Individual Presentations on Weekly Readings
Length:
5 minutes (a 7 minute hard maximum)
Your task for that week is to put together a presentation on the given reading. You can give some powerpoint
slides, What is most important is the content that I expect to see:
a. A brief overview of the key messages from the reading.
b. Include your own insights / opinion / critical analysis on the content. E.g. do you agree with the arguments
given? Has the author missed a key factor? What points resonated with your own experience?
c. Suggest some points for follow-up discussion in the forum. You will need to moderate that discussion.
d. To excel: bring in materials from other sources to compare to your given readings / enhance
discussion / etc., and give references for these in the discussion forum for those who are interested.
You are encouraged to be creative and innovative in your presentation of the material.
You must digest the content of the reading, and deliver the presentation in your own words – if you are
excessively reading / directly repeating the text from the reading you will fail
MARKING GUIDE
0-49%
1. Structure 10
No structure and logical
sequence of information
Difficult or impossible to
understand or follow
50-64%
65-74%
Loose organisation
Information presented in
sequence and easy to follow
Well structured with an
Presentation difficult understand
introduction, main section and
and/or follow
conclusion (that suits the
innovation/creative format of the
presentation)
2. Delivery 10
Good posture, some eye contact,
Reads directly from notes or
slides
Inaudible
Spoken too quickly
Uncomfortable in front of camera
Little eye contact or audience
engagement
Presentation audible but with
Poorly articulated thoughts.
poor articulation and flow
Very good use of pausing
Extreme use of vocalized
Excessive vocalized pauses (uh,
Generally clear articulation of
pauses(uh, well uh, um)
Poor readability of any visual
aids
well uh, um)
Somewhat innapropriate or
unreadable visual aids if used
thoughts but with noticeable
vocalized pauses (uh, well uh,
um)
Clear voice
Correct pronunication
Well-paced delivery
3. Content –
overview 25
No evidence of research.
Content is all or to a significant
degree read directly from the
reading.
Content generally appropriate
but not well developed
Little reference to innovation
themes
Little evidence of relevant
research
Lack of confidence with material
presented
85-100%
Information presented in logical,
interesting sequence
Very well structured with
excellent introduction, clear
purpose, body and conclusion
(that suits the
innovation/creative format of the
Information is highly structured,
facilitating class understanding
Outstanding introduction, clear
and engaging purpose, well
developed body and compelling
conclusion (that suits the
innovation/creative format of the
presentation)
presentation)
Excellent interesting and well-
and rapport with camera
Appropriate visual aids if used
No appropriate content and/or
No grasp of information
presented
75-84%
paced delivery with good use of
voice, tone, diction and pausing
Excellent posture, eye contact
and rapport with camera
Thoughts articulated clearly with
very few vocalized pauses (uh,
well uh, um)
Excellent use of visual aids
Mark
..out of
10
Informative, well paced and
entertaining delivery
Outstanding posture, eye contact
and rapport with camera
10
Thoughts articulated clearly with
engaging delivery
No vocalized pauses noticed
Exemplary use of visual aids
(where used)
where used
Content appropriate to purpose,
and linked to an innovation
themes
Content fully appropriate to
purpose, and linked very well to
innovation themes
Evidence of research relevant to
Evidence of very good research
the topic
relevant to topic
Good understanding and
confidence in material presented
Excellent understanding of and
confidence in material presented
Outstanding selection of
engaging content entirely
appropriate to purpose, and
strongly linked to innovation
themes
Evidence of significant research
fully relevant to topic
Outstanding level of
understanding and confidence in
25
material presented
4. Content –
insights / critical
analysis and use of 35
other sources
No insights / critical analysis of
the material presented. Content
is all or to a significant degree
Rudimentary critical analysis
provided, perhaps of peripheral
issues.
Good critical analysis of a
number of issues, some in depth.
Some comparison to other
Strong critical analysis of the
issues related to the structures
and processes of this
organisation.
No use of other sources for
read directly from the reading.
comparison
Outstanding critical analysis of
the issues related to the
structures and processes of this
organisation, with reference to
best practise, other
35
organisations.
sources
Good comparison to other
sources, appropriately chosen
Excellent use of other
sources/literature to extend the
analysis
5. Innovation /
creativity 5
Delivers a standard presentation,
with no creativity applied to the
Uses some creativity applied to
the standard format, or the
creative structure used is not
Uses a creative concept to
deliver the information, and the
concept is suitable for this
format
6. Modertion of
follow up
discussion /
response to
questions
Comments
Failure to moderate discussion /
15 answer questions about subject
particularly suitable
purpose
Can answer rudimentary
questions from audience.
Rudimentary moderation of
forum
Can answer most questions with
ease but fails to elaborate on
some questions
Prompts some further discussion
Uses a highly creative concept to
Uses a highly creative and
deliver the presentation, and the
concept is very well suited to
memorable concept to deliver
the presentation, and the
convey the information to the
concept is strongly suitable for
audience
this purpose
Can answer all questions with
Elaborates and explains when
ease
answering all questions
Can elaborate on some questions
Good moderation to prompt
Responds confidently and in a
friendly manner
Excellent moderation to steer
further discussion
the discussion
Total
5
15
0.0
100
Innovation management
measurement: A review
XX
81O
Innovation
RIGINAL
management
ARTICLE
measurement:
A review
Blackwell
Oxford,
International
IJMR
©
1460-8545
Blackwell
UK
Publishing
Publishing
Journal
of
Ltd
Management
Ltd
2006
Reviews
Richard Adams,1 John Bessant and Robert Phelps
Measurement of the process of innovation is critical for both practitioners and academics,
yet the literature is characterized by a diversity of approaches, prescriptions and practices that
can be confusing and contradictory. Conceptualized as a process, innovation measurement
lends itself to disaggregation into a series of separate studies. The consequence of this is the
absence of a holistic framework covering the range of activities required to turn ideas into
useful and marketable products. We attempt to address this gap by reviewing the literature
pertaining to the measurement of innovation management at the level of the firm. Drawing
on a wide body of literature, we first develop a synthesized framework of the innovation
management process consisting of seven categories: inputs management, knowledge
management, innovation strategy, organizational culture and structure, portfolio management,
project management and commercialization. Second, we populate each category of the
framework with factors empirically demonstrated to be significant in the innovation process,
and illustrative measures to map the territory of innovation management measurement. The
review makes two important contributions. First, it takes the difficult step of incorporating
a vastly diverse literature into a single framework. Second, it provides a framework against
which managers can evaluate their own innovation activity, explore the extent to which their
organization is nominally innovative or whether or not innovation is embedded throughout
their organization, and identify areas for improvement.
Measuring the Management of
Innovation
A considerable literature has accumulated
on the subject of innovation, which is widely
seen as the basis of a competitive economy
(Porter and Ketels 2003). This literature
includes evidence that competitive success is
dependent upon an organization’s management of the innovation process and proposes
factors that relate to successful management of the innovation process (cf. inter
alia Balachandra and Friar 1997; Cooper
1979a,b; de Brentani 1991; Di Benedetto 1996;
Ernst 2002; Globe et al. 1973; Griffin 1997;
Rothwell 1992).
Quantifying, evaluating and benchmarking
innovation competence and practice is a significant and complex issue for many contemporary organizations (Frenkel et al. 2000). An
important challenge is to measure the complex processes that influence the organization’s innovation capability, in order that they
can be optimally managed (Cordero 1990).
The measurement of innovation is also important from an academic research perspective.
Unless constructs relating to the phenomenon
are measurable using commonly accepted
© Blackwell Publishing Ltd 2006, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA
International Journal of Management Reviews Volume 8 Issue 1 pp. 21–47
21
Innovation management measurement: A review
methods, there is a risk that different operationalizations of the same effect will produce
conflicting findings, and that theoretical
advances become lost in the different terminologies that resist the accumulation of
knowledge.
