, at least partially, through relational debt financing (David et al., 2008). From this perspective, R&D investment creates opportunities for relational debt providers, and thus they may seek to invest in firms that emphasize R&D. However, a very high level of R&D investment may be of concern to relational debt providers. As discussed earlier, innovation is a risk-taking process (Miller and Bromiley, 1990). If a firm has a very high level of R&D investment, its business may be at high risk, resulting in a greater chance of fatal losses (Miller and Friesen, 1982). Although private loan providers may have access to the firm’s proprietary information (Boot, 2000; David et al., 2008; Degryse and Ongena, 2001), they are still outsiders of the firm (Williamson, 1988). A high level of R&D investment suggests that the firm has much tacit knowledge, which is often opaque to outsiders (Grant, 1996). As a result, relational debt providers are unlikely to know exactly what is going on inside the firm, and thus cannot successfully intervene in the conduct of management (Stiglitz and Weiss, 1981). Under such a situation, TCE’s assumption on intervention barriers stands again, suggesting that relational debt providers may tend to avoid too much R&D investment. Further, relational debts are often secured by collateral assets, which R&D intangibles clearly are not (Stiglitz and Weiss, 1981). The preceding suggests that very low or very high levels of R&D investment may constrain the access to relational debt. Hypothesis 4. There is an inverted U-shaped relationship between R&D investment and financing through relational debt. 3. Data and methodology 3.1. Data Data were collected from the Industrial Annual tape and the Segment tape of the COMPUSTAT North America dataset. We focused on the petroleum industry (Standard Industry Classification – SIC 29) for several reasons. First, our major arguments are based on the firm-specificity attribute of R&D investment. To extract additional oil from existing reservoirs, oil companies need to pursue R&D on geological features of their reservoirs. Such R&D investment is likely to be firm-specific since different reservoirs differ in geological features (Helfat, 1994). Second, this study links R&D to financing, which also depends on the availability of internal capital (Myers and Majluf, 1984; Shyam-Sunder and Myers, 1999). Fluctuation of oil prices provides an ideal context to examine their relationships (Helfat, 1997). Within the petroleum industry, we selected all the firms that had total assets exceeding 100 million U.S. dollars during the period 1976–2005. In the COMPUSTAT tape, smaller firms are more likely

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to have missing values and thus are less representative of the population (Hitt et al., 1997). Furthermore, smaller firms may only rely on a particular type of financing (Wright et al., 2006), but we attempt to investigate all the four types of financing instruments (Fig. 1). We excluded firm-year observations before 1975 because the oil crisis in the early 1970s may have resulted in irrational innovation decisions and financing choices. Previous research found that the effect of R&D investment may be sustainable (Ravenscraft and Scherer, 1982; Zahra and Covin, 1995). As such, it is necessary to examine the effects of R&D investment on financing choices over a period of time. In this study, we lagged R&D intensity up to four years, and our longitudinal technique requires at least two observations per firm. For these reasons, we only included firms that have more than five years of consecutive observations. The final sample contains 39 large firms with 457 firm-year observations. As can be seen in Table 1, the majority of these firms focused on the petroleum refining business. They were relatively larger than those that had primary businesses in the other two subindustries: asphalt paying and miscellaneous products. Generally speaking, firms in this sample used all the four types of financing instruments (Hovakimian et al., 2001), suggesting the importance of a comprehensive view on studying financing choices. 3.2. Measures Dependent variables. The Industrial Annual tape of COMPUSTAT provides a data item on common stock. As defined previously, transactional debt refers to corporate bonds (Boot, 2000; David et al., 2008; Degryse and Ongena, 2001), including notes, subordinates, and debentures. To measure transactional debt, we summed these bonds. We measured relational debt using another item that includes bank loans, commercial loans, and other debt that cannot be specified as bonds (Boot, 2000; Degryse and Ongena, 2001). Convertible securities were measured by summing convertible debt and preferred stock. After obtaining common stock, transactional debt, relational debt, and convertible securities, we calculated their ratios to the sum of long-term debt and book value equity (Titman and Wessels, 1988; Vicente-Lorente, 2001).3 R&D intensity. Following prior research (Balakrishnan and Fox, 1993; Titman and Wessels, 1988), we measured R&D intensity by dividing R&D expenditures by net sales. To estimate the U-shaped and inverted U-shaped curves, we constructed the squared term of R&D intensity by means of centering, which may help reduce multicollinearity (Cohen et al., 2003). Control variables. Some industrial, macro, and organizational factors affect financing decisions and thus should be controlled (Balakrishnan and Fox, 1993; O’Brien, 2003; Titman and Wessels, 1988). The 39 firms in our sample primarily belonged to three subindustries. We used two dummies to control for the differences in the three segments. Macro-level factors such as changes in financial markets, interest rates, and business laws may influence financing decisions (Balakrishnan and Fox, 1993). As a result, firms may prefer different financing choices in different years (Baker and Wurgler, 2002). We used 29 dummies to control for the differences in the 30 years. Firm level controls include size, growth rate, non-debt tax shield, collateral assets, profitability, current debt ratio, internal funding, product diversification, and ownership concentration. These variables were extensively studied in prior research (Balakrishnan and Fox, 1993; Barclay and Smith, 2005; Barton and

3 We also calculated their ratios to the sum of long-term debt and market value equity. We found book and market value equity generated qualitatively identical results.

