Firm growth patterns: Examining the associations with firm size and internationalization

International Journal of Hospitality Management 29 (2010) 368–377

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International Journal of Hospitality Management journal homepage: www.elsevier.com/locate/ijhosman

Firm growth patterns: Examining the associations with firm size and internationalization Kwangmin Park 1, SooCheong (Shawn) Jang * Department of Hospitality and Tourism Management, Purdue University, West Lafayette, IN 47907-0327, United States

A R T I C L E I N F O

A B S T R A C T

Keywords: Firm growth Firm size Internationalization Growth strategy Restaurant industry

Understanding the growth patterns of an industry is essential for establishing sustainable growth strategies. However, until recently little had been known about restaurant firm growth patterns. Thus, this study examined the growth patterns of restaurant firms in association with firm size class and internationalization, after controlling for total and long-term debt leverage, retained earnings, and growth opportunity. Overall, the results of this study showed that small restaurant firms grow faster than large restaurant firms but the growth rate decreases as firm size increases. Furthermore, the growth rate of large firms decreased more slowly than small firms. In terms of internationalization, this study found that as firm size increases, the growth rate of small international firms decreases more rapidly than that of small domestic firms. However, the growth rate of large international firms decreases more slowly than that of large domestic firms. These findings indicate the appropriateness of internationalization strategies for large restaurant firms but the inappropriateness of these strategies for small firms. More detailed results and discussion are also provided. ß 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Hughes, 1994). Accordingly, small firms tend to allocate their resources more optimally than large firms to achieve rapid-growth (Cabral, 1995). On the other hand, firms whose sizes are beyond scale economies cannot benefit from additional growth because the longrun average cost curve stops falling. Further, as Storey (1994) stated, firm size class should be considered in firm growth analysis because the long-run average cost curve decreases non-linearly as firm size increases (Hart and Prais, 1956; Samuels, 1965; Evans, 1987; Hall, 1987; Jovanovic, 1982; Rodriguez et al., 2003). Another important subject related to firm growth is internationalization. The restaurant industry has a long history of firm growth via international expansion (Singh et al., 2003). According to Technomic Inc. (2008), the top 100 U.S. restaurant chains increased their international units and sales by 5.6% and 7.6%, respectively, in 2007. These numbers outpaced domestic units and domestic sales growth rates, which were 2.4% and 5.1%, respectively. Bloodgood et al. (1996) also concurred that internationalization is an essential growth strategy. Due to the possibilities opened up by the ubiquity of rapid, low-cost global communication and transportation systems, even small firms are able to internationalize relatively easily (Knight and Cavusgil, 1996; Oviatt and McDougall, 1999). However, there has been no consensus in academia regarding the influence of internationalization on firm growth. Lu and Beamish (2006) and Oulton (1998) claimed that internationalization has a positive effect on firm growth, but Pfaffermayr and Bellak (2002) demonstrated that internationalization has no significant influence on firm growth. Furthermore, Blonigen and

Firm growth is one of the most important issues in business management because growth usually reflects market acceptance and firm success (Feeser and Willard, 1990). In reality, however, it is difficult for companies to maintain consistent growth. Zook and Allen (1999) indicated that only one in seven firms can sustain persistent and profitable growth. Nevertheless, according to one business executive survey (Deloitte and Touch LLP and Wirthline Worldwide, 1996), firm growth is a top strategic priority for the majority of firms, which indicates how important firm growth is to executives. Despite the importance of firm growth to businesses, little is known regarding the nature of firm growth patterns in the U.S. hospitality industry. In order to begin filling the research gap, this study investigated the growth patterns of restaurant firms in association with firm size class and internationalization using Gibrat’s Law, a stochastic firm growth model. Previous studies have revealed that small firms grow faster than their larger competitors (Kumar, 1985; Evans, 1987; Hall, 1987; Dunne and Hughes, 1994; Mata and Portugal, 1994; Wagner, 1994; Baldwin, 1995). The rationale is that small firms try to grow faster to reach a scale economy (Kumar, 1985; Evans, 1987; Hall, 1987; Dunne and

* Corresponding author. Tel.: +1 765 496 3610. E-mail addresses: [email protected] (K. Park), [email protected] (S.(. Jang). 1 Tel.: +1 765 413 7352. 0278-4319/$ – see front matter ß 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhm.2009.10.026

K. Park, S.C.S. Jang / International Journal of Hospitality Management 29 (2010) 368–377

Tomlin (2001) and Siddharthan and Lall (1982) stated that internationalization has a negative influence on firm growth. The contradictory results of previous studies may be due to the fact that firm size class was not considered in their analyses of the relationship between internationalization and firm growth. Unlike previous studies, this study is unique in that it investigated firm growth patterns in association with both firm size class and internationalization strategy. Further, using U.S. restaurant firms, this study intended to find out if inconsistent effect of internationalization on firm growth comes from firm size. This study could also identify the unique effects of firm size and internationalization on firm growth through controlling for important financial variables (e.g., total and long-term debt leverage, retained earnings, and growth opportunity) based on the findings of prior studies. Thus, this study helps explain the nature of firm growth patterns in more depth so that restaurant managers and executives can establish sustainable firm growth strategies. Specifically, the objectives of this study were: (1) to investigate the growth rate patterns of the restaurant industry, (2) to examine growth rates among different firm size classes, (3) to investigate the impact of internationalization on growth rate by comparing domestic and international restaurant firms, and (4) to identify whether internationalization differently impacts the growth rate patterns of small and large firm classes in the restaurant industry. In the next section, we review the prior studies to investigate the effect of firm size and internationalization on firm growth. In Section 3, we explain the models used to examine this study’s hypotheses. Finally, results and conclusions are provided in the last section. 2. Literature review 2.1. Firm growth theories and growth patterns There are two strands of firm growth theories; one is deterministic theory and the other is stochastic theory (Marris, 1979). Deterministic theory explains firm growth as a process of industry concentration based on particular patterns of cause and effect (Marris and Mueller, 1980), while stochastic theory contends that firm growth occurs by chance (Mancke, 1974). Stochastic theory’s validity has been debated in academia for the last several decades. Gibrat’s Law is representative of stochastic firm growth theory. Gibrat (1931) argued that firm size is determined by a firstorder integrated growth process in which firm growth rates are independent, identically distributed random variables (Geroski, 1995; Sutton, 1997; Caves, 1998), which signify that firm growth occurs by chance. That is, Gibrat (1931) claimed that firm growth is independent of firm size. Empirical studies were conducted to verify Gibrat’s Law and early studies generally supported Gibrat’s claim (Hart and Prais, 1956; Hymer and Pashigian, 1962). More recent empirical studies, however, have denied the veracity of Gibrat’s Law (Singh and Whittington, 1975; Chesher, 1979; Kumar, 1985; Evans, 1987; Hall, 1987; Contini and Revelli, 1989; Wagner, 1992; Dunne and Hughes, 1994; Reid, 1995; Hart and Oulton, 1996; Harhoff et al., 1998; Audretsch et al., 1999; Wilson and Morris, 2000; Rufin, 2007; Teruel-Carrizosa, in press). According to Goddard et al. (2006), some firm growth patterns have emerged and they are consistent with trends in the industry or the overall economy. Besides, firm growth patterns have been observed to be similar in both U.S. and Europe (Pryor, 2001, 2002; Davies et al., 2001). Generally, previous studies reporting stylized results derived from empirical evidence have suggested a negative firm size–growth pattern (Geroski, 1995; Sutton, 1997; Caves, 1998; Coad, 2009). This negative pattern means that smaller firms grow faster than larger firms. As noted earlier, Kumar (1985), Evans (1987), Hall (1987), Dunne and Hughes (1994) and Audretsch (1995) explained

