What triggers change and innovation?

Carlos Montalvo*

Institute of Strategy, Technology and Policy, TNO, Netherlands Organisation for Scientific Applied Research,

Schoemakerstraat 97, P.O. Box 6040, 2600JA Delft, The Netherlands

Abstract

Innovation and competitiveness amongst firms are currently seen as some of the main economic multipliers in industrialised and emerging

economies. After 50 years of theoretical and empirical development in innovation studies explaining Why? and How? innovation occurs at

the firm level remains as a prime challenge for academics and practitioners. Innovative behaviour in organisations has been attributed to

dissimilar factors (e.g. institutional arrangements, entrepreneurial or risk taking behaviours, economic opportunities, organisational learning,

technological and organisational capabilities, etc.). The communality of current theories and studies is that they tend to put emphasis on

individual determinants of the innovative behaviour. In consequence, much of the generated knowledge is still rather fragmented because the

diverse insights are not unified in single and testable theoretical body towards the explanation and prediction of innovative behaviours in

firms. This paper introduces and empirically tests a structural model from the behavioural sciences that enables the organisation and

integration of knowledge generated in diverse areas of innovation studies to explore, explain and predict the innovative behaviour of the firm

in specific contexts.

q 2004 Elsevier Ltd. All rights reserved.

Keywords: Innovation; Resistance to change; Propensity; Willingness; Mental models; Corporate change

1. Introduction

Innovation and competitiveness amongst firms are

currently seen as some of the main economic multipliers

in industrialised and emerging economies. Explaining why?

and how? innovation occurs at the firm level remains as a

prime challenge and occupation for academics and prac-

titioners (e.g. Utterback, 1994; Tidd et al., 1997; Colmer

et al., 1999; Damanpour, 1991, 1996; Dewar and Dutton,

1986; Gopalakrishnan and Damanpour, 1994; Grant, 1997;

Tschirky, 1994; van de Ven, 1986; Collins et al., 1988;

Georghiou et al., 1986; Raymond et al., 1996; Brady et al.,

1997). Important advances have been achieved in the

last 50 years offering many theoretical insights that

intend to explain the innovative behaviour of the firm.1

0166-4972/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.technovation.2004.09.003

* Tel.: C31 15 269 5490; C31 61 092 4786 (mobile); fax: C31 15 269

5460.

E-mail addresses: montalvo@stb.tno.nl (C. Montalvo),

http://www.tno.nl (C. Montalvo).1 For literature reviews in the field see Dodgson (1995), Gopalakrishnan

and Damanpour (1997), Miller (1996), Tsang (1997), Tidd et al. (1997),

Kline and Rosenberg (1986) and Berry and Taggart (1994).

However, three fundamental concerns about the current

state of the innovation literature can be raised concerning

the capacity of current models to explain and predict

innovative behaviours.

First, this behaviour has been attributed to a range of

factors such as institutional arrangements (e.g. Hodgson,

1998; Furubotn, 2001; Nelson and Sampat, 2001; Hall et al.,

2001; Cooke et al., 1997; Westall, 1997), entrepreneurial or

risk taking behaviours (e.g. Rotemberg and Salomer, 2000;

Shapira, 1994; Nelson and Winter, 1982; Dosi, 1988; Tidd

et al., 1997; Petts et al., 1998; Andrews, 1998; Kline and

Rosenberg, 1986; Utterback, 1994; van Someren, 1995;

Chattery, 1995; Freeman and Perez, 1988; Roome, 1994;

Schoemaker, 1993b), organisational learning (e.g. Senge,

1990; Nonaka, 1994; Leonard-Barton, 1995; Tsang, 1997;

Argyis and Schon, 1996; Dodgson, 1995; Hippel and Tyre,

1995; Miller, 1996), and technological and organisational

capabilities (e.g. Penrose, 1959; Collins, 1994; Collins et al.,

1988; Leonard-Barton, 1992; Rosenbloom and Christensen,

1994; Teece and Pisano, 1994; Teece et al., 1990; Panda and

Ramanathan, 1996; Grant, 1996), etc. to mention only the

most influential concepts in the field. A commonality of

current theories and studies is that they tend to put emphasis

Technovation 26 (2006) 312–323

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C. Montalvo / Technovation 26 (2006) 312–323 313

on individual factors as determinants of the innovative

behaviour. As a consequence, the generated knowledge is

still rather fragmented due to the lack of the unification of

diverse insights in a single and testable theoretical body

concerning the measurement of the conditions upon which

the firm could be more prone to innovate.

Point two follows on from this. Although there is an

implicit recognition that the factors mentioned above

interact and influence each other, no models have been

provided to facilitate the quantitative empirical test of such

influences. Third, in the innovation literature, it is generally

accepted that there is dissonance between cognition and

behaviour (e.g. Fransman, 1984, Brusoni et al., 2001;

Mahdi, 2002). Yet this literature does not propose

methodologies to assess the origin of such dissonance.

Following the line of thought of early institutional

economists in that “economic theory must be based upon

acceptable theory of human behaviour” (Hamilton in

Hodgson, 2000), this paper addresses these concerns by

introducing a model from social psychology for decision-

making analysis regarding innovation and resistance to

change. The application of this behavioural model helps to

organise the knowledge generated in diverse areas of

innovation studies to explain and predict the conditions

upon which innovative behaviours of organisations in

specific contexts could occur.2

The paper proceeds as follows: first, the theoretical basis

for the approach adopted is briefly explained and compared

with other decision-making schemas. Second, the model is

operated within the context of innovation studies. Finally

some conclusions are drawn on the implications of the

proposed model for innovation and institutional change

studies towards gaining better understanding of what

triggers innovation in organisations.

2. A behavioural approach to explore and predict

innovative behaviours

2.1. Innovation: from cognition, plans and intentions

to actions

Human behaviour at the individual or social levels taken

as a whole is an ongoing process in constant evolution which

is general difficult when not impossible to understand, even

more to predict. In this regard, according to Ajzen (1985,

1996) and Gollwitzer and Bargh (1996), there appears to be

general agreement among social psychologists that most

human behaviour ‘in specific situations’ is goal directed. This

implies that social behaviour can be accurately portrayed as

moving along paths of more or less well formulated plans.

2 The problem of dynamics i.e. changes in the decision-making

structures due to recursive effects of learning in the face of changes on

information, resources and operating context is dealt with elsewhere

(Montalvo, 2003b).

Thus, discounting contingencies, people are expected to

behave according to their intentions, goals or plans.

Underlying this statement there is the assumption that

generally speaking human beings are usually rational and

make use of the information available to them before acting

(Ajzen and Fishbein, 1980; Fransman, 1994). This argument

is more likely to be correct when considering technology

development (or adoption) and strategic planning within the

firm’s context, as strategic planning and technology devel-

opment are based on goals to be achieved (Coates et al.,

2001; Rotemberg and Salomer, 2000). These goals can be

seen as intentions to perform behaviour, that is, the firm’s

planned behaviour. In this sense, the first condition for a firm

to engage in innovative activities is that innovation has to be

contemplated by decisions makers as a strategic planned

behaviour. That is, the firm must be willing to change, to

innovate. Thus ‘willingness’ can be considered the first

predictor of the firm’s innovative behaviour.

