January 18, 2016
Thesis of Msc Business Administration: Strategic Innovation Management Faculty of Economics & Business, University of Groningen, The Netherlands
INSIGHTS IN THE RELATIONSHIP BETWEEN
THE TEMPORAL DIMENSION OF FLEXIBILITY IN
THE NPD PROCESS AND INNOVATION PROJECT
PERFORMANCE: A META-ANALYSIS
Author: Rose Borgeld
Student number: s1619101
First supervisor: dr. J.D. van der Bij Second supervisor: dr W.G Biemans Word count: 7564
1. Introduction
In today’s dynamic and turbulent environment, firms need to be innovative in order to survive in the long run. The process of innovation is a topic in management literature that has been broadly researched. This is not remotely surprising considering that the steps that firms take in order to get from the generation of new ideas to the actual market launch of a new product, are important influencers of the firm’s innovative performance (Griffin, 1997).
Probably the most well-known model for new product development (NPD) is Cooper’s Stage-Gate model (Cooper, 1990), which is characterized by series of sequential project stages and by clearly separating the design and implementation stages. This model is widely recognized and used in NPD literature, but has also been critiqued by various authors over the years. Several studies have mentioned the risk of too easily accepting a normative vision of the NPD processes that would lead to a simplified identification and acceptation of decontextualized “best practices” (Bessant and Francis, 2004; Loch 2000; Phillips et al., 2004).
Development processes that have proven to be suitable in stable conditions might be unsuitable and ineffective in turbulent environments (Song & Montoya-Weiss, 1998). It is argued by several authors that the practices that lead to early and sharp product definition are not suitable in turbulent environment or when innovation is discontinuous and that turbulent and dynamic environments need more flexibility in the NPD process than is accounted for in Cooper’s Stage-Gate model, without completely abandoning the model. These studies however, show conflicting results (Bacon et al 1994; Thomke 1997; Ajamian and Koen, 2002; Bessant and Francis, 2004; Lynn, Morone, and Paulson, 1996; Shenhar, 2001a; Shenhar, 2001b; Veryzer, 1998).
Furthermore Kamoche and Cunha (2001) argue that the established literature show three different NPD models: 1) The traditional Stage-Gate model, 2) a version of the traditional approach tailored to high-speed environments and 3) the flexible model. So apparently several authors perceive that Cooper’s Stage-Gate model could still be applicable in dynamic and turbulent environments if it is tweaked in order to allow certain elements of flexibility, while others maintain that it should coexist with more flexible models that are suitable in more turbulent and dynamic environments. Biazzo (2009) recognizes this and aims to clarify the theoretical implications of adding flexibility to the traditional Stage-Gate model by further specifying the flexibility in NPD processes in three different dimensions of flexibility that all contain certain elements of structuration: the organizational, informational and temporal dimension. The latter dimension is the focus of this paper.
terminology of these elements appear to be used interchangeable and all seem to refer to the degree of temporal simultaneity in the NPD processes. However a meta-analysis is necessary in order to determine that these elements have homogeneous traits and can be synthesized into one driver of innovation performance. Furthermore this paper also discusses two more elements that also have common grounds with the temporal dimension: Time between milestones and formality.
The division of flexibility in the NPD process into three dimensions by Biazzo (2009) is a relatively new and untested theory. Further exploration of his proposed theory is
necessary in order to create a better understanding of the behaviour of different elements of flexibility in the NPD process. Many papers have focussed on different drivers of innovation performance and several elements of flexibility in the NPD process, none however focussed solely on the temporal dimension of flexibility in the NPD process. Furthermore, the studies showed a variety of significant and insignificant results on the relevant elements. In order to contribute towards a broader understanding of the relevant elements of the temporal
dimension proposed by Biazzo (2009) and their impact on NPD performance, this study synthesizes the different characteristics of temporal flexibility in the NPD process in relation to innovation project performance.