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T. Wang, S. Thornhill / Research Policy 39 (2010) 1148–1159

Table 1 Description of the samplea . Mean score

SIC 2911: petroleum refining (32 firms and 372 observations)

SIC 2950: asphalt paving and roofing (5 firms and 40 observations)

SIC2990: miscellaneous products (2 firms and 45 observations)

Total sample: (39 firms and 457 observations)

Total assets Total sales R&D investment Common stock

31,316 36,188 175 1,538

850 813 9 12

943 1,036 50 74

25,659 29,630 149 1,260

42 1,275 993

60 205 26

0 79 49

39 1,064 816

54 52

2 3

0 0

44 43

1,654

140

38

1,362

Transactional debt Subordinates Notes Debentures Convertible securities Preferred stock Convertible debt Relational debt a

Unit: USD (million).

Gordon, 1988; Kochhar and Hitt, 1998; O’Brien, 2003; Titman and Wessels, 1988). Although all the firms in our sample are large, differences in market power (including financial markets) may still exist as a function of size. We measured firm size by the natural log of net sales (Titman and Wessels, 1988). Firms with high growth rates tend to maintain investment flexibility (Balakrishnan and Fox, 1993; Titman and Wessels, 1988). We measured growth rate by (Salest − Salest−1 )/Salest−1 , where Salest and Salest−1 are the firm’s sales in year t and t−1, respectively. Debt can be used to avoid tax, and thus non-debt tax shield should be controlled (Balakrishnan and Fox, 1993; Titman and Wessels, 1988). We used the ratio of depreciation and amortization to total assets to proxy non-debt tax shield. A firm’s collateral assets may influence its access to debt capital (Titman and Wessels, 1988). Generally speaking, debt providers such as banks often collateralize the firm’s assets before providing it with loans (Stiglitz and Weiss, 1981). We had two collateral indicators: one was the ratio of net plants, property, and equipment (PPE) to total assets and the other was the ratio of inventory to total assets. Both PPE and inventory were used to proxy collateral in prior research (O’Brien, 2003; Titman and Wessels, 1988; Vicente-Lorente, 2001). According to the leverage signalling model (Klein et al., 2002), firms with high expected profitability are likely to raise high levels of debt so as to differentiate themselves from low quality firms (Leland and Pyle, 1977; Ross, 1977). Significant debt exposure is associated with potential financial distress (Wruck, 1990), and thus only firms with substantial expected cash flows can be highly leveraged. We measured profitability by dividing operating income before depreciation by total sales (i.e., return on sales – ROS) (Titman and Wessels, 1988).4 According to the trade-off theory, firms maintain optimal financial leverage (Leary and Roberts, 2005). Therefore, how much debt firms incur is determined by their current debt ratios, which we measured by dividing total long-term debt by total assets (Titman and Wessels, 1988). Information asymmetry between firms and capital providers forces firms to use internal funds over external capital (Myers and Majluf, 1984; Shyam-Sunder and Myers, 1999). This pecking-order theory suggests that firms will not raise external capital unless their internal funding is not enough. We measured internal funding by dividing retained earnings by total assets. Diversification, which can be considered a type of internal portfolio management, may reduce a firm’s risk of default (Amihud and

4

We found using return on assets (ROA) generated similar results.