Following this thought, the approach adopted to explain

and predict the innovative behaviour of the firm is based on

a behavioural model, the theory of planned behaviour

(TPB). This theory is designed to understand and predict

human social behaviour concerning a specific action with a

specific target and within a specific time frame and context

(Ajzen, 1991). Taking into account the previous criteria, it

has demonstrated that people’s behaviour in most situations

can be explained and predicted in terms of intentions,

attitudes, subjective norms and behavioural control. The

theory is well supported by empirical evidence; the model

has performed with an explanatory reliability up to 91%

of the variance on behaviour (Ajzen, 1991; Ajzen and

Krebs, 1994).

The TPB has offered a framework to understand and

predict a wide variety of social behaviours (see e.g. Fishbein

et al., 1980a,b,c; Fishbein and Ajzen, 1980; Terry, 1993;

Van Ryn and Vinokur, 1992). Although recently Ajzen’s

model has also been used in decision-making studies in

relation to technology (e.g. Lynne et al., 1995; Taylor and

Todd, 1995; Harrison et al., 1997; Montalvo, 2002; Wehn de

Montalvo, 2003) and organisational change (Metselaar,

1997), its potential application in innovation studies in

general have not yet been discussed. Thus, this paper places

emphasis on the theoretical aspects of its application to

innovation studies. Only the most relevant elements of the

TPB will be briefly presented here. A complete exposition

of the theory can be found in Ajzen (1988, 1991).3

Fishbein and Ajzen (1980) postulated that people’s

intention to perform (or not to perform) a behaviour is the

immediate determinant of that action. The TPB specifies

three major sources of cognition-behaviour inconsistency.

The first source is a change in the initial intention (plan or

goal) before it is carried out. The second source is people’s

3 Critical analyses of the TPB can be found in Conner and Armitage

(1998), Jonas and Doll (1996) and Sutton (1998).

C. Montalvo / Technovation 26 (2006) 312–323314

lack of confidence that the attainment of their behavioural

goal is under their volitional control. Finally, whether an

expectation formed on the basis of such an attainment of

volitional control leads to actual goal attainment, is

contingent on the relation between people’s confidence in

their ability to exercise control over their own action and the

extent to which they actually do control events (Ajzen,

1985). These sources of inconsistency between cognition

and behaviour have been classified and defined as:

†

4

imp

by

Attitude toward the behaviour (A): “is the degree to

which a person has a favourable or unfavourable

evaluation or appraisal of the behaviour in question”.

†

Subjective norm (SN): “is a social factor, is the perceived

social pressure to perform or not to perform the

behaviour”.

†

Perceived behavioural control (PC): “is the perceived

ease or difficulty of performing the behaviour and it is

assumed to reflect past experience as well as anticipated

impediments and obstacles” (Ajzen, 1991:188).

2.2. Determinants of attitudes, subjective norms and

perceived behavioural control

At its most basic level of explanation, the TPB postulates

that behaviour is a function of salient beliefs or information

relevant to the behaviour. The nature of these beliefs can be

explained by looking at how they are shaped. Generally

speaking we form beliefs about an object (or people,

activity, institution, etc.) by associating it with various

characteristics, qualities and attributes. Depending on this

connotative meaning, automatically and simultaneously we

acquire an attitude toward that object (Ajzen, 1991: 191).4

In a similar fashion, we associate our skills, resources, time,

etc. to the control over our own and/or others’ behaviour.

Following the Ajzen model, three kinds of salient beliefs

can be distinguished:

†

behavioural beliefs: which are assumed to influence

attitudes toward the behaviour;

†

normative beliefs: which constitute the underlying

determinants of subjective norms, and

†

control beliefs: which provide the basis for perceptions

of behavioural control.

2.3. Decision-making models in organisations

Various models have been used to predict or understand

strategic decisions in organisations. Following the taxon-

omy of Schoemaker (1993a), the work in decision-making

The connotative meaning of a concept includes all of its suggestive or

licit significance. That is, the concept or object have significance only

association (see Carlsmith et al., 1976).

since the influential works of Bernard (1938), Edwards

(1954) and Simon (1945) can be divided into four

categories. The first refers to the single rational actor that

acts from a clear set of objectives and pursues a rational

strategy with unlimited information processing capacity and

perfect foresight to meet these objectives. Organisations are

seen as monolithic entities that can be understood only in

terms of individual rationality. The second are organis-

ational models, in which multiple players pursue the same

objectives. They practice a differentiated but integrated

division of labour on various activities with shared values

and rationality.

The third category comprises political models in which

individual or departmental goals supersede the overarching

organisational ones. However, there is a fine balance

between individual and organisational goals. This model

uses partisan behaviour in understanding organisational

decision-making. The fourth and last category, the con-

textual view models, hold that organisational environments

are so complex, and human desires so varied, that each

decision context becomes its own reality, with limited

consistency across situations and goals. Therefore, the

particularities of the context are the driving force for the

decision, rather than the super-ordinate goals or compre-

hensive planning.

The TPB falls into the first class according to

Schoemaker’s (1993a) categorisation, as it focuses on

single rational actors. However, it differs from the

traditional linear process normative model of decision-

making based on the subjective expected utility (e.g.

Eduards, 1954; Simon, 1945). The TPB is a structural

descriptive model that aims to gain an understanding of the

predispositional factors by looking at the structural

relationships of the possible determinants of behaviour.5

Although originally the TPB was designed to under-

stand and explain the behaviour of people, from the

above discussion it can be argued that its application to

study the behaviour of organisations coincides with

Schoemaker’s idea of a meta-theory that enables the

integration of elements of the four models mentioned

above (Schoemaker, 1993a). The study of the organis-

ation’s behaviour can be achieved by applying the rule of

specificity or generality of the behavioural criterion. That

is, defining and in consequence modifying the action,

target, context and time according to the study of single

behaviours or categories of behaviour (Ajzen, 1988). The

definition of an appropriate behavioural criterion could

take into account the behaviour of the organisation through

the perception of its managers. Here a simile is made

between the ‘perception’ of the entity ‘the firm’ with the

perceptions of the managers. In principle, these are two

different units of analysis.

5 For a comparison between process and structural models of decision-

making, see Abelson and Levi (1985).

C. Montalvo / Technovation 26 (2006) 312–323 315

Elsewhere it has been widely discussed that decision-

making within the firm is socially constructed (Hickson

et al., 1986). In this construction process the organisations’

managers are considered to be the primary decision makers

and best positioned to express the preferences of their

organisation (Shapira, 1994; Frank et al., 1988; Hickson

et al., 1986). They are the trustees of the strategic vision of

the firm and the hub of information, communication, control

and decision-making (Rotemberg and Salomer, 2000; Benis

and Nanus, 1985; Aguilar, 1988; Mintzberg, 1994; Quiley,

1993). However, far from being absolute optimisers, highly

reflective, strategic or tactical and top-down planners, their

decisions and actions could be seen far from optimal, but

having full rationality. Because of this, managers are here

considered to be the best informed about the internal and

external contexts in which their organisation operates.

Therefore, the assessment of managers’ (or CEOs) percep-

tions in this paper is considered the appropriate proxy to

infer the planned behaviour of the firm.

3. Understanding and predicting the innovative

behaviour of the firm

In policy analysis and design, it is often necessary to

predict firms’ behaviour to solve applied problems or to

make policy decisions. In this regard, Ajzen and Fishbein

(1980) have argued that although prediction of people’s

actions is possible with little or no understanding of the

factors that cause behaviour. If our goal goes beyond

prediction in that we attempt to produce policy recommen-

dations that influence the behaviour of firms, it is necessary

to gain a better understanding of the underpinnings of the

behaviour of interest. In order to reach a deeper under-

standing and predictive reliability it is necessary to examine

the beliefs that generate attitudes, subjective norms and

perceived behavioural control as determinants of intentions

or willingness to engage in innovative activities. Within the

TPB framework behaviour is considered explained once its

determinants have been traced to the underlying belief

system (Ajzen and Fishbein, 1980). The model to analyse

the innovative planned behaviour of the firm is presented in

the following subsections.

