An empirical test of environmental, organizational, and process factors affecting incremental and...

An empirical test of environmental, organizational, and

process factors affecting incremental and radical innovation

Christine S. Koberga, Dawn R. Detienneb,*,1, Kurt A. Heppardc,2

aGraduate School of Business Administration, University of Colorado, Campus Box 419, Boulder,

CO 80309-0419, USAbDepartment of Management and HR, Utah State University, 3555 Old Main Hill, Logan, UT 84322-3555, USA

cDepartment of Management, United States Air Force Academy, 2354 Fairchild Drive, Suite 6H94,

USAF Academy, CO 80840-5701, USA

Accepted 27 November 2002

Abstract

This study examines the influence of environmental, organizational, process, and managerial

characteristics on incremental and radical innovation across three industries (aerospace, electronic

components, and telecommunications). Results show that different mixes of environmental and

organizational variables were significant predictors of incremental and radical innovation. Factors that

favor incremental innovation include environmental dynamism, age and size of the firm (although not

in the expected direction), intrafirm structural linkages, and the age of the CEO. Factors that favor

radical innovation include environmental dynamism, intrafirm linkages, experimentation, and

transitioning or sequencing from one project or product to another.

D 2003 Elsevier Science Inc. All rights reserved.

Keywords: Innovation; Radical; Incremental

1047-8310/03/$ – see front matter D 2003 Elsevier Science Inc. All rights reserved.

doi:10.1016/S1047-8310(03)00003-8

* Corresponding author. Tel.: +1-435-755-5908.

E-mail addresses: [email protected] (C.S. Koberg), [email protected] (D.R. Detienne),

[email protected] (K.A. Heppard).1 Tel.: + 1-303-492-8677; fax: + 1-303-492-5962.2 Tel.: + 1-719-333-4130; fax: + 1-719-333-2944.

Journal of High Technology

Management Research 14 (2003) 21–45

1. Introduction

Innovation is a necessity for firms that compete in environments where change is

pervasive, unpredictable, and continuous (Brown & Eisenhardt, 1997). It has been the

focus of much work in strategy and related fields over the last 20 years (Drazin &

Schoonhoven, 1996; Fiol, 1996; Glynn, 1996; Ibarra, 1993; Shane & Venkataraman,

2000; Van de Ven, Polley, Garud, & Venkataraman, 1999; Walsh & Linton, 2000).

Although there is a volume of research on what strategies and structures make a firm

innovative, there is less evidence on what processes (e.g., improvisation, experimentation,

and transitioning from one project to the next) enable a firm to be continuously or more

or less innovative (Brown & Eisenhardt, 1998). Moreover, studies rarely distinguish

among types of innovation (Damanpour, 1992; Drazin & Schoonhoven, 1996; Klein &

Sorra, 1996).

Though seldom examined, radical innovations (along with incremental innovations) are

important to the economic sustainability of firms in industries that are dependent on

competitive research and development for comparative advantage and long-term survival.

Ettlie, Bridges, and O’Keefe (1984) argue, ‘‘unique strategy and structural arrangements are

necessary for radical innovation’’ (p. 683). This study investigates whether the frequency of

incremental and radical innovations may be explained by different mixes of environmental,

organizational, processes, and managerial forces, and therefore may need to be managed

differently (Rogers, 1995; Van de Ven et al., 1999).

Strategic choice and population ecologists differ about which is the most powerful or

important set of factors (environment, organization, or managerial) in explaining organiza-

tional change and innovation. And yet, researchers generally agree that each set may be

employed to account for organizational changes, including innovation (Hambrick & Finkel-

stein, 1987; Hrebiniak & Joyce, 1985), although researchers rarely investigate whether these

forces operate separately or in combination.

On the assumption that innovations could be categorized as either incremental or radical,

we examined the influence of the following factors on the type of innovation: environment

(dynamism); organization (age and size of the firm); structure (intrafirm structural linkages);

process (improvisation, experimentation, and transitioning across projects); and managerial

characteristics (CEO age and tenure in the position and with the company). We measured

these factors through the perceptions of CEOs in three different industries—aerospace,

electronics, and telecommunications—chosen because they are highly dependent on innova-

tion for competitive advantage and survival.

We begin our study by reviewing the difference between incremental and radical

innovations. We next provide an overview of complexity theory. It is within the context of

this theory that we seek to investigate the research hypotheses concerning the effects of

environmental, organizational, process, and managerial forces for incremental and radical

innovation. This is followed by a discussion of our research design, and by our results of the

tests of the research hypotheses. Finally, we conclude in the discussion section that those

forces favoring incremental innovation do in fact differ from those favoring radical

innovation.

C.S. Koberg et al. / Journal of High Technology Management Research 14 (2003) 21–4522

2. Incremental and radical innovation

Research on innovation has progressed along a variety of course rather than a single one; it

encompasses diverse types varying in scope, depth, and objective. Among the different types

of innovation identified by researchers are administrative and technical, product and process,

technological and architectural, and incremental and radical (Chiesa, Coughian, & Voss,

1996). Differentiating between incremental and radical innovation is not always clear

(Henderson & Clark, 1990). Most innovations simply build on what is already there,

requiring modifications to existing functions and practices, but some innovations change

the entire order of things, making obsolete the old ways (Van de Ven et al., 1999, p. 171).

Tushman and Romanelli (1985) along with other researchers (e.g., Gersick, 1991)

distinguish broadly between two types of organization change: incremental and radical.

Tushman and Romanelli describe incremental changes as those that encourage the status quo,

whereas radical changes are those characterized by ‘‘processes of reorientation wherein

patterns of consistency are fundamentally reordered’’ (p. 174). They describe a hierarchy of

organizational changes, corresponding to how pervasively the change affects an organiza-

tion’s premises or decisions. In his model of organizational changes, Miles (1975) also

described a hierarchy of organizational adjustments or changes, including procedure, process,

structure, and strategic, corresponding to the scope of the change. Innovation, thus,

encompasses multiple types, ranging from innovations in an organization’s control systems,

allocation of resources, technology, and structure to changes in a firm’s strategy, rather than a

single type of change (Tushman, Newman, & Romanelli, 1986).

