A Multi-Study Investigation of Outcomes of Franchisees’ Affective Commitment to Their Franchise Organization

A Multi-Study

& Investigation of

Outcomes of Franchisees’ Affective Commitment to Their Franchise Organization

Karim Mignonac Christian Vandenberghe Rozenn Perrigot Assâad El Akremi Olivier Herrbach

Franchisees’ affective organizational commitment refers to the degree to which franchisees experience an emotional attachment to their franchise organization. Using a social exchange theory perspective, this research reports four studies that explore the relationship between franchisee’s affective commitment and franchisee outcomes. We found that affective com- mitment to the franchise organization was positively related to franchisee objective perfor- mance (Study 1) and intent to acquire additional units (Study 2), and negatively related to franchisee opportunism (Study 3) and intent to leave the franchise organization, particularly when continuance commitment (i.e., commitment based on the cost associated with mem- bership to the franchise) was low (Study 4). The implications of these findings are discussed.

Introduction

Franchising is a popular business arrangement that involves cooperation between an entrepreneurially minded firm—i.e., the franchise organization (or franchisor)—and several individual entrepreneurs—i.e., the franchisees (Combs, Ketchen, Shook, & Short, 2011). Typically, a franchisor that already has a successful product or service enters a continuing contractual relationship with franchisees operating under the franchisor’s trade

Please send correspondence to: Karim Mignonac, tel.: (33) 5-61-63-38-87; e-mail: karim.mignonac@ univ-tlse1.fr, to Christian Vandenberghe at christian.vandenberghe@hec.ca, to Rozenn Perrigot at rozenn. perrigot@univ-rennes1.fr, to Assâad El Akremi at assaad.el-akremi@univ-tlse1.fr, and to Olivier Herrbach at olivier.herrbach@u-bordeaux4.fr.

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However, a broader understanding of the franchisor–franchisee relationship might be gained by recognizing that franchisees “are not single economic actors simply reacting to economic incentive mechanisms but are social actors embedded within a complex set of interpersonal relationships” (Lawrence & Kaufmann, 2011, p. 299). These relationships can be understood through the lens of Social Exchange Theory (SET), which states that exchange interactions result in both economic and social outcomes that engender obli- gations between the parties, expectations of future rewards, and a willingness to invest time and effort in the relationship (Lambe, Wittman, & Spekman, 2001; Thibaut & Kelley, 1959). However, social exchange theorists have often considered economic and social exchange separate forms of relationship. Social exchange differs from economic exchange in that (1) trust and socio-emotional components are central ingredients, (2) personal investment in the relationship is essential to its maintenance over time, and (3) a long-term orientation is required for mutual obligations to be fulfilled (Blau, 1964; Cropanzano & Mitchell, 2005; Shore, Tetrick, Lynch, & Barksdale, 2006). While this separation of economic and social exchange might be relevant to employee–employer relationships, it is less applicable to franchising because franchisee–franchisor relationships are sealed in strong economic inducements from the start. Franchisees make significant idiosyncratic investments (e.g., lump sum payment, annual royalty fee based on sales) and franchisors provide agreed-upon assistance in management, operational procedures, training, and advertising (Shane, 1996). As a result, both parties have a vested interest to maintain the relationship over time. Thus, it is not clear that SET holds to the same extent among franchises, where economic forces might alone be sufficient.

Although economic conditions may appear to suffice to build a mutually beneficial relationship between the franchisee and the franchisor, SET suggests that the social component of the relationship still represents a major driver of the future outcomes associated with it. We focus on affective commitment because prior research shows that it is central to the social component of exchange relationships (Cropanzano & Mitchell, 2005; Lawler & Thye, 1999). Affective commitment refers to an emotional attachment to an organization based on a sense of identification to its goals and values (Meyer & Allen, 1991; Meyer, Becker, & Vandenberghe, 2004). Thus, although franchising is thought to

be an efficient solution to the agency problem of having the principal (i.e., the franchisor) monitor and control the agent (i.e., the franchisee) by sealing the relationship into eco- nomic and contractual constraints (Shane, 1996), franchisees’ affective commitment to the relationship represents an important driver on its own of future outcomes because it reflects a commitment to the franchisor’s goals.

This research breaks new ground by looking at the role of affective commitment in the context of franchising, i.e., a context known to be bound by strong economic incen- tives. Specifically, we address the influence of affective commitment on four franchisee outcomes, namely performance, intent to own additional units, opportunism, and intent to leave the franchisor. Although the relevance of affective commitment as a predictor of a variety of outcomes including performance has been studied on samples of regular employees (e.g., Gong, Law, Chang, & Xin, 2009; Meyer, Stanley, Herscovitch, & Topolnytsky, 2002), its application to franchising emerged only recently (e.g., Meek, Davis-Sramek, Baucus, & Germain, 2011). This paper intends to demonstrate that affec- tive commitment, as an indicator of SET, is a relevant predictor of franchise outcomes

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(i.e., performance, acquisition of new outlets, opportunism, and intent to leave the franchise), even though franchising is characterized by strong economic incentives. We base our reasoning on the idea that economic exchange relationships become mutually beneficial to the extent that relational norms emerge (Lambe et al., 2001), and the current relationship is viewed as more rewarding than other exchange alternatives (Thibaut & Kelley, 1959). This might happen when the franchisee experiences a sense of affective commitment to the franchise.

