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Master Thesis

“Taking a new perspective on the relationship of flexibility and innovation performance on a project level; a meta-analysis” By Anne Roukema 06 27363752 a.j.roukema@student.rug.nl University of Groningen Faculty of Economics and Business

Nettelbosje 2 9747 AE Groningen

Date; June 2014

Master of Science, Business Administration Strategic Innovation Management

First supervisor: Dr. J. D. (Hans) van der Bij Second supervisor: Dr. W.G. (Wim) Biemans

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Abstract:

Flexibility is of vital importance when coping with uncertain dynamic environments like many businesses do today. In order to better understand contingency factors affecting flexibility in new product development and to further the research on flexibility in a meaningful way, it is required to identify different dimensions of flexibility. To do this the paper builds on the organizational dimension as identified by Biazzo (2009), which entails the structuration of the project process. By performing a literature review, structuration practices which correspond to this dimension are identified, and a meta-analysis is conducted using 28 papers and 33 independent samples from the period 1996 to 2010 to analyze how these practices affect innovation performance on a project level of analysis. This resulted in the identification of eight meta-factors (centralization, colocation, formalization, functional integration, goal stability, team stability, process project concurrency and project process structure) with nine homogeneous result in 22 subcategories. For the remaining heterogeneous factors a moderator analysis was performed which resulted in the identification of two moderators. Implications for theory and practice are discussed and future research directions are proposed.

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1. Introduction

Flexibility is of vital importance when coping with uncertain dynamic environments like many businesses do today. In the context of new product development it has been defined as the capacity to adapt to new technological and market information that emerges over the course of a project (Iansiti, 1995; Iansiti & MacCormack, 1997; MacCormack & Verganti, 2003; MacCormack, et al. 2001). Getting to grips with the concept of flexibility has proven to be difficult due to the application of the term in many different settings and its polymorphous nature. That is to say, it does not describe a fixed state with flexibility on one end of the spectrum and inflexibility on the other (Golden & Powell, 2000). It has been argued that flexibility is multidimensional in the sense that an organization can be flexible in some ways and inflexible in others (Suarez, et al., 1995). So in order to better understand contingency factors affecting flexibility and to further the research on flexibility in a meaningful way, it is therefore required to identify different dimensions of flexibility.

One attempt to develop a dimensional conceptual framework to better understand contingency factors in the design of new product development processes (NPD) has been made by Biazzo (2009). In his paper he identifies an; organizational-, informational-, and temporal dimension. The organizational dimension refers to the structuration of the process. The informational dimension deals with classifying the development activities and investigating the firm’s product definition approach (early and sharp mode vs. late freeze mode). And the temporal dimension relates to the execution strategies of development tasks (Biazzo, 2009).

Biazzo’s main reasoning for developing this framework is to overcome the widely accepted dichotomy between flexible structured processes and stage gate processes. Which assumes that these are irreconcilable approaches to NPD because one depends on early sharp product definition whereas the other does not, while both still facilitate the ability to adapt, and hence foster flexibility. Biazzo (2009) effectively states in his paper, that flexibility is only present on the informational dimension whereby he overcomes this supposed dichotomy between process flexibility and process control through structuration in uncertain and dynamic conditions.

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In this paper the organizational dimension as identified by Biazzo (2009) is built upon by identifying structuration practices which correspond to this dimension and analyzing how they affect innovation performance. Thereby obtaining insight for managers of NPD projects on how to combine different structuration practices that foster either flexibility or inflexibility and contributing to the organizational literature on flexibility, NPD characteristics and innovation performance.

To further mark down this research a specific focus on the project level of analysis is taken. This decision has several reasons. First, most of the literature on innovation performance is conducted on the project level (Chen & Damanpour, 2010). Which facilitates the validity of our meta-analysis by providing the biggest number of suitable papers. Secondly, the project level seems the most appropriate for practical reasons, in that it may provide more accurate and applicable insights for project managers. Thirdly, looking at the theory there is a gap concerning the research about structuration practices and innovation performance on a project level (Damanpour, 1991; Montoya-Weiss & Calantone, 1994). In accordance with the afore mentioned gap and the intention to build on the organizational dimension of flexibility this paper poses the following research question;

‘What organizational characteristics reflect flexibility on the organizational dimension and how do these impact innovation performance on a project level?’.

To answer this question a literature review was performed to determine which organizational antecedents of innovation performance apply to the organizational dimension and how these factors reflect flexibility. And secondly, a quantitative literature review (meta-analysis) was performed, based on Hunter and Schmidt’s (1990) tried and tested protocol to integrate the existing quantitative evaluations on how these factors affect innovation performance on a project level.

Based on 28 different studies which represented 33 independent samples from the period 1996 to 2010, this resulted in the identification of eight meta-factors (centralization, colocation, formalization, functional integration, goal stability, team stability, process project concurrency and project process structure).

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discussion of these results and the conclusions and implications. The article will conclude with a description of its limitations and possible future research directions.

2. Theoretical background

In this section the theoretical background for factors in the conceptual model (figure 1) will be explained. Starting with an explanation of flexibility and its dimensionality. Followed by an overview of innovation performance and the measures that were used for this concept. And lastly, an explanation of the various factors that were distilled from the literature that reflect flexibility on the organizational dimension and how these may affect innovation performance according to the literature.

