Investment–cash flow sensitivities of restaurant firms

Abstract

Although some theories argued that investment decisions are irrelevant to financing decisions under the assumption of perfect market, investment decisions and capital structure seem interdependent in real-world circumstances. Further, the past literature also suggested a close relationship between internal cash flows and investment decisions, that is, investment–cash flow sensitivity (ICFS), but this issue has not been closely examined in the restaurant setting. Therefore, the current study first proposes to examine ICFS in the context of the restaurant industry. More importantly, this study also examines a moderating role of franchising to better explain ICFS, considering a major role of franchising in the restaurant industry, based on theories of pecking order, resource scarcity, and risk sharing. Findings of the current study deepens the understanding of ICFS via franchising, making meaningful contributions to not only to existing ICFS literature but also restaurant franchising literature.

Keywords

franchising investment–cash flow sensitivity moderating effect restaurant industry

Introduction

The topic of investment–cash flow sensitivity (ICFS) has attracted the attention of a number of researchers in the fields of corporate finance and financial economics (Alti, 2001; Bhagat et al., 2005; Cleary et al., 2007; Moyen, 2004). According to the foundations for modern finance theory, such as Modigliani and Miller’s Proposition III (1958) and Tobin’s Separation Theorem (1958), investment decisions are irrelevant to financing decisions under the assumption of a perfect market (Modigliani and Miller, 1958). However, the later literature generally suggested that investment decisions and capital structure appear to be interdependent in real-world circumstances, assuming an imperfect capital market (Jensen and Meckling, 1976; Myers and Majluf, 1984; Stiglitz and Weiss, 1981; Stowe et al., 1980). More specifically, information asymmetry may generate cost disadvantages stemming from external financing, potentially creating the “financing hierarchy” described by the pecking order theory (Fazzari et al., 1988; Myers and Majluf, 1984). Consequently, cost disparities occur among internal funds (or internal cash flows), new debt, and equity, favoring most in internal funds over the others. Due to this cost advantage, firms pay a close attention to their internal cash flows when investing in various projects, suggesting a close relationship between internal cash flows and investment decisions (Attig et al., 2012). This relationship is referred to as “ICFS” in the literature.

In the hospitality context, some empirical studies (e.g. Jang and Ryu, 2006; Jang and Kim, 2009) have suggested that the investment and general financing decisions of restaurant firms are relevant to each other based on canonical correlation analyses of the right- and left-hand sides of the balance sheet. However, since the relationship between firms’ internal cash flows and investment (i.e. ICFS) in the restaurant context has been rarely examined, an investigation on this topic should make meaningful contributions to the hospitality literature and the practice.

After seven consecutive years of growth, the US restaurant industry is still expected to report a 4.3% rise in sales for 2017 (Restaurant Industry Outlook, 2017). The US restaurant market is substantial, accounting for about 4% of total gross domestic product of the US economy in 2015 (Restaurant Industry Forecast, 2015). In addition, the industry presents unique features that differentiate itself from other industries, such as comparatively high leverage (Skalpe, 2003) and widely practiced franchising (Sun and Lee, 2013). Considering the significance and idiosyncratic features of the industry and also scant research on ICFS in the restaurant literature, the current study proposes to examine ICFS in the context of the US restaurant industry. This study first examines whether ICFS exists in the restaurant industry by investigating the main effect of internal cash flows on investment based on the argument of information asymmetry under the assumption of an imperfect capital market (Greenwald et al., 1984) and the pecking order theory (Myers and Majluf, 1984).

Second and more importantly, the current study further examines a moderating role of franchising to better explain ICFS. The restaurant industry implements the franchising the most in the US economy (Anwer, 2011; Michael and Combs, 2008), thus the franchising strategy plays a significant role in the restaurant business. Out of several major motivations for firms to franchise, this study relies on two major motivations for franchising to explain the moderating role of franchising on ICFS: resource scarcity and risk-sharing theories. According to the resource scarcity theory (Alone, 2001; Castogiovanni et al., 1993), we propose that restaurant firms with a high degree of franchising would depend more on internal cash flows for investments than other sources because this type of financial resources can be more easily accessible through the franchising system. Moreover, the risk-sharing theory (Dahlstrom and Nygaard, 1994) and the pecking order theory (Myers and Majluf, 1984) further support that heavily franchised restaurant firms would rely more on internal cash flows for their investment due to their more risk-averse tendencies.

The remainder of this study is organized as follows: The study next reviews relevant literature and develops the main hypothesis. The following section describes the data, variables, and the econometrics models. Then, the study reports the results of the analysis, discusses the results, and concludes with limitations and suggestions for future research.

