examining the drivers for franchised chains performance ......michael, and castrogiovanni (2004) and...

Examining the Drivers for Franchised ChainsPerformance through the Lens of the DynamicCapabilities Approach*by Assâad El Akremi, Rozenn Perrigot, and Isabelle Piot-Lepetit

This paper draws on the dynamic capabilities approach to explain the performance of fran-chised chains. This approach is a useful lens to understand why some chains are more likely todrive superior performance than others. Hence, using this theoretical lens, we explore why andhow several characteristics of franchised chains influence sales performance. This study includes189 retail and service chains operating in the United States. Findings show that experience beforefranchising, length of training, chain age, franchising fees, and level of internationalizationpositively impact performance of franchised chains, whereas the proportion of franchised unitshas a curvilinear influence (inverted-U shape) on chains’ performance. Implications for franchis-ing scholars and practitioners are discussed.

IntroductionPerformance has interested many scholars of

the franchising field. Some have been inter-ested in unit-level performance, comparingfranchised units and company-owned units(Anderson et al. 1998; Frazer and Winzar 2005;Johns, Howcroft, and Drake 1997; Morey andDittman 1995; Yoo, Donthu, and Pilling 1998).Others have focused on performance at thechain level (Botti, Briec, and Cliquet 2009;

Michael 2003; Perrigot, Cliquet, and Piot-Lepetit 2009; Shane 1996, 1998a; Sorenson andSørensen 2001; Srinivasan 2006), comparingthe performance of several chains within thesame industry. Despite this research, Combs,Michael, and Castrogiovanni (2004) and Watsonet al. (2005) called for more research on per-formance differences among franchised chains.In early franchising research, resource scarcityand agency theories had focused on thepropensity to franchise and did not predict

*We gratefully acknowledge the support of the French National Research Agency (ANR-12-BSH1-0011-01—ANR FRANBLE) and the Human Sciences Institute in Brittany (MSHB-FRANNET). We thank Editor MarkoGrünhagen and the anonymous reviewers for their comments. We are also grateful to Gérard Cliquet, BegoñaLópez Fernández, and Kelly Prioux for their suggestions, and the participants at the 4th InternationalConference on Economics and Management of Networks (2009) for their comments on a preliminary draft.

Assâad EL Akremi is Associate Professor in Université de Toulouse 1—Capitole, CRM (UMR 5303 CNRS).Rozenn Perrigot is Associate Professor at the Graduate School of Management (IGR-IAE)—University of

Rennes 1, Affiliate Professor at ESC Rennes School of Business, and Research at CREM UMR CNRS 6211.Isabelle Piot-Lepetit is Senior Researcher Fellow in Economics at INRA UMR 1110 MOISA, Montpellier.Address correspondence to: Rozenn Perrigot, Graduate School of Management (IGR-IAE), University of

Rennes 1, 11, rue Jean Macé, CS 70803, 35708 Rennes Cedex, France. E-mail: [email protected].

Journal of Small Business Management 2015 53(1), pp. 145–165

doi: 10.1111/jsbm.12059

EL AKREMI, PERRIGOT, AND PIOT-LEPETIT 145

mailto:[email protected]

mailto:[email protected]

superior performance (Castrogiovanni, Combs,and Justis 2006a; Gillis and Combs 2009).Combs, Michael, and Castrogiovanni (2004)suggested that franchised chains’ performanceis, at best, contingent on factors other thanreducing agency costs or accessing scarceresources, and they called for new theoreticalframeworks to enhance our understanding ofthese other factors.

This paper answers this call by analyzing thedrivers of franchised chains’ performancethrough the lens of the dynamic capabilitiesapproach and by using secondary data from189 U.S. franchised chains in the retail andservice industries. The dynamic capabilitiesapproach is an offshoot of the resource-basedview (Barney 1991; Eisenhardt and Martin2000; Wang and Ahmed 2007) and theknowledge-based view (Easterby-Smith andPrieto 2008; Grant 1996). According to thisapproach, the ability of a firm to “integrate,build, and reconfigure internal and externalcompetencies to address rapidly changing envi-ronments” (Teece, Pisano, and Shuen 1997, p.516) is a critical source of superior perfor-mance. The dynamic capabilities approach is arelevant theoretical lens for deepening ourunderstanding of factors that influence perfor-mance in franchising, for three main reasons.First, this approach shows that the manipula-tion of resources, in particular knowledgeresources, is especially critical in the franchis-ing context. Like other firms, franchised chainsneed to possess distinctive capabilities to makebetter use of their resources (Eisenhardt andMartin 2000; Penrose 1959; Wang and Ahmed2007). The chain’s capacity to integrate, recon-figure and renew knowledge resources, andencapsulate both explicit processes and tacitelements is a necessary condition for superiorperformance. Second, the dynamic capabilitiesapproach emphasizes the importance of repli-cation and learning (Teece, Pisano, and Shuen1997; Zollo and Winter 2002). In the franchis-ing context, previous research has shown thatstrict replication drives superior growth andprofitability, and is based on capabilities androutines used by managers to copy, transfer,and recombine resources within the chain,especially knowledge-based resources (Winterand Szulanski 2001). Third, we derive someuseful elements from the dynamic capabilitiesapproach, allowing us to understand why andhow some chains’ characteristics lead to supe-rior performance.

The contributions of this paper are three-fold. First, there is a lack of theoretical andempirical works on the determinants of perfor-mance for franchised chains (Combs et al. 2011;Combs, Michael, and Castrogiovanni 2004). Inthis paper, we develop a thorough understand-ing of multiple factors that affect the perfor-mance of franchised chains. We use thedynamic capabilities approach to understandwhy and how chains’ characteristics such asexperience before franchising, chain age,length of initial training period, contract dura-tion, franchising fees and franchising royalties,internationalization level, and proportion offranchised units would be effective drivers forsuperior performance.

Second, this study tackles relevant contribu-tions to franchising research. Even thoughthere is ample research on the determinants offranchised chains performance, this studydiffers from prior research in the sense itfocuses on a set of chain characteristics andshows the complexity of the effects of some ofthese characteristics as drivers of performance.For instance, previous research has viewedcompany-owned and franchised units in a sym-biotic manner to theorize the advantages ofplural forms (Perryman and Combs 2012;Sorenson and Sørensen 2001). This studyshows that the relationship between the pro-portion of franchised units and performancecould be curvilinear, and hence the advantagesfrom the mix of franchised and company-owned units may be contingent on the level ofthis mix. Moreover, by simultaneously testingthe effects of various chains’ characteristics,this study may help to further understanding ofwhat factors better explain the performance offranchised chains. Finally, this study coversretail and service chains, contrary to previousstudies that have dealt with specific industriessuch as the hotel industry and the restaurantindustry.

