The influence of project front-end management and project complexity on project success
a contingency approach in project management research
In this study the relationships between project complexity, activities performed in the front-end development (FED) phase, and project success were investigated. A survey was distributed to project managers in the member companies of the NAP network, a competence network of the Dutch process industry. The survey contained questions on the interviewees' most recent finished project and was developed to acquire data on project complexity, FED activities, and project success. Project complexity was measured distinguishing three types of complexity: technical, organizational, and environmental. FED activities were identified from literature, interviews with project managers, including value improving practices (VIPs) and “normal” practices. Criteria on budget and schedule performance were used to measure project success.
Through the survey, data was acquired on 67 projects performed in the NAP network. This data was analyzed using descriptive statistics and multivariate statistical techniques. The analysis framework was based on a theory originating in organizational research, known as contingency theory. This framework allowed the identification of direct relationships as well as moderated relationships between project complexity, activities performed in the FED phase and project success.
The results of the study show the benefits of applying contingency theory in project management research. The relationships found have practical implications for project management in the NAP network and the process industry. It was shown that, when organizational complexity rose, goal-setting, and alignment was applied insufficiently leading to a decreased chance of project success. Further, when technical complexity in the project was high, the steering committee intervened more often in the project, increasing the chance of project success. Also, active monitoring of project goals increased the chance of project success, but this was applied less when technical complexity was high, leading to a decreased chance of project success. The results also showed the importance of creating cohesive and integrated project teams.
Keywords: project complexity, project success, front-end development, contingency theory
The complexity of engineering projects is assumed to be increasing (Baccarini, 1996; Williams, 1999, 2002). Time and cost overruns are regular occurrences in engineering projects (Flyvbjerg, Bruzelius, & Rothengatter, 2003; Hall, 1981; Morris & Hough, 1987; Neleman, 2006; Williams, 2005). Industry-wide, about 40% of capital projects suffer from time or cost overruns (IPA, 2009b). Therefore research is being undertaken to gain insight into the management of these projects and how to increase the chance of delivering projects within budget and schedule. The preparation phase of projects, also known as front-end development (FED) phase, has been suggested as a major factor in increasing the chance of a successful project, though this is as yet based on little empirical data (Morris, 1994; Morris, Crawford, Hodgson, Shepherd, & Thomas, 2006).
The front-end development phase, project complexity as well as project success have been subject to prior research (Arkesteijn, 2009; Bosch-Rekveldt & Mooi, 2008; Bosch-Rekveldt, et al., 2009a; Bosch-Rekveldt, Mooi, Verbraeck, & Bakker, 2009b; Bosch-Rekveldt, Jongkind, Bakker, Mooi, & Verbraeck, 2010; Jongkind, 2008; van der Weijde, 2008). The relationships between these factors (for example, could one adapt the front-end development phase to the particular project complexity in such a way to increase the chance of project success) seem not to have been tested quantitatively so far. Therefore, the current study focuses on a quantitative assessment of these (potential) relationships. To investigate these (potential) relationships, contingency theory was applied to project management, as suggested in literature (Engwall, 2003; Shenhar & Dvir, 1996; Smyth & Morris, 2007; Williams, 2005).
The above considerations are summarized in the following main research question:
How does front-end development relate to project complexity and project success in projects in the process industry and can this successfully be examined using contingency theory?
The study was performed within the Dutch process industry, particularly within the companies that are members of the NAP network (www.napnetwerk.nl). The NAP network is a platform bringing together companies from the entire value chain in the Dutch process industry, including engineering agencies and the academic community (NAP, 2009) and consists of about 100 member organizations.
This paper is structured as follows. The theory used and the hypothesis to be tested are introduced hereafter, followed by a description of the data collection and the methods used. Next, the results are presented and analyzed followed by a discussion section including implications for project management. The paper is ended with conclusions and recommendations.
Theory and Hypothesis Development
Contingency theory originates from organizations studies (Dessler, 1976), with a number of pioneers (Burns, Stalker, & Woodward, 1961;Galbraith, 1973; Lawrence & Lorsch, 1967; Thompson, 1967; Woodward, 1965, and more recently described by Donaldson (2001). According to contingency theory, the organizational structure is made dependent on the contingency factors, with a fit between organizational structure and a contingency factor leading to best organizational performance. In other words, the right combination of contingency factor and organizational structure leads to better organizational performance. That means that organizations utilizing the organizational structure that better suits its contingency factors will perform better than organizations using a structure that does not match the contingency factors. Main (classic) contingency factors identified in organizational research are strategy, rate of change, size, task uncertainty, and technology (Donaldson, 1996).
In organizational contingency theory two characteristics of contingency factors are defined. These characteristics play an important role in applying contingency theory to project management. The first characteristic is that a contingency factor acts as a moderator in a relationship. On an abstract level this means that the relationship between two variables (A and B, see Figure 1) depends on, or is moderated by, the moderating variable C. The effect of A on B differs when C is low or high. For instance, A can have a negative effect on B when C is high and a positive effect when C is low. Thus, C can be seen as the moderating or conditioning variable of this relationship (Bryman & Cramer, 2009; Galtung, 1967).
Figure 1: C As a Moderator of the Relationship Between A and B
Acting as a moderator is a necessary but not sufficient condition for being a contingency factor; a contingency factor also needs to satisfy a second characteristic. The second characteristic is that a contingency factor (C) deals with the relationship between an independent variable (A) and the effectiveness or result (B, the dependent variable): the right combination of A and C, resulting in high B or success, is known as “fit” between variables A and C. Elaborating on the previous example, this would mean that variable B would be a variable indicating success. In order to maximize B, one would have to try to match independent variable A to the contingency variable C. This “fit” between A and C is central to contingency theory.
Applying a contingency approach to project management means that project management would be applied to fit the contingency factor(s), with this fit causing an optimal project result. The (potential) importance of making project management contingent upon its context or environment is stressed in literature (Engwall, 2003; Shenhar & Dvir, 2007; Smyth and Morris, 2007; Williams, 2005). Therefore, in the current research, project complexity was investigated as a possible contingency factor for the (management of the) front-end development phase, meaning that the front-end development phase would vary if different types of complexity were present in a project. The right combination (fit) of activities in front-end development and project complexity should then result in project success. Finding this combination would help to increase the chance of project success in the future, because it would be possible to adapt front end development practices so that it fits to a project complexity “footprint” of the project before it starts.
Possible relationships are mentioned in literature and these are tested quantitatively in the current study. The corresponding conceptual model, including the hypothesized relationships between the separate factors, is shown in Figure 2.
Figure 2: Conceptual Model and Hypotheses
Hypothesis 1: Project complexity has a direct relationship with project success. Project complexity is mentioned in literature as a reason for project failure (Flyvbjerg et al., 2003; Hall, 1981; Morris et al., 1987; Neleman, 2006; Williams, 2002, 2005).
