an empirical study on the drivers of management control systems

An empirical study on the drivers of management controlsystems' 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 exploreswhether 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 centraldimension 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 ®rstmover advantages, fast product introductions,more demanding product functionality, andshortening life cycles has put greater pressure onnew product development (Cooper, 1998). Whilemanufacturing has traditionally been a key repo-sitory of core competencies (Hayes & Abernathy,1980), outperforming competitors in productdevelopment has emerged as a relevant source ofcompetitive advantage.

As the process has gained importance, aca-demics as well as practitioners have voiced theimportance that management control systems playin coordinating and controlling this process(Cooper & Kleinschmidt, 1987; Zirger & Maidique,1990). For example, Clark and Fujimoto (1991),in their study of the product development processin the auto industry, argue that:

Today's e�ective product development organ-ization is characterized not only by creativityand freedom, but also by discipline and con-trol in scheduling, resource use, and productquality (...) The challenge in product devel-opment is not so much unilateral pursuit oforganic structure and permissive managementstyle as a subtle balance of control and free-dom, precision and ¯exibility, individualismand 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 productdevelopment process contrasts with the traditionalview supporting a hands-o� approach (Lothian,1984; McNair & Leibfried, 1992). According tothis latter view, successful new products resultfrom devoting adequate resources to the processand avoiding control procedures that couldrestrict the freedom of engineers. The impact ofmanagement 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 ofmanagement 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 managementcontrol systems as hindering or, at most, beingirrelevant in R&D settings. In contrast, Nixon(1998) o�ers a rich case description of a productdevelopment process where ®nancial control playsa signi®cant role.The importance of new product development

requires the allocation of accounting researchresources 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 systems1

to understand how companies adapt their sys-tems to the particular characteristics of each pro-duct development e�ort. Moreover, the studybrings new evidence to the unsettled issue of therelevance or, alternatively, the lack of relevanceof management control systems in productdevelopment.Several characteristics distinguish this study. In

contrast to previous research, the unit of analysisis the product development project rather than theR&D project. Because R&D projects are very

heterogeneous (National Science Foundation,1976), focusing on one type of project increasesthe power of the research design. The study alsogoes beyond the narrow de®nition of managementcontrol systems around ®nancial informationto add formal but non-®nancial information(Kaplan, 1983; Banker, Potter & Schroeder, 1993).Moreover, the theoretical foundation of the studyleads to an interpretation of management controlsystems di�erent from previous studies and to adi�erent set of independent variables.The study focuses on the medical devices indus-

try to keep the external factors as constant aspossible and avoid confounding e�ects that maycome from di�erences across industries. Thisindustry has several attractive characteristics.First, product development is an important pro-cess: R&D over sales averages more than 5% forthe industry and new products are constantlyintroduced. Therefore, companies have wellthought-out product development processes.Second, the industry is characterized by a lot oftechnological 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 alsodiverse; even products belonging to the samecompany and serving the same product-markethave to adapt their value proposition to di�erentmarket segments ranging from price sensitive toperformance oriented customers. X-ray productsinclude machines designed to take static images ofparts of the body, where price is the key purchas-ing criteria, as well as sophisticated machines thatscan the whole body from di�erent angles, whereperformance and customer interfaces are the keycompetitive 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 research1 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 controlsystems in R&D and presents the theoretical fra-mework underlying the study. Section 3 describesthe phenomenon studied through the descriptionof four representative cases. These cases illustratethe variables in the research as well as thehypotheses of the study. Section 4 develops thehypotheses for the empirical test based on thetheory as well as case ®ndings. Section 5 describesthe research design for the survey study. Section 6discusses the results of the paper and Section 7reaches conclusions.

2. Theory development

2.1. The product development process

The objective of product development is totranslate an idea into a tangible physical asset.The process is structured around well-de®nedphases; each phase ends with a decision-makingmeeting where management decides about thefuture of the project. A typical product develop-ment project starts with a planning phase toestablish the requirements of the project. Duringthis phase, the organization de®nes the targetmarket 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 ofthese characteristics. The second phase Ð conceptdesign Ð goes into more detail to specify the pro-duct speci®cations and the requirements of thedevelopment project: target costs, technologicalperformance, customer interfaces, market releasedates, and organizational resources. The thirdphase Ð product design Ð is the actual develop-ment of the physical product. It is in this phasewhen trade-o�s get resolved and information istransformed into a tangible product. The last twophases Ð testing and production start up Ð con-®rm that the product meets its objectives and pre-pare it for release. The process, even thoughdescribed as linear, is an iterative process: productspeci®cations or even the product concept can bere-evaluated in light of new information generatedthroughout the process.

2.2. Literature review

Past work on management control systems inR&D follows two approaches. One line ofresearch focuses on how R&D departments use®nancial measures (Brownell, 1985; Hayes, 1977;Rockness & Shields, 1988). The consensus fromthese studies reveals that ®nancial measures do nothave an important role in R&D departments otherthan signaling the commitment of the organiza-tion to its R&D e�orts. The perceived importanceof 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). Forexample, Kamm de®nes control as ``the set ofcriteria, policies and procedures established tostandardize operations and to make possible mea-surement of performance to insure achievement oforganizational objectives'' (p. I-12, I-13). Rock-ness and Shields (1984) study the relationshipbetween types of control and project character-istics. Following Ouchi's framework3 (Ouchi,1979), they classify R&D projects according to thelevel of knowledge of the transformation processand the measurability of the output. Next, theypredict a relationship between these characteristicsand the type of control used: input, behavior, andoutput control. These authors ®nd only marginalrelationships between control systems and projectcharacteristics. Similarly, Kamm (1980) concludesthat ``researchers do not necessarily exhibit moreinnovative behavior when they perceive relativelyloose administrative control than when they per-ceive tight control'' (p. IV±11). Abernethy andBrownell (1997), use Perrow's model (Perrow,1970) that relates type of control with taskanalyzability and number of exceptions. Theseauthors conclude that ``reliance on accountingcontrols 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 systemshave, at most, a minor role in product development.Nixon (1998) provides a rich case description on

the balancing role of the controller in assistingengineers during the development of a new copperrod production machine. In contrast to previousstudies, the author reports that the ``®nancialcomponent of the system serves to integrate thedisparate perspectives'' (p. 343).However, management control systems have

