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Technological Innovation in the Pharmaceutical Industry:The Use of Organizational Control in Managing Researchand DevelopmentLaura B. Cardinal,

To cite this article:Laura B. Cardinal, (2001) Technological Innovation in the Pharmaceutical Industry: The Use of Organizational Control inManaging Research and Development. Organization Science 12(1):19-36. http://dx.doi.org/10.1287/orsc.12.1.19.10119

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ORGANIZATION SCIENCE, � 2001 INFORMSVol. 12, No. 1, January–February 2001, pp. 19–36

Technological Innovation in the PharmaceuticalIndustry: The Use of Organizational Control in

Managing Research and Development

Laura B. CardinalKenan-Flagler School of Business, McColl Building, CB #3490, The University of North Carolina at Chapel Hill,

Chapel Hill, North Carolina 27599–[email protected]

AbstractThe literature on the management of R&D professionalsstrongly advocates managing R&D projects on a project-by-project basis. This literature suggests that projects should bemanaged differently depending upon project characteristicssuch as risk, ambiguity, and nonroutineness. While the primaryemphasis of the R&D professional literature has been on projectteams, the purpose of this study is to examine the impact oforganization-wide controls on innovativeness at the firm level.

In a sample of 57 pharmaceutical firms, this study investi-gates the influence of organizational controls on the researchand development activities of R&D professionals. This study isone of a handful of studies that simultaneously explores the useof input, behavior, and output controls. Two categories of in-novation are considered as dependent variables: incremental in-novations in the form of drug enhancements and radical inno-vations in the form of new drugs. Contrary to existing theoryand hypotheses developed in this study, the results show thatinput, behavior, and output control enhanced radical innovation,and input and output controls enhanced incremental innovation.

These results challenge several important features of existingmodels of R&D management and diverge from common beliefsabout R&D management at the project level. While it is com-monly accepted that incremental and radical innovation shouldbe managed differently, the results of this study suggest oth-erwise. In this instance, the management of R&D activities maybe considered more similar than previously thought.(Innovation; R&D; Organizational Control; Pharmaceu-tical Industry)

IntroductionTechnological innovation has become increasingly criti-cal for firms as they struggle to achieve and maintain

competitive advantage. Trends such as globalization, fastproduct-cycle times, greater competition, product com-moditization, and technology fusion have only added tothis importance. Close examination of the pharmaceuticalindustry shows that this industry, while consistently prof-itable, has not been immune from these same forces.Fewer drug introductions and increased R&D expendi-tures, increased popularity of generic substitutes, in-creased foreign competition, an increased number of sig-nificant drugs coming off patent protection, and increasedhealth care reform have simultaneously squeezed profitmargins and limited the selection of drugs made availableto consumers through health plans (Ravenscraft and Long1999, Taggart 1993). All these elements have put a pre-mium on managing R&D processes in an effective andefficient manner.

Insights into understanding the management of tech-nological innovation can be gained by the study of R&Dprofessionals. This study advances our understanding ofthe management of R&D professionals by examiningorganization-wide controls used in R&D laboratories andtheir effects on innovation in the pharmaceutical industry.More precisely, this research explores whether differentkinds of R&D projects require different kinds of controls.Researchers have suggested that because of different lev-els of uncertainty and complexity, a contingency ap-proach should be taken to organizing for innovation(Dewar and Dutton 1986, Duncan 1976, Keller 1994,McDonough and Leifer 1983).

While this study builds on existing research on R&Dprofessionals, it diverges from prior research on severalcritical dimensions. First, the unit of analysis is at theorganization level. Much of what we know about themanagement of R&D professionals has centered on pro-ject team performance and not organization level inno-vation (exceptions include Bailyn 1985, Ettlie et al. 1984,

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LAURA B. CARDINAL Technological Innovation in the Pharmaceutical Industry

20 ORGANIZATION SCIENCE/Vol. 12, No. 1, January–February 2001

and Pelz and Andrews 1976). In spite of the interest inproject teams, the management of formal R&D efforts orthe R&D function per se have received less attention.Second, this study is one of only a handful that have ex-amined the effects of structural control on both incre-mental and radical innovations (Dewar and Dutton 1986,Ettlie et al. 1984, Kaluzny et al. 1974, Nord and Tucker1987). Third, this study moves beyond previous researchby examining a full range of organizational control mech-anisms: input, behavior, and output controls. Not onlyhave most studies focused on more traditional structuralaspects of control (Pelz and Andrews 1976), but the ma-jority of theories concerning control have been developedfor line workers or managers, not R&D professionals andscientists, and not for scientific work.

Formal research efforts in the pharmaceutical industryprovide an excellent setting in which to test determinantsof incremental and radical innovation. These programsare highly dependent upon the success of R&D profes-sionals for the discovery and advancement of knowledgeneeded for the development of new drug innovations.Pharmaceutical firms demonstrate wide variance in suc-cess achieving both incremental innovation in the formof drug enhancements and radical innovation in the formof new drug introductions (Cool and Schendel 1987). Onesource of the variation across firms may be how effec-tively these firms manage the R&D process. The study ofR&D professionals in the pharmaceutical industry isespecially appealing because a large proportion of activ-ity involving the discovery of new knowledge and ad-vances made to existing knowledge occurs in the researchand development laboratories for all high-technology,science-based firms (Cardinal and Lei 2000, Freeman1982, Pavitt 1990).

The paper begins with a discussion of R&D activitiesand innovation in the pharmaceutical industry, followedby a review of the pertinent research on incremental andradical innovation, and then a brief review of the corestructural control and innovation literature is highlighted.A theory of control that builds upon the R&D projectteam literature provides a framework for understandingthe potential effects of control on innovation. After a de-scription of the methodology, the empirical findings arepresented. The discussion relates the findings to the gen-eral question of the differential management of incre-mental versus radical innovation.

R&D Activities and InnovationThe foundation of competitive advantage in the phar-maceutical industry lies in successful innovation. Thepharmaceutical industry spends more as a percentage of

sales on research and development than any other high-tech industry, including electronics, computers, and aer-ospace (Pharmaceutical Research and Manufacturers ofAmerica 1997, Teitelman and Baldo 1989). This studyexamines both drug development and drug discovery ac-tivities.

R&D activities can vary in the degree of radicalness oftheir outputs. Unlike mechanical assembled products in-dustries—such as disk drives, mainframe computers, andautomobiles, which involve complex systems and manycomponents—the pharmaceutical industry’s core productcenters on a molecule (Henderson 1994). The radicalnessof drug innovation is therefore a function of the new tech-nological and scientific knowledge embedded in the drug(Abernathy and Clark 1985). Radical innovations repre-sent major changes in technology involving the discoveryof new knowledge, substantial technical risk, time, andcost (Roussel et al. 1991). Incremental innovations rep-resent minor changes to existing technology involvingsmall advances based on an established foundation ofknowledge (Roussel et al. 1991). In the case of the phar-maceutical industry, ‘‘New Chemical Entities (NCEs) aretotally new drugs which, in most cases, represent signifi-cant therapeutic advances’’ (Cool 1985, p. 250), and aredefined by the Food and Drug Administration (FDA) as‘‘those products representing new chemical structuresnever previously available to treat a particular disease’’(Pharmaceutical Manufacturers Association 1989, p. 22).These new drugs would be considered radical innova-tions.1 Following FDA criteria, drug enhancements in-volve combinations of existing drugs, new dosage forms,new indications, and formula changes, and would be con-sidered incremental innovations. These definitions areconsistent with definitions given by Abernathy and Clark(1985), Banbury and Mitchell (1995), Freeman (1982),and Roussel et al. (1991).

Development of a Theoretical Model andHypotheses

Incremental and Radical InnovationTwo key streams of literature provide insights into themanagement of incremental versus radical innovationprocesses: project team research and innovation adoptionresearch.

The first stream, the project team research, offers themost consistent findings. There has been considerable re-search on the management of R&D professionals with thefocus primarily on project team characteristics and inter-nal team functioning (Allen et al. 1980, Katz 1982, Kellerand Holland 1983). Major themes across this research

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ORGANIZATION SCIENCE/Vol. 12, No. 1, January–February 2001 21

have been team communication patterns, task character-istics of research projects, information processing re-quirements, and project team demography.

This research has produced four main findings. First,project team communication modes vary by the type ofresearch project (Allen et al. 1980, Allen et al. 1979, Katzand Tushman 1979, Tushman 1977). Radical projectswere more successful when all members of the team com-municated outside the team, and incremental projectswere more successful when a single boundary spanner orgatekeeper was responsible for external communication.Second, the source of information varies by project type(Katz and Tushman 1981, Lee and Allen 1982). Radicalinnovation projects required external sources of infor-mation that were dependent upon discipline knowledgeand public science, and incremental innovation projectsrequired internal sources of information that were specificto the firm and unique to specific product-line offeringsand characteristics (Allen et al. 1980). Third, informationprocessing requirements vary by the complexity or rou-tineness of the project (Katz and Tushman 1979, Keller1994). The fit between project routineness and the amountof information processing was found to be an importantpredictor of project team performance, with nonroutineradical projects requiring more information processingand routine incremental projects requiring less informa-tion. Fourth, the demography research shows that teamdemographic characteristics are important predictors ofboth project team dynamics and project team perfor-mance (Ancona and Caldwell 1992; Katz 1982, 1988).Radical project success was facilitated by cross-functional teams that enhanced the permeability of teamboundaries and increased information-processing capac-ities, whereas the cross-functionality created informationoverload for incremental projects.

The findings of the project team literature are quite con-sistent, and a key conclusion drawn from this researchstream is that across R&D projects, teams should not bemanaged in the same way because of their different pro-ject characteristics, such as riskiness, ambiguity, nonrou-tineness, and radicalness. This conclusion has importantimplications for the design of formal R&D programs. IfR&D management is inherently a project-by-project-based process, then organization-wide controls (cross-project organizational controls) utilized in R&D pro-grams would be expected to differentially impactincremental versus radical innovation.

