The antecedents of innovative NPD performance in turbulent environments: an
examination of the most important flexibility constructs and dimensions on a
project level
Abstract
The development of innovative new products is crucial in today’s turbulent environment. Flexibility in the NPD process is needed to cope with this environment. Biazzo (2009) identifies three important
flexibility dimensions, each of which contains flexibility variables identified by previous research. Although researched a fair bit, no research is done to determine which variables or dimensions are
most important for the innovative performance of an NPD project operating in turbulent environments. This research contributes to the innovation flexibility literature field by filling that gap and by relating the OIPT with the NPD process. Based on previous research, a correlation matrix was
made and served as input for a regression analysis done in LISREL to identify the most important variables and dimensions. This study identifies formalization as the most important variable, just ahead of multiple design iterations and less time between milestones. Decentralization, less project process structure and fewer overlapping stages do have effect on the innovative performance of an NPD project, but their effect is weaker. Implications for theory and managers are discussed, and
future research directions are given based on the limitations of this research.
Keywords: organizational flexibility, new product development, flexibility constructs, organizational information processing theory
MSC BA Strategic Innovation Management (SIM) University of Groningen
Faculty of Economics and Business June 2017
2
1. Introduction
Today’s competitive environment is turbulent and dynamic due to technological advancements and the globalization of markets (Ahmed, Hardaker & Carpenter, 1996; Cerny, 1994; Iansiti, 1995). Innovation and the development of new products have been proven to help a firm cope with turbulent environments (Weiss & Heide, 1993; Calantone, Garcia & Dröge, 2003). They are becoming a prerequisite for success (Lee & Trimi, 2016; Acikgoz, Günsel, Kuzey & Zaim, 2016). The current body of literature provides conflicting arguments towards coping with today’s turbulent environment through innovation, and especially new product development (NPD). On the one hand, Cooper (1990) proved that a fixed way of developing new products can be beneficial, while others emphasized flexibility in the new product development process (Iansiti, 1995; MacCormack and Verganti, 2003). Biazzo (2009) identifies two ways: anticipation and reaction (Verganti, 1999; Kalyanaram & Krishnan, 1997). An anticipation strategy involves an early and sharp definition of the product; a famous example is the Stage-Gate model of Cooper (1990). A reaction strategy involves moving the freeze point of the product definition as close to market launch as possible.
Both academics and practitioners see this conflicting nature as a problem: for academics, conflicting results often stem from an underdeveloped theory about the phenomenon. For managers it is difficult to adopt and implement the theory while it is separated into two versions (Biazzo, 2009). This confusion results in high failure rates of new products on the market. Liu, Li & Wei (2009) concluded that almost 50 percent of new products are complete failures, while 70 percent do not achieve the desired goals.
To cope with this confusion, several authors have suggested that being flexible in some processes does not necessarily have to mean that a firm cannot be structured in other processes. Suarez, Cusumano & Fine (1995) concluded that firms can simultaneously be flexible and less flexible, meaning that there is a need for clarification on which dimension a firm should be (less) flexible. Tatikonda & Rosenthal (2000) call this ‘balance of firmness and flexibility’. Biazzo (2009) recognizes this and proposes a conceptual framework that aims to clarify the dichotomy between stage-gate processes (anticipation strategy) and flexible structured process (reaction strategy) in the NPD process in turbulent environment. He identifies three dimensions: the temporal, informational and organizational dimension (Biazzo, 2009). He identifies the temporal dimension as “the execution strategies of development tasks” (p. 338), the informational dimension as “the classification of the development activities into problem-formulation and problem-solving tasks” (p. 338) and the organizational dimension as “the structuration of the process” (p. 338).
3
Borgeld, 2016; van Ark, 2014; Roukema, 2014). Although all three dimensions have been researched, there is no evidence about which of the three dimensions, and it’s variables, have the most impact on the innovative performance of an NPD project operating in turbulent environments. This research aims to achieve this by comparing previous researches about the different variables associated with the dimension identified by Biazzo (2009), and by drawing conclusions about the dimensions based on previous findings. The scope of this research is on the project level, as Biazzo (2009) concludes its article on the same level.
In order to address the identified literature gap and to come to the above mentioned conclusions, this paper is guided by the following research question:
Which flexibility variables, and corresponding dimensions, have the most impact on the innovative performance of an NPD project operating in a turbulent environment?
To answer this question, several variables were selected based on previous research. This previous research contained articles of academic journals and previous research done by students (based on their theses). After establishing the relevant variables, corresponding correlations among the variables as well as with the innovative performance of an NPD project (dependent variable) were searched to complete a correlation matrix. Hypotheses are developed based on previous literature as well as the organizational information processing theory (OIPT). The hypotheses were tested by testing the correlation matrix through a regression analysis performed in LISREL.
The final amount of articles used for the correlation matrix was 15, ranging from a period of 1995 to 2017, and identified six variables for the three dimensions identified by Biazzo (2009): overlapping stages, time between milestones, iterations, formalization, centralization and project process structure. These six variables were tested with the innovative performance of the NPD project indicator.
4
The paper begins by looking at the theoretical background. The main concepts and variables are defined and hypotheses are developed based on available literature. Second, the data collection and research methods are explained. Afterwards, the results are given and explained properly. The paper concludes with a discussion of the results, implications for managers and researchers and directions for future research based on the limitations of this paper.
2. Theoretical background
2.1 Innovation performance
The current body of literature struggles with defining innovation performance. The innovative performance of a firm can be measured through many different instruments (Montoya-Weiss & Calantone, 1994). When looking at the innovative performance of a firm, researchers often look at the NPD processes and outcomes. A distinction can be made between project performance and product performance. Project performance refers to the operational outcomes like cost and quality, while product performance refers to market outcomes like customer satisfaction (Tatikonda & Montoya-Weiss, 2001; Hannachi, 2016). In this research, both product -and project performance will relate to the innovative performance of the NPD project.