Literature review and hypothesis development

ICFS and pecking order theory

ICFS can be defined as the elasticity of cash flows to investment, referring to changes in investment expenditures according to fluctuations in a firm’s internal cash flows (or funds). A seminal study on this topic, Fazzari et al. (1988) criticized Modigliani and Miller’s Propositions III for their unrealistic assumption of a perfect capital market and linked a firm’s investment to the premise of an imperfect capital market. Fazzari et al. (1988) postulated that asymmetric information exists between a firm and potential investors under imperfect capital markets, and as a result, external financing could lead to potential cost disadvantages.

Considering the information asymmetry between managers and outside investors and consequential cost differences among the financing sources, the corporate finance capital structure literature identifies a preference order in terms of firms’ three main financing sources: internal funds, debt, and external equity. A seminal paper in economics, Akerlof (1970), delineates earlier that the information asymmetry causes adverse selection problem in the market since the less-informed outside investors (i.e. buyers) are not necessarily able to differentiate between promising (i.e. peaches) and less-promising (i.e. lemons) investment opportunities, therefore would like to pay the equivalent price between the two types of opportunities. As a result, firms (sellers) tend to offer the less-promising investment opportunities to the market if the prices for both opportunities are same. Eventually, by learning from experience, outside investors are willing to pay less for any investment and sellers with better investment opportunities leave the market. Repeating this mechanism leads to market collapse.

Pecking order theory (Myers and Majluf, 1984) argues that the adverse selection creates a firm’s preference ranking over financing sources: Firms mostly prefer internal funds to external finance. When outside funds are necessary, firms prefer debt to equity because of lower information costs associated with debt issues. Specifically, it is noted that internal funding has no adverse selection problem, and equity is subject to serious adverse selection problems while debt has only less. From the outside investors’ point of view, equity is riskier than debt, therefore they demand a higher rate of return on equity to offset possible losses that may arise from funding “lemons.” From the insiders’ point of view, cost of internal funding is cheaper than debt financing. Accordingly, firms want to fund all projects using internal fund if possible.

Based on the pecking order theory, Fazzari et al. (1988) suggested that a firm’s investments can be affected by internal funds (or internal cash flows) after controlling for Tobin’s q, a proxy for the firm’s investment opportunities. Their findings supported the general relationship between internal cash flows and investment (i.e. ICFS). However, they further examined a moderating role of a firm’s financial constraints by using the Tobin’s q investment demand model and found differences in ICFS among three groups of different degrees of financial constraints. Similar to Fazzari et al.’s findings, although previous studies have generally agreed that investment is highly sensitive to fluctuations in the availability of internal funds due to imperfect capital markets (Kaplan and Zingales, 1997; Cleary, 1999), there are still more rooms to explore to explain ICFS further in details, especially considering a firm’s particular characteristics, such as financial constraints, as potential moderators.

For example, while Fazzari et al. (1988, 2000) argued that the investments of firms with more financial constraints are more sensitive to internal cash flows than less constrained firms, Kaplan and Zingales (1997, 2000) argued for an opposite direction by criticizing Fazzari et al.’s (1988) measurement of a firm’s financial constraints and also their assumption that ICFS increases monotonically as the degree of financial constraint increases. Kaplan and Zingales (1997) questioned whether a firm’s dividend policy can be a reliable measure of financing constraints (Ascioglu et al., 2008; Denis and Sibilkov, 2009; Gomes, 2001). However, Kaplan and Zingale’s (1997) empirical results have been criticized due to an insufficient sample size and subjective classification schemes of financing constraints (Fazzari et al., 2000). Considering these conflicting arguments and findings, some researchers (i.e. Allayannis and Mozumdar, 2004; Cleary et al., 2007; Guarglia, 2008; Moyen, 2004) have attempted to reconcile those mixed claims. For instance, when Cleary et al. (2007) measured financing constraints based on levels of net worth, their findings were consistent with Kaplan and Zingales’ (1997), but when they measured it based on the dividend payout ratio, their findings aligned with those of Fazzari et al. (1988).

Notwithstanding conflicting debates about ICFS along with other moderating factors in ensuing studies, little has been done to address the issue in the US restaurant context. Jang and Ryu (2006) and Jang and Kim (2009) examined the interdependency of investment decisions and capital structure in the US restaurant context by performing a common size balance sheet analysis. However, these studies employed relatively limited sample sizes, for 3 years and 6 years, respectively. Moreover, these studies did not focus primarily on the concept of ICFS but on an overall understanding of the financing behavior according to different types of assets for restaurant firms. Thus, the current study first seeks to close this research gap by specifically investigating the existence of ICFS in the restaurant context. Following the general consensus in the ICFS literature, the current study proposes the following hypothesis:

H1: The investments of US restaurant firms are positively sensitive to fluctuations in internal cash flows. In other words, US restaurant firms’ ICFS is significantly positive.

