The Effect of Flexibility on Performance in NPD Projects and the
Influence of Technological Turbulence
MSc BA Strategic Innovation Management University of Groningen
Faculty of Economics and Business June 25th, 2018
Mark Ottema s2155435
Word count including references and appendix:6610
Abstract
New product development (NPD) project are often carried out in uncertain and turbulent environment. To adapt to this conditions, flexibility in the NPD process is applied. This paper examines three types of NPD project flexibility: Structural flexibility, informational flexibility and temporal flexibility, and their influence on NPD project performance. Furthermore, the effects of a firm's technological environmental turbulence on these relationships is investigated. To test these propositions, data was collected from 49 firms involved in the development of new products. The analysis shows that formalization of the NPD process, which is used to measure structural flexibility, has a positive effect on NPD project performance.
Introduction
Despite its importance, flexibility in developing new products remains an elusive goal for many firms, as evidenced by the high-failure rates of new product launches. Nearly 50 percent of new products introduced in the market are complete failures and more than 70 percent do not reach their sales goals. (Yuan & Zelong, 2009). This flexibility in the new product development (NPD) process is of great importance, because the process is highly susceptible to uncertainty originating from the firm’s environment. (Kettunen, Grushka-Cockayne, Degreave & De Reyck, 2015). However, previous research is contradictory concerning the effects of different types of flexibility in the NPD process. For instance, some scholars argue that formalizing the NPD process, which results in less flexibility, can have beneficial effects for the projects performance. This is due to increased speed of the innovation process, which reduces the time to market (Palmié, Zeschky, Winterhalter, Sauter, Haefner & Gassmann, 2016). Conversely, development processes suitable for situations where technological uncertainty is moderate and stable, might prove to be ineffective in turbulent environments (Biazzo, 2009). The pace of change in many markets and technologies has reached a critical point—product cycles have accelerated to the point where traditional new-product development methods no longer work (Cooper & Sommer, 2018). Flexibility, accommodating to this technological turbulence and high dynamics, might be the best way to sustain a competitive advantage (Hung & Chou, 2013). This suggests a contingent approach is fitting. It is proposed that the degree of technological turbulence plays an important role in determining to what extent NPD process flexibility is beneficial for performance. An information processing theory lens will applied to explain expected differences in performance of NPD project, under different environmental circumstances. The organizational information processing theory (OITP) by Tushman and Nadler (1978) builds on the view of organizations as information processing systems facing uncertainty. Following this perspective, it is proposed that flexibility in the NPD process increases the information processing capacity and technological turbulence increases the needed processing capacities.
temporal dimension. He argues that the degree of flexibility in one dimension does not necessarily cohere with degree of flexibility for another dimension. Variables derived from this theoretical paper are used to empirically test the theory. The variables that are part of the dimensions by Biazzo (2009) have been previously investigated by applying meta-analyses. However, as of yet, these dimensions have not been tested in an empirical way on a project-level. In particular, the effects of these types of flexibility on the performance of a NPD project performance are of interest. Furthermore, this research proposes a contingency approach and will study the moderating effect of different levels of technological turbulence. It is believed this research gap of project flexibility gives an opportunity to be explored for a better understanding of the NPD proficiency, for research and management practice. Based on the identified literature gap and the goals of the research, a research question was formulated: How do different modes of flexibility in the NPD process affect the project performance in different levels of technological turbulence?
Academically, this study will contribute to previous research on flexibility in the NPD process with empirical data. This will help to gain more knowledge about the effectiveness of NPD projects. Furthermore, by investigating the impact of different degrees of technological turbulence on these relationships, this research aims to contribute empirically to the OIPT (Galbraith, 1974; Tushman & Nadler, 1978). Practically, this study can help managers understand the effects of flexibility on NPD processes and act accordingly, which aides in increasing the performance of NPD projects.
This paper has been divided into several parts. First, the theoretical background is discussed, in which the investigated hypotheses are formulated. Then, the methodology of the research is described. Next, the results are discussed. Lastly, the paper concludes with a discussion section, related to the findings of the study.