In the following sections, we present four studies that examine the relationships between franchisee’s affective commitment to the franchisor and franchisee performance (Study 1), intent to acquire additional units (Study 2), opportunism (Study 3), and intent to leave the franchisor (Study 4). Across these studies, our intention is to highlight how affective commitment can work as a driver of franchise outcomes and franchisor– franchisee relationships, which take place in an incentive intensive context.

Theoretical Background and Hypotheses

Much of the research on franchisee–franchisor relationships has been conducted through the lens of economic exchanges whereby franchisees’ behavior is determined by the so-called “carrot-and-stick” approach (the carrot being the residual profits and the stick the loss of ex-post rents; Lawrence & Kaufmann, 2011). This research conceives franchisees’ behavior as being regulated by external rewards. In this view, franchisee’s behavior reflects an externally regulated motivation, i.e., the reason for engaging in the behavior (e.g., staying with the franchise organization or making the franchise profitable) is external to the individual. This approach is consistent with an economic exchange perspective. Economic exchanges are give-and-take relationships, based on tangible inducements, and typically short-term (Blau, 1964; Cropanzano & Mitchell, 2005; Shore et al., 2006).

However, some scholars have recently criticized the exclusive focus on economic exchanges (e.g., Dant, Weaven, Baker, & Jeon, 2013; Weaven, Grace, & Manning, 2009) and recognized that franchisee motivation is “more varied and complex than being simply an expression of profit maximization desires” (Stanworth & Curran, 1999, p. 338). This stance is compatible with the tenets of SET. Contrary to the economic exchange perspec- tive, SET proposes that, in the longer term, the maintenance of constructive relationships requires and is explained by mutual commitments among partners (Blau, 1964; Cropanzano & Mitchell, 2005), which are intrinsically tied to emotional attachment to the relationship. Following this logic, affective commitment to the franchisor, which emerges in the context of ongoing and repeated social exchanges, constitutes an important driver of the franchisee’s willingness to act in the best interests of the franchisor and invest effort into making the relationship efficient in the long term.

Franchising represents an organizational form where the franchisee receives the right to sell a product or service using a brand name in return for a lump sum and annual royalties, and the franchisor is expected in return to provide assistance in management, training, advertising, and operational procedures (Norton, 1988; Shane, 1996). While these conditions create strong economic incentives that encourage parties to cooperate and ensure the relationship is mutually beneficial (hence making exchange alternatives less attractive; Lambe et al., 2001; Thibaut & Kelley, 1959), SET would predict that these inducements would lead to positive outcomes only if relational exchange norms emerge in which partners experience a sense of commitment to the relationship’s goals. For example, if the franchisee feels that the franchisor fails to some extent to provide timely

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Performance

Successful franchise organizations depend heavily on their franchisees to perform well. However, the determination of outlet performance remains poorly understood (Michael & Combs, 2008). SET suggests that the economic and social outcomes tied to the exchange contribute and reinforce one another to create the conditions for outlet performance. As argued above, franchisor–franchisee exchanges are tinged by strong economic inducements that both make exchange alternatives less attractive and encourage franchisees to become self-motivated in managing their business. However, economic incentives are not enough by themselves to ensure that parties invest significant inputs in the future success of the exchange. For this to happen, relational norms need to emerge which are typically reflected in mutual commitments to the relationship’s goals (Cropanzano & Mitchell, 2005; Lambe et al., 2001). In other words, SET states that there should be some balance among the economic inputs generated by the parties over time so that interdependence is achieved and the relationship is mutually rewarding (Lambe et al.). For example, the provision of assistance (e.g., support to advertising and opera- tional procedures) that is proportional to and meets franchisees’ needs may instill the expectation of future positive outcomes. These conditions should lead to a franchisee’s affective commitment to the relationship, and ultimately result in increased effort promoting outlet performance.

Franchisees with high affective commitment may work toward outlet performance through a variety of means. For example, they may engage in customer-focused citizen- ship behavior (such as going out of their way to help a customer or attending to customer needs) that in the end would lead to stronger customer satisfaction and indirectly increased sales performance (e.g., Schneider, Ehrhart, Mayer, Saltz, & Niles-Jolly, 2005). Alterna- tively, they may engage in cooperative behaviors directed at their own employees, taking steps toward creating a service climate in their franchise (e.g., Schneider, White, & Paul, 1998), or developing strong social exchange relationships with employees (Liao & Chuang, 2007). This would indirectly instill a sense of service toward customers among their employees which may lead to higher sales performance.

There is indirect evidence that supports the expected relationship of franchisees’ affective commitment to outlet performance. For example, in a study conducted among Chinese organizations, Gong et al. (2009) found aggregate managers’ affective commit- ment to be positively related to firm performance. Similarly, in a large sample of business firms in the United States, Harter, Schmidt, and Hayes (2002) found employee engage- ment (i.e., captured through a measure that has similarities with a commitment measure) to be positively associated with the business unit outcomes of customer satisfaction,

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Hypothesis 1: Franchisees’ affective commitment to the franchise organization is positively related to franchisees’ performance.