Figure 1. Conceptual model organizational antecedents of innovation performance reflecting flexibility

2.1 Flexibility and the organizational dimension

Although the term ‘flexibility’ is ubiquitous in its use, its meaning is not always clear (Evans, 1991). In this paper we use the definition of flexibility set out by Biazzo (2009);’flexibility is the ability to embrace environmental turbulence rapidly adapting to new technological and market information that emerges over the course of a project’ (Biazzo, 2009; Iansiti, 1995; Iansiti & MacCormack, 1997; MacCormack & Verganti, 2003; MacCormack, et al. 2001).

Due to the fact that flexibility can have many different meanings and organizations can be flexible in some ways and inflexible in others it is considered to be multidimensional (Suarez, et al., 1995). Identifying the different dimension allows the concept of flexibility to be split into its component parts which can be more readily prioritized, measured and improved (Suarez, et al., 1995; Evans, 1991; Upton, 1994). The fairly recent attempt by Biazzo (2009) to develop a dimensional conceptual framework served as the catalyst for this research. The organizational dimension he specifies concerns the structuration of the project process. Biazzo (2009) defines structuration as; “the formal segmentation

Project level Innovation performance 1 Project performance 1a Time 1b Cost 1c Quality 2 Product performance Organizational dimension 1 Centralization 2 Formalization 3 Colocation 4 Functional integration 5 Goal stability 6 Team stability

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of the temporal progression in stages and the definition of the activities that should be occurring in each stage”. This organizational dimension encompasses a variety of structuration practices that do not always seem to be compatible, causing a dichotomy in the literature. Where one side argues that high-speed environments demand flexible, extemporaneous, fast (re)actions facilitated by responsive structures (Thomke and Reinertsen 1998). And the other side argues that the use of structured, more rigid, approaches may lead to superior new product innovations and helps to avoid the risks created by the acceleration of lead times (Kamoche and Cunha, 2001). Biazzo attempts to overcome this dichotomy and to obtain a framework that better fits contingency factors which may lead to better best practices for managers. Following this reasoning this paper builds on the organizational dimension to ascertain which structuration practices reflect flexibility and how they impact innovation performance.

Whereas Biazzo (2009) deems the structuration decision to be a question of process control and not of process flexibility. The literature as well as the definition provided by Biazzo (2009) provide the view that process structuration practices can give us an indication of flexibility in the sense of adapting to new technological and market information and embracing environmental turbulence. The issue at hand seems to be one of semantics because tight process control through for instance formalization can quite clearly and logically be seen to cause the process structure to become inflexible. Leading to earlier specification of process and product details which constrain the project later on when it has to deal with new technological and market information. In this paper we stick to the definition of flexibility by Biazzo (2009).

2.2 Innovation performance

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7 2.3 Organizational flexibility factors

On the basis of a literature review, that was conducted in accordance with the performance of a meta-analysis, eight factors were distilled form the available existing literature on organizational characteristics and innovation performance; centralization, colocation, formalization, functional integration, goal stability, team stability, process project concurrency and project process structure. The distillation process utilized for these factors will be detailed in the data collection and methodology section. The theoretical background for each of these factors, in terms of definitions and supposed effects on flexibility and innovation performance, will now be explained.

2.3.1 Centralization

First, centralization refers to the concentration of power of authority in an organization (Schminke, et al., 2000). It is widely considered to consist out of two subcomponents; participation in decision making, and hierarchy of authority (Gupta, et al., 1986; Hage & Aiken, 1967). The higher the level of hierarchy on which the decision making takes place, and the lower the level of participation in decision making by lower hierarchical levels, the more centralized the project/organization will be.

Centralization is regarded to effect flexibility negatively in uncertain and dynamic environments (Chen, 2007; Lysonski, et al., 1995). A higher degree of centralization causing a decrease in flexibility because the information necessary to make the decisions has to travel up the hierarchy, taking more time resulting in a decreased ability to adapt. Conversely, more decentralization, as the antithesis of centralization is called, may also negatively affect the ability to adapt because of the difficulty of gaining consensus among different people with different problem-solving ideas (Sheremata, 2000).

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unable to recognize problems due to their limited integral understanding of the project resulting from decreased communication among participants (Chen, 2007).

2.3.2 Formalization

Formalization refers to the extent to which explicit rules and procedures govern the new product development process (Chen, 2007; Li & Atuahene-Gima, 1999; Evanschitzky, 2012). According to Bidault and Cummings (1994), highly formalized projects impede the spontaneity and flexibility needed for internal innovation. They are constraint by their existing rules and procedures when adapting to a new situation, reducing the flexibility of the project in terms of responsiveness (Chen, 2007).

Formalization can have a positive impact on information use because formal, structured ties minimize misunderstanding (Li & Atuahene-Gima, 1999; Ayers, 2001). Tatikonda and Montoya-Weiss (2001) find a direct positive relationship with project performance outcomes for higher formalization. A less formalized process is likely to stimulate interaction but it may also require more communication and coordination to get the job done because members discretion on the job is inversely related to the amount of formalized/prescribed behavior (Chen, 2007).

2.3.3 Colocation

Colocation is a term used to indicate the proximity between team members and between team members and supervisors (Carbonell & Rodriguez, 2006; Keller, 2006). High colocation has been found to facilitate easier and more frequent interaction which helps to break down functional and mental barriers (Kahn and McDonough, 1997). And can result in accelerated development, by increasing mutual understanding of constraints, limitations and potential problems (Carbonell & Rodriguez, 2006). Especially in uncertain environments it allows project members to adapt more readily, by enabling rapid feedback, decoding, and synthesis of complex information (Katz & Tushman, 1979). It may also increase learning through enhanced knowledge sharing (Akgün, et al., 2005).

