An Empirical Analysis of the Impact of Flexibility Dimensions and Market
Turbulence on NPD Project Performance
Master Thesis, MscBA, track Strategy & Innovation Management University of Groningen, Faculty of Economics and Business
25th of June, 2018
ELIEN FRANCIS KLUITER S2565269
Supervisor Co-assessor
Dr. J. D. van der Bij Dr. W. G. Biemans
Word Count: 8888
Abstract
In recent literature, there is a lack of clarity on the best practices related to structuration and flexibility in NPD processes. In order to clarify these best practices, Biazzo (2009) identified three dimensions: organizational, informational and temporal. Although previous researchers investigated the effects of several flexibility variables on the performance of NPD projects, there is still no empirical evidence for the relationships between the three flexibility
Introduction
During the last three decades, the competitive environment has become more turbulent than ever due to market globalization and technological developments, such as the introduction of the computer and the internet (Cooper, 2017; D’Aveni, 1994; Iansiti, 1995; Kleinschmidt, de Brentani & Salomo, 2007). Existing literature suggests that innovating is the best way for firms to cope with the current turbulent competitive environment (Weiss & Heide, 1993), because it is necessary to survive (Lee & Trimi, 2016) and to achieve success (Crossan & Apaydin, 2010). However, dealing with environmental turbulence may be challenging for firms in managing innovation or new product development (NPD) processes (Buganza, Dell’Era & Verganti, 2009; Calantone, Garcia & Dröge, 2003). The reason behind this is that a wide range of environmental information on, for example, changing technologies,
competitors, customer needs, and availability of resources needs to be gathered en processed (Buganza, Dell’Era & Verganti, 2009; Gupta, Raj & Wilemon, 1986; Mullins & Sutherland, 1998).
In recent literature, there are contradicting views regarding to the best practices to cope with environmental turbulence in NPD processes. A rough distinction can be made between two different ways of organizing NPD processes. On the one hand, structured Stage-Gate processes and anticipation strategies, in which the product is defined in the initial phase and the development and implementation phases are clearly separated (Cooper, 2008; Kalyanaram & Krishnan, 1997). And on the other hand, more flexible processes and reaction strategies, in which the different phases are overlapping and the product definition is delayed as long as possible to allow the firm to anticipate on new environmental information (Iansiti, 1995; MacCormack & Verganti, 2003; Verganti, 1999).
In order to clarify the interaction between structuration and flexibility in NPD processes, Biazzo (2009) identified three flexibility dimensions: organizational, informational and temporal. The organizational dimension relates to the process structure, the informational dimension refers to the intersection between product definition and product design activities, and the temporal dimension deals with the simultaneity in the execution of the development tasks (Biazzo, 2009). Previous research on flexibility in NPD processes consists mainly of conceptualizations of flexibility and the three analytical dimensions (Borgeld, 2016; Golden & Powell, 2000; Marovska, 2016). Other research based on meta-analyses already provides some empirical evidence for the effects and relative importance of a number of flexibility variables on innovative performance in turbulent environments (Fisher, 2017). However, there is still no empirical evidence for the effects of the three different dimensions on different aspects of NPD project performance. Moreover, there is no evidence for the effect of market turbulence on these relationships.
In order to address the aforementioned literature gap, the effects of the three different dimensions on the innovative performance of NPD projects and the moderating effect of market turbulence will be empirically tested. Therefore, the following research question is formulated:
What is the relationship between the three different flexibility dimensions and NPD project performance and how does market turbulence moderate this relationship?
Six hypotheses were formulated, in accordance with previous research, in order to answer the aforementioned research question. Two Dutch surveys were used to collect real-life data on 50 NPD projects. The hypotheses were tested on the basis of a multiple regression analysis in SPSS. The research results show that there are, in contrast with previous literature, no direct relationships found between the three flexibility dimensions and NPD project performance. However, by this research evidence is found for the moderating effect of market turbulence on the relationships between the flexibility dimensions and NPD project performance for all dimensions. To contribution of this research consists of linking Galbraith’s (1974)
organizational information processing theory (OIPT) with the innovation flexibility literature field, which shows that the creation of lateral relationships actually might be detrimental for NPD project performance in case of high uncertainty. In addition, the managerial implications indicate that managers should reduce the shifting from activities to earlier stages, increase the time to fixation of the product definition and reduce predetermination of stages and
The structure of the paper will be as follows. Firstly, the different concepts will be defined and the hypotheses will be formulated and visualized in a conceptual model. Secondly, methods used to collect data and to analyse will be described. Lastly, the results, the
theoretical and managerial implications of the research, and future research directions will be discussed.