Intent to Acquire Additional Units

From the franchisor’s perspective, multi-unit franchising is a growth strategy (Kaufmann & Dant, 1996) that allows franchisees to open additional units, either one unit at a time (sequential multi-unit strategy), or multiple units at the outset (area devel- opment multi-unit strategy). Scholars have studied how and why franchisors benefit from multi-unit franchising (Hussain & Windsperger, 2009; Perryman & Combs, 2012), but very little is known about the factors affecting franchisees’ intent to contribute to this strategy, that is, to increase the number of units they own within the same franchise organization.

Within a SET perspective, the acquisition of additional units represents a significant investment in the future prospects of the relationship. Such investment can only be granted if the franchisee perceives the relationship with the franchisor as being open- ended, based on mutual commitments, and rewarding in the long term. These conditions, which reflect franchisee affective commitment, should happen when the franchisee expects the exchange to generate (social and economic) benefits that match his/her expected rewards (i.e., a comparison level perspective) and to be more rewarding than other exchange alternatives (i.e., a comparison level of alternatives perspective) (Thibaut & Kelley, 1959). In other words, franchisees with high levels of affective commitment envision the rewarding opportunities that the acquisition of new units affords. Hence, they are likely to see the acquisition of additional outlets as an attractive goal. This leads to the following hypothesis.

Hypothesis 2: Franchisees’ affective commitment to the franchise organization is positively related to franchisees’ intent to acquire additional units.

Opportunism

Franchisee opportunism refers to the extent to which franchisees act according to their self-interests in order to achieve their own goals despite possible damage to franchisors (Jambulingam & Nevin, 1999). Examples of franchisee opportunism include free-riding on the franchisor’s brand name and the efforts of other franchisees (Kidwell, Nygaard, & Silkoset, 2007); withholding information from the franchise organization (El Akremi, Mignonac, & Perrigot, 2011); or not complying with the franchisor’s standards, policies, and relational norms (Davies, Lassar, Manolis, Prince, & Winsor, 2011; El Akremi et al.). Although franchisors may to some degree accept certain forms of opportunism on the part of their franchisees (Cox & Mason, 2007; Kidwell & Nygaard, 2011), franchisee opportunism is largely discouraged because it jeopardizes the performance and survival of both the franchisor and franchisees (Kidwell et al.; Szulanski & Jensen, 2006; Winter, Szulanski, Ringov, & Jensen, 2012). Thus, overcoming opportunism is a major challenge for franchise systems (Davies et al.).

Franchisees with high affective commitment perceive that there is a balance between the efforts they put forth and the benefit and support they gain from the franchisor. In other

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Hypothesis 3: Franchisees’ affective commitment to the franchise organization is negatively related to franchisees’ opportunism.

Intent to Leave the Franchise Organization

We define franchisees’ intent to leave as the perceived likelihood that a franchisee will voluntarily terminate the relationship with the franchisor when his/her current franchise agreement expires. Retaining franchisees is a major challenge for franchise organiza- tions because franchisee exits have detrimental effects on the chain’s growth (e.g., loss of royalties and financial resources to expand) and brand equity (e.g., competing ex-franchisees operating in a similar business; Frazer, Merrilees, & Wright, 2007). Since turnover intention has been found to be a strong antecedent of actual turnover behavior (Griffeth et al., 2000), studying the precursors of franchisees’ turnover intention is impor- tant and allows a franchise organization to take action before the intention turns into actual behavior (Meek et al., 2011).

Affective commitment negatively relates to turnover intention (Mathieu & Zajac, 1990; Meyer et al., 2002), essentially because it denotes an emotional investment into a long-term and rewarding relationship with the organization. At least one study in the franchising context reported a positive association between franchisees’ affective com- mitment and their intent to remain with the franchisor (Morrison, 1997). However, as SET suggests that exchange relationships result in both economic and social outcomes (Lambe et al., 2001), it makes sense to also consider the perceived switching costs associated with leaving the franchisor, which are shaped by the economic and legal constraints associated with the exchanges (Shane, 1996). It is likely that these costs, which basically denote continuance commitment or a commitment based on the perceived cost associated with leaving the organization (Meyer et al., 2004), act as a potential confound in affective commitment’s relationship with intent to leave. In fact, franchisees who perceive that leaving entails high economic costs (e.g., loss of upfront fees) (cf. Powell & Meyer, 2004) may actually decide to stay regardless of their level of affective commitment. Indeed, SET suggests that partners may feel constrained to stay in a low rewarding relationship if the switching costs are high (Thibaut & Kelley, 1959). Conversely, franchisees who perceive that leaving would not require significant personal sacrifice may more readily factor their emotional connection to the franchisor into their decision making. Thus, we argue that continuance commitment, which represents a proxy for the perceived importance of the switching costs associated with leaving (Meyer et al.), will moderate affective commit- ment’s relationship to franchisees’ intent to leave, with this relationship being stronger at low levels of continuance commitment. Therefore, we propose the following, remaining hypothesis.

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Hypothesis 4: Franchisees’ continuance commitment moderates the relationship between franchisees’ affective commitment to the franchise organization and intent to leave, such that this (negative) relationship is stronger when continuance commitment is low vs. high.

