CEO incentives, innovation, and performance in technology-intensive firms: a reconciliation of...

Strategic Management JournalStrat. Mgmt. J. (in press)

Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/smj.560

Received 9 October 2001; Final revision received 1 April 2006

CEO INCENTIVES, INNOVATION, ANDPERFORMANCE IN TECHNOLOGY-INTENSIVE FIRMS:A RECONCILIATION OF OUTCOME ANDBEHAVIOR-BASED INCENTIVE SCHEMES

MARIANNA MAKRI,1* PETER J. LANE2 and LUIS R. GOMEZ-MEJIA3

1 School of Business Administration, University of Miami, Coral Gables, Florida,U.S.A.2 Whittemore School of Business and Economics, University of New Hampshire,Durham, New Hampshire, U.S.A.3 College of Business, Arizona State University, Tempe, Arizona, U.S.A.

Building on the agency view of corporate governance, we propose that technology-intensivefirms use both outcome and behavior-based performance criteria for rewarding CEOs. Usinga sample of 206 firms from 12 U.S. manufacturing industries, we find that as technologicalintensity increases CEO bonuses are more closely linked to financial results and that totalCEO incentives are associated with two indicators of desirable innovation behaviors: inventionresonance and science harvesting. Invention resonance refers to the impact a firm’s inventionshave on other firms’ inventions, while science harvesting reflects a firm’s commitment toscientific research. As technological intensity increases, aligning bonus with financial results,total incentives with invention resonance, and total incentives with science harvesting predictfirm market performance. Copyright 2006 John Wiley & Sons, Ltd.

In the competitive environment of the 21st cen-tury, ‘the most important force of all is the grow-ing power of ideas . . . the power, prestige, andmoney will flow to the companies with indispens-able intellectual property’ (Coy, 2000: 77). Thehigh-technology sector plays a pivotal role in this‘creative economy’ and has become the majorsource of employment and productivity growth.The Bureau of Labor Statistics estimates that thepercentage of workers employed by manufacturing

Keywords: executive compensation; performance; inno-vation; technology; science∗ Correspondence to: Marianna Makri, School of BusinessAdministration, University of Miami, Jenkins Building, 414 L,5250 University Drive, Coral Gables, FL 33146, U.S.A.E-mail: mmakri@miami.edu

industries has fallen below 20 percent, the lowestlevel since 1850. On the other hand, the pro-portion of workers in the computer, electronics,biotechnology, pharmaceutical, and other research-intensive industries has more than tripled sincethe early 1980s. The rising importance of intellec-tual property is reflected in an astonishing statistic:the U.S. Patent and Trademark Office handed out70 percent more patents in 1999 (about 170,000)than it did just a decade earlier, surpassing therate of increase of the prior 40 years combined(Coy, 2000). The Office of Science and Technol-ogy estimates that more than half of economicgrowth during 1945–2002 is attributed to inno-vations within the high-technology sector (Leary,2002). Further, since 2000, the 10 largest U.S.

M. Makri, P. J. Lane and L. R. Gomez-Mejia

companies increased their R&D spending by 42percent, while their capital spending increased bya mere 2 percent (Mandel, Hamm, and Farrell,2006).

While most management scholars would agreethat technological innovation is a key source ofcompetitive advantage for high-technology firms1

and that top executives in these firms shouldbe rewarded accordingly, little is known aboutwhich executive pay policies are most appropri-ate for these organizations. Not only has empiricalresearch been very limited, but scholars have reliedon agency theory to make what purportedly areconflicting predictions about the design of exec-utive pay programs in this sector. Some arguefor tightly coupling pay and financial outcomesin these firms, given high information asymme-tries and the difficulties of monitoring executivebehavior (e.g., Milkovich, Gerhart, and Hannon,1991). Others argue that such a link may induceexecutives to avoid high-risk/high-return innova-tion projects which may damage the organization’slong-term success (Baysinger, Kosnik, and Turk,1991; Hoskisson, Hitt, and Hill, 1991; Eisenmann,2002).

The study reported here makes several impor-tant contributions to the executive compensationliterature. First, research on innovation–CEO paylinkages in high-technology firms has focused onaligning pay with the quantity of innovation inputs(R&D spending) and outputs (number of patents).We extend that research by showing the impor-tance of also considering the quality of innovationoutputs. Second, we help resolve the apparent theo-retical paradox in agency predictions about the nor-mative consequences of performance based pay:it creates a common fate between principal andagent but it also makes the agent overly conser-vative. We argue that for CEO pay–performancerelations in high-technology firms these views arenot incompatible but represent two sides of thesame coin. We propose that these firms can simul-taneously foster incentive alignment and managerisk aversion by rewarding executives using multi-ple performance criteria which include both finan-cial results and behavioral indicators of the quality

1 In this paper, we use the terms ‘high technology,’ ‘technologyintensive,’ and ‘R&D intensive’ interchangeably, a usage con-sistent with much of the literature. Operationally, as described inthe methods section, we use a continuous measure (R&D expen-ditures as a percentage of sales) to assess the firm’s technologicalintensity.

of innovation efforts. We find strong support forour hypotheses: the greater the technological inten-sity of a firm, the more CEO bonuses are tied tofinancial results, and the more CEO total incen-tives are linked to evidence of influential innova-tions (which we call invention resonance) as wellas the firm’s ability to utilize scientific knowl-edge effectively (which we call science harvest-ing). Third, agency theory has been the foundationfor both positive and negative answers to the keyquestion: Does incentive compensation help high-technology firms attain higher subsequent perfor-mance levels? We report empirical evidence thathigh-technology firms that use more holistic out-come based and behavior based performance cri-teria to reward executives exhibit better marketperformance than those that do not.

THEORETICAL BACKGROUND

Despite its long history and wide application,agency theory continues to foster a debate aboutthe inconsistent effects of incentive based pay. Oneside argues that the principal can design a con-tract based on outcomes of the agent’s behaviorthat aligns the preferences of the agent and theprincipal (i.e., reward firm performance). Theseoutcome based incentive (OBI) proponents ‘positpositive incentive effects because performance-based incentives align the interests of executiveswith shareholders, motivate appropriate risk tak-ing, and promote a long-term orientation’ (Sandersand Hambrick, 2004: 2; see related discussion byEisenhardt, 1989; Mehran, 1995; Murphy, 1999).

The other side focuses on the negative con-sequences of incentives, suggesting that greateragent risk bearing induces executives to makedecisions designed to reduce personal risk, notmaximize performance (Beatty and Zajac, 1994;Bloom and Milkovich, 1998; Zajac and Westphal,1994; Miller, Wiseman, and Gomez-Mejia, 2002).OBI may be counterproductive because executivesdepend on one job while shareholders can spreadtheir risk across a diversified portfolio. If the cri-teria outcomes are only partially a function ofagents’ behaviors, risk-averse agents will makedecisions that may sub-optimize the principal’sreturns (Demski and Feltham, 1978). To addressthis problem, proponents of behavior-based incen-tives (BBIs) argue that the principal can invest ininformation to ensure that the agent is behaving

CEO Incentives, Innovation, and Performance

appropriately (i.e., reward certain types of deci-sions believed to be beneficial to the shareholder’sinterests).

The debate over CEO incentives inR&D-intensive companies

The implications of the differing agency viewson incentives are especially addressed by researchon CEO pay in high-technology firms. Someresearchers argue that OBI is especially impor-tant for R&D-intensive firms because R&D exac-erbates owner-manager information asymmetries(Milkovich et al., 1991). Managers in R&D-inten-sive firms hold information about their technicalabilities and behaviors that is not available to prin-cipals, or requires significant costs for principalsto monitor. According to Milkovich et al., prin-cipal–agent information asymmetries represent agreater threat to shareholders’ welfare in high-technology firms than increased CEO risk bearingattributed to CEO pay–financial outcomes ties. Intheir words:

compared to less R&D intensive firms, behavioralmonitoring and collection of such technical data inthe R&D firm would be more costly (if not impos-sible) than measuring outcomes. Thus, managers’behaviors and information cannot be as readilyobserved or controlled in R&D firms due to thevery nature of the innovative process . . . [there-fore] according to agency theory R&D intensivefirms are more likely than others to make use ofhigher ratios of bonuses to base pay to focus man-agers’ decisions on outcomes rather than bear thecosts of monitoring managers’ interim behaviors.(Milkovich et al., 1991: 137)

Other OBI proponents warn that idiosyncratic‘subjectively based’ control processes present amajor challenge to board members who are askedto make informed judgments about highly complexand esoteric projects. To accomplish this, boardmembers of R&D-intensive firms would ‘need adeep understanding of technological and competi-tive risk, the capabilities of different project teams,and the emerging contingencies that may demandlarge scale redirection of resources across multi-ple projects’ (Henderson and Fredrickson, 2001:100). This may be a heroic assumption for mostboards who often only meet for a handful of hoursa year. Even in the absence of these informationconstraints, it is less than obvious that the useof subjective criteria vis-a-vis outcome (financial)

measures by directors intimately familiar with thehigh-technology firm reduces executive risk bear-ing and thus promotes innovation. In fact, Wise-man and Gomez-Mejia (1998: 145) argue that theopposite is just as likely to occur under subjectivemonitoring, given the inherent ambiguity of theappraisal criteria used in the evaluation of seniorexecutives: ‘because of the necessity of reachingconsensus over those [subjective] criteria amonga diverse and varying set of monitors, the use ofjudgmental criteria is likely to increase agent riskbearing, resulting in greater preferences for lowerrisk strategic options.’