Theoretical Background
Flexibility in NPD processes
In the last three decades, a large literature stream on the organization of NPD processes has emerged. In the literature, a rough distinction is made between two ways of organizing NPD processes. NPD processes can be structured on the basis of Stage-Gate models (Cooper, 2008) and anticipation strategies (Kalyanaram & Krishnan, 1997), which can be recognized by an early product definition and a clear separation between the development and implementation phases. Processes with this structure are characterized by a relatively low degree of costly and long-lasting changes and therefore the product could be developed quickly (Bacon et al., 1994; Cooper, & Kleinschmidt, 1994). The use of a structured Stage-Gate NPD process could lead to higher innovative performance (Cooper, 1990), but only in case of relatively low uncertainty, high market price sensitivity, high integrational needs or team inflexibility (Bhattacharya, Krishnan, & Mahajan, 1998). However, the use of an early product definition can result in an outdated product after finishing the project (Thomke, 1997). Therefore, Stage-Gate processes could be outperformed by more flexible processes.
So, NPD processes can also adopt flexible structures and adoption strategies, in which the development and implementation phases are overlapping and the product definition is delayed as long as possible (Iansiti, 1995; MacCormack & Verganti, 2003; Verganti, 1999).
However, the contraposition between structured and flexible processes does not seem to be this clear. Tatikonda & Rosenthal (2000), for instance, state that structure and flexibility can be balanced in product development processes. According to Salerno et al. (2015), there are many other types of NPD processes in-between structured and flexible processes. For example, processes that use the phases of the Stage-Gate model in a different order or processes that create stoppages to be able to wait for technological improvements and/or market growth. In addition, Cooper & Sommer (2016) & Cooper (2017) suggest that Stage-Gate models can be combined with agile development methodologies, which are
characterized by daily face-to-face meetings for planning and control by dedicated project teams, and short-term iterative development cycles used to frequently build, test and revise new products together with stakeholders. Due to improved communication, productivity, and adaptiveness to environmental changes, the use of an Agile-Stage-Gate model leads to more flexibility and speed in the development process, while the structure remains intact.
According to Biazzo (2009), a lack of clarity on the distinction between structure and flexibility indicates an underdeveloped part of the academic field, which is problematic for both academics and managers, since they do not know which practices they should use or rely on. This leads, in turn, to high new product failure rates (Liu, Li & Wei, 2009). Therefore, clarification of the best flexibility practices is needed. In order to clarify these practices in NPD processes, Biazzo (2009) described three flexibility dimensions: organizational,
Organizational Information Processing Theory (OIPT)
The OIPT forms the basis for the hypotheses in this research. The OIPT describes that the effectiveness of a certain organizational design depends on the circumstances of an
organization (Galbraith, 1974; Kitchen & Spickett-Jones, 2003; Tushman & Nadler, 1978). The theory asserts that the predictability of tasks within an organization, which depends on uncertainty, size and interdependency, is a good predictor of organizational design. According to Lawrence and Lorsch (1967), the degree of task predictability, and the subsequent amount of planning changes needed, determines the amount of information that needs to be processed for that task. In addition, the design of an organizational determines its ability to process information (Galbraith, 1974). Therefore, performance is determined by the match between the amount of information that needs to be processed, the information processing requirement, and the organization’s ability to process this information, the information processing capacity (Egelhoff, 1991). This match can be enhanced by improving the information processing capacity and/or reducing the information processing requirement. In this research, Biazzo’s (2009) three flexibility dimensions determine the information processing capacity and the degree of market turbulence determines the information processing requirement.
In the following sections is explained how the three flexibility dimensions and market
turbulence affect the information processing capacity and requirement, and therefore the NPD project performance.
The impact of the temporal dimension on NPD project performance. The temporal dimension deals with the strategies behind the development activities (Biazzo, 2009). In the literature, three factors are associated with the temporal dimension, namely overlapping stages, time between milestones, and gate conditionality (Eisenhardt & Tabrizi, 1995; Sethi & Iqbal, 2008; Zirger & Hartley, 1996). Since most importance is given to overlapping stages in recent literature (Biazzo, 2009; Iansiti, 1995), the research will be focused on this factor. Cooper (2001) defines overlapping stages as the shifting of activities from one stage to an earlier stage. This means that functional tasks, in contrast with structured models, not
necessarily need to be performed in the intended stage or phase of the project, so that changes can be managed (Iansiti, 1995). Some researches state that stage overlap leads to decreased performance due to increased coordination costs and unnecessary rework (Terwiesch et al., 2002; Terwiesch & Loch, 1999a; Terwiesch and Loch; 1999b). According to other
proposes a positive influence of overlapping stages on operational performance. The reason behind this is that stage overlap leads to an increase in knowledge sharing and better insights into risks and opportunities (Biazzo, 2009; Iansiti, 1995; Tatikonda & Montoya-Weiss, 2001). Given that there are better insights into risks, the concept of overlapping stages also leads to faster and improved problem solving and therefore to improved product quality. This is consistent with Galbraith’s (1974) argument that knowledge sharing through the creation of lateral relations, facilitated by the movement of decision making and authority into low hierarchical firm levels, enables firms to respond quickly to unexpected events. Therefore, the creation of lateral relations increases their information processing capacity (Egelhoff, 1991). This leads to a better fit between the information processing requirement and capacity, which in turn leads to an improved performance (Galbraith, 1974). Thus, the overlapping of stages contributes to an increased product quality (Eisenhardt & Tabrizi, 1995; Tatikonda & Montoya-Weiss, 2001), which determines an increased NDP project performance.