Hypothesis 2: The front-end development phase has a direct relationship with project success. Putting significant effort in the front-end development phase is often recommended as a vital part of project management, with a high influence on the final project result (Bakker, 2008; de Groen, Dhillon, Kerkhoven, Janssen, & Bout, 2003; Flyvbjerg et al., 2003; Morris, 1994; Morris et al., 2006; Oosterhuis, Pang, Oostwegel, & de Kleijn, 2008; van der Weijde, 2008).
The third and fourth hypotheses are related to the application of contingency theory to project management. As we will see in the analysis, it is necessary to show a relationship between project complexity and FED activities before a moderated relationship with project success can be found (Donaldson, 2001). Testing the relationship between project complexity and FED also has a practical value because awareness of interaction between the two is required if we ever want to be able to adapt FED to project complexity.
Hypothesis 3: A relationship with unknown direction exists between front-end development and project complexity. Front-end activities might influence project complexity or vice versa.
Hypothesis 4: Project complexity acts as a moderating factor in the relationship between front end development and project success. Applying contingency theory from organizational research to project management has been suggested in literature (Engwall, 2003; Shenhar et al., 1996; Smyth and Morris, 2007; Williams, 2005).
This paper attempts to test these hypotheses in order to answer the main research question stated in the introduction section.
Data and Methodology
To investigate the potential relationships between front-end development, project complexity, and project success, a quantitative approach was followed. Data was collected from a large number of research units by means of a survey study (Baarda & de Goede, 2006; Verschuren & Doorewaard, 1999). For analysis, traditional correlation techniques were used.
Data was collected performing a survey study among companies within the NAP network. The companies in this network are heterogeneous in size and assumed to be dealing with increasing complexity in projects, hence providing an interesting sample for both process industry and the project management community.
The target group of the survey consisted of project managers in companies in the NAP network. The survey was distributed through the database of the NAP network by e-mail. In total about 330 project managers in almost 100 member companies were approached. These project managers were allowed to forward the survey request within their company. The survey was web-based and anonymous. Progress was saved on the participants' computer and measures were taken to prevent double submissions from one participant. The survey was started by a total of 107 project managers, 67 of whom finished it entirely. With 67 completed responses, an acceptable response rate of 20% was achieved.
The unit under investigation was the interviewee's most recently completed project. Some general questions about personal and company data were also included for future analysis purposes. The survey was designed to determine project complexity, FED activities, and project success, as well as indicating some general project characteristics.
Sampling was done on a convenience basis, which limits the generalization possibilities. However, some randomization in the sample was obtained by surveying the most recently completed project of the respondents rather than allowing them to pick their favorable projects. The NAP network contains a broad variety of companies representing the process industry hence allowing some generalization.
The data obtained by the survey was analyzed using both descriptive and multivariate methods. Descriptive techniques were used to review the data concerning the separate variables of the research (project complexity, front-end development, and project success), while the relationships between them were tested using multivariate techniques. The majority of the questions were rated using a 5-point Likert scale. Data obtained from such questions is generally assumed to be suitable for use in multivariate techniques as interval variables (Jaccard & Wan, 1996; Kim, 1975; Labovitz, 1967).
Testing for contingency was done using an analysis framework from organizational research as described by Donaldson (2001):
Step 1: Look for direct relationships between the variables under investigation and result.
Step 2: Look for moderated relationships between the variables:
Step 2a: Look for direct relationships amongst the variables under investigation (excluding result). Relationships found constitute possible fit(s).
Step 2b: Test the relationships found in step 2a to determine whether possible fits are related with result. If so, a moderated relationship exists.
Step 3: Check whether the fit found in step 2b causes result. If so, the moderator is a contingency factor.
Step 3 of testing for causality is often problematic in organizational theory, because causality is challenged as structure and contingency factors undergo simultaneous changes, hence requiring for instance a longitudinal research design (Donaldson, 2001). Note, however, this is different in case the project result is involved, because result simply sequences front end development and project complexity.
To test the hypotheses of the current research (see overview in Figure 2), only step 1 and step 2 of the analysis framework are required. The relationships tested in step 1 cover hypotheses 1 and 2. Step 2a tests hypothesis 3 and step 2b tests hypothesis 4.
The questionnaire was developed based on a literature review and transcripts of interviews performed in previous research (Bosch-Rekveldt et al., 2009a, 2010). The survey contained questions related to:
General characteristics of the project
The project's complexity
Front-end development activities
Identifying the Project's Complexity
In a literature review on project complexity classification methods by Bosch-Rekveldt and Mooi (2008), a tendency to summarize project complexity into a single score was observed. However, recent findings indicate that a multi-dimensional approach seems a more viable solution (Bosch-Rekveldt et al., 2008; Geraldi, 2008; Hass, 2007). Based on literature and case studies such a multi-dimensional approach was developed distinguishing three dimensions in complexity: technical, organizational and environmental complexity (Bosch-Rekveldt et al., 2010), see Appendix B. To characterize project complexity in the current study, these three main dimensions of the framework were used. It could be argued however that more or different dimensions could be necessary to cover all potential aspects of complexity (Remington, Zolin, & Turner, 2009).
What Was Done in the Front-End Development Phase of the Project
To develop survey questions about the front-end development phase of the project, attention was paid toward value improving practices (VIPs). Participants were familiar with the VIPs. VIPs from different sources were compared:
IPA value improving practices (IPA, 2009a)
Construction Industry Institute best practices (CII, 2009)
Transcripts from interviews taken in from previous research (Bosch-Rekveldt et al., 2009b)
VIPs as defined by the NAP network (de Groen et al., 2003; Oosterhuis et al., 2008).
VIPs that appeared in several sources were considered for inclusion in the survey. From the resulting list a division was made distinguishing project management practices and technical practices. Because the aim of the research was in project management, mainly project management practices were included in the survey. The final list of VIPs considered in the survey is given in Figure 3.
Figure 3: Surveyed VIPs
The VIPs were surveyed with four questions per VIP. It was asked how much effort was spent in application of the VIP (none, little, substantial), how the amount of application was perceived (too little, sufficient, too much), whether the VIP was beneficial to the project (strongly agree to strongly disagree) and whether the activity was a burden to the project (strongly agree to strongly disagree).
It was felt that the selected VIPs did not cover all relevant activities in the front-end development phase. Therefore, additional questions were asked to the participants related to “normal” practices or activities that were hypothesized as having a relationship with project complexity and/or project success. Similar to the latter two VIP questions, these “normal” FED practices were surveyed in the form of statements to be answered on a 5-point Likert scale (strongly agree to strongly disagree). The statements were formulated in different directions, meaning agreement could sometimes be seen as positive for the project (e.g., the business and the project team had the same goals in mind) or negative (e.g., more social team building would have been beneficial to the project). This was done in order to keep the interviewee thinking about the answer and to prevent habituation (Verschuren et al., 1999).
