The impact of adding improvisation to sequential NPD processes on cost: the moderating effects of turbulence.
Nunez, Enrique ; Lynn, Gary S.
INTRODUCTION
Coping with a volatile environment is a job requirement for all
modern-day new product development managers as change and uncertainty
characterize today's unpredictable business world (Calatone,
Garcia, & Droge 2003; Lynn & Akgun 1998). A useful taxonomy of
new product development classifies approaches according to the degree to
which they emphasize control (e.g. Bower & Hout 1988; Cooper 1994b,
1998; Eisenhardt & Tabrizi 1995) and the degree to which they
emphasize adaptability (e.g. Iansiti 1995b; Kamoche & Cunha 2001;
Miner, Bassoff, & Moorman 2001). Such a classification is useful as
it highlights the tension in the literature between the amount of
structure necessary to control development, versus the need to adapt to
changes (e.g. Deuten & Rip 2000; Gwynne 1997; Kamoche & Cunha
2001).
However, the problem for business is that NPD approaches that
emphasize control may be too rigid for today's highly dynamic
markets (Chakravarthy 1997; Cooper 1994b; Deuten & Rip 2000; Hoopes
& Postrel 1999; Iansiti 1995a; Imai, Ikujiro, & Takeuchi 1985;
MacCormack & Verganti 2003; Moorman & Miner 1998b; Rosenthal
1992), while approaches that emphasize adaptability are inefficient
(Aram & Walochik 1996; Miner, et al. 2001; Sharkansky &
Zalmanovitch 2000). Therefore, in this study, we test an integrated
approach to new product development that combines control with
adaptability (Nunez & Lynn 2007). We theorize that coupling these
two seemingly divergent approaches offers a way to reinforce the
strengths of each approach, while overcoming their respective
shortcomings.
While examining an integrated approach to new product development
is constructive, this usefulness is diminished if the environment is not
considered as well. New product development is the principal means by
which firms adapt to changing market and technical conditions
(Schoonhoven, Eisenhardt & Lyman 1990), and innovative approaches
have emerged as an effective means by which to manage in unstable
environments (e.g. MacCormack, Verganti & Iansiti 2001). Scholars
have given the environmental context of a project insufficient
consideration in the literature (MacCormack & Verganti 2003). This
research attempts to remedy this deficiency by examining the impact of
novel NPD approaches on a critical development outcome in a turbulent
environment. The following research question drives this study:
How can we best maintain cost effectiveness of a new product
development project under various levels of environmental
turbulence?
To answer this question, we develop and test two hypotheses. We
begin by providing a brief overview of various product development
approaches, describing where each may be lacking under conditions of
rapid change. To address issues with existing NPD approaches, we propose
an integrated approach that builds on the existing literature, and
develop a theoretical framework focusing on a significant aspect of NPD
processes: cost reduction. We then provide our research methodology
which includes our questionnaire design, sampling procedure, process to
ensure measure reliability and validity, and the analytical techniques
performed. Next, we move into an analysis of our results and discuss the
implications of our research to academics and practitioners. Finally, we
discuss the limitations of our research and provide the broad
conclusions we have drawn from our study.
THEORETICAL FRAMEWORK
Sequential NPD Helps to Control Costs with Structure
The highly-structured, sequential NPD framework, characterized by
consecutive product development phases, with each separated by a
decision point (Cooper 1998), is the most pervasive model in the
literature (e.g. Cooper & Kleinschmidt 1991; Millson & Wilemon
2002; and Shepard & Pervaiz 2000). As such, scholars have been
enthusiastic in studying this model, including its impact on controlling
costs. This type of framework supports new product development decisions
and helps reduce the uncertainty inherent in the innovation process
(Dosi 1988) by suggesting a series of steps to be completed
sequentially. Ultimately, the objective of the model is to provide a
clear-cut plan for product development and execution (Cooper 1998).