2.3.4 Functional integration

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al, 2005; Souder et al, 1998). It is also associated with functional diversity which represents the number of departments and external stakeholders on the team (Carbonell & Rodriguez, 2006).

High functional integration can lead to decreased development time by increasing goal congruence and bringing more creative potential to problem solving (Carbonell & Rodriguez, 2006). On the other hand, it may also increase cycle-time because project members from different functions may find it hard to work together effectively and develop a shared purpose (Carbonell & Rodriguez, 2006). One specific integration of functions that has received much attention in NPD research is that between R&D and marketing. Many empirical studies have indicated that R&D and marketing integration is positively related to NPD success (Cooper, 1983; Ottum & Moore, 1997; Souder, 1988; Souder and Song, 1998; Yap and Souder, 1994). While too high a degree of functional integration may cause communication and coordination breakdowns in the project process, it can generally be seen as a flexible structuration practice because it fosters the availability of expertise and creative potential in problem-solving which leads to an increased ability to adapt to new technological and market information (Carbonell & Rodriguez, 2006).

2.3.5 Goal stability

Goal stability refers to the degree in which core objectives, timetables, and resource commitments remain stable over the course of a project (Akgün & Lynn, 2002; Lynn et al., 1999; Salomo, 2007). Low goal stability is associated with increased cost and development time because development activities need to be adjusted to the new goals and previous path dependent investments may become obsolete (Salomo, 2007). On the other hand , absolute adherence to initially defined goals may indicate that the project is rigid and tends not to react adequately to changes, which may also have a negative effect on performance (Thomke & Reinertsen, 1998; Verganti, 1999). According to Salomo et al (2007) a certain degree of goal flexibility is required to react appropriately to shifting conditions. Because internal and external changes cannot be completely anticipated through planning and must be accounted for during project execution. By pre-defining goals and adhering very strictly to them the ability to adapt is diminished and therefore goal stability is considered to be inflexible in essence.

2.3.6 Team stability

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project their accumulated knowledge is no longer available to the project. Therefore team stability can play an important role in the knowledge or learnings collected by a team, the speed of development as well as the overall success of NPD projects (Akgün & Lynn, 2002). Others propose that under organizational crisis, low team stability can provide the organization with new knowledge which can help them to become more successful (Starbuck, 1992; Nystrom & Starbuck, 1984).

Low team stability can also increase flexibility according to Mobley (1982) because it increases commitment of project members and stimulates them in new environments. High team stability is associated with inflexibility especially for technologically complex projects which may require different perspectives and ways of thinking to adapt to their greater radicalness (Carbonell & Rodriguez, 2006). 2.3.7 Project process concurrency

Project process concurrency refers to the degree to which different functional representatives conduct project work simultaneously (Tatikonda & Montoya-Weiss, 2001). It concerns the overlapping of problem-solving between functions (Hauptman & Hirji, 1996). If a downstream function only starts work after the upstream function is finished there is no concurrency. Conversely, when both functions start at the same time there is total concurrency. It can be assessed by the extend of readiness of project members to release incomplete and/or uncertain and/or ambiguous information to their counterparts (Hauptman & Hirji, 1996; Jayaram & Malhotra, 2010). Several studies have found that it leads to greater information sharing between functions resulting in better operational outcomes (Wheelwright & Clark, 1992; Rosenthal, 1992; Adler, 1995). It leads to better mutual understanding of constraints, opportunities, and requirements between up- and downstream functions, which enables joint problem-solving, and better anticipation of technical challenges and conflicts. These benefits can lead to increased time, cost and quality performance (Tatikonda & Montoya-Weiss, 2001). These same benefits also suggest that project process concurrency is a flexible structuration practice because improved anticipation, and joint problem-solving foster the ability to adapt.

2.3.8 Project process structure

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facilitate quality, time and cost performance because it helps to integrate functional perspectives, facilitating communication and coordination (Bonner, et al., 2002). But on the other hand to much project process structuration may impede the autonomy necessary for creativity within the project leading to less innovation and performance (Bonner, et al., 2002). Strict adherence has also been shown to correlate with learning failures which lead to new product failure (Sethi and Iqbal, 2008). It is therefore considered to be inflexible because it predefines a roadmap, milestones and sequence in advance leaving less opportunity to adapt.

3. Data collection and methodology

In order to integrate the previous findings on antecedents of innovation performance that reflect flexibility a meta-analysis is conducted. This enables the establishment of general principles and accumulation of knowledge in a field by applying statistical procedures that are designed for this purpose (Montoya-Weiss, & Calantone, 1994; Song, et al., 2008). In the next section the selection of the studies used in the analysis, the distillation process of the factors and the protocol of the meta-analysis will be explained.

3.1 Select Studies as Input for the Analysis

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studies further search terms were used including; project performance autonomy, control, formality, concurrency and combinations thereof.

For the selection of the studies in this meta-analysis the following criteria were defined in advance. The studies needed to contain; (1) a correlation matrix; (2) have a predictor of innovation performance reflecting flexibility on the organizational dimension; (3) a dependent variable measuring innovation performance; and (4) a project level of analysis. After checking for appropriate usage of these criteria the search yielded 28 papers and 155 possible variables. Appendix 1 shows the papers included in the meta-analysis and appendix 3 shows the publication sources.

3.2 Distillation process

The next step was to categorize the measures found in the papers to form meta-factors. This was initially done individually by the author and another student, M. van Ark. The resulting categorizations were then combined and checked in collaboration with to expert researchers, dr. J.D. van der Bij and dr. W.G. Biemans. Any inconsistencies were debated and resolved in multiple sessions with said experts. Finally resulting in eight meta-factors. The meta-factors were formed based on definitions from the existing literature to make them more interpretable and transparent. Appendix 4 shows the factors and the measures included.

