ACTIONS SPEAK LOUDER THAN MODES:

HOW PARENT IMPLEMENTATION CAPABILITIES AFFECT BUSINESS UNIT PERFORMANCE

Anne Parmigiani Lundquist College of Business

University of Oregon Eugene, OR 97403 Tel: (541) 346-3497 Fax: (541) 346-3341

e-mail: annepa@uoregon.edu

Samuel S. Holloway Dr. Robert B. Pamplin, Jr. School of Business Administration

University of Portland Portland, OR 97203 Tel: (503) 943-7421 Fax: (503) 943-8041

hollowas@up.edu

December 30, 2009

Running head: Actions Speak Louder Than Modes

Keywords (6): governance choice, strategy implementation, resource-based view, capabilities, corporate strategy

For helpful comments and encouragement, the authors thank Trudy Cameron, Aldas Kriauciunas, Cathy Maritan, Alan Meyer,

Joanne Oxley, Miguel Rivera-Santos, Mike Russo, and Suzanne Tilleman.

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ACTIONS SPEAK LOUDER THAN MODES:

HOW PARENT IMPLEMENTATION CAPABILITIES AFFECT BUSINESS UNIT PERFORMANCE

ABSTRACT

Firm boundaries and strategic execution affect the firm’s ability to generate rents, grow,

and survive. Boundaries are determined through governance mode choices, such as whether to

make or buy a particular good or activity. While significant work has addressed the performance

implications of this fit, less attention has been directed toward strategic execution, or

implementation. In particular, the impact of corporate parents has been understudied. We

suggest that parent-level implementation capabilities of operating expertise gained through

related experience and coordination by collocation combine with governance mode choices to

jointly affect performance. By employing theories of organizational economics and testing

predictions in casual dining chains, this paper unpacks the relationship between implementation,

governance mode choice, and performance. Our findings suggest that parent capabilities may be

more important than mode choice fit and that parent benefits are contingent upon mode choice

and type of performance.

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INTRODUCTION Why do firms and their affiliated business units vary in performance? Strategists suggest

two key explanations: environmental fit and strategic execution. Governance mode choices,

such as the prototypical “make-or-buy” decision, establish firm boundaries, create the

environmental interface, and thus determine fit. While significant work has explored the

relationship between governance mode choice and performance (e.g., Williamson, 1985;

Leiblein, Reuer and Dalsace, 2002), less emphasis has been placed on implementation of these

decisions.

We define implementation as taking action through operations to execute strategy (Hill

and Jones, 2007). We focus on business-unit implementation, for which three factors have been

identified that impact effectiveness: managerial characteristics, internal organization, and

corporate influence (Gupta and Govindarajan, 1984). While other studies have focused on

managers (Gupta and Govindarajan, 1984; Adner and Helfat, 2003), we consider internal

organization as determined by governance mode choice and corporate influence as indicated by

parent capabilities. Though both business and corporate-level factors can influence governance

mode choice for a unit, we take this strategic choice as a given. Rather, we seek to understand

how parent implementation capabilities affect governance mode performance.

Several gaps in the literature motivated us to address this question. Research on

corporate diversification suggests that related diversification improves firm performance (Palich,

Cardinal, and Miller, 2000), but it is not clear what parent capabilities drive this performance.

Firms may also use different governance modes for different purposes, such as internalization for

exploitation and outsourcing for exploration (Robins, 1993; Sorenson and Sorenson, 2001).

While specific capabilities have been identified to manage particular modes (e.g., Kale, Dyer,

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and Singh, 2002), perhaps firms need broader, more fungible capabilities to manage the overall

portfolio of businesses and modes in order to obtain anticipated benefits. Organizational

economics theory suggests that firms should align transaction attributes with governance mode

choice (Williamson, 1991; Masten, Meehan, and Snyder, 1991), but it not clear how this

approach scales up to the broader corporation. Answering these questions regarding boundary

choice and strategic execution can thus extend theory by considering multiple levels of analysis

and unpacking their influences on performance.

Although there has been some debate over the degree of impact, strategists concur that

corporate parents significantly influence business unit performance (for a summary, see Bowman

and Helfat, 2001). It is not clear, however, how this occurs. We emphasize the role of parent

capabilities, hearkening back to Penrose, who highlighted the role of firm resources as the basis

for growth opportunities and diversification (Penrose, 1959; Prahalad and Hamel, 1990; Collis

and Montgomery, 1998; Priem and Butler, 2001; Peteraf and Barney, 2003). Recent work

suggests that the key role of a corporate parent is not in selecting and acquiring businesses, but

rather in providing related experience and control systems to better manage their business units

(Fitza, Matusik, and Mosakowski, 2009; Sarkar, Aulakh, and Madhok, 2009). Our work follows

in this spirit, investigating upper level actions of an organization that may explain performance

differences on the front lines.

We consider the performance impact of two types of parent implementation capabilities:

operating expertise from related experience and coordination by collocation. We define

operating expertise as accumulated related experience and knowledge from activities in a

particular industry. Firms can use this understanding as a knowledge platform to gain from

economies of scale and scope, as well as from inter-unit learning. Parents with similar units and

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with greater industry knowledge will be better able to identify, assimilate, and obtain these

benefits (Cohen and Levinthal, 1990; Ingram and Baum, 1997a ; Helfat and Raubitschak, 2000;

Macher and Boerner, 2006; van Wijk, Jansen, and Lyles, 2008). Coordination by collocation

refers to when parents and business units share a headquarters location, which results in

improved coordination of activities (Bouquet and Birkenshaw, 2008). Through operating

expertise from related experience and coordination by collocation, corporate parents can better

create and capture value from synergies between units (Chandler, 1991; Poppo, 2003).

Our work provides several contributions. First, we explore how two distinct parent

implementation capabilities affect different types of business unit performance. We build on

studies that have shown a corporate effect (Fitza, et al., 2009), but unpack this effect into

capabilities involving related operating expertise and coordination. While other studies that

investigate experience and business unit performance implicitly assume that the unit has no

corporate affiliation (e.g., Ingram and Baum, 1997b; Baum, Li, and Usher, 2000; Sorenson and

Sorenson, 2000), we explicitly investigate these affiliations and the resulting differences. We

essentially connect these corporate and experience streams of work by considering fungible,

ordinary parental capabilities that are developed through experience and used in normal business

operations (Winter, 2003; Jacobides and Hitt, 2005). Relative to dynamic capabilities aimed at

broader organizational changes, ordinary capabilities used for implementation are understudied

(for an exception, Schreiner, Kale, and Corsten 2009). Finally, by investigating different types

of business unit performance - growth and quality - we can better understand the nuanced effects

of parental capabilities, including potential trade offs and complementarities.

Further, we explore the contingent effects of operating expertise from related experience

and coordination by collocation upon business unit performance by governance mode. It may

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be the combination of mode choice and parent capabilities that leads to superior performance.

While there is considerable work involving parent capabilities specific to certain modes (e.g.,

Kale, et al., 2002; Mitchell and Shaver, 2003; Zollo and Singh, 2004; Holcomb and Hitt, 2007;

Schreiner, Kale, and Corsten, 2009), it is not clear how parental related experience or collocation

affects performance across different modes. If we assume that business units need to both adapt

and coordinate (Simon, 1957; March, 1991; Puranam, Singh, and Zollo, 2006), can the mode

choice provide one of these functions while the parent provides the other? Or, does effective

implementation of a particular mode require certain parent capabilities? To preview our results,

we find that parent implementation capabilities may be more important than mode choice fit and

that parent benefits are contingent upon mode choice and type of performance.

This paper proceeds as follows. We first present theoretical foundations and propose

hypotheses relating parent implementation capabilities with unit performance and mode choice.

Figure 1 previews the model and hypotheses. After presenting the context of casual dining

chains (e.g., Applebee’s), describing the data, and discussing methods, we offer results from our

analysis based on two-stage models that control for mode choice selection and predict growth

and quality performance. We discuss our findings and conclude with contributions and

extensions.

--------------------------------------- Insert Figure 1 about here

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THEORETICAL FOUNDATIONS AND PREDICTIONS

While certainly of interest to practitioners (e.g., Bossidy and Charan, 2002),

implementation has been somewhat overlooked by strategy scholars (Poppo, 2003). This may be

because processes like implementation are challenging to study, other than through detailed

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surveys or deep case studies, often within a single firm (e.g., Szulanski, 1996; Maritan and

Brush, 2003). In addition, it can be difficult to determine appropriate dependent variables for

such work (Ray, Barney, and Muhanna, 2004). Scholars may also view the key strategic

decisions as selecting the business, determining the governance mode, and integrating the unit

into the corporate structure; if these are done correctly, successful performance will naturally

follow (Masten 1993; Williamson 1991). While seemingly mundane, running a business unit is a

complex task, affected both by unit and parent-level management and capabilities.

In organization theory, implementation involves a firm’s structure, systems, operating

procedures, and social processes. Firms create a structure to match their environment and

strategic goals. Functional structures tend to promote specialization, scale economies, and

stability, while product or divisional structures promote more diverse product offerings and tend

to be more adaptable (Burns and Stalker, 1961; Chandler, 1962; Galbraith, 1973). Systems and

standard operating procedures facilitate tactical decision making, especially at the lower levels,

conserving executive attention for non-recurring and more strategic choices (March and Simon,

1958; Simon, 1957). Firms are inherently social organizations intended to coordinate actions

toward a goal. Thus, informal and interpersonal means of coordination, such as socialization and

persuasion, co-exist with formal authority (Barnard, 1938; Simon, 1957). Due to this

combination of formal and informal mechanisms, firms excel in creating a shared understanding,

developing rich and deep knowledge, and translating this into coordinated actions (Arrow, 1974;

Grant, 1996; Kogut and Zander, 1992; Nelson and Winter, 1982). These learned patterns that

enable the firm to convert inputs into outputs are termed capabilities (Winter, 2003). This

literature suggests that variation in implementation capabilities can explain performance

differences.

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In capabilities research, the effects of corporate parents, relative to unit-level effects,

have been understudied (Armstrong and Shimuzu, 2007). On one hand, this is surprising, as the

effects of corporate parents underpin many classic strategy works (e.g., Ansoff, 1965; Rumelt,

1974; Andrews, 1987). However, the prominent variance decomposition studies of business unit

performance (Schmalensee, 1985; Rumelt, 1991; McGahan and Porter, 1997; Brush, Bromily,

and Hendrickx, 1999; Fitza et al., 2009) may have shifted the focus away from the parent toward

the unit. While these studies have shown a considerable corporate effect on performance

variance, business-level effects captured more of this variance, and thus more of the subsequent

research efforts.

