Organizational performance is an essential factor for the survival and success of any business model (Richard et al., 2009). In the case of franchises, the actions, and skills of both the franchisor and the franchisee are relevant. This happens because the achievements of individual businesses benefit the network in its entirety; and the network, in turn, must be aware of and meet the demands of its franchised units, in a relationship of interdependence (Pazetto & Beuren, 2018).

The relationship between franchisor and franchisee is unique in nature, as they act mutually before the business venture even starts (Roh & Yoon, 2009). In order to achieve a "win-win" situation, collaboration and a balance of benefits between franchisor and franchisee are required (Jang & Park, 2019).

In this sense, considering franchisee satisfaction (Su & Tsai, 2017) is paramount to sustaining the business model and performance, and generating network success and sustainability (Jang & Park, 2019; Jirásek et al., 2022; Ortega et al., 2016). Franchisees who are satisfied with their franchisors are more motivated, engaged, and willing to strive for the best performance of their business (Nijmeijer et al., 2013); as well as "more cooperative and more likely to contribute to the success of the franchise system in its entirety" (Roh & Yoon, 2009, p. 87).

Satisfaction has also been shown to be one of the best predictors of loyalty (Ribeiro et al., 2011), in addition to the fact that it leads to several consequences – from stimulating the implementation of new practices to the decision to leave the network (Jirásek et al., 2022). In other words, in a franchise network, satisfaction is a key point for remaining in the network and the long-term survival of franchised businesses (Gauzente, 2003).

The relationship with the franchisor – herein named "support", offered to franchisees – is a preponderant factor in the decision-making when buying a franchise (Cho, 2004; Ramírez-Hurtado, 2017). In this aspect, franchisee satisfaction grows according to the quality of direct support received, that is, the higher the level of support quality, the higher the satisfaction (Nijmeijer et al., 2013).

For Jirásek et al. (2022), this relationship is one of the three performance dimensions that affect franchisee satisfaction, in general, in a franchise network; and it also mediates the effect of the other dimensions – financial and service performance.

The quality of support offered by franchisors therefore contributes to franchisee satisfaction, and positively impacts business results and financial performance (Guedes & Trigo, 2009; Nijmeijer et al., 2013; Roh & Yoon, 2009), which in turn leads to the success of the network in its entirety (Pazetto & Beuren, 2018).

Given the above, as a hypothesis to be tested throughout this study, it is proposed that: The level of franchisee satisfaction presents a positive association with the network’s performance.

The data collection was carried out based on information available in the following yearbooks: (a) "The Franchise Guide", audited by Serasa Experian and published by the "Pequenas Empresas e Grandes Negócios (PEGN)" magazine – which covers more than 1000 franchises, and addresses: the industry trends; the main legal and financial information to invest in a franchise; the tips to attract, train, and retain a qualified team; the secrets of multi-franchisees' success; and the brands evaluated and ranked based on the quality and performance of the network, and franchisee satisfaction (Editora Globo, 2020); and (b) "The Official Franchise Guide", published by the Associação Brasileira de Franchising (ABF, 2020) – the entity is a national reference in the franchise segment, and is responsible for the main indicators of the sector, disclosed in the guide, which also includes a complete record of the franchise brands.

The 2019 editions of the aforementioned yearbooks were used to analyze information related to 106 franchise networks, from 12 distinct sectors, comprising a total of 20,261 franchised units. The selection of the networks was random, based on the information availability (complementary in the two yearbooks researched).

In this sense, it is worth noting that databases, created with information from secondary sources, have been used in previous studies on franchising (Melo et al., 2014). These data are not only reliable and regularly updated, but also comprehensive in their coverage of the rapidly evolving franchise market (Baena, 2018). A descriptive analysis of the sample composition is presented in the results.

To analyze the data, the techniques of univariate and bivariate descriptive statistics were used (Fávero & Belfiore, 2015), with the results of the univariate analysis used as input to create the strata of the categories that enabled the other verifications. To support this process, the Stata software version 14 was used.

