An empirical study on the drivers of management control systems' design in new product dev

Description
New product development has changed signi®cantly over the last decade and management control systems have
played an important role in this transformation. This study draws on Galbraith's concept of uncertainty and investi-
gates the relationship between project uncertainty, product strategy and management control systems. It also explores
whether these systems help or, as argued in the innovation literature, hinder product development performance.

An empirical study on the drivers of management control
systems' design in new product development
Tony Davila*
IESE, University of Navarra, Avenida Pearson, 21, Barcelona 08034, Spain
Abstract
New product development has changed signi®cantly over the last decade and management control systems have
played an important role in this transformation. This study draws on Galbraith's concept of uncertainty and investi-
gates the relationship between project uncertainty, product strategy and management control systems. It also explores
whether these systems help or, as argued in the innovation literature, hinder product development performance.
Results support the relevance of the project uncertainty and product strategy to explain the design of management
control systems. They also show that better cost and design information has a positive association with performance,
but that time information has a negative e?ect. # 2000 Elsevier Science Ltd. All rights reserved.
1. Introduction
New product development has become a central
dimension in the strategies of many companies
(Brown & Eisenhardt, 1995; Clark & Fujimoto,
1991, p. 6; Grant, 1996; Gupta & Wilemon, 1990;
Schilling & Hill, 1998). Current emphasis on ®rst
mover advantages, fast product introductions,
more demanding product functionality, and
shortening life cycles has put greater pressure on
new product development (Cooper, 1998). While
manufacturing has traditionally been a key repo-
sitory of core competencies (Hayes & Abernathy,
1980), outperforming competitors in product
development has emerged as a relevant source of
competitive advantage.
As the process has gained importance, aca-
demics as well as practitioners have voiced the
importance that management control systems play
in coordinating and controlling this process
(Cooper & Kleinschmidt, 1987; Zirger & Maidique,
1990). For example, Clark and Fujimoto (1991),
in their study of the product development process
in the auto industry, argue that:
Today's e?ective product development organ-
ization is characterized not only by creativity
and freedom, but also by discipline and con-
trol in scheduling, resource use, and product
quality (...) The challenge in product devel-
opment is not so much unilateral pursuit of
organic structure and permissive management
style as a subtle balance of control and free-
dom, precision and ¯exibility, individualism
and teamwork (Clark & Fujimoto, p. 169).
0361-3682/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.
PI I : S0361- 3682( 99) 00034- 3
Accounting, Organizations and Society 25 (2000) 383±409
www.elsevier.com/locate/aos
* E-mail address: [email protected] (T. Davila).
However, this emphasis on a structured product
development process contrasts with the traditional
view supporting a hands-o? approach (Lothian,
1984; McNair & Leibfried, 1992). According to
this latter view, successful new products result
from devoting adequate resources to the process
and avoiding control procedures that could
restrict the freedom of engineers. The impact of
management control systems in product develop-
ment performance is, therefore, unclear.
So far, management accounting literature has
devoted scant attention to new product develop-
ment. Most studies have looked at the relevance of
management control systems to the broader pro-
cess of R&D (Abernethy & Brownell, 1997;
Birnberg, 1988; Brownell, 1985; Hayes, 1977;
Kamm, 1980; Rockness & Shields, 1984, 1988).
These studies mainly characterize management
control systems as hindering or, at most, being
irrelevant in R&D settings. In contrast, Nixon
(1998) o?ers a rich case description of a product
development process where ®nancial control plays
a signi®cant role.
The importance of new product development
requires the allocation of accounting research
resources in order to understand the phenomenon.
This study seeks to extend this line of inquiry.
Using a contingency approach, the study investi-
gates the design of management control systems
1
to understand how companies adapt their sys-
tems to the particular characteristics of each pro-
duct development e?ort. Moreover, the study
brings new evidence to the unsettled issue of the
relevance or, alternatively, the lack of relevance
of management control systems in product
development.
Several characteristics distinguish this study. In
contrast to previous research, the unit of analysis
is the product development project rather than the
R&D project. Because R&D projects are very
heterogeneous (National Science Foundation,
1976), focusing on one type of project increases
the power of the research design. The study also
goes beyond the narrow de®nition of management
control systems around ®nancial information
to add formal but non-®nancial information
(Kaplan, 1983; Banker, Potter & Schroeder, 1993).
Moreover, the theoretical foundation of the study
leads to an interpretation of management control
systems di?erent from previous studies and to a
di?erent set of independent variables.
The study focuses on the medical devices indus-
try to keep the external factors as constant as
possible and avoid confounding e?ects that may
come from di?erences across industries. This
industry has several attractive characteristics.
First, product development is an important pro-
cess: R&D over sales averages more than 5% for
the industry and new products are constantly
introduced. Therefore, companies have well
thought-out product development processes.
Second, the industry is characterized by a lot of
technological diversity. Some products Ð syringes,
for example Ð use well-established technology,
while others Ð CT systems, for example Ð com-
pete by bringing to the market the latest technol-
ogy developed. Finally, product strategies are also
diverse; even products belonging to the same
company and serving the same product-market
have to adapt their value proposition to di?erent
market segments ranging from price sensitive to
performance oriented customers. X-ray products
include machines designed to take static images of
parts of the body, where price is the key purchas-
ing criteria, as well as sophisticated machines that
scan the whole body from di?erent angles, where
performance and customer interfaces are the key
competitive dimensions.
2
Both diversity in tech-
nology and product strategies suggest that com-
panies manage product development di?erently.
The remainder of this paper is structured as fol-
lows. The next section reviews previous research
1
The term management control systems is used to name the
design as well as the use of measurement systems in an organi-
zation. Therefore, leaving out other formal procedures that the
organization may use to alter behaviour (Flamholtz, 1983; p.
154). An alternative term is management accounting systems.
However, management accounting systems are sometimes
interpreted as conveying ®nancial information only, while this
paper also investigates non-®nancial measures.
2
The companies in the study include a wide range of medical
products: body-imaging machines, heart devices, orthopedics,
surgical instruments, drug delivery products, diagnostic equip-
ment, blood collection, and therapy products.
384 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
on the design and role of management control
systems in R&D and presents the theoretical fra-
mework underlying the study. Section 3 describes
the phenomenon studied through the description
of four representative cases. These cases illustrate
the variables in the research as well as the
hypotheses of the study. Section 4 develops the
hypotheses for the empirical test based on the
theory as well as case ®ndings. Section 5 describes
the research design for the survey study. Section 6
discusses the results of the paper and Section 7
reaches conclusions.
2. Theory development
2.1. The product development process
The objective of product development is to
translate an idea into a tangible physical asset.
The process is structured around well-de®ned
phases; each phase ends with a decision-making
meeting where management decides about the
future of the project. A typical product develop-
ment project starts with a planning phase to
establish the requirements of the project. During
this phase, the organization de®nes the target
market and the characteristics of the product.
These characteristics include functionality, price,
performance, and expected release time. The out-
come of the initial phase is a broad description of
these characteristics. The second phase Ð concept
design Ð goes into more detail to specify the pro-
duct speci®cations and the requirements of the
development project: target costs, technological
performance, customer interfaces, market release
dates, and organizational resources. The third
phase Ð product design Ð is the actual develop-
ment of the physical product. It is in this phase
when trade-o?s get resolved and information is
transformed into a tangible product. The last two
phases Ð testing and production start up Ð con-
®rm that the product meets its objectives and pre-
pare it for release. The process, even though
described as linear, is an iterative process: product
speci®cations or even the product concept can be
re-evaluated in light of new information generated
throughout the process.
2.2. Literature review
Past work on management control systems in
R&D follows two approaches. One line of
research focuses on how R&D departments use
®nancial measures (Brownell, 1985; Hayes, 1977;
Rockness & Shields, 1988). The consensus from
these studies reveals that ®nancial measures do not
have an important role in R&D departments other
than signaling the commitment of the organiza-
tion to its R&D e?orts. The perceived importance
of budgets ``decreases monotonically from plan-
ning to monitoring, monitoring to evaluating,
and evaluating to rewarding'' (Rockness & Shields,
p. 571).
Another line of research adopts a broader view
of control systems (Abernethy & Brownell, 1997;
Kamm, 1980; Rockness & Shields, 1984). For
example, Kamm de®nes control as ``the set of
criteria, policies and procedures established to
standardize operations and to make possible mea-
surement of performance to insure achievement of
organizational objectives'' (p. I-12, I-13). Rock-
ness and Shields (1984) study the relationship
between types of control and project character-
istics. Following Ouchi's framework
3
(Ouchi,
1979), they classify R&D projects according to the
level of knowledge of the transformation process
and the measurability of the output. Next, they
predict a relationship between these characteristics
and the type of control used: input, behavior, and
output control. These authors ®nd only marginal
relationships between control systems and project
characteristics. Similarly, Kamm (1980) concludes
that ``researchers do not necessarily exhibit more
innovative behavior when they perceive relatively
loose administrative control than when they per-
ceive tight control'' (p. IV±11). Abernethy and
Brownell (1997), use Perrow's model (Perrow,
1970) that relates type of control with task
analyzability and number of exceptions. These
authors conclude that ``reliance on accounting
controls has signi®cant positive e?ects on perfor-
mance only where task uncertainty is lowest''
while ``behavior controls appear to contribute to
3
See also Thompson (1967, p. 86).
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 385
performance in no situation'' (p. 245).
4
This evi-
dence suggests that management control systems
have, at most, a minor role in product development.
Nixon (1998) provides a rich case description on
the balancing role of the controller in assisting
engineers during the development of a new copper
rod production machine. In contrast to previous
studies, the author reports that the ``®nancial
component of the system serves to integrate the
disparate perspectives'' (p. 343).