proven to be useful tools in environments char-acterized by high levels of uncertainty. Forexample, Khandwalla (1972) ®nds that reliance onformal control systems increases with the intensityof competition. Similarly, Simons (1987) reportsthat high performing prospectors rely on theinformation provided by frequently updated for-mal control systems to drive organizational learn-ing (Dent, 1990). Additional research shows thatmanagers who perceive a higher level of environ-mental uncertainty tend to use broad scope andmore timely information (Chenhall & Morris,1986) as well as more external, non-®nancial, andex-ante information (Gordon & Narayanan,1984). Kren (1992) ®nds that participation in thebudgeting process is related to better performancefor high uncertainty tasks. Finally, Chenhall andLang®eld-Smith (1998) report that di�erent stra-tegic priorities emphasize di�erent formal controlsystems regardless of the uncertainty faced by theorganization.A possible explanation for the apparent contra-

diction between the results for R&D environ-ments, where management control systems seemnot to be relevant, and other environments is adi�erent interpretation of management controlsystems. R&D studies interpret these systems ascontrol tools to reduce goal divergence rather thanas information tools to deal with uncertainty.These ®ndings are in line with the concept of clancontrol (Ouchi, 1979). Clan control emphasizesinformal control mechanisms and relies less on

formal systems. When uncertainty is high, clancontrol is preferred to solve goal congruenceproblems (Merchant, 1982).5

In line with the alternative interpretation ofmanagement control systems as tools to manageuncertainty, studies on target costing all concur onthe 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 andDavila (1998) ®nd that target costing ful®lls aninformation role to facilitate learning and experi-mentation, yet they ®nd no support for targetcosting being used to address goal divergenceproblems or coordination issues.

2.3. Theoretical framework

2.3.1. Management control systems and theconcept of uncertaintyProduct development is an uncertain process.

For example, Gupta and Wilemon (1990) reportthat technological uncertainty is mentioned as areason for delays by 58% of project managerssurveyed. Each new product development processpresents a di�erent set of problems and organiza-tions need information to solve uncertainties asthey emerge. The theoretical background of thepaper 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 ofinformation already possessed by the organiza-tion'' (Galbraith, 1973, p. 5). This paper, in con-trast to previous work in the ®eld, assumes thatthe main role of management control systems inproduct development is to supply informationrequired to reduce uncertainty rather than toreduce goal divergence problems. This alternativeperspective is intended to reconcile the tensionthat exists between the sparse empirical evidenceavailable with the strong recommendations bypractitioners 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).


systems used in this study,6 following Chenhalland Morris (1986) and Gordon and Narayanan(1984), goes beyond the narrow perspectivefocused around accounting numbers Ð cost,pro®tability, and budget Ð to include a broaderinformation set (Kaplan, 1983) capturing cus-tomer, product design, and time-related measures.Management control systems in new product

development are viewed as sources of informationthat are used to close the gap between ``the infor-mation required to perform a task and the amountof information already possessed''. This view isconsistent with Tushman and Nadler (1978) whoargue that management control systems are e�ec-tive tools to manage uncertainty because theysupply 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 fromalternative sources. For instance, it may beobtained through experimentation (Pisano, 1994)or informal communication (Allen, 1977; Dou-gerthy, 1990); if this is the case, then managementcontrol systems may not have any role in the pro-cess and, consequently, not be related to projectuncertainty.Research in new product development

(McGrath, 1995; Shenhar & Dvir, 1996; VonHippel, 1988; Wheelwright & Clark, 1992) hasidenti®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 threetypes of uncertainty shape the design of manage-ment control systems. In addition to the uncer-tainty characterizing the project, the design ofmanagement control systems depends on thestrategy (Govindarajan & Gupta, 1985) as well asthe organizational structure (Bruns & Waterhouse,

1975). Cooper (1995) reports that companies placedi�erent emphasis on target costing proceduresdepending on product strategy. Certainly, thevalue of a piece of information (for instance, costinformation) is contingent upon the importance aswell as the uncertainty related to the competitivedimension addressed (cost leadership). Similarly,organizational structure a�ects the size of theproject team that is associated with the level offormalization (Mintzberg, 1979, pp. 230±235) andthe project manager's responsibilities that a�ectthe allocation of uncertainty. For instance, if themarketing department is responsible for dealingwith market uncertainty, then the project managerwill be insulated from it and he will not demandcustomer-related information, even if it may becritical to the success of the project.

2.3.2. Management control systems and projectperformance

The e�ect of management control systems uponnew product development performance is di�cultto predict. If management control systems supplyinformation relevant for coordination and learn-ing, then a positive relationship between perfor-mance and the use of management control systemsis 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 relationshipdoes not exist or is negative. Management controlsystems, by imposing rules and constrainingbehavior, reduce the level of creativity requiredfrom product development and, thus, negativelya�ect performance (Amabile, 1998).

3. Case studies

To understand how project managers use man-agement control systems, I visited 12 businessunits in seven companies both in Europe and theUnited States. During each of these visits, I inter-viewed one or two project managers, the market-ing manager, the R&D manager, and the generalmanager 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.


the new product development process guidelines.Because existing literature in management controlsystems in product development is still sparse, Ichose to do an exploratory study using casestudies as the preferred methodology to buildknowledge about the phenomenon (Yin, 1988).Interviews were structured around a set of ques-tions about the formal systems and the productdevelopment process itself. The questions wereopen-ended which allowed me to adapt the inter-view to the expertise of each manager withoutlosing the overall direction. Appendix A presentsthe protocol that I used for the interviews withproject managers. Similar protocols were used forthe interviews with other managers.I interviewed an average of ®ve managers in

each business. The use of multiple informersallowed for a triangulation of the data. When amanager's explanation did not agree with thedescription given by previous managers of thesame organization, the di�erences were exploreduntil the reason for this divergence was fullyunderstood.Next, I present four illustrative cases on the

diversity of product development projects and thedesign and use of management control systems.

