Breakthrough innovation: the roles of dynamic innovation capabilities and open innovation activities

Breakthrough innovation: the roles ofdynamic innovation capabilities and open

innovation activitiesColin C.J. Cheng and Ja-Shen Chen

College of Management, Yuan Ze University, Chung-Li, Taiwan

AbstractPurpose – This study aims to examine the roles of dynamic innovation capabilities and open innovation activities in breakthrough innovation. Drawingfrom the absorptive capacity perspective, organizational inertia theory, and open innovation, the authors seek to argue that dynamic innovationcapabilities have a curvilinear effect on breakthrough innovation that is moderated by open innovation activities.Design/methodology/approach – A mail survey was sent to the top 1,000 firms in Taiwan, the target respondents being senior managers withexperience in developing at least three successful breakthrough innovations in the past five years. A total of 218 usable questionnaires were collected,resulting in a respondent rate of 22.9 percent.Findings – The findings support the argument that dynamic innovation capabilities have an inverted U-shape relationship with breakthroughinnovation. Meanwhile, open innovation activities strengthen the positive effects of dynamic innovation capabilities on breakthrough innovation.Research limitations/implications – The findings enrich the existing literature by proposing and confirming empirically that open innovationactivities help firms with effective coordination of dynamic innovation capabilities.Practical implications – Managers must be aware of the limitations of their existing dynamic innovation capabilities in terms of developingbreakthrough innovation.Originality/value – This study not only resolves the conflicting views about the relationship between dynamic capabilities and innovation but alsoadds to the existing literature that indicates the failure of leading firms in the face of rapid environmental change.

Keywords Breakthrough innovation, Dynamic innovation capabilities, Open innovation, Innovation, Taiwan, Managers, Business performance

Paper type Research paper

An executive summary for managers and executivereaders can be found at the end of this article.

Introduction

To enhance breakthrough innovation, proponents of theresource-based view and the dynamic capability view suggestthat firms should invest heavily in developing dynamicinnovation capabilities (Davenport et al., 2006; Song et al.,2005; Teece, 2007). Meanwhile, Chesbrough (2006, 2010)and Gassmann et al. (2010) suggest that firms can use openinnovation to produce radically new products.However, the innovation literature is divided over whether

dynamic innovation capabilities lead to breakthroughinnovation (e.g. Antikainen and Vaataja, 2010; Rosenkopfand Nerkar, 2001). For example, studies rooted in theabsorptive capacity perspective suggest that dynamicinnovation capabilities may foster greater breakthroughinnovation (Rosenkopf and Nerkar, 2001). When a firmbuilds its dynamic innovation capabilities, its absorptivecapacity increases, and as a result it is encouraged to explorenew information and eventually develop breakthrough

innovation (Lavie and Rosenkopf, 2006). However, studies

rooted in organizational inertia theory (Hannan and Freeman,

1984) suggest that dynamic innovation capabilities may

discourage breakthrough innovation (e.g. Benner and

Tushman, 2003; Levinthal and March, 1993). When firms

accumulate more experience and become more efficient at

using their existing knowledge, the self-reinforcing nature of

learning produces more incremental innovation rather than

breakthrough innovation (Benner and Tushman, 2003).

Thus, the relationship between dynamic innovation

capabilities and breakthrough innovation remains unclear.There are equally conflicting views regarding open

innovation (e.g. Lichtenthaler, 2011). Not all firms adopt

open innovation activities because some firms prefer to have

more control over the source breakthrough innovation and

their relationships with innovation partners (Cheng and

Huizingh, 2010; Di Benedetto, 2010; Lichtenthaler, 2011).

In addition, despite research on the effects of open innovation

on innovation performance (e.g. Gassmann et al., 2010;

Gassmann and Zeschky, 2008), not all firms are successful

adopting open innovation activities. For example, the direct

cost of acquiring technology from third parties is often greater

than the indirect value generated by having this technology.

As a result, the net effect on firm performance is negative

(Faems et al., 2009).

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Journal of Business & Industrial Marketing

28/5 (2013) 444–454

q Emerald Group Publishing Limited [ISSN 0885-8624]

[DOI 10.1108/08858621311330281]

Received 29 May 2011Revised 15 May 2012Accepted 25 June 2012

444

Our purpose therefore is to investigate the effect of dynamicinnovation capabilities on breakthrough innovation in light ofthe moderating role of open innovation. This studycontributes to the innovation literature in the followingways. First, it increases understanding of the effects ofdynamic innovation capabilities on breakthrough innovation.Second, it enriches the extant literature by empirically testingwhether open innovation activities help firms strengthen theirbreakthrough innovation. Evidence for positive effects ofbreakthrough performance will further demonstrate thelegitimacy of open innovation as an important researchfield. Finally, an analysis of the performance effects ofbreakthrough innovation is critical to firms that attempt toprofit from using dynamic innovation capabilities and openinnovation activities.The rest of this paper is organized as follows. We begin by

describing the theoretical background and hypotheses. Wethen present the research method and the results of a series ofstatistical assessments. Finally, we discuss our conclusionsand the implications of the findings.

Theoretical background

Breakthrough innovationThe degree of innovation can range from totally new to aminor improvement (Garcia and Calantone, 2002).Depending on their newness, innovations can be categorizedas incremental or breakthrough innovations (Johannessenet al., 2001; O’Connor and De Martino, 2006; Song and DiBenedetto, 2008). Incremental innovations are minor changesin technology, simple product improvements, or lineextensions that minimally improve existing performance. Incontrast, breakthrough innovations involve substantially newtechnology, offer substantially greater customer benefitsrelative to existing products, and demand considerablechanges to consumption or usage patterns (Chandy andTellis, 2000; O’Connor and De Martino, 2006; De Visseret al., 2010). Thus, a breakthrough innovation may imply agreater level of complexity (Rogers, 2003) and may require anew knowledge base and innovation capabilities (Song and DiBenedetto, 2008).

Dynamic innovation capabilitiesDynamic capabilities are an organization’s abilities to“integrate, build, and reconfigure internal and externalcompetencies to address rapidly changing environments”(Teece et al., 1997, p. 516). Several authors, such as Teeceet al. (1997), and most recently Zollo and Winter (2002),have challenged other researchers to further explicate theconcept and build a solid theoretical foundation for it. Thishas led us to classify dynamic capabilities into two majorstreams:1 Teece et al.’s (1997) “ability” view; and2 Zollo and Winter’s (2002) “process and routine”

perspective.

The ability view is clearly in the lineage of Barney (1991), andclaims that dynamic capabilities are unique abilities thatcannot be replicated accurately or fully comprehended. Thiscamp, which is an extension of the resource-based view, iswell represented by Teece et al.’s (1997) work on how somefirms develop and sustain competitive advantages andsuperior profitability. The process and routine perspectiveclaims that dynamic capabilities, as organizational sociallearning processes, are uniquely determined by each firm’sunique history, which confers on each organization specific

know-how and traditions that are not easily replicated. Thesocial learning underlying dynamic capabilities is accumulatedalong the specific and unique path that each firm follows(Zollo and Winter, 2002).Dynamic innovation capabilities are operational capabilities

that include organizational learning processes and routinesrooted in innovation knowledge and that involvetransformation of a firm’s innovation knowledge resourcesand routines. Therefore, following Zollo and Winter (2002),we define dynamic innovation capabilities as those hard-to-transfer and hard-to-imitate innovation capabilities that firmsuse to develop, integrate, and reconfigure existing and newresources and operational capabilities.