A number of authors adopt the behavior based(BBI) agency view and argue against the linkageof financial outcomes and incentives for executivesof R&D-intensive firms (e.g., Baysinger et al.,1991; Graves and Langowitz, 1993; Baysinger andHoskisson, 1990; Hoskisson, Hitt, and Hill, 1993;Duysters and Hagedoorn, 2000). Assuming thatexecutives are risk-averse, these scholars believethat linking CEO pay to financial results in R&D-intensive firms may lead to conservative decisionsthat smother innovation, induce greater cost con-trols at the expense of creative freedom, and resultin fewer resources devoted to capital investmentand R&D activity.

The BBI literature expresses two fundamentalreasons for these concerns. First, R&D executivesenjoy great latitude of action when making deci-sions pertaining to innovation, but these decisionsare inherently risky given that innovation effortsmight fail despite the best intentions of managers.In other words, being wrong is a frequent sideeffect of being innovative. Paying agents for finan-cial results will increase risk bearing and thus makemanagers more risk-averse. In Eisenmann’s words‘to improve the odds that they will meet thesefinancial targets, managers may choose to avoidrisky projects’ (Eisenmann, 2002: 513). Second,there are often multi-year lags between innovation-related expenditures and potential future revenues.In industries where tenure is relatively short, exec-utives may be tempted to maximize the easierto attain and manipulate short-term gains at theexpense of more uncertain long-term returns iftheir pay is linked to observed financial results.

Consequently, the BBI position suggests thatit is crucial to shield executives from the poten-tially adverse profitability consequences of innova-tion efforts (cf. Baysinger and Hoskisson, 1990).If executives do not bear the compensation and

employment risk associated with the unpredictableconsequences of innovation efforts, they would bemore likely to devote resources to risky projectswith potentially high, but uncertain, returns. Inthe words of Balkin, Markman, and Gomez-Mejia(2000: 19), ‘emphasis on innovation implies agreater variability of outcomes and a greater prob-ability of failure . . . because the relationship be-tween senior managers’ actions and firm perfor-mance is very uncertain in high-technology firms,executive compensation packages should be loose-ly linked to observed performance results.’ Follow-ing this agency logic, those who fear the negativeconsequences of incentive alignment in terms ofrisk aversion suggest that cognizant board mem-bers should subjectively evaluate executives ofR&D-intensive firms based on the soundness oftheir decision, rather than on observed financialoutcomes (cf. Baysinger and Hoskisson, 1990).

Reconciling the outcome-based andbehavioral-based perspectives

We believe that these contrasting prescriptions forCEO compensation programs in R&D-intensivefirms can be reconciled as they are derived fromthe same agency theoretical framework. In otherwords, they both raise valid points that are in factcomplementary. We agree with the BBI school thatexclusive reliance on financial evaluative measuresmay reward ‘single-mindedness’ with dysfunc-tional consequences for the firm (e.g., cost controlsat the expense of capital investments). We alsoagree with outcome-based proponents (OBI) thatinformation asymmetries make reliance on purelybehavioral criteria unwise. However, both perspec-tives tend to portray a rather simplistic view of howthe criteria in incentive alignment systems affectexecutive behavior (e.g., linking financial resultsto pay leads to ‘low risk/low returns’ managerialdecisions and, conversely, subjective performanceassessments or ‘strategic controls’ lead to moredesirable, riskier executive decisions). As Gomez-Mejia and Balkin (1992) note in their review ofa large number of experimental and field studies,maximizing a single set of criteria (whether out-come based or behavioral) is perhaps the biggestdanger in poorly designed incentive programs, asthose affected tend to focus their attention on theperformance measures, and are tempted to engagein criterion manipulation (for instance, through

creative accounting and/or impression manage-ment).

We believe that rather than having to choosebetween financial performance criteria (OBI) andinnovation management criteria (BBI), using bothtogether can address these shortcomings providedthat the innovation management criteria are suf-ficiently broad. This approach provides a globalassessment of high-technology executive perfor-mance, focusing managerial efforts on both devel-oping valuable innovations (through BBI) andon capturing their commercial potential (throughOBI). In short, we propose that in R&D-intensivefirms utilizing a comprehensive set of CEO eval-uation criteria can prevent the perverse incentivesthat may derive from exclusive reliance on a nar-row set of performance indicators. The followingsections expand upon this logic and develop thefour hypotheses to be tested in this study.

HYPOTHESES

Outcome-based controls, managerialincentives, and innovation

The OBI position suggests that R&D-intensivefirms should reward executives for financial resultswith annual bonuses for several reasons. First, ahigh-technology firm may produce a large numberof inventions, but these may be of little value toshareholders unless the firm is able to commercial-ize these inventions and derive a profit from them.Furthermore, while there may be a multi-year timelag between a particular invention and its com-mercialization, the R&D firm’s profitability in aparticular year reflects the commercial value of itscumulative efforts in innovation (Deng, Lev, andNarin, 2001). Linking bonuses to financial resultsfocuses executives’ attention on the profitabilityconsequences of innovation efforts.

Second, information is more limited and uncer-tain in high-technology than low-technology con-texts, particularly in the short term (Milkovichet al., 1991). Thus, it may be unfeasible forboards of directors to judge the complex deci-sions made by high-technology executives on aday-to-day basis adequately—a challenge mademore difficult as the number of investment projectsgrows and/or the size and intricacy of the aver-age project increases (Henderson and Fredrickson,2001). Even the most diligent board may be ableto assess only a few projects in detail. In other

words, information asymmetries in the short runare likely to exceed the cognitive ability of theevaluators to appraise the strategic value of R&Defforts accurately and hence behavior monitoringbecomes more difficult in a technologically intensecontext within a short time frame. Alternatively,boards may rely on bonuses tied to financial resultsand hence avoid the need for close monitoring(which would be costly, inefficient and possiblycounterproductive).2

Third, it is unclear that subjective judgments bythe board based on limited and uncertain infor-mation obtained within a compressed timeframewould lead to lower management risk bearing inhigh-technology firms: executives may perceivegreater risk if the judgments are believed to bearbitrary (Wiseman and Gomez-Mejia, 1998). Fur-thermore, executives may engage in ‘impressionformation’ behaviors to influence the monitors(e.g., using selective reporting and better ‘pack-aging’ of information at the end of the reviewperiod to project a more positive image), whichin turn would distort the value of the evaluationprocess (Ferris and Judge, 1991). Thus, from arisk-bearing perspective using financial criteria totrigger bonuses in high technology may be a betteroption in the short term than behavioral monitor-ing since the former is less ambiguous and morecontrollable by executives. And while gaming mayoccur with profitability indicators, it is just aslikely that gaming tactics may be used by execu-tives when subjective judgments are employed bythe board to evaluate short-term performance. Inthe long run, this is a less serious threat for clini-cal or subjective assessments since the board willhave more opportunity to learn about the execu-tive’s strengths and weaknesses.

Fourth, to remain profitable, high-technologyfirms need to be able to commercially capital-ize on new ideas that they develop internally ordiscover externally. The product life cycles ofR&D-intensive firms (which are sometimes mea-sured in months) are far shorter than those of low-technology firms (Balkin and Gomez-Mejia, 1987).

2 Most bonus programs are year-end incentives linked to prof-itability indicators (such as ROA or ROE) during the precedingyear. Empirical evidence regarding the effect of annual bonuseson subsequent performance has been mixed (Sanders and Ham-brick, 2004). These mixed results may be due in part to the influ-ence of contextual factors. In the present study, CEO bonuses arepositively related to future performance as a function of R&Dintensity, consistent with the arguments made above and underHypothesis 4.

This creates a greater need in high-technologyindustries for maintaining a steady flow of newideas and new product introductions. Furthermore,most scholars agree that while executives have lit-tle influence on the firm’s market stock price inthe short run, they have a fair degree of controlover the factors that impact profitability within ashort time horizon (see review by Gomez-Mejiaand Wiseman, 1997). Rewarding executives withannual bonuses linked to profitability reinforcesdecisions that keep them focused on a continuousstream of innovations under extreme time pres-sures.

In summary, given the problems associated withinformation asymmetry, uncertainty, and concernswith alternative assessments of short-term perfor-mance, coupled with the need to hold the executiveaccountable for and focused on the commercialreturns to innovation efforts, R&D-intensive firmsshould link CEO bonuses to financial results. Ourfirst hypothesis follows from these arguments:

Hypothesis 1: The greater the technologicalintensity of a firm, the more CEO bonuses willbe linked to financial results.

Behavior-based controls, managerialincentives, and innovation

To avoid the deleterious effect of exclusive relianceon financial criteria, we propose that R&D-inten-sive firms should also reward executives for behav-ioral evidence of valued knowledge creation asreflected in innovation quality. Prior research fromthe BBI suggests that patent counts are a bettercriterion for short- and long-term CEO incentivesin high-technology firms than financial results, aspatents are more clearly influenced by manage-rial decisions and presumably reflect innovationefforts. ‘Such criterion reduces managerial riskbearing, increasing the probability that agents inhigh-technology firms will channel firm resourcesinto risky R&D projects with uncertain payoffs’(Balkin et al., 2000: 1120).