However, management control systems have
proven to be useful tools in environments char-
acterized by high levels of uncertainty. For
example, Khandwalla (1972) ®nds that reliance on
formal control systems increases with the intensity
of competition. Similarly, Simons (1987) reports
that high performing prospectors rely on the
information provided by frequently updated for-
mal control systems to drive organizational learn-
ing (Dent, 1990). Additional research shows that
managers who perceive a higher level of environ-
mental uncertainty tend to use broad scope and
more timely information (Chenhall & Morris,
1986) as well as more external, non-®nancial, and
ex-ante information (Gordon & Narayanan,
1984). Kren (1992) ®nds that participation in the
budgeting process is related to better performance
for high uncertainty tasks. Finally, Chenhall and
Lang®eld-Smith (1998) report that di?erent stra-
tegic priorities emphasize di?erent formal control
systems regardless of the uncertainty faced by the
organization.
A possible explanation for the apparent contra-
diction between the results for R&D environ-
ments, where management control systems seem
not to be relevant, and other environments is a
di?erent interpretation of management control
systems. R&D studies interpret these systems as
control tools to reduce goal divergence rather than
as information tools to deal with uncertainty.
These ®ndings are in line with the concept of clan
control (Ouchi, 1979). Clan control emphasizes
informal control mechanisms and relies less on
formal systems. When uncertainty is high, clan
control is preferred to solve goal congruence
problems (Merchant, 1982).
5
In line with the alternative interpretation of
management control systems as tools to manage
uncertainty, studies on target costing all concur on
the role of target costing procedures as commu-
nication, problem solving, and learning devices
(Cooper, 1995; Kato, Boer & Chow, 1995; Koga,
1998; Sakurai, 1989; Tani, 1995). Koga and
Davila (1998) ®nd that target costing ful®lls an
information role to facilitate learning and experi-
mentation, yet they ®nd no support for target
costing being used to address goal divergence
problems or coordination issues.
2.3. Theoretical framework
2.3.1. Management control systems and the
concept of uncertainty
Product development is an uncertain process.
For example, Gupta and Wilemon (1990) report
that technological uncertainty is mentioned as a
reason for delays by 58% of project managers
surveyed. Each new product development process
presents a di?erent set of problems and organiza-
tions need information to solve uncertainties as
they emerge. The theoretical background of the
paper is based on the concept of uncertainty as
``the di?erence between the amount of informa-
tion required to perform a task and the amount of
information already possessed by the organiza-
tion'' (Galbraith, 1973, p. 5). This paper, in con-
trast to previous work in the ®eld, assumes that
the main role of management control systems in
product development is to supply information
required to reduce uncertainty rather than to
reduce goal divergence problems. This alternative
perspective is intended to reconcile the tension
that exists between the sparse empirical evidence
available with the strong recommendations by
practitioners and academics in the product devel-
opment ®eld. The concept of management control
4
Accounting control is similar to Rockness and Shields'
(1988) ®nancial measures, while behavior control is related to
the level of formalization of the organizational structure.
5
The dual role of management control systems is common
in the literature, for example Shields and Shields (1998) identify
motivation and information sharing as di?erent reasons for
participative budgeting. See also Barrett and Fraser (1977).
386 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
systems used in this study,
6
following Chenhall
and Morris (1986) and Gordon and Narayanan
(1984), goes beyond the narrow perspective
focused around accounting numbers Ð cost,
pro®tability, and budget Ð to include a broader
information set (Kaplan, 1983) capturing cus-
tomer, product design, and time-related measures.
Management control systems in new product
development are viewed as sources of information
that are used to close the gap between ``the infor-
mation required to perform a task and the amount
of information already possessed''. This view is
consistent with Tushman and Nadler (1978) who
argue that management control systems are e?ec-
tive tools to manage uncertainty because they
supply the data needed to reduce Galbraith's
``information gap''.
However, management control systems are not
necessarily the optimal sources when the informa-
tion that they deliver is not matched to the uncer-
tainty facing the product development manager.
The relevant information may be obtained from
alternative sources. For instance, it may be
obtained through experimentation (Pisano, 1994)
or informal communication (Allen, 1977; Dou-
gerthy, 1990); if this is the case, then management
control systems may not have any role in the pro-
cess and, consequently, not be related to project
uncertainty.
Research in new product development
(McGrath, 1995; Shenhar & Dvir, 1996; Von
Hippel, 1988; Wheelwright & Clark, 1992) has
identi®ed three main types of uncertainty (or
``information gaps'' according to Galbraith's de®-
nition): market-related uncertainty, technology-
related uncertainty, and project scope. These three
types of uncertainty shape the design of manage-
ment control systems. In addition to the uncer-
tainty characterizing the project, the design of
management control systems depends on the
strategy (Govindarajan & Gupta, 1985) as well as
the organizational structure (Bruns & Waterhouse,
1975). Cooper (1995) reports that companies place
di?erent emphasis on target costing procedures
depending on product strategy. Certainly, the
value of a piece of information (for instance, cost
information) is contingent upon the importance as
well as the uncertainty related to the competitive
dimension addressed (cost leadership). Similarly,
organizational structure a?ects the size of the
project team that is associated with the level of
formalization (Mintzberg, 1979, pp. 230±235) and
the project manager's responsibilities that a?ect
the allocation of uncertainty. For instance, if the
marketing department is responsible for dealing
with market uncertainty, then the project manager
will be insulated from it and he will not demand
customer-related information, even if it may be
critical to the success of the project.
2.3.2. Management control systems and project
performance
The e?ect of management control systems upon
new product development performance is dicult
to predict. If management control systems supply
information relevant for coordination and learn-
ing, then a positive relationship between perfor-
mance and the use of management control systems
is expected. Some evidence in the product devel-
opment ®eld exists pointing in this direction
(Koga & Davila, 1998, Nixon, 1998). But argu-
ments as well as evidence (Eisenhardt & Tabrizi,
1995) exist suggesting that such a relationship
does not exist or is negative. Management control
systems, by imposing rules and constraining
behavior, reduce the level of creativity required
from product development and, thus, negatively
a?ect performance (Amabile, 1998).
3. Case studies
To understand how project managers use man-
agement control systems, I visited 12 business
units in seven companies both in Europe and the
United States. During each of these visits, I inter-
viewed one or two project managers, the market-
ing manager, the R&D manager, and the general
manager for each business unit as well as the per-
son in charge of the design and implementation of
6
Simons (1995, p. 5) de®nes management control systems
as the formal, information-based routines and procedures
managers use to maintain or alter patterns in organizational
activities.
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 387
the new product development process guidelines.
Because existing literature in management control
systems in product development is still sparse, I
chose to do an exploratory study using case
studies as the preferred methodology to build
knowledge about the phenomenon (Yin, 1988).
Interviews were structured around a set of ques-
tions about the formal systems and the product
development process itself. The questions were
open-ended which allowed me to adapt the inter-
view to the expertise of each manager without
losing the overall direction. Appendix A presents
the protocol that I used for the interviews with
project managers. Similar protocols were used for
the interviews with other managers.
I interviewed an average of ®ve managers in
each business. The use of multiple informers
allowed for a triangulation of the data. When a
manager's explanation did not agree with the
description given by previous managers of the
same organization, the di?erences were explored
until the reason for this divergence was fully
understood.
Next, I present four illustrative cases on the
diversity of product development projects and the
design and use of management control systems.
3.1. Project manager A
Project manager A worked in an anesthesia
monitoring system. This product was designed to
work together with the company's anesthesia
delivery system. The company's strategy was ``to
work very close to the customer, in that sense we
are not a low cost producer but we focus very
much on customer needs and facilitate customer
interface with the product. We want to be special
in the sense of adapting to the needs of the custo-
mer and understanding the customer well''.
At the beginning of the project, the manager
signed a three page contract with eight goals:
schedule (phases and review points), quality,
usability, manufacturing cost, project budget,
simple description of intended functionality, and
contact points with other projects (the anesthesia
delivery system). The purpose of this contract was
not to evaluate performance ex-post, but to gain
the personal commitment of each person involved
in the project. The contract brought together the
expectations of the various people involved in the
project rather than establish goals to increase
extrinsic motivation.
Project goals were clearly de®ned except for
product speci®cations related to the customer
interface. The product's strategic advantage came
from meeting customer needs and developing the
appropriate customer interface. The ``information
gap'' to be closed during the product design
phase came from the market, in particular from
customer needs.
Because of the relevance of customer informa-
tion, management built ¯exibility into project
goals to incorporate this information during the
execution instead of freezing it at the beginning of
the project. The decision to sketch only certain
product speci®cations at the start of the project
was intended to adapt as much as possible to cus-
tomer feedback: ``there is a need to expose the
product and product concept to the customer and
be ready to change and adapt features and
appearance to their reactions.'' Uncertainty was
purposely left unresolved on the customer dimen-
sion to adapt during the development process, but
it was clearly bounded: ``there is a need to de®ne
¯exibility dimensions up-front (and freeze other
dimensions)''.
During the execution, the project was divided
into smaller sub-projects including ``moving from
the traditional two measures captured in a tradi-
tional anesthesia monitoring system to several
measures, developing the frame to integrate the
various modules of the product, and writing the
product's software''. The project also had mar-
keting sub-projects like ``the product launch pro-
ject including training distributors, promotion
material and marketing concept communication''.
The project manager directly supervised engineers
and marketing people. He was also frequently in
touch with manufacturing people to prepare pro-
duction ramp-up.
Project objectives were periodically reviewed in
the formal review points. However, customer infor-
mation was constantly received: ``There is a constant
communication with doctors, at a certain point a
doctor was working full time for the company
to make sure that the product was user friendly.''
388 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
3.2. Project manager B
Project manager B had developed a new clip
used for brain surgery. Medical doctors used these
clips to keep blood vessels closed while performing
brain surgery. Existing clips were metallic. This
material had the advantage of providing the right
mechanical properties like torsion and resistance
as well as being cost e?ective. But metal had a
signi®cant drawback for certain types of surgery.