Within the literature on the management of
innovation, measures of aspects of innovation management are frequently proposed,
responding to the needs of both firms and
academics to understand the effectiveness
of innovation actions (Barclay 1992; Kim and
Oh 2002). However, their treatment is fragmented. It is possibly a consequence of this
fragmentation that empirical studies have
found many organizations tend to focus only
on the measurement of innovation inputs and
outputs in terms of spend, speed to market
and numbers of new products, and ignore the
processes in-between (Cordero 1990). A generalized measurement framework specified
at the level of the organization would provide
a useful basis for managers to monitor and
evaluate their innovation processes, diagnose
limitations and prescribe remedies (Cebon and
Newton 1999). In an attempt to extend measurement theory and practice beyond a focus
on output performance, this paper reviews the
literature as it relates to the measurement of
innovation management in the context of a
conceptual framework of process that provides the basis for a general measurement
framework. We bring together disparate suggestions for innovation management measurement made in various parts of the literature
and summarize commonly used measures at
different stages of innovation management.
We identify gaps in measurement theory and
practice and point the way toward the development of a comprehensive set of innovation
management measures.
Developing an Analytic Framework for
Innovation Management Measurement
The innovation literature is a fragmented
corpus, and scholars from a diversity of
disciplinary backgrounds adopt a variety of
22
ontological and epistemological positions to
investigate, analyse and report on a phenomenon that is complex and multidimensional
(Wolfe 1994). More specifically, this diversity
is reflected in the multitude of approaches
to measurement and the number of different
measures that can be found. It is difficult to
identify a bounded body of literature in which
a comprehensive discussion of innovation
measurement issues might be located. Representing this diversity within a synthesized
framework is a challenging task.
Indeed, the term ‘innovation’ is notoriously
ambiguous and lacks either a single definition
or measure. We chose to adopt the UK Department of Trade and Industry’s (DTI 1998)
broad definition of innovation, ‘the successful
exploitation of new ideas’, to guide our
review, because it accommodates the range of
innovation types (product/service, process,
administrative, technological, etc.) that one
might reasonably expect to encounter in an
organization. This holistic approach is clearly
important from the practitioner’s perspective,
as it obviates the need to collate measures on
a piecemeal basis from a diverse literature.
So, while this review is broadly specified, its
breadth presents a number of methodological
challenges. We address these challenges by
employing the approach of an adapted systematic review.
The notion of systematic review has
recently gained currency in the management
literature (Denyer and Neely 2004), and the
strategy for this review followed, in many
respects, the methodology detailed by Tranfield et al. (2003). They state that systematic
reviews include: development of clear and
precise aims and objectives; pre-planned
methods; comprehensive search of all potentially relevant articles; use of explicit, reproducible criteria in the selection of articles for
review; appraisal of the quality of the research
and the strength of the findings; synthesis of
individual studies using an explicit analytic
framework; and balanced, impartial and comprehensible presentation of the results. Our
review strategy broadly adopted this model,
© Blackwell Publishing Ltd 2006
March
but with some changes to suit the exigencies
of our question and data sources, notably: first,
the inclusion of a Delphi study and, second, a
relaxation of the requirement for reproducible
criteria for document selection and appraisal.
The authors formed a review panel consisting of domain-relevant experts from a range
of disciplines with an interest in both innovation and measurement. Time was spent outlining the research project, which was articulated
in terms of the fragmented world of competing and contradictory measures addressing
the issue of the management of innovation.
Rather than attempting to generate an exhaustive list, our objectives were defined in terms
of the collation and synthesis of measures,
better reflecting the needs of academics and
practitioners: we ask ‘What are the measures
that have been used, and to what extent do
they adequately populate and dimensionalize
a comprehensive analytic framework?’
McManus et al. (1998), reflecting upon systematic review, identified the limitations of
searching electronic databases as sometimes
uncovering only half the relevant studies. They
attribute this in part to a lack of sensitivity of
electronic databases. Indeed, this may account
for the disappointing outcome in terms of
breadth that Leseure et al. (2004) report from
their search of electronic databases for literature relating to the adoption of promising
practices. Compensating strategies include
either hand searching of journals (which we
had to discount on the grounds of time and
cost) or consultation with appropriate experts
(McManus et al. 1998). The latter are particularly important when ‘performing a systematic review in a developing field that does not
have a clearly defined specialist literature’
(McManus et al. 1998, 1563). Using the Delphi method, a process consisting of structured
design for group communication relating to
complex problems (Linstone and Turoff 2002),
we extended our consultation process to
incorporate the input of external experts.
A list of global experts in innovation and
measurement (n = 100) based on the knowledge
of the review panel was developed. Potential
© Blackwell Publishing Ltd 2006
2006
respondents were contacted by e-mail and asked
to respond to a series of questions relating to
innovation metrics at the level of management practice, particularly addressing aspects
of measuring the existence and effectiveness
of the innovation management process.
There was a high degree of consensus in the
28 responses received. All respondents recognized the existence of a plethora of extant
measures, obviating the need for new measures to be developed. Specifically, an absence
of measures well aligned to the activities of
the innovation process was noted. Further,
our attention was drawn to the absence of
devices to help identify the appropriate
metrics to apply.
Systematic review stresses the importance
of an audit trail in the review process to
ensure clarity and replicability. Significant to
this is the use of explicit, reproducible criteria
in the selection of articles for review and, an
appraisal of the quality of the research and the
strength of the findings (Tranfield et al. 2003).
However, regardless of a study’s methodology
or generalizability, it might still incorporate
measures of innovation management that could
contribute to the construction of a measurement framework, and so grey literature is
included in this review. As we were not
reviewing an evidence base in the normally
accepted use of the term, quality criteria as
used in previously published systematic reviews
in the management literature (e.g. Leseure
et al. 2004; Pittaway et al. 2004), in which the
value of the evidence base is determined by
assessed levels of theory robustness, methodology, implications for practice, generalizability
and contribution, were felt not to be wholly
relevant in this case. We instead adopted a
position akin to Glaser and Strauss’s (1967)
notion of ‘theoretical saturation’ which is
achieved when
no additional data are being found whereby the
researcher can develop properties of the category.
As he sees similar instances over and over again,
the researcher becomes empirically confident that
a category is saturated … when one category is
23
Innovation management measurement: A review
Table 1. Review strategy
Step 1
Step
Step
Step
Step
2
3
4
5
Step 6
Step 7
Establish review team and scope and nature
of the question and search strings
Undertake Delphi investigation
Preliminary search of electronic databases
Develop analytic framework
Secondary search of electronic databases and
Delphi study
Content analysis of data set, sorting of
measures into first order categories defined
by the analytic framework
Review measures against framework for gaps
saturated, nothing remains but to go on to new
groups for data on other categories, and attempt to
saturate these categories also. (Glaser and Strauss
1967, 65)
That is, the incremental contribution of further
sampling is marginal and fails to add significant
value. The steps of our review are presented in
Table 1.
A plethora of studies operationalize measures of aspects of innovation management;
however, to provide some synthesis and identify gaps, agreement regarding the nature of
innovation management is needed. The literature lacks such consensus. The concept is frequently disaggregated into component parts,
and scholars adopt their own partial views. As
a result, the operationalization of measures is
frequently idiosyncratic, owing more to the
predilections of the researcher and the exigencies of the data than to the overarching objectives of synthesis or cumulation. To organize,
compare and contrast such measures, we propose a typology of elements of the innovation
management process.
Many scholars have sought to identify the
key activities of the innovation management
process (Wolfe 1994), some of which are presented as linear models (e.g. Daft 1978), and
others that are dynamic and recursive characterized by feedback and feed-forward loops
(e.g. Schroeder et al. 1989). While useful,
these models are limited from a measurement
perspective: first, there are many competing
models with consensus only evident at abstract
levels; second, models have principally been
24
generated in the context of technology, and so
generalizability is constrained; third, with a
focus on activities, models fail to take account
of the organizational pervasiveness of innovation and its socio-technical connectedness with
all aspects of the organization, or the levels
of integration envisaged in Rothwell’s (1992)
fifth-generation process model. Finally, while
the range and sequence of activities may vary
across organizations and projects, their successful management is affected by a number of
factors. Cebon and Newton (1999) call this
the ‘capacity to make change’, about which
the literature generally is relatively silent
(Neely and Hii 1998). Based on a review of
models, we propose a seven-factor framework
of meaningful categories specified in terms of
the requisite organizational capabilities to
make and manage change (see Table 2).
In a review of the factors associated with
new product development (NPD) success, Ernst
(2002) echoes Cooper and Kleinschmidt’s
(1995) influential five techno-centric factors
for new product performance: NPD process,
NPD strategy, organization, culture and management commitment. This model, though, overlooks innovation in non-technical contexts
and other important factors such as the role of
knowledge (Leonard and Sensniper 1998).
