NONLINEAR EFFECTS OF ENTREPRENEURIALORIENTATION ON SMALL FIRM PERFORMANCE:THE MODERATING ROLE OF RESOURCEORCHESTRATION CAPABILITIES

WILLIAM J. WALES,1* PANKAJ C. PATEL,2 VINIT PARIDA,3,4 andPATRICK M. KREISER5

1Department of Management, James Madison University, Harrisonburg,Virginia, U.S.A.2Miller College of Business, Ball State University, Muncie, Indiana, U.S.A.3Entrepreneurship and Innovation, Luleå University of Technology, Luleå,Sweden4Department of Management, University of Vaasa, Vasa, Finland5College of Business, Ohio University, Athens, Ohio, U.S.A.

This research examines the nature of the relationship between entrepreneurial orientation (EO)and small firm performance. The results from a sample of 258 Swedish small firms indicate aninverted U-shaped relationship between EO and small firm performance. Drawing uponresource orchestration theory, we theorize that information and communication technologycapability and network capability help small firms overcome their resource-related ‘liabilitiesof smallness’ and observe these capabilities to increase the optimal levels and performance-related returns from EO. In the absence of these capabilities, returns to firm performance fromincreasing EO were observed to reach harmful levels. The study implications are discussed.Copyright © 2013 Strategic Management Society.

INTRODUCTION

During the last three decades, entrepreneurial orien-tation (EO) has become one of the most extensivelyresearched topics in the entrepreneurship and strate-gic management literature, with more than 100studies exploring the concept (Lumpkin, 2011;Rauch et al., 2009). Prior studies define EO as‘strategy-making practices, management philoso-phies, and firm-level behaviors that are entrepreneur-ial in nature’ (Anderson, Covin, and Slevin, 2009:220) and suggest that EO is evidenced through thesimultaneous manifestation of innovative, risk-taking, and proactive firm behaviors (Covin and

Slevin, 1989; Miller, 1983). As entrepreneurialbehaviors contribute to new product market entry,a principal focus of prior research has been therelationship between EO and firm performance(Lumpkin and Dess, 1996). In this regard, moststudies generally suggest a positive effect of EO onfirm performance (Rauch et al., 2009). Nonetheless,key knowledge voids remain concerning theEO-performance relationship, particularly in thecontext of small firms (i.e., firms with fewer than 50employees1) that may have difficulty in consistentlyaccessing and making use of the resources necessaryto successfully employ an EO-focused strategicapproach (Su et al., 2011b). Previous research hasyet to consider the potential for nonlinearity within

Keywords: entrepreneurial orientation; small firms; curvilinear-ity; firm performance; firm capabilities; resource orchestration*Correspondence to: William J. Wales, Department of Manage-ment, James Madison University, Harrisonburg, VA 22801,U.S.A. E-mail: waleswj@jmu.edu

1 Firms with fewer than 50 employees have also been referred toas ‘microfirms’ by Rauch et al. (2009). Their meta-analysissuggests that a shared understanding of the EO-performancelinkage remains elusive in this firm context.

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Strategic Entrepreneurship JournalStrat. Entrepreneurship J., 7: 93–121 (2013)

Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/sej.1153

Copyright © 2013 Strategic Management Society

the EO-performance relationship in small firms.Advancing understanding of this relationship prom-ises to provide important theoretical insights intohow this strategic orientation can be effectivelymanaged in resource-constrained firm contexts.

Small firms are subject to several size-relatedliabilities, which may hinder their efforts to translateEO into new or renewed growth trajectories(Freeman, Carroll, and Hannan, 1983). These firmssuffer from lower levels of slack resources,decreased efficiency when using their resources, andinferior legitimacy associated with the products theirresources produce (Stinchcombe, 1965; Thornhilland Amit, 2003). Therefore, it is not surprising thatThornhill and Amit (2003) observed higher failurerates of small firms to frequently result from a lackof key resources and capabilities. For these reasons,achieving high levels of growth becomes a primaryobjective of small firms, as growth affords firms theorganizational slack necessary to potentially bufferresource-related liabilities (Agarwal and Audretsch,2001; Sirmon et al., 2011). The dilemma for smallfirms becomes how they can leverage and supportEO to best facilitate this growth in spite of theirresource limitations.

An emerging stream of research suggests that‘what a firm does with its resources is at least asimportant as which resources it possesses’ (Hansen,Perry, and Reese, 2004: 1280) and the ability of afirm to ‘orchestrate’ its resources facilitates theachievement of its strategic objectives (Sirmon et al.,2011). Foundational work in this area suggests thatcomponents of effective firm resource managementinclude ‘structuring the firm’s resource portfolio,bundling the resources to build capabilities, andleveraging those capabilities with the purpose ofcreating and maintaining value for customersand owners’ (Sirmon, Hitt, and Ireland, 2007: 273).Moreover, EO plays an important role in theresource orchestration process, as it ‘provides themobilizing vision to use firm resources. By directingthe use of resources, EO not only provides an objec-tive, but also helps identify the resources necessaryto support the objective’ (Chirico et al., 2011: 311).Yet, given the resource limitations faced by smallfirms, it is paramount that these firms make the mostout of the firm-level capabilities that may help themrealize the potential benefits of EO. Firm-level capa-bilities represent the glue that binds together a firm’sresources and configures these resources to performa value-producing task or activity (Grant, 1991). Assmall firms may be prevented from releasing the full

potential of their EO due to resource limitations,capabilities that support either gaining access toexternal resources or making use of the entrepre-neurial potential latent within extant resources arevital to the success of small firms (Gulati, 1999).

We extend ‘resource orchestration’ arguments bytheorizing that the ability of small firms to translateEO into heightened performance is dependent ontheir capacity to develop and leverage critical firm-level capabilities that can be effectively utilized inconjunction with EO. Specifically, we propose that asmall firm’s information and communication tech-nology (ICT) capability and network capability (NC)may enable it to more efficiently and effectivelyorchestrate its resources and, thereby, enhance theefficacy of its EO efforts. ICT capability refers to theextent to which a firm is able to utilize informationand communication technologies to improve itsoverall business processes (Johannessen, Olaisen,and Olsen, 1999; Tippins and Sohi, 2003) and NCrefers to a firm’s ability to use relationships toprocure resources held by other actors (Kale, Dyer,and Singh, 2002). These moderating influencesexplore important aspects of resource orchestration.Although both NC and ICT capability affect a firm’sability to bundle its resources into capabilities, NCalso strongly influences the structuring of the firm’sresource portfolio, while ICT capability enables thefirm to better leverage its extant resources and capa-bilities to foster firm performance. Together, theyprovide a more complete perspective concerning therole of synchronized resource orchestration-basedmechanisms in the EO-small firm performance rela-tionship. We posit that ICT capability and NC serveto mitigate the resource constraints that hinder theeffective utilization of EO in small firms and,thereby, alter the nature of the EO-small firm perfor-mance relationship.

This study offers several contributions to theentrepreneurship and strategic management litera-ture. We extend existing theories of EO by offering arationale as to why EO may possess a curvilinearrelationship with firm performance in small firms.While low to moderate levels of EO may enhanceperformance, we argue that the combination of theresource constraints faced by small firms and theresource-intensive nature of EO (Su et al., 2011b)may limit these performance effects at elevatedlevels of EO and may even turn these effects negativeto the point where they are actually harmful (i.e.,result in below zero returns to performance). Thus,we seek to provide insight to the questions of

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whether and how a curvilinear relationship betweenEO and performance is manifest in the small firmcontext. Further, to better understand how curvilin-earity is manifest within the EO-small firm perfor-mance relationship, we explore ICT capability andNC as moderating variables that may influence smallfirms’ ability to orchestrate their limited resourcesmore successfully and enable higher returns fromEO. In exploring these moderating influences, thepresent research provides insight into how theoptimal level of EO may vary as a function of spe-cific firm-level capabilities and whether an interme-diate level of EO is always optimal (Bhuian,Menguc, and Bell, 2005; Tang et al., 2008).

We also aim to address many of the methodologi-cal limitations associated with prior EO research by:(1) utilizing objective secondary measures whencapturing the performance of small firms; (2) mea-suring these indicators longitudinally; and (3) utiliz-ing multiple indicators of firm performance that areall focused on firm growth, an outcome of utmostrelevance to small firms (e.g., Rauch et al., 2009).Although previous research suggests that theEO-performance relationship may be subject todiminishing returns in certain industrial environmen-tal contexts (Su, Xie, and Li, 2011a; Tang et al.,2008), this study represents a starting point in exam-ining potential curvilinearity in the small firmcontext and in establishing whether diminishingreturns to performance may reach harmful, belowzero, levels under certain conditions. Extendingprior research, the present study additionally exam-ines whether curvilinearity occurs within the rangeof EO that is manifest in small firms. Moreover, weprovide detailed information regarding the form andstrength of the curvilinear effect and provide insightsinto how this effect is moderated. Taken together, ourstudy provides theoretical and empirical insight intothe nature of the relationship between EO and firmperformance in small firms, as well as two specificfirm-level capabilities that may influence the natureof this relationship.

THEORY AND HYPOTHESES

EO is evidenced through an organization’s simulta-neous exhibition of innovative, risk-taking, and pro-active firm behaviors (Covin and Slevin, 1989;Miller, 1983). Innovativeness reflects a firm’s will-ingness to support new ideas, creativity, and experi-mentation in the development of internal solutions or

external offerings. Risk taking is associated with afirm’s readiness to make bold and daring resourcecommitments toward organizational initiativeswith uncertain returns. Proactiveness represents aforward-looking and opportunity-seeking perspec-tive that provides the firm an advantage over com-petitors’ actions by anticipating future marketdemands. Prior research suggests that the appropri-ateness of exploring the dimensions of EO sepa-rately or in unison is a matter of theoreticalperspective (Covin, Green, and Slevin, 2006; Covinand Wales, forthcoming). This choice is largelydependent on whether the primary research objectivenecessitates assessing the unique influence of thedimensions of EO (e.g., Lumpkin and Dess, 1996) orassessing EO as an overarching strategic approachindicated by the positive covariance of these dimen-sions (e.g., Covin and Slevin, 1989; Miller, 1983).2

Since the research question being investigated in thisstudy concerns how EO—as a firm-level strategicapproach—is related to performance in small firms,we adopt a composite view of EO.

Nonlinear effects of entrepreneurial orientationin small firms

The relationship between EO and firm performancehas been a central focus of prior entrepreneurshipresearch. As scholars have previously acknowledged(Bhuian et al., 2005; Miller, 1983), the idea that afirm can be too entrepreneurial is rather intriguing.Yet, only a limited number of studies have examinedquestions informative to this research agenda.Bhuian et al. (2005) observed that the effect ofmarket orientation on firm performance is highest atmoderate levels of EO. Tang et al. (2008) observed acurvilinear relationship between EO and firm perfor-mance in the Chinese national context (Tang et al.,2008). In this context, Tang et al. (2008) observedthe positive effect of EO on firm performance todiminish (i.e., firms realize reduced positive gainsfrom high investments in EO). Similarly, Su et al.(2011a) observed the positive effects of EO on firmperformance to diminish within a sample of youngChinese firms. Yet, the existence and strength ofcurvilinearity within the EO-performance relation-ship and whether the influence of EO on small firm

2 It has been noted that the three investigated dimensions of EOare often observed to exhibit moderate to high correlations withone another in practice (Covin et al., 2006; Rauch et al., 2009).