Radical innovations encompass higher order innovations that serve to create new

industries, products, or markets (Herbig, 1994; Meyer, Brooks, & Goes, 1990). They

comprise technological advances so significant that no increase in scale, efficiency, or design

can make older technologies competitive (Tushman & Anderson, 1986). They make obsolete

the old, and permit entire industries and markets to emerge, transform, or disappear (Kaplan,

1999). Whether internally developed or externally generated, radical innovations ‘‘over time,

augment, shift, and change a firm’s technological processes and open up whole new markets

and product applications’’ (Henderson & Clark, 1990, p. 9). Examples from the electronics

industry include self-healing computers; from the aerospace industry, commercial satellites;

and from the telecommunications industry, the wireless Web. In accordance with the work of

Herbig (1994) and Tushman and Anderson (1986), we define radical innovation as strategic

changes in product/services, markets served, and technological breakthroughs used to

produce a product or render a service based on significant innovation.

Herbig (1994) describes three types of incremental or lower order innovations: continuous,

modified, and process. Continuous innovations constitute augmented changes to products

(e.g., product line extensions). Modified innovations comprise slightly more disruptive

innovations such as the introduction of a new technology that performs the same basic

functions as the old one (e.g., updated computer software). Process innovations consist of

improvements in the way an existing product is produced (e.g., TQM and CAD).

In accordance with the work of Herbig (1994), we define incremental (first or lower order)

innovation as low in breadth of impact and comprising the following broad categories:

C.S. Koberg et al. / Journal of High Technology Management Research 14 (2003) 21–45 23

procedural (management-determined innovations in rules and procedures); personnel-related

(innovations in selection and training policies, and in human resource management practices);

process (new methods of production or manufacturing); and structural (modifications to

equipment and facilities and new ways in which work units are structured). We define radical

(higher or second-order) innovation as major in scope and breadth, involving strategic

innovations or the creation of new products, services, or markets.

Executives also distinguish between incremental and radical innovation as noted by one

executive from the telecommunications industry who stated:

It is kind of like the evolution of the PC. The basic structure is the same, but you change a

chip here and a diode there and it gives you better performance. But fiber optic is going to

be a paradigm change. It is going to add another level of technology to our industry. It

won’t completely replace everything, but it will over time change the leading edge

technology of the industry. Fiber optic interconnect capability will route data 1000 times

faster than we do today.

Herbig (1994) found that the conditions that favored high-order or radical innovations

differed from those favorable to incremental innovation. Angle (1989) in his work with the

Minnesota Innovation Research Program (MIRP) found that different cultural characteristics

(individualism vs. collectivism) affect incremental and radical innovations differently. Van de

Ven et al. (1999) argue that some organizations may be better suited to one type of innovation

than the other. Structural variables that increase the degree of incremental innovation may

simultaneously decrease the degree of radical innovation.

Thus, in this study, we distinguish between incremental and radical innovation and seek to

further understand the influence of the environment, organization, structure, processes, and

managerial characteristics on these types of innovation.

3. Complexity theory

Complexity theory provides the orienting framework for our study (Anderson, 1999;

Lewin, 1999; Stacey, 1995). Complexity theory combines open with rational assumptions,

allowing scholars to combine elements of stability, instability, and bounded stability into

behaviors that apply in all human organizations, at the same time, under all conditions

(Stacey, 1995). Organizations carrying on the most complex types of work develop formal

systems that are made to be resistant to change and to sustain the status quo [stability] in the

interest of efficiency. We are all familiar with the well-defined hierarchical structures, the

rules and procedures, and the machinery of repetitive, everyday activities of most organ-

izations. At the same time instability underlies all human organizations; more complicated

than stability, it produces patterns of behavior that are unpredictable.

Nonetheless, although organizational behavior may not be possible to predict in advance,

over the long term it develops uniformity or structure—known as bounded instability.

Instability and bounded instability are, according to complexity theory, the fundamental


properties of innovative and creative systems. ‘‘In order to produce creative, innovative,

continually changeable behavior, systems must operate far from equilibrium where they are

driven by negative and positive feedback to paradoxical states of stability and instability,

predictability, and unpredictability’’ (Stacey, 1995, p. 478).

4. Research hypotheses

Complexity theory argues that human organizations are ‘‘complex adaptive systems’’

characterized by: (1) cognitive structures influencing an agent’s behavior; (2) energy

imported from the outside sustaining self-organizing networks; (3) a pattern of large and

small changes forming a pattern of continuous change; and (4) a system of recombination or

new innovations building on elements of previous innovations. Because organizations are

characterized by continuous large and small changes, and because in larger systems,

significant change occurs exponentially less frequently than in small ones (Anderson,

1999), we expect that,

Hypothesis 1: Organizational policymakers will report incremental innovations with sig-

nificantly greater frequency than they will report radical innovations.

4.1. Relating environmental characteristics to innovation

4.1.1. Environmental dynamism

Complexity theory argues that organizations are sustained by importing energy from the

outside (Anderson, 1999). Shifting environmental pressures, such as changes globally and

domestically by customers, clients, suppliers, and regulatory bodies and agencies, along with

market and technological developments, drive internal organizational change. Organizations

are embedded in environments, both inside and outside the organization, where new ideas are

constantly being developed (Van de Ven & Poole, 1995).

Top managers must constantly adapt to powerful environmental forces that cannot be

foreseen, often at an accelerating pace. To recognize opportunities for innovation, top

managers must reshape the organization, alter its path, and change the ways it adapts and

innovates. One aerospace executive we interviewed explained, ‘‘Our industry changes in

response to other industries. The primary product we make are parts for jet engines, so you

have to ask how fast is the need for change in the industry you are serving.’’ Another

executive from the same industry described the importance of forces outside the organization

as follows: ‘‘If you look at the history of innovations, most major things happen outside the

industry that they made their money in. I think that a lot of times, the idea comes from outside

an industry, and then the industry develops around the idea.’’

Dynamism flows from an information-processing view that treats environments as a source

of information (Dutton, 1993; Huber & Daft, 1987). Strategic choices are therefore explained

by variations in information as filtered by managerial perceptions of the external envir-

onment. From a complexity theory perspective, top managers have ‘‘cognitive structures that


determine what action the agent takes at time t, given the perception of the environ-

ment’’(Anderson, 1999, p. 219). Top managers acquire knowledge about environmental

events, whether threats or opportunities, by noticing, interpreting, and incorporating data

about environmental changes. Ginsberg and Venkatraman (1992) reported that the ‘‘meanings

managers attach to an environmental occurrence, such as the introduction of a new information

technology, explains why they may respond differently to the same event’’ (p. 426).

