Description
Recent theoretical and empirical work indicates that management control systems (MCS) are an important element
in enhancing innovation. We extend this research thrust examining the adoption of MCS in product development,
arguably one of the business processes where innovation plays a major role. Using a sample of 69 early-stage entrepreneurial
companies, data are collected from questionnaires and interviews with each of the CEO, financial officer,
and business development managers pertaining to product development MCS. We examine seven different systems:
project milestones, reports comparing actual progress to plan, budget for development projects, project selection process,
product portfolio roadmap, product concept testing process, and project team composition guidelines.
Reasons for management control systems adoption:
Insights from product development systems choice
by early-stage entrepreneurial companies
Antonio Davila
a,
*
, George Foster
b
, Mu Li
c
a
IESE, University of Navarra, Spain
b
Graduate School of Business, Stanford University, United States
c
Applied Micro Devices, United States
Abstract
Recent theoretical and empirical work indicates that management control systems (MCS) are an important element
in enhancing innovation. We extend this research thrust examining the adoption of MCS in product development,
arguably one of the business processes where innovation plays a major role. Using a sample of 69 early-stage entre-
preneurial companies, data are collected from questionnaires and interviews with each of the CEO, ?nancial o?cer,
and business development managers pertaining to product development MCS. We examine seven di?erent systems:
project milestones, reports comparing actual progress to plan, budget for development projects, project selection pro-
cess, product portfolio roadmap, product concept testing process, and project team composition guidelines. We
address three distinct questions: (1) What are the reasons-for-adoption of these systems? The nature of our sample
allows us to trace back to the adoption point and develop a set of reasons-for-adoption from the analysis of the data.
While MCS ful?ll certain roles as described in the literature, these reasons-for-adoption are distinct from these roles.
Results indicate that certain events lead managers to adopt these systems and address the challenges that they face.
They include contracting and legitimizing the process with external parties and internal reasons-for-adoption such as
managers’ background, learning by doing, need to focus the organization, or reaction to problems. (2) Are these rea-
sons-for-adoption associated with di?erences across companies in the time from their founding date until these sys-
tems are adopted (time-to-adoption)? Prior research has looked at the covariance of various organizational
variables with this timing; this study goes a step further by looking at the e?ect of di?erent reasons-for-adoption
on this timing. Our evidence ?nds an association between these two variables. (3) Are these reasons-for-adoption rel-
evant to performance? We ?nd that the reason-for-adoption is associated with the on-time dimension of product
development performance.
Ó 2008 Elsevier Ltd. All rights reserved.
0361-3682/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.aos.2008.08.002
*
Corresponding author. Tel.: +34 93 602 4183; fax: +34 93 253 43 43.
E-mail address: [email protected] (A. Davila).
Available online at www.sciencedirect.com
Accounting, Organizations and Society 34 (2009) 322–347
www.elsevier.com/locate/aos
Introduction
Formal management control systems (MCS)
have traditionally been associated with mechanistic
organizations (Burns & Stalker, 1961). These systems
support the periodic execution of the same routines
with little if any changes. Their relevance to the
innovation process – a process associated with
uncertainty, with unknown links between inputs
and outputs, with exceptions, and with outputs that
are often hard to evaluate – is less clear. Ouchi
(1979) used a research department to illustrate clan
control where social norms substitute for formal
management systems. Mintzberg’s separation of
planning and managing (Mintzberg, 1976) and
Quinn’s logical incrementalism (Quinn, 1978) also
highlight the limitations of traditional MCS. A fun-
damentally di?erent perspective is that these sys-
tems may provide important discipline to help
manage uncertainty. Recent theoretical develop-
ments o?er various concepts that support the need
for formal management control systems (MCS) in
uncertain settings.
1
For instance, the distinction
between coercive and enabling bureaucracies (Adler
& Borys, 1996) suggests that MCS may be instru-
mental to innovation. Gavetti and Levinthal
(2000) present a learning model where companies
that jointly rely on planning and learning by doing
are predicted to perform better in uncertain envi-
ronments compared to alternative strategies. Thus,
forward looking e?orts typically associated with
MCS complement fast reaction to new information
to improve how organizations deal with uncer-
tainty. Zollo and Winter (2002) argue that the
essence of dynamic capabilities is adaptive routines
– including information-based routines. Simons
(1995) interactive systems concept can have an
explicit role in sparking innovation around strategic
uncertainties.
For the most part, recent empirical evidence
also indicates that innovation processes may gain
from the presence of MCS. Abernathy and Brow-
nell (1999) use Simons’ model to examine the use
of budgets ‘‘as a dialogue, learning and idea
creation machine” during episodes of strategic
change. Cardinal (2001) reports an association
between control and performance in both radical
as well as incremental innovation projects in the
pharmaceutical industry.
2
Ditillo (2004) describes
MCS as a key element in knowledge intensive
?rms.
3
Similarly, Chapman (1998) presents evi-
dence consistent with the relevance of these sys-
tems in uncertain environments.
Based on a sample of 69 technology-based
early-stage companies, the paper examines the
adoption of MCS within an organizational pro-
cess where innovation has a pivotal role: the
product development process.
4
The focus on
product development led to sampling from tech-
nology-based ?rms.
5
Product development is a
key aspect in these ?rms. If MCS are important
to managing innovation, this sample of compa-
nies will be (on average) ahead in their use.
6
Our objective of learning about the adoption of
MCS led to adding the early-stage criteria. The
focus on the adoption stage (rather than the evo-
lution of existing MCS) suggested studying com-
panies that are going through the transition
from birth to early-stage when the MCS are
1
MCS are de?ned as formal management control systems
following Simons’ de?nition: ‘‘formal, information-based rou-
tines and procedures managers use to maintain or alter patterns
in organizational activities” (Simons, 1995, p. 5). Throughout the
paper, MCS is used to refer to this de?nition unless otherwise
noted.
2
Cardinal identi?es input, behavior and output control where
input control (scienti?c diversity and professionalization) might
be interpreted as informal control while behavior and output
control are formal MCS. She ?nds all three types of control being
associated with performance for incremental and radical innova-
tion projects.
3
Ditillo (2004) interprets MCS similarly to the way this paper
uses the term. He describes them as ‘‘the design as well as use of
coordination mechanisms based on the standardization of either
input, action, or results” (p. 402).
4
Because we study MCS in the speci?c process of product
development, MCS speaks to the systems that are used within this
process. In particular we examine the following MCS: project
milestones, reports comparing actual progress to plan, budget for
development projects, project selection process, product portfolio
roadmap, product concept testing process, and project team
composition guidelines. We collect data on actual systems rather
than on particular theoretical constructs associated with the
design and use of these seven systems.
5
We chose to study a speci?c realization of innovation through
looking at product development process rather than the broader
concept of ‘‘organizational innovation” to increase the power of
the research design by reducing the noise and potential con-
founding factors.
6
The objective of the paper is not to examine whether the
presence of MCS is needed for a company to be innovative.
Rather, its objective is to understand why MCS are adopted in a
particular process associated with innovation and whether distinct
reasons for adoption are associated with the time it takes to
formalize these systems and to on-time performance. To probe the
link between the presence of MCS and innovation, one would
need to develop a measure of innovativeness and compute this
measure on a year-by-year basis for each company in our sample.
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 323
adopted for the ?rst time. We conceptualize MCS
in our ?eld research as formal systems particular
to product development including: project mile-
stones, budget for development projects, reports
comparing actual progress to plan, project selec-
tion process, product portfolio roadmap, product
concept testing process and, project team compo-
sition guidelines.
Accordingly, this paper is built at the intersection
of two main research thrusts. (a) The study exam-
ines the product development process – where inno-
vation has a signi?cant role. Building on existing
theory on the relevance of MCS to innovation
processes, it provides new evidence to advance our
conceptual understanding of why MCS are an
important aspect of innovation management. In
particular, it highlights the di?erence between the
reasons-for-adoption of MCS in innovation pro-
cesses and the objectives that these systems pursue.
(b) Our sample of entrepreneurial companies also
speaks to the literature on the emergence of MCS;
thus, the conceptual development around the adop-
tion of MCS in product development processes can
be the basis for empirical work to understand the
adoption of these systems in other organizational
processes in early-stage companies. The study
speaks to the adoption of MCS in a process tradi-
tionally associated with innovation. Because the
study is framed within early-stage entrepreneurial
companies to capture the adoption event, it does
not speak to the evolution of MCS in established
?rms.
We examine three related research questions: (1)
why MCS are ?rst adopted in the product develop-
ment process? In particular, we present various
reasons-for-adoption of MCS, (2) what is the rela-
tionship between these reasons-for-adoption and
speed of adoption? and (3) what is the relationship
between reasons-for-adoption and a key product
development performance measure (on-time
development)? Fig. 1 illustrates these research
questions.
To inform these questions, the literature o?ers
arguments on why organizations adopt MCS.
These arguments have an early antecedent in
Greiner (1972). The ?rst stage of his growth
model deals with the emergence of MCS. It
describes the emergence of MCS as a crisis of
leadership where: ‘‘increased number of employees
cannot be managed exclusively through informal
communication (. . .) and new accounting proce-
dures are needed for ?nancial control.” Yet this
argument was not researched in depth for several
decades.
7
Cardinal, Sitkin, and Long (2004)
observe that much of the literature has ‘‘virtually
ignored the origins and evolution of organiza-
tional control” (p. 411). Recent research has
started to address the origins and evolution issue.
The two main streams of research are: (a) case
studies that describe the rise (and fall in some
cases) of controls over the early years of startup
companies (Cardinal et al., 2004) and (Granlund
& Taipaleenmaki, 2005), and (b) large-sample
based studies of the association between MCS
evolution and organizational variables such as
age, size, company strategy, and the presence of
venture capital (Davila, 2005; Davila & Foster,
2005, 2007; Sandino, 2007). The present study
probes the reasons that led to a di?erential
MCS adoption for 69 technology-based early-
stage companies in the product development
management systems area. Product development
systems are especially important for high-techno-
logy companies given the pivotal role that product
innovation plays in their growth.
Our research design combines qualitative data
from over 200 interviews and quantitative data
gathered using questionnaires. It also triangulates
the data using three di?erent informants per com-
pany. Access to qualitative descriptions of why
MCS were adopted allows us to develop a frame-
work of reasons why such systems are adopted.
We identify two external based reasons (labeled
legitimize and contract) and four internal reasons
(labeled manger’s background, need to focus
8
,
chaos, and learning). One bene?t of the structured
qualitative analysis of interviews is the rich descrip-
tion we can report to illustrate the six di?erent rea-
sons-for-adoption.
The second and third research questions build
upon the framework of reasons-for-adoption
developed in the ?rst question. In particular, the
second research question examines how the six dif-
ferent reasons for adoption in?uence the speed of
adoption of product development systems. Speed
7
MCS in small and medium companies have been studied using
cross-sectional designs (thus not looking at MCS adoption)
(Amat, Carmona, & Roberts, 1994; Romano & Ratnatunga,
1994). Also the evolution of existing MCS has been examined
(Oakes, Townley, & Cooper, 1998). Yet the adoption of these
systems has only received attention recently.
8
‘‘Need to focus” refers to the manager’s adoption of MCS to
focus the company on executing the strategy when the company is
failing to do so because of lack of systems.
324 A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347
of adoption is measured using time elapsed from
the start of the company to the time reported for
the adoption of the project milestones’ system.
9
We ?nd that the reasons-for-adoption associated
with manager’s background, chaos, legitimize,
need to focus, and contract are found to be signif-
icantly related with faster adoption vis-a`-vis a sub-
set of our sample of companies with higher
reliance on informal product development control
systems.
The third research question probes whether the
same six di?erent reasons for MCS adoption in?u-
ence a key product development performance mea-
sure (on-time development). Our results indicate
that when MCS are adopted because of the man-
ager’s background, product development perfor-
mance is enhanced.