Regarding the examination of the relationship between satisfaction and performance, the exploratory technique of Simple Correspondence Analysis (CA) was chosen – useful when confirming the existence of a significant association between two categorical variables, and between the categories of each of them, in addition to determining the coordinates of the categories, for the development of perceptual maps (Fávero & Belfiore, 2015).

The perceptual map is the visual representation of object perception by an individual in two or more dimensions. This map generally has opposite levels of dimensions at the extremes of the x and y axes (Hair et al., 2009). CA provides the tools to examine the relationships between variables, in rows or columns individually, and in rows and columns combined (Fávero & Belfiore, 2015). Therefore, in the scope of this research, the use of this map enables to establish, at different levels, the association between franchisee satisfaction and the performance of franchise networks. The description of the compiled and analyzed variables is presented below (Table 1).

As noted, the dependent variable "network performance" (Richard et al., 2009) is represented by the increase in the revenue rates of the units and by the growth of the networks, in the number of franchised units. The independent variable "franchisee satisfaction" (Kalargyrou et al., 2017) is related to the quality indicators – training, field consulting, and operations manual – and reflects the perception of franchisees regarding the support provided by franchisors.

The sample analyzed is composed of 106 franchise networks, divided into twelve sectors of operation (Table 2). The prevalence of the "general services" sector can be observed (19), followed by: "food" (18); "health and well-being" (17); "training and courses" (11); "cafeteria and confectionary" (8); "cleaning and maintenance services" (8); "language teaching" (8); and "home, decoration, and gifts" (6). On the other hand, there was a lower incidence of observation in the sectors: "vehicles" (5); "cosmetics, perfume, and drugstore" (3); "clothing, footwear, and accessories" (2); and "culture and leisure" (1).

The descriptive statistics for each metric variable in the database are presented below (Table 3). The means of the variables "performance" (6.80) and "satisfaction" (6.72) are similar, and their deviations can be considered relatively low (1.34 and 1.10, respectively).

In contrast, the standard deviations of "network age", "network size" and "franchise fee" are high (9.65, 411.26, and 81.15, respectively), and with significant variance, which highlights the heterogeneity of the sample and the lack of standardization. This can also be observed regarding the "maximum" and "minimum" statistics for each variable. For instance: a maximum age of 65, and a minimum of two; a maximum network size of 3,720 units, and a minimum of four. Regarding the franchise investment rate, it ranges from 3 thousand to 790 thousand Reais. It is worth mentioning that the values found in the medians were used as one of the criteria to categorize the variables.

In addition to breaking down the database, these analyses allowed the creation of the category strata that were used as input for the bivariate statistics technique and CA. Thus, considering the position and dispersion measures of the sample, the number of categories was established (in parentheses) for each variable: "network performance" (4); "franchisee satisfaction" (4); "network age" (3); "network size" (4); and "franchise fee" (3), which are described below (Tables 4, 5, 6, 7, and 8, respectively).

As observed (Table 4), it can be noted that most samples are in the medium-high stratum (27.36%), followed by: medium-low (26.42%), low (26.42%) and, finally, high (19.81%). Therefore, there is an approximate distribution of companies among the categories.

Regarding "franchisee satisfaction" (Table 5), most companies considered themselves "not very satisfied" (33.96%), followed by "satisfied" (31.13%); "unsatisfied" (25.47%); and "very satisfied" (only 9.43%).

Regarding the "network age" – the variable that measures the franchise's age, counting the years since its adoption by the franchising system – (Table 6) the predominance of networks with more than 10 years can be noted (50% of the sample), followed by franchises with 5 to 10 years (30.19%); and with up to 5 years in the system (19.81%). Therefore, the samples are not composed of franchises in their first years of existence.

Regarding the "network size", that is, the number of units in each franchise network (Table 7), the majority is represented by networks with up to 50 franchises (34.91%). Considering collectively the range between 50 and 200 units, there is a total of 42.45%; and the smallest contingent of the sample studied (22.64%) comprises larger networks, with more than 200 units.

