How different modes of project flexibility affect NPD project performance,
and the influence of technological and market turbulence
by
EDWARD DE BRUIJN S2193655
Reitemakersrijge 6-32 9711HT Groningen
MScBA, specialization Strategic Innovation Management University of Groningen
Faculty of Economics and Business
June 25, 2018
First supervisor: dr. van der Bij Co-assessor: dr. Biemans
Abstract
Managers of the new product development (NPD) process are faced with dynamic and turbulent environments that requires flexibility to cope with changing speed of technology and market demands. This study examines how three different modes of project flexibility in the NPD process affect innovation project performance, and subsequently how these relationships are influenced by different degrees of technological and market turbulence. The research uses the organization information processing theory (OIPT) and its contingency perspective to understand the relationships. Data from a cross-sectional sample of 50 completed product development projects is analyzed via a hierarchical moderated regression approach. The findings show that: (1) formalizing the NPD process positively impacts the NPD project effectiveness; (2) too much overlap between the stages in the NPD process is detrimental for the NPD project efficiency. Therefore, this report sets an empirical basis for future research on the influence of different dimensions of project flexibility on the NPD project performance.
Introduction
Nowadays, managers of the new product development (NPD) projects are faced with fundamental challenges (MacCormack et al., 2001). Particularly, coping with turbulent environments is in the forefront for managers seeking guidance on how to reduce the risk of failure of either the project or the resulting product (Akgün et al., 2007; Calantone et al., 2003). Especially, considering the problem that the failure rates for new products remain alarmingly high, averaging 50% (Clancy & Stone, 2005; Liu et al., 2009; Stockstrom & Herstatt, 2008).
With such a poor rate of NPD project success and the increasing rate of organizational change and environmental uncertainty (Belassi et al., 2007), a closer look at the determinants of NPD project performance is necessary to ensure continued organizational survival and growth.
Several studies suggest different ways of coping with environmental turbulence (i.e., uncertainty) through innovation, and notably the NPD project structure. Uncertainty is associated with the absence of information related to decision making (Daft & Lengel, 1986; Karimi et al., 2004). Thus, there are three NPD strategies to cope with uncertainty: (1) anticipation; (2) reaction; (3) agile product development (Kalyanaram & Krishnan, 1997; Verganti, 1999; Thomke & Reinertsen, 1998).
First, anticipation entails the early and sharp definition of the product. For example, a well-known and popular sequential approach is the Stage-Gate model (Cooper, 1990). However, despite the claims of inflexibility of the sequential models (e.g., Sethi & Iqbal, 2008), in practice firms experiment with implementing Agile-Stage-Gate hybrids. Some firms report significant improvements, i.a., faster responses to changing market conditions and customer needs (Cooper & Sommer, 2018). Second, the reaction strategy involves moving the freeze milestone of the product definition as close to the market launch as possible. Third, agility improves development efficiency to make teams more capable of responding to change in a timely fashion and maximizing their throughput (Cockburn & Highsmith, 2001).
NPD process with to achieve optimal innovation project performance (Calantone et al., 1995). Thus, contingent upon the nature of the turbulence, Biazzo (2009) identified three different flexibility dimensions, which were researched in meta-analyses. Further, in this study, the three dimensions are used to empirically test how the different modes of project flexibility in the NPD process affect NPD project performance. Additionally, uncertainty is caused by technological turbulence and market turbulence (Calantone et al., 2003). Therefore, we examine how both types of different degrees of turbulence moderate the relationship between different modes of project flexibility and NPD project performance.
As of yet, these dimensions are not tested in an empirical way on a project-level. Especially, to gain more insight about the effects of project flexibility have on project innovation performance. Thus, the current literature consists of mainly conceptual works (e.g., Biazzo, 2009) and the empirical evidence is limited. This research uses the organizational information processing theory (OIPT) and its contingency perspective to explain the relationships. The theory builds on the notion that organizational decision making is a process governed by uncertainty (Melville & Ramirez, 2008). We believe this research gap of project flexibility forms an opportunity to be explored for a deeper understanding and broader explanatory power of the NPD proficiency for research and management practice. Bear in mind, that NPD projects can be a risky undertaking (Calantone et al., 1995).