When the doctor, while performing surgery, had
to do a scan of the patient's brain, the clip created
shades in the picture and, more important, mag-
netic ®elds could move the clip with possibly
devastating consequences for the patient. In doing
some tests on his own, project manager B found
that a new material, titanium, could solve this
problem. Titanium was more expensive but it
would become the only product available to per-
form scans during surgery. The company esti-
mated a signi®cant market for the product and
funded the project.
According to the project manager the main
question during the project was to get the
mechanical properties right: ``in this product,
technology was critical''. Technology was the
main source of uncertainty as well as the key fac-
tor for product success. He did not care about the
cost of the product, because it would have a vir-
tual monopoly in its segment: doctors did not
have alternative products and competitors were
unlikely to develop the required mechanical know-
how to copy the clip in the short term.
At the beginning of the project, the project
manager talked to doctors and was present in
several surgeries to see how the clip was used.
These visits allowed him to understand customer
needs. In addition, the project was not subject to
time pressure because no other company was
investing in a similar product. Only when the
technology was well understood, did the company
decide on a deadline. During the 4 years that the
project lasted, all the attention of the project
manager was focused on ®nding the right combi-
nation of materials and the appropriate design to
meet the mechanical requirements: ``because it was
intense in technology, it was hard to see problems
and it was also hard to calculate timing''.
Because technology was the paramount variable
in this project, project manager B worked together
with a team made up of researchers. Only a mar-
keting manager was supporting the teamto facilitate
contact with doctors. The project plan was simple,
the timing for the various phases of the product
were loosely speci®ed as was the budget and the
expected product cost. The fact that the CEO had
come from the R&D function and kept close con-
tact with product development people reinforced
an informal control on the project. Through
monthly meetings, the CEO evaluated whether the
project was moving according to expectations
without the help of a formal project plan.
The management control system for project
manager B was almost non-existent. He got all the
relevant information from prototyping ``to assure
manufacturability''. He built more than two
thousand prototypes before he found the right mix
of materials and design. Any other information,
like timing or cost, was irrelevant to him. The new
clip was a success when it hit the market.
3.3. Project manager C
Project manager C worked for the same com-
pany as project manager B. He was in charge of
developing a hip endoprothesis for an Asian
country. The product was similar to an existing
one, but the marketing department had found that
the body geometry of people in the main ethnic
group of the country was di?erent. The company
saw this fact as a relevant dimension for competi-
tive advantage. The project took a year to
develop. Because the product was similar to an
existing one, few doubts existed regarding product
cost and technology: ``we knew a lot about the
structure of the development of this product''.
The pressure points were time-to-market and
project budget. Time-to-market characterized the
strategy of this product. Time pressure came
through the scheduling of the introduction date:
``time-to-market was the most important factor
because the group in the country had already
started to sell the product''. The budget was
equivalent to the number of prototypes because of
the direct relationship between prototypes and
investment: ``there was a monthly overview meeting
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 389
to compare project costs and budget, it was impor-
tant to reduce as much as possible the number of
prototypes to save development money. There was
a trade-o? between safety and investment''.
The main task for project manager C was to
coordinate the e?ort of engineers to meet the tight
schedule. The source of uncertainty came mainly
fromproject scope. The management control system
provided detailed information on how the project
progressed in terms of schedule and budget.
The project manager did not have direct contact
with the customer. In fact, his supervisor talked to
the marketing people in the Asian country and the
marketing people talked to doctors. The project
manager did not see this lack of direct access to
the customer as a problem because the product
was well understood, the only relevant issue being
a change in geometry. Moreover, the contacts with
the marketing people were mostly related to pro-
duct launch, not to customer needs.
Finally, product costs were also well under-
stood. However, the manufacturing person
involved in the development team periodically
estimated product cost to make sure that it was on
target: ``the project would have stopped only if
manufacturing costs had been too high''.
Management control systems in this project
were focused around time-to-market and project
budget. The latter information was re¯ected in the
project manager's decisions concerning whether to
build a newprototype. Product costs, even if critical
to project success, were managed by exception.
3.4. Project manager D
Project manager D developed a multi-purpose
X-ray machine. The product had two critical
components, the X-ray camera and the examina-
tion table for the patient. The technology for the
X-ray machine was well understood and devel-
oped in-house. But, the table was a complex
mechanical device. Because the machine allowed
an X-ray picture to be taken of any part of the
body, the table was large and, as a consequence,
hard to develop. In addition, the doctor could
choose the angle for the picture that (s)he con-
sidered most appropriate. This capability meant
that the table had to move at least 180 degrees in
each of the three spatial axes with a high degree of
precision. The main source of uncertainty for this
product came from mechanical technology.
The X-ray division had recently reassessed its
strategy after several years of disappointing ®nan-
cial performance. According to the marketing
manager: ``We are stripping down the number of
products because now there are too many and it is
expensive to deliver and service such an extensive
line of products. We are not satisfying customers
per se, we are also looking at pro®tability. The
current product line is based only on satisfying
customer needs and this is why there is so much
proliferation of products''. This new emphasis on
cost a?ected project manager D, even if technol-
ogy was the key source of uncertainty.
Product development was a linear process at the
division. It started in the marketing department
with product de®nition, then customer require-
ments were translated into system speci®cations,
system speci®cations into component speci®ca-
tions, then components were integrated at the sys-
tem integration phase, and ®nally the product was
launched. The role of the project manager was
limited in this division to the supervision of com-
ponent development. His main task was to break
down the project into small work packages fully
speci®ed in terms of budget, time, component
speci®cations, and component cost and make sure
that plans were met. In the terms of Wheelwright
and Clark (1992), he was a ``lightweight'' project
manager with no people reporting directly to him,
but only coordinating the development e?ort. The
project manager mentioned: ``I never talk to cus-
tomers, they talk to the marketing people but not
to me''.
Because of the recent focus on cost that the
new division imposed, a cross-functional group
re-estimated product costs ``every time new parts
become available''. However, the most time con-
suming issue for the project manager was an Italian
OEM in charge of developing the examination
table for the X-ray machine: ``It took them too
long and they made too many mistakes in devel-
oping the table. We did the design and wrote the
software for the table. The Italian company was in
charge of the mechanics''. His attention was
devoted to managing the relationship with this
390 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
supplier, provide support to their people, and try
to minimize the e?ect of these problems in project
scheduling and cost. He did not care as much
about budget because ``development cost is similar
to time because it is basically time multiplied by
price''. Even if the product achieved its objectives,
the project was not considered a success because
of delays and budget overruns.
3.5. Discussion of case studies
The previous cases provide a diverse set of pro-
duct development experiences and di?erent roles
for management control systems. Each project
manager required di?erent information depending
on product strategy and type of uncertainty. For
project A, meeting customer needs was the key
success factor as well as the main source of uncer-
tainty. Management purposely left customer-
related uncertainties to be resolved during the
development process through close contact with
the customer. The structure of management con-
trol systems emphasized customer interaction.
Time, budget, and product cost were managed by
exception. Because the project never hit these
constraints, the project manager devoted his
attention to customer information. The project
team integrated both engineers and marketing
people with a looser coordination with the manu-
facturing function. This structure re¯ected the
management belief that the project manager
should be in charge of marketing.
In contrast, project B was all technology. Time
was not a constraint, nor was budget nor product
cost. In fact, the formal systems were loose com-
pared to the detailed project plans and review
points used in the other projects. The project
manager focused his attention on prototyping as
the most ecient way of coping with technological
uncertainty. Project B exempli®es a situation
where detailed formal management control sys-
tems could undermine performance. Prototyping
gave project manager B the information that he
needed Ð any other source of information would
have been a burden and undermined performance.
His team was composed of R&D people only and
he reported to the CEO who had a background in
R&D.
Project C illustrates the development process
most similar to a manufacturing process where
uncertainty resides in coordination Ð project
scope. The cause±e?ect relationships were well
understood and product functionality was well
de®ned. Project manager's attention was mainly
devoted to time-to-market and budget. He did not
interact with customers, nor did he devote much
attention to costs (controlled by exception), but he
was constantly thinking what needed to be done to
meet the deadline and assessing whether he could
save development costs by reducing prototyping.
It is interesting to observe how project manager C
used a non-®nancial measure Ð number of proto-
types Ð as a substitute for a ®nancial measure Ð
project investment. Again, this project manager
was in charge of an R&D team. Interestingly,
his contacts with marketing were not related to
customer needs but to product launch because of
its importance to the strategy of the product.
Finally, project manager D worked at a com-
pany where costs had become a key dimension
because product proliferation had led the com-
pany into disappointing ®nancial performance.
This emphasis was translated into frequent cost
estimations. Unfortunately, the main source of
uncertainty for project manager D came from
technology. The design of a key part of the pro-
duct was subcontracted out and ran into prob-
lems. Project manager D had to devote most of
his attention to this unexpected issue that a?ected
the timing, functionality, and budget of the pro-
ject. In this case, management control systems
informed the project manager about technology
only by exception even if it may have required
more frequent updating. Project manager D did
not have a team reporting to him, he only coordi-
nated the technical part of the project. Table 1
summarizes these ®ndings.
4. Development of the research hypotheses
4.1. Uncertainty and the design of management
control systems
The theoretical discussion and case descriptions
suggest that uncertainty is a driving force in the
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 391
design and use of management control systems.
Case discussions illustrate how the sources of
uncertainty vary across projects. Also, product
development literature and management account-
ing literature identify di?erent types of project
uncertainty. To be as close as possible to the phe-
nomenon studied, I rely on the classi®cation of
uncertainty used in the product development lit-
erature. Uncertainty is classi®ed as market uncer-
tainty, technological uncertainty, and project
scope (Shenhar & Dvir, 1996).