How Did the Project Perform?
Several studies have been done on how to define the success of a project (Arkesteijn, 2009; Balachandra & Friar, 1997; Shenhar, Dvir, Levy, & Maltz, 2001). One project might be successful for one stakeholder and at the same time very unsuccessful in the eyes of another stakeholder. The current study was interested in how a project performed and hence a rather narrow definition of project success could be used. Project success was therefore defined as delivering the project within 10% of the budget and the schedule agreed at the final investment decision (further referred to as agreed budget or agreed schedule). A project that was delivered within 10% of only one of these resources was named unsuccessful and a project that suffered overruns of more than 10% on both budget and schedule was considered a failed project. One could argue that a project delivered more than 10% under time or budget should be considered successful rather than unsuccessful, because it saves the company money or time. However, resources were reserved for a project while they could have been used elsewhere. Overestimating the costs or time required for a project is therefore considered undesirable and a project being delivered more than 10% under budget or schedule is considered unsuccessful.
Next to questions to measure the project performance in terms of budget and schedule, the survey included questions toward a broader definition of project success but this is outside the scope of the current research.
The survey was mainly filled in by project managers (52% of interviewees), business representatives (22%), and team members (16%), from different industrial sectors. Capital expenditure of the projects largely ranged between 10k Euro and 7500M Euro. The data (67 projects) contained 22 successful projects and 32 unsuccessful projects, 10 of which failed completely. Of the remaining 13 projects data was missing on the result regarding budget, schedule or both. Because our study is focused on the relations between front-end development, project complexity, and project success, only those descriptive results are presented here that contribute to the understanding of these relations. Further descriptive results are given in Appendix A.
Activities in Front-end Development: Value Improving Practices
The VIPs were surveyed in four questions of which the results are given in Table 1. The table indicates the number of cases in which a particular answer was given; highlighted results are discussed hereafter.
1. How much effort was spent in the activity?
The majority of the VIPs was substantially applied in every project. Two activities stood out as being applied less than the others: external benchmarking and stakeholder management. External benchmarking was not applied at all in many projects and little in most others. Stakeholder management was usually lightly applied.
2. Was this application of the VIPs sufficient, too little, or too much?
The amount of application was considered sufficient according to the majority of respondents. Too much application appears to have been very rare, but some VIPs are considered to have been applied too little. VIPs that were considered to be applied too little by more than 25% of the interviewees were: value engineering, operations implementations planning, goal-setting and alignment, and stakeholder management.
3. Was the activity beneficial to the project?
When asked about the benefits of the VIPs for the project result the majority were considered beneficial (agree) or very beneficial (strongly agree). The most beneficial VIPs according to the interviewees were: project team building, constructability review, risk management, and to a lesser degree project quality control.
4. Was the activity a burden to the project?
None of the VIPs was considered a burden to the project by more than seven interviewees. Project team building, constructability review, and risk management are least considered a burden, compared to the other activities (>25% of interviewees strongly disagreed with the activity being a burden to the project).
Combining the Answers on the Above Questions
The two least applied VIPs are stakeholder management and external benchmarking. Stakeholder management was considered to be applied too little by 22 (32.8%) interviewees, one of the highest rates of all VIPs. External benchmarking was considered to be applied too little in only nine cases (13.4%). This leads to the conclusion that not applying external benchmarking is often considered sufficient, but stakeholder management should be applied more thoroughly in the eyes of many of the interviewees. The VIPs that were considered the most beneficial to the project were also the ones that were considered the least of a burden to the project: project team building, constructability review, risk management, and project quality control. It was therefore to be expected that these were also the most applied of all VIPs: indeed all of them were applied substantially in more than 40 cases.
Table 1: Survey Results of VIP Questions in Numbers of Cases in Which Particular Answer Was Given
Other Activities in Front-End Development: “Normal” Practices
The FED normal practices were surveyed in the form of statements to be answered (strongly disagree – strongly agree). Results are given in Table 2. For the majority of the “normal” practices a dominant answer, usually agree or disagree depending on the direction of the statement, was found. Three statements had more than one dominant answer: scope changes occurring late in the FED phase, active risk register monitoring, and the project suffering from late entry of parties (see highlighted cells in the table).
Table 2: Survey Results of Questions About the “Normal” FED Practices in Numbers of Cases
|Strongly disagree||Disagree||Neutral||Agree||Strongly agree||Missing|
|The business and the project team had the same goals in mind||0||6||11||33||15||2|
|Project goals were actively monitored||0||8||16||27||11||5|
|Substantial scope changes occurred in a late stage of FED||0||18||11||25||9||4|
|Project manager had a say in human resourcing of the project||1||7||10||36||11||2|
|Project team incorporated all necessary disciplines||0||3||4||45||13||2|
|A lack of team cohesion endangered the project outcome||6||35||10||13||2||1|
|Foreseen project complexity was taken into account when selecting the project manager||5||12||11||27||10||2|
|More social team building would have been beneficial for the project||0||14||29||17||4||3|
|Project manager cooperated closely with the steering committee||1||6||9||38||10||3|
|Steering committee regularly intervened in the project||3||27||21||12||2||2|
|Cooperation with the (sub)contractors was hampered by main contract type||9||26||12||9||4||7|
|Project manager regularly intervened in the work of the (sub)contractors||5||14||17||23||3||5|
|The (sub)contractors worked closely together with the project manager||1||8||17||32||5||4|
|Too many project meetings were held during the project||2||37||16||6||3||3|
|Risk register was actively used to monitor risks throughout the project||6||21||8||22||7||3|
|Project suffered from late entry of parties||5||19||19||19||3||2|
Late scope changes and late entry of parties may be affected by external factors and are not always completely controlled by a project manager. The process of risk management on the other hand is within the control of a project manager. Note the discrepancy between risk management as a VIP and the active use of a risk register to control risks throughout the project: The VIP risk management was usually applied substantially, sufficiently and was considered very beneficial, but actively monitoring risks during the whole FED phase was done as often as not.
Multivariate Analysis of the Results: Establishing Relationships
In the analysis below, the following “rule of thumb” was applied for the strength of a correlation: below 0.19 is very low; 0.20 to 0.39 is low; 0.40 to 0.69 is modest; 0.70 to 0.89 is high and 0.90 to 1 is very high (Cohen & Holliday, 1982).
Step 1: Direct correlations with project success (hypotheses 1 and 2)
Starting at step 1 the correlations between the variables under investigation (Complexity and FED) and project success were calculated. Several significant results were found; see Table 3 and Table 4. The percentage of significant relations of all possible relations should be higher than 5% to make the study relevant, which is the case in our study. With correlations one should be careful not to infer causality, but in this case causality seems present due to the fact that the independent variables are correlated to project success, which occurs later in time. The significant correlations therefore likely indicate that the variables have a direct influence on project success.