Based on the identified literature gap and the goals of our research, we have formulated a research question: How do different modes of project flexibility in the NPD process affect innovation project performance in different degrees of technological and market turbulence?
impact NPD processes to achieve optimal NPD performance. Accordingly, to mitigate risk and uncertainty in coping with environmental turbulence.
The remainder of this paper is organized as follows: First, we elaborate on the theoretical background of the study. Then, we introduce the conceptual model and develop research hypotheses. Subsequently, we describe our research methodology and present the results of our statistical analysis. The paper concludes with a discussion of the key findings and implications, and we address study limitations and identify future research avenues.
Theoretical background Organizational information processing theory
Organizational information processing theory (OIPT) is conceptually underpinned by the contingency theory (Moser et al., 2017). According to Drazin and van de Ven (1985), contingency theory relies on the premise that context and structure must fit together to increase organizational performance. In this sense, OIPT stipulates that an adequate information level of managers determines their decision-making effectiveness and thereby firm performance (Galbraith, 1974; Melville & Ramirez, 2008; Moser et al., 2017; Tushman & Nadler, 1978; Wang, 2003). Therefore, this study draws on the conceptualization of OIPT and its contingency perspective in the context of organizational decision making.
viewed as composed of sets of groups or subunits. The three sources of work-related uncertainty are the unit task characteristics, unit task environment, and inter-unit task interdependence. Thus, as the amount of uncertainty which a subunit faces increases, so does the information processing requirements (Tushman & Nadler, 1978). On the other hand, the information processing capabilities are affected by the organic or mechanistic design of subunits and the coordination and control mechanisms. Then, to achieve optimal performance, there should be a fit between information processing requirements and information processing capabilities of the organization (Tushman & Nadler, 1978). Mismatch in capacity and requirements should be associated with lower organizational performance (Tushman & Nadler, 1978).
Figure 1: Information Processing Framework (adapted from Tushman & Nadler, 1978).
Technological and market turbulence
processing activities (Glazer & Weiss, 1993). Ergo, the necessity to recognize potential changes in the industry environment, further affects managers’ information needs. In this case, unexpected events can quickly render existent strategic plans obsolete (Moser et al., 2017). Then, when environmental turbulence is high, the need for innovation increases (Liu & Woywode, 2013). So, if uncertainty increases, information processing requirements increase as well (e.g., Glazer & Weiss, 1993; Moser et al., 2017; Peng & Luo, 2000).
This research draws on technological and market turbulence as the moderators within the analysis framework, because a firm’s innovation processes are embedded in its environmental context (Huang et al., 2018; Jansen et al., 2006; Lichtenthaler, 2009). Especially, to high levels of technological turbulence, which result in increased project flexibility as a common reaction (Candi et al., 2013). Moreover, this line of reasoning is in alignment with OIPT and its contingency perspective. Organizations in dynamic and uncertain environments require more advanced approaches for searching, collecting, and processing information (Galbraith, 1974; Tushman & Nadler, 1978). In turn, these organizations adopt a less centralized, and more organic structure. Thus, a fit or match between information processing requirements and information processing capabilities balances uncertainty with investments into required information processing capabilities. Accordingly, such a fit is considered to support the effectiveness of decision making and overall firm performance (Moser et al., 2017).
Following the studies of Calantone et al. (2003) and Sethi and Iqbal (2008), technological and market turbulence are also considered additional direct antecedents to the NPD performance. Then, technological turbulence refers to the rate of technology change and unpredictability, which rapidly makes a firm’s existing technological knowledge obsolete. Market turbulence refers to degree of variation in customer preference and demand (Jaworski & Kohli, 1993).
Flexibility in the NPD process
According to the current literature, researchers have identified three different approaches to structure an NPD process, and subsequently how to cope with different degrees of environmental turbulence.
technology and market needs. In other words, the focus is on developing a structured process with clearly defined and sequential phases, through which the future product is defined, designed, transferred to manufacturing plant, and rolled out to the market (Iansiti, 1995). Additionally, when implementing changes during the specification phase of the product could be costly and time-consuming (Kalyanaram & Krishnan, 1997; Verganti, 1999). These traditional processes are also known as Stage-Gate processes, and originates from the Stage-Gate model of Cooper (1990). However, according to Cooper and Sommer (2018), the pace of change in many technologies and markets has reached a peak point. Specifically, product cycles have accelerated to the critical point where traditional new product development methods no longer work. Because many of the gating systems that firms use today have their roots in the 1980s and 1990s, and are too cumbersome, too linear, and too rigid to deal with the realities of today’s fast-paced world (Cooper, 2017). Therefore, a handful of leading firms have reinvented their innovation processes by building in agile methods from the information technology industry. Thereby, creating an Agile-Stage-Gate hybrid model, which makes the process more iterative and adaptive (Cooper, 2014; Cooper, 2016). For example, frequently cited benefits include faster response to changing market conditions and customer needs and higher project team morale (Cooper & Sommer, 2018).