Therefore this paper aims to address the following research question: What are the characteristics of the NPD process that reflect temporal flexibility and how do these
characteristics impact innovation performance on a project level? The research question is addressed by means of conducting a meta-analysis, similar to the method used by Song et al (2008).
The study is conducted on a project level. The most important reason for this decision is that a project level of analysis generates more practical and applicable insights for
managers than any other unit of analysis, since it contains performance drivers that can be influenced by individual managers. Furthermore, different levels of analysis have different performance predicting relationships. A new product can be a failure at the project level, but that same product can have important learning effects and strategic capabilities at the firm level (Pattikawa, Verwaal & Commandeur, 2006).
This meta-analysis identified three meta-factors (Overlap, time between milestones and process formality). Nine different papers from 1995-2011 on innovation performance at a project level, representing nine different samples formed the input for this analysis.
This paper attempts to make several contributions to the innovation literature stream: 1) It synthesizes different temporal flexibility variables in relation to innovation performance; 2) It answers Biazzo’s (2009) call for further research into the different elements of flexibility in the NPD process; 3) It helps with gaining a better understanding of the temporal dimension of flexibility in the NPD process and 4) This meta-analysis will be the first study to focus solely on the temporal dimension of flexibility.
2. Theoretical background
2.1 Innovation project performance
According to Montoya-Weiss & Cantone (1994) it is apparent that it is difficult to define innovation performance, since there are several aspects of innovation performance to consider. Tatikonda and Montoya-Weiss (2001) argue that in literature there is a division between operational outcomes and market outcomes. Marketing literature concerning NPD performance is focussed on the marketplace and assesses NPD performance from an external perspective. It is mostly concerned with marketplace outcomes such as: customer
satisfaction, sales, market share and profitability (Cooper & Kleinschmidt, 1993; Griffin & Page, 1996; Song & Parry 1997; Shankar, 1999. Operational outcomes are more concerned with internal NPD performance.
The most commonly used indicators for internal innovation or NPD performance are product quality, unit cost and time to market (Wheelwright and Clark, 1992; Hauptman and Hirji, 1996; Smith and Reinertsen, 1998). Several researchers agree that NPD management is susceptible to a performance trade-off of these three performance indicators. They identified three potential pairs of trade-off for NPD performance: 1) speed - quality, 2) time - cost and 3) time - quality (Graves, 1989; Karlsson and Ahlstrom, 1999; Calantone and di Benetto, 2000; Harter, Krischnan & Slaughter, 2000). The constant element that all three trade-offs have in common is the temporal element. This is not entirely surprising. The time that it takes to get a product from the idea generation and design phase to market launch has been considered to be an important factor for success (Blackburn, 1992; Wheelwright and Clark, 1992).
Firms that are able to develop new products quickly and bring them to the market in time are often rewarded by the market. When firms operate faster they waste fewer resources (Stalk and Hout, 1990; Clark and Fujimoto, 1991) and the returns in the marketplace are often higher (Vessey, 1991; Hendricks and Singhal, 1997). This suggests that higher speed can diminish the negative results from the above mentioned trade-off effects.
2.2 Flexibility in the NPD process
As mentioned before, the traditional view of the NPD process appears to leave little room for flexibility (Lynn, Morone, and Paulson, 1996). The studies carried out by Iansiti,
MacCormack and Verganti identified five basic characterizations of flexibility in the NPD process (Iansiti, 1995; Iansiti and MacCormack, 1997; MacCormack and Verganti, 2003): 1) Sensing the market regarding change in needs and customer need fulfilment; 2)Rapid and early experimentation in order to explore the effect of product concept decisions; 3) Controlling of product evolution by using technological and organization integration
mechanisms; 4)Exploitation of the generational experience of people working with different product generations; 5) Search for modular product architectures that demonstrate critical elements of system performance, even when the modules are not entirely finished.
and containing overlapping stages, leaving little room for the traditional sequential
characteristics of the Stage-Gate model. The reactive quality of the NPD is considered to be adequate in turbulent and dynamic environments (Iansiti, 1995; MacCormack, Verganti & Iansiti, 2001).