2.2 Organizational information processing theory (OIPT)
Galbraith (1974) developed the organizational information processing theory (1974), based on the basic proposition that more uncertainty leads to greater information processing during task execution. He identified that whenever the task is well understood prior to performing it, planning is a useful tool to assess the needed activities. However, when the task is uncertain, more knowledge is acquired during the task execution, leading to changes in resource allocations, schedules and priorities. To accompany these changes, information processing during task performance is needed. The OIPT therefore is best described as: “the greater the task uncertainty, the greater the amount of information that must be processed among decision makers during task execution in order to achieve a given level of performance.” (Galbraith, 1974, p.28) Whenever a firm faces a change in uncertainty, integrating mechanisms should be adopted. Galbraith (1974) identifies three integrating mechanisms for a firm: (1) coordination by rules or programs should help when facing routine and predictable tasks, (2) infrequent situations are handled by those in the hierarchy that have a global perspective for all affected subunits: they adjust the original plan, and (3) coordination by targets or goals is used whenever the uncertainty of the organization’s task increases.
5
reducing the amount of information that needs to be processed, Galbraith (1974) identifies: creation of slack resources and creation of self-contained tasks. Increasing the information processing capacity can be done by: investment in vertical information systems and creation of lateral relationships (Galbraith, 1974). These strategies are aimed to obtain the needed information processing capabilities, so that non-routine events can be anticipated on.
Creation of slack resources contains creating a buffer, like buffer inventories, extension of deadlines and budget targets. In uncertain environments, exceptions and non-routine events occur more frequently, overloading those employees with a global perspective in the hierarchy. By creating a buffer, the chance of having unforeseen uncertainties is reduced, as well as the need for adjustment and processing of information. This is because the amount of interdependence between subunits is reduced, as there are less simultaneous factors that need to be considered by the appropriate decision makers when an exception occurs. Galbraith (1974) summarizes the effect of slack resources: “The greater the uncertainty, the greater the magnitude of the inventory, lead time or budget needed to reduce an overload” (p. 30). However, more slack resources results in higher costs, as inventories increase and customers get delayed products. Thus, in order to keep a balance between costs and the ability to reduce information, firms should decide which factors (lead time, overtime etc.) to change and by what amount (Galbraith, 1974).
Creation of self-contained tasks entails giving a group all the inputs they need in order to supply the output. This strategy shifts the focus from input to output, resulting in less information processing through several mechanisms. First, the output diversity is reduced faced by a single collection of resources because all groups have their own needed specialties. Second, information reduction occurs through less specialization of the employees, and a reduced division of labor. Instead of acquiring a specialist, current employees can learn the needed skills (Galbraith, 1974). Both mechanisms are the foundation of a more self-contained structure for the groups in an organization. Galbraith (1974) concludes that: “The greater the degree of uncertainty, other things equal, the greater the degree of self-containment” (p. 31).
Investment in vertical information systems. The hierarchy is tasked to change the original plans whenever unanticipated events occur, this can be done by incremental changes or by generating a new plan. Replanning is needed when the newly acquired information is substantial. More information means more work for the employees with a global perspective in the hierarchy, thus leading to an overload of the hierarchy. To cope with this overload, investments in vertical information systems like computers and other man-machine combinations, help to increase the capacity of the decision makers (Galbraith, 1974).
6
hierarchy in the occasion of unanticipated events. First, direct contact between managers who share a problem helps with preventing an upward referral, as they find a solution together. Second, liaison roles can be incorporated to handle communication between two collaborating departments. Third, tasks forces are introduced whenever multiple departments collaborate to solve joint problems. These consist of representatives from each of the affected departments, which exist as long as the problem remains. Fourth, task forces can be permanent when certain problems consistently arise, called teams. Fifth, teams can be reinforced with integrating roles. These roles acquire information, equalize power differences in the different department representatives and increase trust and the quality of the joint decision process. Sixth, alongside the integrating roles, managerial linking roles can stimulate the quality and effectiveness of the teams. They deal with forming the budget and allocate budget to those that need it. Finally, a matrix organization can be formed where teams have more than one reporting line (Galbraith, 1974).
2.3 Flexibility in the NPD process
In the current body of literature, researchers have identified two ways of structuring the NPD process: a structured process defined by an early freeze of the product definition, and a flexible product development process defined by a late freeze of the definition (Biazzo, 2009). Freezing the product definition early in the process copes with uncertainties by imposing discipline on the product development process, thereby increasing the speed of development (Cooper & Kleinschmidt, 1994; Bacon, Beckman, Mowery & Wilson, 1994). Furthermore, implementing changes in the specification of the product can be costly and time-consuming (Verganti, 1999; Kalyanaram & Krishnan, 1997; Cooper & Kleinschmidt, 1994). Guiding the process while avoiding uncertainties can result in better innovative performance of the organization in turbulent environments. These structured processes are often called stage-gate processes, based on the Stage-Gate model of Cooper (1990).
7
Krishan & Battacharya (2002) concluded that an early definition of the product was only beneficial if there is low uncertainty, the market price is sensitive, when there is low risk or when the team itself is not flexible. In all other situations, a flexible product definition increased the organization’s profits. Thomke (1997) also concluded that flexible design technologies outperformed projects using inflexible technologies.
Although researched a fair bit, flexibility still has many different meanings, making it a multidimensional concept (Suarez, Cusumano & Fine, 1995). In order to measure and improve this concept, different dimensions should be identified and assessed whether flexibility in this dimension is profitable for the innovative performance of a firm. Biazzo (2009) identified three dimensions of flexibility in the NPD process: temporal, informational and organizational dimension.