Hypotheses developmentTwo theories can be used to describe dynamic innovationcapabilities:1 absorptive capacity; and2 organizational inertia (Davenport et al., 2006; Zahra and

George, 2002).

Absorptive capacity refers to a firm’s ability to “recognize thevalue of new information, assimilate it, and apply it tocommercial ends” (Cohen and Levinthal, 1990, p. 128).Absorptive capacity is primarily a function of a firm’s priorknowledge and is especially related to how well it can use newknowledge to achieve desired innovation (Volberda et al.,2010; Lewin et al., 2011). Each firm has its own specificinnovation capabilities. Thus, dynamic innovation capabilitiesare capabilities that organizations already have or have newlydeveloped to manage the process of innovation (Hertog et al.,2010; Davenport et al., 2006). When dynamic innovationcapabilities become embedded in organizational routines overtime, they become more valuable, inimitable, and non-substitutable, therefore representing an important source ofabsorptive capacity (Davenport et al., 2006).When a firm builds its dynamic innovation capabilities, it

invests substantial resources in new product development,which involves discovering radically new product ideas,accumulating state-of-the-art knowledge, and trainingpersonnel (Afuah, 2002). The accumulation ofbreakthrough innovation knowledge increases the firm’sability to evaluate and use new techniques and skills inbreakthrough innovation (O’Connor, 2009; Zahra andGeorge, 2002). Thus, the firm can quickly identifybreakthrough innovation trends, experiment with emergingdesigns, and engage in breakthrough innovations beyondcurrent innovation boundaries (Gassmann et al., 2010;Rosenkopf and Nerkar, 2001). Accordingly, theaccumulation of dynamic innovation capabilities facilitatesbreakthrough innovation.The opposite perspective can be found in the organizational

inertia literature. Organizational inertia refers to the stabilityin innovation development that underlies insufficientadaptation to changes in the environment (Hannan andFreeman, 1984). Organizations in the throes of organizationalinertia often establish routines to maximize the efficiency.When these routines become embedded within anorganization over time, they decrease the firm’s innovationcapabilities in response to demands in the environment and asa result create strong internal resistance against radical change(Benner and Tushman, 2003; Nelson and Winter, 1982).Thus, firms’ dynamic innovation capabilities develop over

time and accumulate as a result of their past experience. Theyreflect firms’ abilities to use existing resources (Christensenet al., 2005; Afuah, 2002). Levinthal and March (1993) and

Breakthrough innovation: the roles of capabilities and activities

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Christensen (1997) suggest that firms with a superiorcapability in a particular field are more likely to search formore current information and use their existing knowledgestores to achieve immediate advantage. Empirical evidenceprovides support for this argument. For example, Benner andTushman (2003) indicate that process managementtechniques and skills promote exploitative learning and as aresult facilitate incremental innovation.Thus, the accumulation of innovation expertise enables a

firm to better understand and recognize the value of newproduct development in the existing innovation trajectory,which in turn provides insights into how to exploit currentknowledge and skills. When the firm accumulates knowledge,the self-reinforcing nature of learning makes the firm moreefficient at integrating additional skills into its existingknowledge base (Lieberman and Montgomery, 1998). As aresult, dynamic innovation capabilities can facilitate greaterexploitation of existing knowledge that thus producesincremental innovation (Lavie and Rosenkopf, 2006).Therefore, we propose the following:

H1. There is an inverted U-shape relationship betweendynamic innovation capability and breakthroughinnovation.

To enhance breakthrough innovation, it is necessary for firmsto break down institutional boundaries (Gassmann et al.,2010; Chesbrough, 2007). A recently proposed model forboundary breaking is open innovation (Chesbrough, 2003),which refers to “the use of purposive inflows and outflows ofknowledge to accelerate internal innovation, and expand themarkets for external use of innovation, respectively”(Chesbrough, 2006, p. 2). Because open innovationemphasizes the flexible use of resources and thereconfiguration of innovation processes with third parties, itenables firms to achieve a competitive advantage throughopen coordination (Chesbrough, 2010). Following this newapproach, firms are beginning to share or sell their internalresources to third parties to create value (namely, inside-outprocesses). Meanwhile, firms are also able to incorporateexternal resources into their own innovation activities (namelyoutside-in processes; Gassmann et al., 2010; Chesbrough,2007).Open innovation may create activities that support

breakthrough innovation (Chesbrough, 2007, 2010; Deck,2008). However, because open innovation activities serve asan organizing principle for coordinating various resources andfunctional units (Wirtz et al., 2010; Chesbrough, 2006), theymay not directly affect a firm’s innovation output bythemselves. Instead, they may enhance dynamic innovationcapabilities in breakthrough innovation. Although currentresearch has yet to determine how a firm integrates openinnovation activities into dynamic innovation capabilities, weargue that open innovation activities strengthen the positiveeffect of dynamic innovation capabilities on breakthroughinnovation.Firms that actively seek out external resources to

supplement their own innovation projects (outside-inprocesses) can enjoy more advantages than firms thatchoose to do everything in house, from research anddevelopment productivity to radical innovation (Dodgsonet al., 2006; Gassmann et al., 2010; Rohrbeck et al., 2009). Inaddition, because of the dramatic increase in the number andmobility of knowledge workers in various industries, firms thatengage in open innovation activities can not onlycommercialize new products themselves but also license or

sell their breakthrough ideas (inside-out processes) to increasetheir potential profit and enhance their radical innovationperformance (De Man and Duysters, 2005; Chesbrough andAppleyard, 2007; Gassmann et al., 2010).More specifically, high open innovation activities require

that a firm openly cooperates with its third parties, and thisactive cooperation process increases the chance of the firmfinding the adequate breakthrough ideas it requires. Inaddition, this open cooperation can help the firm tosuccessfully integrate the internal and external resourcesthat breakthrough innovation requires. Therefore, it isreasonable to assume that open innovation activities canhave an enhanced effect on dynamic innovation capabilities,leading to greater breakthrough innovation.

H2. Open innovation activities strengthen the positiveeffect of dynamic innovation capabilities onbreakthrough innovation.

Research method

Questionnaire developmentThe questionnaire used in this study was developed overseveral stages, following Churchill (1979), Gerbing andAnderson (1988), and Adams et al. (2006). We began byconducting a search of the relevant literature (e.g. Hertoget al., 2010; Vrande et al., 2009; Chesbrough, 2003, 2010;Laursen and Salter, 2006) and explored the perspectives ofpractitioners and academics specializing in the resource-basedview, the dynamic capability view, and open innovation. Wealso consulted documents such as annual reports, pressreleases and financial statements. By doing so, we were able tocross-check the empirical findings, which led to more preciseresults (Eisenhardt, 1989; Yin, 2009).Because some items were originally written in English

(e.g. breakthrough innovation and the control variables), wehad to translate them into Chinese. We used a double-translation method to ensure conceptual equivalence(Hoskisson et al., 2000; Song and Parry, 1996). One of theauthors translated the items into Chinese, and then two otheracademics translated the Chinese version back into English.The original items and the back-translated items were thencompared by a third academic to check for consistency of thetranslation. The translation was confirmed by a fourthacademic.To assess the quality of the measured items, we conducted a

pilot test. The scale was tested using a convenience sample of67 senior managers with experience in developingbreakthrough innovation. Based upon their feedback, a fewconcerns were raised and adjustments were made in terms ofwording and formatting. The final questionnaire contained 26items, each measured using a seven-point Likert scale. Theentire scale is shown in the Appendix.