Study 1: Relationship With Performance

Sample and Data Collection

We obtained the agreement of a French franchise organization that retails beauty care products globally to conduct a study about their franchisees’ attitudes and performance. In early 2009, we mailed a questionnaire to all 164 franchisees located in France, with a cover letter outlining the franchisor’s support for the research. We received 92 completed questionnaires, which represents a 56.1% response rate. Franchise headquarters provided data about product sales revenue and retail space for each franchisee for years 2008 and 2009. Usable data on franchisee responses, sales revenue, and retail space were available for 79 franchisees (48.17% of the original sample). To estimate the likelihood of a nonresponse bias, we compared respondents and nonrespondents on franchise organization-provided variables (i.e., product sales revenue and retail space). We found no significant differences in the responses (p > .05), which suggests that nonresponse bias was not a major concern in this study.

Measures

All items (see Appendix) were measured using a 5-point Likert-type scale (1 = com- pletely disagree; 5 = completely agree) unless specified otherwise. Except for product sales revenue and retail space data that were collected from franchisors, all the other data were collected from franchisees.

Affective Commitment. We measured affective commitment to the franchise organization by slightly modifying the French version of Meyer, Allen, and Smith’s (1993) 6-item scale (Vandenberghe & Bentein, 2009). We replaced the word “organization” with “franchise organization” in all items. To minimize measurement errors due to careless responding or fatigue, we positively worded all items (Merritt, 2012). A sample item is “I really feel that

I belong in this franchise organization” ( a = .92). Performance. We operationalized franchisee performance through two measures: (1) the

ratio between the yearly revenue earned from store product sales (in euros) and the total area of retail space (in square meters); this ratio represents sales per unit and is a measure of productivity commonly used in the retail industry (Fenwick & Strombom, 1998; Litz & Stewart, 2000); and (2) yearly sales revenue, which represents an absolute measure of sales performance.

Control Variables. We controlled for multi-unit ownership because research suggests that multi-unit franchisees can benefit from the experience of their previously opened outlets, and thus be more productive than single-unit franchisees (Darr, Argote, & Epple, 1995). We also controlled for the number of employees, because this may explain variations in store performance (Arnold, Palmatier, Grewal, & Sharma, 2009). In addition, we controlled for age and tenure in the franchise organization, in order to hold constant the

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Results

Table 1 presents the means, standard deviations, reliabilities, and correlations for Study 1 variables. Of interest, affective commitment was positively related to 2008 and 2009 sales revenue per square meter (r = .26, p < .05, and r = .32, p < .01, respectively) but not to 2008 and 2009 sales revenue (r = .08, ns, and r = .08, ns, respectively). Thus, the productivity measure (sales revenue per square meter) was significantly related to fran- chisee affective commitment, plausibly because this measure better reflects whether the outlet is well managed, while the measure of overall performance (sales revenue), which may be more sensitive to market potential, was not. Hypothesis 1 predicted that franchi- sees’ affective commitment would be positively related to franchisee performance. We tested hypothesis 1 using hierarchical ordinary least squares (OLS) regression analyses. We entered the control variables in Model 1 and added affective commitment in Model 2. As can be seen from Table 2 (Model 2), affective commitment was significantly and positively related to 2009 sales revenue per square meter ( b = .22, p < .01) and 2009 sales revenue ( b = .19, p < .01), controlling for all the other variables, namely 2008 measures of these outcomes. Thus, hypothesis 1 was supported.

Study 2: Relationship With Intent to Acquire Additional Units Sample and Data Collection

As part of a larger project, the research team personally contacted 100 franchise organizations at their leadership meetings or at franchise exhibitions. We targeted fran- chisors listed in the 2008 directory of the French Franchise Federation with at least 10 units and operating in France for more than 5 years. We approached franchisors and asked them whether they would agree to participate in a study of business strategies in franchise organizations. We also asked them if they would accept that we survey their franchisees about their work attitudes. We offered a free report of the study’s results to franchisors in return for their participation (which involved completing a questionnaire—see Measures subsection for a description of franchisor variables—and the provision of a list of their members). Eighty franchisors returned usable questionnaires and supplied contact infor- mation for all their franchisees. Key informants among franchisors were chief executive officers, executive vice presidents of franchising, or directors of franchise development. T -tests and chi-square statistics showed no significant differences (p > .10) between respondents and nonrespondents among key informants on franchise organization age, size, and industry, proportion of franchised outlets, and level of internationalization.

We mailed questionnaires to the 2,405 franchisees belonging to the 80 franchise organizations from which usable responses were obtained, and received 829 completed and usable questionnaires (for a response rate of 34.47%). The number of respondents from franchise chains ranged from 3 to 33, with an average of 10.45 (SD = 6.64). We assessed possible nonresponse bias by comparing early and late respondents on demo- graphic information and study constructs. No significant differences were found (p > .05).

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Table 1 Study 1 and 3 Means, Standard Deviations, Reliabilities, and Correlations

Study 1 (N = 79) 1. Age

2. Tenure in the franchise organization (years)

3. Multi-unit ownership (1 = multi-unit franchisee)

4. Number of employees

5. Market area (log. population size)

6. Retail space (square meter)

7. Affective commitment to the franchise organization

— 9. 2009 Product sales revenue per square meter

8. 2008 Product sales revenue per square meter

.87** — 10. 2008 Sales revenue

.46** .41** — 11. 2009 Sales revenue

.08 .35** .72** — Study 3 (N = 529) 1. Age

2. Gender (1 = male)

3. Tenure in the franchise organization (years)

4. Multi-unit ownership (1 = multi-unit franchisee)

5. Positive affectivity

6. Negative affectivity

7. Self-reported performance

8. Affective commitment to the franchise organization

* p < .05; ** p < .01 Note: Pearson product moment correlations are reported for pairs of continuous variables, Spearman rank correlations are reported for pairs of continuous and dichotomous variables, and Phi correlations are reported for pairs of dichotomous variables. Internal consistency values (Cronbach’s alphas) appear across the diagonal in parentheses.