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that small firms strive to grow faster in order to reach scale economies. However, the more a firm grows, the less motivated it is to grow (Dobbs and Hamilton, 2007). On the other hand, Audretsch et al. (2004) have criticized most prior studies for being based on manufacturing industries and argued that growth patterns could be different for manufacturing and service industries. For example, they showed that Gibrat’s Law is accepted for the Dutch hospitality industry. In general, manufacturing involves large-scale production, aimed at exploiting scale economies to yield storable output (Stanback, 1979). However, service is intangible, highly differentiated, perishable and involves simultaneity in consumption and production (Akehurst, 1987; Britton et al., 1992). Thus, in manufacturing industries, it is difficult for small firms to survive because of the disadvantage of scale economies. Yet, this may not be the case for small firms in service industries due to relatively small economies of scale. However, the industrial composition of the Dutch hospitality industry may be different from the U.S. restaurant industry. As explained in Audretsch et al. (2004), Dutch hospitality firms consisted mainly of family-owned and small independent businesses. Thus, the level of scale economies is relatively small and most small firms in the industry operate at a low level of scale economies. In contrast, the U.S. restaurant industry includes many large chain restaurants. Thus, the level of scale economies is much larger than the Dutch sample. Accordingly, the growth pattern of the U.S. restaurant industry may be more similar to the manufacturing industry’s growth pattern. Therefore, this study hypothesized to reject Gibrat’s Law, proposing a negative relationship between firm size and growth rate for the restaurant industry. In other words, it was hypothesized that small firms grow faster than large firms, or large firms grow more slowly than small firms, in the U.S. restaurant industry. Hypothesis 1. There is a negative relationship between firm size and firm growth rate in the restaurant industry. 2.2. Firm size–growth patterns in different size classes Several previous studies (e.g., Hart and Prais, 1956; Mansfield, 1962; Acs and Audretsch, 1990; Almus and Nerlinger, 2000; Klomp et al., 2003; Jovanovic, 1982; Audretsch et al., 2004) have shown that Gibrat’s Law is rejected for all size classes. However, Evans (1987) and Hall (1987) argued that Gibrat’s Law can be accepted or rejected among different size classes, depending on the heteroskedastic errors of the firm growth rates. Theoretically, firm size–growth patterns among size classes can also be explained by economies of scale. When a firm size is less than a scale economy, the firm can obtain benefits from decreasing costs per unit as the firm grows. Decreasing cost means the improvement of a firm’s efficiency. On the other hand, firms that are larger than the scale of economy do not receive benefits from additional growth because the long-run average cost curve is flat or even increasing, which indicates diseconomies of scale. Another previously overlooked point is the non-linearity of the long-run average cost curve, which is known as an ‘‘L-shaped’’ or ‘‘U-shaped’’ pattern. The long-run average cost curve can be divided into three different phases (decreasing, constant, and increasing) as firm size increases (Maurice and Thomas, 1999). The different phases of long-run average cost imply differences in growth potential, which suggests that size–growth patterns are more homogeneous within the same size class but more heterogeneous among different size classes (Giordano, 2003). Furthermore, as far as a firm remains in the scale economy phase the non-linear long-run average cost (L-shaped) suggests that size–growth patterns are negative in smaller class sizes, less negative in the medium size class, and much less negative for the

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larger size class (Giordano, 2003). In other words, even though small firms grow faster than large firms, the growth rates are not the same in different firm size classes. When a firm is small, it grows faster but its growth rate is decreasing more quickly as well because the improvement of the long-run average cost is quickly decreasing. As a firm becomes larger, it grows more slowly. Its decreasing growth rate becomes slower as well because the improvement of the long-turn average cost slows down. Thus, growth rates slowly decrease as class size goes up. Therefore, it was hypothesized that the relationship between firm size and firm growth rate in the restaurant industry is less negative as firm class size increases. It can also be interpreted that the growth rate of a large firm class decreases more slowly than that of a small firm class. Hypothesis 2. The relationship between firm size and firm growth rate in the restaurant industry is less negative as firm class size increases. 2.3. Firm size–growth patterns between domestic and international firms There are various perspectives that could explain motives for internationalization: international product life cycle theory, strategic behavior theory, and profitability. International product life cycle theory (Vernon, 1966) describes internationalization as a chain of four stages where a firm internationally expands at a time when it needs to grow its product sales. Thus, the fundamental driving force of internationalization is the desire to grow. Strategic behavior theory (Knickerbocker, 1973) argues that companies tend to minimize business risk through internationalization. The last motive for international expansion is profitability. According to this perspective, international firms can attain profitability by spreading their fixed costs to international markets and flexibly accessing business resources (Goerzen and Beamish, 2003). This motivation is along the same lines as scale economies on the firm level (Brainard, 1997). In the manufacturing industry, scale economies at the plant level are a driving force of firm growth, while in the service industry scale economies at the firm level might be a driving force of firm growth as well as internationalization (Markusen and Venables, 1999). Campbell and Verbeke (1994) also indicated that international service companies could be better situated to achieve scale economies than domestic firms due to their access to wider international markets. Consequently, internationalization provides an opportunity to increase sales and the capacity to benefit from global scale economies. Along similar lines, Robson and Bennett (2000) and Beck et al. (2005) also reported that internationalization has a positive effect on firm growth. Based on the above explanations, internationalization is expected to positively influence firm growth. Siddharthan and Lall (1982) and Lu and Beamish (2006), however, empirically evidenced that the relationship between firm size and firm growth is negative for international firms as well, as stated in Hypothesis 1. Their results indicated that despite the potential positive impact of internationalization on firm growth rates, the nature of firm size– firm growth patterns remains negative. That is, both domestic and international small firms grew faster than their larger competitors. Nevertheless, the firm size–growth relationship of international firms may be less negative than of domestic firms, ceteris paribus, due to the positive impact of internationalization. As firm size increases, the growth rate of international firms decreases more slowly than that of domestic firms. Hypothesis 3. As firm size increases the relationship between firm size and firm growth rate of international firms is less negative than that of domestic firms.