Lev, 1981) and thus affects its financing choices (Kochhar and Hitt, 1998). Although the 39 firms in our sample primarily belonged to one of the three sub-industries, they had businesses in different segments. We collected their business segment information from the Segment tape of the COMPUSTAT, and then measured product diversification by counting their number of business segments. This count measure has been widely used in diversification research (Hoskisson et al., 1993). We also compared this measure with the entropy (Hoskisson et al., 1993) and the refined entropy indices (Raghunathan, 1995), and we found they generated similar results. We finally used the count measure given that it is more conceptually straightforward. A firm’s financing choices may depend on its ownership structure (Kochhar, 1996; Kochhar and Hitt, 1998). While making financing decisions, firms tend to negotiate with influential owners (or their representatives on the board) who possess substantial portions of shares (Kochhar, 1996). To control for this effect, we added ownership concentration, which was measured by number of shares outstanding/number of shareholders. The higher this ratio is, the higher the likelihood that the firm has influential owners. 3.3. Model selection We used general least squares (GLS) for longitudinal data and specified panel-specific first-order autocorrelations to test our hypotheses. This specification allows different firms to have different autocorrelation patterns, resulting in reliable results. In the GLS model, we lagged all the control variables by one year to address causality. We lagged R&D intensity by1–4 years (RD1, RD2, RD3, and RD4) to examine how the effects of R&D investment on firm financing change over time. We did not lag more years due to sample size restrictions. 4. Empirical evidence Descriptive statistics and bivariate correlations of all the variables are shown in Table 2. Transactional debt and long-term debt ratio were highly correlated (r = .79), suggesting transactional debt is an important element of long-term debt. R&D intensity in different years tends to highly associate with each other, suggesting these petroleum firms had consistent R&D investment over years (Helfat, 1994, 1997). Hierarchical regressions were used to estimate the effects of R&D investment on financing choices. In Table 3, the ratio of common stock is the dependent variable of the five GLS regression models. Model 1 includes all the control variables. In Models 2–5,

Table 2 Descriptive statistics and bivariate correlationsa,b,c . Mean

S.D.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

.09 .22 .01 .09 .19 .01 .00 .01 .00 .01 .00 .01 .00 9.22 .09 .06 .57 .10 .15 .34 3.77 5.72

.08 .19 .02 .11 .14 .01 .00 .01 .00 .01 .00 .01 .00 1.72 .19 .02 .15 .06 .07 .15 1.74 9.42

−.23 −.07 −.11 −.26 .06 .02 .06 .01 .06 .01 .06 .01 −.07 −.06 .04 .14 −.02 .16 .00 .05 .12

.17 −.08 .79 −.12 −.13 −.11 −.12 −.11 −.11 −.10 −.11 −.28 −.04 −.10 .02 .01 −.15 −.59 −.14 .05

.07 .14 −.14 −.10 −.14 −.10 −.14 −.10 −.14 −.10 −.00 −.05 −.06 .03 .11 −.24 −.19 −.15 −.16

.31 −.01 −.11 −.02 −.12 −.02 −.13 −.03 −.11 .02 −.10 −.02 −.20 −.00 −.08 −.34 .06 −.15

−.13 −.17 −.13 −.18 −.13 −.18 −.12 −.17 −.26 −.02 −.11 −.05 .01 −.09 −.73 −.13 −.01

.91 .99 .90 .98 .88 .97 .87 −.48 −.11 −.22 −.54 .39 .01 .33 −.30 .05

.90 .93 .89 .90 .89 .91 −.43 −.05 −.21 −.45 .37 −.04 .35 −.29 .09

.91 .99 .90 .98 .89 −.49 −.08 −.22 −.55 .38 .01 .33 −.30 .06

.90 .94 .90 .91 −.44 −.04 −.21 −.45 .36 −.04 .36 −.30 .08

.91 .99 .91 −.49 −.06 −.23 −.55 .39 .00 .32 −.30 .07

.90 .93 −.43 −.03 −.21 −.45 .36 −.03 .35 −.30 .08

.92 −.50 −.06 −.23 −.55 .39 −.01 .31 −.31 .07

−.44 −.04 −.22 −.46 .36 −.04 .34 −.29 .09

.11 .45 .51 −.60 .21 .13 .47 −.13

−.15 −.04 −.05 .18 −.02 .09 .09

.60 −.43 .29 −.03 .35 −.06

−.56 .32 −.04 .32 .06

−.39 .07 −.42 −.10

.16 .26 .13

.00 .02

−.05

T. Wang, S. Thornhill / Research Policy 39 (2010) 1148–1159

1. Common stock 2. Transactional debt 3. Convertible securities 4. Relational debt 5. Current debt ratio 6. RD1 7. RD12 8. RD2 9. RD22 10. RD3 11. RD32 12. RD4 13. RD42 14. Firm size 15. Growth rate 16. Non-debt shield 17. Collateral (PPE) 18. Collateral (Inventory) 19. Return on sales (ROS) 20. Retained earnings 21. Diversification 22. Ownership concentration a

N = 457. Correlation coefficients ≥ .09 are significant at p < .05 (two-tailed tests). c The unit of “Firm size” (natural log of net sales) is USD (million), the unit of “Ownership concentration” is the number of shares (thousand), the unit of “Diversification” is the number business segments, and the units of all the other variables are ratios. b

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Table 3 GLS of the ratio of common stocka,b .