Table 2 Study 1 Ordinary Lead Square (OLS) Regression Analyses Predicting Franchisee

Performance

2009 Product sales revenue per square meter 2009 Sales revenue

Model 1

Model 2

Model 1 Model 2

-.03 -.38 -.08 -1.22 Multi-unit ownership

1.55 .13 2.43* .19 3.52** Number of employees

-.08 -.79 -.13 -1.36 Market area

.19 2.85** .19 3.11** 2008 Product sales revenue

per square meter 2008 Sales revenue

.43 4.43** .48 5.30** Retail space

.52 8.05** .51 8.48** Affective commitment

Adjusted R 2 .77

* p < .05; ** p < .01 Note: Standardized regression coefficients (

b) are reported. 2008 and 2009 product sales revenue per square meter and sales revenue were standardized using z-scores due to large variance on these variables. As the distribution of market area scores was severely skewed, scores on this variable were log-transformed before running the regression analysis.

Measures

All items (see Appendix) were measured using a 5-point Likert-type scale (1 = com- pletely disagree; 5 = completely agree) unless specified otherwise. We collected data regarding affective commitment to the franchise organization, intent to acquire additional units, demographics, and self-reported performance from franchisees’ questionnaires. Additionally, data regarding franchisors’ strategies for multi-unit ownership were col- lected from franchisors’ questionnaires. Finally, we gathered basic information about franchisors, such as age, size, contract length, and minimum cash required to open a franchise unit, directly from the French Franchise Federation directory.

Affective Commitment. We measured affective commitment to the franchise organization using the same scale as in Study 1 ( a = .93).

Intent to Acquire Additional Units. We developed a 3-item scale to measure franchisees’ intent to acquire additional units. A sample item is “I intend to own one or several additional units of this franchise organization within the next two years” ( a = .86).

Franchisee-Level Control Variables. Control variables were chosen based on their potential influence on growth intentions among small business owners: these included

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Franchise Organization-Level Control Variables. In order to account for the variance potentially explained by idiosyncratic characteristics of the franchise organizations, we controlled for age and size of franchise organizations, length of franchise contracts, minimum cash required to open a franchise unit, and strategy for multi-unit franchising. We developed a 4-item scale to assess franchisors’ multi-unit strategy (e.g., “We encour- age our franchisees to own more than one unit”; a = .74).

Measurement Analyses

We compared two nested confirmatory factor analysis (CFA) models. The baseline two-factor measurement model comprising affective commitment and intent to own additional units yielded a satisfactory fit to the data: c 2 (26) = 118.92, p < .01; comparative fit index (CFI) = .99; non-normed fit index (NNFI) = .98; root mean square error of approximation (RMSEA) = .076; standardized root mean square residual (SRMR) = .025.

This model proved superior to a single-factor model combining the two constructs ( Dc 2 [1] = 794.73, p < .01). These results supported the convergent and discriminant validity of our study measures.

To test for common method variance, we compared our theoretical model to a model that also included an orthogonal method factor on which all items had a loading (in addition to a loading on their intended factor). The model that included the method factor

resulted in a good fit ( c 2 [17] = 37.62, p < .01; CFI = .99; NNFI = .99; RMSEA = .045; SRMR = .016), and outperformed the model with no method factor (Dc 2 [9] = 127.57, p < .01). Further analyses of factor loadings revealed that 22% of the variance was accounted for by the method factor, which is similar to percentages typically found in other studies (Lance, Dawson, Birkelbach, & Hoffman, 2010). The poor fit of the one-factor model (NNFI = .80; RMSEA = .232) also suggests that common method vari- ance does not appear to be an excessive threat to this study. Hence, we proceeded with hypothesis testing.

Results

Table 3 presents the means, standard deviations, reliabilities, and correlations for Study 2 variables. As can be seen, age and tenure correlated negatively with the intention to acquire additional outlets (r = -.17, p < .01, and r = -.16, p < .01, respectively), sug- gesting that those who expand their units are younger and less tenured. In contrast, being a male franchisee (r = .06, p < .05), being a multi-unit franchisee (r = .17, p < .01), number of units (r = .12, p < .01), perceived performance (r = .24, p < .01), and affective commitment (r = .39, p < .01) were all positively associated with intent to own more units.

