Market Vision and Market Visioning Competence: Impact on

Early Performance for Radically New, High-Tech Products�

Susan E. Reid and Ulrike de Brentani

Having the ‘‘right’’ market vision (MV) in new product scenarios involving high degrees of uncertainty has been

shown to help firms achieve a significant competitive advantage, which can ultimately lead to superior financial

results. Despite today’s increased rate of radical innovation, and hence the importance of effective vision, relatively

little research has been undertaken to improve our understanding of this phenomenon. The exploratory and empirical

investigation undertaken herewith responds to this research gap by focusing on MV and its precursor, market

visioning competence (MVC), for radically new, high-tech products. MV is a clear and specific mental model/image

that organizational members have of a desired and important product-market for a new advanced technology, and

MVC is a set of individual and organizational capabilities that enable the linking of advanced technologies to a

future market opportunity. Based on samples of high-tech firms involved in early technology developments, the

measurement study indicates that five factors comprise MV (i.e., clarity, magnetism, specificity, form, and scope)

and that four factors underlie MVC (i.e., networking, idea driving, proactive market orientation, and market

learning tools). Structural equation modeling is used to demonstrate that MVC significantly and positively impacts

MV and that each of these constructs significantly and positively influences certain aspects of early performance

(EP) in new product development. This is the first empirical study to develop a comprehensive set of scales to

measure these constructs and then to combine them in a model by which to examine their interrelationships.

Introduction

Why is it that some firms seem to be in the

right place at the right time when the next

big technology comes down the pipe? Is it

just luck, or is there something firms can learn or ac-

tively do to improve their chances of success with

choosing the next ‘‘killer app’’? The objective of the

research presented in this paper is to determine

whether firms can be proactive in creating or select-

ing a market vision (MV) for a new advanced tech-

nology in a way that can lead to superior

performance. This question is particularly important

because involvement with radical innovation is high

risk but can also bring great rewards (Urban and

Hauser, 1993; Veryzer, 1998) both in terms of the

competitive advantage achieved and long-term profit-

ability (Kleinschmidt and Cooper, 1991). Further,

because the pace of technological advances can be ex-

pected to increase in frequency and radicalness

(Chandy and Tellis, 2000), firms must learn to de-

velop effective market vision if they are to cope in this

turbulent environment (O’Connor and Veryzer,

2001).

A key challenge at the ‘‘fuzzy’’ front end of devel-

opment of a radical new technology is the associated

high level of risk. This is due to the high degree of

uncertainty about the direction of the technology and

its options for potential market application (Burgel-

man and Sayles, 1986; Ettlie, 2000; Reid and de Brent-

ani, 2004) as well as the potential loss associated with

cannibalizing previous investments (Tellis, 2006). In

this research it is suggested that to reduce risk and

improve chances of success with radical innovation,

companies must create an effective market vision

(MV)—that is, a clear and specific mental model or

image that organizational members have of a desired

and important product-market for a new advanced tech-

nology. In the conceptual model, an a priori assump-

tion is made that MV results from market visioning

competence (MVC), or the ability of individuals in or-

ganizations to link advanced technologies to market

opportunities of the future (O’Connor and Veryzer,

2001). This assumption is based on the perspective

forwarded in the resource-based view (RBV) of the

firm, as proposed in the dynamic capabilities litera-

�The authors would like to thank TechnoCap (Richard Prytula),Concordia University, and the Product Development andManagementAssociation for their financial assistance and support of this project.

Address correspondence to: Dr. Susan E. Reid, Department ofMarketing, Williams School of Business, Bishop’s University, Len-noxville, Canada J1M 1Z7. Tel.: (819) 822-9600. Fax: (819) 822-9760.E-mail: sreid@ubishops.ca.

J PROD INNOV MANAG 2010;27:500–518r 2010 Product Development & Management Association

ture, which states that learning results in firm-specific

competitive advantage, which leads to superior per-

formance. As such, this paper provides a discussion of

how the concepts of MV and MVC are interrelated

and how the constructs in question are operational-

ized. Further, through structural equation modeling,

it is demonstrated that MV and MVC lead to impor-

tant firm advantages with the ability to impact early

performance (EP) in the development of radically new

product innovations.

The Model

This research focuses on how organizations develop

firm-specific new product exploration capabilities and

how they share and integrate new information to re-

spond to shifts or discontinuities in the environment.

Cast in RBV, exploratory learning processes—that is,

knowledge creation and building through sharing

(Henderson, 1994; Iansiti and Clark, 1994)—are re-

source-based dynamic capabilities that allow firms

operating in dynamic environments such as those pro-

vided by radical innovation to build further capabil-

ities and resources (Teece, Pisano, and Shuen, 1997).

These, in turn, provide defensible competitive advan-

tages that result in ‘‘economic rents’’ or profits

(Grant, 1991; Wernerfelt, 1984). The capabilities of

interest are those that enable firms, through various

exploratory learning processes (as reflected in MVC),

to build an understanding of a viable and potentially

successful future product-market option (as reflected

in MV). To be successful, MVC requires a subtle bal-

ance between the dynamic learning capabilities of in-

dividuals (e.g., networking, idea driving) and of the

organization in which they participate (e.g., proactive

market orientation, market learning tools). The mar-

ket visioning competence, which is reflected in these

capabilities, allows organizational members to use

ideas stemming from early technology development

in creating an effective mental image, or MV, toward

a radically new, high-tech product for future markets.

Research in the areas of organizational and project

vision, described later in this paper, provides clues

about the factors comprising MV. Most of these are

of an extrinsic nature in that they describe elements

that are external to the vision itself. When considering

the essence of vision, however, it is the intrinsic com-

ponents that are of interest. These denote what the

vision or image looks like and what it represents to

organizational members. Figure 1 illustrates the pro-

posed basic relationships.

Theoretical Foundations

To date, MV and MVC have received only limited

attention from researchers. Despite their importance

in enhancing the potential for success of radically new

products involving advanced technologies, a review of

the literature indicates that only a handful of quali-

tative studies have dealt with these concepts, thus

offering limited theoretical evidence as to their defi-

nition and operationalization. As stated by Brown

and Eisenhardt (1995, p. 370) in their meta-review of

NPD, ‘‘. . . our understanding of exactly what vision is

. . . is very weak,’’ and, according to Crawford and Di

Benedetto (2000), surprisingly little research has been

conducted to reduce this gap between the need to

know and the dearth of knowledge developed to date.

Indeed, in the new product development (NPD) liter-

ature, what are shown by the research presented in

this paper to be two separate constructs are often used

interchangeably. Hence, a broad literature review cov-

ering writings in economics, organization/manage-

ment, and marketing was undertaken to provide a

basis for defining MV and MVC and in articulating

the model for testing their relationship to each other

and to EP.

BIOGRAPHICAL SKETCHES

Dr. Susan E. Reid is associate professor of marketing in the Wil-

liams School of Business at Bishop’s University and has research

interests including front-end strategy for new products and services,

new product development with emergent technologies, the modeling

of individual choice, decision making of gatekeepers and boundary

spanners involved in high-tech businesses, and marketing history.

She has published in refereed journals including Journal of Product

Innovation Management, World Development, and International

Journal of Technology Marketing. She has more than 15 years of

experience working and consulting for the biopharmaceutical and

nanotechnology industries.

Dr. Ulrike de Brentani is professor of marketing at the JohnMolson

School of Business at Concordia University. She holds an M.B.A.

and a Ph.D. in business administration and has research interests in

the areas of new product/service development, new product evalu-

ation, and new product/service marketing in the business-to-busi-

ness sector. She has received several Awards of Excellence for her

research, which is published in refereed journals including Journal of

Product Innovation Management, International Journal of Research

in Marketing, Journal of the Academy of Marketing Science, Indus-

trial Marketing Management, and European Journal of Marketing.