3.3 Protocol for Meta-analysis

The actual meta-analysis was conducted in three stages: the main effects testing, the moderator existence testing, and the moderating effects testing.

Hunter and Schmidt’s (1990) protocol was used for the first stage. This protocol has been extensively used in previous research (Chen & Damanpour, 2010; Song et al., 2008). As input for the study Pearson correlations between meta-factors and dependent variables were used. This is strongly advised by Hunter and Schmidt because correlations between two variables are independent of other variables in the model. After selecting the meta-factors they were corrected for sample size differences, and measurement errors.

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13  = ∑ 

 

∑  

Where Ni is the sample size of the primary study i. To remedy measurement errors, Cronbach alphas were used. The correlation coefficient was divided by the product of the square root of the reliability of the meta-factor and the square root of the reliability of performance (Song et al. 2008). In case the reliabilities were not reported for all the measures in a study, averages were used of the other studies in the meta-factor. Second, the correction for measurement error was performed according to the following formula:

= ̅ =  

 ∗  

Where Ā is the compound reliability correction factor; √Rxx is the average of the square roots of reliabilities of independent variables composing a given meta-factor; and √Ryy is the average of the square roots of reliabilities of dependent variables composing a given meta-factor (Song et al. 2008). Appendix 2 shows a complete overview of the formulas used in the meta-analysis.

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4. Analysis and results

The meta-analysis used eight meta-factors of which the definitions are shown in table 1.

Table 1. Definitions of the meta-factors

Meta-factors Flexible/Inflexible Definition Sources

Centralization Inflexible the concentration of power of authority in an organization

Schminke, et al., 2000 Colocation Flexible indicates the proximity between team

members and between team members and supervisors

Carbonell & Rodriguez, 2006; Keller, 2006

Formalization Inflexible the extent to which explicit rules and procedures govern the new product development process Chen, 2007; Li & Atuahene-Gima, 1999; Evanschitzky, 2012 Functional integration

Flexible the degree of integration between functions, measured by level of contact, level of information flow, level of friction between technical and commercial entities, level of participation in problem definition by commercial entities, and level of participation in problem definition by technical entities

Lee et al, 2000; Sherman et al, 2005; Souder et al, 1998

Goal stability Inflexible the degree to which core objectives, timetables, and resource commitments remained stable over the course of a project

Akgün & Lynn, 2002; Lynn et al., 1999; Salomo, 2007

Project process concurrency

Flexible the degree to which different functional representatives conduct project work simultaneously

Tatikonda & Montoya-Weiss, 2001

Project process structure

Inflexible the usage of a systematic NPD process which consist of a roadmap with measurable milestones and a logical sequence; including idea generation, screening and evaluation, development, testing, and launch stages

Lynn et al., 1999

Team stability Inflexible the degree to which team members and managers, who were active in the project pre-prototype, remained on until the project was launched

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15 4.1 Main effects

The main effects of the meta-analysis are shown in table 2. N represents the aggregate sample size, K the number of correlations that constitute a meta-factor, ρ represents an estimate of the real population correlation and Xs represents the ‘file drawer’. The studies were counted only once, and averages were used when more than one correlation was present for a given sample. For the spread of the real correlation variance a 95 percent confidence interval is given.

Table 2. Results of the meta-analysis

Metafactor Ntotal K ρ

95% Confidence

Interval Varreal (%) a Moderators Xs b

Organizational practice Innovation performance dimension

1 Centralization 1 Project performance 325 3 -0.29 (-0.37,-0.08) 53 Yes 9

2 Product performance 289 4 -0.30 (-0.37,-0.23) 28 Yes 30

2 Colocation 1 Project performance 752 6 0.03 (-0.05,0.11) 46 Yes** 13

2 Product performance 451 4 0.02 (-0.06,0.10) 40 Yes 2

3 Formalization 1 Project performance 1929 12 0.33 (0.14,0.52) 86 Yes 715

1a Time 1227 9 0.28 (0.05,0.51) 89 Yes 394

1b Cost 300 3 0.25* 0 30

1c Quality 238 2 0.25* 0 7

2 Product performance 1731 10 0.33 (-0.03,0.68) 91 Yes** 331

4 Functional integration 1 Project performance 842 4 0.10* (0.05,0.14) 25 10

2 Product performance 135 2 0.64* 0 29

5 Goal stability 1 Project performance 438 3 0.54* (0.51,0.58) 24 91

2 Product performance 306 2 0.45* 0 36

6 Project process concurrency 1 Project performance 920 6 0.19 (0.06,0.33) 74 Yes 76

1a Time 920 6 0.23 (0.11,0.34) 68 Yes 95

1b Cost 602 3 0.05 (-0.01,0.10) 37 Yes 0

1c Quality 602 3 0.06 (-0.03,0.15) 63 Yes 3

2 Product performance 688 3 0.10* 0 4

7 Project process structure 1 Project performance 1286 8 0.21 (0.05,0.38) 82 Yes 206

2 Product performance 425 3 0.24 (0.12,0.36) 68 Yes 22

8 Team stability 1 Project performance 463 3 0.44* 0 90

2 Product performance 280 2 0.32* (0.28,0.36) 22 13

aVarreal as a percentage of the total variance, where Varreal (%) above 25% means that the meta-factor has moderators.

b Xs indicates the ‘file drawer’

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Results in Table 2 reveal nine success factors that are homogeneous and positively significant meta-factors correlated to innovation performance.