Because our sample comprises early-stage com-
panies, their level of MCS adoption and product
development performance are likely to have signi?-
cant variation. The sample selection is designed to
capture these companies’ transition phase into
MCS; moreover, their product development pro-
cesses are still evolving – for instance, through the
adoption of MCS – and thus expected to show dif-
ferent performance levels. This fact allows us to
study the relationship between reason-for-adoption
and speed of adoption and performance.
The paper brings new evidence to the growing lit-
erature on the relevance of MCS to enhance the per-
formance of ?rms employing organic structures
(Kalagnanam & Lindsay, 1999) and in particular
to innovation processes (Bisbe & Otley, 2004; Granl-
und & Taipaleenmaki, 2005). The main ?ndings
include: (1) we identi?ed six reasons for MCS
adoption. Two reasons-for-adoption are externally
related: legitimizing the company and contracting
with external parties. This evidence highlights the
role of external parties in shaping management sys-
tems internal to the ?rm (Pfe?er & Salancik, 1978);
an in?uence that is typically associated with ?nancial
reporting. We also identify four internal reasons-for-
adoption, two of them proactive: managers’ back-
Time to Adoption of
MCS
Innovation Process
Performance
Organizational
Performance
Reasons-for-Adoption
External Reasons
Legitimize
Contract
Internal Reasons: Proactive
Manager Background
Need to Focus
Internal Reasons: Reactive
To Chaos
To Learning
Organizational Covariates Such As:
First Research Question
Second Research
Question
Third Research
Question
: Prior Work : Current Work
Legend
Size
Age
Strategy
VC Funding
Founder Replaced
MCS Roles
Fig. 1. Overview of research questions and related literature. First research question: what are the reasons-for-adoption of product
development MCS? Second research question: are these reasons-for-adoption associated with di?erences across companies in the time from
their founding date until these systems are adopted (time-to-adoption)? Third research question: are these reasons-for-adoption relevant to
performance?
9
We adopt this measure because ‘‘project milestones” is the
system adopted most often. We also ?nd similar results for
‘‘reports comparing actual progress to plan” and ‘‘budgets for
development projects” as the speed of adoption measure.
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 325
ground, focus the attention of the organization on
executing the strategy (need to focus), and two reac-
tive: react to problems, and code learning (associ-
ated with formalizing repetitive yet non-formalized
processes). Our evidence also provides qualitative
data consistent with MCS roles identi?ed in the
literature including stimulating dialogue and idea
creation; controlling execution through diagnostic
systems; and stabilizing an environment that, by
the nature of the innovation process, is already rich
in opportunities. (2) Using these six new reasons for-
adoption categories, the study then examines the
impact of di?erent reasons-for-adoption on the
speed of adoption of MCS. We ?nd that managers’
background is associated with the fastest time-
to-adoption. (3) Finally, we ?nd that MCS adopted
to code learning or because of the managers’
background is associated with better on-time
development (an important dimension of product
development performance).
The next section (‘Conceptual underpinnings
and literature linkages’) of this paper reviews the
literature on innovation and MCS. We highlight
how the literature has emphasized the di?erent
roles that MCS may have; thus, what purpose do
MCS ful?ll in organizations. Yet, these roles are
not necessarily associated with the reasons why
MCS are adopted. These reasons are associated
with events that do not have a one-to-one relation-
ship with the roles that these systems play. There-
fore, a particular event may trigger the adoption
of a system with a particular role in a certain envi-
ronment and a system with a di?erent role in
another environment. The reasons why systems
are adopted are distinct from the roles that the sys-
tems ultimately play. This section also presents the
evolution of the literature from an initial notion
that innovation and MCS were incompatible to
the new broader support for MCS having a positive
role in promoting innovation. ‘Field research
design’ presents the research design. The study is
based on a multi-case, multi-method research
design. We sent questionnaires and interviewed
three managers in each of 69 high-growth technol-
ogy entrepreneurial companies. This design allows
for triangulation of the data and combining quali-
tative and quantitative data to develop a frame-
work and use statistical generalization of the
?ndings. The following three sections present the
results to our three research questions. ‘Discussion’
relates these ?ndings to the existing literature and
discusses limitations and future research.
Conceptual underpinnings and literature linkages
Fig. 1 provides an overview of our three research
questions and its links to the existing literature. This
section explores those links to highlight our contri-
butions. The initial arguments in the literature
(‘Limitations of management control systems’) saw
MCS as constraining innovation and therefore
incompatible with it. Over time, evidence started
to accumulate on the bene?ts of MCS on innovation
(‘Theoretical justi?cations for and empirical evi-
dence on the role of MCS in innovative settings’)
and theoretical arguments emerged to explain this
evidence. To address our research questions, ‘Rea-
sons/events and roles of MCS in product develop-
ment’ reviews the di?erent roles of MCS proposed
in the literature. These roles are contrasted against
the reasons-for-adoption that we identify from our
empirical evidence. ‘The emergence of MCS in
entrepreneurial companies’ presents recent empiri-
cal work on the emergence of MCS in entrepreneur-
ial companies. This work provides the framework to
analyze our two additional research questions: the
relationship between (i) reason-to-adopt and time-
to-adoption (our second research question), and
(ii) reason-to-adopt and performance (our third
research question).
Limitations of management control systems
Management control systems can sti?e innova-
tion (Ouchi, 1979). If not designed to deal with
uncertainty, they may constrain cross-functional
interaction, limit communication to established
patterns, penalize deviations, and di?use leader-
ship. Mintzberg’s work (1976, 1979, 1994) high-
lights the separation between planning and
managing as two separate processes, the ?rst ruled
by formal systems and the second more informal
and decoupled from MCS. Quinn (1978) argues
that the restrictions imposed by organizations’ for-
mal systems limit the innovation abilities to ‘‘logi-
cal incrementalism.” Damanpour’s (1991) meta-
analysis of empirical work on organizational deter-
minants of innovation reports a negative associa-
tion between innovation and formalization. These
MCS are most useful when task analyzability is
high and the number of exceptions is low (Perrow,
1970) such as in low innovation settings. In high-
innovation environments, they may diminish the
intrinsic motivation and freedom that innovation
requires (Amabile, 1998). Empirical studies have
326 A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347
con?rmed these predictions. Abernethy and Lillis
(1995) ?nd that ‘‘spontaneous contact and ‘‘inte-
grative liaison devices” which allow regular, per-
sonal and intensive contact” are more prevalent
in ?exible manufacturing ?rms while traditional
performance measurement systems are de-empha-
sized. Abernethy and Brownell (1997) report higher
reliance on personnel control in research and
development departments. Rockness and Shields
(1988) echo these conclusions and ?nd that the rel-
evance of budgets in R&D settings is highest for
planning purposes and decreases monotonically
for monitoring, evaluating, and rewarding.
While these arguments do not support the need
for management control systems in innovative set-
tings, recent empirical evidence and theoretical
arguments provide a di?erent perspective. Innova-
tion management appears to bene?t from having a
balance between ‘‘tight” and ‘‘loose” controls to
provide both the support and direction for innova-
tion. The positive impact of these controls on inno-
vation highlights the need to understand why these
systems are adopted in processes where innovation
has a relevant role.
Theoretical justi?cations for and empirical evidence
on the role of MCS in innovative settings
Simons’ typology (Simons, 1995) identi?es
interactive systems as information-based routines
to identify knowledge required to address strate-
gic uncertainties. One attractive feature of the
interactive systems concept is that it allows top
management to guide the search stage of the
innovation process, without falling into the cyber-
netic model. Recent empirical studies (Abernethy
& Brownell, 1997; Bisbe & Otley, 2004) rely on
interactive systems to examine MCS in uncertain
environments.
While interactive systems speak to the front end
of the innovation process, the concept of enabling
bureaucracy (Adler & Borys, 1996) addresses the
role of MCS throughout the stages of assimilation
and exploitation of knowledge. Enabling bureau-
cracy is designed to ‘‘enhance the users’ capabilities
and to leverage their skills and intelligence” (p. 68)
rather than with ‘‘a fool-proo?ng and deskilling
rationale.” Thus, organizations assimilate and exploit
the knowledge accumulated in the ?rst stage through
?exible, transparent, user-friendly routines. Ahrens
and Chapman (2004) apply the concept of enabling
bureaucracy to analyze the role of MCS in a ?eld
study setting. They describe how managers rely on
an enabling use of these systems to cope with the
uncertainty of day-to-day operations.
The concept of adaptive routines (Weick, Sutc-
li?e, & Obstfeld, 1999) describes routines as resilient
because of their capacity to adapt to unexpected
events. This concept portrays routines as ?exible
to absorb novelty rather than rigidly to suppress
it. They o?er organizational members a stable
framework to interpret and communicate when fac-
ing unexpected events. They ‘‘usefully constrain the
direction of subsequent experiential search” (Gav-
etti & Levinthal, 2000, p. 113). Reliability rather
than replicability identify routines in uncertain set-
tings. Feldman and Rafaeli (2002) extend this argu-
ment to include routines as drivers of key patterns
of communication among organizational members.
Miner, Basso?, and Moorman (2001) describe the
constant interaction between routine activities and
improvisation in product development. Routines
provide the background for improvisation to hap-
pen and learning to accumulate.
These concepts highlight the positive e?ect that
MCS may have on innovation. MCS are viewed as
?exible and dynamic frames adapting and evolving
to the unpredictability of innovation, but stable to
frame cognitive models, communication patterns,
and actions.
Evidence is accumulating of the positive e?ects
from adopting MCS in uncertain settings. For
instance, managing environmental uncertainty has
repeatedly been associated with MCS (Chenhall &
Morris, 1986; Khandwalla, 1972; Simons, 1987).
Chenhall and Morris (1995) identify extensive use
of management accounting systems with superior
performance in companies following entrepreneur-
ial as opposed to conservative strategies. Henri
(2006) ?nds that the dynamic tension that emerges
as systems are used both in a diagnostic and interac-
tive way is associated with improved performance.
Chapman (1998) used four case studies to conclude
‘‘that accounting does have a bene?cial role in
highly uncertain conditions” (p. 738). Howard-
Grenville (2003) used an ethnographic approach in
one high-technology company to document the rel-
evance of organizational routines to confront uncer-
tain and complex situations.
Within product development, prescriptive recom-
mendations to practitioners emphasize the impor-
tance of MCS (McGrath, 1995). Several research
studies have found that planning and well-coordi-
nated project execution are associated with product
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 327
success (Cooper, 1995; Zirger & Maidique, 1990).
These studies hint to a role of MCS although they
fail to provide a theoretical justi?cation for their
?ndings (Brown & Eisenhardt, 1995). Using a con-
trol framework, Cardinal (2001) found that the
three types of control systems – input, behavior,
and output control – enhance radical innovation –
arguably the most uncertain type of innovation.
Davila (2000) reports a positive association between
the use of management accounting information
and product development performance. Brown
and Eisenhardt (1997) describe successful product
innovation as blending ‘‘limited structure around
responsibilities and priorities with extensive com-
munication and design freedom” so that ‘‘this com-
bination is neither so structured that change cannot
occur nor so unstructured that chaos ensues” (p. 1).
This evidence highlights the importance of under-
standing MCS in product development.
The emergence of MCS in entrepreneurial companies
The previous sub-sections argue for the
importance of MCS to the innovation process
from theory and evidence perspectives. This
sub-section reviews another stream of literature
relevant to our research question – the literature
on the emergence of MCS in entrepreneurial
companies.
Our research questions examine the adoption of
MCS in a particular innovation process, product
development. Thus, both literatures o?er relevant
frames of reference. While the innovation literature
is relevant to the need for MCS in product develop-
ment, the MCS emergence literature examines in
more detail variables associated with how fast these
systems are adopted. These ideas are most relevant
to our second and third research questions relating
time-to-adoption and performance to reasons-for-
adoption.