In their technical innovation audit tool,
Chiesa et al. (1996) describe process and
performance as the two foci of innovation
management measures. They overlay ‘core
processes’ with a set of ‘enabling processes’,
the latter describing the deployment of
resources, and the effective use of appropriate
systems and tools governed by top management leadership and direction. Finding this
applicable only to ‘hard’ innovations, Verhaeghe and Kfir (2002) extended the audit
tool to an investigation of the processes that
support and enable both ‘hard’ and ‘soft’ (e.g.
a research or consultancy project) innovation.
The changes they made may appear semantic,
for instance relabelling ‘process innovation’ as
‘technology transfer’. However, the important
implication is that the study extends the application of the instrument to service contexts.
© Blackwell Publishing Ltd 2006
© Blackwell Publishing Ltd 2006
Table 2. Innovation management models and organizing framework
Cooper and
Kleinschmidt (1995)
Chiesa
et al. (1996)
Cormican and
O’Sullivan (2004)
Inputs
Creativity and
human resources
Knowledge
management
Resource
provision
Strategy
NPD strategy
Organization and
culture
Organizational
culture
Management
commitment
NPD process
Portfolio
management
Project management
Commercialization
Goffin and
Pfeiffer (1999)
Leadership
Systems
and tools
Strategy and
leadership
Culture and
climate
Innovation
strategy
Planning and
selection
Communication
and collaboration
Structure and
performance
Portfolio
management
Project
management
Burgelman et al. (2004)
Resource availability
Understand relevant technological
developments and competitor
strategies
Strategic management
Verhaeghe and
Kfir (2002)
Idea generation
Technology acquisition
Networking
Structural and cultural context of
the organization
Development
Commercialization
March
2006
25
Innovation management measurement: A review
Furthermore, they explicitly bounded their
model with notions of inputs and commercialized outputs.
Cormican and O’Sullivan (2004, 820), reflecting earlier studies’ coverage of the organization
of innovation, conceive of product innovation
as a continuous and cross-functional process
‘involving and integrating a growing number
of different competencies [inside the organization]’. So, effective management of the process
requires successful adoption and adaption of a
socio-technical systems approach to all aspects
of the organization, critically including people
and process as well as technology-related
issues.
While there are areas of commonality
across these innovation management models,
no one model covers every dimension. This
suggests a need for a synthetic and integrative
framework to promote comparability and to
enable future work to build on results found in
previous studies. In column 1 of Table 2, we
present such a framework derived from a
synthesis of the studies presented in the other
columns of the table. The framework consists
of seven categories: inputs, knowledge management, strategy, organization and culture, portfolio management, project management and
commercialization.
In the following sections, we use these seven
inductively derived categories as the organizing framework for a discussion of innovation
management measurement. For each of these
categories, we review the diverse literature
relevant to the measurement of that typological
category. Within each category, a series of subdimensions of measurement focus are identified, reflecting the distinctions and emphases
in the literature (see Table 3).
Measures of Innovation Management
Inputs Management
Inputs management is concerned with the
resourcing of innovation activities and includes
factors ranging from finance, to human and
physical resources, to generating new ideas.
26
Table 3. Innovation management measurement
areas
Framework category
Measurement areas
Inputs
People
Physical and financial resources
Tools
Idea generation
Knowledge repository
Information flows
Strategic orientation
Strategic leadership
Culture
Structure
Risk/return balance
Optimization tool use
Project efficiency
Tools
Communications
Collaboration
Market research
Market testing
Marketing and sales
Knowledge
management
Innovation strategy
Organization and
culture
Portfolio
management
Project management
Commercialization
The construct research and development (R&D)
intensity has frequently been used as a global
measure of input. Typically, it is expressed as
a ratio between expenditure (e.g. Parthasarthy
and Hammond 2002) or numbers employed
in R&D roles (Kivimäki et al. 2000) and some
expression of output. The relationship between
R&D intensity and firm or innovation performance has been empirically demonstrated
in several studies (e.g. Deeds 2001; Greve
2003; Parthasarthy and Hammond 2002).
However, there is some equivocality in the literature. Stock et al. (2001) note an inverted-U
relationship between R&D intensity and NPD
performance, and Bougrain and Haudeville
(2002) point out that it does not influence the
future prospects of a project and is, indeed, an
imperfect measure of innovation activity.
Further, R&D is only one of several inputs into
the innovation process and, therefore, cannot
be regarded as an adequate proxy; it also does
not appear to be a very useful measure for
small and medium-sized enterprises (SMEs),
which may not have formal R&D activities or
may not record them (Kleinknecht 1987), or,
indeed, for service industries, which tend to have
low R&D intensity (Hipp and Grupp 2005).
© Blackwell Publishing Ltd 2006
March
High levels of R&D intensity are therefore
not necessarily evidence of good innovation
practice: they may simply mask process inefficiencies (Cebon and Newton 1999; Dodgson
and Hinze 2000). However, adequate funding
is clearly a critical input into the innovation
process. Expenditure data have long been a
popular proxy measure of innovation input,
largely because of their ready availability.
Several different approaches, both qualitative
and quantitative, to measuring funding can
be identified: total expenditure, expenditure
expressed as a proportion of sales or revenues,
and expenditure by item (organizational department, patent, innovation or scientist) (Geisler
1995; Oliver et al. 1999). Also, there is a
series of perceptual measures that attempt to
determine the adequacy of funding (AtuaheneGima 1995). Measures, though, tend principally to be quantitative and express little other
than funding level; in particular, few measures
attempt to determine the adequacy of funding
for the innovation project. Kerssens-van
Drongelen and de Weerd-Nederhof (1999)
point to a lack of measurement procedures
to help managers diagnose poor innovation
performance or support improvement.
A more helpful measure of inputs may
therefore be obtained by disaggregating inputs
into different types and measuring each independently, before aggregating back to a measure of overall inputs management. Brown and
Svenson (1988, 30) define the inputs into the
R&D system as ‘the raw materials or stimuli
a system receives and processes’, including
people, equipment, facilities and funds. This
fundamental distinction between people, tools
and physical and financial resources is widely
mirrored in the literature.
People factors have been measured as the
number of people committed to the innovation
task (absolute numbers and relative to total
employees), in terms of the mix of types of
people regarding cosmopolitanism and propensity to innovate and, in terms of skills,
experience and education. Damanpour (1991)
argues that a diversity of skills and experience
permits more differentiated units from which
© Blackwell Publishing Ltd 2006
2006
collaborative relationships can emerge and add
significant value to innovation outcomes.
Although Baldridge and Burnham (1975) argue
that demographic characteristics (sex, age,
cosmopolitanism, education) do not appear
to influence innovative behaviour among
individuals, more recent research suggests that
innovating groups should comprise individuals
with a mix of these characteristics (Amabile
1998). Members with high levels of education
and self-esteem increase the effectiveness of
R&D project teams (Kessler and Chakrabarti
1996), and individuals of greater educational
attainment with diverse backgrounds have
been associated with more innovative teams
(Bantel and Jackson 1989).
The propensity of an individual to innovate
has received considerable attention, though it
is difficult to measure. Kirton (1976) developed a 32-item questionnaire designed to
establish an individual’s position on an
‘adaptor–innovator’ continuum. Scott and
Bruce (1994) operationalized an ‘innovative
behaviour measure’ consisting of six items
against which subordinates are measured by
hierarchical superiors. Finally, the Innovation
Potential Indicator (Patterson 2003) provides
a framework for investigating individual
behaviours that might promote or inhibit
innovation in the workplace. This measure
is constructed around four dimensions: an
individual’s motivation to change, challenging behaviour, preferred approach to work,
and preference for tried and trusted
methods of work as opposed to doing things
differently.
Facilities, or physical resources, is a broad
category that captures a range of inputs from
buildings to computer equipment. Physical
resources can be counted or measured in
dollar terms. However, one important general
measure of facilities, which cannot be so readily measured, is slack. Slack resources or
unused capacity can be regarded as an important catalyst for innovation. Slack allows
failures to be absorbed, provides the opportunity for diversification, and fosters a culture
of experimentation and protects against the
27
Innovation management measurement: A review
uncertainty of project failure (Kimberly 1981).