4.2 Moderator effects

As stated in the previous section, a heterogeneous meta-factor in the main effect analysis suggests the existence of one or more moderator variables. In case of a heterogeneous result the independent variable, dependent variable, and sample were therefore analyzed for possible moderators. This resulted in the testing of various possible moderators. In some cases however, this did not lead to homogeneous moderators or the number of correlations necessary to perform a meta-analysis was diminished to unusable levels for meta-analysis. The results are shown in table 3.

Table 3. Results moderator analysis

Meta-factor Moderator ρ Ntotal K

95% confidence interval Varreal (%) a Xs Organizational practice

Innovation performance dimension

3 Formalization 2 Product performance Financial outcomes 0.284 1549 8 (0.04,0.52) 91 300 Non-financial outcomes 0.07* 182 2 0 0 2 Colocation 1 Project performance Performance operationalization: Quality based 0.012* 187 2 0 13 Non-quality based 0.007 752 6 (-0.06, 0.10) 48 12

a Varreal as a percentage of the total variance, where Varreal (%) above 25% means that the meta-factor has moderators.

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17 4.3 Major research results

The results are summarized in figure 2. They can be categorized in three main blocks: significant homogeneous factors, heterogeneous factors with moderators and heterogeneous factors without moderators. The dotted lines indicate heterogeneous relationships and the solid lines homogeneous relationships. Positive significant relationships are indicated with a + sign and moderators are shown in italic. The results show that nine relationships out of a total of 22 are homogeneous and significant. This includes homogenous significant results for four out of the eight meta-factors. In addition two moderators variables were found through the moderator analysis. After this analysis eleven relationships remained heterogeneous and require more research.

Project performance Centralization

Formalization

Project process concurrency

Project process structure

Centralization

Project process structure

Colocation

Project process concurrency

Team stability Goal stability Functional integration Time Cost Quality Product performance Formalization heterogeneous homogeneous significant positive + + + + + Formalization

Project process concurrency

Quality based = 1 Non-quality based = 0 Financial outcomes=0 Non-financial outcomes=1 + + Colocation +

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5. Discussion

The primary objective of this meta-analysis was to accumulate and integrate the empirical results of current research on the relationship of innovation performance and its organizational dimension antecedents that reflect flexibility on a project level. In order to (1) gain a better understanding of the phenomenon, and (2) identify directions for future research to increase understanding (Chen & Damanpour, 2010). Below the results concerning the implications for research are first discussed. Followed by implications for practice, limitations, and lastly future research directions based on this study’s findings are offered.

5.1 Implications for research

First, the organizational practice of centralization. For this meta-factor both the relationship with project and product performance was heterogeneous and no moderators were found. Which suggest that there must be other moderators for this meta-factor which have not been reported in existing research studies.

For the second meta-factor colocation the relationship with project and product performance was also heterogeneous. However, for project performance a successful moderator was found in terms of quality based performance operationalization. It suggests that colocation positively impacts project quality performance. This was not an expected outcome discussed in the literature reviewed in this paper and would therefore merit extra research. Although it might be explained through the existence of an availability bias, as the ‘file drawer’ is relatively low (13), it may be in line with the theory. Because increased mutual understanding of constraints, limitations and potential problems (Carbonell & Rodriguez, 2006) can logically be seen to enhance project quality performance.

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nature of formalization, leading to increased performance for lower internal and external uncertainty but decreased or inconclusive project performance in high uncertainty environments where more flexibility is required.

For the relationship between formalization and product performance the distinction between financial and non-financial outcomes was found to be a moderator. Where formalization was positively and significantly related to non-financial product performance. For financial outcomes the result remained heterogeneous which implies the relationship between formalization and financial product performance is moderated but these factors could not be ascertained by looking at methodology of the selected studies.

The relationship of functional integration was found to be homogeneous for both project en product performance. Indicating that it has a generally effect on innovation performance both concerning internal as external outcomes. The supposed negative effect on cycle time could not be researched due to a lack of studies, but the general homogeneousness of the relationship with project performance as a whole does not indicate that this is likely.

The meta-factors team- and goal-stability both provided homogeneous and significant relations with project and process performance. For team-stability this raises questions about the literature that states high team-stability has a negative effect on innovation performance because projects may require new knowledge, different perspectives and ways of thinking which are provided by low team-stability. This is not supported on the basis of this research and therefore begs further research.

For project process concurrency only product performance turned up a homogeneous result. Project process structure did not provide a homogeneous outcome for either project or product performance and no moderators were found. This may be explained because the sample of project process structure was too heterogeneous.

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between flexible and inflexible structuration of NPD processes because it does not show a clear divide when it comes to innovation performance.

5.2 Implications for practice

One of the objectives of this paper was to obtain insight for managers of NPD projects on how to combine different structuration practices that foster either flexibility or inflexibility. Based on the success-factors that were found several guidelines for managers can be provided. The first implication for managers is that a variety of more or less flexible structuration practices can have beneficial effects so adhering to a strict flexible versus inflexible structuration approach is unsupported. In general goal stability, team stability and functional integration are indicated to have a beneficial effect on innovation performance. When project managers have to decide how to structure their project process these findings indicate that they should obtain a high level of functional integration through stimulating information sharing, and participation of different functions in problem definition (Lee et al, 2000; Sherman et al, 2005; Souder et al, 1998). And high levels of goal and team stability by keeping team composition, core objectives, timetables, and resource commitments stable over the course of the project (Akgün & Lynn, 2002; Lynn et al., 1999; Salomo, 2007). When cost and quality objectives are important the results indicate that formalization is a preferred structuration practice. Project process concurrency should be considered by managers if they assign weight to product performance outcomes specifically. Furthermore the moderator analysis indicated formalization is beneficial for product performance in terms of non-financial outcomes such as customer satisfaction. And colocation is indicated to be beneficial for project performance in terms of quality. For managers whose strategy involves an emphasis on non-financial outcomes or quality, this indicates that formalization and colocation should be supported respectively.