Her current research deals with new product and service develop-

ment for global markets and market vision for new-to-the-world

products.

MARKET VISION AND MARKET VISIONING COMPETENCE J PROD INNOV MANAG2010;27:500–518

501

Market Vision and the Radical Innovation Context

The NPD scenario of radical innovation is proposed to

be the condition under which MVC andMV have their

greatest impact on firm-level performance, particularly

during the very early stages, or the fuzzy front end, of

the NPD effort. This can be explained by the fact that

early on in a given technology life cycle, when radical

innovation is most prevalent (Sahal, 1981), different

firms’ visions of possible market applications tend to be

quite divergent. In effect, divergent thought patterns

occur when search initiates in different directions, and

this leads to wide-ranging ideas (Baer, 1993). This pro-

cess likely occurs more often early in the technology life

cycle where a large number of potential applications

still remain untapped, where there are few competitors

with the same vision, and therefore where company

influence on the marketplace has the greatest potential.

Due to this, there is a greater likelihood that some of

the ideas and opportunities will be unusual to the mar-

ket and therefore in the range considered as ‘‘radical’’

(i.e., 5 to 10 times improvement in benefits or 30% cost

reduction compared with the previous generation of

the technology; see O’Connor and Veryzer, 2001). Over

time, as firms gain a similar understanding of evolving

markets and product applications, convergence of

MVs occurs, often resulting in a ‘‘dominant design’’

(Abernathy and Utterback, 1978). This, in turn, leads

to higher awareness of what is now a relatively nar-

rowly defined view of a technology, and, as competi-

tion for these specific markets and applications

increases, the nature of NPD moves to the ‘‘incremen-

tal’’ end of the innovation spectrum.

Given that MV is likely to have a greater impact on

firm performance early in a technology life cycle, the

present research focuses on the radical innovation con-

text. The challenge, however, is that it is precisely in this

scenario—the front end of radical innovation—that

establishing a MV is the most difficult and where com-

panies have the least capability in this regard (Gupta

and Wilemon, 1996). Therefore, enhancing our knowl-

edge of MV and how it evolves through MVC strength-

ens our understanding of the fuzzy front end of radical

innovations and can help companies cope better with

the difficult decisions that characterize this scenario.

Types and Levels of Vision

When a technological discontinuity occurs, the initial

wave of radical innovation typically diffuses through

the firm from the technology level to the product-

market level and then to the project and organiza-

tional levels. Radical technologies often meet with

more resistance from firms than incremental ones be-

cause of the uncertainties they entail and also because

of the difficulties in visualizing how such new tech-

nologies might be incorporated into products and

used in the marketplace (Rogers, 1983). Figure 2

presents a schematic representation of the process

that for the firm determines which products will be

pursued for which markets and based on which tech-

nologies. The capability to visualize (MVC) how to

move from the technology to the product-market level

(MV) tends to be a reflection of individuals working

within the firm. Some individuals can overcome resis-

tance to uncertainty through their abilities to network

externally and to rally support for new ideas within

the firm. This operates in combination with firm-level

proactive market learning capabilities and market

learning tools. MVC is considered to be a composite

of these underlying capabilities. As such, the resultant

MV (product-market level) is proposed to serve as a

facilitator of the organizational adoption process

whereby a project-level formalization ultimately re-

sults in an updated organizational-level vision.

A Generic View of Vision

Idea exploration and evaluation in the radical inno-

vation context have been referred to as emergent, or

Market Visioning Competence Market Vision

IndividualDimensions

ExtrinsicDimensions

IntrinsicDimensions

OrganizationalDimensions

Performance

Figure 1: Key Relationships Investigated in the Literature Review

502 J PROD INNOV MANAG2010;27:500–518

S. E. REID AND U. DE BRENTANI

‘‘bottom-up,’’ processes (Burgelman and Sayles, 1986;

Reid and de Brentani, 2004). This is because the pro-

cess of idea evaluation typically starts at the individ-

ual technical level, then through championing moves

upward to small groups or teams (Crossan, Lane, and

White, 1999) operating at the product-market level,

then if approved moves to the project level (Lynn and

Akgun, 2001) for implementation, and ultimately

ends up at the corporate level for updating the orga-

nizational vision (Collins and Porras, 1991). As shown

in Figure 2, visions start with the recognition of a

technological discontinuity and become more elabo-

rated, supported, and shared as more people become

involved. Although a vision evolves as it emerges in

the organization, some aspects of vision are generic

and may be shared across these levels.

Vision implies knowledge, insight, and foresight as

well as an image of a desired future state (Jolly, 1997;

Rice et al., 1998). Stokes (1991, p. 118) explained that a

vision is a description of what a desired state should

look like or a description of ‘‘conditions as we would

like them to be.’’ This generic description of vision

suggests three aspects of vision that may be shared

across market, project, and organizational levels. First,

vision is related to the goal but not to the action steps a

strategy must outline to proceed to a goal. Crawford

and Di Benedetto (2000), who use Crawford’s (1980)

Product Innovation Charter (PIC) to describe vision in

terms of team directions, goals, and objectives, support

this understanding of generic vision. A second shared

component is that the goals are represented through an

image. The descriptors of the vision itself—conditions

as we would like them (i.e., desirability), how grandiose

it is, and the specific conditions of the image—are in-

trinsic dimensions. They are intrinsic because they can-

not be completely divorced from the mental image, or

vision, itself. In some way they are related specifically

to what the vision is or looks like. Third, if vision is

‘‘foresight,’’ then clearly there is uncertainty regarding

what is being described. Uncertainty about an image—

whether related to clarity or ability to gain support—

suggests that an image itself is separate from thinking

directed toward that image. The image is seen differ-

ently over time. As such, these dimensions—clarity and

support—are separate from the image itself and are

considered extrinsic dimensions.

According to Stokes (1991), a vision need not be

grandiose. This view, however, has been challenged,

particularly by those who describe ‘‘vision’’ from the

perspectives of the sociology and material culture lit-

eratures. For example, in his discussion of the building

of the Brooklyn Bridge, Trachtenberg (1964, p. 3) de-

scribed the remarks of General J. Johnson: ‘‘the mag-

nitude of the undertaking gave it a visionary aspect.’’

Further, Stokes’s use of the word grandiose is broader

in its meaning than just magnitude; it implies a range of

possibilities. That is, grandiose suggests both magni-

tude and range of possibilities, which together imply

scope. This disparity points to an important question:

is scope a key component of vision? Because past re-

search has not investigated scope as a component of

vision, it is included in the present study.

Organizational and Project Vision

Organizational Vision. Song and Montoya-Weiss

(1998) found that strategic planning, which they relate

to organizational vision or how the organization

envisions its future, is positively associated with new

product success in the radical case. Given the bottom-

up directionality suggested for radical innovations,

Focus Level Vision Level Linking Mechanism

Market Vision (MV)

Organizational Level Organizational Vision (prior)

Technology Level Technology Vision

Market Visioning Competence (MVC)

Technology Discontinuity

Project Formalization

Formal NPD Process

Product-Market Level

Project Level Project Vision

Organizational Level Updated Organizational Vision

Figure 2: Diffusion of a Radical Innovation through the firm

MARKET VISION AND MARKET VISIONING COMPETENCE J PROD INNOV MANAG2010;27:500–518

503

for industry incumbents (i.e., existing firms that

already have an extant organizational vision), the

elements of strategic planning are likely related to

putting the capabilities and processes in place that will

enable effective visioning and vision to unfold

throughout the spectrum outlined in Figure 2. As

Collins and Porras (1991) suggested, organizational

vision is not about predicting but rather about con-

structing the future.

Collins and Porras (1991) contribute to our under-

standing of the intrinsic components of organizational

vision, which they outlined as (1) a guiding philoso-

phy (made up of core beliefs/values and purpose) and

(2) a tangible image (made up of the mission or goal of

the organization and a vivid description of that goal).

