A quantitative empirical study into NPD project flexibility and NPD project performance
“How flexibility in terms of organizational and informational flexibility leads to a better NPD project performance and the moderating effect of environmental turbulence”
Master Thesis
Martijn Diekmann S2374838
MSs BA Strategic Innovation Management June 2018
Supervisor: dr. W.G. Biemans Co-assessor: dr. J. D. van der Bij
Word count: 11.081 ABSTRACT
Flexibility is of great importance when coping with uncertain dynamic environments of today’s business. Lots of research has been done about the influence of flexibility on NPD performance. This study builds on Biazzo’s (2009) framework of flexibility dimensions (temporal, informational and organizational flexibility) in relationship with NPD project performance. This quantitative study of empirical data gathered in the Netherlands focusses on the informational and organizational dimension using the informational processing theory. The effect of customer feedback, iterations, centralization and formalization on NPD project performance is measured. A positive relation between customer feedback and NPD project performance was found. Formalization and centralization show contradictory outcomes in relation to NPD project performance. This study found no evidence for a moderation effect of environmental turbulence on the relationship between the two dimensions and NPD project performance.
Keywords: Informational flexibility, organizational flexibility, organizational informational
1 Introduction
Due to the rapid rate of technological change during the last decades, the length of the product
life cycle has decreased and the global competition has increased. Therefore, the importance of
new product development (Henceforth, NPD) has become a critical concern for companies
(Buganza, T., Gerst, M., & Verganti, R. 2010). However, developing new products is a
complex and risky decision-making process (Wang, 2009; Ragatz, Handfield, & Scannell,
1997). Changes to processes and structures, in order to increase the NPD success rates, are an
iterative process, with management continuously trying to improve this process. Therefore, it
is important to understand which factors are responsible for the success of a new product and
the efficiency of this NPD process (Cooper & Kleinschmidt, 1995). Many studies have analyzed
which factors improve the NPD project performance. One of these factors in NPD project
performance is NPD project flexibility. Over the past decades, flexibility has been a main focus
in many studies (Iansiti & MacCormack, 1997; Boschma, 2005; Georgsdottir & Getz, 2004).
According to Biazzo (2009), flexibility in a NPD project is defined as ‘the ability to embrace environmental turbulence rapidly adapting to new technological and market information that
emerges over the course of a project’ (p. 337).
Biazzo (2009) distinguishes three different dimensions of flexibility: organizational flexibility,
temporal flexibility and informational flexibility. Organizational flexibility relates the
structuration of the NPD process, hence how strictly NPD projects are organized in terms of
centralization and formalization. Informational flexibility refers to the classification of the
development activities and the product solution. The last dimension Biazzo (2009) describes in
his framework is the temporal dimension, this dimension concerns the execution strategies of
the development tasks and the time between milestones. Although prior research has been done
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effectiveness of the three dimensions that Biazzo (2009) describes on NPD project
performance. This paper will therefore study the effectiveness of two of the dimensions
identified by Biazzo (2009), namely the organizational and informational dimension. Only
these two dimensions have been included in this study to ensure a deep understanding of the
influence of both. According to Biazzo (2009), the informational dimension consists of:
iterations, changes after customer feedback, freezing time and product alternatives. The
organizational dimension consists of: centralization, formalization, process structure and the
number of milestones within a NPD project. This study will focus on two variables per
dimension, these will be iterations and customer feedback (informational); formalization and
centralization (organizational), since these variables are seen as most important related to NPD
project performance (Jansen & Van Den Bosch, 2006; Thomke, 2001). The aim of this study is
to observe a relationship between these variables and NPD project performance. Additionally,
earlier studies have demonstrated that the rate of environmental change impacts on various
variables that predict the relationship between NPD performance (York & Venkatraman, 2010;
Hage & Dewar, 1973; Leonard, 2011; Tatikonda & Rosenthal 2000). For this reason, this study
will look at the moderating effect of environmental turbulence on these relationships.
Environmental turbulence contains the uncertainties of the technological developments and the
market. Furthermore, the conceptual framework of this paper is underpinned by the
organizational informational processing theory (OIPT), conducted by Galbraith (1974). This
theory states that firms should gather and process more information when uncertainty is high
and is therefore closely related to Biazzo’s (2009) organizational and informational flexibility dimension.
An earlier meta-analysis, based on previous academic studies from the period 1995 to 2017,
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of the effectiveness of Biazzo’s (2009) different flexibility dimensions on NPD performance
(Fisher, 2017; Borgeld, 2016; Roukema, 2014). One of the main reasons for the disappointing
outcomes was the lack of rich empirical data. That is why this study will use recently collected
(2018) empirical data about the effectiveness of organizational and informational flexibility on
the NPD project performance. In doing so, this research will enhance the current literature in
the field of innovation by determining the importance of different elements of Biazzo’s (2009)
organizational and informational flexibility dimensions on NPD project performance. Earlier
studies mainly focussed on time-to-market as the most important performance indicator. Due
to the heavy emphasis placed on cycle time reduction, less emphasis has been given to other
important performance measures (Cooper, Edgett & Kleinschmidt, 2004). Therefore, this study
will focus on product quality, technological performance and the commercial success of the
product as performance indicators. From a managerial point of view this research will help
managers understand the effectiveness of organizational and informational flexibility during
the NPD process and the role of environmental turbulence. This will aid companies to get a
higher NPD project performance and by doing so, a better competitive advantage to survive the
current uncertain and dynamic market conditions. This paper will start with an explanation of
the theoretical background. Here, the main concepts and all variables are defined and
hypotheses are developed. This is followed by a description of the data collection and
methodology of this research. Afterwards the results of the analysis are presented and
explained. These results will be discussed in the discussion section. After this, the theoretical
and managerial implications of the findings are given. The paper will conclude with a direction
4 Literature review
New product development performance
As discussed earlier, numerous research have been conducted on new product development
(NPD) and these researches vary in different research disciplines and theoretical perspectives
such as – strategy, organization theory and design, organizational behavior, marketing,
operation management, sociology of organization (Krishnan & Ulrich, 2001). According to
Armstrong et al. (2015), New product development refers to original products, product
improvements, product modifications, and new brands developed from research and
development. To measure the success of NPD projects, it is important to track the performance.