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performance is ever harmful (i.e, produces belowzero returns) is still poorly understood. Drawingupon resource orchestration theory, we examineinternal organizational resource effects as a noveltheoretic lens in explaining the existence of dimin-ishing, even harmful, returns associated withincreasing levels of EO in small firms.

The results of previous research on the EO-performance relationship have been mixed in thecontext of small firms. While several studies haveevidenced a positive relationship (Smart and Conant,1994; Wiklund and Shepherd, 2005; Yamada andEshima, 2009), others have found only a marginallypositive relationship (Lee, Lee, and Pennings, 2001)or no effect of EO on firm performance (Baker andSinkula, 2009; Messersmith and Wales, 2013;Runyan, Droge, and Swinney, 2008; Stam andElfring, 2008) in this context. Yamada and Eshima(2009) observed EO to have a lagged effect on theperformance of firms with a mean size of 33 employ-ees. This correlates with Wiklund’s (1999) study,which investigated small firms over a three-yearperiod. Given varied support for the contention thatEO is beneficial to the performance of small firms, itis surprising that the potential for EO to harm per-formance in small firms has been a largely neglectedarea of inquiry. Our study seeks to contribute toresearch in this area by examining the potentiallycurvilinear nature of the relationship between EOand firm performance in small firms.

To understand curvilinearity in the small firmcontext, it is useful to distinguish between the mar-ginal benefits and costs associated with increases ina small firm’s level of EO. If the marginal costsincrease more quickly than the marginal benefits, theperformance-related returns derived from EO willdiminish and become negative. The primary mar-ginal benefits of pursuing higher levels of EO are anincrease in the discovery of new entry opportunities(Lumpkin and Dess, 1996) and enhanced motivationto exploit these opportunities (Wiklund andShepherd, 2003). The broader pool of new entryalternatives produced through higher EO are likelyto enhance a firm’s decision-making processes,thereby enabling the small firm to improve its overallcompetitive capability and ultimately secure a morefavorable strategic position when pursuing growth(Ireland, Covin, and Kuratko, 2009) and combating‘liabilities of smallness’ (Freeman et al., 1983). Still,the primary marginal cost of higher levels of EO insmall firms is a greater expenditure of limited firmresources (Covin and Slevin, 1991), which are

consumed in the process of experimenting with newentry possibilities (Wiklund and Shepherd, 2011).Firms with high EO tend to expend resources byembracing experimentation through new productdevelopment and new market entry (Covin andSlevin, 1989).

Given that a primary strategic goal of small firmsis the effective utilization of their relatively limitedresources, the question of curvilinearity in relation toEO-small firm performance is whether the potentialcosts of resource-intensive efforts to explore newentry opportunities outweigh the potential benefitsassociated with the discoveries they create. Webelieve that the answer to this question is contextual—dependent upon the firm’s ability to orchestrate itsvarious resources. Resource orchestration theoryposits that firms must develop proficiency at struc-turing, bundling, and leveraging their organizationalresources toward new opportunities (Sirmon et al.,2007; Sirmon et al., 2011). While a variety of stra-tegic activities can be utilized to enact each of theseprocesses (Sirmon et al., 2011), the conceptualframework of resource orchestration suggests thatorganizations—particularly those more prone tosuffer from resource-related liabilities—are depen-dent on their ability to efficiently and effectivelystructure, bundle, and leverage their resources.

Small firms, therefore, provide a particularly rel-evant context for exploring resource orchestrationeffects since these firms are frequently constrainedby ‘liabilities of smallness’ resulting from (1) theirlimited levels of slack resources and (2) potentialinefficiencies in using their resources (Stinchcombe,1965; Thornhill and Amit, 2003). As such, the abilityof small firms to orchestrate their resources is likelyto represent a primary driver in enhancing and/ordiminishing performance levels within these firms.In relation to EO, we contend that, ceteris paribus(i.e., holding constant all capabilities which mayinfluence the efficiency or effectiveness with which afirm orchestrates its resources), EO requires an esca-lating commitment of firm resources that small firmsuse to support new product market initiatives. Tobetter understand this effect, we now examine thebenefits and costs of EO in small firms at (1) low tomoderate levels of EO and (2) high levels of EO.

At low to moderate levels of EO, the associatedbenefits are likely to outweigh the associated costs insmall firms. Although low to moderate EO may notgenerate the maximum quality or quantity of newentry alternatives possible, by encouraging experi-mentation, or to be more general, variation within a

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firm’s value producing activities, it is likely thatsome of the innovations uncovered will exceed thefirm’s criteria for selection and be pursued aspossible sources of new value creation. Further,as EO drives new entry frequency (Lumpkin andDess, 1996), with low to moderate EO, the processesof search and selection (Helfat et al., 2007) resultin a firm pursuing a more focused number ofnew entry opportunities. Moreover, with lower EO,these new entries should occur through less riskyand more incremental innovations. As such, newentry opportunities stemming from low to moderatelevels of EO should be fewer in number, less specu-lative, less resource intensive, and, thus, more man-ageable for resource-constrained small firms toachieve the growth potential offered by their newentry efforts.

However, at high levels of EO, the marginal costsare likely to outweigh the marginal benefits in smallfirms for several reasons. To begin, the resourceconstraints within a small firm limit the firm’seffectiveness in pursuing many simultaneous expe-riments with new strategies to their conclusion orperforming large-scale research and development(Cooper, 1995). Yet, high EO implies an extensivereliance on large-scale, ‘bold strokes’ or ‘coura-geous moves’ within the firm (Mintzberg, 1973:45). At higher levels of EO, a firm is likely topursue a greater number of more dramatic (asopposed to incremental) innovations. Launchingmany new, and oftentimes radical, product/marketdevelopment efforts requires significant resourceinvestment, which may prove challenging to sustainor fully exploit given the additional tax each effortplaces upon a small firm’s already thinly stretchedresources. Further, firm flexibility to orchestrateresources is weakened as firm resources arestretched—a key competitive capability of smallfirms (Wiklund, Patzelt, and Shepherd, 2009). Athigh levels of EO in a small firm, resources oftenmust be withdrawn from ongoing initiatives tosupport further ‘bold strokes’ and new entries. Inmany cases, short time horizons and asset invest-ment specificity make such transitions unattainableor undesirable in the small firm context, as opposedto allowing ongoing initiatives the time necessary tomatriculate, produce returns, and ultimately provetheir full growth potential.

The efficiency with which a firm orchestrates itsresources is also likely to decrease at higher levels ofEO. Atuahene-Gima and Ko (2001: 56) note that ‘anunbridled entrepreneurship orientation may blind the

firm into the erroneous belief that technologicalsuperiority is a sufficient condition for new productsuccess.’ In a later study, Bhuian et al. (2005)observed that at higher levels of EO, firms begin touse market intelligence only selectively and sym-bolically to satisfy their entrepreneurial agendas.As such, increasing levels of EO may eventuallylead to the bundling of product market offeringswhich are principally driven by technologicalmyopia as opposed to consumer-oriented marketsignals. Resource orchestration efficiency declinesas new entry opportunities with lower probabilitiesof solving relevant consumer market needs arepursued. Taken together, the preceding argumentssuggest that high levels of EO are likely to havenegative performance ramifications for small firms.Therefore, we propose the following hypothesis:

Hypothesis 1: The relationship between EO andfirm performance is inverted U-shaped for smallfirms.

EO and information and communicationtechnology capability

Consistent with resource orchestration theory, theefficiency and effectiveness with which small firmsstructure, bundle, and leverage their resources mayalter the proposed inverted U-shaped relationshipbetween EO and firm performance. We posit thatinformation and communication technology (ICT)capability is essential in allowing small firms tomore efficiently and effectively orchestrate theirrelatively limited resources. The extent to which afirm utilizes its ICT capability represents a strategicasset by enhancing the firm’s business processes inthe pursuit of competitive advantage (Johannessenet al., 1999; Martin and Matlay, 2001). Consistentwith our focus on resource orchestration effects, weexamine a firm’s utilization of ICT to support itsbusiness processes, not merely its general possessionof ICT tools or infrastructure. Prior research sug-gests that a firm develops ICT capability for anumber of strategic applications (Johannessen et al.,1999). A firm utilizes ICT to increase its internalefficiency through enabling better informationaccess, strategic planning, cost saving, and employeedevelopment. ICT may also be employed to enhancefirm collaboration among employees and businesspartners. Moreover, ICT may be used to improveknowledge integration through enabling a better

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flow of information within and across firm bound-aries (Tippins and Sohi, 2003).

ICT capability is likely to affect the marginal costsand benefits of increasing levels of EO on perfor-mance in small firms. First, concerning the marginalcosts associated with increasing levels of EO, ICTcapability is likely to increase the efficiency withwhich resources are bundled and leveraged whenexperimenting with new entry opportunities. A highdegree of ICT capability has been shown to stream-line business processes and reduce the costs of pro-duction (Levy, Powell, and Yetton, 2001).Furthermore, small firms may employ ICT for awide variety of applications and processes leading togreater efficiency. At a basic level, ICT capabilitymay contribute to the efficiency with which firmresources are leveraged as organizations improvetheir document handling and asset tracking bymaking use of financial, inventory, and accountingapplications (Acar et al., 2005). Moreover, ICTcapability enables improved data management,virtual prototyping, and computer-aided design,which can accelerate the schedule and reduce thecosts of new product development (Thomke, VonHippel, and Franke, 1998).

ICT capability can also enable firms to effectivelyleverage a wider variety of business experiments,with much lower resource costs, than would be pos-sible in its absence. For instance, Thomke et al.(1998) observed that the integration of computersimulation within design processes allows a firm toenhance its innovation effectiveness by increasingthe frequency of its problem-solving cycles. Routinequality control functions become more expensiveand time consuming when the experiments must beconducted on physical prototypes as opposed to ICT-derived virtual models (Thomke, 1998). Further, thestrategic use of ICT represents a direct substitutionfor capital and labor as factors of production(Cordella, 2006). Accordingly, a firm can accom-plish greater resource leveraging without having toincrease its size to do so—a critical capability insmall firms. For instance, Thomke (1997) observedthat ICT-enabled projects using flexible technologiesoutperform projects using less flexible approachesby a factor of 2.2 person months. Thus, firm ICTcapability can enable higher tolerance for experi-mentation with design risks as demanded by higherlevels of EO. A firm making less use of its ICT willrequire significantly higher resource investments todeal with project design changes. Taken together, weargue that the marginal costs of EO in small firms are

lower as a result of the greater efficiency ICT capa-bility provides when bundling and leveraging firmresources.