H1: An increase in the extent to which tasks are performed simultaneously results in an increase of the performance of NPD projects.
The impact of the informational dimension on NPD project performance. The informational dimension refers to the intersection between product definition and product design activities (Biazzo, 2009). Four factors for the informational dimension are identified in the literature, namely iterations, customer feedback, product alternatives and freezing (Biazzo, 2009; Eisenhardt & Tabrizi, 1995). The most important factor from the informational
dimension is freezing, whichis defined as the fixation of the product definition and the decisions with regards to the process (Biazzo, 2009; Souder & Moenaert, 1992). As mentioned before early freezing can lead to an outdated product at the market launch
H2: An increase in the extent to which the project was completed at the time of freezing results in an increase of the innovation performance of NPD projects.
The impact of the organizational dimension on NPD project performance. The organizational dimension relates to the process structure (Biazzo, 2009). In the literature, four concepts are associated with the organizational dimension, namely formalization,
centralization, milestones and project process structure (Jansen, van den Bosch & Volberda, 2006; Kleinschmidt, de Brentani & Salomo, 2007). In recent literature, most importance is given to project process structure, which can be defined as the degree to which the project is divided into stages and the activities per stage are predetermined (Biazzo, 2009). A structured process can have a positive influence on product quality, since it gives employees direction and motivation (Cooper, 1990; Eisenhardt & Tabrizi, 1995). However, this process is only effective in case of relatively low load of uncertainty, high market price sensitivity, high integrational needs or team inflexibility (Bhattacharya, Krishnan, & Mahajan, 1998). In other cases, more flexible processes are effective, since they allow firms to respond to new
environmental information, which prevents the firm from introducing outdated products (Bhattacharya, Krishnan, & Mahajan, 1998, Thomke, 1997). As mentioned before, the IOPT has described the creation of slack resources, by loosening targets, in order to deal with task uncertainty. By creating slack resources, the information processing requirement is reduced, which increased the likelihood of unexpected events or problems to be solved (Galbraith, 1974). So, by making use of flexible processes, product performance is increased
(Bhattacharya, Krishnan, & Mahajan, 1998, Thomke, 1997), which in turn leads to an
increase of NPD project performance. Thus, project process structure has a negative influence on NPD project performance.
H3: An increase in the extent to which project stages and stage activities are
predetermined results in a decrease of the innovation performance of NPD projects.
capacity perform better (Galbraith, 1974). Therefore, firms that manage to effectively process a wide range of environmental information on, for example, customer needs are more
successful than firms than cannot manage this (Buganza, Dell’Era & Verganti, 2009; Gupta, Raj & Wilemon, 1986; Mullins & Sutherland, 1998). As previously described, implementing flexibility in NPD processes allows firms to use this information in their NPD processes (Bacon et al., 1994; Biazzo, 2009; Chesbrough, 2007; Ricciardi, Zardini & Rossignoli, 2016) and to increase innovation performance (Bhattacharya, Krishnan, & Mahajan, 1998, Iansiti, 1995; Tatikonda & Montoya-Weiss, 2001; Thomke, 1997). Furthermore, the processing of this information could lead to learning effects, which results in an additional increase in firm performance (Kim & Atuahene-Gima, 2010). So, firms that implement flexibility into their NPD processes are better prepared to deal with market turbulence and therefore outperform inflexible firms. Thus, market turbulence moderates the relationships between flexibility and NPD project performance and this is operationalised as follows:
H4a: Turbulence strengthens the positive relationship between the extent to which tasks are performed simultaneously and NPD project performance.
H4b: Turbulence strengthens the positive relationship between the extent to which the project was completed at the time of freezing and NPD project performance.
H4c: Turbulence strengthens the negative relationship between the extent to which project stages and stage activities are predetermined and NPD project performance.
Control variables
Previous literature shows that the performance of NPD project is, besides flexibility and turbulence, affected by many factors. Therefore, four additional factors - project group size, project duration, firm size, and firm age - will be used as control variables in the analysis. Project group size has been found to positively and negatively affect NPD project
Conceptual model
Based on the hypotheses, the following conceptual model is composed:
Figure 1 – Conceptual model
Methods
Data collection and sample
The basis of the data collection for this research was located in the article of Biazzo (2009) and the research of former students (Fischer, 2017; Marovska, 2016). Although the
number of criteria were used to reduce the population. Firstly, the firms should at least have two or more employees working on the NPD project. Firms with projects executed by less than 2 staff members were excluded, because these firms require less structure than firms with more staff and are therefore not comparable. And secondly, the firms should have launched the new product to the market so recently that sufficient knowledge was available to answer the survey. Upon approval, the surveys were personally or by e-mail distributed to two different persons in the firms. Firms that did not respond were sent up to three reminders. Finally, 50 fully completed surveys were collected and used in the analysis.
Measurement
All measures used in this research were based on well-validated measures. Table I in the Appendix provides a comprehensive overview of the original survey items used in this research. A number of this survey items were dropped when constructing measures due to validity or reliability reasons, which will be discussed later. The measures that are used to measure the independent, dependent and control variables will be separately described below.
