Findings

The findings of this research are presented in two sections namely, Delphi survey results and cross-impact analysis. In the Delphi survey results, the data that are elicited from the experts are analyzed. In the second section, the multi-level cross-impact model is utilized in order to assess the impacts of IT on the maturity levels of the project management knowledge areas in the design process of a building project.

The multi-level cross-impact analysis model developed in this research demands data from experts in a variety of disciplines (i.e., designers, construction managers, property managers, and information technologists). Hence the model solely depends on the intuitive judgment of the experts and the model's output can be only as good as this input. Therefore, the selection of these experts is very important. To that end, a Delphi survey of the members of the Project Management Institute's (PMI ) Midwest Chapter in the United States (US) was conducted. The endorsement of the Chapter's Board of Directors was obtained. Volunteers were sought through an announcement in the Chapter's monthly newsletter. Later, members were invited via email to join the study. A total of thirty-four experts accepted the offer to participate. Data presented in Tables 1–3 were elicited from these experts.

Delphi Results

The results of the Delphi survey show the impacts of ITs on project management knowledge areas (Table 4) and the forecasted maturity levels of the project management knowledge areas (Table 3) in the design phase of a building project. First, the significant interdependencies between ITs and project management knowledge areas will be discussed. Then forecasted maturity levels of project management knowledge areas for each scene (i.e., years 2000–2005, 2005–2010, and 2010–2015) will be presented.

Table 4 is designed to exhibit the impacts of breakthrough developments in ITs on the maturity levels of project management knowledge areas. Each cell contains a rating between 0 and 3, where 0 is no impact, 1 is slight impact, 2 is moderate impact, and 3 is significant impact. The sum of the total impacts is recorded at the end of each row (i.e., impacting ITs) and column (i.e., impacted project management knowledge areas). The sum of the impacts at the end of a row indicates that particular IT's total impact on the project management knowledge areas, whereas, the sum of the impacts at the end of a column indicates the total impact coming from all the ITs to that particular project management knowledge area. For example, Table 4 shows that project scope management (column 3) is the most impacted project management knowledge area by ITs with a total impact rating of 13. On the other hand, artificial intelligence is the IT with the most impact on project management knowledge areas, with a total impact rating of 20 (column 11). The last row in Table 4 indicates that the most impacted project management knowledge areas by ITs are project scope management (rating = 13), project time management (rating = 12), project integration management (rating = 11), and project communications management (rating = 11). The least affected project management knowledge areas are project cost management (rating = 6), project quality management (rating = 6), and project human resources management (rating = 6). On the other hand, column 11 shows that the ITs with greatest impact on project management knowledge areas are artificial intelligence (rating = 20), project management software (rating = 18), and virtual reality (rating = 13), whereas the ITs with least impact are GIS/GPS (rating = 2) and wireless communication technologies (rating = 5).

The results of this Delphi survey indicate that the maturity levels of project management knowledge areas can be enhanced by the use of some ITs. Indeed virtual reality, CAD, and artificial intelligence technologies can actually help both the customer and designer to understand the actual building better before it is built. This ability can positively impact scope management activities in the design phase of a building project. Artificial intelligence and project management software can be utilized for more accurate project time management. On the other hand, project integration management is very important in the design phase because many problems related to the integration of the many disciplines and knowledge areas throughout the building process can be solved in the design phase. This may enhance the constructability and quality of the design. Project communications management in the design phase is significantly impacted by breakthrough developments in Internet/intranet technologies. The building design process highly depends on information from sources as diverse as material dealers, technology developers, equipment manufacturers, technical consultants, and so on. More and more business communications are done via the Internet/intranet. Blueprints and other information can be transferred between the parties by electronic means. Increasing integration of the software and hardware technologies makes this possible. This creates many opportunities for design firms. It eliminates to some extent the barrier of geography; design firms can work with other professionals with more flexible schedules and with fewer limitations. According to Buckley (1994), ITs also support concurrent engineering activities that can, if done correctly, reduce lead-times from concept to customer and increase quality performance. ITs with the most impact on project communication management are artificial intelligence, project management software, and Internet/intranet services.