An alternative view, however, suggests a negative relationship: slack can become synonymous
with waste and represents a cost that should be
eliminated (Nohria and Gulati 1996). Typically,
financial measures of slack are used (Damanpour 1991), although Miller and Friesen (1982)
use both financial and human measures of
slack.
Use of systems and tools is an important
input to the innovation process (Bessant and
Francis 1997; Cooper et al. 2004). Measures
identified tend to relate to whether or not the
organization has or makes use of formal systems and tools in support of innovation. These
can be of various sorts, such as the availability
and use of tools and techniques for promoting
creativity (Amabile 1998; Rickards 1991;
Rochford 1991; Thompson 2003) or the availability and use of systems of quality control
ranging from informal methods to specific
techniques such as total quality management
(TQM) (Souitaris 2002). While tool use can
be evaluated on a binary or degree of use scale,
there is little other than Chiesa et al.’s (1996)
technical innovation audit that attempts to
measure whether or not tool use is consistent
with a firm’s innovation requirements and is
integrated into its processes.
We conclude that, while there has been a
concentration on financial measurement of
inputs, there is less emphasis on measuring
other aspects of the category. Even within
financial measures, there are few that attempt
to determine the adequacy of funding for the
innovation project. Further, most measures
reflect a preoccupation with R&D and NPD
rather than other forms of innovation (e.g.
process, business model). In particular, the
softer inputs of skills and knowledge are
poorly represented by measurement instruments. Tacit knowledge input appears not to
be well captured by extant measures, and no
measures of appropriate skill levels have been
developed. This is an imbalance that needs
to be addressed by further work to develop
a balanced set of measures covering all
sub-dimensions of the input category.
28
Knowledge Management
Knowledge absorption, an organization’s
ability to identify, acquire and utilize external
knowledge, can be critical to a firm’s successful operation (Zahra and George 2002). The
concept of knowledge has received much
attention in recent years (e.g. Blackler 1995;
McAdam 1999; Nonaka 1991) and has been
asserted to play a critical role in the innovation process (Hull et al. 2000). Knowledge
management is concerned with obtaining
and communicating ideas and information
that underlie innovation competencies, and
includes idea generation, absorptive capacity
and networking. Knowledge management
covers the management of explicit and
implicit knowledge held by the organization
(Davis 1998; Nonaka 1991) as well as the
processes of gathering and using information. The three areas within knowledge
management of importance for innovation
management identified in the literature
are: idea generation, knowledge repository
(including the management of implicit
and explicit knowledge) and information
flows (including information gathering and
networking).
The importance of generating sufficient
numbers of ideas has been well established in
the literature. Ideas are the raw materials for
innovation; it is relatively inexpensive to
generate and screen ideas, yet this can have
significant impact on ultimate success or
failure (Cooper 1988). Several authors have
conceptualized the early stages of the innovation process as a somewhat fuzzy period
(Kim and Wilemon 2002; Moenaert et al.
1995; Verworn 2002), including opportunity
identification, opportunity analysis, idea genesis, idea selection and concept development
(Koen et al. 2001).
At the commencement of the innovation
process, when ideas are generated and
explored, measures tend mostly to be quantitative, inexpensive and rapid. As the process
progresses and uncertainties with regard to
appropriateness, feasibility and business case
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are reduced, measurement approaches become
increasingly qualitative and possibly more
costly and time-consuming to deploy. Measures imply an assumption that the objective is
to generate as many ideas as possible through
the use of generative tools. Several measures
attempt to count the number of ideas generated in a period (cf. Chiesa et al. 1996; Lee
et al. 1996), while others probe the extent to
which organizations are using different generative tools and techniques (cf. Cebon and
Newton 1999; Chiesa et al. 1996; Loch et al.
1996; Rochford 1991; Szakonyi 1994;
Thompson 2003).
If knowledge is fundamental to innovation,
then it should be possible to measure the
accumulated knowledge of the firm, its
knowledge repository. One aspect of innovation relates to combinations of new and
existing knowledge, which privileges the contribution of internal and external knowledge
and the mechanisms by which it flows into
and within an organization (Galunic and
Rodan 1998; Nonaka and Takeuchi 1995; Pitt
and Clarke 1999). Central to this perspective
is the idea of ‘absorptive capacity’, the firm’s
ability to absorb and put to use new knowledge,
and involving ‘an ability to recognize the
value of new, external knowledge, assimilate
it, and apply it to commercial ends’ (Cohen
and Levinthal 1990, 128). Absorptive capacity
results from a prolonged process of investment and knowledge accumulation within the
firm, and its development is path dependent.
Firms with strong absorptive capabilities are
more likely to acquire knowledge and learn
effectively from outside. Higher levels of
absorptive capacity appear to be positively
related to innovation and performance (Chen
2004; Tsai 2001), but it is impossible to
predict what is the ‘right’ level of investment
in absorptive capacity for any individual
firm (Cohen and Levinthal 1990), meaning
that it is not readily amenable to international benchmarking. However, the conceptual
development and empirical studies infer or
imply a range of organizational knowledge
states.
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Several quantitative approaches have been
developed for the measurement of imported
tangible knowledge. The most frequently used
approach counts numbers or value of patents
brought in. However, this restricts its application to contexts in which patents are significant, and overlooks those industries where
they do not feature. For a while, patent data
was widely accepted as a proxy measure for
innovation. More recently, however, the validity of patent statistics has been questioned:
patents vary in their utility for organizations,
and so their input value to the innovation cannot adequately be judged in terms of a cash
price (Griliches 1990; Pakes and Griliches
1980). Only a few studies have attempted to
devise measures for other contexts. For example, Kleinknecht (1987) constructed a question designed to capture the informal hours of
R&D work that are hypothesized to be hidden
within other activities or to take place outside
formal working hours.
Patents represent codified knowledge, but
the importance of tacit knowledge to organizational innovation is underscored in the
resource-based approach (Barney 1991; Galunic and Rodan 1998; Grant 1991; Leonard
and Sensiper 1998). Tacit knowledge is an
important resource when it is has value for the
organization, is difficult for competitors to
imitate, is rare, and can be operationalized by
the organization. Of particular importance is
its acquisition and use (Bess 1998). It can be
acquired opportunistically or by a deliberate
policy of search.
Tacit knowledge is notoriously difficult to
measure in organizational research, and
methodologies for its investigation and
measurement can be complex. Ambrosini
and Bowman (2001), for example, propose
an approach that consists of causal mapping
facilitated by story-telling and the use of metaphor. Other attempts to capture tacit knowledge and group memory have been reported
by Oliver et al. (1999). At the level of the
organization, Sveiby (1997) suggests the
difference between market value and net
book value as an indicator of the value to an
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Innovation management measurement: A review
organization of intangible knowledge assets.
The first method is resource intensive, while
in the second it is not clear how much of the
identified value can be attributed to inputs into
innovation. Tacit knowledge input appears not
to be well captured by extant measures.
Information flows into and within the firm
are important in sparking ideas and in allowing the development of innovative concepts.
Three measurement approaches to information flows can be identified: first, measures of
the linkages that the innovating group maintains with external organizations and sources;
second, measures of internal information
gathering processes. Third, measures of customer information contacts.
Linkage measures determine whether or not
the organization has and maintains external
linkages with other organizations or sources
of information, e.g. through participation in
research projects, university links or attendance at trade shows (Atuahene-Gima 1995;
Tipping and Zeffren 1995). These are principally dichotomous measures, only infrequently is there a measure that implies some
sort of qualitative assessment of the nature of
the linkages. Cebon and Newton’s (1999)
measures suggest that quality and diversity of
linkage might be an important factor, for
example, visits to exemplary projects.
Statistics on the use of formal methods of
information gathering such as project reviews
and the use of technical reports (Oliver et al.
1999) provide a frequently used approach to
the measurement of information gathering.
Cebon and Newton (1999) suggest benchmarking information gathering against competitors’ activities, to gauge how well the
activity is performed. Another important area
about which a firm needs understanding and
information is customers; Atuahene-Gima
(1995) lists a series of measures specifically
aimed at examining the extent to which organizations make use of customers as a source of
information, and include measures assessing
the adequacy of information and customer
contact time (Lee et al. 1996; Miller and
Friesen 1982).