Measures of NPD performance can be grouped into three different categories: (1) financial
success; (2) customer-based success; and (3)technical performance (Ledwith & O’Dwyer,
2009). Griffin & Page (1996) group 18 different performance metrics into these three categories
and found that the usefulness of measures depends on the strategy of companies.
Organizational information processing theory
In 1974, Galbraith conducted the organizational information processing theory (OIPT). Many
scholars still use constructs of this theory (Song, van der Bij & Weggeman, 2005; Ling, Tee &
Eze, 2013; Gattiker & Goodhue, 2004; Tatikonda & Rosenthal, 2000). The OIPT is used in this
paper because the NPD process is often described as an exercise in information processing
(Tatikonda & Rosenthal, 2000). The OIPT is based on the proposition that a higher task
uncertainty leads to a higher amount of information that needs to be processed between the
decision-makers during the execution of a task. In this paper the NPD project will be considered
as the ‘task’. When uncertainty about the execution of the task is low, a lot of activities in the
process can be planned ex ante. When a task is not well understood, there is more knowledge
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of resources, priorities and schedules. So, all the information must be spread between decision
makers in uncertain conditions when the company strives to get a high level of NPD
performance. Therefore, Galbraith (1974) states that companies are limited in the ability to
pre-plan or to make decisions ex ante due to uncertainty. The strategies of companies to pre-pre-plan
activities or be more flexible vary in different circumstances. Through the application of OIPT
we can identify these different circumstances and their costs. When a company faces a change
in uncertainty, the adoption of integrating mechanisms is required. According to Galbraith
(1974), three possible integrating mechanisms a company could use are:
Coordination by rules or programs: for predictable tasks with a low degree of uncertainty, the use of rules or programs are useful to coordinate the behavior between
interdependent subtasks.
Hierarchy: when organizations are facing more uncertainties, employees can encounter situations for which no pre-specified rules exist. Infrequent situations are handled by
managers in the hierarchy that have a global perspective of the company.
Coordination by targets or goals: the coordination by specifying outputs, targets or goals is easier in uncertain conditions instead of specifying specific behavior of
employees.
The ability of companies to successfully use the above mentioned integrating mechanisms
depends on the frequency of exception and the capacity of the hierarchy to handle them. In
order to use the three different mechanisms properly, a firm should rethink its organizational
design when uncertainty increases. This rethinking of the organizational design should increase
the capability of the company to process more information or to reduce the amount of
information that needs to be processed within the company. In order to increase the capability
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Creation of slack resources: by creating buffers, the uncertainties will reduce a possible overload and interdependencies of sub-units. For example extension of deadlines, buffer
inventories and budget targets.
Creation of self-contained tasks: here is a shift from input to output by giving employees sufficient input in order to supply output. This results in a decreased need for
information processing.
On the other hand, to increase the capability of information processing, Galbraith (1974)
recommends the following two strategies:
Investment in vertical information systems: if more information needs to be processed through the entire hierarchy, investments in vertical information systems are needed.
An investment in a vertical information system could be for example a CRM system.
Creation of lateral relationships: this strategy moves the level of decision-making down in the organization. Thus, decentralization takes place in the decision-making process.
Flexibility and NPD performance
Although flexibility as being an important factor in the NPD process, the term flexibility still
has many different meanings (Suarez, Cusumano & Fine, 1995). For that reason Biazzo (2009)
conducted a framework of different flexibility related to NPD performance. As earlier
mentioned, Biazzo (2009) describes the following three dimensions; organizational flexibility,
temporal flexibility and informational flexibility. These three dimensions give a better
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Scientist of new product development has been focussed on two types of NPD process
strategies. Biazzo (2009) calls these strategies: reaction and anticipation. The anticipation
strategy is based on the stage-gate model of Cooper (1990); this model is characterized by
sequential project stages with a freezing point after the concept development stage. There is an
early freeze in the product definition. After setting this freezing point there will be no more
modifications during the implementation stage. According to Bacon et al. (1994), an early
product definition helps companies to cope with uncertainties about the NPD process during
the process. This lack of uncertainties will speed up a NPD project and thereby the market
launch of the new product (Verganti, 1999). The second strategy is the reaction strategy, and
here is an overlap between the concept development and the implementation stage.
Modifications are possible during the entire process in the reaction strategy (Iansiti &
MacCormack, 1997). So, the reaction strategy contains a more flexible NPD project approach
with a later freeze of the product definition (Biazzo, 2009). When the freezing point is placed
closer to the market launch, the product can be better defined and there could be more feedback
from suppliers, customers and the market be taken into account. This will improve the quality
of the final product (Ricciardi, Zardini & Rossignoli, 2016).