Table 3: Overview of Direct Significant Correlations
|Relations between||Number of possible relations||Number of significant relations||Percentage of significant relations|
|Complexity – Project success||3||2||67%|
|FED – Project success||64||6||9%|
Table 4: Significant Correlations Between Project Success and Project Complexity; Project Success and FED Activities
|Varl||Var2||Strength||N||Meaning of correlation|
|Project success||Technical complexity||-0,340*||54||Increased technical complexity decreases the chance of project success|
|Organizational complexity||-0,345*||54||Increased organizational complexity decreases the chance of project success|
|Project success||Amount of project team building||0,279*||50||Sufficient application of project team building increases the chance of project success|
|Amount of application of constructability review||0,325*||49||Sufficient application of constructability review increases the chance of project success|
|Benefit of operations implementation planning||0,317*||45||Increased perceived benefits of operations implementation planning coincide with increased project success|
|Project goals were actively monitored||0,455**||53||Active monitoring of project goals increases the chance of project success|
|A lack of team cohesion endangered the project outcome||-0,364**||54||Increased lack of team cohesion decreases the chance of project success|
|Social team building would have been beneficial to project outcome||-0,419**||53||Increased application of social team building increases the chance of project success|
*significant at 0,05 level, ** significant at 0,01 level
Table 4 shows significant relationships between project success and two types of project complexity (strength is low), which means hypothesis 1 is partially supported. No significant correlation was found between environmental complexity and project success. The current study was limited to the distinction of technical complexity, organizational complexity, and environmental complexity—the highest level of the TOE framework. A deeper analysis into the subcategories and elements of the TOE framework could help to discover the reason for the absence of the relationship between environmental complexity and project success, but this was outside the scope of the current research.
Table 4 also shows significant correlations between FED practices and project success, with strengths of low to modest. The existence of these correlations means that hypothesis 2 is also partially supported. The strongest correlation (0.455, the correlation between actively monitoring project goals and project success) was significant on a 0.01 level. Three of the six significant correlations found relate to team building. The amount of project team building, the lack of team cohesion, and social team building all show a direct relation to project success. Apparently more project and social team building enhance team cohesion, thus increasing the chance of project success.
Summarizing the step 1 analysis, it can be concluded that both hypothesis 1 and 2 are partially supported.
Step 2a: Associations between front-end development and project complexity (hypothesis 3)
The number of possible relations between FED and project complexity was 192 (64 FED variables times three complexity variables). In total 16 significant relations were found (again > 5% of the total number of possible relations). Significant results indicated a direct relationship between the particular FED activity or VIP and project complexity, hence also hypothesis 3 is partially supported. However, no conclusions can be drawn on causality because project complexity might be influenced by FED activities but FED activities might also have influenced project complexity (Bryman & Cramer, 2009).
Table 5 shows the significant correlations between technical complexity and FED. The strength of all of these correlations is low and sample sizes are all over 60. One correlation is significant at the 0.01 level: the amount of application of risk management. Increased technical complexity coincides with lower active monitoring of project goals. However, the opposite would be expected, because increased technical complexity would make reaching the project goals more difficult, hence requiring more active monitoring of these goals to reach the goals anyway.
Table 5 also shows the correlations found between organizational complexity and FED. Like technical complexity the correlation strengths are all low and sample sizes high. Two correlations are significant at the 0.01 level: the amount of goal-setting and alignment and the project suffering from late entry of parties. The latter was also correlated to technical complexity, but less strongly and less significant.
Table 5: Significant Correlations Between Complexity and FED
|Varl||Var2||Strength||N||Meaning of correlation|
|Technical complexity||Amount of application of risk management||-0,327**||62||Increased technical complexity coincides with lower sufficiency of application of risk management|
|Amount of application of lessons learned||-0,266*||62||Increased technical complexity coincides with lower sufficiency of application of lessons learned|
|Business and project team had the same goals in mind||-0,255*||65||Increased technical complexity coincides with less agreement on goals between business and project team|
|Project goals were actively monitored||-0,302*||62||Increased technical complexity coincides with lower active monitoring of project goals|
|The project suffered from late entry of parties||0,253*||65||Increased technical complexity coincides with more problems due to late entry of parties|
|Organizational complexity||Amount of application of goal-setting and alignment||-0,344**||62||Increased organizational complexity coincides with lower sufficiency of application of goal-setting and alignment|
|The project suffered from late entry of parties||0,335**||65||Increased organizational complexity coincides with more problems due to late entry of parties|
|Perceived burden of constructability review||-0,308*||58||Increased organizational complexity coincides with lower perceived burden of constructability review|
|A lack of team cohesion endangered the project outcome||0,309*||66||Increased organizational complexity coincides with more problems due to lack of team cohesion|
|The steering committee regularly intervened in the project||0,260*||65||Increased organizational complexity coincides with more interventions by the steering committee|
|The project manager regularly intervened in the work of the (sub)contractors||0,253*||62||Increased organizational complexity coincides with more interventions by the project manager in the work of (sub)contractors|
|The (sub)contractors worked closely together with the project manager||-0,266*||63||Increased organizational complexity coincides with less cooperation between project manager and (sub)contractors|
|Environmental complexity||Perceived burden of external benchmarking||-0,622**||30||Increased environmental complexity coincides with lower perceived burden of external benchmarking|
|Amount of application of external benchmarking||-0,316*||46||Increased environmental complexity coincides with lower sufficiency of application of external benchmarking|
|A lack of team cohesion endangered the project outcome||0,244*||66||Increased environmental complexity coincides with more problems due to lack of team cohesion|
|The project manager cooperated closely with the steering committee||-0,284*||64||Increased environmental complexity coincides with lower sufficiency of application of goal-setting and alignment|
*significant at 0,05 level, ** significant at 0,01 level
There are several significant correlations between organizational complexity and cooperation with different parties. With rising organizational complexity more problems due to lack of team cohesion appear, the steering committee intervenes more often in the project and the project manager intervenes in the work of the (sub)contractors instead of cooperating with them.
Finally, Table 5 shows the correlations found between environmental complexity and FED. The strongest correlation of all is found between the perceived burden of external benchmarking and environmental complexity. This correlation is also significant at the 0.01 level. The amount of external benchmarking is also correlated to environmental complexity, suggesting a clear relationship between environmental complexity and external benchmarking. The sample sizes these correlations are based on are relatively low due to the fact that external benchmarking was not applied in many of the surveyed projects. The other correlation strengths are all low and their sample sizes higher. Lack of team cohesion causing problems in the project is related to environmental complexity, but less strongly than to organizational complexity.
Summarizing the step 2a analysis, it can be concluded that also hypothesis 3 is partially supported.