They provided an excellent example of the vivid

description of Henry Ford’s mission (p. 47):

I will build a motor car for the great multitude. . . . It

will be so low in price that no man making a good salary

will be unable to own one—and enjoy with his family

the blessing of hours of pleasure in God’s great open

spaces. . . . When I’m through everybody will be able to

afford one, and everyone will have one. The horse will

have disappeared from our highways, the automobile

will be taken for granted . . . (and we will) give a large

number of men employment at good wages.

Collins and Porras also indirectly support the idea

that scope is an organizational vision component. For

example, they use the term ‘‘big, hairy, audacious

goal’’ (p. 42) when describing John F. Kennedy’s 1961

vision of NASA landing a man on the moon and re-

turning him to Earth before the end of the decade.

The extrinsic side of vision is highlighted in Hamel

and Prahalad’s (1994) case studies of several well-

known companies in which they identified three key

components of an effective organizational vision: (1)

it must be clear; (2) supported by others in the orga-

nization; and (3) stable. These studies clearly point to

the importance of clarity, support, and stability when

it comes to articulating the characteristics of an

organizational vision. The concepts themselves, how-

ever, are not elaborated by the authors through

operationalization.

Project Vision. In the case of radical innovations,

project vision occurs only after an MV has been for-

malized but prior to the potential updating of an on-

going organizational vision. After the MV of a

particular new product-market scenario is elaborated,

formalized, and accepted for further development, the

project vision tends to be quite specific. Indeed, pro-

ject vision serves as a connection point between vision

and the action steps of project strategy by meshing an

organization’s competencies and strategies with the

needs of the market to create an effective product

concept (Brown and Eisenhardt, 1995). Once a prod-

uct concept is clear enough to include more detailed

aspects of project strategy (e.g., the answers to how,

when, and where questions), then it has moved

through the spectrum shown in Figure 2 from being

a market vision to a project vision. Thus, project vi-

sion provides the necessary action steps to form the

link between the specific product and the market. This

is different fromMV, which enables the image of what

the product-market interface looks like but which

does not involve action steps (how, when, where). It

is this inclusion of strategic action steps that most

distinguishes project vision from other types of vision.

For their project-level research, Lynn and Akgun

(2001) developed scales and definitions for Hamel and

Prahalad’s (1994) three extrinsic components—

clarity, support, and stability—and tested these for

impact on performance of radical innovations. Their

findings indicate that project vision clarity is signifi-

cantly associated with new product success while pro-

ject vision stability is not. According to Lynn and

Akgun, vision stability at the project level may not be

critical because there are many paths for achieving the

designated ends, and these may be unknown or un-

knowable at the outset of projects where conditions

can be quite uncertain. Given this and the fact that

MV occurs earlier than project vision, it is unlikely

that stability is a critical phenomenon at this even

more uncertain stage of the vision development pro-

cess. Thus, stability was not considered to be a rele-

vant dimension in the current study. In the case of

project vision support, the link to new product success

has been found to depend on where the support comes

from (i.e., team members, team managers, or top

management) and the findings on this issue are equiv-

ocal (Lynn and Akgun, 2001; O’Connor and Veryzer,

2001; Rice et al., 1998). This may be the result of

variance in testing methodologies, pointing to the

need to further investigate the support dimension.

Market Vision and Market Visioning Competence

To develop an effective market vision, organizations

require market visioning competence. According to

504 J PROD INNOV MANAG2010;27:500–518

S. E. REID AND U. DE BRENTANI

case studies by O’Connor and Veryzer (2001), MVC

comprises a set of capabilities that enable the linking

of advanced technologies to a future market oppor-

tunity. This results in a shared mental model of future

potential product-markets (the MV itself). In the case

of conventional, less discontinuous NPD, the capa-

bilities needed to bridge technological competencies

and market need are well understood (Cooper, 1984;

Cooper and Kleinschmidt, 1995; Crawford, 1997).

But key questions remain about how to choose spe-

cific product-market applications for a radically new

technology. The process of visioning helps to answer

the question regarding which market applications to

pursue, and it does this by focusing on individual

capabilities (i.e., networking, idea driving) and on or-

ganizational capabilities (i.e., proactive market orien-

tation, market learning tools) whose combined impact

is an effective MV.

Market Vision: Intrinsic Components

The intrinsic components of generic vision (form,

scope, and magnetism) were shown by Collins and

Porras (1991) to be related to goals through an image

or description of the vision. From the organizational

and project vision literatures, the description of a

vision may potentially include the specific conditions

of the image, how grandiose it is, and how desired it

is. These descriptors suggest that form (specific con-

ditions of the image), scope (target market and target

magnitude), and magnetism (how compelling or

desirable the vision is) are intrinsic components of

MV that are shared across all visions.

Market Vision Form. Vision form refers to the spe-

cific condition of the image and is most effective when

it is tangible. Tangibility is created when the mission

or goal provides a clear focus or a ‘‘vivid description’’

that helps to make it more alive (Collins and Porras,

1991). Applied to MV, such a description should in-

corporate the market goal. Because ‘‘market’’ is de-

fined as ‘‘all actual and potential buyers of a product

or service’’ (Kotler and Keller, 2005, p. 10), a descrip-

tion of the potential or desired market (the MV)

should therefore articulate as part of the image an

important market focal point, or goal. MV form can

also be viewed as including the product design, prod-

uct concept, and product in use. Product design in-

volves the idea of what the product components will

be and how they will be integrated (Ulrich and Ep-

pinger, 1995); these may be espoused through a pro-

totype (Lynn, 1993; Shanklin and Ryans, 1984).

Product concept involves the relationship between an-

ticipated product features and customer benefits. An-

ticipating product fit with market needs has been

shown to be critical to success for any product (Coo-

per, 1979; Crawford, 1980; de Brentani, 1989). Prod-

uct in use is the idea of how the product and users will

interact and what the system of interaction will look

like. Understanding how a product fits into an overall

system of use with other products and into the use

environment has been shown to be critical to success

(Rogers, 1983; Sengupta, 1998; Tripsas, 2000).

Market Vision Scope. MV scope incorporates both

target market and target magnitude. Target market

refers to the specific market goal for the product to be

developed (Cooper, 1993; Crawford, 1980). The target

business areas described in Crawford’s (1980) PIC

(product type, end-user activity, technology, interme-

diate- or end-user group) offer a useful classification

of target markets. Target magnitude involves the

scope or breadth of the envisioned market. This is

potentially an important component of MV because

markets that have good potential size typically offer

better outcomes (Cooper, 1981; Cooper and Klein-

schmidt, 1995). Because, similar to MV form, MV

scope provides for goal focus, it can be expected to

enhance innovation success.

Market Vision Magnetism. MV magnetism refers

to how compelling, important, or desirable the vision

is in the eyes of members of the organization (McA-

lister, 1998). Magnetism operates in a manner similar

to how people are drawn to a charismatic leader. In

the case of MV, it entails the way people are drawn to

an idea pertaining to a product-market. The concepts

of importance and desirability are related to the no-

tion of guiding philosophy because people are attracted

to ideas that relate to their own core beliefs, values,

and purpose (Collins and Porras, 1991). Thus, a

vision with a magnetic quality infuses value into the

organization (Selznick, 1957); this is because for or-

ganizations to move in a coordinated direction toward

their vision of the product-market interface, they must

inherently believe in and identify with that

vision. Therefore, the power to motivate individuals

to strive for and attain a given goal has inherent

‘‘value’’ both for the organization and the market be-

cause ‘‘organizations become infused with value as

MARKET VISION AND MARKET VISIONING COMPETENCE J PROD INNOV MANAG2010;27:500–518

505

they come to symbolize the community’s aspirations’’

(ibid., p. 19).