MeasuresDynamic innovation capabilities were measured with fiveitems adapted from Hertog et al. (2010), Davenport et al.(2006), and Song et al. (2005). Open innovation activitieswere measured by five items adapted from Vrande et al.(2009), Chesbrough (2003, 2010), and Laursen and Salter(2006). Breakthrough innovation was measured with six itemsadapted from Zhou et al. (2005) and Gatignon and Xuereb(1997).As recommended by innovation researchers (Zhou et al.,

2005; Im and Workman, 2004; Atuahene-Gima and Ko,




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2001), we used subjective performance measures to overcomethe difficulties inherent in asking respondents to revealsensitive information. In addition, the relative method wasused to overcome difficulties associated with comparingdifferent sectors and sizes of firms (Van Egeren andO’Connor, 1998). Both approaches have been widely usedin innovation research (Zhou et al., 2005; Im and Workman,2004; Blindenbach-Driessen et al., 2010).To account for the effects of extraneous variables, we

considered firm size a control variable. Following similarresearch (Menguc et al., 2007), we used the logarithmictransformation of the number of full-time employees tomeasure firm size, because it is widely believed that large firmscan access key resources and are able to take on morebreakthrough innovation (Wagner and Hansen, 2005; Sheferand Frenkel, 2005). In addition, market turbulence,technological turbulence, and competitive intensity (adaptedfrom Citrin et al., 2007; Zhou et al., 2005; Han et al., 1998)were considered control variables because of their effects oninnovation-related outcomes (Zhou et al., 2005; Atuahene-Gima and Wei, 2011).

Sampling and data collectionData were drawn from the top 1,000 Taiwanese firms in termsof total revenue (China Credit Information Service, 2009). Asin similar studies of innovation and dynamic capabilities(e.g. Morgan et al., 2009; Zhou et al., 2005; Narver et al.,2004), senior managers were selected. We first called eachfirm to identify a senior manager to be the key respondent.We then screened the key respondent to ensure that he or shepossessed sufficient knowledge about the firm’s variousfunctional areas and was committed to cooperating with theresearch project. Furthermore, to check whether each firmhad engaged in breakthrough innovations, we sent a copy ofour description of incrementally and radically new products/services (Baker and Sinkula, 2007) to each firm, askingwhether the firm had introduced at least three successfullyand radically new products/services over the past five years(e.g. De Brentani and Kleinschmidt, 2004).Using Dillman’s (2000) total design method for mail

surveys, we mailed questionnaires with preaddressed postage-paid envelopes and a cover letter explaining the purpose of thestudy. This procedure yielded 218 usable questionnaires,representing a response rate of 22.9 percent. This responserate is within the acceptable range for surveys of top managers(Homburg et al., 1999; Menon et al., 1996). The firmssampled represented six industries:1 electronics (21.5 percent);2 consumer products (20.5 percent);3 information technologies (19.2 percent);4 software (17.7 percent);5 telecommunications (17.6 percent); and6 other (3.5 percent).

The firms’ annual sales ranged from $US2.3m to $US8.3bn.Finally, the number of firm employees ranged from 1,534 to26,473, with 70.6 percent of units reporting more than 1,000full-time employees.

Nonresponse biasAs nonrespondents have been found to resemble laterespondents (Armstrong and Overton, 1977), we assessednonresponse bias by comparing early and late respondents(late responses were those received after a reminder mailing,representing 30.8 percent of the total respondents) on allitems. Results of t tests showed no significant difference

between the two groups, indicating no systematic differencesbetween early and late respondents.

Common method biasBecause the data for dependent and independent constructswere measured using the same method, there is a potential forcommon method bias (Podsakoff et al., 2003). If commonmethod bias exists, a confirmatory factor analysis containingall constructs should produce a single method factor(Podsakoff and Organ, 1986). The goodness-of-fit indices(x2=df ¼ 33:5, root mean square error of approximationðRMSEAÞ ¼ 0:45, comparative fit index ðCFIÞ ¼ 0:34,nonnormed fit index ðNNFIÞ ¼ 0:21, parsimony-adjustednormal fit index ðPNFIÞ ¼ 0:35) indicate a poor fit for thesingle-factor model, which suggests that bias from commonmethod variance is unlikely.

Analyses and results

Validation of measuresTo determine the factor structure, we conducted a principalcomponent analysis with Varimax rotation and used theeigenvalues to identify the number of factors to retain.Following the suggestions of Hair et al. (2006), an item wasremoved if;. the factor loading was less than 0.5;. the item loaded on two different factors at the same time;

or. the item did not load in the group to which it belonged.

The remaining items loaded on six factors as expected. Thus,these results indicate the unidimensionality of the variousconstructs. Reliability was then measured; the Cronbach’s avalues for all measures are well above the threshold value of0.7 that Nunnally (1978) recommended (see the Appendix).

Measurement modelsWe further evaluated measurement properties by running aconfirmatory factor analysis. Following similar studies (Bakerand Sinkula, 1999; Hult et al., 2004), we divided the variablesinto related groups. Each item was set to load only on itsrespective latent construct, and the latent constructs wereallowed to be correlated. The results indicate that themeasurement model of dynamic innovation capabilities fitsthe data satisfactorily (x2=df ¼ 1:32, RMSEA ¼ 0:04,CFI ¼ 0:92, NNFI ¼ 0:95, PNFI ¼ 0:83). The openinnovation activities measures are represented satisfactorily(x2=df ¼ 1:45, RMSEA ¼ 0:05, CFI ¼ 0:94, NNFI ¼ 0:95,PNFI ¼ 0:85), and breakthrough innovation measures also fitthe data satisfactorily (x2=df ¼ 1:62, RMSEA ¼ 0:05,CFI ¼ 0:92, NNFI ¼ 0:93, PNFI ¼ 0:86). The factorloading of indicators is significant (p , 0:01) and well abovethe recommended level of 0.45 (Joreskog and Sorbom, 1993).

Convergent and discriminant validityWe next examined construct convergent and discriminantvalidity. Composite reliability is an indicator of sharedvariance among the set of observed variables used asindicators of a latent construct (Kandemir et al., 2006;Fornell and Larcker, 1981). As shown in the Appendix, thecomposite reliabilities of all constructs exceed the usual 0.60benchmark (Bagozzi and Yi, 1988). The results provide thenecessary evidence that all constructs exhibit convergentvalidity.We then examined discriminant validity using a procedure

suggested by Fornell and Larcker (1981) that has been widely




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used in other studies (e.g. Kandemir et al., 2006). Wecomputed the average variance extracted by the indicatorscorresponding to each of the six factors and compared thiswith the variance that each factor shared with the otherfactors in the model. The results in Table I indicate that alldiagonal elements representing the square root of the averagevariance extracted are greater than the highest shared variance(the off-diagonal correlations).We also examined discriminant validity using an alternative

approach recommended by Anderson and Gerbing (1988).The x2 values for the unconstrained models, which allowedeach pair of constructs to covary freely, were alwayssignificantly lower than those of the constrained models,which constrained the estimated correlation of each pair ofestimated constructs to 1. In this study, the value of theunconstrained model is significantly lower than that of theconstrained model in all cases (e.g. for the pair of constructsdynamic innovation capabilities and breakthrough innovation,the unconstrained model has a x2 of 34.9 and the constrainedmodel has a x2 of 114.3. The x2 difference (i.e. 79.4) issignificant at p , 0:001).As the criteria for both approaches are satisfied, an

inference error of multicollinearity is unlikely (Grewal et al.,2004). Accordingly, the measurement model fits the datasatisfactorily and exhibits unidimensionality and convergentand discriminant validity.