Unfortunately, patent generation is very suscep-tible to ‘window dressing’ as it is subject to howaggressive management wishes to be in patentingnew products or services (Gopalakrishnan, 2000).3

3 Also, the overall correlation between patent counts and currentor subsequent firm performance, while statistically significant, isvery weak (Hall, 1998; Griliches, 1986, 1990, 1995, 1998). As

As noted by Szakonji (1997: 101), ‘patent countsmay not measure the productivity of researchersbut that of patent attorneys . . . determined by thenumber of patent applications they complete andship off to Washington.’ Therefore, while usingpatent counts in addition to financial measuresfor rewarding CEOs is a more comprehensiveapproach than relying on either one, incentivizingexecutives for greater patent counts may inadver-tently reinforce the wrong behaviors, a case of‘rewarding A while hoping for B’ (Kerr, 1975).

Further, not all patents are created equal. AsHarhoff et al. (1999) suggest, some patents arevery valuable, while some are worth almost noth-ing. This suggests that in order to be effectiveBBI for innovation management must reward thequality of the firm’s patents as well as the quan-tity of patents that it produces. Separate streamsof research in economics (Pakes and Griliches,1984; Griliches, 1986, 1990, 1995, 1998) and sci-entometrics (the study of scientific communicationpatterns; see Albert et al., 1991; Narin, 1994), sug-gest two, broad behavioral dimensions to innova-tion quality. The first dimension reflects an inven-tion’s ability to stimulate subsequent inventions:the degree to which one observes a ‘ripple effect’as an invention stimulates subsequent inventions.We refer to this dimension of innovation qualityas ‘invention resonance.’ The second dimension ofinnovation quality reflects evidence that an inno-vation that builds on basic scientific research tendsto produce breakthrough technologies and discon-tinuous technological shifts, a process that we call‘science harvesting.’ We propose that both typesof behavioral criteria should be reinforced througha combination of short- and long-term managerialincentives.

Invention resonance and managerial incentives

Some inventions are dead ends or only have a nar-row range of potential applications, while othersstimulate waves of new ideas and additional inven-tions (Dosi, 1988; Podolny and Stuart, 1995; Sahal,1985). While firms may need a mix of narrow-and broad-impact inventions to meet their short-and long-term competitive needs, it is in theirbest interest to nurture a portfolio of influential

Hall, Jaffe, and Trajtenberg (1999) note, ‘measuring innovationby simply counting the number of patents issued generates anindex of technological progress with a great deal of noise.’

inventions that are widely utilized and extendedby subsequent inventions. The extent to which afirm’s inventions resonate with other researchersis an important behavioral indicator of its capac-ity to generate ideas that have both clear utilityand wide applicability (Achilladelis, Schwarzkopf,and Cines, 1990; DeCarolis and Deeds, 1999).Prior research suggests that such inventions cre-ate considerable economic value for the invent-ing firm (e.g., Hall, Jaffe, and Trajtenberg, 2005;Rosenkopf and Nerkar, 2001; Deng et al., 2001;Lanjouw and Schankerman, 2004). For instance,Hall et al. (2005) find that patent citations are posi-tively related to Tobin’s q with an extra citation perpatent boosting market value by 3 percent. Further,Deng et al. (2001) show that citation intensity issignificantly associated with subsequent market-to-book ratios and stock returns, while Lanjouw andSchankerman (2004) concur that patent citation ispositively related to market value.

Thus, the greater the resonance of the ideasin a firm’s inventions, the more likely it is thatthe firm will produce a stream of commerciallyvaluable inventions in the future. Note that it isimportant to consider how inventions resonate withall researchers, not just the firm’s own researchers.When other firms partake in an innovation rippleand build on an idea in an invention, it reflectsan external validation of the value of that idea.Thus, when assessing its innovative capabilities, itis the overall magnitude of the innovation ripplesthat a firm’s inventions create that matters, and notjust the internal and external ripples that the firmcaptures for itself.

The resonance of inventions can manifest itselfover a range of time horizons. Some inventionsproduce an immediate effect on an industry. Theystimulate a rapid flurry of additional inventions,but quickly become obsolete as the technologicalfrontier moves beyond them. Other inventions canstimulate a slow but steady stream of inventionsthat last for over a decade (Ahuja and Lampert,2001; Tushman and Anderson, 1986). What mat-ters most is not the time horizon of the ripples, buttheir cumulative magnitude. The more subsequentinventions a firm’s stock of inventions stimulate(i.e., the greater the resonance), the more economicvalue the firm is creating (Trajtenberg, 1990; Denget al., 2001). Thus, boards are likely to induceexecutives to focus on producing high-value inven-tions by rewarding them for evidence of invention

resonance using a combination of short- and long-term incentives (hereafter termed total incentives).

Hypothesis 2: The greater the technologicalintensity of a firm, the more total CEO incentiveswill be linked to invention resonance.

Science harvesting and managerial incentives

In today’s highly competitive environment, thewinners are the firms that are able to disrupt thestatus quo and create a series of temporary advan-tages (D’Aveni, 1994). For R&D-intensive firmsin particular, this demands breaking out of estab-lished technological trajectories to offer new con-ceptualizations of components, products, or sys-tems. Novel ideas are continually needed to helpdevelop successive generations of valuable andunique products, because a firm’s ability to meetmarket opportunities diminishes as its technologyplatform matures (Kim and Kogut, 1996). Whilesome inventions typically consist of reconfigur-ing existing technology and technical knowledge,others attempt to create or significantly redefinemarkets through the introduction of fundamentallynew products or services. Both require new ideasthat allow for market development and creation,and some may call for major technological leaps.

The utilization of scientific research contributesgreatly to R&D-intensive firms’ efforts to arriveat novel inventions. Firms often engage in sci-entific research because of a concern that thefundamental knowledge they need to advance islacking and unlikely to come from the academicsector (Stephan, 1996). While they sometimesutilize internally developed scientific knowledge,more frequently, they build on scientific knowl-edge developed by others (Cohen and Levinthal,1990; Lane and Lubatkin, 1998). More specifi-cally, Cohen and Levinthal (1989) argue that firmsmay conduct basic research [science] less for par-ticular results than to be able to identify andexploit potentially useful scientific and technolog-ical knowledge generated by universities or gov-ernment laboratories and thereby gain a first moveradvantage in exploiting new technologies. We referto this process as ‘science harvesting.’

Scientific knowledge plays a unique role inthe innovation process because it deals with gen-eral theories about fundamental relationships aswell as the methods for testing them (Burgel-man, Madique, and Wheelwright, 1996). Changes

in these theories can overturn the core design con-cepts underlying existing technologies, products,and services (Henderson and Clark, 1990). Firmsthat are first to capitalize on new scientific ideasare often able to develop novel new products andservices that could not be developed by build-ing on prior innovations alone (Allen, 1984, 1997;Nightingale, 1998). These novel products and ser-vices can allow the science-harvesting firm to cre-ate first- and second-mover pioneering advantagesfor itself and to create a disadvantage for its rivalsby making their products and services obsolete(Tushman and Anderson, 1986). Simply put, firmsuse science because it enhances the process ofinvestigation: ‘it may transform invention froma relatively haphazard search process to a moredirected identification of useful new combinations’(Fleming and Sorenson, 2004). Such science har-vesting makes product or service novelty muchmore likely (Nightingale, 1998), and that noveltycan create economic value for the firm (Ahuja andLampert, 2001; Deng et al., 2001).

Industries vary considerably in their historic useof science in innovation. For example, the phar-maceutical and chemical industries have been themost heavily reliant on science, while electronicshas made limited use of science (Narin, Hamil-ton, and Olivastro, 1997). A recent cross-industrystudy examining all U.S. patents granted in Mayand June 1990 suggests that science is most valu-able for innovation when different areas of knowl-edge are being combined (Fleming and Sorenson,2004). Scientific knowledge acts as a ‘map’ to helpresearchers navigate through the new interdepen-dencies that emerge in such situations. However,that same study found that after controlling for thetypes of technologies, the type and degree of inter-dependencies and other factors, patents which citescience were more valuable than patents which donot. Thus, while the impact of science to innova-tion varies by industry, it typically creates valuefor a firm.

Science harvesting, like invention resonance,can manifest itself over a range of time horizons. Insome cases, science can yield inventions relativelyfast when technological breakthroughs provide sci-ence with tools that open new areas of inquiryor speed up the pace of scientific exploration.This has been the case in biotechnology, whereadvances in computers and software made high-throughput screening possible (Nightingale, 1998).In other cases, turning science into inventions is

often a journey of discovery requiring many years,because the two are not symmetrical and involvemuch trial and error. This process involves multi-ple, and often overlapping time frames. It wouldbe difficult, if not impossible, for boards to discernthese ‘timing’ issues; thus, R&D-intensive firmsare likely to reward executives for evidence of sci-ence harvesting using a combination of short- andlong-term incentives (total incentives).

Hypothesis 3: The greater the technologicalintensity of a firm, the more total CEO incentiveswill be linked to science harvesting.