2.3.1 Temporal dimension. The temporal dimension of flexibility relates to the execution strategies of the different tasks of development (Biazzo, 2009). Previous research identified two variables that influence the temporal dimension of flexibility: overlapping stages and time between milestones throughout the NPD process (Borgeld, 2016). Overlapping stages is defined as: “the degrees to which different organizational functions simultaneously conduct project work” (Tatikonda & Montoya-Weiss, 2001, p. 155). Overlap in stages is possible when the NPD processes are both loosely coupled as well as characterized by high degree of modularity allowing them to function autonomously and concurrently (Sanchez & Mahoney, 1996). Overlapping stages have been proven beneficial for operational outcomes as it enables greater information sharing, better understanding of constraints and opportunities, and it enables joint problem solving. The problems can be solved earlier, as they are recognized and anticipated on earlier in the process (Tatikonda & Montoya-Weiss, 2001). This is especially important in turbulent environments (Calantone et al, 2003). Furthermore, due to the joint problem solving technique, the quality of the products improves as well. This is in line with the OIPT, which emphasizes adapting the internal structure of a firm to the given task uncertainty. It also emphasizes the importance of lateral relations in the form of inter-departmental collaboration as a way to cope with high uncertain, turbulent environments. It reduces the load on the decision makers of an organization, by bringing the decision making level down to the collaborating departments. Fewer loads on the hierarchy mean an increase in information processing capacity during task execution, thus resulting in better performance in turbulent environments (Galbraith, 1974). A more flexible internal structure can therefore be achieved through overlapping stages (Cuijpers, Guenter & Hussinger, 2011). They have thus been recognized as an important way to reduce development times and improve quality in turbulent environments (Tatikonda & Montoya-Weiss, 2001; Eisenhardt & Tabrizi, 1995; Terwiesch & Loch, 1999), thereby increasing the innovative performance of an NPD project.
8
Time between milestones is the time between formal project review points (Eisenhardt & Tabrizi, 1995). Frequent milestones let developers assess their work more often, making off-sets earlier visible in the process. That way, whenever an off-set is identified, the amount of redesign should be kept to its minimum (Terwiesch & Loch, 1999; Eisenhardt & Tabrizi, 1995). However, Sethi & Iqbal (2008) showed that frequent evaluations decreased the flexibility of the project. Inflexibility increases the learning failure in the product development team and is worsened when the project is being developed in a turbulent environment. This is because the NPD team has less time to evaluate the past, as they are continuously working towards the next milestone (Sethi & Iqbal, 2008). The extension of evaluation points, or deadlines, is identified as a way to cope with task uncertainty by the OIPT. The delay in deadlines helps with not overloading those employees in the hierarchy that have the appropriate perspective to make decisions. When deadlines are extended, the decision makers need to account for less simultaneous factors, resulting in a reduced chance of unforeseen events. This is beneficial for the performance of a project as delayed projects can be adjusted during task execution, therefore limiting last minute adjustments or obsolete products (Galbraith, 1974). More time between milestones therefore, is beneficial for the innovative performance of an NPD project operating in turbulent environments (Tschang & Szczypula, 2006; Laine, Suomala & Nørreklit, 2013).
H1b: More time between milestones in the NPD process increases the innovative performance of an NPD project operating in turbulent environments.
9
increases the chance for a product or service to be successful: the chance of scoring a ‘hit’ is higher when there are multiple designs (Eisenhardt & Tabrizi, 1995). All help with adapting the new product or service to the needs of the market (reaction strategy), leading to a better accepted innovation, thus increasing the innovative performance of an NPD project.
H2: The use of multiple design iterations in the NPD process increases the innovative performance of an NPD project operating in turbulent environments.
2.3.3 Organizational dimension. The organizational dimension deals with the structuration of the process. Biazzo (2009) defines this dimension as: “the formal segmentation of the temporal progression in stages and the definition of the activities that should be occurring in each stage” (p. 338). Roukema (2014) and van Ark (2014) concluded that sticking to a strict flexible or inflexible organizational structure would yield little results, as some flexible characteristics did not positively influence innovative performance. A mix of firmness (structure) with flexibility is needed when organizing for an NPD innovation project. Previous research has identified several variables; three are highlighted in this research: formalization, centralization and project process structure (Roukema, 2014; van Ark, 2014). Formalization is defined as “the existence of formal rules and regulations that correspond also to the degree to which decisions and working relationships are governed by standard policies and procedures” (Belbaly, Benbya & Meissonier, 2007). Formalization decreases the time-to-market as it uses rules and procedures that are efficient and based on previous experiences (Palmie et al., 2016). Furthermore, it reduces the time-to-market by mitigating project uncertainty and it reduces unit cost (Naveh, 2007; Tatikonda & Montoya-Weiss, 2001). However, Sethi & Iqbal (2008) concluded that using strict processes and criteria will lead to an increase in learning failure, especially when coping with a turbulent environment. The OIPT recognizes this: having strict rules can lead to a learning trap, and ultimately to organizational inertia. The OIPT emphasizes the importance of matching the external environment with the internal environment of the firm. Thus, NPD projects operating in fast changing environments should not have rigid process structures (Galbraith, 1974). This will ultimately lead to worse project performance. Despite the contrasting results in the current body of literature, this research follows the OIPT and thus hypothesizes:
H3a: A high level of formalization in the NPD process decreases the innovative performance of an organization in turbulent environments.
