Methodology


The computerized model that is the subject of this chapter forecasts the impact of major breakthrough developments in seven information technologies (virtual reality, computer-aided design/computer-aided manufacturing [CAD/CAM], artificial intelligence, geographical information systems/global positioning systems [GIS/GPS], Internet/intranet, project management software, and wireless communication technology) on the maturity levels of the nine project management knowledge areas defined by A Guide to the Project Management Body of Knowledge (PMBOK Guide) (Project Management Institute 1996)—project integration management, project scope management, project time management, project cost management, project quality management, project human resources management, project communications management, project risk management, and project procurement management—of organizations involved in building design projects.

In the research reported in this chapter, a modified Delphi approach was used over the Internet to elicit expert opinion. Three types of information were collected. First, the forecasted (next fifteen years) maturity levels of the nine project management knowledge areas were obtained from building designers (for the conceptual and design phases). Second, the forecasted (next fifteen years) probabilities of occurrence of major breakthrough developments in the seven information technologies were obtained from information technologists. Third, the assessments of the cross impacts between the variables of the model were obtained from relevant respective parties. The data collected were subjected to a multi-level cross-impact analysis, which is a method typically used for long-term forecasting in financial markets. Cross-impact analysis was used for analyzing the interdependencies between the variables of the model by means of a simulation model built into the system.

Delphi Method

The objective of most Delphi applications is the reliable and creative exploration of ideas or the production of suitable information for decision-making. The Delphi method is based on a structured process for collecting and distilling knowledge from a group of experts by means of a series of questionnaires interspersed with controlled opinion feedback (Adler and Ziglio 1996). According to Helmer (1977) Delphi represents a useful communication device among a group of experts and, thus, facilitates the formation of a group judgment. Wissema (1982) underlines the importance of the Delphi method as a monovariable exploration technique for technology forecasting. He further states that the Delphi method has been developed in order to make discussion between experts possible, without permitting a certain social interactive behavior as happens during a normal group discussion and hampers opinion forming. Baldwin (1975) asserts that lacking full scientific knowledge, decision-makers have to rely on their own intuition or on expert opinion. The Delphi method has been widely used to generate forecasts in technology, education, and other fields (Cornish 1977; Gatewood and Gatewood 1983; Huss 1988). The Delphi method recognizes human judgment as legitimate and useful inputs in generating forecasts. Later, the notion of cross impacts was introduced to overcome the shortcomings of this simplistic approach (Helmer 1977).

In the original Delphi process, the key elements were 1) structuring of information flow, 2) feedback to the participants, and 3) anonymity for the participants. Clearly, these characteristics may offer distinct advantages over the conventional face-to-face conference as a communication tool. Goldschmidt (1975) agrees that there have been many poorly conducted Delphi projects. However, he warns that it is a fundamental mistake to equate the applications of the Delphi method with the Delphi method itself, as too many critics do. On the other hand, there have been several studies (Fowles 1978; Helmer 1981; Wissema 1982; Reuven and Dongchui 1995) supporting the Delphi method.

In general, the Delphi method is useful in answering one specific, single-dimension question. An improvement in forecasting reliability of the Delphi method was thought to be attainable by taking into consideration the possibility that the occurrence of one event may cause an increase or decrease in the probability of occurrence of other events included in the survey (Helmer 1977). Therefore cross-impact analysis has developed as an extension of Delphi method.

Cross-Impact Analysis

A basic limitation of many forecasting methods and the Delphi method is that they produce only isolated forecasts; that is, events and trends are projected one by one, without explicit reference to their possible influence on each other. Most events and developments, however, are in some way connected to each other. Interdependencies between these events and developments can be taken into consideration for more consistent and accurate forecasts. Cross-impact analysis addresses the Delphi method's lack of a mechanism for discovering mutually exclusive or conflicting outcomes. Thus, some outcomes forecasted by the Delphi method could be impossible to obtain simultaneously (Reuven and Dongchui 1995): for example full employment and a low rate of inflation. Cross-impact analysis addresses this problem directly by analyzing conditional probabilities—for example, the likelihood that inflation will be low if full employment is achieved. It examines the interactions of forecasted items (Gordon and Hayward 1968).