To summarize our discussion so far, agency theorymay be used to argue that the high-technology firmshould link CEO incentives to both outcome (i.e.,financial returns) and behavioral (i.e., innovationquality) measures, on the premise that expandingthe scope of the evaluation criteria for incentivealignment purposes improves principal’s welfare.This contextualized interpretation of agency theorymeans that the high-technology firm can simulta-neously use outcome and behavioral controls effec-tively in a dual, complementary fashion, ratherthan as substitutes for each other (as implied inthe ongoing debate discussed earlier). The nor-mative implications of our first three hypothesesare addressed next, leading to our last hypothesis(Hypothesis 4).

Incentive alignment, managerial risk bearing,and firm performance

As noted earlier, a classical concern in the agencyliterature is how to balance the principal’s incen-tive alignment needs with the managerial riskinherent in pay-for-performance programs (Eisen-hardt, 1989). To the extent that managers arepenalized for performance variance that is notunder their control, ‘they may unwittingly sabo-tage the long-term earning power of the business’by acting in a risk-averse fashion (Rajagopalan,1997: 767). In other words, ‘inefficient risk shar-ing’ leads to missed opportunities and lower per-formance (Eisenmann, 2002). This danger maybe greater in high-technology firms because ofinformation asymmetries, low programmability oftasks, high decision ambiguity in cause–effectrelations, high outcome uncertainty, and the multi-plicity of choices open to managers (Balkin et al.,2000). Thus, the key issue is whether incentive

alignment leads to better performance (by engen-dering a common fate between principal–agent)or lower performance (as a result of inefficientrisk sharing). Our last hypothesis argues thattechnology-intensive firms that adopt risk-sharingpolicies considering both outcome (Hypothesis 1)and behavioral (Hypotheses 2–3) criteria shouldperform better than those that do not. There areseveral interrelated reasons why this should be thecase.

A holistic set of evaluation criteria

As per the arguments underlying Hypotheses 1–3,the incentive plans that are likely to be most ben-eficial for high-technology firms are those thatreward both high-quality research and develop-ment of commercial value from the technologiesthat they have created. By utilizing broader, moreholistic evaluation criteria, risk bearing is spreadacross multiple indicators (i.e., addressing financialand research quality aspects). Thus, the multidi-mensionality of performance criteria can strike abalance between the incentive alignment problemfaced by the principal (i.e., creating a commonbond of interest between principal and agent) andthe risk-bearing problem faced by the managers(i.e., the agent’s welfare is not tied to variationsin a single performance indicator) (Gomez-Mejiaand Wiseman, 1997).

It is important to note that the three criteriawe propose in Hypotheses 1–3 are presented asan interdependent set, not as a list from whichto pick and choose. Each criterion has weak-nesses which the other criteria counter. For exam-ple, a high-technology firm that relies solely onfinancial performance-based incentives encouragesrisk-averse decision making that can weaken afirm over time. Combining a financial performanceincentive with rewards based on innovation qual-ity can encourage more risk taking while provid-ing guidance on the types of innovation that aremost helpful to the firm. Conversely, a firm withhigh innovation resonance is generating a lot ofvaluable ideas, but it may or may not be benefit-ing from them. If the firm also has high financialperformance, then it is likely that it is commercial-izing or licensing many of its own ideas. Similarly,inventions which build on science tend to be morenovel and more difficult for other firms to followthan inventions which are incremental, but science-based inventions do not benefit the firm unless it

commercializes or licenses them. Again, scienceharvesting needs to be evaluated in light of thefirm’s financial performance.

Innovation as a risk reduction strategy

As argued by Grenadier and Weiss (1997), high-technology firms actually reduce business risk byallocating resources to innovation-related activitiesbecause doing so enables them to compete moreeffectively. Thus, to the extent that the incentivealignment system establishes a link between payand innovation, it would not be in the executive’sself-interest to reallocate resources away frominnovation-related efforts (as this would tend tojeopardize the firm’s future success, with its con-comitant employment and compensation risk forthe incumbent). Relatedly, high-technology CEOsmay not see R&D investments as risky, as theyare often portrayed in the literature. A recent sur-vey of CEOs of IPOs reveals that increased R&Dinvestment was ranked lowest in terms of the rel-ative risk of various strategic options facing theexecutive, lagging behind other actions such asentry into new markets, greater long-term debt,increased promotion and advertising, introductionof new products and the like (Larraza-Kintanaet al., 2000). The low perceived risk associatedwith R&D investments by these CEOs challengesprior studies that use R&D as a proxy for risk tak-ing (e.g., Hoskisson et al., 1991, 1993). In short,we have good reasons to suspect that increasedCEO risk bearing (through an innovation-basedincentive alignment system) may not lead to a‘low-risk/low-return’ strategy among high-techno-logy firms that compete through innovation.

Greater control over process

While it is true that there is high ambiguity aboutthe likelihood of success in any research endeavor,particularly that aimed at obtaining significantinventions, rewarding innovation quality probablyinvolves less uncertainty for the executive than ifthe firm were to rely exclusively on financial out-come measures. This is because executives exer-cise more control over the magnitude and directionof a firm’s research agenda than over any commer-cial gains which may or may not be forthcoming(Balkin et al., 2000). In other words, compensa-tion risk bearing should not be less efficient in thecase of innovation-related criteria (particularly at

the aggregate level, considering all projects car-ried out by a firm) than in the case of outcomemeasures (such as firm performance; Balkin et al.,2000). As noted earlier, the alternative of rely-ing on purely subjective, clinical judgments bythe board (what Baysinger and Hoskisson, 1990,refer to as ‘strategic controls’) is unlikely to bemore risk-efficient for the CEO because of greaterambiguity in the evaluation criteria, which maybe perceived as arbitrary and dependent on boardcomposition (Gomez-Mejia and Wiseman, 1997).

Innovation as a path-dependent journey

It may be rather difficult to determine if the rela-tive strength or weakness of particular innovationefforts can be attributed exclusively to the focalCEO; there may be a lag of 10 years or morebetween a CEO’s decision to concentrate on a cer-tain kind of research and the ability to assess theconsequences of that research. Holding the CEOaccountable for research contributions in a giventime period increases risk bearing as these con-tributions probably grow out of the accumulateddecisions made by the current and prior CEOs. Yet,the fact that the agent does not have full controlof the causal factors leading to desired results isnot unique to research. This is probably unavoid-able with almost all relevant firm-level perfor-mance criteria that may be used in top managementincentive alignment plans. For instance, marketshare (Kalyanaram and Urban, 1992), stock mar-ket returns (Lee and Swaminathan, 2000), earnings(Barber, Kang, and Kumar, 1998), product repu-tation, brand loyalties (Wernerfelt, 1991), and thelike all reflect cumulative, multi-year decisions thathave evolved in a path-dependent fashion.

Path dependency implies that CEOs’ contribu-tions in high-technology firms is not so much amatter of how well they can create somethingbrand new, but how well they can protect, buildon, and improve what was in place before, per-haps prior to their arrival. The resource-based viewof the firm supports this interpretation: knowledgecreation and invention as a source of competi-tive advantage emanate in a cumulative, additivefashion from intangible resources that may takeyears to create and that often involve the stew-ardship of several CEOs (Gopalakrishnan, Bierly,and Kessler, 1999; Godfrey and Gregersen, 1999).This means that the focal CEO should be rewardedfor sustaining the firm’s ability to innovate, even

though it may not be possible to pin the relativesuccess or failure of particular innovation effortson discrete decisions made by the incumbent.

Firm-level contingencies

Related to the prior point, independent of who is incharge at the moment, several studies using a con-tingency theory perspective suggest that the greaterthe fit between the firm’s executive compensationpolicies and the organization’s idiosyncratic char-acteristics, including its technological intensity, themore these pay policies will contribute to firm per-formance (Balkin and Gomez-Mejia, 1987, 1990;Gomez-Mejia, 1992; Rajagopalan, 1996; Gerhart,2000). In other words, better-performing firmsreward CEOs based on what is most congruentor appropriate for the firm (in our case financialand behavioral quality indicators) and not neces-sarily for results that are directly traceable to theexecutive in charge (Rajagopalan, 1997).

All of the above arguments lead to our lasthypothesis:

Hypothesis 4: The greater the technologicalintensity of a firm, the more important it is forsubsequent performance to align CEO incen-tives with financial results and behavioral indi-cators of innovation quality.

Because innovation is a set of future-focused activ-ities and processes whose value must be assessedover the long term, we use the market value of thefirm to assess the market’s expectations of firms’long-term innovation returns. This conceptualiza-tion of long-term performance relies on the factthat publicly traded firms are bundles of tangibleand intangible assets whose values are determinedin the financial markets (Hall, 1998).

METHODS

Sample and data collection

We tested our hypotheses with a sample of 206U.S. publicly traded firms provided by CHI Re-search Inc., the source of the patent analyses usedin the National Science Foundation’s bi-annual sci-ence and engineering reports. The firms in oursample represented a variety of industries such asdrugs (26 firms), chemicals (56 firms), electronics

(94 firms), and a variety of other manufacturingindustries (30 firms spread over 12 industries).CHI Research Inc. provided data on the number ofU.S. patents granted annually to each firm during1992–95 as well as measures of key patent port-folio characteristics that were used as indicators ofinvention resonance and science harvesting. Datafor CEO pay and stock ownership were collectedfor 1992–95 from the firms’ proxy statements. Per-formance, sales, and R&D spending data for theperiod 1992–95 were downloaded from COMPU-STAT.