The commonly accepted wisdom in the second streamof research, the innovation adoption literature, is that in-cremental and radical innovation involve separate anddistinct adoption processes (Damanpour 1991). This sec-ond stream has not only focused almost exclusively on

innovation adoption rather than product innovation,2 butthe majority of predictors studied concern structural as-pects of control. Further, only four studies have actuallymeasured both incremental and radical innovation (seeDamanpour 1991 for a complete review of the determi-nants of innovation).3 Ettlie et al. (1984) studied radicaland incremental process innovations and radical pack-aging innovations in the food-processing industry. Theyfound that adoption of radical innovations occurred morefrequently in firms with an aggressive technology policyand a concentration of technical specialists, while incre-mental innovations were adopted more frequently in firmsthat were more decentralized and formalized. A criticalmass of technical specialists was found to be importantfor incremental innovation, but was more predictive ofradical adoptions. In a later study, Dewar and Dutton(1986) examined radical and incremental innovations inthe footwear industry and found that technical specialistswere critical for both radical and incremental adoptions,but were more predictive of radical adoptions. Kaluznyet al. (1974) studied the number of new hospital andhealth services adopted. While professionalization was apositive predictor of both incremental and radical inno-vation in health services, it was positive for incrementalinnovation only in hospitals. In the case of radical pro-grams in hospitals, formalization was the primary predic-tor—in a positive direction. Finally, Nord and Tucker(1987) studied the adoption of NOW accounts by banks.Firms adopting incremental innovations were more con-cerned with consistency and not interrupting daily rou-tines, whereas firms adopting radical innovations adaptedtheir organizational routines to accommodate the inno-vation.

In conclusion, these studies suggest that incrementaland radical innovation adoptions may be more similarthan previously thought. Damanpour’s (1991) meta-analytic review, which is the most comprehensive reviewto date, concludes that the predictors for radical and in-cremental innovation are the same, not different as pre-viously argued. Careful interpretation of these studiessuggests that the difference may be one of magnituderather than direction when discussing innovation adop-tion.

Implications of the R&D Project Team andInnovation Adoption LiteraturesCombining both the R&D project team and the innova-tion adoption literatures provides a starting point for un-derstanding how managers can systematically influencethe organization-wide R&D process over time. The find-ings for R&D project research are directly applicable tothe management of R&D professionals and technological

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innovation; however, the conclusion from the adoptionstudies offers additional guidelines as to how managersmight influence product innovation. While the R&D pro-ject team research focuses on informal and emergent rou-tines of R&D teams, the innovation adoption influencesrepresent formal levers designed by managers.

If you look at the pattern of influences on incrementaland radical innovation adoptions, many of these influ-ences center on some form of control. Some studies havefocused on ‘‘people policies’’ or input control (Dewarand Dutton 1986, Ettlie et al. 1984), and others have fo-cused on structural control, also referred to as bureau-cratic or behavior control (Aiken et al. 1980, Aiken andHage 1968, Nord and Tucker 1987). The conventionalwisdom on control is that one form of control should beused over another depending upon the given activity(Eisenhardt 1985, Ouchi 1979, Snell 1992). While schol-ars have analyzed how organizations choose one form ofcontrol over another (Mintzberg 1979, 1983; Ouchi1977), typically a single form of control does not exist inisolation; rather, controls are combined to influence theattainment of a specific goal (Jaworski 1988, Kirsch1996). However, thinking about how organizations con-trol multiple stages of the transformation process—frominputs, to behaviors and activities, to outputs—providesa broader and more realistic perspective on control.

As a central concept in organization theory, the role oforganization control in the attainment of technologicalinnovation in R&D processes is not well understood. Thepurpose here is to examine how well our theories of con-trol apply to R&D professionals and R&D activities.

Organizational ControlControl can be defined as any process by which managersdirect attention, motivate, and encourage organizationalmembers to act in desired ways to meet the firm’s objec-tives (Eisenhardt 1985; Govindarajan and Gupta 1985;Jaeger and Baliga 1985; Kerr 1985, Langfield-Smith1997; Ouchi 1977, 1979; Snell 1992). Scholars have iden-tified several forms of control: structural (Blau and Scott1962, Lebas and Weigenstein 1986), market (Ouchi 1979,Williamson 1975), cultural (Arvey 1979, Wanous 1980),input (Merchant 1985; Mintzberg 1979, 1983), output(Jaworski 1988, Merchant 1985), and integrative(Cardinal et al. 1999, Roth et al. 1994); of these, structuralforms of control have been the object of considerable re-search (Barker 1993). Structural control, also referred toas bureaucratic or behavior control, regulates activitiesand behaviors and is most often implemented in the formof rules and procedures.

Because a focus on behavior control has dominated ourtheories and empirical work, our field has adopted a bias

against control, especially as it pertains to innovation. Bu-reaucratic control—assumed to be predominantly a mat-ter of behavior controls—has been predominantly viewedas a mechanism that stifles creativity and fosters em-ployee dissatisfaction (Adler and Borys 1996). A broaderperspective conceptualization of control might be par-ticularly effective in analyzing R&D activities. R&D ac-tivities can be described as unpredictable, labor intensive,long-term and multistaged, idiosyncratic, risky, highly un-certain, cumulative, and highly differentiated (Hendersonand Cockburn 1994, March 1991, Ravenscraft and Long1999). Given that we cannot escape control in formal or-ganizations, it is prudent then to understand what sorts ofcontrol mechanisms are effective in settings such as R&Dlaboratories which fuel innovation in science-based in-dustries.

Interestingly researchers such as Jelinek and Schoon-hoven (1990) demonstrate that structure and formality areimportant to innovation in high-technology settings. Ad-ler and Borys (1996) show that bureaucracy has beneficialeffects in organizations, and Bailyn (1985) illustrates thatscientists are provided with too little behavior control intheir initial jobs, creating job dissatisfaction. She rec-ommends that less autonomy and some behavioral con-trols are important in early career stages. This small butsignificant stream of research implies that the relationshipbetween control and innovation may be more complexthan previously thought.

Few phenomena have been studied where all threeforms of control (input, behavior, and output) have beenexamined simultaneously (exceptions include Kirsch1996 and Snell 1992), and we have not explored the roleof all three types of control in the R&D process. Morenotably, output control is conspicuously absent in boththe innovation adoption literature and the R&D projectteam literature. This study adopts a broader view of con-trol and seeks to understand its impact on multifaceted,complex, and uncertain activities such as R&D.

Hypothesis DevelopmentTo structure the hypotheses and empirical analysis, I usethe classical distinction between the three broad classesof control: input, behavior, and output control. Each classof control consists of control mechanisms that representelements or modular units of control (e.g., policies,norms, procedures, processes, etc.). Specific controlmechanisms can be distinguished based on what they areintended to influence. Input control can be considered aform of resource allocation because it regulates the an-tecedent conditions of performance. In the case of R&Dprofessionals, it can be used to create the type of ‘‘knowl-edge environment’’ desired by firms by manipulating the

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degree and variety of core knowledge, skills, experiences,and attitudes displayed on the job (Mintzberg 1979,1983). Behavior control consists of monitoring ongoingemployee activities and behaviors and regulating howwork gets done (Eisenhardt 1985, Ouchi 1977, Snell1992), and output control regulates outcomes and resultsas opposed to the means by which outcomes are achieved(Eisenhardt 1985, Ouchi 1977, Snell 1992). In applyingoutput control, firms define the dimensions of desired re-sults, measure how well output aligns with the set stan-dards, and provide respective rewards and punishment forsuccess and failure in goal attainment (Merchant 1985).

Input Control. The distribution of knowledge acrossR&D professionals is particularly important in the phar-maceutical industry because many drug therapies dependupon specific scientific disciplines and access to leading-edge breakthroughs. Innovative potential can be managedby controlling the antecedent conditions, in particular byselecting a broad range of scientific specialties while en-suring that scientists maintain close ties with the scientificcommunity.

Input control through scientific diversity offers severaladvantages in the pharmaceutical industry. First, scien-tific diversity aids in general creativity and brainstormingprocesses. Diversity of perspectives, backgrounds, andtraining facilitate the generation of new ideas (Bantel andJackson 1989, Wiersema and Bantel 1992). Dougherty(1992, p. 195) found that innovation required collectiveaction and ‘‘efforts to bring together disparate perspec-tives.’’ The availability of multiple scientific areas of ex-pertise can aid R&D professionals in developing newknowledge and expanding existing knowledge basesthrough the cross-fertilization of ideas. Second, scientificdiversity has become more critical to drug research, sci-entific knowledge, has rapidly expanded, and new spe-cializations have emerged. As drug research has ad-vanced, it has become more dependent on a broad arrayof scientific disciplines such as chemistry, physiology,toxicology, pharmacology, medicine, and biology(Henderson 1994, Spilker 1989). Because the problem-solving skills required in drug research are inherently di-verse, diversity of scientific personnel facilitates complexproblem solving.

While scientific diversity directly affects the availableknowledge in R&D laboratories, input control throughprofessionalization affects the exposure to and acquisitionof new scientific knowledge. Relative to other industries,the pharmaceutical industry places a high value on theexchange of scientific information through communica-tion and publication. This is consistent with the researchon R&D professionals that finds that project performance

is positively linked to boundary-spanning activities andgatekeepers who stay in contact with colleagues and re-main informed of current scientific developments (Allen1977, Allen et al. 1980, Katz 1982, Tushman 1977). R&Dprofessionals remain current concerning scientific devel-opments by reading key scientific journals, attending pro-fessional meetings, and obtaining additional training andeducation. Staying current in science-based industriessuch as the pharmaceutical industry is critical where ad-vances in the basic sciences are occurring at a rapid paceand technological obsolescence can occur quickly. Also,firms that are not plugged into the scientific network arelimited in their ability to exploit new scientific develop-ments. Gambardella (1992) found that firms that have in-house research programs and publish in the scientific lit-erature were better able to exploit available publicscientific knowledge. As argued by Cohen and Levinthal(1989), firms’ in-house R&D programs not only providea direct input to innovation but also increase firms’ ca-pacities to absorb and exploit new technologies. There-fore, professionalization exposes R&D professionals tonew technologies and serves as a link to the scientificnetwork-enhancing innovation capabilities.