3.1. Behavioural beliefs and attitudes toward the

engagement in innovative activities

As defined above, attitude is the degree to which people

have a favourable or unfavourable evaluation or appraisal of

a specific behaviour. Following this definition, in the realm

of the behaviour of the firm, the attitude towards innovation

is an index of the degree to which the firms’ manager like or

dislike (approve or disapprove of, agree or disagree with,

etc.) any aspect arising from the engagement in innovative

activities. Each behavioural belief links specific behaviour

to an outcome or an attribute that is valued positively or

negatively. In this way, it can be expected that managers

will tend to prefer behaviours believed to have desirable

consequences. The attitude towards the behaviour results

from the accumulated connotative load associated with the

behavioural salient beliefs or relevant information regarding

the implications of the planned innovation.

Examples of negative attitudinal salient beliefs are: a

new technology can be unreliable, costly and lengthy to

develop. Such a belief implies negative connotations of

obtaining negative outcomes. This belief can be expected to

contribute to the formation of a negative attitude towards the

engagement on innovation. A negative attitude is likely to

prevent any engagement in innovative activities. The

contrary can be expected with the perception of positive

outcomes or the presence of a positive attitude. An index of

attitude (A) can be obtained, as shown in Eq. (1), by

multiplying the subjective evaluation (e) of each belief

attribute and the strength (b) of each salient belief, with the

resulting products summed over the n salient beliefs.

AfXn

iZ1

biei (1)

where

A

is the manager’s attitude toward the engagement in

innovative activities;

bi

is the belief (subjective probability) that the engagement

in innovation will lead to outcome i;

eiP

is the evaluation of the outcome i, and;

is the sum of the n salient behavioural beliefs.

3.2. Normative beliefs and subjective norms

According to Ajzen (1991), the subjective norm is an

index of the importance that people give to their important

referents (e.g. individuals, groups, or firms) and whether

they are perceived to approve or disapprove of the

behaviour in question. In the case of firms’ behaviour, the

subjective norm can be conceptualised as the social pressure

or social norm that arises from the context in which the firm

operates. Here we can define the firm’s perceived social

norm (SN) as the importance that the firm’s manager gives

to different crucial referents to engage or not to engage in

innovative activities. It results from the accumulated

connotative load of normative beliefs that managers may

hold.

That is, this refers to how the managers perceive their

important referents within the firm to be thinking about what

their firms behaviour should be (e.g. staff suggestions,

shareholder expectations) and the external referents (e.g.

behaviour of competitors, pace of technological innovation

in the sector, customers expectations, legal requirements,

public perceptions, and industry standards and norms). It is

assumed that those firms with high perceived social pressure

C. Montalvo / Technovation 26 (2006) 312–323316

will be more willing to engage in innovative activities, as

these are perceived as a necessity to maintain its competitive

edge or public legitimacy. However, such a perception may

or may not reflect what the important referents really expect

from or think about the behaviour of the firm, or reflect the

real condition of the market.

This index can be calculated by multiplying the

strength of each normative belief (nj) with the managers’

motivation (or perceived necessity) to comply with or

follow the referent in question (mj). The social norm is

hypothesised to be directly proportional to the sum of the

resulting products across the n salient beliefs, as shown

in Eq. (2).

SNfXn

jZ1

bjmj (2)

where

SN

is the perceived social norm;

bj

is the normative belief concerning referent j;

mj

is the organisation’s motivation to comply with, follow

or anticipate to the preferences of the referent j, and

P is the sum of the n salient normative beliefs to produce

an index of the overall perception of social pressure and

the need to engage in innovation.

3.3. Control beliefs and perceived control over the

innovation process

The perceived behavioural control is defined as the

perceived ease or difficulty of performing the behaviour

(Ajzen, 1991) This index enables us to differentiate between

behaviours that are under volitional control and those that

are not. In the firm context, innovation can be considered as

a behaviour that in many cases is not under volitional

control of its managers. The perceived control over any

innovation process (PC) is an index of the presence or

absence of requisite resources and opportunities to carry out

innovative activities. These beliefs may be based on past

experience, second hand information or any other factors

that increase or reduce the perceived difficulty or feasibility

of a specific innovation project.

The overall perceived control over the innovation

process arises from the accumulated connotative load of

beliefs with regard to the perceived ease or difficulty to

perform and to achieve the expected outcome as planned.

Depending on the perceived control over technological or

organisational change, the willingness of the firm to

innovate can be expected to be strong or weak. An index

of the perceived control over the innovation process can be

estimated by multiplying the control belief strength (ci) with

the perceived power (pi) of the specific factor that facilitates

or inhibits the performance of the action. The resulting

product is summed across the n salient beliefs as shown

in Eq. (3)

PCfXn

iZ1

cipi (3)

where

PC

is the perceived control over the innovative activity;

ci

is the control belief strength;

pi

is the perceived power of the particular control factor to

facilitate or inhibit the performance of innovation;

P is the sum of the n salient control beliefs to produce an

index of the overall perception of control over the

innovation process.

Finally, following Ajzen’s (1991) model, in order to

integrate the above constructs Eq. (4) suggests that the

strategic or planned innovative behaviour of the firm is a

function of the three indexes presented above. The form of

the function of attitude, social norms and perceived control

over innovation with the willingness to engage and the

performance of innovations must be determined empirically

I wW Z WðA;SN;PCÞ (4)

where

I

is the overt behaviour, the engagement of the firm in a

specific innovative activity;

W

is the willingness, plan or intention to engage in

innovation;

A

is the manager’s attitude toward the engagement on

innovative activities;

SN

is the manager’s perceived social norm concerning the

engagement on innovation;

PC

is the manager’s perceived control over the innovation

process;

w

suggests that willingness is expected to predict

behaviour.

3.4. Defining the innovative behaviour of interest

As well as it is difficult to understand or predict human

behaviour as a whole the same is applicable to the

innovative behaviour of the firm. In this regard, the first

step towards understanding the behaviour firms is to define

clearly the behaviour of interest. In order to achieve a clear

definition of a specific behaviour, the TPB proposes four

criteria. The first is related to the problem of distinguishing

between behaviours and events that may be the outcomes of

those behaviours. To do this, the TPB divides behaviour into

single actions and behavioural categories. Single actions are

specific behaviours performed by individuals in a firm (e.g.

drawing, reading, writing, calculating, etc.), while beha-

vioural categories are composed of a set of single actions

(e.g. metal casting, product integration, process design,

C. Montalvo / Technovation 26 (2006) 312–323 317

developing new products or services, etc.). Outcomes are

the result of single or behavioural categories (e.g. better

product performance, higher organisational flexibility,

increases on the firm’s market share, etc.). The second

criterion is the target (i.e. object, new routines and

organisational arrangements, new product, process or

service concepts, etc.) towards which the action is directed.

The third is the time when the action should or would occur.

The fourth is the context in which the innovation occurs or is

supposed to occur. These four criteria (i.e. action, target,

time and context) help us to generate a behavioural criterion

essential for the study of any innovative behaviour (Ajzen,

1991). So, following these criteria it is possible to explore,

understand and predict not all innovations but those directed

to specific target in a given time and context.