Constant Segment 1 Segment 2 Current debt ratio Firm size Growth rate Non-debt tax shield Collateral (PPE) Collateral (inventory) ROS Retained earnings Diversification Ownership concentration RD1 RD2 RD3 RD4 Wald test (1 D.F.) a

Model 1

Model 2

Model 3

Model 4

Model 5

.21*** .07* −.01 −.11*** −.02*** −.00 −.16 .05† .02 .20*** −.11*** .00 −.00

.17*** .14*** .02 −.11*** −.02*** .00 −.21 .05† .07 .21*** −.13*** .00 −.00 1.96**

.17*** .14*** .03 −.11*** −.02*** −.00 −.23 .04 .05 .20*** −.12*** .00 .00

.18*** .11*** .01 −.12*** −.02*** −.00 −.27† .05* .02 .24*** −.14*** .00 −.00

.18*** .12*** .04 −.12*** −.02*** −.00 −.30* .05* .00 .23*** −.13*** .00 −.00

1.75** 1.45* NA

9.39**

7.19**

4.86*

1.42* 5.03*

29 Year dummies were included but not reported to save space. p < .10, *p < .05, **p < .01, ***p < .001 (two-tailed tests).

b †

RD1, RD2, RD3, and RD4 were added, respectively. Wald test suggests that Models 2–5 fitted the data significantly better than did Model 1. R&D intensity had positive coefficients in Models 2–5, providing supportive evidence for Hypothesis 1, which posits that there is a positive relationship between R&D investment and the use of common equity to raise capital. In Table 4, the ratio of transactional debt is the dependent variable of the five GLS models. Model 6 includes all the control variables. In Models 7–10, RD1, RD2, RD3, and RD4 were added one by one. As can be seen in Table 4, these R&D intensity terms exhibited positive coefficients, but only the coefficient of RD4 was marginally significant (p < .10). Therefore, Hypothesis 2, which suggests that there is a negative relationship between R&D investment and the use of transactional debt to raise capital, was not supported.5 Hypothesis 2 is based on the TCE argument that bond investors do not like firm-specific assets (Williamson, 1988) and thus require higher interest premiums for firms with higher R&D investment (Balakrishnan and Fox, 1993; O’Brien, 2003; VicenteLorente, 2001). A possible explanation of findings here is that R&D investment helps firms achieve superior performance (Ravenscraft and Scherer, 1982; Zahra and Covin, 1995), which translates into rich cash flows that make debt financing relatively cheaper.6 As a result, this positive effect may offset the negative effect predicted by TCE. In Table 5, the ratio of convertible securities is the dependent variable of the five GLS models. Model 11 includes all the control variables. In Models 12–15, the simple and the squared terms of RD1, RD2, RD3, and RD4 were added, respectively. Wald test suggests that Models 12, 13, and 15 fitted the data better than did the nested Model 11. In Models 12, 13 and 15, the coefficients of R&D intensity were negative, and those of the squared terms were positive. This pattern is consistent with a U-shaped curve. Given the high correlations between the simple and squared terms of R&D, it is possible that multicollinearity deflated or inflated the regression coefficients and/or their standard errors (O’Brien, 2007), resulting in biased results. To solve this potential issue, we conducted sub-group analyses by dividing our sample into two groups. Group A includes all the observations with R&D investment

5 We also explored possible curvilinear relationships by adding the squared terms of RD1, RD2, RD3, and RD4. We found the coefficients of the squared terms were not statistically significant. 6 We thank an anonymous reviewer for raising this point.

lower than its mean, and Group B includes all the observations with R&D investment higher than its mean. A negative relationship in Group A and a positive relationship in Group B will demonstrate the presence of a U-shaped curve. As reported in Table 6, this inverted U-shaped pattern was supported in all the models, and both the negative and the positive coefficients were significant in Models 18 and 20. In Table 7, the ratio of relational debt is the dependent variable of the five GLS models. Model 21 includes all the control variables. In Models 22–25, the simple and the squared terms of RD1, RD2, RD3, and RD4 were added. In Models 22–24, the simple terms exhibited positive and the squared terms exhibited negative coefficients. This pattern supported Hypothesis 4, which suggests that there is an inverted U-shaped relationship between R&D investment and the use of relational debt financing. Multicollinearity may bias regression results (O’Brien, 2007). We conducted subgroup analyses by dividing our sample into two groups at the mean level of R&D intensity as we did for the ratio of convertible securities. In Models 27–30 (Table 8), the regression coefficients of R&D intensity were positive in Group A and were negative in Group B. All the coefficients were strongly significant, providing clear evidence for Hypothesis 4. Given the growing concern of limited oil resources, the role that R&D plays in extracting additional oil has become more and more important (Helfat, 1994, 1997). We examined whether and how the effects of R&D investment on financing choices change during the 30 years by splitting our sample at the midpoint of our time series data and replicating our analyses for the periods of 1976–1990 and 1991–2005. As summarized in Table 97 , the relationships between R&D intensity and the ratios of transactional debt, convertible securities, and relational debt were stable over the two periods, while the association between R&D intensity and the ratio of common equity was significant only in the second period (1991–2005). The early 1990s were noteworthy for not only the Gulf War, but also for a change in the U.S. production-import balance such that imported oil overtook domestic production (U.S. Energy Information Administration, 2009). Under such a situation, the role of R&D in extracting additional oil (Helfat, 1994, 1997) became an important investment criterion for common shareholders, and thus the effect of R&D intensity on common equity financing became significant.