5.3 Limitations

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especially in de moderator analysis, should be considered with caution because the meta-analysis is reduced in power when the number of samples is small (Chen & Damanpour, 2010). Second, the moderator analysis was also limited because many studies did not offer adequate documentation of the characteristics of research methods, contextual and organizational conditions. Which is something that other meta-studies have also encountered and limits the results (Chen & Damanpour, 2010). Third, a general limitation to meta-studies using Pearson correlations is the observance of a vivid curvilinear relationship between variables whilst there exist zero correlation (Song, et al., 2008).

5.4 Directions for future research

The results of this study provide some possible directions for future research. First, the large amount of heterogeneous factors indicate the existence of moderators that have not been identified. Some reasons why this research did not find these moderators have been discussed in the limitations. Future research should nonetheless try to find more moderators of the antecedents of innovation performance on a project level to get a more clear understanding of the relationships between structuration practices and innovation performance. For instance the type of NPD project in terms of its radicalness with regard to innovation could be investigated or geographical differences.

Secondly, future research should further address the different outcomes of innovation performance. As the results of this analysis and theory suggest very different outcomes in terms of quality, cost and time for different structuration practices, but lack the required amount of studies to perform a more focused analysis.

Thirdly, another avenue of research that might be explored in the future is the existence of moderators based on cultural differences. Because people from different cultures may prefer other types of organizational structures because they hold different fundamental values about people and the way they should behave in organizations (Shane, 1994).

Acknowledgements

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References:

References marked with an asterisk were used in the meta-analysis.

Adler, P. S. (1995). Interdepartmental interdependence and coordination: The case of the design/manufacturing interface. Organization Science, 6(2), 147-167.

*Akgün, A.E., & Lynn, G.S. (2002). Antecedents and consequences of team stability on new product development performance. Journal of Engineering and Technology Management, 19 (3/4), 263-286.

*Akgün, A.E., Byrne, J., Keskin, H., Lynn, G.S., & Imamoglu, S.Z. (2005). Knowledge

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*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.

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Appendix 1. Papers included in meta-analysis

Articles Sample Independent Dependent

Akgün & Lynn (2002) 211 F Ts Gs Pj t Pd

Akgün et al (2005) 69 Co Ts Pj t Pd

Atuahene-Gima (2003) 103 Ce F Pj t q

Barczak et al (2008) - Dutch sample 118 Co Pps t Pd

Barczak et al (2008) - US sample 212 Co Pps t Pd

Bonner et al (2002) 95 F Pps Pj

Bstieler (2005) - Canadian sample 82 Ppc t

Bstieler (2005) – Australian sample 100 Ppc t

Carbonell & Rodriguez (2006) 183 Co Ts Fi Pj t

Chen (2007) 102 Ce F Pj t Pd

Gomes et al (2003) 92 Fi t c q

Hauptman & Hirji (1996) 50 Ppc Pj t c q

Hoegl et al (2004) - time 1 39 t c q

Hoegl et al (2004) - time 2 39 t c q

Hong et al (2004) 205 F t

Jayaram & Malhotra (2010) 432 Ppc t c q Pd

Keller (2006) - sample 1 52 F Co t Pd

Keller (2006) - sample 2 118 F Co t c q

Lee et al (2000) - Korean sample 51 Ce F Fi Pd

Lee et al (2000) - US sample 86 Ce F Fi Pd Li & Atuahene-Gima (1999) 128 F t Pd Lynn et al (1999) 95 F Gs Pps t Pd McNally et al (2010) 444 F Pd Naveh (2007) 62 F t c Pd Salomo et al (2007) 132 F Gs Pps Pj

Sarin & McDermott (2003) 229 F Pps t

Sherman et al (2005) 466 Fi t

Sicotte & Bourgault (2008) 302 Pps t c q

Souder et al (1998) 101 Fi t

Stockstrom & Herstatt (2008) 475 F Pj Pd

Swink (2000) 136 Ppc t q Pd

Tatikonda & Montoya-Weiss (2001) 120 F Ppc t c q Pd

Tatikonda & Rosenthal (2000) 120 Ce F Pj

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Appendix 2. Formulas used in meta-study (A) Correct sampling error

F1. Correction for sample size

 = ∑   

∑  

where

Ni = sample size in the original studies, and r0i= correlation coefficient reported in the original studies (B) Correct measurement error

F2. Real population correlation

= ̅ =  

 ∗ 

where

̅ = compound reliability correction factor,  = average of the square roots of reliabilities of independent variables composing a given meta-factor, and  = average of the square roots of reliabilities of dependent variables composing a given meta-factor

(C) Calculate variances

F3. Total variance of observed correlations from primary studies

 !"!#$= ∑ %&− ( )* 



∑  

where  = observed correlation of the primary study i, and  = weighted average of the observed  correlations of the primary studies, so that



 =∑  ∑ 

  

F4. Variance due to artefacts

 #+!,= ) ̅) = ) = )- ./



 + . 1 / F5. Variance due to sampling error

 2.4.= .1 −  )/)

 − 1 where

 = average samples size of primary studies F6. Real variance of the population correlation

 +4#$=  !"!#$−  #+!,−  2.4.