3.5. Behavioural domains definition and exploration of

managers’ beliefs

Once the innovative behaviour, willingness to inno-

vate, attitude, the social norm and the perceived control

are defined consistently with the behavioural criterion

(i. e. action, target, context and time), then it is necessary

to search for the beliefs that might determine behaviour.

It is at this point that the TPB works as a meta-theory to

integrate several bodies of theory enabling a deeper

understanding of the behaviour of interest. The first step

to explore and gather the relevant beliefs is to define and

map the behavioural domains that underlie attitudes, the

perceived social pressure, and the perceived control over

the innovation process. The behavioural domains are

defined as “the specific areas of experience and

knowledge from which the salient beliefs arise”

(Ajzen, 1988: Ch. 1).6

3.5.1. Beliefs and behavioural domains underlying attitude

toward innovation

Concerning the beliefs that underlie attitudes the

question here is, which are the areas of experience and

knowledge that might influence the managers’ attitude

towards innovation? Attitude was defined above as the

degree to which the firm’s manager expects positive or

negative outcomes arising from the engagement in innova-

tive activities. This favourable or unfavourable evaluation is

proposed to arise from two behavioural domains: the

perceived social impact (or social desirability) of the

innovation and the perceived economic risk for the firm

(including gain and losses). When exploring the perceived

social outcomes, the aim is to gain deeper understanding of

what the manager of the firm would perceive to be the

benefits or costs for the society as a whole derived from

the innovation. Here it is assumed that those managers

6 Further examples and elaboration of behavioural domain definitions can

be found in Montalvo (2002) and Wehn de Montalvo (2001).

that foresee highly desirable social outcomes would have

a higher propensity to lead their firms into innovative

activities.

Regarding the perceived economic risk, in innovation

studies uncertainty is considered a fundamental problem

that organises innovative activities (e.g. Kline and

Rosenberg, 1986; Dosi, 1988; Nelson and Winter, 1982).

In order to gain a better understanding of what constitutes

uncertainty over innovative activities, four aspects require

exploration: economic opportunities, appropiability of

benefits, technical feasibility and financial risk. These

concepts can be individually operated in terms of the key

questions that management of risk in any project entails

(i.e. Who are the parties involved? What do the parties

want to achieve? What is it the parties interested in? How

is it to be done? What are the resources required? and

When does it need to be done?) (Chapman and Ward,

1997). Responding to these questions would allow us

to understand better what constitutes uncertainly and

how managers conceptualise economic risk in specific

innovative projects.

3.5.2. Normative beliefs and behavioural domains

underlying the perceived social norm

The hypothesised second construct to influence the

propensity of the firm to engage in innovation is the

perception of the social context in which the firm operates,

that is, the social norm (SN). In order to define its

behavioural domains, the leading question is: What are the

important referents that may be dictating the norm that

would motivate the firm to innovate? Here we consider three

main sources; the market, the regulatory regime and the

communities to whom the firm serves and benefits from.

Concerning the market pressures, there is a vast body of

research emphasising the importance of innovation as a

determinant of firm’s competitiveness (e.g. Clark and Guy,

1998; Fagerberg, 1996; Tidd et al., 1997; Teece and Pisano,

1994; Hamel and Prahalad, 1994).

Following the works of Miller (1987), Boyd et al. (1993),

and Zahra (1996) the perceptions of the competitive context

firm and its relation with innovation can be explored along

three key dimensions: dynamism, hostility and heterogen-

eity. In terms of market dynamism, it is necessary to include

questions about consumer preferences, competitors’ beha-

viour, the rate of technical change and growth opportunities

in a specific industry (Miller, 1987). Market hostility can be

explored by looking at how intense competition is perceived

due to an unfavourable business climate, market saturation

or recessionary conditions (Boyd et al., 1993). Exploring

heterogeneity could indicate the need of the firm to

differentiate itself when it faces highly diverse and complex

market segmentation (Zahra, 1996). These three aspects of

the competitive environment have the potential to provide

an index of how pressured and motivated the firm could be

to remain a follower or to become a pioneer on the markets

that it serves.

C. Montalvo / Technovation 26 (2006) 312–323318

Regarding the perceived community pressure elsewhere

it has been argued that strategic decisions in organisations

are socially constructed (e.g. Eisenhardt and Zbraracki,

1992; Payne et al., 1992; Frank et al., 1988; Beach and

Mitchell, 1990; Hickson et al., 1986; Shapira, 1994).

Complex organisations have both a planned process where

managers play a crucial role as well as an evolutionary and

iterative process in which diverse members of the

organisation and external referents can influence strategy

(Burgelman, 1983, 1991). In this construction process

important internal referents that might influence the firm

to engage in innovative activities are the lobbying generated

by individual staff members, or groups like shareholders,

departments, divisions, unions, etc. that could push (or be

perceived to push) a specific vision concerning the

behaviour of the firm. External referents include amongst

others consumers, suppliers, NGOs, local communities,

consumer reports, etc.

With respect to the regulatory regime, a number of

authors have indicated that it can be an important driver of

innovation (e.g. Clayton et al., 1999; Hawkins et al., 1995).

Perceived pressures that might motivate the firm to innovate

could arise from the need to comply with or to be ahead of

new standards affecting products production processes, or

services concerning safety, quality, performance, and the

environment.

3.5.3. Control beliefs and behavioural domains underlying

the perceived control over the innovation process

Concerning the exploration of the behavioural domains

underlying the perception of control, the guiding question

here is, what are the areas of experience and knowledge

that might influence the perceived ease or difficulty to carry

out the innovation? The perceived control over the

innovation process was defined above to be depending on

perceived ease or difficulty to carry out innovation. This

was argued to be dependant on the availability of

resources, past experience, skills and timing. In the

literature of ‘the resource view of the firm’ the most

frequently mentioned determinants of the innovative

capacity are: technological capabilities, the availability of

technological opportunities, collaboration with research

institutions, collaboration and influence with suppliers,

technology users involvement, perceived internal control

of the firm, and organisational learning capability (e.g.

Penrose, 1959; Prahalad and Hamel, 1994; Huber, 1996;

Teece and Pisano, 1994; Leonard-Barton, 1995; Grant,

1996; Panda and Ramanathan, 1996; Tidd et al., 1997).

All these factors have in common the underlying notion

of control over innovation. Upon consideration of these

factors it is proposed that the perceived control arise from

two domains of the internal sphere of firm. One domain

consists of perceived organisational capabilities that the

firm holds to guide and implement technological change.

The other internal domain refers to the perceived techno-

logical capabilities within the firm. The external domain

captures the technological opportunities that the market has

to offer. The success in assessing perceived control over the

innovation process depends on knowing what activities are

needed to carry out a specific innovation project, i.e.

relevant technological expertise for the specific area of

innovation under consideration. This expertise provides the

necessary information about what types of technological

capabilities, learning skills, strategic alliances and networks

of collaboration are needed. Then it is possible to assess the

extent to which firm perceive to have control over carrying

these activities.

3.6. Hypotheses

The discussion up to this point can be summarised by

proposing two hypotheses to test the presented model. The

first concerns the first level of explanation of the willingness

to engage in innovative activities, that is, its dependence on

attitudes, social pressure and perceived behavioural control.

The reasoning behind both hypotheses is to test whether,

within the innovation and technology policy realm, it is

possible to link the perceptions of managers at the

behavioural domain level to the willingness of their firm

to engage in specific innovations and, ultimately, to

behaviour.

H1:

The firms’ willingness (W) to engage in innovation can

be explained in terms of the managers’ attitude towards

the engagement on innovative activities (A), the

perceived social norm to engage in innovation (SN)

and the control over the innovation process (PC) as

perceived by their managers.