2.4. Effects of internationalization in different size classes Penrose (1959) argued that firm growth is closely related to managerial capability and organizational learning capacity (Slater, 1980; Jovanovic, 1982; Cabral and Mata, 2003; Oliveira and Fortunato, 2008). If small firms are assumed to have low managerial capability and a limited capacity for organizational learning, internationalization could negatively influence the growth of small firms. Moreover, due to a lack of managerial capability and low organizational learning levels, internationalization can damage the ability of small firms to optimally allocate their resources in the business. Consequently, it can be presumed that small international firms tend not to use their resources as optimally as small domestic firms. Small domestic firms usually know their markets and customers well. But small international firms often do not understand foreign markets and due to their limited organizational learning capacity it can take a long time for small international firms to adjust to foreign markets. Thus, small international firms may operate less effectively than small domestic firms, indicating that internationalization negatively influences small firm growth when compared to small domestic firms. Thus, this study hypothesized that the relationship between firm size and firm growth rate in small international firms is more negative than that in small domestic firms. In other words, as firm size increases the growth rates of small international firms decrease more rapidly than those of small domestic firms. In contrast, large firms usually are better situated in terms of managerial capacity and organizational learning than small firms. Large firms can take advantage of the previously explained benefits of internationalization. Accordingly, internationalization can also have a positive effect on firm growth. Thus, it was hypothesized that as firm size increases the relationship between firm size and firm growth rate in large international firms is less negative than in large domestic firms. That is, the growth rate of large international firms decreases more slowly than that of large domestic firms. Hypothesis 41. As firm size increases, the relationship between firm size and firm growth rate of small international firms is more negative than that of small domestic firms. Hypothesis 42. As firm size increases, the relationship between firm size and firm growth rate of large international firms is less negative than that of large domestic firms. 3. Methodology 3.1. Data The data used in this study was collected from the COMPUSTAT Industrial Annual and Segment database using the SIC 5812 (eating places). The data covers the U.S. restaurant industry between fiscal year 1995 and 2006. The information on internationalization was downloaded from the COMPUSTAT Segment database. A total of 180 U.S. restaurant firms were included in the sample: 130 domestic firms, 20 international firms, and 30 unidentifiable firms, which were unable to be categorized as either domestic or international firms. During the sample period, three restaurant firms expanded their business into the international market, while four restaurant firms withdrew from international markets. The seven firms that changed the nature of their business were included as unidentifiable firms. This study included all restaurant firms whether or not they survived the sample period. International firms were defined in this study as those firms that reported international sales revenue. Using the distribution of annual total sales, this study categorized the firms into four size classes: small (bottom 25% in terms of size), medium-small (the

K. Park, S.C.S. Jang / International Journal of Hospitality Management 29 (2010) 368–377

next 25%), medium-large (the next 25%), and large (top 25% in terms of size). As a result, 51 restaurant companies were classified as small size firms; 45 firms fell into the medium-small size class; 42 firms were classified as medium-large size firms; and 42 firms fell into the large size class. 3.2. Models and variables Audretsch et al. (2004) argued that it is inappropriate to directly compare the empirical results of all studies using Gibrat’s Law because they utilize different samples and methodologies. Further, they suggested dividing past studies of Gibrat’s Law into two types: static analysis and temporal analysis. Most static analyses (e.g., Mansfield, 1962; Hymer and Pashigian, 1962; Singh and Whittington, 1975; Acs and Audretsch, 1990; Piergiovanni et al., 2003; Audretsch et al., 2004) divided samples into groups based on firm size and tested whether or not growth rates among the size groups were the same. Other static analyses (e.g., Mansfield, 1962; Evans, 1987; Contini and Revelli, 1989; Hall, 1987; Dunne and Hughes, 1994) used regression analyses to explain growth rates in different firm sizes, but they did not include the autoregressive terms of the growth rates. On the other hand, temporal analyses have tested the persistence of growth using the autoregressive terms of the growth rates (Chesher, 1979; Singh and Whittington, 1975; Kumar, 1985; Bottazzi et al., 2001; Goddard et al., 2002; Pfaffermayr and Bellak, 2002; Oliveira and Fortunato, 2008). To test the proposed hypotheses, this study is based on a temporal analysis. Prior empirical studies based on temporal analysis used an OLS estimator with logarithmic growth and lagged growth (Chesher, 1979). However, Goddard et al. (2002) contended that OLS regression produced biased estimates and the significance test would suffer from a loss of power if there were heterogeneous individual firm effects. Bond (2002) also demonstrated the inconsistency of the OLS estimator using the AR(1) model as follows: yi;t ¼ ayi;t1 þ ðhi þ vi;t Þ

ðjaj < 1;

i ¼ 1; 2; . . . ; N;