Greiner (1972)’s growth model describes a ?rst
transition (labeled crisis of control) where he
argues for the adoption of MCS. At the end of
the ?rst growth phase informal management no
longer works and MCS are required. Moores and
Yuen (2001) provide initial evidence on the emer-
gence of MCS. They ?nd, as they expected, that
management systems are adopted in the growth
stage of the ?rm. Davila (2005) in his study of
the emergence of HR systems and Davila and Fos-
ter (2005, 2007) in their research of management
accounting systems’ adoption focus on time-to-
adoption as their main research variable.
10
They
?nd that faster adoption of these systems is associ-
ated with the presence of venture capital funding,
size, age of the ?rm, and the replacement of the
founder as CEO. Sandino (2007) examines how
time-to-adoption of MCS in retail ?rms depends
on their strategy given a set of common systems
being adopted. Cardinal et al. (2004) describe
how the control approach evolves from informal
to formal in a case study of a growth ?rm. These
studies look at the emergence of MCS and they
rely on time-to-adoption as their proxy to address
this question.
Organizational characteristics such as age, strat-
egy, lifecycle stage, or size are covariates in vari-
ous studies; yet the co-variation between time-to-
adoption and organizational variables is mediated
by events (reasons-for-adoption) that are absent in
these studies. Simons (1995) provides arguments
indicating that a potential event leading to MCS
adoption is a breakdown in processes – such as
failure to meet deadlines or quality problems. In
this case, the reason-for-adoption is an organiza-
tional failure, an event that can be associated to
di?erent MCS roles – such as either a making
goals explicit role or a coordination role. In other
instances, MCS may be triggered by an event
associated with one MCS role such as adopting
MCS to legitimize the company vis-a`-vis potential
customers. However, no explicit arguments have
been provided to answer the question of why are
MCS adopted (Cardinal et al., 2004). One objec-
tive of this paper is to answer this question
grounded in the qualitative evidence that the data-
base provides.
As described in the previous paragraph, prior
literature on the emergence of MCS has focused
on two main variables: time-to-adoption and orga-
nizational performance. Time-to-adoption mea-
sures the speed to MCS adoption and thus is
relevant to understand the phenomenon. Organiza-
tional performance addresses the issue of whether
MCS adoption is relevant. Davila and Foster
(2005, 2007) ?nd that faster adoption of budgets
and of a broader measure of MCS adoption is
associated with faster company growth as mea-
sured by number of employees. This result suggests
that faster adoption is relevant to organizational
performance. While this result is important, it does
10
They de?ne time-to-adoption in a similar way: time from the
inception of the company to adoption of a particular system.
328 A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347
not address the question of whether di?erent rea-
sons-for-adoption are associated with faster adop-
tion; it also does not address the question of
whether these reasons are related to performance.
While no theory exists to guide our priors, the
empirical evidence provides an exploratory answer
to these questions.
11,12
Reasons/events and roles of MCS in product
development
Existing knowledge on MCS focuses on the roles
that these systems ful?ll; in other words, why these
systems exist. Yet, understanding the reasons why
these systems are adopted in the ?rst place is
another important aspect of MCS research. This
paper presents new evidence on the reasons MCS
are ?rst adopted within the context of product
development. This is a di?erent lens to view MCS
than the extensive literature on the roles that MCS
can play. We show that in some cases the reason
for adoption is unrelated to a particular role. More-
over, the same reason for adoption can lead to the
adoption of MCS with di?erent roles across compa-
nies. To fully position the reasons-for-adoption that
emerge from the empirical study in this paper, the
following paragraphs describe the MCS roles that
the existing literature identi?es:
(1) Make goals explicit and stable. Amabile
(1998) indicates that innovation is enhanced
when people are granted freedom to achieve
goals that are clear and stable for a su?ciently
long period of time. She states ‘‘it is far more
important that whoever sets the goals also
makes them clear to the organization and that
these goals remain stable for a meaningful per-
iod of time” (p. 80). Uncertainty provokes a
constant shift of priorities that may undermine
the innovation process. MCS typically state
explicit goals. This increases their stability
and visibility, facilitates convergence in mean-
ing across organizational actors, and provides
the clarity that creativity is argued to require.
(2) Code learning from past. Lundberg (1995)
indicates that procedures help innovation by
coding learning from past experience (Levitt
& March, 1988). Coded routines facilitate
the di?usion across the organization and over
time of organizational capabilities (Nelson &
Winter, 1982).
(3) Help coordination. Lundberg (1995) also points
out the importance of coordinating di?erent
innovation e?orts across the organization.
MCS decouple the e?orts of organizational
actors and reduce coordination costs through
the explicit negotiation of local goals.
(4) Plan the sequence of steps. Process planning
clari?es the sequence of steps to achieve cer-
tain organizational goals and provides a blue-
print for coordinating the innovation e?ort
over time (Cohen et al., 1996).
(5) Promote accountability and facilitate control.
MCS facilitate control by exception (Simons,
1995) where managerial attention is required
only if innovation results deviate from
expectations.
(6) Contract with external parties. External con-
stituencies, such as partners, may impose
MCS to enhance their monitoring within the
?rm. These intermediate milestones facilitate
contracting with outside partners (Powell,
1998). Pfe?er and Salancik (1978) highlight
the relevance of the external context in
explaining how ?rms are organized.
(7) Symbols to legitimize. Finally, new institution-
alism (Carruthers, 1995; Powell & DiMaggio,
1991) views cognitive processes as relevant to
explain management systems. It identi?es
formal processes as symbolic to externally
legitimize the innovation process of the orga-
nization through an appearance of compe-
tency. Management systems do not ful?ll as
much a technical need as conforming to
external demands decoupled from technical
needs.
11
While there are no theoretical models to guide our priors,
their future development is important because there is no clear
directional prediction. For instance, it might be argued that MCS
that are adopted because of managers’ prior experience will
emerge before MCS that code existing learning; because manag-
ers do not have to go through the learning process. However, the
opposite argument can hold: coding existing learning can lead to
faster emergence because the hiring of managers’ with prior MCS
experience may only happen when the company codes and
realizes the limitations of the MCS that it has put in place.
12
The relationship between reasons for adoption and perfor-
mance also has no clear directional predictions. For instance, if
the knowledge about how to design MCS is not in the company
(for instance, when systems are designed as a reaction to
problems), the design is likely to be worse than if the knowledge
exists (for instance, when systems are designed based on the prior
knowledge of managers) and impact performance. Alternatively,
MCS developed as a reaction to problems may be better adapted
than MCS developed based on an experience di?erent from the
particular needs of the company.
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 329
Appendix A illustrates these di?erent roles of
MCS using quotes from interviews with executives
of our early-stage entrepreneurial company sample.
These quotes reinforce the importance of these dif-
ferent roles. ‘Results for the adoption of manage-
ment control systems in product development’
presents the reasons-for-adoption identi?ed in the
empirical study and compares them to the MCS
roles described in the literature.
Field research design
To capture the richness needed to explore why
companies adopt MCS in the product development
process, we adopt a cross-sectional, multi-method,
multi-case ?eld research design. The aim of the
cross-sectional multi-case design is to gather a large
enough variation to probe our research questions,
to capture the detail required to answer the ques-
tions, and to link contextual variables to the adop-
tion of MCS. The multi-method design relies on
qualitative data to identify patterns of behavior
and quantitative data to examine covariates that
may inform the research questions. Our data
include questionnaire and semi-structured inter-
views on the adoption and role of MCS in young
technology companies. Capturing the quality,
depth, and richness to understand the experience
of the actors (Seidman, 1998) demands detailed
descriptions of the phenomena (Kvale, 1996; Mar-
shall & Rossman, 1995). The focus of the study on
product development as a relevant aspect of innova-
tion processes drives the decision to sample among a
population of technology companies. We expect
product development to be a signi?cant enough
aspect of their strategy to have received manage-
ment attention. The sampling of a population of
young ?rms is intended to capture the point in time
when formal systems, if any, are adopted. Managers
in young ?rms are more likely to identify the process
that led to the adoption these systems were recently
adopted compared to more mature ?rms where
MCS have been around for several decades.
13
This
transition point is likely to be a recent event in the
life of these ?rms and thus managers are expected
to be able to better articulate the reasons-for-adop-
tion (as well as identify the time of adoption).
Data sources
The three main information sources for each com-
pany are public data, three questionnaires, and three
interviews. We ?rst collected as much information as
possible from public sources – such as company’s
web pages and press releases from Lexis-Nexis. This
information was used to familiarize the research
team with the characteristics and products of each
company. Next, each company received three ques-
tionnaires – one for the CEO, another for the CFO,
and a third one for the business development man-
ager. The purpose of the questionnaire was to collect
quanti?able information about the company and its
processes – for instance, whether product develop-
ment projects were early, on-time, or late as a mea-
sure of product development performance or the
time of adoption of each of seven product develop-
ment management systems. The questionnaire was
sent in advance to have additional information to
prepare the interviews, to provide managers with
guidance of what the research was about, and to have
the opportunity to clarify any questions regarding
questionnaire’s answers. Appendix B reproduces
the relevant questions for this study.
This use of questionnaires is somewhat atypical
in the literature. Rather than having the question-
naire measuring the underlying variables using psy-
chometric tools, it was used to collect factual data.
Because the variables in the research had not been
previously used in an empirical study, interview
data were more appropriate to identify them. The
objective was to infer these variables from the
descriptions provided in the interviews and examine
whether they matched the theoretical variables.
The ?nal phase of the data collection included
separate semi-structured interviews with the CEO,
CFO, and business development manager of each
company. The objective of these interviews was to
gain detail about the company, its history, its strat-
egy and the adoption, design, and use of MCS. Each
interview lasted between forty-?ve and ninety min-
utes. Over 200 such interviews were conducted for
the sample examined in this paper. The interviews
relied on a detailed protocol listing the questions
to be addressed. The protocol insured that the main
topics of the research were systematically covered
13
The sampling of young ?rms has to do with the focus of the
research on the adoption of MCS. Managers of younger ?rms are
more likely to have lived through the adoption of these systems.
An alternative is to examine the adoption of accounting
innovations (such as Beyond Budgeting, ABC, or Balanced
Scorecard) in mature ?rms (Innes, Mitchell, & Sinclair, 2000) or
the evolution of MCS as a reaction to changes in the objective
and power structure of external constituencies (Oakes et al.,
1998).
330 A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347
during the conversation, but the semi-structured
nature of the interview gave the ?exibility of follow
up questions to clarify the particular practices at
each company (Marshall & Rossman, 1995). The
relevant protocol questions are reproduced in
Appendix C. Interviews were conducted in person
or by phone and at least two researchers were pres-
ent in every interview. Interviews were taped and
then transcribed. The questionnaire, sent prior to
the interview, facilitated focusing the interview
around the key aspects of interest.
Sample description
The ?nal sample includes 69 young, high technol-
ogy companies. We construct the sampled popula-
tion using the following selection criteria: (1) high
technology, (2) less than 10 years old, (3) between
50 and 150 employees, (4) independent, and (5) in
a limited geographic area.
14
These criteria identi?ed
companies where product development is likely to
be a foundation of their competitive advantage.
They also identify companies more likely to have
recently and independently transitioned through
the stage of formalizing product development pro-
cesses, rather than companies that have had systems
in place for a long time or systems imposed by a
parent company. We did not restrict ?rms to be
public or private, foreign owned, or venture funded;
however the majority of ?rms were private, domes-
tically owned, and venture funded. The population
of ?rms was sourced from the CorpTech Internet
directory of technology companies. We accessed the
database in January and June 2002 and build our
sample from the following industries (using Corp-
Tech industry classi?cation): biotech (BIO), com-
puter hardware (COM), manufacturing (MAN),
medical equipment (MED), pharmaceuticals
(PHA), photonics (PHO), computer software
(SOF), subassemblies (SUB), test & measuring
equipment (TAM), and telecommunications
(TEL).