Third, highlighting the drivers of perfor-mance of franchised chains has practicalimplications. Indeed, franchising as an entre-preneurial partnership is a cooperative arrange-ment between interdependent types of entre-preneurs (Gonzalez-Diaz and Solis-Rodriguez2012; Kaufmann and Dant 1999). Contrary tocompany-owned chains, “[f]ranchised chains[. . .] operate in sectors where establishmentstend to be smaller and more specialized”(Kosová and Lafontaine 2012, p. 2). Likewise,recent studies have used multilevel models to

JOURNAL OF SMALL BUSINESS MANAGEMENT146

show how group membership influences theperformance of firms, and members of strategicgroups, through a cascading effect (Pereira-Moliner, Claver-Cortés, and Molina-Azorín2011). “Because channel participants performcomplementary roles, a firm’s success dependson its own performance and its partner’s per-formance” (Lewis and Lambert 1991, p. 207).Nested into franchised chains, franchisees’ per-formance is thus impacted by franchised chainsperformance, and vice versa, underscoringthe interest of exploring the drivers of thisperformance.

Further, many franchisors—both well-established and new ones—can be consideredentrepreneurs. As they strive to improve theperformance of their chain, it is important toknow which factors are effective. For new fran-chisors, a study such as this may provide anoverview of the drivers for their future perfor-mance and success. For instance, knowingthat more experience before franchising willincrease performance may convince new chainoperators to take their time developing con-cepts and knowledge before franchising theirbusiness. Also, the drivers for higher perfor-mance of franchised chains can be consideredby prospective franchisees in the process ofselecting a chain, as relevant signals to choosethe appropriate chain.

The paper is organized as follows. In thenext section, we present the theoretical frame-work and the research hypotheses. Then, wedescribe the method before presenting and dis-cussing the main findings of this research.

Theoretical Frameworkand Hypotheses

To develop a better understanding of whyand how some franchised chains’ characteristicsimpact performance, our theoretical framework,in the most general sense, draws on the dynamiccapabilities approach (Ambrosini and Bowman2009; Eisenhardt and Martin 2000; Macherand Mowery 2009; Teece, Pisano, and Shuen1997; Zollo and Winter 2002). This approachemphasizes the importance of a firm’s capabili-ties in creating, reconfiguring, and leverag-ing resources to obtain superior performanceand outperform competitors (Ambrosini andBowman 2009). “Dynamic capabilities are thefirm’s processes that use resources—specificallythe processes to integrate, reconfigure, gain andrelease resources—to match and even createmarket change. Dynamic capabilities thus are

the organizational and strategic routines bywhich firms achieve new resource configura-tions and markets emerge, collide, split, evolve,and die” (Eisenhardt and Martin 2000, p. 1107).Dynamic capabilities consist of specific strate-gic, organizational, and managerial processessuch as strategic decision making, new businessunits’ development, alliances creation, andproduct development, which create value for thefirm within dynamic markets by manipulatingand reconfiguring resources (Ambrosini andBowman 2009; Eisenhardt and Martin 2000;Teece, Pisano, and Shuen 1997).

There is evidence that franchised chains canachieve a competitive advantage and outper-form their competitors by leveraging dynamiccapabilities to recognize, integrate, transfer, andexploit resources that further enhance theircapabilities across business processes andcreate unique value (Combs et al. 2011; Combs,Michael, and Castrogiovanni 2004; Covin andSlevin 1991; Grewal et al. 2011). Franchisorsalso possess dynamic capabilities that theyreconfigure and redeploy within their chain tocreate additional resources and new knowledge.These capabilities refer to the extent to which afranchised chain is able to pool, use, and rede-ploy resources to formulate and implement acompetitively superior strategy, to respondbetter to the market, and outperform competi-tors (Gillis and Combs 2009; Griffith, Noble, andChen 2006). Moreover, the process of learning isa central element in the creation and renewal ofdynamic capabilities (Easterby-Smith and Prieto2008). Prior research in the franchising contexthas emphasized the importance of learning andthe ability of franchisors to create and transferknowledge to franchisees in order to developsuperior performance (Argote 1999; Darr,Argote, and Epple 1995; Sorenson and Sørensen2001).

Dynamic capability is a process that impactsresources and focuses on their reconfiguration(Ambrosini and Bowman 2009). For franchisedchains, knowledge is the most strategically sig-nificant resource, and thus a source of competi-tive advantage that drives economic growth(Gillis and Combs 2009; Grant 1996; Kogut andZander 1996). Both general and system-specificknowledge play a crucial role in the franchisingsector (Darr, Argote, and Epple 1995; Knott2003). In this paper, we define franchisingknowledge as original and proven businessconcepts, as well as operational, technical, andmarketing know-how (Argote and Darr 2000;


Dant 1995; Gillis and Combs 2009; Knott 2003).The primary task of a franchisor is to create andmaximize value through optimally transferringknowledge to franchisees. Any knowledge pos-sessed must be effectively organized and dis-seminated within the chain. Franchisorsprovide their franchisees with two major stra-tegic assets—brand name and business prac-tices. Specifically, they provide manufacturingcapabilities, regulatory know-how, informationon managing and deploying their sales forces,and standard operating processes to managefranchised units effectively (Hoang andRothaermel 2010; Knott 2003; Watson andStanworth 2006). Thus, dynamic capabilities ofa franchisor to recognize, assimilate, transfer,and apply knowledge are critical in the pursuitof improved performance.

Dynamic capabilities are chain-specific andare developed over time through complexinteractions between resources, specificallyknowledge and managerial expertise (Amit andSchoemaker 1993; Combs, Ketchen, andHoover 2004). Chains that develop and lever-age such capabilities have specific characteris-tics. Dynamic capabilities are shaped byenabling and inhibiting variables that charac-terize franchised chains and drive their perfor-mance (Ambrosini and Bowman 2009).Variables such as experience before franchis-ing, chain age, length of initial training period,contract duration, franchising fees and fran-chising royalties, internationalization level,and proportion of franchised units can lead tosuperior performance for franchised chains.

The Impact of Experience beforeFranchising and Chain Maturity

According to Zollo and Winter (2002),“dynamic capabilities emerge from the co-evolution of tacit experience accumulation pro-cesses with explicit knowledge articulation andcodification activities” (p. 344). These dynamiccapabilities result from experience and learningwithin the organization, and their developmentand deployment unfold over time (Ambrosiniand Bowman 2009). Teece, Pisano, and Shuen(1997) argued that the process of learning isbased on repetition and experimentation thatpermit tasks to be performed better and quicker.Repeated practice is thus an important learningmechanism for the development of dynamiccapabilities. In the franchising context, practicehelps franchisors to understand processes morefully and to develop more effective routines

(Argote 1999; Eisenhardt and Martin 2000).Winter and Szulanski (2001) used the concept of“Arrow core” to show that over time, a franchi-sor can learn about what works and what doesnot work with the concept.

