ORGANIZING FOR NEW PRODUCT DEVELOPMENT : ALIGNING PROJECT ...

ORGANIZING FOR NEW PRODUCT DEVELOPMENT: ALIGNING PROJECT ENVIRONMENT AND DESIGN




Michael J. Fern
Faculty of Business
University of Victoria
Victoria, British Columbia, V8W2Y2 Canada
Phone: 250-721-6403
Fax: 250-721-6067
mfern@uvic.ca

Laura B. Cardinal
A. B. Freeman School of Business
Tulane University
7 McAlister Drive
New Orleans, LA 70118
Phone: 504-865-5667
Fax: 504-862-8716
cardinal@tulane.edu

Scott F. Turner
R. H. Smith School of Business
University of Maryland
Van Munching Hall
College Park, MD 20742
Phone: 301-405-7186
Fax: 301-314-8787
sturner@rhsmith.umd.edu

Richard M. Burton
Fuqua School of Business
Duke University
Box 90120
Durham, NC 27708
Phone: 919-660-7847
Fax: 919-681-6245
rmb2@mail.duke.edu







This research was funded in part by the Center for Innovation Management Studies (CIMS). Their
support is greatly acknowledged. Burton’s efforts were supported, in part, by the Hartman Fund.

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ORGANIZING FOR NEW PRODUCT DEVELOPMENT: ALIGNING PROJECT ENVIRONMENT AND DESIGN



Abstract

In this research we adopt a multi-method approach to better understand new product development
processes in different environments. Drawing on research that distinguishes among three industrial
contexts for product development, we develop three congruent project designs using information
processing theory. Using our theoretical arguments as a foundation, we develop a simulation to both
formalize and extend our initial predictions. Concurrently, we carried out a qualitative field study to add
additional texture to the simple theoretical arguments and the sanitized simulation. Drawing together
findings from these three approaches yields substantial new insight. First, our research suggests that
different project environments involve different performance priorities, different project designs yield
different performance outcomes, and there is a fit/misfit relationship between project environments and
designs. Second, we observe the benefits of midrange product development processes, which incorporate
moderate communication richness and moderate concurrency, in complex and uncertain environments. As
part of our contribution, we also identify the mechanics that underlie both the fit/misfit relationships and
the advantages of the midrange product development processes. Our research has significant implications
for theory development in the area of new product development as well as for practitioners involved with
project design choice for new product development efforts.




Keywords: new product development, organizational design, contingency theory, simulation, qualitative
research

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Product development entails the processes through which an initial idea is transformed into a
commercialized product. The development process can generate competitive advantage by enabling
organizations to condense time to market, increase product quality, and reduce the overall costs of
development. Failure to employ requisite processes, however, can have severe consequences -- most
notably rendering an inventive capability irrelevant. Although new product development serves as a
fundamental source for organizational change, growth, and capability development (Brown and
Eisenhardt 1995, Danneels 2002), these efforts are characterized by substantial failure rates (Berggren
and Nacher 2001, Schilling 2005, Stevens and Burley 1997). The objective of this research is to gain
further insight into the relationship between new product development (NPD) processes, the environment
for new product development, and the related performance outcomes.
We begin by developing a model of new product development that aligns project design with project
environment. Project design refers to the structure and process for new product development, while
project environment refers to the industrial context for product development. The project design
dimension of our model builds on classic organizational design and information processing theory (e.g.,
Burton and Obel 2004, Galbraith 1977, Thompson 1967, Van de Ven et al. 1976, Victor and Blackburn
1987). The project environment dimension stems from a specialized stream of economics-based
innovation research, led by scholars at the Science Policy Research Unit at the University of Sussex (Dosi
1988, Pavitt 1984, Pavitt et al. 1989). This research identifies three dominant industrial environments that
are distinguished by the nature of the technology: science-based, scale-based, and specialized. While we
provide greater depth in the next section, the following technological characteristics and industries are
representative of the archetype environments: science-based (dominant role of scientific knowledge in the
public domain, specialty chemicals), scale-based (presence of dominant product and process design
shared among competing firms, consumer foods), and specialized (absence of a stable technological base,
industrial controls).
Each environment generates a unique set of challenges for an innovating organization, and we argue
that these challenges require distinct design characteristics to achieve successful new product
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development. This emphasis on linking project design and industrial context is consistent with a recent
call from Krishnan and Ulrich (2001: 15): “Insights on customizing product development practices to
diverse [industrial] environments should also help increase the relevance and applicability of the
[product] development literature.”
In the spirit of McGrath’s (1982) notion of triangulation, we employ a multi-method approach to
validate and extend our initial theoretical arguments. Using the arguments as our initial base, we turn to a
simulation experiment. The simulation experiment crystallized the theoretical arguments, provided a
preliminary set of findings, and offered insight into underlying mechanics driving the performance
outcomes of our contingency model. Concurrently with the simulation experiment, we carried out a
qualitative study. Drawing on extensive field observations and archival data, we were able to add
considerable texture to the simple theory and sanitized simulation. The qualitative study provided insight
into critical mechanisms underlying performance that were not evident based on the theoretical arguments
or the simulation. Collectively, this process of theory building through triangulation enabled us to develop
a rich understanding of new product development processes, interrelationships with project environment,
and the associated performance outcomes.1
Fit and Misfit in New Product Development
Contingency Theory
Contingency theory is founded on the notion that congruence or fit among a set of organizational and
environmental elements will yield superior performance (Burton et al. 2002, Donaldson 2001, Shenhar
2001). Fit among activities or elements within an organization represents an internal dimension of fit,
while congruence between an organization and its environment refers to external fit (Siggelkow 2001).
While contingency models traditionally have been applied at the organization level (Lawrence and Lorsch
1967, Thompson 1967, Woodward 1965), more recent research has expanded into the context of new
product development projects (Bhuiyan et al. 2004, Eisenhardt and Tabrizi 1995, Griffin 1997, Liker et al.