In considering the capabilities or resource-based view, the seminal work of Penrose

(1959) suggests the importance of corporate level resources as the basis for growth and

diversification. Terms for these capabilities include core competencies (Prahalad and Hamel,

1990), parenting advantages (Campbell, Goold, and Alexander, 1995), and dynamic managerial

capabilities (Adner and Helfat, 2003). These concepts invoke the idea of synergistic benefits

originating from economies of scale that arise from combined volume requirements, economies

of scope through greater utilization of shared assets, or learning that occurs when a corporation

owns multiple units (Capron, 1999; Collis and Montgomery, 1998; Hill and Hoskisson, 1987).

Corporate parents create value through knowledge-based synergies among their business units

and through oversight of their activities (Chandler, 1991; Golden, 1992). In this way, parents

take on dual roles of being entrepreneurial and administrative, creating routines and capabilities

to carry out these responsibilities (Knott, 2001). For example, Argyres (1995) explored the

implementation of a new technology platform and found the roles of the parent were to select the

technology and to manage incentive systems to facilitate adoption. Likewise, Poppo’s (2003)

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investigation of product strategy decisions found the parents’ roles were to protect the firm’s

reputation while settling interdivisional conflicts. Thus, unique corporate capabilities in

managing their units can lead to a sustainable competitive advantage (Barney, 1991; Peteraf and

Barney, 2003).

A significant stream of research has investigated corporate-level capabilities toward the

creation and management of specific interfirm relationships, such as acquisitions, alliances, and

outsourcing. These relationships can be key to a firm’s competitive advantage as they expand

the firm’s range of activities (Dyer and Singh 1998; Capron, 1999; Kale et al 2002; Hult,

Ketchen, and Arrfelt, 2007). Parent capabilities include selecting the best type of relationship

(Capron and Mitchell, 2009; Dyer, Kale, and Singh, 2004), choosing appropriate partners and

scope of activities (Mitchell and Shaver, 2003; Kale, Dyer, and Singh, 2001; Carr and Pearson,

1999), designing governance and other structures (Puranam et al., 2006; Oxley, 1997; Argyres

and Mayer, 2007), executing activities (Zollo and Singh, 2004; Capron, 1999; Reuer, Zollo, and

Singh, 2002; Dyer, 2000), and managing the portfolio of relationships (Laamanen and Keil,

2008; Wessmer, 2009; Hult et al., 2007). Generally, this work has indicated that having

experience in a particular type of relationship is necessary but not sufficient for success. Firms

also need a dedicated function to gather, understand, and incorporate best practices across

business units (Chaudhuri and Tabrizi, 1999; Zollo and Winter, 2004; Kale et al., 2002; Holcomb

and Hitt, 2007). This may explain why even large firms that engage in a variety of relationships

tend to specialize in one mode (Villalonga and McGahan, 2005).

One wonders, however, whether there might be fungible parent-level capabilities that

could be deployed within and across firm boundaries to facilitate implementation at the business

unit level and thus gain purported benefits of related diversification. Unlike the dynamic

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parental capabilities mentioned above that are deliberately developed and often aimed at the

creation and design of specific modes, our focus is on more ordinary capabilities that are

developed through and used in normal business operations (Winter, 2003; Jacobides and Hitt,

2005). These parental implementation capabilities are developed rather passively. Winter

(2003:992) termed capabilities “ordinary” when they accumulate through experience and focus

on ongoing and seemingly mundane operations that enable firms to “earn a living now”.

Ordinary capabilities offer organizations economies of scope benefits from prior investments

(Danneels, 2008; Helfat, 1997) and corporations gain a parenting advantage from their

deployment across business units (Collis and Montgomery, 1998; Campbell et al., 1995).

Through learning by doing, parents create and improve operational routines that can be shared

between business units (Nelson and Winter, 1982; Ethiraj et al., 2005).

Relevant parental implementation capabilities would likely involve the parent’s sharing

of knowledge and resources, as knowledge sharing can help units reduce costs and extend market

coverage, and thus improve performance. Successful knowledge transfer requires some overlap

between the sender and recipient in order for it to be understood and used (Cohen and Levinthal,

1990; Lane and Lubatkin, 1998). Van Wijk and colleagues (2008) recently reviewed the

literature on intrafirm and interfirm knowledge transfer and comment: “Our review clearly

demonstrates that prior experience and related knowledge contributes to transferring knowledge

both within and between organizations” (van Wijk et al 2008: 844, italics added). A recent

study of ordinary, implementation capabilities in alliances further suggests that coordination and

communication are necessary for knowledge transfer and performance (Schreiner et al., 2009).

Based upon the importance of knowledge transfer, we thus identify two parent

implementation capabilities that affect business unit performance: operating expertise based on

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related experience and coordination by collocation. Our depiction of parent implementation

capabilities follows work by Teece and colleagues (1994) who described technical and

organizational competences and explained how these lead to corporate coherence. Related

operating expertise enables value creation through scale and scope economies by combining the

activities of different units. These can include technological and scientific knowledge that can

be exploited across multiple products; marketing expertise such as branding, customer

knowledge, and channel management; and management of external stakeholders, such as

government agencies (Helfat and Raubitschak, 2000; Schultz, 2003; Macher and Boerner, 2006;

Capron, 1999). Coordination involves the formal and informal processes by which parents

manage resources, including capabilities to administer and control the units, such as through

standard operating procedures or budgeting processes (Sarkar et al., 2009). Collocation will

improve the unit’s understanding of formal processes and strengthen informal processes, thus

improving coordination (Jansen, Van Den Bosch, and Volberda, 2006; Puranam, Singh, and

Chaudhuri, 2009). Together, these parent capabilities provide the unit with the resources and

support to effectively implement its strategy.

Since we are taking governance modes as given and emphasizing related operating

expertise from experience and coordination by collocation, our predictions are more appropriate

to some organizations than others. Specifically, our work is best applied to related diversifiers

with similarly sized units in relatively mature settings. This includes traditional corporations,

venture capital firms (Fitza et al., 2009), and chain organizations (Baum et al., 2000) in both

manufacturing and service settings. It applies to firms that can identify a core of activities to be

replicated within and across several units, in which parents determine the most valuable business

model, thereby creating a template which they replicate in order to expand quickly and broadly.

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This replication involves both considerable related operating experience involving an extensive

set of routines and practices, as well as centralized coordination to support rapid and precise

replication (Winter and Szulanski, 2001). However, our predictions also apply to related

diversifiers with a broader portfolio of businesses, as long as governance modes are stable and

some knowledge platform exists that can be applied across multiple units.

Our predictions may be less appropriate for industries or technologies that are unstable or

rapidly changing, as firms may be unable to identify or develop a common knowledge platform

of related operating experience. In these settings, governance modes may also be unstable due to

high uncertainty (Nickerson and Zenger, 2002; Balakrishnan and Wernerfelt, 1986). In high

technology sectors, large firms often make multiple acquisitions or alliances of small targets,

using a real options strategy to cover different potential trajectories (Bowman and Hurry, 1993;

Puranam et al., 2006). These firms may later divest some units, as they did not plan on

incorporating them all permanently (Karim, 2009; Villalonga and McGahan, 2005). Thus, these

smaller units may not benefit from collocation or related operating experience; these units might

not be deemed worthy of parental attention (Birkenshaw and Bouquet, 2008). Therefore, since

we are interested in how parents affect the ongoing business of their units, we focus on related

diversifiers in mature industries in which parents manage all of their units similarly as they

intend to operate them indefinitely. Due to slow market growth, effective implementation is

critical in these settings to gain an advantage over competitors.

Our setting, casual dining restaurants, satisfies these conditions nicely. This service-

based industry sector is mature with stable technologies. Corporate parents often own and

operate several chains, providing them with operating expertise based upon related experience

that they can share among business units. Outsourcing in the form of franchising is common, but

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not universal, such that we can contrast the impact of parental implementation capabilities over

different governance modes. In the hypotheses that follow, we include the terms business unit

and chain to refer to the subordinate divisions of the corporate parent. In the context section, we

further describe this setting and we consider generalizability issues in the discussion.

Parent Implementation Capabilities: Operating Expertise from Related Experience

Operating expertise from related experience allows a firm to create value through scale

and scope economies, as well as learning, which is created by the overall corporation and can

benefit a business unit. Scale economies can arise from volume discounts due to combined

purchasing across units for raw materials, common equipment (e.g., computer hardware or

software), advertising, and other goods (Pindyck and Rubenfeld, 1995). Scope economies,

efficiencies from producing multiple related goods simultaneously through better use of

complementary or shared assets, originate from technical, marketing, or other knowledge that

can be leveraged across multiple products or processes and are frequently embodied in

equipment, human resources, and organizational routines (Panzar and Willig, 1979; Teece,

1982). Accumulated learning within the parent can be disseminated to different business units,

helping them resolve production, engineering, and marketing issues, resulting in product and

process improvements as well as entry into new markets. The parent’s combination of core

product knowledge and integrative knowledge that connects activities provides the foundation

for growth through related diversification (Capron, 1999; Helfat and Raubitschek, 2000).

Related operating expertise based on experience can improve performance through

several mechanisms. Via cumulative production, firms can reduce costs through learning, scale

economies, and process innovations (Darr, Argote, and Epple, 1995; Sinclair, Klepper, and

Cohen, 2000). Continued operating experience in a single sector can also help firms better

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understand the market, survive, and grow (Ingram and Baum, 1997b). Firms that develop

technical experience can leverage this knowledge by introducing new, related products and

enjoying knowledge spillovers (Macher and Boerner, 2006; Nerkar and Roberts, 2004).

Experience that is more relevant and local will be easier to transfer within the firm and thus more

beneficial (Kalnins and Mayer, 2004; Darr et al., 1995).

For a particular business unit, operating expertise from related experience will be most

useful, enhance knowledge transfer, and improve performance. Therefore, units that have

parents with considerable experience in the same industry should benefit more than units whose

parents are conglomerates, holding companies, or unrelated diversifiers (Palich et al., 2000).

Further, units can benefit from having multiple siblings in related businesses, with which they

can exchange resources and share knowledge. Knowledge from related siblings is likely to be

seen as more relevant and potentially useful (Schultz, 2003). Parents with deep industry-specific

knowledge develop a more thorough understanding of technical processes and thereby help units

reduce causal ambiguity, which can be detrimental to interdivisional learning (Szulanski, 1996).

These parents may also be more likely to develop a common template that can be leveraged

across multiple units (Winter and Szulanski, 2001). Related operating experience from the

parent can thus provide a common ground and facilitate learning between different units

(Puranam et al., 2009). This provides the basis for our first hypothesis:

Hypothesis 1a: The greater the parent’s related operating experience, the better the performance of the business unit (chain).

Parent Implementation Capabilities: Coordination through Collocation

While a parent’s related operating experience provides a platform of knowledge that units

can access and improve performance, coordination between the parent and unit will also

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influence implementation performance. Coordination by a corporate parent involves

organizational design, incentive systems, and socialization processes to promote the best use of

resources and provide a parenting advantage (Govindarajan, 1988; Chandler, 1991; Campbell et

al., 1995). Information processing systems, standardized procedures, and codified routines allow

firms to standardize best practices and disseminate these among all business units (Kale et al.,

2002; Sarkar et al., 2009). This standardization helps business units diagnose and fix problems

as they expand (Szulanski and Jensen, 2006).