Hypothesis 2 predicted that franchisees’ affective commitment would relate positively to franchisees’ intent to own additional units. We used hierarchical linear modeling

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Table 3 Study 2 Means, Standard Deviations, Reliabilities, and Correlations

Mean SD

Franchisee level (N = 829) 1. Age

2. Gender (1 = male)

.48 -.08

3. Tenure in the franchise organization (years)

5.48 5.29 .33** -.02

4. Multi-unit ownership (1 = multi-unit franchisee)

— 5. Number of units owned

.15** .79** — 6. Self-reported performance

1.18 .63 -.01

.06 .05 (.90) 7. Affective commitment to the franchise

.06 .07* .30** (.93) organization 8. Intent to own additional units

-.16** .17** .12** .24** .39** (.86) Franchise organization level (N = 80) 1. Age (years)

2. Size (number of units)

3. Contract length (years)

5.90 2.14 -.04

4. Minimum cash required (1,000 euros)

— 5. Multi-unit franchising strategy

* p < .05; ** p < .01 Note: Pearson product moment correlations are reported for pairs of continuous variables, Spearman rank correlations are reported for pairs of continuous and dichotomous variables, and Phi correlations are reported for pairs of dichotomous variables. Internal consistency coefficients (Cronbach’s alphas) appear across the diagonal in parentheses.

(HLM) to account for the multi-level and nested structure of the data (Raudenbush & Bryk, 2002). To justify HLM as the appropriate analytic technique for testing this hypoth-

esis, 1 we first ran a null model with no predictors and intent to acquire additional units as the dependent variable. This analysis revealed that franchisees’ intent to own additional

units varied significantly across franchise organizations ( 2 c = 184.72 [70], p < .01; ICC(1) = .13), with 13% of its variance being explained by the franchise organization to

which franchisees belonged. Consequently, we proceeded to test hypothesis 2 using HLM. We entered control variables in Model 1 and added affective commitment in Model 2. The results for Model 2, as reported in Table 4, indicated a positive and statistically significant relationship between affective commitment and intent to own additional units ( g = .45, p < .01), controlling for all other variables. Thus, hypothesis 2 was supported.

Study 3: Relationship With Opportunism

Sample and Data Collection

The sampling frame used for this study was a database of 12,000 franchisee contacts located in France. We developed this database from 737 business-format franchise

1. In the present study, franchisees (Level 1) were nested within franchise organizations (Level 2). HLM allows an examination of the relationships across levels of analysis by simultaneously estimating both within-organization and between-organization variances in the dependent variable (i.e., intent to own addi- tional units). However, if there is little or no variance in the dependent variable across franchise organizations, the assumptions of OLS regression are not violated, and HLM is not required to examine cross-level relationships (Raudenbush & Bryk, 2002).

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Table 4 Study 2 HLM Analyses Predicting Intent to Own Additional Units

2.51 40.91** Level 1 controls Age

-.01 -2.49* Gender

-.09 -.99 Tenure

-.02 -2.60* Multi-unit ownership

.76 3.74** Number of units owned

-.22 -2.78** Self-reported performance

.21 4.75** Level 2 controls System age

-.30 -4.41** System size

.04 .50 Contract length

.03 1.70 † Minimum cash required

-.01 -.15 Multi-unit franchising strategy

.18 2.52* Level 1 independent variable Affective commitment

Pseudo R 2 .10

p < .10; * p < .05; ** p < .01 Note: Fixed effects (

g) are reported. Pseudo R 2 is calculated based on proportional reduction of error variance due to predictors in the models of Table 4 (Snijders & Bosker, 1999).

organizations listed in the 2008 directory of the Fédération Française de la Franchise (i.e., the French Franchise Federation). We retained only those franchisors with at least 10 units and operating in France for more than 5 years. We obtained the names and addresses of franchisees from their respective websites and the Yellow Pages phone directory. To avoid employees or managers of the franchisee mistakenly receiving and completing the survey questionnaire, we included in the database only prospective participants for whom complete professional and contact information were available. Moreover, questionnaires were addressed to the franchisees personally. In total, 3,000 franchisees were randomly selected from the database and invited to participate in the survey in April 2009.

We received 529 completed and usable questionnaires, corresponding to a response rate of 17.6%. To estimate the likelihood of a nonresponse bias, we compared early and late respondents on all of the variables listed in Table 1 and found no significant differ- ences in the responses (p > .05), which suggests that nonresponse bias was not a major concern in this study.

Measures

All items were measured using a 5-point Likert-type scale (1 = completely disagree;

5 = completely agree) unless specified otherwise. Scale items are reported in the Appen- dix. All of the data were collected from the franchisees.

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Affective Commitment. We measured affective commitment using the same scale as in Study 1 ( a = .80).

Opportunism. We used Jambulingam and Nevin’s (1999) 7-item scale to measure franchisee opportunism. We translated this measure into French, and followed back-

translation procedures (Brislin, 1980). A sample item is “I sometimes withhold informa- tion that would help my franchisor to run his/her business” ( a = .72).

Control Variables. Control variables included age, gender, tenure in the franchise orga- nization, and multi-unit ownership, because they account for variance in franchisee opportunism (El Akremi et al., 2011; Jambulingam & Nevin, 1999). We also controlled for franchisee affectivity (i.e., stable individual differences in the tendency to experience positive and negative mood states), because of its demonstrated relationships with work- place deviance (Lee & Allen, 2002), and thus its likely relationship with our outcome variable. In addition, controlling for affectivity may help reduce potential common- method effects (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). We assessed trait positive (5 items; a = .81) and negative (5 items; a = .80) affect using the items from the International Positive and Negative Affect Schedule Short Form (Thompson, 2007). We also controlled for franchisees’ perceptions of performance, because performance may

be related to franchisee opportunism (Dunlop & Lee, 2004). We used the same scale of self-reported performance as in Study 2 (Samaha et al., 2011) ( a = .90).