Franchising strategy and ICFS in the restaurant industry

In addition to confirming the existence of ICFS in the US restaurant context, the current study further attempts to explore franchising strategy as a potential moderator that may have an impact on the tendency of restaurant firms to rely on internal cash flows for investments. Franchising has been heavily implemented as a dominant growth strategy for the restaurant industry (Koh et al., 2009). In fact, the restaurant industry employs the franchising strategy more than any other industries in the US economy (Anwer, 2011). For example, about 13% of full service and 56% of quick service restaurants are franchised, respectively (Hsu et al., 2010).

There are four main motivations for restaurant firms to franchise. First, restaurant firms may franchise to reduce monitoring costs, based on the agency theory (Carney and Gedajlovic, 1991; Roh and Kwag, 1997). Second, according to the resource scarcity theory, restaurants have a propensity to franchise when they possess less financial and managerial resources because franchisees provide their own capital for the business, including fixed assets (e.g. property, plant, and equipment) (Alon, 2001; Kaufmann and Dant, 1996; Shane, 2005). Third, the risk-sharing theory suggests that franchising may help restaurant firms to better control cash flow fluctuations by providing a stable franchise fee income, thus facilitating low-cost access to financial markets (Dahlstrom and Nygaard, 1994). Last, franchising can be an efficient way to transfer specific knowledge between franchisors (e.g. knowledge and systems providers) and franchisees (e.g. knowledge and systems recipients) (Hoover et al., 2003; Roh and Yoon, 2009). Among these four main motivations, the current study bases its arguments about franchising as a potential moderator on the resource scarcity theory and risk-sharing theory.

According to the resource scarcity theory, restaurant firms franchise in order to utilize franchisees’ capital and to obtain managerial resources, so that they can accelerate growth (Hsu et al., 2010; Kaufmann and Dant, 1996). In particular, firms can gain franchisees’ financial capital through ongoing fee income, and this source of income can become an important internal source of financing to the firms. This internal funding injected through ongoing franchising fee income may help franchisors alleviate the need of external financing based on the suggestion of the pecking order theory (Myers and Majluf, 1984) that firms mostly prefer internal funds. Therefore, as the firms increase their involvement in franchising, the cash inflow by franchising will become a more substantial source of internal funding. Franchising helps firms better able to make their investment by facilitating the internal funding and consequently, the internal funding can become a critical source of investment expenditure.

Further, based on the risk-sharing theory, franchising plays an important role in lowering a firm’s risk by securing stable internal cash flows (i.e. franchising fees) at relatively low costs (Dahlstrom and Nygaard, 1994; Hsu et al., 2010; Koh et al., 2015; Sun and Lee, 2016). Specifically, Koh et al. (2015) pointed out that the stable fee income received from franchising fees and royalties mitigates franchisors’ risks by alleviating earnings volatility and fluctuations in cash flow, and thereby makes firms less vulnerable to the economic condition. The role of stable cash flow with respect to the corporate investment is also addressed in the corporate risk management literature that stable cash flows are less likely to require external financing (Froot et al., 1993). The risk reducing role of franchising and its extended influence on the use of financing methods can be particularly apparent for the restaurant firms due to the high level of risks of the industry. Restaurant industry is considered as being risky because of restaurant firms’ high proportion of fixed assets (Upneja and Dalbor, 1999) and their reliance on consumers’ discretionary spending (Kumcu and Kaufman, 2011; Singal, 2012). For this reason, the risk-reducing role of franchising could be significantly attractive to restaurant firms and encourage them to be involved more in franchising. Grounded on this line of theoretical foundation, we argue that franchising plays an important role in lowering firms’ risks by providing stable fee income, and the stable fee income brings in stable cash inflow to the firms, which mitigates the need of external funding. Therefore, we propose that franchisors will be able to take advantage of using the internal funding, generated from franchising, for their investment, and this leads them to being more sensitive to internal cash flow.

H2: Franchising moderates the relationship between internal cash flows and investment for US restaurant firms. In other words, as US restaurant firms franchise more, their reliance on internal cash flows for investment (i.e. ICFS) increases.

Methodology

Data and samples

This study samples publicly traded US restaurant firms for the period of 2000–2014 to conduct a main panel regression analysis. The sample period of 15 years embraces all economic cycles, including an economic boom and a bust cycle. Collected unbalanced panel data mainly comes from: (1) the COMPUSTAT database, based on the standard industrial classification (SIC) code of 5812 and (2) 10K reports in the US Securities and Exchange Commission database. Restaurant firms’ 10K filings and official websites were utilized to determine the degree of franchising and the firm age calculation. After eliminating outliers based on the criteria of a studentized residual value of 4 (Younger, 1979), the study includes a total of 58 restaurant firms with 615 firm-year observations for the main analysis.