Theoretical framework
Contingency and the organizational information processing theory
Figure 1
. The Information Processing Model. Adapted from : “Information Processing as an
Integrating Concept in Organizational Design,”
by M. L. Tushman and D. A. Nadler, 1978,
The academy of Management Review
, 3(3), 613-624
Technological turbulence
Flexibility in the NPD process
A very popular and most known instrument in the new product development process is the Stage-Gate model. The Stage-Gate method is recognized all over the world, and helps organizations bring order in a chaotic product development process. The model was developed by Robert Cooper in 1990 and proposed a system from idea to market launch. The process is divided in multiple stages. Between each stage, there is a quality control checkpoint or gate (Cooper, 1990). Although the Stage-Gate model was highly adopted by firms involved in NPD, the model has received critique from scholars as well. The gates might be too rigorous and works in a sequential or linear manner. This increases efficiency, but might be less effective in certain circumstances (du Preez & Louw, 2008). For instance, Sethi and Iqbal (2008) propose that a Stage-Gate process could be inappropriate when developing radical innovative products, because such innovation does not follow the traditional product development process. Their research shows that strict gate review criteria are positively related to product inflexibility.
Another relevant factor concerning NPD flexibility, is degree of technological turbulence of the firms environment. Although environmental turbulence can vary from market to market, it is believed that environmental turbulence is an increasing phenomenon worldwide (Carbonell & Rodríguez-Escudero, 2009). A rm involved in innovation may enjoy only temporary competitive advantage, as product obsolescence occurs more quickly. This may also cause competitors to frequently enter and exit the market as they gain and lose protable competitive advantage. Monitoring and reacting to technological turbulence have been cited as major factors in conguring the NPD process (Calantone et al., 2003). Braun and Eisenhardt (1995) argue that under conditions of uncertainty it is not helpful to plan. Rather, maintaining flexibility and learning quickly through improvisation and experience yield effective process performance. The term ‘NPD flexibility’ was introduced in literature. Flexibility in NPD is seen by Iansiti (1995) as the ability to gather, and rapidly respond to, new knowledge about technical and market information as a project evolves.
general assumptions and incomplete expectations to lead to large outcomes and effective action. More recent, some leading manufacturers in North America and Europe are experimenting with integrating elements of the Agile method into their existing NPD process. This new model has the advantage of providing the company’s existing stage-and-gate system, which provides focus, structure, and control, with the benefits of an Agile approach and mindset, namely speed, agility, and productivity. Thus, creating a hybrid process. Agile originates from the software industry and uses adaptive planning and evolutionary delivery through a time-boxed, iterative approach. It emphasizes rapid delivery of incremental components of a product and frequent communication among team members and with stakeholders. This results in greater flexibility and a more adaptive NPD process (Cooper, 2017).
Some studies propose a dichotomy between the Stage-Gate model and flexible processes. Iansiti (1995), states that Stage-Gate processes are characterized by early and sharp product definition and clear separation between concept development and implementation. Contrasting, flexible processes seek to delay the ‘concept freeze point’ and overlap NPD stages. Biazzo (2009) proposes that the contraposition between NPD flexibility, or flexible processes, and the Stage-Gate model, is misleading. He states states that the contradictory nature of results in research, is because of the lack of accuracy of the description of the phenomena being studied. In his research, he states that this lack of accuracy is due to treating flexibility a one dimension variable. Instead, he identifies three different dimension of flexibility in NPD processes:
● Temporal dimension: Relates to the execution strategies of development tasks and refers to task scheduling.
● Informational dimension: Deals with classifying the development activities and investigating the firm’s product definition approach (early and sharp mode vs. late freeze mode).
● Organizational dimension: Refers to the structuration of the process.
dimensions, have different influences on the performance of the NPD projects. Each dimension can be measured by using multiple variables. Yet, because of the limited scope of this study, one variable for each dimension is explored.