Independent variables
Temporal dimension. The independent variable representing the temporal dimension
is the degree of stage overlap, in other words the extent to which tasks are performed simultaneously. The measurement scale for the degree of stage overlap is inspired by Zirger and Hartley (1996). The variable is measured on the basis of the survey item “The different tasks during the project were carried out”. This item was measured on a seven-point scale
ranging from “fully sequentially” to “fully simultaneously”.
Informational dimension. The informational dimension is presented by the
independent variable “timing of freezing”. The measurement scale corresponding with this variable is adopted from Biazzo (2009) and Eisenhardt and Tabrizi (1995) and consists of the percentage of completion of the development process at the time of determination of the project definition and the decisions with regards to the process.
Organizational dimension. The independent variable representing the organizational
project specific activities were listed and defined for each stage of the project”. All four items were measured on a seven-point Likert-type scale. The scores on the four different items were averaged in order to arrive at the construct project process structure.
Market turbulence. A two-item, seven-point Likert-type scale was adapted from
Jaworski & Kohli (1993) to measure market turbulence, which can be described as the degree of uncertainty and instability concerning the composition and needs of customers. An
example of an item used to measure market turbulence is “in our kind of business customers’ product preferences change quite a bit over time”. The average of the scores on both items
represents the construct market turbulence.
Dependent variable
The performance of NPD projects in this research consists of seven sub variables. NPD project performance is measured by a seven-point scale ranging from “significantly worse than initial expectations” to “significantly better than initial expectations” inspired by Schleimer and Faems (2015) and Ahmad, Mallick and Schroeder (2012). The items used are “product development costs”, “product quality”, “technical performance with respect to specifications”, “time-to-market”, “market share”, “overall profitability of the product” and “overall commercial success of the product”. A construct of NPD project performance was calculated by averaging the scores of the seven different items. However, the scores on the independent items were also used in the analysis.
Control variables
The control variables were measured based on simple scales. The project group size is measured by the number of people that participated in the project. The measure of project duration consists of the number of months that the project lasted. Firm size is measured by the total amount of employees working in the firm. Finally, firm age is measured by the number of years the firm exists.
Measurement validity & reliability
1994). These values should be above .6 (Boyer and Pagell, 2000). An overview of the final results of the validity and reliability analysis can be found in Table II, while an overview of the original survey items is provided in Table I in the Appendix. As can be seen from Table II, the final variability and reliability scores of the constructs used in this research are above the aforementioned recommended values. Therefore, they are assumed to be sufficient for the continuation of the analysis.
Table II
Validity & reliability of constructs
Survey Items NPD Project Performance Project Process Structure Market Turbulence
Perf2 .78 .19 -.13 Perf5 .76 .12 .20 Perf7 .74 .08 .27 Perf3 .70 -.10 -.05 Perf6 .69 -.10 .26 Perf4 .66 -.26 -.35 Perf1 .56 -.30 -.20 Struc1 .06 .92 .05 Struc3 .05 .89 -.08 Struc2 -.10 .70 .01 Struc4 -.06 .69 -.08 MT2 -.12 .07 .85 MT1 .27 -.24 .74 Cronbach’s α .81 .84 .67
The meanings of the different survey items can be found in table I in the Appendix. A bold number shows that the measure loaded on a specific factor.
Results
Descriptive statistics
TABLE III
Descriptive statistics for and correlations between research variables
Variables Mean Standard
Deviation 1 2 3 4 5 6 7 8 9 1. NPD Performance - Production Costs - Quality - Technical Specifications - Time to market - Market Share - Profitability - Commercial success 4.45 3.76 5.04 4.81 3.53 4.54 4.51 4.94 0.85 1.38 0.99 1.04 1.52 1.22 1.16 1.23 -.10 -.36* .00 -.13 -.30* .17 .04 .20 -.14 -.34* .13 .08 -.31* -.05 -.02 .05 .03 -.33* .18 .16 -.27† .31* .08 .21 .05 .04 -.07 -.14 -.04 .03 .06 .30* -.08 -.32* -.10 -.12 -.09 .21 -.02 .08 .14 -.03 .21 .09 .10 .06 .12 .01 -.10 -.24† .11 -.11 -.22 .08 -.11 .03 .09 -.04 -.06 .07 -.17 .15 .17 .17
2. Project Group Size 14.78 39.59 .46** .48** .03 .35* -.01 .24† .06
3. Project Duration 17.65 21.28 .16 -.13 .32* .23 .11 -.10 4. Firm Size 5783.29 20192.59 .23 .30* -.17 .18 .05 5. Firm Age 70.33 93.50 -.24 -.18 .09 -.04 6. Overlapping Stages 4.30 1.40 .27† .00 .11 7. Freezing 41.02 31.45 .30* .04 8. Process Structure 4.92 1.25 .05 9. Market Turbulence 4.83 1.16
Notes. † = p < .10, * = p < .05, ** = p < .01. N’s vary from 45 to 49 because of accidental missing data. For independent variables measured by
However, they also indicate a potentially negative relationship between overlapping stages and NPD project performance in terms of production costs in contrast with the expectations.