The distribution of companies by category (Table 8), based on the franchise fee (paid when acquiring the enterprise) show the prevalent (71.7%) of the franchises that had a low investment (up to 50 thousand reais), being followed by companies (23.58%) with an investment from 50 to 100 thousand reais (considered medium). The minority (4.72%) is from the high investment group (above 100 thousand reais). This analysis reflects, in a sense, a profile of franchisees that can be classified as micro and small entrepreneurs, who have lower investment funds.

For the bivariate analyses, the variables "performance" and "franchisee satisfaction" were related to the variables "network age," "network size," and "franchise fee" (Tables 9, 10, and 11; and 12, 13, and 14).

According to the data (Table 9), the higher the network performance (medium-high and high), the fewer the number of newer networks aged up to 5 years old (2 and 1, respectively). And the number of networks increases along the year intervals: from 5 to 10 years (9 and 8); and above 10 years (18 and 12). In contrast, it is also possible to observe companies with low performance in networks that are more than 10 years old (12), and 5 to 10 years old (11).

Networks with 50 to 100 units have a homogeneous performance level among the categories (6, 6, 6, and 4, representing low, medium-low, medium-high, and high, respectively). In contrast, networks with up to 50 franchises have companies with low and medium-low performance levels, totaling 26 observations. Meanwhile, regarding the networks with more than 200 units, these have equal numbers (8) of companies with low and high performance. Based on the analysis, therefore, it is not possible to verify if there is a relationship between performance and network size (Table 10).

In the analysis of the variables "network performance" and "franchise fee" (Table 11), it is noted that in medium-high and high performance levels of the networks, when the fee values increase, there are fewer observations. This indicates that better network performances are not related to higher franchise fees. Furthermore, it is not possible to state that low franchise fees lead to lower performance, given the average distribution among the categories. There is, however, a slight decrease in observations as the network performance increases, with low (24), medium-low (19), medium-high (18), and high (15) investment in the franchise fee.

When relating the "franchisee satisfaction" and the "network age" (Table 12), it can be observed that, in networks up to 5 years old, the "unsatisfied" (8 mentions) and "not very satisfied" (6) conditions of the franchisee are more recurrent than the "satisfied" (4) and "very satisfied" (3) conditions. In networks with more than 10 years, there is also a prevalence of lower levels of satisfaction: "unsatisfied" (11) and "not very satisfied" (18). The "satisfied" category corresponds to a significant part of the data, and it is possible to note that, over the years, the number of networks increases. Nevertheless, based on the information, no association was found between these variables.

According to the analysis (Table 13), networks with up to 50 units have more "unsatisfied" (13) and "not very satisfied" (12) franchisees. In addition, in the "unsatisfied" category, it is possible to notice that, as the size of the network grows, the dissatisfaction tends to decrease.

According to the data collected, regarding "satisfaction" and "franchise fee" (Table 14), networks with lower levels of satisfaction – "unsatisfied" (19) and "not very satisfied" (32) – are related to the low franchise fee. In the medium franchise fee category (in thousands), the condition "satisfied" prevails (10 networks); and, regarding the high franchise fee, the data were insufficient to verify if a relationship between the variables exists.

Correspondence analysis (CA)

According to the analysis guideline proposed by Fávero and Belfiore (2015, p. 246), before proceeding to the correspondence analysis:

1. it is initially recommended to perform the χ2 test for the verification of the existence of dependence between the two variables and, consequently, for the evaluation of the adequacy of CA application.

CA uses the χ2 test to analyze the distribution of absolute frequencies by category and evaluate the statistical significance of the association between two variables (Hair et al., 2009). Then, based on a contingency table, the expected frequencies and the χ2 value for each cell are calculated, considering the differences between the observed and expected frequencies (Table 15).

Given the X² test result, it can be stated that, at a significance level of 1% and 9 degrees of freedom, there is a statistically significant association between the variables "network performance" and "franchisee satisfaction", since X² = 62,86 and Prob. X²calc < 0,01%, which renders the use of CA viable. Following the guidelines proposed by Fávero and Belfiore (2015), the association between pairs of categories of these variables ("network performance" and "franchisee satisfaction") was subsequently examined by adjusted standardize residual analysis, aiming to study the relationships between all their categories in a simple correspondence analysis (bivariate), as presented below (Table 16).