7

Detailed results are available from the corresponding author upon request.

T. Wang, S. Thornhill / Research Policy 39 (2010) 1148–1159

1155

Table 4 GLS of the ratio of transactional debta,b .

Constant Segment 1 Segment 2 Current debt ratio Firm size Growth rate Non-debt tax shield Collateral (PPE) Collateral (inventory) ROS Retained earnings Diversification Ownership concentration RD1 RD2 RD3 RD4 Wald test (1 D.F.) a

Model 6

Model 7

Model 8

Model 9

Model 10

.36*** .07 −.04 .82*** −.03*** −.00 −.59 .15* −.02 −.10 −.06 .00 −.002*

.34*** .09 −.03 .83*** −.03*** −.00 −.61 .17* −.01 −.10 −.05 .00 −.002* .52

.33*** .10 −.02 .84*** −.03*** −.00 −.62 .17* −.02 −.10 −.05 .00 −.002*

.29** .14* −.02 .84*** −.03*** −.00 −.66† .18* −.03 −.11 −.04 .00 −.002*

.28** .16* .03 .84*** −.03*** −.00 −.69† .19** −.05 −.11 −.04 .00 −.002*

.70 1.87 NA

.21

.35

2.46

2.24† 3.51†

29 Year dummies were included but not reported to save space. p < .10, *p < .05, **p < .01, ***p < .001 (two-tailed tests).

b †

Table 5 GLS of the ratio of convertible securitiesa,b .

Constant Segment 1 Segment 2 Current debt ratio Firm size Growth rate Non-debt tax shield Collateral (PPE) Collateral (inventory) ROS Retained earnings Diversification Ownership concentration RD1 RD12 RD2 RD22 RD3 RD32 RD4 RD42 Wald test (2 D.F.) a

Model 11

Model 12

Model 13

−.05** −.01 .03*** −.03* .004* −.00 −.04 .03† .07** .02 −.01 −.00 −.00

−.03 −.04* .01 −.03* .006** −.00 −.05 .02 .09** .03 −.01 −.00 −.00 −.89* 4.84

−.01 −.04* .01 −.03† .004* −.00 −.05 .02 .07** .02† −.00 −.00 −.00

Model 14

Model 15

−.03 −.02 .02 −.03* .004* −.00 −.05 .03† .07** .02 −.01 −.00 −.00

−.00 −.05** −.00 −.02† .004** −.00 −.04 .02 .09*** .02 −.01 −.00 −.00

−.76* 1.25 −.26 −2.84

NA

5.82†

6.50*

2.25

−1.31*** 8.80† 14.64***

29 Year dummies were included but not reported to save space. p < .10, *p < .05, **p < .01, ***p < .001 (two-tailed tests).

b †

Some control variables produced interesting results. Firm size exhibited strong negative associations with the ratios of common stock (Table 3) and transactional debt (Table 4), which are major sources of external capital. According to the pecking order theory, firms prefer using internal funds over external capital (Myers and Majluf, 1984; Shyam-Sunder and Myers, 1999). In the petroleum industry, larger firms generally possess more reservoirs, which are limited in nature and thus may be a source of competitive advantage (Barney, 1991). As such, large firms may have relatively larger sales (our measure of firm size) and obtain abundant internal funds, which enable them to finance internally. In effect, this explanation was supported by the positive relationships between firm size, return on sales, and retained earnings (Table 2). Collateral assets measured by net plant, property, and equipment (PPE) exhibited a positive relationship with the ratio of transactional debt (Table 4) and a negative association with the ratio of relational debt (Table 7). Transactional debt providers do not have access to the firm’s proprietary information (Williamson, 1988). As a result, they have to pay much attention to the firm’s

asset conditions and net PPE may be an important investment criterion. The negative relationship between net PPE and relational debt is quite surprising, given that private loan providers often require collateral for lending (Boot, 2000). A possible explanation is that in the petroleum industry, PPE may be firm-specific and therefore poorly suited to serve as collateral assets. Facilities used in reservoirs may not be redeployed somewhere else because each reservoir has its own geographical features (Helfat, 1994). Our finding that PPE exhibited opposite relationships with transactional and relational debts further supports the view that these two types of debt are different in nature (Boot, 2000). The negative relationship between retained earnings and common equity financing (Table 3) seems to support the pecking order theory, which posits that firms prefer internal fund over external capital (Myers and Majluf, 1984; Shyam-Sunder and Myers, 1999). ROS exhibited a strong positive effect on common equity financing (Table 3). A possible explanation is that common stock investors would like to invest in firms that have high residual value. To a large extent, ROS captures how much residual value a firm

