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420 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 55, NO. 3, AUGUST 2008

Strategic Capabilities and Radical Innovation:An Empirical Study in Three Countries

C. Anthony Di Benedetto, Wayne S. DeSarbo, and Michael Song

Abstract—This paper examines strategic capabilities as driversof the development and launch of radical innovations. We constructa theoretical framework relating five strategic capabilities (mar-keting, market linking, technology, information technology, andmanagement-related capabilities) to radical innovation. From thisframework, we derive hypotheses concerning a division’s propen-sity to engage in radical innovation. Using empirical data derivedfrom a research study of 376 firms in the United States, Japan, andChina, we apply analysis of variance and negative binomial distri-bution (NBD) regression techniques to test our hypotheses. We findevidence that, overall, technology and information technology ca-pabilities are significantly and positively related to radical productinnovation. We also find some significant differences among thethree country samples concerning drivers of radical innovation.Marketing capability is more significantly and positively relatedto radical innovation in the United States than in Japan; and, inChina, the only capability that is significantly and positively relatedto radical innovation is technology. All of the findings completelyor partially support our research hypotheses. We conclude witha discussion of the managerial implications of our findings, anddirections for future research.

Index Terms—Cross-national analysis, Japan and China, neg-ative binomial distribution regression models, radical innovation,strategic capabilities.

I. INTRODUCTION

F IRMS and strategic business units rely on innovation as adriver of growth. Recent studies of new product managers

find that, on average, nearly 30% of sales and profits are obtainedfrom products that are less than five years old; the best productinnovating firms derive almost half of their sales and profitsfrom new products [3]. Many managers recognize the particu-lar importance of “radical innovation” to long-term growth [75,p. 34]. Radical innovations are usually defined as innovationsthat use new technologies and/or create new markets [2]; thetechnologies employed are different enough from existing prac-tice so as to obsolete existing technology [5]. These innovationstherefore can displace or obsolete current products, creating en-tirely new product categories [77]. As a result, firms that excelin radical innovation consistently outperform competitors [75,p. 34]. Business writer Gary Hamel asserts that building compa-

Manuscript received January 1, 2007; revised May 1, 2007 and July 1, 2007.Review of this manuscript was arranged by Department Editor P. E. Bierly.

C. A. Di Benedetto is with Temple University, Philadelphia, PA 19140 USA.W. S. DeSarbo is with Smeal College of Business, Pennsylvania State Uni-

versity, University Park, PA 16802 USA.M. Song is with the Institute for Entrepreneurship and Innovation, University

of Missouri, Kansas City, MO 64110 USA.Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TEM.2008.922645

nies that can systemically create radical innovation is “the mostimportant business issue of our time” [43].

Some extant research has specifically examined the precur-sors of radical innovation, including firm size [14], [29], [60],environmental dynamism and organizational structure [28], orwillingness to cannibalize specialized assets [13], but withmixed results. Many studies in the marketing and managementliterature have examined strategic capabilities that are linked tocompetitive advantage and long-term success [18], [23], [25],[53], [81]. While the number of strategic capabilities identifiedin these studies is too large to list, five of them are most oftenidentified in the literature as significant precursors or criticaldrivers of competitive advantage. These include marketing ca-pabilities (such as skill in segmentation and targeting, and im-plementing marketing programs), technology capabilities (in-cluding skill in technology and product development as well asproduction and manufacturing process skills), market linkingcapabilities (market sensing, customer linking, and technologymonitoring skill), information technology capabilities (skill indiffusing technical and market information throughout all func-tional areas), and management-related capabilities (includinghuman resource management, financial management, and fore-casting) [18], [24], [26], [31], [40], [51], [69], [73], [81], [89].

Henderson and Clark [47] proposed that radical innovationsinvolve changes in core concepts and also in linkages betweenthe core concepts and components. Innovation involving nochange in core competence, but rather affecting the linkagesbetween concepts and components, are termed “architectural in-novations.” Firms often have difficulty adapting to architecturalinnovation and may confuse them with true radical innovation.The capabilities that drive successful architectural innovationmay differ from those that drive radical innovation [47]. A fewother research studies [12], [19], [74] have examined whetherthe drivers of radical innovation differ from those of incrementalinnovation (i.e., innovations that build upon existing technologyrather than making it obsolete), again with mixed results (seediscussion next). To our knowledge, no studies have attemptedto identify the specific strategic competencies that drive radicalinnovation or to determine if there are significant cross-nationaleffects.

In this paper, we gather data from strategic business units inJapan and China, as well as in the United States. We includeJapan and China in our paper because of their importance in theglobal economy: in addition to being the two largest East Asianeconomies, they make up two of the three largest economies inthe world as measured by purchasing power parity (PPP), theother being the United States [89]. Japan is one of the dominantglobal economies (the others being North America and Western

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DI BENEDETTO et al.: STRATEGIC CAPABILITIES AND RADICAL INNOVATION 421

Europe), and due to its population, changing economic policies,and projected economic growth, China is seen as among themost important big emerging markets (BEM) projected to bemajor forces in the global economy in upcoming decades.

Both Japan and China are marked by major cultural differ-ences such as higher collectivism and a longer-term orientation.Further, the business environment in Japan is characterized bythe substantial role of government, in the form of the Ministryof Economy, Trade, and Industry (METI), and interorganiza-tional relationships (keiretsu). The Chinese business environ-ment is marked by a high percentage of state-owned enterprisesand centralized decision making, although decentralization isoccurring in many industries. Both nations have prioritized in-vestment in technology and in radical innovation as a means toincrease global competitiveness. Given the importance of thesetwo economies in the global market, it is important to understandthe key drivers of radical innovation in these countries.

Our research objective is to understand the effect of strategiccapabilities possessed at the divisional level as precursors tothe development and launch of radical innovations. Specifically,our first objective is to build a theoretical framework relatingstrategic capabilities to the development and launch of radicalinnovations, and to test hypotheses derived from this model. Asa second objective, we determine if the relationship betweenstrategic capabilities and radical innovation is moderated bycountry, and we test two cross-national hypotheses. Our em-pirical data are derived from an extensive empirical researchstudy conducted with 376 firms in the United States, Japan,and China, who collectively developed a total of 380 radicalinnovations.

To accomplish our objectives, we first conduct an analysis ofvariance to determine if there are significant differences in levelsof radical innovation by country. Next, using negative binomialdistribution (NBD) regression techniques, we determine the re-lationships between radical innovation and the five strategiccapabilities. We run NBD regressions with these various strate-gic capabilities as the independent variables. We determine ifthe relationships are moderated by country, and examine empir-ical factor changes by capability to study country heterogeneity.Finally, we discuss managerial implications and future researchdirections.