Step 2b: Test for moderated relationships (hypothesis 4)
To test for moderated relationships, subgroup analysis was used (Donaldson, 2001). Subgroup analysis involves breaking down the data into subgroups based on fit or result. The interpretation of this analysis is based on a simple principle: if a moderated relationship exists, the associations found in the different subgroups should significantly differ (Bryman & Cramer, 2009; Donaldson, 2001). In our study subgroups were defined based on project success:
Successful projects (within 10% of cost- and schedule estimates)
Unsuccessful projects (more than 10% over or under cost or schedule estimate)
Failed projects (more than 10% over or under the cost- and schedule estimates).
For the subgroup analysis, only the FED variables relating to application of the activity, the sufficiency of application and the “normal” practices (Table 2) were included. The relationships that were found to be significant in each of the subgroups are summarized in Table 6.
Table 6: Significant Correlations Within Subgroups
|Subgroup||Var1||Var2||Strength||N||Meaning of correlation|
|Successful projects||Technical complexity||A lack of team cohesion endangered the project outcome||0,462*||22||Increased technical complexity coincides with more problems due to lack of team cohesion|
|Technical complexity||Amount of application of risk management||-0,443*||21||Increased technical complexity coincides with lower sufficiency of application of risk management|
|Technical complexity||The steering committee regularly intervened in the project||0,540**||22||Increased technical complexity coincides with more interventions by the steering committee|
|Environmental complexity||Amount of application of design-to-capacity||-0,520*||20||Increased environmental complexity coincides with lower sufficiency of application of design-to-capacity|
|Environmental complexity||Amount of application of external benchmarking||-0,568*||15||Increased environmental complexity coincides with lower sufficiency of application of external benchmarking|
|Unsuccessful projects||Technical complexity||Project goals were actively monitored||-0,354*||32||Increased technical complexity coincides with lower active monitoring of project goals|
|Organizational complexity||Application of operations implementation planning||-0,431*||31||Increased organizational complexity coincides with lower sufficiency of application of operations implementation planning|
|Organizational complexity||Amount of application of goal-setting and alignment||-0,363*||31||Increased organizational complexity coincides with lower sufficiency of application of goal-setting and alignment|
|Organizational complexity||The project manager regularly intervened in the work of the (sub)contractors||0,393*||32||Increased organizational complexity coincides with more interventions by the project manager in the work of (sub)contractors|
|Failed projects||Organizational complexity||The (sub)contractors worked closely together with the project manager||-0,648*||10||Increased organizational complexity coincides with less cooperation between project manager and (sub)contractors|
*significant at 0,05 level, ** significant at 0,01 level
The correlations found in the data were all low (0.20 to 0.39) or modest (0.40 to 0.69). The strongest of all correlations was found in the failed projects group between organizational complexity and the cooperation between project manager and (sub)contractors. Note, however, that the sample size here was only 10 projects, hence reducing the value of this result. Due to the calculation of the result variable and division into subgroups, the size of the samples for testing became lower than for the previous analyses. Most correlations were significant within a significance level of 0.05, with a few being significant on a 0.01 level.
If a moderated relationship exists, the correlation strength should significantly differ between the subgroups. Correlations were calculated within the subgroups, but none of the associations found in step 2a was significant in both subgroups. Correlations that are significant in one subgroup and not in the other do indicate a difference in correlation strength. Therefore the significant correlations in Table 6 indicate moderated relationships between the investigated variables and hypothesis 4 can be partially supported (partially because it is not valid for all relationships under investigation).
Overview of Relationships Found
Combining all correlations found made it possible to create an overview of the relationships between FED, project complexity, and project success. The following criteria were used to map these relationships:
If a direct relationship existed (step 1), there was no need to look for a moderated relationship (Bryman & Cramer, 2009; Donaldson, 2001).
If a correlation between any type of project complexity and FED was found in step 2a and only in one of the subgroups of step 2b it was considered a moderated relationship.
An example of a direct relationship found by using these criteria is the active monitoring of project goals; this activity was directly correlated to project success (see Table 3), so there was no need to look for a moderated relationship. An example of a moderated relationship is goal-setting and alignment; this activity was not directly correlated to project success. However, an association was found in step 2a between goal-setting and alignment and organizational complexity (see Table 5) and again in the subgroup unsuccessful projects in step 2b (see Table 6). The criteria were applied similarly to all the results of the multivariate analyses to map all direct and moderated relationships.
The relationships that influence project success are shown in Figure 4. In Figure 4, variables related to the same FED activity were taken together to describe a single relationship. Team building consists of the variables project team building, social team building, and lack of team cohesion. Management of (sub)contractors consists of the interventions by the project manager in the work of the (sub)contractors and the cooperation between these two parties. The three variables related to project complexity are taken together.
Figure 4 shows the direct influence of project complexity on project success (drawn arrow right) as well as the direct influence of several FED activities (drawn arrow to the right). These FED activities are all in turn related to project complexity (dashed arrow), but as indicated before it is not clear which factor is a cause, and which factor a result in that relationship. These FED activities might influence project complexity or vice versa.
The dotted arrows indicate the more complicated moderated relationships. Project complexity acts as a moderator in the relationship between the FED activities and project success. This means the relationship varies when project complexity varies. For instance goal-setting and alignment is applied less when organizational complexity rises, decreasing the chance of project success. Other than the FED activities in the box on the left, the relationships between the FED activities on the right and project success only exist in combination with (a certain type of) project complexity.
Figure 4: Relationships Influencing Project Success
The following moderated relationships were found:
When organizational complexity rises, cooperation between the project manager and the (sub)contractors goes down, while the number of interventions by the project manager in the work of the (sub)contractors goes up, reducing the chance of project success.
When environmental complexity rises, the interviewees consider the application of external benchmarking less of a burden and find that it is applied too little. This opinion exists especially in successful projects. In other words when environmental complexity rises, the interviewees believe external benchmarking could help to increase the chance of project success.
When organizational complexity rises, goal-setting and alignment is applied insufficiently leading to a decreased chance of project success.
When technical complexity is high, the steering committee intervenes more regularly in the project, increasing the chance of project success.
When technical complexity rises, more risk management is required, increasing the chance of project success.
Also, the following direct relationships were found:
Achieving team cohesion increases the chance of project success. This becomes more difficult to achieve when any type of complexity rises.
Performing a thorough constructability review increases the chance of project success. The burden of performing this VIP was considered by the interviewees to be lower when organizational complexity is high.
Active monitoring of project goals increases the chance of project success, but is applied less when technical complexity is high, leading to a decreased chance of project success.
Performing operations implementation planning increases the chance of project success, but is applied less when organizational complexity is high, leading to a decreased chance of project success.
Implications for Project Management
Several practical implications for project management were derived from the current study, related to teambuilding, cooperation, and some VIPs. These are all discussed next.
This study underlines the importance of teambuilding. The VIP project team building is directly linked to project success, as well as social team building and team cohesion. This finding is in line with findings by Independent Project Analysis (IPA), a leading project management benchmarking and consulting firm. In their project database, team development was identified as a main driver for project success (IPA, 2009b).