Model

In order to investigate the proposed relationships, the current study, following previous studies (Attig et al., 2012; Fazzari et al., 1988; Kaplan and Zingales, 1997), augments the Tobin’s q investment demand model in which a restaurant firm’s investment is the dependent variable, and Tobin’s q and cash flow serve as independent variables to represent the firm’s investment opportunities and internal funds, respectively. The study further includes a firm’s degree of franchising to test its moderating role and adds control variables to the model to enhance the internal validity of the obtained results.

Specifically, to examine the moderating effect of franchising on ICFS, this study includes an interaction term of franchising and internal cash flow in the model. In other words, this study’s main regression specification is augmented from previous studies’ standard Tobin’s q investment model (Cleary, 1999; Kapan and Zingales, 1997) by including the franchising variable and an interaction term of franchising and internal cash flow. To alleviate the potential multicollinearity issue, the included variables related to interaction terms, such as internal cash flow and degree of franchising, are mean-centered (Aiken et al. 1991).

To test the proposed hypotheses, this study performed a two-way fixed effects panel regression model by firm and year to effectively accommodate a possible problem with unobserved effects in the panel data (Woodridge, 2002). This study conducted Hausman test to determine whether a fixed effects model is more appropriate than a random effects model. The models to test the proposed hypotheses are as follow:

The cash flow effect on investment model specification

INVEST = β_{0} + β_{1} Q_{i t} + β_{2} ICF + f (control variables) + α_{i} + α_{t} + ε_{i t}

The moderating effect of franchising model specification

INVEST = β_{0} + β_{1} Q_{i t} + β_{2} ICF + β_{3} {DFRA}_{i t} + β_{4} ICF \times {DFRA}_{i t} + f (control variables) + α_{i} + α_{t} + ε_{i t}

where i indexes individual firms; t indexes years; INVEST represents a firm’s investment, measured by capital expenditures scaled by total assets at the beginning of the year; ICF represents a firm’s internal cash flows, measured by operating cash flow scaled by total assets at the beginning of the year; α_i indexes known as the individual-specific unobserved effect; α_t indexes a trend term to allow for a shift of the intercept over time; ε_it indexes the idiosyncratic errors or disturbance term; Q indexes Chung and Pruitt’s (1994) measure of approximate Tobin’s q; and DFRA indexes the degree of franchising.

Since the collected data is an unbalanced panel data, the results from the pooled ordinary least squares (OLS) specification may be biased due to overlooked time- and firm-specific heterogeneities as well as autocorrelation (Gujarati, 2003). In order to mitigate these errors, a panel data analysis is performed in this study. The panel analysis model controlling for the unobserved heterogeneity helps obtain efficient estimates (Wooldridge, 2002).

The current study conducted the Breusch–Pagan Lagrange Multiplier (LM) test and Hausman test to determine an appropriate model among pooled OLS, fixed effects model, and random effects model (Wooldridge, 2002). Results of the LM test recommend the random effects model over the pooled OLS. After that, this study performed the Hausman specification test to determine whether a fixed effects or random effects model is more appropriate. The null hypothesis of the Hausman specification is that the random effects model is preferred, and the result of the test is a p value less than 0.05 (χ ² = 25.60, p value < 0.005), which means that a fixed effects model is more efficient than a random effects model (Greene, 2003; Hausman, 1978).

Dependent variable

This study uses capital expenditure as a measure of a firm’s investment (INVEST). Some researchers (e.g. Cleary, 1999; Kaplan and Zingale, 1997) measured a firm’s investment by dividing capital expenditure by net property, plant, and equipment at the beginning of the fiscal year, while Attig et al. (2014) and Attig and Cleary (2014) measured investment by scaling capital expenditure by total assets at the beginning of the fiscal year. The current study employs the measurement of the latter studies in order to be consistent with measures of other variables included in the model; Tobin’s q is scaled by total assets and internal cash flows is also divided by total assets.

Independent variable

The main factor of the current study is internal cash flows (ICF), and this study uses cash flow based on previous studies (e.g. Attig and Cleary, 2014; Attig et al., 2014; Fazzari et al., 1988; Kaplan and Zingales, 1997) as a proxy for ICF which is measured by earnings before extraordinary items, plus depreciation and amortizations, and the coefficient of ICF represents potential ICFS. Therefore, when the coefficient is significant, it means ICFS exists after controlling for Tobin’s q, a proxy for investment opportunities. Operating cash flow is divided by the total assets at the beginning of the year.

