Knowledge, routines, and cognitive effects in nonmarket selection environments: An examination of the regulatory review of innovations

• Author: Francisco Polidoro
• DOI: http://doi.org/10.1002/smj.3196
• Published date: 01 December 2020
• Date: 01 December 2020

RESEARCH ARTICLE

Knowledge, routines, and cognitive effects in

nonmarket selection environments: An

examination of the regulatory review of

innovations

Francisco Polidoro Jr.

Department of Management, The

University of Texas at Austin, McCombs

School of Business, Austin, Texas

Correspondence

Francisco Polidoro Jr, Department of

Management, The University of Texas at

Austin, McCombs School of Business,

CBA 4.230, 2110 Speedway Stop B6300,

Austin, TX 78712-1282, USA.

Email: francisco.polidoro@mccombs.

utexas.edu

Abstract

Research summary: Evolutionary models of techno-

logical evolution highlight the cognitive underpinning

of routines that shape organizational adaptation.

However, research thus far has overlooked the possi-

bility that cognitive effects might also shape selection.

This study redresses this imbalance by examining

nonmarket selection, focusing for that purpose on the

regulatory review of innovations. It proposes that the

more knowledge about different technologies is avail-

able to regulatory agencies, the more evaluation

incongruities they face when evaluating a focal inno-

vation, which increases the time for its regulatory

review. It also proposes that this effect is attenuated

when regulatory agencies are more frequently con-

fronted with innovations drawing on new technolo-

gies. By elucidating cognitive effects that shape

nonmarket selection, this study has theoretical impli-

cations for research on technological evolution and

organizational learning.

Managerial summary: This study highlights influ-

ences on the regulatory review of innovations, an

important hurdle that firms in many industries must

clear before launching innovations into the market.

The regulatory review of an innovation is largely

thought to be facilitated by knowledge about that inno-

vation and the technology on which it builds. But, this

Received: 12 November 2019 Revised: 2 May 2020 Accepted: 4 May 2020 Published on: 6 July 2020

DOI: 10.1002/smj.3196

2400 © 2020 John Wiley & Sons, Ltd. Strat Mgmt J. 2020;41:2400–2435.wileyonlinelibrary.com/journal/smj

view overlooks that knowledge about other technolo-

gies that exist in the same domain of an innovation can

create evaluation incongruities that hamper its regula-

tory review, extending its regulatory review time. This

effect is attenuated when regulatory agencies are more

frequently confronted with new technologies, which

makes them more aware of distinctions that different

technologies entail, thus reducing incongruities in the

review of subsequent innovations.

KEYWORDS

evaluation routines, nonmarket selection, organizational cognition,

organizational learning, technological evolution

1|INTRODUCTION

Evolutionary models have provided a fruitful perspective in the study of technological evo-

lution. The evolutionary perspective underscores the role of routines, which refer to “repeti-

tive pattern[s] of activity”(Nelson & Winter, 1982, p. 97), in shaping organizational

adaptation. As a firm accumulates experience in performing an activity, an important part

of its specific operational knowledge becomes stored in its routines (Nelson & Winter, 1982,

pp. 96–136; Gavetti & Levinthal, 2000). This cognitive underpinning of routines facilitates

local adaptation, encouraging a firm to search for innovations in the proximity of what it

already knows (e.g., Cohen & Levinthal, 1990; Cyert & March, 1963; Helfat, 1994a, 1994b;

Katila & Ahuja, 2002), while hampering its efforts to adapt to new technologies (e.g.,

Christensen & Bower, 1996; Henderson & Clark, 1990; Kapoor & Klueter, 2015). In contrast

with great strides in elucidating the cognitive underpinning of routines that affects adapta-

tion, research thus far has stopped short of exploring the effects it might have on selection.