Team cohesion is linked to every kind of project complexity (T, O, and E) in the results of this study. When any type of complexity rises, more problems due to lack of team cohesion are perceived by the interviewees. This is to be expected, because when complexity rises, team cohesion can help to deal with it. A cohesive team will communicate and cooperate more easily, enabling it to be proactive in signalling potential problems and dealing with them. The results of the survey show that social team building could help to achieve team cohesion, increasing the chance of project success.
Cooperation with certain key stakeholders was also shown to influence the chance of project success, moderated by organizational complexity. When organizational complexity rises, the number of interventions by the project manager in the work of (sub)contractors goes up while the perceived cooperation between project manager and (sub)contractor goes down. This means that the project manager is “policing” instead of working together with the (sub)contractors. Project managers from one of the NAP member companies confirmed the occurrence of this phenomenon from personal experience. The data shows that this kind of relationship leads to a decrease in the chance of project success. Again this result is in line with IPA findings. IPA has shown that integrated project teams are more successful than “traditional” teams. These integrated teams include (sub)contractors in the team rather than seeing them as a separate party, implying cooperation rather than policing. This is also supported by the finding that stakeholder management was often considered to be applied too little by the interviewees. This VIP consists of identifying and involving all relevant stakeholders, which is apparently not done sufficiently.
While a policing relationship with (sub)contractors was identified as a reason for decreased chance of project success, interventions by the steering committee were shown to increase the chance of project success in technically complex projects. A possible explanation is that project managers can lose sight of the project goals in a technically complex project. Steering committee interventions could then force the project manager to review the goals and regain the right focus. This explanation is supported by findings on the monitoring of project goals.
The VIP goal-setting and alignment was often considered to have been applied too little by interviewees. This VIP was shown to have a moderated relationship to project success: when organizational complexity is high, goal-setting and alignment is considered to be applied insufficiently by the interviewees, leading to a decreased chance of project success. This suggests that the VIP was performed, but the interviewees felt they were not actually aligned and this led to a decreased chance of project success.
The results of the study show it is important to actively monitor the goals after having set the goals. Active monitoring of project goals directly improves the chance of project success. However, when technical complexity rises, active monitoring of project goals happened less, leading to decreased agreement on these goals and a decreased chance of project success. A possible reason for this is the fact that making estimates about a technically complex project are difficult to make up front, especially when new technology is being applied. That would make the chance of reaching the estimated budget or schedule (project success) lower. Another explanation that was offered by project managers is a tendency by project managers to lose sight of the project goals by getting too involved in the technical details of the project or by not understanding them. This might be a reason why interventions by the steering committee are beneficial to the chance of project success. An intervention by the steering committee likely forces the project team to verify whether they are on course for reaching the project goals.
Three other VIPs were found to influence the chance of project success. First, sufficient application of constructability review increases the chance of project success. Constructability review was the most applied of all VIPs and only the perceived burden of applying the VIP declines when organizational complexity increases. The amount of application is not related to project complexity. This suggests that the benefit of this VIP is recognized by the interviewees and is confirmed by the data.
Operations implementation planning (OIP) also has a direct positive influence on project success. However, this VIP is more often considered to be applied too little by interviewees. This was confirmed by the data, because when organizational complexity increased, the application of OIP was applied less, leading to a decreased chance of project success. A reason for this could be the problematic relationship with (sub)contractors when organizational complexity is high. If (sub)contractors were a part of a cohesive integrated team they would likely be more able and willing to provide input for OIP.
The third VIP related to project success was external benchmarking. This VIP was the least applied of all VIPs, which was considered sufficient by the interviewees. Only when environmental complexity increased interviewees did find the application insufficient, especially in successful projects. This suggests that they see external benchmarking as a way of dealing with environmental complexity.
Suggestions to Project Managers
Based on this study, some “do's” and “things to take care of” are suggested for project managers specifically:
Create a cohesive and integrated team, involving all key stakeholders. Hold frequent meetings and social team buildings to increase or maintain team cohesion and involve key stakeholders like (sub)contractors as part of the team.
Make goals clear at the outset and actively monitor them during the lifetime of the project. The VIPs operations implementation planning and constructability review should always be performed thoroughly to increase the chance of project success. When environmental complexity is high, applying external benchmarking should be considered.
Take risk management seriously as it was shown that, when technical complexity rises, more risk management is required to increase the chance on project success. However, this study also showed that active monitoring of risks was done as often as not.
Take care of project complexity. Whenever any type of complexity is high, team cohesion is more difficult to achieve. High technical complexity increases the likelihood of not monitoring project goals adequately. High organizational complexity coincides with problems in the relationship between the project manager and (sub)contractors and insufficient initial goal-setting and alignment. Performing operations implementation planning is also problematic when organizational complexity is high.
Conclusions and Further Research
The main research question to be answered in this paper was: how does front-end development relate to project complexity and project success and can this be examined using contingency theory? This study showed a novel and successful application of contingency theory in project management research, which resulted in the finding of relationships between FED, project complexity and project success in projects performed by companies in the NAP network. The analysis framework based on contingency theory resulted in the identification of several direct and moderated relationships between the variables under investigation. The moderated relationships would likely have been missed if normal direct multivariate analyses would have been used. This shows the added value of using this contingency theory based analysis in project management research.
An extensive data set consisting of data from 67 projects allowed a quantitative analysis, which resulted in several relationships between components of the front-end development phase in projects, project complexity, and project success, amongst others:
When organizational complexity rises, goal-setting and alignment is applied insufficiently leading to a decreased chance of project success.
When technical complexity is high, the steering committee intervenes more regularly in the project, increasing the chance of project success.
Active monitoring of project goals increases the chance of project success, but is applied less when technical complexity is high, leading to a decreased chance of project success.
Results showed the importance of creating cohesive and integrated project teams; confirming findings by the leading project management benchmarking company IPA. Also constructability reviews and operations implementation planning were shown to positively influence the chance on project success.
Using the current analysis framework, the conclusions of this study could be further elaborated with an even larger data set, for example from different industry sectors. Future research could also be focused on refinements of the conceptual model. Such refinements could include, on the one hand, a limitation to specific FED activities, and, on the other hand, inclusion of (more) different dimensions of project complexity. One of the ultimate goals of future research is to adapt project management to the project complexity up front or during a project. Because of the assumed dynamic character of project complexity, also a longitudinal research design could be required: gaining information at several points in time and monitor changes.
Arkesteijn, R. (2009). Present perspectives on project success - MSc Thesis. Delft: Delft University of Technology.
Baarda, D. B., & de Goede, M. P. M. (2006). Basisboek methoden en technieken. Groningen: Wolters-Noordhoff.
Baccarini, D. (1996). The concept of project complexity—A review. International Journal of Project Management, 14(4), 201–204.
Bakker, H. L. M. (2008). Management of projects: A people process (inaugural address). Delft: Delft University of Technology.