Before franchising their concept, entrepre-neurs usually own and manage one or moreunits. This experience contributes to creatingand designing original, valuable, and provenknowledge. Indeed, this is how entrepreneurslearn about customers, local environments,competitors, etc., which they use to improvetheir organizational and managerial capabilities(Sorenson and Sørensen 2001). Lafontaine andShaw (1998) asserted that experience prior tofranchising positively and significantly impactsa franchisor’s longevity in franchising. Theirfindings suggest that “franchisors that spendmore time developing their prototype and theiroperating procedures and documentation aremore likely to succeed in franchising” (p. 108).Here, “prototype, operating procedures anddocumentation” refer to formalized knowledge.This valuable knowledge, gradually createdand improved before franchising the concept,reflects an improvement of the franchisor’sdynamic capabilities.

Besides, franchisors with experience mayforesee market trends and technologicaladvances better than their rivals. Their knowl-edge helps them detect valuable resources thatthey acquire at lower costs compared with theircompetitors (Michael 2002, 2003; Shane 1996).Knowledge structures of well-performing fran-chised chains “are not direct imprints of reality,but a result of complex selection, sorting,manipulation, and conversion processes shapedby experience and existing knowledge” (Kuncand Morecroft 2010, p. 1166). Maturity alsoenhances franchisors’ ability to design advanta-geous contracts and to manage relationshipswith franchisees (Argyres and Mayer 2007).

In the same way, the maturity of a fran-chised chain indicates superior organizationalcapabilities and then contributes to higher per-formance (Bradach 1998), as mature chains aremore able to transfer and exploit organizationalroutines than new chains. Further, the codifi-cation of these routines through formal proce-dures enhances sustainability, and thus achain’s capabilities become robust with age(Argote 1999). Experience encountered tooquickly may overwhelm franchisors, leading totheir inability to transfer experience into mean-ingful learning (Eisenhardt and Martin 2000).


Yet, as time passes, firms become increasinglyaware about what they know and what works,and are better able to develop and deploy capa-bilities (Zahra, Sapienza, and Davidson 2006).Thus, the chain’s maturity allows franchisors tobetter exploit dynamic capabilities and earnreturns above those of their competitors.

Also, older chains have more relationship-specific skills and shared understandings thatfacilitate knowledge transfers. Because complexbodies of both explicit and tacit system-specificknowledge move among franchisors and fran-chisees (Spender and Grant 1996), knowledgetransfer increases when partners spend signifi-cant time together and maintain stable relation-ships (Kotabe, Martin, and Domoto 2003;Li, Poppo, and Zhou 2010). As a chain growsand becomes mature, the average cost ofdeploying and exploiting capabilities falls,making the chain more profitable (Shane,Shankar, and Aravindakshan 2006). Hence, weargue that franchisors with higher prior experi-ence and higher maturity will outperform theircounterparts.

H1a: In franchised chains, more experiencebefore franchising is positively correlatedwith higher performance.

H1b: The age of a franchised chain is positivelycorrelated with higher performance.

The Impact of Initial Training EffortsResearch has highlighted the importance of

training practices in the franchising context(Brand and Croonen 2010; Truss 2004).According to Truss (2004), training throughoutthe franchised chains is generally controlled bythe franchisor. Developing stronger trainingcapabilities is important for the success of fran-chised chains. A franchised chain’s knowledgeis usually codified, then taught and transferredto franchisees and their employees. Some of achain’s know-how is tacit and difficult tocodify. This know-how is then transferred tofranchisees over time through complex socialexchanges (Gillis and Combs 2009; Gorovaiaand Windsperger 2010; Paswan and Wittmann2009; Windsperger 2004). However, initialtraining remains a critical means throughwhich the franchisor systematically transfersthe operational, technical, and marketingknow-how to franchisees in order to improvethe effectiveness of the whole chain. Accord-ingly, franchisors devote considerable effort to

socializing and training franchisees (Bradach1997). Training can be costly but is necessaryto maintain chain uniformity, brand reputation,and therefore performance. Initial training maysucceed as a means for a replication strategy,and Winter and Szulanski (2001) showed thatreplication can breed superior growth andprofitability by leveraging knowledge assets.Michael and Combs (2008) found that franchi-sors’ investments in training improved units’survival, and hence the chain’s performance.Shane (2001) showed that training was ameans to standardizing products and services,while it also reduces failure and increasesperformance.

Initial training is a time-consuming processthat requires organizational and managerialprocesses such as demonstrations, coaching,and hands-on experience (Hansen 1999, 2002;Zander and Kogut 1995). It takes considerabletime to transfer tacit knowledge that is contex-tually embedded and not easily transferredthrough manuals, standard operating proce-dures, or information systems (Kogut andZander 1992, 1996). Most of all, when franchi-sors have dynamic capabilities that affordmaking costly investments and mobilizingexpert knowledge for intensive and longertraining, the organizational and managerialprocesses would favor organizational learning.These processes also enable tasks to be per-formed better and more quickly by the majorityof the chain’s members, and thus positivelyinfluence chain performance.

H2: In franchised chains, longer periods ofinitial training are positively correlatedwith higher performance.

The Impact of FranchisingContract Duration

Dynamic capabilities contain processes suchas strategic decision making and the creation ofsustainable partnerships and alliances that gen-erate value for firms (Ambrosini and Bowman2009; Eisenhardt and Martin 2000; Teece,Pisano, and Shuen 1997). Thus, choosing theappropriate contract length and developingstrong relational norms and common goalsamong partners within a franchised chain rep-resent dynamic capabilities that would favorperformance. Further, such processes representisolating mechanisms that prevent the leak of afranchisor’s valuable knowledge that causesimitation by competitors.


Replication drives superior growth and prof-itability within franchised chains (Winter andSzulanski 2001). It involves transferring knowl-edge to franchisees. Yet, when a franchisor’sknowledge is transferred to franchisees, it mustbe kept secret from competitors. Contracts infranchised chains usually stipulate conditionsthat prevent franchisees from transferringknowledge to competitors, thus deterringknowledge leakage (Argote and Darr 2000;Darr, Argote, and Epple 1995; Knott 2003;Mariz-Pérez and García-Álvarez 2009). It isimportant that franchisees keep transferredknowledge and information confidential and donot allow competitors to copy it. Franchisors usethe franchising contract to protect their knowl-edge, and thus maintain their differentiation.During the contract period and beyond, franchi-sees are forbidden to divulge any aspect of theknowledge to anyone. Over time, sharing clas-sified information allows franchisors and fran-chisees to feel secure about their futurerelationship (Dant and Nasr 1998). Longer con-tracts are associated with keeping knowledgewithin a chain, where it is secret and well-used,allowing the chain to fully exploit its competi-tive advantage.

Paswan and Wittmann (2009) suggested thatfranchised chains have social networks thatpromote shared identities, and a sense of com-munity that structures learning and knowledgetransfer (Kogut and Zander 1996). Long-termcontracts feature strong relational norms andcommon goals that develop over time. As fran-chisors and franchisees become increasinglyinterdependent, both the franchisor and fran-chisees develop cooperative strategies, makeadjustments, and learn about the others’ values,procedures, and interests (Blut et al. 2011;Tikoo 2002) so that long-term contracts safe-guard against opportunism (Dyer and Singh1998). Indeed, longer contracts increase finan-cial interdependence between the franchisorand franchisees, creating a shared purpose andidentification with a chain’s interests. Blut et al.(2011) used the life cycle approach to showthat, as relationships between franchisor and

franchisees become longer, ties betweenparties grow stronger, and cooperation andperformance improve.

