THE EFFECTS OF FRANCHISE OWNERSHIP AND PRICE ON SURVIVAL: BEYOND RESOURCE SCARCITY

Ilan Alon*Cornell Professor of International Business

Rollins College 1000 Holt Ave, Winter Park, Florida, USA

Email : ialon@rollins.edu

M. Victoria Bordonaba-JusteAssociate Professor

Dept. Economy and Business AdministrationUniversity of Zaragoza (Spain)

Email: vbordon@unizar.es

Laura Lucia-PalaciosAssistant Professor

Dept. Economy and Business AdministrationUniversity of ZaragozaZaragoza, 50005, Spain

Tel: +34 976 761000Fax: +34 976 761767

Email: llucia@unizar.es

Yolanda Polo-RedondoProfessor in Marketing

Dept. Economy and Business AdministrationUniversity of Zaragoza (Spain)

Email: ypolo@unizar.es

* Corresponding author

Keywords: Franchising survival, Network survival, Cox Analysis, Franchising Pricing, Proportion of Franchising

This research was financed by a project from the regional government GENERÉS (S09) and PI 138/08; and from the Spanish Government (CICYT ECO 2008-04704).

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THE EFFECTS OF FRANCHISE OWNERSHIP AND PRICE ON SURVIVAL: BEYOND RESOURCE SCARCITY

ABSTRACT

Going beyond resource scarcity, this article develops a multi-theoretic framework that explains the relationship between ownership and pricing strategy and two types of franchising survival (franchise use survival and company survival). We used a large sample longitudinal data related to the franchisors that operated in the US between 1994 and 2008 to test multiple variables and their interactions. The results show that, in general, survivors are older, larger chains with lower proportions of company-owned units, higher royalty rates, and greater international expansion than non-survivors. We also found that the negative effect from having a high proportion of company-owned units on survival is greater for larger chains than it is for smaller chains. The results suggest that theories of franchising success/failure need to go beyond a simple examination of the resources and into explanations rooted in agency, signaling and dependency theories.

THE EFFECTS OF FRANCHISE OWNERSHIP AND PRICE ON SURVIVAL

1. IntroductionFranchising has been an important type of business in the economy over the last decades. According to statistics of the International Franchise Association (IFA 2005), franchised businesses created nearly 21 million jobs (15 percent of all US private-sector jobs) and produced $2.31 trillion in annual output (11.4 percent of all private-sector output in the US). In the service industries, like fast-food restaurants, franchising consisted of over 56 percent of all establishments. In the US, the size and impact of franchising is formidable, with direct output in excess of $835 billion (IFA, 2009).

Although franchising has become a mainstream economic activity in the United States, little is known about the key factors for success and survival (Alon 2006). In particular, the International Franchising Association has claimed that franchising reduces the risk of failure, but the empirical evidence has not always supported this contention (e.g., Shane and Foo 1999; Stanworth et al., 2001). Furthermore, how ownership and pricing strategies impact franchise survival is not well articulated. Therefore, our research question is twofold: how do (1) the ownership strategy and (2) the pricing strategy of franchising firms affect their survival. Our research is unique in investigating two kind franchising failure explanations: firms who stop using franchising and firms who no longer exist altogether. Theoretical speculation and empirical evidence are presented.

The extant franchising research has focused on which theory better explains different aspects of ownership and pricing decisions, such as the antecedents of using dual distribution, ownership redirection (Lafontaine and Kaufman 1994; Dant et al. 1996; Dant and Kaufman 2003; Combs and Ketchen 2003; Combs et al. 2004; Castrogiovanni et al. 2006), or the dynamism of the pricing strategy (Lafontaine and Shaw 1999; Kaufman and Dant 2001; Vázquez, 2005). Agency theory (Brickley and Dark 1987; Rubin 1978; Brickley et al. 1991; Lafontaine 1992; Shane 1998; Alon 2001) and resource scarcity theory (Oxenfeldt and Kelly 1968/1969; Caves and Murphy 1976; Norton 1988; Dant et al. 1992; Dant and Paswan 1998) are the main explanations. Combs and Ketchen, 2003; Alon, 2006).