In addition to these formal mechanisms, coordination can be facilitated through informal

mechanisms. Shared culture and relationships between personnel in different units will improve

communication and information flows, reducing conflict and facilitating action (March and

Simon, 1958; Puranam et al., 2009). Understanding these mechanisms is important, as

effectiveness is contingent upon the fit between operating capabilities and administrative systems

(Govindarajan, 1988; Roth, Schweiger, and Morrison, 1991; Golden 1992).

From the business unit’s viewpoint, a close relationship with the parent should improve

performance due to improved coordination and preferential access to resources. One indication

of a close relationship is sharing headquarters with the parent. Collocating should facilitate an

enhanced understanding of formal systems, as well as provide more opportunities for informal

relationship building, both of which will improve coordination. Collocation should also result in

greater positive attention, since attention is based upon a business unit’s strategic significance,

initiative, and geographic distance from the parent (Bouquet and Birkenshaw, 2008). Since

knowledge transfer is facilitated through frequent, interpersonal contact, it follows that units that

are physically close to parents should enjoy more effective inflows of knowledge to assist in

implementation (Szulanski, 1996; Hoopes and Madsen, 2008). This may be particularly true for

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inter-unit knowledge transfer, as this knowledge flows vertically from units to parents and then

down to sibling units, rather than directly and horizontally between units (Schultz, 2003).

Collocation should improve relationships between the unit’s top managers and those from the

parent. Collocation facilitates greater communication, reduces conflict, and aligns goals based

upon mutual knowledge, shared context, and shared category membership (Canella, Park, and

Lee, 2008). These arguments support the following hypothesis:

Hypothesis 1b: Collocation of parent and business unit (chain) headquarters will improve business unit (chain) performance.

Governance Mode Choice

Governance modes are discrete, structural alternatives which are efficiently chosen to

match attributes of the transaction and internal characteristics of the firm. The key transaction

attribute is specific investment; transaction cost theory suggests that internalizing transactions

high in specific investment will lead to improved performance (Williamson 1985; Williamson

1991; Masten 1993). From a capabilities standpoint, firms internally produce goods for which

they have relevant equipment and expertise (Argyres, 1996; Conner and Prahalad, 1996).

However, capabilities and investments (e.g., complementary assets) are unlikely to only relate to

a single product, but rather can be deployed across several products (Teece, 1982; Wernerfelt,

1984). In this way, firms tend to focus on producing related products, leveraging their expertise

and other assets. Conversely, firms use outsourcing for more peripheral activities, to take

advantage of autonomous incentives of the market, and to adapt to new opportunities. Thus,

firms tend to exploit through internalization and explore via outsourcing1 (March, 1991; Robins,

1993; Sorensen and Sorensen, 2001; Puranam et al., 2006).

1 Outsourcing refers to firms engaging with suppliers to provide a strategically important good or activity over a relatively long time period requiring some degree of mutual adaptation. This is in contrast to arms-length exchanges of trivial, routine, and commodity-type goods (Holcomb and Hitt, 2007).

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In considering the choice of governance mode, firms need to consider not only the focal

activity, but also other related activities and accumulated attributes of the business unit and

parent firm. Often there are interdependencies between activities that affect the choice of

governance mode, such as knowledge or reputation spillovers (Kang, Mahoney, and Tan, 2009;

Parmigiani and Mitchell, 2009). Moreover, there can be other, unobserved factors that lead to

both a particular mode choice and improved performance (Shaver, 1998; Hamilton and

Nickerson, 2003). These factors may be based upon parent capabilities, unit capabilities, or

some combination of these, such as technical expertise, selection capabilities, or relationship

management skills (Dyer, 1997; Kale et al., 2002; Capron and Mitchell, 2009). Cost reductions

and related benefits from these factors can motivate the unit to make or buy, but also

independently contribute to improved performance. Thus, complementary capabilities and assets

can develop as a result of and simultaneously with a series of governance mode decisions for

related goods (Teece, 1982; Argyres and Zenger, 2008; Reuer et al., 2002). In this way, it can be

difficult to unpack what actually causes superior performance – is it an efficient choice of

governance mode, a superior capability, or the interaction between them?

For our purposes, we focus on the implementation of the governance mode, rather than

the governance mode choice. We assume that the parent firm and business unit have taken into

account all relevant factors and have chosen correctly and efficiently. Based upon this

assumption, differences in performance would thus arise from implementation, particularly since

governance mode choices tend to be stable and difficult to change (Lafontaine and Shaw 2005;

Leibeskind and Argyres 1999; Nickerson and Zenger 2002; Puranam et al., 2006). To

empirically control for endogeneity2, in which unobservable attributes like capabilities can

2 Endogeneity refers to the problem of self-selection, such that firms will choose strategies based upon their own attributes. Empirical models that predict performance, but do not account for this self-selection, are misspecified

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influence both strategic choices and performance, we follow prior scholars in employing two-

stage econometric models (Masten, 1993; Shaver, 1998; Leiblein et al., 2002; Nickerson and

Silverman, 2003; Sampson 2004; Mayer and Nickerson, 2005). By conceptually and empirically

controlling for factors relating to governance mode choice, as well as for unit-level factors that

may affect performance, our approach provides a relatively clean investigation of how parent-

level implementation capabilities affect unit-level performance by governance mode.

Implementation of Governance Modes

We now turn to the contingent value of parent implementation capabilities on governance

mode performance. While both related operating experience and collocation are likely to benefit

both internalization and outsourcing, we focus on the most significant relationships. When units

outsource, they rely on market forces to manage supplier relationships, suggesting benefits from

leveraging a parent’s related businesses to strengthen their position. Therefore, related operating

experience of parents will assist outsourcing units. Conversely, when units conduct activities

internally, they need to acquire personnel, equipment, and other resources to create products and

services. These units will benefit from parental support in obtaining these resources,

highlighting the importance of coordination. Next, we flesh out these arguments and provide

associated hypotheses.

When business units outsource, they must select suppliers, negotiate the terms of trade,

and monitor product and information flows to protect against possible opportunism. Units that

have parents in related businesses can leverage this relationship as the broader market power and

business potential of the unit’s parent helps influence suppliers to offer lower prices, more

and lead to incorrect conclusions. For a classic example, see Shaver’s study of foreign direct investment via greenfield sites vs. acquisitions (Shaver, 1998).

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features, and better terms (van Weele, 1994). Suppliers may also be attracted by the parent’s

reputation and therefore provide better service to the unit (Dyer, 1997).

Formal contracts, relational governance, and technical expertise have all been shown to

assist in managing suppliers (Williamson, 1985; Dyer and Singh, 1998; Mayer and Salomon,

2006). Units whose parents have operating experience in the same industry can create better

contracts, as contracting capabilities may be more likely to reside at the parent rather than at the

unit level (Argyres and Mayer, 2007). Relational governance is based on industry norms

(Macauley, 1963); units with experienced parents should be better versed in these norms.

Technical expertise is critical in governing these relationships, as skilled firms can craft more

relevant contracts and better monitor suppliers (Mayer and Salomon, 2006). Units with

knowledgeable parents will also be able to learn more from suppliers and offer their own and

their parent’s knowledge as an incentive to promote long term relationships and interfirm

learning (Bradach, 1997; Krause, Handfeld, and Scannell, 1998).

Outsourcing allows firms greater potential for adaptation and exploration, due to a

broader set of relationships with suppliers and the accompanying, more diverse inflows of

knowledge. Through a myriad of customer relationships, suppliers can develop best practices

for their particular activity which can benefit the buying firm (Holcomb and Hitt, 2007; Mayer

and Teece, 2008). However, to manage suppliers successfully and gain from supplier

knowledge, firms need technical expertise. Tiwana and Keil (2007) refer to this as “peripheral

knowledge” and discuss its importance in specifying and enforcing supply agreements. Arend

(2006) further suggests that broad industry knowledge promotes deeper supply relationships.

Studies of automotive, aircraft, and chemical firms suggest that outsourcing firms need to

maintain a “cognitive overlap” with suppliers to manage inter-dependencies among purchased

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components, particularly when these are parts of larger systems (Brusoni, Prencipe, and Pavitt,

2001). This cognitive overlap, peripheral knowledge, and industry understanding can reside at

the corporate parent level as evidenced by related operating experience. This experience can be

shared among related business units and accessed as needed. Thus, we propose:

Hypothesis 2a: Parent’s related operating experience will have a greater positive impact on the performance of business units (chains) that outsource than on those that do not outsource. When internalizing activities, business units must acquire the necessary resources to

produce goods and services, including personnel, procedures, equipment, and other essential

assets. Given the size and scope of the investments required, corporate parents may be involved

to provide capital. Parents may also facilitate cooperation among units through capacity

utilization or personnel transfers. The monitoring and administration function of parents will

thus be invoked to ensure that corporate resources are used wisely.

Internalized units will strive to exploit efficiencies and improve processes, relying on

formal and informal coordination mechanisms. Parents can assist in these efforts by providing

standardized routines, infrastructure, and control systems (Baum et al., 2000). Through creating

and enforcing operating routines, parents can improve performance among multiple units (Knott,

2001). These administrative routines will result in less performance variation between units and

better performance overall (He and Wong, 2004). Parents that provide more formal, codified

procedures will help units better leverage and exploit parental resources and knowledge (Jansen

et al., 2006). Particularly for organizations with similar units, parents provide centralized

capabilities for quick and precise replication, which allows for rapid expansion and market

penetration (Winter and Szulanski, 2001).

21

When producing internally, business units that are collocated with parents should more

effectively obtain required resources, due to an increased understanding of formal systems and

enhanced participation in informal exchanges. In-house production does not guarantee improved

coordination, as units can be decentralized and compared against suppliers, potentially gaining

few of the anticipated benefits of vertical integration (Walker and Poppo, 1991). However,

collocation of the unit and parent should improve this coordination and make a positive

comparison more likely. For resources that are shared across business units, some act like public

goods which can be easily shared among units without conflict. Others, however, act like private

goods whereby use by one unit affects how other units may use the resource (Collis and

Montgomery, 1998). Parents may choose not to outsource in order to better deploy and

coordinate the use of these private goods resources. Moreover, units that are collocated with

parents should be in a better position to both gain their share of these resources and promote

positive spillovers. Communication between the unit and parent should be more robust such that

the parent understands specifically what resources the unit requires, approves of their decisions,

and allocates resources as needed. Inevitable glitches and slowdowns in the initial stages of

production or expansion will be more easily explained and resolved between the unit and parent

due to improved collaboration. Collocation will improve both the formal and informal

coordination mechanisms, since unit managers will be more proficient in understanding

corporate procedures, as they will benefit from richer communication channels and “hall talk”

(Puranam et al., 2009; Jansen et al., 2006). These ideas support our final hypothesis:

Hypothesis 2b: Collocation of the parent and business unit (chain) headquarters will have a greater positive impact on the performance of business units (chains) that do not outsource than on those that outsource.