¼ 1; 2; 3; . . . ; TÞ

tically efficient because the model is over-identified with T > 3 and the first-differenced error term Dvi;t has a first-order moving average form of serial correlation. However, the first-differenced generalized method of moments (GMM)-system estimator for AR(1) panel data provided no serial correlation between the time varying components of the error terms (Blundell and Bond, 1998). Thus, the estimator eliminates firm-specific effects through the first-difference equation model and lagged differences of each variable are valid as instruments (Oliveira and Fortunato, 2008). The validity of instruments can be tested by the Sargan test of overidentifying restriction (Bond, 2002). Therefore, this study applied the panel data model with fixed effects estimated with the firstdifferenced GMM-system estimator, originally developed by Blundell and Bond (1998). Additionally, this study conducted the Arellano and Bond (1991) test, which was originally designed as an autocorrelation test for a particular linear GMM dynamic panel data estimator. Since the AR(1) process was proposed for this study, the first-order autocorrelation test (m1) should be significant but the second order autocorrelation test (m2) should not be significant. Based on Gibrat’s Law, Wilson and Morris (2000) developed the OLS regression model to empirically estimate firm size–growth rate relationships in UK’s manufacturing and service firms. The benefit of Wilson and Morris’s (2000) model is that it allows for variations in the slopes of both the preceding firm size and the preceding firm growth rate according to different dummy variables. As noted, however, the OLS estimator is biased. Thus, this study improved Wilson and Morris’s (2000) model by using the first-differenced GMM-system estimator. To test the specific hypotheses, Wilson and Morris’s (2000) model was modified as follows: Model 1

DSi;t ¼ a1 þ a2 Si;t1 þ a3 DSi;t1 þ u1 TDLi;t1 þ u2 LTDLi;t1 þ u 3 REi;t1 þ u 4 Q i;t1 þ

t

371

t X

ui DYEARi þ ui;t

i

(1)

Model 2

where yi,t is an observed variable for firm i at time t; hi is an unobserved firm-specific time-invariant effect, which allows for heterogeneity in the means of the yi,t across firms. vi;t is a disturbance term. A key assumption in this equation is that the disturbance term vi;t is independent across firms and serially uncorrelated. Bond (2002) indicated that the OLS estimator of a is inconsistent since yi,t1 is positively correlated with the error term (hi þ vi;t ) due to the presence of firm-specific effects. Even though the sample size is sufficiently large, the correlation has not vanished. Thus, the OLS estimator is biased even in large samples. Further, although the Within Groups estimator can eliminate this inconsistency by diminishing the firm-specific term hi, there is a negative correlation between the lagged dependent variable and error term, which results in bias as well. Consequently, Bond (2002) suggested the first-difference model to eliminate the firmspecific effect hi from the model as follows:

DSi;t ¼ a1 þ a2 Si;t1 þ a3 DSi;t1 þ g 1 DS1i;t1 þ g 2 DS1i;t1 Si;t1

Dyi;t ¼ aDyi;t1 þ Dvi;t

DSi;t ¼ a1 þ a2 Si;t1 þ a3 DSi;t1 þ b1 DIi;t þ b2 DIi;t Si;t1

¼ 3; 4; . . . ; TÞ

ðjaj < 1;

i ¼ 1; 2; . . . ; N;

t (2)

where Dyi,t is yi,t  yi,t1, indicates the difference of the observed variable at time t. However, the OLS estimates of a are also biased in the first-difference equation model, because Dvi;t term depends on vi;t (Bond, 2002). In addition, there is an endogeneity problem due to the correlation of the lagged independent variable (Dyi,t1) and the error term (Dvi;t ) (Oliveira and Fortunato, 2008). According to Bond (2002), the 2SLS method is consistent but not asympto-

þ g 3 DS1i;t1 DSi;t1 þ d1 DS2i;t1 þ d2 DS2i;t1 Si;t1 þ d3 DS2i;t1 DSi;t1 þ z1 DS3i;t1 þ z2 DS3i;t1 Si;t1 þ z3 DS3i;t1 DSi;t1 þ u1 TDLi;t1 þ u2 LTDLi;t1 þ u3 REi;t1 þ u 4 Q i;t1 þ

t X

ui DYEARi þ ui;t

i

Model 3

DSi;t ¼ a1 þ a2 Si;t1 þ a3 DSi;t1 þ b1 DIi;t þ b2 DIi;t Si;t1 þ b3 DIi;t DSi;t1 þ u1 TDLi;t1 þ u 2 LTDLi;t1 þ u3 REi;t1 þ u 4 Q i;t1 þ

t X

ui DYEARi þ ui;t

i

Model 4

þ b3 DIi;t DSi;t1 þ g 1 DS1i;t1 þ g 2 DS1i;t1 Si;t1 þ g 3 DS1i;t1 DSi;t1 þ d1 DS2i;t1 þ d2 DS2i;t1 Si;t1 þ d3 DS2i;t1 DSi;t1 þ z1 DS3i;t1 þ z2 DS3i;t1 Si;t1 þ z3 DS3i;t1 DSi;t1 þ u1 TDLi;t1 þ u2 LTDLi;t1 þ u3 REi;t1 þ u 4 Q i;t1 þ

t X

ui DYEARi þ ui;t

i

K. Park, S.C.S. Jang / International Journal of Hospitality Management 29 (2010) 368–377

372

Model 5-1

DSi;t ¼ a1 þ a2 Si;t1 þ a3 DSi;t1 þ b1 DIi;t þ b2 DIi;t Si;t1 þ b3 DIi;t DSi;t1 þ g 1 DS1i;t1 þ g 2 DS1i;t1 Si;t1 þ g 3 DS1i;t1 DSi;t1 þ d1 DS2i;t1 þ d2 DS2i;t1 Si;t1 þ d3 DS2i;t1 DSi;t1 þ z1 DS3i;t1 þ z2 DS3i;t1 Si;t1 þ z3 DS3i;t1 DSi;t1 þ h1 CSDIi;t1 þ h2 CSDIi;t1 Si;t1 þ h3 CSDIi;t1 DSi;t1 þ u 1 TDLi;t1 þ u2 LTDLi;t1 þ u3 REi;t1 þ u4 Q i;t1 þ