H3: In franchised chains, longer contractsare positively correlated with higherperformance.

The Impact of Franchising Feesand Royalties1

If a firm possesses and utilizes dynamic capa-bilities, it can continually reconfigure, renew,and redeploy its resources and knowledge tocapture additional value (Liao, Kickul, and Ma2009; Teece, Pisano, and Shuen 1997). At thesame time, “without a critical mass of resources,a firm lacks the basic ingredients or buildingblocks for its integrative capability—an essentialtype of dynamic capability—to leverage withand exercise on” (Liao, Kickul, and Ma 2009, p.270). In the franchising context, fees (as ex anterents) and royalties (as ex post rents) representimportant financial resources used to developcapabilities that allow franchisors to reconfig-ure, renew, and redeploy knowledge. In theabsence of new resources, franchised chainsmay find it difficult to perform in challengingenvironments and to match the changing oppor-tunities. The higher the resources of a franchisedchain, in terms of fees and royalties, the higherthe capability of the chain to efficiently exploitand explore its knowledge resources.

March (1991) suggested that both exploita-tion and exploration are necessary to developorganizational learning and determine firms’outcomes. This is consistent with the dynamiccapabilities approach that postulates that anappropriate balance between exploration andexploitation is needed for deploying, exploit-ing, and leveraging organizational processesand routines (Zollo and Winter 2002). Sorensonand Sørensen (2001) showed that franchisorsthat balance exploitation and explorationperform better than those that do not. In orderto maintain competitiveness, franchisors incre-mentally improve existing know-how and rou-tines before transferring them to franchisees

1The relationship between fees/royalties and performance can be bidirectional. High fees/royalties allowfranchised chains to have access to more financial resources that can improve the chain’s performance. Yetfranchised chains that outperform their competitors can afford to ask for high fees and royalties. In this paper,only the former argument is relevant for the dynamic capabilities approach. We thank an anonymousreviewer for pointing this out.


to assure their assimilation (Argote 1999; Darr,Argote, and Epple 1995; Sorenson andSørensen 2001). When franchising partnerswork together creatively, subsequent updatingleads to chain-specific knowledge, which keepsa franchised chain competitive and improvesperformance (Darr, Argote, and Epple 1995;Hoang and Rothaermel 2010).

To perform well, franchisors have to updateexisting knowledge and create new knowledge.This requires investing time and money onResearch and Development, hiring new manag-ers with specific profiles and backgrounds,using external benchmarking, etc. To financethese investments and provide knowledgeupdates (Sen 1993), franchisors use franchisingfees paid at the beginning of the contract andalso royalties paid regularly (Combs et al.2011). Royalties can be driven by both a fran-chisor’s cost of brand building and a brand’sunderlying market value (Combs and Ketchen2003). High royalties increase franchisors’profits (Lafontaine 1992) and provide incen-tives to maintain the brand (Rubin 1978) bycontinuous innovation and updates. Roh (2002)suggested that franchised chains reduce thevariability of their cash flows because royaltiesreceived from one unit exhibit less varianceover time than the revenue of that unit. Therisks of franchised chains are thus reduced, andtheir performance is enhanced. Although pre-vious research has shown that the effects offees and royalties on performance are complexand contingent upon factors such as chain ageand size (Benjamin, Chinloy, and Winkler 2007;Grünhagen and Dorsch 2003; Shane, Shankar,and Aravindakshan 2006), we argue that highfees and royalties generate resources used todevelop and renew capabilities, and to improvechain performance.

H4a: In franchised chains, higher franchisingfees are positively correlated with higherperformance.

H4b: In franchised chains, higher franchisingroyalties are positively correlated withhigher performance.

The Impact of Chains’Internationalization2

Grewal et al. (2011) suggested that thespeed, scale, and scope of international growthare related to an “alliance capability” thatinsures mutual sense of dependence in fran-chise relationships (Combs, Ketchen, and Short2011). Franchisors’ strategic decisions toexpand internationally, and to outperformglobally, are based on the ability to assessadvantages, but also risks, of internationalexpansion (Aliouche and Schlentrich 2011;Preble and Hoffman 2006). Presence in foreignmarkets may require adapting to new environ-ments and/or new constraints. Yet the successof international expansion depends on the fran-chisors’ ability to leverage expansion advan-tages and manage risks and constraints. Evenmore than in domestic markets, feedback frominternational markets enables franchisors toincrease their competitive advantage in severalgeographical areas. Research shows that higherregional diversification positively enhancesperformance (Qian et al. 2010).

Preble and Hoffman (2006) showed thatfranchisors can experiment their businessconcept with international franchisees prior toengaging in active international franchising.Feedback about consumers, local markets, andcompetitors reinforces knowledge, and can beused to maintain a competitive advantage andenhance performance. Sorenson and Sørensen(2001) showed that as chains became geo-graphically dispersed, their rate of learning andgrowth also increased. Acquiring local knowl-edge in host countries strengthens firm perfor-mance. Franchisors rely on internationalexpansion to collect local knowledge to adapttheir products, select market segments, andupgrade their technology for local markets (Li,Poppo, and Zhou 2010; Mariz-Pérez andGarcía-Álvarez 2009). Kalnins and Mayer (2004)found that the knowledge of local marketsdetermines the success of new units opened inthe same geographical area. Internationalexpansion can make franchisors leverage newknowledge and adapt to different markets(Gillis and Combs 2009; Mariz-Pérez and

2The relationship between chain internationalization and performance can be bidirectional. Franchisors canuse their success on the domestic market, that is, their high level of performance, to expand their businessat the international level. Yet franchisors can use their international experience to reinforce their success onthe domestic market, that is, achieving high levels of performance. In this paper, the latter argument seemsmore relevant according to the dynamic capabilities approach. We thank the editor for pointing this out.


García-Álvarez 2009), and develop innovationsthat increase performance and prevent chaindecline.

In the international franchising domain,prior research shows that the relationshipsbetween the franchisor and franchisees locatedin foreign markets are quite different fromthose in domestic environments (Huszagh,Huszagh, and McIntyre 1992; Qian et al. 2010).Grewal et al. (2011) suggested that interna-tional expansion is akin to an interfirm partner-ship rather than a contract between afranchisor and an individual franchisee. “Thus,franchisors that expand internationally andintra-regionally would experience lower adap-tation, monitoring, and management costs indealing with a limited number of master fran-chisees, which recoups investment costs morequickly for accelerated and profitable expan-sion. Geographic expansion offers performanceadvantages as capabilities are leveraged to apoint before excessive investments and coordi-nation costs erode profits” (Grewal et al. 2011,p. 548).

H5: A higher level of internationalization of afranchised chain is positively correlatedwith higher performance.