Notwithstanding the problem of determining the accuracy of managerial perceptions of

knowing whether environmental perceptions are a consequence rather than a cause of

innovation, the present study is in keeping with both an information processing and

complexity approach to organization environments. These two approaches consider percep-

tions of the environment useful for predicting choices among decisions about strategy and

innovation, even though they may be insufficient for predicting the success of those choices

or ultimately how well they perform for the organization (Dutton, 1993).

Top managers of ‘‘organizations in environments with substantial technological and/or

legal/social uncertainty’’ tend to undertake quantum (radical) changes (Tushman & Roma-

nelli, 1985, p. 207). High-velocity environments with short product cycles require firms to

innovate rapidly and continuously to survive (Brown & Eisenhardt, 1997). The more dynamic

the policymakers perceive the external environment to be, the more favorable they perceive it

to be to innovators (Miller & Friesen, 1982; Scott & Bruce, 1994). We agree that,

Hypothesis 2: Perceived environmental dynamism will be a greater positive predictor of the

frequency of radical than of incremental innovation.

4.2. Relating organizational characteristics to innovation

4.2.1. Age and size of firm

According to the tenets of complexity theory, ‘‘by its very nature an efficient formal system

in organization is not changeable—it is meant to resist change and sustain the status quo

[stability] in the interest of efficiency’’ (Stacey, 1995, p. 484). Older organizations are less

able to change because they have had time to formalize relationships and standardize routines

(Kelly & Amburgey, 1991). As firms age and become larger, structural rigidity and inertial

forces increase, potentially constraining the ability of the organization to innovate. With

increasing size and age come bureaucratic procedures that often ‘‘constrain innovation unless

special systems are put in place to motivate and enable innovative behavior’’ (Van de Ven et

al., 1999, p. 201). Larger and older organizations are likely to have in place the routines to

elicit procedural, personnel-related or process innovations, but less likely to be able to change

quickly enough to develop radical innovation. Accordingly,

Hypothesis 3a: The age of the firm will be a greater positive predictor of the frequency of

incremental than of radical innovation.

Hypothesis 3b: The size of the firm will be a greater positive predictor of the frequency of

incremental than of radical innovation.


4.3. Relating structural characteristics to innovation

4.3.1. Intrafirm structural linkages

Complexity theory argues that the work of organizations is carried out by agents who are

‘‘partially connected to one another, so that the behavior of a particular agent depends on the

behavior (or state) of some subset of all the agents in the system’’ (Anderson, 1999, p. 219).

Intrafirm linkages are defined as cross-functional and coordination mechanisms, designed to

increase integration. These linkages structurally link individuals from different functional

units, thereby facilitating the exchange of ideas and the collaborative efforts of a variety of

people concurrently working on different aspects of a project (Legnick-Hall, 1992; Spender &

Kessler, 1995). Kolodny, Stymne, and Denis (1996) studied the introduction of innovative

flexible technologies in 12 companies in Sweden, France, and Canada and found that

successful companies moved towards flatter organizational structures with more horizontal

communication. Muffato and Panizzolo (1996) studied product development managers from

firms in the Italian motorcycle industry and found innovation was associated with program

managers who coordinated cross-functional integration. One electronics executive we

interviewed, in describing the innovation process in his firm, remarked:

When we form teams, we group ’em together so there’s a project manager, and there is an

engineering team. With all of the disciplines, and the disciplines are pretty varied here from

electrical to mechanical to product design to the optics to the thermal part, it’s a big issue.

So we put the disciplines together in team areas and work with them there.

Of course, all innovation does not occur through collective or joint action, although a lack

of interplay of ideas among individuals can impede all types of innovation. Lateral relation-

ships and a widening of task boundaries within organizations create an environment favorable

to innovation (Brown & Eisenhardt, 1997), both incremental and radical. Accordingly, we

predict:

Hypothesis 4: The greater the number of intrafirm structural linkages, the greater the

frequency of both incremental and radical innovation.

4.4. Relating processes to innovation

4.4.1. Improvisation

According to complexity theory, innovation cannot be planned but must evolve; it requires

experimentation, openness, and improvisation (Lewin, 1999, p. 215). ‘‘Over time a firm is a

combination of frequent small changes made in an improvisational way that occasionally

cumulate into radical strategic innovations, changing the terms of competition fundament-

ally’’ (Anderson, 1999, p. 224). Brown and Eisenhardt (1998), in their groundbreaking study

of firms in the computer industry, found that successful innovators in the computer industry

were able to improvise, experiment, and choreograph transitions from one project or product

to the next.


A manager who is able to improvise is able to balance the structures and procedures

necessary for meeting budgets and schedules with flexible ways of working necessary for

ensuring creative and innovative behavior. Clearly, some coordination is required, but too

much or tight coordination impedes an organization’s capacity for innovation, given the

uncertainty and technical complexity of innovative work. One aerospace executive described

his firm’s review process for funding innovative ideas as follows:

We have a 6 or 7 member board made up of all the operating systems managers who

review each idea people within the company want to propose, called R&D Bid &

Proposal (due in September). In addition, we have four R&D teams (2 people each),

who can commit based on their signature. If someone has an idea, they can come up to

their team and say here’s my idea. If the team decides, they don’t even have to fill out

a piece of paper. Those are what we call our ‘wild-hair projects.’ This is just what it is

for . . . to let people have a way of trying things without having to deal with the

bureaucracy.

4.4.2. Experimentation

A manager who is able to experiment is able to proactively pursue and recognize new

opportunities early while being able at the same time to react to today’s moves by rivals

(Brown & Eisenhardt, 1998). Ideally, managers gather information necessary to envision the

future while simultaneously maintaining an ability to see unexpected opportunities in the

present and to move quickly and shift strategies in response to unforeseen market,

competitive, technological, and regulatory changes.

Experimentation and improvisation are separate but not mutual exclusive constructs. Both

imply ‘‘making it up as you go,’’ ‘‘trial and error learning,’’ and ‘‘coevolution to the edge of

chaos’’ (Brown & Eisenhardt, 1995, p. 15). An aerospace executive described how

experimentation and improvisation were carried out in his firm as follows:

If you fire enough missiles, you’ll get one. If someone tries long enough, one of the ideas

is going to work, but if you never have an idea, it’s not going to work. So you take the guys

who are willing to go out on a limb, you look at them a little more favorably. And of

course, if their ideas hit, you reward them . . .. When you’re operating on the frontier, a lot

of things can happen. We’re operating in areas where we’ve never been before so you

don’t know what you’re going to get. So there are a lot of stops and starts and a lot of blind

alleys.