H1 : W Z WðA;SN;PCÞ

The second hypothesis brings into focus the coherence of

the theoretical framework. If the managers’ attitudes, the

perceived social norm and the perceived control over the

innovation process arise from their respective behavioural

domains, then the perceptions in these specific areas of

experience and knowledge should also explain the will-

ingness of the firms to engage in innovative activities.

Thus:

H2:

The firms’ willingness (W) to engage in innovation can

be explained in terms of the perceptions of: expected

social outcomes (SO), economic risk (ER), community

pressure (CP), market pressure (MP), regulatory

pressure (RP), technological capabilities (TC), and

organisational capabilities (OC).

H2 : W Z WðSO;ER;CP;MP;RP;TC;OCÞ

3.7. Data collection procedure

The chief executive officer or highest ranking execu-

tive in 154 small and medium size manufacturing firms

C. Montalvo / Technovation 26 (2006) 312–323 319

were contacted by phone followed-up by personalised

letters. The firms approached are located in the USA–

Mexico border. Three industrial sectors were selected:

electrical and electronics; metal-mechanics and plastics.

In total 97 questionnaires were included in the analysis.

Data were collected through the face-to-face adminis-

tration of a self-report questionnaire.7 The respondents

answered to a total of nine scales, each scale correspond

to one of the constructs included in hypothesis two.8 In

turn, each scale considered rational expectations for four

points in time (moment of the survey, short, medium and

long term).

3.8. Findings

Elsewhere has established that people’s behaviour can be

expressed in the following form (Ajzen, 1991):

BwIfw0 Cw1A Cw2SN Cw3PBC

Hypothesis H1 intends to test whether the main prop-

osition of Ajzen can be applied to describe the firms’

willingness to innovate The results of the regression (see

Table A1 in Appendix A) indicate satisfactory values. The

coefficient of determination (R2Z0.85); the standard error

of the estimate (SEZ1.36); the F-test (FZ191.65); and

the significance of the probability levels (Sig.Z0.000)

indicate that the goodness of fit of the data to the

proposed model is satisfactory. From these results it is

possible to accept H1. That is, the willingness of the firm

to engage innovation can be explained in terms of the

attitudes toward the innovation, the perceived social

pressure and the perceived control upon the innovation

process.

Although the test of H1 is relevant to establish the

validity of the adaptation of the Ajzen model to assess

willingness to innovate, hypothesis H2 tests the coherence

and appropriateness of the proposed definitional system to

integrate diverse determinants into a single model. In

addition, with respect to policy analysis, it is more useful

to carry out the analysis at the determinants level. In this

regard, the values of the coefficient of determination

(R2Z0.87); the standard error of the estimate (SEZ1.25);

the F-test (FZ77.43); and the probability level of

significance of (Sig.Z0.000) indicate that the fit of the

data to the proposed model is satisfactory. That is, the

willingness of the firm to develop clean technologies can

be explained in terms of: social outcomes (SO) and

economic risk (ER) perceptions; the perceived social

norms arising from the community (CP), the market (MP),

and regulatory institutions (RP); and the perceived

7 The questionnaire can be obtained by requesting a copy to the author.8 The assessment of the construct ‘organisational capabilities’ was done

with three separated scales. These were organisational learning, strategic

alliances and networks of collaboration (see Montalvo, 2002).

technological (TC), learning (OL), strategic alliances

(AL) and networks of collaboration (NWK) capabilities

to develop clean technologies.

The results shown in Table A3 (see Appendix A)

indicate that perceived control over the development of

clean technologies accounted for 46.5% of the explained

variance. Within this control component, technological

capabilities (TC) accounted for 39.8% of the variance and

capabilities in organisational learning (OL) explain 6.6%

of the variance on willingness. The domains of strategic

alliances (AL) and networks of collaboration (NWK)

showed minimal explanatory significance. The attitudinal

component of the model explained 27.4% of the variance.

Economic risk (ER) accounted for 21.38% of the variance

on willingness, while social outcomes (SO) explained only

6.04%. The least important component to influence the

willingness to innovate was found to be the perceived

social pressure (PSP). Together, the three domains

proposed under social pressure explained only 14.67%

of the variance in the willingness to innovate. The

pressures arising from the market (MP) explained

5.75%, the perceived community pressure (CP) accounted

for 8.22%, and the perceived regulatory pressure

accounted for less that one percentile of the willingness

to innovate.

3.9. Can we explain and predict what triggers innovation?

Returning to our initial question, what triggers change

and innovation in firms? It is clear that a wide variety of

factors depending on the type of innovation in question

and the internal and external contexts of the firm can

trigger innovation. The empirical test has shown that

with the application of the structural model presented

above we can systematically explore the determinants of

the innovative behaviour of the firm in specific situations.

Provided that the empirical stage of the research render

well behaved data and a good fit to the model, many

scenarios possible can be generated. We can have a firm

that is highly motivated to innovate by normative aspects

of behaviour (i.e. either by market, community or

regulatory pressures), in addition the firm could be

facing good economic opportunities in combination with

laudable social outcomes. Taking into account only these

aspects concerning attitudes and social norms might lead

to wrong conclusions if we do not take into account past

experience and the current control over the innovation

process (i.e. economic resources, timing and capabilities).

Similarly, a firm could be highly motivated to innovate

by attitudinal aspects of behaviour (i.e. economic

opportunities and good appropriability conditions)

coupled with high capabilities to innovate. Being both

aspects optimal still normative aspects (e.g. community

and regulatory pressures) could stop the innovative

process. The case of genetically modified crops provides

an example of this case.

Table A1

Model summary

Model R R2 Adjusted R2 Std. error of

the estimatea

1 0.928 0.861 0.856 1.3624

Predictors: (constant), PC, PSP, A. Dependent variable: W.a The interval of the scales to assess willingness ranges from 2 to 14. The

standard error of the estimate should be compared against this value.

C. Montalvo / Technovation 26 (2006) 312–323320

Compared to previous models that explain the

innovative behaviour of firms, the structural model

proposed here not only enables to comprehensively

explore the internal and external operating contexts of

the firm. In addition, it allows assessing the influences

between the predictors and the propensity of the firm to

innovate and explore what type of relationship could exist

among variables. For example, the relationship between

capabilities and perceived economic risk or market

pressures and attitudes towards innovation is expected to

be negative, as technological feasibility is directly related

to capital risk.

The model in addition enables scenario building to find

the conditions upon which a sample of firms could be

more prone to innovate or to engage in specific

technological development. The scenario building can be

done at three levels of explanation of behaviour. First,

combining changes in attitudes, social pressures and

perceived control. Second, permuting changes in the

behavioural domains of social and economic risk out-

comes; community, market and regulatory pressures; and

changes in technological and organisational capabilities.

Finally, combining changes in the salient beliefs at the

domain level. In general, the model allows us to

explore the origin of dissonance between cognition,

motivations, plans and actions in the realm of the

behaviour of the firm.

An additional contribution of the research strategy

proposed for policy analysis is that it evolves from a

positive to a normative approach by proceeding from the

explanation to the prediction of the willingness of firms.

It is positive insofar as it first explores and describes the

possible determinants of the dependent variable. It is only

after testing the association between dependent and

independent variables through empirical validation that

it could use at a normative level. The practical uses of the

developed model arise from the diagnosis of the different

degrees of predisposition to innovate. It is possible to

differentiate between firms and industrial sectors. As a

consequence, it is possible to allocate the policy effort to

those areas of policy intervention that result in being

more relevant to the achievement of socially desirable

goals.