15
We also purposefully over-sampled bio-
technology ?rms because of their potential rele-
vance as a growth industry. This sub-sample was
extended using three additional databases particular
to the industry: Rich’s High-Tech Business Guide to
Silicon Valley and Northern California (2000/2002),
BioScan (Oct. 2001), and the US Business Browser
(c. 2001).
A letter addressed to the CEO was sent to every
?rm in the sample. The letter described the purpose
of the research, the research process, and the bene-
?ts of participating – a half-day conference where
participating companies were invited to a presenta-
tion of the managerial implications of the research
project and a written document of the ?ndings.
The letter was followed up with a phone call to
entice participation; a company was dropped from
the sample if it had not accepted or declined partic-
ipation after ?ve phone calls. The sample selection
process is detailed in Panel A, Table 1. Excluding
companies that were acquired, went out of business
or are ineligible, the response rate is 20%.
16
Compa-
nies acquired or that went out of business were sig-
ni?cantly younger than the eligible sample but
comparable in terms of sales and number of
employees. Within the eligible sample, we compared
companies that participated to those that did not, in
order to assess potential self-selection bias; we
found no signi?cant di?erences in sales, number of
employees and age.
17
The ?nal sample includes 11
biotechnology companies, 48 information technol-
ogy companies, and 10 companies in other indus-
tries; in addition 59 received venture capital. Panel
B of Table 1 provides additional descriptive statis-
tics on the sample.
Data analysis
The analysis of why ?rms adopt MCS for prod-
uct development was structured in two stages. In
the ?rst stage, interview data were coded – to sum-
14
The main reason for the geographic criterion was research
funding (more than 50% of the interviews were done at the
companies’ premises). While this decision reduces the potential
impact of omitted variables that may vary with geography, it
limits the generalizability of results.
15
We excluded from these lists any companies that were also
listed as ‘‘Energy,” ‘‘Environment,” ‘‘Chemical,” ‘‘Defense”,
‘‘Transportation” or ‘‘Non”. ‘‘Non” companies are not primarily
high-tech companies. The other industries are excluded because
they face a di?erent regulatory and/or institutional environment.
We also excluded organizations cross-listed in these industries.
16
The 20% is a conservative estimate. It assumes that all the
companies that did not respond (to the ?ve telephone contacts)
(which means that they did not pick up the phone) were eligible.
The percentage of companies that participated over the percent-
age of companies that were contacted is 38%. The demands on
company resources were much higher than a traditional mail
survey; we asked each company to ?ll three questionnaires and
have separate one-hour interviews with each of three di?erent
managers.
17
We compared means and medians of sales, employees, and
age (variables available from the databases that we accessed) for
both groups in our sample. We also use the non-parametric
Mann–Whitney test on these variables with identical conclusions.
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 331
marize, interpret, and classify the information. To
limit the potential bias inherent in the analysis of
qualitative data, three researchers coded each one
of the interviews. To systematically proceed through
the coding process, each researcher used the Nvivo
qualitative coding software. This software details
the analysis from the raw data to the theoretical
propositions, thus providing an auditable trail of
the analysis. Because of the exploratory nature, each
researcher may potentially identify di?erent con-
structs that explain the observed patterns. To iden-
tify common constructs, the coding was done
following a structured process. The sample was
divided into two groups. The three researchers inde-
pendently identi?ed the main topics covered in each
interview for one of the groups. The result was the
dissection and reorganization of the original tran-
scripts into broad topics. Then, the researchers
met to contrast the topics that each one identi?ed,
agree in a common set of terms to identify them,
and discussed any di?erences in interpretation of
the transcripts. Next, the second group of interviews
was independently coded using the common termi-
nology. Finally, at the end of the process the team
met to contrast the results of this second coding
e?ort and discuss di?erences and new topics, if
any. The objective of sub-dividing the sample into
two groups is to contrast the model that emerges
from the ?rst analysis using a hold-up sample. The
same process was iteratively used to analyze with
increasing detail each of the topics until a stable
set of constructs were identi?ed that explained the
phenomenon examined. The process evolved in an
iterative and non-linear fashion where the topics
and constructs where revised to better capture the
insights of the independent analyses. The end result
is a set of typologies that describe di?erent aspects
of the adoption of MCS in product development
(Marshall & Rossman, 1995).
The second stage of the analysis combines the
quantitative data collected through the question-
naires with the qualitative data derived from the
analysis of the interviews. The product develop-
ment systems included in the questionnaire were
selected based on a review of the product develop-
ment literature (such as Ulrich and Eppinger
(1995)). Those systems more frequently cited were
included in the questionnaire. The unit of analysis
is the company; therefore each observation com-
bines qualitative-interview data with quantitative-
questionnaire data. Interviews and questionnaires
were designed to collect di?erent types of data
from the companies. The objective of analyzing
these two sources of data is to establish patterns
leading to a framework of reasons-for-adoption
(Strauss & Corbin, 1990). The ?nal dataset com-
bines survey data with the variables identi?ed in
the coding of the qualitative data to propose rela-
tionships among these variables (King, Keohane,
& Verba, 1994). The ?ndings reported in the paper
are the end process of the analysis; however the
audit trail documentation allows tracking the con-
clusions to the raw data.
Table 1
Early-stage entrepreneurial company sample: sample construc-
tion and descriptive statistics on ?nal sample
Panel A: sample construction
Companies in the initial database 624
Minus
Companies that went out of business 94
Companies acquired 88
Companies ineligible in some other way
a
102
Companies that did not respond
b
158
Companies that declined participation 113
Final sample of companies 69
Mean Std.
dev.
Quartile
1 (0.25)
Median
(0.50)
Quartile
3 (0.75)
Panel B: sample descriptive statistics on ?nal sample
Number of
CEOs
1.73 0.80 1 2 2
Age (in
years)
7.42 2.33 5 7 9
Employees
c
119 62.6 72 114 160
R&D
intensity
(%)
d
0.39 0.26 0.16 0.38 0.61
Revenues
($‘000)
e
10,691 11,711 2468 7140 15,156
Income
($‘000)
e
À10,175 15,598 À12,139 À5400 À18
Funding
($‘000)
f
52,441 59,865 8963 39,300 72,750
a
These companies are too small, too old, subsidiaries of other
companies, or with no signi?cant product development activity.
b
These are companies that did not respond to the ?ve tele-
phone contacts.
c
Number of employees is calculated at the peak of each com-
pany’s size.
d
R&D employees are estimated as a percentage of total
employees de?ned as the sum of R&D employees for each of the
years reported divided by the sum of total employees for those
same years. Only companies that reported R&D employees are
included.
e
Revenues and income are for the last year of data available.
f
Funding is the total external funding for each company.
332 A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347
Results for the adoption of management control
systems in product development
Table 2 (Panel A) provides descriptive statistics
on the percentage of companies adopting each of
the product development systems identi?ed for this
research and the time-to-adoption since the found-
ing of the company (Appendix B reproduces the rel-
evant questionnaire items). Project milestones are
the system most companies have adopted and the
fastest to adopt. Time-to-adoption is signi?cantly
di?erent from the other systems at least at the
10% (two-tailed t-test) in pair-wise comparisons,
18
except for product concept testing process where
the di?erence in time of adoption is not signi?cant.
Companies that adopted product concept testing
process, while relatively fewer (45%), did so earlier
in their lives. Project selection process is adopted
signi?cantly slower than all other systems except
product portfolio roadmaps. Roadmaps are also
slower than the other systems except product con-
cept testing process. These systems are likely to be
adopted later because they require having various
products considered in the development plan; some-
thing that is likely to happen later on in the life of a
company, once the initial product has been devel-
oped and released to the market.
19
Fig. 2 shows
the adoption time evolution; it plots the percentage
of companies that had adopted each of the systems
as a function of company age. Fig. 3 plots the same
information but against company size (proxied by
number of employees).
Panel B in Table 2 summarizes the results to the
open-ended question ‘‘what are the three most
important measures that top management uses to
evaluate the progress of a development e?ort?”
included in the questionnaire. The ?ve measures’
descriptions were coded by two researchers indepen-
dently. Panel B in Table 2 gives the frequency distri-
Table 2
Early stage entrepreneurial company sample: product development descriptive statistics
Companies adopting (%) Time-to-adoption (years since company founding)
Mean Quartile 1 (0.25) Median (0.50) Quartile 3 (0.75)
Panel A: descriptive statistics on product development systems
Project milestones 81 2.70 1 2 5
Reports comparing actual progress to plan 71 2.88 1 3 4
Budget for development projects 68 3.02 1 2 5
Project selection process 65 3.44 1 3 5
Product portfolio roadmap 61 3.36 1 3 5
Product concept testing process 45 2.74 1 2 5
Project team composition guidelines 39 3.00 1 2 5
Type of measures Respondents Updating frequency (per month)
Mean Quartile 1 (0.25) Median (0.50) Quartile 3 (0.75)
Panel B: descriptive statistics on product development measures
Time 62 2.81 1 4 4
Budget/?nancial 30 2.36 1 1 4
Product functionality 30 2.08 0.83 2 4
Customer 24 1.78 1 1 2
Quality 15 2.90 2 4 4
Panel A: Companies adopting each system are the percentage of companies that had adopted the system at the point of data collection.
Time-to-system is the mean number of years since founding to the adoption of the particular system for those companies that adopted each
system.
Panel B: The table reports the number of respondents that listed each type of measure among the three top measures for managing product
development and the frequency (times per month) that the measures are updated.
18
We also performed an ANOVA test that was signi?cant at the
1% level. We also tested for di?erences using Wilcoxon matched-
pairs signed-ranks test and testing whether the median of the
di?erences were di?erent from zero. The conclusions remained
are the same as those using the parametric t-test.
19
We also tested for di?erences in percentage of adoption.
‘‘project milestones” is signi?cantly di?erent from all other
systems except ‘‘comparing actual progress to plan” at least at the
5% level (two-tailed). ‘‘Comparing actual progress to plan,”
‘‘budget for development projects,” ‘‘project selection process,”
and ‘‘product portfolio roadmap” are signi?cantly di?erent from
‘‘project team composition guidelines” and ‘‘product concept
testing process” at least at the 10% level (two-tailed).
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 333
bution for the 69 companies of the types of measures
used to track product development and how often
they are updated. A time-related measure is the most
frequently used with 62 respondents (89.9%) refer-
ring to it with updates averaging 2.81 times per
month on average. Budgets and product functional-
ity related measures are mentioned by 30 of the
respondents (43.5%) while customer-related mea-
sures are reported by 24 respondents (34.8%) and
quality-related measures 15 respondents (21.7%).
The iterative analysis of interview data identi?ed
six di?erent reasons-for-adoption of MCS. While
we began our data analysis aware of the seven
MCS roles identi?ed in the literature (‘Reasons/
events and roles of MCS in product development’),
the iterative analysis quickly converged to a
0
0.2
0.4
0.6
0.8
Company Age (years)
P
e
r
c
e
n
t
a
g
e
o
f
C
o
m
p
a
n
i
e
s
A
d
o
p
t
e
d
Project Milestones
Actual Progress to Plan Project Budget
Project Selection
Product Portfolio Roadmap Product Concept Testing
Project Team Composition
0 1
2 3 4
5
Fig. 2. Early-stage entrepreneurial company sample: evolution of product development management control systems over time. Each of
the seven systems (project milestones, reports comparing actual progress to plan, budget for development projects, project selection
process, product portfolio roadmap, product concept testing process, project team composition guidelines) is coded yearly as 1 (if the
company reports having adopted the system) and 0 otherwise. For each year since funding (x-axis) we add the number of companies that
have adopted the system over the total population to obtain the percentage in the y-axis.