In the current literature researchers disagree about the dichotomy between a stage-gate structure
and a flexible structure (Biazzo, 2009). Some propose that there is a dichotomy between both
(Iansiti, 1995; MacCormack & Verganti, 2003). Others argue that a rigid structure can coexist
with a flexible structure (Biazo, 2009; Suarez, Cusumano & Fine, 1995). Biazzo (2009)
attempts to overcome this dichotomy and conducted a framework that better fits the
combination of a rigid and flexibility process with contingency factors. Suarez, Cusumano &
Fine (1995), state that a combination of a flexible and rigid process is possible. They define the
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activities and not an act of organizational design. Thus, it seems a contingency approach is
needed to determine the impact of organizational and informational flexibility on NPD
performance. First it is important to clearly describe the impact of the informational and
organizational dimensions on NPD performance.
Informational flexibility
The informational flexibility dimension distinguishes the activities of an NPD project into two
categories, problem formulation activities and problem-solving activities (Biazzo, 2009).
Problem formulation activities are those that are dealing with the product definition. These
problem formulation activities define the output of the product, but also the intended end-user
or market segment. Problem formulation activities consist of descriptive elements of the
product, for example: the features and functionality of the product, the price of the product and
the technologies where the product is based on (Biazzo, 2009). Problem formulation has long
been acknowledged as a core activity in strategic decision making. However, recently focus is
shifted to problem-solving activities (Baer et al. 2013; Witte, 1972; Shrivastava & Grant, 1985).
Problem-solving activities define the product through the entire engineering design. The
number of intersections during the NPD process, after the problem formulation phase, can be
high or low. When there are a lot of intersections during the problem formulation and
problem-solving activities in the process, a lot of oscillations can be observed (Biazzo, 2009). The
formulation of the problem can move back and forward during the NPD project. This will direct
the search for possible solutions. If the quality evaluation of the product is insufficient, the
problem formulation can be changed. As a result, the quality and technical performance of the
final product will improve (Sieg et al., 2010). A major challenge for managers in problem
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al. (1976) argue that a good formulation of the problem is crucial in the strategic decision
making. The OIPT states that more information needs to be processed if uncertainty is high. If
companies face many uncertainties during the NPD project, more iterations and feedback from
customers are needed. It depends on the ability of companies to manage the processing of this
information (Galbraith, 1974). As mentioned before, this paper puts emphasis on iterations and
customer feedback as part of the informational dimension. Therefore, these variables and their
possible relation with NPD project performance will now be discussed.
Iteration is the most important variable connected to the informational dimension (Thomke, 2001). From an experiential perspective, a good way to increase the quality and technical
performance of a product is through frequent iterations, also called prototyping. Including
iterations is an experimental process among different stages during the NPD process. The
purpose of this experimentation is to learn whether a product or a technological solution helps
to address a new problem or customer need. When this experiment has been done during the
project the information can be incorporated in the next stage so that ultimately the quality of
the final product will be enhanced (Thomke, 2001). Iterations also contribute to a better
understood product definition and help companies to deal with uncertainties of the NPD project
(MacCormack et al., 2001; Verganti, 1999; Cui & Wu, 2017). More iterations cause more
information that needs to be processed. The OIPT supports this view and states under uncertain
conditions, a firm should acquire more information when there is task uncertainty. Companies
acquire this information from the feedback they receive from every single iteration (Galbraith,
1974). Based on previous literature, it is therefore expected that the use of iterations will
10 H1a: The use of multiple design iterations in the NPD process increases the innovative performance of an NPD project.
Customer feedback has become a major point of attention for companies since recent years.To
innovate successfully, firms should create knowledge faster than their competitors (Song, van
der Bij & Weggeman, 2015). One way of doing that, is by using customer feedback. A
traditional product-centric view is therefore moving towards a more customer-centric view.
Frequent interactions between companies and customers have enabled individual customers to
co-create with companies and share unique experiences and creative ideas. If companies are
able to manage this process efficiently, it can give a company an advantage against competition
(Prahalad & Ramaswamy, 2004). Therefore, many companies nowadays use external gathered
resources from customers in the development of new products and with that improve the NPD
project performance (Verona 1999; Morash & Lynch 2002). Chesbrough (2006) uses the term
‘open innovation’ and defines this as: "the use of purposive inflows and outflows of knowledge to accelerate internal innovation and expand the markets for external use of innovation" (p. 20).
By opening companies’ boundaries, valuable information from outside can flow into the
company. Veryzer & Mozota (2005) argue that open innovation therefore can contribute to a
more flexible process of product innovation and will get more customer acceptance. They state
that user-oriented designs have a positive effect on the commercial success of new products.
This is because the needs of customers are complex and to provide customers with meaningful
products, a deep understanding of the needs of these customers is essential (Lau et al. 2010;
Menguc et al. 2014). Especially in the early stage of NPD projects it is important to use
customer feedback. In this stage, the new products are generated and transformed into new
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more information from customers and other actors in the market when there is task uncertainty.
Companies must try to process the information gathered from the feedback efficiently
(Galbraith, 1974). Based on current literature, there might be a positive relationship between
customer feedback and NPD project performance. Therefore, the next hypothesis is as follows:
H1b: The use of customer feedback during project execution in the NPD process increases the innovative performance of an NPD project.
Organizational flexibility
The organizational flexibility dimension of Biazzo (2009) is about the structuration of the NPD
project process. Biazzo (2009) defines the organizational dimension as follows: “the formal
segmentation of the temporal progression in stages and the definition of the activities that
should be occurring in each stage” (p. 338). The organizational structure of a company influences the capability of an organization to implement an NPD project successfully
(Gosselin, 1997; Damanpour, 1991). Many large organizational structures are too inflexible due
to decreased communication within the company. This lack of communication and the existence
of depersonalization makes these companies rigid and this negatively influences the NPD
project performance (Rosenfeld & Servo, 1990). Companies should therefore alter their
organizational structure to a more flexible structure to enhance NPD project performance
(Georgsdottir & Getz 2004). Stimulating creativity and innovations within companies, requires freedom and a climate of support to employees (Amabile, 1988). But researchers agree that
there should be some kind of systematic approach within an NPD project to succeed (
Damanpour & Aravind, 2012). The two most common variables of organizational structures,
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2006). Therefore we will now discuss these variables and their relation with NPD project
performance.