3.10. Theoretical implications for innovation studies

At the outset of this paper, it was argued that there are

several shortcomings in the innovation literature: empha-

sis on individual determinants of innovative behaviours;

insufficient explanation of dissonance between cognition,

motivations and plans and behaviour; lack of differen-

tiation of effects and gauging influences among determi-

nants The behavioural model to explain and predict the

propensity of the firm to engage in innovative activities

that has been proposed in this paper addressed these

concerns as follows. First, rather than focusing on

individual determinants the proposed model takes a

holistic approach. It includes cognitive AZA(SI, ER),

motivational SNZSN(MP, CP, RP) and instrumental

PCZPC(TC, OC) aspects that may affect the behaviour

of the firm BwWZW(A, SN, PC). This system of

definitions facilitates the classification and integration of

diverse insights from innovation studies into a single and

testable theoretical body concerning the measurement of

the conditions upon which the firm could be more prone

to innovate, to change.

Second, concerning the explanation of dissonance

between cognition, motivations, plans and behaviour,

contrary to mainstream models, the proposed model does

not assumes that behaviour should be consistent with the

optimisation principle. Instead, it relies upon internal

consistency of beliefs with the aggregated variables (i.e.

A, SN, and PC), and these aggregated variables with

willingness, and willingness with behaviour. Thus, the

model is a definitional system that prompts for the

inclusion of variables such as beliefs, expectancies, values,

plans, past experience and control as moderators between

cognition, motivation and behaviour. It takes into account

that the relationship between cognition, motivation and

action is not straightforward. This provides the opportunity

to look at managers (firms, groups and organisations) as

dynamic social actors searching for change but perhaps

encountering many obstacles that hamper the achievement

of an ideal goal.

Lastly, at broader level following the line of thought of

early institutional economists in that economic theory must

be based upon acceptable theory of human behaviour, the

paper proposed a theoretical and methodological approach

that enables the integration of insights from diverse areas

of innovation studies towards the explanation and predic-

tion of innovative behaviour of the firm in specific

contexts.

Appendix A. ANOVA

Willingness to innovate against attitude (A), the per-

ceived social presure (PSP) and perceived control (PC)

(Tables A1 and A2).

Willingness to innovate in CT against SO, ER, MP, CP,

RP, TC, OL, AL, and NWK (Tables A3–A5).

Table A5

Correlation analysis

Model W EVR ER MP CP RP TCPP OL AL

Attitudes SO 0.320**

ER K0.529** K0.202

Social pressure MP 0.502** 0.380** K0.355**

CP 0.109 0.136 0.089 0.397**

RP 0.301** 0.353** K0.089 0.335** 0.355**

Control over

innovation

TC 0.657** 0.095 K0.614** 0.304** 0.127 0.260*

OL 0.484** K0.115 K0.413** 0.194 0.124 0.397** 0.554**

AL 0.707** 0.318 K0.538** 0.305** 0.184 0.517** 0.645** 0.543**

NWK 0.732** 0.107 K0.459** 0.313** 0.097 0.429** 0.526** 0.602** 0.690**

**Correlation is significant at the 0.001 level. nZ97.

Table A4

ANOVA

Sum of squares df Mean square F Sig.

Reg. 1102.176 9 122.464 77.443 0.000

Res. 137.577 87 1.581

Total 1239.753 96

Table A3

Model summary

Predictors SEE Adj. R2 % Explains

Attitudes SOZsocial outcomes 3.5016 0.051 6.04

ERZeconomic risk perception 3.0939 0.259 21.38

Social pressure MPZperceived market pressure 2.9847 0.310 5.75

CPZperceived community pressure 2.8102 0.388 8.22

RPZperceived regulatory pressure 2.8086 0.389 0.07

Control over innovation TCPPZtechnological capabilites 1.5797 0.807 39.79

OLZorganisational learning 1.2661 0.876 6.61

OALZstrategic alliances 1.2520 0.879 0.38

NWKZnetworks of collaboration 1.2575 0.878 0.02

Variance explained by the model 87.8%

Dependent variable: W. Predictors: (constant), EVR, CP, ER, RP, MP, TCCP, OL, AL, NWK. EVR is a weighted variable [EVRZevr2!(0.14evr1)]

Table A2

ANOVA

Model Sum of squares df Mean square F Sig.

1 Regression 1067.141 3 355.714 191.652 0.000

Residual 172.612 93 1.856

Total 1239.753 96

Predictors: (constant), PC, PSP, A; Dependent variable: W.

C. Montalvo / Technovation 26 (2006) 312–323 321

References

Abelson, R.P., Levi, A., 1985. Decision making and decision theory, in:

Lindzy, G., Aronson, E. (Eds.),, third ed The Handbook of Social

Psychology, vol. 1. Randon House, New York, pp. 231–309.

Aguilar, F.J., 1988. General Managers in Action. Oxford University press,

New York.

Ajzen, I., 1985. From intentions to actions: a theory of planned behavior, in:

Kuhl, J., Beckmann, J. (Eds.), Action-control: From Cognition to

Behavior. Springer, Heilderberg, pp. 11–39.

Ajzen, I., 1988. Attitudes, Personality, and Behavior. Dorsey Press,

Chicago.

Ajzen, I., 1991. The theory of planned behavior. Organizational Behavior

and Human Decision Process 50, 179–211.

Ajzen, I., 1996. The moderating effects of attitude in decision making, in:

Gollwitzer, P.M., Bargh, J.A. (Eds.), The Psychology of Action: Linking

Cognition and Motivation to Behavior. The Guilford Press, New York.

Ajzen, I., Fishbein, M., 1980. Understanding Attitudes and Predicting

Social Behavior. Prentice Hall, Englewood Cliffs, NJ.

Andrews, C.J., 1998. Environmental business strategy: corporate leaders

perceptions. Society and Natural Resources 11, 531–540.

Argyis, C., Schon, D.A., 1996. Organizational Learning II: Theory, Method

and Practice. Addison-Wesley, Reading, MA.

Beach, L.R., Mitchell, T.R., 1990. Image theory; a behavioral theory of

decision making in organizations. Research in Organizational Behavior

12, 1–41.

Bernard, C.I., 1938. The Functions of the Executive. Harvard University

Press, Cambridge, MA.

C. Montalvo / Technovation 26 (2006) 312–323322

Berry, M.M.J., Taggart, J.H., 1994. Managing technology and innovation: a

review. R&D Management 24 (4), 341–353.

Boyd, B., Dess, G.G., Rasheed, A., 1993. Divergence between archival and

perceptual measures of the environment: causes and consequenses.

Academy Management Review 18, 204–226.

Brady, T., Rush, H., Hobbday, M., Davis, A., Probert, D., Banerjee, S.,

1997. Tools for technology management: an academic perspective.

Technovation 17 (18), 417–426.

Brusoni, S., Prencipe, A., Pavitt, K., 2001. Knowledge specialisation,

organizational coupling and the boundaries of the firm: why do firms

know more than they make?. Administrative Science Quarterly 46 (4),

597–621.

Burgelman, R., 1983. A model of the interaction of strategic behaviour,

corporate context, and the context of strategy. Academy Management

Review 8, 61–70.

Carlsmith, J.M., Ellsworth, P.C., Aroson, E., 1976. Methods of Research in

Social Psychology. Addison Wesley, Reading, MA.

Chapman, C., Ward, S., 1997. Project Risk Management: Processes,

Techniques and Insights. Wiley, Chichester.