Second, practices that support flexibility in product specifications to deal with uncertain environments are sometimes referred to as agile product development (Thomke & Reinertsen, 1998). The agile development process emerged from the software industry, where it has delivered positive results since the 1990s (Rigby et al., 2016). According to Beck et al. 2001, agile product development practices value collaboration, response to change, and a working product. Accordingly, agile methods address these values by using adaptive planning and evolutionary delivery through a time-boxed, iterative approach. As a result, continually evolving the product definition, which implies short-term rigid project planning coupled with flexible project specifications (Candi et al., 2013; Cooper & Sommer, 2018). Thus, flexibility intertwines with rigidity in different stages of the NPD process.
complexity is a significant issue that firms continually face in their development projects, due to rapid technological and market changes (Jongbae & Wilemon, 2007). Then, the NPD processes need to be flexible and responsive (Iansite, 1995). In addition, flexible design technologies outperform projects using inflexible technologies, especially in unstable environments (Thomke, 1997). Appropriately, project flexibility has been proposed as a means to cope with high levels of environmental turbulence (Candi et al., 2013).
Thus, as mentioned before, there are three different strategies to structure an NPD process. Namely, (Agile)-Stage-Gate model (i.e., anticipation strategy), agile product development, and project flexibility (i.e., reaction strategy). Biazzo (2009) explained the contradicting views to the lack of descriptive accuracy from the studied phenomena.
This empirical study focuses on the particular effects between three different dimensions of project flexibility to better understand how each relates to innovation project performance. Additionally, how different degrees of technological and market turbulence moderate these relationships. Ergo, under which conditions the three types of project flexibility are likely to be chosen to deal with uncertainty. Thus, Biazzo (2009) identified the three dimensions of flexibility in NPD processes:
1. Temporal dimension 2. Informational dimension
3. Organizational dimension
Temporal dimension
information exchange, is regarded as a core technique for faster product development (Lin et al., 2010).
Further, as reported by Krishnan et al. (1997), frequent exchange of design information enables the concurrent execution of coupled activities in an overlapped process - as long as the downstream activity begins earlier by using preliminary information (Krishnan et al., 1997). Along these lines, overlapping is considered to avoid costly late changes or product obsolescence. Besides, overlapping leads to an overall reduction in costs (Roemer & Ahmadi, 2004). However, for example, when parts of the upstream-generated information are finalized early, the upstream activity loses the flexibility to make future changes during the development process (Krishnan et al., 1997). Consequently, this can result in quality loss, unintended schedule delay, and loss of motivation (Clark & Fuijmoto, 1991; Krishnan et al., 1997).
Moreover, overlapping is particularly promising if the outside world is highly dynamic and characterized by uncertainty (Roemer & Ahmadi, 2004). Conversely, Eisenhardt and Tabrizi (1995) argue that compressing the development process through activity overlaps only yield a time reduction if the market environment is stable and predictable. The dichotomy could be interpreted as trade-offs in choosing the appropriate overlap level (Krishnan et al., 1997; Terwiesch & Loch, 1999). In such a contingency, if the uncertainty over the duration of the project increases for overlapping activities, the project organization has to search for other means to cope with environmental turbulence. For example, using frequent iterations.
increasing NPD project performance. Thus, we hypothesize that overlapping stages will contribute positively to innovation project performance.
H1a:
The degree of overlapping stages in the NPD process positively influences the NPD
project performance.
H1b:
Technological and market turbulence negatively moderate the relationship between the
degree of overlapping stages in the NPD process and the NPD project performance.