Balachandra, R., & Friar, J. H. (1997). Factors for success in R&D projects and new product innovation: A contextual framework. IEEE Transactions on Engineering Management, 44(3), 276–287.
Bosch-Rekveldt, M., Gulden, C., Wolsing, B., Mooi, H., Verbraeck, A., & Sjoer, E. (2009a). Mapping project manager's competences to project complexity. Paper presented at the IPMA 23rd WorldCongress, Research Track Human Side of Projects in Modern Business, Helsinki.
Bosch-Rekveldt, M. G. C., & Mooi, H. G. (2008). Research into project complexity classification methods. Paper presented at the IPMA Research conference 2008, Rome.
Bosch-Rekveldt, M. G. C., Mooi, H. G., Verbraeck, A., & Bakker, H. L. M. (2009b). Perspectives of project professionals on project complexity in the process and energy industry. Paper presented at the IRNOP IX, Berlin.
Bosch-Rekveldt, M., Jongkind, Y., Bakker, H., Mooi, H., & Verbraeck, A. (2010). Grasping project complexity in large engineering projects. Revised submission to International journal of project management.
Bryman, A., & Cramer, D. (2009). Quantitative data analysis with SPSS 14, 15 & 16. Hove: Routledge.
Burns, T., Stalker, G. M., & Woodward, J. (1961). The management of innovation. London: Tavistock.
CII. (2009). Construction Industry Institute Best Practices, http://www.construction-institute.org/scriptcontent/bp.cfm?section=aboutcii.
Cohen, L., & Holliday, M. (1982). Statistics for social scientists. London: Harper & Row.
de Groen, T., Dhillon, J., Kerkhoven, H., Janssen, J., & Bout, J. (2003). 2x2: A guide for key decision makers in the process industry. Nijkerk: NAP.
Dessler, G. (1976). Organization and management—A contingency approach. Englewood Cliffs, NJ: Prentice-Hall.
Donaldson, L. (1996). The normal science of structural contingency theory. London: Sage publications.
Donaldson, L. (2001). The contingency theory of organizations. Thousand Oaks: Sage Publications.
Engwall, M. (2003). No project is an island: Linking projects to history and context. Research Policy, 32(5), 789–808.
Flyvbjerg, B., Bruzelius, N., & Rothengatter, W. (2003). Megaprojects and risk. An anatomy of ambition. Cambridge: Cambridge University Press.
Galbraith, J. (1973). Designing complex organizations. Addison-Wesley.
Galtung, J. (1967). Theory and methods of social research. Oslo: Universitetsforlaget.
Geraldi, J. (2008). Patterns of complexity: the thermometer of complexity. Project Perspectives, 29, 4–9.
Hall, P. G. (1981). Great planning disasters. London: Weidenfeld and Nicholson.
Hass, K. (2007). Introducing the project complexity model—A new approach to diagnosing and manageing projects. PM World Today, 9(7), 12–19.
IPA. (2009a). Independent Project Analysis—Value Improving Practices Workshop, http://www.ipainstitute.com/home/programs/description.aspx?id=15.
IPA. (2009b). Industry Benchmarking Consortium 2009: IPA.
Jaccard, J., & Wan, C. K. (1996). LISREL approaches to interaction effects in multiple regressions. Thousand Oaks: Sage.
Jongkind, Y. (2008). Rubik's cube in project complexity - MSc Thesis. Delft: Delft University of Technology.
Kim, J. O. (1975). Multivariate analysis of ordinal variables. American Journal of Sociology, 81, 261–298.
Labovitz, S. (1967). Some observations on measurement and statistics. Social Forces, 46, 151–160.
Lawrence, P. R., & Lorsch, J. W. (1967). Organization and environment. Cambridge MA Harvard Graduate School of Business Administration.
Morris, P. W. G. (1994). The management of projects. London: Thomas Telford.
Morris, P. W. G., Crawford, L., Hodgson, D., Shepherd, M. M., & Thomas, J. (2006). Exploring the role of formal bodies of knowledge in defining a profession—The case of project management. International Journal of Project Management, 24(8), 710–721.
Morris, P. W. G., & Hough, G. H. (1987). The anatomy of major projects: A study of the reality of project management. Chichester: John Wiley.
NAP. (2009). NAP—The process industry competence network. www.napnetwerk.nl.
Neleman. (2006). Shell gaat diep. FEM Business, 9(4), 30–34.
Oosterhuis, E. J., Pang, Y., Oostwegel, E., & de Kleijn, J. P. (2008). Front-end loading strategy: A strategy to achieve 2x2 goals. Nijkerk: NAP.
Remington, K., Zolin, R., & Turner, J. R. (2009). A model of project complexity: distinguishing dimensions of complexity from severity. Paper presented at the IRNOP IX, Berlin.
Shenhar, A., Dvir, D., Levy, O., & Maltz, A. (2001). Project success: A multidimensional strategic concept. Long Range Planning, 34, 699–725.
Shenhar, A. J., & Dvir, D. (1996). Toward a typological theory of project management. Research Policy, 25(4), 607–632.
Shenhar, A. J., & Dvir, D. (2007). Project management research: The challenge and opportunity. Project Management Journal, 38(2), 93–99.
Smyth, H. J., & Morris, P. W. G. (2007). An epistemological evaluation of research into projects and their management: Methodological issues. International Journal of Project Management, 25(4), 423–436.
Thompson, J. D. (1967). Organization in action. New York: McGraw-Hill.
van der Weijde, G. (2008). Front-end loading in the oil and gas industry: Towards a fit front-end development phase - MSc Thesis. Delft: Delft University of Technology.
Verschuren, P., & Doorewaard, H. (1999). Designing a research project. Utrecht: Lemma.
Williams, T. M. (1999). The need for new paradigms for complex projects. International journal of project management, 17(5), 269–273.
Williams, T. M. (2002). Modelling complex projects. London: John Wiley & Sons.
Williams, T. M. (2005). Assessing and moving on from the dominant project management discourse in the light of project overruns. IEEE Transactions on Engineering Management, 52(4), 497–508.
Woodward. (1965). Industrial organisation: Theory and practice. Oxford, UK: Oxford University Press.
APPENDIX A: Descriptive results of survey study
The final 67 projects were performed in several industrial sectors, see Figure 6. The survey was mainly filled in by project managers (52% of interviewees), business representatives (22.5%), and team members (16.5%). The data contained 22 successful projects and 32 unsuccessful projects, 10 of which failed completely, see Figure 7. Of the remaining 13 projects data was missing on the result regarding budget, schedule or both. Figure 7 shows very few projects came in more than 10% under schedule and/or budget. All of the failed projects suffered budget and schedule overruns. This confirms what is stated in literature: failure due to overruns is much more likely than overestimation (7; 8; 6). Figure 8 shows the distributions of project complexity in the surveyed projects. The shapes of the histograms approach normal distributions, which is important for the multivariate analysis that uses correlations assuming normal distribution of the variables.