The Curvilinear Impact of the Proportionof Franchised Units

According to the “plural form symbiosis”(Bradach and Eccles 1989; Combs, Ketchen,and Short 2011; Perryman and Combs 2012),the symbiotic advantage is based on the abilityof a franchisor to manage complementaritiesbetween franchised and company-owned units,and to use both organizational forms in waysthat each form balances the weaknesses of theother. Complementarities can produce a virtu-ous cycle for the development of knowledgethat is then shared among chain members(Perryman and Combs 2012). Yet “the correctquestion for franchising scholars to ask is notwhy do firms franchise or why they are notcompletely company-owned, but what is theoptimal proportion of franchised units givenother firm characteristics? ” (Shane 1998b, p.736). Barthélemy (2008) found that the perfor-mance of chains with strong brand names andtacit business practices worsened if they usedhigh levels of franchising. Combs, Ketchen,and Hoover (2004) showed that chains per-formed well when an optimal proportion offranchised units reduced both the monitoring

costs of company-owned units’ managers andthe risks of franchisees’ free-riding. Also,Castrogiovanni, Combs, and Justis (2006b)found a nonlinear impact of franchise propor-tion on performance.

Using plural forms allows franchisors tobenefit from sharing routines and knowledgeacross units, while spreading the costs of gen-erating and updating knowledge (Argote,Beckman, and Epple 1990). For instance, fran-chisees help franchisors by making substantialinvestments in their units, providing manage-rial skills to update the chain’s knowledge,fostering innovations by using knowledge onlocal markets, making new capabilities avail-able, and hence fueling franchisor growth(Castrogiovanni, Justis, and Julian 1993; Hsuand Jang 2009; Lafontaine and Kaufmann 1994;Shane 1998b). Company ownership, in con-trast, promotes standardization, guaranteesvaluable replication, and thus delivers perfor-mance (Bradach 1997; Combs, Ketchen, andShort 2011; Winter and Szulanski 2001).

Once the chain is developed, expansionthrough franchising is no longer necessary andmay become risky (Oxenfeldt and Kelly1968–1969). Franchising is useful when demandis growing and uncertainty exists, because itcreates new knowledge and intellectual assetsthat increase a chain’s performance (Hsu andJang 2009; Srinivasan 2006). As the proportionof franchised units increases, knowledge updat-ing and performance improve. However, at acertain point, performance can begin todecrease. According to the dynamic capabilitiesapproach, the symbiotic advantage of pluralform depends on the ability of a franchisor tobalance high risks of monitoring company-owned units and risks of free-riding in fran-chised units, and capabilities development(Perryman and Combs 2012). However, a dis-proportionate increase of the proportion of fran-chised units can make it difficult for thefranchisor to reap additional values by leverag-ing the advantages of each organizational form.

Increasing the number of franchised unitscan become excessive if franchisees jeopardizea franchisor’s knowledge and brand’s reputa-tion, thus negatively impacting performance.When franchisors enhance the value and repu-tation of units by creating valuable knowledge,the potential for franchisees to free-rideincreases (Michael 2000; Vázquez 2007). Fran-chisors’ risk of losing control over businessprocesses grows also. Indeed, prior research


has shown that the more valuable the intan-gible assets of a chain (brand name, know-how,reputation, etc.), the higher the potential forfree-riding by franchisees (Barthélemy 2008;Brickley and Dark 1987; Lafontaine and Shaw2005; Norton 1988). Franchisor’s inputsbecome increasingly valuable as they continu-ously develop and update knowledge, so theyhave an incentive to reduce the number offranchised units and increase the number ofcompany-owned units (Michael 2000). Conse-quently, risks of knowledge leakage, noncom-pliance to standards, and opportunism increaseand may deteriorate chain performance (ElAkremi, Mignonac, and Perrigot 2011). Toomuch franchising can increase variabilityamong units to the point that it weakens afranchisor’s ability to sustain and leverageknowledge (Gillis and Combs 2009). Sorensonand Sørensen (2001) suggested that chainsbenefit from knowledge transfer, updating, andinnovation by having both franchised andcompany-owned units. In equilibrium, a chainwill exhibit the most efficient proportion offranchised versus company-owned units(Vázquez 2007). In the case of the restaurantindustry, Hsu and Jang (2009) found a curvilin-ear (inverted-U shape) relationship betweenthe proportion of franchised units and chainprofitability.

H6: There is a curvilinear relationship(inverted-U shape) between the proportion offranchised units and chain performance.

MethodSample and Data Collection

We used two complementary sources togather data on the performance of U.S. fran-chised chains. The 2009 Top 200 FranchiseSystems published by Franchise Times Maga-zine provides information such as worldwidesales in millions of dollars, number of units inthe domestic market, total number of units,proportion of units that are franchised, andindustry. Although the Top 200 FranchiseSystems provides only the top 200 chains interms of worldwide sales, there is enough vari-ance in our sample to ensure the significance ofour results. The sample includes chains withdifferent performance levels (from $67 millionof annual worldwide sales to $70,693 million)and different number of units (minimum of 43units in the U.S. market). As reminded by

Kosová, Lafontaine, and Zhao (2011), whorecently used this data source for exploringhow chain scale and parent company scopeaffect chain performance, “despite being com-prised of only largest chains, the size range ofthe chains is quite wide” in the Top 200 Fran-chise Systems ranking (p. 13). Finally, in abenchmarking perspective, the examination ofthe performance levels of well-performing fran-chisors is relevant for understanding the driversof such levels of performance.

The 2009 Annual Franchise 500 publishedby Entrepreneur Magazine offers additionalinformation about franchised chains, such ascompany age, chain age, franchising fees, roy-alties, and contract duration. Both rankings arebased on U.S. franchisor surveys, and pub-lished figures only include franchisors whoresponded to the surveys; usually new franchi-sors or those looking to attract new franchi-sees. They do not provide an exhaustive list offranchised chains within the United States and,like all secondary data sources, present a selec-tion bias (Lafontaine 1995). Nevertheless,Entrepreneur Magazine and Franchise TimesMagazine check the figures provided by fran-chisors before publishing them. The informa-tion is generally objective because franchisorsdo not have any particular interest in providingincorrect information that would be quicklydetected by prospective franchisees (Scott1995). To sum up, these databases remain reli-able sources of data for the U.S. market andhave been used in previous research in fran-chising (Castrogiovanni and Justis 2002; Combsand Castrogiovanni 1994; Dant, Perrigot, andCliquet 2008; Elango 2007; Kosová, Lafontaine,and Zhao 2011; Lafontaine 1992). Our sampleconsists of 189 chains, those present in both2009 rankings for which we have maximuminformation. This sample covers both retailingand services.