II. THEORETICAL BACKGROUND

A. Radical Innovation

It is recognized that some innovations pose greater risks forcompanies than others due to the uncertainties involved in thecreation of new technologies and new markets [75, p. 33]. Werefer to these as radical innovations, and use the definition ofLiefer et al. [60, pp. 5–6]: these are innovations that “involve thediscovery of new technologies and the creation of new markets.”

This definition is similar to that of Abernathy and Utter-back [2], who defined radical innovations as those involving thecreation of new technologies and/or markets and that “are con-ceptual shifts that make history.” Liefer et al.’s [60] definitionis consistent with that used by later authors studying radicalinnovation [20], [36], [64], [70], [72], [74], [77]. It is also con-

sistent with the “organizational view” of classifying innovations(see [5]). Under this view, radical innovations require techno-logical discontinuities that can be competence-destroying (thetechnology is so different from existing knowledge as to ren-der existing technology obsolete) or competence-enhancing (thetechnology, while different from existing knowledge, ultimatelystrengthens the position of existing competitors) [87]. The tran-sistor was a competence-destroying technology, for example(as it made vacuum tubes obsolete), while the jet engine was acompetence-enhancing one (since it strengthened the competi-tive position of existing aircraft makers).

To test the appropriateness of the Liefer et al. [60] definitionof radical innovation, we conducted field research to compareit to other definitions used in the literature. We presented 42well-known radical innovations, and about the same numberof incremental innovations, to a panel of three experts withconsulting experience in innovation management in the UnitedStates, China, and Japan. We supplied the experts with four ma-jor definitions of radical innovation, and asked them to classifythe innovations as radical or incremental using each definition.Liefer et al.’s [60] definition had the highest percentage of cor-rect classifications. Therefore, based on both academic literatureand field research, we adopt Liefer et al.’s [60] definition forthis paper.

A significant literature exists that examines antecedent factorsto radical innovation. The role of firm size has been investigatedin a few studies. Chandy and Tellis [14] noted that many man-agers accept the notion of the “incumbent’s curse” (that is, largeincumbent firms are less adept at radical innovations than smalloutsider firms), but their empirical research did not support thisnotion (see also [60]). In fact, it has been suggested that largerfirms are more likely to adopt radical innovation, because theyhave more technical specialists available [29].

Other organizational drivers of radical innovation have alsoreceived research attention. In a study of the food processingindustry, different organizational strategies and structures werefound to support different types of innovation [33]. Some driversof radical innovation in the pharmaceutical industry (such asextent of organizational control) were found to be the same asthose that drove incremental innovation [12]; see also [19]. Otherresearch comparing radical to incremental innovation, however,did find differences in innovation drivers [74]. For example,some marketing-related capabilities (such as market potentialand growth potential) drove incremental innovation but wereunimportant for radical innovation.

B. Strategic Capabilities and Radical Innovation

Strategic capabilities have been defined as “complex bundlesof skills and accumulated knowledge that enable firms to co-ordinate activities and make use of their assets” [23, p. 38].A broad literature exists in theory development and/or empiri-cal research into strategic capabilities. Many empirical studiestake the resource-based view (RBV) in which capabilities areseen as the way by which firms deploy their resources in or-der to achieve a desirable objective, such as sustainable com-petitive advantage [7], [31]. Central to the RBV is the notion



that the marginal payoffs among capabilities must be under-stood, so that the firm can make optimal decisions on acquiringcapabilities [34]. Recent literature provides evidence of a dy-namic relationship between capabilities and innovation: inno-vation may require a firm to draw on existing competencies, orto invest in new ones [20], [45]. As noted earlier, we focus onstrategic capabilities that the marketing and management litera-ture has linked to competitive advantage and long-term success(e.g., [18], [23], [25], [53], and [81]). These include:

1) Marketing capabilities: segmentation, targeting, pricingand advertising skills, knowledge of customers and com-petition, and skill at integration of marketing activities.These permit the firm to better implement its marketingprograms [18], [26], [51], [69].

2) Technology capabilities: technology or R&D develop-ment, product development, production process, manu-facturing process, technological change forecasting, andlogistics. These capabilities allow a firm to maintain costsand/or achieve product differentiation, and often developin response to external challenges and opportunities pre-sented by the market, the competition, and the environ-ment [24], [31].

3) Market linking capabilities: market sensing, channel link-ing, customer linking, and technology monitoring. Theseallow a firm to increase competitiveness by detectingchanges in the market environment early so that it canrespond efficiently to changing customer needs [24].

4) Information technology (IT) capabilities: those that allowfor the diffusion of technical and market information effec-tively throughout all relevant functional areas and increasestrategic flexibility [6], [24], [40], [41], [73], [81].

5) Management-related capabilities: other capabilities thatalso affect profit performance and increase effective ex-ecution of strategy. These include human resource man-agement, financial management, profit forecasting, andrevenue forecasting [26], [88].

This set of important capabilities is consistent with the “twinstream” new product development literature [8], [24], [82], [83],which notes that successful product innovation (innovation thatmeets market share or profitability goals) typically requiresstrengths in both marketing and technology, as well as a matchbetween a technology and an unmet market need.

The literature has been equivocal on the relationships betweenmarketing or market-linking capabilities and radical innovation.Some literature suggests no relationship, or even a negative rela-tionship, between these under some circumstances. Accordingto Christensen [15], firms that are overly focused on currentcustomers (i.e., highly linked with their current markets) maybecome overly risk averse, and thereby produce fewer radicalinnovations. Song and Montoya-Weiss [81] found that improv-ing proficiency in market opportunity analysis improves prof-itability for incremental products only and could be detrimentalto profitability for radical innovations. In Leonard-Barton’s re-search [59], the more radical the innovation, the less likely thecustomer will have relevant experience, ability, or professionalknowledge, and the less willing the customer will be to provideaccurate knowledge and feedback.

An opposing point of view, however, suggests that market-ing plays a paradoxical role in supporting radical innovation(e.g., [20] and [21]). While technology capabilities may be mostimportant in radical innovation, the first applications of emerg-ing technology are often in marginal markets, so it is criticalfor the incumbent firm launching a radical innovation to be ableto develop this marginal market (i.e., through its marketing andmarket-linking capabilities) in order to succeed. This abilityto build the resources to serve new markets has been called a“second-order marketing competence” [20]. The ability to iden-tify the customers making up the new market, and to establisheffective interaction (market linking) with them and to adapt tomarket changes, is critical in the development of radical newproducts [15], [59], [63], [71], as is the ability to monitor andrespond to competitors’ actions [50].