Second, ICT capability serves to increase the mar-ginal benefits of EO in small firms by improving theefficiency of bundling efforts through the discoveryof higher quality new entry opportunities. ICTcapability plays an important role in facilitating inter-nal and external organizational social exchange(Huseman and Miles, 1988), which leads to informa-tion and resources. Simsek et al. (2009) observed thata firm’s information systems promote entrepreneurialalertness and, in turn, organizational entrepreneurialefforts. Sophisticated and increasingly inexpensiveand customizable ICT infrastructures, such as virtu-ally private intranets and extranets, serve as importanttechnological platforms for communication withinand outside the firm. These platforms provide a con-stant inflow and outflow of information to importantstakeholders (Venkatraman, 1994). Additionally,through these platforms, the scope of collaboration isextended as a small firm is better able to facilitate thelocation and integration of external informationalresources in a cost-efficient manner, regardless of itsphysical location (Nieto and Fernandez, 2006). ICTcapability also allows a firm to become more quicklyaware of relevant information in its external environ-ment and better utilize the information for strategicdecision making. In this way, ICT capability providesorganizational actors with additional information thathelps them generate novel insights concerning waysto better bundle their resources toward the pursuit ofnew entry opportunities.

Moreover, ICT capability helps heighten the effi-cacy of firm resource bundling efforts. This occurs asmanagers improve their decision-making processesand perspectives due to quicker access to customizedcritical market information and signals (Tippins andSohi, 2003). Such information exchange helpsengender the vital insights imperative when takingcalculated risks and proactively responding toemerging threats (Porter, 2001). As such, engender-ing a high level of ICT capability helps a firm aggre-gate and integrate the primary market informationnecessary to reduce the development of asymmetriesbetween the perceptions of organizational managersand the realities of markets when exhibiting EO.

In summary, ICT capability enables small firmsto simultaneously mitigate the marginal costs andenhance the marginal benefits associated withincreasing levels of EO by more effectively andefficiently enabling the bundling of firm resources

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into capabilities that can be leveraged with lessresource investment. Therefore, we hypothesize thefollowing:

Hypothesis 2: ICT capability positively moder-ates the curvilinear relationship between EO andsmall firm performance. Specifically, we expectthat increasing ICT capability increases the posi-tive effect of low levels of EO and reduces thenegative effect of high levels of EO.

EO and network capability

While ICT capability primarily affects resource bun-dling and leveraging, we posit that network capabil-ity allows small firms to better structure and bundletheir resources and reap further benefits from EO.Difficulty in accessing valuable external resourcesrepresents a significant factor that may prevent asmall firm from achieving growth at higher EO. Thedevelopment of strong exchange relationships iscritical in allowing a small firm that is high in EO toacquire valuable external resources (De Clercq,Dimov, and Thongpapanl, 2009; Stam and Elfring,2008). Such relationships act as a resource bufferagainst market changes and enable a small firm toundertake risky initiatives and proactively developinnovative products that would be much more chal-lenging to accomplish without collaboration (Blackand Boal, 1994). However, when a small firm devel-ops innovative products, it must build legitimacy forits new offerings (Aldrich and Foil, 1994). Due totheir size-related liabilities (Stinchcombe, 1965),these firms generally possess low levels of perceivedlegitimacy, making it more difficult to project them-selves as attractive partners. In effect, a small firmfinds it challenging to secure a strategic position in anetwork that provides opportunities for structuringand bundling critical resources (Baum, Calabrese,and Silverman, 2000; Stuart, 2000).

Kale et al. (2002) note that it is not enough simplyto have access to a network; it is also vital for a firmto successfully utilize and manage its networks. Afirm with heightened NC is able to improve itsoverall position and develop a superior ability tomanage key relationships (Ritter and Gemünden,2003; 2004). Network capability encompasses afirm’s ability to make use of interorganizational rela-tionships to gain access to various resources held byother actors (Kale et al., 2002). Although previouslyexplored by Walter, Auer, and Ritter (2006), webelieve that a firm’s NC plays an essential role in

understanding nonlinearity (as opposed to linearity)in the performance of small firms. This may helpexplain why a nonsignificant main effect of EO onperformance was observed by Walter et al. (2006)and in many similar studies of small firms (mostnotably, Stam and Elfring, 2008). Therefore, we seekto extend the work of Walter et al. (2006) by theo-rizing that NC moderates the curvilinear relationshipby enhancing a small firm’s ability to structure andbundle resources, thereby altering the shape of theEO-small firm performance relationship.

According to Walter et al. (2006), a firm’s NC isreflected through several dimensions: (1) the firm’scoordination activities with collaborating firms;(2) the firm’s relational skills to facilitate interper-sonal exchange; (3) its partner knowledge, i.e., pos-sessing organized and structured information aboutcollaborating firms; and (4) the firm’s internal com-munication to assist in the transfer of organizationalknowledge between collaborators. Taken together,these mutually reinforcing elements play an impor-tant role in impacting the marginal costs and benefitsassociated with increasing levels of EO on perfor-mance in small firms.

First, NC increases the efficiency and effective-ness of resource structuring within small firms byfacilitating access to a larger resource base. NC mayhelp enable a small firm to better develop andmanage the boundary-spanning relationships neces-sary to acquire much-needed resources (Ritter andGemünden, 2004). Higher NC improves the progressof resource sharing and structuring by enabling afirm to build stronger and closer relationships withits partners (Sorenson, Folker, and Brigham, 2008).Greater partner knowledge sharing serves to reducefurther information asymmetry among trading part-ners. This is important for avoiding opportunism incollaborative relationships designed to support afirm’s innovation projects and initiatives (Dickson,Weaver, and Hoy, 2006). In turn, this also servesto increase the efficiency of resource structuringthrough a reduction in transaction costs (Williamson,1985), thereby further accommodating the marginalcosts of EO. Therefore, a small firm with a high NCcan more advantageously gain access to externalresources to support its EO and achieve performance(Stam and Elfring, 2008; Stuart, 2000).

Second, NC increases the marginal benefits of EOin small firms by improving the discovery of higherquality new entry opportunities. EO drives the devel-opment of new routines, competences, and technolo-gies, the value of which are enhanced through

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bridging ties with different and new network part-ners from other industries (Stam and Elfring, 2008).Establishing a diverse set of bridging ties enables asmall firm to engender a higher potential EO byallowing the firm to access information corridorswith which the small firm would not normally havecontact (Hayek, 1945). With higher NC, a firm isable to more effectively make decisions concerningthe bundling of its limited resources to exploit inno-vation strategies through the use of increased infor-mation concerning customers’ needs and suppliers’availabilities gained from better relationshipsthroughout a firm’s value chain (Porter, 2001).Further, NC enables access to relation-specificresources that are not easily transferable and, thus,serve as a potentially powerful source of competitiveadvantage (Dyer and Hatch, 2006).

NC further facilitates higher quality new entrydiscoveries by enabling a firm to better manageinterpersonal exchanges critical to effective interfirmresource and knowledge sharing (Simsek andHeavey, 2011). With higher NC, a small firm candevelop stronger exchange relationships that allowfor more situation-specific resource structuring withpartners residing within the value chain. This capa-bility provides a basis for more proactive andsolution-oriented approaches to conflict manage-ment with partners. In doing so, NC facilitates theformation of trust necessary for encouraging effec-tive innovation in collaborative relationships(Nielsen and Nielsen, 2009) and, by extension, thediscovery of how firm resources may be bundled intoproductive capabilities. Moreover, greater trustmakes a firm less likely to be concerned with theopportunity for ‘leakage’ of its intellectual assets tohorizontal competitors when vertically sharing withupstream supplier partners or downstream consumerpartners (Li, 2002). These various factors serve toincrease the ability of the focal firm to productivelybundle knowledge-based and information-relatedresources acquired from its partners.

In sum, possessing well-developed relationalskills and the ability to coordinate partners into sup-portive interactions allows small firms to more effec-tively enhance their competitive positions. Thecapability to develop relationships and facilitateresource exchange between network partners allowssmall firms to gain access to additional resources thatcan be structured and bundled in order to help miti-gate the marginal costs and enhance the marginalbenefits associated with increasing levels of EO. Assuch, we offer the following hypothesis:

Hypothesis 3: Network capability positively mod-erates the curvilinear relationship between EOand small firm performance. Specifically, weexpect that increasing network capabilityincreases the positive effect of low levels of EOand reduces the negative effect of high levels ofEO.

METHODS

For this study, we collected data from two differentsources—perceptual data from a respondent surveyand objective performance data from secondarysources. Consistent with previous research (Covinand Slevin, 1989; Miller, 1983) and to capture aholistic view of firm operations, we employed akey decision maker framework by utilizing subjec-tive reports from CEOs to measure entrepreneurialorientation, NC, and ICT capability. Survey datawere gathered via a mail survey of Swedishtechnology-based small firms. The sampled firmswere drawn from technology-based industries inthe following Swedish industry index code: SNIcode 72,220.

The sampling context provided several advan-tages. First, focusing on a single industry limitedpotential confounding effects due to industry factorsin facilitating or debilitating returns from EO.Second, by sampling high-technology firms, weincreased the likelihood of identifying some firmswithin our sample that were able to achieve highlevels of growth, which is sometimes more difficultfor small firms (Davidsson, 2005). Third, while amajority of studies on EO have utilized perceivedfinancial performance as an outcome variable(Rauch et al., 2009), the Swedish governmentrequires firms—irrespective of size and age—toreport financial performance certified by a charteredaccountant. In most other countries, objectiveperformance data from small private firms isunavailable.

The sampled firms were located by querying theSwedish business database Affärsdata.

We applied two filters: (1) firms with 50 or feweremployees; and (2) firms with one million SwedishSEK (approximately €100,000) or more in sales asan indicator that the firm is actively engaged in busi-ness operations. Based on these filters, we were leftwith 3,737 active firms. Stratification was based onfirm size, measured as the number of employees. Thesampled firms were stratified based on five firm-size

100 W. J. Wales et al.

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

brackets: one employee (885 firms), two employees(983 firms), three to five employees (473 firms), sixto nine employees (698 firms), and 10 to 49 employ-ees (698 firms). In order to balance survey cost andscope, while accounting for a required sample size atthe statistical power of 0.80 and in light of traditionalsurvey response rates in Sweden, a random sampleof 1,471 firms was selected.