While it appears that input controls are critical to rad-ical innovation, the role of input controls in influencingincremental innovation is less clear. Unlike other indus-tries, pharmaceutical firms cannot introduce this kind ofinnovation unless they have already introduced majornew drugs. Whether input controls are also important forincremental innovation depends on the knowledge re-quirements of the activity and the original knowledgebase. Anecdotal evidence (Levy 1990) suggests that evendrug enhancements depend upon a broad array of disci-plines and benefit from access to external information.

In summary, input control through scientific diversityand professionalization increases the variety of ideas pro-duced and exposure to scientific knowledge. Both typesof input control mechanisms forge knowledge linkagesbetween different scientific disciplines and with the sci-entific community. Thus, the use of input control isclearly expected to enhance innovation processes with re-gard to nonroutine drug technologies. Further, it may alsoplay a role in routine drug technologies. Following fromthe above arguments, the following two hypotheses arepresented.

HYPOTHESIS1A. As the use of input control increases,the likelihood of drug enhancement innovation increases.

HYPOTHESIS1B. As the use of input control increases,the likelihood of new drug innovation increases.

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Behavior Control. Behavior control is expected to in-fluence innovation activities in a variety of ways, de-pending upon the nature of the research task (Allen et al.1980, Duncan 1976, Katz and Tushman 1983, Zaltmanet al. 1973). Research directed toward the developmentof new knowledge, such as the discovery of new drugs,involves uncertainty, complexity, and nonroutineness;whereas research focused on routine and narrow activitiesinvolves less ambiguity, more certainty, and less risk.

The regulation function of behavior control—exercisedthrough centralization and formalization and often re-ferred to as ‘‘structural’’ or ‘‘bureaucratic’’ control(Aiken et al. 1980)—has been frequently studied and hasa long history in the innovation literature (Damanpour1991). At the same time, the empirical evidence concern-ing the effects of centralization and formalization hasbeen mixed. Some have argued that increased regula-tion enhances the implementation of innovation but ham-pers the generation of new ideas and the creativity stagesof innovation (Duncan 1976, Zaltman et al. 1973).Damanpour’s (1991) meta-analytic review demonstratesthat formalization is positively related, and centralizationis negatively related, to product innovation. However,Damanpour’s relationships are nonsignificant for bothradical and incremental innovations. Unfortunately, be-cause there are very few studies that examine both incre-mental and radical innovation, Damanpour’s subsampledoes not differentiate between product and process in-novations. Other researchers have also found different re-sults depending on the innovation process or the type ofstructure studied (Birnbaum-More 1993, Jelinek andSchoonhoven 1990). Birnbaum-More, in his study ofproduct-cycle times, found that centralization and for-malization did not affect product response time for U.S.firms. Jelinek and Schoonhoven’s (1990) work on high-tech firms is more in line with that of Duncan (1976) andMcDonough and Leifer (1983), supporting the need forstructural flexibility. They find that firms often changestructural forms, defined as design architecture, as thefirm’s organizing requirements change. The preponder-ance of structure literature has argued that mechanisticorganizations are detrimental to radical innovation(Damanpour 1991, Dewar and Dutton 1986).

Formalization through rules and procedures governingthe approach taken in problem solving restricts scientists’ability to deal effectively with the high level of uncer-tainty inherent in the drug research process. As a result,the greater the formalization the less likely experimen-tation will occur (March and Simon 1958). The presenceof many rules, and the reliance on them, will reduce the

likelihood that organizational members deviate from es-tablished behavior patterns (Weick 1979). Further, cen-tralization of decision-making narrows channels of com-munication. If information flows follow prescribedchannels of communication, the probability that a projectinvolving a radically new technology will be screened outif it does not ‘‘fit’’ the status quo will also increase. Asstated by Thompson (1967), it becomes easier for the in-novation to get vetoed. Tushman (1979) found that non-routine research projects with decentralized communi-cation, and routine technical service projects performedbetter with centralized communication.

When a project such as a drug enhancement involvesexploiting familiar skills and familiar problems buildingon existing drug platforms, formalization and centrali-zation can improve information-processing efficiency,and thereby increase the likelihood of drug enhance-ments. However, increases in the regulation aspect of be-havior control could be detrimental to the pursuit of rad-ical innovation because in this case, informationprocessing is less predictable in its content and pathways.Pharmaceutical research depends on new chemical enti-ties, which are the basis for proprietary drug patents andare unavoidably uncertain (Cardinal and Lei 2000, Sitkinet al. 1994).

The monitoring function of behavior control—ex-pressed in performance appraisals—while equally impor-tant to innovation processes has received less attention.Frequent monitoring can interfere with research activitiesand may reduce the likelihood that R&D professionalswill pursue nonroutine and radical changes that involvehigher probabilities of failure. Monitoring of behavior byfrequent performance appraisals may cause R&D profes-sionals to focus their efforts on small improvements soas to demonstrate productivity for bureaucratic gatekeep-ers. When behavior is under close scrutiny, organizationalmembers will feel pressure to avoid making mistakes andto ‘‘play it safe’’ (Burgelman and Sayles 1986, Sitkin1992). Similarly, frequent monitoring may discourage ex-perimentation on projects where short-term performancemay suffer. Employees would be expected to engage ininfluence activities by making the necessary efforts tolook good under increased monitoring, but to divert thoseefforts in ways that are actually less effective (Holmstrom1982, Merchant 1985).

In summary, the regulatory and monitoring aspects ofbehavior control are expected to encourage productivityfor routine drug improvements, but dampen effort onmore revolutionary and uncertain drug technologies. Fol-lowing from the above arguments, it is hypothesized that:

HYPOTHESIS 2A. As the use of behavior control in-creases, the likelihood of drug enhancement innovationincreases.

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HYPOTHESIS 2B. As the use of behavior control in-creases, the likelihood of new drug innovation decreases.

Output Control. The use of output control offers in-teresting challenges for R&D projects in the pharmaceu-tical industry because of these projects’ idiosyncraticcharacteristics. However, output controls have not beenstudied in either the literature on the management of tech-nological innovation or the literature on innovation adop-tion.

The majority of new chemical compounds (NCEs)never make it to a point in the research process wherethey can be labeled a potential drug. Most chemical com-pounds do not reach potential drug status and are consid-ered failures (Henderson 1994, Spilker 1989), yet the in-dustry’s reliance on NCEs for proprietary breakthroughdrugs is the basis of economic ‘‘blockbusters’’ and futureR&D funding. Several issues, then, must be confrontedif output controls are to be used effectively in R&D set-tings (Merchant 1985). First, the results of projects mustbe in the control of the individual being controlled. Sec-ond, results need to be measured effectively. Finally,measurement, feedback, and rewards need to be timely.All three of these are difficult to deal with in the contextof drug outcomes.

R&D projects vary in terms of their return patterns—some have faster payback periods and others are moredistant. Both managers and R&D professionals mayfavor assignments based on how conducive they are toearly returns, which frequently correspond to less risk(Holmstrom 1989). Organizations that emphasize quan-tity of output may inadvertently emphasize projects withshorter time horizons, thereby encouraging risk-averseand imitative behavior (Ouchi 1977). Merchant (1985, p.72) suggests that control displacement occurs in situa-tions with ‘‘an overreliance on easily quantified results.’’Consequently, activities with the greatest importance tothe firm do not necessarily receive the most attention andeffort.4

Project goals can vary greatly from quite narrow toextremely broad in R&D laboratories (Spilker 1989).Narrower and more specific goals would be expected toresult in less variance across outcomes produced becauseall projects are submitted to similar standards. The free-dom to pursue scientific ideas, however, is one of thenorms espoused by professionals (Friedson 1984). Marchand Simon (1958) suggest that innovation is idiosyncraticand difficult to predict and that plans for innovation canbe overly specific, enhancing routine innovations butdriving out novel innovations.

Output controls in R&D activities are difficult to de-sign. The risk is high that these controls lead staff to put

too much emphasis on more incremental projects withmore predictable outcomes and faster returns—even ifthese returns are smaller in the long run. Because ex anteassessments of the potential payoff for any given chem-ical compound are difficult to determine, the wrong pro-jects get emphasized. Therefore, an increased emphasison outputs in evaluation and rewards may create an at-mosphere where scientists focus on project success orcompletion at the expense of developing revolutionaryknowledge with longer payback periods and higherchances for failure.

Thus, output controls would be projected to be moreeffective in contexts where desired results are shorterterm and easily measurable. Output controls would beexpected to be beneficial for incremental projects but det-rimental to radical projects. Following from the abovearguments:

HYPOTHESIS3A. As the use of output control increases,the likelihood of drug enhancement innovation increases.

HYPOTHESIS3B.As the use of output control increases,the likelihood of new drug innovation decreases.

Data and MethodsMethods Overview and SampleThe pharmaceutical industry was selected to provide asingle-industry study of innovation. The sample was alsorestricted to U.S. drug firms. Two primary data collectionmethods were used for this study: archival and question-naire. An archival data methodology was used to collectdata for the dependent variables: new drugs and drug en-hancements. A retrospective questionnaire methodologywas used to collect data for the independent variables:size and the organizational control mechanisms. Usinginformants for the independent variables and an objectiveset of archival data for the dependent variables avoids theproblem of common method bias that occurs when a sin-gle key informant is used for both independent and de-pendent variables. CEOs and R&D directors served askey informants for the questionnaire data. They provideddata in 1988; however, they responded to questions ret-rospectively concerning the time period 1979–1983.

In the pharmaceutical industry the R&D process, in-cluding both drug discovery and development, is lengthy,averaging eight years from concept to market (Buzzell1983, Pharmaceutical Council Panel 1983, U.S. IndustryOutlook 1986). Because random variations influence in-novation outcomes in any single year and the actual R&Dprocess is lengthy, a 10-year period was chosen to capturethe average R&D cycle for a new drug. This smoothesout the random fluctuations and provides a stable estimate

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of innovation (Bettis and Hall 1982, Gomez-Mejia et al.1987). Data for the innovation variables were collectedfrom 1984–1993.