Market Vision: Extrinsic Components

As discussed already, organizational visions typically

have high levels of clarity, support, and stability,

whereas project visions tend to have clarity and sup-

port from some levels within the organization but

not stability. Because MV occurs even earlier in time

these extrinsic components may be part of MV but are

not likely to be as strong as with project vision. For

example, in 1937, when Bill Hewlett and Dave Pack-

ard formed HP, they did not have a clear market vi-

sion. Indeed, for advanced technologies, vision is

likely to be the result of an emergent strategic pro-

cess where clarity, support, and stability are extrinsic

components of generic vision, which strengthen over

time as the vision evolves.

Market Vision Clarity. Lynn and Akgun (2001, p.

375) in their study of project vision, defined vision

clarity as ‘‘having a well-articulated, easy-to-under-

stand target—a very specific goal that provides direc-

tion to others in the organization.’’ Ackoff (1970) and

Drucker (1954) also emphasized the need for creative

objectives to be made operationally meaningful and

tangible. Tangibilization of image ultimately aids the

extrinsic vision components of clarity and support.

Thus, to ensure for clarity in MV, the image of a

product-market interface needs to be tangibilized so

that mental models are vivid, particularly with regard

to the market form and scope.

Market Vision Support. Vision support in the case

of project vision ‘‘implies securing the commitment

from people throughout an organization for what the

company is trying to do. It indicates that people are

willing to pitch in to help accomplish the vision—to

do whatever it takes to achieve their goal’’ (Lynn and

Akgun, 2001, p. 375; see also Rice et al., 1998). To

secure support, mental models must be shared be-

tween individuals and the organizational system

(Stacey, 2001), which could be a small group of indi-

viduals, a project team, or the entire corporation.

Given the importance of sharing when it comes to

moving a market vision forward, despite equivocal

findings in the project vision literature, it was decided

that vision support could potentially be an extrinsic

component of market vision.

Market Visioning Competence: Individual-LevelDimensions

MVC Networking. External webs of relationships

developed by individuals of the firm, or networking, is

a key element in creating effective visioning competence

(O’Connor and Veryzer, 2001; Lynn, 1993). From the

firm’s perspective, individuals involved in such net-

works are referred to as ‘‘boundary spanners,’’ or per-

sons who operate at the periphery of a permeable

organization, performing organizationally relevant

tasks, and relating the organization with

elements in the external environment (Keller and Hol-

land, 1975; Leifer and Delbecq, 1978; Reid and de

Brentani, 2004). In the context of MVC, it is primarily

the networks of boundary spanners that are relevant.

Rather than focusing on current customers and mar-

kets, these networks help to broaden thinking by giving

individuals the opportunity to draw on new and differ-

ent areas of knowledge and product applications. This

process is called vision migration and also divergent vi-

sioning (O’Connor and Veryzer; Lynn). Due to the

large number and variety of relationships that can be

cultivated, the key to the study of MVC is not ‘‘who’’ is

involved but ‘‘what’’ the structural features of the ex-

ternal network are and how quickly structural advan-

tages can be capitalized upon (Burt, 1992; Granovetter,

1983). The aspects of breadth (or size), variety, and

centrality (Berkowitz, 1982; Granovetter; Scott, 1990)

are the structural characteristics of a network, which

exist regardless of who is part of the network and which

have the potential to provide competitive advantage.

Idea Driving. The literature supports the notion of

the champions as the persons responsible for pushing

ideas forward from the individual level up through the

organization for radical innovations (Howell and Hig-

gins, 1990; Schon, 1967). A champion is generally seen

as an individual who informally emerges in an orga-

nization and who makes a decisive contribution to an

innovation by actively and enthusiastically promoting

its progress through critical stages, particularly during

the more uncertain phase early on in the NPD process

(Achilladelis, Jervis, and Robertson, 1971; Burgelman

and Sayles, 1986). As such, ‘‘champions,’’ or ‘‘idea

drivers,’’ provide important individual-level capabili-

ties that are part of MVC (O’Connor and Veryzer,

2001). It is important for idea champions to know

how to gain—even accelerate—the commitment and

involvement of management for a proposed idea. This

is critical because innovation at its core is a political

506 J PROD INNOV MANAG2010;27:500–518

S. E. REID AND U. DE BRENTANI

and social process of change (Frost and Egri, 1991).

This makes ‘‘politicking’’ necessary during visioning

and likely directly related to driving a MV forward.

Market Visioning Competence: Organizational-Level Dimensions

Proactive Market Orientation. Narver and Slater

(1990) stated that market orientation is measured in

terms of three behavioral components: (1) customer

orientation; (2) competitor orientation; and (3) inter-

functional coordination. Because these measures are

focused on customers’ expressed needs, they are con-

sidered to represent ‘‘reactive market orientation’’ be-

cause the producer responds to the requests of

customers. Due to the criticism that there are penal-

ties to firms that listen too closely to or that focus only

on customers’ ‘‘expressed needs’’ (Christensen, 1997),

Narver, Slater, and MacLachlan (2000) developed a

new construct, labeled ‘‘proactive market orienta-

tion,’’ which focuses on customer latent needs (either

solutions to unarticulated customer needs or discov-

ering new needs) and which operates as the first or-

ganizational dimension of MVC.

Market Learning Tools. A second organizational

dimension of MVC is the market learning tools avail-

able to firms involved in the task of probing and

learning about new markets in the case of radical in-

novation. In many cases, deep interaction with cus-

tomers is simply not possible or not particularly

productive during the fuzzy front end with radical in-

novations (de Brentani, 2001; Song and Montoya-

Weiss, 1998). Here, other techniques for market learn-

ing need to be employed. Tools shown to be most

successful with radical innovation are those that play

on combinations and scenarios for the future: for ex-

ample, scenario analysis and planning (Schoemaker,

1995), technology opportunity analysis (Porter, 1994),

roadmapping (Kostoff and Schaller, 2000), and back-

casting (Noori et al., 1999). Further, market learning

tools have been shown to be most effective when used

in combination (Meade and Islam, 1998). Other tech-

niques developed for market learning under radical

innovation scenarios involve learning by using (Ro-

senberg, 1982). Several researchers (e.g., Hamel and

Prahalad, 1994; Leifer et al., 2000) suggest that the

imagination underlying all successful technology-

based innovations—that is, the ‘‘techno-market in-

sight’’—comes from how a problem is approached

technically and from an ability to identify compelling

benefits of that technology and to characterize these in

terms of a market that may not yet exist. Jolly (1997)

called this ‘‘marketing flair’’; Hamel and Prahalad

called it ‘‘visioning’’ the future market. Through play-

ing with a technology, market insight may come

through the continuous interaction between the

user/developer and the technology. For example,

Lynn (1993) suggestsed that use of ‘‘patsy markets’’

(p. 278) is a good ‘‘learning by using method’’ (p. 272)

for designing and testing different network webs and

potential applications.

Market Vision, Market Visioning Competence, andEarly Performance

To develop insights about the likelihood of new product

success engendered by MV and MVC at the fuzzy front

end of radical new product development, this research

requires an early performance metric. Typically, project-

level measures of success include market size and share,

revenue, revenue growth, profitability, return on invest-

ment (ROI), break-even time, speed to market, devel-

opment cost, and time to launch (Griffin and Page,

1996). But, because MV and MVC deal with the early,

pre-project stages of radical innovation involving ad-

vanced technology, most of these standard measures of

project-level performance are inappropriate. Not only

are most of them temporally far removed from the fuzzy

front end of NPD, but many companies taking part in

this research had yet to launch a product, making the

traditional performance measures nonrelevant.