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In recent years, new approaches have been proposed. A property-rights view (Windsperger and Dant 2006) and a risk-based explanation (Bürkle and Posselt 2008) attempt to explain the use of plural forms (Dant et al. 1992; Bradach 1997). The signaling model (Gallini and Lutz 1992; Lafontaine 1993) and the reputation model (Mathewson and Winter 1985) are new approaches to understand the relationship between the pricing terms and their dynamism. We use the extant literature to develop a multi-theoretic approach to explain the impacts of ownership and pricing on survival.

Despite the existing research on ownership and pricing, several issues remain unanswered. Little is still known about the performance implications of dual distribution, pricing, and its dynamism over the organizational life cycle (Heide 2003; Srinivasan, 2006; Shane et al. 2006). Performance in franchising has been measured in very different ways, but a more recent measure has been the network growth measure (Michael 2003; Shane et al. 2006; Castrogiovanni et al. 2006; Dant et al. 2007) and, oftentimes, survival (Castrogiovanni et al. 1993; Stanworth et al. 1998; Bates 1995; 1998; Lafontaine and Shaw 1998; Shane and Foo 1999; Shane 2001; Holmberg and Morgan 2003).

Researchers have also made a theoretical effort to suggest hypotheses about the relationship between ownership and pricing and growth to identify supporting theories. However, few studies applied a theoretical approach and proposed hypotheses about ownership and pricing in relation to franchisor survival (Shane and Foo1999; Shane 2001). Therefore, we still do not know whether classical theories (resource scarcity or agency explanations) or new approaches (signaling theory, the plural forms perspective, or the double-sided moral hazard) better explain the link between ownership and pricing and survival.

In relation to ownership structure, agency theory is mainly applied (Shane 1996; Shane 1998; Shane and Foo 1999), although the results of ownership redirection only partially confirm the theory (Shane and Foo 1999). In most of these studies no hypotheses are proposed about the relationship between ownership and survival (Castrogiovanni et al. 1993; Lafontaine and Shaw 1998; Stanworth et al. 1998; Shane 2001). Greater ambiguity was found in the franchising literature about the underlying theory behind the relationship of the pricing strategy and survival. Results of previous studies have found a positive (Shane 1998; Shane 2001), negative (Lafontaine and Shaw 1998), and non-significant effects (Shane and Foo 1999). There is still little evidence of the effect of the pricing dynamism on survival (Shane 2001). Our article contributes to the extant literature by examining, in part, the impact of pricing on survival, with survival being measured in two different ways: survival of franchising as a model and the system as a whole. The second type of failure is the total failure of the system and is, thus, more significant.

In this paper we compare agency theory and the signaling model with resource scarcity theory and the plural forms perspective to explain the effects of ownership and pricing dynamics on survival. These theories are contradictory in that they propose different effects as a franchise matures or becomes more established. We analyze these dynamisms in terms of age and size, using the hypothesized effects as moderators.

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In the next section we analyze the literature of franchising success/failure. This is followed by hypotheses development, data gathering and analysis, results and conclusions and discussion.

2. Franchise success/failureIn the last decades, researchers have paid attention to franchise survival, a field in which a great heterogeneity of studies is found. Some studies have adopted the franchisee point of view. They focused on comparing the failure risk of franchisees and entrepreneurs (Bates 1995a, 1995b; 1998); others have analyzed the franchisee success or failure (Falbe and Welsch, 1998), comparing the discontinuances and failures (Tuunanen and Torikka, 2008) or suggesting a multistage process (Holmberg and Morgan, 2007). Most of the researchers have analyzed only organizational mortality (Bates 1995a, 1995b; 1998; Falbe and Welsch, 1998; Michael and Combs 2008) and, to a lesser extent, discontinuation of franchising as an organizational form (Frazer, 2001; Frazer and Winzar, 2005). Our article does both, since each represents a different type of market exit.