Measures

CEO short- and long-term incentives

CEO short-term incentives were measured usingthe CEO’s annual bonus. Long-term income wasestimated as the number of options granted to theCEO multiplied by 25 percent of their exerciseprice. Lambert, Larker, and Weigelt (1993) andFinkelstein and Boyd (1998) found this approachto be highly correlated (0.98) with values derivedusing the more cumbersome Black–Scholes for-mula. Total incentives were calculated by addingCEO annual bonus and long-term income.

Firm technology intensity

Consistent with much of the prior literature, tech-nology intensity was measured as the ratio ofannual R&D expenditures to sales (Balkin et al.,2000; Carpenter and Wade, 2002; Milkovich et al.,1991).

Firm performance

In keeping with prior research on the relation-ship between innovation and firm performance(Hirschey, 1985; Jose, Nichols, and Stevens,1986; Lustgarten and Thomadakis, 1987; Morck,Shleifer, and Vishny, 1988; Morck and Yeung,1991), the latter was operationalized as firms’market-to-book ratio for the period 1992–95. Priorresearch has suggested (Lindenberg and Ross,1981; Wright et al., 1996; Brush, Bromiley, andHendrickx, 2000) that firms with ratios >1 areunder-invested, demonstrating the existence ofgrowth opportunities. In addition, Brush et al.(2000) suggested that these growth opportunitiescan be attributed in part to technological superi-ority. Firms with ratios <1, on the other hand,

can be judged as over-invested, lacking a set ofprofitable new investment projects. Based on thesearguments, using firms’ market-to-book ratio is anappropriate indicator of future performance.

Financial outcomes

Financial results represent more immediate assess-ments of a firm’s ability to generate profits forshareholders. In keeping with prior research onexecutive compensation (Finkelstein and Boyd,1998; Westphal and Zajac, 1994; Sanders and Car-penter, 1998), each firm’s financial results weremeasured using its annual return on equity (ROE).

Behavioral innovation quality measures

These were assessed using certain patent charac-teristics. A patent is a legal document which spec-ifies how an invention works, how it is unique,what prior knowledge it builds on, and who ownsthe rights to the ideas embodied in it. The keyprior knowledge (termed ‘prior art’ in the patentworld) must be identified on the front page ofeach patent. These prior art citations typically fallinto two categories: previously granted patents andscientific papers. This creates a chain of citationsbetween patents over time and a means of trackingthe knowledge generation behaviors underlying thefirm’s patenting efforts. While citations reflect theknowledge flow among firms, citation frequencyindicates the value of an invention. Hence, thevalue of a firm’s patent portfolio can be capturedby measuring the extent to which a firm’s patentsare being cited by others, what we refer to asinvention resonance. Making references to scien-tific papers suggests that a firm is building on basicresearch, what we refer to as science harvesting.These variables are described in detail below.

Invention resonance. The more widely cited afirm’s patent portfolio, the more valuable its intel-lectual capital is. Furthermore, a portfolio of influ-ential patents provides a firm with a ‘powerfullever in negotiating access’ to other firms’ tech-nologies through cross-licensing and ‘may lead tosignificant royalty earnings or, at a minimum, toreduced payments to others’ (Grindley and Teece,1997: 10). We measured invention resonance asthe number of times a firm’s previous five years ofpatents are cited in the current year. This count wasconverted to an index by dividing it by the average

for all U.S. patents that year from the same Interna-tional Patent Classification (IPC) 4-digit subclass.4

Thus, a value of 1.0 represents average citationfrequency; a value of 2.0 represents twice averagecitation frequency; and 0.25 represents 25 percentof average citation frequency.

Science harvesting. Using science in the innova-tion process can help firms develop new technol-ogy platforms and accomplish more radical inno-vations (Lane and Makri, 2003). The more a firm’spatents make references to basic research, the morelikely it is that the firm will produce major inno-vations. Counting the number of references to sci-entific papers reveals how closely linked a patentis to cutting-edge scientific research (Breitzman,Thomas, and Cheney, 2002). Simply put, the morea firm’s patents cite scientific papers, the greaterthe firm’s ability to assimilate and effectively uti-lize scientific knowledge. We measured scienceharvesting behaviors by using the number of ref-erences in the firm’s own patent applications toscientific papers.5 This count was converted to anindex by dividing it by the average number of sci-ence reference for the IPC subclass. This createda measure which controls for differences acrossindustries and technologies in the extent to whichscience is used in innovation.

As is common in much strategy research, schol-ars typically use observable characteristics as prox-ies for underlying theoretical attributes. Theseinclude, for instance, CEO age and organiza-tional tenure as indicators of cognitive fram-ing (Hambrick and Finkelstein, 1995; Datta andRajagopalan, 1998); degree of diversification asa measure of organizational complexity (Hender-son and Fredrickson, 1996); R&D expenditures asa reflection of risky decisions (Hoskisson et al.,1993); and the presence of a stockholder with5 percent or more of the firm’s shares as evi-dence of external control (Hambrick and Finkel-stein, 1995) or monitoring intensity (Tosi andWerner, 1995). By definition, proxies are inferen-tial rather than direct measures of the constructs ofinterest, and thus are imperfect representations oftheoretical attributes. In our particular study, the

4 For those IPC subclasses with fewer than 10 patents, thecomparison figure was obtained by averaging all the patents inthe U.S. patent system for that year.5 CHI Research codes as a scientific paper any reference in apatent to a scientific article, book, working paper, or relateddocument.

patent-based indexes are also inferential indicatorsof innovation quality as it is impossible to mea-sure quality directly across multiple firms. Theseproxies have several advantages from a researchperspective.

First, patent citation data can be readily obtainedfrom unobtrusive, secondary sources. Hence, froma practical standpoint, they can be used in acost efficient fashion. Second, there is a signif-icant body of evidence that these are reason-ably valid measures of innovation quality (Pakesand Griliches, 1984; Griliches, 1986, 1990, 1995,1998; Albert et al., 1991; Narin, 1994; DeCarolisand Deeds, 1999; Nightingale, 1998; Deng et al.,2001). Third, patent citation data permit compar-isons across different studies and facilitate futurereplication and cumulative theory building. Lastly,we recognize that boards of directors are unlikelyto apply these numerical measures precisely ina mechanistic fashion to assess innovation qual-ity. To get at this, boards may rely instead oninterviews, formal and informal reports, reputationamong influential players in an industry and thelike. Yet, tapping the board’s cognitive process andthe sources it may use to assess innovation qual-ity in each firm would be almost impossible. Thiswould require close personal involvement of theresearchers in board deliberations, something thatwould not be very feasible and that, in the best ofcases, would yield very small samples with limitedstatistical power and generalizability. Our proxiescapture some of this evaluative process and make itpossible to test the hypotheses; the fact that empir-ical results support theoretical expectations lendscredence to this belief.

Control variables

Several measures were used as control variables.Firm size was represented by the level of totalassets. Further, CEO salary, the fixed componentof pay, was included as a control variable. Build-ing on the findings of Balkin et al. (2000), thenumber of patents granted to a firm each year wasincluded in the models. For the above three controlvariables we used a logarithmic transformation toaccount for the skewness of the distribution. Fol-lowing Deng et al. (2001), firms were classifiedinto four major industry groups: drugs, chemicals,electronics, and ‘others.’

A set of three control variables representing thefirms’ governance structure was also included for

testing Hypotheses 1–3. These control variablesconsisted of (1) CEO tenure, a measure of CEOinfluence or entrenchment (Hill and Phan, 1991);(2) inside director’s ratio (Baysinger et al., 1991;Westphal and Zajac, 1994); and (3) individual orcorporate owner control, a measure of ownershipconcentration (O’Reilly, Main and Crystal, 1988;Tosi et al., 1999). A dummy variable coded onewas used to note the presence of one or more indi-vidual or corporate owners with 5 percent or moreownership of the company stock. Firms without thepresence of individuals or corporate owners withstock holdings of 5 percent or more were coded aszero. The governance structure data for the insidedirector’s ratio were collected from Standard andPoor’s Register of Corporations, Directors, andExecutives for 1993. The CEO tenure variable,the individual ownership and corporate ownershipvariables were taken from proxy statements for1993.

Analysis

To test our hypotheses, we used panel data anal-ysis (Hsiao, 1986; Baltagi, 1995). This techniqueis appropriate for analyzing a dataset composedof multiple firms observed at multiple points intime. To account for heteroskedasticity and auto-correlated error terms, we used a fixed-effectsintercept model offered in LIMDEP econometricsoftware (Greene, 1995a). Compared to ordinaryleast squares (OLS) or generalized least squares(GLS) approaches, fixed-effects models have theadvantage of explicitly modeling unaccounted andunobserved heterogeneity and also allowing thepossibility that the omitted variables captured inthe firm-specific intercepts may be correlated withother independent variables in the model (Baltagi,1995). For each equation, the dependent variableswere measured the year after the independent vari-ables.