In light of these concerns, von Hippel’s (1978)

‘‘lead user’’ concept and findings from Griffin and

Page’s (1996) project-level success measurement study

were used for developing one of two EP metrics—

specifically, early success with customers (ESC)—for

measuring in a meaningful way outcomes of MV in

the case of radical innovation. Griffin and Page found

that such an outcome (measured by both satisfaction

and acceptance of a new product idea) is particularly

important for measuring success with ‘‘new-to-the-

world’’ products (i.e., if customers do not accept a

product in the first place, no sales will result). A sec-

ond EP measure used in this study is the ability to at-

tract capital (AAC). This is an early prelaunch

measure of success with a new technology that comes

from the literature dealing with the importance of

venture capital (Zider, 1998), particularly for entre-

preneurial start-ups (Timmons and Spinelli, 2004).

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507

Hypotheses

Firms achieve success in the marketplace with radical

new technologies by doing two things well. First, they

choose a specific market application with which they

are ahead of competitors and can best leverage their

technical and marketing capabilities and which ulti-

mately is profitable. Second, they engineer the poten-

tial for an effective interface between the new product

(within which resides the new technology) and the

customer. As such, the delivery of products to the

marketplace results in variance (i.e., through a variety

of market applications and product-market interfaces

enacted by all firms involved with the generic tech-

nology) and in a resultant selection in the environ-

ment such that some firms are successful and some are

not (Nelson and Winter, 1982). It is important, there-

fore, for firms to focus on the capabilities that give

them the best possible chance to select the best mar-

kets and to envision product-market interfaces that

will result in success with radical innovation (Hunt

and Morgan, 1996). Appropriate and faster-than-

competition market application selection (MV) can

be strongly impacted by an effective MVC. Based on a

co-evolutionary approach—that is, both firms and

environments impact future successes (Baum and

Singh, 1994)—MVC involves learning quickly from

the environment while at the same time impacting the

environment by initiating disruptive variance through

effective market selection and product-market inter-

faces (MV) that are considered successful by custom-

ers (ESC) and investors (AAC). These relationships

are summarized in Figure 3.

MV facilitates the firm’s ability to respond to dis-

ruption in its environment as it provides a rallying

point, or end-state, around which people can begin to

build an understanding of a potential product-market

interface for a new technology. As described under

Theoretical Foundations, various perspectives have

been used to build a conceptual framework for un-

derstanding what MV is potentially composed of: the

intrinsic factors that capture the essence of the vision,

including MV form, MV scope, and MV magnetism;

and the extrinsic factors that are separate from the

image itself, including MV clarity and MV support.

One of the main objectives of this research is to test

the validity and reliability of this second-order factor

structure (see Figure 3).

H1: MV is a multidimensional, second-order, construct

reflected by five dimensions: MV form, MV scope, MV

magnetism, MV clarity, and MV support.

MVC entails the process and ability to undertake

rapid learning from the environment (Baum and

Singh, 1994). Such learning involves technical learn-

ing and market learning, which are facilitated through

networking by individual boundary spanners within

the firm. In the case of market learning, using a va-

riety of organization-based forecasting techniques,

maintaining strategic flexibility with respect to new

markets (i.e., market learning tools), and proactively

Legend:

AAC = Ability to Attract Capital ESC = Early Success with Customers MVC = Market Visioning Competence MV = Market Vision NW = Networking CL = Market Vision ClarityID = Idea Driving Sp = Market Vision SpecificityMO = Proactive Market Orientation MG = Market Vision Magnetism ML = Market Learning Tools FO = MarketVisionForm

Sc = MarketVisionScopeHypothesized significant relationship Hypothesized non-significant relationship

MV

AAC

(H5b) (H4b)

MVC(H3)

NW ID MO ML CL Sp MG FO Sc

(H2) (H1)

ESC

(H5a) (H4a)

Figure 3: Conceptual Model of Impact of Market Visioning Competence and Market Vision on Early Performance

508 J PROD INNOV MANAG2010;27:500–518

S. E. REID AND U. DE BRENTANI

dealing with potential markets (i.e., proactive market

orientation) are primary facilitators. Further, driving

forward new ideas within the organization so that

they ultimately lead to products in the marketplace

(i.e., idea driving) impacts learning in the environ-

ment. Thus, MVC is hypothesized to be reflected in

the nurturing and development of these capabilities.

H2: MVC is a multidimensional, second-order, con-

struct reflected by four dimensions: networking, idea

driving, proactive market orientation, and market

learning tools.

The elements of MVC can be used by firms to create a

shared focus for the future. This is because firms that

are competent with market visioning are good at the

exploratory learning process and are therefore able to

move more quickly to shared mental models of future

markets (MV). Therefore,

H3: MVC has a positive and significant impact on MV.

Having a vision of the point of interaction between

potential customer and potential product—that is, the

MV FORM—enables the firm to develop new prod-

ucts that are likely to meet customer needs and wants,

especially those of lead users (von Hippel, 1978, 1986).

Such users are positioned to benefit significantly by

obtaining a solution to what are seen to be ahead-of-

the-trend needs. At the same time, the magnetism el-

ement of MV helps to ensure this because individuals

in the firm are more likely to be attracted to a goal

that has a good possibility of impacting the market, as

evidenced by MV scope. Much of the research in

NPD supports this idea that pursuing large and im-

portant target markets plays out in terms of success

(e.g., Cooper, 1979, 1984; Cooper and Kleinschmidt,

1995; de Brentani, 1989). While some researchers sug-

gest that large markets do not exist for discontinuous

or radically new products (e.g., Christensen, 1997),

this is likely to be only a short-term phenomenon. It is

the ability to forecast the long-term potential for a

market in terms of size and importance that is impor-

tant, particularly in terms of firing up employees’

imaginations. In addition, the clarity and support el-

ements of MV should play an important role in mov-

ing the firm more quickly to a shared vision of the

future. In sum, an effective MV allows individuals in

the company to focus on delivering unique benefits to

the customer. If individuals in firms are focused on

delivering this MV, it has a high likelihood of playing

out in terms of early success with customers (ESC).

H4a: MV has a significant and positive impact on ESC.

In contrast to the hypothesized positive link to ESC,

MV is not believed to impact the firm’s AAC. This is

because investors tend to focus on management’s ability

to take advantage of technology and markets rather

than on the markets themselves (see following discus-

sion). Therefore, the elements of MVC, rather than

MV, are seen as tied to the ability to secure capital. By

the time MV is crystallized, capital may already be at-

tracted to the initial technology and broader range of

possibilities, prior to the formation of a specific MV.

H4b: MV has no significant impact on AAC.

The primary impact of MVC, in terms of EP, can be

expected to be related to attracting sources of financing

for the innovation. More specifically, the capabilities

comprising MVC—that is, networking, idea driving,

market learning tools, and proactive market orienta-

tion—are all considered important for gaining the at-

tention of financiers. Idea driving, for example, helps

draw attention to opportunities internally (Howell and

Higgins, 1990; Howell and Shea, 2001), and this may

translate into gaining external acceptance with finan-

ciers due to the larger number of people who are likely

to become involved with the external networking pro-

cess to bring potential financiers (e.g., venture capital-

ists or investment bankers) to the table (Zider, 1998).

Proactive market orientation within the firm, together

with external networking, has the potential to draw fi-

nancial resources to the firm through the collection of

new and interesting market information (Narver et al.,

2000). Lastly, market learning tools, because they help

to identify several potential markets for a given tech-

nology (Porter, 1994; Schoemaker, 1995), can excite in-

vestors as they may see both short- and long-term

market opportunities for the technology. As such, a

strong MVC ensures that a company is able to secure

the necessary capital to continue with the venture.

H5a: MVC has a significant and positive impact on

AAC.

Without a specific product-market focus (MV), the

capabilities comprising MVC are not directed in a

way that enables success with customers. Thus, clearly

it is the potential of a new technology to solve a cus-

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509

tomer problem (as described by MV) that plays the

primary role in attracting markets. Therefore, MVC

does not impact ESC unless mediated by MV.

H5b: MVC has no direct, significant impact on ESC.