Most of the franchise survival research has focused on franchisor or network failure. This could be explained by the idea that network survival may reflect network performance. If the network survives, this means that the network is able to react and to adapt to the different competitive environments (Perrigot et al 2004). Early research focused on analyzing franchisors failure rates compared to other business (Castrogiovanni et al., 1993; Stanworth et al., 1998). Additionally, the failure rate of new franchisors was also analyzed (Shane 1996; Stanworth et al., 2001) and the entry to and exit from the franchise market (Lafontaine and Shaw 1998). In the last years, much research paid a special attention to the drivers of franchisor failure (Lafontaine and Shaw 1998; Shane and Foo, 1999; Shane 2001; Azoulay and Shane, 2001; Bordonaba et al., 2009). As in franchisee survival research, most of the studies have focused on organizational mortality. However, other possibilities of market exit could be distinguished. Holmberg and Morgan (2003) examined different stages of network failure distinguishing between franchise “turnover” than franchise “failure”. They found different results from previous research. By using two measures of market exits, our this article enriches the discussion of success/failure of franchising as well as the debate of its determining factors.

3. Ownership Redirection

Ownership in franchise systems has long been a focus of franchising research. Two theories have been dominant is previous explanations: resource scarcity and agency theories. According to resource scarcity theory, entrepreneurs use franchising to gain access to strategic resources in early stages (Oxenfeldt and Kelly 1968/1969; Norton 1988). Young firms need three types of capital: financial, managerial, and informational (Norton 1988). Franchisees are a source of these three types of capital and they are a less expensive option than going public, for example (Hunt 1973; Caves and Murphy 1976; Lafontaine 1992; Dant 1995).

Although franchisees can help the growth of a company, they can also create some conflicts. According to the franchisors, franchisees may work hard in the wrong way. This type of problem is only solved by using control mechanisms within company-owned units (Shane 1999). Therefore, according to the resource scarcity theory, franchisors prefer to manage company-owned units rather than franchised units, but

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franchise to extend limited managerial and financial resources. Thus, franchisors use franchised units at the early stages to solve their capital constraints, but at mature stages, the network reverts to more company-owned units (Oxenfeldt and Kelly 1968/1969; Hunt 1973). Some authors have found evidence of a curvilinear relationship between company-owned units and size (Shane 1998). There is an apparent conflict between resource-based and agency theories in explaining the proportion of franchising or company ownership. Moreover, the impact of company ownership on survival in the presence of experience is even less known. Following the logic that as firms get more resources, they will need less franchising, we can hypothesize that as franchise networks get older and bigger, the positive effect of the proportion of company owned on failure will be weaker.

In contrast, agency and the signaling theories propose a preference toward franchised units. Agency theory suggests that franchised units are more efficient because the franchisor does not need to rely on incentives (Shane 1996). Franchisees put in the right amount of effort in managing the store because they will receive a large part of the profit of the outlet (Rubin, 1978; Brickley and Dark, 1987). So, the costs of monitoring and motivation of franchisees are lower than those of the managers. This benefit is emphasized when the new outlets are far from the headquarters (Brickley and Dark, 1987; Norton 1988). Thus, this theory suggests that larger and older franchise networks will reinforce the use of franchised units for their sustainable growth.

Signaling theory also supports a preference for franchised units, but the basis of the theory is different. The signaling theory is based on market asymmetries and the lack of knowledge (Gallini and Lutz 1992; Lafontaine 1992). Franchisors have to find a way to show the potential franchisees the quality of the business. According to this theory, franchisors at early stages of development use company-owned units to show the quality of their business ideas, but this preference will change at the mature stages. Prospective franchisees will be more likely to trust older and larger systems than newer and smaller networks. Those older and larger networks have more experience in the market and with the product, and prospective franchisees may observe the market share of each outlet. Evidence to support this theory is mixed. Gallini and Lutz (1992) and Shane et al. (2006) found support for this theory, but Lafontaine (1992) did not find evidence of this dynamism. On the basis of these streams of research, we can hypothesize that as franchise networks become older and larger, the positive effect of the percentage of company ownership on failure will be stronger. That is, franchisees will rather trust large networks that also have a high proportion of franchising outlets as these networks will be seen as more successful.

Finally, in contrast to agency theory and signaling theory, dependence theory takes a plural perspective (Dant, Paswan and Kaufmann 1996; Bradach 1997; Cliquet 2000; Cliquet and Nguyen 2004; Lafontaine and Shaw 2005; Bürkle and Posselt 2008). This perspective is based on the fact that both types of units are needed to be successful (Sorenson and Sorensen 2001). On the one hand, company-owned outlets are related to higher brand-name capital (Minkler and Park 1994). On the other hand, when the monitoring costs rise due to uncertainties, opportunism, and dispersion, franchised outlets are more efficient. Lafontaine and Shaw (2005) found evidence of a stable proportion of company-owned units over the organizational life cycle. Thus, in this situation, we should not find any dynamism over time as organizations grow, and can hypothesize that the percentage of company ownership has no moderating effect on size and age in relation to survival. Companies reach a steady state of franchise ownership

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which is optimal for their system/industry and this relative ownership is stable over time.