While the literature offers support for both viewpoints, ourexpectation is that marketing and market linking capabilitiesare unlikely to make a firm less capable at radical innovation,but rather will improve the firm’s ability to bring the radicalinnovation to the newly emerging markets where it initially isadopted [21]. Indeed, several authors have commented on theneed for firms to be able to understand emerging customer needsfor commercially successful radical innovation [35], [46], [66],[80] and to focus on the future market [85]. This standpoint isconsistent with Liefer’s definition [60], in which radical inno-vation involves the use of new technology in the creation of anew market. The firm must have marketing and market-linkingcapabilities to find the emerging markets that will be the firstto accept the new technology; that is, the firm must be able tocreate “market pull” for a project that may have been largely orcompletely “technology push” in origin.1

We express our expectations about the directional relation-ships between capabilities and frequency of radical innovationas follows:

H1a: Marketing capability will be significantly positively re-lated to radical innovation.

H1b: Technology capability will be significantly positively re-lated to radical innovation.

H1c: Information Technology capability will be significantlypositively related to radical innovation.

H1d: Market Linking capability will be significantly positivelyrelated to radical innovation.

H1e: Management-Related capability will be significantly pos-itively related to radical innovation.

C. Technology-Related Capabilities and Radical Innovation

Abernathy and Utterback [2] proposed a dynamic model ofproduct and process innovation. As a technology evolves, theindustry moves through three phases. In the first fluid phase,there are high levels of technological and market uncertain-ties. Products are often custom-designed to suit market niches,and the designs are subject to much change as more is learnedabout marketplace needs, and as customers learn more about the

1We are grateful to a reviewer for this explanation and for the insight on therole of market linking in supporting radical innovation.



emerging technology. Eventually, the transitional phase isreached: technological and market uncertainties subside, anda dominant form of the product emerges. The product andits components become standardized, process innovations takeprecedence, and firms begin to compete on the basis of productdifferentiation. In the third specific phase, process innovations,especially of the cost-reducing kind, predominate. Different ca-pabilities are required for firms to profit from technology as itmoves through the dynamic model stages [5]. In earlier stages,technical-related capabilities will be most critical, while at laterstages, the ability to differentiate products and to reduce costswill grow in importance (see discussion of technological versusmarket capabilities in [1]).

Based on this process model, we note that we must con-sider the differential effects of market- and technology-relatedcapabilities on innovation. To assess these effects, we grouptechnology and information technology capabilities togetheras technology-related capabilities, and group marketing andmarket-linking capabilities together as marketing-related capa-bilities. We expect that technology-related capabilities will bethe most critical to radical innovation, due to the “technology-push” nature of most radical innovations; this expectation isconsistent with the findings of O’Connor [74], who determinedthat technology-related capabilities are relatively more impor-tant than marketing-related capabilities in the case of radicalinnovation. The most important role of marketing in radicalinnovation is often to identify possible markets ([2]; [72, p.36]). Customers are unlikely to be able to articulate desiresor preferences, as the technology and its possible benefits willbe unfamiliar to them. Still, the firm’s ability to understand cus-tomer needs and match them to emerging technologies is criticalto converting the technology into a successful innovation. Thishypothesis is consistent with [74], especially regarding the rel-atively greater importance of technology-related capabilities inearliest stages of radical innovation. It is also consistent withDanneels [20], [21], who accepted that technological capabili-ties are most important to radical product development, yet the“second-order marketing competence” is also important in culti-vating the small, emerging market most interested in the radicalinnovation.

We also expect that management-related capabilities will besignificant drivers as management needs to make appropriateallocation decisions regarding financial and human resources,and accurately foresee the revenue and profits that could begenerated by the investment in radical innovation, to provideenough funding and support to the radical innovation (such asadequate promotion for the innovative and early adopter markets[20], [21], [26], [88]).

We state our expectations here as a series of testable hypothe-ses:

H2a: Technology-related capabilities will be more strongly pos-itively related to radical innovation than marketing-relatedcapabilities.

H2b: Management-related capabilities will be more stronglypositively related to radical innovation than marketing-related capabilities.

D. Cross-National Effects

Due to cultural environment differences between the UnitedStates, Japan, and China, one would expect differences amongthe drivers of radical innovation. There are very few cross-national research studies on drivers of radical innovation, otherthan Chandy and Tellis [14]. Nevertheless, we use the literatureon the business and cultural environments in these countriesto develop and test two cross-national hypotheses as describednext.

Japanese and Chinese cultures are collectivistic and long-term oriented, and value group harmony and cohesiveness. Bycomparison, the United States is more individualistic and short-term oriented: freedom of choice and competition are valuedover group cohesiveness [49], [86].

The Japanese business environment reflects these culturaltraits. The Ministry of International Trade and Industry (MITI),recently renamed the Ministry of Economy, Trade, and Indus-try (METI), encourages heavy investment in selected technolo-gies, and strong competition among Japanese firms in key in-dustries [54], in order to increase Japan’s global competitive-ness. Since 2001, METI has broadened its policies to promote agreater role for information technology and the development ofenvironmentally-friendly products [32]. MITI and METI havealso protected Japanese technology against foreign-developedproduct forms. Keiretsu (interorganizational business groups)are also seen in many Japanese industries. These comprise co-operative relationships between a major Japanese manufacturerand its suppliers and distributors (vertical keiretsu) [56], [61], orbetween several Japanese manufacturers across various relatedindustries (horizontal keiretsu) [56], [70]. Consortia of Japanesefirms may work cooperatively to perfect a new technology, asin the development of the Sony global positioning system [11].Technology development by Japanese firms is strongly sup-ported by government policy and the keiretsu, and this is stillthe case even after the emergence of the METI [32]. WhileJapanese manufacturers in industries such as carmaking aretypically strong at consumer research, it is excellence in tech-nology capabilities, prioritized by government policy and by thekeiretsu, which will have the greatest impact on radical innova-tion. We state the hypothesis:

H3a: Relative to the United States, technology capability willbe more positively related to radical innovation in Japan.