22

Collectively, these hypotheses suggest that parent implementation capabilities and

governance mode choice jointly affect business unit performance. For a summary of our

hypotheses and a preview of our results, see Table 1. To test these predictions, we now turn to

the casual dining industry. Restaurant chains in this sector use one of two governance modes,

franchising or company ownership, and vary in their corporate parentage.

--------------------------------------- Insert Table 1 about here

---------------------------------------

CONTEXT, METHODOLOGY, AND DATA

To investigate the relationship between governance mode choice, implementation, and

performance, we focus on the casual dining industry. This sector includes restaurants such as

Ruby Tuesday or Olive Garden that generally operate at lunch and dinner hours, have table

service, and offer complete meals with alcoholic beverages. We equate restaurant chains with

business units, as these are generally managed independently and have diverse corporate

parentage. Our data include information on 72 restaurant chains from 1998-2007.

We chose this industry for several reasons. First, only two governance modes are

common and co-exist: franchising and company ownership. Franchising reflects a type of

outsourcing in which the franchisors engage in long term relationships with independent

franchisees to create and manage restaurants. As with outsourcing, franchisors must manage

relationships with external firms through careful selection, contracting, and monitoring. In

return for royalty fees, franchisors obtain diverse local market knowledge and rapid geographic

expansion. Franchisees gain an established business model, training and advertising support, and

profits above these fees (Blair and Lafontaine, 2005). In our data, 55% of the chains use

franchising, while the rest operate all outlets through company ownership. While the extent of

23

franchising can vary considerably between chains, the percentage of franchising within a chain

stabilizes after about seven years (Lafontaine and Shaw, 2005). All but six of the chains in the

data are older than seven years, and those that franchise use franchisees for an average of 35% of

their restaurants.

We are interested in how parental implementation capabilities affect business unit

performance, but we cannot compare pure outsourcing with internalization in this context.

Restaurant business units only operate single chains, therefore if the unit were completely

outsourced, it would essentially be spun off with no remaining parental connection. However,

we can compare modes that differ in their degree of market influence by contrasting fully

internalized units with those that choose a plural governance mode, like franchising, since this

involves simultaneously conducting an activity both internally and externally (Bradach and

Eccles, 1989). In this way, our work follows Harrigan (1986) who investigated the degree and

extent of tapered integration within strategic business units. Since restaurant chains typically

don’t choose complex governance modes like joint ventures, our context also provides a clean

comparison between units’ choice of two governance modes: franchised and fully company-

owned. This makes our study more conceptually focused and econometrically tractable.

We acknowledge that there are several key differences between franchising and

traditional strategic outsourcing, including the lack of a pure outsourcing option, the service

sector context, and suppliers who conduct a relatively broad set of activities (Blair and

Laftontaine, 2005). Typically, firms can choose between internal production, total outsourcing,

or some hybrid form, often a mix of these options. In franchising, these choices are limited to

internal ownership or dual distribution, operating outlets internally and through franchisees.

Franchising occurs in the service sector, which generally is not technologically intensive,

24

requires few physical investments but considerable advertising and related intangible

investments, and is a relatively mature and stable context. Unlike traditional outsourcing of a

discrete good or activity, franchising entails outsourcing the whole business model, relying on

suppliers to conduct a system of activities including staffing, production, and marketing.

However, these differences are more likely to affect the choice to franchise or outsource,

not the implementation and management of the governance mode. Traditional firms have at least

three options for obtaining goods and services, whereas our firms typically have just two, which

may change the decision calculus regarding governance mode choice. But once selected, both

traditional firms who outsource and franchisors must work with suppliers who have considerable

influence on their end products. The decision to outsource or franchise depends upon different

types of specific investment, but once the decision is made, in both cases these investments are

mutually enhanced and developed. Contracting, relational governance, and technical expertise

have been found to be important tools in managing traditional outsourcing (Poppo and Zenger,

2002; Mayer and Salomon 2006; Dyer 2000); these tools are likely even more important in

franchising as the good being outsourced is actually a number of interconnected activities, all of

which need to be monitored and enhanced over time.

Therefore, parent implementation capabilities, particularly operating expertise based on

related experience, will impact the effectiveness of franchising. Related operating experience

from parents will help units select and manage franchisees since these franchisees may work

with sister divisions. More experienced parents can help chains that franchise better manage

mutual investments, such as through assistance in determining advertising rates in contracts and

in providing tools for monitoring franchisees. Related experience also provides the basis to

better create contracts, to have richer relationships, and to learn more from franchisees. As in

25

traditional outsourcing, franchisors benefit from having an understanding of operations and

technical requirements as they can better learn from franchisees (Bradach 1997; Brusoni et al.,

2001; Mayer and Salomon 2006). Related experience from parents can provide the foundation

of this understanding.

By studying franchising, we build upon a considerable body of scholarly work (for

reviews, see Elango and Fried, 1997; Combs and Ketchen, 2003). Like many of these studies,

our work is grounded in organizational economics and focuses on business unit performance,

which in this context equates to chain performance (Carney and Gedajlovic, 1991; Shane, 1996;

Michael, 2000a; Barthelemey, 2008). Several scholars have investigated learning and have

found that knowledge transfer is easier within versus between franchise systems (Darr, et al.,

1995; Bradach, 1997; Ingram and Baum, 1997b; Sorenson and Sorenson, 2001; Kalnins and

Mayer, 2004). We build upon these ideas, as we predict that parent’s related operating

experience will improve chain performance. We also build upon findings that specific

investments and more complex strategies are negatively related to franchising (Minkler and Park,

1994; Yin and Zajac, 2004; Lafontaine and Shaw, 2005). Our work also connects well with

studies on replication in franchise chains (Winter and Szulanski, 2001; Winter, et al., 2008), as

well as Knott’s work (2001, 2003) on dual routines within franchise systems. All of these studies

highlight the importance of franchisor knowledge and control systems which may operate at the

parental level.

However, several aspects of our study differ from prior research. In contrast to some

studies (Lafontaine, 1992; Combs and Ketchen, 1999), we take the franchise form as a discrete

choice rather than as a continuum such that we do not emphasize the degree of franchising,

although we control for this in our analysis. We view the decision to franchise or not as an initial

26

strategic decision and a discrete choice of governance mode, treating the subsequent choice of

which specific restaurants to operate internally versus externally as a tactical decision with

different antecedents (Zollo and Winter, 2004; Parmigiani, 2007). While some studies consider

outlet-level performance within a single chain (Knott, 2001; Szulanski and Jensen, 2006), and

others compare performance of chains in many different sectors (Lafontaine and Shaw, 2005),

our approach considers the performance of different chains within a focused sector. The key

distinction between our work and others’ is our emphasis on parent capabilities.

In addition to the existence of both franchised and company-owned systems, we also

chose the casual dining context because operating experience and coordination are critical to

success, as these chains strive to grow through replication (Winter and Szulanski, 2001).

Expertise relating to service flows, food procurement, financing, contracting, and human

resources are important for quality and growth; knowledge in these areas often is transferred

from parent firms and franchisors to individual outlets (Bradach, 1997; Khan, 1999). Scale

economies in purchasing food and other inputs can also be consolidated by parents and passed

along to chains (Khan, 1999). Coordination capabilities provide systems for training and

monitoring, as well as the standardization necessary for consistency among restaurants, in order

to diagnose problems and improve quality and efficiency (Szulanski and Jensen, 2006). Greater

coordination between the chain and corporate parent would likely result in tighter replication of a

template to individual outlets, which should improve performance (Winter, et al., 2008).

Another reason for studying this context is that while chains in this sector have a variety

of parents, their effects are rarely studied. In these data, there are 83 distinct parents, with 34

chains changing parents in the ten year period. Some of these parents are single-chain operators

(Buffalo Wild Wings, Inc.), while others manage several chains (Landry’s Restaurants). Some

27

are conglomerates (Carlson Companies) or investment firms (Castle Harlan Partners III LLP).

See Table 2 for a listing of casual dining chains in 2007 and Table 3 for a summary of these

chains and their parentage.

--------------------------------------- Insert Tables 2 and 3 about here ---------------------------------------

Thus far, franchise scholars have neglected parent or firm-level influences, essentially

assuming that the chain and firm are equivalent. Quoting Combs and colleagues (2004: 913):

“A complete model of franchising will require researchers to also understand firm-level reasons why firms initiate franchising…No studies at the firm–level contrast franchisors with non-franchisors. Taking an example from the restaurant industry, Ruby Tuesday and Applebee’s are two chains operating in the casual dining segment. The décor, service, and menu of the two chains are quite similar, yet Ruby Tuesday grew for over 20 years to 365 outlets before initiating franchising in a foreign market in 1996. In contrast, Applebee’s has always franchised more than 75% of its outlets. To date, we have no theory to explain why such similar business models develop such different organizational forms.”

Our work begins to address this question by considering parent effects on implementation, as

well as to control for mode choice selection based on chain-level attributes. Our approach of

incorporating parent firm effects in a franchising context is novel, as scholars typically equate

the firm and chain, implicitly assuming single business firms and neglecting parent effects.

Finally, complete and reliable data were available for these chains primarily from the

trade publication, Nation’s Restaurant News (NRN). Each year, they publish a “Top 200” list of

the top 200 restaurant chains by system-wide US sales. This list identifies the sector of each

chain (e.g., fast food, casual dining), chain sales, headquarters, units, and the extent of

franchising. It also lists each chain’s parent firm along with parent firm headquarters

information, food service sales, and all food-service businesses affiliated with the parent. By

using this list as the basis for these data, we can reliably connect the chains and parents.

28

Methodology

We use a two-stage technique in which we first model governance mode choice,

franchise or not, and then separately model three types of performance. This technique has

become increasingly common for similarly structured data (Leiblein et al., 2002; Mayer and

Nickerson, 2005; Morrow et al., 2007). Unlike these studies, we have panel data, but the same

approach can be used (Hamilton and Nickerson, 2003). The first stage involves a random effects

probit model for time series data. This specification controls for chain and year effects and

produces normally-distributed residuals, which are required to create the inverse Mills ratio for

the second stage. Note that we include variables for parent-level capabilities in the first stage,

which helps to separate the effects of these variables on mode choice from their effects on

performance. Our instrument, brand value as measured by advertising spending at the chain

level, is included only in the first stage and is not highly correlated with performance; this

improves the reliability of second stage estimates (Hamilton and Nickerson, 2003). Results for

this stage are presented in Appendix 1.