7
Von Hippel (1988, chapter 2) describes the
importance of the organization's experience with
the targeted customer segment. When the organi-
zation already serves the target customers, their
needs and requirements are well understood and
uncertainty is low. In contrast, when the organiza-
tion enters a new market, uncertainty surrounding
Table 1
Summary of the case studies
Project manager A
Anesthesia monitoring
system
Project manager B
Brain clip
Project manager C
Endoprothesis for an
Asian country
Project manager D
Multipurpose X-ray
machine
Type of
uncertainty
Market-related uncertainty
Product speci®cations
were clearly de®ned except
for customer interface.
Technology-related
uncertainty
The project manager built
more than 2,000
prototypes.
Project scope
Pressure came from
coordinating e?orts to
meet the expected market
introduction date.
Technology-related
uncertainty
The project included
complex mechanical
parts.
Product strategy Customer-focused strategy
``We focus very much on
customer needs and
facilitate customer
interface with the
product.''
Technology-focused
strategy
``In this product,
technology was critical.''
Time-to-market strategy
``Time-to-market was the
most important factor
because they had already
started to sell the
product.''
Low cost strategy
``Product costs are
estimated every time new
parts become available.''
Organizational
structure
Cross-functional team
Including engineers and
marketing people.
Engineers-only
The project manager
worked only with
engineers.
Engineers-only
His supervisor managed
relationship with
marketing.
Lightweight project
manager
Nobody reported directly
to him, he only
coordinated e?orts.
Purpose of
management
control systems
Information purpose
Management control
systems were designed to
focus management
attention on customer
needs.
Information purpose
Management control
systems used sparsely,
experimentation was the
main vehicle to reduce
uncertainty.
Information purpose
Management control
systems used constantly to
monitor schedule and by
exception for cost and
budget.
Information purpose
Management control
systems used by exception
to detect potential
problems.
Performance The alignment between
project uncertainty,
customer-focused strategy
and management control
systems' design led to a
successful project.
The low emphasis on
time, cost, or customer
information allowed
project manager to focus
on experimentation and
develop a successful
product.
Low uncertainty related
to technology and
product speci®cations
allowed attention to be
focussed on time-to-
market to meet
introduction date.
Misalignment between
uncertainty, strategy, and
project manager's
authority led to poor
performance re¯ected in
problems with an OEM
supplier.
7
A parallelism can be established between both classi®ca-
tions (without implying that the concepts are the same). Envir-
onmental uncertainty (Chenhall & Morris, 1986; Gordon &
Narayanan, 1984) is similar to market uncertainty and can be
managed through organizational interfaces with the environ-
ment (Thompson, 1967, p. 20). Task uncertainty (Kren, 1992;
Abernethy & Stoelwinder, 1991) is inherent to the task per-
formed and can be equated to technological uncertainty (Brownell
& Dunk, 1991). Finally, project scope is related to the organi-
zational structure of the project.
392 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
customer preferences increases (market uncer-
tainty). In the latter case, information about cus-
tomers is expected to help in reducing market
uncertainty.
H1a:
8
Customer information is used more
intensively as market uncertainty increases.
The sources of product technology can range
from existing, well-known bodies of knowledge
(low uncertainty) to unknown and yet-to-be
developed technologies (high uncertainty)
(McGrath, 1995; Shenhar & Dvir, 1996; Wheel-
wright & Clark, 1992). When technology is the
main source of uncertainty, project team members
focus their attention on resolving the problems
associated with technology. Product design and
functionality information can help in addressing
this type of uncertainty. However, case study B
suggests that project managers may obtain the
relevant information from experimentation and
prototyping (Clark & Fujimoto, 1991; Pisano,
1994), and then the relationship between technol-
ogy uncertainty and the use of management con-
trol systems is non-existent or even negative.
H1b: Management control systems are used less
intensively as technological uncertainty increases.
Finally, project scope is related to e?ort that the
project manager has to devote to coordinating the
input from di?erent constituencies. Project scope
depends on the number of people involved in the
project. A small project, possibly because the pro-
duct is simple or because it only involves a small
group of engineers, will have low demands on
formal systems for coordination. In contrast, a
large project with ®fty people dispersed in several
departments around the company will need to rely
much more on formal systems for coordination
(Mintzberg, 1979).
The coordination e?ort will also depend on the
project manager's responsibility. For example,
project manager A was responsible for customer
interaction as well as technology development,
while project manager B only supervised R&D
people. There is ample evidence on the relation-
ship between organizational structure and the
design of management control systems (Baiman,
Larker & Rajan, 1995; Bruns & Waterhouse, 1975;
Merchant, 1981). Therefore, the empirical tests
need to control for the organizational structure.
H1c: Management control systems are used
more intensively as project scope increases.
4.2. Product strategy and the design of
management control systems
The relationship between strategy and manage-
ment control systems' design has been well docu-
mented at the business strategy level (Govindarajan
& Fisher, 1990; Kaplan & Norton, 1996; Lang-
®eld-Smith, 1997; Merchant, 1985; Simons, 1987).
The ®ndings of these studies are robust in terms of
the typology of strategy used. Simons (1987) uses
the strategy types de®ned by Miles and Snow
(1978); Merchant (1985) follows the typology sug-
gested by MacMillan (1982); while Govindarajan
and Fisher (1990) rely on Porter's (1980) concept
of competitive strategy. If these results are gen-
eralized to product development, then it is expec-
ted that product strategies will be related to
management control systems' design. However,
this relationship is only a conjecture empty of any
empirical evidence. Even if cost may be critical to
the success of a product competing on price,
meeting initial speci®cations may satisfy this
objective and the project manager can safely
ignore cost information. The typology of product
strategies selected for the research is based on
Miller and Roth (1994) who identify price, time-
to-market, and customer focus as di?erent product
strategies.
9
If management control systems provide
useful information to deal with relevant project
uncertainties, then project managers designing
low-price products will value product cost infor-
mation more highly, while time information may
be more valuable for products that would stand to
bene®t from ®rst mover advantages. The following
hypotheses capture these arguments:
8
Hypotheses are stated in positive terms for clarity, but the
no-hypotheses are tested.
9
Technology-based strategy is sometimes included as an
additional product strategy. As illustrated in the case study of
project manager B, when technology is the most relevant
dimension, management control systems play a minor role in
the product development process.
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 393
H2a: Cost information will be used more inten-
sively as the importance of a low cost product
strategy increases.
H2b: Time information will be used more inten-
sively as the importance of a time-to-market pro-
duct strategy increases.
H2c: Customer information will be used more
intensively as the importance of a customer
focused product strategy increases.
4.3. Management control systems and project
performance
The aim of most managerial activities is to
improve the performance of the organization.
Therefore, it is relevant to know whether manage-
ment control systems a?ect project performance.
Notice, however, that the absence of a relation-
ship between management control systems and
performance does not necessarily mean that these
systems are irrelevant. An alternative interpreta-
tion is that companies have optimally designed
systems. If all companies have precisely the man-
agement control systems that they require, then
performance will not be related to these systems.
In contrast, if such a relationship exists, then it
can be concluded that management control sys-
tems are related to project performance and that
some companies are not using optimal systems.
The relationship between management control
systems and project performance will be positive if
projects bene®t from more structured systems. On
the other hand, if systems are too structured and
sti¯e the ability of the development team to
respond to demands particular to the project, then
the relationship will be negative.
Moreover, the relationship between manage-
ment control systems and project performance
may be contingent upon certain project character-
istics.
10
In particular, strategy has been frequently
identi®ed as a?ecting the design of management
control systems (Govindarajan & Gupta, 1985;
Lang®eld-Smith, 1997). The following hypotheses
capture the main e?ect (H3a) as well as contingent
relationships (H3b, H3c, H3d).
H3a: More intense use of management control
systems has a positive e?ect on project performance.
H3b: More intense use of customer information
has a positive e?ect upon performance for pro-
ducts following a customer-focused strategy.
H3c: More intense use of cost information has
a positive e?ect upon performance for products
following a low cost strategy.
H3d: More intense use of time information has
a positive e?ect upon performance for products
following a time strategy.
Finally, the detail reported at the beginning of
the product design phase may also a?ect project
performance. However, existing evidence is con-
tradictory. Eisenhardt and Tabrizi (1995) ®nd that
the amount of planning has no e?ect upon devel-
opment time. In contrast, Gupta and Wilemon
(1990) report that the ®rst reason for product
delays is a poor de®nition of product requirements
(71% of the respondents). A more general argu-
ment supporting the importance of planning is
provided by Bruns and McKinnon (1992) who
found a positive association between clear goals
and improved performance. The last hypothesis
captures these arguments and relates them to pro-
duct development.
11
H3e: Detailed project objectives are associated
with improved performance.
5. Research and survey design
Management control systems in product devel-
opment vary over time and across the organiza-
tion's hierarchy. They vary over time because
information needs are di?erent for the planning,
concept design, product design, and testing and
start up phases. Similarly, management control
systems span the whole organization, from the
formal systems used by top management, to the
routines that shape the work of a recently hired
engineer. This variation in the research setting can
10
I thank one of the referees for pointing out this interesting
extension.
11
Similarly to the discussion for project performance, the
e?ectiveness of having a detailed plan may be contingent upon
project characteristics. However, the theory developed in the
paper does not identify these contingencies. Future research
may fruitfully explore this ®eld.
394 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
decrease signi®cantly the power of the research
design. To increase as much as possible this
power, the research design includes three speci®c
decisions. First, the study focuses on the product
design phase only. Because this phase requires
more structured information to evaluate trade-
o?s, the relationships predicted by the theory will
be especially strong in this phase. Also, the start
and the end of this phase are clearly de®ned,
thereby eliminating ambiguity about which data
are required from project managers. Moreover,
focusing on one phase reduces the noise that
would result from asking for and interpreting data
related to multiple phases.