Testing of the hypothesesAfter the preliminary analyses we tested hypotheses usinghierarchical moderated regression analyses (Aiken and West,1991). Hierarchical moderated regression analyses offer somecomplementary benefits to structural equation modeling,such as the ability to easily assess differences between nestedmodels and calibrate the relative impact of the interactionbetween dynamic innovation capabilities and open innovationactivities in H2 (Hair et al., 2006). As shown in Table II, threehierarchical regressions were estimated:1 one including the control variables only;2 one adding dynamic innovation capabilities, open

innovation activities, and dynamic innovation capabilitiessquared; and

3 one adding the dynamic innovation capabilities £ openinnovation activities interaction and the dynamicinnovation capabilities squared £ open innovationactivities interaction.

To address possible multicollinearity, we mean-centered eachscale that constituted an interaction term and created theinteraction terms by multiplying the relevant mean-centeredscales (Aiken and West, 1991). The results show that thelargest variance inflation factor in any of hierarchical

regressions is 1.25 (below the cutoff of 10), indicating thatno multicollinearity concerns exist (Mason and Perreault,1991).The key to whether a relationship is U-shaped or inverted

U-shaped lies in the second derivative, which contains onlythe coefficient for the squared term. A positive sign for thecoefficient of the squared term indicates a U-shapedrelationship, whereas a negative sign indicates an invertedU-shaped relationship (Aiken and West, 1991).As shown in Table II (Model 2), dynamic innovation

capabilities are positively related to breakthrough innovation(b ¼ 0:32, p , 0:01), and the coefficient for dynamicinnovation capabilities squared is negative and significant(b ¼ 20:19, p , 0:05). We further explored this curvilinearrelationship through a partial derivative of the regressionfunction. As shown in Figure 1, the results(Y ¼ 20:19X2 þ 0:32X , where Y is breakthroughinnovation and X is dynamic innovation capabilities)indicate that the regression function reaches its maximumwhen dynamic innovation capabilities ¼ 0:84. This suggeststhat for values less than 0.84, there is a positive relationshipbetween dynamic innovation capabilities and breakthroughinnovation. However, beyond that, the relationship turnsnegative. Thus, there is evidence of an inverted U-shapedrelationship between dynamic innovation capabilities andbreakthrough innovation. H1 is supported.We then assessed the model with the interaction variable of

open innovation activities. As Model 3 in Table II shows, theinteraction between open innovation activities and dynamicinnovation capabilities positively affects breakthroughinnovation (b ¼ 0:21, p , 0:05), and dynamic innovationcapabilities squared interacts is negatively related tobreakthrough innovation (b ¼ 20:18, p , 0:05). The resultsindicate that open innovation activities strengthen the positiveeffects of dynamic innovation capabilities on breakthroughinnovation.To better understand the interaction effects, we performed

simple slope tests and plotted the relationships followingAiken and West (1991). We first split open innovationactivities into high and low levels (standard deviation above/below the mean). Then we estimated the effect of dynamicinnovation capabilities on breakthrough innovation for bothlevels. Hypothetically speaking, when open innovationactivities are high, dynamic innovation capabilities have astronger positive effect on breakthrough innovation. Theresults show that the effect of dynamic innovation capabilitieson breakthrough innovation is stronger when open innovationactivities are high (b ¼ 0:36, p , 0:01) than when they are low(b ¼ 0:21, p , 0:05). The optimal level of dynamic innovationcapabilities for breakthrough innovation is moderate whenopen innovation activities are low, whereas when open

Table I Basic descriptive statistics and correlation matrix

Variable Mean SD 1 2 3 4 5 6

1. Dynamic innovation capabilities 5.12 0.25 0.77

2. Open innovation activities 5.04 0.61 0.34 * * 0.83

3. Breakthrough innovation 5.25 .62 0.29 * * 0.31 * * 0.84

4. Market turbulence 4.58 0.66 0.10 0.03 0.09 0.75

5. Technological turbulence 4.46 0.48 0.22 * * 0.19 * 0.25 * * 0.08 0.85

6. Competitive intensity 4.61 0.62 20.09 0.04 20.04 20.11 20.02 0.78

7. Firm size 4.39 .56 20.15 * 20.26 * * 20.05 0.15 * 0.02 0.14 *

Notes: *p , 0:05; * *p , 0:01




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innovation activities are high, the optimal level shifts to ahigher point. These results suggest that H2 is supported.Finally, the regression results indicate that the relationships

are not significantly affected by market turbulence,technological turbulence, competitive intensity, or firm size.

Discussion

Drawing from the absorptive capacity perspective andorganizational inertia theory, we tested the impact ofdynamic innovation capabilities on breakthrough innovationin light of the moderating role of open innovation activities.We find that dynamic innovation capabilities have an invertedU-shaped relationship with breakthrough innovation. We alsofind that open innovation activities enhance the positiverelationship between dynamic innovation capabilities andbreakthrough innovation.Our results contribute to the existing literature in the

following ways. Our findings provide a better understanding

of the curvilinear effects of dynamic innovation capabilities onbreakthrough innovation. The previous literature highlightsthe role of dynamic capabilities in new product successbecause a firm’s dynamic capabilities to deal with rapidchanges in the environment are critical for product innovation(e.g. Morgan et al., 2009; Verona and Ravasi, 2003; Danneels,2004). Extending this logic, we find that dynamic innovationcapabilities have an inverted U-shape relationship withbreakthrough innovation. That is, at the beginning stage,dynamic innovation capabilities relate to the highest degree ofbreakthrough innovation, whereas in later stages, dynamicinnovation capabilities prevent breakthrough innovation (seeFigure 1). More specifically, the longer firms have dynamicinnovation capabilities, the more rooted firms may become inexisting environments such that they might overlook emergingchanges in the environment; moreover, the longer firms havedynamic innovation capabilities, the more they are unable tomanage changes in the environment. Organizational inertiafurther discourages breakthrough innovation. As a result, thelonger firms have dynamic innovation capabilities, the lessfirms intend to develop breakthrough innovation.These findings enrich the existing literature by

demonstrating the possible problems associated withdynamic innovation capabilities: When firms hold ontodynamic innovation capabilities longer, they may be lessable to sense changes in the environment. These findings notonly resolve the conflicting views about the relationshipbetween dynamic capabilities and innovation (e.g. Rosenkopfand Nerkar, 2001) but also add to the existing literature thatindicates the failure of leading firms in the face of rapidenvironmental change (Christensen, 2006).Moreover, we enrich the existing literature by proposing

and confirming empirically that open innovation activitieshelp firms with effective coordination of dynamic innovationcapabilities. As noted by Eisenhardt and Martin (2000),researchers need to identify dynamic capabilities that firmscan use to adapt, integrate, and reconfigure their resourcesand competencies in response to changing environments.Lavie (2006) further points out that there is a lack ofempirical evidence for dynamic capabilities. In particular,Chesbrough (2010) suggests that open innovation activitiespromote the open coordination of resources to supportvarious developments in innovation. Thus, we argue that open

Table II Hierarchical moderated regression results (t values)

Breakthrough innovation

Model 1 Model 2 Model 3

Market turbulence 0.09 (1.04) 0.11 (1.44) 0.11 (1.15)