As the model is often referred to as the Tobin’s q investment demand model, the model includes Tobin’s q (Q), which represents a firm’s investment opportunities (Carpenter and Petersen, 2002; Fazzari et al., 1988). Tobin’s q in the current study is measured according to Chung and Pruitt’s (1994) study, which proposes approximate Tobin’s q, a practice widely accepted in the financial management literature (Kang and Lee, 2014). The approximate Tobin’s q is calculated as follows

Approximate Tobin’s q = \frac{MVE + PS + DEBT}{TA}

where MVE is the number of shares of common stock outstanding multiplied by a firm’s share price; PS represents the firm’s outstanding preferred stock’s liquidating value; DEBT stands for the net value of short-term liabilities of short-term assets and the book value of the firm’s long-term debt; and TA is the book value of a firm’s total assets (Chung and Pruitt, 1994).

As the third independent variable, this study measures degree of franchising (DFRA) by scaling the number of franchised units by the number of total units including owned units (Hsu and Jang, 2009; Koh et al., 2009) to examine the impact of franchising on ICFS. As explained earlier, ICF, DFRA, and their interaction (ICF × DFRA) variables are mean-centered to mitigate a potential multicollinearity issue.

Control variables

The current study considers five additional control variables—size, age, leverage, financial slack, and dividend payout—to improve internal validity, based on the previous literature (Almeida et al., 2004; Attig and Cleary, 2014; Hovakimian, 2009).

The model includes size (SIZE) as a variable to control for the expected effect of a firm’s size on ICFS. SIZE is measured by the natural logarithm of a firm’s revenues to consider the restaurant industry’s unique feature of franchising as a significant growth strategy (Sun and Lee, 2013). A log transformation is frequently used to improve the normality of the variable’s distribution (Koh et al., 2009). Regarding the effect of a firm’s size on ICFS, an expected effect is still mixed. Some studies (e.g. Attig et al., 2014; Myers and Majluf, 1984) argued that larger firms tend to easily opt for external financing for investment, suggesting low ICFS, while other studies (e.g. Attig and Cleary, 2014; Vogt, 1994) found that larger firms tend to experience high ICFS. Further in the hospitality context, Jang and Kim (2009) found that there is no difference between small restaurant firms and large restaurant firms in terms of investment financing behavior.

A firm’s age (AGE) is included in the current study to control for the effect of AGE on the relationship between internal funding and investment. Older firms are likely to have lower growth opportunities than younger firms (Alti, 2003) and are less likely to face higher external financing costs (Fazzari et al., 1988). Consequently, older firms may tend to use more external funds than internal funds (i.e. internal cash flows). Thus, AGE is expected to have a negative relationship with ICFS. AGE is measured by the number of years that a firm has operated. The data to measure AGE were hand-collected from restaurant firms’ websites and annual reports (10Ks).

In an effort to control the influence of a firm’s risk stemming from capital structure, financial leverage (LEV) is included in the model. According to Modigliani and Miller (1958), financial leverage has a positive relationship with cost of capital, and it may also directly affect a firm’s investment spending (Hovakimian, 2009). However, leverage may also stimulate overinvestment by managers (Lang et al., 1996). Therefore, the direction of the effect of leverage on investment is still mixed. This study uses total debt ratio as a proxy for leverage (Hovakimian, 2009).

Financial slack (FINSLK), is used in this study’s model as a proxy for internal liquidity. Internal liquidity reflects a firm’s ability to afford internal financing and may influence a firm’s investment behavior (Wang, 2014). More specifically, abundant financial slack in internal funds may lower a firm’s dependence on external resources, thus influencing future investment. Much of the existing literature has found a positive relationship between FINSLK and ICFS (Shin and Kim, 2002; Stulz, 1990; Wang, 2014). The current study measures FINSLK as the sum of the current ratio (the total of current assets divided by the total of current liabilities) and debt-to-equity ratio, based on the existing literature (Daniel et al., 2004; Wang, 2014).

Finally, the study includes dividend payout (DIV) in the model. Traditionally, DIV, measured as the ratio of cash dividends paid to net income, has been regarded as a signal of a firm’s future cash flows, based on the dividend signaling model in corporate finance investment literature (Ross et al., 2002). DIV is also a well-known proxy for a firm’s financial constraints in the ICFS literature. For instance, a firm with a lower dividend payout faces more financial constraints, thus increasing ICFS (Allaynnis and Mozumdar, 2004; Fazzari et al., 1988; Hoshi et al., 1991).

Results

Descriptive statistics

Table 1 summarizes the descriptive statistics of all the variables included in this study. Means, standard deviations, and range information are included in the descriptive summary table. Investment (INVEST), a dependent variable, has a mean value of approximately 12% of total assets, and the range is from 0% to 50% of total assets. As independent variables, approximate Tobin’s q (Q), internal cash flows (ICF), and degree of franchise (DFRA) have mean values of 1.94%, 0.35%, and 35.22%, respectively. Mean values of control variables, natural logarithm of the firm’s revenue, a proxy for firm size (SIZE), firm age (AGE), leverage (LEV), financial slack (FINSLK), and dividend payout (DIV), are 6.0852, about 34 years old, 0.2924, −11.8465, and −1.1898, respectively.