The cross-impact concept originated with Olaf Helmer and Theodore Gordon in conjunction with the design of a forecasting game for Kaiser-Aluminum (Helmer 1977). It represented an effort to extend the forecasting techniques of the Delphi method. In 1968 at the University of California at Los Angeles (UCLA), Gordon and Hayward developed a computer-based approach to cross-impact analysis and they published their findings in the paper titled Initial Experiments with the Cross-Impact Matrix Method of Forecasting (Gordon and Hayward 1968). In this approach, events were recorded on an orthogonal matrix and at each matrix intersection the question was asked: If the event in the row were to occur, how would it affect the probability of occurrence of the event in the column? The judgments were entered in the matrix cells. Cornish (1977) states that most forecasting methods may not consider many reactions between forecasted events. Cross-impact analysis, however, attempts to reveal the conditional probability of an event given that various events have or have not occurred. Duval, Fontela, and Gabus (1975) claim that cross-impact analysis differs from both probability theory and mathematical statistics; a cross-impact analysis is concerned with the identification of possible outcomes rather than with an understanding of what is or what was. They define cross-impact analysis as a systematic way to examine possible future developments and their interactions.

Technology forecasting does not follow a fixed methodological pattern. However, the way in which the study is approached and the choice of methods depend on the individual researcher (Wissema 1982). Several versions of cross-impact analysis have been developed by researchers (Gordon and Hayward 1968; Duval, Fontela, and Gabus 1975; Helmer 1977; Sarin 1978; Novak and Lorant 1978; Wissema and Benes 1980; Hanson and Ramani 1988; Fargionne, 1997). The evaluation of the technique has not followed a single path, but has produced a variety of different methods for constructing, utilizing, and evaluating cross-impact matrices.

There are several methodologies for different applications. Gordon and Hayward (1968) define three modes of connection between variables. Assume event E1 occurs. A second event, E2, may be completely unaffected by E1; it may be enhanced by the occurrence of E1; or it may be inhibited by the occurrence of E1. Thus, E1 may affect E2 as follows:

  • Unrelated

  • Enhancing

  • Inhibiting.

The impact of breakthrough developments in IT in the building construction process can be measured in terms of the deviations in the maturity levels of project management knowledge areas. The project management knowledge areas that are described in the PMBOK Guide are used in this study. The reason for the selection of these knowledge areas is their clear definition in terms of processes and their wide acceptance among project management professionals. The life cycle of a building project consists of the conceptual, design, construction, and operation phases. This holistic approach may benefit all stakeholders in building projects including the suppliers, the processors, and finally the customers of the construction industry. In this research three modules are taken into consideration (Figure 1):

  • Module 1: Information technology module

  • Module 2: Building processes module

  • Module 3: Project management functions module

click to expand
Figure 1: Conceptual Model of Cross-Impact Analysis

In these modules, events, trends, and phases are defined for causal cross-impact analysis. Module 1 defines the events; these are the breakthrough developments in information technologies. Module 2 identifies the trends; these are changes in the maturity level of the project management functions of the PMBOK Guide. Module 3 covers the phases of a building project. Occurrence of the events in Module 1 has impacts on the trends in Module 2; these impacts are assessed within the framework of Module 3.

The modular design enhances the flexibility of the model. Therefore the model can be easily modified according to the needs of its user by adding or deleting modules. This multifunctional, multi-user, modular approach enables the model to develop without strict limitations and costly modifications.

Three five-year scenes are proposed for the years 2000–2005, 2005–2010, and 2010–2015 to define the time horizon in this study. The reasons for the selection of the time horizon of fifteen years and the three time intervals of five years each, namely scenes are as follows:

  • They are easy to understand.

  • They are long enough to allow experts not to make trend-based forecasting.

  • They are not too long for practical and planning purposes.

  • They are short enough that inter-event and inter-trend impacts in the same scene can be ignored.

  • Time intervals are suitable for long-term forecasting.

Classifying the causal cross impacts into four groups makes the model easier to comprehend (Figure 1). These causal cross impacts between and within the modules are defined as:

  1. Inter-module impacts

  2. Inter-event impacts

  3. Inter-trend impacts

  4. Inter-phase impacts

Inter-event impacts assess the impacts of ITs on ITs. The occurrence of an IT may decrease or increase the probability of occurrence of the other ITs in the following scenes. These impacts are elicited from IT experts by means of a Delphi process.

Inter-trend impacts state the interdependencies between the project management knowledge areas. Fluctuation of the maturity level of any project management knowledge area may have impacts on the others in the next scene. These interdependencies are obtained from project management professionals.