The second research design decision is to specify
the hierarchical level inside the organization. The
project manager is the person in charge of moving
a product development project from an idea to a
physical object and thus, (s)he is the key person
for the success of the project. This person is selec-
ted as the unit of analysis.
12
The third design choice is to limit the study to
the medical devices industry. Eleven companies
participated in the second part of the study. A
contact person in each company selected a group
of projects as heterogeneous as possible in terms
of size and product strategies. The data were col-
lected using a questionnaire mailed to project
managers that had recently ®nished the develop-
ment of a new product.
The questionnaire was designed to collect as
much quantitative data as possible to avoid per-
ceptual biases. However, recall bias Ð possibly
driven by ex-post rationalization Ð could be a
threat to the integrity of the data.
The response rate was 77% (56 out of 73 mailed
questionnaires). This high response rate was
accomplished by following several procedures
(Dillman, 1983). The questionnaire was initially
pre-tested among academics with previous experi-
ence in questionnaire design. Some of the items
were shifted to facilitate answering the questions,
and to avoid, as much as possible, respondents
rationalizing their behavior. Then, a group of
ten project managers tested the questionnaire.
Two of these managers had the questionnaire
administered in person. In the other cases, man-
agers completed the questionnaire and commented
on it in a telephone conversation. Only minor
wording changes were necessary after the second
pre-test.
Each questionnaire was personally addressed to
the project manager. The package included a
cover letter, the questionnaire, a pre-paid envel-
ope, and a copy of an article for practitioners that
could be of interest to the respondents as a token
of appreciation for their e?ort Ð completing the
questionnaire took 35 to 45 min. The letter o?ered
a copy of the aggregate results of the study to the
companies as well as to each respondent. The
support of the contact people in the companies
was also a very important element in achieving the
high response rate.
5.1. Dependent variables
Preliminary interviews with product development
managers identi®ed the six types of information
most frequently reported through the organiza-
tions' formal systems: product cost, product
design, time-related, customer-related, resource
input (budgets), and pro®tability. The design of
management control systems for each of the six
types of information is measured through three
characteristics (Merchant, 1981; Simons, 1995):
1. Level of detail in the information reported is
measured on a ®ve-point scale with three
anchor points exemplifying measures ranging
from low to medium and high detail. For
example, cost information has low detail if
the system only reports material and labor
costs, and it has high detail when, in addition,
the systems include related manufacturing,
marketing, and administrative costs. Simi-
larly, customer information has low detail if
it comes only from an initial assessment of
the marketing department, and it has high
detail when, in addition, the project team
interacts directly with the customer. Appen-
dix B reproduces the anchor points used.
12
Results not reported in this paper show that the project
manager uses information more intensively than his superior.
This ®nding reinforces the adequacy of choosing him as the
unit of analysis (Davila, 1998).
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 395
2. Frequency of information updating is mea-
sured, for each type of information, on a
®ve-point scale ranging from (1) weekly or
less, (2) twice a month, (3) monthly, (4)
quarterly, and (5) longer than quarterly.
3. Usage pattern of information is measured on
a ®ve-point scale anchored with two sentences:
``the information was used to monitor the
project, but it was not discussed with my
team except when it reported events that fell
below plans or expectations'' (diagnostic
system) and ``the information was used con-
stantly in the interactions with my team.
Frequently it was the main topic of our con-
versation'' (interactive system).
These three characteristics have the same pur-
pose: providing information in order to reduce
uncertainty. Therefore, they represent the same
underlying latent variable. This variable is identi-
®ed using principal component factor analysis as
described in Table 2.
13
Table 2 also describes the
variance explained, the ®rst eigenvalue, as well as
the Cronbach alpha measure of reliability (Cron-
bach, 1951). The inter-item reliability estimates
meet Nunnally's (1967) standards for exploratory
research.
Project performance (Perf ) is a multidimensional
variable (Shenhar, Dvir & Levy, 1997) and the
importance of each dimension changes across
projects. Meeting cost objectives can be critical for
certain products while secondary for others.
Moreover, the success of a product may not be
correctly assessed for a long time after its market
introduction. Financial success is not a good
measure of performance (Cooper & Kleinschmidt,
1987): consider companies entering new markets
Ð their early products are intended to facilitate
learning rather than to make big pro®ts.
A set of questions developed by Shenhar and
Dvir (1996) were adapted to measure project per-
formance. The instrument includes eleven items
that capture di?erent aspects of product develop-
ment (see Appendix C). The respondent rates the
importance of each item on a seven-point scale
from ``not important'' to ``extremely important''.
(S)he also rates performance for each item on a
seven-point scale from ``extremely poor'' to ``extre-
mely good''. The overall measure of performance
(Perf ) is the weighted average of these 11 items.
The drawback of using a self-reported measure
is that it may be a?ected by perceptual biases. On
the other hand, it has the advantage that it cap-
tures the dimensions most relevant to the project
and takes into account expectations for the pro-
ject. For example, a delay of one month in intro-
ducing a new product is bad for time-sensitive
projects, but it is not important for projects
focused on other dimensions.
5.2. Independent variables
To measure product strategy, principal compo-
nent factor analysis with varimax rotation is used
on nine questionnaire items intended to measure
these variables. One set of items asks the respondent
to allocate 100 points among di?erent possible
strategies. The other six items require respondents
to rate the importance for the company and for
the customer of each strategy in a seven-point
scale ranging from ``not important'' to ``extremely
important''. Three factors are identi®ed re¯ecting
three possible strategies: cost-related questions
load onto the ®rst factor, this factor identi®es the
importance of cost strategy; the second factor
re¯ects time strategy; and the third factor represents
the importance of customer strategy (see Table 3).
Project uncertainty includes three variables: market
uncertainty, technological uncertainty and project
scope. Market uncertainty (Mkt-X) and technologi-
cal uncertainty (Tech-X) are multidimensional con-
cepts, constructed both as dummy variables. When
the project is below the median in each of the
questions that de®ne Mkt-X (Tech-X), the variable
takes a value of zero, it takes a value of one if one
of the questions is above the median, and so on (see
Appendix D for a description of the questionnaire
items) (see Table 4 for descriptive statistics).
The number of people involved in the project
(People) and the number of new parts in the
13
Both, principal factor analysis and maximum likelihood
factor analysis were used. The factor loadings were comparable
in both cases. Principal factor analysis' loadings were kept
because they are more robust to the underlying properties of
the distribution. Because only one factor is used, the technique
is free from the possible arbitrariness of a rotation.
396 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
product (New-Parts) represent project scope.
However, New-Parts may also re¯ect technologi-
cal uncertainty if it is argued that products with
more parts are also technologically more complex.
I use three variables to control for organiza-
tional structure. The ®rst one is the level of cross-
functional integration (Function) that exists in the
project team. This variable is measured by the
number of functions reporting to the project
manager. The e?ect of this variable on perfor-
mance has strong support in the product develop-
ment literature (Clark & Fujimoto, 1991; Zirger &
Table 2
Principal factor analysis for the construction of management control systems' variables
a
Variable Name of
variable
Items in questionnaire Loading on
®rst factor
Variance
explained
Eigenvalue Cronbach
alpha
Customer information CUSTI . Detail of customer info. 0.70
. Updating frequency of customer info. (*) 0.74 0.55 1.655 0.60
. Interactive use of customer info. 0.74
Product design DESI . Detail of product info. 0.83
information . Updating frequency of product info. (*) 0.78 0.57 1.721 0.61
. Interactive use of product info. 0.62
Time information TIMEI . Detail of schedule info. 0.65
. Updating frequency of schedule info. (*) 0.82 0.61 1.823 0.67
. Interactive use of schedule info. 0.76
Cost information COSTI . Detail of cost info. 0.86
. Updating frequency of cost info. (*) 0.76 0.62 1.859 0.68
. Interactive use of cost info. 0.62
Resources information BUDI . Detail of resources info. 0.66
. Updating frequency of resources info. (*) 0.68 0.58 1.747 0.64
. Interactive use of resources info. 0.83
Pro®tability information PROFI . Detail of pro®t info. 0.81
. Updating frequency of pro®t info. (*) 0.85 0.68 2.043 0.76
. Interactive use of pro®t info. 0.72
a
Loadings based on the principal factor analysis, this speci®cation is more robust to the underlying properties of the probability
distribution of the variables. One factor is retained for each construct. Loading signs for questions that are worded in reverse (denoted
by * in the table) have been changed. In all cases the value of the second eigenvalue is less than 1.
Table 3
Factor analysis on independent variables
Items in questionnaire First factor (Cost-Str) Second factor (Time-Str) Third factor (Cust-Str) Uniqueness
Design a low cost product 0.74 À0.16 À0.28 0.35
Meet unit cost objectives 0.79 0.04 0.06 0.38
Target customers value price 0.82 0.02 0.12 0.31
Reduce time to market À0.11 0.74 À0.27 0.36
Meet timing goals À0.03 0.81 À0.05 0.35
Target customers value time 0.06 0.74 0.20 0.40
Design a customer friendly product À0.17 À0.25 0.64 0.50
Ful®ll customer needs À0.10 0.22 0.77 0.34
Target customers value ease of use 0.17 À0.16 0.81 0.29
Eigenvalue 2.11 1.97 1.65
Variance explained 23% 22% 19%
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 397
Maidique, 1990). The second variable represents
the hierarchical level of the project manager's
superior (Hierarchy). This variable takes values
from one to four depending on the position of the
superior (see Appendix E). This variable is rele-
vant because arguably managers with a higher
hierarchical position may be busier and thus dele-
gate more decision making to the project manager.
