Resource allocation strategy for innovation portfolio management
Published date | 01 February 2014 |
Author | Ronald Klingebiel,Christian Rammer |
DOI | http://doi.org/10.1002/smj.2107 |
Date | 01 February 2014 |
Strategic Management Journal
Strat. Mgmt. J.,35: 246– 268 (2014)
Published online EarlyView 22 May 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smj.2107
Received 26 October 2011;Final revision received 11 February 2013
RESOURCE ALLOCATION STRATEGY FOR
INNOVATION PORTFOLIO MANAGEMENT
RONALD KLINGEBIEL1*and CHRISTIAN RAMMER2
1
Warwick Business School, University of Warwick, Coventry, UK
2
Centre for European Economic Research, Mannheim, Germany
Our study demonstrates empirically that the choice of resource allocation strategy affects
innovation performance. Allocating resources to a broaderrange of innovation projects increases
new product sales, an effect that appears to outweigh that of resource intensity. In addition, we
find that the performance benefit of breadth is higher for firms that allocate resources selectively
at later stages of the innovation process. This breadth-selectiveness effect is greatest for firms
intending to create relatively more novel products, departing further from their knowledge base.
Based on these results, we theorize that breadth increases performance because it spreads
firms’ bets on unproven innovative endeavors. Limiting resource commitments by selecting out
deteriorating projects prevents an escalation in the costs of breadth. This advantage increases
with the uncertainty implicit in greater innovative intent. The paper thus contributes to theory
of how resource allocation strategies influence performance outcomes of innovation project
portfolios. Copyright 2013 John Wiley & Sons, Ltd.
INTRODUCTION
In today’s fast-moving markets, new products
are more likely to fail than succeed. Nonethe-
less, competitive pressure requires firms to con-
tinue investing in product innovation projects,
even if, initially, little is known about their com-
mercial viability (Brown and Eisenhardt, 1997;
Hauser, Tellis, and Griffin, 2006). Allocating
scarce resources to uncertain innovation endeav-
ors is thus a daunting task for many organizational
decision makers.
Despite its managerial relevance, resource allo-
cation strategy has scarcely featured in research
on innovation performance. Standard input-output
models do not account for heterogeneity in
Keywords: strategic management of innovation; resource
allocation; flexibility; innovation portfolios; new product
development process
*Correspondence to: Ronald Klingebiel, Warwick Business
School, Coventry CV4 7AL, UK. E-mail: ronald.klingebiel@
wbs.ac.uk
Copyright 2013 John Wiley & Sons, Ltd.
resource allocation (c.f. Cr´
epon, Duguet, and
Mairesse, 1998; Mairesse and Mohnen, 2002). The
models’ principal input factor tends to be innova-
tion expenditure, which conceals variations in how
these resources are allocated. However, pouring
more money into bad projects does not necessarily
increase performance.
Our aim in this paper is to test the effect of dif-
ferent resource allocation strategies on innovation
performance, particularly in terms of new product
sales. As resource allocation is a core activity for
managers of innovations portfolios, this study adds
to a growing body of literature that delineates how
organizational differences in the strategic manage-
ment of innovation impact upon performance (Cas-
siman and Veugelers, 2006; Laursen and Salter,
2006; Leiponen and Helfat, 2010, 2011; Li and
Atuahene-Gima, 2001).
One strategy available to managers is to allo-
cate resources to a broad range of innovation
projects. Greater breadth might cover a greater
spectrum of future consumer preferences, hedging
bets on individual new product projects (Sorenson,
Resource Allocation Strategy for Innovation Portfolio Management 247
2000). Unfortunately, the paucity of available data
has limited empirical analysis of resource allo-
cation breadth and potential performance effects.
Related research found that innovation perfor-
mance increases the wider companies search for
information (Laursen and Salter, 2006; Leiponen
and Helfat, 2010). That greater breadth may be
advantageous is also an implicit assumption in
conceptual and computational models of the new
product development process (Cooper, Edgett, and
Kleinschmidt, 2001; Ding and Eliashberg, 2002;
Roberts and Weitzman, 1981).