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Appendix 3. Publication Sources of the articles used in the meta-analysis

Publication source Number of studies in analysis

Academy of Management Journal 1

Decision Sciences 2

IEEE Transactions on Engineering Management 2

Information & Management 1

International Journal of Operations & Production Management 1

Journal of Applied Psychology 1

Journal of Business Research 1

Journal of Engineering and Management 1

Journal of International Marketing 1

Journal of Operations Management 2

Journal of Product Innovation Management 9

Management Science 1

Organization Science 1

R&D Management 2

Technovation 2

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31 Appendix 4. Variables and measures table

Organizational dimension Independent variable Measure Article

Centralization Centralization Three items to indicate the extent of employee’s autonomy; participation in the

decision-making process; search for problem solutions from many channels

Chen (2007)

Decentralization We had to ask a higher manager before we could do almost anything*; We had to

ask a higher manager before we could do almost anything*; There could be little action taken in the project until a higher manager approved*; Any decision we made had to have a higher manager's approval*; A team member who wanted to make his/her own decisions would be quickly discouraged*

Atuahene-Gima (2003)

Level of authority concentration Part of the organizational process category, measured by five-point Likert-type scale

Lee et al (2000) Level of participation in

decision making

Part of the organizational process category, measured by five-point Likert-type scale

Lee et al (2000) Project management autonomy With respect to upper management, how free was project management to:

Determine interim schedule targets; Determine the project management approach; Choose the format of progress reviews; Reallocate financial resources during the project; Reallocate personnel resources during the project; and Reallocate equipment resources during the project.

Tatikonda & Rosenthal (2000)

Formalization Clarity of project targets The project mission was well communicated to all team members; This product

development team had a well-defined mission; A clear set of project targets guided development efforts; Project targets were clearly understood by all team members; Project targets were clearly communicated to all team members; Project targets were clear

Hong et al (2004)

Degree of organizational organicity

Part of the organizational process category, measured by five-point Likert-type scale

Lee et al (2000)

Formality To what degree: Were project management rules and procedures formalized via

documentation such as contract books, sign-off forms, and such?; Were formal project management rules and procedures actually followed for this project?; Were formal progress reviews held (sometimes also called design, gate, phase, or stage reviews)?; Were there rules, policies, and procedures governing the project work activities?; Were the rules, policies, and procedure levels specified not only in terms of ‘‘what to do’’ but also in terms of ‘‘how to do’’ project work activities?

Naveh (2007)

Formality To what degree were project management rules and procedures formalized via

documents such as contract book, 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)?

Tatikonda & Rosenthal (2000)

Formalization Explicit work rules and clearly defined task procedures Chen (2007)

Goal clarity The team had a clear goal of the required product feature; The team had clear goal

of the target market (user); The team had a clear understanding of target customers’ needs and wants; The technical goals were clear

Akgün & Lynn (2002)

Goal structure Our team leader lets the team know what is expected of them; Our team leader

makes his/her attitudes clear to the team members; Our team leader makes sure that

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his/her part in the team is understood by the team members.

Initiating structure Six items from the Leader Behavior Description Questionnaire-Form XII (Stogdill, 1963)

Keller (2006) Intensity of planning The project was broken into work packages; Timings were assigned to the work

packages; Resources (personnel, financial) were assigned to the work packages; There was a detailed cost plan for the project; Responsibilities of team members were assigned at the beginning of the project.

Stockstrom & Herstatt (2008)

Level of procedure formality Part of the organizational process category, measured by five-point Likert-type scale Lee et al (2000)

Organizational formalization Five item scale adopted from Kerr and Jermier (1978) Keller (2006)

Output control There were clear, planned goals and objectives set for this project team by upper

management; Upper management specified objectives for quality management and standards for this project; Upper management specified product performance objectives for this project; Upper management specified product quality objectives for this project

Bonner et al (2002)

Process formality Process managed according to milestone plan; strategic decisions were taken at

milestones; project team was assigned clear performance targets; clear, predefined criteria existed for strategic decisions

Salomo et al (2007)

Process formality An adaptation of Oldham's and Hackman's (1981) formality measure: To what

degree were project management rules and procedures formalizedv ia documents such as contract books, sign-off forms, and such?; To what degree 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)?

Tatikonda & Montoya-Weiss (2001)

Project formalization Standard operation procedures have been established for new product projects; The terms of departmental relationships within the project have been explicitly verbalized or discussed; Formal communication channels are followed.

Li & Atuahene-Gima (1999)

Project protocol To what extent was the target market (precisely who the intended customer was)

defined prior to embarking into the development phase of the project?; To what extent were the benefits to be delivered defined prior to embarking into the development phase of the project?; To what extent was the positioning strategy (how the product would be positioned in the market versus competitive products) defined prior to embarking into the development phase of the project?

McNally et al (2010)

Colocation Colocation What percent of core team members were located in the same building? Barczak et al (2008)

Spatial distance from supervisor Three item scale adopted from Kerr and Jermier (1978) Keller (2006)

Team member proximity The core engineers on this team were located within a short walk of the core marketers; The core engineers on this team were located so close to the core marketers that they could talk to one another without using a telephone

Akgün et al (2005)

Team proximity / Colocation Proximity amongst the team members Carbonell & Rodriguez

(2006) Functional integration Degree of R&D / Marketing

integration

Measured by the arithmetic mean of five items: level of contact, level of information flow, level of friction between technical and commercial entities, level of

participation in problem definition by commercial entities, and level of participation in problem definition by technical entities

Lee et al (2000)

Functional diversity Number of departments and external stakeholders represented on the team Carbonell & Rodriguez

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33 Functional integration:

collaboration

Three dimensions of cooperation: interpersonal relations, communication and task orientation.