Throughout much of this literature, researchers have consistently
found that the structure associated with sequential NPD processes help
to manage costs (e.g. Barrett 1998; Cooper & Kleinschmidt 1991;
Shepard & Pervaiz 2000). Indeed, the literature notes that the chief
contribution of sequential new product development processes is to
control costs (Cooper & Kleinschmidt 1991). As a rule, if an
objective of a new product development initiative is efficiency, then
the development process must avoid changes and uncertainty by using
well-structured mechanisms to manage development such as uniform
processes, firm objectives, and centralized control (Barrett 1998;
Bhattacharya, Krishnan & Mahajan 1998; Cooper & Kleinschmidt
1991). Subsequently, under conditions of relative stability, an
effective way to control new product development costs is through the
use of a sequential approach. But what are new product development
managers to do when change is unavoidable? As the literature notes,
innovation under low levels of environmental instability is well-suited
to development using a structured, cost reduction orientation (Lynn
1998)--in other words, sequential NPD. But is this same approach as
effective as environmental instability increases?
Navigating Environmental Turbulence
Tumultuous change and uncertainty typify virtually all industries
in today's volatile business environment (Lynn & Akgun 1998).
Environments that change rapidly can destroy the value of existing
competencies (Tushman & Anderson 1986). Moreover, the consequence of
fast changing environments is the generation of uncertainty--uncertainty
that makes it difficult to predict which knowledge will be important to
future success (Marsh & Stock 2003). Thus, under environmental
uncertainty, organizations must search for ways to adjust to a dearth of
information (Scott 1987). Consequently, approaches that facilitate rapid
product development, and alleviate uncertainty are of fundamental
interest to scholars and practitioners alike. Yet, the literature
indicates that fast changing environments pose challenges that
traditional sequential NPD approaches are not well-suited to address
(Chakravarthy 1997; Cooper 1994b; Deuten & Rip 2000; Hoopes &
Postrel 1999; Iansiti 1995a; Imai, et al. 1985; MacCormack &
Verganti 2003; Moorman & Miner 1998b; Rosenthal 1992; Trygg 1993).
While there is a growing realization that NPD approaches based on
traditional mechanisms are counterproductive in a turbulent environment
(Freedman 1992), firms that have embraced more flexible product
development approaches are becoming more competitive (Iansiti &
MacCormack 1997). Firms are beginning to realize that to be successful,
different types of projects carried out in different environments are
likely to require different development processes (MacCormack &
Verganti 2003).
Fortunately, improvisation has emerged as one technique to help
contend with the contemporary requirements of increased speed and
reduced uncertainty. Nevertheless, as the literature asserts,
improvisation alone is not enough. Scholars contend that improvisation
is costlier than traditional planning. Whereas planning is aimed at
solving problems in an optimal manner, improvisation endeavors to
adequately manage them (Sharkansky & Zalmanovitch 2000). Thus,
following a wholly improvisational approach is prone to missteps that
cost more than following an established plan. In a study of new product
development activities, Miner, Bassoff & Moorman (2001) found that
while team members viewed improvisation as a tool for flexibility and
adaptability, their use of improvisation was limited. In addition, other
areas within the firm shunned improvisation, viewing it as a source of
inefficiency and costly errors. This follows another study that
concluded that improvising sometimes brings about negative
organizational outcomes (Aram & Walochik 1996). Both studies support
Barrett's contention that "risky explorative attempts are
likely to produce errors" (1998), as well as Crossan that maintains
that "improvisation requires some tolerance for error" (1998).
So how can NPD practitioners utilize improvisation to help speed
product development and reduce uncertainty while still maintaining
costs? We believe that in order to realize cost-containment benefits
with improvisation under conditions of environmental instability,
improvisation must be coupled with a structured process, such as that
found in sequential NPD.
How Might the Addition of Improvisation to Sequential NPD Affect
Outcomes?
Researchers have noted that improvisation often occurs within a
larger structural context. Moorman and Miner contend that improvisation
occurs along a continuum upper-limited by spontaneous action and
lower-limited by entirely planned action (1998b). Cunha, Cunha, and
Kamoche also note that improvisation can happen in environments rich
with direct supervision and standardization (1999). Eisenhardt and
Tabrizi observe that by establishing basic aspects of process such as
milestones, firms can provide a sense of structure and routinization to
improvisational activities often perceived to result from chaos and
disorder (1995). Indeed, some believe that fostering improvisation
within a larger structural context should be the objective to encourage
certain types of innovation. In referring to breakthrough innovation,
Mascitelli states that the goal includes establishing an environment in
which improvisation merges with practical demands of the product
development process (2000).
As scholars have observed, managers of successful firms must
balance mechanistic and organic elements (Brown & Eisenhardt 1997).