3.1. Project manager A

Project manager A worked in an anesthesiamonitoring system. This product was designed towork together with the company's anesthesiadelivery system. The company's strategy was ``towork very close to the customer, in that sense weare not a low cost producer but we focus verymuch on customer needs and facilitate customerinterface with the product. We want to be specialin 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, andcontact points with other projects (the anesthesiadelivery system). The purpose of this contract wasnot to evaluate performance ex-post, but to gainthe personal commitment of each person involved

in the project. The contract brought together theexpectations of the various people involved in theproject rather than establish goals to increaseextrinsic motivation.Project goals were clearly de®ned except for

product speci®cations related to the customerinterface. The product's strategic advantage camefrom meeting customer needs and developing theappropriate customer interface. The ``informationgap'' to be closed during the product designphase came from the market, in particular fromcustomer needs.Because of the relevance of customer informa-

tion, management built ¯exibility into projectgoals to incorporate this information during theexecution instead of freezing it at the beginning ofthe project. The decision to sketch only certainproduct speci®cations at the start of the projectwas intended to adapt as much as possible to cus-tomer feedback: ``there is a need to expose theproduct and product concept to the customer andbe ready to change and adapt features andappearance to their reactions.'' Uncertainty waspurposely left unresolved on the customer dimen-sion to adapt during the development process, butit was clearly bounded: ``there is a need to de®ne¯exibility dimensions up-front (and freeze otherdimensions)''.During the execution, the project was divided

into smaller sub-projects including ``moving fromthe traditional two measures captured in a tradi-tional anesthesia monitoring system to severalmeasures, developing the frame to integrate thevarious modules of the product, and writing theproduct's software''. The project also had mar-keting sub-projects like ``the product launch pro-ject including training distributors, promotionmaterial and marketing concept communication''.The project manager directly supervised engineersand marketing people. He was also frequently intouch 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 constantcommunication with doctors, at a certain point adoctor was working full time for the companyto make sure that the product was user friendly.''


3.2. Project manager B

Project manager B had developed a new clipused for brain surgery. Medical doctors used theseclips to keep blood vessels closed while performingbrain surgery. Existing clips were metallic. Thismaterial had the advantage of providing the rightmechanical properties like torsion and resistanceas well as being cost e�ective. But metal had asigni®cant drawback for certain types of surgery.When the doctor, while performing surgery, hadto do a scan of the patient's brain, the clip createdshades in the picture and, more important, mag-netic ®elds could move the clip with possiblydevastating consequences for the patient. In doingsome tests on his own, project manager B foundthat a new material, titanium, could solve thisproblem. Titanium was more expensive but itwould become the only product available to per-form scans during surgery. The company esti-mated a signi®cant market for the product andfunded the project.According to the project manager the main

question during the project was to get themechanical properties right: ``in this product,technology was critical''. Technology was themain source of uncertainty as well as the key fac-tor for product success. He did not care about thecost of the product, because it would have a vir-tual monopoly in its segment: doctors did nothave alternative products and competitors wereunlikely 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 inseveral surgeries to see how the clip was used.These visits allowed him to understand customerneeds. In addition, the project was not subject totime pressure because no other company wasinvesting in a similar product. Only when thetechnology was well understood, did the companydecide on a deadline. During the 4 years that theproject lasted, all the attention of the projectmanager was focused on ®nding the right combi-nation of materials and the appropriate design tomeet the mechanical requirements: ``because it wasintense in technology, it was hard to see problemsand it was also hard to calculate timing''.

Because technology was the paramount variablein this project, project manager B worked togetherwith a team made up of researchers. Only a mar-keting manager was supporting the team to facilitatecontact with doctors. The project plan was simple,the timing for the various phases of the productwere loosely speci®ed as was the budget and theexpected product cost. The fact that the CEO hadcome from the R&D function and kept close con-tact with product development people reinforcedan informal control on the project. Throughmonthly meetings, the CEO evaluated whether theproject was moving according to expectationswithout the help of a formal project plan.The management control system for project

manager B was almost non-existent. He got all therelevant information from prototyping ``to assuremanufacturability''. He built more than twothousand prototypes before he found the right mixof materials and design. Any other information,like timing or cost, was irrelevant to him. The newclip 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 ofdeveloping a hip endoprothesis for an Asiancountry. The product was similar to an existingone, but the marketing department had found thatthe body geometry of people in the main ethnicgroup of the country was di�erent. The companysaw this fact as a relevant dimension for competi-tive advantage. The project took a year todevelop. Because the product was similar to anexisting one, few doubts existed regarding productcost and technology: ``we knew a lot about thestructure of the development of this product''.The pressure points were time-to-market and

project budget. Time-to-market characterized thestrategy of this product. Time pressure camethrough the scheduling of the introduction date:``time-to-market was the most important factorbecause the group in the country had alreadystarted to sell the product''. The budget wasequivalent to the number of prototypes because ofthe direct relationship between prototypes andinvestment: ``there was a monthly overview meeting


to compare project costs and budget, it was impor-tant to reduce as much as possible the number ofprototypes to save development money. There wasa trade-o� between safety and investment''.The main task for project manager C was to

coordinate the e�ort of engineers to meet the tightschedule. The source of uncertainty came mainlyfrom project scope. Themanagement control systemprovided detailed information on how the projectprogressed in terms of schedule and budget.The project manager did not have direct contact

with the customer. In fact, his supervisor talked tothe marketing people in the Asian country and themarketing people talked to doctors. The projectmanager did not see this lack of direct access tothe customer as a problem because the productwas well understood, the only relevant issue beinga change in geometry. Moreover, the contacts withthe marketing people were mostly related to pro-duct launch, not to customer needs.Finally, product costs were also well under-

stood. However, the manufacturing personinvolved in the development team periodicallyestimated product cost to make sure that it was ontarget: ``the project would have stopped only ifmanufacturing costs had been too high''.Management control systems in this project

were focused around time-to-market and projectbudget. The latter information was re¯ected in theproject manager's decisions concerning whether tobuild a new prototype. Product costs, even if criticalto project success, were managed by exception.

3.4. Project manager D

Project manager D developed a multi-purposeX-ray machine. The product had two criticalcomponents, the X-ray camera and the examina-tion table for the patient. The technology for theX-ray machine was well understood and devel-oped in-house. But, the table was a complexmechanical device. Because the machine allowedan X-ray picture to be taken of any part of thebody, the table was large and, as a consequence,hard to develop. In addition, the doctor couldchoose the angle for the picture that (s)he con-sidered most appropriate. This capability meantthat the table had to move at least 180 degrees in

each of the three spatial axes with a high degree ofprecision. The main source of uncertainty for thisproduct came from mechanical technology.The X-ray division had recently reassessed its

strategy after several years of disappointing ®nan-cial performance. According to the marketingmanager: ``We are stripping down the number ofproducts because now there are too many and it isexpensive to deliver and service such an extensiveline of products. We are not satisfying customersper se, we are also looking at pro®tability. Thecurrent product line is based only on satisfyingcustomer needs and this is why there is so muchproliferation of products''. This new emphasis oncost 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 departmentwith 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 waslaunched. The role of the project manager waslimited in this division to the supervision of com-ponent development. His main task was to breakdown the project into small work packages fullyspeci®ed in terms of budget, time, componentspeci®cations, and component cost and make surethat plans were met. In the terms of Wheelwrightand Clark (1992), he was a ``lightweight'' projectmanager with no people reporting directly to him,but only coordinating the development e�ort. Theproject manager mentioned: `Ì never talk to cus-tomers, they talk to the marketing people but notto me''.Because of the recent focus on cost that the