10
buyer learning of a firm. Additionally, centralizing the development process of a firm often results in a sluggish, difficult to change environment that yields negative results in fast changing, turbulent markets (Dewar & Dutton, 1986). Furthermore, centralization decreases the team’s creativity because of the lack of autonomy of the individuals (Leenders, van Engelen & Kratzer, 2007; Amabile, 1988). Because all the autonomy is within a central team, non-central individuals lack autonomy to be creative. Amabile (1988) showed that autonomy is needed for creative achievements of a team. Giving autonomy to the team is in line with the OIPT: providing autonomy to the team helps with increasing the capacity to process information. Decentralization therefore can be seen as the creation of lateral relations where the autonomy is moved downwards in the organization, resulting in fewer load for the hierarchy by increasing the information processing capacity of teams during task execution, and a reduced chance of facing unanticipated events. Furthermore, decentralization of the autonomy can also be linked with the creation of self-contained tasks: teams operating lower in the organization are provided with all the input they need to supply the output. This reduces the information that needs to be processed. This ultimately helps with the performance of a project, as new information is easily processed and incorporated (Galbraith, 1974). Therefore this research hypothesizes:
H3b: A high level of centralization in the NPD process decreases the innovative performance of an NPD project operating in turbulent environments
Lynn, Skov & Abel (1999) identified project process structure as the degree to which the NPD processes follow a clear plan - a roadmap with measurable milestones, where mechanisms track the project’s progress and costs, and aims to complete the process in a logical sequence. It has been proven beneficial for solution quality and product quality by creating focus and motivation for the development teams (Atuahene-Gima, 2003; Eisenhardt & Tabrizi, 1995). However, extensive planning in a turbulent market potentially wastes time as the generated products/services can become obsolete fast (Eisenhardt & Tabrizi, 1995). Glazer and Weiss (1993) conclude that planning in fast changing, turbulent environments often lead to inferior performance. Less planning in turbulent environments has been identified by the OITP to reduce the task uncertainty. They emphasize the importance of slack resources as a way to cope with uncertainty. This entails creating a buffer by extending deadlines during task execution in order to reduce unforeseen events, as well as reducing the need for adjustments. In turbulent environments, formal planning slows down the decision-making process and negatively influences the real-time decision making capability of a firm. Real-time decision making helps with processing information during task execution, leading to a well suited product or service for the market (Galbraith, 1974; Galbraith, 1977). Less process structure thus results in better innovative performance of an NPD project.
11
The following model, based on the hypotheses, has been made to guide this research.
Figure 1 Conceptual model
3. Methodology
A theory testing approach is used to answer the research question. Theory testing is used when the literature field is rather mature, however, literature gaps still exist that need to be empirically tested (van Aken, Berends & van der Bij, 2012). The literature about flexibility in NPD processes is mature, however, showed by the research question in the introduction, a literature gap still exists. This research followed the theory testing steps identified by van Aken et al. (2012): (1) Identify the literature gap, (2) identification of important variables and the generation of hypotheses and a conceptual model, (3) data collection and statistical data analysis, and (4) listing the results, formulate theoretical and practical implications and give directions for future research.
3.1 Data Collection
In this research, previous research was used as primary data, both from former students (Marovska, 2016; Borgeld, 2016; van Ark, 2014; Roukema, 2014) and additional data gathered from the current literature field.
12
former students only looked at the relationship between the variables and the dependent variable (innovative performance of an NPD project). In order to draw conclusions about the most important dimension and corresponding variables, a correlation matrix was completed where each variable is tested against each other. This is necessary to reveal potential synergies and the relative importance of each variable. After identifying the variables for each dimension, the correlations between the variables and the correlations with the dependent variable were looked for in the literature field. Data was found through Google Scholar and Business Source Premier. Several keywords were used to acquire additional papers. To ensure reliability of the search process, three basic keywords were used: two keywords about the constructs of interest combined with the words ‘correlation’, ‘project’ or ‘NPD’. These made sure that the articles found were on the project level or contained a correlation matrix, making them relevant for this research. Whenever the chosen keywords did not yield any results, synonyms were used. When the synonyms did not yield any results either, only one variable (or synonym) was searched for combined with ‘correlation’, ‘project’ or ‘NPD’. The used search terms are listed in appendix 1.
This research aimed to incorporate every variable identified by the former students, however, not all yielded results when tested with each other. Because of these missing links, some variables were left out (colocation, functional integration, goal stability, team stability, frequency), while others were fused together (iterations & feedback) to complete the correlation matrix.
3.2 Sample
Above search strategy yielded a total of 136 articles in Business Source Premier (BSP). Google Scholar was also used whenever BSP did not yield any results.
13
Table 1 Inclusion / Exclusion criteria data
Inclusion criteria Exclusion criteria
Peer-reviewed articles from scientific journals
Congress report
Research on project level Master / bachelor thesis
Quantitative study with correlation matrix
Books
Known sample size
3.3 Measurements
The measurement of each variable in this study is outlined next. A distinction will be made between the dependent and the independent variables. The independent variables consist of the three different dimensions; each dimension contains several variables. The dependent variable, innovative performance of an NPD project, often entails different variables. However, in this research all variables associated with either product or project performance are taken together.
3.3.1 Independent variables.
3.3.1.1 Temporal dimension. Two variables are identified by Borgeld (2016): overlapping
stages and time between milestones. Overlapping stages was measured similarly across all studies. They all divide the number of overlapping activities in subsequent phases by the gross duration of the project (Eisenhardt & Tabrizi, 1995; Tatikonda & Montoya-Weiss, 2001; Terwiesch & Loch, 1998; Zirger & Hartley, 1996).
Time between milestones was also measured similarly. All authors measured time between milestones by averaging the number of weeks between two officially scheduled project reviews (or the number of gates). To account for any differences between different industries, the time was adjusted to the average time of the industry segment (Eisenhardt & Tabrizi, 1995; Sethi & Iqbal, 2008; Terwiesch & Loch, 1998).
3.3.1.2 Informational dimension. There are two variables identified by previous research:
14
For instance, Eisenhardt & Tabrizi (1995) asked the respondents the number of iterations that occurred in the development process. This then was then adjusted by the average number of iterations in the industry to account for any differences. Cui & Wu (2017) looked at the degree to which customers were actively involved in the development activities: the interaction with the development team, the input provided, and the portion of customers’ involvement in the development effort. The number of iterations that occurred was adjusted by the average number of iterations in the industry
3.3.1.3 Organizational dimension. Roukema (2014) and van Ark (2014) identified seven
variables that influence organizational flexibility. Three of those are included in this research: centralization, formalization and project process structure. Centralization was measured as the degree to which decisions are taken by either top management or lower level employees and the degree of hierarchy in task authority (Atuahene-Gima, 2003; Eisenhardt & Tabrizi, 1995; Lin & Germain, 2004; Nakata & Im, 2010; Palmie et al., 2016; Tatikonda & Rosenthal, 2000; Zirger & Hartley, 1996). The different authors adapted their measurement from either Van de Ven and Ferry (1980) or Amabile et al. (1996).