The Chinese business environment has also emerged in along-term-oriented, highly collectivistic culture, and is differ-ent from the business environments of the United States andJapan. A large number of Chinese firms are state-owned enter-prises (SOEs), typified by both administrative (firm) and partyauthority [78]. Since the 1970s, investments in technology andinnovation have been made to stimulate growth in the Chi-nese economy and boost global competitiveness. In more re-cent years, some decentralization has occurred: the SOE hasobtained more decision-making authority and also more prof-itability relative to that gained by the state [55]. Today, the SOEhas much of latitude in making decisions on product mix, prices,and outputs, among others [48], [78], and smaller enterprises



such as collectives are even less controlled by government [76].Still, government policy in China has for many years priori-tized investment in technology capability to stimulate growthand increase global competitiveness. Due to central governmentprioritization and funding, radical innovation in China will bedriven more by technology, and less by marketing and marketlinking capabilities, than is the case in the United States. Inthe literature on Chinese SOEs, information technology is notexpressly prioritized or funded by the Chinese government, sowe do not hypothesize that information technology will be asignificant driver of radical innovation in China. We state:

H3b: Relative to the United States, technology capability willbe more positively related to radical innovation in China.

III. RESEARCH DESIGN

A. Measurement Scales

Well-defined constructs should be based on theory, and theoperationalization of these constructs through measures withhigh degrees of validity and reliability is a prerequisite for anystudy [16]. We used scales developed and validated in DeSarboet al. [27] to assess the impact of five strategic capabilities(marketing, technology, information technology, market link-ing, and management) on radical innovation. The procedure isbriefly summarized next.

The constructs for the capabilities were defined based on com-petitive capability theory [24]. However, a review of the market-ing and management literature ( [6], [18], [23]–[25], [51], [53],[67], [73], [81], [88], and others) found no existing scales forthe capabilities. A three-step instrument development procedurewas used to develop appropriate scales.

1) Step 1: Measurement Items for the Capabilities: An ini-tial pool of scale items was obtained by scanning the extantliterature and compiling all items used in these articles. Theseitems were each classified into one of five categories, each cor-responding to one of the strategic types. To this pool of items,new items were added in cases where the dimensions of theconstruct were not sufficiently covered.

To ensure content validity and appropriateness of items, werefined the scales through in-depth focus interviews in twostrategic business units (SBUs). The interviews consisted ofthree parts. First, executives were asked their opinions regard-ing salient issues in SBU capabilities. In particular, we wantedto investigate the best way to measure capabilities. Second, theexecutives were asked to evaluate whether our study hypothe-ses described their own experiences adequately. The third partof the interviews addressed executives’ perceptions of the rele-vance and completeness of scale items drawn from our literaturereview and earlier case studies.

2) Step 2: Scale Development: Following [16], we assessedconstruct validity of the scales being developed, correcting anyscale items that may still be ambiguous and identifying subsetsof items that possessed “different shades of meaning” to infor-mants. Seven judges (two professors and five doctoral studentswith background in measurement development) were asked tosort the items from the first step into the five capability scales,

following Davis’s [22] procedure. First, the judges were pre-sented with the construct definition of each capability type, andasked to assess how well the items developed in step 1 fit the con-struct definitions. Second, a set of index cards with each scaleitem on a card were shuffled into random order and presentedto the judges, who had been read a standard set of instructions.Working independently, the judges sorted the cards into thecapability types. Then, construct convergence and divergencewere examined by assessing interrater reliability.

Interrater reliability was assessed in two ways. First, the per-centage of correct placement of items was calculated as theproportion of items placed by the seven judges within the in-tended theoretical construct. Higher percentages indicate higherdegree of construct validity, and a higher potential for good reli-ability. The minimum percentage obtained was 84%. Five itemswere responsible for “incorrect” placement, and were deletedfrom the pool. Second, we calculated Cohen’s Kappa [17] foreach pair of judges to measure their level of agreement in cat-egorizing items into capability types and product competitiveadvantages. The Kappa scores ranged from 0.97 to 0.82, ex-ceeding the acceptable level of 0.65 [52]. We concluded thatthe scale items were consistently placed within the correct con-structs. Therefore, the items demonstrated convergent validitywith the related capability, and discriminant validity across thecapabilities. Furthermore, because the judges’ categorizations ofitems into strategic types were consistent, we concluded that thescales demonstrated convergent and discriminant validity [22].

3) Step 3: Instrument Pretesting: Based on step 2 results,we reexamined all scale items and eliminated inappropriate,ambiguous, or inconsistently classified items. The scales werecombined into an overall instrument for additional pretesting.The instrument was distributed to 32 managers in the two SBUsto further assess scale reliability and validity. This pretest re-sulted in two further items being deleted. Then, the instrumentwas distributed to 41 EMBA students taking a new product de-velopment class. The following five capability scales were de-veloped by this procedure (see Appendix A for the scale itemsproduced by the procedure).

4) Marketing Capabilities (MKTG): Marketing capabilitieswere measured using a set of scale items drawn from the Conantet al. [18] study of marketing capability and strategic type. Theseinclude knowledge of customers and competitors, integration ofmarketing activities, skills in segmentation and targeting, andeffectiveness of pricing and advertising programs.

5) Technology Capabilities (TECH): These capabilities arerelated to technology development and new product devel-opment, as well as production processes. These were ratedaccording to scale items originally drawn from Day’s [24]set of such capabilities. The items measure relative capabili-ties in technology, new product development, and productionfacilities.

6) Information Technology Capabilities (ITECH): These re-fer to relative capabilities that support new product developmentprojects, and facilitate cross-functional communication flow.DeSarbo et al. [27] developed items that measure the possessionof information technology systems for product development andcross-functional integration.



7) Market Linking Capabilities (MLINK): The market link-ing capabilities, focusing on market sensing capabilities outsidethe organization, were rated on scale items originally developedby Day [24]. These items measure the relative capabilities increating and managing durable customer relationships, marketsensing, and creating durable relationships with suppliers.

8) Management-Related Capabilities (MR): These are othercapabilities that impact profit performance and allow firms toexecute strategy more effectively. They include human resourcemanagement, financial management, profit and revenue fore-casting, and others [26], [27], [88].

The aforesaid steps served only as a starting point for ourscale development. We then took the following steps to en-sure we avoided a North American bias. To ensure accuratetranslation, we used a double-translation method to translate thequestionnaire into Japanese and Chinese [4], [30], [79]. That is,it was first translated into the foreign language by a translatorand then translated back into English by a different translatorto ensure translation equivalence. A comparison of the resultingquestionnaires revealed considerable consistency across trans-lators.