Chattery, D., 1995. Achieving leadership in environmental R&D. R&D

Management March–April, 37–42.

Clark, J., Guy, K., 1998. Innovation and competitiveness: a review.

Technology Analysis and Strategic Management 10 (3), 363–395.

Clayton, A., Spinardi, G., Williams, R., 1999. Policies for Cleaner

Technology. Earthscan, London.

Coates, V., Farooque, M., Klavans, R., Lapid, K., Linstone, H.A.,

Pistorius, C., Porter, A.L., 2001. On the future of technological

forecasting. Technological Forecasting and Social Change 67, 1–17.

Collins, D.J., 1994. Research note: how valuable are organizational

capabilities?. Strategic Management Journal 15, 143–152.

Collins, P.D., Hage, J., Hull, F.M., 1988. Organizational and technological

predictors of change in automacity. Academy of Management Journal

September, 512–536.

Colmer, G., Dunkley, M., Gray, K., Pugh, P., Williamson, A., 1999.

Estimating the cost of new technology products. International Journal of

Technology Management 17 (7-8), 840–846.

Conner, M., Armitage, C.J., 1998. Extending the theory of planned

behavior: a review and avenues for further research. Journal of Applied

of Applied Social Psychology 28 (15), 1429–1464.

Cooke, P., Uranga, M.G., Etxebarria, G., 1997. Regional innovation

systems: institutional and organisational dimensions. Research Policy

26 (4–5), 475–491.

Damanpour, F., 1991. Organizational innovation: a meta-analysis of the

effects of determinants and moderators. Academy of Management

Journal 34, 555–590.

Damanpour, F., 1996. Organizational complexity and innovation: devel-

oping and testing multiple contingency models. Journal of Management

Science 42 (5), 693–716.

Dewar, R.D., Dutton, J., 1986. The adoption of radical and incremental

innovations. Management Science 32 (11), 1422–1433.

Dodgson, M., 1995. Organizational learning: a review of some literatures.

Organization Studies 14 (3), 375–394.

Dosi, G., 1988. The nature of the innovative process, in: Dosi, G.,

Freeman, C., Nelson, R., Silverberg, G., Soete, L. (Eds.), Technical

Change and Economic Theory. Printer, London, pp. 221–238.

Eduards, W., 1954. The theory of decision making. Psychological Bulletin

51, 380–417.

Eisenhardt, K.M., Zbraracki, M.J., 1992. Strategic decision making.

Strategic Management Journal 13, 17–37.

Fagerberg, J., 1996. Technology and competitiveness. Oxford Review of

Economic Policy 12 (3), 39–51.

Fishbein, M., Ajzen, I., 1980. Predicting and understanding consumer

behavior: attitude behavior correspondence, in: Ajzen, I., Fishbein, M.

(Eds.), Understanding Attitudes and Predicting Social Behavior.

Prentice Hall, Englewood Cliffs, NJ, pp. 149–172.

Fishbein, M., Ajzen, I., Hinkle, R., 1980a. Predicting and understanding

voting in American elections: effects of external variables, in: Ajzen, I.,

Fishbein, M. (Eds.), Understanding Attitudes and Predicting Social

Behavior. Prentice Hall, Englewood Cliffs, NJ, pp. 174–216.

Fishbein, M., Ajzen, I., McArdle, J., 1980b. Changing the behavior of

alcoholics: effects of persuasive communication, in: Ajzen, I.,

Fishbein, M. (Eds.), Understanding Attitudes and Predicting Social

Behavior. Prentice Hall, Englewood Cliffs, NJ, pp. 218–242.

Fishbein, M., Jaccard, J.J., Davison, A.R., Ajzen, I., Loken, B., 1980c.

Predicting and understanding family planning behaviors: beliefs,

attitudes and intentions, in: Ajzen, I., Fishbein, M. (Eds.), Under-

standing Attitudes and Predicting Social Behavior. Prentice Hall,

Englewood Cliffs, NJ, pp. 130–147.

Frank, H., Drenth, P., Koopman, P., Rus, V., 1988. Decisions in

Organizations. Sage, London.

Fransman, M., 1994. Information, Knowledge, vision and theories of the

firm. Industrial and Corporate Change 3 (3), 713–757.

Freeman, C., Perez, C., 1988. Structural crisis of adjustment, business

cycles and investment behavior, in: Dosi, G., Freeman, C., Nelson, R.,

Silverberg, G., Soete, L. (Eds.), Technical Change and Economic

Theory. Printer, London.

Furubotn, E.G., 2001. The new institutional economics and the theory of the

firm. Journal of Economic Behaviour and Organization 45, 133–153.

Georghiou, L.J., Metcalfe, S., Gibbons, M., Ray, T., Evans, J., 1986. Post-

innovation Performance: Technological Development and Competition.

MacMillan, London.

Gollwitzer, P.M., Bargh, J.A. (Eds.), 1996. The Psychology of Action: Linking

Cognition and Motivation to Behavior. The Guilford Press, New York.

Gopalakrishnan, S., Damanpour, F., 1994. Patterns of generation and

adoption of innovation in organizations: contingency models of

innovation attributes. Journal of Engineering and Technology Manage-

ment 11 (2), 95–116.

Gopalakrishnan, S., Damanpour, F., 1997. A review of innovation research

in economics, sociology and technology management. Omega Inter-

national Journal of Management Science 25 (1), 15–28.

Grant, R., 1996. Prospering in dynamically-competitive environments:

organizational capability as knowledge integration. Organization

Science 7 (4), 357–387.

Grant, R.M., 1997. The knowledge-based view of the firm: implications for

management practice. Long Range Planning 30 (3), 450–454.

Hall, A., Bockett, G., Taylor, S., Sivamohan, M.V.K., Clark, N., 2001. Why

research partnerships really matter: Innovation theory, institutional

arrangements and implications for developing new technology for the

poor. World Development 29 (5), 783–797.

Hamel, G., Prahalad, C.K., 1994. Competing for the future. Harvard

Business School Press.

Harrison, D.A., Mykytyn, P.P., Riemenschneider, C.K., 1997. Executive

decisions about adoption of information technology in small business:

theory and empirical tests. Journal Systems Research 8 (2), 171–195.

Hawkins, R., Mansell, R.E., Skea, J. (Eds.), 1995. Standards, innovation

and competitiveness: the politics and economics of standards in natural

and technical environments. Edward Elgar, Brookfield, VT.

Hickson, D.J., Butler, R.J., Cray, D., Mallory, G.R., Wilson, D.C., 1986.

Top Decision: Strategic Decision-making in Organization. Basil

Blackwell, Oxford.

Hippel, E., Tyre, M.J., 1995. How learning by doing in done: problem

identification in novel process equipment. Research Policy 24, 1–12.

Hodgson, G.M., 1998. The approach of institutional economics. Journal of

Economic Literature 36, 166–192.

Hodgson, G.M., 2000. What is the essence of institutional economics?.

Journal of Economic Issues 34 (2), 317–359.

Huber, G.P., 1996. Organizational learning: a guide for executives in

technology-critical organisations. International Journal of Technology

Management 11 (7/8), 821–832.

Jonas, K., Doll, J., 1996. A critical evaluation of the theory of reasoned

action and the theory of planned behavior. Zeitchrift fur Sozialpsychol-

ogies 27 (1), 18–31.

C. Montalvo / Technovation 26 (2006) 312–323 323

Kline, S.J., Rosenberg, N., 1986. An overview of innovation, in:

Landau, R., Rosenberg, N. (Eds.), The Positive Sum Strategy. National

Academy Press, Washington, DC.