t X DYEARi þ ui;t i

Model 5-2

DSi;t ¼ a1 þ a2 Si;t1 þ a3 DSi;t1 þ b1 DIi;t þ b2 DIi;t Si;t1 þ b3 DIi;t DSi;t1 þ g 1 DS1i;t1 þ g 2 DS1i;t1 Si;t1 þ g 3 DS1i;t1 DSi;t1 þ d1 DS2i;t1 þ d2 DS2i;t1 Si;t1 þ d3 DS2i;t1 DSi;t1 þ z1 DS3i;t1 þ z2 DS3i;t1 Si;t1 þ z3 DS3i;t1 DSi;t1 þ r1 CLDIi;t1 þ r2 CLDIi;t1 Si;t1 þ r3 CLDIi;t1 DSi;t1 þ u1 TDLi;t1 þ u2 LTDLi;t1 þ u 3 REi;t1 þ u4 Q i;t1 þ

t X DYEARi þ ui;t i

where, ui,t is a random error term with E(ui,t) = 0 and Varðui;t Þ ¼ s 2i;t . Coefficient a2 indicates the influence of the preceding year’s firm size on the current year’s firm growth. a3 is the impact of the preceding year’s firm growth on the current year’s firm growth. Table 1 lists descriptions of the variables. The firm size variable was measured using net sales and was calculated as ln(Net Sales) of firm i at time t, which is denoted by Si,t. If a2 is not significant, preceding firm size has no impact on growth. In this case Si,t follows Table 1 Variables description. Variables

Description

Si,t

Firm size variable, which is calculated as ln(Net Sales) of firm i at time t Firm growth variable, which is calculated as Si,t  Si,t1 Internationalization dummy variable: 1 if firm i is international firm and 0 if firm i is domestic firm at time t Medium-small size dummy variable: 1 if firm i is included in the medium-small class (between 1st quartile and median based on the every year firm size distribution) at time t  1; 0 otherwise Medium-large size dummy variable: 1 if firm i is included in the medium-large class (between median and 3rd quartile based on the every year firm size distribution) at time t  1; 0 otherwise Large size dummy variable: 1 if firm i is included in the large class (larger than 3rd quartile based on the every year firm size distribution) at time t  1; 0 otherwise Contrasts of small domestic and small international firms: 1 if firm i is small domestic firms and 1 if firm i is small international firms at time t  1; 0 otherwise Contrasts of large domestic and large international firms: 1 if firm i is large domestic firms and 1 if firm i is large international firms at time t  1; 0 otherwise Total debt leverage, which is calculated as ln(Total Debt/ Total Assets) Long-term debt leverage, which is calculated as ln(Long-term Debt/Total Assets) Retained earnings, which is calculated as retained earnings divided by total assets Tobin’s q, which is ln(Tobin’s q). Tobin’s q is calculated as market value of common stock plus preferred stock and total debt divided by book value of total assets Year dummy variables

DSi,t DIi,t DS1i;t1

DS2i;t1

DS3i;t1

CSDIi,t1

CLDIi,t1

TDLi,t1 LTDLi,t1 REi,t1 Qi,t1

DYEAR

random walk, indicating that firm growth happens by chance. If a2 is significant and less than 0, then small firms grow faster than their larger competitors. However, if a2 is significant and greater than 0, then large firms grow faster than small firms. a3 indicates the extent to which growth in one period is correlated with growth in the preceding year. If a3 is not significant, it means there is no correlation between growth in successive years. If a3 is significant and greater than 0, it implies positive or negative growth in the preceding year continuing into the current year. Whereas, if a3 is significant and negative, it means positive or negative growth in the preceding year tends to be followed by growth in the opposite direction in the current year (e.g., negative growth if the preceding was positive or positive growth if the preceding was negative). When the joint hypothesis (a2 a3) = (0 0) is accepted, Gibrat’s Law is accepted, which signifies that firm growth happens by chance. DIi,t, DS1i;t1 , DS2i;t1 , and DS3i;t1 are dummy variables used to identify domestic/international firms and the four firm size classes. If b2 is positive and significant, the relationship between firm size and growth of international firms is less negative than it is for domestic firms. Conversely, if b2 is negative and significant, it implies that the firm size–growth relationship of international firms is more negative than it is for domestic firms. For firm size class, if g2 is significant and greater than 0 (d2 > 0, and z1 > 0), the firm size–growth pattern of medium-small (medium-large, and large) firms is less negative than that of small firms. If g2 is significant and less than 0 (d2 < 0, and z1 < 0), the firm size– growth relationship of median-small (medium-large, and large) firms is more negative than that of small firms. Thus, models 1, 2, and 3 were used to test Hypotheses 1, 2, and 3, respectively. To check the validity of models 2 and 3, this study incorporated both models 2 and 3 into model 4. Finally, models 5-1 and 5-2 were used to test Hypotheses 4-1 and 4-2. Based on prior studies (Opler and Titman, 1994; Lang et al., 1996; Carpenter and Petersen, 2002; Oliveira and Fortunato, 2008), this study included total debt leverage (TDL), long-term debt leverage (LTDL), retained earnings (RE), and Tobin’s q as control variables. According to the pecking-order theory, firms prefer internal financing sources rather than external financing sources (Myers and Majluf, 1984). Thus, retained earnings are the -preferred financing source for firm growth. Thus, this study expected a positive relationship between the preceding year’s retained earnings (RE) and firm growth in the current year. On the other hand, Myers and Majluf (1984) argued that firms prefer debt financing to equity because equity financing changes ownership structure. However, debt financing can be risky if a firm has excessive debt due to the probability of a bankruptcy. Thus, firms with high total debt leverage tend to suffer from high financial distress costs and, thus, may have difficulties in making additional investments for growth. Therefore, a negative relationship between total debt leverage and firm growth was expected (Opler and Titman, 1994; Ushijima, 2005; Oliveira and Fortunato, 2008). Nevertheless, prior studies have revealed that long-term debt leverage is positively associated with firm growth. Longterm debt is highly related to long-term investment, which has a positive influence on firm growth. Hence, this study expected a positive relationship between long-term debt leverage and firm growth. Since Tobin’s q could be considered as growth opportunities, Carpenter and Petersen (2002) used one year lagged Tobin’s q as a control variable in his firm growth model. Along the same lines, this study expected a positive relationship between lagged Tobin’s q and firm growth. In addition, year dummies were included to control for yearly variations in the dynamic panel GMM-estimation. However, the results of the year dummies were not reported because they were not of direct concern to this study. All variables of interest and control variables are presented in Table 1.