The retrospective approach was used to provide a lagbetween the independent variables (control mechanisms)and the dependent variables (innovation outcomes). Ret-rospective reports can be a useful research tool if the mea-sures are reliable (Miller et al. 1997). If respondents areasked about concrete facts and events rather than opinionsor beliefs, the measures are less prone to cognitive biasesand impression management (Chen et al. 1993, Glick etal. 1990, Golden 1992, Howell and Higgins 1990, Huberand Power 1987, Miller et al. 1997, Nisbett and Wilson1977).5

Sample SelectionThe organizations studied were pharmaceutical drug unitsand their corresponding research programs. In most casesthese were strategic business units (SBUs) or single di-visions.6 One-hundred forty-eight relevant SBUs wereidentified as potential participants.7 CEOs and R&D Di-rectors from 57 SBUs agreed to participate, for a responserate of 38.5%.8 Only firms with R&D programs were ofinterest for this research. In the selected sample, 15 firmswere members of the National Pharmaceutical Council(NPC), which represents the large R&D intensive firmsresponsible for the majority of the new drugs developedin the industry (there were 30 NPC firms when the datawere collected). There were no significant differences be-tween the participating SBUs (N� 57) and the nonpar-ticipating SBUs (N� 91) on the dependent variables(Wilks’s lambda� 0.98, F� 1.52, n.s.).

Data Sources On Pharmaceutical Firms

Archival Data—Innovation Outcomes.Innovationwas measured using data drawn fromThe FDC Reports.Innovation outcomes were classified as either drug en-hancements or new drugs. New chemical entities (NCEs)were coded as new drugs by FDA criteria in this study.‘‘New Chemical Entities are totally new drugs which, inmost cases, represent significant therapeutic advances’’(Cool 1985, p. 250) and are defined by the FDA as ‘‘thoseproducts representing new chemical structures never pre-viously available to treat a particular disease’’ (Pharma-ceutical Manufacturers Association 1989, p. 22). Follow-ing FDA criteria, drugs involving combinations ofexisting drugs, new dosage forms, new indications, andformula changes were coded as drug enhancements.9 TheFDA criteria data allow for differentiation between gen-uinely new drugs and modifications to or combinationsof existing drugs. For each SBU, the total number of newdrugs and the total number of drug enhancements werecalculated for the 1984–1993 time period.

Questionnaire Data. The measures used in the ques-tionnaires and the scale internal reliabilities are describedbelow. Scale reliabilities ranged from 0.74 to 0.94, withan average reliability of 0.88. The scale for each controlmechanism represents a modular unit of control and be-longs to one of the three classes of control: input, behav-ior, and output. Appendix A lists the specific items foreach scale. Means, standard deviations, and correlationsfor all of the variables are reported in Table 1.10

Input Control Mechanisms.Scientific diversity wasassessed as the extent to which different scientific spe-cialties were represented in the R&D program. This mea-sure was based on empirical work by Aiken and Hage(1968), Hage and Dewar (1973), and Reimann (1973).R&D directors were asked to characterize the distributionof R&D knowledge by indicating the percentage of pro-fessionals/scientists working in each of ten general areasof scientific expertise (e.g., chemistry, biomedical sci-ences). They were then asked to check all specific areasof scientific expertise that corresponded to each generalexpertise area. The number of specific specialties repre-sented in R&D laboratories could range from 1 to 59.11

The number of specific scientific specialties representedin each R&D laboratory was used to measure scientificdiversity. Professionalization was assessed as the propen-sity of R&D personnel to seek contact with professionalcolleagues and exposure to information external to theorganization. This measure was based on empirical workby Aiken and Hage (1968) and Kimberly and Evanisko(1981), and was measured with four 7-point items (alpha� 0.91).

Behavior Control Mechanisms.Centralization wasassessed as the extent to which decision-making authoritywas at lower levels of the R&D laboratory hierarchy. Thismeasure was based on empirical work by Aiken and Hage(1968) and Khandwalla (1974) and was measured withnine 7-point items (alpha� 0.94). Formalization was as-sessed as the extent to which rules governing behaviorwere precisely and explicitly formulated. This measurewas based on empirical work by Aiken and Hage (1968),Dewar and Werbel (1979), and Hall (1968) and was mea-sured with five 7-point items (alpha� 0.82). Frequencyof performance appraisals was assessed as the extent towhich behavior was measured and evaluated. This mea-sure was based on empirical work by Abbey (1982) andwas measured with three 7-point items (alpha� 0.85).

Output Control Mechanisms.Goal specificity was as-sessed as the extent to which goals were explicit, clearlydefined, and provided unambiguous criteria for selectingamong alternatives. This measure was based on empirical

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Table 1 Means, Standard Deviations, and Correlations for Variables

Variables

Variables Mean s.d. 1 2 3 4 5 6 7 8 9 10 11

1. Number of Drug Enhancements 11.75 33.682. Number of New Drugs 1.58 2.86 .47***3. Specialist Diversity 17.24 14.74 0.55*** 0.77***4. Professionalization 18.04 6.17 0.02 0.28* 0.32*5. Centralization 41.86 15.73 0.12 0.11 0.17 0.33*6. Formalization 14.12 5.75 0.03 �0.03 0.13 0.42** 0.177. Frequency of Performance Appraisal 13.74 4.13 �0.08 0.06 0.06 0.53*** 0.30* 0.40**8. Goal Specificity 14.28 5.31 �0.04 �0.05 0.08 0.33* 0.21 0.52*** 0.39**9. Emphasis on Output 28.85 12.82 0.14 0.38** 0.42** 0.53*** 0.34** 0.12 0.37** 0.11

10. Professional Emphasis on Output 8.43 3.66 �0.01 0.26* 0.34** 0.62*** 0.37** 0.16 0.33* 0.19 0.61***11. Rewards and Recognition 11.62 3.86 0.15 0.24† 0.28* 0.54*** 0.38** 0.23† 0.37** 0.29* 0.43*** 0.36**12. ln (SBU Size) 5.64 2.17 0.38** 0.57*** 0.66*** 0.22 0.26† �0.05 0.06 �0.06 0.32* 0.21 0.18

†p � 0.10*p � 0.05**p � 0.01***p � 0.001

work by Van de Ven and Ferry (1980) and Zanzi (1987).It was measured with three 7-point items (alpha� 0.90).Emphasis on output was assessed two ways. First, a moregeneral measure captured the extent to which the quantityof outcomes was emphasized in performance appraisalsand rewards. This measure was based on empirical workby Abbey (1982) and Newman (1977) and was measuredwith six 7-point items (alpha� 0.94). Second, there wasa measure that examined whether professional forms ofoutput was emphasized. In most university and many cor-porate research laboratories, presentations and publishedpapers are the primary outputs of research. This measurewas based on empirical work by Abbey (1982) and New-man (1977) and was measured with two 7-point items(alpha� 0.92). Rewards and recognition were assessedas the extent to which R&D professionals received publicrecognition for outstanding achievements. This measurewas based on empirical work by Abbey (1982), Ivancev-ich (1983), Kopelman (1976), and conceptual work byNorthrup and Malin (1985), and was measured with three7-point items (alpha� 0.74).

Statistical Control Variable. In addition to control-ling for a single industry, size was entered as statisticalcontrol variables in each model. Size was defined andmeasured as the SBU’s total number of employees duringthe time period 1979–1983. A natural logarithm transfor-mation was used for the size variable.

Empirical Results

Methods Overview and SampleSeveral complications were encountered for the hypoth-esis-testing portion of this study.

First, the distribution of the dependent variables, thenumber of drug enhancements, and the number of newdrugs exhibited a moderate level of positive skewness. Ahigh level of skewness is frequently found in samples ofhigh-technology firms when examining patents and newproducts (Bound et al. 1984, Cardinal and Opler 1995,Graves and Langowitz 1993, Hausman et al. 1984). Inthis research, skewness is representative of the pharma-ceutical industry and was not obtained through samplingerror. The OLS assumption of normally distributed errorsis violated, given the high skewness of the distribution ofdrug improvements and new drugs. The simplest regres-sion model for discrete variables such as innovative out-put variables is the Poisson regression. Unless the strin-gent assumption that the variance and the mean numberof events are equal is met, the simple Poisson producesincorrect estimates of its variance terms and misleadinginferences about the regression, and creates a dispersionproblem (Frone 1997, Gardner et al. 1995, King 1988).Given the frequency of the dispersion problem found us-ing innovation count data (i.e., patents, new product an-nouncements) with the Poisson specification, followingGardner et al. (1995), the results will be also be analyzed

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Table 2 Determinants of Drug Enhancement Innovation

Poisson withDispersion Parameter

(1a)

NegativeBinomial

(1b)

ln (SBU Size) 0.077*** 0.077***(0.021) (0.021)

Specialist Diversity 0.182*** 0.181***(0.039) (0.039)

Professionalization 0.141*** 0.141***(0.037) (0.038)

Centralization 0.076* 0.076*(0.037) (0.012)

Formalization �0.067† �0.066†

(0.038) (0.038)Frequency of Performance 0.056 0.057Appraisals (0.038) (0.038)Goal Specificity 0.035 0.036

(0.039) (0.039)Emphasis on Output 0.105** 0.105**

(0.039) (0.039)Emphasis on 0.059† 0.060†

Professional Output (0.036) (0.036)Rewards and Recognition 0.266*** 0.266***

(0.039) (0.039)Intercept 2.195*** 2.196***Log-likelihood �147.446 �147.430Pseudo R 2 0.362*** 0.217***

†p � 0.10*p � 0.05**p � 0.01***p � 0.001

using the Poisson regression with the dispersion param-eter and the negative binomial.12 In the Poisson disper-sion model a factor is calculated that corrects the infer-ential statistics of the Poisson regression model, and inthe negative binomial model a random term reflecting un-explained between-subject differences is included in theregression model (Gardner et al. 1995, p. 393).

Second, an extreme outlier was discovered for one de-pendent variable, number of drug enhancements. A dis-tribution analysis was performed to determine the influ-ence of each observation on the regression estimates(Belsley et al. 1980). Influence diagnostics were run foreach of the regression models with and without the po-tential outlier. The extreme outlier was confirmed for asingle SBU on the dependent variable, number of drugenhancements. Consistent with Belsley et al. (1980), theoutlier was deleted from the analyses for drug enhance-ments because the DFFITS scores were greater than 2.0.