Leonard-Barton, D., 1992. Core capabilities and core rigidities: a paradox

in managing new product development. Strategic Management Journal

13, 111–125.

Leonard-Barton, D., 1995. Wellspring of Knowledge: Building and Sustaining

the Sources of Innovation. Harvard School Press, Boston, MA.

Lynne, G.D., Casey, C.F., Hodges, A., Rahmani, M., 1995. Conservation

technology adoption decisions and the theory of the planned behavior.

Journal of Economic Psychology 16 (4), 581–598.

Mahdi, S., 2002. Search strategy on product innovation process: theory and

evidence from the evolution of agrochemical lead discovery process SPRU—

Science and Technology Policy Research, Electronic Working Papers Series ..

Metselaar, E.E., 1997. Assessing the Willingness to Change-Construction

and Validation of the Dinamo. Faculty fo Psychology and Pedagogy,

University of Amsterdam, Amsterdam.

Miller, D., 1987. The structural and environmental correlates of business

strategy. Strategic Management Journal 8, 55–76.

Miller, D., 1996. A preliminary typology or organizational learning:

synthesising the literature. Journal of Management 22 (3), 485–505.

Mintzberg, H., 1994. The Rise and Fall of Strategic Planning. The Free

Press, New York.

Montalvo, C.C., 2002. Environmental Policy and Technological Inno-

vation: Why do Firms Adopt or Reject New Technologies?. Eduard

Elgar, Cheltenham, UK.

Montalvo, C.C., 2003a. Sustainable production and consumption systems-

cooperation for change: assessing and simulating the willingness

of the firm to adopt/develop cleaner technologies. The case of the In-Bond

industry in northern Mexico. Journal of Cleaner Production 11, 411–426.

Montalvo C.C., 2003b. Clean production: governance and regulation under

interdependence and power assymmetry. In the proceedings of The 11th

International Conference of the Greening of Industry Network, October

12–15; 2003.

Nelson, R.R., Sampat, B.N., 2001. Making sense of institutions as a factor

shping economic performance. Journal of Economic Behavior and

Organization 44, 31–54.

Nelson, R.R., Winter, S.G., 1982. An evolutionary theory of economic

change. Harvard University Press/Belknap Press, Cambridge, MA.

Nonaka, I., 1994. A dynamic theory of knowledge creation. Organization

Science 5 (1), 14–37.

Panda, H., Ramanathan, K., 1996. Technological capability assessment of a

firm in the electricity sector. Technovation 16 (10), 561–588.

Payne, J., Bettman, J.R., Johnson, E.J., 1992. Behavioral decision research:

a constructive processing perspective. Annual Review of Psychology

43, 87–131.

Penrose, E., 1959. The Theory of the Growth of the Firm. Basil Blackwell,

London.

Petts, J., Herd, H., O’Heocha, M., 1998. Environmental responsiveness,

individuals and organizational learning: SME experience. Journal of

Environmental Planning and Management 4 (6), 711–730.

Quiley, J.V., 1993. Vision: How Leaders Develop it, Share it, and Sustain it.

McGraw Hill, New York.

Raymond, L., Julien, P.A., Carriere, B., Lachance, R., 1996. Managing

technological change in manufacturing SMEs: a multiple case analysis.

International Journal of Technology Management 11 (3-4), 270–285.

Roome, N., 1994. Business strategy, R&D management and environmental

imperatives. R&D Management 24 (1), 65–82.

Rosenbloom, R.S., Christensen, C.M., 1994. Technological discontinuities,

organizational capabilities, and strategic commitments. Industrial and

Corporate Change 3 (3), 655–683.

Rotemberg, J.J., Salomer, G., 2000. Visionaries, Managers and strategic

direction. Rand Journal of Economics 31 (4), 693–716.

Schoemaker, P.J.H., 1993a. Strategic decisions in organisations: rational

and behavioral views. Journal of Management Studies 30 (1), 107–129.

Schoemaker, P.J.H., 1993b. Determinants of risk-taking: behavioral and

economic views. Journal of Risk and Uncertainty 6, 49–73.

Senge, P.M., 1990. The Fifth Discipline. Century Business, London.

Shapira, Z., 1994. Risk Taking: a Managerial Perspective. Russell Sage

Foundation, New York.

Simon, H.A., 1945. Administrative Behavior. Free Press, New York.

Sutton, S., 1998. Predicting and explaining intentions and behavior: how

well are we doing?. Journal of Applied of Applied Social Psychology 28

(15), 1317–1338.

Taylor, S., Todd, P.A., 1995. Understanding information technology usage: a

test of competing models. Information Systems Research 6 (2), 144–176.

Teece, D.J., Pisano, G., 1994. The dynamic capabilities of firms: an

introduction. Industrial and Corporate Change 3 (3), 537–556.

Teece, J.D., Pisano, G., Shuen, A., 1990. Firm capabilities, resources and the

concept of strategy. Consortium on Competitiveness and Cooperation,

Working Paper No. 90-8. University of California at Berkeley.

Terry, D.J., 1993. Self-efficacy expectancies and the theory of the reasoned

action, in: Terry, D.J., Gallois, C., McCamish, M. (Eds.), The Theory of

Reasoned Action: Its Application to AIDS-preventive Behavior.

Penguin, London.

Tidd, J., Bessant, J., Pavitt, K., 1997. Managing Innovation: Integrating

Technological, Market and Organizational Change. Wiley, Chichester.

Tsang, E.W.K., 1997. Organizational learning and the learning organiz-

ation: A dichotomy between descriptive and prescriptive research.

Human Relations 50 (1), 73–89.

Utterback, J.M., 1994. Mastering the Dynamics of Innovations. Harvard

Business School Press, Boston.

van de Ven, A.H., 1986. Central problems in the management of

innovation. Management Science 32 (5), 590–607.

van Ryn, M., Vinokur, A., 1992. How did it work? An examination of the

mechanisms through which a community intervention influenced job-

search behavior among an unemployed sample. American Journal of

Community Psychology 33, 793–802.

van Someren, T.C.R., 1995. Sustainable development and the firm:

organizational innovations and environmental strategy. Business

Strategy and the Environment 4, 23–33.

Wehn de Montalvo, U.W.C., 2003. Mapping the Determinants of Spatial

Data Sharing. Ashgate, Oxon.

Westall, O.M., 1997. Invisible, visible and direct hands: an institutional

interpretation of organisational structure and change in British general

insurance. Business History 39 (4), 44–68.

Zahra, S.A., 1996. Technology strategy and financial performance:

examining the moderating role of the firm’s competitive environment.

Journal of Business Venturing 11, 189–219.

Dr Montalvo works at TNO-STB as

Senior Advisor for Innovation Policy and

Management. He holds a BSc in Electro-

mechanical Engineering and a MPhil in

Industrial Economics. He completed a

DPhil in Science and Technology Policy

at SPRU, University of Sussex (UK). Dr

Montalvo has extensive industry experi-

ence as an engineer and in project and

product management. His is an authority

on environmental technology innovation

and management. Previous to joining TNO-STB, Dr Montalvo held a

number of engineering and management positions in industry and most

recently held the post of Economic Affairs Officer, at the United

Nations Conference for Trade and Development (UNCTAD) in

Geneva. His current research activities and interest focus on the

application of behavioural and system dynamics models to explore the

interaction between regulatory systems and technological innovation,

competition policy in markets with rapid innovation and strategic

prospective intelligence.