The three competing theoretical streams allow us to test which theory is best suited to explain franchising failure. We, thus, propose that if resource-scarcity is dominant, PCO will have a negative impact on the relationship between size and age and survival. If, on the other hand, the relationship will have a positive PCO moderating effect, agency and signaling theories prevail. Finally, if no impact is noted, dependency theory of plural perspective takes the lead in explanation.

4. Pricing Strategy

Pricing in franchising is related to the decision of two components: upfront franchise fees and royalty rates. Most franchising research has focused on the relationship between both contract terms (Vázquez 2005), their evolution over time (Lafontaine and Shaw 1999), or their impact on internationalization (Alon, 2010). The results about the dynamism over the contract term are mixed. Some researchers have found evidence of some evolution over time (Lafontaine 1992) and a correlation between the contract terms and the age of the system (Vázquez 2005). Others found a relationship between high fees in relation to royalties and internationalization. Although there is no agreement in the literature about the dynamism of royalty rates and upfront fees, most models agree that an increase in the royalty rates should be accompanied by a decline in the upfront fees (Lafontaine and Shaw 1999). That is, there is an inverse relation between royalty rates and franchise fees.

Although the pricing strategy is an important decision that affects the attraction of prospective franchisees (Shane et al. 2006), little is known about the effect and relevance of these contract terms on performance measures. In the franchisor survival research both terms are usually included as independent variables, with a negative direct effect on failure.

In analyzing the antecedents of franchise fees and royalty rates from an agency perspective, on the one hand, the more valuable the continuous services provided for the franchisors, the higher the royalty rates. On the other hand, the franchise fees are explained mainly by the initial costs related to the opening of new outlets: training, selecting new franchisees, and locating. As franchisors become larger, the costs of continuous services after the outlet becomes operational, such as marketing campaigns or ongoing training increase (Vázquez 2005). As the franchise network increases in size, the franchisor’s effort to maintain the brand name is higher, partly due to monitoring costs (Rubin 1978; Shane 1996). The franchisor are compensated for the additional effort by increasing the royalty rates. To compensate the franchisees for the increase in royalty rates, firms can reduce their upfront fees (Lafontaine and Shaw 1999; Brickley 2002; Vázquez 2005), suggesting a double-sided moral hazard. Thus, following agency theory, we can hypothesize that as a franchise network becomes bigger and larger, the negative effect of upfront fee will be weaker and the negative effect of royalty rate will be stronger on the possibility of failure.

However, using information asymmetry and signaling models may suggest a different argument (Baucus et al. 1993; Shane et al. 2006). Based on the information asymmetry of the signaling models (Gallini and Lutz 1992), the franchisor has to use some instruments to show the market the profitability of its business. One way to do this is

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through the ownership of some units, as already noted. Another way is through upfront fees. Some studies suggest that the upfront fees cover the brand-name value and the reputation of the franchise network (Bhattacharyya and Lafontaine 1995; Brickley 2002). There is evidence of a positive relation between system size and age and the upfront fees (Baucus et al. 1993). As a firm operates in the market, it develops a reputation that is used to attract franchisees (Mathewson and Winter 1985). As a result, franchisors no longer need to keep upfront fees low so as to attract prospective franchisees. Thus, franchisors can afford to charge higher upfront fees (Shane et al. 2006).

As mentioned before, as the upfront fees increase, the royalty rates should decrease. There are additional arguments to support this strategy. First, charging lower royalty rates may be interpreted as an incentive to the franchisee to improve the business. Evidence shows that most innovations and improvements are proposed by franchised units rather than by company-owned units (Darr et al. 1995; Sorenson and Sorensen 2001). Second, as the franchisor increases the size, the market becomes more saturated, thus reducing the franchisee’s profits. A franchisor reduces the royalty rates to compensate the franchisees for the saturation of the market and for adding new outlets (Shane et al. 2006). Therefore, using signaling and information asymmetry models, we can hypothesize that as the franchise network becomes bigger and older the negative effect of upfront fee will be stronger, while the negative effect of royalty rates will become weaker.