Formalization was measured by the degree to which the project followed formal project management rules and procedures and the degree to which these rules are strictly enforced (Ettlie & Elsenbach, 2007; Naveh, 2007; Palmie et al., 2016; Sethi & Iqbal, 2008; Tatikonda & Montoya-Weiss, 2001; Tatikonda & Rosenthal, 2000).
Project process structure was measured either by asking whether the new product development processes followed a well-defined, structured process (Ettlie & Elsenbach, 2007), by a 4 or 7 point Likert scale on statements like: “The criteria for gate reviews are strictly adhered to” (Nakata & Im, 2010; Sethi & Iqbal, 2008) or by calculating the time spent planning in comparison with the total elapsed time (Eisenhardt & Tabrizi, 1995).
3.3.2 Dependent variable.
15
customer satisfaction (Naveh, 2007; Tatikonda & Montoya-Weiss, 2001), management evaluation (Reid et al., 2016), efficient team collaboration (Reid et al., 2016).
3.3 Analysis
The described data collection strategy resulted in the completion of the correlation matrix. In some occasions, multiple results were found for one relation. In these instances, a small meta-analysis was done. This analysis consisted of two corrections: sample size correction and a measurement error correction (based on the Cronbach alpha). Results from meta-analyses are often more reliable than data of individual researches, because they draw conclusions based on a variety of different individual studies (Wanous, Reichers & Hudy, 1997). In this research, the following relations resulted in multiple correlations and were thus calculated through small meta-analyses: centralization – formalization, overlap – iteration, time-between-milestones – iterations, feedback – iterations (meta-analysis was done before the fusion), feedback – centralization, centralization – project process structure and overlap – time-between-milestones.
Sample size correction (ncor) = (𝑟1𝑛1) +(𝑟2𝑛2)
𝑛1+𝑛2 (r = correlation, n = sample size)
Measurement error correction = 𝑛𝑐𝑜𝑟
(√𝛼1+(√𝛼2) 2 ).(
√𝛽1+(√𝛽2)
2 )
(α = Cronbach Alpha independent, β = Cronbach Alpha dependent)
Furthermore, single correlation results were corrected for measurement errors as well to keep the results reliable.
Measurement error correction = 𝑟
√𝛼√𝛽 (α = Cronbach Alpha independent, β = Cronbach Alpha dependent)
16
in Chi Square announced whether there was a significant difference between the two variables: for each extra degree of freedom, a difference of 3.84 in Chi Square (p = 0.05) means a significant difference between the two variables (Bollen & Stine, 1992). Based on these differences, a hierarchy can be made to identify the most important variables. The same thing was done with the different dimensions. First, the correlations across the dimensions were averaged to create a correlation matrix with only the three identified dimensions. After this, the ranking of the dimensions was determined by performing the same steps mentioned above.
4. Results
Table 2 shows the used matrix with the correlations among the different variables. None of the correlations between the independent variables exceed 0.65, meaning that the estimations are not likely biased by multicollinearity problems (Tabachnick & Fidell, 1996).
Table 2 Used correlation matrix
Variable 1 2 3 4 5 6 7
1. Iterations 1.00
2. Overlapping stages 0.02 1.00
3. Formalization -0.46 0.18 1.00
4. Centralization 0.12 0.20 0.24 1.00
5. Project process structure 0.07 0.10 0.62 -0.08 1.00
6. Time between milestones 0.12 -0.20 0.51 0.06 0.51 1.00
7. Performance 0.22 0.10 0.18 0.03 0.15 0.26 1.00
To test the formulated hypotheses, a regression analysis was conducted. Before interpreting the results, the fit between the model and the data was tested according to the different goodness of fit statistics. This research’s model fits the data well with a Chi-Square of 0.080 (p = 0.96), (𝜒2
𝑑𝑓) = 0.040, RMSEA = 0.0, CFI = 1.00, NFI = 1.00, GFI = 1.00 and AGFI = 0.99 (Hu & Bentler, 1999).
17
innovative performance of an NPD project (β = -0.35, p < 0.05) was not hypothesized. Although the relation is weak, it does contradict hypothesis H1a. Time between milestones was hypothesized to have a positive relation with innovative NPD project performance (H1b), however, the results show a negative relation (β = -1.15, p < 0.01). Furthermore, although hypothesized that formalization would have a negative effect on innovative NPD project performance (H3a), this research found a strong positive relation between formalization and innovation performance (β = 1.88, p < 0.01). All unexpected findings will be explained in the discussion section later on.
Table 3 Regression analysis results: variables impacting innovative performance
* p<0.05 ** p<0.01 *** p<0.001
One of the reasons to conduct a regression analysis with LISREL was to identify the most important variables that influence the innovative performance of a firm. In order to conclude which variable has the most impact on the innovative performance of an NPD project in turbulent environments, the unstandardized regression coefficients were compared with each other. By comparing those that were close to each other, a hierarchy was determined. The most important variable was analyzed by comparing iterations with formalization. The Chi Square changed with a value of 17.28, making it a significant difference: formalization is the most important variable. To determine the second most important variable, iterations was tested with time between milestones (negative predictor). The difference in Chi Square was 2.99, thus the difference between iterations and time between milestones is not significant. They share the second place. Calculated with time between milestones all other results were significantly lower with a Chi Square difference of: centralization (29.63; negative predictor), overlapping stages (37.91; negative predictor) and project process structure (26.44; negative predictor). None of these three variables were significantly different from each other; where centralization – project process structure resulted in a Chi Square difference of 0.27, overlapping
Variable Hypothesis T-value P-value β
Overlapping stages H1a -4.41 0.048 -0.35*
18
stages – centralization in 2.28 and overlapping stages – project process structure in 2.16. These three share the third place.