0
0.2
0.4
0.6
0.8
Company Size (Number of Employees)
P
e
r
c
e
n
t
a
g
e
o
f
C
o
m
p
a
n
i
e
s
A
d
o
p
t
e
d
Project Milestones Actual Progress to Plan Project Budget
Project Selection Product Portfolio Roadmap Product Concept Testing
Project Team Composition
Recent theoretical and empirical work indicates that management control systems (MCS) are an important element
in enhancing innovation. We extend this research thrust examining the adoption of MCS in product development,
arguably one of the business processes where innovation plays a major role. Using a sample of 69 early-stage entrepreneurial
companies, data are collected from questionnaires and interviews with each of the CEO, financial officer,
and business development managers pertaining to product development MCS. We examine seven different systems:
project milestones, reports comparing actual progress to plan, budget for development projects, project selection process,
product portfolio roadmap, product concept testing process, and project team composition guidelines.
Reasons for management control systems adoption:
Insights from product development systems choice
by early-stage entrepreneurial companies
Antonio Davila
a,
*
, George Foster
b
, Mu Li
c
a
IESE, University of Navarra, Spain
b
Graduate School of Business, Stanford University, United States
c
Applied Micro Devices, United States
Abstract
Recent theoretical and empirical work indicates that management control systems (MCS) are an important element
in enhancing innovation. We extend this research thrust examining the adoption of MCS in product development,
arguably one of the business processes where innovation plays a major role. Using a sample of 69 early-stage entre-
preneurial companies, data are collected from questionnaires and interviews with each of the CEO, ?nancial o?cer,
and business development managers pertaining to product development MCS. We examine seven di?erent systems:
project milestones, reports comparing actual progress to plan, budget for development projects, project selection pro-
cess, product portfolio roadmap, product concept testing process, and project team composition guidelines. We
address three distinct questions: (1) What are the reasons-for-adoption of these systems? The nature of our sample
allows us to trace back to the adoption point and develop a set of reasons-for-adoption from the analysis of the data.
While MCS ful?ll certain roles as described in the literature, these reasons-for-adoption are distinct from these roles.
Results indicate that certain events lead managers to adopt these systems and address the challenges that they face.
They include contracting and legitimizing the process with external parties and internal reasons-for-adoption such as
managers’ background, learning by doing, need to focus the organization, or reaction to problems. (2) Are these rea-
sons-for-adoption associated with di?erences across companies in the time from their founding date until these sys-
tems are adopted (time-to-adoption)? Prior research has looked at the covariance of various organizational
variables with this timing; this study goes a step further by looking at the e?ect of di?erent reasons-for-adoption
on this timing. Our evidence ?nds an association between these two variables. (3) Are these reasons-for-adoption rel-
evant to performance? We ?nd that the reason-for-adoption is associated with the on-time dimension of product
development performance.
Ó 2008 Elsevier Ltd. All rights reserved.
0361-3682/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.aos.2008.08.002
*
Corresponding author. Tel.: +34 93 602 4183; fax: +34 93 253 43 43.
E-mail address: [email protected] (A. Davila).
Available online at www.sciencedirect.com
Accounting, Organizations and Society 34 (2009) 322–347
www.elsevier.com/locate/aos
Introduction
Formal management control systems (MCS)
have traditionally been associated with mechanistic
organizations (Burns & Stalker, 1961). These systems
support the periodic execution of the same routines
with little if any changes. Their relevance to the
innovation process – a process associated with
uncertainty, with unknown links between inputs
and outputs, with exceptions, and with outputs that
are often hard to evaluate – is less clear. Ouchi
(1979) used a research department to illustrate clan
control where social norms substitute for formal
management systems. Mintzberg’s separation of
planning and managing (Mintzberg, 1976) and
Quinn’s logical incrementalism (Quinn, 1978) also
highlight the limitations of traditional MCS. A fun-
damentally di?erent perspective is that these sys-
tems may provide important discipline to help
manage uncertainty. Recent theoretical develop-
ments o?er various concepts that support the need
for formal management control systems (MCS) in
uncertain settings.
1
For instance, the distinction
between coercive and enabling bureaucracies (Adler
& Borys, 1996) suggests that MCS may be instru-
mental to innovation. Gavetti and Levinthal
(2000) present a learning model where companies
that jointly rely on planning and learning by doing
are predicted to perform better in uncertain envi-
ronments compared to alternative strategies. Thus,
forward looking e?orts typically associated with
MCS complement fast reaction to new information
to improve how organizations deal with uncer-
tainty. Zollo and Winter (2002) argue that the
essence of dynamic capabilities is adaptive routines
– including information-based routines. Simons
(1995) interactive systems concept can have an
explicit role in sparking innovation around strategic
uncertainties.
For the most part, recent empirical evidence
also indicates that innovation processes may gain
from the presence of MCS. Abernathy and Brow-
nell (1999) use Simons’ model to examine the use
of budgets ‘‘as a dialogue, learning and idea
creation machine” during episodes of strategic
change. Cardinal (2001) reports an association
between control and performance in both radical
as well as incremental innovation projects in the
pharmaceutical industry.
2
Ditillo (2004) describes
MCS as a key element in knowledge intensive
?rms.
3
Similarly, Chapman (1998) presents evi-
dence consistent with the relevance of these sys-
tems in uncertain environments.
Based on a sample of 69 technology-based
early-stage companies, the paper examines the
adoption of MCS within an organizational pro-
cess where innovation has a pivotal role: the
product development process.
4
The focus on
product development led to sampling from tech-
nology-based ?rms.
5
Product development is a
key aspect in these ?rms. If MCS are important
to managing innovation, this sample of compa-
nies will be (on average) ahead in their use.
6
Our objective of learning about the adoption of
MCS led to adding the early-stage criteria. The
focus on the adoption stage (rather than the evo-
lution of existing MCS) suggested studying com-
panies that are going through the transition
from birth to early-stage when the MCS are
1
MCS are de?ned as formal management control systems
following Simons’ de?nition: ‘‘formal, information-based rou-
tines and procedures managers use to maintain or alter patterns
in organizational activities” (Simons, 1995, p. 5). Throughout the
paper, MCS is used to refer to this de?nition unless otherwise
noted.
2
Cardinal identi?es input, behavior and output control where
input control (scienti?c diversity and professionalization) might
be interpreted as informal control while behavior and output
control are formal MCS. She ?nds all three types of control being
associated with performance for incremental and radical innova-
tion projects.
3
Ditillo (2004) interprets MCS similarly to the way this paper
uses the term. He describes them as ‘‘the design as well as use of
coordination mechanisms based on the standardization of either
input, action, or results” (p. 402).
4
Because we study MCS in the speci?c process of product
development, MCS speaks to the systems that are used within this
process. In particular we examine the following MCS: project
milestones, reports comparing actual progress to plan, budget for
development projects, project selection process, product portfolio
roadmap, product concept testing process, and project team
composition guidelines. We collect data on actual systems rather
than on particular theoretical constructs associated with the
design and use of these seven systems.
5
We chose to study a speci?c realization of innovation through
looking at product development process rather than the broader
concept of ‘‘organizational innovation” to increase the power of
the research design by reducing the noise and potential con-
founding factors.
6
The objective of the paper is not to examine whether the
presence of MCS is needed for a company to be innovative.
Rather, its objective is to understand why MCS are adopted in a
particular process associated with innovation and whether distinct
reasons for adoption are associated with the time it takes to
formalize these systems and to on-time performance. To probe the
link between the presence of MCS and innovation, one would
need to develop a measure of innovativeness and compute this
measure on a year-by-year basis for each company in our sample.
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 323
adopted for the ?rst time. We conceptualize MCS
in our ?eld research as formal systems particular
to product development including: project mile-
stones, budget for development projects, reports
comparing actual progress to plan, project selec-
tion process, product portfolio roadmap, product
concept testing process and, project team compo-
sition guidelines.
Accordingly, this paper is built at the intersection
of two main research thrusts. (a) The study exam-
ines the product development process – where inno-
vation has a signi?cant role. Building on existing
theory on the relevance of MCS to innovation
processes, it provides new evidence to advance our
conceptual understanding of why MCS are an
important aspect of innovation management. In
particular, it highlights the di?erence between the
reasons-for-adoption of MCS in innovation pro-
cesses and the objectives that these systems pursue.
(b) Our sample of entrepreneurial companies also
speaks to the literature on the emergence of MCS;
thus, the conceptual development around the adop-
tion of MCS in product development processes can
be the basis for empirical work to understand the
adoption of these systems in other organizational
processes in early-stage companies. The study
speaks to the adoption of MCS in a process tradi-
tionally associated with innovation. Because the
study is framed within early-stage entrepreneurial
companies to capture the adoption event, it does
not speak to the evolution of MCS in established
?rms.
We examine three related research questions: (1)
why MCS are ?rst adopted in the product develop-
ment process? In particular, we present various
reasons-for-adoption of MCS, (2) what is the rela-
tionship between these reasons-for-adoption and
speed of adoption? and (3) what is the relationship
between reasons-for-adoption and a key product
development performance measure (on-time
development)? Fig. 1 illustrates these research
questions.
To inform these questions, the literature o?ers
arguments on why organizations adopt MCS.
These arguments have an early antecedent in
Greiner (1972). The ?rst stage of his growth
model deals with the emergence of MCS. It
describes the emergence of MCS as a crisis of
leadership where: ‘‘increased number of employees
cannot be managed exclusively through informal
communication (. . .) and new accounting proce-
dures are needed for ?nancial control.” Yet this
argument was not researched in depth for several
decades.
7
Cardinal, Sitkin, and Long (2004)
observe that much of the literature has ‘‘virtually
ignored the origins and evolution of organiza-
tional control” (p. 411). Recent research has
started to address the origins and evolution issue.
The two main streams of research are: (a) case
studies that describe the rise (and fall in some
cases) of controls over the early years of startup
companies (Cardinal et al., 2004) and (Granlund
& Taipaleenmaki, 2005), and (b) large-sample
based studies of the association between MCS
evolution and organizational variables such as
age, size, company strategy, and the presence of
venture capital (Davila, 2005; Davila & Foster,
2005, 2007; Sandino, 2007). The present study
probes the reasons that led to a di?erential
MCS adoption for 69 technology-based early-
stage companies in the product development
management systems area. Product development
systems are especially important for high-techno-
logy companies given the pivotal role that product
innovation plays in their growth.
Our research design combines qualitative data
from over 200 interviews and quantitative data
gathered using questionnaires. It also triangulates
the data using three di?erent informants per com-
pany. Access to qualitative descriptions of why
MCS were adopted allows us to develop a frame-
work of reasons why such systems are adopted.
We identify two external based reasons (labeled
legitimize and contract) and four internal reasons
(labeled manger’s background, need to focus
8
,
chaos, and learning). One bene?t of the structured
qualitative analysis of interviews is the rich descrip-
tion we can report to illustrate the six di?erent rea-
sons-for-adoption.
The second and third research questions build
upon the framework of reasons-for-adoption
developed in the ?rst question. In particular, the
second research question examines how the six dif-
ferent reasons for adoption in?uence the speed of
adoption of product development systems. Speed
7
MCS in small and medium companies have been studied using
cross-sectional designs (thus not looking at MCS adoption)
(Amat, Carmona, & Roberts, 1994; Romano & Ratnatunga,
1994). Also the evolution of existing MCS has been examined
(Oakes, Townley, & Cooper, 1998). Yet the adoption of these
systems has only received attention recently.
8
‘‘Need to focus” refers to the manager’s adoption of MCS to
focus the company on executing the strategy when the company is
failing to do so because of lack of systems.
324 A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347
of adoption is measured using time elapsed from
the start of the company to the time reported for
the adoption of the project milestones’ system.
9
We ?nd that the reasons-for-adoption associated
with manager’s background, chaos, legitimize,
need to focus, and contract are found to be signif-
icantly related with faster adoption vis-a`-vis a sub-
set of our sample of companies with higher
reliance on informal product development control
systems.
The third research question probes whether the
same six di?erent reasons for MCS adoption in?u-
ence a key product development performance mea-
sure (on-time development). Our results indicate
that when MCS are adopted because of the man-
ager’s background, product development perfor-
mance is enhanced.