The IT that has significant and moderate impact on all the four project management knowledge areas (project scope management, project time management, project integration management, and project communications management) appears to be artificial intelligence. Indeed artificial intelligence may help designers in a variety of ways; for example, artificial intelligence programs given the design constraints can generate design alternatives. Generation and evaluation of the design alternatives may enhance the designer's creativity to address potential design problems. Of course, the other advantage of artificial intelligence is the reduction of the time required for the decision-making process. Traditional sketch drawings may take days to generate a few alternatives, whereas, numerous alternatives can be generated and evaluated by the artificial intelligence technologies in matter of minutes.

One of the three project management knowledge areas that is least impacted by ITs is project cost management (total rating = 6). The IT that impacts project cost management the most is project management software technologies, which seems to have a relatively moderate impact on the maturity level of the project cost management knowledge area. This is because costs can be tracked and controlled in the design phase by cost control software. Project quality management, which also has the lowest rating of 6 is not impacted significantly by any of the ITs except for virtual reality, which has a moderate impact on it; it is either slightly or not impacted at all by the remaining ITs. The moderate impact of virtual reality on project quality management may come from the importance of virtual reality presentations in order to get more customer input and solve potential conflicts in the design phase before they actually occur. This may increase the quality of the building project. Project human resources management is the third knowledge area that is least impacted by ITs (total rating = 6). The strongest impact comes from project management software technologies that have a moderate impact on the maturity level of this knowledge area. While these knowledge areas do not receive direct impacts from ITs, they might be impacted by secondary impacts via other project management knowledge areas as discussed later.

On the other hand, artificial intelligence and project management software technologies significantly and moderately impact project procurement management, respectively. Indeed the processes required in acquiring goods and services from outside the performing organization and decision-making processes may benefit from both artificial intelligence and project management software technologies. Artificial intelligence, in this case, can help decision-makers in the selection process of sources or identifying the potential risk areas. Project management software technologies may help to keep track of procurement planning and contract administration.

Breakthrough developments in artificial intelligence have the highest impact (total rating = 20) on the maturity levels of project management knowledge areas in the design phase. Indeed the design process is an iterative process; therefore, artificial intelligence technologies may assist designers in their decision-making process. Developments in artificial intelligence may enhance the efficiency of CAD and virtual reality technologies.

The second part of the Delphi survey was designed to retrieve the forecasted values of the maturity levels of project management knowledge areas in the design phase. The results are presented in Table 3. The total change in the maturity level of each project management knowledge area is presented in column 6. The total change indicates the percentage increase in the maturity levels from scene 0 (i.e., year 1997) to scene 3 (i.e., years 2010–2015). The total changes show the experts' perceptions of potential improvement areas relative to each other. The highest increases can be seen in the maturity levels of project quality management (total change 41 percent) and project risk management (total change 31 percent); Kwak (1997) had measured the maturity levels of project quality management and project risk management as 2.9 in 1997, the lowest among the others. The maturity levels of project scope management and project cost management on the other hand show small increases of 9 percent and 11 percent, respectively. The predictions of the experts show that attaining full maturity in the industry (i.e., a maturity level of 5) is not reachable within the time horizon of this study. That is why companies that deal with the design of building projects can differentiate themselves from their competitors by enhancing their project management maturity levels.

Cross-Impact Analysis

A multi-level cross-impact analysis uses the data that were elicited from experts, as described in the preceding sections. A computer run of 10,000 replications of the model produced the results that consist of forecasted maturity levels of project management knowledge areas and the probabilities of major breakthrough developments in ITs in the given time horizon (i.e., years 2000–2005, 2005–2010, and 2010–2015). Hence the trigger actions of the model in the first scene can only affect the second scene (i.e., 2005–2010); the forecasted values for the first scene (i.e., 2000–2005) are limited to the Delphi survey results. The second and the third scenes (i.e., 2005–2010 and 2010–2015) have cross-impact analysis values along with the Delphi results. Because of the nature of the study, the relationships between the variables are all positive; therefore, the results of the Delphi survey are either positive (i.e., significant, moderate, and slight) impacts or no impacts. Lack of negative impacts naturally makes the model generate higher forecasts then the Delphi results. This is because the model generates new estimates based on the Delphi estimates and sums up all the negative and positive impacts around the Delphi results. Indeed, if there were negative impacts, the model might have generated lower values than the estimated Delphi results (in cases where the negative impacts overcame the positive impacts). For example, if we assume that the events (trigger actions) are not occurring at all, then the model should and actually does present the original Delphi estimates (i.e., forecasted maturity levels and forecasted probabilities), since there are no impacts either positive or negative on the estimated trend and event values. The model's performance depends on the Delphi estimates; indeed the results of the model can be as good as the inputs.