Individuals operating in open systems, which promote improvisation and experimentation,

are more likely to discover radical innovation, whereas individuals operating in more

formalized systems are more likely to discover incremental innovation. Therefore, we

propose

Hypothesis 5a: Improvisation will be a greater positive predictor of the frequency of radical

than of incremental innovation.


Hypothesis 5b: Experimentation will be a greater positive predictor of the frequency of

radical than of incremental innovation.

4.4.3. Transitioning across projects

Managers operate at a place between order and chaos. As noted earlier, complexity theory

argues that organizations encompass the paradoxical states of predictability and unpredict-

ability, stability, and instability. Stumbling blocks to innovation are created by an absence of

well-established routines that provide predictability for leaving old businesses (Brown &

Eisenhardt, 1997). In successful firms, managers coordinate across projects and ensure they

have things in the ‘‘pipeline.’’ Successful managers smoothly transition from one project to

another, creating well-established routines for leaving old business areas and following

explicit procedures for developing new projects. Most successful firms create a multistage

process whereby projects pass through a structured sequence of steps from concept to

development and promotion. As each step is completed, the project is passed to the next step.

In this way, successful managers increase the speed with which an innovation hits the market.

We predict:

Hypothesis 5c: Transitioning across projects will be positively related to the frequency of

both incremental and radical innovation.

4.5. Relating managerial characteristics to innovation

4.5.1. Age and tenure of organizational policymakers

According to complexity theory, organizations are characterized by cognitive structures

that influence an individual’s behavior. Virtually all managers develop an ideology or

schema (a cognitive structure) that influences the choices of managers. One electronics

executive we interviewed described his decision rule or ‘‘schema’’ as follows: ‘‘I have

always been interested in the innovative thing—always interested in developing new

products and new technologies . . . some people are real interested in production or

manufacturing—that’s not me; I am interested in R&D.’’ By virtue of their personal

characteristics, managers will vary in the degree to which they develop and promote

different types of innovation.

Upper echelon research holds that cognitive biases, values, and perceptions, measured by

such proxies as tenure and age, influence the choice of managers. Research shows, for

example, that long tenure is associated with performance conformity and strategic

persistence (Finkelstein & Hambrick, 1990) that may discourage innovation. By contrast,

age and short tenure have been linked to risky strategies (Bantel & Jackson, 1989), because

younger managers typically have less commitment to the status quo (Wiersema & Bantel,

1992).

In his work on scientific discovery, Kuhn (1970) proposes that people who are very young

or very new to the field are those most likely to discover paradigm-breaking discoveries

because these individuals are not committed to the traditional rules of normal science. These

individuals that have less invested in the status quo are more likely to see that the current


rules no longer define a playable game and conceive another set that can replace them.

Accordingly, we expect that:

Hypothesis 6a: The lower the age of organizational policymakers, the greater the

frequency of radical innovation and the lower the frequency of incremental innova-

tion.

Hypothesis 6b: The shorter the tenure in the position and with the company of organizational

policymakers, the greater the frequency of radical innovation and the lower the frequency of

incremental innovation.

5. Methods

5.1. Organizations and executives

The sample frame for this study was selected from the 1998 and 2000 editions of Ward’s

Business Directory of U.S. Private and Public Companies. Ward’s Directory contains a

complete list of public companies and is also a leading source of information about

companies that are not publicly traded or are subsidiaries of larger companies. The

directory contains information on over 132,500 companies (90% of which are private),

listing CEO names, addresses, sales information, employee figures, and five and six digit

Standard Industry Classification (SIC) codes. To select the sample, we used a two-step

approach. First, we selected three industry strata: (1) aerospace (SIC 336411 through

336415, and 336419); (2) electronic components and superconductors (SIC 334415 through

334419); and (3) telecommunications (SIC 513321, 513322, and 51331 through 51333).

We then randomly selected samples of 300, 350, and 250, respectively, from the three

industry strata, for a total of 900 firms. Although these industries vary in technical

development and batch processing, all are highly dependent on innovation for competitive

advantage and survival.

5.2. Aerospace

Whereas sales of planes, jets, missiles, and other space vehicles have declined since

1991, space and defense spending has recently increased (Business Week, 1999a, p. 114),

and military exports and the civilian side of the business offer promise for growth and

innovation in this industry. Aerospace companies are actively seeking to develop

wireless telecommunications, laser communications, and antennas for commercial mar-

kets. Menes (1998) summarizes the aerospace and high-technology industries as follows:

‘‘The ten fastest growing manufacturing industries are heavily dominated by high-tech

industries. At the top is computer equipment, followed by the six aerospace industries.

This is a dramatic turnaround for these industries since, with the exception of aircraft

engines, they all ranked near the bottom in growth for 1995 and 1996’’ (p. xxxiii).


5.3. Electronic components and chips

The electronic and superconductor industries have seen rapid technological progress and

innovation. In the microprocessor industry, R&D can amount to 25–30% of firm sales (Oster,

1994). Examples of innovations in this industry are ‘‘handheld PCs, digital videodisk players,

smart phones, digital TVs, and set-top boxes’’ (Business Week, 1998a, p. 89).

5.4. Telecommunications

The Telecommunications Act of 1996 has opened up competition and made possible new

kinds of service for new classes of customers in this industry. Wireless, an unregulated

service, continues to explode (Business Week, 1999b, pp. 98–99), with the ‘‘US government

lending a hand, funneling money for wireless research to universities’’ (Business Week,

2000a, p. 68). Telecommunication firms want to carry data of all sorts—voice, video, and

graphics—and provide faster access to the Internet (Fortune, 1998, pp. 144–148).

5.5. Survey design

In accordance with the Total Design Method (TDM) described by Dillman (1978), we

mailed questionnaires, accompanied by postpaid return envelopes and cover letters, to the

chief executive officers (chairman, CEO, and president) of the 900 firms in the sample frame.

The cover letters served to identify the sponsor of the study and to explain its purpose and

importance. We assured executives of confidentiality and promised them a report of the

aggregated findings once the study was completed. A reminder letter with a replacement

survey questionnaire was mailed 3 weeks after the initial mailing.