To identify the most important dimension, the unstandardized regression coefficients were compared among the three dimensions. None of the three Chi Square differences were significant, with informational – organizational resulting in a Chi Square difference of 0.32, informational – temporal (negative predictor) had a difference of 0.69 and temporal – organizational resulted in a Chi Square difference of 0.072.
5. Discussion
The primary objective of this research was to further investigate the different flexibility dimensions of Biazzo (2009): temporal, informational and organizational flexibility dimension. Previous research (van Ark, 2014; Roukema, 2014; Marovska, 2016; Borgeld, 2016) identified several variables that characterized the different dimensions: iterations, overlapping stages, time between milestones, formalization, centralization and project process structure. Although they identified which variables were important for the innovative performance of a firm, no research has been done to identify the most important variables across the different dimensions. This research aimed to fill that gap.
Contrary to the ideas proposed in this research, formalization was identified as the most important flexibility variable to increase the NPD project’s innovation performance in turbulent environments. This is in contrast with the article of Sethi & Iqbal (2008);it seems that strict rules and procedures are beneficial for an NPD project’s innovative performance in turbulent environments. A possible explanation for this surprising result is the importance of development speed in turbulent environments. Moreno-Moya & Munuera-Aleman (2015) concluded that development speed is important because firms face the risk of taking too long and losing to competitors that develop products quicker. In fast moving markets, this can lead to the loss of potential market share and further benefits of being the first mover in a market. To avoid falling behind, formalization can help with decreasing the time-to-market as well as limiting the development costs (Palmie et al., 2016).
Munuera-19
Aleman, 2015). Moreover, Salomo, Weise & Gemünden (2007) showed that frequent evaluations can have a significant impact on the success of the innovation, as long as it is not a highly innovative product or service.
The three least influential variables were: centralization, overlapping stages and project process structure. Although not having a relatively strong effect on innovation performance, both project process structure and centralization had the predictive negative effect. Centralizing the development process results in a sluggish and difficult to change environment that often yields negative results in fast changing markets (Dewar & Dutton, 1986). Decentralizing the decision making structure of an organization helps with NPD process success (Carbonell & Rodriguez Escudero, 2016). Project process structure is also not beneficial for fast changing turbulent environments. Having the development process follow a predefined path, results in slow decision making processes and it negatively influences the real-time decision making capability of a firm, which is important in turbulent environments (Glazer & Weiss, 1993; Galbraith, 1974). Overlapping stages was predicted as having a positive influence on the innovative performance of a firm. However, the opposite was proven (weak effect). Making commitments before data is available from the upstream function can result in large downstream rework; slowing down the development process and increasing the development costs (Tatikonda & Montoya-Weiss, 2001).
Nothing can be said about the ranking of the three dimensions. While comparing, none of them differed significantly from the others. Although part of the research question, this research cannot conclude which of the three dimensions is most important for the innovative performance of an NPD project operating in turbulent environments.
5.1 Theoretical implications
20
The innovation literature field is also complemented because some of the surprising results can be linked with the complexity theory. This theory suggests that the organization’s key to survival is operating on ‘the edge of chaos’ (Burnes, 2005). This entails having both order and disorder in the organization. Too little order may result in a chaotic system, where many changes can overwhelm the organization resulting in less performance. Too much order makes the organization rigid, where nothing changes and the organization becomes obsolete. This edge of chaos helps “(…) exhibit the most prolific, complex and continuous change” (Brown & Eisenhardt, 1997, p. 29) in an organization. Although the complexity theory mostly speaks about organizational change, Brown & Eisenhardt (1997) link continuous change with the development of new products. Thus, the complexity theory can be applied to the NPD process.
The results of this research can therefore be seen as flexibility constructs that stimulate the order, or the disorder of the NPD process. Formalization is a good example of an ‘order’ variable where strict rules and procedures guide the chaotic process that is an NPD process. Same goes with the negative relation of overlapping stages; concurrency can cause too much chaos through large downstream rework (Tatikonda & Montoya-Weiss, 2001). Furthermore, frequent milestones help guide the chaotic NPD process, especially when developing radically new projects (Lakemond & Berggren, 2006). The other results found in this research represent the disorder side of the ‘edge of chaos’, where multiple iterations result in lots of new information (Eisenhardt & Tabrizi, 1995), and decentralization and less project process structure represent the chaos needed in order to create the culture and climate for innovation (Ahmed, 1998). Thus, in this research, three variables are identified as brining order, and three variables as bringing disorder to the NPD process.
5.2 Managerial implications
21 5.3 Limitations and future research
There are several limitations to this study. First, some variables were left out due to missing data. In order to give a proper ranking of flexibility dimensions and corresponding variables, these left out variables should be included. The ranking of the flexibility dimensions is done through the available variables, but none of them differed significantly. In order to conclude which dimension is most important, more variables are needed; especially in the informational dimension. The informational dimension is only assessed through one variable, while the organizational and temporal dimensions are measured by three and two variables respectively. Further research should try to incorporate more variables for each dimension, making it less likely for the results to be biased.
Second, and related to the first limitation, the combined variable of iterations and feedback in the informational dimension should be separated. Although both result in additional feedback, the type of feedback often differs. Feedback on iterations is most likely coming from inside the organization, while ‘normal’ feedback often originates from the market or consumer. Both can have a substantial different effect on development speed or innovative performance overall and should thus be separated in future research.
Third, further research should focus on splitting up the term ‘innovation performance’ into more specific performance measures. Although the combination of all performance indicators can give a general image of improving the performance of a firm, it is often more interesting to see the different effects of the variables on different performance indicators. For instance, whenever a firm is doing well on delivering qualitative products, they can lack speedy development. For these firms it is beneficial to know which flexibility dimensions and variables help with increasing the speed of the NPD process. Moreover, the surprising result of formalization and time between milestones can be linked with the lack of distinction in the ‘innovation performance’ indicator. In this research, therefore, the results can be biased, as a significant amount of used performance indicators contained development speed or similar measures like time-to-market. A distinction between the different performance indicators can not only help firms focus on whichever they need to improve, it also helps with the generalizability of this research.