Third, the independent variables exhibited multicollin-earity. One of the outcomes of multicollinearity is thatthe likelihood of Type II errors increases (the failure todetect a ‘‘significant’’ predictor). Even slight collinearitycan result in severe Type II errors. As advocated by re-searchers (Boya 1981, Fava and Velicer 1992, Mason andPerreault 1991), factor scores with a varimax rotationwere used rather than the scales to deal with the multi-collinearity.

Drug Enhancements.Table 2 presents the results forModel 1 for drug enhancements. The results for the Pois-son model with the dispersion parameter (1a) and the neg-ative binomial model (1b) were consistent. The resultsdemonstrate the importance of input and output controlsfor drug enhancement innovation. The results in Model 1suggest that after controlling for size, input control in theform of scientific diversity and professionalization waspositively related to the likelihood of drug enhancements.The results for the regulation function of behavior controlwere mixed. Centralization was positively related to thelikelihood of drug enhancements as predicted, while for-malization was negatively related to the likelihood ofdrug enhancements in the opposite direction as hypoth-esized. The monitoring function of behavior control, thefrequency of performance appraisals, was not significant.Finally, output controls received consistent support, andoverall were positively related to the likelihood of drugenhancements. Goal specificity, an emphasis on output,an emphasis on professional output, and rewards and rec-ognition, were all positively related to the likelihood ofdrug enhancements.

New Drugs. Table 3 presents the results for Model 2for new drug outputs. The results for the Poisson model

with the dispersion parameter (2a), and the negative bi-nomial model (2b) were consistent as well. The resultshighlight the importance of all three classes of controlinput, behavior, and output for new drug innovation. Af-ter controlling for size, input control in the form of sci-entific diversity and professionalization was significantlyrelated to the likelihood of new drugs. Surprisingly, andin the opposite direction hypothesized, behavior controlwas positively related to the likelihood of new drugs. Allthree of the behavior control mechanisms—centraliza-tion, formalization, and frequency of performance ap-praisals—were positively related to the likelihood of newdrugs. The findings for output control were also positive,but in the opposite direction as hypothesized. Goal spec-ificity, an emphasis on output, and rewards tied to outputwere positively related to the likelihood of new drug in-novation. An emphasis on professional output was notsignificant.

In summary, the results suggest that control plays an

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Table 3 Determinants of New Drug innovation

Poisson withDispersion Parameter

(2a)

NegativeBinomial

(2b)

ln (SBU Size) 0.003 0.003(0.011) (0.013)

Specialist Diversity 0.050* 0.050†

(0.022) (0.026)Professionalization 0.084*** 0.084**

(0.021) (0.025)Centralization 0.053* 0.053*

(0.021) (0.024)Formalization 0.044* 0.044†

(0.022) (0.025)Frequency of Performance 0.082*** 0.082**Appraisals (0.021) (0.025)Goal Specificity 0.062** 0.062*

(0.022) (0.025)Emphasis on Output 0.062** 0.062**

(0.021) (0.025)Emphasis on 0.021 0.021Professional Output (0.021) (0.024)Rewards and Recognition 0.177*** 0.177***

(0.021) (0.025)Intercept 3.437*** 3.437***Log-likelihood �167.365 �167.365Pseudo R2 0.228*** 0.185***

†p � 0.10*p � 0.05**p � 0.01***p � 0.001

important role in the R&D process in the pharmaceuticalindustry. As hypothesized, input controls and output con-trols were important to incremental innovation. The rolethat behavior control plays in incremental innovation isless clear. Unexpectedly though, all three classes of con-trol, input, behavior, and output were found to be impor-tant for radical innovation. The results from this researchare consistent across models and are robust.

DiscussionThis research extends the work on R&D professionals bylooking at the role that organizational control has on re-search and development and the likelihood of innovationin the pharmaceutical industry. Several critical lessonscan be gleaned from these findings to guide future re-search. First, contrary to the project team literature, thegeneral conclusion of this study is that for drug enhance-ments and new drugs the technological process is quite

similar in the pharmaceutical industry. Second, althoughmost theoretical discourse suggests that behavior and out-put controls are detrimental to radical innovation, thisstudy lends support for the use of both behavior and out-put controls.

Drug Enhancements Versus New DrugsThe pharmaceutical industry was selected based on itshigh level of variance between firms on the outputs oftheir R&D activities—the dependent variables of drug en-hancements and new drugs. However, in this case thehigh level of variance on the dependent variable may nothave translated into variance across research and devel-opment activities. The underlying assumption that thelevel of uncertainty would vary greatly between the twosets of activities appears, in hindsight, not to hold. Whilepharmaceutical firms heavily invest in research efforts di-rected toward the pursuit of new drugs with discernableadvancements in scientific knowledge and therapeutic ap-plications, there are many more failures than successes.Inherent in the process is unique, nonrepetitive varianceand a large proportion of errors. Thus, compared to rou-tine manufacturing, both innovative activities are perhapshighly uncertain, helping to explain the high level of sim-ilarity between the two models. Sitkin et al. (1994) sug-gest that in the case of more uncertain technologies, learn-ing occurs through error-induced discoveries. Looking atthe evolution of several critical drugs and their corollar-ies, Levy (1990) demonstrates that the feedback loopscreated by both incremental and radical innovations arereciprocal, with both leading to subsequent learning.Moreover, one could argue that pharmaceutical firmsmake heavy investments in R&D but that these effortsbuild upon scientific subdisciplines and applications thatthe firm has experience in (Weiss and Birnbaum 1989).13

In spite of the similarity between the two models, it isimportant to note that the drug enhancement model ismore predictive than the model for new drugs.

An alternative viewpoint explaining why these resultsdiffer from the prior project team literature centers on therole of formal organizational control versus emergentteam attributes. The focus of this study was on formallydesigned control mechanisms, whereas the project teamliterature could be characterized as accentuating emergentteam characteristics, such as boundary spanners, the useof gatekeepers, and internal and external informationflows. While formal control mechanisms did not differ-entiate between incremental and radical innovations, it isplausible that teams adapt their current activities and rou-tines according to project-by-project demands. Theseteam adaptations may supplant the formal controls ormerely coexist. Research presented by McDonough and

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Leifer (1983) indicates that in order to perform effec-tively, work units adapt to the situation as the need arises.Further understanding the coexistence of informal andformal controls may be relevant here, and it requires di-rect empirical testing.

The Role of Formal ControlFrom a managerial perspective these results highlight theimportance of input, behavior, and output controls forradical innovation, and the importance of input and outputcontrols for incremental innovation. Within the contextof the pharmaceutical industry, formal control mecha-nisms coexist with strong informal professional normsthat also influence values, activities, and outcomes. In thissetting, input, behavior, and output forms of control mayenable scientists to effectively conduct their work andthus align with professional norms and goals—serving inan enabling role (Adler and Borys 1996, Sobek et al.1998). Further, the nature of scientific work for new drugsdictates the correct scientific procedures and processes,thus lending its own structure to the nature of R&D work.This could be quite different in other industry sectors(Pavitt 1984, Senker and Faulkner 1992). This may ex-plain the unanticipated positive role served by behaviorand output controls in enhancing radical innovation. Inthe case of the mixed results for behavior control for drugenhancements, not only does the nature of scientific workdictate some of the processes and procedures, but the aug-mentation of existing drug knowledge may further limitthe variance of activities that can be pursued. So, somedimensions of behavior control may enhance this existingtrajectory of work, and other dimensions could detract.

Oftentimes in the context of R&D professionals, whilethe organization is controlling behaviors and outcomes,individuals are simultaneously getting information fromexternal sources. These various sources of informationare capable of providing conflicting signals. Differentcombinations of control have the potential to generatepositive synergy or negative tensions. More than likely,scientists will choose to serve professional norms overthose of the organization. Consistent with this line ofreasoning, Sitkin and Sutcliffe (1991) found that whenpharmacists were faced with conflicting controls theychose to serve the professional norms. Adler and Borys(1996) emphasize that properly designed forms of struc-ture can be viewed positively by employees. They suggestthat in cases where formalization captures prior learningit can enhance innovation, particularly when employeessee overlap with their own goals and those of the orga-nization. While the research presented here did not dif-ferentiate between enabling versus coercive aspects oforganizational control, the positive aspects of organiza-tional control as it pertains to innovation may have been

unintentionally captured here. In the presence of stronginformal professional norms, the best course of action isto couple output controls with the informal professionalnorms. Incremental and especially radical innovations areimportant indicators of professional success and valuedby the scientific community. Because it will be more dif-ficult for the firm to alter R&D professional allegiances,the firm should concentrate on output controls that alignwith the professional community values and tap into ex-isting professional controls with input and behavior con-trols. Alternatively, standard organization theory assumesthat more formalization is needed to control more routinetasks because it assumes a conflict between the goals oforganizational members and the organization. Under con-ditions of high input control and moderately certain tasks(i.e., ones that are still sufficiently intrinsically motivat-ing) the organization may not need much formalization.14

The firms in this study have found ways to ensure thattheir behavior controls are enabling. Further research ex-amining the interplay between input, behavior, and outputcontrols and professional standards, and the role of en-abling versus coercive control, warrants further study.

Interestingly, the underlying assumptions of norms ofopportunistic rationality by agency theory are thrown intoquestion by these results. Consistent with agency theory,radical innovation was predicted not to be in the agent’sbest interest when output controls were present becauseof long-run time horizons, high failure rates, and lowprobability of earlier returns. Thus, the agent was ex-pected to seek as much utility as possible for the leastexpenditure, basically favoring incremental innovationwith fast paybacks. Surprisingly, an emphasis on outputcontrols enhanced both incremental and radical innova-tion. While some might interpret these results as supportfor stewardship theory (Davis et al. 1997), this does notexplain the full picture either. In the case of stewardshiptheory, a strong relationship exists between the steward’ssatisfaction and the success of the organization. Thisstrong relationship is driven by intrinsic motivation ratherthan by extrinsic motivation as predicted by agency the-ory. To lend full support for stewardship theory, the im-pact of output controls would be expected to be neutral.