A self-administrated questionnaire was developedto test the three research hypotheses. To enhanceexternal validity, the questionnaire was checked forany problems or irregularities through pretestingwith several chief executive officers (CEOs) of smallfirms in a similar industry. The questionnaires weremailed from May to July 2007. The questionnairewas addressed to the CEO of the firm, and wasaccompanied by a descriptive letter explaining thepurpose of the study. From the sample of 1,471firms, 93 questionnaires were removed since theydid not meet both of our aforementioned samplingcriteria (50 or fewer employees and one million ormore Swedish SEK in sales). Furthermore, six ques-tionnaires did not reach the identified firms and itwas not possible to contact these firms. This reducedthe sample size to 1,372 firms. We received 306replies to our survey. Of these responses, four werefilled in incorrectly, one was a duplicate, and 10came from firms where the CEO addressed anotherentity than the one targeted (for example, a group offirms instead of a single firm). Thus, the usablenumber of questionnaires was reduced to 291, ofwhich 117 were technology service firms and theremaining 174 were technology manufacturingfirms. This provided an effective response rate of 21percent. A nonresponse analysis was performed bycomparing pertinent variables for all firms, such asthe firm’s age, size, sales, sales per employee,profitability, and solidity (i.e., the degree of inter-nally funded capital). The analysis showed nosignificant differences between respondents andnonrespondents.

Finally, to avoid potential issues related tocommon method bias, we collected objective firmperformance data from business registers. We col-lected secondary data on the performance variables,such as sales growth, operating profit growth, andreturn on assets, from the Swedish business databaseAffärsdata. We collected three-year prospective per-formance (2007 to 2009) data on the 291 firms basedon their unique organization codes from the initialsampling frame. Of the 291 firms, 33 firms failedfrom 2007 to 2009. Thus, our final sample consists

of 258 firms.3 The dependent variable is measuredfrom archival information on firm performance from2007 to 2009. The three independent variables arebased on survey responses from 2007.

Independent variable

EO was measured using a nine-item scale developedby Miller (1983) and Covin and Slevin (1989). ThisEO measure is well established in the literature(Rauch et al., 2009). The scale measures threedimensions of EO: innovativeness, risk taking, andproactiveness. Each item was measured using aseven-point scale ranging from ‘1—strongly dis-agree’ to ‘7—strongly agree.’ The results of anexploratory factor analysis (EFA) of EO showed noirregularities (see Appendix Table A2), and the scaleachieved satisfactory levels of construct reliability(a = 0.85). Moreover, the average variance extracted(AVE) by the EO measure was 0.68, which is abovethe recommended threshold level of 0.50 suggestedby Bagozzi and Yi (1988).

Moderating variables

Information and communicationtechnology capability

Based on Johannessen et al. (1999), we identified 13strategic activities or uses of ICT in technology-based small firms. These activities were furtherrefined based on relevance using a case studyapproach. The case study involved three technology-based small firms and the interviews were conductedwith the owner/manager or the equivalent withresponsibility for ICT in the firm. The intervieweeswere asked to explain in detail what they viewed asthe strategic employment of ICT for their firms andfor other small firms in their industry. We were ableto recognize 10 strategic activities or uses of ICT fortechnology-based small firms, which together repre-sent ICT capability. The next step involved employ-ing EFA for data reduction and exploring theoretical

3 To assess whether exclusion of failed firms had a significanteffect on our results, we included an inverse-Mill’s ratio basedon firm age, firm size, firm sector, firm sales in 2007, environ-mental dynamism, environmental hostility, labor productivitygrowth, EO, network capability, and ICT capability. Theinverse-Mill’s ratio in the main regression was statisticallyinsignificant (b = 0.052, p = 0.348). Furthermore, t-tests on theaforementioned variables used to calculate the inverse-Mill’sratio did not show significant differences between surviving andfailed firms.

Nonlinear Effects of EO on Small Firm Performance 101

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

structures (see Table A2). The results from theanalysis indicated three underlying dimensions ofthe ICT capability construct—i.e., ICT internal use,ICT collaboration, and ICT communication—withloadings ranging from 0.73 to 0.89.

A four-item scale was used to measure the internaluse of ICT (a = 0.82), which refers to those activitiesthat are closely related to achieving internal effi-ciency through accessing information, strategicplanning, cost saving, and developing employees’skills. ICT collaboration (a = 0.84) was measuredbased on a three-item scale reflecting the role of ICTin maintaining and establishing new relationshipswith different actors. Finally, ICT communication(a = 0.79) was measured using a three-item scalereflecting the role of ICT in enabling a better flow ofinformation inside and outside the firm. Each of the10 items was measured using a seven-point scaleranging from ‘1—not at all’ to ‘7—to a large extent.’EFA revealed the three dimensions of ICT capabilityto load on a single factor with reasonably high load-ings (Table A2). Moreover, the Cronbach alpha forICT capability was 0.79 and the AVE was 0.61,which are both above the recommended levels.

Network capability

Network capability was measured using the scaleproposed by Walter et al. (2006). The scale concep-tualizes NC as a higher-order construct that is relatedto four dimensions (coordination activities, relation-ship skills, partner knowledge, and internal commu-nication). Three-item scales were used to measurecoordination activities (a = 0.81), relationship skills(a = 0.93), partner knowledge (a = 0.86), and inter-nal communication (a = 0.84). Each item was mea-sured using a seven-point scale ranging from‘1—strongly disagree’ to ‘7—strongly agree.’ EFArevealed the four NC dimensions to load on a singlefactor with reasonably high loadings, ranging from0.75 to 0.94. Moreover, the Cronbach alpha for NCwas 0.83 and the AVE was 0.66, which are bothabove recommended levels.

Dependent variables

As widely acknowledged in the literature (e.g.,Davidsson, Delmar, and Wiklund, 2008), firmgrowth is a key performance indicator for smallfirms. Additionally, growth has been described as aprincipal performance outcome of relevance in rela-tion to EO (Lumpkin and Dess, 1996). Research

suggests that the effects of EO on firm performancemay take several years to manifest (Yamada andEshima, 2009; Zahra and Covin, 1995). Given theprevalence of cross-sectional models in previous EOresearch, more research is needed assessing theimpact of EO on firm performance over time (Rauchet al., 2009). Thus, a focus on the effects of EO ongrowth following the measurement of EO is impor-tant to the internal validity of the study findings. Inaddition, others have proposed firm growth as a mul-tidimensional measure with potential trade-offsbetween different indicators of growth (Baum et al.,2000) and, therefore, assessing growth on multipledimensions may lead to more robust conclusions.Drawing on the performance measurement criterianecessary for capturing the effects of EO in thecontext of small firms, we collected secondary datafor three prospective annual compounded growthrates: sales, operating profit, and return on asset over2007, 2008, and 2009.

Operating profit is measured as sales minus costof goods sold, and return on assets is the ratio of netprofit divided by total assets. Compounded growth ismeasured as:

Compounded Growth

Performance in

Performance in=

⎛⎝⎜

⎞⎠⎟

2009

2007

1

33

1years

⎛⎝⎜

⎞⎠⎟

−

For example, if sales grew from 200,000 SEK to400,000 SEK from 2007 to 2009, then the com-pounded annual growth rate over three years is 25.99percent.

Controls

Several control variables were utilized in the model.Firm age and firm size were obtained from Affärs-data. We use and report the mean values of firm ageand firm size over three years (2007, 2008, and2009). As small firms age, they develop moreadequate routines and processes and acquire moreresources (Kazanjian and Drazin, 1989). Mitigatingliabilities of newness enhances firm performanceover time. Firm size is also an important contributorto firm growth. Larger firms have more resourcesand market power to leverage and, hence, are able toincrease their performance. The environment of thefirm also plays an important role in altering theeffectiveness of EO (Lumpkin and Dess, 1996) and

102 W. J. Wales et al.

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

firm networks (Stam and Elfring, 2008). Based onsurvey responses in 2007, we measured environmen-tal hostility and environmental dynamism using self-reported scales taken from Miller and Friesen(1982). Small firms in dynamic environments aremore likely to engage in entrepreneurial activities.Additionally, environmental hostility affects col-laboration intensity and entrepreneurial behavior(Covin and Slevin, 1989).

Labor productivity growth may be an importantcontributor to small firm growth due to high-performance work practices. Based on past researchin the human resources literature, we operationalizedlabor productivity as the ratio of natural log of salesto natural log of number of employees (Datta,Guthrie, and Wright, 2005). To ensure that welimited the effects of other firm-specific factors thatcould contribute to small firm growth, we controlledfor labor productivity growth over a three-yearperiod (2007, 2008, and 2009) using data fromAffärsdata. While EO focuses on enhancing innova-tive practices, labor productivity focuses on embed-ded processes that are a result of efficiency-orientedefforts. An entrepreneur’s equity stake is also anindicator of the effort they may expend in the firm.We calculated the average equity stake of the ownersreported in Affärsdata from 2007, 2008, 2009.

A broader geographical focus could lead toeconomies of scope for increasing sales and leverag-ing existing resources to enhance profit and assetperformance. Based on survey responses in 2007,we created an index of self-reported measures foreach of the geographical markets—local, regional,national, and international—that a small firm mightoperate in. If the firm operated in any of these fourgeographical markets, we assigned a score of ‘1’ foreach respective market. Otherwise, the firm wasassigned a score of ‘0’ for that particular market.Given that each firm had to operate in at least onemarket, the maximum score on this index was ‘4’and the minimum score was ‘1.’ Additionally,diversity in firm exchange activities, such as manu-facturing, services, and trading, could explain per-formance differences. Small firms operating innumerous value-added activities could create econo-mies of scope for internal resources and processes.We created a market focus index assigning a score of‘1’ if a venture operated in one of the following areasof value-adding activities: manufacturing, services,and/or trading. Since firms could operate in multipleareas, the maximum score for this measure was ‘3’and the minimum score was ‘1.’

To mitigate the effects of past performance, wecontrolled for prior growth rate during the years2004, 2005, and 2006. We used the mean com-pounded growth rates of sales, operating profit, andreturn on assets for the three years prior to the surveycollection. Controlling for past performance iscentral to prospectively assessing direct and modera-tion effects. This design feature, although it does notimply causality, helps identify unique effects over2007, 2008, and 2009. Finally, our sample consistsof technology firms in both the service sector (= 0)and manufacturing sector (= 1). We controlled forsector membership using dummy variables. Infor-mation on sector membership was drawn fromreported SIC codes in Affärsdata during 2007.

Additional validity tests

To begin, we estimated individual measurementmodels. Table A2 (Appendix A) reports the results ofour exploratory factor analysis (EFA). In Table A1,we show item loadings for the scales. In the nextstep, we conducted a second-order factor analysis foreach of the variables to assess whether the subcon-structs could be combined into one construct. Asshown in Table A3, we found support for combiningall subconstructs into their respective second-orderconstructs.

Drawing on Anderson and Gerbing (1988), wetested the discriminant validity between EO, NC,and ICT capability. We used discriminant analyses tocompare the two-factor model with the one-factormodel. While the independent variables were com-bined into a second-order model, we conducted ameasurement model with second-order factorsamong the three constructs. Due to space limitations,we did not list all the changes in the chi-squarevalues. However, the lowest chi-square differencewas between ICT (Dc2 = 5.307; p < 0.001).