It is however a dangerous simplification to state that flexible development processes are not compatible with the Stage-Gate model. Different paths toward flexibility can coexist. The notion that flexibility cannot coexist with structure is caused by the lack of clear
definition and theorization of the concept of flexibility in the NPD process (Biazzo, 2009). The mutual exclusion of Stage-Gate processes and flexible NPD processes with overlapping stages is not that apparent in several studies that are concerned with the relationship between anticipation and reaction strategies in turbulent environments. These studies show that even though certain elements of flexibility are added to the NPD process, structuration similar to structuration in the Stage-Gate model is still clearly recognizable in the NPD process. These studies also show that there is some overlap in the NPD processes, but this is mostly in the delay of a formalized design and it is not apparent that stages overlap one another entirely, in the way discussed by Iansiti and colleages (Bacon et al, 1994; Bhattacharya, Krishnan & Mahajan, 1998; Thomke, 1997; Thomke & Reintertsen, 1998; Biazzo, 2009).
As previously mentioned, Biazzo (2009) strives to contribute to the clarification of the flexibility terminology in NPD processes by separating the organizational, informational and temporal domains of flexibility. The organizational dimension of flexibility refers to the structuration of the NPD process and considers the design of the organizational stages. The informational dimension is concerned with the investigation of the firm’s product definition approach and the classification of the development activities. The temporal dimension refers to task scheduling in the NPD process. In this study we focus on the temporal dimension of flexibility. Biazzo (2009) discusses three types of task scheduling in the temporal dimension: 1)sequential, 2)overlapping and 3) concurrent.
2.3 The temporal domain of flexibility in the NPD process
Temporal elements of flexibility in the NPD process have already been added to the original Stage-Gate model by Cooper himself when he introduced the concept fluidity to the original model (Cooper, 1994). ‘‘Some activities, normally done in the next stage, will begin before the previous stage is completed; long lead time activities can be brought forward from one stage to an earlier one’’ (Cooper, 2001, p. 147). Fluidity refers to the shifting of certain NPD activities along the temporal timeline with respect to certain gates. The shifting of activities from one stage to another stage is also recognized as overlap of activities and is not as rigorous as the overlap suggested by Iansiti and colleages (Biazzo, 2009). The temporal element of overlap can be defined as different tasks that are simultaneously carried out by different groups of people (Krishnan, Eppinger & Whitney, 1997).
Temporal flexibility: Overlap
development in multiple industries (Clark, Chew and Fujimoto, 1989; Hayes, Wheelwright & Clark, 1988; Clark and Fujimoto, 1991; Cusumano & Selby, 1995; Sabbagh, 1996; ). Overlap reduces the hurdles that products must pass between the stages and it helps in identification and solution of problems in a more timely manner . Furthermore, by overlapping predictable steps and by accomplishing more tasks parallel, the waiting time between steps can be eliminated and the overall duration of a development project can be reduced. (Cooper & Kleinschmidt, 1986; Tatikonda & Montoya-Weiss, 2001). Overlap is made possible when the NPD processes are loosely coupled and are characterized by a high degree of modularity, that allows each participating component of the development unit to function autonomously and concurrently.
On the other hand, when NPD processes are tightly coupled the NPD process is much more iterative and less modular. When modularity is low, the NPD process requires a more sequential form of task scheduling. Otherwise the iterations would slow down the NPD process and that would have a negative impact on time and cost performance (Sanchez and Mahoney, 1996). According to Terwiesch & Loch (1998) overlapping activities in the NPD process require a situation with limited uncertainty and with foreseeable and controllable changes. Or else, the overlap will cause substantial rework in other aspects of the NPD process which will outweigh the time gained from overlapping.