The second stage analysis tests our predictions and involves a time series OLS regression

with random effects controlling for chain and year effects. Based upon work in labor economics

(Laird and Ware, 1982; Hedeker and Gibbons, 1997), we use random rather than fixed effects in

our second stage, since our data is an unbalanced panel. Some chains exit and enter the sample;

thus chains are not always measured over the same number of time points. The random effects

specification estimates a chain's trend across time on the basis of available chain data, augmented

by the time trend estimated for the whole sample.

29

Key Dependent and Explanatory Variables Performance. We use three measures for restaurant chain performance. From the NRN

Top 200 lists, we calculate sales and unit growth compared to the prior year ((Sales t – Sales t-1 )/

Sales t-1 ). Growth is the goal of many chains and may be more appropriate than profitability in

this mature, competitive context (Winter and Szulanski 2006; Helfat et al., 2007). Our quality

measure is from the Consumer Reports survey of roughly 70,000 readers which they conduct

approximately every three years for restaurants. Each chain must have at least 250 visits to be

included in their results. We took their reader score, which reflects overall satisfaction, as our

quality measure which follows the approach of Michael (2000b) who investigated franchising

and restaurant quality.

Parent’s related operating experience. To measure the parent’s related operating

experience, we follow Ingram and Baum (1997a) and use cumulative related operating

experience, measured by the past three years of parent food service sales (all revenue from

domestic restaurant operations and franchise revenue), discounted by age. Specifically, we

calculate this variable as: 32t21 −− ++ tt SalesSalesSales . Unlike past studies, our measure is richer and

more precise in that it is specific to the chain’s industry (as compared to age or other general

measures) at the parent level (as compared to cumulative production volumes or similar metrics

at the unit or chain level), and not a simple count variable (e.g., number of patents).

Related operating experience has been shown to significantly improve business unit

performance among hotel chains (Baum and Ingram, 1998; Ingram and Baum, 1997a),

pharmaceutical firms (Macher and Boerner, 2006), and pizza franchises (Darr et al., 1995;

Kalnins and Mayer, 2004; Kalnins, Swaminathan, and Mitchell, 2006). Several scholars have

shown that the benefits business units derive from a parent or chain’s cumulative operating

30

experience decays over time ( Darr, et al., 1995; Ingram and Baum, 1997a ; Baum and Ingram,

1998; Kalnins and Mayer, 2004; Kalnins et al., 2006). While most of these studies examined

business unit failure and included experience from founding (e.g., age), studies that considered

more fine-grained measures of performance (e.g., sales) suggest that decay is relatively rapid.

As it is close to our empirical setting, we follow Darr and colleagues (1995), who studied pizza

restaurant franchises, and found past experience could completely decay in about six months

due to turnover and a resulting lack of knowledge retention and replenishment. They also found

that when past experience is replenished with new knowledge, the decay is better modeled

annually. Since our data involves restaurant chains with parents that actively maintain and

replenish related operating knowledge that is embodied within parental routines, we chose a

three year window of experience to represent this knowledge stock, which we discounted linearly

on an annual basis. We felt three years was an appropriate middle ground in our mature,

service industry sector, as it was in between very rapid decay found in individual restaurants due

to high personnel turnover (e.g., six months) and slower decay, such as long-lived knowledge

embedded in technology or equipment that may last ten years or more (e.g., Nerkar and Roberts,

2004). Although the sector is different, our construct is similar to Puranam and colleagues’

(2009) concept of common ground between acquirers and targets based upon technological

subclasses; they used the past three years to measure this dimension. As noted by Barkema and

Schijven (2008), there is little agreement in the strategy literature regarding how to measure

experience or its depreciation rate, thus we used our best judgment, given our context and the

limitations of our data. In a later section on robustness, we discuss alternate methods of

computing this variable and associated results.

31

Collocation. From parent and business unit headquarters data published in the NRN, we

created a binary variable, coding “1” when the chain and parent share the same city and state

headquarters and “0” otherwise.

Governance Mode Self-Selection and Other Control Variables Governance Mode Self Selection Correction. To capture potential endogeneity effects

of unobserved variables on governance mode choice and performance, we created a variable

called “Self Selection Correction” by computing the inverse Mills ratio based on the residuals of

the first stage equation that predicts mode choice. This ratio, jiλ , is computed differently for

franchised ( ) ( )( )' '1 /i i iX Xλ φβ β= Φ ( )i.e., 1j = versus company-owned

( ) ( )( )' '0 / 1i i iX Xλ φ β β⎡ ⎤= − −Φ⎣ ⎦ chains ( )i.e., 0j = . In both cases, ( )φ ⋅ is the standard

normal pdf, and ( )Φ ⋅ is the standard normal cdf. Second stage models that incorporate this

correction provide consistent unbiased estimates. We followed Shaver’s study of foreign direct

investment, in which he showed that the entry mode choice of greenfield vs. acquisition had no

effect on survival once he included the variable that took into account the firm’s unobserved

attributes leading toward a particular mode choice (Shaver, 1998). This approach has become

increasingly common in the strategy literature (Leiblein et al., 2002; Sampson, 2004; Mayer and

Nickerson, 2005; Morrow, et. al., 2007).

Mode choice misfit. To measure the alignment or fit between the environment and the

choice of governance mode, we followed prior work and created a continuous variable to

compare the output of the first stage model (franchise or not) with the actual mode choice for

each chain (Leiblein et al., 2002).

32

Parent level controls. We include the number of food service siblings for each chain to

control for differences between single-business corporations and those that operate multiple

chains, since this may affect the potential for scope economies, learning, and parental attention.

For example, Darden Restaurants operates the Olive Garden, Red Lobster, Bahama Breeze,

Smokey Bones Barbeque and Seasons 52 chains, while Max & Erma’s operates only its

namesake chain. Therefore, Olive Garden has four food service siblings while Max & Erma’s

has none. We include a binary control for whether a parent is publicly held, as this could

improve its access to financial resources. We also include a binary variable indicating if the

parent is new for the chain in that year.

Chain level controls. We include chain age and size (number of outlets, logged), as

these can proxy for unit-level capabilities or experience (Sorensen and Sorensen, 2001). We also

include average outlet size. This indicates tangible specific investments that may affect parental

attention and resources. Finally, we include franchise variables (franchise or not and percentage

franchised) to further control for governance mode choice on performance. We also include a

change in franchising strategy variable to indicate whether a chain switched from all company-

owned restaurants to franchising (or vice versa) as this may cause a disruption in operations and

perhaps reduce performance for that particular time period. If there was such a change, we

coded this variable as “1”; if the chain kept the same mode as the prior year, we coded this

variable as “0”. Table 4 presents descriptive statistics of our key variables.

--------------------------------------- Insert Table 4 about here

---------------------------------------

33

RESULTS

Tables 5, 6, and 7 report the relationships between the three performance measures and

the explanatory variables. Model 1 includes all the variables from the first stage except

advertising spending (our instrument), along with controls for new parent, mode choice, and

change of mode. Hypothesis 1a relates parental operating experience with performance; we find

mixed results for this prediction. Supporting H1a, parent food service sales is positively related

to quality, but contrary to this prediction, this variable is negatively related to both sales and unit

growth. Perhaps chains rely on help from skilled parents to improve quality, but parents with

broader interests or too many units deter growth. Hypothesis 1b predicts collocation of chain

and parent headquarters will improve performance; we find support for H1b for all three

performance measures.

Model 2 adds a self-selection variable for governance mode choice to Model 1,

controlling for potential endogenity of mode choice due to any unobserved internal attributes

such as chain-level capabilities. As in the first model, hypothesis 1a was supported for quality,

but not for growth and hypothesis 1b was supported for sales growth, unit growth, and quality.

To understand the impact of fit between the governance mode and external environment, Model

3 adds the variable governance misfit. Earlier results for hypotheses 1a and 1b are replicated.

To test hypotheses 2a and 2b, Model 4 further unpacks the relationship between

governance mode and performance by running separate models for franchised and company-

owned chains. This relaxes restrictions implicit in earlier models that parameter estimates must

be the same for both modes (Shaver, 1998). Both expected and surprising results occurred

regarding the two hypotheses relating the contingent value of specific modes and implementation

capabilities. As expected and consistent with hypothesis 2a, parent’s operating experience

34

improved quality for franchised chains. However, this effect was negative, but non-significant

for sales and unit growth. Moreover, we found a negative and significant relationship between

parent operating experience and unit growth for company-owned chains. Consistent with

hypothesis 2b, shared headquarters improved sales growth and quality for company-owned

chains. We found a positive, but non-significant, relationship between collocation and unit

growth for company-owned chains. Surprisingly, however, shared headquarters also improved

unit growth for franchised chains, highlighting the importance of coordination when franchising

(Winter and Szulanski, 2001).

Two of our key control variables involved the choice of governance mode: the self-

selection correction and misfit measures. Since the self-selection variable is positive and

significant for both sales and unit growth, unobserved factors that affect the decision to franchise

also affect growth (Hausman and Taylor, 1981). So, for example, it may be that superior chain-

level relational capabilities may improve the likelihood that the chain franchises and also

improves growth, since the chain excels at both selecting and managing franchisees. This result

matches that of other authors, which suggest that firms know their own competences and select

the “right” mode to match their skills (Leiblein et al., 2002; Capron and Mitchell, 2009).

Interestingly, however, the self-selection variable was not significant for quality, suggesting

more independence between the determinants of mode choice and this kind of performance.

According to Model 3, the coefficient for governance misfit is not significant, thus misfit

does not appear to adversely affect sales growth, unit growth, or quality. This suggests that firms

somehow compensate for ill-fitting governance modes. As such, the alignment of the mode

with attributes of the chain, parent, or environment appears to matter less than how it is executed.

35

This is intriguing as it is contrary to discriminating alignment theory (Williamson, 1991), as well

as to prior empirical results (Leiblein et al., 2002).

To explore this further, we created a binary variable indicating whether the chain’s

predicted versus actual governance mode was the same (appropriately aligned) or not

(inappropriately aligned). We then conducted t-tests to compare performance for appropriately

vs. inappropriately aligned chains. No significant statistical difference existed for average

performance for unit growth (n=292 for appropriate and n=181 for appropriate; difference in

mean unit growth: 1.33% ; t=-1.28; p=0.20) or for sales growth (n=293 for appropriate and

n=181 for inappropriate; mean difference=1.16%, t = -0.99; p=0.32). Note that these are simple

t-tests, without incorporating any of the explanatory and control variables. The range of sales

growth for appropriately matched chains was wider than that for inappropriate chains. These

sales growth results are depicted in Appendix 2. Rather than the appropriate curve always being

better than the inappropriate one (as predicted by theory), or even intersecting curves (as implied

by Sampson (2004), note that the appropriate curve encompasses the inappropriate one.

The results investigating quality performance were even more intriguing. Inappropriately

matched chains actually had better performance than appropriate ones (n=260 for appropriate

and n=165 for inappropriate; difference in mean quality score: -2.20, t = - 4.93; p=0.00).