Variables and MeasuresDependent Variable. Chain performance is thedependent variable in this study. We usedworldwide sales as a measure of performancefor franchised chains. Although there are otherperformance measures (for instance, profits,return on assets [ROA], return on equity [ROE],and average growth), no source systematicallycompiles cost data for franchised chains(Sorenson and Sørensen 2001). Moreover, it wasdifficult to acquire enough reliable informationto create a composite measure of performance at


the chain level. Indeed, there is a lack of publiclyavailable financial data on franchised chains, asthese are mostly privately owned (Shane 2001).Previous research about franchised chains usedsales to measure performance (Botti, Briec, andCliquet 2009; Combs, Ketchen, and Hoover2004; Perrigot, Cliquet, and Piot-Lepetit 2009;Sorenson and Sørensen 2001). According to Yinand Zajac (2004, p. 374), sales are an importantindicator of performance as “profit is highlycorrelated with stores sales.” Moreover, usingsales has the advantage of being applicableacross industries (especially in multi-industrysettings), and to both private and public firms.We used the logarithm for sales to adjust fordecreasing returns to scale and to correct for thenormal distribution and central tendency of themeasure.

Independent Variables. Various independentvariables may explain the performance of fran-chised chains through the lens of the dynamiccapabilities approach. Experience before fran-chising was the number of years between whenthe company started its business and when itbecame a franchise. Chain age was the numberof years from when the chain was establishedand the present data year (2009). Franchisingfees were entry fees, in thousands of dollars,paid by franchisees to their franchisor at thebeginning of their contract. Franchising royal-ties were fees, based on the percentage of unitsales, paid regularly (monthly, weekly, etc.) byfranchisees to their franchisor. Contract dura-tion was the length of the franchising contractin years. Length of training was the number ofdays for initial training that each new franchi-see received at chain headquarters. Interna-tionalization level was the proportion of unitslocated outside the domestic market. Propor-tion of franchised units was the proportion ofunits owned by franchisees, compared withcompany-owned units.

Control Variables. To strengthen empiricaltests, we controlled for industry-sector effects.Industry was a dummy variable with zero valuefor chains in the service sector, and unity value

for chains in the retail sector. Specific designs inmanaging knowledge characterize service andretail sectors and, therefore, indicate how theyachieve high performance levels (Homburg,Hoyer, and Fassnacht 2002). We also controlledfor chain size in the domestic market becausesize influences performance—large franchisedchains can exploit economies of scale, allowingthem to perform more cost efficiently than smallchains (Hsu and Jang 2009).

Data AnalysisBefore testing our hypotheses, we checked

the normality of the variables. In order toaccount for decreasing marginal effects andnon-normality (Barthélemy 2008; Sorenson andSørensen 2001), we used logarithms for chainage, experience before franchising, fees androyalties, and length of training. Second, wechecked for multicollinearity. The varianceinflation factors were all less than 2, indicatingvery little likelihood of any multicollinearity onthe parameter estimates (Hair et al. 2006). Wealso tested for heteroscedasticity using theWhite test (Hsu and Jang 2009). The resultsindicated that our parameter estimates had sig-nificant heteroscedasticity. We then used abootstrap method to adjust for non-normalityand heteroscedasticity (Efron 1979, 1987).Bootstrapping draws thousands of randomsamples from the collected data. Research hasproven that bootstrapping produces betterresults than simply applying regression analysisto an original sample (Efron 1979; Efron andTibshirani 1993). The bootstrap procedure toestimate the statistical significance of regres-sion coefficients may also mitigate potentialproblems of nonlinearity and non-normality ofthe quadratic term—curvilinear relationshipbetween the proportion of franchised units andchain performance (Efron 1979, 1987; Efronand Tibshirani 1993; Paige, Trindade, andFernando 2009). We created a sampling distri-bution with 5,000 bootstrap resamples (fromthe original sample of 189 franchised chains),using a stratified procedure to maintain theintegrity of the original data. Bootstrapt-statistics are reported throughout our tables.3

3As the sample size was acceptable, we reran the analyses using hierarchical regressions without bootstrap-ping. Results had similar significance levels and negligible changes in coefficient values. Based on therecommendations of Efron and Tibshirani (1993), and Paige, Trindade, and Fernando (2009) concerning thenon-normality of the quadratic terms, we decided to retain results of bootstrapping.We thank an anonymousreviewer for pointing this out.


Following the recommendations of Aikenand West (1991), we used three-stage hierarchi-cal regressions to test for hypothesized linearand quadratic effects. First, we entered thecontrol variables to deal with potential spuriouseffects of industry type and chain size on chainperformance. Second, we tested the lineareffects of independent variables (experiencebefore franchising, chain age, franchising fees,franchising royalties, contract duration, lengthof training period, and internationalizationlevel) on chain performance. Although we didnot hypothesize that the proportion of fran-chised units would affect chain performance, itwas important to test for simple linear effects inorder to follow the recommendations for the testof curvilinear relationships (Aiken and West1991; Cohen et al. 2003). Third, we entered thesquared form of the standardized measures ofthe proportion of franchised units. A negativecoefficient would indicate an inverted-U shapedrelationship (Hair et al. 2006).

ResultsTable 1 shows the descriptive statistics along

with correlation values for all variables. Thecorrelation value between chain performanceand chain size in the domestic market (acontrol variable) was relatively high (r = 0.66,p < .01), and comparable to those reported byothers (Sorenson and Sørensen 2001). Largefranchised chains tend to perform better thansmall chains as they benefit from advantages incosts and market value (Hsu and Jang 2009).All correlations were in the expected directions.

Table 2 presents the results of the three-stage hierarchical regression models with 5,000bootstrap resamples. Bootstrap t-statistics sup-ported H1a, H1b, H2, H4a, H5, and H6 but notH3 and H4b.

H1a posits that more experience before fran-chising correlates significantly and positivelywith higher chain performance. The test oflinear effects in step 2 supports this hypothesis(β = 0.110, p < .10). As hypothesized, chain agehad a significant positive effect on chain per-formance (β = 0.148, p < .05), which supportsH1b. The hypothesized relationship betweenlength of initial training period and chainperformance was significant and positive(β = 0.133, p < .05), thus supporting H2. H3posits that the longer the contract duration, thehigher the chain performance. This hypothesiswas not supported (β = −0.055, ns.). Franchis-ing fees and chain performance correlated posi-

tively and significantly (β = 0.224, p < .01).However, the relationship between franchisingroyalties and chain performance was signifi-cantly negative (β = −0.199, p < .05). Thus, H4awas supported, but H4b was not.

There was also a statistically significant andpositive relationship between the international-ization level and chain performance (β = 0.204,p < .01), which supports H5. There was finally asignificant quadratic relationship between theproportion of franchised units and chain per-formance (β = −0.408, p < .01).

To gain more insight into this curvilinearrelationship, Figure 1 plots the proportion offranchised units on the x-axis against world-wide sales as a measure of performance forfranchised chains on the y-axis. We used onestandard deviation (S.D. = 16.43, Table 1)above and below the mean to capture high andlow proportion of franchised units. In drawingFigure 1, we used the results in Table 2. Thisfigure shows a clear inverted-U shape linkbetween the proportion of franchised units andchain performance. The left-hand side of thegraph shows that when there is low proportionof franchised units, the chain performance islow as the franchisor may have few resourcesand cannot increase the exploitation of thechain’s knowledge assets. As the proportion offranchised units increases, we enter an area ofbest performance where the franchisor effi-ciently exploits the chain’s knowledge andexplores new areas of knowledge. However,the right hand side of the graph shows that thisgradually decreases, and above a certain levelthe impact of the proportion of franchised unitsswitches. In sum, the plot shows that the dataexhibited the expected inverted-U shape. Thus,H6 was supported.