30
Innovation Strategy
Ramanujam and Mensch (1985) define
innovation strategy as a timed sequence of
internally consistent and conditional resource
allocation decisions that are designed to fulfil
an organization’s objectives. Activities must
be consistent with an overarching organizational strategy that implies that management
must take conscious decisions regarding
innovation goals (Sundbo 1997). Innovation
strategy is generally understood to describe an
organization’s innovation posture with regard
to its competitive environment in terms of its
new product and market development plans
(Dyer and Song 1998). This techno-centric
view predominates in the literature and
overlooks those innovative initiatives that are
internally focused (for example, the adoption
of new management techniques and practices). In the conceptualization of innovation
strategy as an articulation of the organization’s
commitment to the development of products
that are new to itself and/or to its markets,
and because strategy does not operate in a
vacuum, but requires a structural context, two
complementary approaches to its measurement, which have been described as objective
and subjective (Li and Atuahene-Gima 2001),
can be identified.
In the literature, scholars principally have
adapted measures from strategic management
research to explore the existence, nature and
extent of innovation strategy. Richard et al.
(2003) use three scale items drawn from the
strategic posture scale devised by Covin and
Slevin (1989), who, in turn, draw on Miles
and Snow’s (1978) ‘prospectors’ and Mintzberg’s (1978) ‘entrepreneurial’ organizations,
for their assessment of bank innovativeness.
These studies classify organizations based on
their approach to innovation using classifications that are ontologically grounded in the
assumption that innovation orientation can be
deciphered from quantitative interpretations
of new product and market activity.
Other objective evidence of an organization’s
innovation strategic posture may include many
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of the input measures (such as level of R&D
expenditure) that we have already discussed.
It is argued that it is not their absolute magnitude but their magnitude relative to industry
rivals that is significant. As has been noted,
although these may be useful indicators of
commitment or intent, they could mask process inefficiencies. However, O’Brien’s (2003)
observation that an interaction between
intended strategy and slack will influence performance suggests that process inefficiencies
are less likely to occur where an innovation
strategy is not merely nominally adopted, but
is embedded in the culture, behaviours and
actions of the organization.
In Li and Atuahene-Gima’s (2001) terms,
the evidence for an embedded innovation
strategy is subjective and may include evaluations of an organization’s emphasis on NPD,
such as resource allocation. Saleh and Wang
(1993) describe this as consisting of three
main components: risk-taking, proactiveness
and persistent commitment to innovation.
These include top management responsibility
for innovation within the organization, including specifying and communicating a direction
for innovation. Cooper (1984) demonstrated
that new product performance is largely
decided by the strategy that top management
adopts. The key components are the link
between innovation strategy and overall business goals (strategic orientation) and the
provision of leadership to make innovation
happen via a strong vision (Pinto and Prescott
1988) for innovation, a long-term commitment to innovation and a clear allocation of
resources (Cooper et al. 2004). This distinction between strategic planning or orientation
and strategic vision or leadership is frequently
replicated in the literature.
Two distinct types of strategic orientation
measures can be identified in the literature.
First, those that measure whether the organization has an innovation strategy; this can be
evaluated in several ways, such as commitment to differentiated funding (White 2002),
explicit expression (does the organization
have an innovation strategy?) (Miller and
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Friesen 1982) and identifiable roles for new
products and services (Cooper and Kleinschmidt 1990; Geisler 1995; Hauser and
Zettelmeyer 1997; Tipping and Zeffren 1995).
The second type of measure regards strategy
as a dynamic instrument that shapes and
guides innovation in the organization. These
measures assume that strategy exists and asks
questions about how effective it is in shaping
and guiding: ‘are structures and systems
aligned’ (Bessant 2003), ‘do innovation goals
match strategic objectives’ (Tipping and
Zeffren 1995) and further similar measures of
strategic fit (Bessant 2003).
From a strategic leadership perspective,
Dougherty and Cohen (1995) found the
behaviour of senior managers to be influential.
Those chief executives most likely to make
innovation happen are those with a clear
vision of the future operation and direction of
organizational change and creativity (Shin and
McClomb 1998). Senior management are
responsible for developing and communicating a vision for innovation, being supportive
and adopting an attitude tolerant to change
and championing the notion of innovation
within the organization. Managerial tolerance
to change creates the right climate for the
implementation stage of innovation, where
conflict resolution might be necessary
(Damanpour 1991).
Managerial attitude is also reflected in
norms or support for innovation. These are the
expectation, approval and practical support of
attempts to introduce new and improved ways
of doing things in the work environment.
Measures are mostly qualitative in nature and
explore perceptions, mostly about the extent
to which respondents recognize factors to be
present (or absent).
Relatively few measures of championing
and leadership have been uncovered in this
review other than those that simply ask the
question whether or not one or other exists,
perhaps through evidence of signs of commitment in annual reports (Chiesa et al. 1996) or
levels of concern of top management (Coughlan
et al. 1994) or whether or not an individual
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Innovation management measurement: A review
has been physically assigned to a particular
role (Souitaris 2002). Shane et al. (1995) provide a series of reflective questions designed
to allow organizations to determine for themselves their expectations and understanding
of the role of champion. For example, ‘to what
extent should the organization make it possible
for the people working on an innovation to
bend the rules of the organization to develop
the innovation, or be allowed to bypass certain
personnel procedures to get people committed
to an innovation?’
It must be emphasized that the dominant
perspective on strategy in the literature is
one that examines the relationship between
strategy and performance. However, a small
number of studies have examined the nature
of the transitions of these states as organizations adopt and embed an innovation strategy.
This sub-section of the literature is underpinned by the assumption that firms that follow innovation strategies differ from other
firms in several respects. It is apparent that
more innovative firms adopt different operational strategies to accommodate flexibility
and quality capabilities (Alegre-Vidal et al.
2004), have different capital management
practices to facilitate slack resources (O’Brien
2003), are more tolerant of internal conflict in
support of creativity (Dyer and Song 1998),
and maintain organizational structures that are
in the ‘intermediate zone between order and
disorder’ (Brown and Eisenhardt 1997, 22).
What is clear from this literature is that any
transition towards an innovation strategy will
necessarily take several years because of the
resources and energy that are necessary before
such a transformation can even be triggered
(Hope Hailey 2001).
Organizational Culture and Structure
Burns and Stalker (1961) described a contingency approach to innovation management,
later developed to include concepts of functional differentiation, specialization and integration (Lawrence and Lorsch 1967), which
suggests that environmental change prompts a
32
realignment of the fit between strategy and
structure. That is, to perform effectively, an
organization must be appropriately differentiated, specialized and integrated. Pugh et al.
(1969) argued that the structure of an organization is strongly related to the context within
which it functions. Our closing discussion in
the previous section, that the characteristics of
organizations following an innovation-focused
strategy will differ from those that do not, is
consistent with this contingency approach and,
in the following, we focus on those dimensions of culture and structure that have been
identified to differentiate between innovative
and non-innovative organizations.
Organizational culture and structure concern
the way staff are grouped and the organizational culture within which they work. There
has been considerable work on the situational
and psychological factors supportive of innovation in organizations. Indeed, it has been
widely demonstrated that the perceived work
environment (comprising both structural and
cultural elements) does make a difference to
the level of innovation in organizations (Amabile et al. 1996; Ekvall 1996). Creative and
innovative behaviours appear to be promoted
by work environment factors (Mathisen and
Einarsen 2004). Indeed, it is clear that organizations can create environments in which
innovation can be encouraged or hampered
(Dougherty and Cohen 1995; Tidd et al.
1997). A common theme is that of the polychronic organization – one with the capacity
to be in two states at once (Becker and
Whisler 1973). Shepard (1967) describes this
as a two-state organization manoeuvreing
between loose and tight, and Mitroff (1987)
as business-as-usual versus business-not-asusual. More prosaically, this means organizations need to be able, for example, to provide
sufficient freedom to allow for the exploration
of creative possibilities, but sufficient control
to manage innovation in an effective and efficient fashion.
Ernst (2002) specifies a range of generic
characteristics for the dedicated project group
assigned the innovation task: multidisciplinarity,
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dedicated project leader, inter-functional communication and co-operation, qualifications
and know-how of the project leader, team
autonomy and responsibility for the process.