Measurement Analyses

We tested and compared the fit of a series of nested CFA models. The baseline four-factor model included affective commitment (AC), positive affectivity (PA), negative affectivity (NA), self-reported performance (PF), and opportunism (OP): it yielded

a satisfactory fit to the data: c 2 (289) = 829.54, p < .01; CFI = .95; NNFI = .94; RMSEA = .061; SRMR = .056. This model proved superior to simpler representations of the data, including three-factor models obtained by combining AC and PA

( Dc 2 [4] = 1,131.27, p < .01); AC and NA (Dc 2 [4] = 1,011.76, p < .01); AC and PF (Dc 2 [4]

= 774.57, p < .01); PA and NA (Dc 2 [4] = 717.53, p < .01); a two-factor model com- bining AC, PA, NA, and PF ( Dc 2 [9] = 3,015.30, p < .01); and a single-factor model

combining all the study constructs ( Dc 2 [11] = 3,286.68, p < .01). In sum, these results support the convergent and discriminant validity of our measures. To test for common method variance, we added an uncorrelated method factor to the 4-factor model (Podsakoff et al., 2003). We allowed all items to load on their intended construct and on the method factor. The model that included the method factor resulted

in a good fit ( 2 c [263] = 647.99, p < .01; CFI = .96; NNFI = .96; RMSEA = .054; SRMR = .047) and improved over the fit obtained for the measurement model alone

( 2 Dc [26] = 204.19, p < .01), thus indicating the presence of a method effect. Further analyses of factor loadings revealed that 14% of the variance was accounted for by the

method factor, a proportion which compares favorably to estimates of the pervasiveness of method effects in organizational research (Lance et al., 2010). Thus, common method variance did not seriously affect our ability to test our hypotheses.

Results

Table 1 shows the means, standard deviations, reliabilities and correlations for Study 3 variables. Interestingly, and as expected, self-reported performance and affective

474 ENTREPRENEURSHIP THEORY and PRACTICE

Table 5 Study 3 Regression Analyses Predicting Opportunism

-.09 -2.14* Gender

1.19 .05 1.12 Multi-unit ownership

.03 .82 Positive affectivity

-.14 -3.35** Negative affectivity

.10 2.37* Self-reported performance

-.05 -1.09 Affective commitment

Adjusted R 2 .11

* p < .05; ** p < .01 Note: Standardized regression coefficients (

b) are reported.

commitment correlated negatively with franchisees’ opportunism (r = -.24, p < .01, and r = -.46, p < .01, respectively). Hypothesis 3 predicted that there would be a negative relationship between franchisees’ affective commitment and opportunism. We tested hypothesis 3 using hierarchical OLS regression analyses. We entered control variables in Model 1 and added affective commitment in Model 2. Table 5 shows that in Model 2 there is a negative and significant relationship between affective commitment and opportunism ( b = -.41, p < .01), controlling for all the other variables. Thus, hypothesis 3 was supported.

Study 4: Relationship With Intent to Leave

Sample and Data Collection

For purposes of this study, we drew a separate and independent sample of 3,000 contacts from the same database of 12,000 franchisee contacts already used in Study 3 (i.e., the 3,000 contacts used in Study 3 were excluded). We collected data in June 2009 using the same procedure as in Study 3. In total, we received 417 completed and usable questionnaires, for a response rate of 13.90%. We compared early and late respondents on the study variables and found no significant differences (p > .05), which suggests that nonresponse bias was not a concern.

Measures

All items (see Appendix) were measured using a 5-point Likert-type scale (1 = completely disagree; 5 = completely agree) unless specified otherwise. All of the data were collected from the franchisees.

May, 2015 475

Affective Commitment. We measured affective commitment to the franchise organization using the same scale as in the previous three studies ( a = .81).

Continuance Commitment. We assessed franchisees’ continuance commitment using the revised 6-item scale of Continuance Organizational Commitment (Powell & Meyer,

2004). This scale measures the high-sacrifice component of continuance commitment, which represents the core essence of the construct (Powell & Meyer). We replaced the word “organization” with “franchise organization” in all items. A sample item was “Leaving this franchise organization now would require considerable personal sacrifice” ( a = .84).

Intent to Leave. We developed two items to measure franchisees’ intent to leave (e.g., “How likely is it that you will voluntarily leave your franchise organization when your current franchise agreement expires?”). The scale anchors ranged from very unlikely (1) to very likely (5). The Pearson correlation (r) between these two items was .55 (p < .01). Note that although this scale was an ad hoc measure of intent to leave and comprised only two items, it closely parallels similar 2-item measures of the construct (e.g., Bentein, Vandenberg, Vandenberghe, & Stinglhamber, 2005; Hom & Griffeth, 1991).

Control Variables. We included franchisee’s age, gender, and tenure in the franchise organization as controls as prior research found them to be correlated with affective commitment and turnover intention (Jaros, 1997; Meyer et al., 2002). As research sug- gests that single-unit and multi-unit franchisee owners differ in terms of disposition toward their franchisors (Dant et al., 2013), we also controlled for multi-unit ownership. Finally, we controlled for self-reported performance using a 3-item scale that we specifi- cally developed for this study ( a = .85). This allowed controlling for the possibility that franchisees who perceive their outlets as high performing would not consider leaving their franchise organization. Two items were rated on a 5-point Likert-type scale ranging from

1 (completely disagree) to 5 (completely agree): “Compared to other franchisees in this chain, my outlet(s) is/are performing well” and “To date, I have been able to meet the business objectives for my outlets,” while the third item read as follows: “Based on the franchisor’s forecasts, the performance of my outlet(s) is (1) poor, (2) below expectations, (3) average, (4) above expectations, (5) outstanding.”