Technological turbulence 0.02 (0.10) 0.04 (0.41) 0.06 (0.83)

Competitive intensity 0.04 (0.35) 0.01 (0.11) 0.07 (0.97)

Firm size 0.11 (1.34) 0.08 (1.03) 0.03 (0.14)

Dynamic innovation capabilities (DIC) 0.32 * * (3.29) 0.44 * * * (5.41)

Open innovation activities (OIA) 0.20 * (2.32) 0.16 * (2.01)

DIC2 20.19 * (22.20) 20.15 * (21.83)

DIC3 OIA 0.21 * (2.42)

DIC2 3 OIA 20.18 * (22.04)

Adjusted R2 0.34 0.39 0.45

F 5.42 * * * 5.61 * * * 5.84 * * *

Incremental R2 0.05 * * 0.06 * *

Notes: *p , 0:05; * *p , 0:01; * * *p , 0:001

Figure 1 Inverted U-shape




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innovation activities as an organizing principle may notdirectly affect breakthrough innovation. In accordance withour arguments, the present findings indicate that openinnovation activities enhance the positive effect of dynamicinnovation capabilities on breakthrough innovation. Morespecifically, open innovation activities strengthen the positiveinfluence of dynamic innovation capabilities. Therefore, openinnovation activities are one type of enhancement to dynamicinnovation capabilities that enables firms to achievebreakthrough innovation.Our findings have some implications for managers. Firms

must be aware of the limitations of their existing dynamicinnovation capabilities in terms of developing breakthroughinnovation. For example, firms with strong dynamicinnovation capabilities should understand that althoughtheir dynamic innovation capabilities increasingly enhancetheir breakthrough innovation, they may trap them inincremental innovation, causing them to focus on existingcustomers and preventing them from exploring breakthroughinnovation. To overcome such obstacles, firms with strongdynamic innovation capabilities could use open innovationactivities to coordinate their resources with third parties. Suchopen innovation activities stimulate greater breakthroughinnovation, which may help firms avoid this disadvantage.Our results should be interpreted in light of some of the

limitations of the study. First, our analysis of breakthroughinnovation is limited to the new product domain. Furtherresearch should examine breakthrough innovation in otherdomains (e.g. new services) and investigate the role ofdynamic innovation capabilities in those contexts. Second, allof our measures relied on senior managers’ subjectivejudgments. Objective data would be useful for validating ourhypotheses. Third, the results based on the perspectives ofsenior managers. A potential limitation of this is possible biasfrom collecting data from a single key informant. Althoughmeasures were taken to reduce such bias, the use of multiplerespondents would have been preferable. Future researchcould examine similar characteristics using data provided bylower level managers. Fourth, the results of this studyrepresent a cross-section of senior manager perceptions.However, the cross-sectional nature of this research intobreakthrough innovation allows us to analyze firms’ conductat only one specific point in time, not over a period of time.Thus, further longitudinal evaluation may be needed. Finally,while the response rate is less than we had hoped, we believethis is thanks partly to the very sensitive nature of thequestions. Future research could attempt to obtain data onthe dependent and independent variables from multiplesources.

References

Adams, R., Bessant, J. and Phelps, R. (2006), “Innovationmanagement measurement: a review”, International Journalof Management Review, Vol. 8 No. 1, pp. 21-47.

Afuah, A. (2002), “Mapping technological capabilities intoproduct markets and competitive advantage: the case ofcholesterol drugs”, Strategic Management Journal, Vol. 23No. 2, pp. 171-179.

Aiken, L. and West, S. (1991), Multiple Regression: Testing andInterpreting Interactions, Sage Publications, Newbury Park,CA.

Anderson, J.C. and Gerbing, D. (1988), “Structural equationmodeling in practice: a review and recommended two step

approach”, Psychological Bulletin, Vol. 103 No. 3,pp. 411-423.

Antikainen, M. and Vaataja, H. (2010), “Rewarding in openinnovation communities-how to motivate members”,International Journal of Entrepreneurship and InnovationManagement, Vol. 11 No. 4, pp. 440-456.

Armstrong, S. and Overton, T. (1977), “Estimating non-response in mailed surveys”, Journal of Marketing Research,Vol. 14 No. 3, pp. 396-402.

Atuahene-Gima, K. and Ko, A. (2001), “An empiricalinvestigation of the effect of market orientation andentrepreneurship orientation alignment on productinnovation”, Organization Science, Vol. 12 No. 1, pp. 54-71.

Atuahene-Gima, K. and Wei, Y. (2011), “The vital role ofproblem-solving competence in new product success”,Journal of Product Innovation Management, Vol. 28 No. 1,pp. 81-98.

Bagozzi, R. and Yi, Y. (1988), “On the evaluation ofstructural equation models”, Journal of the Academy ofMarketing Science, Vol. 14 No. 3, pp. 74-94.

Baker, W.E. and Sinkula, J.M. (1999), “Learning orientation,market orientation, and innovation: integrating andextending models of organizational performance”, Journalof Market Focused Management, Vol. 3 No. 4, pp. 295-308.

Baker, W.E. and Sinkula, J.M. (2007), “Does marketorientation facilitate balanced innovation programs? Anorganizational learning perspective”, Journal of ProductInnovation Management, Vol. 24 No. 4, pp. 316-334.

Barney, J. (1991), “Firm resources and sustained competitiveadvantage”, Journal of Management, Vol. 17 No. 1,pp. 99-120.

Benner, M. and Tushman, M. (2003), “Exploitation,exploration, and process management: the productivitydilemma revisited”, Academy of Management Review, Vol. 28No. 2, pp. 238-256.

Blindenbach-Driessen, F., van Dalen, J. and van den Ende, J.(2010), “Subjective performance assessment of innovationprojects”, Journal of Product Innovation Management, Vol. 27No. 4, pp. 572-592.

Chandy, R. and Tellis, G. (2000), “The incumbent’s curse?Incumbency, size, and radical product innovation”, Journalof Marketing, Vol. 64 No. 3, pp. 1-17.

Cheng, C. and Huizingh, K. (2010), “Open innovation toincrease innovation performance: evidence from a largesurvey”, in Huizingh, K.R.E., Conn, S., Torkelli, M. andBitran, I. (Eds), Proceedings of the XXI ISPIM InternationalConference, Bilbao, June 6-9.

Chesbrough, H. (2003), “The era of open innovation”, MITSloan Management Review, Vol. 44 No. 3, pp. 35-41.

Chesbrough, H. (2010), “Business model innovation:opportunities and barriers”, Long Range Planning, Vol. 43Nos 2/3, pp. 354-363.

Chesbrough, H. and Appleyard, M. (2007), “Openinnovation and strategy”, California Management Review,Vol. 50 No. 1, pp. 57-76.

Chesbrough, H.W. (2006), Open Business Models. How toThrive in the New Innovation Landscape, Harvard BusinessSchool Press, Boston, MA.

Chesbrough, H.W. (2007), “Why companies should haveopen business models”, MIT Sloan Management Review,Vol. 48 No. 2, pp. 22-28.

Christensen, C. (2006), “The ongoing process of building atheory of disruption”, Journal of Product InnovationManagement, Vol. 23 No. 1, pp. 39-55.




Volume 28 · Number 5 · 2013 · 444–454

450

Christensen, C., Cook, S. and Hall, T. (2005), “Marketingmalpractice”, Harvard Business Review, Vol. 83 No. 12,pp. 74-83.