The last variable is the authority of the project
manager over marketing decisions (Mkt-Dec)
( ? 0:78). The questionnaire items used to mea-
sure marketing authority are adapted from Keat-
ing (1997), the respondent evaluates his authority
over a set of decisions on a seven-point scale ran-
ging from ``I (or my team) took action without
consulting other people in the company'' to ``other
people in the company decided what action to
take, my opinion was not solicited, but the deci-
sion was explained to me''.
The detail of project objectives (Plan) is mea-
sured as a weighted average. First, the respondent
is asked to evaluate the ``level of detail in the pro-
ject plan prepared before the start of the design
phase'' for each of the six types of information on
Table 4
Descriptive statistics on variables and related questionnaire items
a
Theoretical Actual
Min Max Min Max Mean Std. Dev.
Project performance 1 7 3.3 6.3 4.67 0.72
Management control systems design
Updating of customer related information 1 5 1 5 3.16 1.15
Updating of product design information 1 5 1 5 2.00 1.12
Updating of product schedule information 1 5 1 4 1.98 0.93
Updating of product cost information 1 5 1 5 3.73 1.03
Updating of product resources information 1 5 1 5 2.85 1.19
Updating of pro®tability information 1 5 1 5 4.17 0.85
Product strategy
Low cost strategy (%) 0% 100% 0% 50% 16.1% 14.1%
Time-based strategy (%) 0% 100% 0% 75% 23.3% 16.2%
Customer focused strategy (%) 0% 100% 0% 70% 32.7% 17.6%
Project uncertainty
Technology uncertainty 0 3 0 3 1.2 0.80
Market uncertainty 0 3 0 3 1.2 0.93
Percentage of new parts 0% 100% 10% 100% 56.1% 27.5%
Number of people in the project 0 1 0 106 16.8 21.4
Organizational structure
Functions under the poroject manager 0 1 0 4 1.3 0.94
Position of supervisor 1 4 1 4 2.6 0.95
Authority over marketing decisions 4 28 12 27 18.0 3.70
Detailed project objectives
Plan 0 5 0.14 4.48 2.62 0.84
a
Project performance is the weighted average performance including the dimensions described in Appendix C. Updating of man-
agement control systems is one if the questions related to the design of management control systems; the anchor points for this ques-
tions are: 1-weekly or less, 2-twice a month, 3-monthly, 4-quarterly, and 5-longer than quarterly. Product strategy is a representative
question used to operationalize this variable and illustrates the importance of various strategies; this question asked the respondent to
allocate 100 points among ®ve strategies (the other two strategies not included here are technology and minimize budget). Technology
uncertainty and market uncertainty are dummy variables. Percentage of new parts is newly designed parts over total number of parts.
Number of people is average number of people working for the project. Functions under the project manager counts the number of
functions reporting to the project manager. Position of supervisor indicates the hierarchical level of the supervisor (see Appendix E).
Finally, authority over marketing decisions is the addition of the four questions used to build this item, 4 is low authority while 28 is
high authority.
398 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
the same ®ve point-scale used for the level of
information reported. Then, the respondent rates
the importance of each type of information. Plan
is the weighted average of the detail for each type
of information.
6. Results
6.1. Descriptive statistics
Table 4 gives descriptive statistics on variables
and representative items in the questionnaire.
Time-information receives the most attention
(with a mean of 1.98 which means that this infor-
mation is updated more frequently than twice a
month). This result, corroborated during company
visits, indicates that management control systems
in product development, following project man-
agement techniques, are focused around time. It
is also important to observe that traditional
accounting measures Ð cost and pro®tability
information Ð are the ones used less frequently
(with means of 3.73 and 4.17 respectively). In
particular, pro®tability information Ð even if it is
the ultimate goal of a product development e?ort
Ð is on average updated quarterly or even longer
and it is the least discussed measure in meetings.
Project managers explained this apparent paradox
arguing that the ®nancial attractiveness of a pro-
ject is studied before the actual development
starts; once the development e?ort is under way,
®nancial performance is expected to follow from
sound non-®nancial performance. Project man-
agers also mentioned that they do not explicitly
include pro®tability issues when evaluating design
trade-o?s.
14
Also notice that this observation
reinforces existing evidence (Abernethy & Brownell,
1997; Brownell, 1985; Rockness & Shields, 1988)
regarding the low importance of traditional
accounting measures in these types of organiza-
tional processes.
Even if time information is used most often,
time strategy (exempli®ed by the relative impor-
tance of the various strategies) is not as important
as customer focus (32.7% for customer focus ver-
sus 23.3% for time and 16.1% for cost). Achieving
low cost has little importance for the sample
studied, which suggests that price pressures in the
health industry have not yet a?ected product
development in the medical devices companies.
15
On average, more than 50% of the parts
designed are new and the number of people
involved ranges from 0 (nobody devoted full time
to the project) to 106, with an average of 17.
Finally, the number of functions reporting to the
project manager is only 1.3 (median=1) indicating
that companies still use a functional structure even
if current research advocates for cross-functional
teams. The lack of cross-functional integration
was also con®rmed in ®eld visits. It is quite common
to have project managers supervising engineers
only and reporting to the R&D manager. How-
ever, cross-functional teams did exist; for example,
in one of the companies visited not only were
teams formed with people from di?erent func-
tions, but also the project leader could come from
any function including human resources or
accounting.
Table 5 presents the pairwise correlation matrix
among independent variables. Technological
uncertainty (Tech-X) is correlated with customer
strategy (Cust-Str) (0.29) indicating that this
strategy is likely to require higher product perfor-
mance compared to time and cost strategies. Pro-
jects with high technological uncertainty (Tech-X)
are also positively correlated with the scope of the
project (People) (0.33). Companies in the sample
have strong technological capabilities with sizable
R&D departments. These capabilities allow them
to tackle technologically complex projects by
assigning more people.
Managers supervising projects with a high
number of new parts (New-Parts) report to a per-
son more senior in the hierarchy (0.25) possibly
because these products tend to represent a more
14
There is little discussion in the literature as to why non-
®nancial measures are used more often than ®nancial measures.
If the ®nal goal of organizational decisions is pro®tability, it
seems reasonable to expect decisions to be made according to
this criterium. The evidence suggests otherwise.
15
The two additional product objectives included in this
question were: importance of technology (mean of 19.56%) and
importance of minimizing project investment (7.70%).
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 399
signi®cant e?ort by the company, thus requiring
top management attention.
Authority over marketing decisions is correlated
with market uncertainty (0.23) indicating that
project managers perceiving a more complex mar-
ket also have more authority to deal with this type
of uncertainty. Finally, authority over marketing
decisions is correlated with the hierarchical posi-
tion of the superior (0.23). This correlation may
just con®rm that as the project manager's span of
attention expands (in this case to include market-
ing decisions), (s)he is supervised by a more senior
person. Or, alternatively, projects with high mar-
ket uncertainty are newer to the organization and,
as such, they demand more attention from top
management.
6.2. The design of management control systems
To test the hypotheses relating management
control systems to project uncertainty and product
strategy, I use the following regression model:
Management Control Systems Characteristics
=f(Company Dummies, Product Strategy, Product
Uncertainty, Organizational Structure)
Table 6 shows the results from OLS regressions
for the six types of information reported in the man-
agement accounting system. The variance in¯ation
factors and the condition indexes are within the
expected ranges Ð thus, multicollinearity is not a
problem.
Hypothesis H1a predicted a positive relation-
ship between more intense use of customer infor-
mation and market uncertainty. The ®rst column
in Table 6 (CUSTI) supports this claim and the
coecient for market uncertainty (Mkt-X) is
positive and signi®cant (the coecient has a value
of 0.362 and is signi®cant at the 1% level). In
addition, the coecient for authority over mar-
keting decisions (Mkt-Dec) is also positive and
signi®cant (0.363). In other words, the project
manager uses customer information more often
when he is responsible for marketing decisions
possibly because he faces a higher degree of market
uncertainty.
Hypothesis H1b predicted a negative relationship
between technological uncertainty and manage-
ment control systems. Supporting this relationship,
I ®nd three regressions (DESI, TIMEI, and
BUDI) where the coecient for technological
uncertainty (Tech-X) is negative and signi®cant.
This result is in line with management control
systems being a poor vehicle to reduce technology-
related uncertainty.
Hypothesis H1c related project scope with
management control systems being more detailed
Table 5
Correlation matrix
a,
*
Cost-Str Time-Str Cust-Str Tech-X Mkt-X New-parts People Function Heirarchy Mkt-dec
Time-Str 0.00
Cust-Str 0.00 0.00
Tech-X À0.03 0.17 0.29**
Mkt-X 0.03 0.10 À0.09 À0.07
New-Parts 0.01 0.12 0.09 0.35** 0.12
People À0.05 À0.05 0.11 0.33** À0.08 0.00
X-Function À0.06 À0.03 0.02 À0.15 À0.02 0.00 À0.21
Hierarchy À0.09 À0.05 À0.19 0.26* 0.21 0.25* À0.07 0.04
Mkt-Dec 0.01 0.08 À0.01 À0.20 0.23* 0.15 0.03 0.02 0.23*
Plan 0.25* À0.15 À0.06 À0.04 0.01 À0.17 0.03 À0.09 0.06 À0.14
*10% Con®dence level; **5% Con®dence level.
a
Cost-Str: importance of cost to the success of the product, Time-Str: importance of time to the success of the product, Cust-Str:
importance of functionality (customer demands) to the success of the product, Tech-X: level of technological uncertainty, Mkt-X: level
of market uncertainty, New-Parts: percentage of new parts, People: number of people involved in the project, Hierarchy: hierarchical
level of project manager's superior, Function: level of cross-functional integration, Mkt-Dec: projects manager's authority over mar-
keting decisions.