Centralization refers to the delegation of decision-making authority in an organization (Kirca, Jayachandran & Bearden, 2005). When there is a high degree of centralization, decisions are
made at the top management. On the other hand, with a low degree of centralization the decision
making takes place at lower echelons of authority (Zaffane, 1989). With a high degree of
centralization, there is a concentration of decision-making. This decision-making concentration
hampers companies from being innovative, because a dispersion of power within a company is
necessary for a good NPD project performance (Thompson, 1965). Another consequence of
centralized companies is that the communication channels are smaller. This decreases the
quantity and quality of creative ideas from knowledge that is retrieved from problem solving,
this in turn will hamper the NPD project performance (Palmie et al., 2016). Next to this,
centralization narrows the sense of control over work and lowers the possibility that employees
will search for new innovative solutions (Jansen & van den Bosch, 2006). A work environment
where employees participate in the decision-making increases the commitment, awareness and
involvement of those employees. This participative nature of a company stimulates the making
of new products (Damanpour, 1991). The OIPT agrees with these arguments and claims that a
high degree of uncertainty will increase the information that needs to be processed, a low degree
of centralization is therefore required. When there is a high degree of centralization, members
of an NPD project could receive information that is necessary for their task too late. This may
require them to throw away already finished work in light of this new information and start over
again (Leenders, Van Engelen & Kratzer, 2007). The degree of recommended centralization
strongly depends on the frequency of exception and the capacity of the hierarchy to handle
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centralization and NPD performance is expected. Accordingly, the following hypothesis is
formulated:
H2a: A high degree of centralization decreases the innovative performance of an NPD project.
Formalization of the NPD project can be defined as ‘a system of rules covering the rights and duties of positional incumbents; a system of procedures for dealing with work situations’
(Walsh & Dewar, 1987, p. 217). Formalization is just as centralization, a form of bureaucratic
control within an organization. When there is a high extent of formalization, there are a lot of
procedures and rules within the company and within the NPD projects. A low extent of
formalization will permit openness (Damanpour, 1991; Zeffane, 1989). To encourage
innovative behavior, a company should facilitate flexibility and give low emphasis on rules
during an NPD project (Aiken & Hage, 1971). When companies rely on procedures and existing
rules, ad hoc problem-solving efforts and experimentation will be reduced. A high degree of
formalization also causes employees to deviate less from their structured behavior during NPD
projects and this will lead to organizational inertia (Jansen & van den Bosch, 2006). This is also
in line with the OIPT, which states that companies operating in a turbulent environment should
have a more organic structure without too many rules and strict procedures. Especially if the
degree of uncertainty is too high and the company is not able to process all the information due
to high hierarchical formal rules. Therefore, just as centralization, a low degree of formalization
is required (Galbraith, 1974). Much research points towards the existence of a negative
relationship between formalization and NPD performance. Therefore, the hypothesis is as
follows:
14 Environmental turbulence
In several studies researchers demonstrate that the rate of environmental change impacts various
variables that predict the relationship between NPD project performance (York &
Venkatraman, 2010; Hage & Dewar, 1973; Leonard, 2011; Tatikonda & Rosenthal 2000). The
way the NPD process is structured (organizational dimension) by companies is often a complex
task for companies, because of the uncertainties regarding the market needs and the
technological turbulence in the market (Biazzo, 2009). Market uncertainty and technological
uncertainty together form the environment uncertainty. According to Biazzo (2009); market
uncertainty is caused by the difficulty to understand the customer needs and the translation of
these needs into a functional product for customers. Technological uncertainty depends on the
degree of novelty of the problem, but also on the knowledge that is already in the company.
Hence, firms need to understand the degree of uncertainty they are facing in such a way that
they can execute NPD projects appropriately (Tatikonda & Rosenthal, 2000). For the
informational dimension, an early definition of products in the development phase is only
beneficial when there is a low degree of environmental turbulence. With a high degree of
environmental turbulence, a flexible definition of the product definition will strengthen the
NPD project performance (Krishan & Battacharya, 2002). For the organizational dimension,
many studies suggests a more organic structure when there is high uncertainty. A mechanic
structure, characterized by a high degree of centralization and formalization, could be more
useful under less uncertain conditions (Damanpour & Gopalakrishnan, 1998). Thus, it seems a
contingency approach is needed, and the environmental turbulence is an important factor for
companies to determine a good strategy (Tushman & Nadler, 1978). Uncertainty is a main focus
of the OIPT and is closely related to the environmental turbulence. The OIPT acknowledges
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during the task that needs to be performed (Galbraith, 1974). Looking at the above-mentioned
arguments it is expected that environmental turbulence strengthens the relationship between the
informational and organizational dimensions of Biazzo (2009) and specifically their parameters
(informational dimension: iterations and customer feedback; organizational dimension:
centralization and formalization). Accordingly, the following hypotheses are defined as
follows:
H3a: The use of multiple design iterations in the NPD process increases the innovative performance of an NPD project, this relationship is strengthened by environmental turbulence.
H3b: The use of customer feedback in the NPD process increases the innovative performance of an NPD project, this relationship is strengthened by environmental turbulence.
H3c: A high degree of centralization decreases the innovative performance of an NPD project, this relationship is more negative when there is environmental turbulence.