Inter-phase impacts assess the impact of phases on each other. Any fluctuation of the maturity level of any project management knowledge area may not only have impacts on the same project management knowledge area in the next scene, but may also have impacts on the same project management knowledge area in the other phases of a building project. For example, any improvement in the project quality management knowledge area in the design phase may also affect the project quality management knowledge area in the construction phase in the following scene. In other words, project management knowledge areas may be impacted by other project management knowledge areas in other phases of the project.

The impacts are classified in terms of intuitive perceptions. The reason for this is to make use of experts in a most convenient way. The scale in Table 1 is developed to collect expert judgment. Actual cross-impact coefficients to be inserted in the matrix are also listed.

Table 1: Scale Developed for Cross Impacts

Coefficient

Scale

Meaning

+3

SIG

Significantly High Impact

+2

MOD

Moderate Impact

+1

SLI

Slight Impact

0

NO

No Impact

1

NEG

Negative Impact

+3 and 1 are not absolute limits; occasionally, for extremely large impacts, numbers could be employed whose absolute value is in excess of 3 or less than 1. This flexible scale lets the experts judge their intuitive interpretations' effects on the model.

The modules and the causal relations between them are defined in the following sections. In this study, causal cross-impact analysis is inevitably conducted in a domain of what might be called "soft data" and "soft laws." As Helmer (1977) stated, dependence on intuitive judgment is not just a temporary expedient, but in fact a mandatory requirement. Therefore, instead of firm observational data, this model utilizes judgmental inputs; in place of well-confirmed empirical laws, the model makes use of intuitively perceived regularities. Therefore, reliance on expert opinion is essential. Even though the model can be used with data that are elicited from a single expert, a Delphi process is also included for added reliability.

In the Delphi method two types of data are elicited from the experts:

  1. Predictions for the future scenes: event probabilities and trend values are elicited in respective scenes. These include:

    • Predictions of the probability of occurrence of major breakthrough developments in ITs in respective scenes (Table 2).

      Table 2: Forecasted Probability of Major Breakthrough Developments by Delphi Process

      Information Technologies (1)

      Scene 1 Years 2000–2005 (2)

      Scene 2 Years 2005–2010 (3)

      Scene 3 Years 2010–2015 (4)

      Virtual Reality

      0.6

      0.7

      0.7

      CAD/CAM

      0.7

      0.8

      0.8

      Artificial Intelligence

      0.6

      0.8

      0.8

      GIS/GPS

      0.8

      0.8

      0.8

      Internet/Intranet

      0.8

      0.8

      0.8

      PM Software

      0.5

      0.6

      0.7

      Wireless Communication

      0.8

      0.8

      0.9

    • Predictions of the expected maturity levels of the PMBOK Guide project management knowledge areas in the design phase in respective scenes (Table 3).

      Table 3: Maturity Levels of Project Management Knowledge Areas in the Design Phase of a Building Project Forecasted by Delphi Process

      PM Knowledge Area (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

      3.7

      3.9

      18

      Scope Management

      3.5

      3.6

      3.7

      3.8

      9

      Time Management

      3.6

      3.7

      3.8

      4.1

      14

      Cost Management

      3.7

      3.8

      3.9

      4.1

      11

      Quality Management

      2.9

      3.5

      3.9

      4.1

      41

      Human Resources Management

      3.1

      3.2

      3.4

      4

      29

      Communications Management

      3.5

      3.7

      4

      4.4

      26

      Risk Management

      2.9

      3.2

      3.5

      3.8

      31

      Procurement Management

      3.3

      3.4

      3.6

      3.9

      18

      1 = Zero Maturity, 2 = Low Maturity, 3 = Moderate Maturity, 4 = High Maturity, 5 = Complete Maturity

  2. Assessments for the cross impacts: impacts of the variables (events, trends, and phases) on each other are elicited by means of the intuitive scale described above. These include:

    • Assessments of the cross impacts of ITs on project management knowledge areas in the design phase (Table 4).