Informational dimension
The informational dimension deals with the classification of the development activities and with the investigation of a firm’s product-definition approach (Biazzo, 2009). Marovaska (2016) identified iterations as the main variable of the informational dimension. Iteration refers to the repetition, or rework, of activities to account for changes in their inputs. Therefore, it can be represented by feedback loops, or cycles, in the product development process (Browning, 1998; Yang et al., 2014). Most definitions of iterations suggest that it consists more than repetition and it is associated with the improvement, evolution, or refinement of a design (Eppinger et al., 1997). In this research, iterations are defined as the successful generation of concepts and ideas for the new product and subsequently the proposed design concept and solution (Reid et al., 2016).
Moreover, when uncertainty reigns, changing market conditions and customer needs will affect the NPD process structure, which leads to more iterations building flexibility into understanding (Hong-bae & Hyo-Won, 2008). For example, the agile product development strategy includes a high amount of iterations during the NPD process. Then, highly experimental NPD processes are open to multiple possibilities, rather than guided by a single direction (Cui & Wu, 2017). So, in uncertain environments, multiple design iterations decreases the probability of selecting only one particular design variation instead of multiple ones. In addition, improve cognitive abilities to shift with new information and to adjust accordingly (Eisenhardt & Tabrizi, 1995; Terwiesch & Loch, 1999), by relying on iterations, flexibility, and improvisation.
Related to OIPT, individual development activities are the information-processing units that receive information from their preceding activities and transform it into new information to be passed on subsequent activities (Lin et al., 2008). Therefore, if task uncertainty increases, the amount of information that must be processed among decision makers during task execution increases as well (Galbraith, 1974). In other words, some tasks need to be reworked, because they start on incorrect information from upstream phases (Lin et al., 2008). Thus, upstream development errors are continuously rectified, according to the feedback information from downstream phases (Lin et al., 2008). Subsequently, rework, due to iterations, reduces task uncertainty, i.e. design information, continuously until acceptable solutions are obtained (Hong-bae & Hyo-Won, 2008; Suss & Thomsons, 2012). However, a drawback, each additional iteration takes more time.
Concluding, multiple design iterations accelerate product design (i.e., reduce time-to-market) to a certain extent, by offering more opportunities for a “hit” and decrease uncertainty (Eisenhardt & Tabrizi, 1995). Based on the research discussed above, we hypothesize for a positive contribution of more multiple design iterations to increase the probability of the NPD project success, quality, and performance.
H2a:
The number of design iterations in the NPD process positively influences the NPD
H2b:
Technological and market turbulence positively moderate the relationship between
multiple design iterations in the NPD process and the NPD project performance.
Organizational dimension
Biazzo defines the organizational dimension as: “the formal segmentation of the temporal progression in stages and the definition of the activities that should be occurring in each stage”. Roukema (2014) identified formalization as one of the variables for the organizational dimension. Formalization is the degree to which rules, procedures, instructions, responsibility, and communications are formalized or written down (Deshpandé & Zaltman, 1982).
It has been widely believed that formalization reduces work ambiguity, enable individual focus, learning and decision making, decrease the cost of coordination, and increase efficiency (Perrow, 1986). Subsequently, formalization decreases the time-to-market and unit cost, and increases product quality (Tatikonda & Montoya-Weiss, 2001). It has been shown that a high degree of formalization facilitates the implementation stage of the innovation process (Rogers & Agarwala-Rogers, 1976). Accordingly, a higher unit’s formalization leads to a higher level of exploitative innovation (Jansen et al., 2006).
However, if units face a changing environment, then fixed rules and standard operating procedures will not be able to deal effectively with environmental uncertainty (Tushman & Nadler, 1978). In turbulent times, formalization undermines fruitful collaboration, e.g., lateral relations. Because it can undermine the workforce’s flexibility and adaptability (Moenaert et al., 2000; Ramus et al., 2017). Thus, in complex institutional environments, formalization processes may foster learning traps and lead to organizational inertia (Ramus et al., 2017; Wei et al., 2011).
According to the contingency theory, a firm should design its organization to match its environmental conditions to gain optimal performance (Wang, 2003). Thus, NPD strategy formality can be seen as a contingency factor (Gumusluoglu & Acur, 2016).