In order to perform Harman’s one-factor analysis,we entered all of the self-reported variables into afactor analysis. Six factors (with eigenvalues > 1.0)accounted for 81.39 percent of the variance and thefirst factor accounted for 19.78 percent of the vari-ance. Next, a common method factor introduced tocontrol for any influence from a method bias was notsignificant (R2 explained = 1.30 percent) (Podsakoffet al., 2003). The highest variance inflation factor(VIF) was 3.7, which is well below the acceptedtolerance of 4.0 (O’Brien, 2007). While commonmethod bias and multicollinearity cannot be fully

Nonlinear Effects of EO on Small Firm Performance 103

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

ruled out, these tests suggest no significant presenceof common methods bias or multicollinearity.

RESULTS

The variables used in the current model are a mix ofcontinuous measures (e.g., growth rates) and surveyresponses based on interval data. As Pearson corre-lations require continuous data, we use Spearman’srank order correlation coefficients. Furthermore, assmall firms exhibit skewed performance outcomes,Pearson correlations are less reliable as they areinfluenced by outliers, unequal variances, non-normality, and nonlinearity (Young et al., 1978). TheSpearman rank order correlations and descrip-tive statistics are listed in Table 1. Preliminary evi-dence in the correlation table suggests support forthe direction of the hypothesized relationships.Although the correlations among the growth mea-sures are significant, they indicate low to mediumeffect sizes. In their review of the venture growthconcept, Gilbert, McDougall, and Audretsch (2006)suggest that due to liabilities of newness and small-ness, ventures tend to experience varying growthrates along different dimensions of growth. Thus,lower to medium effect sizes in correlations amongthe various growth dimensions in the present studyare to be expected. Compared to their older andlarger counterparts, small firms may not grow at anequal pace (i.e., strong correlations) on multiplegrowth dimensions.

To test our hypotheses, we used moderated hier-archical regression for each of the three performancevariables. Based on Aiken and West (1991), wemean centered EO, ICT capability, and NC. Weemployed three models for each performancevariable—a direct effects model, a nonlinear model,and a moderation model. To assess model signifi-cance, we tested differences adjusted-R2 values. Asshown in Table 2, each additional model signifi-cantly added to the explanatory power.

Effect size and prognostic tests

Before conducting our main analysis with OLSregression, we assessed the relevance of the pro-posed relationships using effect size estimates andprognostic tests. Assessing changes in effect sizesupon removal of a direct and moderation effect helpsassess the relative importance of individual effects.

As shown in Table 2, we report Cohen’s effect size(f 2) for direct and moderation effects. The effect sizef 2 indicates whether an exogenous latent variablemakes a large (f 2 > = 0.35), medium (0.35 < f 2 = <0.15), or weak (0.02 < f 2 < 0.15) contribution towardexplaining the variance of an endogenous variable—venture growth. Relative effect size importance iscalculated by comparing the coefficient of determi-nation of an endogenous variable, accounting for theexogenous variable ( Rincl

2 ) and not accounting forthis exogenous variable ( Rexcl

2 ). As shown in Table 2,the direct and moderation effects show medium(lowest effect size = 0.21) to large effect sizes(highest effect size = 0.59) and, hence, are importantin the overall model. Additionally, we calculateeffect sizes based on prognostic relevance. TheStone-Geisser test (Q2), calculated using a blindfold-ing algorithm, shows the change in Stone–Geisser Q2

when a latent exogenous variable is removed fromthe model (Tenenhaus et al., 2005). The inferenceswere similar to Cohen’s effect size (f 2).

Hierarchical OLS regression

Hypothesis 1 predicted an inverse U-shaped rela-tionship between EO and small firm performance.Traditionally, organizational research has examinedinverted U-shaped hypotheses using significancelevels for the squared term of the variable of interest.Specifically, if the squared term is negative andsignificant, the effect of a variable is consideredto exhibit an inverse U-shaped relationship. Theresults of the hierarchical OLS regression arereported in Table 2. We found support for an inverseU-shaped relationship (EO-squared): sales growth:b = -0.13, p < 0.05, DAdjusted-R2 = 0.04; operatingprofit growth: b = -0.12, p < 0.05, DAdjusted-R2 =0.04; return on assets growth: b = -0.18, p < 0.05,DAdjusted-R2 = 0.05).

Hypothesis 2 proposed that ICT capability posi-tively moderates the curvilinear relationshipbetween EO and small firm performance. We foundsupport for the moderation effect of ICT capabilityon the nonlinear effects of EO (sales growth:b = 0.06; p < 0.05; operating profit growth: b = 0.07;p < 0.05; return on assets growth: b = 0.07; p <0.05). Hypothesis 3 proposed that NC positivelymoderates the curvilinear relationship between EOand small firm performance. We also found supportfor the moderation effect of NC on the nonlineareffects of EO (sales growth: b = 0.09; p < 0.05; oper-ating profit growth: b = 0.11; p < 0.05; return on

104 W. J. Wales et al.

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

Tabl

e1.

Spea

rman

’sra

nkor

der

corr

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mat

rix

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0.10

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07∧

Nonlinear Effects of EO on Small Firm Performance 105

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

Tabl

e1.

(Con

tinu

ed)

1314

1516

1718

1920

2122

23

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vativ

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0.20

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.22*

*0.

11*

0.14

*0.

18**

0.33

***

0.16

**0.

13*

0.25

***

0.28

***

Not

es:

N=

258;

�p

<0.

10,*

p<

0.05

,**p

<0.

01,*

**p

<0.

001.

106 W. J. Wales et al.

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

Tabl

e2.

Step

wis

em

oder

ated

OL

Sre

gres

sion

for

sale

sgr

owth

Sale

sgr

owth

Ope

ratin

gpr

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grow

thR

etur

non

asse

tsgr

owth

Mod

el1:

dire

ctef

fect

sM

odel

2:no

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ear

effe

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el3:

mod

erat

ion

effe

cts

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ect

size

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ohen

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2 )4M

odel

1:di

rect

effe

cts

Mod

el2:

nonl

inea

ref

fect

s

Mod

el3:

mod

erat

ion

effe

cts

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ect

size

—C

ohen

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2 )M

odel

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rect

effe

cts

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el2:

nonl

inea

ref

fect

s

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el3:

mod

erat

ion

effe

cts

Eff

ect

size

—C

ohen

’s(f

2 )

Inte

rcep

t0.

19*

0.19

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18*

0.11

*0.

10*

0.09

*0.

11*

0.11

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09*

Con

trol

sFi

rmag

e0.

060.

040.

030.

080.

060.

050.

10*

0.08

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rmsi

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22*

0.20

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19*

0.23

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22*

0.19

*0.

20*

0.20

*0.

18*

Env

iron

men

tal

dyna

mis

m-0

.16*

-0.1

4*-0

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-0.0

9*-0

.14*

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Env

iron

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tal

host

ility

-0.1

7*-0

.16*

-0.1

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-0.0

9*-0

.08

-0.0

9*-0

.08*

-0.0

6L

abor

prod

uctiv

itygr

owth

0.23

*0.

22*

0.19

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20*

0.21

*0.

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0.13

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0.15

*

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ityst

ake

0.16

*0.

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0.16

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0.12

*0.

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*0.

12*

Geo

grap

hica

lfo

cus

0.07

0.02

0.01

0.05

0.04

0.03

0.03

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0.02

Mar

ket

sect

or0.

020.

020.

020.

030.

030.

020.

050.

040.

03Pa

stpe

rfor

man

ce0.

50**

*0.

50**

*0.

43**

*0.

42**

*0.

37**

*0.

36**

*0.

32**

*0.

31**

*0.

30**

*Te

chno

logy

man

ufac

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ctor

(=1)

0.03

0.03

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0.01

0.03

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Dir

ect

effe

cts

Ent

repr

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rial

orie

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ion

0.34

***

0.33

***

0.32

***

0.59

0.32

***

0.32

***

0.32

***

0.51

0.37

***

0.37

***

0.38

***

0.59

Net

wor

kca

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lity

0.23

*0.

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0.21

*0.

370.

25**

0.25

**0.

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0.42

0.31

***

0.18

*0.

17*

0.46

ICT

capa

bilit

y0.

15*

0.15

*0.

12*

0.33

0.19

*0.

18*

0.16

*0.

360.

11*

0.17

*0.

17*

0.37

Non

line

aref

fect

sE

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e(H

1)—

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.13*

0.21

—-0

.12*

-0.1

0*0.

22—

-0.1

8*-0

.15*

0.24

Mod

erat

ion

effe

cts

EO

¥ne

twor

kca

pabi

lity

——

0.12

*0.

36—

—0.

13*

0.35

——

0.14

*0.

35

EO

¥IC

Tca

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lity

——

0.08

∧0.

25—

—0.

00*

0.31

——

0.12

∧0.

31E

O-s

quar

e¥

ICT

capa

bilit

y(H

2)—

—0.

06*

0.29

——

0.07

*0.

30—

—0.

07*

0.36

EO

-squ

are

¥ne

twor

kca

pabi

lity

(H3)

——

0.09

*0.

27—

—0.

11*

0.28

——

0.11

*0.

26

Adj

uste

d-R

20.

150.

190.

290.

150.

190.

280.

130.

180.

24DA

djus

ted-

R2

0.04

(1)*

**0.

10(4

)***

0.04

(1)*

**0.

09(4

)***

0.05

(1)*

**0.

06(4

)***

Sam

ple

size

requ

ired

for

pow

er=

0.95

;a

=0.

05

176

136

106

185

165

130

184

156

131

Not

es:

N=

258;

�p

<0.

10,*

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0.05

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<0.

01,*

**p

<0.

001;

unst

anda

rdiz

edre

gres

sion

coef

ficie

nts

(tw

o-ta

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test

s).

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tsi

zew

asco

nsis

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prog

nost

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leva

nce

(q2 );

resu

ltsav

aila

ble

from

auth

ors.

Nonlinear Effects of EO on Small Firm Performance 107

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

assets growth: b = 0.11; p < 0.05). Thus, we foundsupport for all three hypotheses.

Robustness tests for invertedU-shaped relationships

Blanchflower (2007) and Lind and Mehlum (2009)propose a series of tests to more robustly inferinverted U-shaped relationships. Without these tests,it is difficult to ascertain whether the extreme point(or the inflection point) is within the bounds of thedata and whether a higher order function (cubic, or ahigher power function) is present in the data. Thetests begin with a Wald test to assess whether thelinear and squared terms are jointly significant.Unless listed otherwise, Wald tests are based on esti-mates of mean-centered variables used in the regres-sion. Next, the higher and lower values are identifiedfrom the data. In the current data, the low and highvalues of EO were 1.153 and 6.537, respectively.The testing setup is shown in Table B1 and Fig-ure B1 in Appendix B. In the next step, the directionsof the slopes at low and high values of EO wereestimated. If the slope at the low value of EO ispositive and if the slope at the high value of EO isnegative, then preliminary evidence of an invertedU-shaped relationship is present. It is necessary totest slopes at these bounds to ensure that the invertedU-shaped relationship is representative of the dataand not a statistical artifact.