400 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
and more intensively used. The evidence in Table 6
is weak. Only the coecient for new parts (New-
Parts) is positive and signi®cant for design infor-
mation (DESI) and the coecient for the number
of people (People) is signi®cant for budget infor-
mation (BUDI) (value of 0.013 signi®cant at
the 5% level). The signi®cance of the number of
new parts may just re¯ect the fact that more
Table 6
Results on the design of management control systems in product development
d
Dependent variable
b
CUSTI
a
DESI
a
TIMEI
a
COSTI
a
BUDI
a
PROFI
a
Variance
c
in¯ation
factors
Intercept 0.147 0.349 0.369 À0.04 0.423 0.101
Prob(T) 0.74 0.51 0.47 0.94 0.43 0.85
Product uncertainty
Tech-X 0.145 À0.578*** À0.378* 0.018 À0.400* 0.194 1.90
prob(t) 0.44 0.01 0.09 0.93 0.07 0.39
Mkt-X 0.362*** À0.020 À0.288* 0.034 À0.129 0.102 1.42
prob(t) 0.01 0.90 0.06 0.84 0.44 0.55
New-Parts 0.003 0.017*** 0.081 0.003 0.008 0.002 1.45
prob(t) 0.62 0.00 0.12 0.57 0.16 0.74
People 0.004 0.005 0.000 0.006 0.013** À0.003 1.52
prob(t) 0.51 0.43 0.99 0.41 0.05 0.67
Product strategy
Cost-Str À0.160 À0.152 À0.031 0.331** À0.031 0.305** 1.06
prob(t) 0.16 0.25 0.81 0.02 0.81 0.03
Time-Str 0.287** 0.188 0.361*** 0.026 0.012 0.021 1.22
prob(t) 0.02 0.21 0.01 0.86 0.94 0.89
Cust-Str 0.208 0.212 0.191 0.203 0.194 À0.027 1.41
prob(t) 0.14 0.20 0.24 0.23 0.24 0.87
Organizational structure
Function À0.077 0.046 0.045 0.073 0.423 0.065 1.11
prob(t) 0.53 0.75 0.75 0.61 0.44 0.66
Hierarchy À0.160 À0.128 0.034 À0.174 À0.097 À0.213 1.44
prob(t) 0.24 0.44 0.82 0.32 0.56 0.21
Mkt-Dec 0.363*** À0.191 À0.040 0.119 0.175 0.349** 1.20
prob(t) 0.01 0.18 0.77 0.411 0.22 0.02
R
2
54.5% 43.3% 39.5% 30.6% 37.1% 28.1%
Adjusted R
2
38.5% 22.8% 20.4% 10.5% 14.4% 8.8%
N 51 50 51 50 50 52
a
The condition index in all regression is around 11.70 (small di?erences are due to di?erent data points). *10% con®dence level.
**5% con®dence level. ***1% con®dence level. In all the regressions, dummies are used to control for companies with more than 5
projects in the sample.
b
CUSTI: use of customer information, DESI: use of product design information, TIMEI: use of time information, COSTI: use of
cost information, BUDI: use of budget information, PROFI: use of pro®tability information, Cost-Str: importance of cost to the
success of the product, Time-Str: importance of time to the success of the product, Cust-Str: importance of functionality (customer
demands) to the success of the product, Tech-X: level of technological uncertainty, Mkt-X: level of market uncertainty, New-Parts:
percentage of new parts, People: number of people involved in the project, Hierarchy: hierarchical level of project manager's superior,
Mkt-Dec: project manager's authority over marketing decisions, Function: level of cross-functional integration.
c
Variance in¯ation factor is de®ned as the inverse of 1 minus the correlation of the independent variable on the rest of independent
variables. Multicollinearity is considered to be a problem when the variance in¯ation factor is above 100 (A & Clark, 1990, p. 162).
d
Condition index (or number) is the square root of the relationship of the largest to the smallest eigenvalues of the normalized
matrix of independent variables (XX
0
). Condition numbers in excess of 20 are a signal of potential multicollinearity problems (Greene,
1993, p. 269).
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 401
information on design is required as the number of
parts increases. Therefore, this ®nding should be
interpreted with care. The signi®cance of number
of people in explaining the use of budget infor-
mation (BUDI) suggests that project managers use
budget information to coordinate and control as
the project grows (Lukka, 1988).
Next, I turn to the hypothesized relationships
between product strategy and management con-
trol systems. Hypothesis H2a related cost infor-
mation with the importance of low cost product
strategy. In support of such a relationship, the
coecient for low cost product strategy (Cost-Str)
is positive (0.331) and signi®cant (at the 5% level)
in the regression for cost information (COSTI).
Also in support of hypothesis H2b, the coecient
for time-to-market product strategy (Time-Str) is
positive (0.361) and signi®cant (at the 1% level)
for time-related information (TIMEI). In contrast,
Table 6 shows no support for hypothesis H2c. If
customer information is related to customer strat-
egy, then the coecient for Cust-Str would be
positive and signi®cant in the ®rst regression
(CUSTI). Contrary to the hypothesis, the coe-
cient is non-signi®cant.
6.3. The e?ect of management control systems on
project performance
To test for the relationship between manage-
ment control systems and project performance, I
use the following regression model:
16
Project Performance=f(Company Dummies,
Plan, Product Strategy, Product Uncertainty,
Organizational Structure, Management Control
Systems Characteristics, Interaction Terms)
Table 7 presents the results relating manage-
ment control systems and project performance.
17
The table includes the variance in¯ation factors
and the condition indexes to test for multi-
collinearity. Because they are within the expected
ranges, multicollinearity can be dismissed as a
threat to the results.
The ®rst regression presents the main e?ect.
Supporting the main e?ect hypothesis (H3a)
between management control systems and
improved product development performance, the
coecients for design (DESI) and cost informa-
tion (COSTI) are positive and signi®cant. How-
ever, the coecient for time information (TIMEI)
is negative (coecient À0.225 signi®cant at the
10%).
18
This last ®nding is against hypothesis
H3a and agrees with the argument that manage-
ment control systems can be detrimental to project
performance. The widespread recommendation
that decreasing development time is ``always
good'' to gain competitive advantage (Patterson,
1993) may not always hold.
The second regression includes interaction terms
to test for contingencies. Two interaction terms
are signi®cant. More intense use of customer
information for products following a customer-
focused strategy has a positive impact on perfor-
mance (as hypothesis H3b predicted). In a similar
way, more intense use of cost information is asso-
ciated with better performance as the importance
of a low cost strategy increases (hypothesis H3c).
In contrast, I ®nd no support for hypothesis H3d
relating time information and performance as the
importance of time-to-market increases.
As predicted in hypothesis H3e, detailed project
planning is associated with improved performance
(signi®cant at the 5 and 10% level). Finally, it is
relevant to point out that the coecient for Func-
tion is positive and signi®cant (0.300 and 0.290
both signi®cant at the 1% level), in line with pre-
vious research that found that cross-functional
integration bene®ts product development (Clark &
Fujimoto, 1991).
7. Discussion
This study sought to explore the drivers of
management control systems design in new pro-
duct development. The theoretical foundations are
16
I also tested the model using Euclidean distances as pro-
posed by Drazin and Van de Ven (1985). Results do not
change.
17
Only relevant independent variables are included to save
degrees of freedom, results are robust to alternative speci®ca-
tions. None of the product uncertainty variables was relevant.
18
I also included a quadratic term for TIMEI to test for
non-linearities. The quadratic term was not signi®cant.
402 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
Table 7
Management control systems and project performance
Perf
a
Variance in¯ation factors Perf
a
Variance in¯ation factors
c
Intercept 3.896 3.876
0.00 0.00
Management control systems
b
CUSTI 0.074 1.68 0.146 1.89
prob(t) 0.49 0.18
DESI 0.254*** 1.38 0.190* 1.49
prob(t) 0.01 0.06
COSTI 0.263** 1.83 0.211* 1.89
prob(t) 0.02 0.06
TIMEI À0.225* 1.90 À0.160 2.03
prob(t) 0.06 0.16
BUDI À0.160 2.02 À0.085 2.14
prob(t) 0.19 0.46
PROFI À0.165 1.95 À0.206* 2.04
prob(t) 0.17 0.07
Organizational structure and strategy
Function 0.300*** 1.10 0.290*** 1.11
prob(t) 0.00 0.00
Cost-Str À0.068 1.54 0.016 1.90
prob(t) 0.52 0.88
Time-Str À0.064 1.33 À0.002 1.49
prob(t) 0.52 0.98
Cust-Str 0.074 1.21 0.129 1.35
prob(t) 0.44 0.18
Interaction terms
Cust-Str*CUSTI 0.153* 1.38
prob(t) 0.062
Cost-Str*COSTI 0.190** 1.59
prob(t) 0.02
Time-Str*TIMEI À0.091 1.46
prob(t) 0.40
Detail in project plan
Plan 0.249** 1.42 0.215* 1.44
prob(t) 0.04 0.06
R
2
52.6% 61.9%
Adjusted R
2
34.1% 42.3%
N 51 51
Condition index
d
9.58 10.21
a
*10% Con®dence level; **5% con®dence level; ***1% con®dence level. In all the regressions, dummies are used to control for
companies with more than 5 projects in the sample.
b
PERF: project performance, Plan: detail of information at the beginning of the project, CUSTI: use of customer, DESI: use of
product design information, TIMEI: use of timer information, COSTI: use of cost information, BUDI: use of budget information,
PROFI: use of pro®tability information, Cost-Str: importance of cost to the success of the product, Time-Str: importance of time to
the success of the product, Cust-Str: importance of functionality (customer demands) to the success of the product, Function: level of
cross-functional integration.
c
Variance in¯ation factor is de®ned as the inverse of 1 minus the correlation of the independent variable on the rest of independent
variables. Multicollinearity is considered to be a problem when the variance in¯ation factor is above 100 (A & Clark, 1990, p. 162).
d
Condition index (or number) is the square root of the relationship of the largest to the smallest eigenvalue of the normalized
matrix of independent variables (X X
0
). Condition numbers in excess of 20 are a signal of potential multicollinearity problems
(Greene, 1993, p. 269).