22
6. Conclusion
23
Literature
Açıkgöz, A., Günsel, A., Kuzey, C., & Zaim, H. (2016). Team foresight in new product development projects. Group Decision and Negotiation, 25(2), 289-323.
Ahmad, S., Mallick, D. N., & Schroeder, R. G. (2013). New product development: impact of project characteristics and development practices on performance. Journal of Product Innovation Management, 30(2), 331-348.
Ahmed, P. K. (1998). Culture and climate for innovation. European journal of innovation management, 1(1), 30-43.
Ahmed, P. K., Hardaker, G., & Carpenter, M. (1996). Integrated flexibility—key to competition in a turbulent environment. Long range planning, 29(4), 562-571.
Amabile, T. M. (1988). A model of creativity and innovation in organizations. Research in organizational behavior, 10(1), 123-167.
Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the work environment for creativity. Academy of management journal, 39(5), 1154-1184.
Atuahene-Gima, K. (2003). The effects of centrifugal and centripetal forces on product development speed and quality: How does problem solving matter?. Academy of Management Journal, 46(3), 359-373.
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.
Belbaly, N., Benbya, H., & Meissonier, R. (2007, January). An empirical investigation of the customer Knowledge creation impact on NPD Performance. In System Sciences, 2007. HICSS 2007. 40th Annual Hawaii International Conference on (pp. 193a-193a). IEEE.
Biazzo, S. (2009). Flexibility, structuration, and simultaneity in new product development. Journal of product innovation management, 26(3), 336-353.
Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods & Research, 21(2), 205-229.
24
Brown, S. L., & Eisenhardt, K. M. (1997). The art of continuous change: Linking complexity theory and time-paced evolution in relentlessly shifting organizations. Administrative science quarterly, 1-34. Burnes, B. (2005). Complexity theories and organizational change. International Journal of Management Reviews, 7(2), 73-90.
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. Carbonell, P., & Rodriguez Escudero, A. I. (2016). The Effects of Decentralization in Strategy‐ Making and National Culture on NPD Portfolio Planning. Journal of Product Innovation Management, 33(S1), 101-116.
Cerny, P. G. (1994). The dynamics of financial globalization: Technology, market structure, and policy response. Policy Sciences, 27(4), 319-342.
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. (1994). Perspective third-generation new product processes. Journal of product innovation management, 11(1), 3-14.
Cooper, R. G., & Kleinschmidt, E. J. (1994). Determinants of timeliness in product development. Journal of Product Innovation Management, 11(5), 381-396.
Cui, A. S., & Wu, F. (2017). The Impact of Customer Involvement on New Product Development: Contingent and Substitutive Effects. Journal of Product Innovation Management, 34(1), 60-80.
Cuijpers, M., Guenter, H., & Hussinger, K. (2011). Costs and benefits of inter-departmental innovation collaboration. Research Policy, 40(4), 565-575.
Dewar, R. D., & Dutton, J. E. (1986). The adoption of radical and incremental innovations: An empirical analysis. Management science, 32(11), 1422-1433.
Eisenhardt, K. M., & Tabrizi, B. N. (1995). Accelerating adaptive processes: Product innovation in the global computer industry. Administrative science quarterly, 84-110.
25
Fantazy, K. A., & Salem, M. (2016). The value of strategy and flexibility in new product development: The impact on performance. Journal of Enterprise Information Management, 29(4), 525-548.
Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28-36. Galbraith, J. R. (1977). Organizational design. Addison Wesley Publishing Company.
Glazer, R., & Weiss, A. M. (1993). Marketing in turbulent environments: decision processes and the time-sensitivity of information. Journal of Marketing Research, 509-521.
Golden, W., & Powell, P. (2000). Towards a definition of flexibility: in search of the Holy Grail?. Omega, 28(4), 373-384.
Hannachi, Y. (2016). The Impact of the Learning Organization on Product Innovation Performance: an Empirical Assessment in the French Biotechnology Industry. Management International/International Management/Gestión Internacional, 20(3).
Hong, P., Nahm, A. Y., & Doll, W. J. (2004). The role of project target clarity in an uncertain project environment. International Journal of Operations & Production Management, 24(12), 1269-1291. Iansiti, M. (1995). Shooting the rapids: Managing product development in turbulent environments. California Management Review, 38(1), 37-58.
John, G., & Martin, J. (1984). Effects of organizational structure of marketing planning on credibility and utilization of plan output. Journal of Marketing Research, 170-183.
Kalyanaram, G., & Krishnan, V. (1997). Deliberate product definition: Customizing the product definition process. Journal of Marketing Research, 276-285.
Laine, T., Suomala, P., & Nørreklit, H. (2013). NPD accounting: unveiling the potential through pragmatic constructivism. Proceedings of Pragmatic Constructivism, 3(1), 23-34.
Lakemond, N., & Berggren, C. (2006). Co-locating NPD? The need for combining project focus and organizational integration. Technovation, 26(7), 807-819.
Lee, S. M., & Trimi, S. (2016). Innovation for creating a smart future. Journal of Innovation & Knowledge.
26
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.
Lin, X., & Germain, R. (2004). Antecedents to Customer Involvement in Product Development:: Comparing US and Chinese Firms. European Management Journal, 22(2), 244-255.
Lynn, G. S., Reilly, R. R., & Akgun, A. E. (2000). Knowledge management in new product teams: practices and outcomes. IEEE transactions on Engineering Management, 47(2), 221-231.
Lynn, G. S., Skov, R. B., & Abel, K. D. (1999). Practices that support team learning and their impact on speed to market and new product success. Journal of Product Innovation Management, 16(5), 439-454.
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.
Marovska, N. (2016). The impact of information flexibility on the project-level innovation performance: a meta-analysis. Groningen: University of Groningen.