Analysis
The hypotheses have been tested on the basis of a linear regression analysis. In order to test the moderation effects, a number of interaction variables have been created. To counteract possible multicollinearity problems, mean-centred independent and interaction variables were used (Cronbach, 1987; Jaccard et al., 1990). There was no evidence for an unacceptable degree of multicollinearity, since all independent variables scored below the norm of 5 (Hair et al., 2006) on the Variance Inflation Factor (VIF). This factor describes the increase in variance of a regression coefficient on the basis of correlations between independent variables (Marquardt, 1970).
Table IV shows the regression results, which facilitates the comparison of the different effects of the independent and moderator variables on NPD project performance. Regression model C shows the effects of the control, independent en moderation variables on the construct
performance variable, while regression models 1 to 7 each show the effects of these variables on one of the seven different dependent performance variables. Since models C, 1, 2, 4 and 6 are insignificant, only the effects on the performance variables technical specifications (model 3, F = 2.06, p < .1), market share (model 5, F = 2.16, p < .05) and commercial success (model 7, F = 2.00, p < .1) will be discussed.
Hypotheses 1, 2 and 3 assumed different direct relationships between the three flexibility dimensions (temporal, informational and organizational) and NPD project performance. A direct positive relationship was expected between overlapping stages and performance (H1) and freezing and performance (H2). Moreover, a direct negative relationship was expected between project process structure and performance (H3). Regression models 3, 5 and 7 show that no empirical evidence has been found for these relationships. Therefore, hypotheses 1, 2 and 3 will be rejected.
has a positive impact on two performance variables: market share (β = .02, p < .05) and commercial success (β = .01, p < .10). This means that hypothesis 4b is partially accepted. Regression models 3 and 5 imply that the interaction between project process structure an market turbulence has a significant negative relationship with NPD project performance in terms of technical specifications (β = -.25, p < .05) and market share (β = -.38, p < .01). This is in line with the expectations, which results in the partially acceptance of hypothesis 4c.
TABLE IV
Results of regression analysis
Variables (C) (1) (2) (3) (4) (5) (6) (7)
Intercept 4.31** 4.02** 5.05** 4.60** 3.90** 4.35** 4.14** 4.36** Project Group Size -.00 -.02† -.01 -.01† .00 .01 .00 .01 Project Duration .00 -.01 .01 .02† -.02 -.01 .00 .00 Firm Size .00 .00 .00† .00* .00 .00 .00 .00 Firm Age .00 .00 .00 .00 .00 .00 .00 .01** Overlapping Stages -.01 -.03 -.11 -.20 .10 .21 -.02 .18 Freezing .01 .00 .01† .01 .01 .01 .01 .00 Process Structure -.11 -.34† -.01 -.10 -.29 .10 -.24 -.09 Market Turbulence .27† .22 .10 .18 .01 .26 .38* .42* Overlapping Stages * Market Turbulence -.15 -.09 -.21† -.09 -.09 -.32* -.21 -.28† Freezing * Market Turbulence .01 .00 .01 .01 .01 .02* .00 .01† Process Structure * Market Turbulence -.11 .21 -.15 -.25* -.20 -.38** .09 -.22 R squared .35 .37 .33 .45 .31 .45 .25 .42 F value 1.29 1.60 1.32 2.06† 1.25 2.16* .91 2.00† Highest VIF 4.39 4.17 4.17 4.40 4.17 4.17 4.17 4.17 Notes. † = p < .10, * = p < .05, ** = p < .01.
Discussion
The results from this research did not fully match the expectations. The empirical findings have, for example, shown that the degree of stage overlap, the timing of freezing and the degree project process structure did not appear to have a direct relationship with NPD project performance. This is not in line with previous literature, which describes that overlapping stages and freezing are positively associated with different performance measures (Eisenhardt & Tabrizi, 1995; Iansiti, 1995; Tatikonda & Montoya-Weiss, 2001), while project process structure is negatively associated with performance (Bhattacharya, Krishnan, & Mahajan, 1998, Thomke, 1997). A potential explanation for these unexpected results can be found in the article of Biazzo (2009). In this article is stated that there is a “need for a contingent approach in the design of new product development processes” (Biazzo, 2009, p. 336),
because development process designs that are effective in certain environments do not have to be equally effective in uncertain environments (Mullins & Sutherland, 1998; Song &
Montoya-Weiss, 1998). According to Biazzo (2009), the concept of flexibility is especially relevant in dynamic and uncertain environments. Given that firms were not selected on the basis of their environment, the sample of this research consists of firms with relatively certain environments as well as firms with relatively uncertain and dynamic environments. Therefore, flexibility may not be relevant for this sample, which could explain the surprising results.
The lack of evidence for direct relationships between the three flexibility variables and NPD project performance, however, does not indicate that flexibility is not relevant for NPD projects at all. The results have shown that flexibility can be relevant in case of market turbulence. However, the impact of flexibility on NPD projects in case of a turbulent market is also not entirely in line with the expectations. The results indicated a negative impact of the interaction between the degree of stage overlap and market turbulence on NPD project
Loch; 1999b). In addition, some authors state that stage overlap should only be implemented in case of low environmental uncertainty (Cordero, 1991; Lincke, 1995), since the degree of uncertainty indicates the amount of changes that will occur, which become increasingly difficult to implement over time (Loch & Terwiesch, 1998). A subsequent increase in development time could result in a competitive disadvantage, which potentially clarifies the negative effect of stage overlap and market turbulence on market share and commercial success.