16 Conceptual model
Methodology
Since the aim of this study is to collect more empirical data into the field of flexibility in terms
of NPD project performance, an empirical approach has been taken. Although there has been
prior research done about the impact of flexibility on NPD project performance, there is still a
gap about what the effect is of the organizational and informational dimensions that Biazzo
(2009) describes related to NPD project performance in turbulent environments.
Data collection
In this research empirical data are collected using a survey. This information is gathered from
49 companies in the Netherlands that have recently launched an NPD project. The companies
that participate are selected using the Orbis database as the source. All participants were Dutch
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only select NPD projects with a market launch of at least one year. Another requirement is that
the firms have more than 30 staff members. This is required in order to have a clear distinction
between the management and the members of the NPD project. The survey is filled out by the
project leaders or highly involved project members and their managers. Project members/highly
involved members are asked to answer the questions about project flexibility variables,
performance variables and the setting of the firm. While the managers received questions about
the NPD project performance question and the environmental turbulence. The independent
variables are measured by the input from project leaders and the dependent variable is based on
the answers of the managers. This has been done to avoid the common method bias that can
result from using response of just one respondent that measures the independent and dependent
variables.
Measures
The measures are adapted from validated scales in the current literature. The following
measurements are part of the questionnaire:
variable Scale Adapted from
Independent
NPD Performance (quality, technological performance, commercial success)
(7-point) Likert-scale Schleimer & Faems, (2016); Ahmad, Mallick, &
Schroeder, (2012)
Independent
Iterations Biazzo (2009); Eisenhart &
Tabrizi (1995)
Customer feedback (7-point) Likert-scale Biazzo, (2009)
Centralization (7-point) Likert-scale Jansen, van den Bosch &
Volberda, (2006)
Formalization (7-point) Likert-scale Jansen, van den Bosch &
Volberda, (2006)
Moderator
Environmental turbulence Four-item Likert-type scale Jaworksi & Kohli (1993)
Controls
Firm age Number of
18 Analysis and results
After the questionnaires have been filled out by the project leaders and managers of the
companies, the data was structured by using the statistical program SPSS. The first step is to
clean the data, the scores of coded items that were negatively worded were reversed. By doing
this, the values indicated the same type of response on every item. Table 1 provides the
descriptive statistics of the used data.
Table 1: Descriptive statistics
Mean (Standard Deviation) 1 2 3 4 5 6 7 8 9 1. Firm age 71,54 1.00 (94,01) 2. Number of employees 5903,15 0,231 1.00 (20388,66) 3. NPD project Performance 4,9 0.034 0,237 1.00 (1,08) 4. Customer feedback 4,71 -0,183 -0,016 0,213 1.00 (1,936) 5. Iterations 6,41 -0,031 -1,091 0,054 0,141 1.00 (13,893) 6. Centralization 2,54 0,045 -0,138 0,076 0,031 -0,092 1.00 (1,41) 7. Formalization 3,28 -0,126 0,193 -0,008 1,259* 0,114 0,026 1.00 (1,8) 8. Technological turbulence 2,46 -0,227 0,27** 0,09 -0,081 0,067 -0,063 0,085 1.00 (1,4) 9. Market turbulence 4,81 0,093 0,092 0,079 -0,16 -0,208 -0,261** -0,05 0,019 1.00 (1,34)
A factor analysis was implemented on multi-item scales in order to describe the variability
among observed, correlated variables. The criteria used for each construct was as follows: (1)
each factor must have an eigenvalue of greater than 1; (2) the explained variance must be above
60% in total; (3) the communality of each measure in the construct must have a loading of more
than 0,50; (4) all measures must load in the correct factor. The factor loadings from the factor
analysis are presented in Table 2. The meaning of all the measured that used are shown in the
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Table 2: Factor loadings from factor analysis
Factor 1 Factor2 Factor 3 Factor 4 Factor 5 Communality
Perf. 1 (Quality) .819 0,746
Perf. 2 (Technological performance) .845 0,820
Perf. 3 (Commercial success) .698 0,642
Centralization 1 .795 0,731 Centralization 2 .691 0,624 Centralization 3 .936 0,892 Centralization 4 .846 0,782 Centralization 5 .872 0,778 Formalization 1 .853 0,776 Formalization 2 .882 0,853 Formalization 3 .636 0,583 Technological turbulence 1 .762 0,665 Technological turbulence 2 .777 0,647 Technological turbulence 3 .661 0,526 Technological turbulence 4 .706 0,567 Market turbulence 1 .792 0,782 Market turbulence 2 .829 0,701 % explained variance 25,342 16,22 13,468 8,657 7,581 Eigenvalue 4,308 2,757 2,920 1,472 1,289
If the Error terms are not normally distributed, there is a bias in the predictive model.
Non-normal error terms could also signal the presence of influences not controlled by the predictors.
In order to test for non-normal error terms, the residuals were plotted. Plot A1 (Appendix)
shows that the residual is normally distributed for the main model and thus no bias from
non-normal error terms are observed. After the factor analysis, the Cronbach's alpha () was used
to look at the internal consistency of the variables and the inter-correlations among test items.
The criteria used for the Cronbach’s alpha in this study is that it must be higher than 0.6. The parameters used to measure the NPD performance are: product quality, technical performance
and commercial success. The Cronbach’s alpha () of these NPD performance measures
together is .72. Thus, these three measures of NPD Performance were put together in a new
variable: NPD performance. Centralization contains 5 measures and has a Cronbach’s alpha ()
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turbulence has been split into technological turbulence and market turbulence. The Cronbach’s
alpha () of technological turbulence and market turbulence are .70 and .66 respectively. A
model with similar predictors runs the risk of biased results due to multi-collinearity. In order
to estimate the magnitude of multi-collinearity in the model, the variance inflation factor (VIF)
of each predictor was calculated. A VIF-score below 5 indicates a little to no multi-collinearity.