Some researchers question the validity of studies that rely on a single informant’s

perceptions (Lant, Milliken, & Batra, 1992). However, there is little convincing research

that either supports or contradicts the generally accepted belief that CEOs and top admin-

istrators can provide reliable information about the basic environmental and organizational

characteristics of their firms. Our approach of using one informant per organization has been

supported when survey instruments were well-designed and executed (Jennings & Lumpkin,

1992; Russell & Russell, 1992; Starbuck & Mezias, 1996).

The response rate for mail surveys sent to top managers typically is lower than for other

mail surveys, because top managers have relatively less discretionary time to devote to

completing questionnaires sent to them by academic researchers. We obtained response rates

of 24.3%, 23.7%, and 14.8%, respectively, for the aerospace, electronic components, and

telecommunications industries; these response rates are consistent with Stimpert’s (1992)

report that in studies using CEOs as addresses, response rates range from 14% to 34%.

Nonresponse bias is always a concern when response is voluntary; nonresponding firms,

however, did not differ significantly from responding firms in annual sales or geographic

area.

Although the responding firms, based on a chi-square analysis, did not differ significantly

from nonresponding firms in geographic location (eastern, plains, and western) (c2 = 4.36,


df = 3, ns) and annual sales, a disproportionate number of nonresponding firms were from the

telecommunications industry. This industry is experiencing a complete overhaul and shake-

out, with many small wireless operators going out of business because of a decline in the

price of wireless minutes, and an increased competition for capital (Business Week, 1998b,

p. 93, 2000a, p. 96). As one executive from the telecommunications industry commented,

‘‘There are too many telecommunications companies and not enough banks.’’

The executives who responded included 182 males and 10 females, and had a mean age of

41 to 50 years. They had been in their present position a mean of 6 to 10 years or more, and

had been with the company a mean of 11 to 15 years or more. A large number held a college

(n= 73) or advanced degree (n= 84); the others held an associate degree (n = 19) or had a high

school education (n = 16).

The analysis is undertaken at an organizational level. Approximately 24% (n= 46) of the

responding firms employed fewer than 100 workers, with 19% (n = 37) employing over 500

workers. The majority of firms (n = 109) had between 100 and 500 employees. The mean age

of the companies sampled was 34.36 years. A small number of firms, approximately 13%

(n= 25), reported declining sales over the last 3 years. About 18% of the firms (n= 34)

reported a stable growth rate; 24% (n = 46) reported a growth rate of less than 10%. A larger

percentage of firms, approximately 33% (n = 64), reported a growth rate between 10% and

25% annually, and a smaller number of firms, approximately 12% (n = 23), reported an annual

growth rate over 25%.

We conducted semistructured follow-up interviews of a total of 25 executives who

volunteered to talk with us. We conducted interviews to help generate descriptive indicators

of incremental and radical innovation, to avoid misrepresentation or misinterpretation of the

results from the questionnaire data, and to help strengthen the results of the quantitative

analyses. The interviews were conducted on the telephone and lasted between 30 and 40

minutes. All interviews were taped and transcribed.

6. Measures

We employed scales that had been standardized and validated by other researchers (e.g.,

Brown & Eisenhardt, 1997, 1998; Duncan, 1972).

6.1. Environmental dynamism

Dynamism, the extent of unpredictable change in the external environment, is considered a

perceptual phenomenon, inasmuch as the uncertainty resides ‘‘in the perceptions and minds of

managers in terms of their ability to predict future environmental states’’ (Bluedorn, 1993, p.

166). One executive describing the unpredictable change in the electronics industry stated,

‘‘As far as the future we only go out 2 or 3 years because things are happening so fast that it is

impossible to predict. We would make terrible communists—a 5-year plan would not work!’’

We measured perceived dynamism using 10 items developed by Duncan (1972). Using a

scale anchored from 1 = very frequent change to 5 = very rare change (reverse scored), we


asked executives to rate the frequency of change in their firm’s external environment sectors,

including distributors of their product or services; users of products or services; suppliers of

equipment, materials, and parts; supply of labor (all types); competitors for customers;

government regulatory control; public political attitude toward the industry; and development

of new or improved production methods and new or improved products and services. To test

the dimensionality of the scale (alpha coefficient of .70), the 10 items were factor analyzed

using principal components analysis with varimax rotation, yielding one identifiable factor

(eigenvalue of 2.81) and loadings ranging from .42 to .74.

6.2. Size and age of firm

We measured the size of the firm in terms of the total number of employees. A logarithm of

the number of employees was computed to represent size, because a logarithmic transforma-

tion provides the most generally useful procedure for effecting linearity. The age of the firm

was determined by the firm’s founding date.

6.3. Intrafirm structural linkages

We measured intrafirm structural linkages, defined as cross-functional and coordination

mechanisms, designed to increase integration, by eight 5-point Likert items based on the

works of Brown and Eisenhardt (1998) and Muffatto and Panizzolo (1996). Executives rate

the accuracy (1 = very accurate to 5 = very inaccurate) (reverse scored) of each of eight

statements:

1. There are formal cross-project meetings.

2. There is frequent cross-project communication.

3. There are explicit project priorities.

4. There is a hierarchy of project managers.

5. A small core team is formed that carries a project through product concept.

6. A project coordinator leads project transitions.

7. As a version of an existing project is finished, team members transition to working on new

projects.

8. New teams are a mix of old and new team members.

We obtained an alpha coefficient of .76, and one identifiable factor (eigenvalue of 3.14),

with loadings ranging from .42 to .69.

6.4. Improvisation and experimentation

To measure improvisation and experimentation, we employed a group of paired statements

developed by Brown and Eisenhardt (1998) from their in-depth study of firms in the

computer industry in Asia, Europe, and North America. We asked executives to rate the

extent to which the paired statements, separated by a 5-point Likert scale, best approximated


or described their firm. Improvisation, defined as too much versus too little structure,

consisted of the following five paired statements:

1. Change is expected (vs. problematic).

2. Priorities are clear (vs. ambiguous).

3. Priorities drive resources always (vs. never).

4. Communication is constant (vs. infrequent).

5. Communication is channeled (vs. chaotic).

We obtained an alpha coefficient of .73 and one identifiable factor with an eigenvalue of

2.48 and loadings ranging from .27 to .67.

Experimentation, defined as being able to focus on both the present and the future without

losing the flexibility required to react to unexpected events (Brown & Eisenhardt, 1998,

p. 131), consisted of six paired statements:

1. The collective vision of our business is clear (vs. ambiguous).

2. Our future is based on careful planning (vs. reacting to future development).

3. Our attention to the future is constant (vs. rare).

4. We have several (vs. no) meaningful experimental products and future-oriented strategic

alliances.