The Research

Item Generation

The first research phase was of an exploratory nature

whereby the domains for MV, MVC, and EP were

defined on the basis of constructs and definitions

available in the literature. To provide content valid-

ity, these were refined with input from 20 in-depth

interviews with high-tech industry experts (Bearden,

Hardesty, and Rose, 2001; Richins and Dawson,

1992). This item generation process resulted in 40

items to measure MV, 61 items to measure MVC, and

6 items to measure EP. Certain measurement scales

existed previously and were adopted for use in this

context, including strength of championing (Howell

and Shea, 2001), proactive market orientation (Nar-

ver et al., 2000), vision clarity, and vision support

(Lynn and Akgun, 2001). Four manipulation check

questions were used to verify that the innovations

were radical, according to definitions provided by

O’Connor and Veryzer (2001) and by Garcia and

Calantone (2002). Additionally, one question was

used to verify that the companies were high-tech (%

of sales spent on research and development [R&D]

� 6%; Richie, Hecker, and Burgan, 1983). Eight de-

mographic items and 19 other questions were used for

further classification purposes. In total, 139 items

were used in the administration of the questionnaire

to the first sample in Study 1.

Study 1: Item Purification

The survey using the refined items was administered

to an international sample of firms, broad based

across several high-tech industries including pharma-

ceuticals, biotechnology, chemicals, electronics, de-

fense, and aerospace (n5 109; response in terms of

eligible questionnaires5 28%; North American and

European firms). Individuals with a high likelihood of

involvement in the early stage development of ‘‘rad-

ical innovations’’ were contacted over a four-month

period. The aim was to reach chief technology officers

(CTOs), chief executive officers (CEOs), and R&D

directors, as these individuals have a good knowledge

of the firm’s NPD activities. In small organizations,

the person who worked directly with the technology

was typically the CTO or CEO. In large organiza-

tions, CTOs or CEOs responded if they were familiar

with the development of the technology but otherwise

forwarded the questionnaire to an individual closer to

the actual development of the technology. The inclu-

sion of multiple perspectives from within the firm and

from multiple industries helped ensure generalizabil-

ity. Using seven-point Likert scales, respondents rated

the items.

Following methods suggested by Bearden et al.

(2001), Bollen and Lennox (1991), and DeVellis

(1991), analyses were performed using SPSS (Versions

11.5 and 12) and EQS (Version 6.1). Exploratory fac-

tor analyses (EFAs) and further expert interviews led

to a refined list of 36 items to measure MVC, MV, and

EP. Principal components analysis on each latent con-

struct suggested adequate dimensionality and reliabil-

ity for the first-order constructs (a � 0.75) with the

exception of MV form (a5 0.68). This was probably

due to only two items loading on this factor; hence, a

second round of expert interviews resulted in the iden-

tification of additional MV form items for inclusion in

Study 2.

Overall, the results of the EFAs and internal con-

sistency analyses provided initial support for the hy-

pothesized domain structure (H1 and H2). MV

support was the only exception, as it did not come

out as a factor. It is possible that support for a vision

at this early stage is separate from the vision itself

because support may come from external players (e.g.,

financiers, suppliers, customers) or may be given for

things such as the idea, the technology, or the people

involved and, therefore, is not tied specifically to the

market vision. Therefore, MV support was excluded

from the second-order construct of MV.

Study 2: Measurement Model Specification

Companies and respondents were selected for Study 2

from a large sample of North American nanotechnol-

ogy firms. It was believed that this newly emergent

field would provide a good perspective on the dimen-

sions and relationships in question. Respondents were

contacted by telephone or e-mail over a six-month

period. Those who agreed to participate were directed

to a Web-based survey link (condition of participa-

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S. E. REID AND U. DE BRENTANI

tion: ‘‘high-tech’’ and ‘‘radical’’ according to the defi-

nitions mentioned).

Data collection for use in Study 2 included two

samples of similar size (Churchill, 1979), which were

collected from different segments from within the

same industry (Herzberg, 1969). As such, one sample

from Study 2 consisted of a broad base of nanotech-

nology firms mostly focused on nano-systems, while

the other sample involved companies developing ad-

vanced materials and nano-materials. The data set

was used to provide evidence of reliability and to test

the factor structures using cross-validation across the

two samples from the same industry population (Mo-

zier, 1951). For the ‘‘systems’’ sample, 180 of 495 ini-

tial contacts responded to the survey (initial response

rate: 36%). After eliminating unusable question-

naires, the final sample comprised 110 respondents

(final response rate: 22%). For the ‘‘materials’’ sam-

ple, there were 539 initial contacts, 146 responses

(27%), and a final sample of 117 usable question-

naires (22%).

Based on the construct with the largest number of

items (MV, 17 items) and no evidence of significant

kurtosis based on Mardias tests, the final sample sizes

of 110 and 117 respondents were considered adequate

(i.e., moderate response ratio of 1:6; DeVellis, 1991;

Hinkin, 1995; Reinecke Flynn and Pearcy, 2001).

This data set was further used to rerun EFAs on

MV form and MV clarity, because these factors had

new items added after the second round of expert in-

terviews and further literature review/personal inter-

pretation. In the case of MV form, which in Study 1

had only two items loading, new items were generated

from the literature and industry experts. The new

EFA resulted in two additional items: ‘‘the product’s

relationship to the customer use environment’’ and

‘‘the potential for standardizing the design.’’ Thus, a

total of four items were used to measure MV form,

with acceptable reliability (a5 .79). Further, with the

addition of new items from the second round of expert

interviews, the MV clarity dimension separated into

two different factors: MV clarity and MV specificity.

Specifically, two new items were added: ‘‘even in the

very early stages of development, prior to formal pro-

ject status, the market vision was very specific’’ and

‘‘even in the very early stages of development, prior to

formal project status, the market vision was able to

provide direction to others in the organization.’’ As a

result, a new factor emerged—MV specificity—which

is more general and at a higher level of abstraction

pertaining to specificity of the overall vision (e.g.,

form, scope, clarity). The revised MV clarity factor

now represents clarity only of the MV form itself (i.e.,

‘‘in the very early stages of this technology’s develop-

ment it was clear what target customers’ needs would

be’’).

The final set of items used for the measurement

models for MV and MVC are presented in Appendix

1, along with the respective loadings and reliabilities

(all � 0.70) resulting from a confirmatory factor

analysis (CFA) performed on the total data set. Com-

bination of the ‘‘systems’’ and ‘‘materials’’ samples

was considered acceptable because multigroup analy-

sis of the two samples showed full configural and

metric invariance. CFAs (maximum likelihood) were

performed on the latent constructs, with results (com-

parative fit index [CFI]40.96; root mean square error

of approximation [RMSEA]o0.05) suggesting a good

fit to the data and that performing structural equation

modeling (SEM) was appropriate. Testing of compet-

ing models showed that the five-factor model for MV

and the four-factor model for MVC offer the best fit

(see Table 1), thereby confirming H1 and H2.

In addition, CFA was performed to test the reli-

ability of EP: three items for each of ESC and AAC.

The results indicate that the measures are reliable

(a5 0.84 for ESC; a5 0.81 for AAC). Finally, disc-

riminant analysis was performed between MV and

MVC. The w2 test showed a statistically significant

difference, demonstrating that MV and MVC are in-

deed two distinct constructs. First-order factors com-

prising MV and MVC also showed discriminant and

convergent validity.

Table 1: Comparative Model Fit Tests for H1 and H2a

Model v2 Df a v2 Diff. CFIb RMSEAc

Market Vision

Null 1153.246 136 n/a n/a n/a1 Factor 505.755 119 647.49� .62 .1422 Factor Uncorrelated 430.965 118 74.79� .69 .1282 Factor Correlated 413.284 118 17.68� .71 .1245 Factor Uncorrelated 254.164 117 159.12� .87 .0855 Factor Correlated 163.152 109 248.11� .95 .055Second-Order Model 168.502 113 5.35b .95 .055

Market Visioning Competence

Null 542.714 78 n/a n/a n/a1 Factor 321.491 65 221.22� .45 .1484 Factor Uncorrelated 111.883 65 209.61� .90 .0634 Factor Correlated 65.570 59 46.31� .99 .025Second-Order Model 63.148 60 2.42d .99 .017

aDf, degrees of freedom.bCFI, comparative fit index.cRMSEA, root mean square error of approximation.dNonsignificant.� po.05.