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 403
grounded in Galbraith's de®nition of uncertainty
as ``the information required to perform a task
and the amount of information already possessed'',
and Tushman and Nadler's interpretation of
management control systems. The results indicate
that these systems are relevant and, moreover,
managers in product development use them to
obtain information needed to reduce uncertainty.
This ®nding contrasts with previous studies which
assumed that control systems are tools to reduce
goal divergence and found that control systems
are only marginally relevant to product develop-
ment. Further research may fruitfully adopt this
alternative interpretation of management control
systems.
The study reinforces a broader de®nition of
management control systems to go beyond ®nan-
cial measures and also include non-®nancial mea-
sures. Descriptive statistics show that project
managers rely on non-®nancial measures much
more than they do on ®nancial ones. This ®nding
suggests that researching management control
systems in new product development cannot be
restricted to traditional accounting measures, but
needs to encompass a broader set of measures.
This is so because managers work with the implicit
assumption that good performance in non-®nan-
cials will drive good ®nancial performance. As the
theory predicted, uncertainty and product strategy
are related to the design and use of management
control systems. The fact that the results are
anchored in existing theory reinforces the ade-
quacy of this theory to understand reality. How-
ever, a word of caution related to the limitations
of the research design is needed. To increase the
power of the design, the study focuses on the
medical devices industry. This research choice has
the drawback that the conclusions cannot be gen-
eralized beyond this industry. Moreover, the unit
of analysis is the project manager and conclusions
may not be valid for other hierarchical levels such
as, for example, the project manager's supervisor.
Consistent with previous studies in product
development, cross-functional integration is sig-
ni®cantly related to performance. But more inter-
estingly the use of cost, time, and product design
information is also signi®cantly related to per-
formance. In other words, management control
systems' design is related to performance, in con-
trast to other variables including product strategy
that have no such relationship. Even if it has been
argued that formal systems may be detrimental,
the results in the study suggest a more complex
picture. Cost and design information has a posi-
tive e?ect upon performance. In contrast, time
information hinders performance supporting the
argument that too much emphasis on formal sys-
tems limits innovation. A possible explanation is
that current emphasis on reducing time-to-market
may not be appropriate for certain projects.
A potential limitation of the study is that per-
formance was constructed as a subjective, self-
reported measure to take into account that this
variable is multidimensional. But this design is
subject to potential biases related to self-reported
measures.
The study provides evidence supporting a con-
tingency theory of management control systems
in product development. In particular, the align-
ment between the design and use of these systems
and product strategy is signi®cantly related to
performance.
The signi®cant relationship between detailed
project objectives and performance is consistent
with previous research (Bruns & McKinnon,
1992). But the actual planning of a product devel-
opment process is not well understood. Project
manager A planned uncertainty. He tried to
remove it as soon as possible except in the case of
customer-related uncertainty. He kept this uncer-
tainty intentionally unresolved until the appro-
priate time in the development e?ort: ambiguity
was planned. Further research is needed to under-
stand when planning is appropriate and when too
much detail can hinder performance.
Case studies illustrate the diversity that exists
among the design and use of management control
systems in new product development. The four
case studies describe how di?erent project man-
agers use these systems (or fail to use these
systems) depending on project characteristics.
Supporting the literature in new product develop-
ment, prototyping seems to replace management
control systems when technology is the main source
of uncertainty. When uncertainty comes from the
market or from project scope, management control
404 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
systems are a good vehicle to reduce uncertainty
rather than to monitor and control. This observa-
tion provides further evidence to support an
information perspective rather than a goal diver-
gence approach to this ®eld.
The study also makes a contribution to research
methodology. It develops three measures for
determining the information content of manage-
ment control systems: the detail of the information
reported, the frequency with which it is updated,
and the use managers make of it. Nonetheless, the
instrument can be improved to increase its relia-
bility. A possible way would be to develop addi-
tional questions keeping in mind that in product
development, the design and use of management
control systems re¯ect a single variable.
This research can be extended in several direc-
tions. Management control systems are important
for the performance of the project, but the
research does not reveal why, nor provides the
detail on how these systems are designed. Con-
tingency relationships for project performance and
project planning have been partially studied and
represent a promising area for future research.
This paper is just the ®rst step in the advancement
of knowledge about management control systems
in new product development. Additional emp-
irical evidence and theoretical concepts are
required to fully understand the implications of
this research.
Acknowledgements
I appreciate the comments from my thesis advisors
Robert S. Kaplan, Robert Simons, Clayton
Christensen, and V. G. Narayanan. I am also
grateful for the comments of Kentaro Koga, Wil-
liam Bruns, Srikant Datar, Amy Hutton, partici-
pants in the Harvard Accounting Seminar, and
participants in the European Accounting Asso-
ciation Annual Meeting in Antwerp, 1998. Also
my sincere appreciation to two anonymous
AOS referees that provided excellent comments.
This research received ®nancial support from the
Division of Research at the Harvard Business
School and from IESE, University of Navarra.
Appendix A
Interview protocol for project managers
1. Product strategy and product characteristics
1.1. How would you relate the product
developed to previous products in the
company?
1.2. Why did the company decided to invest
into this project?
1.3. What was the target market for the
product and the key success factors?
1.4. What technology did you use. How
familiar was the organization with it?
1.5. How familiar was the organization
with the market being targeted?
1.6. What were the most challenging issues
in the development process?
2. Organizational structure
2.1. How was the project organized? Who
did you report to? Who did the people
involved in the project reported to?
What was their time commitment?
2.2. How many functions were included in
your team?
2.3. How much autonomy did you have in
decisions related to the project? How
frequently did you got guidance from
your supervisor?
2.4. What information did you report to
your supervisor?
3. Management control systems
3.1. How detailed were project objectives?
3.2. What reports did you use over the
course of the project? (Examples)
3.3. Howdetailed was project schedule? How
often did you updated it? (Example)
3.4. What information did you receive to
track project costs? How were resour-
ces allocated to the project? Who was
in charge of managing resource demands
across projects?
3.5. What type of product costs did you
consider? How did you get this infor-
mation? How did you use it?
T. Davila / Accounting, Organizations and Society 25 (2000) 383±409 405
3.6. Did you use any kind of ®nancial
measures? When did you use them?
What decisions involved this type of
information?
3.7. What information was used for pro-
duct performance?
3.8. How did you make sure that customer
needs were incorporated into the pro-
duct? How did you get this information
(®lter)?
3.9. Did you get information on competitors?
3.10. What were the variables you focused
on for competitors? What other type of
information did you use that you con-
sider important and it is not re¯ected in
the questionnaire.
3.11. How would you change the informa-
tion environment to improve the
project?
4. Performance evaluation
4.1. How was your performance evaluated?
4.2. Did you have any type of rewards
linked to project performance?
4.3. How are promotions decided.
5. Project performance
5.1. Are you satis®ed with the performance
of the project?
5.2. Do you think it met the objectives?
5.3. How is it performing in the market
with respect to competitors?
5.4. What would you change if you were to
do this project again?
Appendix B
Anchor points used to measure the level of detail
Scheduling Information Ð information related to
project schedule.
Very low detail (level 1): Expected date for
market introduction.
Medium detail (level 3): Milestones to be
accomplished monthly.
Very high detail (level 5): Milestones to be
accomplished weekly.
Customer Information Ð information related to
customer needs, preferences and acceptance.
Very low detail (level 1): General information
about the product from
marketing people.
Medium detail (level 3): Salesforce assessment
of customer reactions
to product concept/
design.
Very high detail (level 5): Detailed customer
evaluation of product
features, prototypes,
and marketing plans
through interaction
with the development
team.
Project Resources Information Ð information
related to project resources such as budgets, man-
power,. . .
Very low detail (level 1): Total budget for the
product development
project.
Medium detail (level 3): Budget speci®ed per
department
(engineering,
marketing,. . .).
Very high detail (level 5): Detailed description of
resources from all parts
of the organization and
suppliers.
Product Performance Information Ð information
related to the technical and technological aspects
of the product.
Very low detail (level 1): General product
speci®cations.
Medium detail (level 3): Description of the
features of major parts.
Very high detail (level 5): Detailed description of
the design of all the
product's parts.
Product Cost Estimates Ð information related to
unit costs of producing and delivering the product.
Very low detail (level 1): Material and labor
costs for the product.
Medium detail (level 3): Manufacturing costs
for the product
including overhead
costs.
406 T. Davila / Accounting, Organizations and Society 25 (2000) 383±409
Appendix C
Project performance dimensions
1. Meet product speci®cations.
2. Meet unit cost objectives.
3. Meet timing goals.
4. Meet project's budget goals.
5. Ful®ll customers' needs.
6. Be a business success.
7. Capture a high market share.
8. Create a new market.
9. Create a new product line.
10. Develop new technology.
11. Enhance skills to handle new technology.
Appendix D
Questionnaire item used to measure market and
technology uncertainty
Market uncertainty
How would you describe the experience of the
organization with the target market.
How would you describe the experience of the
organization with competitors.
What was the importance of creating a new
product line.
What was the importance of creating a new
market.
Technological uncertainty
Which of the following sentences best describes
the product developed:
The product was a simple redesign of an exist-
ing derivative.
The product was a new derivative based on an
existing platform.
The product was a major redesign of an existing
platform.
The product was a new platform replacing an
existing platform.
The product was a completely new platform.
What was the importance of developing a new
technology.
What importance did target customers give to
technological performance.
Appendix E
Questionnaire item used to measure the
hierarchical level
Which of the following best describes whom you
reported to during the design phase:
Division general manager.
More than one functional department head.
One of the functional department heads.
A manager below the functional department
head level.
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