Because our sample comprises early-stage com-
panies, their level of MCS adoption and product
development performance are likely to have signi?-
cant variation. The sample selection is designed to
capture these companies’ transition phase into
MCS; moreover, their product development pro-
cesses are still evolving – for instance, through the
adoption of MCS – and thus expected to show dif-
ferent performance levels. This fact allows us to
study the relationship between reason-for-adoption
and speed of adoption and performance.
The paper brings new evidence to the growing lit-
erature on the relevance of MCS to enhance the per-
formance of ?rms employing organic structures
(Kalagnanam & Lindsay, 1999) and in particular
to innovation processes (Bisbe & Otley, 2004; Granl-
und & Taipaleenmaki, 2005). The main ?ndings
include: (1) we identi?ed six reasons for MCS
adoption. Two reasons-for-adoption are externally
related: legitimizing the company and contracting
with external parties. This evidence highlights the
role of external parties in shaping management sys-
tems internal to the ?rm (Pfe?er & Salancik, 1978);
an in?uence that is typically associated with ?nancial
reporting. We also identify four internal reasons-for-
adoption, two of them proactive: managers’ back-
Time to Adoption of
MCS
Innovation Process
Performance
Organizational
Performance
Reasons-for-Adoption
External Reasons
Legitimize
Contract
Internal Reasons: Proactive
Manager Background
Need to Focus
Internal Reasons: Reactive
To Chaos
To Learning
Organizational Covariates Such As:
First Research Question
Second Research
Question
Third Research
Question
: Prior Work : Current Work
Legend
Size
Age
Strategy
VC Funding
Founder Replaced
MCS Roles
Fig. 1. Overview of research questions and related literature. First research question: what are the reasons-for-adoption of product
development MCS? Second research question: are these reasons-for-adoption associated with di?erences across companies in the time from
their founding date until these systems are adopted (time-to-adoption)? Third research question: are these reasons-for-adoption relevant to
performance?
9
We adopt this measure because ‘‘project milestones” is the
system adopted most often. We also ?nd similar results for
‘‘reports comparing actual progress to plan” and ‘‘budgets for
development projects” as the speed of adoption measure.
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 325
ground, focus the attention of the organization on
executing the strategy (need to focus), and two reac-
tive: react to problems, and code learning (associ-
ated with formalizing repetitive yet non-formalized
processes). Our evidence also provides qualitative
data consistent with MCS roles identi?ed in the
literature including stimulating dialogue and idea
creation; controlling execution through diagnostic
systems; and stabilizing an environment that, by
the nature of the innovation process, is already rich
in opportunities. (2) Using these six new reasons for-
adoption categories, the study then examines the
impact of di?erent reasons-for-adoption on the
speed of adoption of MCS. We ?nd that managers’
background is associated with the fastest time-
to-adoption. (3) Finally, we ?nd that MCS adopted
to code learning or because of the managers’
background is associated with better on-time
development (an important dimension of product
development performance).
The next section (‘Conceptual underpinnings
and literature linkages’) of this paper reviews the
literature on innovation and MCS. We highlight
how the literature has emphasized the di?erent
roles that MCS may have; thus, what purpose do
MCS ful?ll in organizations. Yet, these roles are
not necessarily associated with the reasons why
MCS are adopted. These reasons are associated
with events that do not have a one-to-one relation-
ship with the roles that these systems play. There-
fore, a particular event may trigger the adoption
of a system with a particular role in a certain envi-
ronment and a system with a di?erent role in
another environment. The reasons why systems
are adopted are distinct from the roles that the sys-
tems ultimately play. This section also presents the
evolution of the literature from an initial notion
that innovation and MCS were incompatible to
the new broader support for MCS having a positive
role in promoting innovation. ‘Field research
design’ presents the research design. The study is
based on a multi-case, multi-method research
design. We sent questionnaires and interviewed
three managers in each of 69 high-growth technol-
ogy entrepreneurial companies. This design allows
for triangulation of the data and combining quali-
tative and quantitative data to develop a frame-
work and use statistical generalization of the
?ndings. The following three sections present the
results to our three research questions. ‘Discussion’
relates these ?ndings to the existing literature and
discusses limitations and future research.
Conceptual underpinnings and literature linkages
Fig. 1 provides an overview of our three research
questions and its links to the existing literature. This
section explores those links to highlight our contri-
butions. The initial arguments in the literature
(‘Limitations of management control systems’) saw
MCS as constraining innovation and therefore
incompatible with it. Over time, evidence started
to accumulate on the bene?ts of MCS on innovation
(‘Theoretical justi?cations for and empirical evi-
dence on the role of MCS in innovative settings’)
and theoretical arguments emerged to explain this
evidence. To address our research questions, ‘Rea-
sons/events and roles of MCS in product develop-
ment’ reviews the di?erent roles of MCS proposed
in the literature. These roles are contrasted against
the reasons-for-adoption that we identify from our
empirical evidence. ‘The emergence of MCS in
entrepreneurial companies’ presents recent empiri-
cal work on the emergence of MCS in entrepreneur-
ial companies. This work provides the framework to
analyze our two additional research questions: the
relationship between (i) reason-to-adopt and time-
to-adoption (our second research question), and
(ii) reason-to-adopt and performance (our third
research question).
Limitations of management control systems
Management control systems can sti?e innova-
tion (Ouchi, 1979). If not designed to deal with
uncertainty, they may constrain cross-functional
interaction, limit communication to established
patterns, penalize deviations, and di?use leader-
ship. Mintzberg’s work (1976, 1979, 1994) high-
lights the separation between planning and
managing as two separate processes, the ?rst ruled
by formal systems and the second more informal
and decoupled from MCS. Quinn (1978) argues
that the restrictions imposed by organizations’ for-
mal systems limit the innovation abilities to ‘‘logi-
cal incrementalism.” Damanpour’s (1991) meta-
analysis of empirical work on organizational deter-
minants of innovation reports a negative associa-
tion between innovation and formalization. These
MCS are most useful when task analyzability is
high and the number of exceptions is low (Perrow,
1970) such as in low innovation settings. In high-
innovation environments, they may diminish the
intrinsic motivation and freedom that innovation
requires (Amabile, 1998). Empirical studies have
326 A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347
con?rmed these predictions. Abernethy and Lillis
(1995) ?nd that ‘‘spontaneous contact and ‘‘inte-
grative liaison devices” which allow regular, per-
sonal and intensive contact” are more prevalent
in ?exible manufacturing ?rms while traditional
performance measurement systems are de-empha-
sized. Abernethy and Brownell (1997) report higher
reliance on personnel control in research and
development departments. Rockness and Shields
(1988) echo these conclusions and ?nd that the rel-
evance of budgets in R&D settings is highest for
planning purposes and decreases monotonically
for monitoring, evaluating, and rewarding.
While these arguments do not support the need
for management control systems in innovative set-
tings, recent empirical evidence and theoretical
arguments provide a di?erent perspective. Innova-
tion management appears to bene?t from having a
balance between ‘‘tight” and ‘‘loose” controls to
provide both the support and direction for innova-
tion. The positive impact of these controls on inno-
vation highlights the need to understand why these
systems are adopted in processes where innovation
has a relevant role.
Theoretical justi?cations for and empirical evidence
on the role of MCS in innovative settings
Simons’ typology (Simons, 1995) identi?es
interactive systems as information-based routines
to identify knowledge required to address strate-
gic uncertainties. One attractive feature of the
interactive systems concept is that it allows top
management to guide the search stage of the
innovation process, without falling into the cyber-
netic model. Recent empirical studies (Abernethy
& Brownell, 1997; Bisbe & Otley, 2004) rely on
interactive systems to examine MCS in uncertain
environments.
While interactive systems speak to the front end
of the innovation process, the concept of enabling
bureaucracy (Adler & Borys, 1996) addresses the
role of MCS throughout the stages of assimilation
and exploitation of knowledge. Enabling bureau-
cracy is designed to ‘‘enhance the users’ capabilities
and to leverage their skills and intelligence” (p. 68)
rather than with ‘‘a fool-proo?ng and deskilling
rationale.” Thus, organizations assimilate and exploit
the knowledge accumulated in the ?rst stage through
?exible, transparent, user-friendly routines. Ahrens
and Chapman (2004) apply the concept of enabling
bureaucracy to analyze the role of MCS in a ?eld
study setting. They describe how managers rely on
an enabling use of these systems to cope with the
uncertainty of day-to-day operations.
The concept of adaptive routines (Weick, Sutc-
li?e, & Obstfeld, 1999) describes routines as resilient
because of their capacity to adapt to unexpected
events. This concept portrays routines as ?exible
to absorb novelty rather than rigidly to suppress
it. They o?er organizational members a stable
framework to interpret and communicate when fac-
ing unexpected events. They ‘‘usefully constrain the
direction of subsequent experiential search” (Gav-
etti & Levinthal, 2000, p. 113). Reliability rather
than replicability identify routines in uncertain set-
tings. Feldman and Rafaeli (2002) extend this argu-
ment to include routines as drivers of key patterns
of communication among organizational members.
Miner, Basso?, and Moorman (2001) describe the
constant interaction between routine activities and
improvisation in product development. Routines
provide the background for improvisation to hap-
pen and learning to accumulate.
These concepts highlight the positive e?ect that
MCS may have on innovation. MCS are viewed as
?exible and dynamic frames adapting and evolving
to the unpredictability of innovation, but stable to
frame cognitive models, communication patterns,
and actions.
Evidence is accumulating of the positive e?ects
from adopting MCS in uncertain settings. For
instance, managing environmental uncertainty has
repeatedly been associated with MCS (Chenhall &
Morris, 1986; Khandwalla, 1972; Simons, 1987).
Chenhall and Morris (1995) identify extensive use
of management accounting systems with superior
performance in companies following entrepreneur-
ial as opposed to conservative strategies. Henri
(2006) ?nds that the dynamic tension that emerges
as systems are used both in a diagnostic and interac-
tive way is associated with improved performance.
Chapman (1998) used four case studies to conclude
‘‘that accounting does have a bene?cial role in
highly uncertain conditions” (p. 738). Howard-
Grenville (2003) used an ethnographic approach in
one high-technology company to document the rel-
evance of organizational routines to confront uncer-
tain and complex situations.
Within product development, prescriptive recom-
mendations to practitioners emphasize the impor-
tance of MCS (McGrath, 1995). Several research
studies have found that planning and well-coordi-
nated project execution are associated with product
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 327
success (Cooper, 1995; Zirger & Maidique, 1990).
These studies hint to a role of MCS although they
fail to provide a theoretical justi?cation for their
?ndings (Brown & Eisenhardt, 1995). Using a con-
trol framework, Cardinal (2001) found that the
three types of control systems – input, behavior,
and output control – enhance radical innovation –
arguably the most uncertain type of innovation.
Davila (2000) reports a positive association between
the use of management accounting information
and product development performance. Brown
and Eisenhardt (1997) describe successful product
innovation as blending ‘‘limited structure around
responsibilities and priorities with extensive com-
munication and design freedom” so that ‘‘this com-
bination is neither so structured that change cannot
occur nor so unstructured that chaos ensues” (p. 1).
This evidence highlights the importance of under-
standing MCS in product development.
The emergence of MCS in entrepreneurial companies
The previous sub-sections argue for the
importance of MCS to the innovation process
from theory and evidence perspectives. This
sub-section reviews another stream of literature
relevant to our research question – the literature
on the emergence of MCS in entrepreneurial
companies.
Our research questions examine the adoption of
MCS in a particular innovation process, product
development. Thus, both literatures o?er relevant
frames of reference. While the innovation literature
is relevant to the need for MCS in product develop-
ment, the MCS emergence literature examines in
more detail variables associated with how fast these
systems are adopted. These ideas are most relevant
to our second and third research questions relating
time-to-adoption and performance to reasons-for-
adoption.