Exploring this possibility can expand understanding about technological evolution, since

selection processes interact with adaptation to shape evolution (Levinthal, 1991;

Levinthal, 1997). Ultimately, the success of innovations that firms create depends on responses

they elicit in the selection environment. In contrast with a stylized notion of selection driven by

omniscient consumers making decisions about innovations, selection is oftentimes driven by

organizations that do not engage in direct market-based exchanges with innovating firms but

whose evaluations shape the success of innovations in the marketplace (Nelson & Winter, 1982,

pp. 262–272). Nonmarket selectors, such as regulatory agencies and professional communities,

mitigate uncertainty that could otherwise result in market failure (Akerlof, 1970; Noll, 1989)—

when consumers lack sufficient information to evaluate innovations, they can rely on the “pro-

fessional stamp of approval”that these organizations confer to innovations (Nelson & Win-

ter, 1982, p. 270). Unlike innovating firms, which have discretion about the technology they

decide to focus on to create innovations, nonmarket selectors do not decide which technologies

the innovations they review build on. Moreover, unlike innovating firms, which store in their

POLIDORO JR 2401

routines operational knowledge that is relevant to a particular activity

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(Nelson & Winter, 1982,

pp. 96–136), nonmarket selectors are expected to function as experts who keep abreast of the

frontier of knowledge available in a domain (Rindova, Williamson, Petkova, & Sever, 2005) and

understandings about different technologies likely provide the backdrop against which they

evaluate an innovation (Hargadon & Douglas, 2001). As a result, nonmarket selectors might be

subject to cognitive effects that are qualitatively distinct from those that prior research has

shown to shape firms' adaptation.

This study explores this possibility by examining the following research question—How

does knowledge about different technologies that exist in a domain affect the time it takes for

regulatory agencies to review an innovation in that domain? This outcome is consequential to

innovating firms: it affects the capitalized costs of innovations and the extent to which firms

can obtain time-based advantages by swiftly launching innovations in the market

(Eisenhardt, 1989; Lieberman & Montgomery, 1988). Following the tradition in evolutionary

theory to focus on routines as important conceptual building blocks, this study draws on the

insight that regulatory agencies, as organizations dedicated to the review of innovations, rely on

evaluation routines (Garud & Rappa, 1994). As the theory section further elaborates, under-

standings that regulatory agencies have developed about other technologies in the same domain

of a focal innovation induce them to view this innovation through the prism of evaluation rou-

tines that are not necessarily applicable to its review, thereby increasing the number of issues

that they need to sift through and accordingly extending the time they need to complete their

task. This study also argues that this effect is attenuated when regulatory agencies are more fre-

quently confronted with new technologies, which makes them more aware of distinctions that

different technologies entail, thus reducing incongruities in the review of subsequent

innovations.

To test these arguments, this study uses data about the regulatory review of new drugs. Sev-

eral features of this setting make it particularly appropriate for this study. First, nonmarket

selection is critical in this context, as the introduction of new drugs in the U.S. market is subject

to regulatory approval by the Food and Drug Administration (FDA; Hilts, 2003). Second, in

contrast with the high failure rates typical of drug discovery and development, the FDA

approves the vast majority of new drug applications (GAO, 2012, p. 7; Barber IV & Diestre, 2019,

p. 1197), such that the key variation is not whether or not a new drug is approved but rather

the time between application and approval (Carpenter, 2002; Carpenter, 2004). This variation in

FDA review times is consequential to firms, as each day's delay in obtaining FDA approval has

been estimated to inflict on the respective firm an average loss of one million U.S. dollars (Abra-

ham, 2002). Third, a drug's mechanism of action, which describes the process by which it func-

tions to produce the intended pharmacological effect (Nature, 2020), captures the knowledge

base on which it builds (Reuben & Wittcoff, 1989; Scriabine, 1999). Thus, in this setting it is

possible to observe the technology underlying an innovation (i.e., the pharmacological mecha-

nism underlying a new drug) and other technologies that exist in the same domain (i.e., other

mechanisms underlying drugs in the same therapeutic class). Finally, the regulatory review of

new drugs is informed by scientific evidence and knowledge available in scientific publications

provides a collectively shared framework that assists those reviews (Bodewitz, Buurma, &

Devries, 1987; Sherman, Davies, Robb, Hunter, & Califf, 2017). This enables the use of top

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Sometimes firms entering a new product domain benefit from knowledge accumulated in another domain, but this

effect occurs precisely because such prior knowledge is directly relevant to their activities in the new domain (e.g.,

Carroll, Bigelow, Seidel, & Tsai, 1996; Cattani, 2005; Klepper & Simons, 2000).

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