Mechanistic organizations implement structures such as plans and
schedules that produce clear responsibilities and priorities, while
organic organizations foster creativity through extensive communication
and decentralized decision-making (Brown & Eisenhardt 1997; Burns
& Stalker 1961). Kamoche and Cunha express a similar sentiment in
stating that firms that succeed in realizing the synthesis of control
and adaptability seem to be effective in the management of NPD projects
in turbulent environments. As they have noted, the critical NPD
challenge is to achieve a balance between control and adaptability
(Kamoche & Cunha 2001). This is the issue we have sought to address
with our study.
Our approach to studying the new product development process
combines the structure inherent in sequential approaches, with
improvisation, thereby allowing for the robust control mechanisms
associated with sequential models, with the adaptability inherent in
improvisational methods. The use of the sequential model provides for a
unifying framework throughout the development process, while the
improvisational techniques offer a mechanism for product developers to
adapt to changes. Thus, this approach aims to maintain the strengths of
traditional sequential NPD, namely structure, while introducing
adaptability into the process, thereby preserving each model's
advantages while addressing drawbacks.
HYPOTHESIS DEVELOPMENT
As the rigidity of traditional sequential NPD is considered
inappropriate for fast changing environments (Chakravarthy 1997; Cooper
1994b; Deuten & Rip 2000; Hoopes & Postrel 1999; Iansiti 1995a;
Imai, et al. 1985; MacCormack & Verganti 2003; Moorman & Miner
1998b; Rosenthal 1992; Trygg 1993), we would expect sequential NPD to
become less important as environmental change increases. However, the
structure intrinsic to sequential NPD becomes more significant under
fast changing environments when viewed from another perspective. As fast
changing environments generate uncertainty (Marsh & Stock 2003), a
new product development process focused on meeting cost expectations in
highly uncertain environments would need to reduce the level of
uncertainty. A factor such as planning reduces uncertainty (Dosi 1988;
Sharkansky & Zalmanovitch 2000). Therefore, when viewed from this
perspective, sequential NPD with its emphasis on planning, would become
more important to meeting cost objectives as environmental instability
increases. Consequently, we expect these two perspectives to create
equilibrium in fast-changing environments.
While teams may abandon a rigid implementation of sequential NPD
under conditions of instability, we anticipate these forces to be
tempered by the need for planning to reduce the uncertainty inherent in
unstable environments. Thus, we anticipate that development teams will
follow the various sequential NPD phases to a medium degree as
environmental change increases. We also expect development teams to
follow sequential NPD phases closely under low levels of environmental
change, as the structure intrinsic to sequential NPD remains important
to maintaining cost expectations.
Although improvisation is considered costly (Sharkansky &
Zalmanovitch 2000; Miner, et al. 2001; Aram & Walochik 1996; Barrett
1998; Crossan 1998) and inappropriate for slow changing environments
(e.g. Moorman & Miner 1998b), it allows the generation of situation
specific knowledge (Eisenhardt & Martin 2000) which in, turn reduces
uncertainty (Morabito, Sack, & Bhate 1999). Improvisation can also
help speed development in fast changing environments (Akgun & Lynn
2002), thus allowing a project to be impacted by fewer changes.
Therefore, while we expect improvisation to always negatively impact
meeting cost expectations; we expect this penalty to be less severe as
environmental change increases. As a result, we expect to see medium
levels of improvisation as environmental change increases. Consequently,
we anticipate that the regression equations that will result from our
statistical analysis will change as the level of turbulence changes. Put
more formally:
H1: Development teams will improvise modestly and follow a
sequential NPD approach closely under conditions of low environmental
turbulence.
H2: Development teams will demonstrate a medium level of
improvisation, and will not follow a sequential NPD approach closely
under conditions of high environmental turbulence.
METHODOLOGY
Classifying a Turbulent Environment
To analyze this integrated approach within an environmental
context, we used a previously developed matrix to categorize and test
turbulence (Lynn 1998) (see Figure 1).
[FIGURE 1 OMITTED]
The Turbulence Matrix is based on Jaworski and Kohli's work on
market and technological turbulence. They defined market turbulence as
the rate of change in the composition of customers and their
preferences, and technological turbulence as technological change
(1993). The Turbulence Matrix classifies environmental turbulence by
dividing the environment into four quadrants, with each quadrant
indicating a degree a degree of turbulence. A slowly changing technology
and market characterize the Incremental quadrant. Thus, products in this
quadrant are likely to remain stable as customer preferences scarcely
change.