Situations with fast evolution are expected to have more overlap related benefits than situations with slow evolution, in the event of fast evolution it is expected that uncertainty is reduced early in the process. This leads to more opportunities for concurrent
engineering/overlap in the NPD process (Krishnan, Eppinger & Whitney, 1997). The concept of uncertainty resolution is also described as the distribution of engineering changes over the course of the project. When engineering changes occur later in the NPD project it takes more time to adjust the work of concurrently performed activities. In this situation the rework activities nullify the time gains that result from overlapping activities (Loch and Terwiesch, 1996).
Temporal flexibility: Time between milestones / frequency of evaluation
Frequent milestones or more formal project evaluation points can accelerate product
development. The presence of frequent milestones and more formal project evaluation points suggests that the current state of progress is frequently reassessed and this forces people to check often what they are doing exactly and if the development process runs out of course and should be adjusted (Eisenhardt & Tabrizi, 1995).
Milestones are useful as a tool for creating order and routine in chaotic activities such as iteration and testing (Bastien & Hostager, 1988; Weick, 1993). This is particularly
important in uncertain situations where frequent milestones can be useful for checking current progress against evolving markets and technologies (Gersick, 1994). Furthermore, milestones can shorten development time because their frequency can work as a motivator. It gives developers a sense of control and accomplishment. It also creates a sense of urgency, that keeps people from procrastinating (McClelland, 1961; Langer, 1975; Gersick, 1988). The opposite is true for milestones that are infrequent.
Project process can skid of off track because problems are discovered later in the NPD process, when it has become harder and more time consuming to readjust. Lack of
developers. The NPD process itself can become too unstructured and chaotic (Waldrop, 1992).
Temporal flexibility: Process formality
Process formality might not be the first thing a person thinks about when considering the temporal dimension. However, the measurements used to measure the degree of process formality usually contain temporal elements (Tatikonda & Montoya-Weiss, 2001; Naveh, 2007; Salomo, Weise & Gemünden, 2007).
Process formality offers a general framework that designates the milestones that need to be met in the process and provides a sequence of steps that show what needs to be done. When process formality is high, the dates in the NPD procedure are specifically set and the project has written procedures that need to be strictly followed, when it is low, the dates are not specified in advance and procedures do not have to be strictly followed (Naveh, 2007). Process formality shows similar traits as project inflexibility. According to Sethi & Iqbal (2008, p121) the definition of project inflexibility is: ‘The extent to which project parameters (e.g. product definition,concept, specifications, architecture) are rigid and unchangeable when the project is approved after review at the initial gates.’
The structure and sequence that process formality provides to the work that needs to be done can help with the development effectiveness. It can also reduce the ambiguity for developers when deciding what to do and when to do it. Furthermore process formality can be beneficial for cross-functional communication and coordination (Cooper & Kleinschmidt 1990; Rosenthal 1992; O’Connor 1994).
There is also a downside to a high amount of process formality. Conducting reviews and following rules in an pre-set manner can lower the fulfilment of “real work” (Rosenthal 1992) and there is also the possibility that it forces the NPD project in one predetermined direction. This reduces the ability to deal with uncertainties that arise during the project (Eisenhardt & Tabrizi, 1995).
3. Methodology
3.1 Data collection
In order to perform this study, empirical studies were selected that: 1) Are performed at a project level, 2) include innovation project performance as the dependent variable and 3) incorporated antecedents regarding the temporal dimension of flexibility in NPD processes. The search process to gain this data was as follows:
1. Searches of keywords in Google scholar and electronic databases using terms such as “Simultaneity NPD”, “Concurrent NPD”, “Sequential NPD”, “Acceleration NPD”, “Modular NPD”. (A more elaborate list of keywords and its results are available from author)
2. “Snowbal searches” by checking the references of studies already.
4. Using Google scholar’s “relevant article” option that identifies articles that are similar to the initially found articles.