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T. Wang, S. Thornhill / Research Policy 39 (2010) 1148–1159

Table 6 GLS of the ratio of convertible securities: subgroup analysisa,b . Model 16

Constant Segment 1 Segment 2 Current debt ratio Firm size Growth rate Non-debt tax shield Collateral (PPE) Collateral (inventory) ROS Retained earnings Diversification Ownership concentration RD1 RD2 RD3 RD4 Wald test (1 D.F.) a

Model 17

Model 18

Model 19

Model 20

Group A

Group B

Group A

Group B

Group A

Group B

Group A

Group B

Group A

Group B

−.13*** NAc .04*** −.05* .01** −.00 −.05 .06** .14*** .06** −.01 −.00 −.00

−.00 −.01 .01* −.02* .00 .01† .03 .01 −.00 −.03 −.02* −.00 −.00

−.14*** NAc .06*** −.05* .01*** −.00 −.08 .06** .13*** .07** −.01 −.00 −.00 −1.81*

−.00 −.00 .02* −.02* .00 .01* .03 .02† −.01 −.04† −.02* −.00 −.00 .15

−.13*** NAc .05*** −.05* .01*** .00 −.07 .06** .14*** .08*** −.01 −.00 −.00

−.01 .01 .02*** −.03** .00 .01* .03 .02† −.00 −.02 −.02** −.00 −.00

−.14*** NAc .05*** −.05** .01*** −.00 −.05 .06** .13*** .06* −.01 −.00 −.00

−.01 .01 .02** −.03** .00 .01† .05 .01 −.00 −.02 −.02** −.00 −.00

−.12*** NAc .04*** −.05** .01*** −.00 −.04 .06** .16*** .07** −.02 −.00 −.00

−.01 .02 .03*** −.02** .00 .01† .04 .02 −.01 −.02 −.02** −.00 −.00

−1.90*

.42* −1.95*** 14.43***

.55** 9.18**

−.30 NA

NA

5.74*

.77

7.33*

5.93*

.28

.35* 3.78*

29 Year dummies were included but not reported to save space. p < .10, *p < .05, **p < .01, ***p < .001 (two-tailed tests). Dropped due to collinearity.

b † c

Table 7 GLS of the ratio of relational debta,b .

Constant Segment 1 Segment 2 Current debt ratio Firm size Growth rate Non-debt tax shield Collateral (PPE) Collateral (inventory) ROS Retained earnings Diversification Ownership concentration RD1 RD12 RD2 RD22 RD3 RD32 RD4 RD42 Wald test (2 D.F.) a b

Model 21

Model 22

.04 .05 .08* .15** .01 −.01 .25 −.24*** −.00 .05 −.05 .00 .00

.03 .09† .11* .15** .00 −.01 .17 −.22*** −.03 .04 −.04 .00 .00 .99 −1.72

Model 23

Model 24

.01 .09 .11* .13* .00 −.02 .25 −.21*** −.02 .01 −.05 .00 .00

.02 .09 .11* .13* .00 −.02 .33 −.23*** −.03 .02 −.05 .00 .00

Model 25 .00 .11* .13** .15* .00 −.01 .18 −.23*** −.04 .04 −.04 .00 .00

2.70* −52.57** 2.17 −34.43†

NA

.98

8.72*

3.97

1.11 11.51 2.27

29 Year dummies were included but not reported to save space. † p < .10, *p < .05, **p < .01, ***p < .001 (two-tailed tests).

may accumulate over time, and thus is a major factor for common stock investors to make investment decisions. In contrast, returns of transactional and relational debt are predetermined interests (Williamson, 1988). As long as the firm pays its principal and interest, debt investors do not pay much attention to the residual value of the firm. This may explain why ROS did not have significant coefficients on the ratios of transactional debt (Table 4) and relational debt (Table 7). 5. Discussion and conclusion 5.1. Contributions We employ a comprehensive perspective to examine financing through different instruments. Empirical results based on data from the petroleum industry suggest that R&D investment has a positive effect on the use of common stock, a U-shaped effect on the use

of convertible securities, and an inverted U-shaped effect on the use of relational debt to raise capital. We also noticed that these effects are sustained over several years, supporting the long-term aspect of R&D investment (Ravenscraft and Scherer, 1982; Zahra and Covin, 1995). The first implication of these findings is that strategic resources built through R&D investment also affect firms’ access to different sources of financial capital. Firms are comprised of different resources (Penrose, 1959). Some resources are firm-specific and inherently strategic (Barney, 1991; Dierickx and Cool, 1989; Wernerfelt, 1984). Since strategic resources are unlikely to be available in the market, firms need to develop such resources through strategic investment (e.g., R&D). Although financial capital may not be strategic in nature given that it is a general asset, access to it may depend on a firm’s strategic characteristics (David et al., 2008; Kaivanto and Stoneman, 2007; O’Brien, 2003; Wright et al., 2006). As such, there is value in developing a deeper understanding of