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373

Table 2 Descriptive statistics. Variables

Sales

All sample

758.16 (1362)

Small firms Medium-small firms Medium-large firms Large firms

21.39 96.81 317.75 2564.29

(337) (341) (339) (345)

251.05***

ANOVA (F-value) Domestic firms International firms Unidentifiable firms

312.17 (726) 3229.59 (145) 687.77 (491)

t-Statistics (between domestic and international firms)

DGR

GR a

7.50***

11.62 (1292) 8.41 15.02 13.07 9.91

(310) (324) (324) (334)

5.40 (1122) 10.46 4.72 4.28 2.45

3.98***

(270) (277) (278) (297)

2.49*

10.08 (724) 6.16 (145) 16.14 (423) 1.93*

TDL

RE

0.61 (1358)

0.28 (1358)

0.66 0.52 0.64 0.62

0.23 0.22 0.35 0.33

(336) (340) (339) (343)

6.09***

6.46 (669) 1.27 (136) 4.93 (317) 2.31**

LTDL

0.61 (726) 0.69 (145) 0.59 (487) 2.04**

(336) (340) (339) (343)

.044 (1343) 1.49 0.29 0.15 0.17

18.78*** 0.27 (726) 0.38 (145) 0.27 (487) 3.36***

Tobin’s q

(336) (330) (335) (342)

43.43*** 0.53 (720) 0.34 (145) 0.33 (478) 1.26

1.76 (1185) 1.99 1.33 1.72 2.00

(305) (303) (274) (303)

10.35*** 1.63 (669) 2.19 (125) 1.86 (391) 4.05***

a

Parenthesis is the number of observations. p < 0.10. p < 0.05. *** p < 0.01. *

**

associated with the four firm size classes and domestic/international firms. In Table 3, the upper left side of each cell is the firm growth rate (static growth) and the bottom right side of each cell is the change of firm growth rates between time t and time t  1 (dynamic growth). Static firm growth rates for domestic firms across firm size classes showed convex shapes (5.22%, 13.86%, 11.59%, and 9.64%). On the other hand, static firm growth rates for international firms fluctuated across firm size classes (4.62%, 13.91%, 2.63%, and 5.38%). However, the growth rate change for small firms, 9.00%, was the most negative of the four size classes. Overall, international firms’ growth rate change (1.27%) was less negative than domestic firms’ growth rate change (6.46%). Interestingly, growth rate change for large domestic firms was 1.55%, but growth rate change for large international firms was 1.89%. This is different from the expectation provided in the literature review section. However, since the descriptive statistics do not fully reflect other confounding control variables, it is necessary to investigate a causal model in more detail to understand the impact of internationalization.

4. Results 4.1. Descriptive statistics Table 2 shows the descriptive statistics of the variables. Growth rates (GR) vary as firm size class increases (8.41%, 15.02%, 13.07%, and 9.91%), showing a convex shape. However, changes in the growth rates (DGR) were 10.46%, 4.72%, 4.28%, and 2.45%, respectively. This revealed that the growth rate of restaurant firms decreased less rapidly as firm class increased. The average size (net sales, million U.S. dollars) of international restaurant firms was about 10 times larger than the average size of domestic restaurant firms. Overall growth rate of the restaurant industry was 11.62%. The growth rates of domestic and international restaurant firms were 10.08% and 6.16%, respectively. On the other hand, the changes in growth rates (DGR) for domestic and international firms were 6.46% and 1.27%, respectively, which demonstrated that the growth rate of international firms decreased less rapidly than that of domestic firms. Average total debt leverage and long-term debt leverage of overall restaurant firms were 61% and 28%. International firms’ total and long-term debt leverage was larger than that of domestic firms. The average industry level of retained earnings was negative, indicating that the profitability level of the restaurant industry has not been positive. In particular, the retained earnings (RE) of small firms were the worst. The mean RE of large firms, however, was positive. Tobin’s q for large firms was the largest and international firms’ Tobin’s q was significantly larger than that of domestic firms.

4.3. Testing hypotheses As explained earlier, this study tested the proposed hypotheses by using Blundell and Bond’s (1998) GMM-system estimator, which could be viewed as temporal analysis. As presented in model 1 of Table 4, this study showed that preceding firm size had a negative and significant influence on current firm growth, supporting Hypothesis 1. Model 1 suggests that the joint hypothesis (a2 a3) = (0 0) was rejected, meaning that Gibrat’s Law was rejected. Thus, the overall size–growth pattern of restaurant firms was not following random walk but instead a systematic process. The results related to Hypothesis 1 were consistent with prior studies using temporal analyses (Mansfield,

4.2. Static and dynamic growth rates To investigate static and dynamic changes in firm growth rates in a more detailed manner, this study examined a two-way table Table 3 Two-way table for firm growth rates and change of firm growth rates. Small firmsa GR Domestic firms

5.22% (215)b

International firms 4.62% (7) All sample a

5.20% (222)

Medium-small firms

DGR

GR

DGR

Medium-large firms GR

DGR

Large firms GR

Total sample

DGR

GR

DGR

8.80% (200) 13.86% (215) 6.11% (194) 11.59% (185) 7.18% (169) 9.64% (109) 1.55% (106) 10.08% (724) 6.46% (669) 15.66% (6)

13.91% (24)

3.41% (21)

2.63% (31)

0.19% (30)

5.38% (83)

1.89% (79)

9.00% (206) 13.86% (239) 5.18% (215) 10.30% (216) 6.10% (199) 7.80% (192) 1.70% (185)

6.16% (145) 1.27% (136) 9.43% (869) 5.59% (805)

Class of small firms is defined as firms less than 1st quartile based on the every year net sales distribution. Medium-small firms are between 1st quartile and median net sales. Medium-large firms are between median and 3rd quartile net sales. Large firms are the largest quartile of net sales. b Parenthesis is the number of observations.