DiscussionBuilding on the dynamic capabilities

approach as a broad theoretical lens, our study,which explored the impact of franchisedchains’ characteristics on sales performance,contributes to franchising research and practicein several ways. The dynamic capabilitiesapproach is a relevant framework in thecontext of franchising as it focuses on theimportance of organizational learning and rep-lication (Zollo and Winter 2002). It is consistentwith the works of Sorenson and Sørensen(2001), and Winter and Szulanski (2001). Thisapproach also shows that the development ofhigh-performing processes and routines is


Tab

le1

Mea

ns,

Sta

ndar

dD

evia

tions,

and

Corr

elat

ions

Val

ues

Mea

nS.D

.1

23

45

67

89

10

11

1.Chai

nper

form

ance

1,73

9.77

6,74

0

2.Exp

erie

nce

bef

ore

fran

chis

ing

8.03

13.7

20.

195*

3.Contr

act

dura

tion

13.5

66.

100.

253*

*−0

.051

4.Le

ngt

hof

trai

nin

gper

iod

11.8

211

.15

−0.0

930.

005

0.07

4

5.Chai

nag

e26

.56

13.6

50.

428*

*−0

.041

0.26

6**

0.03

86.

Fran

chis

ing

fees

30.9

114

.94

0.05

3−0

.158

*0.

216*

*0.

169*

−0.0

117.

Fran

chis

ing

roya

ltie

s5.

421.

68−0

.095

−0.1

43*

−0.1

24−0

.091

0.00

6−0

.033

8.Pro

port

ion

of

fran

chis

edunits

9.01

16.4

30.

446*

*0.

082

0.09

6−0

.029

0.38

5**

0.07

0−0

.145

*

9.In

tern

atio

nal

izat

ion

leve

l12

.40

18.2

60.

307*

*0.

345*

*0.

143*

−0.1

20−0

.001

0.05

20.

084

−0.0

71

10.

Chai

nsi

zein

the

dom

estic

mar

ket

1,19

0.68

2,37

5.94

0.62

3**

−0.0

230.

134*

−0.1

56*

0.29

9**

−0.0

880.

171*

0.10

90.

192*

*

11.

Indust

rya

0.28

0.45

−0.1

69*

0.08

3−0

.160

*−0

.063

−0.1

46*

0.07

70.

127

−0.0

850.

016

−0.0

73

n=

189;

a For

Indust

ry:

0=

Serv

ices

,1

=Ret

ailing.

Tw

o-s

ided

t-te

st:

*p<

.05,

**p

<.0

1.


path-dependent and embedded in chains andchains’ characteristics (Eisenhardt and Martin2000). It highlights the importance of learningmechanisms in terms of experience accumula-tion, knowledge articulation, and knowledgecodification and redeployment in franchisedchains (Argote 1999; Argote and Darr 2000;Combs et al. 2011; Zollo and Winter 2002).Indeed, the term “dynamic capabilities” empha-sizes the key role of franchisors in appropri-ately creating, integrating, transferring, andreconfiguring knowledge resources (Teece,Pisano, and Shuen 1997).

Contributions for Franchising ResearchThis study contributes to franchising

research in multiple ways. First, it corroboratesprior research (Argote and Darr 2000; Argyresand Mayer 2007; Hossain and Wang 2008)which has shown that a franchisor’s experienceis cumulative and embedded in processes, rou-tines, structures, and codified innovations. Thiscumulative process allows more experienced

franchisors to outperform their counterparts.Our findings on the significant impact of expe-rience before franchising on chain performancehighlight the importance of both explorationand exploitation of knowledge resourcesthrough different stages of franchising lifecycles (Kalnins and Mayer 2004; Lafontaine andShaw 1998; Sorenson and Sørensen 2001). Inthe same vein, Cochet and Garg (2008) foundthat the ability of franchisors to develop appro-priate contracts required time and depended onmanagerial expertise, repeated practice, andtrial and error.

Second, this study showed that the length ofthe franchising contract did not significantlyinfluence chain’s performance. This is contraryto the study of Brickley, Misra, and Van Horn(2006) that showed that more experienced fran-chisors used longer contracts than less experi-enced franchisors in order to reinforce theinterests’ interdependence with franchisees.Yet contract duration is only one aspect ofgovernance that minimizes transaction costs

Table 2Tests of Linear and Curvilinear Relationships

Variables Chain Performance: Sales

Step 1 Step 2 Step 3

ControlsChain size in the domestic market 0.660*** 0.694*** 0.716***Industry −0.065 −0.064 −0.087

Linear effectsExperience before franchising – 0.110* 0.134**Chain age – 0.148** 0.147**Length of training period – 0.133** 0.101*Contract duration – −0.055 −0.043Franchising fees – 0.224*** 0.180**Franchising royalties – −0.199** −0.138**Internationalization level – 0.204*** 0.217***Proportion of franchised units – 0.227*** 0.609***

Quadratic effects(Proportion of franchised units)2 – – −0.408***Adjusted R2 0.492 0.729 0.742ΔR2 – 0.257*** 0.021***ΔF 48.877 11.879*** 8.517***

*p < .10**p < .05***p < .01, two-tailed tests.


and opportunism risks. Dyer and Singh (1998)found that self-enforcing agreements, such astrust and reputation, were more effective thanthird-party enforcement agreements, such aslegal contracts. Self-enforcement was associ-ated with both maximizing value-creation ini-tiatives and minimizing opportunistic threats(Dyer and Singh 1998; El Akremi, Mignonac,and Perrigot 2011). Regardless of a contract’slength, franchisors use both stern contractualclauses and regular monitoring to assure com-pliance with standards. The results of this studysuggest that contract duration, reflecting strongrelational norms, common goals and sharedidentity, does not guarantee chain perfor-mance. Rather, legal conditions such as penal-ties, competition clauses, and process/resultscontrols such as field audits and mystery shop-pers, could be the most efficient mechanismsfor maintaining established standards anddriving performance (Bradach 1998; Sorenson

and Sørensen 2001; Spinelli and Birley 1996).Further, with longer contracts, conflicts mayincrease as a result of wealth distribution (Nair,Tikoo, and Liu 2009). Franchisees’ demand forself-determination and autonomy may alsoamplify as the age of the relationships increases(Blut et al. 2011). These demands may consti-tute a boundary condition for the relationshipbetween contract duration and chain perfor-mance. Future research should continue toexamine the role of contract duration and itsboundary conditions in the franchising context.