Christensen, C.M. (1997), The Innovator’s Dilemma, HarvardBusiness School Press, Boston, MA.

Churchill, J.G. (1979), “A paradigm for developing bettermeasures of marketing constructs”, Journal of MarketingResearch, Vol. 16 No. 1, pp. 64-73.

Citrin, A., Lee, R. and McCullough, J. (2007), “Informationuse and new product outcomes: the contingent role ofstrategy type”, Journal of Product Innovation Management,Vol. 24 No. 3, pp. 259-273.

Cohen, W.M. and Levinthal, D.A. (1990), “Absorptivecapacity: a new perspective on learning and innovation”,Administrative Science Quarterly, Vol. 35 No. 1, pp. 128-152.

Danneels, E. (2004), “Disruptive technology reconsidered: acritique and research agenda”, Journal of Product InnovationManagement, Vol. 21 No. 4, pp. 246-258.

Davenport, T., Leibold, M. and Voelpel, S. (2006), StrategicManagement in the Innovation Economy. Strategy Approachesand Tools for Dynamic Innovation Capabilities, Wiley, NewYork, NY.

De Brentani, U. and Kleinschmidt, E. (2004), “Corporateculture and commitment: impact on performance ofinternational new product development programs”,Journal of Product Innovation Management, Vol. 21 No. 5,pp. 309-333.

De Man, A. and Duysters, G. (2005), “Collaboration andinnovation: a review of the effects of mergers, acquisitionsand alliances on innovation”, Technovation, Vol. 25 No. 12,pp. 1377-1387.

De Visser, M., de Weerd-Nederhof, P., Faems, D, Song, M.,Van Looy, B. and Visscher, K. (2010), “Structuralambidexterity in NPD processes: a firm-level assessmentof the impact of differentiated structures on innovationperformance”, Technovation, Vol. 30 Nos 5/6, pp. 291-299.

Deck, M.J. (2008), “Open business models: how to thrive inthe new innovation landscape by Henry Chesbrough”,Journal of Product Innovation Management, Vol. 25 No. 4,pp. 406-408.

Di Benedetto, A. (2010), “Comment on ‘Is open innovation afield of study or a communication barrier to theorydevelopment?’”, Technovation, Vol. 30 Nos 11/12, p. 557.

Dillman, D.A. (2000), Mail and Internet Surveys: The TailoredDesign Method, Wiley, New York, NY.

Dodgson, M., Gann, D. and Salter, A. (2006), “The role oftechnology in the shift towards open innovation: the case ofProcter & Gamble”, R&D Management, Vol. 36 No. 3,pp. 333-346.

Eisenhardt, K. (1989), “Building theories from case studies”,Academy of Management Review, Vol. 14 No. 4, pp. 532-550.

Eisenhardt, K. and Martin, J. (2000), “Dynamic capabilities:what are they?”, Strategic Management Journal, Vol. 21Nos 10/11, pp. 1105-1121.

Faems, D., De Visser, M. and Van Looy, B. (2009),“Technology alliance portfolios and financial performance:disentangling value enhancing and cost increasing effects ofopen innovation”, Proceedings of the 16th InternationalProduct Development Management Conference, Twente, June7-9.

Fornell, C. and Larcker, F. (1981), “Evaluating structuralequation models with unobservable variables andmeasurement errors”, Journal of Marketing Research,Vol. 18 No. 1, pp. 39-50.

Garcia, R. and Calantone, R. (2002), “A critical look attechnological innovation typology and innovativenessterminology: a literature review”, Journal of ProductInnovation Management, Vol. 19 No. 2, pp. 110-132.

Gassmann, O. and Zeschky, M. (2008), “Opening up thesolution space: the role of analogical thinking forbreakthrough product innovation”, Creativity andInnovation Management, Vol. 17 No. 2, pp. 97-106.

Gassmann, O., Enkel, E. and Chesbrough, H. (2010), “Thefuture of open innovation”, R&D Management, Vol. 40No. 3, pp. 213-221.

Gatignon, H. and Xuereb, J. (1997), “Strategic orientation ofthe firm and new product performance”, Journal ofMarketing Research, Vol. 34 No. 1, pp. 77-90.

Gerbing, D. and Anderson, J. (1988), “An updated paradigmfor scale development incorporating unidimensionality andits assessment”, Journal of Marketing Research, Vol. 25 No. 2,pp. 186-192.

Grewal, R., Cote, J. and Hans, B. (2004), “Multicollinearityand measurement error in structural equation models:implications for theory testing”, Marketing Science, Vol. 23No. 4, pp. 519-529.

Hair, J., Tatham, R., Anderson, R. and Black, W. (2006),Multivariate Data Analysis, 5th ed., Prentice Hall, UpperSaddle River, NJ.

Han, J., Kim, N. and Srivastava, R. (1998), “Marketorientation and organizational performance: Is innovationa missing link?”, Journal of Marketing, Vol. 62 No. 4,pp. 30-45.

Hannan, M.T. and Freeman, J.H. (1984), “Structural inertiaand organizational change”, American Sociological Review,Vol. 49 No. 2, pp. 149-164.

Hertog, P., Aa, W. and Jong, M. (2010), “Capabilities formanaging service innovation: towards a conceptualframework”, Journal of Service Management, Vol. 21 No. 4,pp. 490-514.

Homburg, C., Workman, J. and Krohmer, H. (1999),“Marketing’s influence within the firm”, Journal ofMarketing, Vol. 63 No. 4, pp. 1-17.

Hoskisson, R., Eden, L., Lau, C. and Wright, M. (2000),“Strategy in emerging economies”, Academy of ManagementJournal, Vol. 43 No. 3, pp. 249-267.

Hult, G., Hurley, R. and Knight, G. (2004), “Innovativeness:its antecedents and impact on business performance”,Industrial Marketing Management, Vol. 33 No. 5,pp. 429-438.

Im, S. and Workman, J. (2004), “Market orientation,creativity, and new product performance in high-technology firms”, Journal of Marketing, Vol. 68 No. 2,pp. 114-132.

Johannessen, J., Olsen, B. and Lumpkin, G. (2001),“Innovation as newness: what is new, how new, and newto whom?”, European Journal of Innovation Management,Vol. 4 No. 1, pp. 20-31.

Joreskog, K. and Sorbom, D. (1993), LISREL 8: StructuralEquation Modeling with the SIMPLIS Command Language,Lawrence Erlbaum Associates, Hillsdale, NJ.

Kandemir, D., Yaprak, A. and Cavusgil, S.T. (2006),“Alliance orientation: conceptualization, measurement,and impact on market performance”, Journal of theAcademy of Marketing Science, Vol. 34 No. 3, pp. 324-341.

Laursen, K. and Salter, A. (2006), “Open for innovation: therole of openness in explaining innovation performanceamong UK manufacturing firms”, Strategic ManagementJournal, Vol. 27 No. 2, pp. 131-150.




Volume 28 · Number 5 · 2013 · 444–454

451

Lavie, D. (2006), “The competitive advantage ofinterconnected firms: an extension of the resource-basedview”, Academy of Management Review, Vol. 31 No. 3,pp. 638-658.

Lavie, D. and Rosenkopf, L. (2006), “Balancing explorationand exploitation in alliance formation”, Academy ofManagement Journal, Vol. 49 No. 4, pp. 797-818.

Levinthal, D.A. and March, J.G. (1993), “The myopia oflearning”, Strategic Management Journal, Vol. 14, S2,pp. 95-112.