In accordance with OIPT, organic organizations characterized by greater decentralized and less formal structure tend to perform better when facing high information processing requirements caused by environmental turbulence (Wang, 2003). Because decentralized and less formalized firms are more likely to make greater use of market research information (Deshpandé & Zaltman, 1982). Respectively, where the nature of the unit’s work is highly certain, small amounts of information are sufficient. Little new information or information processing are required during task performance (Tushman & Nadler, 1978). So, when formalization is low, informal communication can be fostered easily. Then, employees can be motivated to process information about environmental changes in broader perspectives, or search scopes, than their job responsibility (Wei et al., 2011), which is essential during the initiation stage of the innovation process (Zaltman et al., 1973). Vertical mechanisms like formalization require less investments, but their information capacity to facilitate information processing is lower than that of lateral mechanisms (Trautmann et al., 2009).
In stable environments, we hypothesize that formalization will contribute positively to new product performance. However, when facing technological and market turbulence, increasing information processing requirements are detrimental for vertical mechanisms as formalization. Because mechanistic units are not flexible enough to have sufficient information capacity to deal with dynamic environments.
H3a:
Higher levels of formalization in the NPD process positively influences NPD project
performance.
H3b:
Technological and market turbulence negatively moderate the relationship between
Conceptual model
Figure 2: Conceptual model.
Methodology Sample & data collection
The first questionnaire was for senior managers and focused on market success and firm-level variables. The second questionnaire was for project leaders, or highly involved project members, and covered the development of a specific new product and its project-level variables. By collecting data for the independent and dependent variables from different respondents, the potential problem of common method bias was likely mitigated (Candi et al., 2013).
Our sample contained 50 matched pairs of project leaders and senior manager responses, which were used to test the research hypotheses. The sample includes firms belonging to a wide range of industries, such as chemicals, healthcare, information and communication, manufacturing, and technology. The firms ranged in size from very small < 30 employees to very large > 110000 employees, and the median size was 88 employees. Furthermore, most of the participating firms were established, domestically oriented companies with a focus on the business-to-business (B2B) customer segment (M = 4.4), and selling products (M = 3.8), rather than business-to-customer activities and providing services.
Measurements
Based on previous studies, we developed scale items to measure variables. Each construct was measured using one or multiple item(s) and a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree).
Dependent variable
Project performance
is adapted from Ahmad et al. (2013) and Schleimer and Faems (2016).
Independent variables
Temporal dimension.
The overlapping stages measurement is adapted from the research of
Zinger and Hartley (1996). Ergo, overlapping stages addressed the number of overlapped activities in the development process. From this data, the number of overlapped development activities were divided by the total possible number of overlapping activities for that project.
Informational dimension.
The number of design iterations measurement is adapted from
Eisenhardt and Tabrizi (1995). Therefore, the respondents were asked how many iterations occurred in the development of the product. Particularly, an iteration was defined as a redesign of at least 10 percent of a product’s parts.
Organizational dimension.
The measurement of formalization, in and around the NPD
project, is derived from the research of Deshpandé and Zaltman (1982). Thus, formalization measured: (1) whatever situation arose during the project, written procedures were available for dealing with; (2) rules and procedures occupied a central place in the project organization; (3) written records were kept of everyone’s performance; (4) project members were hardly checked for rule violations; (5) written job descriptions were formulated for positions in the project team. The mean of these items (composite score) was used as the measure of formalization (Jansen et al., 2006).
Moderating variables
Technological and market turbulence.
The measurements of technological and market
turbulence were adapted from Jaworski and Kohli (1993). Two items were measured regarding market turbulence and three regarding technological turbulence. The items for the market turbulence assessed the extent to which the composition and preferences of an organization’s customers tended to change over time, and technological turbulence refers to the change associated with new product technologies (Jaworski & Kohli, 1993).
Control variables
Because age builds up resources for innovation, but long timespan since previous product innovation builds inertia and hinders future innovation (Coad et al., 2018). Correspondingly, there is evidence that firm age affect innovation project performance (Hansen, 1999; Sivadas & Dwyer, 2000). Moreover, B2B firms interact with a network of customers during the early stages of NPD to reduce uncertainty and increase the likelihood of new product success (Lynch et al., 2016). Thus, the initial phase proficiency can improve NPD effectiveness and NPD efficiency (e.g., time-to-market), if moderated by intensive communication throughout the entire firm (Durmusoglu et al., 2017).
Analysis
inflation factor was 1.80, which is well below the threshold of 5 (Marquardt, 1970). Third, the interaction terms were entered as a block to analyze the full model.