And these factors are echoed throughout the
literature. Rothwell (1992) refers to them as
‘corporate conditions’, Chiesa et al. (1996)
‘enabling processes’ and O’Reilly and Tushman
(1997) ‘norms for innovation and change’.
However, despite their perceived importance,
there is little guidance on how to measure
them.
Volberda (1996) developed a conceptual
model of alternative flexible organizational
forms that are argued to initiate or respond
better to different types of competition. Yet, in
spite of its importance, there is relatively little
evidence of extant measures of flexibility in
the literature. Rothwell (1992) and Ekvall
(1996) propose a range of foci for measures of
organizational and production flexibility, such
as ‘corporate flexibility and responsiveness to
change’; Coughlan (1994) considers the flexibility of resource allocation. Several measures
of personnel flexibility are evident, such as
‘the adaptiveness of R&D personnel to technology changes’ (Lee et al. 1996) and ‘the
willingness to try new procedures and to
experiment with change, so as to improve a
situation or a process’ (Abbey and Dickson
1983). At the level of the firm, Liao et al.
(2003) introduce a similar construct – ‘organizational responsiveness’.
Organizational complexity, the amount of
specialization and task differentiation reportedly have a positive relationship (Damanpour
1996), though Wolfe (1994) argues that it may
favour initiation at the expense of implementation. Administrative intensity, that is the
ratio of managers to total employees, favours
administrative innovation (Damanpour 1991,
1996), but possibly at the cost of other product or technological innovations (Dougherty
and Hardy 1996). Centralization, the concentration of decision-making authority at the top
of the organizational hierarchy, and formalization, the degree of emphasis on following rules
and procedures in role performance, have both
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been shown to have a negative impact on organizational innovation (Burns and Stalker 1961;
Damanpour 1991). Indeed, rigidity in rules
and procedures may prohibit organizational
decision-makers from seeking new sources of
information (Vyakarnam and Adams 2001).
Underlying the concept of the workplace
environment are issues related to the management of human resources and the creation of
a culture or climate in which individuals perceive innovation to be a desired and supported
organizational objective. There has been considerable empirical work on organizational
climates supportive of the innovation process,
and several measurement instruments have
been developed, which Mathisen and Einarsen
(2004) review. The Team Climate Inventory
(TCI) (Anderson and West 1996, 1998) and
the KEYS instrument for assessing the work
environment for creativity (Amabile et al. 1996)
have been found to be robust and rigorous.
The TCI has been applied and validated in
several studies (Agrell and Gustafson 1994;
Anderson and West 1998; Kivimäki et al.
1997; West and Anderson 1996). Replication
studies using the TCI have found it can
explain a large part of the variance in teams’
innovative performance (Agrell and Gustafson
1994). The TCI is based around four main
factors: participative safety (how participative
is the team in decision-making procedures,
and how psychologically secure do team
members feel about proposing new and
improved ways of doing things), support for
innovation (the degree of practical support for
innovation attempts contrasted with the rhetoric of professed support by senior management), vision (how clearly, defined, shared,
attainable and valued are the team’s objectives
and vision) and task orientation (the commitment of the team to achieve the highest
possible standards of task performance, including the use of constructive progress monitoring procedures) (Anderson and West 1996).
Kivimäki et al. (1997) suggested a fifth factor,
‘interaction frequency’ relating to the regularity
of contact and communication within the
project team.
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Innovation management measurement: A review
There is general agreement about the
importance of individual and group autonomy
in the innovation process (Amabile 1998).
Zien and Buckler (1997) emphasize the need
for freedom to experiment and the creation of
safe havens, without which innovation outcome might be constrained. Measures of
autonomy mix both qualitative and quantitative approaches. For example, ‘the degree of
freedom personnel have in day-to-day operating decisions such as when to work and
how to solve job problems, including freedom
from constant evaluation and close supervision’ (Abbey and Dickson 1983) or ‘percentage of R&D portfolio with explicit business
unit and/or corporate business management
sign-off’ (Tipping and Zeffren 1995). Additionally, several authors have proposed general
measures of autonomy such as ‘freedom to
make operating decisions’ (Abbey and Dickson 1983) or ‘degree of empowerment’ (de
Leede et al. 1999; Tipping and Zeffren
1995).
Morale and motivation are dimensions of
the innovative organization that can be measured as they pertain to individuals – ‘the
extent to which personnel are well rewarded’
(Abbey and Dickson 1983); to groups – ‘our
reward system is more group-based than
individual-based’ (Parthasarthy and Hammond
2002); and to the organization as a whole –
‘the degree to which the organization attempts
to excel’ (Abbey and Dickson 1983). Aspects
of morale and motivation that have been
measured include trust (Miller and Friesen
1982) and job satisfaction, which has been
measured by Keller (1986) on a 20-item scale.
Organizational culture may include a shared
vision, and it has been argued that, the clearer
the vision, the more effective it is as a facilitator
of innovation, as it enables focused development of new ideas that can be assessed more
precisely (West 1990). Pinto and Prescott
(1988) found that the only factor to have predictive power in terms of potential for success
at all stages of the innovation process (conception, planning, execution and termination)
was having a clearly stated mission /vision,
34
while West (1990) contends that the quality of
innovation is partly a function of vision.
Keller (1986) adopts Kirton’s (1976) 32-item
adopter–innovator inventory to determine
individual and team orientation to innovation.
Vision is operationalized in the team climate
inventory (cf. Agrell and Gustafson 1994;
Anderson and West 1996, 1998; West and
Anderson 1996) and is deconstructed into a
series of sub-dimensions such as clarity, sharedness, attainability and value with regard to
team objectives.
Another aspect of culture is the propensity
to take risks. Saleh and Wang (1993) describe
this as a willingness to confront risky opportunities and tolerate failure, and learn from
doing so rather than recklessly gambling.
Similarly, West (1990) demonstrated that
higher levels of participative safety facilitate
innovation. Participative safety is nonjudgmental, supportive and characterized by
socio-emotional cohesiveness. The attractiveness of the organization as a place to work
and undertake innovative activities is used by
Geisler (1995) as an indicator of the climate
for innovation, measured by numbers of candidates applying for positions, and the age
profile of scientists and engineers. Keller
(1986) offers a slightly different perspective
on participative safety with the construct
‘group cohesiveness’, which he operationalizes using an established five-item measure.
This review has described a wide variety of
measures proposed for organizational culture
and structure. Unlike some other aspects of
innovation management, this area has received
extensive measurement attention. However,
Holbek (1988) argues that innovating organizations must adopt contrasting structures and
climates as they move from the initiation to
the implementation stages of innovation.
Chesborough and Teece (1996) and Burns and
Stalker (1961) argue that there is a relationship between organizational design and type
of innovation. It is a significant gap in innovation measurement that there appears to be no
measures that adequately capture or articulate
this sense of structural shift.
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Portfolio Management
The importance of portfolio management to
successful product innovation has recently
emerged as a key theme in the literature. It
is important because of the rapidity at which
resources are consumed in the innovation
process and the need for these to be managed
(Cebon and Newton 1999). The effectiveness
with which an organization manages its R&D
portfolio is often a key determinant of its
competitive advantage (Bard et al. 1988). The
focus of portfolio management is on making
strategic, technological and resource choices
that govern project selection and the future
shape of the organization (Cooper et al. 1999).
The problems of allocation of resources, evaluation, selection and termination of projects in
achieving the optimal portfolio have been
extensively investigated. The models all have
the objective of devising means to allocate
resources to projects to obtain the optimal
balance in the product development portfolio,
that is, arriving at a portfolio that optimizes
the trade-off between returns and risks.
The process of selecting innovation projects
requires evaluation and resource allocation
under uncertain conditions. It is argued that a
systematic process guided by clear selection
criteria can help optimize the use of limited
resources and enhance an organization’s
competitive position (Hall and Nauda 1990).
The earliest models used return on investment
as the primary decision criteria (Bard et al. 1988).
Following this, increasingly sophisticated
mathematical tools were developed to resolve
what Schmidt and Freeland (1992) describe as
the constrained optimization problem, that is,
to maximize the output (according to specified
criteria) from a subset of available inputs.
These project selection models ‘have been
virtually ignored by industry’ (Schmidt and
Freeland 1992, 190). Part of the reason for
this is the inherent complexity of some of
these models, but also that they fail to take
into account organizational decision and
communication processes. More recently,
models have tried to take account of these
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more qualitative factors involved in decision
processes.