After translation, we conducted field research in six Japanesefirms and two Chinese firms. The purposes of the field re-search were: to establish content validity of the concepts andthe hypothesized relationships among the constructs; to estab-lish equivalence of the constructs, concepts, measures, and sam-ples; and to assess the possibility of cultural or response-formatbias [30]. The field research was done over a nine-month periodwith multiple visits to the firms.

Our field research studies allowed us to assess constructequivalence (conceptual, functional, and category equivalence).They also indicated that (with minor modifications) the mea-surement scales were appropriate for studying capability andradical innovation in Japanese and Chinese contexts. The fieldresearch results suggested that it is more appropriate to ask therespondents to rate their SBU on each capability scale item rel-ative to their major competitors, and to use 11-point Likert-typescales (from 0 to 10) to measure capabilities (as recommendedin [82] and [83]), where 0 = much worse than our competitorsand 10 = much better than our competitors.

The questionnaire also required respondents to list the numberof radical and incremental (nonradical) innovations developedand launched by the division within the past three years, and alsoto provide a brief description of each innovation. For clarity, therespondents were given an abridged version of the Liefer et al.[60] definition of radical innovations (“innovations that employnew technologies and create new markets”). Finally, data onseveral control variables were also collected, as suggested by[73]: buyer power, supplier power, seller concentration, ease ofentry, market growth rate, and rate of technology change.

B. Data Collection Procedures

The data were obtained via a large mail survey of the com-panies listed in the Ward’s Business Directory, the Directory ofCorporate Affiliations, and the World Marketing Directory. Aproportionate-stratified random sample of 800 firms was drawn

from each country (United States, Japan, and China), using in-dustries as strata. The first stage of data collection was a presur-vey, in which we sent a one-page survey and an introductoryletter requesting participation, and offered a list of availableresearch reports to participating firms. Each firm was asked toselect a division for participation and provide a contact personin that division. Of the 2400 firms contacted, 1173 firms (392in the United States, 429 in Japan, and 352 in China) agreed toparticipate and provided the necessary contacts at the divisionlevel.

In the second stage of data collection, we sent the question-naire to the division managers, followed by a three-wave mail-ing. In this stage, we obtained usable data on relative capabili-ties from 152, 140, and 84 divisions in the United States, Japan,and China, respectively. These sample sizes represent responserates of 19.0% in the United States, 17.5% in Japan, and 10.5%in China. The final sample includes the following industries:computer-related products, electronics, electric equipment andhousehold appliances, pharmaceuticals, drugs and medicines,machinery, telecommunications equipment, instruments and re-lated products, air conditioning, chemicals and related products,and transportation equipment. Most participating divisions hadannual sales of $11–750 million and 100–12500 employees.

To test for nonresponse biases, we performed multivariateanalysis of variance (MANOVA) analysis on the means of ca-pabilities comparing the early and late response groups. Wefound no significant differences between early and late responsegroups. Thus, we conclude that nonresponse bias is not a majorproblem. We then tested for possible industry effects across thethree countries (due, for example, to some industries being over-or underrepresented in some countries). We tested for all of theindustry control variables mentioned earlier (supplier power,buyer power, etc.), again using MANOVA analysis. No signifi-cant differences were found across the three countries; thus, weconclude that industry effects are also not a major problem.

To further verify correctness of the classification of radi-cal innovations by companies, we conducted follow-up inter-views with a sample of companies (12 selected Chinese firms, 8Japanese firms, and 14 U.S. firms) to identify the radical inno-vations reported by these companies. We presented these data tothe three experts with consulting experience who had previouslyassisted in the selection of the definition of radical innovation.These experts were requested to classify the innovations inde-pendently as either radical or incremental. The classifications bythe experts were consistent in all cases (i.e., the experts were allin agreement in their judgments) except four in China and threein Japan. In the seven cases where the experts disagreed, thedisagreements were resolved using a simple majority rule. Wealso checked the sample of respondent firms against secondarysources (in cases where secondary data were available to us),again to determine if radical innovations had been correctlyclassified. All remaining discussion includes only those self-reported radical innovations that were verified by the expertsand by comparison against secondary sources.2

2All means, standard deviations, and correlations are given in Appendix B.



Fig. 1. Descriptive frequency histogram and statistics for a number of radicalinnovations.

IV. EMPIRICAL RESULTS

A. Traditional Analyses

Fig. 1 shows the frequencies for the number of radical inno-vations (NRAD). As shown, NRAD is a discrete count type ofvariable and a separate literature exists on the analysis of suchdata (cf., [9], [35]). As Long [62] notes, the use of the tradi-tional linear model for count outcomes can result in inefficient,inconsistent, and biased estimates. Fig. 1 also provides somedetailed statistics for NRAD, and a number of characteristicsbecome quite evident. One, the variance is much larger than themean for NRAD, and thus, a situation of overdispersion existsfor which models like Poisson regression that assume equalityof the mean and variance may not prove appropriate. Two, forNRAD, over half of the sample of counts occur at zero newradical innovations—a condition often described as zero infla-tion [37], [57], where concern is expressed over whether theprocesses leading to zero outcomes are the same as those lead-ing to nonzero outcomes. Three, there is evidence of potentialoutliers in the data. Finally, as with any model, the issue ofpotential unobserved heterogeneity requires exploration.

Table I presents an ANOVA using country as an independentfactor in examining difference in mean levels of the numberof radical innovations by country. As shown by the ANOVAtest, there are significant differences in these means across thethree countries. In particular, by examining the various posthoccontrasts provided, the mean number of radical innovations issignificantly higher for Japan (mean = 1.26) as compared to theUnited States (0.78) and China (0.89), the last two of which arenot significantly different.

B. Analysis of Radical Innovations (All Countries Combined)

Given the prevalence of inflated counts at zero and the factthat the variance is much larger than the mean for the depen-dent count variable (number of radical innovations), we se-quentially applied Poisson regression, negative binomial distri-bution (NBD) regression, and ZIP NBD regression using thefive summary capability measures as independent variables andthe number of radical innovations as the dependent variable.

The LIMDEP V8.0 software system was used. Cameron andTrevedi [9] overdispersion tests suggest that the Poisson regres-sion model is not appropriate given the overdispersion problem.The Vuong test was used to compare ordinary (unaltered) NBDregression with the ZIP NBD model, and this test fails to rejectthe ordinary NBD regression model. In addition, the ordinaryNBD model has a higher likelihood value (log likelihoods ofthe NBD and ZIP NBD models are −494.64 and −498.86, re-spectively). Therefore, we select the ordinary NBD model forour analysis.