A rapidly changing market, but slowly changing technological
environment represents the Evolutionary Market quadrant, where
technological innovation remains modest, but customer preferences change
quickly. A slowly changing market accompanied by a rapidly changing
technological environment characterizes the Evolutionary Technology
quadrant. Rapid technological and market change characterized the
radical quadrant; thus, this quadrant represents a virtually
unpredictable environment.
Our hypotheses state that the regression equations representing how
closely development teams follow the sequential NPD approach and level
of improvisation in the development process will differ under varying
conditions of turbulence. That is, we expect development teams to follow
the sequential NPD approach closely under low levels of environmental
change (i.e. "High" Sequential NPD) and not as closely as
environmental change increases (i.e. "Medium" Sequential NPD).
We also anticipate moderate levels of improvisation under both low and
high levels of environmental turbulence (i.e. "Medium"
Improvisation).
Figure 2 graphically on the following page depicts the degree to
which we anticipate development teams to follow the sequential NPD
approach while incorporating improvisation as the amount of
environmental turbulence varies.
Questionnaire Design and Sampling Procedure
To test our hypotheses, we developed a questionnaire based on
previous research (e.g. Jaworski & Kohli 1993; Moorman & Miner
1998a, b) and distributed it to members of the senior management team in
a number of northeastern US-based technology companies. As suggested by
Huber and Power (1985), we informed participants that their responses
would remain anonymous and would not be linked to their companies or
products to encourage cooperation without fear of reprisal. Respondents
to our questionnaire were product / project managers, department
managers and directors who were on a NPD project from pre-prototype
through launch.
Of the 579 respondents invited to participate, 454 of them
completed and returned a questionnaire--a 78% response rate. Of the 454,
there were 414 fully completed questionnaires. The reason for the high
response rate is because in each company, we personally knew each
respondent through executive workshops that we gave or from a referral
that we knew in each company who provided us a name and contact
information of an appropriate individual who was personally contacted.
The responses represented the following industries: telecommunications,
pharmaceuticals, computer and electronics, fabricated metal products,
chemical manufacturing, information services, food manufacturing and
machinery manufacturing.
[FIGURE 2 OMITTED]
Measure Reliability and Validity
We used a Likert scale (0 = Strongly Disagree to 10 = Strongly
Agree) to operationalize the constructs. An initial reliability analysis
was run to determine internal consistency of each scale and the extent
to which the items in the questionnaire were related to each other, and
to identify problem items that should be excluded from the scale. A
confirmatory factor analysis was then run to retest the validity of the
constructs. A Principal Component Analysis using a Varimax rotation
method was used as the extraction method. The scales used in the
analysis (see Appendix--Table 6) loaded onto factors as expected. The
following provides a brief summary of the measures.
Analytical Techniques Performed
Table 2 shows the one-tailed correlation coefficients for
independent and dependent variables, as well as for our environmental
variables. Here, we discuss some of the more significant findings.
As theorized, we found a significant positive correlation (p <
.01) between dependent variable Cost Expectations and independent
variable Sequential NPD. Also as theorized, we also found a negative
correlation between Cost Expectations and Team Improvisation, although
it was not significant. As theorized, we also found significant positive
correlations (p < .01) between Team Improvisation and different
classifications of turbulence, thus bolstering previous assertions that
improvisation may be appropriate under changing environments.
Also as theorized, we found negative correlations (both significant
and insignificant) between Cost Expectations and different
classifications of turbulence, thus supporting our assertions that
meeting cost expectations under changing environments may be more
difficult than under stable environments.
Before running our regression for Team Improvisation and Sequential
NPD as predictors of Cost Expectations under varying degrees of
turbulence, we decided that it would be useful to determine if each
approach added significantly to the other. For example, would adding
Team Improvisation to Sequential NPD help significantly with meeting
Cost Expectations (without taking the variability of the environment
into account), or would an NPD team be better off simply using a
sequential approach on its own?
To determine this, we ran two hierarchical regressions to
establish: (1) whether Sequential NPD adds significantly to Team
Improvisation while maintaining cost expectations, and conversely (2)
whether Team Improvisation adds significantly to Sequential NPD.