The results are presented in two sections. First, the results of the basic computer run are discussed. Then sensitivity analyses are conducted in order to determine the sensitivity of the project management knowledge areas in each phase to major breakthrough developments in ITs.

Results of the Basic Run

A basic computer run of the model consists of 10,000 replications. Computer runs of 1,000, 4,000, and 10,000 replications resulted in standard errors of 0.0038, 0.0019, and 0.0012, respectively, for the project integration management knowledge area with similar results in the other project management knowledge areas. For comparative policy analysis and scenario generating purposes, replications of 1,000 and above are reasonable given the low standard errors. The results consist of the forecasted maturity levels of project management knowledge areas in the design phase of a building project (Table 5) and the forecasted probabilities of the major breakthrough developments in ITs. Since the model produces values only for the second and the third scenes, the analysis will be conducted in the second (i.e., 2005–2010) and the third (i.e., 2010–2015) scenes. The values in the first scene (i.e., 2000-2005) are the Delphi forecasts. As discussed earlier, the model produces higher estimates than the Delphi forecasts in the last two scenes. A ranking of the mean values of the estimates or a ranking of the percentage increases are useful in this case.

Table 5: Maturity Levels of Project Management Knowledge Areas in the Design Phase of a Building Project Forecasted by the Model

PM Knowledge Areas (1)	Scene 0 Year 1997 (2)	Scene 1 Years 2000–2005 (3)	Scene 2 Years 2005–2010 (4)	Scene 3 Years 2010–2015 (5)	Total Change (%) (6)
Integration Management	3.3	3.4	4.0	4.3	30
Scope Management	3.5	3.6	4.1	4.2	20
Time Management	3.6	3.7	4.1	4.4	22
Cost Management	3.7	3.8	4.1	4.3	16
Quality Management	2.9	3.7	4.1	4.3	48
Human Resources Management	3.1	3.2	3.6	4.2	35
Communications Management	3.5	3.7	4.2	4.5	29
Risk Management	2.9	3.2	3.8	4.1	41
Procurement Management	3.3	3.4	3.9	4.2	27
1 = Zero Maturity, 2 = Low Maturity, 3 = Moderate Maturity, 4 = High Maturity, 5 = Complete Maturity

In the design phase, Tables 3 and 5 are used for comparative analysis. The model forecasts more increases than the Delphi forecast for reasons discussed earlier. The differences can be calculated easily by subtracting the respective values of total change in column 6 of Table 3 from column 6 of Table 5. For example, while total change in project integration management is forecasted by experts as 18 percent, the model forecasts a 30 percent total change. The difference of 12 percent shows that experts' estimates are not consistent with the model's forecasts. The same situation can be observed in project scope management with an 11 percent higher increase forecasted by the model. In contrast, the forecasts generated by the model for project communications management (a difference of 3 percent) and project cost management (a difference of 5 percent) are much closer to the predictions made by experts. The mean of the differences of total changes between the Delphi forecasts and model forecasts is 8 percent in the design phase.

Sensitivity Analysis

Sensitivity of each project management knowledge area to an IT is important in order to understand the impacts of ITs on project management knowledge areas. The design of the model enables sensitivity analysis. For example, the sensitivity of the project management knowledge areas to major breakthrough developments in the Internet/intranet technologies can be observed by setting all the IT events' occurrence probabilities to 0 except for Internet/intranet technologies. Then running the model will show the impact of Internet/intranet technologies on the project management knowledge areas without the other ITs' interactions.

For each sensitivity run, the model starts at the same random number entry which guarantees that, at analogous, stochastic decision points, the likelihood of major breakthrough developments will be as "lucky" or "unlucky" as in the preceding basic run. Each basic run consists of 10,000 replications. The sensitivity analysis is conducted for each of the seven ITs defined in this study.