Count data of this type are typically analyzed with Poisson re-gression in the presence of such covariates. The assumed equal-ity of the conditional mean and variance functions is typicallytaken to be the major shortcoming of the Poisson regressionmodel. Perhaps, the most common alternative procedure is thenegative binomial model, which arises from a natural formu-lation of cross-section heterogeneity. One can generalize thePoisson model by introducing an individual, unobserved effectinto the conditional mean

ln µi = x′iβ + εi = ln λi + ln ui (1)

where the disturbance εi reflects either specification error asin the classical regression model or the kind of cross-sectionalheterogeneity that normally characterizes many types of data.Then, the distribution of yi conditioned on xi and ui (i.e., εi)remains Poisson with conditional mean and variance µi

f(yi |xi, ui) =e−λi u i (λiui)yi

yi !. (2)

The unconditional distribution f(yi |xi) is the expected value(over ui) of f(yi |xi, ui)

f(yi |xi) =∫ ∞

0

e−λi u i (λiui)yi

yi !g(ui)dui. (3)

The choice of a density for ui defines the unconditional dis-tribution. For mathematical convenience, a gamma distributionis usually assumed for ui = exp(εi). With appropriate normal-ization

g(ui) =θθ

Γ(θ)e−θui uθ−1

i . (4)

The density for yi is then

f(yi |xi) =∫ ∞

0

e−λi u i (λiui)yi

yi !θθuθ−1

i e−θui

Γ(θ)dui (5)

=θθλ

yi

i

Γ(yi + 1)Γ(θ)

∫ ∞

0e−(λi +θ)ui uθ+yi −1

i dui (6)

=θθλ

yi

i Γ(θ + yi)Γ(yi + 1)Γ(θ)(λi + θ)θ+yi

(7)

=Γ(θ + yi)

Γ(yi + 1)Γ(θ)ryi

i (1 − ri)θ (8)

where

ri =λi

λi + θ



TABLE IANOVA OF MEAN DIFFERENCES IN NRAD BY COUNTRY

TABLE IINEGATIVE BINOMIAL REGRESSION, MAXIMUM LIKELIHOOD ESTIMATES:

AGGREGATE SAMPLE

which is one form of the NBD. The distribution has conditionalmean λi and conditional variance λi (1+(1/θ) λi).3 The negativebinomial model is estimated by maximum likelihood.

Table II shows the NBD regression coefficients and their sig-nificances for each of the five capabilities for the aggregate sam-ple (all three countries combined), and also lists the marginaleffects. These marginal effects are calculated as partial deriva-tives for the NBD model. Since the NBD model is not linear,one cannot interpret the estimated model coefficients themselveslike regression coefficients in multiple regression. Rather, thecorrect way to assess relative impacts of unit changes in eachindependent variable for the NBD model is to compute marginaleffects.

We first examine the NBD regression coefficients in Table IIto determine which capabilities are significantly related to rad-

3Adapted from Greene [39].

ical innovation. These coefficients show that only technologyand information technology capabilities (0.0724 and 0.0974,respectively) are significantly and positively related to radicalinnovation at the 0.05 level, while market linking capabilities(−0.1289) show a significant and negative relationship, whichis contrary to H1d (significant at the 0.01 level). Marketing andmanagement capabilities are not found to be significant for theaggregate sample. Hence, H1a, d, and e are not supported, butH1b and c are supported in this aggregate analysis.

Second, to obtain the relative magnitudes of the relationshipsbetween capabilities and radical innovation, one must examinethe marginal effects (shown in the right column of Table II),and not the raw NBD coefficients. The marginal effects showthat information technology has the largest significant and pos-itive relationship to radical innovation, and technology has thesecond largest (marginal effects = 0.0958 and 0.0712, respec-tively), though the difference between the two is not great. Theresults support H2a (technology-related capabilities are morepositively related to radical innovation than marketing-relatedcapabilities), but not H2b (management-related capabilities aremore positively related to radical innovation than marketing-related capabilities).

C. Cross-National Comparisons

The Table II results combine the results for all three countries,and this could mask important cross-national differences (i.e.,



TABLE IIINEGATIVE BINOMIAL REGRESSION, MAXIMUM LIKELIHOOD

ESTIMATES BY COUNTRY

heterogeneity). Oftentimes, heterogeneity could be the cause ofthe overdispersion problem. To alleviate this problem, the anal-ysis was conducted by country. Thus, the same NBD regressionprocedure was then applied to the data for each country individ-ually. Table III contrasts the coefficients and marginal effectsfor the United States, Japanese, and Chinese samples (fullerstatistical details are available from the authors).

The NBD regression results for the United States are shownin the first panel of Table III. The regression coefficients formarketing, technology, and information technology are 0.1216,0.0783, and 0.1916, respectively, all significant at 0.05 orbetter; the other capabilities are not found to be significant.Thus, H1a, b, and c are supported in the U.S. sample, but notH1d or e. Second, examining the marginal effects, informa-tion technology capability has the largest positive relationshipwith radical innovation, followed by marketing and technol-ogy (marginal effects are 0.1500, 0.0952, and 0.0613, respec-tively). These results partially support H2a, which hypothesizedthat both technology-related capabilities would have strongerpositive relationships to radical innovation than the marketing-related capabilities. No evidence was found to support H2b,since the management-related capability was not found to besignificant.

Now consider the results obtained for the Japanese sample(Table III, second panel). To make comparisons between theUnited States and Japanese samples, we observe which capabil-ities have significant positive effects in each country, and alsoconsider the relative sizes of the marginal effects. Only tech-nology and marketing capabilities are found to be significantin Japan (the coefficients are 0.2740 and 0.0965, respectively,both significant at 0.05 level or better), so H1a and b only aresupported (though H1c is only supported at the 0.10 level).Furthermore, the marginal effects show a different order of im-

portance than that found in the United States (coefficient =0.161). Technology capabilities have a larger marginal effectthan marketing capabilities (0.3491 and 0.1229, respectively).Information technology capabilities also have a large marginaleffect (0.2138), but as noted earlier, its NBD regression coeffi-cient is not found to be significant. Since technology capabilityhas a larger marginal effect than marketing, H2a is partially sup-ported in Japan. Management-related capabilities are not foundto be significant for Japan and, as was the case for the U.S.sample, there is no evidence supporting H2b in Japan. Hypoth-esis 3a is supported: technology capability has the largest effecton radical innovation in Japan but has only the third greatesteffect on radical innovation in the United States. We also obtaina few surprising results in Japan: we do not find a significant ef-fect for market linking, management, or information technologycapabilities.