Measurement Analyses

We tested and compared a series of nested CFA models. The baseline four-factor measurement model, that comprised affective commitment (AC), continuance commit- ment (CC), self-reported performance (PF), and intent to leave (IL), yielded a satisfactory

fit to the data: 2 c (113) = 427.18, p < .01; CFI = .97; NNFI = .96; RMSEA = .082; SRMR = .058. This model proved superior to simpler representations of the data, includ-

2 2 = 112.05, p < .01), AC and IL ( Dc [3] = 20.45, p < .01), CC and IL (Dc [3]

ing three-factor models obtained by combining AC and CC ( 2 Dc [3]

2 factor model combining all the study constructs ( = 31.06, p < .01), and a single- Dc [6] = 331.82, p < .01). In sum, these

analyses attest to the convergent and discriminant validity of the measures used in this study.

To test for the presence of common-method variance, we repeated the procedure used in Studies 2 and 3. The model with the method factor resulted in a good fit ( 2 c [112] = 426.24, p < .01; CFI = .97; NNFI = .96; RMSEA = .051; SRMR = .058) but did not improve over the fit obtained for the measurement model alone ( 2 Dc [1] = .94,

476 ENTREPRENEURSHIP THEORY and PRACTICE

Table 6 Study 4 Means, Standard Deviations, Reliabilities, and Correlations

Mean SD

1. Age

2. Gender (1 = male)

3. Tenure in the franchise

organization (years) 4. Multi-unit ownership

(1 = multi-unit franchisee) 5. Self-reported performance

(.85) 6. Affective commitment to the

.50** (.81) franchise organization 7. Continuance commitment to the

.41** .42** (.84) franchise organization 8. Intent to leave the franchise

-.48** -.63** -.60** (.55) organization

* p < .05; ** p < .01 Note: N = 417. Pearson product moment correlations are reported for pairs of continuous variables, Spearman rank correlations are reported for pairs of continuous and dichotomous variables, and Phi correlations are reported for pairs of dichotomous variables. Internal consistency values (Cronbach’s alphas or correlation for the 2-item scale of intent to leave) appear across the diagonal in parentheses.

p > .10). Analyses of factor loadings revealed that only 2.1% of the variance was accounted for by the method factor, which suggests that common-method variance did not

significantly affect our ability to test study hypotheses.

Results

Table 6 presents the means, standard deviations, reliabilities, and correlations for Study 4 variables. As can be seen, multi-unit ownership (r = -.17, p < .01), self-reported performance (r = -.48, p < .01), and affective (r = -.63, p < .01) and continuance (r = -.60, p < .01) commitment were significantly and negatively related to the intention to leave the franchise organization.

Hypothesis 4 predicted that low levels of franchisees’ continuance commitment would be associated with a stronger relationship between franchisees’ affective commit- ment and intent to leave. We tested hypothesis 4 using hierarchical OLS regression analyses and the moderated multiple regression procedures recommended by Aguinis and Gottfredson (2010). More precisely, we entered control variables in Model 1, added centered affective and continuance commitment in Model 2, and the affective ¥ continuance commitment product term in Model 3. The results, which are displayed in Table 7, indicated a statistically significant interaction between affective and continuance commitment in predicting intent to leave (

b = .24, p < .01). 2

2. Note that, although not reported, we also measured normative commitment (i.e., commitment based on a sense of obligation toward the organization; Meyer & Allen, 1991) in Study 4. Controlling for normative commitment and for its interaction with affective commitment and continuance commitment did not change the results presented in Table 7. Moreover, none of these terms was significant.

May, 2015 477

Table 7 Study 4 OLS Regression Analyses Predicting Intent to Leave

1.08 .02 .57 Tenure in the franchise organization

.02 .60 Multi-unit ownership

-.03 -.87 Self-reported performance

-4.43** -.18 -4.43** Affective commitment (AC)

-6.96** -.26 -5.14** Continuance commitment (CC)

-6.16** -.27 -5.83** AC ¥ CC

Adjusted R 2 .23

* p < .05; ** p < .01 Note: Standardized regression coefficients (

b) are reported.

To interpret the form of this interaction, we plotted the simple slopes at one standard deviation above and below the mean of continuance commitment (Aguinis & Gottfredson, 2010). As displayed in Figure 1, affective commitment was negatively related to intent to leave at low but not high levels of continuance commitment. To test this interpretation statistically, we compared each of the simple slopes to zero. When continuance commit- ment was low, the relationship between affective commitment and intent to leave was negative and statistically significant (b = -.43, t = -8.70, p < .001). In contrast, when continuance commitment was high, the relationship between affective commitment and intent to leave did not differ significantly from zero (b = -.08, t = -1.27, p = .22). These results support hypothesis 4.

Discussion

This research reports four studies showing that franchisees’ affective commitment to the franchisor is positively related to franchisee objective performance (Study 1) and intent to acquire additional units (Study 2), and negatively related to opportunism (Study