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Table 4 Results of generalized method of moments (GMM)-system estimation. Variables

Model 1 ***

Model 2 ***

Model 3

Model 4 ***

Model 5-1 ***

Model 5-2 0.6805*** 0.2291*** 0.0009 0.1075 0.0141 0.1722 0.2868* 0.1245*** 0.0981 0.0928 0.1025*** 0.3389*** 0.2985 0.1496*** 0.2162

(Constant) Si,t1 DSi,t1 DIi,t DIi,tSi,t1 DIi,tDSi,t1 DS1i;t1 DS1i;t1 Si;t1 DS1i;t1 DSi;t1 DS2i;t1 DS2i;t1 Si;t1 DS2i;t1 DSi;t1 DS3i;t1 DS3i;t1 Si;t1 DS3i;t1 DSi;t1 CSDIi,t1 CSDIi,t1Si,t1 CSDIi,t1DSi,t1 CLDIi,t1 CLDIi,t1Si,t1 CLDIi,t1DSi,t1 TDLi,t1 LTDLi,t1 REi,t1 Qi,t1

0.3140 0.0656*** 0.0606*

0.1070*** 0.0031 0.0707*** 0.1847***

0.0740*** 0.0065 0.0800*** 0.1410***

0.1507*** 0.0157 0.0499*** 0.1138***

0.0766*** 0.0119* 0.0752*** 0.1149***

0.0468** 0.0135** 0.0689*** 0.1116***

0.5739*** 0.0747*** 0.2208 0.0707*** 0.0138 0.0733*** 0.1233***

a

119.58 m1: 3.16*** m2: 0.57 16.31*** 885 Si,t2 DSi,t2 DTDLi,t2 DLTDLi,t2 DREi,t2 DQi,t2 DDYEAR

118.44 m1: 3.94*** m2: 0.39 87.29*** 885 Si,t2 DSi,t2 DTDLi,t2 DLTDLi,t2 DREi,t2 DQi,t2 DDYEAR

107.80 m1: 3.06** m2: 1.11 5.36* 628 Si,t2 DSi,t2 DTDLi,t2 DLTDLi,t2 DREi,t2 DQi,t2 DDYEAR DIi,t1

84.38 m1: 3.39*** m2: 1.16 69.42*** 628 Si,t2 DSi,t2 DTDLi,t2 DLTDLi,t2 DREi,t2 DQi,t2 DDYEAR DIi,t1

91.62 m1: 3.02*** m2: 0.84 248.89*** 628 Si,t2 DSi,t2 DTDLi,t2 DLTDLi,t2 DREi,t2 DQi,t2 DDYEAR DIi,t1

80.59 m1: 3.02*** m2: 0.73 68.88*** 628 Si,t2 DSi,t2 DTDLi,t2 DLTDLi,t2 DREi,t2 DQi,t2 DDYEAR DIi,t1

Sargan test Arellano–Bond test

b

c

(a2 a3) = (0 0) # of Obs. Instrumental variables

0.8214 0.2540*** 0.0298

0.0301 0.0207 0.0838** 0.0570 0.0129 0.0690

0.3104** 0.1282*** 0.0867 0.1055 0.1081*** 0.2114** 0.3993** 0.1703*** 0.3285***

0.6884 0.2359*** 0.0014 0.0284 0.0081 0.0201 0.2427 0.1166*** 0.0797 0.0063 0.0907** 0.2738*** 0.1434 0.1332*** 0.1117

4.2318 1.4240*** 1.9412*** 0.1223 0.0189 0.1992* 3.4209*** 1.2315*** 2.0235*** 3.5765*** 1.2831*** 2.2305*** 3.6600*** 1.3180*** 2.0519*** 3.7912*** 1.2616*** 1.9438***

a Sargan test is to test the over-identification of instruments in the model. If the Sargan test shows significance, it indicates that the model is over-identified, which can make biased estimation results. b Arellano–Bond test is the autocorrelation test. m1 is the first and m2 is the second order autocorrelation test. c Joint hypothesis test (a2 a3) = (0 0), is tested the Gibrat’s Law. If the joint hypothesis test is accepted, the Gibrat’s Law is accepted. If the Law is rejected, firm growth pattern is systematic. * p < 0.10. ** p < 0.05. *** p < 0.01.

1962; Contini and Revelli, 1989; Wagner, 1992; Harhoff et al., 1998; Hart and Oulton, 1999; Machado and Mata, 2000; Singh and Whittington, 1975; Chesher, 1979; Kumar, 1985; Goddard et al., 2002; Oliveira and Fortunato, 2008). The results were consistent with recent static analysis studies as well (Evans, 1987; Contini and Revelli, 1989; Dunne and Hughes, 1994; Hall, 1987; Johansson, 2004). To test Hypothesis 2, this study created model 2. The coefficients of the interaction variables, which were three size dummies multiplied with the preceding firm size (g2, d2, and z2), are of interest for Hypothesis 2. If g2 and d2 are significantly positive, it means that the growth rates of medium-small and medium-large firms decreased less rapidly than the growth rate of small firms. If g2 and d2 are significantly negative, it implies that the growth rates of medium-small and medium-large firms decreased more rapidly than the growth rate of small firms. Likewise, if z2 is significantly positive (or negative), it means that the growth rate of large firms decreased less (or more) rapidly than that of small firms. Model 2 shows that the coefficients g2, d2, and z2 were significantly positive, supporting Hypothesis 2. These results implied that the speed at which growth rates decrease slowed down as firm size increased. Considering the static growth

rates of each size class, the coefficients of DS1i;t1 and DS3i;t1 were negatively significant. Therefore, even though the average level of growth rates of medium-small and large firms were less than that of small firms, the growth rates of those two classes decreased less rapidly than that of small firms. These results implied that firm growth rate should be analyzed at the same level of size class. Further, from a sustainable growth perspective the decreasing speed of growth rate is more important than the static average of growth rate. To test Hypothesis 3 for the impact of internationalization on changes in firm growth rate, this study incorporated an internationalization dummy and its interaction with preceding firm size and firm growth into model 3. The preceding size variable was negative but not significant and the joint hypothesis (a2 a3) = (0 0) was not rejected, which meant that Gibrat’s Law was accepted. Moreover, the internationalization dummy and all interaction terms were not significant. However, a different result was found in model 4, which included the internationalization and size class variables. Thus, model 4 addressed the potential problem of model misspecification and could represent a more finely tune model. In other words, this may suggest that model 4 was more reliable than model 3. Model 4 showed that preceding firm size