Table 1a. Exploratory factor analysis loadings
Effectiveness Efficiency M_Perf5 .86 .02 M_Perf7 .84 .09 M_Perf6 .70 .25 M_Perf3 .60 .36 M_Perf2 .59 .51 M_Perf1 -.01 .90 M_Perf4 .30 .71
Bold numbers indicate that the measures loaded to the factor. Table 1b. Exploratory factor analysis loadings
Formalization Technological turbulence Market turbulence
P_Formal1 .84 .06 .18 P_Formal2 .76 -.25 .18 P_Formal3 .74 -.10 -.01 P_Formal4 .59 .17 -.20 P_Formal5 .48 -.07 -.23 M_TT1 .12 .85 .23 M_TT4 -.07 .80 -.12 M_TT2 -.15 .73 .15 M_MT1 .09 .16 .85 M_MT2 -.13 .03 .81
Results
Table 2 presents the descriptive statistics and correlations for the study variables. We found that formalization was positively correlated with NPD project effectiveness (r
= .25, p< .1).
Accordingly, an increase in the degree of formalization is significantly related with an increase in the NPD project effectiveness. Further, there was a significant positive relationship between technological turbulence and NPD project effectiveness (r
= .26, p< .1).
Then, an increase in the level of technological turbulence is significantly related with an increase in NPD project effectiveness. However, we also revealed a significant negative relationship between firm age and %B2B (r
= -.32, p< .05). None of the correlations between
the study variables exceed .65, which leads to less biased estimations by multicollinearity problems (Tabachnick & Fidell, 1996).
This study used the SPSS 25.0 statistical software to carry out the hierarchical moderated regression analysis. Table 3 presents the results of the analyses for overlapping stages, iterations, formalization, technological turbulence, market turbulence, and NPD project efficiency and effectiveness.
Table 2. Descriptive statistics and correlation matrix
Variables M (SD) 1 2 3 4 5 6 7 8 9
1. Efficiency 3.64 (1.24) (.67) 2. Effectiveness 4.77 (.85) .42*** (.82) 3. Overlapping stages 4.30 (1.40) -.23 .03 n.a.
4. Iterations 6.30 (13.34) -.10 -.04 .07 n.a.
5. Formalization 3.60 (1.24) -.18 .25* .08 .15 (.72) 6. Technological turbulence 5.32 (1.11) -.05 .26* .12 -.00 -.08 (.70) 7. Market turbulence 4.83 (1.15) -.12 .13 .11 -.17 -.03 .19 (.67) 8. %B2B 4.35 (1.04) -.22 -.12 -.20 -.03 -.03 .06 -.15 n.a. 9. Firm age 70 (93.50) .00 .06 -.24 .02 .02 -.19 -.03 -.32** n.a. n
= 50. Cronbach’s alphas of the composite scales are reported along the diagonal.
* p
The results of the hierarchical regression analyses offer some support for the research hypotheses. H1a, while suggesting a positive relationship between the degree of overlapping stages in the NPD process and NPD project performance, is not supported. Instead, we demonstrated a significant and negative relationship with NPD project efficiency (b
= -.40, p
< .1). Additionally, H1b, suggesting the same relationship with technological or market turbulence as a negative interaction effect with the degree of overlapping stages on NPD project performance, is also not supported.
Further, the results do not support H2a, proposing a positive relationship between multiple design iterations in the NPD process and NPD project performance. Besides, the same relationship with technological or market turbulence as a positive interaction effect with multiple design iterations on NPD project performance was found to be insignificant and negative, not supporting H2b.
H3a hypothesizes a positive relationship between the level of formalization in the NPD process and NPD project performance. The results support this hypothesis (b
= .23, p< .01).
In particular, with NPD project effectiveness. Also, we found a significant and positive relationship with technological turbulence and NPD project effectiveness (b
= .26, p< .1).
However, H3b proposed a negative interaction role of technological or market turbulence on the relationship between a high degree of formalization and NPD project performance, is not supported.