The calculated VIF-scores of the model are all below 3, therefore there is no risk of bias due to
multi-collinearity. The next step is the hypotheses testing. With regards to the conceptual model
of the NPD project performance research and the NPD flexibility dimensions, a multiple
regression analysis was made to test the hypotheses. To test the interaction effects, all variables
are mean-centered, as recommended by Aiken & West (1991). Furthermore, to test a possible
moderating effect of environmental turbulence, the moderation procedure of Baron & Kenny
(1986) is used. Table 3 shows the results of the hierarchical regression analysis. Results of
three hierarchical regressions for the NPD project performance are reported. Model 1 includes
the control variables (firm age and number of employees). Model 1 has an R2 of .012 and the
F-statistic (.22) is insignificant. model 2 contains the control variables and the independent
variables. The R2 Of model 2 is .188 and the F-statistic (.898) is insignificant. In model 3, the
interaction variables are added. The R2 of model 3 is .547 and the F-statistic (1.737) is
21
Table 3: Hierarchical Regression Analyses Evaluating Predictors of NPD project performance
Model 1 Model 2 Model 3
Constant -.127 -.173 -.245 .203 .204 .199 Firm age .000 .000 .001 .002 .002 .002 N employees .000 .000 .000 .000 .000 .000 Customer feedback .077 .135 .088 .097 Iterations .010 -.016 .011 .033 Formalization -.305 -.393 .195 .243 Centralization .030 .376* .192 .217 Market Turbulence (MT) .329* .161 .184 .299 Technological Turbulence (TT) -.105 -.323 .193 .215 MT x customer feedback -.082 .099 MT x iterations -.032 .048 MT x formalization .372 .284 MT x centralization .527** .189 TT x customer feedback .052 .100 TT x iterations .013 .028 TT x formalization -.427 .268 TT x centralization -.020 .226 R2 .012 .188 .547 adj-R2 -.042 -.021 .232 ΔF .220 .898 1.737 N 40 40 40 * p < .10, ** p < .05, *** p < .01
The F-statistics of all three models are insignificant, which means that all hypothesis cannot be
rejected. Therefore, an alternative regression model is incorporated to find a better model. A
22
Input from this stepwise regression method was used to make a new model. Results of the
alternative hierarchical regression analyses are shown in table 4.
Table 4: Alternative hierarchical regression analyses evaluating predictors of NPD project performance
Model 1 Model 2 Model 3
23
The alternative regression analysis contains some differences compared to the analysis in table
3. First, formalization and centralization are split into loose measures and not based on the
construct after the factor analysis (table 2). Second, instead of calculating technological and
market turbulence separate, both measures are combined to a new variable: environmental
turbulence (Cronbach’s alpha () of .62). Model 1 includes the control variables (firm age and number of employees). Model 1 has an R2 of .056 and the F-statistic (1.255) is insignificant.
model 2 contains the control variables and the independent variables. The R2 of model 2 is .331
and the F-statistic (2.040) is significant (p<0.10). In model 3, the interaction variables are
added. The R2 of model 3 is .496 and the F-statistic (1.971) is significant (p<0.10).
The results of the alternative hierarchical regression analysis indicate different outcomes. None
of the control variables were statistically significant. In model 2; customer feedback,
formalization and centralization were found to be significant. Customer feedback shows a direct
positive relationship with NPD project performance thus, in the alternative model H1b is
supported. Formalization 2 shows a negative direct relation whereas formalization 3 is
positively related to NPD project performance. This contradictory is also found for
centralization (centralization 1 negative and centralization 5 positive). Therefore, the
hypotheses of the organizational dimensions (formalization and centralization) cannot be
confirmed, using this model. Iterations was not found to be a significant predictor of NPD
project performance, hypothesis 1a can therefore not be confirmed. In model 3 environmental
turbulence is included as moderator. This model only contains a significant positive relationship
with centralization 1. Environmental turbulence positively influences the relationship between
centralization 1 and NPD performance. After the alternative hierarchical regression method was
used on all three performance measures, the effects of the three different measures are
calculated. Table 5 shows the outcomes of the three different dependent variables of NPD
24
Table 5: Alternative hierarchical regression analyses separating NPD project performance indicators
DV = Product quality (Model A) DV = Technical performance (Model B) DV= Commercial success (Model C)
Model 1A Model 2A Model 3A Model 1B Model 2B Model 3B Model 1C Model 2C Model 3C
Constant 4,993 4,942 4,97 4,731 4,734 4.773 4,576 4,486 4,406 (.239) (.257) (.274) (.193) (.215) (.218) (.225) (.219) (.209) Firm age -.002 -.001 -.001 -.001 -.001 -.002 .004* .006*** .006*** (.002) (.002) (.003) (.002) (.002) (.002) (.002) (.002) (.002) Number of employees .000 .000 .000* .000*** .000** .000 .000 .000 .000 (.000) (.000) (.000) (.000) (.000) (.000) (.000) (.000) (.000) Customer feedback .103 .059 .037 -.142 .286** .354*** (.124) (.152) (.104) (.121) (.105) (.113) Iterations .011 .014 .004 .006 -.021 -.020 (.016) (.017) (.013) (.013) (.013) (.013) Formalization 2 -.062 -.029 .015 .033 -.236* -.054 (.147) (.178) (.123) (.142) (.126) (.136) Formalization 3 .075 .090 .015 .028 .186* .166 (.129) (.150) (.108) (.120) (.109) (.117) Centralization 1 .022 -.119 .073 .206 -.248 -.170 (.202) (.241) (.169) (.192) (.174) (.212) Centralization 5 .130 -.047 -.062 -.231 .263 .125 (.224) (.263) (.187) (.209) (.194) (.220) ET x Customer feedback -.010 -.148 -.243** (.147) (.117) (.109) ET x Iterations .015 -.005 .010 (.029) (.023) (.022) ET x Formalization 2 -.192 .278 .024 (.241) (.192) (.183) ET x Formalization 3 .122 .018 -.115 (.149) (.119) (.112) ET x Centralization 1 .341 .151 .164 (.213) (.169) (.197) ET x Centralization 5 -.183 -.424 .149 (.252) (.200) (.224)
Environ. turbulence (ET) -.546* -.236 0.48
25
In model A, the quality of the product is the dependent variable. In this model there is a direct
significant relationship between environmental turbulence and product quality. The model has