Furthermore, the empirical findings of this research provided support for the hypothesized impacts of the interactions between market turbulence and two flexibility dimensions on NPD project performance. The findings show that the interactions between freezing and market turbulence and between project process structure and market turbulence were respectively positively and negatively associated with NPD project performance.
On the basis of the foregoing, it can be concluded that NPD project performance is, especially in case of market turbulence, positively influenced by the informational dimension and
negatively influenced by the temporal and organizational dimensions.
Theoretical & managerial implications
This research contributes to the literature and practice in different ways. Firstly, empirical evidence for the influences of the interactions between the three flexibility dimensions of Biazzo (2009) and market turbulence on different NPD project performance variables was provided. On the basis of this evidence, the non-existence of a clear distinction between structured and flexible NPD processes, as proposed by Biazzo (2009), was clarified.
definition are recommended in order to increase market share and commercial success. Additionally, a decrease in the predetermination of stages and corresponding activities is recommended in order to increase technical specifications and market share in case of high market turbulence. Finally, it is recommended to take no action in case of a low degree of market turbulence.
Limitations and future research
The first limitation to this study was the small sample size of 50 firms. A relatively large part of the regression results was insignificant and this is possibly due to the small sample size. To prevent for a lack of statistical power, the sample size should be sufficiently large. Therefore, future research should aim for a larger sample size.
The second limitation was the large amount of missing data. Consistent with the first limitation, a large amount of missing data could result in a lack of statistical power and significant regression results. Future research should therefore manage the data collection process more strictly. For instance, by personally distributing (and checking) surveys or by making one person responsible for the data collection. However, the advantage of spreading the responsibility for data collection is that more firms can be approached.
The third limitation was located in the measurement of NPD project performance. In
accordance with many studies on NPD projects, this research made use of measurement scales based on expectations or project goals (Mallick & Schroeder, 2005). The use of scales based on expectations or goals can be problematic, since they are subjective and can easily be adjusted. Therefore, the use of more objective measures, such as competitive ones, would be recommended for future research.
The fourth and last limitation was the omission of other variables of the flexibility
dimensions. Variables, such as iterations, were omitted in this research because of a small sample size and complexity. However, using such variables in the regression analysis could result in more precise conclusions. Therefore, future research should consider to use more (or all) flexibility variables.
References
Bacon, G., Beckman, S., Mowery, D., & Wilson, E. (1994). Managing product definition in high-technology industries: A pilot study. California Management Review, 36 (3), 32-56.
Bhattacharya, S., Krishnan, V., & Mahajan, V. (1998). Managing New Product Definition in Highly Dynamic Environments. Management Science, 44 (11), 50–64.
Biazzo, S. (2009). Flexibility, structuration, and simultaneity in new product development. Journal of Product Innovation Management, 26 (3), 336-353.
Borgeld, R. (2016). ‘Insights in the Relationship between the Temporal Dimension of Flexibility in the NPD process and Innovation Project Management: a Meta-Analysis’. Msc Thesis, University of Groningen, Groningen.
Boyer, K. K., & Pagell, M. (2000). Measurement issues in empirical research:
Improving measures of operations strategy and advanced manufacturing technology. Journal of Operations Management, 18 (3), 361-374.
Buganza, T., Dell’Era, C., & Verganti, R. (2009). Exploring the Relationships Between Product Development and Environmental Turbulence: The Case of Mobile TLC Services. Journal of Product Innovation Management, 26 (3), 308-312.
Calantone, R., Garcia, R., & Dröge, C. (2003). The effects of environmental turbulence on new product development strategy planning. Journal of Product Innovation Management, 20 (2), 90-103.
Chesbrough, H. (2007). Business model innovation: it's not just about technology anymore. Strategy & leadership, 35 (6), 12-17.
Cooper, R. G. (1990). Stage-Gate systems: a new tool for managing new products. Business horizons, 33 (3), 44-54.
Cooper, R. G. (2001). Winning at New Products. Accelerating the Process from Idea to Launch. Cambridge, MA: Perseus Publishing.
Cooper, R. G. (2017). Idea-to-Launch Gating Systems: Better, Faster and More Agile. Research-Technology Management, 60 (1), 48-52.
Cooper, R. G., & Kleinschmidt, E. J. (1994). Determinants of timeliness in product development. Journal of Product Innovation Management, 11 (5), 381-396.
Cooper, R. G. & Sommer, A. F. (2016). From Experience: The Agile–Stage-Gate Hybrid Model: A Promising New Approach and a New Research Opportunity. Journal of Product Innovation Management, 33 (5), 513-526.
Cordero, R. (1991). Managing for speed to avoid product obsolescence: A survey of techniques. Journal of Product Innovation Management, 8 (4), 289-294.