5. Data and Methodology

To test our hypotheses we have an unbalanced panel of 2,474 US franchise systems that were operating between 1994 and 2008. The source of the data is Bond’s Franchising Report (World Franchising 2008), which is among the most comprehensive available datasets on franchising over time and used by multiple researchers in the past (Mehta et al. 1999; Michael 1999, Clarkin et al. 2007).

The information collected includes the starting year of franchising, the year of the firm set-up, the number of company-owned units and franchised units, the number of units overseas, and the sector in which the firm operates. Each franchise system’s information was collected annually.

From our sample, a total of 1,458 firms exited the market (58.93 percent) among a total of 10,978 observations. Two kinds of exits are identified and analyzed: (1) exiting the franchising business and (2) exiting the business altogether. Cox regression analysis was used to test the effects of the covariates on franchisor failure. Cox regression allows for the control of right censoring and left truncation effects (to solve the problem generated by firms that were already in business before 1994).

The hazard function of a firm hi (t) is expressed as: hi (t) = h0 (t) exp [(βXi) +λXi(t)] , where λ0 (t) is an arbitrary and unspecified baseline hazard function reflecting the probability of failure conditional on the firm having survived until time t after entry into the market, Xi is a vector of measured explanatory variables for the ith firm (time-varying and constant covariants), and is the vector of unknown regression parameters to be estimated (as coefficients or hazard ratios).

We can linearize the model by dividing both sides of the equation by h0(t) and taking

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logarithms on both sides:

The dependent variable is the probability of franchisor failure. We define failure as not requesting the survey and non-reemergence in the survey at a later date. Firms that were alive during the year of the survey were coded as 0 and firms that were no longer listed in the survey were coded as 1. To verify whether a firm is in existence, we have checked their website searching for the franchise by name on Google. If the firm was no longer showing on the franchise list and no website existed, we assumed that the firm is no longer in existence. For firms in existence, we also checked their website and called their phone numbers to see if they still franchising. If the firm stopped franchising, we considered it as a second type of failure, or market exit. If the franchise system did not fail during the period of our study, it was treated as right censored.

Following previous franchising research, we operationalize PCO as the proportion of company-owned units that the system has each year (company owned units/total units). Previous research has also measured the percentage of franchise ownership (Alon, 2001), which is the same scale in reverse. Either conceptualization is acceptable, but we chose the former because of our emphasis on the franchising company as the unit of analysis. We measure the royalty rate as the percentage of sales that franchisees pay to the franchisor on an ongoing basis. The upfront franchise fee is the amount that the franchisee pays to the franchisor to open an outlet.

Other variables we included in the analysis are the age of the firm, as the number of years since the firm was set up, the age at which the firm started to franchise (package time), and franchisee investment as the amount of expenditures incurred by the franchisee to open the outlet. We add a set of dummy variables for each sector of study and a dummy variable that takes the value 1 for the years 1994-1999 of our study.We take the natural logarithms of all the independent variables (in this way we measure the elasticity of the coefficients) except the dummy variables.

Table 1 shows the means, standard deviation, and maximum and minimum of the variables. It shows the skewed nature of some variables, so we decided to transform the variables, taking logarithms. Table 2 provides a correlation matrix. Because of the relatively low level of correlations among the non-interaction independent variables, multicollinearity was not identified as a problem.

Insert Table 1 and 2 About Here

6. Results

Table 3 presents a mean comparison test between survivors and non-survivors. Differences are found in the level of experience, time to franchising, age, total size, company-owned units, royalty rates, and the number of units overseas. Survivors are older, larger chains with lower proportions of company-owned units, higher royalty rates, and greater international expansion as compared to non-survivors.

Insert Table 3 About Here

Table 4 shows the results of the Cox regressions predicting franchisor failure, both in terms of discontinuing franchising activity and total organizational failure. The

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regressions measure the effects of the independent variables on the likelihood that the franchisor will fail, both in terms of main effects and testing for interactions.