Temporal dimension
The temporal dimension is associated with the decisions made regarding the temporal interrelationships, between the various tasks assigned to organizational actors (Biazzo, 2009). One previously identified variable is ‘time between milestones’ in the NPD process (Borgeld, 2016). Milestones are formal project review points, they reassess the current state of progress. The time between milestones is the average time between these formal review points during a development project (Eisenhardt & Tabrizi, 1995). Limited previous research shows that shorter time between milestones can benefit NPD project performance. Eisenhardt and Tabrizi (1995) claim that less time between milestones is associated with shorter development time. This is because the milestones force people to look often at what they are doing so that if actions are off-course, they can be corrected earlier in the process. A lower development time leads to lower costs, and could be beneficial for the (financial) performance of the developed product. Moreover, frequent formal reviews enable critical assessments that inform major decisions, this allows managers to adjust project resources and objectives if necessary (Salomo, Weise & Gemünden, 2007). On the contrary, short time between milestones might be detrimental in a technological turbulent environment. According to OIPT, technological turbulent environment increases the uncertainty and information requirements. Less the time between milestones will decrease the temporal flexibility. Therefore the information processing capacity can be too low, which is detrimental for performance.
Hypothesis 1a: Time between milestones in the NPD process negatively influences the NPD project performance.
Informational dimension
The informational dimension considers the classication of the development activities and with the investigation of a rm’s product denition approach. (Biazzo, 2009) The main variable that is associated with the informational dimension is design iterations, or prototypes (Marovska, 2016). Design iterations are defined as the amount and quality of ideas and concepts generated for the new product and its proposed design and solution (Reid, Hultink, Marion & Barczak, 2016). Prototyping is a typical example of such a type of iteration (Terwiesch & Loch, 1999). Iterations are one way to speed up product development. Just as catalysts and heat accelerate chemical reactions by creating more opportunities for reactions to occur (Curtis & Barnes, 1989). These design iterations can be simultaneous, alternative designs, designs that are iterations of previous designs, or some combination of the two. Regardless of the actual iteration pattern, simply increasing the number of design iterations improves the odds of success and thus accelerates the process, particularly when predictable paths do not exist. (Eisenhardt & Tabrizi, 1995). One approach of developing new product, which include high amount of iterations during the process, is the Agile method. One study shows that industrial companies can gain substantial performance benefit for new product development from implementing Agile processes into a their existing Stage-Gate process (Sommer, Hedegaard, Dukovska-Popovska & Steger-Jensen, 2015). Concluding, design iterations increase the informational flexibility and lead to reduction in time to market, thus leading to a higher NPD project performance. Higher technological turbulence strengthens this relationship, because more multiple design iterations allows to adapt to technological changes that can influence the product in development.
Technological risk stems from uncertainty about whether a new product will
Hypothesis 2a: The number of design iterations during the NPD process positively influences the NPD project performance.
Hypothesis 2b: Technological turbulence positively moderates the positive relationship between increasing the number of design iterations during the NPD process and the NPD project performance.
Organizational dimension
turbulence increases the need for information processing. Therefore, the chances of a ‘fit’ decrease.
Hypothesis 3a: A higher level of formalization in the NPD process positively influences the NPD project performance.
Hypothesis 3b: Technological turbulence negatively moderates the positive relationship between formalization in the NPD process and the NPD project performance.
Figure 2. Conceptual model
Methodology
Sampleprocess had to be recently launched and the market performance of product had to available. The size of the firms in terms of employees ranged from small (<30) to very large (112.000). The sample includes firms operating in a broad variety of industries, such as chemicals, communication, healthcare, information, manufacturing, and technology. On average, most of the generated revenue by the firms were from Business-to-Business activities, as opposed to Business-to-Consumer (4.33 on a 1-5 scale) and from services, instead of products(3.78 on a 1-5 scale).
Procedure
Surveys were used to gain information about the projects. The first knowledge source was gained by a survey filled in by projects leaders or highly involved project members. The questions provide information on the project flexibility variables, performance variables and setting of the firm. The second information source comes from surveys provided by the senior general, marketing, or innovation managers, responsible for the project. Using two different information sources for dependent and independent variables will reduce the chance of common method bias (Chang, Witteloostuijn, Eden, 2010).
Measurements
Independent variables.
The time between milestones is derived from the research of
Eisenhardt and Tabrizi (1995). First the respondents group were asked to provide the number of formal milestones for the project. To calculate the time between milestones, the duration of the innovation project was divided by the number of milestones. This resulted in the average time between milestones during the NPD process.
The number of design iterations measurement is adapted from Eisenhardt and Tabrizi (1995). It is measured by asking directly how many iterations occured in the development of the product. An iteration is defined as a redesign of at least 10 percent of a products’ parts.