an insignificant F-value on all three models. Thus, it is not possible to reject the hypothesis of
model A. In model B the technological performance is the independent variable. Model 1B,
which only includes the control variables, has a significant F-value (p<0.05) and the number of
employees is significantly related to technological performance. No further significant
relationships are found in model B. The dependent variable of model C is based on the
commercial success of the product. All F-values of model C are significant (model 1C (p<0.10);
model 2C (p<0.10); model 3C (p<0.05)). Customer feedback is positively direct related to the
commercial success of the product. A negative effect between formalization 2 and commercial
success is observed. Lastly, a positive relationship between formalization 3 and commercial
success can be found (Model 3B). Including the environmental turbulence as a moderator, the
positive relationship between customer feedback and commercial success is weakened (Model
3C). Hence, this research revealed a moderating effect of environmental turbulence in the
relationship between customer feedback and commercial success. But this moderating effect is
not in line with hypothesis 3b. Therefore, all hypotheses 3, which expected a strengthened
relationship between the independent variables and NPD project performance (quality,
technological performance and commercial success), are rejected.
Discussion
The primary objective of this research is to empirically test the effectiveness of two of the three
flexibility dimensions of Biazzo’s (2009) framework, namely: the informational and
organizational flexibility dimension. In the original model no statistical evidence was found.
26 Customer feedback
A positive direct significant relationship is observed between the feedback of customers and
NPD project performance. This is in line with previous research, which states that customer
feedback directly influences the NPD project performance of a firm (Lau et al. 2010; Menguc
et al. 2014; Veryzer & Mozota (2005). Inconsistent with current literature is the relationship
between separate NPD project performance indicators. Veryzer & Mozota (2005) found a
significant positive relationship between customer feedback and quality of the final product.
This relationship was not found in this empirical study. However, they also found a positive
relationship between customer feedback and commercial success. This study supports this
positive relationship. Furthermore, a positive moderating effect of environmental turbulence on
this relationship was expected, surprisingly and not in line with the current literature, a negative
moderating effect was observed in the relationship between customer feedback and the
commercial success of the product. The relative low number of respondents could be a possible
explanation.
Iterations
Literature suggest that there is a positive relationship between the number of iterations and NPD
project performance (MacCormack et al., 2001; Verganti, 1999; Cui & Wu, 2017; Thomke,
2001). However, in this study iterations do not show a significant direct positive relation to
NPD project performance. Earlier research has mainly focused on time-to-market as a
performance indicator (Thomke, 2001). This study did not include this performance indicator
as a dependent variable, but focused only on the quality of products, the technological
performance and commercial success of an NPD project. It is possible that iterations do not
positively influence the NPD project performance metrics on these indicators. Bhuiyan, Gerwin
27
conditions. The negative effects of more iterations outweighs the positive effects of having
somewhat fewer phase iterations under high uncertainty. Therefore a moderating of
environmental turbulence was expected. This study did not found a moderating effect on the
relationship between iterations and NPD project performance. It is possible that due to the
divergent outcomes (mean: 6,41; standard deviation: 13,893) of respondents no moderating
effect is found.
Centralization
As mentioned earlier, there is a contradiction in the outcomes in both of included the variables
of centralization in the alternative model. The first centralization (centralization 1) parameter
has, just as expected, a negative influence on NPD project performance. The second parameter
of centralization (centralization 5) was positively related to NPD project performance and is
not in line with the hypothesis. A possible explanation for this contradictory outcome could be
the sort of question that measures the variable (see Appendix). It could be that a high score on
centralization 1 is a better predictor of centralization than centralization 5. Hence, this suggests
that some degree of centralization contributes to a higher NPD project performance whereas
too much centralization leads to a lower performance. This can be an indication for a non-linear
relationship between centralization and NPD project performance. This is also in line with the
OIPT which states that centralization will increase the information that needs to be processed.
When the uncertainty grows the positive effect of hierarchy (centralization) will hamper the
performance and therefore a lower degree of centralization is needed (Galbraith, 1974).
Therefore, the expected outcome of this study is that the relationship between centralization
and NPD project performance is more negative when there is environmental turbulence (H3c).
However, results of this study show no consistent moderating effect of environmental
28
include a significant amount of respondents and participating companies are from numerous
industries. It is also possible that other missing moderating effects play an important role. For
instance, O’Reilly & Tushman (2004) proved in their study that a more mechanistic organizational structure should be applied for the exploitation-oriented business units. On the
other hand, more organic structures can be implemented in the exploration-oriented business
units. Since the orientation of the business unit is not measured in this paper, their impact is not
clear. Another possible factor that could play a role is the radicalness of the innovation.
According to Bonner et al. (2002) decentralization and participative decision processes are
more positively related to various performance outcomes for relatively high innovative NPD
project than for less innovative ones.