Insert Table 4 About Here

A negative coefficient means that the independent variable has a negative effect on failure, or positive effect on survival. Results differ between the two types of market exit: organizational failure and discontinuous franchising. Regression results are shown in 4 columns, first using only main effect then testing for interactions.

The first column depicts total organizational failure, controlling for industry and time. Most of the main effect variables are significant, and the model as a whole is also significant. The model suggests that age, size, overseas units, and upfront fee all have a positive impact on survival (negative impact on failure), while the percentage of company owned units and time to franchising have negative and significant impact on survival (positive impact on failure).

Examining a comparable model in column 3 for discontinuous franchising reveals similar coefficients on significant variables, although less of the same variables are significant. That is, age and size are positively and significantly related to franchising survival, while time to franchising and percentage of company ownership are negatively related to percentage of company ownership. The rest of the main effect variables were not significant.

What can be concluded about the main effects from both models of failure, in column 1 and 3, is that older and bigger firms, with more franchising outlets as a percentage of total outlets, are more likely to survive.

Examining the two hypothesized relationships, between percentage of company ownership as well as pricing and survival allows us to test the theoretical antecedents of failure. Looking specifically at the effect of the level of PCO on survival, we found that the PCO has a positive effect on franchisor organizational failure (0.632 p<000) and on discontinuous franchising (0.592 p<000). This positive effect is stronger as the franchise system becomes larger (0.298 p<0.5 and 0.928 p<0.000).

The effect of the pricing strategy is mixed. Both the upfront fee and the royalty rates show a significant negative effect (-0.278 p<0.01) and (-0.146 p<0.05) respectively on organizational failure. However, these variables show a non-significant influence on discontinuous franchising. The only interaction term that is significant is the upfront fee x size, showing a positive effect (0.076 p<0.1) on organizational failure. This means that the negative direct effect of the upfront fee is diluted as the franchise system becomes larger.

7. Conclusions and Discussion

This study presents empirical evidence from the US franchise sector regarding what theory better fits the relationship between ownership and pricing dynamics and survival. In this paper we distinguish between organizational failure and discontinuous franchising. As suggested by the data analysis, franchise networks that face a greater likelihood of organizational failure are those that increased their proportion of company-owned units and their upfront fees as the firm became larger. In the analysis

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of discontinuous franchising the proportion of company-owned units is the main concern.

Positive effect between PCO and failure become stronger as franchising become bigger. This relationship supports agency and signaling theories. Age, on the other hand, does not have a significant impact on the relationship. The lack of significance for the moderating effect of age supports the dependence theory perspective. As the firm becomes older, the effect of PCO is neither reinforced nor weakened.

A large franchise system with a high upfront fee will have a greater likelihood of failure, thus supporting signaling and information asymmetry models. The royalty rate has no significant effect in any of the interaction terms included in the analyses. All the hypotheses related to the moderating effect of age are rejected. We find no evidence for the dynamism of the upfront fee and the royalty rate over time.

Although some of our results are consistent with previous research, many of our findings extend prior research and offer some counterintuitive insights. The results partially support signaling theory and agency theory. According to these theories, franchisors should increase their level of franchised units as the firm becomes more established as a signal of the profitability of the business. We have included two measurements for the extent of the firm establishment: network size and age. These theories are only confirmed when we measure the level of reputation or establishment in the market as network size. However, when we use age as the moderator of the influence of ownership structure on survival, it is not significant.

Previous studies that support the idea of an ownership redirection have analyzed this effect on franchising dynamics in terms of age (Martin 1998; Thompson 1994; Lafontaine and Kaufman 1994), and only a few studies include age and size to analyze the ownership redirection (Dant et al. 1996; Alon, 2001; Dant and Kaufman 2003). In our study, we did not find any effect of this ownership redirection; nor do we find that the use of franchising is reinforced as the firm matures. Thus, the variation in the ownership structure is mainly moderated by the size of the network. As the firm expands geographically, it is more difficult for the franchisor to control the outlet and the costs of monitoring and controlling outlets far away from headquarters are high. The best option is to use franchised units. This decision should be optimal for any level of experience of the franchisor. Whether a firm is young or mature does not affect the choice of the optimal type of outlet. For both type of firms, company-owned outlets over franchised units have the same effect on survival.