Scoring models require respondents to
specify the merit of any project proposal
according to a set of a priori criteria which
may be objective or subjective (Hall and
Nauda 1990). Economic and benefit models
attempt to compute the cost/benefit or financial risk of pursuing a specific project, while
mathematical programming approaches seek
to optimize some objective function(s) subject
to specified resource constraints. Algorithms
of varying complexity exist, requiring monitoring and significant data entry, and, while
many are conceptually attractive, surveys do
not show widespread utilization of these
techniques (Hall and Nauda 1990). These
approaches are all based on financial measures such as internal rate of return, net present
value and return on investment. At the other
end of the spectrum, qualitative approaches
such as peer review and mental checklists
rely on subjective perceptions and measures
of portfolio balance (Cooper et al. 2001;
Henriksen and Traynor 1999).
Cooper et al. (1999) find that best performers
use explicit formalized tools and consistently apply them to all projects considered to
belong to a portfolio. A series of measures can
be identified that evaluate the whole portfolio
of innovation projects to answer questions
such as ‘is it balanced in terms of quantity of
short- and long term-projects?’ and ‘is there a
balance between high and low risk projects
and large and small projects?’ (Brenner 1994;
Cooper et al. 2001). Another set seeks to identify the extent to which portfolio evaluation
measures are formalized within the organization’s processes (Cebon and Newton 1999;
Chiesa et al. 1996; Farrukh et al. 2000; Miller
and Friesen 1982). Yet another approach is to
view project evaluation and selection as an
organizational capability and attempt to determine a level of proficiency (Szakonyi 1994).
Finally, there is a series of post hoc measures
of the appropriateness of project selections in
the light of results and alignment with business objectives (Lee et al. 1996).
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Innovation management measurement: A review
Project Management
Project management is concerned with the
processes that turn the inputs into a marketable innovation. The innovation process is
complex, comprising a myriad of events and
activities some of which can be identified as a
sequence and some of which occur concurrently, and it is clearly possible that innovation processes will differ to some degree,
across organizations and even within organizations on a project-by-project basis. Having
an efficient process that is able to manage the
ambiguity of the innovation is universally agreed
to be critical to innovation (Globe et al. 1973).
Various approaches have been taken to
modelling innovation processes: as a series of
events (Zaltman et al. 1973), as a social interaction (Voss et al. 1999), as a series of transactions
(Nelson and Winter 1982) and as a process of
communication (Farrukh et al. 2000). The history of project management research is partly
characterized by a debate regarding the extent
to which events and activities within the process
occur in linearly sequential, discrete, identifiable
stages (Zaltman et al. 1973) or whether events
are more disorganized (King 1992) or even
chaotic (Koput 1997). However, despite these
different viewpoints, there are a number of
common elements that can be summarized as
the major components of the innovation project
management. These are project efficiency,
tools, communications and collaboration.
Several studies make efforts to measure
project management efficiency, mostly in the
form of comparisons between budget and actual
(project costs, project duration, revenue forecasting). Another measure of project management success is speed. Innovation speed has
been positively correlated with product quality or the degree to which it satisfies customer
requirements; measures include speed (Hauser
and Zettelmeyer 1997), performance against
schedule (Chiesa and Masella 1994), and duration of the process (Cebon and Newton 1999).
To achieve efficiency, it is widely recommended that organizations seeking to innovate
should establish formal processes for innovat36
ing and make use of tools and techniques
that may facilitate innovative endeavours. The
stage-gate process (Cooper 1990) is possibly
the most familiar of these, but other methodologies for innovation project management
exist, including Phased Development, Product
and Cycle-time Excellence and Total Design
(see Jenkins et al. (1997) for a discussion).
These methodologies have in common the
separation of the product development process
into structured and discrete stages, which each
have milestones in the form of quality control
checkpoints at which stop/go decisions are made
with regard to the progress of the project. These
highly structured approaches to the management of the innovation process began to
emerge in the 1990s (Veryzer 1998). The use
of structured tools and processes can be
measured by project process evaluations, for
example the use of a formal problem-finding/
problem-solving cycle (Bessant 2003), the use
of formal post-launch evaluation procedures
(Atuahene-Gima 1995) or the use of certified
processes (Chiesa et al. 1996). These rather
general process measures have been supplemented by measures of the use of specific
instruments and tools, such as interactive
CAD or CAM (Maylor 2001) or the use of
computer-integrated manufacturing processes
(Parthasarthy and Hammond 2002), but we
note that these measures betray the technological
and NPD heritage that underpins many of the
innovation measures identified in this review.
Equivalents for service industry, public or
not-for-profit sector innovations are somewhat
lacking in the literature and constitute a
research gap.
Communications are important in project
management. Damanpour (1991) demonstrated the existence of a positive relationship
between internal communication and innovation. Internal communication facilitates the
dispersion of ideas within an organization,
increases the diversity and also contributes to
the team ‘climate’. Communication can be
measured by various integration mechanisms,
e.g. committees, numbers of meetings and
contacts (Damanpour 1991). There are also
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measures of external communications, which
tend to focus on whether communication is
taking place, the level at which it occurs and
with whom (Cebon and Newton 1999; Lee
et al. 1996; Rothwell 1992; Souitaris 2002).
These measures of internal and external communication in the literature are based on both
subjective evaluations and objective frequency
counts. Subjective measures include: ‘we always
consult suppliers/customers on new product
ideas’ (Parthasarthy and Hammond 2002) and
‘degree of organization members involved and
participating in extra-organizational professional
activities’ (Damanpour 1991). Objective counts
include (Damanpour 1991) ‘extent of communication amongst organizational units or groups
measured by various integrating mechanisms
such as numbers of committees and frequency
of meetings’, (Anderson and West 1998)
‘frequency of formal meetings concerning
new ideas’, (Souitaris 2002; Szakonyi 1994)
‘how well do the technical and finance people
communicate with one another’.
It is widely recognized that collaborating
with suppliers (Bessant 2003) and customers
(von Hippel 1986) can also make a significant
contribution to the innovation process.
Measures of collaborative working include
the use of guest engineers (Maylor 2001), the
percentage of projects in co-operation with
third parties (Kerssens-van Drongelen and
Bilderbeek 1999) and the extent to which
decision-making at top levels is characterized
by cross-functional discussions (Miller and
Friesen 1982). In addition, Jassawalla and
Sashittal (1999) identify some characteristics
of internal collaboration, i.e. that teams are
characterized by their mindfulness, levels of
at-stakeness, synergy and transparency, but
they do not suggest how these aspects might
be measured, which constitutes a further
research gap.
Commercialization
Commercialization can be considered to be
the second of the two phases in Zaltman et al.’s
(1973) conceptualization of the innovation
© Blackwell Publishing Ltd 2006
2006
process, that is, ‘implementation’. This can
mean taking an innovation to market (Chakravorti 2004), but may also include convincing
production managers to adopt a series of new
techniques available to them (Single and
Spurgeon 1996). Indeed, the successful introduction of new products and services into
markets is important for the survival and
growth of organizations. Kelm et al. (1995)
regard commercialization as a transitional
phase in which the organization becomes
less reliant on its technological capabilities
(important during the activities of initiation),
but more dependent on market dynamics.
Commercialization is concerned with making
the innovative process or product a commercial success; it includes issues such as marketing, sales, distribution and joint ventures.
While technical capabilities are important for
the early stages of the innovation process and
development activities, for the launch and
implementation stage it is marketing capabilities (market investigation, market testing,
promotion etc.) that are significant (Calantone
and di Benedetto 1988; Globe et al. 1973).
Verhaeghe and Kfir (2002) consider aspects
of commercialization under the headings of
market analysis and monitoring, reaching the
customer and market planning.
Firm-level measures of the launch or commercialization process appear to be relatively
thin. In their description of the R&D process,
Balachandra and Brockhoff (1995) characterize the commercialization stage as requiring
‘big bucks’, market reviews and organizational commitment. Most measures appear to
be not much more sophisticated than this.
Measures are frequently restricted to numbers
of products launched in a given period (e.g.