Table 1.

Summary of descriptive statistics.

Variable	N	Mean	SD	Min	Max
INVEST	615	0.1214	0.0908	0	0.5013
Q	615	1.938435	1.614612	0.042014	18.42616
ICF	615	0.121435	0.0907835	0	0.5012818
DFRA	615	35.76073	34.22446	0	100
SIZE	615	6.085207	1.893889	0.0751075	10.24373
AGE	615	33.87805	21.85441	2	98
LEV	615	0.2924031	0.4582486	0	4.21372
DIV	615	−1.189845	36.72189	−906.25	80.55421
FINSLK	615	−11.8465	315.5341	−7810.624	101.0214

Note: INVEST represents a firm’s investment, measured by capital expenditure divided by total assets at the beginning of the year; Q represents approximate Tobin’s q; ICF represents internal cash flows, measured by earnings before extraordinary items, plus depreciation and amortizations divided by total assets at the beginning of the year; DFRA represents degree of franchising, measured by the number of franchised units divided by the number of total units; SIZE represents a firm’s size, measured by logarithm of total revenue; AGE represents a firm’s age; LEV represent a firm’s leverage, measured by total liabilities divided by total equity; DIV represents dividend payout ratio, measured by dividend payout divided by net income per share; and FINSLK represents financial slack, measured by the sum of current ration and debt-to-equity ratio.

Table 2 shows the results of Pearson and Spearman correlations with significance level. The upper right-angled triangle from the diagonal line shows Spearman correlations, and the lower left-angled triangle from the diagonal line shows Pearson correlations. The correlation between INVEST and Q is significantly positive at the 5% significance level while the correlation between INVEST and ICF is significantly positive at the 1% significance level. Degree of franchising (DFRA), age (AGE), and leverage (LEV) are negatively correlated with INVEST at the 1% significance level, while firm size (SIZE), dividend payout ratio (DIV), and financial slack (FINSLK) are not significantly correlated with INVEST at the 5% significance level.

Table 2.

Summary of Pearson and Spearman correlations.

Variable	INVEST	Q	ICF	DFRA	SIZE	AGE	LEV	DIV	FINSLK
INVEST	1.0000	0.2906**	0.3651**	−0.2117**	0.0920*	−0.2503**	0.2996**	−0.0502	−0.0885*
Q	0.1252*	1.0000	0.6433**	0.1853*	0.3637**	0.0095	−0.1053**	0.0892*	0.0309
ICF	0.1629**	0.3746**	1.0000	0.3128**	0.4998**	0.1219**	0.0001	0.2249**	−0.0635
DFRA	−0.2417**	0.1938**	0.3089**	1.0000	0.1302**	0.3225**	0.2868**	−0.0215	0.1263**
SIZE	0.0112	0.1971**	0.5201**	0.1764**	1.0000	0.3053**	0.2722**	0.3617**	0.0791
AGE	−0.2971**	−0.0246	0.1404**	0.3795**	0.2172**	1.0000	0.2342**	0.2275**	0.0405
LEV	−0.2137**	0.3572**	0.3010**	0.3193**	0.1120	0.1869**	1.0000	0.0896*	0.1633**
DIV	−0.0855	0.0126	−0.0065	0.0449	0.0282	-0.0199	0.0309	1.0000	0.0214
FINSLK	−0.0300	0.0286	0.0081	0.0373	0.0263	0.0323	−0.0380	−0.0013	1.0000

Note: Upper right-angled triangle from the diagonal is the Spearman correlation, and lower right-angled triangle from the diagonal is the Pearson correlation. INVEST represents a firm’s investment, measured by capital expenditure divided by total assets at the beginning of the year; Q represents approximate Tobin’s q; ICF represents internal cash flows, measured by earnings before extraordinary items, plus depreciation and amortizations divided by total assets at the beginning of the year; DFRA represents degree of franchising, measured by the number of franchised units divided by the number of total units; SIZE represents a firm’s size, measured by logarithm of total revenue; AGE represents a firm’s age; LEV represent a firm’s leverage, measured by total liabilities divided by total equity; DIV represents dividend payout ratio, measured by dividend payout divided by net income per share; and FINSLK represents financial slack, measured by the sum of current ration and debt-to-equity ratio.

*5% significance level.