T. Wang, S. Thornhill / Research Policy 39 (2010) 1148–1159

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Table 8 GLS of the ratio of relational debt: subgroup analysisa,b . Model 26

Constant Segment 1 Segment 2 Current debt ratio Firm size Growth rate Non-debt tax shield Collateral (PPE) Collateral (inventory) ROS Retained earnings Diversification Ownership concentration RD1 RD2 RD3 RD4 Wald test (1 D.F.) a

Model 27

Model 28

Group A

Group B

Group A

Group B

Group A

.06 NAc .02 .08 .01 .00 .36 −.18** .21* .06 −.04 .00 −.00

−.31 .14 .12† .49*** .03† −.20*** .1.63† −.68*** .30 −.06 .24† .01 .005*

.12 NAc .01 .06 .00 .01 .40 −.16** .17† −.02 −.04 −.00 −.00 7.62***

−.01 −.16 −.15* .48*** .04* −.23*** .36 −.62*** .45† −.05 .17 .02* .004† −6.96**

.11 NAc .01 .05 .00 −.00 .38 −.17** .16 −.03 −.04 .00 −.00 5.37**

NA

NA

10.57**

9.63**

6.01*

Model 29 Group B .02 −.07 −.13 .57*** .02 −.20*** .08 −.54*** .31 −.20 .20 .02† .00

Group A

Model 30 Group B

.09 NAc .01 .07 .00 −.00 .37 −.17** .13 −.02 −.02 .00 −.00

.06 −.28* −.25** .54*** .04** −.18** .06 −.52*** .43† −.19 .17 .02* .00

6.33***

−9.52***

12.54***

14.84***

Group A

Group B

.06 NAc .03 .06 .00 −.00 .30 −.17** .17 .00 −.02 .00 −.00

.10 −.30* −.25* .50*** .04* −.20** −13 −.49*** .41 −.19 .18 .02† .00

−5.48*

4.99*

5.97*** 12.62***

−9.95*** 14.75***

29 Year dummies were included but not reported to save space. p < .10, *p < .05, **p < .01, ***p < .001 (two-tailed tests). Dropped due to collinearity

b † c

how strategic investment such as R&D affects a firm’s financing choices (Balakrishnan and Fox, 1993; Barton and Gordon, 1988; Vicente-Lorente, 2001). Compared with prior studies that exclusively focused on the dyadic distinction between debt and equity financing (Balakrishnan and Fox, 1993; O’Brien, 2003; Titman and Wessels, 1988; Vicente-Lorente, 2001), we employ a comprehensive perspective by categorizing major financing instruments into four groups: common equity, convertible securities, transactional debt, and relational debt. This categorization provides refined distinctions between different financing instruments, which enable researchers to reveal detailed associations between strategic and financial factors. Further, our categorization is based on intervention barriers and appropriation discrepancy between capital providers and the firm. Since these two contingencies are major assumptions of TCE (Williamson, 1988), our categorization helps clarify the boundary of TCE in financing research. Another merit of this research lies in our incorporation of different theories. TCE has been widely used to investigate why and how R&D investment influences the choice between debt and equity financing (Balakrishnan and Fox, 1993; O’Brien, 2003; Titman and Wessels, 1988; Vicente-Lorente, 2001). Although TCE has provided an insightful view on the difference between debt and equity holders regarding their information and interest conflicts, TCE does not consider how firm success benefits debt holders. In this study, we incorporate the view that R&D investment helps firms build competitive advantage and achieve superior performance (Ravenscraft and Scherer, 1982; Zahra and Covin, 1995). As a result, we have provided a more comprehensive explanation on how R&D investment influences financing choices. The findings of this study also have managerial implications. In order to realize future performance superiority, firms