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was negatively significant and coefficients, g2, d2, and z2 were significantly positive, again supporting Hypotheses 1 and 2. The joint hypothesis (a2 a3) = (0 0) was rejected, which meant that Gibrat’s Law was rejected and small firms grew faster than their larger competitors. However, the internationalization dummy and its interactions were still not significant in model 4, which did not support Hypothesis 3. Consistent with Pfaffermayr and Bellak (2002), the results indicated that internationalization had no significant influence on restaurant firm growth. These results may come as a surprise to the restaurant industry because restaurateurs usually consider internationalization a good strategy for consistent sales growth. It is difficult to clearly explain the reasons behind these results. Thus, further examination is necessary to figure out the effect of internationalization. Thus, as the next step, this study considered firm class (contrasting small and large firms) in examining the effects of internationalization on firm growth as proposed in Hypotheses 4-1 and 4-2. Model 5-1 includes CSDI, which is the contrast variable of small domestic versus small international firms and the interactions with preceding firm size and firm growth. As expected, the coefficient of CSDIi,t1Si,t1 was negatively significant, meaning that the growth rate of small international firms decreased more rapidly than that of small domestic firms. Thus, internationalization had a negative effect on the growth rate of small restaurant firms, supporting Hypothesis 4-1. The results indicated that it may be hard for small restaurant firms to sustain their growth rate if they go into the international market. Model 5-2 includes the contrast variable of large domestic versus large international firms (CLDI) and its interactions with preceding firm size and firm growth. The coefficient of CLDIi,t1Si,t1 was positively significant, signifying that the growth rate of large international firms decreased more slowly than that of large domestic firms. The result supports proposed Hypothesis 4-2. Thus, internationalization had a significant positive impact on the growth of large firms, revealing that sustainable growth is more likely for large international firms than for large domestic firms. Overall, models 5-1 and 5-2 demonstrate that internationalization has different effects on restaurant firm growth patterns according to firm size. The varied influences of internationalization on firm growth between small and large size classes could explain the insignificant effects of internationalization in models 3 and 4. The hypothesis associated with models 3 and 4 proposed that internationalization had a linear and positive influence on firm growth. However, models 5-1 and 5-2 showed that the effects of internationalization on firm growth produced opposite results in small firms and large

Fig. 1. Firm size–growth patterns of small domestic versus small international firms.

375

Fig. 2. Firm size–growth patterns of large domestic versus large international firms.

firms. Thus, without controlling for firm size class, the overall effect of internationalization on firm growth appeared insignificant. To further examine the dynamic growth patterns of domestic and international restaurant firms, Figs. 1 and 2 are depicted. Fig. 1 shows that the dynamic growth rate of small international firms decreased more rapidly than that of small domestic firms, while the dynamic growth rate of large international firms decreased less rapidly (more slowly) than that of large domestic firms. Interestingly, the variables of CSDI and CLDI in models 5-1 and 5-2 were significantly positive and negative, respectively, signifying that the static growth rate of small international firms was greater than that of small domestic firms but the static growth rate of large international firms was less than that of large domestic firms. Consequently, internationalization affects static and dynamic firm growth in opposite directions in small and large firm classes. Thus, the contradictory results of previous internationalization studies may occur due to the mixed use of static and dynamic firm growth and the opposite impacts of internationalization on small and large firm classes. 5. Conclusion This study investigated firm growth patterns in association with firm size classes and internationalization in the U.S. restaurant industry. This study verified an overall negative relationship between firm size and growth rate for the restaurant industry, confirming that small restaurant firms grew faster than large firms. It was also found that the static growth rate of large firms was lower than that of small firms, ceteris paribus. These results indicated a non-linear relationship between firm size and firm growth as firm size class increased. Practically, even though small firms show higher static growth rates, their growth rates decrease quite rapidly. Consequently, if they cannot obtain sufficient market shares during stages of high growth, the probability of failure is high. This may be one of the reasons why small firms show higher rates of bankruptcy. Regarding the effect of internationalization on firm growth, this study did not initially find significant results for the overall restaurant industry. But when considering size classes we found meaningful results: the growth rate of small international firms decreased more rapidly than that of small domestic firms, whereas the growth rate of large international firms decreased less rapidly (more slowly) than that of large domestic firms. Thus, internationalization impacted the decreasing growth speed of restaurant firms both positively and negatively, depending on firm size classes. Consistent with Bellak (2004), internationalization had a

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positive effect on the dynamic change of firm growth only in large firms. However, internationalization by small firms can accelerate the decreasing speed of growth rate. Thus, going international can be a harmful strategy for creating sustainable growth for small firms. Low managerial capability, small organizational learning capacity, and limited resources are more rapidly exhausted in small restaurant firms when they go international market. Consequently, small international firms could not operate efficiently. In this respect, Blonigen and Tomlin’s (2001) argument for the negative impact of internationalization on firm growth when first entering an uncertain foreign market with lower capital intensity is plausible. The results of this study are meaningful academically as well as practically. Academically, this study distinguished the concepts of static and dynamic firm growth associated with firm size classes and internationalization. Internationalization had different effects on firm growth across firm sizes. These results provide a hint as to why previous internationalization studies produced contradictory results. In practical terms this study evidenced that internationalization may not be a useful growth strategy for small restaurant firms. Therefore, small restaurant firms should be careful when they consider international expansion. This study is not free from limitations. One distinct limitation is the very small sample size of small international firms in analyzing small domestic versus small international firms for Hypothesis 4-1. The small sample size can weaken the statistical power of the analysis. Another limitation is that this study analyzed only restaurant firms whose data was available through the COMPUSTAT database even though many more firms are very small and are not included in the database. Thus, although some firms were categorized as small firms in this study, they are not absolutely small but relatively small. This study focused on the relationship between firm size and growth rate using Gibrat’s Law. Even though this study investigated the influence of size classes and international strategies, other factors should additionally be considered such as experience in foreign markets, firm age, diversification and various business strategies. Future studies could also deal with intangible assets, such as brand value, in understanding firm growth and could address brand name strategies to explain the differences in internationalization effects between small and large restaurant firms.

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