Yoon and Lilien 1985). There is, though,
some focus on market analysis and monitoring (Verhaeghe and Kfir 2002). Song and
Parry (1996) employ a set of measures of
launch proficiency (salesforce, distributional
and promotional support) that directly address
the ‘adequacy’ of the organization’s facilities in
these areas, as do Avlonitis et al. (2001). And
there is a limited mention of the commercial
37
Innovation management measurement: A review
intentions of an organization’s competitors
(Calantone and di Benedetto 1988). The
themes of proficiency of personnel in this area
(e.g. Atuahene-Gima 1995; Cebon and Newton 1999), of adherence to a commercialization schedule (e.g. Griffin and Page 1993) and
of formal post-launch reviews (AtuaheneGima 1995; von Zedtwitz 2002) can also be
found in the literature.
Even in the work that addresses postproject reviews, very little attention is directed
towards measuring innovation launch or
commercialization. However, this comes as
little surprise, as Hultink et al. (2000) recently
observed that little work has been done in the
area, perhaps because, at least in the innovation literature, launch activities appear to be
considered the domain of other specialists,
particularly marketers, and the process is seen
as separate from innovation. In their study,
they investigated launch decisions associated
with success for consumer and business-tobusiness products. Much of the literature that
considers launch and commercialization does
so from the viewpoint of the adopter, and
assesses rates of adoption and diffusion of
innovations over time across populations
(Kessler and Chakrabarti 1996; Van Den
Bulte 2000).
The area of commercialization appears to
be the least developed of the issues involved
in innovation management. This is a huge gap
because, without this last step, the previous
steps of assembling inputs, project management, etc. will not result in a commercially
viable outcome for the firm. We believe that
this area of innovation is in urgent need of
further development, from both theory and
measurement viewpoints.
Discussion
Innovation management measurement is a
critical discipline for both academics and
practitioners. The capacity of organizations to
innovate is determined by multiple factors that
relate both to their own internal organization
and to their market environment (Rothwell
38
et al. 1974) and the task of generating and
then converting ideas into usable and marketable products requires high levels of interfunctional co-ordination and integration. This
paper opened with the general observation
that innovation measurement does not appear
to take place routinely within management
practice and that, where it does, it tends to
focus on output measures. Further, from the
relatively small number of empirical studies
of measurement in practice, measurement of
innovation management appears to be undertaken infrequently, in an ad hoc fashion, and
relies on dated, unbalanced or under-specified
models of the innovation management phenomenon. This suggests that a large part of
the contemporary conceptualization of the
innovation management phenomenon is overlooked in practitioners’ measurement practices and, consequently, that opportunities for
the more efficient and effective management
of the innovation process are not realized.
Some possible reasons for this state of affairs
are: failure of academics to communicate adequately, inconsistency, inaccessibility and
complexity of measures and poor synthesis
and packaging.
Following a review and synthesis of the
literature, we proposed a seven-dimensional
conceptualization of the innovation management phenomenon and applied it to an examination of the measurement problem. Through
the application of this framework to their own
particular context, it is suggested that practitioners will be able to conduct an evaluation
of their own innovation management activity,
identify gaps, weaknesses or deficiencies, and
also improvement potential. Further, it is
hoped that organizations applying the framework will be able to tease out areas where
innovation is only nominally adopted in their
processes and identify areas where attention
and resources might be focused.
From the perspective of its management, it
is no longer sufficient to treat innovation as a
linear process where resources are channelled
at one end, from which emerges a new product or process. The measurement framework
© Blackwell Publishing Ltd 2006
March
presented in Table 2 shows the breadth and
variety of elements of innovation management
that ideally need to be measured. There have
been several studies that have investigated the
limitations of various approaches to measurement (e.g. Werner and Souder 1997), and of
specific measures (e.g. Trajtenberg 1990) as
they relate to the practice of innovation. The
choice of an appropriate R&D measurement
metric depends on the user’s needs in terms of
comprehensiveness of measurement, type of
R&D being measured, available data and
amount of effort the user can afford to allocate
to the exercise. Nonetheless, the common
citing across various papers of the measures
noted in the innovation management framework presented here suggests that a base set
of innovation management measures is
implicitly present in the literature, even
though they may be fragmented in appearance
and presentation. By pulling together the
innovation management framework from
diverse sources, this paper aims to bring such
issues into the open and identify relevant
research gaps. Table 2 can be viewed as the
basis for a balanced scorecard (Kaplan and
Norton 1992) for innovation management,
that is, as a balanced set of areas that need to
be measured in order to gain insight into an
organization’s holistic ability to manage innovation. Such multidimensional approaches to
measurement have been found in other areas
of management to be an improvement on simple one-dimensional measures and to be able
to capture both short- and long-term aspects
of value creation in the firm (Phelps 2004).
We have identified a large number of measures and approaches in a variety of innovation
management areas, and we have constructed a
generic set of innovation management measurement areas to act as a framework for balanced measurement. However, in several of
these areas, measurement gaps have been
identified. These gaps are of two types: validity gaps and omission gaps. Validity gaps arise
where there is insufficient evidence that proposed measures actually do capture drivers or
outputs of innovation management; for example,
© Blackwell Publishing Ltd 2006
2006
we have noted the objections to counts of
patents or increasing expenditure on R&D being
used as measures of innovative organizations.
Similarly, there is an absence of evidence that
the large number of subjective perceptions of
innovation management practices proposed
(‘we do A’, ‘we are good at B’) actually relate
to innovation management performance.
Omission gaps occur where the importance
of an aspect of innovation management is supported in the literature, but measures for this
aspect are lacking. Omission gaps are particularly prevalent in the elements of innovation
management such as knowledge management,
innovation strategy and commercialization
that do not feature strongly in the literature on
technological and manufacturing-based innovation. Another area traditionally not given
much emphasis in this literature is organization and culture, which, fortunately, is better
treated by the organizational behaviour literature, where a number of measures have been
developed. Much of the current state of measurement practice can be traced back to the discipline’s early manufacturing, R&D and NPD
focus. While product innovation is undoubtedly important, it is only one dimension of an
organization’s innovation agenda. Process and
organizational innovations are recognized,
too, as critical for competitiveness, yet these
perspectives are inadequately represented in
measurement terms.
General areas of omission in this field
relate to an over-reliance on financial measures rather than process measures: for example, the use of financial measures of portfolio
optimization, but an absence of portfolio
capability measures; a similar reliance on
codified knowledge such as patents to the
exclusion of more intangible measures such
as tacit knowledge; the measurement of levels
of resources or activities with no indication of
what an optimal level would be; the measurement of drivers of innovation without measures of whether these drivers are aligned with
each other and with firm strategy; measures of
presence that do not measure quality (e.g. the
use of dichotomous (yes/no) measures that do
39
Innovation management measurement: A review
not indicate how well an action is implemented,
and measures of the presence of leadership
(such as champions), but not of quality of
leadership); a general absence of measures
for important properties of organizational
structures such as flexibility, and a lack of
measures of the match between structure and
environment; a technological and NPD bias to
project management measures and a relative
absence of measures for service sectors; a
paucity of measures for internal communications; and a general lack of measures for
commercialization management – in particular,
around marketing and sales capabilities for
innovative products and services. All these
gaps provide the potential for further research
in innovation management measurement.
The absence of an accepted framework of
innovation management measures leads not
only to the gaps identified above, but also to
other problems in the literature. One is that it
is frequently unclear whether or not the metrics used are of the researchers’ own devising,
drawn from the literature or are what are used
by the organizations being studied. Nor is it
always clear when metrics are devised or
recommended, whether they are intended for
use in a research capacity or for management
tasks. Further, the measures proposed in the
literature often seem to be proposed abstractly,
with little consideration given to the use of
measures as a management tool in the day-today context of managing innovation. In the
absence of a comprehensive framework for
innovation management measurement, organizations will inevitably resort to ad hoc and
partial metrics, which can encourage wasteful
practice (e.g. measuring innovation management
capability according to annual R&D spend).
We hope that the framework constructed in
this paper will be useful in the construction
of comprehensive measures of innovation
management.
Acknowledgements
The research reported here was supported by
a grant from Cranfield School of Management,
40
the authors gratefully acknowledge this support.
Further, the authors are grateful to the editors
and two anonymous reviewers for their helpful
and insightful comments on previous versions
of this paper.
Note
1 Corresponding author. Telephone: +44 (0) 20
7594 9137; e-mail: r.adams@imperial.ac.uk
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