As for the control variables, we revealed that the %B2B activities have a significant negative relationship with NPD project efficiency (b
= -.43, p< .1), which indicates that an increase in
Table 3. Hierarchical regression results
Hypotheses 1a - 3b
Efficiency Effectiveness
Model 1a Model 1b Model 1c Model 2a Model 2b Model 2c
Steps and variables B B B B B B
Control variables %B2B -.28 -.43** -.41** -.09 -.10 -.10 Firm age -.00 -.00 -.00 .00 .00 .00 Main effects Overlapping stages -.40** -.41** .01 .01 Iterations -.18 -.25 -.04 -.18 Formalization -.24 -.22 .23* .23* Moderators Technological turbulence -.08 -.11 .26** .26** Market turbulence -.23 -.26 .11 .12 Interaction terms Overlapping stages * TT -.21 -.10 Iterations * TT -.28 -.08 Formalization * TT -.19 .00 Overlapping stages * MT .19 -.02 Iterations * MT -.11 -.23 Formalization * MT -.24 -.10 R Square .05 .16 .18 .02 .16 .16 F-value 1.19 2.19* 1.93 .35 2.17* 1.70 Maximum VIF 1.08 1.21 1.80 1.08 1.21 1.80
Standardized regression coefficients are reported. * p
Discussion
This research aimed to study how three different modes of project flexibility in the NPD processes affect innovation project performance. Then, how these relationships are impacted by interactions with technological and market turbulence.
Theoretical implications
The results of the research have several theoretical implications. First, this research contributes to the study of Tatikonda and Montoya-Weiss (2001) by explaining how formalization, i.e., the extent of rules and procedures, positively relates to NPD performance. Particularly, implicating that formalizing the structure of the NPD process, leads to an increase in NPD effectiveness performance. For example, product quality. In this way, our study bears similarities with insights that rules and procedures are established to incrementally improve processes and outputs (Jansen et al., 2006). Besides, formalization was measured encompassing multiple aspects, which is in alignment with the current literature (e.g., Deshpandé & Zaltman, 1982; Jansen et al., 2006). Moreover, formalization does not simply produce learning traps and organizational inertia. Considering, formalization can facilitate collaborative efforts that aim to develop new practices through recombining competing logics. Thus, this observation reinforces the notion that well-defined formal rules and procedures can reduce work ambiguity, enable individual focus, learning and decision making, decrease the cost of coordination, and increase project effectiveness, i.e., product innovation (Fréchet & Goy, 2017; Perrow, 1986; Ramus et al., 2017).
effectiveness) (Krishnan et al., 1997). Thus, this supports the proposition of Cordero (1991), who argues that overlapping should be used mainly for moderate levels of innovation.
Third, this study contributes by inspecting how higher levels of technological turbulence result in an increase of NPD project effectiveness. Respectively, there was no interaction effect, but only a main effect. In addition, our report has resemblance with the concept that implies technological turbulence as a direct antecedent to NPD program performance (e.g., Calantone et al., 2003). It has been widely cited, monitoring and reacting to technological turbulence as major factors in configuring the NPD process (Calantone et al., 2003). Accordingly, technological turbulence can be considered a constant variable. Further, our research also has similarities with the study from Wu and Shanley (2009). They confirmed that in turbulent environments, companies are stipulated to explore new ideas and develop new knowledge. Especially, in order to keep up with technological changes. Then, could it be possible that the degree of technological turbulence has to be moderated by other interaction terms. Because Biazzo (2009) noted it as well, there are more opportunities for other interaction effects with the relationship between different modes of project flexibility in the NPD process and innovation project performance. For example, with product modularity, project radicalness, management support, and customer and supplier involvement. Thus, this implication strengthens the importance of high technological turbulence, which affects NPD project effectiveness.
Managerial implications
manage trade-offs in the course of the NPD process in order to achieve a certain NPD performance outcome.
Second, formalizing the NPD process is beneficial for the NPD project effectiveness. Ergo, project management needs to pay attention to integrating higher levels of formalization (e.g., organizational tasks and roles), take targeted measures, and track the process. Thus, formalizing the NPD process will be advantageous for succeeding in the development of new products.
Third, in practice, it is important for managers to realize that that too much overlap between the stages of the NPD process is detrimental to NPD project efficiency. Beforehand, it is already quite costly and time-consuming to integrate the concept of overlapping with lateral relations. Afterwards, thus with an imbalance in the degree of overlapping stages, can result in quality loss, unintended schedule delay, and loss of motivation (Clark & Fuijmoto, 1991; Krishnan et al., 1997).
Finally, higher levels of technological turbulence contribute to NPD project effectiveness. In this sense, the project management should recognize the potentiality of increasing the NPD project effectiveness by embracing technological turbulent environments. Then, encourage managers the need to make risky investments and sometimes risky decisions in NPD planning (Calantone et al., 2003), which may lead to higher levels of product innovativeness and market outcomes. For example, commercial success of the developed product.