In order to make it possible to conduct a meta-analysis on the identified studies, the selected studies needed the following elements: 1) the correlation coefficient between the
independent and dependent variables, 2) the sample size of the study, 3) the Cronbach alpha of the constructs in the study needed to be listed or otherwise easily identified. Empirical data were only used once per meta-analysis, when multiple papers shared the same data, only one study was selected for the meta-analysis.
Initial application of these criteria resulted in nine papers and 14 correlation coefficients that were relevant for the studied topic.
3.2 Coding procedure
Data were coded by the author and checked by the first supervisor who is also a research expert, dr. J.D. van der Bij. Inconsistencies and uncertainties were discussed and the studies were re-examined in order to determine its suitability and reach agreement. Furthermore the author and the first supervisor discussed and agreed on possible meta-factors. In
determining the meta-factors the measures and definitions were used instead of the names of the variables. The meta-factors and their corresponding variables and measures can be found in the Appendix.
3.3 Protocol for Meta-Analysis
The protocol developed by Hunter and Schmidt (1990) was used for the meta-analysis. The first step was correcting for sampling size, the second step was correcting for measurement error, in step three, four and five the total variance, the artificial variance and the variance in sampling error were computed. In step six the outcomes of step four and step five were subtracted from the outcome of step three in order to compute the real variance due to heterogeneity of the meta-factor. It is assumed that the meta-factor is homogeneous if the real variance is less than 30 percent of the total variance. For meta-factors that are considered to be heterogeneous, a moderator analysis was conducted. In order to do this, the data were divided into subgroups according to research context and research context (Henard & Szymanski, 2001).
4. Analysis and Results
4.1 Antecedents for the temporal dimension of flexibility
This study revealed three meta-factors on the temporal dimension of flexibility as drivers of innovation performance. Overlap, time between milestones and process formality. Earlier we discussed the definitions of performance and the several aspects that have to be considered when determining performance (Montoya-Weiss & Catantone, 1994). Most of the
external performance data. A performance measure that almost all of the studies had in common was the internal performance measure time. Therefor this performance measure was picked as the dependent variable for NPD project performance.
Table 1: Results of the meta-analysis
Meta-factor N-tot K p 95% conf interval VARreal (%) Moderators Overlap 649 6 -0,22* 0 Time between Milestones 332 3 0,34 (0.25; 0,43) 47% Yes Process formality 372 4 0,098 (-0,97; 0,29) 80% Yes
4.2 Moderator analysis
One out of three analysed meta-factors showed a homogeneous correlation. The other two showed heterogeneous c orrelations. Therefore, it was necessary to conduct a moderator analysis. For the meta-factor time between Milestones, performance was measured
Table 2: Moderator Analysis Meta-factor N-tot K p 95% conf interval VARreal (%) 1. Time between Milestones 212 2 0,41 0 (Similar performance measures) 2. Process formality 182 2 0,15 0 (Similar performance measures) 3. Process formality (remaining studies) 252 2 0,29 (0,21; 0,38) 48 (Different performance measures)
5. Conclusions and implications
5.1 Major research results
This research started with the research question: What are the characteristics of the NPD process that reflect temporal flexibility and how do these characteristics impact innovation performance on a project level? In answer to this research question this meta-analysis identified three meta-factors: Overlap, Time between milestones and process formality. These meta-factors were then investigated.
The meta-analysis conducted in this study shows interesting results. Only one of the three investigated meta-factors shows homogeneous and significant results. Moderator analysis showed that the heterogeneity in the other two investigated relationships was
milestones and process formality showed negative relationships with innovation performance.