Table 3 lists the details of this hierarchical regression. Model 1
lists the statistics for the regression with only the Team Improvisation
variable entered, and Model 2 represents the regression after entering
the Sequential NPD variable. The purpose of this table is to demonstrate
the changes in the variables as new variables are entered into the
regression equation.
Here, our Adjusted [R.sup.2] indicates that an estimated 12.1% of
the variance in Cost Expectations is accounted for after Sequential NPD
has been added to Team Improvisation. In addition, our [R.sup.2] Change
indicates that the inclusion of Sequential NPD explains an additional
12.4% of the variance. For these data, our F-ratio is 58.065, and is
significant (i.e. Sig F Change (p) < .01). Note that the F-ratio is a
measure of how much the model has improved the prediction of Cost
Expectations. We can therefore conclude that Sequential NPD does indeed
add significantly to Team Improvisation for dependent variable Cost
Expectations.
Table 4 lists the details of our next hierarchical regression.
Model 1 lists the statistics for the regression with only the Sequential
NPD variable entered, and Model 2 represents the regression after
entering the Team Improvisation variable. As before, the purpose of this
table is to demonstrate the changes in the variables as new variables
are entered into the regression equation.
As expected, our Adjusted [R.sup.2] has not changed and indicates
that an estimated 12.1% of the variance in Cost Expectations is
accounted for after Team Improvisation has been added to Sequential NPD.
In addition, our [R.sup.2] Change indicates that the inclusion of Team
Improvisation does not help explain any additional portion of the
variance. For these data, our F-ratio is .078, and is not significant.
We can therefore conclude that Team Improvisation does not add
significantly to Sequential NPD. This outcome is not surprising as we
expect improvisation to negatively impact meeting cost expectations.
Next, we ran a hierarchical regression for Cost Expectations while
splitting the file by Environmental Turbulence.
RESULTS
The literature notes that the most important contribution of
sequential NPD approaches is to control costs. However, as rigid,
highly-structured approaches are inappropriate for fast changing
environments, we anticipated that development teams would not follow the
phases of the sequential NPD approach closely as environmental change
increases. Conversely, we expected development teams to rigidly follow
the phases of the sequential NPD approach as environmental change
decreases, as doing so is important to maintaining cost expectations.
Scholars contend that improvisation is costlier than traditional
planning. However, as improvisation can help speed development in fast
changing environments, we theorized that it might allow a project to be
impacted by fewer changes. Therefore, while we expected improvisation to
always negatively impact meeting cost expectations; we expected this
penalty to be less severe as environmental change increases.
A review of Table 5 seems to bear out our theory. This table lists
the details of the hierarchical regression for dependent variable Cost
Expectations under a turbulent environment. Model 1 lists the statistics
for the overall regression, Model 2 represents the intercepts, and Model
3 lists details of the regression weights.
As the table demonstrates, the regression equations do indeed
differ significantly for dependent variable Cost Expectations under a
turbulent environment. For these data, F is 3.080, and is significant
(i.e. Sig F Change (p) < .01). We can therefore conclude that our
regression model overall does predict Cost Expectations under a
Turbulent environment significantly well.
Our Adjusted [R.sup.2] indicates that an estimated 15% of the
variance in Cost Expectations is accounted for by Sequential NPD and
Team Improvisation. Thus, our hypotheses are supported.
DISCUSSION
When we began our research, one of the issues we observed with new
product development was that highly-structured NPD approaches were too
rigid for dynamic environments, while less-structured approaches were
inefficient for stable ones. As we developed ideas to address this
issue, we quickly recognized that a "middle of the road"
approach would not work--that is, one with a "medium" amount
of structure. Such an approach would only work to compromise the
integrity of each model, while ignoring the outlying environmental
conditions that reveal the strength of each approach. What we felt was
required was an approach that preserved the strengths of the existing
models, even as it addressed their shortcomings. This insight led us to
the development of the integrated approach to new product development.
In developing this approach, we theorized that by coupling
structure with improvisation we could offer a way to reinforce the
strengths of each approach, while overcoming their respective
weaknesses. In studying this issue, we hoped to demonstrate that an
integrated approach to NPD influences outcomes as we had hypothesized.