new division imposed, a cross-functional groupre-estimated product costs `èvery time new partsbecome available''. However, the most time con-suming issue for the project manager was an ItalianOEM in charge of developing the examinationtable for the X-ray machine: `Ìt took them toolong and they made too many mistakes in devel-oping the table. We did the design and wrote thesoftware for the table. The Italian company was incharge of the mechanics''. His attention wasdevoted to managing the relationship with this


supplier, provide support to their people, and tryto minimize the e�ect of these problems in projectscheduling and cost. He did not care as muchabout budget because ``development cost is similarto time because it is basically time multiplied byprice''. Even if the product achieved its objectives,the project was not considered a success becauseof 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 rolesfor management control systems. Each projectmanager required di�erent information dependingon product strategy and type of uncertainty. Forproject A, meeting customer needs was the keysuccess factor as well as the main source of uncer-tainty. Management purposely left customer-related uncertainties to be resolved during thedevelopment process through close contact withthe customer. The structure of management con-trol systems emphasized customer interaction.Time, budget, and product cost were managed byexception. Because the project never hit theseconstraints, the project manager devoted hisattention to customer information. The projectteam integrated both engineers and marketingpeople with a looser coordination with the manu-facturing function. This structure re¯ected themanagement belief that the project managershould be in charge of marketing.In contrast, project B was all technology. Time

was not a constraint, nor was budget nor productcost. In fact, the formal systems were loose com-pared to the detailed project plans and reviewpoints used in the other projects. The projectmanager focused his attention on prototyping asthe most e�cient way of coping with technologicaluncertainty. Project B exempli®es a situationwhere detailed formal management control sys-tems could undermine performance. Prototypinggave project manager B the information that heneeded Ð any other source of information wouldhave been a burden and undermined performance.His team was composed of R&D people only andhe reported to the CEO who had a background inR&D.

Project C illustrates the development processmost similar to a manufacturing process whereuncertainty resides in coordination Ð projectscope. The cause±e�ect relationships were wellunderstood and product functionality was wellde®ned. Project manager's attention was mainlydevoted to time-to-market and budget. He did notinteract with customers, nor did he devote muchattention to costs (controlled by exception), but hewas constantly thinking what needed to be done tomeet the deadline and assessing whether he couldsave development costs by reducing prototyping.It is interesting to observe how project manager Cused a non-®nancial measure Ð number of proto-types Ð as a substitute for a ®nancial measure Ðproject investment. Again, this project managerwas in charge of an R&D team. Interestingly,his contacts with marketing were not related tocustomer needs but to product launch because ofits importance to the strategy of the product.Finally, project manager D worked at a com-

pany where costs had become a key dimensionbecause product proliferation had led the com-pany into disappointing ®nancial performance.This emphasis was translated into frequent costestimations. Unfortunately, the main source ofuncertainty for project manager D came fromtechnology. 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 ofhis attention to this unexpected issue that a�ectedthe timing, functionality, and budget of the pro-ject. In this case, management control systemsinformed the project manager about technologyonly by exception even if it may have requiredmore frequent updating. Project manager D didnot have a team reporting to him, he only coordi-nated the technical part of the project. Table 1summarizes these ®ndings.

4. Development of the research hypotheses

4.1. Uncertainty and the design of managementcontrol systems

The theoretical discussion and case descriptionssuggest that uncertainty is a driving force in the


design and use of management control systems.Case discussions illustrate how the sources ofuncertainty vary across projects. Also, productdevelopment literature and management account-ing literature identify di�erent types of projectuncertainty. To be as close as possible to the phe-nomenon studied, I rely on the classi®cation ofuncertainty used in the product development lit-erature. Uncertainty is classi®ed as market uncer-tainty, technological uncertainty, and projectscope (Shenhar & Dvir, 1996).7

Von Hippel (1988, chapter 2) describes theimportance of the organization's experience withthe targeted customer segment. When the organi-

zation already serves the target customers, theirneeds and requirements are well understood anduncertainty 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.


customer preferences increases (market uncer-tainty). In the latter case, information about cus-tomers is expected to help in reducing marketuncertainty.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-bedeveloped technologies (high uncertainty)(McGrath, 1995; Shenhar & Dvir, 1996; Wheel-wright & Clark, 1992). When technology is themain source of uncertainty, project team membersfocus their attention on resolving the problemsassociated with technology. Product design andfunctionality information can help in addressingthis type of uncertainty. However, case study Bsuggests that project managers may obtain therelevant information from experimentation andprototyping (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 theinput from di�erent constituencies. Project scopedepends on the number of people involved in theproject. A small project, possibly because the pro-duct is simple or because it only involves a smallgroup of engineers, will have low demands onformal systems for coordination. In contrast, alarge project with ®fty people dispersed in severaldepartments around the company will need to relymuch 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 customerinteraction as well as technology development,while project manager B only supervised R&Dpeople. 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 testsneed 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 ofmanagement 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 ofthe typology of strategy used. Simons (1987) usesthe strategy types de®ned by Miles and Snow(1978); Merchant (1985) follows the typology sug-gested by MacMillan (1982); while Govindarajanand Fisher (1990) rely on Porter's (1980) conceptof competitive strategy. If these results are gen-eralized to product development, then it is expec-ted that product strategies will be related tomanagement control systems' design. However,this relationship is only a conjecture empty of anyempirical evidence. Even if cost may be critical tothe success of a product competing on price,meeting initial speci®cations may satisfy thisobjective and the project manager can safelyignore cost information. The typology of productstrategies selected for the research is based onMiller and Roth (1994) who identify price, time-to-market, and customer focus as di�erent productstrategies.9 If management control systems provideuseful information to deal with relevant projectuncertainties, then project managers designinglow-price products will value product cost infor-mation more highly, while time information maybe more valuable for products that would stand tobene®t from ®rst mover advantages. The followinghypotheses 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.


H2a: Cost information will be used more inten-sively as the importance of a low cost productstrategy 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 customerfocused product strategy increases.