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511

Research Results

The Measurement Model

The measurement model was tested using the nano-

technology data set of complete questionnaires. Univ-

ariate outliers were removed prior to estimation

(Byrne, 1994), thereby resulting in a final data set,

for testing the measurement and structural models of

180 respondents. Estimation of the model was per-

formed using EQS (version 6.1), which produced ac-

ceptable results (w2 5 754.97, 579 df; adjusted

w2 5 1.3; CFI5 0.92; RMSEA5 0.041), with all stan-

dardized loadings on the respective latent factors

significant at the p � .05 level. This supports the qual-

ity of the measurement model as suggested by the

previously described results of individual latent con-

struct factor analyses (H1 and H2). The EQS stan-

dardized estimates of the parameters with respective

t-values for the measurement model are presented

in Table 2.

The Structural Model

The structural model was evaluated by including the

hypothesized structural relationships between the

constructs evaluated in the measurement model. No

correlations between errors of the constructs or items

were allowed, thereby giving a conservative estimate

of the model (Kline, 1998). The structural model pro-

duced acceptable results (w2 5 774.43, 580 df; ad-

justed w2 5 1.3; CFI5 0.91; RMSEA5 0.043), with

standardized loadings on the respective latent factors

significant at the po.05 level (one-tailed test) (Kendall

and Stuart, 1979). These results indicate a good fit of

the model to the data, supporting the hypothesized

structural relationships. The standardized parameter

estimates and their respective t-values are presented in

Table 2.

As hypothesized (H3), the results indicate that

MVC has a significant and positive impact on MV

(l5 0.320, t5 2.47, po.01, one-tailed test). This

finding highlights the importance of developing a

MVC within the firm as an essential resource and

precursor for developing a MV. The underlying first-

order factors of MVC and MV provide meaning to

this result. For example, to develop links in people’s

minds between potential products and markets (i.e.,

MV form, MV scope), it is necessary to use MVC by

first gleaning information from the external environ-

ment. This is achieved by networking and the proac-

tive use of market learning tools and then by driving

these new product-market ideas through the organi-

zation. Also, MVC dimensions related to how well

the idea is presented, driven, and championed in the

organization impact MV magnetism (i.e., the impor-

tance and desirability of the vision), MV clarity, and

MV specificity.

The research results support both H4a and H5b.

On one hand, MV has a moderately positive and sig-

nificant impact on ESC (l5 0.237, t5 1.75, po.05,

one-tailed test), supporting H4a. On the other hand,

the standardized estimate for the link between MVC

Table 2: Standardized Estimates and Fit Results for Mea-surement and Structural Modelsa

Measurement Model: PathTested

Standardized Estimate (t-; p-values)

Networking to MVC (H1) k 5 0.693 (no statistic: set to 1)Idea Driving to MVC (H1) k 5 0.528 (t5 3.456; po.01)Proactive Market Orientationto MVC (H1)

k 5 0.576 (t5 3.476; po.01)

Market Learning Tools toMVC (H1)

k 5 0.432 (t5 3.139; po.01)

MV Clarity to MV (H2) k 5 0.737 (no statistic: set to 1)MV Specificity to MV (H2) k 5 0.824 (t5 6.804; po.01)MV Magnetism to MV (H2) k 5 0.652 (t5 5.638; po.01)MV Form to MV (H2) k 5 0.399 (t5 3.571; po.01)MV Scope to MV (H2) k 5 0.549 (t5 5.353; po.01)Early Customers were Satisfiedto ESC

k 5 0.804 (no statistic: set to 1)

Early Customers AcceptedProducts from the Tech to ESC

k 5 0.944 (t5 11.851; po.01)

Customer Needs Will Be BetterSatisfied to ESC

k 5 0.670 (t5 9.498; po.01)

Average Growth in CompanyEmployment to AAC

k 5 0.732 (no statistic: set to 1)

Cash Flow to AAC k 5 0.747 (t5 7.628; po.01)Ability to Attract Capitalto AAC

k 5 0.683 (t5 7.370; po.01)

Structural Model: PathTested

Standardized Estimate (t-;p-values)

MVC to MV (H3) k 5 0.320 (t5 2.47; po.01)MV to ESC (H4a) k 5 0.237 (t5 1.75; po.05)MVC to AAC (H5a) k 5 0.227 (t5 2.37; po.01)MV to AAC (H4b) k 5 0.096 (t5 0.903; p4.05)MVC to ESC (H5b) k 5 0.015 (t5 0.137; p4.05)

Fit Indicators Model Fit

CFI 0.92 (Meas. Model); 0.91(Structural Model)

RMSEA 0.041 (Meas. Model); 0.043(Structural Model)

Adj. v2 1.3 (Meas. Model); 1.3(Structural Model)

a CFI, comparative fit index. RMSEA, root mean square error ofapproximation.

512 J PROD INNOV MANAG2010;27:500–518

S. E. REID AND U. DE BRENTANI

and ESC is only 0.015 (t5 0.137, p5n.s., one-tailed

test), supporting H5b that there is no direct link be-

tween MVC and ESC. It is evident from these results

that MV is necessary to fulfill ESC and as such fully

mediates the relationship between MVC and ESC.

These findings, together with the underlying dimen-

sions of MV, demonstrate that market vision is fo-

cused on delivering unique benefits and value to

customers rather than on how to attract capital.

This suggests that individuals in the firm not only

must emphasize the essence of the vision but also must

develop a MV that is magnetic, clear, specific, and

with the ‘‘right’’ form and scope so that it has a max-

imum effect on EP in terms of early customer interest

(ESC).

The results also demonstrate that MVC has a

strong impact on EP in terms of AAC (l5 0.227,

t5 2.37, po.01; one-tailed test), supporting H5a.

MVC entails networking, persuasion through idea

driving, and market learning facilitated by proactive

market orientation and market learning tools. Excel-

ling in these competencies is important if the firm is to

gain the attention of financiers, internal stakeholders

and ‘‘the best’’ new hires, which put the firm in a bet-

ter position to succeed in the venture. As hypothe-

sized, it is the MVC and not the MV that is tied to the

ability to secure venture capital. This is because by the

time MV is crystallized, capital has typically already

been attracted to the venture through the firm’s MVC

activities. This supports H4b that MV does not sig-

nificantly impact AAC.

Theoretical Implications

Development of a Scale to Measure Market Vision

Prior to this research, to the best of our knowledge, no

qualitative or quantitative research has been carried

out specifically to deal with the topic of market vision

(MV). Hence, the theoretical development and artic-

ulation of this construct is completely new, and de-

veloping and testing a scale for MV represents an

important contribution to the field. The development

of a valid and reliable multidimensional measurement

scale, as is presented in this study, not only demon-

strates the depth and complexity of the MV construct

itself but also provides some insight about why a given

MV may lead to better or poorer early performance in

the case of radically discontinuous innovations. Also,

the set of five first-order factors comprising MV pro-

vides evidence that the concept of market vision is

more than just an intrinsic form of mental model; in-

deed, it is shown to have both intrinsic dimensions as

well as extrinsic components, which allow it to be

better understood, forwarded, or shared and eventu-

ally to be adopted by others.

Development of a Scale to Measure MarketVisioning Competence

In addition to MV, a second theoretical construct,

MVC, was also elaborated in the form of a scale. The

research results demonstrate that MV and MVC are

two distinct constructs. This is important because

most past research treats the concepts of visioning

and vision interchangeably. It is important to show, as

has been done in this study, that these are distinct

concepts entailing different underlying dimensions

and involving very different concerns for the firm.