Results from our study demonstrate that an integrated approach is indeed
viable in helping achieve certain outcomes. We also hoped to establish
that environmental turbulence moderates our integrated approach to NPD.
This implies that as the environment changes from low (market and
technological) turbulence to higher levels of turbulence, the mixture of
structure and improvisation would also need to change in order to
maintain optimal results.
The data supported our hypotheses that anticipated development
teams would follow the sequential NPD approach closely under low levels
of environmental change and not as closely as environmental change
increases. We also anticipated moderate levels of improvisation under
both low and high levels of environmental turbulence. That is, we
theorized that structure becomes less important as environmental change
increases. Conversely, structure with its cost containment benefits
(Barrett 1998; Cooper 1983) was thought to become more important to
maintaining cost expectations as environmental change decreases. In
addition, as improvisation can help speed development in fast changing
environments, we thought it might allow a project to be impacted by
fewer changes as environmental change increases. Our results support
this theory.
This study is the first of its kind to analyze NPD using these two
seemingly divergent approaches. Our study supports prior findings
indicating that following a structured approach helps NPD teams achieve
cost containment outcomes. In our study, we have found that structure
adds significantly to maintaining costs, and that it has more influence
than improvisation. Nevertheless, our research sought to achieve more
than establish a single, dominant approach--our study ventured to
address the call by scholars for balancing structure with adaptability.
For this study, we have defined environmental turbulence in terms
of change in the market and technology, and found that the degree of
structure and improvisation in an integrated approach differs for cost
expectations under different degrees of turbulence. This implies that as
the environment changes from low (market and technological) turbulence
to higher levels of turbulence, the mixture of structure and
improvisation would also need to change in order to maintain optimal
costs.
Various studies have found that following a structured approach
helps NPD teams achieve a variety of outcomes. Our study supports these
findings. However, critics have argued that under certain circumstances,
a more flexible approach may be advantageous. In this respect, our study
has broken new ground by establishing highly-structured approaches as a
foundation on which to build. Specifically, we have found that under
conditions where NPD teams are using a structured approach but require
additional flexibility, adding improvisation to structure is productive.
For example, a team may consider adopting a "throttled"
approach to innovation, whereby control could be loosened to allow for
more improvisation where fruitful (e.g. design), while tightening
control in other stages (e.g. testing). For the practitioner, this
implies that by intelligently coupling structure with improvisation, our
solution plays to the strength of each, while offering a way of
overcoming many of the shortcomings of either approach.
LIMITATIONS AND FUTURE RESEARCH
To test our hypotheses, we developed a questionnaire and
distributed it to members of the senior management team in a number of
northeastern US-based technology companies. Although single sourcing is
a potential limitation, we consider these issues with to be moderated by
research demonstrating that a single source is more reliable and
accurate than averaging multiple sources (Huber & Power 1985). In
addition, further research demonstrates that managers rely on their own
self-reports and offer reliable and objective data (Lucas & Ferrel
2000; Podsakoff & Organ 1986).
For this study, the phenomenon we sought to understand was the
integrated NPD approach. Using this as our guide, we seek to bolster our
research findings in a systematic, iterative manner. Given our focus on
cost outcomes, we recognize the need for further research to identify
other areas in which an integrated approach is useful. As our current
analysis has focused on immediate, tangible outcomes, a future
examination could center on the less tangible opportunities created by
using an integrated approach versus another, less adaptable method.
While our current analysis has concentrated on issues that are
important to new product development, we could also extend our research
to help shed some light on higher level strategic issues with firm level
implications. An initial way to do this could include assessing how
senior management expectations were met when using an integrated
approach versus other methods. We could also assess how senior
management's role in driving new product development differs
according to which approach is used.
Another useful analysis would identify those stages wherein
improvisation is most productive and most inefficient when coupled with
structure. A "throttled" approach to innovation would seek to
loosen control and allow more improvisation where fruitful (e.g.
design), while tightening control in other stages (e.g. testing).
APPENDIX
Table 6: Scales
Sequential NPD New Product Development Process: Reflecting back
(Cooper, et al. on this project from concept to launch ...
2002)
* Initial screening of the product idea (first
review of the venture).
* Translating the product concept into business
terms (such as market share, profitability, etc.).
* Preparing the written proposal of the product
concept.
* Determining the desired product features.