Greiner (1972)’s growth model describes a ?rst
transition (labeled crisis of control) where he
argues for the adoption of MCS. At the end of
the ?rst growth phase informal management no
longer works and MCS are required. Moores and
Yuen (2001) provide initial evidence on the emer-
gence of MCS. They ?nd, as they expected, that
management systems are adopted in the growth
stage of the ?rm. Davila (2005) in his study of
the emergence of HR systems and Davila and Fos-
ter (2005, 2007) in their research of management
accounting systems’ adoption focus on time-to-
adoption as their main research variable.
10
They
?nd that faster adoption of these systems is associ-
ated with the presence of venture capital funding,
size, age of the ?rm, and the replacement of the
founder as CEO. Sandino (2007) examines how
time-to-adoption of MCS in retail ?rms depends
on their strategy given a set of common systems
being adopted. Cardinal et al. (2004) describe
how the control approach evolves from informal
to formal in a case study of a growth ?rm. These
studies look at the emergence of MCS and they
rely on time-to-adoption as their proxy to address
this question.
Organizational characteristics such as age, strat-
egy, lifecycle stage, or size are covariates in vari-
ous studies; yet the co-variation between time-to-
adoption and organizational variables is mediated
by events (reasons-for-adoption) that are absent in
these studies. Simons (1995) provides arguments
indicating that a potential event leading to MCS
adoption is a breakdown in processes – such as
failure to meet deadlines or quality problems. In
this case, the reason-for-adoption is an organiza-
tional failure, an event that can be associated to
di?erent MCS roles – such as either a making
goals explicit role or a coordination role. In other
instances, MCS may be triggered by an event
associated with one MCS role such as adopting
MCS to legitimize the company vis-a`-vis potential
customers. However, no explicit arguments have
been provided to answer the question of why are
MCS adopted (Cardinal et al., 2004). One objec-
tive of this paper is to answer this question
grounded in the qualitative evidence that the data-
base provides.
As described in the previous paragraph, prior
literature on the emergence of MCS has focused
on two main variables: time-to-adoption and orga-
nizational performance. Time-to-adoption mea-
sures the speed to MCS adoption and thus is
relevant to understand the phenomenon. Organiza-
tional performance addresses the issue of whether
MCS adoption is relevant. Davila and Foster
(2005, 2007) ?nd that faster adoption of budgets
and of a broader measure of MCS adoption is
associated with faster company growth as mea-
sured by number of employees. This result suggests
that faster adoption is relevant to organizational
performance. While this result is important, it does
10
They de?ne time-to-adoption in a similar way: time from the
inception of the company to adoption of a particular system.
328 A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347
not address the question of whether di?erent rea-
sons-for-adoption are associated with faster adop-
tion; it also does not address the question of
whether these reasons are related to performance.
While no theory exists to guide our priors, the
empirical evidence provides an exploratory answer
to these questions.
11,12
Reasons/events and roles of MCS in product
development
Existing knowledge on MCS focuses on the roles
that these systems ful?ll; in other words, why these
systems exist. Yet, understanding the reasons why
these systems are adopted in the ?rst place is
another important aspect of MCS research. This
paper presents new evidence on the reasons MCS
are ?rst adopted within the context of product
development. This is a di?erent lens to view MCS
than the extensive literature on the roles that MCS
can play. We show that in some cases the reason
for adoption is unrelated to a particular role. More-
over, the same reason for adoption can lead to the
adoption of MCS with di?erent roles across compa-
nies. To fully position the reasons-for-adoption that
emerge from the empirical study in this paper, the
following paragraphs describe the MCS roles that
the existing literature identi?es:
(1) Make goals explicit and stable. Amabile
(1998) indicates that innovation is enhanced
when people are granted freedom to achieve
goals that are clear and stable for a su?ciently
long period of time. She states ‘‘it is far more
important that whoever sets the goals also
makes them clear to the organization and that
these goals remain stable for a meaningful per-
iod of time” (p. 80). Uncertainty provokes a
constant shift of priorities that may undermine
the innovation process. MCS typically state
explicit goals. This increases their stability
and visibility, facilitates convergence in mean-
ing across organizational actors, and provides
the clarity that creativity is argued to require.
(2) Code learning from past. Lundberg (1995)
indicates that procedures help innovation by
coding learning from past experience (Levitt
& March, 1988). Coded routines facilitate
the di?usion across the organization and over
time of organizational capabilities (Nelson &
Winter, 1982).
(3) Help coordination. Lundberg (1995) also points
out the importance of coordinating di?erent
innovation e?orts across the organization.
MCS decouple the e?orts of organizational
actors and reduce coordination costs through
the explicit negotiation of local goals.
(4) Plan the sequence of steps. Process planning
clari?es the sequence of steps to achieve cer-
tain organizational goals and provides a blue-
print for coordinating the innovation e?ort
over time (Cohen et al., 1996).
(5) Promote accountability and facilitate control.
MCS facilitate control by exception (Simons,
1995) where managerial attention is required
only if innovation results deviate from
expectations.
(6) Contract with external parties. External con-
stituencies, such as partners, may impose
MCS to enhance their monitoring within the
?rm. These intermediate milestones facilitate
contracting with outside partners (Powell,
1998). Pfe?er and Salancik (1978) highlight
the relevance of the external context in
explaining how ?rms are organized.
(7) Symbols to legitimize. Finally, new institution-
alism (Carruthers, 1995; Powell & DiMaggio,
1991) views cognitive processes as relevant to
explain management systems. It identi?es
formal processes as symbolic to externally
legitimize the innovation process of the orga-
nization through an appearance of compe-
tency. Management systems do not ful?ll as
much a technical need as conforming to
external demands decoupled from technical
needs.
11
While there are no theoretical models to guide our priors,
their future development is important because there is no clear
directional prediction. For instance, it might be argued that MCS
that are adopted because of managers’ prior experience will
emerge before MCS that code existing learning; because manag-
ers do not have to go through the learning process. However, the
opposite argument can hold: coding existing learning can lead to
faster emergence because the hiring of managers’ with prior MCS
experience may only happen when the company codes and
realizes the limitations of the MCS that it has put in place.
12
The relationship between reasons for adoption and perfor-
mance also has no clear directional predictions. For instance, if
the knowledge about how to design MCS is not in the company
(for instance, when systems are designed as a reaction to
problems), the design is likely to be worse than if the knowledge
exists (for instance, when systems are designed based on the prior
knowledge of managers) and impact performance. Alternatively,
MCS developed as a reaction to problems may be better adapted
than MCS developed based on an experience di?erent from the
particular needs of the company.
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 329
Appendix A illustrates these di?erent roles of
MCS using quotes from interviews with executives
of our early-stage entrepreneurial company sample.
These quotes reinforce the importance of these dif-
ferent roles. ‘Results for the adoption of manage-
ment control systems in product development’
presents the reasons-for-adoption identi?ed in the
empirical study and compares them to the MCS
roles described in the literature.
Field research design
To capture the richness needed to explore why
companies adopt MCS in the product development
process, we adopt a cross-sectional, multi-method,
multi-case ?eld research design. The aim of the
cross-sectional multi-case design is to gather a large
enough variation to probe our research questions,
to capture the detail required to answer the ques-
tions, and to link contextual variables to the adop-
tion of MCS. The multi-method design relies on
qualitative data to identify patterns of behavior
and quantitative data to examine covariates that
may inform the research questions. Our data
include questionnaire and semi-structured inter-
views on the adoption and role of MCS in young
technology companies. Capturing the quality,
depth, and richness to understand the experience
of the actors (Seidman, 1998) demands detailed
descriptions of the phenomena (Kvale, 1996; Mar-
shall & Rossman, 1995). The focus of the study on
product development as a relevant aspect of innova-
tion processes drives the decision to sample among a
population of technology companies. We expect
product development to be a signi?cant enough
aspect of their strategy to have received manage-
ment attention. The sampling of a population of
young ?rms is intended to capture the point in time
when formal systems, if any, are adopted. Managers
in young ?rms are more likely to identify the process
that led to the adoption these systems were recently
adopted compared to more mature ?rms where
MCS have been around for several decades.
13
This
transition point is likely to be a recent event in the
life of these ?rms and thus managers are expected
to be able to better articulate the reasons-for-adop-
tion (as well as identify the time of adoption).
Data sources
The three main information sources for each com-
pany are public data, three questionnaires, and three
interviews. We ?rst collected as much information as
possible from public sources – such as company’s
web pages and press releases from Lexis-Nexis. This
information was used to familiarize the research
team with the characteristics and products of each
company. Next, each company received three ques-
tionnaires – one for the CEO, another for the CFO,
and a third one for the business development man-
ager. The purpose of the questionnaire was to collect
quanti?able information about the company and its
processes – for instance, whether product develop-
ment projects were early, on-time, or late as a mea-
sure of product development performance or the
time of adoption of each of seven product develop-
ment management systems. The questionnaire was
sent in advance to have additional information to
prepare the interviews, to provide managers with
guidance of what the research was about, and to have
the opportunity to clarify any questions regarding
questionnaire’s answers. Appendix B reproduces
the relevant questions for this study.
This use of questionnaires is somewhat atypical
in the literature. Rather than having the question-
naire measuring the underlying variables using psy-
chometric tools, it was used to collect factual data.
Because the variables in the research had not been
previously used in an empirical study, interview
data were more appropriate to identify them. The
objective was to infer these variables from the
descriptions provided in the interviews and examine
whether they matched the theoretical variables.
The ?nal phase of the data collection included
separate semi-structured interviews with the CEO,
CFO, and business development manager of each
company. The objective of these interviews was to
gain detail about the company, its history, its strat-
egy and the adoption, design, and use of MCS. Each
interview lasted between forty-?ve and ninety min-
utes. Over 200 such interviews were conducted for
the sample examined in this paper. The interviews
relied on a detailed protocol listing the questions
to be addressed. The protocol insured that the main
topics of the research were systematically covered
13
The sampling of young ?rms has to do with the focus of the
research on the adoption of MCS. Managers of younger ?rms are
more likely to have lived through the adoption of these systems.
An alternative is to examine the adoption of accounting
innovations (such as Beyond Budgeting, ABC, or Balanced
Scorecard) in mature ?rms (Innes, Mitchell, & Sinclair, 2000) or
the evolution of MCS as a reaction to changes in the objective
and power structure of external constituencies (Oakes et al.,
1998).
330 A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347
during the conversation, but the semi-structured
nature of the interview gave the ?exibility of follow
up questions to clarify the particular practices at
each company (Marshall & Rossman, 1995). The
relevant protocol questions are reproduced in
Appendix C. Interviews were conducted in person
or by phone and at least two researchers were pres-
ent in every interview. Interviews were taped and
then transcribed. The questionnaire, sent prior to
the interview, facilitated focusing the interview
around the key aspects of interest.
Sample description
The ?nal sample includes 69 young, high technol-
ogy companies. We construct the sampled popula-
tion using the following selection criteria: (1) high
technology, (2) less than 10 years old, (3) between
50 and 150 employees, (4) independent, and (5) in
a limited geographic area.
14
These criteria identi?ed
companies where product development is likely to
be a foundation of their competitive advantage.
They also identify companies more likely to have
recently and independently transitioned through
the stage of formalizing product development pro-
cesses, rather than companies that have had systems
in place for a long time or systems imposed by a
parent company. We did not restrict ?rms to be
public or private, foreign owned, or venture funded;
however the majority of ?rms were private, domes-
tically owned, and venture funded. The population
of ?rms was sourced from the CorpTech Internet
directory of technology companies. We accessed the
database in January and June 2002 and build our
sample from the following industries (using Corp-
Tech industry classi?cation): biotech (BIO), com-
puter hardware (COM), manufacturing (MAN),
medical equipment (MED), pharmaceuticals
(PHA), photonics (PHO), computer software
(SOF), subassemblies (SUB), test & measuring
equipment (TAM), and telecommunications
(TEL).
15
We also purposefully over-sampled bio-
technology ?rms because of their potential rele-
vance as a growth industry. This sub-sample was
extended using three additional databases particular
to the industry: Rich’s High-Tech Business Guide to
Silicon Valley and Northern California (2000/2002),
BioScan (Oct. 2001), and the US Business Browser
(c. 2001).