Curious, we selected the 13 worst cases of quality performance (score < 71) and the 17 best cases

(score > 85). Eight of the 13 poor performers had an appropriate governance mode, while twelve

of the 17 outstanding performers had an inappropriate mode. Together, these results suggest that

governance mode alignment does not appear to affect performance. Although there may be

alternate explanations, perhaps specific to our context, it appears that other factors – such as

those related to implementation – affect performance and may compensate for mode choice.

36

By considering and interpreting some of our other control variables, we get a better

picture of how parent and chain attributes affect performance. For chain growth, public parents

who are focused and chains that are large, young, and have sizeable outlets perform better. This

suggests that perhaps an important parental resource for chain growth is capital, which publicly

held parents can provide. This finding is supported by prior literature on resource scarcity,

although rather than franchisees we suggest that parents are a key source of needed managerial

and financial capital (Oxenfeldt and Kelly, 1968; Combs and Ketchen, 2003). For company-

owned chains, new parents improve unit growth and collocation has no effect. This suggests that

perhaps chains get overtaxed by managing both the demands of franchisees and those of new

parents. However, company-owned chains may see an infusion of new attention and resources,

thus benefiting from a new parent. For chains striving for high quality, older chains with larger

outlets performed better. Together, our findings suggest that different types of parental

capabilities and resources (industry experience vs. capital) drive different types of performance

(quality vs. growth). It also suggests that governance mode and collocation jointly affect

performance. For franchised chains, collocation helps growth, while for company-owned chains,

collocation improves quality. This finding implies that collocation and coordination between

chains and parents differ by mode.

We conducted several tests for model robustness and sensitivity. For model robustness,

we ran each model using two-stage least squares regression (2SLS). This instrumental variable

technique uses simultaneous equations to calculate estimates for the endogenous variables in the

first stage and then uses these estimated values and the exogenous variables as regressors in a

second stage OLS regression (Kennedy, 2003). This two-stage technique produces consistent

estimates and is insensitive to issues with multicollinearity and specification errors (Kennedy,

37

2003). Additionally, 2SLS allows calculating test statistics to check for autocorrelated

disturbances. Our results from 2SLS regression mirror those found using the manual two-stage

Heckman selection method for all models. Since some franchise studies treat the degree of

franchising as a continuous variable, we also conducted a two-stage analysis with the outcome of

the first stage being percentage franchised, using all of the other variables mentioned previously.

This analysis indicated that shared headquarters improves all three types of performance,

supporting H1b. Regarding autocorrelation, we calculated a test statistic for unbalanced panel

data using random effects, according to Baltagi and Wu (1999). We calculated this statistic for

each of the three dependent variables, with the largest statistic value being for Unit Growth (F =

2.09, d.f. = 1, p=0.385). This largest statistic does not reject the null hypothesis of zero

autocorrelation, thus we can be confident that our results do not suffer from autocorrelated

disturbances. Since we found largely consistent results across all models using both a two-stage

Heckman selection correction method and 2SLS, we are confident our results are robust and

unbiased.

We also wondered if our results were affected by franchised chains’ differences in their

degree of franchising. Some chains franchise to a very small degree, less than 10%, and may not

represent franchised chains overall. When omitting these cases (n=48), the results for

hypotheses 1a, 1b, 2a, and 2b were replicated. Only two cases involved an extensive use of

franchising (over 90%), thus we do not believe these affected our estimates. Therefore, our

results truly reflect differences between franchised and company-owned chains.

We conducted several sensitivity analyses for our variables. To investigate various

measures of cumulative operating experience, we began by examining the number of years to

include. Previous work that examines chain performance used all prior years of experience (e.g.,

38

age) leading up to the founding or acquisition of a new chain (Ingram and Baum, 1997a; Kalnins

and Mayer, 2004; Kalnins et al., 2006). However, these studies examined a drastic performance

measure – organizational failure. In studies modeling more fine-grained measures of

performance, such as sales or cost per unit sold (Darr et al., 1995; Sorenson and Sorenson, 2001),

more current operating experience is much more relevant due to decay. Thus, we examined the

effects of current year related operating experience as well as the cumulative experience of the

current year plus the previous one and two years. Our results indicated that three years

cumulative operating experience (current year plus the previous two) produced the most

consistent estimates and, in general, higher values of overall R-squared. Results using one and

two years of cumulative experience supported Hypotheses 1a, 1b, 2a, and 2b for quality

performance and were consistent with reported results. Since we have ten years of unbalanced

panel data, adding additional years to our experience variable would have introduced problems

with missing data and did not seem appropriate in this mature, service industry setting.

To test how the benefits of prior related operating experience may decay over time, we

followed Ingram and Baum (1997a) and tried several discount rates. We modeled three discount

rates of annual parent food service sales, including no discount (Salest + Salest-1+ Salest-2), linear

annual depreciation ( )1 2t 2 3

t tSales SalesSales − −+ + , and a simple average t t-1 t-2 3

Sales Sales Sales+ +⎛ ⎞⎜ ⎟⎝ ⎠

.

We were skeptical of using either the cumulative, no discount figure or the simple average, as we

expected the decay of related operating experience to be quicker than in studies of organizational

failure (Darr et al., 1995; Sorenson and Sorensen, 2001). We felt our variable should be more

aligned with Macher and Boerner’s (2006) study of technological experience in pharmaceutical

firms which used two years of data, discounting the older experience by 20%. However, we

39

wanted to be confident in our results and reran all models using the cumulative, no discount

figure and the simple average; once again, Hypotheses 1a, 1b, 2a, and 2b were supported.

Finally, the following sensitivity analyses were performed for Collocation and

Governance Misfit. Our results held when same state headquarters was substituted for same

city/state headquarters, suggesting that close proximity and collocation have similar effects.

When we used a discrete rather than continuous measure of governance mode fit, we replicated

our results for Hypotheses 1a, 1b, 2a, and 2b.

In summary, parent’s related operating experience improved quality, especially for

franchised chains, supporting hypotheses 1a and 2a. However, parental operating experience

detracted from both sales and unit growth, especially in company-owned chains. Collocation of

chain and parent headquarters enhanced sales growth, unit growth, and quality, supporting

hypothesis 1b; this was especially true for company-owned chains, supporting hypothesis 2b.

Collocation was also surprisingly important for unit growth of franchised chains.

DISCUSSION AND CONCLUSION

This study illustrates how parents enable business units to implement strategy, specifying

parental capabilities that affect performance and the contingent effects of these capabilities by

governance mode. Our attempt to discover parent implementation capabilities that could be

deployed over multiple governance modes was successful, but surprising. As expected, a

parent’s operating expertise from related experience improved quality; however, this capability

detracted from growth, particularly unit growth for internal units. Coordination based upon

collocation of parents and business units improved performance both in terms of growth and

quality, especially for internal units. Unexpectedly, we found that collocation also improved unit

growth of outsourced units.

40

Our work sheds light on the effect of corporate parents and how they diversify. Parents

with operating expertise from related experience can help units reduce costs, learn, and enter new

markets. This broad experience can be a knowledge platform upon which firms pursue programs

of successful diversification, as more closely related acquisitions, new ventures, alliances, and

joint ventures are more successful (Helfat and Raubitschek, 2000; Palich et al., 2000; Keil et al.,

2008). While other studies have focused on experience in a particular business, with a partner,

or with a specific governance mode, our work indicates that corporate related operating

experience can span across governance modes and business units to affect performance. We

also find that the structural decision of sharing headquarters has a powerful and positive impact

on business unit performance, due to an enhanced understanding and engagement with formal

and informal coordination mechanisms.

We find interesting joint effects of parent implementation capabilities and governance

mode on performance. If we assume that units need to both adapt and coordinate, it appears that

in the case of unit growth, firms use outsourcing for adaptation and a shared headquarters for

coordination. In other cases, however, units seem to need parental capabilities to gain the

benefits from a chosen governance mode, such as our findings of related operating experience

and outsourcing improving quality and shared headquarters improving sales growth and quality

of internal units. In the former case, the parental capability and governance mode are substitutes,

while in the latter cases, they are complements. This suggests that firms which use different

governance modes for different purposes, such as internalization for exploitation and outsourcing

for exploration (Robins, 1993; Sorenson and Sorenson, 2001; Puranam et al., 2006), should

expect performance trade offs. Interesting questions for future work could investigate whether

41

the exploration/exploitation balance occurs at the business unit level, corporate parent level, or

both and what capabilities at various levels are required to capture anticipated benefits.

Although we found evidence that firms select the governance mode that best suit them,

our results did not indicate that governance mode alignment drove unit performance. While

prior studies have shown a positive relationship between governance mode alignment and

performance, there have been caveats that our study helps to address by incorporating parent-

level capabilities. For example, Nickerson and Silverman (2003) found that alignment led to

improved profitability, but noted that firms were slow to adopt new governance modes; this

inertia may be due to the parent’s lack of implementation capabilities. Prior studies have also

found asymmetric effects of misalignment, but in different directions. Sampson (2004) found

that excessive bureaucracy was worse than contracting hazards such that firms are better off

outsourcing, but other scholars have found that excessive outsourcing was more problematic

(Leiblein et al., 2002; Mayer and Nickerson, 2005). Our findings regarding contingent effects of

parent capabilities by governance mode help to resolve these confounding results.

The context of this study has limitations which may suggest alternate explanations but

also provide opportunities for further work. The industry sector of casual dining restaurants is

mature, relatively non-technical, and service-based with franchising and internal operation as the

governance mode choices. Since a low level of physical specific investment is required, it may

be that mode choice is less important than in other settings; this may have led to our null result

for mode alignment affecting performance. Franchising is also a specific form of outsourcing

that entails the whole business model rather than individual goods or activities. One could

extend our study to include more governance modes, such as pure outsourcing or alliances, to

manufacturing sectors, and to high technology settings to test the generalizability of our findings.

42

Second, we focus only on related operating experience and collocation, but other parental

influences and interactions between units may also affect unit performance. While we did

control for the number of siblings and whether a parent was publicly held, we could not flesh out

a more complete understanding of the parent’s entire portfolio of capabilities and businesses

since our parents tended to be relatively small, private firms. Extending this work to Fortune 500

firms, perhaps using survey methods to obtain fine grained details, would add insights. Third,

while we did allow for experience to decay over time, we also did not investigate the more

dynamic nature of parental capabilities on unit performance. Our industry sector is mature and

stable; in other sectors, parental capabilities may need to be more flexible and change frequently.

We leave this for future scholars to create deep archival panel datasets or extended case studies

perhaps in multiple settings which could address this issue.