4.3. Management control systems and projectperformance

The aim of most managerial activities is toimprove 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 andperformance does not necessarily mean that thesesystems are irrelevant. An alternative interpreta-tion is that companies have optimally designedsystems. If all companies have precisely the man-agement control systems that they require, thenperformance will not be related to these systems.In contrast, if such a relationship exists, then itcan be concluded that management control sys-tems are related to project performance and thatsome companies are not using optimal systems.The relationship between management control

systems and project performance will be positive ifprojects bene®t from more structured systems. Onthe other hand, if systems are too structured andsti¯e the ability of the development team torespond to demands particular to the project, thenthe relationship will be negative.Moreover, the relationship between manage-

ment control systems and project performancemay be contingent upon certain project character-istics.10 In particular, strategy has been frequentlyidenti®ed as a�ecting the design of managementcontrol systems (Govindarajan & Gupta, 1985;Lang®eld-Smith, 1997). The following hypothesescapture the main e�ect (H3a) as well as contingentrelationships (H3b, H3c, H3d).

H3a: More intense use of management controlsystems 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 productsfollowing a low cost strategy.H3d: More intense use of time information has

a positive e�ect upon performance for productsfollowing a time strategy.Finally, the detail reported at the beginning of

the product design phase may also a�ect projectperformance. However, existing evidence is con-tradictory. Eisenhardt and Tabrizi (1995) ®nd thatthe amount of planning has no e�ect upon devel-opment time. In contrast, Gupta and Wilemon(1990) report that the ®rst reason for productdelays is a poor de®nition of product requirements(71% of the respondents). A more general argu-ment supporting the importance of planning isprovided by Bruns and McKinnon (1992) whofound a positive association between clear goalsand improved performance. The last hypothesiscaptures these arguments and relates them to pro-duct development.11

H3e: Detailed project objectives are associatedwith 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 becauseinformation needs are di�erent for the planning,concept design, product design, and testing andstart up phases. Similarly, management controlsystems span the whole organization, from theformal systems used by top management, to theroutines that shape the work of a recently hiredengineer. 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.


decrease signi®cantly the power of the researchdesign. To increase as much as possible thispower, the research design includes three speci®cdecisions. First, the study focuses on the productdesign phase only. Because this phase requiresmore structured information to evaluate trade-o�s, the relationships predicted by the theory willbe especially strong in this phase. Also, the startand the end of this phase are clearly de®ned,thereby eliminating ambiguity about which dataare required from project managers. Moreover,focusing on one phase reduces the noise thatwould result from asking for and interpreting datarelated to multiple phases.The second research design decision is to specify

the hierarchical level inside the organization. Theproject manager is the person in charge of movinga product development project from an idea to aphysical object and thus, (s)he is the key personfor 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 tothe medical devices industry. Eleven companiesparticipated in the second part of the study. Acontact person in each company selected a groupof projects as heterogeneous as possible in termsof size and product strategies. The data were col-lected using a questionnaire mailed to projectmanagers 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 possiblydriven by ex-post rationalization Ð could be athreat to the integrity of the data.The response rate was 77% (56 out of 73 mailed

questionnaires). This high response rate wasaccomplished by following several procedures(Dillman, 1983). The questionnaire was initiallypre-tested among academics with previous experi-ence in questionnaire design. Some of the itemswere shifted to facilitate answering the questions,and to avoid, as much as possible, respondents

rationalizing their behavior. Then, a group often project managers tested the questionnaire.Two of these managers had the questionnaireadministered in person. In the other cases, man-agers completed the questionnaire and commentedon it in a telephone conversation. Only minorwording changes were necessary after the secondpre-test.Each questionnaire was personally addressed to

the project manager. The package included acover letter, the questionnaire, a pre-paid envel-ope, and a copy of an article for practitioners thatcould be of interest to the respondents as a tokenof appreciation for their e�ort Ð completing thequestionnaire took 35 to 45 min. The letter o�ereda copy of the aggregate results of the study to thecompanies as well as to each respondent. Thesupport of the contact people in the companieswas also a very important element in achieving thehigh response rate.

5.1. Dependent variables

Preliminary interviews with product developmentmanagers identi®ed the six types of informationmost frequently reported through the organiza-tions' formal systems: product cost, productdesign, time-related, customer-related, resourceinput (budgets), and pro®tability. The design ofmanagement control systems for each of the sixtypes of information is measured through threecharacteristics (Merchant, 1981; Simons, 1995):

1. Level of detail in the information reported ismeasured on a ®ve-point scale with threeanchor points exemplifying measures rangingfrom low to medium and high detail. Forexample, cost information has low detail ifthe system only reports material and laborcosts, and it has high detail when, in addition,the systems include related manufacturing,marketing, and administrative costs. Simi-larly, customer information has low detail ifit comes only from an initial assessment ofthe marketing department, and it has highdetail when, in addition, the project teaminteracts 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).


2. Frequency of information updating is mea-sured, for each type of information, on a®ve-point scale ranging from (1) weekly orless, (2) twice a month, (3) monthly, (4)quarterly, and (5) longer than quarterly.

3. Usage pattern of information is measured ona ®ve-point scale anchored with two sentences:``the information was used to monitor theproject, but it was not discussed with myteam except when it reported events that fellbelow plans or expectations'' (diagnosticsystem) 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 reduceuncertainty. Therefore, they represent the sameunderlying latent variable. This variable is identi-®ed using principal component factor analysis asdescribed in Table 2.13 Table 2 also describes thevariance explained, the ®rst eigenvalue, as well asthe Cronbach alpha measure of reliability (Cron-bach, 1951). The inter-item reliability estimatesmeet Nunnally's (1967) standards for exploratoryresearch.Project performance (Perf ) is a multidimensional

variable (Shenhar, Dvir & Levy, 1997) and theimportance of each dimension changes acrossprojects. Meeting cost objectives can be critical forcertain products while secondary for others.Moreover, the success of a product may not becorrectly assessed for a long time after its marketintroduction. Financial success is not a goodmeasure of performance (Cooper & Kleinschmidt,1987): consider companies entering new marketsÐ their early products are intended to facilitatelearning 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 theimportance of each item on a seven-point scalefrom ``not important'' to `èxtremely important''.(S)he also rates performance for each item on aseven-point scale from `èxtremely poor'' to `èxtre-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. Onthe other hand, it has the advantage that it cap-tures the dimensions most relevant to the projectand 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-sensitiveprojects, but it is not important for projectsfocused on other dimensions.