The analysis indicated dependence between the categories: "low performance" and "unsatisfied"; "medium-low performance" and "not very satisfied"; and "medium-high performance" and "very satisfied". There was also an association of "high performance" with "satisfied," since the standardized residuals of the corresponding cells are, respectively, equal to 5.495, 2.554, 2.433, and 4.454 (positive and > 1.96).

With this, it was possible to generate the graphic representation of these associations, through the following perceptual map (Figure 1), which demonstrates some positive associations between the levels of "network performance" and "franchisee satisfaction", such as dimensions 1 and 2, which explain, respectively, 87.6% and 9.0% of the total primary inertia.

Analyzing the map, it is also possible to observe a strong association between the categories "low performance" and "low satisfaction", since the "unsatisfied" franchisees present "low performance levels"; and the "not very satisfied" franchisees follow the same logic, presenting "medium-low performance".

In contrast, as expected, the "medium-high" and "high" levels of "performance" are also associated with high levels of "franchisee satisfaction," ranking between "medium-high" and "high."

Thus, considering the associations present in the researched sample, the null hypothesis that "satisfaction" and "performance" are randomly associated is rejected, and the hypothesis defended in this study is confirmed – that there is a statistically significant association between these variables; and more than that, there is a logical association between the categories considered negative ("low performance" and "low satisfaction"), and positive ("high performance" and "high satisfaction").

The hypothesis defended and confirmed in this study – that satisfaction and performance are associated under a positive logic, in which networks with more satisfied franchisees perform better – corroborates the inferences of Guedes & Trigo (2009), Jang & Park (2019), Jirásek et al. (2022), Nijmeijer et al. (2013), Ortega et al. (2016), Ramírez-Hurtado (2017), and Roh & Yoon (2009).

Therefore, satisfaction is essential to network success (de Faria Olivo et al., 2020; Ortega et al., 2016; Melo et al., 2022); and it hinges on the support received by and the relationship established with the franchisor (Ramírez-Hurtado, 2017; Su & Tsai, 2019), which is also an important factor in the decision to acquire a franchise (Cho, 2004).

In addition, the price of the "franchise fee" helps to cover the expenses with training, support, and implementation of the new unit (Central do Franqueado, 2020). Following this logic, it is possible to understand that franchises with lower investment fees tend to have less satisfied franchisees, since their satisfaction is related to the support offered by the network (Jang & Park, 2019; Nijmeijer et al., 2013; Su & Tsai, 2019), which is covered by the franchise fee. Based on the collected data, it was observed that franchisee satisfaction in businesses with "low investment" is lower, according to the categorization into "unsatisfied" (19 networks) and "not very satisfied" (32 networks).

The network performance can also be related to the franchise fee, since the fee helps to cover the expenses with support (Central do Franqueado, 2020). Thus, it is considered that the support received contributes to satisfaction (Roh & Yoon, 2009) and its quality positively affects the franchisee's results (Jang & Park, 2019; Nijmeijer et al., 2013; Su & Tsai, 2019).

In contrast, higher franchise fees do not lead to higher performance, since in "medium-high performance" and "high performance" the number of networks decreases as the fee increases. At "low franchise fees", the data indicate 18 and 15 networks; at "medium fees", 10 and 4; and at "high fees", 1 and 2, respectively.

Another point raised is the relationship between "network size" and "franchisee satisfaction," where the older the network, the more consolidated it is, and it has better processes and support, raising the levels of satisfaction.

In addition, the number of "unsatisfied" franchisees decreases as the "size of the network" increases. Thus, networks consisting of up to 50 units have 13 "unsatisfied" companies; networks from 50 to 100 have 8; and networks from 100 to 200 and above 200 have 8 networks each.

Finally, one of the advantages of franchising for the franchisee lies in the assistance received for maintaining the business in the first years (Carvalho & Viana, 2015). In the data, franchises with more than 5 years of life survive in networks with more than 50 units. As the networks are continuously growing and staying in the market (Redecker, 2020), the franchisee can survive the first years of the company's life.

Corresponding author: anaazevedons@gmail.com