With the preliminary evidence suggesting thepresence of an inverted U-shaped relationship, theSasabuchi test (based on a likelihood ratio test) wasused as an intersection union test that assesseswhether: (1) the effect of EO on growth is increasingat low values of EO and (2) the effect of EO ongrowth is decreasing at high values of EO. Signifi-cant values indicate the presence of an invertedU-shaped relationship. To further assess if theextreme point is within the upper and lower boundsof EO, Lind and Mehlum (2009) propose the Fiellerand Delta approaches to estimating confidence inter-vals around the extreme points. If the confidenceintervals are within the bounds of the low and highvalues of EO, then it is further evidence of the pres-ence of inverted U-shaped relationship in the data.

Table 3 shows the results of the tests for EO andinverted U-shaped curves for both high and lowlevels of the moderators. The inverted U-shaped rela-tionship was significant for all three types of growthoutcomes and the moderation effects. Moreover, inline with our moderation hypotheses, NC and ICT

capability are observed to increase the value of EOthat maximizes small firm performance.

Interaction effects

Drawing upon the recommendations of Cohen et al.(2003) regarding the plotting of nonlinear modera-tion effects, we graphically depicted the moderatedrelationships in Figure 1. We explain Cohen et al.’s(2003) nonlinear moderation effects in the context ofNC. The equations could similarly be extended to themoderating effect of ICT capability.

Firm Performance EO EO NCEO NC EO NC

= + + + +× + ×

β β β ββ β

0 1 22

3

4 52

(1)

As stated earlier, we mean centered the values ofEO, ICT capability, and NC. The three curves forhigh, average, and low values of NC are plotted bysubstituting high (+1 s.d.), average (mean), and low(-1 s.d.) values of NC in Equation 1. The figuressuggest that higher levels of ICT capability or NCaffect the optimal levels and performance returnsfrom EO. Alternatively, at lower levels of these capa-bilities, the effect of EO on performance is consis-tently harmful across all dependent variables andfollows a negative path to below zero returns in firmperformance.

Conditional moderation effects

Recently, Brambor, Clark, and Golder (2006),Preacher, Curran, and Bauer (2006), and others havequestioned whether the conditional effects of a mod-erator at +1 s.d. and -1 s.d. are sufficient to inferconditional effects at all values of a moderator. Inother words, traditional interaction plots depict con-ditional effects at high and low values of the mod-erator. However, it is also of interest to demonstratethe magnitude, direction, and significance of themoderator over its range. In the current context, con-ditional effects of ICT capability and NC couldprovide a more robust assessment of its effects onventure growth.

The conditional effects of NC over a range ofvalues of EO from Equation 1 is given by taking thefirst-order derivative with respect to EO:

∂∂

= +( ) + × +( ) + ( )

( )

( )

Firm Performance

EO

NC NC EO NCβ β β β β1 4 2 5 32

108 W. J. Wales et al.

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

Table 3. Test of an inversely U-shaped relationship between EO and performance

Table 3(a). Test of inverse-U relationship between EO and sales growth—direct and conditional effects

Sales growth

Maineffects

High ICTcapability

Low ICTcapability

High networkcapability

Low networkcapability

Test of joint significance of EOvariables [EO and EO-squared];(p-value)a (H0: bEO = bEO-square = 0)

0.023 0.004 0.009 0.013 0.014

Slope at EOlow 0.123** 0.215* 0.064* 0.217* 0.024*Slope at EOhigh -0.132*** -0.184* -0.063* -0.207* -0.022*Sasabuchi-test of inverse U-shape in

EO (p-value)b0.013 0.020 0.016 0.023 0.017

Estimated extreme point 2.663 4.615 2.080 5.620 1.21495% confidence interval—Fieller

methodLow 1.471 2.641 1.357 3.635 1.280High 3.273 5.629 2.897 6.055 2.657

95% confidence interval—Deltamethod

Low 1.533 2.671 1.350 3.703 1.240High 3.139 5.666 2.270 5.238 2.683

Test of joint significance of controlvariables (p-value)

0.025 0.026 0.020 0.016 0.026

Test of joint significance ICT-usagedirect and moderation effects(p-value)

0.026 0.021

Test of joint significance networkcapability direct and moderationeffects (p-value)

0.025 0.026

Test of joint significance of allvariables in the model

0.024 0.024 0.021 0.021 0.022

Table 3(b). Test of inverse-U relationship between EO and operating profit growth—direct and conditional effects

Operating profit growth

Maineffects

High ICTcapability

Low ICTcapability

High networkcapability

Low networkcapability

Test of joint significance of EOvariables [EO and EO-squared](p-value); (H0: bEO = bEO-square = 0)

0.020 0.024 0.026 0.022 0.025

Slope at EOlow 0.117*** 0.180* 0.065* 0.209* 0.021*Slope at EOhigh -0.138** -0.163* -0.057* -0.168* -0.082*Sasabuchi-test of inverse U-shape in EO

(p-value)0.024 0.017 0.019 0.024 0.014

Estimated extreme point 2.236 3.819 2.119 4.821 1.84795% confidence interval—Fieller

methodLow 1.263 2.678 1.402 3.678 1.276High 3.841 4.851 2.843 6.021 2.623

95% confidence interval—Delta method Low 2.077 2.673 1.328 3.680 1.327High 3.678 4.256 2.839 5.237 2.542

Test of joint significance of controlvariables (p-value)

0.014 0.026 0.027 0.017 0.027

Test of joint significance ICT-usagedirect and moderation effects(p-value)

0.017 0.016

Test of joint significance networkcapability direct and moderationeffects (p-value)

0.025 0.018

Test of joint significance of all variablesin the model

0.019 0.018 0.025 0.026 0.019

Nonlinear Effects of EO on Small Firm Performance 109

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

To calculate the standard errors, we use the varianceof the conditional effects as

ˆ ˆ ˆ ˆ

ˆ ˆ

σ β β β

β β

∂∂

= ( ) + +( ) +

( ) +

y

x

var EO var NC

NC var EO NC

21

22 5

24

2 25

4

4 (( ) +

( ) + ( ) + ⋅

( ) + ⋅

4 2 4

4

1 2 1 4

2 4

EOcov NCcov EO

NCcov EO NC

ˆ ˆ ˆ ˆ

ˆ ˆ

β β β β

β β ccov

EO NCcov EO NC cov

ˆ ˆ

ˆ ˆ ˆ ˆ

β β

β β β β1 5

22 5

24 58 4

( ) +

⋅ ( ) + ⋅ ( )A similar approach is used for the equation

pertaining to the conditional effects of ICT capabil-ity. Drawing on the Johnson-Neyman technique,Preacher et al. (2006) recommend using bootstrapsamples to measure the conditional effects. Based ontheir recommendation, conditional effects based on1,000 bootstrap samples are plotted in Figure 2. Notethat the presented figures are along the ‘fit’ lineor along the diagonal cross-section of a three-dimensional figure with EO on the X-axis, the

moderator on the Y-axis, and ∂∂

( )

( )

Firm Performance

EOon the Z-axis. The plots in Figure 2 can be inter-preted as follows: the X-axis indicates how aone unit increase in EO and a one unit increasein NC (or ICT capability) affects marginal perfor-mance on the Y-axis. An inverted-U shaped relation-ship along the ‘fit’ line (a unitary increase in EO andnetwork/ICT capability) indicates that the jointeffects increase up to a certain point and declineafterward. The conditional effects plot further con-firms the inverted U-shaped relationship over arange of values of EO. As shown in Figure 2, thezone of significance starts from the dashed line andcontinues onward toward the right. At lower valuesof the unitary increase in EO and NC (or ICT capa-bility), the nonlinear effects are not significant.However, as EO and NC (or ICT capability) jointlyincrease, the nonlinear effects become significant,thereby indicating significant nonlinear marginaleffects.

Table 3. (Continued)

Table 3(c). Test of inverse-U relationship between EO and ROA growth—direct and conditional effects

ROA growth

Maineffects

High ICTcapability

Low ICTcapability

High networkcapability

Low networkcapability

Test of joint significance of EOvariables [EO and EO-squared](p-value); (H0: bEO = bEO-square = 0)

0.027 0.026 0.017 0.017 0.013

Slope at EOlow 0.103*** 0.160* 0.062* 0.162* 0.063*Slope at EOhigh -0.117*** -0.154* -0.066* -0.154* -0.084*Sasabuchi-test of inverse U-shape

in EO (p-value)0.015 0.021 0.023 0.023 0.022

Estimated extreme point 3.480 4.872 1.435 5.165 1.22195% confidence interval—Fieller

methodLow 2.203 3.653 1.234 4.120 1.077High 4.165 5.901 2.139 5.235 2.654

95% confidence interval—Delta method

Low 2.421 3.833 1.239 4.318 1.259High 4.646 5.240 2.038 6.046 2.679

Test of joint significance of controlvariables (p-value)

0.017 0.025 0.018 0.027 0.026

Test of joint significance ICT-usagedirect and moderation effects(p-value)

0.013 0.024

Test of joint significance networkcapability direct and moderationeffects (p-value)

0.019 0.026

Test of joint significance of allvariables in the model

0.017 0.025 0.018 0.027 0.026

aAll joint significance tests are based on Wald test.bSasabuchi (1980) proposed a test of composite null hypothesis that the relationship is increasing at low values of the EO interval and/or isdecreasing at high values.*p < 0.05; **p < 0.01; ***p < 0.001.

110 W. J. Wales et al.

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

Figure 1. Interaction plots7

Figure 2. Conditional moderation effects

7 Based on standardized estimates and standardized outcome variables—sales growth, operating profit growth, and return on assetgrowth.

Nonlinear Effects of EO on Small Firm Performance 111

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

Robustness checks

We conducted several robustness tests to assesswhether our findings were sensitive to model speci-fications. First, we further tested the relative signifi-cance of the linear moderation effects (EO ¥ NC andEO ¥ ICT capability) to the nonlinear moderationeffects (EO-square ¥ NC and EO-square ¥ ICT capa-bility). We found that the differences between thelinear moderation effects (Sales Growth: Adjusted-R2=0.24, p < 0.001; Operating Profit Growth:Adjusted-R2 = 0.24, p < 0.001; Return on AssetsGrowth: Adjusted-R2 = 0.20, p < 0.001) and the non-linear moderation effects (Adjusted-R2 values listedin the full model in Table 3) were statistically sig-nificant (Sales Growth: DAdjusted-R2 = 0.05, p <0.01; Operating Profit Growth: DAdjusted-R2 = 0.04,p < 0.01; Return on Assets Growth: DAdjusted-R2 = 0.04, p < 0.01).