Cronbach, L. (1987). Statistical tests for moderator variables. Psych. Bull, 102, 414-417.
Crossan, M. M., & Apaydin, M. (2010). A Multi-Dimensional Framework of Organizational Innovation: A Systematic Review of the Literature. Journal of Management Studies, 47 (6), 1154-1191.
D’Aveni, R. (1994). Hypercompetition. New York: Free Press.
Egelhoff, W. G. (1991). Information-processing theory and the multinational enterprise. Journal of International Business Studies, 22 (3), 341–68.
Eisenhardt, K. M., & Tabrizi, B. N. (1995). Accelerating adaptive processes: Product innovation in the global computer industry. Administrative Science Quarterly, 40 (1), 84-110.
Fischer, E. (2017). ‘The antecedents of innovative NPD performance in turbulent environments: an examination of the most important flexibility constructs and dimensions on a project level’. Msc Thesis, University of Groningen, Groningen.
Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4 (3), 28-36.
Golden, W., & Powell, P. (2000). Towards a definition of flexibility: in search of the Holy Grail?. Omega, 28 (4), 373-384.
Gupta, A. K., Raj, S. P. & Wilemon, D. (1986). A model for studying R&D-marketing interface in the product innovation process. Journal of Marketing, 50 (2), 7–17.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. Upper Saddle River, NJ: Prentice-Hall.
Hair, J. F. J., Black W. C., Babin B. J., Anderson R. E., & Tatham R. L. (2006). Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall.
Hoegl, M. Paraboteaah, K. P., & Munson, C. L. (2003). Team-level antecedents of individuals’ knowledge networks. Decision Sciences, 34 (4), 741-770.
Iansiti, M. (1995). Shooting the Rapids: Managing Product Development in Turbulent Environments. California Management Review, 38 (1), 37–58.
Jaccard, J., Turrisi, R., Wan, C. K. (1990). Interaction Effects in Multiple Regression. Sage Publication, Newbury Park, C. A.
Jansen, J. J. P., van den Bosch, F. A. J., & Volberda, H. W. (2006). Exploratory Innovation, Exploitative Innovation, and Performance: Effects of Organizational Antecedents and Environmental Moderators. Management Science, 52 (11), 1661-1674.
Jaworski, B., & Kohli, A. K. (1993), Market Orientation: Antecedents and Consequences. Journal of Marketing, 57 , 53–70.
Kalyanaram, G., & Krishnan, V. (1997). Deliberate product definition: Customizing the product definition process. Journal of Marketing Research, 276-285.
Kim, N., & Atuahene-Gima, K. (2010) Using Exploratory and Exploitative Market Learning for New Product Development. Journal of Product Innovation Management, 27 (4), 519-536.
Kitchen, P. J., & Spickett-Jones, G. (2003). Information processing: A critical
literature review and future research directions. International Journal of Market Research, 45 (1), 73–98.
Kleinschmidt, E. J., de Brentani, U., & Salomo, S. (2007). Performance of Global New Product Development Programs: A Resource-Based View. Journal of Product Innovation Management, 24 (5), 419-441.
Lee, S. M., & Trimi, S. (2016). Innovation for creating a smart future. Journal of Innovation & Knowledge.
Lincke, W. (1995). Simultaneous Engineering. Munich: Hanser.
Liu, Y., Li, Y., & Wei, Z. (2009). How organizational flexibility affects new product development in an uncertain environment: Evidence from China. International Journal of Production Economics, 120 (1), 18-29.
Loch, C. H., & Terwiesch, C. (1998). Communication and uncertainty in concurrent engineering. Management Science, 44 (8), 1032-1048.
MacCormack, A., & Verganti, R. (2003). Managing the sources of uncertainty: Matching process and context in software development. Journal of Product Innovation Management, 20 (3), 217-232.
Mallick, D. N., & Schroeder, R. G. (2005). An integrated framework for measuring product development performance in high technology industries. Production and Operations Management, 14 (2), 142-158.
Marovska, N. (2016). ‘The impact of information flexibility on the project-level innovation performance: a meta-analysis’. Msc Thesis, University of Groningen, Groningen.
Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12, 591–612.
Mullins, J.W. and Sutherland, D.J (1998). New product development in rapidly changing markets: an exploratory study. Journal of Product Innovation Management, 15 (3), 224–236.
Nunnally, J. C., & I. H. Bernstein. (1994). Psychometric theory. NewYork: McGraw-Hill.
Ricciardi, F., Zardini, A., & Rossignoli, C. (2016). Organizational dynamism and adaptive business model innovation: The triple paradox configuration. Journal of Business Research, 69 (11), 5487-5493.
Schleimer, S. C., & Faems, D. (2015). Connecting Interfirm and Intrafirm Collaboration in NPD Projects: Does Innovation Context Matter? Journal of Product Innovation Management, 33 (2), 154-165.
Sethi, R., & Iqbal, Z. (2008) Stage-Gate controls, learning failure, and adverse effect on novel new products. Journal of Marketing, 72 (1), 118-134.