Limitations and Future ResearchSeveral limitations warrant mentioning in this research.Despite these limitations, this study demonstrates someimportant lessons to consider and future avenues to ex-plore. First, there is reason to be cautious in generalizingfrom this industry because these findings may not applyto other industries. The critical drivers of innovationwould be hypothesized to vary based upon the locus ofcore technological activities—spanning from upstream to

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downstream activities (Cardinal and Lei 2000; Pavitt1986, 1990). Therefore, understanding the role of controlin other product development settings is important. Howcontrol influences innovation could look quite differentdepending upon the character of product development ac-tivity (i.e., science-based versus production-intensiveR&D activities) (Pavitt 1984, Senker and Faulkner 1992).Second, innovation output measures in this study are lim-ited to a single measure of technological outputs: FDAdrug approvals. Other intermediate measures of techno-logical output exist, such as important patents, overallpatent count, Investigational New Drug Applications(INDs), sales per drug, journal publications, journal ci-tations, and R&D efficiency measures. Future studiesshould explore different effectiveness measures of inno-vation. Finally, the focus of this study was on formal con-trols, and not informal or emergent controls. Examiningdifferent combinations of control is worth pursuing. Fu-ture research should explore how formal and informalaspects of control coexist and interact. Current research-ers still treat formality of control as a single dimension—ranging from high to low (Makhija and Ganesh 1997—or either examine only formal (as in this study) or onlyinformal control, but not both simultaneously.

These findings provide a useful foundation to beginreexamining the benefits of control in technological in-novation. As stated in a business press article, ‘‘Fixingresearch and development also depends on punctuatingthe myth that it’s inherently unmanageable’’ (BusinessWeek1993, p. 104). The pervasiveness of the view thatcontrol is inherently negative has limited our ability tofurther explore how control may help solve the uniquechallenges of managing and channeling R&D activities.In closing, the results provide evidence that technologicalinnovation in fact can be managed with some forms oforganizational control previously thought to be detrimen-tal to the innovation process.

AcknowledgmentsThis research was supported by the National Science Foundation. The au-thor would like to thank Dave Arnott, Richard Bettis, Robert Burgelman,Fariborz Damanpour, Michael Dowling, William Glick, Donald Hatfield,Robert Hoskisson, David Jemison, Kenneth Kirk, Chris Long, Chet Miller,Les Palich, Andreas Pleil, Al Segars, Sim Sitkin, Scott Turner, and AndrewVan de Ven for their invaluable contributions to this research. The authoralso thanks theOrganization Sciencesenior editors Dorothy Leonard andPaul Adler, and three anonymous reviewers, for their thoughtful feedback.

Endnotes1I recognize that radicalness is a relative term. These innovations aremore radical / less incremental than drug enhancements.2Product innovation or product introduction refers to innovations cre-ated by the organization, whereas adopted innovation refers to inno-vations imported into the organization and created else where.

3Ettlie et al. (1984) is one of the few exceptions to examine structuralcharacteristics and new product introductions; opposed to innovationadoption, but the study does not differentiate incremental and radicalinnovations.4Kotter et al. (1979) describe an example of displacement in a corporateresearch laboratory that implemented output control with the numberof patents filed as an indicator of research effectiveness. The scientistsran up the number of patents while actually harming project effective-ness.5These scales were read, tested, and assessed by managers in the phar-maceutical industry. During the pretest and assessment sessions man-agers were asked to assess their ability to recall this type of information.They did not express difficulty with the methodology.6For the large focused pharmaceutical houses, the SBU and firm wereoften one and the same. The same holds for small firms with a singleSBU. However, in diversified health care firms the SBU of interest wasthe pharmaceutical drug unit.7A comprehensive list of U.S. pharmaceutical strategic business units(SBUs) was developed using multiple sources: the National Pharma-ceutical Council’s (NPC) membership list, the Pharmaceutical Manu-facturers Association’s membership list,Medical Advertising News(Top 50 Pharmaceutical Firms Special Issue), Paul de Haen’sNewProduct SurveyandNew Product Index, Pharmaceutical Manufactur-ers of the United States, R&D Laboratories in the U.S., Physician’sDesk Reference, andApproved Drug Products with Therapeutic Equiv-alence Evaluations. A population of 300 SBUs was identified. Afterexcluding SBUs that no longer existed, duplicate listings, and SBUswith no in-house R&D, a final sample population of 148 SBUs re-mained.8Firms that underwent major restructuring during the time period ofstudy declined to be in the study or were not included in the sampleafter initial phone calls to the SBU.9‘‘While drug enhancements such as new dosage forms may appear atfirst to be unimportant, or even trivial, they are important avenues oflearning for firms. Incremental progress gives rise to families, or classesof related drugs. Although several agents within a class may have thesame general action, they often differ significantly in specific actions,side effects, and suitability for individual patients’’ (Levy 1990, p. v).Furthermore, these innovations lead to further learning and a betterunderstanding of adverse reactions. Banbury and Mitchell (1995) havedemonstrated the importance of incremental innovation to marketshare.10Confirmatory factor analyses were used in evaluating the a prioriscales for discriminant validity (Allen and Yen 1979). A loading of 0.4or greater was used to assign an item to a specific factor. If an itemhad a loading less than 0.4, the item was dropped. In cases where afactor loaded greater than 0.4 on two factors, the item was also dropped.The confirmatory factor analyses yielded 9 out of 9 theoretically as-signed factors and were considered highly successful.11The general scientific categories represented in R&D laboratories inthe pharmaceutical industry are biomedical sciences, chemistry, statis-tics, process development, medical and clinical sciences, agriculturaland veterinary sciences, administration, pharmacology/toxicology, bio-pharmaceutics and metabolism, and quality assurance and control.Each general scientific category had specific specialties listed for thatcategory (i.e., for the general category of biomedical sciences therewere 15 specific areas of expertise: biochemistry, biology, biophysics,

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cancer research, endocrinology, enzymology, genetic engineering, ge-netics, immunology, life sciences, microbiology, molecular genetics,physiology, and virology).12For both dependent variables, drug enhancements and new drugs, thedata were slightly underdispersed.

13Because the R&D activities along the technological value chain forboth radical and incremental innovations were the same, Christensen(1997) would refer to these as radical and incremental sustaining tech-nologies.14I would like to thank Paul Adler for this insight.

Appendix A Questionnaire Itemsa,b

Variables Items Reference(s) Source Type

Input Control Mechanisms:Specialist Diversity The number of R&D professionals/scientists in:

biomedical sciences, chemistry, statistics, processdevelopment, medical & clinical sciences, agricultural &veterinary sciences, administration, pharmacology/toxicology, biopharmaceutics & metabolism, and qualityassurance & control.

Aiken & Hage (1968)Hage & Dewar (1973)Reimann (1973)

EmpiricalEmpiricalEmpirical

Professionalization To what extent did your firm encourage R&Dprofessionals/scientists to engage in the followingactivities?. . . becoming members in professional organizations.. . . attending professional meetings.. . . acquiring additional in-house educational/developmental training.. . . acquiring additional external/degree education.

Aiken & Hage (1968)Kimberly & Evanisko (1981)

EmpiricalEmpirical

Behavior Control Mechanisms:Centralization To what extent did your firm delegate decision making

authority to R&D professionals/scientists concerning thefollowing issues?. . . choosing R&D projects to work on.. . . choosing employee assignments for projects.. . . hiring and firing R&D staff.. . . promoting R&D staff.. . . administering the salary administration system.. . . allocating raises.. . . making major capital expenditures.. . . making major non-capital expenditures.. . . making minor non-capital expenditures.

Aiken & Hage (1968)Khandwalla (1974)

EmpiricalEmpirical

Formalization To what extent were the following statements true for yourR&D laboratory(ies)?. . . written rules about laboratory procedures existed.. . . we had rules and procedures stating how to performnormal daily activities.. . . there were standard procedures for individual tasks.. . . there was strict enforcement of written rules andprocedures.

Aiken & Hage (1968)Dewar & Werbel (1979)Hall (1968)


Frequency of PerformanceAppraisal

How frequently did managers in your firm evaluate R&Dprofessionals’ activities and performance? To what extentdid they. . .. . . have the opportunity to frequently observeperformance?. . . informally evaluate R&D professionals’ performanceon a frequent basis?. . . provide frequent informal feedback concerningperformance?

Abbey (1982) Empirical

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Appendix A (contd.)

Output Control Mechanisms:Goal Specificity How specific were R&D goals with respect to the

following criteria?Overall R&D laboratory goals concerning. . .. . . drug indications were. . .Specific R&D project goals concerning. . .. . . drug indications were. . .

Van de Ven & Ferry (1980)Zanzi (1987)

EmpiricalEmpirical

Emphasis on Output To what extent were the following statements trueconcerning outputs produced by R&D professionals/scientists in your firm?. . . The quantity of produced was reflectedin performance appraisals.

. . . new ideas submitted

. . . new patents

. . . publications

. . . new drugs. . . The quantity of produced was reflectedin rewards received.

. . . new ideas submitted

. . . new patents

. . . publications

. . . new drugs

Abbey (1982)Newman (1977)

EmpiricalEmpirical

Rewards and Recognition To what extent were the following statements true abouthow R&D professionals/scientists were recognized andrewarded in your firm?R&D professionals/scientists. . .. . . got personal mention in oral and written reports forexcellent work.. . . received financial rewards for good ideas/accomplishments.. . . received non-financial rewards for good ideas/accomplishments.

Abbey (1982)Ivancevich (1983)Kopelman (1976)


Emphasis on ProfessionalOutput

To what extent did your firm encourage R&Dprofessionals/scientists to engage in the followingactivities?. . . presenting papers.. . . publishing in journals.

Aiken & Hage (1968)Kimberly & Evanisko (1981)

EmpiricalEmpirical

Statistical Control Variable:ln (SBU Size) The natural logarithm of the total number of employees in

the SBU.Huber et al. (1990) Empirical

Notes.aThe items were based on empirical work done by the references listed. However, the items were changed where appropriate to fit the

context (R&D laboratories in the pharmaceutical industry).bThe items were not only based on existing empirical work but in some cases reflected conceptual input from the references listed.

ReferencesAbbey, A. 1982.Technological Innovation: R&D Work Environment.