5. We extensively (vs. never) use experimentation.

6. Our business is considered to be a leader (vs. follower).

We obtained an alpha coefficient of .80, and one identifiable factor with an eigenvalue of

3.05 and loadings of .51, .45, .66, .63, .34, and .45. The factor analysis indicated to us that the

items for measuring improvisation and experimentation appear to capture aspects of the

processes that were considered theoretically and practically meaningful by Brown and

Eisenhardt (1997, 1998).

6.5. Transitioning across projects

We measured transitioning, defined as transitioning or sequencing from one project to

another, by three items developed by Brown and Eisenhardt (1997, p. 21). Using a 5-point

Likert scale anchored from 1 = never to 5 = frequently, the executives were asked to rate the

frequency with which:

1. New product or services are introduced at predictable intervals.

2. New product or service performance is measured with well-defined metrics.

3. Well-established routines are used for leaving old business areas.

4. The firm has explicit procedures for development projects.

One identifiable factor (eigenvalue of 2.29) was produced with factor loadings of .54, .65,

.52 and .59. We obtained a reliability coefficient of .75.


6.6. Incremental and radical innovation

A major drawback of any innovation research is measurement; there is no easy way or

standardized way to measure organizational innovation. We asked executives to indicate the

frequency of their firm’s incremental and radical innovation. We told executives that organ-

izational innovations refer to a broad range of innovations, either internally developed or

externally acquired, that firms use to meet customer demand and to maintain or improve

performance. Incremental innovation was subdivided into four categories: (1) procedural

(innovations in rules, work procedures, work schedules, etc.); (2) personnel (innovations in

human resource management; creative changes in selection and training policies, etc); (3)

process (new methods of production or manufacturing processes or significant technology

enhancements in the organization’s operations that are used to produce a product or render a

service); and (4) structural (new innovations or creative modifications to equipment and

facilities; innovative redesign of departments, divisions, and/or projects, etc). Radical innova-

tion has only one category, strategic (innovations in basic product or service programs offered or

market served; creation of newmajor product/service programs leading to expansion of current

markets). We asked executives to report the frequency of each category over the last 3-year

period. The responses are 1 = never, 2 = rarely (1 or 2 times), 3 = sometimes (3 to 5 times), 4 =

frequently (6 to 10 times), and 5 = very frequently (more than 10 times). We computed a single

composite overall measure of incremental innovation (alpha coefficient of .73) by averaging the

unweighted score on each of the four incremental variables. The items were factor analyzed,

producing one significant eigenvalue of 2.25 and individual loadings of .49, .63, .62, and .52.

We acknowledge that field studies using self-report, cross-sectional data are particularly

susceptible to errors resulting from consistency, priming, and problems associated with

common method variance (Podsakoff & Organ, 1986). Factual data of which the respondent

possesses direct knowledge pose less serious problems, since such data are in principle

verifiable. Some of the data collected in the present study (age and size of the firm) were of

this type. Also, Spector (1987) proposed that method variance might well be more of a

problem with single items or poorly designed scales and less of a problem with multi-item

and well-designed scales. An indication of the portion of variance attributable to functional

relationships and the portion to the use of common methods is desirable in survey research.

Podsakoff and Organ (1986) suggested a number of statistical procedures whereby common

method variance can be checked. Among these procedures is Harman’s single-factor test.

This test assumes that the first rotated factor provides a good approximation of common

method variance. Factor analysis of the study research variables revealed a first unrotated

factor that accounted for 17.3% of the variance, suggesting problems of common method

should therefore be somewhat attenuated.

7. Analysis of data and research findings

Data on the distributional characteristics of the scaled variables, along with the bivariate

relationships (correlations) among the variables (without controlling for the effects of other


Table 1

Means, standard deviations, and zero-order correlations among the study variables

Means S.D. Zero-order correlations

1 2 3 4 5 6 7 8 9 10 11 12 13

1. Environmental

dynamisma

2.55 .49 –

2. Age of the firm 34.36 20.81 � .01 –

3. Size of firm 2186.76 113.72 .18 .37** –

4. Intrafirm structural

linkagesa3.54 .62 .11 .01 .17 * –

5. Improvisationa 3.59 .55 � .04 .07 .06 .32** –

6. Experimentationa 3.46 .69 .08 � .06 .09 .43** .56** –

7. Transitioning 2.79 .77 .05 � .01 .11 .46** .23** .31** –

8. CEO ageb 3.64 .59 � .14 � .06 � .13 .04 � .03 .04 .05 –

9. Tenure in

position

4.40 1.8 � .03 � .05 � 28** � .03 � .11 .001 � .03 .36** –

10. Tenure with the

companyc5.55 1.64 � .04 .17 * .05 � .05 � .02 � .02 .05 .16 * .57** –

11. Educationa 3.26 1.03 � .04 .05 .20** .06 .04 .08 .03 � .01 � .10 � .20** –

12. Incremental

Innovationa3.14 .75 .22** .24** 34** .27** .06 .11 .20** � .21** � .17 * .01 .08 –

13. Radical

Innovationa2.98 .91 .28** � .06 .11 .25** .10 .24** .26** � .08 � .10 .004 .04 .37** –

a 5-point scale.b 4-point scale.c 7-point scale.

* P=.05.

** P=.01.

C.S.Koberg

etal./JournalofHighTech

nologyManagem

entResea

rch14(2003)21–45

36

variables) are given in Table 1. Predictably, the process variables (experimentation,

improvisation, and transitioning), age and size of the firm, and the two innovation

variables (incremental and radical) were positively related to each other. Intrafirm

structural linkages were positively related to size of the firm and to the three process

variables. Age of the CEO was positively related to tenure in the position and tenure with

the company.

We performed analyses of variance to determine whether tests of the research

hypotheses had to be conducted while controlling for the effects of industry. We found

no significant differences across the industries in the research variables; therefore, we

aggregated data across the three industries for the purposes of data analysis and for

testing of the research hypotheses. Van de Ven et al. (1999, p. 17) assert that the

processes of innovation are fundamentally the same across very different organizational

structures and settings.