Gomes et al (2003) Functional integration:

interaction

Five areas of interaction between R&D and marketing: budgeting; planning and scheduling; concept generation and screening; product development, testing and commercialisation; and post-commercialisation monitoring and service.

Gomes et al (2003)

R&D / Marketing integration What was the level of contact between the technical and commercial entities?; What was the level of information flow between the commercial and technical entities?; What was the level of participation in problem definition by the commercial entities?; What was the level of participation in problem definition by the technical entities?

Sherman et al (2005)

R&D / Marketing integration Items asses the level of contact, amount of information flow, participation and interaction between R&D and marketing parties frequency

Souder et al (1998)

Team stability Team stability The project manager who started this project remained on from pre-prototype

through launch; Department managers who were on the team remained on it from pre-prototype through launch; Team members who were on the team remained on it from pre-prototype through launch

Akgün & Lynn (2002); Akgün et al (2005)

Team stability Team members who were on the team remained on it through completion; The

project manager who started this project remained on through completion

Carbonell & Rodriguez (2006)

Project process structure NPD process The team followed a clear plan—a road map with measurable milestones:

Pre-launch, there were adequate mechanisms to track the project’s progress; Pre-Pre-launch, there were adequate mechanisms to track the project’s costs; Idea generation, screening and evaluation, development, testing, and launch were all completed; The above five phases in the new product process were proficiently completed.

Lynn et al (1999)

NPD process formalization Please indicate the type of NPD process you used for product development: no process used;

informal process; formal, sequential process; functional, Stage-Gates; cross-functional,

facilitated Stage-Gates; cross-functional third-generation Stage-Gates; other

Barczak et al (2008)

Process control Upper management specified the processes or procedures by which this team was to

achieve its goals; Upper management determined the team’s work process; Upper management specified procedures used by the team; Upper management determined work assignments for team members

Bonner et al (2002)

Process structure Our team leader encourages the use of uniform procedures; Our team leader decides

what shall be done and how it will be done; Our team leader schedules the work to be done; Our team leader maintains definite standards of performance; Our team leader asks the team members to follow standard rules and regulations.

Sarin & McDermott (2003)

Project methods XXX Sicotte & Bourgault

(2008) Proficiency of project planning We designed and used a work breakdown structure; we designed and used a

milestone plan; we designed and used a resource plan

Salomo et al (2007)

Temporal pacing The project used a formal process with frequent reviews of progress; The project

used a formal process with different stages for major activities; The progress towards the project objectives was reviewed at specific stages; The project team followed a documented process with specific milestones for each activity

Atuahene-Gima (2003)

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34

overlapping problem-solving manufacturing representatives on the CE team. (1996)

Degree of concurrency: release of incomplete and uncertain information

The extent of readiness of R&D/engineering and manufacturing representatives on the CE team to release incomplete and/or uncertain and/or ambiguous information to their counterparts.

Hauptman & Hirji (1996)

Degree of concurrency: two-way communication

The extent of two-way information flow between R&D/engineering and manufacturing representatives on the CE team

Hauptman & Hirji (1996)

Degree of concurrency: use of incomplete and uncertain information

The extent of incomplete, uncertain, and/or ambiguous information usage by R&D/engineering and manufacturing representatives on the CE team, released by their counterparts for decision making.

Hauptman & Hirji (1996)

Design integration (concurrency, CAD)

Most people on the project were quite accessible; Project activities were overlapped (performed concurrently) to a great degree; Teamwork and information sharing are highly valued in our division; Data systems used by different groups in our division are compatible; Degree of use of cross-functional teams

Swink (2000)

Downstream coordination: completeness of product design when manufacturing gave feedback on production feasibility

Completeness of product design when manufacturing gave feedback on production feasibility

Jayaram & Malhotra (2010)

Downstream coordination: completeness of product design when manufacturing made formal cost estimates

Completeness of product design when manufacturing made formal cost estimates Jayaram & Malhotra (2010)

Downstream coordination: completeness of product design when manufacturing made purchasing commitments

Completeness of product design when manufacturing made purchasing commitments

Jayaram & Malhotra (2010)

Process compression Dividing process completeness by actual project duration (measured in months

indicating the time frame from the first ‘‘go-decision’’ to product launch) resulted in an activity-per-month ratio, so process compression is measured as the average number of process activities done per month (a higher ratio indicated greater compression)

Bstieler (2005)

Process concurrency What percentage of the design engineering effort was complete when manufacturing

engineering started active involvement in this project?

Tatikonda & Montoya-Weiss (2001)

Goal stability Goal stability The pre-prototype design goals remained stable through launch; The pre-prototype

technical goals remained stable through launch; The pre-prototype goal of this project remained stable through launch

Akgün & Lynn (2002); Lynn et al (1999)

Goal stability Core objectives and timetable only infrequently altered; resource commitments only

infrequently adjusted; current project goals still correspond to original ones

Salomo et al (2007)

Innovation dimension Dependent variable Measure Article

Project performance NPD performance (managerial

performance)

The managerial performance factor is reflected by firm’s satisfaction with yield rate, productivity, quality, and utilization of new technologies

Chen (2007)

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