A letter addressed to the CEO was sent to every
?rm in the sample. The letter described the purpose
of the research, the research process, and the bene-
?ts of participating – a half-day conference where
participating companies were invited to a presenta-
tion of the managerial implications of the research
project and a written document of the ?ndings.
The letter was followed up with a phone call to
entice participation; a company was dropped from
the sample if it had not accepted or declined partic-
ipation after ?ve phone calls. The sample selection
process is detailed in Panel A, Table 1. Excluding
companies that were acquired, went out of business
or are ineligible, the response rate is 20%.
16
Compa-
nies acquired or that went out of business were sig-
ni?cantly younger than the eligible sample but
comparable in terms of sales and number of
employees. Within the eligible sample, we compared
companies that participated to those that did not, in
order to assess potential self-selection bias; we
found no signi?cant di?erences in sales, number of
employees and age.
17
The ?nal sample includes 11
biotechnology companies, 48 information technol-
ogy companies, and 10 companies in other indus-
tries; in addition 59 received venture capital. Panel
B of Table 1 provides additional descriptive statis-
tics on the sample.
Data analysis
The analysis of why ?rms adopt MCS for prod-
uct development was structured in two stages. In
the ?rst stage, interview data were coded – to sum-
14
The main reason for the geographic criterion was research
funding (more than 50% of the interviews were done at the
companies’ premises). While this decision reduces the potential
impact of omitted variables that may vary with geography, it
limits the generalizability of results.
15
We excluded from these lists any companies that were also
listed as ‘‘Energy,” ‘‘Environment,” ‘‘Chemical,” ‘‘Defense”,
‘‘Transportation” or ‘‘Non”. ‘‘Non” companies are not primarily
high-tech companies. The other industries are excluded because
they face a di?erent regulatory and/or institutional environment.
We also excluded organizations cross-listed in these industries.
16
The 20% is a conservative estimate. It assumes that all the
companies that did not respond (to the ?ve telephone contacts)
(which means that they did not pick up the phone) were eligible.
The percentage of companies that participated over the percent-
age of companies that were contacted is 38%. The demands on
company resources were much higher than a traditional mail
survey; we asked each company to ?ll three questionnaires and
have separate one-hour interviews with each of three di?erent
managers.
17
We compared means and medians of sales, employees, and
age (variables available from the databases that we accessed) for
both groups in our sample. We also use the non-parametric
Mann–Whitney test on these variables with identical conclusions.
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 331
marize, interpret, and classify the information. To
limit the potential bias inherent in the analysis of
qualitative data, three researchers coded each one
of the interviews. To systematically proceed through
the coding process, each researcher used the Nvivo
qualitative coding software. This software details
the analysis from the raw data to the theoretical
propositions, thus providing an auditable trail of
the analysis. Because of the exploratory nature, each
researcher may potentially identify di?erent con-
structs that explain the observed patterns. To iden-
tify common constructs, the coding was done
following a structured process. The sample was
divided into two groups. The three researchers inde-
pendently identi?ed the main topics covered in each
interview for one of the groups. The result was the
dissection and reorganization of the original tran-
scripts into broad topics. Then, the researchers
met to contrast the topics that each one identi?ed,
agree in a common set of terms to identify them,
and discussed any di?erences in interpretation of
the transcripts. Next, the second group of interviews
was independently coded using the common termi-
nology. Finally, at the end of the process the team
met to contrast the results of this second coding
e?ort and discuss di?erences and new topics, if
any. The objective of sub-dividing the sample into
two groups is to contrast the model that emerges
from the ?rst analysis using a hold-up sample. The
same process was iteratively used to analyze with
increasing detail each of the topics until a stable
set of constructs were identi?ed that explained the
phenomenon examined. The process evolved in an
iterative and non-linear fashion where the topics
and constructs where revised to better capture the
insights of the independent analyses. The end result
is a set of typologies that describe di?erent aspects
of the adoption of MCS in product development
(Marshall & Rossman, 1995).
The second stage of the analysis combines the
quantitative data collected through the question-
naires with the qualitative data derived from the
analysis of the interviews. The product develop-
ment systems included in the questionnaire were
selected based on a review of the product develop-
ment literature (such as Ulrich and Eppinger
(1995)). Those systems more frequently cited were
included in the questionnaire. The unit of analysis
is the company; therefore each observation com-
bines qualitative-interview data with quantitative-
questionnaire data. Interviews and questionnaires
were designed to collect di?erent types of data
from the companies. The objective of analyzing
these two sources of data is to establish patterns
leading to a framework of reasons-for-adoption
(Strauss & Corbin, 1990). The ?nal dataset com-
bines survey data with the variables identi?ed in
the coding of the qualitative data to propose rela-
tionships among these variables (King, Keohane,
& Verba, 1994). The ?ndings reported in the paper
are the end process of the analysis; however the
audit trail documentation allows tracking the con-
clusions to the raw data.
Table 1
Early-stage entrepreneurial company sample: sample construc-
tion and descriptive statistics on ?nal sample
Panel A: sample construction
Companies in the initial database 624
Minus
Companies that went out of business 94
Companies acquired 88
Companies ineligible in some other way
a
102
Companies that did not respond
b
158
Companies that declined participation 113
Final sample of companies 69
Mean Std.
dev.
Quartile
1 (0.25)
Median
(0.50)
Quartile
3 (0.75)
Panel B: sample descriptive statistics on ?nal sample
Number of
CEOs
1.73 0.80 1 2 2
Age (in
years)
7.42 2.33 5 7 9
Employees
c
119 62.6 72 114 160
R&D
intensity
(%)
d
0.39 0.26 0.16 0.38 0.61
Revenues
($‘000)
e
10,691 11,711 2468 7140 15,156
Income
($‘000)
e
À10,175 15,598 À12,139 À5400 À18
Funding
($‘000)
f
52,441 59,865 8963 39,300 72,750
a
These companies are too small, too old, subsidiaries of other
companies, or with no signi?cant product development activity.
b
These are companies that did not respond to the ?ve tele-
phone contacts.
c
Number of employees is calculated at the peak of each com-
pany’s size.
d
R&D employees are estimated as a percentage of total
employees de?ned as the sum of R&D employees for each of the
years reported divided by the sum of total employees for those
same years. Only companies that reported R&D employees are
included.
e
Revenues and income are for the last year of data available.
f
Funding is the total external funding for each company.
332 A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347
Results for the adoption of management control
systems in product development
Table 2 (Panel A) provides descriptive statistics
on the percentage of companies adopting each of
the product development systems identi?ed for this
research and the time-to-adoption since the found-
ing of the company (Appendix B reproduces the rel-
evant questionnaire items). Project milestones are
the system most companies have adopted and the
fastest to adopt. Time-to-adoption is signi?cantly
di?erent from the other systems at least at the
10% (two-tailed t-test) in pair-wise comparisons,
18
except for product concept testing process where
the di?erence in time of adoption is not signi?cant.
Companies that adopted product concept testing
process, while relatively fewer (45%), did so earlier
in their lives. Project selection process is adopted
signi?cantly slower than all other systems except
product portfolio roadmaps. Roadmaps are also
slower than the other systems except product con-
cept testing process. These systems are likely to be
adopted later because they require having various
products considered in the development plan; some-
thing that is likely to happen later on in the life of a
company, once the initial product has been devel-
oped and released to the market.
19
Fig. 2 shows
the adoption time evolution; it plots the percentage
of companies that had adopted each of the systems
as a function of company age. Fig. 3 plots the same
information but against company size (proxied by
number of employees).
Panel B in Table 2 summarizes the results to the
open-ended question ‘‘what are the three most
important measures that top management uses to
evaluate the progress of a development e?ort?”
included in the questionnaire. The ?ve measures’
descriptions were coded by two researchers indepen-
dently. Panel B in Table 2 gives the frequency distri-
Table 2
Early stage entrepreneurial company sample: product development descriptive statistics
Companies adopting (%) Time-to-adoption (years since company founding)
Mean Quartile 1 (0.25) Median (0.50) Quartile 3 (0.75)
Panel A: descriptive statistics on product development systems
Project milestones 81 2.70 1 2 5
Reports comparing actual progress to plan 71 2.88 1 3 4
Budget for development projects 68 3.02 1 2 5
Project selection process 65 3.44 1 3 5
Product portfolio roadmap 61 3.36 1 3 5
Product concept testing process 45 2.74 1 2 5
Project team composition guidelines 39 3.00 1 2 5
Type of measures Respondents Updating frequency (per month)
Mean Quartile 1 (0.25) Median (0.50) Quartile 3 (0.75)
Panel B: descriptive statistics on product development measures
Time 62 2.81 1 4 4
Budget/?nancial 30 2.36 1 1 4
Product functionality 30 2.08 0.83 2 4
Customer 24 1.78 1 1 2
Quality 15 2.90 2 4 4
Panel A: Companies adopting each system are the percentage of companies that had adopted the system at the point of data collection.
Time-to-system is the mean number of years since founding to the adoption of the particular system for those companies that adopted each
system.
Panel B: The table reports the number of respondents that listed each type of measure among the three top measures for managing product
development and the frequency (times per month) that the measures are updated.
18
We also performed an ANOVA test that was signi?cant at the
1% level. We also tested for di?erences using Wilcoxon matched-
pairs signed-ranks test and testing whether the median of the
di?erences were di?erent from zero. The conclusions remained
are the same as those using the parametric t-test.
19
We also tested for di?erences in percentage of adoption.
‘‘project milestones” is signi?cantly di?erent from all other
systems except ‘‘comparing actual progress to plan” at least at the
5% level (two-tailed). ‘‘Comparing actual progress to plan,”
‘‘budget for development projects,” ‘‘project selection process,”
and ‘‘product portfolio roadmap” are signi?cantly di?erent from
‘‘project team composition guidelines” and ‘‘product concept
testing process” at least at the 10% level (two-tailed).
A. Davila et al. / Accounting, Organizations and Society 34 (2009) 322–347 333
bution for the 69 companies of the types of measures
used to track product development and how often
they are updated. A time-related measure is the most
frequently used with 62 respondents (89.9%) refer-
ring to it with updates averaging 2.81 times per
month on average. Budgets and product functional-
ity related measures are mentioned by 30 of the
respondents (43.5%) while customer-related mea-
sures are reported by 24 respondents (34.8%) and
quality-related measures 15 respondents (21.7%).
The iterative analysis of interview data identi?ed
six di?erent reasons-for-adoption of MCS. While
we began our data analysis aware of the seven
MCS roles identi?ed in the literature (‘Reasons/
events and roles of MCS in product development’),
the iterative analysis quickly converged to a
0
0.2
0.4
0.6
0.8
Company Age (years)
P
e
r
c
e
n
t
a
g
e
o
f
C
o
m
p
a
n
i
e
s
A
d
o
p
t
e
d
Project Milestones
Actual Progress to Plan Project Budget
Project Selection
Product Portfolio Roadmap Product Concept Testing
Project Team Composition
0 1
2 3 4
5
Fig. 2. Early-stage entrepreneurial company sample: evolution of product development management control systems over time. Each of
the seven systems (project milestones, reports comparing actual progress to plan, budget for development projects, project selection
process, product portfolio roadmap, product concept testing process, project team composition guidelines) is coded yearly as 1 (if the
company reports having adopted the system) and 0 otherwise. For each year since funding (x-axis) we add the number of companies that
have adopted the system over the total population to obtain the percentage in the y-axis.
0
0.2
0.4
0.6
0.8
Company Size (Number of Employees)
P
e
r
c
e
n
t
a
g
e
o
f
C
o
m
p
a
n
i
e
s
A
d
o
p
t
e
d
Project Milestones Actual Progress to Plan Project Budget
Project Selection Product Portfolio Roadmap Product Concept Testing
Project Team Composition