Table 2

Results of hierarchical regression of incremental and radical innovation

Independent variables Innovation variables (standardized regression coefficients)

Incremental Radical

Environment

Environmental dynamism .22** * .28** *

Adjusted R2 .05** * .08** *

Organization

Age of the firm .18** * � .09

Size of the firm .21** * .08

Intrafirm structural linkages .22** * .22** *

Change in adjusted R2 .17** * .06** *

Process

Improvisation � .03 � .10

Experimentation .01 .22** *

Transitioning .08 .13 *

Change in adjusted R2 .01 .04* *

Managerial

CEO age � .16* * � .04

Tenure in position � .07 � .14

Tenure with company .05 .11

Change in adjusted R2 .03 .02

F= 5.34, df = 11, 180, P=.001 F= 3.98, df = 11, 180, P=.001

Multiple R=.51, R2=.26 Multiple R=.45, R2=.20

* P=.10.

* * P=.05.

*** P=.01.


7.1. Results: research hypotheses

A correlated (paired sample) t test showed that incremental innovations occurred with

significantly greater frequency than did radical innovations (t = 2.37, df = 191, P=.02). This

finding supports Hypothesis 1.

We used hierarchical regression, an efficient analysis alternative that allows blocking on

variables, to test our research hypotheses that the environmental, organizational, structural,

process, and managerial variables would all be related to incremental and radical innovation

(see Table 2).

Hypothesis 2 predicted that dynamism would be a greater positive predictor of radical than

of incremental innovation. An examination of the beta coefficients in Table 2 shows that

dynamism was a greater significant predictor of radical than of incremental innovation.

Hypotheses 3a and 3b hypothesized that older and larger firms would be a greater positive

predictor of incremental innovation. Both these hypotheses were supported. Hypothesis 4

related intrafirm structural linkages to innovation. Intrafirm linkages were a positive predictor

of both types of innovation. 5a, 5b, and 5c related the process variables (improvisation,

experimentation, and transitioning) to innovation. As hypothesized (Hypothesis 5b), experi-

mentation was a positive predictor of radical innovation. Transitioning (Hypothesis 5c) also

predicted radical innovation, but at a lower significance level (.10). Lastly, Hypotheses 6a and

6b related managerial characteristics to innovation. CEO age was a significant predictor of

incremental innovation, but not in the expected direction. In summary, there is support for 1,

2, 3a, 3b, 4, and 5b, partial support for Hypotheses 5c and 6a, and no support for Hypotheses

5a and 6b.

To determine the unique contribution of the environmental, organizational, process, and

managerial variables to innovation, we examined the change in the adjusted R2. Table 2

reveals that environmental and organizational variables were a significant predictor of

incremental and radical innovation. Additionally, the process variables were a significant

predictor of radical innovation, and the managerial variables were a significant predictor of

incremental innovation.

8. Discussion and conclusions

This study makes four important contributions to the study of innovation. First, we

distinguish between two general types of innovation (incremental and radical) as opposed to

focusing simply on product or process innovation. Our results suggest that in future

research projects, it is important for researchers to distinguish the scope of the innovation.

Second, the results of this study suggest that factors favoring the frequency of incremental

and radical innovation include environmental, organizational, process, and managerial

factors, which are all interrelated and subject to change. These findings agree with a

suggestion by Ettlie et al. (1984) that the ‘‘strategy–structure causal sequence for radical

innovation is markedly different from the strategy–structure sequence for incremental

innovation’’ (p. 692). Our results suggest a hierarchy of innovations, and that factors in the


environment and in the organization appear to limit or favor strategists’ efforts to innovate.

Factors that favor the frequency of incremental innovation include environmental dynam-

ism, age and size of the firm, intrafirm structural linkages, and the age of the CEO. Factors

that favor the frequency of radical innovation include environmental dynamism, intrafirm

linkages, experimentation, and transitioning or sequencing from project or product to

another.

Third, this study represents a first empirical test of Brown and Eisenhardt’s (1997, 1998)

statement that firms that are multiple innovators exhibit underlying processes of improvisa-

tion, experimentation, and transitioning or sequencing across projects. Fourth, our results

complement complexity theory, which suggests that strategists are connected and inter-

dependent with the environment, and that organizations exhibit a continuous pattern of large

and small changes (Anderson, 1999; Lewin, 1999; Stacey, 1995).

Over a 3-year period, strategic managers reported incremental innovations most frequently

and radical innovations least frequently. Among the incremental innovations identified by the

aerospace executives we interviewed were efforts to identify the ‘‘best practices’’ throughout

their firm to improve efficiency (a procedural innovation). They also created new divisions

that allowed an ‘‘entrepreneurial spirit’’ to flourish (a structural innovation), and designed

parts digitally rather than on paper (a technological innovation). Further, they reported

offering satellite communications services to farmers and real estate agents, a market or

radical innovation. Among the incremental innovations identified by executives from the

electronics/telecommunications industry were the use of copper-circuitry for making chips (a

technological innovation), and allowing employees remote access to all their computing

applications so they could work at home either occasionally or routinely (a personnel

innovation). These executives also reported expanding into mobile commerce and devices

that combined voice and Internet access, a strategic innovation.

Our results are consistent with complexity theory in that the organizational processes and

structures conducive to innovation are embedded in environments characterized by uncer-

tainty and rapid and unpredictable market and technical changes. Our results show that

radical (in addition to incremental) innovation increased as perceived environmental

dynamism increased, a finding that is consistent with Tushman and Romanelli’s (1985)

suggestion that ‘‘effective organizations in environments with substantial technological and/

or legal/social uncertainty’’ tend to undertake reorientations or quantum (radical) changes (p.

207). Whether environmental or organizational variables most strongly affected innovation

remains open to question. Executives from all three industries commented that several trends

appeared in the external environment whose impacts were not immediately discernible as

enhancing or inhibiting, but changing over time, and often opening up new possibilities for

innovation.

Among the environmental trends described by executives in both the aerospace and

electronics/telecommunications industries were global economic fluctuations and uncertainty,

cyclical demand, the Asian crisis, increased partnering across industries, double-digit growth,

and the broadband revolution. More than 10 years ago, Ansoff (1988) predicted that those

organizational decision makers who failed to anticipate for discontinuities and trends in their

economic, competitive, technological, or sociopolitical environments were ‘‘very likely to be


left behind in the competitive race’’ (p. 12). One electronics executive commented on the

importance of recognizing opportunities for innovation early:

We are kind of like the old Indian chief listening for buffalo—one footstep you can’t hear

but if you put your ear to the ground you can hear that there is a herd coming toward you.