1 We highlight that the scope of our examination includes the performance of the product development process for an
environment at a specific point in time. We stress that this does not include organizational decisions in terms of market selection
(i.e., preceding the product development process), nor does it include the organization’s ability to appropriate returns from the
developed product (i.e., following the product development process).
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1999, Song and Montoya-Weiss 2001, Tatikonda and Montoya-Weiss 2001).
This emerging area of contingency-based research suggests that uncertainty plays an important role in
new product development. Eisenhardt and Tabrizi (1995) tested competing models of fast product
development. They found that uncertainty moderated acceleration time when an experiential strategy was
used for uncertain projects, but that the results for compression strategies were less clear. In a study that
examined the effects of operational outcomes on new product market success, Tatikonda and Montoya-
Weiss (2001) predicted that external uncertainty would moderate the relationship, but found only main
effects. While supportive of a contingency approach, these studies also highlight that our understanding of
the role of project environment and contingency relationships for new product development is incomplete.
To further develop the concept of contingency in new product development, we turn to the literature on
industrial context and information processing.
Project Environment
In industrial economics, researchers have identified three distinct environmental contexts based on the
nature of the underlying technology: science-based, scale-based, and specialized (Dosi 1988, Pavitt 1984,
Pavitt et al. 1989). This perspective is consistent with recent treatments in the NPD literature that
emphasize the importance of differentiating the nature of technology in understanding innovation
processes (Argyres 1999, Cardinal and Lei 2000, Eisenhardt and Tabrizi 1995, Ulrich and Eppinger
2004). Technology is defined as (a) the knowledge underlying the design and evolution of products and
processes and (b) the technical artifacts themselves (Dosi 1982, Pavitt 1984). The source of technology
identifies the origin of knowledge and technical artifacts that form the basis for the innovation activity.
The nature of technology in the three environments affects the degree and locus of complexity and
uncertainty for product development. Complexity refers to the number of factors from the environment,
and the interdependencies among them, that must be addressed during product development (Duncan
1972, Tung 1979). Uncertainty refers to the availability of information in the environment required for
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project execution (Duncan 1972, Galbraith 1977)2. Locus refers to the distinct areas of product
development. In this paper we distinguish between upstream (R&D) activities that concentrate on factors
relating to the development of the product, and downstream (manufacturing) activities that focus
primarily on the development of the manufacturing process.
Project Design
According to the information processing view of organizations, an increase in uncertainty and
complexity requires an increase in information processing among project participants to achieve a given
level of performance (Galbraith 1977). Distinct levels of information processing are achieved by adopting
certain project design mechanisms (Galbraith 1977, Tushman and Nadler 1978). Specifically, the
following design dimensions affect information processing in organizations. First, specialization is the
partitioning of the overall product development task into subtasks performed by units within the
organization. Second, subtask integration is the means of reintegrating the subtasks into the completion of
the overall project development task (Galbraith 1977). Within subtask integration, task sequencing
encompasses the order in which tasks are executed, and communication refers to the mechanisms for
transferring information to facilitate integration. Collectively, these design dimensions define the
structural mechanisms through which knowledge is generated, subsequently integrated and coordinated,
and then applied to form a physical artifact, thus completing the cycle of product development.
In the subsequent sections, we develop our contingency-based model around the three environments
for new product development: science-based, scale-based, and specialized (Pavitt 1984). For each section,
we provide an overview of the project environment, followed by a discussion of the extent and locus of
complexity and uncertainty. We then discuss the corresponding design characteristics that align with the
project environment, focusing initially on specialization and sequencing, followed by richness of
communication mechanisms.