The deviations of the results (i.e., results of sensitivity runs) from the Delphi forecasts are used for sensitivity analysis. In order to accomplish the objective of this study, deviations from the Delphi estimates are calculated and summarized in terms of percentages for the design phase (Table 6). Table 6 shows the sensitivity (percent change) of each project management knowledge area to the given IT. For example, the maturity level of project integration management in the third scene of the design phase is 3.9 (Table 3). Considering breakthrough developments only in virtual reality, the maturity level of project integration management will be 4.0 in the third scene (result of the model). The difference is 0.1, which is a 2.6 percent increase from the Delphi estimate. The highest deviation in this study is found to be 5.7 percent. This result indicates that breakthrough developments in an IT taken individually do not impact the maturity level of any one project management knowledge area significantly, whereas they effect the maturity level of all project management knowledge areas through primary (tangible) and secondary (intangible) impacts. This finding can be used to maximize the benefits of IT deployment and, therefore, decreas the cost and failure rates at the initial phases of reengineering/restructuring efforts. The impact of an individual IT on the maturity level of all project management knowledge areas can be observed from the bottom row (total change percent) of Table 6.

Table 6: Sensitivity of the Maturity Level of Project Management Knowledge Areas to Major Breakthrough Developments in IT in the Design Phase of a Building Project in Terms of Percentage Change

PM Knowledge Areas(1)	Virtual Reality(2)	CAD/CAM(3)	Artificial Intelligence(4)	GIS/GPS(5)	Internet/Intranet(6)	PM Software(7)	Wireless Communication(8)
Integration Management	2.6	2.6	2.6	0.0	2.6	5.1	0.0
Scope Management	5.3	2.6	5.3	0.0	2.6	5.3	0.0
Time Management	2.4	0.0	2.4	0.0	2.4	2.4	0.0
Cost Management	2.4	0.0	2.4	0.0	0.0	2.4	0.0
Quality Management	2.4	0.0	2.4	0.0	0.0	2.4	0.0
Human Resources Management	2.5	0.0	2.5	0.0	0.0	2.5	0.0
Communications Management	0.0	0.0	0.0	0.0	2.3	0.0	0.0
Risk Management	2.6	2.6	2.6	0.0	0.0	2.6	0.0
Procurement Management	2.6	2.6	2.6	0.0	0.0	2.6	0.0
Total Change (%)	22.8	10.4	22.8	0.0	9.9	25.3	0.0

The sensitivity analyses consider both primary and secondary impacts of IT. The primary impacts are the ones that affect the maturity level of a project management knowledge area directly. The secondary impacts are the impacts coming from the other knowledge areas and/or the impacts coming from the same knowledge area in the different phases. If only the primary impacts are considered, the results should and actually do reflect the Delphi results about the cross impacts of ITs on project management knowledge areas. However secondary impacts make this model more realistic because it considers the interdependencies between the variables.

The summary of the results for the design phase is presented in Table 6. Project management knowledge areas are sensitive to breakthrough developments in project management software (total change 25.3 percent), virtual reality (total change 22.8 percent), and artificial intelligence (total change 22.8 percent). The overall maturity level of project management knowledge areas in a design firm may be enhanced most by investing in these ITs. Focusing on one project management knowledge area will not help the organization to reach higher overall maturity levels. These results indicate that IT investments should be considered in a holistic environment. The secondary impacts of improvements will spread benefits of IT deployment throughout all project management knowledge areas.

Studies conducted by Alter (1996) and Callon (1996) conclude that IT investments are useless if the current business processes are not analyzed and redesigned to perform best with the new ITs. The different sensitivity of each project management knowledge area to an IT in the different phases of a project support this idea. Indeed, project management knowledge areas aim to achieve the same goals in each phase of a building project, but the tools to achieve these objectives should be and actually are different from each other. For example, project human resources management in the design phase has different procedures than project human resources management in the construction phase. While a relatively small number of employees dominate the process in a design firm, a large labor force may be typical in a construction company. To deal with these differences project management knowledge areas should utilize different ITs for their specific cases.

The modular design of the model allows the researcher to add other ITs in order to test the sensitivity of the maturity levels of project management knowledge areas in the process of a building project for those specific ITs. As the results of this study indicate, the pace of the developments in ITs is very fast. Change breeds change, and each change in IT brings different opportunities for the building design process. Further and more detailed studies in this area may help building construction professionals to redesign their processes for better performance in the light of the new ITs.