The results also suggest that mature and large networks will have a lower probability of exiting the market. This result reinforces the idea that it is not only the young and small networks that are interested in franchising. Franchising is an efficient way of managing a large network. The resource scarcity theory is again rejected. The advantages related to agency theory seem to be underscored in our results.

There is no consensus in the literature about the evolution and dynamics of pricing policy. However, most previous research suggested that both terms are negatively related. The results about pricing strategy presented in this article support previous evidence on the lower variability of the upfront fee and the royalty rate as a firm ages (Banerji and Simon 1991; Lafontaine 1992). Lafontaine (1992) found that although the upfront fee increases over time and the royalty rate decreases over time, neither effect is

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statistically significant herein. In another research article, Bhattacharyya and Lafontaine (1995) showed that over periods of up to 10 years, these terms are relatively stable. Franchise firms do not systematically change their upfront fees and royalty rates over time (Lafontaine and Shaw 1999).

Confirming previous results (Kaufmann and Dant 2001), the royalty rates are not significant. However, when the dynamics in the pricing policy are analyzed, as the firm increases its size, our results suggest that the upfront fees are more likely to increase the likelihood of failure. This finding is contrary to the idea that the upfront fees include the value of the brand name and the firm’s reputation (Baucus et al. 1993; Bhattacharyya and Lafontaine 1995; Brickley 2002). Therefore, our results about pricing strategy reject the signaling model. Our research on organizational failure only partially confirms the agency model and the double-sided moral hazard because we did not find evidence of the other part of the double-sided moral hazard model: an increase in royalty rates as the firm becomes larger. Although a negative relationship between the two terms has been established in several articles (e.g., Lafontaine and Shaw 1999), no such evidence was found here.

This article’s findings contribute to the franchising literature in several ways. We have analyzed which of the theories and approaches better fit the link between ownership and pricing dynamics and network survival. Ownership dynamics is explained by signaling and agency reasons, while pricing dynamics partially follow agency theory.

Second, through our results we have analyzed the effect of age and network size as moderators of the effect of the ownership structure and the pricing policy on survival. Both strategies have been included previously in franchisor survival analysis but, as we have found, size is an important moderator of the effect that ownership and upfront fees have on franchisor survival. Although most previous research has focused on the effect of age as a moderator, we found that the extent of network size leads franchisors to adapt and change strategies.

The findings have important managerial implications. Survival is the basic aim of any firm. Looking at the results, managers should increase the proportion of franchisees as their network becomes larger. Agency and monitoring costs should be evaluated by the franchisor, using the pricing policy as a control instrument. Although survival is not influenced by the royalty rate, managers should take into account the effect of their upfront fee on firm success. As we have observed, franchisors should decrease their upfront fee as the network becomes larger.

Limitations and Future Research

This study is not without limitations. First, we study only a small number of strategic actions that firms use to survive. The variables included in the analysis are objective and as in previous studies, with a low level of significance. In the future, other strategic decisions might be included as well as subjective variables. Additionally, our research provides evidence that should be confirmed in other countries.

Future research should consider network size in analyses of franchising and pricing dynamics. An additional contribution of this study is related to the analysis of two types of market exit and to the use of data from different industries. Although most

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franchising research has been focused on the fast-food industry, recent efforts have been made to include other sectors and to provide general results.

Future research should analyze evidence of the royalty dynamics. Although we have not found a significant effect of a variation in the royalty rates, we have found a negative but non-significant correlation between the royalty rates and the upfront fees. Thus, increasing the royalty rates as the size of the network increases may not hurt the firm’s expansion objectives.

Of course, there is always a difficulty of projecting causation in the social sciences, especially when data utilized is secondary. One could make a case that survival, age, size of network, percentage of franchised outlets, percentage of international activity and even size of royalties are all items comprising a construct called “success”. A firm might have a higher percentage of franchisees because of success; be able to expand overseas based on domestic success and be able to charge higher on going royalties because of perceived value added due to success. In an extreme case they could all be endogenous. We attempted to define success/failure in its strictest forms. When organizations stop operating, organizational failure, or when the franchising stops operating, franchise system failure. Future research should attempt to measure success using other factors, aside from lack of failure. Collectively, these studies will contribute to our understanding of the key success factors for franchising.

