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