The measurement for formalization
in and around the NPD project is derived from
Despandé and Zaltman (1982). Statements were used, which respondents have to rate from 1-7 in what degree they agree with them. The five statements encompass written procedures, rules and procedures in place, keeping track of individual performance, monitoring sticking to the rules and the use of job descriptions for team members.
Moderating variable.
Technological turbulence was translated from Jaworski and
Kohli (1993). It is measured by asking questions about the rate of change of technology, the degree to which technological changes offers chances for the company and the degree to which ideas in the company came from technological change. A 1-7 likert scale was used.
Control variable.
Firm agewill be included as control variables. Firms age is known
to influence innovation performance. Entering firms and firms of the youngest cohorts are, conditional on the peculiarities of their activity and size, prone to innovate more, and the oldest ones propend to innovate less than entrants (Huergo & Jaumandreu, 2003). It is argued that this control variables also affect innovation on a team level.
Analysis and results
the development costs and the time to market. This variable was titled “efficiency performance”. The second new performance variable relates to product quality, technical performance, market share, profitability and commercial success. This variable was titled “product performance”. Tabel 1 indicate that all multiple-item constructs have sufficient reliabilities with, coefcient between 0.67 and 0.82. A multicollinearity diagnostic test was conducted which shows there is no case of multicollinearity in the regression models, as VIF values are all lower than the proposed threshold of 5 (Marquardt, 1970) . The means, standard deviations, Pearson correlation (two-tailed) coefficients and the Cronbach’s alphas for all variables are presented in table 1.
Table 1: Descriptive statistics and correlation matrix
Variable M SD 1 2 3 4 5 6 7 1. Process efficiency 3.63 1.26 (.67) 2. Product performance 4.74 .85 .420*** (.82) 3. Time between milestones 3.77 2.84 -.11 .13 (n.a.) 4. Iterations 6.41 13.89 -.11 -.03 -.18 (n.a.) 5. Formalization 3.29 1.49 -.16 .25* -.01 .08 (.77) 6. Technological turbulence 5.27 1.13 -.08 .25* .06 .02 -.05 (.70) 7. Firm age 71.54 94.09 .01 .09 -.10 -.03 -.07 -.17 (n.a.)
n=49.
Cronbach’s alphas of the composite scales are reported along the diagonal.
* p
< .10, ** p < .05, *** < .01 (two tailed)
For the testing of the hypotheses a three step hierarchical linear regression was conducted. There were some missing values in the dataset. The missing values were replaced by the means of the variables. Table 3 presents the results shown of the testing of all hypotheses. The dependent variables were first mean-centered to calculate the moderating effect.
variables. It was hypothesized that the time between milestones was negatively related to NPD project performance. The results shows no support for hypothesis 1a. Next, it was hypothesized that technological turbulence negatively moderates this relationship. There was no support found for hypothesis 1b. Furthermore, no support was found for hypothesis 2a, which states that the number of design iterations positively affect NPD project performance. Hypothesis 2b states that this positive effect was strengthened by high technological turbulence. The results show no support for hypothesis 2b. Hypothesis 3a states that a higher formalization leads to higher NPD performance. Model 3b shows a significant positive relationship with product performance (b=0.22, p
< 0.1), with an F value that is significant
(p
< .1). This shows support for hypothesis 3a. Hypothesis 3b stated that this effect was
negatively moderated by technological turbulence. The results show no support for hypothesis 3b. Furthermore, the analysis shows a significant positive relationship between technological turbulence and product performance (b=0.22, p < 0.1), with a significant F value (p
< 0.1).
Table 2a: Factor Loadings from Factor Analysis
Table 2b: Factor Loadings from Factor Analysis Efficiency performance Product performance Formali- zation Technological turbulence M_Perf1 .79 .16 P_Formal1 .84 .11 M_Perf4 .71 .19 P_Formal2 .85 .16 M_Perf2 .01 .89 P_Formal3 .69 .02 M_Perf3 .06 .88 M_TT1 .10 .87 M_Perf5 .19 .71 M_TT2 .03 .81 M_Perf6 .26 .79 M_TT4 .27 .68 M_Perf7 .28 .81
Table 3: Results from Hierarchical Regression Analyses
Efficiency performance Product performance Steps and
variable
Model 1a Model 1b Model 1c Model 2a Model 2b Model 2c
Discussion
Theoretical implicationThe aim of this study was to investigate the following research question: How do
different modes of flexibility in the NPD process affect the project performance in different levels of technological turbulence?