5.2. Independent variables

To measure product strategy, principal compo-nent factor analysis with varimax rotation is usedon nine questionnaire items intended to measurethese variables. One set of items asks the respondentto allocate 100 points among di�erent possiblestrategies. The other six items require respondentsto rate the importance for the company and forthe customer of each strategy in a seven-pointscale ranging from ``not important'' to `èxtremelyimportant''. Three factors are identi®ed re¯ectingthree possible strategies: cost-related questionsload onto the ®rst factor, this factor identi®es theimportance of cost strategy; the second factorre¯ects time strategy; and the third factor representsthe importance of customer strategy (see Table 3).Project uncertainty includes three variables: market

uncertainty, technological uncertainty and projectscope. Market uncertainty (Mkt-X) and technologi-cal uncertainty (Tech-X) are multidimensional con-cepts, constructed both as dummy variables. Whenthe project is below the median in each of thequestions that de®ne Mkt-X (Tech-X), the variabletakes a value of zero, it takes a value of one if oneof the questions is above the median, and so on (seeAppendix D for a description of the questionnaireitems) (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.


product (New-Parts) represent project scope.However, New-Parts may also re¯ect technologi-cal uncertainty if it is argued that products withmore 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 theproject team. This variable is measured by thenumber of functions reporting to the projectmanager. 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' variablesa

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 ÿ0.16 ÿ0.28 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 ÿ0.11 0.74 ÿ0.27 0.36

Meet timing goals ÿ0.03 0.81 ÿ0.05 0.35

Target customers value time 0.06 0.74 0.20 0.40

Design a customer friendly product ÿ0.17 ÿ0.25 0.64 0.50

Ful®ll customer needs ÿ0.10 0.22 0.77 0.34

Target customers value ease of use 0.17 ÿ0.16 0.81 0.29

Eigenvalue 2.11 1.97 1.65

Variance explained 23% 22% 19%


Maidique, 1990). The second variable representsthe hierarchical level of the project manager'ssuperior (Hierarchy). This variable takes valuesfrom one to four depending on the position of thesuperior (see Appendix E). This variable is rele-vant because arguably managers with a higherhierarchical position may be busier and thus dele-gate more decision making to the project manager.The last variable is the authority of the projectmanager 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 authorityover a set of decisions on a seven-point scale ran-ging from ``I (or my team) took action withoutconsulting other people in the company'' to ``otherpeople in the company decided what action totake, 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 respondentis asked to evaluate the ``level of detail in the pro-ject plan prepared before the start of the designphase'' for each of the six types of information on

Table 4

Descriptive statistics on variables and related questionnaire itemsa

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.


the same ®ve point-scale used for the level ofinformation reported. Then, the respondent ratesthe importance of each type of information. Planis the weighted average of the detail for each typeof information.

6. Results

6.1. Descriptive statistics

Table 4 gives descriptive statistics on variablesand 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 amonth). This result, corroborated during companyvisits, indicates that management control systemsin product development, following project man-agement techniques, are focused around time. Itis also important to observe that traditionalaccounting measures Ð cost and pro®tabilityinformation Ð are the ones used less frequently(with means of 3.73 and 4.17 respectively). Inparticular, pro®tability information Ð even if it isthe ultimate goal of a product development e�ortÐ is on average updated quarterly or even longerand it is the least discussed measure in meetings.Project managers explained this apparent paradoxarguing that the ®nancial attractiveness of a pro-ject is studied before the actual developmentstarts; once the development e�ort is under way,®nancial performance is expected to follow fromsound non-®nancial performance. Project man-agers also mentioned that they do not explicitlyinclude pro®tability issues when evaluating designtrade-o�s.14 Also notice that this observationreinforces existing evidence (Abernethy & Brownell,1997; Brownell, 1985; Rockness & Shields, 1988)regarding the low importance of traditionalaccounting 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 importantas customer focus (32.7% for customer focus ver-sus 23.3% for time and 16.1% for cost). Achievinglow cost has little importance for the samplestudied, which suggests that price pressures in thehealth industry have not yet a�ected productdevelopment in the medical devices companies.15

On average, more than 50% of the partsdesigned are new and the number of peopleinvolved ranges from 0 (nobody devoted full timeto the project) to 106, with an average of 17.Finally, the number of functions reporting to theproject manager is only 1.3 (median=1) indicatingthat companies still use a functional structure evenif current research advocates for cross-functionalteams. The lack of cross-functional integrationwas also con®rmed in ®eld visits. It is quite commonto have project managers supervising engineersonly and reporting to the R&D manager. How-ever, cross-functional teams did exist; for example,in one of the companies visited not only wereteams formed with people from di�erent func-tions, but also the project leader could come fromany function including human resources oraccounting.Table 5 presents the pairwise correlation matrix

among independent variables. Technologicaluncertainty (Tech-X) is correlated with customerstrategy (Cust-Str) (0.29) indicating that thisstrategy 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 theproject (People) (0.33). Companies in the samplehave strong technological capabilities with sizableR&D departments. These capabilities allow themto tackle technologically complex projects byassigning 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) possiblybecause 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%).


signi®cant e�ort by the company, thus requiringtop management attention.Authority over marketing decisions is correlated

with market uncertainty (0.23) indicating thatproject managers perceiving a more complex mar-ket also have more authority to deal with this typeof uncertainty. Finally, authority over marketingdecisions is correlated with the hierarchical posi-tion of the superior (0.23). This correlation mayjust con®rm that as the project manager's span ofattention expands (in this case to include market-ing decisions), (s)he is supervised by a more seniorperson. Or, alternatively, projects with high mar-ket uncertainty are newer to the organization and,as such, they demand more attention from topmanagement.

6.2. The design of management control systems

To test the hypotheses relating managementcontrol systems to project uncertainty and productstrategy, I use the following regression model:Management Control Systems Characteristics

=f(Company Dummies, Product Strategy, ProductUncertainty, 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 theexpected ranges Ð thus, multicollinearity is not aproblem.Hypothesis H1a predicted a positive relation-

ship between more intense use of customer infor-mation and market uncertainty. The ®rst columnin Table 6 (CUSTI) supports this claim and thecoe�cient for market uncertainty (Mkt-X) ispositive and signi®cant (the coe�cient has a valueof 0.362 and is signi®cant at the 1% level). Inaddition, the coe�cient for authority over mar-keting decisions (Mkt-Dec) is also positive andsigni®cant (0.363). In other words, the projectmanager uses customer information more oftenwhen he is responsible for marketing decisionspossibly because he faces a higher degree of marketuncertainty.HypothesisH1b predicted a negative relationship

between technological uncertainty and manage-ment control systems. Supporting this relationship,I ®nd three regressions (DESI, TIMEI, andBUDI) where the coe�cient for technologicaluncertainty (Tech-X) is negative and signi®cant.This result is in line with management controlsystems being a poor vehicle to reduce technology-related uncertainty.Hypothesis H1c related project scope with

management control systems being more detailed

Table 5

Correlation matrixa,*

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 ÿ0.03 0.17 0.29**

Mkt-X 0.03 0.10 ÿ0.09 ÿ0.07New-Parts 0.01 0.12 0.09 0.35** 0.12

People ÿ0.05 ÿ0.05 0.11 0.33** ÿ0.08 0.00

X-Function ÿ0.06 ÿ0.03 0.02 ÿ0.15 ÿ0.02 0.00 ÿ0.21Hierarchy ÿ0.09 ÿ0.05 ÿ0.19 0.26* 0.21 0.25* ÿ0.07 0.04