Second, we tested whether our estimates wereconsistent over different outcome specifications.Many previous studies on EO have used perceivedperformance measures such as return on sales(ROS), return on equity (ROE), and return on assets(ROA) (Rauch et al., 2009). Based on objectiveinformation available in Affärsdata, we utilized thegeometric mean of ROS, ROE, and ROA for 2007 to2009. Our inferences did not change in magnitude ordirection.

Third, we operationalized compounded annualgrowth rate for employees (2007 to 2009) and laborproductivity (2007 to 2009). While the effect sizeswere smaller for both outcome measures than thoseof the currently used growth rate measures, our infer-ences did not change. Fourth, we tested whether theindividual components of EO—innovativeness, risktaking, and proactiveness—displayed different rela-tionships with firm growth. Although the estimateswere lower in magnitude, they did not differ in direc-tion or statistical significance. Fifth, we conducted astatistical power analysis in two modes. We drew onCohen et al.’s (2003) approach and also used therecent bootstrapping approach. Table 2 lists power atthe 0.05 levels of significance. The overall modelpower for operating sales growth, operating profitgrowth, and return on assets growth was 0.87, 0.86,and 0.80, respectively.

DISCUSSION

Our results contribute to the existing literature bysuggesting that the relationship between EO and per-

formance in small firms (1) does not hold indefinitely,(2) is maximized at differing levels of EO as a func-tion of firm-level capabilities, and (3) can producebelow zero returns when these capabilities are defi-cient. Although prior research has uncovered poten-tial nonlinear and contingency effects related to EO,more work is warranted in both areas (Rauch et al.,2009; Tang et al., 2008; Su et al., 2011a). For smallfirms, our results suggest that increasing levels of EOappear beneficial to a point, after which positivereturns cease and performance begins to decline.Intriguingly, these results suggest that the maximumpositive effect of EO on performance, at least in termsof small firm growth, occurs at lower levels of EOthan would be expected based on previous researchefforts that have suggested either a linear relationship(e.g., Rauch et al., 2009) or diminishing performancegains at high levels of EO (Tang et al., 2008). Weobserved inflection points of 2.663, 2.236, and 3.480for the three growth dependent variables, respec-tively. This indicates that, on average, a relatively lowto moderate level of EO produces the highest growthin small firms. Further, by controlling for past perfor-mance, our results capture distinct direct and modera-tion effects above and beyond past growth trends.

Drawing upon the tenets of resource orchestrationtheory, we posit internal organizational resourceeffects as a novel theoretic lens in explaining theexistence of diminishing, even harmful, returns asso-ciated with increasing levels of EO in small firms.Our results suggest that curvilinearity within theEO-small firm performance relationship occurs whenfirms lack specific capabilities that enable them toorchestrate their resource bases more effectively.Optimum levels of EO appear to vary as a function oforganizational capabilities and, by extension, anintermediate level of EO is not always optimal, asprior work would suggest (Bhuian et al., 2005; Tanget al., 2008). The optimal level of EO varies based onthe presence of these organizational capabilities. Astriking contrast noted in Table 3 and observed inFigure 1 concerns how the estimated extreme valuesof EO—or the inflection points within the invertedU-shaped curve—appear to shift in the presence ofthe moderators. In the presence of high levels of firmresource orchestration capabilities, such as NC orICT capability, the optimum value of EO for enhanc-ing firm performance occurs at significantly higherlevels than when these capabilities are deficient. Theshift in the optimal levels of EO based upon themoderating factors represents an intriguing studyimplication. This finding suggests that it is not simply

112 W. J. Wales et al.

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

the level of moderators (or context) in which EOoccurs that must be accounted for as prior researchhas described, but also that the level of EO itself mustbe managed based upon the presence (or absence) ofsuch moderating conditions.

Although the inflection points address what levelsof EO can be reached before diminishing returns toperformance commence, discussion should also bedevoted to the overall magnitude of the influence onperformance. Our results suggest that the effect ofEO on small firm performance across average levelsof the moderators is generally marginal. Modestlevels of NC and ICT capability appear insufficientto fully reap the value of firm investments in EO.However, as hypothesized, high levels of ICT capa-bility and NC allow small firms to achieve higherperformance returns from EO. A key theoreticalimplication of these findings is that resource orches-tration capabilities appear to play an important rolein maximizing the utility of EO in small firms. More-over, when these capabilities are deficient in smallfirms, increasing investments in EO beyond moder-ate levels are associated with notably damaging orharmful (i.e., below zero) returns to performance. Tospeculate, it would appear that high EO is a poten-tially hazardous strategic posture in small firmswhen resource orchestration capabilities are absentand, thus, resource orchestration capabilities may, inthis regard, be viewed as critical boundary condi-tions as opposed to simply enabling factors whichenhance EO’s influence.

Additionally, while NC and ICT capability focuson internal capabilities geared toward enhancingentrepreneurial efforts through a more effectiveorchestration of resources, the overall resource con-straints faced by firms due to their ‘liabilities ofsmallness’ could limit them from realizing returnsfrom EO. To further test our theorized model, weconducted a post hoc analysis to explore the contin-gent effects of firm size on the effects of EO on firmgrowth.8 The results of this post hoc analysis furthersupport the logic of our theoretic rationale inexplaining the main effect relationship. The EO-performance inflection point occurs at higher levelsof EO within larger small firms than in smaller smallfirms.9 Thus, even among small firms, smaller size

leads to experiencing the harmful effects of increas-ing EO more rapidly and significantly.

There are several practical implications of ourfindings which generate useful and actionableinsights for managers of small firms. First, it wouldseem prudent for managers to recognize that, whennot accompanied by critical firm-level resourceorchestration capabilities, increasing levels of EOare less beneficial, even harmful, to small firm per-formance. Second, our post hoc analysis suggeststhat managers would be wise to increase EO intandem with firm size. Study findings suggest thatincreasing EO too far ahead of growth in firm sizewill likely have notably harmful effects on perfor-mance. To elaborate, this suggests a need for man-agers to balance EO with ICT capability, NC, andfirm size to best enhance firm performance.

While the results of this study are instructive in anumber of ways, they are nonetheless subject tosome limitations. To begin, while the present studyoffers a unique theoretical lens through which wemay better understand nonlinearity in the EO-smallfirm performance relationship, we did not offer adirect test of the resource availability hypothesisbeyond our post hoc analysis, which posits firm sizeas a proxy for firm resource levels. Rather, our focushas been on understanding the nature of the EO-small firm performance relationship drawing uponresource orchestration theory as an exemplar. Futurework should seek to test the effect of various aspectsof firm resources upon the EO-firm performancerelationship and perhaps to examine whether thefindings herein are generalizable to midsize andlarger firm contexts to further enhance our under-standing of the EO-firm performance relationship.10

Furthermore, it is important to note that while ICTcapability and NC may allow small firms to over-come their resource limitations, their developmentmay carry costs as well. In this vein, the relationshipbetween absorptive capacity and firm financial per-formance has been shown to be subject to diminish-ing returns (Wales, Parida, and Patel, forthcoming).11

8 Post hoc analysis results and figures for the firm size tests areavailable from the authors upon request.9 We refer to organizations one standard deviation above themean firm size value as ‘larger small-sized firms’ and organi-zations one standard deviation below the mean firm size valueas ‘smaller small-sized firms.’

10 We wish to thank an anonymous reviewer for helping recog-nize this future research direction.11 Wales et al. (forthcoming) examines the influence of absorp-tive capacity on firm financial performance based upon datacollection efforts around the same time as the present investiga-tion with overlapping firm observations. The Wales et al. (forth-coming) study specifically examined SMEs as opposed to smallfirms, i.e., firms with less than 250 as opposed to 50 employees.It was found that the ACAP-financial performance relationshipis subject to diminishing returns and EO plays a critical role inenabling the translation of ACAP into SME performance.

Nonlinear Effects of EO on Small Firm Performance 113

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Thus, ICT capability and NC cannot be increasedindefinitely as a means of enhancing the relation-ship between EO and small firm performance.However, the present research suggests that thesefirm-level capabilities and EO are indeed com-plementary and that their mutual manifestationenables higher firm performance. Since the mani-festation of EO and firm-level capabilities requiresa portion of a firm’s investable resources, futureresearch exploring the possibility of coevolu-tionary development may prove fruitful. Specifically,studying the balance between such resource-based capabilities and EO, as well as between firmresource costs and consumption, represents anintriguing area for further research and suggeststhe potential for more complex rationale to explainand build upon the observation of a positiveinteraction.

Future research should also continue to investi-gate the means through which small firms mayfacilitate higher levels of productive EO. Forinstance, are there specific types of resources (i.e.,human, social, technological, etc.), organizationalcontexts (internal, external, strategic, etc.), or addi-tional capabilities that serve to influence theEO-performance inflection point, and with whateffects? Moreover, considerations beyond resourceorchestration capabilities that may influence thetemporal lag between when resource costs areincurred and benefits realized by firms should beexplored in future research. Life cycle modelssuggest that more formalized systems, structures,and decision making emerge as organizations grow(Kazanjian and Drazin, 1989). For instance, ambi-dextrous capabilities, which allow organizations topartition their exploratory and exploitative activitiesmore effectively, may play an important role in theeffective manifestation of increasing levels of pro-ductive EO as small firms become larger and con-tinue to face a strategic imperative to successfullyorchestrate their resources (Raisch and Birkinshaw,2008).

In conclusion, while the positive effects of EOare generally well established in the literature(Rauch et al., 2009), the present research observesthat, ceteris paribus, high levels of EO serve todiminish (as opposed to facilitate) firm perfor-mance in the small firm context. It is our hopethat this study will inspire additional researchin the vein of scholarly inquiry linking EO tomoderating conditions and nonlinear performanceoutcomes.

ACKNOWLEDGEMENTS

The authors would like to thank Michael Hitt, coedi-tor of SEJ, and two anonymous reviewers for theirextremely helpful thoughts concerning the developmentof this manuscript. We would also like to thank JohanWiklund for his comments on earlier versions of themanuscript.