By contrast, the results for the China sample (Table III, thirdpanel) are widely different from those found earlier. We also seea significant overall fit, as shown by the chi-square statistic. InChina, technology capability has a significant positive relation-ship with radical innovation (coefficient = 0.3169, significantat the 0.01 level), while both market linking and marketing havesignificant negative relationships (coefficients are −0.4734 and−0.2406, respectively, both significant at 0.05 level or better).Neither information technology nor management-related capa-bilities are significant in China. Examining the marginal effects,the capability with the largest significant positive relationshipwith radical innovation is technology (0.2845). Market link-ing and marketing both have large negative marginal effects(−0.4260 and −0.2160, respectively). In China, we do not findmuch support for H1 a–e: only H1b is supported. We find onevery surprising result in China: marketing and market linking aresignificantly negatively related to radical innovation, which is inthe direction counterintuitive to H1a and H1d. We will furtherdiscuss this finding in the next section. H2a is partially supportedin the China sample, since technology capability has a strongerpositive relationship to radical innovation than the marketing-related capabilities, but information technology capability hasno significant effect. Finally, the finding that technology capa-bility is significantly positively related to radical innovation inChina supports Hypothesis 3b.

We then performed a likelihood ratio test (akin to a Chow test)to compare the aggregate, pooled regression solution (acrossthree countries) against the three individual country solutionsas a nested models test, since the aggregate solution can beshown to be a special restricted case of the three-country-model solutions. The resulting chi-square statistic was 63.42(significant at p < 0.001), thus we reject the hypothesis thatall the countries have the same intercepts and slopes (that is,we find evidence that the aggregate sample results do not holdfor all three countries and that there is heterogeneity acrossthe three countries). We reject the aggregate model solutionin favor of the three-country solution. Thus, cultural or coun-try effects account for much of the heterogeneity seen in thispaper.

We summarize all of our hypotheses tests (overall and bycountry) in Table IV.



TABLE IVSUMMARY OF FINDINGS

V. DISCUSSION AND CONCLUSION

There4 is little doubt that firm management understands thecritical role of radical innovation in sustaining long-term salesgrowth and profitability. Radical innovation initiatives have animportant role in the firm’s portfolio of new product projects.There is also a literature relating strategic capabilities to inno-vation propensity. In spite of this, there have been relatively fewempirical studies that specifically examined strategic capabili-ties as drivers of radical innovation development and launch, andthose studies that have done so have sometimes resulted in con-flicting results. Also, little research has examined cross-nationaldifferences. We present the results of an analysis conducted over376 firms in the United States, Japan, and China to test hypothe-ses regarding the relationships between various strategic capa-bilities and the development and launch of radical innovations.We find evidence that the capabilities that have the largest mag-nitude relationship with radical innovation differ by country. Inparticular, we find that technology and information technologycapabilities are significantly related to radical innovation whenthe three countries are combined (consistent with Hypotheses1b and c). But when the sample is disaggregated by country, onefinds substantial differences across the countries. In the United

4We thank the anonymous reviewers for several insightful suggestions foundin this section.

States, marketing, technology, and information technology ca-pabilities are significant, while only technology and marketingcapabilities are significant in Japan and only technology capabil-ity is significant and positive in China. Thus, in the United Statesand Japan, we find evidence of the role of marketing capabili-ties in radical product innovation. This supports the viewpoint ofDanneels [20], [21] and others who noted that radical innovationrequires an understanding of customer needs and competitive ac-tions [35], [46], [50], [59], [63], [72], [80]. Our results suggestthat the influences on radical innovation in China are different(see next).

Also, when the country samples are separated, partial supportis found for H2a (the importance of technology-related capabil-ities in supporting radical innovation) in all three countries, butno support is found for H2b (the importance of managementcapability in supporting radical innovation) in any of the threecountries. Technology capability is found to be significantly re-lated to radical innovation in both Japan and China, and in fact,in both of these countries, it is the capability with the largestfactor effect. While the effect is also significant in the UnitedStates, technology capability does not have the largest effect onradical innovation. This finding is consistent with Hypothesis3a and 3b.

We obtain surprising findings in the Chinese sample. Whilewe hypothesize that marketing and market linking capabilities



will not be related strongly to radical innovation in China,we actually find that these capabilities were significantly andnegatively related to radical innovation. Though the resultsdo not refute Hypothesis 3b (the effect of technology capa-bility on radical innovation should be more positive than theeffect of marketing-related capabilities in China), the signifi-cant negative effect was not expected. The China sample re-sults seem to lend support to the views of Christensen [15],i.e., overemphasis on marketing or market linking capabilitiesmay result in risk-averse behavior and an avoidance of radi-cal innovation. The results may be due to the fact that Chi-nese firms are often suppliers to foreign countries. Relative tothe Japan and U.S. sample, the China sample may overrep-resent suppliers to foreign firms. It is possible that marketingand market linking capabilities are less important to firms thatact largely as suppliers to firms who work for foreign busi-ness clients, than to firms that produce for local private cus-tomers. If this is the case, investment in marketing-related ca-pabilities beyond some minimal level is wasteful and wouldhave been better off invested in improving technology capa-bilities. If invested in the latter, there would be a significantpositive effect on radical innovation. Investment in informa-tion technology, beyond some base level, by Chinese firmshas neither a significant positive nor negative relationship and,while not detrimental to radical innovation, would be relativelywasteful.

The results in Japan and the United States are close to what isstated in the hypotheses. The U.S. firms seem to require strongattention to technology capabilities, while also not ignoring theimportance of marketing-related capabilities, as these are morepositively related to radical innovation. This finding is consistentwith the “dual drive” stream in the new product developmentliterature (e.g., [24], [82], and [83]). Marketing capabilities aswell as technology-related capabilities are significantly relatedto radical innovation also in Japan, but, as hypothesized in H3a,the magnitude of the relationship between technology capabilityand radical innovation was found to be greater in Japan than inthe United States. Incidentally, though they examined differentdrivers of radical innovation, our results are consistent with thoseof Chandy and Tellis [14] who found differences in patternsbetween radical innovations developed in Japan relative to theUnited States.