      Table 4: Cross Impacts of Information Technologies on Project Management Knowledge Areas in the Design Phase

      Impacting Information Technologies(1)

      Integration Management(2)

      Scope Management(3)

      Time Management(4)

      Cost Management(5)

      Quality Management(6)

      Human Resources Management(7)

      Communications Management(8)

      Risk Management(9)

      Procurement Management(10)

      Total Ratings(11)

      Virtual Reality

      1

      3

      1

      1

      2

      1

      2

      1

      1

      13

      CAD/CAM

      1

      3

      1

      1

      1

      0

      1

      1

      1

      10

      Artificial Intelligence

      3

      3

      3

      1

      1

      1

      2

      3

      3

      20

      GIS/GPS

      0

      0

      1

      0

      0

      0

      0

      1

      0

      2

      Internet/Intranet

      2

      2

      2

      1

      0

      1

      3

      0

      1

      12

      PM Software

      3

      2

      3

      2

      1

      2

      2

      1

      2

      18

      Wireless Communication

      1

      0

      1

      0

      1

      1

      1

      0

      0

      5

      Total Ratings

      11

      13

      12

      6

      6

      6

      11

      7

      7

      80

      0 = No Impact, 1 = Slight Impact, 2 = Moderate Impact, 3 = Significant Impact

    • Assessments of the cross impacts of ITs on ITs.

    • Assessments of the cross impacts of trends (project management knowledge areas) on trends.

    • Assessments of cross impacts of building project phases (conceptual, design, construction, and operation phases) on phases.

Data elicitation for all cross impacts was obtained via Internet forms that were submitted to experts with required feedback for the Delphi process.

Information Technology Module

The IT module is basically a technology-forecasting module. From the beginning of this study, IT's rapid development led us to consider a model which does not strictly depend on specific ITs, but rather depends on a flexible technology module that can be changed and modified easily. This property of the model makes sure that the model will not be outdated because any development can be easily integrated into this modular model. Seven ITs are defined as events of the module. These ITs will be described later.

The IT module is the ignition point of the model (Figure 1). This module defines the likelihood of the occurrence of breakthrough developments in IT. When the model runs, it is this module that defines the scenarios based on the occurrence and nonoccurrence of the events in the respective scenes. Probabilities of the occurrence of breakthrough developments in these events are elicited from IT experts (Table 2). Each scenario consists of three scenes (years 2000–2005, 2005–2010, and 2010–2015) and the seven ITs defined in the following sections. Currently this scenario-generating module considers only the occurrence and nonoccurrence of the events in the defined time horizon; therefore 221 (2,097,152) scenarios can be generated.

Project Management Knowledge Areas Module

The project management knowledge areas module defines and measures the trends in project management knowledge areas in the process of building projects (Figure 1). The maturity levels of project management knowledge areas are defined and measured by Kwak (1997) on a scale of 1–5 (1 being no conformity, 5 being complete conformity). Kwak (1997) reports on current maturity levels of project management knowledge areas in the United States (US) construction industry. Future trends in the maturity levels of project management knowledge areas are elicited from experts by means of a Delphi method (Table 3) in the design phase of a building project. The trends' cross impacts on each other are also elicited from experts. The nine PMBOK Guide project management knowledge areas are used in this model and defined as trends.

Other trends throughout the building design process can also be simulated for different events. Modules such as productivity, safety, quality, and so on, can replace this module or be added to the model. This way, the effects of technological and environmental developments can be forecasted on several aspects of building projects. This modular design makes this methodology a generic forecasting frame for the construction industry.

Building Processes Module

The building processes module includes the phases of the life cycle of a building project. This module basically forms the backbone of the model (Figure 1). All project management knowledge areas and ITs are tested in the frame that is defined by this module. One can easily eliminate one or more phases or can easily add subphases. This brings the advantage of multipurpose use; one can focus on any phase or phases as this research focuses on the design phase of a building project. This module can be replaced by modules such as a management level module (strategic, tactical, and operational), a management hierarchy module (site or office practices), and so forth.

The building processes module also analyzes the relationships between the phases of a building project. Interdependencies between these phases are defined in terms of cross impacts of the maturity levels of project management knowledge areas. If one considers a construction project as a whole system, any development in any phase of the process may have an impact on the other phases. Departments responsible for the processes are in fact internal customers of the company, and they provide and get both services and products to and from other departments. Their sensitivity to the other departments of the company has to be analyzed for their efficiency, in addition to the efficiency of their relationships with external customers. This module therefore explicitly defines these interdependencies and helps professionals to understand their organization as a whole system.




The Frontiers of Project Management Research
The Frontiers of Project Management Research
ISBN: 1880410745
EAN: 2147483647
Year: 2002
Pages: 207

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