Mkt-Dec 0.01 0.08 ÿ0.01 ÿ0.20 0.23* 0.15 0.03 0.02 0.23*

Plan 0.25* ÿ0.15 ÿ0.06 ÿ0.04 0.01 ÿ0.17 0.03 ÿ0.09 0.06 ÿ0.14

*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.


and more intensively used. The evidence in Table 6is 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 atthe 5% level). The signi®cance of the number ofnew parts may just re¯ect the fact that more

Table 6

Results on the design of management control systems in product developmentd

Dependent variableb CUSTIa DESIa TIMEIa COSTIa BUDIa PROFIa Variancec in¯ation

factors

Intercept 0.147 0.349 0.369 ÿ0.04 0.423 0.101

Prob(T) 0.74 0.51 0.47 0.94 0.43 0.85

Product uncertainty

Tech-X 0.145 ÿ0.578*** ÿ0.378* 0.018 ÿ0.400* 0.194 1.90

prob(t) 0.44 0.01 0.09 0.93 0.07 0.39

Mkt-X 0.362*** ÿ0.020 ÿ0.288* 0.034 ÿ0.129 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** ÿ0.003 1.52

prob(t) 0.51 0.43 0.99 0.41 0.05 0.67

Product strategy

Cost-Str ÿ0.160 ÿ0.152 ÿ0.031 0.331** ÿ0.031 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 ÿ0.027 1.41

prob(t) 0.14 0.20 0.24 0.23 0.24 0.87

Organizational structure

Function ÿ0.077 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 ÿ0.160 ÿ0.128 0.034 ÿ0.174 ÿ0.097 ÿ0.213 1.44

prob(t) 0.24 0.44 0.82 0.32 0.56 0.21

Mkt-Dec 0.363*** ÿ0.191 ÿ0.040 0.119 0.175 0.349** 1.20

prob(t) 0.01 0.18 0.77 0.411 0.22 0.02

R2 54.5% 43.3% 39.5% 30.6% 37.1% 28.1%

Adjusted R2 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 (XX0). Condition numbers in excess of 20 are a signal of potential multicollinearity problems (Greene,

1993, p. 269).


information on design is required as the number ofparts increases. Therefore, this ®nding should beinterpreted with care. The signi®cance of numberof people in explaining the use of budget infor-mation (BUDI) suggests that project managers usebudget information to coordinate and control asthe 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 productstrategy. In support of such a relationship, thecoe�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�cientfor time-to-market product strategy (Time-Str) ispositive (0.361) and signi®cant (at the 1% level)for time-related information (TIMEI). In contrast,Table 6 shows no support for hypothesis H2c. Ifcustomer information is related to customer strat-egy, then the coe�cient for Cust-Str would bepositive 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 onproject performance

To test for the relationship between manage-ment control systems and project performance, Iuse the following regression model:16

Project Performance=f(Company Dummies,Plan, Product Strategy, Product Uncertainty,Organizational Structure, Management ControlSystems Characteristics, Interaction Terms)Table 7 presents the results relating manage-

ment control systems and project performance.17

The table includes the variance in¯ation factorsand the condition indexes to test for multi-collinearity. Because they are within the expected

ranges, multicollinearity can be dismissed as athreat to the results.The ®rst regression presents the main e�ect.

Supporting the main e�ect hypothesis (H3a)between management control systems andimproved product development performance, thecoe�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 ÿ0.225 signi®cant at the10%).18 This last ®nding is against hypothesisH3a and agrees with the argument that manage-ment control systems can be detrimental to projectperformance. The widespread recommendationthat decreasing development time is ``alwaysgood'' to gain competitive advantage (Patterson,1993) may not always hold.The second regression includes interaction terms

to test for contingencies. Two interaction termsare signi®cant. More intense use of customerinformation for products following a customer-focused strategy has a positive impact on perfor-mance (as hypothesis H3b predicted). In a similarway, more intense use of cost information is asso-ciated with better performance as the importanceof a low cost strategy increases (hypothesis H3c).In contrast, I ®nd no support for hypothesis H3drelating time information and performance as theimportance 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 isrelevant to point out that the coe�cient for Func-tion is positive and signi®cant (0.300 and 0.290both signi®cant at the 1% level), in line with pre-vious research that found that cross-functionalintegration bene®ts product development (Clark &Fujimoto, 1991).

7. Discussion

This study sought to explore the drivers ofmanagement 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.


Table 7

Management control systems and project performance

Perf a Variance in¯ation factors Perf a Variance in¯ation factorsc

Intercept 3.896 3.876

0.00 0.00

Management control systemsb

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 ÿ0.225* 1.90 ÿ0.160 2.03

prob(t) 0.06 0.16

BUDI ÿ0.160 2.02 ÿ0.085 2.14

prob(t) 0.19 0.46

PROFI ÿ0.165 1.95 ÿ0.206* 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 ÿ0.068 1.54 0.016 1.90

prob(t) 0.52 0.88

Time-Str ÿ0.064 1.33 ÿ0.002 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 ÿ0.091 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

R2 52.6% 61.9%

Adjusted R2 34.1% 42.3%

N 51 51

Condition indexd 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 X0). Condition numbers in excess of 20 are a signal of potential multicollinearity problems

(Greene, 1993, p. 269).