Lewin, A., Massini, S. and Peeters, C. (2011), “The microfoundations of internal and external absorptive capacityroutines”, Organization Science, Vol. 22 No. 1, pp. 81-98.

Lichtenthaler, U. (2011), “Open innovation: past research,current debates, and future directions”, The Academy ofManagement Perspectives, Vol. 25 No. 1, pp. 75-93.

Lieberman, M.B. and Montgomery, D.B. (1998), “First-mover (dis)advantages: retrospective and link with theresource-based view”, Strategic Management Journal, Vol. 19No. 12, pp. 1111-1125.

Mason, C. and Perreault, W. (1991), “Collinearity, power,and interpretation of multiple regression analysis”, Journalof Marketing Research, Vol. 28 No. 3, pp. 268-280.

Menguc, B., Auh, S. and Shih, E. (2007), “Transformationalleadership and market orientation: Implications for theimplementation of competitive strategies and business unitperformance”, Journal of Business Research, Vol. 60 No. 4,pp. 314-321.

Menon, A., Bharadwj, S. and Howell, R. (1996), “Thequality and effectiveness of marketing strategy: effects offunctional and dysfunctional conflict in intraorganizationalrelationships”, Journal of the Academy of Marketing Science,Vol. 24 No. 4, pp. 299-313.

Morgan, N., Vorhies, D. and Mason, C. (2009), “Marketorientation, marketing capabilities, and firm performance”,Strategic Management Journal, Vol. 30 No. 8, pp. 909-920.

Narver, J., Slater, S. and MacLachlan, D. (2004), “Low andhigh market orientation and new product success”, Journalof Product Innovation Management, Vol. 21 No. 5,pp. 334-347.

Nelson, R. and Winter, S. (1982), An Evolutionary Theory ofEconomic Change, Belknap Press, Cambridge, MA.

Nunnally, J.C. (1978), Psychometric Theory, McGraw-Hill,New York, NY.

O’Connor, G. (2009), “Sustaining breakthrough innovation”,Research Technology Management, Vol. 52 No. 3, pp. 12-14.

O’Connor, G. and De Martino, R. (2006), “Organizing forradical innovation: an exploratory study of the structuralaspects of ri management systems in large establishedfirms”, Journal of Product Innovation Management, Vol. 23No. 6, pp. 475-497.

Podsakoff, P., MacKenzie, S., Lee, J. and Podsakoff, N.(2003), “Common method biases in behavioral research: acritical review of the literature and recommendedremedies”, Journal of Applied Psychology, Vol. 88 No. 5,pp. 879-903.

Podsakoff, P.M. and Organ, D.W. (1986), “Self-reports inorganizational research: problems and prospects”, Journalof Management, Vol. 12 No. 4, pp. 531-544.

Rogers, E. (2003), Diffusion of Innovations, 5th ed., The FreePress, New York, NY.

Rohrbeck, R., Holzle, K. and Gemunden, H. (2009),“Opening up for competitive advantage – how Deutsche

Telekom creates an open innovation ecosystem”, R&DManagement, Vol. 39 No. 4, pp. 420-430.

Rosenkopf, L. and Nerkar, A. (2001), “Beyond local search:boundary-spanning, exploration, and impact in the opticaldisk industry”, Strategic Management Journal, Vol. 22 No. 4,pp. 287-306.

Shefer, D. and Frenkel, A. (2005), “R&D, firm size andinnovation: an empirical analysis”, Technovation, Vol. 25No. 1, pp. 25-32.

Song, M. and Di Benedetto, A. (2008), “Supplier’sinvolvement and success of radical new productdevelopment in new ventures”, Journal of OperationsManagement, Vol. 26 No. 1, pp. 1-22.

Song, M. and Parry, M.E. (1996), “What separates Japanesenew product winners from losers”, Journal of ProductInnovation Management, Vol. 13 No. 5, pp. 422-439.

Song, M., Droge, C., Hanvanich, S. and Calantone, R.(2005), “Marketing and technology resourcecomplementarity: an analysis of their interaction effect intwo environmental contexts”, Strategic Management Journal,Vol. 26 No. 3, pp. 259-276.

Teece, D. (2007), “Explicating dynamic capabilities: thenature and micro foundations of (sustainable) enterpriseperformance”, Strategic Management Journal, Vol. 28No. 13, pp. 1319-1350.

Teece, D., Pisano, G. and Shuen, A. (1997), “Dynamiccapabilities and strategic management”, StrategicManagement Journal, Vol. 18 No. 7, pp. 509-535.

Van Egeren, M. and O’Connor, S. (1998), “Drivers of marketorientation and performance in service firms”, Journal ofService Marketing, Vol. 12 No. 1, pp. 39-58.

Verona, G. and Ravasi, D. (2003), “Unbundling dynamiccapabilities: an exploratory study of continuous productinnovation”, Industrial and Corporate Change, Vol. 12 No. 3,pp. 577-606.

Volberda, H.W., Foss, N. and Lyles, M. (2010), “Absorbingthe concept of absorptive capacity: how to realize itspotential in the organization field”, Organization Science,Vol. 21, pp. 931-951.

Vrande, V., Jong, J., Vanhaverbeke, W. and Rochemont, M.(2009), “Open innovation in SMEs: trends, motives andmanagement challenges”, Technovation, Vol. 29 Nos 6/7,pp. 23-437.

Wagner, E. and Hansen, E. (2005), “Innovation in largeversus small companies: insights from the US woodproducts industry”, Management Decision, Vol. 43 Nos 5/6, pp. 837-850.

Wirtz, B., Schilke, O. and Ullrich, S. (2010), “Strategicdevelopment of business models: implications of the Web2.0 for creating value on the internet”, Long RangePlanning, Vol. 43 Nos 2/3, pp. 272-290.

Yin, R. (2009), Case Study Research: Design and Methods, 4thed., Sage Publications, Newbury Park, CA.

Zahra, S. and George, G. (2002), “Absorptive capacity: areview, reconceptualization and extension”, Academy ofManagement Review, Vol. 27 No. 2, pp. 185-203.

Zhou, K., Yim, B. and Tse, D. (2005), “The effects ofstrategic orientations on technology- and market-basedbreakthrough innovations”, Journal of Marketing, Vol. 69No. 2, pp. 42-60.

Zollo, M. and Winter, S. (2002), “Deliberate learning and theevolution of dynamic capabilities”, Organization Science,Vol. 13 No. 3, pp. 339-351.




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Appendix

About the authors

Colin C.J. Cheng is an Associate Professor of College ofManagement in Yuan Ze University, Taiwan. He received hisPhD in Commerce from the University of Birmingham, UK.His primary research interests include innovation and newproduct/service development.Ja-Shen Chen is currently a Professor and Dean of College

of Management in Yuan Ze University, Taiwan. He holds MSand PhD both in Decision Sciences from Rensselaer

Polytechnic Institute, NY. His research interests include

service innovation, customer relationship management, and e-

business management. He has published a number of research

articles including recent ones appearing in Information and

Management, Industrial and Marketing Management, Journal of

Service Research and OMEGA. He also actively associates with

industries as a consultant or a principal project investigator.