* Conducting a market study or marketing research.
* Assessing the required investment, time and
risks of the product concept. * Conducting
preliminary engineering, technical and
manufacturing assessments.
* Building the product to the designated or
revised specifications.
* Specifying a detailed program for full-scale
manufacturing.
* Selecting customers for testing market
acceptance.
* Submitting products to customers for testing.
* Interpreting the findings from customer trials,
test markets and market surveys.
* Completing the final plans for manufacturing.
* Completing the final plans for marketing.
* Launching the product in the
marketplace--selling, promoting and distributing.
Team * The team figured out the new product development
Improvisation process vs. strictly following the plan. * The
(Moorman & team improvised in developing the product vs.
Miner 1998b) strictly following the plan.
* The team improvised in commercializing the
product vs. strictly following the plan.
Cost This project ...
Expectations
(Griffin * Was launched within or under the original budget.
1997)
* Came in at or below cost estimate for development.
* Came in at or below cost estimate for production.
Environmental Technical * The technology in the industry
Turbulence was changing rapidly.
(Jaworksi &
Kohli 1993) * A large number of new product
ideas have been made possible
through technological breakthroughs
in the industry.
* Technological
changes provided big opportunities
in the industry.
Market * Customers'
preferences changed quite a bit
over time.
* Customers tended to look for new
products all the time.
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Table 1: Summary of Measures
Independent Variables
Sequential We asked fifteen questions to
NPD operationalize Sequential NPD. Items
roughly correlate to the five stages
that are found in most sequential models
(e.g. Cooper, Edgett & Kleinschmidt
2002). Items loaded onto one factor and
the mean was used as the variable.
Team We asked three questions to
Improvisation operationalize team improvisation. Items
were adapted from Moorman and Miner
(1998b). All items loaded onto one
factor related to improvisation and the
mean of these items was used as the
variable.
Dependent Variable
Cost We asked three questions to
Expectations operationalize cost expectations. All
items loaded onto one factor and the
mean was used as the variable. Items
were adapted from Griffin (1997).
Environmental Variable
Turbulence Technical We asked three questions to
operationalize technical turbulence. All
items loaded onto one factor and the
mean was used as the variable. Items
were adapted from Jaworski & Kohli
(1993).
Market We asked two questions to operationalize
market turbulence. All items loaded onto
one factor and the mean was used as the
variable. Items were adapted from
Jaworski & Kohli (1993).
Table 2: Correlation Coefficients
Variables 1 2 3
1 Sequential NPD 1.000
2 Team Improvisation -.058 1.000
3 Cost Expectations .368 *** -.033 1.000
4 Technical Turbulence .052 .183 *** -.047
5 Market Turbulence .028 118 *** -107 ***
6 Total Turbulence .049 .186 *** -.079 **
Variables 4 5 6
1 Sequential NPD
2 Team Improvisation
3 Cost Expectations
4 Technical Turbulence 1.000
5 Market Turbulence .346 *** 1.000
6 Total Turbulence .907 *** 718 *** 1.000
Significance (one-tailed test): * p < .1; ** p < .05; *** p < .01.
Table 3: Determine if Structure Adds Significantly to Improvisation
Change Statistics
Adjusted [R.sup.2] Sig. F
Model [R.sup.2] Change F Change Change df1 df2
1 a -.001 .001 .481 .488 1 412
2 b .121 .124 58.065 *** .000 1 411
Significance of F (one-tailed test): * p < .1; ** p < .05; *** p < .01.
Table 4: Determine if Improvisation
Adds Significantly to Structure
Change Statistics
Adjusted [R.sup.2] Sig. F
Model [R.sup.2] Change F Change Change df1 df2
1 a .123 .125 58.667 *** .000 1 412
2 b .121 .000 .078 .781 1 411
Significance of F (one-tailed test): * p < .1; ** p < .05; *** p < .01.
Table 5: Turbulence Hierarchical
Regression Model Summary for Cost
Change Statistics
Adjusted [R.sup.2] Sig. F
Model [R.sup.2] Change F Change Change df1 df2
1 a .120 .125 28.559 *** .000 2 401
2 b .123 .010 1.478 .220 3 398
3 c .150 .039 3.080 *** .006 6 392
Significance of F (one-tailed test): * p < .1; ** p < .05; *** p < .01.