Song, X. M., & Montoya-Weiss, M. M. (1998). Critical Development Activities for Really New versus Incremental Products. Journal of Product Innovation Management, 15 (2), 124–135.
Sorensen, J. B., & Stuart, T. E. (2000). Aging, Obsolescence and Organizational Innovation. Administrative Science Quarterly, 45, 81-113.
Souder, W. E., & Moenaert, R. K. (1992). Integrating Marketing and R&D Project Personnel within Innovation Projects: An Information Uncertainty Model. Journal of Management Studies, 29 (4), 485– 512.
Tatikonda, M. V., & Montoya-Weiss, M. M. (2001). Integrating operations and marketing perspectives of product innovation: The influence of organizational process factors and capabilities on development performance. Management Science, 47 (1), 151-172.
Tatikonda, M. V., & Rosenthal, S. R. (2000). Successful execution of product
development projects: balancing firmness and flexibility in the innovation process. Journal of Operations Management, 18, 401-425.
Terwiesch, C., & Loch, C. H. (1999a). Managing the Process of Engineering Change Orders: The Case of the Climate Control System in Automobile Development. Journal of Product Innovation Management, 16 (2), 160–172.
Terwiesch, C., & Loch, C. H. (1999b). Measuring the Effectiveness of Overlapping Development Activities. Management Science, 45 (4), 455–465.
Terwiesch, C., Loch, C. H., & De Meyer, A. (2002). Exchanging Preliminary Information in Concurrent Engineering: Alternative Coordination Strategies. Organization Science, 13 (4), 402– 419.
Thomke, S. H., & Reinertsen, D. (1998). Agile Product Development: Managing Flexibility in Uncertain Environments. California Management Review, 41 (1), 8-30.
Tushman, M. L., & Nadler, D. A. (1978). Information Processing as an Integrating Concept in Organizational Design. Academy of Management Review, 3 (3), 613-624.
Verganti, R. (1999). Planned flexibility: linking anticipation and reaction in product development projects. Journal of Product Innovation Management, 16 (4), 363-376.
Wang, Y. & Li-Ying, J. (2014). When does inward technology licensing facilitate firms’ NPD performance? A contingency perspective. Technovation, 34 (1), 44-53.
Weiss, A. M. & Heide, J. B. (1993). The nature of organizational search in high technology markets. Journal of Marketing Research, 30 (2), 220–233.
Zhao, Z. J., & Anand, J. (2009). A multilevel perspective on knowledge transfer: Evidence from the Chinese automotive industry. Strategic Management Journal, 30 (9), 959-983.
Zirger, B. J., & Hartley, J. L. (1996). The effect of acceleration techniques on product development time. IEEE Transactions on Engineering Management, 43 (2), 143-152.
Appendix
Table I
Research measures
Survey Items Measure and scale Dependent variables
NPD Project performance
Perf1 The success of the project in terms of product development costs. (1 = significantly worse than initial expectations; 7 = significantly better than initial expectations)
Perf2 The success of the project in terms of product quality. (1 =
significantly worse than initial expectations; 7 = significantly better than initial expectations)
Perf4 The success of the project in terms of time to market. (1 =
significantly worse than initial expectations; 7 = significantly better than initial expectations)
Perf5 The success of the project in terms of market share. (1 = significantly worse than initial expectations; 7 = significantly better than initial expectations)
Perf6 The success of the project in terms of overall profitability of the product. (1 = significantly worse than initial expectations; 7 = significantly better than initial expectations)
Perf7 The success of the project in terms of overall commercial success of the product. (1 = significantly worse than initial expectations; 7 = significantly better than initial expectations)
Independent variables
Overlapping Stages
Stageoverlap The different tasks during the project were carried out (1 = fully sequentially; 7 = fully simultaneously)
Freezing
Freezing Extent to which the development process was completed at the time of determination of the project definition and the decisions with regards to the process (in %)
Project Process Structure
Struc1 During the project a standardized set of stages and go/no go decisions guided the activities from idea to launch. (1 = fully disagree; 7 = fully agree)
Struc2 In the NPD project specific activities were listed and defined for each stage of the project. (1 = fully disagree; 7 = fully agree)
Struc3 The NPD project had clearly defined go/no go decision points after each stage. (1 = fully disagree; 7 = fully agree)
Struc4 In the NPD project there were clearly defined gatekeepers who reviewed the project at each stage gate and made go/no go decisions. (1 = fully disagree; 7 = fully agree)
Market Turbulence
MT1 In our kind of business customers’ product preferences change quite a bit over time. (1 = fully disagree; 7 = fully agree)
MT3* We are witnessing demand for our products from customers who never bought them before. (1 = fully disagree; 7 = fully agree) MT4* New customers tend to have product-related needs that are different
from those of our existing customers. (1 = fully disagree; 7 = fully agree)
MT5* We cater to many of the same customers that we used in the past. (1 = fully disagree; 7 = fully agree)
Control Variables
Project Group Size
Groupsize Number of people that participated in the project
Project Duration
PDuration Number of months that the project lasted.
Firm Size
FSize Total amount of employees working in the firm.
Firm Age
FAge The number of years the firm exists.