Ann Arbor, MI.Abernathy, W. J., K. B. Clark. 1985. Innovation: Mapping the winds

of creative destruction.Res. Policy14 3–22.Adler, P. S., B. Borys. 1996. Two types of bureaucracy: Enabling and

coercive.Admin. Sci. Quart., 41 61–89.Aiken, M., S. B. Bacharach, J. L. French. 1980. Organizational struc-

ture, work process, and proposal making in administrative bu-reaucracies.Acad. Management J.23 631–652.

——, J. Hage. 1968. Organizational interdependence and intra-organizational structure.Amer. Soc. Rev.33 912–930.

Allen, M. J., W. M. Yen. 1979.Introduction to Measurement Theory.Brooks/Cole Publishing Company, Monterey, CA.

Allen, T. J. 1977.Managing the Flow of Technology.MIT Press, Cam-bridge, MA.

Dow

nloa

ded

from

info

rms.

org

by [

128.

172.

10.1

94]

on 0

3 Ju

ne 2

014,

at 0

0:05

. Fo

r pe

rson

al u

se o

nly,

all

righ

ts r

eser

ved.



——, D. M. S. Lee, M. L. Tushman. 1980. R&D performance as afunction of internal communication, project management, and thenature of work.IEEE Trans. Eng. Management27 2–12.

——, M. L. Tushman, D. M. S. Lee. 1979. Modes of technology trans-fer as a function of position in the spectrum from research throughdevelopment to technical services.Acad. Management J.22 694–708.

Ancona, D. G., D. F. Caldwell. 1992. Demography and design: Pre-dictors of new product team performance.Org. Sci.3 321–341.

Arvey, R. D. 1979.Fairness in Selecting Employees.Addison Wesley,Reading, MA.

Bailyn, L. 1985. Autonomy in the industrial R&D lab.Human Re-source Management24 129–146.

Banbury, C. M., W. Mitchell. 1995. The effect of introducing importantincremental innovations on market share and business survival.Strategic Management J.16 161–182.

Bantel, K. A., S. E. Jackson. 1989. Top management and innovationsin banking: Does the composition of the top team make a differ-ence?Strategic Management J.10 107–114.

Barker, J. R. 1993. Tightening the iron cage: Concertive control in self-managing teams.Admin. Sci. Quart.38 408–437.

Belsley, D. A., E. Kuh, R. E. Welsch. 1980.Regression Diagnostics.John Wiley and Sons, New York.

Bettis, R. A., W. K. Hall. 1982. Diversification strategy, accountingdetermined risk, and accounting determined return.Acad. Man-agement J.25 265–298.

Birnbaum-More, P. H. 1993. New product development time: A cross-national study. Final Report, Center for Innovation ManagementStudies, Lehigh University, Bethlehem, PA.

Blau, P., R. Scott. 1962.Formal Organizations: A Comparative Ap-proach.Chandler Publishing Company, San Francisco, CA.

Bound, J., C. Cummins, Z. Griliches, B. H. Hall, A. Jaffe. 1984. Whodoes R&D and who patents? InPatents and Productivity.Z. Gril-iches (ed.), University of Chicago Press, Chicago, IL, 21–54.

Boya, U. O. 1981. Some issues in measures of predictor variable im-portance based in orthogonal decompositions in multiple regres-sions.AMA Proc.American Marketing Association, Chicago, IL,306–308.

Burgelman, R. A., L. R. Sayles. 1986.Inside Corporate Innovation:Strategy, Structure, and Managerial Skills.The Free Press, NewYork.

Business Week.1993. Business Week R&D Scoreboard. (June 28) 102–104.

Buzzell, R. D. 1983.The World Pharmaceutical Industry: Prospectsfor the 1980s.Harvard Business School, #9–583–168, HBS Ser-vices, Cambridge, MA.

Cardinal, L. B., D. Lei. 2000. Structuring research and developmentteams in the technological conversion process,Advances in In-terdisciplinary Studies of Work Teams.Vol. 5. M. M. Beyerlein,D. A. Johnson, S. T. Beyerlein, eds., JAI Press, Greenwich, CT,31–62.

——, T. C. Opler. 1995. Corporate diversification and innovative ef-ficiency: An empirical study.J. Accounting and Econom.19 365–381.

——, S. B. Sitkin, C. P. Long. 1999. Mixing oil and water: Sequencingcontrol system adaptation to create effective control. Paper pre-sented at the Strategic Management Society, Berlin, Germany.

Chen, M. J., J. L. Farh, I. C. MacMillan. 1993. An exploration of theexpertness of outside informants.Acad. Management J.36 1614–1632.

Christensen, C. M. 1997.The Innovator’s Dilemma.Harvard BusinessSchool Press, Boston, MA.

Cohen, W. M., R. C. Levinthal. 1989. Empirical studies of innovationand market structure.Handbook of Industrial Organization,Vol.2, R. Schmalensee, R. Willig, eds. Elsevier Science, New York,1060–1107.

Cool, K. 1985. Strategic group formation and strategic group shifts: Alongitudinal analysis of the U.S. pharmaceutical industry, 1963–1982. Unpublished doctoral dissertation, Purdue University, WestLafayette, IN.

——, D. Schendel. 1987. Strategic group formation and performance:The case of the U.S. pharmaceutical industry, 1963–1982.Man-agement Sci.33 1102–1124.

Damanpour, F. 1991. Organizational innovation: A meta-analysis ofeffects of determinants and moderators.Acad. Management J.34555–590.

Davis, J. H., F. D. Schoorman, L. Donaldson. 1997. Toward a stew-ardship theory of management.Acad. Management Rev.22 20–47.

Dewar, R. D., J. E. Dutton. 1986. The adoption of radical and incre-mental innovations: An empirical analysis.Management Sci.321422–1433.

——, J. Werbel. 1979. Universalities and contingency predictions ofemployee satisfaction and conflict.Admin. Sci. Quart.24 426–448.

Dougherty, D. 1992. Interpretive barriers to successful product inno-vation in large firms.Organization Sci.3 179–202.

Duncan, R. 1976. The ambidextrous organization: Designing dualstructures for innovation.The Management of Organization De-sign, R. H. Kilmann, L. R. Pondy, D. P. Slevin, eds., Vol. 1,North-Holland, New York, 167–188.

Eisenhardt, K. M. 1985. Control: Organizational and economic ap-proaches.Management Sci.31 134–149.

Ettlie, J. E., W. P. Bridges, R. D. O’Keefe. 1984. Organization strategyand structural differences for radical versus incremental innova-tion. Management Sci.30 682–695.

Fava J. L., W. F. Velicer. 1992. An empirical comparison of factor,image, component, and scale scores.Multivariate Behavioral Res.27 301–322.

Freeman, C. 1982.The Economics of Industrial Innovation.The MITPress, Cambridge, MA.

Friedson, E. 1984. The changing nature of professional control.Ann.Rev. Soc.10 1–20.

Frone, M. R. 1997. Regression models for discrete and limited depen-dent variables.RM Forum2 3–6.

Gambardella, A. 1992. Competitive advantages from in-house scien-tific research: The US pharmaceutical industry in the 1980s.Res.Policy 21 391–407.

Gardener, W., E. P. Mulvey, E. C. Shaw. 1995. Regression analysesof counts and rates: Poisson, overdispersed Poisson, and negativebinomial models.Psych. Bull.118 392–404.

Glick, W. H., G. P. Huber, C. C. Miller, H. D. Doty, K. S. Sutcliffe.1990. Studying changes in organizational design and effective-ness: Retrospective event histories and periodic assessments.Org.Sci.1 293–312.

Dow

nloa

ded

from

info

rms.

org

by [

128.

172.

10.1

94]

on 0

3 Ju

ne 2

014,

at 0

0:05

. Fo

r pe

rson

al u

se o

nly,

all

righ

ts r

eser

ved.



Golden, B. R. 1992. The past is the past—or is it? The use of retro-spective accounts as indicators of past strategy.Acad. Manage-ment J.35 848–860.

Gomez-Mejia, L. R., H. Tosi, T. Hinkin. 1987. Managerial control,performance, and executive compensation.Acad. Management J.30 51–70.

Govindarajan, V., A. K. Gupta. 1985. Linking control systems to busi-ness unit strategy: Impact on performance.Accounting, Organ.and Society10 51–56.

Graves, S. B., N. S. Langowitz. 1993. Innovative productivity and re-turns to scale in the pharmaceutical industry.Strategic Manage-ment J.14 593–605.

Hage, J., R. Dewar. 1973. Elite values versus organizational structurein predicting innovation.Admin. Sci. Quart.18 279–290.

Hall, R. H. 1968. Professionalization and bureaucratization.Amer. Soc.Rev.33 92–104.

Hausman, J., Z. Griliches, B. H. Hall. 1984. Econometric models forcount data with an application to the patents-R&D relationship.Econometrica52 909–938.

Henderson, R. M. 1994. The evolution of integrative capability: In-novation in cardiovascular drug discovery.Indust. CorporateChange3(3) 607–630.

——, I. Cockburn. 1994. Measuring competence? Exploring firm ef-fects in pharmaceutical research.Strategic Management J.15 63–84.

Holmstrom, B. R. 1982. Managerial incentives—a dynamic perspec-tive. Essays in Honors of Lars Wahlbeck, Swedish School of Eco-nomics, Helsinki, Finalnd.

——. 1989. Agency costs and innovation.J. Econom. Behavior andOrgan.12 305–327.

Howell, J. M., C. A. Higgins. 1990. Champions of technological in-novations.Admin. Sci. Quart.35 317–341.

Huber, G. P., C. C. Miller, W. H. Glick. 1990. Developing more en-compassing theories about organizations: The centralization-effectiveness relationship as an example.Organ. Sci.1 11–40.

——, D. J. Power. 1987. Retrospective reports of strategic level man-agers: Guidelines for increasing their accuracy.Strategic Man-agement J.6 171–180.

Ivancevich, J. M. 1983. Contrast effects in performance evaluation andreward practices.Acad. Management J.26 465–476.

Jaeger, A. M., B. R. Baliga. 1985. Control systems and strategic ad-aptation: Lessons from the japanese experience.Strategic Man-agement J.6 115–134.