Other limitations were based on our study design but again offer several opportunities for

fruitful extensions. Due to our study involving both parents and business units, it is possible

there are sources of unobserved heterogeneity that we do not capture, such as business-unit level

capabilities or some underlying factors that influence both mode choice and performance. We

do our best to control for these econometrically, but we cannot untangle the “chicken-or-egg”

conundrum around parent capabilities, unit capabilities, and governance mode choice. Case

studies are a vital tool to study these issues and document the origination of capabilities and

initial governance mode choices. Second, our measures of performance were multi-faceted,

covering customer satisfaction (quality), expansion (unit growth), and volume and price

increases (sales growth). It could very well be that some of these measures are more important

for some businesses than others. Our measures were also absolute; it may be that some

businesses target relative performance within their peer group. While our aggregate results

43

suggest trade offs, these may be ameliorated if business units are only concerned about one

metric in relation to a small number of other firms. We did not include financial measures, such

as profitability, or technical indicators, such as innovation, which are keenly important in many

sectors. Through detailed case studies and surveys, future scholars could dig into which

performance metrics matter to managers and how parental capabilities and governance mode

choices affect these. Finally, our focus was on the direct effects of operating expertise from

related experience and coordination by collocation, but it is likely that these jointly impact

performance much as technical expertise and mutual coordination from relationships jointly

influence supplier performance (Dyer, 2000). Future work could investigate these joint effects

and consider whether they vary by governance mode.

Our work has several practical implications. Business unit managers should welcome

collocation of their headquarters with their corporate parents, as this can improve both quality

and growth. This collocation improves communication, which may assist in understanding and

deploying the business template across the system (Winter and Szulanski, 2001). It also may

improve contracting and the management of resources to promote a community of interest; this is

particularly important for the health of a franchised system (Michael and Combs, 2008).

Managers should recognize, however, that there are trade offs with leveraging a parent’s related

operating experience. While this experience may improve quality, it may limit growth, although

this negative effect may be abated through outsourcing. For growth, access to capital through

publicly held parents appears to be more important than related experience. Finally, managers

should not be discouraged if environmental constraints limit their choice of governance mode, as

this does not appear to be the overriding factor influencing performance.

44

In conclusion, our evidence indicates that the actions of corporate parents regarding

implementation may have a greater impact on business unit performance than governance mode

choice. Ordinary, fungible parental capabilities based upon related experience and collocation

affect business unit growth and quality performance and these effects are contingent upon

governance mode. There is still much we do not understand about the complex and multi-level

connections between capabilities, governance modes, and performance. We hope our insights

enrich future conversations about these relationships.

45

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51

FIGURE 1: A Model of Governance Mode Choice, Implementation,

and Performance

H2

H1

Business Unit Attributes (Specific Investments - Alignment)

Governance Mode Choice

(Internal/External)

Business Unit

Performance

Parent Firm Implementation Capabilities (Operating Expertise from Related Experience, Coordination by Collocation)

52

TABLE 1: Hypotheses Summary: Parent Capabilities and Business Unit Performance

Overall Franchised Company Owned Parent’s Operating Expertise from Related Experience

H1a: The greater the parent’s related operating experience, the better the performance of the business unit (chain). Rationale: Gain economies of scope and scale; can learn from parent Results: Supported for Quality

H2a: Parent’s related operating experience will have a greater positive impact on the performance of business units (chains) that outsource than on those that do not outsource. Rationale: Helps with exploration, adaptation Results: Supported for Quality

Coordination by Collocation

H1b: Collocation of parent and business unit (chain) headquarters will improve business unit (chain) performance. Rationale: Improved coordination and control Results: Supported for Quality, Sales Growth, and Unit Growth

H2b: Collocation of the parent and business unit (chain) headquarters will have a greater positive impact on the performance of business units (chains) that do not outsource than on those that outsource. Rationale: Helps with exploitation, replication Results: Supported for Quality and Sales Growth

53

TABLE 2: Casual Dining Chains in 2007

Chain Chain Sls # Units % Fran 2007 Parent ParentFoodSls Applebee's $ 4,505.4 1864 72.6`% Dine Equity Inc. $ 239.4 Beef O'Brady's Family Sports Pub 202.9 239 98.3% Family Sports Concepts, Inc. ?? Benihana of Tokyo 247.0 70 15.7% Benihana Inc. 296.4 Bennigan's 643.0 255 37.6% Metromedia Inc. 685.3 Bertucci's Brick Oven Pizzeria 219.2 92 0.0% Jacobson Partners 245.0 BJ's Restaurant & Brewery 318.7 69 1.4% BJ's Restaurants, Inc. 316.1 Bonefish Grill 390.0 140 4.3% Bain Capital LLC 2,200.0 Buca di Beppo 245.6 90 0.0% Buca Inc. 245.6 Buffalo Wild Wings Grill & Bar 1,017.3 493 67.3% Buffalo Wild Wings Inc. 329.7 California Pizza Kitchen 642.0 209 8.1% California Pizza Kitchen Inc. 627.0 Carrabba's Italian Grills 705.0 238 0.0% Bain Capital LLC 2,200.0 Champps Americana 240.0 60 20.0% F&H Acquisition Corp. 200.0 Cheesecake Factory, The 1,327.4 140 0.0% The Cheesecake Factory Inc. 1,448.3 Chevys Fresh Mex 272.7 97 29.9% Sun Capital Partners Inc. 1,622.0 Chili's Grill & Bar 3,880.0 1305 31.6% Brinker Intl Inc. 4,258.0 Claim Jumper 294.0 44 0.0% Leonard Green and Partners LP 294.0 Dave & Buster's 283.1 47 0.0% Wellspring Capital Management, LLC 527.0 El Torito 221.4 79 0.0% Sun Capital Partners Inc. 1,622.0 Elephant Bar 182.0 44 0.0% Apax Partners Worldwide LLP 225.0 Famous Dave's 430.0 164 73.2% Famous Dave's of America, Inc. 125.9 Fleming's Prime Steakhouse 221.0 54 0.0% Bain Capital LLC 2,200.0 Fuddruckers 342.0 230 47.8% Magic Restaurants LLC 191.0 Hard Rock Café 187.3 43 0.0% Seminole Tribe of FL 180.0 Hooters 953.0 392 68.9% Hooters of America Inc. 397.0 Houlihan's 260.3 92 66.3% Houlihan's Restaurants Inc. 140.7 Houston's 264.0 30 0.0% Hillstone Restaurant Group 350.0 Joe's Crab Shack 365.0 120 0.0% JH Whitney Capital Partners Inc. 315.0 Jonny Carino's Italian 361.1 159 53.5% Fired Up Inc. 178.8 Legal Sea Foods 215.0 36 0.0% Legal Sea Foods Inc. 215.0 Logan's Roadhouse 557.8 182 14.3% LRI Holdings Inc. 304.5 Lone Star Steakhouse 387.0 195 2.1% Lone Star Funds ?? Longhorn Steakhouse 831.2 305 0.0% Darden Restaurants Inc 6,653.0 Maggiano's Little Italy 377.0 42 0.0% Brinker Intl Inc. 4,258.0 Marie Callendar's Restaurant 304.0 134 32.1% Castle Harlan Partners III LP 535.0 Max & Erma's 235.0 103 23.3% Max & Erma's Restaurants Inc. 174.9 Mc Cormick & Schmick's 290.0 65 0.0% McCormick & Schmick's Restaurants 338.6 Mimi's Café 411.0 132 0.0% Bob Evans Farms Inc. 1,425.0 Morton's of Chicago 327.0 72 0.0% Morton's Restaurant Group, Inc 337.0 Ninety Nine Restaurant & Pub 311.3 115 0.0% O'Charley's Inc. 970.0 O'Charley's 630.0 240 4.6% O'Charley's Inc. 970.0 Old Chicago 217.1 95 33.7% Rock Bottom Restaurants 310.4 Olive Garden 3,048.0 647 0.0% Darden Restaurants Inc 6,653.0 On the Border 434.0 165 18.8% Brinker Intl Inc. 4,258.0 Outback Steakhouse 2,637.0 795 13.5% Bain Capital LLC 2,200.0 PF Chang's China Bistro 853.3 173 0.6% PF Chang's China Bistro Inc. 1,095.3 Pizzeria Uno Chicago Bar & Grill 448.6 201 40.3% Centre Partners Management, LLC 287.8 Rainforest Café 226.5 26 0.0% Landry's Restaurants Inc. 880.0 Red Lobster 2,567.0 651 0.0% Darden Restaurants Inc 6,653.0 Red Robin Burger & Spirits 1,102.3 366 32.0% Red Robin Gourmet Burgers Inc. 762.0 Romano's Macaroni Grill 690.0 201 3.0% Brinker Intl Inc. 4,258.0 Ruby Tuesday 1,704.2 904 19.8% Ruby Tuesday Inc. 1,358.6 Ruth's Chris Steak House 498.0 106 42.5% Ruth's Hospitality Group Inc. 319.2 Smokey Bones Barbeque & Grill 200.0 73 0.0% Sun Capital Partners Inc. 1,622.0 Stuart Anderson's Black Angus 230.0 82 0.0% Versa Capital Management Inc. ?? Texas Roadhouse Grill 1,020.0 285 28.4% Texas Roadhouse Inc. 735.1 TGI Friday's 2,028.9 595 48.7% Carlson Cos. Inc. 1,326.0 The Capital Grille 225.0 32 0.0% Darden Restaurants Inc 6,653.0

Note: Parent food sales can be less than chain sales due to a high degree of franchising by the chain since the parent receives only the royalty rate (typically 5% of sales) whereas the chain sales figure includes all revenues. In some cases, parent food sales is unknown (“??” above) since it was too low to be included in the NRN data.