To test Hypotheses 1–3 we use the followingtwo regression models:

Bonusi,t+1 = a1Assetsit + a2Tenureit

+ a3Insidersit + a4MjIndit

+ a5MjCorpit + a6Salaryit

+ a7R&Dit + a8NPatit

+ a9ROEit + a10Resit + a11Sciit

+ a12(R&Dit × ROEit )

Incentivesi,t+1= a1Assetsit

+ a2Tenureit + a3Insidersit

+ a4MjIndit + a5MjCorpit

+ a6Salaryit + a7R&Dit

+ a8NPatit + a9ROEit

+ a10Resit + a11Sciit

+ a12(R&Dit × Sciit )

+ a13(R&Dit × Resit )

Bonus is the CEO’s bonus in year t + 1(1993–95);

Total incentives is the CEO’s total variablecompensation (Bonus and long-term pay) inyear t + 1 (1993–95);

Assets is the logarithm of the total assets of afirm in year t (1992–94);

Tenure reflects the number of years the execu-tive has held his position in year t ;

Insiders reflects the ratio of board members thatare employed by the firm (insiders) to the totalnumber of board members in year t ;

MjInd and MjCorp are dummy variables indi-cating the presence of one or more individualsor corporate stockholders, respectively, thathold 5 percent or more of the firm’s stock inyear t ;

Salary reflects the logarithm of the CEO’s fixedpay in year t ;

R&D is a measure of technological intensityand refers to the degree of investment inresearch and development (in millions of dol-lars) divided by sales in year t ;

NPat is the logarithm of a firm’s number ofpatents granted in year t ;

ROE is a firm’s reported return on equity forthat fiscal year in year t ;

Res is invention resonance in year t ; andSci is science harvesting in year t .

To test Hypothesis 4, we use a third regressionmodel:

M/Bi,t+1 = a1ROEit + a2Assetsit + a3R&Dit

+ a4NPatit + a5Resit + a6Sciit

+ a7(R&Dit × ROEit )

+ a8(R&Dit × Resit )

+ a9(R&Dit × Sciit )

+ a10(R&Dit × ROEit × Bonusit )

+ a11(R&Dit × Resit

× Total incentivesit )

+ a12(R&Dit × Sciit

× Total incentivesit )

where M/Bi,t+1 is the firm’s ratio of market valueto book value in year t + 1.

RESULTS

Table 1 presents the descriptive statistics and cor-relations of the variables for 1994 (the median yearof the time period examined). In that year, thefirms in our sample had an average ROE of 21.45and an average market-to-book ratio of 3.32. Theyspent an average of $313.4 million on R&D andwere granted an average of 118 patents. The CEOsof those firms averaged $666,000 in bonuses and$1.19 million in stock options.

Technology intensity, financial results andCEO bonus

Table 2 summarizes the fixed-effects model analy-ses for testing Hypothesis 1. That hypothesis pre-dicted that the greater the technological intensityof the firm, the more closely CEO bonuses at t + 1would be linked to financial results at t . Model Ashows the general effect of financial results andinnovation quality indicators on CEO bonus, andModel B shows their effects with the hypothesizedtechnological intensity interaction. The interactionterm for financial results and technology intensityis significant at p ≤ 0.001 for CEO bonus, thusstrongly supporting Hypothesis 1.

Consistent with prior research, firm size is pos-itively associated with bonuses (p < 0.05). Thenumber of patents that a firm is granted annu-ally (p < 0.10) has a positive association withCEO bonus, in keeping with Balkin et al.’s (2000)finding that executives are rewarded for the quan-tity of their firm’s innovative outputs. Finally, asexpected, CEO salary is positively and signifi-cantly associated with CEO bonus (p ≤ 0.001).

∗∗

03−0

.22∗∗

−0.1

0∗−0

.20∗∗

13∗∗

39−0

−0.3

4∗∗∗

−0.0

.32∗∗

16∗∗

29∗∗

∗∗∗

−0.0

9∗−0

−0.0

72∗∗

−0.1

3†−0

−0.2

5∗∗−0

−0.0

.20∗∗

∗∗

∗∗−0

−0.1

.23∗∗

−0.0

49∗∗

∗∗

∗∗−0

−0.0

.35∗∗

−0.1

9∗∗−0

∗∗0.

44∗∗

∗∗

71∗∗

−0.0

.17∗

−0.3

1∗∗−0

.15∗

59∗∗

∗∗0.

77∗∗

∗∗

12∗∗

−0.0

9†−0

.18∗∗

22∗∗

∗∗∗

−0.0

∗∗∗

†−0

.18∗

−0.0

∗∗p

∗∗∗p

Table 2. Technology intensity, financial results and CEO bonus

CEO bonus (log - 1 year lag)

Model A Model B

ControlsFirm size (log assets) 80247.53∗ (31596.18) 74071.51∗ (31273.44)CEO tenure 5698.08 (4059.86) 5151.26 (4015.02)Insiders −61499.93 (224456.63) −102042.29 (222112.09)Major individual owners 116798.9 (69085.97)† 108792.20 (68309.46)Major corporate owners 51668.25 (63109.73) 35498.59 (62536.67)# Patents (log) 55511.58 (32341.84)† 55536.07 (31960.35)†

Technology intensity 127.89∗∗ (39.34) 101.01∗ (39.63)CEO salary (log) 476366.59∗∗∗ (82821.22) 482333.48∗∗∗ (81862.07)Financial results 760.02 (786.50) 84.14 (800.92)

Innovation qualityInvention resonance 62691.98 (48915.26) 74888.46 (48464.06)Science harvesting −47665.66 (24315.33)† −46887.59 (24029.54)†

InteractionsTech. intensity × Financial results 3.29∗∗∗ (0.94)F -value 19.28∗∗∗ 19.28∗∗∗

R2 0.38 0.39Adjusted R2 0.36 0.38

N = 484. Standard errors are in parentheses.†p < 0.10; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001

Technology intensity, innovation quality andCEO total incentives

Table 3 summarizes the fixed-effects model anal-yses for testing Hypotheses 2 and 3: the greaterthe technological intensity of the firm, the moreclosely the CEO’s total incentives at t + 1 wouldbe linked to invention resonance (Hypothesis 2)and science harvesting (Hypothesis 3) at t . Asbefore, Model A shows the general effect of inven-tion resonance and science harvesting on CEO totalincentives. Model B shows the hypothesized tech-nological intensity × invention resonance interac-tion and suggests that it has a significant effect onCEO total incentives (p < 0.05), which supportsHypothesis 2. Model C shows the hypothesizedtechnological intensity × science harvesting inter-action and suggests that it has a significant positiveassociation with CEO total incentives (p < 0.05),which supports Hypothesis 3.

As before, firm size (p < 0.05) has a positiveeffect on total CEO incentives. Consistent withmuch of the prior literature on ownership struc-ture (Hambrick and Finkelstein, 1995; Werner andTosi, 1995; David, Kochhar, and Levitas, 1998;Tosi et al., 1999) the presence of major corporate

Table 3. Technology intensity, innovation quality &CEO total incentives

CEO bonus and long-termpay (log −1 year lag)

Model A

ControlsFirm size (log assets) 270015.00∗ (129450.31)CEO tenure −1281.16 (18671.01)Insiders −969294.73 (934027.68)Major individual owners −136503.84 (291112.94)Major corporate owners −577394.89∗ (252067.04)# Patents (log) 184602.00 (132807.32)

Technology intensity 202.22 (151.68)CEO salary (log) 825864.32∗ (350076.54)Financial results 14903.86∗ (6201.41)

Innovation qualityInvention resonance 213443.25 (192407.81)Science harvesting 45623.85 (93989.93)F -value 9.12∗∗∗

R2 0.26Adjusted R2 0.23

(Table 3 continued on next page)

stockholders has a negative and significant effect(p < 0.05) on the level of total incentive pay.

Table 3. (Continued )

CEO bonus and long-term pay (log −1 year lag)

Model B Model C

ControlsFirm size (log assets) 286570.61∗ (128962.36) 292753.51∗ (129044.61)CEO tenure −768.86 (18572.66) 556.62 (18577.42)Insiders −845811.48 (930607.50) −913879.97 (928834.13)Major individual owners −117437.72 (289678.39) −125952.97 (289437.16)Major corporate owners −597868.83∗ (250880.78) −586597.71∗ (250616.46)# Patents (log) 161757.75 (132475.21) 145185.50 (133063.28)

Technology intensity −740.72 (439.02)† −115.78 (201.53)CEO salary (log) 791051.97∗ (348539.67) 759221.21∗ (349146.50)Financial results 17081.25∗∗ (6241.33) 15082.97∗ (6165.45)

Innovation qualityInvention resonance 47554.75 (204663.51) 254612.31 (192059.28)Science harvesting 37045.52 (93563.26) −91555.94 (109804.28)

InteractionsTech. intensity × Invention resonance 751.79∗ (328.70)Tech intensity × Science harvesting 427.55∗ (179.75)F -value 8.97∗∗∗ 9.01∗∗∗

R2 0.27 0.27Adjusted R2 0.24 0.23

N = 410. Standard errors are in parentheses.†p < 0.10; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001.