The findings of this study implicate that a more rigid
approach of structuring the process benefits the NPD performance. More concrete, this implies incorporating written rules and procedures about the development process, and keeping track of individual performance during the process. The present study provides additional evidence with respect to previous research which emphasize the advantages of a more formal process of developing new products (Palmié et al., 2016; Thamhain, 1990; Tatidonka & Montoya-Weiss, 2001; Barczak et. al., 2009). No other relationships between the variables underlying the informational and the temporal dimension and NPD performance were found. There could be theoretical reasons for not finding this relationships. For instance, Biazzo (2009) shows that different moderators might be relevant when researching flexibility, that have not been included in the model, tested in this research. Examples of described moderators are market turbulence, product modularity and customer involvement.
Another finding from the analysis, is the direct positive relationship between technological turbulence and (product) performance. One explanation for this interesting finding is provided by previous research. In environmental turbulent environments, companies are stimulated to develop new ideas, in order to keep up with technological changes. This enhances the relative NPD performance in comparison to firms that operate in a technological stable environment (Wu and Shanley, 2007)
Limitations and future research
have been a selection bias. Not all industries and product types are appropriate or suitable for patents (Hall, Jaffe, Trajtenberg, 1995). Further, only firms who operate in the Netherlands were included. This implies the results may not be valid for other countries. In future studies, this methodological limitation could be overcome by taking more time to increase the sample. Third, to avoid common method bias, different respondents were asked to assess the independent variable and the dependent variable. It is possible that both respondents did not have the exact same project in mind when filling in the survey, since a large portion of the companies have ‘smaller’ products, which are part of a larger project. This makes is difficult to assess if both respondents rated the exact same projects, since both respondents filled in the survey independently. Fourth, for the scope of this research, only one variable for each flexibility dimension was included. Therefore, the variables might not be representative for the whole construct of a dimension. In future studies, more underlying variables could be included in the model. Fifth, performance was rated with regard to the initial expectations, this might distort the results of the performance variables. Sixth, this study takes into account technological turbulence as a moderating effect. Investigating more variables of the flexibility variables can provide more clarity about the relationship between NPD flexibility and performance.
Managerial implication
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Appendix: Study Measures
The survey was conducted in the Dutch language.Dependent Variables
NPD project performance.
Als u de uitkomsten van het project vergelijkt met de verwachtingen bij aanvang van het project, hoe heeft het project dan gepresteerd op:
(Likert 1-7 scale, where 1= considerably
worse and 7=considerably better) M_Perf1. Productontwikkelingskosten. M_Perf2. Productkwaliteit
M_Perf3. Technische prestaties met betrekking tot productspecificaties M_Perf 4. Tijd tot marktintroductie
M_Perf 5. Marktaandeel
Independent Variables
Time between milestones
P_Duration. Wat is de duur van het project in maanden?
P_Milestone. Hoeveel formele mijlpalen werden gebruikt in het project vanaf de start tot en met de marktintroductie?
Iterations
P_Freezing2. Hoe vaak is het ontwerp van een product alternatief tenminste 10% veranderd gedurende het project?
Formalization
(Likert 1-7 scale, where 1= fully agree, 7=fully disagree).
P_Formal1. Welke situatie zich ook voordeed, er waren altijd schriftelijke procedures beschikbaar die beschrijven hoe in een situatie gehandeld moest worden.
P_Formal2. Regels en procedures speelden een belangrijke rol tijdens dit project.
P_Formal3. Van iedereen die bij het project betrokken was werden de prestaties schriftelijk vastgelegd.
Moderating variables
Technological Turbulence
(Likert 1-7 scale, where 1= fully agree, 7=fully disagree). M_TT1. In onze bedrijfstak verandert de technologie snel.
M_TT2. In onze bedrijfstak bieden technologische veranderingen grote kansen
M_TT4. In onze bedrijfstak zijn de technologische ontwikkelingen beperkt. (reverse coded).
Control variable
Firm Age