Third, this study underscores the compleximpact of the proportion of franchised unitsversus company-owned units on chain perfor-mance. By using a larger sample and focusingon several industries, we add to Hsu and Jang’s(2009) findings regarding the “optimal” level ofcompany-owned units. This optimal level mayoccur at different points. According to Bradach(1997), the optimal point is at the intersectionof innovation benefits and franchisees’ controlcosts. For resource scarcity scholars, theoptimal level occurs at the point where thebenefits of resource access are balanced byforegone profits (Oxenfeldt and Kelly 1968).This study supports existing research about theadvantages of plural forms (Bradach 1998;Ehrmann and Spranger 2004; Madanoglu, Lee,and Castrogiovanni 2011; Perryman and Combs2012). Sorenson and Sørensen (2001, p. 723)postulated that “finding the right mix of gover-nance structures in a chain importantly affectsthe success of both the franchiser and its fran-chisees.” This result is also consistent with theentrepreneurship literature in franchising.which suggests that some franchisees progres-sively lose their commitment to a franchisor’sknowledge and standards. As their franchisingexperience grows, they become increasinglyconfident in their own skills and comply lesswith operating routines (Davies et al. 2011).“As a result, the perceived contribution androle of the franchisor becomes marginalizedand its relative knowledge-based powererodes” (Davies et al. 2011, p. 2). Michael(2003) found that the more franchised units achain has, the lower the quality rating of thatchain, which may weaken performance.

Implications for PractitionersThis study has practical implications for

franchisors, new franchisors, and also pro-spective franchisees. On the one hand, theresearch findings can help franchisors and new

Figure 1Curvilinear Relationship

between the Proportion ofFranchised Units and Chain

Performance

*Logarithm for sales performance was used

to adjust for decreasing returns to scale and

to correct for the normal distribution and

central tendency of the measure. The axis is

graduated from 0 to 12 (logarithm) repre-

senting the sales that vary from 67.00 to

70,693.00 million dollars.

PFU, proportion of franchised units.


franchisors to look for the “optimal” proportionlevel of franchised units within their chains. Forinstance, using the data of our sample, it seemsthat on average, 62.2 percent may be the“optimal” proportion of franchised units withina chain.4 This proportion is specific to oursample and does not hold for all franchisedchains. Practitioners should therefore interpretthis proportion with utmost caution. It should beused as an indication rather than as a target toachieve. Of course, other parameters may causethis percentage to vary. There are probablysome franchised chains that outperform theircompetitors by using a higher or lower percent-age of franchised units. Further, fully franchisedchains can succeed—an example is the fran-chised chain Subway in the fast-food/sandwichsegment. Nevertheless, our empirical studyestablishes the advantage of mixed ownership interms of a chain’s sales performance. Therefore,well-established franchisors and new franchi-sors should develop plural forms that havemultiple advantages. These advantages includespecific aspects of a chain’s management, suchas adding new units, protecting the brand bymaintaining concept uniformity, reacting locallyto threats or opportunities, and evolving andadapting service and/or product concepts(Bradach 1998). Besides, more internationalizedchains outperform less internationalized ones.Transfer of knowledge in various countries,according to specific market conditions, encour-ages learning, improves knowledge, andincreases a chain’s performance. As a result,well-established franchisors and new franchi-sors should consider internationalization as astrategy source of higher performance.

On the other hand, these findings can helpprospective franchisees. They should realizethat more mature (in terms of experiencebefore franchising and age) and more interna-tional franchised chains have strategic assetsand knowledge resources that help them out-perform competitors and offer a safer invest-ment. Other drivers to be taken intoconsideration for joining a high-performingfranchisor are the length of the initial training,franchising fees, and the proportion of fran-chised units. The elements that positively influ-

ence chain performance can serve as signalsand can contribute to the process of selecting asuccessful franchised chain for prospectivefranchisees, and thus becoming small businessowners.

Limitations and Suggestions forFurther Research

This paper has some limitations that futureresearch can address. As recently reminded byKosová, Lafontaine, and Zhao (2011) who usedTop 200 Franchise Systems—Franchise TimesMagazine as a source of data, “the populationof franchisors operating in the U.S and theamount of activity they engage in each yearare not so straightforward to assess” (pp.12–13). Moreover, this list focuses on alreadywell-performing franchised chains. Besides,these data sources, Annual Franchise 500—Entrepreneur Magazine and Top 200 FranchiseSystems—Franchise Times Magazine, includeonly information from franchisors whoresponded to surveys, and who tend to be newfranchisors, or those looking to attract newfranchisees. These sources do not provide anexhaustive list of franchised chains within theUnited States, and like all secondary datasources present a selection bias (Lafontaine1995). Even if the size of the sample is limitedto 189 chains, it covers both retail and servicechains. It also enlarges the academic literatureon franchising performance often based onvery limited samples (for instance, 16 chains inthe Botti, Briec, and Cliquet (2009) study, 15chains in the Perrigot, Cliquet, and Piot-Lepetit(2009) study) and/or a specific industry, mostlythe fast-food restaurant and hotel industries.

The scope of the study is also limited to theextent of available data, especially the use ofworldwide sales as a measure of the franchisedchains’ performance. Worldwide sales may bean inaccurate measure of the real performanceof franchised chains. Indeed, a chain’s salesdepend on various contingency factors such asthe state of the economy, the intensity of com-petition, and different countries’ institutionalcharacteristics. It is therefore difficult to con-sider the homogeneous nature of sales mea-sures across franchised chains. Researchers

4The value is determined based on the first derivative of the dependent variable regarding the independentvariable (proportion of franchised units). The computing formula is explained in Silberberg E. and W. Suen(2001), The Structure of Economics—A Mathematical Analysis, 3rd ed. Singapore: McGraw-Hill HigherEducation.


should recognize the distinctive profiles ofchains when analyzing data on performance(Inma and Debowski 2006). Besides, world-wide sales are just one possible measure of afranchised chain’s performance, and thismeasure does not include any aspect of coststructure. Previous studies have used variablessuch as return on sales, ROA (Barthélemy2008), or sales growth (Combs, Ketchen, andHoover 2004). Yet Spanos and Lioukas (2001)showed that financial performance and salesperformance were highly correlated. Theyfound that the impact of firm assets on finan-cial profitability (ROE, profit margin, and netprofits relative to competition) is mediated bymarket performance (absolute sales volume,market share, and increase in market share andsales). Finally, franchised chains are, ingeneral, privately held companies. This makesit very difficult to access market and financialperformance data (Madanoglu, Lee, andCastrogiovanni 2011).

Difficulties associated with collecting infor-mation about performance explain the use ofsecondary data. Future research could there-fore use other data sources, such asquestionnaire-based surveys, to measure theperformance of franchised chains. Scales couldmeasure how franchisors and franchisees per-ceive their chain’s performance. Moreover, thecross-sectional approach used in this paperdoes not capture all the dynamics that explainhigh performance. Longitudinal data analysiscould be used to provide a description of theevolution of performance in franchised chains.Finally, the relationship between a chain’scharacteristics and its performance is acomplex one. Future research should incorpo-rate intermediating variables that refer, forexample, to organizational learning and repli-cation processes.

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examining the drivers for franchised chains performance ......michael, and castrogiovanni (2004) and...

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