Limitations and Future research
The empirical data on which this research is based have the strength of being derived from dual respondents. Project managers answered questions about the NPD process, while senior managers answered questions about the success of the same NPD projects. However, this study has some limitations and it offers several avenues for future research as well.
performance. Moreover, it would enable analyzing innovation project performance implications at different points in time to contrast the effects of project flexibility. In this way, a longitudinal research method could be used to get more objective results about how different project flexibility modes affect NPD project performance.
Second, our sample size is relatively small, which is prone to low statistical power. As a consequence, this study increased the likelihood that a statistically significant finding represents a false positive result (Dumas-Mallet et al., 2017). Then, future research could benefit from larger sample sizes that may allow for alternative modeling approaches and more refined assessment of contingency factors.
Third, the sample consists mainly of Dutch companies. Then, the question of external validity to other countries remain open. In addition, this research does not take into account the differences between various industries, which could have impact on the nature of the relationships between different types of project flexibility and NPD project performance. Thus, future research should investigate how such conditions impact the studied phenomena.
Fourth, the moderators technological and market turbulence were measured through a separate composite score of their items. However, due to the insignificant interaction effects of this research, it may be a solution to treat technological and market turbulence together in future research. In addition, as mentioned before, the future research should investigate how other moderators might be relevant as well (Biazzo, 2009). For example, supplier involvement and management support.
Fifth, we investigated only product innovation in this study. Other innovations were not considered. For example, new service development and software development. Thus, future research should consider a focus on improvisation in different types of innovation projects and industries, which may lead to generalizability beyond assembled products.
This report provides a detailed description of the process used to collect data from a random sample of innovation-active firms in a wide range of industries as well as details about survey items and variables. So, the article should provide sufficient information for replication in other contexts.
Conclusion
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Appendix. Study measures Dependent variable
NPD project performance (adapted from Ahmad et al., 2013 and Schleimer & Faems, 2015) If you compare the outcomes of the project with the expectations at the start of the project, how did the project perform? Please evaluate the following statements on a scale of 1 (strongly disagree) to 7 (strongly agree).
1. Production costs (M_Perf1). 2. Product quality (M_Perf2).
3. Technical performance with regard to product specifications (M_Perf3). 4. Time-to-market (M_Perf4).
5. Market share (M_Perf5).
6. Profitability of the product (M_Perf6).
7. Commercial success of the product (M_Perf7). Independent variables
Overlapping stages (adapted from Zinger & Hartley, 1996)
Please evaluate the following statement on on a scale of 1 (always sequentially) to 7 (fully simultaneously).
1. Each project consists of different tasks, carried out by separate groups of people. Thus, to what extent were the different tasks performed simultaneously or sequentially? (P_TaskOverlap)
Iterations (adapted from Eisenhardt & Tabrizi, 1995).
1. How many times has the design of the product changed at least 10% during the project? (P_Freezing2)
Formalization (adapted from Deshpandé & Zaltman, 1982 and Jansen et al., 2006).
Please evaluate the following statements on a scale of 1 (strongly disagree) to 7 (strongly agree).
1. Whatever situation occurs, written procedures were always available, which describe how to act in a particular situation (P_Formal1).
2. Rules and procedures played an important role during the project (P_Formal2).
3. The performances of everyone who was involved in the project were recorded in writing (P_Formal3).
4. Project members were hardly checked for not following the rules (P_Formal4). 5. Job descriptions were defined for the various positions within the project team
Moderating variables
Technological turbulence (adapted from Jaworski & Kohli, 1993).
Please evaluate the following statements on a scale of 1 (strongly disagree) to 7 (strongly agree).
1. In our industry, the technology is rapidly changing (M_TT1).
2. In our industry, technological changes offer great opportunities (M_TT2). 3. In our industry, technological developments are limited (M_TT4).
Market turbulence (adapted from Jaworski & Kohli, 1993).
Please evaluate the following statements on a scale of 1 (strongly disagree) to 7 (strongly agree).
1. In our industry, the needs of the customers are constantly changing (M_MT1). 2. Our customers are constantly looking for new products (M_MT2).
Control variables Firm age
1. How old is your company in years? (M_FirmAge) %B2B
The following statement is about the turnover of the company.