2 Our project environment dimension, while derived from an industrial environment perspective (Pavitt and colleagues), becomes
an exogenous variable when imported to the development project unit of analysis. Our approach is similar to that of the use of
uncertainty in organization theory where organization theorists examine technology uncertainty and structure relationships
(Miller et al. 1991) with technology at the organization unit level and environment as beyond the organization level.
Organizational theories also examine environmental uncertainty as an exogenous variable to organizations. As summarized by
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Science-Based New Product Development
Project environment. The direct relevance of scientific knowledge in the public domain is a
discriminating feature of science-based contexts (Dosi 1988, Pavitt 1984). Scientific knowledge, once
codified and disseminated via journals and conferences, becomes a public good in which others cannot
easily be excluded from its use (Dasgupta and David 1994, Nelson 1959). In these environments,
organizations combine public domain knowledge with internally-developed knowledge.
With science-based product development, the primary source of complexity and uncertainty is located
in upstream activities. Environmental complexity is high in the R&D function, as this function must
consider a large number of interacting external and internal factors when designing a new product
(Nightingale 2000, Spilker 1989). After internal and external knowledge have been combined, a codified
knowledge output (e.g., chemical formula) serves as the input for downstream activities (Spilker 1989,
Winter 1987). Given that product and process specifications are set in the R&D stage, there are few
remaining environmental factors that need attention in downstream activities. As such, environmental
complexity is low in downstream activities and between upstream and downstream activities. Similar to
complexity, task uncertainty is largely isolated within the R&D function. This function performs the trial
and error experimentation and learning before codifying the product and transferring the technical
specifications to downstream activities.
Project design. With few interdependencies among upstream and downstream activities, there are low
information processing requirements across areas (Galbraith 1977, Thompson 1967). This enables
organizations to take advantage of specialization through high division of labor across functional areas
(Grant 1996). With high specialization, subtasks are integrated in sequential steps (Thompson 1967). The
downstream tasks are dependent upon upstream output, suggesting a serial product development process
(i.e., R&D followed by manufacturing across time).
Within the R&D function, organizations require rich modes of communication, given the high level
of complexity and uncertainty at this stage. Richness can be viewed as the learning capacity of a means of

Burton and Obel (2004: 246), “at a more abstract level, environmental and technological uncertainty poses the same
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communication (Daft and Lengel 1986). After product specifications are set in upstream activities, there
is limited uncertainty and complexity in downstream activities. Therefore, organizations can attain
efficiencies by employing communication mechanisms that are low in richness within manufacturing
activities and between upstream and downstream activities (Daft and Lengel 1986, Keller 1994). In
summary, the dominant characteristics of this project design are lean coordination across functional areas,
rich communication modes within R&D and lean modes within manufacturing, high functional
specialization, and sequential development (i.e., a lean-sequential (LS) project design). From an
information processing perspective, we suggest that the LS project design provides an appropriate fit for
attaining high project performance in science-based environments.
Scale-Based New Product Development
Project environment. Dominant designs are the central feature that characterize technology within
scale-based environments (Abernathy 1978, Pavitt 1984). A dominant design represents a specific design
path that attains predominance, relative to alternatives, within the market (Suarez and Utterback 1995).
The dominant design generally represents a synthesis of technological innovations introduced in previous
product variations (Abernathy and Utterback 1978, Utterback and Suarez 1993), and it imposes product
and process standardization that permeates the offerings of a majority of competing firms (Abernathy
1978).
In the scale-based product development environment, complexity and uncertainty are present at
moderate levels throughout the system. The establishment of a dominant design indicates a stable
technological foundation for product development (Abernathy 1978, Abernathy and Utterback 1978). As
such, project members contend with fewer factors from the external environment. Interdependencies
between upstream and downstream activities are moderate. This reflects increasing interdependence
between product and process design with the emergence of the dominant design (Abernathy, 1978), but
also acknowledges that product and process innovations do not move in lock step (Utterback, 1994). Task
uncertainty is also moderate within functional areas. By exploiting a dominant product and process design