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APPENDIX ATable A1. Item loadings

Entrepreneurial orientation In our firm . . . Factorloading

t-value

Risk taking(a = 0.87; CR = 0.81; AVE = 0.63)

. . . we see bold, wide-ranging acts are necessaryto achieve the firm’s objectives

0.796 9.659

. . . we have a strong aptitude for high-riskprojects (with chances of high returns)

0.812 10.776

. . . my firm typically adopts a bold posture whenconfronted with decisions involving uncertainty,to maximize the exploitation of opportunities

0.927 21.916

Proactiveness(a = 0.91; CR = 0.82; AVE = 0.65)

. . . we tend to be ahead of competitors regardingintroduction of products and ideas

0.781 8.901

. . . we typically initiate actions which competitorsthen respond to

0.899 19.313

. . . we are often the first to introduce newproducts and services, new ways to producethese, or new administrative methods

0.954 23.951

Innovativeness(a = 0.84; CR = 0.79; AVE = 0.72)

. . . we have a strong emphasis on R&D,technological leadership, and innovations

0.820 12.382

. . . changes in product or service lines haveusually been quite dramatic to achievecompetitive advantage

0.863 18.426

. . . one of the main goals is to launch many newlines of products/services in next three years

0.900 19.629

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Table A1. (Continued)

Entrepreneurial orientation In our firm . . . Factorloading

t-value

Entrepreneurial orientation(a = 0.85; CR = 0.71; AVE = 0.68)

Risk taking 0.783 9.305Proactiveness 0.807 10.642Innovativeness 0.692 7.408

Network capability In our firm . . .Coordination(a = 0.81; CR = 0.76; AVE = 0.68)

. . . we analyze what we would like and desire toachieve with each partner

0.853 15.174

. . . we develop relations with each partner basedon what they can contribute

0.772 8.935

. . . we discuss regularly with our partners howwe can support each other

0.753 7.904

Relational skills(a = 0.93; CR = 0.84; AVE = 0.66)

. . . we have the ability to build good personalrelationships with our business partners

0.940 20.251

. . . we can deal flexibly with our partners 0.884 19.633

. . . we almost always solve problemsconstructively with our partners

0.912 20.856

Partner knowledge(a = 0.86; CR = 0.91; AVE = 0.73)

. . . we know our partners’ markets 0.901 21.100

. . . we know our partners’products/procedures/services

0.845 12.538

. . . we know our partners’ strengths andweaknesses

0.842 12.256

Internal communication(a = 0.84; CR = 0.80; AVE = 0.65)

. . . we have regular meetings for every project 0.897 20.784

. . . employees develop informal contacts amongthemselves

0.852 15.833

. . . managers and employees often give feedbackto each other

0.803 10.996

Networking capability(a = 0.83; CR = 0.82; AVE = 0.66)

Coordination 0.923 21.807Relational skills 0.736 8.761Partner knowledge 0.748 9.214Internal communication 0.739 9.055

ICT capability The extent to which your company uses ICT inthis area . . .

ICT internal use(a = 0.82; CR = 0.77; AVE = 0.63)

. . . access information (e.g., market, customer) 0.895 18.519

. . . enable strategic planning 0.798 9.237

. . . enable cost savings 0.789 9.516

. . . enable competence/skills development foremployees

0.793 10.410

ICT collaboration(a = 0.84; CR = 0.81; AVE = 0.71)

. . . maintain collaboration with existing businesspartners

0.734 6.884

. . . establish business collaborations with newpartners

0.856 14.582

. . . enable work flexibility (e.g., work outside theoffice)

0.819 11.654

ICT communication(a = 0.79; CR = 0.76; AVE = 0.68)

. . . handle communication within the firm (e.g.,intranet)

0.784 8.372

. . . handle external communication with thefirm’s stakeholders (e.g., extranet)

0.822 12.622

. . . promote marketing activities 0.847 14.365ICT capability(a = 0.79; CR = 0.73; AVE = 0.61)

ICT internal use 0.753 8.724ICT collaboration 0.801 11.658ICT communication 0.786 8.487

a—Cronbach’s alpha, CR—composite reliability, AVE—average variance extracted (AVE).

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Table A2. Exploratory factor analysis

ICTintern. use

ICTcollab.

ICTcomm.

NCcoord.

NC rel.skills

NC partnerknowledge

NC int.comm.

EO risktaking

EOproact.

EOinnov.

ICT internal use 112 0.766 0.179 0.236 0.152 0.043 0.053 0.188 0.161 0.181 0.175ICT internal use 2 0.588 0.213 0.166 0.116 0.085 0.020 0.152 0.204 0.170 0.141ICT internal use 3 0.657 0.314 0.028 0.129 0.025 0.043 0.123 0.147 0.206 0.099ICT internal use 4 0.525 0.168 0.137 0.154 0.124 0.176 0.216 0.177 0.163 0.200ICT collaboration 1 0.167 0.601 0.138 0.157 0.130 0.144 0.147 0.104 0.145 0.080ICT collaboration 2 0.129 0.703 0.167 0.032 0.204 0.065 0.169 0.194 0.311 0.035ICT collaboration 3 0.184 0.516 0.196 0.158 0.105 0.174 0.167 0.183 0.065 0.210ICT communication 1 0.040 0.188 0.538 0.167 0.173 0.021 0.154 0.033 0.166 0.194ICT communication2 0.066 0.154 0.563 0.191 0.072 0.174 0.121 0.034 0.127 0.140ICT communication3 0.140 0.144 0.549 0.180 0.150 0.202 0.042 0.209 0.163 0.126NC coordination 1 0.154 0.159 0.123 0.539 0.145 0.109 0.156 0.134 0.170 0.117NC coordination 2 0.110 0.154 0.020 0.602 0.208 0.186 0.091 0.119 0.203 0.077NC coordination 3 0.159 0.189 0.091 0.693 0.206 0.145 0.187 0.204 0.124 0.053NC rel. skills 1 0.076 0.147 0.148 0.143 0.679 0.035 0.173 0.088 0.031 0.147NC rel. skills 2 0.118 0.019 0.089 0.176 0.690 0.194 0.119 0.178 0.073 0.143NC rel. skills 3 0.262 0.149 0.120 0.183 0.640 0.140 0.150 0.160 0.137 0.170NC partner

knowledge 10.023 0.170 0.025 0.177 0.129 0.597 0.178 0.091 0.205 0.044

NC partnerknowledge 2

0.163 0.142 0.078 0.201 0.165 0.530 0.164 0.131 0.085 0.101

NC partnerknowledge 3

0.173 0.230 0.193 0.159 0.202 0.460 0.193 0.122 0.169 0.148

NC int. comm. 1 0.053 0.169 0.143 0.193 0.120 0.192 0.567 0.164 0.194 0.146NC int. comm. 2 0.148 0.036 0.030 0.165 0.041 0.149 0.611 0.202 0.028 0.109NC int. comm. 3 0.232 0.182 0.152 0.197 0.189 0.182 0.657 0.183 0.204 0.108EO risk taking 1 0.195 0.185 0.134 0.237 0.181 0.093 0.185 0.667 0.168 0.132EO risk taking 2 0.183 0.174 0.186 0.181 0.140 0.119 0.201 0.631 0.051 0.161EO risk taking 3 0.064 0.179 0.171 0.206 0.078 0.146 0.159 0.540 0.211 0.190EO proactiveness 1 0.138 0.142 0.166 0.145 0.123 0.132 0.134 0.210 0.461 0.154EO proactiveness 2 0.244 0.177 0.041 0.041 0.132 0.029 0.200 0.189 0.786 0.167EO proactiveness 3 0.161 0.120 0.172 0.133 0.071 0.146 0.189 0.205 0.647 0.167EO innovativeness 1 0.116 0.144 0.101 0.195 0.086 0.179 0.151 0.125 0.137 0.673EO innovativeness 2 0.032 0.138 0.156 0.191 0.194 0.170 0.125 0.199 0.151 0.680EO innovativeness 3 0.062 0.155 0.176 0.118 0.200 0.131 0.173 0.122 0.201 0.678

Kaiser-Meyer-Olkin measure of sample adequacy = 0.907.Bartlett’s test of sphericity = c2/df = 11.103, p = 0.000.Confirmatory factor analysis = c2 = 671.334; df = 427; CFI = 0.93; TLI = 0.92; RMSEA = 0.061; RMSR = 0.017.

Table A3. Second-order factor results

Model Normed c2 NNFI TLI CFI RMSEA

Entrepreneurial orientationModel 1 (one-factor model) 4.442 0.952 0.816 0.773 0.198Model 2 (three uncorrelated factors) 5.608 0.923 0.92 0.961 0.126Model 3 (three correlated factors) 2.371 0.943 0.915 0.994 0.078Model 4 (one second-order factor) 1.983 1.101 1.003 0.986 0.052

Network capabilityModel 1 (one-factor model) 5.648 0.841 0.797 0.882 0.285Model 2 (four uncorrelated factors) 5.034 0.928 0.934 0.918 0.255Model 3 (four correlated factors) 4.922 1.018 0.962 0.994 0.119Model 4 (one second-order factor) 2.994 1.057 1.057 1.052 0.056

12 The numbering of scale items corresponds to item listing in Table A1.

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Table A3. (Continued)

Model Normed c2 NNFI TLI CFI RMSEA

ICT capabilityModel 1 (one-factor model) 8.702 0.88 0.916 0.838 0.213Model 2 (three uncorrelated factors) 7.314 0.953 0.910 0.886 0.191Model 3 (three correlated factors) 5.335 0.959 0.954 0.935 0.073Model 4 (one second-order factor) 1.217 1.104 1.054 1.007 0.064

Normed c2 = c2/df; NNFI—non-normed fit index (recommended value > 0.9); TLI—Tucker Lewis index; CFI; RMSEA—root meansquare error aggregate.

APPENDIX BTable B1. Inverted U-shaped relationship tests

Test Purpose Output Inference

Test of jointsignificance ofestimates

A Wald-test assesses jointsignificance of variables; (e.g.,based on Equation (1)—H0:b1 = b2 = b3 = b4 =b5 = b6 = b7 = b8 = 0)

F-stat (n, df) If the null is rejected (p < 0.05),then at least one variable in thetest cannot be dropped from theregression.

Slope at lower(or, higher) limit

Estimates direction, magnitude,and significance of a linetangent to the low (or, higher)point of EO

b(s.e.) If the slope is positive andsignificant on the lower limitand if the slope is negative onthe upper limit, then preliminaryevidence ofan inverted U-shapedrelationship is present by thedata points represented withinthe dataset.

Sasabuchi test For inverted U-shapedrelationships, the Sasabuchi testassesses the composite nullhypothesis that the relationshipis decreasing at low values ofthe X interval and increasing athigh values. Or, formally stated:H0: b = gù′(xi) � 0 andb = gù′(xh) � 0

Likelihood ratiotest withp-values

When testing for an inverted-Uhypothesis, the null hypothesistests the presence of a U-shapedrelationship. If the null isrejected, then the curve isinverted U-shaped. TheSasabuchi test is anintersection-union likelihoodratio test based on asimultaneous test of inequalitybetween two estimates.

Fieller (or delta)confidenceinterval

Constructs a confidence intervalaround the estimated maximumpoint to check whether theestimated confidence interval iscontained within the dataset[EOlow, EOhigh].

If the confidence intervals arewithin the bounds of the upperand lower values of EO in thedataset, then the maximumpoint is within the lower andhigher limits of EO. This furtherconfirms the inverted U-shapedrelationship by substantiatingthat the inflection point iswithin the lower and higherlimits of EO in the dataset.

120 W. J. Wales et al.

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej

Figure B1. Interpretation of inverted U-shaped relationship tests

Nonlinear Effects of EO on Small Firm Performance 121

Copyright © 2013 Strategic Management Society Strat. Entrepreneurship J., 7: 93–121 (2013)DOI: 10.1002/sej