Table 4. The predictors of market expectations for future performance

Market-to-book ratio (1 year lag)

Model A Model B

ControlsFirm size (log assets) −0.301∗∗∗ (0.069) −0.275∗∗∗ (0.068)# Patents (log) 0.284∗∗∗ (0.085) 0.238∗∗ (0.084)

Technology intensity 0.0002∗ (0.0001) 0.0002 (0.0003)Financial results 0.042∗∗∗ (0.004) 0.050∗∗∗ (0.004)

Innovation qualityInvention resonance 0.314∗ (0.127) 0.275∗ (0.135)Science harvesting 0.109 (0.062)† −0.0004 (0.072)

InteractionsTech. intensity × Financial results −0.013∗∗∗ (0.0026)Tech. intensity × Invention resonance −0.079 (0.238)Tech Intensity × Science harvesting 0.334∗∗ (0.120)F -value 28.36∗∗∗ 25.65∗∗∗

R2 0.37 0.42Adjusted R2 0.36 0.39

(Table 4 continued on next page)

N = 486. Standard errors are in parentheses. For the interaction terms, standard errors andcoefficients have been multiplied by E +3 for simplicity.† p < 0.10; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001

Table 4. (Continued )

Market-to-book ratio (1 year lag)

Model C

ControlsFirm size (log assets) −0.294∗∗∗ (0.076)# Patents (log) 0.192∗ (0.097)

Technology intensity −0.0005∗∗ (0.0002)Financial results 0.051∗∗∗ (0.005)

Innovation qualityInvention resonance −0.288∗ (0.138)Science harvesting 0.013 (0.070)

InteractionsTech. intensity × Financial results −0.015∗∗∗ (0.0044)Tech. intensity × Invention resonance 0.231 (0.369)Tech. intensity × Science harvesting 0.174 (0.216)Tech. intensity × Financial results × Bonus −0.056∗∗ (0.016)Tech. intensity × Invention resonance × Total incentives 0.357 (0.221)†Tech. intensity × Science harvesting × Total incentives 0.497∗ (0.227)F -value 21.43∗∗∗

R2 0.42Adjusted R2 0.39

For simplicity’s sake, the two-way interaction terms, standard errors, and coefficients have been multiplied byE +3 and the three-way interaction terms, standard errors and coefficients have been multiplied by E +10.

The predictors of market expectations forfuture performance

Table 4 summarizes the fixed-effects model anal-yses for testing Hypothesis 4: the greater the fitbetween the firm’s executive compensation poli-cies and its technological intensity, the more thesepay policies will contribute to the market’s expec-tations for future performance. We tested thishypothesis using the firm’s market-to-book ratio asa measure of market performance and three-wayinteraction terms to capture the proposed align-ments. Once again, Model A shows the maineffects, while Model B includes the two-way inter-actions.

In Model C, the three-way interaction for theincreased importance of aligning bonuses withfinancial results, given increased technologicalintensity, has a significant association with mar-ket performance (p < 0.01). Further, rewardingexecutives through total incentives for evidence ofinvention resonance has a significant associationwith market performance as technological inten-sity increases (p < 0.10), and aligning total CEOincentives with science harvesting, given increasedtechnological intensity, has a significant effect onmarket performance (p < 0.05). Thus, Hypothesis4 is supported.

In order to explore the performance implicationsof incentive alignment further, we divided oursample, based on the median market-to-book value,and tested Hypotheses 1–3 on two sub-samples ofhigh- and low-performing firms.6 We found thataligning CEO bonuses with financial results andtotal incentives with science harvesting as techno-logical intensity increases is associated with higherperformance. These analyses provide additionalsupport for Hypothesis 4, substantiating the argu-ment that better performing R&D-intensive firmsreward CEOs based on a combination of financialand behavioral quality indicators.

DISCUSSION AND CONCLUSION

Intellectual capital and innovation have becomethe key sources of competitive advantage in awide range of industries. While CEOs may not beinvolved in selecting specific technologies, the lit-erature on leadership suggests that CEOs play an

6 We have also plotted the interactive effect of technologicalintensity, total incentives and science linkage on market perfor-mance. These plots suggest that as technological intensity, sci-ence linkage, and total incentives increase together, the market-to-book ratio increases as well. The sub-sample analyses and theplots are available upon request from the first author.

important role in creating a corporate environmentthat fosters or inhibits innovation (Daft, 2002).The CEO can either encourage basic researchby creating well paid research fellow positions(IBM and DuPont7 adopted this) and by promot-ing partnerships with universities or he/she canimplement strategies that impair the productivityof scientists (for example, by tying salary increasesto the assumption of managerial responsibilities)(Stephan, 1996). Simply put, effective innovationrequires that resources are routed to basic researchand CEOs have great power to influence innova-tion decisions since they are the central strategicdecision maker (e.g., Zahra and Pearce, 1989).

Therefore, understanding the relationships be-tween CEO pay, innovation, and firm performanceis of critical importance to the effective corporategovernance of public corporations. The predomi-nant view of corporate governance considers theevaluation and reward of a CEO’s performancea pivotal control mechanism to prevent oppor-tunistic behaviors and to align the CEO’s interestswith those of the firm’s shareholders (cf. Car-penter, 2000). The agency-based CEO compen-sation literature emphasizes the substitutability ofoutcome vs. behavior-based criteria, with diver-gent prescriptions made by various scholars (cf.Sanders and Hambrick, 2004). In this study, wehave argued that a fundamental concern in solv-ing this apparent theoretical paradox is to exam-ine the context in which these agency predictionsare made. We proposed that technology-intensivefirms can be more effective if they base CEOincentives on a combination of short-term financialresults (ROE) and behavioral indicators of long-term innovation quality (invention resonance andscience harvesting). Such a compensation systemleverages the strengths of each approach and off-sets their weaknesses. It encourages a CEO to com-mercialize innovations but also reinforces behav-iors that enhance the firm’s ability to innovate in

7 The historic relationship between DuPont’s top management,scientific R&D policies, and firm outcomes has been studied indetail in the book Science and Corporate Strategy: Dupont R&D1902–1980 (Hounshell and Smith, 1988) and in a subsequentempirical paper by McMillan and Hicks (2001). Hounshell andSmith found that R&D intensity, areas of scientific researchand approaches to conducting research changed when DuPontchanged its head of R&D, a change made at the CEO’s direction.McMillan and Hicks updated Hounshell and Smith’s work usingempirical studies based on patent and scientific publications data.They uncovered a number of dramatic shifts in DuPont’s R&Defforts during the 1980s that can be explained in part by a changein top management.

the future. Accordingly, we argued that the greaterthe technology intensity of the firm, the more CEOincentives would be linked with all three of theseperformance criteria. We also predicted that align-ing CEO incentives with each of these performancecriteria would be positively associated with subse-quent firm performance.

We tested our theory using a sample of 206publicly traded firms from 12 U.S. manufactur-ing industries and found support for all of ourhypotheses. Financial results, invention resonanceand science harvesting each have their predictedpositive association with CEO incentives as tech-nological intensity increases. The interaction oftechnological intensity, CEO incentives, and finan-cial results as well as the interaction term betweentechnological intensity, CEO incentives, and sci-ence harvesting are positively associated with thefirm’s market performance. However, the interac-tion between technological intensity, CEO incen-tives, and invention resonance is only weakly sig-nificant. Taken together, these results provide sub-stantial support for our contention that technology-intensive firms perform better when their CEOs’incentives are linked to a holistic set of finan-cial results and innovation quality indicators, whilethe same is not true for less technology-intensivefirms. This again suggests that for agency predic-tions to be meaningful in terms of the determinantsand consequences of CEO pay design, it is criticalto take context into account.

Implications for research

This paper examines a phenomenon at the intersec-tion of corporate governance, compensation, andinnovation research: the relationship between CEOincentives, innovation, and firm performance. Ourfindings have important theoretical and method-ological implications for all three research streams.They also have implications for other areas ofstrategic management research.

Corporate governance research implications

Boards of directors are a crucial element of cor-porate governance, having as their reason for exis-tence the representation of shareholder interests.Under this rubric fall two primary roles: a mon-itoring role, which includes setting CEO pay andmotivating the CEO to act as a steward on behalfof the owner, and a service role, which consists

of decisions affecting the strategic direction of thefirm (e.g., Johnson, Daily, and Ellstrand, 1996).The dimensions of innovation quality introducedhere could affect the way researchers examine therole of boards in two ways.

First, innovation quality is an important com-pensation factor that boards may want to considerwhen gathering information about the CEO’s per-formance in a knowledge-intensive context. Wehave argued that in the context of high-technologyfirms where information asymmetries betweenboards of directors and executives are high, boardeffectiveness can be enhanced if board mem-bers proactively gather information relevant to thefirm’s innovation efforts. Our constructs can shedsome light on intermediary factors that affect thebottom line, and help boards reward the right pro-cesses in addition to the right outcomes.

Second, our research has important implicationsfor the service role played by boards in high-technology organizations. Sanders and Carpenter(1998) argued that firms may increase the sizeand diversity of their board members when facedwith greater information-processing demands inorder to accommodate those demands. Our theo-retical framework suggests that in R&D-intensivefirms, where information processing is high, hav-ing innovation-specific knowledge may improvethe CEO evaluation process.