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Table 1. Descriptive Statistics of the Main VariablesVariables Mean Std deviation Min MaxFailure 0.11 0.32 0 1Age 23.01 16.04 0 143Time franchising 15.63 11.7 0 89Time till franchising (Package) 7.378 12.13 0 129Total units 342.82 1467.56 0 31439Company-owned/total units 0.185 0.88 0 69.07Franchise fee 35.88 805.32 0 77500Royalty 4.92 3.92 0 7Units overseas 76.43 809.27 0 24300Total investment 337.93 1640.9 0.9 75007

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Table 2. Correlation Matrix failure age package fee royalty units overseas size PCO Investment sxx

failure 1.000age -0.177 1.000package -0.001 -0.084 1.000fee -0.050 0.013 0.023 1.000royalty -0.032 -0.005 -0.079 0.160 1.000units overseas -0.091 0.267 -0.050 0.082 0.004 1.000size -0.222 0.524 -0.063 0.071 -0.007 0.545 1.000PCO 0.134 -0.256 0.229 0.061 -0.005 -0.127 -0.296 1.000Investment -0.053 0.196 0.054 0.447 -0.060 0.103 0.195 0.172 1.000sxx 0.131 -0.247 0.040 -0.105 -0.031 -0.080 -0.165 0.084 -0.131 1.000

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Table 3. Mean Differences Between Survivors and Non-survivors (ANOVA)Variables Survivors Non-survivors Discontinuous

franchisingF

Time franchising 16.17 8.86 9.59 194.8***Time till franchising (package) 7.42 6.05 8.62 6.55***Age 23.59 14.91 18.21 120.08***Total units 365.86 66.25 56.18 22.05 ***Company-owned/total units 17.58 30.31 28.66 9.77***Franchise fee 37.05 21.67 21.09 0.18Royalty rate 4.94 4.73 4.57 2.42*Units overseas 74.49 4.22 6.32 4.35**Total investment 345.84 261.66 192.03 2.25 * significant at 10%, ** significant at 5% and *** significant a 1% level

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Table 4. Cox Regression Model to Explain Firm FailureVariables Organizational

failureOrganizational

failureDiscontinuous

franchisingDiscontinuous

franchisingAge -0.641***

(0.109)-0.549***

(0.137)-0.277*(0.162)

-0.308*(0.183)

Age squared 0.067(0.06)

-0.035(0.100)

Time till franchising 0.148**(0.068)

0.122*(0.06)

0.289***(0.106)

0.276**(0.108)

Size -0.458***(0.044)

-0.380***(0.124)

-0.434***(0.070)

-0.327**(0.149)

Units overseas -0.033(0.060)

-0.095(0.215)

-0.093(0.090)

-0.115(0.349)

Upfront fee -0.278***(0.069)

-0.249(0.207)

0.022(0.092)

-0.091(0.237)

Royalty rate -0.146**(0.076)

-0.478**(0.2489

-0.158(0.099)

-0.269(0.307)

Total investment 0.079(0.05)

0.078(0.051)

-0.070(0.075)

-0.108(0.074)

PCO 0.632***(0.202)

1.567**(0.676)

0.592*(0.353)

1.409(0.997)

PCO squared -0.2184(0.183)

1.431(1.475)

PCO x Age -0.147(0.263)

0.106(0.333)

PCO x Size 0.2984**(0.149)

0.928***(0.193)

Upfront fee x Age 0.022(0.080)

0.023(0.099)

Upfront fee x Size 0.0762*(0.04)

-0.082(0.052)

Royalty rate x Age 0.114(0.098)

0.044(0.119)

Royalty rate x Size -0.087(0.061)

-0.023(0.072)

Size x Age -0.053(0.058)

-0.108*(0.060)

Units overseas x Age

0.031(0.079)

0.033(0.128)

XX century vs XXI century

0.822***(0.931)

0.809***(0.093)

0.656***(0.128)

0.659***(0.128)

Dummy industry Included Included Included IncludedNumber of subjects/ failures

2168/544 2168/544 2169/247 2169/247

Log pseudolikelihood

-3069.9029 -3063.3476 -1392.854 -1380.2149

Wald Chi2 483.52*** 559.34*** 242.42*** 255.19***Standard robust errors are in parentheses.* significant at 10%, ** significant at 5% and *** significant a 1% level

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