Scores for technical complexity, organizational complexity, and environmental complexity were obtained by taking the mean value of several subscores. Figure 5: Distribution of Project Complexity Scores in Surveyed Projects
Table 7 shows that technical complexity has the highest mean value of the three types of complexity as well as the highest standard deviation. This means that technical complexity was highest on average, but also the most varied across the surveyed projects. Environmental complexity was the least varied of the three.
Figure 6: Industrial Sectors in Which Surveyed Projects Were Performed
Figure 7: Project Performance in Surveyed Projects
Figure 8: Distribution of Project Complexity Scores in Surveyed Projects
Table 7: Descriptive Statistics of Project Complexity (T, O and E Dimensions) in Surveyed Projects
|Technical complexity||Organizational complexity||Environmental complexity|
APPENDIX B: TOE framework (Bosch-Rekveldt et al 2010)
Table 4: TOE Framework (50 Elements in Total)
|TOE||Sub-ordering||ID||Source L/E/B1||Elements defined||Explanation|
|T||Goals||TG1||L||Number of goals||What is the number of strategic project goals?|
|T||Goals||TG2||B||Goal alignment||Are the project goals aligned?|
|T||Goals||TG3||B||Clarity of goals||Are the project goals clear amongst the project team?|
|T||Scope||TS1||B||Scope largeness||What is the largeness of the scope, e.g. the number of official deliverables involved in the project?|
|T||Scope||TS2||B||Uncertainties in scope||Are there uncertainties in the scope?|
|T||Scope||TS3||E||Quality requirements||Are there strict quality requirements regarding the project deliverables?|
|T||Tasks||TT1||B||Number of tasks||What is the number of tasks involved?|
|T||Tasks||TT2||B||Variety of tasks||Does the project have a variety of tasks (e.g. different types of tasks)?|
|T||Tasks||TT3||B||Dependencies between tasks||What is the number and nature of dependencies between the tasks?|
|T||Tasks||TT4||B||Uncertainty in methods||Are there uncertainties in the technical methods to be applied?|
|T||Tasks||TT5||B||Interrelations between technical processes||To what extent do technical processes in this project have interrelations with existing processes?|
|T||Tasks||TT6||B||Conflicting norms and standards||Are there conflicting design standards and country specific norms involved in the project?|
|T||Experience||TE1||B||Newness of technology (worldwide)||Did the project make use of new technology, e.g. non-proven technology (technology which is new in the world, not only new to the company!)?|
|T||Experience||TE2||B||Experience with technology||Do the involved parties have experience with the technology involved?|
|T||Risk||TR1||B||Technical risks||Do you consider the project being high risk (number, probability and/or impact of) in terms of technical risks?|
|O||Size||OS1||L||Project duration||What is the planned duration of the project?|
|O||Size||OS2||B||Compatibility of different project management methods and tools||Do you expect compatibility issues regarding project management methodology or project management tools?|
|O||Size||OS3||B||Size in CAPEX||What is the estimated CAPEX of the project?|
|O||Size||OS4||B||Size in Engineering hours||What is the (expected) amount of engineering hours in the project?|
|O||Size||OS5||B||Size of project team||How many persons are within the project team?|
|O||Size||OS6||E||Size of site area||What is the size of the site area in square meters?|
|O||Size||OS7||B||Number of locations||How many site locations are involved in the project, including contractor sites?|
|O||Resources||ORE1||B||Project drive||Is there strong project drive (cost, quality, schedule)?|
|O||Resources||ORE2||B||Resource & Skills availability||Are the resources (materials, personnel) and skills required in the project, available?|
|O||Resources||ORE3||B||Experience with parties involved||Do you have experience with the parties involved in the project (JV partner, contractor, supplier, etc)?|
|O||Resources||ORE4||E||HSSE awareness||Are involved parties aware of health, safety, security and environment (HSSE) importance?|
|O||Resources||ORE5||B||Interfaces between different disciplines||Are there interfaces between different disciplines involved in the project (mechanical, electrical, chemical, civil, finance, legal, communication, accounting, etc) that could lead to interface problems?|
|O||Resources||ORE6||B||Number of financial resources||How many financial resources does the project have (e.g. own investment, bank investment, JV-parties, subsidies, …)?|
|O||Resources||ORE7||B||Contract types||Are there different main contract types involved?|
|O||Project team||OP1||B||Number of different nationalities||What is the number of different nationalities involved in the project team?|
|O||Project team||OP2||B||Number of different languages||How many different languages were used in the project for work or work related communication?|
|O||Project team||OP3||B||Cooperation JV partner||Do you cooperate with a JV partner in the project?|
|O||Project team||OP4||B||Overlapping office hours||How many overlapping office hours does the project have because of different time zones involved?|
|O||Trust||OT1||B||Trust in project team||Do you trust the project team members (incl JV partner if applicable)|
|O||Trust||OT2||B||Trust in contractor||Do you trust the contractor(s)?|
|O||Risk||OR1||B||Organizational risks||Do you consider the project being high risk (number, probability and/or impact of) in terms of organizational risks?|
|E||Stakeholders||ES1||B||Number of stakeholders||What is the number of stakeholders (all parties (internal and external) around the table, pm=1, project team=1, NGOs, suppliers, contractors, governments)?|
|E||Stakeholders||ES2||B||Variety of stakeholders' perspectives||Do different stakeholders have different perspectives?|
|E||Stakeholders||ES3||B||Dependencies on other stakeholders||What is the number and nature of dependencies on other stakeholders?|
|E||Stakeholders||ES4||B||Political influence||Does the political situation influence the project?|
|E||Stakeholders||ES5||B||Company internal support||Is there internal support (management support) for the project?|
|E||Stakeholders||ES6||B||Required local content||What is the required local content?|
|E||Location||EL1||E||Interference with existing site||Do you expect interference with the current site or the current use of the (foreseen) project location?|
|E||Location||EL2||E||Weather conditions||Do you expect unstable and/or extreme weather conditions; could they potentially influence the project progress?|
|E||Location||EL3||E||Remoteness of location||How remote is the location?|
|E||Location||EL4||E||Experience in the country||Do the involved parties have experience in that country?|
|E||Market conditions||EM1||E||Internal strategic pressure||Is there internal strategic pressure from the business?|
|E||Market conditions||EM2||B||Stability project environment||Is the project environment stable (e.g. exchange rates, raw material pricing)?|
|E||Market conditions||EM3||B||Level of competition||What is the level of competition (e.g. related to market conditions)?|
|E||Risk||ER1||B||Risks from environment||Do you consider the project being high risk (number, probability and/or impact of) in terms of risk from the environment?|
1 L = based on literature data, E = based on empirical data, B = based on both literature and empirical data.
© 2010 Project Management Institute