Jaworski, B. J. 1988. Toward a theory of marketing control: Environ-mental context, control types, and consequences.J. Marketing5223–39.

Jelinek, M., C. B. Schoonhoven, 1990.Innovation Marathon: LessonsFrom High Technology Firms, Basil Blackwell, Cambridge, MA.

Kaluzny, A. D., J. E. Veney, J. T. Gentry. 1974. Innovation of healthservices: A comparative study of hospitals and health depart-ments.Health and Society52 51–82.

Katz, R. 1982. The effects of group longevity on project communica-tion and performance.Admin. Sci. Quart.27 81–104.

——. 1988. High performance research teams.Managing Profession-als in Innovative Organizations, Ballinger Publishing Co., Cam-bridge, MA.

——, M. L. Tushman. 1979. Communication patterns, project perfor-mance and task characteristics: An empirical evaluation and in-tegration in an R&D setting.Organ. Behavior and Human Per-formance23 139–162.

——, ——. 1981. An investigation into the managerial roles and careerpaths of gatekeepers and project supervisors in a major R&D fa-cility. R&D Management11 103–110.

——, ——. 1983. A longitudinal study of the effects of boundary span-ning supervision on turnover and promotion in research and de-velopment.Acad. Management J.26 437–456.

Keller, R. T. 1994. Technology-information professing fit and the per-formance of R&D project groups: A test of contingency theory.Acad. Management J.37 167–179.

——, W. E. Holland. 1983. Communicators and innovators in researchand development organizations.Acad. Management J.26 742–749.

Kerr, J. L. 1985. Diversification strategies and managerial rewards: Anempirical investigation.Acad. Management J.28 155–179.

Khandwalla, P. N. 1974.The Design of Organizations. Harcourt BraceJovanovich, Inc., New York.

Kimberly, J. R., M. J. Evanisko. 1981. Organizational innovation: Theinfluence of individual, organizational, and contextual factors inhospital adoption of technical and administrative innovations.Acad. Management J.24 689–713.

King, G. 1988. Statistical models for political science event counts:Bias in conventional procedures and evidence for the exponentialPoisson regression model.Amer. J. Political Sci.32 838–863.

Kirsch, L. J. 1996. The management of complex tasks in organizations:Controlling the systems development process.Organ. Sci.7 1–20.

Kopelman, R. E. 1976. Organizational control responsiveness, expec-tancy theory constructs and work motivation.Personnel Psych.,29 205–220.

Kotter, J. P., L. A. Schlesinger, V. Sathe. 1979.Organization: Text,Cases, and Readings on the Management of Organizational De-sign. Vol. 25. Richard D. Irwin, Homewood, IL.

Langfield-Smith, K. 1997. Management control systems and strategy:A critical review.Accounting, Organ. and Society22 207–232.

Lebas, M., J. Weigenstein. 1986. Management control: The roles ofrules, markets, and culture.J. Management Stud.23 259–272.

Lee, D. M., T. J. Allen. 1982. Integrating new technical staff: Impli-cations for acquiring new technology.Management Sci.28 1405–1420.

Levy, R. A. 1990.Pharmaceutical Research: Therapeutic and Eco-nomic Value of Incremental Innovations.National PharmaceuticalCouncil, Reston, VA.

Makhija, M. V., U. Ganesh. 1997. The relationship between controland partner learning in learning-related joint ventures.Organ. Sci.8 508–527.

March, J. G. 1991. Exploration and exploitation in organizational learn-ing. Organ. Sci.2 71–87.

——, H. A. Simon. 1958.Organizations.John Wiley and Sons, NewYork.

Mason, C. H., W. D. Perreault. 1991. Multicollinearity, power, andinterpretation of multiple regression analysis.J. Marketing Res.28 268–280.

Dow

nloa

ded

from

info

rms.

org

by [

128.

172.

10.1

94]

on 0

3 Ju

ne 2

014,

at 0

0:05

. Fo

r pe

rson

al u

se o

nly,

all

righ

ts r

eser

ved.



McDonough, E. F., III, R. Leifer. 1983. Using simultaneous structuresto cope with uncertainty.Acad. Management J.26 727–735.

Merchant, K. A. 1985.Control in Business Organizations. Pitman,Boston, MA.

Miller, C. C., L. B. Cardinal, W. H. Glick. 1997. Retrospective reportsin organizational research: A reexamination of recent evidence.Acad. Management J.40 189–204.

Mintzberg, H. T. 1979.The Structuring of Organizations.Prentice-Hall, Englewood Cliffs, NJ.

——. 1983. Structure in Fives: Designing Effective Organizations.Prentice-Hall, Englewood Cliffs, NJ

Newman, J. E. 1977. Development of a measure of perceived workenvironment.Acad. Management J.20 520–534.

Nisbett, R. E., T. D. Wilson. 1977. Telling more than we can know:Verbal reports on mental processes.Psych. Rev.814 231–259.

Nord, W. R, S. Tucker. 1987.Implementing Routine and Radical In-novations.Lexington Books, Lexington, MA.

Northrup, H. R., M. E. Malin. 1985.Personnel Policies for Engineersand Scientists: An Analysis of Major Corporate Practice.Uni-versity of Pennsylvania, Philadelphia, PA.

Ouchi, W. G. 1977. The relationship between organizational structureand organizational control.Admin. Sci. Quart.22 95–113.

——. 1979. A conceptual framework for the design of organizationalcontrol mechanisms.Admin. Sci. Quart.24 833–848.

Pavitt, K. 1984. Sectoral patterns of technological change: Towards ataxonomy and a theory.Res. Policy13 343–374.

——. 1986. Comment on Tushman and Anderson. A. Pettigrew, Ed.The Management of Strategic Change.Oxford, UK.

——. 1990. What we know about the strategic management of tech-nology.California Management Rev.32 17–26.

Pelz, D. C., F. M. Andrews. 1976.Scientists in Organizations: Pro-ductive Climates for Research and Development. Institute for So-cial Research, The University of Michigan, Ann Arbor, MI.

Pharmaceutical Council Panel. 1983.The Competitive Status of theU.S. Pharmaceutical Industry. National Academy Press, Wash-ington, D.C.

Pharmaceutical Manufacturers Association. 1989.America’s Phar-maceutical Research Companies: A Cost Effective Source of Im-portant New Medicines. November.

Pharmaceutical Research and Manufacturers of America. 1997.PhRMA Annual Survey.

Ravenscraft, D. J., W. F. Long. 1999. Paths to creating value in phar-maceutical mergers.Mergers and Productivity. NBER. S. N. Kap-lan, ed. The University of Chicago Press, Chicago, IL.

Reimann, B. G. 1973. On the dimensions of bureaucratic structure: Anempirical reappraisal.Admin. Sci. Quart.18 462–476.

Roth, N. L., S. B. Sitkin, A. House. 1994. Stigma as determinant oflegalization.The Legalistic Organization. S. B. Sitkin, R. J. Bies,eds. Sage Publications, Thousand Oaks, CA.

Roussel, P. A., K. N. Saad, T. J. Erickson. 1991.Third GenerationR&D: Managing the Link to Corporate Strategy. Harvard Busi-ness School Press, Boston, MA.

Senker, J., W. Faulkner. 1992. Industrial use of public sector researchin advanced technologies: A comparison of biotechnology andceramics.R&D Management22 157–175.

Sitkin, S. B. 1992. Learning through failure: The strategy of smalllosses.Research in Organizational Behavior, Vol. 14, B. M.Staw, L. L. Cummings, eds. 231–266.

——, K. M. Sutcliffe. 1991. Dispensing legitimacy: The influence ofprofessional, organizational, and legal controls on pharmacists be-havior.Res. Soc. Organ.8 269–295.

——, ——, R. G. Schroeder. 1994. Distinguishing control from learn-ing in total quality management: A contingency perspective.Acad. Management Rev.19 537–564.

Snell, S. A. 1992. Control theory in strategic human resource manage-ment: The mediating effect of administrative information.Acad.Management J.35 1–36.

Sobek, D. K. III, J. K. Liker, A. C Ward. 1998. Another look at howToyota integrates product development.Harvard Bus. Rev.7636–48.

Spilker, B. 1989.Multinational Drug Companies: Issues in Drug Dis-covery and Development.Raven Press, New York.

Taggart, J. 1993.The World Pharmaceutical Industry.Routledge, Lon-don, U.K.

Taylor, F. W. 1911.The Principles of Scientific Management.Harper,New York.

Teitelman, R., A. Baldo. 1989. Grading R&D.Financial World(Jan-uary 24). 22–24.

Thompson, J. D. 1967.Organizations in Action.McGraw-Hill, NewYork.

Tushman, M. L. 1977. Communication across organizational bound-aries: Special boundary roles in the innovation process.Admin.Sci. Quart.22 587–605.

——. 1979. Work characteristics and solo unit communication struc-ture: A contingency analysis.Admin. Sci. Quart.24 82–98.

U.S. Industry Outlook. 1986. January.Van de Ven, A. H., D. L. Ferry. 1980.Measuring and Assessing Or-

ganizations.John Wiley and Sons, New York.Wanous, J. P. 1980.Organizational Entry: Recruitment, Selection and

Socialization of Newcomers.Addison-Wesley, Reading, MA.Weick, K. E. 1979.The Social Psychology of Organizing. Addison-

Wesley Publishing Company, Reading, MA.Weiss, A. R., P. H. Birnbaum. 1989. Technological infrastructure and

the implementation of technological strategies.Management Sci.35 1014–1026.

Wiersema, M. F., K. A. Bantel. 1992. Top management team demog-raphy and corporate strategic change.Acad. Management J.3591–121.

Williamson, O. E. 1975.Market and Hierarchies: Analysis and Anti-trust Implications.Free Press, New York.

Zaltman, G., R. Duncan. J. Holbek. 1973.Innovation and Organiza-tions.John Wiley and Sons, New York.

Zanzi, A. 1987. How organic is your organization?—Determinants oforganic/mechanistic tendencies in a public accounting firm.J.Management Stud.24 125–142.

Accepted by Paul Adler; received August 18, 2000.

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r pe

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technological innovation in the pharmaceutical industry: the use of organizational control in...

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