54

TABLE 3: An Overview of Casual Dining:

2007 Chains by Governance Mode and Parent Type (Number of Chains, with Examples and Parents’ Names in Italics)

Parent Type Franchised Chains Company Owned Chains Total Single Chain Parent

11 Ruby Tuesday (Ruby Tuesday Inc.)

3 The Cheesecake Factory (The Cheesecake Factory Inc.)

14

Multiple Chain Parent (all Food Business Units)

13 Chili’s (Brinker Intl.)

11 Olive Garden (Darden Restaurants Inc.)

24

Parent with Non-Food Business Units

8 TGI Friday’s (Carlson Co.)

11 Bertucci’s (Jacobson Partners)

19

Total

32

25

57

55

Table 4: Descriptive Statistics and Correlations

Mean S.D. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)

(1) Chain Sales Growth 0.08 0.12 1.00 (2) Chain Quality 77.50 4.59 0.21* 1.00 (3) Chain Unit Growth 5.63 10.88 0.78* 0.23* 1.00 (4) Parent Operating Experience 6.92 1.04 0.06 0.06 0.11* 1.00 (5) Food Service Siblings 2.17 2.10 -0.14* 0.01 -0.12* 0.51* 1.00 (6) Parent/Chain Same HQ 0.68 0.47 0.18* 0.16* 0.17* -0.01 -0.20* 1.00 (7) Chain Age 23.92 13.34 -0.25* -0.03 -0.27* -0.26* -0.06 -0.12* 1.00 (8) Chain Size (Units)a 4.82 0.93 0.02 -0.28* 0.05 0.52* 0.01 0.14* 0.03 1.00 (9) Parent Public 0.58 0.49 0.29* 0.14 0.33* 0.44* 0.04 0.34* -0.35* 0.16* 1.00 (10) Outlet Size (Sales Per Unit)b 3.36 1.78 0.21* 0.40* 0.21* 0.06 0.11* 0.02 -0.10* -0.47* 0.15* 1.00 (11) New Parent 0.08 0.28 -0.08 0.03 -0.04 0.05 0.20* -0.28* 0.00 -0.09* -0.20* 0.00 1.00 (12) Advertising Spendingc 7.05 2.26 0.08 -0.17* 0.06 0.50* 0.12* 0.18* -0.12* 0.65* 0.27* -0.18* -0.09 1.00 (13) Use Franchising 0.55 0.50 0.00 -0.18* -0.02 -0.08 -0.13* 0.02 0.07 0.41* -0.14* -0.43* -0.04 0.08 1.00 (14) Changed to/from Franchising 0.02 0.13 0.03 0.09 0.03 0.07 0.07 0.06 -0.05 0.02 0.05 0.01 0.02 0.02 0.02 1.00 (15) Percent Franchise Out 0.20 0.25 0.00 -0.31* -0.04 -0.26* -0.24* -0.01 0.13* 0.40* -0.30* -0.39* -0.03 0.10* 0.70* -0.08 1.00 (16) Self Selection (Inverse Mills) -0.11 1.15 0.15* 0.08 0.13* 0.14* 0.28* -0.09 -0.16* -0.08 0.01 0.02 0.11* 0.07 0.57* 0.06 0.26* 1.00 (17) Governance Misfit -1.08 2.62 -0.08 0.23* -0.11* 0.09 0.01 0.09 -0.00 -0.01 -0.04 -0.26* -0.06 -0.01 0.01 0.00 -0.25* 0.02 1.00 Sample size n=545 * p<0.05 a Used Log Total Units b Used Log Sales Per Unit c Used Log Ad Spending

56

TABLE 5: Time Series OLS Regression Models for Sales Growth of the Chain

Model 4 Model 1 Model 2 Model 3 Franchise Internal Parent Related Op Exper -2.81 ** -2.82 ** -3.01 *** -2.08 -0.84

( H1a, H2a) (1.24) (1.21) (1.24) (2.27) (2.23) Parent/Chain Same HQ 1.98 * 3.28 ** 3.15 ** 2.34 3.63 *

(H1b, H2b) (1.51) (1.53) (1.56) (2.02) (2.75) Food Service Siblings -0.37 -0.75 ** -0.70 ** -0.61 -0.60

(0.37) (0.39) (0.40) (0.75) (0.83) Parent Public 3.69 ** 2.79 ** 2.81 ** 2.62 3.39 (1.68) (1.67) (1.68) (2.32) (2.73) New Parent 0.27 -0.49 -0.46 -2.83 1.42 (1.99) (1.99) (1.99) (2.93) (2.92) Chain Age -0.13 ** -0.11 ** -0.11 ** -0.15 ** -0.06

(0.07) (0.07) (0.07) (0.08) (0.12) Chain Size (Units) 3.83 *** 4.49 *** 4.71 *** 2.59 2.60 (1.40) (1.46) (1.50) (3.20) (3.68) Outlet Size 2.79 *** 1.81 *** 1.94 *** 2.85 ** 1.70 ** (Sales Per Unit) (0.59) (0.59) (0.62) (1.28) (0.81) Use Franchising 0.38 -4.73 ** -4.73 ** (1.73) (2.61) (2.62) Changed to/from 0.01 1.03 1.13 3.56 5.90 Franchising (3.49) (3.64) (3.64) (4.04) (9.18) Percent Franchise Out 5.57 (7.34) Self Selection Correction 2.41 *** 2.40 *** 3.30 ** 2.56 * (Inverse Mills Ratio) (0.94) (0.94) (1.82) (1.64) Governance Misfit 0.18 -0.59 0.62

(0.28) (1.12) (1.36) Number of chains 61 59 59 35 30 Number of observations 365 334 334 184 150 Wald Chi-Squared (dof) 54.35(10) 58.27(11) 58.24(12) 33.59(12) 29.04(11) Overall R-squared 0.170 0.204 0.204 0.208 0.228 Constants omitted; OLS time series regression with random errors; *** p<.001 ** p<.01 * p<0.05 one tailed tests

57

TABLE 6: Time Series OLS Regression Models for Unit Growth of the Chain

Model 4 Model 1 Model 2 Model 3 Franchise Internal Parent Related Op Exper -2.65 *** -3.01 *** -2.96 *** -1.56 -4.64 ***

( H1a, H2a) (1.06) (1.06) (1.09) (1.86) (1.97) Parent/Chain Same HQ 2.12 ** 3.20 *** 3.28 *** 3.72 ** 1.61

(H1b, H2b) (1.27) (1.33) (1.35) (1.63) (2.43) Food Service Siblings 0.06 -0.13 -0.15 -0.46 0.01

(0.31) (0.33) (0.34) (0.60) (0.73) Parent Public 4.16 *** 4.01 *** 3.98 *** 3.79 ** 4.04 ** (1.42) (1.45) (1.46) (1.87) (2.41) New Parent 2.93 ** 3.89 ** 3.86 ** -1.43 9.05 *** (1.63) (1.69) (1.69) (2.30) (2.54) Chain Age -0.15 *** -0.12 ** -0.12 ** -0.17 *** -0.05

(0.06) (0.06) (0.06) (0.07) (0.10) Chain Size (Units) 3.79 *** 3.80 *** 3.73 *** 1.71 7.18 ** (1.22) (1.29) (1.33) (2.61) (3.26) Outlet Size 1.85 *** 1.45 *** 1.40 *** 0.87 1.97 *** (Sales Per Unit) (0.52) (0.52) (0.55) (1.07) (0.73) Use Franchising -0.22 -2.71 -2.71 (1.48) (2.27) (2.28) Changed to/from 0.42 0.80 0.74 3.29 5.84 Franchising (2.85) (3.08) (3.08) (3.17) (7.98) Percent Franchise Out 0.29 (6.13) Self Selection Correction 1.16 * 1.15 * 1.89 1.26 (Inverse Mills Ratio) (0.81) (0.81) (1.49) (1.45) Governance Misfit -0.09 -0.54 -0.23

(0.25) (0.91) (1.21) Number of chains 61 59 59 35 30 Number of observations 365 334 334 184 150 Wald Chi-Squared (dof) 51.72(10) 55.65(11) 55.34(12) 47.00(12) 32.10(11) Overall R-squared 0.200 0.212 0.214 0.275 0.266 Constants omitted; OLS time series regression with random errors; *** p<.001 ** p<.01 * p<0.05 one tailed tests

58

TABLE 7: Time Series OLS Regression Models for Consumer Reports Quality Score for the Chain

Model 4 Model 1 Model 2 Model 3 Franchise Internal Parent Related Op Exper 0.69 ** 0.75 ** 0.77 ** 0.89 * 0.76

( H1a, H2a) (0.34) (0.36) (0.36) (0.57) (0.60) Parent/Chain Same HQ 0.77 ** 0.72 ** 0.75 ** 0.27 2.05 ***

(H1b, H2b) (0.33) (0.37) (0.37) (0.46) (0.75) Food Service Siblings 0.06 0.07 0.06 0.03 0.05

(0.07) (0.08) (0.08) (0.16) (0.22) Parent Public 0.13 0.16 0.16 0.09 -0.71 (0.38) (0.39) (0.40) (0.47) (0.79) New Parent 0.36 0.29 0.31 -0.13 0.69 (0.37) (0.40) (0.41) (0.60) (0.60) Chain Age 0.09 *** 0.07 ** 0.07 ** 0.01 0.09 **

(0.03) (0.03) (0.03) (0.03) (0.05) Chain Size (Units) -0.52 -0.91 ** -0.96 ** -1.31 * -0.43 (0.49) (0.54) (0.53) (0.82) (1.22) Outlet Size 0.93 *** 0.94 *** 0.91 *** 1.81 *** 0.56 ** (Sales Per Unit) (0.22) (0.24) (0.24) (0.40) (0.33) Use Franchising 0.37 0.82 * 0.82 * (0.42) (0.63) (0.63) Changed to/from -0.35 -0.90 * -0.92 * -1.18 ** -0.53 Franchising (0.57) (0.63) (0.64) (0.69) (1.66) Percent Franchise Out -1.90 (2.21) Self Selection Correction -0.02 -0.02 -0.34 0.11 (Inverse Mills Ratio) (0.20) (0.20) (0.41) (0.45) Governance Misfit -0.04 0.11 -0.12

(0.07) (0.27) (0.40) Number of chains 58 56 56 33 29 Number of observations 322 296 296 163 133 Wald Chi-Squared (dof) 64.43(10) 56.02(11) 56.07(12) 51.73(12) 27.84(11) Overall R-squared 0.118 0.121 0.116 0.439 0.086 Constants omitted; OLS time series regression with random errors; *** p<.001 ** p<.01 * p<0.05 one tailed tests

59

APPENDIX 1: Time Series Probit Models for Franchise (1) or Not, Casual Dining Chains, 1998-2007

Model 1 Model 2 Parent Related Op Experience -2.15 *** 0.71 (0.54) (0.59) Parent/Chain Same HQ 1.05 * 3.58 *** (0.79) (1.16) Food Service Siblings 0.35 ** 0.00 (0.18) (0.24) Chain Age 0.09 ** 0.07 ** (0.04) (0.03) Chain Size (Units) 4.57 *** 2.46 *** (0.88) (0.72) Public Parent 0.05 -2.81 ** (0.91) (1.33) Outlet Size (Sales Per Unit) -0.40 *

(0.28) Advertising Spending -0.36 **

(0.18) Constant -10.91 *** -12.22 ***

(2.82) (4.05) LogLikelihood -70.39 -66.96 -2(L(Model 1)- Model 2)) 6.86 ** Wald Chi-Squared (dof) 28.14(6) 26.08(8) Number of chains f59 59 Number of observations 334 334 Probit for panel data with random errors *** p<.001 ** p<.01 * p<0.05 one tailed tests

60

APPENDIX 2: Expected and Actual Performance Implications of Appropriate

Governance Mode Choice

Figure 1: Expected distribution of performance by governance mode, assuming heterogeneity in implementation capabilities such that generally appropriately matched modes perform better, but that inferior (superior) implementation may detract from (compensate for) (in)appropriate governance mode choice (e.g., Sampson 2004).

Figure 2: Actual distribution of sales growth performance by governance mode. Note that the appropriate and inappropriate governance mode curves overlap, suggesting factors other than governance mode are driving performance.

0

10

20

30

40

50

#

-20 0 20 40 60SlsGrowth%

Inappropriate Mode

Appropriate Mode

Inappropriate Governance Mode

Appropriate Governance Mode

Performance

# of

Cas

es