grounded in Galbraith's de®nition of uncertaintyas ``the information required to perform a taskand the amount of information already possessed'',and Tushman and Nadler's interpretation ofmanagement control systems. The results indicatethat these systems are relevant and, moreover,managers in product development use them toobtain information needed to reduce uncertainty.This ®nding contrasts with previous studies whichassumed that control systems are tools to reducegoal divergence and found that control systemsare only marginally relevant to product develop-ment. Further research may fruitfully adopt thisalternative interpretation of management controlsystems.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 projectmanagers rely on non-®nancial measures muchmore than they do on ®nancial ones. This ®ndingsuggests that researching management controlsystems in new product development cannot berestricted to traditional accounting measures, butneeds to encompass a broader set of measures.This is so because managers work with the implicitassumption that good performance in non-®nan-cials will drive good ®nancial performance. As thetheory predicted, uncertainty and product strategyare related to the design and use of managementcontrol systems. The fact that the results areanchored in existing theory reinforces the ade-quacy of this theory to understand reality. How-ever, a word of caution related to the limitationsof the research design is needed. To increase thepower of the design, the study focuses on themedical devices industry. This research choice hasthe drawback that the conclusions cannot be gen-eralized beyond this industry. Moreover, the unitof analysis is the project manager and conclusionsmay not be valid for other hierarchical levels suchas, 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 designinformation 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 strategythat have no such relationship. Even if it has beenargued that formal systems may be detrimental,the results in the study suggest a more complexpicture. Cost and design information has a posi-tive e�ect upon performance. In contrast, timeinformation hinders performance supporting theargument that too much emphasis on formal sys-tems limits innovation. A possible explanation isthat current emphasis on reducing time-to-marketmay 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 thisvariable is multidimensional. But this design issubject to potential biases related to self-reportedmeasures.The study provides evidence supporting a con-

tingency theory of management control systemsin product development. In particular, the align-ment between the design and use of these systemsand product strategy is signi®cantly related toperformance.The signi®cant relationship between detailed

project objectives and performance is consistentwith previous research (Bruns & McKinnon,1992). But the actual planning of a product devel-opment process is not well understood. Projectmanager A planned uncertainty. He tried toremove it as soon as possible except in the case ofcustomer-related uncertainty. He kept this uncer-tainty intentionally unresolved until the appro-priate time in the development e�ort: ambiguitywas planned. Further research is needed to under-stand when planning is appropriate and when toomuch detail can hinder performance.Case studies illustrate the diversity that exists

among the design and use of management controlsystems in new product development. The fourcase studies describe how di�erent project man-agers use these systems (or fail to use thesesystems) depending on project characteristics.Supporting the literature in new product develop-ment, prototyping seems to replace managementcontrol systems when technology is the main sourceof uncertainty. When uncertainty comes from themarket or from project scope, management control


systems are a good vehicle to reduce uncertaintyrather than to monitor and control. This observa-tion provides further evidence to support aninformation perspective rather than a goal diver-gence approach to this ®eld.The study also makes a contribution to research

methodology. It develops three measures fordetermining the information content of manage-ment control systems: the detail of the informationreported, the frequency with which it is updated,and the use managers make of it. Nonetheless, theinstrument can be improved to increase its relia-bility. A possible way would be to develop addi-tional questions keeping in mind that in productdevelopment, the design and use of managementcontrol systems re¯ect a single variable.This research can be extended in several direc-

tions. Management control systems are importantfor the performance of the project, but theresearch does not reveal why, nor provides thedetail on how these systems are designed. Con-tingency relationships for project performance andproject planning have been partially studied andrepresent a promising area for future research.This paper is just the ®rst step in the advancementof knowledge about management control systemsin new product development. Additional emp-irical evidence and theoretical concepts arerequired to fully understand the implications ofthis research.

Acknowledgements

I appreciate the comments from my thesis advisorsRobert S. Kaplan, Robert Simons, ClaytonChristensen, and V. G. Narayanan. I am alsograteful for the comments of Kentaro Koga, Wil-liam Bruns, Srikant Datar, Amy Hutton, partici-pants in the Harvard Accounting Seminar, andparticipants in the European Accounting Asso-ciation Annual Meeting in Antwerp, 1998. Alsomy sincere appreciation to two anonymousAOS referees that provided excellent comments.This research received ®nancial support from theDivision of Research at the Harvard BusinessSchool and from IESE, University of Navarra.

Appendix A

Interview protocol for project managers

1. Product strategy and product characteristics1.1. How would you relate the product

developed to previous products in thecompany?

1.2. Why did the company decided to investinto this project?

1.3. What was the target market for theproduct and the key success factors?

1.4. What technology did you use. Howfamiliar was the organization with it?

1.5. How familiar was the organizationwith the market being targeted?

1.6. What were the most challenging issuesin the development process?

2. Organizational structure2.1. How was the project organized? Who

did you report to? Who did the peopleinvolved in the project reported to?What was their time commitment?

2.2. How many functions were included inyour team?

2.3. How much autonomy did you have indecisions related to the project? Howfrequently did you got guidance fromyour supervisor?

2.4. What information did you report toyour supervisor?

3. Management control systems3.1. How detailed were project objectives?3.2. What reports did you use over the

course of the project? (Examples)3.3. How detailed 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 wasin charge of managing resource demandsacross projects?

3.5. What type of product costs did youconsider? How did you get this infor-mation? How did you use it?


3.6. Did you use any kind of ®nancialmeasures? When did you use them?What decisions involved this type ofinformation?

3.7. What information was used for pro-duct performance?

3.8. How did you make sure that customerneeds 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 ofinformation did you use that you con-sider important and it is not re¯ected inthe questionnaire.

3.11. How would you change the informa-tion environment to improve theproject?

4. Performance evaluation4.1. 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 performance5.1. 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 toproject 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 tocustomer needs, preferences and acceptance.Very low detail (level 1): General information

about the product frommarketing people.

Medium detail (level 3): Salesforce assessmentof customer reactionsto product concept/design.

Very high detail (level 5): Detailed customerevaluation of productfeatures, prototypes,and marketing plansthrough interactionwith the developmentteam.

Project Resources Information Ð informationrelated to project resources such as budgets, man-power,. . .Very low detail (level 1): Total budget for the

product developmentproject.

Medium detail (level 3): Budget speci®ed perdepartment(engineering,marketing,. . .).

Very high detail (level 5): Detailed description ofresources from all partsof the organization andsuppliers.

Product Performance Information Ð informationrelated to the technical and technological aspectsof 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 theproduct's parts.

Product Cost Estimates Ð information related tounit 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 productincluding overheadcosts.


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 andtechnology uncertainty

Market uncertainty

How would you describe the experience of theorganization with the target market.

How would you describe the experience of theorganization with competitors.

What was the importance of creating a newproduct line.

What was the importance of creating a newmarket.

Technological uncertainty

Which of the following sentences best describesthe product developed:

The product was a simple redesign of an exist-ing derivative.

The product was a new derivative based on anexisting platform.

The product was a major redesign of an existingplatform.

The product was a new platform replacing anexisting platform.

The product was a completely new platform.What was the importance of developing a newtechnology.

What importance did target customers give totechnological performance.

Appendix E

Questionnaire item used to measure thehierarchical level

Which of the following best describes whom youreported 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 departmenthead level.

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an empirical study on the drivers of management control systems

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