But literature also suggests that there may be
disadvantages for firms that undertake greater
numbers of innovation attempts, including reduced
managerial attention to individual projects, dimin-
ished strategic focus, heightened organizational
complexity, and lowered incentives (Boudreau,
Lacetera, and Lakhani, 2011; Klingebiel, 2010;
Sull, 2003). It is thus worth asking not only
whether resource allocation breadth improves
innovation performance but also under which con-
ditions it is more likely to do so.
The first consideration is timing. The disadvan-
tages of breadth are pronounced in the later stages
of the development process, where commitment
and resource requirements are more substantial
(Loch and Kavadias, 2007). Firms that explore a
broad range of early-stage projects but select out
some projects in later stages avoid an escalation
of the disadvantages of breadth. Later-stage selec-
tion decisions should be better informed, as more
becomes known about projects’ commercial via-
bility as time goes on. This paper thus tests for
the performance effect of breadth conditional on
selectiveness.
A second consideration is the innovative intent
associated with the project portfolio, i.e. how
ambitious the firms’ innovation objectives are.
More innovative intent indicates a greater share
of novel projects, which are relatively distant
from the firm’s established knowledge and capa-
bility base. This poses a problem for resource-
allocation decision making. Firms engaged in
projects intended to upgrade existing products ben-
efit from signals that are clearer and easier to
interpret than firms aiming to extend product lines
or expand into new market segments (Normann,
1971). As a result, innovative endeavors of the
latter, more ambitious firms frequently generate
disappointing sales, even if returns to the occa-
sional success are higher (Hauser et al., 2006;
Shane and Ulrich, 2004). We therefore posit that
the higher risk of decision-making error makes
firms with greater innovative intent benefit more
from a broader project portfolio than their less
ambitious peers. And since there is greater scope
for learning from uncertainty resolution when pur-
suing novel projects (e.g. Eggers, 2012; Huchzer-
meier and Loch, 2001), we argue that they also
benefit more from complementing breadth with
later-stage selectiveness.
To test these predictions, we use data from
the German section of the EU-wide Community
Innovation Survey (CIS). CIS data are appropri-
ate because they include direct measures of firms’
innovation performance, namely sales generated
from new products. We were fortunate in being
able to append further questions to the standard
questionnaire, capturing firms’ strategies for allo-
cating resources to projects in their innovation
portfolio.
Results show that breadth has a significant pos-
itive direct impact on innovation performance.
Interestingly, its performance effect is more sig-
nificant than that of increased project investment.
Beyond this, we show that the effect of breadth
is context-dependent. Firms achieve greater per-
formance improvement if they allocate broadly at
first and then also discontinue projects in later
stages. When comparing sales generated from
improved, new-to-firm, and new-to-market prod-
ucts, the effects are most pronounced for new-to-
market sales. Testing for differences in the effect
of breadth with regard to innovative intent cor-
roborates this finding: more ambitious innovators
derive greater benefit from breadth than their peers
with lower innovative intent. The use they can
make of breadth increases further if they also
engage in selectiveness.
Our theoretical contribution lies in the delin-
eation of how heterogeneity in firms’ resource
allocation policies explains variance in perfor-
mance outcomes. Innovation performance is not
only determined by the amount of resources spent
on innovation, but also by the way in which
these resources are allocated. We theorize how
resource allocation breadth leads to higher new
product sales and how this effect varies depend-
ing on selectiveness and innovative intent. The
combination of breadth plus selectiveness pro-
vides a resource allocation strategy for coping with
the challenge of incomplete knowledge in inno-
vation portfolio management. This is particularly
Copyright 2013 John Wiley & Sons, Ltd. Strat. Mgmt. J.,35: 246– 268 (2014)
DOI: 10.1002/smj
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