5.2 Theoretical implications
This meta-analyis studied the results of empirical research on the relationship between Biazzo’s (2009) temporal dimension of flexibility and innovation project performance and integrated these results. Overall research that show results concerning the temporal
dimension of flexibility in the NPD process show mostly operational outcomes (Naveh, 2007; Tatikonda & Montoya-Weiss, 2001) of an innovation project, in particular ‘time to market performance’. This is defined as project performance in this meta-analysis. However, one study only showed market performance as a performance outcome. Tatikonda & Montoya-Weiss (2001) predicted a relationship between operational outcomes and market outcomes. Because of this prediction, the initial conducted meta-analysis did not differentiate between operational outcomes and market outcomes. This resulted however in a heterogeneous result.
Further analysis and differentiating between operational outcomes and market outcomes resulted in a homogeneous relationship of the remaining samples. This evidence is not strong enough to rule out a relationship between operational outcomes and market outcomes such as predicted by Tatikonda & Montoya Weiss (2001) but it does suggest that this relationship is not strong enough enable pooling of the operational and market project performance outcomes. Further research into the relationship between operational outcomes and market outcomes is necessary, since this relationship was not further investigated in this study. The results of the meta-analysis was also congruent with the foreseen trade-off between time, quality and cost elements of operational performance (Graves, 1989; Karlsson and Ahlstrom, 1999; Calantone and di Benetto, 2000; Harter et al, 2000). In the moderator analysis a meta-factor that initially showed heterogeneous results, showed homogeneous results after deleting a study that showed performance as a combination of time, cost and quality instead of only time performance which was common in the other studies.
When focusing on each meta-factor and the relationship of that factor with the operational outcome ‘time to market’ it shows that these meta-factors behave in accordance with what is predicted in innovation literature. The effect of overlapping activities ‘Overlap’ in the NPD-process shows a positive relationship with time to market performance, meaning that when overlap in the NPD process is higher, the time to market is shorter and therefore the performance of time to market is higher. The relationship between time between
milestones and time to market performance is negative. This means that when there is more time between milestones in a NPD project, the duration of that project is longer and thus the time to market performance is lower. The third meta-factor, process formality, also shows a negative relationship between process formality and time to market performance. So NPD projects that have more formalized processes take longer to complete than NPD projects with less formalized processes.
Another interesting theoretical implication that did not come directly from
conducting the analysis, but from building the theoretical framework prior to the meta-analysis was the following finding. The most common view of the use of flexibility in the NPD process is that it provides firms with the ability to rapidly embrace environmental
turbulence. And that it help with adapting to new emerging technological and market
in the NPD process is associated with dynamic and turbulent environments with high amounts of uncertainty and structuration of the NPD process is associated with stable environments. For the temporal dimension of flexibility in the NPD process it has become clear that the opposite of this statement is true. Characteristics that have been associated with flexibility in the NPD process such as overlap and concurrency will be more successful in stable environments (Biazzo, 2009).
5.3 Managerial implications
This research showed that managers can slow down the innovation process when there is a lot of time between milestones or when the NPD process is highly formalized and managers can speed up the innovation process by using overlapping activities in the NPD process. Speeding up the innovation process however is not a one on one substitute for overall innovation performance. For overall performance there are several aspects of innovation performance that need to be considered (Montoya-Weiss & Calantone, 1994). Although Tatikonda & Montoya-Weiss (2001) predict a direct relationship between operational outcomes and market outcomes, this meta-analysis showed that managers should be aware that operational performance outcomes and market performance outcomes are influenced differently by temporal flexibility factors. This study also confirmed that managers should be aware that even when performance measures are concentrated on operational outcomes, there is still the trade-off between time, cost and quality elements of operational
performance, such as foreseen by several researchers (Graves, 1989; Karlsson and Ahlstrom, 1999; Calantone and di Benetto, 2000; Harter et al, 2000). Furthermore, managers should also realize that even though flexibility is usually linked to dynamic and turbulent
environments, temporal flexibility strategies such as overlap in activities is more suited in a stable and predictive environment (Biazzo, 2009).