**1% significance level.

Also, Tobin’s q (Q) is significantly correlated with ICF, DFRA, SIZE, and LEV at the 1% significance level while ICF is positively correlated with DFRA, SIZE, AGE, and LEV at the 1% significance level. In particular, the correlation between DFRA and ICF implies that restaurant firms which heavily franchise are likely to have more internal cash flows. At the same time, DFRA is positively correlated with SIZE, AGE, and LEV at the 1% significance level. In addition, SIZE is positively correlated with AGE, and AGE is positively correlated with LEV. SIZE is not directly associated with LEV, and DIV and FINSLK do not correlate with other variables.

Main results

Table 3 reports the results of the main regression analyses. χ ² values confirm overall goodness of fit of the models at the 1% significance level. To check multicollinearity within the regression specification, the study estimates the values of the variance inflation factor (VIF), and all VIF values are below 2.0 (mean VIF = 1.40), which allows this study to assume no significant multicollinearity problem (Belsely et al., 2005).

Table 3.

Summary of regression analyses results.

Estimation variable interaction term	FE without interaction	FE with interaction
Q	0.0046 (1.94)	0.0015 (0.65)
ICF	0.1997 (5.25**)	0.2216 (5.93**)
DFRA	−0.0010 (−2.91*)	−0.0010 (−2.96*)
ICF × DFRA		0.0054 (5.48**)
SIZE	−0.0206 (−3.40**)	−0.0269 (−3.67**)
AGE	−0.0030 (−2.55*)	−0.0037 (−3.19**)
LEV	−0.0206 (−1.97*)	−0.0173547 (−1.70)
DIV	0.0000 (0.05)	−0.0008 (−0.12)
FINSLK	−6.16e−06 (−0.81)	−8.25e−06 (−1.11)
Intercept	0.3407 (7.79**⁾	0.3607 (9.15**)
Observation	615	615
Wald χ ²	989.29**	1067.49**

Note: INVEST represents a firm’s investment, measured by capital expenditure divided by total assets at the beginning of the year; Q represents approximate Tobin’s q; ICF represents internal cash flows, measured by earnings before extraordinary items, plus depreciation and amortizations divided by total assets at the beginning of the year; DFRA represents degree of franchising, measured by the number of franchised units divided by the number of total units; ICF × DFRA represents an interaction term of ICF and DFRA; SIZE represents a firm’s size, measured by logarithm of total revenue; AGE represents a firm’s age; LEV represent a firm’s leverage, measured by total liabilities divided by total equity; DIV represents dividend payout ratio, measured by dividend payout divided by net income per share; and FINSLK represents financial slack, measured by the sum of current ration and debt-to-equity ratio.

*5% significance level.

**1% significance level.

As presented in the first column (Fixed-Effects (FE) model without interaction) of Table 3, a restaurant firm’s coefficient of internal cash flows (ICF) shows a positively significant result, with a z value of 5.25 at the 1% significance level. This result supports hypothesis 1 of a significant ICFS, as there is a main and positive effect of internal cash flows on investment. The coefficient of approximate Tobin’s q (Q) is not statistically significant. Consequently, hypothesis 1 in this study can be supported by this result, suggesting that restaurant firms’ capital expenditures have a positive relationship with their ICF rather than Tobin’s q as a proxy for investment opportunities.

The main objective of this study is to investigate the moderating role of franchising in the investment decisions of US restaurant firms. As shown in the second column of Table 3, the interaction term of ICF and DFRA (ICF × DFRA) shows a significantly positive coefficient, with a z value of 5.48 at the 1% significance level. This result supports the positive moderating role of franchising on ICFS proposed in hypothesis 2, suggesting that an increase in franchising leads to an increase in ICFS. In other words, as a restaurant firm franchises more, the positive effect of the firm’s internal cash flows on investment increases.

Some control variables, such as firm size (SIZE) and firm age (AGE), have a significant and negative impact on INVEST at the 1% significance level. However, other control variables, such as firm leverage (LEV), dividend payout (DIV), and financial slack (FINSL), show an insignificant impact on INVEST.

Discussion and conclusions

The purpose of this study is to examine the relationship between investment and internal cash flows by employing the widely accepted concept of ICFS within the US restaurant context. The previous literature argued that a firm’s investments tend to depend on internal cash flows, assuming imperfect capital markets. In the US restaurant context, existing research also suggested that investment and financing decisions show a certain relationship. Nevertheless, the restaurant literature has not paid much attention to ICFS issues, thus the current study attempts to fill this void.

In addition to investigating the existence of ICFS in the restaurant context, this study further examines the moderating role of franchising on the ICFS of US restaurant firms to provide a more comprehensive picture of ICFS. It is no exaggeration to say that the restaurant industry is at the zenith of franchising prosperity (see the “Franchising strategy and ICFS in the restaurant industry” section). In this regard, the franchising strategy in the restaurant industry has long been a topic of great interest among hospitality researchers. Yet most studies on the restaurant franchising feature a heavy emphasis on performance or motivation, missing out the opportunity to relate the franchising strategy to the concept of ICFS. Based on the pecking order theory, as well as the resource scarcity and risk-sharing theories for franchising, the current study proposes that franchising will increase ICFS for restaurant companies.