Compensation research implications

Our study contributes to the ongoing debate inthe agency-based executive compensation liter-ature between the outcome-based controls andbehavioral monitoring schools of thought. Recallthat the OBI perspective argues that because of theincreased information asymmetries between own-ers and managers, reliance on outcome-based mea-sures is preferable, while the behavioral monitor-ing school suggests that such an approach can leadto decisions that smother innovation, thereby argu-ing for behavioral measures of performance. Ourstudy found that technology-intensive firms couldtake advantage of the complementarities of thesetwo perspectives by rewarding executives usingboth outcome-based and behavior-based indicatorsof performance.

Even though we have limited ourselves to CEOincentives, clearly, other managers are also invol-ved in facilitating innovation. The relationshipsexamined here should be explored in the context of

the top management team as a whole, as well asin the context of divisional and R&D managers.Most likely there is a cascading effect from theCEO down to lower levels within the technology-intensive firm. Werner and Tosi (1995) found thatthe incentive system at the top tends to have a cas-cading effect across different management layers.It would be interesting to study how this cascadingprocess operates in the technology-intensive firmas CEOs are rewarded for outcomes and behavioralmeasures using a combination of short- and long-term incentives.

Innovation research implications

If invention resonance and science harvesting arebehavioral indicators of innovation performance,then what structures, policies, or processes influ-ence them? For example, Lane and Lubatkin(1998) found that formalization and centralizationof decision-making influenced R&D-related learn-ing between alliance partners. Do those factorsalso influence invention resonance and science har-vesting? Are they influenced by the geographicdispersion of a firm’s R&D activities or by theway in which it manages its absorptive capacity?The strong effect of the science harvesting–CEOincentives alignment on market performance sug-gests that how firms manage scientific knowledgeis a strategically important question.

We have focused our attention on technologi-cal innovations, in part because they are the eas-iest to track and evaluate because of the patent-ing process. However, firms innovate in otherareas not suited for patent protection, such asmanagement processes, marketing strategies, andhuman resources policies. These types of innova-tions can also influence firm performance. Futureresearch should attempt to track these innovationsand examine their relationship to managerial incen-tives and firm performance. Given the firm-specificnature of many of these innovations, this may needto be done as qualitative rather than quantitativeresearch.

Other research implications

Our theoretical framework may open up newavenues of research on absorptive capacity andorganizational learning. The aspects of innovationon which a firm chooses to focus through its com-pensation scheme are likely to affect the types of

knowledge developed. In turn, this would influencethe focus of its absorptive capacity, and hence, thetypes of knowledge it will be able to learn in thefuture. Firms that focus more on basic researchand that reward their managers for investments inscience are more likely to develop different capa-bilities and different types of innovations fromthose that engage in applied research. Simply put,a firm’s compensation design can affect the typesof knowledge a firm creates internally and acquiresexternally.

The relationships we studied should also berevisited in non-U.S. contexts. Implicit in ourhypotheses about CEO pay and firm performanceis the U.S. model of corporate governance. Recentresearch suggests that the socialization of man-agers, shareholders, and directors that the U.S.governance model assumes may be far from uni-versal (Thomsen and Pedersen, 1996; Pedersen andThomsen, 1997). In addition, there is evidencethat firms in different nations approach innovationdifferently (Meyer-Krahmer and Schmoch, 1998;Mowery, 1998). This may mean that the relativeimportance of the behavioral and outcome criteriaused here could vary across nations.

Managerial implications and caveats

Our citation-based measures are rough proxiesfor desirable innovation-related behaviors and notdirect measures of the behavior themselves. Wehave used these measures ex post to operationalizeresearch quality, enabling us to test our theoreti-cal predictions. This does not mean, however, thata board of directors necessarily should use thesetypes of indexes ex ante to structure a compensa-tion contract around them.8 Citations to the firm’sown prior patents or to scientific papers may beopen to manipulation. If CEOs know ex ante thatincreasing either of these two types of citations willbe rewarded, they may attempt to encourage thearbitrary addition of citations to patents. However,the risk that managers will try to ‘game’ citationbased incentives is mitigated by the vigilance ofthe firms own patent lawyers and by governmen-tal patent examiners who routinely delete citationswhich they determine to be of little or no relevanceto the patents’ claims (Meyer, 2002).

8 The authors are grateful to an anonymous reviewer for raisingthese concerns.

There is also the risk that adding spuriouscitations to a patent can reduce the scope of itsclaim and hence its value (Meyer, 2002). Thisis especially true for citations to prior patentswhich limit a patent’s novelty and inventive step(nonobvious) claims. The risk of value loss is farless for additional science citations. The USPTOpatent examiner’s manual discusses science cita-tions solely in the context of supporting utilityclaims, showing that what the inventor claims canbe done is supported by science. Simply put, citingscience to support your utility claims means thatyou are venturing into areas where prior patentshave not gone before. This is the logic that guidespatent examiners who view patents citing scienceas working on the leading edge of technology(Mogee and Kolar, 1994).

Most importantly, the lag between a CEO’sinnovation management decisions and their mani-festation in their firms’ patents will be a minimumof 3 or 4 years. This makes patent citation mea-sures such as those used in our study difficult forboards to utilize when designing CEO compensa-tion contracts, but does not mean that our findingslack practical value. The basic message of thisstudy for practitioners is that it is important forhigh-technology firms to consider both financialresults and research quality when rewarding execu-tives, and that how the incentive alignment systemis designed may have important firm performanceconsequences. The challenge lies on how to mea-sure research quality in a way that does not leadto some of the problems noted earlier, particularlygaming. While it is beyond the scope of this paperto delve into operational details, prior research inrelated areas suggests that there a number of otherways that boards could use when assessing thefirm’s innovation quality, methods that are moreamenable to compensation design.

The human resource literature offers a num-ber of tools that may be used by the board(Gomez-Mejia, Balkin, and Cardy, 2006). Forexample, the board could use the so called 360°

appraisal, whereby input about the executives’innovation quality-related decisions and imple-mentation is sought from internal sources (sub-ordinates, key scientists and engineers, ‘insider’board members, peers, employee surveys and thelike), cognizant external parties that have busi-ness ties with the firm (and thus are more inti-mately familiar with its operations such as cus-tomers and vendors), and outside experts (which

may include consultants, colleagues in other orga-nizations, university faculty, and technical staffof private/public sector research centers). Anotherway of evaluating executives is the ‘reputationalapproach,’ which focuses on measuring execu-tive effectiveness in initiating and implementingquality initiatives from the perspective of multipleconstituencies (Tsui and Gomez-Mejia, 1988; Tsui,1987). Both of these approaches involve subjectivejudgments but these tend to be more accurate thegreater the length of time involved in the reviewcycle and the more data points are used (e.g., mul-tiple feedback sources).

The innovation management literature suggeststhat evaluators can gather objective indicators ofthe quality of the knowledge upon which theyare building, especially in science. The number ofpapers a firm’s scientists publish and their exter-nal reputation are positively related to the rate ofnew product development in the pharmaceuticaland chemical industries (Henderson and Cockburn,1994) Companies that have a good understandingof the science relevant to their technology tend tofocus their R&D spending on topics which havean established network of international researchersand are rapidly developing new knowledge (Nor-ling et al., 2000). Third party assessments of howwell a firm is ‘placing its science bets’ are avail-able (McMillan, Klavans, and Hamilton, 1995) andwould be difficult for the executive to manipulatethem across the board.

As noted several times in this paper, in the end,one of the greatest dangers in pay-for-performancesystems is that individuals tend to maximize thecriteria on which they are being evaluated, andsince most criteria are contaminated or imper-fect, the most prudent approach is to collect asmuch information as possible. This may be atime-consuming process, but when it comes downto assessing research quality performance dimen-sions for rewarding the high-technology CEO whomakes key strategic and resource allocation deci-sions for the firm, our study suggests that it isworth the effort.

Conclusion

The pace of innovation has increased dramati-cally over the past two decades. Where it usedto take years to exploit the potential of a techno-logical trajectory, it now often takes only months.Today, the cornerstone of a high-technology firm’s

competitive advantage is its ability to innovatein ways that allow it to jump to new trajecto-ries where its rivals cannot easily follow. Thisstudy emphasizes that it is crucial for firms toreward their executives for supporting their firms’innovation efforts and suggests that an effectiveway to accomplish this is by rewarding execu-tives using multiple performance criteria whichinclude both financial results and behavioral indi-cators of innovation quality. We also reconcile theoutcome and behavioral-based incentive views onincentive alignment in technology-intensive firmsby suggesting that these two views complementeach other.

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CEO incentives, innovation, and performance in technology-intensive firms: a reconciliation of...

Documents

Maluku: Anomie to Reconciliation

Bank Reconciliation Documentation - Edmunds GovTech

RO_Details.pdf - CEO Madhya Pradesh

Report - RECOM Reconciliation Network

BHOLA ROY_Winner - CEO Bihar

CEO Worldwide Expert File

Incentive systems - OECD

Boiragipara - CEO Meghalaya

DETAILED RESULTS - CEO , JK

Incentive theory

15 - MAWLAI - CEO Meghalaya

Incentive Mechanisms for Participatory Sensing

Narendra Singh - CEO Bihar

Balwan Singh - CEO , JK

Index Reconciliation Fundamentals

FOSTERING RECONCILIATION THROUGH TREE PLANTING

- s. 100 - :: Ceo-Telangana ::

Reconciliation and Founding Wounds

R.K Anand - CEO - Haryana

State Name - CEO - Haryana