That is, something is coming toward you and it has mass to it. What are the drivers to these

technological changes? Who needs it? How many people are interested in it?

Our results suggest that firms with intrafirm structural linkages have an enhanced ability to

innovate, regardless of the type of innovation (incremental or radical). When there are

substantial interdependencies across parts of a firm, intrafirm linkages cut across projects and

product lines, providing a free-flowing exchange and cross-pollination of information. The

fact that some coordinating and integrating mechanisms are needed for innovation has long

been noted (Galbraith, 1973). Innovation depends on team rather than individual effort, and

the cross-flow of information among a variety of people working concurrently on different

aspects of a project is essential for developing an environment conducive to innovation,

whether incremental or radical (Scott & Bruce, 1994; Spender & Kessler, 1995).

Some scholars argue that older and larger firms experience rigidity and inertial pressures.

Our results suggest that older and larger firms are more likely to produce incremental

innovations. As organizations age, they often develop excess capacity; innovation is one way

to use that excess capacity (Oster, 1994).

Our findings suggest that the ability of managers to experiment and to move quickly and

smoothly from one project or product to another is an important factor in explaining radical

innovation. Experimentation is a mechanism that builds in flexibility (strategic responsive-

ness); it creates a momentum for change and improves the capacity of the firm to react

quickly to a changing environment while maintaining a focus on the present. The ability of

strategic managers to move from one project or product to another and to experiment may

play a smaller role in incremental innovation because it ‘‘introduces relatively minor changes

to the existing product, and exploits the potential of the established design’’ (Henderson &

Clark, 1990, p. 9).

We also found that as the age of the CEO decreased, incremental innovation increased

suggesting that younger CEOs were associated with higher incidents of incremental

innovation. Although we had hypothesized that older CEOs would be more likely to be

associated with incremental innovation, there are a couple of possible explanations for our

finding. First, younger managers in large firms may not feel that they have the ability to

influence or change the status quo. Therefore, they may be more comfortable with assuming a

less aggressive stance. Another explanation may be that younger managers are under more

pressure to avoid being seen as reckless or as ‘‘risk takers.’’ Therefore, initially at least,

younger managers may be content to make incremental changes. Future research should

concentrate on this area as previous literature (Bantel & Jackson, 1989; Wiersema & Bantel,

1992) suggests the opposite relationship.

Our findings are limited by the difficulties inherent in performing a cross-sectional study

and disentangling cause and effect. With such a variety of factors and relationships to be


considered, an assessment of the factors influencing innovation is particularly complex, and

defining causal relations among component factors is difficult. However, Stacey (1995)

argues that rather than ‘‘looking for causes and effects, it is necessary to look for patterns and

their systematic implications’’ (p. 493). Also, though some researchers question the validity

of studies using self-report data, Kahn and Manopichetwattana (1989) and Jennings and

Young (1990) reported a strong correlation between perceptual and objective measures of

innovation. Also, realizing the difficulties associated with common method bias, every

attempt was made to minimize the methodological difficulties of this bias by using both

quantitative and qualitative data from our sample organizations.

Lastly, we did not examine the relationship between innovation and firm performance,

despite its importance to both scholars and practitioners. Determining the antecedents of firm

performance continues to be an intractable but enduring problem to organizational researchers.

Retrospective studies that use self-report performance data are subject to criticism because top

managers can retrospectively distort accounts of performance to present themselves in a

favorable light (March & Sutton, 1997, p. 698). Because the majority of firms in our sample

are not publicly traded, reliable performance data are difficult or impossible to obtain. In

addition, it may not make sense to measure performance because of the lag time between the

development of an innovative product that causes large R&D expenditures and the income

earned from the sale of the product. Innovative products—those requiring substantial

investment—typically require more time before they earn back their original investment for

the firm. As one electronic executive commented, ‘‘often times research is not something that

will positively affect the next quarter or the next year.’’ In addition, the amount of time before

payback varies considerably among firms. For these reasons, our research remained focused

on the environmental and organizational processes that enable firms to undertake incremental

and radical innovations and did not measure performance of the firms.

Future research is needed to investigate other environmental and organizational determi-

nants of incremental and radical innovation, such as strategic alliances, external contracts, and

other types of interorganizational relationships that may accelerate radical innovation, for

example (Kessler & Chakrabarti, 1996). Future researchers should investigate the perform-

ance consequences of how often top managers roll out a new product or technology, how

quickly it is brought to market (time-to-market), and how quickly other firms are able to copy

it (speed of diffusion) (Oster, 1994). Research is needed to investigate whether radical as

opposed to incremental innovation is externally acquired versus internally developed

(Legnick-Hall, 1992). Last, future research is indicated to investigate whether, in accord

with prospect theory, strategic managers typically take greater risks and make larger, more

radical changes in response to threats than in response to opportunities (Dutton & Jackson,

1987). From prospect theory we can infer that top managers undertake risks and innovations

of greater magnitude when organizations face environments characterized by threats as

opposed to opportunities (Dutton & Jackson, 1987).

Our findings have implications for managers who seek to innovate in fast-paced and highly

competitive environments. Highly innovative firms, those generating new and enhanced

products, manufacturing processes, and services, require policymakers who can successfully

manage the innovation process (Oster, 1994; Shane & Venkataraman, 2000). Commenting on


the importance of innovation to the economic viability of his integrated circuits/program-

mable logic business, an electronics executive remarked:

If you don’t stretch and innovate and your competitor does, you will be out of business.

What worked as recently as two years age today will not be effective either because it is

too slow or more importantly too expensive. A year is a long time and if your judgement is

not going as to where things are going you will move from first to second to tenth in a

hurry.

‘‘Management cannot ensure innovation success but can influence its odds’’ (Van de Ven

et al., 1999, p. 11). The age and size of the firm, and the external environment, are generally

outside the scope of the manager’s duties. But the organizational policymakers can increase

innovation potential, whether incremental or radical, by fostering intrafirm linkages: cross-

project communications, the mixing of old and new team members, or other creative ideas.

Further, our findings suggest that in the face of substantial uncertainty (Eisenhardt & Brown,

1998; Eisenhardt & Tabrizi, 1995), strategic managers can foster the frequency of radical

innovation by encouraging experimentation and by ensuring smooth transitions from one

project to another. Strategic managers vary considerably in their speed and skill in doing

this.

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