The findings of the current study make salient contributions not only to the general ICFS and franchising literature but specifically to the restaurant literature. First, this study empirically demonstrates that ICFS exists in the restaurant industry. According to the result of the regression analysis to test hypothesis 1, internal cash flow has a positive and significant impact on a restaurant firm’s investment decisions, while Tobin’s q (as a proxy for investment opportunity) does not have a significant effect on investment decisions. These findings may suggest that restaurant firms consider internal cash flows more important than investment opportunities perceived by the financial market when they make investments. From the theoretical spectrum, the pecking order theory (Myers and Majluf, 1984) may provide a more appropriate theoretical framework to explain corporate investment and financing decisions in the restaurant context. These findings may suggest that it is possible for restaurant firms to exhibit a tendency to overinvest, but this speculation requires further specific empirical examinations.

Next, and perhaps most importantly, this article is the first to deal with the issue of the moderating role of franchising on ICFS, and the findings of this study support this moderating effect, contributing to the general ICFS literature as well as enriching the restaurant literature. Our results support the theory that restaurant firms may heavily implement franchising as a growth strategy due to two major motivating factors: the risk-sharing theory (Alone, 2001; Dahlstrom and Nyggard, 1994) and resource scarcity theory (Kaufman and Dant, 1996). The risk-sharing theory argues that firms (i.e. franchisors) are motivated to franchise because it will lead to sharing business risk with franchisees. Therefore, these restaurant firms heavily involved in franchising should possess a strong motivation to share business risk with their franchisees, suggesting these firms are likely to be risk averse. The general risk-averse tendencies of restaurant firms engaged in heavy franchising are likely linked to these firms’ relatively heavier reliance on internal cash flows for investments, as internal funds are considered a safer resource according to the pecking order theory (Myers and Majluf, 1984).

On the other hand, the resources scarcity theory may suggest that these restaurant firms which are heavily involved in franchising should receive major resources, including internal cash flows, from franchisees. Franchise fees, including royalties and marketing and advertising fees, represent a major component of the resources from franchisees that will feed into franchisors’ internal funds. Therefore, these restaurant firms (franchisors) should be able to generate sufficient internal cash flows from stable fee income (Alone, 2001; Brown, 1998; Castrogiovanni et al., 1993). Thus, as restaurant firms franchise more, the demand on external financing may relatively decrease and the firms may tend to rely more on internal cash flows because internal cash flows are safer and less costly than external funding, according to the pecking order theory (Myers and Majluf, 1984). In other words, restaurant firms engaged in heavy franchising are likely to take advantage of the easy and cheap financing option represented by internal cash flows, thereby increasing ICFS.

The findings of the current study also provide practical insights for US restaurant managers and business decision makers in the real world. According to our findings, restaurant firms may look more into their available internal cash flows rather than sufficiently evaluating growth opportunities when considering their investments. Since this practice may lead to overinvestment, restaurant firms may need to spend more time and resources to analyze and evaluate their investment opportunities in making their investment and financing decisions to avoid possible overinvestment. Further, restaurant managers especially of those restaurants with a heavy franchising involvement may need to pay more attention to the current study’s findings. Based on our findings regarding the moderating role of franchising on ICFS, franchising restaurant firms may experience more ICFS compared to non-franchising restaurant firms. They should bear in mind that ICFS increases as degree of franchising increases. Thus, restaurant executives may need to develop a diverse array of efficient, cost-effective channels to access external financing resources, especially in the long term, because their heavy involvement in franchising may have steered them away from efficient utilization of external funding. Based on the trade-off theory (Kraus and Litzenberger, 1973), restaurant firms may achieve optimal benefit from balancing the use of internal and external financing.

Limitations and suggestions for future research

Although this study provides meaningful contributions to academia and the business world, it is not free from limitations. This study only investigates publicly traded US restaurant firms from 2000 to 2014. Therefore, we caution careful application of this study’s findings to the private restaurant sector or other countries’ publicly traded restaurant firms due to the limited generalizability issue. First, the market environments in other countries could be different from the US restaurant market. Second, private restaurant firms have different external funding situations than public firms. Therefore, future studies are encouraged to use diverse samples in order to enhance external validity. In addition, future studies may consider other potential moderators, such as macroeconomic factors. The current study introduced the franchising strategy, a firm-specific factor, as a moderator. However, other non-firm-specific factors may impact ICFS. Future studies may delve into examining how some macroeconomic factors affect ICFS.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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