The research suggests that managers of firms prioritizing rad-ical innovation can take steps to insure that their scarce financialresources are allocated in favor of boosting technology, infor-mation technology, and/or marketing capabilities; and that therelative importances of these radical innovation drivers differacross countries. In the extreme, we found evidence that toomuch investment in marketing-related capability actually de-creases the rate of radical innovation rate in China; more often,ill-advised investment in capabilities is wasteful. The researchraises several questions, however, which are potentially direc-tions for future research.

Several cross-national research findings were obtained in ad-dition to our findings supporting the relative importance oftechnology capability in Japan and China (H3a and H3b). For

example, information technology capability has the largest ef-fect on radical innovation in the United States, and has oneof the largest factor effects in Japan, yet is insignificant inChina. Further cross-national research is warranted. Also, asnoted earlier, the results suggest not investing in certain ca-pabilities (marketing or market linking in China, for exam-ple), due to the apparent significant, negative effects. But thisdoes not mean that the capabilities are unimportant, or that afirm with zero marketing-related capability would be successfulwith radical innovation. Rather, the results suggest that thereis a minimal or threshold level of marketing expertise, with-out which the firm is incapable of successful launch. Once thisthreshold is attained, if the firm has additional financial (or hu-man) resources to allocate, these are better aimed at boostingtechnology-related capabilities. Our research does not permitus to pinpoint what these threshold levels of capabilities are,or if they differ across countries. This is an avenue for furtherresearch.

Another unanswered question is how the relative effects ofthe capability drivers may change through time. The Japanese orChinese business environments will probably continue to evolveand change, as they have in recent years. The role of METI inJapanese business has already shifted, as noted earlier, to favortechnology and information technology capability development.This trend may continue, or possibly METI may refocus atten-tion to encourage Japanese firms to build marketing capabilitiesto better penetrate developing and third-world markets. Like-wise, the current business environment in China is in flux asthe government moves toward encouraging more competitionwhile still doing some central planning. As the business envi-ronments continue to evolve, the relative differences betweenJapan, China, and the United States may diminish, or possiblyeven become more pronounced. It would be interesting to repli-cate this research in several years’ time after more governmentpolicy changes have occurred.

Another possible factor to be explored in future research isthe experience of the firm. We have not explicitly consideredeither the experience of the firm (i.e., institutional experience)in generating radical innovations, or the personal experienceof team members or leaders involved in the radical innovation.Possibly the firm with the greatest institutional or personal ex-perience has an advantage, though this could not be ascertainedin the current research.

Finally, we note that we have studied antecedents to allradical innovations developed and launched by the respon-dent firms. Having successfully developed the radical inno-vation and launched it in the marketplace, of course, doesnot guarantee ultimate commercial success of the innovation,and we have not distinguished the commercially successfulradical innovations from unsuccessful ones. Thus, while wemake a contribution by understanding the relationships be-tween strategic capabilities and the successful development andlaunch of radical innovations, future research could seek toidentify how strategic capabilities ought to be best deployedin order to increase the likelihood of ultimate commercialsuccess.



APPENDIX A

ITEM-TO-TOTAL CORRELATIONS AND RELIABILITIES

APPENDIX B

MEANS, STANDARD DEVIATIONS, AND CORRELATIONS

ACKNOWLEDGMENT

The authors wish to thank Editor in Chief G. Farris, Depart-ment Editor P. Bierly, and the anonymous reviewers for theirhelpful comments.

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C. Anthony Di Benedetto received the B.Sc. de-gree in chemistry, the MBA degree in marketing,and the Ph.D. degree in marketing and managementscience, all from McGill University, Montreal, QC,Canada.

He is currently a Professor of Marketing and Se-nior Crawford Research Fellow at Temple University,Philadelphia, PA, and Editor-in-Chief of the Journalof Product Innovation Management. He is the authoror coauthor of more than 70 articles published in ref-ereed journals, and his research appears in Manage-

ment Science, Strategic Management Journal, Journal of International BusinessStudies, Journal of Product Innovation Management, the IEEE TRANSACTIONS

ON ENGINEERING MANAGEMENT, and elsewhere. He is the coauthor of the text-book New Products Management (New York: McGraw-Hill, 2008). His currentresearch interests include new product development and launch, and interna-tional marketing strategy.

Prof. Di Benedetto is a New Product Development Professional and a mem-ber of the Product Development & Management Association.

Wayne S. DeSarbo received the Ph.D. degree in mar-keting and statistics from the University of Penn-sylvania, Philadelphia, where he also completedthe Postdoctoral work in operations research andeconometrics.

Currently, he is the Mary Jean and Frank P. SmealDistinguished Professor of Marketing at the SmealCollege of Business, Pennsylvania State University,University Park. He serves on the review boards ofMarketing Science, Journal of Consumer Research,Journal of Marketing, and Journal of Marketing Re-

search. He is the author or coauthor of more than 130 articles published injournals such as the Journal of Marketing Research, Psychometrika, Journal ofConsumer Research, Journal of Mathematical Psychology, Marketing Science,Journal of Classification, Journal of Marketing, Management Science, and De-cision Sciences. His current research interests include multidimensional scaling,classification, and multivariate statistics, especially as they pertain to substantivemarketing problems in positioning, market structure, consumer choice, marketsegmentation, and competitive strategy.

Michael Song received the M.S. degree from CornellUniversity, Ithaca, NY, and the MBA and Ph.D. de-grees in business administration from Darden School,University of Virginia, Charlottesville.

He is currently the Charles N. Kimball,MRI/Missouri Endowed Chair in Management ofTechnology and Innovation, is Professor of Market-ing, and is the Founder and Executive Director ofInstitute for Entrepreneurship and Innovation, Uni-versity of Missouri, Kansas City. He is the author orcoauthor of more than 60 articles in Management Sci-

ence, Strategic Management Journal, Academy of Management Journal, Journalof Marketing Research, Marketing Science, Journal of Marketing, Journal of theAcademy of Marketing Science, Journal of International Business Studies, Jour-nal of Operations Management, Journal of Product Innovation Management,Journal of International Marketing, the IEEE TRANSACTIONS ON ENGINEERING

MANAGEMENT, and others. His current research interests include managementof technology and innovation, technology entrepreneurship, valuation of newventures and emerging technologies, risk assessment, methods for measuringvalues of technology and R&D projects, and technology portfolio management.

Prof. Song was the recipient of the 2005 Excellence in Research Award,presented by the American Marketing Association, and his published papershave received six Best Paper Awards.


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