Formalization
Consistent with the current literature, a negative direct significant relationship is observed
between the formalization 2 and NPD project performance (Damanpour, 1991; Zeffane, 1989;
Aiken & Hage, 1971; Jansen et al., 2006). But, the peculiar finding of this research is that
another contradiction in outcomes is found between two metrics of the formalization.
Formalization 3 is, instead of what is expected, positively related to NPD performance. Here
the same reasoning counts as for centralization. Contradictory outcomes could be due to the
sort of question that measures the variable (see Appendix). The OIPT underscores the
importance of uncertainty. When the uncertainty grows the positive effect of hierarchy
(formalization) will hamper the performance and therefore a lower degree of formalization is
needed (Galbraith, 1974). There is no significant moderating effect found of environmental
turbulence on the relationship between formalization and all NPD performance measures. This
29
lack of statistical power. But, it could also be that other factors play an important role. For
example, Jansen et al. (2006) emphasize the importance of different business units. They found
that exploitative business units perform better with high levels of formalization whereas
explorative business units do not perform better with a high emphasis on rules and procedures.
So, it seems that the orientation (explorative or exploitative) of the NPD project plays a
substantial role in the organizational structure that the companies use. Firms apply different
structures (i.e., formalization and centralization) for different kinds of NPD projects (De Visser
et al. 2010). Next to orientation of the companies, another possible factor that influences the
relationship between formalization and NPD performance is the type of innovation. Pullen et
al. (2009) Argue that high levels of formalization are positively related to the development of
incremental new products, while low levels of formalization are needed for successful radical
product NPD projects.
Theoretical implications
This quantitative research provides empirical results and theoretical insights into the framework
of flexibility dimensions conducted by Biazzo (2009). This study emphasizes the relationship
between Biazzo’s (2009) informational and organizational dimension of flexibility and NPD project performance. Two important elements of both dimensions are included. For the
informational dimension: iterations and customer feedback. The variables of the organizational
elements that are included in this paper are centralization and formalization. Results of the study
clarify the importance of customer feedback on NPD project performance. Another
determination is the contradictory outcomes of different metrics of centralization and
formalization. The study therefore contributes to the debate about what degree of centralization
30
contingency approach (Pullen et al. 2009; De Visser et al. 2010). Therefore, a contingent
framework, where environmental turbulence moderates these relationships was tested. Biazzo
(2009) mentioned the effect of environmental turbulence but does not emphasizes the possible
moderating effect. But, no earlier research has been conducted about the moderating effect on
the framework of Biazzo (2009).
Managerial implications
Managers can use the information of this research to determine the effectiveness of customer
feedback, iterations, centralization and formalization (independent variables) on NPD project
performance. Three different performance measures are included in this paper. These are the
quality, the technological performance and the commercial success of the product (dependent
variable). Managers can use the influence of the independent variables on every separate
performance indicator and look at the combined effect on the three performance measures.
Customer feedback during the NPD project has a positive impact on NPD project performance,
especially the effect on the commercial success is high. So, managers should use the feedback
from customers to enhance the NPD project performance. However, when there is a high
degree of environmental turbulence, the positive relationship between customer feedback and
commercial success is weakened. It is therefore important for managers to first determine the
degree of environmental turbulence, before making use of customer feedback. The contingency
model suggests that managers should be aware of the contradiction between the outcomes of
centralization and formalization. Environmental turbulence does not show a moderating effect
31 Limitations and future research
As with any study, this study should be evaluated by a couple of limitations. The first limitation
has to do with the lack of empirical data and the limited scope of the data. Unfortunately, this
study is only based on empirical data of 49 companies in the Netherlands from numerous
industries. Hence, due to the limited number of respondents it does not give enough statistical
power to draw general conclusions. Next to this, the data was collected among different
industries from Dutch companies and therefore results could be not generalizable to other
industries and countries. Second, to identify the most important dimension of Biazzo’s (2009)
framework, all three dimensions should be measured. This study has been focusing only on two
of the three dimensions, namely the organizational and informational dimension. Future
research should include the temporal dimension of the framework to see if there are significant
differences in the impact on NPD performance between the three different dimensions. Another
limitation of this study are the inconsistent findings about the relationship of formalization and
centralization on NPD project performance. Future research should pay attention to the possible
non-linear relationship of these variables. Next to this, future research should pay attention to
other parameters that are included in Biazzo’s (2009) framework. These are for the
organizational dimension: process structure and number of milestones during the NPD project.
The informational variables that are excluded from this study are: freezing time and product
alternatives. Furthermore, there has not been found a moderating effect on the relationships
between both flexibility dimensions and NPD performance. Other possible moderators should
be considered in future research, for example the orientation of companies. This can be an
explorative or exploitative orientation (De Visser et al. 2010). Lastly, the influence of
radicalness of the innovation could play a role between the flexibility dimensions and NPD
32 Conclusion
Environmental turbulence has caused companies to increasingly focus on the development of
new products. Since it is important for companies to efficiently coordinate the NPD process,
underlying mechanisms that influence the NPD project performance are discussed in this paper.
The framework of Biazzo’s (2009) flexibility dimensions has served as a building block of the concepts that are measured in this paper. Included variables of the informational dimension are:
customer feedback and iteration; the organizational dimension: centralization an formalization.
This paper finds a direct positive relationship between customer feedback and NPD project
performance. Contradictory results have been found about direct impact of formalization and
centralization on NPD project performance. Some measures of centralization and formalization
indicate a positive direct impact on NPD project performance, whereas other parameters show
a negative relation. This indicates that a contingency approach could be needed to score high
on NPD performance. Although earlier research suggests a moderating effect of environmental
turbulence on NPD project performance, this study did not find a moderating effect.
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