Further impetus for developing the MVC scale was to

provide evidence of nomological validity for the study

of MV. To test nomological validity, the main con-

struct of interest—that is, market vision—was tested

in a framework with a related and antecedent con-

struct, Market visioning competence.

Development of an Early Performance Metric

Firm performance in new product development has

typically been measured using standard postlaunch

metrics such as ROI or profitability. Extensive review

of the literature indicates that no comprehensive met-

ric for measuring EP—that is, performance in the

prerevenue phase—was previously used in NPD re-

search. This presented a challenge in studying MV for

firms operating with real-time emergent technologies.

This challenge was met through the advancement of

two metrics for measuring EP: ESC and AAC. In ad-

dition to their development, these EP scales were used

in a final structural equation model as outcome vari-

ables dependent on MVC and MV. As such, not only

do the EP metrics test the nomological validity of

MV, but they also provide specific outcome measures

for use in studies of early performance with discon-

tinuous innovations.

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513

A Real-Time Analysis of a High-Tech, EmergingIndustry

Surveys of truly emergent phenomena are typically

conducted post hoc—indeed, often decades after an

innovation has unfolded. Carrying out such research

in real time has provided a rare opportunity to learn

about and report on the early stages of an emergent

technology. Because the research covers what is hap-

pening in real time (i.e., not retrospectively), there is a

greater likelihood that the information obtained, al-

though based on recall, is more accurate and true to

reality. Respondents who took part in this study were

describing their current technologies in terms of real-

time phases of development. This is in contrast to

most studies of high-technology products, typically

conducted after launch, which have the potential of

inaccurate and biased recall.

Managerial Implications

The Importance of Market Visioning Competenceand Market Vision

Operationalization of the MVC and MV constructs

articulates for companies what the key components

are that comprise these two dimensions. In many

cases, firms involved with radical innovations tend

to be too focused on technical solutions and are not

aware of many of the marketing activities that need to

be carried out early on in the process to be successful.

A good understanding of the variables that comprise

each of the MVC and MV constructs can allow com-

panies to begin to focus on developing and empha-

sizing key nontechnical resources and competences

that are linked to achieving success when developing

radically new products in highly uncertain technolog-

ical and market environments.

Benchmarking Tools for Evaluating MVC and MVStrength, and EP

A key challenge often described by practitioners is

their inability to realistically gauge how well they are

performing during the very early stages of involve-

ment with a radical innovation. The MV and MVC

scales developed in this research and the model show-

ing their relationship to EP provide an opportunity

for firms to assess, in real terms, their current empha-

sis and performance in this regard. Additionally,

given that success with financiers and lead users are

indications of excellent MVC and MV, it may be a

good idea for firms to seek validation as early as pos-

sible from these stakeholders who are the ultimate

early-stage judges of NPD success.

Future Research

Recent research in marketing encourages the use of

longitudinal studies (Golder, 2000). Market vision as

well as other types of vision are typically elaborated

over time and may change due to external pressures

(e.g., venture capital pressures to pursue ‘‘lower-hang-

ing fruit’’ than initially intended) or intentional stra-

tegic decisions (e.g., as in the case of ‘‘patsy markets’’

where the plan is to use short-term product-market

ideas to leverage longer-term opportunities). As such,

a longitudinal study would provide further insights

regarding changes in the nature and valence of each of

the underlying dimensions of MV over time. Such a

study could also lead to a better understanding of the

types of external and internal factors that impact the

course of the originally intended vision. Finally, while

this research focused specifically on MV, it was pro-

posed in the theoretical discussion that market vision

is likely to lead to a specific project vision, which ul-

timately dictates or reorients the much broader orga-

nizational vision of the firm. These ideas need to be

further elaborated and tested to provide a compre-

hensive understanding of the role of vision at various

levels of analysis.

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Appendix 1: Standardized Loadings and Reliabilities of Constructs: Confirmatory Factor Analysis for MeasurementModel of Market Vision

Factor Name Items 1 2 3 4 5

MV Clarity (Extrinsic)(a5 0.88)

. . . it was clear how the product would beused. (MVCRUSE)

.894

Preamble:‘‘In the very early stages of this technology’sdevelopment . . .’’

. . . it was clear who the target market (user)would be. (MVCRUSER)

.786

. . . it was clear what target customers’ needswould be. (MVCRNEED)

.835

MV Magnetism (Intrinsic)(a5 0.78)

. . . the market vision was desirable.(MVDESIRE)

.648

Preamble:‘‘In the very early stages of this technology’sdevelopment . . .’’

. . . the market vision was attractive.(MVATTRAC)

.836

. . . the market vision was important(MVIMPORT)

.750

MV Specificity (Extrinsic)(a5 0.89)

. . . the market vision was clear (MVCLEAR) .864

Preamble:‘‘In the very early stages of this technology’sdevelopment . . .’’

. . . the market vision was tangible (e.g., easy tovisualize) (MVTANG)

.869

. . . the market vision was very specific(MVSPEC)

.725

. . . the market vision was able to providedirection to others in the organization(MVDIR)

.839

MV Form (Intrinsic)(a5 0.74)

. . . how end-user would ultimately interactwith and use the product (MVUSE)

.782

Preamble: ‘‘When we first started thinking aboutwhat specific markets would benefit from thetechnology, we spent most of our time thinkingand talking about . . .’’

. . . how product would fit into an overallsystem of use for potential customers.(MVSYSTEM)

.558

. . . the product’s relationship to the customeruse environment. (MVUENVT)

.710

. . . the potential for standardizing the design(MVSTANDARD)

.543

MV Scope (Intrinsic)(a5 0.86)

. . . what the most profitable target marketwould be. (MVPROFTM)

.847

Preamble: ‘‘When we first started thinking aboutwhat specific markets would benefit from thetechnology, we spent most of our time thinkingand talking about . . .’’

. . . what the largest target market would be.(MVLGETM)

.759

. . . what the most important target marketwould be. (MVIMPTM)

.842

Factor Name Items 1 2 3 4

MVC (Individual)Idea Driving

(a5 0.80)Preamble: ‘‘The person who first championed thistechnology in our firm . . .’’

. . . got key decision makers in our firminvolved. (IDDMKS)

.745

. . . secured the required senior management-level support. (IDSRMGR)

.756

. . . shared information and campaigned forsupport very quickly with senior management.(IDSHRSM)

.779

MVC (Individual)Networking

(a5 0.70)Preamble: ‘‘The person who first championed thistechnology in our firm . . .’’

. . . had a broad network of relationshipsoutside of our company. (NWBROAD)

.738

. . . had a network made up of people with avariety of different backgrounds (e.g.,different industries, different disciplines,different functions). (NWVARIET)

.788

. . . was at the center of the network growingup around the technology. (NWCENTRL)

.484

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MVC (Organizational)Market Learning Tools

(a5 0.77)

We tried to keep our market opportunityoptions open as long as possible for the newtechnology. (MLOPTIONS)

.682

We tried to develop several potentialtechnological scenarios before choosingmarket(s) to pursue. (MLSCENAR)

.707

We use forecasting and market estimationtechniques before making a market selection.(MLMEFORE)

.651

We use several forecasting and marketestimation techniques in combination beforemarket selection. (MLCOMBO)

.648

MVC (Organizational)Proactive Market Orientation

(a5 0.79)

We continuously try to discover additionalneeds of our customers of which they areunaware. (MLADNEED)

.774

We incorporate solutions to unarticulatedcustomer needs in our new products andservices. (MLUNART)

.777

We brainstorm on how customers use ourproducts and services. (MLBRAIN)

.689

Appendix 1: (Contd.)

Factor Name Items 1 2 3 4

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