Montoya-Weiss, M. M., & Calantone, R. (1994). Determinants of new product performance: A review and meta-analysis. Journal of product innovation management, 11(5), 397-417.
Moreno‐Moya, M., & Munuera‐Aleman, J. L. (2015). The differential effect of development speed and launching speed on new product performance: An analysis in SMEs. Journal of Small Business Management.
Nakata, C., & Im, S. (2010). Spurring cross‐functional integration for higher new product performance: A group effectiveness perspective. Journal of Product Innovation Management, 27(4), 554-571.
Naveh, E. (2007). Formality and discretion in successful R&D projects. Journal of Operations Management, 25(1), 110-125.
Palmié, M., Zeschky, M., Winterhalter, S., Sauter, P. W., Haefner, N., & Gassmann, O. (2016). Coordination mechanisms for international innovation in SMEs: effects on time-to-market and R&D task complexity as a moderator. Small Business Economics, 46(2), 273-294.
27
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. Roukema, A. (2014). Taking a new perspective on the relationship of flexibility and innovation performance on a project. Groningen: University of Groningen.
Salomo, S., Weise, J., & Gemünden, H. G. (2007). NPD planning activities and innovation performance: the mediating role of process management and the moderating effect of product innovativeness. Journal of product innovation management, 24(4), 285-302.
Sanchez, R., & Mahoney, J. T. (1996). Modularity, flexibility, and knowledge management in product and organization design. Strategic management journal, 17(S2), 63-76.
Schmidt, F. L., & Hunter, J. E. (2014). Methods of meta-analysis: Correcting error and bias in research findings. Sage publications.
Sethi, R., & Iqbal, Z. (2008). Stage-gate controls, learning failure, and adverse effect on novel new products. Journal of Marketing, 72(1), 118-134.
Sheremata, W. A. (2000). Centrifugal and centripetal forces in radical new product development under time pressure. Academy of management review, 25(2), 389-408.
Suarez, F. F., Cusumano, M. A., & Fine, C. H. (1995). An Empirical Study of Flexibility in Manufacturing. Sloan Management Review, 37(1), 25-32.
Tabachnick, B. Fidell (1996), Using Multivariate Statistics.
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. (1999). Measuring the effectiveness of overlapping development activities. Management science, 45(4), 455-465.
28
Tschang, F. T., & Szczypula, J. (2006). Idea creation, constructivism and evolution as key characteristics in the videogame artifact design process. European Management Journal, 24(4), 270-287.
van Aken, J., Berends, H., & Van der Bij, H. (2012). Problem solving in organizations: A methodological handbook for business and management students. Cambridge University Press. van Ark, M. (2014). Understanding the Relationship between Organizational Structure and Innovation Performance on a Project level: a Meta-Analysis. Groningen: University of Groningen. Van de Ven, A. H., & Ferry, D. L. (1980). Measuring and assessing organizations. John Wiley & Sons.
Verganti, R. (1999). Planned flexibility: linking anticipation and reaction in product development projects. Journal of Product Innovation Management, 16(4), 363-376.
Wanous, J. P., Reichers, A. E., & Hudy, M. J. (1997). Overall job satisfaction: how good are single-item measures?.
Weiss, A.M. and Heide, J.B. (1993). The nature of organizational search in high technology markets. Journal of Marketing Research, 30(2):220–233.
Yan, T., & Nair, A. (2015). Structuring Supplier Involvement in New Product Development: A China– US Study. Decision Sciences.
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.
29
Appendix
Appendix 1: Variables and their search terms
Variables Search terms
Iterations Iterations
Feedback
Customer involvement Prototype (-ing) Experimenting
Overlapping stages Overlap
Concurrent stages Process concurrency Formalization Formalization Process formality Strict rules Centralization Centralization Decentralization (Team) Authority Empowerment
Project process structure Process structure
Stage-Gate Planning
Temporal pacing
Time between milestones Time between milestones
Frequency of evaluation
Appendix 2: found articles and their variables
Article Sample It Ov Fo Ce Pps Tbm
Atuahene-Gima (2003) 104 X X
Cui & Wu (2017) 236 X
Eisenhardt & Tabrizi (1995) 72 X X X X X
Ettlie & Elsenbach (2007) 60 X X
Lin & Germain (2004) 227/231 X X
Naveh (2007) 62 X X
Nakata & Im (2010) 206 X X
Palmie et al. (2016) 103 X X
Reid et al. (2016) 152 X
30
Tatikonda & Montoya-Weiss (2001) 120 X X
Tatikonda & Rosenthal (2000) 120 X X
Terwiesch & Loch (1998) 140 X X X
Tih, Wong & Reilly (2016) 341 X
Zirger & Hartley (1996) 44 X X
Note: It = Iteration, Ov = Overlapping stages, Fo = Formalization, Ce = Centralization, Pps = Project process structure, Tbm = Time between milestones.
Appendix 3: SIMPLIS input LISREL Observed variables:
iter overl form centr pstruc time perf CORRELATION MATRIX 1.00 0.02 1.00 -0.46 0.18 1.00 0.12 0.20 0.24 1.00 0.07 0.10 0.62 -0.08 1.00 0.12 -0.20 0.51 0.06 0.51 1.00 0.22 0.10 0.18 0.03 0.15 -0.26 1.00 Sample size=44 Latent variables
ITER OVERL FORM CENTR PSTRUC TIME PERF Relationships iter = 1*ITER overl = 1*OVERL form = 1*FORM centr = 1*CENTR pstruc = 1*PSTRUC time = 1*TIME perf = 1*PERF PERF=ITER+OVERL+FORM+CENTR+PSTRUC+TIME
set the error variance of iter to 0 set the error variance of overl to 0 set the error variance of form to 0 set the error variance of centr to 0 set the error variance of pstruc to 0 set the error variance of time to 0 set the error variance of perf to 0
set the covariance between OVERL and FORM to 0.22 set the covariance between ITER and OVERL to 0.02 lisrel output: sc mi ad=off