Ja-Shen Chen is the corresponding author and can be

contacted at: [email protected]

Table AI The scale

Factor loading t-value

Dynamic innovation capabilities (a5 0:92, CR5 0:88, AVE5 0:60)

Compared to your major competitors, how would you evaluate your firm’s dynamic innovation capabilities in the following areas? (15much

worse; 75much better)

Acquiring important new product information 0.71 9.26

Responding to new product changes 0.72 8.95

Mastering state-of-the-art new products 0.81 10.84

Developing a series of new products constantly 0.81 11.22

Identifying new product opportunities 0.82 11.34

Open innovation activities (a5 0:90, CR5 0:91, AVE5 0:69) (15 strongly disagree; 75 strongly agree)

Our firm produces new products in open ways 0.78 10.07

Our firm brings together new participants 0.82 11.22

Our firm links participants to transactions in open ways 0.89 13.08

The richness (i.e. quality and depth) of our links with participants is open 0.86 12.29

Overall, our firm uses any possible external sources 0.80 10.88

Breakthroughinnovation (a5 0:89, CR5 0:93, AVE5 0:70) (15 strongly disagree; 75 strongly agree)

Our new products are highly innovative, replacing inferior alternatives 0.87 12.29

Our new products incorporate radically new technological knowledge 0.84 12.01

Our new product concepts are difficult for mainstream customers to understand 0.83 11.64

Our new products involve high switching costs for mainstream customers 0.81 11.22

The use of our new products requires a major learning effort on the part of mainstream customers 0.82 11.34

It takes a long time for mainstream customers to understand our new products’ benefits 0.85 12.19

Market turbulence (a 5 0.90, CR5 0:84, AVE5 0:57) (1 5 none; 75 very much)

Extent of turbulence in the market 0.72 9.27

Frequent changes in customer preferences 0.79 10.78

Ability to reduce market uncertainty 0.76 10.22

Ability to respond to market opportunities 0.75 9.67

Technological turbulence (a5 0:87, CR 5 0.89, AVE 5 0.73) (15 strongly disagree; 75 strongly agree)

The technology in this industry is changing rapidly 0.81 8.97

Technological changes provide big opportunities in our industry 0.89 13.58

A large number of new product ideas have been made through technological breakthroughs 0.86 11.13

Competitiveintensity (a5 0:81, CR5 0:83, AVE5 0:61) (15 strongly disagree; 75 strongly agree)

There are too many similar products in the market 0.76 9.52

It is very difficult to differentiate our products 0.78 10.22

This market is too competitive 0.71 8.26

Notes: Overall model: x2=df ¼ 1:43, RMSEA ¼ 0:04, CFI ¼ 0:94, NNFI ¼ 0:95, PNFI ¼ 0:85; RMSEA, root mean square error of approximation; CFI,comparative fit index; NNFI, non-normed fit index; PNFI, parsimony-adjusted normal fit index; CR, composite reliability; AVE, average variance extracted




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Executive summary and implications formanagers and executives

This summary has been provided to allow managers and executivesa rapid appreciation of the content of the article. Those with aparticular interest in the topic covered may then read the article intoto to take advantage of the more comprehensive description of theresearch undertaken and its results to get the full benefit of thematerial present.

Innovation is one word that you can guarantee will be spokenwhen anyone is talking about how companies can flourish,particularly when those organizations might be very similar inwhat products or services they supply, either to otherbusinesses or individual consumers. Standing still is not anoption – no matter how good your product or service.Constant innovation is key.Recognizing this is the easy bit. Knowing how to achieve

that innovation is rather more complex, especially when youhave to grapple with the concepts of breakthrough innovation,dynamic innovation, open innovation, incremental innovationetc., and also because there are conflicting opinions abouthow to mix and match such notions to achieve what yourorganization needs to secure its innovative credentials.To enhance breakthrough innovation, some say firms

should invest heavily in developing dynamic innovationcapabilities. Others suggest that firms can use openinnovation to produce radically new products. It is said thatdynamic innovation capabilities may foster greaterbreakthrough innovation. When a firm builds its dynamicinnovation capabilities, its absorptive capacity increases, andas a result it is encouraged to explore new information andeventually develop breakthrough innovation. However, othersclaim that dynamic innovation capabilities may discouragebreakthrough innovation. When firms accumulate moreexperience and become more efficient at using their existingknowledge, the self-reinforcing nature of learning producesmore incremental innovation rather than breakthroughinnovation.There are equally conflicting views regarding open

innovation. Not all firms adopt open innovation activitiesbecause they prefer to have more control over the sourcebreakthrough innovation and their relationships withinnovation partners. In addition, not all firms are successfulin adopting open innovation activities. For example, the directcost of acquiring technology from third parties is often greaterthan the indirect value generated by having this technology.Aiming to shed some light on the effect of dynamic

innovation capabilities on breakthrough innovation in thelight of the moderating role of open innovation, Colin C.J.Cheng and Ja-Shen Chen warn that firms must be aware ofthe limitations of their existing dynamic innovationcapabilities in terms of developing breakthrough innovation.In “Breakthrough innovation: The roles of dynamicinnovation capabilities and open innovation activities” theysay: “For example, firms with strong dynamic innovationcapabilities should understand that although their dynamicinnovation capabilities increasingly enhance their

breakthrough innovation, they may trap them in incrementalinnovation, causing them to focus on existing customers andpreventing them from exploring breakthrough innovation.“To overcome such obstacles, firms with strong dynamic

innovation capabilities could use open innovation activities tocoordinate their resources with third parties. Such openinnovation activities stimulate greater breakthroughinnovation, which may help firms avoid this disadvantage.”Dynamic innovation capabilities are operational capabilities

that include organizational learning processes and routinesrooted in innovation knowledge and that involvetransformation of a firm’s innovation knowledge resourcesand routines. The authors describe them as those hard-to-transfer and hard-to-imitate innovation capabilities that firmsuse to develop, integrate, and reconfigure existing and newresources and operational capabilities.The degree of innovation can range from totally new to a

minor improvement. Depending on their newness,innovations can be categorized as incremental orbreakthrough. Incremental innovations are minor changes intechnology, simple product improvements, or line extensionsthat minimally improve existing performance. In contrast,breakthrough innovations involve substantially newtechnology, offer substantially greater customer benefitsrelative to existing products, and demand considerablechanges to consumption or usage patterns. A breakthroughinnovation may imply a greater level of complexity and mayrequire a new knowledge base and innovation capabilities.The study finds that dynamic innovation capabilities have

an inverted U-shape relationship with breakthroughinnovation. That is, at the beginning, dynamic innovationcapabilities relate to the highest degree of breakthroughinnovation, whereas in later stages they prevent breakthroughinnovation. More specifically, the longer firms have dynamicinnovation capabilities, the more rooted they may become inexisting environments such that they might overlook emergingchanges in the environment. Moreover, the longer firms havedynamic innovation capabilities, the more they are unable tomanage changes in the environment. Organizational inertiafurther discourages breakthrough innovation. As a result, thelonger firms have dynamic innovation capabilities, the lessfirms intend to develop breakthrough innovation.Cheng and Chen argue that open innovation activities as an

organizing principle may not directly affect breakthroughinnovation. Their findings indicate that open innovationactivities enhance the positive effect of dynamic innovationcapabilities on breakthrough innovation. More specifically,open innovation activities strengthen the positive influence ofdynamic innovation capabilities. Therefore, open innovationactivities are one type of enhancement to dynamic innovationcapabilities that enable firms to achieve breakthroughinnovation.

(A precis of the article “Breakthrough innovation: the roles ofdynamic innovation capabilities and open innovation activities”.Supplied by Marketing Consultants for Emerald.)




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