organizational challenge.”
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over time, organizations within the industry have accumulated knowledge concerning important cause
and effect relationships.
Project design. When the interdependencies among upstream and downstream activities are moderate,
there are moderate information processing requirements between functional areas (Galbraith 1977,
Thompson 1967). Under these conditions, organizations do not strictly allocate tasks to specialists in
distinct functional areas. Rather the process involves overlapping functional areas, given the moderate
level of interdependence among units. Here, reciprocal interdependence facilitates sub-task integration
among organizational units (Thompson 1967). This represents frequent interaction between functional
areas for significant amounts of time throughout product development.
Given moderate uncertainty and complexity within functional areas, moderately rich communication
mechanisms are appropriate. Further, the structural mechanisms that bridge functional areas are moderate
in richness (Daft and Lengel 1986, Keller 1994). Under these conditions, to efficiently achieve the
requisite level of coordination between functional areas, organizations integrate by mutual adjustment
(Grant 1996, Thompson 1967). In sum, this project design is characterized by moderately rich modes of
communication within and between functional areas, overlapping functional specialization, and reciprocal
development (i.e., a meso-overlapping (MO) project design). Our information processing argument
suggests that in scale-based environments, the MO project design will achieve high project performance.
Specialized New Product Development
Project environment. The source of technology in the specialized environment emerges from the
techniques employed by focal end users, as the catalyst for innovation is generally the existing production
system of established firms (Dosi 1988, Pavitt 1984). Distinct from science-based and scale-based
environments, which are characterized by stable technology (e.g., established body of scientific
knowledge and dominant design, respectively), the source of technology for the specialized environment
is idiosyncratic because it pivots on developments in alternative sectors (Pavitt 1984). Distinct from
science-based and scale-based contexts in which the core technology is largely codified, technology
within specialized sectors remains largely tacit.
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With specialized new product development, complexity and uncertainty are high. In this type of
environment, product development resembles a complex technology-brokering system, with large
interdependencies between upstream and downstream activities (Hargadon and Sutton, 1997). Relevant
factors include aspects of the customer production system, awareness of the appropriateness and
availability of potential components, and capacity of component suppliers. In addition, task uncertainty is
high throughout the project. The innovating organization has neither an established body of public
scientific knowledge nor a dominant product or process design to guide its development efforts.
Project design. High levels of complexity and uncertainty result in high information processing
requirements within and across functional areas. These conditions are consistent with reciprocal
interdependence, where outputs for one functional unit serve as inputs for another unit and vice versa
(Thompson 1967). However, given the extent of required interaction, maintaining distinct functional units
may prove too costly. In fact, researchers suggest that when the product development task is complex,
involving high interdependence and frequent interaction between units, a low division of labor across
functional areas has a positive effect on performance (Nonaka and Takeuchi 1995, Van de Ven 1986). As
such, attaining the requisite level of integration in an efficient manner extends beyond reciprocal
interdependence to a concurrent development process, where all members are assigned to a dedicated
project team.
Rich coordination mechanisms support high information processing requirements (Daft and Lengel
1986, Keller 1994), and while the coordination costs are also high (Grant 1996), continuous interaction
among functional areas with rich mechanisms is required for product development. Therefore,
communication richness within and across functional areas is high. In summary, the aforementioned
project design is characterized by rich modes of communication within and across functional areas, low
functional specialization, and concurrent development (i.e., a rich-concurrent (RC) project design). Our
information processing argument suggests that in specialized environments, the RC project design will
achieve high project performance.
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