5.4 Limitations
All research has its limitations, this meta-analysis is no exception. The first limitation lies in the used data-set for this meta-analysis. The input for the meta-analysis existed of empirical data on projects that finished successfully. Projects that are unsuccessful in the sense of being unfinished were not included in the empirical results of the data used in this meta-analysis. This means that there must be a bias toward project successes. It can be misleading to only depict strategies that seemingly deliver the best performance (Song eta al., 2008). Failures should also be studied in order to end up with better, less biased data.
The sample size of the meta-analysis itself is a second limitation. According to
data input in the meta-analysis, since some studies did not depict a correlation matrix. It will be helpful if in the future all research would report correlations (Chen et al, 2010).
The third limitation is that this meta-analysis did not depict a holistic view of innovation project performance. As mentioned before, there are several aspects of performance that should be considered and these aspects are not homogeneous. There is market performance and operational performance and operational performance contains a trade-off between time, cost and quality. This meta-analysis only covered the time aspect of operational performance.
5.5 Future research directions
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APPENDIX: META-FACTORS AND
VARIABLES FROM PRIMARY
STUDIES AND CORRESPONDING MEASURES
Meta-factor Independent variable Measure Article
Overlap NPD-concurrency
Process concurrency
% Overlapping development activities
Managers were asked to rate the following statement: Project activities were overlapped (performed concurrently) to a great degree. (Strongly disagree----strongly agree).
The process concurrency scale is based on cross-functional integration concepts of
Wheelwright and Clark (1992). Managers were asked to answer the following question: What percentage of the design
engineering effort was complete when manufacturing engineering started active involvement in this project? (Scale 1-7, Where 1=0%, 4=50% and 7=100%
The number of overlapped activities in the development process were measured. Activities were identified and selected from a list of 11 activities and divided by the total possible number of overlapping activities.
Swink, 2003
Tatikonda & Montoya-Weiss, 2001
Overlap
Overlap
Task interdependence
Overlap was measured as the sum of the overlaps between
subsequent phases divided by the gross duration of the project without deducting overlap (i.e. the sum of the development phases.
Overlap was measured by the sum of the overlaps (in months) across the six phases described above of the development project, divided by the total development time. Higher percentages of overlap reflected more project overlap. Task interdependence was measured by three items developed for this study, which asked respondents whether marketing-related and technology-related functions depend on each other to complete their development tasks, seven point likert-type scales were used
Terwiesch & Loch, 1998
Eisenhardt & Tabrizi, 1995
Ma et al, 2011
Time between Milestones Time between Milestones Time between milestones was measured by first asking the respondent group to provide the number of formal milestones for the project, with a milestone defined as an officially scheduled project review point. The
respondents were then asked the average time (weeks) between these milestones during the
Time between Milestones
Frequency of evaluation
project.
Time between milestones was measured by the average number of weeks between two officially scheduled project reviews. Only milestones with a detailed project review were included.
The respondents were asked to report the total number of gates in their company’s Stage-Gate process as a measure of frequency of evaluation
Terwiesch & Loch, 1998
Sethi & Iqbal, 2008
Process formality Process formality
Formality
Formality
The process formality scale is derived from measures previously tested by other authors such as Montoya-Weiss (2001). Four items were used to assess the degree to which product
development was managed as a formal top-down process. This study also refers to
measuring of formality similar to the scale derived by Montoya-Weiss (2001)
They used a 7 point Likert scale and asked managers to assess the following: -To what degree were project management rules and procedures formalized via documents sucha as contract
Salomo et al, 2007
Naveh, 2007
Strictly enforced gate review criteria
books, sign-off forms and such? – To what degree were formal project management rules and procedures actually followed for this project?- To what degree were formal progress reviews held (sometimes also called
design,gate, phase or stage reviews?)
The authors developed a new measure for this construct to assess the degree to which the gate review criteria were formally established and used and strictly enforced. The items in the
measure were based on the Stage-Gate literature (Cooper 1994, 2001)