need to pursue R&D investment (Ravenscraft and Scherer, 1982; Zahra and Covin, 1995). R&D is resource consuming (Nohria and Gulati, 1996), and thus many firms need to raise external capital (Balakrishnan and Fox, 1993; Baldwin and Johnson, 1996). Our findings suggest that managers need to consider the consequences of R&D investment on financing choices. The positive relationship between R&D intensity and the ratio of common stock implies that if a firm has a high level of R&D investment, it may be appropriate for the firm to utilize common equity financing. Our results also suggest that firms have optimal R&D investment points regarding their access to relational debt. At their optimal R&D investment points, they can raise the highest level of relational debt to support their R&D expenditures. 5.2. Limitations and future research Our empirical results are based on the petroleum industry and our sampled firms were all publicly listed corporations. As such, our results may not be generalizeable to other firms in the industry (e.g., private or state-owned oil companies) and firms in other industries. We consider this as a limitation and encourage future research to replicate our study in other settings. Another limitation of this study is that we were unable to distinguish between different types of R&D investment. Prior research found that internally developed R&D projects and externally developed R&D projects have different effects on the use of debt (VicenteLorente, 2001). Internal R&D investment is likely to generate firm-specific assets and create information opacity to potential debt investors, and thus has a negative effect on debt financing. In contrast, external R&D investment does not generate firmspecific assets, and thus has no effect on access to debt capital (Vicente-Lorente, 2001). Future research may improve this study’s model by considering different types of R&D investment such as

Table 9 Summary of empirical results. Hypotheses

Overall (1976–2005)

Period 1 (1976–1990)

Period 2 (1991–2005)

H1: A positive relationship between R&D intensity and the ratio of common stock H2: A negative relationship between R&D intensity and the ratio of transaction debt H3: A U-shaped relationship between R&D intensity and the ratio of convertible securities H4: An inverted U-shaped relationship between R&D intensity and ratio of relational debt

Supported Not supported Supported Supported

Not supported Not supported Generally supported Supported

Supported Not supported Generally supported Supported

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internal vs. external, product vs. process, and incremental vs. radical. Researchers have pointed out that returns of R&D investment have a long-term property (Ravenscraft and Scherer, 1982; Zahra and Covin, 1995). R&D generates future profits, which will translate into cash flows, making debt financing relatively cheaper. Although R&D investment develops firm-specific assets (Helfat, 1997) that may intensify information asymmetry and intervention barriers (Williamson, 1988), R&D projects launched long time ago may have lost this property, at least for investors who have access to the firm’s proprietary information (e.g., shareholders and relational debt providers). These ideas suggest that the effects of R&D investment on different financing choices may change differently over time. In this study, we only lagged R&D intensity up to four years (due to sample size limitations). This four-year window may be too short given that R&D projects in the petroleum industry may have longer effects (Helfat, 1994, 1997). We encourage researchers with more comprehensive data to explore the patterns of the effects of R&D on different financial choices overtime. 5.3. Conclusion By categorizing financial instruments based on the degree of intervention barriers and appropriation discrepancy between capital providers and the firm, we provide a refined distinction between major financing instruments and clarify boundary conditions for TCE in explaining financing choices. By combining the perspectives of TCE and other lenses, we offer a more comprehensive mechanism on how R&D investment influences financing choices. Our empirical results suggest that R&D investment has a positive effect on the use of common stock, a U-shaped effect on the use of convertible securities, and an inverted U-shaped effect on the use of relational debt to raise capital. These findings add new evidence to the existing literature and provide meaningful implications for decision makers. Acknowledgements We thank June Cotte, Jijun Gao, Glenn Rowe, Natalie Slawinski, and Jianyun Tang for providing valuable feedback on early versions of this paper. We have substantially benefited from the detailed comments from two anonymous reviewers, and we are grateful for Ben Martin for his guidance and support during the review process. References Ahuja, G., Lampert, C.M., 2001. Entrepreneurship in the large corporation: A longitudinal study of how established firms create breakthrough inventions. Strategic Management Journal 22 (6/7), 521–543. Amihud, Y., Lev, B., 1981. Risk reduction as a managerial motive for conglomerate mergers. Bell Journal of Economics 12 (2), 605–617. Baker, M., Wurgler, J., 2002. Market timing and capital structure. Journal of Finance 57 (1), 1–32. Balakrishnan, S., Fox, I., 1993. Asset specificity, firm heterogeneity and capital structure. Strategic Management Journal 14 (1), 3–16. Baldwin, J.R., Johnson, J., 1996. Business strategies in more- and less-innovative firms in Canada. Research Policy 25 (5), 785–804. Barclay, M.J., Smith, C.W., 2005. The capital structure puzzle: The evidence revisited. Journal of Applied Corporate Finance 17 (1), 8–17. Barney, J.B., 1991. Firm resources and sustained competitive advantage. Journal of Management 17 (1), 99–120. Barton, S.L., Gordon, P.J., 1987. Corporate strategy: Useful perspective for the study of capital structure? Academy of Management Review 12 (1), 67–75. Barton, S.L., Gordon, P.J., 1988. Corporate strategy and capital structure. Strategic Management Journal 9 (6), 623–632. Bettis, R.A., 1983. Modern financial theory, corporate strategy and public policy: Three conundrums. Academy of Management Review 8 (3), 406–415. Boot, A.W.A., 2000. Relationship banking: What do we know? Journal of Financial Intermediation 9, 7–25.

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