Internal Validity External Validity

128 of expanding any claims of causality from a studied sample to other ones. Lewin 2005 asserts that the obtained results should be the same results which have come from the sample in order to be able to generalize the findings. Moreover, Yin 2003 expressed that external validity depends on possibility of generalizing the findings of a study to an immediate case study. This study uses theories and models from prior literature to extend propositions for this study. Some of these opinions are explained below: - Paulson 1976, Jin Levitt 1996 and Ibrahim and Paulson 2008 stated that incomplete and inefficient knowledge transfer causes unnecessary rework delay and lost revenue in the construction industry. - Valkenburg 1998 expressed that lack of synchronization causes serious problems for team members in both interactions and communications, and results in misunderstandings and uncoordinated actions. - Martinez 1998 stated that when an organization lacks a heightened degree of knowledge sharing, knowledge leaks are the results which cause repeated mistakes, dependence on a few key individuals, duplicated work, lack of sharing of good ideas, and slow introduction of new products or market solutions. - Macmillan 2001 believed that the conceptual stage of the design process is particularly difficult to be specified because its phases cannot be described as isolated activities. Additionally, the way in which the activities are described 129 is highly ambiguous, with individuals from various disciplines using a variety of terminology to recount the same occurrence. - Shelborn 2006 highlights that effective knowledge management can reduce project time and cost, improve the quality, and provide a major source of competitive advantage for the construction organizations. - Ibrahim and Paulson 2008 and Ibrahim and Nissen 2007 found that one characteristic which contributes to the knowledge loss phenomenon is the different dominating knowledge type for each lifecycle phase. According to the findings from the abovementioned studies, the researcher attempted to apply such findings to the conceptual design stage of a building project. To evaluate applicability of the generalized proposition, the proposed model will be simulated using SimVision tool.

3.4.4 Reliability

As mentioned before, Yin 2003 reasons that the use of multiple resource of evidence which provides multiple measurements of the same phenomena, can address construct validity and also ensure reliability. Since multiple sources of data are employed in this research, reliability is addressed sufficiently. The first source of data was the data collected through a case study during the conceptual design stage of a building project. As mentioned earlier in section 3.3.3, the study follows Nissen and 130 Levitt 2002 to have the 4 th year students trained on the green building technologies to improve knowledge of team members about mechanical and electrical requirements. The second source of data was basic mechanicalelectrical requirement standard Burberry, 1997 for building projects. Because the selected case study was a green building project, green building index GBI Assessment criteria for Non-Residential New Construction, 2009 was also used as the third source of the data to ensure the completeness of mechanicalelectrical considerations for the proposed concept design. Finally, computational organizational tool was utilized to compare the results after simulating Macmillan 2001 framework and the framework proposed by this study.

3.5 Limitation of Study

This study is limited to the 4 th year architect students in a public university. Team members are led by professionals and experts from architectural and mechanicalelectrical field. Indeed, architect students play the role of mechanicalelectrical experts by studying the required technologies for Green Building projects See section 3.3.3. Moreover, this research focuses on the conceptual design phase of a Green building project. Therefore, this study is limited to the AEC field. Finally, for reliability validation, computational modeling has